diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6c10135 --- /dev/null +++ b/.gitignore @@ -0,0 +1,23 @@ +*.png +*.jpg +*.csv +*.mp4 + +*.env + +*.zip + +__pycache__/ +*.pyc + +data/ + +*.pt +*.pth + +*.wav +*.tar +*.bin + +.conda/ +.venv/ \ No newline at end of file diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..6cdee9f --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "fairseq"] + path = fairseq + url = https://github.com/facebookresearch/fairseq.git \ No newline at end of file diff --git a/README.md b/README.md deleted file mode 100644 index 77200ba..0000000 --- a/README.md +++ /dev/null @@ -1,6 +0,0 @@ -# NetfLips -[2025-2] textless direct audio-video speech translation ---- - -This repository is built upon [AV2AV](https://github.com/choijeongsoo/av2av?tab=readme-ov-file) and [Fairseq](https://github.com/pytorch/fairseq). We appreciate the open-source of the projects. - diff --git a/README_environment.md b/README_environment.md new file mode 100644 index 0000000..b239083 --- /dev/null +++ b/README_environment.md @@ -0,0 +1,28 @@ +# 1. 환경 설정 +```bash +# 1. 레포지토리 클론 +git clone https://github.com/Prometheus-AI-3team/NetfLips.git + +cd NetfLips + +# 2. 서브모듈(fairseq) update +git submodule init +git submodule update + +# 2. Conda 기본 환경 생성 +conda env create -f environment.yml +conda activate unit2a + +# 3. Pip 다운그레이드 (메타데이터 에러 방지) +pip install "pip<24.1" + +# 4. PyTorch 설치 (CUDA 11.7 기준) +pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117 + +# 5. 나머지 라이브러리 설치 +pip install -r requirements.txt + +# 6. Fairseq 설치 +cd av2av-main/fairseq +pip install -e . +``` \ No newline at end of file diff --git a/av2unit/avhubert/__init__.py b/av2unit/avhubert/__init__.py new file mode 100644 index 0000000..6cb0629 --- /dev/null +++ b/av2unit/avhubert/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .hubert import * # noqa +# from .hubert_asr import * # noqa +from .hubert_dataset import * +from .hubert_pretraining import * +# from .hubert_criterion import * diff --git a/av2unit/avhubert/hubert.py b/av2unit/avhubert/hubert.py new file mode 100644 index 0000000..30830ea --- /dev/null +++ b/av2unit/avhubert/hubert.py @@ -0,0 +1,797 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os,sys +import logging +from typing import Dict, List, Optional, Tuple + +import numpy as np + +import torch +import torch.nn as nn +from dataclasses import dataclass, field +from fairseq import utils +from fairseq.data.data_utils import compute_mask_indices +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.wav2vec.wav2vec2 import ( + LAYER_TYPE_CHOICES, + ConvFeatureExtractionModel, + TransformerEncoder, +) +from fairseq.modules import GradMultiply, LayerNorm +from copy import deepcopy + +DBG=True if len(sys.argv) == 1 else False + +if DBG: + from hubert_pretraining import ( + AVHubertPretrainingConfig, + AVHubertPretrainingTask, + ) + from resnet import ResEncoder + logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, + ) + from utils import compute_mask_indices + # from decoder import TransformerDecoder + +else: + from .hubert_pretraining import ( + AVHubertPretrainingConfig, + AVHubertPretrainingTask, + ) + from .resnet import ResEncoder + from .utils import compute_mask_indices + # from .decoder import TransformerDecoder + +from omegaconf import II + +logger = logging.getLogger(__name__) + +EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"]) +MASKING_DISTRIBUTION_CHOICES = ChoiceEnum( + ["static", "uniform", "normal", "poisson"] +) + + +@dataclass +class AVHubertConfig(FairseqDataclass): + label_rate: int = II("task.label_rate") + input_modality: str = II("task.input_modality") + extractor_mode: EXTRACTOR_MODE_CHOICES = field( + default="default", + metadata={ + "help": "mode for feature extractor. default has a single group " + "norm with d groups in the first conv block, whereas layer_norm " + "has layer norms in every block (meant to use with normalize=True)" + }, + ) + encoder_layers: int = field( + default=12, metadata={"help": "num encoder layers in the transformer"} + ) + encoder_embed_dim: int = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + encoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "encoder embedding dimension for FFN"} + ) + encoder_attention_heads: int = field( + default=12, metadata={"help": "num encoder attention heads"} + ) + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="gelu", metadata={"help": "activation function to use"} + ) + + # dropouts + dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for the transformer"}, + ) + attention_dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for attention weights"}, + ) + activation_dropout: float = field( + default=0.0, + metadata={"help": "dropout probability after activation in FFN"}, + ) + encoder_layerdrop: float = field( + default=0.0, + metadata={"help": "probability of dropping a tarnsformer layer"}, + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + dropout_features: float = field( + default=0.0, + metadata={ + "help": "dropout to apply to the features (after feat extr)" + }, + ) + + final_dim: int = field( + default=0, + metadata={ + "help": "project final representations and targets to this many " + "dimensions. set to encoder_embed_dim is <= 0" + }, + ) + untie_final_proj: bool = field( + default=False, + metadata={"help": "use separate projection for each target"}, + ) + layer_norm_first: bool = field( + default=False, + metadata={"help": "apply layernorm first in the transformer"}, + ) + conv_feature_layers: str = field( + default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", + metadata={ + "help": "string describing convolutional feature extraction " + "layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + conv_bias: bool = field( + default=False, metadata={"help": "include bias in conv encoder"} + ) + logit_temp: float = field( + default=0.1, metadata={"help": "temperature to divide logits by"} + ) + target_glu: bool = field( + default=False, metadata={"help": "adds projection + glu to targets"} + ) + feature_grad_mult: float = field( + default=1.0, + metadata={"help": "multiply feature extractor var grads by this"}, + ) + + # masking + mask_length_audio: int = field(default=10, metadata={"help": "mask length"}) + mask_prob_audio: float = field( + default=0.65, + metadata={"help": "probability of replacing a token with mask"}, + ) + mask_length_image: int = field(default=10, metadata={"help": "mask length"}) + mask_prob_image: float = field( + default=0.65, + metadata={"help": "probability of replacing a token with mask"}, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose mask length"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: int = field( + default=1, + metadata={ + "help": "min space between spans (if no overlap is enabled)" + }, + ) + + # channel masking + mask_channel_length: int = field( + default=10, + metadata={"help": "length of the mask for features (channels)"}, + ) + mask_channel_prob: float = field( + default=0.0, + metadata={"help": "probability of replacing a feature with 0"}, + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, + metadata={"help": "whether to allow channel masks to overlap"}, + ) + mask_channel_min_space: int = field( + default=1, + metadata={ + "help": "min space between spans (if no overlap is enabled)" + }, + ) + + # positional embeddings + conv_pos: int = field( + default=128, + metadata={ + "help": "number of filters for convolutional positional embeddings" + }, + ) + conv_pos_groups: int = field( + default=16, + metadata={ + "help": "number of groups for convolutional positional embedding" + }, + ) + + latent_temp: Tuple[float, float, float] = field( + default=(2, 0.5, 0.999995), + metadata={"help": "legacy (to be removed)"}, + ) + + # loss computation + skip_masked: bool = field( + default=False, + metadata={"help": "skip computing losses over masked frames"}, + ) + skip_nomask: bool = field( + default=False, + metadata={"help": "skip computing losses over unmasked frames"}, + ) + resnet_relu_type: str = field(default='prelu', metadata={"help": 'relu type for resnet'}) + resnet_weights: Optional[str] = field(default=None, metadata={"help": 'resnet weights'}) + sim_type: str = field(default='cosine', metadata={"help": 'similarity type'}) + + sub_encoder_layers: int = field(default=0, metadata={'help': 'number of transformer layers for single modality'}) + audio_feat_dim: int = field(default=-1, metadata={'help': 'audio feature dimension'}) + modality_dropout: float = field(default=0, metadata={'help': 'drop one modality'}) + audio_dropout: float = field(default=0, metadata={'help': 'drop audio feature'}) + modality_fuse: str = field(default='concat', metadata={'help': 'fusing two modalities: add,concat'}) + selection_type : str = field(default='same_other_seq', metadata={'help': 'type of selectig images, same_other_seq: replace masked span with span from another sequence, same_seq: repace masked span with span of the same sequence'}) + masking_type : str = field(default='input', metadata={'help': 'input or feature masking'}) + + decoder_embed_dim: int = field( + default=768, metadata={"help": "decoder embedding dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field( + default=6, metadata={"help": "num of decoder layers"} + ) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "decoder layerdrop chance"} + ) + decoder_attention_heads: int = field( + default=4, metadata={"help": "num decoder attention heads"} + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + decoder_normalize_before: bool = field( + default=False, + metadata={"help": "apply layernorm before each decoder block"}, + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings " + "(outside self attention)" + }, + ) + decoder_dropout: float = field( + default=0.1, metadata={"help": "dropout probability in the decoder"} + ) + decoder_attention_dropout: float = field( + default=0.1, + metadata={ + "help": "dropout probability for attention weights " + "inside the decoder" + }, + ) + decoder_activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN " + "inside the decoder" + }, + ) + max_target_positions: int = field( + default=2048, metadata={"help": "max target positions"} + ) + share_decoder_input_output_embed: bool = field( + default=False, + metadata={"help": "share decoder input and output embeddings"}, + ) + no_scale_embedding: bool = field(default=True, metadata={'help': 'scale embedding'}) + + layer_type: LAYER_TYPE_CHOICES = field( + default="transformer", metadata={"help": "layer type in encoder"} + ) + + checkpoint_activations: bool = field( + default=False, + metadata={"help": "recompute activations and save memory for extra compute"}, + ) + + # FP16 optimization + required_seq_len_multiple: int = field( + default=1, + metadata={ + "help": "pad the input to encoder such that the sequence length is divisible by multiple" + }, + ) + +class SubModel(nn.Module): + def __init__(self, resnet=None, input_dim=None, cfg=None): + super().__init__() + self.resnet = resnet + self.proj = nn.Linear(input_dim, cfg.encoder_embed_dim) + self.encoder = TransformerEncoder(cfg) if cfg.encoder_layers > 0 else None + + def forward(self, x): + if self.resnet is not None: + x = self.resnet(x) + x = self.proj(x.transpose(1, 2)) + if self.encoder is not None: + x = self.encoder(x)[0].transpose(1, 2) + else: + x = x.transpose(1, 2) + return x + +@register_model("av_hubert", dataclass=AVHubertConfig) +class AVHubertModel(BaseFairseqModel): + def __init__( + self, + cfg: AVHubertConfig, + task_cfg: AVHubertPretrainingConfig, + dictionaries: List[Dictionary], + **kwargs + ) -> None: + super().__init__() + logger.info(f"HubertModel Config: {cfg}") + + feature_ds_rate = 1 + self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / task_cfg.sample_rate + sub_cfg = deepcopy(cfg) + sub_cfg.encoder_layers = sub_cfg.sub_encoder_layers + resnet = ResEncoder(relu_type=cfg.resnet_relu_type, weights=cfg.resnet_weights) + self.feature_extractor_audio = SubModel(resnet=None, input_dim=cfg.audio_feat_dim, cfg=sub_cfg) + self.feature_extractor_video = SubModel(resnet=resnet, input_dim=resnet.backend_out, cfg=sub_cfg) + self.modality_dropout, self.audio_dropout = cfg.modality_dropout, cfg.audio_dropout + self.modality_fuse = cfg.modality_fuse + self.encoder_embed_dim = cfg.encoder_embed_dim + if self.modality_fuse == 'concat': + self.embed = cfg.encoder_embed_dim * 2 + elif self.modality_fuse == 'add': + self.embed = cfg.encoder_embed_dim + self.post_extract_proj = ( + nn.Linear(self.embed, cfg.encoder_embed_dim) + if self.embed != cfg.encoder_embed_dim + else None + ) + + self.mask_prob_image, self.mask_prob_audio = cfg.mask_prob_image, cfg.mask_prob_audio + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length_image, self.mask_length_audio = cfg.mask_length_image, cfg.mask_length_audio + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + self.logit_temp = cfg.logit_temp + self.skip_masked = cfg.skip_masked + self.skip_nomask = cfg.skip_nomask + self.sim_type = cfg.sim_type + self.selection_type = cfg.selection_type + self.masking_type = cfg.masking_type + + final_dim = ( + cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim + ) + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.audio_feat_dim).uniform_() if self.masking_type == 'input' else torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + + self.encoder = TransformerEncoder(cfg) + self.layer_norm = LayerNorm(self.embed) + + self.target_glu = None + if cfg.target_glu: + self.target_glu = nn.Sequential( + nn.Linear(final_dim, final_dim * 2), nn.GLU() + ) + + self.untie_final_proj = cfg.untie_final_proj + if self.untie_final_proj: + self.final_proj = nn.Linear( + cfg.encoder_embed_dim, final_dim * len(dictionaries) + ) + else: + self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) + + # modules below are not needed during fine-tuning + if any([d is None for d in dictionaries]): + logger.info( + "cannot find dictionary. assume will be used for fine-tuning" + ) + else: + self.num_classes = [len(d) for d in dictionaries] + self.label_embs_concat = nn.Parameter( + torch.FloatTensor(sum(self.num_classes), final_dim) + ) + nn.init.uniform_(self.label_embs_concat) + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: AVHubertConfig, task: AVHubertPretrainingTask): + """Build a new model instance.""" + + kwargs = {} + model = AVHubertModel(cfg, task.cfg, task.dictionaries, **kwargs) + return model + + def apply_input_mask(self, x, padding_mask, target_list): + B, C, T = x.shape[:3] + is_audio = True if len(x.shape) == 3 else False + if is_audio: + mask_prob, mask_length = self.mask_prob_audio, self.mask_length_audio + else: + mask_prob, mask_length = self.mask_prob_image, self.mask_length_image + if mask_prob > 0: + + mask_indices, starts, ends, batch_indexes = compute_mask_indices( + (B, T), + padding_mask, + mask_prob, + mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices_np = mask_indices + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x = x.transpose(1, 2).contiguous() # [B, T, C, H, W] + if B == 1: + x[mask_indices] = 0 + elif is_audio: + x[mask_indices] = self.mask_emb + elif self.selection_type == 'same_other_seq': + perm = (torch.arange(B) + torch.randint(low=1, high=B, size=(1,))) % B + x_perm = x[perm] + x[mask_indices] = x_perm[mask_indices] + elif self.selection_type == 'same_seq': + batch_indexes_, other_indexes = [], [] + for batch_index, start, end in zip(batch_indexes, starts, ends): + length = end-start + other_start = np.setdiff1d(np.arange(T), np.arange(max(0, start-length), end)) + if len(other_start) > 0: + other_start = np.random.choice(other_start, size=1) + else: + other_start = 0 + other_end = other_start + length + other_indexes.append(np.arange(other_start, other_end).clip(max=T-1)) + batch_indexes_.append(np.zeros([length], dtype=np.int64)+batch_index) + batch_indexes, other_indexes = np.concatenate(batch_indexes_), np.concatenate(other_indexes) + x[mask_indices] = x[batch_indexes, other_indexes] + + x = x.transpose(1, 2).contiguous() + else: + mask_indices = None + + if self.mask_channel_prob > 0: + logger.info(f"No mask channel prob for input masking") + return x, mask_indices + + def apply_feature_mask(self, x, padding_mask, target_list): + B, T, C = x.shape + assert self.mask_prob_audio == self.mask_prob_image and self.mask_length_audio == self.mask_length_image, f"masking prob/length for image/audio be same for feature masking" + mask_prob, mask_length = self.mask_prob_audio, self.mask_length_image + if mask_prob > 0: + mask_indices, _, _, _ = compute_mask_indices( + (B, T), + padding_mask, + mask_prob, + mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb + else: + mask_indices = None + + if self.mask_channel_prob > 0: + mask_channel_indices, _, _, _ = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def forward_features(self, source: torch.Tensor, modality: str) -> torch.Tensor: + extractor = eval(f"self.feature_extractor_{modality}") + if self.feature_grad_mult > 0: + features = extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = extractor(source) + return features + + def forward_targets( + self, features: torch.Tensor, mask_indices: torch.Tensor, target_list: List[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Trim features to ensure labels exist and then get aligned labels + feat_tsz = features.size(2) + targ_tsz = min([t.size(1) for t in target_list]) + if self.feat2tar_ratio * feat_tsz > targ_tsz: + feat_tsz = int(targ_tsz / self.feat2tar_ratio) + features = features[..., :feat_tsz] + if mask_indices is not None: + mask_indices = mask_indices[..., :feat_tsz] + target_inds = torch.arange(feat_tsz).float() * self.feat2tar_ratio + target_list = [t[:, target_inds.long()] for t in target_list] + return features, mask_indices, target_list + + def forward_padding_mask( + self, features: torch.Tensor, padding_mask: torch.Tensor, + ) -> torch.Tensor: + extra = padding_mask.size(1) % features.size(1) + if extra > 0: + padding_mask = padding_mask[:, :-extra] + padding_mask = padding_mask.view( + padding_mask.size(0), features.size(1), -1 + ) + padding_mask = padding_mask.all(-1) + return padding_mask + + def compute_logits(self, feats, emb_mat): + # feats: [B, T, F], emb_mat: [V, F] + if self.sim_type == 'dot': + logits = torch.matmul(feats, emb_mat.transpose(0, 1)) + elif self.sim_type == 'cosine': + batch_size, timesteps, emb_dim = feats.size() + feats_ = feats.view(-1, emb_dim) + nom = (feats_.unsqueeze(dim=1) * emb_mat.unsqueeze(dim=0)).sum(dim=-1) # [B*T, V] + denom = (feats_**2).sum(dim=-1).sqrt().unsqueeze(dim=1) * (emb_mat**2).sum(dim=-1).sqrt().unsqueeze(dim=0) # [B*T, V] + logits = (nom/denom.clamp(min=1e-6)).view(batch_size, timesteps, -1) + else: + raise NotImplementedError + logits = logits / self.logit_temp + return logits + + def forward( + self, + source: torch.Tensor, + target_list: Optional[List[torch.Tensor]] = None, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = True, + features_only: bool = False, + output_layer: Optional[int] = None + ) -> Dict[str, torch.Tensor]: + """output layer is 1-based""" + src_audio, src_video = source['audio'], source['video'] + if mask and self.masking_type == 'input': + src_video, mask_indices_video = self.apply_input_mask(src_video, padding_mask, target_list) + src_audio, mask_indices_audio = self.apply_input_mask(src_audio, padding_mask, target_list) + mask_indices = torch.logical_or(mask_indices_audio, mask_indices_video) + else: + src_audio, src_video, mask_indices = src_audio, src_video, None + + features_audio = self.forward_features(src_audio, modality='audio') # features: [B, F, T] + features_video = self.forward_features(src_video, modality='video') + modality_drop_prob, audio_drop_prob = np.random.random(), np.random.random() + if self.training: + if modality_drop_prob < self.modality_dropout: + if audio_drop_prob < self.audio_dropout: + features_audio = 0 * features_audio + else: + features_video = 0 * features_video + if self.modality_fuse == 'concat': + features = torch.cat([features_audio, features_video], dim=1) + elif self.modality_fuse == 'add': + features = features_audio + features_video + if target_list is not None: + features, mask_indices, target_list = self.forward_targets(features, mask_indices, target_list) + + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + + if padding_mask is not None: + padding_mask = self.forward_padding_mask(features, padding_mask) + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + if self.masking_type == 'feature' and mask: + x, mask_indices = self.apply_feature_mask(features, padding_mask, target_list) + else: + x = features + + # feature: (B, T, D), float + # target: (B, T), long + # x: (B, T, D), float + # padding_mask: (B, T), bool + # mask_indices: (B, T), bool + x, _ = self.encoder( + x, + padding_mask=padding_mask, + layer=None if output_layer is None else output_layer - 1 + ) + + if features_only: + return {"x": x, "padding_mask": padding_mask, "features": features} + + label_embs_list = self.label_embs_concat.split(self.num_classes, 0) + proj_x = self.final_proj(x) + if self.untie_final_proj: + proj_x_list = proj_x.chunk(len(self.num_classes), dim=-1) + else: + proj_x_list = [proj_x for _ in self.num_classes] + logit_list = [self.compute_logits(proj, emb).view(-1, num_class) for proj, emb, num_class in zip(proj_x_list, label_embs_list, self.num_classes)] # [[B*T, V]] + mask, unmask = torch.logical_and(mask_indices, ~padding_mask).view(-1), torch.logical_and(~mask_indices, ~padding_mask).view(-1) # [B*T] + logit_m_list, logit_u_list = [logit[mask] for logit in logit_list], [logit[unmask] for logit in logit_list] + target_m_list, target_u_list = [target.view(-1)[mask].long() for target in target_list], [target.view(-1)[unmask].long() for target in target_list] + result = { + "logit_m_list": logit_m_list, + "logit_u_list": logit_u_list, + "target_m_list": target_m_list, + "target_u_list": target_u_list, + "padding_mask": padding_mask, + "features_pen": features_pen, + } + return result + + def extract_features( + self, + source: torch.Tensor, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = False, + ret_conv: bool = False, + output_layer: Optional[int] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + res = self.forward( + source, + padding_mask=padding_mask, + mask=mask, + features_only=True, + output_layer=output_layer, + ) + feature = res["features"] if ret_conv else res["x"] + return feature, res["padding_mask"] + + def extract_finetune(self, source, padding_mask=None, mask=False, ret_conv=False, output_layer=None): + src_audio, src_video = source['audio'], source['video'] + if mask and self.masking_type == 'input': + src_video, mask_indices_video = self.apply_input_mask(src_video, padding_mask, target_list=None) + src_audio, mask_indices_audio = self.apply_input_mask(src_audio, padding_mask, target_list=None) + mask_indices = torch.logical_or(mask_indices_audio, mask_indices_video) # mask_indices not used in fine-tuning + else: + src_audio, src_video, mask_indices = src_audio, src_video, None + + if src_audio is not None and src_video is None: + features_audio = self.forward_features(src_audio, modality='audio') # features: [B, F, T] + features_video = features_audio.new_zeros(features_audio.size(0), self.encoder_embed_dim, features_audio.size(-1)) + elif src_audio is None and src_video is not None: + features_video = self.forward_features(src_video, modality='video') + features_audio = features_video.new_zeros(features_video.size(0), self.encoder_embed_dim, features_video.size(-1)) + elif src_audio is not None and src_video is not None: + features_video = self.forward_features(src_video, modality='video') + features_audio = self.forward_features(src_audio, modality='audio') # features: [B, F, T] + + if self.modality_fuse == 'concat': + features = torch.cat([features_audio, features_video], dim=1) + elif self.modality_fuse == 'add': + features = features_audio + features_video + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + unmasked_features = features.clone() + + if padding_mask is not None: + padding_mask = self.forward_padding_mask(features, padding_mask) + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + x = features + mask_indices = None + + # feature: (B, T, D), float + # target: (B, T), long + # x: (B, T, D), float + # padding_mask: (B, T), bool + # mask_indices: (B, T), bool + x, _ = self.encoder( + x, + padding_mask=padding_mask, + layer=None if output_layer is None else output_layer - 1 + ) + + return x, padding_mask + + + def get_extra_losses(self, net_output): + extra_losses = [] + names = [] + if "features_pen" in net_output: + extra_losses.append(net_output["features_pen"]) + names.append("features_pen") + + return extra_losses, names + + def remove_pretraining_modules(self): + self.target_glu = None + self.final_proj = None + + def get_logits(self, net_output, is_masked=True): + raise NotImplementedError + + def get_targets(self, net_output, is_masked=True): + raise NotImplementedError + + def compute_nce(self, x, pos, negs): + neg_is_pos = (pos == negs).all(-1) + pos = pos.unsqueeze(0) + targets = torch.cat([pos, negs], dim=0) + + logits = torch.cosine_similarity( + x.float(), targets.float(), dim=-1 + ).type_as(x) + logits /= self.logit_temp + if neg_is_pos.any(): + logits[1:][neg_is_pos] = float("-inf") + logits = logits.transpose(0, 1) # (num_x, num_cls+1) + return logits diff --git a/av2unit/avhubert/hubert_dataset.py b/av2unit/avhubert/hubert_dataset.py new file mode 100644 index 0000000..e80895f --- /dev/null +++ b/av2unit/avhubert/hubert_dataset.py @@ -0,0 +1,529 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import logging +import os +import sys +import time +from typing import Any, List, Optional, Union + +import numpy as np + +import torch +import torch.nn.functional as F +from fairseq.data import data_utils +from fairseq.data.fairseq_dataset import FairseqDataset +from python_speech_features import logfbank +from scipy.io import wavfile + +DBG=True if len(sys.argv) == 1 else False + +if DBG: + import utils as custom_utils + logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "DEBUG").upper(), + stream=sys.stdout, + ) +else: + from . import utils as custom_utils + +logger = logging.getLogger(__name__) + + +def load_audio_visual(manifest_path, max_keep, min_keep, frame_rate, label_paths, label_rates, tol=0.1): + def is_audio_label_aligned(audio_dur, label_durs): + return all([abs(audio_dur - label_dur) max_keep: + n_long += 1 + elif (not is_seq_label) and (not is_audio_label_aligned(sz/frame_rate, dur_from_label_list[ind])): + n_unaligned += 1 + else: + video_path = items[1] + audio_path = items[2] + audio_id = items[0] + names.append((video_path, audio_path+':'+audio_id)) + inds.append(ind) + sizes.append(sz) + tot = ind + 1 + logger.info( + ( + f"max_keep={max_keep}, min_keep={min_keep}, " + f"loaded {len(names)}, skipped {n_short} short and {n_long} long and {n_unaligned} unaligned, " + f"longest-loaded={max(sizes)}, shortest-loaded={min(sizes)}" + ) + ) + return root, names, inds, tot, sizes + +def load_label(label_path, inds, tot): + with open(label_path) as f: + labels = [line.rstrip() for line in f] + assert ( + len(labels) == tot + ), f"number of labels does not match ({len(labels)} != {tot})" + labels = [labels[i] for i in inds] + return labels + + +def load_label_offset(label_path, inds, tot): + with open(label_path) as f: + code_lengths = [len(line.encode("utf-8")) for line in f] + assert ( + len(code_lengths) == tot + ), f"number of labels does not match ({len(code_lengths)} != {tot})" + offsets = list(itertools.accumulate([0] + code_lengths)) + offsets = [(offsets[i], offsets[i + 1]) for i in inds] + return offsets + + +def verify_label_lengths( + audio_sizes, + audio_rate, + label_path, + label_rate, + inds, + tot, + tol=0.1, # tolerance in seconds +): + if label_rate < 0: + logger.info(f"{label_path} is sequence label. skipped") + return + + with open(label_path) as f: + lengths = [len(line.rstrip().split()) for line in f] + assert len(lengths) == tot + lengths = [lengths[i] for i in inds] + num_invalid = 0 + for i, ind in enumerate(inds): + dur_from_audio = audio_sizes[i] / audio_rate + dur_from_label = lengths[i] / label_rate + if abs(dur_from_audio - dur_from_label) > tol: + logger.warning( + ( + f"audio and label duration differ too much " + f"(|{dur_from_audio} - {dur_from_label}| > {tol}) " + f"in line {ind+1} of {label_path}. Check if `label_rate` " + f"is correctly set (currently {label_rate}). " + f"num. of samples = {audio_sizes[i]}; " + f"label length = {lengths[i]}" + ) + ) + num_invalid += 1 + if num_invalid > 0: + logger.warning( + f"total {num_invalid} (audio, label) pairs with mismatched lengths" + ) + + +class AVHubertDataset(FairseqDataset): + def __init__( + self, + manifest_path: str, + sample_rate: float, + label_paths: List[str], + label_rates: Union[List[float], float], # -1 for sequence labels + pad_list: List[str], + eos_list: List[str], + label_processors: Optional[List[Any]] = None, + max_keep_sample_size: Optional[int] = None, + min_keep_sample_size: Optional[int] = None, + max_sample_size: Optional[int] = None, + shuffle: bool = True, + pad_audio: bool = False, + normalize: bool = False, + store_labels: bool = True, + random_crop: bool = False, + single_target: bool = False, + stack_order_audio: int=1, + skip_verify: bool=False, + image_mean: float=0, + image_std: float=1, + image_crop_size: int=88, + image_aug: bool=False, + modalities: Optional[List[str]]=None, + is_s2s=False, + noise_fn=None, + noise_prob=0, + noise_snr=0, + noise_num=1 + ): + self.label_rates = ( + [label_rates for _ in range(len(label_paths))] + if isinstance(label_rates, int) + else label_rates + ) + self.modalities = set(modalities) + self.audio_root, self.names, inds, tot, self.sizes = load_audio_visual(manifest_path, max_keep_sample_size, min_keep_sample_size, frame_rate=sample_rate, label_paths=label_paths, label_rates=self.label_rates) + self.sample_rate = sample_rate + self.stack_order_audio = stack_order_audio + self.shuffle = shuffle + self.random_crop = random_crop + + self.num_labels = len(label_paths) + self.pad_list = pad_list + self.eos_list = eos_list + self.label_processors = label_processors + self.single_target = single_target + self.store_labels = store_labels + self.is_s2s = is_s2s + self.noise_wav, self.noise_prob, self.noise_snr, self.noise_num = [ln.strip() for ln in open(noise_fn).readlines()] if noise_fn is not None else [], noise_prob, noise_snr, noise_num + + assert self.single_target == (self.label_rates[0] == -1), f"single target should be equivalent to sequence label (label_rate==-1)" + if store_labels: + self.label_list = [load_label(p, inds, tot) for p in label_paths] + else: + self.label_paths = label_paths + self.label_offsets_list = [ + load_label_offset(p, inds, tot) for p in label_paths + ] + assert ( + label_processors is None + or len(label_processors) == self.num_labels + ) + if not skip_verify: + for label_path, label_rate in zip(label_paths, self.label_rates): + verify_label_lengths(self.sizes, self.sample_rate, label_path, label_rate, inds, tot) + else: + logger.info(f"Skip label alignment verifying") + + self.max_sample_size = ( + max_sample_size if max_sample_size is not None else sys.maxsize + ) + self.pad_audio = pad_audio + self.normalize = normalize + if image_aug: + self.transform = custom_utils.Compose([ + custom_utils.Normalize( 0.0,255.0 ), + custom_utils.RandomCrop((image_crop_size, image_crop_size)), + custom_utils.HorizontalFlip(0.5), + custom_utils.Normalize(image_mean, image_std) ]) + else: + self.transform = custom_utils.Compose([ + custom_utils.Normalize( 0.0,255.0 ), + custom_utils.CenterCrop((image_crop_size, image_crop_size)), + custom_utils.Normalize(image_mean, image_std) ]) + logger.info(f"image transform: {self.transform}") + + logger.info( + f"pad_audio={pad_audio}, random_crop={random_crop}, " + f"normalize={normalize}, max_sample_size={self.max_sample_size}, " + f"seqs2seq data={self.is_s2s},") + logger.info( + f"Noise wav: {noise_fn}->{len(self.noise_wav)} wav, Prob: {self.noise_prob}, SNR: {self.noise_snr}, Number of mixture: {self.noise_num}" + ) + + def get_label(self, index, label_idx): + if self.store_labels: + label = self.label_list[label_idx][index] + else: + with open(self.label_paths[label_idx]) as f: + offset_s, offset_e = self.label_offsets_list[label_idx][index] + f.seek(offset_s) + label = f.read(offset_e - offset_s) + + if self.label_processors is not None: + label = self.label_processors[label_idx](label) + return label + + def get_labels(self, index): + return [self.get_label(index, i) for i in range(self.num_labels)] + + def load_feature(self, mix_name): + """ + Load image and audio feature + Returns: + video_feats: numpy.ndarray of shape [T, H, W, 1], audio_feats: numpy.ndarray of shape [T, F] + """ + def stacker(feats, stack_order): + """ + Concatenating consecutive audio frames + Args: + feats - numpy.ndarray of shape [T, F] + stack_order - int (number of neighboring frames to concatenate + Returns: + feats - numpy.ndarray of shape [T', F'] + """ + feat_dim = feats.shape[1] + if len(feats) % stack_order != 0: + res = stack_order - len(feats) % stack_order + res = np.zeros([res, feat_dim]).astype(feats.dtype) + feats = np.concatenate([feats, res], axis=0) + feats = feats.reshape((-1, stack_order, feat_dim)).reshape(-1, stack_order*feat_dim) + return feats + video_fn, audio_fn = mix_name + if 'video' in self.modalities: + video_feats = self.load_video(video_fn) # [T, H, W, 1] + else: + video_feats = None + if 'audio' in self.modalities: + audio_fn = audio_fn.split(':')[0] + sample_rate, wav_data = wavfile.read(audio_fn) + assert sample_rate == 16_000 and len(wav_data.shape) == 1 + if np.random.rand() < self.noise_prob: + wav_data = self.add_noise(wav_data) + audio_feats = logfbank(wav_data, samplerate=sample_rate).astype(np.float32) # [T, F] + audio_feats = stacker(audio_feats, self.stack_order_audio) # [T/stack_order_audio, F*stack_order_audio] + else: + audio_feats = None + if audio_feats is not None and video_feats is not None: + diff = len(audio_feats) - len(video_feats) + if diff < 0: + audio_feats = np.concatenate([audio_feats, np.zeros([-diff, audio_feats.shape[-1]], dtype=audio_feats.dtype)]) + elif diff > 0: + audio_feats = audio_feats[:-diff] + return video_feats, audio_feats + + def load_video(self, audio_name): + feats = custom_utils.load_video(os.path.join(self.audio_root, audio_name)) + feats = self.transform(feats) + feats = np.expand_dims(feats, axis=-1) + return feats + + def select_noise(self): + rand_indexes = np.random.randint(0, len(self.noise_wav), size=self.noise_num) + noise_wav = [] + for x in rand_indexes: + noise_wav.append(wavfile.read(self.noise_wav[x])[1].astype(np.float32)) + if self.noise_num == 1: + return noise_wav[0] + else: + min_len = min([len(x) for x in noise_wav]) + noise_wav = [x[:min_len] for x in noise_wav] + noise_wav = np.floor(np.stack(noise_wav).mean(axis=0)) + return noise_wav + + def add_noise(self, clean_wav): + clean_wav = clean_wav.astype(np.float32) + noise_wav = self.select_noise() + if type(self.noise_snr) == int or type(self.noise_snr) == float: + snr = self.noise_snr + elif type(self.noise_snr) == tuple: + snr = np.random.randint(self.noise_snr[0], self.noise_snr[1]+1) + clean_rms = np.sqrt(np.mean(np.square(clean_wav), axis=-1)) + if len(clean_wav) > len(noise_wav): + ratio = int(np.ceil(len(clean_wav)/len(noise_wav))) + noise_wav = np.concatenate([noise_wav for _ in range(ratio)]) + if len(clean_wav) < len(noise_wav): + start = 0 + noise_wav = noise_wav[start: start + len(clean_wav)] + noise_rms = np.sqrt(np.mean(np.square(noise_wav), axis=-1)) + adjusted_noise_rms = clean_rms / (10**(snr/20)) + adjusted_noise_wav = noise_wav * (adjusted_noise_rms / noise_rms) + mixed = clean_wav + adjusted_noise_wav + + #Avoid clipping noise + max_int16 = np.iinfo(np.int16).max + min_int16 = np.iinfo(np.int16).min + if mixed.max(axis=0) > max_int16 or mixed.min(axis=0) < min_int16: + if mixed.max(axis=0) >= abs(mixed.min(axis=0)): + reduction_rate = max_int16 / mixed.max(axis=0) + else : + reduction_rate = min_int16 / mixed.min(axis=0) + mixed = mixed * (reduction_rate) + mixed = mixed.astype(np.int16) + return mixed + + def __getitem__(self, index): + video_feats, audio_feats = self.load_feature(self.names[index]) + audio_feats, video_feats = torch.from_numpy(audio_feats.astype(np.float32)) if audio_feats is not None else None, torch.from_numpy(video_feats.astype(np.float32)) if video_feats is not None else None + if self.normalize and 'audio' in self.modalities: + with torch.no_grad(): + audio_feats = F.layer_norm(audio_feats, audio_feats.shape[1:]) + labels = self.get_labels(index) + fid = self.names[index][1].split(':')[1] + return {"id": index, 'fid': fid, "video_source": video_feats, 'audio_source': audio_feats, "label_list": labels} + + def __len__(self): + return len(self.sizes) + + def crop_to_max_size(self, wav, target_size, start=None): + size = len(wav) + diff = size - target_size + if diff <= 0: + return wav, 0 + # longer utterances + if start is None: + start, end = 0, target_size + if self.random_crop: + start = np.random.randint(0, diff + 1) + end = size - diff + start + else: + end = start + target_size + return wav[start:end], start + + def collater(self, samples): + samples = [s for s in samples if s["id"] is not None] + if len(samples) == 0: + return {} + + audio_source, video_source = [s["audio_source"] for s in samples], [s["video_source"] for s in samples] + if audio_source[0] is None: + audio_source = None + if video_source[0] is None: + video_source = None + if audio_source is not None: + audio_sizes = [len(s) for s in audio_source] + else: + audio_sizes = [len(s) for s in video_source] + if self.pad_audio: + audio_size = min(max(audio_sizes), self.max_sample_size) + else: + audio_size = min(min(audio_sizes), self.max_sample_size) + if audio_source is not None: + collated_audios, padding_mask, audio_starts = self.collater_audio(audio_source, audio_size) + else: + collated_audios, audio_starts = None, None + if video_source is not None: + collated_videos, padding_mask, audio_starts = self.collater_audio(video_source, audio_size, audio_starts) + else: + collated_videos = None + targets_by_label = [ + [s["label_list"][i] for s in samples] + for i in range(self.num_labels) + ] + targets_list, lengths_list, ntokens_list = self.collater_label( + targets_by_label, audio_size, audio_starts + ) + source = {"audio": collated_audios, "video": collated_videos} + net_input = {"source": source, "padding_mask": padding_mask} + batch = { + "id": torch.LongTensor([s["id"] for s in samples]), + "net_input": net_input, + "utt_id": [s['fid'] for s in samples] + } + + if self.single_target: + batch["target_lengths"] = lengths_list[0] + batch["ntokens"] = ntokens_list[0] + if self.is_s2s: + batch['target'], net_input['prev_output_tokens'] = targets_list[0][0], targets_list[0][1] + else: + batch["target"] = targets_list[0] + else: + batch["target_lengths_list"] = lengths_list + batch["ntokens_list"] = ntokens_list + batch["target_list"] = targets_list + return batch + + def collater_audio(self, audios, audio_size, audio_starts=None): + audio_feat_shape = list(audios[0].shape[1:]) + collated_audios = audios[0].new_zeros([len(audios), audio_size]+audio_feat_shape) + padding_mask = ( + torch.BoolTensor(len(audios), audio_size).fill_(False) # + ) + start_known = audio_starts is not None + audio_starts = [0 for _ in audios] if not start_known else audio_starts + for i, audio in enumerate(audios): + diff = len(audio) - audio_size + if diff == 0: + collated_audios[i] = audio + elif diff < 0: + assert self.pad_audio + collated_audios[i] = torch.cat( + [audio, audio.new_full([-diff]+audio_feat_shape, 0.0)] + ) + padding_mask[i, diff:] = True + else: + collated_audios[i], audio_starts[i] = self.crop_to_max_size( + audio, audio_size, audio_starts[i] if start_known else None + ) + if len(audios[0].shape) == 2: + collated_audios = collated_audios.transpose(1, 2) # [B, T, F] -> [B, F, T] + else: + collated_audios = collated_audios.permute((0, 4, 1, 2, 3)).contiguous() # [B, T, H, W, C] -> [B, C, T, H, W] + return collated_audios, padding_mask, audio_starts + + def collater_frm_label( + self, targets, audio_size, audio_starts, label_rate, pad + ): + assert label_rate > 0 + s2f = label_rate / self.sample_rate # num label per sample + frm_starts = [int(round(s * s2f)) for s in audio_starts] + frm_size = int(round(audio_size * s2f)) + if not self.pad_audio: + rem_size = [len(t) - s for t, s in zip(targets, frm_starts)] + frm_size = min(frm_size, *rem_size) + targets = [t[s: s + frm_size] for t, s in zip(targets, frm_starts)] + logger.debug(f"audio_starts={audio_starts}") + logger.debug(f"frame_starts={frm_starts}") + logger.debug(f"frame_size={frm_size}") + + lengths = torch.LongTensor([len(t) for t in targets]) + ntokens = lengths.sum().item() + targets = data_utils.collate_tokens( + targets, pad_idx=pad, left_pad=False + ) + return targets, lengths, ntokens + + def collater_seq_label(self, targets, pad): + lengths = torch.LongTensor([len(t) for t in targets]) + ntokens = lengths.sum().item() + targets = data_utils.collate_tokens( + targets, pad_idx=pad, left_pad=False + ) + return targets, lengths, ntokens + + def collater_seq_label_s2s(self, targets, pad): + lengths = torch.LongTensor([len(t) for t in targets]) + ntokens = lengths.sum().item() + pad, eos = self.label_processors[0].dictionary.pad(), self.label_processors[0].dictionary.eos() + targets_ = data_utils.collate_tokens(targets, pad_idx=pad, eos_idx=eos, left_pad=False) + prev_output_tokens = data_utils.collate_tokens(targets, pad_idx=pad, eos_idx=eos, left_pad=False, move_eos_to_beginning=True) + return (targets_, prev_output_tokens), lengths, ntokens + + def collater_label(self, targets_by_label, audio_size, audio_starts): + targets_list, lengths_list, ntokens_list = [], [], [] + itr = zip(targets_by_label, self.label_rates, self.pad_list) + for targets, label_rate, pad in itr: + if label_rate == -1: + if self.is_s2s: + targets, lengths, ntokens = self.collater_seq_label_s2s(targets, pad) + else: + targets, lengths, ntokens = self.collater_seq_label(targets, pad) + else: + targets, lengths, ntokens = self.collater_frm_label( + targets, audio_size, audio_starts, label_rate, pad + ) + targets_list.append(targets) + lengths_list.append(lengths) + ntokens_list.append(ntokens) + return targets_list, lengths_list, ntokens_list + + def num_tokens(self, index): + return self.size(index) + + def size(self, index): + if self.pad_audio: + return self.sizes[index] + return min(self.sizes[index], self.max_sample_size) + + def ordered_indices(self): + if self.shuffle: + order = [np.random.permutation(len(self))] + else: + order = [np.arange(len(self))] + + order.append(self.sizes) + return np.lexsort(order)[::-1] diff --git a/av2unit/avhubert/hubert_pretraining.py b/av2unit/avhubert/hubert_pretraining.py new file mode 100644 index 0000000..3c3b42e --- /dev/null +++ b/av2unit/avhubert/hubert_pretraining.py @@ -0,0 +1,400 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os, glob +import sys +from typing import Dict, List, Optional, Tuple + +import numpy as np + +from dataclasses import dataclass, field +from fairseq import metrics, search +from fairseq.data import Dictionary, encoders +from fairseq.dataclass.configs import FairseqDataclass +from fairseq.tasks import register_task +from fairseq.tasks.fairseq_task import FairseqTask +from omegaconf import MISSING, II +import numpy as np +from argparse import Namespace + +DBG=True if len(sys.argv) == 1 else False + +if DBG: + from hubert_dataset import AVHubertDataset + # from sequence_generator import SequenceGenerator +else: + from .hubert_dataset import AVHubertDataset + # from .sequence_generator import SequenceGenerator + +logger = logging.getLogger(__name__) + + +class LabelEncoder(object): + def __init__(self, dictionary: Dictionary) -> None: + self.dictionary = dictionary + + def __call__(self, label: str) -> List[str]: + return self.dictionary.encode_line( + label, append_eos=False, add_if_not_exist=False, + ) + +class LabelEncoderS2SToken(object): + def __init__(self, dictionary: Dictionary, bpe_tokenizer) -> None: + self.bpe_tokenizer = bpe_tokenizer + self.dictionary = dictionary + + def __call__(self, label: str) -> List[str]: + label = self.bpe_tokenizer.encode(label.lower()) + return self.dictionary.encode_line( + label, append_eos=True, add_if_not_exist=False, + ).long() + + def decode(self, tok, symbols_ignore=None): + tok = self.dictionary.string(tok, extra_symbols_to_ignore=symbols_ignore) + if self.bpe_tokenizer: + tok = self.bpe_tokenizer.decode(tok) + return tok + +@dataclass +class AVHubertPretrainingConfig(FairseqDataclass): + data: str = field( + default=MISSING, metadata={"help": "path to data directory"} + ) + labels: List[str] = field( + default_factory=lambda: ["ltr"], + metadata={ + "help": ( + "extension of the label files to load, frame-level labels for" + " pre-training, and sequence-level label for fine-tuning" + ) + }, + ) + label_dir: Optional[str] = field( + default=None, + metadata={ + "help": "if set, looks for labels in this directory instead", + }, + ) + label_rate: int = field( + default=-1, + metadata={"help": "label frame rate. -1 for sequence label"}, + ) + + sample_rate: int = field( + default=16_000, + metadata={ + "help": "target sample rate. audio files will be up/down " + "sampled to this rate" + }, + ) + normalize: bool = field( + default=False, + metadata={ + "help": "if set, normalizes input to have 0 mean and unit variance" + }, + ) + enable_padding: bool = field( + default=False, + metadata={"help": "pad shorter samples instead of cropping"}, + ) + max_sample_size: Optional[int] = field( + default=None, + metadata={"help": "max sample size to keep in training"}, + ) + min_sample_size: Optional[int] = field( + default=None, + metadata={"help": "min sample size to keep in training"}, + ) + max_trim_sample_size: Optional[int] = field( + default=II("task.max_sample_size"), + metadata={"help": "max sample size to trim to for batching"}, + ) + single_target: Optional[bool] = field( + default=False, + metadata={ + "help": "if set, AddTargetDatasets outputs same keys " + "as AddTargetDataset" + }, + ) + random_crop: Optional[bool] = field( + default=True, + metadata={"help": "always crop from the beginning if false"}, + ) + pad_audio: Optional[bool] = field( + default=False, + metadata={"help": "pad audio to the longest one in the batch if true"}, + ) + pdb: Optional[bool] = field( + default=False, + metadata={"help": "pdb"}, + ) + stack_order_audio: int = field( + default=1, + metadata={"help": "concatenate n consecutive audio frames for one step"}, + ) + skip_verify: Optional[bool] = field( + default=False, + metadata={"help": "skip verifying label-audio alignment"}, + ) + image_aug: bool = field(default=False, metadata={'help': 'image data augmentation'}) + image_crop_size: int = field( + default=88, metadata={"help": "image ROI size"}) + image_mean: float = field( + default=0.421, metadata={"help": "image mean"}) + image_std: float = field( + default=0.165, metadata={"help": "image std"}) + modalities: Optional[List[str]] = field(default_factory=lambda: ["audio", "video"], metadata={'help': 'modalities to load'}) + is_s2s: bool=field(default=False, metadata={'help': 'seq2seq fine-tuning only'}) + tokenizer_bpe_name: Optional[str] = field(default=None, metadata={'help': 'tokenizer model name'}) + tokenizer_bpe_model: Optional[str] = field(default=None, metadata={'help': 'tokenizer model path'}) + noise_wav: Optional[str] = field(default=None, metadata={'help': 'manifest of noise wav files (one wav file path per line)'}) + noise_prob: float = field(default=0, metadata={'help': 'noise probability'}) + noise_snr: Optional[str] = field(default='0', metadata={'help': 'noise SNR in audio'}) + noise_num: int = field(default=1, metadata={'help': 'number of noise wav files to mix'}) + fine_tuning: bool = field(default=False, metadata={"help": "set to true if fine-tuning AV-Hubert"}) + +@register_task("av_hubert_pretraining", dataclass=AVHubertPretrainingConfig) +class AVHubertPretrainingTask(FairseqTask): + + cfg: AVHubertPretrainingConfig + + def __init__( + self, + cfg: AVHubertPretrainingConfig, + ) -> None: + super().__init__(cfg) + + logger.info(f"current directory is {os.getcwd()}") + logger.info(f"AVHubertPretrainingTask Config {cfg}") + + self.fine_tuning = cfg.fine_tuning + if cfg.fine_tuning: + self.state.add_factory("target_dictionary", self.load_dictionaries) + if cfg.is_s2s: + self.state.add_factory("s2s_tokenizer", self.load_tokenizer) + else: + self.state.add_factory("dictionaries", self.load_dictionaries) + + self.blank_symbol = "" + + @property + def source_dictionary(self) -> Optional[Dictionary]: + return None # self._source_dictionary + + @property + def target_dictionary(self) -> Optional[Dictionary]: + return self.state.target_dictionary # self._target_dictionary + + @property + def dictionaries(self) -> List[Dictionary]: + return self.state.dictionaries + + def load_dictionaries(self): + label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir + dictionaries = [ + Dictionary.load(f"{label_dir}/dict.{label}.txt") + for label in self.cfg.labels + ] + return dictionaries[0] if self.cfg.fine_tuning else dictionaries + + def load_tokenizer(self): + bpe_args = Namespace(**{'bpe': self.cfg.tokenizer_bpe_name, f"{self.cfg.tokenizer_bpe_name}_model": self.cfg.tokenizer_bpe_model}) + bpe_tokenizer = encoders.build_bpe(bpe_args) + return bpe_tokenizer + + @property + def s2s_tokenizer(self): + return self.state.s2s_tokenizer + + @classmethod + def setup_task( + cls, cfg: AVHubertPretrainingConfig, **kwargs + ) -> "AVHubertPretrainingTask": + if cfg.pdb: + import pdb + pdb.set_trace() + return cls(cfg) + + def get_label_dir(self) -> str: + if self.cfg.label_dir is None: + return self.cfg.data + return self.cfg.label_dir + + def load_dataset(self, split: str, **kwargs) -> None: + manifest = f"{self.cfg.data}/{split}.tsv" + dictionaries = [self.target_dictionary] if self.fine_tuning else self.dictionaries + pad_list = [dictionary.pad() for dictionary in dictionaries] + eos_list = [dictionary.eos() for dictionary in dictionaries] + if not self.cfg.is_s2s: + procs = [LabelEncoder(dictionary) for dictionary in dictionaries] + else: + logger.info(f"Using tokenizer") + bpe_tokenizer = self.s2s_tokenizer + procs = [LabelEncoderS2SToken(dictionary, bpe_tokenizer) for dictionary in dictionaries] + paths = [ + f"{self.get_label_dir()}/{split}.{l}" for l in self.cfg.labels + ] + image_aug = self.cfg.image_aug if split == 'train' else False + noise_fn, noise_snr = f"{self.cfg.noise_wav}/{split}.tsv" if self.cfg.noise_wav is not None else None, eval(self.cfg.noise_snr) + noise_num = self.cfg.noise_num # + self.datasets[split] = AVHubertDataset( + manifest, + sample_rate=self.cfg.sample_rate, + label_paths=paths, + label_rates=self.cfg.label_rate, + pad_list=pad_list, + eos_list=eos_list, + label_processors=procs, + max_keep_sample_size=self.cfg.max_sample_size, + min_keep_sample_size=self.cfg.min_sample_size, + max_sample_size=self.cfg.max_trim_sample_size, + pad_audio=self.cfg.pad_audio, + normalize=self.cfg.normalize, + store_labels=False, + random_crop=self.cfg.random_crop, + single_target=self.cfg.single_target, + stack_order_audio=self.cfg.stack_order_audio, + skip_verify=self.cfg.skip_verify, + image_mean=self.cfg.image_mean, + image_std=self.cfg.image_std, + image_crop_size=self.cfg.image_crop_size, + image_aug=image_aug, + modalities=self.cfg.modalities, + is_s2s=self.cfg.is_s2s, + noise_fn=noise_fn, + noise_prob=self.cfg.noise_prob, + noise_snr=noise_snr, + noise_num=noise_num + ) + + def max_positions(self) -> Tuple[int, int]: + return (sys.maxsize, sys.maxsize) + + def filter_indices_by_size( + self, indices: np.array, *args, **kwargs + ) -> np.array: + return indices + + def build_generator( + self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None, + ): + """ + Build a :class:`~fairseq.SequenceGenerator` instance for this + task. + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + args (fairseq.dataclass.configs.GenerationConfig): + configuration object (dataclass) for generation + extra_gen_cls_kwargs (Dict[str, Any]): extra options to pass + through to SequenceGenerator + prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]]): + If provided, this function constrains the beam search to + allowed tokens only at each step. The provided function + should take 2 arguments: the batch ID (`batch_id: int`) + and a unidimensional tensor of token ids (`inputs_ids: + torch.Tensor`). It has to return a `List[int]` with the + allowed tokens for the next generation step conditioned + on the previously generated tokens (`inputs_ids`) and + the batch ID (`batch_id`). This argument is useful for + constrained generation conditioned on the prefix, as + described in "Autoregressive Entity Retrieval" + (https://arxiv.org/abs/2010.00904) and + https://github.com/facebookresearch/GENRE. + """ + if getattr(args, "score_reference", False): + from fairseq.sequence_scorer import SequenceScorer + + return SequenceScorer( + self.target_dictionary, + compute_alignment=getattr(args, "print_alignment", False), + ) + + # Choose search strategy. Defaults to Beam Search. + sampling = getattr(args, "sampling", False) + sampling_topk = getattr(args, "sampling_topk", -1) + sampling_topp = getattr(args, "sampling_topp", -1.0) + diverse_beam_groups = getattr(args, "diverse_beam_groups", -1) + diverse_beam_strength = getattr(args, "diverse_beam_strength", 0.5) + match_source_len = getattr(args, "match_source_len", False) + diversity_rate = getattr(args, "diversity_rate", -1) + constrained = getattr(args, "constraints", False) + if prefix_allowed_tokens_fn is None: + prefix_allowed_tokens_fn = getattr(args, "prefix_allowed_tokens_fn", None) + if ( + sum( + int(cond) + for cond in [ + sampling, + diverse_beam_groups > 0, + match_source_len, + diversity_rate > 0, + ] + ) + > 1 + ): + raise ValueError("Provided Search parameters are mutually exclusive.") + assert sampling_topk < 0 or sampling, "--sampling-topk requires --sampling" + assert sampling_topp < 0 or sampling, "--sampling-topp requires --sampling" + + if sampling: + search_strategy = search.Sampling( + self.target_dictionary, sampling_topk, sampling_topp + ) + elif diverse_beam_groups > 0: + search_strategy = search.DiverseBeamSearch( + self.target_dictionary, diverse_beam_groups, diverse_beam_strength + ) + elif match_source_len: + # this is useful for tagging applications where the output + # length should match the input length, so we hardcode the + # length constraints for simplicity + search_strategy = search.LengthConstrainedBeamSearch( + self.target_dictionary, + min_len_a=1, + min_len_b=0, + max_len_a=1, + max_len_b=0, + ) + elif diversity_rate > -1: + search_strategy = search.DiverseSiblingsSearch( + self.target_dictionary, diversity_rate + ) + elif constrained: + search_strategy = search.LexicallyConstrainedBeamSearch( + self.target_dictionary, args.constraints + ) + elif prefix_allowed_tokens_fn: + search_strategy = search.PrefixConstrainedBeamSearch( + self.target_dictionary, prefix_allowed_tokens_fn + ) + else: + search_strategy = search.BeamSearch(self.target_dictionary) + + extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} + if seq_gen_cls is None: + if getattr(args, "print_alignment", False): + seq_gen_cls = SequenceGeneratorWithAlignment + extra_gen_cls_kwargs["print_alignment"] = args.print_alignment + else: + seq_gen_cls = SequenceGenerator + + return seq_gen_cls( + models, + self.target_dictionary, + beam_size=getattr(args, "beam", 5), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 200), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + search_strategy=search_strategy, + **extra_gen_cls_kwargs, + ) diff --git a/av2unit/avhubert/resnet.py b/av2unit/avhubert/resnet.py new file mode 100644 index 0000000..e584f2b --- /dev/null +++ b/av2unit/avhubert/resnet.py @@ -0,0 +1,169 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +import torch.nn as nn +import pdb + + +logger = logging.getLogger(__name__) + +def conv3x3(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +def downsample_basic_block( inplanes, outplanes, stride ): + return nn.Sequential( + nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(outplanes), + ) + +def downsample_basic_block_v2( inplanes, outplanes, stride ): + return nn.Sequential( + nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False), + nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(outplanes), + ) + + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, relu_type = 'relu' ): + super(BasicBlock, self).__init__() + + assert relu_type in ['relu','prelu'] + + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + + if relu_type == 'relu': + self.relu1 = nn.ReLU(inplace=True) + self.relu2 = nn.ReLU(inplace=True) + elif relu_type == 'prelu': + self.relu1 = nn.PReLU(num_parameters=planes) + self.relu2 = nn.PReLU(num_parameters=planes) + else: + raise Exception('relu type not implemented') + + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + out = self.conv1(x) + out = self.bn1(out) + out = self.relu1(out) + out = self.conv2(out) + out = self.bn2(out) + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu2(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, relu_type = 'relu', gamma_zero = False, avg_pool_downsample = False): + self.inplanes = 64 + self.relu_type = relu_type + self.gamma_zero = gamma_zero + self.downsample_block = downsample_basic_block_v2 if avg_pool_downsample else downsample_basic_block + + super(ResNet, self).__init__() + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + if self.gamma_zero: + for m in self.modules(): + if isinstance(m, BasicBlock ): + m.bn2.weight.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + + + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = self.downsample_block( inplanes = self.inplanes, + outplanes = planes * block.expansion, + stride = stride ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, relu_type = self.relu_type)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes, relu_type = self.relu_type)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + return x + +class ResEncoder(nn.Module): + def __init__(self, relu_type, weights): + super(ResEncoder, self).__init__() + self.frontend_nout = 64 + self.backend_out = 512 + frontend_relu = nn.PReLU(num_parameters=self.frontend_nout) if relu_type == 'prelu' else nn.ReLU() + self.frontend3D = nn.Sequential( + nn.Conv3d(1, self.frontend_nout, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3), bias=False), + nn.BatchNorm3d(self.frontend_nout), + frontend_relu, + nn.MaxPool3d( kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1))) + self.trunk = ResNet(BasicBlock, [2, 2, 2, 2], relu_type=relu_type) + if weights is not None: + logger.info(f"Load {weights} for resnet") + std = torch.load(weights, map_location=torch.device('cpu'))['model_state_dict'] + frontend_std, trunk_std = OrderedDict(), OrderedDict() + for key, val in std.items(): + new_key = '.'.join(key.split('.')[1:]) + if 'frontend3D' in key: + frontend_std[new_key] = val + if 'trunk' in key: + trunk_std[new_key] = val + self.frontend3D.load_state_dict(frontend_std) + self.trunk.load_state_dict(trunk_std) + + def forward(self, x): + B, C, T, H, W = x.size() + x = self.frontend3D(x) + Tnew = x.shape[2] + x = self.threeD_to_2D_tensor(x) + x = self.trunk(x) + x = x.view(B, Tnew, x.size(1)) + x = x.transpose(1, 2).contiguous() + return x + + def threeD_to_2D_tensor(self, x): + n_batch, n_channels, s_time, sx, sy = x.shape + x = x.transpose(1, 2).contiguous() + return x.reshape(n_batch*s_time, n_channels, sx, sy) diff --git a/av2unit/avhubert/utils.py b/av2unit/avhubert/utils.py new file mode 100644 index 0000000..60d57fa --- /dev/null +++ b/av2unit/avhubert/utils.py @@ -0,0 +1,298 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import cv2 +import torch +import random +import numpy as np +from typing import Dict, List, Optional, Tuple + +def load_video(path): + for i in range(3): + try: + cap = cv2.VideoCapture(path) + frames = [] + while True: + ret, frame = cap.read() + if ret: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + frames.append(frame) + else: + break + frames = np.stack(frames) + return frames + except Exception: + print(f"failed loading {path} ({i} / 3)") + if i == 2: + raise ValueError(f"Unable to load {path}") + + +class Compose(object): + """Compose several preprocess together. + Args: + preprocess (list of ``Preprocess`` objects): list of preprocess to compose. + """ + + def __init__(self, preprocess): + self.preprocess = preprocess + + def __call__(self, sample): + for t in self.preprocess: + sample = t(sample) + return sample + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.preprocess: + format_string += '\n' + format_string += ' {0}'.format(t) + format_string += '\n)' + return format_string + + +class Normalize(object): + """Normalize a ndarray image with mean and standard deviation. + """ + + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, frames): + """ + Args: + tensor (Tensor): Tensor image of size (C, H, W) to be normalized. + Returns: + Tensor: Normalized Tensor image. + """ + frames = (frames - self.mean) / self.std + return frames + + def __repr__(self): + return self.__class__.__name__+'(mean={0}, std={1})'.format(self.mean, self.std) + +class CenterCrop(object): + """Crop the given image at the center + """ + def __init__(self, size): + self.size = size + + def __call__(self, frames): + """ + Args: + img (numpy.ndarray): Images to be cropped. + Returns: + numpy.ndarray: Cropped image. + """ + t, h, w = frames.shape + th, tw = self.size + delta_w = int(round((w - tw))/2.) + delta_h = int(round((h - th))/2.) + frames = frames[:, delta_h:delta_h+th, delta_w:delta_w+tw] + return frames + + +class RandomCrop(object): + """Crop the given image at the center + """ + + def __init__(self, size): + self.size = size + + def __call__(self, frames): + """ + Args: + img (numpy.ndarray): Images to be cropped. + Returns: + numpy.ndarray: Cropped image. + """ + t, h, w = frames.shape + th, tw = self.size + delta_w = random.randint(0, w-tw) + delta_h = random.randint(0, h-th) + frames = frames[:, delta_h:delta_h+th, delta_w:delta_w+tw] + return frames + + def __repr__(self): + return self.__class__.__name__ + '(size={0})'.format(self.size) + +class HorizontalFlip(object): + """Flip image horizontally. + """ + + def __init__(self, flip_ratio): + self.flip_ratio = flip_ratio + + def __call__(self, frames): + """ + Args: + img (numpy.ndarray): Images to be flipped with a probability flip_ratio + Returns: + numpy.ndarray: Cropped image. + """ + t, h, w = frames.shape + if random.random() < self.flip_ratio: + for index in range(t): + frames[index] = cv2.flip(frames[index], 1) + return frames + +def compute_mask_indices( + shape: Tuple[int, int], + padding_mask: Optional[torch.Tensor], + mask_prob: float, + mask_length: int, + mask_type: str = "static", + mask_other: float = 0.0, + min_masks: int = 0, + no_overlap: bool = False, + min_space: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape + Args: + shape: the the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements + mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_type: how to compute mask lengths + static = fixed size + uniform = sample from uniform distribution [mask_other, mask_length*2] + normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element + poisson = sample from possion distribution with lambda = mask length + min_masks: minimum number of masked spans + no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping + min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans + """ + + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + all_num_mask = int( + # add a random number for probabilistic rounding + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + + all_num_mask = max(min_masks, all_num_mask) + + mask_idcs = [] + for i in range(bsz): + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + num_mask = int( + # add a random number for probabilistic rounding + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + sz = all_sz + num_mask = all_num_mask + + if mask_type == "static": + lengths = np.full(num_mask, mask_length) + elif mask_type == "uniform": + lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask) + elif mask_type == "normal": + lengths = np.random.normal(mask_length, mask_other, size=num_mask) + lengths = [max(1, int(round(x))) for x in lengths] + elif mask_type == "poisson": + lengths = np.random.poisson(mask_length, size=num_mask) + lengths = [int(round(x)) for x in lengths] + else: + raise Exception("unknown mask selection " + mask_type) + + if sum(lengths) == 0: + lengths[0] = min(mask_length, sz - 1) + + if no_overlap: + mask_idc = [] + + def arrange(s, e, length, keep_length): + span_start = np.random.randint(s, e - length) + mask_idc.extend(span_start + i for i in range(length)) + + new_parts = [] + if span_start - s - min_space >= keep_length: + new_parts.append((s, span_start - min_space + 1)) + if e - span_start - keep_length - min_space > keep_length: + new_parts.append((span_start + length + min_space, e)) + return new_parts + + parts = [(0, sz)] + min_length = min(lengths) + for length in sorted(lengths, reverse=True): + lens = np.fromiter( + (e - s if e - s >= length + min_space else 0 for s, e in parts), + np.int, + ) + l_sum = np.sum(lens) + if l_sum == 0: + break + probs = lens / np.sum(lens) + c = np.random.choice(len(parts), p=probs) + s, e = parts.pop(c) + parts.extend(arrange(s, e, length, min_length)) + mask_idc = np.asarray(mask_idc) + else: + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + + mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) + + mask_idc = np.asarray( + [ + mask_idc[j] + offset + for j in range(len(mask_idc)) + for offset in range(lengths[j]) + ] + ) + + mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) + + min_len = min([len(m) for m in mask_idcs]) + batch_indexes, starts, ends = [], [], [] + for i, mask_idc in enumerate(mask_idcs): + if len(mask_idc) > min_len: + mask_idc = np.random.choice(mask_idc, min_len, replace=False) + mask[i, mask_idc] = True + vals, run_starts, run_lengths = find_runs(mask[i]) + start_indices, lengths = run_starts[vals == True], run_lengths[vals == True] + starts.append(start_indices) + ends.append(start_indices+lengths) + batch_indexes.append(np.zeros([len(start_indices)])+i) + return mask, np.concatenate(starts).astype(np.int64), np.concatenate(ends).astype(np.int64), np.concatenate(batch_indexes).astype(np.int64) + +def find_runs(x): + """Find runs of consecutive items in an array.""" + + # ensure array + x = np.asanyarray(x) + if x.ndim != 1: + raise ValueError('only 1D array supported') + n = x.shape[0] + + # handle empty array + if n == 0: + return np.array([]), np.array([]), np.array([]) + + else: + # find run starts + loc_run_start = np.empty(n, dtype=bool) + loc_run_start[0] = True + np.not_equal(x[:-1], x[1:], out=loc_run_start[1:]) + run_starts = np.nonzero(loc_run_start)[0] + + # find run values + run_values = x[loc_run_start] + + # find run lengths + run_lengths = np.diff(np.append(run_starts, n)) + + return run_values, run_starts, run_lengths diff --git a/av2unit/inference.py b/av2unit/inference.py new file mode 100644 index 0000000..823d184 --- /dev/null +++ b/av2unit/inference.py @@ -0,0 +1,79 @@ +import os +import argparse +import numpy as np +import torch +import torch.nn.functional as F + +from fairseq import checkpoint_utils, utils + +from util import process_units, save_unit, extract_audio_from_video +from av2unit.task import AVHubertUnitPretrainingTask + +def load_model(model_path, modalities, use_cuda=False): + models, cfg, task = checkpoint_utils.load_model_ensemble_and_task([model_path]) + + for model in models: + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: + model.cuda() + model.prepare_for_inference_(cfg) + + task.cfg.modalities = modalities.split(",") + task.load_dataset() + + return models[0], task + +def main(args): + use_cuda = torch.cuda.is_available() and not args.cpu + + model, task = load_model(args.av2unit_path, args.modalities, use_cuda=use_cuda) + + temp_audio_path = os.path.splitext(args.in_vid_path)[0]+".temp.wav" + lip_video_path = os.path.splitext(args.in_vid_path)[0]+".lip.mp4" + extract_audio_from_video(args.in_vid_path, temp_audio_path) + + video_feats, audio_feats = task.dataset.load_feature((lip_video_path, temp_audio_path)) + audio_feats, video_feats = torch.from_numpy(audio_feats.astype(np.float32)) if audio_feats is not None else None, torch.from_numpy(video_feats.astype(np.float32)) if video_feats is not None else None + if task.dataset.normalize and 'audio' in task.dataset.modalities: + with torch.no_grad(): + audio_feats = F.layer_norm(audio_feats, audio_feats.shape[1:]) + + collated_audios, _, _ = task.dataset.collater_audio([audio_feats], len(audio_feats)) + collated_videos, _, _ = task.dataset.collater_audio([video_feats], len(video_feats)) + + sample = {"source": { + "audio": collated_audios, "video": collated_videos, + }} + sample = utils.move_to_cuda(sample) if use_cuda else sample + + pred = task.inference( + model, + sample, + ) + pred_str = task.dictionaries[0].string(pred.int().cpu()) + + save_unit(pred_str, args.out_unit_path) + os.remove(temp_audio_path) + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-vid-path", type=str, required=True, help="File path of source video input" + ) + parser.add_argument( + "--out-unit-path", type=str, required=True, help="File path of target unit output" + ) + parser.add_argument( + "--av2unit-path", type=str, required=True, help="path to the mAV-HuBERT pre-trained model" + ) + parser.add_argument( + "--modalities", type=str, default="audio,video", help="input modalities", + choices=["audio,video","audio","video"], + ) + parser.add_argument("--cpu", action="store_true", help="run on CPU") + + args = parser.parse_args() + + main(args) + +if __name__ == "__main__": + cli_main() diff --git a/av2unit/task.py b/av2unit/task.py new file mode 100644 index 0000000..330fb80 --- /dev/null +++ b/av2unit/task.py @@ -0,0 +1,68 @@ +import torch + +from av2unit.avhubert.hubert_pretraining import * +from av2unit.avhubert.hubert_dataset import * + +class AVHubertUnitDataset(AVHubertDataset): + def __init__( + self, + sample_rate: float, + normalize: bool = False, + stack_order_audio: int=1, + image_mean: float=0, + image_std: float=1, + image_crop_size: int=88, + image_aug: bool=False, + modalities: Optional[List[str]]=None, + noise_prob=0, + ): + self.audio_root = "" + + self.modalities = set(modalities) + self.sample_rate = sample_rate + self.stack_order_audio = stack_order_audio + + self.noise_prob = noise_prob + + self.normalize = normalize + if image_aug: + self.transform = custom_utils.Compose([ + custom_utils.Normalize( 0.0,255.0 ), + custom_utils.RandomCrop((image_crop_size, image_crop_size)), + custom_utils.HorizontalFlip(0.5), + custom_utils.Normalize(image_mean, image_std) ]) + else: + self.transform = custom_utils.Compose([ + custom_utils.Normalize( 0.0,255.0 ), + custom_utils.CenterCrop((image_crop_size, image_crop_size)), + custom_utils.Normalize(image_mean, image_std) ]) + logger.info(f"image transform: {self.transform}") + +@register_task("av_hubert_unit_pretraining", dataclass=AVHubertPretrainingConfig) +class AVHubertUnitPretrainingTask(AVHubertPretrainingTask): + def load_dataset(self) -> None: + self.dataset = AVHubertUnitDataset( + sample_rate=self.cfg.sample_rate, + normalize=self.cfg.normalize, + stack_order_audio=self.cfg.stack_order_audio, + image_mean=self.cfg.image_mean, + image_std=self.cfg.image_std, + image_crop_size=self.cfg.image_crop_size, + modalities=self.cfg.modalities, + ) + def inference(self, model, sample): + x, padding_mask = model.extract_finetune(**sample) + + label_embs_list = model.label_embs_concat.split(model.num_classes, 0) + proj_x = model.final_proj(x) + if model.untie_final_proj: + proj_x_list = proj_x.chunk(len(model.num_classes), dim=-1) + else: + proj_x_list = [proj_x for _ in model.num_classes] + logit_list = [model.compute_logits(proj, emb).view(-1, num_class) for proj, emb, num_class in zip(proj_x_list, label_embs_list, model.num_classes)] # [[B*T, V]] + + pred_even = logit_list[0].argmax(dim=-1).cpu() + pred_odd = logit_list[1].argmax(dim=-1).cpu() + pred = torch.stack([pred_even, pred_odd]).transpose(0,1).reshape(-1) + + return pred diff --git a/dict.txt b/dict.txt new file mode 100644 index 0000000..dd3cccd --- /dev/null +++ b/dict.txt @@ -0,0 +1,1000 @@ +0 1 +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 1 +21 1 +22 1 +23 1 +24 1 +25 1 +26 1 +27 1 +28 1 +29 1 +30 1 +31 1 +32 1 +33 1 +34 1 +35 1 +36 1 +37 1 +38 1 +39 1 +40 1 +41 1 +42 1 +43 1 +44 1 +45 1 +46 1 +47 1 +48 1 +49 1 +50 1 +51 1 +52 1 +53 1 +54 1 +55 1 +56 1 +57 1 +58 1 +59 1 +60 1 +61 1 +62 1 +63 1 +64 1 +65 1 +66 1 +67 1 +68 1 +69 1 +70 1 +71 1 +72 1 +73 1 +74 1 +75 1 +76 1 +77 1 +78 1 +79 1 +80 1 +81 1 +82 1 +83 1 +84 1 +85 1 +86 1 +87 1 +88 1 +89 1 +90 1 +91 1 +92 1 +93 1 +94 1 +95 1 +96 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+955 1 +956 1 +957 1 +958 1 +959 1 +960 1 +961 1 +962 1 +963 1 +964 1 +965 1 +966 1 +967 1 +968 1 +969 1 +970 1 +971 1 +972 1 +973 1 +974 1 +975 1 +976 1 +977 1 +978 1 +979 1 +980 1 +981 1 +982 1 +983 1 +984 1 +985 1 +986 1 +987 1 +988 1 +989 1 +990 1 +991 1 +992 1 +993 1 +994 1 +995 1 +996 1 +997 1 +998 1 +999 1 diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..9b48f0a --- /dev/null +++ b/environment.yml @@ -0,0 +1,9 @@ +name: netflips +channels: + - conda-forge + - defaults +dependencies: + - python=3.8 + - pip + - ffmpeg + - ninja \ No newline at end of file diff --git a/fairseq/.github/CODEOWNERS b/fairseq/.github/CODEOWNERS new file mode 100644 index 0000000..b79aa2f --- /dev/null +++ b/fairseq/.github/CODEOWNERS @@ -0,0 +1,21 @@ +# Setting up CODEOWNERS for UST related codebase +# Documentation for open sourced models relevant to UST +examples/speech_to_text @kahne @sravyapopuri388 @jmp84 +examples/speech_to_speech @an918tw @sravyapopuri388 @jmp84 +examples/speech_synthesis @kahne @jmp84 +examples/simultaneous_translation @kahne @jmp84 +examples/speech_text_joint_to_text @yuntang @jmp84 + +# Speech related models relevant to UST +fairseq/models/speech_to_speech @sravyapopuri388 @jmp84 +fairseq/models/speech_to_text @kahne @sravyapopuri388 @jmp84 +fairseq/models/text_to_speech @kahne @jmp84 + +# CONFORMER IMPLEMENTATION +fairseq/modules/conformer_layer.py @sravyapopuri388 @jmp84 +fairseq/modules/espnet_multihead_attention.py @sravyapopuri388 @jmp84 +fairseq/modules/rotary_positional_embedding.py @sravyapopuri388 @jmp84 +fairseq/modules/positional_encoding.py @sravyapopuri388 @jmp84 + +# Machine Translation/NLLB +fairseq/tasks/translation.py @gwenzek diff --git a/fairseq/.github/ISSUE_TEMPLATE.md b/fairseq/.github/ISSUE_TEMPLATE.md new file mode 100644 index 0000000..5c4c449 --- /dev/null +++ b/fairseq/.github/ISSUE_TEMPLATE.md @@ -0,0 +1,3 @@ +## 👉 [Please follow one of these issue templates](https://github.com/pytorch/fairseq/issues/new/choose) 👈 + +Note: to keep the backlog clean and actionable, issues may be immediately closed if they do not follow one of the above issue templates. diff --git a/fairseq/.github/ISSUE_TEMPLATE/bug_report.md b/fairseq/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..aa15123 --- /dev/null +++ b/fairseq/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,43 @@ +--- +name: 🐛 Bug Report +about: Submit a bug report to help us improve +labels: 'bug, needs triage' +--- + +## 🐛 Bug + + + +### To Reproduce + +Steps to reproduce the behavior (**always include the command you ran**): + +1. Run cmd '....' +2. See error + + + + +#### Code sample + + +### Expected behavior + + + +### Environment + + - fairseq Version (e.g., 1.0 or main): + - PyTorch Version (e.g., 1.0) + - OS (e.g., Linux): + - How you installed fairseq (`pip`, source): + - Build command you used (if compiling from source): + - Python version: + - CUDA/cuDNN version: + - GPU models and configuration: + - Any other relevant information: + +### Additional context + + diff --git a/fairseq/.github/ISSUE_TEMPLATE/documentation.md b/fairseq/.github/ISSUE_TEMPLATE/documentation.md new file mode 100644 index 0000000..3a6e2e9 --- /dev/null +++ b/fairseq/.github/ISSUE_TEMPLATE/documentation.md @@ -0,0 +1,15 @@ +--- +name: 📚 Documentation/Typos +about: Report an issue related to documentation or a typo +labels: 'documentation, needs triage' +--- + +## 📚 Documentation + +For typos and doc fixes, please go ahead and: + +1. Create an issue. +2. Fix the typo. +3. Submit a PR. + +Thanks! diff --git a/fairseq/.github/ISSUE_TEMPLATE/feature_request.md b/fairseq/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..93c8668 --- /dev/null +++ b/fairseq/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,24 @@ +--- +name: 🚀 Feature Request +about: Submit a proposal/request for a new feature +labels: 'enhancement, help wanted, needs triage' +--- + +## 🚀 Feature Request + + +### Motivation + + + +### Pitch + + + +### Alternatives + + + +### Additional context + + diff --git a/fairseq/.github/ISSUE_TEMPLATE/how-to-question.md b/fairseq/.github/ISSUE_TEMPLATE/how-to-question.md new file mode 100644 index 0000000..04f3f15 --- /dev/null +++ b/fairseq/.github/ISSUE_TEMPLATE/how-to-question.md @@ -0,0 +1,33 @@ +--- +name: ❓ Questions/Help +about: If you have questions, please first search existing issues and docs +labels: 'question, needs triage' +--- + +## ❓ Questions and Help + +### Before asking: +1. search the issues. +2. search the docs. + + + +#### What is your question? + +#### Code + + + +#### What have you tried? + +#### What's your environment? + + - fairseq Version (e.g., 1.0 or main): + - PyTorch Version (e.g., 1.0) + - OS (e.g., Linux): + - How you installed fairseq (`pip`, source): + - Build command you used (if compiling from source): + - Python version: + - CUDA/cuDNN version: + - GPU models and configuration: + - Any other relevant information: diff --git a/fairseq/.github/PULL_REQUEST_TEMPLATE.md b/fairseq/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..d005e2d --- /dev/null +++ b/fairseq/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,16 @@ +# Before submitting + +- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) +- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)? +- [ ] Did you make sure to update the docs? +- [ ] Did you write any new necessary tests? + +## What does this PR do? +Fixes # (issue). + +## PR review +Anyone in the community is free to review the PR once the tests have passed. +If we didn't discuss your PR in Github issues there's a high chance it will not be merged. + +## Did you have fun? +Make sure you had fun coding 🙃 diff --git a/fairseq/.github/stale.yml b/fairseq/.github/stale.yml new file mode 100644 index 0000000..b12867d --- /dev/null +++ b/fairseq/.github/stale.yml @@ -0,0 +1,30 @@ +# Configuration for probot-stale - https://github.com/probot/stale +# Mostly copied from github.com/facebook/react/blob/master/.github/stale.yml +# Number of days of inactivity before an issue becomes stale +daysUntilStale: 90 +# Number of days of inactivity before a stale issue is closed +daysUntilClose: 7 +# Issues with these labels will never be considered stale +exemptLabels: + - bug +# Label to use when marking an issue as stale +staleLabel: stale +issues: + # Comment to post when marking an issue as stale. + markComment: > + This issue has been automatically marked as stale. + **If this issue is still affecting you, please leave any comment** (for example, "bump"), and we'll keep it open. + We are sorry that we haven't been able to prioritize it yet. If you have any new additional information, please include it with your comment! + # Comment to post when closing a stale issue. + closeComment: > + Closing this issue after a prolonged period of inactivity. If this issue is still present in the latest release, please create a new issue with up-to-date information. Thank you! +pulls: + # Comment to post when marking a pull request as stale. + markComment: > + This pull request has been automatically marked as stale. + **If this pull request is still relevant, please leave any comment** (for example, "bump"), and we'll keep it open. + We are sorry that we haven't been able to prioritize reviewing it yet. Your contribution is very much appreciated. + # Comment to post when closing a stale pull request. + closeComment: > + Closing this pull request after a prolonged period of inactivity. If this issue is still present in the latest release, please ask for this pull request to be reopened. Thank you! + diff --git a/fairseq/.github/workflows/build.yml b/fairseq/.github/workflows/build.yml new file mode 100644 index 0000000..036233d --- /dev/null +++ b/fairseq/.github/workflows/build.yml @@ -0,0 +1,81 @@ +name: build + +on: + # Trigger the workflow on push to main or any pull request + push: + branches: + - main + pull_request: + +jobs: + build: + + strategy: + max-parallel: 4 + matrix: + platform: [ubuntu-latest, macos-latest] + python-version: [3.8, 3.9] + + runs-on: ${{ matrix.platform }} + + steps: + - uses: actions/checkout@v2 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Conditionally install pytorch + if: matrix.platform == 'windows-latest' + run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html + + - name: Install locally + run: | + python -m pip install --upgrade pip + git submodule update --init --recursive + python -m pip install . + + - name: Check installation + working-directory: /tmp + run: python $GITHUB_WORKSPACE/scripts/check_installation.py + + - name: Install optional test requirements + run: | + python -m pip install '.[dev,docs]' + python -m pip install iopath transformers pyarrow + python -m pip install git+https://github.com/facebookresearch/fairscale.git@main + python -m pip install pygit2 pgzip + + - name: Install xformers for Macos + if: matrix.platform == 'macos-latest' + run: | + brew install llvm libomp + CC=/usr/local/opt/llvm/bin/clang CXX=clang++ pip install git+https://github.com/facebookresearch/xformers.git@main + + - name: Install xformers for non-MacOS + if: matrix.platform != 'macos-latest' + run: | + python -m pip install --progress-bar off git+https://github.com/facebookresearch/xformers.git@main + + - name: Lint with black + run: black --check --diff . + + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + + - name: Build doc + run: make singlehtml + working-directory: docs/ + + - name: Run tests + # When installing in non-editable mode, the .so files will be generated in 'site-packages/fairseq'. + # But by default, pytest import machinery will load local fairseq, and won't see the .so. + # Use --import-mode=append to favorize the 'site-packages/fairseq'. + # https://docs.pytest.org/en/7.1.x/explanation/pythonpath.html + run: pytest --import-mode=append -vvv tests/ + diff --git a/fairseq/.github/workflows/depreview.yml b/fairseq/.github/workflows/depreview.yml new file mode 100644 index 0000000..032edde --- /dev/null +++ b/fairseq/.github/workflows/depreview.yml @@ -0,0 +1,14 @@ +name: 'Dependency Review' +on: [pull_request] + +permissions: + contents: read + +jobs: + dependency-review: + runs-on: ubuntu-latest + steps: + - name: 'Checkout Repository' + uses: actions/checkout@v4 + - name: Dependency Review + uses: actions/dependency-review-action@v4 diff --git a/fairseq/.github/workflows/release.yml b/fairseq/.github/workflows/release.yml new file mode 100644 index 0000000..241b74b --- /dev/null +++ b/fairseq/.github/workflows/release.yml @@ -0,0 +1,161 @@ +name: Fairseq Release + +on: + workflow_dispatch: + inputs: + name: + description: 'Release Type' + default: 'patch' + required: true + +jobs: + + get_next_version: + runs-on: ubuntu-latest + steps: + - name: checkout-repo-content + uses: actions/checkout@v2 + + - name: setup-python + uses: actions/setup-python@v2 + with: + python-version: 3.8 + + - name: get next version and tag + id: get-next-version-and-tag + run: | + output=$(python3 release_utils.py --release-type ${{ github.event.inputs.name }}) + echo $output + new_version=$(echo $output | awk '{print $1}') + new_tag=$(echo $output | awk '{print $2}') + echo "new version is $new_version" + echo "new tag is $new_tag" + echo ::set-output name=version::$new_version + echo ::set-output name=tag::$new_tag + echo ::set-output name=branch_name::$new_version-release + echo "NEW_TAG=$new_tag" >> $GITHUB_ENV + echo "NEW_BRANCH=$new_version-release" >> $GITHUB_ENV + + + # update the version number in version.txt + - name: update version + id: update-version + run : | + echo "current folder = $PWD" + echo "current branch = $(git branch --show-current)" + output=$(python3 release_utils.py --release-type ${{ github.event.inputs.name }} --update-version) + + - name: add and commit + uses: EndBug/add-and-commit@v9 + with: + author_name: ${{ secrets.AUTHOR_NAME }} + author_email: ${{ secrets.AUTHOR_EMAIL }} + + # TODO: change this to main once shipit is disabled. + new_branch: '${{ env.NEW_BRANCH }}' + default_author: github_actor + message: '${{ env.NEW_TAG }} release' + pathspec_error_handling: exitAtEnd + + # Arguments for the git pull command. Use NO-PULL to avoid the action pulling at all. + # pull: 'NO-PULL' + tag: '${{ env.NEW_TAG }}' + + outputs: + new_version: ${{ steps.get-next-version-and-tag.outputs.version }} + new_tag: ${{ steps.get-next-version-and-tag.outputs.tag }} + branch_name: ${{ steps.get-next-version-and-tag.outputs.branch_name }} + + create_sdist: + runs-on: ubuntu-latest + name: Create Source Distribution + needs: get_next_version + steps: + - uses: actions/checkout@v3 + with: + ref: ${{ needs.get_next_version.outputs.branch_name }} + + - name: Install Python + uses: actions/setup-python@v2 + with: + python-version: '3.8' + + - name: Upgrade pip + run: | + python3 -m pip install --upgrade pip + + - name: Create Source Distribution + run: | + python3 -m pip install setuptools wheel twine torch + python3 setup.py sdist + + - uses: actions/upload-artifact@v2 + with: + path: dist/*.tar.gz + + build_wheels: + name: Build wheels on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + needs: get_next_version + strategy: + matrix: + os: [ubuntu-latest, macos-latest] + + steps: + - uses: actions/checkout@v3 + with: + ref: ${{ needs.get_next_version.outputs.branch_name }} + + - name: Install Python + uses: actions/setup-python@v2 + with: + python-version: '3.8' + + - name: Upgrade pip + run: | + python3 -m pip install --upgrade pip + + - name: Install cibuildwheel + run: | + python3 -m pip install cibuildwheel + + - name: Build wheels for CPython + run: | + python3 -m cibuildwheel --output-dir dist + env: + CIBW_BUILD: "cp38-*64" + CIBW_MANYLINUX_X86_64_IMAGE: manylinux1 + CIBW_BEFORE_BUILD: git submodule update --init --recursive && pip install . + # Install system library + CIBW_BEFORE_BUILD_LINUX: (yum install -y libffi-devel || apt-get install -y libffi-devel || apk add --update --no-cache libffi-devel || true) && (yum install -y libc6 || apt-get install -y libc6 || apk add --update --no-cache libc6 || true) + CIBW_ENVIRONMENT: "PIP_ONLY_BINARY=numpy" + CIBW_SKIP: "*musllinux*" + + - uses: actions/upload-artifact@v2 + with: + path: dist + + upload: + name: Upload to PyPi and create release + runs-on: ubuntu-latest + needs: [build_wheels, create_sdist, get_next_version] + steps: + - uses: actions/download-artifact@v2 + with: + name: artifact + path: dist + + # build the PyPI package and upload it + - name: upload + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + pip install setuptools wheel twine + python3 -m twine upload --repository pypi dist/* + + # create the release on github + - name: create release on github + uses: ncipollo/release-action@v1 + with: + tag: '${{ needs.get_next_version.outputs.new_tag }}' diff --git a/fairseq/.gitignore b/fairseq/.gitignore new file mode 100644 index 0000000..4be1363 --- /dev/null +++ b/fairseq/.gitignore @@ -0,0 +1,141 @@ +# JetBrains PyCharm IDE +.idea/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# macOS dir files +.DS_Store + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Checkpoints +checkpoints + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# Generated files +/fairseq/temporal_convolution_tbc +/fairseq/modules/*_layer/*_forward.cu +/fairseq/modules/*_layer/*_backward.cu +/fairseq/version.py + +# data +data-bin/ + +# reranking +/examples/reranking/rerank_data + +# Cython-generated C++ source files +/fairseq/data/data_utils_fast.cpp +/fairseq/data/token_block_utils_fast.cpp + +# VSCODE +.vscode/ftp-sync.json +.vscode/settings.json + +# Experimental Folder +experimental/* + +# Weights and Biases logs +wandb/ + +# Hydra artifacts +nohup.out +multirun +outputs diff --git a/fairseq/.gitmodules b/fairseq/.gitmodules new file mode 100644 index 0000000..07a55d4 --- /dev/null +++ b/fairseq/.gitmodules @@ -0,0 +1,4 @@ +[submodule "fairseq/model_parallel/megatron"] + path = fairseq/model_parallel/megatron + url = https://github.com/ngoyal2707/Megatron-LM + branch = fairseq diff --git a/fairseq/.pre-commit-config.yaml b/fairseq/.pre-commit-config.yaml new file mode 100644 index 0000000..6b1d6ae --- /dev/null +++ b/fairseq/.pre-commit-config.yaml @@ -0,0 +1,40 @@ +exclude: 'build|stubs' + +default_language_version: + python: python3 + +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.1.0 + hooks: + - id: trailing-whitespace + - id: check-ast + - id: check-merge-conflict + - id: no-commit-to-branch + args: ['--branch=master'] + - id: check-added-large-files + args: ['--maxkb=500'] + - id: end-of-file-fixer + +- repo: https://github.com/ambv/black + rev: 22.3.0 + hooks: + - id: black + language_version: python3.8 + +- repo: https://gitlab.com/pycqa/flake8 + rev: 3.9.2 + hooks: + - id: flake8 + args: [ + # only error for syntax errors and undefined names + "--select=E9,F63,F7,F82", + ] + +- repo: https://github.com/pycqa/isort + rev: 5.10.1 + hooks: + - id: isort + exclude: README.md + additional_dependencies: [toml] + args: ["--profile", "black"] diff --git a/fairseq/CODE_OF_CONDUCT.md b/fairseq/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..a0cbeaa --- /dev/null +++ b/fairseq/CODE_OF_CONDUCT.md @@ -0,0 +1,77 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq + diff --git a/fairseq/CONTRIBUTING.md b/fairseq/CONTRIBUTING.md new file mode 100644 index 0000000..60e9025 --- /dev/null +++ b/fairseq/CONTRIBUTING.md @@ -0,0 +1,82 @@ +# Contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq) +We want to make contributing to this project as easy and transparent as +possible. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from `main`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. +5. Make sure your code lints. +6. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +## License +By contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq), +you agree that your contributions will be licensed under the LICENSE file in +the root directory of this source tree. + +## Pre-commit hooks +In order to ensure your code lints, there are pre-commit hooks configured in the repository which you can install. +After installation, they will automatically run each time you commit. +An abbreviated guide is given below; for more information, refer to [the offical pre-commit documentation](https://pre-commit.com/). + +### Installation +``` +pip install pre-commit +pre-commit install +``` + +### Usage +Just commit your changes: +``` +git commit -m "My informative commit message" +``` + +If there was a failure, you will get feedback +``` +[INFO] Initializing environment for https://github.com/PyCQA/flake8. +[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks. +[INFO] Once installed this environment will be reused. +[INFO] This may take a few minutes... +[INFO] Installing environment for https://github.com/PyCQA/flake8. +[INFO] Once installed this environment will be reused. +[INFO] This may take a few minutes... +Trim Trailing Whitespace.................................................Failed +- hook id: trailing-whitespace +- exit code: 1 +- files were modified by this hook +Fixing examples/nllb/modeling/wmt15_benchmark/eval_langs2.sh +Fix End of Files.........................................................Failed +- hook id: end-of-file-fixer +- exit code: 1 +- files were modified by this hook +Fixing examples/few_shot/scripts/schedule_jobs_few_shot.py +flake8...................................................................Passed +``` + +Certain hooks modify your files to comply. +To include these modifications, you will need to add them (i.e. `git add ...`) and commit again. + +If all is well, you should see something like: +``` +Trim Trailing Whitespace.................................................Passed +Fix End of Files.........................................................Passed +flake8...................................................................Passed +[gshard-fix-ci 8698644e1] Fix lint, add pre-commit hooks + 10 files changed, 148 insertions(+), 110 deletions(-) + create mode 100644 .flake8 + create mode 100644 .pre-commit-config.yaml + rename examples/nllb/modeling/wmt15_benchmark/{eval_langs2.py => eval_langs2.sh} (99%) + ``` diff --git a/fairseq/LICENSE b/fairseq/LICENSE new file mode 100644 index 0000000..b96dcb0 --- /dev/null +++ b/fairseq/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Facebook, Inc. and its affiliates. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/fairseq/MANIFEST.in b/fairseq/MANIFEST.in new file mode 100644 index 0000000..4f719da --- /dev/null +++ b/fairseq/MANIFEST.in @@ -0,0 +1 @@ +include fairseq/version.txt diff --git a/fairseq/README.md b/fairseq/README.md new file mode 100644 index 0000000..1150c66 --- /dev/null +++ b/fairseq/README.md @@ -0,0 +1,242 @@ +

+ +
+
+ Support Ukraine + MIT License + Latest Release + Build Status + Documentation Status + CicleCI Status +

+ +-------------------------------------------------------------------------------- + +Fairseq(-py) is a sequence modeling toolkit that allows researchers and +developers to train custom models for translation, summarization, language +modeling and other text generation tasks. + +We provide reference implementations of various sequence modeling papers: + +
List of implemented papers

+ +* **Convolutional Neural Networks (CNN)** + + [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md) + + [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) + + [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) + + [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) + + [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) +* **LightConv and DynamicConv models** + + [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) +* **Long Short-Term Memory (LSTM) networks** + + Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015) +* **Transformer (self-attention) networks** + + Attention Is All You Need (Vaswani et al., 2017) + + [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) + + [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) + + [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/README.adaptive_inputs.md) + + [Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)](examples/constrained_decoding/README.md) + + [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (Dai et al., 2019)](examples/truncated_bptt/README.md) + + [Adaptive Attention Span in Transformers (Sukhbaatar et al., 2019)](examples/adaptive_span/README.md) + + [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) + + [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) + + [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) + + [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md ) + + [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) + + [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) + + [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) + + [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) + + [Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)](examples/pointer_generator/README.md) + + [Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)](examples/linformer/README.md) + + [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) + + [Deep Transformers with Latent Depth (Li et al., 2020)](examples/latent_depth/README.md) + + [Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)](https://arxiv.org/abs/2006.13979) + + [Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020)](https://arxiv.org/abs/2010.11430) + + [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)](https://arxiv.org/abs/2104.01027) + + [Unsupervised Speech Recognition (Baevski, et al., 2021)](https://arxiv.org/abs/2105.11084) + + [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021)](https://arxiv.org/abs/2109.11680) + + [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (Xu et. al., 2021)](https://arxiv.org/pdf/2109.14084.pdf) + + [VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding (Xu et. al., 2021)](https://aclanthology.org/2021.findings-acl.370.pdf) + + [NormFormer: Improved Transformer Pretraining with Extra Normalization (Shleifer et. al, 2021)](examples/normformer/README.md) +* **Non-autoregressive Transformers** + + Non-Autoregressive Neural Machine Translation (Gu et al., 2017) + + Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018) + + Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019) + + Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019) + + [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) +* **Finetuning** + + [Better Fine-Tuning by Reducing Representational Collapse (Aghajanyan et al. 2020)](examples/rxf/README.md) + +

+ +### What's New: +* May 2023 [Released models for Scaling Speech Technology to 1,000+ Languages (Pratap, et al., 2023)](examples/mms/README.md) +* June 2022 [Released code for wav2vec-U 2.0 from Towards End-to-end Unsupervised Speech Recognition (Liu, et al., 2022)](examples/wav2vec/unsupervised/README.md) +* May 2022 [Integration with xFormers](https://github.com/facebookresearch/xformers) +* December 2021 [Released Direct speech-to-speech translation code](examples/speech_to_speech/README.md) +* October 2021 [Released VideoCLIP and VLM models](examples/MMPT/README.md) +* October 2021 [Released multilingual finetuned XLSR-53 model](examples/wav2vec/README.md) +* September 2021 [`master` branch renamed to `main`](https://github.com/github/renaming). +* July 2021 [Released DrNMT code](examples/discriminative_reranking_nmt/README.md) +* July 2021 [Released Robust wav2vec 2.0 model](examples/wav2vec/README.md) +* June 2021 [Released XLMR-XL and XLMR-XXL models](examples/xlmr/README.md) +* May 2021 [Released Unsupervised Speech Recognition code](examples/wav2vec/unsupervised/README.md) +* March 2021 [Added full parameter and optimizer state sharding + CPU offloading](examples/fully_sharded_data_parallel/README.md) +* February 2021 [Added LASER training code](examples/laser/README.md) +* December 2020: [Added Adaptive Attention Span code](examples/adaptive_span/README.md) +* December 2020: [GottBERT model and code released](examples/gottbert/README.md) +* November 2020: Adopted the [Hydra](https://github.com/facebookresearch/hydra) configuration framework + * [see documentation explaining how to use it for new and existing projects](docs/hydra_integration.md) +* November 2020: [fairseq 0.10.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.10.0) +* October 2020: [Added R3F/R4F (Better Fine-Tuning) code](examples/rxf/README.md) +* October 2020: [Deep Transformer with Latent Depth code released](examples/latent_depth/README.md) +* October 2020: [Added CRISS models and code](examples/criss/README.md) + +
Previous updates

+ +* September 2020: [Added Linformer code](examples/linformer/README.md) +* September 2020: [Added pointer-generator networks](examples/pointer_generator/README.md) +* August 2020: [Added lexically constrained decoding](examples/constrained_decoding/README.md) +* August 2020: [wav2vec2 models and code released](examples/wav2vec/README.md) +* July 2020: [Unsupervised Quality Estimation code released](examples/unsupervised_quality_estimation/README.md) +* May 2020: [Follow fairseq on Twitter](https://twitter.com/fairseq) +* April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md) +* April 2020: [Quant-Noise code released](examples/quant_noise/README.md) +* April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md) +* March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md) +* February 2020: [mBART model and code released](examples/mbart/README.md) +* February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/main/examples/backtranslation#training-your-own-model-wmt18-english-german) +* December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0) +* November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example) +* November 2019: [CamemBERT model and code released](examples/camembert/README.md) +* November 2019: [BART model and code released](examples/bart/README.md) +* November 2019: [XLM-R models and code released](examples/xlmr/README.md) +* September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md) +* August 2019: [WMT'19 models released](examples/wmt19/README.md) +* July 2019: fairseq relicensed under MIT license +* July 2019: [RoBERTa models and code released](examples/roberta/README.md) +* June 2019: [wav2vec models and code released](examples/wav2vec/README.md) + +

+ +### Features: + +* multi-GPU training on one machine or across multiple machines (data and model parallel) +* fast generation on both CPU and GPU with multiple search algorithms implemented: + + beam search + + Diverse Beam Search ([Vijayakumar et al., 2016](https://arxiv.org/abs/1610.02424)) + + sampling (unconstrained, top-k and top-p/nucleus) + + [lexically constrained decoding](examples/constrained_decoding/README.md) (Post & Vilar, 2018) +* [gradient accumulation](https://fairseq.readthedocs.io/en/latest/getting_started.html#large-mini-batch-training-with-delayed-updates) enables training with large mini-batches even on a single GPU +* [mixed precision training](https://fairseq.readthedocs.io/en/latest/getting_started.html#training-with-half-precision-floating-point-fp16) (trains faster with less GPU memory on [NVIDIA tensor cores](https://developer.nvidia.com/tensor-cores)) +* [extensible](https://fairseq.readthedocs.io/en/latest/overview.html): easily register new models, criterions, tasks, optimizers and learning rate schedulers +* [flexible configuration](docs/hydra_integration.md) based on [Hydra](https://github.com/facebookresearch/hydra) allowing a combination of code, command-line and file based configuration +* [full parameter and optimizer state sharding](examples/fully_sharded_data_parallel/README.md) +* [offloading parameters to CPU](examples/fully_sharded_data_parallel/README.md) + +We also provide [pre-trained models for translation and language modeling](#pre-trained-models-and-examples) +with a convenient `torch.hub` interface: + +``` python +en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model') +en2de.translate('Hello world', beam=5) +# 'Hallo Welt' +``` + +See the PyTorch Hub tutorials for [translation](https://pytorch.org/hub/pytorch_fairseq_translation/) +and [RoBERTa](https://pytorch.org/hub/pytorch_fairseq_roberta/) for more examples. + +# Requirements and Installation + +* [PyTorch](http://pytorch.org/) version >= 1.10.0 +* Python version >= 3.8 +* For training new models, you'll also need an NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl) +* **To install fairseq** and develop locally: + +``` bash +git clone https://github.com/pytorch/fairseq +cd fairseq +pip install --editable ./ + +# on MacOS: +# CFLAGS="-stdlib=libc++" pip install --editable ./ + +# to install the latest stable release (0.10.x) +# pip install fairseq +``` + +* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library: + +``` bash +git clone https://github.com/NVIDIA/apex +cd apex +pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \ + --global-option="--deprecated_fused_adam" --global-option="--xentropy" \ + --global-option="--fast_multihead_attn" ./ +``` + +* **For large datasets** install [PyArrow](https://arrow.apache.org/docs/python/install.html#using-pip): `pip install pyarrow` +* If you use Docker make sure to increase the shared memory size either with `--ipc=host` or `--shm-size` + as command line options to `nvidia-docker run` . + +# Getting Started + +The [full documentation](https://fairseq.readthedocs.io/) contains instructions +for getting started, training new models and extending fairseq with new model +types and tasks. + +# Pre-trained models and examples + +We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, +as well as example training and evaluation commands. + +* [Translation](examples/translation/README.md): convolutional and transformer models are available +* [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available + +We also have more detailed READMEs to reproduce results from specific papers: + +* [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale (Babu et al., 2021)](examples/wav2vec/xlsr/README.md) +* [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) +* [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) +* [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) +* [Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)](examples/quant_noise/README.md) +* [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) +* [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) +* [Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)](examples/layerdrop/README.md) +* [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md) +* [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) +* [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) +* [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) +* [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) +* [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) +* [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) +* [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) +* [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) +* [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) +* [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) +* [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) +* [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/README.conv.md) + +# Join the fairseq community + +* Twitter: https://twitter.com/fairseq +* Facebook page: https://www.facebook.com/groups/fairseq.users +* Google group: https://groups.google.com/forum/#!forum/fairseq-users + +# License + +fairseq(-py) is MIT-licensed. +The license applies to the pre-trained models as well. + +# Citation + +Please cite as: + +``` bibtex +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` diff --git a/fairseq/RELEASE.md b/fairseq/RELEASE.md new file mode 100644 index 0000000..79480a1 --- /dev/null +++ b/fairseq/RELEASE.md @@ -0,0 +1,13 @@ +# Creating a New Release + +In order to create a new release: + +1. Navigate to the [Fairseq Workflows](https://github.com/facebookresearch/fairseq/actions) and find the one named _Fairseq Release_. + +2. Under _Run Workflow_ choose the branch `main` and for _Release Type_ enter either `major`, `minor`, or `patch`. + +3. A branch named `$new_version-release` will be created where the `version.txt` file is updated. Merge those changes into `main`. + +4. Make sure that a [new PYPI package](https://pypi.org/project/fairseq/) has been uploaded. + +5. Make sure that a [new github release](https://github.com/facebookresearch/fairseq/releases) has been created. diff --git a/fairseq/docs/Makefile b/fairseq/docs/Makefile new file mode 100644 index 0000000..c2f5b1a --- /dev/null +++ b/fairseq/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = python -msphinx +SPHINXPROJ = fairseq +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/fairseq/docs/command_line_tools.rst b/fairseq/docs/command_line_tools.rst new file mode 100644 index 0000000..c16300f --- /dev/null +++ b/fairseq/docs/command_line_tools.rst @@ -0,0 +1,85 @@ +.. _Command-line Tools: + +Command-line Tools +================== + +Fairseq provides several command-line tools for training and evaluating models: + +- :ref:`fairseq-preprocess`: Data pre-processing: build vocabularies and binarize training data +- :ref:`fairseq-train`: Train a new model on one or multiple GPUs +- :ref:`fairseq-generate`: Translate pre-processed data with a trained model +- :ref:`fairseq-interactive`: Translate raw text with a trained model +- :ref:`fairseq-score`: BLEU scoring of generated translations against reference translations +- :ref:`fairseq-eval-lm`: Language model evaluation + + +.. _fairseq-preprocess: + +fairseq-preprocess +~~~~~~~~~~~~~~~~~~ +.. automodule:: fairseq_cli.preprocess + + .. argparse:: + :module: fairseq.options + :func: get_preprocessing_parser + :prog: fairseq-preprocess + + +.. _fairseq-train: + +fairseq-train +~~~~~~~~~~~~~ +.. automodule:: fairseq_cli.train + + .. argparse:: + :module: fairseq.options + :func: get_training_parser + :prog: fairseq-train + + +.. _fairseq-generate: + +fairseq-generate +~~~~~~~~~~~~~~~~ +.. automodule:: fairseq_cli.generate + + .. argparse:: + :module: fairseq.options + :func: get_generation_parser + :prog: fairseq-generate + + +.. _fairseq-interactive: + +fairseq-interactive +~~~~~~~~~~~~~~~~~~~ +.. automodule:: fairseq_cli.interactive + + .. argparse:: + :module: fairseq.options + :func: get_interactive_generation_parser + :prog: fairseq-interactive + + +.. _fairseq-score: + +fairseq-score +~~~~~~~~~~~~~ +.. automodule:: fairseq_cli.score + + .. argparse:: + :module: fairseq_cli.score + :func: get_parser + :prog: fairseq-score + + +.. _fairseq-eval-lm: + +fairseq-eval-lm +~~~~~~~~~~~~~~~ +.. automodule:: fairseq_cli.eval_lm + + .. argparse:: + :module: fairseq.options + :func: get_eval_lm_parser + :prog: fairseq-eval-lm diff --git a/fairseq/docs/conf.py b/fairseq/docs/conf.py new file mode 100644 index 0000000..0bc049f --- /dev/null +++ b/fairseq/docs/conf.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# fairseq documentation build configuration file, created by +# sphinx-quickstart on Fri Aug 17 21:45:30 2018. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. + +import os +import sys +from fairseq import __version__ + + +# source code directory, relative to this file, for sphinx-autobuild +sys.path.insert(0, os.path.abspath("..")) + +source_suffix = [".rst"] + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.intersphinx", + "sphinx.ext.viewcode", + "sphinx.ext.napoleon", + "sphinxarg.ext", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# The master toctree document. +master_doc = "index" + +# General information about the project. +project = "fairseq" +copyright = "Facebook AI Research (FAIR)" +author = "Facebook AI Research (FAIR)" + +github_doc_root = "https://github.com/pytorch/fairseq/tree/main/docs/" + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = __version__ +# The full version, including alpha/beta/rc tags. +release = __version__ + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "sphinx" +highlight_language = "python" + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +html_theme = "classic" + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = { + "numpy": ("http://docs.scipy.org/doc/numpy/", None), + "python": ("https://docs.python.org/", None), + "torch": ("https://pytorch.org/docs/master/", None), +} diff --git a/fairseq/docs/criterions.rst b/fairseq/docs/criterions.rst new file mode 100644 index 0000000..d6b8ca6 --- /dev/null +++ b/fairseq/docs/criterions.rst @@ -0,0 +1,31 @@ +.. role:: hidden + :class: hidden-section + +.. _Criterions: + +Criterions +========== + +Criterions compute the loss function given the model and batch, roughly:: + + loss = criterion(model, batch) + +.. automodule:: fairseq.criterions + :members: + +.. autoclass:: fairseq.criterions.FairseqCriterion + :members: + :undoc-members: + +.. autoclass:: fairseq.criterions.adaptive_loss.AdaptiveLoss + :members: + :undoc-members: +.. autoclass:: fairseq.criterions.composite_loss.CompositeLoss + :members: + :undoc-members: +.. autoclass:: fairseq.criterions.cross_entropy.CrossEntropyCriterion + :members: + :undoc-members: +.. autoclass:: fairseq.criterions.label_smoothed_cross_entropy.LabelSmoothedCrossEntropyCriterion + :members: + :undoc-members: diff --git a/fairseq/docs/data.rst b/fairseq/docs/data.rst new file mode 100644 index 0000000..6a390cb --- /dev/null +++ b/fairseq/docs/data.rst @@ -0,0 +1,58 @@ +.. role:: hidden + :class: hidden-section + +.. module:: fairseq.data + +Data Loading and Utilities +========================== + +.. _datasets: + +Datasets +-------- + +**Datasets** define the data format and provide helpers for creating +mini-batches. + +.. autoclass:: fairseq.data.FairseqDataset + :members: +.. autoclass:: fairseq.data.LanguagePairDataset + :members: +.. autoclass:: fairseq.data.MonolingualDataset + :members: + +**Helper Datasets** + +These datasets wrap other :class:`fairseq.data.FairseqDataset` instances and +provide additional functionality: + +.. autoclass:: fairseq.data.BacktranslationDataset + :members: +.. autoclass:: fairseq.data.ConcatDataset + :members: +.. autoclass:: fairseq.data.ResamplingDataset + :members: +.. autoclass:: fairseq.data.RoundRobinZipDatasets + :members: +.. autoclass:: fairseq.data.TransformEosDataset + :members: + + +Dictionary +---------- + +.. autoclass:: fairseq.data.Dictionary + :members: + + +Iterators +--------- + +.. autoclass:: fairseq.data.CountingIterator + :members: +.. autoclass:: fairseq.data.EpochBatchIterator + :members: +.. autoclass:: fairseq.data.GroupedIterator + :members: +.. autoclass:: fairseq.data.ShardedIterator + :members: diff --git a/fairseq/docs/docutils.conf b/fairseq/docs/docutils.conf new file mode 100644 index 0000000..526acff --- /dev/null +++ b/fairseq/docs/docutils.conf @@ -0,0 +1,2 @@ +[writers] +option-limit=0 diff --git a/fairseq/docs/fairseq.gif b/fairseq/docs/fairseq.gif new file mode 100644 index 0000000..5782fdb Binary files /dev/null and b/fairseq/docs/fairseq.gif differ diff --git a/fairseq/docs/getting_started.rst b/fairseq/docs/getting_started.rst new file mode 100644 index 0000000..09cc21e --- /dev/null +++ b/fairseq/docs/getting_started.rst @@ -0,0 +1,230 @@ +Evaluating Pre-trained Models +============================= + +First, download a pre-trained model along with its vocabularies: + +.. code-block:: console + + > curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf - + +This model uses a `Byte Pair Encoding (BPE) +vocabulary `__, so we'll have to apply +the encoding to the source text before it can be translated. This can be +done with the +`apply\_bpe.py `__ +script using the ``wmt14.en-fr.fconv-cuda/bpecodes`` file. ``@@`` is +used as a continuation marker and the original text can be easily +recovered with e.g. ``sed s/@@ //g`` or by passing the ``--remove-bpe`` +flag to :ref:`fairseq-generate`. Prior to BPE, input text needs to be tokenized +using ``tokenizer.perl`` from +`mosesdecoder `__. + +Let's use :ref:`fairseq-interactive` to generate translations interactively. +Here, we use a beam size of 5 and preprocess the input with the Moses +tokenizer and the given Byte-Pair Encoding vocabulary. It will automatically +remove the BPE continuation markers and detokenize the output. + +.. code-block:: console + + > MODEL_DIR=wmt14.en-fr.fconv-py + > fairseq-interactive \ + --path $MODEL_DIR/model.pt $MODEL_DIR \ + --beam 5 --source-lang en --target-lang fr \ + --tokenizer moses \ + --bpe subword_nmt --bpe-codes $MODEL_DIR/bpecodes + | loading model(s) from wmt14.en-fr.fconv-py/model.pt + | [en] dictionary: 44206 types + | [fr] dictionary: 44463 types + | Type the input sentence and press return: + Why is it rare to discover new marine mammal species? + S-0 Why is it rare to discover new marine mam@@ mal species ? + H-0 -0.0643349438905716 Pourquoi est-il rare de découvrir de nouvelles espèces de mammifères marins? + P-0 -0.0763 -0.1849 -0.0956 -0.0946 -0.0735 -0.1150 -0.1301 -0.0042 -0.0321 -0.0171 -0.0052 -0.0062 -0.0015 + +This generation script produces three types of outputs: a line prefixed +with *O* is a copy of the original source sentence; *H* is the +hypothesis along with an average log-likelihood; and *P* is the +positional score per token position, including the +end-of-sentence marker which is omitted from the text. + +Other types of output lines you might see are *D*, the detokenized hypothesis, +*T*, the reference target, *A*, alignment info, *E* the history of generation steps. + +See the `README `__ for a +full list of pre-trained models available. + +Training a New Model +==================== + +The following tutorial is for machine translation. For an example of how +to use Fairseq for other tasks, such as :ref:`language modeling`, please see the +``examples/`` directory. + +Data Pre-processing +------------------- + +Fairseq contains example pre-processing scripts for several translation +datasets: IWSLT 2014 (German-English), WMT 2014 (English-French) and WMT +2014 (English-German). To pre-process and binarize the IWSLT dataset: + +.. code-block:: console + + > cd examples/translation/ + > bash prepare-iwslt14.sh + > cd ../.. + > TEXT=examples/translation/iwslt14.tokenized.de-en + > fairseq-preprocess --source-lang de --target-lang en \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/iwslt14.tokenized.de-en + +This will write binarized data that can be used for model training to +``data-bin/iwslt14.tokenized.de-en``. + +Training +-------- + +Use :ref:`fairseq-train` to train a new model. Here a few example settings that work +well for the IWSLT 2014 dataset: + +.. code-block:: console + + > mkdir -p checkpoints/fconv + > CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt14.tokenized.de-en \ + --optimizer nag --lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \ + --arch fconv_iwslt_de_en --save-dir checkpoints/fconv + +By default, :ref:`fairseq-train` will use all available GPUs on your machine. Use the +``CUDA_VISIBLE_DEVICES`` environment variable to select specific GPUs and/or to +change the number of GPU devices that will be used. + +Also note that the batch size is specified in terms of the maximum +number of tokens per batch (``--max-tokens``). You may need to use a +smaller value depending on the available GPU memory on your system. + +Generation +---------- + +Once your model is trained, you can generate translations using +:ref:`fairseq-generate` **(for binarized data)** or +:ref:`fairseq-interactive` **(for raw text)**: + +.. code-block:: console + + > fairseq-generate data-bin/iwslt14.tokenized.de-en \ + --path checkpoints/fconv/checkpoint_best.pt \ + --batch-size 128 --beam 5 + | [de] dictionary: 35475 types + | [en] dictionary: 24739 types + | data-bin/iwslt14.tokenized.de-en test 6750 examples + | model fconv + | loaded checkpoint trainings/fconv/checkpoint_best.pt + S-721 danke . + T-721 thank you . + ... + +To generate translations with only a CPU, use the ``--cpu`` flag. BPE +continuation markers can be removed with the ``--remove-bpe`` flag. + +Advanced Training Options +========================= + +Large mini-batch training with delayed updates +---------------------------------------------- + +The ``--update-freq`` option can be used to accumulate gradients from +multiple mini-batches and delay updating, creating a larger effective +batch size. Delayed updates can also improve training speed by reducing +inter-GPU communication costs and by saving idle time caused by variance +in workload across GPUs. See `Ott et al. +(2018) `__ for more details. + +To train on a single GPU with an effective batch size that is equivalent +to training on 8 GPUs: + +.. code-block:: console + + > CUDA_VISIBLE_DEVICES=0 fairseq-train --update-freq 8 (...) + +Training with half precision floating point (FP16) +-------------------------------------------------- + +.. note:: + + FP16 training requires a Volta GPU and CUDA 9.1 or greater + +Recent GPUs enable efficient half precision floating point computation, +e.g., using `Nvidia Tensor Cores +`__. +Fairseq supports FP16 training with the ``--fp16`` flag: + +.. code-block:: console + + > fairseq-train --fp16 (...) + +Distributed training +-------------------- + +Distributed training in fairseq is implemented on top of ``torch.distributed``. +The easiest way to launch jobs is with the `torch.distributed.launch +`__ tool. + +For example, to train a large English-German Transformer model on 2 nodes each +with 8 GPUs (in total 16 GPUs), run the following command on each node, +replacing ``node_rank=0`` with ``node_rank=1`` on the second node and making +sure to update ``--master_addr`` to the IP address of the first node: + +.. code-block:: console + + > python -m torch.distributed.launch --nproc_per_node=8 \ + --nnodes=2 --node_rank=0 --master_addr="192.168.1.1" \ + --master_port=12345 \ + $(which fairseq-train) data-bin/wmt16_en_de_bpe32k \ + --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \ + --lr 0.0005 \ + --dropout 0.3 --weight-decay 0.0 --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --max-tokens 3584 \ + --max-epoch 70 \ + --fp16 + +On SLURM clusters, fairseq will automatically detect the number of nodes and +GPUs, but a port number must be provided: + +.. code-block:: console + + > salloc --gpus=16 --nodes 2 (...) + > srun fairseq-train --distributed-port 12345 (...). + + +.. warning:: + + PyTorch Distributed features used in fairseq are intended for internal + communication only. They are not built for use in untrusted environments or + networks. + + For performance reasons, none of the PyTorch Distributed primitives include + any authorization protocol and will send messages unencrypted. They accept + connections from anywhere, and execute the workload sent without performing + any checks. Therefore, if you run a distributed fairseq job on your network, + anybody with access to the network can execute arbitrary code with the + privileges of the user running the job. + +Sharding very large datasets +---------------------------- + +It can be challenging to train over very large datasets, particularly if your +machine does not have much system RAM. Most tasks in fairseq support training +over "sharded" datasets, in which the original dataset has been preprocessed +into non-overlapping chunks (or "shards"). + +For example, instead of preprocessing all your data into a single "data-bin" +directory, you can split the data and create "data-bin1", "data-bin2", etc. +Then you can adapt your training command like so: + +.. code-block:: console + + > fairseq-train data-bin1:data-bin2:data-bin3 (...) + +Training will now iterate over each shard, one by one, with each shard +corresponding to an "epoch", thus reducing system memory usage. diff --git a/fairseq/docs/hydra_integration.md b/fairseq/docs/hydra_integration.md new file mode 100644 index 0000000..6a15298 --- /dev/null +++ b/fairseq/docs/hydra_integration.md @@ -0,0 +1,284 @@ +## Hydra + +[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python +framework that simplifies the development of research and other complex +applications. The key feature is the ability to dynamically create a +hierarchical configuration by composition and override it through config files +and the command line. The name Hydra comes from its ability to run multiple +similar jobs - much like a Hydra with multiple heads. + +## Motivation + +Until recently, all components in fairseq were configured through a shared +`args` namespace that was created at application startup. Components declared +their own `add_args` method to update the argparse parser, hoping that the names +would not clash with arguments from other components. While this model works for +smaller applications, as fairseq grew and became integrated into other +applications, this became problematic. In order to determine how to configure +each component, one needed to a) examine what args were added by this component, +and b) read the code to figure out what shared arguments it is using that were +added in other places. Reproducing models involved sharing commands that often +contained dozens of command line switches. + +The model described above is still supported by fairseq for backward +compatibility, but will be deprecated some time in the future. + +New components in fairseq should now create a dataclass that encapsulates all +parameters required to configure this component. The dataclass is registered +along with the component, and fairseq takes care of constructing and providing +this configuration object to the component's constructor. Note that sharing +parameters can optionally still work, but one has to explicitly point to the +"source of truth" (see inheritance example below). These changes make components +in fairseq more independent and re-usable by other applications: all that is +needed to create a component is to initialize its dataclass and overwrite some +of the defaults. + +While configuring fairseq through command line (using either the legacy argparse +based or the new Hydra based entry points) is still fully supported, you can now +take advantage of configuring fairseq completely or piece-by-piece through +hierarchical YAML configuration files. These files can also be shipped as +examples that others can use to run an identically configured job. + +Additionally, Hydra has a rich and growing [library of +plugins](https://github.com/facebookresearch/hydra/tree/master/plugins) that +provide functionality such as hyperparameter sweeping (including using bayesian +optimization through the [Ax](https://github.com/facebook/Ax) library), job +launching across various platforms, and more. + +## Creating or migrating components + +In general, each new (or updated) component should provide a companion +[dataclass](https://www.python.org/dev/peps/pep-0557/). These dataclass are +typically located in the same file as the component and are passed as arguments +to the `register_*()` functions. Top-level configs that should be present in +every fairseq application are placed in the +[global](fairseq/dataclass/configs.py) config file and added to the +`FairseqConfig` object. + +Each dataclass is a plain-old-data object, similar to a `NamedTuple`. These +classes are decorated with a `@dataclass` decorator, and typically inherit from +`FairseqDataclass` (which adds some functionality for backward compatibility). +Each field must have a type, and generally has metadata (such as a help string) +and a default value. Only primitive types or other config objects are allowed as +data types for each field. + +#### Example: + +```python +from dataclasses import dataclass, field +from fairseq.dataclass import FairseqDataclass + +@dataclass +class InteractiveConfig(FairseqDataclass): + buffer_size: int = field( + default=0, + metadata={ + "help": "read this many sentences into a buffer before processing them" + }, + ) + input: str = field( + default="-", + metadata={"help": "file to read from; use - for stdin"}, + ) +``` + +### Inherting values + +Some components require sharing a value. For example, a learning rate scheduler +and an optimizer may both need to know the initial learning rate value. One can +declare a field that, by default, will inherit its value from another config +node in the same hierarchy: + +```python +@dataclass +FairseqAdamConfig(FairseqDataclass): + ... + lr: List[float] = II("optimization.lr") + ... +``` + +`II("optimization.lr")` is syntactic sugar for `"${optimization.lr}"`, which is +the value one can use in a YAML config file or through command line to achieve +the same effect. Note that this assumes that there is an "optimization" config +object in the root config and it has a field called "lr". + +### Tasks and Models + +Creating Tasks and Models works same as before, except that legacy +implementations now inherit from `LegacyFairseq*` base classes, while new +components inherit from `FairseqTask` and `FairseqModel` and provide a dataclass +to the `register_*()` functions. + +#### Task example: + +```python +@dataclass +class LanguageModelingConfig(FairseqDataclass): + data: Optional[str] = field( + default=None, metadata={"help": "path to data directory"} + ) + ... + +@register_task("language_modeling", dataclass=LanguageModelingConfig) +class LanguageModelingTask(FairseqTask): + ... + @classmethod + def setup_task(cls, cfg: LanguageModelingConfig): + ... +``` + +#### Model example: + +```python +@dataclass +class TransformerLanguageModelConfig(FairseqDataclass): + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="relu", metadata={"help": "activation function to use"} + ) + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + ... + +@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig) +class TransformerLanguageModel(FairseqLanguageModel): + ... + @classmethod + def build_model(cls, cfg: TransformerLanguageModelConfig, task: FairseqTask): + ... +``` + +### Other components + +Other components work as before, but they now take their configuration dataclass +as the only constructor argument: + +```python +@dataclass +class MosesTokenizerConfig(FairseqDataclass): + source_lang: str = field(default="en", metadata={"help": "source language"}) + ... + +@register_tokenizer("moses", dataclass=MosesTokenizerConfig) +class MosesTokenizer(object): + def __init__(self, cfg: MosesTokenizerConfig): + ... +``` + +Note that if you are adding a new registry for a new set of components, you need +to add it to the `FairseqConfig` object in `fairseq/dataclass/configs.py`: + +```python +@dataclass +class FairseqConfig(object): + ... + my_new_registry: Any = None +``` + +## Training with `fairseq-hydra-train` + +To fully take advantage of configuration flexibility offered by Hydra, you may +want to train new models using the `fairseq-hydra-train` entry point. Legacy CLI +tools such as `fairseq-train` will remain supported for the foreseeable future +but will be deprecated eventually. + +On startup, Hydra will create a configuration object that contains a hierarchy +of all the necessary dataclasses populated with their default values in the +code. The default values are overwritten by values found in YAML files in +`fairseq/config` directory (which currently sets minimal defaults) and then +further overwritten by values provided through command line arguments. + +Some of the most common use cases are shown below: + +### 1. Override default values through command line: + +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_world_size=1 \ + dataset.batch_size=2 \ + task.data=data-bin \ + model=transformer_lm/transformer_lm_gpt \ + task=language_modeling \ + optimization.max_update=5000 +``` + +Note that along with explicitly providing values for parameters such as +`dataset.batch_size`, this also tells Hydra to overlay configuration found in +`fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml` over the default +values in the dataclass. If you want to train a model without specifying a +particular architecture you can simply specify `model=transformer_lm`. This only +works for migrated tasks and models. + +### 2. Replace bundled configs with an external config: + +```shell script +$ fairseq-hydra-train \ + --config-dir /path/to/external/configs \ + --config-name wiki103 +``` + +where `/path/to/external/configs/wiki103.yaml` contains: + +```yaml +# @package _group_ + +model: + _name: transformer_lm +distributed_training: + distributed_world_size: 1 +dataset: + batch_size: 2 +task: + _name: language_modeling + data: /path/to/data + add_bos_token: false + max_target_positions: 1024 +optimization: + max_update: 50000 + lr: [ 0.25 ] +criterion: cross_entropy +optimizer: adam +lr_scheduler: + _name: cosine +``` + +Note that here bundled configs from `fairseq/config` directory are not used, +however the defaults from each dataclass will still be used (unless overwritten +by your external config). + +Additionally you can choose to break up your configs by creating a directory +structure in the same location as your main config file, with the names of the +top-level fields (such as "model", "dataset", etc), and placing config files +with meaningful names that would populate that specific section of your +top-level config file (for example, you might have +`model/small_transformer_lm.yaml`, `model/big_transformer_lm.yaml`, etc). You +can then specify the correct configuration via command line, defaults in the +main config, or even launch all of them as a sweep (see Hydra documentation on +how to do this). + +### 3. Add an external config directory to Hydra search path: + +This allows combining default configuration (including using any bundled config +files), while specifying your own config files for some parts of the +configuration. + +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_world_size=1 \ + dataset.batch_size=2 \ + task.data=/path/to/data/ \ + model=transformer_lm/2_layers \ + task=language_modeling \ + optimization.max_update=5000 \ + --config-dir /path/to/external/configs +``` + +where `/path/to/external/configs` has the following structure: +``` +. ++-- model +| +-- transformer_lm +| | +-- 2_layers.yaml +``` + +and `2_layers.yaml` contains a copy of `transformer_lm_gpt.yaml` but with +`decoder_layers` set to 2. You can add other configs to configure other +components as well. diff --git a/fairseq/docs/index.rst b/fairseq/docs/index.rst new file mode 100644 index 0000000..591db86 --- /dev/null +++ b/fairseq/docs/index.rst @@ -0,0 +1,49 @@ +.. fairseq documentation master file, created by + sphinx-quickstart on Fri Aug 17 21:45:30 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +:github_url: https://github.com/pytorch/fairseq + + +fairseq documentation +===================== + +Fairseq is a sequence modeling toolkit written in `PyTorch +`_ that allows researchers and developers to +train custom models for translation, summarization, language modeling and other +text generation tasks. + +.. toctree:: + :maxdepth: 1 + :caption: Getting Started + + getting_started + command_line_tools + +.. toctree:: + :maxdepth: 1 + :caption: Extending Fairseq + + overview + tutorial_simple_lstm + tutorial_classifying_names + +.. toctree:: + :maxdepth: 2 + :caption: Library Reference + + tasks + models + criterions + optim + lr_scheduler + data + modules + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/fairseq/docs/lr_scheduler.rst b/fairseq/docs/lr_scheduler.rst new file mode 100644 index 0000000..bbc09dc --- /dev/null +++ b/fairseq/docs/lr_scheduler.rst @@ -0,0 +1,34 @@ +.. role:: hidden + :class: hidden-section + +.. _Learning Rate Schedulers: + +Learning Rate Schedulers +======================== + +Learning Rate Schedulers update the learning rate over the course of training. +Learning rates can be updated after each update via :func:`step_update` or at +epoch boundaries via :func:`step`. + +.. automodule:: fairseq.optim.lr_scheduler + :members: + +.. autoclass:: fairseq.optim.lr_scheduler.FairseqLRScheduler + :members: + :undoc-members: + +.. autoclass:: fairseq.optim.lr_scheduler.cosine_lr_scheduler.CosineSchedule + :members: + :undoc-members: +.. autoclass:: fairseq.optim.lr_scheduler.fixed_schedule.FixedSchedule + :members: + :undoc-members: +.. autoclass:: fairseq.optim.lr_scheduler.inverse_square_root_schedule.InverseSquareRootSchedule + :members: + :undoc-members: +.. autoclass:: fairseq.optim.lr_scheduler.reduce_lr_on_plateau.ReduceLROnPlateau + :members: + :undoc-members: +.. autoclass:: fairseq.optim.lr_scheduler.triangular_lr_scheduler.TriangularSchedule + :members: + :undoc-members: diff --git a/fairseq/docs/make.bat b/fairseq/docs/make.bat new file mode 100644 index 0000000..baa9d02 --- /dev/null +++ b/fairseq/docs/make.bat @@ -0,0 +1,36 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=python -msphinx +) +set SOURCEDIR=. +set BUILDDIR=_build +set SPHINXPROJ=fairseq + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The Sphinx module was not found. Make sure you have Sphinx installed, + echo.then set the SPHINXBUILD environment variable to point to the full + echo.path of the 'sphinx-build' executable. Alternatively you may add the + echo.Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/fairseq/docs/models.rst b/fairseq/docs/models.rst new file mode 100644 index 0000000..054622d --- /dev/null +++ b/fairseq/docs/models.rst @@ -0,0 +1,104 @@ +.. role:: hidden + :class: hidden-section + +.. module:: fairseq.models + +.. _Models: + +Models +====== + +A Model defines the neural network's ``forward()`` method and encapsulates all +of the learnable parameters in the network. Each model also provides a set of +named *architectures* that define the precise network configuration (e.g., +embedding dimension, number of layers, etc.). + +Both the model type and architecture are selected via the ``--arch`` +command-line argument. Once selected, a model may expose additional command-line +arguments for further configuration. + +.. note:: + + All fairseq Models extend :class:`BaseFairseqModel`, which in turn extends + :class:`torch.nn.Module`. Thus any fairseq Model can be used as a + stand-alone Module in other PyTorch code. + + +Convolutional Neural Networks (CNN) +----------------------------------- + +.. module:: fairseq.models.fconv +.. autoclass:: fairseq.models.fconv.FConvModel + :members: +.. autoclass:: fairseq.models.fconv.FConvEncoder + :members: + :undoc-members: +.. autoclass:: fairseq.models.fconv.FConvDecoder + :members: + + +Long Short-Term Memory (LSTM) networks +-------------------------------------- + +.. module:: fairseq.models.lstm +.. autoclass:: fairseq.models.lstm.LSTMModel + :members: +.. autoclass:: fairseq.models.lstm.LSTMEncoder + :members: +.. autoclass:: fairseq.models.lstm.LSTMDecoder + :members: + + +Transformer (self-attention) networks +------------------------------------- + +.. module:: fairseq.models.transformer +.. autoclass:: fairseq.models.transformer.TransformerModel + :members: +.. autoclass:: fairseq.models.transformer.TransformerEncoder + :members: +.. autoclass:: fairseq.models.transformer.TransformerEncoderLayer + :members: +.. autoclass:: fairseq.models.transformer.TransformerDecoder + :members: +.. autoclass:: fairseq.models.transformer.TransformerDecoderLayer + :members: + + +Adding new models +----------------- + +.. currentmodule:: fairseq.models +.. autofunction:: fairseq.models.register_model +.. autofunction:: fairseq.models.register_model_architecture +.. autoclass:: fairseq.models.BaseFairseqModel + :members: + :undoc-members: +.. autoclass:: fairseq.models.FairseqEncoderDecoderModel + :members: + :undoc-members: +.. autoclass:: fairseq.models.FairseqEncoderModel + :members: + :undoc-members: +.. autoclass:: fairseq.models.FairseqLanguageModel + :members: + :undoc-members: +.. autoclass:: fairseq.models.FairseqMultiModel + :members: + :undoc-members: +.. autoclass:: fairseq.models.FairseqEncoder + :members: +.. autoclass:: fairseq.models.CompositeEncoder + :members: +.. autoclass:: fairseq.models.FairseqDecoder + :members: + + +.. _Incremental decoding: + +Incremental decoding +-------------------- + +.. autoclass:: fairseq.models.FairseqIncrementalDecoder + :members: + :undoc-members: diff --git a/fairseq/docs/modules.rst b/fairseq/docs/modules.rst new file mode 100644 index 0000000..9631c93 --- /dev/null +++ b/fairseq/docs/modules.rst @@ -0,0 +1,9 @@ +Modules +======= + +Fairseq provides several stand-alone :class:`torch.nn.Module` classes that may +be helpful when implementing a new :class:`~fairseq.models.BaseFairseqModel`. + +.. automodule:: fairseq.modules + :members: + :undoc-members: diff --git a/fairseq/docs/optim.rst b/fairseq/docs/optim.rst new file mode 100644 index 0000000..c332645 --- /dev/null +++ b/fairseq/docs/optim.rst @@ -0,0 +1,38 @@ +.. role:: hidden + :class: hidden-section + +.. _optimizers: + +Optimizers +========== + +Optimizers update the Model parameters based on the gradients. + +.. automodule:: fairseq.optim + :members: + +.. autoclass:: fairseq.optim.FairseqOptimizer + :members: + :undoc-members: + +.. autoclass:: fairseq.optim.adadelta.Adadelta + :members: + :undoc-members: +.. autoclass:: fairseq.optim.adagrad.Adagrad + :members: + :undoc-members: +.. autoclass:: fairseq.optim.adafactor.FairseqAdafactor + :members: + :undoc-members: +.. autoclass:: fairseq.optim.adam.FairseqAdam + :members: + :undoc-members: +.. autoclass:: fairseq.optim.fp16_optimizer.FP16Optimizer + :members: + :undoc-members: +.. autoclass:: fairseq.optim.nag.FairseqNAG + :members: + :undoc-members: +.. autoclass:: fairseq.optim.sgd.SGD + :members: + :undoc-members: diff --git a/fairseq/docs/overview.rst b/fairseq/docs/overview.rst new file mode 100644 index 0000000..026b3b5 --- /dev/null +++ b/fairseq/docs/overview.rst @@ -0,0 +1,74 @@ +Overview +======== + +Fairseq can be extended through user-supplied `plug-ins +`_. We support five kinds of +plug-ins: + +- :ref:`Models` define the neural network architecture and encapsulate all of the + learnable parameters. +- :ref:`Criterions` compute the loss function given the model outputs and targets. +- :ref:`Tasks` store dictionaries and provide helpers for loading/iterating over + Datasets, initializing the Model/Criterion and calculating the loss. +- :ref:`Optimizers` update the Model parameters based on the gradients. +- :ref:`Learning Rate Schedulers` update the learning rate over the course of + training. + +**Training Flow** + +Given a ``model``, ``criterion``, ``task``, ``optimizer`` and ``lr_scheduler``, +fairseq implements the following high-level training flow:: + + for epoch in range(num_epochs): + itr = task.get_batch_iterator(task.dataset('train')) + for num_updates, batch in enumerate(itr): + task.train_step(batch, model, criterion, optimizer) + average_and_clip_gradients() + optimizer.step() + lr_scheduler.step_update(num_updates) + lr_scheduler.step(epoch) + +where the default implementation for ``task.train_step`` is roughly:: + + def train_step(self, batch, model, criterion, optimizer, **unused): + loss = criterion(model, batch) + optimizer.backward(loss) + return loss + +**Registering new plug-ins** + +New plug-ins are *registered* through a set of ``@register`` function +decorators, for example:: + + @register_model('my_lstm') + class MyLSTM(FairseqEncoderDecoderModel): + (...) + +Once registered, new plug-ins can be used with the existing :ref:`Command-line +Tools`. See the Tutorial sections for more detailed walkthroughs of how to add +new plug-ins. + +**Loading plug-ins from another directory** + +New plug-ins can be defined in a custom module stored in the user system. In +order to import the module, and make the plugin available to *fairseq*, the +command line supports the ``--user-dir`` flag that can be used to specify a +custom location for additional modules to load into *fairseq*. + +For example, assuming this directory tree:: + + /home/user/my-module/ + └── __init__.py + +with ``__init__.py``:: + + from fairseq.models import register_model_architecture + from fairseq.models.transformer import transformer_vaswani_wmt_en_de_big + + @register_model_architecture('transformer', 'my_transformer') + def transformer_mmt_big(args): + transformer_vaswani_wmt_en_de_big(args) + +it is possible to invoke the :ref:`fairseq-train` script with the new architecture with:: + + fairseq-train ... --user-dir /home/user/my-module -a my_transformer --task translation diff --git a/fairseq/docs/tasks.rst b/fairseq/docs/tasks.rst new file mode 100644 index 0000000..5f65c3c --- /dev/null +++ b/fairseq/docs/tasks.rst @@ -0,0 +1,61 @@ +.. role:: hidden + :class: hidden-section + +.. module:: fairseq.tasks + +.. _Tasks: + +Tasks +===== + +Tasks store dictionaries and provide helpers for loading/iterating over +Datasets, initializing the Model/Criterion and calculating the loss. + +Tasks can be selected via the ``--task`` command-line argument. Once selected, a +task may expose additional command-line arguments for further configuration. + +Example usage:: + + # setup the task (e.g., load dictionaries) + task = fairseq.tasks.setup_task(args) + + # build model and criterion + model = task.build_model(args) + criterion = task.build_criterion(args) + + # load datasets + task.load_dataset('train') + task.load_dataset('valid') + + # iterate over mini-batches of data + batch_itr = task.get_batch_iterator( + task.dataset('train'), max_tokens=4096, + ) + for batch in batch_itr: + # compute the loss + loss, sample_size, logging_output = task.get_loss( + model, criterion, batch, + ) + loss.backward() + + +Translation +----------- + +.. autoclass:: fairseq.tasks.translation.TranslationTask + +.. _language modeling: + +Language Modeling +----------------- + +.. autoclass:: fairseq.tasks.language_modeling.LanguageModelingTask + + +Adding new tasks +---------------- + +.. autofunction:: fairseq.tasks.register_task +.. autoclass:: fairseq.tasks.FairseqTask + :members: + :undoc-members: diff --git a/fairseq/docs/tutorial_classifying_names.rst b/fairseq/docs/tutorial_classifying_names.rst new file mode 100644 index 0000000..de099f0 --- /dev/null +++ b/fairseq/docs/tutorial_classifying_names.rst @@ -0,0 +1,415 @@ +Tutorial: Classifying Names with a Character-Level RNN +====================================================== + +In this tutorial we will extend fairseq to support *classification* tasks. In +particular we will re-implement the PyTorch tutorial for `Classifying Names with +a Character-Level RNN `_ +in fairseq. It is recommended to quickly skim that tutorial before beginning +this one. + +This tutorial covers: + +1. **Preprocessing the data** to create dictionaries. +2. **Registering a new Model** that encodes an input sentence with a simple RNN + and predicts the output label. +3. **Registering a new Task** that loads our dictionaries and dataset. +4. **Training the Model** using the existing command-line tools. +5. **Writing an evaluation script** that imports fairseq and allows us to + interactively evaluate our model on new inputs. + + +1. Preprocessing the data +------------------------- + +The original tutorial provides raw data, but we'll work with a modified version +of the data that is already tokenized into characters and split into separate +train, valid and test sets. + +Download and extract the data from here: +`tutorial_names.tar.gz `_ + +Once extracted, let's preprocess the data using the :ref:`fairseq-preprocess` +command-line tool to create the dictionaries. While this tool is primarily +intended for sequence-to-sequence problems, we're able to reuse it here by +treating the label as a "target" sequence of length 1. We'll also output the +preprocessed files in "raw" format using the ``--dataset-impl`` option to +enhance readability: + +.. code-block:: console + + > fairseq-preprocess \ + --trainpref names/train --validpref names/valid --testpref names/test \ + --source-lang input --target-lang label \ + --destdir names-bin --dataset-impl raw + +After running the above command you should see a new directory, +:file:`names-bin/`, containing the dictionaries for *inputs* and *labels*. + + +2. Registering a new Model +-------------------------- + +Next we'll register a new model in fairseq that will encode an input sentence +with a simple RNN and predict the output label. Compared to the original PyTorch +tutorial, our version will also work with batches of data and GPU Tensors. + +First let's copy the simple RNN module implemented in the `PyTorch tutorial +`_. +Create a new file named :file:`fairseq/models/rnn_classifier.py` with the +following contents:: + + import torch + import torch.nn as nn + + class RNN(nn.Module): + + def __init__(self, input_size, hidden_size, output_size): + super(RNN, self).__init__() + + self.hidden_size = hidden_size + + self.i2h = nn.Linear(input_size + hidden_size, hidden_size) + self.i2o = nn.Linear(input_size + hidden_size, output_size) + self.softmax = nn.LogSoftmax(dim=1) + + def forward(self, input, hidden): + combined = torch.cat((input, hidden), 1) + hidden = self.i2h(combined) + output = self.i2o(combined) + output = self.softmax(output) + return output, hidden + + def initHidden(self): + return torch.zeros(1, self.hidden_size) + +We must also *register* this model with fairseq using the +:func:`~fairseq.models.register_model` function decorator. Once the model is +registered we'll be able to use it with the existing :ref:`Command-line Tools`. + +All registered models must implement the :class:`~fairseq.models.BaseFairseqModel` +interface, so we'll create a small wrapper class in the same file and register +it in fairseq with the name ``'rnn_classifier'``:: + + from fairseq.models import BaseFairseqModel, register_model + + # Note: the register_model "decorator" should immediately precede the + # definition of the Model class. + + @register_model('rnn_classifier') + class FairseqRNNClassifier(BaseFairseqModel): + + @staticmethod + def add_args(parser): + # Models can override this method to add new command-line arguments. + # Here we'll add a new command-line argument to configure the + # dimensionality of the hidden state. + parser.add_argument( + '--hidden-dim', type=int, metavar='N', + help='dimensionality of the hidden state', + ) + + @classmethod + def build_model(cls, args, task): + # Fairseq initializes models by calling the ``build_model()`` + # function. This provides more flexibility, since the returned model + # instance can be of a different type than the one that was called. + # In this case we'll just return a FairseqRNNClassifier instance. + + # Initialize our RNN module + rnn = RNN( + # We'll define the Task in the next section, but for now just + # notice that the task holds the dictionaries for the "source" + # (i.e., the input sentence) and "target" (i.e., the label). + input_size=len(task.source_dictionary), + hidden_size=args.hidden_dim, + output_size=len(task.target_dictionary), + ) + + # Return the wrapped version of the module + return FairseqRNNClassifier( + rnn=rnn, + input_vocab=task.source_dictionary, + ) + + def __init__(self, rnn, input_vocab): + super(FairseqRNNClassifier, self).__init__() + + self.rnn = rnn + self.input_vocab = input_vocab + + # The RNN module in the tutorial expects one-hot inputs, so we can + # precompute the identity matrix to help convert from indices to + # one-hot vectors. We register it as a buffer so that it is moved to + # the GPU when ``cuda()`` is called. + self.register_buffer('one_hot_inputs', torch.eye(len(input_vocab))) + + def forward(self, src_tokens, src_lengths): + # The inputs to the ``forward()`` function are determined by the + # Task, and in particular the ``'net_input'`` key in each + # mini-batch. We'll define the Task in the next section, but for + # now just know that *src_tokens* has shape `(batch, src_len)` and + # *src_lengths* has shape `(batch)`. + bsz, max_src_len = src_tokens.size() + + # Initialize the RNN hidden state. Compared to the original PyTorch + # tutorial we'll also handle batched inputs and work on the GPU. + hidden = self.rnn.initHidden() + hidden = hidden.repeat(bsz, 1) # expand for batched inputs + hidden = hidden.to(src_tokens.device) # move to GPU + + for i in range(max_src_len): + # WARNING: The inputs have padding, so we should mask those + # elements here so that padding doesn't affect the results. + # This is left as an exercise for the reader. The padding symbol + # is given by ``self.input_vocab.pad()`` and the unpadded length + # of each input is given by *src_lengths*. + + # One-hot encode a batch of input characters. + input = self.one_hot_inputs[src_tokens[:, i].long()] + + # Feed the input to our RNN. + output, hidden = self.rnn(input, hidden) + + # Return the final output state for making a prediction + return output + +Finally let's define a *named architecture* with the configuration for our +model. This is done with the :func:`~fairseq.models.register_model_architecture` +function decorator. Thereafter this named architecture can be used with the +``--arch`` command-line argument, e.g., ``--arch pytorch_tutorial_rnn``:: + + from fairseq.models import register_model_architecture + + # The first argument to ``register_model_architecture()`` should be the name + # of the model we registered above (i.e., 'rnn_classifier'). The function we + # register here should take a single argument *args* and modify it in-place + # to match the desired architecture. + + @register_model_architecture('rnn_classifier', 'pytorch_tutorial_rnn') + def pytorch_tutorial_rnn(args): + # We use ``getattr()`` to prioritize arguments that are explicitly given + # on the command-line, so that the defaults defined below are only used + # when no other value has been specified. + args.hidden_dim = getattr(args, 'hidden_dim', 128) + + +3. Registering a new Task +------------------------- + +Now we'll register a new :class:`~fairseq.tasks.FairseqTask` that will load our +dictionaries and dataset. Tasks can also control how the data is batched into +mini-batches, but in this tutorial we'll reuse the batching provided by +:class:`fairseq.data.LanguagePairDataset`. + +Create a new file named :file:`fairseq/tasks/simple_classification.py` with the +following contents:: + + import os + import torch + + from fairseq.data import Dictionary, LanguagePairDataset + from fairseq.tasks import LegacyFairseqTask, register_task + + + @register_task('simple_classification') + class SimpleClassificationTask(LegacyFairseqTask): + + @staticmethod + def add_args(parser): + # Add some command-line arguments for specifying where the data is + # located and the maximum supported input length. + parser.add_argument('data', metavar='FILE', + help='file prefix for data') + parser.add_argument('--max-positions', default=1024, type=int, + help='max input length') + + @classmethod + def setup_task(cls, args, **kwargs): + # Here we can perform any setup required for the task. This may include + # loading Dictionaries, initializing shared Embedding layers, etc. + # In this case we'll just load the Dictionaries. + input_vocab = Dictionary.load(os.path.join(args.data, 'dict.input.txt')) + label_vocab = Dictionary.load(os.path.join(args.data, 'dict.label.txt')) + print('| [input] dictionary: {} types'.format(len(input_vocab))) + print('| [label] dictionary: {} types'.format(len(label_vocab))) + + return SimpleClassificationTask(args, input_vocab, label_vocab) + + def __init__(self, args, input_vocab, label_vocab): + super().__init__(args) + self.input_vocab = input_vocab + self.label_vocab = label_vocab + + def load_dataset(self, split, **kwargs): + """Load a given dataset split (e.g., train, valid, test).""" + + prefix = os.path.join(self.args.data, '{}.input-label'.format(split)) + + # Read input sentences. + sentences, lengths = [], [] + with open(prefix + '.input', encoding='utf-8') as file: + for line in file: + sentence = line.strip() + + # Tokenize the sentence, splitting on spaces + tokens = self.input_vocab.encode_line( + sentence, add_if_not_exist=False, + ) + + sentences.append(tokens) + lengths.append(tokens.numel()) + + # Read labels. + labels = [] + with open(prefix + '.label', encoding='utf-8') as file: + for line in file: + label = line.strip() + labels.append( + # Convert label to a numeric ID. + torch.LongTensor([self.label_vocab.add_symbol(label)]) + ) + + assert len(sentences) == len(labels) + print('| {} {} {} examples'.format(self.args.data, split, len(sentences))) + + # We reuse LanguagePairDataset since classification can be modeled as a + # sequence-to-sequence task where the target sequence has length 1. + self.datasets[split] = LanguagePairDataset( + src=sentences, + src_sizes=lengths, + src_dict=self.input_vocab, + tgt=labels, + tgt_sizes=torch.ones(len(labels)), # targets have length 1 + tgt_dict=self.label_vocab, + left_pad_source=False, + # Since our target is a single class label, there's no need for + # teacher forcing. If we set this to ``True`` then our Model's + # ``forward()`` method would receive an additional argument called + # *prev_output_tokens* that would contain a shifted version of the + # target sequence. + input_feeding=False, + ) + + def max_positions(self): + """Return the max input length allowed by the task.""" + # The source should be less than *args.max_positions* and the "target" + # has max length 1. + return (self.args.max_positions, 1) + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary`.""" + return self.input_vocab + + @property + def target_dictionary(self): + """Return the target :class:`~fairseq.data.Dictionary`.""" + return self.label_vocab + + # We could override this method if we wanted more control over how batches + # are constructed, but it's not necessary for this tutorial since we can + # reuse the batching provided by LanguagePairDataset. + # + # def get_batch_iterator( + # self, dataset, max_tokens=None, max_sentences=None, max_positions=None, + # ignore_invalid_inputs=False, required_batch_size_multiple=1, + # seed=1, num_shards=1, shard_id=0, num_workers=0, epoch=1, + # data_buffer_size=0, disable_iterator_cache=False, + # ): + # (...) + + +4. Training the Model +--------------------- + +Now we're ready to train the model. We can use the existing :ref:`fairseq-train` +command-line tool for this, making sure to specify our new Task (``--task +simple_classification``) and Model architecture (``--arch +pytorch_tutorial_rnn``): + +.. note:: + + You can also configure the dimensionality of the hidden state by passing the + ``--hidden-dim`` argument to :ref:`fairseq-train`. + +.. code-block:: console + + > fairseq-train names-bin \ + --task simple_classification \ + --arch pytorch_tutorial_rnn \ + --optimizer adam --lr 0.001 --lr-shrink 0.5 \ + --max-tokens 1000 + (...) + | epoch 027 | loss 1.200 | ppl 2.30 | wps 15728 | ups 119.4 | wpb 116 | bsz 116 | num_updates 3726 | lr 1.5625e-05 | gnorm 1.290 | clip 0% | oom 0 | wall 32 | train_wall 21 + | epoch 027 | valid on 'valid' subset | valid_loss 1.41304 | valid_ppl 2.66 | num_updates 3726 | best 1.41208 + | done training in 31.6 seconds + +The model files should appear in the :file:`checkpoints/` directory. + + +5. Writing an evaluation script +------------------------------- + +Finally we can write a short script to evaluate our model on new inputs. Create +a new file named :file:`eval_classifier.py` with the following contents:: + + from fairseq import checkpoint_utils, data, options, tasks + + # Parse command-line arguments for generation + parser = options.get_generation_parser(default_task='simple_classification') + args = options.parse_args_and_arch(parser) + + # Setup task + task = tasks.setup_task(args) + + # Load model + print('| loading model from {}'.format(args.path)) + models, _model_args = checkpoint_utils.load_model_ensemble([args.path], task=task) + model = models[0] + + while True: + sentence = input('\nInput: ') + + # Tokenize into characters + chars = ' '.join(list(sentence.strip())) + tokens = task.source_dictionary.encode_line( + chars, add_if_not_exist=False, + ) + + # Build mini-batch to feed to the model + batch = data.language_pair_dataset.collate( + samples=[{'id': -1, 'source': tokens}], # bsz = 1 + pad_idx=task.source_dictionary.pad(), + eos_idx=task.source_dictionary.eos(), + left_pad_source=False, + input_feeding=False, + ) + + # Feed batch to the model and get predictions + preds = model(**batch['net_input']) + + # Print top 3 predictions and their log-probabilities + top_scores, top_labels = preds[0].topk(k=3) + for score, label_idx in zip(top_scores, top_labels): + label_name = task.target_dictionary.string([label_idx]) + print('({:.2f})\t{}'.format(score, label_name)) + +Now we can evaluate our model interactively. Note that we have included the +original data path (:file:`names-bin/`) so that the dictionaries can be loaded: + +.. code-block:: console + + > python eval_classifier.py names-bin --path checkpoints/checkpoint_best.pt + | [input] dictionary: 64 types + | [label] dictionary: 24 types + | loading model from checkpoints/checkpoint_best.pt + + Input: Satoshi + (-0.61) Japanese + (-1.20) Arabic + (-2.86) Italian + + Input: Sinbad + (-0.30) Arabic + (-1.76) English + (-4.08) Russian diff --git a/fairseq/docs/tutorial_simple_lstm.rst b/fairseq/docs/tutorial_simple_lstm.rst new file mode 100644 index 0000000..f529885 --- /dev/null +++ b/fairseq/docs/tutorial_simple_lstm.rst @@ -0,0 +1,518 @@ +Tutorial: Simple LSTM +===================== + +In this tutorial we will extend fairseq by adding a new +:class:`~fairseq.models.FairseqEncoderDecoderModel` that encodes a source +sentence with an LSTM and then passes the final hidden state to a second LSTM +that decodes the target sentence (without attention). + +This tutorial covers: + +1. **Writing an Encoder and Decoder** to encode/decode the source/target + sentence, respectively. +2. **Registering a new Model** so that it can be used with the existing + :ref:`Command-line tools`. +3. **Training the Model** using the existing command-line tools. +4. **Making generation faster** by modifying the Decoder to use + :ref:`Incremental decoding`. + + +1. Building an Encoder and Decoder +---------------------------------- + +In this section we'll define a simple LSTM Encoder and Decoder. All Encoders +should implement the :class:`~fairseq.models.FairseqEncoder` interface and +Decoders should implement the :class:`~fairseq.models.FairseqDecoder` interface. +These interfaces themselves extend :class:`torch.nn.Module`, so FairseqEncoders +and FairseqDecoders can be written and used in the same ways as ordinary PyTorch +Modules. + + +Encoder +~~~~~~~ + +Our Encoder will embed the tokens in the source sentence, feed them to a +:class:`torch.nn.LSTM` and return the final hidden state. To create our encoder +save the following in a new file named :file:`fairseq/models/simple_lstm.py`:: + + import torch.nn as nn + from fairseq import utils + from fairseq.models import FairseqEncoder + + class SimpleLSTMEncoder(FairseqEncoder): + + def __init__( + self, args, dictionary, embed_dim=128, hidden_dim=128, dropout=0.1, + ): + super().__init__(dictionary) + self.args = args + + # Our encoder will embed the inputs before feeding them to the LSTM. + self.embed_tokens = nn.Embedding( + num_embeddings=len(dictionary), + embedding_dim=embed_dim, + padding_idx=dictionary.pad(), + ) + self.dropout = nn.Dropout(p=dropout) + + # We'll use a single-layer, unidirectional LSTM for simplicity. + self.lstm = nn.LSTM( + input_size=embed_dim, + hidden_size=hidden_dim, + num_layers=1, + bidirectional=False, + batch_first=True, + ) + + def forward(self, src_tokens, src_lengths): + # The inputs to the ``forward()`` function are determined by the + # Task, and in particular the ``'net_input'`` key in each + # mini-batch. We discuss Tasks in the next tutorial, but for now just + # know that *src_tokens* has shape `(batch, src_len)` and *src_lengths* + # has shape `(batch)`. + + # Note that the source is typically padded on the left. This can be + # configured by adding the `--left-pad-source "False"` command-line + # argument, but here we'll make the Encoder handle either kind of + # padding by converting everything to be right-padded. + if self.args.left_pad_source: + # Convert left-padding to right-padding. + src_tokens = utils.convert_padding_direction( + src_tokens, + padding_idx=self.dictionary.pad(), + left_to_right=True + ) + + # Embed the source. + x = self.embed_tokens(src_tokens) + + # Apply dropout. + x = self.dropout(x) + + # Pack the sequence into a PackedSequence object to feed to the LSTM. + x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True) + + # Get the output from the LSTM. + _outputs, (final_hidden, _final_cell) = self.lstm(x) + + # Return the Encoder's output. This can be any object and will be + # passed directly to the Decoder. + return { + # this will have shape `(bsz, hidden_dim)` + 'final_hidden': final_hidden.squeeze(0), + } + + # Encoders are required to implement this method so that we can rearrange + # the order of the batch elements during inference (e.g., beam search). + def reorder_encoder_out(self, encoder_out, new_order): + """ + Reorder encoder output according to `new_order`. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + `encoder_out` rearranged according to `new_order` + """ + final_hidden = encoder_out['final_hidden'] + return { + 'final_hidden': final_hidden.index_select(0, new_order), + } + + +Decoder +~~~~~~~ + +Our Decoder will predict the next word, conditioned on the Encoder's final +hidden state and an embedded representation of the previous target word -- which +is sometimes called *teacher forcing*. More specifically, we'll use a +:class:`torch.nn.LSTM` to produce a sequence of hidden states that we'll project +to the size of the output vocabulary to predict each target word. + +:: + + import torch + from fairseq.models import FairseqDecoder + + class SimpleLSTMDecoder(FairseqDecoder): + + def __init__( + self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128, + dropout=0.1, + ): + super().__init__(dictionary) + + # Our decoder will embed the inputs before feeding them to the LSTM. + self.embed_tokens = nn.Embedding( + num_embeddings=len(dictionary), + embedding_dim=embed_dim, + padding_idx=dictionary.pad(), + ) + self.dropout = nn.Dropout(p=dropout) + + # We'll use a single-layer, unidirectional LSTM for simplicity. + self.lstm = nn.LSTM( + # For the first layer we'll concatenate the Encoder's final hidden + # state with the embedded target tokens. + input_size=encoder_hidden_dim + embed_dim, + hidden_size=hidden_dim, + num_layers=1, + bidirectional=False, + ) + + # Define the output projection. + self.output_projection = nn.Linear(hidden_dim, len(dictionary)) + + # During training Decoders are expected to take the entire target sequence + # (shifted right by one position) and produce logits over the vocabulary. + # The *prev_output_tokens* tensor begins with the end-of-sentence symbol, + # ``dictionary.eos()``, followed by the target sequence. + def forward(self, prev_output_tokens, encoder_out): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (Tensor, optional): output from the encoder, used for + encoder-side attention + + Returns: + tuple: + - the last decoder layer's output of shape + `(batch, tgt_len, vocab)` + - the last decoder layer's attention weights of shape + `(batch, tgt_len, src_len)` + """ + bsz, tgt_len = prev_output_tokens.size() + + # Extract the final hidden state from the Encoder. + final_encoder_hidden = encoder_out['final_hidden'] + + # Embed the target sequence, which has been shifted right by one + # position and now starts with the end-of-sentence symbol. + x = self.embed_tokens(prev_output_tokens) + + # Apply dropout. + x = self.dropout(x) + + # Concatenate the Encoder's final hidden state to *every* embedded + # target token. + x = torch.cat( + [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)], + dim=2, + ) + + # Using PackedSequence objects in the Decoder is harder than in the + # Encoder, since the targets are not sorted in descending length order, + # which is a requirement of ``pack_padded_sequence()``. Instead we'll + # feed nn.LSTM directly. + initial_state = ( + final_encoder_hidden.unsqueeze(0), # hidden + torch.zeros_like(final_encoder_hidden).unsqueeze(0), # cell + ) + output, _ = self.lstm( + x.transpose(0, 1), # convert to shape `(tgt_len, bsz, dim)` + initial_state, + ) + x = output.transpose(0, 1) # convert to shape `(bsz, tgt_len, hidden)` + + # Project the outputs to the size of the vocabulary. + x = self.output_projection(x) + + # Return the logits and ``None`` for the attention weights + return x, None + + +2. Registering the Model +------------------------ + +Now that we've defined our Encoder and Decoder we must *register* our model with +fairseq using the :func:`~fairseq.models.register_model` function decorator. +Once the model is registered we'll be able to use it with the existing +:ref:`Command-line Tools`. + +All registered models must implement the +:class:`~fairseq.models.BaseFairseqModel` interface. For sequence-to-sequence +models (i.e., any model with a single Encoder and Decoder), we can instead +implement the :class:`~fairseq.models.FairseqEncoderDecoderModel` interface. + +Create a small wrapper class in the same file and register it in fairseq with +the name ``'simple_lstm'``:: + + from fairseq.models import FairseqEncoderDecoderModel, register_model + + # Note: the register_model "decorator" should immediately precede the + # definition of the Model class. + + @register_model('simple_lstm') + class SimpleLSTMModel(FairseqEncoderDecoderModel): + + @staticmethod + def add_args(parser): + # Models can override this method to add new command-line arguments. + # Here we'll add some new command-line arguments to configure dropout + # and the dimensionality of the embeddings and hidden states. + parser.add_argument( + '--encoder-embed-dim', type=int, metavar='N', + help='dimensionality of the encoder embeddings', + ) + parser.add_argument( + '--encoder-hidden-dim', type=int, metavar='N', + help='dimensionality of the encoder hidden state', + ) + parser.add_argument( + '--encoder-dropout', type=float, default=0.1, + help='encoder dropout probability', + ) + parser.add_argument( + '--decoder-embed-dim', type=int, metavar='N', + help='dimensionality of the decoder embeddings', + ) + parser.add_argument( + '--decoder-hidden-dim', type=int, metavar='N', + help='dimensionality of the decoder hidden state', + ) + parser.add_argument( + '--decoder-dropout', type=float, default=0.1, + help='decoder dropout probability', + ) + + @classmethod + def build_model(cls, args, task): + # Fairseq initializes models by calling the ``build_model()`` + # function. This provides more flexibility, since the returned model + # instance can be of a different type than the one that was called. + # In this case we'll just return a SimpleLSTMModel instance. + + # Initialize our Encoder and Decoder. + encoder = SimpleLSTMEncoder( + args=args, + dictionary=task.source_dictionary, + embed_dim=args.encoder_embed_dim, + hidden_dim=args.encoder_hidden_dim, + dropout=args.encoder_dropout, + ) + decoder = SimpleLSTMDecoder( + dictionary=task.target_dictionary, + encoder_hidden_dim=args.encoder_hidden_dim, + embed_dim=args.decoder_embed_dim, + hidden_dim=args.decoder_hidden_dim, + dropout=args.decoder_dropout, + ) + model = SimpleLSTMModel(encoder, decoder) + + # Print the model architecture. + print(model) + + return model + + # We could override the ``forward()`` if we wanted more control over how + # the encoder and decoder interact, but it's not necessary for this + # tutorial since we can inherit the default implementation provided by + # the FairseqEncoderDecoderModel base class, which looks like: + # + # def forward(self, src_tokens, src_lengths, prev_output_tokens): + # encoder_out = self.encoder(src_tokens, src_lengths) + # decoder_out = self.decoder(prev_output_tokens, encoder_out) + # return decoder_out + +Finally let's define a *named architecture* with the configuration for our +model. This is done with the :func:`~fairseq.models.register_model_architecture` +function decorator. Thereafter this named architecture can be used with the +``--arch`` command-line argument, e.g., ``--arch tutorial_simple_lstm``:: + + from fairseq.models import register_model_architecture + + # The first argument to ``register_model_architecture()`` should be the name + # of the model we registered above (i.e., 'simple_lstm'). The function we + # register here should take a single argument *args* and modify it in-place + # to match the desired architecture. + + @register_model_architecture('simple_lstm', 'tutorial_simple_lstm') + def tutorial_simple_lstm(args): + # We use ``getattr()`` to prioritize arguments that are explicitly given + # on the command-line, so that the defaults defined below are only used + # when no other value has been specified. + args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256) + args.encoder_hidden_dim = getattr(args, 'encoder_hidden_dim', 256) + args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256) + args.decoder_hidden_dim = getattr(args, 'decoder_hidden_dim', 256) + + +3. Training the Model +--------------------- + +Now we're ready to train the model. We can use the existing :ref:`fairseq-train` +command-line tool for this, making sure to specify our new Model architecture +(``--arch tutorial_simple_lstm``). + +.. note:: + + Make sure you've already preprocessed the data from the IWSLT example in the + :file:`examples/translation/` directory. + +.. code-block:: console + + > fairseq-train data-bin/iwslt14.tokenized.de-en \ + --arch tutorial_simple_lstm \ + --encoder-dropout 0.2 --decoder-dropout 0.2 \ + --optimizer adam --lr 0.005 --lr-shrink 0.5 \ + --max-tokens 12000 + (...) + | epoch 052 | loss 4.027 | ppl 16.30 | wps 420805 | ups 39.7 | wpb 9841 | bsz 400 | num_updates 20852 | lr 1.95313e-05 | gnorm 0.218 | clip 0% | oom 0 | wall 529 | train_wall 396 + | epoch 052 | valid on 'valid' subset | valid_loss 4.74989 | valid_ppl 26.91 | num_updates 20852 | best 4.74954 + +The model files should appear in the :file:`checkpoints/` directory. While this +model architecture is not very good, we can use the :ref:`fairseq-generate` script to +generate translations and compute our BLEU score over the test set: + +.. code-block:: console + + > fairseq-generate data-bin/iwslt14.tokenized.de-en \ + --path checkpoints/checkpoint_best.pt \ + --beam 5 \ + --remove-bpe + (...) + | Translated 6750 sentences (153132 tokens) in 17.3s (389.12 sentences/s, 8827.68 tokens/s) + | Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146) + + +4. Making generation faster +--------------------------- + +While autoregressive generation from sequence-to-sequence models is inherently +slow, our implementation above is especially slow because it recomputes the +entire sequence of Decoder hidden states for every output token (i.e., it is +``O(n^2)``). We can make this significantly faster by instead caching the +previous hidden states. + +In fairseq this is called :ref:`Incremental decoding`. Incremental decoding is a +special mode at inference time where the Model only receives a single timestep +of input corresponding to the immediately previous output token (for teacher +forcing) and must produce the next output incrementally. Thus the model must +cache any long-term state that is needed about the sequence, e.g., hidden +states, convolutional states, etc. + +To implement incremental decoding we will modify our model to implement the +:class:`~fairseq.models.FairseqIncrementalDecoder` interface. Compared to the +standard :class:`~fairseq.models.FairseqDecoder` interface, the incremental +decoder interface allows ``forward()`` methods to take an extra keyword argument +(*incremental_state*) that can be used to cache state across time-steps. + +Let's replace our ``SimpleLSTMDecoder`` with an incremental one:: + + import torch + from fairseq.models import FairseqIncrementalDecoder + + class SimpleLSTMDecoder(FairseqIncrementalDecoder): + + def __init__( + self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128, + dropout=0.1, + ): + # This remains the same as before. + super().__init__(dictionary) + self.embed_tokens = nn.Embedding( + num_embeddings=len(dictionary), + embedding_dim=embed_dim, + padding_idx=dictionary.pad(), + ) + self.dropout = nn.Dropout(p=dropout) + self.lstm = nn.LSTM( + input_size=encoder_hidden_dim + embed_dim, + hidden_size=hidden_dim, + num_layers=1, + bidirectional=False, + ) + self.output_projection = nn.Linear(hidden_dim, len(dictionary)) + + # We now take an additional kwarg (*incremental_state*) for caching the + # previous hidden and cell states. + def forward(self, prev_output_tokens, encoder_out, incremental_state=None): + if incremental_state is not None: + # If the *incremental_state* argument is not ``None`` then we are + # in incremental inference mode. While *prev_output_tokens* will + # still contain the entire decoded prefix, we will only use the + # last step and assume that the rest of the state is cached. + prev_output_tokens = prev_output_tokens[:, -1:] + + # This remains the same as before. + bsz, tgt_len = prev_output_tokens.size() + final_encoder_hidden = encoder_out['final_hidden'] + x = self.embed_tokens(prev_output_tokens) + x = self.dropout(x) + x = torch.cat( + [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)], + dim=2, + ) + + # We will now check the cache and load the cached previous hidden and + # cell states, if they exist, otherwise we will initialize them to + # zeros (as before). We will use the ``utils.get_incremental_state()`` + # and ``utils.set_incremental_state()`` helpers. + initial_state = utils.get_incremental_state( + self, incremental_state, 'prev_state', + ) + if initial_state is None: + # first time initialization, same as the original version + initial_state = ( + final_encoder_hidden.unsqueeze(0), # hidden + torch.zeros_like(final_encoder_hidden).unsqueeze(0), # cell + ) + + # Run one step of our LSTM. + output, latest_state = self.lstm(x.transpose(0, 1), initial_state) + + # Update the cache with the latest hidden and cell states. + utils.set_incremental_state( + self, incremental_state, 'prev_state', latest_state, + ) + + # This remains the same as before + x = output.transpose(0, 1) + x = self.output_projection(x) + return x, None + + # The ``FairseqIncrementalDecoder`` interface also requires implementing a + # ``reorder_incremental_state()`` method, which is used during beam search + # to select and reorder the incremental state. + def reorder_incremental_state(self, incremental_state, new_order): + # Load the cached state. + prev_state = utils.get_incremental_state( + self, incremental_state, 'prev_state', + ) + + # Reorder batches according to *new_order*. + reordered_state = ( + prev_state[0].index_select(1, new_order), # hidden + prev_state[1].index_select(1, new_order), # cell + ) + + # Update the cached state. + utils.set_incremental_state( + self, incremental_state, 'prev_state', reordered_state, + ) + +Finally, we can rerun generation and observe the speedup: + +.. code-block:: console + + # Before + + > fairseq-generate data-bin/iwslt14.tokenized.de-en \ + --path checkpoints/checkpoint_best.pt \ + --beam 5 \ + --remove-bpe + (...) + | Translated 6750 sentences (153132 tokens) in 17.3s (389.12 sentences/s, 8827.68 tokens/s) + | Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146) + + # After + + > fairseq-generate data-bin/iwslt14.tokenized.de-en \ + --path checkpoints/checkpoint_best.pt \ + --beam 5 \ + --remove-bpe + (...) + | Translated 6750 sentences (153132 tokens) in 5.5s (1225.54 sentences/s, 27802.94 tokens/s) + | Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146) diff --git a/fairseq/examples/.gitignore b/fairseq/examples/.gitignore new file mode 100644 index 0000000..1ef816f --- /dev/null +++ b/fairseq/examples/.gitignore @@ -0,0 +1,2 @@ +!*/*.sh +!*/*.md diff --git a/fairseq/examples/MMPT/.gitignore b/fairseq/examples/MMPT/.gitignore new file mode 100644 index 0000000..70a255d --- /dev/null +++ b/fairseq/examples/MMPT/.gitignore @@ -0,0 +1,139 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ +runs +data +pretrained_models +projects/mmfusion_* +log_test +third-party +python_log +slurm_snapshot_code +lightning_logs +demos diff --git a/fairseq/examples/MMPT/CONFIG.md b/fairseq/examples/MMPT/CONFIG.md new file mode 100644 index 0000000..bbd1403 --- /dev/null +++ b/fairseq/examples/MMPT/CONFIG.md @@ -0,0 +1,41 @@ +### Config Files Explained + +Taking `projects/mfmmlm.yaml` for example, which run pretraining using masked frame model (MFM) and masked language model (MLM) on a single BERT: + +```yaml +project_dir: mfmmlm # specify the project dir for this baseline. +run_task: + - how2.yaml # run pretraining on how2 when launching `projects/taskmfmmlm.yaml` + - [vtt.yaml, vttcap.yaml, vttqa.yaml, youcook.yaml, youcookcap.yaml, crosstask.yaml, coin.yaml] # run fine-tuning tasks. +base_dir: task # a global template folder to specify each training task. +task_group: + pretrain: # section for pretraining. Most baselines differs in this section. + task_list: + - how2.yaml # reconfig `projects/task/how2.yaml` + dataset: + aligner: MFMMLMAligner # overwrite the aligner for MFMMLM training task. + model: + model_cls: MMFusionMFMMLM # overwrite the model, which constructs negative examples for MFM on-the-fly. + loss: + loss_cls: MFMMLM # overwrite the loss as MFMMLM, which combines MFM and MLM together. + fairseq: # all fairseq args can be expecified under this name. + dataset: + batch_size: 128 + finetune: # section for fine-tuning tasks, we don't need to change anything here mostly since we want to see how pretraining can contribute to finetuning. + task_list: # specify the list of downstream tasks, e.g., copy `projects/task/vtt.yaml` to `projects/mfmmlm`. + - vtt.yaml + - vttqa.yaml + - youcook.yaml + - youcookcap.yaml + - crosstask.yaml + - coin.yaml + test: # section for testing. + task_list: + - test_vtt.yaml + - test_vttqa.yaml + - test_youcook.yaml + - test_youcookcap.yaml + - test_crosstask.yaml + - test_crosstask_zs.yaml + - test_coin.yaml +``` diff --git a/fairseq/examples/MMPT/DATASET.md b/fairseq/examples/MMPT/DATASET.md new file mode 100644 index 0000000..930403e --- /dev/null +++ b/fairseq/examples/MMPT/DATASET.md @@ -0,0 +1,34 @@ +# Dataset + +We understand video data are challenging to download and process. For videos, we provide our preprocessing scripts under `scripts/video_feature_extractor` (deeply adapted from `https://github.com/antoine77340/video_feature_extractor`); for text, we pre-tokenizing scripts under `scripts/text_token_extractor`. + +### S3D Feature Extraction +We use pre-trained [S3D](https://github.com/antoine77340/S3D_HowTo100M) for video feature extraction. Please place the models as `pretrained_models/s3d_dict.npy` and `pretrained_models/s3d_howto100m.pth`. + +We implement a `PathBuilder` to automatically track video ids, source video paths to their feature locations (you may need `conda install -c anaconda pandas`). Decoding may need `pip install ffmpeg-python`. + +### Howto100M +[Howto100M](https://www.di.ens.fr/willow/research/howto100m/) is a large-scale video pre-training datasets. You may download videos by yourself and run preprocessing of our scripts. + +Several key differences of our preprocessing from existing papers: (1) we use `raw_caption.json` instead of `caption.json` to have pure self-supervision on text (`caption.json` has manual removal of stop words); (2) we remove partially duplicated texts that are originally designed for real-time readability (see `mmpt/processors/dedupprocessor.py`); (3) then we shard video/text features using `SharedTensor` in `mmpt/utils/shardedtensor.py` for fast loading during training (faster than `h5py`). + +#### Steps +##### video +To extract video features: edit and run `bash scripts/video_feature_extractor/how2/s3d.sh`. (consider to run this on multiple machines; by default, we store features in fp16 to save space and also for faster training). + +Split available video ids as `data/how2/how2_s3d_train.lst` and `data/how2/how2_s3d_val.lst`. + +Lastly, pack video features into `ShardedTensor` using `python scripts/video_feature_extractor/shard_feature.py`. + +##### text +Clean captions using `python -m mmpt.processors.dedupprocessor`. + +Tokenize dedupped captions `data/how2/raw_caption_dedup.pkl` into sharded numpy arrays: +``` +python scripts/text_token_extractor/pretokenization.py scripts/text_token_extractor/configs/bert-base-uncased.yaml +``` + +### Youcook, MSRVTT etc. +We use the version of Youcook and MSRVTT come with Howto100M and MILNCE. Please download the data to `data/youcook` and `data/msrvtt` accordingly, you can also check `projects/task/youcook.yaml` and `projects/task/vtt.yaml` etc. in details. +We extract features for Youcook, MSRVTT similar to the first step of Howto100M but we read text from meta data directly and perform on-the-fly tokenization. + diff --git a/fairseq/examples/MMPT/README.md b/fairseq/examples/MMPT/README.md new file mode 100644 index 0000000..4a84819 --- /dev/null +++ b/fairseq/examples/MMPT/README.md @@ -0,0 +1,166 @@ +# VideoCLIP and VLM + +You just find this toolkit for multimodal video understanding! It contains implementation of two recent multi-modal video understanding papers [VideoCLIP](https://arxiv.org/pdf/2109.14084.pdf) (EMNLP, 2021) and [VLM](https://aclanthology.org/2021.findings-acl.370.pdf) (ACL Findings, 2021), along with high-performance toolkits that are typically lacking in existing codebase. The toolkit is desigend to contain generic performance-tuned components that can be potentially adapted to other frameworks (we initially use fairseq). + +VideoCLIP is a contrastive learning model for zero-shot transfer to retrieval/classification/sequence labeling style tasks. + + + +VLM is a masked language model style pre-training using only one encoder with masked modality model (MMM) for retrieval/generation/sequence labeling style tasks. + + + +### News +[Oct. 2021] Initial release of implementation for the following papers: +[VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://arxiv.org/pdf/2109.14084.pdf) (Xu et. al., EMNLP 2021) +[VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding](https://aclanthology.org/2021.findings-acl.370.pdf) (Xu et. al., ACL Findings 2021) + + +### Installation +We aim to minimize the dependency of this repo on other packages. +We use fairseq as the main trainer (no models/datasets dependency on fairseq. We will support other trainer in future): +``` +git clone https://github.com/pytorch/fairseq +cd fairseq +pip install -e . # also optionally follow fairseq README for apex installation for fp16 training. +export MKL_THREADING_LAYER=GNU # fairseq may need this for numpy. +``` + +Then install this toolkit: +``` +cd examples/MMPT # MMPT can be in any folder, not necessarily under fairseq/examples. +pip install -e . +``` + +The code is developed under Python=3.8.8, Pytorch=1.8, cuda=11.0 with fairseq=1.0.0a0+af0389f and tested under Python=3.8.8 pytorch=1.9 cuda=11.0 fairseq=1.0.0a0+8e7bc73 during code release. +Most models require `transformers==3.4` for API compatibility `pip install transformers==3.4`. +In addition, some downstream tasks may need `conda install pandas`. + + +### Usage +#### Download Checkpoints +We use pre-trained [S3D](https://github.com/antoine77340/S3D_HowTo100M) for video feature extraction. Please place the models as `pretrained_models/s3d_dict.npy` and `pretrained_models/s3d_howto100m.pth`. + +Download VideoCLIP checkpoint `https://dl.fbaipublicfiles.com/MMPT/retri/videoclip/checkpoint_best.pt` to `runs/retri/videoclip` or VLM checkpoint `https://dl.fbaipublicfiles.com/MMPT/mtm/vlm/checkpoint_best.pt` to `runs/mtm/vlm`. + +#### Demo of Inference +run `python locallaunch.py projects/retri/videoclip.yaml --dryrun` to get all `.yaml`s for VideoCLIP. + +```python +import torch + +from mmpt.models import MMPTModel + + +model, tokenizer, aligner = MMPTModel.from_pretrained( + "projects/retri/videoclip/how2.yaml") + +model.eval() + + +# B, T, FPS, H, W, C (VideoCLIP is trained on 30 fps of s3d) +video_frames = torch.randn(1, 2, 30, 224, 224, 3) +caps, cmasks = aligner._build_text_seq( + tokenizer("some text", add_special_tokens=False)["input_ids"] +) + +caps, cmasks = caps[None, :], cmasks[None, :] # bsz=1 + +with torch.no_grad(): + output = model(video_frames, caps, cmasks, return_score=True) +print(output["score"]) # dot-product +``` + +#### Data Preparation +See [dataset](DATASET.md) for each dataset. + +#### Global Config for Training Pipeline +We organize a global config file for a training/testing pipeline under projects (see a detailed [explanation](CONFIG.md)). For example, VideoCLIP in `projects/retri/videoclip.yaml` and VLM is in `projects/mtm/vlm.yaml`. + +We wrap all cmds into `locallaunch.py` and `mmpt_cli/localjob.py`. You can check concrete cmds by `--dryrun` and then drop it for actual run. + +First, run `python locallaunch.py projects/retri/videoclip.yaml --dryrun` will generate configs for all configs of pre-training, zero-shot evaluation, fine-tuning and testing, for VideoCLIP under `projects/retri/videoclip`. + +Then each (either training or evaluation) process will be configed by a concrete config file (we save all complex arguments into the concrete config file for reproducibility, including fairseq args). For example, run zero-shot evaluation on youcook, +``` +python locallaunch.py projects/retri/videoclip/test_youcook_zs.yaml --jobtype local_predict # zero-shot evaluation. +python locallaunch.py projects/retri/videoclip/youcook_videoclip.yaml --jobtype local_single --dryrun # fine-tuning: use --dryrun to check cmds and drop it to make an actual run; local_small will run on two gpus (as in paper). +python locallaunch.py projects/retri/videoclip/test_youcook_videoclip.yaml --jobtype local_predict # testing on fine-tuned model. +``` + +Pretraining can be run as: +``` +python locallaunch.py projects/retri/videoclip/how2.yaml --jobtype local_single --dryrun # check then drop dryrun; paper is ran on local_big as 8 gpus. +``` +You may need to change `--jobtype`, check/extend `LocalJob` in `mmpt_cli/localjob.py` for multi-gpu/multi-node pre-training. + +The detailed instructions of pretraining and fine-tuning can be found at [pretraining instruction](pretraining.md) and [finetuning instruction](endtask.md). + + +### Development +Several components of this toolkit can be re-used for future research (and also our ongoing research). + +#### Framework Wrapper +We currently only support fairseq, but most components can be easily fit into other frameworks like huggingface. This repo is a `--user-dir` of fairseq with fairseq wrapper. For example, `mmpt/tasks` includes a `FairseqMMTTask`, which manages `mmpt/datasets` with `FairseqDataset`, `mmpt/models` with `FairseqModel`, `mmpt/losses` with `FairseqCriterion`. + +#### Processors +**Multi**modal research introduces the complexity on modality alignment from different input sources to losses. Inspired by [MMF](https://github.com/facebookresearch/mmf), this toolkit leverages `mmpt/processors` to handle various needs of data preprocessing and loading, **alleviating** the needs of multiple `torch.data.utils.Dataset` (that can be tricky for ablation study). +Processors can also be decoupled from `torch.data.utils.Dataset` for offline preprocessing instead of on-the-fly data preprocessing. + +We decouple a `mmpt.MMDataset` as 3 types of processors: `MetaProcessor`, `VideoProcessor`, `TextProcessor` and `Aligner`. They can be configed in `dataset` field of a config file (e.g., see `projects/task/how2.yaml`). +`MetaProcessor` is used to load the meta data about a dataset, aka, all video_ids of how2 dataset. +`VideoProcessor` is used to load the video features about a dataset. For example, S3D features for each second of a video. +`TextProcessor` is used to load the text (feature). For example, BERT pre-tokenized text clips for how2 dataset (with `start`s, `end`s of timestamps and `cap` for `token_ids`). +`Aligner` is the core class for different baselines that prepares the training data. For example, sampling a clip, masking tokens for MLM, etc. + +#### Performance-tuned Components +To speed up pre-training, this toolkit uses sharded features stored in mmaped numpy, backed by `ShardedTensor` in `mmpt/utils/shardedtensor.py` (adopted from MARGE paper). This reduces the loads of IO for multi-GPU training without loading all features for a video into the memory each time and `ShardedTensor` ensure features are stored in continuous disk space for near random access. This is used for both How2 video features and texts in `mmpt/processors/how2processor.py`. + + +### Citation +If this codebase is useful for your work, please cite the following papers: + +```BibTeX +@inproceedings{xu-etal-2021-videoclip, + title = "{VideoCLIP}: Contrastive Pre-training for\\Zero-shot Video-Text Understanding", + author = "Xu, Hu and + Ghosh, Gargi and + Huang, Po-Yao and + Okhonko, Dmytro and + Aghajanyan, Armen and + Metze, Florian and + Zettlemoyer, Luke and + Feichtenhofer, Christoph", + booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", + month = nov, + year = "2021", + address = "Online", + publisher = "Association for Computational Linguistics", +} + +@inproceedings{xu-etal-2021-vlm, + title = "{VLM}: Task-agnostic Video-Language Model Pre-training for Video Understanding", + author = "Xu, Hu and + Ghosh, Gargi and + Huang, Po-Yao and + Arora, Prahal and + Aminzadeh, Masoumeh and + Feichtenhofer, Christoph and + Metze, Florian and + Zettlemoyer, Luke", + booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", + month = aug, + year = "2021", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.findings-acl.370", + doi = "10.18653/v1/2021.findings-acl.370", + pages = "4227--4239", +} +``` + +### Bug Reports +This repo is in its initial stage, welcome bug reports to huxu@fb.com + +### Copyright +The majority of Multimodal Pre-training (MMPT) is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Evaluation Codes/Models: Howto100M and HuggingFace Transformers are licensed under the Apache2.0 license; COIN and NLG-eval are licensed under the MIT license; CrossTask is licensed under the BSD-3; DiDeMo is licensed under the BSD-2 license. diff --git a/fairseq/examples/MMPT/endtask.md b/fairseq/examples/MMPT/endtask.md new file mode 100644 index 0000000..7690955 --- /dev/null +++ b/fairseq/examples/MMPT/endtask.md @@ -0,0 +1,41 @@ +# Zero-shot Transfer and Finetuning + +(If you are new to the ideas of `mmpt.processors`, see [README](README.md) first.) +All finetuning datasets (specifically `processors`) are defined in `mmpt.processors.dsprocessor`. +Given the complexity of different types of finetuning tasks, each task may have their own meta/video/text/aligner processors and `mmpt/evaluators/{Predictor,Metric}`. + +### Tasks + +Currently, we support 5 end datasets: `MSRVTT`, `Youcook`, `COIN`, `Crosstask` and `DiDeMo` with the following tasks: +text-video retrieval: `MSRVTT`, `Youcook`, `DiDeMo`; +video captioning: `Youcook`; +Video Question and Answering: `MSRVTT-QA`. + +To add your own dataset, you can specify the corresponding processors and config them in the `dataset` field of a config file, such as `projects/task/vtt.yaml`. + +### Zero-shot Transfer (no Training) +Zero-shot transfer will run the pre-trained model (e.g., VideoCLIP) directly on testing data. Configs with pattern: `projects/task/*_zs_*.yaml` are dedicated for zero-shot transfer. + +### Fine-tuning + +The training of a downstream task is similar to pretraining, execept you may need to specify the `restore_file` in `fairseq.checkpoint` and reset optimizers, see `projects/task/ft.yaml` that is included by `projects/task/vtt.yaml`. + +We typically do finetuning on 2 gpus (`local_small`). + +### Testing +For each finetuning dataset, you may need to specify a testing config, similar to `projects/task/test_vtt.yaml`. + +We define `mmpt.evaluators.Predictor` for different types of prediction. For example, `MSRVTT` and `Youcook` are video-retrieval tasks and expecting to use `RetrievalPredictor`. You may need to define your new type of predictors and specify that in `predictor` field of a testing config. + +Each task may also have their own metric for evaluation. This can be created in `mmpt.evaluators.Metric` and specified in the `metric` field of a testing config. + +Launching a testing is as simple as training by specifying the path of a testing config: +```python locallaunch.py projects/mfmmlm/test_vtt.yaml``` +Testing will be launched locally by default since prediction is computationally less expensive. + +### Third-party Libraries +We list the following finetuning tasks that require third-party libraries. + +Youcook captioning: `https://github.com/Maluuba/nlg-eval` + +CrossTask: `https://github.com/DmZhukov/CrossTask`'s `dp` under `third-party/CrossTask` (`python setup.py build_ext --inplace`) diff --git a/fairseq/examples/MMPT/locallaunch.py b/fairseq/examples/MMPT/locallaunch.py new file mode 100644 index 0000000..e20fd81 --- /dev/null +++ b/fairseq/examples/MMPT/locallaunch.py @@ -0,0 +1,148 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import argparse +import os + +from omegaconf import OmegaConf + +from mmpt.utils import recursive_config, overwrite_dir +from mmpt_cli.localjob import LocalJob + + +class JobLauncher(object): + JOB_CONFIG = { + "local": LocalJob, + } + + def __init__(self, yaml_file): + self.yaml_file = yaml_file + job_key = "local" + + if yaml_file.endswith(".yaml"): + config = recursive_config(yaml_file) + if config.task_type is not None: + job_key = config.task_type.split("_")[0] + else: + raise ValueError("unknown extension of job file:", yaml_file) + self.job_key = job_key + + def __call__(self, job_type=None, dryrun=False): + if job_type is not None: + self.job_key = job_type.split("_")[0] + print("[JobLauncher] job_key", self.job_key) + job = JobLauncher.JOB_CONFIG[self.job_key]( + self.yaml_file, job_type=job_type, dryrun=dryrun) + return job.submit() + + +class Pipeline(object): + """a job that loads yaml config.""" + + def __init__(self, fn): + """ + load a yaml config of a job and save generated configs as yaml for each task. + return: a list of files to run as specified by `run_task`. + """ + if fn.endswith(".py"): + # a python command. + self.backend = "python" + self.run_yamls = [fn] + return + + job_config = recursive_config(fn) + if job_config.base_dir is None: # single file job config. + self.run_yamls = [fn] + return + + self.project_dir = os.path.join("projects", job_config.project_dir) + self.run_dir = os.path.join("runs", job_config.project_dir) + + if job_config.run_task is not None: + run_yamls = [] + for stage in job_config.run_task: + # each stage can have multiple tasks running in parallel. + if OmegaConf.is_list(stage): + stage_yamls = [] + for task_file in stage: + stage_yamls.append( + os.path.join(self.project_dir, task_file)) + run_yamls.append(stage_yamls) + else: + run_yamls.append(os.path.join(self.project_dir, stage)) + self.run_yamls = run_yamls + configs_to_save = self._overwrite_task(job_config) + self._save_configs(configs_to_save) + + def __getitem__(self, idx): + yaml_files = self.run_yamls[idx] + if isinstance(yaml_files, list): + return [JobLauncher(yaml_file) for yaml_file in yaml_files] + return [JobLauncher(yaml_files)] + + def __len__(self): + return len(self.run_yamls) + + def _save_configs(self, configs_to_save: dict): + # save + os.makedirs(self.project_dir, exist_ok=True) + for config_file in configs_to_save: + config = configs_to_save[config_file] + print("saving", config_file) + OmegaConf.save(config=config, f=config_file) + + def _overwrite_task(self, job_config): + configs_to_save = {} + self.base_project_dir = os.path.join("projects", job_config.base_dir) + self.base_run_dir = os.path.join("runs", job_config.base_dir) + + for config_sets in job_config.task_group: + overwrite_config = job_config.task_group[config_sets] + if ( + overwrite_config.task_list is None + or len(overwrite_config.task_list) == 0 + ): + print( + "[warning]", + job_config.task_group, + "has no task_list specified.") + # we don't want this added to a final config. + task_list = overwrite_config.pop("task_list", None) + for config_file in task_list: + config_file_path = os.path.join( + self.base_project_dir, config_file) + config = recursive_config(config_file_path) + # overwrite it. + if overwrite_config: + config = OmegaConf.merge(config, overwrite_config) + overwrite_dir(config, self.run_dir, basedir=self.base_run_dir) + save_file_path = os.path.join(self.project_dir, config_file) + configs_to_save[save_file_path] = config + return configs_to_save + + +def main(args): + job_type = args.jobtype if args.jobtype else None + # parse multiple pipelines. + pipelines = [Pipeline(fn) for fn in args.yamls.split(",")] + + for pipe_id, pipeline in enumerate(pipelines): + if not hasattr(pipeline, "project_dir"): + for job in pipeline[0]: + job(job_type=job_type, dryrun=args.dryrun) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("yamls", type=str) + parser.add_argument( + "--dryrun", + action="store_true", + help="run config and prepare to submit without launch the job.", + ) + parser.add_argument( + "--jobtype", type=str, default="", + help="force to run jobs as specified.") + args = parser.parse_args() + main(args) diff --git a/fairseq/examples/MMPT/mmpt/__init__.py b/fairseq/examples/MMPT/mmpt/__init__.py new file mode 100644 index 0000000..6ff86dd --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +try: + # fairseq user dir + from .datasets import FairseqMMDataset + from .losses import FairseqCriterion + from .models import FairseqMMModel + from .tasks import FairseqMMTask +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/datasets/__init__.py b/fairseq/examples/MMPT/mmpt/datasets/__init__.py new file mode 100644 index 0000000..2578235 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/datasets/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .mmdataset import * + +try: + from .fairseqmmdataset import * +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/datasets/fairseqmmdataset.py b/fairseq/examples/MMPT/mmpt/datasets/fairseqmmdataset.py new file mode 100644 index 0000000..02c4914 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/datasets/fairseqmmdataset.py @@ -0,0 +1,57 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +TODO (huxu): fairseq wrapper class for all dataset you defined: mostly MMDataset. +""" + +from collections import OrderedDict + +from torch.utils.data import Dataset +from torch.utils.data.dataloader import default_collate +from fairseq.data import FairseqDataset, data_utils + + +class FairseqMMDataset(FairseqDataset): + """ + A wrapper class for MMDataset for fairseq. + """ + + def __init__(self, mmdataset): + if not isinstance(mmdataset, Dataset): + raise TypeError("mmdataset must be of type `torch.utils.data.dataset`.") + self.mmdataset = mmdataset + + def set_epoch(self, epoch, **unused): + super().set_epoch(epoch) + self.epoch = epoch + + def __getitem__(self, idx): + with data_utils.numpy_seed(43211, self.epoch, idx): + return self.mmdataset[idx] + + def __len__(self): + return len(self.mmdataset) + + def collater(self, samples): + if hasattr(self.mmdataset, "collator"): + return self.mmdataset.collator(samples) + if len(samples) == 0: + return {} + if isinstance(samples[0], dict): + batch = OrderedDict() + for key in samples[0]: + if samples[0][key] is not None: + batch[key] = default_collate([sample[key] for sample in samples]) + return batch + else: + return default_collate(samples) + + def size(self, index): + """dummy implementation: we don't use --max-tokens""" + return 1 + + def num_tokens(self, index): + """dummy implementation: we don't use --max-tokens""" + return 1 diff --git a/fairseq/examples/MMPT/mmpt/datasets/mmdataset.py b/fairseq/examples/MMPT/mmpt/datasets/mmdataset.py new file mode 100644 index 0000000..3d07283 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/datasets/mmdataset.py @@ -0,0 +1,111 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from collections import OrderedDict + +from torch.utils.data import Dataset +from torch.utils.data.dataloader import default_collate + +from ..utils import set_seed + + +class MMDataset(Dataset): + """ + A generic multi-modal dataset. + Args: + `meta_processor`: a meta processor, + handling loading meta data and return video_id and text_id. + `video_processor`: a video processor, + handling e.g., decoding, loading .np files. + `text_processor`: a text processor, + handling e.g., tokenization. + `aligner`: combine the video and text feature + as one training example. + """ + + def __init__( + self, + meta_processor, + video_processor, + text_processor, + align_processor, + ): + self.split = meta_processor.split + self.meta_processor = meta_processor + self.video_processor = video_processor + self.text_processor = text_processor + self.align_processor = align_processor + + def __len__(self): + return len(self.meta_processor) + + def __getitem__(self, idx): + if self.split == "test": + set_seed(idx) + video_id, text_id = self.meta_processor[idx] + video_feature = self.video_processor(video_id) + text_feature = self.text_processor(text_id) + output = self.align_processor(video_id, video_feature, text_feature) + # TODO (huxu): the following is for debug purpose. + output.update({"idx": idx}) + return output + + def collater(self, samples): + """This collator is deprecated. + set self.collator = MMDataset.collater. + see collator in FairseqMMDataset. + """ + + if len(samples) == 0: + return {} + if isinstance(samples[0], dict): + batch = OrderedDict() + for key in samples[0]: + if samples[0][key] is not None: + batch[key] = default_collate( + [sample[key] for sample in samples]) + # if torch.is_tensor(batch[key]): + # print(key, batch[key].size()) + # else: + # print(key, len(batch[key])) + return batch + else: + return default_collate(samples) + + def print_example(self, output): + print("[one example]", output["video_id"]) + if ( + hasattr(self.align_processor, "subsampling") + and self.align_processor.subsampling is not None + and self.align_processor.subsampling > 1 + ): + for key in output: + if torch.is_tensor(output[key]): + output[key] = output[key][0] + + # search tokenizer to translate ids back. + tokenizer = None + if hasattr(self.text_processor, "tokenizer"): + tokenizer = self.text_processor.tokenizer + elif hasattr(self.align_processor, "tokenizer"): + tokenizer = self.align_processor.tokenizer + if tokenizer is not None: + caps = output["caps"].tolist() + if isinstance(caps[0], list): + caps = caps[0] + print("caps", tokenizer.decode(caps)) + print("caps", tokenizer.convert_ids_to_tokens(caps)) + + for key, value in output.items(): + if torch.is_tensor(value): + if len(value.size()) >= 3: # attention_mask. + print(key, value.size()) + print(key, "first", value[0, :, :]) + print(key, "last", value[-1, :, :]) + else: + print(key, value) + print("[end of one example]") diff --git a/fairseq/examples/MMPT/mmpt/evaluators/__init__.py b/fairseq/examples/MMPT/mmpt/evaluators/__init__.py new file mode 100644 index 0000000..2d06b9d --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/evaluators/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .metric import * +from .evaluator import * + + +# experimental. +try: + from .expmetric import * +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/evaluators/evaluator.py b/fairseq/examples/MMPT/mmpt/evaluators/evaluator.py new file mode 100644 index 0000000..94d9c5e --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/evaluators/evaluator.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import glob +import numpy as np + +from . import metric as metric_path +from . import predictor as predictor_path + + +class Evaluator(object): + """ + perform evaluation on a single (downstream) task. + make this both offline and online. + TODO(huxu) saving evaluation results. + """ + + def __init__(self, config, eval_dataloader=None): + if config.metric is None: + raise ValueError("config.metric is", config.metric) + metric_cls = getattr(metric_path, config.metric) + self.metric = metric_cls(config) + if config.predictor is None: + raise ValueError("config.predictor is", config.predictor) + predictor_cls = getattr(predictor_path, config.predictor) + self.predictor = predictor_cls(config) + self.eval_dataloader = eval_dataloader + + def __call__(self): + try: + print(self.predictor.pred_dir) + for pred_file in glob.glob( + self.predictor.pred_dir + "/*_merged.npy"): + outputs = np.load(pred_file) + results = self.metric.compute_metrics(outputs) + self.metric.print_computed_metrics(results) + + outputs = np.load(os.path.join( + self.predictor.pred_dir, "merged.npy")) + results = self.metric.compute_metrics(outputs) + return {"results": results, "metric": self.metric} + except FileNotFoundError: + print("\n[missing]", self.predictor.pred_dir) + return {} + + def evaluate(self, model, eval_dataloader=None, output_file="merged"): + if eval_dataloader is None: + eval_dataloader = self.eval_dataloader + outputs = self.predictor.predict_loop( + model, eval_dataloader, output_file) + results = self.metric.compute_metrics(**outputs) + return results diff --git a/fairseq/examples/MMPT/mmpt/evaluators/metric.py b/fairseq/examples/MMPT/mmpt/evaluators/metric.py new file mode 100644 index 0000000..163724b --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/evaluators/metric.py @@ -0,0 +1,313 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import json + + +class Metric(object): + def __init__(self, config, metric_names): + self.metric_names = metric_names + + def best_metric(self, metric): + return metric[self.metric_names[0]] + + def save_metrics(self, fn, metrics): + with open(fn, "w") as fw: + json.dump(fw, metrics) + + def print_computed_metrics(self, metrics): + raise NotImplementedError + + +class RetrievalMetric(Metric): + """ + this is modified from `howto100m/metrics.py`. + History of changes: + refactor as a class. + add metric_key in __init__ + """ + + def __init__(self, config, metric_names=["R1", "R5", "R10", "MR"]): + super().__init__(config, metric_names) + self.error = False # TODO(huxu): add to config to print error. + + def compute_metrics(self, outputs, texts, **kwargs): + x = outputs + sx = np.sort(-x, axis=1) + d = np.diag(-x) + d = d[:, np.newaxis] + ind = sx - d + ind = np.where(ind == 0) + ind = ind[1] + metrics = {} + metrics["R1"] = float(np.sum(ind == 0)) / len(ind) + metrics["R5"] = float(np.sum(ind < 5)) / len(ind) + metrics["R10"] = float(np.sum(ind < 10)) / len(ind) + metrics["MR"] = np.median(ind) + 1 + + max_idx = np.argmax(outputs, axis=1) + if self.error: + # print top-20 errors. + error = [] + for ex_idx in range(20): + error.append((texts[ex_idx], texts[max_idx[ex_idx]])) + metrics["error"] = error + return metrics + + def print_computed_metrics(self, metrics): + r1 = metrics["R1"] + r5 = metrics["R5"] + r10 = metrics["R10"] + mr = metrics["MR"] + print( + "R@1: {:.4f} - R@5: {:.4f} - R@10: {:.4f} - Median R: {}".format( + r1, r5, r10, mr + ) + ) + if "error" in metrics: + print(metrics["error"]) + + +class DiDeMoMetric(Metric): + """ + History of changes: + python 2.x to python 3.x. + merge utils.py into eval to save one file. + reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py + Code to evaluate your results on the DiDeMo dataset. + """ + def __init__(self, config, metric_names=["rank1", "rank5", "miou"]): + super().__init__(config, metric_names) + + def compute_metrics(self, outputs, targets, **kwargs): + assert len(outputs) == len(targets) + rank1, rank5, miou = self._eval_predictions(outputs, targets) + metrics = { + "rank1": rank1, + "rank5": rank5, + "miou": miou + } + return metrics + + def print_computed_metrics(self, metrics): + rank1 = metrics["rank1"] + rank5 = metrics["rank5"] + miou = metrics["miou"] + # print("Average rank@1: %f" % rank1) + # print("Average rank@5: %f" % rank5) + # print("Average iou: %f" % miou) + + print( + "Average rank@1: {:.4f} Average rank@5: {:.4f} Average iou: {:.4f}".format( + rank1, rank5, miou + ) + ) + + def _iou(self, pred, gt): + intersection = max(0, min(pred[1], gt[1]) + 1 - max(pred[0], gt[0])) + union = max(pred[1], gt[1]) + 1 - min(pred[0], gt[0]) + return float(intersection)/union + + def _rank(self, pred, gt): + return pred.index(tuple(gt)) + 1 + + def _eval_predictions(self, segments, data): + ''' + Inputs: + segments: For each item in the ground truth data, rank possible video segments given the description and video. + In DiDeMo, there are 21 posible moments extracted for each video so the list of video segments will be of length 21. + The first video segment should be the video segment that best corresponds to the text query. + There are 4180 sentence in the validation data, so when evaluating a model on the val dataset, + segments should be a list of lenght 4180, and each item in segments should be a list of length 21. + data: ground truth data + ''' + average_ranks = [] + average_iou = [] + for s, d in zip(segments, data): + pred = s[0] + ious = [self._iou(pred, t) for t in d['times']] + average_iou.append(np.mean(np.sort(ious)[-3:])) + ranks = [self._rank(s, t) for t in d['times'] if tuple(t) in s] # if t in s] is added for s, e not in prediction. + average_ranks.append(np.mean(np.sort(ranks)[:3])) + rank1 = np.sum(np.array(average_ranks) <= 1)/float(len(average_ranks)) + rank5 = np.sum(np.array(average_ranks) <= 5)/float(len(average_ranks)) + miou = np.mean(average_iou) + + # print("Average rank@1: %f" % rank1) + # print("Average rank@5: %f" % rank5) + # print("Average iou: %f" % miou) + return rank1, rank5, miou + + +class NLGMetric(Metric): + def __init__( + self, + config, + metric_names=[ + "Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4", + "METEOR", "ROUGE_L", "CIDEr" + ] + ): + super().__init__(config, metric_names) + # please install NLGEval from `https://github.com/Maluuba/nlg-eval` + from nlgeval import NLGEval + self.nlg = NLGEval() + + def compute_metrics(self, outputs, targets, **kwargs): + return self.nlg.compute_metrics( + hyp_list=outputs, ref_list=targets) + + def print_computed_metrics(self, metrics): + Bleu_1 = metrics["Bleu_1"] + Bleu_2 = metrics["Bleu_2"] + Bleu_3 = metrics["Bleu_3"] + Bleu_4 = metrics["Bleu_4"] + METEOR = metrics["METEOR"] + ROUGE_L = metrics["ROUGE_L"] + CIDEr = metrics["CIDEr"] + + print( + "Bleu_1: {:.4f} - Bleu_2: {:.4f} - Bleu_3: {:.4f} - Bleu_4: {:.4f} - METEOR: {:.4f} - ROUGE_L: {:.4f} - CIDEr: {:.4f}".format( + Bleu_1, Bleu_2, Bleu_3, Bleu_4, METEOR, ROUGE_L, CIDEr + ) + ) + + +class QAMetric(Metric): + def __init__( + self, + config, + metric_names=["acc"] + ): + super().__init__(config, metric_names) + + def compute_metrics(self, outputs, targets, **kwargs): + from sklearn.metrics import accuracy_score + return {"acc": accuracy_score(targets, outputs)} + + def print_computed_metrics(self, metrics): + print("acc: {:.4f}".format(metrics["acc"])) + + +class COINActionSegmentationMetric(Metric): + """ + COIN dataset listed 3 repos for Action Segmentation. + Action Sets, NeuralNetwork-Viterbi, TCFPN-ISBA. + The first and second are the same. + https://github.com/alexanderrichard/action-sets/blob/master/eval.py + + Future reference for the third: + `https://github.com/Zephyr-D/TCFPN-ISBA/blob/master/utils/metrics.py` + """ + def __init__(self, config, metric_name=["frame_acc"]): + super().__init__(config, metric_name) + + def compute_metrics(self, outputs, targets): + n_frames = 0 + n_errors = 0 + n_errors = sum(outputs != targets) + n_frames = len(targets) + return {"frame_acc": 1.0 - float(n_errors) / n_frames} + + def print_computed_metrics(self, metrics): + fa = metrics["frame_acc"] + print("frame accuracy:", fa) + + +class CrossTaskMetric(Metric): + def __init__(self, config, metric_names=["recall"]): + super().__init__(config, metric_names) + + def compute_metrics(self, outputs, targets, **kwargs): + """refactored from line 166: + https://github.com/DmZhukov/CrossTask/blob/master/train.py""" + + recalls = self._get_recalls(Y_true=targets, Y_pred=outputs) + results = {} + for task, rec in recalls.items(): + results[str(task)] = rec + + avg_recall = np.mean(list(recalls.values())) + results["recall"] = avg_recall + return results + + def print_computed_metrics(self, metrics): + print('Recall: {0:0.3f}'.format(metrics["recall"])) + for task in metrics: + if task != "recall": + print('Task {0}. Recall = {1:0.3f}'.format( + task, metrics[task])) + + def _get_recalls(self, Y_true, Y_pred): + """refactored from + https://github.com/DmZhukov/CrossTask/blob/master/train.py""" + + step_match = {task: 0 for task in Y_true.keys()} + step_total = {task: 0 for task in Y_true.keys()} + for task, ys_true in Y_true.items(): + ys_pred = Y_pred[task] + for vid in set(ys_pred.keys()).intersection(set(ys_true.keys())): + y_true = ys_true[vid] + y_pred = ys_pred[vid] + step_total[task] += (y_true.sum(axis=0) > 0).sum() + step_match[task] += (y_true*y_pred).sum() + recalls = { + task: step_match[task] / n for task, n in step_total.items()} + return recalls + + +class ActionRecognitionMetric(Metric): + def __init__( + self, + config, + metric_names=["acc", "acc_splits", "r1_splits", "r5_splits", "r10_splits"] + ): + super().__init__(config, metric_names) + + def compute_metrics(self, outputs, targets, splits, **kwargs): + all_video_embd = outputs + labels = targets + split1, split2, split3 = splits + accs = [] + r1s = [] + r5s = [] + r10s = [] + for split in range(3): + if split == 0: + s = split1 + elif split == 1: + s = split2 + else: + s = split3 + + X_pred = all_video_embd[np.where(s == 2)[0]] + label_test = labels[np.where(s == 2)[0]] + logits = X_pred + X_pred = np.argmax(X_pred, axis=1) + acc = np.sum(X_pred == label_test) / float(len(X_pred)) + accs.append(acc) + # compute recall. + sorted_pred = (-logits).argsort(axis=-1) + label_test_sp = label_test.reshape(-1, 1) + + r1 = np.mean((sorted_pred[:, :1] == label_test_sp).sum(axis=1), axis=0) + r5 = np.mean((sorted_pred[:, :5] == label_test_sp).sum(axis=1), axis=0) + r10 = np.mean((sorted_pred[:, :10] == label_test_sp).sum(axis=1), axis=0) + r1s.append(r1) + r5s.append(r5) + r10s.append(r10) + + return {"acc": accs[0], "acc_splits": accs, "r1_splits": r1s, "r5_splits": r5s, "r10_splits": r10s} + + def print_computed_metrics(self, metrics): + for split, acc in enumerate(metrics["acc_splits"]): + print("Top 1 accuracy on split {}: {}; r1 {}; r5 {}; r10 {}".format( + split + 1, acc, + metrics["r1_splits"][split], + metrics["r5_splits"][split], + metrics["r10_splits"][split], + ) + ) diff --git a/fairseq/examples/MMPT/mmpt/evaluators/predictor.py b/fairseq/examples/MMPT/mmpt/evaluators/predictor.py new file mode 100644 index 0000000..2ffef6a --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/evaluators/predictor.py @@ -0,0 +1,595 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import random +import json +import numpy as np +import torch +import pickle +import math + +from tqdm import tqdm + + +class Predictor(object): + """this base class is used to save predictions to disk + (and being called by a evaluator later). + Predictor has minimum support of single gpu prediction. + """ + def __init__(self, config): + self.pred_dir = None # on-the-fly eval does not save the results. + if hasattr(config, "eval") and config.eval is not None: + self.pred_dir = config.eval.save_path + os.makedirs(self.pred_dir, exist_ok=True) + + def __call__(self, outputs): + """extract the prediction and save it.""" + raise NotImplementedError + + def predict_loop(self, model, eval_dataloader, output_file=None): + """on-the-fly prediction on a single gpu.""" + self.full_scores = [] + model.eval() + model = model.to(0) + with torch.no_grad(): + for data in eval_dataloader: + data = self.to_ctx(data) + outputs = model(**data) + outputs.update(data) + self(outputs) + return self.finalize(output_file) + + def finalize(self, output_file): + pass + + def to_ctx(self, data, ctx=0, dtype=None): + if isinstance(data, dict): + for key in data: + if torch.is_tensor(data[key]): + if dtype is not None and data[key].dtype == torch.float32: + data[key] = data[key].to(dtype) + data[key] = data[key].to(ctx) + return data + else: + raise ValueError("non-dict type of batch is not supported yet.") + + +class NLGPredictor(Predictor): + """Predicting Text from MMFusion models.""" + """TODO: make a context.""" + def __init__(self, config): + super().__init__(config) + from transformers import AutoTokenizer + + self.tokenizer = AutoTokenizer.from_pretrained( + config.dataset.bert_name, + bos_token="[CLS]", eos_token="[SEP]") + self.bos_token_id = self.tokenizer.bos_token_id + self.eos_token_id = self.tokenizer.eos_token_id + + def predict_loop(self, model, eval_dataloader, output_file=None): + """TODO: refactor base classes.""" + ctx = 0 + outputs = {"outputs": [], "targets": [[]]} + model.eval() + model = model.to(ctx) + with torch.no_grad(): + for data in tqdm(eval_dataloader): + data = self.to_ctx(data, ctx) + self(data, model, outputs) + return self.finalize(outputs, output_file) + + def __call__(self, data, model, outputs): + data.update({ + "bos_token_id": self.bos_token_id, + "eos_token_id": self.eos_token_id + }) + + output = model.generate(**data) + assert len(output) == len(data["ref"]) + for idx, _output in enumerate(output): + generated_text = self.tokenizer.decode( + _output, skip_special_tokens=True) + if generated_text == "": + generated_text = "none" + outputs["outputs"].append(generated_text) + outputs["targets"][0].append(data["ref"][idx]) + if random.random() < 0.001: + print("_output", _output) + print("generated_text", generated_text) + print("ref", data["ref"][idx]) + + def finalize(self, outputs, output_file=None): + if output_file is not None: + with open(os.path.join( + self.pred_dir, output_file + ".json"), "w") as fw: + json.dump(outputs, fw, indent=4) + return outputs + + +class RetrievalPredictor(Predictor): + """generated `pooled_video` and `pooled_text`.""" + def __init__(self, config): + super().__init__(config) + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained( + config.dataset.bert_name) + + def predict_loop( + self, + model, + eval_dataloader, + output_file="retrieval.npy" + ): + """on-the-fly prediction on a single gpu.""" + full_scores = [] + texts = [] + model.eval() + model = model.cuda() + with torch.no_grad(): + for data in eval_dataloader: + # convert to dict. + if not isinstance(data, dict): + data = { + "caps": data[0], + "cmasks": data[1], + "vfeats": data[2], + "vmasks": data[3], + "video_id": data[4] + } + data = self.to_ctx(data) + outputs = model(**data) + outputs.update(data) + self(outputs, full_scores) + for _cap in data["caps"]: + texts.append( + self.tokenizer.decode(_cap, skip_special_tokens=True) + ) + + return self.finalize(full_scores, texts, output_file) + + def __call__(self, sample, full_scores): + scores = self._get_pooled_outputs(sample) + self._append_scores(scores, full_scores) + + def finalize(self, full_scores, texts, output_file=None): + outputs = self._aggregate_scores(full_scores) + if output_file is not None: + np.save(os.path.join(self.pred_dir, output_file + ".npy"), outputs) + return {"outputs": outputs, "texts": texts} + + def _get_pooled_outputs(self, outputs): + if "pooled_video" in outputs: + return outputs["pooled_video"], outputs["pooled_text"] + else: + raise ValueError("unknown format of outputs.") + + def _append_scores(self, scores, full_scores): + assert len(scores) == 2 + if len(full_scores) == 0: + full_scores.append([]) + full_scores.append([]) + full_scores[0].append(scores[0].cpu().detach().numpy()) + full_scores[1].append(scores[1].cpu().detach().numpy()) + + def _aggregate_scores(self, scores): + assert len(scores) == 2 + video_hidden = np.concatenate(scores[0], axis=0) + text_hidden = np.concatenate(scores[1], axis=0) + # clear up. + self.full_scores = [] + return np.matmul(text_hidden, video_hidden.T) + + +class QAPredictor(Predictor): + """generated `pooled_video` and `pooled_text`.""" + def __init__(self, config): + super().__init__(config) + """predictor maintains scores and aggregate them.""" + + def predict_loop(self, model, eval_dataloader, output_file="qa.npy"): + """on-the-fly prediction on a single gpu.""" + self.full_scores = [] + model.eval() + model = model.cuda() + with torch.no_grad(): + for data in eval_dataloader: + # reshape ans and dup video 5 times. + v_len = data["vfeats"].size(1) + hidden_size = data["vfeats"].size(2) + data["vfeats"] = data["vfeats"].unsqueeze(1).repeat(1, 5, 1, 1).view(-1, v_len, hidden_size) + data["vmasks"] = data["vmasks"].unsqueeze(1).repeat(1, 5, 1).view(-1, v_len) + + t_len = data["caps"].size(-1) + data["caps"] = data["caps"].view(-1, t_len) + data["cmasks"] = data["cmasks"].view(-1, t_len) + + data = self.to_ctx(data) + outputs = model(**data) + outputs.update(data) + self(outputs) + return self.finalize(output_file) + + def __call__(self, sample): + hidden_size = sample["pooled_video"].size(-1) + pooled_video = sample["pooled_video"].view(-1, 5, hidden_size) + pooled_text = sample["pooled_text"].view(-1, 5, hidden_size) + scores = torch.bmm(pooled_video, pooled_text.transpose(2, 1)) + scores = scores.argmax(-1) + self._append_scores(scores[:, 0], sample["answers"], self.full_scores) + + def finalize(self, output_file=None): + outputs, targets = self._aggregate_scores(self.full_scores) + if output_file is not None: + np.save(os.path.join(self.pred_dir, output_file + ".npy"), outputs) + return {"outputs": outputs, "targets": targets} + + def _append_scores(self, scores, answers, full_scores): + if len(full_scores) == 0: + full_scores.append([]) + full_scores.append([]) + full_scores[0].append(scores.cpu().detach().numpy()) + full_scores[1].append(answers.cpu().detach().numpy()) + + def _aggregate_scores(self, scores): + assert len(scores) == 2 + outputs = np.concatenate(scores[0], axis=0) + targets = np.concatenate(scores[1], axis=0) + # clear up. + self.full_scores = [] + return outputs, targets + + +class CrossTaskPredictor(Predictor): + """ + CrossTaskPredictor needs to compute the average of logits + for overlapped sliding-window. + """ + def __init__(self, config): + super().__init__(config) + self.lsm = torch.nn.LogSoftmax(dim=1) + self.max_video_len = config.dataset.max_video_len + self.sliding_window = config.dataset.sliding_window + self.sliding_window_size = config.dataset.sliding_window_size + self.annotation_path = config.dataset.annotation_path + + def predict_loop(self, model, eval_dataloader, output_file="result.pkl"): + """refactored from line 144: + https://github.com/DmZhukov/CrossTask/blob/master/train.py + """ + ctx = 0 + model.eval() + model = model.to(ctx) + # this is not a loss but just compute neg_log_prob. + Y_pred = {} + Y_true = {} + with torch.no_grad(): + for batch in eval_dataloader: + self(batch, model, Y_pred, Y_true) + return self.finalize(Y_pred, Y_true, output_file) + + def __call__(self, sample, model, Y_pred, Y_true): + # please install dp from `https://github.com/DmZhukov/CrossTask` + from dp import dp + vid, task = sample['video_id'][0], sample['task'][0] + sample = self.to_ctx(sample) + # compute the average logits over sliding windows. + output = model(**sample) + batch_logits = output["logits"].cpu() + + video_len = sample["video_len"][0] + + # the following version is slow. + logits = torch.zeros((video_len, batch_logits.size(1))) + logits_counts = torch.zeros((video_len, 1), dtype=torch.long) + # use the same loop as aligner to recover. + batch_logit_idx = 0 + for window_start in range(0, video_len, self.sliding_window): + video_end = min(video_len - window_start, self.sliding_window_size) + logits[window_start: window_start + video_end] += batch_logits[ + batch_logit_idx: batch_logit_idx + video_end] + batch_logit_idx += video_end + logits_counts[window_start: window_start + video_end] += torch.ones((video_end, 1), dtype=torch.long) + + if (video_len - window_start) <= self.sliding_window_size: + break + + logits /= logits_counts + assert logits.size() == (video_len, batch_logits.size(1)), "{}, {}".format(logits.size(), video_len) + + O = self.lsm(logits) + y = np.zeros(O.size(), dtype=np.float32) + dp(y, -O.detach().cpu().numpy()) + if task not in Y_pred: + Y_pred[task] = {} + Y_pred[task][vid] = y + annot_path = os.path.join( + self.annotation_path, task+'_'+vid+'.csv') + if os.path.exists(annot_path): + if task not in Y_true: + Y_true[task] = {} + Y_true[task][vid] = self._read_assignment( + *y.shape, annot_path) + + def finalize(self, Y_pred, Y_true, output_file=None): + if output_file is not None: + with open( + os.path.join(self.pred_dir, output_file + ".pkl"), + "wb") as fw: + pickle.dump( + {"Y_pred": Y_pred, "Y_true": Y_true}, fw, + protocol=pickle.HIGHEST_PROTOCOL) + return {"outputs": Y_pred, "targets": Y_true} + + def _read_assignment(self, T, K, path): + """ + refactored from https://github.com/DmZhukov/CrossTask/blob/master/data.py + Howto interpret contraints on loss that is going to be minimized: + lambd is a big number; + self.lambd * C is a big number for all valid position (csv stores invalids) + + def forward(self, O, Y, C): + return (Y*(self.lambd * C - self.lsm(O))).mean(dim=0).sum() + + This will load the csv file and fill-in the step col from start to end rows. + """ + + Y = np.zeros([T, K], dtype=np.uint8) + with open(path, 'r') as f: + for line in f: + step, start, end = line.strip().split(',') + start = int(math.floor(float(start))) + end = int(math.ceil(float(end))) + step = int(step) - 1 + Y[start:end, step] = 1 + return Y + + +class COINPredictor(Predictor): + """ + COINPredictor is similar to CrossTask on sliding windows. + """ + def __init__(self, config): + super().__init__(config) + self.max_video_len = config.dataset.max_video_len + self.sliding_window = config.dataset.sliding_window + self.sliding_window_size = config.dataset.sliding_window_size + + def predict_loop(self, model, eval_dataloader, output_file="result.pkl"): + """refactored from line 144: + https://github.com/DmZhukov/CrossTask/blob/master/train.py + """ + ctx = 0 + model.eval() + model = model.to(ctx) + # this is not a loss but just compute neg_log_prob. + Y_pred = [] + Y_true = [] + with torch.no_grad(): + for batch in eval_dataloader: + self(batch, model, Y_pred, Y_true) + return self.finalize(Y_pred, Y_true, output_file) + + def __call__(self, sample, model, Y_pred, Y_true): + sample = self.to_ctx(sample) + # compute the average logits over sliding windows. + output = model(**sample) + logits = self._merge_windows(sample, output) + Y_pred.append(logits.argmax(dim=1)) + Y_true.append(sample["video_targets"].squeeze(0).cpu()) + + def _merge_windows(self, sample, output): + targets = sample["targets"].reshape(-1).cpu() + valid_mask = targets != -100 + targets = targets[valid_mask] + batch_logits = output["logits"].cpu() + batch_logits = batch_logits.reshape(-1, batch_logits.size(-1)) + batch_logits = batch_logits[valid_mask] + + video_len = sample["video_len"][0] + + # the following version is slow. + logits = torch.zeros((video_len, batch_logits.size(1))) + logits_counts = torch.zeros((video_len, 1), dtype=torch.long) + # use the same loop as aligner to recover. + batch_logit_idx = 0 + for window_start in range(0, video_len, self.sliding_window): + video_end = min(video_len - window_start, self.sliding_window_size) + logits[window_start: window_start + video_end] += batch_logits[ + batch_logit_idx: batch_logit_idx + video_end] + batch_logit_idx += video_end + logits_counts[window_start: window_start + video_end] += torch.ones((video_end, 1), dtype=torch.long) + if (video_len - window_start) <= self.sliding_window_size: + break + logits /= logits_counts + assert logits.size() == (video_len, batch_logits.size(1)), "{}, {}".format(logits.size(), video_len) + return logits + + def finalize(self, Y_pred, Y_true, output_file=None): + Y_pred = torch.cat(Y_pred, dim=0).numpy() + Y_true = torch.cat(Y_true, dim=0).numpy() + assert len(Y_pred) == len(Y_true) + + error_mask = Y_pred != Y_true + print("sample error", Y_pred[error_mask][:10], Y_true[error_mask][:10]) + print("sample error", Y_pred[error_mask][10:20], Y_true[error_mask][10:20]) + + if output_file is not None: + with open( + os.path.join(self.pred_dir, output_file + ".pkl"), + "wb") as fw: + pickle.dump( + {"Y_pred": Y_pred, "Y_true": Y_true}, fw, + protocol=pickle.HIGHEST_PROTOCOL) + return {"outputs": Y_pred, "targets": Y_true} + + +class COINZSPredictor(COINPredictor): + """ + COINZSPredictor for COIN zero-shot prediction. + """ + + def __init__(self, config): + super().__init__(config) + self.dataset_config = config.dataset + + def predict_loop(self, model, eval_dataloader, output_file="result.pkl"): + """refactored from line 144: + https://github.com/DmZhukov/CrossTask/blob/master/train.py + """ + ctx = 0 + model.eval() + model = model.to(ctx) + + with torch.no_grad(): + outputs = eval_dataloader.dataset.meta_processor.meta_text_labels( + self.dataset_config) + outputs = self.to_ctx(outputs, ctx) + label_hidden_states = model.forward_text(**outputs).cpu() + label_sim = label_hidden_states @ label_hidden_states.t() + num_labels = label_sim.size(0) + eye_mask = ~torch.eye(num_labels, dtype=torch.bool) + label_sim = label_sim.masked_select(eye_mask).view(num_labels, num_labels - 1) + lbd = label_sim.max() + + # this is not a loss but just compute neg_log_prob. + Y_pred = [] + Y_true = [] + with torch.no_grad(): + for batch in eval_dataloader: + self(batch, label_hidden_states, model, lbd, Y_pred, Y_true) + return self.finalize(Y_pred, Y_true, output_file) + + def reshape_subsample(self, sample): + for key in sample: + if torch.is_tensor(sample[key]): + sample[key] = self.flat_subsample(sample[key]) + return sample + + def flat_subsample(self, tensor): + if len(tensor.size()) > 1 and tensor.size(0) == 1: + tensor = tensor.squeeze(0) + return tensor + + def __call__(self, sample, label_hidden_states, model, lbd, Y_pred, Y_true): + sample = self.reshape_subsample(sample) + sample = self.to_ctx(sample) + # compute the average logits over sliding windows. + sample["output_hidden_states"] = True + video_outputs = model.forward_video(**sample).cpu() + output = {"logits": video_outputs[:, 1:sample["vmasks"].size(1)+1] @ label_hidden_states.t()} + logits = self._merge_windows(sample, output) + # logic of zero-shot for sequence labeling. + logits_argmax = logits.argmax(dim=1) + 1 # 0 is "O" label. + logits_max = logits.max(dim=1)[0] + + pred = torch.zeros_like(logits_argmax) + label_select = logits_max > lbd # 73 or 74 + pred[label_select] = logits_argmax[label_select] + + Y_pred.append(pred) + Y_true.append(sample["video_targets"].squeeze(0).cpu()) + + def finalize(self, Y_pred, Y_true, output_file=None): + Y_pred = torch.cat(Y_pred, dim=0).numpy() + Y_true = torch.cat(Y_true, dim=0).numpy() + assert len(Y_pred) == len(Y_true) + + error_mask = Y_pred != Y_true + print("sample error", Y_pred[error_mask][:10], Y_true[error_mask][:10]) + print("sample error", Y_pred[error_mask][10:20], Y_true[error_mask][10:20]) + + if output_file is not None: + with open( + os.path.join(self.pred_dir, output_file + ".pkl"), + "wb") as fw: + pickle.dump( + {"Y_pred": Y_pred, "Y_true": Y_true}, fw, + protocol=pickle.HIGHEST_PROTOCOL) + return {"outputs": Y_pred, "targets": Y_true} + + +class DiDeMoPredictor(Predictor): + """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py + https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py + """ + def __init__(self, config): + super().__init__(config) + # load targets. + with open(config.dataset.test_path) as data_file: + self.test_data = json.load(data_file) + + def predict_loop(self, model, eval_dataloader, output_file="didemo.npy"): + """ + TODO: two solutions here. + """ + import itertools + # 21 chunks. + self.possible_segments = [(0,0), (1,1), (2,2), (3,3), (4,4), (5,5)] + for i in itertools.combinations(range(6), 2): + self.possible_segments.append(i) + # pick segments from a video. + + """on-the-fly prediction on a single gpu.""" + self.full_scores = [] + model.eval() + model = model.cuda() + with torch.no_grad(): + for data in eval_dataloader: + # TODO special forwarding logic here. + data = self.to_ctx(data) + data["output_hidden_states"] = True + hidden_video = model.forward_video(**data) + data["output_hidden_states"] = False + pooled_text = model.forward_text(**data) + outputs = { + "hidden_video": hidden_video, + "pooled_text": pooled_text + } + outputs.update(data) + self(outputs) + return self.finalize(output_file) + + def __call__(self, sample): + # TODO: make an index select from self.possible_segments. + hidden_video = sample["hidden_video"] + pooled_text = sample["pooled_text"] + vmasks = sample["vmasks"] + # probably maintain valid results here. + + hidden_video = hidden_video[:, 1:-1, :] + # probably maintain valid results here. + pooled_video = [] + for s, e in self.possible_segments: + pooled_video.append( + torch.mean( + hidden_video[:, int(s*5):int((e+1)*5), :], + dim=1, keepdim=True) + ) + pooled_video = torch.cat(pooled_video, dim=1) + scores = torch.bmm( + pooled_video, pooled_text.unsqueeze(-1)).squeeze(-1).cpu() + + ranks = scores.argsort(dim=-1, descending=True) + + for batch_idx, rank in enumerate(ranks): + rank_of_moment = [] + for m_idx, moment in enumerate(rank): + s, e = self.possible_segments[moment.item()] + if torch.any( + vmasks[batch_idx, int(s*5):int((e+1)*5)] + ): + rank_of_moment.append((s, e)) + self.full_scores.append(rank_of_moment) + + def finalize(self, output_file=None): + outputs = self._aggregate_scores(self.full_scores) + if output_file is not None: + np.save(os.path.join(self.pred_dir, output_file + ".npy"), outputs) + return {"outputs": outputs, "targets": self.test_data} + + def _aggregate_scores(self, scores): + self.full_scores = [] + return scores diff --git a/fairseq/examples/MMPT/mmpt/losses/__init__.py b/fairseq/examples/MMPT/mmpt/losses/__init__.py new file mode 100644 index 0000000..8dc32c9 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/losses/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .loss import * +from .nce import * + +try: + from .fairseqmmloss import * +except ImportError: + pass + +try: + from .expnce import * +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/losses/fairseqmmloss.py b/fairseq/examples/MMPT/mmpt/losses/fairseqmmloss.py new file mode 100644 index 0000000..a95e5ec --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/losses/fairseqmmloss.py @@ -0,0 +1,63 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +TODO (huxu): a general fairseq criterion for all your pre-defined losses. +""" + +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.logging import metrics + + +@register_criterion("mmloss") +class MMCriterion(FairseqCriterion): + def __init__(self, task): + super().__init__(task) + # TODO (huxu): wrap forward call of loss_fn and eval_fn into task. + self.mmtask = task.mmtask + + def forward(self, model, sample): + """Compute the loss for the given sample. + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + outputs = self.mmtask(model, sample) + + loss, loss_scalar, max_len, batch_size, sample_size = ( + outputs["loss"], + outputs["loss_scalar"], + outputs["max_len"], + outputs["batch_size"], + outputs["sample_size"], + ) + + logging_output = { + "loss": loss_scalar, + "ntokens": max_len * batch_size, # dummy report. + "nsentences": batch_size, # dummy report. + "sample_size": sample_size, + } + + return loss, 1, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + """since we use NCE, our actual batch_size is 1 per GPU. + Then we take the mean of each worker.""" + loss_sum = sum(log.get("loss", 0.0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + metrics.log_scalar("loss", loss_sum / sample_size, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/examples/MMPT/mmpt/losses/loss.py b/fairseq/examples/MMPT/mmpt/losses/loss.py new file mode 100644 index 0000000..99c05d0 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/losses/loss.py @@ -0,0 +1,87 @@ +# Copyright (c) Facebook, Inc. All Rights Reserved + +import torch + +from torch import nn + + +class Loss(object): + def __call__(self, *args, **kwargs): + raise NotImplementedError + + +# Dummy Loss for testing. +class DummyLoss(Loss): + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__(self, logits, targets, **kwargs): + return self.loss(logits, targets) + + +class DummyK400Loss(Loss): + """dummy k400 loss for MViT.""" + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__(self, logits, targets, **kwargs): + return self.loss( + logits, torch.randint(0, 400, (logits.size(0),), device=logits.device)) + + +class CrossEntropy(Loss): + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__(self, logits, targets, **kwargs): + return self.loss(logits.reshape(-1, logits.size(-1)), targets.reshape(-1)) + + +class ArgmaxCrossEntropy(Loss): + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__(self, logits, targets, **kwargs): + return self.loss(logits, targets.argmax(dim=1)) + + +class BCE(Loss): + def __init__(self): + self.loss = nn.BCEWithLogitsLoss() + + def __call__(self, logits, targets, **kwargs): + targets = targets.squeeze(0) + return self.loss(logits, targets) + + +class NLGLoss(Loss): + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__(self, logits, text_label, **kwargs): + targets = text_label[text_label != -100] + return self.loss(logits, targets) + + +class MSE(Loss): + def __init__(self): + self.loss = nn.MSELoss() + + def __call__(self, logits, targets, **kwargs): + return self.loss(logits, targets) + + +class L1(Loss): + def __init__(self): + self.loss = nn.L1Loss() + + def __call__(self, logits, targets, **kwargs): + return self.loss(logits, targets) + + +class SmoothL1(Loss): + def __init__(self): + self.loss = nn.SmoothL1Loss() + + def __call__(self, logits, targets, **kwargs): + return self.loss(logits, targets) diff --git a/fairseq/examples/MMPT/mmpt/losses/nce.py b/fairseq/examples/MMPT/mmpt/losses/nce.py new file mode 100644 index 0000000..ed7be8d --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/losses/nce.py @@ -0,0 +1,156 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +softmax-based NCE loss, used by this project. +""" + +import torch + +from torch import nn + +from .loss import Loss + + +class NCE(Loss): + def __init__(self): + # TODO (huxu): define temperature. + self.loss = nn.CrossEntropyLoss() + + def __call__(self, align_scores, **kargs): + # note: we reuse the same shape as cls head in BERT (batch_size, 2) + # but NCE only needs one logits. + # (so we drop all weights in the second neg logits.) + align_scores = align_scores[:, :1] + # duplicate negative examples + batch_size = align_scores.size(0) // 2 + pos_scores = align_scores[:batch_size] + neg_scores = align_scores[batch_size:].view(1, batch_size).repeat( + batch_size, 1) + scores = torch.cat([pos_scores, neg_scores], dim=1) + return self.loss( + scores, + torch.zeros( + (batch_size,), + dtype=torch.long, + device=align_scores.device), + ) + + +class T2VContraLoss(Loss): + """NCE for MM joint space, on softmax text2video matrix. + """ + def __init__(self): + # TODO (huxu): define temperature. + self.loss = nn.CrossEntropyLoss() + + def __call__(self, pooled_video, pooled_text, **kargs): + batch_size = pooled_video.size(0) + logits = torch.mm(pooled_text, pooled_video.transpose(1, 0)) + targets = torch.arange( + batch_size, + dtype=torch.long, + device=pooled_video.device) + return self.loss(logits, targets) + + +class V2TContraLoss(Loss): + """NCE for MM joint space, with softmax on video2text matrix.""" + + def __init__(self): + # TODO (huxu): define temperature. + self.loss = nn.CrossEntropyLoss() + + def __call__(self, pooled_video, pooled_text, **kargs): + batch_size = pooled_video.size(0) + logits = torch.mm(pooled_video, pooled_text.transpose(1, 0)) + targets = torch.arange( + batch_size, + dtype=torch.long, + device=pooled_video.device) + return self.loss(logits, targets) + + +class MMContraLoss(Loss): + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__(self, pooled_video, pooled_text, **kwargs): + logits_per_video = pooled_video @ pooled_text.t() + logits_per_text = pooled_text @ pooled_video.t() + + targets = torch.arange( + pooled_video.size(0), + dtype=torch.long, + device=pooled_video.device) + loss_video = self.loss(logits_per_video, targets) + loss_text = self.loss(logits_per_text, targets) + return loss_video + loss_text + + +class MTM(Loss): + """Combination of MFM and MLM.""" + + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__( + self, + video_logits, + text_logits, + video_label, + text_label, + **kwargs + ): + text_logits = torch.cat([ + text_logits, + torch.zeros( + (text_logits.size(0), 1), device=text_logits.device) + ], dim=1) + vt_logits = torch.cat([video_logits, text_logits], dim=0) + # loss for video. + video_label = torch.zeros( + (video_logits.size(0),), + dtype=torch.long, + device=video_logits.device + ) + + # loss for text. + text_label = text_label.reshape(-1) + labels_mask = text_label != -100 + selected_text_label = text_label[labels_mask] + + vt_label = torch.cat([video_label, selected_text_label], dim=0) + return self.loss(vt_logits, vt_label) + + +class MFMMLM(Loss): + """Combination of MFM and MLM.""" + + def __init__(self): + self.loss = nn.CrossEntropyLoss() + + def __call__( + self, + video_logits, + text_logits, + video_label, + text_label, + **kwargs + ): + # loss for video. + video_label = torch.zeros( + (video_logits.size(0),), + dtype=torch.long, + device=video_logits.device + ) + masked_frame_loss = self.loss(video_logits, video_label) + + # loss for text. + text_label = text_label.reshape(-1) + labels_mask = text_label != -100 + selected_text_label = text_label[labels_mask] + masked_lm_loss = self.loss(text_logits, selected_text_label) + return masked_frame_loss + masked_lm_loss diff --git a/fairseq/examples/MMPT/mmpt/models/__init__.py b/fairseq/examples/MMPT/mmpt/models/__init__.py new file mode 100644 index 0000000..825250c --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/models/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .mmfusion import * +from .transformermodel import * +from .mmfusionnlg import * + +try: + from .fairseqmmmodel import * +except ImportError: + pass + +try: + from .expmmfusion import * +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/models/fairseqmmmodel.py b/fairseq/examples/MMPT/mmpt/models/fairseqmmmodel.py new file mode 100644 index 0000000..b7dd643 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/models/fairseqmmmodel.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.models import ( + BaseFairseqModel, + register_model, + register_model_architecture +) + + +@register_model("mmmodel") +class FairseqMMModel(BaseFairseqModel): + """a fairseq wrapper of model built by `task`.""" + + @classmethod + def build_model(cls, args, task): + return FairseqMMModel(task.mmtask.model) + + def __init__(self, mmmodel): + super().__init__() + self.mmmodel = mmmodel + + def forward(self, *args, **kwargs): + return self.mmmodel(*args, **kwargs) + + def upgrade_state_dict_named(self, state_dict, name): + + super().upgrade_state_dict_named(state_dict, name) + + keys_to_delete = [] + + for key in state_dict: + if key not in self.state_dict(): + keys_to_delete.append(key) + for key in keys_to_delete: + print("[INFO]", key, "not used anymore.") + del state_dict[key] + + # copy any newly defined parameters. + for key in self.state_dict(): + if key not in state_dict: + print("[INFO] adding", key) + state_dict[key] = self.state_dict()[key] + + +# a dummy arch, we config the model. +@register_model_architecture("mmmodel", "mmarch") +def mmarch(args): + pass diff --git a/fairseq/examples/MMPT/mmpt/models/mmfusion.py b/fairseq/examples/MMPT/mmpt/models/mmfusion.py new file mode 100644 index 0000000..2509e26 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/models/mmfusion.py @@ -0,0 +1,926 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Copyright (c) Facebook, Inc. All Rights Reserved + + +import torch + +from torch import nn + +try: + from transformers import AutoConfig, AutoTokenizer +except ImportError: + pass + +from . import transformermodel + + +class MMPTModel(nn.Module): + """An e2e wrapper of inference model. + """ + @classmethod + def from_pretrained(cls, config, checkpoint="checkpoint_best.pt"): + import os + from ..utils import recursive_config + from ..tasks import Task + config = recursive_config(config) + mmtask = Task.config_task(config) + checkpoint_path = os.path.join(config.eval.save_path, checkpoint) + mmtask.build_model(checkpoint=checkpoint_path) + # TODO(huxu): make the video encoder configurable. + from ..processors.models.s3dg import S3D + video_encoder = S3D('pretrained_models/s3d_dict.npy', 512) + video_encoder.load_state_dict( + torch.load('pretrained_models/s3d_howto100m.pth')) + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained( + config.dataset.bert_name, use_fast=config.dataset.use_fast + ) + from ..processors import Aligner + aligner = Aligner(config.dataset) + return ( + MMPTModel(config, mmtask.model, video_encoder), + tokenizer, + aligner + ) + + def __init__(self, config, model, video_encoder, **kwargs): + super().__init__() + self.max_video_len = config.dataset.max_video_len + self.video_encoder = video_encoder + self.model = model + + def forward(self, video_frames, caps, cmasks, return_score=False): + bsz = video_frames.size(0) + assert bsz == 1, "only bsz=1 is supported now." + seq_len = video_frames.size(1) + video_frames = video_frames.view(-1, *video_frames.size()[2:]) + vfeats = self.video_encoder(video_frames.permute(0, 4, 1, 2, 3)) + vfeats = vfeats['video_embedding'] + vfeats = vfeats.view(bsz, seq_len, vfeats.size(-1)) + padding = torch.zeros( + bsz, self.max_video_len - seq_len, vfeats.size(-1)) + vfeats = torch.cat([vfeats, padding], dim=1) + vmasks = torch.cat([ + torch.ones((bsz, seq_len), dtype=torch.bool), + torch.zeros((bsz, self.max_video_len - seq_len), dtype=torch.bool) + ], + dim=1 + ) + output = self.model(caps, cmasks, vfeats, vmasks) + if return_score: + output = {"score": torch.bmm( + output["pooled_video"][:, None, :], + output["pooled_text"][:, :, None] + ).squeeze(-1).squeeze(-1)} + return output + + +class MMFusion(nn.Module): + """a MMPT wrapper class for MMBert style models. + TODO: move isolated mask to a subclass. + """ + def __init__(self, config, **kwargs): + super().__init__() + transformer_config = AutoConfig.from_pretrained( + config.dataset.bert_name) + self.hidden_size = transformer_config.hidden_size + self.is_train = False + if config.dataset.train_path is not None: + self.is_train = True + # 0 means no iso; 1-12 means iso up to that layer. + self.num_hidden_layers = transformer_config.num_hidden_layers + self.last_iso_layer = 0 + if config.dataset.num_iso_layer is not None: + self.last_iso_layer = config.dataset.num_iso_layer - 1 + 1 + + if config.model.mm_encoder_cls is not None: + mm_encoder_cls = getattr(transformermodel, config.model.mm_encoder_cls) + model_config = AutoConfig.from_pretrained(config.dataset.bert_name) + model_config.max_video_len = config.dataset.max_video_len + # TODO: a general way to add parameter for a model. + model_config.use_seg_emb = config.model.use_seg_emb + self.mm_encoder = mm_encoder_cls.from_pretrained( + config.dataset.bert_name, config=model_config) + elif config.model.video_encoder_cls is not None\ + and config.model.text_encoder_cls is not None: + video_encoder_cls = getattr(transformermodel, config.model.video_encoder_cls) + model_config = AutoConfig.from_pretrained(config.dataset.bert_name) + model_config.max_video_len = config.dataset.max_video_len + # TODO: make each model a set of config class. + if hasattr(model_config, "num_layers"): + model_config.num_layers = config.model.num_hidden_video_layers + else: + model_config.num_hidden_layers = config.model.num_hidden_video_layers + self.video_encoder = video_encoder_cls.from_pretrained( + config.dataset.bert_name, config=model_config) + # exact same NLP model from Huggingface. + text_encoder_cls = getattr(transformermodel, config.model.text_encoder_cls) + self.text_encoder = text_encoder_cls.from_pretrained( + config.dataset.bert_name) + else: + raise ValueError("the encoder must be either MM or two backbones.") + + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + **kwargs + ): + raise NotImplementedError( + "Please derive MMFusion module." + ) + + def _mm_on_the_fly( + self, + cmasks, + vmasks, + attention_mask + ): + """helper function for mask, seg_ids and token_type_ids.""" + if attention_mask is None: + attention_mask = self._mm_attention_mask(cmasks, vmasks) + + """ + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + """ + token_type_ids = torch.cat( + [ + torch.zeros( + (vmasks.size(0), vmasks.size(1) + 2), + dtype=torch.long, + device=vmasks.device, + ), + torch.ones( + (cmasks.size(0), cmasks.size(1) - 2), + dtype=torch.long, + device=cmasks.device, + ), + ], + dim=1, + ) + return attention_mask, token_type_ids + + def _mm_attention_mask(self, cmasks, vmasks): + assert cmasks.size(0) == vmasks.size(0), "{}, {}, {}, {}".format( + str(cmasks.size()), + str(vmasks.size()), + str(cmasks.size(0)), + str(vmasks.size(0)), + ) + + mm_mask = torch.cat([cmasks[:, :1], vmasks, cmasks[:, 1:]], dim=1) + if self.last_iso_layer == 0: + # hard attention mask. + return mm_mask + else: + # a gpu iso mask; 0 : num_iso_layer is isolated; + # num_iso_layer: are MM-fused. + # make an iso layer + batch_size = cmasks.size(0) + iso_mask = self._make_iso_mask(batch_size, cmasks, vmasks) + mm_mask = mm_mask[:, None, :].repeat(1, mm_mask.size(-1), 1) + iso_mm_masks = [] + # hard attention mask. + iso_mask = iso_mask[:, None, :, :].repeat( + 1, self.last_iso_layer, 1, 1) + iso_mm_masks.append(iso_mask) + if self.last_iso_layer < self.num_hidden_layers: + mm_mask = mm_mask[:, None, :, :].repeat( + 1, self.num_hidden_layers - self.last_iso_layer, 1, 1 + ) + iso_mm_masks.append(mm_mask) + iso_mm_masks = torch.cat(iso_mm_masks, dim=1) + return iso_mm_masks + + def _make_iso_mask(self, batch_size, cmasks, vmasks): + cls_self_mask = torch.cat( + [ + torch.ones( + (batch_size, 1), dtype=torch.bool, device=cmasks.device), + torch.zeros( + (batch_size, cmasks.size(1) + vmasks.size(1) - 1), + dtype=torch.bool, device=cmasks.device) + ], dim=1) + + iso_video_mask = torch.cat( + [ + # [CLS] is not used. + torch.zeros( + (batch_size, 1), dtype=torch.bool, device=cmasks.device + ), + vmasks, + # assume to be 1. + cmasks[:, 1:2], + # 2 means [CLS] + [SEP] + torch.zeros( + (batch_size, cmasks.size(1) - 2), + dtype=torch.bool, + device=cmasks.device, + ), + ], + dim=1, + ) + iso_text_mask = torch.cat( + [ + torch.zeros( + (batch_size, 2 + vmasks.size(1)), + dtype=torch.bool, + device=cmasks.device, + ), # [CLS] is not used. + cmasks[:, 2:], # assume to be 1. + ], + dim=1, + ) + cls_self_mask = cls_self_mask[:, None, :] + iso_video_mask = iso_video_mask[:, None, :].repeat( + 1, vmasks.size(1) + 1, 1) + iso_text_mask = iso_text_mask[:, None, :].repeat( + 1, cmasks.size(1) - 2, 1) + return torch.cat([cls_self_mask, iso_video_mask, iso_text_mask], dim=1) + + def _pooling_vt_layer( + self, + layered_sequence_output, + cmasks, + vmasks + ): + layer_idx = self.last_iso_layer \ + if self.last_iso_layer > 0 else self.num_hidden_layers + hidden_state = layered_sequence_output[layer_idx] + # also output pooled_video and pooled_text. + batch_size = cmasks.size(0) + # pool the modality. + text_offset = vmasks.size(1) + 2 # [CLS] + [SEP] + # video tokens + [SEP] + video_outputs = hidden_state[:, 1:text_offset] + video_attention_mask = torch.cat( + [ + vmasks, + torch.ones( + (batch_size, 1), dtype=torch.bool, device=vmasks.device), + ], + dim=1, + ) + assert video_outputs.size(1) == video_attention_mask.size(1) + pooled_video = torch.sum( + video_outputs * video_attention_mask.unsqueeze(-1), dim=1 + ) / video_attention_mask.sum(1, keepdim=True) + # pooled_video = torch.mean(video_outputs[0], dim=1) + + # text tokens + [SEP] + text_attention_mask = cmasks[:, 2:] + text_outputs = hidden_state[:, text_offset:] + assert text_outputs.size(1) == text_attention_mask.size(1) + pooled_text = torch.sum( + text_outputs * text_attention_mask.unsqueeze(-1), dim=1 + ) / text_attention_mask.sum(1, keepdim=True) + return pooled_video, pooled_text + + +class MMFusionMFMMLM(MMFusion): + """forward function for MFM and MLM.""" + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + attention_mask=None, + video_label=None, + text_label=None, + **kwargs + ): + output_hidden_states = False if self.is_train else True + + target_vfeats, non_masked_frame_mask = None, None + if video_label is not None: + target_vfeats = vfeats.masked_select( + video_label.unsqueeze(-1)).view( + -1, vfeats.size(-1) + ) + # mask video token. + vfeats[video_label] = 0.0 + non_masked_frame_mask = vmasks.clone() + non_masked_frame_mask[video_label] = False + + attention_mask, token_type_ids = self._mm_on_the_fly( + cmasks, vmasks, attention_mask) + + outputs = self.mm_encoder( + input_ids=caps, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + masked_frame_labels=video_label, + target_video_hidden_states=target_vfeats, + non_masked_frame_mask=non_masked_frame_mask, + masked_lm_labels=text_label, + output_hidden_states=output_hidden_states, + ) + + video_logits, text_logits = outputs[0], outputs[1] + + if self.is_train: # return earlier for training. + return { + "video_logits": video_logits, + "text_logits": text_logits, + } + + pooled_video, pooled_text = self._pooling_vt_layer( + outputs[2], cmasks, vmasks) + return {"pooled_video": pooled_video, "pooled_text": pooled_text} + + +class MMFusionMTM(MMFusionMFMMLM): + def __init__(self, config, **kwargs): + super().__init__(config) + """ + For reproducibility: + self.mm_encoder will be initialized then discarded. + """ + from .transformermodel import MMBertForMTM + model_config = AutoConfig.from_pretrained(config.dataset.bert_name) + model_config.max_video_len = config.dataset.max_video_len + model_config.use_seg_emb = config.model.use_seg_emb + self.mm_encoder = MMBertForMTM.from_pretrained( + config.dataset.bert_name, config=model_config) + + +class MMFusionShare(MMFusion): + """A retrival wrapper using mm_encoder as both video/text backbone. + TODO: move formally. + """ + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + attention_mask=None, + video_label=None, + text_label=None, + output_hidden_states=False, + **kwargs + ): + pooled_video = self.forward_video( + vfeats, + vmasks, + caps, + cmasks, + output_hidden_states + ) + + pooled_text = self.forward_text( + caps, + cmasks, + output_hidden_states + ) + + return {"pooled_video": pooled_video, "pooled_text": pooled_text} + + def forward_video( + self, + vfeats, + vmasks, + caps, + cmasks, + output_hidden_states=False, + **kwargs + ): + input_ids = caps[:, :2] + + attention_mask = torch.cat([ + cmasks[:, :1], + vmasks, + cmasks[:, 1:2] + ], dim=1) + + token_type_ids = torch.zeros( + (vmasks.size(0), vmasks.size(1) + 2), + dtype=torch.long, + device=vmasks.device) + + outputs = self.mm_encoder( + input_ids=input_ids, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=True + ) + video_outputs = outputs[0] + + if output_hidden_states: + return video_outputs + + batch_size = cmasks.size(0) + + video_attention_mask = torch.cat( + [ + torch.zeros( + (batch_size, 1), dtype=torch.bool, device=vmasks.device), + vmasks, + torch.ones( + (batch_size, 1), dtype=torch.bool, device=vmasks.device), + ], + dim=1, + ) + assert video_outputs.size(1) == video_attention_mask.size(1) + + video_attention_mask = video_attention_mask.type(video_outputs.dtype) \ + / video_attention_mask.sum(1, keepdim=True) + + pooled_video = torch.bmm( + video_outputs.transpose(2, 1), + video_attention_mask.unsqueeze(2) + ).squeeze(-1) + return pooled_video # video_outputs + + def forward_text( + self, + caps, + cmasks, + output_hidden_states=False, + **kwargs + ): + input_ids = torch.cat([ + caps[:, :1], caps[:, 2:], + ], dim=1) + + attention_mask = torch.cat([ + cmasks[:, :1], + cmasks[:, 2:] + ], dim=1) + + token_type_ids = torch.cat([ + torch.zeros( + (cmasks.size(0), 1), + dtype=torch.long, + device=cmasks.device), + torch.ones( + (cmasks.size(0), cmasks.size(1) - 2), + dtype=torch.long, + device=cmasks.device) + ], dim=1) + + outputs = self.mm_encoder( + input_ids=input_ids, + input_video_embeds=None, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=True + ) + text_outputs = outputs[0] + + if output_hidden_states: + return text_outputs + + batch_size = caps.size(0) + # text tokens + [SEP] + text_attention_mask = torch.cat([ + torch.zeros( + (batch_size, 1), dtype=torch.bool, device=cmasks.device), + cmasks[:, 2:] + ], dim=1) + + assert text_outputs.size(1) == text_attention_mask.size(1) + + text_attention_mask = text_attention_mask.type(text_outputs.dtype) \ + / text_attention_mask.sum(1, keepdim=True) + + pooled_text = torch.bmm( + text_outputs.transpose(2, 1), + text_attention_mask.unsqueeze(2) + ).squeeze(-1) + return pooled_text # text_outputs + + +class MMFusionSeparate(MMFusionShare): + def forward_video( + self, + vfeats, + vmasks, + caps, + cmasks, + output_hidden_states=False, + **kwargs + ): + input_ids = caps[:, :2] + + attention_mask = torch.cat([ + cmasks[:, :1], + vmasks, + cmasks[:, 1:2] + ], dim=1) + + token_type_ids = torch.zeros( + (vmasks.size(0), vmasks.size(1) + 2), + dtype=torch.long, + device=vmasks.device) + + outputs = self.video_encoder( + input_ids=input_ids, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=True + ) + video_outputs = outputs[0] + + if output_hidden_states: + return video_outputs + + batch_size = cmasks.size(0) + + video_attention_mask = torch.cat( + [ + torch.zeros( + (batch_size, 1), dtype=torch.bool, device=vmasks.device), + vmasks, + torch.ones( + (batch_size, 1), dtype=torch.bool, device=vmasks.device), + ], + dim=1, + ) + assert video_outputs.size(1) == video_attention_mask.size(1) + + video_attention_mask = video_attention_mask.type(video_outputs.dtype) \ + / video_attention_mask.sum(1, keepdim=True) + + pooled_video = torch.bmm( + video_outputs.transpose(2, 1), + video_attention_mask.unsqueeze(2) + ).squeeze(-1) + return pooled_video # video_outputs + + def forward_text( + self, + caps, + cmasks, + output_hidden_states=False, + **kwargs + ): + input_ids = torch.cat([ + caps[:, :1], caps[:, 2:], + ], dim=1) + + attention_mask = torch.cat([ + cmasks[:, :1], + cmasks[:, 2:] + ], dim=1) + # different from sharing, we use all-0 type. + token_type_ids = torch.zeros( + (cmasks.size(0), cmasks.size(1) - 1), + dtype=torch.long, + device=cmasks.device) + + outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=True + ) + text_outputs = outputs[0] + + if output_hidden_states: + return text_outputs + + batch_size = caps.size(0) + # text tokens + [SEP] + text_attention_mask = torch.cat([ + torch.zeros( + (batch_size, 1), dtype=torch.bool, device=cmasks.device), + cmasks[:, 2:] + ], dim=1) + + assert text_outputs.size(1) == text_attention_mask.size(1) + + text_attention_mask = text_attention_mask.type(text_outputs.dtype) \ + / text_attention_mask.sum(1, keepdim=True) + + pooled_text = torch.bmm( + text_outputs.transpose(2, 1), + text_attention_mask.unsqueeze(2) + ).squeeze(-1) + return pooled_text # text_outputs + + +class MMFusionJoint(MMFusion): + """fine-tuning wrapper for retrival task.""" + + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + attention_mask=None, + video_label=None, + text_label=None, + **kwargs + ): + # TODO (huxu): other ways to do negative examples; move the following + # into your criterion forward. + output_hidden_states = True + + attention_mask, token_type_ids = self._mm_on_the_fly( + cmasks, vmasks, attention_mask) + + separate_forward_split = ( + None if self.is_train else vmasks.size(1) + 2 + ) # [CLS] + [SEP] + + outputs = self.mm_encoder( + input_ids=caps, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=output_hidden_states, + separate_forward_split=separate_forward_split, + ) + + pooled_video, pooled_text = self._pooling_vt_layer( + outputs[2], cmasks, vmasks) + return {"pooled_video": pooled_video, "pooled_text": pooled_text} + + +class MMFusionActionSegmentation(MMFusion): + """Fine-tuning wrapper for action segmentation. + TODO: rename this for VLM. + """ + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + attention_mask=None, + **kwargs + ): + # ActionLocalization assume of batch_size=1, squeeze it. + caps = caps.view(-1, caps.size(-1)) + cmasks = cmasks.view(-1, cmasks.size(-1)) + vfeats = vfeats.view(-1, vfeats.size(2), vfeats.size(3)) + vmasks = vmasks.view(-1, vmasks.size(-1)) + + # this may not cover all shapes of attention_mask. + attention_mask = attention_mask.view( + -1, attention_mask.size(2), attention_mask.size(3)) \ + if attention_mask is not None else None + + # TODO (huxu): other ways to do negative examples; move the following + # into your criterion forward. + output_hidden_states = True + + # video forwarding, text is dummy; never use attention_mask. + attention_mask, token_type_ids = self._mm_on_the_fly( + cmasks, vmasks, attention_mask) + + logits = self.mm_encoder( + input_ids=caps, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=output_hidden_states, + ) + return {"logits": logits[0][:, 1:vmasks.size(1)+1]} + + +class MMFusionActionLocalization(MMFusion): + """fine-tuning model for retrival task.""" + + def __init__(self, config, **kwargs): + super().__init__(config) + tokenizer = AutoTokenizer.from_pretrained( + config.dataset.bert_name) + self.cls_token_id = tokenizer.cls_token_id + self.sep_token_id = tokenizer.sep_token_id + self.pad_token_id = tokenizer.pad_token_id + + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + attention_mask=None, + **kwargs + ): + # ActionLocalization assume of batch_size=1, squeeze it. + caps = caps.squeeze(0) + cmasks = cmasks.squeeze(0) + vfeats = vfeats.squeeze(0) + vmasks = vmasks.squeeze(0) + attention_mask = attention_mask.squeeze(0) if attention_mask is not None else None + + # TODO (huxu): other ways to do negative examples; move the following + # into your criterion forward. + output_hidden_states = True + + # a len1 dummy video token. + dummy_vfeats = torch.zeros( + (caps.size(0), 1, vfeats.size(-1)), device=vfeats.device, dtype=vfeats.dtype) + dummy_vmasks = torch.ones( + (caps.size(0), 1), dtype=torch.bool, + device=vfeats.device) + + dummy_caps = torch.LongTensor( + [[self.cls_token_id, self.sep_token_id, + self.pad_token_id, self.sep_token_id]], + ).to(caps.device).repeat(vfeats.size(0), 1) + dummy_cmasks = torch.BoolTensor( + [[0, 1, 0, 1]] # pad are valid for attention. + ).to(caps.device).repeat(vfeats.size(0), 1) + + # video forwarding, text is dummy; never use attention_mask. + attention_mask, token_type_ids = self._mm_on_the_fly( + dummy_cmasks, vmasks, None) + + outputs = self.mm_encoder( + input_ids=dummy_caps, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=output_hidden_states, + ) + + layer_idx = self.last_iso_layer \ + if self.last_iso_layer > 0 else self.num_hidden_layers + + video_seq = outputs[2][layer_idx][:, 1:vmasks.size(1)+1].masked_select( + vmasks.unsqueeze(-1) + ).view(-1, self.hidden_size) + + # text forwarding, video is dummy + attention_mask, token_type_ids = self._mm_on_the_fly( + cmasks, dummy_vmasks, None) + + outputs = self.mm_encoder( + input_ids=caps, + input_video_embeds=dummy_vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + output_hidden_states=output_hidden_states, + ) + + _, pooled_text = self._pooling_vt_layer( + outputs[2], cmasks, dummy_vmasks) + # this line is not right. + logits = torch.mm(video_seq, pooled_text.transpose(1, 0)) + return {"logits": logits} + + +# --------------- MMFusionSeparate for end tasks --------------- + +class MMFusionSeparateActionSegmentation(MMFusionSeparate): + """Fine-tuning wrapper for action segmentation.""" + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + attention_mask=None, + **kwargs + ): + # ActionLocalization assume of batch_size=1, squeeze it. + caps = caps.view(-1, caps.size(-1)) + cmasks = cmasks.view(-1, cmasks.size(-1)) + vfeats = vfeats.view(-1, vfeats.size(2), vfeats.size(3)) + vmasks = vmasks.view(-1, vmasks.size(-1)) + logits = self.forward_video( + vfeats, + vmasks, + caps, + cmasks, + output_hidden_states=True + ) + return {"logits": logits[:, 1:vmasks.size(1)+1]} + + +class MMFusionSeparateActionLocalization(MMFusionSeparate): + def __init__(self, config, **kwargs): + super().__init__(config) + tokenizer = AutoTokenizer.from_pretrained( + config.dataset.bert_name) + self.cls_token_id = tokenizer.cls_token_id + self.sep_token_id = tokenizer.sep_token_id + self.pad_token_id = tokenizer.pad_token_id + + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + **kwargs + ): + # ActionLocalization assume of batch_size=1, squeeze it. + caps = caps.squeeze(0) + cmasks = cmasks.squeeze(0) + vfeats = vfeats.squeeze(0) + vmasks = vmasks.squeeze(0) + + # TODO (huxu): other ways to do negative examples; move the following + # into your criterion forward. + dummy_caps = torch.LongTensor( + [[self.cls_token_id, self.sep_token_id, + self.pad_token_id, self.sep_token_id]], + ).to(caps.device).repeat(vfeats.size(0), 1) + dummy_cmasks = torch.BoolTensor( + [[0, 1, 0, 1]] # pad are valid for attention. + ).to(caps.device).repeat(vfeats.size(0), 1) + + outputs = self.forward_video( + vfeats, + vmasks, + dummy_caps, + dummy_cmasks, + output_hidden_states=True + ) + + video_seq = outputs[:, 1:vmasks.size(1)+1].masked_select( + vmasks.unsqueeze(-1) + ).view(-1, self.hidden_size) + + pooled_text = self.forward_text( + caps, + cmasks, + output_hidden_states=False + ) + + # this line is not right. + logits = torch.mm(video_seq, pooled_text.transpose(1, 0)) + return {"logits": logits} + + +class MMFusionShareActionLocalization(MMFusionShare): + def __init__(self, config, **kwargs): + super().__init__(config) + tokenizer = AutoTokenizer.from_pretrained( + config.dataset.bert_name) + self.cls_token_id = tokenizer.cls_token_id + self.sep_token_id = tokenizer.sep_token_id + self.pad_token_id = tokenizer.pad_token_id + + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + **kwargs + ): + # ActionLocalization assume of batch_size=1, squeeze it. + caps = caps.squeeze(0) + cmasks = cmasks.squeeze(0) + vfeats = vfeats.squeeze(0) + vmasks = vmasks.squeeze(0) + + # TODO (huxu): other ways to do negative examples; move the following + # into your criterion forward. + dummy_caps = torch.LongTensor( + [[self.cls_token_id, self.sep_token_id, + self.pad_token_id, self.sep_token_id]], + ).to(caps.device).repeat(vfeats.size(0), 1) + dummy_cmasks = torch.BoolTensor( + [[0, 1, 0, 1]] # pad are valid for attention. + ).to(caps.device).repeat(vfeats.size(0), 1) + + outputs = self.forward_video( + vfeats, + vmasks, + dummy_caps, + dummy_cmasks, + output_hidden_states=True + ) + + video_seq = outputs[:, 1:vmasks.size(1)+1].masked_select( + vmasks.unsqueeze(-1) + ).view(-1, self.hidden_size) + + pooled_text = self.forward_text( + caps, + cmasks, + output_hidden_states=False + ) + + # this line is not right. + logits = torch.mm(video_seq, pooled_text.transpose(1, 0)) + return {"logits": logits} diff --git a/fairseq/examples/MMPT/mmpt/models/mmfusionnlg.py b/fairseq/examples/MMPT/mmpt/models/mmfusionnlg.py new file mode 100644 index 0000000..9207e77 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/models/mmfusionnlg.py @@ -0,0 +1,999 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Copyright (c) Facebook, Inc. All Rights Reserved + + +import torch + +from torch.nn import functional as F + +from typing import Optional, Iterable + +try: + from transformers import BertPreTrainedModel + from transformers.modeling_bert import BertOnlyMLMHead + + from transformers.file_utils import ModelOutput + from transformers.modeling_outputs import CausalLMOutput + from transformers.generation_utils import ( + BeamHypotheses, + top_k_top_p_filtering + ) +except ImportError: + pass + +from .mmfusion import MMFusion +from .transformermodel import MMBertModel +from ..modules import VideoTokenMLP + + +class MMFusionNLG(MMFusion): + def __init__(self, config, **kwargs): + super().__init__(config) + if config.model.max_decode_length is not None: + self.max_length = min( + config.model.max_decode_length, + config.dataset.max_len - config.dataset.max_video_len - 3 + ) + else: + self.max_length = \ + config.dataset.max_len - config.dataset.max_video_len - 3 + self.gen_param = config.gen_param if config.gen_param is not None \ + else {} + + def forward( + self, + caps, + cmasks, + vfeats, + vmasks, + attention_mask, + video_label=None, + text_label=None, + **kwargs + ): + """use pre-trained LM header for generation.""" + attention_mask, token_type_ids = self._mm_on_the_fly( + cmasks, vmasks, attention_mask) + + outputs = self.mm_encoder( + input_ids=caps, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + masked_lm_labels=text_label, + ) + return {"logits": outputs[0]} + + @torch.no_grad() + def generate( + self, + caps, cmasks, vfeats, vmasks, + attention_mask=None, + bos_token_id=None, + eos_token_id=None, + **kwargs + ): + # a simplified interface from + # https://huggingface.co/transformers/v3.4.0/_modules/transformers/generation_utils.html#GenerationMixin.generate + + # caps now only have + # [CLS], [SEP] (for video) and [CLS] (as bos_token) + assert caps.size(1) == 3 + + attention_mask, token_type_ids = self._mm_on_the_fly( + cmasks, vmasks, attention_mask) + + output = self.mm_encoder.generate( + input_ids=caps, + input_video_embeds=vfeats, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + max_length=self.max_length, + **self.gen_param + ) + return output + + +class MMBertForNLG(BertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.bert = MMBertModel(config) + self.videomlp = VideoTokenMLP(config) + # we do not use `BertGenerationOnlyLMHead` + # because we can reuse pretraining. + self.cls = BertOnlyMLMHead(config) + self.hidden_size = config.hidden_size + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def forward( + self, + input_ids=None, + input_video_embeds=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + masked_lm_labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + # similar to MMBertForMFMMLM without MFM. + video_tokens = self.videomlp(input_video_embeds) + outputs = self.bert( + input_ids, + video_tokens, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + prediction_scores = None + if masked_lm_labels is not None: + text_offset = input_video_embeds.size(1) + 1 # [CLS] + # recover caps format: [CLS] [SEP] text [SEP] + text_sequence_output = torch.cat( + [sequence_output[:, :1], sequence_output[:, text_offset:]], + dim=1 + ) + + # only compute select tokens to training to speed up. + hidden_size = text_sequence_output.size(-1) + # masked_lm_labels = masked_lm_labels.reshape(-1) + labels_mask = masked_lm_labels != -100 + + selected_text_output = text_sequence_output.masked_select( + labels_mask.unsqueeze(-1) + ).view(-1, hidden_size) + prediction_scores = self.cls(selected_text_output) + + if not return_dict: + output = ( + prediction_scores, + ) + outputs[2:] + return output + + # for generation. + text_offset = input_video_embeds.size(1) + 2 # [CLS] + text_sequence_output = sequence_output[:, text_offset:] + prediction_scores = self.cls(text_sequence_output) + return CausalLMOutput( + loss=None, + logits=prediction_scores, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + input_video_embeds, + attention_mask=None, + token_type_ids=None, + **model_kwargs + ): + # must return a dictionary. + seq_len = input_ids.size(1) + input_video_embeds.size(1) + if attention_mask is not None: + if len(attention_mask.size()) == 4: + attention_mask = attention_mask[:, :, :seq_len, :seq_len] + elif len(attention_mask.size()) == 3: + attention_mask = attention_mask[:, :seq_len, :seq_len] + else: + attention_mask = attention_mask[:, :seq_len] + if token_type_ids is not None: + token_type_ids = token_type_ids[:, :seq_len] + + return { + "input_ids": input_ids, + "input_video_embeds": input_video_embeds, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + + @torch.no_grad() + def generate( + self, + input_ids: Optional[torch.LongTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + max_length: Optional[int] = None, + min_length: Optional[int] = None, + do_sample: Optional[bool] = None, + early_stopping: Optional[bool] = None, + num_beams: Optional[int] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + repetition_penalty: Optional[float] = None, + bad_words_ids: Optional[Iterable[int]] = None, + bos_token_id: Optional[int] = None, + pad_token_id: Optional[int] = None, + eos_token_id: Optional[int] = None, + length_penalty: Optional[float] = None, + no_repeat_ngram_size: Optional[int] = None, + num_return_sequences: Optional[int] = None, + attention_mask: Optional[torch.LongTensor] = None, + decoder_start_token_id: Optional[int] = None, + use_cache: Optional[bool] = None, + **model_kwargs + ) -> torch.LongTensor: + r""" + Generates sequences for models with a language modeling head. The method currently supports greedy decoding, + beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling. + Adapted in part from `Facebook's XLM beam search code + `__. + Apart from :obj:`input_ids` and :obj:`attention_mask`, all the arguments below will default to the value of the + attribute of the same name inside the :class:`~transformers.PretrainedConfig` of the model. The default values + indicated are the default values of those config. + Most of these parameters are explained in more detail in `this blog post + `__. + Parameters: + input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + The sequence used as a prompt for the generation. If :obj:`None` the method initializes + it as an empty :obj:`torch.LongTensor` of shape :obj:`(1,)`. + decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + initial input_ids for the decoder of encoder-decoder type models. If :obj:`None` then only + decoder_start_token_id is passed as the first token to the decoder. + max_length (:obj:`int`, `optional`, defaults to 20): + The maximum length of the sequence to be generated. + min_length (:obj:`int`, `optional`, defaults to 10): + The minimum length of the sequence to be generated. + do_sample (:obj:`bool`, `optional`, defaults to :obj:`False`): + Whether or not to use sampling ; use greedy decoding otherwise. + early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`): + Whether to stop the beam search when at least ``num_beams`` sentences are finished per batch or not. + num_beams (:obj:`int`, `optional`, defaults to 1): + Number of beams for beam search. 1 means no beam search. + temperature (:obj:`float`, `optional`, defaults tp 1.0): + The value used to module the next token probabilities. + top_k (:obj:`int`, `optional`, defaults to 50): + The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p (:obj:`float`, `optional`, defaults to 1.0): + If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or + higher are kept for generation. + repetition_penalty (:obj:`float`, `optional`, defaults to 1.0): + The parameter for repetition penalty. 1.0 means no penalty. See `this paper + `__ for more details. + pad_token_id (:obj:`int`, `optional`): + The id of the `padding` token. + bos_token_id (:obj:`int`, `optional`): + The id of the `beginning-of-sequence` token. + eos_token_id (:obj:`int`, `optional`): + The id of the `end-of-sequence` token. + length_penalty (:obj:`float`, `optional`, defaults to 1.0): + Exponential penalty to the length. 1.0 means no penalty. + Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in + order to encourage the model to produce longer sequences. + no_repeat_ngram_size (:obj:`int`, `optional`, defaults to 0): + If set to int > 0, all ngrams of that size can only occur once. + bad_words_ids(:obj:`List[int]`, `optional`): + List of token ids that are not allowed to be generated. In order to get the tokens of the words that + should not appear in the generated text, use :obj:`tokenizer.encode(bad_word, add_prefix_space=True)`. + num_return_sequences(:obj:`int`, `optional`, defaults to 1): + The number of independently computed returned sequences for each element in the batch. + attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on padding token indices. Mask values are in ``[0, 1]``, 1 for + tokens that are not masked, and 0 for masked tokens. + If not provided, will default to a tensor the same shape as :obj:`input_ids` that masks the pad token. + `What are attention masks? <../glossary.html#attention-mask>`__ + decoder_start_token_id (:obj:`int`, `optional`): + If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token. + use_cache: (:obj:`bool`, `optional`, defaults to :obj:`True`): + Whether or not the model should use the past last key/values attentions (if applicable to the model) to + speed up decoding. + model_kwargs: + Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. + Return: + :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: + The generated sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or + shorter if all batches finished early due to the :obj:`eos_token_id`. + Examples:: + tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer + model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache. + outputs = model.generate(max_length=40) # do greedy decoding + print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True))) + tokenizer = AutoTokenizer.from_pretrained('openai-gpt') # Initialize tokenizer + model = AutoModelWithLMHead.from_pretrained('openai-gpt') # Download model and configuration from S3 and cache. + input_context = 'The dog' + input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context + outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog' + for i in range(3): # 3 output sequences were generated + print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True))) + tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer + model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache. + input_context = 'The dog' + input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context + outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True) # generate 3 candidates using sampling + for i in range(3): # 3 output sequences were generated + print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True))) + tokenizer = AutoTokenizer.from_pretrained('ctrl') # Initialize tokenizer + model = AutoModelWithLMHead.from_pretrained('ctrl') # Download model and configuration from S3 and cache. + input_context = 'Legal My neighbor is' # "Legal" is one of the control codes for ctrl + input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context + outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2) # generate sequences + print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True))) + tokenizer = AutoTokenizer.from_pretrained('gpt2') # Initialize tokenizer + model = AutoModelWithLMHead.from_pretrained('gpt2') # Download model and configuration from S3 and cache. + input_context = 'My cute dog' # "Legal" is one of the control codes for ctrl + bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']] + input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context + outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids) # generate sequences without allowing bad_words to be generated + """ + + # We cannot generate if the model does not have a LM head + if self.get_output_embeddings() is None: + raise AttributeError( + "You tried to generate sequences with a model that does not have a LM Head." + "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )" + ) + + max_length = max_length if max_length is not None else self.config.max_length + min_length = min_length if min_length is not None else self.config.min_length + do_sample = do_sample if do_sample is not None else self.config.do_sample + early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping + use_cache = use_cache if use_cache is not None else self.config.use_cache + num_beams = num_beams if num_beams is not None else self.config.num_beams + temperature = temperature if temperature is not None else self.config.temperature + top_k = top_k if top_k is not None else self.config.top_k + top_p = top_p if top_p is not None else self.config.top_p + repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty + bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id + pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id + eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id + length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty + no_repeat_ngram_size = ( + no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size + ) + bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids + num_return_sequences = ( + num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences + ) + decoder_start_token_id = ( + decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id + ) + + if input_ids is not None: + batch_size = input_ids.shape[0] # overriden by the input batch_size + else: + batch_size = 1 + + assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer." + assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer." + assert isinstance(do_sample, bool), "`do_sample` should be a boolean." + assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean." + assert isinstance(use_cache, bool), "`use_cache` should be a boolean." + assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer." + assert temperature > 0, "`temperature` should be strictly positive." + assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer." + assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1." + assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1." + assert input_ids is not None or ( + isinstance(bos_token_id, int) and bos_token_id >= 0 + ), "If input_ids is not defined, `bos_token_id` should be a positive integer." + assert pad_token_id is None or ( + isinstance(pad_token_id, int) and (pad_token_id >= 0) + ), "`pad_token_id` should be a positive integer." + assert (eos_token_id is None) or ( + isinstance(eos_token_id, int) and (eos_token_id >= 0) + ), "`eos_token_id` should be a positive integer." + assert length_penalty > 0, "`length_penalty` should be strictly positive." + assert ( + isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0 + ), "`no_repeat_ngram_size` should be a positive integer." + assert ( + isinstance(num_return_sequences, int) and num_return_sequences > 0 + ), "`num_return_sequences` should be a strictly positive integer." + assert ( + bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list) + ), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated" + + if input_ids is None: + assert isinstance(bos_token_id, int) and bos_token_id >= 0, ( + "you should either supply a context to complete as `input_ids` input " + "or a `bos_token_id` (integer >= 0) as a first token to start the generation." + ) + input_ids = torch.full( + (batch_size, 1), + bos_token_id, + dtype=torch.long, + device=next(self.parameters()).device, + ) + else: + assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)." + + # not allow to duplicate outputs when greedy decoding + if do_sample is False: + if num_beams == 1: + # no_beam_search greedy generation conditions + assert ( + num_return_sequences == 1 + ), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1" + + else: + # beam_search greedy generation conditions + assert ( + num_beams >= num_return_sequences + ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences" + + # create attention mask if necessary + # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140 + if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids): + attention_mask = input_ids.ne(pad_token_id).long() + elif attention_mask is None: + attention_mask = input_ids.new_ones(input_ids.shape) + + # set pad_token_id to eos_token_id if not set. Important that this is done after + # attention_mask is created + if pad_token_id is None and eos_token_id is not None: + print( + "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id) + ) + pad_token_id = eos_token_id + + # vocab size + if hasattr(self.config, "vocab_size"): + vocab_size = self.config.vocab_size + elif ( + self.config.is_encoder_decoder + and hasattr(self.config, "decoder") + and hasattr(self.config.decoder, "vocab_size") + ): + vocab_size = self.config.decoder.vocab_size + else: + raise ValueError("either self.config.vocab_size or self.config.decoder.vocab_size needs to be defined") + + # set effective batch size and effective batch multiplier according to do_sample + if do_sample: + effective_batch_size = batch_size * num_return_sequences + effective_batch_mult = num_return_sequences + else: + effective_batch_size = batch_size + effective_batch_mult = 1 + + if self.config.is_encoder_decoder: + if decoder_start_token_id is None: + # see if BOS token can be used for decoder_start_token_id + if bos_token_id is not None: + decoder_start_token_id = bos_token_id + elif ( + hasattr(self.config, "decoder") + and hasattr(self.config.decoder, "bos_token_id") + and self.config.decoder.bos_token_id is not None + ): + decoder_start_token_id = self.config.decoder.bos_token_id + else: + raise ValueError( + "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation" + ) + + assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self) + assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder) + + # get encoder and store encoder outputs + encoder = self.get_encoder() + encoder_outputs: ModelOutput = encoder(input_ids, attention_mask=attention_mask, return_dict=True) + + # Expand input ids if num_beams > 1 or num_return_sequences > 1 + if num_return_sequences > 1 or num_beams > 1: + # TODO: make this a call-back function. + # input_ids=caps, + # input_video_embeds=vfeats, + # attention_mask=attention_mask, + # token_type_ids=token_type_ids, + input_video_embeds = model_kwargs.pop("input_video_embeds", None) + token_type_ids = model_kwargs.pop("token_type_ids", None) + + input_ids_len = input_ids.shape[-1] + input_ids = input_ids.unsqueeze(1).expand( + batch_size, effective_batch_mult * num_beams, input_ids_len) + + input_video_embeds_len, input_video_embeds_hidden = input_video_embeds.size(1), input_video_embeds.size(2) + input_video_embeds = input_video_embeds.unsqueeze(1).expand( + batch_size, effective_batch_mult * num_beams, input_video_embeds_len, input_video_embeds_hidden) + + attention_mask_from_len, attention_mask_to_len = attention_mask.size(1), attention_mask.size(2) + attention_mask = attention_mask.unsqueeze(1).expand( + batch_size, effective_batch_mult * num_beams, attention_mask_from_len, attention_mask_to_len + ) + + token_type_ids_len = token_type_ids.size(1) + token_type_ids = token_type_ids.unsqueeze(1).expand( + batch_size, effective_batch_mult * num_beams, token_type_ids_len + ) + + # contiguous ... + input_ids = input_ids.contiguous().view( + effective_batch_size * num_beams, input_ids_len + ) # shape: (batch_size * num_return_sequences * num_beams, cur_len) + + input_video_embeds = input_video_embeds.contiguous().view( + effective_batch_size * num_beams, input_video_embeds_len, input_video_embeds_hidden) + + attention_mask = attention_mask.contiguous().view( + effective_batch_size * num_beams, attention_mask_from_len, attention_mask_to_len + ) # shape: (batch_size * num_return_sequences * num_beams, cur_len) + + token_type_ids = token_type_ids.contiguous().view( + effective_batch_size * num_beams, token_type_ids_len + ) + + model_kwargs["input_video_embeds"] = input_video_embeds + model_kwargs["token_type_ids"] = token_type_ids + + if self.config.is_encoder_decoder: + device = next(self.parameters()).device + if decoder_input_ids is not None: + # give initial decoder input ids + input_ids = decoder_input_ids.repeat(effective_batch_size * num_beams, 1).to(device) + else: + # create empty decoder input_ids + input_ids = torch.full( + (effective_batch_size * num_beams, 1), + decoder_start_token_id, + dtype=torch.long, + device=device, + ) + cur_len = input_ids.shape[-1] + + assert ( + batch_size == encoder_outputs.last_hidden_state.shape[0] + ), f"expected encoder_outputs.last_hidden_state to have 1st dimension bs={batch_size}, got {encoder_outputs.last_hidden_state.shape[0]} " + + # expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1) + expanded_batch_idxs = ( + torch.arange(batch_size) + .view(-1, 1) + .repeat(1, num_beams * effective_batch_mult) + .view(-1) + .to(input_ids.device) + ) + + # expand encoder_outputs + encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select( + 0, expanded_batch_idxs + ) + + # save encoder_outputs in `model_kwargs` + model_kwargs["encoder_outputs"] = encoder_outputs + + else: + cur_len = input_ids.shape[-1] + + assert ( + cur_len < max_length + ), f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`" + + if num_beams > 1: + output = self._generate_beam_search( + input_ids, + cur_len=cur_len, + max_length=max_length, + min_length=min_length, + do_sample=do_sample, + early_stopping=early_stopping, + temperature=temperature, + top_k=top_k, + top_p=top_p, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + bad_words_ids=bad_words_ids, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + batch_size=effective_batch_size, + num_return_sequences=num_return_sequences, + length_penalty=length_penalty, + num_beams=num_beams, + vocab_size=vocab_size, + attention_mask=attention_mask, + use_cache=use_cache, + model_kwargs=model_kwargs, + ) + else: + output = self._generate_no_beam_search( + input_ids, + cur_len=cur_len, + max_length=max_length, + min_length=min_length, + do_sample=do_sample, + temperature=temperature, + top_k=top_k, + top_p=top_p, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + bad_words_ids=bad_words_ids, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + batch_size=effective_batch_size, + attention_mask=attention_mask, + use_cache=use_cache, + model_kwargs=model_kwargs, + ) + + return output + + def _generate_beam_search( + self, + input_ids, + cur_len, + max_length, + min_length, + do_sample, + early_stopping, + temperature, + top_k, + top_p, + repetition_penalty, + no_repeat_ngram_size, + bad_words_ids, + pad_token_id, + eos_token_id, + batch_size, + num_return_sequences, + length_penalty, + num_beams, + vocab_size, + attention_mask, + use_cache, + model_kwargs, + ): + """Generate sequences for each example with beam search.""" + + # generated hypotheses + generated_hyps = [ + BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping) + for _ in range(batch_size) + ] + + # scores for each sentence in the beam + beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device) + + # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times + if do_sample is False: + beam_scores[:, 1:] = -1e9 + beam_scores = beam_scores.view(-1) # shape (batch_size * num_beams,) + + # cache compute states + past = None + + # done sentences + done = [False for _ in range(batch_size)] + + while cur_len < max_length: + model_inputs = self.prepare_inputs_for_generation( + input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs + ) + outputs = self(**model_inputs, return_dict=True) # (batch_size * num_beams, cur_len, vocab_size) + next_token_logits = outputs.logits[:, -1, :] # (batch_size * num_beams, vocab_size) + + # if model has past, then set the past variable to speed up decoding + if "past_key_values" in outputs: + past = outputs.past_key_values + elif "mems" in outputs: + past = outputs.mems + + if self.config.is_encoder_decoder and do_sample is False: + # TODO (PVP) still a bit hacky here - there might be a better solution + next_token_logits = self.adjust_logits_during_generation( + next_token_logits, cur_len=cur_len, max_length=max_length + ) + + scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size) + + scores = self.postprocess_next_token_scores( + scores=scores, + input_ids=input_ids, + no_repeat_ngram_size=no_repeat_ngram_size, + bad_words_ids=bad_words_ids, + cur_len=cur_len, + min_length=min_length, + max_length=max_length, + eos_token_id=eos_token_id, + repetition_penalty=repetition_penalty, + batch_size=batch_size, + num_beams=num_beams, + ) + + assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format( + scores.shape, (batch_size * num_beams, vocab_size) + ) + + if do_sample: + _scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size) + # Temperature + if temperature != 1.0: + _scores = _scores / temperature + # Top-p/top-k filtering + _scores = top_k_top_p_filtering( + _scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2 + ) # (batch_size * num_beams, vocab_size) + # re-organize to group the beam together to sample from all beam_idxs + _scores = _scores.contiguous().view( + batch_size, num_beams * vocab_size + ) # (batch_size, num_beams * vocab_size) + + # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search) + probs = F.softmax(_scores, dim=-1) + next_tokens = torch.multinomial(probs, num_samples=2 * num_beams) # (batch_size, num_beams * 2) + # Compute next scores + next_scores = torch.gather(_scores, -1, next_tokens) # (batch_size, num_beams * 2) + # sort the sampled vector to make sure that the first num_beams samples are the best + next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1) + next_tokens = torch.gather(next_tokens, -1, next_scores_indices) # (batch_size, num_beams * 2) + + else: + next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size) + + # re-organize to group the beam together (we are keeping top hypothesis accross beams) + next_scores = next_scores.view( + batch_size, num_beams * vocab_size + ) # (batch_size, num_beams * vocab_size) + + next_scores, next_tokens = torch.topk(next_scores, 2 * num_beams, dim=1, largest=True, sorted=True) + + assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams) + + # next batch beam content + next_batch_beam = [] + + # for each sentence + for batch_idx in range(batch_size): + + # if we are done with this sentence, add a pad token + if done[batch_idx]: + assert ( + len(generated_hyps[batch_idx]) >= num_beams + ), "Batch can only be done if at least {} beams have been generated".format(num_beams) + assert ( + eos_token_id is not None and pad_token_id is not None + ), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined" + next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch + continue + + # next sentence beam content, this will get added to next_batch_beam + next_sent_beam = [] + + # next tokens for this sentence + for beam_token_rank, (beam_token_id, beam_token_score) in enumerate( + zip(next_tokens[batch_idx], next_scores[batch_idx]) + ): + # get beam and token IDs + beam_id = beam_token_id // vocab_size + token_id = beam_token_id % vocab_size + + effective_beam_id = batch_idx * num_beams + beam_id + # add to generated hypotheses if end of sentence + if (eos_token_id is not None) and (token_id.item() == eos_token_id): + # if beam_token does not belong to top num_beams tokens, it should not be added + is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams + if is_beam_token_worse_than_top_num_beams: + continue + generated_hyps[batch_idx].add( + input_ids[effective_beam_id].clone(), + beam_token_score.item(), + ) + else: + # add next predicted token since it is not eos_token + next_sent_beam.append((beam_token_score, token_id, effective_beam_id)) + + # once the beam for next step is full, don't add more tokens to it. + if len(next_sent_beam) == num_beams: + break + + # Check if we are done so that we can save a pad step if all(done) + done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done( + next_scores[batch_idx].max().item(), cur_len + ) + + # update next beam content + assert len(next_sent_beam) == num_beams, "Beam should always be full" + next_batch_beam.extend(next_sent_beam) + assert len(next_batch_beam) == num_beams * (batch_idx + 1), "We should have added num_beams each step" + + # stop when we are done with each sentence + if all(done): + break + + # sanity check / prepare next batch + assert len(next_batch_beam) == batch_size * num_beams + beam_scores = beam_scores.new([x[0] for x in next_batch_beam]) + beam_tokens = input_ids.new([x[1] for x in next_batch_beam]) + beam_idx = input_ids.new([x[2] for x in next_batch_beam]) + + # re-order batch and update current length + input_ids = input_ids[beam_idx, :] + input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1) + cur_len = cur_len + 1 + + # re-order internal states + if past is not None: + past = self._reorder_cache(past, beam_idx) + + # extend attention_mask for new generated input if only decoder + # (huxu): move out since we trim attention_mask by ourselves. + # if self.config.is_encoder_decoder is False: + # attention_mask = torch.cat( + # [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + # ) + + # finalize all open beam hypotheses and add to generated hypotheses + for batch_idx in range(batch_size): + if done[batch_idx]: + continue + + # test that beam scores match previously calculated scores if not eos and batch_idx not done + if eos_token_id is not None and all( + (token_id % vocab_size).item() != eos_token_id for token_id in next_tokens[batch_idx] + ): + assert torch.all( + next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx] + ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format( + next_scores[:, :num_beams][batch_idx], + beam_scores.view(batch_size, num_beams)[batch_idx], + ) + + # need to add best num_beams hypotheses to generated hyps + for beam_id in range(num_beams): + effective_beam_id = batch_idx * num_beams + beam_id + final_score = beam_scores[effective_beam_id].item() + final_tokens = input_ids[effective_beam_id] + generated_hyps[batch_idx].add(final_tokens, final_score) + + # depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch + output_batch_size = batch_size if do_sample else batch_size * num_return_sequences + output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences + + # select the best hypotheses + sent_lengths = input_ids.new(output_batch_size) + best = [] + + # retrieve best hypotheses + for i, hypotheses in enumerate(generated_hyps): + sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0]) + for j in range(output_num_return_sequences_per_batch): + effective_batch_idx = output_num_return_sequences_per_batch * i + j + best_hyp = sorted_hyps.pop()[1] + sent_lengths[effective_batch_idx] = len(best_hyp) + best.append(best_hyp) + + # prepare for adding eos + sent_max_len = min(sent_lengths.max().item() + 1, max_length) + decoded = input_ids.new(output_batch_size, sent_max_len) + # shorter batches are padded if needed + if sent_lengths.min().item() != sent_lengths.max().item(): + assert pad_token_id is not None, "`pad_token_id` has to be defined" + decoded.fill_(pad_token_id) + + # fill with hypotheses and eos_token_id if the latter fits in + for i, hypo in enumerate(best): + decoded[i, : sent_lengths[i]] = hypo + if sent_lengths[i] < max_length: + decoded[i, sent_lengths[i]] = eos_token_id + + return decoded + + def _generate_no_beam_search( + self, + input_ids, + cur_len, + max_length, + min_length, + do_sample, + temperature, + top_k, + top_p, + repetition_penalty, + no_repeat_ngram_size, + bad_words_ids, + pad_token_id, + eos_token_id, + batch_size, + attention_mask, + use_cache, + model_kwargs, + ): + """Generate sequences for each example without beam search (num_beams == 1). + All returned sequence are generated independantly. + """ + # length of generated sentences / unfinished sentences + unfinished_sents = input_ids.new(batch_size).fill_(1) + sent_lengths = input_ids.new(batch_size).fill_(max_length) + + past = None + while cur_len < max_length: + model_inputs = self.prepare_inputs_for_generation( + input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs + ) + + outputs = self(**model_inputs, return_dict=True) + next_token_logits = outputs.logits[:, -1, :] + scores = self.postprocess_next_token_scores( + scores=next_token_logits, + input_ids=input_ids, + no_repeat_ngram_size=no_repeat_ngram_size, + bad_words_ids=bad_words_ids, + cur_len=cur_len, + min_length=min_length, + max_length=max_length, + eos_token_id=eos_token_id, + repetition_penalty=repetition_penalty, + batch_size=batch_size, + num_beams=1, + ) + + # if model has past, then set the past variable to speed up decoding + if "past_key_values" in outputs: + past = outputs.past_key_values + elif "mems" in outputs: + past = outputs.mems + + if do_sample: + # Temperature (higher temperature => more likely to sample low probability tokens) + if temperature != 1.0: + scores = scores / temperature + # Top-p/top-k filtering + next_token_logscores = top_k_top_p_filtering(scores, top_k=top_k, top_p=top_p) + # Sample + probs = F.softmax(next_token_logscores, dim=-1) + next_token = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + # Greedy decoding + next_token = torch.argmax(next_token_logits, dim=-1) + + # print(next_token_logits[0,next_token[0]], next_token_logits[0,eos_token_id]) + + # update generations and finished sentences + if eos_token_id is not None: + # pad finished sentences if eos_token_id exist + tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents) + else: + tokens_to_add = next_token + + # add token and increase length by one + input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1) + cur_len = cur_len + 1 + + if eos_token_id is not None: + eos_in_sents = tokens_to_add == eos_token_id + # if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length + is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool() + sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len) + # unfinished_sents is set to zero if eos in sentence + unfinished_sents.mul_((~eos_in_sents).long()) + + # stop when there is a in each sentence, or if we exceed the maximul length + if unfinished_sents.max() == 0: + break + + + # extend attention_mask for new generated input if only decoder + # if self.config.is_encoder_decoder is False: + # attention_mask = torch.cat( + # [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + # ) + + return input_ids diff --git a/fairseq/examples/MMPT/mmpt/models/transformermodel.py b/fairseq/examples/MMPT/mmpt/models/transformermodel.py new file mode 100644 index 0000000..6acc419 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/models/transformermodel.py @@ -0,0 +1,734 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Copyright (c) Facebook, Inc. All Rights Reserved + +import torch + +from torch import nn + +try: + from transformers.modeling_bert import ( + BertPreTrainedModel, + BertModel, + BertEncoder, + BertPredictionHeadTransform, + ) +except ImportError: + pass + +from ..modules import VideoTokenMLP, MMBertEmbeddings + + +# --------------- fine-tuning models --------------- +class MMBertForJoint(BertPreTrainedModel): + """A BertModel with isolated attention mask to separate modality.""" + + def __init__(self, config): + super().__init__(config) + self.videomlp = VideoTokenMLP(config) + self.bert = MMBertModel(config) + self.init_weights() + + def forward( + self, + input_ids=None, + input_video_embeds=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + next_sentence_label=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + separate_forward_split=None, + ): + return_dict = ( + return_dict if return_dict is not None + else self.config.use_return_dict + ) + video_tokens = self.videomlp(input_video_embeds) + + outputs = self.bert( + input_ids, + video_tokens, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + separate_forward_split=separate_forward_split, + ) + + return outputs + + +class MMBertForTokenClassification(BertPreTrainedModel): + """A BertModel similar to MMJointUni, with extra wrapper layer + to be fine-tuned from other pretrained MMFusion model.""" + + def __init__(self, config): + super().__init__(config) + self.videomlp = VideoTokenMLP(config) + self.bert = MMBertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # TODO(huxu): 779 is the number of classes for COIN: move to config? + self.classifier = nn.Linear(config.hidden_size, 779) + self.init_weights() + + def forward( + self, + input_ids=None, + input_video_embeds=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + next_sentence_label=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + separate_forward_split=None, + ): + return_dict = ( + return_dict if return_dict is not None + else self.config.use_return_dict + ) + + video_tokens = self.videomlp(input_video_embeds) + outputs = self.bert( + input_ids, + video_tokens, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + separate_forward_split=separate_forward_split, + ) + + return (self.classifier(outputs[0]),) + + +# ------------ pre-training models ---------------- + +class MMBertForEncoder(BertPreTrainedModel): + """A BertModel for Contrastive Learning.""" + def __init__(self, config): + super().__init__(config) + self.videomlp = VideoTokenMLP(config) + self.bert = MMBertModel(config) + self.init_weights() + + def forward( + self, + input_ids=None, + input_video_embeds=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + return_dict = ( + return_dict if return_dict is not None + else self.config.use_return_dict + ) + if input_video_embeds is not None: + video_tokens = self.videomlp(input_video_embeds) + else: + video_tokens = None + + outputs = self.bert( + input_ids, + video_tokens, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + return outputs + + +class MMBertForMFMMLM(BertPreTrainedModel): + """A BertModel with shared prediction head on MFM-MLM.""" + def __init__(self, config): + super().__init__(config) + self.videomlp = VideoTokenMLP(config) + self.bert = MMBertModel(config) + self.cls = MFMMLMHead(config) + self.hidden_size = config.hidden_size + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def forward( + self, + input_ids=None, + input_video_embeds=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + masked_frame_labels=None, + target_video_hidden_states=None, + non_masked_frame_mask=None, + masked_lm_labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + return_dict = ( + return_dict if return_dict is not None + else self.config.use_return_dict + ) + if input_video_embeds is not None: + video_tokens = self.videomlp(input_video_embeds) + else: + video_tokens = None + + if target_video_hidden_states is not None: + target_video_hidden_states = self.videomlp( + target_video_hidden_states) + + non_masked_frame_hidden_states = video_tokens.masked_select( + non_masked_frame_mask.unsqueeze(-1) + ).view(-1, self.hidden_size) + + outputs = self.bert( + input_ids, + video_tokens, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + mfm_scores, prediction_scores = None, None + if masked_frame_labels is not None and masked_lm_labels is not None: + # split the sequence. + text_offset = masked_frame_labels.size(1) + 1 # [CLS] + video_sequence_output = sequence_output[ + :, 1:text_offset + ] # remove [SEP] as not in video_label. + text_sequence_output = torch.cat( + [sequence_output[:, :1], sequence_output[:, text_offset:]], + dim=1 + ) + + hidden_size = video_sequence_output.size(-1) + selected_video_output = video_sequence_output.masked_select( + masked_frame_labels.unsqueeze(-1) + ).view(-1, hidden_size) + + # only compute select tokens to training to speed up. + hidden_size = text_sequence_output.size(-1) + # masked_lm_labels = masked_lm_labels.reshape(-1) + labels_mask = masked_lm_labels != -100 + + selected_text_output = text_sequence_output.masked_select( + labels_mask.unsqueeze(-1) + ).view(-1, hidden_size) + mfm_scores, prediction_scores = self.cls( + selected_video_output, + target_video_hidden_states, + non_masked_frame_hidden_states, + selected_text_output, + ) + + output = ( + mfm_scores, + prediction_scores, + ) + outputs + return output + + +class BertMFMMLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear( + config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly + # resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward( + self, + video_hidden_states=None, + target_video_hidden_states=None, + non_masked_frame_hidden_states=None, + text_hidden_states=None, + ): + video_logits, text_logits = None, None + if video_hidden_states is not None: + video_hidden_states = self.transform(video_hidden_states) + non_masked_frame_logits = torch.mm( + video_hidden_states, + non_masked_frame_hidden_states.transpose(1, 0) + ) + masked_frame_logits = torch.bmm( + video_hidden_states.unsqueeze(1), + target_video_hidden_states.unsqueeze(-1), + ).squeeze(-1) + video_logits = torch.cat( + [masked_frame_logits, non_masked_frame_logits], dim=1 + ) + + if text_hidden_states is not None: + text_hidden_states = self.transform(text_hidden_states) + text_logits = self.decoder(text_hidden_states) + return video_logits, text_logits + + +class MFMMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertMFMMLMPredictionHead(config) + + def forward( + self, + video_hidden_states=None, + target_video_hidden_states=None, + non_masked_frame_hidden_states=None, + text_hidden_states=None, + ): + video_logits, text_logits = self.predictions( + video_hidden_states, + target_video_hidden_states, + non_masked_frame_hidden_states, + text_hidden_states, + ) + return video_logits, text_logits + + +class MMBertForMTM(MMBertForMFMMLM): + def __init__(self, config): + BertPreTrainedModel.__init__(self, config) + self.videomlp = VideoTokenMLP(config) + self.bert = MMBertModel(config) + self.cls = MTMHead(config) + self.hidden_size = config.hidden_size + self.init_weights() + + +class BertMTMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + self.decoder = nn.Linear( + config.hidden_size, config.vocab_size, bias=False) + + def forward( + self, + video_hidden_states=None, + target_video_hidden_states=None, + non_masked_frame_hidden_states=None, + text_hidden_states=None, + ): + non_masked_frame_hidden_states = non_masked_frame_hidden_states.transpose(1, 0) + video_logits, text_logits = None, None + if video_hidden_states is not None: + video_hidden_states = self.transform(video_hidden_states) + + masked_frame_logits = torch.bmm( + video_hidden_states.unsqueeze(1), + target_video_hidden_states.unsqueeze(-1), + ).squeeze(-1) + + non_masked_frame_logits = torch.mm( + video_hidden_states, + non_masked_frame_hidden_states + ) + video_on_vocab_logits = self.decoder(video_hidden_states) + video_logits = torch.cat([ + masked_frame_logits, + non_masked_frame_logits, + video_on_vocab_logits], dim=1) + + if text_hidden_states is not None: + text_hidden_states = self.transform(text_hidden_states) + # text first so label does not need to be shifted. + text_on_vocab_logits = self.decoder(text_hidden_states) + text_on_video_logits = torch.mm( + text_hidden_states, + non_masked_frame_hidden_states + ) + text_logits = torch.cat([ + text_on_vocab_logits, + text_on_video_logits + ], dim=1) + + return video_logits, text_logits + + +class MTMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertMTMPredictionHead(config) + + def forward( + self, + video_hidden_states=None, + target_video_hidden_states=None, + non_masked_frame_hidden_states=None, + text_hidden_states=None, + ): + video_logits, text_logits = self.predictions( + video_hidden_states, + target_video_hidden_states, + non_masked_frame_hidden_states, + text_hidden_states, + ) + return video_logits, text_logits + + +class MMBertModel(BertModel): + """MMBertModel has MMBertEmbedding to support video tokens.""" + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + # overwrite embedding + self.embeddings = MMBertEmbeddings(config) + self.encoder = MultiLayerAttentionMaskBertEncoder(config) + self.init_weights() + + def forward( + self, + input_ids=None, + input_video_embeds=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + separate_forward_split=None, + ): + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None + else self.config.use_return_dict + ) + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids " + "and inputs_embeds at the same time" + ) + elif input_ids is not None: + if input_video_embeds is not None: + input_shape = ( + input_ids.size(0), + input_ids.size(1) + input_video_embeds.size(1), + ) + else: + input_shape = ( + input_ids.size(0), + input_ids.size(1), + ) + elif inputs_embeds is not None: + if input_video_embeds is not None: + input_shape = ( + inputs_embeds.size(0), + inputs_embeds.size(1) + input_video_embeds.size(1), + ) + else: + input_shape = ( + input_ids.size(0), + input_ids.size(1), + ) + else: + raise ValueError( + "You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None \ + else inputs_embeds.device + + if attention_mask is None: + attention_mask = torch.ones(input_shape, device=device) + if token_type_ids is None: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions + # [batch_size, from_seq_length, to_seq_length] + # ourselves in which case + # we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = \ + self.get_extended_attention_mask( + attention_mask, input_shape, device) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to + # [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = ( + encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones( + encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or + # [num_hidden_layers x num_heads] + # and head_mask is converted to shape + # [num_hidden_layers x batch x num_heads x seq_length x seq_length] + + head_mask = self.get_head_mask( + head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids, + input_video_embeds, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + ) + + if separate_forward_split is not None: + split_embedding_output = \ + embedding_output[:, :separate_forward_split] + split_extended_attention_mask = extended_attention_mask[ + :, :, :, :separate_forward_split, :separate_forward_split + ] + split_encoder_outputs = self.encoder( + split_embedding_output, + attention_mask=split_extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + assert ( + len(split_encoder_outputs) <= 2 + ), "we do not support merge on attention for now." + encoder_outputs = [] + encoder_outputs.append([split_encoder_outputs[0]]) + if len(split_encoder_outputs) == 2: + encoder_outputs.append([]) + for _all_hidden_states in split_encoder_outputs[1]: + encoder_outputs[-1].append([_all_hidden_states]) + + split_embedding_output = \ + embedding_output[:, separate_forward_split:] + split_extended_attention_mask = extended_attention_mask[ + :, :, :, separate_forward_split:, separate_forward_split: + ] + + split_encoder_outputs = self.encoder( + split_embedding_output, + attention_mask=split_extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + assert ( + len(split_encoder_outputs) <= 2 + ), "we do not support merge on attention for now." + encoder_outputs[0].append(split_encoder_outputs[0]) + encoder_outputs[0] = torch.cat(encoder_outputs[0], dim=1) + if len(split_encoder_outputs) == 2: + for layer_idx, _all_hidden_states in enumerate( + split_encoder_outputs[1] + ): + encoder_outputs[1][layer_idx].append(_all_hidden_states) + encoder_outputs[1][layer_idx] = torch.cat( + encoder_outputs[1][layer_idx], dim=1 + ) + encoder_outputs = tuple(encoder_outputs) + else: + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + return (sequence_output, pooled_output) + encoder_outputs[1:] + + def get_extended_attention_mask(self, attention_mask, input_shape, device): + """This is borrowed from `modeling_utils.py` with the support of + multi-layer attention masks. + The second dim is expected to be number of layers. + See `MMAttentionMaskProcessor`. + Makes broadcastable attention and causal masks so that future + and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, + zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, \ + with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions + # [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable + # to all heads. + if attention_mask.dim() == 4: + extended_attention_mask = attention_mask[:, :, None, :, :] + extended_attention_mask = extended_attention_mask.to( + dtype=self.dtype + ) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) \ + * -10000.0 + return extended_attention_mask + else: + return super().get_extended_attention_mask( + attention_mask, input_shape, device + ) + + +class MultiLayerAttentionMaskBertEncoder(BertEncoder): + """extend BertEncoder with the capability of + multiple layers of attention mask.""" + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=False, + ): + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + layer_head_mask = head_mask[i] if head_mask is not None else None + + layer_attention_mask = ( + attention_mask[:, i, :, :, :] + if attention_mask.dim() == 5 + else attention_mask + ) + + if getattr(self.config, "gradient_checkpointing", False): + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + layer_attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + layer_attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions, + ) + hidden_states = layer_outputs[0] + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + return tuple( + v + for v in [hidden_states, all_hidden_states, all_attentions] + if v is not None + ) diff --git a/fairseq/examples/MMPT/mmpt/modules/__init__.py b/fairseq/examples/MMPT/mmpt/modules/__init__.py new file mode 100644 index 0000000..4c78594 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/modules/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .mm import * + +try: + from .expmm import * +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/modules/mm.py b/fairseq/examples/MMPT/mmpt/modules/mm.py new file mode 100644 index 0000000..5d97773 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/modules/mm.py @@ -0,0 +1,145 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Copyright (c) Facebook, Inc. All Rights Reserved + + +import torch + +from torch import nn + +try: + from transformers.modeling_bert import ( + BertEmbeddings, + ACT2FN, + ) +except ImportError: + pass + + +class VideoTokenMLP(nn.Module): + def __init__(self, config): + super().__init__() + input_dim = config.input_dim if hasattr(config, "input_dim") else 512 + self.linear1 = nn.Linear(input_dim, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size) + self.activation = ACT2FN[config.hidden_act] + self.linear2 = nn.Linear(config.hidden_size, config.hidden_size) + + def forward(self, hidden_states): + hidden_states = self.linear1(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + hidden_states = self.linear2(hidden_states) + return hidden_states + + +class MMBertEmbeddings(BertEmbeddings): + def __init__(self, config): + super().__init__(config) + self.max_video_len = config.max_video_len + if hasattr(config, "use_seg_emb") and config.use_seg_emb: + """the original VLM paper uses seg_embeddings for temporal space. + although not used it changed the randomness of initialization. + we keep it for reproducibility. + """ + self.seg_embeddings = nn.Embedding(256, config.hidden_size) + + def forward( + self, + input_ids, + input_video_embeds, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + ): + input_tensor = input_ids if input_ids is not None else inputs_embeds + if input_video_embeds is not None: + input_shape = ( + input_tensor.size(0), + input_tensor.size(1) + input_video_embeds.size(1), + ) + else: + input_shape = (input_tensor.size(0), input_tensor.size(1)) + + if position_ids is None: + """ + Auto skip position embeddings for text only case. + use cases: + (1) action localization and segmentation: + feed in len-1 dummy video token needs text part to + skip input_video_embeds.size(1) for the right + position_ids for video [SEP] and rest text tokens. + (2) MMFusionShare for two forward passings: + in `forward_text`: input_video_embeds is None. + need to skip video [SEP] token. + + # video_len + 1: [CLS] + video_embed + # self.max_video_len + 1: [SEP] for video. + # self.max_video_len + 2: [SEP] for video. + # self.max_video_len + input_ids.size(1): rest for text. + """ + if input_video_embeds is not None: + video_len = input_video_embeds.size(1) + starting_offset = self.max_video_len + 1 # video [SEP] + ending_offset = self.max_video_len + input_ids.size(1) + else: + video_len = 0 + starting_offset = self.max_video_len + 2 # first text token. + ending_offset = self.max_video_len + input_ids.size(1) + 1 + position_ids = torch.cat([ + self.position_ids[:, :video_len + 1], + self.position_ids[:, starting_offset:ending_offset] + ], dim=1) + + if token_type_ids is None: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + """ + the format of input_ids is [CLS] [SEP] caption [SEP] padding. + the goal is to build [CLS] video tokens [SEP] caption [SEP] . + """ + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + if input_video_embeds is not None: + inputs_mm_embeds = torch.cat([ + inputs_embeds[:, :1], input_video_embeds, inputs_embeds[:, 1:] + ], dim=1) + else: + # text only for `MMFusionShare`. + inputs_mm_embeds = inputs_embeds + + position_embeddings = self.position_embeddings(position_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_mm_embeds + position_embeddings + embeddings += token_type_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class AlignHead(nn.Module): + """this will load pre-trained weights for NSP, which is desirable.""" + + def __init__(self, config): + super().__init__() + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, dropout_pooled_output): + logits = self.seq_relationship(dropout_pooled_output) + return logits diff --git a/fairseq/examples/MMPT/mmpt/modules/retri.py b/fairseq/examples/MMPT/mmpt/modules/retri.py new file mode 100644 index 0000000..d1b288f --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/modules/retri.py @@ -0,0 +1,429 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import numpy as np +import pickle +import time + +try: + import faiss +except ImportError: + pass + +from collections import defaultdict + +from ..utils import get_local_rank, print_on_rank0 + + +class VectorRetriever(object): + """ + How2 Video Retriver. + Reference usage of FAISS: + https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py + """ + + def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train): + if db_type == "flatl2": + quantizer = faiss.IndexFlatL2(hidden_size) # the other index + self.db = faiss.IndexIVFFlat( + quantizer, hidden_size, cent, faiss.METRIC_L2) + elif db_type == "pq": + self.db = faiss.index_factory( + hidden_size, f"IVF{cent}_HNSW32,PQ32" + ) + else: + raise ValueError("unknown type of db", db_type) + self.train_thres = cent * examples_per_cent_to_train + self.train_cache = [] + self.train_len = 0 + self.videoid_to_vectoridx = {} + self.vectoridx_to_videoid = None + self.make_direct_maps_done = False + + def make_direct_maps(self): + faiss.downcast_index(self.db).make_direct_map() + + def __len__(self): + return self.db.ntotal + + def save(self, out_dir): + faiss.write_index( + self.db, + os.path.join(out_dir, "faiss_idx") + ) + with open( + os.path.join( + out_dir, "videoid_to_vectoridx.pkl"), + "wb") as fw: + pickle.dump( + self.videoid_to_vectoridx, fw, + protocol=pickle.HIGHEST_PROTOCOL + ) + + def load(self, out_dir): + fn = os.path.join(out_dir, "faiss_idx") + self.db = faiss.read_index(fn) + with open( + os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr: + self.videoid_to_vectoridx = pickle.load(fr) + + def add(self, hidden_states, video_ids, last=False): + assert len(hidden_states) == len(video_ids), "{}, {}".format( + str(len(hidden_states)), str(len(video_ids))) + assert len(hidden_states.shape) == 2 + assert hidden_states.dtype == np.float32 + + valid_idx = [] + for idx, video_id in enumerate(video_ids): + if video_id not in self.videoid_to_vectoridx: + valid_idx.append(idx) + self.videoid_to_vectoridx[video_id] = \ + len(self.videoid_to_vectoridx) + + hidden_states = hidden_states[valid_idx] + if not self.db.is_trained: + self.train_cache.append(hidden_states) + self.train_len += hidden_states.shape[0] + if self.train_len < self.train_thres: + return + self.finalize_training() + else: + self.db.add(hidden_states) + + def finalize_training(self): + hidden_states = np.concatenate(self.train_cache, axis=0) + del self.train_cache + local_rank = get_local_rank() + if local_rank == 0: + start = time.time() + print("training db on", self.train_thres, "/", self.train_len) + self.db.train(hidden_states[:self.train_thres]) + if local_rank == 0: + print("training db for", time.time() - start) + self.db.add(hidden_states) + + def search( + self, + query_hidden_states, + orig_dist, + ): + if len(self.videoid_to_vectoridx) != self.db.ntotal: + raise ValueError( + "cannot search: size mismatch in-between index and db", + len(self.videoid_to_vectoridx), + self.db.ntotal + ) + + if self.vectoridx_to_videoid is None: + self.vectoridx_to_videoid = { + self.videoid_to_vectoridx[videoid]: videoid + for videoid in self.videoid_to_vectoridx + } + assert len(self.vectoridx_to_videoid) \ + == len(self.videoid_to_vectoridx) + + # MultilingualFaissDataset uses the following; not sure the purpose. + # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) + queried_dist, index = self.db.search(query_hidden_states, 1) + queried_dist, index = queried_dist[:, 0], index[:, 0] + + outputs = np.array( + [self.vectoridx_to_videoid[_index] + if _index != -1 else (-1, -1, -1) for _index in index], + dtype=np.int32) + outputs[queried_dist <= orig_dist] = -1 + return outputs + + def search_by_video_ids( + self, + video_ids, + retri_factor + ): + if len(self.videoid_to_vectoridx) != self.db.ntotal: + raise ValueError( + len(self.videoid_to_vectoridx), + self.db.ntotal + ) + + if not self.make_direct_maps_done: + self.make_direct_maps() + + if self.vectoridx_to_videoid is None: + self.vectoridx_to_videoid = { + self.videoid_to_vectoridx[videoid]: videoid + for videoid in self.videoid_to_vectoridx + } + assert len(self.vectoridx_to_videoid) \ + == len(self.videoid_to_vectoridx) + + query_hidden_states = [] + vector_ids = [] + for video_id in video_ids: + vector_id = self.videoid_to_vectoridx[video_id] + vector_ids.append(vector_id) + query_hidden_state = self.db.reconstruct(vector_id) + query_hidden_states.append(query_hidden_state) + query_hidden_states = np.stack(query_hidden_states) + + # MultilingualFaissDataset uses the following; not sure the reason. + # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) + _, index = self.db.search(query_hidden_states, retri_factor) + outputs = [] + for sample_idx, sample in enumerate(index): + # the first video_id is always the video itself. + cands = [video_ids[sample_idx]] + for vector_idx in sample: + if vector_idx >= 0 \ + and vector_ids[sample_idx] != vector_idx: + cands.append( + self.vectoridx_to_videoid[vector_idx] + ) + outputs.append(cands) + return outputs + + +class VectorRetrieverDM(VectorRetriever): + """ + with direct map. + How2 Video Retriver. + Reference usage of FAISS: + https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py + """ + + def __init__( + self, + hidden_size, + cent, + db_type, + examples_per_cent_to_train + ): + super().__init__( + hidden_size, cent, db_type, examples_per_cent_to_train) + self.make_direct_maps_done = False + + def make_direct_maps(self): + faiss.downcast_index(self.db).make_direct_map() + self.make_direct_maps_done = True + + def search( + self, + query_hidden_states, + orig_dist, + ): + if len(self.videoid_to_vectoridx) != self.db.ntotal: + raise ValueError( + len(self.videoid_to_vectoridx), + self.db.ntotal + ) + + if not self.make_direct_maps_done: + self.make_direct_maps() + if self.vectoridx_to_videoid is None: + self.vectoridx_to_videoid = { + self.videoid_to_vectoridx[videoid]: videoid + for videoid in self.videoid_to_vectoridx + } + assert len(self.vectoridx_to_videoid) \ + == len(self.videoid_to_vectoridx) + + # MultilingualFaissDataset uses the following; not sure the reason. + # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) + queried_dist, index = self.db.search(query_hidden_states, 1) + outputs = [] + for sample_idx, sample in enumerate(index): + # and queried_dist[sample_idx] < thres \ + if sample >= 0 \ + and queried_dist[sample_idx] < orig_dist[sample_idx]: + outputs.append(self.vectoridx_to_videoid[sample]) + else: + outputs.append(None) + return outputs + + def search_by_video_ids( + self, + video_ids, + retri_factor=8 + ): + if len(self.videoid_to_vectoridx) != self.db.ntotal: + raise ValueError( + len(self.videoid_to_vectoridx), + self.db.ntotal + ) + + if not self.make_direct_maps_done: + self.make_direct_maps() + if self.vectoridx_to_videoid is None: + self.vectoridx_to_videoid = { + self.videoid_to_vectoridx[videoid]: videoid + for videoid in self.videoid_to_vectoridx + } + assert len(self.vectoridx_to_videoid) \ + == len(self.videoid_to_vectoridx) + + query_hidden_states = [] + vector_ids = [] + for video_id in video_ids: + vector_id = self.videoid_to_vectoridx[video_id] + vector_ids.append(vector_id) + query_hidden_state = self.db.reconstruct(vector_id) + query_hidden_states.append(query_hidden_state) + query_hidden_states = np.stack(query_hidden_states) + + # MultilingualFaissDataset uses the following; not sure the reason. + # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) + _, index = self.db.search(query_hidden_states, retri_factor) + outputs = [] + for sample_idx, sample in enumerate(index): + # the first video_id is always the video itself. + cands = [video_ids[sample_idx]] + for vector_idx in sample: + if vector_idx >= 0 \ + and vector_ids[sample_idx] != vector_idx: + cands.append( + self.vectoridx_to_videoid[vector_idx] + ) + outputs.append(cands) + return outputs + + +class MMVectorRetriever(VectorRetrieverDM): + """ + multimodal vector retriver: + text retrieve video or video retrieve text. + """ + + def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train): + super().__init__( + hidden_size, cent, db_type, examples_per_cent_to_train) + video_db = self.db + super().__init__( + hidden_size, cent, db_type, examples_per_cent_to_train) + text_db = self.db + self.db = {"video": video_db, "text": text_db} + self.video_to_videoid = defaultdict(list) + + def __len__(self): + assert self.db["video"].ntotal == self.db["text"].ntotal + return self.db["video"].ntotal + + def make_direct_maps(self): + faiss.downcast_index(self.db["video"]).make_direct_map() + faiss.downcast_index(self.db["text"]).make_direct_map() + + def save(self, out_dir): + faiss.write_index( + self.db["video"], + os.path.join(out_dir, "video_faiss_idx") + ) + faiss.write_index( + self.db["text"], + os.path.join(out_dir, "text_faiss_idx") + ) + + with open( + os.path.join( + out_dir, "videoid_to_vectoridx.pkl"), + "wb") as fw: + pickle.dump( + self.videoid_to_vectoridx, fw, + protocol=pickle.HIGHEST_PROTOCOL + ) + + def load(self, out_dir): + fn = os.path.join(out_dir, "video_faiss_idx") + video_db = faiss.read_index(fn) + fn = os.path.join(out_dir, "text_faiss_idx") + text_db = faiss.read_index(fn) + self.db = {"video": video_db, "text": text_db} + with open( + os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr: + self.videoid_to_vectoridx = pickle.load(fr) + self.video_to_videoid = defaultdict(list) + + def add(self, hidden_states, video_ids): + """hidden_states is a pair `(video, text)`""" + assert len(hidden_states) == len(video_ids), "{}, {}".format( + str(len(hidden_states)), str(len(video_ids))) + assert len(hidden_states.shape) == 3 + assert len(self.video_to_videoid) == 0 + + valid_idx = [] + for idx, video_id in enumerate(video_ids): + if video_id not in self.videoid_to_vectoridx: + valid_idx.append(idx) + self.videoid_to_vectoridx[video_id] = \ + len(self.videoid_to_vectoridx) + + batch_size = hidden_states.shape[0] + hidden_states = hidden_states[valid_idx] + + hidden_states = np.transpose(hidden_states, (1, 0, 2)).copy() + if not self.db["video"].is_trained: + self.train_cache.append(hidden_states) + train_len = batch_size * len(self.train_cache) + if train_len < self.train_thres: + return + + hidden_states = np.concatenate(self.train_cache, axis=1) + del self.train_cache + self.db["video"].train(hidden_states[0, :self.train_thres]) + self.db["text"].train(hidden_states[1, :self.train_thres]) + self.db["video"].add(hidden_states[0]) + self.db["text"].add(hidden_states[1]) + + def get_clips_by_video_id(self, video_id): + if not self.video_to_videoid: + for video_id, video_clip, text_clip in self.videoid_to_vectoridx: + self.video_to_videoid[video_id].append( + (video_id, video_clip, text_clip)) + return self.video_to_videoid[video_id] + + def search( + self, + video_ids, + target_modality, + retri_factor=8 + ): + if len(self.videoid_to_vectoridx) != len(self): + raise ValueError( + len(self.videoid_to_vectoridx), + len(self) + ) + + if not self.make_direct_maps_done: + self.make_direct_maps() + if self.vectoridx_to_videoid is None: + self.vectoridx_to_videoid = { + self.videoid_to_vectoridx[videoid]: videoid + for videoid in self.videoid_to_vectoridx + } + assert len(self.vectoridx_to_videoid) \ + == len(self.videoid_to_vectoridx) + + src_modality = "text" if target_modality == "video" else "video" + + query_hidden_states = [] + vector_ids = [] + for video_id in video_ids: + vector_id = self.videoid_to_vectoridx[video_id] + vector_ids.append(vector_id) + query_hidden_state = self.db[src_modality].reconstruct(vector_id) + query_hidden_states.append(query_hidden_state) + query_hidden_states = np.stack(query_hidden_states) + + # MultilingualFaissDataset uses the following; not sure the reason. + # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) + _, index = self.db[target_modality].search( + query_hidden_states, retri_factor) + outputs = [] + for sample_idx, sample in enumerate(index): + cands = [] + for vector_idx in sample: + if vector_idx >= 0: + cands.append( + self.vectoridx_to_videoid[vector_idx] + ) + outputs.append(cands) + return outputs diff --git a/fairseq/examples/MMPT/mmpt/modules/vectorpool.py b/fairseq/examples/MMPT/mmpt/modules/vectorpool.py new file mode 100644 index 0000000..d2b23d2 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/modules/vectorpool.py @@ -0,0 +1,246 @@ +# Copyright (c) Facebook, Inc. All Rights Reserved + +import torch +import os +import numpy as np +import pickle + +from . import retri +from ..utils import get_local_rank + + +class VectorPool(object): + """ + Base class of retrieval space. + """ + + def __init__(self, config): + from transformers import AutoConfig + self.hidden_size = AutoConfig.from_pretrained( + config.dataset.bert_name).hidden_size + self.retriever_cls = getattr(retri, config.retriever_cls) + + def __call__(self, sample, **kwargs): + raise NotImplementedError + + def build_retriver( + self, + retriever_cls=None, + hidden_size=None, + centroids=512, + db_type="flatl2", + examples_per_cent_to_train=48 + ): + + """merge results from multiple gpus and return a retriver..""" + self.retriver = retriever_cls( + hidden_size, centroids, db_type, examples_per_cent_to_train) + return self.retriver + + def __repr__(self): + if hasattr(self, "retriver"): + retriver_name = str(len(self.retriver)) + else: + retriver_name = "no retriver field yet" + return self.__class__.__name__ \ + + "(" + retriver_name + ")" + + +class VideoVectorPool(VectorPool): + """ + average clips of a video as video representation. + """ + def __init__(self, config): + super().__init__(config) + self.build_retriver(self.retriever_cls, self.hidden_size) + + def __call__(self, sample, subsampling, **kwargs): + hidden_states = ( + sample["pooled_video"] + sample["pooled_text"]) / 2. + hidden_states = hidden_states.view( + -1, subsampling, + hidden_states.size(-1)) + hidden_states = torch.mean(hidden_states, dim=1) + hidden_states = hidden_states.cpu().detach().numpy() + video_ids = [] + for offset_idx, video_id in enumerate(sample["video_id"]): + if isinstance(video_id, tuple) and len(video_id) == 3: + # a sharded video_id. + video_id = video_id[0] + video_ids.append(video_id) + assert len(video_ids) == len(hidden_states) + self.retriver.add( + hidden_states.astype("float32"), + video_ids + ) + + +class DistributedVectorPool(VectorPool): + """ + support sync of multiple gpus/nodes. + """ + def __init__(self, config): + super().__init__(config) + self.out_dir = os.path.join( + config.fairseq.checkpoint.save_dir, + "retri") + os.makedirs(self.out_dir, exist_ok=True) + self.hidden_states = [] + self.video_ids = [] + + def build_retriver( + self, + retriever_cls=None, + hidden_size=None, + centroids=4096, + db_type="flatl2", + examples_per_cent_to_train=48 + ): + if retriever_cls is None: + retriever_cls = self.retriever_cls + if hidden_size is None: + hidden_size = self.hidden_size + """merge results from multiple gpus and return a retriver..""" + if torch.distributed.is_initialized(): + self.save() + # sync saving. + torch.distributed.barrier() + world_size = torch.distributed.get_world_size() + else: + world_size = 1 + self.retriver = retriever_cls( + hidden_size, centroids, db_type, examples_per_cent_to_train) + # each gpu process has its own retriever. + for local_rank in range(world_size): + if get_local_rank() == 0: + print("load local_rank", local_rank) + hidden_states, video_ids = self.load(local_rank) + hidden_states = hidden_states.astype("float32") + self.retriver.add(hidden_states, video_ids) + return self.retriver + + def load(self, local_rank): + hidden_states = np.load( + os.path.join( + self.out_dir, + "hidden_state" + str(local_rank) + ".npy" + ) + ) + + with open( + os.path.join( + self.out_dir, "video_id" + str(local_rank) + ".pkl"), + "rb") as fr: + video_ids = pickle.load(fr) + return hidden_states, video_ids + + def save(self): + hidden_states = np.vstack(self.hidden_states) + assert len(hidden_states) == len(self.video_ids), "{}, {}".format( + len(hidden_states), + len(self.video_ids) + ) + local_rank = torch.distributed.get_rank() \ + if torch.distributed.is_initialized() else 0 + + np.save( + os.path.join( + self.out_dir, + "hidden_state" + str(local_rank) + ".npy"), + hidden_states) + + with open( + os.path.join( + self.out_dir, + "video_id" + str(local_rank) + ".pkl"), + "wb") as fw: + pickle.dump( + self.video_ids, + fw, + protocol=pickle.HIGHEST_PROTOCOL + ) + + +class DistributedVideoVectorPool(DistributedVectorPool): + """ + average clips of a video as video representation. + """ + def __call__(self, sample, subsampling, **kwargs): + hidden_states = ( + sample["pooled_video"] + sample["pooled_text"]) / 2. + hidden_states = hidden_states.view( + -1, subsampling, + hidden_states.size(-1)) + hidden_states = torch.mean(hidden_states, dim=1) + hidden_states = hidden_states.cpu().detach().numpy() + video_ids = [] + for offset_idx, video_id in enumerate(sample["video_id"]): + if isinstance(video_id, tuple) and len(video_id) == 3: + # a sharded video_id. + video_id = video_id[0] + video_ids.append(video_id) + assert len(video_ids) == len(hidden_states) + self.hidden_states.append(hidden_states) + self.video_ids.extend(video_ids) + + +# ------------ the following are deprecated -------------- + +class TextClipVectorPool(VectorPool): + def __init__(self, config): + from transformers import AutoConfig + hidden_size = AutoConfig.from_pretrained( + config.dataset.bert_name).hidden_size + retriever_cls = getattr(retri, config.retriever_cls) + self.build_retriver(retriever_cls, hidden_size) + + def __call__(self, sample, **kwargs): + clip_meta = sample["clip_meta"].cpu() + assert torch.all(torch.le(clip_meta[:, 4], clip_meta[:, 5])) + text_meta = [tuple(item.tolist()) for item in clip_meta[:, 3:]] + + if hasattr(self, "retriver"): + # build_retriver is called. + self.retriver.add( + sample["pooled_text"].cpu().numpy().astype("float32"), + text_meta + ) + else: + raise NotImplementedError + + +class MMClipVectorPool(VectorPool): + """ + Multimodal Clip-level vector pool. + """ + def __init__(self, out_dir): + """use hidden_states to store `(video, text)`.""" + """use video_ids to store `(video_id, start, end)`.""" + super().__init__(out_dir) + + def __call__(self, sample, **kwargs): + pooled_video = sample["pooled_video"].cpu().unsqueeze(1).numpy() + pooled_text = sample["pooled_text"].cpu().unsqueeze(1).numpy() + + self.hidden_states.append( + np.concatenate([pooled_video, pooled_text], axis=1) + ) + + video_starts = sample["video_start"].cpu() + video_ends = sample["video_end"].cpu() + assert torch.all(torch.le(video_starts, video_ends)) + + text_starts = sample["text_start"].cpu() + text_ends = sample["text_end"].cpu() + assert torch.all(torch.le(text_starts, text_ends)) + subsample_size = sample["pooled_video"].size(0) // len(sample["video_id"]) + video_ids = [video_id for video_id in sample["video_id"] + for _ in range(subsample_size) + ] + for video_id, video_start, video_end, text_start, text_end in zip( + video_ids, video_starts, video_ends, text_starts, text_ends): + self.video_ids.append(( + video_id, + (int(video_start), int(video_end)), + (int(text_start), int(text_end)) + )) diff --git a/fairseq/examples/MMPT/mmpt/processors/__init__.py b/fairseq/examples/MMPT/mmpt/processors/__init__.py new file mode 100644 index 0000000..434d1d9 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/processors/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .processor import * + +from .how2processor import * +from .how2retriprocessor import * + +from .dsprocessor import * + +try: + from .rawvideoprocessor import * + from .codecprocessor import * + from .webvidprocessor import * + from .expprocessor import * + from .exphow2processor import * + from .exphow2retriprocessor import * + from .expcodecprocessor import * + from .expfeatureencoder import * + from .expdsprocessor import * +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/processors/dedupprocessor.py b/fairseq/examples/MMPT/mmpt/processors/dedupprocessor.py new file mode 100644 index 0000000..8a1ad40 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/processors/dedupprocessor.py @@ -0,0 +1,242 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import json +import pickle +from tqdm import tqdm +import os +import numpy as np + + +class CaptionDedupProcessor(object): + """remove overlapping of caption sentences(clip). + Some statistics: + caption: + {'t_clip_len': 246.6448431320854, + 'video_len': 281.09174795676245, + 'clip_tps': 0.8841283727427481, + 'video_tps': 0.7821156477732097, + 'min_clip_len': 0.0, + 'max_clip_len': 398.3, + 'mean_clip_len': 3.196580003006861, + 'num_clip': 77.15897706301081} + + raw_caption: + {'t_clip_len': 238.95908778424115, + 'video_len': 267.5914859862507, + 'clip_tps': 2.4941363624267963, + 'video_tps': 2.258989769647173, + 'min_clip_len': 0.0, + 'max_clip_len': 398.3, + 'mean_clip_len': 3.0537954186814265, + 'num_clip': 78.24986779481756} + """ + + def __init__(self, pkl_file): + with open(pkl_file, "rb") as fd: + self.data = pickle.load(fd) + self.stat = { + "t_clip_len": [], + "video_len": [], + "clip_tps": [], + "video_tps": [], + "clip_len": [], + } + + def __call__(self): + for idx, video_id in enumerate(tqdm(self.data)): + caption = json.loads(self.data[video_id]) + caption = self._dedup(caption) + if idx < 4096: # for the first 4096 examples, compute the statistics. + self.save_stat(video_id, caption) + self.data[video_id] = json.dumps(caption) + self.print_stat() + + def single(self, video_id): + caption = json.loads(self.data[video_id]) + for clip_idx, (start, end, text) in enumerate( + zip(caption["start"], caption["end"], caption["text"]) + ): + print(start, end, text) + print("@" * 100) + caption = self._dedup(caption) + for clip_idx, (start, end, text) in enumerate( + zip(caption["start"], caption["end"], caption["text"]) + ): + print(start, end, text) + print("#" * 100) + self.save_stat(video_id, caption) + self.print_stat() + + def finalize(self, tgt_fn): + with open(tgt_fn, "wb") as fw: + pickle.dump(self.data, fw, pickle.HIGHEST_PROTOCOL) + + def save_stat(self, video_id, caption): + video_fn = os.path.join( + "data/feat/feat_how2_s3d", video_id + ".npy" + ) + if os.path.isfile(video_fn): + with open(video_fn, "rb", 1) as fr: # 24 is the buffer size. buffered + version = np.lib.format.read_magic(fr) + shape, fortran, dtype = np.lib.format._read_array_header(fr, version) + video_len = shape[0] + + t_clip_len = 0.0 + t_tokens = 0 + for idx, (start, end, text) in enumerate( + zip(caption["start"], caption["end"], caption["text"]) + ): + clip_len = ( + (end - max(caption["end"][idx - 1], start)) + if idx > 0 + else end - start + ) + t_clip_len += clip_len + t_tokens += len(text.split(" ")) + self.stat["clip_len"].append(clip_len) + self.stat["t_clip_len"].append(t_clip_len) + self.stat["video_len"].append(video_len) + self.stat["clip_tps"].append(t_tokens / t_clip_len) + self.stat["video_tps"].append(t_tokens / video_len) + + def print_stat(self): + result = { + "t_clip_len": np.mean(self.stat["t_clip_len"]), + "video_len": np.mean(self.stat["video_len"]), + "clip_tps": np.mean(self.stat["clip_tps"]), + "video_tps": np.mean(self.stat["video_tps"]), + "min_clip_len": min(self.stat["clip_len"]), + "max_clip_len": max(self.stat["clip_len"]), + "mean_clip_len": np.mean(self.stat["clip_len"]), + "num_clip": len(self.stat["clip_len"]) / len(self.stat["video_tps"]), + } + print(result) + + def _dedup(self, caption): + def random_merge(end_idx, start, end, text, starts, ends, texts): + if random.random() > 0.5: + # print(clip_idx, "[PARTIAL INTO PREV]", end_idx) + # overlapped part goes to the end of previous. + ends[-1] = max(ends[-1], start) # ? + rest_text = text[end_idx:].strip() + if rest_text: + starts.append(max(ends[-1], start)) + ends.append(max(end, starts[-1])) + texts.append(rest_text) + else: # goes to the beginning of the current. + # strip the previous. + left_text = texts[-1][:-end_idx].strip() + if left_text: + # print(clip_idx, "[PREV PARTIAL INTO CUR]", end_idx) + ends[-1] = min(ends[-1], start) + texts[-1] = left_text + else: + # print(clip_idx, "[PREV LEFT NOTHING ALL INTO CUR]", end_idx) + starts.pop(-1) + ends.pop(-1) + texts.pop(-1) + starts.append(start) + ends.append(end) + texts.append(text) + + starts, ends, texts = [], [], [] + for clip_idx, (start, end, text) in enumerate( + zip(caption["start"], caption["end"], caption["text"]) + ): + if not isinstance(text, str): + continue + text = text.replace("\n", " ").strip() + if len(text) == 0: + continue + starts.append(start) + ends.append(end) + texts.append(text) + break + + for clip_idx, (start, end, text) in enumerate( + zip( + caption["start"][clip_idx + 1:], + caption["end"][clip_idx + 1:], + caption["text"][clip_idx + 1:], + ) + ): + if not isinstance(text, str): + continue + text = text.replace("\n", " ").strip() + if len(text) == 0: + continue + + # print(clip_idx, texts[-5:]) + # print(clip_idx, start, end, text) + if texts[-1].endswith(text): # subset of prev caption -> merge + # print(clip_idx, "[MERGE INTO PREV]") + ends[-1] = max(ends[-1], end) + elif text.startswith(texts[-1]): # superset of prev caption -> merge + # print(clip_idx, "[PREV MERGE INTO CUR]") + texts[-1] = text + starts[-1] = min(starts[-1], start) + ends[-1] = max(ends[-1], end) + else: # overlapping or non-overlapping. + for end_idx in range(1, len(text) + 1): + if texts[-1].endswith(text[:end_idx]): + random_merge(end_idx, start, end, text, starts, ends, texts) + break + else: + starts.append(start) + ends.append(end) + texts.append(text) + + assert (ends[-1] + 0.001) >= starts[-1] and len( + texts[-1] + ) > 0, "{} {} {} <- {} {} {}, {} {} {}".format( + str(starts[-1]), + str(ends[-1]), + texts[-1], + caption["start"][clip_idx - 1], + caption["end"][clip_idx - 1], + caption["text"][clip_idx - 1], + str(start), + str(end), + text, + ) + + return {"start": starts, "end": ends, "text": texts} + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="dedup how2 caption") + parser.add_argument('--how2dir', default="data/how2") + args = parser.parse_args() + + raw_caption_json = os.path.join(args.how2dir, "raw_caption.json") + raw_caption_pickle = os.path.join(args.how2dir, "raw_caption.pkl") + raw_caption_dedup_pickle = os.path.join(args.how2dir, "raw_caption_dedup.pkl") + + def convert_to_pickle(src_fn, tgt_fn): + with open(src_fn) as fd: + captions = json.load(fd) + + for video_id in captions: + captions[video_id] = json.dumps(captions[video_id]) + + with open(tgt_fn, "wb") as fw: + pickle.dump(captions, fw, pickle.HIGHEST_PROTOCOL) + + if not os.path.isfile(raw_caption_pickle): + convert_to_pickle(raw_caption_json, raw_caption_pickle) + + deduper = CaptionDedupProcessor(raw_caption_pickle) + deduper() + deduper.finalize(raw_caption_dedup_pickle) + + """ + # demo + deduper = CaptionDedupProcessor("data/how2/raw_caption.pkl") + deduper.single("HfIeQ9pzL5U") + """ diff --git a/fairseq/examples/MMPT/mmpt/processors/dsprocessor.py b/fairseq/examples/MMPT/mmpt/processors/dsprocessor.py new file mode 100644 index 0000000..ecebf0e --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/processors/dsprocessor.py @@ -0,0 +1,848 @@ +# Copyright (c) Facebook, Inc. All Rights Reserved + +""" +Processors for all downstream (ds) tasks. +""" + +import json +import os +import pickle +import random +import math +import numpy as np +import torch + +from collections import defaultdict + +from .processor import ( + MetaProcessor, + VideoProcessor, + TextProcessor, + Aligner, + MMAttentionMask2DProcessor, +) + +from .how2processor import TextGenerationProcessor + + +# ------------- A General Aligner for all downstream tasks----------------- + + +class DSAligner(Aligner): + """ + Downstream (DS) aligner shared by all datasets. + """ + + def __call__(self, video_id, video_feature, text_feature, wps=0.7): + # random sample a starting sec for video. + video_start = 0 + video_end = min(len(video_feature), self.max_video_len) + # the whole sequence is a single clip. + video_clips = {"start": [video_start], "end": [video_end]} + + text_feature = { + "cap": [text_feature], + "start": [video_start], + "end": [len(text_feature) / wps], + } + text_clip_indexs = [0] + + vfeats, vmasks = self._build_video_seq( + video_feature, video_clips + ) + caps, cmasks = self._build_text_seq( + text_feature, text_clip_indexs + ) + + return { + "caps": caps, + "cmasks": cmasks, + "vfeats": vfeats, + "vmasks": vmasks, + "video_id": video_id, + } + + +class NLGTextProcessor(TextProcessor): + """ + Also return the original text as ref. + """ + def __call__(self, text_id): + return super().__call__(text_id), text_id + + +class DSNLGAligner(DSAligner): + """extend with the capability of 2d mask for generation.""" + def __init__(self, config): + super().__init__(config) + self.attnmasker = MMAttentionMask2DProcessor() + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained( + self.bert_name, use_fast=self.use_fast, + bos_token="[CLS]", eos_token="[SEP]" + ) + self.tokenizer = tokenizer + self.bos_token_id = tokenizer.bos_token_id + self.eos_token_id = tokenizer.eos_token_id + self.textgen = TextGenerationProcessor(tokenizer) + + def __call__(self, video_id, video_feature, text_feature): + output = super().__call__(video_id, video_feature, text_feature[0]) + if self.split == "test": + # output.update({"ref": text_feature[1]}) + output.update({"ref": self.tokenizer.decode( + output["caps"], skip_special_tokens=True)}) + text_label = output["caps"] + cmasks = torch.BoolTensor([1] * text_label.size(0)) + caps = torch.LongTensor([ + self.cls_token_id, + self.sep_token_id, + self.bos_token_id]) + else: + caps, text_label = self.textgen(output["caps"]) + cmasks = output["cmasks"] + + attention_mask = self.attnmasker( + output["vmasks"], cmasks, "textgen") + + output.update({ + "caps": caps, + "cmasks": cmasks, + "text_label": text_label, + "attention_mask": attention_mask, + }) + return output + + +# -------------------- MSRVTT ------------------------ + + +class MSRVTTMetaProcessor(MetaProcessor): + """MSRVTT dataset. + reference: `howto100m/msrvtt_dataloader.py` + """ + + def __init__(self, config): + super().__init__(config) + import pandas as pd + data = pd.read_csv(self._get_split_path(config)) + # TODO: add a text1ka flag. + if config.split == "train" \ + and config.full_test_path is not None \ + and config.jsfusion_path is not None: + # add testing videos from full_test_path not used by jfusion. + additional_data = pd.read_csv(config.full_test_path) + jsfusion_data = pd.read_csv(config.jsfusion_path) + + for video_id in additional_data["video_id"]: + if video_id not in jsfusion_data["video_id"].values: + data = data.append( + {"video_id": video_id}, ignore_index=True) + + if config.dup is not None and config.split == "train": + data = data.append([data] * (config.dup - 1), ignore_index=True) + self.data = data + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + """slightly modify with if condition to combine train/test.""" + vid, sentence = None, None + vid = self.data["video_id"].values[idx] + if "sentence" in self.data: # for testing. + sentence = self.data["sentence"].values[idx] + else: # for training. + sentence = vid + return vid, sentence + + +class MSRVTTTextProcessor(TextProcessor): + """MSRVTT dataset. + reference: `msrvtt_dataloader.py` `MSRVTT_TrainDataLoader`. + TODO (huxu): add max_words. + """ + + def __init__(self, config): + super().__init__(config) + self.sentences = None + if config.json_path is not None and config.split == "train": + with open(config.json_path) as fd: + self.data = json.load(fd) + self.sentences = defaultdict(list) + for s in self.data["sentences"]: + self.sentences[s["video_id"]].append(s["caption"]) + + def __call__(self, text_id): + if self.sentences is not None: + rind = random.randint(0, len(self.sentences[text_id]) - 1) + sentence = self.sentences[text_id][rind] + else: + sentence = text_id + caption = self.tokenizer(sentence, add_special_tokens=False) + return caption["input_ids"] + + +class MSRVTTNLGTextProcessor(MSRVTTTextProcessor): + """TODO: change dsaligner and merge to avoid any NLG text processor.""" + def __call__(self, text_id): + if self.sentences is not None: + rind = random.randint(0, len(self.sentences[text_id]) - 1) + sentence = self.sentences[text_id][rind] + else: + sentence = text_id + caption = self.tokenizer(sentence, add_special_tokens=False) + return caption["input_ids"], sentence + + +class MSRVTTQAMetaProcessor(MetaProcessor): + """MSRVTT-QA: retrieval-based multi-choice QA from JSFusion dataset. + For simplicity, we use the train retrieval model. + reference: `https://github.com/yj-yu/lsmdc` + """ + + def __init__(self, config): + super().__init__(config) + import pandas as pd + csv_data = pd.read_csv(self._get_split_path(config), sep="\t") + data = [] + for video_id, a1, a2, a3, a4, a5, answer in zip( + csv_data["vid_key"].values, + csv_data["a1"].values, + csv_data["a2"].values, + csv_data["a3"].values, + csv_data["a4"].values, + csv_data["a5"].values, + csv_data["answer"].values): + video_id = video_id.replace("msr", "video") + data.append((video_id, (answer, [a1, a2, a3, a4, a5]))) + self.data = data + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + +class MSRVTTQATextProcessor(TextProcessor): + """MSRVTT-QA dataset. + text_ans is of format `(answer, [a1, a2, a3, a4, a5])`. + """ + + def __call__(self, text_ans): + for ans_idx, ans in enumerate(text_ans[1]): + if isinstance(ans, str): + text_ans[1][ans_idx] = self.tokenizer(ans, add_special_tokens=False)["input_ids"] + return text_ans + + +class MSRVTTQAAligner(DSAligner): + """MSRVTT dataset. + similar to sample in how2. + we call __call__ multiple times. + """ + + def __call__(self, video_id, video_feature, text_feature, wps=0.7): + caps = [] + cmasks = [] + answer = text_feature[0] + for ans_idx, _text_feature in enumerate(text_feature[1]): + output = super().__call__( + video_id, video_feature, _text_feature, wps) + caps.append(output["caps"]) + cmasks.append(output["cmasks"]) + output.update({ + "caps": torch.stack(caps), + "cmasks": torch.stack(cmasks), + "answers": torch.LongTensor([answer]), + }) + return output + + +# -------------------- Youcook ----------------------- + + +class YoucookMetaProcessor(MetaProcessor): + """Youcook dataset. + reference: `howto100m/youcook_dataloader.py` + note that the data can be different as the + (1) some videos already in Howto100m are removed. + (2) stop words are removed from caption + TODO (huxu): make a flag to load the original caption. + (see youcookii_annotations_trainval.json). + + The max_video_len can be 264 and text can be 64 tokens. + In reality we may not need that long. see projects/task/youcook.yaml + """ + + def __init__(self, config): + super().__init__(config) + vfeat_dir = config.vfeat_dir + print(self._get_split_path(config)) + with open(self._get_split_path(config), "rb") as fd: + data = pickle.load(fd) + all_valid_video_ids = set( + [os.path.splitext(fn)[0] for fn in os.listdir(vfeat_dir)] + ) + recs = [] + video_ids = set() + valid_video_ids = set() + for rec in data: # filter videos not available. + udl_idx = rec["id"].rindex("_") + video_id = rec["id"][:udl_idx] + video_ids.add(video_id) + if video_id in all_valid_video_ids: + valid_video_ids.add(video_id) + recs.append(rec) + print("total video_ids in .pkl", len(video_ids)) + print("valid video_ids in .pkl", len(valid_video_ids)) + print("please verify {train,val}_list.txt") + data = recs + self.data = data + + with open(config.trainval_annotation) as fd: + self.youcook_annotation = json.load(fd)["database"] + if config.use_annotation_text is True: + print("using text in annotation.") + self.use_annotation_caption = True + else: + self.use_annotation_caption = False + + def __getitem__(self, idx): + def _get_video_and_caption(rec): + vid = rec["id"] + udl_idx = vid.rindex("_") + video_id, clip_id = vid[:udl_idx], int(vid[udl_idx + 1:]) + clip = self.youcook_annotation[video_id]["annotations"][clip_id] + start, end = clip["segment"] + if self.use_annotation_caption: + caption = clip["sentence"] + else: + caption = rec["caption"] + return (video_id, start, end), caption + + rec = self.data[idx] + video_info, text_info = _get_video_and_caption(rec) + return video_info, text_info + + +class YoucookVideoProcessor(VideoProcessor): + """video_fn is a tuple of (video_id, start, end) now.""" + + def __call__(self, video_fn): + video_id, start, end = video_fn + feat = np.load(os.path.join(self.vfeat_dir, video_id + ".npy")) + return feat[start:end] + + +class YoucookNLGMetaProcessor(MetaProcessor): + """NLG uses the original split: + `train_list.txt` and `val_list.txt` + """ + + def __init__(self, config): + super().__init__(config) + vfeat_dir = config.vfeat_dir + print(self._get_split_path(config)) + with open(self._get_split_path(config)) as fd: + video_ids = [ + line.strip().split("/")[1] for line in fd.readlines()] + print("total video_ids in train/val_list.txt", len(video_ids)) + + all_valid_video_ids = set( + [os.path.splitext(fn)[0] for fn in os.listdir(vfeat_dir)] + ) + video_ids = [ + video_id for video_id in video_ids + if video_id in all_valid_video_ids] + + print("valid video_ids in train/val_list.txt", len(video_ids)) + with open(config.trainval_annotation) as fd: + self.youcook_annotation = json.load(fd)["database"] + + data = [] + for video_id in video_ids: + for clip in self.youcook_annotation[video_id]["annotations"]: + start, end = clip["segment"] + caption = clip["sentence"] + data.append(((video_id, start, end), caption)) + self.data = data + + def __getitem__(self, idx): + return self.data[idx] + + +# --------------------- CrossTask ------------------------- + +class CrossTaskMetaProcessor(MetaProcessor): + def __init__(self, config): + super().__init__(config) + np.random.seed(0) # deterministic random split. + task_vids = self._get_vids( + config.train_csv_path, + config.vfeat_dir, + config.annotation_path) + + val_vids = self._get_vids( + config.val_csv_path, + config.vfeat_dir, + config.annotation_path) + + # filter out those task and vids appear in val_vids. + task_vids = { + task: [ + vid for vid in vids + if task not in val_vids or vid not in val_vids[task]] + for task, vids in task_vids.items()} + + primary_info = self._read_task_info(config.primary_path) + test_tasks = set(primary_info['steps'].keys()) + + # if args.use_related: + related_info = self._read_task_info(config.related_path) + task_steps = {**primary_info['steps'], **related_info['steps']} + n_steps = {**primary_info['n_steps'], **related_info['n_steps']} + # else: + # task_steps = primary_info['steps'] + # n_steps = primary_info['n_steps'] + all_tasks = set(n_steps.keys()) + # filter and keep task in primary or related. + task_vids = { + task: vids for task, vids in task_vids.items() + if task in all_tasks} + # vocab-by-step matrix (A) and vocab (M) + # (huxu): we do not use BoW. + # A, M = self._get_A(task_steps, share="words") + + train_vids, test_vids = self._random_split( + task_vids, test_tasks, config.n_train) + print("train_num_videos", sum(len(vids) for vids in train_vids.values())) + print("test_num_videos", sum(len(vids) for vids in test_vids.values())) + # added by huxu to automatically determine the split. + split_map = { + "train": train_vids, + "valid": test_vids, + "test": test_vids + } + task_vids = split_map[config.split] + + self.vids = [] + for task, vids in task_vids.items(): + self.vids.extend([(task, vid) for vid in vids]) + self.task_steps = task_steps + self.n_steps = n_steps + + def __getitem__(self, idx): + task, vid = self.vids[idx] + n_steps = self.n_steps[task] + steps = self.task_steps[task] + assert len(steps) == n_steps + return (task, vid, steps, n_steps), (task, vid, steps, n_steps) + + def __len__(self): + return len(self.vids) + + def _random_split(self, task_vids, test_tasks, n_train): + train_vids = {} + test_vids = {} + for task, vids in task_vids.items(): + if task in test_tasks and len(vids) > n_train: + train_vids[task] = np.random.choice( + vids, n_train, replace=False).tolist() + test_vids[task] = [ + vid for vid in vids if vid not in train_vids[task]] + else: + train_vids[task] = vids + return train_vids, test_vids + + def _get_vids(self, path, vfeat_dir, annotation_path): + """refactored from + https://github.com/DmZhukov/CrossTask/blob/master/data.py + changes: add `vfeat_dir` to check if the video is available. + add `annotation_path` to check if the video is available. + """ + + task_vids = {} + with open(path, 'r') as f: + for line in f: + task, vid, url = line.strip().split(',') + # double check the video is available. + if not os.path.exists( + os.path.join(vfeat_dir, vid + ".npy")): + continue + # double check the annotation is available. + if not os.path.exists(os.path.join( + annotation_path, + task + "_" + vid + ".csv")): + continue + if task not in task_vids: + task_vids[task] = [] + task_vids[task].append(vid) + return task_vids + + def _read_task_info(self, path): + titles = {} + urls = {} + n_steps = {} + steps = {} + with open(path, 'r') as f: + idx = f.readline() + while idx != '': + idx = idx.strip() + titles[idx] = f.readline().strip() + urls[idx] = f.readline().strip() + n_steps[idx] = int(f.readline().strip()) + steps[idx] = f.readline().strip().split(',') + next(f) + idx = f.readline() + return { + 'title': titles, + 'url': urls, + 'n_steps': n_steps, + 'steps': steps + } + + def _get_A(self, task_steps, share="words"): + raise ValueError("running get_A is not allowed for BERT.") + """Step-to-component matrices.""" + if share == 'words': + # share words + task_step_comps = { + task: [step.split(' ') for step in steps] + for task, steps in task_steps.items()} + elif share == 'task_words': + # share words within same task + task_step_comps = { + task: [[task+'_'+tok for tok in step.split(' ')] for step in steps] + for task, steps in task_steps.items()} + elif share == 'steps': + # share whole step descriptions + task_step_comps = { + task: [[step] for step in steps] for task, steps in task_steps.items()} + else: + # no sharing + task_step_comps = { + task: [[task+'_'+step] for step in steps] + for task, steps in task_steps.items()} + # BERT tokenizer here? + vocab = [] + for task, steps in task_step_comps.items(): + for step in steps: + vocab.extend(step) + vocab = {comp: m for m, comp in enumerate(set(vocab))} + M = len(vocab) + A = {} + for task, steps in task_step_comps.items(): + K = len(steps) + a = torch.zeros(M, K) + for k, step in enumerate(steps): + a[[vocab[comp] for comp in step], k] = 1 + a /= a.sum(dim=0) + A[task] = a + return A, M + + +class CrossTaskVideoProcessor(VideoProcessor): + def __call__(self, video_fn): + task, vid, steps, n_steps = video_fn + video_fn = os.path.join(self.vfeat_dir, vid + ".npy") + feat = np.load(video_fn) + return feat + + +class CrossTaskTextProcessor(TextProcessor): + def __call__(self, text_id): + task, vid, steps, n_steps = text_id + step_ids = [] + for step_str in steps: + step_ids.append( + self.tokenizer(step_str, add_special_tokens=False)["input_ids"] + ) + return step_ids + + +class CrossTaskAligner(Aligner): + """ + TODO: it's not clear yet the formulation of the task; finish this later. + """ + def __init__(self, config): + super().__init__(config) + self.annotation_path = config.annotation_path + self.sliding_window = config.sliding_window + self.sliding_window_size = config.sliding_window_size + + def __call__(self, video_id, video_feature, text_feature): + task, vid, steps, n_steps = video_id + annot_path = os.path.join( + self.annotation_path, task + '_' + vid + '.csv') + video_len = len(video_feature) + + labels = torch.from_numpy(self._read_assignment( + video_len, n_steps, annot_path)).float() + + vfeats, vmasks, targets = [], [], [] + # sliding window on video features and targets. + for window_start in range(0, video_len, self.sliding_window): + video_start = 0 + video_end = min(video_len - window_start, self.sliding_window_size) + video_clip = {"start": [video_start], "end": [video_end]} + + vfeat, vmask = self._build_video_seq( + video_feature[window_start: window_start + video_end], + video_clip + ) + + target = labels[window_start: window_start + video_end] + assert len(vfeat) >= len(target), "{},{}".format(len(vfeat), len(target)) + # TODO: randomly drop all zero targets for training ? + # if self.split == "train" and target.sum() == 0: + # continue + vfeats.append(vfeat) + vmasks.append(vmask) + targets.append(target) + + if (video_len - window_start) <= self.sliding_window_size: + break + + vfeats = torch.stack(vfeats) + vmasks = torch.stack(vmasks) + targets = torch.cat(targets, dim=0) + + caps, cmasks = [], [] + for step in text_feature: + step_text_feature = {"start": [0], "end": [1], "cap": [step]} + step_text_clip_index = [0] + cap, cmask = self._build_text_seq( + step_text_feature, step_text_clip_index + ) + caps.append(cap) + cmasks.append(cmask) + caps = torch.stack(caps) + cmasks = torch.stack(cmasks) + + return { + "caps": caps, + "cmasks": cmasks, + "vfeats": vfeats, # X for original code. + "vmasks": vmasks, + "targets": targets, + "video_id": vid, + "task": task, + "video_len": video_len # for later checking. + } + + def _read_assignment(self, T, K, path): + """ + refactored from https://github.com/DmZhukov/CrossTask/blob/master/data.py + Howto interpret contraints on loss that is going to be minimized: + lambd is a big number; + self.lambd * C is a big number for all valid position (csv stores invalids) + + def forward(self, O, Y, C): + return (Y*(self.lambd * C - self.lsm(O))).mean(dim=0).sum() + + This will load the csv file and fill-in the step col from start to end rows. + """ + + Y = np.zeros([T, K], dtype=np.uint8) + with open(path, 'r') as f: + for line in f: + step, start, end = line.strip().split(',') + start = int(math.floor(float(start))) + end = int(math.ceil(float(end))) + step = int(step) - 1 + Y[start:end, step] = 1 + return Y + + +# --------------------- COIN ------------------------- + +class MetaTextBinarizer(Aligner): + def __call__(self, text_feature): + text_feature = { + "cap": [text_feature], + "start": [0.], + "end": [100.], + } + text_clip_indexs = [0] + + caps, cmasks = self._build_text_seq( + text_feature, text_clip_indexs + ) + return {"caps": caps, "cmasks": cmasks} + + +class COINActionSegmentationMetaProcessor(MetaProcessor): + split_map = { + "train": "training", + "valid": "testing", + "test": "testing", + } + + def __init__(self, config): + super().__init__(config) + with open(self._get_split_path(config)) as fr: + database = json.load(fr)["database"] + id2label = {} + data = [] + # filter the data by split. + for video_id, rec in database.items(): + # always use testing to determine label_set + if rec["subset"] == "testing": + for segment in rec["annotation"]: + id2label[int(segment["id"])] = segment["label"] + # text_labels is used for ZS setting + self.text_labels = ["none"] * len(id2label) + for label_id in id2label: + self.text_labels[label_id-1] = id2label[label_id] + + id2label[0] = "O" + print("num of labels", len(id2label)) + + for video_id, rec in database.items(): + if not os.path.isfile(os.path.join(config.vfeat_dir, video_id + ".npy")): + continue + if rec["subset"] == COINActionSegmentationMetaProcessor.split_map[self.split]: + starts, ends, labels = [], [], [] + for segment in rec["annotation"]: + start, end = segment["segment"] + label = int(segment["id"]) + starts.append(start) + ends.append(end) + labels.append(label) + data.append( + (video_id, {"start": starts, "end": ends, "label": labels})) + self.data = data + + def meta_text_labels(self, config): + from transformers import default_data_collator + from ..utils import get_local_rank + + text_processor = TextProcessor(config) + binarizer = MetaTextBinarizer(config) + # TODO: add prompts to .yaml. + text_labels = [label for label in self.text_labels] + + if get_local_rank() == 0: + print(text_labels) + + outputs = [] + for text_label in text_labels: + text_feature = text_processor(text_label) + outputs.append(binarizer(text_feature)) + return default_data_collator(outputs) + + def __getitem__(self, idx): + return self.data[idx] + + +class COINActionSegmentationTextProcessor(TextProcessor): + def __call__(self, text_label): + return text_label + + +class COINActionSegmentationAligner(Aligner): + def __init__(self, config): + super().__init__(config) + self.sliding_window = config.sliding_window + self.sliding_window_size = config.sliding_window_size + + def __call__(self, video_id, video_feature, text_feature): + starts, ends, label_ids = text_feature["start"], text_feature["end"], text_feature["label"] + # sliding window. + video_len = len(video_feature) + + vfeats, vmasks, targets = [], [], [] + # sliding window on video features and targets. + for window_start in range(0, video_len, self.sliding_window): + video_start = 0 + video_end = min(video_len - window_start, self.sliding_window_size) + video_clip = {"start": [video_start], "end": [video_end]} + vfeat, vmask = self._build_video_seq( + video_feature[window_start: window_start + video_end], + video_clip + ) + # covers video length only. + target = torch.full_like(vmask, -100, dtype=torch.long) + target[vmask] = 0 + for start, end, label_id in zip(starts, ends, label_ids): + if (window_start < end) and (start < (window_start + video_end)): + start_offset = max(0, math.floor(start) - window_start) + end_offset = min(video_end, math.ceil(end) - window_start) + target[start_offset:end_offset] = label_id + vfeats.append(vfeat) + vmasks.append(vmask) + targets.append(target) + if (video_len - window_start) <= self.sliding_window_size: + break + + vfeats = torch.stack(vfeats) + vmasks = torch.stack(vmasks) + targets = torch.stack(targets) + video_targets = torch.full((video_len,), 0) + for start, end, label_id in zip(starts, ends, label_ids): + start_offset = max(0, math.floor(start)) + end_offset = min(video_len, math.ceil(end)) + video_targets[start_offset:end_offset] = label_id + + caps = torch.LongTensor( + [[self.cls_token_id, self.sep_token_id, + self.pad_token_id, self.sep_token_id]], + ).repeat(vfeats.size(0), 1) + cmasks = torch.BoolTensor( + [[0, 1, 0, 1]] # pad are valid for attention. + ).repeat(vfeats.size(0), 1) + return { + "caps": caps, + "cmasks": cmasks, + "vfeats": vfeats, # X for original code. + "vmasks": vmasks, + "targets": targets, + "video_id": video_id, + "video_len": video_len, # for later checking. + "video_targets": video_targets + } + + +class DiDeMoMetaProcessor(MetaProcessor): + """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py + https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py + """ + def __init__(self, config): + super().__init__(config) + + assert "test" in self._get_split_path(config), "DiDeMo only supports zero-shot testing for now." + + with open(self._get_split_path(config)) as data_file: + json_data = json.load(data_file) + + data = [] + for record in json_data: + data.append((record["video"], record["description"])) + self.data = data + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + +class DiDeMoTextProcessor(TextProcessor): + """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py + https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py + """ + + def __call__(self, text): + return self.tokenizer(text, add_special_tokens=False)["input_ids"] + + +class DiDeMoAligner(DSAligner): + """ + check video length. + """ + + def __call__(self, video_id, video_feature, text_feature): + # print(video_feature.shape[0]) + return super().__call__(video_id, video_feature, text_feature) diff --git a/fairseq/examples/MMPT/mmpt/processors/how2processor.py b/fairseq/examples/MMPT/mmpt/processors/how2processor.py new file mode 100644 index 0000000..bed2168 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/processors/how2processor.py @@ -0,0 +1,887 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Copyright (c) Facebook, Inc. All Rights Reserved + + +import torch +import math +import pickle +import random +import os +import numpy as np + +from collections import deque +from typing import Optional, Tuple, List +from .processor import ( + Processor, + MetaProcessor, + TextProcessor, + Aligner, + MMAttentionMask2DProcessor +) + +from ..utils import ShardedTensor + + +class How2MetaProcessor(MetaProcessor): + def __init__(self, config): + super().__init__(config) + path = self._get_split_path(config) + with open(path) as fd: + self.data = [line.strip() for line in fd] + + def __getitem__(self, idx): + video_id = self.data[idx] + return video_id, video_id + + +class ShardedHow2MetaProcessor(How2MetaProcessor): + def __init__(self, config): + super().__init__(config) + self.split = str(config.split) + self.vfeat_dir = config.vfeat_dir + self._init_shard() + + def _init_shard(self): + if self.split == "train": + meta_fn = os.path.join(self.vfeat_dir, "train" + "_meta.pkl") + with open(meta_fn, "rb") as fr: + meta = pickle.load(fr) + elif self.split == "valid": + meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl") + with open(meta_fn, "rb") as fr: + meta = pickle.load(fr) + elif self.split == "test": + print("use how2 val as test.") + meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl") + with open(meta_fn, "rb") as fr: + meta = pickle.load(fr) + else: + raise ValueError("unsupported for MetaProcessor:", self.split) + video_id_to_shard = {} + for shard_id in meta: + for video_idx, video_id in enumerate(meta[shard_id]): + video_id_to_shard[video_id] = (shard_id, video_idx) + self.video_id_to_shard = video_id_to_shard + + def __getitem__(self, idx): + video_id, video_id = super().__getitem__(idx) + shard_id, shard_idx = self.video_id_to_shard[video_id] + meta = (video_id, idx, shard_id, shard_idx) + return meta, meta + + +class ShardedVideoProcessor(Processor): + """ + mmaped shards of numpy video features. + """ + + def __init__(self, config): + self.split = str(config.split) + self.vfeat_dir = config.vfeat_dir + + def __call__(self, video_id): + _, _, shard_id, video_idx = video_id + if self.split == "train": + shard = ShardedTensor.load( + os.path.join(self.vfeat_dir, "train" + "_" + str(shard_id)), + "r" + ) + elif self.split == "valid": + shard = ShardedTensor.load( + os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)), + "r" + ) + elif self.split == "test": + shard = ShardedTensor.load( + os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)), + "r" + ) + else: + raise ValueError("unknown split", self.split) + feat = shard[video_idx] + return feat + + +class ShardedTextProcessor(Processor): + def __init__(self, config): + self.tfeat_dir = str(config.tfeat_dir) + self.split = str(config.split) + + def __call__(self, video_id): + _, _, shard_id, shard_idx = video_id + if self.split == "train": + target_path = self.tfeat_dir + "train" + "_" + str(shard_id) + elif self.split == "valid": + target_path = self.tfeat_dir + "val" + "_" + str(shard_id) + elif self.split == "test": + target_path = self.tfeat_dir + "val" + "_" + str(shard_id) + else: + raise ValueError("unknown split", self.split) + + startend = ShardedTensor.load( + target_path + ".startends", "r")[shard_idx] + cap_ids = ShardedTensor.load( + target_path + ".caps_ids", "r")[shard_idx] + cap = [] + for clip_idx in range(len(cap_ids)): + clip = cap_ids[clip_idx] + cap.append(clip[clip != -1].tolist()) + start, end = startend[:, 0].tolist(), startend[:, 1].tolist() + return {"start": start, "end": end, "cap": cap} + + +class FixedLenAligner(Aligner): + """ + In the model we assume text is on the left (closer to BERT formulation) + and video is on the right. + We fix the total length of text + video. + max_video_len is in number of secs. + max_text_len is in number of tokens. + + special tokens formats: + we use the format [CLS] [SEP] text tokens [SEP] [PAD] ... + [CLS] will be splitted out into: + [CLS] video tokens [SEP] text tokens [SEP] [PAD] ... + token_type_ids will be generated by the model (for now). + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + so each sequence owns a [SEP] token for no-ops. + """ + + def __init__(self, config): + super().__init__(config) + self.text_clip_sampler = TextClipSamplingProcessor( + self.max_len - self.max_video_len - 3 + ) + """ + decide subsampling: + `config.subsampling` will change batch_size in trainer. + `config.clip_per_video` (used by RetriTask) doesn't + change batch_size in trainer. + """ + subsampling = config.subsampling \ + if config.subsampling is not None else None + if config.clip_per_video is not None: + subsampling = config.clip_per_video + self.subsampling = subsampling + + def _get_text_maxlen(self): + # use max text len + return self.text_clip_sampler.max_text_len + + def __call__(self, video_id, video_feature, text_feature): + from transformers import default_data_collator + video_idx = video_id[1] + if self.subsampling is not None and self.subsampling >= 1: + batch = [] + for _ in range(self.subsampling): + centerclip_idx = random.randint( + 0, len(text_feature["start"]) - 1) + batch.append( + self.sampling( + video_idx, + video_feature, + text_feature, + centerclip_idx, + self._get_text_maxlen() + )) + batch = self.batch_post_processing(batch, video_feature) + batch = default_data_collator(batch) + else: + raise ValueError( + "dataset.subsampling must be >= 1 for efficient video loading.") + batch = self.sampling(video_idx, video_feature, text_feature) + batch = self.batch_post_processing(batch, video_feature) + + batch["video_id"] = video_id if isinstance(video_id, str) \ + else video_id[0] + # e2e: make sure frame ids is into tensor. + assert torch.is_tensor(batch["vfeats"]) + return batch + + def sampling( + self, + video_idx, + video_feature, + text_feature, + centerclip_idx=None, + sampled_max_text_len=None, + ): + text_clip_indexs = self.text_clip_sampler( + text_feature, centerclip_idx, + sampled_max_text_len + ) + if isinstance(video_feature, np.ndarray): + video_len = len(video_feature) + else: + video_len = math.ceil(text_feature["end"][-1]) + + video_end = min( + math.ceil(text_feature["end"][text_clip_indexs[-1]]), + video_len + ) + video_start = max( + min( + math.floor(text_feature["start"][text_clip_indexs[0]]), + video_end), + 0 + ) + + video_clips = {"start": [video_start], "end": [video_end]} + + # tensorize. + vfeats, vmasks = self._build_video_seq( + video_feature, video_clips + ) + caps, cmasks = self._build_text_seq( + text_feature, text_clip_indexs + ) + + text_start = text_clip_indexs[0] + text_end = text_clip_indexs[-1] + 1 + + return { + "caps": caps, + "cmasks": cmasks, + "vfeats": vfeats, + "vmasks": vmasks, + "video_start": video_start, + "video_end": video_end, + "text_start": text_start, + "text_end": text_end, + } + + +class VariedLenAligner(FixedLenAligner): + def __init__(self, config): + super().__init__(config) + self.sampled_min_len = config.sampled_min_len + self.sampled_max_len = config.sampled_max_len + + def _get_text_maxlen(self): + return random.randint(self.sampled_min_len, self.sampled_max_len) + + +class StartClipAligner(VariedLenAligner): + def sampling( + self, + video_idx, + video_feature, + text_feature, + centerclip_idx=None, + sampled_max_text_len=None, + ): + return super().sampling( + video_idx, video_feature, text_feature, 0) + + +class OverlappedAligner(VariedLenAligner): + """video clip and text clip has overlappings + but may not be the same start/end.""" + def __init__(self, config): + super().__init__(config) + self.sampled_video_min_len = config.sampled_video_min_len + self.sampled_video_max_len = config.sampled_video_max_len + + self.video_clip_sampler = VideoClipSamplingProcessor() + + def _get_video_maxlen(self): + return random.randint( + self.sampled_video_min_len, self.sampled_video_max_len) + + def sampling( + self, + video_idx, + video_feature, + text_feature, + centerclip_idx=None, + sampled_max_text_len=None, + ): + text_clip_indexs = self.text_clip_sampler( + text_feature, centerclip_idx, + sampled_max_text_len + ) + if isinstance(video_feature, np.ndarray): + video_len = len(video_feature) + else: + video_len = math.ceil(text_feature["end"][-1]) + low = math.floor(text_feature["start"][text_clip_indexs[0]]) + high = math.ceil(text_feature["end"][text_clip_indexs[-1]]) + if low < high: + center = random.randint(low, high) + else: + center = int((low + high) // 2) + center = max(0, min(video_feature.shape[0] - 1, center)) + + assert 0 <= center < video_feature.shape[0] + + video_clips = self.video_clip_sampler( + video_len, self._get_video_maxlen(), center + ) + video_start = video_clips["start"][0] + video_end = video_clips["end"][0] + + # tensorize. + vfeats, vmasks = self._build_video_seq( + video_feature, video_clips + ) + caps, cmasks = self._build_text_seq( + text_feature, text_clip_indexs + ) + + text_start = text_clip_indexs[0] + text_end = text_clip_indexs[-1] + 1 + + return { + "caps": caps, + "cmasks": cmasks, + "vfeats": vfeats, + "vmasks": vmasks, + "video_start": video_start, + "video_end": video_end, + "text_start": text_start, + "text_end": text_end, + } + + +class MFMMLMAligner(FixedLenAligner): + """ + `FixedLenAligner` with Masked Language Model and Masked Frame Model. + """ + + def __init__(self, config): + super().__init__(config) + keep_prob = config.keep_prob if config.keep_prob is not None else 1.0 + self.text_clip_sampler = TextClipSamplingProcessor( + self.max_len - self.max_video_len - 3, keep_prob + ) + self.sampled_min_len = config.sampled_min_len + self.sampled_max_len = config.sampled_max_len + self.masked_token_sampler = TextMaskingProcessor(config) + self.mm_type = config.mm_type \ + if config.mm_type is not None else "full" + self.attnmasker = MMAttentionMask2DProcessor() \ + if self.mm_type == "textgen" else None + self.masked_frame_sampler = FrameMaskingProcessor(config) + self.lazy_vfeat_mask = ( + False if config.lazy_vfeat_mask is None else config.lazy_vfeat_mask + ) + self.mm_prob = config.mm_prob if config.mm_prob is not None else 0. + + def __call__(self, video_id, video_feature, text_feature): + from transformers import default_data_collator + if self.subsampling is not None and self.subsampling > 1: + batch = [] + for _ in range(self.subsampling): + centerclip_idx = random.randint( + 0, len(text_feature["start"]) - 1) + sampled_max_text_len = random.randint( + self.sampled_min_len, self.sampled_max_len + ) + batch.append( + self.sampling( + video_id, + video_feature, + text_feature, + centerclip_idx, + sampled_max_text_len, + ) + ) + batch = self.batch_post_processing(batch, video_feature) + batch = default_data_collator(batch) + else: + batch = self.sampling(video_id, video_feature, text_feature) + batch = self.batch_post_processing(batch, video_feature) + batch["video_id"] = video_id if isinstance(video_id, str) \ + else video_id[0] + return batch + + def sampling( + self, + video_id, + video_feature, + text_feature, + centerclip_idx=None, + sampled_max_text_len=None, + ): + output = FixedLenAligner.sampling(self, + video_id, video_feature, text_feature, + centerclip_idx, sampled_max_text_len) + + masking_text, masking_video = None, None + if random.random() < self.mm_prob: + if random.random() > 0.5: + masking_text, masking_video = self.mm_type, "no" + else: + masking_text, masking_video = "no", "full" + video_feats = output["vfeats"] if not self.lazy_vfeat_mask else None + video_label = self.masked_frame_sampler( + output["vmasks"], masking_video, vfeats=video_feats) + caps, text_label = self.masked_token_sampler( + output["caps"], masking_text) + + output.update({ + "caps": caps, + "video_label": video_label, + "text_label": text_label, + }) + + if self.attnmasker is not None: + attention_mask = self.attnmasker( + output["vmasks"], output["cmasks"], masking_text) + output.update({ + "attention_mask": attention_mask + }) + return output + + +class FrameMaskingProcessor(Processor): + def __init__(self, config): + self.mfm_probability = 0.15 + if config.mfm_probability is not None: + self.mfm_probability = config.mfm_probability + + def __call__(self, vmasks, modality_masking=None, vfeats=None): + """ + We perform lazy masking to save data transfer time. + It only generates video_labels by default and MFM model + will do actualy masking. + Return: `video_label` is a binary mask. + """ + video_label = vmasks.clone() + if modality_masking is not None: + if modality_masking == "full": + probability_matrix = torch.full(video_label.shape, 1.) + elif modality_masking == "no": + probability_matrix = torch.full(video_label.shape, 0.) + elif modality_masking == "inverse": + probability_matrix = torch.full( + video_label.shape, 1. - self.mfm_probability) + else: + raise ValueError("unknown modality masking.", modality_masking) + else: + probability_matrix = torch.full( + video_label.shape, self.mfm_probability) + masked_indices = torch.bernoulli(probability_matrix).bool() + # We only compute loss on masked tokens + video_label[~masked_indices] = 0 + if vfeats is not None: + vfeats[video_label, :] = 0.0 + return video_label + + +class TextGenerationProcessor(Processor): + def __init__(self, tokenizer): + self.bos_token_id = tokenizer.bos_token_id + self.pad_token_id = tokenizer.pad_token_id + + def __call__(self, inputs): + labels = inputs.clone() + # [CLS] [SEP] for video + labels[:2] = -100 + # keep [SEP] for text. + pad_mask = labels == self.pad_token_id + labels[pad_mask] = -100 + inputs[2:] = torch.cat([ + torch.LongTensor([self.bos_token_id]), + inputs[2:-1]]) + inputs[pad_mask] = self.pad_token_id + assert len(inputs) == len(labels) + return inputs, labels + + +class TextMaskingProcessor(Processor): + def __init__(self, config): + """this function is borrowed from + `transformers/data/data_collator.DataCollatorForLanguageModeling`""" + self.mlm_probability = 0.15 + if config.mlm_probability is not None: + self.mlm_probability = config.mlm_probability + self.bert_name = config.bert_name + # [CLS] is used as bos_token and [SEP] is used as eos_token. + # https://huggingface.co/transformers/master/model_doc/bertgeneration.html + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained( + self.bert_name, bos_token="[CLS]", eos_token="[SEP]") + self.textgen = TextGenerationProcessor(self.tokenizer) + + def __call__( + self, inputs: torch.Tensor, + modality_masking=None, + special_tokens_mask: Optional[torch.Tensor] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + expand modality_masking into + None: traditional bert masking. + "no": no masking. + "full": all [MASK] token for generation. + "gen": autoregressive generation. + """ + """ + Prepare masked tokens inputs/labels for masked language modeling: + 80% MASK, 10% random, 10% original. + """ + labels = inputs.clone() + # We sample a few tokens in each sequence for MLM training + # (with probability `self.mlm_probability`) + if modality_masking is not None: + if modality_masking == "full": + probability_matrix = torch.full(labels.shape, 1.) + elif modality_masking == "no": + probability_matrix = torch.full(labels.shape, 0.) + elif modality_masking.startswith("textgen"): + # [CLS] [SEP] ... + inputs, labels = self.textgen(inputs) + if "mask" not in modality_masking: + return inputs, labels + inputs = self.mask_input(inputs, special_tokens_mask) + return inputs, labels + elif modality_masking == "mask": + inputs = self.mask_input(inputs, special_tokens_mask) + labels = torch.full(inputs.shape, -100) + return inputs, labels + elif modality_masking == "inverse": + probability_matrix = torch.full(labels.shape, 1. - self.mlm_probability) + else: + raise ValueError("unknown modality masking.", modality_masking) + else: + probability_matrix = torch.full(labels.shape, self.mlm_probability) + + if special_tokens_mask is None: + special_tokens_mask = self.get_special_tokens_mask( + labels.tolist(), already_has_special_tokens=True + ) + special_tokens_mask = torch.tensor( + special_tokens_mask, dtype=torch.bool) + else: + special_tokens_mask = special_tokens_mask.bool() + + probability_matrix.masked_fill_(special_tokens_mask, value=0.0) + masked_indices = torch.bernoulli(probability_matrix).bool() + labels[~masked_indices] = -100 # We only compute loss on masked tokens + + # 80% of the time, + # we replace masked input tokens with tokenizer.mask_token ([MASK]) + indices_replaced = ( + torch.bernoulli( + torch.full(labels.shape, 0.8)).bool() & masked_indices + ) + inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids( + self.tokenizer.mask_token + ) + + # 10% of the time, we replace masked input tokens with random word + indices_random = ( + torch.bernoulli(torch.full(labels.shape, 0.5)).bool() + & masked_indices + & ~indices_replaced + ) + random_words = torch.randint( + len(self.tokenizer), labels.shape, dtype=torch.long + ) + inputs[indices_random] = random_words[indices_random] + + # The rest of the time (10% of the time) we keep the masked input + # tokens unchanged + return inputs, labels + + def mask_input(self, inputs, special_tokens_mask=None): + # the following is new with masked autoregressive. + probability_matrix = torch.full( + inputs.shape, self.mlm_probability) + if special_tokens_mask is None: + special_tokens_mask = self.get_special_tokens_mask( + inputs.tolist(), already_has_special_tokens=True + ) + special_tokens_mask = torch.tensor( + special_tokens_mask, dtype=torch.bool) + else: + special_tokens_mask = special_tokens_mask.bool() + probability_matrix.masked_fill_(special_tokens_mask, value=0.0) + masked_indices = torch.bernoulli(probability_matrix).bool() + indices_replaced = ( + torch.bernoulli( + torch.full(inputs.shape, 0.8)).bool() & masked_indices + ) + inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids( + self.tokenizer.mask_token + ) + + # 10% of the time, we replace masked input tokens with random word + indices_random = ( + torch.bernoulli(torch.full(inputs.shape, 0.5)).bool() + & masked_indices + & ~indices_replaced + ) + random_words = torch.randint( + len(self.tokenizer), inputs.shape, dtype=torch.long + ) + inputs[indices_random] = random_words[indices_random] + return inputs + + def get_special_tokens_mask( + self, token_ids_0: List[int], + token_ids_1: Optional[List[int]] = None, + already_has_special_tokens: bool = False + ) -> List[int]: + """ + Note: the version from transformers do not consider pad + as special tokens. + """ + + if already_has_special_tokens: + if token_ids_1 is not None: + raise ValueError( + "You should not supply a second sequence if" + "the provided sequence of " + "ids is already formated with special tokens " + "for the model." + ) + return list(map(lambda x: 1 if x in [ + self.tokenizer.sep_token_id, + self.tokenizer.cls_token_id, + self.tokenizer.pad_token_id] else 0, token_ids_0)) + + if token_ids_1 is not None: + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + + +class TextClipSamplingProcessor(Processor): + def __init__(self, max_text_len, keep_prob=1.0): + self.max_text_len = max_text_len + self.max_video_len = 256 # always hold. + self.keep_prob = keep_prob + + def __call__( + self, + text_feature, + centerclip_idx=None, + sampled_max_text_len=None, + sampled_max_video_len=None, + ): + # Let's use all caps for now and see if 256 can cover all of them. + if sampled_max_text_len is not None: + max_text_len = sampled_max_text_len + else: + max_text_len = self.max_text_len + if sampled_max_video_len is not None: + max_video_len = sampled_max_video_len + else: + max_video_len = self.max_video_len + + t_num_clips = len(text_feature["start"]) + + if centerclip_idx is None: + centerclip_idx = random.randint(0, t_num_clips - 1) + + start_idx, end_idx = centerclip_idx, centerclip_idx + 1 + text_clip_indexs = deque() + text_clip_indexs.append(start_idx) + text_len = len(text_feature["cap"][start_idx]) + + video_len = max( + 0, + text_feature["end"][start_idx] + - text_feature["start"][start_idx], + ) + + while ( + (start_idx > 0 or end_idx < t_num_clips) + and text_len < max_text_len + and video_len < max_video_len + ): + if random.random() > 0.5 and end_idx < t_num_clips: + # skip the next one? + if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips: + end_idx = end_idx + 1 + text_clip_indexs.append(end_idx) + text_len += len(text_feature["cap"][end_idx]) + end_idx += 1 + elif start_idx > 0: + if random.random() > self.keep_prob and (start_idx - 1) > 0: + start_idx = start_idx - 1 + start_idx -= 1 + text_clip_indexs.insert(0, start_idx) + text_len += len(text_feature["cap"][start_idx]) + else: + if end_idx < t_num_clips: + if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips: + end_idx = end_idx + 1 + text_clip_indexs.append(end_idx) + text_len += len(text_feature["cap"][end_idx]) + end_idx += 1 + else: + return text_clip_indexs + video_len = max( + 0, + text_feature["end"][text_clip_indexs[-1]] + - text_feature["start"][text_clip_indexs[0]], + ) + return text_clip_indexs + + +class VideoClipSamplingProcessor(Processor): + def __call__(self, video_len, max_video_len, center): + """ + `video_len`: length of the video. + `max_video_len`: maximum video tokens allowd in a sequence. + `center`: initial starting index. + """ + assert center >= 0 and center < video_len + t_clip_len = 0 + start, end = center, center + while (start > 0 or end < video_len) and t_clip_len < max_video_len: + # decide the direction to grow. + if start <= 0: + end += 1 + elif end >= video_len: + start -= 1 + elif random.random() > 0.5: + end += 1 + else: + start -= 1 + t_clip_len += 1 + return {"start": [start], "end": [end]} + + +class How2MILNCEAligner(FixedLenAligner): + """reference: `antoine77340/MIL-NCE_HowTo100M/video_loader.py`""" + + def __init__(self, config): + super().__init__(config) + self.num_candidates = 4 + self.min_time = 5.0 + self.num_sec = 3.2 + # self.num_sec = self.num_frames / float(self.fps) num_frames=16 / fps = 5 + # self.num_frames = 16 + + def sampling( + self, + video_id, + video_feature, + text_feature, + centerclip_idx=None, # will be ignored. + sampled_max_text_len=None # will be ignored. + ): + text, start, end = self._get_text(text_feature) + video = self._get_video(video_feature, start, end) + + vfeats = torch.zeros((self.max_video_len, video_feature.shape[1])) + vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool) + vfeats[: video.shape[0]] = torch.from_numpy(np.array(video)) + vmasks[: video.shape[0]] = 1 + + caps, cmasks = [], [] + for words in text: + cap, cmask = self._build_text_seq(text_feature, words) + caps.append(cap) + cmasks.append(cmask) + caps = torch.stack(caps) + cmasks = torch.stack(cmasks) + # video of shape: (video_len) + # text of shape (num_candidates, max_text_len) + + return { + "caps": caps, + "cmasks": cmasks, + "vfeats": vfeats, + "vmasks": vmasks, + # "video_id": video_id, + } + + def _get_video(self, video_feature, start, end): + start_seek = random.randint(start, int(max(start, end - self.num_sec))) + # duration = self.num_sec + 0.1 + return video_feature[start_seek : int(start_seek + self.num_sec)] + + def _get_text(self, cap): + ind = random.randint(0, len(cap["start"]) - 1) + if self.num_candidates == 1: + words = [ind] + else: + words = [] + cap_start = self._find_nearest_candidates(cap, ind) + for i in range(self.num_candidates): + words.append([max(0, min(len(cap["cap"]) - 1, cap_start + i))]) + + start, end = cap["start"][ind], cap["end"][ind] + # TODO: May need to be improved for edge cases. + # expand the min time. + if end - start < self.min_time: + diff = self.min_time - end + start + start = max(0, start - diff / 2) + end = start + self.min_time + return words, int(start), int(end) + + def _find_nearest_candidates(self, caption, ind): + """find the range of the clips.""" + start, end = ind, ind + #diff = caption["end"][end] - caption["start"][start] + n_candidate = 1 + while n_candidate < self.num_candidates: + # the first clip + if start == 0: + return 0 + # we add () in the following condition to fix the bug. + elif end == (len(caption["start"]) - 1): + return start - (self.num_candidates - n_candidate) + elif (caption["end"][end] - caption["start"][start - 1]) < ( + caption["end"][end + 1] - caption["start"][start] + ): + start -= 1 + else: + end += 1 + n_candidate += 1 + return start + + +class PKLJSONStrTextProcessor(TextProcessor): + """`caption.json` from howto100m are preprocessed as a + dict `[video_id, json_str]`. + Json parsing tokenization are conducted on-the-fly and cached into dict. + """ + + def __init__(self, config, max_clip_text_len=96): + print("[Warning] PKLJSONStrTextProcessor is slow for num_workers > 0.") + self.caption_pkl_path = str(config.caption_pkl_path) + with open(self.caption_pkl_path, "rb") as fd: + self.data = pickle.load(fd) + self.max_clip_text_len = max_clip_text_len + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained( + str(config.bert_name), use_fast=config.use_fast + ) + + def __call__(self, video_id): + caption = self.data[video_id] + if isinstance(caption, str): + import json + caption = json.loads(caption) + cap = [] + for clip_idx, text_clip in enumerate(caption["text"]): + clip_ids = [] + if isinstance(text_clip, str): + clip_ids = self.tokenizer( + text_clip[: self.max_clip_text_len], + add_special_tokens=False + )["input_ids"] + cap.append(clip_ids) + caption["cap"] = cap + caption.pop("text") # save space. + self.data[video_id] = caption + return caption diff --git a/fairseq/examples/MMPT/mmpt/processors/how2retriprocessor.py b/fairseq/examples/MMPT/mmpt/processors/how2retriprocessor.py new file mode 100644 index 0000000..b5a7730 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/processors/how2retriprocessor.py @@ -0,0 +1,100 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .how2processor import ( + ShardedHow2MetaProcessor, + ShardedVideoProcessor, + ShardedTextProcessor, + VariedLenAligner, + OverlappedAligner +) + + +class ShardedHow2VideoRetriMetaProcessor(ShardedHow2MetaProcessor): + def __init__(self, config): + super().__init__(config) + self.num_video_per_batch = config.num_video_per_batch + self.cands = [ + self.data[batch_offset:batch_offset + self.num_video_per_batch] + for batch_offset in + range(0, (len(self.data) // (8 * self.num_video_per_batch)) * 8 * self.num_video_per_batch, self.num_video_per_batch)] + + def __len__(self): + return len(self.cands) + + def set_candidates(self, cands): + # no changes on num of batches. + print(len(self.cands), "->", len(cands)) + # assert len(self.cands) == len(cands) + self.cands = cands + + def __getitem__(self, idx): + video_ids = self.cands[idx] + assert isinstance(video_ids, list) + sharded_video_idxs = [] + for video_id in video_ids: + shard_id, video_idx = self.video_id_to_shard[video_id] + sharded_video_idxs.append((video_id, -1, shard_id, video_idx)) + return sharded_video_idxs, sharded_video_idxs + + +class ShardedVideoRetriVideoProcessor(ShardedVideoProcessor): + """In retrival case the video_id + is a list of tuples: `(shard_id, video_idx)` .""" + + def __call__(self, sharded_video_idxs): + assert isinstance(sharded_video_idxs, list) + cand_feats = [] + for shared_video_idx in sharded_video_idxs: + feat = super().__call__(shared_video_idx) + cand_feats.append(feat) + return cand_feats + + +class ShardedVideoRetriTextProcessor(ShardedTextProcessor): + """In retrival case the video_id + is a list of tuples: `(shard_id, video_idx)` .""" + + def __call__(self, sharded_video_idxs): + assert isinstance(sharded_video_idxs, list) + cand_caps = [] + for shared_video_idx in sharded_video_idxs: + caps = super().__call__(shared_video_idx) + cand_caps.append(caps) + return cand_caps + + +class VideoRetriAligner(VariedLenAligner): + # Retritask will trim dim-0. + def __call__(self, sharded_video_idxs, video_features, text_features): + from transformers import default_data_collator + batch, video_ids = [], [] + for video_id, video_feature, text_feature in \ + zip(sharded_video_idxs, video_features, text_features): + sub_batch = super().__call__(video_id, video_feature, text_feature) + batch.append(sub_batch) + if isinstance(video_id, tuple): + video_id = video_id[0] + video_ids.append(video_id) + batch = default_data_collator(batch) + batch["video_id"] = video_ids + return batch + + +class VideoRetriOverlappedAligner(OverlappedAligner): + # Retritask will trim dim-0. + def __call__(self, sharded_video_idxs, video_features, text_features): + from transformers import default_data_collator + batch, video_ids = [], [] + for video_id, video_feature, text_feature in \ + zip(sharded_video_idxs, video_features, text_features): + sub_batch = super().__call__(video_id, video_feature, text_feature) + batch.append(sub_batch) + if isinstance(video_id, tuple): + video_id = video_id[0] + video_ids.append(video_id) + batch = default_data_collator(batch) + batch["video_id"] = video_ids + return batch diff --git a/fairseq/examples/MMPT/mmpt/processors/models/s3dg.py b/fairseq/examples/MMPT/mmpt/processors/models/s3dg.py new file mode 100644 index 0000000..6c7a691 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/processors/models/s3dg.py @@ -0,0 +1,336 @@ +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Contains a PyTorch definition for Gated Separable 3D network (S3D-G) +with a text module for computing joint text-video embedding from raw text +and video input. The following code will enable you to load the HowTo100M +pretrained S3D Text-Video model from: + A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic and A. Zisserman, + End-to-End Learning of Visual Representations from Uncurated Instructional Videos. + https://arxiv.org/abs/1912.06430. + +S3D-G was proposed by: + S. Xie, C. Sun, J. Huang, Z. Tu and K. Murphy, + Rethinking Spatiotemporal Feature Learning For Video Understanding. + https://arxiv.org/abs/1712.04851. + Tensorflow code: https://github.com/tensorflow/models/blob/master/research/slim/nets/s3dg.py + +The S3D architecture was slightly modified with a space to depth trick for TPU +optimization. +""" + +import torch as th +import torch.nn.functional as F +import torch.nn as nn +import os +import numpy as np +import re + + +class InceptionBlock(nn.Module): + def __init__( + self, + input_dim, + num_outputs_0_0a, + num_outputs_1_0a, + num_outputs_1_0b, + num_outputs_2_0a, + num_outputs_2_0b, + num_outputs_3_0b, + gating=True, + ): + super(InceptionBlock, self).__init__() + self.conv_b0 = STConv3D(input_dim, num_outputs_0_0a, [1, 1, 1]) + self.conv_b1_a = STConv3D(input_dim, num_outputs_1_0a, [1, 1, 1]) + self.conv_b1_b = STConv3D( + num_outputs_1_0a, num_outputs_1_0b, [3, 3, 3], padding=1, separable=True + ) + self.conv_b2_a = STConv3D(input_dim, num_outputs_2_0a, [1, 1, 1]) + self.conv_b2_b = STConv3D( + num_outputs_2_0a, num_outputs_2_0b, [3, 3, 3], padding=1, separable=True + ) + self.maxpool_b3 = th.nn.MaxPool3d((3, 3, 3), stride=1, padding=1) + self.conv_b3_b = STConv3D(input_dim, num_outputs_3_0b, [1, 1, 1]) + self.gating = gating + self.output_dim = ( + num_outputs_0_0a + num_outputs_1_0b + num_outputs_2_0b + num_outputs_3_0b + ) + if gating: + self.gating_b0 = SelfGating(num_outputs_0_0a) + self.gating_b1 = SelfGating(num_outputs_1_0b) + self.gating_b2 = SelfGating(num_outputs_2_0b) + self.gating_b3 = SelfGating(num_outputs_3_0b) + + def forward(self, input): + """Inception block + """ + b0 = self.conv_b0(input) + b1 = self.conv_b1_a(input) + b1 = self.conv_b1_b(b1) + b2 = self.conv_b2_a(input) + b2 = self.conv_b2_b(b2) + b3 = self.maxpool_b3(input) + b3 = self.conv_b3_b(b3) + if self.gating: + b0 = self.gating_b0(b0) + b1 = self.gating_b1(b1) + b2 = self.gating_b2(b2) + b3 = self.gating_b3(b3) + return th.cat((b0, b1, b2, b3), dim=1) + + +class SelfGating(nn.Module): + def __init__(self, input_dim): + super(SelfGating, self).__init__() + self.fc = nn.Linear(input_dim, input_dim) + + def forward(self, input_tensor): + """Feature gating as used in S3D-G. + """ + spatiotemporal_average = th.mean(input_tensor, dim=[2, 3, 4]) + weights = self.fc(spatiotemporal_average) + weights = th.sigmoid(weights) + return weights[:, :, None, None, None] * input_tensor + + +class STConv3D(nn.Module): + def __init__( + self, input_dim, output_dim, kernel_size, stride=1, padding=0, separable=False + ): + super(STConv3D, self).__init__() + self.separable = separable + self.relu = nn.ReLU(inplace=True) + assert len(kernel_size) == 3 + if separable and kernel_size[0] != 1: + spatial_kernel_size = [1, kernel_size[1], kernel_size[2]] + temporal_kernel_size = [kernel_size[0], 1, 1] + if isinstance(stride, list) and len(stride) == 3: + spatial_stride = [1, stride[1], stride[2]] + temporal_stride = [stride[0], 1, 1] + else: + spatial_stride = [1, stride, stride] + temporal_stride = [stride, 1, 1] + if isinstance(padding, list) and len(padding) == 3: + spatial_padding = [0, padding[1], padding[2]] + temporal_padding = [padding[0], 0, 0] + else: + spatial_padding = [0, padding, padding] + temporal_padding = [padding, 0, 0] + if separable: + self.conv1 = nn.Conv3d( + input_dim, + output_dim, + kernel_size=spatial_kernel_size, + stride=spatial_stride, + padding=spatial_padding, + bias=False, + ) + self.bn1 = nn.BatchNorm3d(output_dim) + self.conv2 = nn.Conv3d( + output_dim, + output_dim, + kernel_size=temporal_kernel_size, + stride=temporal_stride, + padding=temporal_padding, + bias=False, + ) + self.bn2 = nn.BatchNorm3d(output_dim) + else: + self.conv1 = nn.Conv3d( + input_dim, + output_dim, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=False, + ) + self.bn1 = nn.BatchNorm3d(output_dim) + + def forward(self, input): + out = self.relu(self.bn1(self.conv1(input))) + if self.separable: + out = self.relu(self.bn2(self.conv2(out))) + return out + + +class MaxPool3dTFPadding(th.nn.Module): + def __init__(self, kernel_size, stride=None, padding="SAME"): + super(MaxPool3dTFPadding, self).__init__() + if padding == "SAME": + padding_shape = self._get_padding_shape(kernel_size, stride) + self.padding_shape = padding_shape + self.pad = th.nn.ConstantPad3d(padding_shape, 0) + self.pool = th.nn.MaxPool3d(kernel_size, stride, ceil_mode=True) + + def _get_padding_shape(self, filter_shape, stride): + def _pad_top_bottom(filter_dim, stride_val): + pad_along = max(filter_dim - stride_val, 0) + pad_top = pad_along // 2 + pad_bottom = pad_along - pad_top + return pad_top, pad_bottom + + padding_shape = [] + for filter_dim, stride_val in zip(filter_shape, stride): + pad_top, pad_bottom = _pad_top_bottom(filter_dim, stride_val) + padding_shape.append(pad_top) + padding_shape.append(pad_bottom) + depth_top = padding_shape.pop(0) + depth_bottom = padding_shape.pop(0) + padding_shape.append(depth_top) + padding_shape.append(depth_bottom) + return tuple(padding_shape) + + def forward(self, inp): + inp = self.pad(inp) + out = self.pool(inp) + return out + + +class Sentence_Embedding(nn.Module): + def __init__( + self, + embd_dim, + num_embeddings=66250, + word_embedding_dim=300, + token_to_word_path="dict.npy", + max_words=16, + output_dim=2048, + ): + super(Sentence_Embedding, self).__init__() + self.word_embd = nn.Embedding(num_embeddings, word_embedding_dim) + self.fc1 = nn.Linear(word_embedding_dim, output_dim) + self.fc2 = nn.Linear(output_dim, embd_dim) + self.word_to_token = {} + self.max_words = max_words + token_to_word = np.load(token_to_word_path) + for i, t in enumerate(token_to_word): + self.word_to_token[t] = i + 1 + + def _zero_pad_tensor_token(self, tensor, size): + if len(tensor) >= size: + return tensor[:size] + else: + zero = th.zeros(size - len(tensor)).long() + return th.cat((tensor, zero), dim=0) + + def _split_text(self, sentence): + w = re.findall(r"[\w']+", str(sentence)) + return w + + def _words_to_token(self, words): + words = [ + self.word_to_token[word] for word in words if word in self.word_to_token + ] + if words: + we = self._zero_pad_tensor_token(th.LongTensor(words), self.max_words) + return we + else: + return th.zeros(self.max_words).long() + + def _words_to_ids(self, x): + split_x = [self._words_to_token(self._split_text(sent.lower())) for sent in x] + return th.stack(split_x, dim=0) + + def forward(self, x): + x = self._words_to_ids(x) + x = self.word_embd(x) + x = F.relu(self.fc1(x)) + x = th.max(x, dim=1)[0] + x = self.fc2(x) + return {'text_embedding': x} + + +class S3D(nn.Module): + def __init__(self, dict_path, num_classes=512, gating=True, space_to_depth=True): + super(S3D, self).__init__() + self.num_classes = num_classes + self.gating = gating + self.space_to_depth = space_to_depth + if space_to_depth: + self.conv1 = STConv3D( + 24, 64, [2, 4, 4], stride=1, padding=(1, 2, 2), separable=False + ) + else: + self.conv1 = STConv3D( + 3, 64, [3, 7, 7], stride=2, padding=(1, 3, 3), separable=False + ) + self.conv_2b = STConv3D(64, 64, [1, 1, 1], separable=False) + self.conv_2c = STConv3D(64, 192, [3, 3, 3], padding=1, separable=True) + self.gating = SelfGating(192) + self.maxpool_2a = MaxPool3dTFPadding( + kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME" + ) + self.maxpool_3a = MaxPool3dTFPadding( + kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME" + ) + self.mixed_3b = InceptionBlock(192, 64, 96, 128, 16, 32, 32) + self.mixed_3c = InceptionBlock( + self.mixed_3b.output_dim, 128, 128, 192, 32, 96, 64 + ) + self.maxpool_4a = MaxPool3dTFPadding( + kernel_size=(3, 3, 3), stride=(2, 2, 2), padding="SAME" + ) + self.mixed_4b = InceptionBlock( + self.mixed_3c.output_dim, 192, 96, 208, 16, 48, 64 + ) + self.mixed_4c = InceptionBlock( + self.mixed_4b.output_dim, 160, 112, 224, 24, 64, 64 + ) + self.mixed_4d = InceptionBlock( + self.mixed_4c.output_dim, 128, 128, 256, 24, 64, 64 + ) + self.mixed_4e = InceptionBlock( + self.mixed_4d.output_dim, 112, 144, 288, 32, 64, 64 + ) + self.mixed_4f = InceptionBlock( + self.mixed_4e.output_dim, 256, 160, 320, 32, 128, 128 + ) + self.maxpool_5a = self.maxPool3d_5a_2x2 = MaxPool3dTFPadding( + kernel_size=(2, 2, 2), stride=(2, 2, 2), padding="SAME" + ) + self.mixed_5b = InceptionBlock( + self.mixed_4f.output_dim, 256, 160, 320, 32, 128, 128 + ) + self.mixed_5c = InceptionBlock( + self.mixed_5b.output_dim, 384, 192, 384, 48, 128, 128 + ) + self.fc = nn.Linear(self.mixed_5c.output_dim, num_classes) + self.text_module = Sentence_Embedding(num_classes, + token_to_word_path=dict_path) + + def _space_to_depth(self, input): + """3D space to depth trick for TPU optimization. + """ + B, C, T, H, W = input.shape + input = input.view(B, C, T // 2, 2, H // 2, 2, W // 2, 2) + input = input.permute(0, 3, 5, 7, 1, 2, 4, 6) + input = input.contiguous().view(B, 8 * C, T // 2, H // 2, W // 2) + return input + + def forward(self, inputs): + """Defines the S3DG base architecture.""" + if self.space_to_depth: + inputs = self._space_to_depth(inputs) + net = self.conv1(inputs) + if self.space_to_depth: + # we need to replicate 'SAME' tensorflow padding + net = net[:, :, 1:, 1:, 1:] + net = self.maxpool_2a(net) + net = self.conv_2b(net) + net = self.conv_2c(net) + if self.gating: + net = self.gating(net) + net = self.maxpool_3a(net) + net = self.mixed_3b(net) + net = self.mixed_3c(net) + net = self.maxpool_4a(net) + net = self.mixed_4b(net) + net = self.mixed_4c(net) + net = self.mixed_4d(net) + net = self.mixed_4e(net) + net = self.mixed_4f(net) + net = self.maxpool_5a(net) + net = self.mixed_5b(net) + net = self.mixed_5c(net) + net = th.mean(net, dim=[2, 3, 4]) + return {'video_embedding': self.fc(net), 'mixed_5c': net} diff --git a/fairseq/examples/MMPT/mmpt/processors/processor.py b/fairseq/examples/MMPT/mmpt/processors/processor.py new file mode 100644 index 0000000..98edb05 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/processors/processor.py @@ -0,0 +1,274 @@ +# Copyright (c) Facebook, Inc. All Rights Reserved + +import numpy as np +import os +import torch + + +class Processor(object): + """ + A generic processor for video (codec, feature etc.) and text. + """ + + def __call__(self, **kwargs): + raise NotImplementedError + + +class MetaProcessor(Processor): + """ + A meta processor is expected to load the metadata of a dataset: + (e.g., video_ids, or captions). + You must implement the `__getitem__` (meta datasets are rather diverse.). + """ + + def __init__(self, config): + self.split = config.split + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + raise NotImplementedError + + def _get_split_path(self, config): + splits = { + "train": config.train_path, + "valid": config.val_path, + "test": config.test_path, + } + if config.split is not None: + return splits[config.split] + return config.train_path + + +class TextProcessor(Processor): + """ + A generic Text processor: rename this as `withTokenizer`. + tokenize a string of text on-the-fly. + Warning: mostly used for end tasks. + (on-the-fly tokenization is slow for how2.) + TODO(huxu): move this class as a subclass. + """ + + def __init__(self, config): + self.bert_name = str(config.bert_name) + self.use_fast = config.use_fast + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained( + self.bert_name, use_fast=self.use_fast + ) + + def __call__(self, text_id): + caption = self.tokenizer(text_id, add_special_tokens=False) + return caption["input_ids"] + + +class VideoProcessor(Processor): + """ + A generic video processor: load a numpy video tokens by default. + """ + + def __init__(self, config): + self.vfeat_dir = config.vfeat_dir + + def __call__(self, video_fn): + if isinstance(video_fn, tuple): + video_fn = video_fn[0] + assert isinstance(video_fn, str) + video_fn = os.path.join(self.vfeat_dir, video_fn + ".npy") + feat = np.load(video_fn) + return feat + + +class Aligner(object): + """ + An alignprocessor align video and text and output a dict of tensors (for a model). + """ + def __init__(self, config): + """__init__ needs to be light weight for more workers/threads.""" + self.split = config.split + self.max_video_len = config.max_video_len + self.max_len = config.max_len + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained( + str(config.bert_name), use_fast=config.use_fast + ) + self.cls_token_id = tokenizer.cls_token_id + self.sep_token_id = tokenizer.sep_token_id + self.pad_token_id = tokenizer.pad_token_id + self.mask_token_id = tokenizer.mask_token_id + + def __call__(self, video_id, video_feature, text_feature): + raise NotImplementedError + + def _build_video_seq(self, video_feature, video_clips=None): + """ + `video_feature`: available video tokens. + `video_clips`: video clip sequence to build. + """ + if not isinstance(video_feature, np.ndarray): + raise ValueError( + "unsupported type of video_feature", type(video_feature) + ) + + if video_clips is None: + # this is borrowed from DSAligner + video_start = 0 + video_end = min(len(video_feature), self.max_video_len) + # the whole sequence is a single clip. + video_clips = {"start": [video_start], "end": [video_end]} + + vfeats = np.zeros( + (self.max_video_len, video_feature.shape[1]), dtype=np.float32 + ) + vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool) + video_len = 0 + for start, end in zip(video_clips["start"], video_clips["end"]): + clip_len = min(self.max_video_len - video_len, (end - start)) + if clip_len > 0: + vfeats[video_len: video_len + clip_len] = video_feature[ + start: start + clip_len + ] + vmasks[video_len: video_len + clip_len] = 1 + video_len += clip_len + vfeats = torch.from_numpy(vfeats) + + return vfeats, vmasks + + def _build_text_seq(self, text_feature, text_clip_indexs=None): + """ + `text_feature`: all available clips. + `text_clip_indexes`: clip sequence to build. + """ + if text_clip_indexs is None: + text_clip_indexs = [0] + + full_caps = [] + if isinstance(text_feature, dict): + for clip_idx in text_clip_indexs: + full_caps.extend(text_feature["cap"][clip_idx]) + else: + full_caps = text_feature + max_text_len = self.max_len - self.max_video_len - 3 + full_caps = full_caps[:max_text_len] + full_caps = ( + [self.cls_token_id, self.sep_token_id] + full_caps + [self.sep_token_id] + ) + text_pad_len = self.max_len - len(full_caps) - self.max_video_len + padded_full_caps = full_caps + [self.pad_token_id] * text_pad_len + caps = torch.LongTensor(padded_full_caps) + cmasks = torch.zeros((len(padded_full_caps),), dtype=torch.bool) + cmasks[: len(full_caps)] = 1 + + return caps, cmasks + + def batch_post_processing(self, batch, video_feature): + return batch + + +class MMAttentionMask2DProcessor(Processor): + """text generation requires 2d mask + that is harder to generate by GPU at this stage.""" + + def __call__(self, vmask, cmask, mtype): + if mtype == "textgen": + return self._build_textgeneration_mask(vmask, cmask) + elif mtype == "videogen": + return self._build_videogeneration_mask(vmask, cmask) + else: + return self._build_mm_mask(vmask, cmask) + + def _build_mm_mask(self, vmask, cmask): + mask_1d = torch.cat([cmask[:1], vmask, cmask[1:]], dim=0) + return mask_1d[None, :].repeat(mask_1d.size(0), 1) + + def _build_videogeneration_mask(self, vmask, cmask): + # cls_mask is only about text otherwise it will leak generation. + cls_text_mask = torch.cat([ + # [CLS] + torch.ones( + (1,), dtype=torch.bool, device=cmask.device), + # video tokens and [SEP] for video. + torch.zeros( + (vmask.size(0) + 1,), dtype=torch.bool, device=cmask.device), + cmask[2:] + ], dim=0) + + # concat horizontially. + video_len = int(vmask.sum()) + video_masks = torch.cat([ + # [CLS] + torch.ones( + (video_len, 1), dtype=torch.bool, device=cmask.device + ), + torch.tril( + torch.ones( + (video_len, video_len), + dtype=torch.bool, device=cmask.device)), + # video_padding + torch.zeros( + (video_len, vmask.size(0) - video_len), + dtype=torch.bool, device=cmask.device + ), + # [SEP] for video (unused). + torch.zeros( + (video_len, 1), dtype=torch.bool, device=cmask.device + ), + cmask[2:].unsqueeze(0).repeat(video_len, 1) + ], dim=1) + + text_masks = cls_text_mask[None, :].repeat( + cmask.size(0) - 2, 1) + video_padding_masks = cls_text_mask[None, :].repeat( + vmask.size(0) - video_len, 1) + + return torch.cat([ + cls_text_mask[None, :], + video_masks, + video_padding_masks, + torch.cat([cmask[:1], vmask, cmask[1:]], dim=0)[None,:], + text_masks + ], dim=0) + + def _build_textgeneration_mask(self, vmask, cmask): + # cls_mask is only about video otherwise it will leak generation. + cls_video_mask = torch.cat([ + # [CLS] + torch.ones( + (1,), dtype=torch.bool, device=cmask.device), + vmask, + # [SEP] + torch.ones((1,), dtype=torch.bool, device=cmask.device), + torch.zeros( + (cmask.size(0)-2,), dtype=torch.bool, device=cmask.device) + ], dim=0) + + # concat horizontially. + text_len = int(cmask[2:].sum()) + text_masks = torch.cat([ + # [CLS] + torch.ones( + (text_len, 1), dtype=torch.bool, device=cmask.device + ), + vmask.unsqueeze(0).repeat(text_len, 1), + # [SEP] for video. + torch.ones( + (text_len, 1), dtype=torch.bool, device=cmask.device + ), + torch.tril( + torch.ones( + (text_len, text_len), + dtype=torch.bool, device=cmask.device)), + # padding. + torch.zeros( + (text_len, cmask.size(0) - text_len - 2), + dtype=torch.bool, device=cmask.device + ) + ], dim=1) + + cls_video_masks = cls_video_mask[None, :].repeat( + vmask.size(0) + 2, 1) + text_padding_masks = cls_video_mask[None, :].repeat( + cmask.size(0) - text_len - 2, 1) + return torch.cat([ + cls_video_masks, text_masks, text_padding_masks], dim=0) diff --git a/fairseq/examples/MMPT/mmpt/tasks/__init__.py b/fairseq/examples/MMPT/mmpt/tasks/__init__.py new file mode 100644 index 0000000..e2e9323 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/tasks/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .task import * +from .vlmtask import * +from .retritask import * + +try: + from .fairseqmmtask import * +except ImportError: + pass + +try: + from .milncetask import * +except ImportError: + pass + +try: + from .expretritask import * +except ImportError: + pass diff --git a/fairseq/examples/MMPT/mmpt/tasks/fairseqmmtask.py b/fairseq/examples/MMPT/mmpt/tasks/fairseqmmtask.py new file mode 100644 index 0000000..f6b6115 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/tasks/fairseqmmtask.py @@ -0,0 +1,104 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +make a general fairseq task for MM pretraining. +""" + +import random + +from fairseq.tasks import LegacyFairseqTask, register_task + +from .task import Task +from .retritask import RetriTask +from ..datasets import FairseqMMDataset +from .. import utils + + +@register_task("mmtask") +class FairseqMMTask(LegacyFairseqTask): + @staticmethod + def add_args(parser): + # Add some command-line arguments for specifying where the data is + # located and the maximum supported input length. + parser.add_argument( + "taskconfig", + metavar="FILE", + help=("taskconfig to load all configurations" "outside fairseq parser."), + ) + + @classmethod + def setup_task(cls, args, **kwargs): + return FairseqMMTask(args) + + def __init__(self, args): + super().__init__(args) + config = utils.load_config(args) + self.mmtask = Task.config_task(config) + self.mmtask.build_dataset() + self.mmtask.build_model() + self.mmtask.build_loss() + + def load_dataset(self, split, **kwargs): + split_map = { + "train": self.mmtask.train_data, + "valid": self.mmtask.val_data, + "test": self.mmtask.test_data, + } + if split not in split_map: + raise ValueError("unknown split type.") + if split_map[split] is not None: + self.datasets[split] = FairseqMMDataset(split_map[split]) + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + data_buffer_size=0, + disable_iterator_cache=False, + skip_remainder_batch=False, + grouped_shuffling=False, + update_epoch_batch_itr=False, + ): + random.seed(epoch) + if dataset.mmdataset.split == "train" and isinstance(self.mmtask, RetriTask): + if epoch >= self.mmtask.config.retri_epoch: + if not hasattr(self.mmtask, "retri_dataloader"): + self.mmtask.build_dataloader() + self.mmtask.retrive_candidates(epoch) + + return super().get_batch_iterator( + dataset, + max_tokens, + max_sentences, + max_positions, + ignore_invalid_inputs, + required_batch_size_multiple, + seed, + num_shards, + shard_id, + num_workers, + epoch, + data_buffer_size, + disable_iterator_cache, + grouped_shuffling, + update_epoch_batch_itr, + ) + + @property + def source_dictionary(self): + return None + + @property + def target_dictionary(self): + return None diff --git a/fairseq/examples/MMPT/mmpt/tasks/milncetask.py b/fairseq/examples/MMPT/mmpt/tasks/milncetask.py new file mode 100644 index 0000000..61b6ab0 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/tasks/milncetask.py @@ -0,0 +1,27 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from .task import Task + + +class MILNCETask(Task): + def reshape_subsample(self, sample): + if ( + hasattr(self.config.dataset, "subsampling") + and self.config.dataset.subsampling is not None + and self.config.dataset.subsampling > 1 + ): + for key in sample: + if torch.is_tensor(sample[key]): + tensor = self.flat_subsample(sample[key]) + if key in ["caps", "cmasks"]: + size = tensor.size() + batch_size = size[0] * size[1] + expanded_size = (batch_size,) + size[2:] + tensor = tensor.view(expanded_size) + sample[key] = tensor + return sample diff --git a/fairseq/examples/MMPT/mmpt/tasks/retritask.py b/fairseq/examples/MMPT/mmpt/tasks/retritask.py new file mode 100644 index 0000000..b43f20f --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/tasks/retritask.py @@ -0,0 +1,253 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import torch +import pickle +import random + +from tqdm import tqdm +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from ..processors import ( + ShardedHow2MetaProcessor, + ShardedVideoProcessor, + ShardedTextProcessor, + VariedLenAligner, +) + +from ..datasets import MMDataset +from .task import Task +from ..modules import vectorpool +from ..evaluators.predictor import Predictor +from ..utils import set_seed, get_local_rank, get_world_size + + +class RetriTask(Task): + """abstract class for task with retrival.""" + + def reshape_subsample(self, sample): + for key in sample: + if torch.is_tensor(sample[key]): + sample[key] = self.flat_subsample(sample[key]) + return sample + + def flat_subsample(self, tensor): + if tensor.size(0) == 1: + tensor = tensor.squeeze(0) + return tensor + + def build_dataloader(self): + """called by `get_batch_iterator` in fairseqmmtask. """ + # TODO: hard-code dataloader for retri for now and configurable in .yaml. + # reuse the `train.lst`. + self.config.dataset.split = "train" + meta_processor = ShardedHow2MetaProcessor(self.config.dataset) + video_processor = ShardedVideoProcessor(self.config.dataset) + text_processor = ShardedTextProcessor(self.config.dataset) + + aligner = VariedLenAligner(self.config.dataset) + aligner.subsampling = self.config.dataset.clip_per_video + + self.retri_data = MMDataset( + meta_processor, video_processor, text_processor, aligner + ) + + retri_sampler = DistributedSampler(self.retri_data) + infer_scale = 16 + batch_size = self.config.dataset.num_video_per_batch \ + * infer_scale + + self.retri_dataloader = DataLoader( + self.retri_data, + collate_fn=self.retri_data.collater, + batch_size=batch_size, + shuffle=False, + sampler=retri_sampler, + num_workers=self.config.fairseq.dataset.num_workers + ) + return self.retri_dataloader + + def retrive_candidates(self, epoch, dataloader=None): + if get_local_rank() == 0: + print("running retrieval model.") + out_dir = os.path.join( + self.config.fairseq.checkpoint.save_dir, "retri") + os.makedirs(out_dir, exist_ok=True) + + if not os.path.isfile( + os.path.join( + out_dir, "batched_e" + str(epoch) + "_videos0.pkl") + ): + if dataloader is None: + dataloader = self.retri_dataloader + + self.model.eval() + self.model.is_train = False + + assert self.retri_data.meta_processor.data == \ + self.train_data.meta_processor.data # video_ids not mutated. + + self._retri_predict(epoch, dataloader) + + self.model.train() + self.model.is_train = True + + torch.distributed.barrier() + output = self._retri_sync(epoch, out_dir) + torch.distributed.barrier() + self.train_data.meta_processor.set_candidates(output) + return output + + +class VideoRetriTask(RetriTask): + """RetriTask on video level.""" + + def reshape_subsample(self, sample): + if ( + hasattr(self.config.dataset, "clip_per_video") + and self.config.dataset.clip_per_video is not None + and self.config.dataset.clip_per_video > 1 + ): + for key in sample: + if torch.is_tensor(sample[key]): + sample[key] = self.flat_subsample(sample[key]) + return sample + + def flat_subsample(self, tensor): + if tensor.size(0) == 1: + tensor = tensor.squeeze(0) + return Task.flat_subsample(self, tensor) + + def _retri_predict(self, epoch, dataloader): + set_seed(epoch) + # save for retrival. + predictor = VideoPredictor(self.config) + predictor.predict_loop( + self.model, dataloader) + set_seed(epoch) # get the same text clips. + # retrival. + retri_predictor = VideoRetriPredictor( + self.config) + retri_predictor.predict_loop( + self.model, predictor.vecpool.retriver, epoch) + del predictor + del retri_predictor + + def _retri_sync(self, epoch, out_dir): + # gpu do the same merge. + batched_videos = [] + for local_rank in range(get_world_size()): + fn = os.path.join( + out_dir, + "batched_e" + str(epoch) + "_videos" + str(local_rank) + ".pkl") + with open(fn, "rb") as fr: + batched_videos.extend(pickle.load(fr)) + print( + "[INFO] batched_videos", + len(batched_videos), len(batched_videos[0])) + return batched_videos + + +class VideoPredictor(Predictor): + def __init__(self, config): + vectorpool_cls = getattr(vectorpool, config.vectorpool_cls) + self.vecpool = vectorpool_cls(config) + + def predict_loop( + self, + model, + dataloader, + early_stop=-1, + ): + with torch.no_grad(): + if get_local_rank() == 0: + dataloader = tqdm(dataloader) + for batch_idx, batch in enumerate(dataloader): + if batch_idx == early_stop: + break + self(batch, model) + return self.finalize() + + def __call__(self, sample, model, **kwargs): + param = next(model.parameters()) + dtype = param.dtype + device = param.device + subsample = sample["vfeats"].size(1) + sample = self.to_ctx(sample, device, dtype) + for key in sample: + if torch.is_tensor(sample[key]): + size = sample[key].size() + if len(size) >= 2: + batch_size = size[0] * size[1] + expanded_size = ( + (batch_size,) + size[2:] if len(size) > 2 + else (batch_size,) + ) + sample[key] = sample[key].view(expanded_size) + + outputs = model(**sample) + sample.update(outputs) + self.vecpool(sample, subsample) + + def finalize(self): + print("[INFO]", self.vecpool) + if not self.vecpool.retriver.db.is_trained: + self.vecpool.retriver.finalize_training() + return self.vecpool.retriver + + +class VideoRetriPredictor(Predictor): + """ + Online Retrieval Predictor for Clips (used by RetriTask). + TODO: merge this with VisPredictor? + """ + + def __init__(self, config): + self.pred_dir = os.path.join( + config.fairseq.checkpoint.save_dir, + "retri") + self.num_cands = config.num_cands + self.num_video_per_batch = config.dataset.num_video_per_batch + + def predict_loop( + self, + model, + retriver, + epoch, + early_stop=-1 + ): + # a fake loop that only try to recover video vector + # from video_id. + batched_videos = [] + # obtain available video_ids. + video_ids = list(retriver.videoid_to_vectoridx.keys()) + + dataloader = random.sample( + video_ids, + len(video_ids) // self.num_video_per_batch + ) + + if get_local_rank() == 0: + dataloader = tqdm(dataloader) + for batch_idx, batch in enumerate(dataloader): + # batch is one video id. + if batch_idx == early_stop: + break + video_ids = retriver.search_by_video_ids( + [batch], self.num_cands)[0] + if len(video_ids) > self.num_video_per_batch: + # we moved the center to make cluster robust. + video_ids = random.sample(video_ids, self.num_video_per_batch) + batched_videos.append(video_ids) + return self.finalize(batched_videos, epoch) + + def finalize(self, batched_videos, epoch): + fn = os.path.join( + self.pred_dir, + "batched_e" + str(epoch) + "_videos" + str(get_local_rank()) + ".pkl") + with open(fn, "wb") as fw: + pickle.dump(batched_videos, fw, pickle.HIGHEST_PROTOCOL) + return batched_videos diff --git a/fairseq/examples/MMPT/mmpt/tasks/task.py b/fairseq/examples/MMPT/mmpt/tasks/task.py new file mode 100644 index 0000000..8bb50f2 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/tasks/task.py @@ -0,0 +1,184 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import torch + +from .. import tasks +from .. import models +from .. import losses +from ..datasets import MMDataset +from .. import processors + + +class Task(object): + """ + A task refers to one generic training task (e.g., training one model). + """ + + @classmethod + def config_task(cls, config): + """ + determine whether to load a hard-coded task or config from a generic one. + via if a task string is available in config. + """ + if config.task is not None: + # TODO (huxu): expand the search scope. + task_cls = getattr(tasks, config.task) + return task_cls(config) + else: + return Task(config) + + def __init__(self, config): + self.config = config + self.train_data = None + self.val_data = None + self.test_data = None + + self.model = None + self.loss_fn = None + self.eval_fn = None + + def build_dataset(self): + """TODO (huxu): move processor breakdown to MMDataset.""" + """fill-in `self.train_data`, `self.val_data` and `self.test_data`.""" + + meta_processor_cls = getattr( + processors, self.config.dataset.meta_processor) + video_processor_cls = getattr( + processors, self.config.dataset.video_processor) + text_processor_cls = getattr( + processors, self.config.dataset.text_processor) + aligner_cls = getattr( + processors, self.config.dataset.aligner) + + if self.config.dataset.train_path is not None: + self.config.dataset.split = "train" + # may be used by meta processor. + # meta_processor controls different dataset. + meta_processor = meta_processor_cls(self.config.dataset) + video_processor = video_processor_cls(self.config.dataset) + text_processor = text_processor_cls(self.config.dataset) + aligner = aligner_cls(self.config.dataset) + self.train_data = MMDataset( + meta_processor, video_processor, text_processor, aligner + ) + print("train_len", len(self.train_data)) + output = self.train_data[0] + self.train_data.print_example(output) + if self.config.dataset.val_path is not None: + self.config.dataset.split = "valid" + # may be used by meta processor. + meta_processor = meta_processor_cls(self.config.dataset) + video_processor = video_processor_cls(self.config.dataset) + text_processor = text_processor_cls(self.config.dataset) + aligner = aligner_cls(self.config.dataset) + self.val_data = MMDataset( + meta_processor, video_processor, text_processor, aligner + ) + print("val_len", len(self.val_data)) + output = self.val_data[0] + self.val_data.print_example(output) + + if self.config.dataset.split == "test": + # the following is run via lauching fairseq-validate. + meta_processor = meta_processor_cls(self.config.dataset) + video_processor = video_processor_cls(self.config.dataset) + text_processor = text_processor_cls(self.config.dataset) + + self.test_data = MMDataset( + meta_processor, video_processor, text_processor, aligner + ) + print("test_len", len(self.test_data)) + output = self.test_data[0] + self.test_data.print_example(output) + + def build_model(self, checkpoint=None): + if self.model is None: + model_cls = getattr(models, self.config.model.model_cls) + self.model = model_cls(self.config) + if checkpoint is not None: + self.load_checkpoint(checkpoint) + return self.model + + def load_checkpoint(self, checkpoint): + if self.model is None: + raise ValueError("model is not initialized.") + state_dict = torch.load(checkpoint) + state_dict = self._trim_state_dict(state_dict) + self.model.load_state_dict(state_dict, strict=False) + # if it's a fp16 model, turn it back. + if next(self.model.parameters()).dtype == torch.float16: + self.model = self.model.float() + return self.model + + def _trim_state_dict(self, state_dict): + from collections import OrderedDict + + if "state_dict" in state_dict: + state_dict = state_dict["state_dict"] + if "model" in state_dict: # fairseq checkpoint format. + state_dict = state_dict["model"] + ret_state_dict = OrderedDict() + for ( + key, + value, + ) in state_dict.items(): + # remove fairseq wrapper since this is a task. + if key.startswith("mmmodel"): + key = key[len("mmmodel."):] + ret_state_dict[key] = value + return ret_state_dict + + def build_loss(self): + if self.loss_fn is None and self.config.loss is not None: + loss_cls = getattr(losses, self.config.loss.loss_cls) + self.loss_fn = loss_cls() + return self.loss_fn + + def flat_subsample(self, tensor): + size = tensor.size() + if len(size) >= 2: + batch_size = size[0] * size[1] + expanded_size = ( + (batch_size,) + size[2:] if len(size) > 2 + else (batch_size,) + ) + tensor = tensor.view(expanded_size) + return tensor + + def reshape_subsample(self, sample): + if ( + hasattr(self.config.dataset, "subsampling") + and self.config.dataset.subsampling is not None + and self.config.dataset.subsampling > 1 + ): + for key in sample: + if torch.is_tensor(sample[key]): + sample[key] = self.flat_subsample(sample[key]) + return sample + + def __call__(self, model, sample): + loss = None + loss_scalar = float("inf") + + sample = self.reshape_subsample(sample) + outputs = self.model(**sample) + sample.update(outputs) + if self.loss_fn is not None: + loss = self.loss_fn(**sample) + loss_scalar = loss.item() + + batch_size = sample["caps"].size(0) + sample_size = 1 + return { + "loss": loss, + "loss_scalar": loss_scalar, + "max_len": self.config.dataset.max_len, + "batch_size": batch_size, + "sample_size": sample_size, + } + + def build_dataloader(self): + """only used for trainer that lacks building loaders.""" + raise NotImplementedError diff --git a/fairseq/examples/MMPT/mmpt/tasks/vlmtask.py b/fairseq/examples/MMPT/mmpt/tasks/vlmtask.py new file mode 100644 index 0000000..57dc4c9 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/tasks/vlmtask.py @@ -0,0 +1,27 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import torch + +from .task import Task + + +class VLMTask(Task): + """A VLM task for reproducibility. + the collator split subsamples into two sub-batches. + This has should have no logic changes. + but changed the randomness in frame masking. + """ + + def flat_subsample(self, tensor): + size = tensor.size() + if len(size) >= 2: + batch_size = size[0] * (size[1] // 2) + expanded_size = ( + (batch_size, 2) + size[2:] if len(size) > 2 + else (batch_size, 2) + ) + tensor = tensor.view(expanded_size) + tensor = torch.cat([tensor[:, 0], tensor[:, 1]], dim=0) + return tensor diff --git a/fairseq/examples/MMPT/mmpt/utils/__init__.py b/fairseq/examples/MMPT/mmpt/utils/__init__.py new file mode 100644 index 0000000..2429ee3 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/utils/__init__.py @@ -0,0 +1,68 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import random +import numpy as np +import torch + +from .shardedtensor import * +from .load_config import * + + +def set_seed(seed=43211): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if torch.backends.cudnn.enabled: + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + + +def get_world_size(): + if torch.distributed.is_initialized(): + world_size = torch.distributed.get_world_size() + else: + world_size = 1 + return world_size + + +def get_local_rank(): + return torch.distributed.get_rank() \ + if torch.distributed.is_initialized() else 0 + + +def print_on_rank0(func): + local_rank = get_local_rank() + if local_rank == 0: + print("[INFO]", func) + + +class RetriMeter(object): + """ + Statistics on whether retrieval yields a better pair. + """ + def __init__(self, freq=1024): + self.freq = freq + self.total = 0 + self.replace = 0 + self.updates = 0 + + def __call__(self, data): + if isinstance(data, np.ndarray): + self.replace += data.shape[0] - int((data[:, 0] == -1).sum()) + self.total += data.shape[0] + elif torch.is_tensor(data): + self.replace += int(data.sum()) + self.total += data.size(0) + else: + raise ValueError("unsupported RetriMeter data type.", type(data)) + + self.updates += 1 + if get_local_rank() == 0 and self.updates % self.freq == 0: + print("[INFO]", self) + + def __repr__(self): + return "RetriMeter (" + str(self.replace / self.total) \ + + "/" + str(self.replace) + "/" + str(self.total) + ")" diff --git a/fairseq/examples/MMPT/mmpt/utils/load_config.py b/fairseq/examples/MMPT/mmpt/utils/load_config.py new file mode 100644 index 0000000..ede4f94 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/utils/load_config.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import omegaconf +from omegaconf import OmegaConf + + +def load_config(args=None, config_file=None, overwrite_fairseq=False): + """TODO (huxu): move fairseq overwrite to another function.""" + if args is not None: + config_file = args.taskconfig + config = recursive_config(config_file) + + if config.dataset.subsampling is not None: + batch_size = config.fairseq.dataset.batch_size // config.dataset.subsampling + print( + "adjusting batch_size to {} due to subsampling {}.".format( + batch_size, config.dataset.subsampling + ) + ) + config.fairseq.dataset.batch_size = batch_size + + is_test = config.dataset.split is not None and config.dataset.split == "test" + if not is_test: + if ( + config.fairseq.checkpoint is None + or config.fairseq.checkpoint.save_dir is None + ): + raise ValueError("fairseq save_dir or save_path must be specified.") + + save_dir = config.fairseq.checkpoint.save_dir + os.makedirs(save_dir, exist_ok=True) + if config.fairseq.common.tensorboard_logdir is not None: + tb_run_dir = suffix_rundir( + save_dir, config.fairseq.common.tensorboard_logdir + ) + config.fairseq.common.tensorboard_logdir = tb_run_dir + print( + "update tensorboard_logdir as", config.fairseq.common.tensorboard_logdir + ) + os.makedirs(save_dir, exist_ok=True) + OmegaConf.save(config=config, f=os.path.join(save_dir, "config.yaml")) + + if overwrite_fairseq and config.fairseq is not None and args is not None: + # flatten fields. + for group in config.fairseq: + for field in config.fairseq[group]: + print("overwrite args." + field, "as", config.fairseq[group][field]) + setattr(args, field, config.fairseq[group][field]) + return config + + +def recursive_config(config_path): + """allows for stacking of configs in any depth.""" + config = OmegaConf.load(config_path) + if config.includes is not None: + includes = config.includes + config.pop("includes") + base_config = recursive_config(includes) + config = OmegaConf.merge(base_config, config) + return config + + +def suffix_rundir(save_dir, run_dir): + max_id = -1 + for search_dir in os.listdir(save_dir): + if search_dir.startswith(run_dir): + splits = search_dir.split("_") + cur_id = int(splits[1]) if len(splits) > 1 else 0 + max_id = max(max_id, cur_id) + return os.path.join(save_dir, run_dir + "_" + str(max_id + 1)) + + +def overwrite_dir(config, replace, basedir): + for key in config: + if isinstance(config[key], str) and config[key].startswith(basedir): + config[key] = config[key].replace(basedir, replace) + if isinstance(config[key], omegaconf.dictconfig.DictConfig): + overwrite_dir(config[key], replace, basedir) diff --git a/fairseq/examples/MMPT/mmpt/utils/shardedtensor.py b/fairseq/examples/MMPT/mmpt/utils/shardedtensor.py new file mode 100644 index 0000000..2424f36 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt/utils/shardedtensor.py @@ -0,0 +1,46 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import pickle +import numpy as np + + +class ShardedTensor(object): + def __init__(self, data, starts): + self.data = data + self.starts = starts + assert self.starts[0] == 0 + assert self.starts[-1] == len(self.data) + assert (self.starts[1:] >= self.starts[:-1]).all() + assert (self.starts > -1).all() + + @staticmethod + def from_list(xs): + starts = np.full((len(xs) + 1,), -1, dtype=np.long) + data = np.concatenate(xs, axis=0) + starts[0] = 0 + for i, x in enumerate(xs): + starts[i + 1] = starts[i] + x.shape[0] + assert (starts > -1).all() + return ShardedTensor(data, starts) + + def __getitem__(self, i): + return self.data[self.starts[i] : self.starts[i + 1]] + + def __len__(self): + return len(self.starts) - 1 + + def lengths(self): + return self.starts[1:] - self.starts[:-1] + + def save(self, path): + np.save(path + "_starts", self.starts) + np.save(path + "_data", self.data) + + @staticmethod + def load(path, mmap_mode=None): + starts = np.load(path + "_starts.npy", mmap_mode) + data = np.load(path + "_data.npy", mmap_mode) + return ShardedTensor(data, starts) diff --git a/fairseq/examples/MMPT/mmpt_cli/localjob.py b/fairseq/examples/MMPT/mmpt_cli/localjob.py new file mode 100644 index 0000000..2675d35 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt_cli/localjob.py @@ -0,0 +1,117 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os + +from mmpt.utils import recursive_config + + +class BaseJob(object): + def __init__(self, yaml_file, dryrun=False): + self.yaml_file = yaml_file + self.config = recursive_config(yaml_file) + self.dryrun = dryrun + + def submit(self, **kwargs): + raise NotImplementedError + + def _normalize_cmd(self, cmd_list): + cmd_list = list(cmd_list) + yaml_index = cmd_list.index("[yaml]") + cmd_list[yaml_index] = self.yaml_file + return cmd_list + + +class LocalJob(BaseJob): + + CMD_CONFIG = { + "local_single": [ + "fairseq-train", "[yaml]", "--user-dir", "mmpt", + "--task", "mmtask", "--arch", "mmarch", + "--criterion", "mmloss", + ], + "local_small": [ + "fairseq-train", "[yaml]", "--user-dir", "mmpt", + "--task", "mmtask", "--arch", "mmarch", + "--criterion", "mmloss", + "--distributed-world-size", "2" + ], + "local_big": [ + "fairseq-train", "[yaml]", "--user-dir", "mmpt", + "--task", "mmtask", "--arch", "mmarch", + "--criterion", "mmloss", + "--distributed-world-size", "8" + ], + "local_predict": ["python", "mmpt_cli/predict.py", "[yaml]"], + } + + def __init__(self, yaml_file, job_type=None, dryrun=False): + super().__init__(yaml_file, dryrun) + if job_type is None: + self.job_type = "local_single" + if self.config.task_type is not None: + self.job_type = self.config.task_type + else: + self.job_type = job_type + if self.job_type in ["local_single", "local_small"]: + if self.config.fairseq.dataset.batch_size > 32: + print("decreasing batch_size to 32 for local testing?") + + def submit(self): + cmd_list = self._normalize_cmd(LocalJob.CMD_CONFIG[self.job_type]) + if "predict" not in self.job_type: + # append fairseq args. + from mmpt.utils import load_config + + config = load_config(config_file=self.yaml_file) + for field in config.fairseq: + for key in config.fairseq[field]: + if key in ["fp16", "reset_optimizer", "reset_dataloader", "reset_meters"]: # a list of binary flag. + param = ["--" + key.replace("_", "-")] + else: + if key == "lr": + value = str(config.fairseq[field][key][0]) + elif key == "adam_betas": + value = "'"+str(config.fairseq[field][key])+"'" + else: + value = str(config.fairseq[field][key]) + param = [ + "--" + key.replace("_", "-"), + value + ] + cmd_list.extend(param) + + print("launching", " ".join(cmd_list)) + if not self.dryrun: + os.system(" ".join(cmd_list)) + return JobStatus("12345678") + + +class JobStatus(object): + def __init__(self, job_id): + self.job_id = job_id + + def __repr__(self): + return self.job_id + + def __str__(self): + return self.job_id + + def done(self): + return False + + def running(self): + return False + + def result(self): + if self.done(): + return "{} is done.".format(self.job_id) + else: + return "{} is running.".format(self.job_id) + + def stderr(self): + return self.result() + + def stdout(self): + return self.result() diff --git a/fairseq/examples/MMPT/mmpt_cli/predict.py b/fairseq/examples/MMPT/mmpt_cli/predict.py new file mode 100644 index 0000000..4071e19 --- /dev/null +++ b/fairseq/examples/MMPT/mmpt_cli/predict.py @@ -0,0 +1,113 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import glob +import argparse +import pprint +import omegaconf + +from omegaconf import OmegaConf +from torch.utils.data import DataLoader + +from mmpt.utils import load_config, set_seed +from mmpt.evaluators import Evaluator +from mmpt.evaluators import predictor as predictor_path +from mmpt.tasks import Task +from mmpt import processors +from mmpt.datasets import MMDataset + + +def get_dataloader(config): + meta_processor_cls = getattr(processors, config.dataset.meta_processor) + video_processor_cls = getattr(processors, config.dataset.video_processor) + text_processor_cls = getattr(processors, config.dataset.text_processor) + aligner_cls = getattr(processors, config.dataset.aligner) + + meta_processor = meta_processor_cls(config.dataset) + video_processor = video_processor_cls(config.dataset) + text_processor = text_processor_cls(config.dataset) + aligner = aligner_cls(config.dataset) + + test_data = MMDataset( + meta_processor, + video_processor, + text_processor, + aligner, + ) + print("test_len", len(test_data)) + output = test_data[0] + test_data.print_example(output) + + test_dataloader = DataLoader( + test_data, + batch_size=config.fairseq.dataset.batch_size, + shuffle=False, + num_workers=6, + collate_fn=test_data.collater, + ) + return test_dataloader + + +def main(args): + config = load_config(args) + + if isinstance(config, omegaconf.dictconfig.DictConfig): + print(OmegaConf.to_yaml(config)) + else: + pp = pprint.PrettyPrinter(indent=4) + pp.print(config) + + mmtask = Task.config_task(config) + mmtask.build_model() + + test_dataloader = get_dataloader(config) + checkpoint_search_path = os.path.dirname(config.eval.save_path) + results = [] + + prefix = os.path.basename(args.taskconfig) + if prefix.startswith("test"): + # loop all checkpoint for datasets without validation set. + if "best" not in config.fairseq.common_eval.path: + print("eval each epoch.") + for checkpoint in glob.glob(checkpoint_search_path + "/checkpoint*"): + model = mmtask.load_checkpoint(checkpoint) + ckpt = os.path.basename(checkpoint) + evaluator = Evaluator(config) + output = evaluator.evaluate( + model, test_dataloader, ckpt + "_merged") + results.append((checkpoint, output)) + # use the one specified by the config lastly. + model = mmtask.load_checkpoint(config.fairseq.common_eval.path) + evaluator = Evaluator(config) + output = evaluator.evaluate(model, test_dataloader) + results.append((config.fairseq.common_eval.path, output)) + + best_result = None + best_metric = 0. + for checkpoint, result in results: + print(checkpoint) + evaluator.metric.print_computed_metrics(result) + best_score = evaluator.metric.best_metric(result) + if best_score > best_metric: + best_result = (checkpoint, result) + best_metric = best_score + print("best results:") + print(best_result[0]) + evaluator.metric.print_computed_metrics(best_result[1]) + + elif prefix.startswith("vis"): + model = mmtask.load_checkpoint(config.fairseq.common_eval.path) + predictor_cls = getattr(predictor_path, config.predictor) + predictor = predictor_cls(config) + predictor.predict_loop(model, test_dataloader, mmtask, None) + else: + raise ValueError("unknown prefix of the config file", args.taskconfig) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("taskconfig", type=str) + args = parser.parse_args() + main(args) diff --git a/fairseq/examples/MMPT/pretraining.md b/fairseq/examples/MMPT/pretraining.md new file mode 100644 index 0000000..8f8e6d0 --- /dev/null +++ b/fairseq/examples/MMPT/pretraining.md @@ -0,0 +1,29 @@ +# Pretraining + +(If you are new to the ideas of `mmpt.processors`, see [README](README.md) first.) +We mostly use [howto100M](https://github.com/antoine77340/howto100m) dataset for pretraining (other datasets are coming). So you are less likely to write a new `MetaProcessor`, `VideoProcessor` or `TextProcessor` but only working on a new `Aligner`, a new model and loss. + +### Data Sharding +Pretraining on Howto100M is heavy on IO since we have millions of videos or captions on the hard disk that cannot be fit into the memory. +It is desirable to have an optimized preprocessing step before the actual dataloading. + +We support data sharding to pack multiple videos into a shards of training data for both videos and captions. (see [dataset](DATASET.md) for preprocessing). +These shards will be mapped into memory to reduce the frequency of IO access on millions of files. See (processors starting with `Sharded*`). +This will be the default config for a how2 dataset `projects/task/how2.yaml`. + +Great thanks to Dmytro Okhonko for sharing the code from MARGE project. + +### Training +Pretraining on Howto100m is expected on one or multiple nodes, where each node has 8 GPUS with 32 GB mem. +launching a pretraing on MFM+MLM can be done, via: +```python locallaunch.py projects/mfmmlm/how2.yaml``` + +### Pre-training with a Retrieval Model (VideoCLIP) +This projects now support alternatively run a retrieval model and pre-training. +We implement a basic retrieval model that is built on the hidden states of a video and faiss. + +You may need to install faiss via `conda install faiss-cpu -c pytorch`. + +Right now, the hidden states of a video is computed as the average of 8 clips of their pooled visual/text hidden states. +See `mmpt/tasks/retritask.py` for more details. +The `.yaml` config for running pre-training with a retrieval model can be found at `projects/retri/videoretri.yaml`. diff --git a/fairseq/examples/MMPT/projects/mfmmlm.yaml b/fairseq/examples/MMPT/projects/mfmmlm.yaml new file mode 100644 index 0000000..0f3450a --- /dev/null +++ b/fairseq/examples/MMPT/projects/mfmmlm.yaml @@ -0,0 +1,59 @@ +project_dir: mfmmlm +run_task: + - how2.yaml + - [vtt.yaml, vttcap.yaml, vttqa.yaml, youcook.yaml, youcookcap.yaml, crosstask.yaml, coin.yaml] +base_dir: task +task_group: + pretrain: + task_list: + - how2.yaml + dataset: + subsampling: 32 + sampled_min_len: 10 + sampled_max_len: 64 + max_video_len: 32 + max_len: 96 + aligner: MFMMLMAligner + lazy_vfeat_mask: True + mfm_probability: 0.15 + mlm_probability: 0.15 + mm_prob: 0.5 + model: + model_cls: MMFusionMFMMLM + mm_encoder_cls: MMFusionForMFMMLM + loss: + loss_cls: MFMMLM + fairseq: + common: + fp16: true + dataset: + batch_size: 256 + optimization: + max_epoch: 15 + finetune: + task_list: + - vtt.yaml + - vttqa.yaml + - youcook.yaml + - youcookcap.yaml + - crosstask.yaml + - coin.yaml + dataset: + max_video_len: 32 + max_len: 96 + fairseq: + common: + fp16: true + # do not write any model or loss here (they are expected to be fixed in mmfusion). + test: + task_list: + - test_vtt.yaml + - test_vttqa.yaml + - test_youcook.yaml + - test_youcookcap.yaml + - test_crosstask.yaml + - test_crosstask_zs.yaml + - test_coin.yaml + dataset: + max_video_len: 32 + max_len: 96 diff --git a/fairseq/examples/MMPT/projects/mtm/mmfusionmtm.yaml b/fairseq/examples/MMPT/projects/mtm/mmfusionmtm.yaml new file mode 100644 index 0000000..337d66a --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/mmfusionmtm.yaml @@ -0,0 +1,19 @@ +includes: projects/mfmmlm.yaml +project_dir: mtm/mmfusionmtm +task_group: + pretrain: + task: VLMTask # reproducible + dataset: + aligner: MFMMLMAligner + model: + use_seg_emb: True # reproducible + model_cls: MMFusionMTM + mm_encoder_cls: MMBertForMFMMLM + loss: + loss_cls: MTM + finetune: + model: + use_seg_emb: True # reproducible + test: + model: + use_seg_emb: True # reproducible diff --git a/fairseq/examples/MMPT/projects/mtm/vlm.yaml b/fairseq/examples/MMPT/projects/mtm/vlm.yaml new file mode 100644 index 0000000..022a262 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm.yaml @@ -0,0 +1,8 @@ +includes: projects/mtm/mmfusionmtm.yaml +project_dir: mtm/vlm +task_group: + pretrain: + dataset: + sampled_min_len: 8 + loss: + loss_cls: MTM diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/coin.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/coin.yaml new file mode 100644 index 0000000..48fd64a --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/coin.yaml @@ -0,0 +1,47 @@ +dataset: + video_processor: VideoProcessor + bert_name: bert-base-uncased + meta_processor: COINActionSegmentationMetaProcessor + train_path: data/coin/COIN.json + val_path: data/coin/COIN.json + vfeat_dir: data/feat/feat_coin_s3d + text_processor: COINActionSegmentationTextProcessor + aligner: COINActionSegmentationAligner + num_iso_layer: 12 + sliding_window: 8 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 1 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 8 + checkpoint: + restore_file: runs/mtm/vlm/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/mtm/vlm/coin +task_type: sweep_big +model: + model_cls: MMFusionActionSegmentation + mm_encoder_cls: MMBertForTokenClassification + use_seg_emb: true +loss: + loss_cls: CrossEntropy diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/crosstask.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/crosstask.yaml new file mode 100644 index 0000000..4e706b5 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/crosstask.yaml @@ -0,0 +1,53 @@ +dataset: + video_processor: CrossTaskVideoProcessor + bert_name: bert-base-uncased + meta_processor: CrossTaskMetaProcessor + train_path: data/crosstask/crosstask_release/videos.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + text_processor: CrossTaskTextProcessor + aligner: CrossTaskAligner + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 1 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 5 + checkpoint: + restore_file: runs/mtm/vlm/checkpoint11.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/mtm/vlm/crosstask +task_type: sweep_small +model: + model_cls: MMFusionActionLocalization + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +loss: + loss_cls: BCE diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/how2.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/how2.yaml new file mode 100644 index 0000000..7ca40ad --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/how2.yaml @@ -0,0 +1,55 @@ +dataset: + video_processor: ShardedVideoProcessor + bert_name: bert-base-uncased + meta_processor: ShardedHow2MetaProcessor + train_path: data/how2/how2_s3d_train.lst + val_path: data/how2/how2_s3d_val.lst + vfeat_dir: data/feat/feat_how2_s3d_shard_small + text_processor: ShardedTextProcessor + tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased. + aligner: MFMMLMAligner + subsampling: 32 + sampled_min_len: 8 + sampled_max_len: 64 + max_video_len: 32 + max_len: 96 + lazy_vfeat_mask: true + mfm_probability: 0.15 + mlm_probability: 0.15 + mm_prob: 0.5 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 256 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 1000 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 15 + checkpoint: + save_dir: runs/mtm/vlm + save_interval_updates: 1024 + keep_interval_updates: 2 + keep_last_epochs: 30 +task_type: sweep_big +slurm_config: big +eval: + save_path: runs/mtm/vlm +model: + model_cls: MMFusionMTM + mm_encoder_cls: MMBertForMFMMLM + use_seg_emb: true +loss: + loss_cls: MTM +task: VLMTask diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/test_coin.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/test_coin.yaml new file mode 100644 index 0000000..8df2e66 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/test_coin.yaml @@ -0,0 +1,31 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: COINActionSegmentationAligner + bert_name: bert-base-uncased + test_path: data/coin/COIN.json + meta_processor: COINActionSegmentationMetaProcessor + vfeat_dir: data/feat/feat_coin_s3d + text_processor: COINActionSegmentationTextProcessor + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 1 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/mtm/vlm/coin/checkpoint_best.pt +model: + model_cls: MMFusionActionSegmentation + mm_encoder_cls: MMBertForTokenClassification + use_seg_emb: true +eval: + save_path: runs/mtm/vlm/coin/eval +metric: COINActionSegmentationMetric +predictor: COINPredictor diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/test_crosstask.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/test_crosstask.yaml new file mode 100644 index 0000000..d159847 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/test_crosstask.yaml @@ -0,0 +1,38 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: CrossTaskVideoProcessor + aligner: CrossTaskAligner + bert_name: bert-base-uncased + meta_processor: CrossTaskMetaProcessor + test_path: data/crosstask/crosstask_release/videos_val.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + text_processor: CrossTaskTextProcessor + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 1 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/mtm/vlm/crosstask/checkpoint_best.pt +model: + model_cls: MMFusionActionLocalization + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +eval: + save_path: runs/mtm/vlm/crosstask/eval +metric: CrossTaskMetric +predictor: CrossTaskPredictor diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/test_crosstask_zs.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/test_crosstask_zs.yaml new file mode 100644 index 0000000..59833c5 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/test_crosstask_zs.yaml @@ -0,0 +1,38 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: CrossTaskVideoProcessor + aligner: CrossTaskAligner + bert_name: bert-base-uncased + meta_processor: CrossTaskMetaProcessor + test_path: data/crosstask/crosstask_release/videos_val.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + text_processor: CrossTaskTextProcessor + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 1 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/mtm/vlm/checkpoint_best.pt +model: + model_cls: MMFusionActionLocalization + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +eval: + save_path: runs/mtm/vlm/crosstask_zs/eval +metric: CrossTaskMetric +predictor: CrossTaskPredictor diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/test_vtt.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/test_vtt.yaml new file mode 100644 index 0000000..a41557d --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/test_vtt.yaml @@ -0,0 +1,29 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: DSAligner + bert_name: bert-base-uncased + meta_processor: MSRVTTMetaProcessor + test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/mtm/vlm/vtt/checkpoint_last.pt +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +eval: + save_path: runs/mtm/vlm/vtt/eval +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/test_vttqa.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/test_vttqa.yaml new file mode 100644 index 0000000..abf3309 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/test_vttqa.yaml @@ -0,0 +1,29 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: MSRVTTQAAligner + bert_name: bert-base-uncased + meta_processor: MSRVTTQAMetaProcessor + test_path: data/msrvtt-qa/MSR_MC_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTQATextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/mtm/vlm/vttqa/checkpoint_last.pt +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +eval: + save_path: runs/mtm/vlm/vttqa/eval +metric: QAMetric +predictor: QAPredictor diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/test_youcook.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/test_youcook.yaml new file mode 100644 index 0000000..3a57d25 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/test_youcook.yaml @@ -0,0 +1,31 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: YoucookVideoProcessor + aligner: DSAligner + bert_name: bert-base-uncased + meta_processor: YoucookMetaProcessor + test_path: data/youcook/youcook_val.pkl + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + use_annotation_text: true + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: TextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/mtm/vlm/youcook/checkpoint_last.pt +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +eval: + save_path: runs/mtm/vlm/youcook/eval +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/test_youcookcap.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/test_youcookcap.yaml new file mode 100644 index 0000000..b2595d7 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/test_youcookcap.yaml @@ -0,0 +1,32 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: YoucookVideoProcessor + aligner: DSNLGAligner + bert_name: bert-base-uncased + meta_processor: YoucookNLGMetaProcessor + test_path: data/youcook/val_list.txt + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: NLGTextProcessor + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/mtm/vlm/youcookcap/checkpoint_best.pt +model: + model_cls: MMFusionNLG + mm_encoder_cls: MMBertForNLG + max_decode_length: 24 + use_seg_emb: true +eval: + save_path: runs/mtm/vlm/youcookcap/eval +metric: NLGMetric +predictor: NLGPredictor +gen_param: + num_beams: 5 diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/vtt.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/vtt.yaml new file mode 100644 index 0000000..c6c5b1a --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/vtt.yaml @@ -0,0 +1,49 @@ +dataset: + video_processor: VideoProcessor + bert_name: bert-base-uncased + meta_processor: MSRVTTMetaProcessor + train_path: data/msrvtt/MSRVTT_train.csv + jsfusion_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + full_test_path: data/msrvtt/MSRVTT_FULL_test.csv + dup: 20 + val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + json_path: data/msrvtt/MSRVTT_data.json + aligner: DSAligner + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 256 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 10 + checkpoint: + restore_file: runs/mtm/vlm/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/mtm/vlm/vtt +task_type: sweep_small +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +loss: + loss_cls: T2VContraLoss diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/vttqa.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/vttqa.yaml new file mode 100644 index 0000000..0a440c7 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/vttqa.yaml @@ -0,0 +1,47 @@ +dataset: + video_processor: VideoProcessor + bert_name: bert-base-uncased + meta_processor: MSRVTTMetaProcessor + train_path: data/msrvtt/MSRVTT_train.csv + dup: 20 + val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + json_path: data/msrvtt/MSRVTT_data.json + aligner: DSAligner + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 128 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 5 + checkpoint: + restore_file: runs/mtm/vlm/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/mtm/vlm/vttqa +task_type: sweep_small +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +loss: + loss_cls: V2TContraLoss diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/youcook.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/youcook.yaml new file mode 100644 index 0000000..9ee82b8 --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/youcook.yaml @@ -0,0 +1,47 @@ +dataset: + video_processor: YoucookVideoProcessor + bert_name: bert-base-uncased + meta_processor: YoucookMetaProcessor + train_path: data/youcook/youcook_train.pkl + val_path: data/youcook/youcook_val.pkl + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + use_annotation_text: true + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: TextProcessor + aligner: DSAligner + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 128 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 10 + checkpoint: + restore_file: runs/mtm/vlm/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/mtm/vlm/youcook +task_type: sweep_small +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint + use_seg_emb: true +loss: + loss_cls: T2VContraLoss diff --git a/fairseq/examples/MMPT/projects/mtm/vlm/youcookcap.yaml b/fairseq/examples/MMPT/projects/mtm/vlm/youcookcap.yaml new file mode 100644 index 0000000..d29dfad --- /dev/null +++ b/fairseq/examples/MMPT/projects/mtm/vlm/youcookcap.yaml @@ -0,0 +1,45 @@ +dataset: + video_processor: YoucookVideoProcessor + bert_name: bert-base-uncased + meta_processor: YoucookNLGMetaProcessor + train_path: data/youcook/train_list.txt + val_path: data/youcook/val_list.txt + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: NLGTextProcessor + aligner: DSNLGAligner + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 128 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 10 + checkpoint: + restore_file: runs/mtm/vlm/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/mtm/vlm/youcookcap +task_type: sweep_small +model: + model_cls: MMFusionNLG + mm_encoder_cls: MMBertForNLG + use_seg_emb: true +loss: + loss_cls: NLGLoss diff --git a/fairseq/examples/MMPT/projects/retri/videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip.yaml new file mode 100644 index 0000000..afd040a --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip.yaml @@ -0,0 +1,10 @@ +includes: projects/retri/videoretri.yaml +project_dir: retri/videoclip +task_group: + pretrain: + model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/coin_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/coin_videoclip.yaml new file mode 100644 index 0000000..aaed5e4 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/coin_videoclip.yaml @@ -0,0 +1,49 @@ +dataset: + video_processor: VideoProcessor + bert_name: bert-base-uncased + meta_processor: COINActionSegmentationMetaProcessor + train_path: data/coin/COIN.json + val_path: data/coin/COIN.json + vfeat_dir: data/feat/feat_coin_s3d + text_processor: COINActionSegmentationTextProcessor + aligner: COINActionSegmentationAligner + num_iso_layer: 12 + sliding_window: 8 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 1 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 8 + checkpoint: + restore_file: runs/retri/videoclip/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/retri/videoclip/coin +task_type: sweep_big +model: + model_cls: MMFusionSeparateActionSegmentation + mm_encoder_cls: null + video_encoder_cls: MMBertForTokenClassification + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +loss: + loss_cls: CrossEntropy diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/crosstask_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/crosstask_videoclip.yaml new file mode 100644 index 0000000..758601e --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/crosstask_videoclip.yaml @@ -0,0 +1,55 @@ +dataset: + video_processor: CrossTaskVideoProcessor + bert_name: bert-base-uncased + meta_processor: CrossTaskMetaProcessor + train_path: data/crosstask/crosstask_release/videos.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + text_processor: CrossTaskTextProcessor + aligner: CrossTaskAligner + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 1 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 5 + checkpoint: + restore_file: runs/retri/videoclip/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/retri/videoclip/crosstask +task_type: sweep_small +model: + model_cls: MMFusionSeparateActionLocalization + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +loss: + loss_cls: BCE diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/how2.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/how2.yaml new file mode 100644 index 0000000..b49581e --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/how2.yaml @@ -0,0 +1,65 @@ +dataset: + video_processor: ShardedVideoRetriVideoProcessor + bert_name: bert-base-uncased + meta_processor: ShardedHow2VideoRetriMetaProcessor + train_path: data/how2/how2_s3d_train.lst + val_path: data/how2/how2_s3d_val.lst + vfeat_dir: data/feat/feat_how2_s3d_shard_small + text_processor: ShardedVideoRetriTextProcessor + tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased. + aligner: VideoRetriOverlappedAligner + subsampling: 1 + sampled_min_len: 8 + sampled_max_len: 64 + max_video_len: 32 + max_len: 96 + lazy_vfeat_mask: true + mfm_probability: 0.15 + mlm_probability: 0.15 + mm_prob: 0.5 + sampled_video_min_len: 3 + sampled_video_max_len: 32 + num_video_per_batch: 32 + clip_per_video: 16 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 1 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 1000 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 25 + checkpoint: + save_dir: runs/retri/videoclip + save_interval_updates: 1024 + keep_interval_updates: 2 + keep_last_epochs: 30 +task_type: sweep_big +slurm_config: big +eval: + save_path: runs/retri/videoclip +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +loss: + loss_cls: MMContraLoss +task: VideoRetriTask +retri_epoch: 1 +vectorpool_cls: VideoVectorPool +retriever_cls: VectorRetriever +num_cands: 64 diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_coin_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_coin_videoclip.yaml new file mode 100644 index 0000000..4099062 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_coin_videoclip.yaml @@ -0,0 +1,33 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: COINActionSegmentationAligner + bert_name: bert-base-uncased + test_path: data/coin/COIN.json + meta_processor: COINActionSegmentationMetaProcessor + vfeat_dir: data/feat/feat_coin_s3d + text_processor: COINActionSegmentationTextProcessor + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 1 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/coin/checkpoint_best.pt +model: + model_cls: MMFusionSeparateActionSegmentation + mm_encoder_cls: null + video_encoder_cls: MMBertForTokenClassification + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/coin/eval +metric: COINActionSegmentationMetric +predictor: COINPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_coin_zs.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_coin_zs.yaml new file mode 100644 index 0000000..b33739c --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_coin_zs.yaml @@ -0,0 +1,33 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: COINActionSegmentationAligner + bert_name: bert-base-uncased + test_path: data/coin/COIN.json + meta_processor: COINActionSegmentationMetaProcessor + vfeat_dir: data/feat/feat_coin_s3d + text_processor: COINActionSegmentationTextProcessor + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 1 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/checkpoint_best.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/coin_zs/eval +metric: COINActionSegmentationMetric +predictor: COINZSPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_videoclip.yaml new file mode 100644 index 0000000..e82f54f --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_videoclip.yaml @@ -0,0 +1,40 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: CrossTaskVideoProcessor + aligner: CrossTaskAligner + bert_name: bert-base-uncased + meta_processor: CrossTaskMetaProcessor + test_path: data/crosstask/crosstask_release/videos_val.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + text_processor: CrossTaskTextProcessor + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 1 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/crosstask/checkpoint_best.pt +model: + model_cls: MMFusionSeparateActionLocalization + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/crosstask/eval +metric: CrossTaskMetric +predictor: CrossTaskPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_zs_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_zs_videoclip.yaml new file mode 100644 index 0000000..6fc357c --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_zs_videoclip.yaml @@ -0,0 +1,40 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: CrossTaskVideoProcessor + aligner: CrossTaskAligner + bert_name: bert-base-uncased + meta_processor: CrossTaskMetaProcessor + test_path: data/crosstask/crosstask_release/videos_val.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + text_processor: CrossTaskTextProcessor + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 1 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/checkpoint_best.pt +model: + model_cls: MMFusionSeparateActionLocalization + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/crosstask_zs/eval +metric: CrossTaskMetric +predictor: CrossTaskPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_didemo_zs.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_didemo_zs.yaml new file mode 100644 index 0000000..8dc7168 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_didemo_zs.yaml @@ -0,0 +1,31 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: DiDeMoAligner + bert_name: bert-base-uncased + meta_processor: DiDeMoMetaProcessor + test_path: data/didemo/test_data.json + vfeat_dir: data/feat/feat_didemo_s3d + text_processor: DiDeMoTextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/checkpoint_best.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/didemo_zs/eval +metric: DiDeMoMetric +predictor: DiDeMoPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_videoclip.yaml new file mode 100644 index 0000000..19321ad --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_videoclip.yaml @@ -0,0 +1,31 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: DSAligner + bert_name: bert-base-uncased + meta_processor: MSRVTTMetaProcessor + test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/vtt/checkpoint_last.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/vtt/eval +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_zs.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_zs.yaml new file mode 100644 index 0000000..d149fa3 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_zs.yaml @@ -0,0 +1,31 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: DSAligner + bert_name: bert-base-uncased + meta_processor: MSRVTTMetaProcessor + test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/checkpoint_best.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/vtt_zs/eval +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_videoclip.yaml new file mode 100644 index 0000000..295aeed --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_videoclip.yaml @@ -0,0 +1,31 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: MSRVTTQAAligner + bert_name: bert-base-uncased + meta_processor: MSRVTTQAMetaProcessor + test_path: data/msrvtt-qa/MSR_MC_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTQATextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/vttqa/checkpoint_last.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/vttqa/eval +metric: QAMetric +predictor: QAPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_zs.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_zs.yaml new file mode 100644 index 0000000..7a876c8 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_zs.yaml @@ -0,0 +1,31 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: MSRVTTQAAligner + bert_name: bert-base-uncased + meta_processor: MSRVTTQAMetaProcessor + test_path: data/msrvtt-qa/MSR_MC_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTQATextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/checkpoint_best.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/vttqa_zs/eval +metric: QAMetric +predictor: QAPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_youcook_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_youcook_videoclip.yaml new file mode 100644 index 0000000..86a4ab2 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_youcook_videoclip.yaml @@ -0,0 +1,33 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: YoucookVideoProcessor + aligner: DSAligner + bert_name: bert-base-uncased + meta_processor: YoucookMetaProcessor + test_path: data/youcook/youcook_val.pkl + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + use_annotation_text: true + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: TextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/youcook/checkpoint_last.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/youcook/eval +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/test_youcook_zs.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/test_youcook_zs.yaml new file mode 100644 index 0000000..fd29417 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/test_youcook_zs.yaml @@ -0,0 +1,33 @@ +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: YoucookVideoProcessor + aligner: DSAligner + bert_name: bert-base-uncased + meta_processor: YoucookMetaProcessor + test_path: data/youcook/youcook_val.pkl + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + use_annotation_text: true + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: TextProcessor + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 + common_eval: + path: runs/retri/videoclip/checkpoint_best.pt +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/retri/videoclip/youcook_zs/eval +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/vtt_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/vtt_videoclip.yaml new file mode 100644 index 0000000..d8b4079 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/vtt_videoclip.yaml @@ -0,0 +1,51 @@ +dataset: + video_processor: VideoProcessor + bert_name: bert-base-uncased + meta_processor: MSRVTTMetaProcessor + train_path: data/msrvtt/MSRVTT_train.csv + jsfusion_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + full_test_path: data/msrvtt/MSRVTT_FULL_test.csv + dup: 20 + val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + json_path: data/msrvtt/MSRVTT_data.json + aligner: DSAligner + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 224 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 10 + checkpoint: + restore_file: runs/retri/videoclip/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/retri/videoclip/vtt +task_type: sweep_small +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +loss: + loss_cls: T2VContraLoss diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/vttqa_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/vttqa_videoclip.yaml new file mode 100644 index 0000000..f0566d7 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/vttqa_videoclip.yaml @@ -0,0 +1,49 @@ +dataset: + video_processor: VideoProcessor + bert_name: bert-base-uncased + meta_processor: MSRVTTMetaProcessor + train_path: data/msrvtt/MSRVTT_train.csv + dup: 20 + val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + json_path: data/msrvtt/MSRVTT_data.json + aligner: DSAligner + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 128 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 5 + checkpoint: + restore_file: runs/retri/videoclip/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/retri/videoclip/vttqa +task_type: sweep_small +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +loss: + loss_cls: V2TContraLoss diff --git a/fairseq/examples/MMPT/projects/retri/videoclip/youcook_videoclip.yaml b/fairseq/examples/MMPT/projects/retri/videoclip/youcook_videoclip.yaml new file mode 100644 index 0000000..c2b13e5 --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoclip/youcook_videoclip.yaml @@ -0,0 +1,49 @@ +dataset: + video_processor: YoucookVideoProcessor + bert_name: bert-base-uncased + meta_processor: YoucookMetaProcessor + train_path: data/youcook/youcook_train.pkl + val_path: data/youcook/youcook_val.pkl + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + use_annotation_text: true + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: TextProcessor + aligner: DSAligner + num_iso_layer: 12 + max_video_len: 32 + max_len: 96 +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + fp16: true + dataset: + num_workers: 4 + batch_size: 128 + optimization: + lr: + - 5.0e-05 + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 + warmup_updates: 122 + weight_decay: 0.0 + ddp_backend: no_c10d + max_epoch: 10 + checkpoint: + restore_file: runs/retri/videoclip/checkpoint_best.pt + reset_optimizer: true + reset_dataloader: true + reset_meters: true + save_dir: runs/retri/videoclip/youcook +task_type: sweep_small +model: + model_cls: MMFusionSeparate + mm_encoder_cls: null + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +loss: + loss_cls: T2VContraLoss diff --git a/fairseq/examples/MMPT/projects/retri/videoretri.yaml b/fairseq/examples/MMPT/projects/retri/videoretri.yaml new file mode 100644 index 0000000..969e1fb --- /dev/null +++ b/fairseq/examples/MMPT/projects/retri/videoretri.yaml @@ -0,0 +1,51 @@ +includes: projects/mfmmlm.yaml +project_dir: retri/videoretri +run_task: + - how2.yaml +task_group: + pretrain: + task: VideoRetriTask + retri_epoch: 1 + vectorpool_cls: VideoVectorPool + retriever_cls: VectorRetriever + num_cands: 64 + dataset: + train_path: data/how2/how2_s3d_train.lst + meta_processor: ShardedHow2VideoRetriMetaProcessor + video_processor: ShardedVideoRetriVideoProcessor + text_processor: ShardedVideoRetriTextProcessor + aligner: VideoRetriOverlappedAligner + sampled_video_min_len: 3 + sampled_video_max_len: 32 + sampled_min_len: 8 + sampled_max_len: 64 + num_video_per_batch: 32 + # do not use subsampling as it changes fairseq batch_size. + subsampling: 1 # disable subsampling + clip_per_video: 16 + fairseq: + dataset: + batch_size: 1 + optimization: + max_epoch: 25 + model: + model_cls: MMFusionShare + mm_encoder_cls: MMBertForEncoder + loss: + loss_cls: MMContraLoss + finetune: + task_list: [vtt_videoclip.yaml, youcook_videoclip.yaml, vttqa_videoclip.yaml, crosstask_videoclip.yaml, coin_videoclip.yaml] + test: + task_list: + - test_youcook_zs.yaml + - test_vtt_zs.yaml + - test_vttqa_zs.yaml + - test_crosstask_zs_videoclip.yaml + - test_coin_zs.yaml + - test_didemo_zs.yaml + - test_youcook_videoclip.yaml + - test_vtt_videoclip.yaml + - test_vttqa_videoclip.yaml + - test_crosstask_videoclip.yaml + - test_coin_videoclip.yaml + diff --git a/fairseq/examples/MMPT/projects/task/coin.yaml b/fairseq/examples/MMPT/projects/task/coin.yaml new file mode 100644 index 0000000..e777248 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/coin.yaml @@ -0,0 +1,25 @@ +includes: projects/task/ft.yaml +task_type: sweep_big +dataset: + meta_processor: COINActionSegmentationMetaProcessor + train_path: data/coin/COIN.json + val_path: data/coin/COIN.json + vfeat_dir: data/feat/feat_coin_s3d + video_processor: VideoProcessor + text_processor: COINActionSegmentationTextProcessor + aligner: COINActionSegmentationAligner + num_iso_layer: 12 + sliding_window: 8 + sliding_window_size: 32 +model: + model_cls: MMFusionActionSegmentation + mm_encoder_cls: MMBertForTokenClassification +loss: + loss_cls: CrossEntropy +fairseq: + dataset: + batch_size: 1 + optimization: + max_epoch: 8 + checkpoint: + save_dir: runs/task/coin diff --git a/fairseq/examples/MMPT/projects/task/coin_videoclip.yaml b/fairseq/examples/MMPT/projects/task/coin_videoclip.yaml new file mode 100644 index 0000000..69988bc --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/coin_videoclip.yaml @@ -0,0 +1,7 @@ +includes: projects/task/coin.yaml +model: + model_cls: MMFusionSeparateActionSegmentation + mm_encoder_cls: + video_encoder_cls: MMBertForTokenClassification + text_encoder_cls: BertModel # dummy, not used. + num_hidden_video_layers: 6 diff --git a/fairseq/examples/MMPT/projects/task/crosstask.yaml b/fairseq/examples/MMPT/projects/task/crosstask.yaml new file mode 100644 index 0000000..cb4dbb0 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/crosstask.yaml @@ -0,0 +1,31 @@ +includes: projects/task/ft.yaml +dataset: + meta_processor: CrossTaskMetaProcessor + train_path: data/crosstask/crosstask_release/videos.csv # dummy + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv # dummy + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + video_processor: CrossTaskVideoProcessor + text_processor: CrossTaskTextProcessor + aligner: CrossTaskAligner + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 +model: + model_cls: MMFusionActionLocalization + mm_encoder_cls: MMBertForJoint +loss: + loss_cls: BCE +fairseq: + dataset: + batch_size: 1 + optimization: + max_epoch: 5 + checkpoint: + save_dir: runs/task/crosstask + restore_file: runs/task/checkpoint11.pt # for VLM diff --git a/fairseq/examples/MMPT/projects/task/crosstask_videoclip.yaml b/fairseq/examples/MMPT/projects/task/crosstask_videoclip.yaml new file mode 100644 index 0000000..6ec613c --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/crosstask_videoclip.yaml @@ -0,0 +1,10 @@ +includes: projects/task/crosstask.yaml +model: + model_cls: MMFusionSeparateActionLocalization + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel # dummy, not used. + num_hidden_video_layers: 6 +fairseq: + checkpoint: + restore_file: runs/task/checkpoint_best.pt # overwrite the default of VLM. diff --git a/fairseq/examples/MMPT/projects/task/default.yaml b/fairseq/examples/MMPT/projects/task/default.yaml new file mode 100644 index 0000000..087fef7 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/default.yaml @@ -0,0 +1,20 @@ +# this yaml cannot be run alone. you must use `how2.yaml`, `vtt.yaml` etc for training. +dataset: + video_processor: VideoProcessor + bert_name: bert-base-uncased +fairseq: + common: + tensorboard_logdir: run + log_interval: 1000 + dataset: + num_workers: 4 + optimization: + lr: [ 0.00005 ] + clip_norm: 2.0 + optimizer: adam + adam_betas: (0.9, 0.98) + lr_scheduler: polynomial_decay + total_num_update: 1000000 # backward compatible on fairseq 1.0.0a0+af0389f for reproducibility. + warmup_updates: 1000 + weight_decay: 0.0 + ddp_backend: no_c10d diff --git a/fairseq/examples/MMPT/projects/task/ft.yaml b/fairseq/examples/MMPT/projects/task/ft.yaml new file mode 100644 index 0000000..c93b8a7 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/ft.yaml @@ -0,0 +1,13 @@ +includes: projects/task/default.yaml +# all derived config will be run by fairseq-train. +task_type: sweep_small +fairseq: + optimization: + warmup_updates: 122 # copied from roberta glue: https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.glue.md + checkpoint: + # save_interval_updates: 512 + # borrowed from Roberta script. + restore_file: runs/task/checkpoint_best.pt + reset_optimizer: True + reset_dataloader: True + reset_meters: True diff --git a/fairseq/examples/MMPT/projects/task/how2.yaml b/fairseq/examples/MMPT/projects/task/how2.yaml new file mode 100644 index 0000000..094dd04 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/how2.yaml @@ -0,0 +1,22 @@ +includes: projects/task/default.yaml +task_type: sweep_big +slurm_config: big +dataset: + meta_processor: ShardedHow2MetaProcessor + train_path: data/how2/how2_s3d_train.lst + val_path: data/how2/how2_s3d_val.lst + video_processor: ShardedVideoProcessor + vfeat_dir: data/feat/feat_how2_s3d_shard_small + text_processor: ShardedTextProcessor + tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased. + aligner: FixedLenAligner +# disable direct running of this yaml +eval: + save_path: runs/task +fairseq: + checkpoint: + save_dir: runs/task + save_interval_updates: 1024 + keep_interval_updates: 2 + keep_last_epochs: 30 + diff --git a/fairseq/examples/MMPT/projects/task/test.yaml b/fairseq/examples/MMPT/projects/task/test.yaml new file mode 100644 index 0000000..0a98445 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test.yaml @@ -0,0 +1,13 @@ +# this yaml cannot be run alone: implement a test_${dataset}.yaml +slurm_config: big +task_type: local_predict +dataset: + split: test + video_processor: VideoProcessor + aligner: DSAligner + bert_name: bert-base-uncased +fairseq: + dataset: + batch_size: 256 + valid_subset: test + num_workers: 2 diff --git a/fairseq/examples/MMPT/projects/task/test_coin.yaml b/fairseq/examples/MMPT/projects/task/test_coin.yaml new file mode 100644 index 0000000..6d919df --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_coin.yaml @@ -0,0 +1,24 @@ +includes: projects/task/test.yaml +dataset: + split: test + test_path: data/coin/COIN.json + meta_processor: COINActionSegmentationMetaProcessor + vfeat_dir: data/feat/feat_coin_s3d + video_processor: VideoProcessor + text_processor: COINActionSegmentationTextProcessor + aligner: COINActionSegmentationAligner + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 +model: + model_cls: MMFusionActionSegmentation + mm_encoder_cls: MMBertForTokenClassification +eval: + save_path: runs/task/coin/eval +fairseq: + dataset: + batch_size: 1 + common_eval: + path: runs/task/coin/checkpoint_best.pt +metric: COINActionSegmentationMetric +predictor: COINPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_coin_videoclip.yaml b/fairseq/examples/MMPT/projects/task/test_coin_videoclip.yaml new file mode 100644 index 0000000..b41f5bc --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_coin_videoclip.yaml @@ -0,0 +1,7 @@ +includes: projects/task/test_coin.yaml +model: + model_cls: MMFusionSeparateActionSegmentation + mm_encoder_cls: + video_encoder_cls: MMBertForTokenClassification + text_encoder_cls: BertModel # dummy, not used. + num_hidden_video_layers: 6 diff --git a/fairseq/examples/MMPT/projects/task/test_coin_zs.yaml b/fairseq/examples/MMPT/projects/task/test_coin_zs.yaml new file mode 100644 index 0000000..5d19b09 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_coin_zs.yaml @@ -0,0 +1,13 @@ +includes: projects/task/test_coin.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/task/coin_zs/eval +fairseq: + common_eval: + path: runs/task/checkpoint_best.pt +predictor: COINZSPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_crosstask.yaml b/fairseq/examples/MMPT/projects/task/test_crosstask.yaml new file mode 100644 index 0000000..6dd778e --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_crosstask.yaml @@ -0,0 +1,32 @@ +includes: projects/task/test.yaml +dataset: + split: test + meta_processor: CrossTaskMetaProcessor + test_path: data/crosstask/crosstask_release/videos_val.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv # dummy + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + video_processor: CrossTaskVideoProcessor + text_processor: CrossTaskTextProcessor + aligner: CrossTaskAligner + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 +model: + model_cls: MMFusionActionLocalization + mm_encoder_cls: MMBertForJoint +eval: + save_path: runs/task/crosstask/eval +fairseq: + # read code and find what is the checkpoint arg. + dataset: + batch_size: 1 + common_eval: + path: runs/task/crosstask/checkpoint_best.pt +metric: CrossTaskMetric +predictor: CrossTaskPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_crosstask_videoclip.yaml b/fairseq/examples/MMPT/projects/task/test_crosstask_videoclip.yaml new file mode 100644 index 0000000..df12535 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_crosstask_videoclip.yaml @@ -0,0 +1,7 @@ +includes: projects/task/test_crosstask.yaml +model: + model_cls: MMFusionSeparateActionLocalization + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel # dummy, not used. + num_hidden_video_layers: 6 diff --git a/fairseq/examples/MMPT/projects/task/test_crosstask_zs.yaml b/fairseq/examples/MMPT/projects/task/test_crosstask_zs.yaml new file mode 100644 index 0000000..19386e4 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_crosstask_zs.yaml @@ -0,0 +1,32 @@ +includes: projects/task/test.yaml +dataset: + split: test + meta_processor: CrossTaskMetaProcessor + test_path: data/crosstask/crosstask_release/videos_val.csv + train_csv_path: data/crosstask/crosstask_release/videos.csv + val_path: data/crosstask/crosstask_release/videos_val.csv # dummy + val_csv_path: data/crosstask/crosstask_release/videos_val.csv + primary_path: data/crosstask/crosstask_release/tasks_primary.txt + related_path: data/crosstask/crosstask_release/tasks_related.txt + vfeat_dir: data/feat/feat_crosstask_s3d + annotation_path: data/crosstask/crosstask_release/annotations + n_train: 30 + video_processor: CrossTaskVideoProcessor + text_processor: CrossTaskTextProcessor + aligner: CrossTaskAligner + num_iso_layer: 12 + sliding_window: 16 + sliding_window_size: 32 +model: + model_cls: MMFusionActionLocalization + mm_encoder_cls: MMBertForJoint +eval: + save_path: runs/task/crosstask_zs/eval +fairseq: + # read code and find what is the checkpoint arg. + dataset: + batch_size: 1 + common_eval: + path: runs/task/checkpoint_best.pt # load the best from how2 on ACL submission: runs/task/checkpoint11.pt +metric: CrossTaskMetric +predictor: CrossTaskPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_crosstask_zs_videoclip.yaml b/fairseq/examples/MMPT/projects/task/test_crosstask_zs_videoclip.yaml new file mode 100644 index 0000000..7f01982 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_crosstask_zs_videoclip.yaml @@ -0,0 +1,7 @@ +includes: projects/task/test_crosstask_zs.yaml +model: + model_cls: MMFusionSeparateActionLocalization + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel # dummy, not used. + num_hidden_video_layers: 6 diff --git a/fairseq/examples/MMPT/projects/task/test_didemo_zs.yaml b/fairseq/examples/MMPT/projects/task/test_didemo_zs.yaml new file mode 100644 index 0000000..4b53dca --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_didemo_zs.yaml @@ -0,0 +1,23 @@ +includes: projects/task/test.yaml +dataset: + meta_processor: DiDeMoMetaProcessor + test_path: data/didemo/test_data.json + video_processor: VideoProcessor + vfeat_dir: data/feat/feat_didemo_s3d + text_processor: DiDeMoTextProcessor + aligner: DiDeMoAligner + num_iso_layer: 12 +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/task/didemo_zs/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/checkpoint_best.pt +metric: DiDeMoMetric +predictor: DiDeMoPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_vtt.yaml b/fairseq/examples/MMPT/projects/task/test_vtt.yaml new file mode 100644 index 0000000..2f809b3 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_vtt.yaml @@ -0,0 +1,19 @@ +includes: projects/task/test.yaml +dataset: + meta_processor: MSRVTTMetaProcessor + test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + video_processor: VideoProcessor + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + num_iso_layer: 12 +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint +eval: + save_path: runs/task/vtt/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/vtt/checkpoint_last.pt +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_vtt_videoclip.yaml b/fairseq/examples/MMPT/projects/task/test_vtt_videoclip.yaml new file mode 100644 index 0000000..cb65643 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_vtt_videoclip.yaml @@ -0,0 +1,8 @@ +includes: projects/task/test_vtt.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 + diff --git a/fairseq/examples/MMPT/projects/task/test_vtt_zs.yaml b/fairseq/examples/MMPT/projects/task/test_vtt_zs.yaml new file mode 100644 index 0000000..5734092 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_vtt_zs.yaml @@ -0,0 +1,13 @@ +includes: projects/task/test_vtt.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/task/vtt_zs/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/checkpoint_best.pt diff --git a/fairseq/examples/MMPT/projects/task/test_vttqa.yaml b/fairseq/examples/MMPT/projects/task/test_vttqa.yaml new file mode 100644 index 0000000..ddf813c --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_vttqa.yaml @@ -0,0 +1,20 @@ +includes: projects/task/test.yaml +dataset: + meta_processor: MSRVTTQAMetaProcessor + test_path: data/msrvtt-qa/MSR_MC_test.csv + video_processor: VideoProcessor + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTQATextProcessor + aligner: MSRVTTQAAligner + num_iso_layer: 12 +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint +eval: + save_path: runs/task/vttqa/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/vttqa/checkpoint_last.pt +metric: QAMetric +predictor: QAPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_vttqa_videoclip.yaml b/fairseq/examples/MMPT/projects/task/test_vttqa_videoclip.yaml new file mode 100644 index 0000000..32a41e8 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_vttqa_videoclip.yaml @@ -0,0 +1,8 @@ +includes: projects/task/test_vttqa.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 + diff --git a/fairseq/examples/MMPT/projects/task/test_vttqa_zs.yaml b/fairseq/examples/MMPT/projects/task/test_vttqa_zs.yaml new file mode 100644 index 0000000..5e0e29d --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_vttqa_zs.yaml @@ -0,0 +1,13 @@ +includes: projects/task/test_vttqa.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/task/vttqa_zs/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/checkpoint_best.pt diff --git a/fairseq/examples/MMPT/projects/task/test_youcook.yaml b/fairseq/examples/MMPT/projects/task/test_youcook.yaml new file mode 100644 index 0000000..092b680 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_youcook.yaml @@ -0,0 +1,22 @@ +includes: projects/task/test.yaml +dataset: + meta_processor: YoucookMetaProcessor + test_path: data/youcook/youcook_val.pkl + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + use_annotation_text: True + video_processor: YoucookVideoProcessor + vfeat_dir: data/feat/feat_youcook_s3d # /checkpoint/huxu/feat/youcook_vmz # /checkpoint/prarora/berniehuang/feat_youcook_vmz + text_processor: TextProcessor + aligner: DSAligner + num_iso_layer: 12 +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint +eval: + save_path: runs/task/youcook/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/youcook/checkpoint_last.pt +metric: RetrievalMetric +predictor: RetrievalPredictor diff --git a/fairseq/examples/MMPT/projects/task/test_youcook_videoclip.yaml b/fairseq/examples/MMPT/projects/task/test_youcook_videoclip.yaml new file mode 100644 index 0000000..b85ea43 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_youcook_videoclip.yaml @@ -0,0 +1,8 @@ +includes: projects/task/test_youcook.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 + diff --git a/fairseq/examples/MMPT/projects/task/test_youcook_zs.yaml b/fairseq/examples/MMPT/projects/task/test_youcook_zs.yaml new file mode 100644 index 0000000..0a5875b --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_youcook_zs.yaml @@ -0,0 +1,13 @@ +includes: projects/task/test_youcook.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +eval: + save_path: runs/task/youcook_zs/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/checkpoint_best.pt diff --git a/fairseq/examples/MMPT/projects/task/test_youcookcap.yaml b/fairseq/examples/MMPT/projects/task/test_youcookcap.yaml new file mode 100644 index 0000000..24f6518 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/test_youcookcap.yaml @@ -0,0 +1,23 @@ +includes: projects/task/test.yaml +dataset: + meta_processor: YoucookNLGMetaProcessor + test_path: data/youcook/val_list.txt + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + video_processor: YoucookVideoProcessor + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: NLGTextProcessor + aligner: DSNLGAligner +model: + model_cls: MMFusionNLG + mm_encoder_cls: MMBertForNLG + max_decode_length: 24 +eval: + save_path: runs/task/youcookcap/eval +fairseq: + # read code and find what is the checkpoint arg. + common_eval: + path: runs/task/youcookcap/checkpoint_best.pt +metric: NLGMetric +predictor: NLGPredictor +gen_param: + num_beams: 5 diff --git a/fairseq/examples/MMPT/projects/task/vtt.yaml b/fairseq/examples/MMPT/projects/task/vtt.yaml new file mode 100644 index 0000000..395e2ee --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/vtt.yaml @@ -0,0 +1,25 @@ +includes: projects/task/ft.yaml +dataset: + meta_processor: MSRVTTMetaProcessor + train_path: data/msrvtt/MSRVTT_train.csv + jsfusion_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + full_test_path: data/msrvtt/MSRVTT_FULL_test.csv + dup: 20 + val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + json_path: data/msrvtt/MSRVTT_data.json + aligner: DSAligner + num_iso_layer: 12 +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint +loss: + loss_cls: T2VContraLoss +fairseq: + dataset: + batch_size: 256 + optimization: + max_epoch: 10 + checkpoint: + save_dir: runs/task/vtt diff --git a/fairseq/examples/MMPT/projects/task/vtt_videoclip.yaml b/fairseq/examples/MMPT/projects/task/vtt_videoclip.yaml new file mode 100644 index 0000000..a9892ca --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/vtt_videoclip.yaml @@ -0,0 +1,12 @@ +includes: projects/task/vtt.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 +fairseq: + dataset: + batch_size: 224 +# model_cls: MMFusionShare +# mm_encoder_cls: MMBertForEncoder diff --git a/fairseq/examples/MMPT/projects/task/vttqa.yaml b/fairseq/examples/MMPT/projects/task/vttqa.yaml new file mode 100644 index 0000000..56d578e --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/vttqa.yaml @@ -0,0 +1,23 @@ +includes: projects/task/ft.yaml +dataset: + meta_processor: MSRVTTMetaProcessor + train_path: data/msrvtt/MSRVTT_train.csv + dup: 20 + val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv + vfeat_dir: data/feat/feat_vtt_s3d + text_processor: MSRVTTTextProcessor + json_path: data/msrvtt/MSRVTT_data.json + aligner: DSAligner + num_iso_layer: 12 +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint +loss: + loss_cls: V2TContraLoss +fairseq: + dataset: + batch_size: 128 + optimization: + max_epoch: 5 + checkpoint: + save_dir: runs/task/vttqa diff --git a/fairseq/examples/MMPT/projects/task/vttqa_videoclip.yaml b/fairseq/examples/MMPT/projects/task/vttqa_videoclip.yaml new file mode 100644 index 0000000..2d484ca --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/vttqa_videoclip.yaml @@ -0,0 +1,10 @@ +includes: projects/task/vttqa.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 + +# model_cls: MMFusionShare +# mm_encoder_cls: MMBertForEncoder diff --git a/fairseq/examples/MMPT/projects/task/youcook.yaml b/fairseq/examples/MMPT/projects/task/youcook.yaml new file mode 100644 index 0000000..e0cd841 --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/youcook.yaml @@ -0,0 +1,25 @@ +includes: projects/task/ft.yaml +dataset: + meta_processor: YoucookMetaProcessor + train_path: data/youcook/youcook_train.pkl + val_path: data/youcook/youcook_val.pkl + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + use_annotation_text: True + video_processor: YoucookVideoProcessor + vfeat_dir: data/feat/feat_youcook_s3d # /checkpoint/huxu/feat/youcook_vmz # /checkpoint/prarora/berniehuang/feat_youcook_vmz + text_processor: TextProcessor + aligner: DSAligner + num_iso_layer: 12 +model: + model_cls: MMFusionJoint + mm_encoder_cls: MMBertForJoint +loss: + loss_cls: T2VContraLoss +fairseq: + dataset: + batch_size: 128 + optimization: + max_epoch: 10 + checkpoint: + save_dir: runs/task/youcook + diff --git a/fairseq/examples/MMPT/projects/task/youcook_videoclip.yaml b/fairseq/examples/MMPT/projects/task/youcook_videoclip.yaml new file mode 100644 index 0000000..e3e901c --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/youcook_videoclip.yaml @@ -0,0 +1,9 @@ +includes: projects/task/youcook.yaml +model: + model_cls: MMFusionSeparate + mm_encoder_cls: + video_encoder_cls: MMBertForEncoder + text_encoder_cls: BertModel + num_hidden_video_layers: 6 + # model_cls: MMFusionShare + # mm_encoder_cls: MMBertForEncoder diff --git a/fairseq/examples/MMPT/projects/task/youcookcap.yaml b/fairseq/examples/MMPT/projects/task/youcookcap.yaml new file mode 100644 index 0000000..047735f --- /dev/null +++ b/fairseq/examples/MMPT/projects/task/youcookcap.yaml @@ -0,0 +1,23 @@ +# finetuning for youcook captioning. +includes: projects/task/ft.yaml +dataset: + meta_processor: YoucookNLGMetaProcessor + train_path: data/youcook/train_list.txt + val_path: data/youcook/val_list.txt + trainval_annotation: data/youcook/youcookii_annotations_trainval.json + video_processor: YoucookVideoProcessor + vfeat_dir: data/feat/feat_youcook_s3d + text_processor: NLGTextProcessor + aligner: DSNLGAligner +model: + model_cls: MMFusionNLG + mm_encoder_cls: MMBertForNLG +loss: + loss_cls: NLGLoss +fairseq: + dataset: + batch_size: 128 + optimization: + max_epoch: 10 + checkpoint: + save_dir: runs/task/youcookcap diff --git a/fairseq/examples/MMPT/scripts/text_token_extractor/configs/bert-base-uncased.yaml b/fairseq/examples/MMPT/scripts/text_token_extractor/configs/bert-base-uncased.yaml new file mode 100644 index 0000000..473dd9b --- /dev/null +++ b/fairseq/examples/MMPT/scripts/text_token_extractor/configs/bert-base-uncased.yaml @@ -0,0 +1,5 @@ +dataset: + bert_name: bert-base-uncased + caption_pkl_path: data/how2/raw_caption_dedup.pkl + use_fast: true + target_dir: data/feat/feat_how2_s3d_shard_small diff --git a/fairseq/examples/MMPT/scripts/text_token_extractor/pretokenization.py b/fairseq/examples/MMPT/scripts/text_token_extractor/pretokenization.py new file mode 100644 index 0000000..29ae5dc --- /dev/null +++ b/fairseq/examples/MMPT/scripts/text_token_extractor/pretokenization.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import pickle +import os +import argparse +import numpy as np + +from torch.utils.data import Dataset, DataLoader +from mmpt.processors import PKLJSONStrTextProcessor +from mmpt.utils import ShardedTensor, recursive_config + + +class TokenizerDataset(Dataset): + def __init__(self, config): + self.text_processor = PKLJSONStrTextProcessor(config) + self.video_ids = list(self.text_processor.data.keys()) + + def __getitem__(self, idx): + video_id = self.video_ids[idx] + return video_id, self.text_processor(video_id) + + def __len__(self): + return len(self.video_ids) + + +def numpify(shard_idx, video_ids, captions, target_dir, split, prefix, max_cap_len=32): + startends = [] + caps_ids = [] + for video_id in video_ids: + caption = captions[video_id] + startend = [] + cap_ids = [] + for start, end, cap in zip( + caption["start"], caption["end"], caption["cap"]): + startend.append(np.array([start, end]).astype("float32")) + cap_id = np.full((max_cap_len,), -1, dtype=np.int32) + cap = cap[:max_cap_len] + cap_id[:len(cap)] = cap + cap_ids.append(cap_id) + startends.append(np.stack(startend)) + caps_ids.append(np.stack(cap_ids)) + + startends = ShardedTensor.from_list(startends) + target_path = os.path.join( + target_dir, + prefix + split + "_" + str(shard_idx) + ) + print("save to", target_path) + startends.save(target_path + ".startends") + caps_ids = ShardedTensor.from_list(caps_ids) + caps_ids.save(target_path + ".caps_ids") + + +def sharding(config, out_file): + with open(out_file, "rb") as fr: + captions = pickle.load(fr) + target_dir = config.target_dir + prefix = os.path.basename( + os.path.splitext(config.caption_pkl_path)[0] + ) + "." + config.bert_name + "." + for split in ["train", "val"]: + target_path = os.path.join(target_dir, split + "_meta") + with open(target_path + ".pkl", "rb") as fr: + meta = pickle.load(fr) + print("load meta", target_path, len(meta)) + for shard_id in meta: + numpify( + shard_id, meta[shard_id], captions, + target_dir, split, prefix + ) + + +def tokenize(config, out_file): + def collator(samples): + return samples + dataset = TokenizerDataset(config) + data = {} + for idx, batch in enumerate( + DataLoader(dataset, collate_fn=collator, num_workers=16)): + for video_id, caption in batch: + data[video_id] = caption + if idx % 5000 == 0: + print(idx) + with open(out_file, "wb") as fw: + pickle.dump(data, fw, pickle.HIGHEST_PROTOCOL) + + +def main(args): + config = recursive_config(args.config).dataset + + out_file = os.path.splitext(config.caption_pkl_path)[0] \ + + "." + config.bert_name + ".pkl" + if not os.path.isfile(out_file): + tokenize(config, out_file) + sharding(config, out_file) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="pretokenize (raw_)caption.json into pkl.") + parser.add_argument('config', type=str) + args = parser.parse_args() + main(args) diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/extract.py b/fairseq/examples/MMPT/scripts/video_feature_extractor/extract.py new file mode 100644 index 0000000..b5ee7b7 --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/extract.py @@ -0,0 +1,157 @@ +# Copyright Howto100M authors. +# Copyright (c) Facebook, Inc. All Rights Reserved + +import torch as th +import torch.nn.functional as F +import math +import numpy as np +import argparse + +from torch.utils.data import DataLoader +from model import get_model +from preprocessing import Preprocessing +from random_sequence_shuffler import RandomSequenceSampler + +from tqdm import tqdm +from pathbuilder import PathBuilder +from videoreader import VideoLoader + + +parser = argparse.ArgumentParser(description='Easy video feature extractor') + +parser.add_argument('--vdir', type=str) +parser.add_argument('--fdir', type=str) +parser.add_argument('--hflip', type=int, default=0) + +parser.add_argument('--batch_size', type=int, default=64, + help='batch size') +parser.add_argument('--type', type=str, default='2d', + help='CNN type') +parser.add_argument('--half_precision', type=int, default=0, + help='output half precision float') +parser.add_argument('--num_decoding_thread', type=int, default=4, + help='Num parallel thread for video decoding') +parser.add_argument('--l2_normalize', type=int, default=1, + help='l2 normalize feature') +parser.add_argument('--resnext101_model_path', type=str, default='model/resnext101.pth', + help='Resnext model path') +parser.add_argument('--vmz_model_path', type=str, default='model/r2plus1d_34_clip8_ig65m_from_scratch-9bae36ae.pth', + help='vmz model path') + +args = parser.parse_args() + + +# TODO: refactor all args into config. (current code is from different people.) +CONFIGS = { + "2d": { + "fps": 1, + "size": 224, + "centercrop": False, + "shards": 0, + }, + "3d": { + "fps": 24, + "size": 112, + "centercrop": True, + "shards": 0, + }, + "s3d": { + "fps": 30, + "size": 224, + "centercrop": True, + "shards": 0, + }, + "vmz": { + "fps": 24, + "size": 112, + "centercrop": True, + "shards": 0, + }, + "vae": { + "fps": 2, + "size": 256, + "centercrop": True, + "shards": 100, + } +} + +config = CONFIGS[args.type] + + +video_dirs = args.vdir +feature_dir = args.fdir + +video_dict = PathBuilder.build(video_dirs, feature_dir, ".npy", config["shards"]) + +dataset = VideoLoader( + video_dict=video_dict, + framerate=config["fps"], + size=config["size"], + centercrop=config["centercrop"], + hflip=args.hflip +) +n_dataset = len(dataset) +sampler = RandomSequenceSampler(n_dataset, 10) +loader = DataLoader( + dataset, + batch_size=1, + shuffle=False, + num_workers=args.num_decoding_thread, + sampler=sampler if n_dataset > 10 else None, +) +preprocess = Preprocessing(args.type) +model = get_model(args) + +with th.no_grad(): + for k, data in tqdm(enumerate(loader), total=loader.__len__(), ascii=True): + input_file = data['input'][0] + output_file = data['output'][0] + if len(data['video'].shape) > 3: + video = data['video'].squeeze() + if len(video.shape) == 4: + video = preprocess(video) + n_chunk = len(video) + if args.type == 'vmz': + n_chunk = math.ceil(n_chunk/float(3)) + features = th.cuda.FloatTensor(n_chunk, 512).fill_(0) + elif args.type == 's3d': + features = th.cuda.FloatTensor(n_chunk, 512).fill_(0) + elif args.type == "vae": + features = th.cuda.LongTensor(n_chunk, 1024).fill_(0) + else: + features = th.cuda.FloatTensor(n_chunk, 2048).fill_(0) + n_iter = int(math.ceil(n_chunk / float(args.batch_size))) + for i in range(n_iter): + factor = 1 + if args.type == 'vmz': + factor = 3 + min_ind = factor * i * args.batch_size + max_ind = factor * (i + 1) * args.batch_size + video_batch = video[min_ind:max_ind:factor].cuda() + if args.type == '2d': + batch_features = model(video_batch) # (51, 487), (51, 512) + elif args.type == 's3d': + batch_features = model(video_batch) + batch_features = batch_features['video_embedding'] + elif args.type == "vae": + # image_code. + batch_features = model(video_batch) + else: + batch_pred, batch_features = model(video_batch) # (51, 487), (51, 512) + if args.l2_normalize: + batch_features = F.normalize(batch_features, dim=1) + features[i*args.batch_size:(i+1)*args.batch_size] = batch_features + features = features.cpu().numpy() + if args.half_precision: + if args.type == "vae": + features = features.astype(np.int16) + else: + features = features.astype('float16') + else: + if args.type == "vae": + features = features.astype(np.int32) + else: + features = features.astype('float32') + np.save(output_file, features) + else: + print('Video {} error.'.format(input_file)) diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/how2/s3d.sh b/fairseq/examples/MMPT/scripts/video_feature_extractor/how2/s3d.sh new file mode 100644 index 0000000..90102c8 --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/how2/s3d.sh @@ -0,0 +1,8 @@ +#!/bin/bash + + +python scripts/video_feature_extractor/extract.py \ + --vdir \ + --fdir data/feat/feat_how2_s3d \ + --type=s3d --num_decoding_thread=4 \ + --batch_size 32 --half_precision 1 diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/model.py b/fairseq/examples/MMPT/scripts/video_feature_extractor/model.py new file mode 100644 index 0000000..ac266e8 --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/model.py @@ -0,0 +1,58 @@ +# Copyright (c) Howto100M authors and Facebook, Inc. All Rights Reserved + +import torch as th + +from torch import nn + + +class GlobalAvgPool(nn.Module): + def __init__(self): + super(GlobalAvgPool, self).__init__() + + def forward(self, x): + return th.mean(x, dim=[-2, -1]) + + +def get_model(args): + assert args.type in ['2d', '3d', 'vmz', 's3d', 'vae'] + if args.type == '2d': + print('Loading 2D-ResNet-152 ...') + import torchvision.models as models + model = models.resnet152(pretrained=True) + model = nn.Sequential(*list(model.children())[:-2], GlobalAvgPool()) + model = model.cuda() + elif args.type == 'vmz': + print('Loading VMZ ...') + from vmz34 import r2plus1d_34 + model = r2plus1d_34(pretrained_path=args.vmz_model_path, pretrained_num_classes=487) + model = model.cuda() + elif args.type == 's3d': + # we use one copy of s3d instead of dup another one for feature extraction. + from mmpt.processors.models.s3dg import S3D + model = S3D('pretrained_models/s3d_dict.npy', 512) + model.load_state_dict(th.load('pretrained_models/s3d_howto100m.pth')) + model = model.cuda() + + elif args.type == '3d': + print('Loading 3D-ResneXt-101 ...') + from videocnn.models import resnext + model = resnext.resnet101( + num_classes=400, + shortcut_type='B', + cardinality=32, + sample_size=112, + sample_duration=16, + last_fc=False) + model = model.cuda() + model_data = th.load(args.resnext101_model_path) + model.load_state_dict(model_data) + elif args.type == 'vae': + from openaivae import OpenAIParallelDiscreteVAE + model = OpenAIParallelDiscreteVAE() + model = model.cuda() + else: + raise ValueError("model not supported yet.") + + model.eval() + print('loaded') + return model diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/pathbuilder.py b/fairseq/examples/MMPT/scripts/video_feature_extractor/pathbuilder.py new file mode 100644 index 0000000..2392d6d --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/pathbuilder.py @@ -0,0 +1,89 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import urllib.parse +import json +import pandas as pd + +from tqdm import tqdm + + +# TODO: extending to other datasets. +supported_formats = {} + + +class PathBuilder(object): + @classmethod + def build(cls, video_dirs, feature_dir, ext, shards=0, split=None): + meta_fn = os.path.join(feature_dir, "meta_plan.json") + os.makedirs(feature_dir, exist_ok=True) + if os.path.isfile(meta_fn): + with open(meta_fn) as fr: + meta = json.load(fr) + return meta + print("searching videos...") + + video_id_to_path = {} + for video_dir in video_dirs.split(","): + # TODO: add supports of recursive listdir. + if video_dir in supported_formats: + supported_formats[video_dir].load(video_dir, video_id_to_path) + else: + for idx, fn in enumerate(tqdm(os.listdir(video_dir))): + video_fn = os.path.join(video_dir, fn) + if os.path.isfile(video_fn): + video_id = os.path.splitext(fn)[0] + video_id_to_path[video_id] = video_fn + elif os.path.isdir(video_fn): + # shards of folders. + shard_dir = video_fn + for idx, fn in enumerate(os.listdir(shard_dir)): + video_fn = os.path.join(shard_dir, fn) + if os.path.isfile(video_fn): + video_id = os.path.splitext(fn)[0] + video_id_to_path[video_id] = video_fn + + video_path, feature_path = [], [] + valid_ext = set() + for idx, video_id in enumerate(video_id_to_path): + video_path.append(video_id_to_path[video_id]) + if ext is None: + # use original file ext for format compatibility. + video_id_to_path[video_id] + path = urllib.parse.urlparse(video_id_to_path[video_id]).path + ext = os.path.splitext(path)[1] + if ext not in valid_ext: + valid_ext.add(ext) + print("adding", ext) + if shards: + shard_id = str(idx % shards) + feature_fn = os.path.join( + feature_dir, shard_id, video_id + ext) + else: + feature_fn = os.path.join( + feature_dir, video_id + ext) + feature_path.append(feature_fn) + + print("targeting", len(feature_path), "videos") + meta = { + "video_path": video_path, "feature_path": feature_path} + with open(meta_fn, "w") as fw: + json.dump(meta, fw) + + if split is not None: + splits = split.split("/") + assert len(splits) == 2 + cur, total = int(splits[0]), int(splits[1]) + assert cur < total + import math + chunk = math.ceil(len(meta["video_path"]) / total) + start = cur * chunk + end = (cur + 1) * chunk + meta = { + "video_path": meta["video_path"][start:end], + "feature_path": meta["feature_path"][start:end] + } + + return meta diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/preprocessing.py b/fairseq/examples/MMPT/scripts/video_feature_extractor/preprocessing.py new file mode 100644 index 0000000..fa0cec3 --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/preprocessing.py @@ -0,0 +1,57 @@ +# Copyright Howto100m authors. +# Copyright (c) Facebook, Inc. All Rights Reserved + +import torch as th + +class Normalize(object): + + def __init__(self, mean, std): + self.mean = th.FloatTensor(mean).view(1, 3, 1, 1) + self.std = th.FloatTensor(std).view(1, 3, 1, 1) + + def __call__(self, tensor): + tensor = (tensor - self.mean) / (self.std + 1e-8) + return tensor + +class Preprocessing(object): + + def __init__(self, type): + self.type = type + if type == '2d': + self.norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + elif type == '3d': + self.norm = Normalize(mean=[110.6, 103.2, 96.3], std=[1.0, 1.0, 1.0]) + elif type == 'vmz': + self.norm = Normalize(mean=[110.201, 100.64, 95.997], std=[58.1489, 56.4701, 55.3324]) + + def _zero_pad(self, tensor, size): + n = size - len(tensor) % size + if n == size: + return tensor + else: + z = th.zeros(n, tensor.shape[1], tensor.shape[2], tensor.shape[3]) + return th.cat((tensor, z), 0) + + def __call__(self, tensor): + if self.type == '2d': + tensor = tensor / 255.0 + tensor = self.norm(tensor) + elif self.type == 'vmz': + #tensor = self._zero_pad(tensor, 8) + tensor = self._zero_pad(tensor, 10) + tensor = self.norm(tensor) + #tensor = tensor.view(-1, 8, 3, 112, 112) + tensor = tensor.view(-1, 10, 3, 112, 112) + tensor = tensor.transpose(1, 2) + elif self.type == '3d': + tensor = self._zero_pad(tensor, 16) + tensor = self.norm(tensor) + tensor = tensor.view(-1, 16, 3, 112, 112) + tensor = tensor.transpose(1, 2) + elif self.type == 's3d': + tensor = tensor / 255.0 + tensor = self._zero_pad(tensor, 30) + tensor = tensor.view(-1, 30, 3, 224, 224) # N x 30 x 3 x H x W + tensor = tensor.transpose(1, 2) # N x 3 x 30 x H x W + # for vae do nothing + return tensor diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/random_sequence_shuffler.py b/fairseq/examples/MMPT/scripts/video_feature_extractor/random_sequence_shuffler.py new file mode 100644 index 0000000..1f3e4ac --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/random_sequence_shuffler.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. All Rights Reserved + +import numpy as np + +from torch.utils.data.sampler import Sampler + + +class RandomSequenceSampler(Sampler): + + def __init__(self, n_sample, seq_len): + self.n_sample = n_sample + self.seq_len = seq_len + + def _pad_ind(self, ind): + zeros = np.zeros(self.seq_len - self.n_sample % self.seq_len) + ind = np.concatenate((ind, zeros)) + return ind + + def __iter__(self): + idx = np.arange(self.n_sample) + if self.n_sample % self.seq_len != 0: + idx = self._pad_ind(idx) + idx = np.reshape(idx, (-1, self.seq_len)) + np.random.shuffle(idx) + idx = np.reshape(idx, (-1)) + return iter(idx.astype(int)) + + def __len__(self): + return self.n_sample + (self.seq_len - self.n_sample % self.seq_len) diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/shard_feature.py b/fairseq/examples/MMPT/scripts/video_feature_extractor/shard_feature.py new file mode 100644 index 0000000..f75e1df --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/shard_feature.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import numpy as np +import os +import pickle + +from mmpt.utils import ShardedTensor + + +class Shard(object): + def __init__( + self, + vfeat_dir, + tfeat_dir, + target_dir, + file_paths, + shard_size=4096 + ): + self.vfeat_dir = vfeat_dir + self.tfeat_dir = tfeat_dir + self.target_dir = target_dir + self.video_ids = {} + for split, file_path in zip(["train", "val"], file_paths): + with open(file_path) as fr: + self.video_ids[split] = [ + line.strip() for line in fr.readlines()] + self.shard_size = shard_size + + def __call__(self, split="train"): + for split in ["train", "val"]: + meta = {} + for shard_idx, shard_offset in enumerate( + range(0, len(self.video_ids[split]), self.shard_size) + ): + print(shard_idx) + meta_shard = [] + video_shard = [] + for video_id in self.video_ids[split][shard_offset:shard_offset+self.shard_size]: + meta_shard.append(video_id) + npy_file = os.path.join(self.vfeat_dir, video_id + ".npy") + video_shard.append(np.load(npy_file)) + + meta[shard_idx] = meta_shard + video_shard = ShardedTensor.from_list(video_shard) + target_path = os.path.join( + self.target_dir, split + "_" + str(shard_idx)) + video_shard.save(target_path) + + target_path = os.path.join(self.target_dir, split + "_meta") + with open(target_path + ".pkl", "wb") as fw: + pickle.dump(meta, fw, pickle.HIGHEST_PROTOCOL) + + +if __name__ == "__main__": + shard = Shard( + "data/feat/feat_how2_s3d", + "data/how2/raw_caption_dedup.bert-base-uncased", + "data/feat/feat_how2_s3d_shard_small", + ["data/how2/how2_s3d_train.lst", "data/how2/how2_s3d_val.lst"] + ) + + shard() diff --git a/fairseq/examples/MMPT/scripts/video_feature_extractor/videoreader.py b/fairseq/examples/MMPT/scripts/video_feature_extractor/videoreader.py new file mode 100644 index 0000000..429e05f --- /dev/null +++ b/fairseq/examples/MMPT/scripts/video_feature_extractor/videoreader.py @@ -0,0 +1,242 @@ +# Copyright Howto100M authors. +# Copyright (c) Facebook, Inc. All Rights Reserved + +import torch as th +import pandas as pd +import os +import numpy as np +import ffmpeg +import random + +from torch.utils.data import Dataset + + +class VideoLoader(Dataset): + """modified from how2's video_feature_extractor.""" + def __init__( + self, + csv=None, + video_dict=None, + framerate=1, + size=112, + centercrop=False, + hflip=False, + **kwargs + ): + if csv is None and video_dict is None: + raise ValueError("csv and video_dict cannot be both None.") + if csv is not None: + self.csv = pd.read_csv(csv) + if video_dict is not None: + self.csv = pd.DataFrame.from_dict(video_dict) + + self.centercrop = centercrop + self.size = size + self.framerate = framerate + self.hflip = hflip + + def __len__(self): + return len(self.csv) + + def _get_video_dim(self, video_path): + probe = ffmpeg.probe(video_path) + video_stream = next((stream for stream in probe['streams'] + if stream['codec_type'] == 'video'), None) + width = int(video_stream['width']) + height = int(video_stream['height']) + return height, width + + def _get_video_info(self, video_path): + probe = ffmpeg.probe(video_path) + video_stream = next((stream for stream in probe['streams'] + if stream['codec_type'] == 'video'), None) + return video_stream + + def _get_output_dim(self, h, w): + if isinstance(self.size, tuple) and len(self.size) == 2: + return self.size + elif h >= w: + return int(h * self.size / w), self.size + else: + return self.size, int(w * self.size / h) + + def __getitem__(self, idx): + video_path = self.csv['video_path'].values[idx] + output_file = self.csv['feature_path'].values[idx] + return self._decode(output_file, video_path) + + def _decode(self, output_file, video_path): + if not(os.path.isfile(output_file)) and os.path.isfile(video_path): + try: + h, w = self._get_video_dim(video_path) + except Exception: + print('ffprobe failed at: {}'.format(video_path)) + return {'video': th.zeros(1), 'input': video_path, + 'output': output_file} + try: + os.makedirs(os.path.dirname(output_file), exist_ok=True) + height, width = self._get_output_dim(h, w) + + cmd = ( + ffmpeg + .input(video_path) + .filter('fps', fps=self.framerate) + .filter('scale', width, height) + ) + if self.hflip: + cmd = cmd.filter('hflip') + + if self.centercrop: + x = int((width - self.size) / 2.0) + y = int((height - self.size) / 2.0) + cmd = cmd.crop(x, y, self.size, self.size) + video = self._run(cmd, output_file) + except Exception: + video = th.zeros(1) + else: + video = th.zeros(1) + + return {'video': video, 'input': video_path, 'output': output_file} + + def _run(self, cmd, output_file): + out, _ = ( + cmd.output('pipe:', format='rawvideo', pix_fmt='rgb24') + .run(capture_stdout=True, quiet=True) + ) + if self.centercrop and isinstance(self.size, int): + height, width = self.size, self.size + video = np.frombuffer(out, np.uint8).reshape([-1, height, width, 3]) + video = th.from_numpy(video.astype('float32')) + return video.permute(0, 3, 1, 2) + + +class VideoVerifier(VideoLoader): + def __getitem__(self, idx): + video_path = self.csv['video_path'].values[idx] + try: + return self._get_video_info(video_path) + except Exception: + # print('ffprobe failed at: {}'.format(video_path)) + return None + + +class VideoCompressor(VideoLoader): + def __init__( + self, + csv=None, + video_dict=None, + framerate=1, + size=112, + centercrop=False, + hflip=False, + crf=32, + **kwargs + ): + super().__init__( + csv, + video_dict, + framerate, + size, + centercrop, + hflip + ) + self.crf = crf + + def _run(self, cmd, output_file): + out, _ = ( + cmd.output(filename=output_file, crf=self.crf) + .run(quiet=True) + ) + video = None + return video + + +class VideoDownloader(VideoCompressor): + """download""" + def __getitem__(self, idx): + video_path = self.csv['video_path'].values[idx] + output_file = self.csv['feature_path'].values[idx] + if not(os.path.isfile(output_file)): + os.makedirs(os.path.dirname(output_file), exist_ok=True) + cmd = "wget -O" + output_file + " " + video_path + # import subprocess + # subprocess.check_output( + # cmd, + # stderr=subprocess.STDOUT, shell=True) + os.system(cmd) + return {'video': None, 'input': video_path, 'output': output_file} + + +class AvKeyframeVideoCompressor(VideoLoader): + """extract keyframes from a video and save it as jpg. + TODO: consider to merge with `CodecProcessor`. + """ + def __init__( + self, + csv=None, + video_dict=None, + framerate=1, + size=112, + centercrop=False, + max_num_frames=5, + **kwargs + ): + super().__init__(csv, video_dict, framerate, size, centercrop) + self.max_num_frames = max_num_frames + + def _get_video_dim(self, video_fn): + """decord cannot probe the size of a video, we use pyav instead.""" + import av + with av.open(video_fn) as container: + height = container.streams.video[0].codec_context.height + width = container.streams.video[0].codec_context.width + return height, width + + def _get_output_dim(self, height, width): + """ + keep the shorter side be `self.size`, strech the other. + """ + if height >= width: + return int(height * self.size / width), self.size + else: + return self.size, int(width * self.size / height) + + def __getitem__(self, idx): + import av + video_path = self.csv['video_path'].values[idx] + output_file = self.csv['feature_path'].values[idx] + if not(os.path.isdir(output_file)) and os.path.isfile(video_path): + try: + h, w = self._get_video_dim(video_path) + except Exception: + print('probe failed at: {}'.format(video_path)) + return {'video': th.zeros(1), 'input': video_path, + 'output': output_file} + + try: + height, width = self._get_output_dim(h, w) + + # new for av. + with av.open(video_path) as container: + container.streams.video[0].thread_type = "AUTO" + container.streams.video[0].codec_context.height = height + container.streams.video[0].codec_context.width = width + if self.framerate == 0: # keyframe. + container.streams.video[0].codec_context.skip_frame = 'NONKEY' + frames = [] + for frame in container.decode(video=0): + frames.append(frame) + frames = random.sample(frames, self.max_num_frames) + + os.makedirs(output_file, exist_ok=True) + for frame in frames: + frame.to_image().save( + os.path.join( + output_file, + "%04d.jpg" % frame.index)) + except Exception: + print('extract failed at: {}'.format(video_path)) + return {'video': th.zeros(1), 'input': video_path, + 'output': output_file} + video = th.zeros(1) + return {'video': video, 'input': video_path, 'output': output_file} diff --git a/fairseq/examples/MMPT/setup.py b/fairseq/examples/MMPT/setup.py new file mode 100644 index 0000000..a9a8229 --- /dev/null +++ b/fairseq/examples/MMPT/setup.py @@ -0,0 +1,24 @@ +import setuptools + +with open("README.md", "r") as fh: + long_description = fh.read() + +setuptools.setup( + name="mmpt", + version="0.0.1", + author="Hu Xu, Po-yao Huang", + author_email="huxu@fb.com", + description="A package for multimodal pretraining.", + long_description=long_description, + long_description_content_type="text/markdown", + url="https://github.com/pytorch/fairseq/examples/MMPT", + packages=setuptools.find_packages(), + install_requires=[ + ], + classifiers=[ + "Programming Language :: Python :: 3", + "License :: CC-BY-NC", + "Operating System :: OS Independent", + ], + python_requires='>=3.6', +) diff --git a/fairseq/examples/__init__.py b/fairseq/examples/__init__.py new file mode 100644 index 0000000..44bb24a --- /dev/null +++ b/fairseq/examples/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +try: + from fairseq.version import __version__ # noqa +except ImportError: + pass diff --git a/fairseq/examples/adaptive_span/README.md b/fairseq/examples/adaptive_span/README.md new file mode 100644 index 0000000..d5224fb --- /dev/null +++ b/fairseq/examples/adaptive_span/README.md @@ -0,0 +1,90 @@ +# Adaptive Span + +Adaptive Span is a novel self-attention mechanism that can learn its optimal +attention span. This allows us to extend significantly the maximum context size +used in Transformer, while maintaining control over their memory footprint +and computational time. It uses the Truncated BPTT technique for training, +as in [transformerXL](https://github.com/pytorch/fairseq/blob/main/examples/truncated_bptt/README.md). + +Adaptive Span was introduced by paper: +[Adaptive Attention Span in Transformers](https://arxiv.org/abs/1905.07799), +which achieved state-of-the-art language modeling results at the time of publication. + +We manage to reproduce their result in fairseq and keep most of the +[original implementation](https://github.com/facebookresearch/adaptive-span) untouched. +You can refer to the their sweep file as well if any combination of hyperparameter is not clear. + +##### 0. Setup + +First you need to process the Enwik8 dataset, we use the pre-tokenized dataset +from [adaptive span paper](https://github.com/facebookresearch/adaptive-span/blob/master/get_data.sh). +You can download the dataset, and then run: +```bash +fairseq-preprocess --only-source --trainpref ~/data/enwik8/train.txt \ + --validpref ~/data/enwik8/valid.txt --testpref ~/data/enwik8/test.txt \ + --destdir ~/data/enwik8/data-bin/ --joined-dictionary --workers 20 +``` + +##### 1. Train a Adaptive Span model on Enwik8 + +We will train a 12-layer Adaptive Span model following the [hyperparameters +used in the original +paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh). + +The following command assumes 4 GPUs, so that the total batch size is 64 +sequences (4 x 16). Training should take 2-3 days on 4 V100 GPUs: +```bash +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ + --user-dir examples/adaptive_span \ + --data ~/data/enwik8/data-bin/ \ + --fp16 --fp16-no-flatten-grads --max-update 600000 \ + --task truncated_bptt_lm --tokens-per-sample 512 --arch adaptive_span \ + --n-layer 12 --d-model 512 --n-head 8 --d-inner 2048 --dropout 0.3 \ + --attn-span 8192 --optimizer adagrad_with_grad_clip --adagrad-clip 0.03 \ + --validate-interval-updates 1000 \ + --lr-scheduler fixed --warmup-updates 32000 --batch-size-valid 32 \ + --lr 0.07 --criterion adaptive_span_loss --batch-size 16 --update-freq 1 \ + --seed 2 --log-format json --log-interval 25 --aux-loss-scaler 5e-07 +``` +This should land around 1.05 on validation, 1.03 on test. You can lower the +--aux-loss-scaler for better performance (longer span). It gives ~0.03 bpc +improvement to the transformerXL baseline here. +If training on a single GPU, set `--update-freq=4` to accumulate 4x gradients +and simulate training on 4 GPUs. +You can also reproduce the transformerXL result on enwik8 using this code base. +It should land around 1.06 on test,matching the [original paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_enwik8_base.sh). +You can try by +```bash +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ + --user-dir examples/truncated_bptt \ + ~/data/enwik8/data-bin/ \ + --task truncated_bptt_lm --fp16 --max-update 400000 \ + --tokens-per-sample 512 --arch transformer_xl --n-layer 12 \ + --d-model 512 --n-head 8 --d-head 64 --d-inner 2048 --dropout 0.1 \ + --dropatt 0.0 --mem-len 512 --optimizer adam --clip-norm 0.25 \ + --lr-scheduler cosine --warmup-updates 0 \ + --lr 0.0 --lr 0.00025 --batch-size 15 \ + --update-freq 1 --seed 2 --log-format json --log-interval 25 \ + --fp16 +``` + +##### 2. Evaluate +For Adaptive Span: +```bash +fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \ + --user-dir examples/adaptive_span \ + --task truncated_bptt_lm --batch-size 8 --tokens-per-sample 512 --gen-subset test +``` +For Transformer-XL evaluation: +```bash +fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \ + --user-dir examples/truncated_bptt/ --task truncated_bptt_lm --batch-size 8 \ + --tokens-per-sample 80 \ + --model-overrides '{"mem_len":2100,"clamp_len":820,"same_length":True}' \ + --gen-subset valid +``` + +*Note:* During training the model saw 512 tokens of context +(``--tokens-per-sample=512``), with batch size 8. These settings match the evaluation +settings from [the original +paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh). diff --git a/fairseq/examples/adaptive_span/__init__.py b/fairseq/examples/adaptive_span/__init__.py new file mode 100644 index 0000000..e0a142a --- /dev/null +++ b/fairseq/examples/adaptive_span/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + +# automatically import any Python files in the current directory +cur_dir = os.path.dirname(__file__) +for file in os.listdir(cur_dir): + path = os.path.join(cur_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + mod_name = file[: file.find(".py")] if file.endswith(".py") else file + module = importlib.import_module(__name__ + "." + mod_name) diff --git a/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py b/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py new file mode 100644 index 0000000..585ce18 --- /dev/null +++ b/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from torch.optim import Adagrad + +from fairseq.optim import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("adagrad_with_grad_clip") +class FairseqAdagradWithGradClip(LegacyFairseqOptimizer): + def __init__(self, args, params): + super().__init__(args) + self._optimizer = AdagradWithGradClip(params, **self.optimizer_config) + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + parser.add_argument('--adagrad-clip', default=0.0, type=float, metavar='D', + help='internal grad clip') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.args.lr[0], + "weight_decay": self.args.weight_decay, + "grad_clip": self.args.adagrad_clip, + } + + @property + def supports_flat_params(self): + return False + + +def _clip_grad(clr, grad, group_grad_clip): + if group_grad_clip > 0: + norm = grad.norm(2).item() + if norm > group_grad_clip: + clr *= group_grad_clip / (norm + 1e-10) + return clr + + +class AdagradWithGradClip(Adagrad): + """Adagrad algorithm with custom gradient clipping""" + + def __init__( + self, + params, + lr=1e-2, + lr_decay=0, + weight_decay=0, + initial_accumulator_value=0, + grad_clip=0, + ): + Adagrad.__init__( + self, + params, + lr=lr, + lr_decay=lr_decay, + weight_decay=weight_decay, + initial_accumulator_value=initial_accumulator_value, + ) + self.defaults["grad_clip"] = grad_clip + self.param_groups[0].setdefault("grad_clip", grad_clip) + + def step(self, closure=None): + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + + grad = p.grad.data + state = self.state[p] + + state["step"] += 1 + + if group["weight_decay"] != 0: + if p.grad.data.is_sparse: + raise RuntimeError( + "weight_decay option is " + "not compatible with sparse " + "gradients" + ) + grad = grad.add(group["weight_decay"], p.data) + + clr = group["lr"] / (1 + (state["step"] - 1) * group["lr_decay"]) + + # clip + clr = _clip_grad(clr=clr, grad=grad, group_grad_clip=group["grad_clip"]) + + if grad.is_sparse: + # the update is non-linear so indices must be unique + grad = grad.coalesce() + grad_indices = grad._indices() + grad_values = grad._values() + size = grad.size() + + def make_sparse(values): + constructor = grad.new + if grad_indices.dim() == 0 or values.dim() == 0: + return constructor().resize_as_(grad) + return constructor(grad_indices, values, size) + + state["sum"].add_(make_sparse(grad_values.pow(2))) + std = state["sum"]._sparse_mask(grad) + std_values = std._values().sqrt_().add_(1e-10) + p.data.add_(-clr, make_sparse(grad_values / std_values)) + else: + state["sum"].addcmul_(1, grad, grad) + std = state["sum"].sqrt().add_(1e-10) + p.data.addcdiv_(-clr, grad, std) + + return loss diff --git a/fairseq/examples/adaptive_span/adaptive_span_attention.py b/fairseq/examples/adaptive_span/adaptive_span_attention.py new file mode 100644 index 0000000..07f757b --- /dev/null +++ b/fairseq/examples/adaptive_span/adaptive_span_attention.py @@ -0,0 +1,160 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class AdaptiveMask(nn.Module): + """Soft masking function for adaptive size. + It masks out the last K values of an input. The masking value + goes from 1 to 0 gradually, so K can be learned with + back-propagation. + Args: + max_size: maximum size (i.e. input dimension) + ramp_size: size of the ramp going from 0 to 1 + init_val: initial size proportion not to be masked out + shape: learn multiple sizes independent of each other + """ + + def __init__(self, max_size, ramp_size, init_val=0, shape=(1,)): + nn.Module.__init__(self) + self._max_size = max_size + self._ramp_size = ramp_size + self.current_val = nn.Parameter(torch.zeros(*shape) + init_val) + mask_template = torch.linspace(1 - max_size, 0, steps=max_size) + self.register_buffer("mask_template", mask_template) + + def forward(self, x): + mask = self.mask_template.float() + self.current_val.float() * self._max_size + mask = mask / self._ramp_size + 1 + mask = mask.clamp(0, 1) + if x.size(-1) < self._max_size: + # the input could have been trimmed beforehand to save computation + mask = mask.narrow(-1, self._max_size - x.size(-1), x.size(-1)) + x = (x * mask).type_as(x) + return x + + def get_current_max_size(self, include_ramp=True): + current_size = math.ceil(self.current_val.max().item() * self._max_size) + if include_ramp: + current_size += self._ramp_size + current_size = max(0, min(self._max_size, current_size)) + return current_size + + def get_current_avg_size(self, include_ramp=True): + current_size = math.ceil( + self.current_val.float().mean().item() * self._max_size + ) + if include_ramp: + current_size += self._ramp_size + current_size = max(0, min(self._max_size, current_size)) + return current_size + + def clamp_param(self): + """this need to be called after each update""" + self.current_val.data.clamp_(0, 1) + + +class AdaptiveSpan(nn.Module): + """Adaptive attention span for Transformerself. + This module learns an attention span length from data for each + self-attention head. + Args: + attn_span: maximum attention span + adapt_span_loss: loss coefficient for the span length + adapt_span_ramp: length of the masking ramp + adapt_span_init: initial size ratio + adapt_span_cache: adapt cache size to reduce memory usage + """ + + def __init__( + self, + attn_span, + adapt_span_ramp, + adapt_span_init, + n_head, + adapt_span_layer, + **kargs + ): + nn.Module.__init__(self) + self._max_span = attn_span + self._n_head = n_head + self._adapt_span_layer = adapt_span_layer + if self._adapt_span_layer: + self._mask = AdaptiveMask( + max_size=self._max_span, + ramp_size=adapt_span_ramp, + init_val=adapt_span_init, + ) + else: + self._mask = AdaptiveMask( + max_size=self._max_span, + ramp_size=adapt_span_ramp, + init_val=adapt_span_init, + shape=(n_head, 1, 1), + ) + + def forward(self, attn, normalize=True): + """mask attention with the right span""" + # batch and head dimensions are merged together, so separate them first + self.clamp_param() + if self._adapt_span_layer: + attn = self._mask(attn) + else: + B = attn.size(0) # batch size + M = attn.size(1) # block size + attn = attn.reshape(B // self._n_head, self._n_head, M, -1) + attn = self._mask(attn) + attn = attn.view(B, M, -1) + return attn + + def get_trim_len(self): + """how much of memory can be trimmed to reduce computation""" + L = self._max_span + trim_len = min(L - 1, L - self._mask.get_current_max_size()) + # too fine granularity might be bad for the memory management + trim_len = math.floor(trim_len / 64) * 64 + return trim_len + + def trim_memory(self, query, key, value, key_pe): + """trim out unnecessary memory beforehand to reduce computation""" + trim_len = self.get_trim_len() + cache_size = key.size(1) - query.size(1) + trim_len_cache = trim_len - (self._max_span - cache_size) + if trim_len_cache > 0: + key = key[:, trim_len_cache:, :] + value = value[:, trim_len_cache:, :] + elif trim_len_cache < 0: + # cache is too short! this happens when validation resumes + # after a lot of updates. + key = F.pad(key, [0, 0, -trim_len_cache, 0]) + value = F.pad(value, [0, 0, -trim_len_cache, 0]) + if trim_len > 0: + if key_pe is not None: + key_pe = key_pe[:, :, trim_len:] + return key, value, key_pe + + def get_cache_size(self): + """determine how long the cache should be""" + trim_len = self.get_trim_len() + # give a buffer of 64 steps since a span might increase + # in future updates + return min(self._max_span, self._max_span - trim_len + 64) + + def get_loss(self): + """a loss term for regularizing the span length""" + return self._max_span * self._mask.current_val.float().mean() + + def get_current_max_span(self): + return self._mask.get_current_max_size() + + def get_current_avg_span(self): + return self._mask.get_current_avg_size() + + def clamp_param(self): + self._mask.clamp_param() diff --git a/fairseq/examples/adaptive_span/adaptive_span_loss.py b/fairseq/examples/adaptive_span/adaptive_span_loss.py new file mode 100644 index 0000000..fe95b0d --- /dev/null +++ b/fairseq/examples/adaptive_span/adaptive_span_loss.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass + +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import register_criterion +from fairseq.criterions.cross_entropy import CrossEntropyCriterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II + + +@dataclass +class AdaptiveSpanCriterionConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + + +@register_criterion("adaptive_span_loss", dataclass=AdaptiveSpanCriterionConfig) +class AdaptiveSpanCriterion(CrossEntropyCriterion): + def __init__(self, task, sentence_avg): + super().__init__(task, sentence_avg) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss here is summed, different from the adaptive span code + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + loss, aux_loss, avg_span, max_span = self.compute_loss( + model, net_output, sample, reduce=reduce + ) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + loss /= sample_size + total_loss = loss + aux_loss + sample_size = 1 + + logging_output = { + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + "total_loss": total_loss.data, + "avg_span": avg_span * sample_size, + "max_span": max_span * sample_size, + } + return total_loss, sample_size, logging_output + + def compute_loss(self, model, net_output, sample, reduce=True): + loss, _ = super().compute_loss(model, net_output, sample, reduce) + aux_loss = model.get_aux_loss() + avg_span = model.get_current_avg_span() + max_span = model.get_current_max_span() + return loss, aux_loss, avg_span, max_span + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + total_loss_sum = sum(log.get("total_loss", 0) for log in logging_outputs) + avg_span_sum = sum(log.get("avg_span", 0) for log in logging_outputs) + max_span_sum = sum(log.get("max_span", 0) for log in logging_outputs) + + # we divide by log(2) to convert the loss from base e to base 2 + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar("avg_span", avg_span_sum / sample_size, sample_size, round=3) + metrics.log_scalar("max_span", max_span_sum / sample_size, sample_size, round=3) + # total loss contains the L1 norm on adaptive-span + metrics.log_scalar( + "total_loss", + total_loss_sum / sample_size / math.log(2), + sample_size, + round=3, + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + else: + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/examples/adaptive_span/adaptive_span_model.py b/fairseq/examples/adaptive_span/adaptive_span_model.py new file mode 100644 index 0000000..d96c95b --- /dev/null +++ b/fairseq/examples/adaptive_span/adaptive_span_model.py @@ -0,0 +1,263 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq.modules.layer_norm import LayerNorm + +from .adaptive_span_attention import AdaptiveSpan + +# Size notations: +# B = batch_size, H = d_model, M = block_size, L = attn_span + + +def _skew(X, pad_value): + """shift every row 1 step to right""" + # X = B x M x L + B, M, L = X.size() + X = F.pad(X, (0, M + 1), value=pad_value) # B x M x (L+M+1) + X = X.view(B, -1) # B x ML+MM+M + X = X[:, :-M] # B x ML+MM + X = X.view(B, M, M + L) # B x M x L+M + return X + + +def _unskew(X): + """reverse _skew operation""" + # X = B x M x L+M + B, M, L = X.size() + L -= M + X = X.view(B, -1) # B x ML+MM + X = F.pad(X, (0, M)) # B x ML+MM+M + X = X.view(B, M, M + L + 1) # B x M x L+M+1 + X = X[:, :, :L] # B x M x L + return X + + +class SeqAttention(nn.Module): + """Sequential self-attention layer. + Each token will attend to its previous fixed number of steps. + Note that attention doesn't include the current step itself. + """ + + def __init__(self, d_model, n_head, attn_span, dropout, adapt_span_layer, **kargs): + nn.Module.__init__(self) + self.dropout = nn.Dropout(dropout) + self.d_model = d_model # size of a single head + self.attn_span = attn_span + self.adaptive_span = AdaptiveSpan( + attn_span=attn_span, + n_head=n_head, + adapt_span_layer=adapt_span_layer, + **kargs + ) + + def forward(self, query, key, value, key_pe): + # query size = B x M x H + # key, value sizes = B x (M+L) x H + + key, value, key_pe = self.adaptive_span.trim_memory(query, key, value, key_pe) + + # compute attention from context + # B x M (dest) x (M+L) (src) + attn_cont = torch.matmul(query, key.transpose(-1, -2)) + attn_cont = _unskew(attn_cont) # B x M x L + + # compute the effect of position embedding + attn_pos = torch.matmul(query, key_pe) # B x M x L_pos + attn = attn_cont + attn_pos + + attn = attn / math.sqrt(self.d_model) # B x M X L_pos + + attn = F.softmax(attn.float(), dim=-1).type_as(attn) + + # trim attention lengths according to the learned span + attn = self.adaptive_span(attn) + + attn = self.dropout(attn) # B x M X L_pos + + attn_cont = _skew(attn, 0) # B x M X (L+M) + out = torch.matmul(attn_cont, value) # B x M x H + return out + + def get_cache_size(self): + return self.adaptive_span.get_cache_size() + + +class MultiHeadSeqAttention(nn.Module): + def __init__(self, d_model, n_head, **kargs): + nn.Module.__init__(self) + assert d_model % n_head == 0 + self.n_head = n_head + self.head_dim = d_model // n_head + self.attn = SeqAttention(d_model=self.head_dim, n_head=n_head, **kargs) + self.proj_query = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_query.weight) + self.proj_out = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_out.weight) + self.proj_val = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_val.weight) + self.proj_key = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_key.weight) + + def head_reshape(self, x): + K = self.n_head + D = self.head_dim + x = x.view(x.size()[:-1] + (K, D)) # B x (M+L) x K x D + x = x.transpose(1, 2).contiguous() # B x K x (M+L) x D + x = x.view(-1, x.size(-2), x.size(-1)) # B_K x (M+L) x D + return x + + def forward(self, query, key, value, key_pe): + B = query.size(0) + K = self.n_head + D = self.head_dim + M = query.size(1) + + query = self.proj_query(query) + query = self.head_reshape(query) + value = self.proj_val(value) + value = self.head_reshape(value) + key = self.proj_key(key) + key = self.head_reshape(key) + + out = self.attn(query, key, value, key_pe) # B_K x M x D + out = out.view(B, K, M, D) # B x K x M x D + out = out.transpose(1, 2).contiguous() # B x M x K x D + out = out.view(B, M, -1) # B x M x K_D + out = self.proj_out(out) + return out + + +class FeedForwardLayer(nn.Module): + def __init__(self, d_model, d_inner, dropout, **kargs): + nn.Module.__init__(self) + self.fc1 = nn.Linear(d_model, d_inner) + self.fc2 = nn.Linear(d_inner, d_model) + nn.init.xavier_uniform_(self.fc1.weight) + nn.init.xavier_uniform_(self.fc2.weight) + self.dropout = nn.Dropout(dropout) + + def forward(self, h): + h1 = F.relu(self.fc1(h)) + h1 = self.dropout(h1) + h2 = self.fc2(h1) + return h2 + + +class TransformerSeqLayer(nn.Module): + def __init__(self, d_model, **kargs): + nn.Module.__init__(self) + self.attn = MultiHeadSeqAttention(d_model=d_model, **kargs) + self.norm1 = LayerNorm(d_model) + self.ff = FeedForwardLayer(d_model=d_model, **kargs) + self.norm2 = LayerNorm(d_model) + + def forward(self, h, h_cache, key_pe): + # h = B x M x H + # h_cache = B x L x H + h_all = torch.cat([h_cache, h], dim=1) # B x (M+L) x H + attn_out = self.attn(h, h_all, h_all, key_pe) + h = self.norm1(h + attn_out) # B x M x H + if self.ff is not None: + ff_out = self.ff(h) + out = self.norm2(h + ff_out) # B x M x H + else: + out = h + return out + + def get_cache_size(self): + return self.attn.attn.get_cache_size() + + +class TransformerSeq(nn.Module): + def __init__( + self, + vocab_size, + d_model, + n_head, + n_layer, + attn_span, + emb_dropout, + aux_loss_scaler, + adapt_span_layer, + **kargs + ): + nn.Module.__init__(self) + # token embeddings + self.in_emb = nn.Embedding(vocab_size, d_model) + nn.init.normal_(self.in_emb.weight, mean=0, std=d_model ** -0.5) + self.out_emb = nn.Linear(d_model, vocab_size) + self.aux_loss_scaler = aux_loss_scaler + if emb_dropout > 0: + self.emb_dropout = nn.Dropout(emb_dropout) + else: + self.emb_dropout = None + # position embeddings + self.key_pe = nn.Parameter(torch.randn(1, d_model // n_head, attn_span)) + + self.layers = nn.ModuleList() + self.layers.extend( + TransformerSeqLayer( + d_model=d_model, + n_head=n_head, + attn_span=attn_span, + adapt_span_layer=adapt_span_layer, + **kargs + ) + for _ in range(n_layer) + ) + + def forward(self, x, h_cache, target=None): + # x size = B x M + block_size = x.size(1) + h = self.in_emb(x) # B x M x H + if self.emb_dropout is not None: + h = self.emb_dropout(h) + + h_cache_next = [] + for l, layer in enumerate(self.layers): + cache_size = layer.attn.attn.get_cache_size() + if cache_size > block_size: + h_cache_next_l = torch.cat( + [h_cache[l][:, -cache_size + block_size :, :], h], dim=1 + ).detach() + else: + h_cache_next_l = h[:, -cache_size:, :].detach() + h_cache_next.append(h_cache_next_l) + h = layer(h, h_cache[l], self.key_pe) # B x M x H + + if self.emb_dropout is not None: + h = self.emb_dropout(h) + + out = F.log_softmax(self.out_emb(h).float(), dim=-1).type_as(h) + dummy_loss = None + + return out, h_cache_next, dummy_loss + + def get_aux_loss(self): + loss = 0.0 + for layer in self.layers: + loss += layer.attn.attn.adaptive_span.get_loss() + return self.aux_loss_scaler * loss + + def get_current_max_span(self): + max_span = 0.0 + for layer in self.layers: + max_span = max( + max_span, layer.attn.attn.adaptive_span.get_current_max_span() + ) + return max_span + + def get_current_avg_span(self): + avg_span = 0.0 + for layer in self.layers: + avg_span += layer.attn.attn.adaptive_span.get_current_avg_span() + return avg_span / len(self.layers) diff --git a/fairseq/examples/adaptive_span/adaptive_span_model_wrapper.py b/fairseq/examples/adaptive_span/adaptive_span_model_wrapper.py new file mode 100644 index 0000000..5b147fe --- /dev/null +++ b/fairseq/examples/adaptive_span/adaptive_span_model_wrapper.py @@ -0,0 +1,145 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass +from typing import Dict, List, Optional + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.models import ( + FairseqIncrementalDecoder, + FairseqLanguageModel, + register_model, +) +from .adaptive_span_model import TransformerSeq as AdaptiveSpanTransformerModel + + +logger = logging.getLogger(__name__) + + +@dataclass +class AdaptiveSpanSmallConfig(FairseqDataclass): + # defaults come from https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8_small.sh + vocab_size: int = 50 + d_model: int = 256 + n_head: int = 4 + d_inner: int = 1024 + n_layer: int = 8 + attn_span: int = 1024 + dropout: float = 0.0 + emb_dropout: float = 0.0 + adapt_span_ramp: int = 32 + adapt_span_init: float = 0.0 + aux_loss_scaler: float = 0.000002 + adapt_span_layer: bool = False + + +@register_model("adaptive_span", dataclass=AdaptiveSpanSmallConfig) +class AdaptiveSpanTransformer(FairseqLanguageModel): + @classmethod + def build_model(cls, cfg: AdaptiveSpanSmallConfig, task): + return cls(AdaptiveSpanDecoder(cfg, task)) + + def get_aux_loss(self): + return self.decoder.get_aux_loss() + + def get_current_max_span(self): + return self.decoder.get_current_max_span() + + def get_current_avg_span(self): + return self.decoder.get_current_avg_span() + + +class AdaptiveSpanDecoder(FairseqIncrementalDecoder): + def __init__(self, cfg, task): + + super().__init__(task.target_dictionary) + + self.config = cfg + config = AdaptiveSpanSmallConfig( + vocab_size=len(task.target_dictionary), + d_model=cfg.d_model, + n_head=cfg.n_head, + d_inner=cfg.d_inner, + n_layer=cfg.n_layer, + attn_span=cfg.attn_span, + dropout=cfg.dropout, + emb_dropout=cfg.emb_dropout, + adapt_span_ramp=cfg.adapt_span_ramp, + adapt_span_init=cfg.adapt_span_init, + aux_loss_scaler=cfg.aux_loss_scaler, + adapt_span_layer=cfg.adapt_span_layer, + ) + logger.info(config) + self.model = AdaptiveSpanTransformerModel(**config.__dict__) + + self._mems = None + + def forward( + self, + src_tokens, + incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, + encoder_out=None, + ): + bsz = src_tokens.size(0) + if incremental_state is not None: # used during inference + mems = self.get_incremental_state("mems") + src_tokens = src_tokens[:, -1:] # only keep the most recent token + else: + mems = self._mems + + if mems is None: + # first time init + mems = self.init_hid_cache(bsz) + output = self.model(x=src_tokens, h_cache=mems,) + if incremental_state is not None: + self.set_incremental_state(incremental_state, "mems", output[1]) + else: + self._mems = output[1] + return (output[0],) + + def max_positions(self): + return self.config.attn_span + + def init_hid_cache(self, batch_sz): + hid = [] + for layer in self.model.layers: + param = next(self.model.parameters()) + h = torch.zeros( + batch_sz, + layer.get_cache_size(), + self.config.d_model, + dtype=param.dtype, + device=param.device, + ) + hid.append(h) + return hid + + def get_aux_loss(self): + return self.model.get_aux_loss() + + def get_current_max_span(self): + return self.model.get_current_max_span() + + def get_current_avg_span(self): + return self.model.get_current_avg_span() + + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]], + new_order: torch.Tensor, + ): + """Reorder incremental state. + + This will be called when the order of the input has changed from the + previous time step. A typical use case is beam search, where the input + order changes between time steps based on the selection of beams. + """ + raise NotImplementedError("This is required for generation/beam search") + # mems = self.get_incremental_state(incremental_state, "mems") + # if mems is not None: + # new_mems = [mems_i.index_select(1, new_order) for mems_i in mems] + # self.set_incremental_state(incremental_state, "mems", new_mems) diff --git a/fairseq/examples/adaptive_span/truncated_bptt_lm_task.py b/fairseq/examples/adaptive_span/truncated_bptt_lm_task.py new file mode 100644 index 0000000..a92da3a --- /dev/null +++ b/fairseq/examples/adaptive_span/truncated_bptt_lm_task.py @@ -0,0 +1 @@ +../truncated_bptt/truncated_bptt_lm_task.py \ No newline at end of file diff --git a/fairseq/examples/attention_head_selection/README.md b/fairseq/examples/attention_head_selection/README.md new file mode 100644 index 0000000..2434f1f --- /dev/null +++ b/fairseq/examples/attention_head_selection/README.md @@ -0,0 +1,161 @@ +# Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling (Gong et al., 2021) + +[https://arxiv.org/pdf/2106.10840.pdf](https://arxiv.org/pdf/2106.10840.pdf) + +## Introduction + +We present attention head selection strategies in multilingual and multi-domain sequence modeling including text translation, speech recognition and speech translation tasks. + +Below is an example of training multilingual/multi-domain speech recognition models. + +## Data Preparation +Prepare mTEDx data as in [mTEDx example](https://github.com/fairinternal/fairseq-py/blob/0d9c5851e6fac40f9e366b3633ccd615c2901788/examples/speech_to_text/docs/mtedx_example.md) and CoVoST data as in [CoVoST example](https://github.com/fairinternal/fairseq-py/blob/0d9c5851e6fac40f9e366b3633ccd615c2901788/examples/speech_to_text/docs/covost_example.md). Similarly prepare EuroParl data. + + +## Training a multilingual ASR model with attention head selection + +```bash +data_dir= +train_subset="train_ar_ar_tedx,train_de_de_tedx,train_el_el_tedx,train_es_es_tedx,train_fr_fr_tedx,train_it_it_tedx,train_pt_pt_tedx,train_ru_ru_tedx" +valid_subset="valid_ar_ar_tedx,valid_de_de_tedx,valid_el_el_tedx,valid_es_es_tedx,valid_fr_fr_tedx,valid_it_it_tedx,valid_pt_pt_tedx,valid_ru_ru_tedx" +strateg= + +fairseq-train ${data_dir} \ + --user-dir examples/attention_head_selection/src \ + --train-subset "${train_subset}" \ + --valid-subset "${valid_subset}" \ + --config-yaml 'config_asr.yaml' \ + --arch 'head_selection_s2t_transformer_s' \ + --task 'speech_to_text_head_selection' \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --lr-scheduler 'inverse_sqrt' --stop-min-lr -1.0 --warmup-updates 10000 \ + --lr 5e-4 \ + --clip-norm 10.0 \ + --seed 1 \ + --max-epoch 400 \ + --max-tokens 32000 \ + --ignore-prefix-size 1 \ + --dropout 0.3 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --skip-invalid-size-inputs-valid-test \ + --encoder-attn-head-select \ + --total-encoder-attention-heads 8 \ + --decoder-self-attn-head-select \ + --total-decoder-attention-heads 8 \ + --attn-head-select-strategy ${strategy} \ + --task-type lang \ +``` + +## Training a multi-domain ASR model with attention head selection + +```bash +data_dir= +train_subset="train_es_es_tedx,train_fr_fr_tedx,train_pt_pt_tedx,train_it_it_tedx,train_ru_ru_tedx,train_el_el_tedx,train_ar_ar_tedx,train_de_de_tedx,train_ar_ar_cv,train_de_de_cv,train_es_es_cv,train_fr_fr_cv,train_it_it_cv,train_pt_pt_cv,train_ru_ru_cv,train_de_de_ep,train_es_es_ep,train_fr_fr_ep,train_it_it_ep,train_pt_pt_ep" +valid_subset="dev_es_es_tedx,dev_fr_fr_tedx,dev_pt_pt_tedx,dev_it_it_tedx,dev_ru_ru_tedx,dev_el_el_tedx,dev_ar_ar_tedx,dev_de_de_tedx,dev_ar_ar_cv,dev_de_de_cv,dev_es_es_cv,dev_fr_fr_cv,dev_it_it_cv,dev_pt_pt_cv,dev_ru_ru_cv,dev_de_de_ep,dev_es_es_ep,dev_fr_fr_ep,dev_it_it_ep,dev_pt_pt_ep" +strateg= + +fairseq-train ${data_dir} \ + --user-dir examples/attention_head_selection/src \ + --train-subset "${train_subset}" \ + --valid-subset "${valid_subset}" \ + --config-yaml 'config_asr.yaml' \ + --arch head_selection_s2t_transformer_s \ + --task speech_to_text_head_selection \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --lr-scheduler 'inverse_sqrt' --stop-min-lr -1.0 --warmup-updates 10000 \ + --lr 5e-4 \ + --clip-norm 10.0 \ + --seed 1 \ + --max-epoch 400 \ + --max-tokens 32000 \ + --ignore-prefix-size 1 \ + --dropout 0.3 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --skip-invalid-size-inputs-valid-test \ + --encoder-attn-head-select \ + --total-encoder-attention-heads 8 \ + --decoder-self-attn-head-select \ + --total-decoder-attention-heads 8 \ + --attn-head-select-strategy ${strategy} \ + --task-type domain +``` + +## Inference in multilingual setting + +```bash +MODEL_DIR= +data_dir= +gen_subset= +train_subset="train_ar_ar_tedx,train_de_de_tedx,train_el_el_tedx,train_es_es_tedx,train_fr_fr_tedx,train_it_it_tedx,train_pt_pt_tedx,train_ru_ru_tedx" +last_n=10 +CHECKPOINT_FILENAME="avg_last_${last_n}_checkpoint.pt" +CHECKPOINT="_avg" +RESULTS="${MODEL_DIR}/ckpt${CHECKPOINT}" +if [ ! -d $RESULTS ]; then + mkdir -p $RESULTS +fi; + +python scripts/average_checkpoints.py \ + --inputs ${MODEL_DIR} --num-epoch-checkpoints ${last_n} \ + --output "${MODEL_DIR}/${CHECKPOINT_FILENAME}" + +fairseq-generate ${data_dir} \ + --user-dir examples/attention_head_selection/src \ + --arch 'head_selection_s2t_transformer_s' \ + --task 'speech_to_text_head_selection' \ + --train-subset ${train_subset} \ + --gen-subset ${gen_subset} \ + --path "${MODEL_DIR}/${CHECKPOINT_FILENAME}" \ + --config-yaml 'config_asr.yaml' \ + --prefix-size 1 \ + --max-tokens 40000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --results-path ${RESULTS} \ + --scoring wer --wer-tokenizer 13a \ + --wer-lowercase --wer-remove-punct --remove-bpe +``` + +## Inference in multi-domain setting + +```bash +MODEL_DIR= +data_dir= +gen_subset= +train_subset="train_es_es_tedx,train_fr_fr_tedx,train_pt_pt_tedx,train_it_it_tedx,train_ru_ru_tedx,train_el_el_tedx,train_ar_ar_tedx,train_de_de_tedx,train_ar_ar_cv,train_de_de_cv,train_es_es_cv,train_fr_fr_cv,train_it_it_cv,train_pt_pt_cv,train_ru_ru_cv,train_de_de_ep,train_es_es_ep,train_fr_fr_ep,train_it_it_ep,train_pt_pt_ep" +last_n=10 +CHECKPOINT_FILENAME="avg_last_${last_n}_checkpoint.pt" +CHECKPOINT="_avg" +RESULTS="${MODEL_DIR}/ckpt${CHECKPOINT}" +if [ ! -d $RESULTS ]; then + mkdir -p $RESULTS +fi; + +python scripts/average_checkpoints.py \ + --inputs ${MODEL_DIR} --num-epoch-checkpoints ${last_n} \ + --output "${MODEL_DIR}/${CHECKPOINT_FILENAME}" + +fairseq-generate ${data_dir} \ + --user-dir examples/attention_head_selection/src \ + --arch 'head_selection_s2t_transformer_s' \ + --task 'speech_to_text_head_selection' \ + --train-subset ${train_subset} \ + --gen-subset ${gen_subset} \ + --path "${MODEL_DIR}/${CHECKPOINT_FILENAME}" \ + --config-yaml 'config_asr.yaml' \ + --prefix-size 1 \ + --max-tokens 40000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --results-path ${RESULTS} \ + --scoring wer --wer-tokenizer 13a \ + --wer-lowercase --wer-remove-punct --remove-bpe +``` + +## Citation +```bibtex +@article{gong2021pay, + title={Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling}, + author={Gong, Hongyu and Tang, Yun and Pino, Juan and Li, Xian}, + journal={arXiv preprint arXiv:2106.10840}, + year={2021} +} +''' diff --git a/fairseq/examples/attention_head_selection/src/__init__.py b/fairseq/examples/attention_head_selection/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/attention_head_selection/src/loss/__init__.py b/fairseq/examples/attention_head_selection/src/loss/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/attention_head_selection/src/loss/attention_head_selection.py b/fairseq/examples/attention_head_selection/src/loss/attention_head_selection.py new file mode 100644 index 0000000..4ba3395 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/loss/attention_head_selection.py @@ -0,0 +1,27 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from torch.nn.modules.loss import _Loss + + +class HeadSelectionLoss(_Loss): + + def __init__(self, args): + super().__init__() + self.args = args + self.kl_weight = getattr(args, "kl_weight", 0.0) + + def forward(self, head_samples, sample_sizes, prior=0.5, eps=1e-7): + """ + head_scores: (num_tasks, num_layers, num_heads) + sample_sizes: (num_tasks, ) + """ + kl_loss = (head_samples * (torch.log(head_samples + eps) - math.log(prior))).sum(-1).sum(-1) + kl_loss /= (torch.numel(head_samples) / head_samples.size(0)) + kl_loss = self.kl_weight * torch.matmul(kl_loss, sample_sizes) + return kl_loss diff --git a/fairseq/examples/attention_head_selection/src/models/__init__.py b/fairseq/examples/attention_head_selection/src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/attention_head_selection/src/models/head_selection_s2t_transformer.py b/fairseq/examples/attention_head_selection/src/models/head_selection_s2t_transformer.py new file mode 100644 index 0000000..2c7ed89 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/models/head_selection_s2t_transformer.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import Dict, List, Optional +from pathlib import Path +import torch.nn as nn +from torch import Tensor +from fairseq import checkpoint_utils + +from fairseq.models import register_model, register_model_architecture +from fairseq.utils import safe_hasattr +from fairseq.models.speech_to_text.s2t_transformer import ( + S2TTransformerModel, + S2TTransformerEncoder, + TransformerDecoderScriptable +) +from fairseq.models.speech_to_text.s2t_transformer import base_architecture as s2t_base_architecture + +from ..modules.attn_head_selector import AttnHeadSelector +from ..modules.head_selection_transformer_layer import HeadSelectionTransformerEncoderLayer +from .head_selection_transformer import HeadSelectionTransformerDecoder + + +logger = logging.getLogger(__name__) + + +@register_model("head_selection_s2t_transformer") +class HeadSelectionS2TTransformerModel(S2TTransformerModel): + """ + Head selection implemented in S2TTransformer + """ + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + S2TTransformerModel.add_args(parser) + # encoder head selection + parser.add_argument( + "--encoder-attn-head-select", + action="store_true", + default=False, + help="encoder head selection" + ) + parser.add_argument( + "--total-encoder-attention-heads", + type=int, + help="total number of encoder attention heads" + ) + # decoder self attention selection + parser.add_argument( + "--decoder-self-attn-head-select", + action="store_true", + default=False, + help="decoder self-attention head selection" + ) + # decoder-encoder attention selection + parser.add_argument( + "--dec-enc-attn-head-select", + action="store_true", + default=False, + help="decoder-encoder attention head selection" + ) + parser.add_argument( + "--total-decoder-attention-heads", + type=int, + help="total number of decoder attention heads" + ) + # selection strategy + parser.add_argument( + "--attn-head-select-strategy", + type=str, + help="attention head selection strategy, subset or group" + ) + + @classmethod + def build_encoder(cls, args): + if safe_hasattr(args, "encoder_attn_head_select") and args.encoder_attn_head_select: + encoder = HeadSelectionS2TTransformerEncoder(args) + else: + encoder = S2TTransformerEncoder(args) + pretraining_path = getattr(args, "load_pretrained_encoder_from", None) + if pretraining_path is not None: + if not Path(pretraining_path).exists(): + logger.warning( + f"skipped pretraining because {pretraining_path} does not exist" + ) + else: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=pretraining_path + ) + logger.info(f"loaded pretrained encoder from: {pretraining_path}") + return encoder + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + if (safe_hasattr(args, "decoder_self_attn_head_select") and args.decoder_self_attn_head_select) or (safe_hasattr(args, "dec_enc_attn_head_select") and args.dec_enc_attn_head_select): + return HeadSelectionTransformerDecoderScriptable(args, task.target_dictionary, embed_tokens) + else: + return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens) + + +class HeadSelectionS2TTransformerEncoder(S2TTransformerEncoder): + + def __init__(self, args): + super().__init__(args) + self.attn_head_selector = AttnHeadSelector( + args.encoder_tasks, + args.encoder_layers, + args.total_encoder_attention_heads, + args.encoder_attention_heads, + args.attn_head_select_strategy, + ) + self.task_ids = None + self.transformer_layers = nn.ModuleList([ + HeadSelectionTransformerEncoderLayer(args, layer_idx, attn_head_selector=self.attn_head_selector) for layer_idx in range(args.encoder_layers) + ]) + + def set_task_ids(self, task_ids): + self.task_ids = task_ids + + def _forward(self, src_tokens, src_lengths, return_all_hiddens=False): + self.attn_head_selector.head_select(self.task_ids) + return super()._forward(src_tokens, src_lengths, return_all_hiddens) + + +class HeadSelectionTransformerDecoderScriptable(HeadSelectionTransformerDecoder): + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + # call scriptable method from parent class + x, _ = self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + return x, None + + +@register_model_architecture(model_name="head_selection_s2t_transformer", arch_name="head_selection_s2t_transformer") +def base_architecture(args): + s2t_base_architecture(args) + args.encoder_attn_head_select = getattr(args, "encoder_attn_head_select", False) + args.decoder_self_attn_head_select = getattr(args, "decoder_self_attn_head_select", False) + args.dec_enc_attn_head_select = getattr(args, "dec_enc_attn_head_select", False) + args.total_encoder_attention_heads = getattr(args, "total_encoder_attention_heads", 8) + args.total_decoder_attention_heads = getattr(args, "total_decoder_attention_heads", 8) + args.attn_head_select_strategy = getattr(args, "attn_head_select_strategy", "group") + + +@register_model_architecture("head_selection_s2t_transformer", "head_selection_s2t_transformer_s") +def head_selection_s2t_transformer_s(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + base_architecture(args) diff --git a/fairseq/examples/attention_head_selection/src/models/head_selection_transformer.py b/fairseq/examples/attention_head_selection/src/models/head_selection_transformer.py new file mode 100644 index 0000000..b9d5956 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/models/head_selection_transformer.py @@ -0,0 +1,215 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Any, List, Dict, Optional +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq.utils import safe_hasattr +from fairseq.models.transformer import ( + TransformerModel, + TransformerEncoder, + TransformerDecoder +) + +from ..modules.attn_head_selector import AttnHeadSelector +from ..modules.head_selection_transformer_layer import ( + HeadSelectionTransformerEncoderLayer, + HeadSelectionTransformerDecoderLayer +) + + +class HeadSelectionTransformerModel(TransformerModel): + def __init__(self, args, encoder, decoder): + super().__init__(args, encoder, decoder) + + @staticmethod + def add_args(parser): + TransformerModel.add_args(parser) + # encoder head selection + parser.add_argument( + "--encoder-attn-head-select", + action="store_true", + default=False, + help="encoder head selection" + ) + parser.add_argument( + "--total-encoder-attention-heads", + type=int, + help="total number of encoder attention heads" + ) + # decoder self attention + parser.add_argument( + "--decoder-self-attn-head-select", + action="store_true", + default=False, + help="decoder self-attention head selection" + ) + # decoder-encoder attention + parser.add_argument( + "--dec-enc-attn-head-select", + action="store_true", + default=False, + help="decoder-encoder attention head selection" + ) + parser.add_argument( + "--total-decoder-attention-heads", + type=int, + help="total number of decoder attention heads" + ) + # selection strategy + parser.add_argument( + "--attn-head-select-strategy", + type=str, + help="attention head selection strategy, subset or group" + ) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + if safe_hasattr(args, "encoder_attn_head_select") and args.encoder_attn_head_select: + return HeadSelectionTransformerEncoder( + args, src_dict, embed_tokens + ) + else: + return TransformerEncoder(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + if (safe_hasattr(args, "decoder_self_attn_head_select") and args.decoder_self_attn_head_select) or (safe_hasattr(args, "dec_enc_attn_head_select") and args.dec_enc_attn_head_select): + return HeadSelectionTransformerDecoder( + args, tgt_dict, embed_tokens + ) + else: + return TransformerDecoder(args, tgt_dict, embed_tokens) + + +class HeadSelectionTransformerEncoder(TransformerEncoder): + + def __init__(self, args, dictionary, embed_tokens): + self.num_tasks = args.encoder_tasks + self.num_layers = args.encoder_layers + self.total_num_heads = args.total_encoder_attention_heads + self.num_heads = args.encoder_attention_heads + self.select_strategy = args.attn_head_select_strategy + + super().__init__(args, dictionary, embed_tokens) + self.attn_head_selector = AttnHeadSelector( + self.num_tasks, + self.num_layers, + self.total_num_heads, + self.num_heads, + self.select_strategy + ) + self.task_ids = None + self.layers = nn.ModuleList( + [self.build_encoder_layer(args, i) for i in range(args.encoder_layers)] + ) + + def set_task_ids(self, task_ids): + self.task_ids = task_ids + + def build_encoder_layer(self, args, layer_idx=None): + return HeadSelectionTransformerEncoderLayer( + args, + layer_idx, + attn_head_selector=self.attn_head_selector + ) + + def forward( + self, + src_tokens, + src_lengths: Optional[torch.Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[torch.Tensor] = None, + ): + self.attn_head_selector.head_select(self.task_ids) + return super().forward(src_tokens, src_lengths, return_all_hiddens, token_embeddings) + + +class HeadSelectionTransformerDecoder(TransformerDecoder): + + def __init__( + self, + args, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=None, + ): + self.num_tasks = args.decoder_tasks + self.num_layers = args.decoder_layers + self.total_num_heads = args.total_decoder_attention_heads + self.num_heads = args.decoder_attention_heads + self.select_strategy = args.attn_head_select_strategy + super().__init__( + args, dictionary, embed_tokens, + no_encoder_attn=no_encoder_attn, + output_projection=output_projection + ) + self.self_attn_head_selector = None + self.enc_attn_head_selector = None + if safe_hasattr(args, "decoder_self_attn_head_select") and args.decoder_self_attn_head_select: + self.self_attn_head_selector = AttnHeadSelector( + self.num_tasks, + self.num_layers, + self.total_num_heads, + self.num_heads, + self.select_strategy + ) + if safe_hasattr(args, "dec_enc_attn_head_select") and args.dec_enc_attn_head_select: + self.enc_attn_head_selector = AttnHeadSelector( + self.num_tasks, + self.num_layers, + self.total_num_heads, + self.num_heads, + self.select_strategy + ) + self.task_ids = None + self.layers = nn.ModuleList( + [ + self.build_head_selection_decoder_layer(args, no_encoder_attn, idx) for idx in range(args.decoder_layers) + ] + ) + + def set_task_ids(self, task_ids): + self.task_ids = task_ids + + def build_head_selection_decoder_layer(self, args, no_encoder_attn=False, layer_idx=None): + return HeadSelectionTransformerDecoderLayer( + args, + layer_idx, + self.self_attn_head_selector, + self.enc_attn_head_selector, + no_encoder_attn=no_encoder_attn + ) + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + if self.self_attn_head_selector is not None: + self.self_attn_head_selector.head_select(self.task_ids) + if self.enc_attn_head_selector is not None: + self.enc_attn_head_selector.head_select(self.task_ids) + return super().forward( + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + features_only=features_only, + full_context_alignment=full_context_alignment, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + src_lengths=src_lengths, + return_all_hiddens=return_all_hiddens + ) diff --git a/fairseq/examples/attention_head_selection/src/modules/__init__.py b/fairseq/examples/attention_head_selection/src/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/attention_head_selection/src/modules/attn_head_selector.py b/fairseq/examples/attention_head_selection/src/modules/attn_head_selector.py new file mode 100644 index 0000000..346fc62 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/modules/attn_head_selector.py @@ -0,0 +1,81 @@ +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import math + + +class AttnHeadSelector(nn.Module): + """ + Latent variable modeling of attention head selection + """ + def __init__( + self, num_tasks, num_layers, + total_num_heads, num_heads, + select_strategy="group", + head_select_temp=5.0 + ): + super(AttnHeadSelector, self).__init__() + self.num_tasks = num_tasks + self.num_layers = num_layers + self.total_num_heads = total_num_heads + self.num_heads = num_heads + self.select_strategy = select_strategy + self.temp = head_select_temp + + self.head_logits = torch.nn.Parameter( + torch.Tensor(self.num_tasks, self.num_layers, total_num_heads), + requires_grad=True + ) + nn.init.uniform_( + self.head_logits, a=math.log(0.01), + b=math.log(1.0) + ) + + def gumbel_sample(self, logits, tau=1.0): + gumbels1 = -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log() + gumbels2 = -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log() + gumbels1 = (logits + gumbels1 - gumbels2) / tau + y_soft = gumbels1.sigmoid() + return y_soft + + def subset_select(self, y_soft, topk, dim=-1): + top_values, top_inds = torch.topk(y_soft, k=topk, dim=dim) + top_ret = 1.0 - top_values.detach() + top_values + return top_inds.detach(), top_ret + + def group_selet(self, y_soft, topk, dim=-1): + # top_values: (num_tasks, num_layers, topk) + top_values, top_inds = torch.max( + y_soft.view(self.num_tasks, self.num_layers, -1, topk), dim=2 + ) + top_inds = top_inds * topk + torch.arange(topk, device=top_inds.device).unsqueeze(0).unsqueeze(1) + top_ret = 1.0 - top_values.detach() + top_values + return top_inds.detach(), top_ret + + def head_select(self, task_ids=None): + # gumbel_sample + self.head_samples = self.gumbel_sample(self.head_logits, tau=self.temp) + # head select + if self.select_strategy == "subset": + self.subset_heads, self.subset_weights = self.subset_select( + self.head_samples, + topk=self.num_heads, + ) + elif self.select_strategy == "group": + self.subset_heads, self.subset_weights = self.group_selet( + self.head_samples, + topk=self.num_heads, + ) + else: + raise ValueError("{} is not supported".format(self.select_strategy)) + + self.batch_subset = self.subset_heads[task_ids, :, :] + self.batch_weights = self.subset_weights[task_ids, :, :] + + def forward(self, layer_idx): + assert layer_idx is not None + batch_subset = self.batch_subset[:, layer_idx, :] + batch_weights = self.batch_weights[:, layer_idx, :] + return batch_subset, batch_weights diff --git a/fairseq/examples/attention_head_selection/src/modules/head_selection_transformer_layer.py b/fairseq/examples/attention_head_selection/src/modules/head_selection_transformer_layer.py new file mode 100644 index 0000000..c792143 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/modules/head_selection_transformer_layer.py @@ -0,0 +1,92 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.utils import safe_getattr +from fairseq.modules import TransformerEncoderLayer, TransformerDecoderLayer +from ..modules.multihead_attention_selection import MultiheadAttentionSelection + + +class HeadSelectionTransformerEncoderLayer(TransformerEncoderLayer): + + def __init__(self, args, layer_idx, attn_head_selector=None): + super().__init__(args) + self.layer_idx = layer_idx + self.self_attn = self.build_self_attention_selection( + self.embed_dim, args, attn_head_selector + ) + + def build_self_attention_selection(self, embed_dim, args, attn_head_selector=None): + return MultiheadAttentionSelection( + embed_dim, + args.total_encoder_attention_heads, + args.encoder_attention_heads, + dropout=args.attention_dropout, + self_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + layer_idx=self.layer_idx, + attn_head_selector=attn_head_selector + ) + + +class HeadSelectionTransformerDecoderLayer(TransformerDecoderLayer): + + def __init__( + self, + args, + layer_idx, + self_attn_head_selector=None, + enc_attn_head_selector=None, + no_encoder_attn=False, + add_bias_kv=False, + add_zero_attn=False, + ): + self.layer_idx = layer_idx + super().__init__(args, no_encoder_attn, add_bias_kv, add_zero_attn) + if self_attn_head_selector is not None: + self.self_attn = self.build_self_attention_selection( + self.embed_dim, args, + self_attn_head_selector=self_attn_head_selector, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn + ) + if enc_attn_head_selector is not None: + self.encoder_attn = self.build_encoder_attention_selection( + self.embed_dim, args, + enc_attn_head_selector=enc_attn_head_selector + ) + + def build_self_attention_selection( + self, embed_dim, args, self_attn_head_selector=None, + add_bias_kv=False, add_zero_attn=False + ): + return MultiheadAttentionSelection( + embed_dim, + args.total_decoder_attention_heads, + args.decoder_attention_heads, + dropout=args.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=not safe_getattr(args, "cross_self_attention"), + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + layer_idx=self.layer_idx, + attn_head_selector=self_attn_head_selector, + ) + + def build_encoder_attention_selection(self, embed_dim, args, enc_attn_head_selector=None): + return MultiheadAttentionSelection( + embed_dim, + args.total_decoder_attention_heads, + args.decoder_attention_heads, + kdim=args.encoder_embed_dim, + vdim=args.encoder_embed_dim, + dropout=args.attention_dropout, + encoder_decoder_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + layer_idx=self.layer_idx, + attn_head_selector=enc_attn_head_selector, + ) diff --git a/fairseq/examples/attention_head_selection/src/modules/multihead_attention_selection.py b/fairseq/examples/attention_head_selection/src/modules/multihead_attention_selection.py new file mode 100644 index 0000000..566ad82 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/modules/multihead_attention_selection.py @@ -0,0 +1,355 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, Optional, Tuple +import torch +from fairseq import utils +from fairseq.modules.quant_noise import quant_noise +from torch import Tensor, nn +from torch.nn import Parameter + +from fairseq.modules.multihead_attention import MultiheadAttention +from ..modules.multihead_functional import multi_head_attention_forward + + +class MultiheadAttentionSelection(MultiheadAttention): + + def __init__( + self, + embed_dim, + total_num_heads, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + q_noise=0.0, + qn_block_size=8, + layer_idx=0, + attn_head_selector=None + ): + super().__init__( + embed_dim, + num_heads, + kdim=kdim, + vdim=vdim, + dropout=dropout, + bias=bias, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=self_attention, + encoder_decoder_attention=encoder_decoder_attention, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + self.layer_idx = layer_idx + self.attn_head_selector = attn_head_selector + self.total_num_heads = total_num_heads + self.total_embed_dim = self.head_dim * total_num_heads + self.k_proj = quant_noise( + nn.Linear(self.kdim, self.total_embed_dim, bias=bias), q_noise, qn_block_size + ) + self.v_proj = quant_noise( + nn.Linear(self.vdim, self.total_embed_dim, bias=bias), q_noise, qn_block_size + ) + self.q_proj = quant_noise( + nn.Linear(embed_dim, self.total_embed_dim, bias=bias), q_noise, qn_block_size + ) + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, self.total_embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, self.total_embed_dim)) + else: + self.bias_k = self.bias_v = None + self.reset_parameters() + + def forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + before_softmax: bool = False, + need_head_weights: bool = False, + # subset_heads: Optional[Tensor] = None, + # subset_weights: Optional[Tensor] = None + ) -> Tuple[Tensor, Optional[Tensor]]: + if need_head_weights: + need_weights = True + + is_tpu = query.device.type == "xla" + + subset_heads, subset_weights = self.attn_head_selector(self.layer_idx) + + tgt_len, bsz, embed_dim = query.size() + src_len = tgt_len + assert list(query.size()) == [tgt_len, bsz, self.embed_dim] + if key is not None: + src_len, key_bsz, _ = key.size() + if not torch.jit.is_scripting(): + assert key_bsz == bsz + assert value is not None + assert src_len, bsz == value.shape[:2] + + if ( + not self.onnx_trace + and not is_tpu # don't use PyTorch version on TPUs + and incremental_state is None + and not static_kv + # A workaround for quantization to work. Otherwise JIT compilation + # treats bias in linear module as method. + and not torch.jit.is_scripting() + ): + assert key is not None and value is not None + return multi_head_attention_forward( + query, + key, + value, + self.embed_dim, + self.total_num_heads, + self.num_heads, + torch.empty([0]), + torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), + self.bias_k, + self.bias_v, + self.add_zero_attn, + self.dropout_module.p, + self.out_proj.weight, + self.out_proj.bias, + self.training or self.dropout_module.apply_during_inference, + key_padding_mask, + need_weights, + attn_mask, + use_separate_proj_weight=True, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + subset_heads=subset_heads, + subset_weights=subset_weights + ) + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(key_padding_mask.size(0), 1), + ], + dim=1, + ) + + q = ( + q.contiguous() + .view(tgt_len, bsz * self.total_num_heads, self.head_dim) + .transpose(0, 1) + ) + if k is not None: + k = ( + k.contiguous() + .view(-1, bsz * self.total_num_heads, self.head_dim) + .transpose(0, 1) + ) + if v is not None: + v = ( + v.contiguous() + .view(-1, bsz * self.total_num_heads, self.head_dim) + .transpose(0, 1) + ) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + prev_key = _prev_key.view(bsz * self.total_num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + src_len = k.size(1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + prev_value = _prev_value.view(bsz * self.total_num_heads, -1, self.head_dim) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + + saved_state["prev_key"] = k.view(bsz, self.total_num_heads, -1, self.head_dim) + saved_state["prev_value"] = v.view(bsz, self.total_num_heads, -1, self.head_dim) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + assert k.size(1) == src_len + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.dim() == 0: + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if self.add_zero_attn: + assert v is not None + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + torch.zeros(key_padding_mask.size(0), 1).type_as( + key_padding_mask + ), + ], + dim=1, + ) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + + assert list(attn_weights.size()) == [bsz * self.total_num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + if self.onnx_trace: + attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) + attn_weights += attn_mask + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view(bsz, self.total_num_heads, tgt_len, src_len) + if not is_tpu: + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + else: + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if before_softmax: + return attn_weights, v + + attn_weights_float = utils.softmax( + attn_weights, dim=-1, onnx_trace=self.onnx_trace + ) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = self.dropout_module(attn_weights) + + assert v is not None + + # evaluation + if subset_heads is not None and subset_heads.numel() == 1: + subset_heads = subset_heads.repeat(bsz) + subset_weights = subset_weights.repeat(bsz) + + if subset_heads is None: + attn = torch.bmm(attn_probs, v) + else: + # training with head selection + mixed_attn = torch.bmm(attn_probs, v).contiguous().view(bsz, self.total_num_heads, tgt_len, self.head_dim) + attn = torch.stack( + [mixed_attn[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1 + ) + attn = attn * subset_weights.unsqueeze(2).unsqueeze(3) + attn = attn.contiguous().view(bsz * self.num_heads, tgt_len, self.head_dim) + + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + if self.onnx_trace and attn.size(1) == 1: + # when ONNX tracing a single decoder step (sequence length == 1) + # the transpose is a no-op copy before view, thus unnecessary + attn = attn.contiguous().view(tgt_len, bsz, embed_dim) + else: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn = self.out_proj(attn) + attn_weights: Optional[Tensor] = None + if need_weights: + if subset_heads is None: + attn_weights = attn_weights_float.view( + bsz, self.num_heads, tgt_len, src_len + ).transpose(1, 0) + else: + mixed_attn_weights = attn_weights_float.view( + bsz, self.total_num_heads, tgt_len, src_len + ) + attn_weights = torch.stack( + [mixed_attn_weights[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1 + ).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + + return attn, attn_weights diff --git a/fairseq/examples/attention_head_selection/src/modules/multihead_functional.py b/fairseq/examples/attention_head_selection/src/modules/multihead_functional.py new file mode 100644 index 0000000..d5edc77 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/modules/multihead_functional.py @@ -0,0 +1,278 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional, Tuple +import torch +from torch import Tensor +from torch.nn.functional import ( + linear, softmax, dropout, pad, + has_torch_function, + handle_torch_function, + _in_projection_packed, +) +import math +import warnings + + +def _scaled_dot_product_attention( + q: Tensor, + k: Tensor, + v: Tensor, + attn_mask: Optional[Tensor] = None, + dropout_p: float = 0.0, + bsz: int = 1, + subset_heads: Optional[Tensor] = None, + subset_weights: Optional[Tensor] = None, +) -> Tuple[Tensor, Tensor]: + B, Nt, E = q.shape + q = q / math.sqrt(E) + # B: bsz * total_num_heads + # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns) + attn = torch.bmm(q, k.transpose(-2, -1)) + if attn_mask is not None: + attn += attn_mask + attn = softmax(attn, dim=-1) + if dropout_p > 0.0: + attn = dropout(attn, p=dropout_p) + if subset_heads is None: + # (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E) + output = torch.bmm(attn, v) + else: + mixed_output = torch.bmm(attn, v).contiguous().view(bsz, -1, Nt, E) + output = torch.stack( + [mixed_output[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], + dim=1 + ) + output = output * subset_weights.unsqueeze(2).unsqueeze(3) + output = output.contiguous().view(-1, Nt, E) + if subset_heads is not None: + _, Nt, Ns = attn.size() + mixed_attn = attn.view(bsz, -1, Nt, Ns) + attn = torch.stack( + [mixed_attn[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1 + ) + return output, attn + + +def _in_projection( + q: Tensor, + k: Tensor, + v: Tensor, + w_q: Tensor, + w_k: Tensor, + w_v: Tensor, + b_q: Optional[Tensor] = None, + b_k: Optional[Tensor] = None, + b_v: Optional[Tensor] = None, +) -> Tuple[Tensor, Tensor, Tensor]: + return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v) + + +def multi_head_attention_forward( + query: Tensor, + key: Tensor, + value: Tensor, + embed_dim_to_check: int, + total_num_heads: int, + num_heads: int, + in_proj_weight: Tensor, + in_proj_bias: Optional[Tensor], + bias_k: Optional[Tensor], + bias_v: Optional[Tensor], + add_zero_attn: bool, + dropout_p: float, + out_proj_weight: Tensor, + out_proj_bias: Optional[Tensor], + training: bool = True, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + use_separate_proj_weight: bool = False, + q_proj_weight: Optional[Tensor] = None, + k_proj_weight: Optional[Tensor] = None, + v_proj_weight: Optional[Tensor] = None, + static_k: Optional[Tensor] = None, + static_v: Optional[Tensor] = None, + subset_heads: Optional[Tensor] = None, + subset_weights: Optional[Tensor] = None, +): + tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias) + if has_torch_function(tens_ops): + return handle_torch_function( + multi_head_attention_forward, + tens_ops, + query, + key, + value, + embed_dim_to_check, + total_num_heads, + num_heads, + in_proj_weight, + in_proj_bias, + bias_k, + bias_v, + add_zero_attn, + dropout_p, + out_proj_weight, + out_proj_bias, + training=training, + key_padding_mask=key_padding_mask, + need_weights=need_weights, + attn_mask=attn_mask, + use_separate_proj_weight=use_separate_proj_weight, + q_proj_weight=q_proj_weight, + k_proj_weight=k_proj_weight, + v_proj_weight=v_proj_weight, + static_k=static_k, + static_v=static_v, + subset_heads=subset_heads, + subset_weights=subset_weights + ) + + # set up shape vars + tgt_len, bsz, embed_dim = query.shape + src_len, _, _ = key.shape + assert embed_dim == embed_dim_to_check, \ + f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}" + if isinstance(embed_dim, torch.Tensor): + # embed_dim can be a tensor when JIT tracing + head_dim = embed_dim.div(num_heads, rounding_mode='trunc') + else: + head_dim = embed_dim // num_heads + assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}" + if use_separate_proj_weight: + # allow MHA to have different embedding dimensions when separate projection weights are used + assert key.shape[:2] == value.shape[:2], \ + f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}" + else: + assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}" + + # + # compute in-projection + # + if not use_separate_proj_weight: + q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias) + else: + assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None" + assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None" + assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None" + if in_proj_bias is None: + b_q = b_k = b_v = None + else: + b_q, b_k, b_v = in_proj_bias.chunk(3) + q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v) + + # prep attention mask + if attn_mask is not None: + if attn_mask.dtype == torch.uint8: + warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.") + attn_mask = attn_mask.to(torch.bool) + else: + assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \ + f"Only float, byte, and bool types are supported for attn_mask, not {attn_mask.dtype}" + # ensure attn_mask's dim is 3 + if attn_mask.dim() == 2: + correct_2d_size = (tgt_len, src_len) + if attn_mask.shape != correct_2d_size: + raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.") + attn_mask = attn_mask.unsqueeze(0) + elif attn_mask.dim() == 3: + correct_3d_size = (bsz * total_num_heads, tgt_len, src_len) + if attn_mask.shape != correct_3d_size: + raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.") + else: + raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported") + + # prep key padding mask + if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8: + warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.") + key_padding_mask = key_padding_mask.to(torch.bool) + + # add bias along batch dimension (currently second) + if bias_k is not None and bias_v is not None: + assert static_k is None, "bias cannot be added to static key." + assert static_v is None, "bias cannot be added to static value." + k = torch.cat([k, bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = pad(attn_mask, (0, 1)) + if key_padding_mask is not None: + key_padding_mask = pad(key_padding_mask, (0, 1)) + else: + assert bias_k is None + assert bias_v is None + + # + # reshape q, k, v for multihead attention and make em batch first + # + q = q.contiguous().view(tgt_len, bsz * total_num_heads, head_dim).transpose(0, 1) + if static_k is None: + k = k.contiguous().view(k.shape[0], bsz * total_num_heads, head_dim).transpose(0, 1) + else: + # TODO finish disentangling control flow so we don't do in-projections when statics are passed + assert static_k.size(0) == bsz * total_num_heads, \ + f"expecting static_k.size(0) of {bsz * total_num_heads}, but got {static_k.size(0)}" + assert static_k.size(2) == head_dim, \ + f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}" + k = static_k + if static_v is None: + v = v.contiguous().view(v.shape[0], bsz * total_num_heads, head_dim).transpose(0, 1) + else: + # TODO finish disentangling control flow so we don't do in-projections when statics are passed + assert static_v.size(0) == bsz * total_num_heads, \ + f"expecting static_v.size(0) of {bsz * total_num_heads}, but got {static_v.size(0)}" + assert static_v.size(2) == head_dim, \ + f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}" + v = static_v + + # add zero attention along batch dimension (now first) + if add_zero_attn: + zero_attn_shape = (bsz * total_num_heads, 1, head_dim) + k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1) + v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1) + if attn_mask is not None: + attn_mask = pad(attn_mask, (0, 1)) + if key_padding_mask is not None: + key_padding_mask = pad(key_padding_mask, (0, 1)) + + # update source sequence length after adjustments + src_len = k.size(1) + + # merge key padding and attention masks + if key_padding_mask is not None: + assert key_padding_mask.shape == (bsz, src_len), \ + f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}" + key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \ + expand(-1, total_num_heads, -1, -1).reshape(bsz * total_num_heads, 1, src_len) + if attn_mask is None: + attn_mask = key_padding_mask + elif attn_mask.dtype == torch.bool: + attn_mask = attn_mask.logical_or(key_padding_mask) + else: + attn_mask = attn_mask.masked_fill(key_padding_mask, float("-inf")) + + # convert mask to float + if attn_mask is not None and attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=torch.float) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + + # adjust dropout probability + if not training: + dropout_p = 0.0 + + # + # (deep breath) calculate attention and out projection + # + attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, bsz, subset_heads, subset_weights) + attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn_output = linear(attn_output, out_proj_weight, out_proj_bias) + + if need_weights: + # average attention weights over heads + attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) + return attn_output, attn_output_weights.sum(dim=1) / num_heads + else: + return attn_output, None diff --git a/fairseq/examples/attention_head_selection/src/speech_to_text_head_selection.py b/fairseq/examples/attention_head_selection/src/speech_to_text_head_selection.py new file mode 100644 index 0000000..6e0ce11 --- /dev/null +++ b/fairseq/examples/attention_head_selection/src/speech_to_text_head_selection.py @@ -0,0 +1,180 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq.optim.amp_optimizer import AMPOptimizer +from fairseq.tasks import register_task +from fairseq.tasks.speech_to_text import SpeechToTextTask + +from .data.speech_to_text_dataset_with_domain import SpeechToTextDatasetCreatorWithDomain +from .loss.attention_head_selection import HeadSelectionLoss + + +@register_task("speech_to_text_head_selection") +class SpeechToTextHeadSelectionTask(SpeechToTextTask): + + @classmethod + def add_args(cls, parser): + SpeechToTextTask.add_args(parser) + parser.add_argument( + "--task-type", + type=str, + default="lang", + help="task type for head selection, lang or domain" + ) + parser.add_argument( + "--kl-weight", + type=float, + default=0.0, + help="the weight of KL loss" + ) + + def __init__(self, args, tgt_dict): + super().__init__(args, tgt_dict) + self.task_type = args.task_type + assert self.task_type in ["lang", "domain"], "invalid task_type: {}, should be either lang or domain".format(self.task_type) + self.map_task_to_id(args.train_subset) + self.encoder_head_prior = float(args.decoder_attention_heads) / args.total_decoder_attention_heads + self.decoder_head_prior = float(args.encoder_attention_heads) / args.total_encoder_attention_heads + self.kl_loss = HeadSelectionLoss(args) + + def map_task_to_id(self, train_subset): + src_lang_set, tgt_lang_set, domain_set = set(), set(), set() + for split in train_subset.split(","): + seq = split.split("_") + assert len(seq) == 4, "subset {} should be in the format of train_src_tgt_domain".format(split) + _, src_lang, tgt_lang, domain = seq + src_lang_set.add(src_lang) + tgt_lang_set.add(tgt_lang) + domain_set.add(domain) + src_langs = sorted(src_lang_set) + tgt_langs = sorted(tgt_lang_set) + domains = sorted(domain_set) + self.src_lang_map = {src_lang: i for (i, src_lang) in enumerate(src_langs)} + self.tgt_lang_map = {tgt_lang: i for (i, tgt_lang) in enumerate(tgt_langs)} + self.domain_map = {domain: i for (i, domain) in enumerate(domains)} + if self.task_type == "lang": + self.encoder_tasks = len(self.src_lang_map) + self.decoder_tasks = len(self.tgt_lang_map) + elif self.task_type == "domain": + self.encoder_tasks = len(self.domain_map) + self.decoder_tasks = len(self.domain_map) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + self.datasets[split] = SpeechToTextDatasetCreatorWithDomain.from_tsv( + self.args.data, + self.data_cfg, + split, + self.tgt_dict, + pre_tokenizer, + bpe_tokenizer, + is_train_split=is_train_split, + epoch=epoch, + seed=self.args.seed, + src_lang_map=self.src_lang_map, + tgt_lang_map=self.tgt_lang_map, + domain_map=self.domain_map, + speaker_to_id=self.speaker_to_id + ) + + def build_model(self, args): + args.encoder_tasks = self.encoder_tasks + args.decoder_tasks = self.decoder_tasks + return super(SpeechToTextHeadSelectionTask, self).build_model(args) + + def get_sample_sizes(self, sample, task_ids, num_tasks): + """ + task_ids: (bsz,) + get sample sizes for each task + """ + bsz = task_ids.size(0) + mat = torch.zeros((num_tasks, bsz), device=task_ids.device) + mat[task_ids, torch.arange(bsz)] = 1.0 + ntokens = torch.sum(sample['target'] != 1, dim=-1) + sample_sizes = torch.matmul(mat, ntokens.float()) + return sample_sizes + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + model.train() + model.set_num_updates(update_num) + # task ids + if self.task_type == "lang": + encoder_task_ids = sample["src_lang_ids"] + decoder_task_ids = sample["tgt_lang_ids"] + elif self.task_type == "domain": + encoder_task_ids = sample["domain_ids"] + decoder_task_ids = sample["domain_ids"] + model.encoder.set_task_ids(encoder_task_ids) + model.decoder.set_task_ids(decoder_task_ids) + + with torch.autograd.profiler.record_function("forward"): + with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))): + loss, sample_size, logging_output = criterion(model, sample) + # KL loss + if self.args.encoder_attn_head_select: + sample_sizes = self.get_sample_sizes(sample, encoder_task_ids, self.encoder_tasks) + loss += self.kl_loss( + model.encoder.attn_head_selector.head_samples, + sample_sizes, + self.encoder_head_prior + ) + if self.args.decoder_self_attn_head_select: + sample_sizes = self.get_sample_sizes(sample, decoder_task_ids, self.decoder_tasks) + loss += self.kl_loss( + model.decoder.self_attn_head_selector.head_samples, + sample_sizes, + self.decoder_head_prior + ) + if self.args.dec_enc_attn_head_select: + sample_sizes = self.get_sample_sizes(sample, decoder_task_ids, self.decoder_tasks) + loss += self.kl_loss( + model.decoder.enc_attn_head_selector.head_sampes, + sample_sizes, + self.decoder_head_prior + ) + + if ignore_grad: + loss *= 0 + with torch.autograd.profiler.record_function("backward"): + optimizer.backward(loss) + return loss, sample_size, logging_output + + def valid_step(self, sample, model, criterion): + model.eval() + # task ids + if self.task_type == "lang": + encoder_task_ids = sample["src_lang_ids"] + decoder_task_ids = sample["tgt_lang_ids"] + elif self.task_type == "domain": + encoder_task_ids = sample["domain_ids"] + decoder_task_ids = sample["domain_ids"] + model.encoder.set_task_ids(encoder_task_ids) + model.decoder.set_task_ids(decoder_task_ids) + with torch.no_grad(): + loss, sample_size, logging_output = criterion(model, sample) + return loss, sample_size, logging_output + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + # task ids + if self.task_type == "lang": + encoder_task_ids = sample["src_lang_ids"][:1] + decoder_task_ids = sample["tgt_lang_ids"][:1] + elif self.task_type == "domain": + encoder_task_ids = sample["domain_ids"][:1] + decoder_task_ids = sample["domain_ids"][:1] + for model in models: + model.encoder.set_task_ids(encoder_task_ids) + model.decoder.set_task_ids(decoder_task_ids) + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, constraints=constraints + ) diff --git a/fairseq/examples/audio_nlp/nlu/README.md b/fairseq/examples/audio_nlp/nlu/README.md new file mode 100644 index 0000000..a11b3f3 --- /dev/null +++ b/fairseq/examples/audio_nlp/nlu/README.md @@ -0,0 +1,53 @@ +# End-to-end NLU + +End-to-end spoken language understanding (SLU) predicts intent directly from audio using a single model. It promises to improve the performance of assistant systems by leveraging acoustic information lost in the intermediate textual representation and preventing cascading errors from Automatic Speech Recognition (ASR). Further, having one unified model has efficiency advantages when deploying assistant systems on-device. + +This page releases the code for reproducing the results in [STOP: A dataset for Spoken Task Oriented Semantic Parsing](https://arxiv.org/abs/2207.10643) + +The dataset can be downloaded here: [download link](https://dl.fbaipublicfiles.com/stop/stop.tar.gz) + +The low-resource splits can be downloaded here: [download link](http://dl.fbaipublicfiles.com/stop/low_resource_splits.tar.gz) + +## Pretrained models end-to-end NLU Models + +| Speech Pretraining | ASR Pretraining | Test EM Accuracy | Tesst EM-Tree Accuracy | Link | +| ----------- | ----------- |----------|----------|----------| +| None | None | 36.54 | 57.01 | [link](https://dl.fbaipublicfiles.com/stop/end-to-end-nlu-none-none.pt) | +| Wav2Vec | None | 68.05 | 82.53 | [link](https://dl.fbaipublicfiles.com/stop/end-to-end-nlu-wav2vec-none.pt) | +| HuBERT | None | 68.40 | 82.85 | [link](https://dl.fbaipublicfiles.com/stop/end-to-end-nlu-hubert-none.pt) | +| Wav2Vec | STOP | 68.70 | 82.78 | [link](https://dl.fbaipublicfiles.com/stop/end-to-end-nlu-wav2vec-stop.pt) | +| HuBERT | STOP | 69.23 | 82.87 | [link](https://dl.fbaipublicfiles.com/stop/end-to-end-nlu-hubert-stop.pt) | +| Wav2Vec | Librispeech | 68.47 | 82.49 | [link](https://dl.fbaipublicfiles.com/stop/end-to-end-nlu-wav2vec-ls.pt) | +| HuBERT | Librispeech | 68.70 | 82.78 | [link](https://dl.fbaipublicfiles.com/stop/end-to-end-nlu-hubert-ls.pt) | + +## Pretrained models ASR Models +| Speech Pre-training | ASR Dataset | STOP Eval WER | STOP Test WER | dev\_other WER | dev\_clean WER | test\_clean WER | test\_other WER | Link | +| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | +| HuBERT | Librispeech | 8.47 | 2.99 | 3.25 | 8.06 | 25.68 | 26.19 | [link](https://dl.fbaipublicfiles.com/stop/ctc-asr-hubert-ls.pt) | +| Wav2Vec | Librispeech | 9.215 | 3.204 | 3.334 | 9.006 | 27.257 | 27.588 | [link](https://dl.fbaipublicfiles.com/stop/ctc-asr-wav2vec-ls.pt) | +| HuBERT | STOP | 46.31 | 31.30 | 31.52 | 47.16 | 4.29 | 4.26 | [link](https://dl.fbaipublicfiles.com/stop/ctc-asr-hubert-stop.pt) | +| Wav2Vec | STOP | 43.103 | 27.833 | 28.479 | 28.479 | 4.679 | 4.667 | [link](https://dl.fbaipublicfiles.com/stop/ctc-asr-wav2vec-stop.pt) | +| HuBERT | Librispeech + STOP | 9.015 | 3.211 | 3.372 | 8.635 | 5.133 | 5.056 | [link](https://dl.fbaipublicfiles.com/stop/ctc-asr-hubert-ls-stop.pt) | +| Wav2Vec | Librispeech + STOP | 9.549 | 3.537 | 3.625 | 9.514 | 5.59 | 5.562 | [link](https://dl.fbaipublicfiles.com/stop/ctc-asr-wav2vec-ls-stop.pt) | + +## Creating the fairseq datasets from STOP + +First, create the audio file manifests and label files: + +``` +python examples/audio_nlp/nlu/generate_manifests.py --stop_root $STOP_DOWNLOAD_DIR/stop --output $FAIRSEQ_DATASET_OUTPUT/ +``` + + +Run `./examples/audio_nlp/nlu/create_dict_stop.sh $FAIRSEQ_DATASET_OUTPUT` to generate the fairseq dictionaries. + + +## Training an End-to-end NLU Model + + +Download a wav2vec or hubert model from [link](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert) or [link](https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec) + + +``` +python fairseq_cli/hydra-train --config-dir examples/audio_nlp/nlu/configs/ --config-name nlu_finetuning task.data=$FAIRSEQ_DATA_OUTPUT model.w2v_path=$PRETRAINED_MODEL_PATH +``` diff --git a/fairseq/examples/audio_nlp/nlu/configs/nlu_finetuning.yaml b/fairseq/examples/audio_nlp/nlu/configs/nlu_finetuning.yaml new file mode 100644 index 0000000..bb90f45 --- /dev/null +++ b/fairseq/examples/audio_nlp/nlu/configs/nlu_finetuning.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 10 + tensorboard_logdir: tb + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: em_error + save_interval: 10 + +task: + _name: nlu_finetuning + data: ??? + labels: parse + eval_wer_parse: true + autoregressive: true + +dataset: + num_workers: 6 + max_tokens: 1600000 + skip_invalid_size_inputs_valid_test: true + valid_subset: eval,test + train_subset: train + validate_interval: 10 + +criterion: + _name: label_smoothed_cross_entropy + +optimization: + max_update: 320000 + lr: [0.0001] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_seq2seq + w2v_path: ??? + autoregressive: true + apply_mask: true + mask_prob: 0.5 + mask_channel_prob: 0.5 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 0 diff --git a/fairseq/examples/audio_nlp/nlu/create_dict_stop.sh b/fairseq/examples/audio_nlp/nlu/create_dict_stop.sh new file mode 100644 index 0000000..7533932 --- /dev/null +++ b/fairseq/examples/audio_nlp/nlu/create_dict_stop.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +### Script handling creation of data binaries +### for model training within fairseq + + +fairseq_root="." + +data_root=$1 +train_prefix="${data_root}/train" +valid_prefix="${data_root}/eval" +test_prefix="${data_root}/test" + +dest_dir="$data_root/" + +#echo "src dict: $src_dict" > "$dest_dir/src_dict.txt" +#echo "trg dict: $tgt_dict" > "$dest_dir/tgt_dict.txt" + + #--tgtdict $tgt_dict \ +PYTHONPATH=$fairseq_root \ + python $fairseq_root/fairseq_cli/preprocess.py \ + --source-lang "parse" \ + --trainpref "$train_prefix" \ + --validpref "$valid_prefix" \ + --destdir "$dest_dir" \ + --only-source \ + --dict-only \ + --workers 60; + +PYTHONPATH=$fairseq_root \ + python $fairseq_root/fairseq_cli/preprocess.py \ + --source-lang "ltr" \ + --trainpref "$train_prefix" \ + --validpref "$valid_prefix" \ + --destdir "$dest_dir" \ + --only-source \ + --dict-only \ + --workers 60; diff --git a/fairseq/examples/audio_nlp/nlu/generate_manifests.py b/fairseq/examples/audio_nlp/nlu/generate_manifests.py new file mode 100644 index 0000000..e217609 --- /dev/null +++ b/fairseq/examples/audio_nlp/nlu/generate_manifests.py @@ -0,0 +1,83 @@ +import argparse +from pathlib import Path +import soundfile + +def get_insl_frame(parse): + out = [] + def is_ont_token(tok): + return tok[0] in ["[", "]"]; + + res = [] + x = [] + for tok in parse.split(): + if is_ont_token(tok): + res.extend('_'.join(x)) + x = [] + res.append(tok.upper()) + else: + x.append(tok.upper()) + + return " ".join(res) + ' | ' + +def sequencify_utterance(utterance): + utterance = utterance.upper() + utterance = utterance.replace(' ', '|') + '|' + utterance = list(utterance) + utterance = ' '.join(utterance) + return utterance + + +def generate_fairseq_manifests(manifest, output_path, audio_root=None): + + with open(manifest, 'r') as i: + parses = [] + utterances = [] + filepaths = [] + keys = None + for (idx, line) in enumerate(i): + if idx == 0: keys = line.strip().split('\t') + else: + data = { k: v for (k, v) in zip(keys, line.split('\t'))} + parses.append(get_insl_frame(data['decoupled_normalized_seqlogical'])) + utterances.append(sequencify_utterance(data['normalized_utterance'])) + filepaths.append(data['file_id']) + + parses_fp = output_path.with_suffix('.parse') + with open(str(parses_fp), 'w') as o: + for p in parses: + o.write(p + '\n') + + utterances_fp = output_path.with_suffix('.ltr') + with open(str(utterances_fp), 'w') as o: + for u in utterances: + o.write(u + '\n') + + filepaths_fp = output_path.with_suffix('.tsv') + with open(str(filepaths_fp), 'w') as o: + o.write(str(audio_root) + '\n') + for f in filepaths: + fullpath = audio_root / f + assert fullpath.exists(), f'{fullpath}' + frames = soundfile.info(fullpath).frames + o.write(f'{f}\t{frames}\n') + +def main(args): + + splits = ['train', 'eval', 'test'] + root = Path(args.stop_root) + output_root = Path(args.output) + + for split in splits: + stop_manifest_path = root / 'manifests' / (split + '.tsv') + output_path = output_root / (split) + + generate_fairseq_manifests(stop_manifest_path, output_path, root) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Process some integers.') + parser.add_argument('--stop_root', type=str, + help='path to stop root directory') + parser.add_argument('--output', type=str, + help='output directory') + args = parser.parse_args() + main(args) diff --git a/fairseq/examples/backtranslation/README.md b/fairseq/examples/backtranslation/README.md new file mode 100644 index 0000000..73675f1 --- /dev/null +++ b/fairseq/examples/backtranslation/README.md @@ -0,0 +1,297 @@ +# Understanding Back-Translation at Scale (Edunov et al., 2018) + +This page includes pre-trained models from the paper [Understanding Back-Translation at Scale (Edunov et al., 2018)](https://arxiv.org/abs/1808.09381). + +## Pre-trained models + +Model | Description | Dataset | Download +---|---|---|--- +`transformer.wmt18.en-de` | Transformer
([Edunov et al., 2018](https://arxiv.org/abs/1808.09381))
WMT'18 winner | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz)
See NOTE in the archive + +## Example usage (torch.hub) + +We require a few additional Python dependencies for preprocessing: +```bash +pip install subword_nmt sacremoses +``` + +Then to generate translations from the full model ensemble: +```python +import torch + +# List available models +torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt18.en-de', ... ] + +# Load the WMT'18 En-De ensemble +en2de_ensemble = torch.hub.load( + 'pytorch/fairseq', 'transformer.wmt18.en-de', + checkpoint_file='wmt18.model1.pt:wmt18.model2.pt:wmt18.model3.pt:wmt18.model4.pt:wmt18.model5.pt', + tokenizer='moses', bpe='subword_nmt') + +# The ensemble contains 5 models +len(en2de_ensemble.models) +# 5 + +# Translate +en2de_ensemble.translate('Hello world!') +# 'Hallo Welt!' +``` + +## Training your own model (WMT'18 English-German) + +The following instructions can be adapted to reproduce the models from the paper. + + +#### Step 1. Prepare parallel data and optionally train a baseline (English-German) model + +First download and preprocess the data: +```bash +# Download and prepare the data +cd examples/backtranslation/ +bash prepare-wmt18en2de.sh +cd ../.. + +# Binarize the data +TEXT=examples/backtranslation/wmt18_en_de +fairseq-preprocess \ + --joined-dictionary \ + --source-lang en --target-lang de \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/wmt18_en_de --thresholdtgt 0 --thresholdsrc 0 \ + --workers 20 + +# Copy the BPE code into the data-bin directory for future use +cp examples/backtranslation/wmt18_en_de/code data-bin/wmt18_en_de/code +``` + +(Optionally) Train a baseline model (English-German) using just the parallel data: +```bash +CHECKPOINT_DIR=checkpoints_en_de_parallel +fairseq-train --fp16 \ + data-bin/wmt18_en_de \ + --source-lang en --target-lang de \ + --arch transformer_wmt_en_de_big --share-all-embeddings \ + --dropout 0.3 --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --max-tokens 3584 --update-freq 16 \ + --max-update 30000 \ + --save-dir $CHECKPOINT_DIR +# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a +# different number of GPUs. +``` + +Average the last 10 checkpoints: +```bash +python scripts/average_checkpoints.py \ + --inputs $CHECKPOINT_DIR \ + --num-epoch-checkpoints 10 \ + --output $CHECKPOINT_DIR/checkpoint.avg10.pt +``` + +Evaluate BLEU: +```bash +# tokenized BLEU on newstest2017: +bash examples/backtranslation/tokenized_bleu.sh \ + wmt17 \ + en-de \ + data-bin/wmt18_en_de \ + data-bin/wmt18_en_de/code \ + $CHECKPOINT_DIR/checkpoint.avg10.pt +# BLEU4 = 29.57, 60.9/35.4/22.9/15.5 (BP=1.000, ratio=1.014, syslen=63049, reflen=62152) +# compare to 29.46 in Table 1, which is also for tokenized BLEU + +# generally it's better to report (detokenized) sacrebleu though: +bash examples/backtranslation/sacrebleu.sh \ + wmt17 \ + en-de \ + data-bin/wmt18_en_de \ + data-bin/wmt18_en_de/code \ + $CHECKPOINT_DIR/checkpoint.avg10.pt +# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 29.0 60.6/34.7/22.4/14.9 (BP = 1.000 ratio = 1.013 hyp_len = 62099 ref_len = 61287) +``` + + +#### Step 2. Back-translate monolingual German data + +Train a reverse model (German-English) to do the back-translation: +```bash +CHECKPOINT_DIR=checkpoints_de_en_parallel +fairseq-train --fp16 \ + data-bin/wmt18_en_de \ + --source-lang de --target-lang en \ + --arch transformer_wmt_en_de_big --share-all-embeddings \ + --dropout 0.3 --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --max-tokens 3584 --update-freq 16 \ + --max-update 30000 \ + --save-dir $CHECKPOINT_DIR +# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a +# different number of GPUs. +``` + +Let's evaluate the back-translation (BT) model to make sure it is well trained: +```bash +bash examples/backtranslation/sacrebleu.sh \ + wmt17 \ + de-en \ + data-bin/wmt18_en_de \ + data-bin/wmt18_en_de/code \ + $CHECKPOINT_DIR/checkpoint_best.py +# BLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 34.9 66.9/41.8/28.5/19.9 (BP = 0.983 ratio = 0.984 hyp_len = 63342 ref_len = 64399) +# compare to the best system from WMT'17 which scored 35.1: http://matrix.statmt.org/matrix/systems_list/1868 +``` + +Next prepare the monolingual data: +```bash +# Download and prepare the monolingual data +# By default the script samples 25M monolingual sentences, which after +# deduplication should be just over 24M sentences. These are split into 25 +# shards, each with 1M sentences (except for the last shard). +cd examples/backtranslation/ +bash prepare-de-monolingual.sh +cd ../.. + +# Binarize each shard of the monolingual data +TEXT=examples/backtranslation/wmt18_de_mono +for SHARD in $(seq -f "%02g" 0 24); do \ + fairseq-preprocess \ + --only-source \ + --source-lang de --target-lang en \ + --joined-dictionary \ + --srcdict data-bin/wmt18_en_de/dict.de.txt \ + --testpref $TEXT/bpe.monolingual.dedup.${SHARD} \ + --destdir data-bin/wmt18_de_mono/shard${SHARD} \ + --workers 20; \ + cp data-bin/wmt18_en_de/dict.en.txt data-bin/wmt18_de_mono/shard${SHARD}/; \ +done +``` + +Now we're ready to perform back-translation over the monolingual data. The +following command generates via sampling, but it's possible to use greedy +decoding (`--beam 1`), beam search (`--beam 5`), +top-k sampling (`--sampling --beam 1 --sampling-topk 10`), etc.: +```bash +mkdir backtranslation_output +for SHARD in $(seq -f "%02g" 0 24); do \ + fairseq-generate --fp16 \ + data-bin/wmt18_de_mono/shard${SHARD} \ + --path $CHECKPOINT_DIR/checkpoint_best.pt \ + --skip-invalid-size-inputs-valid-test \ + --max-tokens 4096 \ + --sampling --beam 1 \ + > backtranslation_output/sampling.shard${SHARD}.out; \ +done +``` + +After BT, use the `extract_bt_data.py` script to re-combine the shards, extract +the back-translations and apply length ratio filters: +```bash +python examples/backtranslation/extract_bt_data.py \ + --minlen 1 --maxlen 250 --ratio 1.5 \ + --output backtranslation_output/bt_data --srclang en --tgtlang de \ + backtranslation_output/sampling.shard*.out + +# Ensure lengths are the same: +# wc -l backtranslation_output/bt_data.{en,de} +# 21795614 backtranslation_output/bt_data.en +# 21795614 backtranslation_output/bt_data.de +# 43591228 total +``` + +Binarize the filtered BT data and combine it with the parallel data: +```bash +TEXT=backtranslation_output +fairseq-preprocess \ + --source-lang en --target-lang de \ + --joined-dictionary \ + --srcdict data-bin/wmt18_en_de/dict.en.txt \ + --trainpref $TEXT/bt_data \ + --destdir data-bin/wmt18_en_de_bt \ + --workers 20 + +# We want to train on the combined data, so we'll symlink the parallel + BT data +# in the wmt18_en_de_para_plus_bt directory. We link the parallel data as "train" +# and the BT data as "train1", so that fairseq will combine them automatically +# and so that we can use the `--upsample-primary` option to upsample the +# parallel data (if desired). +PARA_DATA=$(readlink -f data-bin/wmt18_en_de) +BT_DATA=$(readlink -f data-bin/wmt18_en_de_bt) +COMB_DATA=data-bin/wmt18_en_de_para_plus_bt +mkdir -p $COMB_DATA +for LANG in en de; do \ + ln -s ${PARA_DATA}/dict.$LANG.txt ${COMB_DATA}/dict.$LANG.txt; \ + for EXT in bin idx; do \ + ln -s ${PARA_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train.en-de.$LANG.$EXT; \ + ln -s ${BT_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train1.en-de.$LANG.$EXT; \ + ln -s ${PARA_DATA}/valid.en-de.$LANG.$EXT ${COMB_DATA}/valid.en-de.$LANG.$EXT; \ + ln -s ${PARA_DATA}/test.en-de.$LANG.$EXT ${COMB_DATA}/test.en-de.$LANG.$EXT; \ + done; \ +done +``` + + +#### 3. Train an English-German model over the combined parallel + BT data + +Finally we can train a model over the parallel + BT data: +```bash +CHECKPOINT_DIR=checkpoints_en_de_parallel_plus_bt +fairseq-train --fp16 \ + data-bin/wmt18_en_de_para_plus_bt \ + --upsample-primary 16 \ + --source-lang en --target-lang de \ + --arch transformer_wmt_en_de_big --share-all-embeddings \ + --dropout 0.3 --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr 0.0007 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --max-tokens 3584 --update-freq 16 \ + --max-update 100000 \ + --save-dir $CHECKPOINT_DIR +# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a +# different number of GPUs. +``` + +Average the last 10 checkpoints: +```bash +python scripts/average_checkpoints.py \ + --inputs $CHECKPOINT_DIR \ + --num-epoch-checkpoints 10 \ + --output $CHECKPOINT_DIR/checkpoint.avg10.pt +``` + +Evaluate BLEU: +```bash +# tokenized BLEU on newstest2017: +bash examples/backtranslation/tokenized_bleu.sh \ + wmt17 \ + en-de \ + data-bin/wmt18_en_de \ + data-bin/wmt18_en_de/code \ + $CHECKPOINT_DIR/checkpoint.avg10.pt +# BLEU4 = 32.35, 64.4/38.9/26.2/18.3 (BP=0.977, ratio=0.977, syslen=60729, reflen=62152) +# compare to 32.35 in Table 1, which is also for tokenized BLEU + +# generally it's better to report (detokenized) sacrebleu: +bash examples/backtranslation/sacrebleu.sh \ + wmt17 \ + en-de \ + data-bin/wmt18_en_de \ + data-bin/wmt18_en_de/code \ + $CHECKPOINT_DIR/checkpoint.avg10.pt +# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 31.5 64.3/38.2/25.6/17.6 (BP = 0.971 ratio = 0.971 hyp_len = 59515 ref_len = 61287) +``` + + +## Citation +```bibtex +@inproceedings{edunov2018backtranslation, + title = {Understanding Back-Translation at Scale}, + author = {Edunov, Sergey and Ott, Myle and Auli, Michael and Grangier, David}, + booktitle = {Conference of the Association for Computational Linguistics (ACL)}, + year = 2018, +} +``` diff --git a/fairseq/examples/backtranslation/deduplicate_lines.py b/fairseq/examples/backtranslation/deduplicate_lines.py new file mode 100644 index 0000000..50e4583 --- /dev/null +++ b/fairseq/examples/backtranslation/deduplicate_lines.py @@ -0,0 +1,41 @@ +#!/usr/bin/python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import fileinput +import hashlib +import sys +from multiprocessing import Pool + + +def get_hashes_and_lines(raw_line): + hash = hashlib.md5(raw_line).hexdigest() + return hash, raw_line + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--workers", type=int, default=10) + parser.add_argument("files", nargs="*", help="input files") + args = parser.parse_args() + + seen = set() + with fileinput.input(args.files, mode="rb") as h: + pool = Pool(args.workers) + results = pool.imap_unordered(get_hashes_and_lines, h, 1000) + for i, (hash, raw_line) in enumerate(results): + if hash not in seen: + seen.add(hash) + sys.stdout.buffer.write(raw_line) + if i % 1000000 == 0: + print(i, file=sys.stderr, end="", flush=True) + elif i % 100000 == 0: + print(".", file=sys.stderr, end="", flush=True) + print(file=sys.stderr, flush=True) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/backtranslation/extract_bt_data.py b/fairseq/examples/backtranslation/extract_bt_data.py new file mode 100644 index 0000000..e766391 --- /dev/null +++ b/fairseq/examples/backtranslation/extract_bt_data.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import fileinput + +from tqdm import tqdm + + +def main(): + parser = argparse.ArgumentParser( + description=( + "Extract back-translations from the stdout of fairseq-generate. " + "If there are multiply hypotheses for a source, we only keep the first one. " + ) + ) + parser.add_argument("--output", required=True, help="output prefix") + parser.add_argument( + "--srclang", required=True, help="source language (extracted from H-* lines)" + ) + parser.add_argument( + "--tgtlang", required=True, help="target language (extracted from S-* lines)" + ) + parser.add_argument("--minlen", type=int, help="min length filter") + parser.add_argument("--maxlen", type=int, help="max length filter") + parser.add_argument("--ratio", type=float, help="ratio filter") + parser.add_argument("files", nargs="*", help="input files") + args = parser.parse_args() + + def validate(src, tgt): + srclen = len(src.split(" ")) if src != "" else 0 + tgtlen = len(tgt.split(" ")) if tgt != "" else 0 + if ( + (args.minlen is not None and (srclen < args.minlen or tgtlen < args.minlen)) + or ( + args.maxlen is not None + and (srclen > args.maxlen or tgtlen > args.maxlen) + ) + or ( + args.ratio is not None + and (max(srclen, tgtlen) / float(min(srclen, tgtlen)) > args.ratio) + ) + ): + return False + return True + + def safe_index(toks, index, default): + try: + return toks[index] + except IndexError: + return default + + with open(args.output + "." + args.srclang, "w") as src_h, open( + args.output + "." + args.tgtlang, "w" + ) as tgt_h: + for line in tqdm(fileinput.input(args.files)): + if line.startswith("S-"): + tgt = safe_index(line.rstrip().split("\t"), 1, "") + elif line.startswith("H-"): + if tgt is not None: + src = safe_index(line.rstrip().split("\t"), 2, "") + if validate(src, tgt): + print(src, file=src_h) + print(tgt, file=tgt_h) + tgt = None + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/backtranslation/prepare-de-monolingual.sh b/fairseq/examples/backtranslation/prepare-de-monolingual.sh new file mode 100644 index 0000000..5e67b2b --- /dev/null +++ b/fairseq/examples/backtranslation/prepare-de-monolingual.sh @@ -0,0 +1,98 @@ +#!/bin/bash + +SCRIPTS=mosesdecoder/scripts +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl +BPEROOT=subword-nmt/subword_nmt + + +BPE_CODE=wmt18_en_de/code +SUBSAMPLE_SIZE=25000000 +LANG=de + + +OUTDIR=wmt18_${LANG}_mono +orig=orig +tmp=$OUTDIR/tmp +mkdir -p $OUTDIR $tmp + + +URLS=( + "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2007.de.shuffled.gz" + "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2008.de.shuffled.gz" + "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2009.de.shuffled.gz" + "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2010.de.shuffled.gz" + "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2011.de.shuffled.gz" + "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2012.de.shuffled.gz" + "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2013.de.shuffled.gz" + "http://www.statmt.org/wmt15/training-monolingual-news-crawl-v2/news.2014.de.shuffled.v2.gz" + "http://data.statmt.org/wmt16/translation-task/news.2015.de.shuffled.gz" + "http://data.statmt.org/wmt17/translation-task/news.2016.de.shuffled.gz" + "http://data.statmt.org/wmt18/translation-task/news.2017.de.shuffled.deduped.gz" +) +FILES=( + "news.2007.de.shuffled.gz" + "news.2008.de.shuffled.gz" + "news.2009.de.shuffled.gz" + "news.2010.de.shuffled.gz" + "news.2011.de.shuffled.gz" + "news.2012.de.shuffled.gz" + "news.2013.de.shuffled.gz" + "news.2014.de.shuffled.v2.gz" + "news.2015.de.shuffled.gz" + "news.2016.de.shuffled.gz" + "news.2017.de.shuffled.deduped.gz" +) + + +cd $orig +for ((i=0;i<${#URLS[@]};++i)); do + file=${FILES[i]} + if [ -f $file ]; then + echo "$file already exists, skipping download" + else + url=${URLS[i]} + wget "$url" + fi +done +cd .. + + +if [ -f $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} ]; then + echo "found monolingual sample, skipping shuffle/sample/tokenize" +else + gzip -c -d -k $(for FILE in "${FILES[@]}"; do echo $orig/$FILE; done) \ + | shuf -n $SUBSAMPLE_SIZE \ + | perl $NORM_PUNC $LANG \ + | perl $REM_NON_PRINT_CHAR \ + | perl $TOKENIZER -threads 8 -a -l $LANG \ + > $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} +fi + + +if [ -f $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} ]; then + echo "found BPE monolingual sample, skipping BPE step" +else + python $BPEROOT/apply_bpe.py -c $BPE_CODE \ + < $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} \ + > $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} +fi + + +if [ -f $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} ]; then + echo "found deduplicated monolingual sample, skipping deduplication step" +else + python deduplicate_lines.py $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} \ + > $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} +fi + + +if [ -f $OUTDIR/bpe.monolingual.dedup.00.de ]; then + echo "found sharded data, skipping sharding step" +else + split --lines 1000000 --numeric-suffixes \ + --additional-suffix .${LANG} \ + $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} \ + $OUTDIR/bpe.monolingual.dedup. +fi diff --git a/fairseq/examples/backtranslation/prepare-wmt18en2de.sh b/fairseq/examples/backtranslation/prepare-wmt18en2de.sh new file mode 100644 index 0000000..f6fd275 --- /dev/null +++ b/fairseq/examples/backtranslation/prepare-wmt18en2de.sh @@ -0,0 +1,135 @@ +#!/bin/bash +# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh + +echo 'Cloning Moses github repository (for tokenization scripts)...' +git clone https://github.com/moses-smt/mosesdecoder.git + +echo 'Cloning Subword NMT repository (for BPE pre-processing)...' +git clone https://github.com/rsennrich/subword-nmt.git + +SCRIPTS=mosesdecoder/scripts +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +CLEAN=$SCRIPTS/training/clean-corpus-n.perl +NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl +BPEROOT=subword-nmt/subword_nmt +BPE_TOKENS=32000 + +URLS=( + "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz" + "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz" + "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz" + "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz" + "http://data.statmt.org/wmt17/translation-task/dev.tgz" + "http://statmt.org/wmt14/test-full.tgz" +) +FILES=( + "training-parallel-europarl-v7.tgz" + "training-parallel-commoncrawl.tgz" + "training-parallel-nc-v13.tgz" + "rapid2016.tgz" + "dev.tgz" + "test-full.tgz" +) +CORPORA=( + "training/europarl-v7.de-en" + "commoncrawl.de-en" + "training-parallel-nc-v13/news-commentary-v13.de-en" + "rapid2016.de-en" +) + +if [ ! -d "$SCRIPTS" ]; then + echo "Please set SCRIPTS variable correctly to point to Moses scripts." + exit 1 +fi + +OUTDIR=wmt18_en_de + +src=en +tgt=de +lang=en-de +prep=$OUTDIR +tmp=$prep/tmp +orig=orig + +mkdir -p $orig $tmp $prep + +cd $orig + +for ((i=0;i<${#URLS[@]};++i)); do + file=${FILES[i]} + if [ -f $file ]; then + echo "$file already exists, skipping download" + else + url=${URLS[i]} + wget "$url" + if [ -f $file ]; then + echo "$url successfully downloaded." + else + echo "$url not successfully downloaded." + exit 1 + fi + if [ ${file: -4} == ".tgz" ]; then + tar zxvf $file + elif [ ${file: -4} == ".tar" ]; then + tar xvf $file + fi + fi +done +cd .. + +echo "pre-processing train data..." +for l in $src $tgt; do + rm $tmp/train.tags.$lang.tok.$l + for f in "${CORPORA[@]}"; do + cat $orig/$f.$l | \ + perl $NORM_PUNC $l | \ + perl $REM_NON_PRINT_CHAR | \ + perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l + done +done + +echo "pre-processing test data..." +for l in $src $tgt; do + if [ "$l" == "$src" ]; then + t="src" + else + t="ref" + fi + grep '\s*//g' | \ + sed -e 's/\s*<\/seg>\s*//g' | \ + sed -e "s/\’/\'/g" | \ + perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l + echo "" +done + +echo "splitting train and valid..." +for l in $src $tgt; do + awk '{if (NR%100 == 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l + awk '{if (NR%100 != 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l +done + +TRAIN=$tmp/train.de-en +BPE_CODE=$prep/code +rm -f $TRAIN +for l in $src $tgt; do + cat $tmp/train.$l >> $TRAIN +done + +echo "learn_bpe.py on ${TRAIN}..." +python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE + +for L in $src $tgt; do + for f in train.$L valid.$L test.$L; do + echo "apply_bpe.py to ${f}..." + python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f + done +done + +perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250 +perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250 + +for L in $src $tgt; do + cp $tmp/bpe.test.$L $prep/test.$L +done diff --git a/fairseq/examples/backtranslation/sacrebleu.sh b/fairseq/examples/backtranslation/sacrebleu.sh new file mode 100644 index 0000000..a70da23 --- /dev/null +++ b/fairseq/examples/backtranslation/sacrebleu.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +if [ $# -ne 5 ]; then + echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]" + exit +fi + + +DATASET=$1 +LANGPAIR=$2 +DATABIN=$3 +BPECODE=$4 +MODEL=$5 + +SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1) +TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2) + + +BPEROOT=examples/backtranslation/subword-nmt/subword_nmt +if [ ! -e $BPEROOT ]; then + BPEROOT=subword-nmt/subword_nmt + if [ ! -e $BPEROOT ]; then + echo 'Cloning Subword NMT repository (for BPE pre-processing)...' + git clone https://github.com/rsennrich/subword-nmt.git + fi +fi + + +sacrebleu -t $DATASET -l $LANGPAIR --echo src \ +| sacremoses tokenize -a -l $SRCLANG -q \ +| python $BPEROOT/apply_bpe.py -c $BPECODE \ +| fairseq-interactive $DATABIN --path $MODEL \ + -s $SRCLANG -t $TGTLANG \ + --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \ +| grep ^H- | cut -f 3- \ +| sacremoses detokenize -l $TGTLANG -q \ +| sacrebleu -t $DATASET -l $LANGPAIR diff --git a/fairseq/examples/backtranslation/tokenized_bleu.sh b/fairseq/examples/backtranslation/tokenized_bleu.sh new file mode 100644 index 0000000..c6d6aaa --- /dev/null +++ b/fairseq/examples/backtranslation/tokenized_bleu.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +if [ $# -ne 5 ]; then + echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]" + exit +fi + + +DATASET=$1 +LANGPAIR=$2 +DATABIN=$3 +BPECODE=$4 +MODEL=$5 + +SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1) +TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2) + + +BPEROOT=examples/backtranslation/subword-nmt/subword_nmt +if [ ! -e $BPEROOT ]; then + BPEROOT=subword-nmt/subword_nmt + if [ ! -e $BPEROOT ]; then + echo 'Cloning Subword NMT repository (for BPE pre-processing)...' + git clone https://github.com/rsennrich/subword-nmt.git + fi +fi + + +TMP_REF=$(mktemp) + +sacrebleu -t $DATASET -l $LANGPAIR --echo ref -q \ +| sacremoses normalize -l $TGTLANG -q \ +| sacremoses tokenize -a -l $TGTLANG -q \ +> $TMP_REF + +sacrebleu -t $DATASET -l $LANGPAIR --echo src -q \ +| sacremoses normalize -l $SRCLANG -q \ +| sacremoses tokenize -a -l $SRCLANG -q \ +| python $BPEROOT/apply_bpe.py -c $BPECODE \ +| fairseq-interactive $DATABIN --path $MODEL \ + -s $SRCLANG -t $TGTLANG \ + --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \ +| grep ^H- | cut -f 3- \ +| fairseq-score --ref $TMP_REF + +rm -f $TMP_REF diff --git a/fairseq/examples/bart/README.glue.md b/fairseq/examples/bart/README.glue.md new file mode 100644 index 0000000..a010934 --- /dev/null +++ b/fairseq/examples/bart/README.glue.md @@ -0,0 +1,99 @@ +# Fine-tuning BART on GLUE tasks + +### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands: +```bash +wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py +python download_glue_data.py --data_dir glue_data --tasks all +``` + +### 2) Preprocess GLUE task data (same as RoBERTa): +```bash +./examples/roberta/preprocess_GLUE_tasks.sh glue_data +``` +`glue_task_name` is one of the following: +`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}` +Use `ALL` for preprocessing all the glue tasks. + +### 3) Fine-tuning on GLUE task: +Example fine-tuning cmd for `RTE` task +```bash +TOTAL_NUM_UPDATES=2036 # 10 epochs through RTE for bsz 16 +WARMUP_UPDATES=61 # 6 percent of the number of updates +LR=1e-05 # Peak LR for polynomial LR scheduler. +NUM_CLASSES=2 +MAX_SENTENCES=16 # Batch size. +BART_PATH=/path/to/bart/model.pt + +CUDA_VISIBLE_DEVICES=0,1 fairseq-train RTE-bin/ \ + --restore-file $BART_PATH \ + --batch-size $MAX_SENTENCES \ + --max-tokens 4400 \ + --task sentence_prediction \ + --add-prev-output-tokens \ + --layernorm-embedding \ + --share-all-embeddings \ + --share-decoder-input-output-embed \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --init-token 0 \ + --arch bart_large \ + --criterion sentence_prediction \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --max-epoch 10 \ + --find-unused-parameters \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric; +``` + +For each of the GLUE task, you will need to use following cmd-line arguments: + +Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B +---|---|---|---|---|---|---|---|--- +`--num-classes` | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 1 +`--lr` | 5e-6 | 1e-5 | 1e-5 | 1e-5 | 5e-6 | 2e-5 | 2e-5 | 2e-5 +`bsz` | 128 | 32 | 32 | 32 | 128 | 64 | 64 | 32 +`--total-num-update` | 30968 | 33112 | 113272 | 1018 | 5233 | 1148 | 1334 | 1799 +`--warmup-updates` | 1858 | 1986 | 6796 | 61 | 314 | 68 | 80 | 107 + +For `STS-B` additionally add `--regression-target --best-checkpoint-metric loss` and remove `--maximize-best-checkpoint-metric`. + +**Note:** + +a) `--total-num-updates` is used by `--polynomial_decay` scheduler and is calculated for `--max-epoch=10` and `--batch-size=32/64/128` depending on the task. + +b) Above cmd-args and hyperparams are tested on Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. + +### Inference on GLUE task +After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet: + +```python +from fairseq.models.bart import BARTModel + +bart = BARTModel.from_pretrained( + 'checkpoints/', + checkpoint_file='checkpoint_best.pt', + data_name_or_path='RTE-bin' +) + +label_fn = lambda label: bart.task.label_dictionary.string( + [label + bart.task.label_dictionary.nspecial] +) +ncorrect, nsamples = 0, 0 +bart.cuda() +bart.eval() +with open('glue_data/RTE/dev.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[1], tokens[2], tokens[3] + tokens = bart.encode(sent1, sent2) + prediction = bart.predict('sentence_classification_head', tokens).argmax().item() + prediction_label = label_fn(prediction) + ncorrect += int(prediction_label == target) + nsamples += 1 +print('| Accuracy: ', float(ncorrect)/float(nsamples)) +``` diff --git a/fairseq/examples/bart/README.md b/fairseq/examples/bart/README.md new file mode 100644 index 0000000..4050a72 --- /dev/null +++ b/fairseq/examples/bart/README.md @@ -0,0 +1,228 @@ +# BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension + +[https://arxiv.org/abs/1910.13461](https://arxiv.org/abs/1910.13461) + +## Introduction + +BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match [RoBERTa](../roberta) results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details. + +## Pre-trained models + +Model | Description | # params | Download +---|---|---|--- +`bart.base` | BART model with 6 encoder and decoder layers | 140M | [bart.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz) +`bart.large` | BART model with 12 encoder and decoder layers | 400M | [bart.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz) +`bart.large.mnli` | `bart.large` finetuned on `MNLI` | 400M | [bart.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz) +`bart.large.cnn` | `bart.large` finetuned on `CNN-DM` | 400M | [bart.large.cnn.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz) +`bart.large.xsum` | `bart.large` finetuned on `Xsum` | 400M | [bart.large.xsum.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz) + +## Results + +**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)** +_(dev set, single model, single-task finetuning)_ + +Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B +---|---|---|---|---|---|---|---|--- +`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4 +`bart.large` | 89.9 | 94.9 | 92.5 | 87.0 | 96.6 | 90.4 | 62.8 | 91.2 + +**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)** +_(dev set, no additional data used)_ + +Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1 +---|---|--- +`roberta.large` | 88.9/94.6 | 86.5/89.4 +`bart.large` | 88.8/94.6 | 86.1/89.2 + +**[CNN/Daily Mail](http://nlpprogress.com/english/summarization.html)** +_(test set, no additional data used)_ + +Model | R1 | R2 | RL +---|---|---|--- +`BERTSUMEXTABS` | 42.13 | 19.60 | 39.18 +`bart.large` | 44.16 | 21.28 | 40.90 + +## Example usage + +##### Load BART from torch.hub (PyTorch >= 1.1): +```python +import torch +bart = torch.hub.load('pytorch/fairseq', 'bart.large') +bart.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Load BART (for PyTorch 1.0 or custom models): +```python +# Download bart.large model +wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz +tar -xzvf bart.large.tar.gz + +# Load the model in fairseq +from fairseq.models.bart import BARTModel +bart = BARTModel.from_pretrained('/path/to/bart.large', checkpoint_file='model.pt') +bart.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Apply Byte-Pair Encoding (BPE) to input text: +```python +tokens = bart.encode('Hello world!') +assert tokens.tolist() == [0, 31414, 232, 328, 2] +bart.decode(tokens) # 'Hello world!' +``` + +##### Extract features from BART: +```python +# Extract the last layer's features +last_layer_features = bart.extract_features(tokens) +assert last_layer_features.size() == torch.Size([1, 5, 1024]) + +# Extract all layer's features from decoder (layer 0 is the embedding layer) +all_layers = bart.extract_features(tokens, return_all_hiddens=True) +assert len(all_layers) == 13 +assert torch.all(all_layers[-1] == last_layer_features) +``` + +##### Use BART for sentence-pair classification tasks: +```python +# Download BART already finetuned for MNLI +bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli') +bart.eval() # disable dropout for evaluation + +# Encode a pair of sentences and make a prediction +tokens = bart.encode('BART is a seq2seq model.', 'BART is not sequence to sequence.') +bart.predict('mnli', tokens).argmax() # 0: contradiction + +# Encode another pair of sentences +tokens = bart.encode('BART is denoising autoencoder.', 'BART is version of autoencoder.') +bart.predict('mnli', tokens).argmax() # 2: entailment +``` + +##### Register a new (randomly initialized) classification head: +```python +bart.register_classification_head('new_task', num_classes=3) +logprobs = bart.predict('new_task', tokens) +``` + +##### Batched prediction: +```python +import torch +from fairseq.data.data_utils import collate_tokens + +bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli') +bart.eval() + +batch_of_pairs = [ + ['BART is a seq2seq model.', 'BART is not sequence to sequence.'], + ['BART is denoising autoencoder.', 'BART is version of autoencoder.'], +] + +batch = collate_tokens( + [bart.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1 +) + +logprobs = bart.predict('mnli', batch) +print(logprobs.argmax(dim=1)) +# tensor([0, 2]) +``` + +##### Using the GPU: +```python +bart.cuda() +bart.predict('new_task', tokens) +``` + +#### Filling masks: + +BART can be used to fill multiple `` tokens in the input. +```python +bart = torch.hub.load('pytorch/fairseq', 'bart.base') +bart.eval() +bart.fill_mask(['The cat on the .'], topk=3, beam=10) +# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))]] +``` + +Note that by default we enforce the output length to match the input length. +This can be disabled by setting ``match_source_len=False``: +``` +bart.fill_mask(['The cat on the .'], topk=3, beam=10, match_source_len=False) +# [[('The cat was on the ground.', tensor(-0.6185)), ('The cat was asleep on the couch.', tensor(-0.6276)), ('The cat was on the floor.', tensor(-0.6800))]] +``` + +Example code to fill masks for a batch of sentences using GPU +``` +bart.cuda() +bart.fill_mask(['The cat on the .', 'The dog on the .'], topk=3, beam=10) +# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))], [('The dog was on the ground.', tensor(-0.6190)), ('The dog lay on the ground.', tensor(-0.6711)), +('The dog was asleep on the couch', tensor(-0.6796))]] +``` + +#### Evaluating the `bart.large.mnli` model: + +Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set. +```python +label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'} +ncorrect, nsamples = 0, 0 +bart.cuda() +bart.eval() +with open('glue_data/MNLI/dev_matched.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[8], tokens[9], tokens[-1] + tokens = bart.encode(sent1, sent2) + prediction = bart.predict('mnli', tokens).argmax().item() + prediction_label = label_map[prediction] + ncorrect += int(prediction_label == target) + nsamples += 1 + print('| Accuracy: ', float(ncorrect)/float(nsamples)) +# Expected output: 0.9010 +``` + +#### Evaluating the `bart.large.cnn` model: +- Follow instructions [here](https://github.com/abisee/cnn-dailymail) to download and process into data-files such that `test.source` and `test.target` has one line for each non-tokenized sample. +- For simpler preprocessing, you can also `wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz`, although there is no guarantee of identical scores +- `huggingface/transformers` has a simpler interface that supports [single-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_eval.py) and [multi-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_distributed_eval.py) beam search. + In `huggingface/transformers`, the BART models' paths are `facebook/bart-large-cnn` and `facebook/bart-large-xsum`. + +In `fairseq`, summaries can be generated using: + +```bash +cp data-bin/cnn_dm/dict.source.txt checkpoints/ +python examples/bart/summarize.py \ + --model-dir pytorch/fairseq \ + --model-file bart.large.cnn \ + --src cnn_dm/test.source \ + --out cnn_dm/test.hypo +``` + +For calculating rouge, install `files2rouge` from [here](https://github.com/pltrdy/files2rouge). + +```bash +export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar + +# Tokenize hypothesis and target files. +cat test.hypo | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.tokenized +cat test.target | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.target +files2rouge test.hypo.tokenized test.hypo.target +# Expected output: (ROUGE-2 Average_F: 0.21238) +``` + + +## Finetuning + +- [Finetuning on GLUE](README.glue.md) +- [Finetuning on CNN-DM](README.summarization.md) + +## Citation + +```bibtex +@article{lewis2019bart, + title = {BART: Denoising Sequence-to-Sequence Pre-training for Natural +Language Generation, Translation, and Comprehension}, + author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and + Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov + and Luke Zettlemoyer }, + journal={arXiv preprint arXiv:1910.13461}, + year = {2019}, +} +``` diff --git a/fairseq/examples/bart/README.summarization.md b/fairseq/examples/bart/README.summarization.md new file mode 100644 index 0000000..8727584 --- /dev/null +++ b/fairseq/examples/bart/README.summarization.md @@ -0,0 +1,102 @@ +# Fine-tuning BART on CNN-Dailymail summarization task + +### 1) Download the CNN and Daily Mail data and preprocess it into data files with non-tokenized cased samples. + +Follow the instructions [here](https://github.com/abisee/cnn-dailymail) to download the original CNN and Daily Mail datasets. To preprocess the data, refer to the pointers in [this issue](https://github.com/pytorch/fairseq/issues/1391) or check out the code [here](https://github.com/artmatsak/cnn-dailymail). + +Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to download the original Extreme Summarization datasets, or check out the code [here](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset), Please keep the raw dataset and make sure no tokenization nor BPE on the dataset. + +### 2) BPE preprocess: + +```bash +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' + +TASK=cnn_dm +for SPLIT in train val +do + for LANG in source target + do + python -m examples.roberta.multiprocessing_bpe_encoder \ + --encoder-json encoder.json \ + --vocab-bpe vocab.bpe \ + --inputs "$TASK/$SPLIT.$LANG" \ + --outputs "$TASK/$SPLIT.bpe.$LANG" \ + --workers 60 \ + --keep-empty; + done +done +``` + +### 3) Binarize dataset: +```bash +fairseq-preprocess \ + --source-lang "source" \ + --target-lang "target" \ + --trainpref "${TASK}/train.bpe" \ + --validpref "${TASK}/val.bpe" \ + --destdir "${TASK}-bin/" \ + --workers 60 \ + --srcdict dict.txt \ + --tgtdict dict.txt; +``` + +### 4) Fine-tuning on CNN-DM summarization task: +Example fine-tuning CNN-DM +```bash +TOTAL_NUM_UPDATES=20000 +WARMUP_UPDATES=500 +LR=3e-05 +MAX_TOKENS=2048 +UPDATE_FREQ=4 +BART_PATH=/path/to/bart/model.pt + +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train cnn_dm-bin \ + --restore-file $BART_PATH \ + --max-tokens $MAX_TOKENS \ + --task translation \ + --source-lang source --target-lang target \ + --truncate-source \ + --layernorm-embedding \ + --share-all-embeddings \ + --share-decoder-input-output-embed \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --arch bart_large \ + --criterion label_smoothed_cross_entropy \ + --label-smoothing 0.1 \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \ + --clip-norm 0.1 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --update-freq $UPDATE_FREQ \ + --skip-invalid-size-inputs-valid-test \ + --find-unused-parameters; +``` +Above is expected to run on `1` node with `8 32gb-V100`. +Expected training time is about `5 hours`. Training time can be reduced with distributed training on `4` nodes and `--update-freq 1`. + +Use TOTAL_NUM_UPDATES=15000 UPDATE_FREQ=2 for Xsum task + +### Inference for CNN-DM test data using above trained checkpoint. +After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using `eval_cnn.py`, for example + +```bash +cp data-bin/cnn_dm/dict.source.txt checkpoints/ +python examples/bart/summarize.py \ + --model-dir checkpoints \ + --model-file checkpoint_best.pt \ + --src cnn_dm/test.source \ + --out cnn_dm/test.hypo +``` +For XSUM, which uses beam=6, lenpen=1.0, max_len_b=60, min_len=10: +```bash +cp data-bin/cnn_dm/dict.source.txt checkpoints/ +python examples/bart/summarize.py \ + --model-dir checkpoints \ + --model-file checkpoint_best.pt \ + --src cnn_dm/test.source \ + --out cnn_dm/test.hypo \ + --xsum-kwargs +``` diff --git a/fairseq/examples/bart/summarize.py b/fairseq/examples/bart/summarize.py new file mode 100644 index 0000000..04435f8 --- /dev/null +++ b/fairseq/examples/bart/summarize.py @@ -0,0 +1,100 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq.models.bart import BARTModel +import argparse + +XSUM_KWARGS = dict(beam=6, lenpen=1.0, max_len_b=60, min_len=10, no_repeat_ngram_size=3) +CNN_KWARGS = dict(beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) + + +@torch.no_grad() +def generate(bart, infile, outfile="bart_hypo.txt", bsz=32, n_obs=None, **eval_kwargs): + count = 1 + + # if n_obs is not None: bsz = min(bsz, n_obs) + + with open(infile) as source, open(outfile, "w") as fout: + sline = source.readline().strip() + slines = [sline] + for sline in source: + if n_obs is not None and count > n_obs: + break + if count % bsz == 0: + hypotheses_batch = bart.sample(slines, **eval_kwargs) + for hypothesis in hypotheses_batch: + fout.write(hypothesis + "\n") + fout.flush() + slines = [] + + slines.append(sline.strip()) + count += 1 + + if slines != []: + hypotheses_batch = bart.sample(slines, **eval_kwargs) + for hypothesis in hypotheses_batch: + fout.write(hypothesis + "\n") + fout.flush() + + +def main(): + """ + Usage:: + + python examples/bart/summarize.py \ + --model-dir $HOME/bart.large.cnn \ + --model-file model.pt \ + --src $HOME/data-bin/cnn_dm/test.source + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "--model-dir", + required=True, + type=str, + default="bart.large.cnn/", + help="path containing model file and src_dict.txt", + ) + parser.add_argument( + "--model-file", + default="checkpoint_best.pt", + help="where in model_dir are weights saved", + ) + parser.add_argument( + "--src", default="test.source", help="text to summarize", type=str + ) + parser.add_argument( + "--out", default="test.hypo", help="where to save summaries", type=str + ) + parser.add_argument("--bsz", default=32, help="where to save summaries", type=int) + parser.add_argument( + "--n", default=None, help="how many examples to summarize", type=int + ) + parser.add_argument( + "--xsum-kwargs", + action="store_true", + default=False, + help="if true use XSUM_KWARGS else CNN_KWARGS", + ) + args = parser.parse_args() + eval_kwargs = XSUM_KWARGS if args.xsum_kwargs else CNN_KWARGS + if args.model_dir == "pytorch/fairseq": + bart = torch.hub.load("pytorch/fairseq", args.model_file) + else: + bart = BARTModel.from_pretrained( + args.model_dir, + checkpoint_file=args.model_file, + data_name_or_path=args.model_dir, + ) + bart = bart.eval() + if torch.cuda.is_available(): + bart = bart.cuda().half() + generate( + bart, args.src, bsz=args.bsz, n_obs=args.n, outfile=args.out, **eval_kwargs + ) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/byte_level_bpe/README.md b/fairseq/examples/byte_level_bpe/README.md new file mode 100644 index 0000000..6570926 --- /dev/null +++ b/fairseq/examples/byte_level_bpe/README.md @@ -0,0 +1,88 @@ +# Neural Machine Translation with Byte-Level Subwords + +https://arxiv.org/abs/1909.03341 + +We provide an implementation of byte-level byte-pair encoding (BBPE), taking IWSLT 2017 Fr-En translation as +example. + +## Data +Get data and generate fairseq binary dataset: +```bash +bash ./get_data.sh +``` + +## Model Training +Train Transformer model with Bi-GRU embedding contextualization (implemented in `gru_transformer.py`): +```bash +# VOCAB=bytes +# VOCAB=chars +VOCAB=bbpe2048 +# VOCAB=bpe2048 +# VOCAB=bbpe4096 +# VOCAB=bpe4096 +# VOCAB=bpe16384 +``` +```bash +fairseq-train "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ + --arch gru_transformer --encoder-layers 2 --decoder-layers 2 --dropout 0.3 --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --log-format 'simple' --log-interval 100 --save-dir "checkpoints/${VOCAB}" \ + --batch-size 100 --max-update 100000 --update-freq 2 +``` + +## Generation +`fairseq-generate` requires bytes (BBPE) decoder to convert byte-level representation back to characters: +```bash +# BPE=--bpe bytes +# BPE=--bpe characters +BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe2048.model +# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe2048.model +# BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe4096.model +# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe4096.model +# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe16384.model +``` + +```bash +fairseq-generate "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ + --source-lang fr --gen-subset test --sacrebleu --path "checkpoints/${VOCAB}/checkpoint_last.pt" \ + --tokenizer moses --moses-target-lang en ${BPE} +``` +When using `fairseq-interactive`, bytes (BBPE) encoder/decoder is required to tokenize input data and detokenize model predictions: +```bash +fairseq-interactive "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ + --path "checkpoints/${VOCAB}/checkpoint_last.pt" --input data/test.fr --tokenizer moses --moses-source-lang fr \ + --moses-target-lang en ${BPE} --buffer-size 1000 --max-tokens 10000 +``` + +## Results +| Vocabulary | Model | BLEU | +|:-------------:|:-------------:|:-------------:| +| Joint BPE 16k ([Kudo, 2018](https://arxiv.org/abs/1804.10959)) | 512d LSTM 2+2 | 33.81 | +| Joint BPE 16k | Transformer base 2+2 (w/ GRU) | 36.64 (36.72) | +| Joint BPE 4k | Transformer base 2+2 (w/ GRU) | 35.49 (36.10) | +| Joint BBPE 4k | Transformer base 2+2 (w/ GRU) | 35.61 (35.82) | +| Joint BPE 2k | Transformer base 2+2 (w/ GRU) | 34.87 (36.13) | +| Joint BBPE 2k | Transformer base 2+2 (w/ GRU) | 34.98 (35.43) | +| Characters | Transformer base 2+2 (w/ GRU) | 31.78 (33.30) | +| Bytes | Transformer base 2+2 (w/ GRU) | 31.57 (33.62) | + + +## Citation +``` +@misc{wang2019neural, + title={Neural Machine Translation with Byte-Level Subwords}, + author={Changhan Wang and Kyunghyun Cho and Jiatao Gu}, + year={2019}, + eprint={1909.03341}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + + +## Contact +Changhan Wang ([changhan@fb.com](mailto:changhan@fb.com)), +Kyunghyun Cho ([kyunghyuncho@fb.com](mailto:kyunghyuncho@fb.com)), +Jiatao Gu ([jgu@fb.com](mailto:jgu@fb.com)) diff --git a/fairseq/examples/byte_level_bpe/get_bitext.py b/fairseq/examples/byte_level_bpe/get_bitext.py new file mode 100644 index 0000000..6ac1eee --- /dev/null +++ b/fairseq/examples/byte_level_bpe/get_bitext.py @@ -0,0 +1,254 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import argparse +import os +import os.path as op +from collections import namedtuple +from multiprocessing import cpu_count +from typing import List, Optional + +import sentencepiece as sp +from fairseq.data.encoders.byte_bpe import ByteBPE +from fairseq.data.encoders.byte_utils import byte_encode +from fairseq.data.encoders.bytes import Bytes +from fairseq.data.encoders.characters import Characters +from fairseq.data.encoders.moses_tokenizer import MosesTokenizer +from fairseq.data.encoders.sentencepiece_bpe import SentencepieceBPE + + +SPLITS = ["train", "valid", "test"] + + +def _convert_xml(in_path: str, out_path: str): + with open(in_path) as f, open(out_path, "w") as f_o: + for s in f: + ss = s.strip() + if not ss.startswith("", "").split('">') + assert len(ss) == 2 + f_o.write(ss[1].strip() + "\n") + + +def _convert_train(in_path: str, out_path: str): + with open(in_path) as f, open(out_path, "w") as f_o: + for s in f: + ss = s.strip() + if ss.startswith("<"): + continue + f_o.write(ss.strip() + "\n") + + +def _get_bytes(in_path: str, out_path: str): + with open(in_path) as f, open(out_path, "w") as f_o: + for s in f: + f_o.write(Bytes.encode(s.strip()) + "\n") + + +def _get_chars(in_path: str, out_path: str): + with open(in_path) as f, open(out_path, "w") as f_o: + for s in f: + f_o.write(Characters.encode(s.strip()) + "\n") + + +def pretokenize(in_path: str, out_path: str, src: str, tgt: str): + Args = namedtuple( + "Args", + [ + "moses_source_lang", + "moses_target_lang", + "moses_no_dash_splits", + "moses_no_escape", + ], + ) + args = Args( + moses_source_lang=src, + moses_target_lang=tgt, + moses_no_dash_splits=False, + moses_no_escape=False, + ) + pretokenizer = MosesTokenizer(args) + with open(in_path) as f, open(out_path, "w") as f_o: + for s in f: + f_o.write(pretokenizer.encode(s.strip()) + "\n") + + +def _convert_to_bchar(in_path_prefix: str, src: str, tgt: str, out_path: str): + with open(out_path, "w") as f_o: + for lang in [src, tgt]: + with open(f"{in_path_prefix}.{lang}") as f: + for s in f: + f_o.write(byte_encode(s.strip()) + "\n") + + +def _get_bpe(in_path: str, model_prefix: str, vocab_size: int): + arguments = [ + f"--input={in_path}", + f"--model_prefix={model_prefix}", + f"--model_type=bpe", + f"--vocab_size={vocab_size}", + "--character_coverage=1.0", + "--normalization_rule_name=identity", + f"--num_threads={cpu_count()}", + ] + sp.SentencePieceTrainer.Train(" ".join(arguments)) + + +def _apply_bbpe(model_path: str, in_path: str, out_path: str): + Args = namedtuple("Args", ["sentencepiece_model_path"]) + args = Args(sentencepiece_model_path=model_path) + tokenizer = ByteBPE(args) + with open(in_path) as f, open(out_path, "w") as f_o: + for s in f: + f_o.write(tokenizer.encode(s.strip()) + "\n") + + +def _apply_bpe(model_path: str, in_path: str, out_path: str): + Args = namedtuple("Args", ["sentencepiece_model"]) + args = Args(sentencepiece_model=model_path) + tokenizer = SentencepieceBPE(args) + with open(in_path) as f, open(out_path, "w") as f_o: + for s in f: + f_o.write(tokenizer.encode(s.strip()) + "\n") + + +def _concat_files(in_paths: List[str], out_path: str): + with open(out_path, "w") as f_o: + for p in in_paths: + with open(p) as f: + for r in f: + f_o.write(r) + + +def preprocess_iwslt17( + root: str, + src: str, + tgt: str, + bpe_size: Optional[int], + need_chars: bool, + bbpe_size: Optional[int], + need_bytes: bool, +): + # extract bitext + in_root = op.join(root, f"{src}-{tgt}") + for lang in [src, tgt]: + _convert_train( + op.join(in_root, f"train.tags.{src}-{tgt}.{lang}"), + op.join(root, f"train.{lang}"), + ) + _convert_xml( + op.join(in_root, f"IWSLT17.TED.dev2010.{src}-{tgt}.{lang}.xml"), + op.join(root, f"valid.{lang}"), + ) + _convert_xml( + op.join(in_root, f"IWSLT17.TED.tst2015.{src}-{tgt}.{lang}.xml"), + op.join(root, f"test.{lang}"), + ) + # pre-tokenize + for lang in [src, tgt]: + for split in SPLITS: + pretokenize( + op.join(root, f"{split}.{lang}"), + op.join(root, f"{split}.moses.{lang}"), + src, + tgt, + ) + # tokenize with BPE vocabulary + if bpe_size is not None: + # learn vocabulary + concated_train_path = op.join(root, "train.all") + _concat_files( + [op.join(root, "train.moses.fr"), op.join(root, "train.moses.en")], + concated_train_path, + ) + bpe_model_prefix = op.join(root, f"spm_bpe{bpe_size}") + _get_bpe(concated_train_path, bpe_model_prefix, bpe_size) + os.remove(concated_train_path) + # apply + for lang in [src, tgt]: + for split in SPLITS: + _apply_bpe( + bpe_model_prefix + ".model", + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.bpe{bpe_size}.{lang}"), + ) + # tokenize with bytes vocabulary + if need_bytes: + for lang in [src, tgt]: + for split in SPLITS: + _get_bytes( + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.bytes.{lang}"), + ) + # tokenize with characters vocabulary + if need_chars: + for lang in [src, tgt]: + for split in SPLITS: + _get_chars( + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.chars.{lang}"), + ) + # tokenize with byte-level BPE vocabulary + if bbpe_size is not None: + # learn vocabulary + bchar_path = op.join(root, "train.bchar") + _convert_to_bchar(op.join(root, "train.moses"), src, tgt, bchar_path) + bbpe_model_prefix = op.join(root, f"spm_bbpe{bbpe_size}") + _get_bpe(bchar_path, bbpe_model_prefix, bbpe_size) + os.remove(bchar_path) + # apply + for lang in [src, tgt]: + for split in SPLITS: + _apply_bbpe( + bbpe_model_prefix + ".model", + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.bbpe{bbpe_size}.{lang}"), + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--root", type=str, default="data") + parser.add_argument( + "--bpe-vocab", + default=None, + type=int, + help="Generate tokenized bitext with BPE of size K." + "Default to None (disabled).", + ) + parser.add_argument( + "--bbpe-vocab", + default=None, + type=int, + help="Generate tokenized bitext with BBPE of size K." + "Default to None (disabled).", + ) + parser.add_argument( + "--byte-vocab", + action="store_true", + help="Generate tokenized bitext with bytes vocabulary", + ) + parser.add_argument( + "--char-vocab", + action="store_true", + help="Generate tokenized bitext with chars vocabulary", + ) + args = parser.parse_args() + + preprocess_iwslt17( + args.root, + "fr", + "en", + args.bpe_vocab, + args.char_vocab, + args.bbpe_vocab, + args.byte_vocab, + ) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/byte_level_bpe/get_data.sh b/fairseq/examples/byte_level_bpe/get_data.sh new file mode 100644 index 0000000..c3d55d4 --- /dev/null +++ b/fairseq/examples/byte_level_bpe/get_data.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +PY_BIN_ROOT= + +# PyPI dependency +${PY_BIN_ROOT}pip install sentencepiece sacremoses + +# Get data +if [ ! -d "data" ]; then + mkdir data +fi + +if [ ! -f "data/fr-en.tgz" ]; then + wget https://wit3.fbk.eu/archive/2017-01-trnted/texts/fr/en/fr-en.tgz -P data + tar xvf data/fr-en.tgz -C data +fi +${PY_BIN_ROOT}python get_bitext.py --bpe-vocab 16384 --byte-vocab --char-vocab +for VOCAB_SIZE in 2048 4096; do + ${PY_BIN_ROOT}python get_bitext.py --bpe-vocab ${VOCAB_SIZE} --bbpe-vocab ${VOCAB_SIZE} +done +rm -r data/fr-en data/fr-en.tgz + +# Generate binary dataset +${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bpe16384 --joined-dictionary \ + --workers "$(nproc)" --trainpref data/train.moses.bpe16384 --validpref data/valid.moses.bpe16384 \ + --testpref data/test.moses.bpe16384 + +${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bytes --joined-dictionary \ + --workers "$(nproc)" --trainpref data/train.moses.bytes --validpref data/valid.moses.bytes \ + --testpref data/test.moses.bytes + +${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_chars --joined-dictionary \ + --workers "$(nproc)" --trainpref data/train.moses.chars --validpref data/valid.moses.chars \ + --testpref data/test.moses.chars + +for VOCAB_SIZE in 2048 4096; do + for TYPE in bbpe bpe; do + ${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir "data/bin_${TYPE}${VOCAB_SIZE}" \ + --joined-dictionary --workers "$(nproc)" --trainpref "data/train.moses.${TYPE}${VOCAB_SIZE}" \ + --validpref "data/valid.moses.${TYPE}${VOCAB_SIZE}" --testpref "data/test.moses.${TYPE}${VOCAB_SIZE}" + done +done diff --git a/fairseq/examples/byte_level_bpe/gru_transformer.py b/fairseq/examples/byte_level_bpe/gru_transformer.py new file mode 100644 index 0000000..d4efa93 --- /dev/null +++ b/fairseq/examples/byte_level_bpe/gru_transformer.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch.nn.functional as F +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer import TransformerEncoder, TransformerModel + + +@register_model("gru_transformer") +class GRUTransformerModel(TransformerModel): + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return GRUTransformerEncoder(args, src_dict, embed_tokens) + + +class GRUTransformerEncoder(TransformerEncoder): + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens) + self.emb_ctx = nn.GRU( + input_size=embed_tokens.embedding_dim, + hidden_size=embed_tokens.embedding_dim // 2, + num_layers=1, + bidirectional=True, + ) + + def forward_embedding(self, src_tokens): + # embed tokens and positions + x = embed = self.embed_scale * self.embed_tokens(src_tokens) + if self.embed_positions is not None: + x = embed + self.embed_positions(src_tokens) + + # contextualize embeddings + x = x.transpose(0, 1) + x = self.dropout_module(x) + x, _ = self.emb_ctx.forward(x) + x = x.transpose(0, 1) + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + x = self.dropout_module(x) + return x, embed + + +@register_model_architecture("gru_transformer", "gru_transformer") +def gru_transformer_base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.no_cross_attention = getattr(args, "no_cross_attention", False) + args.cross_self_attention = getattr(args, "cross_self_attention", False) + args.layer_wise_attention = getattr(args, "layer_wise_attention", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + + +@register_model_architecture("gru_transformer", "gru_transformer_big") +def gru_transformer_big(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) + gru_transformer_base_architecture(args) diff --git a/fairseq/examples/camembert/README.md b/fairseq/examples/camembert/README.md new file mode 100644 index 0000000..5ef4fe3 --- /dev/null +++ b/fairseq/examples/camembert/README.md @@ -0,0 +1,75 @@ +# CamemBERT: a Tasty French Language Model + +## Introduction + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a pretrained language model trained on 138GB of French text based on RoBERTa. + +Also available in [github.com/huggingface/transformers](https://github.com/huggingface/transformers/). + +## Pre-trained models + +| Model | #params | Download | Arch. | Training data | +|--------------------------------|---------|--------------------------------------------------------------------------------------------------------------------------|-------|-----------------------------------| +| `camembert` / `camembert-base` | 110M | [camembert-base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz) | Base | OSCAR (138 GB of text) | +| `camembert-large` | 335M | [camembert-large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz) | Large | CCNet (135 GB of text) | +| `camembert-base-ccnet` | 110M | [camembert-base-ccnet.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz) | Base | CCNet (135 GB of text) | +| `camembert-base-wikipedia-4gb` | 110M | [camembert-base-wikipedia-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz) | Base | Wikipedia (4 GB of text) | +| `camembert-base-oscar-4gb` | 110M | [camembert-base-oscar-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz) | Base | Subsample of OSCAR (4 GB of text) | +| `camembert-base-ccnet-4gb` | 110M | [camembert-base-ccnet-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz) | Base | Subsample of CCNet (4 GB of text) | + +## Example usage + +### fairseq +##### Load CamemBERT from torch.hub (PyTorch >= 1.1): +```python +import torch +camembert = torch.hub.load('pytorch/fairseq', 'camembert') +camembert.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Load CamemBERT (for PyTorch 1.0 or custom models): +```python +# Download camembert model +wget https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz +tar -xzvf camembert.tar.gz + +# Load the model in fairseq +from fairseq.models.roberta import CamembertModel +camembert = CamembertModel.from_pretrained('/path/to/camembert') +camembert.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Filling masks: +```python +masked_line = 'Le camembert est :)' +camembert.fill_mask(masked_line, topk=3) +# [('Le camembert est délicieux :)', 0.4909118115901947, ' délicieux'), +# ('Le camembert est excellent :)', 0.10556942224502563, ' excellent'), +# ('Le camembert est succulent :)', 0.03453322499990463, ' succulent')] +``` + +##### Extract features from Camembert: +```python +# Extract the last layer's features +line = "J'aime le camembert !" +tokens = camembert.encode(line) +last_layer_features = camembert.extract_features(tokens) +assert last_layer_features.size() == torch.Size([1, 10, 768]) + +# Extract all layer's features (layer 0 is the embedding layer) +all_layers = camembert.extract_features(tokens, return_all_hiddens=True) +assert len(all_layers) == 13 +assert torch.all(all_layers[-1] == last_layer_features) +``` + +## Citation +If you use our work, please cite: + +```bibtex +@inproceedings{martin2020camembert, + title={CamemBERT: a Tasty French Language Model}, + author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, + booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, + year={2020} +} +``` diff --git a/fairseq/examples/constrained_decoding/README.md b/fairseq/examples/constrained_decoding/README.md new file mode 100644 index 0000000..e04b8b6 --- /dev/null +++ b/fairseq/examples/constrained_decoding/README.md @@ -0,0 +1,123 @@ +# (Vectorized) Lexically constrained decoding with dynamic beam allocation + +This page provides instructions for how to use lexically constrained decoding in Fairseq. +Fairseq implements the code described in the following papers: + +* [Fast Lexically Constrained Decoding With Dynamic Beam Allocation](https://www.aclweb.org/anthology/N18-1119/) (Post & Vilar, 2018) +* [Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting](https://www.aclweb.org/anthology/N19-1090/) (Hu et al., 2019) + +## Quick start + +Constrained search is enabled by adding the command-line argument `--constraints` to `fairseq-interactive`. +Constraints are appended to each line of input, separated by tabs. Each constraint (one or more tokens) +is a separate field. + +The following command, using [Fairseq's WMT19 German--English model](https://github.com/pytorch/fairseq/blob/main/examples/wmt19/README.md), +translates the sentence *Die maschinelle Übersetzung ist schwer zu kontrollieren.* with the constraints +"hard" and "to influence". + + echo -e "Die maschinelle Übersetzung ist schwer zu kontrollieren.\thard\ttoinfluence" \ + | normalize.py | tok.py \ + | fairseq-interactive /path/to/model \ + --path /path/to/model/model1.pt \ + --bpe fastbpe \ + --bpe-codes /path/to/model/bpecodes \ + --constraints \ + -s de -t en \ + --beam 10 + +(tok.py and normalize.py can be found in the same directory as this README; they are just shortcuts around Fairseq's WMT19 preprocessing). +This will generate the following output: + + [snip] + S-0 Die masch@@ in@@ elle Über@@ setzung ist schwer zu kontrollieren . + W-0 1.844 seconds + C-0 hard + C-0 influence + H-0 -1.5333266258239746 Mach@@ ine trans@@ lation is hard to influence . + D-0 -1.5333266258239746 Machine translation is hard to influence . + P-0 -0.5434 -0.1423 -0.1930 -0.1415 -0.2346 -1.8031 -0.1701 -11.7727 -0.1815 -0.1511 + +By default, constraints are generated in the order supplied, with any number (zero or more) of tokens generated +between constraints. If you wish for the decoder to order the constraints, then use `--constraints unordered`. +Note that you may want to use a larger beam. + +## Implementation details + +The heart of the implementation is in `fairseq/search.py`, which adds a `LexicallyConstrainedBeamSearch` instance. +This instance of beam search tracks the progress of each hypothesis in the beam through the set of constraints +provided for each input sentence. It does this using one of two classes, both found in `fairseq/token_generation_contstraints.py`: + +* OrderedConstraintState: assumes the `C` input constraints will be generated in the provided order +* UnorderedConstraintState: tries to apply `C` (phrasal) constraints in all `C!` orders + +## Differences from Sockeye + +There are a number of [differences from Sockeye's implementation](https://awslabs.github.io/sockeye/inference.html#lexical-constraints). + +* Generating constraints in the order supplied (the default option here) is not available in Sockeye. +* Due to an improved beam allocation method, there is no need to prune the beam. +* Again due to better allocation, beam sizes as low as 10 or even 5 are often sufficient. +* [The vector extensions described in Hu et al.](https://github.com/edwardjhu/sockeye/tree/trie_constraints) (NAACL 2019) were never merged + into the main Sockeye branch. + +## Citation + +The paper first describing lexical constraints for seq2seq decoding is: + +```bibtex +@inproceedings{hokamp-liu-2017-lexically, + title = "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search", + author = "Hokamp, Chris and + Liu, Qun", + booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2017", + address = "Vancouver, Canada", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/P17-1141", + doi = "10.18653/v1/P17-1141", + pages = "1535--1546", +} +``` + +The fairseq implementation uses the extensions described in + +```bibtex +@inproceedings{post-vilar-2018-fast, + title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation", + author = "Post, Matt and + Vilar, David", + booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", + month = jun, + year = "2018", + address = "New Orleans, Louisiana", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/N18-1119", + doi = "10.18653/v1/N18-1119", + pages = "1314--1324", +} +``` + +and + +```bibtex +@inproceedings{hu-etal-2019-improved, + title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting", + author = "Hu, J. Edward and + Khayrallah, Huda and + Culkin, Ryan and + Xia, Patrick and + Chen, Tongfei and + Post, Matt and + Van Durme, Benjamin", + booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", + month = jun, + year = "2019", + address = "Minneapolis, Minnesota", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/N19-1090", + doi = "10.18653/v1/N19-1090", + pages = "839--850", +} +``` diff --git a/fairseq/examples/constrained_decoding/normalize.py b/fairseq/examples/constrained_decoding/normalize.py new file mode 100644 index 0000000..4ae2b51 --- /dev/null +++ b/fairseq/examples/constrained_decoding/normalize.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +from sacremoses.normalize import MosesPunctNormalizer + + +def main(args): + normalizer = MosesPunctNormalizer(lang=args.lang, penn=args.penn) + for line in sys.stdin: + print(normalizer.normalize(line.rstrip()), flush=True) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--lang", "-l", default="en") + parser.add_argument("--penn", "-p", action="store_true") + args = parser.parse_args() + + main(args) diff --git a/fairseq/examples/constrained_decoding/tok.py b/fairseq/examples/constrained_decoding/tok.py new file mode 100644 index 0000000..b1f888a --- /dev/null +++ b/fairseq/examples/constrained_decoding/tok.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +import sacremoses + + +def main(args): + """Tokenizes, preserving tabs""" + mt = sacremoses.MosesTokenizer(lang=args.lang) + + def tok(s): + return mt.tokenize(s, return_str=True) + + for line in sys.stdin: + parts = list(map(tok, line.split("\t"))) + print(*parts, sep="\t", flush=True) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--lang", "-l", default="en") + parser.add_argument("--penn", "-p", action="store_true") + parser.add_argument("--fields", "-f", help="fields to tokenize") + args = parser.parse_args() + + main(args) diff --git a/fairseq/examples/conv_seq2seq/README.md b/fairseq/examples/conv_seq2seq/README.md new file mode 100644 index 0000000..95fe7e7 --- /dev/null +++ b/fairseq/examples/conv_seq2seq/README.md @@ -0,0 +1,25 @@ +# Convolutional Sequence to Sequence Learning (Gehring et al., 2017) + +## Pre-trained models + +Description | Dataset | Model | Test set(s) +---|---|---|--- +Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2)
newstest2012/2013:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2) +Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2) +Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2) + +## Example usage + +See the [translation README](../translation/README.md) for instructions on reproducing results for WMT'14 En-De and +WMT'14 En-Fr using the `fconv_wmt_en_de` and `fconv_wmt_en_fr` model architectures. + +## Citation + +```bibtex +@inproceedings{gehring2017convs2s, + title = {Convolutional Sequence to Sequence Learning}, + author = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N}, + booktitle = {Proc. of ICML}, + year = 2017, +} +``` diff --git a/fairseq/examples/criss/README.md b/fairseq/examples/criss/README.md new file mode 100644 index 0000000..4689ed7 --- /dev/null +++ b/fairseq/examples/criss/README.md @@ -0,0 +1,61 @@ +# Cross-lingual Retrieval for Iterative Self-Supervised Training + +https://arxiv.org/pdf/2006.09526.pdf + +## Introduction + +CRISS is a multilingual sequence-to-sequnce pretraining method where mining and training processes are applied iteratively, improving cross-lingual alignment and translation ability at the same time. + +## Requirements: + +* faiss: https://github.com/facebookresearch/faiss +* mosesdecoder: https://github.com/moses-smt/mosesdecoder +* flores: https://github.com/facebookresearch/flores +* LASER: https://github.com/facebookresearch/LASER + +## Unsupervised Machine Translation +##### 1. Download and decompress CRISS checkpoints +``` +cd examples/criss +wget https://dl.fbaipublicfiles.com/criss/criss_3rd_checkpoints.tar.gz +tar -xf criss_checkpoints.tar.gz +``` +##### 2. Download and preprocess Flores test dataset +Make sure to run all scripts from examples/criss directory +``` +bash download_and_preprocess_flores_test.sh +``` + +##### 3. Run Evaluation on Sinhala-English +``` +bash unsupervised_mt/eval.sh +``` + +## Sentence Retrieval +##### 1. Download and preprocess Tatoeba dataset +``` +bash download_and_preprocess_tatoeba.sh +``` + +##### 2. Run Sentence Retrieval on Tatoeba Kazakh-English +``` +bash sentence_retrieval/sentence_retrieval_tatoeba.sh +``` + +## Mining +##### 1. Install faiss +Follow instructions on https://github.com/facebookresearch/faiss/blob/master/INSTALL.md +##### 2. Mine pseudo-parallel data between Kazakh and English +``` +bash mining/mine_example.sh +``` + +## Citation +```bibtex +@article{tran2020cross, + title={Cross-lingual retrieval for iterative self-supervised training}, + author={Tran, Chau and Tang, Yuqing and Li, Xian and Gu, Jiatao}, + journal={arXiv preprint arXiv:2006.09526}, + year={2020} +} +``` diff --git a/fairseq/examples/criss/download_and_preprocess_flores_test.sh b/fairseq/examples/criss/download_and_preprocess_flores_test.sh new file mode 100644 index 0000000..ed4b390 --- /dev/null +++ b/fairseq/examples/criss/download_and_preprocess_flores_test.sh @@ -0,0 +1,64 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +SPM_ENCODE=flores/scripts/spm_encode.py +DATA=data_tmp +SPM_MODEL=criss_checkpoints/sentence.bpe.model +DICT=criss_checkpoints/dict.txt + +download_data() { + CORPORA=$1 + URL=$2 + + if [ -f $CORPORA ]; then + echo "$CORPORA already exists, skipping download" + else + echo "Downloading $URL" + wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA + if [ -f $CORPORA ]; then + echo "$URL successfully downloaded." + else + echo "$URL not successfully downloaded." + rm -f $CORPORA + fi + fi +} + +if [[ -f flores ]]; then + echo "flores already cloned" +else + git clone https://github.com/facebookresearch/flores +fi + +mkdir -p $DATA +download_data $DATA/wikipedia_en_ne_si_test_sets.tgz "https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz" +pushd $DATA +pwd +tar -vxf wikipedia_en_ne_si_test_sets.tgz +popd + + +for lang in ne_NP si_LK; do + datadir=$DATA/${lang}-en_XX-flores + rm -rf $datadir + mkdir -p $datadir + TEST_PREFIX=$DATA/wikipedia_en_ne_si_test_sets/wikipedia.test + python $SPM_ENCODE \ + --model ${SPM_MODEL} \ + --output_format=piece \ + --inputs ${TEST_PREFIX}.${lang:0:2}-en.${lang:0:2} ${TEST_PREFIX}.${lang:0:2}-en.en \ + --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX + + # binarize data + fairseq-preprocess \ + --source-lang ${lang} --target-lang en_XX \ + --testpref $datadir/test.bpe.${lang}-en_XX \ + --destdir $datadir \ + --srcdict ${DICT} \ + --joined-dictionary \ + --workers 4 +done diff --git a/fairseq/examples/criss/download_and_preprocess_tatoeba.sh b/fairseq/examples/criss/download_and_preprocess_tatoeba.sh new file mode 100644 index 0000000..7ed64f0 --- /dev/null +++ b/fairseq/examples/criss/download_and_preprocess_tatoeba.sh @@ -0,0 +1,46 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +SPM_ENCODE=flores/scripts/spm_encode.py +DATA=data_tmp +SPM_MODEL=criss_checkpoints/sentence.bpe.model +DICT=criss_checkpoints/dict.txt + +if [[ -f flores ]]; then + echo "flores already cloned" +else + git clone https://github.com/facebookresearch/flores +fi +if [[ -f LASER ]]; then + echo "LASER already cloned" +else + git clone https://github.com/facebookresearch/LASER +fi +mkdir -p data_tmp +declare -A lang_tatoeba_map=( ["ar_AR"]="ara" ["de_DE"]="deu" ["es_XX"]="spa" ["et_EE"]="est" ["fi_FI"]="fin" ["fr_XX"]="fra" ["hi_IN"]="hin" ["it_IT"]="ita" ["ja_XX"]="jpn" ["ko_KR"]="kor" ["kk_KZ"]="kaz" ["nl_XX"]="nld" ["ru_RU"]="rus" ["tr_TR"]="tur" ["vi_VN"]="vie" ["zh_CN"]="cmn") +for lang in ar_AR de_DE es_XX et_EE fi_FI fr_XX hi_IN it_IT ja_XX kk_KZ ko_KR nl_XX ru_RU tr_TR vi_VN zh_CN; do + lang_tatoeba=${lang_tatoeba_map[$lang]} + echo $lang_tatoeba + datadir=$DATA/${lang}-en_XX-tatoeba + rm -rf $datadir + mkdir -p $datadir + TEST_PREFIX=LASER/data/tatoeba/v1/tatoeba + python $SPM_ENCODE \ + --model ${SPM_MODEL} \ + --output_format=piece \ + --inputs ${TEST_PREFIX}.${lang_tatoeba}-eng.${lang_tatoeba} ${TEST_PREFIX}.${lang_tatoeba}-eng.eng \ + --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX + + # binarize data + fairseq-preprocess \ + --source-lang ${lang} --target-lang en_XX \ + --testpref $datadir/test.bpe.${lang}-en_XX \ + --destdir $datadir \ + --srcdict ${DICT} \ + --joined-dictionary \ + --workers 4 +done diff --git a/fairseq/examples/criss/mining/mine.py b/fairseq/examples/criss/mining/mine.py new file mode 100644 index 0000000..c872da1 --- /dev/null +++ b/fairseq/examples/criss/mining/mine.py @@ -0,0 +1,240 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import argparse +import glob +from subprocess import check_call + +try: + import faiss + + has_faiss = True +except ImportError: + has_faiss = False +import numpy as np + + +GB = 1024 * 1024 * 1024 + + +def call(cmd): + print(cmd) + check_call(cmd, shell=True) + + +def get_batches(directory, lang, prefix="all_avg_pool"): + print(f"Finding in {directory}/{prefix}.{lang}*") + files = glob.glob(f"{directory}/{prefix}.{lang}*") + emb_files = [] + txt_files = [] + for emb_fi in files: + emb_files.append(emb_fi) + txt_fi = emb_fi.replace(prefix, "sentences") + txt_files.append(txt_fi) + return emb_files, txt_files + + +def load_batch(emb_file, dim): + embeddings = np.fromfile(emb_file, dtype=np.float32) + num_rows = int(embeddings.shape[0] / dim) + embeddings = embeddings.reshape((num_rows, dim)) + faiss.normalize_L2(embeddings) + return embeddings + + +def knnGPU_sharded(x_batches_f, y_batches_f, dim, k, direction="x2y"): + if not has_faiss: + raise ImportError("Please install Faiss") + sims = [] + inds = [] + xfrom = 0 + xto = 0 + for x_batch_f in x_batches_f: + yfrom = 0 + yto = 0 + x_batch = load_batch(x_batch_f, dim) + xto = xfrom + x_batch.shape[0] + bsims, binds = [], [] + for y_batch_f in y_batches_f: + y_batch = load_batch(y_batch_f, dim) + neighbor_size = min(k, y_batch.shape[0]) + yto = yfrom + y_batch.shape[0] + print("{}-{} -> {}-{}".format(xfrom, xto, yfrom, yto)) + idx = faiss.IndexFlatIP(dim) + idx = faiss.index_cpu_to_all_gpus(idx) + idx.add(y_batch) + bsim, bind = idx.search(x_batch, neighbor_size) + + bsims.append(bsim) + binds.append(bind + yfrom) + yfrom += y_batch.shape[0] + del idx + del y_batch + bsims = np.concatenate(bsims, axis=1) + binds = np.concatenate(binds, axis=1) + aux = np.argsort(-bsims, axis=1) + sim_batch = np.zeros((x_batch.shape[0], k), dtype=np.float32) + ind_batch = np.zeros((x_batch.shape[0], k), dtype=np.int64) + for i in range(x_batch.shape[0]): + for j in range(k): + sim_batch[i, j] = bsims[i, aux[i, j]] + ind_batch[i, j] = binds[i, aux[i, j]] + sims.append(sim_batch) + inds.append(ind_batch) + xfrom += x_batch.shape[0] + del x_batch + sim = np.concatenate(sims, axis=0) + ind = np.concatenate(inds, axis=0) + return sim, ind + + +def score(sim, fwd_mean, bwd_mean, margin): + return margin(sim, (fwd_mean + bwd_mean) / 2) + + +def score_candidates( + sim_mat, candidate_inds, fwd_mean, bwd_mean, margin, verbose=False +): + print(" - scoring {:d} candidates".format(sim_mat.shape[0])) + scores = np.zeros(candidate_inds.shape) + for i in range(scores.shape[0]): + for j in range(scores.shape[1]): + k = int(candidate_inds[i, j]) + scores[i, j] = score(sim_mat[i, j], fwd_mean[i], bwd_mean[k], margin) + return scores + + +def load_text(files): + all_sentences = [] + for fi in files: + with open(fi) as sentence_fi: + for line in sentence_fi: + all_sentences.append(line.strip()) + print(f"Read {len(all_sentences)} sentences") + return all_sentences + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Mine bitext") + parser.add_argument("--src-lang", help="Source language") + parser.add_argument("--tgt-lang", help="Target language") + parser.add_argument( + "--dict-path", help="Path to dictionary file", default="dict.txt" + ) + parser.add_argument( + "--spm-path", help="Path to SPM model file", default="sentence.bpe.model" + ) + parser.add_argument("--dim", type=int, default=1024, help="Embedding dimension") + parser.add_argument("--mem", type=int, default=5, help="Memory in GB") + parser.add_argument("--src-dir", help="Source directory") + parser.add_argument("--tgt-dir", help="Target directory") + parser.add_argument("--output", help="Output path") + parser.add_argument( + "--neighborhood", type=int, default=4, help="Embedding dimension" + ) + parser.add_argument( + "--threshold", type=float, default=1.06, help="Threshold on mined bitext" + ) + parser.add_argument( + "--valid-size", + type=int, + default=2000, + help="Number of sentences used for validation set", + ) + parser.add_argument( + "--min-count", + type=int, + default=50000, + help="Min num sentences used for each language", + ) + args = parser.parse_args() + + x_batches_f, x_sents_f = get_batches(args.src_dir, args.src_lang) + y_batches_f, y_sents_f = get_batches(args.tgt_dir, args.tgt_lang) + margin = lambda a, b: a / b + y2x_sim, y2x_ind = knnGPU_sharded( + y_batches_f, x_batches_f, args.dim, args.neighborhood, direction="y2x" + ) + x2y_sim, x2y_ind = knnGPU_sharded( + x_batches_f, y_batches_f, args.dim, args.neighborhood, direction="x2y" + ) + + x2y_mean = x2y_sim.mean(axis=1) + y2x_mean = y2x_sim.mean(axis=1) + fwd_scores = score_candidates(x2y_sim, x2y_ind, x2y_mean, y2x_mean, margin) + bwd_scores = score_candidates(y2x_sim, y2x_ind, y2x_mean, x2y_mean, margin) + fwd_best = x2y_ind[np.arange(x2y_sim.shape[0]), fwd_scores.argmax(axis=1)] + bwd_best = y2x_ind[np.arange(y2x_sim.shape[0]), bwd_scores.argmax(axis=1)] + indices = np.stack( + ( + np.concatenate((np.arange(x2y_ind.shape[0]), bwd_best)), + np.concatenate((fwd_best, np.arange(y2x_ind.shape[0]))), + ), + axis=1, + ) + scores = np.concatenate((fwd_scores.max(axis=1), bwd_scores.max(axis=1))) + + x_sentences = load_text(x_sents_f) + y_sentences = load_text(y_sents_f) + + threshold = args.threshold + min_count = args.min_count + seen_src, seen_trg = set(), set() + directory = args.output + call(f"mkdir -p {directory}") + src_out = open( + f"{directory}/all.{args.src_lang}", + mode="w", + encoding="utf-8", + errors="surrogateescape", + ) + tgt_out = open( + f"{directory}/all.{args.tgt_lang}", + mode="w", + encoding="utf-8", + errors="surrogateescape", + ) + scores_out = open( + f"{directory}/all.scores", mode="w", encoding="utf-8", errors="surrogateescape" + ) + count = 0 + for i in np.argsort(-scores): + src_ind, trg_ind = indices[i] + if src_ind not in seen_src and trg_ind not in seen_trg: + seen_src.add(src_ind) + seen_trg.add(trg_ind) + if scores[i] > threshold or count < min_count: + if x_sentences[src_ind]: + print(scores[i], file=scores_out) + print(x_sentences[src_ind], file=src_out) + print(y_sentences[trg_ind], file=tgt_out) + count += 1 + else: + print(f"Ignoring sentence: {x_sentences[src_ind]}") + src_out.close() + tgt_out.close() + scores_out.close() + + print(f"Found {count} pairs for threshold={threshold}") + with open(f"{directory}/all.{args.src_lang}") as all_s, open( + f"{directory}/all.{args.tgt_lang}" + ) as all_t, open(f"{directory}/valid.{args.src_lang}", "w") as valid_s, open( + f"{directory}/valid.{args.tgt_lang}", "w" + ) as valid_t, open( + f"{directory}/train.{args.src_lang}", "w" + ) as train_s, open( + f"{directory}/train.{args.tgt_lang}", "w" + ) as train_t: + count = 0 + for s_line, t_line in zip(all_s, all_t): + s_line = s_line.split("\t")[1] + t_line = t_line.split("\t")[1] + if count >= args.valid_size: + train_s.write(s_line) + train_t.write(t_line) + else: + valid_s.write(s_line) + valid_t.write(t_line) + count += 1 diff --git a/fairseq/examples/criss/mining/mine_example.sh b/fairseq/examples/criss/mining/mine_example.sh new file mode 100644 index 0000000..ace995a --- /dev/null +++ b/fairseq/examples/criss/mining/mine_example.sh @@ -0,0 +1,103 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +source_lang=kk_KZ +target_lang=en_XX +MODEL=criss_checkpoints/criss.3rd.pt +SPM=criss_checkpoints/sentence.bpe.model +SPLIT=test +LANG_DICT=criss_checkpoints/lang_dict.txt +SPM_ENCODE=flores/scripts/spm_encode.py +SAVE_ENCODER=save_encoder.py +ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL +DICT=criss_checkpoints/dict.txt +THRESHOLD=1.02 +MIN_COUNT=500 + +DATA_DIR=data_tmp +SAVE_DIR=mining/${source_lang}_${target_lang}_mined +ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang} +INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba + +mkdir -p $ENCODER_SAVE_DIR/${target_lang} +mkdir -p $ENCODER_SAVE_DIR/${source_lang} +mkdir -p $SAVE_DIR + +## Save encoder outputs + +# Save encoder outputs for source sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --task translation_multi_simple_epoch \ + --lang-pairs ${source_lang}-${target_lang} \ + --lang-dict ${LANG_DICT} \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + -s ${source_lang} -t ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang} + +## Save encoder outputs for target sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --lang-pairs ${source_lang}-${target_lang} \ + --lang-dict ${LANG_DICT} \ + --task translation_multi_simple_epoch \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + -t ${source_lang} -s ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang} + +## Mining +python mining/mine.py \ + --src-lang ${source_lang} \ + --tgt-lang ${target_lang} \ + --dim 1024 \ + --mem 10 \ + --neighborhood 4 \ + --src-dir ${ENCODER_SAVE_DIR}/${source_lang} \ + --tgt-dir ${ENCODER_SAVE_DIR}/${target_lang} \ + --output $SAVE_DIR \ + --threshold ${THRESHOLD} \ + --min-count ${MIN_COUNT} \ + --valid-size 100 \ + --dict-path ${DICT} \ + --spm-path ${SPM} \ + + +## Process and binarize mined data +python $SPM_ENCODE \ + --model ${SPM} \ + --output_format=piece \ + --inputs mining/${source_lang}_${target_lang}_mined/train.${source_lang} mining/${source_lang}_${target_lang}_mined/train.${target_lang} \ + --outputs mining/${source_lang}_${target_lang}_mined/train.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/train.bpe.${target_lang} + +python $SPM_ENCODE \ + --model ${SPM} \ + --output_format=piece \ + --inputs mining/${source_lang}_${target_lang}_mined/valid.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.${target_lang} \ + --outputs mining/${source_lang}_${target_lang}_mined/valid.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.bpe.${target_lang} + + +fairseq-preprocess \ + --source-lang ${source_lang} \ + --target-lang ${target_lang} \ + --trainpref mining/${source_lang}_${target_lang}_mined/train.bpe \ + --validpref mining/${source_lang}_${target_lang}_mined/valid.bpe \ + --destdir mining/${source_lang}_${target_lang}_mined \ + --srcdict ${DICT} \ + --joined-dictionary \ + --workers 8 diff --git a/fairseq/examples/criss/save_encoder.py b/fairseq/examples/criss/save_encoder.py new file mode 100644 index 0000000..24a842e --- /dev/null +++ b/fairseq/examples/criss/save_encoder.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Translate pre-processed data with a trained model. +""" + +import numpy as np +import torch +from fairseq import checkpoint_utils, options, progress_bar, tasks, utils +from fairseq.sequence_generator import EnsembleModel +from fairseq.utils import safe_hasattr + + +def get_avg_pool( + models, sample, prefix_tokens, src_dict, remove_bpe, has_langtok=False +): + model = EnsembleModel(models) + + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" + } + + # compute the encoder output for each beam + encoder_outs = model.forward_encoder(encoder_input) + np_encoder_outs = encoder_outs[0].encoder_out.cpu().numpy().astype(np.float32) + encoder_mask = 1 - encoder_outs[0].encoder_padding_mask.cpu().numpy().astype( + np.float32 + ) + encoder_mask = np.expand_dims(encoder_mask.T, axis=2) + if has_langtok: + encoder_mask = encoder_mask[1:, :, :] + np_encoder_outs = np_encoder_outs[1, :, :] + masked_encoder_outs = encoder_mask * np_encoder_outs + avg_pool = (masked_encoder_outs / encoder_mask.sum(axis=0)).sum(axis=0) + return avg_pool + + +def main(args): + assert args.path is not None, "--path required for generation!" + assert ( + not args.sampling or args.nbest == args.beam + ), "--sampling requires --nbest to be equal to --beam" + assert ( + args.replace_unk is None or args.raw_text + ), "--replace-unk requires a raw text dataset (--raw-text)" + + args.beam = 1 + utils.import_user_module(args) + + if args.max_tokens is None: + args.max_tokens = 12000 + print(args) + use_cuda = torch.cuda.is_available() and not args.cpu + + # Load dataset splits + task = tasks.setup_task(args) + task.load_dataset(args.gen_subset) + + # Set dictionaries + try: + src_dict = getattr(task, "source_dictionary", None) + except NotImplementedError: + src_dict = None + tgt_dict = task.target_dictionary + + # Load ensemble + print("| loading model(s) from {}".format(args.path)) + models, _model_args = checkpoint_utils.load_model_ensemble( + args.path.split(":"), + arg_overrides=eval(args.model_overrides), + task=task, + ) + + # Optimize ensemble for generation + for model in models: + model.make_generation_fast_( + beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, + need_attn=args.print_alignment, + ) + if args.fp16: + model.half() + if use_cuda: + model.cuda() + + # Load alignment dictionary for unknown word replacement + # (None if no unknown word replacement, empty if no path to align dictionary) + align_dict = utils.load_align_dict(args.replace_unk) + + # Load dataset (possibly sharded) + itr = task.get_batch_iterator( + dataset=task.dataset(args.gen_subset), + max_tokens=args.max_tokens, + max_positions=utils.resolve_max_positions( + task.max_positions(), + ), + ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=args.required_batch_size_multiple, + num_shards=args.num_shards, + shard_id=args.shard_id, + num_workers=args.num_workers, + ).next_epoch_itr(shuffle=False) + + num_sentences = 0 + source_sentences = [] + shard_id = 0 + all_avg_pool = None + encoder_has_langtok = ( + safe_hasattr(task.args, "encoder_langtok") + and task.args.encoder_langtok is not None + and safe_hasattr(task.args, "lang_tok_replacing_bos_eos") + and not task.args.lang_tok_replacing_bos_eos + ) + with progress_bar.build_progress_bar(args, itr) as t: + for sample in t: + if sample is None: + print("Skipping None") + continue + sample = utils.move_to_cuda(sample) if use_cuda else sample + if "net_input" not in sample: + continue + + prefix_tokens = None + if args.prefix_size > 0: + prefix_tokens = sample["target"][:, : args.prefix_size] + + with torch.no_grad(): + avg_pool = get_avg_pool( + models, + sample, + prefix_tokens, + src_dict, + args.post_process, + has_langtok=encoder_has_langtok, + ) + if all_avg_pool is not None: + all_avg_pool = np.concatenate((all_avg_pool, avg_pool)) + else: + all_avg_pool = avg_pool + + if not isinstance(sample["id"], list): + sample_ids = sample["id"].tolist() + else: + sample_ids = sample["id"] + for i, sample_id in enumerate(sample_ids): + # Remove padding + src_tokens = utils.strip_pad( + sample["net_input"]["src_tokens"][i, :], tgt_dict.pad() + ) + + # Either retrieve the original sentences or regenerate them from tokens. + if align_dict is not None: + src_str = task.dataset(args.gen_subset).src.get_original_text( + sample_id + ) + else: + if src_dict is not None: + src_str = src_dict.string(src_tokens, args.post_process) + else: + src_str = "" + + if not args.quiet: + if src_dict is not None: + print("S-{}\t{}".format(sample_id, src_str)) + + source_sentences.append(f"{sample_id}\t{src_str}") + + num_sentences += sample["nsentences"] + if all_avg_pool.shape[0] >= 1000000: + with open( + f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", + "w", + ) as avg_pool_file: + all_avg_pool.tofile(avg_pool_file) + with open( + f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", + "w", + ) as sentence_file: + sentence_file.writelines(f"{line}\n" for line in source_sentences) + all_avg_pool = None + source_sentences = [] + shard_id += 1 + + if all_avg_pool is not None: + with open( + f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", "w" + ) as avg_pool_file: + all_avg_pool.tofile(avg_pool_file) + with open( + f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", "w" + ) as sentence_file: + sentence_file.writelines(f"{line}\n" for line in source_sentences) + return None + + +def cli_main(): + parser = options.get_generation_parser() + parser.add_argument( + "--encoder-save-dir", + default="", + type=str, + metavar="N", + help="directory to save encoder outputs", + ) + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py b/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py new file mode 100644 index 0000000..b41bfbe --- /dev/null +++ b/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import argparse +import glob + +import numpy as np + + +DIM = 1024 + + +def compute_dist(source_embs, target_embs, k=5, return_sim_mat=False): + target_ids = [tid for tid in target_embs] + source_mat = np.stack(source_embs.values(), axis=0) + normalized_source_mat = source_mat / np.linalg.norm( + source_mat, axis=1, keepdims=True + ) + target_mat = np.stack(target_embs.values(), axis=0) + normalized_target_mat = target_mat / np.linalg.norm( + target_mat, axis=1, keepdims=True + ) + sim_mat = normalized_source_mat.dot(normalized_target_mat.T) + if return_sim_mat: + return sim_mat + neighbors_map = {} + for i, sentence_id in enumerate(source_embs): + idx = np.argsort(sim_mat[i, :])[::-1][:k] + neighbors_map[sentence_id] = [target_ids[tid] for tid in idx] + return neighbors_map + + +def load_embeddings(directory, LANGS): + sentence_embeddings = {} + sentence_texts = {} + for lang in LANGS: + sentence_embeddings[lang] = {} + sentence_texts[lang] = {} + lang_dir = f"{directory}/{lang}" + embedding_files = glob.glob(f"{lang_dir}/all_avg_pool.{lang}.*") + for embed_file in embedding_files: + shard_id = embed_file.split(".")[-1] + embeddings = np.fromfile(embed_file, dtype=np.float32) + num_rows = embeddings.shape[0] // DIM + embeddings = embeddings.reshape((num_rows, DIM)) + + with open(f"{lang_dir}/sentences.{lang}.{shard_id}") as sentence_file: + for idx, line in enumerate(sentence_file): + sentence_id, sentence = line.strip().split("\t") + sentence_texts[lang][sentence_id] = sentence + sentence_embeddings[lang][sentence_id] = embeddings[idx, :] + + return sentence_embeddings, sentence_texts + + +def compute_accuracy(directory, LANGS): + sentence_embeddings, sentence_texts = load_embeddings(directory, LANGS) + + top_1_accuracy = {} + + top1_str = " ".join(LANGS) + "\n" + for source_lang in LANGS: + top_1_accuracy[source_lang] = {} + top1_str += f"{source_lang} " + for target_lang in LANGS: + top1 = 0 + top5 = 0 + neighbors_map = compute_dist( + sentence_embeddings[source_lang], sentence_embeddings[target_lang] + ) + for sentence_id, neighbors in neighbors_map.items(): + if sentence_id == neighbors[0]: + top1 += 1 + if sentence_id in neighbors[:5]: + top5 += 1 + n = len(sentence_embeddings[target_lang]) + top1_str += f"{top1/n} " + top1_str += "\n" + + print(top1_str) + print(top1_str, file=open(f"{directory}/accuracy", "w")) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Analyze encoder outputs") + parser.add_argument("directory", help="Source language corpus") + parser.add_argument("--langs", help="List of langs") + args = parser.parse_args() + langs = args.langs.split(",") + compute_accuracy(args.directory, langs) diff --git a/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh b/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh new file mode 100644 index 0000000..0428d8b --- /dev/null +++ b/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh @@ -0,0 +1,59 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +source_lang=kk_KZ +target_lang=en_XX +MODEL=criss_checkpoints/criss.3rd.pt +SPM=criss_checkpoints/sentence.bpe.model +SPLIT=test +LANG_DICT=criss_checkpoints/lang_dict.txt +ENCODER_ANALYSIS=sentence_retrieval/encoder_analysis.py +SAVE_ENCODER=save_encoder.py +ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL + + + +DATA_DIR=data_tmp +INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba +ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang} +mkdir -p $ENCODER_SAVE_DIR/${target_lang} +mkdir -p $ENCODER_SAVE_DIR/${source_lang} + +# Save encoder outputs for source sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --task translation_multi_simple_epoch \ + --lang-dict ${LANG_DICT} \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + --lang-pairs ${source_lang}-${target_lang} \ + -s ${source_lang} -t ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang} + +# Save encoder outputs for target sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --lang-dict ${LANG_DICT} \ + --task translation_multi_simple_epoch \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + --lang-pairs ${target_lang}-${source_lang} \ + -t ${source_lang} -s ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang} + +# Analyze sentence retrieval accuracy +python $ENCODER_ANALYSIS --langs "${source_lang},${target_lang}" ${ENCODER_SAVE_DIR} diff --git a/fairseq/examples/criss/unsupervised_mt/eval.sh b/fairseq/examples/criss/unsupervised_mt/eval.sh new file mode 100644 index 0000000..03b773e --- /dev/null +++ b/fairseq/examples/criss/unsupervised_mt/eval.sh @@ -0,0 +1,37 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +SRC=si_LK +TGT=en_XX +MODEL=criss_checkpoints/criss.3rd.pt + +MULTIBLEU=mosesdecoder/scripts/generic/multi-bleu.perl +MOSES=mosesdecoder +REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl +NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl +TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl +GEN_TMP_DIR=gen_tmp +LANG_DICT=criss_checkpoints/lang_dict.txt + +if [ ! -d "mosesdecoder" ]; then + git clone https://github.com/moses-smt/mosesdecoder +fi +mkdir -p $GEN_TMP_DIR +fairseq-generate data_tmp/${SRC}-${TGT}-flores \ + --task translation_multi_simple_epoch \ + --max-tokens 2000 \ + --path ${MODEL} \ + --skip-invalid-size-inputs-valid-test \ + --beam 5 --lenpen 1.0 --gen-subset test \ + --remove-bpe=sentencepiece \ + --source-lang ${SRC} --target-lang ${TGT} \ + --decoder-langtok --lang-pairs 'en_XX-ar_AR,en_XX-de_DE,en_XX-es_XX,en_XX-fr_XX,en_XX-hi_IN,en_XX-it_IT,en_XX-ja_XX,en_XX-ko_KR,en_XX-nl_XX,en_XX-ru_RU,en_XX-zh_CN,en_XX-tr_TR,en_XX-vi_VN,en_XX-ro_RO,en_XX-my_MM,en_XX-ne_NP,en_XX-si_LK,en_XX-cs_CZ,en_XX-lt_LT,en_XX-kk_KZ,en_XX-gu_IN,en_XX-fi_FI,en_XX-et_EE,en_XX-lv_LV,ar_AR-en_XX,cs_CZ-en_XX,de_DE-en_XX,es_XX-en_XX,et_EE-en_XX,fi_FI-en_XX,fr_XX-en_XX,gu_IN-en_XX,hi_IN-en_XX,it_IT-en_XX,ja_XX-en_XX,kk_KZ-en_XX,ko_KR-en_XX,lt_LT-en_XX,lv_LV-en_XX,my_MM-en_XX,ne_NP-en_XX,nl_XX-en_XX,ro_RO-en_XX,ru_RU-en_XX,si_LK-en_XX,tr_TR-en_XX,vi_VN-en_XX,zh_CN-en_XX,ar_AR-es_XX,es_XX-ar_AR,ar_AR-hi_IN,hi_IN-ar_AR,ar_AR-zh_CN,zh_CN-ar_AR,cs_CZ-es_XX,es_XX-cs_CZ,cs_CZ-hi_IN,hi_IN-cs_CZ,cs_CZ-zh_CN,zh_CN-cs_CZ,de_DE-es_XX,es_XX-de_DE,de_DE-hi_IN,hi_IN-de_DE,de_DE-zh_CN,zh_CN-de_DE,es_XX-hi_IN,hi_IN-es_XX,es_XX-zh_CN,zh_CN-es_XX,et_EE-es_XX,es_XX-et_EE,et_EE-hi_IN,hi_IN-et_EE,et_EE-zh_CN,zh_CN-et_EE,fi_FI-es_XX,es_XX-fi_FI,fi_FI-hi_IN,hi_IN-fi_FI,fi_FI-zh_CN,zh_CN-fi_FI,fr_XX-es_XX,es_XX-fr_XX,fr_XX-hi_IN,hi_IN-fr_XX,fr_XX-zh_CN,zh_CN-fr_XX,gu_IN-es_XX,es_XX-gu_IN,gu_IN-hi_IN,hi_IN-gu_IN,gu_IN-zh_CN,zh_CN-gu_IN,hi_IN-zh_CN,zh_CN-hi_IN,it_IT-es_XX,es_XX-it_IT,it_IT-hi_IN,hi_IN-it_IT,it_IT-zh_CN,zh_CN-it_IT,ja_XX-es_XX,es_XX-ja_XX,ja_XX-hi_IN,hi_IN-ja_XX,ja_XX-zh_CN,zh_CN-ja_XX,kk_KZ-es_XX,es_XX-kk_KZ,kk_KZ-hi_IN,hi_IN-kk_KZ,kk_KZ-zh_CN,zh_CN-kk_KZ,ko_KR-es_XX,es_XX-ko_KR,ko_KR-hi_IN,hi_IN-ko_KR,ko_KR-zh_CN,zh_CN-ko_KR,lt_LT-es_XX,es_XX-lt_LT,lt_LT-hi_IN,hi_IN-lt_LT,lt_LT-zh_CN,zh_CN-lt_LT,lv_LV-es_XX,es_XX-lv_LV,lv_LV-hi_IN,hi_IN-lv_LV,lv_LV-zh_CN,zh_CN-lv_LV,my_MM-es_XX,es_XX-my_MM,my_MM-hi_IN,hi_IN-my_MM,my_MM-zh_CN,zh_CN-my_MM,ne_NP-es_XX,es_XX-ne_NP,ne_NP-hi_IN,hi_IN-ne_NP,ne_NP-zh_CN,zh_CN-ne_NP,nl_XX-es_XX,es_XX-nl_XX,nl_XX-hi_IN,hi_IN-nl_XX,nl_XX-zh_CN,zh_CN-nl_XX,ro_RO-es_XX,es_XX-ro_RO,ro_RO-hi_IN,hi_IN-ro_RO,ro_RO-zh_CN,zh_CN-ro_RO,ru_RU-es_XX,es_XX-ru_RU,ru_RU-hi_IN,hi_IN-ru_RU,ru_RU-zh_CN,zh_CN-ru_RU,si_LK-es_XX,es_XX-si_LK,si_LK-hi_IN,hi_IN-si_LK,si_LK-zh_CN,zh_CN-si_LK,tr_TR-es_XX,es_XX-tr_TR,tr_TR-hi_IN,hi_IN-tr_TR,tr_TR-zh_CN,zh_CN-tr_TR,vi_VN-es_XX,es_XX-vi_VN,vi_VN-hi_IN,hi_IN-vi_VN,vi_VN-zh_CN,zh_CN-vi_VN' \ + --lang-dict ${LANG_DICT} --lang-tok-style 'mbart' --sampling-method 'temperature' --sampling-temperature '1.0' > $GEN_TMP_DIR/${SRC}_${TGT}.gen +cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^T-" | cut -f2 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.hyp +cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^H-" | cut -f3 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.ref +${MULTIBLEU} $GEN_TMP_DIR/${SRC}_${TGT}.ref < $GEN_TMP_DIR/${SRC}_${TGT}.hyp diff --git a/fairseq/examples/cross_lingual_language_model/README.md b/fairseq/examples/cross_lingual_language_model/README.md new file mode 100644 index 0000000..af9128e --- /dev/null +++ b/fairseq/examples/cross_lingual_language_model/README.md @@ -0,0 +1,77 @@ +# Cross-Lingual Language Model Pre-training + +Below are some details for training Cross-Lingual Language Models (XLM) - similar to the ones presented in [Lample & Conneau, 2019](https://arxiv.org/pdf/1901.07291.pdf) - in Fairseq. The current implementation only supports the Masked Language Model (MLM) from the paper above. + +## Downloading and Tokenizing Monolingual Data + +Pointers to the monolingual data from wikipedia, used for training the XLM-style MLM model as well as details on processing (tokenization and BPE) it can be found in the [XLM Github Repository](https://github.com/facebookresearch/XLM#download--preprocess-monolingual-data). + +Let's assume the following for the code snippets in later sections to work +- Processed data is in the folder: monolingual_data/processed +- Each language has 3 files for train, test and validation. For example we have the following files for English: + train.en, valid.en +- We are training a model for 5 languages: Arabic (ar), German (de), English (en), Hindi (hi) and French (fr) +- The vocabulary file is monolingual_data/processed/vocab_mlm + + +## Fairseq Pre-processing and Binarization + +Pre-process and binarize the data with the MaskedLMDictionary and cross_lingual_lm task + +```bash +# Ensure the output directory exists +DATA_DIR=monolingual_data/fairseq_processed +mkdir -p "$DATA_DIR" + +for lg in ar de en hi fr +do + + fairseq-preprocess \ + --task cross_lingual_lm \ + --srcdict monolingual_data/processed/vocab_mlm \ + --only-source \ + --trainpref monolingual_data/processed/train \ + --validpref monolingual_data/processed/valid \ + --testpref monolingual_data/processed/test \ + --destdir monolingual_data/fairseq_processed \ + --workers 20 \ + --source-lang $lg + + # Since we only have a source language, the output file has a None for the + # target language. Remove this + + for stage in train test valid + + sudo mv "$DATA_DIR/$stage.$lg-None.$lg.bin" "$stage.$lg.bin" + sudo mv "$DATA_DIR/$stage.$lg-None.$lg.idx" "$stage.$lg.idx" + + done + +done +``` + +## Train a Cross-lingual Language Model similar to the XLM MLM model + +Use the following command to train the model on 5 languages. + +``` +fairseq-train \ +--task cross_lingual_lm monolingual_data/fairseq_processed \ +--save-dir checkpoints/mlm \ +--max-update 2400000 --save-interval 1 --no-epoch-checkpoints \ +--arch xlm_base \ +--optimizer adam --lr-scheduler reduce_lr_on_plateau \ +--lr-shrink 0.5 --lr 0.0001 --stop-min-lr 1e-09 \ +--dropout 0.1 \ +--criterion legacy_masked_lm_loss \ +--max-tokens 2048 --tokens-per-sample 256 --attention-dropout 0.1 \ +--dataset-impl lazy --seed 0 \ +--masked-lm-only \ +--monolingual-langs 'ar,de,en,hi,fr' --num-segment 5 \ +--ddp-backend=legacy_ddp +``` + +Some Notes: +- Using tokens_per_sample greater than 256 can cause OOM (out-of-memory) issues. Usually since MLM packs in streams of text, this parameter doesn't need much tuning. +- The Evaluation workflow for computing MLM Perplexity on test data is in progress. +- Finetuning this model on a downstream task is something which is not currently available. diff --git a/fairseq/examples/data2vec/README.md b/fairseq/examples/data2vec/README.md new file mode 100644 index 0000000..a0ff21b --- /dev/null +++ b/fairseq/examples/data2vec/README.md @@ -0,0 +1,261 @@ +# data2vec 2.0 + +data2vec 2.0 improves the training efficiency of the original data2vec algorithm. We make the following improvements for efficiency considerations - we forward only the unmasked timesteps through the encoder, we use convolutional decoder and we use multimasking to amortize the compute overhead of the teacher model. You can find details in the paper [Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language](https://arxiv.org/abs/2212.07525) and our [blog post](https://ai.facebook.com/blog/ai-self-supervised-learning-data2vec/). + +## Pretrained and finetuned models +### Vision +| Model | Finetuning split | Link +|---|---|--- +data2vec ViT-B | No fine-tuning | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/base_imagenet.pt) +data2vec ViT-B | Imagenet-1K | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/base_imagenet_ft.pt) +data2vec ViT-L | No fine-tuning | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/large_imagenet.pt) +data2vec ViT-L | Imagenet-1K | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/large_imagenet_ft.pt) +data2vec ViT-H | No fine-tuning | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/huge_imagenet.pt) +data2vec ViT-H | Imagenet-1K | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/huge_imagenet_ft.pt) + +Vision models only are license under CC-BY-NC. +### Speech + +| Model | Finetuning split | Dataset | Link +|---|---|---|--- +data2vec Base | No fine-tuning | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/base_libri.pt) +data2vec Base | 960 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/base_libri_960h.pt) +data2vec Large | No fine-tuning | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/large_vox.pt) +data2vec Large | 960 hours | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/large_vox_960h.pt) + +### NLP + +| Model | Fine-tuning data | Dataset | Link | Dict | BPE +|---|---|---|---|---|--- +data2vec Base | No fine-tuning | Books + Wiki | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec2/nlp_base.pt) | [dict](https://dl.fbaipublicfiles.com/fairseq/data2vec2/dict.txt) | [encoder](https://dl.fbaipublicfiles.com/fairseq/data2vec2/encoder.json) / [vocab](https://dl.fbaipublicfiles.com/fairseq/data2vec2/vocab.bpe) + +[//]: # (## Data Preparation) + +[//]: # () +[//]: # (### Vision) + +[//]: # (add details) + +[//]: # (### Speech) + +[//]: # (add details) + +[//]: # () +[//]: # (### NLP) + +[//]: # (add details) + + +## Commands to train different models using data2vec 2.0 + +### Vision + +Commands to pretrain different model configurations +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \ +--config-name base_images_only_task task.data=/path/to/dir +``` + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \ +--config-name large_images_only_task task.data=/path/to/dir +``` + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \ +--config-name huge_images14_only_task task.data=/path/to/dir +``` + +Commands to finetune different model configurations + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/vision/finetuning \ +--config-name mae_imagenet_clean task.data=/path/to/dir model.model_path=/path/to/pretrained/model +``` + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/vision/finetuning \ +--config-name mae_imagenet_large_clean task.data=/path/to/dir model.model_path=/path/to/pretrained/model +``` + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/vision/finetuning \ +--config-name mae_imagenet_huge_clean task.data=/path/to/dir model.model_path=/path/to/pretrained/model +``` + +### Speech + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \ +--config-name base_audio_only_task task.data=/path/to/manifests +``` + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \ +--config-name large_audio_only_task task.data=/path/to/manifests +``` + +Finetuning: + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/wav2vec/config/finetuning --config-name vox_10h \ +task.data=/path/to/manifests model.w2v_path=/path/to/pretrained/model common.user_dir=examples/data2vec +``` + +Replace vox_10h with the right config depending on your model and fine-tuning split. +See examples/wav2vec/config/finetuning for all available configs. + +### NLP + +Commands to pretrain +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \ +--config-name base_text_only_task task.data=/path/to/file +``` + +Commands to fine-tune all GLUE tasks +```shell script +$ task=cola # choose from [cola|qnli|mrpc|rte|sst_2|mnli|qqp|sts_b] +$ lr=1e-5 # sweep [1e-5|2e-5|4e-5|6e-5] for each task +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2/text_finetuning \ +--config-name $task task.data=/path/to/file model.model_path=/path/to/pretrained/model "optimization.lr=[${lr}]" +``` + +# data2vec + +data2vec is a framework for self-supervised representation learning for images, speech, and text as described in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language (Baevski et al., 2022)](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language). The algorithm uses the same learning mechanism for different modalities. + + +## Pre-trained models + +### Vision + +Code and pre-trained models for data2vec visions can be found [here](https://github.com/facebookresearch/data2vec_vision/tree/main/beit). + +### Speech + +| Model | Finetuning split | Dataset | Link +|---|---|---|--- +data2vec Base | No fine-tuning | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls.pt) +data2vec Base | 10 minutes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls_10m.pt) +data2vec Base | 100 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls_100h.pt) +data2vec Base | 960 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls_960h.pt) +data2vec Large | No fine-tuning | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_pretrained.pt) +data2vec Large | 10 minutes | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_10m.pt) +data2vec Large | 100 hours | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_100h.pt) +data2vec Large | 960 hours | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_960h.pt) +--- + +### NLP + +Model | Fine-tuning data | Dataset | Link +|---|---|---|---| +data2vec Base | No fine-tuning | Books + Wiki | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt) + +## Training a new speech model with the CLI tools + +Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length) + +### Prepare training data manifest: + +First, install the `soundfile` library: +```shell script +pip install soundfile +``` + +Next, run: + +```shell script +$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext $ext --valid-percent $valid +``` + +$ext should be set to flac, wav, or whatever format your dataset happens to use that soundfile can read. + +$valid should be set to some reasonable percentage (like 0.01) of training data to use for validation. +To use a pre-defined validation set (like dev-other from librispeech), set to it 0 and then overwrite valid.tsv with a +separately pre-processed manifest file. + +### Train a data2vec Base model: + +This configuration was used for the base model trained on the Librispeech dataset in the data2vec paper + +Note that the input is expected to be single channel, sampled at 16 kHz + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/audio/pretraining \ +--config-name base_librispeech task.data=/path/to/manifests common.user_dir=examples/data2vec +``` + +Note: you can simulate 16 GPUs by using k GPUs and adding command line parameters +`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 16/k + +### Fine-tune a pre-trained model with CTC: + +Fine-tuning a model requires parallel audio and labels file, as well as a vocabulary file in fairseq format. +A letter vocabulary can be downloaded [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). +An example [script](../wav2vec/libri_labels.py) that generates labels for the Librispeech dataset from the tsv file produced by wav2vec_manifest.py can be used as follows: + +```shell script +split=train +$ python libri_labels.py /path/to/tsv --output-dir /output/dir --output-name $split +``` + +Fine-tuning on 100h of Librispeech with letter targets: +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_port=$PORT \ + task.data=/path/to/data \ + model.w2v_path=/path/to/model.pt \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/finetuning \ + --config-name base_100h common.user_dir=examples/data2vec +``` + +There are other config files in the config/finetuning directory that can be used to fine-tune on other splits. +You can specify the right config via the `--config-name` parameter. + +Decoding with a language model during training requires flashlight [python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter). +If you want to use a language model, add `+criterion.wer_args='[/path/to/kenlm, /path/to/lexicon, 2, -1]'` to the command line. + +### Evaluating a CTC model: + +Evaluating a CTC model with a language model requires [flashlight python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter) to be installed. + +Fairseq transformer language model used in the wav2vec 2.0 paper can be obtained from the [wav2letter model repository](https://github.com/facebookresearch/wav2letter/tree/master/recipes/sota/2019). +Be sure to upper-case the language model vocab after downloading it. + +Letter dictionary for pre-trained models can be found [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). + +Next, run the evaluation command: + +```shell script +python examples/speech_recognition/new/infer.py --config-dir examples/speech_recognition/new/conf \ +--config-name infer task=audio_finetuning task.data=/path/to/manifests common.user_dir=examples/data2vec \ +task.labels=ltr decoding.type=kenlm \ +decoding.lmweight=${lmweight} decoding.wordscore=${wordscore} decoding.silweight=${silscore} \ +decoding.lexicon=/path/to/lexicon \ +decoding.lmpath=/path/to/lm decoding.unique_wer_file=True \ +dataset.gen_subset=dev_clean,dev_other,test_clean,test_other \ +common_eval.path=/path/to/checkpoint.pt decoding.beam=1500 distributed_training.distributed_world_size=${num_gpus} +``` + +To get raw numbers, use decoding.type=viterbi and omit the lexicon. To use the transformer language model, use decoding.type=fairseqlm. + +## Training a new NLP model with the CLI tools + +Please follow the [RoBERTa](../roberta/README.md) instructions to preprocess your data. To train a data2vec model on run: + +```shell script +$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/text/pretraining \ +--config-name base task.data=/path/to/data common.user_dir=examples/data2vec +``` + +As for speech models, you can simulate 16 gpus by using the update_freq parameter. + +### Finetuning data2vec-text on GLUE + +Please use a command similar to this: + +```shell +$ python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task task.data=$data_path checkpoint.restore_file="${/path/to/pretrained/model.pt}" +``` diff --git a/fairseq/examples/data2vec/__init__.py b/fairseq/examples/data2vec/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/data2vec/config/audio/classification/base_classification.yaml b/fairseq/examples/data2vec/config/audio/classification/base_classification.yaml new file mode 100644 index 0000000..fdb9c8d --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/classification/base_classification.yaml @@ -0,0 +1,70 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + all_gather_list_size: 70000 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + +checkpoint: + save_interval: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: mAP + maximize_best_checkpoint_metric: true + +task: + _name: audio_classification + data: ??? + normalize: true + labels: lbl + +dataset: + num_workers: 6 + max_tokens: 2560000 + skip_invalid_size_inputs_valid_test: true + valid_subset: eval + validate_interval: 5 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: model + can_sum: false + log_keys: + - _predictions + - _targets + +optimization: + max_update: 30000 + lr: [0.00006] # scratch 53-5 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: cosine + warmup_updates: 5000 + +model: + _name: audio_classification + model_path: ??? + apply_mask: true + mask_prob: 0.6 + mask_length: 5 # scratch 1 + mask_channel_prob: 0 + mask_channel_length: 64 + layerdrop: 0.1 + dropout: 0.1 + activation_dropout: 0.1 + attention_dropout: 0.2 + feature_grad_mult: 0 # scratch 1 + label_mixup: true + source_mixup: 0.5 + prediction_mode: lin_softmax # scratch average_sigmoid + diff --git a/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_1.yaml b/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_1.yaml new file mode 100644 index 0000000..881a158 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_1.yaml @@ -0,0 +1,35 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_1g.yaml b/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_1g.yaml new file mode 100644 index 0000000..de7894d --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_1g.yaml @@ -0,0 +1,35 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 1 + tasks_per_node: 1 + mem_gb: 100 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_2.yaml b/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_2.yaml new file mode 100644 index 0000000..b016cac --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/classification/run_config/slurm_2.yaml @@ -0,0 +1,35 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/audioset.yaml b/fairseq/examples/data2vec/config/audio/pretraining/audioset.yaml new file mode 100644 index 0000000..dd30fbe --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/audioset.yaml @@ -0,0 +1,91 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec + +checkpoint: + save_interval: 1 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: /private/home/abaevski/data/audioset + max_sample_size: 320000 + min_sample_size: 32000 + normalize: true + +dataset: + num_workers: 6 + max_tokens: 3400000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 24 + ddp_backend: legacy_ddp + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var +# - avg_self_attn +# - weights + +optimization: + max_update: 200000 + lr: [0.0005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: cosine + warmup_updates: 10000 + +model: + _name: data2vec_audio + extractor_mode: layer_norm + encoder_layerdrop: 0.05 + dropout_input: 0.0 + dropout_features: 0.0 + feature_grad_mult: 1.0 + encoder_embed_dim: 768 + + mask_prob: 0.65 + mask_length: 10 + + loss_beta: 0 + loss_scale: null + + instance_norm_target_layer: true + layer_norm_targets: true + average_top_k_layers: 12 + + self_attn_norm_type: deepnorm + final_norm_type: deepnorm + + pos_conv_depth: 5 + conv_pos: 95 + + ema_decay: 0.999 + ema_end_decay: 0.9999 + ema_anneal_end_step: 30000 + ema_transformer_only: true + ema_layers_only: false + + require_same_masks: true + mask_dropout: 0 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/base_librispeech.yaml b/fairseq/examples/data2vec/config/audio/pretraining/base_librispeech.yaml new file mode 100644 index 0000000..c332c5a --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/base_librispeech.yaml @@ -0,0 +1,83 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 320000 + min_sample_size: 32000 + normalize: true + +dataset: + num_workers: 6 + max_tokens: 3800000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 16 + ddp_backend: legacy_ddp + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + +optimization: + max_update: 400000 + lr: [0.0005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.03,0.9,0.07] + +model: + _name: data2vec_audio + extractor_mode: layer_norm + encoder_layerdrop: 0.05 + dropout_input: 0.0 + dropout_features: 0.0 + feature_grad_mult: 1.0 + encoder_embed_dim: 768 + + mask_prob: 0.65 + mask_length: 10 + + loss_beta: 0 + loss_scale: null + + instance_norm_target_layer: true + average_top_k_layers: 8 + + pos_conv_depth: 5 + conv_pos: 95 + + ema_decay: 0.999 + ema_end_decay: 0.9999 + ema_anneal_end_step: 30000 + ema_transformer_only: true + ema_layers_only: true + + require_same_masks: true + mask_dropout: 0 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/local.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_1.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_1.yaml new file mode 100644 index 0000000..732f018 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_1.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_1_aws.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_1_aws.yaml new file mode 100644 index 0000000..e2bab56 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_1_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_2.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_2.yaml new file mode 100644 index 0000000..ec53dc2 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_2.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_2_aws.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_2_aws.yaml new file mode 100644 index 0000000..70cc8cb --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_2_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - task.post_save_script + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_3.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_3.yaml new file mode 100644 index 0000000..14b47d1 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_3.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 3 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_4.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_4.yaml new file mode 100644 index 0000000..c54d735 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_4.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_4_aws.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_4_aws.yaml new file mode 100644 index 0000000..0231b26 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_4_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - task.post_save_script + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_6_aws.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_6_aws.yaml new file mode 100644 index 0000000..9a4e43a --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_6_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 6 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_8_aws.yaml b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_8_aws.yaml new file mode 100644 index 0000000..78c9f57 --- /dev/null +++ b/fairseq/examples/data2vec/config/audio/pretraining/run_config/slurm_8_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 8 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/text/pretraining/base.yaml b/fairseq/examples/data2vec/config/text/pretraining/base.yaml new file mode 100644 index 0000000..c6b07c4 --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/base.yaml @@ -0,0 +1,77 @@ +# @package _group_ +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + no_epoch_checkpoints: true + save_interval_updates: 50000 + keep_interval_updates: 1 + +distributed_training: + distributed_world_size: 16 + ddp_backend: legacy_ddp + +task: + _name: masked_lm + data: ??? + sample_break_mode: complete_doc + tokens_per_sample: 512 + include_target_tokens: true + random_token_prob: 0 + leave_unmasked_prob: 0 + mask_prob: 0.35 + mask_multiple_length: 4 + +criterion: model + +dataset: + max_tokens: 8192 + ignore_unused_valid_subsets: true + skip_invalid_size_inputs_valid_test: true + +optimizer: + _name: adam + weight_decay: 0.01 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: cosine + warmup_updates: 10000 + +optimization: + clip_norm: 5 + lr: [0.0002] + max_update: 1000000 + update_freq: [1] + +model: + _name: data2vec_text + head_layers: 2 + average_top_k_layers: 10 + layer_norm_target_layer: true + loss_scale: 1 + ema_decay: 0.999 + ema_end_decay: 0.9999 + ema_anneal_end_step: 300000 + loss_beta: 4 + ema_transformer_layers_only: true + + transformer: + dropout: 0.1 + attention_dropout: 0.1 + layernorm_embedding: true + activation_fn: gelu + no_scale_embedding: true + max_source_positions: 512 + encoder: + embed_dim: 768 + ffn_embed_dim: 3072 + layers: 12 + attention_heads: 12 + normalize_before: false + learned_pos: true + layerdrop: 0 diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/local.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_1_aws.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_1_aws.yaml new file mode 100644 index 0000000..4bac45a --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_1_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: '_' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir}/submitit + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec + max_num_timeout: 30 + exclude: a100-st-p4d24xlarge-471 diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_2.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_2.yaml new file mode 100644 index 0000000..006a0f2 --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_2.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_2_aws.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_2_aws.yaml new file mode 100644 index 0000000..4292198 --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_2_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: '_' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir}/submitit + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec + max_num_timeout: 30 + exclude: a100-st-p4d24xlarge-471 diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_3.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_3.yaml new file mode 100644 index 0000000..0e1555d --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_3.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 3 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_4.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_4.yaml new file mode 100644 index 0000000..c54d735 --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_4.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_4_aws.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_4_aws.yaml new file mode 100644 index 0000000..5df84cd --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_4_aws.yaml @@ -0,0 +1,41 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: '_' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir}/submitit + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec + max_num_timeout: 30 + exclude: a100-st-p4d24xlarge-471 + +distributed_training: + distributed_world_size: 32 + ddp_backend: legacy_ddp diff --git a/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_8_aws.yaml b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_8_aws.yaml new file mode 100644 index 0000000..5b32c23 --- /dev/null +++ b/fairseq/examples/data2vec/config/text/pretraining/run_config/slurm_8_aws.yaml @@ -0,0 +1,41 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: '_' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir}/submitit + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 8 + name: pt + partition: wav2vec + max_num_timeout: 30 + exclude: a100-st-p4d24xlarge-471 + +distributed_training: + distributed_world_size: 64 + ddp_backend: legacy_ddp diff --git a/fairseq/examples/data2vec/config/v2/base_audio_only_task.yaml b/fairseq/examples/data2vec/config/v2/base_audio_only_task.yaml new file mode 100644 index 0000000..65a9ab3 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/base_audio_only_task.yaml @@ -0,0 +1,113 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + fp16_no_flatten_grads: false + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + save_interval: 1 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: /private/home/abaevski/data/librispeech/full + max_sample_size: 320000 + min_sample_size: 32000 + normalize: true + precompute_mask_config: {} + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 8 + ddp_backend: legacy_ddp + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +optimization: + max_update: 400000 + lr: [0.00075] + debug_param_names: true + +optimizer: + _name: adam + adam_betas: [ 0.9,0.98 ] + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: cosine + warmup_updates: 8000 + +model: + _name: data2vec_multi + + loss_beta: 0 + loss_scale: null + + depth: 12 + embed_dim: 768 + clone_batch: 8 + + ema_decay: 0.999 + ema_end_decay: 0.99999 + ema_anneal_end_step: 75000 + ema_encoder_only: false + + average_top_k_layers: 8 + instance_norm_target_layer: true + layer_norm_target_layer: false + layer_norm_targets: false + + layerdrop: 0.05 + norm_eps: 1e-5 + + supported_modality: AUDIO + + modalities: + audio: + feature_encoder_spec: '[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]' + conv_pos_depth: 5 + conv_pos_width: 95 + conv_pos_groups: 16 + prenet_depth: 0 + mask_prob: 0.5 + mask_prob_adjust: 0.05 + inverse_mask: false + mask_length: 5 + mask_noise_std: 0.01 + mask_dropout: 0 + add_masks: false + ema_local_encoder: false + use_alibi_encoder: true + prenet_layerdrop: 0.05 + prenet_dropout: 0.1 + learned_alibi_scale: true + learned_alibi_scale_per_head: true + decoder: + input_dropout: 0.1 + decoder_dim: 384 + decoder_groups: 16 + decoder_kernel: 7 + decoder_layers: 4 diff --git a/fairseq/examples/data2vec/config/v2/base_images_only_task.yaml b/fairseq/examples/data2vec/config/v2/base_images_only_task.yaml new file mode 100644 index 0000000..ff0c247 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/base_images_only_task.yaml @@ -0,0 +1,116 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: mae_image_pretraining + data: /datasets01/imagenet_full_size/061417/ + rebuild_batches: true + local_cache_path: /scratch/cache_abaevski/imagenet + key: source + precompute_mask_config: {} + +dataset: + num_workers: 10 + batch_size: 16 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +optimization: + max_update: 375300 + lr: [ 0.001 ] + debug_param_names: true + clip_norm: 4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 1e-3 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 50040 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + ema_decay: 0.9998 + ema_end_decay: 0.99999 + ema_anneal_end_step: 100000 + instance_norm_target_layer: true + layer_norm_target_layer: false + layer_norm_targets: true + end_of_block_targets: false + + depth: 10 + average_top_k_layers: 10 + clone_batch: 16 + + norm_eps: 1e-6 + + min_target_var: 0 + min_pred_var: 0 + + encoder_dropout: 0 + post_mlp_drop: 0 + attention_dropout: 0 + activation_dropout: 0 + + supported_modality: IMAGE + cls_loss: 0.01 + + ema_encoder_only: false + + modalities: + image: + inverse_mask: true + mask_prob: 0.8 + mask_prob_adjust: 0.07 + mask_length: 3 + mask_noise_std: 0.01 + prenet_depth: 2 + ema_local_encoder: true + num_extra_tokens: 1 + init_extra_token_zero: false + use_alibi_encoder: false + decoder: + decoder_dim: 768 + decoder_groups: 16 + decoder_kernel: 3 + decoder_layers: 6 + input_dropout: 0 \ No newline at end of file diff --git a/fairseq/examples/data2vec/config/v2/base_text_only_task.yaml b/fairseq/examples/data2vec/config/v2/base_text_only_task.yaml new file mode 100644 index 0000000..62f22eb --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/base_text_only_task.yaml @@ -0,0 +1,112 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + no_epoch_checkpoints: true + save_interval_updates: 50000 + keep_interval_updates: 1 + +distributed_training: + distributed_world_size: 16 + ddp_backend: legacy_ddp + +task: + _name: masked_lm + data: /fsx-wav2vec/abaevski/data/nlp/bookwiki_aml-full-mmap2-bin + sample_break_mode: none + tokens_per_sample: 512 + include_target_tokens: true + random_token_prob: 0 + leave_unmasked_prob: 0 + include_index: True + skip_masking: True + d2v2_multi: True + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +dataset: + batch_size: 4 + ignore_unused_valid_subsets: true + skip_invalid_size_inputs_valid_test: true + disable_validation: true + +optimization: + clip_norm: 1 + lr: [0.0002] + max_update: 1000000 + update_freq: [1] + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 0.0002 + optimizer: + _name: adam + adam_betas: [0.9,0.98] + adam_eps: 1e-06 + weight_decay: 0.01 + lr_scheduler: + _name: cosine + warmup_updates: 4000 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + loss_beta: 0 + loss_scale: 1 + + depth: 12 + embed_dim: 768 + clone_batch: 8 + + ema_decay: 0.9999 + ema_end_decay: 0.99999 + ema_anneal_end_step: 100000 + ema_encoder_only: true + + average_top_k_layers: 12 + layer_norm_target_layer: false + instance_norm_target_layer: true + batch_norm_target_layer: false + instance_norm_targets: false + layer_norm_targets: false + + layerdrop: 0 + norm_eps: 1e-5 + + supported_modality: TEXT + + modalities: + text: + mask_prob: 0.48 + mask_length: 1 + mask_noise_std: 0.01 + prenet_depth: 0 + decoder: + input_dropout: 0.1 + decoder_dim: 768 + decoder_groups: 1 + decoder_kernel: 9 + decoder_layers: 5 + decoder_residual: false + projection_layers: 2 + projection_ratio: 2.0 diff --git a/fairseq/examples/data2vec/config/v2/huge_images14_only_task.yaml b/fairseq/examples/data2vec/config/v2/huge_images14_only_task.yaml new file mode 100644 index 0000000..a8a1525 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/huge_images14_only_task.yaml @@ -0,0 +1,122 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: mae_image_pretraining + data: /datasets01/imagenet_full_size/061417/ + rebuild_batches: true + local_cache_path: /scratch/cache_abaevski/imagenet + key: source + precompute_mask_config: {} + +dataset: + num_workers: 10 + batch_size: 8 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 32 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +optimization: + max_update: 500000 + lr: [ 0.0004 ] + debug_param_names: true + clip_norm: 4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 4e-4 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 50040 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + ema_decay: 0.9998 + ema_end_decay: 1 + ema_anneal_end_step: 300000 + instance_norm_target_layer: true + layer_norm_target_layer: false + layer_norm_targets: true + end_of_block_targets: false + + depth: 32 + embed_dim: 1280 + num_heads: 16 + + average_top_k_layers: 24 + clone_batch: 16 + + norm_eps: 1e-6 + + min_target_var: 0 + min_pred_var: 0 + + encoder_dropout: 0 + post_mlp_drop: 0 + attention_dropout: 0 + activation_dropout: 0 + + supported_modality: IMAGE + cls_loss: 0.01 + + ema_encoder_only: false + + modalities: + image: + patch_size: 14 + inverse_mask: true + mask_prob: 0.75 + mask_prob_adjust: 0.1 + mask_length: 3 + mask_noise_std: 0.01 + prenet_depth: 0 + ema_local_encoder: true + num_extra_tokens: 1 + init_extra_token_zero: false + use_alibi_encoder: false + embed_dim: 1280 + decoder: + decoder_dim: 1024 + decoder_groups: 16 + decoder_kernel: 5 + decoder_layers: 3 + final_layer_norm: false + input_dropout: 0 \ No newline at end of file diff --git a/fairseq/examples/data2vec/config/v2/huge_images_only_task.yaml b/fairseq/examples/data2vec/config/v2/huge_images_only_task.yaml new file mode 100644 index 0000000..7a352ac --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/huge_images_only_task.yaml @@ -0,0 +1,120 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: mae_image_pretraining + data: /datasets01/imagenet_full_size/061417/ + rebuild_batches: true + local_cache_path: /scratch/cache_abaevski/imagenet + key: source + precompute_mask_config: {} + +dataset: + num_workers: 10 + batch_size: 8 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +optimization: + max_update: 375300 + lr: [ 0.0004 ] + debug_param_names: true + clip_norm: 4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 4e-4 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 50040 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + ema_decay: 0.9998 + ema_end_decay: 0.99995 + ema_anneal_end_step: 150000 + instance_norm_target_layer: true + layer_norm_target_layer: false + layer_norm_targets: true + end_of_block_targets: false + + depth: 32 + embed_dim: 1280 + num_heads: 16 + + average_top_k_layers: 24 + clone_batch: 16 + + norm_eps: 1e-6 + + min_target_var: 0 + min_pred_var: 0 + + encoder_dropout: 0 + post_mlp_drop: 0 + attention_dropout: 0 + activation_dropout: 0 + + supported_modality: IMAGE + cls_loss: 0.01 + + ema_encoder_only: false + + modalities: + image: + inverse_mask: true + mask_prob: 0.75 + mask_prob_adjust: 0.1 + mask_length: 3 + mask_noise_std: 0.01 + prenet_depth: 0 + ema_local_encoder: true + num_extra_tokens: 1 + init_extra_token_zero: false + use_alibi_encoder: false + embed_dim: 1280 + decoder: + decoder_dim: 1024 + decoder_groups: 16 + decoder_kernel: 5 + decoder_layers: 3 + input_dropout: 0 \ No newline at end of file diff --git a/fairseq/examples/data2vec/config/v2/large_audio_only_task.yaml b/fairseq/examples/data2vec/config/v2/large_audio_only_task.yaml new file mode 100644 index 0000000..3f61589 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/large_audio_only_task.yaml @@ -0,0 +1,122 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + save_interval: 1 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: /fsx-wav2vec/abaevski/data/librivox/no_silence + max_sample_size: 320000 + min_sample_size: 32000 + normalize: true + precompute_mask_config: {} + +dataset: + num_workers: 8 + max_tokens: 320000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 48 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +optimization: + max_update: 600000 + debug_param_names: true + clip_norm: 1 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 0.0004 + optimizer: + _name: adam + adam_betas: [0.9,0.98] + adam_eps: 1e-06 + weight_decay: 0.01 + lr_scheduler: + _name: cosine + warmup_updates: 10000 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + loss_beta: 0 + loss_scale: null + + depth: 16 + embed_dim: 1024 + num_heads: 16 + + clone_batch: 12 + + ema_decay: 0.9997 + ema_end_decay: 1 + ema_anneal_end_step: 300000 + ema_encoder_only: false + + average_top_k_layers: 16 + instance_norm_target_layer: true + layer_norm_target_layer: false + layer_norm_targets: false + + layerdrop: 0 + norm_eps: 1e-5 + + supported_modality: AUDIO + + modalities: + audio: + feature_encoder_spec: '[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]' + conv_pos_depth: 5 + conv_pos_width: 95 + conv_pos_groups: 16 + prenet_depth: 8 + mask_prob: 0.55 + mask_prob_adjust: 0.1 + inverse_mask: false + mask_length: 5 + mask_noise_std: 0.01 + mask_dropout: 0 + add_masks: false + ema_local_encoder: false + use_alibi_encoder: true + prenet_layerdrop: 0 + prenet_dropout: 0.1 + learned_alibi_scale: true + learned_alibi_scale_per_head: true + decoder: + input_dropout: 0.1 + decoder_dim: 768 + decoder_groups: 16 + decoder_kernel: 7 + decoder_layers: 4 diff --git a/fairseq/examples/data2vec/config/v2/large_images_only_task.yaml b/fairseq/examples/data2vec/config/v2/large_images_only_task.yaml new file mode 100644 index 0000000..6b957fc --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/large_images_only_task.yaml @@ -0,0 +1,120 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: mae_image_pretraining + data: /datasets01/imagenet_full_size/061417/ + rebuild_batches: true + local_cache_path: /scratch/cache_abaevski/imagenet + key: source + precompute_mask_config: {} + +dataset: + num_workers: 10 + batch_size: 8 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +optimization: + max_update: 375300 + lr: [ 0.0004 ] + debug_param_names: true + clip_norm: 4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 4e-4 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 50040 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + ema_decay: 0.9998 + ema_end_decay: 0.99999 + ema_anneal_end_step: 150000 + instance_norm_target_layer: true + layer_norm_target_layer: false + layer_norm_targets: true + end_of_block_targets: false + + depth: 24 + embed_dim: 1024 + num_heads: 16 + + average_top_k_layers: 18 + clone_batch: 16 + + norm_eps: 1e-6 + + min_target_var: 0 + min_pred_var: 0 + + encoder_dropout: 0 + post_mlp_drop: 0 + attention_dropout: 0 + activation_dropout: 0 + + supported_modality: IMAGE + cls_loss: 0.01 + + ema_encoder_only: false + + modalities: + image: + inverse_mask: true + mask_prob: 0.75 + mask_prob_adjust: 0.1 + mask_length: 3 + mask_noise_std: 0.01 + prenet_depth: 0 + ema_local_encoder: true + num_extra_tokens: 1 + init_extra_token_zero: false + use_alibi_encoder: false + embed_dim: 1024 + decoder: + decoder_dim: 1024 + decoder_groups: 16 + decoder_kernel: 5 + decoder_layers: 3 + input_dropout: 0 \ No newline at end of file diff --git a/fairseq/examples/data2vec/config/v2/large_text_only_task.yaml b/fairseq/examples/data2vec/config/v2/large_text_only_task.yaml new file mode 100644 index 0000000..fd69048 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/large_text_only_task.yaml @@ -0,0 +1,112 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + min_loss_scale: 1e-6 + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + save_interval_updates: 50000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: masked_lm + data: /fsx-wav2vec/abaevski/data/nlp/bookwiki_aml-full-mmap2-bin + sample_break_mode: none + tokens_per_sample: 512 + include_target_tokens: true + random_token_prob: 0 + leave_unmasked_prob: 0 + include_index: True + skip_masking: True + d2v2_multi: True + +dataset: + batch_size: 2 + ignore_unused_valid_subsets: true + skip_invalid_size_inputs_valid_test: true + disable_validation: true + +distributed_training: + distributed_world_size: 32 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +optimization: + max_update: 600000 + clip_norm: 1 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 0.0001 + optimizer: + _name: adam + adam_betas: [0.9,0.98] + adam_eps: 1e-06 + weight_decay: 0.01 + lr_scheduler: + _name: cosine + warmup_updates: 4000 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + loss_beta: 0 + loss_scale: 1 + + depth: 24 + num_heads: 16 + embed_dim: 1024 + clone_batch: 8 + + ema_decay: 0.9999 + ema_end_decay: 0.99999 + ema_anneal_end_step: 100000 + ema_encoder_only: true + + average_top_k_layers: 24 + layer_norm_target_layer: true + instance_norm_target_layer: false + batch_norm_target_layer: false + instance_norm_targets: true + layer_norm_targets: false + + layerdrop: 0 + norm_eps: 1e-5 + + supported_modality: TEXT + + modalities: + text: + mask_prob: 0.5 + mask_length: 1 + mask_noise_std: 0.01 + prenet_depth: 0 + decoder: + input_dropout: 0.1 + decoder_dim: 768 + decoder_groups: 1 + decoder_kernel: 9 + decoder_layers: 5 + decoder_residual: false + projection_layers: 2 + projection_ratio: 2.0 diff --git a/fairseq/examples/data2vec/config/v2/large_text_only_task_pgrp_1M.yaml b/fairseq/examples/data2vec/config/v2/large_text_only_task_pgrp_1M.yaml new file mode 100644 index 0000000..739e6f6 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/large_text_only_task_pgrp_1M.yaml @@ -0,0 +1,123 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + fp16_no_flatten_grads: true + user_dir: ${env:PWD}/examples/data2vec + +checkpoint: + no_epoch_checkpoints: true + save_interval_updates: 50000 + keep_interval_updates: 1 + +distributed_training: + distributed_world_size: 32 + ddp_backend: legacy_ddp + +task: + _name: masked_lm + data: /fsx-wav2vec/abaevski/data/nlp/bookwiki_aml-full-mmap2-bin + sample_break_mode: none + tokens_per_sample: 512 + include_target_tokens: true + random_token_prob: 0 + leave_unmasked_prob: 0 + include_index: True + skip_masking: True + d2v2_multi: True + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + - model_norm + - ema_norm + - masked_pct + +dataset: + batch_size: 2 + ignore_unused_valid_subsets: true + skip_invalid_size_inputs_valid_test: true + disable_validation: true + +optimization: + clip_norm: 1 + lr: [3e-4] + max_update: 1000000 + update_freq: [1] + +optimizer: + _name: composite + groups: + default: + lr_float: 1e-4 + optimizer: + _name: adam + adam_betas: [0.9,0.98] + adam_eps: 1e-06 + weight_decay: 0.01 + lr_scheduler: + _name: cosine + warmup_updates: 4000 + decoder: + lr_float: 1e-4 + optimizer: + _name: adam + adam_betas: [0.9,0.98] + adam_eps: 1e-06 + weight_decay: 0.01 + lr_scheduler: + _name: cosine + warmup_updates: 4000 + +lr_scheduler: pass_through + +model: + _name: data2vec_multi + + loss_beta: 4 + loss_scale: 1 + + depth: 24 + num_heads: 16 + embed_dim: 1024 + clone_batch: 8 + + ema_decay: 0.9999 + ema_end_decay: 0.99999 + ema_anneal_end_step: 100000 + ema_encoder_only: true + + average_top_k_layers: 24 + layer_norm_target_layer: true + instance_norm_target_layer: false + batch_norm_target_layer: false + instance_norm_targets: true + layer_norm_targets: false + + layerdrop: 0 + norm_eps: 1e-5 + + supported_modality: TEXT + decoder_group: true + + modalities: + text: + mask_prob: 0.5 + mask_length: 1 + mask_noise_std: 0.01 + prenet_depth: 0 + decoder: + input_dropout: 0.1 + decoder_dim: 768 + decoder_groups: 1 + decoder_kernel: 9 + decoder_layers: 5 + decoder_residual: false + projection_layers: 2 + projection_ratio: 2.0 diff --git a/fairseq/examples/data2vec/config/v2/run_config/local.yaml b/fairseq/examples/data2vec/config/v2/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_1.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_1.yaml new file mode 100644 index 0000000..732f018 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_1.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_1_aws.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_1_aws.yaml new file mode 100644 index 0000000..b2184f8 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_1_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.local_cache_path + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_2.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_2.yaml new file mode 100644 index 0000000..ec53dc2 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_2.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_2_aws.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_2_aws.yaml new file mode 100644 index 0000000..5537655 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_2_aws.yaml @@ -0,0 +1,39 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.local_cache_path + - task.data + - task.post_save_script + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + - model.model_path + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 12 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_3.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_3.yaml new file mode 100644 index 0000000..14b47d1 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_3.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 3 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_4.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_4.yaml new file mode 100644 index 0000000..c54d735 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_4.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_4_aws.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_4_aws.yaml new file mode 100644 index 0000000..a77f62a --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_4_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - task.post_save_script + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 12 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_6_aws.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_6_aws.yaml new file mode 100644 index 0000000..20e0658 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_6_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 12 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 6 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_8.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_8.yaml new file mode 100644 index 0000000..e3ec2c2 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_8.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 8 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/run_config/slurm_8_aws.yaml b/fairseq/examples/data2vec/config/v2/run_config/slurm_8_aws.yaml new file mode 100644 index 0000000..a9dce87 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/run_config/slurm_8_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 12 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 8 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/cola.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/cola.yaml new file mode 100644 index 0000000..d4ac4ec --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/cola.yaml @@ -0,0 +1,60 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: mcc + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + report_mcc: True + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 320 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 5336 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/mnli.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/mnli.yaml new file mode 100644 index 0000000..1a9d6e5 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/mnli.yaml @@ -0,0 +1,60 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 3 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + valid_subset: valid,valid1 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 7432 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 123873 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/mrpc.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/mrpc.yaml new file mode 100644 index 0000000..8f93d9d --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/mrpc.yaml @@ -0,0 +1,60 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: acc_and_f1 + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + report_acc_and_f1: True + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 137 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 2296 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/qnli.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/qnli.yaml new file mode 100644 index 0000000..739fb53 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/qnli.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 1986 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 33112 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/qqp.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/qqp.yaml new file mode 100644 index 0000000..9accbaa --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/qqp.yaml @@ -0,0 +1,60 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: acc_and_f1 + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + report_acc_and_f1: True + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 28318 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 113272 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/rte.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/rte.yaml new file mode 100644 index 0000000..ea07764 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/rte.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 122 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 2036 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/run_config/local.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/sst_2.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/sst_2.yaml new file mode 100644 index 0000000..a273e5b --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/sst_2.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 1256 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 20935 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/v2/text_finetuning/sts_b.yaml b/fairseq/examples/data2vec/config/v2/text_finetuning/sts_b.yaml new file mode 100644 index 0000000..fb009ab --- /dev/null +++ b/fairseq/examples/data2vec/config/v2/text_finetuning/sts_b.yaml @@ -0,0 +1,61 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + user_dir: ${env:PWD}/examples/data2vec + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 1 + max_positions: 512 + d2v2_multi: True + +checkpoint: + best_checkpoint_metric: pearson_and_spearman + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +criterion: + _name: sentence_prediction + regression_target: true + report_pearson_and_spearman: True + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + num_workers: 1 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 214 + +optimization: + clip_norm: 0.0 + lr: [4e-05] + max_update: 3598 + max_epoch: 10 + +model: + _name: data2vec_text_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/vision/finetuning/imagenet.yaml b/fairseq/examples/data2vec/config/vision/finetuning/imagenet.yaml new file mode 100644 index 0000000..d6d4864 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/imagenet.yaml @@ -0,0 +1,52 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + save_interval: 1 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: accuracy + +task: + _name: image_classification + data: /datasets01/imagenet_full_size/061417 + +dataset: + num_workers: 6 + batch_size: 64 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 1 + valid_subset: val + +distributed_training: + distributed_world_size: 8 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - correct + +optimization: + max_update: 100000 + lr: [0.0005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: cosine + warmup_updates: 10000 + +model: + _name: data2vec_image_classification + model_path: ??? diff --git a/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_clean.yaml b/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_clean.yaml new file mode 100644 index 0000000..17d4c0a --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_clean.yaml @@ -0,0 +1,65 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + fp16_no_flatten_grads: true + +checkpoint: + save_interval: 1 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + +task: + _name: mae_image_classification + data: /datasets01/imagenet_full_size/061417 + +dataset: + num_workers: 6 + batch_size: 32 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 2 + valid_subset: val + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - correct + +optimization: + max_update: 250200 + lr: [0.001] + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 0.001 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 16000 + min_lr: 1e-6 + + +lr_scheduler: pass_through + +model: + _name: mae_image_classification + mixup: 0.7 + mixup_prob: 0.9 + + model_path: ??? diff --git a/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_huge_clean.yaml b/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_huge_clean.yaml new file mode 100644 index 0000000..2d2eb57 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_huge_clean.yaml @@ -0,0 +1,68 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + fp16_no_flatten_grads: true + +checkpoint: + save_interval: 1 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + +task: + _name: mae_image_classification + data: /datasets01/imagenet_full_size/061417 + +dataset: + num_workers: 6 + batch_size: 32 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 2 + valid_subset: val + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - correct + +optimization: + max_update: 125200 + lr: [0.0005] + clip_norm: 4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 0.0005 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 16000 + min_lr: 1e-20 + + +lr_scheduler: pass_through + +model: + _name: mae_image_classification + mixup: 0.7 + mixup_prob: 0.9 + layer_decay: 0.75 + drop_path_rate: 0.2 + + model_path: ??? diff --git a/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_large_clean.yaml b/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_large_clean.yaml new file mode 100644 index 0000000..3a9413c --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/mae_imagenet_large_clean.yaml @@ -0,0 +1,68 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + fp16_no_flatten_grads: true + +checkpoint: + save_interval: 1 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + +task: + _name: mae_image_classification + data: /datasets01/imagenet_full_size/061417 + +dataset: + num_workers: 6 + batch_size: 32 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 2 + valid_subset: val + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - correct + +optimization: + max_update: 125200 + lr: [0.0005] + clip_norm: 4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 0.0005 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 16000 + min_lr: 1e-7 + + +lr_scheduler: pass_through + +model: + _name: mae_image_classification + mixup: 0.7 + mixup_prob: 0.9 + layer_decay: 0.75 + drop_path_rate: 0.2 + + model_path: ??? diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/local.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_1.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_1.yaml new file mode 100644 index 0000000..732f018 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_1.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_1_aws.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_1_aws.yaml new file mode 100644 index 0000000..e2bab56 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_1_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_2.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_2.yaml new file mode 100644 index 0000000..c8b0f02 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_2.yaml @@ -0,0 +1,38 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + - task.local_cache_path + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_2_aws.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_2_aws.yaml new file mode 100644 index 0000000..93d0d9c --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_2_aws.yaml @@ -0,0 +1,38 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + - task.local_cache_path + - model.model_path + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_3.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_3.yaml new file mode 100644 index 0000000..14b47d1 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_3.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 3 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_4.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_4.yaml new file mode 100644 index 0000000..c54d735 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_4.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_4_aws.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_4_aws.yaml new file mode 100644 index 0000000..d5d11cb --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_4_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_6_aws.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_6_aws.yaml new file mode 100644 index 0000000..906f08a --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_6_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 6 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_8_aws.yaml b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_8_aws.yaml new file mode 100644 index 0000000..d60e13f --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/finetuning/run_config/slurm_8_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 8 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/base_imagenet.yaml b/fairseq/examples/data2vec/config/vision/pretraining/base_imagenet.yaml new file mode 100644 index 0000000..9bfc0f3 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/base_imagenet.yaml @@ -0,0 +1,52 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: image_pretraining + data: /datasets01/imagenet_full_size/061417/ + +dataset: + num_workers: 6 + batch_size: 64 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + +optimization: + max_update: 400000 + lr: [0.0005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: cosine + warmup_updates: 10000 + +model: + _name: data2vec_vision diff --git a/fairseq/examples/data2vec/config/vision/pretraining/base_imagenet_d2v1.yaml b/fairseq/examples/data2vec/config/vision/pretraining/base_imagenet_d2v1.yaml new file mode 100644 index 0000000..5fd399b --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/base_imagenet_d2v1.yaml @@ -0,0 +1,64 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: image_pretraining + data: /datasets01/imagenet_full_size/061417 + +dataset: + num_workers: 6 + batch_size: 128 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 2 + disable_validation: true + +distributed_training: + distributed_world_size: 16 + ddp_backend: legacy_ddp + +criterion: + _name: model + log_keys: + - ema_decay + - target_var + - pred_var + +optimization: + max_update: 375300 #300*1251 + lr: [0.0005] + clip_norm: 3.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.999) + adam_eps: 1e-08 + weight_decay: 0.05 + +lr_scheduler: + _name: cosine + warmup_updates: 12510 # it should be 10 epochs + +model: + _name: data2vec_vision + + attention_dropout: 0.05 + + ema_decay: 0.999 + ema_end_decay: 0.9998 + layer_norm_targets: True + average_top_k_layers: 6 + + loss_beta: 2.0 + + drop_path: 0.25 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/base_mae_imagenet.yaml b/fairseq/examples/data2vec/config/vision/pretraining/base_mae_imagenet.yaml new file mode 100644 index 0000000..d7872b5 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/base_mae_imagenet.yaml @@ -0,0 +1,64 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + fp16_no_flatten_grads: true + +checkpoint: + save_interval: 5 + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: mae_image_pretraining + data: /datasets01/imagenet_full_size/061417/ + rebuild_batches: true + +dataset: + num_workers: 6 + batch_size: 64 + skip_invalid_size_inputs_valid_test: true + required_batch_size_multiple: 1 + disable_validation: true + +distributed_training: + distributed_world_size: 16 + ddp_backend: c10d + +criterion: + _name: model + +optimization: + max_update: 375300 + lr: [0.0006] + +optimizer: + _name: composite + groups: + with_decay: + lr_float: 6e-4 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0.05 + lr_scheduler: + _name: cosine + warmup_updates: 50040 + no_decay: + lr_float: 6e-4 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + weight_decay: 0 + lr_scheduler: + _name: cosine + warmup_updates: 50040 + +lr_scheduler: pass_through + +model: + _name: mae diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/local.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_1.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_1.yaml new file mode 100644 index 0000000..732f018 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_1.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_1_aws.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_1_aws.yaml new file mode 100644 index 0000000..e2bab56 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_1_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_2.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_2.yaml new file mode 100644 index 0000000..c8b0f02 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_2.yaml @@ -0,0 +1,38 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + - task.local_cache_path + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_2_aws.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_2_aws.yaml new file mode 100644 index 0000000..032e53a --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_2_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + - task.local_cache_path + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_3.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_3.yaml new file mode 100644 index 0000000..14b47d1 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_3.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 3 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_4.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_4.yaml new file mode 100644 index 0000000..c54d735 --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_4.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_4_aws.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_4_aws.yaml new file mode 100644 index 0000000..d5d11cb --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_4_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_6_aws.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_6_aws.yaml new file mode 100644 index 0000000..906f08a --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_6_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 6 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_8_aws.yaml b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_8_aws.yaml new file mode 100644 index 0000000..d60e13f --- /dev/null +++ b/fairseq/examples/data2vec/config/vision/pretraining/run_config/slurm_8_aws.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 8 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/data2vec/fb_convert_beit_cp.py b/fairseq/examples/data2vec/fb_convert_beit_cp.py new file mode 100644 index 0000000..cf42ace --- /dev/null +++ b/fairseq/examples/data2vec/fb_convert_beit_cp.py @@ -0,0 +1,165 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import torch + +from omegaconf import OmegaConf + +from fairseq.criterions.model_criterion import ModelCriterionConfig +from fairseq.dataclass.configs import FairseqConfig + +from tasks import ImageClassificationConfig, ImagePretrainingConfig +from models.data2vec_image_classification import ( + Data2VecImageClassificationConfig, + Data2VecImageClassificationModel, +) +from models.data2vec_vision import Data2VecVisionConfig, Data2VecVisionModel + + +def get_parser(): + parser = argparse.ArgumentParser( + description="convert beit checkpoint into data2vec - vision checkpoint" + ) + # fmt: off + parser.add_argument('checkpoint', help='checkpoint to convert') + parser.add_argument('--output', required=True, metavar='PATH', help='where to output converted checkpoint') + parser.add_argument('--type', type=str, choices=['vision', 'image_classification'], default='image_classification', help='type of model to upgrade') + parser.add_argument('--inception_norms', action='store_true', default=False) + # fmt: on + + return parser + + +def update_checkpoint(model_dict, prefix, is_nested): + + replace_paths = { + "cls_token": "model.cls_emb" if is_nested else "cls_emb", + "patch_embed": "model.patch_embed" if is_nested else "patch_embed", + "mask_token": "mask_emb", + } + + starts_with = { + "patch_embed.proj": "model.patch_embed.conv" + if is_nested + else "patch_embed.conv", + "lm_head": "final_proj", + "fc_norm": "fc_norm", + "head": "head", + } + + partial = { + "mlp.fc1": "mlp.0", + "mlp.fc2": "mlp.2", + } + + for k in list(model_dict.keys()): + for sw, r in starts_with.items(): + if k.startswith(sw): + replace_paths[k] = k.replace(sw, r) + for p, r in partial.items(): + if p in k: + replace_paths[k] = prefix + k.replace(p, r) + + if prefix != "": + for k in list(model_dict.keys()): + if k not in replace_paths: + replace_paths[k] = prefix + k + + for k in list(model_dict.keys()): + if k in replace_paths: + model_dict[replace_paths[k]] = model_dict[k] + if k != replace_paths[k]: + del model_dict[k] + + return model_dict + + +def main(): + parser = get_parser() + args = parser.parse_args() + + cp = torch.load(args.checkpoint, map_location="cpu") + + cfg = FairseqConfig( + criterion=ModelCriterionConfig(_name="model", log_keys=["correct"]), + ) + + if args.type == "image_classification": + + cfg.task = ImageClassificationConfig( + _name="image_classification", + data=".", + ) + + if args.inception_norms: + cfg.task.normalization_mean = [0.5, 0.5, 0.5] + cfg.task.normalization_std = [0.5, 0.5, 0.5] + + cfg.model = Data2VecImageClassificationConfig( + _name="data2vec_image_classification", + ) + cfg.model.pretrained_model_args = FairseqConfig( + model=Data2VecVisionConfig( + _name="data2vec_vision", shared_rel_pos_bias=False + ), + task=ImagePretrainingConfig( + _name="image_pretraining", + ), + ) + + cfg = OmegaConf.create(cfg) + + state = { + "cfg": OmegaConf.to_container(cfg, resolve=True, enum_to_str=True), + "model": cp["module"], + "best_loss": None, + "optimizer": None, + "extra_state": {}, + } + + model = Data2VecImageClassificationModel(cfg.model) + model.load_state_dict( + update_checkpoint(state["model"], prefix="model.encoder.", is_nested=True), + strict=True, + ) + elif args.type == "vision": + cfg.task = ImagePretrainingConfig( + _name="image_pretraining", + data=".", + ) + + if args.inception_norms: + cfg.task.normalization_mean = [0.5, 0.5, 0.5] + cfg.task.normalization_std = [0.5, 0.5, 0.5] + + cfg.model = Data2VecVisionConfig( + _name="data2vec_vision", + ) + cfg = OmegaConf.create(cfg) + + state = { + "cfg": OmegaConf.to_container(cfg, resolve=True, enum_to_str=True), + "model": cp["model"], + "best_loss": None, + "optimizer": None, + "extra_state": {}, + } + + model = Data2VecVisionModel(cfg.model) + model.load_state_dict( + update_checkpoint(state["model"], prefix="encoder.", is_nested=False), + strict=True, + ) + else: + raise Exception("unsupported type " + args.type) + + print(state["cfg"], state.keys()) + torch.save(state, args.output) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/data2vec/models/__init__.py b/fairseq/examples/data2vec/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/data2vec/models/audio_classification.py b/fairseq/examples/data2vec/models/audio_classification.py new file mode 100644 index 0000000..06d2158 --- /dev/null +++ b/fairseq/examples/data2vec/models/audio_classification.py @@ -0,0 +1,614 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import logging +import re +from dataclasses import dataclass, field +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from omegaconf import II, MISSING, open_dict + +from fairseq import checkpoint_utils, tasks +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models import ( + BaseFairseqModel, + register_model, +) +from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES +from fairseq.modules import TransposeLast +from fairseq.tasks import FairseqTask + +logger = logging.getLogger(__name__) + + +@dataclass +class AudioClassificationConfig(FairseqDataclass): + model_path: str = field( + default=MISSING, metadata={"help": "path to wav2vec 2.0 model"} + ) + no_pretrained_weights: bool = field( + default=False, metadata={"help": "if true, does not load pretrained weights"} + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + final_dropout: float = field( + default=0.0, + metadata={"help": "dropout after transformer and before final projection"}, + ) + dropout: float = field( + default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"} + ) + attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights inside wav2vec 2.0 model" + }, + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN inside wav2vec 2.0 model" + }, + ) + + # masking + apply_mask: bool = field( + default=False, metadata={"help": "apply masking during fine-tuning"} + ) + mask_length: int = field( + default=10, metadata={"help": "repeat the mask indices multiple times"} + ) + mask_prob: float = field( + default=0.5, + metadata={ + "help": "probability of replacing a token with mask (normalized by length)" + }, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose masks"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: Optional[int] = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + require_same_masks: bool = field( + default=True, + metadata={ + "help": "whether to number of masked timesteps must be the same across all " + "examples in a batch" + }, + ) + mask_dropout: float = field( + default=0.0, + metadata={"help": "percent of masks to unmask for each sample"}, + ) + + # channel masking + mask_channel_length: int = field( + default=10, metadata={"help": "length of the mask for features (channels)"} + ) + mask_channel_prob: float = field( + default=0.0, metadata={"help": "probability of replacing a feature with 0"} + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, metadata={"help": "whether to allow channel masks to overlap"} + ) + freeze_finetune_updates: int = field( + default=0, metadata={"help": "dont finetune wav2vec for this many updates"} + ) + feature_grad_mult: float = field( + default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"} + ) + layerdrop: float = field( + default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"} + ) + mask_channel_min_space: Optional[int] = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + mask_channel_before: bool = False + normalize: bool = II("task.normalize") + data: str = II("task.data") + # this holds the loaded wav2vec args + d2v_args: Any = None + offload_activations: bool = field( + default=False, metadata={"help": "offload_activations"} + ) + min_params_to_wrap: int = field( + default=int(1e8), + metadata={ + "help": "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + }, + ) + + checkpoint_activations: bool = field( + default=False, + metadata={"help": "recompute activations and save memory for extra compute"}, + ) + ddp_backend: str = II("distributed_training.ddp_backend") + + prediction_mode: str = "lin_softmax" + eval_prediction_mode: Optional[str] = None + conv_kernel: int = -1 + conv_stride: int = 1 + two_convs: bool = False + extreme_factor: float = 1.0 + + conv_feature_layers: Optional[str] = field( + default=None, + metadata={ + "help": "string describing convolutional feature extraction layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + + mixup_prob: float = 1.0 + source_mixup: float = -1 + same_mixup: bool = True + label_mixup: bool = False + + gain_mode: str = "none" + + +@register_model("audio_classification", dataclass=AudioClassificationConfig) +class AudioClassificationModel(BaseFairseqModel): + def __init__(self, cfg: AudioClassificationConfig, num_classes): + super().__init__() + + self.apply_mask = cfg.apply_mask + self.cfg = cfg + + arg_overrides = { + "dropout": cfg.dropout, + "activation_dropout": cfg.activation_dropout, + "dropout_input": cfg.dropout_input, + "attention_dropout": cfg.attention_dropout, + "mask_length": cfg.mask_length, + "mask_prob": cfg.mask_prob, + "require_same_masks": getattr(cfg, "require_same_masks", True), + "mask_dropout": getattr(cfg, "mask_dropout", 0), + "mask_selection": cfg.mask_selection, + "mask_other": cfg.mask_other, + "no_mask_overlap": cfg.no_mask_overlap, + "mask_channel_length": cfg.mask_channel_length, + "mask_channel_prob": cfg.mask_channel_prob, + "mask_channel_before": cfg.mask_channel_before, + "mask_channel_selection": cfg.mask_channel_selection, + "mask_channel_other": cfg.mask_channel_other, + "no_mask_channel_overlap": cfg.no_mask_channel_overlap, + "encoder_layerdrop": cfg.layerdrop, + "feature_grad_mult": cfg.feature_grad_mult, + "checkpoint_activations": cfg.checkpoint_activations, + "offload_activations": cfg.offload_activations, + "min_params_to_wrap": cfg.min_params_to_wrap, + "mixup": -1, + } + + if cfg.conv_feature_layers is not None: + arg_overrides["conv_feature_layers"] = cfg.conv_feature_layers + + if cfg.d2v_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu( + cfg.model_path, arg_overrides + ) + d2v_args = state.get("cfg", None) + if d2v_args is None: + d2v_args = convert_namespace_to_omegaconf(state["args"]) + d2v_args.criterion = None + d2v_args.lr_scheduler = None + cfg.d2v_args = d2v_args + + logger.info(d2v_args) + + else: + state = None + d2v_args = cfg.d2v_args + + model_normalized = d2v_args.task.get( + "normalize", d2v_args.model.get("normalize", False) + ) + assert cfg.normalize == model_normalized, ( + "Fine-tuning works best when data normalization is the same. " + "Please check that --normalize is set or unset for both pre-training and here" + ) + + if hasattr(cfg, "checkpoint_activations") and cfg.checkpoint_activations: + with open_dict(d2v_args): + d2v_args.model.checkpoint_activations = cfg.checkpoint_activations + + d2v_args.task.data = cfg.data + task = tasks.setup_task(d2v_args.task) + model = task.build_model(d2v_args.model, from_checkpoint=True) + + model.remove_pretraining_modules() + + if state is not None and not cfg.no_pretrained_weights: + self.load_model_weights(state, model, cfg) + + d = d2v_args.model.encoder_embed_dim + + self.d2v_model = model + + self.final_dropout = nn.Dropout(cfg.final_dropout) + self.freeze_finetune_updates = cfg.freeze_finetune_updates + self.num_updates = 0 + + for p in self.parameters(): + p.param_group = "pretrained" + + if cfg.prediction_mode == "proj_avg_proj": + self.proj = nn.Linear(d, d * 2) + self.proj2 = nn.Linear(d * 2, num_classes) + + for p in self.proj.parameters(): + p.param_group = "projection" + for p in self.proj2.parameters(): + p.param_group = "projection" + elif self.cfg.prediction_mode == "summary_proj": + self.proj = nn.Linear(d // 3, num_classes) + for p in self.proj.parameters(): + p.param_group = "projection" + elif self.cfg.conv_kernel > 1 and not self.cfg.two_convs: + self.proj = nn.Sequential( + TransposeLast(), + nn.Conv1d(d, num_classes, kernel_size=self.cfg.conv_kernel, stride=self.cfg.conv_stride), + TransposeLast(), + ) + for p in self.proj.parameters(): + p.param_group = "projection" + elif self.cfg.conv_kernel > 0 and self.cfg.two_convs: + self.proj = nn.Sequential( + TransposeLast(), + nn.Conv1d(d, d, kernel_size=self.cfg.conv_kernel, stride=self.cfg.conv_stride), + TransposeLast(), + nn.GELU(), + nn.Linear(d, num_classes), + ) + for p in self.proj.parameters(): + p.param_group = "projection" + else: + self.proj = nn.Linear(d, num_classes) + for p in self.proj.parameters(): + p.param_group = "projection" + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: AudioClassificationConfig, task: FairseqTask): + """Build a new model instance.""" + + assert hasattr(task, "labels"), f"Task {task} must have an attribute 'labels'" + + return cls(cfg, len(task.labels)) + + def load_model_weights(self, state, model, cfg): + if cfg.ddp_backend == "fully_sharded": + from fairseq.distributed import FullyShardedDataParallel + + for name, module in model.named_modules(): + if "encoder.layers" in name and len(name.split(".")) == 3: + # Only for layers, we do a special handling and load the weights one by one + # We dont load all weights together as that wont be memory efficient and may + # cause oom + new_dict = { + k.replace(name + ".", ""): v + for (k, v) in state["model"].items() + if name + "." in k + } + assert isinstance(module, FullyShardedDataParallel) + with module.summon_full_params(): + module.load_state_dict(new_dict, strict=True) + module._reset_lazy_init() + + # Once layers are loaded, filter them out and load everything else. + r = re.compile("encoder.layers.\d.") + filtered_list = list(filter(r.match, state["model"].keys())) + + new_big_dict = { + k: v for (k, v) in state["model"].items() if k not in filtered_list + } + + model.load_state_dict(new_big_dict, strict=False) + else: + if "_ema" in state["model"]: + del state["model"]["_ema"] + model.load_state_dict(state["model"], strict=False) + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def compute_gain(self, sound, fs=16_000, min_db=-80.0, mode="A_weighting"): + if fs == 16000: + n_fft = 2048 + elif fs == 44100: + n_fft = 4096 + else: + raise Exception("Invalid fs {}".format(fs)) + stride = n_fft // 2 + + def a_weight(fs, n_fft, min_db=-80.0): + freq = np.linspace(0, fs // 2, n_fft // 2 + 1) + freq_sq = np.power(freq, 2) + freq_sq[0] = 1.0 + weight = 2.0 + 20.0 * ( + 2 * np.log10(12194) + + 2 * np.log10(freq_sq) + - np.log10(freq_sq + 12194 ** 2) + - np.log10(freq_sq + 20.6 ** 2) + - 0.5 * np.log10(freq_sq + 107.7 ** 2) + - 0.5 * np.log10(freq_sq + 737.9 ** 2) + ) + weight = np.maximum(weight, min_db) + + return weight + + gain = [] + for i in range(0, len(sound) - n_fft + 1, stride): + if mode == "RMSE": + g = np.mean(sound[i : i + n_fft] ** 2) + elif mode == "A_weighting": + spec = np.fft.rfft(np.hanning(n_fft + 1)[:-1] * sound[i : i + n_fft]) + power_spec = np.abs(spec) ** 2 + a_weighted_spec = power_spec * np.power(10, a_weight(fs, n_fft) / 10) + g = np.sum(a_weighted_spec) + else: + raise Exception("Invalid mode {}".format(mode)) + gain.append(g) + + gain = np.array(gain) + gain = np.maximum(gain, np.power(10, min_db / 10)) + gain_db = 10 * np.log10(gain) + + return gain_db + + # adapted from https://github.com/mil-tokyo/bc_learning_sound/blob/master/utils.py + def compute_gain_torch(self, sound, fs=16_000, min_db=-80.0, mode="A_weighting"): + if fs == 16000: + n_fft = 2048 + elif fs == 44100: + n_fft = 4096 + else: + raise Exception("Invalid fs {}".format(fs)) + + if mode == "A_weighting": + if not hasattr(self, f"a_weight"): + self.a_weight = {} + + if fs not in self.a_weight: + + def a_weight(fs, n_fft, min_db=-80.0): + freq = np.linspace(0, fs // 2, n_fft // 2 + 1) + freq_sq = freq ** 2 + freq_sq[0] = 1.0 + weight = 2.0 + 20.0 * ( + 2 * np.log10(12194) + + 2 * np.log10(freq_sq) + - np.log10(freq_sq + 12194 ** 2) + - np.log10(freq_sq + 20.6 ** 2) + - 0.5 * np.log10(freq_sq + 107.7 ** 2) + - 0.5 * np.log10(freq_sq + 737.9 ** 2) + ) + weight = np.maximum(weight, min_db) + + return weight + + self.a_weight[fs] = torch.from_numpy( + np.power(10, a_weight(fs, n_fft, min_db) / 10) + ).to(device=sound.device) + + sound = sound.unfold(-1, n_fft, n_fft // 2) + + if mode == "RMSE": + sound = sound ** 2 + g = sound.mean(-1) + elif mode == "A_weighting": + w = torch.hann_window(n_fft, device=sound.device) * sound + spec = torch.fft.rfft(w) + power_spec = spec.abs() ** 2 + a_weighted_spec = power_spec * self.a_weight[fs] + g = a_weighted_spec.sum(-1) + else: + raise Exception("Invalid mode {}".format(mode)) + + gain = torch.maximum(g, torch.tensor(10 ** (min_db / 10), device=g.device)) + gain_db = 10 * torch.log10(gain) + + return gain_db + + def forward(self, source, padding_mask, label=None, **kwargs): + + if self.cfg.source_mixup >= 0 and self.training and self.cfg.mixup_prob > 0: + with torch.no_grad(): + mixed_source = source + mix_mask = None + if self.cfg.mixup_prob < 1: + mix_mask = ( + torch.empty((source.size(0),), device=source.device) + .bernoulli_(self.cfg.mixup_prob) + .bool() + ) + mixed_source = source[mix_mask] + + r = ( + torch.FloatTensor( + 1 if self.cfg.same_mixup else mixed_source.size(0) + ) + .uniform_(max(1e-6, self.cfg.source_mixup), 1) + .to(dtype=source.dtype, device=source.device) + ) + + mixup_perm = torch.randperm(source.size(0)) + s2 = source[mixup_perm] + + if self.cfg.gain_mode == "none": + p = r.unsqueeze(-1) + if mix_mask is not None: + s2 = s2[mix_mask] + else: + if self.cfg.gain_mode == "naive_rms": + G1 = source.pow(2).mean(dim=-1).sqrt() + else: + G1, _ = self.compute_gain_torch( + source, mode=self.cfg.gain_mode + ).max(-1) + G1 = G1.to(dtype=source.dtype) + + G2 = G1[mixup_perm] + + if mix_mask is not None: + G1 = G1[mix_mask] + G2 = G2[mix_mask] + s2 = s2[mix_mask] + + p = 1 / (1 + 10 ** ((G1 - G2) / 20) * (1 - r) / r) + p = p.unsqueeze(-1) + + mixed = (p * mixed_source) + (1 - p) * s2 + + if mix_mask is None: + source = mixed / torch.sqrt(p ** 2 + (1 - p) ** 2) + else: + source[mix_mask] = mixed / torch.sqrt(p ** 2 + (1 - p) ** 2) + + if label is not None and self.cfg.label_mixup: + r = r.unsqueeze(-1) + if mix_mask is None: + label = label * r + (1 - r) * label[mixup_perm] + else: + label[mix_mask] = ( + label[mix_mask] * r + (1 - r) * label[mixup_perm][mix_mask] + ) + + d2v_args = { + "source": source, + "padding_mask": padding_mask, + "mask": self.apply_mask and self.training, + } + + ft = self.freeze_finetune_updates <= self.num_updates + + with torch.no_grad() if not ft else contextlib.ExitStack(): + res = self.d2v_model.extract_features(**d2v_args) + + x = res["x"] + padding_mask = res["padding_mask"] + if padding_mask is not None: + x[padding_mask] = 0 + + x = self.final_dropout(x) + + if self.training or ( + self.cfg.eval_prediction_mode is None or self.cfg.eval_prediction_mode == "" + ): + prediction_mode = self.cfg.prediction_mode + else: + prediction_mode = self.cfg.eval_prediction_mode + + if prediction_mode == "average_before": + x = x.mean(dim=1) + + if prediction_mode != "summary_mha" and prediction_mode != "summary_proj" and prediction_mode != "cls": + x = self.proj(x) + + logits = True + if prediction_mode == "lin_softmax": + x = F.logsigmoid(x.float()) + x = torch.logsumexp(x + x, dim=1) - torch.logsumexp(x, dim=1) + x = x.clamp(max=0) + x = x - torch.log(-(torch.expm1(x))) + elif prediction_mode == "extremized_odds": + x = x.float().sum(dim=1) + x = x * self.cfg.extreme_factor + elif prediction_mode == "average_before": + x = x.float() + elif prediction_mode == "average": + x = x.float().mean(dim=1) + elif prediction_mode == "average_sigmoid": + x = torch.sigmoid(x.float()) + x = x.mean(dim=1) + logits = False + elif prediction_mode == "max": + x, _ = x.float().max(dim=1) + elif prediction_mode == "max_sigmoid": + x = torch.sigmoid(x.float()) + x, _ = x.float().max(dim=1) + logits = False + elif prediction_mode == "proj_avg_proj": + x = x.mean(dim=1) + x = self.proj2(x) + elif prediction_mode == "summary_mha" or prediction_mode == "summary_proj": + x = self.d2v_model.summary( + x, padding_mask, proj=prediction_mode == "summary_proj" + ) + x = x.type_as(source) + x = self.proj(x) + elif prediction_mode == "cls": + x = x[:,0] + x = self.proj(x) + else: + raise Exception(f"unknown prediction mode {prediction_mode}") + + if label is None: + return torch.sigmoid(x) if logits else x + + x = torch.nan_to_num(x) + + if logits: + loss = F.binary_cross_entropy_with_logits( + x, label.float(), reduction="none" + ) + else: + loss = F.binary_cross_entropy(x, label.float(), reduction="none") + + result = { + "losses": { + "main": loss, + }, + "sample_size": label.sum(), + } + + if not self.training: + result["_predictions"] = torch.sigmoid(x) if logits else x + result["_targets"] = label + + return result diff --git a/fairseq/examples/data2vec/models/data2vec2.py b/fairseq/examples/data2vec/models/data2vec2.py new file mode 100644 index 0000000..0c61b37 --- /dev/null +++ b/fairseq/examples/data2vec/models/data2vec2.py @@ -0,0 +1,813 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from dataclasses import dataclass, field +from typing import Optional, Callable +from functools import partial +import numpy as np + +from omegaconf import II + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.distributed as dist + +from fairseq.modules import EMAModule, EMAModuleConfig + +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model + +from examples.data2vec.data.modality import Modality + +from examples.data2vec.models.modalities.base import ( + MaskSeed, + D2vModalityConfig, + ModalitySpecificEncoder, + get_annealed_rate, +) +from examples.data2vec.models.modalities.modules import ( + D2vDecoderConfig, + AltBlock, + Decoder1d, +) + +from examples.data2vec.models.modalities.audio import ( + D2vAudioConfig, + AudioEncoder, +) +from examples.data2vec.models.modalities.images import ( + D2vImageConfig, + ImageEncoder, +) +from examples.data2vec.models.modalities.text import ( + D2vTextConfig, + TextEncoder, +) + +logger = logging.getLogger(__name__) + + +@dataclass +class D2vModalitiesConfig(FairseqDataclass): + audio: D2vAudioConfig = D2vAudioConfig() + image: D2vImageConfig = D2vImageConfig() + text: D2vTextConfig = D2vTextConfig() + + +@dataclass +class Data2VecMultiConfig(FairseqDataclass): + + loss_beta: float = field( + default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"} + ) + loss_scale: Optional[float] = field( + default=None, + metadata={ + "help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)" + }, + ) + + depth: int = 8 + start_drop_path_rate: float = 0 + end_drop_path_rate: float = 0 + num_heads: int = 12 + norm_eps: float = 1e-6 + norm_affine: bool = True + encoder_dropout: float = 0.1 + post_mlp_drop: float = 0.1 + attention_dropout: float = 0.1 + activation_dropout: float = 0.0 + dropout_input: float = 0.0 + layerdrop: float = 0.0 + embed_dim: int = 768 + mlp_ratio: float = 4 + layer_norm_first: bool = False + + average_top_k_layers: int = field( + default=8, metadata={"help": "how many layers to average"} + ) + + end_of_block_targets: bool = False + + clone_batch: int = 1 + + layer_norm_target_layer: bool = False + batch_norm_target_layer: bool = False + instance_norm_target_layer: bool = False + instance_norm_targets: bool = False + layer_norm_targets: bool = False + + ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"}) + ema_same_dtype: bool = True + log_norms: bool = True + ema_end_decay: float = field( + default=0.9999, metadata={"help": "final ema decay rate"} + ) + + # when to finish annealing ema decay rate + ema_anneal_end_step: int = II("optimization.max_update") + + ema_encoder_only: bool = field( + default=True, + metadata={ + "help": "whether to momentum update only the shared transformer encoder" + }, + ) + + max_update: int = II("optimization.max_update") + + modalities: D2vModalitiesConfig = D2vModalitiesConfig() + + shared_decoder: Optional[D2vDecoderConfig] = None + + min_target_var: float = field( + default=0.1, metadata={"help": "stop training if target var falls below this"} + ) + min_pred_var: float = field( + default=0.01, + metadata={"help": "stop training if prediction var falls below this"}, + ) + + supported_modality: Optional[Modality] = None + mae_init: bool = False + + seed: int = II("common.seed") + + skip_ema: bool = False + + cls_loss: float = 0 + recon_loss: float = 0 + d2v_loss: float = 1 + + decoder_group: bool = False + + +@register_model("data2vec_multi", dataclass=Data2VecMultiConfig) +class Data2VecMultiModel(BaseFairseqModel): + def make_modality_encoder( + self, + cfg: D2vModalityConfig, + embed_dim: int, + make_block: Callable[[float], nn.ModuleList], + norm_layer: Callable[[int], nn.LayerNorm], + layer_norm_first: bool, + alibi_biases, + task, + ) -> ModalitySpecificEncoder: + if cfg.type == Modality.AUDIO: + enc_cls = AudioEncoder + elif cfg.type == Modality.IMAGE: + enc_cls = ImageEncoder + elif cfg.type == Modality.TEXT: + enc_cls = TextEncoder + if hasattr(task, "text_task"): + task = task.text_task + else: + raise Exception(f"unsupported modality {cfg.type}") + + return enc_cls( + cfg, + embed_dim, + make_block, + norm_layer, + layer_norm_first, + alibi_biases, + task, + ) + + def __init__(self, cfg: Data2VecMultiConfig, modalities, skip_ema=False, task=None): + super().__init__() + self.cfg = cfg + self.modalities = modalities + self.task = task + + make_layer_norm = partial( + nn.LayerNorm, eps=cfg.norm_eps, elementwise_affine=cfg.norm_affine + ) + + def make_block(drop_path, dim=None, heads=None): + return AltBlock( + cfg.embed_dim if dim is None else dim, + cfg.num_heads if heads is None else heads, + cfg.mlp_ratio, + qkv_bias=True, + drop=cfg.encoder_dropout, + attn_drop=cfg.attention_dropout, + mlp_drop=cfg.activation_dropout, + post_mlp_drop=cfg.post_mlp_drop, + drop_path=drop_path, + norm_layer=make_layer_norm, + layer_norm_first=cfg.layer_norm_first, + ffn_targets=not cfg.end_of_block_targets, + ) + + self.alibi_biases = {} + self.modality_encoders = nn.ModuleDict() + for mod in self.modalities: + mod_cfg = getattr(cfg.modalities, mod.name.lower()) + enc = self.make_modality_encoder( + mod_cfg, + cfg.embed_dim, + make_block, + make_layer_norm, + cfg.layer_norm_first, + self.alibi_biases, + task, + ) + self.modality_encoders[mod.name] = enc + + self.ema = None + + self.average_top_k_layers = cfg.average_top_k_layers + self.loss_beta = cfg.loss_beta + self.loss_scale = cfg.loss_scale + + self.dropout_input = nn.Dropout(cfg.dropout_input) + + dpr = np.linspace(cfg.start_drop_path_rate, cfg.end_drop_path_rate, cfg.depth) + + self.blocks = nn.ModuleList([make_block(dpr[i]) for i in range(cfg.depth)]) + + self.norm = None + if cfg.layer_norm_first: + self.norm = make_layer_norm(cfg.embed_dim) + + if self.cfg.mae_init: + self.apply(self._init_weights) + else: + from fairseq.modules.transformer_sentence_encoder import init_bert_params + + self.apply(init_bert_params) + + for mod_enc in self.modality_encoders.values(): + mod_enc.reset_parameters() + + if not skip_ema: + self.ema = self.make_ema_teacher(cfg.ema_decay) + self.shared_decoder = ( + Decoder1d(cfg.shared_decoder, cfg.embed_dim) + if self.cfg.shared_decoder is not None + else None + ) + if self.shared_decoder is not None: + self.shared_decoder.apply(self._init_weights) + + self.recon_proj = None + if cfg.recon_loss > 0: + self.recon_proj = nn.Linear(cfg.embed_dim, cfg.embed_dim) + + for pn, p in self.named_parameters(): + if len(p.shape) == 1 or pn.endswith(".bias") or "alibi_scale" in pn: + p.optim_overrides = {"optimizer": {"weight_decay_scale": 0}} + if cfg.decoder_group and "decoder" in pn: + p.param_group = "decoder" + + self.num_updates = 0 + + def _init_weights(self, m): + + try: + from apex.normalization import FusedLayerNorm + + fn = FusedLayerNorm + except: + fn = nn.LayerNorm + + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm) or isinstance(m, fn): + if m.bias is not None: + nn.init.constant_(m.bias, 0) + if m.weight is not None: + nn.init.constant_(m.weight, 1.0) + + @torch.no_grad() + def make_ema_teacher(self, ema_decay): + ema_config = EMAModuleConfig( + ema_decay=ema_decay, + ema_fp32=True, + log_norms=self.cfg.log_norms, + add_missing_params=False, + ) + + model_copy = self.make_target_model() + + return EMAModule( + model_copy, + ema_config, + copy_model=False, + ) + + def make_target_model(self): + logger.info("making target model") + + model_copy = Data2VecMultiModel( + self.cfg, self.modalities, skip_ema=True, task=self.task + ) + + if self.cfg.ema_encoder_only: + model_copy = model_copy.blocks + for p_s, p_t in zip(self.blocks.parameters(), model_copy.parameters()): + p_t.data.copy_(p_s.data) + else: + for p_s, p_t in zip(self.parameters(), model_copy.parameters()): + p_t.data.copy_(p_s.data) + + for mod_enc in model_copy.modality_encoders.values(): + mod_enc.decoder = None + if not mod_enc.modality_cfg.ema_local_encoder: + mod_enc.local_encoder = None + mod_enc.project_features = None + + model_copy.requires_grad_(False) + return model_copy + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + + if self.ema is not None and ( + (self.num_updates == 0 and num_updates > 1) + or self.num_updates >= num_updates + ): + pass + elif self.training and self.ema is not None: + ema_weight_decay = None + if self.cfg.ema_decay != self.cfg.ema_end_decay: + if num_updates >= self.cfg.ema_anneal_end_step: + decay = self.cfg.ema_end_decay + else: + decay = get_annealed_rate( + self.cfg.ema_decay, + self.cfg.ema_end_decay, + num_updates, + self.cfg.ema_anneal_end_step, + ) + self.ema.set_decay(decay, weight_decay=ema_weight_decay) + if self.ema.get_decay() < 1: + self.ema.step(self.blocks if self.cfg.ema_encoder_only else self) + + self.num_updates = num_updates + + def state_dict(self, destination=None, prefix="", keep_vars=False): + state = super().state_dict(destination, prefix, keep_vars) + + if self.ema is not None: + state[prefix + "_ema"] = self.ema.fp32_params + + return state + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + k = prefix + "_ema" + if self.ema is not None: + assert k in state_dict + self.ema.restore(state_dict[k], True) + del state_dict[k] + elif k in state_dict: + del state_dict[k] + + return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + @classmethod + def build_model(cls, cfg: Data2VecMultiConfig, task=None): + """Build a new model instance.""" + if task is None or not hasattr(task, "supported_modalities"): + modalities = ( + [cfg.supported_modality] + if cfg.supported_modality is not None + else [ + Modality.AUDIO, + Modality.IMAGE, + Modality.TEXT, + ] + ) + else: + modalities = task.supported_modalities + return cls(cfg, modalities, task=task, skip_ema=cfg.skip_ema) + + def forward( + self, + source, + target=None, + id=None, + mode=None, + padding_mask=None, + mask=True, + features_only=False, + force_remove_masked=False, + remove_extra_tokens=True, + precomputed_mask=None, + ): + if mode is None: + assert self.cfg.supported_modality is not None + mode = self.cfg.supported_modality + + if isinstance(mode, Modality): + mode = mode.name + + feature_extractor = self.modality_encoders[mode] + + mask_seeds = None + if id is not None: + mask_seeds = MaskSeed(seed=self.cfg.seed, update=self.num_updates, ids=id) + + extractor_out = feature_extractor( + source, + padding_mask, + mask, + remove_masked=not features_only or force_remove_masked, + clone_batch=self.cfg.clone_batch if not features_only else 1, + mask_seeds=mask_seeds, + precomputed_mask=precomputed_mask, + ) + + x = extractor_out["x"] + encoder_mask = extractor_out["encoder_mask"] + masked_padding_mask = extractor_out["padding_mask"] + masked_alibi_bias = extractor_out.get("alibi_bias", None) + alibi_scale = extractor_out.get("alibi_scale", None) + + if self.dropout_input is not None: + x = self.dropout_input(x) + + layer_results = [] + for i, blk in enumerate(self.blocks): + if ( + not self.training + or self.cfg.layerdrop == 0 + or (np.random.random() > self.cfg.layerdrop) + ): + ab = masked_alibi_bias + if ab is not None and alibi_scale is not None: + scale = ( + alibi_scale[i] + if alibi_scale.size(0) > 1 + else alibi_scale.squeeze(0) + ) + ab = ab * scale.type_as(ab) + + x, lr = blk( + x, + padding_mask=masked_padding_mask, + alibi_bias=ab, + ) + if features_only: + layer_results.append(lr) + + if self.norm is not None: + x = self.norm(x) + + if features_only: + if remove_extra_tokens: + x = x[:, feature_extractor.modality_cfg.num_extra_tokens :] + if masked_padding_mask is not None: + masked_padding_mask = masked_padding_mask[ + :, feature_extractor.modality_cfg.num_extra_tokens : + ] + + return { + "x": x, + "padding_mask": masked_padding_mask, + "layer_results": layer_results, + "mask": encoder_mask, + } + + xs = [] + + if self.shared_decoder is not None: + dx = self.forward_decoder( + x, + feature_extractor, + self.shared_decoder, + encoder_mask, + ) + xs.append(dx) + if feature_extractor.decoder is not None: + dx = self.forward_decoder( + x, + feature_extractor, + feature_extractor.decoder, + encoder_mask, + ) + xs.append(dx) + orig_x = x + + assert len(xs) > 0 + + p = next(self.ema.model.parameters()) + device = x.device + dtype = x.dtype + ema_device = p.device + ema_dtype = p.dtype + + if not self.cfg.ema_same_dtype: + dtype = ema_dtype + + if ema_device != device or ema_dtype != dtype: + logger.info(f"adjusting ema dtype to {dtype} and device to {device}") + self.ema.model = self.ema.model.to(dtype=dtype, device=device) + ema_dtype = dtype + + def to_device(d): + for k, p in d.items(): + if isinstance(d[k], dict): + to_device(d[k]) + else: + d[k] = p.to(device=device) + + to_device(self.ema.fp32_params) + tm = self.ema.model + + with torch.no_grad(): + tm.eval() + + if self.cfg.ema_encoder_only: + assert target is None + ema_input = extractor_out["local_features"] + ema_input = feature_extractor.contextualized_features( + ema_input.to(dtype=ema_dtype), + padding_mask, + mask=False, + remove_masked=False, + ) + ema_blocks = tm + else: + ema_blocks = tm.blocks + if feature_extractor.modality_cfg.ema_local_encoder: + inp = ( + target.to(dtype=ema_dtype) + if target is not None + else source.to(dtype=ema_dtype) + ) + ema_input = tm.modality_encoders[mode]( + inp, + padding_mask, + mask=False, + remove_masked=False, + ) + else: + assert target is None + ema_input = extractor_out["local_features"] + ema_feature_enc = tm.modality_encoders[mode] + ema_input = ema_feature_enc.contextualized_features( + ema_input.to(dtype=ema_dtype), + padding_mask, + mask=False, + remove_masked=False, + ) + + ema_padding_mask = ema_input["padding_mask"] + ema_alibi_bias = ema_input.get("alibi_bias", None) + ema_alibi_scale = ema_input.get("alibi_scale", None) + ema_input = ema_input["x"] + + y = [] + ema_x = [] + extra_tokens = feature_extractor.modality_cfg.num_extra_tokens + for i, blk in enumerate(ema_blocks): + ab = ema_alibi_bias + if ab is not None and alibi_scale is not None: + scale = ( + ema_alibi_scale[i] + if ema_alibi_scale.size(0) > 1 + else ema_alibi_scale.squeeze(0) + ) + ab = ab * scale.type_as(ab) + + ema_input, lr = blk( + ema_input, + padding_mask=ema_padding_mask, + alibi_bias=ab, + ) + y.append(lr[:, extra_tokens:]) + ema_x.append(ema_input[:, extra_tokens:]) + + y = self.make_targets(y, self.average_top_k_layers) + orig_targets = y + + if self.cfg.clone_batch > 1: + y = y.repeat_interleave(self.cfg.clone_batch, 0) + + masked = encoder_mask.mask.unsqueeze(-1) + masked_b = encoder_mask.mask.bool() + y = y[masked_b] + + if xs[0].size(1) == masked_b.size(1): + xs = [x[masked_b] for x in xs] + else: + xs = [x.reshape(-1, x.size(-1)) for x in xs] + + sample_size = masked.sum().long() + + result = { + "losses": {}, + "sample_size": sample_size, + } + + sample_size = result["sample_size"] + + if self.cfg.cls_loss > 0: + assert extra_tokens > 0 + cls_target = orig_targets.mean(dim=1) + if self.cfg.clone_batch > 1: + cls_target = cls_target.repeat_interleave(self.cfg.clone_batch, 0) + cls_pred = x[:, extra_tokens - 1] + result["losses"]["cls"] = self.d2v_loss(cls_pred, cls_target) * ( + self.cfg.cls_loss * sample_size + ) + + if self.cfg.recon_loss > 0: + + with torch.no_grad(): + target = feature_extractor.patchify(source) + mean = target.mean(dim=-1, keepdim=True) + var = target.var(dim=-1, keepdim=True) + target = (target - mean) / (var + 1.0e-6) ** 0.5 + + if self.cfg.clone_batch > 1: + target = target.repeat_interleave(self.cfg.clone_batch, 0) + + if masked_b is not None: + target = target[masked_b] + + recon = xs[0] + if self.recon_proj is not None: + recon = self.recon_proj(recon) + + result["losses"]["recon"] = ( + self.d2v_loss(recon, target.float()) * self.cfg.recon_loss + ) + + if self.cfg.d2v_loss > 0: + for i, x in enumerate(xs): + reg_loss = self.d2v_loss(x, y) + n = f"{mode}_regression_{i}" if len(xs) > 1 else f"{mode}_regression" + result["losses"][n] = reg_loss * self.cfg.d2v_loss + + suffix = "" if len(self.modalities) == 1 else f"_{mode}" + with torch.no_grad(): + if encoder_mask is not None: + result["masked_pct"] = 1 - ( + encoder_mask.ids_keep.size(1) / encoder_mask.ids_restore.size(1) + ) + for i, x in enumerate(xs): + n = f"pred_var{suffix}_{i}" if len(xs) > 1 else f"pred_var{suffix}" + result[n] = self.compute_var(x.float()) + if self.ema is not None: + for k, v in self.ema.logs.items(): + result[k] = v + + y = y.float() + result[f"target_var{suffix}"] = self.compute_var(y) + + if self.num_updates > 5000: + if result[f"target_var{suffix}"] < self.cfg.min_target_var: + logger.error( + f"target var is {result[f'target_var{suffix}'].item()} < {self.cfg.min_target_var}, exiting ({mode})" + ) + raise Exception( + f"target var is {result[f'target_var{suffix}'].item()} < {self.cfg.min_target_var}, exiting ({mode})" + ) + + for k in result.keys(): + if k.startswith("pred_var") and result[k] < self.cfg.min_pred_var: + logger.error( + f"{k} is {result[k].item()} < {self.cfg.min_pred_var}, exiting ({mode})" + ) + raise Exception( + f"{k} is {result[k].item()} < {self.cfg.min_pred_var}, exiting ({mode})" + ) + + result["ema_decay"] = self.ema.get_decay() * 1000 + + return result + + def forward_decoder( + self, + x, + feature_extractor, + decoder, + mask_info, + ): + x = feature_extractor.decoder_input(x, mask_info) + x = decoder(*x) + + return x + + def d2v_loss(self, x, y): + x = x.view(-1, x.size(-1)).float() + y = y.view(-1, x.size(-1)) + + if self.loss_beta == 0: + loss = F.mse_loss(x, y, reduction="none") + else: + loss = F.smooth_l1_loss(x, y, reduction="none", beta=self.loss_beta) + + if self.loss_scale is not None: + scale = self.loss_scale + else: + scale = 1 / math.sqrt(x.size(-1)) + + reg_loss = loss * scale + + return reg_loss + + def make_targets(self, y, num_layers): + + with torch.no_grad(): + target_layer_results = y[-num_layers:] + + permuted = False + if self.cfg.instance_norm_target_layer or self.cfg.batch_norm_target_layer: + target_layer_results = [ + tl.transpose(1, 2) for tl in target_layer_results # BTC -> BCT + ] + permuted = True + if self.cfg.batch_norm_target_layer: + target_layer_results = [ + F.batch_norm( + tl.float(), running_mean=None, running_var=None, training=True + ) + for tl in target_layer_results + ] + if self.cfg.instance_norm_target_layer: + target_layer_results = [ + F.instance_norm(tl.float()) for tl in target_layer_results + ] + if permuted: + target_layer_results = [ + tl.transpose(1, 2) for tl in target_layer_results # BCT -> BTC + ] + if self.cfg.layer_norm_target_layer: + target_layer_results = [ + F.layer_norm(tl.float(), tl.shape[-1:]) + for tl in target_layer_results + ] + + y = target_layer_results[0].float() + for tl in target_layer_results[1:]: + y.add_(tl.float()) + y = y.div_(len(target_layer_results)) + + if self.cfg.layer_norm_targets: + y = F.layer_norm(y, y.shape[-1:]) + + if self.cfg.instance_norm_targets: + y = F.instance_norm(y.transpose(1, 2)).transpose(1, 2) + + return y + + @staticmethod + def compute_var(y): + y = y.view(-1, y.size(-1)) + if dist.is_initialized(): + zc = torch.tensor(y.size(0)).cuda() + zs = y.sum(dim=0) + zss = (y**2).sum(dim=0) + + dist.all_reduce(zc) + dist.all_reduce(zs) + dist.all_reduce(zss) + + var = zss / (zc - 1) - (zs**2) / (zc * (zc - 1)) + return torch.sqrt(var + 1e-6).mean() + else: + return torch.sqrt(y.var(dim=0) + 1e-6).mean() + + def extract_features( + self, source, mode=None, padding_mask=None, mask=False, remove_extra_tokens=True + ): + res = self.forward( + source, + mode=mode, + padding_mask=padding_mask, + mask=mask, + features_only=True, + remove_extra_tokens=remove_extra_tokens, + ) + return res + + def remove_pretraining_modules(self, modality=None, keep_decoder=False): + self.ema = None + self.cfg.clone_batch = 1 + self.recon_proj = None + + if not keep_decoder: + self.shared_decoder = None + + modality = modality.lower() if modality is not None else None + for k in list(self.modality_encoders.keys()): + if modality is not None and k.lower() != modality: + del self.modality_encoders[k] + else: + self.modality_encoders[k].remove_pretraining_modules( + keep_decoder=keep_decoder + ) + if not keep_decoder: + self.modality_encoders[k].decoder = None diff --git a/fairseq/examples/data2vec/models/data2vec_audio.py b/fairseq/examples/data2vec/models/data2vec_audio.py new file mode 100644 index 0000000..261c2f1 --- /dev/null +++ b/fairseq/examples/data2vec/models/data2vec_audio.py @@ -0,0 +1,537 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from dataclasses import dataclass, field +from typing import Optional + +from omegaconf import II + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.distributed as dist + +from fairseq.modules import EMAModule, EMAModuleConfig +from fairseq.data.data_utils import compute_mask_indices +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.wav2vec import ( + ConvFeatureExtractionModel, + Wav2Vec2Config, + TransformerEncoder, +) +from fairseq.modules import ( + GradMultiply, + LayerNorm, +) +from fairseq.utils import index_put + + +logger = logging.getLogger(__name__) + + +@dataclass +class Data2VecAudioConfig(Wav2Vec2Config): + + loss_beta: float = field( + default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"} + ) + loss_scale: Optional[float] = field( + default=None, + metadata={ + "help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)" + }, + ) + average_top_k_layers: int = field( + default=8, metadata={"help": "how many layers to average"} + ) + + layer_norm_target_layer: bool = False + instance_norm_target_layer: bool = False + instance_norm_targets: bool = False + layer_norm_targets: bool = False + batch_norm_target_layer: bool = False + group_norm_target_layer: bool = False + + ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"}) + ema_end_decay: float = field( + default=0.9999, metadata={"help": "final ema decay rate"} + ) + + # when to finish annealing ema decay rate + ema_anneal_end_step: int = II("optimization.max_update") + + ema_transformer_only: bool = field( + default=True, + metadata={"help": "whether to momentum update only the transformer"}, + ) + ema_layers_only: bool = field( + default=True, + metadata={"help": "whether to momentum update only the transformer layers"}, + ) + + max_update: int = II("optimization.max_update") + + min_target_var: float = field( + default=0.1, metadata={"help": "stop training if target var falls below this"} + ) + min_pred_var: float = field( + default=0.01, + metadata={"help": "stop training if prediction var falls below this"}, + ) + + +def get_annealed_rate(start, end, curr_step, total_steps): + r = end - start + pct_remaining = 1 - curr_step / total_steps + return end - r * pct_remaining + + +@register_model("data2vec_audio", dataclass=Data2VecAudioConfig) +class Data2VecAudioModel(BaseFairseqModel): + def __init__(self, cfg: Data2VecAudioConfig): + super().__init__() + self.cfg = cfg + + feature_enc_layers = eval(cfg.conv_feature_layers) + self.extractor_embed = feature_enc_layers[-1][0] + + self.ema = None + self.embed = cfg.encoder_embed_dim + + self.average_top_k_layers = cfg.average_top_k_layers + self.loss_beta = cfg.loss_beta + self.loss_scale = cfg.loss_scale + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + + self.post_extract_proj = nn.Linear(self.extractor_embed, cfg.encoder_embed_dim) + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_before = cfg.mask_channel_before + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + + self.encoder = TransformerEncoder(cfg) + self.layer_norm = LayerNorm(self.extractor_embed) + + self.final_proj = nn.Linear(self.embed, self.embed) + + self.num_updates = 0 + + def make_ema_teacher(self): + ema_config = EMAModuleConfig( + ema_decay=self.cfg.ema_decay, + ema_fp32=True, + ) + skip_keys = set() + if self.cfg.ema_layers_only: + self.cfg.ema_transformer_only = True + for k, _ in self.encoder.pos_conv.named_parameters(): + skip_keys.add(f"pos_conv.{k}") + + self.ema = EMAModule( + self.encoder if self.cfg.ema_transformer_only else self, + ema_config, + skip_keys=skip_keys, + ) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + + if self.ema is None and self.final_proj is not None: + logger.info(f"making ema teacher") + self.make_ema_teacher() + elif self.training and self.ema is not None: + if self.cfg.ema_decay != self.cfg.ema_end_decay: + if num_updates >= self.cfg.ema_anneal_end_step: + decay = self.cfg.ema_end_decay + else: + decay = get_annealed_rate( + self.cfg.ema_decay, + self.cfg.ema_end_decay, + num_updates, + self.cfg.ema_anneal_end_step, + ) + self.ema.set_decay(decay) + if self.ema.get_decay() < 1: + self.ema.step(self.encoder if self.cfg.ema_transformer_only else self) + + self.num_updates = num_updates + + def state_dict(self, destination=None, prefix="", keep_vars=False): + state = super().state_dict(destination, prefix, keep_vars) + + if self.ema is not None: + state[prefix + "_ema"] = self.ema.fp32_params + + return state + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + if self.ema is not None: + k = prefix + "_ema" + assert k in state_dict + self.ema.restore(state_dict[k], True) + del state_dict[k] + return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + @classmethod + def build_model(cls, cfg: Data2VecAudioConfig, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def apply_mask( + self, + x, + padding_mask, + mask_indices=None, + mask_channel_indices=None, + ): + B, T, C = x.shape + + if self.mask_channel_prob > 0 and self.mask_channel_before: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + if self.mask_prob > 0: + if mask_indices is None: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=1, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + require_same_masks=self.cfg.require_same_masks, + mask_dropout=self.cfg.mask_dropout, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x = index_put(x, mask_indices, self.mask_emb) + else: + mask_indices = None + + if self.mask_channel_prob > 0 and not self.mask_channel_before: + if mask_channel_indices is None: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x = index_put(x, mask_channel_indices, 0) + + return x, mask_indices + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + return torch.floor((input_length - kernel_size) / stride + 1) + + conv_cfg_list = eval(self.cfg.conv_feature_layers) + + for i in range(len(conv_cfg_list)): + input_lengths = _conv_out_length( + input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2] + ) + + return input_lengths.to(torch.long) + + def forward( + self, + source, + padding_mask=None, + mask=True, + features_only=False, + layer=None, + mask_indices=None, + mask_channel_indices=None, + padding_count=None, + ): + features = source + + if self.feature_grad_mult > 0: + features = self.feature_extractor(features) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(features) + + features = features.transpose(1, 2) + + features = self.layer_norm(features) + + orig_padding_mask = padding_mask + + if padding_mask is not None and padding_mask.any(): + input_lengths = (1 - padding_mask.long()).sum(-1) + # apply conv formula to get real output_lengths + output_lengths = self._get_feat_extract_output_lengths(input_lengths) + + padding_mask = torch.zeros( + features.shape[:2], dtype=features.dtype, device=features.device + ) + + # these two operations makes sure that all values + # before the output lengths indices are attended to + padding_mask[ + ( + torch.arange(padding_mask.shape[0], device=padding_mask.device), + output_lengths - 1, + ) + ] = 1 + padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool() + else: + padding_mask = None + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + pre_encoder_features = None + if self.cfg.ema_transformer_only: + pre_encoder_features = features.clone() + + features = self.dropout_input(features) + + if mask: + x, mask_indices = self.apply_mask( + features, + padding_mask, + mask_indices=mask_indices, + mask_channel_indices=mask_channel_indices, + ) + else: + x = features + mask_indices = None + + x, layer_results = self.encoder( + x, + padding_mask=padding_mask, + layer=layer, + ) + + if features_only: + return { + "x": x, + "padding_mask": padding_mask, + "layer_results": layer_results, + } + + result = { + "losses": {}, + } + + with torch.no_grad(): + self.ema.model.eval() + + if self.cfg.ema_transformer_only: + y, layer_results = self.ema.model.extract_features( + pre_encoder_features, + padding_mask=padding_mask, + min_layer=self.cfg.encoder_layers - self.average_top_k_layers, + ) + y = { + "x": y, + "padding_mask": padding_mask, + "layer_results": layer_results, + } + else: + y = self.ema.model.extract_features( + source=source, + padding_mask=orig_padding_mask, + mask=False, + ) + + target_layer_results = [l[2] for l in y["layer_results"]] + + permuted = False + if self.cfg.instance_norm_target_layer or self.cfg.batch_norm_target_layer: + target_layer_results = [ + tl.permute(1, 2, 0) for tl in target_layer_results # TBC -> BCT + ] + permuted = True + + if self.cfg.batch_norm_target_layer: + target_layer_results = [ + F.batch_norm( + tl.float(), running_mean=None, running_var=None, training=True + ) + for tl in target_layer_results + ] + + if self.cfg.instance_norm_target_layer: + target_layer_results = [ + F.instance_norm(tl.float()) for tl in target_layer_results + ] + + if permuted: + target_layer_results = [ + tl.transpose(1, 2) for tl in target_layer_results # BCT -> BTC + ] + + if self.cfg.group_norm_target_layer: + target_layer_results = [ + F.layer_norm(tl.float(), tl.shape[-2:]) + for tl in target_layer_results + ] + + if self.cfg.layer_norm_target_layer: + target_layer_results = [ + F.layer_norm(tl.float(), tl.shape[-1:]) + for tl in target_layer_results + ] + + y = sum(target_layer_results) / len(target_layer_results) + + if self.cfg.layer_norm_targets: + y = F.layer_norm(y.float(), y.shape[-1:]) + + if self.cfg.instance_norm_targets: + y = F.instance_norm(y.float().transpose(1, 2)).transpose(1, 2) + + if not permuted: + y = y.transpose(0, 1) + + y = y[mask_indices] + + x = x[mask_indices] + x = self.final_proj(x) + + sz = x.size(-1) + + if self.loss_beta == 0: + loss = F.mse_loss(x.float(), y.float(), reduction="none").sum(dim=-1) + else: + loss = F.smooth_l1_loss( + x.float(), y.float(), reduction="none", beta=self.loss_beta + ).sum(dim=-1) + + if self.loss_scale is not None: + scale = self.loss_scale + else: + scale = 1 / math.sqrt(sz) + + result["losses"]["regression"] = loss.sum() * scale + + if "sample_size" not in result: + result["sample_size"] = loss.numel() + + with torch.no_grad(): + result["target_var"] = self.compute_var(y) + result["pred_var"] = self.compute_var(x.float()) + + if self.num_updates > 5000 and result["target_var"] < self.cfg.min_target_var: + logger.error( + f"target var is {result['target_var'].item()} < {self.cfg.min_target_var}, exiting" + ) + raise Exception( + f"target var is {result['target_var'].item()} < {self.cfg.min_target_var}, exiting" + ) + if self.num_updates > 5000 and result["pred_var"] < self.cfg.min_pred_var: + logger.error( + f"pred var is {result['pred_var'].item()} < {self.cfg.min_pred_var}, exiting" + ) + raise Exception( + f"pred var is {result['pred_var'].item()} < {self.cfg.min_pred_var}, exiting" + ) + + if self.ema is not None: + result["ema_decay"] = self.ema.get_decay() * 1000 + + return result + + @staticmethod + def compute_var(y): + y = y.view(-1, y.size(-1)) + if dist.is_initialized(): + zc = torch.tensor(y.size(0)).cuda() + zs = y.sum(dim=0) + zss = (y ** 2).sum(dim=0) + + dist.all_reduce(zc) + dist.all_reduce(zs) + dist.all_reduce(zss) + + var = zss / (zc - 1) - (zs ** 2) / (zc * (zc - 1)) + return torch.sqrt(var + 1e-6).mean() + else: + return torch.sqrt(y.var(dim=0) + 1e-6).mean() + + def extract_features( + self, source, padding_mask, mask=False, layer=None + ): + res = self.forward( + source, + padding_mask, + mask=mask, + features_only=True, + layer=layer, + ) + return res + + def remove_pretraining_modules(self, last_layer=None): + self.final_proj = None + self.ema = None + if last_layer is not None: + self.encoder.layers = nn.ModuleList( + l for i, l in enumerate(self.encoder.layers) if i <= last_layer + ) diff --git a/fairseq/examples/data2vec/models/data2vec_image_classification.py b/fairseq/examples/data2vec/models/data2vec_image_classification.py new file mode 100644 index 0000000..851c9ce --- /dev/null +++ b/fairseq/examples/data2vec/models/data2vec_image_classification.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# The code in this file is adapted from the BeiT implementation which can be found here: +# https://github.com/microsoft/unilm/tree/master/beit + +import logging + +from dataclasses import dataclass +from typing import Any + +from omegaconf import II, MISSING + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import checkpoint_utils, tasks + +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model + + +logger = logging.getLogger(__name__) + + +@dataclass +class Data2VecImageClassificationConfig(FairseqDataclass): + model_path: str = MISSING + no_pretrained_weights: bool = False + num_classes: int = 1000 + mixup: float = 0.8 + cutmix: float = 1.0 + label_smoothing: float = 0.1 + + pretrained_model_args: Any = None + data: str = II("task.data") + + +@register_model( + "data2vec_image_classification", dataclass=Data2VecImageClassificationConfig +) +class Data2VecImageClassificationModel(BaseFairseqModel): + def __init__(self, cfg: Data2VecImageClassificationConfig): + super().__init__() + self.cfg = cfg + + if cfg.pretrained_model_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu(cfg.model_path, {}) + pretrained_args = state.get("cfg", None) + pretrained_args.criterion = None + pretrained_args.lr_scheduler = None + cfg.pretrained_model_args = pretrained_args + + logger.info(pretrained_args) + else: + state = None + pretrained_args = cfg.pretrained_model_args + + pretrained_args.task.data = cfg.data + task = tasks.setup_task(pretrained_args.task) + model = task.build_model(pretrained_args.model, from_checkpoint=True) + + model.remove_pretraining_modules() + + self.model = model + + if state is not None and not cfg.no_pretrained_weights: + self.load_model_weights(state, model, cfg) + + self.fc_norm = nn.LayerNorm(pretrained_args.model.embed_dim) + self.head = nn.Linear(pretrained_args.model.embed_dim, cfg.num_classes) + + self.head.weight.data.mul_(1e-3) + self.head.bias.data.mul_(1e-3) + + self.mixup_fn = None + + if cfg.mixup > 0 or cfg.cutmix > 0: + from timm.data import Mixup + + self.mixup_fn = Mixup( + mixup_alpha=cfg.mixup, + cutmix_alpha=cfg.cutmix, + cutmix_minmax=None, + prob=1.0, + switch_prob=0.5, + mode="batch", + label_smoothing=cfg.label_smoothing, + num_classes=cfg.num_classes, + ) + + def load_model_weights(self, state, model, cfg): + if "_ema" in state["model"]: + del state["model"]["_ema"] + model.load_state_dict(state["model"], strict=True) + + @classmethod + def build_model(cls, cfg: Data2VecImageClassificationConfig, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def forward( + self, + img, + label=None, + ): + if self.training and self.mixup_fn is not None and label is not None: + img, label = self.mixup_fn(img, label) + + x = self.model(img, mask=False) + x = x[:, 1:] + x = self.fc_norm(x.mean(1)) + x = self.head(x) + + if label is None: + return x + + if self.training and self.mixup_fn is not None: + loss = -label * F.log_softmax(x.float(), dim=-1) + else: + loss = F.cross_entropy( + x.float(), + label, + label_smoothing=self.cfg.label_smoothing if self.training else 0, + reduction="none", + ) + + result = { + "losses": {"regression": loss}, + "sample_size": img.size(0), + } + + if not self.training: + with torch.no_grad(): + pred = x.argmax(-1) + correct = (pred == label).sum() + result["correct"] = correct + + return result diff --git a/fairseq/examples/data2vec/models/data2vec_text.py b/fairseq/examples/data2vec/models/data2vec_text.py new file mode 100644 index 0000000..cb3c8b3 --- /dev/null +++ b/fairseq/examples/data2vec/models/data2vec_text.py @@ -0,0 +1,517 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +from typing import Optional +import logging +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.modules import EMAModule, EMAModuleConfig +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderModel, + register_model, +) +from fairseq.models.roberta.model import RobertaLMHead, RobertaClassificationHead +from fairseq.models.transformer import TransformerEncoder, TransformerConfig +from fairseq.modules.transformer_sentence_encoder import init_bert_params + +logger = logging.getLogger(__name__) + + +@dataclass +class Data2VecTextConfig(FairseqDataclass): + max_positions: int = II("task.tokens_per_sample") + + head_layers: int = 1 + + transformer: TransformerConfig = TransformerConfig() + + load_checkpoint_heads: bool = field( + default=False, + metadata={"help": "(re-)register and load heads when loading checkpoints"}, + ) + + loss_beta: float = field( + default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"} + ) + loss_scale: Optional[float] = field( + default=None, + metadata={ + "help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)" + }, + ) + average_top_k_layers: int = field( + default=8, metadata={"help": "how many layers to average"} + ) + + layer_norm_target_layer: bool = False + instance_norm_target_layer: bool = False + batch_norm_target_layer: bool = False + instance_norm_targets: bool = False + layer_norm_targets: bool = False + + ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"}) + ema_end_decay: float = field( + default=0.9999, metadata={"help": "final ema decay rate"} + ) + + # when to finish annealing ema decay rate + ema_anneal_end_step: int = II("optimization.max_update") + + ema_transformer_layers_only: bool = field( + default=True, + metadata={"help": "whether to momentum update only the transformer layers"}, + ) + + +def get_annealed_rate(start, end, curr_step, total_steps): + r = end - start + pct_remaining = 1 - curr_step / total_steps + return end - r * pct_remaining + + +@register_model("data2vec_text", dataclass=Data2VecTextConfig) +class Data2VecTextModel(FairseqEncoderModel): + def __init__(self, cfg: Data2VecTextConfig, encoder): + super().__init__(encoder) + self.cfg = cfg + + # We follow BERT's random weight initialization + self.apply(init_bert_params) + + self.classification_heads = nn.ModuleDict() + + @classmethod + def build_model(cls, cfg, task): + """Build a new model instance.""" + + encoder = Data2VecTextEncoder(cfg, task.source_dictionary, task.cfg.data) + + return cls(cfg, encoder) + + def forward( + self, + src_tokens, + target_tokens=None, + features_only=False, + return_all_hiddens=False, + classification_head_name=None, + **kwargs, + ): + if classification_head_name is not None: + features_only = True + + res = self.encoder( + src_tokens, target_tokens, features_only, return_all_hiddens, **kwargs + ) + + if isinstance(res, tuple): + x, extra = res + else: + return res + + if classification_head_name is not None: + x = self.classification_heads[classification_head_name](x) + return x, extra + + def get_normalized_probs(self, net_output, log_probs, sample=None): + """Get normalized probabilities (or log probs) from a net's output.""" + logits = net_output[0].float() + if log_probs: + return F.log_softmax(logits, dim=-1) + else: + return F.softmax(logits, dim=-1) + + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): + """Register a classification head.""" + if name in self.classification_heads: + prev_num_classes = self.classification_heads[name].out_proj.out_features + prev_inner_dim = self.classification_heads[name].dense.out_features + if num_classes != prev_num_classes or inner_dim != prev_inner_dim: + logger.warning( + 're-registering head "{}" with num_classes {} (prev: {}) ' + "and inner_dim {} (prev: {})".format( + name, num_classes, prev_num_classes, inner_dim, prev_inner_dim + ) + ) + self.classification_heads[name] = RobertaClassificationHead( + input_dim=self.cfg.transformer.encoder.embed_dim, + inner_dim=inner_dim or self.cfg.transformer.encoder.embed_dim, + num_classes=num_classes, + activation_fn="tanh", + pooler_dropout=0, + ) + + @property + def supported_targets(self): + return {"self"} + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + + # rename decoder -> encoder before upgrading children modules + for k in list(state_dict.keys()): + if k.startswith(prefix + "decoder"): + new_k = prefix + "encoder" + k[len(prefix + "decoder") :] + state_dict[new_k] = state_dict[k] + del state_dict[k] + + # rename emb_layer_norm -> layernorm_embedding + for k in list(state_dict.keys()): + if ".emb_layer_norm." in k: + new_k = k.replace(".emb_layer_norm.", ".layernorm_embedding.") + state_dict[new_k] = state_dict[k] + del state_dict[k] + + if self.encoder.regression_head is not None: + if ".lm_head." in k: + new_k = k.replace(".lm_head.", ".regression_head.") + state_dict[new_k] = state_dict[k] + del state_dict[k] + else: + if ".regression_head." in k: + del state_dict[k] + + # upgrade children modules + super().upgrade_state_dict_named(state_dict, name) + + # Handle new classification heads present in the state dict. + current_head_names = ( + [] + if not hasattr(self, "classification_heads") + or self.classification_heads is None + else self.classification_heads.keys() + ) + keys_to_delete = [] + for k in state_dict.keys(): + if not k.startswith(prefix + "classification_heads."): + continue + + head_name = k[len(prefix + "classification_heads.") :].split(".")[0] + num_classes = state_dict[ + prefix + "classification_heads." + head_name + ".out_proj.weight" + ].size(0) + inner_dim = state_dict[ + prefix + "classification_heads." + head_name + ".dense.weight" + ].size(0) + + if self.cfg.load_checkpoint_heads: + if head_name not in current_head_names: + self.register_classification_head(head_name, num_classes, inner_dim) + else: + if head_name not in current_head_names: + logger.warning( + "deleting classification head ({}) from checkpoint " + "not present in current model: {}".format(head_name, k) + ) + keys_to_delete.append(k) + elif ( + num_classes + != self.classification_heads[head_name].out_proj.out_features + or inner_dim + != self.classification_heads[head_name].dense.out_features + ): + logger.warning( + "deleting classification head ({}) from checkpoint " + "with different dimensions than current model: {}".format( + head_name, k + ) + ) + keys_to_delete.append(k) + for k in keys_to_delete: + del state_dict[k] + + # Copy any newly-added classification heads into the state dict + # with their current weights. + if ( + hasattr(self, "classification_heads") + and self.classification_heads is not None + and len(self.classification_heads) > 0 + ): + cur_state = self.classification_heads.state_dict() + for k, v in cur_state.items(): + if prefix + "classification_heads." + k not in state_dict: + logger.info("Overwriting " + prefix + "classification_heads." + k) + state_dict[prefix + "classification_heads." + k] = v + + for k in list(state_dict.keys()): + if k.startswith(prefix + "encoder.lm_head.") or k.startswith( + prefix + "encoder.emb_head." + ): + del state_dict[k] + + self.encoder.lm_head = None + + if self.encoder.target_model is None: + for k in list(state_dict.keys()): + if k.startswith(prefix + "encoder.target_model."): + del state_dict[k] + + if (self.encoder.ema is None) and (prefix + "encoder._ema" in state_dict): + del state_dict[prefix + "encoder._ema"] + + def remove_pretraining_modules(self, last_layer=None): + self.encoder.lm_head = None + self.encoder.regression_head = None + self.encoder.ema = None + self.classification_heads = None + + if last_layer is not None: + self.encoder.sentence_encoder.layers = nn.ModuleList( + l + for i, l in enumerate(self.encoder.sentence_encoder.layers) + if i <= last_layer + ) + self.encoder.sentence_encoder.layer_norm = None + + +class Data2VecTextEncoder(FairseqEncoder): + def __init__(self, cfg: Data2VecTextConfig, dictionary, task_data): + super().__init__(dictionary) + + self.cfg = cfg + + embed_tokens = self.build_embedding( + len(dictionary), cfg.transformer.encoder.embed_dim, dictionary.pad() + ) + + self.sentence_encoder = self.build_encoder(cfg, dictionary, embed_tokens) + self.mask_idx = dictionary.index("") + assert self.mask_idx != dictionary.unk(), dictionary.symbols + + self.ema = None + self.average_top_k_layers = cfg.average_top_k_layers + self.loss_scale = cfg.loss_scale + + assert self.cfg.head_layers >= 1 + + embed_dim = cfg.transformer.encoder.embed_dim + curr_dim = embed_dim + projs = [] + for i in range(self.cfg.head_layers - 1): + next_dim = embed_dim * 2 if i == 0 else curr_dim + projs.append(nn.Linear(curr_dim, next_dim)) + projs.append(nn.GELU()) + curr_dim = next_dim + + projs.append(nn.Linear(curr_dim, embed_dim)) + self.regression_head = nn.Sequential(*projs) + + self.num_updates = 0 + + def build_embedding(self, vocab_size, embedding_dim, padding_idx): + return nn.Embedding(vocab_size, embedding_dim, padding_idx) + + def build_encoder(self, cfg, dictionary, embed_tokens): + encoder = TransformerEncoder(cfg.transformer, dictionary, embed_tokens, return_fc=True) + encoder.apply(init_bert_params) + return encoder + + def build_lm_head(self, embed_dim, output_dim, activation_fn, weight): + return RobertaLMHead(embed_dim, output_dim, activation_fn, weight) + + def make_ema_teacher(self): + ema_config = EMAModuleConfig( + ema_decay=self.cfg.ema_decay, + ema_fp32=True, + ) + skip_keys = set() + if self.cfg.ema_transformer_layers_only: + for k, _ in self.sentence_encoder.embed_positions.named_parameters(): + skip_keys.add(f"embed_tokens.{k}") + for k, _ in self.sentence_encoder.embed_positions.named_parameters(): + skip_keys.add(f"embed_positions.{k}") + if self.sentence_encoder.layernorm_embedding is not None: + for ( + k, + _, + ) in self.sentence_encoder.layernorm_embedding.named_parameters(): + skip_keys.add(f"layernorm_embedding.{k}") + if self.sentence_encoder.layer_norm is not None: + for k, _ in self.sentence_encoder.layer_norm.named_parameters(): + skip_keys.add(f"layernorm_embedding.{k}") + + self.ema = EMAModule( + self.sentence_encoder, + ema_config, + skip_keys=skip_keys, + ) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + + if self.ema is None and self.regression_head is not None: + logger.info(f"making ema teacher") + self.make_ema_teacher() + elif self.training and self.ema is not None: + if self.cfg.ema_decay != self.cfg.ema_end_decay: + if num_updates >= self.cfg.ema_anneal_end_step: + decay = self.cfg.ema_end_decay + else: + decay = get_annealed_rate( + self.cfg.ema_decay, + self.cfg.ema_end_decay, + num_updates, + self.cfg.ema_anneal_end_step, + ) + self.ema.set_decay(decay) + if self.ema.get_decay() < 1: + self.ema.step(self.sentence_encoder) + + def state_dict(self, destination=None, prefix="", keep_vars=False): + state = super().state_dict(destination, prefix, keep_vars) + if self.ema is not None: + state[prefix + "_ema"] = self.ema.fp32_params + return state + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + if self.ema is not None: + k = prefix + "_ema" + assert k in state_dict + self.ema.restore(state_dict[k], True) + del state_dict[k] + return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + def forward( + self, + src_tokens, + target_tokens=None, + features_only=False, + return_all_hiddens=False, + masked_tokens=None, + **unused, + ): + """ + Args: + src_tokens (LongTensor): input tokens of shape `(batch, src_len)` + features_only (bool, optional): skip LM head and just return + features. If True, the output will be of shape + `(batch, src_len, embed_dim)`. + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + + Returns: + tuple: + - the LM output of shape `(batch, src_len, vocab)` + - a dictionary of additional data, where 'inner_states' + is a list of hidden states. Note that the hidden + states have shape `(src_len, batch, vocab)`. + """ + + x, extra = self.extract_features( + src_tokens, return_all_hiddens=return_all_hiddens + ) + + if features_only: + return x, extra + + assert target_tokens is not None + + with torch.no_grad(): + # use EMA parameter as the teacher + self.ema.model.eval() + + encoder_out = self.ema.model( + target_tokens, + return_all_hiddens=True, + ) + y = encoder_out["fc_results"] + + y = y[-self.average_top_k_layers :] + + permuted = False + if self.cfg.instance_norm_target_layer or self.cfg.batch_norm_target_layer: + y = [tl.permute(1, 2, 0) for tl in y] # TBC -> BCT + permuted = True + + if self.cfg.batch_norm_target_layer: + y = [ + F.batch_norm( + tl.float(), running_mean=None, running_var=None, training=True + ) + for tl in y + ] + + if self.cfg.instance_norm_target_layer: + y = [F.instance_norm(tl.float()) for tl in y] + + if permuted: + y = [tl.transpose(1, 2) for tl in y] # BCT -> BTC + + if self.cfg.layer_norm_target_layer: + y = [F.layer_norm(tl.float(), tl.shape[-1:]) for tl in y] + + y = sum(y) / len(y) + + if not permuted: + y = y.transpose(0, 1) + + if self.cfg.layer_norm_targets: + y = F.layer_norm(y.float(), y.shape[-1:]) + + if self.cfg.instance_norm_targets: + y = F.instance_norm(y.transpose(1, 2)).transpose(1, 2) + + masked_indices = src_tokens.eq(self.mask_idx) + + x = x[masked_indices] + y = y[masked_indices] + + x = self.regression_head(x) + + sz = x.size(-1) + if self.cfg.loss_beta == 0: + loss = F.mse_loss(x.float(), y.float(), reduction="none").sum(dim=-1) + else: + loss = F.smooth_l1_loss( + x.float(), y.float(), reduction="none", beta=self.cfg.loss_beta + ).sum(dim=-1) + + result = { + "losses": { + "main": loss.sum() / math.sqrt(sz) + if self.loss_scale <= 0 + else loss.sum() * self.loss_scale, + }, + "sample_size": loss.numel(), + } + + # logging other values + other_logs = { + "ema_decay": self.ema.get_decay() * 1000 + } + result["logs"] = other_logs + return result + + def extract_features(self, src_tokens, return_all_hiddens=False, **kwargs): + encoder_out = self.sentence_encoder( + src_tokens, + return_all_hiddens=return_all_hiddens, + token_embeddings=kwargs.get("token_embeddings", None), + ) + # T x B x C -> B x T x C + features = encoder_out["encoder_out"][0].transpose(0, 1) + inner_states = encoder_out["encoder_states"] if return_all_hiddens else None + return features, { + "inner_states": inner_states, + "encoder_embedding": encoder_out["encoder_embedding"][0], + } + + def output_layer(self, features, masked_tokens=None, **unused): + return self.lm_head(features, masked_tokens) + + def max_positions(self): + """Maximum output length supported by the encoder.""" + return self.cfg.max_positions diff --git a/fairseq/examples/data2vec/models/data2vec_text_classification.py b/fairseq/examples/data2vec/models/data2vec_text_classification.py new file mode 100644 index 0000000..e787b91 --- /dev/null +++ b/fairseq/examples/data2vec/models/data2vec_text_classification.py @@ -0,0 +1,141 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# The code in this file is adapted from the BeiT implementation which can be found here: +# https://github.com/microsoft/unilm/tree/master/beit + +import logging + +from dataclasses import dataclass +from typing import Any + +from omegaconf import II, MISSING + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import checkpoint_utils, tasks + +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.roberta.model import RobertaClassificationHead + +from examples.data2vec.data.modality import Modality + + +logger = logging.getLogger(__name__) + + +@dataclass +class Data2VecTextClassificationConfig(FairseqDataclass): + pooler_dropout: float = 0.0 + pooler_activation_fn: str = "tanh" + quant_noise_pq: int = 0 + quant_noise_pq_block_size: int = 8 + spectral_norm_classification_head: bool = False + + model_path: str = MISSING + no_pretrained_weights: bool = False + + pretrained_model_args: Any = None + + +@register_model( + "data2vec_text_classification", dataclass=Data2VecTextClassificationConfig +) +class Data2VecTextClassificationModel(BaseFairseqModel): + def __init__(self, cfg: Data2VecTextClassificationConfig): + super().__init__() + self.cfg = cfg + + if cfg.pretrained_model_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu(cfg.model_path, {}) + pretrained_args = state.get("cfg", None) + pretrained_args.criterion = None + pretrained_args.lr_scheduler = None + cfg.pretrained_model_args = pretrained_args + + logger.info(pretrained_args) + else: + state = None + pretrained_args = cfg.pretrained_model_args + + task = tasks.setup_task(pretrained_args.task) + model = task.build_model(pretrained_args.model, from_checkpoint=True) + + model.remove_pretraining_modules() + + self.model = model + + if state is not None and not cfg.no_pretrained_weights: + self.load_model_weights(state, model, cfg) + + self.classification_heads = nn.ModuleDict() + + + def load_model_weights(self, state, model, cfg): + for k in list(state["model"].keys()): + if ( + k.startswith("shared_decoder") or + k.startswith("_ema") or + "decoder" in k + ): + logger.info(f"Deleting {k} from checkpoint") + del state["model"][k] + model.load_state_dict(state["model"], strict=True) + + @classmethod + def build_model(cls, cfg: Data2VecTextClassificationConfig, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): + """Register a classification head.""" + if name in self.classification_heads: + prev_num_classes = self.classification_heads[name].out_proj.out_features + prev_inner_dim = self.classification_heads[name].dense.out_features + if num_classes != prev_num_classes or inner_dim != prev_inner_dim: + logger.warning( + 're-registering head "{}" with num_classes {} (prev: {}) ' + "and inner_dim {} (prev: {})".format( + name, num_classes, prev_num_classes, inner_dim, prev_inner_dim + ) + ) + embed_dim = self.cfg.pretrained_model_args.model.embed_dim + self.classification_heads[name] = RobertaClassificationHead( + input_dim=embed_dim, + inner_dim=inner_dim or embed_dim, + num_classes=num_classes, + activation_fn=self.cfg.pooler_activation_fn, + pooler_dropout=self.cfg.pooler_dropout, + q_noise=self.cfg.quant_noise_pq, + qn_block_size=self.cfg.quant_noise_pq_block_size, + do_spectral_norm=self.cfg.spectral_norm_classification_head, + ) + + def forward( + self, + source, + id, + padding_mask, + features_only=True, + remove_extra_tokens=True, + classification_head_name=None, + ): + encoder_out = self.model( + source, + id=id, + mode=Modality.TEXT, + padding_mask=padding_mask, + mask=False, + features_only=features_only, + remove_extra_tokens=remove_extra_tokens + ) + logits = self.classification_heads[classification_head_name](encoder_out["x"]) + return logits, encoder_out diff --git a/fairseq/examples/data2vec/models/data2vec_vision.py b/fairseq/examples/data2vec/models/data2vec_vision.py new file mode 100644 index 0000000..2f89894 --- /dev/null +++ b/fairseq/examples/data2vec/models/data2vec_vision.py @@ -0,0 +1,727 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# The code in this file is adapted from the BeiT implementation which can be found here: +# https://github.com/microsoft/unilm/tree/master/beit + +import logging +import math +import numpy as np +import random + +from dataclasses import dataclass, field +from typing import Optional + +from omegaconf import II + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.distributed as dist + +from fairseq.modules import EMAModule, EMAModuleConfig +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model + + +logger = logging.getLogger(__name__) + + +@dataclass +class Data2VecVisionConfig(FairseqDataclass): + layer_scale_init_value: float = field( + default=1e-4, metadata={"help": "rescale layer outputs, 0 to disable"} + ) + num_mask_patches: int = field( + default=75, + metadata={"help": "number of the visual tokens/patches need be masked"}, + ) + min_mask_patches_per_block: int = 16 + max_mask_patches_per_block: int = 196 + image_size: int = 224 + patch_size: int = 16 + in_channels: int = 3 + + shared_rel_pos_bias: bool = True + + drop_path: float = 0.1 + attention_dropout: float = 0.0 + + depth: int = 12 + embed_dim: int = 768 + num_heads: int = 12 + mlp_ratio: int = 4 + + loss_beta: float = field( + default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"} + ) + loss_scale: Optional[float] = field( + default=None, + metadata={ + "help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)" + }, + ) + average_top_k_layers: int = field( + default=8, metadata={"help": "how many layers to average"} + ) + + end_of_block_targets: bool = True + layer_norm_target_layer: bool = False + instance_norm_target_layer: bool = False + batch_norm_target_layer: bool = False + instance_norm_targets: bool = False + layer_norm_targets: bool = False + + ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"}) + ema_end_decay: float = field( + default=0.9999, metadata={"help": "final ema decay rate"} + ) + + # when to finish annealing ema decay rate + ema_anneal_end_step: int = II("optimization.max_update") + + ema_transformer_only: bool = field( + default=True, + metadata={"help": "whether to momentum update only the transformer layers"}, + ) + + +def get_annealed_rate(start, end, curr_step, total_steps): + r = end - start + pct_remaining = 1 - curr_step / total_steps + return end - r * pct_remaining + + +@register_model("data2vec_vision", dataclass=Data2VecVisionConfig) +class Data2VecVisionModel(BaseFairseqModel): + def __init__(self, cfg: Data2VecVisionConfig): + super().__init__() + self.cfg = cfg + + self.ema = None + + self.average_top_k_layers = cfg.average_top_k_layers + self.loss_beta = cfg.loss_beta + self.loss_scale = ( + cfg.loss_scale + if cfg.loss_scale is not None + else 1 / math.sqrt(cfg.embed_dim) + ) + + self.patch_embed = PatchEmbed( + img_size=cfg.image_size, + patch_size=cfg.patch_size, + in_chans=cfg.in_channels, + embed_dim=cfg.embed_dim, + ) + + patch_size = self.patch_embed.patch_size + self.window_size = ( + cfg.image_size // patch_size[0], + cfg.image_size // patch_size[1], + ) + + self.cls_emb = nn.Parameter(torch.FloatTensor(1, 1, cfg.embed_dim)) + self.mask_emb = nn.Parameter(torch.FloatTensor(1, 1, cfg.embed_dim)) + + nn.init.trunc_normal_(self.cls_emb, 0.02) + nn.init.trunc_normal_(self.mask_emb, 0.02) + + self.encoder = TransformerEncoder(cfg, self.patch_embed.patch_shape) + + self.final_proj = nn.Linear(cfg.embed_dim, cfg.embed_dim) + self.num_updates = 0 + + def make_ema_teacher(self): + ema_config = EMAModuleConfig( + ema_decay=self.cfg.ema_decay, + ema_fp32=True, + ) + self.ema = EMAModule( + self.encoder if self.cfg.ema_transformer_only else self, + ema_config, + ) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + + if self.ema is None and self.final_proj is not None: + logger.info(f"making ema teacher") + self.make_ema_teacher() + elif self.training and self.ema is not None: + if self.cfg.ema_decay != self.cfg.ema_end_decay: + if num_updates >= self.cfg.ema_anneal_end_step: + decay = self.cfg.ema_end_decay + else: + decay = get_annealed_rate( + self.cfg.ema_decay, + self.cfg.ema_end_decay, + num_updates, + self.cfg.ema_anneal_end_step, + ) + self.ema.set_decay(decay) + if self.ema.get_decay() < 1: + self.ema.step(self.encoder if self.cfg.ema_transformer_only else self) + + self.num_updates = num_updates + + def state_dict(self, destination=None, prefix="", keep_vars=False): + state = super().state_dict(destination, prefix, keep_vars) + + if self.ema is not None: + state[prefix + "_ema"] = self.ema.fp32_params + + return state + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + if self.ema is not None: + k = prefix + "_ema" + assert k in state_dict + self.ema.restore(state_dict[k], True) + del state_dict[k] + return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + @classmethod + def build_model(cls, cfg: Data2VecVisionConfig, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def make_mask(self, bsz, num_masks, min_masks, max_masks): + height, width = self.window_size + + masks = np.zeros(shape=(bsz, height, width), dtype=np.int) + + for i in range(bsz): + mask = masks[i] + mask_count = 0 + + min_aspect = 0.3 + max_aspect = 1 / min_aspect + log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect)) + + def _mask(mask, max_mask_patches): + delta = 0 + for attempt in range(10): + target_area = random.uniform(min_masks, max_mask_patches) + aspect_ratio = math.exp(random.uniform(*log_aspect_ratio)) + h = int(round(math.sqrt(target_area * aspect_ratio))) + w = int(round(math.sqrt(target_area / aspect_ratio))) + if w < width and h < height: + top = random.randint(0, height - h) + left = random.randint(0, width - w) + + num_masked = mask[top : top + h, left : left + w].sum() + # Overlap + if 0 < h * w - num_masked <= max_mask_patches: + for i in range(top, top + h): + for j in range(left, left + w): + if mask[i, j] == 0: + mask[i, j] = 1 + delta += 1 + + if delta > 0: + break + return delta + + while mask_count < num_masks: + max_mask_patches = min(num_masks - mask_count, max_masks) + + delta = _mask(mask, max_mask_patches) + if delta == 0: + break + else: + mask_count += delta + + return torch.from_numpy(masks) + + def forward( + self, + img, + mask: bool = True, + layer_results: bool = False, + ): + x = self.patch_embed(img) + batch_size, seq_len, _ = x.size() + + if mask: + mask_indices = self.make_mask( + img.size(0), + self.cfg.num_mask_patches, + self.cfg.min_mask_patches_per_block, + self.cfg.max_mask_patches_per_block, + ) + bool_mask = mask_indices.view(mask_indices.size(0), -1).bool() + else: + mask_indices = bool_mask = None + + cls_tokens = self.cls_emb.expand(batch_size, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + if self.ema is not None: + with torch.no_grad(): + self.ema.model.eval() + + if self.cfg.ema_transformer_only: + y = self.ema.model( + x, + layer_results="end" if self.cfg.end_of_block_targets else "fc", + ) + else: + y = self.ema.model( + img, + mask=False, + layer_results=True, + ) + + y = y[-self.cfg.average_top_k_layers :] + + permuted = False + if self.cfg.instance_norm_target_layer or self.cfg.batch_norm_target_layer: + y = [tl.transpose(1, 2) for tl in y] # BTC -> BCT + permuted = True + + if self.cfg.batch_norm_target_layer: + y = [ + F.batch_norm( + tl.float(), running_mean=None, running_var=None, training=True + ) + for tl in y + ] + + if self.cfg.instance_norm_target_layer: + y = [F.instance_norm(tl.float()) for tl in y] + + if permuted: + y = [tl.transpose(1, 2) for tl in y] # BCT -> BTC + + if self.cfg.layer_norm_target_layer: + y = [F.layer_norm(tl.float(), tl.shape[-1:]) for tl in y] + + y = sum(y) / len(y) + + if self.cfg.layer_norm_targets: + y = F.layer_norm(y.float(), y.shape[-1:]) + + if self.cfg.instance_norm_targets: + y = F.instance_norm(y.float().transpose(1, 2)).transpose(1, 2) + + y = y[bool_mask].float() + + if mask_indices is not None: + mask_token = self.mask_emb.expand(batch_size, seq_len, -1) + w = mask_indices.view(mask_indices.size(0), -1, 1).type_as(mask_token) + x[:, 1:] = x[:, 1:] * (1 - w) + mask_token * w + + if layer_results: + enc_layer_results = "end" if self.cfg.end_of_block_targets else "fc" + else: + enc_layer_results = None + + x = self.encoder(x, layer_results=enc_layer_results) + if layer_results or mask_indices is None: + return x + + x = x[bool_mask].float() + + if self.loss_beta == 0: + loss = F.mse_loss(x, y, reduction="none").sum(dim=-1) + else: + loss = F.smooth_l1_loss(x, y, reduction="none", beta=self.loss_beta).sum( + dim=-1 + ) + + if self.loss_scale > 0: + loss = loss * self.loss_scale + + result = { + "losses": {"regression": loss.sum()}, + "sample_size": loss.numel(), + "target_var": self.compute_var(y), + "pred_var": self.compute_var(x), + "ema_decay": self.ema.get_decay() * 1000, + } + return result + + @staticmethod + def compute_var(y): + y = y.view(-1, y.size(-1)) + if dist.is_initialized(): + zc = torch.tensor(y.size(0)).cuda() + zs = y.sum(dim=0) + zss = (y ** 2).sum(dim=0) + + dist.all_reduce(zc) + dist.all_reduce(zs) + dist.all_reduce(zss) + + var = zss / (zc - 1) - (zs ** 2) / (zc * (zc - 1)) + return torch.sqrt(var + 1e-6).mean() + else: + return torch.sqrt(y.var(dim=0) + 1e-6).mean() + + def remove_pretraining_modules(self, last_layer=None): + self.final_proj = None + self.ema = None + self.encoder.norm = nn.Identity() + self.mask_emb = None + if last_layer is not None: + self.encoder.layers = nn.ModuleList( + l for i, l in enumerate(self.encoder.layers) if i <= last_layer + ) + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + if isinstance(img_size, int): + img_size = img_size, img_size + if isinstance(patch_size, int): + patch_size = patch_size, patch_size + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.conv = nn.Conv2d( + in_chans, embed_dim, kernel_size=patch_size, stride=patch_size + ) + + def forward(self, x): + # BCHW -> BTC + x = self.conv(x).flatten(2).transpose(1, 2) + return x + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + attn_drop=0.0, + proj_drop=0.0, + window_size=None, + attn_head_dim=None, + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = head_dim ** -0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * ( + 2 * window_size[1] - 1 + ) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = torch.zeros( + size=(window_size[0] * window_size[1] + 1,) * 2, + dtype=relative_coords.dtype, + ) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, rel_pos_bias=None): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + ( + self.q_bias, + torch.zeros_like(self.v_bias, requires_grad=False), + self.v_bias, + ) + ) + # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if self.relative_position_bias_table is not None: + assert 1==2 + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + print("attn.size() :", attn.size()) + print("rel_pos_bias.size() :", rel_pos_bias.size()) + if rel_pos_bias is not None: + attn = attn + rel_pos_bias + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class RelativePositionBias(nn.Module): + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * ( + 2 * window_size[1] - 1 + ) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads) + ) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = torch.zeros( + size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype + ) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + def forward(self): + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, + -1, + ) # Wh*Ww,Wh*Ww,nH + print("self.window_size :", self.window_size) + print("self.num_relative_distance :", self.num_relative_distance) + print("self.relative_position_index :", self.relative_position_index.size(), self.relative_position_index) + print("relative_position_bias.size(), relative_position_bias :",relative_position_bias.size(), relative_position_bias) + print("self.relative_position_bias_table.size(), self.relative_position_bias_table :",self.relative_position_bias_table.size(), self.relative_position_bias_table) + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + if self.drop_prob == 0.0 or not self.training: + return x + keep_prob = 1 - self.drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() + output = x.div(keep_prob) * random_tensor + return output + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class Block(nn.Module): + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + init_values=None, + window_size=None, + ): + super().__init__() + + self.norm1 = nn.LayerNorm(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + attn_drop=attn_drop, + proj_drop=drop, + window_size=window_size, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = nn.LayerNorm(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + + self.mlp = nn.Sequential( + nn.Linear(dim, mlp_hidden_dim), + nn.GELU(), + nn.Linear(mlp_hidden_dim, dim), + nn.Dropout(drop), + ) + + if init_values > 0: + self.gamma_1 = nn.Parameter( + init_values * torch.ones((dim)), requires_grad=True + ) + self.gamma_2 = nn.Parameter( + init_values * torch.ones((dim)), requires_grad=True + ) + else: + self.gamma_1, self.gamma_2 = None, None + + def forward(self, x, rel_pos_bias=None): + print("inside block :", x.size()) + if self.gamma_1 is None: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)) + fc_feature = self.drop_path(self.mlp(self.norm2(x))) + x = x + fc_feature + else: + x = x + self.drop_path( + self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias) + ) + fc_feature = self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + x = x + fc_feature + return x, fc_feature + + +class TransformerEncoder(nn.Module): + def __init__(self, cfg: Data2VecVisionConfig, patch_shape): + super().__init__() + + self.rel_pos_bias = None + if cfg.shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias( + window_size=patch_shape, num_heads=cfg.num_heads + ) + + dpr = [ + x.item() for x in torch.linspace(0, cfg.drop_path, cfg.depth) + ] # stochastic depth decay rule + + print("TransformerEncoder > patch_shape :", patch_shape) + self.blocks = nn.ModuleList( + Block( + dim=cfg.embed_dim, + num_heads=cfg.num_heads, + attn_drop=cfg.attention_dropout, + drop_path=dpr[i], + init_values=cfg.layer_scale_init_value, + window_size=patch_shape if not cfg.shared_rel_pos_bias else None, + ) + for i in range(cfg.depth) + ) + + self.norm = nn.LayerNorm(cfg.embed_dim) + + self.apply(self.init_weights) + self.fix_init_weight() + + def init_weights(self, m): + std = 0.02 + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=std) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, nn.Conv2d): + nn.init.trunc_normal_(m.weight, std=std) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + rescale(layer.mlp[2].weight.data, layer_id + 1) + + def extract_features(self, x, layer_results): + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + + z = [] + for i, blk in enumerate(self.blocks): + x, fc_feature = blk(x, rel_pos_bias=rel_pos_bias) + if layer_results == "end": + z.append(x) + elif layer_results == "fc": + z.append(fc_feature) + + return z if layer_results else self.norm(x) + + def forward(self, x, layer_results=None): + x = self.extract_features(x, layer_results=layer_results) + if layer_results: + return [z[:, 1:] for z in x] + + x = x[:, 1:] + return x diff --git a/fairseq/examples/data2vec/models/mae.py b/fairseq/examples/data2vec/models/mae.py new file mode 100644 index 0000000..a3b5f72 --- /dev/null +++ b/fairseq/examples/data2vec/models/mae.py @@ -0,0 +1,829 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# The code in this file is adapted from the BeiT implementation which can be found here: +# https://github.com/microsoft/unilm/tree/master/beit + +import logging +from dataclasses import dataclass +from functools import partial + +from timm.models.vision_transformer import PatchEmbed, Block + +import torch +import torch.nn as nn + +import numpy as np + +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.wav2vec.wav2vec2 import TransformerSentenceEncoderLayer + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = nn.LayerNorm + +import torch.nn.functional as F + + +logger = logging.getLogger(__name__) + + +@dataclass +class MaeConfig(FairseqDataclass): + input_size: int = 224 + in_chans: int = 3 + patch_size: int = 16 + embed_dim: int = 768 + depth: int = 12 + num_heads: int = 12 + decoder_embed_dim: int = 512 + decoder_depth: int = 8 + decoder_num_heads: int = 16 + mlp_ratio: int = 4 + norm_eps: float = 1e-6 + + drop_path_rate: float = 0.0 + + mask_ratio: float = 0.75 + norm_pix_loss: bool = True + + w2v_block: bool = False + alt_block: bool = False + alt_block2: bool = False + alt_attention: bool = False + block_dropout: float = 0 + attention_dropout: float = 0 + activation_dropout: float = 0 + layer_norm_first: bool = False + + fused_ln: bool = True + end_of_block_targets: bool = True + + no_decoder_embed: bool = False + no_decoder_pos_embed: bool = False + mask_noise_std: float = 0 + + single_qkv: bool = False + use_rel_pos_bias: bool = False + no_cls: bool = False + + +def modify_relative_position_bias(orig_bias, bsz, mask): + if mask is None: + return orig_bias.unsqueeze(0).repeat( + bsz, 1, 1, 1 + ) # heads x seq_len x seq_len => bsz x heads x seq_len x seq_len + heads, max_seq_len, max_seq_len = orig_bias.shape # includes CLS token + mask_for_rel_pos_bias = torch.cat( + (torch.zeros(bsz, 1, dtype=mask.dtype, device=mask.device), mask), dim=1 + ).bool() # bsz x seqlen (add CLS token) + unmasked_for_rel_pos_bias = ~mask_for_rel_pos_bias + unmasked_for_rel_pos_bias = unmasked_for_rel_pos_bias.unsqueeze(1).repeat( + 1, heads, 1 + ) # bsz x seq_len => bsz x heads x seq_len + b_t_t_rel_pos_bias = orig_bias.unsqueeze(0).repeat( + bsz, 1, 1, 1 + ) # heads x seq_len x seq_len => bsz x heads x seq_len x seq_len + b_t_t_rel_pos_bias = b_t_t_rel_pos_bias.masked_select( + unmasked_for_rel_pos_bias.unsqueeze(-1) + ) + b_t_t_rel_pos_bias = b_t_t_rel_pos_bias.view(bsz, heads, -1, max_seq_len) + new_len = b_t_t_rel_pos_bias.size(-2) + b_t_t_rel_pos_bias = b_t_t_rel_pos_bias.masked_select( + unmasked_for_rel_pos_bias.unsqueeze(-2) + ) + b_t_t_rel_pos_bias = b_t_t_rel_pos_bias.view(bsz, heads, new_len, new_len) + return b_t_t_rel_pos_bias + + +class AltBlock(nn.Module): + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=True, + ffn_targets=False, + use_rel_pos_bias=False, + window_size=None, + alt_attention=False, + ): + super().__init__() + + self.layer_norm_first = layer_norm_first + self.ffn_targets = ffn_targets + + from timm.models.vision_transformer import Attention, DropPath, Mlp + + self.norm1 = norm_layer(dim) + self.use_rel_pos_bias = use_rel_pos_bias + if use_rel_pos_bias: + self.attn = AltAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + window_size=window_size, + ) + else: + if alt_attention: + from .multi.modules import AltAttention as AltAttention2 + self.attn = AltAttention2( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + else: + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + def forward(self, x, rel_pos_bias=None, pos_mask=None): + if self.layer_norm_first: + if self.use_rel_pos_bias: + x = x + self.drop_path( + self.attn( + self.norm1(x), rel_pos_bias=rel_pos_bias, pos_mask=pos_mask + ) + ) + else: + x = x + self.drop_path(self.attn(self.norm1(x))) + t = self.mlp(self.norm2(x)) + x = x + self.drop_path(t) + if not self.ffn_targets: + t = x + return x, t + else: + if self.use_rel_pos_bias: + x = x + self.drop_path( + self.attn(x, rel_pos_bias=rel_pos_bias, pos_mask=pos_mask) + ) + else: + x = x + self.drop_path(self.attn(x)) + r = x = self.norm1(x) + x = self.mlp(x) + t = x + x = self.norm2(r + self.drop_path(x)) + if not self.ffn_targets: + t = x + return x, t + + +class AltAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + window_size=None, + attn_head_dim=None, + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * ( + 2 * window_size[1] - 1 + ) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = torch.zeros( + size=(window_size[0] * window_size[1] + 1,) * 2, + dtype=relative_coords.dtype, + ) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, rel_pos_bias=None, pos_mask=None): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + ( + self.q_bias, + torch.zeros_like(self.v_bias, requires_grad=False), + self.v_bias, + ) + ) + # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if self.relative_position_bias_table is not None: + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + modify_relative_position_bias( + relative_position_bias, x.size(0), pos_mask + ) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class RelativePositionBias(nn.Module): + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * ( + 2 * window_size[1] - 1 + ) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads) + ) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = torch.zeros( + size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype + ) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + def forward(self): + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, + -1, + ) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token: + pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000 ** omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +def interpolate_pos_embed(model, checkpoint_model): + if "pos_embed" in checkpoint_model: + pos_embed_checkpoint = checkpoint_model["pos_embed"] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.patch_embed.num_patches + num_extra_tokens = model.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print( + "Position interpolate from %dx%d to %dx%d" + % (orig_size, orig_size, new_size, new_size) + ) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape( + -1, orig_size, orig_size, embedding_size + ).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, + size=(new_size, new_size), + mode="bicubic", + align_corners=False, + ) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + checkpoint_model["pos_embed"] = new_pos_embed + + +@register_model("mae", dataclass=MaeConfig) +class MaeModel(BaseFairseqModel): + def __init__(self, cfg: MaeConfig): + super().__init__() + self.cfg = cfg + + self.mask_ratio = cfg.mask_ratio + + # -------------------------------------------------------------------------- + # MAE encoder specifics + self.patch_embed = PatchEmbed( + cfg.input_size, cfg.patch_size, cfg.in_chans, cfg.embed_dim + ) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, cfg.embed_dim)) if not cfg.no_cls else None + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches + int(not cfg.no_cls), cfg.embed_dim), requires_grad=False + ) # fixed sin-cos embedding + + norm_layer = partial(nn.LayerNorm, eps=cfg.norm_eps) + + dpr = [ + x.item() for x in torch.linspace(0, cfg.drop_path_rate, cfg.depth) + ] # stochastic depth decay rule + + def make_block(drop_path): + if cfg.w2v_block: + return TransformerSentenceEncoderLayer( + embedding_dim=cfg.embed_dim, + ffn_embedding_dim=cfg.embed_dim * cfg.mlp_ratio, + num_attention_heads=cfg.num_heads, + dropout=cfg.block_dropout, + attention_dropout=cfg.attention_dropout, + activation_dropout=cfg.activation_dropout, + activation_fn="gelu", + layer_norm_first=cfg.layer_norm_first, + drop_path=drop_path, + norm_eps=1e-6, + single_qkv=cfg.single_qkv, + fused_ln=cfg.fused_ln, + ) + elif cfg.alt_block: + window_size = ( + cfg.input_size // self.patch_embed.patch_size[0], + cfg.input_size // self.patch_embed.patch_size[1], + ) + return AltBlock( + cfg.embed_dim, + cfg.num_heads, + cfg.mlp_ratio, + qkv_bias=True, + qk_scale=None, + norm_layer=norm_layer, + drop_path=drop_path, + layer_norm_first=cfg.layer_norm_first, + ffn_targets=not cfg.end_of_block_targets, + use_rel_pos_bias=cfg.use_rel_pos_bias, + window_size=window_size + if (self.cfg.use_rel_pos_bias and not self.cfg.shared_rel_pos_bias) + else None, + alt_attention=cfg.alt_attention, + ) + elif cfg.alt_block2: + from .multi.modules import AltBlock as AltBlock2 + return AltBlock2( + cfg.embed_dim, + cfg.num_heads, + cfg.mlp_ratio, + qkv_bias=True, + qk_scale=None, + norm_layer=norm_layer, + drop_path=drop_path, + layer_norm_first=cfg.layer_norm_first, + ffn_targets=not cfg.end_of_block_targets, + ) + else: + return Block( + cfg.embed_dim, + cfg.num_heads, + cfg.mlp_ratio, + qkv_bias=True, + qk_scale=None, + norm_layer=norm_layer, + drop_path=drop_path, + ) + + self.blocks = nn.ModuleList([make_block(dpr[i]) for i in range(cfg.depth)]) + self.norm = norm_layer(cfg.embed_dim) + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MAE decoder specifics + self.decoder_embed = ( + nn.Linear(cfg.embed_dim, cfg.decoder_embed_dim, bias=True) + if not cfg.no_decoder_embed + else None + ) + + self.mask_token = ( + nn.Parameter( + torch.zeros( + 1, + 1, + cfg.decoder_embed_dim + if not cfg.no_decoder_embed + else cfg.embed_dim, + ) + ) + if cfg.mask_noise_std <= 0 + else None + ) + + self.decoder_pos_embed = ( + nn.Parameter( + torch.zeros( + 1, + num_patches + 1, + cfg.decoder_embed_dim + if not cfg.no_decoder_embed + else cfg.embed_dim, + ), + requires_grad=False, + ) + if not cfg.no_decoder_pos_embed + else None + ) + + self.decoder_blocks = nn.ModuleList( + [ + Block( + cfg.decoder_embed_dim, + cfg.decoder_num_heads, + cfg.mlp_ratio, + qkv_bias=True, + qk_scale=None, + norm_layer=norm_layer, + ) + for _ in range(cfg.decoder_depth) + ] + ) + + self.decoder_norm = norm_layer(cfg.decoder_embed_dim) + self.decoder_pred = nn.Linear( + cfg.decoder_embed_dim, cfg.patch_size ** 2 * cfg.in_chans, bias=True + ) # decoder to patch + # -------------------------------------------------------------------------- + + self.norm_pix_loss = cfg.norm_pix_loss + + self.initialize_weights() + + for pn, p in self.named_parameters(): + if len(p.shape) == 1 or pn.endswith(".bias"): + p.param_group = "no_decay" + else: + p.param_group = "with_decay" + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed( + self.pos_embed.shape[-1], + int(self.patch_embed.num_patches ** 0.5), + cls_token=not self.cfg.no_cls, + ) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + if self.decoder_pos_embed is not None: + decoder_pos_embed = get_2d_sincos_pos_embed( + self.decoder_pos_embed.shape[-1], + int(self.patch_embed.num_patches ** 0.5), + cls_token=not self.cfg.no_cls, + ) + self.decoder_pos_embed.data.copy_( + torch.from_numpy(decoder_pos_embed).float().unsqueeze(0) + ) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + if self.cls_token is not None: + torch.nn.init.normal_(self.cls_token, std=0.02) + + if self.mask_token is not None: + torch.nn.init.normal_(self.mask_token, std=0.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm) or isinstance(m, FusedLayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def patchify(self, imgs): + """ + imgs: (N, 3, H, W) + x: (N, L, patch_size**2 *3) + """ + p = self.patch_embed.patch_size[0] + assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0 + + h = w = imgs.shape[2] // p + x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p)) + x = torch.einsum("nchpwq->nhwpqc", x) + x = x.reshape(shape=(imgs.shape[0], h * w, p ** 2 * 3)) + return x + + def unpatchify(self, x): + """ + x: (N, L, patch_size**2 *3) + imgs: (N, 3, H, W) + """ + p = self.patch_embed.patch_size[0] + h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, 3)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p)) + return imgs + + def random_masking(self, x, mask_ratio): + """ + Perform per-sample random masking by per-sample shuffling. + Per-sample shuffling is done by argsort random noise. + x: [N, L, D], sequence + """ + N, L, D = x.shape # batch, length, dim + len_keep = int(L * (1 - mask_ratio)) + + noise = torch.rand(N, L, device=x.device) # noise in [0, 1] + + # sort noise for each sample + ids_shuffle = torch.argsort( + noise, dim=1 + ) # ascend: small is keep, large is remove + ids_restore = torch.argsort(ids_shuffle, dim=1) + + # keep the first subset + ids_keep = ids_shuffle[:, :len_keep] + x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) + + # generate the binary mask: 0 is keep, 1 is remove + mask = torch.ones([N, L], device=x.device) + mask[:, :len_keep] = 0 + # unshuffle to get the binary mask + mask = torch.gather(mask, dim=1, index=ids_restore) + + return x_masked, mask, ids_restore # x_masked is actually unmasked x + + @classmethod + def build_model(cls, cfg: MaeConfig, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def forward_encoder(self, x, mask_ratio): + # embed patches + x = self.patch_embed(x) + + # add pos embed w/o cls token + # if self.cls_token is not None: + # x = x + self.pos_embed + # else: + x = x + self.pos_embed[:, 1:, :] + + # masking: length -> length * mask_ratio + if mask_ratio > 0: + x, mask, ids_restore = self.random_masking(x, mask_ratio) + else: + mask = ids_restore = None + + # append cls token + if self.cls_token is not None: + cls_token = self.cls_token + self.pos_embed[:, :1, :] + cls_tokens = cls_token.expand(x.shape[0], -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # apply Transformer blocks + for blk in self.blocks: + x = blk(x) + + if self.norm is not None: + x = self.norm(x) + + return x, mask, ids_restore + + def forward_decoder(self, x, ids_restore): + # embed tokens + x = self.decoder_embed(x) + + # append mask tokens to sequence + mask_tokens = self.mask_token.repeat( + x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1 + ) + if self.cls_token is not None: + x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1) # no cls token + else: + x_ = torch.cat([x, mask_tokens], dim=1) # no cls token + + x_ = torch.gather( + x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]) + ) # unshuffle + + if self.cls_token is not None: + x = torch.cat([x[:, :1, :], x_], dim=1) # append cls token + + # add pos embed + x = x + self.decoder_pos_embed + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + x = self.decoder_norm(x) + + # predictor projection + x = self.decoder_pred(x) + + if self.cls_token is not None: + # remove cls token + x = x[:, 1:, :] + + return x + + def forward_loss(self, imgs, pred, mask): + """ + imgs: [N, 3, H, W] + pred: [N, L, p*p*3] + mask: [N, L], 0 is keep, 1 is remove, + """ + target = self.patchify(imgs) + if self.norm_pix_loss: + mean = target.mean(dim=-1, keepdim=True) + var = target.var(dim=-1, keepdim=True) + target = (target - mean) / (var + 1.0e-6) ** 0.5 + + loss = (pred - target) ** 2 + loss = loss.mean(dim=-1) # [N, L], mean loss per patch + + loss = (loss * mask).sum() + return loss, mask.sum() + + def forward(self, imgs, predictions_only=False): + latent, mask, ids_restore = self.forward_encoder( + imgs, self.mask_ratio if not predictions_only else 0 + ) + + if predictions_only: + return latent + + pred = self.forward_decoder(latent, ids_restore) # [N, L, p*p*3] + loss, sample_size = self.forward_loss(imgs, pred, mask) + + result = { + "losses": {"regression": loss}, + "sample_size": sample_size, + } + return result + + def remove_pretraining_modules(self): + self.decoder_embed = None + self.decoder_blocks = None + self.decoder_norm = None + self.decoder_pos_embed = None + self.decoder_pred = None + self.mask_token = None + if self.cfg.layer_norm_first: + self.norm = None diff --git a/fairseq/examples/data2vec/models/mae_image_classification.py b/fairseq/examples/data2vec/models/mae_image_classification.py new file mode 100644 index 0000000..e304618 --- /dev/null +++ b/fairseq/examples/data2vec/models/mae_image_classification.py @@ -0,0 +1,386 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# The code in this file is adapted from the BeiT implementation which can be found here: +# https://github.com/microsoft/unilm/tree/master/beit + +import logging + +from dataclasses import dataclass +from enum import Enum, auto +from typing import Any, Optional + +import numpy as np +from omegaconf import II, MISSING + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import checkpoint_utils, tasks +from omegaconf import open_dict + +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from .mae import interpolate_pos_embed + + +logger = logging.getLogger(__name__) + + +class PredictionMode(Enum): + MEAN_POOLING = auto() + CLS_TOKEN = auto() + LIN_SOFTMAX = auto() + + +@dataclass +class MaeImageClassificationConfig(FairseqDataclass): + model_path: str = MISSING + no_pretrained_weights: bool = False + linear_classifier: bool = False + num_classes: int = 1000 + mixup: float = 0.8 + cutmix: float = 1.0 + label_smoothing: float = 0.1 + + drop_path_rate: float = 0.1 + layer_decay: float = 0.65 + + mixup_prob: float = 1.0 + mixup_switch_prob: float = 0.5 + mixup_mode: str = "batch" + + pretrained_model_args: Any = None + data: str = II("task.data") + + norm_eps: Optional[float] = None + + remove_alibi: bool = False + + # regularization overwrites + encoder_dropout: float = 0 + post_mlp_drop: float = 0 + attention_dropout: float = 0 + activation_dropout: float = 0.0 + dropout_input: float = 0.0 + layerdrop: float = 0.0 + + prenet_layerdrop: float = 0 + prenet_dropout: float = 0 + + use_fc_norm: bool = True + prediction_mode: PredictionMode = PredictionMode.MEAN_POOLING + + no_decay_blocks: bool = True + + +def get_layer_id_for_vit(name, num_layers): + """ + Assign a parameter with its layer id + Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33 + """ + if name in ["cls_token", "pos_embed"]: + return 0 + elif name.startswith("patch_embed"): + return 0 + elif name.startswith("rel_pos_bias"): + return num_layers - 1 + elif name.startswith("blocks"): + return int(name.split(".")[1]) + 1 + else: + return num_layers + + +@register_model("mae_image_classification", dataclass=MaeImageClassificationConfig) +class MaeImageClassificationModel(BaseFairseqModel): + def __init__(self, cfg: MaeImageClassificationConfig): + super().__init__() + self.cfg = cfg + + if cfg.pretrained_model_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu(cfg.model_path, {}) + pretrained_args = state.get("cfg", None) + + pretrained_args.criterion = None + pretrained_args.lr_scheduler = None + + logger.info(pretrained_args.model) + + with open_dict(pretrained_args.model): + pretrained_args.model.drop_path_rate = cfg.drop_path_rate + if cfg.norm_eps is not None: + pretrained_args.model.norm_eps = cfg.norm_eps + + cfg.pretrained_model_args = pretrained_args + + logger.info(pretrained_args) + else: + state = None + pretrained_args = cfg.pretrained_model_args + + if "data" in pretrained_args.task: + pretrained_args.task.data = cfg.data + elif "image" in pretrained_args.task: + pretrained_args.task.image.data = cfg.data + + if "modalities" in pretrained_args.model: + prenet_blocks = pretrained_args.model["modalities"]["image"]["prenet_depth"] + model_blocks = pretrained_args.model["depth"] + with open_dict(pretrained_args): + dpr = np.linspace(0, cfg.drop_path_rate, model_blocks).tolist() + pretrained_args.model["modalities"]["image"][ + "start_drop_path_rate" + ] = dpr[0] + pretrained_args.model["modalities"]["image"][ + "end_drop_path_rate" + ] = max(0, dpr[prenet_blocks - 1]) + pretrained_args.model["start_drop_path_rate"] = dpr[prenet_blocks] + pretrained_args.model["end_drop_path_rate"] = dpr[-1] + + if "mae_masking" in pretrained_args.model["modalities"]["image"]: + del pretrained_args.model["modalities"]["image"]["mae_masking"] + + if cfg.remove_alibi: + pretrained_args.model["modalities"]["image"][ + "use_alibi_encoder" + ] = False + if ( + state is not None + and "modality_encoders.IMAGE.alibi_bias" in state["model"] + ): + del state["model"]["modality_encoders.IMAGE.alibi_bias"] + + pretrained_args.model["encoder_dropout"] = cfg.encoder_dropout + pretrained_args.model["post_mlp_drop"] = cfg.post_mlp_drop + pretrained_args.model["attention_dropout"] = cfg.attention_dropout + pretrained_args.model["activation_dropout"] = cfg.activation_dropout + pretrained_args.model["dropout_input"] = cfg.dropout_input + pretrained_args.model["layerdrop"] = cfg.layerdrop + + pretrained_args.model["modalities"]["image"][ + "prenet_layerdrop" + ] = cfg.prenet_layerdrop + pretrained_args.model["modalities"]["image"][ + "prenet_dropout" + ] = cfg.prenet_dropout + else: + # not d2v multi + with open_dict(pretrained_args): + pretrained_args.model["drop_path_rate"] = cfg.drop_path_rate + pretrained_args.model["block_dropout"] = cfg.encoder_dropout + pretrained_args.model["attention_dropout"] = cfg.attention_dropout + pretrained_args.model["activation_dropout"] = cfg.activation_dropout + + task = tasks.setup_task(pretrained_args.task) + model = task.build_model(pretrained_args.model, from_checkpoint=True) + + self.d2v_multi = "data2vec_multi" in pretrained_args.model._name + self.linear_classifier = cfg.linear_classifier + + self.model = model + + if state is not None and not cfg.no_pretrained_weights: + interpolate_pos_embed(model, state) + + if "modality_encoders.IMAGE.positional_encoder.pos_embed" in state["model"]: + state["model"][ + "modality_encoders.IMAGE.positional_encoder.positions" + ] = state["model"][ + "modality_encoders.IMAGE.positional_encoder.pos_embed" + ] + del state["model"][ + "modality_encoders.IMAGE.positional_encoder.pos_embed" + ] + if "modality_encoders.IMAGE.encoder_mask" in state["model"]: + del state["model"]["modality_encoders.IMAGE.encoder_mask"] + + model.load_state_dict(state["model"], strict=True) + + if self.d2v_multi: + model.remove_pretraining_modules(modality="image") + else: + model.remove_pretraining_modules() + + if self.linear_classifier: + model.requires_grad_(False) + + self.fc_norm = None + if self.cfg.use_fc_norm: + self.fc_norm = nn.LayerNorm(pretrained_args.model.embed_dim, eps=1e-6) + nn.init.constant_(self.fc_norm.bias, 0) + nn.init.constant_(self.fc_norm.weight, 1.0) + + self.head = nn.Linear(pretrained_args.model.embed_dim, cfg.num_classes) + + nn.init.trunc_normal_(self.head.weight, std=0.02) + nn.init.constant_(self.head.bias, 0) + + self.mixup_fn = None + + if cfg.mixup > 0 or cfg.cutmix > 0: + from timm.data import Mixup + + self.mixup_fn = Mixup( + mixup_alpha=cfg.mixup, + cutmix_alpha=cfg.cutmix, + cutmix_minmax=None, + prob=cfg.mixup_prob, + switch_prob=cfg.mixup_switch_prob, + mode=cfg.mixup_mode, + label_smoothing=cfg.label_smoothing, + num_classes=cfg.num_classes, + ) + + if self.model.norm is not None: + for pn, p in self.model.norm.named_parameters(): + if len(p.shape) == 1 or pn.endswith(".bias"): + p.optim_overrides = {"optimizer": {"weight_decay_scale": 0}} + + if self.fc_norm is not None: + for pn, p in self.fc_norm.named_parameters(): + if len(p.shape) == 1 or pn.endswith(".bias"): + p.optim_overrides = {"optimizer": {"weight_decay_scale": 0}} + + for pn, p in self.head.named_parameters(): + if len(p.shape) == 1 or pn.endswith(".bias"): + p.optim_overrides = {"optimizer": {"weight_decay_scale": 0}} + + if self.d2v_multi: + mod_encs = list(model.modality_encoders.values()) + assert len(mod_encs) == 1, len(mod_encs) + blocks = list(mod_encs[0].context_encoder.blocks) + list(model.blocks) + else: + blocks = model.blocks + + num_layers = len(blocks) + 1 + layer_scales = list( + cfg.layer_decay ** (num_layers - i) for i in range(num_layers + 1) + ) + + if self.d2v_multi: + for n, p in self.model.named_parameters(): + optimizer_override_dict = {} + + if len(p.shape) == 1 or n.endswith(".bias"): + optimizer_override_dict["weight_decay_scale"] = 0 + + p.optim_overrides = {"optimizer": optimizer_override_dict} + + if cfg.layer_decay > 0: + for i, b in enumerate(blocks): + lid = i + 1 + if layer_scales[lid] == 1.0: + continue + + for n, p in b.named_parameters(): + optim_override = getattr(p, "optim_overrides", {}) + if "optimizer" not in optim_override: + optim_override["optimizer"] = {} + + if cfg.no_decay_blocks: + optim_override["optimizer"]["lr_scale"] = layer_scales[lid] + p.optim_overrides = optim_override + else: + optim_override["optimizer"] = { + "lr_scale": layer_scales[lid] + } + p.optim_overrides = optim_override + + else: + for n, p in self.model.named_parameters(): + optimizer_override_dict = {} + layer_id = get_layer_id_for_vit(n, num_layers) + + if len(p.shape) == 1 or n.endswith(".bias"): + optimizer_override_dict["weight_decay_scale"] = 0 + + if cfg.layer_decay > 0: + optimizer_override_dict["lr_scale"] = layer_scales[layer_id] + p.optim_overrides = {"optimizer": optimizer_override_dict} + + @classmethod + def build_model(cls, cfg: MaeImageClassificationConfig, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def forward( + self, + imgs, + labels=None, + ): + if self.training and self.mixup_fn is not None and labels is not None: + imgs, labels = self.mixup_fn(imgs, labels) + + if self.linear_classifier: + with torch.no_grad(): + x = self.model_forward(imgs) + else: + x = self.model_forward(imgs) + + if self.cfg.prediction_mode == PredictionMode.MEAN_POOLING: + x = x.mean(dim=1) + elif self.cfg.prediction_mode == PredictionMode.CLS_TOKEN: + x = x[:, 0] + elif self.cfg.prediction_mode == PredictionMode.LIN_SOFTMAX: + dtype = x.dtype + x = F.logsigmoid(x.float()) + x = torch.logsumexp(x + x, dim=1) - torch.logsumexp(x + 1e-6, dim=1) + x = x.clamp(max=0) + x = x - torch.log(-(torch.expm1(x))) + x = torch.nan_to_num(x, nan=0, posinf=0, neginf=0) + x = x.to(dtype=dtype) + else: + raise Exception(f"unknown prediction mode {self.cfg.prediction_mode.name}") + + if self.fc_norm is not None: + x = self.fc_norm(x) + + x = self.head(x) + + if labels is None: + return x + + if self.training and self.mixup_fn is not None: + loss = -labels * F.log_softmax(x.float(), dim=-1) + else: + loss = F.cross_entropy( + x.float(), + labels, + label_smoothing=self.cfg.label_smoothing if self.training else 0, + reduction="none", + ) + + result = { + "losses": {"regression": loss}, + "sample_size": imgs.size(0), + } + + if not self.training: + with torch.no_grad(): + pred = x.argmax(-1) + correct = (pred == labels).sum() + result["correct"] = correct + + return result + + def model_forward(self, imgs): + if self.d2v_multi: + x = self.model.extract_features( + imgs, + mode="IMAGE", + mask=False, + remove_extra_tokens=( + self.cfg.prediction_mode != PredictionMode.CLS_TOKEN + ), + )["x"] + else: + x = self.model(imgs, predictions_only=True) + if ( + "no_cls" not in self.model.cfg or not self.model.cfg.no_cls + ) and not self.cfg.prediction_mode == PredictionMode.CLS_TOKEN: + x = x[:, 1:] + return x diff --git a/fairseq/examples/data2vec/models/modalities/__init__.py b/fairseq/examples/data2vec/models/modalities/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/data2vec/models/modalities/audio.py b/fairseq/examples/data2vec/models/modalities/audio.py new file mode 100644 index 0000000..80d2857 --- /dev/null +++ b/fairseq/examples/data2vec/models/modalities/audio.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from functools import partial +import torch +import torch.nn as nn +import numpy as np +from dataclasses import dataclass, field +from typing import Callable, Dict, Optional +from fairseq.models.wav2vec import ConvFeatureExtractionModel +from fairseq.modules import ( + LayerNorm, + SamePad, + TransposeLast, +) +from fairseq.tasks import FairseqTask +from .base import D2vModalityConfig, ModalitySpecificEncoder, get_alibi_bias +from .modules import BlockEncoder, Decoder1d +from examples.data2vec.data.modality import Modality + + +@dataclass +class D2vAudioConfig(D2vModalityConfig): + type: Modality = Modality.AUDIO + extractor_mode: str = "layer_norm" + feature_encoder_spec: str = field( + default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", + metadata={ + "help": "string describing convolutional feature extraction layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + conv_pos_width: int = field( + default=95, + metadata={"help": "number of filters for convolutional positional embeddings"}, + ) + conv_pos_groups: int = field( + default=16, + metadata={"help": "number of groups for convolutional positional embedding"}, + ) + conv_pos_depth: int = field( + default=5, + metadata={"help": "depth of positional encoder network"}, + ) + conv_pos_pre_ln: bool = False + + +class AudioEncoder(ModalitySpecificEncoder): + + modality_cfg: D2vAudioConfig + + def __init__( + self, + modality_cfg: D2vAudioConfig, + embed_dim: int, + make_block: Callable[[float], nn.ModuleList], + norm_layer: Callable[[int], nn.LayerNorm], + layer_norm_first: bool, + alibi_biases: Dict, + task: Optional[FairseqTask], + ): + + self.feature_enc_layers = eval(modality_cfg.feature_encoder_spec) + feature_embed_dim = self.feature_enc_layers[-1][0] + + local_encoder = ConvFeatureExtractionModel( + conv_layers=self.feature_enc_layers, + dropout=0.0, + mode=modality_cfg.extractor_mode, + conv_bias=False, + ) + + project_features = nn.Sequential( + TransposeLast(), + nn.LayerNorm(feature_embed_dim), + nn.Linear(feature_embed_dim, embed_dim), + ) + + num_pos_layers = modality_cfg.conv_pos_depth + k = max(3, modality_cfg.conv_pos_width // num_pos_layers) + + positional_encoder = nn.Sequential( + TransposeLast(), + *[ + nn.Sequential( + nn.Conv1d( + embed_dim, + embed_dim, + kernel_size=k, + padding=k // 2, + groups=modality_cfg.conv_pos_groups, + ), + SamePad(k), + TransposeLast(), + LayerNorm(embed_dim, elementwise_affine=False), + TransposeLast(), + nn.GELU(), + ) + for _ in range(num_pos_layers) + ], + TransposeLast(), + ) + + if modality_cfg.conv_pos_pre_ln: + positional_encoder = nn.Sequential(LayerNorm(embed_dim), positional_encoder) + + dpr = np.linspace( + modality_cfg.start_drop_path_rate, + modality_cfg.end_drop_path_rate, + modality_cfg.prenet_depth, + ) + context_encoder = BlockEncoder( + nn.ModuleList(make_block(dpr[i]) for i in range(modality_cfg.prenet_depth)), + norm_layer(embed_dim) if not layer_norm_first else None, + layer_norm_first, + modality_cfg.prenet_layerdrop, + modality_cfg.prenet_dropout, + ) + + decoder = ( + Decoder1d(modality_cfg.decoder, embed_dim) + if modality_cfg.decoder is not None + else None + ) + + alibi_bias_fn = partial(get_alibi_bias, alibi_biases=alibi_biases) + + super().__init__( + modality_cfg=modality_cfg, + embed_dim=embed_dim, + local_encoder=local_encoder, + project_features=project_features, + fixed_positional_encoder=None, + relative_positional_encoder=positional_encoder, + context_encoder=context_encoder, + decoder=decoder, + get_alibi_bias=alibi_bias_fn, + ) + + def convert_padding_mask(self, x, padding_mask): + def get_feat_extract_output_lengths(input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + return torch.floor((input_length - kernel_size) / stride + 1) + + for i in range(len(self.feature_enc_layers)): + input_lengths = _conv_out_length( + input_lengths, + self.feature_enc_layers[i][1], + self.feature_enc_layers[i][2], + ) + + return input_lengths.to(torch.long) + + if padding_mask is not None: + input_lengths = (1 - padding_mask.long()).sum(-1) + # apply conv formula to get real output_lengths + output_lengths = get_feat_extract_output_lengths(input_lengths) + + if padding_mask.any(): + padding_mask = torch.zeros(x.shape[:2], dtype=x.dtype, device=x.device) + + # these two operations makes sure that all values + # before the output lengths indices are attended to + padding_mask[ + ( + torch.arange(padding_mask.shape[0], device=padding_mask.device), + output_lengths - 1, + ) + ] = 1 + padding_mask = ( + 1 - padding_mask.flip([-1]).cumsum(-1).flip([-1]) + ).bool() + else: + padding_mask = torch.zeros( + x.shape[:2], dtype=torch.bool, device=x.device + ) + + return padding_mask + + def reset_parameters(self): + super().reset_parameters() + for mod in self.project_features.children(): + if isinstance(mod, nn.Linear): + mod.reset_parameters() + if self.decoder is not None: + self.decoder.reset_parameters() diff --git a/fairseq/examples/data2vec/models/modalities/base.py b/fairseq/examples/data2vec/models/modalities/base.py new file mode 100644 index 0000000..642cc84 --- /dev/null +++ b/fairseq/examples/data2vec/models/modalities/base.py @@ -0,0 +1,684 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from collections import namedtuple +from dataclasses import dataclass +from functools import partial +from omegaconf import MISSING, II +from typing import Optional, Callable +from fairseq.data.data_utils import compute_mask_indices +from fairseq.modules import GradMultiply +from fairseq.utils import index_put +from examples.data2vec.data.modality import Modality +from .modules import D2vDecoderConfig + +logger = logging.getLogger(__name__) + + +@dataclass +class D2vModalityConfig: + type: Modality = MISSING + prenet_depth: int = 4 + prenet_layerdrop: float = 0 + prenet_dropout: float = 0 + start_drop_path_rate: float = 0 + end_drop_path_rate: float = 0 + + num_extra_tokens: int = 0 + init_extra_token_zero: bool = True + + mask_noise_std: float = 0.01 + mask_prob_min: Optional[float] = None + mask_prob: float = 0.7 + inverse_mask: bool = False + mask_prob_adjust: float = 0 + keep_masked_pct: float = 0 + + mask_length: int = 5 + add_masks: bool = False + remove_masks: bool = False + mask_dropout: float = 0.0 + encoder_zero_mask: bool = True + + mask_channel_prob: float = 0.0 + mask_channel_length: int = 64 + + ema_local_encoder: bool = False # used in data2vec_multi + local_grad_mult: float = 1.0 + + use_alibi_encoder: bool = False + alibi_scale: float = 1.0 + learned_alibi: bool = False + alibi_max_pos: Optional[int] = None + learned_alibi_scale: bool = False + learned_alibi_scale_per_head: bool = False + learned_alibi_scale_per_layer: bool = False + + num_alibi_heads: int = II("model.num_heads") + model_depth: int = II("model.depth") + + decoder: Optional[D2vDecoderConfig] = D2vDecoderConfig() + + +MaskSeed = namedtuple("MaskSeed", ["seed", "update", "ids"]) +MaskInfo = namedtuple("MaskInfo", ["x_unmasked", "mask", "ids_restore", "ids_keep"]) + + +class ModalitySpecificEncoder(nn.Module): + def __init__( + self, + modality_cfg: D2vModalityConfig, + embed_dim: int, + local_encoder: nn.Module, + project_features: nn.Module, + fixed_positional_encoder: Optional[nn.Module], + relative_positional_encoder: Optional[nn.Module], + context_encoder: nn.Module, + decoder: nn.Module, + get_alibi_bias: Optional[Callable[[int, int, str, str], torch.Tensor]], + ): + super().__init__() + + self.modality_cfg = modality_cfg + self.local_encoder = local_encoder + self.project_features = project_features + self.fixed_positional_encoder = fixed_positional_encoder + self.relative_positional_encoder = relative_positional_encoder + self.context_encoder = context_encoder + + self.decoder = decoder + self.get_alibi_bias = get_alibi_bias if modality_cfg.use_alibi_encoder else None + + self.local_grad_mult = self.modality_cfg.local_grad_mult + + self.extra_tokens = None + if modality_cfg.num_extra_tokens > 0: + self.extra_tokens = nn.Parameter( + torch.zeros(1, modality_cfg.num_extra_tokens, embed_dim) + ) + if not modality_cfg.init_extra_token_zero: + nn.init.normal_(self.extra_tokens) + elif self.extra_tokens.size(1) > 1: + nn.init.normal_(self.extra_tokens[:, 1:]) + + self.alibi_scale = None + if self.get_alibi_bias is not None: + self.alibi_scale = nn.Parameter( + torch.full( + ( + (modality_cfg.prenet_depth + modality_cfg.model_depth) + if modality_cfg.learned_alibi_scale_per_layer + else 1, + 1, + self.modality_cfg.num_alibi_heads + if modality_cfg.learned_alibi_scale_per_head + else 1, + 1, + 1, + ), + modality_cfg.alibi_scale, + dtype=torch.float, + ), + requires_grad=modality_cfg.learned_alibi_scale, + ) + + if modality_cfg.learned_alibi and self.get_alibi_bias is not None: + assert modality_cfg.alibi_max_pos is not None + alibi_bias = self.get_alibi_bias( + batch_size=1, + time_steps=modality_cfg.alibi_max_pos, + heads=modality_cfg.num_alibi_heads, + scale=1.0, + dtype=torch.float, + device="cpu", + ) + self.alibi_bias = nn.Parameter(alibi_bias) + self.get_alibi_bias = partial( + _learned_alibi_bias, alibi_bias=self.alibi_bias + ) + + def upgrade_state_dict_named(self, state_dict, name): + k = f"{name}.alibi_scale" + if k in state_dict and state_dict[k].dim() == 4: + state_dict[k] = state_dict[k].unsqueeze(0) + + return state_dict + + def convert_padding_mask(self, x, padding_mask): + return padding_mask + + def decoder_input(self, x, mask_info: MaskInfo): + inp_drop = self.modality_cfg.decoder.input_dropout + if inp_drop > 0: + x = F.dropout(x, inp_drop, training=self.training, inplace=True) + + num_extra = self.modality_cfg.num_extra_tokens + + if mask_info is not None: + num_masked = mask_info.ids_restore.shape[1] - x.shape[1] + num_extra + + mask_tokens = x.new_empty( + x.size(0), + num_masked, + x.size(-1), + ).normal_(0, self.modality_cfg.mask_noise_std) + + x_ = torch.cat([x[:, num_extra:], mask_tokens], dim=1) + x = torch.gather(x_, dim=1, index=mask_info.ids_restore) + + if self.modality_cfg.decoder.add_positions_masked: + assert self.fixed_positional_encoder is not None + pos = self.fixed_positional_encoder(x, None) + x = x + (pos * mask_info.mask.unsqueeze(-1)) + else: + x = x[:, num_extra:] + + if self.modality_cfg.decoder.add_positions_all: + assert self.fixed_positional_encoder is not None + x = x + self.fixed_positional_encoder(x, None) + + return x, mask_info + + def local_features(self, features): + if self.local_grad_mult > 0: + if self.local_grad_mult == 1.0: + x = self.local_encoder(features) + else: + x = GradMultiply.apply( + self.local_encoder(features), self.local_grad_mult + ) + else: + with torch.no_grad(): + x = self.local_encoder(features) + + x = self.project_features(x) + return x + + def contextualized_features( + self, + x, + padding_mask, + mask, + remove_masked, + clone_batch: int = 1, + mask_seeds: Optional[torch.Tensor] = None, + precomputed_mask=None, + ): + + if padding_mask is not None: + padding_mask = self.convert_padding_mask(x, padding_mask) + + local_features = x + if mask and clone_batch == 1: + local_features = local_features.clone() + + orig_B, orig_T, _ = x.shape + pre_mask_B = orig_B + mask_info = None + + x_pos = None + if self.fixed_positional_encoder is not None: + x = x + self.fixed_positional_encoder(x, padding_mask) + + if mask: + if clone_batch > 1: + x = x.repeat_interleave(clone_batch, 0) + if mask_seeds is not None: + clone_hash = [ + int(hash((mask_seeds.seed, ind)) % 1e10) + for ind in range(clone_batch - 1) + ] + clone_hash = torch.tensor([0] + clone_hash).long().view(1, -1) + + id = mask_seeds.ids + id = id.repeat_interleave(clone_batch, 0) + id = id.view(-1, clone_batch) + clone_hash.to(id) + id = id.view(-1) + mask_seeds = MaskSeed( + seed=mask_seeds.seed, update=mask_seeds.update, ids=id + ) + if padding_mask is not None: + padding_mask = padding_mask.repeat_interleave(clone_batch, 0) + + x, mask_info = self.compute_mask( + x, + padding_mask, + mask_seed=mask_seeds, + apply=self.relative_positional_encoder is not None or not remove_masked, + precomputed_mask=precomputed_mask, + ) + + if self.relative_positional_encoder is not None: + x_pos = self.relative_positional_encoder(x) + + masked_padding_mask = padding_mask + if mask and remove_masked: + x = mask_info.x_unmasked + if x_pos is not None: + x = x + gather_unmasked(x_pos, mask_info) + + if padding_mask is not None and padding_mask.any(): + masked_padding_mask = gather_unmasked_mask(padding_mask, mask_info) + if not masked_padding_mask.any(): + masked_padding_mask = None + else: + masked_padding_mask = None + + elif x_pos is not None: + x = x + x_pos + + alibi_bias = None + alibi_scale = self.alibi_scale + + if self.get_alibi_bias is not None: + alibi_bias = self.get_alibi_bias( + batch_size=pre_mask_B, + time_steps=orig_T, + heads=self.modality_cfg.num_alibi_heads, + dtype=torch.float32, + device=x.device, + ) + + if alibi_scale is not None: + alibi_scale = alibi_scale.clamp_min(0) + if alibi_scale.size(0) == 1: + alibi_bias = alibi_bias * alibi_scale.squeeze(0).type_as(alibi_bias) + alibi_scale = None + + if clone_batch > 1: + alibi_bias = alibi_bias.repeat_interleave(clone_batch, 0) + + if mask_info is not None and remove_masked: + alibi_bias = masked_alibi(alibi_bias, mask_info) + + if self.extra_tokens is not None: + num = self.extra_tokens.size(1) + x = torch.cat([self.extra_tokens.expand(x.size(0), -1, -1), x], dim=1) + if masked_padding_mask is not None: + # B x T + masked_padding_mask = F.pad(masked_padding_mask, (num, 0)) + if alibi_bias is not None: + # B x H x T x T + alibi_bias = F.pad(alibi_bias, (num, 0, num, 0)) + + x = self.context_encoder( + x, + masked_padding_mask, + alibi_bias, + alibi_scale[: self.modality_cfg.prenet_depth] + if alibi_scale is not None + else None, + ) + + return { + "x": x, + "local_features": local_features, + "padding_mask": masked_padding_mask, + "alibi_bias": alibi_bias, + "alibi_scale": alibi_scale[self.modality_cfg.prenet_depth :] + if alibi_scale is not None and alibi_scale.size(0) > 1 + else alibi_scale, + "encoder_mask": mask_info, + } + + def forward( + self, + features, + padding_mask, + mask: bool, + remove_masked: bool, + clone_batch: int = 1, + mask_seeds: Optional[torch.Tensor] = None, + precomputed_mask=None, + ): + x = self.local_features(features) + return self.contextualized_features( + x, + padding_mask, + mask, + remove_masked, + clone_batch, + mask_seeds, + precomputed_mask, + ) + + def reset_parameters(self): + pass + + def compute_mask( + self, + x, + padding_mask, + mask_seed: Optional[MaskSeed], + apply, + precomputed_mask, + ): + if precomputed_mask is not None: + mask = precomputed_mask + mask_info = self.make_maskinfo(x, mask) + else: + B, T, C = x.shape + cfg = self.modality_cfg + + mask_prob = cfg.mask_prob + + if ( + cfg.mask_prob_min is not None + and cfg.mask_prob_min >= 0 + and cfg.mask_prob_min < mask_prob + ): + mask_prob = np.random.uniform(cfg.mask_prob_min, mask_prob) + + if mask_prob > 0: + if cfg.mask_length == 1: + mask_info = random_masking(x, mask_prob, mask_seed) + else: + if self.modality_cfg.inverse_mask: + mask_prob = 1 - mask_prob + + mask = compute_mask_indices( + (B, T), + padding_mask, + mask_prob, + cfg.mask_length, + min_masks=1, + require_same_masks=True, + mask_dropout=cfg.mask_dropout, + add_masks=cfg.add_masks, + seed=mask_seed.seed if mask_seed is not None else None, + epoch=mask_seed.update if mask_seed is not None else None, + indices=mask_seed.ids if mask_seed is not None else None, + ) + + mask = torch.from_numpy(mask).to(device=x.device) + if self.modality_cfg.inverse_mask: + mask = 1 - mask + mask_info = self.make_maskinfo(x, mask) + else: + mask_info = None + + if apply: + x = self.apply_mask(x, mask_info) + + return x, mask_info + + def make_maskinfo(self, x, mask, shape=None): + if shape is None: + B, T, D = x.shape + else: + B, T, D = shape + + mask = mask.to(torch.uint8) + ids_shuffle = mask.argsort(dim=1) + ids_restore = ids_shuffle.argsort(dim=1).unsqueeze(-1).expand(-1, -1, D) + + len_keep = T - mask[0].sum() + if self.modality_cfg.keep_masked_pct > 0: + len_keep += round((T - int(len_keep)) * self.modality_cfg.keep_masked_pct) + + ids_keep = ids_shuffle[:, :len_keep] + + if shape is not None: + x_unmasked = None + else: + ids_keep = ids_keep.unsqueeze(-1).expand(-1, -1, D) + x_unmasked = torch.gather(x, dim=1, index=ids_keep) + + mask_info = MaskInfo( + x_unmasked=x_unmasked, + mask=mask, + ids_restore=ids_restore, + ids_keep=ids_keep, + ) + return mask_info + + def apply_mask(self, x, mask_info): + cfg = self.modality_cfg + B, T, C = x.shape + + if mask_info is not None: + mask = mask_info.mask + if cfg.encoder_zero_mask: + x = x * (1 - mask.type_as(x).unsqueeze(-1)) + else: + num_masks = mask.sum().item() + masks = x.new_empty(num_masks, x.size(-1)).normal_( + 0, cfg.mask_noise_std + ) + x = index_put(x, mask, masks) + if cfg.mask_channel_prob > 0: + mask_channel = compute_mask_indices( + (B, C), + None, + cfg.mask_channel_prob, + cfg.mask_channel_length, + ) + mask_channel = ( + torch.from_numpy(mask_channel) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x = index_put(x, mask_channel, 0) + return x + + def remove_pretraining_modules(self, keep_decoder=False): + if not keep_decoder: + self.decoder = None + + +def get_annealed_rate(start, end, curr_step, total_steps): + if curr_step >= total_steps: + return end + r = end - start + pct_remaining = 1 - curr_step / total_steps + return end - r * pct_remaining + + +# adapted from MAE +def random_masking(x, mask_ratio, mask_seed: Optional[MaskSeed]): + N, L, D = x.shape # batch, length, dim + len_keep = int(L * (1 - mask_ratio)) + + generator = None + if mask_seed is not None: + seed = int( + hash((mask_seed.seed, mask_seed.update, mask_seed.ids.sum().item())) % 1e6 + ) + generator = torch.Generator(device=x.device) + generator.manual_seed(seed) + + noise = torch.rand(N, L, generator=generator, device=x.device) # noise in [0, 1] + + # sort noise for each sample + ids_shuffle = noise.argsort(dim=1) # ascend: small is keep, large is remove + ids_restore = ids_shuffle.argsort(dim=1) + + # keep the first subset + ids_keep = ids_shuffle[:, :len_keep] + ids_keep = ids_keep.unsqueeze(-1).expand(-1, -1, D) + x_unmasked = torch.gather(x, dim=1, index=ids_keep) + + # generate the binary mask: 0 is keep, 1 is remove + mask = torch.ones([N, L], dtype=x.dtype, device=x.device) + mask[:, :len_keep] = 0 + # unshuffle to get the binary mask + mask = torch.gather(mask, dim=1, index=ids_restore) + + ids_restore = ids_restore.unsqueeze(-1).expand(-1, -1, D) + + return MaskInfo( + x_unmasked=x_unmasked, mask=mask, ids_restore=ids_restore, ids_keep=ids_keep + ) + + +def gather_unmasked(x: torch.Tensor, mask_info: MaskInfo) -> torch.Tensor: + return torch.gather( + x, + dim=1, + index=mask_info.ids_keep, + ) + + +def gather_unmasked_mask(x: torch.Tensor, mask_info: MaskInfo) -> torch.Tensor: + return torch.gather( + x, + dim=1, + index=mask_info.ids_keep[..., 0], # ignore the feature dimension + ) + + +def get_alibi( + max_positions: int, + attention_heads: int, + dims: int = 1, + distance: str = "manhattan", +): + def get_slopes(n): + def get_slopes_power_of_2(n): + start = 2 ** (-(2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio**i for i in range(n)] + + # In the paper, we only train models that have 2^a heads for some + # a. This function has some good properties that only occur when + # the input is a power of 2. To maintain that even when the number + # of heads is not a power of 2, we use this workaround. + if math.log2(n).is_integer(): + return get_slopes_power_of_2(n) + else: + closest_power_of_2 = 2 ** math.floor(math.log2(n)) + return ( + get_slopes_power_of_2(closest_power_of_2) + + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2] + ) + + maxpos = max_positions + attn_heads = attention_heads + slopes = torch.Tensor(get_slopes(attn_heads)) + + if dims == 1: + # prepare alibi position linear bias. Note that wav2vec2 is non + # autoregressive model so we want a symmetric mask with 0 on the + # diagonal and other wise linear decreasing valuees + pos_bias = ( + torch.abs( + torch.arange(maxpos).unsqueeze(0) - torch.arange(maxpos).unsqueeze(1) + ) + * -1 + ) + elif dims == 2: + if distance == "manhattan": + df = lambda x1, y1, x2, y2: abs(x1 - x2) + abs(y1 - y2) + elif distance == "euclidean": + df = lambda x1, y1, x2, y2: math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) + + n = math.sqrt(max_positions) + assert n.is_integer(), n + n = int(n) + + pos_bias = torch.zeros((max_positions, max_positions)) + + for i in range(n): + for j in range(n): + for k in range(n): + for l in range(n): + new_x = i * n + j + new_y = k * n + l + pos_bias[new_x, new_y] = -df(i, j, k, l) + + else: + raise Exception(f"unsupported number of alibi dims: {dims}") + + alibi_bias = slopes.unsqueeze(1).unsqueeze(1) * pos_bias.unsqueeze(0).expand( + attn_heads, -1, -1 + ) + + return alibi_bias + + +def get_alibi_bias( + alibi_biases, + batch_size, + time_steps, + heads, + dtype, + device, + dims=1, + distance="manhattan", +): + cache_key = f"{dims}_{heads}_{distance}" + + buffered = alibi_biases.get(cache_key, None) + + target_size = heads * batch_size + if ( + buffered is None + or buffered.size(0) < target_size + or buffered.size(1) < time_steps + or buffered.dtype != dtype + or buffered.device != device + ): + bt = max(time_steps, buffered.size(1) if buffered is not None else 0) + bn = max(target_size, buffered.size(0) if buffered is not None else 0) // heads + + buffered = ( + get_alibi(bt, heads, dims=dims, distance=distance) + .to(dtype=dtype, device=device) + .repeat(bn, 1, 1) + ) + + alibi_biases[cache_key] = buffered + + b = buffered[:target_size, :time_steps, :time_steps] + b = b.view(batch_size, heads, time_steps, time_steps) + return b + + +def _learned_alibi_bias( + alibi_bias, + batch_size, + time_steps, + heads, + scale, + dtype, + device, +): + assert alibi_bias.size(1) == heads, alibi_bias.shape + assert alibi_bias.dtype == dtype, alibi_bias.dtype + assert alibi_bias.device == device, alibi_bias.device + + if alibi_bias.size(-1) < time_steps: + psz = math.ceil((time_steps - alibi_bias.size(-1)) / 2) + alibi_bias = F.pad(alibi_bias, (psz, psz, psz, psz), mode="replicate") + + alibi_bias = alibi_bias.expand(batch_size, -1, -1, -1) * scale + return alibi_bias[..., :time_steps, :time_steps] + + +def masked_alibi(alibi_bias, mask_info): + H = alibi_bias.size(1) + + orig_bias = alibi_bias + + index = mask_info.ids_keep.unsqueeze(1)[..., 0].unsqueeze(-1) + alibi_bias = torch.gather( + orig_bias, + dim=-2, + index=index.expand(-1, H, -1, mask_info.ids_restore.size(1)), + ) + alibi_bias = torch.gather( + alibi_bias, + dim=-1, + index=index.transpose(-1, -2).expand(-1, H, alibi_bias.size(-2), -1), + ) + + return alibi_bias diff --git a/fairseq/examples/data2vec/models/modalities/images.py b/fairseq/examples/data2vec/models/modalities/images.py new file mode 100644 index 0000000..a6b738c --- /dev/null +++ b/fairseq/examples/data2vec/models/modalities/images.py @@ -0,0 +1,256 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from functools import partial +from dataclasses import dataclass +from typing import Callable, Dict, Optional +from timm.models.layers import to_2tuple +from fairseq.tasks import FairseqTask +from examples.data2vec.models.mae import get_2d_sincos_pos_embed, PatchEmbed +from .base import ( + D2vModalityConfig, + ModalitySpecificEncoder, + get_alibi_bias, + MaskSeed, +) +from .modules import ( + BlockEncoder, + Decoder2d, + FixedPositionalEncoder, + TransformerDecoder, + EncDecTransformerDecoder, +) +from examples.data2vec.data.modality import Modality + + +@dataclass +class D2vImageConfig(D2vModalityConfig): + type: Modality = Modality.IMAGE + + input_size: int = 224 + in_chans: int = 3 + patch_size: int = 16 + embed_dim: int = 768 + + alibi_dims: int = 2 + alibi_distance: str = "manhattan" + + fixed_positions: bool = True + + transformer_decoder: bool = False + enc_dec_transformer: bool = False + + +class ImageEncoder(ModalitySpecificEncoder): + + modality_cfg: D2vImageConfig + + def __init__( + self, + modality_cfg: D2vImageConfig, + embed_dim: int, + make_block: Callable[[float, Optional[int], Optional[int]], nn.ModuleList], + norm_layer: Callable[[int], nn.LayerNorm], + layer_norm_first: bool, + alibi_biases: Dict, + task: Optional[FairseqTask], + ): + + img_size = to_2tuple(modality_cfg.input_size) + patch_size = to_2tuple(modality_cfg.patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + + local_encoder = PatchEmbed( + modality_cfg.input_size, + modality_cfg.patch_size, + modality_cfg.in_chans, + modality_cfg.embed_dim, + ) + + w = local_encoder.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + if modality_cfg.embed_dim != embed_dim: + local_encoder = nn.Sequential( + local_encoder, + nn.Linear(modality_cfg.embed_dim, embed_dim), + ) + + project_features = nn.Identity() + + pos_embed = nn.Parameter( + torch.zeros(1, num_patches, embed_dim), requires_grad=False + ) + + side_n = int(num_patches ** 0.5) + + emb = get_2d_sincos_pos_embed( + pos_embed.shape[-1], + side_n, + cls_token=False, + ) + pos_embed.data.copy_(torch.from_numpy(emb).float().unsqueeze(0)) + fixed_positional_encoder = ( + FixedPositionalEncoder(pos_embed) if modality_cfg.fixed_positions else None + ) + + dpr = np.linspace( + modality_cfg.start_drop_path_rate, + modality_cfg.end_drop_path_rate, + modality_cfg.prenet_depth, + ) + + context_encoder = BlockEncoder( + nn.ModuleList(make_block(dpr[i]) for i in range(modality_cfg.prenet_depth)), + norm_layer(embed_dim) if not layer_norm_first else None, + layer_norm_first, + modality_cfg.prenet_layerdrop, + modality_cfg.prenet_dropout, + ) + + if modality_cfg.transformer_decoder: + if modality_cfg.enc_dec_transformer: + decoder = EncDecTransformerDecoder(modality_cfg.decoder, embed_dim) + else: + dec_enc = BlockEncoder( + nn.ModuleList( + make_block(0, modality_cfg.decoder.decoder_dim, 8) + for _ in range(modality_cfg.decoder.decoder_layers) + ), + None, + layer_norm_first, + 0, + 0, + ) + decoder = TransformerDecoder(modality_cfg.decoder, embed_dim, dec_enc) + else: + decoder = ( + Decoder2d(modality_cfg.decoder, embed_dim, side_n, side_n) + if modality_cfg.decoder is not None + else None + ) + + alibi_bias_fn = partial( + get_alibi_bias, + alibi_biases=alibi_biases, + heads=modality_cfg.num_alibi_heads, + dims=modality_cfg.alibi_dims, + distance=modality_cfg.alibi_distance, + ) + + super().__init__( + modality_cfg=modality_cfg, + embed_dim=embed_dim, + local_encoder=local_encoder, + project_features=project_features, + fixed_positional_encoder=fixed_positional_encoder, + relative_positional_encoder=None, + context_encoder=context_encoder, + decoder=decoder, + get_alibi_bias=alibi_bias_fn, + ) + + def reset_parameters(self): + super().reset_parameters() + if self.decoder is not None: + self.decoder.reset_parameters() + + @torch.no_grad() + def patchify(self, imgs): + """ + imgs: (N, 3, H, W) + x: (N, L, patch_size**2 *3) + """ + p = self.modality_cfg.patch_size + h = w = imgs.shape[2] // p + x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p)) + x = torch.einsum("nchpwq->nhwpqc", x) + x = x.reshape(shape=(imgs.shape[0], h * w, p ** 2 * 3)) + + return x + + @torch.no_grad() + def unpatchify(self, x): + """ + x: (N, L, patch_size**2 *3) + imgs: (N, 3, H, W) + """ + p = self.modality_cfg.patch_size + h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, 3)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p)) + return imgs + + def compute_mask( + self, + x, + padding_mask, + mask_seed: Optional[MaskSeed], + apply, + shape=None, + precomputed_mask=None, + ): + mlen = self.modality_cfg.mask_length + if mlen <= 1: + return super().compute_mask( + x, padding_mask, mask_seed, apply, precomputed_mask + ) + + if precomputed_mask is not None: + mask = precomputed_mask + else: + from fairseq.data.data_utils import compute_block_mask_2d + + if shape is not None: + B, L, D = shape + else: + B, L, D = x.shape + + mask = compute_block_mask_2d( + shape=(B, L), + mask_prob=self.modality_cfg.mask_prob, + mask_length=self.modality_cfg.mask_length, + mask_prob_adjust=self.modality_cfg.mask_prob_adjust, + inverse_mask=self.modality_cfg.inverse_mask, + require_same_masks=True, + mask_dropout=self.modality_cfg.mask_dropout, + ) + + mask_info = self.make_maskinfo(x, mask, shape) + if apply: + x = self.apply_mask(x, mask_info) + + return x, mask_info + + def decoder_input(self, x, mask_info): + if ( + not self.modality_cfg.transformer_decoder + or not self.modality_cfg.enc_dec_transformer + ): + return super().decoder_input(x, mask_info) + + inp_drop = self.modality_cfg.decoder.input_dropout + if inp_drop > 0: + x = F.dropout(x, inp_drop, training=self.training, inplace=True) + + kv = x[:, self.modality_cfg.num_extra_tokens :] + + assert self.fixed_positional_encoder is not None + pos = self.fixed_positional_encoder(x, None).expand(x.size(0), -1, -1) + + mask = mask_info.mask.bool() + if self.modality_cfg.decoder.add_positions_all: + kv = kv + pos[~mask].view(kv.shape) + + q = pos[mask].view(x.size(0), -1, x.size(-1)) + + return q, kv diff --git a/fairseq/examples/data2vec/models/modalities/modules.py b/fairseq/examples/data2vec/models/modalities/modules.py new file mode 100644 index 0000000..a4e1a4e --- /dev/null +++ b/fairseq/examples/data2vec/models/modalities/modules.py @@ -0,0 +1,589 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from dataclasses import dataclass +from fairseq.modules import ( + LayerNorm, + SamePad, + SamePad2d, + TransposeLast, +) + + +@dataclass +class D2vDecoderConfig: + decoder_dim: int = 384 + decoder_groups: int = 16 + decoder_kernel: int = 5 + decoder_layers: int = 5 + input_dropout: float = 0.1 + + add_positions_masked: bool = False + add_positions_all: bool = False + + decoder_residual: bool = True + projection_layers: int = 1 + projection_ratio: float = 2.0 + + +class FixedPositionalEncoder(nn.Module): + def __init__(self, pos_embed): + super().__init__() + self.positions = pos_embed + + def forward(self, x, padding_mask): + return self.positions + + +class TextFeatPositionalEncoder(nn.Module): + """ + Original encoder expects (B, T) long input. This module wraps it to take + local_encoder output which are (B, T, D) float tensors + """ + + def __init__(self, pos_encoder): + super().__init__() + self.pos_encoder = pos_encoder + + def forward(self, x, padding_mask): + # assume padded token embeddings are 0s + # TODO: consider using padding_mask as input + return self.pos_encoder(x[..., 0]) + + +class BlockEncoder(nn.Module): + def __init__(self, blocks, norm_layer, layer_norm_first, layerdrop, dropout): + super().__init__() + self.blocks = blocks + self.norm = norm_layer + self.layer_norm_first = layer_norm_first + self.layerdrop = layerdrop + self.dropout = nn.Dropout(dropout, inplace=True) + + def forward(self, x, padding_mask, alibi_bias, alibi_scale): + if self.norm is not None and not self.layer_norm_first: + x = self.norm(x) + + x = self.dropout(x) + + for i, blk in enumerate(self.blocks): + if ( + not self.training + or self.layerdrop == 0 + or (np.random.random() > self.layerdrop) + ): + ab = alibi_bias + if ab is not None and alibi_scale is not None: + scale = ( + alibi_scale[i] + if alibi_scale.size(0) > 1 + else alibi_scale.squeeze(0) + ) + ab = ab * scale.type_as(ab) + x, _ = blk(x, padding_mask, ab) + + if self.norm is not None and self.layer_norm_first: + x = self.norm(x) + + return x + + +class DecoderBase(nn.Module): + decoder_cfg: D2vDecoderConfig + + def __init__(self, cfg: D2vDecoderConfig): + super().__init__() + + self.decoder_cfg = cfg + + def reset_parameters(self): + for mod in self.proj.modules(): + if isinstance(mod, nn.Linear): + mod.reset_parameters() + + def add_residual(self, x, residual, i, mask_info): + if ( + residual is None + or not self.decoder_cfg.decoder_residual + or residual.size(1) != x.size(1) + ): + return x + + ret = x + residual + + return ret + + +class Decoder1d(DecoderBase): + def __init__(self, cfg: D2vDecoderConfig, input_dim): + super().__init__(cfg) + + def make_block(in_dim): + block = [ + nn.Conv1d( + in_dim, + cfg.decoder_dim, + kernel_size=cfg.decoder_kernel, + padding=cfg.decoder_kernel // 2, + groups=cfg.decoder_groups, + ), + SamePad(cfg.decoder_kernel), + TransposeLast(), + LayerNorm(cfg.decoder_dim, elementwise_affine=False), + TransposeLast(), + nn.GELU(), + ] + + return nn.Sequential(*block) + + self.blocks = nn.Sequential( + *[ + make_block(input_dim if i == 0 else cfg.decoder_dim) + for i in range(cfg.decoder_layers) + ] + ) + + projs = [] + curr_dim = cfg.decoder_dim + for i in range(cfg.projection_layers - 1): + next_dim = int(curr_dim * cfg.projection_ratio) if i == 0 else curr_dim + projs.append(nn.Linear(curr_dim, next_dim)) + projs.append(nn.GELU()) + curr_dim = next_dim + projs.append(nn.Linear(curr_dim, input_dim)) + if len(projs) == 1: + self.proj = projs[0] + else: + self.proj = nn.Sequential(*projs) + + def forward(self, x, mask_info): + + x = x.transpose(1, 2) + + residual = x + + for i, layer in enumerate(self.blocks): + x = layer(x) + x = self.add_residual(x, residual, i, mask_info) + residual = x + + x = x.transpose(1, 2) + x = self.proj(x) + return x + + +class Decoder2d(DecoderBase): + def __init__(self, cfg: D2vDecoderConfig, input_dim, h_size, w_size): + super().__init__(cfg) + + self.h_size = h_size + self.w_size = w_size + + def make_block(in_dim): + block = [ + nn.Conv2d( + in_dim, + cfg.decoder_dim, + kernel_size=cfg.decoder_kernel, + padding=cfg.decoder_kernel // 2, + groups=cfg.decoder_groups, + ), + SamePad2d(cfg.decoder_kernel), + TransposeLast(tranpose_dim=-3), + LayerNorm(cfg.decoder_dim, elementwise_affine=False), + TransposeLast(tranpose_dim=-3), + nn.GELU(), + ] + + return nn.Sequential(*block) + + self.blocks = nn.Sequential( + *[ + make_block(input_dim if i == 0 else cfg.decoder_dim) + for i in range(cfg.decoder_layers) + ] + ) + + self.proj = nn.Linear(cfg.decoder_dim, input_dim) + + def forward(self, x, mask_info): + B, T, C = x.shape + + x = x.transpose(1, 2).reshape(B, C, self.h_size, self.w_size) + + residual = x + + for i, layer in enumerate(self.blocks): + x = layer(x) + x = self.add_residual(x, residual, i, mask_info) + residual = x + + x = x.reshape(B, -1, T).transpose(1, 2) + x = self.proj(x) + return x + + +class TransformerDecoder(nn.Module): + decoder_cfg: D2vDecoderConfig + + def __init__(self, cfg: D2vDecoderConfig, input_dim, encoder): + super().__init__() + + self.decoder_cfg = cfg + + self.input_proj = nn.Linear(input_dim, cfg.decoder_dim) + + self.encoder = encoder + + self.proj = nn.Linear(cfg.decoder_dim, input_dim) + + def reset_parameters(self): + from fairseq.modules.transformer_sentence_encoder import init_bert_params + + self.apply(init_bert_params) + + def forward(self, x, mask_info): + x = self.input_proj(x) + x = self.encoder(x, None, None, 1) + x = self.proj(x) + return x + + +class AltBlock(nn.Module): + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + mlp_drop=0.0, + post_mlp_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=True, + ffn_targets=False, + cosine_attention=False, + ): + super().__init__() + + self.layer_norm_first = layer_norm_first + self.ffn_targets = ffn_targets + + from timm.models.vision_transformer import DropPath, Mlp + + self.norm1 = norm_layer(dim) + self.attn = AltAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + cosine_attention=cosine_attention, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=mlp_drop, + ) + self.post_mlp_dropout = nn.Dropout(post_mlp_drop, inplace=False) + + def forward(self, x, padding_mask=None, alibi_bias=None): + if self.layer_norm_first: + x = x + self.drop_path(self.attn(self.norm1(x), padding_mask, alibi_bias)) + r = x = self.mlp(self.norm2(x)) + t = x + x = r + self.drop_path(self.post_mlp_dropout(x)) + if not self.ffn_targets: + t = x + else: + x = x + self.drop_path(self.attn(x, padding_mask, alibi_bias)) + r = x = self.norm1(x) + x = self.mlp(x) + t = x + x = self.norm2(r + self.drop_path(self.post_mlp_dropout(x))) + if not self.ffn_targets: + t = x + + return x, t + + +class AltAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + cosine_attention=False, + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.cosine_attention = cosine_attention + + if cosine_attention: + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True + ) + + def forward(self, x, padding_mask=None, alibi_bias=None): + B, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) # qkv x B x H x L x D + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + dtype = q.dtype + + if self.cosine_attention: + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01)) + ).exp() + attn = attn * logit_scale + else: + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if alibi_bias is not None: + attn = attn.type_as(alibi_bias) + attn[:, : alibi_bias.size(1)] += alibi_bias + + if padding_mask is not None and padding_mask.any(): + attn = attn.masked_fill( + padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + + attn = attn.softmax(dim=-1, dtype=torch.float32).to(dtype=dtype) + attn = self.attn_drop(attn) + x = (attn @ v).transpose(1, 2) # + x = x.reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class EncDecAttention(nn.Module): + def __init__( + self, + q_dim, + kv_dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + cosine_attention=False, + ): + super().__init__() + self.num_heads = num_heads + head_dim = q_dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.q_proj = nn.Linear(q_dim, q_dim, bias=qkv_bias) + self.kv_proj = nn.Linear(kv_dim, 2 * q_dim, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(q_dim, q_dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.cosine_attention = cosine_attention + + if cosine_attention: + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True + ) + + def forward(self, q, kv, padding_mask=None, alibi_bias=None): + B, N, C = q.shape + + q = ( + self.q_proj(q) + .reshape(B, N, self.num_heads, C // self.num_heads) + .permute(0, 2, 1, 3) + ) # B x H x L x D + kv = ( + self.kv_proj(kv) + .reshape(B, -1, 2, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) # kv x B x H x L x D + k, v = ( + kv[0], + kv[1], + ) # make torchscript happy (cannot use tensor as tuple) + + dtype = q.dtype + + if self.cosine_attention: + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01)) + ).exp() + attn = attn * logit_scale + else: + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if alibi_bias is not None: + attn = attn.type_as(alibi_bias) + attn[:, : alibi_bias.size(1)] += alibi_bias + + if padding_mask is not None and padding_mask.any(): + attn = attn.masked_fill( + padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + + attn = attn.softmax(dim=-1, dtype=torch.float32).to(dtype=dtype) + attn = self.attn_drop(attn) + x = (attn @ v).transpose(1, 2) # + x = x.reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class EncDecBlock(nn.Module): + def __init__( + self, + q_dim, + kv_dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + mlp_drop=0.0, + post_mlp_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=True, + cosine_attention=False, + first_residual=True, + ): + super().__init__() + + self.layer_norm_first = layer_norm_first + + from timm.models.vision_transformer import DropPath, Mlp + + self.norm1 = norm_layer(q_dim) + self.attn = EncDecAttention( + q_dim, + kv_dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + cosine_attention=cosine_attention, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(q_dim) + mlp_hidden_dim = int(q_dim * mlp_ratio) + self.mlp = Mlp( + in_features=q_dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=mlp_drop, + ) + self.post_mlp_dropout = nn.Dropout(post_mlp_drop, inplace=False) + self.first_residual = first_residual + + def forward(self, q, kv, padding_mask=None, alibi_bias=None): + r = q if self.first_residual else 0 + if self.layer_norm_first: + x = r + self.drop_path( + self.attn(self.norm1(q), kv, padding_mask, alibi_bias) + ) + r = x = self.mlp(self.norm2(x)) + x = r + self.drop_path(self.post_mlp_dropout(x)) + else: + x = r + self.drop_path(self.attn(q, kv, padding_mask, alibi_bias)) + r = x = self.norm1(x) + x = self.mlp(x) + x = self.norm2(r + self.drop_path(self.post_mlp_dropout(x))) + + return x + + +class EncDecTransformerDecoder(nn.Module): + def __init__(self, cfg: D2vDecoderConfig, input_dim): + super().__init__() + + self.input_proj = nn.Linear(input_dim, cfg.decoder_dim) + + self.blocks = nn.Sequential( + *[ + EncDecBlock( + q_dim=cfg.decoder_dim, + kv_dim=input_dim, + num_heads=8, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + mlp_drop=0.0, + post_mlp_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=False, + cosine_attention=False, + first_residual=i > 0, + ) + for i in range(cfg.decoder_layers) + ] + ) + + self.proj = nn.Linear(cfg.decoder_dim, input_dim) + + def reset_parameters(self): + from fairseq.modules.transformer_sentence_encoder import init_bert_params + + self.apply(init_bert_params) + + def forward(self, x, kv): + x = self.input_proj(x) + for i, layer in enumerate(self.blocks): + x = layer(x, kv) + + x = self.proj(x) + return x diff --git a/fairseq/examples/data2vec/models/modalities/text.py b/fairseq/examples/data2vec/models/modalities/text.py new file mode 100644 index 0000000..adfac1c --- /dev/null +++ b/fairseq/examples/data2vec/models/modalities/text.py @@ -0,0 +1,161 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass +from functools import partial +from typing import Callable, Dict, Optional + +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from fairseq.modules import PositionalEmbedding, FairseqDropout, LayerNorm +from fairseq.tasks import FairseqTask +from .base import D2vModalityConfig, ModalitySpecificEncoder, get_alibi_bias +from .modules import BlockEncoder, Decoder1d +from examples.data2vec.data.modality import Modality + + +@dataclass +class D2vTextConfig(D2vModalityConfig): + type: Modality = Modality.TEXT + max_source_positions: int = 512 + learned_pos: bool = True + dropout: float = 0.1 # used for both local_encoder and contextualized encoder. tied with global transformer in data2vec_text + + no_scale_embedding: bool = True + layernorm_embedding: bool = True + no_token_positional_embeddings: bool = False + + +class TextEncoder(ModalitySpecificEncoder): + + modality_cfg: D2vTextConfig + + def __init__( + self, + modality_cfg: D2vTextConfig, + embed_dim: int, + make_block: Callable[[float], nn.ModuleList], + norm_layer: Callable[[int], nn.LayerNorm], + layer_norm_first: bool, + alibi_biases: Dict, + task: Optional[FairseqTask], + ): + self.pad_idx = task.source_dictionary.pad() + self.vocab_size = len(task.source_dictionary) + + local_encoder = TextLocalEncoder( + vocab_size=self.vocab_size, + embed_dim=embed_dim, + max_source_positions=modality_cfg.max_source_positions, + pad_idx=self.pad_idx, + no_scale_embedding=modality_cfg.no_scale_embedding, + layernorm_embedding=modality_cfg.layernorm_embedding, + dropout=modality_cfg.dropout, + no_token_positional_embeddings=modality_cfg.no_token_positional_embeddings, + learned_pos=modality_cfg.learned_pos, + ) + dpr = np.linspace( + modality_cfg.start_drop_path_rate, + modality_cfg.end_drop_path_rate, + modality_cfg.prenet_depth, + ) + context_encoder = BlockEncoder( + nn.ModuleList(make_block(dpr[i]) for i in range(modality_cfg.prenet_depth)), + norm_layer(embed_dim) + if not layer_norm_first and modality_cfg.prenet_depth > 0 + else None, + layer_norm_first, + modality_cfg.prenet_layerdrop, + modality_cfg.prenet_dropout if modality_cfg.prenet_depth > 0 else 0.0, + ) + decoder = ( + Decoder1d(modality_cfg.decoder, embed_dim) + if modality_cfg.decoder is not None + else None + ) + + alibi_bias_fn = partial(get_alibi_bias, alibi_biases=alibi_biases) + + super().__init__( + modality_cfg=modality_cfg, + embed_dim=embed_dim, + local_encoder=local_encoder, + project_features=nn.Identity(), + fixed_positional_encoder=None, + relative_positional_encoder=None, + context_encoder=context_encoder, + decoder=decoder, + get_alibi_bias=alibi_bias_fn, + ) + + def reset_parameters(self): + super().reset_parameters() + + def convert_padding_mask(self, x, padding_mask): + if padding_mask is None or padding_mask.size(1) == x.size(1): + return padding_mask + + diff = self.downsample - padding_mask.size(1) % self.downsample + if 0 < diff < self.downsample: + padding_mask = F.pad(padding_mask, (0, diff), value=True) + + padding_mask = padding_mask.view(padding_mask.size(0), -1, self.downsample) + padding_mask = padding_mask.all(-1) + if padding_mask.size(1) > x.size(1): + padding_mask = padding_mask[:, : x.size(1)] + + assert x.size(1) == padding_mask.size( + 1 + ), f"{x.size(1), padding_mask.size(1), diff, self.downsample}" + + return padding_mask + + +class TextLocalEncoder(nn.Module): + def __init__( + self, + vocab_size, + embed_dim, + max_source_positions, + pad_idx, + no_scale_embedding, + layernorm_embedding, + dropout, + no_token_positional_embeddings, + learned_pos, + ): + super().__init__() + self.pad_idx = pad_idx + self.dropout_module = FairseqDropout(dropout) + + self.embed_tokens = nn.Embedding(vocab_size, embed_dim, pad_idx) + self.embed_scale = 1.0 if no_scale_embedding else math.sqrt(embed_dim) + self.embed_positions = ( + PositionalEmbedding( + max_source_positions, + embed_dim, + pad_idx, + learned=learned_pos, + ) + if not no_token_positional_embeddings + else None + ) + self.embed_scale = 1.0 if no_scale_embedding else math.sqrt(embed_dim) + + self.layernorm_embedding = None + if layernorm_embedding: + self.layernorm_embedding = LayerNorm(embed_dim) + + def forward(self, src_tokens): + x = self.embed_scale * self.embed_tokens(src_tokens) + if self.embed_positions is not None: + x = x + self.embed_positions(src_tokens) + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + x = self.dropout_module(x) + return x diff --git a/fairseq/examples/data2vec/models/utils.py b/fairseq/examples/data2vec/models/utils.py new file mode 100644 index 0000000..0e2f240 --- /dev/null +++ b/fairseq/examples/data2vec/models/utils.py @@ -0,0 +1,55 @@ +import math +import torch + +def get_alibi( + max_positions: int, + attention_heads: int, +): + def get_slopes(n): + def get_slopes_power_of_2(n): + start = 2 ** (-(2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio ** i for i in range(n)] + + # In the paper, we only train models that have 2^a heads for some + # a. This function has some good properties that only occur when + # the input is a power of 2. To maintain that even when the number + # of heads is not a power of 2, we use this workaround. + if math.log2(n).is_integer(): + return get_slopes_power_of_2(n) + else: + closest_power_of_2 = 2 ** math.floor(math.log2(n)) + return ( + get_slopes_power_of_2(closest_power_of_2) + + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2] + ) + + maxpos = max_positions + attn_heads = attention_heads + slopes = torch.Tensor(get_slopes(attn_heads)) + # prepare alibi position linear bias. Note that wav2vec2 is non + # autoregressive model so we want a symmetric mask with 0 on the + # diagonal and other wise linear decreasing valuees + pos_bias = ( + torch.abs( + torch.arange(maxpos).unsqueeze(0) - torch.arange(maxpos).unsqueeze(1) + ) + * -1 + ) + alibi_bias = slopes.unsqueeze(1).unsqueeze(1) * pos_bias.unsqueeze(0).expand( + attn_heads, -1, -1 + ) + return alibi_bias + +def masked_alibi(alibi_bias, mask_indices, orig_B, orig_T): + alibi_bias = alibi_bias.view(orig_B, -1, orig_T, orig_T) + H = alibi_bias.size(1) + alibi_mask = mask_indices.unsqueeze(1) + alibi_bias = alibi_bias.masked_select(alibi_mask.unsqueeze(-1)) + alibi_bias = alibi_bias.view(orig_B, H, -1, orig_T) + M = alibi_bias.size(-2) + alibi_bias = alibi_bias.masked_select(alibi_mask.unsqueeze(-2)) + alibi_bias = alibi_bias.view(-1, M, M) + return alibi_bias + + diff --git a/fairseq/examples/data2vec/scripts/convert_audioset_labels.py b/fairseq/examples/data2vec/scripts/convert_audioset_labels.py new file mode 100644 index 0000000..7d720e6 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/convert_audioset_labels.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os + + +def get_parser(): + parser = argparse.ArgumentParser(description="convert audioset labels") + # fmt: off + parser.add_argument('in_file', help='audioset csv file to convert') + parser.add_argument('--manifest', required=True, metavar='PATH', help='wav2vec-like manifest') + parser.add_argument('--descriptors', required=True, metavar='PATH', help='path to label descriptor file') + parser.add_argument('--output', required=True, metavar='PATH', help='where to output converted labels') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + label_descriptors = {} + with open(args.descriptors, "r") as ldf: + next(ldf) + for line in ldf: + if line.strip() == "": + continue + + items = line.split(",") + assert len(items) > 2, line + idx = items[0] + lbl = items[1] + assert lbl not in label_descriptors, lbl + label_descriptors[lbl] = idx + + labels = {} + with open(args.in_file, "r") as ifd: + for line in ifd: + if line.lstrip().startswith("#"): + continue + items = line.rstrip().split(",") + id = items[0].strip() + start = items[1].strip() + end = items[2].strip() + lbls = [label_descriptors[it.strip(' "')] for it in items[3:]] + labels[id] = [start, end, ",".join(lbls)] + + with open(args.manifest, "r") as mf, open(args.output, "w") as of: + next(mf) + for line in mf: + path, _ = line.split("\t") + id = os.path.splitext(os.path.basename(path))[0] + lbl = labels[id] + print("\t".join(lbl), file=of) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_aws_local_lr.sh b/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_aws_local_lr.sh new file mode 100644 index 0000000..41bcd31 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_aws_local_lr.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +set -eu + +job_id="$1" +task_id="$2" +dir="$3" + +echo "job_id: $job_id, task_id: $task_id, dir: $dir" + +mkdir -p "$dir/log" +sbatch_args="-p wav2vec --nodes=1 --ntasks-per-node=1" +sbatch_args="$sbatch_args --gpus-per-node=1 --cpus-per-task=8 --mem=0 --time=24:00:00" +sbatch_args="$sbatch_args -d afterok:$job_id -o $dir/log/decode_sweep_%A.out" +sbatch_args="$sbatch_args -e $dir/log/decode_sweep_%A.err" + +sbatch $sbatch_args examples/data2vec/scripts/multi/finetune_all_fair_local_lr.sh $dir + diff --git a/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_aws_local_lr_nodep.sh b/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_aws_local_lr_nodep.sh new file mode 100644 index 0000000..fc85908 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_aws_local_lr_nodep.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +set -eu + +dir="$1" + +echo "dir: $dir" + +mkdir -p "$dir/log" +sbatch_args="-p wav2vec --nodes=1 --ntasks-per-node=1" +sbatch_args="$sbatch_args --gpus-per-node=1 --cpus-per-task=8 --mem=0 --time=24:00:00" +sbatch_args="$sbatch_args -o $dir/log/decode_sweep_%A.out" +sbatch_args="$sbatch_args -e $dir/log/decode_sweep_%A.err" + +sbatch $sbatch_args examples/data2vec/scripts/multi/finetune_all_fair_local_lr.sh $dir + diff --git a/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_local_lr.sh b/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_local_lr.sh new file mode 100644 index 0000000..1212269 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/multi/finetune_all_fair_local_lr.sh @@ -0,0 +1,28 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" +tasks[mnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MNLI-bin" +tasks[qqp]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QQP-bin" +tasks[sts_b]="/fsx-wav2vec/abaevski/data/nlp/GLUE/STS-B-bin" + +lrs=(5e-6 8e-6 1e-5 2e-5) + +for task data_path in ${(kv)tasks}; do + for lr in $lrs; do + echo $lr $task + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" \ + python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/multi/text_finetuning \ + --config-name $task +run_config=local task.data="$data_path" common.log_interval=200 dataset.num_workers=1 \ + model.model_path="$cp" hydra.sweep.dir="$dir/finetune_lr/$task/$lr" "optimization.lr=[${lr}]" +model=text_wrap + done +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_char_fair_aws_local_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_char_fair_aws_local_lr.sh new file mode 100644 index 0000000..18b862c --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_char_fair_aws_local_lr.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +job_id="$1" +task_id="$2" +dir="$3" + +echo "job_id: $job_id, task_id: $task_id, dir: $dir" + +mkdir -p "$dir/log" +sbatch_args="-p wav2vec --nodes=1 --ntasks-per-node=1" +sbatch_args="$sbatch_args --gpus-per-node=1 --cpus-per-task=8 --mem=0 --time=24:00:00" +sbatch_args="$sbatch_args -d afterok:$job_id -o $dir/log/ft_%A.out" +sbatch_args="$sbatch_args -e $dir/log/ft_%A.err" + +sbatch $sbatch_args examples/data2vec/scripts/text/finetune_all_char_fair_local_lr.sh $dir diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair.sh new file mode 100644 index 0000000..34a2df3 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env zsh + +job_id=$1 +task_id=$2 +dir="$3" +cp="$dir/$task_id/checkpoints/checkpoint_last.pt" + +echo "job_id: $job_id, task_id: $task_id, dir: $dir" + +declare -A tasks +tasks[cola]="/private/home/jgu/data/GLUE/CoLA-bin" +tasks[qnli]="/private/home/jgu/data/GLUE/QNLI-bin" +tasks[mrpc]="/private/home/jgu/data/GLUE/MRPC-bin" +tasks[rte]="/private/home/jgu/data/GLUE/RTE-bin" +tasks[sst_2]="/private/home/jgu/data/GLUE/SST-2-bin" + +for task data_path in ${(kv)tasks}; do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" +hydra.launcher.additional_parameters.dependency="afterok:$job_id" hydra.sweep.dir="$dir/finetune/$task" & +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws.sh new file mode 100644 index 0000000..b417c20 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env zsh + +job_id=$1 +task_id=$2 +dir="$3" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "job_id: $job_id, task_id: $task_id, dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" + +for task data_path in ${(kv)tasks}; do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g_aws task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" +hydra.launcher.additional_parameters.dependency="afterok:$job_id" hydra.sweep.dir="$dir/finetune/$task" & +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws_local_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws_local_lr.sh new file mode 100644 index 0000000..64dbcb1 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws_local_lr.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +job_id="$1" +task_id="$2" +dir="$3" + +echo "job_id: $job_id, task_id: $task_id, dir: $dir" + +mkdir -p "$dir/log" +sbatch_args="-p wav2vec --nodes=1 --ntasks-per-node=1" +sbatch_args="$sbatch_args --gpus-per-node=1 --cpus-per-task=8 --mem=0 --time=24:00:00" +sbatch_args="$sbatch_args -d afterok:$job_id -o $dir/log/decode_sweep_%A.out" +sbatch_args="$sbatch_args -e $dir/log/decode_sweep_%A.err" + +sbatch $sbatch_args examples/data2vec/scripts/text/finetune_all_fair_local_lr.sh $dir diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws_lr.sh new file mode 100644 index 0000000..d75c549 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_aws_lr.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env zsh + +job_id=$1 +task_id=$2 +dir="$3" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "job_id: $job_id, task_id: $task_id, dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" + +for task data_path in ${(kv)tasks}; do + for lr in 5e-6 8e-6 1e-5 2e-5 5e-5 8e-5 1e-4 2e-4; do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g_aws task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" +hydra.launcher.additional_parameters.dependency="afterok:$job_id" hydra.sweep.dir="$dir/finetune_lr/$task/$lr" "optimization.lr=[${lr}]" & + done +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_local_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_local_lr.sh new file mode 100644 index 0000000..8be98c0 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_local_lr.sh @@ -0,0 +1,25 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" + +lrs=(5e-6 8e-6 1e-5 2e-5) + +for task data_path in ${(kv)tasks}; do + for lr in $lrs; do + echo $lr $task + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" \ + python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task +run_config=local task.data="$data_path" common.log_interval=200 dataset.num_workers=1 \ + checkpoint.restore_file="$cp" hydra.sweep.dir="$dir/finetune_lr/$task/$lr" "optimization.lr=[${lr}]" + done +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep.sh new file mode 100644 index 0000000..d02bcc0 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[cola]="/private/home/jgu/data/GLUE/CoLA-bin" +tasks[qnli]="/private/home/jgu/data/GLUE/QNLI-bin" +tasks[mrpc]="/private/home/jgu/data/GLUE/MRPC-bin" +tasks[rte]="/private/home/jgu/data/GLUE/RTE-bin" +tasks[sst_2]="/private/home/jgu/data/GLUE/SST-2-bin" + +for task data_path in ${(kv)tasks}; do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" hydra.sweep.dir="$dir/finetune/$task" & +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws.sh new file mode 100644 index 0000000..7553835 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" + +for task data_path in ${(kv)tasks}; do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g_aws task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" hydra.sweep.dir="$dir/finetune/$task" & +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_local_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_local_lr.sh new file mode 100644 index 0000000..16c1358 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_local_lr.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +set -eu + +dir="$1" + +echo "dir: $dir" + +mkdir -p "$dir/log" +sbatch_args="-p wav2vec --nodes=1 --ntasks-per-node=1" +sbatch_args="$sbatch_args --gpus-per-node=1 --cpus-per-task=8 --mem=0 --time=24:00:00" +sbatch_args="$sbatch_args -o $dir/log/decode_sweep_%A.out" +sbatch_args="$sbatch_args -e $dir/log/decode_sweep_%A.err" + +sbatch $sbatch_args examples/data2vec/scripts/text/finetune_all_fair_local_lr.sh $dir diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_lr.sh new file mode 100644 index 0000000..fb5ddbe --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_lr.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" + +for task data_path in ${(kv)tasks}; do + for lr in 5e-6 8e-6 1e-5 2e-5 5e-5 8e-5 1e-4 2e-4; do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g_aws task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" hydra.sweep.dir="$dir/finetune_lr/$task/$lr" "optimization.lr=[${lr}]" & + done +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_lr_nopos.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_lr_nopos.sh new file mode 100644 index 0000000..1ffab1c --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_fair_nodep_aws_lr_nopos.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" + +for task data_path in ${(kv)tasks}; do + for lr in 5e-6 8e-6 1e-5 2e-5 5e-5 8e-5 1e-4 2e-4; do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g_aws task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" hydra.sweep.dir="$dir/finetune_lr/$task/$lr" "optimization.lr=[${lr}]" +model.encoder_learned_pos=False & + done +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_aws_local_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_aws_local_lr.sh new file mode 100644 index 0000000..c3c58ad --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_aws_local_lr.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +job_id="$1" +task_id="$2" +dir="$3" + +echo "job_id: $job_id, task_id: $task_id, dir: $dir" + +mkdir -p "$dir/log" +sbatch_args="-p wav2vec --nodes=1 --ntasks-per-node=1" +sbatch_args="$sbatch_args --gpus-per-node=1 --cpus-per-task=8 --mem=0 --time=24:00:00" +sbatch_args="$sbatch_args -d afterok:$job_id -o $dir/log/decode_sweep_%A.out" +sbatch_args="$sbatch_args -e $dir/log/decode_sweep_%A.err" + +sbatch $sbatch_args examples/data2vec/scripts/text/finetune_all_large_fair_local_lr.sh $dir diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_local_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_local_lr.sh new file mode 100644 index 0000000..5efb00e --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_local_lr.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[cola]="/fsx-wav2vec/abaevski/data/nlp/GLUE/CoLA-bin" +tasks[qnli]="/fsx-wav2vec/abaevski/data/nlp/GLUE/QNLI-bin" +tasks[mrpc]="/fsx-wav2vec/abaevski/data/nlp/GLUE/MRPC-bin" +tasks[rte]="/fsx-wav2vec/abaevski/data/nlp/GLUE/RTE-bin" +tasks[sst_2]="/fsx-wav2vec/abaevski/data/nlp/GLUE/SST-2-bin" + +lrs=(5e-6 8e-6 1e-5 2e-5) + +for task data_path in ${(kv)tasks}; do + for lr in $lrs; do + echo $lr $task + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" \ + python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task +run_config=local task.data="$data_path" common.log_interval=200 dataset.num_workers=1 \ + checkpoint.restore_file="$cp" hydra.sweep.dir="$dir/finetune_lr/$task/$lr" "optimization.lr=[${lr}]" \ + model._name=roberta_large + done +done diff --git a/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_nodep_aws_local_lr.sh b/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_nodep_aws_local_lr.sh new file mode 100644 index 0000000..4fb21bc --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_all_large_fair_nodep_aws_local_lr.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +set -eu + +dir="$1" + +echo "dir: $dir" + +mkdir -p "$dir/log" +sbatch_args="-p wav2vec --nodes=1 --ntasks-per-node=1" +sbatch_args="$sbatch_args --gpus-per-node=1 --cpus-per-task=8 --mem=0 --time=24:00:00" +sbatch_args="$sbatch_args -o $dir/log/decode_sweep_%A.out" +sbatch_args="$sbatch_args -e $dir/log/decode_sweep_%A.err" + +sbatch $sbatch_args examples/data2vec/scripts/text/finetune_all_large_fair_local_lr.sh $dir diff --git a/fairseq/examples/data2vec/scripts/text/finetune_sst2_qnli_sweep_fair_nodep.sh b/fairseq/examples/data2vec/scripts/text/finetune_sst2_qnli_sweep_fair_nodep.sh new file mode 100644 index 0000000..d7b43be --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/finetune_sst2_qnli_sweep_fair_nodep.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env zsh + +dir="$1" +cp="$dir/checkpoints/checkpoint_last.pt" + +echo "dir: $dir" + +declare -A tasks +tasks[qnli]="/private/home/jgu/data/GLUE/QNLI-bin" +tasks[sst_2]="/private/home/jgu/data/GLUE/SST-2-bin" + +lrs="5e-6 1e-5 2e-5 5e-5 1e-4 2e-4 5e-4 1e-3" + +for task data_path in ${(kv)tasks}; do + for lr in $(echo "$lrs"); do + PYTHONPATH=. PREFIX="${PREFIX}" SUFFIX="" nohup python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ + --config-name $task hydra/launcher=submitit_slurm +run_config=slurm_1g task.data="$data_path" hydra.launcher.name=finetune_${task}_${PREFIX} \ + checkpoint.restore_file="$cp" hydra.sweep.dir="$dir/finetune_sweep/$task/lr_$lr" "optimization.lr=[${lr}]" & + done +done diff --git a/fairseq/examples/data2vec/scripts/text/glue.py b/fairseq/examples/data2vec/scripts/text/glue.py new file mode 100644 index 0000000..5382d31 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/glue.py @@ -0,0 +1,34 @@ +from valids import parser, main as valids_main +import os.path as osp + + +args = parser.parse_args() +args.target = "valid_accuracy" +args.best_biggest = True +args.best = True +args.last = 0 +args.path_contains = None + +res = valids_main(args, print_output=False) + +grouped = {} +for k, v in res.items(): + k = osp.dirname(k) + run = osp.dirname(k) + task = osp.basename(k) + val = v["valid_accuracy"] + + if run not in grouped: + grouped[run] = {} + + grouped[run][task] = val + +for run, tasks in grouped.items(): + print(run) + avg = sum(float(v) for v in tasks.values()) / len(tasks) + avg_norte = sum(float(v) for k,v in tasks.items() if k != 'rte') / (len(tasks) -1) + try: + print(f"{tasks['cola']}\t{tasks['qnli']}\t{tasks['mrpc']}\t{tasks['rte']}\t{tasks['sst_2']}\t{avg:.2f}\t{avg_norte:.2f}") + except: + print(tasks) + print() diff --git a/fairseq/examples/data2vec/scripts/text/glue_lr.py b/fairseq/examples/data2vec/scripts/text/glue_lr.py new file mode 100644 index 0000000..75bdfe0 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/glue_lr.py @@ -0,0 +1,143 @@ +import os.path as osp +import re +from collections import defaultdict + +from valids import parser, main as valids_main + + +TASK_TO_METRIC = { + "cola": "mcc", + "qnli": "accuracy", + "mrpc": "acc_and_f1", + "rte": "accuracy", + "sst_2": "accuracy", + "mnli": "accuracy", + "qqp": "acc_and_f1", + "sts_b": "pearson_and_spearman", +} +TASKS = ["cola", "qnli", "mrpc", "rte", "sst_2", "mnli", "qqp", "sts_b"] + + +def get_best_stat_str(task_vals, show_subdir): + task_to_best_val = {} + task_to_best_dir = {} + for task, subdir_to_val in task_vals.items(): + task_to_best_val[task] = max(subdir_to_val.values()) + task_to_best_dir[task] = max(subdir_to_val.keys(), key=lambda x: subdir_to_val[x]) + + # import pdb; pdb.set_trace() + N1 = len(task_to_best_val) + N2 = len([k for k in task_to_best_val if k != "rte"]) + avg1 = sum(task_to_best_val.values()) / N1 + avg2 = sum(v for task, v in task_to_best_val.items() if task != "rte") / N2 + + try: + msg = "" + for task in TASKS: + dir = task_to_best_dir.get(task, 'null') + val = task_to_best_val.get(task, -100) + msg += f"({dir}, {val})\t" if show_subdir else f"{val}\t" + msg += f"{avg1:.2f}\t{avg2:.2f}" + except Exception as e: + msg = str(e) + msg += str(sorted(task_vals.items())) + return msg + +def get_all_stat_str(task_vals): + msg = "" + for task in [task for task in TASKS if task in task_vals]: + msg += f"=== {task}\n" + for subdir in sorted(task_vals[task].keys()): + msg += f"\t{subdir}\t{task_vals[task][subdir]}\n" + return msg + +def get_tabular_stat_str(task_vals): + """assume subdir is /run_*/0""" + msg = "" + for task in [task for task in TASKS if task in task_vals]: + msg += f"=== {task}\n" + param_to_runs = defaultdict(dict) + for subdir in task_vals[task]: + match = re.match("(.*)/(run_.*)/0", subdir) + assert match, "subdir" + param, run = match.groups() + param_to_runs[param][run] = task_vals[task][subdir] + params = sorted(param_to_runs, key=lambda x: float(x)) + runs = sorted(set(run for runs in param_to_runs.values() for run in runs)) + msg += ("runs:" + "\t".join(runs) + "\n") + msg += ("params:" + "\t".join(params) + "\n") + for param in params: + msg += "\t".join([str(param_to_runs[param].get(run, None)) for run in runs]) + msg += "\n" + # for subdir in sorted(task_vals[task].keys()): + # msg += f"\t{subdir}\t{task_vals[task][subdir]}\n" + return msg + + + +def main(): + parser.add_argument("--show_glue", action="store_true", help="show glue metric for each task instead of accuracy") + parser.add_argument("--print_mode", default="best", help="best|all|tabular") + parser.add_argument("--show_subdir", action="store_true", help="print the subdir that has the best results for each run") + parser.add_argument("--override_target", default="valid_accuracy", help="override target") + + args = parser.parse_args() + args.target = args.override_target + args.best_biggest = True + args.best = True + args.last = 0 + args.path_contains = None + + res = valids_main(args, print_output=False) + grouped_acc = {} + grouped_met = {} # use official metric for each task + for path, v in res.items(): + path = "/".join([args.base, path]) + path = re.sub("//*", "/", path) + match = re.match("(.*)finetune[^/]*/([^/]*)/(.*)", path) + if not match: + continue + run, task, subdir = match.groups() + + if run not in grouped_acc: + grouped_acc[run] = {} + grouped_met[run] = {} + if task not in grouped_acc[run]: + grouped_acc[run][task] = {} + grouped_met[run][task] = {} + + if v is not None: + grouped_acc[run][task][subdir] = float(v.get("valid_accuracy", -100)) + grouped_met[run][task][subdir] = float(v.get(f"valid_{TASK_TO_METRIC[task]}", -100)) + else: + print(f"{path} has None return") + + header = "\t".join(TASKS) + for run in sorted(grouped_acc): + print(run) + if args.print_mode == "all": + if args.show_glue: + print("===== GLUE =====") + print(get_all_stat_str(grouped_met[run])) + else: + print("===== ACC =====") + print(get_all_stat_str(grouped_acc[run])) + elif args.print_mode == "best": + print(f" {header}") + if args.show_glue: + print(f"GLEU: {get_best_stat_str(grouped_met[run], args.show_subdir)}") + else: + print(f"ACC: {get_best_stat_str(grouped_acc[run], args.show_subdir)}") + elif args.print_mode == "tabular": + if args.show_glue: + print("===== GLUE =====") + print(get_tabular_stat_str(grouped_met[run])) + else: + print("===== ACC =====") + print(get_tabular_stat_str(grouped_acc[run])) + else: + raise ValueError(args.print_mode) + print() + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/data2vec/scripts/text/unprocess_data.py b/fairseq/examples/data2vec/scripts/text/unprocess_data.py new file mode 100644 index 0000000..f1acb62 --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/unprocess_data.py @@ -0,0 +1,188 @@ +import json +import os +import tqdm +from fairseq.data import Dictionary, data_utils + + +def load_dictionary(dict_path): + return Dictionary.load(dict_path) + +def load_dataset(split_path, src_dict): + dataset = data_utils.load_indexed_dataset( + split_path, + src_dict, + combine=False, # set to true for loading `train*` + ) + if dataset is None: + raise FileNotFoundError(f"Dataset not found: {split_path}") + return dataset + +def load_bpe(enc_path): + with open(enc_path) as f: + bpe2idx = json.load(f) + idx2bpe = {v: k for k, v in bpe2idx.items()} + return bpe2idx, idx2bpe + +def detokenize(tokens, src_dict, idx2bpe): + raw_inds = map(int, src_dict.string(tokens).split()) + raw_chrs = "".join([idx2bpe[raw_ind] for raw_ind in raw_inds]) + raw_chrs = raw_chrs.replace("\u0120", " ") + return raw_chrs + +def _main(src_root, src_dict_path, src_bpe_path, src_splits, tgt_root, tgt_splits): + src_dict = load_dictionary(src_dict_path) + bpe2idx, idx2bpe = load_bpe(src_bpe_path) + + assert len(src_splits) == len(tgt_splits) + for src_split, tgt_split in zip(src_splits, tgt_splits): + src_dataset = load_dataset(f"{src_root}/{src_split}", src_dict) + tgt_path = f"{tgt_root}/{tgt_split}.txt" + print(f"processing {src_split} (dump to {tgt_path})...") + os.makedirs(os.path.dirname(tgt_path), exist_ok=True) + with open(tgt_path, "w") as f: + for tokens in tqdm.tqdm(src_dataset): + raw_str = detokenize(tokens, src_dict, idx2bpe) + f.write(raw_str + "\n") + +def main_pt(): + src_root = "/datasets01/bookwiki_CC-NEWS_openwebtext_stories-mmap2-bin/121219/bookwiki_CC-NEWS_openwebtext_stories-mmap2-bin" + src_dict_path = f"{src_root}/dict.txt" + src_bpe_path = f"{src_root}/encoder.json" + src_splits = [ + "bookwiki_aml-mmap2-bin/shard0/train", + "bookwiki_aml-mmap2-bin/shard1/train", + "bookwiki_aml-mmap2-bin/shard2/train", + "bookwiki_aml-mmap2-bin/shard3/train", + "bookwiki_aml-mmap2-bin/shard4/train", + "bookwiki_aml-mmap2-bin/valid/valid", + ] + + tgt_root = "/checkpoint/wnhsu/data/data2vec2/data/text/bookwiki_aml-full-mmap2-txt" + tgt_splits = [ + "train0", + "train1", + "train2", + "train3", + "train4", + "valid", + ] + _main(src_root, src_dict_path, src_bpe_path, src_splits, tgt_root, tgt_splits) + +def main_ft(): + src_root = "/fsx-wav2vec/wnhsu/data/data2vec2/data/text/GLUE" + src_dict_path = f"{src_root}/dict.txt" + src_bpe_path = f"{src_root}/encoder.json" + src_splits = [ + "CoLA-bin/input0/train", + "CoLA-bin/input0/valid", + "CoLA-bin/input0/test", + + "MNLI-bin/input0/train", + "MNLI-bin/input0/valid", + "MNLI-bin/input0/test", + "MNLI-bin/input0/test1", + "MNLI-bin/input1/train", + "MNLI-bin/input1/valid", + "MNLI-bin/input1/test", + "MNLI-bin/input1/test1", + + "MRPC-bin/input0/train", + "MRPC-bin/input0/valid", + "MRPC-bin/input0/test", + "MRPC-bin/input1/train", + "MRPC-bin/input1/valid", + "MRPC-bin/input1/test", + + "QNLI-bin/input0/train", + "QNLI-bin/input0/valid", + "QNLI-bin/input0/test", + "QNLI-bin/input1/train", + "QNLI-bin/input1/valid", + "QNLI-bin/input1/test", + + "QQP-bin/input0/train", + "QQP-bin/input0/valid", + "QQP-bin/input0/test", + "QQP-bin/input1/train", + "QQP-bin/input1/valid", + "QQP-bin/input1/test", + + "RTE-bin/input0/train", + "RTE-bin/input0/valid", + "RTE-bin/input0/test", + "RTE-bin/input1/train", + "RTE-bin/input1/valid", + "RTE-bin/input1/test", + + "SST-2-bin/input0/train", + "SST-2-bin/input0/valid", + "SST-2-bin/input0/test", + + "STS-B-bin/input0/train", + "STS-B-bin/input0/valid", + "STS-B-bin/input0/test", + "STS-B-bin/input1/train", + "STS-B-bin/input1/valid", + "STS-B-bin/input1/test", + ] + + tgt_root = "/fsx-wav2vec/wnhsu/data/data2vec2/data/text/GLUE_chr" + tgt_splits = [ + "CoLA-bin/input0/train", + "CoLA-bin/input0/valid", + "CoLA-bin/input0/test", + + "MNLI-bin/input0/train", + "MNLI-bin/input0/valid", + "MNLI-bin/input0/test", + "MNLI-bin/input0/test1", + "MNLI-bin/input1/train", + "MNLI-bin/input1/valid", + "MNLI-bin/input1/test", + "MNLI-bin/input1/test1", + + "MRPC-bin/input0/train", + "MRPC-bin/input0/valid", + "MRPC-bin/input0/test", + "MRPC-bin/input1/train", + "MRPC-bin/input1/valid", + "MRPC-bin/input1/test", + + "QNLI-bin/input0/train", + "QNLI-bin/input0/valid", + "QNLI-bin/input0/test", + "QNLI-bin/input1/train", + "QNLI-bin/input1/valid", + "QNLI-bin/input1/test", + + "QQP-bin/input0/train", + "QQP-bin/input0/valid", + "QQP-bin/input0/test", + "QQP-bin/input1/train", + "QQP-bin/input1/valid", + "QQP-bin/input1/test", + + "RTE-bin/input0/train", + "RTE-bin/input0/valid", + "RTE-bin/input0/test", + "RTE-bin/input1/train", + "RTE-bin/input1/valid", + "RTE-bin/input1/test", + + "SST-2-bin/input0/train", + "SST-2-bin/input0/valid", + "SST-2-bin/input0/test", + + "STS-B-bin/input0/train", + "STS-B-bin/input0/valid", + "STS-B-bin/input0/test", + "STS-B-bin/input1/train", + "STS-B-bin/input1/valid", + "STS-B-bin/input1/test", + ] + _main(src_root, src_dict_path, src_bpe_path, src_splits, tgt_root, tgt_splits) + + +if __name__ == "__main__": + main_pt() + main_ft() diff --git a/fairseq/examples/data2vec/scripts/text/valids.py b/fairseq/examples/data2vec/scripts/text/valids.py new file mode 100644 index 0000000..b2e5cfb --- /dev/null +++ b/fairseq/examples/data2vec/scripts/text/valids.py @@ -0,0 +1,301 @@ +import os, argparse, re, json, copy, math +from collections import OrderedDict +import numpy as np + +parser = argparse.ArgumentParser(description='Process some integers.') +parser.add_argument('base', help='base log path') +parser.add_argument('--file_name', default='train.log', help='the log file name') +parser.add_argument('--target', default='valid_loss', help='target metric') +parser.add_argument('--last', type=int, default=999999999, help='print last n matches') +parser.add_argument('--last_files', type=int, default=None, help='print last x files') +parser.add_argument('--everything', action='store_true', help='print everything instead of only last match') +parser.add_argument('--path_contains', help='only consider matching file pattern') +parser.add_argument('--group_on', help='if set, groups by this metric and shows table of differences') +parser.add_argument('--epoch', help='epoch for comparison', type=int) +parser.add_argument('--skip_empty', action='store_true', help='skip empty results') +parser.add_argument('--skip_containing', help='skips entries containing this attribute') +parser.add_argument('--unique_epochs', action='store_true', help='only consider the last line fore each epoch') +parser.add_argument('--best', action='store_true', help='print the last best result') +parser.add_argument('--avg_params', help='average these params through entire log') +parser.add_argument('--extract_prev', help='extracts this metric from previous line') + +parser.add_argument('--remove_metric', help='extracts this metric from previous line') + +parser.add_argument('--compact', action='store_true', help='if true, just prints checkpoint best val') +parser.add_argument('--hydra', action='store_true', help='if true, uses hydra param conventions') + +parser.add_argument('--best_biggest', action='store_true', help='if true, best is the biggest number, not smallest') +parser.add_argument('--key_len', type=int, default=10, help='max length of key') + +parser.add_argument('--best_only', action='store_true', help='if set, only prints the best value') +parser.add_argument('--flat', action='store_true', help='just print the best results') + + +def main(args, print_output): + ret = {} + + entries = [] + + def extract_metric(s, metric): + try: + j = json.loads(s) + except: + return None + if args.epoch is not None and ('epoch' not in j or j['epoch'] != args.epoch): + return None + return j[metric] if metric in j else None + + + def extract_params(s): + s = s.replace(args.base, '', 1) + if args.path_contains is not None: + s = s.replace(args.path_contains, '', 1) + + if args.hydra: + num_matches = re.findall(r'(?:/|__)([^/:]+):(\d+\.?\d*)', s) + # str_matches = re.findall(r'(?:/|__)([^/:]+):([^\.]*[^\d\.]+)(?:/|__)', s) + str_matches = re.findall(r'(?:/|__)?((?:(?!(?:\:|__)).)+):([^\.]*[^\d\.]+\d*)(?:/|__)', s) + lr_matches = re.findall(r'optimization.(lr):\[([\d\.,]+)\]', s) + task_matches = re.findall(r'.*/(\d+)$', s) + else: + num_matches = re.findall(r'\.?([^\.]+?)(\d+(e\-\d+)?(?:\.\d+)?)(\.|$)', s) + str_matches = re.findall(r'[/\.]([^\.]*[^\d\.]+\d*)(?=\.)', s) + lr_matches = [] + task_matches = [] + + cp_matches = re.findall(r'checkpoint(?:_\d+)?_(\d+).pt', s) + + items = OrderedDict() + for m in str_matches: + if isinstance(m, tuple): + if 'checkpoint' not in m[0]: + items[m[0]] = m[1] + else: + items[m] = '' + + for m in num_matches: + items[m[0]] = m[1] + + for m in lr_matches: + items[m[0]] = m[1] + + for m in task_matches: + items["hydra_task"] = m + + for m in cp_matches: + items['checkpoint'] = m + + return items + + abs_best = None + + sources = [] + for root, _, files in os.walk(args.base): + if args.path_contains is not None and not args.path_contains in root: + continue + for f in files: + if f.endswith(args.file_name): + sources.append((root, f)) + + if args.last_files is not None: + sources = sources[-args.last_files:] + + for root, file in sources: + with open(os.path.join(root, file), 'r') as fin: + found = [] + avg = {} + prev = None + for line in fin: + line = line.rstrip() + if line.find(args.target) != -1 and ( + args.skip_containing is None or line.find(args.skip_containing) == -1): + try: + idx = line.index("{") + line = line[idx:] + line_json = json.loads(line) + except: + continue + if prev is not None: + try: + prev.update(line_json) + line_json = prev + except: + pass + if args.target in line_json: + found.append(line_json) + if args.avg_params: + avg_params = args.avg_params.split(',') + for p in avg_params: + m = extract_metric(line, p) + if m is not None: + prev_v, prev_c = avg.get(p, (0, 0)) + avg[p] = prev_v + float(m), prev_c + 1 + if args.extract_prev: + try: + prev = json.loads(line) + except: + pass + best = None + if args.best: + curr_best = None + for i in range(len(found)): + cand_best = found[i][args.target] if args.target in found[i] else None + + def cmp(a, b): + a = float(a) + b = float(b) + if args.best_biggest: + return a > b + return a < b + + if cand_best is not None and not math.isnan(float(cand_best)) and ( + curr_best is None or cmp(cand_best, curr_best)): + curr_best = cand_best + if abs_best is None or cmp(curr_best, abs_best): + abs_best = curr_best + best = found[i] + if args.unique_epochs or args.epoch: + last_found = [] + last_epoch = None + for i in reversed(range(len(found))): + epoch = found[i]['epoch'] + if args.epoch and args.epoch != epoch: + continue + if epoch != last_epoch: + last_epoch = epoch + last_found.append(found[i]) + found = list(reversed(last_found)) + + if len(found) == 0: + if print_output and (args.last_files is not None or not args.skip_empty): + # print(root.split('/')[-1]) + print(root[len(args.base):]) + print('Nothing') + else: + if not print_output: + ret[root[len(args.base):]] = best + continue + + if args.compact: + # print('{}\t{}'.format(root.split('/')[-1], curr_best)) + print('{}\t{}'.format(root[len(args.base)+1:], curr_best)) + continue + + if args.group_on is None and not args.best_only: + # print(root.split('/')[-1]) + print(root[len(args.base):]) + if not args.everything: + if best is not None and args.group_on is None and not args.best_only and not args.flat: + print(best, '(best)') + if args.group_on is None and args.last and not args.best_only and not args.flat: + for f in found[-args.last:]: + if args.extract_prev is not None: + try: + print('{}\t{}'.format(f[args.extract_prev], f[args.target])) + except Exception as e: + print('Exception!', e) + else: + print(f) + try: + metric = found[-1][args.target] if not args.best or best is None else best[args.target] + except: + print(found[-1]) + raise + if metric is not None: + entries.append((extract_params(root), metric)) + else: + for f in found: + print(f) + if not args.group_on and print_output: + print() + + if len(avg) > 0: + for k, (v, c) in avg.items(): + print(f'{k}: {v/c}') + + if args.best_only: + print(abs_best) + + if args.flat: + print("\t".join(m for _, m in entries)) + + if args.group_on is not None: + by_val = OrderedDict() + for e, m in entries: + k = args.group_on + if k not in e: + m_keys = [x for x in e.keys() if x.startswith(k)] + if len(m_keys) == 0: + val = "False" + else: + assert len(m_keys) == 1 + k = m_keys[0] + val = m_keys[0] + else: + val = e[args.group_on] + if val == "": + val = "True" + scrubbed_entry = copy.deepcopy(e) + if k in scrubbed_entry: + del scrubbed_entry[k] + if args.remove_metric and args.remove_metric in scrubbed_entry: + val += '_' + scrubbed_entry[args.remove_metric] + del scrubbed_entry[args.remove_metric] + by_val.setdefault(tuple(scrubbed_entry.items()), dict())[val] = m + distinct_vals = set() + for v in by_val.values(): + distinct_vals.update(v.keys()) + try: + distinct_vals = {int(d) for d in distinct_vals} + except: + print(distinct_vals) + print() + print("by_val", len(by_val)) + for k,v in by_val.items(): + print(k, '=>', v) + print() + + # , by_val, entries) + raise + from natsort import natsorted + svals = list(map(str, natsorted(distinct_vals))) + print('{}\t{}'.format(args.group_on, '\t'.join(svals))) + sums = OrderedDict({n:[] for n in svals}) + for k, v in by_val.items(): + kstr = '.'.join(':'.join(x) for x in k) + vstr = '' + for mv in svals: + x = v[mv] if mv in v else '' + vstr += '\t{}'.format(round(x, 5) if isinstance(x, float) else x) + try: + sums[mv].append(float(x)) + except: + pass + print('{}{}'.format(kstr[:args.key_len], vstr)) + if any(len(x) > 0 for x in sums.values()): + print('min:', end='') + for v in sums.values(): + min = np.min(v) + print(f'\t{round(min, 5)}', end='') + print() + print('max:', end='') + for v in sums.values(): + max = np.max(v) + print(f'\t{round(max, 5)}', end='') + print() + print('avg:', end='') + for v in sums.values(): + mean = np.mean(v) + print(f'\t{round(mean, 5)}', end='') + print() + print('median:', end='') + for v in sums.values(): + median = np.median(v) + print(f'\t{round(median, 5)}', end='') + print() + + return ret + +if __name__ == "__main__": + args = parser.parse_args() + main(args, print_output=True) \ No newline at end of file diff --git a/fairseq/examples/data2vec/tasks/__init__.py b/fairseq/examples/data2vec/tasks/__init__.py new file mode 100644 index 0000000..a7422e4 --- /dev/null +++ b/fairseq/examples/data2vec/tasks/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .image_pretraining import ImagePretrainingTask, ImagePretrainingConfig +from .image_classification import ImageClassificationTask, ImageClassificationConfig +from .mae_image_pretraining import MaeImagePretrainingTask, MaeImagePretrainingConfig + + +__all__ = [ + "ImageClassificationTask", + "ImageClassificationConfig", + "ImagePretrainingTask", + "ImagePretrainingConfig", + "MaeImagePretrainingTask", + "MaeImagePretrainingConfig", +] \ No newline at end of file diff --git a/fairseq/examples/data2vec/tasks/audio_classification.py b/fairseq/examples/data2vec/tasks/audio_classification.py new file mode 100644 index 0000000..2925a04 --- /dev/null +++ b/fairseq/examples/data2vec/tasks/audio_classification.py @@ -0,0 +1,167 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +import numpy as np +import math +import torch + +from sklearn import metrics as sklearn_metrics +from dataclasses import dataclass + +from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig +from fairseq.tasks import register_task +from fairseq.logging import metrics + +from ..data.add_class_target_dataset import AddClassTargetDataset + + +logger = logging.getLogger(__name__) + + +@dataclass +class AudioClassificationConfig(AudioPretrainingConfig): + label_descriptors: str = "label_descriptors.csv" + labels: str = "lbl" + + +@register_task("audio_classification", dataclass=AudioClassificationConfig) +class AudioClassificationTask(AudioPretrainingTask): + """ """ + + cfg: AudioClassificationConfig + + def __init__( + self, + cfg: AudioClassificationConfig, + ): + super().__init__(cfg) + + self.state.add_factory("labels", self.load_labels) + + def load_labels(self): + labels = {} + path = os.path.join(self.cfg.data, self.cfg.label_descriptors) + with open(path, "r") as ldf: + for line in ldf: + if line.strip() == "": + continue + items = line.split(",") + idx = items[0] + lbl = items[1] + assert lbl not in labels, lbl + labels[lbl] = idx + return labels + + @property + def labels(self): + return self.state.labels + + def load_dataset( + self, split: str, task_cfg: AudioClassificationConfig = None, **kwargs + ): + super().load_dataset(split, task_cfg, **kwargs) + + task_cfg = task_cfg or self.cfg + + data_path = self.cfg.data + label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}") + skipped_indices = getattr(self.datasets[split], "skipped_indices", set()) + labels = [] + with open(label_path, "r") as f: + for i, line in enumerate(f): + if i not in skipped_indices: + lbl_items = line.rstrip().split("\t") + labels.append([int(x) for x in lbl_items[2].split(",")]) + + assert len(labels) == len(self.datasets[split]), ( + f"labels length ({len(labels)}) and dataset length " + f"({len(self.datasets[split])}) do not match" + ) + + self.datasets[split] = AddClassTargetDataset( + self.datasets[split], + labels, + multi_class=True, + add_to_input=True, + num_classes=len(self.labels), + ) + + def calculate_stats(self, output, target): + + classes_num = target.shape[-1] + stats = [] + + # Accuracy, only used for single-label classification such as esc-50, not for multiple label one such as AudioSet + # acc = sklearn_metrics.accuracy_score(np.argmax(target, 1), np.argmax(output, 1)) + + # Class-wise statistics + for k in range(classes_num): + # Average precision + avg_precision = sklearn_metrics.average_precision_score( + target[:, k], output[:, k], average=None + ) + + dict = { + "AP": avg_precision, + } + + # # AUC + # try: + # auc = sklearn_metrics.roc_auc_score(target[:, k], output[:, k], average=None) + # except: + # auc = 0 + # + # # Precisions, recalls + # (precisions, recalls, thresholds) = sklearn_metrics.precision_recall_curve( + # target[:, k], output[:, k] + # ) + # + # # FPR, TPR + # (fpr, tpr, thresholds) = sklearn_metrics.roc_curve(target[:, k], output[:, k]) + # + # save_every_steps = 1000 # Sample statistics to reduce size + # dict = { + # "precisions": precisions[0::save_every_steps], + # "recalls": recalls[0::save_every_steps], + # "AP": avg_precision, + # "fpr": fpr[0::save_every_steps], + # "fnr": 1.0 - tpr[0::save_every_steps], + # "auc": auc, + # # note acc is not class-wise, this is just to keep consistent with other metrics + # "acc": acc, + # } + stats.append(dict) + + return stats + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + return loss, sample_size, logging_output + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + if "_predictions" in logging_outputs[0]: + metrics.log_concat_tensor( + "_predictions", + torch.cat([l["_predictions"].cpu() for l in logging_outputs], dim=0), + ) + metrics.log_concat_tensor( + "_targets", + torch.cat([l["_targets"].cpu() for l in logging_outputs], dim=0), + ) + + def compute_stats(meters): + if meters["_predictions"].tensor.shape[0] < 100: + return 0 + stats = self.calculate_stats( + meters["_predictions"].tensor, meters["_targets"].tensor + ) + return np.nanmean([stat["AP"] for stat in stats]) + + metrics.log_derived("mAP", compute_stats) diff --git a/fairseq/examples/data2vec/tasks/image_classification.py b/fairseq/examples/data2vec/tasks/image_classification.py new file mode 100644 index 0000000..1ea4c2a --- /dev/null +++ b/fairseq/examples/data2vec/tasks/image_classification.py @@ -0,0 +1,129 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import os.path as osp +import logging + +from dataclasses import dataclass +import torch +from torchvision import transforms + +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import register_task +from fairseq.logging import metrics + +try: + from ..data import ImageDataset +except: + import sys + + sys.path.append("..") + from data import ImageDataset + +from .image_pretraining import ( + ImagePretrainingConfig, + ImagePretrainingTask, + IMG_EXTENSIONS, +) + +logger = logging.getLogger(__name__) + + +@dataclass +class ImageClassificationConfig(ImagePretrainingConfig): + pass + + +@register_task("image_classification", dataclass=ImageClassificationConfig) +class ImageClassificationTask(ImagePretrainingTask): + + cfg: ImageClassificationConfig + + @classmethod + def setup_task(cls, cfg: ImageClassificationConfig, **kwargs): + return cls(cfg) + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + cfg = task_cfg or self.cfg + + path_with_split = osp.join(data_path, split) + if osp.exists(path_with_split): + data_path = path_with_split + + from timm.data import create_transform + + if split == "train": + # this should always dispatch to transforms_imagenet_train + transform = create_transform( + input_size=cfg.input_size, + is_training=True, + auto_augment="rand-m9-mstd0.5-inc1", + interpolation="bicubic", + re_prob=0.25, + re_mode="pixel", + re_count=1, + mean=cfg.normalization_mean, + std=cfg.normalization_std, + ) + if not cfg.input_size > 32: + transform.transforms[0] = transforms.RandomCrop( + cfg.input_size, padding=4 + ) + else: + t = [] + if cfg.input_size > 32: + crop_pct = 1 + if cfg.input_size < 384: + crop_pct = 224 / 256 + size = int(cfg.input_size / crop_pct) + t.append( + transforms.Resize( + size, interpolation=3 + ), # to maintain same ratio w.r.t. 224 images + ) + t.append(transforms.CenterCrop(cfg.input_size)) + + t.append(transforms.ToTensor()) + t.append( + transforms.Normalize(cfg.normalization_mean, cfg.normalization_std) + ) + transform = transforms.Compose(t) + logger.info(transform) + + self.datasets[split] = ImageDataset( + root=data_path, + extensions=IMG_EXTENSIONS, + load_classes=True, + transform=transform, + ) + for k in self.datasets.keys(): + if k != split: + assert self.datasets[k].classes == self.datasets[split].classes + + def build_model(self, model_cfg: FairseqDataclass, from_checkpoint=False): + model = super().build_model(model_cfg, from_checkpoint) + + actualized_cfg = getattr(model, "cfg", None) + if actualized_cfg is not None: + if hasattr(actualized_cfg, "pretrained_model_args"): + model_cfg.pretrained_model_args = actualized_cfg.pretrained_model_args + + return model + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + if "correct" in logging_outputs[0]: + zero = torch.scalar_tensor(0.0) + correct = sum(log.get("correct", zero) for log in logging_outputs) + metrics.log_scalar_sum("_correct", correct) + + metrics.log_derived( + "accuracy", + lambda meters: 100 * meters["_correct"].sum / meters["sample_size"].sum, + ) diff --git a/fairseq/examples/data2vec/tasks/image_pretraining.py b/fairseq/examples/data2vec/tasks/image_pretraining.py new file mode 100644 index 0000000..cd688fd --- /dev/null +++ b/fairseq/examples/data2vec/tasks/image_pretraining.py @@ -0,0 +1,110 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import sys +import os.path as osp + +from dataclasses import dataclass, field +from typing import List +from omegaconf import MISSING + +import torch +from torchvision import transforms + +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + +try: + from ..data import ImageDataset +except: + sys.path.append("..") + from data import ImageDataset + +logger = logging.getLogger(__name__) + +IMG_EXTENSIONS = { + ".jpg", + ".jpeg", + ".png", + ".ppm", + ".bmp", + ".pgm", + ".tif", + ".tiff", + ".webp", +} + + +@dataclass +class ImagePretrainingConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + input_size: int = 224 + normalization_mean: List[float] = (0.485, 0.456, 0.406) + normalization_std: List[float] = (0.229, 0.224, 0.225) + + +@register_task("image_pretraining", dataclass=ImagePretrainingConfig) +class ImagePretrainingTask(FairseqTask): + """ """ + + cfg: ImagePretrainingConfig + + @classmethod + def setup_task(cls, cfg: ImagePretrainingConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (AudioPretrainingConfig): configuration of this task + """ + + return cls(cfg) + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + cfg = task_cfg or self.cfg + + path_with_split = osp.join(data_path, split) + if osp.exists(path_with_split): + data_path = path_with_split + + transform = transforms.Compose( + [ + transforms.ColorJitter(0.4, 0.4, 0.4), + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomResizedCrop( + size=cfg.input_size, + interpolation=transforms.InterpolationMode.BICUBIC, + ), + transforms.ToTensor(), + transforms.Normalize( + mean=torch.tensor(cfg.normalization_mean), + std=torch.tensor(cfg.normalization_std), + ), + ] + ) + + logger.info(transform) + + self.datasets[split] = ImageDataset( + root=data_path, + extensions=IMG_EXTENSIONS, + load_classes=False, + transform=transform, + ) + + @property + def source_dictionary(self): + return None + + @property + def target_dictionary(self): + return None + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return sys.maxsize, sys.maxsize diff --git a/fairseq/examples/data2vec/tasks/mae_image_classification.py b/fairseq/examples/data2vec/tasks/mae_image_classification.py new file mode 100644 index 0000000..1bf9358 --- /dev/null +++ b/fairseq/examples/data2vec/tasks/mae_image_classification.py @@ -0,0 +1,100 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import sys +import torch + +from typing import Optional +from dataclasses import dataclass, field +from omegaconf import MISSING + +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task +from fairseq.logging import metrics + +try: + from ..data import MaeFinetuningImageDataset +except: + sys.path.append("..") + from data import MaeFinetuningImageDataset + +logger = logging.getLogger(__name__) + + +@dataclass +class MaeImageClassificationConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + input_size: int = 224 + local_cache_path: Optional[str] = None + + rebuild_batches: bool = True + + +@register_task("mae_image_classification", dataclass=MaeImageClassificationConfig) +class MaeImageClassificationTask(FairseqTask): + """ """ + + cfg: MaeImageClassificationConfig + + @classmethod + def setup_task(cls, cfg: MaeImageClassificationConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (AudioPretrainingConfig): configuration of this task + """ + + return cls(cfg) + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + cfg = task_cfg or self.cfg + + self.datasets[split] = MaeFinetuningImageDataset( + root=data_path, + split=split, + is_train=split == "train", + input_size=cfg.input_size, + local_cache_path=cfg.local_cache_path, + shuffle=split == "train", + ) + + def build_model(self, model_cfg: FairseqDataclass, from_checkpoint=False): + model = super().build_model(model_cfg, from_checkpoint) + + actualized_cfg = getattr(model, "cfg", None) + if actualized_cfg is not None: + if hasattr(actualized_cfg, "pretrained_model_args"): + model_cfg.pretrained_model_args = actualized_cfg.pretrained_model_args + + return model + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + if "correct" in logging_outputs[0]: + zero = torch.scalar_tensor(0.0) + correct = sum(log.get("correct", zero) for log in logging_outputs) + metrics.log_scalar_sum("_correct", correct) + + metrics.log_derived( + "accuracy", + lambda meters: 100 * meters["_correct"].sum / meters["sample_size"].sum, + ) + + @property + def source_dictionary(self): + return None + + @property + def target_dictionary(self): + return None + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return sys.maxsize, sys.maxsize diff --git a/fairseq/examples/data2vec/tasks/mae_image_pretraining.py b/fairseq/examples/data2vec/tasks/mae_image_pretraining.py new file mode 100644 index 0000000..35a1489 --- /dev/null +++ b/fairseq/examples/data2vec/tasks/mae_image_pretraining.py @@ -0,0 +1,119 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import sys + +from typing import Optional, List +from dataclasses import dataclass, field +from omegaconf import MISSING, II + +from fairseq.data import SubsampleDataset +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + +try: + from ..data import MaeImageDataset +except: + sys.path.append("..") + from data import MaeImageDataset + +logger = logging.getLogger(__name__) + + +@dataclass +class ImageMaskingConfig: + patch_size: int = II("model.modalities.image.patch_size") + mask_prob: float = II("model.modalities.image.mask_prob") + mask_prob_adjust: float = II("model.modalities.image.mask_prob_adjust") + mask_length: int = II("model.modalities.image.mask_length") + inverse_mask: bool = II("model.modalities.image.inverse_mask") + mask_dropout: float = II("model.modalities.image.mask_dropout") + clone_batch: int = II("model.clone_batch") + expand_adjacent: bool = False + non_overlapping: bool = False + + +@dataclass +class MaeImagePretrainingConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + multi_data: Optional[List[str]] = None + input_size: int = 224 + local_cache_path: Optional[str] = None + key: str = "imgs" + + beit_transforms: bool = False + target_transform: bool = False + no_transform: bool = False + + rebuild_batches: bool = True + + precompute_mask_config: Optional[ImageMaskingConfig] = None + + subsample: float = 1 + seed: int = II("common.seed") + dataset_type: str = "imagefolder" + + +@register_task("mae_image_pretraining", dataclass=MaeImagePretrainingConfig) +class MaeImagePretrainingTask(FairseqTask): + """ """ + + cfg: MaeImagePretrainingConfig + + @classmethod + def setup_task(cls, cfg: MaeImagePretrainingConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (AudioPretrainingConfig): configuration of this task + """ + + return cls(cfg) + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + cfg = task_cfg or self.cfg + + compute_mask = cfg.precompute_mask_config is not None + mask_args = {} + if compute_mask: + mask_args = cfg.precompute_mask_config + + self.datasets[split] = MaeImageDataset( + root=data_path if cfg.multi_data is None else cfg.multi_data, + split=split, + input_size=cfg.input_size, + local_cache_path=cfg.local_cache_path, + key=cfg.key, + beit_transforms=cfg.beit_transforms, + target_transform=cfg.target_transform, + no_transform=cfg.no_transform, + compute_mask=compute_mask, + dataset_type=cfg.dataset_type, + **mask_args, + ) + + if cfg.subsample < 1: + self.datasets[split] = SubsampleDataset( + self.datasets[split], + cfg.subsample, + shuffle=True, + seed=cfg.seed, + ) + + @property + def source_dictionary(self): + return None + + @property + def target_dictionary(self): + return None + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return sys.maxsize, sys.maxsize diff --git a/fairseq/examples/data2vec/tasks/multimodal.py b/fairseq/examples/data2vec/tasks/multimodal.py new file mode 100644 index 0000000..74648e9 --- /dev/null +++ b/fairseq/examples/data2vec/tasks/multimodal.py @@ -0,0 +1,165 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import sys + +from dataclasses import dataclass +from typing import Optional, List +from omegaconf import II + +from fairseq.data.iterators import GroupedEpochBatchIterator + +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task +from fairseq.tasks.audio_pretraining import AudioPretrainingConfig, AudioPretrainingTask +from fairseq.tasks.masked_lm import MaskedLMConfig, MaskedLMTask +from .mae_image_pretraining import MaeImagePretrainingConfig, MaeImagePretrainingTask +from examples.data2vec.data.modality import Modality + +from fairseq.data.audio.multi_modality_dataset import ( + MultiModalityDataset, + ModalityDatasetItem, +) + + +@dataclass +class MultimodalPretrainingConfig(FairseqDataclass): + audio: Optional[AudioPretrainingConfig] = None + image: Optional[MaeImagePretrainingConfig] = None + text: Optional[MaskedLMConfig] = None + + audio_ratio: float = 1 + image_ratio: float = 1 + text_ratio: float = 1 + + max_tokens: Optional[int] = II("dataset.max_tokens") + batch_size: Optional[int] = II("dataset.batch_size") + update_freq: List[int] = II("optimization.update_freq") + + rebuild_batches: bool = True + + +@register_task("multimodal_pretraining", dataclass=MultimodalPretrainingConfig) +class MultimodalPretrainingTask(FairseqTask): + """ """ + + cfg: MultimodalPretrainingConfig + + def __init__(self, cfg: MultimodalPretrainingConfig): + super().__init__(cfg) + self.audio_task = ( + AudioPretrainingTask(cfg.audio) if cfg.audio is not None else None + ) + self.image_task = ( + MaeImagePretrainingTask(cfg.image) if cfg.image is not None else None + ) + self.text_task = MaskedLMTask(cfg.text) if cfg.text is not None else None + + self.mult_ratios = [] + + @classmethod + def setup_task(cls, cfg: MultimodalPretrainingConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (AudioPretrainingConfig): configuration of this task + """ + + return cls(cfg) + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + datasets = [] + self.mult_ratios = [] + + def load_ds(task, name, ratio): + if task is not None: + task.load_dataset(split) + ds = ModalityDatasetItem( + datasetname=name, + dataset=task.dataset(split), + max_positions=task.max_positions(), + max_tokens=self.cfg.max_tokens, + max_sentences=self.cfg.batch_size, + ) + datasets.append(ds) + self.mult_ratios.append(ratio) + + load_ds(self.audio_task, Modality.AUDIO, self.cfg.audio_ratio) + load_ds(self.image_task, Modality.IMAGE, self.cfg.image_ratio) + load_ds(self.text_task, Modality.TEXT, self.cfg.text_ratio) + + assert len(datasets) > 0 + + self.datasets[split] = MultiModalityDataset(datasets) + + @property + def supported_modalities(self): + modalities = [] + if self.cfg.text is not None: + modalities.append(Modality.TEXT) + if self.cfg.audio is not None: + modalities.append(Modality.AUDIO) + if self.cfg.image is not None: + modalities.append(Modality.IMAGE) + + return modalities + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=0, + data_buffer_size=0, + disable_iterator_cache=False, + skip_remainder_batch=False, + grouped_shuffling=False, + update_epoch_batch_itr=False, + ): + + # initialize the dataset with the correct starting epoch + dataset.set_epoch(epoch) + + batch_samplers = dataset.get_batch_samplers( + self.mult_ratios, required_batch_size_multiple, seed + ) + + # return a reusable, sharded iterator + epoch_iter = GroupedEpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_samplers=batch_samplers, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + mult_rate=max(self.cfg.update_freq), + buffer_size=data_buffer_size, + skip_remainder_batch=skip_remainder_batch, + ) + self.dataset_to_epoch_iter[dataset] = {} # refresh it every epoch + return epoch_iter + + @property + def source_dictionary(self): + return None + + @property + def target_dictionary(self): + return None + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return sys.maxsize, sys.maxsize diff --git a/fairseq/examples/discriminative_reranking_nmt/README.md b/fairseq/examples/discriminative_reranking_nmt/README.md new file mode 100644 index 0000000..b155e85 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/README.md @@ -0,0 +1,202 @@ +# Discriminative Reranking for Neural Machine Translation +https://aclanthology.org/2021.acl-long.563/ + +This folder contains source code for training DrNMT, a discriminatively trained reranker for neural machine translation. + +## Data preparation +1. Follow the instructions under `examples/translation` to build a base MT model. Prepare three files, one with source sentences, one with ground truth target sentences, and one with hypotheses generated from the base MT model. Each line in the file contains one sentence in raw text (i.e. no sentencepiece, etc.). Below is an example of the files with _N_ hypotheses for each source sentence. + +``` +# Example of the source sentence file: (The file should contain L lines.) + +source_sentence_1 +source_sentence_2 +source_sentence_3 +... +source_sentence_L + +# Example of the target sentence file: (The file should contain L lines.) + +target_sentence_1 +target_sentence_2 +target_sentence_3 +... +target_sentence_L + +# Example of the hypotheses file: (The file should contain L*N lines.) + +source_sentence_1_hypo_1 +source_sentence_1_hypo_2 +... +source_sentence_1_hypo_N +source_sentence_2_hypo_1 +... +source_sentence_2_hypo_N +... +source_sentence_L_hypo_1 +... +source_sentence_L_hypo_N +``` + +2. Download the [XLMR model](https://github.com/fairinternal/fairseq-py/tree/main/examples/xlmr#pre-trained-models). +``` +wget https://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz +tar zxvf xlmr.base.tar.gz + +# The folder should contain dict.txt, model.pt and sentencepiece.bpe.model. +``` + +3. Prepare scores and BPE data. +* `N`: Number of hypotheses per each source sentence. We use 50 in the paper. +* `SPLIT`: Name of the data split, i.e. train, valid, test. Use split_name, split_name1, split_name2, ..., if there are multiple datasets for a split, e.g. train, train1, valid, valid1. +* `NUM_SHARDS`: Number of shards. Set this to 1 for non-train splits. +* `METRIC`: The metric for DrNMT to optimize for. We support either `bleu` or `ter`. +``` +# For each data split, e.g. train, valid, test, etc., run the following: + +SOURCE_FILE=/path/to/source_sentence_file +TARGET_FILE=/path/to/target_sentence_file +HYPO_FILE=/path/to/hypo_file +XLMR_DIR=/path/to/xlmr +OUTPUT_DIR=/path/to/output + +python scripts/prep_data.py \ + --input-source ${SOURCE_FILE} \ + --input-target ${TARGET_FILE} \ + --input-hypo ${HYPO_FILE} \ + --output-dir ${OUTPUT_DIR} \ + --split $SPLIT + --beam $N \ + --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ + --metric $METRIC \ + --num-shards ${NUM_SHARDS} + +# The script will create ${OUTPUT_DIR}/$METRIC with ${NUM_SHARDS} splits. +# Under split*/input_src, split*/input_tgt and split*/$METRIC, there will be $SPLIT.bpe and $SPLIT.$METRIC files, respectively. + +``` + +4. Pre-process the data into fairseq format. +``` +# use comma to separate if there are more than one train or valid set +for suffix in src tgt ; do + fairseq-preprocess --only-source \ + --trainpref ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/train.bpe \ + --validpref ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/valid.bpe \ + --destdir ${OUTPUT_DIR}/$METRIC/split1/input_${suffix} \ + --workers 60 \ + --srcdict ${XLMR_DIR}/dict.txt +done + +for i in `seq 2 ${NUM_SHARDS}`; do + for suffix in src tgt ; do + fairseq-preprocess --only-source \ + --trainpref ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix}/train.bpe \ + --destdir ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix} \ + --workers 60 \ + --srcdict ${XLMR_DIR}/dict.txt + + ln -s ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/valid* ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix}/. + done + + ln -s ${OUTPUT_DIR}/$METRIC/split1/$METRIC/valid* ${OUTPUT_DIR}/$METRIC/split${i}/$METRIC/. +done +``` + +## Training + +``` +EXP_DIR=/path/to/exp + +# An example of training the model with the config for De-En experiment in the paper. +# The config uses 16 GPUs and 50 hypotheses. +# For training with fewer number of GPUs, set +# distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 16/k +# For training with fewer number of hypotheses, set +# task.mt_beam=N dataset.batch_size=N dataset.required_batch_size_multiple=N + +fairseq-hydra-train -m \ + --config-dir config/ --config-name deen \ + task.data=${OUTPUT_DIR}/$METRIC/split1/ \ + task.num_data_splits=${NUM_SHARDS} \ + model.pretrained_model=${XLMR_DIR}/model.pt \ + common.user_dir=${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ + checkpoint.save_dir=${EXP_DIR} + +``` + +## Inference & scoring +Perform DrNMT reranking (fw + reranker score) +1. Tune weights on valid sets. +``` +# genrate N hypotheses with the base MT model (fw score) +VALID_SOURCE_FILE=/path/to/source_sentences # one sentence per line, converted to the sentencepiece used by the base MT model +VALID_TARGET_FILE=/path/to/target_sentences # one sentence per line in raw text, i.e. no sentencepiece and tokenization +MT_MODEL=/path/to/mt_model +MT_DATA_PATH=/path/to/mt_data + +cat ${VALID_SOURCE_FILE} | \ + fairseq-interactive ${MT_DATA_PATH} \ + --max-tokens 4000 --buffer-size 16 \ + --num-workers 32 --path ${MT_MODEL} \ + --beam $N --nbest $N \ + --post-process sentencepiece &> valid-hypo.out + +# replace "bleu" with "ter" to optimize for TER +python drnmt_rerank.py \ + ${OUTPUT_DIR}/$METRIC/split1/ \ + --path ${EXP_DIR}/checkpoint_best.pt \ + --in-text valid-hypo.out \ + --results-path ${EXP_DIR} \ + --gen-subset valid \ + --target-text ${VALID_TARGET_FILE} \ + --user-dir ${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ + --bpe sentencepiece \ + --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ + --beam $N \ + --batch-size $N \ + --metric bleu \ + --tune + +``` + +2. Apply best weights on test sets +``` +# genrate N hypotheses with the base MT model (fw score) +TEST_SOURCE_FILE=/path/to/source_sentences # one sentence per line, converted to the sentencepiece used by the base MT model + +cat ${TEST_SOURCE_FILE} | \ + fairseq-interactive ${MT_DATA_PATH} \ + --max-tokens 4000 --buffer-size 16 \ + --num-workers 32 --path ${MT_MODEL} \ + --beam $N --nbest $N \ + --post-process sentencepiece &> test-hypo.out + +# replace "bleu" with "ter" to evaluate TER +# Add --target-text for evaluating BLEU/TER, +# otherwise the script will only generate the hypotheses with the highest scores only. +python drnmt_rerank.py \ + ${OUTPUT_DIR}/$METRIC/split1/ \ + --path ${EXP_DIR}/checkpoint_best.pt \ + --in-text test-hypo.out \ + --results-path ${EXP_DIR} \ + --gen-subset test \ + --user-dir ${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ + --bpe sentencepiece \ + --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ + --beam $N \ + --batch-size $N \ + --metric bleu \ + --fw-weight ${BEST_FW_WEIGHT} \ + --lenpen ${BEST_LENPEN} +``` + +## Citation +```bibtex +@inproceedings{lee2021discriminative, + title={Discriminative Reranking for Neural Machine Translation}, + author={Lee, Ann and Auli, Michael and Ranzato, Marc'Aurelio}, + booktitle={ACL}, + year={2021} +} +``` diff --git a/fairseq/examples/discriminative_reranking_nmt/__init__.py b/fairseq/examples/discriminative_reranking_nmt/__init__.py new file mode 100644 index 0000000..0278f6a --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/__init__.py @@ -0,0 +1 @@ +from . import criterions, models, tasks # noqa diff --git a/fairseq/examples/discriminative_reranking_nmt/config/deen.yaml b/fairseq/examples/discriminative_reranking_nmt/config/deen.yaml new file mode 100644 index 0000000..3fc2d5f --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/config/deen.yaml @@ -0,0 +1,56 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 50 + seed: 2 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: bleu + maximize_best_checkpoint_metric: true + +task: + _name: discriminative_reranking_nmt + data: ??? + num_data_splits: ??? + include_src: true + mt_beam: 50 + eval_target_metric: true + target_metric: bleu + +dataset: + batch_size: 50 + num_workers: 6 + required_batch_size_multiple: 50 + valid_subset: ??? + +criterion: + _name: kl_divergence_rereanking + target_dist_norm: minmax + temperature: 0.5 + +optimization: + max_epoch: 200 + lr: [0.00005] + update_freq: [32] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 8000 + total_num_update: 320000 + +model: + _name: discriminative_nmt_reranker + pretrained_model: ??? + classifier_dropout: 0.2 + +distributed_training: + ddp_backend: no_c10d + distributed_world_size: 16 diff --git a/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py b/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py new file mode 100644 index 0000000..7c257c2 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py @@ -0,0 +1,6 @@ +from .discriminative_reranking_criterion import KLDivergenceRerankingCriterion + + +__all__ = [ + "KLDivergenceRerankingCriterion", +] diff --git a/fairseq/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py b/fairseq/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py new file mode 100644 index 0000000..c8f19e3 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py @@ -0,0 +1,139 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import ChoiceEnum, FairseqDataclass + + +_EPSILON = torch.finfo(torch.float32).eps +TARGET_DIST_NORM_CHOICES = ChoiceEnum(["none", "minmax"]) + + +@dataclass +class KLDivergenceRerankingCriterionConfig(FairseqDataclass): + target_dist_norm: TARGET_DIST_NORM_CHOICES = field( + default="none", + metadata={"help": "method to normalize the range of target scores"}, + ) + temperature: float = field( + default=1.0, + metadata={"help": "temperature in softmax for target distributions"}, + ) + forward_batch_size: int = field( + default=32, + metadata={ + "help": "number of hypotheses per batch for model forward (set a value smaller than --mt-beam to avoid OOM when training with a large beam size)" + }, + ) + + +@register_criterion( + "kl_divergence_rereanking", dataclass=KLDivergenceRerankingCriterionConfig +) +class KLDivergenceRerankingCriterion(FairseqCriterion): + def __init__( + self, task, target_dist_norm, temperature, forward_batch_size, + ): + super().__init__(task) + self.target_dist_norm = target_dist_norm + self.temperature = temperature + self.forward_batch_size = forward_batch_size + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + + sample_size = sample["id"].numel() + assert sample_size % self.task.cfg.mt_beam == 0, ( + f"sample_size ({sample_size}) cannot be divided by beam size ({self.task.cfg.mt_beam})." + f"Please set --required-batch-size-multiple={self.task.cfg.mt_beam}." + ) + + # split into smaller batches for model forward + batch_out = [] + for i in range(0, sample_size, self.forward_batch_size): + j = min(i + self.forward_batch_size, sample_size) + + out = model( + src_tokens=sample["net_input"]["src_tokens"][i:j, :], + src_lengths=sample["net_input"]["src_lengths"][i:j], + ) + + batch_out.append( + model.sentence_forward(out, sample["net_input"]["src_tokens"][i:j, :]) + ) + + batch_out = torch.cat(batch_out, dim=0).view( + self.task.cfg.mt_beam, sample_size // self.task.cfg.mt_beam, -1 + ) # T x B x C + if model.joint_classification == "sent": + batch_out = model.joint_forward(batch_out) + scores = model.classification_forward(batch_out.view(sample_size, 1, -1)).view( + -1, self.task.cfg.mt_beam + ) # input: B x T x C + + loss = self.compute_kl_loss( + scores, sample["target"][:, 0].view(-1, self.task.cfg.mt_beam) + ) + + sample_size = sample_size // self.task.cfg.mt_beam + + logging_output = { + "loss": loss.detach(), + "ntokens": sample["ntokens"], + "nsentences": sample_size * self.task.cfg.mt_beam, + "sample_size": sample_size, + "scores": scores.detach(), + } + + return loss, sample_size, logging_output + + def compute_kl_loss(self, logits, target): + norm_target = target + if self.target_dist_norm == "minmax": + min_v = torch.min(target, 1, keepdim=True).values + max_v = torch.max(target, 1, keepdim=True).values + norm_target = (target - min_v) / (max_v - min_v + _EPSILON) + + target_dist = F.softmax( + norm_target / self.temperature, dim=-1, dtype=torch.float32 + ) + model_dist = F.log_softmax(logits, dim=-1, dtype=torch.float32) + loss = -(target_dist * model_dist - target_dist * target_dist.log()).sum() + return loss + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + loss = loss_sum / sample_size / math.log(2) + metrics.log_scalar("loss", loss, sample_size, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/examples/discriminative_reranking_nmt/drnmt_rerank.py b/fairseq/examples/discriminative_reranking_nmt/drnmt_rerank.py new file mode 100644 index 0000000..2e0fc2b --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/drnmt_rerank.py @@ -0,0 +1,364 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Score raw text with a trained model. +""" + +from collections import namedtuple +import logging +from multiprocessing import Pool +import sys +import os +import random + +import numpy as np +import sacrebleu +import torch + +from fairseq import checkpoint_utils, options, utils + + +logger = logging.getLogger("fairseq_cli.drnmt_rerank") +logger.setLevel(logging.INFO) + +Batch = namedtuple("Batch", "ids src_tokens src_lengths") + + +pool_init_variables = {} + + +def init_loaded_scores(mt_scores, model_scores, hyp, ref): + global pool_init_variables + pool_init_variables["mt_scores"] = mt_scores + pool_init_variables["model_scores"] = model_scores + pool_init_variables["hyp"] = hyp + pool_init_variables["ref"] = ref + + +def parse_fairseq_gen(filename, task): + source = {} + hypos = {} + scores = {} + with open(filename, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line.startswith("S-"): # source + uid, text = line.split("\t", 1) + uid = int(uid[2:]) + source[uid] = text + elif line.startswith("D-"): # hypo + uid, score, text = line.split("\t", 2) + uid = int(uid[2:]) + if uid not in hypos: + hypos[uid] = [] + scores[uid] = [] + hypos[uid].append(text) + scores[uid].append(float(score)) + else: + continue + + source_out = [source[i] for i in range(len(hypos))] + hypos_out = [h for i in range(len(hypos)) for h in hypos[i]] + scores_out = [s for i in range(len(scores)) for s in scores[i]] + + return source_out, hypos_out, scores_out + + +def read_target(filename): + with open(filename, "r", encoding="utf-8") as f: + output = [line.strip() for line in f] + return output + + +def make_batches(args, src, hyp, task, max_positions, encode_fn): + assert len(src) * args.beam == len( + hyp + ), f"Expect {len(src) * args.beam} hypotheses for {len(src)} source sentences with beam size {args.beam}. Got {len(hyp)} hypotheses intead." + hyp_encode = [ + task.source_dictionary.encode_line(encode_fn(h), add_if_not_exist=False).long() + for h in hyp + ] + if task.cfg.include_src: + src_encode = [ + task.source_dictionary.encode_line( + encode_fn(s), add_if_not_exist=False + ).long() + for s in src + ] + tokens = [(src_encode[i // args.beam], h) for i, h in enumerate(hyp_encode)] + lengths = [(t1.numel(), t2.numel()) for t1, t2 in tokens] + else: + tokens = [(h,) for h in hyp_encode] + lengths = [(h.numel(),) for h in hyp_encode] + + itr = task.get_batch_iterator( + dataset=task.build_dataset_for_inference(tokens, lengths), + max_tokens=args.max_tokens, + max_sentences=args.batch_size, + max_positions=max_positions, + ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, + ).next_epoch_itr(shuffle=False) + + for batch in itr: + yield Batch( + ids=batch["id"], + src_tokens=batch["net_input"]["src_tokens"], + src_lengths=batch["net_input"]["src_lengths"], + ) + + +def decode_rerank_scores(args): + if args.max_tokens is None and args.batch_size is None: + args.batch_size = 1 + + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + + # Load ensemble + logger.info("loading model(s) from {}".format(args.path)) + models, _model_args, task = checkpoint_utils.load_model_ensemble_and_task( + [args.path], arg_overrides=eval(args.model_overrides), + ) + + for model in models: + if args.fp16: + model.half() + if use_cuda: + model.cuda() + + # Initialize generator + generator = task.build_generator(args) + + # Handle tokenization and BPE + tokenizer = task.build_tokenizer(args) + bpe = task.build_bpe(args) + + def encode_fn(x): + if tokenizer is not None: + x = tokenizer.encode(x) + if bpe is not None: + x = bpe.encode(x) + return x + + max_positions = utils.resolve_max_positions( + task.max_positions(), *[model.max_positions() for model in models] + ) + + src, hyp, mt_scores = parse_fairseq_gen(args.in_text, task) + model_scores = {} + logger.info("decode reranker score") + for batch in make_batches(args, src, hyp, task, max_positions, encode_fn): + src_tokens = batch.src_tokens + src_lengths = batch.src_lengths + if use_cuda: + src_tokens = src_tokens.cuda() + src_lengths = src_lengths.cuda() + + sample = { + "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}, + } + scores = task.inference_step(generator, models, sample) + + for id, sc in zip(batch.ids.tolist(), scores.tolist()): + model_scores[id] = sc[0] + + model_scores = [model_scores[i] for i in range(len(model_scores))] + + return src, hyp, mt_scores, model_scores + + +def get_score(mt_s, md_s, w1, lp, tgt_len): + return mt_s / (tgt_len ** lp) * w1 + md_s + + +def get_best_hyps(mt_scores, md_scores, hypos, fw_weight, lenpen, beam): + assert len(mt_scores) == len(md_scores) and len(mt_scores) == len(hypos) + hypo_scores = [] + best_hypos = [] + best_scores = [] + offset = 0 + for i in range(len(hypos)): + tgt_len = len(hypos[i].split()) + hypo_scores.append( + get_score(mt_scores[i], md_scores[i], fw_weight, lenpen, tgt_len) + ) + + if (i + 1) % beam == 0: + max_i = np.argmax(hypo_scores) + best_hypos.append(hypos[offset + max_i]) + best_scores.append(hypo_scores[max_i]) + hypo_scores = [] + offset += beam + return best_hypos, best_scores + + +def eval_metric(args, hypos, ref): + if args.metric == "bleu": + score = sacrebleu.corpus_bleu(hypos, [ref]).score + else: + score = sacrebleu.corpus_ter(hypos, [ref]).score + + return score + + +def score_target_hypo(args, fw_weight, lp): + mt_scores = pool_init_variables["mt_scores"] + model_scores = pool_init_variables["model_scores"] + hyp = pool_init_variables["hyp"] + ref = pool_init_variables["ref"] + best_hypos, _ = get_best_hyps( + mt_scores, model_scores, hyp, fw_weight, lp, args.beam + ) + rerank_eval = None + if ref: + rerank_eval = eval_metric(args, best_hypos, ref) + print(f"fw_weight {fw_weight}, lenpen {lp}, eval {rerank_eval}") + + return rerank_eval + + +def print_result(best_scores, best_hypos, output_file): + for i, (s, h) in enumerate(zip(best_scores, best_hypos)): + print(f"{i}\t{s}\t{h}", file=output_file) + + +def main(args): + utils.import_user_module(args) + + src, hyp, mt_scores, model_scores = decode_rerank_scores(args) + + assert ( + not args.tune or args.target_text is not None + ), "--target-text has to be set when tuning weights" + if args.target_text: + ref = read_target(args.target_text) + assert len(src) == len( + ref + ), f"different numbers of source and target sentences ({len(src)} vs. {len(ref)})" + + orig_best_hypos = [hyp[i] for i in range(0, len(hyp), args.beam)] + orig_eval = eval_metric(args, orig_best_hypos, ref) + + if args.tune: + logger.info("tune weights for reranking") + + random_params = np.array( + [ + [ + random.uniform( + args.lower_bound_fw_weight, args.upper_bound_fw_weight + ), + random.uniform(args.lower_bound_lenpen, args.upper_bound_lenpen), + ] + for k in range(args.num_trials) + ] + ) + + logger.info("launching pool") + with Pool( + 32, + initializer=init_loaded_scores, + initargs=(mt_scores, model_scores, hyp, ref), + ) as p: + rerank_scores = p.starmap( + score_target_hypo, + [ + (args, random_params[i][0], random_params[i][1],) + for i in range(args.num_trials) + ], + ) + if args.metric == "bleu": + best_index = np.argmax(rerank_scores) + else: + best_index = np.argmin(rerank_scores) + best_fw_weight = random_params[best_index][0] + best_lenpen = random_params[best_index][1] + else: + assert ( + args.lenpen is not None and args.fw_weight is not None + ), "--lenpen and --fw-weight should be set" + best_fw_weight, best_lenpen = args.fw_weight, args.lenpen + + best_hypos, best_scores = get_best_hyps( + mt_scores, model_scores, hyp, best_fw_weight, best_lenpen, args.beam + ) + + if args.results_path is not None: + os.makedirs(args.results_path, exist_ok=True) + output_path = os.path.join( + args.results_path, "generate-{}.txt".format(args.gen_subset), + ) + with open(output_path, "w", buffering=1, encoding="utf-8") as o: + print_result(best_scores, best_hypos, o) + else: + print_result(best_scores, best_hypos, sys.stdout) + + if args.target_text: + rerank_eval = eval_metric(args, best_hypos, ref) + print(f"before reranking, {args.metric.upper()}:", orig_eval) + print( + f"after reranking with fw_weight={best_fw_weight}, lenpen={best_lenpen}, {args.metric.upper()}:", + rerank_eval, + ) + + +def cli_main(): + parser = options.get_generation_parser(interactive=True) + + parser.add_argument( + "--in-text", + default=None, + required=True, + help="text from fairseq-interactive output, containing source sentences and hypotheses", + ) + parser.add_argument("--target-text", default=None, help="reference text") + parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu") + parser.add_argument( + "--tune", + action="store_true", + help="if set, tune weights on fw scores and lenpen instead of applying fixed weights for reranking", + ) + parser.add_argument( + "--lower-bound-fw-weight", + default=0.0, + type=float, + help="lower bound of search space", + ) + parser.add_argument( + "--upper-bound-fw-weight", + default=3, + type=float, + help="upper bound of search space", + ) + parser.add_argument( + "--lower-bound-lenpen", + default=0.0, + type=float, + help="lower bound of search space", + ) + parser.add_argument( + "--upper-bound-lenpen", + default=3, + type=float, + help="upper bound of search space", + ) + parser.add_argument( + "--fw-weight", type=float, default=None, help="weight on the fw model score" + ) + parser.add_argument( + "--num-trials", + default=1000, + type=int, + help="number of trials to do for random search", + ) + + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/discriminative_reranking_nmt/models/__init__.py b/fairseq/examples/discriminative_reranking_nmt/models/__init__.py new file mode 100644 index 0000000..c593ea5 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/models/__init__.py @@ -0,0 +1,6 @@ +from .discriminative_reranking_model import DiscriminativeNMTReranker + + +__all__ = [ + "DiscriminativeNMTReranker", +] diff --git a/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py b/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py new file mode 100644 index 0000000..e4b5887 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py @@ -0,0 +1,365 @@ +from dataclasses import dataclass, field +import os + +import torch +import torch.nn as nn + +from fairseq import utils +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import ( + BaseFairseqModel, + register_model, +) + +from fairseq.models.roberta.model import RobertaClassificationHead + +from fairseq.modules import ( + LayerNorm, + TransformerSentenceEncoder, + TransformerSentenceEncoderLayer, +) + + +ACTIVATION_FN_CHOICES = ChoiceEnum(utils.get_available_activation_fns()) +JOINT_CLASSIFICATION_CHOICES = ChoiceEnum(["none", "sent"]) +SENTENCE_REP_CHOICES = ChoiceEnum(["head", "meanpool", "maxpool"]) + + +def update_init_roberta_model_state(state): + """ + update the state_dict of a Roberta model for initializing + weights of the BertRanker + """ + for k in list(state.keys()): + if ".lm_head." in k or "version" in k: + del state[k] + continue + # remove 'encoder/decoder.sentence_encoder.' from the key + assert k.startswith("encoder.sentence_encoder.") or k.startswith( + "decoder.sentence_encoder." + ), f"Cannot recognize parameter name {k}" + if "layernorm_embedding" in k: + new_k = k.replace(".layernorm_embedding.", ".emb_layer_norm.") + state[new_k[25:]] = state[k] + else: + state[k[25:]] = state[k] + del state[k] + + +class BaseRanker(nn.Module): + def __init__(self, args, task): + super().__init__() + + self.separator_token = task.dictionary.eos() + self.padding_idx = task.dictionary.pad() + + def forward(self, src_tokens): + raise NotImplementedError + + def get_segment_labels(self, src_tokens): + segment_boundary = (src_tokens == self.separator_token).long() + segment_labels = ( + segment_boundary.cumsum(dim=1) + - segment_boundary + - (src_tokens == self.padding_idx).long() + ) + + return segment_labels + + def get_positions(self, src_tokens, segment_labels): + segment_positions = ( + torch.arange(src_tokens.shape[1]) + .to(src_tokens.device) + .repeat(src_tokens.shape[0], 1) + ) + segment_boundary = (src_tokens == self.separator_token).long() + _, col_idx = (segment_positions * segment_boundary).nonzero(as_tuple=True) + col_idx = torch.cat([torch.zeros(1).type_as(col_idx), col_idx]) + offset = torch.cat( + [ + torch.zeros(1).type_as(segment_boundary), + segment_boundary.sum(dim=1).cumsum(dim=0)[:-1], + ] + ) + segment_positions -= col_idx[segment_labels + offset.unsqueeze(1)] * ( + segment_labels != 0 + ) + + padding_mask = src_tokens.ne(self.padding_idx) + segment_positions = (segment_positions + 1) * padding_mask.type_as( + segment_positions + ) + self.padding_idx + + return segment_positions + + +class BertRanker(BaseRanker): + def __init__(self, args, task): + super(BertRanker, self).__init__(args, task) + + init_model = getattr(args, "pretrained_model", "") + self.joint_layers = nn.ModuleList() + if os.path.isfile(init_model): + print(f"initialize weight from {init_model}") + + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + os.path.dirname(init_model), + checkpoint_file=os.path.basename(init_model), + ) + + in_state_dict = x["models"][0].state_dict() + init_args = x["args"].model + + num_positional_emb = init_args.max_positions + task.dictionary.pad() + 1 + + # follow the setup in roberta + self.model = TransformerSentenceEncoder( + padding_idx=task.dictionary.pad(), + vocab_size=len(task.dictionary), + num_encoder_layers=getattr( + args, "encoder_layers", init_args.encoder_layers + ), + embedding_dim=init_args.encoder_embed_dim, + ffn_embedding_dim=init_args.encoder_ffn_embed_dim, + num_attention_heads=init_args.encoder_attention_heads, + dropout=init_args.dropout, + attention_dropout=init_args.attention_dropout, + activation_dropout=init_args.activation_dropout, + num_segments=2, # add language embeddings + max_seq_len=num_positional_emb, + offset_positions_by_padding=False, + encoder_normalize_before=True, + apply_bert_init=True, + activation_fn=init_args.activation_fn, + freeze_embeddings=args.freeze_embeddings, + n_trans_layers_to_freeze=args.n_trans_layers_to_freeze, + ) + + # still need to learn segment embeddings as we added a second language embedding + if args.freeze_embeddings: + for p in self.model.segment_embeddings.parameters(): + p.requires_grad = False + + update_init_roberta_model_state(in_state_dict) + print("loading weights from the pretrained model") + self.model.load_state_dict( + in_state_dict, strict=False + ) # ignore mismatch in language embeddings + + ffn_embedding_dim = init_args.encoder_ffn_embed_dim + num_attention_heads = init_args.encoder_attention_heads + dropout = init_args.dropout + attention_dropout = init_args.attention_dropout + activation_dropout = init_args.activation_dropout + activation_fn = init_args.activation_fn + + classifier_embed_dim = getattr( + args, "embed_dim", init_args.encoder_embed_dim + ) + if classifier_embed_dim != init_args.encoder_embed_dim: + self.transform_layer = nn.Linear( + init_args.encoder_embed_dim, classifier_embed_dim + ) + else: + self.model = TransformerSentenceEncoder( + padding_idx=task.dictionary.pad(), + vocab_size=len(task.dictionary), + num_encoder_layers=args.encoder_layers, + embedding_dim=args.embed_dim, + ffn_embedding_dim=args.ffn_embed_dim, + num_attention_heads=args.attention_heads, + dropout=args.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + max_seq_len=task.max_positions() + if task.max_positions() + else args.tokens_per_sample, + num_segments=2, + offset_positions_by_padding=False, + encoder_normalize_before=args.encoder_normalize_before, + apply_bert_init=args.apply_bert_init, + activation_fn=args.activation_fn, + ) + + classifier_embed_dim = args.embed_dim + ffn_embedding_dim = args.ffn_embed_dim + num_attention_heads = args.attention_heads + dropout = args.dropout + attention_dropout = args.attention_dropout + activation_dropout = args.activation_dropout + activation_fn = args.activation_fn + + self.joint_classification = args.joint_classification + if args.joint_classification == "sent": + if args.joint_normalize_before: + self.joint_layer_norm = LayerNorm(classifier_embed_dim) + else: + self.joint_layer_norm = None + + self.joint_layers = nn.ModuleList( + [ + TransformerSentenceEncoderLayer( + embedding_dim=classifier_embed_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + ) + for _ in range(args.num_joint_layers) + ] + ) + + self.classifier = RobertaClassificationHead( + classifier_embed_dim, + classifier_embed_dim, + 1, # num_classes + "tanh", + args.classifier_dropout, + ) + + def forward(self, src_tokens, src_lengths): + segment_labels = self.get_segment_labels(src_tokens) + positions = self.get_positions(src_tokens, segment_labels) + + inner_states, _ = self.model( + tokens=src_tokens, + segment_labels=segment_labels, + last_state_only=True, + positions=positions, + ) + + return inner_states[-1].transpose(0, 1) # T x B x C -> B x T x C + + def sentence_forward(self, encoder_out, src_tokens=None, sentence_rep="head"): + # encoder_out: B x T x C + if sentence_rep == "head": + x = encoder_out[:, :1, :] + else: # 'meanpool', 'maxpool' + assert src_tokens is not None, "meanpool requires src_tokens input" + segment_labels = self.get_segment_labels(src_tokens) + padding_mask = src_tokens.ne(self.padding_idx) + encoder_mask = segment_labels * padding_mask.type_as(segment_labels) + + if sentence_rep == "meanpool": + ntokens = torch.sum(encoder_mask, dim=1, keepdim=True) + x = torch.sum( + encoder_out * encoder_mask.unsqueeze(2), dim=1, keepdim=True + ) / ntokens.unsqueeze(2).type_as(encoder_out) + else: # 'maxpool' + encoder_out[ + (encoder_mask == 0).unsqueeze(2).repeat(1, 1, encoder_out.shape[-1]) + ] = -float("inf") + x, _ = torch.max(encoder_out, dim=1, keepdim=True) + + if hasattr(self, "transform_layer"): + x = self.transform_layer(x) + + return x # B x 1 x C + + def joint_forward(self, x): + # x: T x B x C + if self.joint_layer_norm: + x = self.joint_layer_norm(x.transpose(0, 1)) + x = x.transpose(0, 1) + + for layer in self.joint_layers: + x, _ = layer(x, self_attn_padding_mask=None) + return x + + def classification_forward(self, x): + # x: B x T x C + return self.classifier(x) + + +@dataclass +class DiscriminativeNMTRerankerConfig(FairseqDataclass): + pretrained_model: str = field( + default="", metadata={"help": "pretrained model to load"} + ) + sentence_rep: SENTENCE_REP_CHOICES = field( + default="head", + metadata={ + "help": "method to transform the output of the transformer stack to a sentence-level representation" + }, + ) + + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + attention_dropout: float = field( + default=0.0, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN"} + ) + classifier_dropout: float = field( + default=0.0, metadata={"help": "classifier dropout probability"} + ) + embed_dim: int = field(default=768, metadata={"help": "embedding dimension"}) + ffn_embed_dim: int = field( + default=2048, metadata={"help": "embedding dimension for FFN"} + ) + encoder_layers: int = field(default=12, metadata={"help": "num encoder layers"}) + attention_heads: int = field(default=8, metadata={"help": "num attention heads"}) + encoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each encoder block"} + ) + apply_bert_init: bool = field( + default=False, metadata={"help": "use custom param initialization for BERT"} + ) + activation_fn: ACTIVATION_FN_CHOICES = field( + default="relu", metadata={"help": "activation function to use"} + ) + freeze_embeddings: bool = field( + default=False, metadata={"help": "freeze embeddings in the pretrained model"} + ) + n_trans_layers_to_freeze: int = field( + default=0, + metadata={ + "help": "number of layers to freeze in the pretrained transformer model" + }, + ) + + # joint classfication + joint_classification: JOINT_CLASSIFICATION_CHOICES = field( + default="none", + metadata={"help": "method to compute joint features for classification"}, + ) + num_joint_layers: int = field( + default=1, metadata={"help": "number of joint layers"} + ) + joint_normalize_before: bool = field( + default=False, + metadata={"help": "apply layer norm on the input to the joint layer"}, + ) + + +@register_model( + "discriminative_nmt_reranker", dataclass=DiscriminativeNMTRerankerConfig +) +class DiscriminativeNMTReranker(BaseFairseqModel): + @classmethod + def build_model(cls, args, task): + model = BertRanker(args, task) + return DiscriminativeNMTReranker(args, model) + + def __init__(self, args, model): + super().__init__() + + self.model = model + self.sentence_rep = args.sentence_rep + self.joint_classification = args.joint_classification + + def forward(self, src_tokens, src_lengths, **kwargs): + return self.model(src_tokens, src_lengths) + + def sentence_forward(self, encoder_out, src_tokens): + return self.model.sentence_forward(encoder_out, src_tokens, self.sentence_rep) + + def joint_forward(self, x): + return self.model.joint_forward(x) + + def classification_forward(self, x): + return self.model.classification_forward(x) diff --git a/fairseq/examples/discriminative_reranking_nmt/scripts/prep_data.py b/fairseq/examples/discriminative_reranking_nmt/scripts/prep_data.py new file mode 100644 index 0000000..7aa7d37 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/scripts/prep_data.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python + +import argparse +from multiprocessing import Pool +from pathlib import Path + +import sacrebleu +import sentencepiece as spm + + +def read_text_file(filename): + with open(filename, "r") as f: + output = [line.strip() for line in f] + + return output + + +def get_bleu(in_sent, target_sent): + bleu = sacrebleu.corpus_bleu([in_sent], [[target_sent]]) + out = " ".join( + map(str, [bleu.score, bleu.sys_len, bleu.ref_len] + bleu.counts + bleu.totals) + ) + return out + + +def get_ter(in_sent, target_sent): + ter = sacrebleu.corpus_ter([in_sent], [[target_sent]]) + out = " ".join(map(str, [ter.score, ter.num_edits, ter.ref_length])) + return out + + +def init(sp_model): + global sp + sp = spm.SentencePieceProcessor() + sp.Load(sp_model) + + +def process(source_sent, target_sent, hypo_sent, metric): + source_bpe = " ".join(sp.EncodeAsPieces(source_sent)) + hypo_bpe = [" ".join(sp.EncodeAsPieces(h)) for h in hypo_sent] + + if metric == "bleu": + score_str = [get_bleu(h, target_sent) for h in hypo_sent] + else: # ter + score_str = [get_ter(h, target_sent) for h in hypo_sent] + + return source_bpe, hypo_bpe, score_str + + +def main(args): + assert ( + args.split.startswith("train") or args.num_shards == 1 + ), "--num-shards should be set to 1 for valid and test sets" + assert ( + args.split.startswith("train") + or args.split.startswith("valid") + or args.split.startswith("test") + ), "--split should be set to train[n]/valid[n]/test[n]" + + source_sents = read_text_file(args.input_source) + target_sents = read_text_file(args.input_target) + + num_sents = len(source_sents) + assert num_sents == len( + target_sents + ), f"{args.input_source} and {args.input_target} should have the same number of sentences." + + hypo_sents = read_text_file(args.input_hypo) + assert ( + len(hypo_sents) % args.beam == 0 + ), f"Number of hypotheses ({len(hypo_sents)}) cannot be divided by beam size ({args.beam})." + + hypo_sents = [ + hypo_sents[i : i + args.beam] for i in range(0, len(hypo_sents), args.beam) + ] + assert num_sents == len( + hypo_sents + ), f"{args.input_hypo} should contain {num_sents * args.beam} hypotheses but only has {len(hypo_sents) * args.beam}. (--beam={args.beam})" + + output_dir = args.output_dir / args.metric + for ns in range(args.num_shards): + print(f"processing shard {ns+1}/{args.num_shards}") + shard_output_dir = output_dir / f"split{ns+1}" + source_output_dir = shard_output_dir / "input_src" + hypo_output_dir = shard_output_dir / "input_tgt" + metric_output_dir = shard_output_dir / args.metric + + source_output_dir.mkdir(parents=True, exist_ok=True) + hypo_output_dir.mkdir(parents=True, exist_ok=True) + metric_output_dir.mkdir(parents=True, exist_ok=True) + + if args.n_proc > 1: + with Pool( + args.n_proc, initializer=init, initargs=(args.sentencepiece_model,) + ) as p: + output = p.starmap( + process, + [ + (source_sents[i], target_sents[i], hypo_sents[i], args.metric) + for i in range(ns, num_sents, args.num_shards) + ], + ) + else: + init(args.sentencepiece_model) + output = [ + process(source_sents[i], target_sents[i], hypo_sents[i], args.metric) + for i in range(ns, num_sents, args.num_shards) + ] + + with open(source_output_dir / f"{args.split}.bpe", "w") as s_o, open( + hypo_output_dir / f"{args.split}.bpe", "w" + ) as h_o, open(metric_output_dir / f"{args.split}.{args.metric}", "w") as m_o: + for source_bpe, hypo_bpe, score_str in output: + assert len(hypo_bpe) == len(score_str) + for h, m in zip(hypo_bpe, score_str): + s_o.write(f"{source_bpe}\n") + h_o.write(f"{h}\n") + m_o.write(f"{m}\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--input-source", type=Path, required=True) + parser.add_argument("--input-target", type=Path, required=True) + parser.add_argument("--input-hypo", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--split", type=str, required=True) + parser.add_argument("--beam", type=int, required=True) + parser.add_argument("--sentencepiece-model", type=str, required=True) + parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu") + parser.add_argument("--num-shards", type=int, default=1) + parser.add_argument("--n-proc", type=int, default=8) + + args = parser.parse_args() + + main(args) diff --git a/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py b/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py new file mode 100644 index 0000000..2d78ca9 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/tasks/__init__.py @@ -0,0 +1,6 @@ +from .discriminative_reranking_task import DiscriminativeRerankingNMTTask + + +__all__ = [ + "DiscriminativeRerankingNMTTask", +] diff --git a/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py b/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py new file mode 100644 index 0000000..b4ed2a6 --- /dev/null +++ b/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py @@ -0,0 +1,490 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +import itertools +import logging +import os + +import numpy as np +import torch + +from fairseq.logging import metrics +from fairseq.data import ( + ConcatDataset, + ConcatSentencesDataset, + data_utils, + Dictionary, + IdDataset, + indexed_dataset, + NestedDictionaryDataset, + NumSamplesDataset, + NumelDataset, + PrependTokenDataset, + RawLabelDataset, + RightPadDataset, + SortDataset, + TruncateDataset, + TokenBlockDataset, +) +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import FairseqTask, register_task +from omegaconf import II, MISSING + + +EVAL_BLEU_ORDER = 4 +TARGET_METRIC_CHOICES = ChoiceEnum(["bleu", "ter"]) + +logger = logging.getLogger(__name__) + + +@dataclass +class DiscriminativeRerankingNMTConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + num_data_splits: int = field( + default=1, metadata={"help": "total number of data splits"} + ) + no_shuffle: bool = field( + default=False, metadata={"help": "do not shuffle training data"} + ) + max_positions: int = field( + default=512, metadata={"help": "number of positional embeddings to learn"} + ) + include_src: bool = field( + default=False, metadata={"help": "include source sentence"} + ) + mt_beam: int = field(default=50, metadata={"help": "beam size of input hypotheses"}) + eval_target_metric: bool = field( + default=False, + metadata={"help": "evaluation with the target metric during validation"}, + ) + target_metric: TARGET_METRIC_CHOICES = field( + default="bleu", metadata={"help": "name of the target metric to optimize for"} + ) + train_subset: str = field( + default=II("dataset.train_subset"), + metadata={"help": "data subset to use for training (e.g. train, valid, test)"}, + ) + seed: int = field( + default=II("common.seed"), + metadata={"help": "pseudo random number generator seed"}, + ) + + +class RerankerScorer(object): + """Scores the target for a given (source (optional), target) input.""" + + def __init__(self, args, mt_beam): + self.mt_beam = mt_beam + + @torch.no_grad() + def generate(self, models, sample, **kwargs): + """Score a batch of translations.""" + net_input = sample["net_input"] + + assert len(models) == 1, "does not support model ensemble" + model = models[0] + + bs = net_input["src_tokens"].shape[0] + assert ( + model.joint_classification == "none" or bs % self.mt_beam == 0 + ), f"invalid batch size ({bs}) for joint classification with beam size ({self.mt_beam})" + + model.eval() + logits = model(**net_input) + + batch_out = model.sentence_forward(logits, net_input["src_tokens"]) + if model.joint_classification == "sent": + batch_out = model.joint_forward( + batch_out.view(self.mt_beam, bs // self.mt_beam, -1) + ) + scores = model.classification_forward( + batch_out.view(bs, 1, -1) + ) # input: B x T x C + + return scores + + +@register_task( + "discriminative_reranking_nmt", dataclass=DiscriminativeRerankingNMTConfig +) +class DiscriminativeRerankingNMTTask(FairseqTask): + """ + Translation rerank task. + The input can be either (src, tgt) sentence pairs or tgt sentence only. + """ + + cfg: DiscriminativeRerankingNMTConfig + + def __init__(self, cfg: DiscriminativeRerankingNMTConfig, data_dictionary=None): + super().__init__(cfg) + self.dictionary = data_dictionary + self._max_positions = cfg.max_positions + # args.tokens_per_sample = self._max_positions + # self.num_classes = 1 # for model + + @classmethod + def load_dictionary(cls, cfg, filename): + """Load the dictionary from the filename""" + dictionary = Dictionary.load(filename) + dictionary.add_symbol("") # for loading pretrained XLMR model + + return dictionary + + @classmethod + def setup_task(cls, cfg: DiscriminativeRerankingNMTConfig, **kwargs): + # load data dictionary (assume joint dictionary) + data_path = cfg.data + data_dict = cls.load_dictionary( + cfg, os.path.join(data_path, "input_src/dict.txt") + ) + + logger.info("[input] src dictionary: {} types".format(len(data_dict))) + + return DiscriminativeRerankingNMTTask(cfg, data_dict) + + def load_dataset(self, split, epoch=0, combine=False, **kwargs): + """Load a given dataset split (e.g., train, valid, test).""" + if self.cfg.data.endswith("1"): + data_shard = (epoch - 1) % self.cfg.num_data_splits + 1 + data_path = self.cfg.data[:-1] + str(data_shard) + else: + data_path = self.cfg.data + + def get_path(type, data_split): + return os.path.join(data_path, str(type), data_split) + + def make_dataset(type, dictionary, data_split, combine): + split_path = get_path(type, data_split) + + dataset = data_utils.load_indexed_dataset( + split_path, + dictionary, + combine=combine, + ) + return dataset + + def load_split(data_split, metric): + input_src = None + if self.cfg.include_src: + input_src = make_dataset( + "input_src", self.dictionary, data_split, combine=False + ) + assert input_src is not None, "could not find dataset: {}".format( + get_path("input_src", data_split) + ) + + input_tgt = make_dataset( + "input_tgt", self.dictionary, data_split, combine=False + ) + assert input_tgt is not None, "could not find dataset: {}".format( + get_path("input_tgt", data_split) + ) + + label_path = f"{get_path(metric, data_split)}.{metric}" + assert os.path.exists(label_path), f"could not find dataset: {label_path}" + + np_labels = np.loadtxt(label_path) + if self.cfg.target_metric == "ter": + np_labels = -np_labels + label = RawLabelDataset(np_labels) + + return input_src, input_tgt, label + + src_datasets = [] + tgt_datasets = [] + label_datasets = [] + + if split == self.cfg.train_subset: + for k in itertools.count(): + split_k = "train" + (str(k) if k > 0 else "") + prefix = os.path.join(data_path, "input_tgt", split_k) + if not indexed_dataset.dataset_exists(prefix, impl=None): + if k > 0: + break + else: + raise FileNotFoundError(f"Dataset not found: {prefix}") + input_src, input_tgt, label = load_split( + split_k, self.cfg.target_metric + ) + src_datasets.append(input_src) + tgt_datasets.append(input_tgt) + label_datasets.append(label) + else: + input_src, input_tgt, label = load_split(split, self.cfg.target_metric) + src_datasets.append(input_src) + tgt_datasets.append(input_tgt) + label_datasets.append(label) + + if len(tgt_datasets) == 1: + input_tgt, label = tgt_datasets[0], label_datasets[0] + if self.cfg.include_src: + input_src = src_datasets[0] + else: + input_tgt = ConcatDataset(tgt_datasets) + label = ConcatDataset(label_datasets) + if self.cfg.include_src: + input_src = ConcatDataset(src_datasets) + + input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions) + if self.cfg.include_src: + input_src = PrependTokenDataset(input_src, self.dictionary.bos()) + input_src = TruncateDataset(input_src, self.cfg.max_positions) + src_lengths = NumelDataset(input_src, reduce=False) + src_tokens = ConcatSentencesDataset(input_src, input_tgt) + else: + src_tokens = PrependTokenDataset(input_tgt, self.dictionary.bos()) + src_lengths = NumelDataset(src_tokens, reduce=False) + + dataset = { + "id": IdDataset(), + "net_input": { + "src_tokens": RightPadDataset( + src_tokens, + pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": src_lengths, + }, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens, reduce=True), + "target": label, + } + + dataset = NestedDictionaryDataset( + dataset, + sizes=[src_tokens.sizes], + ) + + assert ( + len(dataset) % self.cfg.mt_beam == 0 + ), "dataset size (%d) is not a multiple of beam size (%d)" % ( + len(dataset), + self.cfg.mt_beam, + ) + + # no need to shuffle valid/test sets + if not self.cfg.no_shuffle and split == self.cfg.train_subset: + + # need to keep all hypothese together + start_idx = np.arange(0, len(dataset), self.cfg.mt_beam) + with data_utils.numpy_seed(self.cfg.seed + epoch): + np.random.shuffle(start_idx) + + idx = np.arange(0, self.cfg.mt_beam) + shuffle = np.tile(idx, (len(start_idx), 1)).reshape(-1) + np.tile( + start_idx, (self.cfg.mt_beam, 1) + ).transpose().reshape(-1) + + dataset = SortDataset( + dataset, + sort_order=[shuffle], + ) + + logger.info(f"Loaded {split} with #samples: {len(dataset)}") + + self.datasets[split] = dataset + return self.datasets[split] + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + assert not self.cfg.include_src or len(src_tokens[0]) == 2 + input_src = None + if self.cfg.include_src: + input_src = TokenBlockDataset( + [t[0] for t in src_tokens], + [l[0] for l in src_lengths], + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ) + input_src = PrependTokenDataset(input_src, self.dictionary.bos()) + input_src = TruncateDataset(input_src, self.cfg.max_positions) + + input_tgt = TokenBlockDataset( + [t[-1] for t in src_tokens], + [l[-1] for l in src_lengths], + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ) + input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions) + if self.cfg.include_src: + src_tokens = ConcatSentencesDataset(input_src, input_tgt) + src_lengths = NumelDataset(input_src, reduce=False) + else: + input_tgt = PrependTokenDataset(input_tgt, self.dictionary.bos()) + src_tokens = input_tgt + src_lengths = NumelDataset(src_tokens, reduce=False) + + dataset = { + "id": IdDataset(), + "net_input": { + "src_tokens": RightPadDataset( + src_tokens, + pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": src_lengths, + }, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens, reduce=True), + } + + return NestedDictionaryDataset( + dataset, + sizes=[src_tokens.sizes], + ) + + def build_model(self, cfg: FairseqDataclass, from_checkpoint: bool = False): + return super().build_model(cfg) + + def build_generator(self, args): + return RerankerScorer(args, mt_beam=self.cfg.mt_beam) + + def max_positions(self): + return self._max_positions + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + def create_dummy_batch(self, device): + dummy_target = ( + torch.zeros(self.cfg.mt_beam, EVAL_BLEU_ORDER * 2 + 3).long().to(device) + if not self.cfg.eval_ter + else torch.zeros(self.cfg.mt_beam, 3).long().to(device) + ) + + return { + "id": torch.zeros(self.cfg.mt_beam, 1).long().to(device), + "net_input": { + "src_tokens": torch.zeros(self.cfg.mt_beam, 4).long().to(device), + "src_lengths": torch.ones(self.cfg.mt_beam, 1).long().to(device), + }, + "nsentences": 0, + "ntokens": 0, + "target": dummy_target, + } + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + if ignore_grad and sample is None: + sample = self.create_dummy_batch(model.device) + + return super().train_step( + sample, model, criterion, optimizer, update_num, ignore_grad + ) + + def valid_step(self, sample, model, criterion): + if sample is None: + sample = self.create_dummy_batch(model.device) + + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + + if not self.cfg.eval_target_metric: + return loss, sample_size, logging_output + + scores = logging_output["scores"] + + if self.cfg.target_metric == "bleu": + assert sample["target"].shape[1] == EVAL_BLEU_ORDER * 2 + 3, ( + "target does not contain enough information (" + + str(sample["target"].shape[1]) + + "for evaluating BLEU" + ) + + max_id = torch.argmax(scores, dim=1) + select_id = max_id + torch.arange( + 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam + ).to(max_id.device) + bleu_data = sample["target"][select_id, 1:].sum(0).data + + logging_output["_bleu_sys_len"] = bleu_data[0] + logging_output["_bleu_ref_len"] = bleu_data[1] + + for i in range(EVAL_BLEU_ORDER): + logging_output["_bleu_counts_" + str(i)] = bleu_data[2 + i] + logging_output["_bleu_totals_" + str(i)] = bleu_data[ + 2 + EVAL_BLEU_ORDER + i + ] + + elif self.cfg.target_metric == "ter": + assert sample["target"].shape[1] == 3, ( + "target does not contain enough information (" + + str(sample["target"].shape[1]) + + "for evaluating TER" + ) + + max_id = torch.argmax(scores, dim=1) + select_id = max_id + torch.arange( + 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam + ).to(max_id.device) + ter_data = sample["target"][select_id, 1:].sum(0).data + + logging_output["_ter_num_edits"] = -ter_data[0] + logging_output["_ter_ref_len"] = -ter_data[1] + + return loss, sample_size, logging_output + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + if not self.cfg.eval_target_metric: + return + + def sum_logs(key): + return sum(log.get(key, 0) for log in logging_outputs) + + if self.cfg.target_metric == "bleu": + counts, totals = [], [] + for i in range(EVAL_BLEU_ORDER): + counts.append(sum_logs("_bleu_counts_" + str(i))) + totals.append(sum_logs("_bleu_totals_" + str(i))) + + if max(totals) > 0: + # log counts as numpy arrays -- log_scalar will sum them correctly + metrics.log_scalar("_bleu_counts", np.array(counts)) + metrics.log_scalar("_bleu_totals", np.array(totals)) + metrics.log_scalar("_bleu_sys_len", sum_logs("_bleu_sys_len")) + metrics.log_scalar("_bleu_ref_len", sum_logs("_bleu_ref_len")) + + def compute_bleu(meters): + import inspect + import sacrebleu + + fn_sig = inspect.getfullargspec(sacrebleu.compute_bleu)[0] + if "smooth_method" in fn_sig: + smooth = {"smooth_method": "exp"} + else: + smooth = {"smooth": "exp"} + bleu = sacrebleu.compute_bleu( + correct=meters["_bleu_counts"].sum, + total=meters["_bleu_totals"].sum, + sys_len=meters["_bleu_sys_len"].sum, + ref_len=meters["_bleu_ref_len"].sum, + **smooth, + ) + return round(bleu.score, 2) + + metrics.log_derived("bleu", compute_bleu) + elif self.cfg.target_metric == "ter": + num_edits = sum_logs("_ter_num_edits") + ref_len = sum_logs("_ter_ref_len") + + if ref_len > 0: + metrics.log_scalar("_ter_num_edits", num_edits) + metrics.log_scalar("_ter_ref_len", ref_len) + + def compute_ter(meters): + score = meters["_ter_num_edits"].sum / meters["_ter_ref_len"].sum + return round(score.item(), 2) + + metrics.log_derived("ter", compute_ter) diff --git a/fairseq/examples/emotion_conversion/README.md b/fairseq/examples/emotion_conversion/README.md new file mode 100644 index 0000000..caf22be --- /dev/null +++ b/fairseq/examples/emotion_conversion/README.md @@ -0,0 +1,214 @@ +# Textless speech emotion conversion using decomposed and discrete representations +[Felix Kreuk](https://felixkreuk.github.io), Adam Polyak, Jade Copet, Eugene Kharitonov, Tu-Anh Nguyen, Morgane Rivière, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, [Yossi Adi](https://adiyoss.github.io) + +_abstract_: Speech emotion conversion is the task of modifying the perceived emotion of a speech utterance while preserving the lexical content and speaker identity. In this study, we cast the problem of emotion conversion as a spoken language translation task. We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion. First, we modify the speech content by translating the content units to a target emotion, and then predict the prosodic features based on these units. Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder. Such a paradigm allows us to go beyond spectral and parametric changes of the signal, and model non-verbal vocalizations, such as laughter insertion, yawning removal, etc. We demonstrate objectively and subjectively that the proposed method is superior to the baselines in terms of perceived emotion and audio quality. We rigorously evaluate all components of such a complex system and conclude with an extensive model analysis and ablation study to better emphasize the architectural choices, strengths and weaknesses of the proposed method. Samples and code will be publicly available under the following link: https://speechbot.github.io/emotion. + +## Installation +First, create a conda virtual environment and activate it: +``` +conda create -n emotion python=3.8 -y +conda activate emotion +``` + +Then, clone this repository: +``` +git clone https://github.com/facebookresearch/fairseq.git +cd fairseq/examples/emotion_conversion +git clone https://github.com/felixkreuk/speech-resynthesis +``` + +Next, download the EmoV discrete tokens: +``` +wget https://dl.fbaipublicfiles.com/textless_nlp/emotion_conversion/data.tar.gz # (still in fairseq/examples/emotion_conversion) +tar -xzvf data.tar.gz +``` + +Your `fairseq/examples/emotion_conversion` directory should like this: +``` +drwxrwxr-x 3 felixkreuk felixkreuk 0 Feb 6 2022 data +drwxrwxr-x 3 felixkreuk felixkreuk 0 Sep 28 10:41 emotion_models +drwxr-xr-x 3 felixkreuk felixkreuk 0 Jun 29 05:43 fairseq_models +drwxr-xr-x 3 felixkreuk felixkreuk 0 Sep 28 10:41 preprocess +-rw-rw-r-- 1 felixkreuk felixkreuk 11K Dec 5 09:00 README.md +-rw-rw-r-- 1 felixkreuk felixkreuk 88 Mar 6 2022 requirements.txt +-rw-rw-r-- 1 felixkreuk felixkreuk 13K Jun 29 06:26 synthesize.py +``` + +Lastly, install fairseq and the other packages: +``` +pip install --editable ./ +pip install -r examples/emotion_conversion/requirements.txt +``` + +## Data preprocessing + +### Convert your audio to discrete representations +Please follow the steps described [here](https://github.com/pytorch/fairseq/tree/main/examples/hubert/simple_kmeans). +To generate the same discrete representations please use the following: +1. [HuBERT checkpoint](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) +2. k-means model at `data/hubert_base_ls960_layer9_clusters200/data_hubert_base_ls960_layer9_clusters200.bin` + +### Construct data splits +This step will use the discrete representations from the previous step and split them to train/valid/test sets for 3 tasks: +1. Translation model pre-training (BART language denoising) +2. Translation model training (content units emotion translation mechanism) +3. HiFiGAN model training (for synthesizing audio from discrete representations) + +Your processed data should be at `data/`: +1. `hubert_base_ls960_layer9_clusters200` - discrete representations extracted using HuBERT layer 9, clustered into 200 clusters. +2. `data.tsv` - a tsv file pointing to the EmoV dataset in your environment (Please edit the first line of this file according to your path). + +The following command will create the above splits: +``` +python examples/emotion_conversion/preprocess/create_core_manifest.py \ + --tsv data/data.tsv \ + --emov-km data/hubert_base_ls960_layer9_clusters200/data.km \ + --km data/hubert_base_ls960_layer9_clusters200/vctk.km \ + --dict data/hubert_base_ls960_layer9_clusters200/dict.txt \ + --manifests-dir $DATA +``` +* Set `$DATA` as the directory that will contain the processed data. + +### Extract F0 +To train the HiFiGAN vocoder we need to first extract the F0 curves: +``` +python examples/emotion_conversion/preprocess/extract_f0.py \ + --tsv data/data.tsv \ + --extractor pyaapt \ +``` + +## HiFiGAN training +Now we are all set to train the HiFiGAN vocoder: +``` +python examples/emotion_conversion/speech-resynthesis/train.py + --checkpoint_path \ + --config examples/emotion_conversion/speech-resynthesis/configs/EmoV/emov_hubert-layer9-cluster200_fixed-spkr-embedder_f0-raw_gst.json +``` + +## Translation Pre-training +Before translating emotions, we first need to pre-train the translation model as a denoising autoencoder (similarly to BART). +``` +python train.py \ + $DATA/fairseq-data/emov_multilingual_denoising_cross-speaker_dedup_nonzeroshot/tokenized \ + --save-dir \ + --tensorboard-logdir \ + --langs neutral,amused,angry,sleepy,disgusted,vctk.km \ + --dataset-impl mmap \ + --task multilingual_denoising \ + --arch transformer_small --criterion cross_entropy \ + --multilang-sampling-alpha 1.0 --sample-break-mode eos --max-tokens 16384 \ + --update-freq 1 --max-update 3000000 \ + --dropout 0.1 --attention-dropout 0.1 --relu-dropout 0.0 \ + --optimizer adam --weight-decay 0.01 --adam-eps 1e-06 \ + --clip-norm 0.1 --lr-scheduler polynomial_decay --lr 0.0003 \ + --total-num-update 3000000 --warmup-updates 10000 --fp16 \ + --poisson-lambda 3.5 --mask 0.3 --mask-length span-poisson --replace-length 1 --rotate 0 --mask-random 0.1 --insert 0 --permute-sentences 1.0 \ + --skip-invalid-size-inputs-valid-test \ + --user-dir examples/emotion_conversion/fairseq_models +``` + +## Translation Training +Now we are ready to train our emotion translation model: +``` +python train.py \ + --distributed-world-size 1 \ + $DATA/fairseq-data/emov_multilingual_translation_cross-speaker_dedup/tokenized/ \ + --save-dir \ + --tensorboard-logdir \ + --arch multilingual_small --task multilingual_translation \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --lang-pairs neutral-amused,neutral-sleepy,neutral-disgusted,neutral-angry,amused-sleepy,amused-disgusted,amused-neutral,amused-angry,angry-amused,angry-sleepy,angry-disgusted,angry-neutral,disgusted-amused,disgusted-sleepy,disgusted-neutral,disgusted-angry,sleepy-amused,sleepy-neutral,sleepy-disgusted,sleepy-angry \ + --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --lr 1e-05 --clip-norm 0 --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.01 --warmup-updates 2000 --lr-scheduler inverse_sqrt \ + --max-tokens 4096 --update-freq 1 --max-update 100000 \ + --required-batch-size-multiple 8 --fp16 --num-workers 4 \ + --seed 2 --log-format json --log-interval 25 --save-interval-updates 1000 \ + --no-epoch-checkpoints --keep-best-checkpoints 1 --keep-interval-updates 1 \ + --finetune-from-model \ + --user-dir examples/emotion_conversion/fairseq_models +``` +* To share encoders/decoders use the `--share-encoders` and `--share-decoders` flags. +* To add source/target emotion tokens use the `--encoder-langtok {'src'|'tgt'}` and `--decoder-langtok` flags. + +## F0-predictor Training +The following command trains the F0 prediction module: +``` +cd examples/emotion_conversion +python -m emotion_models.pitch_predictor n_tokens=200 \ + train_tsv="$DATA/denoising/emov/train.tsv" \ + train_km="$DATA/denoising/emov/train.km" \ + valid_tsv="$DATA/denoising/emov/valid.tsv" \ + valid_km="$DATA/denoising/emov/valid.km" +``` +* See `hyra.run.dir` to configure directory for saving models. + +## Duration-predictor Training +The following command trains the duration prediction modules: +``` +cd examples/emotion_conversion +for emotion in "neutral" "amused" "angry" "disgusted" "sleepy"; do + python -m emotion_models.duration_predictor n_tokens=200 substring=$emotion \ + train_tsv="$DATA/denoising/emov/train.tsv" \ + train_km="$DATA/denoising/emov/train.km" \ + valid_tsv="$DATA/denoising/emov/valid.tsv" \ + valid_km="$DATA/denoising/emov/valid.km" +done +``` +* See `hyra.run.dir` to configure directory for saving models. +* After the above command you should have 5 duration models in your checkpoint directory: +``` +❯ ll duration_predictor/ +total 21M +-rw-rw-r-- 1 felixkreuk felixkreuk 4.1M Nov 15 2021 amused.ckpt +-rw-rw-r-- 1 felixkreuk felixkreuk 4.1M Nov 15 2021 angry.ckpt +-rw-rw-r-- 1 felixkreuk felixkreuk 4.1M Nov 15 2021 disgusted.ckpt +-rw-rw-r-- 1 felixkreuk felixkreuk 4.1M Nov 15 2021 neutral.ckpt +-rw-rw-r-- 1 felixkreuk felixkreuk 4.1M Nov 15 2021 sleepy.ckpt +``` + +## Token Generation +The following command uses `fairseq-generate` to generate the token sequences based on the source and target emotions. +``` +fairseq-generate \ + $DATA/fairseq-data/emov_multilingual_translation_cross-speaker_dedup/tokenized/ \ + --task multilingual_translation \ + --gen-subset test \ + --path \ + --beam 5 \ + --batch-size 4 --max-len-a 1.8 --max-len-b 10 --lenpen 1 --min-len 1 \ + --skip-invalid-size-inputs-valid-test --distributed-world-size 1 \ + --source-lang neutral --target-lang amused \ + --lang-pairs neutral-amused,neutral-sleepy,neutral-disgusted,neutral-angry,amused-sleepy,amused-disgusted,amused-neutral,amused-angry,angry-amused,angry-sleepy,angry-disgusted,angry-neutral,disgusted-amused,disgusted-sleepy,disgusted-neutral,disgusted-angry,sleepy-amused,sleepy-neutral,sleepy-disgusted,sleepy-angry \ + --results-path \ + --user-dir examples/emotion_conversion/fairseq_models +``` +* Modify `--source-lang` and `--target-lang` to control for the source and target emotions. +* See [fairseq documentation](https://fairseq.readthedocs.io/en/latest/command_line_tools.html#fairseq-generate) for a full overview of generation parameters (e.g., top-k/top-p sampling). + +## Waveform Synthesis +Using the output of the above command, the HiFiGAN vocoder, and the prosody prediction modules (F0 and duration) we can now generate the output waveforms: +``` +python examples/emotion_conversion/synthesize.py \ + --result-path /generate-test.txt \ + --data $DATA/fairseq-data/emov_multilingual_translation_cross-speaker_dedup/neutral-amused \ + --orig-tsv examples/emotion_conversion/data/data.tsv \ + --orig-km examples/emotion_conversion/data/hubert_base_ls960_layer9_clusters200/data.km \ + --checkpoint-file /g_00400000 \ + --dur-model duration_predictor/ \ + --f0-model pitch_predictor/pitch_predictor.ckpt \ + -s neutral -t amused \ + --outdir ~/tmp/emotion_results/wavs/neutral-amused +``` +* Please make sure the source and target emotions here match those of the previous command. + +# Citation +If you find this useful in your research, please use the following BibTeX entry for citation. +``` +@article{kreuk2021textless, + title={Textless speech emotion conversion using decomposed and discrete representations}, + author={Kreuk, Felix and Polyak, Adam and Copet, Jade and Kharitonov, Eugene and Nguyen, Tu-Anh and Rivi{\`e}re, Morgane and Hsu, Wei-Ning and Mohamed, Abdelrahman and Dupoux, Emmanuel and Adi, Yossi}, + journal={Conference on Empirical Methods in Natural Language Processing (EMNLP)}, + year={2022} +} +``` diff --git a/fairseq/examples/emotion_conversion/emotion_models/__init__.py b/fairseq/examples/emotion_conversion/emotion_models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/emotion_conversion/emotion_models/duration_predictor.py b/fairseq/examples/emotion_conversion/emotion_models/duration_predictor.py new file mode 100644 index 0000000..eb47df0 --- /dev/null +++ b/fairseq/examples/emotion_conversion/emotion_models/duration_predictor.py @@ -0,0 +1,243 @@ +import logging +import os + +import hydra +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops.layers.torch import Rearrange +from torch.utils.data import DataLoader, Dataset + +from .utils import Accuracy + +logger = logging.getLogger(__name__) + + +def save_ckpt(model, path, model_class): + ckpt = { + "state_dict": model.state_dict(), + "padding_token": model.padding_token, + "model_class": model_class, + } + torch.save(ckpt, path) + + +def load_ckpt(path): + ckpt = torch.load(path) + ckpt["model_class"]["_target_"] = "emotion_models.duration_predictor.CnnPredictor" + model = hydra.utils.instantiate(ckpt["model_class"]) + model.load_state_dict(ckpt["state_dict"]) + model.padding_token = ckpt["padding_token"] + model = model.cpu() + model.eval() + return model + + +class Collator: + def __init__(self, padding_idx): + self.padding_idx = padding_idx + + def __call__(self, batch): + x = [item[0] for item in batch] + lengths = [len(item) for item in x] + x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True, padding_value=self.padding_idx) + y = [item[1] for item in batch] + y = torch.nn.utils.rnn.pad_sequence(y, batch_first=True, padding_value=self.padding_idx) + mask = (x != self.padding_idx) + return x, y, mask, lengths + + +class Predictor(nn.Module): + def __init__(self, n_tokens, emb_dim): + super(Predictor, self).__init__() + self.n_tokens = n_tokens + self.emb_dim = emb_dim + self.padding_token = n_tokens + # add 1 extra embedding for padding token, set the padding index to be the last token + # (tokens from the clustering start at index 0) + self.emb = nn.Embedding(n_tokens + 1, emb_dim, padding_idx=self.padding_token) + + def inflate_input(self, batch): + """ get a sequence of tokens, predict their durations + and inflate them accordingly """ + batch_durs = self.forward(batch) + batch_durs = torch.exp(batch_durs) - 1 + batch_durs = batch_durs.round() + output = [] + for seq, durs in zip(batch, batch_durs): + inflated_seq = [] + for token, n in zip(seq, durs): + if token == self.padding_token: + break + n = int(n.item()) + token = int(token.item()) + inflated_seq.extend([token for _ in range(n)]) + output.append(inflated_seq) + output = torch.LongTensor(output) + return output + + +class CnnPredictor(Predictor): + def __init__(self, n_tokens, emb_dim, channels, kernel, output_dim, dropout, n_layers): + super(CnnPredictor, self).__init__(n_tokens=n_tokens, emb_dim=emb_dim) + layers = [ + Rearrange("b t c -> b c t"), + nn.Conv1d(emb_dim, channels, kernel_size=kernel, padding=(kernel - 1) // 2), + Rearrange("b c t -> b t c"), + nn.ReLU(), + nn.LayerNorm(channels), + nn.Dropout(dropout), + ] + for _ in range(n_layers-1): + layers += [ + Rearrange("b t c -> b c t"), + nn.Conv1d(channels, channels, kernel_size=kernel, padding=(kernel - 1) // 2), + Rearrange("b c t -> b t c"), + nn.ReLU(), + nn.LayerNorm(channels), + nn.Dropout(dropout), + ] + self.conv_layer = nn.Sequential(*layers) + self.proj = nn.Linear(channels, output_dim) + + def forward(self, x): + x = self.emb(x) + x = self.conv_layer(x) + x = self.proj(x) + x = x.squeeze(-1) + return x + + +def l2_log_loss(input, target): + return F.mse_loss( + input=input.float(), + target=torch.log(target.float() + 1), + reduce=False + ) + + +class DurationDataset(Dataset): + def __init__(self, tsv_path, km_path, substring=""): + lines = open(tsv_path, "r").readlines() + self.root, self.tsv = lines[0], lines[1:] + self.km = open(km_path, "r").readlines() + logger.info(f"loaded {len(self.km)} files") + + if substring != "": + tsv, km = [], [] + for tsv_line, km_line in zip(self.tsv, self.km): + if substring.lower() in tsv_line.lower(): + tsv.append(tsv_line) + km.append(km_line) + self.tsv, self.km = tsv, km + logger.info(f"after filtering: {len(self.km)} files") + + def __len__(self): + return len(self.km) + + def __getitem__(self, i): + x = self.km[i] + x = x.split(" ") + x = list(map(int, x)) + + y = [] + xd = [] + count = 1 + for x1, x2 in zip(x[:-1], x[1:]): + if x1 == x2: + count += 1 + continue + else: + y.append(count) + xd.append(x1) + count = 1 + + xd = torch.LongTensor(xd) + y = torch.LongTensor(y) + return xd, y + + +def train(cfg): + device = "cuda:0" + model = hydra.utils.instantiate(cfg[cfg.model]).to(device) + optimizer = hydra.utils.instantiate(cfg.optimizer, model.parameters()) + # add 1 extra embedding for padding token, set the padding index to be the last token + # (tokens from the clustering start at index 0) + collate_fn = Collator(padding_idx=model.padding_token) + logger.info(f"data: {cfg.train_tsv}") + train_ds = DurationDataset(cfg.train_tsv, cfg.train_km, substring=cfg.substring) + valid_ds = DurationDataset(cfg.valid_tsv, cfg.valid_km, substring=cfg.substring) + train_dl = DataLoader(train_ds, batch_size=32, shuffle=True, collate_fn=collate_fn) + valid_dl = DataLoader(valid_ds, batch_size=32, shuffle=False, collate_fn=collate_fn) + + best_loss = float("inf") + for epoch in range(cfg.epochs): + train_loss, train_loss_scaled = train_epoch(model, train_dl, l2_log_loss, optimizer, device) + valid_loss, valid_loss_scaled, *acc = valid_epoch(model, valid_dl, l2_log_loss, device) + acc0, acc1, acc2, acc3 = acc + if valid_loss_scaled < best_loss: + path = f"{os.getcwd()}/{cfg.substring}.ckpt" + save_ckpt(model, path, cfg[cfg.model]) + best_loss = valid_loss_scaled + logger.info(f"saved checkpoint: {path}") + logger.info(f"[epoch {epoch}] train loss: {train_loss:.3f}, train scaled: {train_loss_scaled:.3f}") + logger.info(f"[epoch {epoch}] valid loss: {valid_loss:.3f}, valid scaled: {valid_loss_scaled:.3f}") + logger.info(f"acc: {acc0,acc1,acc2,acc3}") + + +def train_epoch(model, loader, criterion, optimizer, device): + model.train() + epoch_loss = 0 + epoch_loss_scaled = 0 + for x, y, mask, _ in loader: + x, y, mask = x.to(device), y.to(device), mask.to(device) + yhat = model(x) + loss = criterion(yhat, y) * mask + loss = torch.mean(loss) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + epoch_loss += loss.item() + # get normal scale loss + yhat_scaled = torch.exp(yhat) - 1 + yhat_scaled = torch.round(yhat_scaled) + scaled_loss = torch.mean(torch.abs(yhat_scaled - y) * mask) + epoch_loss_scaled += scaled_loss.item() + return epoch_loss / len(loader), epoch_loss_scaled / len(loader) + + +def valid_epoch(model, loader, criterion, device): + model.eval() + epoch_loss = 0 + epoch_loss_scaled = 0 + acc = Accuracy() + for x, y, mask, _ in loader: + x, y, mask = x.to(device), y.to(device), mask.to(device) + yhat = model(x) + loss = criterion(yhat, y) * mask + loss = torch.mean(loss) + epoch_loss += loss.item() + # get normal scale loss + yhat_scaled = torch.exp(yhat) - 1 + yhat_scaled = torch.round(yhat_scaled) + scaled_loss = torch.sum(torch.abs(yhat_scaled - y) * mask) / mask.sum() + acc.update(yhat_scaled[mask].view(-1).float(), y[mask].view(-1).float()) + epoch_loss_scaled += scaled_loss.item() + logger.info(f"example y: {y[0, :10].tolist()}") + logger.info(f"example yhat: {yhat_scaled[0, :10].tolist()}") + acc0 = acc.acc(tol=0) + acc1 = acc.acc(tol=1) + acc2 = acc.acc(tol=2) + acc3 = acc.acc(tol=3) + logger.info(f"accs: {acc0,acc1,acc2,acc3}") + return epoch_loss / len(loader), epoch_loss_scaled / len(loader), acc0, acc1, acc2, acc3 + + +@hydra.main(config_path=".", config_name="duration_predictor.yaml") +def main(cfg): + logger.info(f"{cfg}") + train(cfg) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/emotion_conversion/emotion_models/duration_predictor.yaml b/fairseq/examples/emotion_conversion/emotion_models/duration_predictor.yaml new file mode 100644 index 0000000..0e976f4 --- /dev/null +++ b/fairseq/examples/emotion_conversion/emotion_models/duration_predictor.yaml @@ -0,0 +1,48 @@ +train_tsv: "/denoising/emov/train.tsv" +train_km: "/denoising/emov/train.km" +valid_tsv: "/denoising/emov/valid.tsv" +valid_km: "/denoising/emov/valid.km" + +n_tokens: 200 +batch_size: 32 +lr: 0.0001 +epochs: 300 +model: "cnn" +substring: "" + +rnn: + _target_: emotion_models.duration_predictor.RnnPredictor + n_tokens: ${n_tokens} + emb_dim: 128 + rnn_hidden: 128 + output_dim: 1 + dropout: 0 + n_layers: 1 + +optimizer: + _target_: torch.optim.Adam + lr: ${lr} + betas: [0.9, 0.98] + eps: 0.000000001 + weight_decay: 0 + +cnn: + _target_: emotion_models.duration_predictor.CnnPredictor + n_tokens: ${n_tokens} + emb_dim: 128 + channels: 256 + kernel: 3 + output_dim: 1 + dropout: 0.5 + n_layers: 1 + +hydra: + run: + dir: /checkpoint/felixkreuk/experiments/duration_predictor/${hydra.job.override_dirname} + job: + config: + # configuration for the ${hydra.job.override_dirname} runtime variable + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: ['train_tsv', 'train_km', 'valid_tsv', 'valid_km'] diff --git a/fairseq/examples/emotion_conversion/emotion_models/pitch_predictor.py b/fairseq/examples/emotion_conversion/emotion_models/pitch_predictor.py new file mode 100644 index 0000000..4314469 --- /dev/null +++ b/fairseq/examples/emotion_conversion/emotion_models/pitch_predictor.py @@ -0,0 +1,559 @@ +import logging +import os +import random +import sys +from collections import defaultdict + +import hydra +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from einops.layers.torch import Rearrange +from scipy.io.wavfile import read +from scipy.ndimage import gaussian_filter1d +from torch.utils.data import DataLoader, Dataset +from tqdm import tqdm + +dir_path = os.path.dirname(__file__) +resynth_path = os.path.dirname(dir_path) + "/speech-resynthesis" +sys.path.append(resynth_path) +from dataset import parse_speaker, parse_style +from .utils import F0Stat + +MAX_WAV_VALUE = 32768.0 +logger = logging.getLogger(__name__) + + +def quantize_f0(speaker_to_f0, nbins, normalize, log): + f0_all = [] + for speaker, f0 in speaker_to_f0.items(): + f0 = f0.raw_data + if log: + f0 = f0.log() + mean = speaker_to_f0[speaker].mean_log if log else speaker_to_f0[speaker].mean + std = speaker_to_f0[speaker].std_log if log else speaker_to_f0[speaker].std + if normalize == "mean": + f0 = f0 - mean + elif normalize == "meanstd": + f0 = (f0 - mean) / std + f0_all.extend(f0.tolist()) + + hist, bin_x = np.histogram(f0_all, 100000) + cum_hist = np.cumsum(hist) / len(f0_all) * 100 + + bin_offset = [] + bin_size = 100 / nbins + threshold = bin_size + for i in range(nbins - 1): + index = (np.abs(cum_hist - threshold)).argmin() + bin_offset.append(bin_x[index]) + threshold += bin_size + bins = np.array(bin_offset) + bins = torch.FloatTensor(bins) + + return bins + + +def save_ckpt(model, path, model_class, f0_min, f0_max, f0_bins, speaker_stats): + ckpt = { + "state_dict": model.state_dict(), + "padding_token": model.padding_token, + "model_class": model_class, + "speaker_stats": speaker_stats, + "f0_min": f0_min, + "f0_max": f0_max, + "f0_bins": f0_bins, + } + torch.save(ckpt, path) + + +def load_ckpt(path): + ckpt = torch.load(path) + ckpt["model_class"]["_target_"] = "emotion_models.pitch_predictor.CnnPredictor" + model = hydra.utils.instantiate(ckpt["model_class"]) + model.load_state_dict(ckpt["state_dict"]) + model.setup_f0_stats( + ckpt["f0_min"], + ckpt["f0_max"], + ckpt["f0_bins"], + ckpt["speaker_stats"], + ) + return model + + +def freq2bin(f0, f0_min, f0_max, bins): + f0 = f0.clone() + f0[f0 < f0_min] = f0_min + f0[f0 > f0_max] = f0_max + f0 = torch.bucketize(f0, bins) + return f0 + + +def bin2freq(x, f0_min, f0_max, bins, mode): + n_bins = len(bins) + 1 + assert x.shape[-1] == n_bins + bins = torch.cat([torch.tensor([f0_min]), bins]).to(x.device) + if mode == "mean": + f0 = (x * bins).sum(-1, keepdims=True) / x.sum(-1, keepdims=True) + elif mode == "argmax": + idx = F.one_hot(x.argmax(-1), num_classes=n_bins) + f0 = (idx * bins).sum(-1, keepdims=True) + else: + raise NotImplementedError() + return f0[..., 0] + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def l1_loss(input, target): + return F.l1_loss(input=input.float(), target=target.float(), reduce=False) + + +def l2_loss(input, target): + return F.mse_loss(input=input.float(), target=target.float(), reduce=False) + + +class Collator: + def __init__(self, padding_idx): + self.padding_idx = padding_idx + + def __call__(self, batch): + tokens = [item[0] for item in batch] + lengths = [len(item) for item in tokens] + tokens = torch.nn.utils.rnn.pad_sequence( + tokens, batch_first=True, padding_value=self.padding_idx + ) + f0 = [item[1] for item in batch] + f0 = torch.nn.utils.rnn.pad_sequence( + f0, batch_first=True, padding_value=self.padding_idx + ) + f0_raw = [item[2] for item in batch] + f0_raw = torch.nn.utils.rnn.pad_sequence( + f0_raw, batch_first=True, padding_value=self.padding_idx + ) + spk = [item[3] for item in batch] + spk = torch.LongTensor(spk) + gst = [item[4] for item in batch] + gst = torch.LongTensor(gst) + mask = tokens != self.padding_idx + return tokens, f0, f0_raw, spk, gst, mask, lengths + + +class CnnPredictor(nn.Module): + def __init__( + self, + n_tokens, + emb_dim, + channels, + kernel, + dropout, + n_layers, + spk_emb, + gst_emb, + n_bins, + f0_pred, + f0_log, + f0_norm, + ): + super(CnnPredictor, self).__init__() + self.n_tokens = n_tokens + self.emb_dim = emb_dim + self.f0_log = f0_log + self.f0_pred = f0_pred + self.padding_token = n_tokens + self.f0_norm = f0_norm + # add 1 extra embedding for padding token, set the padding index to be the last token + # (tokens from the clustering start at index 0) + self.token_emb = nn.Embedding( + n_tokens + 1, emb_dim, padding_idx=self.padding_token + ) + + self.spk_emb = spk_emb + self.gst_emb = nn.Embedding(20, gst_emb) + self.setup = False + + feats = emb_dim + gst_emb + # feats = emb_dim + gst_emb + (256 if spk_emb else 0) + layers = [ + nn.Sequential( + Rearrange("b t c -> b c t"), + nn.Conv1d( + feats, channels, kernel_size=kernel, padding=(kernel - 1) // 2 + ), + Rearrange("b c t -> b t c"), + nn.ReLU(), + nn.LayerNorm(channels), + nn.Dropout(dropout), + ) + ] + for _ in range(n_layers - 1): + layers += [ + nn.Sequential( + Rearrange("b t c -> b c t"), + nn.Conv1d( + channels, + channels, + kernel_size=kernel, + padding=(kernel - 1) // 2, + ), + Rearrange("b c t -> b t c"), + nn.ReLU(), + nn.LayerNorm(channels), + nn.Dropout(dropout), + ) + ] + self.conv_layer = nn.ModuleList(layers) + self.proj = nn.Linear(channels, n_bins) + + def forward(self, x, gst=None): + x = self.token_emb(x) + feats = [x] + + if gst is not None: + gst = self.gst_emb(gst) + gst = rearrange(gst, "b c -> b c 1") + gst = F.interpolate(gst, x.shape[1]) + gst = rearrange(gst, "b c t -> b t c") + feats.append(gst) + + x = torch.cat(feats, dim=-1) + + for i, conv in enumerate(self.conv_layer): + if i != 0: + x = conv(x) + x + else: + x = conv(x) + + x = self.proj(x) + x = x.squeeze(-1) + + if self.f0_pred == "mean": + x = torch.sigmoid(x) + elif self.f0_pred == "argmax": + x = torch.softmax(x, dim=-1) + else: + raise NotImplementedError + return x + + def setup_f0_stats(self, f0_min, f0_max, f0_bins, speaker_stats): + self.f0_min = f0_min + self.f0_max = f0_max + self.f0_bins = f0_bins + self.speaker_stats = speaker_stats + self.setup = True + + def inference(self, x, spk_id=None, gst=None): + assert ( + self.setup == True + ), "make sure that `setup_f0_stats` was called before inference!" + probs = self(x, gst) + f0 = bin2freq(probs, self.f0_min, self.f0_max, self.f0_bins, self.f0_pred) + for i in range(f0.shape[0]): + mean = ( + self.speaker_stats[spk_id[i].item()].mean_log + if self.f0_log + else self.speaker_stats[spk_id[i].item()].mean + ) + std = ( + self.speaker_stats[spk_id[i].item()].std_log + if self.f0_log + else self.speaker_stats[spk_id[i].item()].std + ) + if self.f0_norm == "mean": + f0[i] = f0[i] + mean + if self.f0_norm == "meanstd": + f0[i] = (f0[i] * std) + mean + if self.f0_log: + f0 = f0.exp() + return f0 + + +class PitchDataset(Dataset): + def __init__( + self, + tsv_path, + km_path, + substring, + spk, + spk2id, + gst, + gst2id, + f0_bins, + f0_bin_type, + f0_smoothing, + f0_norm, + f0_log, + ): + lines = open(tsv_path, "r").readlines() + self.root, self.tsv = lines[0], lines[1:] + self.root = self.root.strip() + self.km = open(km_path, "r").readlines() + print(f"loaded {len(self.km)} files") + + self.spk = spk + self.spk2id = spk2id + self.gst = gst + self.gst2id = gst2id + + self.f0_bins = f0_bins + self.f0_smoothing = f0_smoothing + self.f0_norm = f0_norm + self.f0_log = f0_log + + if substring != "": + tsv, km = [], [] + for tsv_line, km_line in zip(self.tsv, self.km): + if substring.lower() in tsv_line.lower(): + tsv.append(tsv_line) + km.append(km_line) + self.tsv, self.km = tsv, km + print(f"after filtering: {len(self.km)} files") + + self.speaker_stats = self._compute_f0_stats() + self.f0_min, self.f0_max = self._compute_f0_minmax() + if f0_bin_type == "adaptive": + self.f0_bins = quantize_f0( + self.speaker_stats, self.f0_bins, self.f0_norm, self.f0_log + ) + elif f0_bin_type == "uniform": + self.f0_bins = torch.linspace(self.f0_min, self.f0_max, self.f0_bins + 1)[ + 1:-1 + ] + else: + raise NotImplementedError + print(f"f0 min: {self.f0_min}, f0 max: {self.f0_max}") + print(f"bins: {self.f0_bins} (shape: {self.f0_bins.shape})") + + def __len__(self): + return len(self.km) + + def _load_f0(self, tsv_line): + tsv_line = tsv_line.split("\t")[0] + f0 = self.root + "/" + tsv_line.replace(".wav", ".yaapt.f0.npy") + f0 = np.load(f0) + f0 = torch.FloatTensor(f0) + return f0 + + def _preprocess_f0(self, f0, spk): + mask = f0 != -999999 # process all frames + # mask = (f0 != 0) # only process voiced frames + mean = ( + self.speaker_stats[spk].mean_log + if self.f0_log + else self.speaker_stats[spk].mean + ) + std = ( + self.speaker_stats[spk].std_log + if self.f0_log + else self.speaker_stats[spk].std + ) + if self.f0_log: + f0[f0 == 0] = 1e-5 + f0[mask] = f0[mask].log() + if self.f0_norm == "mean": + f0[mask] = f0[mask] - mean + if self.f0_norm == "meanstd": + f0[mask] = (f0[mask] - mean) / std + return f0 + + def _compute_f0_minmax(self): + f0_min, f0_max = float("inf"), -float("inf") + for tsv_line in tqdm(self.tsv, desc="computing f0 minmax"): + spk = self.spk2id[parse_speaker(tsv_line, self.spk)] + f0 = self._load_f0(tsv_line) + f0 = self._preprocess_f0(f0, spk) + f0_min = min(f0_min, f0.min().item()) + f0_max = max(f0_max, f0.max().item()) + return f0_min, f0_max + + def _compute_f0_stats(self): + from functools import partial + + speaker_stats = defaultdict(partial(F0Stat, True)) + for tsv_line in tqdm(self.tsv, desc="computing speaker stats"): + spk = self.spk2id[parse_speaker(tsv_line, self.spk)] + f0 = self._load_f0(tsv_line) + mask = f0 != 0 + f0 = f0[mask] # compute stats only on voiced parts + speaker_stats[spk].update(f0) + return speaker_stats + + def __getitem__(self, i): + x = self.km[i] + x = x.split(" ") + x = list(map(int, x)) + x = torch.LongTensor(x) + + gst = parse_style(self.tsv[i], self.gst) + gst = self.gst2id[gst] + spk = parse_speaker(self.tsv[i], self.spk) + spk = self.spk2id[spk] + + f0_raw = self._load_f0(self.tsv[i]) + f0 = self._preprocess_f0(f0_raw.clone(), spk) + + f0 = F.interpolate(f0.unsqueeze(0).unsqueeze(0), x.shape[0])[0, 0] + f0_raw = F.interpolate(f0_raw.unsqueeze(0).unsqueeze(0), x.shape[0])[0, 0] + + f0 = freq2bin(f0, f0_min=self.f0_min, f0_max=self.f0_max, bins=self.f0_bins) + f0 = F.one_hot(f0.long(), num_classes=len(self.f0_bins) + 1).float() + if self.f0_smoothing > 0: + f0 = torch.tensor( + gaussian_filter1d(f0.float().numpy(), sigma=self.f0_smoothing) + ) + return x, f0, f0_raw, spk, gst + + +def train(cfg): + device = "cuda:0" + # add 1 extra embedding for padding token, set the padding index to be the last token + # (tokens from the clustering start at index 0) + padding_token = cfg.n_tokens + collate_fn = Collator(padding_idx=padding_token) + train_ds = PitchDataset( + cfg.train_tsv, + cfg.train_km, + substring=cfg.substring, + spk=cfg.spk, + spk2id=cfg.spk2id, + gst=cfg.gst, + gst2id=cfg.gst2id, + f0_bins=cfg.f0_bins, + f0_bin_type=cfg.f0_bin_type, + f0_smoothing=cfg.f0_smoothing, + f0_norm=cfg.f0_norm, + f0_log=cfg.f0_log, + ) + valid_ds = PitchDataset( + cfg.valid_tsv, + cfg.valid_km, + substring=cfg.substring, + spk=cfg.spk, + spk2id=cfg.spk2id, + gst=cfg.gst, + gst2id=cfg.gst2id, + f0_bins=cfg.f0_bins, + f0_bin_type=cfg.f0_bin_type, + f0_smoothing=cfg.f0_smoothing, + f0_norm=cfg.f0_norm, + f0_log=cfg.f0_log, + ) + train_dl = DataLoader( + train_ds, + num_workers=0, + batch_size=cfg.batch_size, + shuffle=True, + collate_fn=collate_fn, + ) + valid_dl = DataLoader( + valid_ds, num_workers=0, batch_size=16, shuffle=False, collate_fn=collate_fn + ) + + f0_min = train_ds.f0_min + f0_max = train_ds.f0_max + f0_bins = train_ds.f0_bins + speaker_stats = train_ds.speaker_stats + + model = hydra.utils.instantiate(cfg["model"]).to(device) + model.setup_f0_stats(f0_min, f0_max, f0_bins, speaker_stats) + + optimizer = hydra.utils.instantiate(cfg.optimizer, model.parameters()) + + best_loss = float("inf") + for epoch in range(cfg.epochs): + train_loss, train_l2_loss, train_l2_voiced_loss = run_epoch( + model, train_dl, optimizer, device, cfg, mode="train" + ) + valid_loss, valid_l2_loss, valid_l2_voiced_loss = run_epoch( + model, valid_dl, None, device, cfg, mode="valid" + ) + print( + f"[epoch {epoch}] train loss: {train_loss:.3f}, l2 loss: {train_l2_loss:.3f}, l2 voiced loss: {train_l2_voiced_loss:.3f}" + ) + print( + f"[epoch {epoch}] valid loss: {valid_loss:.3f}, l2 loss: {valid_l2_loss:.3f}, l2 voiced loss: {valid_l2_voiced_loss:.3f}" + ) + if valid_l2_voiced_loss < best_loss: + path = f"{os.getcwd()}/pitch_predictor.ckpt" + save_ckpt(model, path, cfg["model"], f0_min, f0_max, f0_bins, speaker_stats) + best_loss = valid_l2_voiced_loss + print(f"saved checkpoint: {path}") + print(f"[epoch {epoch}] best loss: {best_loss:.3f}") + + +def run_epoch(model, loader, optimizer, device, cfg, mode): + if mode == "train": + model.train() + else: + model.eval() + + epoch_loss = 0 + l1 = 0 + l1_voiced = 0 + for x, f0_bin, f0_raw, spk_id, gst, mask, _ in tqdm(loader): + x, f0_bin, f0_raw, spk_id, gst, mask = ( + x.to(device), + f0_bin.to(device), + f0_raw.to(device), + spk_id.to(device), + gst.to(device), + mask.to(device), + ) + b, t, n_bins = f0_bin.shape + yhat = model(x, gst) + nonzero_mask = (f0_raw != 0).logical_and(mask) + yhat_raw = model.inference(x, spk_id, gst) + expanded_mask = mask.unsqueeze(-1).expand(-1, -1, n_bins) + if cfg.f0_pred == "mean": + loss = F.binary_cross_entropy( + yhat[expanded_mask], f0_bin[expanded_mask] + ).mean() + elif cfg.f0_pred == "argmax": + loss = F.cross_entropy( + rearrange(yhat, "b t d -> (b t) d"), + rearrange(f0_bin.argmax(-1), "b t -> (b t)"), + reduce=False, + ) + loss = rearrange(loss, "(b t) -> b t", b=b, t=t) + loss = (loss * mask).sum() / mask.float().sum() + else: + raise NotImplementedError + l1 += F.l1_loss(yhat_raw[mask], f0_raw[mask]).item() + l1_voiced += F.l1_loss(yhat_raw[nonzero_mask], f0_raw[nonzero_mask]).item() + epoch_loss += loss.item() + + if mode == "train": + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + + print(f"{mode} example y: {f0_bin.argmax(-1)[0, 50:60].tolist()}") + print(f"{mode} example yhat: {yhat.argmax(-1)[0, 50:60].tolist()}") + print(f"{mode} example y: {f0_raw[0, 50:60].round().tolist()}") + print(f"{mode} example yhat: {yhat_raw[0, 50:60].round().tolist()}") + return epoch_loss / len(loader), l1 / len(loader), l1_voiced / len(loader) + + +@hydra.main(config_path=dir_path, config_name="pitch_predictor.yaml") +def main(cfg): + np.random.seed(1) + random.seed(1) + torch.manual_seed(1) + from hydra.core.hydra_config import HydraConfig + + overrides = { + x.split("=")[0]: x.split("=")[1] + for x in HydraConfig.get().overrides.task + if "/" not in x + } + print(f"{cfg}") + train(cfg) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/emotion_conversion/emotion_models/pitch_predictor.yaml b/fairseq/examples/emotion_conversion/emotion_models/pitch_predictor.yaml new file mode 100644 index 0000000..d2dbb86 --- /dev/null +++ b/fairseq/examples/emotion_conversion/emotion_models/pitch_predictor.yaml @@ -0,0 +1,64 @@ +train_tsv: "/denoising/emov/train.tsv" +train_km: "/denoising/emov/train.km" +valid_tsv: "/denoising/emov/valid.tsv" +valid_km: "/denoising/emov/valid.km" + +n_tokens: 200 +batch_size: 64 +lr: 0.0001 +epochs: 1000 + +substring: "" +loss: "l2" +spk: "parent_parent_name" +gst: "emotion" + +f0_bins: 50 +f0_pred: "mean" # [argmax, mean] +f0_smoothing: 0.1 +f0_norm: "mean" +f0_log: false +f0_bin_type: "adaptive" # [uniform, adaptive] + +spk2id: + bea: 0 + jenie: 1 + josh: 2 + sam: 3 + +gst2id: + amused: 0 + angry: 1 + disgusted: 2 + neutral: 3 + sleepy: 4 + +optimizer: + _target_: torch.optim.Adam + lr: ${lr} + +model: + _target_: emotion_models.pitch_predictor.CnnPredictor + n_tokens: ${n_tokens} + emb_dim: 256 + channels: 256 + kernel: 5 + dropout: 0.1 + n_layers: 6 + spk_emb: true + gst_emb: 8 + n_bins: ${f0_bins} + f0_pred: ${f0_pred} + f0_log: ${f0_log} + f0_norm: ${f0_norm} + +hydra: + run: + dir: /checkpoint/felixkreuk/experiments/pitch_predictor/${hydra.job.override_dirname} + job: + config: + # configuration for the ${hydra.job.override_dirname} runtime variable + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: ['train_tsv', 'train_km', 'valid_tsv', 'valid_km'] diff --git a/fairseq/examples/emotion_conversion/emotion_models/utils.py b/fairseq/examples/emotion_conversion/emotion_models/utils.py new file mode 100644 index 0000000..4199c31 --- /dev/null +++ b/fairseq/examples/emotion_conversion/emotion_models/utils.py @@ -0,0 +1,78 @@ +import torch + + +class Stat: + def __init__(self, keep_raw=False): + self.x = 0.0 + self.x2 = 0.0 + self.z = 0.0 # z = logx + self.z2 = 0.0 + self.n = 0.0 + self.u = 0.0 + self.keep_raw = keep_raw + self.raw = [] + + def update(self, new_x): + new_z = new_x.log() + + self.x += new_x.sum() + self.x2 += (new_x**2).sum() + self.z += new_z.sum() + self.z2 += (new_z**2).sum() + self.n += len(new_x) + self.u += 1 + + if self.keep_raw: + self.raw.append(new_x) + + @property + def mean(self): + return self.x / self.n + + @property + def std(self): + return (self.x2 / self.n - self.mean**2) ** 0.5 + + @property + def mean_log(self): + return self.z / self.n + + @property + def std_log(self): + return (self.z2 / self.n - self.mean_log**2) ** 0.5 + + @property + def n_frms(self): + return self.n + + @property + def n_utts(self): + return self.u + + @property + def raw_data(self): + assert self.keep_raw, "does not support storing raw data!" + return torch.cat(self.raw) + + +class F0Stat(Stat): + def update(self, new_x): + # assume unvoiced frames are 0 and consider only voiced frames + if new_x is not None: + super().update(new_x[new_x != 0]) + + +class Accuracy: + def __init__(self): + self.y, self.yhat = [], [] + + def update(self, yhat, y): + self.yhat.append(yhat) + self.y.append(y) + + def acc(self, tol): + yhat = torch.cat(self.yhat) + y = torch.cat(self.y) + acc = torch.abs(yhat - y) <= tol + acc = acc.float().mean().item() + return acc diff --git a/fairseq/examples/emotion_conversion/fairseq_models/__init__.py b/fairseq/examples/emotion_conversion/fairseq_models/__init__.py new file mode 100644 index 0000000..441bc03 --- /dev/null +++ b/fairseq/examples/emotion_conversion/fairseq_models/__init__.py @@ -0,0 +1,226 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq import utils +from fairseq.models import ( + FairseqMultiModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import ( + Embedding, + base_architecture, +) +from fairseq.models.multilingual_transformer import ( + MultilingualTransformerModel, + base_multilingual_architecture, +) +from fairseq.utils import safe_hasattr +from collections import OrderedDict + + +@register_model("multilingual_transformer_from_mbart") +class MultilingualTransformerModelFromMbart(MultilingualTransformerModel): + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + from fairseq.tasks.multilingual_translation import MultilingualTranslationTask + + assert isinstance(task, MultilingualTranslationTask) + + # make sure all arguments are present in older models + base_multilingual_architecture(args) + + if not safe_hasattr(args, "max_source_positions"): + args.max_source_positions = 1024 + if not safe_hasattr(args, "max_target_positions"): + args.max_target_positions = 1024 + + src_langs = [lang_pair.split("-")[0] for lang_pair in task.model_lang_pairs] + tgt_langs = [lang_pair.split("-")[1] for lang_pair in task.model_lang_pairs] + + if args.share_encoders: + args.share_encoder_embeddings = True + if args.share_decoders: + args.share_decoder_embeddings = True + + def build_embedding(dictionary, embed_dim, path=None): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + emb = Embedding(num_embeddings, embed_dim, padding_idx) + # if provided, load from preloaded dictionaries + if path: + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + return emb + + # build shared embeddings (if applicable) + shared_encoder_embed_tokens, shared_decoder_embed_tokens = None, None + if args.share_all_embeddings: + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=task.langs, + embed_dim=args.encoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.encoder_embed_path, + ) + shared_decoder_embed_tokens = shared_encoder_embed_tokens + args.share_decoder_input_output_embed = True + else: + if args.share_encoder_embeddings: + shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=src_langs, + embed_dim=args.encoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.encoder_embed_path, + ) + if args.share_decoder_embeddings: + shared_decoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=tgt_langs, + embed_dim=args.decoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.decoder_embed_path, + ) + + # encoders/decoders for each language + lang_encoders, lang_decoders = {}, {} + + def get_encoder(lang): + if lang not in lang_encoders: + if shared_encoder_embed_tokens is not None: + encoder_embed_tokens = shared_encoder_embed_tokens + else: + encoder_embed_tokens = build_embedding( + task.dicts[lang], + args.encoder_embed_dim, + args.encoder_embed_path, + ) + lang_encoders[lang] = MultilingualTransformerModel._get_module_class( + True, args, task.dicts[lang], encoder_embed_tokens, src_langs + ) + return lang_encoders[lang] + + def get_decoder(lang): + if lang not in lang_decoders: + if shared_decoder_embed_tokens is not None: + decoder_embed_tokens = shared_decoder_embed_tokens + else: + decoder_embed_tokens = build_embedding( + task.dicts[lang], + args.decoder_embed_dim, + args.decoder_embed_path, + ) + lang_decoders[lang] = MultilingualTransformerModel._get_module_class( + False, args, task.dicts[lang], decoder_embed_tokens, tgt_langs + ) + return lang_decoders[lang] + + # shared encoders/decoders (if applicable) + shared_encoder, shared_decoder = None, None + if args.share_encoders: + shared_encoder = get_encoder(src_langs[0]) + if args.share_decoders: + shared_decoder = get_decoder(tgt_langs[0]) + + encoders, decoders = OrderedDict(), OrderedDict() + for lang_pair, src, tgt in zip(task.model_lang_pairs, src_langs, tgt_langs): + encoders[lang_pair] = ( + shared_encoder if shared_encoder is not None else get_encoder(src) + ) + decoders[lang_pair] = ( + shared_decoder if shared_decoder is not None else get_decoder(tgt) + ) + + return MultilingualTransformerModelFromMbart(encoders, decoders) + + def load_state_dict(self, state_dict, strict=True, model_cfg=None): + state_dict_subset = state_dict.copy() + lang_pairs = set([x.split(".")[1] for x in state_dict.keys()]) + finetune_mode = not any("neutral" in lp for lp in lang_pairs) + + if finetune_mode: + # load a pre-trained mBART/BART model + # we need this code because mBART/BART are not of type FairseqMultiModel but FairseqModel + # so we hackishly load the weights by replicating them for all lang pairs + print("loading pre-trained BART") + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + for lang_pair in self.models: + new_key = k if "models." in k else f"models.{lang_pair}.{k}" + # print(new_key) + if self_state_dict[new_key].shape == v.shape: + state_dict_subset[new_key] = v + elif any( + w in k + for w in [ + "encoder.embed_tokens.weight", + "decoder.embed_tokens.weight", + "decoder.output_projection.weight", + ] + ): + # why vocab_size - 5? because there are `vocab_size` tokens from the language + # and 5 additional tokens in the denoising task: eos,bos,pad,unk,mask. + # but in the translation task there are only `vocab_size` + 4 (no mask). + print( + f"{k}: {self_state_dict[new_key].shape} != {v.shape}", + end="", + flush=True, + ) + vocab_size = v.shape[0] - 5 + state_dict_subset[new_key] = self_state_dict[new_key] + state_dict_subset[new_key] = v[: vocab_size + 4] + print(f" => fixed by using first {vocab_size + 4} dims") + else: + raise ValueError("unable to load model due to mimatched dims!") + del state_dict_subset[k] + else: + print("loading pre-trained emotion translation model") + for k, _ in state_dict.items(): + assert k.startswith("models.") + lang_pair = k.split(".")[1] + if lang_pair not in self.models: + del state_dict_subset[k] + + super().load_state_dict(state_dict_subset, strict=strict, model_cfg=model_cfg) + + +@register_model_architecture("transformer", "transformer_small") +def transformer_small(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 3) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 512) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 3) + base_architecture(args) + + +@register_model_architecture( + "multilingual_transformer_from_mbart", "multilingual_small" +) +def multilingual_small(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 3) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 512) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 3) + base_multilingual_architecture(args) diff --git a/fairseq/examples/emotion_conversion/preprocess/__init__.py b/fairseq/examples/emotion_conversion/preprocess/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/emotion_conversion/preprocess/build_hifigan_manifest.py b/fairseq/examples/emotion_conversion/preprocess/build_hifigan_manifest.py new file mode 100644 index 0000000..29c0d79 --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/build_hifigan_manifest.py @@ -0,0 +1,38 @@ +import torchaudio +import argparse +import json + +def main(): + parser = argparse.ArgumentParser(description="example: python create_hifigan_manifest.py --tsv /checkpoint/felixkreuk/datasets/vctk/splits/vctk_16khz/train.tsv --km /checkpoint/felixkreuk/experiments/hubert/hubert_feats/vctk_16khz_km_100/train.km --km_type hubert_100km > ~/tmp/tmp_mani.txt") + parser.add_argument("--tsv", required=True, help="path to fairseq tsv file") + parser.add_argument("--km", required=True, help="path to a km file generated by HuBERT clustering") + parser.add_argument("--km_type", required=True, help="name of the codes in the output json (for example: 'cpc_100km')") + args = parser.parse_args() + + km_lines = open(args.km, "r").readlines() + tsv_lines = open(args.tsv, "r").readlines() + assert len(km_lines) == len(tsv_lines) - 1, "tsv and km files are not of the same length!" + + wav_root = tsv_lines[0].strip() + tsv_lines = tsv_lines[1:] + + for tsv_line, km_line in zip(tsv_lines, km_lines): + tsv_line, km_line = tsv_line.strip(), km_line.strip() + wav_basename, wav_num_frames = tsv_line.split("\t") + wav_path = wav_root + "/" + wav_basename + wav_info = torchaudio.info(wav_path) + assert int(wav_num_frames) == wav_info.num_frames, "tsv duration and actual duration don't match!" + wav_duration = wav_info.num_frames / wav_info.sample_rate + manifest_line = {"audio": wav_path, "duration": wav_duration, args.km_type: km_line} + print(json.dumps(manifest_line)) + +if __name__ == "__main__": + """ + usage: + python create_hifigan_manifest.py \ + --tsv /checkpoint/felixkreuk/datasets/vctk/manifests/vctk_16khz/valid.tsv \ + --km /checkpoint/felixkreuk/datasets/vctk/manifests/vctk_16khz/hubert_km_100/valid.km \ + --km_type hubert \ + > /checkpoint/felixkreuk/datasets/vctk/manifests/vctk_16khz/hubert_km_100/hifigan_valid_manifest.txt + """ + main() diff --git a/fairseq/examples/emotion_conversion/preprocess/build_translation_manifests.py b/fairseq/examples/emotion_conversion/preprocess/build_translation_manifests.py new file mode 100644 index 0000000..d38454a --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/build_translation_manifests.py @@ -0,0 +1,258 @@ +from glob import glob +import argparse +from collections import defaultdict, Counter +from itertools import combinations, product, groupby +from pathlib import Path +import os +from sklearn.utils import shuffle +import numpy as np +import random +from shutil import copy +from subprocess import check_call + +np.random.seed(42) +random.seed(42) + + +def get_fname(s): + return s.split("\t")[0] + +def get_emotion(s): + return get_fname(s).split("_")[0].split("/")[1].lower() + +def get_utt_id(s): + return get_fname(s).split(".")[0].split("_")[-1] + +def dedup(seq): + """ >> remove_repetitions("1 2 2 3 100 2 2 1") + '1 2 3 100 2 1' """ + seq = seq.strip().split(" ") + result = seq[:1] + reps = [] + rep_counter = 1 + for k in seq[1:]: + if k != result[-1]: + result += [k] + reps += [rep_counter] + rep_counter = 1 + else: + rep_counter += 1 + reps += [rep_counter] + assert len(reps) == len(result) and sum(reps) == len(seq) + return " ".join(result) + "\n" #, reps + +def remove_under_k(seq, k): + """ remove tokens that repeat less then k times in a row + >> remove_under_k("a a a a b c c c", 1) ==> a a a a c c c """ + seq = seq.strip().split(" ") + result = [] + + freqs = [(k,len(list(g))) for k, g in groupby(seq)] + for c, f in freqs: + if f > k: + result += [c for _ in range(f)] + return " ".join(result) + "\n" #, reps + + +def call(cmd): + print(cmd) + check_call(cmd, shell=True) + + +def denoising_preprocess(path, lang, dict): + bin = 'fairseq-preprocess' + cmd = [ + bin, + f'--trainpref {path}/train.{lang} --validpref {path}/valid.{lang} --testpref {path}/test.{lang}', + f'--destdir {path}/tokenized/{lang}', + '--only-source', + '--task multilingual_denoising', + '--workers 40', + ] + if dict != "": + cmd += [f'--srcdict {dict}'] + cmd = " ".join(cmd) + call(cmd) + + +def translation_preprocess(path, src_lang, trg_lang, dict, only_train=False): + bin = 'fairseq-preprocess' + cmd = [ + bin, + f'--source-lang {src_lang} --target-lang {trg_lang}', + f'--trainpref {path}/train', + f'--destdir {path}/tokenized', + '--workers 40', + ] + if not only_train: + cmd += [f'--validpref {path}/valid --testpref {path}/test'] + if dict != "": + cmd += [ + f'--srcdict {dict}', + f'--tgtdict {dict}', + ] + cmd = " ".join(cmd) + call(cmd) + + +def load_tsv_km(tsv_path, km_path): + assert tsv_path.exists() and km_path.exists() + tsv_lines = open(tsv_path, "r").readlines() + root, tsv_lines = tsv_lines[0], tsv_lines[1:] + km_lines = open(km_path, "r").readlines() + assert len(tsv_lines) == len(km_lines), ".tsv and .km should be the same length!" + return root, tsv_lines, km_lines + + +def main(): + desc = """ + this script takes as input .tsv and .km files for EMOV dataset, and a pairs of emotions. + it generates parallel .tsv and .km files for these emotions. for exmaple: + ❯ python build_emov_translation_manifests.py \ + /checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz/train.tsv \ + /checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz/emov_16khz_km_100/train.km \ + ~/tmp/emov_pairs \ + --src-emotion amused --trg-emotion neutral \ + --dedup --shuffle --cross-speaker --dry-run + """ + parser = argparse.ArgumentParser(description=desc) + parser.add_argument("data", type=Path, help="path to a dir containing .tsv and .km files containing emov dataset") + parser.add_argument("output_path", type=Path, help="output directory with the manifests will be created") + parser.add_argument("-cs", "--cross-speaker", action='store_true', help="if set then translation will occur also between speakers, meaning the same sentence can be translated between different speakers (default: false)") + parser.add_argument("-dd", "--dedup", action='store_true', help="remove repeated tokens (example: 'aaabc=>abc')") + parser.add_argument("-sh", "--shuffle", action='store_true', help="shuffle the data") + parser.add_argument("-ae", "--autoencode", action='store_true', help="include training pairs from the same emotion (this includes examples of the same sentence uttered by different people and examples where the src and trg are the exact same seq)") + parser.add_argument("-dr", "--dry-run", action='store_true', help="don't write anything to disk") + parser.add_argument("-zs", "--zero-shot", action='store_true', help="if true, the denoising task will train on the same splits as the translation task (split by utterance id). if false, the denoising task will train on randomly sampled splits (not split by utterance id)") + parser.add_argument("--km-ext", default="km", help="") + parser.add_argument("--dict", default="/checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz/fairseq.dict.txt", help="") + args = parser.parse_args() + SPEAKERS = ["bea", "jenie", "josh", "sam", "SAME"] + EMOTIONS = ['neutral', 'amused', 'angry', 'disgusted', 'sleepy'] + + suffix = "" + if args.cross_speaker: suffix += "_cross-speaker" + if args.dedup: suffix += "_dedup" + translation_suffix = "" + if args.autoencode: translation_suffix += "_autoencode" + denoising_suffix = "" + denoising_suffix += "_zeroshot" if args.zero_shot else "_nonzeroshot" + + translation_dir = Path(args.output_path) / ("emov_multilingual_translation" + suffix + translation_suffix) + os.makedirs(translation_dir, exist_ok=True) + denoising_dir = Path(args.output_path) / ("emov_multilingual_denoising" + suffix + denoising_suffix) + os.makedirs(denoising_dir, exist_ok=True) + + denoising_data = [p.name for p in (args.data / "denoising").glob("*") if "emov" not in p.name] + + for split in ["train", "valid", "test"]: + root, tsv_lines, km_lines = load_tsv_km( + tsv_path = args.data / "denoising" / "emov" / f"{split}.tsv", + km_path = args.data / "denoising" / "emov" / f"{split}.{args.km_ext}" + ) + + # generate data for the multilingual denoising task + for EMOTION in EMOTIONS: + print("---") + print(split) + print(f"denoising: {EMOTION}") + emotion_tsv, emotion_km = [], [] + for tsv_line, km_line in zip(tsv_lines, km_lines): + if EMOTION.lower() in tsv_line.lower(): + km_line = km_line if not args.dedup else dedup(km_line) + emotion_tsv.append(tsv_line) + emotion_km.append(km_line) + print(f"{len(emotion_km)} samples") + open(denoising_dir / f"files.{split}.{EMOTION}", "w").writelines([root] + emotion_tsv) + open(denoising_dir / f"{split}.{EMOTION}", "w").writelines(emotion_km) + + for data in denoising_data: + with open(args.data / "denoising" / data / f"{split}.{args.km_ext}", "r") as f1: + with open(denoising_dir / f"{split}.{data}", "w") as f2: + f2.writelines([l if not args.dedup else dedup(l) for l in f1.readlines()]) + + # start of translation preprocessing + root, tsv_lines, km_lines = load_tsv_km( + tsv_path = args.data / "translation" / f"{split}.tsv", + km_path = args.data / "translation" / f"{split}.{args.km_ext}" + ) + + # generate data for the multilingual translation task + for SRC_EMOTION in EMOTIONS: + TRG_EMOTIONS = EMOTIONS if args.autoencode else set(EMOTIONS) - set([SRC_EMOTION]) + for TRG_EMOTION in TRG_EMOTIONS: + # when translating back to the same emotion - we dont want these emotion + # pairs to be part of the validation/test sets (because its not really emotion conversino) + # if SRC_EMOTION == TRG_EMOTION and split in ["valid", "test"]: continue + print("---") + print(split) + print(f"src emotions: {SRC_EMOTION}\ntrg emotions: {TRG_EMOTION}") + + # create a dictionary with the following structure: + # output[SPEAKER][UTT_ID] = list with indexes of line from the tsv file + # that match the speaker and utterance id. for exmaple: + # output = {'sam': {'0493': [875, 1608, 1822], ...}, ...} + # meaning, for speaker 'sam', utterance id '0493', the indexes in tsv_lines + # are 875, 1608, 1822 + spkr2utts = defaultdict(lambda: defaultdict(list)) + for i, tsv_line in enumerate(tsv_lines): + speaker = tsv_line.split("/")[0] + if args.cross_speaker: speaker = "SAME" + assert speaker in SPEAKERS, "unknown speaker! make sure the .tsv contains EMOV data" + utt_id = get_utt_id(tsv_line) + spkr2utts[speaker][utt_id].append(i) + + # create a tsv and km files with all the combinations for translation + src_tsv, trg_tsv, src_km, trg_km = [], [], [], [] + for speaker, utt_ids in spkr2utts.items(): + for utt_id, indices in utt_ids.items(): + # generate all pairs + pairs = [(x,y) for x in indices for y in indices] + # self-translation + if SRC_EMOTION == TRG_EMOTION: + pairs = [(x,y) for (x,y) in pairs if x == y] + # filter according to src and trg emotions + pairs = [(x,y) for (x,y) in pairs + if get_emotion(tsv_lines[x]) == SRC_EMOTION and get_emotion(tsv_lines[y]) == TRG_EMOTION] + + for idx1, idx2 in pairs: + assert get_utt_id(tsv_lines[idx1]) == get_utt_id(tsv_lines[idx2]) + src_tsv.append(tsv_lines[idx1]) + trg_tsv.append(tsv_lines[idx2]) + km_line_idx1 = km_lines[idx1] + km_line_idx2 = km_lines[idx2] + km_line_idx1 = km_line_idx1 if not args.dedup else dedup(km_line_idx1) + km_line_idx2 = km_line_idx2 if not args.dedup else dedup(km_line_idx2) + src_km.append(km_line_idx1) + trg_km.append(km_line_idx2) + assert len(src_tsv) == len(trg_tsv) == len(src_km) == len(trg_km) + print(f"{len(src_tsv)} pairs") + + if len(src_tsv) == 0: + raise Exception("ERROR: generated 0 pairs!") + + if args.dry_run: continue + + # create files + os.makedirs(translation_dir / f"{SRC_EMOTION}-{TRG_EMOTION}", exist_ok=True) + open(translation_dir / f"{SRC_EMOTION}-{TRG_EMOTION}" / f"files.{split}.{SRC_EMOTION}", "w").writelines([root] + src_tsv) + open(translation_dir / f"{SRC_EMOTION}-{TRG_EMOTION}" / f"files.{split}.{TRG_EMOTION}", "w").writelines([root] + trg_tsv) + open(translation_dir / f"{SRC_EMOTION}-{TRG_EMOTION}" / f"{split}.{SRC_EMOTION}", "w").writelines(src_km) + open(translation_dir / f"{SRC_EMOTION}-{TRG_EMOTION}" / f"{split}.{TRG_EMOTION}", "w").writelines(trg_km) + + + # fairseq-preprocess the denoising data + for EMOTION in EMOTIONS + denoising_data: + denoising_preprocess(denoising_dir, EMOTION, args.dict) + os.system(f"cp {args.dict} {denoising_dir}/tokenized/dict.txt") + + # fairseq-preprocess the translation data + os.makedirs(translation_dir / "tokenized", exist_ok=True) + for SRC_EMOTION in EMOTIONS: + TRG_EMOTIONS = EMOTIONS if args.autoencode else set(EMOTIONS) - set([SRC_EMOTION]) + for TRG_EMOTION in TRG_EMOTIONS: + translation_preprocess(translation_dir / f"{SRC_EMOTION}-{TRG_EMOTION}", SRC_EMOTION, TRG_EMOTION, args.dict)#, only_train=SRC_EMOTION==TRG_EMOTION) + os.system(f"cp -rf {translation_dir}/**/tokenized/* {translation_dir}/tokenized") + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/emotion_conversion/preprocess/create_core_manifest.py b/fairseq/examples/emotion_conversion/preprocess/create_core_manifest.py new file mode 100644 index 0000000..b55740e --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/create_core_manifest.py @@ -0,0 +1,91 @@ +from pathlib import Path +import os +import sys +import subprocess +import argparse +from datetime import datetime +import logging + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s [%(levelname)s] %(message)s', + handlers=[logging.FileHandler('debug.log'), logging.StreamHandler()] +) +logger = logging.getLogger(__name__) + + +def verify_dict_size(km, dict): + logger.info(f"verifying: {km}") + dict_size = len(open(dict, "r").readlines()) + km_vocab = set(open(km, "r").read().replace("\n", " ").split(" ")) + if "" in km_vocab: km_vocab.remove("") + km_vocab_size = len(km_vocab) + return dict_size == km_vocab_size + + +def verify_files_exist(l): + for f in l: + if not f.exists(): + logging.error(f"{f} doesn't exist!") + return False + return True + + +def run_cmd(cmd, print_output=True): + try: + out = subprocess.check_output(cmd, stderr=subprocess.STDOUT, universal_newlines=True, shell=True) + if print_output: + logger.info(f"command output:\n{out}") + return out + except subprocess.CalledProcessError as grepexc: + logger.info(f"error executing command!:\n{cmd}") + logger.info(grepexc.output) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--tsv", default="/checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz/data.tsv", type=Path) + parser.add_argument("--emov-km", required=True, type=Path) + parser.add_argument("--km", nargs='+', required=True, type=Path) + parser.add_argument("--seed", type=int, default=1) + parser.add_argument("--dict", default="/checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz/fairseq.dict.txt") + parser.add_argument("--manifests-dir", type=Path, default="/checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz") + args = parser.parse_args() + + manifests_dir = args.manifests_dir + date = datetime.now().strftime('%d%m%y') + outdir = manifests_dir / f"{date}" + + # verify input and create folders + all_kms = args.km + [args.emov_km] + assert verify_files_exist(all_kms), "make sure the km dir contains: train-clean-all.km, blizzard2013.km, data.km" + for codes in all_kms: + assert verify_dict_size(codes, args.dict), "dict argument doesn't match the vocabulary of the km file!" + assert not outdir.exists(), "data dir already exists!" + outdir.mkdir(parents=True, exist_ok=True) + + logger.info("generating denoising split (emov)") + run_cmd(f"python preprocess/split_km_tsv.py {args.tsv} {args.emov_km} --destdir {outdir}/denoising/emov -sh --seed {args.seed}") + for codes in args.km: + codes_name = os.path.basename(codes) + run_cmd(f"python preprocess/split_km.py {codes} --destdir {outdir}/denoising/{codes_name} -sh --seed {args.seed}") + + logger.info("generating translation split") + run_cmd(f"python preprocess/split_emov_km_tsv_by_uttid.py {args.tsv} {args.emov_km} --destdir {outdir}/translation --seed {args.seed}") + + emov_code_name = os.path.basename(args.emov_km) + logger.info("generating hifigan split") + run_cmd( + f"mkdir -p {outdir}/hifigan &&" + f"python preprocess/build_hifigan_manifest.py --km_type hubert --tsv {outdir}/denoising/emov/train.tsv --km {outdir}/denoising/emov/train.km > {outdir}/hifigan/train.txt &&" + f"python preprocess/build_hifigan_manifest.py --km_type hubert --tsv {outdir}/denoising/emov/valid.tsv --km {outdir}/denoising/emov/valid.km > {outdir}/hifigan/valid.txt &&" + f"python preprocess/build_hifigan_manifest.py --km_type hubert --tsv {outdir}/denoising/emov/test.tsv --km {outdir}/denoising/emov/test.km > {outdir}/hifigan/test.txt" + ) + + logger.info("generating fairseq manifests") + run_cmd(f"python preprocess/build_translation_manifests.py {outdir} {outdir}/fairseq-data -dd -cs --dict {args.dict}") + + logger.info(f"finished processing data at:\n{outdir}") + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/emotion_conversion/preprocess/extract_f0.py b/fairseq/examples/emotion_conversion/preprocess/extract_f0.py new file mode 100644 index 0000000..4204aa4 --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/extract_f0.py @@ -0,0 +1,57 @@ +import argparse +from tqdm import tqdm +from multiprocessing import Manager, Pool + +from scipy.io.wavfile import read +from librosa.util import normalize +import numpy as np +import amfm_decompy.pYAAPT as pYAAPT +import amfm_decompy.basic_tools as basic + +MAX_WAV_VALUE = 32768.0 + +parser = argparse.ArgumentParser(description="") +parser.add_argument("tsv", help="") +parser.add_argument("--extractor", choices=["crepe", "pyaapt"], default="pyaapt", help="") +parser.add_argument("--interp", action="store_true", help="") +parser.add_argument("--n_workers", type=int, default=40, help="") +args = parser.parse_args() + +tsv_lines = open(args.tsv, "r").readlines() +root, tsv_lines = tsv_lines[0].strip(), tsv_lines[1:] + + +def extract_f0(tsv_line): + wav_path, _ = tsv_line.split("\t") + wav_path = root.strip() + "/" + wav_path + sr, wav = read(wav_path) + wav = wav / MAX_WAV_VALUE + wav = normalize(wav) * 0.95 + + if args.extractor == "pyaapt": + frame_length = 20.0 + pad = int(frame_length / 1000 * sr) // 2 + wav = np.pad(wav.squeeze(), (pad, pad), "constant", constant_values=0) + signal = basic.SignalObj(wav, sr) + pitch = pYAAPT.yaapt( + signal, + **{ + 'frame_length': frame_length, + 'frame_space': 5.0, + 'nccf_thresh1': 0.25, + 'tda_frame_length': 25.0 + }) + pitch = pitch.samp_interp[None, None, :] if args.interp else pitch.samp_values[None, None, :] + pitch = pitch[0, 0] + f0_path = wav_path.replace(".wav", ".yaapt") + f0_path += ".interp.f0" if args.interp else ".f0" + np.save(f0_path, pitch) + + +def main(): + with Pool(args.n_workers) as p: + r = list(tqdm(p.imap(extract_f0, tsv_lines), total=len(tsv_lines))) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/emotion_conversion/preprocess/process_km.py b/fairseq/examples/emotion_conversion/preprocess/process_km.py new file mode 100644 index 0000000..864a022 --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/process_km.py @@ -0,0 +1,40 @@ +import sys +import argparse +from tqdm import tqdm +from build_emov_translation_manifests import dedup, remove_under_k + + +if __name__ == "__main__": + """ + this is a standalone script to process a km file + specifically, to dedup or remove tokens that repeat less + than k times in a row + """ + parser = argparse.ArgumentParser(description="") + parser.add_argument("km", type=str, help="path to km file") + parser.add_argument("--dedup", action='store_true') + parser.add_argument("--remove-under-k", type=int, default=0) + parser.add_argument("--output", default=None) + args = parser.parse_args() + + if not args.dedup and args.remove_under_k == 0: + print("nothing to do! quitting...") + sys.exit(0) + + km = open(args.km, "r").readlines() + out = [] + for line in tqdm(km): + if args.remove_under_k > 0: + line = remove_under_k(line, args.remove_under_k) + if args.dedup: + line = dedup(line) + out.append(line) + + path = args.km if args.output is None else args.output + if args.remove_under_k > 0: + path = path.replace(".km", f"-k{args.remove_under_k}.km") + if args.dedup: + path = path.replace(".km", f"-deduped.km") + + open(path, "w").writelines(out) + print(f"written to {path}") diff --git a/fairseq/examples/emotion_conversion/preprocess/split_emov_km_tsv_by_uttid.py b/fairseq/examples/emotion_conversion/preprocess/split_emov_km_tsv_by_uttid.py new file mode 100644 index 0000000..94221af --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/split_emov_km_tsv_by_uttid.py @@ -0,0 +1,70 @@ +from pathlib import Path +import os +import sys +import argparse +import random +import numpy as np +from tqdm import tqdm +from sklearn.model_selection import train_test_split +from build_translation_manifests import get_utt_id + + +def train_val_test_split(tsv_lines, km_lines, valid_percent, test_percent, seed=42): + utt_ids = list(sorted(set([get_utt_id(x) for x in tsv_lines]))) + utt_ids, valid_utt_ids, _, _ = train_test_split(utt_ids, utt_ids, test_size=valid_percent, shuffle=True, random_state=seed) + train_utt_ids, test_utt_ids, _, _ = train_test_split(utt_ids, utt_ids, test_size=test_percent, shuffle=True, random_state=seed) + + train_idx = [i for i, line in enumerate(tsv_lines) if get_utt_id(line) in train_utt_ids] + valid_idx = [i for i, line in enumerate(tsv_lines) if get_utt_id(line) in valid_utt_ids] + test_idx = [i for i, line in enumerate(tsv_lines) if get_utt_id(line) in test_utt_ids] + + train_tsv, train_km = [tsv_lines[i] for i in train_idx], [km_lines[i] for i in train_idx] + valid_tsv, valid_km = [tsv_lines[i] for i in valid_idx], [km_lines[i] for i in valid_idx] + test_tsv, test_km = [tsv_lines[i] for i in test_idx], [km_lines[i] for i in test_idx] + + print(f"train {len(train_km)}") + print(f"valid {len(valid_km)}") + print(f"test {len(test_km)}") + + return train_tsv, train_km, valid_tsv, valid_km, test_tsv, test_km + + +if __name__ == "__main__": + """ + this is a standalone script to process a km file + specifically, to dedup or remove tokens that repeat less + than k times in a row + """ + parser = argparse.ArgumentParser(description="") + parser.add_argument("tsv", type=str, help="path to tsv file") + parser.add_argument("km", type=str, help="path to km file") + parser.add_argument("--destdir", required=True, type=str) + parser.add_argument("--valid-percent", type=float, default=0.05, help="percent to allocate to validation set") + parser.add_argument("--test-percent", type=float, default=0.05, help="percent to allocate to test set") + parser.add_argument("--seed", type=int, default=42, help="") + args = parser.parse_args() + + np.random.seed(args.seed) + random.seed(args.seed) + + os.makedirs(args.destdir, exist_ok=True) + km = open(args.km, "r").readlines() + tsv = open(args.tsv, "r").readlines() + root, tsv = tsv[0], tsv[1:] + + assert args.tsv.endswith(".tsv") and args.km.endswith(".km") + assert len(tsv) == len(km) + + train_tsv, train_km, valid_tsv, valid_km, test_tsv, test_km = train_val_test_split(tsv, km, args.valid_percent, args.test_percent, args.seed) + + assert len(train_tsv) + len(valid_tsv) + len(test_tsv) == len(tsv) + assert len(train_tsv) == len(train_km) and len(valid_tsv) == len(valid_km) and len(test_tsv) == len(test_km) + + dir = Path(args.destdir) + open(dir / f"train.tsv", "w").writelines([root] + train_tsv) + open(dir / f"valid.tsv", "w").writelines([root] + valid_tsv) + open(dir / f"test.tsv", "w").writelines([root] + test_tsv) + open(dir / f"train.km", "w").writelines(train_km) + open(dir / f"valid.km", "w").writelines(valid_km) + open(dir / f"test.km", "w").writelines(test_km) + print("done") diff --git a/fairseq/examples/emotion_conversion/preprocess/split_km.py b/fairseq/examples/emotion_conversion/preprocess/split_km.py new file mode 100644 index 0000000..d145fc2 --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/split_km.py @@ -0,0 +1,50 @@ +from pathlib import Path +import os +import argparse +import random +import numpy as np +from sklearn.utils import shuffle + + +if __name__ == "__main__": + """ + this is a standalone script to process a km file + specifically, to dedup or remove tokens that repeat less + than k times in a row + """ + parser = argparse.ArgumentParser(description="") + parser.add_argument("km", type=str, help="path to km file") + parser.add_argument("--destdir", required=True, type=str) + parser.add_argument("--valid-percent", type=float, default=0.05, help="percent to allocate to validation set") + parser.add_argument("--test-percent", type=float, default=0.05, help="percent to allocate to test set") + parser.add_argument("-sh", "--shuffle", action="store_true", help="path to km file") + parser.add_argument("--seed", type=int, default=42, help="") + args = parser.parse_args() + + np.random.seed(args.seed) + random.seed(args.seed) + + os.makedirs(args.destdir, exist_ok=True) + km = open(args.km, "r").readlines() + + if args.shuffle: + km = shuffle(km) + print(f"shuffled") + + N = len(km) + N_tt = int(N * args.test_percent) + N_cv = int(N * args.valid_percent) + N_tr = N - N_tt - N_cv + + train_km = km[:N_tr] + valid_km = km[N_tr:N_tr + N_cv] + test_km = km[N_tr + N_cv:] + + dir = Path(args.destdir) + open(dir / f"train.km", "w").writelines(train_km) + open(dir / f"valid.km", "w").writelines(valid_km) + open(dir / f"test.km", "w").writelines(test_km) + print(f"train: {len(train_km)}") + print(f"valid: {len(valid_km)}") + print(f"test: {len(test_km)}") + print("done") diff --git a/fairseq/examples/emotion_conversion/preprocess/split_km_tsv.py b/fairseq/examples/emotion_conversion/preprocess/split_km_tsv.py new file mode 100644 index 0000000..2113aa7 --- /dev/null +++ b/fairseq/examples/emotion_conversion/preprocess/split_km_tsv.py @@ -0,0 +1,65 @@ +from pathlib import Path +import os +import argparse +import random +import numpy as np +from sklearn.utils import shuffle + + +if __name__ == "__main__": + """ + this is a standalone script to process a km file + specifically, to dedup or remove tokens that repeat less + than k times in a row + """ + parser = argparse.ArgumentParser(description="") + parser.add_argument("tsv", type=str, help="path to tsv file") + parser.add_argument("km", type=str, help="path to km file") + parser.add_argument("--destdir", required=True, type=str) + parser.add_argument("--valid-percent", type=float, default=0.05, help="percent to allocate to validation set") + parser.add_argument("--test-percent", type=float, default=0.05, help="percent to allocate to test set") + parser.add_argument("-sh", "--shuffle", action="store_true", help="path to km file") + parser.add_argument("--seed", type=int, default=42, help="") + args = parser.parse_args() + + np.random.seed(args.seed) + random.seed(args.seed) + + os.makedirs(args.destdir, exist_ok=True) + km = open(args.km, "r").readlines() + tsv = open(args.tsv, "r").readlines() + root, tsv = tsv[0], tsv[1:] + + assert args.tsv.endswith(".tsv") and args.km.endswith(".km") + assert len(tsv) == len(km) + + if args.shuffle: + tsv, km = shuffle(tsv, km) + print(f"shuffled") + + N = len(tsv) + N_tt = int(N * args.test_percent) + N_cv = int(N * args.valid_percent) + N_tr = N - N_tt - N_cv + + train_tsv = tsv[:N_tr] + valid_tsv = tsv[N_tr:N_tr + N_cv] + test_tsv = tsv[N_tr + N_cv:] + train_km = km[:N_tr] + valid_km = km[N_tr:N_tr + N_cv] + test_km = km[N_tr + N_cv:] + + assert len(train_tsv) + len(valid_tsv) + len(test_tsv) == len(tsv) + assert len(train_tsv) == len(train_km) and len(valid_tsv) == len(valid_km) and len(test_tsv) == len(test_km) + + dir = Path(args.destdir) + open(dir / f"train.tsv", "w").writelines([root] + train_tsv) + open(dir / f"valid.tsv", "w").writelines([root] + valid_tsv) + open(dir / f"test.tsv", "w").writelines([root] + test_tsv) + open(dir / f"train.km", "w").writelines(train_km) + open(dir / f"valid.km", "w").writelines(valid_km) + open(dir / f"test.km", "w").writelines(test_km) + print(f"train: {len(train_km)}") + print(f"valid: {len(valid_km)}") + print(f"test: {len(test_km)}") + print("done") diff --git a/fairseq/examples/emotion_conversion/requirements.txt b/fairseq/examples/emotion_conversion/requirements.txt new file mode 100644 index 0000000..fc94c5a --- /dev/null +++ b/fairseq/examples/emotion_conversion/requirements.txt @@ -0,0 +1,11 @@ +scipy +einops +amfm_decompy +joblib +numba +decorator +requests +appdirs +packaging +six +sklearn diff --git a/fairseq/examples/emotion_conversion/synthesize.py b/fairseq/examples/emotion_conversion/synthesize.py new file mode 100644 index 0000000..327fdaf --- /dev/null +++ b/fairseq/examples/emotion_conversion/synthesize.py @@ -0,0 +1,322 @@ +import logging +import argparse +import random +import sys +import os +import numpy as np +import torch +import soundfile as sf +import shutil +import librosa +import json +from pathlib import Path +from tqdm import tqdm +import amfm_decompy.basic_tools as basic +import amfm_decompy.pYAAPT as pYAAPT + +dir_path = os.path.dirname(__file__) +resynth_path = os.path.dirname(os.path.abspath(__file__)) + "/speech-resynthesis" +sys.path.append(resynth_path) + +from models import CodeGenerator +from inference import scan_checkpoint, load_checkpoint, generate +from emotion_models.pitch_predictor import load_ckpt as load_pitch_predictor +from emotion_models.duration_predictor import load_ckpt as load_duration_predictor +from dataset import load_audio, MAX_WAV_VALUE, parse_style, parse_speaker, EMOV_SPK2ID, EMOV_STYLE2ID + + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s [%(levelname)s] %(message)s', + handlers=[logging.FileHandler('debug.log'), logging.StreamHandler()] +) +logger = logging.getLogger(__name__) + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def parse_generation_file(fname): + lines = open(fname).read() + lines = lines.split('\n') + + results = {} + for l in lines: + if len(l) == 0: + continue + + if l[0] == 'H': + parts = l[2:].split('\t') + if len(parts) == 2: + sid, utt = parts + else: + sid, _, utt = parts + sid = int(sid) + utt = [int(x) for x in utt.split()] + if sid in results: + results[sid]['H'] = utt + else: + results[sid] = {'H': utt} + elif l[0] == 'S': + sid, utt = l[2:].split('\t') + sid = int(sid) + utt = [x for x in utt.split()] + if sid in results: + results[sid]['S'] = utt + else: + results[sid] = {'S': utt} + elif l[0] == 'T': + sid, utt = l[2:].split('\t') + sid = int(sid) + utt = [int(x) for x in utt.split()] + if sid in results: + results[sid]['T'] = utt + else: + results[sid] = {'T': utt} + + for d, result in results.items(): + if 'H' not in result: + result['H'] = result['S'] + + return results + + +def get_code_to_fname(manifest, tokens): + if tokens is None: + code_to_fname = {} + with open(manifest) as f: + for line in f: + line = line.strip() + fname, code = line.split() + code = code.replace(',', ' ') + code_to_fname[code] = fname + + return code_to_fname + + with open(manifest) as f: + fnames = [l.strip() for l in f.readlines()] + root = Path(fnames[0]) + fnames = fnames[1:] + if '\t' in fnames[0]: + fnames = [x.split()[0] for x in fnames] + + with open(tokens) as f: + codes = [l.strip() for l in f.readlines()] + + code_to_fname = {} + for fname, code in zip(fnames, codes): + code = code.replace(',', ' ') + code_to_fname[code] = str(root / fname) + + return root, code_to_fname + + +def code_to_str(s): + k = ' '.join([str(x) for x in s]) + return k + + +def get_praat_f0(audio, rate=16000, interp=False): + frame_length = 20.0 + to_pad = int(frame_length / 1000 * rate) // 2 + + f0s = [] + for y in audio.astype(np.float64): + y_pad = np.pad(y.squeeze(), (to_pad, to_pad), "constant", constant_values=0) + signal = basic.SignalObj(y_pad, rate) + pitch = pYAAPT.yaapt(signal, **{'frame_length': frame_length, 'frame_space': 5.0, 'nccf_thresh1': 0.25, + 'tda_frame_length': 25.0}) + if interp: + f0s += [pitch.samp_interp[None, None, :]] + else: + f0s += [pitch.samp_values[None, None, :]] + + f0 = np.vstack(f0s) + return f0 + + +def generate_from_code(generator, h, code, spkr=None, f0=None, gst=None, device="cpu"): + batch = { + 'code': torch.LongTensor(code).to(device).view(1, -1), + } + if spkr is not None: + batch['spkr'] = spkr.to(device).unsqueeze(0) + if f0 is not None: + batch['f0'] = f0.to(device) + if gst is not None: + batch['style'] = gst.to(device) + + with torch.no_grad(): + audio, rtf = generate(h, generator, batch) + audio = librosa.util.normalize(audio / 2 ** 15) + + return audio + + +@torch.no_grad() +def synth(argv, interactive=False): + parser = argparse.ArgumentParser() + parser.add_argument('--result-path', type=Path, help='Translation Model Output', required=True) + parser.add_argument('--data', type=Path, help='a directory with the files: src.tsv, src.km, trg.tsv, trg.km, orig.tsv, orig.km') + parser.add_argument("--orig-tsv", default="/checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz/data.tsv") + parser.add_argument("--orig-km", default="/checkpoint/felixkreuk/datasets/emov/manifests/emov_16khz/core_manifests/emov_16khz_km_100/data.km") + + parser.add_argument('--checkpoint-file', type=Path, help='Generator Checkpoint', required=True) + parser.add_argument('--dur-model', type=Path, help='a token duration prediction model (if tokens were deduped)') + parser.add_argument('--f0-model', type=Path, help='a f0 prediction model') + + parser.add_argument('-s', '--src-emotion', default=None) + parser.add_argument('-t', '--trg-emotion', default=None) + parser.add_argument('-N', type=int, default=10) + parser.add_argument('--split', default="test") + + parser.add_argument('--outdir', type=Path, default=Path('results')) + parser.add_argument('--orig-filename', action='store_true') + + parser.add_argument('--device', type=int, default=0) + a = parser.parse_args(argv) + + seed = 52 + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + if os.path.isdir(a.checkpoint_file): + config_file = os.path.join(a.checkpoint_file, 'config.json') + else: + config_file = os.path.join(os.path.split(a.checkpoint_file)[0], 'config.json') + with open(config_file) as f: + data = f.read() + json_config = json.loads(data) + h = AttrDict(json_config) + + generator = CodeGenerator(h).to(a.device) + if os.path.isdir(a.checkpoint_file): + cp_g = scan_checkpoint(a.checkpoint_file, 'g_') + else: + cp_g = a.checkpoint_file + state_dict_g = load_checkpoint(cp_g) + generator.load_state_dict(state_dict_g['generator']) + + generator.eval() + generator.remove_weight_norm() + + dur_models = { + "neutral": load_duration_predictor(f"{a.dur_model}/neutral.ckpt"), + "amused": load_duration_predictor(f"{a.dur_model}/amused.ckpt"), + "disgusted": load_duration_predictor(f"{a.dur_model}/disgusted.ckpt"), + "angry": load_duration_predictor(f"{a.dur_model}/angry.ckpt"), + "sleepy": load_duration_predictor(f"{a.dur_model}/sleepy.ckpt"), + } + logger.info(f"loaded duration prediction model from {a.dur_model}") + + f0_model = load_pitch_predictor(a.f0_model).to(a.device) + logger.info(f"loaded f0 prediction model from {a.f0_model}") + + # we need to know how to map code back to the filename + # (if we want the original files names as output) + results = parse_generation_file(a.result_path) + _, src_code_to_fname = get_code_to_fname(f'{a.data}/files.{a.split}.{a.src_emotion}', f'{a.data}/{a.split}.{a.src_emotion}') + _, tgt_code_to_fname = get_code_to_fname(f'{a.data}/files.{a.split}.{a.trg_emotion}', f'{a.data}/{a.split}.{a.trg_emotion}') + + # we need the originals (before dedup) to get the ground-truth durations + orig_tsv = open(a.orig_tsv, 'r').readlines() + orig_tsv_root, orig_tsv = orig_tsv[0].strip(), orig_tsv[1:] + orig_km = open(a.orig_km, 'r').readlines() + fname_to_idx = {orig_tsv_root + "/" + line.split("\t")[0]: i for i, line in enumerate(orig_tsv)} + + outdir = a.outdir + outdir.mkdir(parents=True, exist_ok=True) + (outdir / '0-source').mkdir(exist_ok=True) + (outdir / '1-src-tokens-src-style-src-f0').mkdir(exist_ok=True) + (outdir / '2-src-tokens-trg-style-src-f0').mkdir(exist_ok=True) + (outdir / '2.5-src-tokens-trg-style-src-f0').mkdir(exist_ok=True) + (outdir / '3-src-tokens-trg-style-pred-f0').mkdir(exist_ok=True) + (outdir / '4-gen-tokens-trg-style-pred-f0').mkdir(exist_ok=True) + (outdir / '5-target').mkdir(exist_ok=True) + + N = 0 + results = list(results.items()) + random.shuffle(results) + for i, (sid, result) in tqdm(enumerate(results)): + N += 1 + if N > a.N and a.N != -1: + break + + if '[' in result['S'][0]: + result['S'] = result['S'][1:] + if '_' in result['S'][-1]: + result['S'] = result['S'][:-1] + src_ref = src_code_to_fname[code_to_str(result['S'])] + trg_ref = tgt_code_to_fname[code_to_str(result['T'])] + + src_style, trg_style = None, None + src_spkr, trg_spkr = None, None + src_f0 = None + src_audio = (load_audio(src_ref)[0] / MAX_WAV_VALUE) * 0.95 + trg_audio = (load_audio(trg_ref)[0] / MAX_WAV_VALUE) * 0.95 + src_audio = torch.FloatTensor(src_audio).unsqueeze(0).cuda() + trg_audio = torch.FloatTensor(trg_audio).unsqueeze(0).cuda() + + src_spkr = parse_speaker(src_ref, h.multispkr) + src_spkr = src_spkr if src_spkr in EMOV_SPK2ID else random.choice(list(EMOV_SPK2ID.keys())) + src_spkr = EMOV_SPK2ID[src_spkr] + src_spkr = torch.LongTensor([src_spkr]) + trg_spkr = parse_speaker(trg_ref, h.multispkr) + trg_spkr = trg_spkr if trg_spkr in EMOV_SPK2ID else random.choice(list(EMOV_SPK2ID.keys())) + trg_spkr = EMOV_SPK2ID[trg_spkr] + trg_spkr = torch.LongTensor([trg_spkr]) + + src_style = EMOV_STYLE2ID[a.src_emotion] + src_style = torch.LongTensor([src_style]).cuda() + trg_style_str = a.trg_emotion + trg_style = EMOV_STYLE2ID[a.trg_emotion] + trg_style = torch.LongTensor([trg_style]).cuda() + + src_tokens = list(map(int, orig_km[fname_to_idx[src_ref]].strip().split(" "))) + src_tokens = torch.LongTensor(src_tokens).unsqueeze(0) + src_tokens_dur_pred = torch.LongTensor(list(map(int, result['S']))).unsqueeze(0) + src_tokens_dur_pred = dur_models[trg_style_str].inflate_input(src_tokens_dur_pred) + gen_tokens = torch.LongTensor(result['H']).unsqueeze(0) + gen_tokens = dur_models[trg_style_str].inflate_input(gen_tokens) + trg_tokens = torch.LongTensor(result['T']).unsqueeze(0) + trg_tokens = dur_models[trg_style_str].inflate_input(trg_tokens) + + src_f0 = get_praat_f0(src_audio.unsqueeze(0).cpu().numpy()) + src_f0 = torch.FloatTensor(src_f0).cuda() + + pred_src_f0 = f0_model.inference(torch.LongTensor(src_tokens).to(a.device), src_spkr, trg_style).unsqueeze(0) + pred_src_dur_pred_f0 = f0_model.inference(torch.LongTensor(src_tokens_dur_pred).to(a.device), src_spkr, trg_style).unsqueeze(0) + pred_gen_f0 = f0_model.inference(torch.LongTensor(gen_tokens).to(a.device), src_spkr, trg_style).unsqueeze(0) + pred_trg_f0 = f0_model.inference(torch.LongTensor(trg_tokens).to(a.device), src_spkr, trg_style).unsqueeze(0) + + if a.orig_filename: + path = src_code_to_fname[code_to_str(result['S'])] + sid = str(sid) + "__" + Path(path).stem + shutil.copy(src_code_to_fname[code_to_str(result['S'])], outdir / '0-source' / f'{sid}.wav') + + audio = generate_from_code(generator, h, src_tokens, spkr=src_spkr, f0=src_f0, gst=src_style, device=a.device) + sf.write(outdir / '1-src-tokens-src-style-src-f0' / f'{sid}.wav', audio, samplerate=h.sampling_rate) + + audio = generate_from_code(generator, h, src_tokens, spkr=src_spkr, f0=src_f0, gst=trg_style, device=a.device) + sf.write(outdir / '2-src-tokens-trg-style-src-f0' / f'{sid}.wav', audio, samplerate=h.sampling_rate) + + audio = generate_from_code(generator, h, src_tokens_dur_pred, spkr=src_spkr, f0=src_f0, gst=trg_style, device=a.device) + sf.write(outdir / '2.5-src-tokens-trg-style-src-f0' / f'{sid}.wav', audio, samplerate=h.sampling_rate) + + audio = generate_from_code(generator, h, src_tokens_dur_pred, spkr=src_spkr, f0=pred_src_dur_pred_f0, gst=trg_style, device=a.device) + sf.write(outdir / '3-src-tokens-trg-style-pred-f0' / f'{sid}.wav', audio, samplerate=h.sampling_rate) + + audio = generate_from_code(generator, h, gen_tokens, spkr=src_spkr, f0=pred_gen_f0, gst=trg_style, device=a.device) + sf.write(outdir / '4-gen-tokens-trg-style-pred-f0' / f'{sid}.wav', audio, samplerate=h.sampling_rate) + + shutil.copy(tgt_code_to_fname[code_to_str(result['T'])], outdir / '5-target' / f'{sid}.wav') + + logger.info("Done.") + + +if __name__ == '__main__': + synth(sys.argv[1:]) diff --git a/fairseq/examples/fast_noisy_channel/README.md b/fairseq/examples/fast_noisy_channel/README.md new file mode 100644 index 0000000..f2631a8 --- /dev/null +++ b/fairseq/examples/fast_noisy_channel/README.md @@ -0,0 +1,345 @@ +# Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling + +## Introduction +- [Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) introduce a simple and effective noisy channel modeling approach for neural machine translation. However, the noisy channel online decoding approach introduced in this paper is too slow to be practical. +- To address this, [Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 simple approximations to make this approach very fast and practical without much loss in accuracy. +- This README provides intructions on how to run online decoding or generation with the noisy channel modeling approach, including ways to make it very fast without much loss in accuracy. + +## Noisy Channel Modeling + +[Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) applies the Bayes Rule to predict `P(y|x)`, the probability of the target `y` given the source `x`. +```P(y|x) = P(x|y) * P(y) / P(x)``` +- `P(x|y)` predicts the source `x` given the target `y` and is referred to as the **channel model** +- `P(y)` is a **language model** over the target `y` +- `P(x)` is generally not modeled since it is constant for all `y`. + +We use Transformer models to parameterize the direct model `P(y|x)`, the channel model `P(x|y)` and the language model `P(y)`. + +During online decoding with beam search, we generate the top `K2` candidates per beam and score them with the following linear combination of the channel model, the language model as well as the direct model scores. + +```(1 / t) * log(P(y|x) + (1 / s) * ( λ1 * log(P(x|y)) + λ2 * log(P(y) ) )``` +- `t` - Target Prefix Length +- `s` - Source Length +- `λ1` - Channel Model Weight +- `λ2` - Language Model Weight + +The top `beam_size` candidates based on the above combined scores are chosen to continue the beams in beam search. In beam search with a direct model alone, the scores from the direct model `P(y|x)` are used to choose the top candidates in beam search. + +This framework provides a great way to utlize strong target language models trained on large amounts of unlabeled data. Language models can prefer targets unrelated to the source, so we also need a channel model whose role is to ensure that the target preferred by the language model also translates back to the source. + +### Training Translation Models and Language Models + +For training Transformer models in fairseq for machine translation, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/translation) + +For training Transformer models in fairseq for language modeling, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model) + +### Generation with Language Model for German-English translation with fairseq + +Here are instructions to generate using a direct model and a target-side language model. + +Note: +- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) +- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) + +```sh +binarized_data=data_dir/binarized +direct_model=de_en_seed4.pt +lm_model=en_lm.pt +lm_data=lm_data +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} +mkdir -p ${lm_data} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt + +k2=10 +lenpen=0.16 +lm_wt=0.14 +fairseq-generate ${binarized_data} \ + --user-dir examples/fast_noisy_channel \ + --beam 5 \ + --path ${direct_model} \ + --lm-model ${lm_model} \ + --lm-data ${lm_data} \ + --k2 ${k2} \ + --combine-method lm_only \ + --task noisy_channel_translation \ + --lenpen ${lenpen} \ + --lm-wt ${lm_wt} \ + --gen-subset valid \ + --remove-bpe \ + --fp16 \ + --batch-size 10 +``` +### Noisy Channel Generation for German-English translation with fairseq + +Here are instructions for noisy channel generation with a direct model, channel model and language model as explained in section [Noisy Channel Modeling](#noisy-channel-modeling). + +Note: +- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) +- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) + +```sh +binarized_data=data_dir/binarized +direct_model=de_en_seed4.pt +lm_model=en_lm.pt +lm_data=lm_data +ch_model=en_de.big.seed4.pt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} +mkdir -p ${lm_data} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt -O ${ch_model} + +k2=10 +lenpen=0.21 +lm_wt=0.50 +bw_wt=0.30 +fairseq-generate ${binarized_data} \ + --user-dir examples/fast_noisy_channel \ + --beam 5 \ + --path ${direct_model} \ + --lm-model ${lm_model} \ + --lm-data ${lm_data} \ + --channel-model ${ch_model} \ + --k2 ${k2} \ + --combine-method noisy_channel \ + --task noisy_channel_translation \ + --lenpen ${lenpen} \ + --lm-wt ${lm_wt} \ + --ch-wt ${bw_wt} \ + --gen-subset test \ + --remove-bpe \ + --fp16 \ + --batch-size 1 +``` +## Fast Noisy Channel Modeling + +[Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 approximations that speed up online noisy channel decoding - +- Smaller channel models (`Tranformer Base` with 1 encoder and decoder layer each vs. `Transformer Big`) + - This involves training a channel model that is possibly smaller and less accurate in terms of BLEU than a channel model of the same size as the direct model. + - Since the role of the channel model is mainly to assign low scores to generations from the language model if they don't translate back to the source, we may not need the most accurate channel model for this purpose. +- Smaller output vocabulary size for the channel model (~30,000 -> ~1000) + - The channel model doesn't need to score the full output vocabulary, it just needs to score the source tokens, which are completely known. + - This is specified using the arguments `--channel-scoring-type src_vocab --top-k-vocab 500` + - This means that the output vocabulary for the channel model will be the source tokens for all examples in the batch and the top-K most frequent tokens in the vocabulary + - This reduces the memory consumption needed to store channel model scores significantly +- Smaller number of candidates (`k2`) scored per beam + - This is specified by reducing the argument `--k2` + + +### Fast Noisy Channel Generation for German-English translation with fairseq + +Here are instructions for **fast** noisy channel generation with a direct model, channel model and language model as explained in section [Fast Noisy Channel Modeling](#fast-noisy-channel-modeling). The main differences are that we use a smaller channel model, reduce `--k2`, set `--channel-scoring-type src_vocab --top-k-vocab 500` and increase the `--batch-size`. + +Note: +- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) +- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) + +```sh +binarized_data=data_dir/binarized +direct_model=de_en_seed4.pt +lm_model=en_lm.pt +lm_data=lm_data +small_ch_model=en_de.base_1_1.seed4.pt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} +mkdir -p ${lm_data} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt -O ${small_ch_model} + +k2=3 +lenpen=0.23 +lm_wt=0.58 +bw_wt=0.26 +fairseq-generate ${binarized_data} \ + --user-dir examples/fast_noisy_channel \ + --beam 5 \ + --path ${direct_model} \ + --lm-model ${lm_model} \ + --lm-data ${lm_data} \ + --channel-model ${small_ch_model} \ + --k2 ${k2} \ + --combine-method noisy_channel \ + --task noisy_channel_translation \ + --lenpen ${lenpen} \ + --lm-wt ${lm_wt} \ + --ch-wt ${bw_wt} \ + --gen-subset test \ + --remove-bpe \ + --fp16 \ + --batch-size 50 \ + --channel-scoring-type src_vocab --top-k-vocab 500 +``` + +## Test Data Preprocessing + +For preprocessing and binarizing the test sets for Romanian-English and German-English translation, we use the following script - + +```sh +FAIRSEQ=/path/to/fairseq +cd $FAIRSEQ +SCRIPTS=$FAIRSEQ/mosesdecoder/scripts +if [ ! -d "${SCRIPTS}" ]; then + echo 'Cloning Moses github repository (for tokenization scripts)...' + git clone https://github.com/moses-smt/mosesdecoder.git +fi +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +NORMALIZE=$SCRIPTS/tokenizer/normalize-punctuation.perl + +s=de +t=en +test=wmt18 + +mkdir -p data_dir + +# Tokenization +if [ $s == "ro" ] ; then + # Note: Get normalise-romanian.py and remove-diacritics.py from + # https://github.com/rsennrich/wmt16-scripts/tree/master/preprocess + sacrebleu -t $test -l $s-$t --echo src | \ + $NORMALIZE -l $s | \ + python normalise-romanian.py | \ + python remove-diacritics.py | \ + $TOKENIZER -l $s -a -q > data_dir/$test.$s-$t.$s +else + sacrebleu -t $test -l $s-$t --echo src | perl $NORMALIZE -l $s | perl $TOKENIZER -threads 8 -a -l $s > data_dir/$test.$s-$t.$s +fi + +sacrebleu -t $test -l $s-$t --echo ref | perl $NORMALIZE -l $t | perl $TOKENIZER -threads 8 -a -l $t > data_dir/$test.$s-$t.$t + + +# Applying BPE +src_bpe_code=/path/to/source/language/bpe/code +tgt_bpe_code=/path/to/target/language/bpe/code +src_dict=/path/to/source/language/dict +tgt_dict=/path/to/target/language/dict + +FASTBPE=$FAIRSEQ/fastBPE +if [ ! -d "${FASTBPE}" ] ; then + git clone https://github.com/glample/fastBPE.git + # Follow compilation instructions at https://github.com/glample/fastBPE + g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast +fi + +${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${src_bpe_code} +${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${tgt_bpe_code} + +fairseq-preprocess -s $s -t $t \ + --testpref data_dir/bpe.$test.$s-$t \ + --destdir data_dir/binarized \ + --srcdict ${src_dict} \ + --tgtdict ${tgt_dict} +``` + +## Calculating BLEU + +```sh +DETOKENIZER=$SCRIPTS/tokenizer/detokenizer.perl +cat ${generation_output} | grep -P "^H" | sort -V | cut -f 3- | $DETOKENIZER -l $t -q -a | sacrebleu -t $test -l $s-$t +``` + + +## Romanian-English Translation + +The direct and channel models are trained using bitext data (WMT16) combined with backtranslated data (The monolingual data used for backtranslation comes from http://data.statmt.org/rsennrich/wmt16_backtranslations/ (Sennrich et al., 2016c)) + +The backtranslated data is generated using an ensemble of 3 English-Romanian models trained on bitext training data (WMT16) with unrestricted sampling. + +### BPE Codes and Dictionary + +We learn a joint BPE vocabulary of 18K types on the bitext training data which is used for both the source and target. +||Path| +|----------|------| +| BPE Code | [joint_bpe_18k](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/bpe_18k) | +| Dictionary | [dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/dict) | + +### Direct Models +For Ro-En with backtranslation, the direct and channel models use a Transformer-Big architecture. + +| Seed | Model | +|----|----| +| 2 | [ro_en_seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed2.pt) +| 4 | [ro_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed4.pt) +| 6 | [ro_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed6.pt) + +### Channel Models +For channel models, we follow the same steps as for the direct models. But backtranslated data is generated in the opposite direction using [this Romanian monolingual data](http://data.statmt.org/rsennrich/wmt16_backtranslations/). +The best lenpen, LM weight and CH weight are obtained by sweeping over the validation set (wmt16/dev) using beam 5. +| Model Size | Lenpen | LM Weight | CH Weight | Seed 2 | Seed 4 | Seed 6 | +|----|----|----|----|----|----|----| +| `big` | 0.84 | 0.64 | 0.56 | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | +| `base_1_1` | 0.63 | 0.40 | 0.37 | [base_1_1.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed2.pt) | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed6.pt) | + +### Language Model +The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization. +| | Path | +|----|----| +| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/transformer_lm.pt) | +| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/lm_dict) + +## German-English Translation + +### BPE Codes and Dictionaries + +| | Path| +|----------|------| +| Source BPE Code | [de_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_bpe_code_24K) | +| Target BPE Code | [en_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_bpe_code_24K) +| Source Dictionary | [de_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_dict) | +| Target Dictionary | [en_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_dict) | + +### Direct Models +We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs. +We use the Transformer-Big architecture for the direct model. + +| Seed | Model | +|:----:|----| +| 4 | [de_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt) +| 5 | [de_en_seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed5.pt) +| 6 | [de_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed6.pt) + +### Channel Models + +We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs. + +| Model Size | Seed 4 | Seed 5 | Seed 6 | +|----|----|----|----| +| `big` | [big.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt) | [big.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed5.pt) | [big.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed6.pt) | +| `big_1_1` | [big_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed4.pt) | [big_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed5.pt) | [big_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed6.pt) | +| `base` | [base.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed4.pt) | [base.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed5.pt) | [base.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed6.pt) | +| `base_1_1` | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed5.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed6.pt) | +| `half` | [half.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed4.pt) | [half.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed5.pt) | [half.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed6.pt) | +| `half_1_1` | [half_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed4.pt) | [half_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed5.pt) | [half_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed6.pt) | +| `quarter` | [quarter.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed4.pt) | [quarter.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed5.pt) | [quarter.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed6.pt) | +| `quarter_1_1` | [quarter_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed4.pt) | [quarter_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed5.pt) | [quarter_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed6.pt) | +| `8th` | [8th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed4.pt) | [8th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed5.pt) | [8th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed6.pt) | +| `8th_1_1` | [8th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed4.pt) | [8th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed5.pt) | [8th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed6.pt) | +| `16th` | [16th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed4.pt) | [16th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed5.pt) | [16th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed6.pt) | +| `16th_1_1` | [16th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed4.pt) | [16th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed5.pt) | [16th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed6.pt) | + +### Language Model +The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization. +| | Path | +|----|----| +| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt) | +| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/) + + +## Citation + +```bibtex +@inproceedings{bhosale2020language, + title={Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling}, + author={Shruti Bhosale and Kyra Yee and Sergey Edunov and Michael Auli}, + booktitle={Proceedings of the Fifth Conference on Machine Translation (WMT)}, + year={2020}, +} + +@inproceedings{yee2019simple, + title={Simple and Effective Noisy Channel Modeling for Neural Machine Translation}, + author={Yee, Kyra and Dauphin, Yann and Auli, Michael}, + booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, + pages={5700--5705}, + year={2019} +} +``` diff --git a/fairseq/examples/fast_noisy_channel/__init__.py b/fairseq/examples/fast_noisy_channel/__init__.py new file mode 100644 index 0000000..9b248c3 --- /dev/null +++ b/fairseq/examples/fast_noisy_channel/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import noisy_channel_translation # noqa +from . import noisy_channel_sequence_generator # noqa +from . import noisy_channel_beam_search # noqa diff --git a/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py b/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py new file mode 100644 index 0000000..23869eb --- /dev/null +++ b/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq.search import Search + + +class NoisyChannelBeamSearch(Search): + + def __init__(self, tgt_dict): + super().__init__(tgt_dict) + self.fw_scores_buf = None + self.lm_scores_buf = None + + def _init_buffers(self, t): + # super()._init_buffers(t) + if self.fw_scores_buf is None: + self.scores_buf = t.new() + self.indices_buf = torch.LongTensor().to(device=t.device) + self.beams_buf = torch.LongTensor().to(device=t.device) + self.fw_scores_buf = t.new() + self.lm_scores_buf = t.new() + + def combine_fw_bw(self, combine_method, fw_cum, bw, step): + if combine_method == "noisy_channel": + fw_norm = fw_cum.div(step + 1) + lprobs = bw + fw_norm + elif combine_method == "lm_only": + lprobs = bw + fw_cum + + return lprobs + + def step(self, step, fw_lprobs, scores, bw_lprobs, lm_lprobs, combine_method): + self._init_buffers(fw_lprobs) + bsz, beam_size, vocab_size = fw_lprobs.size() + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + fw_lprobs = fw_lprobs[:, ::beam_size, :].contiguous() + bw_lprobs = bw_lprobs[:, ::beam_size, :].contiguous() + # nothing to add since we are at the first step + fw_lprobs_cum = fw_lprobs + + else: + # make probs contain cumulative scores for each hypothesis + raw_scores = (scores[:, :, step - 1].unsqueeze(-1)) + fw_lprobs_cum = (fw_lprobs.add(raw_scores)) + + combined_lprobs = self.combine_fw_bw(combine_method, fw_lprobs_cum, bw_lprobs, step) + + # choose the top k according to the combined noisy channel model score + torch.topk( + combined_lprobs.view(bsz, -1), + k=min( + # Take the best 2 x beam_size predictions. We'll choose the first + # beam_size of these which don't predict eos to continue with. + beam_size * 2, + combined_lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad + ), + out=(self.scores_buf, self.indices_buf), + ) + # save corresponding fw and lm scores + self.fw_scores_buf = torch.gather(fw_lprobs_cum.view(bsz, -1), 1, self.indices_buf) + self.lm_scores_buf = torch.gather(lm_lprobs.view(bsz, -1), 1, self.indices_buf) + # Project back into relative indices and beams + self.beams_buf = self.indices_buf // vocab_size + self.indices_buf.fmod_(vocab_size) + return self.scores_buf, self.fw_scores_buf, self.lm_scores_buf, self.indices_buf, self.beams_buf diff --git a/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py b/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py new file mode 100644 index 0000000..ea8fae9 --- /dev/null +++ b/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py @@ -0,0 +1,842 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional + +import math +import numpy as np + +import torch +import torch.nn.functional as F +from torch import Tensor + +from .noisy_channel_beam_search import NoisyChannelBeamSearch +from fairseq.sequence_generator import EnsembleModel + + +class NoisyChannelSequenceGenerator(object): + def __init__( + self, + combine_method, + tgt_dict, + src_dict=None, + beam_size=1, + max_len_a=0, + max_len_b=200, + min_len=1, + len_penalty=1.0, + unk_penalty=0.0, + retain_dropout=False, + temperature=1.0, + match_source_len=False, + no_repeat_ngram_size=0, + normalize_scores=True, + channel_models=None, + k2=10, + ch_weight=1.0, + channel_scoring_type='log_norm', + top_k_vocab=0, + lm_models=None, + lm_dict=None, + lm_weight=1.0, + normalize_lm_scores_by_tgt_len=False, + ): + """Generates translations of a given source sentence, + using beam search with noisy channel decoding. + + Args: + combine_method (string, optional): Method to combine direct, LM and + channel model scores (default: None) + tgt_dict (~fairseq.data.Dictionary): target dictionary + src_dict (~fairseq.data.Dictionary): source dictionary + beam_size (int, optional): beam width (default: 1) + max_len_a/b (int, optional): generate sequences of maximum length + ax + b, where x is the source length + min_len (int, optional): the minimum length of the generated output + (not including end-of-sentence) + len_penalty (float, optional): length penalty, where <1.0 favors + shorter, >1.0 favors longer sentences (default: 1.0) + unk_penalty (float, optional): unknown word penalty, where <0 + produces more unks, >0 produces fewer (default: 0.0) + retain_dropout (bool, optional): use dropout when generating + (default: False) + temperature (float, optional): temperature, where values + >1.0 produce more uniform samples and values <1.0 produce + sharper samples (default: 1.0) + match_source_len (bool, optional): outputs should match the source + length (default: False) + no_repeat_ngram_size (int, optional): Size of n-grams that we avoid + repeating in the generation (default: 0) + normalize_scores (bool, optional): normalize scores by the length + of the output (default: True) + channel_models (List[~fairseq.models.FairseqModel]): ensemble of models + translating from the target to the source + k2 (int, optional): Top K2 candidates to score per beam at each step (default:10) + ch_weight (int, optional): Weight associated with the channel model score + assuming that the direct model score has weight 1.0 (default: 1.0) + channel_scoring_type (str, optional): String specifying how to score + the channel model (default: 'log_norm') + top_k_vocab (int, optional): If `channel_scoring_type` is `'src_vocab'` or + `'src_vocab_batched'`, then this parameter specifies the number of + most frequent tokens to include in the channel model output vocabulary, + in addition to the source tokens in the input batch (default: 0) + lm_models (List[~fairseq.models.FairseqModel]): ensemble of models + generating text in the target language + lm_dict (~fairseq.data.Dictionary): LM Model dictionary + lm_weight (int, optional): Weight associated with the LM model score + assuming that the direct model score has weight 1.0 (default: 1.0) + normalize_lm_scores_by_tgt_len (bool, optional): Should we normalize LM scores + by the target length? By default, we normalize the combination of + LM and channel model scores by the source length + """ + self.pad = tgt_dict.pad() + self.unk = tgt_dict.unk() + self.eos = tgt_dict.eos() + self.vocab_size = len(tgt_dict) + self.beam_size = beam_size + # the max beam size is the dictionary size - 1, since we never select pad + self.beam_size = min(beam_size, self.vocab_size - 1) + self.max_len_a = max_len_a + self.max_len_b = max_len_b + self.min_len = min_len + self.normalize_scores = normalize_scores + self.len_penalty = len_penalty + self.unk_penalty = unk_penalty + self.retain_dropout = retain_dropout + self.temperature = temperature + self.match_source_len = match_source_len + self.no_repeat_ngram_size = no_repeat_ngram_size + self.channel_models = channel_models + self.src_dict = src_dict + self.tgt_dict = tgt_dict + self.combine_method = combine_method + self.k2 = k2 + self.ch_weight = ch_weight + self.channel_scoring_type = channel_scoring_type + self.top_k_vocab = top_k_vocab + self.lm_models = lm_models + self.lm_dict = lm_dict + self.lm_weight = lm_weight + self.log_softmax_fn = torch.nn.LogSoftmax(dim=1) + self.normalize_lm_scores_by_tgt_len = normalize_lm_scores_by_tgt_len + + self.share_tgt_dict = (self.lm_dict == self.tgt_dict) + self.tgt_to_lm = make_dict2dict(tgt_dict, lm_dict) + + self.ch_scoring_bsz = 3072 + + assert temperature > 0, '--temperature must be greater than 0' + + self.search = NoisyChannelBeamSearch(tgt_dict) + + @torch.no_grad() + def generate( + self, + models, + sample, + prefix_tokens=None, + bos_token=None, + **kwargs + ): + """Generate a batch of translations. + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + sample (dict): batch + prefix_tokens (torch.LongTensor, optional): force decoder to begin + with these tokens + """ + model = EnsembleModel(models) + incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(model.models_size) + ], + ) + if not self.retain_dropout: + model.eval() + + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in sample['net_input'].items() + if k != 'prev_output_tokens' + } + src_tokens = encoder_input['src_tokens'] + src_lengths_no_eos = (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) + input_size = src_tokens.size() + # batch dimension goes first followed by source lengths + bsz = input_size[0] + src_len = input_size[1] + beam_size = self.beam_size + + if self.match_source_len: + max_len = src_lengths_no_eos.max().item() + else: + max_len = min( + int(self.max_len_a * src_len + self.max_len_b), + # exclude the EOS marker + model.max_decoder_positions() - 1, + ) + + # compute the encoder output for each beam + encoder_outs = model.forward_encoder(encoder_input) + new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) + new_order = new_order.to(src_tokens.device).long() + encoder_outs = model.reorder_encoder_out(encoder_outs, new_order) + + src_lengths = encoder_input['src_lengths'] + # initialize buffers + scores = src_tokens.new(bsz * beam_size, max_len + 1).float().fill_(0) + lm_prefix_scores = src_tokens.new(bsz * beam_size).float().fill_(0) + + scores_buf = scores.clone() + tokens = src_tokens.new(bsz * beam_size, max_len + 2).long().fill_(self.pad) + tokens_buf = tokens.clone() + tokens[:, 0] = self.eos if bos_token is None else bos_token + + # reorder source tokens so they may be used as a reference in generating P(S|T) + src_tokens = reorder_all_tokens(src_tokens, src_lengths, self.src_dict.eos_index) + + src_tokens = src_tokens.repeat(1, beam_size).view(-1, src_len) + src_lengths = src_lengths.view(bsz, -1).repeat(1, beam_size).view(bsz*beam_size, -1) + + attn, attn_buf = None, None + nonpad_idxs = None + + # The cands_to_ignore indicates candidates that should be ignored. + # For example, suppose we're sampling and have already finalized 2/5 + # samples. Then the cands_to_ignore would mark 2 positions as being ignored, + # so that we only finalize the remaining 3 samples. + cands_to_ignore = src_tokens.new_zeros(bsz, beam_size).eq(-1) # forward and backward-compatible False mask + + # list of completed sentences + finalized = [[] for i in range(bsz)] + finished = [False for i in range(bsz)] + num_remaining_sent = bsz + + # number of candidate hypos per step + cand_size = 2 * beam_size # 2 x beam size in case half are EOS + + # offset arrays for converting between different indexing schemes + bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens) + cand_offsets = torch.arange(0, cand_size).type_as(tokens) + + # helper function for allocating buffers on the fly + buffers = {} + + def buffer(name, type_of=tokens): # noqa + if name not in buffers: + buffers[name] = type_of.new() + return buffers[name] + + def is_finished(sent, step, unfin_idx): + """ + Check whether we've finished generation for a given sentence, by + comparing the worst score among finalized hypotheses to the best + possible score among unfinalized hypotheses. + """ + assert len(finalized[sent]) <= beam_size + if len(finalized[sent]) == beam_size: + return True + return False + + def finalize_hypos(step, bbsz_idx, eos_scores, combined_noisy_channel_eos_scores): + """ + Finalize the given hypotheses at this step, while keeping the total + number of finalized hypotheses per sentence <= beam_size. + + Note: the input must be in the desired finalization order, so that + hypotheses that appear earlier in the input are preferred to those + that appear later. + + Args: + step: current time step + bbsz_idx: A vector of indices in the range [0, bsz*beam_size), + indicating which hypotheses to finalize + eos_scores: A vector of the same size as bbsz_idx containing + fw scores for each hypothesis + combined_noisy_channel_eos_scores: A vector of the same size as bbsz_idx containing + combined noisy channel scores for each hypothesis + """ + assert bbsz_idx.numel() == eos_scores.numel() + + # clone relevant token and attention tensors + tokens_clone = tokens.index_select(0, bbsz_idx) + tokens_clone = tokens_clone[:, 1:step + 2] # skip the first index, which is EOS + assert not tokens_clone.eq(self.eos).any() + tokens_clone[:, step] = self.eos + attn_clone = attn.index_select(0, bbsz_idx)[:, :, 1:step+2] if attn is not None else None + + # compute scores per token position + pos_scores = scores.index_select(0, bbsz_idx)[:, :step+1] + pos_scores[:, step] = eos_scores + # convert from cumulative to per-position scores + pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] + + # normalize sentence-level scores + if self.normalize_scores: + combined_noisy_channel_eos_scores /= (step + 1) ** self.len_penalty + + cum_unfin = [] + prev = 0 + for f in finished: + if f: + prev += 1 + else: + cum_unfin.append(prev) + + sents_seen = set() + for i, (idx, score) in enumerate(zip(bbsz_idx.tolist(), combined_noisy_channel_eos_scores.tolist())): + unfin_idx = idx // beam_size + sent = unfin_idx + cum_unfin[unfin_idx] + + sents_seen.add((sent, unfin_idx)) + + if self.match_source_len and step > src_lengths_no_eos[unfin_idx]: + score = -math.inf + + def get_hypo(): + + if attn_clone is not None: + # remove padding tokens from attn scores + hypo_attn = attn_clone[i][nonpad_idxs[sent]] + _, alignment = hypo_attn.max(dim=0) + else: + hypo_attn = None + alignment = None + + return { + 'tokens': tokens_clone[i], + 'score': score, + 'attention': hypo_attn, # src_len x tgt_len + 'alignment': alignment, + 'positional_scores': pos_scores[i], + } + + if len(finalized[sent]) < beam_size: + finalized[sent].append(get_hypo()) + + newly_finished = [] + for sent, unfin_idx in sents_seen: + # check termination conditions for this sentence + if not finished[sent] and is_finished(sent, step, unfin_idx): + finished[sent] = True + newly_finished.append(unfin_idx) + return newly_finished + + def noisy_channel_rescoring(lprobs, beam_size, bsz, src_tokens, tokens, k): + """Rescore the top k hypothesis from each beam using noisy channel modeling + Returns: + new_fw_lprobs: the direct model probabilities after pruning the top k + new_ch_lm_lprobs: the combined channel and language model probabilities + new_lm_lprobs: the language model probabilities after pruning the top k + """ + with torch.no_grad(): + lprobs_size = lprobs.size() + if prefix_tokens is not None and step < prefix_tokens.size(1): + probs_slice = lprobs.view(bsz, -1, lprobs.size(-1))[:, 0, :] + cand_scores = torch.gather( + probs_slice, dim=1, + index=prefix_tokens[:, step].view(-1, 1).data + ).expand(-1, beam_size).contiguous().view(bsz*beam_size, 1) + cand_indices = prefix_tokens[:, step].view(-1, 1).expand(bsz, beam_size).data.contiguous().view(bsz*beam_size, 1) + + # need to calculate and save fw and lm probs for prefix tokens + fw_top_k = cand_scores + fw_top_k_idx = cand_indices + k = 1 + else: + # take the top k best words for every sentence in batch*beam + fw_top_k, fw_top_k_idx = torch.topk(lprobs.view(beam_size*bsz, -1), k=k) + eos_idx = torch.nonzero(fw_top_k_idx.view(bsz*beam_size*k, -1) == self.eos)[:, 0] + ch_scores = fw_top_k.new_full((beam_size*bsz*k, ), 0) + src_size = torch.sum(src_tokens[:, :] != self.src_dict.pad_index, dim=1, keepdim=True, dtype=fw_top_k.dtype) + + if self.combine_method != "lm_only": + temp_src_tokens_full = src_tokens[:, :].repeat(1, k).view(bsz*beam_size*k, -1) + not_padding = temp_src_tokens_full[:, 1:] != self.src_dict.pad_index + cur_tgt_size = step+2 + + # add eos to all candidate sentences except those that already end in eos + eos_tokens = tokens[:, 0].repeat(1, k).view(-1, 1) + eos_tokens[eos_idx] = self.tgt_dict.pad_index + + if step == 0: + channel_input = torch.cat((fw_top_k_idx.view(-1, 1), eos_tokens), 1) + else: + # move eos from beginning to end of target sentence + channel_input = torch.cat((tokens[:, 1:step + 1].repeat(1, k).view(-1, step), fw_top_k_idx.view(-1, 1), eos_tokens), 1) + + ch_input_lengths = torch.tensor(np.full(channel_input.size(0), cur_tgt_size)) + ch_input_lengths[eos_idx] = cur_tgt_size-1 + if self.channel_scoring_type == "unnormalized": + ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) + ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) + del ch_encoder_output + ch_intermed_scores = channel_model.decoder.unnormalized_scores_given_target(ch_decoder_output, target_ids=temp_src_tokens_full[:, 1:]) + ch_intermed_scores = ch_intermed_scores.float() + ch_intermed_scores *= not_padding.float() + ch_scores = torch.sum(ch_intermed_scores, dim=1) + elif self.channel_scoring_type == "k2_separate": + for k_idx in range(k): + k_eos_tokens = eos_tokens[k_idx::k, :] + if step == 0: + k_ch_input = torch.cat((fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) + else: + # move eos from beginning to end of target sentence + k_ch_input = torch.cat((tokens[:, 1:step + 1], fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) + k_ch_input_lengths = ch_input_lengths[k_idx::k] + k_ch_output = channel_model(k_ch_input, k_ch_input_lengths, src_tokens) + k_ch_lprobs = channel_model.get_normalized_probs(k_ch_output, log_probs=True) + k_ch_intermed_scores = torch.gather(k_ch_lprobs[:, :-1, :], 2, src_tokens[:, 1:].unsqueeze(2)).squeeze(2) + k_ch_intermed_scores *= not_padding.float() + ch_scores[k_idx::k] = torch.sum(k_ch_intermed_scores, dim=1) + elif self.channel_scoring_type == "src_vocab": + ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) + ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) + + del ch_encoder_output + ch_lprobs = normalized_scores_with_batch_vocab( + channel_model.decoder, + ch_decoder_output, src_tokens, k, bsz, beam_size, + self.src_dict.pad_index, top_k=self.top_k_vocab) + ch_scores = torch.sum(ch_lprobs, dim=1) + elif self.channel_scoring_type == "src_vocab_batched": + ch_bsz_size = temp_src_tokens_full.shape[0] + ch_lprobs_list = [None] * len(range(0, ch_bsz_size, self.ch_scoring_bsz)) + for i, start_idx in enumerate(range(0, ch_bsz_size, self.ch_scoring_bsz)): + end_idx = min(start_idx + self.ch_scoring_bsz, ch_bsz_size) + temp_src_tokens_full_batch = temp_src_tokens_full[start_idx:end_idx, :] + channel_input_batch = channel_input[start_idx:end_idx, :] + ch_input_lengths_batch = ch_input_lengths[start_idx:end_idx] + ch_encoder_output_batch = channel_model.encoder(channel_input_batch, src_lengths=ch_input_lengths_batch) + ch_decoder_output_batch, _ = channel_model.decoder(temp_src_tokens_full_batch, encoder_out=ch_encoder_output_batch, features_only=True) + ch_lprobs_list[i] = normalized_scores_with_batch_vocab( + channel_model.decoder, + ch_decoder_output_batch, src_tokens, k, bsz, beam_size, + self.src_dict.pad_index, top_k=self.top_k_vocab, + start_idx=start_idx, end_idx=end_idx) + ch_lprobs = torch.cat(ch_lprobs_list, dim=0) + ch_scores = torch.sum(ch_lprobs, dim=1) + else: + ch_output = channel_model(channel_input, ch_input_lengths, temp_src_tokens_full) + ch_lprobs = channel_model.get_normalized_probs(ch_output, log_probs=True) + ch_intermed_scores = torch.gather(ch_lprobs[:, :-1, :], 2, temp_src_tokens_full[:, 1:].unsqueeze(2)).squeeze().view(bsz*beam_size*k, -1) + ch_intermed_scores *= not_padding.float() + ch_scores = torch.sum(ch_intermed_scores, dim=1) + + else: + cur_tgt_size = 0 + ch_scores = ch_scores.view(bsz*beam_size, k) + expanded_lm_prefix_scores = lm_prefix_scores.unsqueeze(1).expand(-1, k).flatten() + + if self.share_tgt_dict: + lm_scores = get_lm_scores(lm, tokens[:, :step + 1].view(-1, step+1), lm_incremental_states, fw_top_k_idx.view(-1, 1), torch.tensor(np.full(tokens.size(0), step+1)), k) + else: + new_lm_input = dict2dict(tokens[:, :step + 1].view(-1, step+1), self.tgt_to_lm) + new_cands = dict2dict(fw_top_k_idx.view(-1, 1), self.tgt_to_lm) + lm_scores = get_lm_scores(lm, new_lm_input, lm_incremental_states, new_cands, torch.tensor(np.full(tokens.size(0), step+1)), k) + + lm_scores.add_(expanded_lm_prefix_scores) + ch_lm_scores = combine_ch_lm(self.combine_method, ch_scores, lm_scores, src_size, cur_tgt_size) + # initialize all as min value + new_fw_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) + new_ch_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) + new_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) + new_fw_lprobs[:, self.pad] = -math.inf + new_ch_lm_lprobs[:, self.pad] = -math.inf + new_lm_lprobs[:, self.pad] = -math.inf + + new_fw_lprobs.scatter_(1, fw_top_k_idx, fw_top_k) + new_ch_lm_lprobs.scatter_(1, fw_top_k_idx, ch_lm_scores) + new_lm_lprobs.scatter_(1, fw_top_k_idx, lm_scores.view(-1, k)) + return new_fw_lprobs, new_ch_lm_lprobs, new_lm_lprobs + + def combine_ch_lm(combine_type, ch_scores, lm_scores1, src_size, tgt_size): + if self.channel_scoring_type == "unnormalized": + ch_scores = self.log_softmax_fn( + ch_scores.view(-1, self.beam_size * self.k2) + ).view(ch_scores.shape) + ch_scores = ch_scores * self.ch_weight + lm_scores1 = lm_scores1 * self.lm_weight + + if combine_type == "lm_only": + # log P(T|S) + log P(T) + ch_scores = lm_scores1.view(ch_scores.size()) + elif combine_type == "noisy_channel": + # 1/t log P(T|S) + 1/s log P(S|T) + 1/t log P(T) + if self.normalize_lm_scores_by_tgt_len: + ch_scores.div_(src_size) + lm_scores_norm = lm_scores1.view(ch_scores.size()).div(tgt_size) + ch_scores.add_(lm_scores_norm) + # 1/t log P(T|S) + 1/s log P(S|T) + 1/s log P(T) + else: + ch_scores.add_(lm_scores1.view(ch_scores.size())) + ch_scores.div_(src_size) + + return ch_scores + + if self.channel_models is not None: + channel_model = self.channel_models[0] # assume only one channel_model model + else: + channel_model = None + + lm = EnsembleModel(self.lm_models) + lm_incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(lm.models_size) + ], + ) + + reorder_state = None + batch_idxs = None + for step in range(max_len + 1): # one extra step for EOS marker + # reorder decoder internal states based on the prev choice of beams + if reorder_state is not None: + if batch_idxs is not None: + # update beam indices to take into account removed sentences + corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as(batch_idxs) + reorder_state.view(-1, beam_size).add_(corr.unsqueeze(-1) * beam_size) + model.reorder_incremental_state(incremental_states, reorder_state) + encoder_outs = model.reorder_encoder_out(encoder_outs, reorder_state) + + lm.reorder_incremental_state(lm_incremental_states, reorder_state) + + fw_lprobs, avg_attn_scores = model.forward_decoder( + tokens[:, :step + 1], encoder_outs, incremental_states, temperature=self.temperature, + ) + + fw_lprobs[:, self.pad] = -math.inf # never select pad + fw_lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty + fw_lprobs, ch_lm_lprobs, lm_lprobs = noisy_channel_rescoring(fw_lprobs, beam_size, bsz, src_tokens, tokens, self.k2) + + # handle min and max length constraints + if step >= max_len: + fw_lprobs[:, :self.eos] = -math.inf + fw_lprobs[:, self.eos + 1:] = -math.inf + elif step < self.min_len: + fw_lprobs[:, self.eos] = -math.inf + + # handle prefix tokens (possibly with different lengths) + if prefix_tokens is not None and step < prefix_tokens.size(1): + prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) + prefix_mask = prefix_toks.ne(self.pad) + + prefix_fw_lprobs = fw_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + fw_lprobs[prefix_mask] = -math.inf + fw_lprobs[prefix_mask] = fw_lprobs[prefix_mask].scatter_( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_fw_lprobs + ) + + prefix_ch_lm_lprobs = ch_lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + ch_lm_lprobs[prefix_mask] = -math.inf + ch_lm_lprobs[prefix_mask] = ch_lm_lprobs[prefix_mask].scatter_( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_ch_lm_lprobs + ) + + prefix_lm_lprobs = lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + lm_lprobs[prefix_mask] = -math.inf + lm_lprobs[prefix_mask] = lm_lprobs[prefix_mask].scatter_( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lm_lprobs + ) + + # if prefix includes eos, then we should make sure tokens and + # scores are the same across all beams + eos_mask = prefix_toks.eq(self.eos) + if eos_mask.any(): + # validate that the first beam matches the prefix + first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[:, 0, 1:step + 1] + eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] + target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] + assert (first_beam == target_prefix).all() + + def replicate_first_beam(tensor, mask): + tensor = tensor.view(-1, beam_size, tensor.size(-1)) + tensor[mask] = tensor[mask][:, :1, :] + return tensor.view(-1, tensor.size(-1)) + + # copy tokens, scores and lprobs from the first beam to all beams + tokens = replicate_first_beam(tokens, eos_mask_batch_dim) + scores = replicate_first_beam(scores, eos_mask_batch_dim) + + fw_lprobs = replicate_first_beam(fw_lprobs, eos_mask_batch_dim) + ch_lm_lprobs = replicate_first_beam(ch_lm_lprobs, eos_mask_batch_dim) + lm_lprobs = replicate_first_beam(lm_lprobs, eos_mask_batch_dim) + + if self.no_repeat_ngram_size > 0: + # for each beam and batch sentence, generate a list of previous ngrams + gen_ngrams = [{} for bbsz_idx in range(bsz * beam_size)] + for bbsz_idx in range(bsz * beam_size): + gen_tokens = tokens[bbsz_idx].tolist() + for ngram in zip(*[gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]): + gen_ngrams[bbsz_idx][tuple(ngram[:-1])] = \ + gen_ngrams[bbsz_idx].get(tuple(ngram[:-1]), []) + [ngram[-1]] + + # Record attention scores + if avg_attn_scores is not None: + if attn is None: + attn = scores.new(bsz * beam_size, src_tokens.size(1), max_len + 2) + attn_buf = attn.clone() + nonpad_idxs = src_tokens.ne(self.pad) + attn[:, :, step + 1].copy_(avg_attn_scores) + + scores = scores.type_as(fw_lprobs) + scores_buf = scores_buf.type_as(fw_lprobs) + + self.search.set_src_lengths(src_lengths_no_eos) + + if self.no_repeat_ngram_size > 0: + def calculate_banned_tokens(bbsz_idx): + # before decoding the next token, prevent decoding of ngrams that have already appeared + ngram_index = tuple(tokens[bbsz_idx, step + 2 - self.no_repeat_ngram_size:step + 1].tolist()) + return gen_ngrams[bbsz_idx].get(ngram_index, []) + + if step + 2 - self.no_repeat_ngram_size >= 0: + # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet + banned_tokens = [calculate_banned_tokens(bbsz_idx) for bbsz_idx in range(bsz * beam_size)] + else: + banned_tokens = [[] for bbsz_idx in range(bsz * beam_size)] + + for bbsz_idx in range(bsz * beam_size): + fw_lprobs[bbsz_idx, banned_tokens[bbsz_idx]] = -math.inf + + combined_noisy_channel_scores, fw_lprobs_top_k, lm_lprobs_top_k, cand_indices, cand_beams = self.search.step( + step, + fw_lprobs.view(bsz, -1, self.vocab_size), + scores.view(bsz, beam_size, -1)[:, :, :step], ch_lm_lprobs.view(bsz, -1, self.vocab_size), + lm_lprobs.view(bsz, -1, self.vocab_size), self.combine_method + ) + + # cand_bbsz_idx contains beam indices for the top candidate + # hypotheses, with a range of values: [0, bsz*beam_size), + # and dimensions: [bsz, cand_size] + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + + # finalize hypotheses that end in eos (except for candidates to be ignored) + eos_mask = cand_indices.eq(self.eos) + eos_mask[:, :beam_size] &= ~cands_to_ignore + + # only consider eos when it's among the top beam_size indices + eos_bbsz_idx = torch.masked_select( + cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + + finalized_sents = set() + if eos_bbsz_idx.numel() > 0: + eos_scores = torch.masked_select( + fw_lprobs_top_k[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + combined_noisy_channel_eos_scores = torch.masked_select( + combined_noisy_channel_scores[:, :beam_size], + mask=eos_mask[:, :beam_size], + ) + + # finalize hypo using channel model score + finalized_sents = finalize_hypos( + step, eos_bbsz_idx, eos_scores, combined_noisy_channel_eos_scores) + + num_remaining_sent -= len(finalized_sents) + + assert num_remaining_sent >= 0 + if num_remaining_sent == 0: + break + + if len(finalized_sents) > 0: + new_bsz = bsz - len(finalized_sents) + + # construct batch_idxs which holds indices of batches to keep for the next pass + batch_mask = cand_indices.new_ones(bsz) + batch_mask[cand_indices.new(finalized_sents)] = 0 + batch_idxs = torch.nonzero(batch_mask).squeeze(-1) + + eos_mask = eos_mask[batch_idxs] + cand_beams = cand_beams[batch_idxs] + bbsz_offsets.resize_(new_bsz, 1) + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + + lm_lprobs_top_k = lm_lprobs_top_k[batch_idxs] + + fw_lprobs_top_k = fw_lprobs_top_k[batch_idxs] + cand_indices = cand_indices[batch_idxs] + if prefix_tokens is not None: + prefix_tokens = prefix_tokens[batch_idxs] + src_lengths_no_eos = src_lengths_no_eos[batch_idxs] + cands_to_ignore = cands_to_ignore[batch_idxs] + + scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + scores_buf.resize_as_(scores) + tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + tokens_buf.resize_as_(tokens) + src_tokens = src_tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + src_lengths = src_lengths.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + lm_prefix_scores = lm_prefix_scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1).squeeze() + + if attn is not None: + attn = attn.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, attn.size(1), -1) + attn_buf.resize_as_(attn) + bsz = new_bsz + else: + batch_idxs = None + + # Set active_mask so that values > cand_size indicate eos or + # ignored hypos and values < cand_size indicate candidate + # active hypos. After this, the min values per row are the top + # candidate active hypos. + eos_mask[:, :beam_size] |= cands_to_ignore + active_mask = torch.add( + eos_mask.type_as(cand_offsets) * cand_size, + cand_offsets[: eos_mask.size(1)], + ) + + # get the top beam_size active hypotheses, which are just the hypos + # with the smallest values in active_mask + active_hypos, new_cands_to_ignore = buffer('active_hypos'), buffer('new_cands_to_ignore') + torch.topk( + active_mask, k=beam_size, dim=1, largest=False, + out=(new_cands_to_ignore, active_hypos) + ) + + # update cands_to_ignore to ignore any finalized hypos + cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] + assert (~cands_to_ignore).any(dim=1).all() + + active_bbsz_idx = buffer('active_bbsz_idx') + torch.gather( + cand_bbsz_idx, dim=1, index=active_hypos, + out=active_bbsz_idx, + ) + active_scores = torch.gather( + fw_lprobs_top_k, dim=1, index=active_hypos, + out=scores[:, step].view(bsz, beam_size), + ) + + active_bbsz_idx = active_bbsz_idx.view(-1) + active_scores = active_scores.view(-1) + + # copy tokens and scores for active hypotheses + torch.index_select( + tokens[:, :step + 1], dim=0, index=active_bbsz_idx, + out=tokens_buf[:, :step + 1], + ) + torch.gather( + cand_indices, dim=1, index=active_hypos, + out=tokens_buf.view(bsz, beam_size, -1)[:, :, step + 1], + ) + if step > 0: + torch.index_select( + scores[:, :step], dim=0, index=active_bbsz_idx, + out=scores_buf[:, :step], + ) + torch.gather( + fw_lprobs_top_k, dim=1, index=active_hypos, + out=scores_buf.view(bsz, beam_size, -1)[:, :, step], + ) + torch.gather( + lm_lprobs_top_k, dim=1, index=active_hypos, + out=lm_prefix_scores.view(bsz, beam_size) + ) + + # copy attention for active hypotheses + if attn is not None: + torch.index_select( + attn[:, :, :step + 2], dim=0, index=active_bbsz_idx, + out=attn_buf[:, :, :step + 2], + ) + + # swap buffers + tokens, tokens_buf = tokens_buf, tokens + scores, scores_buf = scores_buf, scores + if attn is not None: + attn, attn_buf = attn_buf, attn + + # reorder incremental state in decoder + reorder_state = active_bbsz_idx + + # sort by score descending + for sent in range(len(finalized)): + finalized[sent] = sorted(finalized[sent], key=lambda r: r['score'], reverse=True) + + return finalized + + +def get_lm_scores(model, input_tokens, incremental_states, cand_tokens, input_len, k): + with torch.no_grad(): + lm_lprobs, avg_attn_scores = model.forward_decoder( + input_tokens, encoder_outs=None, incremental_states=incremental_states, + ) + + lm_lprobs_size = lm_lprobs.size(0) + probs_next_wrd = torch.gather(lm_lprobs.repeat(1, k).view(lm_lprobs_size*k, -1), 1, cand_tokens).squeeze().view(-1) + + return probs_next_wrd + + +def make_dict2dict(old_dict, new_dict): + dict2dict_map = {} + for sym in old_dict.symbols: + dict2dict_map[old_dict.index(sym)] = new_dict.index(sym) + return dict2dict_map + + +def dict2dict(tokens, dict2dict_map): + if tokens.device == torch.device('cpu'): + tokens_tmp = tokens + else: + tokens_tmp = tokens.cpu() + return tokens_tmp.map_( + tokens_tmp, + lambda _, val, dict2dict_map=dict2dict_map : dict2dict_map[float(val)] + ).to(tokens.device) + + +def reorder_tokens(tokens, lengths, eos): + # reorder source tokens so they may be used as reference for P(S|T) + return torch.cat((tokens.new([eos]), tokens[-lengths:-1], tokens[:-lengths]), 0) + + +def reorder_all_tokens(tokens, lengths, eos): + # used to reorder src tokens from [ .. ] to [ ...] + # so source tokens can be used to predict P(S|T) + return torch.stack([reorder_tokens(token, length, eos) for token, length in zip(tokens, lengths)]) + + +def normalized_scores_with_batch_vocab( + model_decoder, features, target_ids, k, bsz, beam_size, + pad_idx, top_k=0, vocab_size_meter=None, start_idx=None, + end_idx=None, **kwargs): + """ + Get normalized probabilities (or log probs) from a net's output + w.r.t. vocab consisting of target IDs in the batch + """ + if model_decoder.adaptive_softmax is None: + weight = model_decoder.output_projection.weight + vocab_ids = torch.unique( + torch.cat( + (torch.unique(target_ids), torch.arange(top_k, device=target_ids.device)) + ) + ) + id_map = dict(zip(vocab_ids.tolist(), range(len(vocab_ids)))) + mapped_target_ids = target_ids.cpu().apply_( + lambda x, id_map=id_map: id_map[x] + ).to(target_ids.device) + expanded_target_ids = mapped_target_ids[:, :].repeat(1, k).view(bsz*beam_size*k, -1) + if start_idx is not None and end_idx is not None: + expanded_target_ids = expanded_target_ids[start_idx:end_idx, :] + logits = F.linear(features, weight[vocab_ids, :]) + log_softmax = F.log_softmax(logits, dim=-1, dtype=torch.float32) + intermed_scores = torch.gather( + log_softmax[:, :-1, :], + 2, + expanded_target_ids[:, 1:].unsqueeze(2), + ).squeeze() + not_padding = expanded_target_ids[:, 1:] != pad_idx + intermed_scores *= not_padding.float() + return intermed_scores + else: + raise ValueError("adaptive softmax doesn't work with " + + "`normalized_scores_with_batch_vocab()`") diff --git a/fairseq/examples/fast_noisy_channel/noisy_channel_translation.py b/fairseq/examples/fast_noisy_channel/noisy_channel_translation.py new file mode 100644 index 0000000..b74bdfd --- /dev/null +++ b/fairseq/examples/fast_noisy_channel/noisy_channel_translation.py @@ -0,0 +1,127 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.tasks.translation import TranslationTask +from fairseq.tasks.language_modeling import LanguageModelingTask +from fairseq import checkpoint_utils +import argparse +from fairseq.tasks import register_task +import torch + + +@register_task("noisy_channel_translation") +class NoisyChannelTranslation(TranslationTask): + """ + Rescore the top k candidates from each beam using noisy channel modeling + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + TranslationTask.add_args(parser) + # fmt: off + parser.add_argument('--channel-model', metavar='FILE', + help='path to P(S|T) model. P(S|T) and P(T|S) must share source and target dictionaries.') + parser.add_argument('--combine-method', default='lm_only', + choices=['lm_only', 'noisy_channel'], + help="""method for combining direct and channel model scores. + lm_only: decode with P(T|S)P(T) + noisy_channel: decode with 1/t P(T|S) + 1/s(P(S|T)P(T))""") + parser.add_argument('--normalize-lm-scores-by-tgt-len', action='store_true', default=False, + help='normalize lm score by target length instead of source length') + parser.add_argument('--channel-scoring-type', default='log_norm', choices=['unnormalized', 'log_norm', 'k2_separate', 'src_vocab', 'src_vocab_batched'], + help="Normalize bw scores with log softmax or return bw scores without log softmax") + parser.add_argument('--top-k-vocab', default=0, type=int, + help='top k vocab IDs to use with `src_vocab` in channel model scoring') + parser.add_argument('--k2', default=50, type=int, + help='the top k2 candidates to rescore with the noisy channel model for each beam') + parser.add_argument('--ch-wt', default=1, type=float, + help='weight for the channel model') + parser.add_argument('--lm-model', metavar='FILE', + help='path to lm model file, to model P(T). P(T) must share the same vocab as the direct model on the target side') + parser.add_argument('--lm-data', metavar='FILE', + help='path to lm model training data for target language, used to properly load LM with correct dictionary') + parser.add_argument('--lm-wt', default=1, type=float, + help='the weight of the lm in joint decoding') + # fmt: on + + def build_generator( + self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None + ): + if getattr(args, "score_reference", False): + raise NotImplementedError() + else: + from .noisy_channel_sequence_generator import NoisyChannelSequenceGenerator + use_cuda = torch.cuda.is_available() and not self.args.cpu + assert self.args.lm_model is not None, '--lm-model required for noisy channel generation!' + assert self.args.lm_data is not None, '--lm-data required for noisy channel generation to map between LM and bitext vocabs' + if self.args.channel_model is not None: + import copy + ch_args_task = copy.deepcopy(self.args) + tmp = ch_args_task.source_lang + ch_args_task.source_lang = ch_args_task.target_lang + ch_args_task.target_lang = tmp + ch_args_task._name = 'translation' + channel_task = TranslationTask.setup_task(ch_args_task) + + arg_dict = {} + arg_dict['task'] = 'language_modeling' + arg_dict['sample_break_mode'] = 'eos' + arg_dict['data'] = self.args.lm_data + arg_dict['output_dictionary_size'] = -1 + lm_args = argparse.Namespace(**arg_dict) + lm_task = LanguageModelingTask.setup_task(lm_args) + lm_dict = lm_task.output_dictionary + + if self.args.channel_model is not None: + channel_models, _ = checkpoint_utils.load_model_ensemble(self.args.channel_model.split(':'), task=channel_task) + + for model in channel_models: + model.make_generation_fast_( + beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, + need_attn=args.print_alignment, + ) + if self.args.fp16: + model.half() + if use_cuda: + model.cuda() + else: + channel_models = None + + lm_models, _ = checkpoint_utils.load_model_ensemble(self.args.lm_model.split(':'), task=lm_task) + + for model in lm_models: + model.make_generation_fast_( + beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, + need_attn=args.print_alignment, + ) + if self.args.fp16: + model.half() + if use_cuda: + model.cuda() + return NoisyChannelSequenceGenerator( + combine_method=self.args.combine_method, + tgt_dict=self.target_dictionary, + src_dict=self.source_dictionary, + beam_size=getattr(args, 'beam', 5), + max_len_a=getattr(args, 'max_len_a', 0), + max_len_b=getattr(args, 'max_len_b', 200), + min_len=getattr(args, 'min_len', 1), + len_penalty=getattr(args, 'lenpen', 1), + unk_penalty=getattr(args, 'unkpen', 0), + temperature=getattr(args, 'temperature', 1.), + match_source_len=getattr(args, 'match_source_len', False), + no_repeat_ngram_size=getattr(args, 'no_repeat_ngram_size', 0), + normalize_scores=(not getattr(args, 'unnormalized', False)), + channel_models=channel_models, + k2=getattr(self.args, 'k2', 50), + ch_weight=getattr(self.args, 'ch_wt', 1), + channel_scoring_type=self.args.channel_scoring_type, + top_k_vocab=self.args.top_k_vocab, + lm_models=lm_models, + lm_dict=lm_dict, + lm_weight=getattr(self.args, 'lm_wt', 1), + normalize_lm_scores_by_tgt_len=getattr(self.args, 'normalize_lm_scores_by_tgt_len', False), + ) diff --git a/fairseq/examples/flores101/README.md b/fairseq/examples/flores101/README.md new file mode 100644 index 0000000..635c13f --- /dev/null +++ b/fairseq/examples/flores101/README.md @@ -0,0 +1,223 @@ +

+ +

+ +# Flores101: Large-Scale Multilingual Machine Translation + +## Introduction + +Baseline pretrained models for small and large tracks of WMT 21 Large-Scale Multilingual Machine Translation competition. + +Flores Task at WMT 21: http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html + +Flores announement blog post: https://ai.facebook.com/blog/flores-researchers-kick-off-multilingual-translation-challenge-at-wmt-and-call-for-compute-grants/ + + + +## Pretrained models + +Model | Num layers | Embed dimension | FFN dimension| Vocab Size | #params | Download +---|---|---|---|---|---|--- +`flores101_mm100_615M` | 12 | 1024 | 4096 | 256,000 | 615M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz +`flores101_mm100_175M` | 6 | 512 | 2048 | 256,000 | 175M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz + + +These models are trained similar to [M2M-100](https://arxiv.org/abs/2010.11125) with additional support for the languages that are part of the WMT Large-Scale Multilingual Machine Translation track. Full list of languages can be found at the bottom. + + +## Example Generation code + +### Download model, sentencepiece vocab + +```bash +fairseq=/path/to/fairseq +cd $fairseq + +# Download 615M param model. +wget https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz + +# Extract +tar -xvzf flores101_mm100_615M.tar.gz +``` + +### Encode using our SentencePiece Model +Note: Install SentencePiece from [here](https://github.com/google/sentencepiece) + + +```bash +fairseq=/path/to/fairseq +cd $fairseq + +# Download example dataset From German to French +sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de +sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr + +for lang in de fr ; do + python scripts/spm_encode.py \ + --model flores101_mm100_615M/sentencepiece.bpe.model \ + --output_format=piece \ + --inputs=raw_input.de-fr.${lang} \ + --outputs=spm.de-fr.${lang} +done +``` + +### Binarization + +```bash +fairseq-preprocess \ + --source-lang de --target-lang fr \ + --testpref spm.de-fr \ + --thresholdsrc 0 --thresholdtgt 0 \ + --destdir data_bin \ + --srcdict flores101_mm100_615M/dict.txt --tgtdict flores101_mm100_615M/dict.txt +``` + +### Generation + + +```bash +fairseq-generate \ + data_bin \ + --batch-size 1 \ + --path flores101_mm100_615M/model.pt \ + --fixed-dictionary flores101_mm100_615M/dict.txt \ + -s de -t fr \ + --remove-bpe 'sentencepiece' \ + --beam 5 \ + --task translation_multi_simple_epoch \ + --lang-pairs flores101_mm100_615M/language_pairs.txt \ + --decoder-langtok --encoder-langtok src \ + --gen-subset test \ + --fp16 \ + --dataset-impl mmap \ + --distributed-world-size 1 --distributed-no-spawn +``` + +### Supported Languages and lang code + +Language | lang code +---|--- +Akrikaans | af +Amharic | am +Arabic | ar +Assamese | as +Asturian | ast +Aymara | ay +Azerbaijani | az +Bashkir | ba +Belarusian | be +Bulgarian | bg +Bengali | bn +Breton | br +Bosnian | bs +Catalan | ca +Cebuano | ceb +Chokwe | cjk +Czech | cs +Welsh | cy +Danish | da +German | de +Dyula| dyu +Greek | el +English | en +Spanish | es +Estonian | et +Persian | fa +Fulah | ff +Finnish | fi +French | fr +Western Frisian | fy +Irish | ga +Scottish Gaelic | gd +Galician | gl +Gujarati | gu +Hausa | ha +Hebrew | he +Hindi | hi +Croatian | hr +Haitian Creole | ht +Hungarian | hu +Armenian | hy +Indonesian | id +Igbo | ig +Iloko | ilo +Icelandic | is +Italian | it +Japanese | ja +Javanese | jv +Georgian | ka +Kachin | kac +Kamba | kam +Kabuverdianu | kea +Kongo | kg +Kazakh | kk +Central Khmer | km +Kimbundu | kmb +Northern Kurdish | kmr +Kannada | kn +Korean | ko +Kurdish | ku +Kyrgyz | ky +Luxembourgish | lb +Ganda | lg +Lingala | ln +Lao | lo +Lithuanian | lt +Luo | luo +Latvian | lv +Malagasy | mg +Maori | mi +Macedonian | mk +Malayalam | ml +Mongolian | mn +Marathi | mr +Malay | ms +Maltese | mt +Burmese | my +Nepali | ne +Dutch | nl +Norwegian | no +Northern Sotho | ns +Nyanja | ny +Occitan | oc +Oromo | om +Oriya | or +Punjabi | pa +Polish | pl +Pashto | ps +Portuguese | pt +Quechua | qu +Romanian | ro +Russian | ru +Sindhi | sd +Shan | shn +Sinhala | si +Slovak | sk +Slovenian | sl +Shona | sn +Somali | so +Albanian | sq +Serbian | sr +Swati | ss +Sundanese | su +Swedish | sv +Swahili | sw +Tamil | ta +Telugu | te +Tajik | tg +Thai | th +Tigrinya | ti +Tagalog | tl +Tswana | tn +Turkish | tr +Ukrainian | uk +Umbundu | umb +Urdu | ur +Uzbek | uz +Vietnamese | vi +Wolof | wo +Xhosa | xh +Yiddish | yi +Yoruba | yo +Chinese| zh +Zulu | zu diff --git a/fairseq/examples/fully_sharded_data_parallel/README.md b/fairseq/examples/fully_sharded_data_parallel/README.md new file mode 100644 index 0000000..b9e44fe --- /dev/null +++ b/fairseq/examples/fully_sharded_data_parallel/README.md @@ -0,0 +1,177 @@ +# Fully Sharded Data Parallel (FSDP) + +## Overview +Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and +[Google](https://arxiv.org/abs/2004.13336) has shown that data parallel +training can be made significantly more efficient by sharding the model +parameters and optimizer state across data parallel workers. These ideas are +encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper provided +by [fairscale](https://github.com/facebookresearch/fairscale/). + +Compared to PyTorch DDP: +* FSDP produces identical results as PyTorch DDP (it's still synchronous data parallel training) +* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs +* FSDP is faster than PyTorch DDP because the optimizer step is sharded, and the communication can be overlapped with the forward pass +* FSDP enables training 13B parameter models on 8 GPUs and 175B parameter models on 128 GPUs + +FSDP is fully supported in fairseq via the following new arguments: +* `--ddp-backend=fully_sharded`: enables full sharding via FSDP +* `--cpu-offload`: offloads the optimizer state and FP32 model copy to CPU (combine with `--optimizer=cpu_adam`) +* `--no-reshard-after-forward`: increases training speed for large models (1B+ params) and is similar to ZeRO stage 2 +* other popular options (`--fp16`, `--update-freq`, `--checkpoint-activations`, `--offload-activations`, etc.) continue to work as normal + +
Limitations

+ +FSDP currently has several limitations compared to fairseq's default DDP backend (PyTorch DDP): +* while FSDP is full compatible with pointwise Optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.), it is not currently compatible with non-pointwise Optimizers (e.g., Adagrad, Adafactor, LAMB, etc.) +* FSDP depends on flattening the parameters, so models that currently require `--fp16-no-flatten-grads` may not be supported + +See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed +explanation of these and other limitations. + +

+ +
How it works

+ +Fully Sharded Data Parallel + +See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed +explanation of how FSDP works. + +

+ +## Example usage + +The following examples illustrate how to train a very large language model with +13 billion parameters on 1 GPU by offloading parameters and optimizer states to +CPU, or on 8 GPUs by fully sharding the params and optimizer states across GPUs. + +These examples use the WikiText-103 dataset for demonstration purposes, but +in practice a much larger dataset will be needed to achieve good results. +Follow the [instructions here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.pretraining.md#1-preprocess-the-data) +to preprocess the WikiText-103 dataset using the GPT-2/RoBERTa vocabulary. + +### 13B params on 1 V100 GPU (with CPU offloading) + +The following command trains a 13B parameter GPT-3 model on a single V100 GPU +using the `--cpu-offload` feature to offload parameters and optimizer states to +CPU. In this setting, the optimizer step (Adam) happens on CPU. We also use the +`--checkpoint-activations` feature (sometimes called [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html)), +which further saves memory in exchange for a small increase in computation. + +**Requirements:** +- Install the latest master version of fairscale: `pip install git+https://github.com/facebookresearch/fairscale.git@master` +- You'll need 32GB of GPU memory and ~256GB of system memory to train the 13B param model. +- If you have less system memory, the 6.7B param model can be trained with ~128GB of system memory, just set `--arch transformer_lm_gpt3_6_7` +- We use the CPU Adam optimizer from [DeepSpeed](https://github.com/microsoft/DeepSpeed), so you'll need to `pip install deepspeed` before running the command. + +**Notes:** +- The command will take ~5 minutes to start training, during which time it will appear to be hung, since randomly initializing 13B weights can be slow. +- The `--cpu-offload` feature requires training in mixed precision (`--fp16`). +- Tune the `OMP_NUM_THREADS` env variable for best performance with CPU offloading. +- The example command below stops training after 10 steps (`--max-update 10`) and does not save checkpoints (`--no-save`). + +```bash +OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0 \ + fairseq-train data-bin/wikitext-103-roberta-bpe-bin \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 2048 --batch-size 8 \ + --arch transformer_lm_gpt3_13 \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 10 --no-save --log-format json --log-interval 1 +``` + +
Example output

+ +``` +(...) +2021-03-08 12:29:51 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) +(...) +2021-03-08 12:29:51 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs) +2021-03-08 12:29:51 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 +(...) +Adam Optimizer #0 is created with AVX2 arithmetic capability. +Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 +(...) +2021-03-08 12:31:36 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.475", "ppl": "91120.8", "wps": "0", "ups": "0", "wpb": "16384", "bsz": "8", "num_updates": "1", "lr": "2e-05", "gnorm": "20.751", "loss_scale": "4", "train_wall": "99", "gb_free": "9.3", "wall": "105"} +2021-03-08 12:32:33 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.446", "ppl": "89281.6", "wps": "288.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "2", "lr": "4e-05", "gnorm": "19.777", "loss_scale": "4", "train_wall": "57", "gb_free": "9.3", "wall": "161"} +2021-03-08 12:33:12 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 +2021-03-08 12:33:51 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 +2021-03-08 12:34:45 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "25.22", "ppl": "3.90691e+07", "wps": "123.4", "ups": "0.01", "wpb": "16384", "bsz": "8", "num_updates": "3", "lr": "6e-05", "gnorm": "131.281", "loss_scale": "1", "train_wall": "133", "gb_free": "9.3", "wall": "294"} +2021-03-08 12:35:43 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.079", "ppl": "276809", "wps": "285.5", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "4", "lr": "8e-05", "gnorm": "13.776", "loss_scale": "1", "train_wall": "57", "gb_free": "9.3", "wall": "351"} +2021-03-08 12:36:35 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "23.729", "ppl": "1.39088e+07", "wps": "316.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "5", "lr": "0.0001", "gnorm": "72.774", "loss_scale": "1", "train_wall": "52", "gb_free": "9.3", "wall": "403"} +2021-03-08 12:37:28 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "20.429", "ppl": "1.41203e+06", "wps": "307.6", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "6", "lr": "8e-05", "gnorm": "60.846", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "456"} +2021-03-08 12:38:27 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.965", "ppl": "511684", "wps": "279.4", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "7", "lr": "6e-05", "gnorm": "22.687", "loss_scale": "1", "train_wall": "59", "gb_free": "9.3", "wall": "515"} +2021-03-08 12:39:18 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.345", "ppl": "332887", "wps": "319.1", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "8", "lr": "4e-05", "gnorm": "8.451", "loss_scale": "1", "train_wall": "51", "gb_free": "9.3", "wall": "566"} +2021-03-08 12:40:11 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "18.262", "ppl": "314336", "wps": "305.9", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "9", "lr": "2e-05", "gnorm": "6.457", "loss_scale": "1", "train_wall": "54", "gb_free": "9.3", "wall": "620"} +2021-03-08 12:41:04 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "17.556", "ppl": "192686", "wps": "311.8", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "10", "lr": "0", "gnorm": "5.796", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "673"} +2021-03-08 12:41:04 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 +2021-03-08 12:41:04 | INFO | fairseq_cli.train | begin validation on "valid" subset +2021-03-08 12:43:15 | INFO | valid | {"epoch": 1, "valid_loss": "17.953", "valid_ppl": "253807", "valid_wps": "1868.4", "valid_wpb": "15400.2", "valid_bsz": "7.6", "valid_num_updates": "10"} +2021-03-08 12:43:15 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) +2021-03-08 12:43:15 | INFO | train | {"epoch": 1, "train_loss": "19.351", "train_ppl": "668509", "train_wps": "210.9", "train_ups": "0.01", "train_wpb": "16384", "train_bsz": "8", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "36.26", "train_loss_scale": "1", "train_train_wall": "667", "train_gb_free": "9.3", "train_wall": "804"} +2021-03-08 12:43:15 | INFO | fairseq_cli.train | done training in 798.6 seconds +``` + +

+ +### 13B params on 8 V100 GPUs (with full parameter + optimizer state sharding) + +FSDP can also shard the parameters and optimizer states across multiple GPUs, +reducing memory requirements significantly. On 8 x 32GB GPUs, sharding enables +training the same 13B parameter model *without offloading the parameters to +CPU*. However, without CPU offloading we'd only be able to fit a batch size of +1 per GPU, which would cause training speed to suffer. + +We obtain the best performance on 8 GPUs by combining full sharding and CPU +offloading. The following command trains the same 13B parameter GPT-3 model as +before on 8 x 32GB V100 GPUs; training speed increases superlinearly from ~310 +words per second to ~3200 words per second. + +```bash +OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ + fairseq-train data-bin/wikitext-103-roberta-bpe-bin \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 2048 --batch-size 8 \ + --arch transformer_lm_gpt3_13 \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 10 --no-save --log-format json --log-interval 1 +``` + +
Example output

+ +``` +(...) +2021-03-08 18:04:09 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) +(...) +2021-03-08 18:04:09 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs) +2021-03-08 18:04:09 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 +(...) +Adam Optimizer #0 is created with AVX2 arithmetic capability. +Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 +(...) +2021-03-08 18:05:06 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "16.408", "ppl": "86945.6", "wps": "0", "ups": "0", "wpb": "131072", "bsz": "64", "num_updates": "1", "lr": "2e-05", "gnorm": "18.27", "loss_scale": "4", "train_wall": "47", "gb_free": "9.3", "wall": "56"} +2021-03-08 18:05:45 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "16.352", "ppl": "83644.3", "wps": "3283.4", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "2", "lr": "4e-05", "gnorm": "18.411", "loss_scale": "4", "train_wall": "40", "gb_free": "9.3", "wall": "96"} +2021-03-08 18:06:21 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 +2021-03-08 18:06:56 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 +2021-03-08 18:07:37 | INFO | train_inner | {"epoch": 1, "update": 0.006, "loss": "23.682", "ppl": "1.34537e+07", "wps": "1176.6", "ups": "0.01", "wpb": "131072", "bsz": "64", "num_updates": "3", "lr": "6e-05", "gnorm": "119.682", "loss_scale": "1", "train_wall": "111", "gb_free": "9.3", "wall": "208"} +2021-03-08 18:08:18 | INFO | train_inner | {"epoch": 1, "update": 0.007, "loss": "18.988", "ppl": "519921", "wps": "3189.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "4", "lr": "8e-05", "gnorm": "14.934", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "249"} +2021-03-08 18:08:59 | INFO | train_inner | {"epoch": 1, "update": 0.008, "loss": "20.08", "ppl": "1.10798e+06", "wps": "3223.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "5", "lr": "0.0001", "gnorm": "59.92", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "289"} +2021-03-08 18:09:39 | INFO | train_inner | {"epoch": 1, "update": 0.009, "loss": "18.323", "ppl": "327980", "wps": "3256.6", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "6", "lr": "8e-05", "gnorm": "37.425", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "330"} +2021-03-08 18:10:20 | INFO | train_inner | {"epoch": 1, "update": 0.01, "loss": "17.264", "ppl": "157354", "wps": "3188.7", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "7", "lr": "6e-05", "gnorm": "10.824", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "371"} +2021-03-08 18:11:01 | INFO | train_inner | {"epoch": 1, "update": 0.011, "loss": "16.794", "ppl": "113647", "wps": "3230", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "8", "lr": "4e-05", "gnorm": "5.616", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "411"} +2021-03-08 18:11:39 | INFO | train_inner | {"epoch": 1, "update": 0.012, "loss": "16.706", "ppl": "106938", "wps": "3384", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "9", "lr": "2e-05", "gnorm": "5.318", "loss_scale": "1", "train_wall": "39", "gb_free": "9.3", "wall": "450"} +2021-03-08 18:12:19 | INFO | train_inner | {"epoch": 1, "update": 0.013, "loss": "16.548", "ppl": "95796.2", "wps": "3274.4", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "10", "lr": "0", "gnorm": "5.22", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "490"} +2021-03-08 18:12:19 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 +2021-03-08 18:12:19 | INFO | fairseq_cli.train | begin validation on "valid" subset +2021-03-08 18:12:45 | INFO | valid | {"epoch": 1, "valid_loss": "16.624", "valid_ppl": "101000", "valid_wps": "10855.9", "valid_wpb": "123202", "valid_bsz": "60.5", "valid_num_updates": "10"} +2021-03-08 18:12:45 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) +2021-03-08 18:12:45 | INFO | train | {"epoch": 1, "train_loss": "18.114", "train_ppl": "283776", "train_wps": "2567.8", "train_ups": "0.02", "train_wpb": "131072", "train_bsz": "64", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "29.562", "train_loss_scale": "1", "train_train_wall": "480", "train_gb_free": "9.3", "train_wall": "516"} +2021-03-08 18:12:45 | INFO | fairseq_cli.train | done training in 509.9 seconds +``` + +

diff --git a/fairseq/examples/gottbert/README.md b/fairseq/examples/gottbert/README.md new file mode 100644 index 0000000..1d58feb --- /dev/null +++ b/fairseq/examples/gottbert/README.md @@ -0,0 +1,64 @@ +# GottBERT: a pure German language model + +## Introduction + +[GottBERT](http://arxiv.org/abs/2012.02110) is a pretrained language model trained on 145GB of German text based on RoBERTa. + +## Example usage + +### fairseq +##### Load GottBERT from torch.hub (PyTorch >= 1.1): +```python +import torch +gottbert = torch.hub.load('pytorch/fairseq', 'gottbert-base') +gottbert.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Load GottBERT (for PyTorch 1.0 or custom models): +```python +# Download gottbert model +wget https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz +tar -xzvf gottbert.tar.gz + +# Load the model in fairseq +from fairseq.models.roberta import GottbertModel +gottbert = GottbertModel.from_pretrained('/path/to/gottbert') +gottbert.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Filling masks: +```python +masked_line = 'Gott ist ! :)' +gottbert.fill_mask(masked_line, topk=3) +# [('Gott ist gut ! :)', 0.3642110526561737, ' gut'), +# ('Gott ist überall ! :)', 0.06009674072265625, ' überall'), +# ('Gott ist großartig ! :)', 0.0370681993663311, ' großartig')] +``` + +##### Extract features from GottBERT + +```python +# Extract the last layer's features +line = "Der erste Schluck aus dem Becher der Naturwissenschaft macht atheistisch , aber auf dem Grunde des Bechers wartet Gott !" +tokens = gottbert.encode(line) +last_layer_features = gottbert.extract_features(tokens) +assert last_layer_features.size() == torch.Size([1, 27, 768]) + +# Extract all layer's features (layer 0 is the embedding layer) +all_layers = gottbert.extract_features(tokens, return_all_hiddens=True) +assert len(all_layers) == 13 +assert torch.all(all_layers[-1] == last_layer_features) +``` +## Citation +If you use our work, please cite: + +```bibtex +@misc{scheible2020gottbert, + title={GottBERT: a pure German Language Model}, + author={Raphael Scheible and Fabian Thomczyk and Patric Tippmann and Victor Jaravine and Martin Boeker}, + year={2020}, + eprint={2012.02110}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/fairseq/examples/hubert/README.md b/fairseq/examples/hubert/README.md new file mode 100644 index 0000000..6695d81 --- /dev/null +++ b/fairseq/examples/hubert/README.md @@ -0,0 +1,116 @@ +# HuBERT + +## Pre-trained and fine-tuned (ASR) models +Model | Pretraining Data | Finetuning Dataset | Model | Quantizer +|---|---|---|---|--- +HuBERT Base (~95M params) | [Librispeech](http://www.openslr.org/12) 960 hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) | [L9 km500](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960_L9_km500.bin) +HuBERT Large (~316M params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt) +HuBERT Extra Large (~1B params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt) +HuBERT Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt) +HuBERT Extra Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt) + +## Load a model +``` +ckpt_path = "/path/to/the/checkpoint.pt" +models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) +model = models[0] +``` + +## Train a new model + +### Data preparation + +Follow the steps in `./simple_kmeans` to create: +- `{train,valid}.tsv` waveform list files +- `{train,valid}.km` frame-aligned pseudo label files. +- `dict.km.txt` a dummy dictionary +The `label_rate` is the same as the feature frame rate used for clustering, +which is 100Hz for MFCC features and 50Hz for HuBERT features by default. + +### Pre-train a HuBERT model + +Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km` +are saved at `/path/to/labels`, and the label rate is 100Hz. + +To train a base model (12 layer transformer), run: +```sh +$ python fairseq_cli/hydra_train.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/pretrain \ + --config-name hubert_base_librispeech \ + task.data=/path/to/data task.label_dir=/path/to/labels task.labels='["km"]' model.label_rate=100 +``` + +### Fine-tune a HuBERT model with a CTC loss + +Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their +corresponding character transcripts `{train,valid}.ltr` are saved at +`/path/to/trans`. + +To fine-tune a pre-trained HuBERT model at `/path/to/checkpoint`, run +```sh +$ python fairseq_cli/hydra_train.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/finetune \ + --config-name base_10h \ + task.data=/path/to/data task.label_dir=/path/to/trans \ + model.w2v_path=/path/to/checkpoint +``` + +### Decode a HuBERT model + +Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of +the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is +saved at `/path/to/checkpoint`. We support three decoding modes: +- Viterbi decoding: greedy decoding without a language model +- KenLM decoding: decoding with an arpa-format KenLM n-gram language model +- Fairseq-LM deocding: decoding with a Fairseq neural language model + + +#### Viterbi decoding + +`task.normalize` needs to be consistent with the value used during fine-tuning. +Decoding results will be saved at +`/path/to/experiment/directory/decode/viterbi/test`. + +```sh +$ python examples/speech_recognition/new/infer.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ + --config-name infer_viterbi \ + task.data=/path/to/data \ + task.normalize=[true|false] \ + decoding.exp_dir=/path/to/experiment/directory \ + common_eval.path=/path/to/checkpoint + dataset.gen_subset=test \ +``` + +#### KenLM / Fairseq-LM decoding + +Suppose the pronunciation lexicon and the n-gram LM are saved at +`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be +saved at `/path/to/experiment/directory/decode/kenlm/test`. + +```sh +$ python examples/speech_recognition/new/infer.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ + --config-name infer_kenlm \ + task.data=/path/to/data \ + task.normalize=[true|false] \ + decoding.exp_dir=/path/to/experiment/directory \ + common_eval.path=/path/to/checkpoint + dataset.gen_subset=test \ + decoding.decoder.lexicon=/path/to/lexicon \ + decoding.decoder.lmpath=/path/to/arpa +``` + +The command above uses the default decoding hyperparameter, which can be found +in `examples/speech_recognition/hydra/decoder.py`. These parameters can be +configured from the command line. For example, to search with a beam size of +500, we can append the command above with `decoding.decoder.beam=500`. +Important parameters include: +- decoding.decoder.beam +- decoding.decoder.beamthreshold +- decoding.decoder.lmweight +- decoding.decoder.wordscore +- decoding.decoder.silweight + +To decode with a Fairseq LM, use `--config-name infer_fsqlm` instead, and +change the path of lexicon and LM accordingly. diff --git a/fairseq/examples/hubert/config/decode/ax_sweep/ngram.yaml b/fairseq/examples/hubert/config/decode/ax_sweep/ngram.yaml new file mode 100644 index 0000000..5a02df1 --- /dev/null +++ b/fairseq/examples/hubert/config/decode/ax_sweep/ngram.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +common_eval: + results_path: ${decoding.exp_dir}/decode/${decoding.decoder.name}_ax/${dataset.gen_subset} + +hydra: + sweeper: + ax_config: + max_trials: 60 + early_stop: + minimize: true + max_epochs_without_improvement: 10 + epsilon: 0.025 + experiment: + name: ${dataset.gen_subset} + objective_name: wer + minimize: true + parameter_constraints: null + outcome_constraints: null + status_quo: null + client: + verbose_logging: false + random_seed: null + params: + decoding.decoder.lmweight: + type: range + bounds: [0.0, 8.0] + decoding.decoder.wordscore: + type: range + bounds: [-5.0, 5.0] + decoding.decoder.silweight: + type: range + bounds: [-10.0, 0.0] diff --git a/fairseq/examples/hubert/config/decode/ax_sweep/transformer.yaml b/fairseq/examples/hubert/config/decode/ax_sweep/transformer.yaml new file mode 100644 index 0000000..85ed3bd --- /dev/null +++ b/fairseq/examples/hubert/config/decode/ax_sweep/transformer.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +common_eval: + results_path: ${decoding.exp_dir}/decode/${decoding.decoder.name}_ax/${dataset.gen_subset} + +hydra: + sweeper: + ax_config: + max_trials: 60 + early_stop: + minimize: true + max_epochs_without_improvement: 10 + epsilon: 0.025 + experiment: + name: ${dataset.gen_subset} + objective_name: wer + minimize: true + parameter_constraints: null + outcome_constraints: null + status_quo: null + client: + verbose_logging: false + random_seed: null + params: + decoding.decoder.lmweight: + type: range + bounds: [0.0, 4.0] + decoding.decoder.wordscore: + type: range + bounds: [-5.0, 5.0] + decoding.decoder.silweight: + type: range + bounds: [-8.0, 0.0] diff --git a/fairseq/examples/hubert/config/decode/infer_fsqlm.yaml b/fairseq/examples/hubert/config/decode/infer_fsqlm.yaml new file mode 100644 index 0000000..026ad8d --- /dev/null +++ b/fairseq/examples/hubert/config/decode/infer_fsqlm.yaml @@ -0,0 +1,36 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + sweep: + dir: ${common_eval.results_path} + subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + +task: + _name: hubert_pretraining + single_target: true + fine_tuning: true + data: ??? + normalize: ??? + +decoding: + type: fairseqlm + lexicon: ??? + lmpath: ??? + beamthreshold: 25 + beam: 500 + lmweight: 2 + wordscore: -1 + silweight: 0 + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/fairseq/examples/hubert/config/decode/infer_kenlm.yaml b/fairseq/examples/hubert/config/decode/infer_kenlm.yaml new file mode 100644 index 0000000..04642ae --- /dev/null +++ b/fairseq/examples/hubert/config/decode/infer_kenlm.yaml @@ -0,0 +1,36 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + sweep: + dir: ${common_eval.results_path} + subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + +task: + _name: hubert_pretraining + single_target: true + fine_tuning: true + data: ??? + normalize: ??? + +decoding: + type: kenlm + lexicon: ??? + lmpath: ??? + beamthreshold: 100 + beam: 500 + lmweight: 2 + wordscore: -1 + silweight: 0 + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/fairseq/examples/hubert/config/decode/infer_viterbi.yaml b/fairseq/examples/hubert/config/decode/infer_viterbi.yaml new file mode 100644 index 0000000..4afc74c --- /dev/null +++ b/fairseq/examples/hubert/config/decode/infer_viterbi.yaml @@ -0,0 +1,29 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/viterbi + sweep: + dir: ${common_eval.results_path} + subdir: viterbi + +task: + _name: hubert_pretraining + single_target: true + fine_tuning: true + data: ??? + normalize: ??? + +decoding: + type: viterbi + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/fairseq/examples/hubert/config/decode/run/submitit_slurm.yaml b/fairseq/examples/hubert/config/decode/run/submitit_slurm.yaml new file mode 100644 index 0000000..0b80658 --- /dev/null +++ b/fairseq/examples/hubert/config/decode/run/submitit_slurm.yaml @@ -0,0 +1,17 @@ +# @package _global_ +hydra: + launcher: + cpus_per_task: ${distributed_training.distributed_world_size} + gpus_per_node: ${distributed_training.distributed_world_size} + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + mem_gb: 200 + timeout_min: 4320 + max_num_timeout: 50 + name: ${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/submitit + +distributed_training: + distributed_world_size: 1 + distributed_no_spawn: true + distributed_port: 29761 diff --git a/fairseq/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml b/fairseq/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml new file mode 100644 index 0000000..2f669f3 --- /dev/null +++ b/fairseq/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml @@ -0,0 +1,17 @@ +# @package _global_ +hydra: + launcher: + cpus_per_task: ${distributed_training.distributed_world_size} + gpus_per_node: ${distributed_training.distributed_world_size} + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + mem_gb: 200 + timeout_min: 4320 + max_num_timeout: 50 + name: ${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/submitit + +distributed_training: + distributed_world_size: 8 + distributed_no_spawn: true + distributed_port: 29761 diff --git a/fairseq/examples/hubert/config/finetune/base_10h.yaml b/fairseq/examples/hubert/config/finetune/base_10h.yaml new file mode 100644 index 0000000..a22c7c0 --- /dev/null +++ b/fairseq/examples/hubert/config/finetune/base_10h.yaml @@ -0,0 +1,100 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + save_interval: 5 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 1 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + normalize: false # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 3200000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 5 + train_subset: train + valid_subset: valid + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 25000 + lr: [2e-5] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: hubert_ctc + w2v_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.w2v_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/hubert/config/finetune/ckpt/it1.yaml b/fairseq/examples/hubert/config/finetune/ckpt/it1.yaml new file mode 100644 index 0000000..2af96b3 --- /dev/null +++ b/fairseq/examples/hubert/config/finetune/ckpt/it1.yaml @@ -0,0 +1,7 @@ +# @package _global_ + +task: + normalize: false + +model: + w2v_path: /checkpoint/wnhsu/w2v/hubert_final/iter1/hubert.km.randcrop.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU400k.s1337.ngpu32/checkpoint_last.pt diff --git a/fairseq/examples/hubert/config/finetune/lm/ls_4gram.yaml b/fairseq/examples/hubert/config/finetune/lm/ls_4gram.yaml new file mode 100644 index 0000000..8c7728a --- /dev/null +++ b/fairseq/examples/hubert/config/finetune/lm/ls_4gram.yaml @@ -0,0 +1,7 @@ +# @package _global_ + +criterion: + wer_kenlm_model: /checkpoint/abdo/old_checkpoint02/datasets/librispeech/4-gram.bin + wer_lexicon: /checkpoint/abdo/old_checkpoint02/datasets/librispeech/10h/raw/lexicon_ltr.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 diff --git a/fairseq/examples/hubert/config/finetune/run/submitit_reg.yaml b/fairseq/examples/hubert/config/finetune/run/submitit_reg.yaml new file mode 100644 index 0000000..2750950 --- /dev/null +++ b/fairseq/examples/hubert/config/finetune/run/submitit_reg.yaml @@ -0,0 +1,20 @@ +# @package _global_ + +hydra: + launcher: + cpus_per_task: 8 + gpus_per_node: 8 + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + comment: null + mem_gb: 384 + timeout_min: 4320 + max_num_timeout: 100 + constraint: volta32gb + name: ${hydra.job.config_name}/${hydra.job.override_dirname} + submitit_folder: ${hydra.sweep.dir}/submitit/%j + +distributed_training: + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 diff --git a/fairseq/examples/hubert/config/pretrain/hubert_base_librispeech.yaml b/fairseq/examples/hubert/config/pretrain/hubert_base_librispeech.yaml new file mode 100644 index 0000000..bd84461 --- /dev/null +++ b/fairseq/examples/hubert/config/pretrain/hubert_base_librispeech.yaml @@ -0,0 +1,97 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 32 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: false # must be consistent with extractor + +dataset: + num_workers: 6 + max_tokens: 1400000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.0005] + clip_norm: 10.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: hubert + label_rate: ??? + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: default + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + final_dim: 256 + encoder_layerdrop: 0.05 + dropout_input: 0.1 + dropout_features: 0.1 + dropout: 0.1 + attention_dropout: 0.1 + feature_grad_mult: 0.1 + untie_final_proj: true + activation_dropout: 0.0 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/hubert/config/pretrain/hubert_large_librivox.yaml b/fairseq/examples/hubert/config/pretrain/hubert_large_librivox.yaml new file mode 100644 index 0000000..a5192b5 --- /dev/null +++ b/fairseq/examples/hubert/config/pretrain/hubert_large_librivox.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 128 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: true # must be consistent with extractor + +dataset: + num_workers: 6 + max_tokens: 900000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.0015] + clip_norm: 1.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: hubert + label_rate: ??? + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + final_dim: 768 + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: layer_norm + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + encoder_layerdrop: 0.0 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + layer_norm_first: true + feature_grad_mult: 1.0 + untie_final_proj: true + activation_dropout: 0.0 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + run: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + sweep: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml b/fairseq/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml new file mode 100644 index 0000000..34e8f2b --- /dev/null +++ b/fairseq/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 256 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: true # must be consistent with extractor + +dataset: + num_workers: 6 + max_tokens: 360000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.003] + clip_norm: 1.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: hubert + label_rate: ??? + encoder_layers: 48 + encoder_embed_dim: 1280 + encoder_ffn_embed_dim: 5120 + encoder_attention_heads: 16 + final_dim: 1024 + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: layer_norm + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + encoder_layerdrop: 0.0 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + layer_norm_first: true + feature_grad_mult: 1.0 + untie_final_proj: true + activation_dropout: 0.0 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + run: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + sweep: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/hubert/config/pretrain/run/submitit_reg.yaml b/fairseq/examples/hubert/config/pretrain/run/submitit_reg.yaml new file mode 100644 index 0000000..46c979c --- /dev/null +++ b/fairseq/examples/hubert/config/pretrain/run/submitit_reg.yaml @@ -0,0 +1,20 @@ +# @package _global_ + +hydra: + launcher: + cpus_per_task: 8 + gpus_per_node: 8 + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 4 + comment: null + mem_gb: 384 + timeout_min: 4320 + max_num_timeout: 100 + constraint: volta32gb + name: ${hydra.job.config_name}/${hydra.job.override_dirname} + submitit_folder: ${hydra.sweep.dir}/submitit/%j + +distributed_training: + distributed_world_size: 32 + distributed_port: 29671 + nprocs_per_node: 8 diff --git a/fairseq/examples/hubert/measure_teacher_quality.py b/fairseq/examples/hubert/measure_teacher_quality.py new file mode 100644 index 0000000..92279b2 --- /dev/null +++ b/fairseq/examples/hubert/measure_teacher_quality.py @@ -0,0 +1,241 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import os.path as op +import re +from tabulate import tabulate +from collections import Counter + + +def comp_purity(p_xy, axis): + max_p = p_xy.max(axis=axis) + marg_p = p_xy.sum(axis=axis) + indv_pur = max_p / marg_p + aggr_pur = max_p.sum() + return indv_pur, aggr_pur + + +def comp_entropy(p): + return (-p * np.log(p + 1e-8)).sum() + + +def comp_norm_mutual_info(p_xy): + p_x = p_xy.sum(axis=1, keepdims=True) + p_y = p_xy.sum(axis=0, keepdims=True) + pmi = np.log(p_xy / np.matmul(p_x, p_y) + 1e-8) + mi = (p_xy * pmi).sum() + h_x = comp_entropy(p_x) + h_y = comp_entropy(p_y) + return mi, mi / h_x, mi / h_y, h_x, h_y + + +def pad(labs, n): + if n == 0: + return np.array(labs) + return np.concatenate([[labs[0]] * n, labs, [labs[-1]] * n]) + + +def comp_avg_seg_dur(labs_list): + n_frms = 0 + n_segs = 0 + for labs in labs_list: + labs = np.array(labs) + edges = np.zeros(len(labs)).astype(bool) + edges[0] = True + edges[1:] = labs[1:] != labs[:-1] + n_frms += len(edges) + n_segs += edges.astype(int).sum() + return n_frms / n_segs + + +def comp_joint_prob(uid2refs, uid2hyps): + """ + Args: + pad: padding for spliced-feature derived labels + """ + cnts = Counter() + skipped = [] + abs_frmdiff = 0 + for uid in uid2refs: + if uid not in uid2hyps: + skipped.append(uid) + continue + refs = uid2refs[uid] + hyps = uid2hyps[uid] + abs_frmdiff += abs(len(refs) - len(hyps)) + min_len = min(len(refs), len(hyps)) + refs = refs[:min_len] + hyps = hyps[:min_len] + cnts.update(zip(refs, hyps)) + tot = sum(cnts.values()) + + ref_set = sorted({ref for ref, _ in cnts.keys()}) + hyp_set = sorted({hyp for _, hyp in cnts.keys()}) + ref2pid = dict(zip(ref_set, range(len(ref_set)))) + hyp2lid = dict(zip(hyp_set, range(len(hyp_set)))) + # print(hyp_set) + p_xy = np.zeros((len(ref2pid), len(hyp2lid)), dtype=float) + for (ref, hyp), cnt in cnts.items(): + p_xy[ref2pid[ref], hyp2lid[hyp]] = cnt + p_xy /= p_xy.sum() + return p_xy, ref2pid, hyp2lid, tot, abs_frmdiff, skipped + + +def read_phn(tsv_path, rm_stress=True): + uid2phns = {} + with open(tsv_path) as f: + for line in f: + uid, phns = line.rstrip().split("\t") + phns = phns.split(",") + if rm_stress: + phns = [re.sub("[0-9]", "", phn) for phn in phns] + uid2phns[uid] = phns + return uid2phns + + +def read_lab(tsv_path, lab_path, pad_len=0, upsample=1): + """ + tsv is needed to retrieve the uids for the labels + """ + with open(tsv_path) as f: + f.readline() + uids = [op.splitext(op.basename(line.rstrip().split()[0]))[0] for line in f] + with open(lab_path) as f: + labs_list = [pad(line.rstrip().split(), pad_len).repeat(upsample) for line in f] + assert len(uids) == len(labs_list) + return dict(zip(uids, labs_list)) + + +def main_lab_lab( + tsv_dir, + lab_dir, + lab_name, + lab_sets, + ref_dir, + ref_name, + pad_len=0, + upsample=1, + verbose=False, +): + # assume tsv_dir is the same for both the reference and the hypotheses + tsv_dir = lab_dir if tsv_dir is None else tsv_dir + + uid2refs = {} + for s in lab_sets: + uid2refs.update(read_lab(f"{tsv_dir}/{s}.tsv", f"{ref_dir}/{s}.{ref_name}")) + + uid2hyps = {} + for s in lab_sets: + uid2hyps.update( + read_lab( + f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample + ) + ) + _main(uid2refs, uid2hyps, verbose) + + +def main_phn_lab( + tsv_dir, + lab_dir, + lab_name, + lab_sets, + phn_dir, + phn_sets, + pad_len=0, + upsample=1, + verbose=False, +): + uid2refs = {} + for s in phn_sets: + uid2refs.update(read_phn(f"{phn_dir}/{s}.tsv")) + + uid2hyps = {} + tsv_dir = lab_dir if tsv_dir is None else tsv_dir + for s in lab_sets: + uid2hyps.update( + read_lab( + f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample + ) + ) + _main(uid2refs, uid2hyps, verbose) + + +def _main(uid2refs, uid2hyps, verbose): + (p_xy, ref2pid, hyp2lid, tot, frmdiff, skipped) = comp_joint_prob( + uid2refs, uid2hyps + ) + ref_pur_by_hyp, ref_pur = comp_purity(p_xy, axis=0) + hyp_pur_by_ref, hyp_pur = comp_purity(p_xy, axis=1) + (mi, mi_norm_by_ref, mi_norm_by_hyp, h_ref, h_hyp) = comp_norm_mutual_info(p_xy) + outputs = { + "ref pur": ref_pur, + "hyp pur": hyp_pur, + "H(ref)": h_ref, + "H(hyp)": h_hyp, + "MI": mi, + "MI/H(ref)": mi_norm_by_ref, + "ref segL": comp_avg_seg_dur(uid2refs.values()), + "hyp segL": comp_avg_seg_dur(uid2hyps.values()), + "p_xy shape": p_xy.shape, + "frm tot": tot, + "frm diff": frmdiff, + "utt tot": len(uid2refs), + "utt miss": len(skipped), + } + print(tabulate([outputs.values()], outputs.keys(), floatfmt=".4f")) + + +if __name__ == "__main__": + """ + compute quality of labels with respect to phone or another labels if set + """ + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("lab_dir") + parser.add_argument("lab_name") + parser.add_argument("--lab_sets", default=["valid"], type=str, nargs="+") + parser.add_argument( + "--phn_dir", + default="/checkpoint/wnhsu/data/librispeech/960h/fa/raw_phn/phone_frame_align_v1", + ) + parser.add_argument( + "--phn_sets", default=["dev-clean", "dev-other"], type=str, nargs="+" + ) + parser.add_argument("--pad_len", default=0, type=int, help="padding for hypotheses") + parser.add_argument( + "--upsample", default=1, type=int, help="upsample factor for hypotheses" + ) + parser.add_argument("--ref_lab_dir", default="") + parser.add_argument("--ref_lab_name", default="") + parser.add_argument("--verbose", action="store_true") + args = parser.parse_args() + + if args.ref_lab_dir and args.ref_lab_name: + main_lab_lab( + args.tsv_dir, + args.lab_dir, + args.lab_name, + args.lab_sets, + args.ref_lab_dir, + args.ref_lab_name, + args.pad_len, + args.upsample, + args.verbose, + ) + else: + main_phn_lab( + args.tsv_dir, + args.lab_dir, + args.lab_name, + args.lab_sets, + args.phn_dir, + args.phn_sets, + args.pad_len, + args.upsample, + args.verbose, + ) diff --git a/fairseq/examples/hubert/simple_kmeans/README.md b/fairseq/examples/hubert/simple_kmeans/README.md new file mode 100644 index 0000000..847475c --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/README.md @@ -0,0 +1,80 @@ +# Sharded Feature Extraction and K-means Application + +This folder contains scripts for preparing HUBERT labels from tsv files, the +steps are: +1. feature extraction +2. k-means clustering +3. k-means application + + +## Data preparation + +`*.tsv` files contains a list of audio, where each line is the root, and +following lines are the subpath for each audio: +``` + + + +... +``` + + +## Feature extraction + +### MFCC feature +Suppose the tsv file is at `${tsv_dir}/${split}.tsv`. To extract 39-D +mfcc+delta+ddelta features for the 1st iteration HUBERT training, run: +```sh +python dump_mfcc_feature.py ${tsv_dir} ${split} ${nshard} ${rank} ${feat_dir} +``` +This would shard the tsv file into `${nshard}` and extract features for the +`${rank}`-th shard, where rank is an integer in `[0, nshard-1]`. Features would +be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`. + + +### HUBERT feature +To extract features from the `${layer}`-th transformer layer of a trained +HUBERT model saved at `${ckpt_path}`, run: +```sh +python dump_hubert_feature.py ${tsv_dir} ${split} ${ckpt_path} ${layer} ${nshard} ${rank} ${feat_dir} +``` +Features would also be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`. + +- if out-of-memory, decrease the chunk size with `--max_chunk` + + +## K-means clustering +To fit a k-means model with `${n_clusters}` clusters on 10% of the `${split}` data, run +```sh +python learn_kmeans.py ${feat_dir} ${split} ${nshard} ${km_path} ${n_cluster} --percent 0.1 +``` +This saves the k-means model to `${km_path}`. + +- set `--precent -1` to use all data +- more kmeans options can be found with `-h` flag + + +## K-means application +To apply a trained k-means model `${km_path}` to obtain labels for `${split}`, run +```sh +python dump_km_label.py ${feat_dir} ${split} ${km_path} ${nshard} ${rank} ${lab_dir} +``` +This would extract labels for the `${rank}`-th shard out of `${nshard}` shards +and dump them to `${lab_dir}/${split}_${rank}_${shard}.km` + + +Finally, merge shards for `${split}` by running +```sh +for rank in $(seq 0 $((nshard - 1))); do + cat $lab_dir/${split}_${rank}_${nshard}.km +done > $lab_dir/${split}.km +``` + + +## Create a dummy dict +To create a dummy dictionary, run +```sh +for x in $(seq 0 $((n_clusters - 1))); do + echo "$x 1" +done >> $lab_dir/dict.km.txt +``` diff --git a/fairseq/examples/hubert/simple_kmeans/dump_hubert_feature.py b/fairseq/examples/hubert/simple_kmeans/dump_hubert_feature.py new file mode 100644 index 0000000..7ea4ea0 --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/dump_hubert_feature.py @@ -0,0 +1,93 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import fairseq +import soundfile as sf +import torch +import torch.nn.functional as F + +from feature_utils import get_path_iterator, dump_feature +from fairseq.data.audio.audio_utils import get_features_or_waveform + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_hubert_feature") + + +class HubertFeatureReader(object): + def __init__(self, ckpt_path, layer, max_chunk=1600000): + ( + model, + cfg, + task, + ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) + self.model = model[0].eval().cuda() + self.task = task + self.layer = layer + self.max_chunk = max_chunk + logger.info(f"TASK CONFIG:\n{self.task.cfg}") + logger.info(f" max_chunk = {self.max_chunk}") + + def read_audio(self, path, ref_len=None): + wav = get_features_or_waveform(path, need_waveform=True, use_sample_rate=self.task.cfg.sample_rate) + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, path, ref_len=None): + x = self.read_audio(path, ref_len=ref_len) + with torch.no_grad(): + x = torch.from_numpy(x).float().cuda() + if self.task.cfg.normalize: + x = F.layer_norm(x, x.shape) + x = x.view(1, -1) + + feat = [] + for start in range(0, x.size(1), self.max_chunk): + x_chunk = x[:, start : start + self.max_chunk] + feat_chunk, _ = self.model.extract_features( + source=x_chunk, + padding_mask=None, + mask=False, + output_layer=self.layer, + ) + feat.append(feat_chunk) + return torch.cat(feat, 1).squeeze(0) + + +def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk): + reader = HubertFeatureReader(ckpt_path, layer, max_chunk) + generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("split") + parser.add_argument("ckpt_path") + parser.add_argument("layer", type=int) + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("--max_chunk", type=int, default=1600000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/fairseq/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py b/fairseq/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py new file mode 100644 index 0000000..941bc1b --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py @@ -0,0 +1,95 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import csv +import io +import logging +import os +import os.path as op +import sys + +from dump_hubert_feature import HubertFeatureReader +from feature_utils import get_shard_range, dump_feature +from fairseq.data.audio.audio_utils import get_features_or_waveform + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_hubert_feature_s2t") + + +class HubertFeatureReaderS2T(HubertFeatureReader): + def read_audio(self, path, ref_len=None): + wav = get_features_or_waveform( + path, need_waveform=True, use_sample_rate=self.task.cfg.sample_rate + ) + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + +def get_path_iterator(root, tsv, nshard, rank, audio_col_name): + with open(tsv) as f: + reader = csv.DictReader( + f, + delimiter="\t", + quotechar=None, + doublequote=False, + lineterminator="\n", + quoting=csv.QUOTE_NONE, + ) + subpaths = [op.join(root, e[audio_col_name]) for e in reader] + start, end = get_shard_range(len(subpaths), nshard, rank) + subpaths = subpaths[start:end] + + def iterate(): + for subpath in subpaths: + yield op.join(root, subpath), None + + return iterate, len(subpaths) + + +def main( + root, + tsv_path, + ckpt_path, + layer, + nshard, + rank, + feat_dir, + split, + max_chunk, + audio_col_name, +): + reader = HubertFeatureReaderS2T(ckpt_path, layer, max_chunk) + generator, num = get_path_iterator(root, tsv_path, nshard, rank, audio_col_name) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("root") + parser.add_argument("tsv_path") + parser.add_argument("ckpt_path") + parser.add_argument("layer", type=int) + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("split") + parser.add_argument("--audio_col_name", type=str, default="audio") + parser.add_argument("--max_chunk", type=int, default=1600000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/fairseq/examples/hubert/simple_kmeans/dump_km_label.py b/fairseq/examples/hubert/simple_kmeans/dump_km_label.py new file mode 100644 index 0000000..8871307 --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/dump_km_label.py @@ -0,0 +1,98 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import numpy as np + +import joblib +import torch +import tqdm + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_km_label") + + +class ApplyKmeans(object): + def __init__(self, km_path): + self.km_model = joblib.load(km_path) + self.C_np = self.km_model.cluster_centers_.transpose() + self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True) + + self.C = torch.from_numpy(self.C_np) + self.Cnorm = torch.from_numpy(self.Cnorm_np) + if torch.cuda.is_available(): + self.C = self.C.cuda() + self.Cnorm = self.Cnorm.cuda() + + def __call__(self, x): + if isinstance(x, torch.Tensor): + dist = ( + x.pow(2).sum(1, keepdim=True) + - 2 * torch.matmul(x, self.C) + + self.Cnorm + ) + return dist.argmin(dim=1).cpu().numpy() + else: + dist = ( + (x ** 2).sum(1, keepdims=True) + - 2 * np.matmul(x, self.C_np) + + self.Cnorm_np + ) + return np.argmin(dist, axis=1) + + +def get_feat_iterator(feat_dir, split, nshard, rank): + feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" + leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" + with open(leng_path, "r") as f: + lengs = [int(line.rstrip()) for line in f] + offsets = [0] + np.cumsum(lengs[:-1]).tolist() + + def iterate(): + feat = np.load(feat_path, mmap_mode="r") + assert feat.shape[0] == (offsets[-1] + lengs[-1]) + for offset, leng in zip(offsets, lengs): + yield feat[offset: offset + leng] + + return iterate, len(lengs) + + +def dump_label(feat_dir, split, km_path, nshard, rank, lab_dir): + apply_kmeans = ApplyKmeans(km_path) + generator, num = get_feat_iterator(feat_dir, split, nshard, rank) + iterator = generator() + + lab_path = f"{lab_dir}/{split}_{rank}_{nshard}.km" + os.makedirs(lab_dir, exist_ok=True) + with open(lab_path, "w") as f: + for feat in tqdm.tqdm(iterator, total=num): + # feat = torch.from_numpy(feat).cuda() + lab = apply_kmeans(feat).tolist() + f.write(" ".join(map(str, lab)) + "\n") + logger.info("finished successfully") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("feat_dir") + parser.add_argument("split") + parser.add_argument("km_path") + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("lab_dir") + args = parser.parse_args() + logging.info(str(args)) + + dump_label(**vars(args)) diff --git a/fairseq/examples/hubert/simple_kmeans/dump_mfcc_feature.py b/fairseq/examples/hubert/simple_kmeans/dump_mfcc_feature.py new file mode 100644 index 0000000..c353778 --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/dump_mfcc_feature.py @@ -0,0 +1,74 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import soundfile as sf +import torch +import torchaudio + +from feature_utils import get_path_iterator, dump_feature +from fairseq.data.audio.audio_utils import get_features_or_waveform + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_mfcc_feature") + + +class MfccFeatureReader(object): + def __init__(self, sample_rate): + self.sample_rate = sample_rate + + def read_audio(self, path, ref_len=None): + wav = get_features_or_waveform(path, need_waveform=True, use_sample_rate=self.sample_rate) + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, path, ref_len=None): + x = self.read_audio(path, ref_len=ref_len) + with torch.no_grad(): + x = torch.from_numpy(x).float() + x = x.view(1, -1) + + mfccs = torchaudio.compliance.kaldi.mfcc( + waveform=x, + sample_frequency=self.sample_rate, + use_energy=False, + ) # (time, freq) + mfccs = mfccs.transpose(0, 1) # (freq, time) + deltas = torchaudio.functional.compute_deltas(mfccs) + ddeltas = torchaudio.functional.compute_deltas(deltas) + concat = torch.cat([mfccs, deltas, ddeltas], dim=0) + concat = concat.transpose(0, 1).contiguous() # (freq, time) + return concat + + +def main(tsv_dir, split, nshard, rank, feat_dir, sample_rate): + reader = MfccFeatureReader(sample_rate) + generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("split") + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("--sample_rate", type=int, default=16000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py b/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py new file mode 100644 index 0000000..a1f0d90 --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py @@ -0,0 +1,95 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import fairseq +import soundfile as sf +import torch +import torch.nn.functional as F + +from feature_utils import get_path_iterator, dump_feature + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_w2v2_feature") + + +class Wav2Vec2FeatureReader(object): + def __init__(self, ckpt_path, layer, max_chunk=1600000): + ( + model, + cfg, + task, + ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) + self.model = model[0].eval().cuda() + self.task = task + self.layer = layer # assume this is 1-based like HuBERT + self.max_chunk = max_chunk + logger.info(f"TASK CONFIG:\n{self.task.cfg}") + logger.info(f" max_chunk = {self.max_chunk}") + logger.info(f" model:\n{self.model}") + + def read_audio(self, path, ref_len=None): + wav, sr = sf.read(path) + assert sr == self.task.cfg.sample_rate, sr + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, path, ref_len=None): + x = self.read_audio(path, ref_len) + with torch.no_grad(): + x = torch.from_numpy(x).float().cuda() + if self.task.cfg.normalize: + x = F.layer_norm(x, x.shape) + x = x.view(1, -1) + + feat = [] + for start in range(0, x.size(1), self.max_chunk): + x_chunk = x[:, start: start + self.max_chunk] + res = self.model.extract_features( + source=x_chunk, + padding_mask=None, + mask=False, + layer=self.layer - 1, + ) + feat_chunk = res["x"] + feat.append(feat_chunk) + return torch.cat(feat, 1).squeeze(0) + + +def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk): + reader = Wav2Vec2FeatureReader(ckpt_path, layer, max_chunk) + generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("split") + parser.add_argument("ckpt_path") + parser.add_argument("layer", type=int) + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("--max_chunk", type=int, default=1600000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/fairseq/examples/hubert/simple_kmeans/feature_utils.py b/fairseq/examples/hubert/simple_kmeans/feature_utils.py new file mode 100644 index 0000000..f80bc45 --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/feature_utils.py @@ -0,0 +1,66 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import tqdm +from npy_append_array import NpyAppendArray + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("feature_utils") + + +def get_shard_range(tot, nshard, rank): + assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}" + start = round(tot / nshard * rank) + end = round(tot / nshard * (rank + 1)) + assert start < end, f"start={start}, end={end}" + logger.info( + f"rank {rank} of {nshard}, process {end-start} " + f"({start}-{end}) out of {tot}" + ) + return start, end + + +def get_path_iterator(tsv, nshard, rank): + with open(tsv, "r") as f: + root = f.readline().rstrip() + lines = [line.rstrip() for line in f] + start, end = get_shard_range(len(lines), nshard, rank) + lines = lines[start:end] + def iterate(): + for line in lines: + subpath, nsample = line.split("\t") + yield f"{root}/{subpath}", int(nsample) + return iterate, len(lines) + + +def dump_feature(reader, generator, num, split, nshard, rank, feat_dir): + iterator = generator() + + feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" + leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" + + os.makedirs(feat_dir, exist_ok=True) + if os.path.exists(feat_path): + os.remove(feat_path) + + feat_f = NpyAppendArray(feat_path) + with open(leng_path, "w") as leng_f: + for path, nsample in tqdm.tqdm(iterator, total=num): + feat = reader.get_feats(path, nsample) + feat_f.append(feat.cpu().numpy()) + leng_f.write(f"{len(feat)}\n") + logger.info("finished successfully") + + diff --git a/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py b/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py new file mode 100644 index 0000000..113ac65 --- /dev/null +++ b/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py @@ -0,0 +1,146 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import numpy as np +from sklearn.cluster import MiniBatchKMeans + +import joblib + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("learn_kmeans") + + +def get_km_model( + n_clusters, + init, + max_iter, + batch_size, + tol, + max_no_improvement, + n_init, + reassignment_ratio, +): + return MiniBatchKMeans( + n_clusters=n_clusters, + init=init, + max_iter=max_iter, + batch_size=batch_size, + verbose=1, + compute_labels=False, + tol=tol, + max_no_improvement=max_no_improvement, + init_size=None, + n_init=n_init, + reassignment_ratio=reassignment_ratio, + ) + + +def load_feature_shard(feat_dir, split, nshard, rank, percent): + feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" + leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" + with open(leng_path, "r") as f: + lengs = [int(line.rstrip()) for line in f] + offsets = [0] + np.cumsum(lengs[:-1]).tolist() + + if percent < 0: + return np.load(feat_path, mmap_mode="r") + else: + nsample = int(np.ceil(len(lengs) * percent)) + indices = np.random.choice(len(lengs), nsample, replace=False) + feat = np.load(feat_path, mmap_mode="r") + sampled_feat = np.concatenate( + [feat[offsets[i]: offsets[i] + lengs[i]] for i in indices], axis=0 + ) + logger.info( + ( + f"sampled {nsample} utterances, {len(sampled_feat)} frames " + f"from shard {rank}/{nshard}" + ) + ) + return sampled_feat + + +def load_feature(feat_dir, split, nshard, seed, percent): + assert percent <= 1.0 + feat = np.concatenate( + [ + load_feature_shard(feat_dir, split, nshard, r, percent) + for r in range(nshard) + ], + axis=0, + ) + logging.info(f"loaded feature with dimension {feat.shape}") + return feat + + +def learn_kmeans( + feat_dir, + split, + nshard, + km_path, + n_clusters, + seed, + percent, + init, + max_iter, + batch_size, + tol, + n_init, + reassignment_ratio, + max_no_improvement, +): + np.random.seed(seed) + feat = load_feature(feat_dir, split, nshard, seed, percent) + km_model = get_km_model( + n_clusters, + init, + max_iter, + batch_size, + tol, + max_no_improvement, + n_init, + reassignment_ratio, + ) + km_model.fit(feat) + joblib.dump(km_model, km_path) + + inertia = -km_model.score(feat) / len(feat) + logger.info("total intertia: %.5f", inertia) + logger.info("finished successfully") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("feat_dir", type=str) + parser.add_argument("split", type=str) + parser.add_argument("nshard", type=int) + parser.add_argument("km_path", type=str) + parser.add_argument("n_clusters", type=int) + parser.add_argument("--seed", default=0, type=int) + parser.add_argument( + "--percent", default=-1, type=float, help="sample a subset; -1 for all" + ) + parser.add_argument("--init", default="k-means++") + parser.add_argument("--max_iter", default=100, type=int) + parser.add_argument("--batch_size", default=10000, type=int) + parser.add_argument("--tol", default=0.0, type=float) + parser.add_argument("--max_no_improvement", default=100, type=int) + parser.add_argument("--n_init", default=20, type=int) + parser.add_argument("--reassignment_ratio", default=0.0, type=float) + args = parser.parse_args() + logging.info(str(args)) + + learn_kmeans(**vars(args)) diff --git a/fairseq/examples/hubert/tests/6313-76958-0021.flac b/fairseq/examples/hubert/tests/6313-76958-0021.flac new file mode 100644 index 0000000..e644b19 Binary files /dev/null and b/fairseq/examples/hubert/tests/6313-76958-0021.flac differ diff --git a/fairseq/examples/hubert/tests/sample.base.L9.km500.km b/fairseq/examples/hubert/tests/sample.base.L9.km500.km new file mode 100644 index 0000000..656eef9 --- /dev/null +++ b/fairseq/examples/hubert/tests/sample.base.L9.km500.km @@ -0,0 +1 @@ +17 17 17 17 296 296 20 20 20 461 461 20 184 20 20 20 184 289 144 445 445 213 213 213 213 252 215 129 401 20 354 180 494 44 416 416 416 192 192 180 180 84 84 84 16 88 88 88 88 319 242 240 348 35 35 117 404 197 226 209 83 55 55 55 322 67 94 199 118 118 118 118 118 118 402 219 219 219 222 222 222 353 59 245 245 251 251 241 241 431 367 367 178 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b/fairseq/examples/hubert/tests/sample.base.L9.len new file mode 100644 index 0000000..7d3028f --- /dev/null +++ b/fairseq/examples/hubert/tests/sample.base.L9.len @@ -0,0 +1 @@ +596 diff --git a/fairseq/examples/hubert/tests/sample.base.L9.npy b/fairseq/examples/hubert/tests/sample.base.L9.npy new file mode 100644 index 0000000..574bef9 Binary files /dev/null and b/fairseq/examples/hubert/tests/sample.base.L9.npy differ diff --git a/fairseq/examples/hubert/tests/sample.large.L20.len b/fairseq/examples/hubert/tests/sample.large.L20.len new file mode 100644 index 0000000..7d3028f --- /dev/null +++ b/fairseq/examples/hubert/tests/sample.large.L20.len @@ -0,0 +1 @@ +596 diff --git a/fairseq/examples/hubert/tests/sample.large.L20.npy b/fairseq/examples/hubert/tests/sample.large.L20.npy new file mode 100644 index 0000000..c58d221 Binary files /dev/null and b/fairseq/examples/hubert/tests/sample.large.L20.npy differ diff --git a/fairseq/examples/hubert/tests/sample.large.hypo.word b/fairseq/examples/hubert/tests/sample.large.hypo.word new file mode 100644 index 0000000..d77a4cf --- /dev/null +++ b/fairseq/examples/hubert/tests/sample.large.hypo.word @@ -0,0 +1 @@ +KEEP A GOING AN IF YOU'RE LUCKY YOU'LL RUN PLUMB INTO THEM WAS THE JEERING ANSWER AS THE SLEEPY COWMEN SPURRED THEIR PONIES ON TOWARD CAMP MUTTERING THEIR DISAPPROVAL OF TAKING ALONG A BUNCH OF BOYS ON A CATTLE DRIVE (None-0) diff --git a/fairseq/examples/hubert/tests/sample.xlarge.L30.len b/fairseq/examples/hubert/tests/sample.xlarge.L30.len new file mode 100644 index 0000000..7d3028f --- /dev/null +++ b/fairseq/examples/hubert/tests/sample.xlarge.L30.len @@ -0,0 +1 @@ +596 diff --git a/fairseq/examples/hubert/tests/sample.xlarge.L30.npy b/fairseq/examples/hubert/tests/sample.xlarge.L30.npy new file mode 100644 index 0000000..29d8c0d Binary files /dev/null and b/fairseq/examples/hubert/tests/sample.xlarge.L30.npy differ diff --git a/fairseq/examples/hubert/tests/sample.xlarge.hypo.word b/fairseq/examples/hubert/tests/sample.xlarge.hypo.word new file mode 100644 index 0000000..53e402d --- /dev/null +++ b/fairseq/examples/hubert/tests/sample.xlarge.hypo.word @@ -0,0 +1 @@ +KEEP A GOIN AND IF YOU'RE LUCKY YOU'LL RUN PLUMB INTO THEM WAS THE JEERING ANSWER AS THE SLEEPY COWMEN SPURRED THEIR PONIES ON TOWARD CAMP MUTTERING THEIR DISAPPROVAL OF TAKING ALONG A BUNCH OF BOYS ON A CATTLE DRIVE (None-0) diff --git a/fairseq/examples/hubert/tests/test_feature_and_unit.sh b/fairseq/examples/hubert/tests/test_feature_and_unit.sh new file mode 100644 index 0000000..8cddb27 --- /dev/null +++ b/fairseq/examples/hubert/tests/test_feature_and_unit.sh @@ -0,0 +1,92 @@ +#!/bin/bash + +set -e + +sizes="base large xlarge" + +declare -A ckpt_urls +ckpt_urls[base]="https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt" +ckpt_urls[large]="https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt" +ckpt_urls[xlarge]="https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt" + +declare -A km_layers +km_layers[base]=9 +km_layers[large]=20 +km_layers[xlarge]=30 + +declare -A km_urls +km_urls[base]="https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960_L9_km500.bin" + +declare -A km_nunits +km_nunits[base]=500 + +test_dir=./examples/hubert/tests +split=sample + +echo -e "${test_dir}\n6313-76958-0021.flac\t190800" > "${test_dir}/${split}.tsv" + +check_feature () { + echo "checking features..." + + size=$1 + ckpt_url=$2 + km_layer=$3 + ckpt_path="$test_dir/$(basename "$ckpt_url")" + + if [ ! -f "$ckpt_path" ]; then + echo "downloading $ckpt_url to $ckpt_path" + wget "$ckpt_url" -O "$ckpt_path" + fi + + python ./examples/hubert/simple_kmeans/dump_hubert_feature.py \ + "${test_dir}" "${split}" "${ckpt_path}" "${km_layer}" 1 0 "${test_dir}" + + if diff -q "${test_dir}/${split}.${size}.L${km_layer}.npy" "${test_dir}/${split}_0_1.npy" &>/dev/null; then + echo "...passed npy check" + else + echo "...failed npy check" + fi + + if diff -q "${test_dir}/${split}.${size}.L${km_layer}.len" "${test_dir}/${split}_0_1.len" &>/dev/null; then + echo "...passed len check" + else + echo "...failed len check" + fi +} + + +check_unit () { + echo "checking units..." + + size=$1 + km_url=$2 + km_layer=$3 + km_nunit=$4 + km_path="$test_dir/$(basename "$km_url")" + + if [ ! -f "$km_path" ]; then + echo "downloading $km_url to $km_path" + wget "$km_url" -O "$km_path" + fi + + python ./examples/hubert/simple_kmeans/dump_km_label.py \ + "${test_dir}" "${split}" "${km_path}" 1 0 "${test_dir}" + + if diff -q "${test_dir}/${split}.${size}.L${km_layer}.km${km_nunit}.km" "${test_dir}/${split}_0_1.km" &>/dev/null; then + echo "...passed unit check" + else + echo "...failed unit check" + fi +} + + +for size in $sizes; do + echo "=== Running unit test for HuBERT $size ===" + check_feature "$size" "${ckpt_urls[$size]}" "${km_layers[$size]}" + + if [ -n "${km_urls[$size]}" ]; then + check_unit "$size" "${km_urls[$size]}" "${km_layers[$size]}" "${km_nunits[$size]}" + fi + + rm -f $test_dir/${split}_0_1.* +done diff --git a/fairseq/examples/hubert/tests/test_finetuned_asr.sh b/fairseq/examples/hubert/tests/test_finetuned_asr.sh new file mode 100644 index 0000000..3c0538b --- /dev/null +++ b/fairseq/examples/hubert/tests/test_finetuned_asr.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +set -e + +sizes="large xlarge" + +declare -A ckpt_urls +ckpt_urls[large]="https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt" +ckpt_urls[xlarge]="https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt" + +test_dir=$(pwd)/examples/hubert/tests +split=sample + +echo -e "${test_dir}\n6313-76958-0021.flac\t190800" > "${test_dir}/${split}.tsv" +echo -e "K E E P | A | G O I N G | A N D | I F | Y O U ' R E | L U C K Y | Y O U ' L L | R U N | P L U M B | I N T O | T H E M | W A S | T H E | J E E R I N G | A N S W E R | A S | T H E | S L E E P Y | C O W M E N | S P U R R E D | T H E I R | P O N I E S | O N | T O W A R D | C A M P | M U T T E R I N G | T H E I R | D I S A P P R O V A L | O F | T A K I N G | A L O N G | A | B U N C H | O F | B O Y S | O N | A | C A T T L E | D R I V E |" > "${test_dir}/${split}.ltr" + +check_asr () { + echo "checking asr outputs..." + + size=$1 + ckpt_url=$2 + ckpt_path="$test_dir/$(basename "$ckpt_url")" + + if [ ! -f "$ckpt_path" ]; then + echo "downloading $ckpt_url to $ckpt_path" + wget "$ckpt_url" -O "$ckpt_path" + fi + + python examples/speech_recognition/new/infer.py \ + --config-dir examples/hubert/config/decode --config-name infer_viterbi \ + common_eval.path="${ckpt_path}" task.data="${test_dir}" task.normalize=true \ + decoding.results_path="${test_dir}/pred" \ + common_eval.results_path="${test_dir}/pred" \ + common_eval.quiet=false dataset.gen_subset="${split}" + + if diff -q "${test_dir}/pred/hypo.word" "${test_dir}/${split}.${size}.hypo.word" &>/dev/null; then + echo "...passed word check" + else + echo "...failed word check" + fi + rm -rf "${test_dir}/pred" +} + +for size in $sizes; do + check_asr "$size" "${ckpt_urls[$size]}" +done diff --git a/fairseq/examples/hubert/update_ckpt.py b/fairseq/examples/hubert/update_ckpt.py new file mode 100644 index 0000000..53c9e74 --- /dev/null +++ b/fairseq/examples/hubert/update_ckpt.py @@ -0,0 +1,22 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +src_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2.pt" +ref_ckpt = "/checkpoint/wnhsu/w2v/hubert_icassp_oss_v3/iter2_km100-400k-grp-L6/oss.km500_p0_1_s334.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU100k.s1337.ngpu32/checkpoint_last.pt" +new_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2_updated.pt" + + +def update_state(state): + state["model"]["label_embs_concat"] = state["model"].pop("label_embs") + state["args"].task = "hubert_pretraining" + state["args"].labels = f"['{state['args'].labels}']" + return state + + +src_state = torch.load(src_ckpt) +src_state = update_state(src_state) +torch.save(src_state, new_ckpt) diff --git a/fairseq/examples/joint_alignment_translation/README.md b/fairseq/examples/joint_alignment_translation/README.md new file mode 100644 index 0000000..cd9c0ea --- /dev/null +++ b/fairseq/examples/joint_alignment_translation/README.md @@ -0,0 +1,89 @@ +# Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019) + +This page includes instructions for training models described in [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](https://arxiv.org/abs/1909.02074). + +## Training a joint alignment-translation model on WMT'18 En-De + +##### 1. Extract and preprocess the WMT'18 En-De data +```bash +./prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh +``` + +##### 2. Generate alignments from statistical alignment toolkits e.g. Giza++/FastAlign. +In this example, we use FastAlign. +```bash +git clone git@github.com:clab/fast_align.git +pushd fast_align +mkdir build +cd build +cmake .. +make +popd +ALIGN=fast_align/build/fast_align +paste bpe.32k/train.en bpe.32k/train.de | awk -F '\t' '{print $1 " ||| " $2}' > bpe.32k/train.en-de +$ALIGN -i bpe.32k/train.en-de -d -o -v > bpe.32k/train.align +``` + +##### 3. Preprocess the dataset with the above generated alignments. +```bash +fairseq-preprocess \ + --source-lang en --target-lang de \ + --trainpref bpe.32k/train \ + --validpref bpe.32k/valid \ + --testpref bpe.32k/test \ + --align-suffix align \ + --destdir binarized/ \ + --joined-dictionary \ + --workers 32 +``` + +##### 4. Train a model +```bash +fairseq-train \ + binarized \ + --arch transformer_wmt_en_de_big_align --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --activation-fn relu\ + --lr 0.0002 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 3500 --label-smoothing 0.1 \ + --save-dir ./checkpoints --log-interval 1000 --max-update 60000 \ + --keep-interval-updates -1 --save-interval-updates 0 \ + --load-alignments --criterion label_smoothed_cross_entropy_with_alignment \ + --fp16 +``` + +Note that the `--fp16` flag requires you have CUDA 9.1 or greater and a Volta GPU or newer. + +If you want to train the above model with big batches (assuming your machine has 8 GPUs): +- add `--update-freq 8` to simulate training on 8x8=64 GPUs +- increase the learning rate; 0.0007 works well for big batches + +##### 5. Evaluate and generate the alignments (BPE level) +```bash +fairseq-generate \ + binarized --gen-subset test --print-alignment \ + --source-lang en --target-lang de \ + --path checkpoints/checkpoint_best.pt --beam 5 --nbest 1 +``` + +##### 6. Other resources. +The code for: +1. preparing alignment test sets +2. converting BPE level alignments to token level alignments +3. symmetrizing bidirectional alignments +4. evaluating alignments using AER metric +can be found [here](https://github.com/lilt/alignment-scripts) + +## Citation + +```bibtex +@inproceedings{garg2019jointly, + title = {Jointly Learning to Align and Translate with Transformer Models}, + author = {Garg, Sarthak and Peitz, Stephan and Nallasamy, Udhyakumar and Paulik, Matthias}, + booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)}, + address = {Hong Kong}, + month = {November}, + url = {https://arxiv.org/abs/1909.02074}, + year = {2019}, +} +``` diff --git a/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh b/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh new file mode 100644 index 0000000..e3efeb2 --- /dev/null +++ b/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh @@ -0,0 +1,118 @@ +#!/bin/bash + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +echo 'Cloning Moses github repository (for tokenization scripts)...' +git clone https://github.com/moses-smt/mosesdecoder.git + +SCRIPTS=mosesdecoder/scripts +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +CLEAN=$SCRIPTS/training/clean-corpus-n.perl +REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl + +URLS=( + "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz" + "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz" + "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz" + "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz" + "http://data.statmt.org/wmt17/translation-task/dev.tgz" + "http://statmt.org/wmt14/test-full.tgz" +) +CORPORA=( + "training/europarl-v7.de-en" + "commoncrawl.de-en" + "training-parallel-nc-v13/news-commentary-v13.de-en" + "rapid2016.de-en" +) + +if [ ! -d "$SCRIPTS" ]; then + echo "Please set SCRIPTS variable correctly to point to Moses scripts." + exit +fi + +src=en +tgt=de +lang=en-de +prep=wmt18_en_de +tmp=$prep/tmp +orig=orig +dev=dev/newstest2012 +codes=32000 +bpe=bpe.32k + +mkdir -p $orig $tmp $prep $bpe + +cd $orig + +for ((i=0;i<${#URLS[@]};++i)); do + url=${URLS[i]} + file=$(basename $url) + if [ -f $file ]; then + echo "$file already exists, skipping download" + else + wget "$url" + if [ -f $file ]; then + echo "$url successfully downloaded." + else + echo "$url not successfully downloaded." + exit 1 + fi + if [ ${file: -4} == ".tgz" ]; then + tar zxvf $file + elif [ ${file: -4} == ".tar" ]; then + tar xvf $file + fi + fi +done +cd .. + +echo "pre-processing train data..." +for l in $src $tgt; do + rm -rf $tmp/train.tags.$lang.tok.$l + for f in "${CORPORA[@]}"; do + cat $orig/$f.$l | \ + perl $REM_NON_PRINT_CHAR | \ + perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/train.tags.$lang.tok.$l + done +done + +echo "pre-processing test data..." +for l in $src $tgt; do + if [ "$l" == "$src" ]; then + t="src" + else + t="ref" + fi + grep '\s*//g' | \ + sed -e 's/\s*<\/seg>\s*//g' | \ + sed -e "s/\’/\'/g" | \ + perl $TOKENIZER -threads 8 -l $l -no-escape > $tmp/test.$l + echo "" +done + +# apply length filtering before BPE +perl $CLEAN -ratio 1.5 $tmp/train.tags.$lang.tok $src $tgt $tmp/train 1 100 + +# use newstest2012 for valid +echo "pre-processing valid data..." +for l in $src $tgt; do + rm -rf $tmp/valid.$l + cat $orig/$dev.$l | \ + perl $REM_NON_PRINT_CHAR | \ + perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/valid.$l +done + +mkdir output +mv $tmp/{train,valid,test}.{$src,$tgt} output + +#BPE +git clone https://github.com/glample/fastBPE.git +pushd fastBPE +g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast +popd +fastBPE/fast learnbpe $codes output/train.$src output/train.$tgt > $bpe/codes +for split in {train,valid,test}; do for lang in {en,de}; do fastBPE/fast applybpe $bpe/$split.$lang output/$split.$lang $bpe/codes; done; done diff --git a/fairseq/examples/language_model/README.adaptive_inputs.md b/fairseq/examples/language_model/README.adaptive_inputs.md new file mode 100644 index 0000000..6650d58 --- /dev/null +++ b/fairseq/examples/language_model/README.adaptive_inputs.md @@ -0,0 +1,39 @@ +# Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018) + +## Pre-trained models + +Description | Parameters | Dataset | Model and Test set(s) +---|---:|---|--- +Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 1026M | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2) +Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 247M | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2) + +## Training an LM with adaptive inputs + +First, see the general [language modeling README](README.md) for instructions on +preprocessing the WikiText-103 data. + +Then use the following training command to train a model with adaptive inputs +using the `transformer_lm_wiki103` model architecture: +```bash +fairseq-train --task language_modeling \ + data-bin/wikitext-103 \ + --save-dir checkpoints/transformer_wikitext-103 \ + --arch transformer_lm_wiki103 \ + --max-update 286000 --lr 1.0 --t-mult 2 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 \ + --warmup-updates 16000 --warmup-init-lr 1e-07 --stop-min-lr 1e-09 --optimizer nag --min-lr 0.0001 --clip-norm 0.1 \ + --criterion adaptive_loss --max-tokens 3072 --update-freq 3 --tokens-per-sample 3072 --seed 1 \ + --sample-break-mode none --skip-invalid-size-inputs-valid-test --ddp-backend=legacy_ddp +``` + +## Citation + +```bibtex +@inproceedings{ + baevski2018adaptive, + title={Adaptive Input Representations for Neural Language Modeling}, + author={Alexei Baevski and Michael Auli}, + booktitle={International Conference on Learning Representations}, + year={2019}, + url={https://openreview.net/forum?id=ByxZX20qFQ}, +} +``` diff --git a/fairseq/examples/language_model/README.conv.md b/fairseq/examples/language_model/README.conv.md new file mode 100644 index 0000000..1ff8635 --- /dev/null +++ b/fairseq/examples/language_model/README.conv.md @@ -0,0 +1,40 @@ +# Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017) + +## Example usage + +First download and preprocess the data following the main [language modeling README](README.md). + +Then to train a convolutional LM using the `fconv_lm_dauphin_wikitext103` +architecture: +```bash +fairseq-train --task language_modeling \ + data-bin/wikitext-103 \ + --save-dir checkpoints/fconv_wikitext-103 \ + --arch fconv_lm_dauphin_wikitext103 \ + --adaptive-softmax-cutoff 10000,20000,200000 \ + --dropout 0.2 \ + --criterion adaptive_loss \ + --optimizer nag --clip-norm 0.1 --weight-decay 5e-06 \ + --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \ + --max-tokens 1024 --tokens-per-sample 1024 \ + --ddp-backend legacy_ddp \ + --max-epoch 35 +``` + +And evaluate with: +```bash +fairseq-eval-lm data-bin/wikitext-103 --path checkpoints/fconv_wiki103/checkpoint_best.pt +``` + +## Citation + +```bibtex +@inproceedings{dauphin2017language, + title={Language Modeling with Gated Convolutional Networks}, + author={Dauphin, Yann N and Fan, Angela and Auli, Michael and Grangier, David}, + booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70}, + pages={933--941}, + year={2017}, + organization={JMLR} +} +``` diff --git a/fairseq/examples/language_model/README.md b/fairseq/examples/language_model/README.md new file mode 100644 index 0000000..e78ea48 --- /dev/null +++ b/fairseq/examples/language_model/README.md @@ -0,0 +1,123 @@ +# Neural Language Modeling + +## Pre-trained models + +Model | Description | Dataset | Download +---|---|---|--- +`transformer_lm.gbw.adaptive_huge` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
1026M params | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2) +`transformer_lm.wiki103.adaptive` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
247M params | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2) +`transformer_lm.wmt19.en` | English LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz) +`transformer_lm.wmt19.de` | German LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz) +`transformer_lm.wmt19.ru` | Russian LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz) + +## Example usage + +We require a few additional Python dependencies for preprocessing: +```bash +pip install fastBPE sacremoses +``` + +To sample from a language model using PyTorch Hub: +```python +import torch + +# List available models +torch.hub.list('pytorch/fairseq') # [..., 'transformer_lm.wmt19.en', ...] + +# Load an English LM trained on WMT'19 News Crawl data +en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe') +en_lm.eval() # disable dropout + +# Move model to GPU +en_lm.cuda() + +# Sample from the language model +en_lm.sample('Barack Obama', beam=1, sampling=True, sampling_topk=10, temperature=0.8) +# "Barack Obama is coming to Sydney and New Zealand (...)" + +# Compute perplexity for a sequence +en_lm.score('Barack Obama is coming to Sydney and New Zealand')['positional_scores'].mean().neg().exp() +# tensor(15.1474) + +# The same interface can be used with custom models as well +from fairseq.models.transformer_lm import TransformerLanguageModel +custom_lm = TransformerLanguageModel.from_pretrained('/path/to/model/dir', 'checkpoint100.pt', tokenizer='moses', bpe='fastbpe') +custom_lm.sample('Barack Obama', beam=5) +# "Barack Obama (...)" +``` + +## Training a transformer language model with the CLI tools + +### 1) Preprocess the data + +First download and prepare the [WikiText-103 dataset](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/): +```bash +cd examples/language_model/ +bash prepare-wikitext-103.sh +cd ../.. +``` + +Next preprocess/binarize the data: +```bash +TEXT=examples/language_model/wikitext-103 +fairseq-preprocess \ + --only-source \ + --trainpref $TEXT/wiki.train.tokens \ + --validpref $TEXT/wiki.valid.tokens \ + --testpref $TEXT/wiki.test.tokens \ + --destdir data-bin/wikitext-103 \ + --workers 20 +``` + +### 2) Train a language model + +Next we'll train a basic transformer language model on wikitext-103. For more +advanced usage, see the [adaptive inputs README](README.adaptive_inputs.md). + +To train a basic LM (assumes 2 GPUs): +``` +$ fairseq-train --task language_modeling \ + data-bin/wikitext-103 \ + --save-dir checkpoints/transformer_wikitext-103 \ + --arch transformer_lm --share-decoder-input-output-embed \ + --dropout 0.1 \ + --optimizer adam --adam-betas '(0.9, 0.98)' --weight-decay 0.01 --clip-norm 0.0 \ + --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ + --tokens-per-sample 512 --sample-break-mode none \ + --max-tokens 2048 --update-freq 16 \ + --fp16 \ + --max-update 50000 +``` + +If you run out of memory, try reducing `--max-tokens` (max number of tokens per +batch) or `--tokens-per-sample` (max sequence length). You can also adjust +`--update-freq` to accumulate gradients and simulate training on a different +number of GPUs. + +### 3) Evaluate + +```bash +fairseq-eval-lm data-bin/wikitext-103 \ + --path checkpoints/transformer_wiki103/checkpoint_best.pt \ + --batch-size 2 \ + --tokens-per-sample 512 \ + --context-window 400 +# | Evaluated 245569 tokens in 56.1s (4379.02 tokens/s) +# | Loss: 3.4164, Perplexity: 30.46 +``` + +*Note:* The `--context-window` option controls how much context is provided to +each token when computing perplexity. When the window size is 0, the dataset is +chunked into segments of length 512 and perplexity is computed over each segment +normally. However, this results in worse (higher) perplexity since tokens that +appear earlier in each segment have less conditioning. When the maximum window +size is used (511 in this case), then we compute perplexity for each token +fully conditioned on 511 tokens of context. This slows down evaluation +significantly, since we must run a separate forward pass for every token in the +dataset, but results in better (lower) perplexity. + + +## Convolutional language models + +Please see the [convolutional LM README](README.conv.md) for instructions on +training convolutional language models. diff --git a/fairseq/examples/language_model/prepare-wikitext-103.sh b/fairseq/examples/language_model/prepare-wikitext-103.sh new file mode 100644 index 0000000..7513021 --- /dev/null +++ b/fairseq/examples/language_model/prepare-wikitext-103.sh @@ -0,0 +1,33 @@ +#!/bin/bash +# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh + +URLS=( + "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip" +) +FILES=( + "wikitext-103-v1.zip" +) + +for ((i=0;i<${#URLS[@]};++i)); do + file=${FILES[i]} + if [ -f $file ]; then + echo "$file already exists, skipping download" + else + url=${URLS[i]} + wget "$url" + if [ -f $file ]; then + echo "$url successfully downloaded." + else + echo "$url not successfully downloaded." + exit -1 + fi + if [ ${file: -4} == ".tgz" ]; then + tar zxvf $file + elif [ ${file: -4} == ".tar" ]; then + tar xvf $file + elif [ ${file: -4} == ".zip" ]; then + unzip $file + fi + fi +done +cd .. diff --git a/fairseq/examples/laser/README.md b/fairseq/examples/laser/README.md new file mode 100644 index 0000000..66acada --- /dev/null +++ b/fairseq/examples/laser/README.md @@ -0,0 +1,144 @@ +# LASER Language-Agnostic SEntence Representations + +LASER is a library to calculate and use multilingual sentence embeddings. + +You can find more information about LASER and how to use it on the official [LASER repository](https://github.com/facebookresearch/LASER). + +This folder contains source code for training LASER embeddings. + + +## Prepare data and configuration file + +Binarize your data with fairseq, as described [here](https://fairseq.readthedocs.io/en/latest/getting_started.html#data-pre-processing). + +Create a json config file with this format: +``` +{ + "src_vocab": "/path/to/spm.src.cvocab", + "tgt_vocab": "/path/to/spm.tgt.cvocab", + "train": [ + { + "type": "translation", + "id": 0, + "src": "/path/to/srclang1-tgtlang0/train.srclang1", + "tgt": "/path/to/srclang1-tgtlang0/train.tgtlang0" + }, + { + "type": "translation", + "id": 1, + "src": "/path/to/srclang1-tgtlang1/train.srclang1", + "tgt": "/path/to/srclang1-tgtlang1/train.tgtlang1" + }, + { + "type": "translation", + "id": 0, + "src": "/path/to/srclang2-tgtlang0/train.srclang2", + "tgt": "/path/to/srclang2-tgtlang0/train.tgtlang0" + }, + { + "type": "translation", + "id": 1, + "src": "/path/to/srclang2-tgtlang1/train.srclang2", + "tgt": "/path/to/srclang2-tgtlang1/train.tgtlang1" + }, + ... + ], + "valid": [ + { + "type": "translation", + "id": 0, + "src": "/unused", + "tgt": "/unused" + } + ] +} +``` +where paths are paths to binarized indexed fairseq dataset files. +`id` represents the target language id. + + +## Training Command Line Example + +``` +fairseq-train \ + /path/to/configfile_described_above.json \ + --user-dir examples/laser/laser_src \ + --log-interval 100 --log-format simple \ + --task laser --arch laser_lstm \ + --save-dir . \ + --optimizer adam \ + --lr 0.001 \ + --lr-scheduler inverse_sqrt \ + --clip-norm 5 \ + --warmup-updates 90000 \ + --update-freq 2 \ + --dropout 0.0 \ + --encoder-dropout-out 0.1 \ + --max-tokens 2000 \ + --max-epoch 50 \ + --encoder-bidirectional \ + --encoder-layers 5 \ + --encoder-hidden-size 512 \ + --decoder-layers 1 \ + --decoder-hidden-size 2048 \ + --encoder-embed-dim 320 \ + --decoder-embed-dim 320 \ + --decoder-lang-embed-dim 32 \ + --warmup-init-lr 0.001 \ + --disable-validation +``` + + +## Applications + +We showcase several applications of multilingual sentence embeddings +with code to reproduce our results (in the directory "tasks"). + +* [**Cross-lingual document classification**](https://github.com/facebookresearch/LASER/tree/master/tasks/mldoc) using the + [*MLDoc*](https://github.com/facebookresearch/MLDoc) corpus [2,6] +* [**WikiMatrix**](https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix) + Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia [7] +* [**Bitext mining**](https://github.com/facebookresearch/LASER/tree/master/tasks/bucc) using the + [*BUCC*](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) corpus [3,5] +* [**Cross-lingual NLI**](https://github.com/facebookresearch/LASER/tree/master/tasks/xnli) + using the [*XNLI*](https://www.nyu.edu/projects/bowman/xnli/) corpus [4,5,6] +* [**Multilingual similarity search**](https://github.com/facebookresearch/LASER/tree/master/tasks/similarity) [1,6] +* [**Sentence embedding of text files**](https://github.com/facebookresearch/LASER/tree/master/tasks/embed) + example how to calculate sentence embeddings for arbitrary text files in any of the supported language. + +**For all tasks, we use exactly the same multilingual encoder, without any task specific optimization or fine-tuning.** + + + +## References + +[1] Holger Schwenk and Matthijs Douze, + [*Learning Joint Multilingual Sentence Representations with Neural Machine Translation*](https://aclanthology.info/papers/W17-2619/w17-2619), + ACL workshop on Representation Learning for NLP, 2017 + +[2] Holger Schwenk and Xian Li, + [*A Corpus for Multilingual Document Classification in Eight Languages*](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf), + LREC, pages 3548-3551, 2018. + +[3] Holger Schwenk, + [*Filtering and Mining Parallel Data in a Joint Multilingual Space*](http://aclweb.org/anthology/P18-2037) + ACL, July 2018 + +[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, + [*XNLI: Cross-lingual Sentence Understanding through Inference*](https://aclweb.org/anthology/D18-1269), + EMNLP, 2018. + +[5] Mikel Artetxe and Holger Schwenk, + [*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*](https://arxiv.org/abs/1811.01136) + arXiv, Nov 3 2018. + +[6] Mikel Artetxe and Holger Schwenk, + [*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464) + arXiv, Dec 26 2018. + +[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, + [*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*](https://arxiv.org/abs/1907.05791) + arXiv, July 11 2019. + +[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin + [*CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB*](https://arxiv.org/abs/1911.04944) diff --git a/fairseq/examples/laser/laser_src/__init__.py b/fairseq/examples/laser/laser_src/__init__.py new file mode 100644 index 0000000..9ffbd65 --- /dev/null +++ b/fairseq/examples/laser/laser_src/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .laser_task import * # noqa +from .laser_lstm import * # noqa +from .laser_transformer import * # noqa diff --git a/fairseq/examples/laser/laser_src/laser_lstm.py b/fairseq/examples/laser/laser_src/laser_lstm.py new file mode 100644 index 0000000..10df90e --- /dev/null +++ b/fairseq/examples/laser/laser_src/laser_lstm.py @@ -0,0 +1,585 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import options, utils + +from fairseq.models import ( + FairseqEncoder, + FairseqIncrementalDecoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) + + +@register_model("laser_lstm") +class LSTMModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens=None, + tgt_tokens=None, + tgt_lengths=None, + target_language_id=None, + dataset_name="", + ): + assert target_language_id is not None + + src_encoder_out = self.encoder(src_tokens, src_lengths, dataset_name) + return self.decoder( + prev_output_tokens, src_encoder_out, lang_id=target_language_id + ) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--dropout", + default=0.1, + type=float, + metavar="D", + help="dropout probability", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-embed-path", + default=None, + type=str, + metavar="STR", + help="path to pre-trained encoder embedding", + ) + parser.add_argument( + "--encoder-hidden-size", type=int, metavar="N", help="encoder hidden size" + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="number of encoder layers" + ) + parser.add_argument( + "--encoder-bidirectional", + action="store_true", + help="make all layers of encoder bidirectional", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-embed-path", + default=None, + type=str, + metavar="STR", + help="path to pre-trained decoder embedding", + ) + parser.add_argument( + "--decoder-hidden-size", type=int, metavar="N", help="decoder hidden size" + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="number of decoder layers" + ) + parser.add_argument( + "--decoder-out-embed-dim", + type=int, + metavar="N", + help="decoder output embedding dimension", + ) + parser.add_argument( + "--decoder-zero-init", + type=str, + metavar="BOOL", + help="initialize the decoder hidden/cell state to zero", + ) + parser.add_argument( + "--decoder-lang-embed-dim", + type=int, + metavar="N", + help="decoder language embedding dimension", + ) + parser.add_argument( + "--fixed-embeddings", + action="store_true", + help="keep embeddings fixed (ENCODER ONLY)", + ) # TODO Also apply to decoder embeddings? + + # Granular dropout settings (if not specified these default to --dropout) + parser.add_argument( + "--encoder-dropout-in", + type=float, + metavar="D", + help="dropout probability for encoder input embedding", + ) + parser.add_argument( + "--encoder-dropout-out", + type=float, + metavar="D", + help="dropout probability for encoder output", + ) + parser.add_argument( + "--decoder-dropout-in", + type=float, + metavar="D", + help="dropout probability for decoder input embedding", + ) + parser.add_argument( + "--decoder-dropout-out", + type=float, + metavar="D", + help="dropout probability for decoder output", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted (in case there are any new ones) + base_architecture(args) + + def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + embed_dict = utils.parse_embedding(embed_path) + utils.print_embed_overlap(embed_dict, dictionary) + return utils.load_embedding(embed_dict, dictionary, embed_tokens) + + pretrained_encoder_embed = None + if args.encoder_embed_path: + pretrained_encoder_embed = load_pretrained_embedding_from_file( + args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim + ) + pretrained_decoder_embed = None + if args.decoder_embed_path: + pretrained_decoder_embed = load_pretrained_embedding_from_file( + args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim + ) + + num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 + + encoder = LSTMEncoder( + dictionary=task.source_dictionary, + embed_dim=args.encoder_embed_dim, + hidden_size=args.encoder_hidden_size, + num_layers=args.encoder_layers, + dropout_in=args.encoder_dropout_in, + dropout_out=args.encoder_dropout_out, + bidirectional=args.encoder_bidirectional, + pretrained_embed=pretrained_encoder_embed, + fixed_embeddings=args.fixed_embeddings, + ) + decoder = LSTMDecoder( + dictionary=task.target_dictionary, + embed_dim=args.decoder_embed_dim, + hidden_size=args.decoder_hidden_size, + out_embed_dim=args.decoder_out_embed_dim, + num_layers=args.decoder_layers, + dropout_in=args.decoder_dropout_in, + dropout_out=args.decoder_dropout_out, + zero_init=options.eval_bool(args.decoder_zero_init), + encoder_embed_dim=args.encoder_embed_dim, + encoder_output_units=encoder.output_units, + pretrained_embed=pretrained_decoder_embed, + num_langs=num_langs, + lang_embed_dim=args.decoder_lang_embed_dim, + ) + return cls(encoder, decoder) + + +class LSTMEncoder(FairseqEncoder): + """LSTM encoder.""" + + def __init__( + self, + dictionary, + embed_dim=512, + hidden_size=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + bidirectional=False, + left_pad=True, + pretrained_embed=None, + padding_value=0.0, + fixed_embeddings=False, + ): + super().__init__(dictionary) + self.num_layers = num_layers + self.dropout_in = dropout_in + self.dropout_out = dropout_out + self.bidirectional = bidirectional + self.hidden_size = hidden_size + + num_embeddings = len(dictionary) + self.padding_idx = dictionary.pad() + if pretrained_embed is None: + self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) + else: + self.embed_tokens = pretrained_embed + if fixed_embeddings: + self.embed_tokens.weight.requires_grad = False + + self.lstm = LSTM( + input_size=embed_dim, + hidden_size=hidden_size, + num_layers=num_layers, + dropout=self.dropout_out if num_layers > 1 else 0.0, + bidirectional=bidirectional, + ) + self.left_pad = left_pad + self.padding_value = padding_value + + self.output_units = hidden_size + if bidirectional: + self.output_units *= 2 + + def forward(self, src_tokens, src_lengths, dataset_name): + if self.left_pad: + # convert left-padding to right-padding + src_tokens = utils.convert_padding_direction( + src_tokens, + self.padding_idx, + left_to_right=True, + ) + + bsz, seqlen = src_tokens.size() + + # embed tokens + x = self.embed_tokens(src_tokens) + x = F.dropout(x, p=self.dropout_in, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # pack embedded source tokens into a PackedSequence + try: + packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist()) + except BaseException: + raise Exception(f"Packing failed in dataset {dataset_name}") + + # apply LSTM + if self.bidirectional: + state_size = 2 * self.num_layers, bsz, self.hidden_size + else: + state_size = self.num_layers, bsz, self.hidden_size + h0 = x.data.new(*state_size).zero_() + c0 = x.data.new(*state_size).zero_() + packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0)) + + # unpack outputs and apply dropout + x, _ = nn.utils.rnn.pad_packed_sequence( + packed_outs, padding_value=self.padding_value + ) + x = F.dropout(x, p=self.dropout_out, training=self.training) + assert list(x.size()) == [seqlen, bsz, self.output_units] + + if self.bidirectional: + + def combine_bidir(outs): + return torch.cat( + [ + torch.cat([outs[2 * i], outs[2 * i + 1]], dim=0).view( + 1, bsz, self.output_units + ) + for i in range(self.num_layers) + ], + dim=0, + ) + + final_hiddens = combine_bidir(final_hiddens) + final_cells = combine_bidir(final_cells) + + encoder_padding_mask = src_tokens.eq(self.padding_idx).t() + + # Set padded outputs to -inf so they are not selected by max-pooling + padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1) + if padding_mask.any(): + x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x) + + # Build the sentence embedding by max-pooling over the encoder outputs + sentemb = x.max(dim=0)[0] + + return { + "sentemb": sentemb, + "encoder_out": (x, final_hiddens, final_cells), + "encoder_padding_mask": encoder_padding_mask + if encoder_padding_mask.any() + else None, + } + + def reorder_encoder_out(self, encoder_out_dict, new_order): + encoder_out_dict["sentemb"] = encoder_out_dict["sentemb"].index_select( + 0, new_order + ) + encoder_out_dict["encoder_out"] = tuple( + eo.index_select(1, new_order) for eo in encoder_out_dict["encoder_out"] + ) + if encoder_out_dict["encoder_padding_mask"] is not None: + encoder_out_dict["encoder_padding_mask"] = encoder_out_dict[ + "encoder_padding_mask" + ].index_select(1, new_order) + return encoder_out_dict + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return int(1e5) # an arbitrary large number + + +class LSTMDecoder(FairseqIncrementalDecoder): + """LSTM decoder.""" + + def __init__( + self, + dictionary, + embed_dim=512, + hidden_size=512, + out_embed_dim=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + zero_init=False, + encoder_embed_dim=512, + encoder_output_units=512, + pretrained_embed=None, + num_langs=1, + lang_embed_dim=0, + ): + super().__init__(dictionary) + self.dropout_in = dropout_in + self.dropout_out = dropout_out + self.hidden_size = hidden_size + + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + if pretrained_embed is None: + self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + else: + self.embed_tokens = pretrained_embed + + self.layers = nn.ModuleList( + [ + LSTMCell( + input_size=encoder_output_units + embed_dim + lang_embed_dim + if layer == 0 + else hidden_size, + hidden_size=hidden_size, + ) + for layer in range(num_layers) + ] + ) + if hidden_size != out_embed_dim: + self.additional_fc = Linear(hidden_size, out_embed_dim) + self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out) + + if zero_init: + self.sentemb2init = None + else: + self.sentemb2init = Linear( + encoder_output_units, 2 * num_layers * hidden_size + ) + + if lang_embed_dim == 0: + self.embed_lang = None + else: + self.embed_lang = nn.Embedding(num_langs, lang_embed_dim) + nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1) + + def forward( + self, prev_output_tokens, encoder_out_dict, incremental_state=None, lang_id=0 + ): + sentemb = encoder_out_dict["sentemb"] + encoder_out = encoder_out_dict["encoder_out"] + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + bsz, seqlen = prev_output_tokens.size() + + # get outputs from encoder + encoder_outs, _, _ = encoder_out[:3] + srclen = encoder_outs.size(0) + + # embed tokens + x = self.embed_tokens(prev_output_tokens) + x = F.dropout(x, p=self.dropout_in, training=self.training) + + # embed language identifier + if self.embed_lang is not None: + lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id) + langemb = self.embed_lang(lang_ids) + # TODO Should we dropout here??? + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # initialize previous states (or get from cache during incremental generation) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is not None: + prev_hiddens, prev_cells, input_feed = cached_state + else: + num_layers = len(self.layers) + if self.sentemb2init is None: + prev_hiddens = [ + x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers) + ] + prev_cells = [ + x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers) + ] + else: + init = self.sentemb2init(sentemb) + prev_hiddens = [ + init[:, (2 * i) * self.hidden_size : (2 * i + 1) * self.hidden_size] + for i in range(num_layers) + ] + prev_cells = [ + init[ + :, + (2 * i + 1) * self.hidden_size : (2 * i + 2) * self.hidden_size, + ] + for i in range(num_layers) + ] + input_feed = x.data.new(bsz, self.hidden_size).zero_() + + attn_scores = x.data.new(srclen, seqlen, bsz).zero_() + outs = [] + for j in range(seqlen): + if self.embed_lang is None: + input = torch.cat((x[j, :, :], sentemb), dim=1) + else: + input = torch.cat((x[j, :, :], sentemb, langemb), dim=1) + + for i, rnn in enumerate(self.layers): + # recurrent cell + hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i])) + + # hidden state becomes the input to the next layer + input = F.dropout(hidden, p=self.dropout_out, training=self.training) + + # save state for next time step + prev_hiddens[i] = hidden + prev_cells[i] = cell + + out = hidden + out = F.dropout(out, p=self.dropout_out, training=self.training) + + # input feeding + input_feed = out + + # save final output + outs.append(out) + + # cache previous states (no-op except during incremental generation) + utils.set_incremental_state( + self, + incremental_state, + "cached_state", + (prev_hiddens, prev_cells, input_feed), + ) + + # collect outputs across time steps + x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) + + # T x B x C -> B x T x C + x = x.transpose(1, 0) + + # srclen x tgtlen x bsz -> bsz x tgtlen x srclen + attn_scores = attn_scores.transpose(0, 2) + + # project back to size of vocabulary + if hasattr(self, "additional_fc"): + x = self.additional_fc(x) + x = F.dropout(x, p=self.dropout_out, training=self.training) + x = self.fc_out(x) + + return x, attn_scores + + def reorder_incremental_state(self, incremental_state, new_order): + super().reorder_incremental_state(incremental_state, new_order) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is None: + return + + def reorder_state(state): + if isinstance(state, list): + return [reorder_state(state_i) for state_i in state] + return state.index_select(0, new_order) + + new_state = tuple(map(reorder_state, cached_state)) + utils.set_incremental_state(self, incremental_state, "cached_state", new_state) + + def max_positions(self): + """Maximum output length supported by the decoder.""" + return int(1e5) # an arbitrary large number + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.uniform_(m.weight, -0.1, 0.1) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def LSTM(input_size, hidden_size, **kwargs): + m = nn.LSTM(input_size, hidden_size, **kwargs) + for name, param in m.named_parameters(): + if "weight" in name or "bias" in name: + param.data.uniform_(-0.1, 0.1) + return m + + +def LSTMCell(input_size, hidden_size, **kwargs): + m = nn.LSTMCell(input_size, hidden_size, **kwargs) + for name, param in m.named_parameters(): + if "weight" in name or "bias" in name: + param.data.uniform_(-0.1, 0.1) + return m + + +def Linear(in_features, out_features, bias=True, dropout=0): + """Weight-normalized Linear layer (input: N x T x C)""" + m = nn.Linear(in_features, out_features, bias=bias) + m.weight.data.uniform_(-0.1, 0.1) + if bias: + m.bias.data.uniform_(-0.1, 0.1) + return m + + +@register_model_architecture("laser_lstm", "laser_lstm") +def base_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_hidden_size = getattr( + args, "encoder_hidden_size", args.encoder_embed_dim + ) + args.encoder_layers = getattr(args, "encoder_layers", 1) + args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False) + args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_hidden_size = getattr( + args, "decoder_hidden_size", args.decoder_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 1) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) + args.decoder_zero_init = getattr(args, "decoder_zero_init", "0") + args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0) + args.fixed_embeddings = getattr(args, "fixed_embeddings", False) diff --git a/fairseq/examples/laser/laser_src/laser_task.py b/fairseq/examples/laser/laser_src/laser_task.py new file mode 100644 index 0000000..9bf2d7a --- /dev/null +++ b/fairseq/examples/laser/laser_src/laser_task.py @@ -0,0 +1,334 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from collections import OrderedDict, defaultdict +import json +import os +import logging +from argparse import ArgumentError + +from fairseq import options, models +from fairseq.data import ( + data_utils, + Dictionary, + LanguagePairDataset, + IndexedDataset, + FairseqDataset, +) +from .multitask_data_utils import ( + MultitaskDatasetWrapper, + MultidatasetEpochBatchIterator, +) + + +from fairseq.tasks import LegacyFairseqTask, register_task + +logger = logging.getLogger(__name__) + + +@register_task("laser") +class LaserTask(LegacyFairseqTask): + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument( + "configfile", metavar="PATH", help="dataset configuration file in json" + ) + parser.add_argument( + "--weighting-alpha", + type=float, + default=None, + help="alpha for automatic weighting", + ) + parser.add_argument( + "--raw-text", action="store_true", help="load raw text dataset" + ) + parser.add_argument( + "--left-pad-source", + default="True", + type=str, + metavar="BOOL", + help="pad the source on the left (default: True)", + ) + parser.add_argument( + "--left-pad-target", + default="False", + type=str, + metavar="BOOL", + help="pad the target on the left (default: False)", + ) + try: + parser.add_argument( + "--max-source-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + except ArgumentError: + # this might have already been defined. Once we transition this to hydra it should be fine to add it here. + pass + + def __init__(self, args, config, src_dictionary, tgt_dictionary, num_tasks): + super().__init__(args) + self.config = config + self.src_dictionary = src_dictionary + self.tgt_dictionary = tgt_dictionary + self.num_tasks = num_tasks + + @classmethod + def setup_task(cls, args, **kwargs): + with open(args.configfile, "r") as f: + config = json.load(f) + num_tasks = max(dataset["id"] for dataset in config["train"]) + 1 + + args.left_pad_source = options.eval_bool(args.left_pad_source) + args.left_pad_target = options.eval_bool(args.left_pad_target) + + src_dictionary = Dictionary.load(config["src_vocab"]) + tgt_dictionary = Dictionary.load(config["tgt_vocab"]) + + logger.info( + "| src Dictionary {} : {} types".format( + config["src_vocab"], len(src_dictionary) + ) + ) + logger.info( + "| tgt Dictionary {} : {} types".format( + config["tgt_vocab"], len(tgt_dictionary) + ) + ) + + return cls(args, config, src_dictionary, tgt_dictionary, num_tasks) + + # Experimental overriding for backtranslation + def build_model(self, args, from_checkpoint=False): + model = models.build_model(args, self) + return model + + def dataset(self, split): + if split not in self.datasets: + raise KeyError("Dataset not loaded: " + split) + return self.datasets[split] + + def load_dataset(self, split, epoch=1, **kwargs): + """Load a dataset split.""" + + def indexed_dataset(path, dictionary): + if self.args.raw_text: + raise Exception("Unable to handle raw text.") + dataset = IndexedDataset(path, fix_lua_indexing=True) + + return dataset + + pair_datasets = OrderedDict() + + if split == "valid": + self.datasets[split] = pair_datasets + return + + if split not in self.config: + raise FileNotFoundError( + "Dataset not found in config file: {}".format(split) + ) + + size_by_corpus = defaultdict(int) + size_sum = 0 + size_sum_with_subsampling = 0 + init_pair_datasets = {} + + for dataset_config in self.config[split]: + src_path = os.path.dirname(dataset_config["src"]) + corpus_name = src_path.split("/")[-2] + language_pair_name = src_path.split("/")[-1] + pair_datasets_key = corpus_name + "-" + language_pair_name + + logger.info(f"loading... {pair_datasets_key}") + if "src" in dataset_config: + src_dataset = indexed_dataset( + dataset_config["src"], self.src_dictionary + ) + else: + src_dataset = None + + if "tgt" in dataset_config: + tgt_dataset = indexed_dataset( + dataset_config["tgt"], self.tgt_dictionary + ) + else: + tgt_dataset = None + + dataset = LanguagePairDataset( + src_dataset, + src_dataset.sizes, + self.src_dictionary, + tgt_dataset, + tgt_dataset.sizes, + self.tgt_dictionary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + ) + + if pair_datasets_key in init_pair_datasets: + logger.warning( + f"Ignoring already added {pair_datasets_key}. " + f"Consider using `sample` key in order to upsample." + ) + else: + init_pair_datasets[pair_datasets_key] = { + "dataset": dataset, + "sample": dataset_config.get("sample", None), + "id": dataset_config.get("id", None), + "len": len(dataset), + } + + length_sum = 0 + weighted_freqs_sum = 0 + freq_per_dataset = {} + vmax = 0 + vmin = 1 + weighted_freq_per_dataset = {} + + if self.args.weighting_alpha: + for key in init_pair_datasets: + if init_pair_datasets[key]["sample"] is None: + length_sum += len(init_pair_datasets[key]["dataset"]) + + for key in init_pair_datasets: + if init_pair_datasets[key]["sample"] is None: + val = float(init_pair_datasets[key]["len"]) / length_sum + freq_per_dataset[key] = val + weighted_freqs_sum += val ** self.args.weighting_alpha + + for key in freq_per_dataset: + val = ( + freq_per_dataset[key] ** self.args.weighting_alpha + / weighted_freqs_sum + ) + vmin = min(vmin, val) + vmax = max(vmax, val) + weighted_freq_per_dataset[key] = val + + for pair_datasets_key in init_pair_datasets: + dataset_config = init_pair_datasets[pair_datasets_key] + dataset = dataset_config["dataset"] + sample = dataset_config["sample"] + if sample is None: + sample = 1.0 + + if pair_datasets_key in weighted_freq_per_dataset: + w = vmax / weighted_freq_per_dataset[pair_datasets_key] + sample = w + + sample = round(sample) + + initial_sample = sample + initial_pair_datasets_key = pair_datasets_key + + while sample >= 1.0: + assert ( + pair_datasets_key not in pair_datasets + ), f"{pair_datasets_key} already in" + size_sum_with_subsampling += len(dataset) + pair_datasets[pair_datasets_key] = MultitaskDatasetWrapper( + dataset, dataset_config.get("id", 0), 1.0, name=pair_datasets_key + ) + size_sum += len(dataset) + sample -= 1.0 + pair_datasets_key += "-up" + + assert sample < 1e-6, f"sample remains > 0 {pair_datasets_key}" + + logger.info( + f"added pair {initial_pair_datasets_key} length {len(dataset)} new_length = {len(dataset)*initial_sample}" + ) + size_by_corpus[corpus_name] += len(dataset) + + self.datasets[split] = pair_datasets + logger.info( + f"Datasets number = {len(self.datasets[split])} size = {size_sum} size_sum_with_subsampling = {size_sum_with_subsampling}" + ) + + @property + def source_dictionary(self): + return self.src_dictionary + + @property + def target_dictionary(self): + return self.tgt_dictionary + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + data_buffer_size=0, + disable_iterator_cache=False, + grouped_shuffling=False, + update_epoch_batch_itr=False, + **kwargs, + ): + + assert isinstance(dataset, OrderedDict) + assert len(dataset) + assert isinstance(dataset[next(iter(dataset))], FairseqDataset) + + # initialize the dataset with the correct starting epoch + for _, dt in dataset.items(): + dt.set_epoch(epoch) + + indices = OrderedDict() + batch_sampler = OrderedDict() + + with data_utils.numpy_seed(seed + epoch): + for key, dt in dataset.items(): + logger.info(f"\t ordered_indices {key}") + indices[key] = dt.ordered_indices() + + # filter examples that are too large + if max_positions is not None: + for key, dt in dataset.items(): + logger.info(f"\t filter_by_size {key}") + indices[key], ignored = dt.filter_indices_by_size( + indices[key], max_positions + ) + + for key, dt in dataset.items(): + logger.info(f"\t batch_by_size {key}") + batch_sampler[key] = data_utils.batch_by_size( + indices[key], + dt.num_tokens, + max_tokens=max_tokens, + max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + + epoch_iter = MultidatasetEpochBatchIterator( + dataset=dataset, + batch_sampler=batch_sampler, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + ) + + return epoch_iter diff --git a/fairseq/examples/laser/laser_src/laser_transformer.py b/fairseq/examples/laser/laser_src/laser_transformer.py new file mode 100644 index 0000000..0be0309 --- /dev/null +++ b/fairseq/examples/laser/laser_src/laser_transformer.py @@ -0,0 +1,354 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +from typing import Any, Dict, List, Optional +from torch import Tensor + +import torch +import torch.nn as nn + +from fairseq.models import ( + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import ( + base_architecture, + Embedding, + TransformerModel, + TransformerEncoder, + TransformerDecoder, +) +from fairseq.modules import ( + TransformerDecoderLayer, +) + +logger = logging.getLogger(__name__) + + +@register_model("laser_transformer") +class LaserTransformerModel(FairseqEncoderDecoderModel): + """Train Transformer for LASER task + + Requires --task laser + """ + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens=None, + tgt_tokens=None, + tgt_lengths=None, + target_language_id=-1, + dataset_name="", + ): + laser_encoder_out = self.encoder(src_tokens, src_lengths) + return self.decoder( + prev_output_tokens, laser_encoder_out, lang_id=target_language_id + ) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + TransformerModel.add_args(parser) + parser.add_argument( + "--decoder-lang-embed-dim", + type=int, + metavar="N", + help="decoder language embedding dimension", + ) + + @classmethod + def build_model(cls, args, task): + base_laser_transformer_architecture(args) + + num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 + + def load_embed_tokens(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + + return Embedding(num_embeddings, embed_dim, padding_idx) + + encoder_embed_tokens = load_embed_tokens( + task.source_dictionary, args.encoder_embed_dim + ) + decoder_embed_tokens = load_embed_tokens( + task.target_dictionary, args.decoder_embed_dim + ) + num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 + + encoder = LaserTransformerEncoder( + args, task.source_dictionary, encoder_embed_tokens + ) + + decoder = LaserTransformerDecoder( + args, + task.target_dictionary, + decoder_embed_tokens, + num_langs=num_langs, + lang_embed_dim=args.decoder_lang_embed_dim, + ) + + return cls(encoder, decoder) + + +class LaserTransformerEncoder(TransformerEncoder): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, src_tokens, *args, **kwargs): + encoder_out = super().forward(src_tokens, *args, **kwargs) + + x = encoder_out["encoder_out"][0] # T x B x C + padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1) + + if padding_mask.any(): + x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x) + + # Build the sentence embedding by max-pooling over the encoder outputs + sentemb = x.max(dim=0)[0] + + # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in + # `foward` so we use a dictionary instead. + # TorchScript does not support mixed values so the values are all lists. + # The empty list is equivalent to None. + return {"sentemb": [sentemb]} # B x C + + @torch.jit.export + def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): + """ + Same as the one in transformer.py, with new_sentemb + """ + if len(encoder_out["sentemb"]) == 0: + new_sentemb = [] + else: + new_sentemb = [encoder_out["sentemb"][0].index_select(0, new_order)] + + return { + "sentemb": new_sentemb, # B x C + } + + +class LaserTransformerDecoder(TransformerDecoder): + def __init__(self, args, dictionary, *kargs, **kwargs): + self.num_langs = kwargs.get("num_langs", 1) + self.lang_embed_dim = kwargs.get("lang_embed_dim", 0) + kwargs.pop("num_langs", None) + kwargs.pop("lang_embed_dim", None) + + super().__init__(args, dictionary, *kargs, **kwargs, no_encoder_attn=True) + + if self.lang_embed_dim == 0: + self.embed_lang = None + else: + self.embed_lang = nn.Embedding(self.num_langs, self.lang_embed_dim) + nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1) + + if self.output_projection is not None: + laser_output_embed_dim = ( + self.output_embed_dim + self.lang_embed_dim + args.encoder_embed_dim + ) + self.output_projection = nn.Linear( + laser_output_embed_dim, len(dictionary), bias=False + ) + nn.init.normal_( + self.output_projection.weight, + mean=0, + std=laser_output_embed_dim ** -0.5, + ) + + def build_decoder_layer(self, args, no_encoder_attn=False): + decoder_embed_dim = args.decoder_embed_dim + args.decoder_embed_dim = ( + decoder_embed_dim + self.lang_embed_dim + args.encoder_embed_dim + ) + res = TransformerDecoderLayer(args, no_encoder_attn=True) + args.decoder_embed_dim = decoder_embed_dim + + return res + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + lang_id: Optional[int] = None, + ): + """ + Similar to *forward* but only return features. + + Includes several features from "Jointly Learning to Align and + Translate with Transformer Models" (Garg et al., EMNLP 2019). + + Args: + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + alignment_layer (int, optional): return mean alignment over + heads at this layer (default: last layer). + alignment_heads (int, optional): only average alignment over + this many heads (default: all heads). + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + if alignment_layer is None: + alignment_layer = self.num_layers - 1 + + # embed positions + positions = ( + self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + bsz, seqlen = prev_output_tokens.size() + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + if self.embed_lang is not None: + lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id) + langemb = self.embed_lang(lang_ids) + langemb = langemb.unsqueeze(0) + repeat_vals = [x.shape[0] // langemb.shape[0]] + [-1] * ( + len(langemb.shape) - 1 + ) + x = torch.cat((x, langemb.expand(*repeat_vals)), dim=-1) + + sentemb = encoder_out["sentemb"][0] + sentemb = sentemb.unsqueeze(0) + + repeat_vals = [x.shape[0] // sentemb.shape[0]] + [-1] * (len(sentemb.shape) - 1) + x = torch.cat((x, sentemb.expand(*repeat_vals)), dim=-1) + + self_attn_padding_mask: Optional[Tensor] = None + if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + + # decoder layers + attn: Optional[Tensor] = None + inner_states: List[Optional[Tensor]] = [x] + for idx, layer in enumerate(self.layers): + if incremental_state is None and not full_context_alignment: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = layer( + x, + None, + None, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + + if attn is not None: + if alignment_heads is not None: + attn = attn[:alignment_heads] + + # average probabilities over heads + attn = attn.mean(dim=0) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x, {"attn": [attn], "inner_states": inner_states} + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + lang_id: Optional[int] = None, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + assert lang_id is not None + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + lang_id=lang_id, + ) + if not features_only: + x = self.output_layer(x) + return x, extra + + +@register_model_architecture("laser_transformer", "laser_transformer") +def base_laser_transformer_architecture(args): + base_architecture(args) + args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0) diff --git a/fairseq/examples/laser/laser_src/multitask_data_utils.py b/fairseq/examples/laser/laser_src/multitask_data_utils.py new file mode 100644 index 0000000..b05caea --- /dev/null +++ b/fairseq/examples/laser/laser_src/multitask_data_utils.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections import OrderedDict + +import numpy as np + +from fairseq.data import BaseWrapperDataset, FairseqDataset, iterators + + +class MultiItr(object): + def __init__(self, itr): + self.itr = itr + self._counts = [0 for x in itr] + + def __len__(self): + return sum(len(itr) for itr in self.itr) + + def __iter__(self): + return self + + def __next__(self): + ratios = [count / len(itr) for count, itr in zip(self._counts, self.itr)] + idx = ratios.index(min(ratios)) + self._counts[idx] += 1 + return next(self.itr[idx]) + + +class MultidatasetEpochBatchIterator(iterators.EpochBatchIterating): + """A wrapper around multiple epoch batch iterators.""" + + def __init__( + self, + dataset, + batch_sampler, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + ): + + assert isinstance(dataset, OrderedDict) + assert len(dataset) + assert isinstance(dataset[next(iter(dataset))], FairseqDataset) + + self.iterators = [] + + self.epoch = epoch + for key, dt in dataset.items(): + epoch_iter = iterators.EpochBatchIterator( + dataset=dt, + collate_fn=dt.collater, + batch_sampler=batch_sampler[key], + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=0, + epoch=epoch, + ) + self.iterators.append(epoch_iter) + + def __len__(self): + return sum(len(itr) for itr in self.iterators) + + def next_epoch_itr(self, shuffle=True, fix_batches_to_gpus=False): + # `self.epoch += 1` should be handled by underlying `EpochBatchIterator`s. + return MultiItr( + [ + itr.next_epoch_itr( + shuffle=shuffle, fix_batches_to_gpus=fix_batches_to_gpus + ) + for itr in self.iterators + ] + ) + + def end_of_epoch(self): + return all(itr.end_of_epoch() for itr in self.iterators) + + @property + def next_epoch_idx(self): + """Return the epoch index after *next_epoch_itr* is called.""" + + epochs = [itr.next_epoch_idx for itr in self.iterators] + self.epoch = epochs[0] + assert all(epoch == self.epoch for epoch in epochs) + + return self.epoch + + @property + def iterations_in_epoch(self): + return sum(itr.iterations_in_epoch for itr in self.iterators) + + def state_dict(self): + return { + "iterators": [it.state_dict() for it in self.iterators], + "epoch": self.epoch, + } + + def load_state_dict(self, state_dict): + self.epoch = state_dict["epoch"] + for it, d in zip(self.iterators, state_dict["iterators"]): + it.load_state_dict(d) + + +class MultitaskDatasetWrapper(BaseWrapperDataset): + """A wrapper for a multitask dataset.""" + + def __init__(self, dataset, target_language_id, sample=1.0, name=""): + super().__init__(dataset) + self.target_language_id = target_language_id + self.sample = sample + self.name = name + + def collater(self, *args, **kwargs): + ans = self.dataset.collater(*args, **kwargs) + if "net_input" in ans: + ans["net_input"]["target_language_id"] = self.target_language_id + ans["net_input"]["dataset_name"] = self.name + return ans + + def num_tokens(self, *args, **kwargs): + return self.dataset.num_tokens(*args, **kwargs) + + def ordered_indices(self, *args, **kwargs): + indices = self.dataset.ordered_indices(*args, **kwargs) + # Hacky solution for sampling + size = int(self.sample * indices.shape[0]) + + return indices.take(np.sort(np.random.permutation(indices.shape[0])[:size])) + + def size(self, index: int): + return self.dataset.size(index) + + @property + def supports_prefetch(self): + """Whether this dataset supports prefetching.""" + return getattr(self.dataset, "supports_prefetch", False) + + def prefetch(self, indices): + return self.dataset.prefetch(indices) diff --git a/fairseq/examples/latent_depth/README.md b/fairseq/examples/latent_depth/README.md new file mode 100644 index 0000000..7774c33 --- /dev/null +++ b/fairseq/examples/latent_depth/README.md @@ -0,0 +1,77 @@ +# Deep Transformers with Latent Depth (Li et al., 2020) + +[https://arxiv.org/abs/2009.13102](https://arxiv.org/abs/2009.13102). + +## Introduction + +We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair. + +## Training a multilingual model with latent depth + +Below is an example of training with latent depth in decoder for one-to-many (O2M) related languages. We use the same preprocessed (numberized and binarized) TED8 dataset as in [Balancing Training for Multilingual Neural Machine Translation (Wang et al., 2020)](https://github.com/cindyxinyiwang/multiDDS), which could be generated by [the script](https://github.com/cindyxinyiwang/multiDDS/blob/multiDDS/util_scripts/prepare_multilingual_data.sh) the author provided. +```bash +lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur" +databin_dir= + +fairseq-train ${databin_dir} \ + --user-dir examples/latent_depth/latent_depth_src \ + --lang-pairs "${lang_pairs_str}" \ + --arch multilingual_transformer_iwslt_de_en \ + --task multilingual_translation_latent_depth \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --share-encoders \ + --share-decoders \ + --decoder-langtok \ + --share-decoder-input-output-embed \ + --dropout 0.3 --attention-dropout 0.3 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --stop-min-lr 1e-9 --warmup-init-lr 1e-7 --warmup-updates 8000 \ + --max-tokens 4096 --update-freq 1 \ + --lr 0.0015 \ + --clip-norm 1.0 \ + --seed 2 \ + --ddp-backend=legacy_ddp \ + --encoder-layers 12 \ + --decoder-layers 24 \ + --decoder-latent-layer \ + --sparsity-weight 0.1 \ + --anneal-updates 5000 \ + --soft-update 500 \ + --target-layers 12 \ + --share-weight 0.1 +``` +## Inference command + +```bash +lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur" +databin_dir= +model_path= +src_lang= +tgt_lang= +gen_data= + +fairseq-generate ${databin_dir} \ + --path ${model_path} \ + --task multilingual_translation_latent_depth \ + --decoder-latent-layer \ + --lang-pairs "${lang_pairs_str}" \ + -s ${src_lang} -t ${tgt_lang} \ + --gen-subset $gen_data \ + --scoring sacrebleu \ + --remove-bpe 'sentencepiece' \ + --lenpen 1.0 \ + --beam 5 \ + --decoder-langtok \ + --max-tokens 4096 +``` + + +## Citation +```bibtex +@article{li2020deep, + title={Deep Transformers with Latent Depth}, + author={Li, Xian and Stickland, Asa Cooper and Tang, Yuqing and Kong, Xiang}, + journal={arXiv preprint arXiv:2009.13102}, + year={2020} +} +``` diff --git a/fairseq/examples/latent_depth/latent_depth_src/__init__.py b/fairseq/examples/latent_depth/latent_depth_src/__init__.py new file mode 100644 index 0000000..c5fa760 --- /dev/null +++ b/fairseq/examples/latent_depth/latent_depth_src/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import multilingual_translation_latent_depth # noqa +from .loss import latent_depth # noqa +from .models import latent_multilingual_transformer # noqa +from .modules import latent_layers # noqa diff --git a/fairseq/examples/latent_depth/latent_depth_src/loss/__init__.py b/fairseq/examples/latent_depth/latent_depth_src/loss/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/latent_depth/latent_depth_src/loss/latent_depth.py b/fairseq/examples/latent_depth/latent_depth_src/loss/latent_depth.py new file mode 100644 index 0000000..a3b9535 --- /dev/null +++ b/fairseq/examples/latent_depth/latent_depth_src/loss/latent_depth.py @@ -0,0 +1,99 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from torch.nn.modules.loss import _Loss + + +class LatentLayersKLLoss(_Loss): + def __init__(self, args): + super().__init__() + self.args = args + + def forward(self, layer_samples, lang_idx, update_num, sample_size): + prior = self.args.prior + samples = layer_samples[lang_idx] + eps = 1e-7 + if prior == "uniform": + # uniform prior + kl_loss = (samples * (torch.log(samples + eps) - math.log(0.5))).sum(-1) + elif prior == "agged_posterior": + # aggregated posterior + y_t = torch.stack([x.detach() for x in layer_samples], dim=0) + agged_q = torch.sum(y_t, dim=0) + row_norm = agged_q.sum(-1) + normed_agg_q = agged_q / row_norm + kl_loss = ( + samples * (torch.log(samples + eps) - torch.log(normed_agg_q + eps)) + ).sum(-1) + else: + raise NotImplementedError("The specified prior is not implemented.") + + # normalized by number of layers + kl_loss /= layer_samples[0].size()[0] + kl_weight = min( + self.args.sparsity_weight, + (update_num - self.args.soft_update) + * self.args.sparsity_weight + / self.args.anneal_updates, + ) + kl_loss *= kl_weight * sample_size + return kl_loss + + +class LatentLayersSparsityLoss(_Loss): + def __init__(self, args): + super().__init__() + self.args = args + + def is_valid(self, update_num): + if self.args.target_layers <= 0: + return False + return update_num > (self.args.soft_update + self.args.anneal_updates) + + def forward(self, layer_samples_list, update_num, sample_size): + batch_loss = 0 + share_loss = 0 + global_sparsity_loss = 0 + layer_samples = torch.stack(layer_samples_list, dim=0) + if ( + self.args.target_layers > 0 or self.args.share_weight > 0 + ) and update_num > (self.args.soft_update + self.args.anneal_updates): + # anneal sparsity weight + if update_num < (self.args.anneal_updates + self.args.soft_update): + weight_anneal = 0 + elif update_num < (2 * self.args.anneal_updates + self.args.soft_update): + weight_anneal = ( + (update_num - self.args.soft_update - self.args.anneal_updates) + * self.args.share_weight + / self.args.anneal_updates + ) + else: + weight_anneal = 1 + # compute ratio among languages + layer_utilization = torch.sum(layer_samples, dim=0) + layer_utilization /= layer_samples.size()[0] + if self.args.share_weight > 0: + # encouraging sharing across languages + share_loss = sum( + -1.0 * v * math.log(v) for v in layer_utilization if v > 0 + ) + batch_loss += ( + weight_anneal * self.args.share_weight * sample_size * share_loss + ) + if self.args.target_layers > 0: + # computed expected number of layers selected + expeted_layers = sum(layer_utilization) + # compute l2 loss wrt target number of layers + global_sparsity_loss = (expeted_layers - self.args.target_layers) ** 2 + batch_loss += ( + weight_anneal + * self.args.share_weight + * sample_size + * global_sparsity_loss + ) + return batch_loss diff --git a/fairseq/examples/latent_depth/latent_depth_src/models/__init__.py b/fairseq/examples/latent_depth/latent_depth_src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py b/fairseq/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py new file mode 100644 index 0000000..9e7b655 --- /dev/null +++ b/fairseq/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py @@ -0,0 +1,76 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.models import register_model, register_model_architecture +from fairseq.models.multilingual_transformer import MultilingualTransformerModel +from fairseq.models.transformer import ( + TransformerDecoder, + TransformerEncoder, + base_architecture, +) +from fairseq.utils import safe_hasattr + +from .latent_transformer import LatentTransformerDecoder, LatentTransformerEncoder + + +@register_model("latent_multilingual_transformer") +class LatentMultilingualTransformerModel(MultilingualTransformerModel): + """A variant of standard multilingual Transformer models which encoder and/or + decoders supports latent depth, as is in "Deep Transformer with Latent Depth" + (https://arxiv.org/abs/2009.13102). + """ + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + MultilingualTransformerModel.add_args(parser) + parser.add_argument( + '--soft-select', + action='store_true', + help='use soft samples in training an inference', + ) + parser.add_argument( + '--sampling-tau', + type=float, + default=5., + help='sampling temperature', + ) + + @classmethod + def _get_module_class(cls, is_encoder, args, lang_dict, embed_tokens, langs): + if is_encoder: + if safe_hasattr(args, "encoder_latent_layer") and args.encoder_latent_layer: + return LatentTransformerEncoder( + args, lang_dict, embed_tokens, num_logits=len(langs) + ) + else: + return TransformerEncoder(args, lang_dict, embed_tokens) + else: + if safe_hasattr(args, "decoder_latent_layer") and args.decoder_latent_layer: + return LatentTransformerDecoder( + args, lang_dict, embed_tokens, num_logits=len(langs) + ) + else: + return TransformerDecoder(args, lang_dict, embed_tokens) + + +@register_model_architecture( + "latent_multilingual_transformer", "latent_multilingual_transformer" +) +def latent_multilingual_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 24) + args.share_encoders = getattr(args, "share_encoders", True) + args.share_decoders = getattr(args, "share_decoders", True) + args.share_encoder_embeddings = getattr(args, "share_encoder_embeddings", True) + args.share_decoder_embeddings = getattr(args, "share_decoder_embeddings", True) + + base_architecture(args) diff --git a/fairseq/examples/latent_depth/latent_depth_src/models/latent_transformer.py b/fairseq/examples/latent_depth/latent_depth_src/models/latent_transformer.py new file mode 100644 index 0000000..6a82530 --- /dev/null +++ b/fairseq/examples/latent_depth/latent_depth_src/models/latent_transformer.py @@ -0,0 +1,156 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Any, Dict, Optional + +import torch.nn as nn +from fairseq.models.fairseq_encoder import EncoderOut +from fairseq.models.transformer import TransformerDecoder, TransformerEncoder +from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer +from torch import Tensor + +from ..modules.latent_layers import LayerSelect + + +class LatentTransformerEncoder(TransformerEncoder): + """Latent depth (https://arxiv.org/abs/2009.13102) implemented in + TransformerEncoder. + """ + + def __init__(self, args, dictionary, embed_tokens, num_logits=1): + self.num_logits = num_logits + self.num_layers = args.encoder_layers + super().__init__(args, dictionary, embed_tokens) + self.layer_select = LayerSelect( + num_layers=self.num_layers, + num_logits=self.num_logits, + soft_select=getattr(args, "soft_select", False), + sampling_tau=getattr(args, "sampling_tau", 5.), + ) + self.lang_idx = None + self.layers = nn.ModuleList( + [self._build_encoder_layer(args, idx) for idx in range(args.encoder_layers)] + ) + + def set_lang_idx(self, lang_idx): + self.lang_idx = lang_idx + + def _build_encoder_layer(self, args, idx=None): + return LatentTransformerEncoderLayer(args, idx, layer_select=self.layer_select) + + def forward(self, src_tokens, src_lengths, return_all_hiddens: bool = False): + self.layer_select.sample(self.lang_idx) + return super().forward(src_tokens, src_lengths, return_all_hiddens) + + +class LatentTransformerEncoderLayer(TransformerEncoderLayer): + """Encoder layer with each (non_residual) block weighted by samples of Bernouli + or Gumbel Signmoid samples. + + Args: + args (argparse.Namespace): parsed command-line arguments from standard + TransformerEncoderLayer. + idx (int): layer index (used to retrieve samples). + layer_select (LayerSelect, optional): instance of LayerSelect module with logits + parameters and sampling method. + """ + + def __init__(self, args, idx, layer_select=None): + super().__init__(args) + self.idx = idx + self.layer_select = layer_select + + def residual_connection(self, x, residual): + return residual + x * self.layer_select(self.idx) + + +class LatentTransformerDecoder(TransformerDecoder): + """Latent depth (https://arxiv.org/abs/2009.13102) implemented in + TransformerDecoder. + """ + + def __init__( + self, args, dictionary, embed_tokens, no_encoder_attn=False, num_logits=1 + ): + self.num_logits = num_logits + self.num_layers = args.decoder_layers + super().__init__( + args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn + ) + self.layer_select = LayerSelect( + num_layers=self.num_layers, + num_logits=self.num_logits, + soft_select=getattr(args, "soft_select", False), + sampling_tau=getattr(args, "sampling_tau", 5.), + ) + self.lang_idx = None + self.layers = nn.ModuleList( + [ + self._build_decoder_layer(args, no_encoder_attn, idx) + for idx in range(args.decoder_layers) + ] + ) + + def set_lang_idx(self, lang_idx): + self.lang_idx = lang_idx + + def _build_decoder_layer(self, args, no_encoder_attn=False, idx=None): + return LatentTransformerDecoderLayer( + args, idx, layer_select=self.layer_select, no_encoder_attn=no_encoder_attn + ) + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[EncoderOut] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + self.layer_select.sample(self.lang_idx) + return super().forward( + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + features_only=features_only, + alignment_layer=alignment_layer, + src_lengths=src_lengths, + return_all_hiddens=return_all_hiddens, + ) + + +class LatentTransformerDecoderLayer(TransformerDecoderLayer): + """Decoder layer with each (non_residual) block weighted by samples of Bernouli + or Gumbel Signmoid samples. + + Args: + args (argparse.Namespace): parsed command-line arguments from standard + TransformerDecoderLayer. + idx (int): layer index (used to retrieve samples). + layer_select (LayerSelect, optional): instance of LayerSelect module with logits + parameters and sampling method. + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + + """ + + def __init__( + self, + args, + idx, + layer_select=None, + no_encoder_attn=False, + add_bias_kv=False, + add_zero_attn=False, + ): + super().__init__(args, no_encoder_attn, add_bias_kv, add_zero_attn) + self.idx = idx + self.layer_select = layer_select + + def residual_connection(self, x, residual): + return residual + x * self.layer_select(self.idx) diff --git a/fairseq/examples/latent_depth/latent_depth_src/modules/__init__.py b/fairseq/examples/latent_depth/latent_depth_src/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/latent_depth/latent_depth_src/modules/latent_layers.py b/fairseq/examples/latent_depth/latent_depth_src/modules/latent_layers.py new file mode 100644 index 0000000..2be05d5 --- /dev/null +++ b/fairseq/examples/latent_depth/latent_depth_src/modules/latent_layers.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + + +class LayerSelect(nn.Module): + """Compute samples (from a Gumbel-Sigmoid distribution) which is used as + either (soft) weighting or (hard) selection of residual connection. + https://arxiv.org/abs/2009.13102 + """ + def __init__(self, num_layers, num_logits, soft_select=False, sampling_tau=5.): + super(LayerSelect, self).__init__() + self.layer_logits = torch.nn.Parameter( + torch.Tensor(num_logits, num_layers), + requires_grad=True, + ) + self.hard_select = not soft_select + self.tau = sampling_tau + self.detach_grad = False + self.layer_samples = [None] * num_logits + + def sample(self, logit_idx): + """To leverage the efficiency of distributed training, samples for all + layers are computed at once for each logit_idx. Logits are parameters + learnt independent of each other. + + Args: + logit_idx: The index of logit parameters used for sampling. + """ + assert logit_idx is not None + self.samples = self._gumbel_sigmoid( + self.layer_logits[logit_idx, :].detach() + if self.detach_grad + else self.layer_logits[logit_idx, :], + dim=-1, + tau=self.tau, + hard=self.hard_select, + ) + self.layer_samples[logit_idx] = self.samples + + def forward(self, i): + sample = self.samples[i] + return sample + + def _gumbel_sigmoid( + self, logits, tau=1, hard=False, eps=1e-10, dim=-1, threshold=0.5 + ): + # ~Gumbel(0,1) + gumbels1 = ( + -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format) + .exponential_() + .log() + ) + gumbels2 = ( + -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format) + .exponential_() + .log() + ) + # Difference of two gumbels because we apply a sigmoid + gumbels1 = (logits + gumbels1 - gumbels2) / tau + y_soft = gumbels1.sigmoid() + if hard: + # Straight through. + y_hard = torch.zeros_like( + logits, memory_format=torch.legacy_contiguous_format + ).masked_fill(y_soft > threshold, 1.0) + ret = y_hard - y_soft.detach() + y_soft + else: + # Reparametrization trick. + ret = y_soft + return ret diff --git a/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py b/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py new file mode 100644 index 0000000..8cc2a71 --- /dev/null +++ b/fairseq/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py @@ -0,0 +1,195 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.tasks import register_task +from fairseq.tasks.multilingual_translation import MultilingualTranslationTask +from fairseq.utils import safe_hasattr + +from .loss.latent_depth import LatentLayersKLLoss, LatentLayersSparsityLoss + + +@register_task("multilingual_translation_latent_depth") +class MultilingualTranslationTaskLatentDepth(MultilingualTranslationTask): + """A task for multiple translation with latent depth. + + See `"Deep Transformer with Latent Depth" + (Li et al., 2020) `_. + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + MultilingualTranslationTask.add_args(parser) + parser.add_argument('--encoder-latent-layer', action='store_true', help='latent layer selection in encoder') + parser.add_argument('--decoder-latent-layer', action='store_true', help='latent layer selection in decoder') + parser.add_argument('--target-layers', default=-1, type=int, + help='number of effective layers to learn; -1 means no constraint') + parser.add_argument('--sparsity-weight', default=0.0, type=float, + help='weight for sparsity loss') + parser.add_argument('--share-weight', default=0.0, type=float, + help='weight for sharing loss') + parser.add_argument('--soft-update', default=1, type=int, + help='number of updates with soft sampling') + parser.add_argument('--anneal-updates', default=1, type=int, + help='number of updates to anneal the KL loss weight') + parser.add_argument('--prior', default="uniform", type=str, + help='prior used for computing KL loss') + # fmt: on + + def __init__(self, args, dicts, training): + super().__init__(args, dicts, training) + self.src_langs, self.tgt_langs = zip( + *[(lang.split("-")[0], lang.split("-")[1]) for lang in args.lang_pairs] + ) + if self.training and self.encoder_latent_layer: + assert self.args.share_encoders + if self.training and self.decoder_latent_layer: + assert self.args.share_decoders + if training or self.encoder_latent_layer or self.decoder_latent_layer: + self.lang_pairs = args.lang_pairs + else: + self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] + self.eval_lang_pairs = self.lang_pairs + self.model_lang_pairs = self.lang_pairs + if self.training and (self.encoder_latent_layer or self.decoder_latent_layer): + self.kl_loss = LatentLayersKLLoss(self.args) + self.sparsity_loss = LatentLayersSparsityLoss(self.args) + + def _per_lang_pair_train_loss( + self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad + ): + src, tgt = lang_pair.split("-") + if self.encoder_latent_layer: + src_lang_idx = self.src_lang_idx_dict[src] + model.models[lang_pair].encoder.set_lang_idx(src_lang_idx) + model.models[lang_pair].encoder.layer_select.hard_select = ( + update_num > self.args.soft_update + ) + if self.decoder_latent_layer: + tgt_lang_idx = self.tgt_lang_idx_dict[tgt] + model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx) + model.models[lang_pair].decoder.layer_select.hard_select = ( + update_num > self.args.soft_update + ) + + loss, sample_size, logging_output = criterion( + model.models[lang_pair], sample[lang_pair] + ) + if self.encoder_latent_layer: + none_samples = sum( + 1 if x is None else 0 + for x in model.models[lang_pair].encoder.layer_select.layer_samples + ) + if none_samples == 0 or self.args.prior != "agged_posterior": + loss += self.kl_loss( + model.models[lang_pair].encoder.layer_select.layer_samples, + src_lang_idx, + update_num, + sample_size, + ) + if self.decoder_latent_layer: + none_samples = sum( + 1 if x is None else 0 + for x in model.models[lang_pair].decoder.layer_select.layer_samples + ) + if none_samples == 0 or self.args.prior != "agged_posterior": + loss += self.kl_loss( + model.models[lang_pair].decoder.layer_select.layer_samples, + tgt_lang_idx, + update_num, + sample_size, + ) + if ignore_grad: + loss *= 0 + + if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num): + # need to retain the graph if sparsity loss needs to be added + loss.backward(retain_graph=True) + else: + optimizer.backward(loss) + + return loss, sample_size, logging_output + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + agg_loss, agg_sample_size, agg_logging_output = super().train_step( + sample, model, criterion, optimizer, update_num, ignore_grad + ) + # compute auxiliary loss from layere sparsity, based on all samples from all languages + if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num): + sparsity_loss = 0 + if self.encoder_latent_layer: + sparsity_loss += self.sparsity_loss( + next( + iter(model.models.values()) + ).encoder.layer_select.layer_samples, + update_num, + agg_sample_size, + ) + if self.decoder_latent_layer: + sparsity_loss += self.sparsity_loss( + next( + iter(model.models.values()) + ).decoder.layer_select.layer_samples, + update_num, + agg_sample_size, + ) + if sparsity_loss > 0: + optimizer.backward(sparsity_loss) + return agg_loss, agg_sample_size, agg_logging_output + + def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample): + src, tgt = lang_pair.split("-") + if self.encoder_latent_layer: + src_lang_idx = self.src_lang_idx_dict[src] + model.models[lang_pair].encoder.set_lang_idx(src_lang_idx) + if self.decoder_latent_layer: + tgt_lang_idx = self.tgt_lang_idx_dict[tgt] + model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx) + loss, sample_size, logging_output = criterion( + model.models[lang_pair], sample[lang_pair] + ) + return loss, sample_size, logging_output + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + if self.encoder_latent_layer or self.decoder_latent_layer: + for model in models: + if self.encoder_latent_layer: + assert model.encoder.layer_select is not None + src_lang_idx = self.src_lang_idx_dict[self.args.source_lang] + model.encoder.set_lang_idx(src_lang_idx) + if self.decoder_latent_layer: + assert model.decoder.layer_select is not None + tgt_lang_idx = self.tgt_lang_idx_dict[self.args.target_lang] + model.decoder.set_lang_idx(tgt_lang_idx) + return super().inference_step( + generator, models, sample, prefix_tokens, constraints + ) + + @property + def encoder_latent_layer(self): + return ( + safe_hasattr(self.args, "encoder_latent_layer") + and self.args.encoder_latent_layer + ) + + @property + def decoder_latent_layer(self): + return ( + safe_hasattr(self.args, "decoder_latent_layer") + and self.args.decoder_latent_layer + ) + + @property + def src_lang_idx_dict(self): + return {lang: lang_idx for lang_idx, lang in enumerate(self.src_langs)} + + @property + def tgt_lang_idx_dict(self): + return {lang: lang_idx for lang_idx, lang in enumerate(self.tgt_langs)} diff --git a/fairseq/examples/layerdrop/README.md b/fairseq/examples/layerdrop/README.md new file mode 100644 index 0000000..4d48ee9 --- /dev/null +++ b/fairseq/examples/layerdrop/README.md @@ -0,0 +1,154 @@ +# Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019) +This page contains information for how to train models with LayerDrop, based on this [paper](https://arxiv.org/abs/1909.11556). + +## Citation: +If you found this technique useful, please cite our paper: +```bibtex +@article{fan2019reducing, + title={Reducing Transformer Depth on Demand with Structured Dropout}, + author={Fan, Angela and Grave, Edouard and Joulin, Armand}, + journal={arXiv preprint arXiv:1909.11556}, + year={2019} +} +``` + +## Pre-trained models + +Model | Description | Download +---|---|--- +`layerdrop_wmt_en_de_12_6` | Transformer + LayerDrop 0.2 trained on WMT16 en-de with 12 encoder and 6 decoder layers | [layerdrop_wmt_en_de_12_6.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/layerdrop_wmt_en_de_12_6.tar.gz) +`roberta_layerdrop.base` | RoBERTa Base + LayerDrop 0.2 | [roberta_layerdrop.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.base.qnli.tar.gz) +`roberta_layerdrop.large` | RoBERTa Large + LayerDrop 0.2 | [roberta_layerdrop.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.tar.gz) +`roberta_layerdrop.large.mnli` | `roberta_layerdrop.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.mnli.tar.gz) +`roberta_layerdrop.large.qnli` | `roberta_layerdrop.large` finetuned on [QNLI](https://arxiv.org/abs/1804.07461) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.qnli.tar.gz) + + +Evaluate performance of these pre-trained models: +```bash +# Example for Machine Translation +fairseq-generate /path/to/bped/wmt/data --path nmt_checkpoint.pt \ + --beam 8 --lenpen 0.4 \ + --batch-size 64 \ + --remove-bpe \ + --gen-subset test > wmt16_gen.txt +bash scripts/compound_split_bleu.sh wmt16_gen.txt +# prints BLEU4 = 30.17 +``` + +```python +# Example for RoBERTa + LayerDrop finetuned on MNLI: +from fairseq.models.roberta import RobertaModel + +roberta_layerdrop = RobertaModel.from_pretrained( + '/path/to/MNLI/model', + checkpoint_file='mnli_checkpoint.pt', + data_name_or_path='/path/to/MNLI/data/MNLI-bin' +) +label_map = {0: 'contradiction', 2: 'neutral', 1: 'entailment'} +ncorrect, nsamples = 0, 0 +roberta_layerdrop.cuda() +roberta_layerdrop.eval() +with open('/path/to/MNLI/data/dev_matched.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[8], tokens[9], tokens[-1] + tokens = roberta_layerdrop.encode(sent1, sent2) + prediction = roberta_layerdrop.predict('sentence_classification_head', tokens).argmax().item() + prediction_label = label_map[prediction] + ncorrect += int(prediction_label == target) + nsamples += 1 +print('| Accuracy: ', float(ncorrect)/float(nsamples)) +# prints | Accuracy: 0.9026999490575649 + + +# Example for RoBERTa + LayerDrop finetuned on QNLI: +roberta = RobertaModel.from_pretrained( + '/path/to/QNLI/model', + checkpoint_file='qnli_checkpoint.pt', + data_name_or_path='/path/to/QNLI/data/QNLI-bin' +) + +label_fn = lambda label: roberta.task.label_dictionary.string( + [label + roberta.task.target_dictionary.nspecial] +) +ncorrect, nsamples = 0, 0 +roberta.cuda() +roberta.eval() +with open('/path/to/QNLI/data/dev.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[1], tokens[2], tokens[3] + tokens = roberta.encode(sent1, sent2) + prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() + prediction_label = label_fn(prediction) + ncorrect += int(prediction_label == target) + nsamples += 1 +print('| Accuracy: ', float(ncorrect)/float(nsamples)) +# prints | Accuracy: 0.9480139117700896 +``` + + +## Example usage + +To train a model with LayerDrop, add the following flags. We recommend 0.2, a value that worked well in our experiments. For Language Models that are decoder-only, you need only the decoder flag. For RoBERTa, an encoder, you need only the encoder flag. The encoder and decoder LayerDrop values can be set differently. +``` +--encoder-layerdrop 0.2 --decoder-layerdrop 0.2 +``` + +To prune a model that has been trained with LayerDrop, add the following flags followed by a comma separated list of which layers you would like to keep. +``` +--encoder-layers-to-keep 0,2,4,6,8,10,12,14 --decoder-layers-to-keep 0,2,4,6,8,10,12,14 +``` +Setting these flags should print a message such as: +``` +| Pruning model to specified layer configuration +``` +You should also see a smaller number of parameters in the model, for example the 16-Layer Transformer Language Model prints: +``` +num. model params: 246933504 +``` +while a model pruned to 8 Layers prints: +``` +num. model params: 146163712 +``` + +If you would like to pick up training with a model that has been pruned, simply adding these flags is sufficient. If you would like to use a script that only does evaluation (no training), you may need to pass an override command. A specific example would be for language modeling: +```bash +fairseq-eval-lm /path/to/wikitext-103 \ + --path /path/to/model/checkpoint.pt \ + --model-overrides "{'decoder_layers_to_keep':'0,2,4,6,8,10,12,14'}" +``` +This model override command overrides the training parameters and updates the model arguments so that the pruned model is run instead of the full model. + +## Reproduce Paper Results + +Looking to reproduce the results in the paper? + +1. For Translation on WMT16 en-de, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/scaling_nmt/README.md) +2. To train RoBERTa, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta) +3. To train Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model) + + +## Tips + +1. If you would like to train large models with better performance, LayerDrop should be set to a smaller value such as 0.1 or 0.2. Too much LayerDrop will mean the model has too much regularization, so may not reach the best performance. Since LayerDrop adds regularization, you may achieve the best performance by slightly reducing the amount of standard dropout (for example, reduce by 0.1). + +2. If you would like to train large models to be pruned and made smaller, LayerDrop should be set to a larger value such as 0.5 if you want to prune very aggressively (such as removing half the network or more). If you would like to prune fewer layers away, LayerDrop can be set to a smaller value such as 0.2. Our experiments were conducted with low values of LayerDrop (such as 0.1 and 0.2), for reference. + +3. When pruning layers at inference time, it is best to spread out the layers remaining so they are evenly spaced throughout the network. For example, if you want to remove 50% of the network, keeping every other layer is good. + + +## FAQ + +1. How did the sharing layers experiment work? In an appendix (https://openreview.net/pdf?id=SylO2yStDr) we added an experiment on Wikitext-103 language modeling that combined LayerDrop with Weight Sharing. We shared chunks of 2 layers such that every other layer had shared weights. For example, if our network has layers 1 through 6, then layer 1 and 2 are shared, layer 3 and 4 are shared, and layer 5 and 6 are shared. + +2. LayerDrop hasn't been helping in my setting? During training time, LayerDrop can help regularize your network. This is most important if your network is already overfitting - if your network is underfitting, it is possible LayerDrop is adding too much regularization. We recommend using smaller values (such as 0.1 or 0.2) and also decreasing the quantity of standard dropout (for example, reduce by 0.1). + +3. Can you train a model without LayerDrop and finetune with LayerDrop (e.g. for BERT)? In our experiments, we did not see great performance. Models such as RoBERTa have trained for a long time in the pre-training setting, so only finetuning with LayerDrop for a few epochs on a downstream task such as MNLI does not achieve the robustness required for successful pruning. + + +## Having an issue or have a question? + +Please open an issue in this repository with the details of your question. Thanks! diff --git a/fairseq/examples/linformer/README.md b/fairseq/examples/linformer/README.md new file mode 100644 index 0000000..f8b36bc --- /dev/null +++ b/fairseq/examples/linformer/README.md @@ -0,0 +1,22 @@ +# Linformer: Self-Attention with Linear Complexity (Wang et al., 2020) + +This example contains code to train Linformer models as described in our paper +[Linformer: Self-Attention with Linear Complexity](https://arxiv.org/abs/2006.04768). + +## Training a new Linformer RoBERTa model + +You can mostly follow the [RoBERTa pretraining README](/examples/roberta/README.pretraining.md), +updating your training command with `--user-dir examples/linformer/linformer_src --arch linformer_roberta_base`. + +## Citation + +If you use our work, please cite: + +```bibtex +@article{wang2020linformer, + title={Linformer: Self-Attention with Linear Complexity}, + author={Wang, Sinong and Li, Belinda and Khabsa, Madian and Fang, Han and Ma, Hao}, + journal={arXiv preprint arXiv:2006.04768}, + year={2020} +} +``` diff --git a/fairseq/examples/linformer/linformer_src/__init__.py b/fairseq/examples/linformer/linformer_src/__init__.py new file mode 100644 index 0000000..1c52f13 --- /dev/null +++ b/fairseq/examples/linformer/linformer_src/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .models import linformer_roberta # noqa diff --git a/fairseq/examples/linformer/linformer_src/models/__init__.py b/fairseq/examples/linformer/linformer_src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/linformer/linformer_src/models/linformer_roberta.py b/fairseq/examples/linformer/linformer_src/models/linformer_roberta.py new file mode 100644 index 0000000..b7bdbb1 --- /dev/null +++ b/fairseq/examples/linformer/linformer_src/models/linformer_roberta.py @@ -0,0 +1,120 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Linformer: Self-Attention with Linear Complexity +""" + +import logging + +import torch +from fairseq import utils +from fairseq.models import register_model, register_model_architecture +from fairseq.models.roberta import ( + init_bert_params, + roberta_base_architecture, + roberta_large_architecture, + RobertaEncoder, + RobertaModel, +) +from fairseq.utils import safe_hasattr + +from ..modules.linformer_sentence_encoder import LinformerTransformerEncoder + + +logger = logging.getLogger(__name__) + + +@register_model("linformer_roberta") +class LinformerModel(RobertaModel): + @staticmethod + def add_args(parser): + RobertaModel.add_args(parser) + + # add args for Linformer + parser.add_argument( + "--compressed", type=int, help="compressed ratio of sequence length" + ) + parser.add_argument( + "--shared-kv-compressed", + type=int, + help="share compressed matrix between k and v, in each layer", + ) + parser.add_argument( + "--shared-layer-kv-compressed", + type=int, + help="share compressed matrix between k and v and across all layers", + ) + parser.add_argument( + "--freeze-compress", + type=int, + help="freeze the parameters in compressed layer", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present + base_architecture(args) + + if not safe_hasattr(args, "max_positions"): + args.max_positions = args.tokens_per_sample + + encoder = LinformerEncoder(args, task.source_dictionary) + return cls(args, encoder) + + +class LinformerEncoder(RobertaEncoder): + """Linformer encoder.""" + + def __init__(self, args, dictionary): + super().__init__(args, dictionary) + self.register_buffer("version", torch.tensor(2)) + + def build_encoder(self, args, dictionary, embed_tokens): + encoder = LinformerTransformerEncoder(args, dictionary, embed_tokens) + encoder.apply(init_bert_params) + return encoder + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + prefix = name + "." if name != "" else "" + + # some old checkpoints had weight sharing implemented incorrectly + # (note: this was correct in the original paper code) + if utils.item(state_dict.get(f"{prefix}version", torch.tensor(1))) < 2: + state_dict[f"{prefix}version"] = torch.tensor(1) + # check if input embeddings and output embeddings were tied + if not torch.allclose( + state_dict[f"{prefix}sentence_encoder.embed_tokens.weight"], + state_dict[f"{prefix}lm_head.weight"], + ): + # they weren't tied, re-init the LM head without weight sharing + self.lm_head = self.build_lm_head( + embed_dim=self.args.encoder_embed_dim, + output_dim=len(self.dictionary), + activation_fn=self.args.activation_fn, + weight=None, # don't share weights + ) + + +@register_model_architecture("linformer_roberta", "linformer_roberta") +def base_architecture(args): + args.compressed = getattr(args, "compressed", 4) + args.shared_kv_compressed = getattr(args, "shared_kv_compressed", 0) + args.shared_layer_kv_compressed = getattr(args, "shared_layer_kv_compressed", 0) + args.freeze_compress = getattr(args, "freeze_compress", 0) + roberta_base_architecture(args) + + +@register_model_architecture("linformer_roberta", "linformer_roberta_base") +def linformer_roberta_base_architecture(args): + base_architecture(args) + + +@register_model_architecture("linformer_roberta", "linformer_roberta_large") +def linformer_roberta_large_architecture(args): + roberta_large_architecture(args) + base_architecture(args) diff --git a/fairseq/examples/linformer/linformer_src/modules/__init__.py b/fairseq/examples/linformer/linformer_src/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py b/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py new file mode 100644 index 0000000..44f7989 --- /dev/null +++ b/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch.nn as nn +from fairseq.models.transformer import TransformerEncoder + +from .linformer_sentence_encoder_layer import LinformerTransformerEncoderLayer + + +class LinformerTransformerEncoder(TransformerEncoder): + """ + Implementation for a Bi-directional Linformer based Sentence Encoder used + in BERT/XLM style pre-trained models. + + This first computes the token embedding using the token embedding matrix, + position embeddings (if specified) and segment embeddings + (if specified). After applying the specified number of + LinformerEncoderLayers, it outputs all the internal states of the + encoder as well as the final representation associated with the first + token (usually CLS token). + + Input: + - tokens: B x T matrix representing sentences + - segment_labels: B x T matrix representing segment label for tokens + + Output: + - a tuple of the following: + - a list of internal model states used to compute the + predictions where each tensor has shape T x B x C + - sentence representation associated with first input token + in format B x C. + """ + + def __init__(self, args, dictionary, embed_tokens): + self.compress_layer = None + super().__init__(args, dictionary, embed_tokens) + + def build_encoder_layer(self, args): + if self.args.shared_layer_kv_compressed == 1 and self.compress_layer is None: + compress_layer = nn.Linear( + self.args.max_positions, + self.args.max_positions // self.args.compressed, + ) + # intialize parameters for compressed layer + nn.init.xavier_uniform_(compress_layer.weight, gain=1 / math.sqrt(2)) + if self.args.freeze_compress == 1: + compress_layer.weight.requires_grad = False + self.compress_layer = compress_layer + + return LinformerTransformerEncoderLayer(args, self.compress_layer) diff --git a/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py b/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py new file mode 100644 index 0000000..7e2caa0 --- /dev/null +++ b/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py @@ -0,0 +1,65 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq import utils +from fairseq.modules import TransformerEncoderLayer + +from .multihead_linear_attention import MultiheadLinearAttention + + +class LinformerTransformerEncoderLayer(TransformerEncoderLayer): + """ + Implements a Linformer Encoder Layer used in BERT/XLM style pre-trained + models. + """ + + def __init__(self, args, shared_compress_layer): + # wrap in a list so it's not automatically registered by PyTorch + self.shared_compress_layer = [shared_compress_layer] + + super().__init__(args) + + self.register_buffer("version", torch.tensor(2)) + + def build_self_attention(self, embed_dim, args): + return MultiheadLinearAttention( + embed_dim, + args.encoder_attention_heads, + dropout=args.dropout, + self_attention=True, + q_noise=args.quant_noise_pq, + qn_block_size=args.quant_noise_pq_block_size, + compressed=args.compressed, + max_seq_len=args.max_positions, + shared_kv_compressed=args.shared_kv_compressed, + shared_compress_layer=self.shared_compress_layer[0], + freeze_compress=args.freeze_compress, + ) + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + prefix = name + "." if name != "" else "" + + # some old checkpoints had weight sharing implemented incorrectly + # (note: this was correct in the original paper code) + if utils.item(state_dict.get(f"{prefix}version", torch.tensor(1))) < 2: + state_dict[f"{prefix}version"] = torch.tensor(1) + # check compression layer sharing + if f"{prefix}shared_compress_layer.weight" in state_dict: + # reinitialize block without sharing compression layer to match + # old behavior + self.shared_compress_layer = [ + torch.nn.Linear( + self.shared_compress_layer[0].weight.size(1), + self.shared_compress_layer[0].weight.size(0), + ) + ] + self.self_attn = self.build_self_attention(self.embed_dim, self.args) + # delete shared_compress_layer, since it's already copied to + # self_attn.compress_k.weight + del state_dict[f"{prefix}shared_compress_layer.weight"] + if f"{prefix}shared_compress_layer.bias" in state_dict: + del state_dict[f"{prefix}shared_compress_layer.bias"] diff --git a/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py b/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py new file mode 100644 index 0000000..6be1007 --- /dev/null +++ b/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py @@ -0,0 +1,481 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, Optional, Tuple + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.quant_noise import quant_noise +from torch import Tensor, nn +from torch.nn import Parameter + + +@with_incremental_state +class MultiheadLinearAttention(nn.Module): + """Multi-headed linformer attention. + + Projects the key and values down to the compressed dimension, before computing self-attention. + + See "Linformer: Self-Attention with Linear Complexity" for more details. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + q_noise=0.0, + qn_block_size=8, + compressed=1, + max_seq_len=256, + shared_kv_compressed=0, + shared_compress_layer=None, + freeze_compress=0, + ): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim ** -0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, ( + "Self-attention requires query, key and " "value to be of the same size" + ) + + self.k_proj = quant_noise( + nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.v_proj = quant_noise( + nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.q_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + # used for compress sequence to subsequence + if shared_compress_layer is None: + self.compress_seq_len = max_seq_len // compressed + self.compress_k = nn.Linear(max_seq_len, self.compress_seq_len, bias=False) + if shared_kv_compressed == 0: + self.compress_v = nn.Linear( + max_seq_len, self.compress_seq_len, bias=False + ) + self.layerwise_sharing = False + else: + self.compress_k = shared_compress_layer + if shared_kv_compressed == 0: + self.compress_v = shared_compress_layer + self.layerwise_sharing = True + self.shared_kv_compressed = shared_kv_compressed + + self.out_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self.reset_parameters() + + if freeze_compress == 1: + self.compress_k.weight.requires_grad = False + if shared_kv_compressed == 0: + self.compress_v.weight.requires_grad = False + + self.onnx_trace = False + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def reset_parameters(self): + if self.qkv_same_dim: + # Empirically observed the convergence to be much better with + # the scaled initialization + nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) + if ( + not self.layerwise_sharing + ): # otherwise, we already initialize the parameters + nn.init.xavier_uniform_(self.compress_k.weight, gain=1 / math.sqrt(2)) + if self.shared_kv_compressed == 0: + nn.init.xavier_uniform_( + self.compress_v.weight, gain=1 / math.sqrt(2) + ) + else: + nn.init.xavier_uniform_(self.k_proj.weight) + nn.init.xavier_uniform_(self.v_proj.weight) + nn.init.xavier_uniform_(self.q_proj.weight) + if ( + not self.layerwise_sharing + ): # otherwise, we already initialize the parameters + nn.init.xavier_uniform_(self.compress_k.weight) + if self.shared_kv_compressed == 0: + nn.init.xavier_uniform_(self.compress_v.weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.out_proj.bias is not None: + nn.init.constant_(self.out_proj.bias, 0.0) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + + def forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + before_softmax: bool = False, + need_head_weights: bool = False, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + + k_input = query.permute(1, 2, 0).contiguous() # B * C * T + k_input = ( + F.linear(k_input, self.compress_k.weight[:, 0:tgt_len]) + .permute(2, 0, 1) + .contiguous() + ) + k = self.k_proj(k_input) + + v_input = query.permute(1, 2, 0).contiguous() # B * C * T + if self.shared_kv_compressed == 0: + v_input = ( + F.linear(v_input, self.compress_v.weight[:, 0:tgt_len]) + .permute(2, 0, 1) + .contiguous() + ) + if self.shared_kv_compressed == 1: # use shared kv compressed linear layer + v_input = ( + F.linear(v_input, self.compress_k.weight[:, 0:tgt_len]) + .permute(2, 0, 1) + .contiguous() + ) + v = self.v_proj(v_input) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(key_padding_mask.size(0), 1), + ], + dim=1, + ) + + q = ( + q.contiguous() + .view(tgt_len, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + if k is not None: + k = ( + k.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + if v is not None: + v = ( + v.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = MultiheadLinearAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + + saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + src_len = k.size(1) + + if self.add_zero_attn: + assert v is not None + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = MultiheadLinearAttention.apply_sparse_mask( + attn_weights, tgt_len, src_len, bsz + ) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + if self.onnx_trace: + attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) + attn_weights += attn_mask + + if before_softmax: + return attn_weights, v + + attn_weights_float = utils.softmax( + attn_weights, dim=-1, onnx_trace=self.onnx_trace + ) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = F.dropout( + attn_weights, + p=self.dropout, + training=self.training, + ) + assert v is not None + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + if self.onnx_trace and attn.size(1) == 1: + # when ONNX tracing a single decoder step (sequence length == 1) + # the transpose is a no-op copy before view, thus unnecessary + attn = attn.contiguous().view(tgt_len, bsz, embed_dim) + else: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn = self.out_proj(attn) + attn_weights: Optional[Tensor] = None + if need_weights: + attn_weights = attn_weights_float.view( + bsz, self.num_heads, tgt_len, src_len + ).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + + return attn, attn_weights + + @staticmethod + def _append_prev_key_padding_mask( + key_padding_mask: Optional[Tensor], + prev_key_padding_mask: Optional[Tensor], + batch_size: int, + src_len: int, + static_kv: bool, + ) -> Optional[Tensor]: + # saved key padding masks have shape (bsz, seq_len) + if prev_key_padding_mask is not None and static_kv: + new_key_padding_mask = prev_key_padding_mask + elif prev_key_padding_mask is not None and key_padding_mask is not None: + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 + ) + # During incremental decoding, as the padding token enters and + # leaves the frame, there will be a time when prev or current + # is None + elif prev_key_padding_mask is not None: + filler = torch.zeros( + (batch_size, src_len - prev_key_padding_mask.size(1)), + device=prev_key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + elif key_padding_mask is not None: + filler = torch.zeros( + (batch_size, src_len - key_padding_mask.size(1)), + device=key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [filler.float(), key_padding_mask.float()], dim=1 + ) + else: + new_key_padding_mask = prev_key_padding_mask + return new_key_padding_mask + + @torch.jit.export + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): + """Reorder buffered internal state (for incremental generation).""" + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + for k in input_buffer.keys(): + input_buffer_k = input_buffer[k] + if input_buffer_k is not None: + if self.encoder_decoder_attention and input_buffer_k.size( + 0 + ) == new_order.size(0): + break + input_buffer[k] = input_buffer_k.index_select(0, new_order) + incremental_state = self._set_input_buffer(incremental_state, input_buffer) + return incremental_state + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ) -> Dict[str, Optional[Tensor]]: + result = self.get_incremental_state(incremental_state, "attn_state") + if result is not None: + return result + else: + empty_result: Dict[str, Optional[Tensor]] = {} + return empty_result + + def _set_input_buffer( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + buffer: Dict[str, Optional[Tensor]], + ): + return self.set_incremental_state(incremental_state, "attn_state", buffer) + + def apply_sparse_mask(attn_weights, tgt_len: int, src_len: int, bsz: int): + return attn_weights + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + items_to_add = {} + keys_to_remove = [] + for k in state_dict.keys(): + if k.endswith(prefix + "in_proj_weight"): + # in_proj_weight used to be q + k + v with same dimensions + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] + items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] + items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] + + keys_to_remove.append(k) + + k_bias = prefix + "in_proj_bias" + if k_bias in state_dict.keys(): + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] + items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][ + dim : 2 * dim + ] + items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] + + keys_to_remove.append(prefix + "in_proj_bias") + + for k in keys_to_remove: + del state_dict[k] + + for key, value in items_to_add.items(): + state_dict[key] = value diff --git a/fairseq/examples/m2m_100/README.md b/fairseq/examples/m2m_100/README.md new file mode 100644 index 0000000..02a68a5 --- /dev/null +++ b/fairseq/examples/m2m_100/README.md @@ -0,0 +1,241 @@ +# Beyond English-Centric Multilingual Machine Translation + +## Introduction +In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively with the best single systems of WMT. + +If you are new to using fairseq, read the following walkthrough. Otherwise, skip to the sections below. + +0. **Generation Data** + +To download the generation data, follow the below commands. Note that all datasets need to be detokenized *before* applying SPM in the data preprocessing step. If you use these evaluation datasets, please cite their associated papers. +```bash +# WMT - use sacrebleu, example here: +sacrebleu -t wmt14 -l fr-en --echo src > wmt.test.fr-en.fr +sacrebleu -t wmt14 -l fr-en --echo ref > wmt.test.fr-en.en + +# WAT +wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip +unzip wat2020.my-en.zip + +# FLORES +# download from: https://github.com/facebookresearch/flores + +# TED - need to detokenize with Moses! +# from: https://github.com/neulab/word-embeddings-for-nmt +wget http://phontron.com/data/ted_talks.tar.gz + +# Autshumato +# request to download: https://repo.sadilar.org/handle/20.500.12185/397 + +# Tatoeba Challenge +# available here: https://github.com/Helsinki-NLP/Tatoeba-Challenge +``` + +1. **Training Data** + +To produce the training data, we use a combination of [CCMatrix](https://arxiv.org/abs/1911.04944) and [CCAligned](https://arxiv.org/abs/1911.06154). Check out the instructions [here](https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix) to download the raw data. + +2. **Preprocess Data** + +After downloading raw data, you will need to postprocess the data, then apply SPM, then binarize. Note that it is very important you run the postprocessing script, because this removes any instance of the evaluation data in the mined training data. + +```bash +# preprocess data + +# remove sentences with more than 50% punctuation +python /path/to/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py + +# deduplicate training data +paste /path/to/datadir/train.$src /path/to/datadir/train.$tgt | awk '!x[$0]++' > /path/to/datadir/train.dedup +echo "keeping $(wc -l /path/to/datadir/train.dedup) bitext out of $(wc -l /path/to/datadir/train.$src)" +cut -f1 /path/to/datadir/train.dedup > /path/to/datadir/train.$src +cut -f2 /path/to/datadir/train.dedup > /path/to/datadir/train.$tgt + +# remove all instances of evaluation data from the training data +python /path/to/fairseq/examples/m2m_100/process_data/dedup_data.py + +# frequency cleaning +wget https://dl.fbaipublicfiles.com/m2m_100/histograms.tar.gz +tar -xvzf histograms.tar.gz +python /path/to/fairseq/examples/m2m_100/process_data/clean_histogram.py --src $src --tgt $tgt --src-file /path/to/source/file --tgt-file /path/to/output/file --src-output-file source_output.$src --tgt-output-file target_output.$tgt --histograms /path/to/histograms + +# apply SPM +wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model +python /path/to/fairseq/scripts/spm_encode.py \ + --model spm.128k.model \ + --output_format=piece \ + --inputs=/path/to/input/file/here \ + --outputs=/path/to/output/file/here + +# length ratio cleaning +perl mosesdecoder/scripts/training/clean-corpus-n.perl --ratio 3 /path/to/training/data/train.spm.$src-$tgt $src $tgt /path/to/output/directory/train.spm.$src-$tgt 1 250 + +# binarize data +wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt +fairseq-preprocess \ + --source-lang $src --target-lang $tgt \ + --testpref spm.$src.$tgt \ + --thresholdsrc 0 --thresholdtgt 0 \ + --destdir data_bin \ + --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt +``` + +3. **Training Scripts** + +To reproduce the training of our models, we train with fairseq-py's multilingual translation [task](https://github.com/pytorch/fairseq/tree/main/examples/multilingual). If you are interested in model parallel training, also check out [fairscale](https://github.com/facebookresearch/fairscale). + +4. **Generation** + +To generate from our models, follow the the commands in the generation section below. + + +If you use any of the resources listed here, please cite: +```bibtex +@article{fan2020beyond, + title={Beyond English-Centric Multilingual Machine Translation}, + author={Fan, Angela and Bhosale, Shruti and Schwenk, Holger and Ma, Zhiyi and El-Kishky, Ahmed and Goyal, Siddharth and Baines, Mandeep and Celebi, Onur and Wenzek, Guillaume and Chaudhary, Vishrav and Goyal, Naman and Birch, Tom and Liptchinsky, Vitaliy and Edunov, Sergey and Grave, Edouard and Auli, Michael and Joulin, Armand}, + journal={arXiv preprint}, + year={2020} +} + +@article{schwenk2019ccmatrix, + title={Ccmatrix: Mining billions of high-quality parallel sentences on the web}, + author={Schwenk, Holger and Wenzek, Guillaume and Edunov, Sergey and Grave, Edouard and Joulin, Armand}, + journal={arXiv preprint arXiv:1911.04944}, + year={2019} +} + +@article{el2019massive, + title={A Massive Collection of Cross-Lingual Web-Document Pairs}, + author={El-Kishky, Ahmed and Chaudhary, Vishrav and Guzman, Francisco and Koehn, Philipp}, + journal={arXiv preprint arXiv:1911.06154}, + year={2019} +} +``` + + +## Trained Models + +### 418M and 1.2B Model +We include the last checkpoint for both of these models. + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt +wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs_small_models.txt + +# 418M parameter model +wget https://dl.fbaipublicfiles.com/m2m_100/418M_last_checkpoint.pt + +# 1.2B parameter model +wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt + +# Generation: +fairseq-generate $binarized_data_path --batch-size 32 --path $path_to_model --fixed-dictionary model_dict.128k.txt -s en -t fr --remove-bpe 'sentencepiece' --beam 5 --task translation_multi_simple_epoch --lang-pairs language_pairs_small_models.txt --decoder-langtok --encoder-langtok src --gen-subset test > gen_out +``` + +### 12B Model +12B parameter model trained on many-to-many training data for 100 languages. We include the last checkpoint, average of last 5 checkpoints, average of last 10 checkpoints. There isn't a universally best choice out of these three, but all three versions are pretty close in accuracy. You can either sweep over the 3 checkpoints on a dev test and use the best performing checkpoint for final testing. Or the last checkpoint can be a good default choice. + +**Model Download Links** +Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs +:--|:--|:--|:--|:-- +Last Checkpoint | [12b_last_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_2_gpus.pt) | [12b_last_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt) | [12b_last_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_6_gpus.pt) | [12b_last_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_8_gpus.pt) +Average of last 5 checkpoints | [12b_avg5_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_2_gpus.pt) | [12b_avg5_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_4_gpus.pt) | [12b_avg5_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_6_gpus.pt) | [12b_avg5_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_8_gpus.pt) +Average of last 10 checkpoints | [12b_avg10_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_2_gpus.pt) | [12b_avg10_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_4_gpus.pt) | [12b_avg10_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_6_gpus.pt) | [12b_avg10_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_8_gpus.pt) + +**Generation Arguments** +Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs +:--|:--|:--|:--|:-- +`--pipeline-encoder-balance` | `[26]` | `[1,15,10]` | `[1,9,9,7]` | `[1,6,6,6,7]` +`--pipeline-encoder-devices` | `[0]` | `[0,1,0]` | `[0,1,2,0]` | `[0,4,5,1,0]` +`--pipeline-decoder-balance` | `[3,22,1]` | `[3,11,11,1]` | `[3,7,7,8,1]` | `[1,6,6,6,6,1]` +`--pipeline-decoder-devices` | `[0,1,0]` | `[0,2,3,0]` | `[0,3,4,5,0]` | `[0,2,6,7,3,0]` + + +## SentencePiece Model + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model +``` + +## Generation with M2M-100 + +### Encode using our SentencePiece Model + +Note: Install SentencePiece from [here](https://github.com/google/sentencepiece) + +```bash +fairseq=/path/to/fairseq +cd $fairseq +sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de +sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr +wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model +for lang in de fr ; do + python scripts/spm_encode.py \ + --model spm.128k.model \ + --output_format=piece \ + --inputs=raw_input.de-fr.${lang} \ + --outputs=spm.de-fr.${lang} +done +``` + +### Binarization + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt +fairseq-preprocess \ + --source-lang de --target-lang fr \ + --testpref spm.de-fr \ + --thresholdsrc 0 --thresholdtgt 0 \ + --destdir data_bin \ + --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt +``` + +### Generation for the 12B model + +Note that generation can currently be run using 2 32GB / 4 16GB / 6 12GB / 8 8GB GPUs, and the corresponding model checkpoints and pipeline arguments can be found in the [12B Model Section](#12b-model). +Generation on CPUs will be added in the future. + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt +wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs.txt +wget https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt +fairseq-generate \ + data_bin \ + --batch-size 1 \ + --path 12b_last_chk_4_gpus.pt \ + --fixed-dictionary model_dict.128k.txt \ + -s de -t fr \ + --remove-bpe 'sentencepiece' \ + --beam 5 \ + --task translation_multi_simple_epoch \ + --lang-pairs language_pairs.txt \ + --decoder-langtok --encoder-langtok src \ + --gen-subset test \ + --fp16 \ + --dataset-impl mmap \ + --distributed-world-size 1 --distributed-no-spawn \ + --pipeline-model-parallel \ + --pipeline-chunks 1 \ + --pipeline-encoder-balance '[1,15,10]' \ + --pipeline-encoder-devices '[0,1,0]' \ + --pipeline-decoder-balance '[3,11,11,1]' \ + --pipeline-decoder-devices '[0,2,3,0]' > gen_out +``` +## Evaluation with M2M-100 + +### Tokenization + +Note: Refer to tokenizers/README.md for more details on tokenization. + +```bash +cd ${fairseq}/examples/m2m_100 +cat ${fairseq}/gen_out | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh fr > hyp +cat ${fairseq}/raw_input.de-fr.fr | sh tok.sh fr > ref +``` + +### BLEU + +```bash +sacrebleu -tok 'none' ref < hyp +``` diff --git a/fairseq/examples/m2m_100/install_dependecies.sh b/fairseq/examples/m2m_100/install_dependecies.sh new file mode 100644 index 0000000..82a1054 --- /dev/null +++ b/fairseq/examples/m2m_100/install_dependecies.sh @@ -0,0 +1,78 @@ +#!/usr/bin/env bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +CWD=`pwd` +INSTALL_PATH=$CWD/tokenizers/thirdparty + +MOSES=$INSTALL_PATH/mosesdecoder +if [ ! -d $MOSES ]; then + echo 'Cloning Moses github repository (for tokenization scripts)...' + git clone https://github.com/moses-smt/mosesdecoder.git $MOSES + cd $MOSES + # To deal with differences in handling ' vs " + git checkout 03578921cc1a03402 + cd - +fi + +WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts +if [ ! -d $WMT16_SCRIPTS ]; then + echo 'Cloning Romanian tokenization scripts' + git clone https://github.com/rsennrich/wmt16-scripts.git $WMT16_SCRIPTS +fi + +KYTEA=$INSTALL_PATH/kytea +if [ ! -f $KYTEA/bin/kytea ]; then + git clone https://github.com/neubig/kytea.git $KYTEA + cd $KYTEA + autoreconf -i + ./configure --prefix=`pwd` + make + make install + cd .. +fi + +export MECAB=$INSTALL_PATH/mecab-0.996-ko-0.9.2 +if [ ! -f $MECAB/bin/mecab ]; then + cd $INSTALL_PATH + curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz + tar zxfv mecab-0.996-ko-0.9.2.tar.gz + cd mecab-0.996-ko-0.9.2/ + ./configure --prefix=`pwd` + make + make install + + cd .. + curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz + tar zxfv mecab-ko-dic-2.1.1-20180720.tar.gz + cd mecab-ko-dic-2.1.1-20180720/ + ./autogen.sh + ./configure --prefix=`pwd` --with-dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic --with-mecab-config=$MECAB/bin/mecab-config + make + sh -c 'echo "dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic" > $MECAB/etc/mecabrc' + make install + cd $CWD +fi + +INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources +if [ ! -d $INDIC_RESOURCES_PATH ]; then + echo 'Cloning indic_nlp_resources' + git clone https://github.com/anoopkunchukuttan/indic_nlp_resources.git $INDIC_RESOURCES_PATH +fi + + +if [ ! -f $INSTALL_PATH/seg_my.py ]; then + cd $INSTALL_PATH + wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip + unzip wat2020.my-en.zip + # switch to python3 + cat wat2020.my-en/myseg.py |sed 's/^sys.std/###sys.std/g' | sed 's/### sys/sys/g' | sed 's/unichr/chr/g' > seg_my.py + cd $CWD +fi + + +pip install pythainlp sacrebleu indic-nlp-library + diff --git a/fairseq/examples/m2m_100/process_data/clean_histogram.py b/fairseq/examples/m2m_100/process_data/clean_histogram.py new file mode 100644 index 0000000..e24e073 --- /dev/null +++ b/fairseq/examples/m2m_100/process_data/clean_histogram.py @@ -0,0 +1,52 @@ +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument('--src', type=str, help='Source language') +parser.add_argument('--tgt', type=str, help='Target language') +parser.add_argument('--src-file', type=str, help='Input source file') +parser.add_argument('--tgt-file', type=str, help='Input target file') +parser.add_argument('--src-output-file', type=str, help='Output source file') +parser.add_argument('--tgt-output-file', type=str, help='Output target file') +parser.add_argument('--threshold', type=float, default=0.5, help='Threshold') +parser.add_argument('--threshold-character', type=str, default=']', help='Threshold character') +parser.add_argument('--histograms', type=str, help='Path to histograms') + +args = parser.parse_args() + + +def read_hist(f): + ch = [] + for line in f: + c = line[0] + if c == args.threshold_character: + break + ch.append(c) + return ch + + +with(open("{}/{}".format(args.histograms, args.src), 'r', encoding='utf8')) as f: + ch1 = read_hist(f) + +with(open("{}/{}".format(args.histograms, args.tgt), 'r', encoding='utf8')) as f: + ch2 = read_hist(f) + +print("Accepted characters for {}: {}".format(args.src, ch1)) +print("Accepted characters for {}: {}".format(args.tgt, ch2)) + +with open(args.src_file, 'r', encoding='utf8') as fs1, open(args.tgt_file, 'r', encoding='utf8') as fs2, open(args.src_output_file, 'w', encoding='utf8') as fos1, open(args.tgt_output_file, 'w', encoding='utf8') as fos2: + ls1 = fs1.readline() + ls2 = fs2.readline() + + while ls1 or ls2: + cnt1 = len([c for c in ls1.strip() if c in ch1]) + cnt2 = len([c for c in ls2.strip() if c in ch2]) + + if cnt1 / len(ls1) > args.threshold and cnt2 / len(ls2) > args.threshold: + fos1.write(ls1) + fos2.write(ls2) + else: + print("{} {} {} \n{} {} {}".format(args.src, cnt1 / len(ls1), ls1.strip(), args.tgt, cnt2 / len(ls2), ls2.strip())) + + ls1 = fs1.readline() + ls2 = fs2.readline() + \ No newline at end of file diff --git a/fairseq/examples/m2m_100/process_data/dedup_data.py b/fairseq/examples/m2m_100/process_data/dedup_data.py new file mode 100644 index 0000000..58d9ed1 --- /dev/null +++ b/fairseq/examples/m2m_100/process_data/dedup_data.py @@ -0,0 +1,91 @@ +import argparse +from collections import namedtuple +import os + +DATADIR = "/path/to/train_data" +DEDUP_FROM_DIR = "/path/to/eval/data" +OUTPUT_DIR = "/path/to/output/data" + + +def main(args): + languages = set() + for language_directory in os.listdir(DATADIR): + if "_" in language_directory: + src, tgt = language_directory.split("_") + languages.add(LanguagePair(src=src, tgt=tgt)) + + data = existing_data() + train_languages = sorted(languages) + for language_pair in train_languages[args.start_index:args.start_index + args.size]: + print(language_pair) + dedup(language_pair, data) + + +LanguagePair = namedtuple("LanguagePair", ["src", "tgt"]) + + +def existing_data(): + data = set() + for file in os.listdir(DEDUP_FROM_DIR): + with open(os.path.join(DEDUP_FROM_DIR, file)) as f: + data |= set(f.readlines()) + return data + +def dedup(language_pair, data, verbose=True, output=True): + train_filenames = LanguagePair( + src=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.src}", + tgt=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.tgt}", + ) + + output_filenames = LanguagePair( + src=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.src}", + tgt=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.tgt}" + ) + + # If output exists, skip this pair. It has already been done. + if (os.path.exists(output_filenames.src) and + os.path.exists(output_filenames.tgt)): + if verbose: + print(f"{language_pair.src}-{language_pair.tgt} already done.") + return + + if verbose: + print(f"{language_pair.src}-{language_pair.tgt} ready, will check dups.") + + # If there is no output, no need to actually do the loop. + if not output: + return + + if os.path.exists(train_filenames.src) and os.path.exists(train_filenames.tgt): + with open(train_filenames.src) as f: + train_source = f.readlines() + + with open(train_filenames.tgt) as f: + train_target = f.readlines() + + # do dedup + new_train_source = [] + new_train_target = [] + for i, train_line in enumerate(train_source): + if train_line not in data and train_target[i] not in data: + new_train_source.append(train_line) + new_train_target.append(train_target[i]) + + assert len(train_source) == len(train_target) + assert len(new_train_source) == len(new_train_target) + assert len(new_train_source) <= len(train_source) + + with open(output_filenames.src, "w") as o: + for line in new_train_source: + o.write(line) + + with open(output_filenames.tgt, "w") as o: + for line in new_train_target: + o.write(line) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--start-index", required=True, type=int) + parser.add_argument("-n", "--size", required=True, type=int) + main(parser.parse_args()) diff --git a/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py b/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py new file mode 100644 index 0000000..6c280de --- /dev/null +++ b/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py @@ -0,0 +1,36 @@ +import gzip +import argparse +from string import punctuation + +def len_no_punc(s, punc): + return len([ch for ch in s if ch in punc]) + +def filter_overpunc(len_npunc, len_sen): + return len_npunc < 0.5*len_sen + +def main(args): + punc = punctuation + "—|–" + print('Processing file {}'.format(args.input)) + with gzip.open(args.input, 'rt', encoding=args.encoding) as tsv: + with open(args.bitext + '.' + args.src_lang, 'wt', encoding=args.encoding) as fsrc: + with open(args.bitext + '.' + args.tgt_lang, 'wt', encoding=args.encoding) as ftgt: + line = tsv.readline() + fields = line.split('\t') + + src, tgt = fields[1], fields[2] + + nchar_npunc_src = len_no_punc(src, punc) + nchar_npunc_tgt = len_no_punc(tgt, punc) + + if filter_overpunc(nchar_npunc_src, len(src)) and filter_overpunc(nchar_npunc_tgt, len(tgt)): + fsrc.write(src.strip() + '\n') + ftgt.write(tgt.strip() + '\n') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--input", required=True, type=str) + parser.add_argument('--encoding', default='utf-8', help='character encoding for input/output') + parser.add_argument('--bitext', type=str, required=True, help='language direction') + parser.add_argument('--src-lang', type=str, required=True, help='Source language') + parser.add_argument('--tgt-lang', type=str, required=True, help='Target language') + main(parser.parse_args()) diff --git a/fairseq/examples/m2m_100/tok.sh b/fairseq/examples/m2m_100/tok.sh new file mode 100644 index 0000000..ba2ec5a --- /dev/null +++ b/fairseq/examples/m2m_100/tok.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env bash +# Copyright (c) 2019-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# + +set -e + +TOKENIZERS_SCRIPTS=tokenizers +INSTALL_PATH=$TOKENIZERS_SCRIPTS/thirdparty + +N_THREADS=8 + +lg=$1 + +MOSES=$INSTALL_PATH/mosesdecoder +REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl +NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl +TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl + +# special tokenization for Romanian +WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts + +NORMALIZE_ROMANIAN=$WMT16_SCRIPTS/preprocess/normalise-romanian.py +REMOVE_DIACRITICS=$WMT16_SCRIPTS/preprocess/remove-diacritics.py + +# Burmese +MY_SEGMENT=$INSTALL_PATH/seg_my.py + +# Arabic +AR_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenizer_ar.sh + +# Korean +KO_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ko.sh + +# Japanese +JA_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ja.sh + +# Indic +IN_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_indic.py +INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources + +# Thai +THAI_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_thai.py + +# Chinese +CHINESE_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_zh.py + +# Chinese +if [ "$lg" = "zh" ]; then + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | python $CHINESE_TOKENIZER +# Thai +elif [ "$lg" = "th" ]; then + cat - | python $THAI_TOKENIZER +# Japanese +elif [ "$lg" = "ja" ]; then + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | ${JA_SEGMENT} +# Korean +elif [ "$lg" = "ko" ]; then + cat - | $REM_NON_PRINT_CHAR | ${KO_SEGMENT} +# Romanian +elif [ "$lg" = "ro" ]; then + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $NORMALIZE_ROMANIAN | $REMOVE_DIACRITICS | $TOKENIZER -no-escape -threads $N_THREADS -l $lg +# Burmese +elif [ "$lg" = "my" ]; then + cat - | python ${MY_SEGMENT} +# Arabic +elif [ "$lg" = "ar" ]; then + cat - | ${AR_TOKENIZER} +# Indic +elif [ "$lg" = "ne" ]; then + cat - | python ${IN_TOKENIZER} $lg +elif [ "$lg" = "si" ]; then + cat - | python ${IN_TOKENIZER} $lg +elif [ "$lg" = "hi" ]; then + cat - | python ${IN_TOKENIZER} $lg +# other languages +else + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape -threads $N_THREADS -l $lg +fi diff --git a/fairseq/examples/m2m_100/tokenizers/README.md b/fairseq/examples/m2m_100/tokenizers/README.md new file mode 100644 index 0000000..e116932 --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/README.md @@ -0,0 +1,18 @@ +# M2M-100 Tokenization + +We apply different tokenization strategies for different languages following the existing literature. Here we provide tok.sh a tokenizer that can be used to reproduce our results. + +To reproduce the results, follow these steps: + +``` +tgt_lang=... +reference_translation=... +cat generation_output | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh $tgt_lang > hyp +cat $reference_translation |sh tok.sh $tgt_lang > ref +sacrebleu -tok 'none' ref < hyp +``` + +## Installation + +Tools needed for all the languages except Arabic can be installed by running install_dependencies.sh +If you want to evaluate Arabic models, please follow the instructions provided here: http://alt.qcri.org/tools/arabic-normalizer/ to install diff --git a/fairseq/examples/m2m_100/tokenizers/seg_ja.sh b/fairseq/examples/m2m_100/tokenizers/seg_ja.sh new file mode 100644 index 0000000..be6f5ca --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/seg_ja.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +SCRIPT=`realpath $0` +KYTEA=`dirname $SCRIPT`/thirdparty/kytea +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$KYTEA/lib:/usr/local/lib +export PATH=$PATH:"$KYTEA/bin" + +cat - | tr -d "[:blank:]" | kytea -notags diff --git a/fairseq/examples/m2m_100/tokenizers/seg_ko.sh b/fairseq/examples/m2m_100/tokenizers/seg_ko.sh new file mode 100644 index 0000000..c523d92 --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/seg_ko.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +SCRIPT=`realpath $0` +MECAB=`dirname $SCRIPT`/thirdparty/mecab-0.996-ko-0.9.2 + +export PATH=$PATH:"$MECAB/bin":"$MECAB/lib" +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:"$MECAB/lib" + +cat - | mecab -O wakati diff --git a/fairseq/examples/m2m_100/tokenizers/thirdparty/.gitignore b/fairseq/examples/m2m_100/tokenizers/thirdparty/.gitignore new file mode 100644 index 0000000..19eb6a9 --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/thirdparty/.gitignore @@ -0,0 +1,12 @@ +seg_my.py +indic_nlp_library/ +indic_nlp_resources/ +kytea/ +mecab-0.996-ko-0.9.2.tar.gz +mecab-0.996-ko-0.9.2/ +mosesdecoder/ +wat2020.my-en.zip +wat2020.my-en/ +wmt16-scripts/ +mecab-ko-dic-2.1.1-20180720/ +mecab-ko-dic-2.1.1-20180720.tar.gz \ No newline at end of file diff --git a/fairseq/examples/m2m_100/tokenizers/tokenize_indic.py b/fairseq/examples/m2m_100/tokenizers/tokenize_indic.py new file mode 100644 index 0000000..a44fad0 --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/tokenize_indic.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Use: echo {text} | python tokenize_indic.py {language} + +import sys + +from indicnlp.normalize.indic_normalize import IndicNormalizerFactory +from indicnlp.tokenize.indic_tokenize import trivial_tokenize + + +factory = IndicNormalizerFactory() +normalizer = factory.get_normalizer( + sys.argv[1], remove_nuktas=False, nasals_mode="do_nothing" +) + +for line in sys.stdin: + normalized_line = normalizer.normalize(line.strip()) + tokenized_line = " ".join(trivial_tokenize(normalized_line, sys.argv[1])) + print(tokenized_line) diff --git a/fairseq/examples/m2m_100/tokenizers/tokenize_thai.py b/fairseq/examples/m2m_100/tokenizers/tokenize_thai.py new file mode 100644 index 0000000..9c72cb8 --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/tokenize_thai.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +from pythainlp import word_tokenize + + +for line in sys.stdin: + print(" ".join(word_tokenize(line.strip()))) diff --git a/fairseq/examples/m2m_100/tokenizers/tokenize_zh.py b/fairseq/examples/m2m_100/tokenizers/tokenize_zh.py new file mode 100644 index 0000000..674b584 --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/tokenize_zh.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import fileinput + +import sacrebleu + + +for line in fileinput.input(): + print(sacrebleu.tokenize_zh(line)) diff --git a/fairseq/examples/m2m_100/tokenizers/tokenizer_ar.sh b/fairseq/examples/m2m_100/tokenizers/tokenizer_ar.sh new file mode 100644 index 0000000..ad35d7a --- /dev/null +++ b/fairseq/examples/m2m_100/tokenizers/tokenizer_ar.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env sh +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# +# Please follow the instructions here http://alt.qcri.org/tools/arabic-normalizer/ +# to install tools needed for Arabic + +echo "Please install Arabic tools: http://alt.qcri.org/tools/arabic-normalizer/" +echo "Then update environment variables in tokenizer_ar.sh" +exit 1 + +SVMTOOL=... +GOMOSESGO=... +QCRI_ARABIC_NORMALIZER=... + +export PERL5LIB="$SVMTOOL/lib":"$GOMOSESGO/bin/MADA-3.2":$PERL5LIB + + +tempfile=$(mktemp) +cat - > $tempfile + +cd $QCRI_ARABIC_NORMALIZER + +bash qcri_normalizer_mada3.2_aramorph1.2.1.sh $tempfile +cat $tempfile.mada_norm-aramorph.europarl_tok diff --git a/fairseq/examples/mbart/README.md b/fairseq/examples/mbart/README.md new file mode 100644 index 0000000..a45e372 --- /dev/null +++ b/fairseq/examples/mbart/README.md @@ -0,0 +1,123 @@ +# MBART: Multilingual Denoising Pre-training for Neural Machine Translation +[https://arxiv.org/abs/2001.08210] + +## Introduction + +MBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. + +## Pre-trained models + +Model | Description | # params | Download +---|---|---|--- +`mbart.CC25` | mBART model with 12 encoder and decoder layers trained on 25 languages' monolingual corpus | 610M | [mbart.CC25.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.v2.tar.gz) +`mbart.ft.ro_en` | finetune mBART cc25 model on ro-en language pairs | 610M | [mbart.cc25.ft.enro.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.ft.enro.tar.gz) + +## Results + +**[WMT16 EN-RO](https://www.statmt.org/wmt16/translation-task.html)** + +_(test set, no additional data used)_ + +Model | en-ro | ro-en +---|---|--- +`Random` | 34.3 | 34.0 +`mbart.cc25` | 37.7 | 37.8 +`mbart.enro.bilingual` | 38.5 | 38.5 + +## BPE data +# download model +wget https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.v2.tar.gz +tar -xzvf mbart.CC25.tar.gz +# bpe data +install SPM [here](https://github.com/google/sentencepiece) +```bash +SPM=/path/to/sentencepiece/build/src/spm_encode +MODEL=sentence.bpe.model +${SPM} --model=${MODEL} < ${DATA}/${TRAIN}.${SRC} > ${DATA}/${TRAIN}.spm.${SRC} & +${SPM} --model=${MODEL} < ${DATA}/${TRAIN}.${TGT} > ${DATA}/${TRAIN}.spm.${TGT} & +${SPM} --model=${MODEL} < ${DATA}/${VALID}.${SRC} > ${DATA}/${VALID}.spm.${SRC} & +${SPM} --model=${MODEL} < ${DATA}/${VALID}.${TGT} > ${DATA}/${VALID}.spm.${TGT} & +${SPM} --model=${MODEL} < ${DATA}/${TEST}.${SRC} > ${DATA}/${TEST}.spm.${SRC} & +${SPM} --model=${MODEL} < ${DATA}/${TEST}.${TGT} > ${DATA}/${TEST}.spm.${TGT} & +``` + +## Preprocess data + +```bash +DICT=dict.txt +fairseq-preprocess \ + --source-lang ${SRC} \ + --target-lang ${TGT} \ + --trainpref ${DATA}/${TRAIN}.spm \ + --validpref ${DATA}/${VALID}.spm \ + --testpref ${DATA}/${TEST}.spm \ + --destdir ${DEST}/${NAME} \ + --thresholdtgt 0 \ + --thresholdsrc 0 \ + --srcdict ${DICT} \ + --tgtdict ${DICT} \ + --workers 70 +``` + +## Finetune on EN-RO +Finetune on mbart CC25 + +```bash +PRETRAIN=mbart.cc25 # fix if you moved the downloaded checkpoint +langs=ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN + +fairseq-train path_2_data \ + --encoder-normalize-before --decoder-normalize-before \ + --arch mbart_large --layernorm-embedding \ + --task translation_from_pretrained_bart \ + --source-lang en_XX --target-lang ro_RO \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler polynomial_decay --lr 3e-05 --warmup-updates 2500 --total-num-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 \ + --restore-file $PRETRAIN \ + --reset-optimizer --reset-meters --reset-dataloader --reset-lr-scheduler \ + --langs $langs \ + --ddp-backend legacy_ddp +``` +## Generate on EN-RO +Get sacrebleu on finetuned en-ro model + +get tokenizer [here](https://github.com/rsennrich/wmt16-scripts) +```bash +wget https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.ft.enro.tar.gz +tar -xzvf mbart.cc25.ft.enro.tar.gz +``` + +```bash +model_dir=MBART_finetuned_enro # fix if you moved the checkpoint + +fairseq-generate path_2_data \ + --path $model_dir/model.pt \ + --task translation_from_pretrained_bart \ + --gen-subset test \ + -t ro_RO -s en_XX \ + --bpe 'sentencepiece' --sentencepiece-model $model_dir/sentence.bpe.model \ + --sacrebleu --remove-bpe 'sentencepiece' \ + --batch-size 32 --langs $langs > en_ro + +cat en_ro | grep -P "^H" |sort -V |cut -f 3- | sed 's/\[ro_RO\]//g' |$TOKENIZER ro > en_ro.hyp +cat en_ro | grep -P "^T" |sort -V |cut -f 2- | sed 's/\[ro_RO\]//g' |$TOKENIZER ro > en_ro.ref +sacrebleu -tok 'none' -s 'none' en_ro.ref < en_ro.hyp +``` + +## Citation + +```bibtex +@article{liu2020multilingual, + title={Multilingual Denoising Pre-training for Neural Machine Translation}, + author={Yinhan Liu and Jiatao Gu and Naman Goyal and Xian Li and Sergey Edunov and Marjan Ghazvininejad and Mike Lewis and Luke Zettlemoyer}, + year={2020}, + eprint={2001.08210}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/fairseq/examples/megatron_11b/README.md b/fairseq/examples/megatron_11b/README.md new file mode 100644 index 0000000..945c96c --- /dev/null +++ b/fairseq/examples/megatron_11b/README.md @@ -0,0 +1,161 @@ +# Megatron-11b + +Megatron-11b is a unidirectional language model with `11B` parameters based on [Megatron-LM](https://arxiv.org/pdf/1909.08053.pdf). Following the original Megatron work, we trained the model using intra-layer model parallelism with each layer's parameters split across 8 GPUs. + +Megatron-11b is trained on the same data and uses the same byte-pair encoding (BPE) as [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf). + +## Pre-trained models + +Model | Description | # params | # filesize | Download +---|---|---|---|--- +`megatron_11b` | megatron_11b unidirectional language model | 11B | 19Gb | [megatron_11b.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/model_parallel/megatron_11b.tar.gz) + +#### Architecture: + +Param | Value +---|--- +embed_dim | 3072 +ffn_dim | 3072 * 6 +layers | 72 +attention heads | 32 + +#### Training details: + +Param | value +---|--- +bsz | 512 +num_updates | 300,000 +peak_lr | 1.5e-04 +lr scheduler | inverse_sqrt +clip norm | 0.0 + + +## Example training command (model parallel) + +Megatron-11b contains too many parameters to train on a single GPU. Following +the original Megatron work, we adopt an intra-layer model parallel training +approach in which each layer's parameters are split across multiple GPUs and +activations and gradients are communicated during the forward/backward pass, +respectively. We similarly split the loss computation using the +`vocab_parallel_cross_entropy` criterion. + +The following training command illustrates how to do model parallel training in +fairseq. We assume that each machine (node) has 8 GPUs among which to split the +model parameters (`--model-parallel-size 8`). If you have access to multiple +nodes, you may combine this with data parallel training by increasing +`--distributed-world-size`. + +To train Megatron-11b on a single node: + + +```bash +fairseq-train \ + --distributed-world-size 8 \ + --memory-efficient-fp16 \ + --num-workers 2 \ + --model-parallel-size 8 \ + --criterion vocab_parallel_cross_entropy \ + --task language_modeling \ + --sample-break-mode none \ + --tokens-per-sample 1024 \ + --arch transformer_lm_megatron_11b \ + --share-decoder-input-output-embed \ + --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 --clip-norm 0.0 \ + --lr-scheduler inverse_sqrt --lr 0.00015 \ + --warmup-updates 3000 --weight-decay 0.01 \ + --dropout 0.1 --attention-dropout 0.1 \ + --batch-size 2 \ + --max-update 300000; +``` + +Note: Above was tested on `DGX-1` box, with `8xV100-32Gb` GPUs. + +## Results + +**[Wikitext103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)** + +Model | Valid perplexity | Test perplexity +---|---|--- +`megatron_11b` | 10.64 | 10.54 + + +## Evaluating `megatron_11b` on Wikitext-103 + +#### 1. Downloading Megatron-11b +```bash +# WARNING: this file is 19GB +wget https://dl.fbaipublicfiles.com/fairseq/models/model_parallel/megatron_11b.tar.gz +tar -xzvf megatron_11b.tar.gz +``` + +#### 2. Download Wikitext-103 +```bash +wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip +unzip wikitext-103-raw-v1.zip +``` + +#### 3. Detokenize test tokens +Megatron-11b uses a byte-level BPE that expects raw (untokenized) input. Since +the wikitext-103 dataset comes tokenized, we apply a simple detokenization +process to restore the untokenized test set: + +```bash +python -m examples.megatron_11b.detok wikitext-103-raw/wiki.test.raw > wikitext-103-raw/wiki.test.detok +``` + +#### 4. BPE encoding +```bash +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' + +python -m examples.roberta.multiprocessing_bpe_encoder \ + --encoder-json encoder.json \ + --vocab-bpe vocab.bpe \ + --inputs "wikitext-103-raw/wiki.test.detok" \ + --outputs "wikitext-103-raw/wiki.test.bpe" \ + --workers 60; +``` + +#### 5. Fairseq binarize +```bash +fairseq-preprocess \ + --only-source \ + --testpref wikitext-103-raw/wiki.test.bpe \ + --srcdict megatron_11b/dict.txt \ + --destdir wikitext103-bin; +``` + +#### 6. Evaluating perplexity. +We can now evaluate perplexity on the test set. Note that because we've modified +the test set (via detokenization and BPE), the perplexity reported by +`fairseq-eval-lm` needs to be renormalized. + +Compute unnormalized perplexity: + +```bash +DATA_PATH=wikitext103-bin/ +fairseq-eval-lm \ + $DATA_PATH \ + --path megatron_11b/model.pt \ + --task language_modeling \ + --gen-subset test \ + --batch-size 8 \ + --criterion cross_entropy \ + --context-window 992 \ + --distributed-world-size 8 \ + --model-parallel-size 8; +# Expected PPL (unnormalized_ppl): [8.46] +# Note: the eval command needs to run on 8 GPUs for the released model +``` +Renormalizing formula: `2 ^ ( log_2(unnormalized_PPL) * (270847 / 245566))`. +PPL After normalization: `10.54` + +To renormalize the perplexity, we must account for the change in token count +after detokenizing and appling BPE. The formula for this is: +`2 ^ ( log_2(unnormalized_PPL) * (new_token_cnt / orig_token_cnt))` + +For the wikitext-103 test set, the original token count is `245566` and the +token count after detokenization and applying BPE is `270847`. + +The perplexity after renormalization is: +`2 ^ ( log_2(8.46) * (270847 / 245566)) = 10.54` diff --git a/fairseq/examples/megatron_11b/detok.py b/fairseq/examples/megatron_11b/detok.py new file mode 100644 index 0000000..49921b2 --- /dev/null +++ b/fairseq/examples/megatron_11b/detok.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import fileinput + +import sacremoses + + +def main(): + parser = argparse.ArgumentParser(description="") + parser.add_argument("files", nargs="*", help="input files") + args = parser.parse_args() + + detok = sacremoses.MosesDetokenizer() + + for line in fileinput.input(args.files, openhook=fileinput.hook_compressed): + print( + detok.detokenize(line.strip().split(" ")) + .replace(" @", "") + .replace("@ ", "") + .replace(" =", "=") + .replace("= ", "=") + .replace(" – ", "–") + ) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/mms/MODEL_CARD.md b/fairseq/examples/mms/MODEL_CARD.md new file mode 100644 index 0000000..63f997f --- /dev/null +++ b/fairseq/examples/mms/MODEL_CARD.md @@ -0,0 +1,63 @@ +# MMS Model Card + +## Model details + +**Organization developing the model** The FAIR team + +**Model version** This is version 1 of the model. + +**Model type** MMS is speech model, based on the transformer architecture. The pre-trained model comes in two sizes: 300M and 1B parameters. We fine-tune the model for speech recognition and make it available in the 1B variant. We also fine-tune the 1B variant for language identification. + +**License** CC BY-NC + +**Where to send questions or comments about the model** Questions and comments about MMS can be sent via the [GitHub repository](https://github.com/pytorch/fairseq/tree/master/examples/mms) of the project , by opening an issue and tagging it as MMS. + +## Uses + +**Primary intended uses** The primary use of MMS is to perform speech processing research for many more languages and to perform tasks such as automatic speech recognition, language identification, and speech synthesis. + +**Primary intended users** The primary intended users of the model are researchers in speech processing, machine learning and artificial intelligence. + +**Out-of-scope use cases** Fine-tuning the pre-pretrained models on other labeled datasets or downstream tasks requires further risk evaluation and mitigation. + +## Bias and Risks + +The MMS models were pre-trained on a blend of data from different domains, including readings of the New Testament. In the paper, we describe two studies analyzing gender bias and the use of religious language which conclude that models perform equally well for both genders and that on average, there is little bias for religious language (section 8 of the paper). + +# Training Details + +## Training Data + +MMS is pre-trained on VoxPopuli (parliamentary speech), MLS (read audiobooks), VoxLingua-107 (YouTube speech), CommonVoice (read Wikipedia text), BABEL (telephone conversations), and MMS-lab-U (New Testament readings), MMS-unlab (various read Christian texts). +Models are fine-tuned on FLEURS, VoxLingua-107, MLS, CommonVoice, and MMS-lab. We obtained the language information for MMS-lab, MMS-lab-U and MMS-unlab from our data soucrce and did not manually verify it for every language. + +## Training Procedure + +Please refer to the research paper for details on this. + +# Evaluation + +## Testing Data, Factors & Metrics + +We evaluate the model on a different benchmarks for the downstream tasks. The evaluation details are presented in the paper. The models performance is measured using standard metrics such as character error rate, word error rate, and classification accuracy. + + +# Citation + +**BibTeX:** + +``` +@article{pratap2023mms, + title={Scaling Speech Technology to 1,000+ Languages}, + author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, + journal={arXiv}, + year={2023} +} + +``` + +# Model Card Contact + +Please reach out to the authors at: [vineelkpratap@meta.com](mailto:vineelkpratap@meta.com) [androstj@meta.com](mailto:androstj@meta.com) [bshi@meta.com](mailto:bshi@meta.com) [michaelauli@meta.com](mailto:michaelauli@gmail.com) + + diff --git a/fairseq/examples/mms/README.md b/fairseq/examples/mms/README.md new file mode 100644 index 0000000..0460dd5 --- /dev/null +++ b/fairseq/examples/mms/README.md @@ -0,0 +1,215 @@ +# MMS: Scaling Speech Technology to 1000+ languages + +The Massively Multilingual Speech (MMS) project expands speech technology from about 100 languages to over 1,000 by building a single multilingual speech recognition model supporting over 1,100 languages (more than 10 times as many as before), language identification models able to identify over [4,000 languages](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html) (40 times more than before), pretrained models supporting over 1,400 languages, and text-to-speech models for over 1,100 languages. Our goal is to make it easier for people to access information and to use devices in their preferred language. + +You can find details in the paper [Scaling Speech Technology to 1000+ languages](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/) and the [blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/). + +An overview of the languages covered by MMS can be found [here](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). + +## 🤗 Transformers + +MMS has been added to Transformers. For more information, please refer to [Transformers' MMS docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). + +[Click here](https://huggingface.co/models?other=mms) to find all MMS checkpoints on the Hub. + +Checkout the demo here [![Open In HF Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm-dark.svg)](https://huggingface.co/spaces/facebook/MMS) + +## Finetuned models +### ASR + +| Model | Languages | Dataset | Model | Dictionary* | Supported languages | | +|---|---|---|---|---|---|--- +MMS-1B:FL102 | 102 | FLEURS | [download](https://dl.fbaipublicfiles.com/mms/asr/mms1b_fl102.pt) | [download](https://dl.fbaipublicfiles.com/mms/asr/dict/mms1b_fl102/eng.txt) | [download](https://dl.fbaipublicfiles.com/mms/asr/mms1b_fl102_langs.html) | [🤗 Hub](https://huggingface.co/facebook/mms-1b-fl102) +MMS-1B:L1107| 1107 | MMS-lab | [download](https://dl.fbaipublicfiles.com/mms/asr/mms1b_l1107.pt) | [download](https://dl.fbaipublicfiles.com/mms/asr/dict/mms1b_l1107/eng.txt) | [download](https://dl.fbaipublicfiles.com/mms/asr/mms1b_l1107_langs.html) | [🤗 Hub](https://huggingface.co/facebook/mms-1b-l1107) +MMS-1B-all| 1162 | MMS-lab + FLEURS
+ CV + VP + MLS | [download](https://dl.fbaipublicfiles.com/mms/asr/mms1b_all.pt) | [download](https://dl.fbaipublicfiles.com/mms/asr/dict/mms1b_all/eng.txt) | [download](https://dl.fbaipublicfiles.com/mms/asr/mms1b_all_langs.html) | [🤗 Hub](https://huggingface.co/facebook/mms-1b-all) + +\* In the `Dictionary` column, we provide the download link for token dictionary in English language. To download token dictionary for a different language supported by the model, modify the language code in the URL appropriately. For example, to get token dictionary of FL102 model for Hindi language, use [this](https://dl.fbaipublicfiles.com/mms/asr/dict/mms1b_fl102/hin.txt) link. + +### TTS +1. Download the list of [iso codes](https://dl.fbaipublicfiles.com/mms/tts/all-tts-languages.html) of 1107 languages. +2. Find the iso code of the target language and download the checkpoint. Each folder contains 3 files: `G_100000.pth`, `config.json`, `vocab.txt`. The `G_100000.pth` is the generator trained for 100K updates, `config.json` is the training config, `vocab.txt` is the vocabulary for the TTS model. +``` +# Examples: +wget https://dl.fbaipublicfiles.com/mms/tts/eng.tar.gz # English (eng) +wget https://dl.fbaipublicfiles.com/mms/tts/azj-script_latin.tar.gz # North Azerbaijani (azj-script_latin) +``` +The above command downloads generator only, which is enough to run TTS inference. If you want the full model checkpoint which also includes the discriminator (`D_100000.pth`) and the optimizer states, download as follows. +``` +# Example (full checkpoint: generator + discriminator + optimizer): +wget https://dl.fbaipublicfiles.com/mms/tts/full_model/eng.tar.gz # English (eng) +``` + + +### LID + +\# Languages | Dataset | Model | Dictionary | Supported languages | | +|---|---|---|---|---|--- +126 | FLEURS + VL + MMS-lab-U + MMS-unlab | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l126.pt) | [download](https://dl.fbaipublicfiles.com/mms/lid/dict/l126/dict.lang.txt) | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l126_langs.html) | [🤗 Hub](https://huggingface.co/facebook/mms-lid-126) +256 | FLEURS + VL + MMS-lab-U + MMS-unlab | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l256.pt) | [download](https://dl.fbaipublicfiles.com/mms/lid/dict/l256/dict.lang.txt) | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l256_langs.html) | [🤗 Hub](https://huggingface.co/facebook/mms-lid-256) +512 | FLEURS + VL + MMS-lab-U + MMS-unlab | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l512.pt) | [download](https://dl.fbaipublicfiles.com/mms/lid/dict/l512/dict.lang.txt) | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l512_langs.html)| [🤗 Hub](https://huggingface.co/facebook/mms-lid-512) +1024 | FLEURS + VL + MMS-lab-U + MMS-unlab | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l1024.pt) | [download](https://dl.fbaipublicfiles.com/mms/lid/dict/l1024/dict.lang.txt) | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l1024_langs.html)| [🤗 Hub](https://huggingface.co/facebook/mms-lid-1024) +2048 | FLEURS + VL + MMS-lab-U + MMS-unlab | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l2048.pt) | [download](https://dl.fbaipublicfiles.com/mms/lid/dict/l2048/dict.lang.txt) | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l2048_langs.html)| [🤗 Hub](https://huggingface.co/facebook/mms-lid-2048) +4017 | FLEURS + VL + MMS-lab-U + MMS-unlab | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l4017.pt) | [download](https://dl.fbaipublicfiles.com/mms/lid/dict/l4017/dict.lang.txt) | [download](https://dl.fbaipublicfiles.com/mms/lid/mms1b_l4017_langs.html)| [🤗 Hub](https://huggingface.co/facebook/mms-lid-4017) + +## Commands to run inference + +### ASR +Run this command to transcribe one or more audio files: +```shell command +cd /path/to/fairseq-py/ +python examples/mms/asr/infer/mms_infer.py --model "/path/to/asr/model" --lang lang_code \ + --audio "/path/to/audio_1.wav" "/path/to/audio_2.wav" "/path/to/audio_3.wav" +``` +We also provide an Ipython notebook example inside `asr/tutorial` folder [ipynb](https://github.com/facebookresearch/fairseq/blob/main/examples/mms/asr/tutorial/MMS_ASR_Inference_Colab.ipynb) or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/fairseq/blob/main/examples/mms/asr/tutorial/MMS_ASR_Inference_Colab.ipynb) + + +For more advance configuration and calculate CER/WER, you could prepare manifest folder by creating a folder with this format: +``` +$ ls /path/to/manifest +dev.tsv +dev.wrd +dev.ltr +dev.uid + +# dev.tsv each line contains `), which corresponds to embedding index `2`. +Thus **the model never saw newline characters during pretraining** and newlines should not be used during few-shot prompting. + +This is more clearly illustrated in the following example, which uses fairseq's Hub Interface to tokenize two documents in the desired format: +```python +from fairseq.models.transformer_lm import TransformerLanguageModel +model_dir = '/path/to/en_dense_lm_125m' +lm = TransformerLanguageModel.from_pretrained(model_dir, bpe='gpt2') + +data = """\ +This is the first paragraph of the first document. +This is the second paragraph of the first document. + +This is the first paragraph of the second document.\ +""" + +# The following is wrong, since it will encode newlines present in `data`. +tokens_bad = lm.score(data)['tokens'] +assert '\n' in lm.decode(tokens_bad) # oops, we encoded a newline + +# Instead pass the replace_newlines_with_eos option to get the correct behavior. +tokens_good = lm.score(data, replace_newline_with_eos=True)['tokens'] +assert '\n' not in lm.decode(tokens_good) # no newlines were encoded +``` + +## Citation + +Coming soon. diff --git a/fairseq/examples/moe_lm/data_card.md b/fairseq/examples/moe_lm/data_card.md new file mode 100644 index 0000000..54e694b --- /dev/null +++ b/fairseq/examples/moe_lm/data_card.md @@ -0,0 +1,221 @@ +# Data card for the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" +## Version 1.0.0 + +We follow the recommendations of Gebru et al. (2018) and provide a datacard for the dataset used to train the 1.1T parameter model. + +## Motivation +* **For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.** +The pre-training data for training the 1.1 T model was created by a union of six English language datasets, including five datasets used by RoBERTa (Liu et al 2019) and the English subset of CC 100. These purpose of creating this dataset was to pre-train the language model. + +* **Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?** +FAIR (Fundamental Artificial Intelligence Research) + +* **Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.** +FAIR (Fundamental Artificial Intelligence Research) + +* **Any other comments?** +No. + +## Composition + +* **What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.** +The instances are textual documents. The overall dataset is composed from a union of the following datasets - + * BookCorpus (Zhu et al., 2019) consists of more than 10K unpublished books (4GB); + * English Wikipedia, excluding lists, tables and headers (12GB); + * CC-News (Nagel,2016) contains 63 million English news articles crawled between September 2016 and February 2019 (76GB); + * OpenWebText (Gokaslan and Cohen, 2019), an open source recreation of the WebText dataset used to train GPT-2 (38GB); + * CC-Stories (Trinh and Le, 2018) contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas (31GB); + * English CC100 (Wenzek et al., 2020), a dataset extracted from CommonCrawl snapshots between January 2018 and December 2018, filtered to match the style of Wikipedia (292GB). + +* **How many instances are there in total (of each type, if appropriate)?** +The training data contains 112B tokens corresponding to 453 GB of data. + +* **Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).** +The English CC100 section of the dataset is a subset of CommonCrawl snapshots extracted between January 2018 to December 2018, filtered to match the style of Wikipedia. The CC-stories dataset contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. + +* **What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.** +Each instance consists of raw text data. + +* **Is there a label or target associated with each instance? If so, please provide a description.** +No. + +* **Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.** +No. + +* **Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? If so, please describe how these relationships are made explicit.** +There are no explicit relationships between individual instances. + +* **Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.** +We hold out a random validation set of approximately 150MB from the pretraining data, sampled proportionally to each dataset's size in the pretraining corpus. + +* **Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.** +N/A + +* **Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?** +It's self-contained. + +* **Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals' non-public communications)? If so, please provide a description.** +The datasets used are publicly available, and the information in them is not considered confidential. + +* **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.** +Parts of the dataset are a subset of public Common Crawl data, which could contain sentences that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety. + +* **Does the dataset relate to people? If not, you may skip the remaining questions in this section.** +Some documents of this data relate to people, such as news articles, Wikipedia descriptions, etc. + +* **Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.** +No. + +* **Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how** +In addition to individuals who have Wikipedia pages (celebrities, politicians, etc.), it may be possible to identify other individuals by their names, Twitter account names, etc. if that information is present in Common Crawl. + +* **Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.** +The training dataset is partially derived from Common Crawl, which may contain some sensitive information. + +* **Any other comments?** +No + + +## Collection Process + +* **How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/ derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.** +N/A. The dataset is a union of six publicly available datasets. + +* **What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated?** +N/A + +* **If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)?** +Please refer to the main document for details. + +* **Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?** +This data is mined, filtered and sampled by machines. + +* **Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.** +Different parts of the dataset were mined over different time periods. +1. The CC-News dataset contains English news articles crawled between September 2016 and February 2019. +2. The English CC-100 dataset was extracted from CommonCrawl snapshots between January 2018 and December 2018. + +* **Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.** +No. + +* **Does the dataset relate to people? If not, you may skip the remainder of the questions in this section.** +No. + +* **Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?** +N/A + +* **Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.** +N/A + +* **Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.** +N/A + +* **If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).** +N/A + +* **Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.** +Some responsible AI related evaluations were performed. Please refer to the main document and the model card for the paper. + +* **Any other comments?** +No + + +## Preprocessing/cleaning/labeling + + +* **Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section.** +The component datasets went through standard cleaning and re-formatting practices, including removing repetitive/non informative text like "Chapter One", or "This ebook by Project Gutenberg". + +* **Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the “raw” data.** +The "raw" component datasets is publicly available in their respective locations (more details can be seen in the respective papers linked in references). + +* **Is the software used to preprocess/clean/label the instances available? If so, please provide a link or other access point.** +The software is proprietary to Meta Platforms and currently unavailable publicly. + +* **Any other comments?** +No + + +## Uses + +* **Has the dataset been used for any tasks already? If so, please provide a description.** +Yes, this dataset was used to pre-train the models described in the paper. + +* **Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point.** +No. + +* **What (other) tasks could the dataset be used for?** +This data can be used to pretrain English language models, which are foundation to many current and future language tasks. + +* **Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?** +The pipeline for creating this dataset paves a way for building a scalable infrastructure for mining datasets to be be used for training large-scale models. + +* **Are there tasks for which the dataset should not be used? If so, please provide a description.** +No. + +* **Any other comments?** +No. + +## Distribution + + +* **Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description.** +No. + +* **How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)?** +N/A + +* **When will the dataset be distributed?** +No. + +* **Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.** +No. + +* **Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.** +No. + +* **Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.** +N/A + +* **Any other comments?** +No. + +## Maintenance + +* **Who is supporting/hosting/maintaining the dataset?** +FAIR (Fundamental Artificial Intelligence Research) + +* **How can the owner/curator/manager of the dataset be contacted (e.g., email address)?** +Refer to the main document. + +* **Is there an erratum? If so, please provide a link or other access point.** +N/A + +* **Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?** +No plan for updating. + +* **If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced.** +N/A + +* **Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to users.** +N/A + +* **If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/ verified? If so, please describe how. If not, why not? Is there a process for communicating/ distributing these contributions to other users? If so, please provide a description.** +No. + +* **Any other comments?** +No. + +## References +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. + +Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2019. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. arXiv:1506.06724. + +Sebastian Nagel. 2016. Cc-news. http: //web.archive.org/save/http: //commoncrawl.org/2016/10/news-dataset-available. + +Aaron Gokaslan and Vanya Cohen. 2019. Openwebtext corpus. http://web.archive.org/save/http://Skylion007.github.io/OpenWebTextCorpus + +Trieu H Trinh and Quoc V Le. 2018. A simple method for commonsense reasoning. arXiv preprint arXiv:1806.02847. + +Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020. CCNet: Extracting high quality monolingual datasets from web crawl data. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4003–4012, Marseille, France. European Language Resources Association. + diff --git a/fairseq/examples/moe_lm/model_card.md b/fairseq/examples/moe_lm/model_card.md new file mode 100644 index 0000000..a1cd681 --- /dev/null +++ b/fairseq/examples/moe_lm/model_card.md @@ -0,0 +1,170 @@ +# Model card for the paper ``Efficient Large Scale Language Modeling with Mixtures of Experts" +## Version 1.0.0 + +### Model developer +FAIR (Fundamental Artificial Intelligence Research) + +### Model type +An autoregressive English language model trained on a union of six English language models. We explore dense and sparse (MoE based) architectures in the paper. +* Dense models - Our dense models range from 125M parameters to 13B parameters. +* Sparse (MoE) models - Our MoE based models range from 15B parameters to 1.1 Trillion parameters. +This model card focuses on the 1.1 Trillion parameter model, but the discussion +applies to all of the models explored in this work. + +### Citation details +Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts + +### Model Feedback Channel +fairseq + +## Intended use +### Primary intended use +For research purposes only, e.g. reproducing model evaluation results. Generation is only used in a limited capacity for explanation/justification or for prompting/probing/priming for class labels. + +### Out of scope uses +The primary purpose of the model is not to generate language, although the model is capable of doing that. + +## Factors influencing model performance +This section discusses potential risks associated with using the model. + +### Relevant factors +Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion). + +### Evaluation factors +The 1.1T model was evaluated on StereoSet and CrowS-Pairs datasets to quantify encoded bias in the model. + +## Metrics +### Model performance measures +The 1.1T parameter model was primarily evaluated on +1. In-domain and out-of-domain language modeling perplexity. +2. Zero-shot and few-shot priming. +3. Fully supervised finetuning. + +### Approaches to handle uncertainty +For few-shot learning, we report the average results across 25 runs, randomly sampling a different set of few-shot examples from the training set each time. + +## Evaluation data +## Zero Shot evaluation + +### HellaSwag +#### Description +HellaSwag is a dataset for evaluating commonsense reasoning. + +### PIQA +#### Description +PIQA is a dataset designed to evaluate reasoning about Physical Commonsense in Natural Language + +### ReCoRd +#### Description +Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) is a large-scale reading comprehension dataset which requires commonsense reasoning. ReCoRD consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news. The goal of ReCoRD is to evaluate a machine's ability of commonsense reasoning in reading comprehension. + +## Few Shot evaluation +### Winogrande +#### Description +Winogrande is a benchmark for commonsense reasoning. The dataset contains pronoun resolution problems originally designed to be unsolvable for statistical models that rely on selectional preferences or word associations. + +### StoryCloze +#### Description +StoryCloze is a new commonsense reasoning framework for evaluating story understanding, story generation, and script learning. This test requires a system to choose the correct ending to a four-sentence story. + +### OpenBookQA +#### Description +OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test), which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel situations. + +## Fully supervised evaluation + +### BoolQ +#### Description +BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring – they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. + +### SST-2 +#### Description +SST-2 (or SST-binary) is a binary classification dataset where the goal is to differentiate between negative or somewhat negative vs somewhat positive or positive. + +### MNLI +#### Description +The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. + +## Responsible AI (RAI) evaluation +### StereoSet +#### Description +A large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion + +#### Motivation for dataset use +The motivation for evaluating the 1.1T parameter model on this dataset is to evaluate the model's stereotype bias in gender, profession, race, and religion + +### CrowS +#### Description +Challenge Dataset for Measuring Social Biases in Masked Language Models + +#### Motivation for dataset use +The motivation for evaluating the 1.1T parameter model on this dataset is to evaluate the model’s bias in the domains of race, religion and age + +---- + +## Training data +### BookCorpus +#### Description +A dataset consisting of more than 10K unpublished books. 4GB in size. (Zhu et al., 2019) + +### English Wikipedia +#### Description +Data from English wikipedia, excluding lists, tables and headers. 12GB in size. + +### CC-News +#### Description +A dataset containing 63 millions English news articles crawled between September 2016 and February 2019. 76GB in size. (Nagel,2016) + +### OpenWebText +#### Description +An open source recreation of the WebText dataset used to train GPT-2. 38GB in size. (Gokaslan and Cohen, 2019) + +### CC-Stories +#### Description +A dataset containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. 31GB in size. (Trinh and Le, 2018) + +### English CC100 +#### Description +A dataset extracted from CommonCrawl snapshots between January 2018 and December 2018, filtered to match the style of Wikipedia following the methodology introduced in CCNet (https://arxiv.org/abs/1911.00359). 292GB in size. (Wenzek et al., 2020) + +## Responsible AI (RAI) Dimensions +### Fairness (Bias and inclusion) +The 1.1T parameter model was evaluated on the StereoSet and CrowS pairs dataset for inherent bias in the model, and bias as a result of the data. Similar to StereoSet, we observe that both the dense and MoE models get worse in terms of the Stereotype Score (SS) with scale. + +### Privacy and security +The 1.1T model did not have any special Privacy and Security considerations. The training data and evaluation data were both public and went through standard Meta privacy and licensing procedures. + +### Transparency and control +In the spirit of transparency and accountability we have created this model card for the 1.1T parameter model and a data card for the training data (referenced in Artetxe et al. (2021)). + +### Efficiency (Green AI) +The 1.1T parameter model is trained as a Mixture of Experts (MoE) model. Mixture of expert (MoE) models are efficient because they leverage sparse computation, i.e., only a small fraction of parameters are active for any given input. For instance, our 1.1T parameter MoE model requires only 30% more FLOPS compared to a 6.7B parameter dense model, i.e., a 160x increase in parameters with only a 30% increase in FLOPS. Notably, MoE models achieve much better validation perplexity for a given compute budget compared to dense models. + +## References +Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019. HellaSwag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791– 4800, Florence, Italy. Association for Computational Linguistics. + +Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020. Piqa: Reasoning about physical commonsense in natural language. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):7432–7439. + +Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2018. ReCoRD: Bridging the gap between human and machine commonsense reading comprehension. arXiv preprint 1810.12885. + +Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020. Winogrande: An adversarial winograd schema challenge at scale. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):8732–8740. + +Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016. A corpus and cloze evaluation for deeper understanding of commonsense stories. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 839–849, San Diego, California. Association for Computational Linguistics. + +Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018. Can a suit of armor conduct electricity? a new dataset for open book question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2381–2391, Brussels, Belgium. Association for Computational Linguistics. + +Christopher Clark and Kenton Lee and Ming-Wei Chang and Tom Kwiatkowski and Michael Collins and Kristina Toutanova. 2019. BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions + +Moin Nadeem, Anna Bethke, and Siva Reddy. 2021. StereoSet: Measuring stereotypical bias in pretrained language models. In Association for Computational Linguistics (ACL). + +Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020. CrowS-pairs: A challenge dataset for measuring social biases in masked language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1953–1967, Online. Association for Computational Linguistics. + +Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2019. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. arXiv:1506.06724. + +Sebastian Nagel. 2016. Cc-news. http: //web.archive.org/save/http: //commoncrawl.org/2016/10/news-dataset-available. + +Aaron Gokaslan and Vanya Cohen. 2019. Openwebtext corpus. http://web.archive.org/save/http://Skylion007.github.io/OpenWebTextCorpus + +Trieu H Trinh and Quoc V Le. 2018. A simple method for commonsense reasoning. arXiv preprint arXiv:1806.02847. + +Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020. CCNet: Extracting high quality monolingual datasets from web crawl data. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4003–4012, Marseille, France. European Language Resources Association. diff --git a/fairseq/examples/mr_hubert/README.md b/fairseq/examples/mr_hubert/README.md new file mode 100644 index 0000000..e72c09c --- /dev/null +++ b/fairseq/examples/mr_hubert/README.md @@ -0,0 +1,187 @@ +# MR-HuBERT + +## Pre-trained models + +### Main models +Model | Pretraining Data | Model | Paper Reference +|---|---|---|--- +MR-HuBERT Base (~97M) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/mono_base/mrhubert_mono_base.pt) | mono\_base +MR-HuBERT Base (~321M) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/mono_large/mrhubert_mono_large.pt) | mono\_large +Multilingual MR-HuBERT Base (~97M) | [Voxpopuli](https://github.com/facebookresearch/voxpopuli) 100k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/multi_base/multi_base.pt) | multi\_base +Multilingual MR-HuBERT Large (~321M) | [Voxpopuli](https://github.com/facebookresearch/voxpopuli) 100k hr | [download 400k steps](https://dl.fbaipublicfiles.com/mrhubert/multi_large/multi_large_400k.pt) or [download 600k steps](https://dl.fbaipublicfiles.com/mrhubert/multi_large/multi_large_600k.pt) | Not in the paper + + +### Abalation models +Model | Pretraining Data | Model | Paper Reference +|---|---|---|--- +MR-HuBERT Base (2-4-6 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b1-a/b1-a.pt) | (B.1)-a +MR-HuBERT Base (5-2-5 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b1-b/b1-b.pt) | (B.1)-b +MR-HuBERT Base (6-4-2 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b1-c/b1-c.pt) | (B.1)-c +MR-HuBERT Base (3res 3-2-2-2-3 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b2-a/b2-a.pt) | (B.2)-a +MR-HuBERT Base (3res 2-2-4-2-2 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b2-b/b2-b.pt) | (B.2)-b +MR-HuBERT Base (3res 2-2-2-2-2 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b2-c/b2-c.pt) | (B.2)-c +MR-HuBERT Base (Simple sampling) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b3-a/b3-a.pt) | (B.3)-a +MR-HuBERT Base (Single target) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b4-a/b4-a.pt) | (B.4)-a +MR-HuBERT Base (Simple Sampling + single target) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b4-b/b4-b.pt) | (B.4)-b +MR-HuBERT Base (Mono-resolution 20ms) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b5-a/b5-a.pt) | (B.5)-a +MR-HuBERT Base (3-3-3 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b6-a/b6-a.pt) | (B.6)-a +MR-HuBERT Base (Mono-resolution 20ms, 3-3-3 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b6-b/b6-b.pt) | (B.6)-b +MR-HuBERT Base (HuBERT 20ms&40ms units) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-a/b7-a.pt) | (B.7)-a +MR-HuBERT Base (Encodec 50Hz unit) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-b/b7-b.pt) | (B.7)-b +MR-HuBERT Base (Encodec 50Hz units and 25Hz units) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-c/b7-c.pt) | (B.7)-c +MR-HuBERT Base (Encodec 50Hz units stream 0&1 ) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-d/b7-d.pt) | (B.7)-d +MR-HuBERT Large (no audio norm) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-a.pt) | (B.8)-a +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-b/b8-b.pt) | (B.8)-b +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-c/b8-c.pt) | (B.8)-c +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-d/b8-d.pt) | (B.8)-d +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-e/b8-e.pt) | (B.8)-e +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-f/b8-f.pt) | (B.8)-f +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-g/b8-g.pt) | (B.8)-g +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-h/b8-h.pt) | (B.8)-h +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-i/b8-i.pt) | (B.8)-i +MR-HuBERT Large (check paper ) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-j/b8-j.pt) | (B.8)-j +Multilingual MR-HuBERT Large (Simple sampling) | [Voxpopuli](https://github.com/facebookresearch/voxpopuli) 100k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/multi_large_simple/multi_large_simple.pt) | Not in paper +MR-HuBERT xLarge (from HuBERT-base label) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/mono_xlarge/v1.pt) | Not in paper +MR-HuBERT xLarge (from HuBERT-large label) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/mono_xlarge/v2.pt) | Not in paper + +## Load a model +``` +ckpt_path = "/path/to/the/checkpoint.pt" +models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) +model = models[0] +``` + +## Train a new model + +### Data preparation + +Follow the steps in `./simple_kmeans` to create: +- `{train,valid}.tsv` waveform list files with length information +``` +/path/to/your/audio/files +file1.wav\t160000 +file2.wav\t154600 +... +filen.wav\t54362 +``` +- `{train,valid}.km` frame-aligned pseudo label files (the order is the same as wavefiles in the tsv file). +``` +44 44 44 48 48 962 962 962 962 962 962 962 962 967 967 967 967 967 967 967 967 370 852 370 ... 18 18 745 745 +44 44 44 48 48 962 962 962 147 147 147 147 147 147 147 147 147 147 147 147 176 176 271 271 ... 27 27 745 745 +... +44 44 44 48 962 962 962 962 962 962 377 377 377 77 77 852 696 694 433 578 578 82 740 622 ... 27 27 745 745 +``` +- `dict.km.txt` a dummy dictionary (first column is id, the second is dummy one) +``` +0 1 +1 1 +2 1 +... +999 1 +``` + +The `label_rate` is the same as the feature frame rate used for clustering, +which is 100Hz for MFCC features and 50Hz for HuBERT features by default. + +### Pre-train a MR-HuBERT model + +Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km` +are saved at `/path/to/labels`, and the label rate is 100Hz. + +To train a base model (12 layer transformer), run: +```sh +$ python fairseq_cli/hydra_train.py \ + --config-dir /path/to/fairseq-py/examples/mr_hubert/config/pretrain \ + --config-name mrhubert_base_librispeech \ + task.data=/path/to/data task.label_dir=/path/to/labels \ + task.labels='["km"]' model.label_rate=100 \ + task.label_rate_ratios='[1, 2]' \ +``` + +Please see sample pre-training scripts `train.sh` for an example script. + +### Fine-tune a MR-HuBERT model with a CTC loss + +Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their +corresponding character transcripts `{train,valid}.ltr` are saved at +`/path/to/trans`. A typical ltr file is with the same order of tsv waveform files as +``` +HOW | ARE | YOU +... +THANK | YOU +``` + +To fine-tune a pre-trained MR-HuBERT model at `/path/to/checkpoint`, run +```sh +$ python fairseq_cli/hydra_train.py \ + --config-dir /path/to/fairseq-py/examples/mr_hubert/config/finetune \ + --config-name base_10h \ + task.data=/path/to/data task.label_dir=/path/to/trans \ + model.w2v_path=/path/to/checkpoint +``` + +Please see sample fine-tuning scripts `finetune.sh` for an example script. + +### Decode a MR-HuBERT model + +Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of +the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is +saved at `/path/to/checkpoint`. + + +We support three decoding modes: +- Viterbi decoding: greedy decoding without a language model +- KenLM decoding: decoding with an arpa-format KenLM n-gram language model +- Fairseq-LM deocding: decoding with a Fairseq neural language model (not fully tested) + + +#### Viterbi decoding + +`task.normalize` needs to be consistent with the value used during fine-tuning. +Decoding results will be saved at +`/path/to/experiment/directory/decode/viterbi/test`. + +```sh +$ python examples/speech_recognition/new/infer.py \ + --config-dir /path/to/fairseq-py/examples/mr_hubert/config/decode \ + --config-name infer \ + task.data=/path/to/data \ + task.normalize=[true|false] \ + decoding.exp_dir=/path/to/experiment/directory \ + common_eval.path=/path/to/checkpoint + dataset.gen_subset=test \ +``` + +#### KenLM / Fairseq-LM decoding + +Suppose the pronunciation lexicon and the n-gram LM are saved at +`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be +saved at `/path/to/experiment/directory/decode/kenlm/test`. + +```sh +$ python examples/speech_recognition/new/infer.py \ + --config-dir /path/to/fairseq-py/examples/mr_hubert/config/decode \ + --config-name infer_lm \ + task.data=/path/to/data \ + task.normalize=[true|false] \ + decoding.exp_dir=/path/to/experiment/directory \ + common_eval.path=/path/to/checkpoint + dataset.gen_subset=test \ + decoding.decoder.lexicon=/path/to/lexicon \ + decoding.decoder.lmpath=/path/to/arpa +``` + +The command above uses the default decoding hyperparameter, which can be found +in `examples/speech_recognition/hydra/decoder.py`. These parameters can be +configured from the command line. For example, to search with a beam size of +500, we can append the command above with `decoding.decoder.beam=500`. +Important parameters include: +- decoding.decoder.beam +- decoding.decoder.beamthreshold +- decoding.decoder.lmweight +- decoding.decoder.wordscore +- decoding.decoder.silweight + +To decode with a Fairseq LM, you may check the usage examples in wav2vec2 or hubert examples. + +Please see sample decoding scripts `decode.sh` for an example script. diff --git a/fairseq/examples/mr_hubert/config/decode/infer.yaml b/fairseq/examples/mr_hubert/config/decode/infer.yaml new file mode 100644 index 0000000..eff3980 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/decode/infer.yaml @@ -0,0 +1,30 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/viterbi + sweep: + dir: ${common_eval.results_path} + subdir: viterbi + +task: + _name: multires_hubert_pretraining + single_target: true + fine_tuning: true + label_rate_ratios: ??? + data: ??? + normalize: false + +decoding: + type: viterbi + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/fairseq/examples/mr_hubert/config/decode/infer_lm.yaml b/fairseq/examples/mr_hubert/config/decode/infer_lm.yaml new file mode 100644 index 0000000..535b950 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/decode/infer_lm.yaml @@ -0,0 +1,37 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + sweep: + dir: ${common_eval.results_path} + subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + +task: + _name: multires_hubert_pretraining + single_target: true + fine_tuning: true + data: ??? + label_rate_ratios: ??? + normalize: ??? + +decoding: + type: kenlm + lexicon: ??? + lmpath: ??? + beamthreshold: 100 + beam: 500 + lmweight: 1.5 + wordscore: -1 + silweight: 0 + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/fairseq/examples/mr_hubert/config/decode/run/submitit_slurm.yaml b/fairseq/examples/mr_hubert/config/decode/run/submitit_slurm.yaml new file mode 100644 index 0000000..0b80658 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/decode/run/submitit_slurm.yaml @@ -0,0 +1,17 @@ +# @package _global_ +hydra: + launcher: + cpus_per_task: ${distributed_training.distributed_world_size} + gpus_per_node: ${distributed_training.distributed_world_size} + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + mem_gb: 200 + timeout_min: 4320 + max_num_timeout: 50 + name: ${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/submitit + +distributed_training: + distributed_world_size: 1 + distributed_no_spawn: true + distributed_port: 29761 diff --git a/fairseq/examples/mr_hubert/config/decode/run/submitit_slurm_8gpu.yaml b/fairseq/examples/mr_hubert/config/decode/run/submitit_slurm_8gpu.yaml new file mode 100644 index 0000000..2f669f3 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/decode/run/submitit_slurm_8gpu.yaml @@ -0,0 +1,17 @@ +# @package _global_ +hydra: + launcher: + cpus_per_task: ${distributed_training.distributed_world_size} + gpus_per_node: ${distributed_training.distributed_world_size} + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + mem_gb: 200 + timeout_min: 4320 + max_num_timeout: 50 + name: ${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/submitit + +distributed_training: + distributed_world_size: 8 + distributed_no_spawn: true + distributed_port: 29761 diff --git a/fairseq/examples/mr_hubert/config/finetune/base_100h.yaml b/fairseq/examples/mr_hubert/config/finetune/base_100h.yaml new file mode 100644 index 0000000..c52a118 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/finetune/base_100h.yaml @@ -0,0 +1,97 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: multires_hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + label_rate_ratios: ??? + normalize: false # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 3200000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 5 + train_subset: train_100h + valid_subset: dev_other + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 80000 + lr: [3e-5] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: multires_hubert_ctc + multires_hubert_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.multires_hubert_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/mr_hubert/config/finetune/base_100h_large.yaml b/fairseq/examples/mr_hubert/config/finetune/base_100h_large.yaml new file mode 100644 index 0000000..1d0c0da --- /dev/null +++ b/fairseq/examples/mr_hubert/config/finetune/base_100h_large.yaml @@ -0,0 +1,97 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: multires_hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + label_rate_ratios: ??? + normalize: true # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 1600000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 5 + train_subset: train_100h + valid_subset: dev_other + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 80000 + lr: [3e-5] + sentence_avg: true + update_freq: [2] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: multires_hubert_ctc + multires_hubert_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.multires_hubert_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/mr_hubert/config/finetune/base_10h.yaml b/fairseq/examples/mr_hubert/config/finetune/base_10h.yaml new file mode 100644 index 0000000..25123e4 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/finetune/base_10h.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + save_interval: 5 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: multires_hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + label_rate_ratios: ??? + normalize: false # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 3200000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 5 + train_subset: train_10h + valid_subset: dev + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 25000 + lr: [2e-5] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: multires_hubert_ctc + multires_hubert_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.multires_hubert_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/mr_hubert/config/finetune/base_10h_large.yaml b/fairseq/examples/mr_hubert/config/finetune/base_10h_large.yaml new file mode 100644 index 0000000..65448c7 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/finetune/base_10h_large.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + save_interval: 5 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: multires_hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + label_rate_ratios: ??? + normalize: true # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 3200000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 5 + train_subset: train_10h + valid_subset: dev + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 25000 + lr: [2e-5] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: multires_hubert_ctc + multires_hubert_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.multires_hubert_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/mr_hubert/config/finetune/base_1h.yaml b/fairseq/examples/mr_hubert/config/finetune/base_1h.yaml new file mode 100644 index 0000000..7459c3f --- /dev/null +++ b/fairseq/examples/mr_hubert/config/finetune/base_1h.yaml @@ -0,0 +1,100 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + save_interval: 50 + keep_interval_updates: 1 + save_interval_updates: 1000 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: multires_hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + label_rate_ratios: ??? + normalize: false # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 3200000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 1000 + train_subset: train_1h + valid_subset: dev_other + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [5e-5] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: multires_hubert_ctc + multires_hubert_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.multires_hubert_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/mr_hubert/config/finetune/base_1h_large.yaml b/fairseq/examples/mr_hubert/config/finetune/base_1h_large.yaml new file mode 100644 index 0000000..34ef4dc --- /dev/null +++ b/fairseq/examples/mr_hubert/config/finetune/base_1h_large.yaml @@ -0,0 +1,99 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + save_interval: 1000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: multires_hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + label_rate_ratios: ??? + normalize: true # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 1280000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 5 + train_subset: train_10h + valid_subset: dev + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 25000 + lr: [3e-4] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: multires_hubert_ctc + multires_hubert_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.multires_hubert_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/mr_hubert/config/pretrain/mrhubert_base_librispeech.yaml b/fairseq/examples/mr_hubert/config/pretrain/mrhubert_base_librispeech.yaml new file mode 100644 index 0000000..16a35d3 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/pretrain/mrhubert_base_librispeech.yaml @@ -0,0 +1,103 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + min_loss_scale: 1e-8 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 32 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: multires_hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + label_rate_ratios: ??? + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: false # must be consistent with extractor + # max_keep_size: 300000 + # max_keep_size: 50000 + + +dataset: + num_workers: 0 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.0005] + clip_norm: 10.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: multires_hubert + label_rate: ??? + label_rate_ratios: ${task.label_rate_ratios} + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: default + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + final_dim: 256 + encoder_layers: 4 + encoder_layerdrop: 0.05 + dropout_input: 0.1 + dropout_features: 0.1 + dropout: 0.1 + attention_dropout: 0.1 + feature_grad_mult: 0.1 + untie_final_proj: true + activation_dropout: 0.0 + conv_adapator_kernal: 1 + use_single_target: true + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '/' + exclude_keys: + - run + - task.data + - task.label_dir + - common.min_loss_scale + - common.log_interval + - optimization.clip_norm diff --git a/fairseq/examples/mr_hubert/config/pretrain/mrhubert_large_librilight.yaml b/fairseq/examples/mr_hubert/config/pretrain/mrhubert_large_librilight.yaml new file mode 100644 index 0000000..423f3b2 --- /dev/null +++ b/fairseq/examples/mr_hubert/config/pretrain/mrhubert_large_librilight.yaml @@ -0,0 +1,107 @@ +# @package _group_ + +common: + memory_efficient_fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 128 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: multires_hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + label_rate_ratios: ??? + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: true # must be consistent with extractor + # max_keep_size: 50000 + +dataset: + num_workers: 0 + max_tokens: 300000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.0015] + clip_norm: 1.0 + update_freq: [3] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: multires_hubert + label_rate: ??? + label_rate_ratios: ${task.label_rate_ratios} + encoder_layers: 8 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + final_dim: 768 + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: layer_norm + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + encoder_layerdrop: 0.0 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + layer_norm_first: true + feature_grad_mult: 1.0 + untie_final_proj: true + activation_dropout: 0.0 + conv_adapator_kernal: 1 + use_single_target: true + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + run: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + sweep: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/fairseq/examples/mr_hubert/config/pretrain/run/submitit_reg.yaml b/fairseq/examples/mr_hubert/config/pretrain/run/submitit_reg.yaml new file mode 100644 index 0000000..46c979c --- /dev/null +++ b/fairseq/examples/mr_hubert/config/pretrain/run/submitit_reg.yaml @@ -0,0 +1,20 @@ +# @package _global_ + +hydra: + launcher: + cpus_per_task: 8 + gpus_per_node: 8 + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 4 + comment: null + mem_gb: 384 + timeout_min: 4320 + max_num_timeout: 100 + constraint: volta32gb + name: ${hydra.job.config_name}/${hydra.job.override_dirname} + submitit_folder: ${hydra.sweep.dir}/submitit/%j + +distributed_training: + distributed_world_size: 32 + distributed_port: 29671 + nprocs_per_node: 8 diff --git a/fairseq/examples/mr_hubert/decode.sh b/fairseq/examples/mr_hubert/decode.sh new file mode 100644 index 0000000..1ff423a --- /dev/null +++ b/fairseq/examples/mr_hubert/decode.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +FAIRSEQ= # Setup your fairseq directory + +config_dir=${FAIRSEQ}/examples/mr_hubert/config +config_name=mr_hubert_base_librispeech + + +# Prepared Data Directory + +data_dir=librispeech +# -- data_dir +# -- test.tsv +# -- test.ltr +# -- dict.ltr.txt + + +exp_dir=exp # Target experiments directory (where you have your pre-trained model with checkpoint_best.pt) +ratios="[1, 2]" # Default label rate ratios + +_opts= + +# If use slurm, uncomment this line and modify the job submission at +# _opts="${_opts} hydra/launcher=submitit_slurm +hydra.launcher.partition=${your_slurm_partition} +run=submitit_reg" + +# If want to set additional experiment tag, uncomment this line +# _opts="${_opts} hydra.sweep.subdir=${your_experiment_tag}" + +# If use un-normalized audio, uncomment this line +# _opts="${_opts} task.normalize=false" + + + +PYTHONPATH=${FAIRSEQ} +python examples/speech_recognition/new/infer.py \ + --config-dir ${config_dir} \ + --config-name infer_multires \ + ${_opts} \ + task.data=${data_dir} \ + task.label_rate_ratios='${ratios}' \ + common_eval.results_path=${exp_dir} \ + common_eval.path=${exp_dir}/checkpoint_best.pt \ + dataset.max_tokens=2000000 \ + dataset.gen_subset=test \ + dataset.skip_invalid_size_inputs_valid_test=true + diff --git a/fairseq/examples/mr_hubert/finetune.sh b/fairseq/examples/mr_hubert/finetune.sh new file mode 100644 index 0000000..31ba645 --- /dev/null +++ b/fairseq/examples/mr_hubert/finetune.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +FAIRSEQ= # Setup your fairseq directory + +config_dir=${FAIRSEQ}/examples/mr_hubert/config +config_name=mr_hubert_base_librispeech + +# override configs if need +max_tokens=3200000 +max_sample_size=1000000 +max_update=50000 + + +# Prepared Data Directory + +data_dir=librispeech +# -- data_dir +# -- train.tsv +# -- train.ltr +# -- valid.tsv +# -- valid.ltr +# -- dict.ltr.txt + + +exp_dir=exp # Target experiments directory +ratios="[1, 2]" # Default label rate ratios +hubert_path=/path/of/your/hubert.pt + +_opts= + +# If use slurm, uncomment this line and modify the job submission at +# _opts="${_opts} hydra/launcher=submitit_slurm +hydra.launcher.partition=${your_slurm_partition} +run=submitit_reg" + +# If want to set additional experiment tag, uncomment this line +# _opts="${_opts} hydra.sweep.subdir=${your_experiment_tag}" + + +python ${FAIRSEQ}/fairseq_cli/hydra_train.py \ + -m --config-dir ${config_dir} --config-name ${config_name} ${_opts} \ + task.data=${data_dir} +task.max_sample_size=${max_sample_size} \ + task.label_dir=${data_dir} \ + task.label_rate_ratios='${ratios}' \ + dataset.max_tokens=${max_tokens} \ + optimization.max_update=${max_update} \ + model.multires_hubert_path=${hubert_path} \ + hydra.sweep.dir=${exp_dir} & diff --git a/fairseq/examples/mr_hubert/simple_kmeans b/fairseq/examples/mr_hubert/simple_kmeans new file mode 100644 index 0000000..4f95545 --- /dev/null +++ b/fairseq/examples/mr_hubert/simple_kmeans @@ -0,0 +1 @@ +../hubert/simple_kmeans \ No newline at end of file diff --git a/fairseq/examples/mr_hubert/train.sh b/fairseq/examples/mr_hubert/train.sh new file mode 100644 index 0000000..da561eb --- /dev/null +++ b/fairseq/examples/mr_hubert/train.sh @@ -0,0 +1,45 @@ +#!/bin/bash + +FAIRSEQ= # Setup your fairseq directory + +config_dir=${FAIRSEQ}/examples/mr_hubert/config +config_name=mr_hubert_base_librispeech + +# Prepared Data Directory +data_dir=librispeech +# -- data_dir +# -- train.tsv +# -- valid.tsv + +label_dir=labels +# -- label_dir +# -- train.km +# -- valid.km +# -- dict.km.txt + + +exp_dir=exp # Target experiments directory +ratios="[1, 2]" # Default label rate ratios +label_rate=50 # Base label rate + + +_opts= + +# If use slurm, uncomment this line and modify the job submission at +# _opts="${_opts} hydra/launcher=submitit_slurm +hydra.launcher.partition=${your_slurm_partition} +run=submitit_reg" + +# If want to set additional experiment tag, uncomment this line +# _opts="${_opts} hydra.sweep.subdir=${your_experiment_tag}" + + +python ${FAIRSEQ}/fairseq_cli/hydra_train.py \ + -m --config-dir ${config_dir} --config-name ${config_name} ${_opts} \ + task.data=${data_dir} \ + task.label_dir=${label_dir} \ + task.labels='["km"]' \ + model.label_rate=${label_rate} \ + task.label_rate_ratios='${ratios}' \ + hydra.sweep.dir=${exp_dir} & + + + diff --git a/fairseq/examples/multilingual/ML50_langs.txt b/fairseq/examples/multilingual/ML50_langs.txt new file mode 100644 index 0000000..558abbc --- /dev/null +++ b/fairseq/examples/multilingual/ML50_langs.txt @@ -0,0 +1,52 @@ +ar_AR +cs_CZ +de_DE +en_XX +es_XX +et_EE +fi_FI +fr_XX +gu_IN +hi_IN +it_IT +ja_XX +kk_KZ +ko_KR +lt_LT +lv_LV +my_MM +ne_NP +nl_XX +ro_RO +ru_RU +si_LK +tr_TR +vi_VN +zh_CN +af_ZA +az_AZ +bn_IN +fa_IR +he_IL +hr_HR +id_ID +ka_GE +km_KH +mk_MK +ml_IN +mn_MN +mr_IN +pl_PL +ps_AF +pt_XX +sv_SE +sw_KE +ta_IN +te_IN +th_TH +tl_XX +uk_UA +ur_PK +xh_ZA +gl_ES +sl_SI \ No newline at end of file diff --git a/fairseq/examples/multilingual/README.md b/fairseq/examples/multilingual/README.md new file mode 100644 index 0000000..46ff9c3 --- /dev/null +++ b/fairseq/examples/multilingual/README.md @@ -0,0 +1,158 @@ +# Multilingual Translation + +[[Multilingual Translation with Extensible Multilingual Pretraining and Finetuning, https://arxiv.org/abs/2008.00401]](https://arxiv.org/abs/2008.00401) + +## Introduction + +This work is for training multilingual translation models with multiple bitext datasets. This multilingual translation framework supports (see [[training section]](#Training) and [[finetuning section]](#Finetuning) for examples) + +* temperature based sampling over unbalancing datasets of different translation directions + - --sampling-method' with + choices=['uniform', 'temperature', 'concat'] + - --sampling-temperature +* configurable to automatically add source and/or target language tokens to source/target sentences using data which are prepared in the same way as bilignual training + - --encoder-langtok with choices=['src', 'tgt', None] to specify whether to add source or target language tokens to the source sentences + - --decoder-langtok (binary option) to specify whether to add target language tokens to the target sentences or not +* finetuning mBART pretrained models for multilingual translation + - --finetune-from-model to specify the path from which to load the pretrained model + +## Preprocessing data +Multilingual training requires a joint BPE vocab. Please follow [mBART's preprocessing steps](https://github.com/pytorch/fairseq/tree/main/examples/mbart#bpe-data) to reuse our pretrained sentence-piece model. + +You can also train a joint BPE model on your own dataset and then follow the steps in [[link]](https://github.com/pytorch/fairseq/tree/main/examples/translation#multilingual-translation). + +## Training + + +```bash +lang_pairs= +path_2_data= +lang_list= + +fairseq-train $path_2_data \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --sampling-method "temperature" \ + --sampling-temperature 1.5 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 +``` + +## Finetuning +We can also finetune multilingual models from a monolingual pretrained models, e.g. [mMBART](https://github.com/pytorch/fairseq/tree/main/examples/mbart). +```bash +lang_pairs= +path_2_data= +lang_list= +pretrained_model= + +fairseq-train $path_2_data \ + --finetune-from-model $pretrained_model \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --sampling-method "temperature" \ + --sampling-temperature 1.5 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 +``` +## Generate +The following command uses the multilingual task (translation_multi_simple_epoch) to generate translation from $source_lang to $target_lang on the test dataset. During generaton, the source language tokens are added to source sentences and the target language tokens are added as the starting token to decode target sentences. Options --lang-dict and --lang-pairs are needed to tell the generation process the ordered list of languages and translation directions that the trained model are awared of; they will need to be consistent with the training. + +```bash +model= +source_lang= +target_lang= + +fairseq-generate $path_2_data \ + --path $model \ + --task translation_multi_simple_epoch \ + --gen-subset test \ + --source-lang $source_lang \ + --target-lang $target_lang + --sacrebleu --remove-bpe 'sentencepiece'\ + --batch-size 32 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" > ${source_lang}_${target_lang}.txt +``` +Fairseq will generate translation into a file {source_lang}_${target_lang}.txt with sacreblue at the end. + +You can also use costomized tokenizer to compare the performance with the literature. For example, you get a tokenizer [here](https://github.com/rsennrich/wmt16-scripts) and do the following: +```bash +TOKENIZER= +TOK_CMD=<"$TOKENIZER $target_lang" or cat for sacrebleu> + +cat {source_lang}_${target_lang}.txt | grep -P "^H" |sort -V |cut -f 3- |$TOK_CMD > ${source_lang}_${target_lang}.hyp +cat {source_lang}_${target_lang}.txt | grep -P "^T" |sort -V |cut -f 2- |$TOK_CMD > ${source_lang}_${target_lang}.ref +sacrebleu -tok 'none' -s 'none' ${source_lang}_${target_lang}.ref < ${source_lang}_${target_lang}.hyp +``` + +# mBART50 models + +* [mMBART 50 pretrained model](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.pretrained.tar.gz). +* [mMBART 50 finetuned many-to-one](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.n1.tar.gz). +* [mMBART 50 finetuned one-to-many](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.1n.tar.gz). +* [mMBART 50 finetuned many-to-many](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.nn.tar.gz). + +Please download and extract from the above tarballs. Each tarball contains +* The fairseq model checkpoint: model.pt +* The list of supported languages: ML50_langs.txt +* Sentence piece model: sentence.bpe.model +* Fairseq dictionary of each language: dict.{lang}.txt (please replace lang with a language specified in ML50_langs.txt) + +To use the trained models, +* use the tool [binarize.py](./data_scripts/binarize.py) to binarize your data using sentence.bpe.model and dict.{lang}.txt, and copy the dictionaries to your data path +* then run the generation command: +```bash +path_2_data= +model=/model.pt +lang_list=/ML50_langs.txt +source_lang= +target_lang= + +fairseq-generate $path_2_data \ + --path $model \ + --task translation_multi_simple_epoch \ + --gen-subset test \ + --source-lang $source_lang \ + --target-lang $target_lang + --sacrebleu --remove-bpe 'sentencepiece'\ + --batch-size 32 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" +``` + +## Citation + +```bibtex +@article{tang2020multilingual, + title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, + author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, + year={2020}, + eprint={2008.00401}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/fairseq/examples/multilingual/data_scripts/README.md b/fairseq/examples/multilingual/data_scripts/README.md new file mode 100644 index 0000000..cc610c0 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/README.md @@ -0,0 +1,24 @@ + +# Install dependency +```bash +pip install -r requirement.txt +``` + +# Download the data set +```bash +export WORKDIR_ROOT= + +``` +The downloaded data will be at $WORKDIR_ROOT/ML50 + +# preprocess the data +Install SPM [here](https://github.com/google/sentencepiece) +```bash +export WORKDIR_ROOT= +export SPM_PATH= +``` +* $WORKDIR_ROOT/ML50/raw: extracted raw data +* $WORKDIR_ROOT/ML50/dedup: dedup data +* $WORKDIR_ROOT/ML50/clean: data with valid and test sentences removed from the dedup data + + diff --git a/fairseq/examples/multilingual/data_scripts/binarize.py b/fairseq/examples/multilingual/data_scripts/binarize.py new file mode 100644 index 0000000..ee54c6a --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/binarize.py @@ -0,0 +1,200 @@ +import shutil +import os, sys +from subprocess import check_call, check_output +import glob +import argparse +import shutil +import pathlib +import itertools + +def call_output(cmd): + print(f"Executing: {cmd}") + ret = check_output(cmd, shell=True) + print(ret) + return ret + +def call(cmd): + print(cmd) + check_call(cmd, shell=True) + + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +SPM_PATH = os.environ.get('SPM_PATH', None) + +if SPM_PATH is None or not SPM_PATH.strip(): + print("Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting...") + sys.exit(-1) + + +SPM_MODEL = f'{WORKDIR_ROOT}/sentence.bpe.model' +SPM_VOCAB = f'{WORKDIR_ROOT}/dict_250k.txt' + +SPM_ENCODE = f'{SPM_PATH}' + +if not os.path.exists(SPM_MODEL): + call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/sentence.bpe.model -O {SPM_MODEL}") + + +if not os.path.exists(SPM_VOCAB): + call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/dict_250k.txt -O {SPM_VOCAB}") + + + +def get_data_size(raw): + cmd = f'wc -l {raw}' + ret = call_output(cmd) + return int(ret.split()[0]) + +def encode_spm(model, direction, prefix='', splits=['train', 'test', 'valid'], pairs_per_shard=None): + src, tgt = direction.split('-') + + for split in splits: + src_raw, tgt_raw = f'{RAW_DIR}/{split}{prefix}.{direction}.{src}', f'{RAW_DIR}/{split}{prefix}.{direction}.{tgt}' + if os.path.exists(src_raw) and os.path.exists(tgt_raw): + cmd = f"""python {SPM_ENCODE} \ + --model {model}\ + --output_format=piece \ + --inputs {src_raw} {tgt_raw} \ + --outputs {BPE_DIR}/{direction}{prefix}/{split}.bpe.{src} {BPE_DIR}/{direction}{prefix}/{split}.bpe.{tgt} """ + print(cmd) + call(cmd) + + +def binarize_( + bpe_dir, + databin_dir, + direction, spm_vocab=SPM_VOCAB, + splits=['train', 'test', 'valid'], +): + src, tgt = direction.split('-') + + try: + shutil.rmtree(f'{databin_dir}', ignore_errors=True) + os.mkdir(f'{databin_dir}') + except OSError as error: + print(error) + cmds = [ + "fairseq-preprocess", + f"--source-lang {src} --target-lang {tgt}", + f"--destdir {databin_dir}/", + f"--workers 8", + ] + if isinstance(spm_vocab, tuple): + src_vocab, tgt_vocab = spm_vocab + cmds.extend( + [ + f"--srcdict {src_vocab}", + f"--tgtdict {tgt_vocab}", + ] + ) + else: + cmds.extend( + [ + f"--joined-dictionary", + f"--srcdict {spm_vocab}", + ] + ) + input_options = [] + if 'train' in splits and glob.glob(f"{bpe_dir}/train.bpe*"): + input_options.append( + f"--trainpref {bpe_dir}/train.bpe", + ) + if 'valid' in splits and glob.glob(f"{bpe_dir}/valid.bpe*"): + input_options.append(f"--validpref {bpe_dir}/valid.bpe") + if 'test' in splits and glob.glob(f"{bpe_dir}/test.bpe*"): + input_options.append(f"--testpref {bpe_dir}/test.bpe") + if len(input_options) > 0: + cmd = " ".join(cmds + input_options) + print(cmd) + call(cmd) + + +def binarize( + databin_dir, + direction, spm_vocab=SPM_VOCAB, prefix='', + splits=['train', 'test', 'valid'], + pairs_per_shard=None, +): + def move_databin_files(from_folder, to_folder): + for bin_file in glob.glob(f"{from_folder}/*.bin") \ + + glob.glob(f"{from_folder}/*.idx") \ + + glob.glob(f"{from_folder}/dict*"): + try: + shutil.move(bin_file, to_folder) + except OSError as error: + print(error) + bpe_databin_dir = f"{BPE_DIR}/{direction}{prefix}_databin" + bpe_dir = f"{BPE_DIR}/{direction}{prefix}" + if pairs_per_shard is None: + binarize_(bpe_dir, bpe_databin_dir, direction, spm_vocab=spm_vocab, splits=splits) + move_databin_files(bpe_databin_dir, databin_dir) + else: + # binarize valid and test which will not be sharded + binarize_( + bpe_dir, bpe_databin_dir, direction, + spm_vocab=spm_vocab, splits=[s for s in splits if s != "train"]) + for shard_bpe_dir in glob.glob(f"{bpe_dir}/shard*"): + path_strs = os.path.split(shard_bpe_dir) + shard_str = path_strs[-1] + shard_folder = f"{bpe_databin_dir}/{shard_str}" + databin_shard_folder = f"{databin_dir}/{shard_str}" + print(f'working from {shard_folder} to {databin_shard_folder}') + os.makedirs(databin_shard_folder, exist_ok=True) + binarize_( + shard_bpe_dir, shard_folder, direction, + spm_vocab=spm_vocab, splits=["train"]) + + for test_data in glob.glob(f"{bpe_databin_dir}/valid.*") + glob.glob(f"{bpe_databin_dir}/test.*"): + filename = os.path.split(test_data)[-1] + try: + os.symlink(test_data, f"{databin_shard_folder}/{filename}") + except OSError as error: + print(error) + move_databin_files(shard_folder, databin_shard_folder) + + +def load_langs(path): + with open(path) as fr: + langs = [l.strip() for l in fr] + return langs + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--data_root", default=f"{WORKDIR_ROOT}/ML50") + parser.add_argument("--raw-folder", default='raw') + parser.add_argument("--bpe-folder", default='bpe') + parser.add_argument("--databin-folder", default='databin') + + args = parser.parse_args() + + DATA_PATH = args.data_root #'/private/home/yuqtang/public_data/ML50' + RAW_DIR = f'{DATA_PATH}/{args.raw_folder}' + BPE_DIR = f'{DATA_PATH}/{args.bpe_folder}' + DATABIN_DIR = f'{DATA_PATH}/{args.databin_folder}' + os.makedirs(BPE_DIR, exist_ok=True) + + raw_files = itertools.chain( + glob.glob(f'{RAW_DIR}/train*'), + glob.glob(f'{RAW_DIR}/valid*'), + glob.glob(f'{RAW_DIR}/test*'), + ) + + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + + for direction in directions: + prefix = "" + splits = ['train', 'valid', 'test'] + try: + shutil.rmtree(f'{BPE_DIR}/{direction}{prefix}', ignore_errors=True) + os.mkdir(f'{BPE_DIR}/{direction}{prefix}') + os.makedirs(DATABIN_DIR, exist_ok=True) + except OSError as error: + print(error) + spm_model, spm_vocab = SPM_MODEL, SPM_VOCAB + encode_spm(spm_model, direction=direction, splits=splits) + binarize(DATABIN_DIR, direction, spm_vocab=spm_vocab, splits=splits) diff --git a/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py b/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py new file mode 100644 index 0000000..f8e2eb0 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py @@ -0,0 +1,67 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os, sys +import subprocess +import re +from subprocess import check_call, check_output + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ") +def run_eval_bleu(cmd): + output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip() + print(output) + bleu = -1.0 + for line in output.strip().split('\n'): + m = BLEU_REGEX.search(line) + if m is not None: + bleu = m.groups()[0] + bleu = float(bleu) + break + return bleu + +def check_data_test_bleu(raw_folder, data_lang_pairs): + not_matchings = [] + for sacrebleu_set, src_tgts in data_lang_pairs: + for src_tgt in src_tgts: + print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}') + src, tgt = src_tgt.split('-') + ssrc, stgt = src[:2], tgt[:2] + if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'): + # reversed direction may have different test set + test_src = f'{raw_folder}/test.{tgt}-{src}.{src}' + else: + test_src = f'{raw_folder}/test.{src}-{tgt}.{src}' + cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""' + test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}' + cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""' + bleu1 = run_eval_bleu(cmd1) + if bleu1 != 100.0: + not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}') + bleu2 = run_eval_bleu(cmd2) + if bleu2 != 100.0: + not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}') + return not_matchings + +if __name__ == "__main__": + to_data_path = f'{WORKDIR_ROOT}/iwsltv2' + not_matching = check_data_test_bleu( + f'{to_data_path}/raw', + [ + ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']), + ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']), + ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]), + ] + ) + if len(not_matching) > 0: + print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching)) + diff --git a/fairseq/examples/multilingual/data_scripts/check_self_overlaps.py b/fairseq/examples/multilingual/data_scripts/check_self_overlaps.py new file mode 100644 index 0000000..07b338d --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/check_self_overlaps.py @@ -0,0 +1,103 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import glob +import argparse +from utils.dedup import deup +import sys + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +def get_directions(folder): + raw_files = glob.glob(f'{folder}/train*') + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + return directions + +def diff_list(lhs, rhs): + return set(lhs).difference(set(rhs)) + +def check_diff( + from_src_file, from_tgt_file, + to_src_file, to_tgt_file, +): + seen_in_from = set() + seen_src_in_from = set() + seen_tgt_in_from = set() + from_count = 0 + with open(from_src_file, encoding='utf-8') as fsrc, \ + open(from_tgt_file, encoding='utf-8') as ftgt: + for s, t in zip(fsrc, ftgt): + seen_in_from.add((s, t)) + seen_src_in_from.add(s) + seen_tgt_in_from.add(t) + from_count += 1 + common = 0 + common_src = 0 + common_tgt = 0 + to_count = 0 + seen = set() + + with open(to_src_file, encoding='utf-8') as fsrc, \ + open(to_tgt_file, encoding='utf-8') as ftgt: + for s, t in zip(fsrc, ftgt): + to_count += 1 + if (s, t) not in seen: + if (s, t) in seen_in_from: + common += 1 + if s in seen_src_in_from: + common_src += 1 + seen_src_in_from.remove(s) + if t in seen_tgt_in_from: + common_tgt += 1 + seen_tgt_in_from.remove(t) + seen.add((s, t)) + return common, common_src, common_tgt, from_count, to_count + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--folder", type=str, required=True, + help="the data folder ") + parser.add_argument("--split", type=str, default='test', + help="split (valid, test) to check against training data") + parser.add_argument('--directions', type=str, default=None, required=False) + + args = parser.parse_args() + + if args.directions is None: + directions = set(get_directions(args.folder)) + directions = sorted(directions) + else: + directions = args.directions.split(',') + directions = sorted(set(directions)) + + results = [] + print(f'checking where {args.split} split data are in training') + print(f'direction\tcommon_count\tsrc common\ttgt common\tfrom_size\tto_size') + + for direction in directions: + src, tgt = direction.split('-') + from_src_file = f'{args.folder}/{args.split}.{src}-{tgt}.{src}' + from_tgt_file = f'{args.folder}/{args.split}.{src}-{tgt}.{tgt}' + if not os.path.exists(from_src_file): + # some test/valid data might in reverse directinos: + from_src_file = f'{args.folder}/{args.split}.{tgt}-{src}.{src}' + from_tgt_file = f'{args.folder}/{args.split}.{tgt}-{src}.{tgt}' + to_src_file = f'{args.folder}/train.{src}-{tgt}.{src}' + to_tgt_file = f'{args.folder}/train.{src}-{tgt}.{tgt}' + if not os.path.exists(to_src_file) or not os.path.exists(from_src_file): + continue + r = check_diff(from_src_file, from_tgt_file, to_src_file, to_tgt_file) + results.append(r) + print(f'{direction}\t', '\t'.join(map(str, r))) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/multilingual/data_scripts/check_valid_test_overlaps.py b/fairseq/examples/multilingual/data_scripts/check_valid_test_overlaps.py new file mode 100644 index 0000000..40fa9ae --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/check_valid_test_overlaps.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import argparse +import pandas as pd +import sys + + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +def load_langs(path): + with open(path) as fr: + langs = [l.strip() for l in fr] + return langs + + + +def load_sentences(raw_data, split, direction): + src, tgt = direction.split('-') + src_path = f"{raw_data}/{split}.{direction}.{src}" + tgt_path = f"{raw_data}/{split}.{direction}.{tgt}" + if os.path.exists(src_path) and os.path.exists(tgt_path): + return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())] + else: + return [] + +def swap_direction(d): + src, tgt = d.split('-') + return f'{tgt}-{src}' + +def get_all_test_data(raw_data, directions, split='test'): + test_data = [ + x + for dd in directions + for d in [dd, swap_direction(dd)] + for x in load_sentences(raw_data, split, d) + ] + # all_test_data = {s for _, d in test_data for s in d} + all_test_data = {} + for lang, d in test_data: + for s in d: + s = s.strip() + lgs = all_test_data.get(s, set()) + lgs.add(lang) + all_test_data[s] = lgs + return all_test_data, test_data + + +def check_train_sentences(src_path, tgt_path, direction, all_test_data, mess_up_train={}): + # src, tgt = direction.split('-') + print(f'check training data for {direction} in {src_path} and {tgt_path}') + size = 0 + overlapped_size_counted_dup = 0 + if not os.path.exists(tgt_path) or not os.path.exists(src_path): + return mess_up_train, size, overlapped_size_counted_dup + + with open(src_path) as f, open(tgt_path) as g: + for src_line, tgt_line in zip(f, g): + s = src_line.strip() + t = tgt_line.strip() + size += 1 + if s in all_test_data: + langs = mess_up_train.get(s, set()) + langs.add(direction) + mess_up_train[s] = langs + overlapped_size_counted_dup += 1 + if t in all_test_data: + langs = mess_up_train.get(t, set()) + langs.add(direction) + mess_up_train[t] = langs + overlapped_size_counted_dup += 1 + print(f'{direction}: size={size}, overlapped={overlapped_size_counted_dup}') + return mess_up_train, size, overlapped_size_counted_dup + +def check_train_all(raw_data, directions, all_test_data): + mess_up_train = {} + data_sizes = {} + # raw_data = '~chau/data-bin/MineBART/multilingual_mined_100M/en_XX/et_EE-en_XX/all.{en_XX, et_EE}' + print(f'checking training data againsts # {len(all_test_data)} sentences') + print(f'example test data: ', [s for i, s in enumerate(all_test_data.keys()) if i < 10]) + for direction in directions: + src, tgt = direction.split('-') + path = f'{raw_data}/en_XX/{direction}/all' + src_path = f'{path}.{src}' + tgt_path = f'{path}.{tgt}' + print(f'checking {src_path} {tgt_path}') + _, size, overlapped_size_counted_dup = check_train_sentences(src_path, tgt_path, direction, all_test_data, mess_up_train) + data_sizes[direction] = (size, overlapped_size_counted_dup) + return mess_up_train, data_sizes + + + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--folder", type=str, required=True, + help="the data folder ") + parser.add_argument("--test-data", type=str, required=True, + help="the test data folder ") + parser.add_argument('--directions', type=str, default=None, required=False) + + args = parser.parse_args() + directions = args.directions.split(',') + directions = sorted(set(directions)) + + results = [] + # print(f'checking where {args.split} split data are in training') + # print(f'direction\tcommon_count\tsrc common\ttgt common\tfrom_size\tto_size') + raw_data = args.folder + all_test_data, test_data = get_all_test_data(args.test_data, directions, split='test') + mess_up_train, data_sizes = check_train_all(raw_data, directions, all_test_data) + print(data_sizes) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/multilingual/data_scripts/dedup_all.py b/fairseq/examples/multilingual/data_scripts/dedup_all.py new file mode 100644 index 0000000..ef39c05 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/dedup_all.py @@ -0,0 +1,52 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + + +import os +import glob +import argparse +from utils.dedup import deup + +import sys +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--from-folder", type=str, required=True, + help="the data folder to be dedup") + parser.add_argument("--to-folder", type=str, required=True, + help="the data folder to save deduped data") + parser.add_argument('--directions', type=str, default=None, required=False) + + args = parser.parse_args() + + if args.directions is None: + raw_files = glob.glob(f'{args.from_folder}/train*') + + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + else: + directions = args.directions.split(',') + directions = sorted(set(directions)) + + for direction in directions: + src, tgt = direction.split('-') + src_file = f'{args.from_folder}/train.{src}-{tgt}.{src}' + tgt_file = f'{args.from_folder}/train.{src}-{tgt}.{tgt}' + src_file_out = f'{args.to_folder}/train.{src}-{tgt}.{src}' + tgt_file_out = f'{args.to_folder}/train.{src}-{tgt}.{tgt}' + assert src_file != src_file_out + assert tgt_file != tgt_file_out + print(f'deduping {src_file}, {tgt_file}') + deup(src_file, tgt_file, src_file_out, tgt_file_out) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh b/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh new file mode 100644 index 0000000..99fbc75 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh @@ -0,0 +1,30 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + +# first run download_wmt20.sh; it will install a few useful tools for other scripts +# TODO: need to print out instructions on downloading a few files which requires manually authentication from the websites +bash ./download_wmt20.sh + +python ./download_wmt19_and_before.py +bash ./download_wat19_my.sh +python ./download_ted_and_extract.py +bash ./download_lotus.sh +bash ./download_iitb.sh +bash ./download_af_xh.sh + + +# IWSLT downloading URLs have changed in between; TODO: fix them: +bash ./download_iwslt_and_extract.sh + +# TODO: globalvoices URLs changed; need to be fixed +bash ./download_flores_data.sh diff --git a/fairseq/examples/multilingual/data_scripts/download_af_xh.sh b/fairseq/examples/multilingual/data_scripts/download_af_xh.sh new file mode 100644 index 0000000..a78fbbb --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_af_xh.sh @@ -0,0 +1,164 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# set -x -e + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +# put intermediate files +TMP_DIR=$WORKDIR_ROOT/temp/af_xhv2 +# output {train,valid,test} files to dest +DEST=${WORKDIR_ROOT}/ML50/raw + + + +ROOT=${WORKDIR_ROOT} +UTILS=$PWD/utils +TMX2CORPUS="${UTILS}/tmx2corpus" +TMX_TOOL="python ${TMX2CORPUS}/tmx2corpus.py" + +mkdir -p $TMP_DIR +mkdir -p $DEST +mkdir -p $UTILS + +function download_opus(){ + src=$1 + tgt=$2 + subset=$3 + ulr=$4 + + mkdir extract_$subset.$src-$tgt + pushd extract_$subset.$src-$tgt + if [ ! -f "$subset.$src-$tgt.tmx.gz" ]; then + wget $url -O "$subset.$src-$tgt.tmx.gz" + gzip -d "$subset.$src-$tgt.tmx.gz" + f=$subset.$src-$tgt.tmx + $TMX_TOOL $f + mv bitext.$src ../$subset.$src-$tgt.$src + mv bitext.$tgt ../$subset.$src-$tgt.$tgt + fi + popd +} + +function concat_subsets(){ + src=$1 + tgt=$2 + subsets=$3 + src_train=raw_train.$src-$tgt.$src + tgt_train=raw_train.$src-$tgt.$tgt + > $src_train + > $tgt_train + for subset in $subsets; do + cat $subset.$src-$tgt.$src >> $src_train + cat $subset.$src-$tgt.$tgt >> $tgt_train + done +} + + + +function get_seeded_random() +{ + seed="$1" + openssl enc -aes-256-ctr -pass pass:"$seed" -nosalt \ + /dev/null +} + +function split_train_valid(){ + src=$1 + tgt=$2 + raw_src_train=raw_train.$src-$tgt.$src + raw_tgt_train=raw_train.$src-$tgt.$tgt + + shuf --random-source=<(get_seeded_random 43) $raw_src_train > shuffled.$src-$tgt.$src + shuf --random-source=<(get_seeded_random 43) $raw_tgt_train > shuffled.$src-$tgt.$tgt + + head -n 1500 shuffled.$src-$tgt.$src > valid.$src-$tgt.$src + head -n 1500 shuffled.$src-$tgt.$tgt > valid.$src-$tgt.$tgt + + tail +1501 shuffled.$src-$tgt.$src > train.$src-$tgt.$src + tail +1501 shuffled.$src-$tgt.$tgt > train.$src-$tgt.$tgt +} + +function copy2dst(){ + lsrc=$1 + ltgt=$2 + src=${lsrc:0:2} + tgt=${ltgt:0:2} + + + cp valid.$src-$tgt.$src $DEST/valid.$lsrc-$ltgt.$lsrc + cp valid.$src-$tgt.$tgt $DEST/valid.$lsrc-$ltgt.$ltgt + + cp train.$src-$tgt.$src $DEST/train.$lsrc-$ltgt.$lsrc + cp train.$src-$tgt.$tgt $DEST/train.$lsrc-$ltgt.$ltgt +} + + + + +#for xh-en +declare -A xh_en_urls +xh_en_urls=( + [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/en-xh.tmx.gz + [wikimedia]=https://object.pouta.csc.fi/OPUS-wikimedia/v20190628/tmx/en-xh.tmx.gz + [memat]=https://object.pouta.csc.fi/OPUS-memat/v1/tmx/en-xh.tmx.gz + [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/en-xh.tmx.gz + [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/en-xh.tmx.gz + [XhosaNavy]=https://object.pouta.csc.fi/OPUS-XhosaNavy/v1/tmx/en-xh.tmx.gz + [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/en-xh.tmx.gz + [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/en-xh.tmx.gz +) + +mkdir $TMP_DIR/xh-en +pushd $TMP_DIR/xh-en +for k in "${!xh_en_urls[@]}" +do + name=$k + url=${xh_en_urls[$k]} + echo "$name: $url" + download_opus xh en $name $ulr +done +concat_subsets xh en "${!xh_en_urls[@]}" +split_train_valid xh en +copy2dst xh_ZA en_XX +popd + + +## +#for af-en +declare -A af_en_urls +af_en_urls=( + [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/af-en.tmx.gz + [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/af-en.tmx.gz + [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/af-en.tmx.gz + [QED]=https://object.pouta.csc.fi/OPUS-QED/v2.0a/tmx/af-en.tmx.gz + [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/af-en.tmx.gz + [OpenSubtitles]=https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/tmx/af-en.tmx.gz + [SPC]=https://object.pouta.csc.fi/OPUS-SPC/v1/tmx/af-en.tmx.gz + [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/af-en.tmx.gz +) + +mkdir $TMP_DIR/af-en +pushd $TMP_DIR/af-en +for k in "${!af_en_urls[@]}" +do + name=$k + url=${af_en_urls[$k]} + echo "$name: $url" + download_opus af en $name $ulr +done +concat_subsets af en "${!af_en_urls[@]}" +split_train_valid af en +copy2dst af_ZA en_XX +popd + + diff --git a/fairseq/examples/multilingual/data_scripts/download_flores_data.sh b/fairseq/examples/multilingual/data_scripts/download_flores_data.sh new file mode 100644 index 0000000..e6175ce --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_flores_data.sh @@ -0,0 +1,246 @@ +#!/bin/bash + +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +set -e +set -o pipefail + +SRC=en +SI_TGT=si +NE_TGT=ne + +DESTDIR=${WORKDIR_ROOT}/ML50/raw/ + +ROOT=${WORKDIR_ROOT}/tmp +mkdir -p $ROOT +DATA=$ROOT/data +NE_ROOT=$DATA/all-clean-ne +SI_ROOT=$DATA/all-clean-si + +mkdir -p $DATA $NE_ROOT $SI_ROOT + +SI_OPUS_DATASETS=( + "$SI_ROOT/GNOME.en-si" + "$SI_ROOT/Ubuntu.en-si" + "$SI_ROOT/KDE4.en-si" + "$SI_ROOT/OpenSubtitles.en-si" +) + +SI_OPUS_URLS=( + "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-si.txt.zip" + "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-si.txt.zip" + "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-si.txt.zip" + "https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/moses/en-si.txt.zip" +) + +NE_OPUS_DATASETS=( + "$NE_ROOT/GNOME.en-ne" + "$NE_ROOT/Ubuntu.en-ne" + "$NE_ROOT/KDE4.en-ne" +) + +NE_OPUS_URLS=( + "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-ne.txt.zip" + "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-ne.txt.zip" + "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-ne.txt.zip" +) + +REMOVE_FILE_PATHS=() + +# Download data +download_data() { + CORPORA=$1 + URL=$2 + + if [ -f $CORPORA ]; then + echo "$CORPORA already exists, skipping download" + else + echo "Downloading $URL" + wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA + if [ -f $CORPORA ]; then + echo "$URL successfully downloaded." + else + echo "$URL not successfully downloaded." + rm -f $CORPORA + exit -1 + fi + fi +} + +# Example: download_opus_data $LANG_ROOT $TGT +download_opus_data() { + LANG_ROOT=$1 + TGT=$2 + + if [ "$TGT" = "si" ]; then + URLS=("${SI_OPUS_URLS[@]}") + DATASETS=("${SI_OPUS_DATASETS[@]}") + else + URLS=("${NE_OPUS_URLS[@]}") + DATASETS=("${NE_OPUS_DATASETS[@]}") + fi + + # Download and extract data + for ((i=0;i<${#URLS[@]};++i)); do + URL=${URLS[i]} + CORPORA=${DATASETS[i]} + + download_data $CORPORA $URL + unzip -o $CORPORA -d $LANG_ROOT + REMOVE_FILE_PATHS+=( $CORPORA $CORPORA.xml $CORPORA.ids $LANG_ROOT/README $LANG_ROOT/LICENSE ) + done + + cat ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$SRC + cat ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$TGT + + REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC ) + REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT ) +} + +download_opus_data $SI_ROOT $SI_TGT +cp ${SI_OPUS_DATASETS[3]}.$SRC $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SRC +cp ${SI_OPUS_DATASETS[3]}.$SI_TGT $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SI_TGT +REMOVE_FILE_PATHS+=( ${SI_OPUS_DATASETS[3]}.$SRC ${SI_OPUS_DATASETS[3]}.$SI_TGT ) + +download_opus_data $NE_ROOT $NE_TGT + + +# Download and extract Global Voices data +GLOBAL_VOICES="$NE_ROOT/globalvoices.2018q4.ne-en" +GLOBAL_VOICES_URL="http://www.casmacat.eu/corpus/global-voices/globalvoices.ne-en.xliff.gz" + +download_data $GLOBAL_VOICES.gz $GLOBAL_VOICES_URL +gunzip -Nf $GLOBAL_VOICES.gz + +sed -ne 's?.*\(.*\).*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$NE_TGT +sed -ne 's?.*]*>\(.*\).*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$SRC + +REMOVE_FILE_PATHS+=( $GLOBAL_VOICES ) + +# Download and extract the bible dataset +BIBLE_TOOLS=bible-corpus-tools +XML_BIBLES=XML_Bibles +XML_BIBLES_DUP=XML_Bibles_dup + +if [ ! -e $BIBLE_TOOLS ]; then + echo "Cloning bible-corpus-tools repository..." + git clone https://github.com/christos-c/bible-corpus-tools.git +fi + +mkdir -p $BIBLE_TOOLS/bin $XML_BIBLES $XML_BIBLES_DUP +javac -cp "$BIBLE_TOOLS/lib/*" -d $BIBLE_TOOLS/bin $BIBLE_TOOLS/src/bible/readers/*.java $BIBLE_TOOLS/src/bible/*.java + +download_data bible.tar.gz "https://github.com/christos-c/bible-corpus/archive/v1.2.1.tar.gz" +tar xvzf bible.tar.gz + +cp bible-corpus-1.2.1/bibles/{Greek.xml,English.xml,Nepali.xml} $XML_BIBLES/ +cp bible-corpus-1.2.1/bibles/{Greek.xml,English-WEB.xml,Nepali.xml} $XML_BIBLES_DUP/ + +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES_DUP +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES_DUP + +cat $XML_BIBLES/aligned/*/English.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$SRC +cat $XML_BIBLES/aligned/*/Nepali.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$NE_TGT +cat $XML_BIBLES_DUP/aligned/*/English-WEB.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$SRC +cat $XML_BIBLES_DUP/aligned/*/Nepali.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$NE_TGT +REMOVE_FILE_PATHS+=( bible-corpus-1.2.1 bible.tar.gz $BIBLE_TOOLS $XML_BIBLES $XML_BIBLES_DUP ) + +# Download and extract the Penn Treebank dataset +NE_TAGGED=$ROOT/new_submissions_parallel_corpus_project_Nepal +NE_TAGGED_URL="http://www.cle.org.pk/Downloads/ling_resources/parallelcorpus/NepaliTaggedCorpus.zip" +EN_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.en.patch" +NE_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.ne.patch" +MOSES=mosesdecoder +MOSES_TOK=$MOSES/scripts/tokenizer +EN_PATCH_REGEX="{s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}" +NE_PATCH_REGEX="{s:\p{Cf}::g;s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}" + +download_data $DATA/nepali-penn-treebank.$SRC.patch $EN_TAGGED_PATCH_URL +download_data $DATA/nepali-penn-treebank.$NE_TGT.patch $NE_TAGGED_PATCH_URL +download_data original.zip $NE_TAGGED_URL +unzip -o original.zip -d $ROOT + +cat $NE_TAGGED/00.txt $NE_TAGGED/01.txt $NE_TAGGED/02.txt > $NE_TAGGED/nepali-penn-treebank.$SRC +cat $NE_TAGGED/00ne_revised.txt $NE_TAGGED/01ne_revised.txt $NE_TAGGED/02ne_revised.txt > $NE_TAGGED/nepali-penn-treebank.$NE_TGT + +patch $NE_TAGGED/nepali-penn-treebank.$SRC -i $DATA/nepali-penn-treebank.$SRC.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$SRC +patch $NE_TAGGED/nepali-penn-treebank.$NE_TGT -i $DATA/nepali-penn-treebank.$NE_TGT.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT + +if [ ! -e $MOSES ]; then + echo "Cloning moses repository..." + git clone https://github.com/moses-smt/mosesdecoder.git +fi + +cat $NE_TAGGED/nepali-penn-treebank-patched.$SRC | \ + perl -anpe "$EN_PATCH_REGEX" | \ + $MOSES_TOK/tokenizer.perl -l $SRC | \ + $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$SRC + +cat $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT | \ + perl -CIO -anpe "$NE_PATCH_REGEX" | \ + $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$NE_TGT + + +# Download nepali dictionary data +NE_DICT=$NE_ROOT/dictionaries +download_data $NE_DICT "http://www.seas.upenn.edu/~nlp/resources/TACL-data-release/dictionaries.tar.gz" +tar xvzf $NE_DICT +cp dictionaries/dict.ne $NE_ROOT/dictionary.$NE_TGT-$SRC +REMOVE_FILE_PATHS+=( $NE_DICT dictionaries ) + +REMOVE_FILE_PATHS+=( $MOSES $NE_TAGGED original.zip $DATA/nepali-penn-treebank.$SRC.patch $DATA/nepali-penn-treebank.$NE_TGT.patch ) + + +# Remove the temporary files +for ((i=0;i<${#REMOVE_FILE_PATHS[@]};++i)); do + rm -rf ${REMOVE_FILE_PATHS[i]} +done + +# Copy the training data +si=si_LK +ne=ne_NP +en=en_XX +cat $SI_ROOT/GNOMEKDEUbuntu.en-si.si $SI_ROOT/OpenSubtitles2018.en-si.si > $DESTDIR/train.$si-$en.$si +cat $SI_ROOT/GNOMEKDEUbuntu.en-si.en $SI_ROOT/OpenSubtitles2018.en-si.en > $DESTDIR/train.$si-$en.$en + +cat $NE_ROOT/bible_dup.en-ne.ne $NE_ROOT/bible.en-ne.ne $NE_ROOT/globalvoices.2018q4.ne-en.ne $NE_ROOT/GNOMEKDEUbuntu.en-ne.ne $NE_ROOT/nepali-penn-treebank.ne > $DESTDIR/train.$ne-$en.$ne +cat $NE_ROOT/bible_dup.en-ne.en $NE_ROOT/bible.en-ne.en $NE_ROOT/globalvoices.2018q4.ne-en.en $NE_ROOT/GNOMEKDEUbuntu.en-ne.en $NE_ROOT/nepali-penn-treebank.en > $DESTDIR/train.$ne-$en.$en + + +#Download the test sets +wget https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz +tar -xvzf wikipedia_en_ne_si_test_sets.tgz + +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.ne $DESTDIR/valid.$ne-$en.$ne +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.en $DESTDIR/valid.$ne-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.si $DESTDIR/valid.$si-$en.$si +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.en $DESTDIR/valid.$si-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.ne $DESTDIR/devtest.$ne-$en.$ne +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.en $DESTDIR/devtest.$ne-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.si $DESTDIR/devtest.$si-$en.$si +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.en $DESTDIR/devtest.$si-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.ne $DESTDIR/test.$ne-$en.$ne +cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.en $DESTDIR/test.$ne-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.si $DESTDIR/test.$si-$en.$si +cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.en $DESTDIR/test.$si-$en.$en + +rm -rf wikipedia_en_ne_si_test_sets.tgz wikipedia_en_ne_si_test_sets diff --git a/fairseq/examples/multilingual/data_scripts/download_iitb.sh b/fairseq/examples/multilingual/data_scripts/download_iitb.sh new file mode 100644 index 0000000..a884e20 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_iitb.sh @@ -0,0 +1,35 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + +IITB=$WORKDIR_ROOT/IITB +mkdir -p $IITB +pushd $IITB + +wget http://www.cfilt.iitb.ac.in/~moses/iitb_en_hi_parallel/iitb_corpus_download/parallel.tgz +tar -xvzf parallel.tgz + +wget http://www.cfilt.iitb.ac.in/~moses/iitb_en_hi_parallel/iitb_corpus_download/dev_test.tgz +tar -xvzf dev_test.tgz + +DESTDIR=${WORKDIR_ROOT}/ML50/raw/ + +cp parallel/IITB.en-hi.en $DESTDIR/train.hi_IN-en_XX.en_XX +cp parallel/IITB.en-hi.hi $DESTDIR/train.hi_IN-en_XX.hi_IN + +cp dev_test/dev.en $DESTDIR/valid.hi_IN-en_XX.en_XX +cp dev_test/dev.hi $DESTDIR/valid.hi_IN-en_XX.hi_IN + +cp dev_test/test.en $DESTDIR/test.hi_IN-en_XX.en_XX +cp dev_test/test.hi $DESTDIR/test.hi_IN-en_XX.hi_IN +popd \ No newline at end of file diff --git a/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh b/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh new file mode 100644 index 0000000..ca3591b --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh @@ -0,0 +1,225 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +#echo 'Cloning Moses github repository (for tokenization scripts)...' +#git clone https://github.com/moses-smt/mosesdecoder.git + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + + +data_root=${WORKDIR_ROOT}/iwsltv2 +DESTDIR=${WORKDIR_ROOT}/ML50/raw + + +langs="ar_AR it_IT nl_XX ko_KR vi_VN" +echo "data_root: $data_root" + +download_path=${data_root}/downloads +raw=${DESTDIR} +tmp=${data_root}/tmp +orig=${data_root}/orig + +mkdir -p $download_path $orig $raw $tmp +####################### +download_iwslt(){ + iwslt_key=$1 + src=$2 + tgt=$3 + save_prefix=$4 + pushd ${download_path} + if [[ ! -f ${save_prefix}$src-$tgt.tgz ]]; then + wget https://wit3.fbk.eu/archive/${iwslt_key}/texts/$src/$tgt/$src-$tgt.tgz -O ${save_prefix}$src-$tgt.tgz + [ $? -eq 0 ] && return 0 + fi + popd +} + +extract_iwslt(){ + src=$1 + tgt=$2 + prefix=$3 + pushd $orig + tar zxvf ${download_path}/${prefix}$src-${tgt}.tgz + popd +} + +generate_train(){ + lsrc=$1 + ltgt=$2 + src=${lsrc:0:2} + tgt=${ltgt:0:2} + for ll in $lsrc $ltgt; do + l=${ll:0:2} + f="$orig/*/train.tags.$src-$tgt.$l" + f_raw=$raw/train.$lsrc-$ltgt.$ll + cat $f \ + | grep -v '' \ + | grep -v '' \ + | grep -v '' \ + | grep -v '' \ + | grep -v '' \ + | sed -e 's///g' \ + | sed -e 's/<\/title>//g' \ + | sed -e 's/<description>//g' \ + | sed -e 's/<\/description>//g' \ + | sed 's/^\s*//g' \ + | sed 's/\s*$//g' \ + > $f_raw + [ $? -eq 0 ] && echo "extracted $f to $f_raw" + done + return 0 +} + +convert_valid_test(){ + src=$1 + tgt=$2 + for l in $src $tgt; do + echo "lang: ${l}" + for o in `ls $orig/*/IWSLT*.TED*.$src-$tgt.$l.xml`; do + fname=${o##*/} + f=$tmp/${fname%.*} + echo "$o => $f" + grep '<seg id' $o \ + | sed -e 's/<seg id="[0-9]*">\s*//g' \ + | sed -e 's/\s*<\/seg>\s*//g' \ + | sed -e "s/\’/\'/g" \ + > $f + echo "" + done + done +} + +generate_subset(){ + lsrc=$1 + ltgt=$2 + src=${lsrc:0:2} + tgt=${ltgt:0:2} + subset=$3 + prefix=$4 + for ll in $lsrc $ltgt; do + l=${ll:0:2} + f=$tmp/$prefix.${src}-${tgt}.$l + if [[ -f $f ]]; then + cp $f $raw/$subset.${lsrc}-$ltgt.${ll} + fi + done +} +################# + +echo "downloading iwslt training and dev data" +# using multilingual for it, nl +download_iwslt "2017-01-trnmted" DeEnItNlRo DeEnItNlRo +download_iwslt "2017-01-trnted" ar en +download_iwslt "2017-01-trnted" en ar +download_iwslt "2017-01-trnted" ko en +download_iwslt "2017-01-trnted" en ko +download_iwslt "2015-01" vi en +download_iwslt "2015-01" en vi + +echo "donwloading iwslt test data" +download_iwslt "2017-01-mted-test" it en "test." +download_iwslt "2017-01-mted-test" en it "test." +download_iwslt "2017-01-mted-test" nl en "test." +download_iwslt "2017-01-mted-test" en nl "test." + +download_iwslt "2017-01-ted-test" ar en "test." +download_iwslt "2017-01-ted-test" en ar "test." +download_iwslt "2017-01-ted-test" ko en "test." +download_iwslt "2017-01-ted-test" en ko "test." +download_iwslt "2015-01-test" vi en "test." +download_iwslt "2015-01-test" en vi "test." + +echo "extract training data tar balls" +extract_iwslt DeEnItNlRo DeEnItNlRo +extract_iwslt ar en +extract_iwslt en ar +extract_iwslt ko en +extract_iwslt en ko +extract_iwslt vi en +extract_iwslt en vi + + +echo "extracting iwslt test data" +for lang in $langs; do + l=${lang:0:2} + extract_iwslt $l en "test." + extract_iwslt en $l "test." +done + +echo "convert dev and test data" +for lang in $langs; do + s_lang=${lang:0:2} + convert_valid_test $s_lang en + convert_valid_test en $s_lang +done + + + +echo "creating training data into $raw" +for lang in $langs; do + generate_train $lang en_XX + generate_train en_XX $lang +done + +echo "creating iwslt dev data into raw" +generate_subset en_XX vi_VN valid "IWSLT15.TED.tst2013" +generate_subset vi_VN en_XX valid "IWSLT15.TED.tst2013" + +generate_subset en_XX ar_AR valid "IWSLT17.TED.tst2016" +generate_subset ar_AR en_XX valid "IWSLT17.TED.tst2016" +generate_subset en_XX ko_KR valid "IWSLT17.TED.tst2016" +generate_subset ko_KR en_XX valid "IWSLT17.TED.tst2016" + + +generate_subset en_XX it_IT valid "IWSLT17.TED.tst2010" +generate_subset it_IT en_XX valid "IWSLT17.TED.tst2010" +generate_subset en_XX nl_XX valid "IWSLT17.TED.tst2010" +generate_subset nl_XX en_XX valid "IWSLT17.TED.tst2010" + +echo "creating iswslt test data into raw" +generate_subset en_XX vi_VN test "IWSLT15.TED.tst2015" +generate_subset vi_VN en_XX test "IWSLT15.TED.tst2015" + +generate_subset en_XX ar_AR test "IWSLT17.TED.tst2017" +generate_subset ar_AR en_XX test "IWSLT17.TED.tst2017" +generate_subset en_XX ko_KR test "IWSLT17.TED.tst2017" +generate_subset ko_KR en_XX test "IWSLT17.TED.tst2017" + +generate_subset en_XX it_IT test "IWSLT17.TED.tst2017.mltlng" +generate_subset it_IT en_XX test "IWSLT17.TED.tst2017.mltlng" +generate_subset en_XX nl_XX test "IWSLT17.TED.tst2017.mltlng" +generate_subset nl_XX en_XX test "IWSLT17.TED.tst2017.mltlng" + +# normalze iwslt directions into x-en +pushd $raw +for lang in $langs; do + for split in test valid; do + x_en_f1=$split.$lang-en_XX.en_XX + x_en_f2=$split.$lang-en_XX.${lang} + + en_x_f1=$split.en_XX-$lang.en_XX + en_x_f2=$split.en_XX-$lang.${lang} + + if [ -f $en_x_f1 ] && [ ! -f $x_en_f1 ]; then + echo "cp $en_x_f1 $x_en_f1" + cp $en_x_f1 $x_en_f1 + fi + if [ -f $x_en_f2 ] && [ ! -f $x_en_f2 ]; then + echo "cp $en_x_f2 $x_en_f2" + cp $en_x_f2 $x_en_f2 + fi + done +done +popd \ No newline at end of file diff --git a/fairseq/examples/multilingual/data_scripts/download_lotus.sh b/fairseq/examples/multilingual/data_scripts/download_lotus.sh new file mode 100644 index 0000000..c08c701 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_lotus.sh @@ -0,0 +1,46 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +SRCDIR=$WORKDIR_ROOT/indic_languages_corpus +DESTDIR=${WORKDIR_ROOT}/ML50/raw/ +mkdir -p $SRCDIR +mkdir -p $DESTDIR + +cd $SRCDIR +wget http://lotus.kuee.kyoto-u.ac.jp/WAT/indic-multilingual/indic_languages_corpus.tar.gz +tar -xvzf indic_languages_corpus.tar.gz + +SRC_EXTRACT_DIR=$SRCDIR/indic_languages_corpus/bilingual + +cp $SRC_EXTRACT_DIR/ml-en/train.ml $DESTDIR/train.ml_IN-en_XX.ml_IN +cp $SRC_EXTRACT_DIR/ml-en/train.en $DESTDIR/train.ml_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ml-en/dev.ml $DESTDIR/valid.ml_IN-en_XX.ml_IN +cp $SRC_EXTRACT_DIR/ml-en/dev.en $DESTDIR/valid.ml_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ml-en/test.ml $DESTDIR/test.ml_IN-en_XX.ml_IN +cp $SRC_EXTRACT_DIR/ml-en/test.en $DESTDIR/test.ml_IN-en_XX.en_XX + +cp $SRC_EXTRACT_DIR/ur-en/train.ur $DESTDIR/train.ur_PK-en_XX.ur_PK +cp $SRC_EXTRACT_DIR/ur-en/train.en $DESTDIR/train.ur_PK-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ur-en/dev.ur $DESTDIR/valid.ur_PK-en_XX.ur_PK +cp $SRC_EXTRACT_DIR/ur-en/dev.en $DESTDIR/valid.ur_PK-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ur-en/test.ur $DESTDIR/test.ur_PK-en_XX.ur_PK +cp $SRC_EXTRACT_DIR/ur-en/test.en $DESTDIR/test.ur_PK-en_XX.en_XX + +cp $SRC_EXTRACT_DIR/te-en/train.te $DESTDIR/train.te_IN-en_XX.te_IN +cp $SRC_EXTRACT_DIR/te-en/train.en $DESTDIR/train.te_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/te-en/dev.te $DESTDIR/valid.te_IN-en_XX.te_IN +cp $SRC_EXTRACT_DIR/te-en/dev.en $DESTDIR/valid.te_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/te-en/test.te $DESTDIR/test.te_IN-en_XX.te_IN +cp $SRC_EXTRACT_DIR/te-en/test.en $DESTDIR/test.te_IN-en_XX.en_XX diff --git a/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py b/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py new file mode 100644 index 0000000..eb75668 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py @@ -0,0 +1,338 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import itertools +import os +import csv +from collections import defaultdict +from six.moves import zip +import io +import wget +import sys + +from subprocess import check_call, check_output + +# scripts and data locations +CWD = os.getcwd() +UTILS = f"{CWD}/utils" + +MOSES = f"{UTILS}/mosesdecoder" + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +# please donwload mosesdecoder here: +detok_cmd = f'{MOSES}/scripts/tokenizer/detokenizer.perl' + + +def call(cmd): + print(f"Executing: {cmd}") + check_call(cmd, shell=True) + +class MultiLingualAlignedCorpusReader(object): + """A class to read TED talk dataset + """ + + def __init__(self, corpus_path, delimiter='\t', + target_token=True, bilingual=True, corpus_type='file', + lang_dict={'source': ['fr'], 'target': ['en']}, + eval_lang_dict=None, zero_shot=False, + detok=True, + ): + + self.empty_line_flag = 'NULL' + self.corpus_path = corpus_path + self.delimiter = delimiter + self.bilingual = bilingual + self.lang_dict = lang_dict + self.lang_set = set() + self.target_token = target_token + self.zero_shot = zero_shot + self.eval_lang_dict = eval_lang_dict + self.corpus_type = corpus_type + self.detok = detok + + for list_ in self.lang_dict.values(): + for lang in list_: + self.lang_set.add(lang) + + self.data = dict() + self.data['train'] = self.read_aligned_corpus(split_type='train') + self.data['test'] = self.read_aligned_corpus(split_type='test') + self.data['dev'] = self.read_aligned_corpus(split_type='dev') + + def read_data(self, file_loc_): + data_list = list() + with io.open(file_loc_, 'r', encoding='utf8') as fp: + for line in fp: + try: + text = line.strip() + except IndexError: + text = self.empty_line_flag + data_list.append(text) + return data_list + + def filter_text(self, dict_): + if self.target_token: + field_index = 1 + else: + field_index = 0 + data_dict = defaultdict(list) + list1 = dict_['source'] + list2 = dict_['target'] + for sent1, sent2 in zip(list1, list2): + try: + src_sent = ' '.join(sent1.split()[field_index: ]) + except IndexError: + src_sent = 'NULL' + + if src_sent.find(self.empty_line_flag) != -1 or len(src_sent) == 0: + continue + + elif sent2.find(self.empty_line_flag) != -1 or len(sent2) == 0: + continue + + else: + data_dict['source'].append(sent1) + data_dict['target'].append(sent2) + return data_dict + + def read_file(self, split_type, data_type): + return self.data[split_type][data_type] + + def save_file(self, path_, split_type, data_type, lang): + tok_file = tok_file_name(path_, lang) + with io.open(tok_file, 'w', encoding='utf8') as fp: + for line in self.data[split_type][data_type]: + fp.write(line + '\n') + if self.detok: + de_tok(tok_file, lang) + + def add_target_token(self, list_, lang_id): + new_list = list() + token = '__' + lang_id + '__' + for sent in list_: + new_list.append(token + ' ' + sent) + return new_list + + def read_from_single_file(self, path_, s_lang, t_lang): + data_dict = defaultdict(list) + with io.open(path_, 'r', encoding='utf8') as fp: + reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) + for row in reader: + data_dict['source'].append(row[s_lang]) + data_dict['target'].append(row[t_lang]) + + if self.target_token: + text = self.add_target_token(data_dict['source'], t_lang) + data_dict['source'] = text + + return data_dict['source'], data_dict['target'] + + def read_aligned_corpus(self, split_type='train'): + data_dict = defaultdict(list) + iterable = [] + s_list = [] + t_list = [] + + if self.zero_shot: + if split_type == "train": + iterable = zip(self.lang_dict['source'], self.lang_dict['target']) + else: + iterable = zip(self.eval_lang_dict['source'], self.eval_lang_dict['target']) + + elif self.bilingual: + iterable = itertools.product(self.lang_dict['source'], self.lang_dict['target']) + + for s_lang, t_lang in iterable: + if s_lang == t_lang: + continue + if self.corpus_type == 'file': + split_type_file_path = os.path.join(self.corpus_path, + "all_talks_{}.tsv".format(split_type)) + s_list, t_list = self.read_from_single_file(split_type_file_path, + s_lang=s_lang, + t_lang=t_lang) + data_dict['source'] += s_list + data_dict['target'] += t_list + new_data_dict = self.filter_text(data_dict) + return new_data_dict + + +def read_langs(corpus_path): + split_type_file_path = os.path.join(corpus_path, 'extracted', + "all_talks_dev.tsv") + with io.open(split_type_file_path, 'r', encoding='utf8') as fp: + reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) + header = next(reader) + return [k for k in header.keys() if k != 'talk_name'] + +def extra_english(corpus_path, split): + split_type_file_path = os.path.join(corpus_path, + f"all_talks_{split}.tsv") + output_split_type_file_path = os.path.join(corpus_path, + f"all_talks_{split}.en") + with io.open(split_type_file_path, 'r', encoding='utf8') as fp, io.open(output_split_type_file_path, 'w', encoding='utf8') as fw: + reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) + for row in reader: + line = row['en'] + fw.write(line + '\n') + de_tok(output_split_type_file_path, 'en') + + + +def tok_file_name(filename, lang): + seps = filename.split('.') + seps.insert(-1, 'tok') + tok_file = '.'.join(seps) + return tok_file + +def de_tok(tok_file, lang): + # seps = tok_file.split('.') + # seps.insert(-1, 'detok') + # de_tok_file = '.'.join(seps) + de_tok_file = tok_file.replace('.tok.', '.') + cmd = 'perl {detok_cmd} -l {lang} < {tok_file} > {de_tok_file}'.format( + detok_cmd=detok_cmd, tok_file=tok_file, + de_tok_file=de_tok_file, lang=lang[:2]) + call(cmd) + +def extra_bitex( + ted_data_path, + lsrc_lang, + ltrg_lang, + target_token, + output_data_path, +): + def get_ted_lang(lang): + long_langs = ['pt-br', 'zh-cn', 'zh-tw', 'fr-ca'] + if lang[:5] in long_langs: + return lang[:5] + elif lang[:4] =='calv': + return lang[:5] + elif lang in ['pt_BR', 'zh_CN', 'zh_TW', 'fr_CA']: + return lang.lower().replace('_', '-') + return lang[:2] + src_lang = get_ted_lang(lsrc_lang) + trg_lang = get_ted_lang(ltrg_lang) + train_lang_dict={'source': [src_lang], 'target': [trg_lang]} + eval_lang_dict = {'source': [src_lang], 'target': [trg_lang]} + + obj = MultiLingualAlignedCorpusReader(corpus_path=ted_data_path, + lang_dict=train_lang_dict, + target_token=target_token, + corpus_type='file', + eval_lang_dict=eval_lang_dict, + zero_shot=False, + bilingual=True) + + os.makedirs(output_data_path, exist_ok=True) + lsrc_lang = lsrc_lang.replace('-', '_') + ltrg_lang = ltrg_lang.replace('-', '_') + obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", + split_type='train', data_type='source', lang=src_lang) + obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", + split_type='train', data_type='target', lang=trg_lang) + + obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", + split_type='test', data_type='source', lang=src_lang) + obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", + split_type='test', data_type='target', lang=trg_lang) + + obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", + split_type='dev', data_type='source', lang=src_lang) + obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", + split_type='dev', data_type='target', lang=trg_lang) + + +def bar_custom(current, total, width=80): + print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r') + + +def download_and_extract(download_to, extract_to): + url = 'http://phontron.com/data/ted_talks.tar.gz' + filename = f"{download_to}/ted_talks.tar.gz" + if os.path.exists(filename): + print(f'{filename} has already been downloaded so skip') + else: + filename = wget.download(url, filename, bar=bar_custom) + if os.path.exists(f'{extract_to}/all_talks_train.tsv'): + print(f'Already extracted so skip') + else: + extract_cmd = f'tar xzfv "{filename}" -C "{extract_to}"' + call(extract_cmd) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--ted_data_path', type=str, default=WORKDIR_ROOT, required=False) + parser.add_argument( + '--direction-list', + type=str, + # default=None, + #for ML50 + default=( + "bn_IN-en_XX,he_IL-en_XX,fa_IR-en_XX,id_ID-en_XX,sv_SE-en_XX,pt_XX-en_XX,ka_GE-en_XX,ka_GE-en_XX,th_TH-en_XX," + "mr_IN-en_XX,hr_HR-en_XX,uk_UA-en_XX,az_AZ-en_XX,mk_MK-en_XX,gl_ES-en_XX,sl_SI-en_XX,mn_MN-en_XX," + #non-english directions + # "fr_XX-de_DE," # replaced with wmt20 + # "ja_XX-ko_KR,es_XX-pt_XX,ru_RU-sv_SE,hi_IN-bn_IN,id_ID-ar_AR,cs_CZ-pl_PL,ar_AR-tr_TR" + ), + required=False) + parser.add_argument('--target-token', action='store_true', default=False) + parser.add_argument('--extract-all-english', action='store_true', default=False) + + args = parser.parse_args() + + import sys + import json + + # TED Talks data directory + ted_data_path = args.ted_data_path + + download_to = f'{ted_data_path}/downloads' + extract_to = f'{ted_data_path}/extracted' + + #DESTDIR=${WORKDIR_ROOT}/ML50/raw/ + output_path = f'{ted_data_path}/ML50/raw' + os.makedirs(download_to, exist_ok=True) + os.makedirs(extract_to, exist_ok=True) + os.makedirs(output_path, exist_ok=True) + download_and_extract(download_to, extract_to) + + + if args.extract_all_english: + for split in ['train', 'dev', 'test']: + extra_english(ted_data_path, split) + exit(0) + if args.direction_list is not None: + directions = args.direction_list.strip().split(',') + directions = [tuple(d.strip().split('-', 1)) for d in directions if d] + else: + langs = read_langs(ted_data_path) + # directions = [ + # '{}.{}'.format(src, tgt) + # for src in langs + # for tgt in langs + # if src < tgt + # ] + directions = [('en', tgt) for tgt in langs if tgt != 'en'] + print(f'num directions={len(directions)}: {directions}') + + for src_lang, trg_lang in directions: + print('--working on {}-{}'.format(src_lang, trg_lang)) + extra_bitex( + extract_to, + src_lang, + trg_lang, + target_token=args.target_token, + output_data_path=output_path + ) diff --git a/fairseq/examples/multilingual/data_scripts/download_wat19_my.sh b/fairseq/examples/multilingual/data_scripts/download_wat19_my.sh new file mode 100644 index 0000000..c1e2d47 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_wat19_my.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +SRCDIR=$WORKDIR_ROOT/indic_languages_corpus +DESTDIR=$WORKDIR_ROOT/ML50/raw +mkdir -p $SRCDIR +mkdir -p $DESTDIR + +WAT_MY_EN=wat2020.my-en.zip +cd $SRCDIR +# please refer to http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/ for latest URL if the following url expired +#- The data used for WAT2020 are identical to those used in WAT2019. +wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/$WAT_MY_EN +unzip $WAT_MY_EN + + +SRC_EXTRACT_DIR=$SRCDIR/wat2020.my-en/alt + +cp $SRC_EXTRACT_DIR/train.alt.en $DESTDIR/train.my_MM-en_XX.en_XX +cp $SRC_EXTRACT_DIR/train.alt.my $DESTDIR/train.my_MM-en_XX.my_MM +cp $SRC_EXTRACT_DIR/dev.alt.en $DESTDIR/valid.my_MM-en_XX.en_XX +cp $SRC_EXTRACT_DIR/dev.alt.my $DESTDIR/valid.my_MM-en_XX.my_MM +cp $SRC_EXTRACT_DIR/test.alt.en $DESTDIR/test.my_MM-en_XX.en_XX +cp $SRC_EXTRACT_DIR/test.alt.my $DESTDIR/test.my_MM-en_XX.my_MM diff --git a/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py b/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py new file mode 100644 index 0000000..3465731 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py @@ -0,0 +1,899 @@ +from typing import NamedTuple, List +from urllib.parse import urlparse +import os, sys +import subprocess +from subprocess import check_call, check_output +import glob +import wget +import re +import multiprocessing as mp +from functools import partial +import pathlib +from collections import OrderedDict + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +# scripts and data locations +CWD = os.getcwd() +UTILS = f"{CWD}/utils" + +MOSES = f"{UTILS}/mosesdecoder" +SGM_TOOL = f'{MOSES}/scripts/ems/support/input-from-sgm.perl' + +TMX2CORPUS = f"{UTILS}/tmx2corpus" +TMX_TOOL = f'python {TMX2CORPUS}/tmx2corpus.py' + +to_data_path = f'{WORKDIR_ROOT}/wmt' +download_to = f'{to_data_path}/downloads' +manually_downloads = f'{to_data_path}/downloads' +extract_to = f'{to_data_path}/extracted' +#DESTDIR=${WORKDIR_ROOT}/ML50/raw/ +raw_data = f'{WORKDIR_ROOT}/ML50/raw' +#### + +class DLDataset(NamedTuple): + name: str + train_urls: List[str] + valid_urls: List[str] + test_urls: List[str] + train_files_patterns: List[str] = [] + valid_files_patterns: List[str] = [] + test_files_patterns: List[str] = [] + + + +def bar_custom(current, total, width=80): + print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r') + +def get_downloaded_file(dl_folder, url): + if isinstance(url, tuple): + url, f = url + else: + url_f = urlparse(url) + # f = os.path.split(url_f.path)[-1] + f = '_'.join(url_f.path.split('/')[1:]) + return url, f"{dl_folder}/{f}" + +def download_parts_and_combine(dl_folder, urls, filename): + parts = [] + for url_record in urls: + url, part_file = get_downloaded_file(dl_folder, url_record) + if os.path.exists(part_file): + print(f'{part_file} has already been downloaded so skip') + else: + part_file = wget.download(url, part_file, bar=bar_custom) + parts.append(part_file) + + def get_combine_cmd(parts): + #default as tar.gz.?? + return f'cat {" ".join(parts)} > {filename}' + + combine_cmd = get_combine_cmd(parts) + call(combine_cmd, debug=True) + return filename + +def download_a_url(dl_folder, url): + url, filename = get_downloaded_file(dl_folder, url) + if os.path.exists(filename): + print(f'{filename} has already been downloaded so skip') + return filename + + print(f'downloading {url} to {filename}') + if isinstance(url, list) or isinstance(url, tuple): + download_parts_and_combine(dl_folder, url, filename) + else: + wget.download(url, filename, bar=bar_custom) + print(f'dowloaded: {filename}') + return filename + +def download_files(dl_folder, urls, completed_urls={}): + for url_record in urls: + url, _ = get_downloaded_file(dl_folder, url_record) + filename = download_a_url(dl_folder, url_record) + completed_urls[str(url)] = filename + return completed_urls + +def check_need_manual_downalod(dl_folder, to_manually_download_urls): + to_be_manually_dowloaded = [] + manually_completed_urls = {} + for url_record, instruction in to_manually_download_urls: + url, filename = get_downloaded_file(dl_folder, url_record) + if not os.path.exists(filename): + print(f'{url} need to be download manually, please download it manually following {instruction}; and copy it to {filename}') + to_be_manually_dowloaded.append((url, filename)) + else: + manually_completed_urls[url] = filename + # if len(to_be_manually_dowloaded) > 0: + # raise ValueError('Missing files that need to be downloaded manually; stop the process now.') + return to_be_manually_dowloaded + +def download_dataset(to_folder, dl_dataset, completed_urls={}): + download_files(to_folder, dl_dataset.train_urls, completed_urls) + download_files(to_folder, dl_dataset.valid_urls, completed_urls) + download_files(to_folder, dl_dataset.test_urls, completed_urls) + print('completed downloading') + return completed_urls + +def call(cmd, debug=False): + if debug: + print(cmd) + check_call(cmd, shell=True) + + +def get_extract_name(file_path): + path = os.path.split(file_path) + return path[-1] + '_extract' #.split('.')[0] + +def extract_file(downloaded_file, extract_folder, get_extract_name=get_extract_name, debug=False): + extract_name = get_extract_name(downloaded_file) + extract_to = f'{extract_folder}/{extract_name}' + os.makedirs(extract_to, exist_ok=True) + if os.path.exists(f'{extract_to}/DONE'): + print(f'{downloaded_file} has already been extracted to {extract_to} so skip') + return extract_to + def get_extract_cmd(filename): + if filename.endswith('.tgz') or filename.endswith('tar.gz'): + return f'tar xzfv {filename} -C {extract_to}' + elif filename.endswith('.gz.tar'): + return f'tar xfv {filename} -C {extract_to}; (cd {extract_to}; gzip -d *.gz; [ $? -eq 0 ] || gzip -d */*.gz)' + elif filename.endswith('.tar'): + return f'tar xfv {filename} -C {extract_to}' + elif filename.endswith('.gz'): + return f'cp {filename} {extract_to}; (cd {extract_to}; gzip -d *.gz)' + elif filename.endswith('.zip'): + return f'unzip {filename} -d {extract_to}' + extract_cmd = get_extract_cmd(downloaded_file) + print(f'extracting {downloaded_file}') + if isinstance(extract_cmd, list): + for c in extract_cmd: + call(c, debug=debug) + else: + call(extract_cmd, debug=debug) + call(f'echo DONE > {extract_to}/DONE') + return extract_to + + +def extract_all_files( + completed_urls, extract_folder, + get_extract_name=get_extract_name, + completed_extraction={}, + debug=False): + extracted_folders = OrderedDict() + for url, downloaded_file in set(completed_urls.items()): + if downloaded_file in completed_extraction: + print(f'{downloaded_file} is already extracted; so skip') + continue + folder = extract_file(downloaded_file, extract_folder, get_extract_name, debug) + extracted_folders[url] = folder + return extracted_folders + + +def my_glob(folder): + for p in [f'{folder}/*', f'{folder}/*/*', f'{folder}/*/*/*']: + for f in glob.glob(p): + yield f + + +def sgm2raw(sgm, debug): + to_file = sgm[0:len(sgm) - len('.sgm')] + if os.path.exists(to_file): + debug and print(f'{sgm} already converted to {to_file}; so skip') + return to_file + cmd = f'{SGM_TOOL} < {sgm} > {to_file}' + call(cmd, debug) + return to_file + +def tmx2raw(tmx, debug): + to_file = tmx[0:len(tmx) - len('.tmx')] + to_folder = os.path.join(*os.path.split(tmx)[:-1]) + if os.path.exists(f'{to_folder}/bitext.en'): + debug and print(f'{tmx} already extracted to {to_file}; so skip') + return to_file + cmd = f'(cd {to_folder}; {TMX_TOOL} {tmx})' + call(cmd, debug) + return to_file + +CZENG16_REGEX = re.compile(r'.*?data.plaintext-format/0[0-9]train$') +WMT19_WIKITITLES_REGEX = re.compile(r'.*?wikititles-v1.(\w\w)-en.tsv.gz') +TSV_REGEX = re.compile(r'.*?(\w\w)-(\w\w).tsv$') + + + +def cut_wikitles(wiki_file, debug): + # different languages have different file names: + if wiki_file.endswith('wiki/fi-en/titles.fi-en'): + to_file1 = f'{wiki_file}.fi' + to_file2 = f'{wiki_file}.en' + BACKSLASH = '\\' + cmd1 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f1 |awk '{{$1=$1}};1' > {to_file1}" + cmd2 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f2 |awk '{{$1=$1}};1' > {to_file2}" +# elif WMT19_WIKITITLES_REGEX.match(wiki_file): +# src = WMT19_WIKITITLES_REGEX.match(wiki_file).groups()[0] +# to_file1 = f'{wiki_file}.{src}' +# to_file2 = f'{wiki_file}.en' +# cmd1 = f"cat {wiki_file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}" +# cmd2 = f"cat {wiki_file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}" + else: + return None + if os.path.exists(to_file1) and os.path.exists(to_file2): + debug and print(f'{wiki_file} already processed to {to_file1} and {to_file2}; so skip') + return wiki_file + + call(cmd1, debug=debug) + call(cmd2, debug=debug) + return wiki_file + +def cut_tsv(file, debug): + m = TSV_REGEX.match(file) + if m is None: + raise ValueError(f'{file} is not matching tsv pattern') + src = m.groups()[0] + tgt = m.groups()[1] + + to_file1 = f'{file}.{src}' + to_file2 = f'{file}.{tgt}' + cmd1 = f"cat {file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}" + cmd2 = f"cat {file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}" + if os.path.exists(to_file1) and os.path.exists(to_file2): + debug and print(f'{file} already processed to {to_file1} and {to_file2}; so skip') + return file + + call(cmd1, debug=debug) + call(cmd2, debug=debug) + return file + + +def convert_file_if_needed(file, debug): + if file.endswith('.sgm'): + return sgm2raw(file, debug) + elif file.endswith('.tmx'): + return tmx2raw(file, debug) + elif file.endswith('wiki/fi-en/titles.fi-en'): + return cut_wikitles(file, debug) +# elif WMT19_WIKITITLES_REGEX.match(file): +# return cut_wikitles(file, debug) + elif file.endswith('.tsv'): + return cut_tsv(file, debug) + elif CZENG16_REGEX.match(file): + return convert2czeng17(file, debug) + else: + return file + + +def convert_files_if_needed(extracted_foldrs, my_glob=my_glob, debug=False): + return { + url: list(sorted(set(convert_file_if_needed(f, debug)) for f in sorted(set(my_glob(folder))))) + for url, folder in extracted_foldrs.items() + } + +def match_patt(file_path, file_pattern, src, tgt, lang): + return file_pattern.format(src=src, tgt=tgt, lang=lang) in file_path + +def match_patts(file_path, file_patterns, src, tgt, lang): + for file_pattern in file_patterns: + params = { k: v for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] if k in file_pattern} + matching = file_pattern.format(**params) + + if isinstance(file_pattern, tuple): + pattern, directions = file_pattern + if f'{src}-{tgt}' in directions and matching in file_path: + return True + else: + if matching in file_path: + return True + return False + +def extracted_glob(extracted_folder, file_patterns, src, tgt, lang): + def get_matching_pattern(file_pattern): + params = { + k: v + for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] + if '{' + k + '}' in file_pattern + } + file_pattern = re.sub(r'{src:(.*?)}', r'\1' if lang == src else '', file_pattern) + file_pattern = re.sub(r'{tgt:(.*?)}', r'\1' if lang == tgt else '', file_pattern) + file_pattern = file_pattern.format(**params) + return file_pattern + for file_pattern in file_patterns: + if isinstance(file_pattern, tuple): + file_pattern, lang_pairs = file_pattern + if f'{src}-{tgt}' not in lang_pairs: + continue +# print('working on pattern: ', file_pattern, lang_pairs ) + matching_pattern = get_matching_pattern(file_pattern) + if matching_pattern is None: + continue + glob_patterns = f'{extracted_folder}/{matching_pattern}' +# print('glob_patterns: ', glob_patterns) + for f in glob.glob(glob_patterns): + yield f + +# for debug usage +def all_extracted_files(split, src, tgt, extracted_folders, split_urls): + def get_url(url): + if isinstance(url, tuple): + url, downloaded_file = url + return url + return [ + f + for url in split_urls + for f in my_glob(extracted_folders[str(get_url(url))]) + ] + +def concat_files(split, src, tgt, extracted_folders, split_urls, path_patterns, to_folder, debug=False): +# if debug: +# print('extracted files to be filtered by patterns: ', +# '\n\t'.join(sorted(all_extracted_files(split, src, tgt, extracted_folders, split_urls)))) + for lang in [src, tgt]: + to_file = f'{to_folder}/{split}.{src}-{tgt}.{lang}' + s_src, s_tgt, s_lang = src.split('_')[0], tgt.split('_')[0], lang.split('_')[0] + files = [] + for url in split_urls: + if isinstance(url, tuple): + url, downloaded_file = url + if str(url) not in extracted_folders: + print(f'warning: {url} not in extracted files') + for extracted_file in set( + extracted_glob( + extracted_folders[str(url)], path_patterns, + s_src, s_tgt, s_lang)): + files.append(extracted_file) + if len(files) == 0: + print('warning: ', f'No files found for split {to_file}') + continue + files = sorted(set(files)) + print(f'concating {len(files)} files into {to_file}') + cmd = ['cat'] + [f'"{f}"' for f in files] + [f'>{to_file}'] + cmd = " ".join(cmd) + call(cmd, debug=debug) + +UTILS = os.path.join(pathlib.Path(__file__).parent, 'utils') +LID_MODEL = f'{download_to}/lid.176.bin' +LID_MULTI = f'{UTILS}/fasttext_multi_filter.py' + +def lid_filter(split, src, tgt, from_folder, to_folder, debug=False): + if not os.path.exists(LID_MODEL): + call(f'wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O {LID_MODEL}') + from_prefix = f'{from_folder}/{split}.{src}-{tgt}' + to_prefix = f'{to_folder}/{split}.{src}-{tgt}' + if os.path.exists(f'{from_prefix}.{src}') and os.path.exists(f'{from_prefix}.{tgt}'): + s_src, s_tgt = src.split('_')[0], tgt.split('_')[0] + cmd = ( + f'python {LID_MULTI} --model {LID_MODEL} --inputs {from_prefix}.{src} {from_prefix}.{tgt} ' + f'--langs {s_src} {s_tgt} --outputs {to_prefix}.{src} {to_prefix}.{tgt}' + ) + print(f'filtering {from_prefix}') + call(cmd, debug=debug) + +def concat_into_splits(dl_dataset, src, tgt, extracted_folders, to_folder, debug): + to_folder_tmp = f"{to_folder}_tmp" + os.makedirs(to_folder_tmp, exist_ok=True) + concat_files('train', src, tgt, + extracted_folders, + split_urls=dl_dataset.train_urls, + path_patterns=dl_dataset.train_files_patterns, + to_folder=to_folder_tmp, debug=debug) + lid_filter('train', src, tgt, to_folder_tmp, to_folder, debug) + + concat_files('valid', src, tgt, + extracted_folders, + split_urls=dl_dataset.valid_urls, + path_patterns=dl_dataset.valid_files_patterns, + to_folder=to_folder, debug=debug) + concat_files('test', src, tgt, + extracted_folders, + split_urls=dl_dataset.test_urls, + path_patterns=dl_dataset.test_files_patterns, + to_folder=to_folder, debug=debug) + + +def download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=False): + pool = mp.Pool(processes=num_processes) + download_f = partial(download_a_url, dl_folder) + downloaded_files = pool.imap_unordered(download_f, urls) + pool.close() + pool.join() + +BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ") +def run_eval_bleu(cmd): + output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip() + print(output) + bleu = -1.0 + for line in output.strip().split('\n'): + m = BLEU_REGEX.search(line) + if m is not None: + bleu = m.groups()[0] + bleu = float(bleu) + break + return bleu + +def check_wmt_test_bleu(raw_folder, wmt_lang_pairs): + not_matchings = [] + for wmt, src_tgts in wmt_lang_pairs: + for src_tgt in src_tgts: + print(f'checking test bleus for: {src_tgt} at {wmt}') + src, tgt = src_tgt.split('-') + ssrc, stgt = src[:2], tgt[:2] + if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'): + # reversed direction may have different test set + test_src = f'{raw_folder}/test.{tgt}-{src}.{src}' + else: + test_src = f'{raw_folder}/test.{src}-{tgt}.{src}' + cmd1 = f'cat {test_src} | sacrebleu -t "{wmt}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""' + test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}' + cmd2 = f'cat {test_tgt} | sacrebleu -t "{wmt}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""' + bleu1 = run_eval_bleu(cmd1) + if bleu1 != 100.0: + not_matchings.append(f'{wmt}:{src_tgt} source side not matching: {test_src}') + bleu2 = run_eval_bleu(cmd2) + if bleu2 != 100.0: + not_matchings.append(f'{wmt}:{src_tgt} target side not matching: {test_tgt}') + return not_matchings + +def download_and_extract( + to_folder, lang_pairs, dl_dataset, + to_manually_download_urls, + completed_urls={}, completed_extraction={}, + debug=False): + + dl_folder = f'{to_folder}/downloads' + extract_folder = f'{to_folder}/extracted' + raw_folder = f'{to_folder}/raw' + lid_filtered = f'{to_folder}/lid_filtered' + + os.makedirs(extract_folder, exist_ok=True) + os.makedirs(raw_folder, exist_ok=True) + os.makedirs(lid_filtered, exist_ok=True) + + + to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls) + + completed_urls = download_dataset( + dl_folder, dl_dataset, completed_urls) + if debug: + print('completed urls: ', completed_urls) + + + extracted_folders = extract_all_files( + completed_urls, + extract_folder=extract_folder, + completed_extraction=completed_extraction, + debug=debug) + if debug: + print('download files have been extracted to folders: ', extracted_folders) + + converted_files = convert_files_if_needed(extracted_folders, debug=False) + for src_tgt in lang_pairs: + print(f'working on {dl_dataset.name}: {src_tgt}') + src, tgt = src_tgt.split('-') + concat_into_splits(dl_dataset, + src=src, tgt=tgt, + extracted_folders=extracted_folders, + to_folder=raw_folder, debug=debug) + print('completed data into: ', raw_folder) + +def download_czang16(download_to, username=None): + wgets = [ + f'wget --user={username} --password=czeng -P {download_to} http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar' + for i in range(10)] + cmds = [] + for i, cmd in enumerate(wgets): + filename = f'{download_to}/data-plaintext-format.{i}.tar' + if os.path.exists(filename): + print(f'{filename} has already been downloaded; so skip') + continue + cmds.append(cmd) + if cmds and username is None: + raise ValueError('No czeng username is given; please register at http://ufal.mff.cuni.cz/czeng/czeng16 to obtain username to download') + for cmd in cmds: + call(cmd) + print('done with downloading czeng1.6') + +def download_czeng17_script(download_to, extract_folder, debug=False): + url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip' + filename = f'{download_to}/convert_czeng16_to_17.pl.zip' + extract_to = f'{extract_folder}/{get_extract_name(filename)}' + script_path = f'{extract_to}/convert_czeng16_to_17.pl' + + if not os.path.exists(script_path): + wget.download(url, filename, bar=bar_custom) + extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug) + return script_path + +czeng17_script_path = "" +def convert2czeng17(file, debug): + en_file = f'{file}.en' + cs_file = f'{file}.cs' + + if not os.path.exists(en_file) or not os.path.exists(cs_file): + cs_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f3 > {cs_file}' + en_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f4 > {en_file}' + call(cs_cmd, debug) + call(en_cmd, debug) + else: + print(f'already extracted: {en_file} and {cs_file}') + return file + +def extract_czeng17(extract_folder, debug=False): + url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip' + filename = f'{download_to}/convert_czeng16_to_17.pl.zip' + extract_to = f'{extract_folder}/{get_extract_name(filename)}' + script_path = f'{extract_to}/convert_czeng16_to_17.pl' + + if not os.path.exists(script_path): + wget.download(url, filename, bar=bar_custom) + extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug) + return script_path + +######### +# definitions of wmt data sources +# for es-en +# Punctuation in the official test sets will be encoded with ASCII characters (not complex Unicode characters) as much as possible. You may want to normalize your system's output before submission. You are able able to use a rawer version of the test sets that does not have this normalization. +# script to normalize punctuation: http://www.statmt.org/wmt11/normalize-punctuation.perl +wmt13_es_en = DLDataset( + name='wmt13_es-en', + train_urls=[ + 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://www.statmt.org/wmt13/training-parallel-un.tgz', + 'http://www.statmt.org/wmt13/training-parallel-nc-v8.tgz', + ], + valid_urls=[ + ('http://www.statmt.org/wmt13/dev.tgz', 'wmt13_dev.tgz') + ], + test_urls=[ + ('http://www.statmt.org/wmt13/test.tgz', 'wmt13_test.tgz') + ], + train_files_patterns=[ + ('*/europarl-v7.{src}-{tgt}.{lang}', ['es-en']), + ('*commoncrawl.{src}-{tgt}.{lang}', ['es-en']), + ('*/news-commentary-v8.{src}-{tgt}.{lang}', ['es-en']), + ('un/*undoc.2000.{src}-{tgt}.{lang}', ['es-en']), + ] , + valid_files_patterns=[ + ('dev/newstest2012.{lang}', ['es-en']) + ], + test_files_patterns=[ + ('test/newstest*.{lang}', ['es-en']) + ], +) + +wmt14_de_fr_en = DLDataset( + name='wmt14_de_fr_en', + train_urls=[ + 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://www.statmt.org/wmt13/training-parallel-un.tgz', + 'http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz', + ('http://www.statmt.org/wmt10/training-giga-fren.tar', 'training-giga-fren.gz.tar'), #it is actuall a gz.tar + ], + valid_urls=[ + ('http://www.statmt.org/wmt14/dev.tgz', 'wmt14_dev.tgz'), + ], + test_urls=[ + ('http://www.statmt.org/wmt14/test-full.tgz', 'wmt14_test_full.tgz'), # cleaned test sets + ], + train_files_patterns=[ + ('*/europarl-v7.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), + ('*commoncrawl.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), + ('*/*news-commentary-v9.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), + ('un/undoc.2000.{src}-{tgt}.{lang}', ['fr-en']), + ('*giga-{src}{tgt}*{lang}', ['fr-en']) + ], + valid_files_patterns=[ + ('dev/newstest2013.{lang}', ['fr-en', 'de-en']) + ], + test_files_patterns=[ + ('test-full/newstest*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['en-de', 'de-en', 'fr-en', 'en-fr']), + ], +) + +# pip install git+https://github.com/amake/tmx2corpus.git +wmt16_ro_en = DLDataset( + name='wmt16_ro-en', + train_urls=[ + ('http://data.statmt.org/wmt16/translation-task/training-parallel-ep-v8.tgz', 'wmt16_training-parallel-ep-v8.tgz'), + ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz', 'en-ro.tmx.gz'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt16/translation-task/dev-romanian-updated.tgz', 'wmt16_dev.tgz') + ], + test_urls=[ + ('http://data.statmt.org/wmt16/translation-task/test.tgz', 'wmt16_test.tgz') + ], + train_files_patterns=[ + ('*/*europarl-v8.{src}-{tgt}.{lang}', ['ro-en']), + ('bitext.{lang}', ['ro-en']) #setimes from tmux + ] , + valid_files_patterns=[ + ('dev/newsdev2016*{src}{tgt}*.{lang}', ['ro-en', 'ro-en']) + ], + test_files_patterns=[ + ('test/newstest*{src}{tgt}*.{lang}', ['ro-en', 'en-ro']) + ], +) + +cwmt_wmt_instruction = 'cwmt download instruction at: http://nlp.nju.edu.cn/cwmt-wmt' +wmt17_fi_lv_tr_zh_en_manual_downloads = [ + # fake urls to have unique keys for the data + ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), cwmt_wmt_instruction), +] +wmt17_fi_lv_tr_zh_en = DLDataset( + name='wmt17_fi_lv_tr_zh_en', + train_urls=[ + ('http://data.statmt.org/wmt17/translation-task/training-parallel-ep-v8.tgz', 'wmt17_training-parallel-ep-v8.tgz'), + 'http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz', + 'http://www.statmt.org/wmt15/wiki-titles.tgz', + ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-tr.tmx.gz', 'en-tr.tmx.gz'), + ('http://data.statmt.org/wmt17/translation-task/rapid2016.tgz', 'wmt17_rapid2016.tgz'), + 'http://data.statmt.org/wmt17/translation-task/leta.v1.tgz', + 'http://data.statmt.org/wmt17/translation-task/dcep.lv-en.v1.tgz', + 'http://data.statmt.org/wmt17/translation-task/books.lv-en.v1.tgz', + (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00', + 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01',), 'UNv1.0.en-zh.tar.gz'), + #manually download files: + ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt17/translation-task/dev.tgz', 'wmt17_dev.tgz'), + ], + test_urls=[ + #NEW: Improved translations for zh test sets + ('http://data.statmt.org/wmt17/translation-task/test-update-1.tgz', 'wmt17_test_zh_en.tgz'), + ('http://data.statmt.org/wmt17/translation-task/test.tgz', 'wmt17_test_others.tgz') + ], + train_files_patterns=[ + ('casict*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ), + ('casia*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ), + ('dataum*/Book*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en']), + ('neu*/NEU*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en'] ), + ('*/*UNv1.0.en-zh.{src:zh}{tgt:en}', ['zh-en']), + ('training/*news-commentary-v12.{src}-{tgt}.{lang}', ['zh-en', ]), + + ('*/*europarl-v8.{src}-{tgt}.{lang}', ['fi-en', 'lv-en']), + ('wiki/fi-en/titles.{src}-{tgt}.{lang}', ['fi-en', ]), + ('rapid2016.{tgt}-{src}.{lang}', ['fi-en', 'lv-en']), + ('*/leta.{lang}', ['lv-en']), + ('*/dcep.{lang}', ['lv-en']), + ('*/farewell.{lang}', ['lv-en']), + ('bitext.{lang}', ['tr-en']), + ] , + valid_files_patterns=[ + ('dev/newsdev2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'fi-en', 'lv-en', 'tr-en', 'zh-en', + 'en-fi', 'en-lv', 'en-tr', 'en-zh' + ]), + ('dev/newstest2016*{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'fi-en', 'tr-en', + 'en-fi', 'en-tr', + ]), + ], + test_files_patterns=[ + ('test/newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'fi-en', 'lv-en', 'tr-en', + 'en-fi', 'en-lv', 'en-tr', + ]), + ('newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'zh-en', + 'en-zh' + ]), + ], +) + +czeng_instruction = 'download instruction at: http://ufal.mff.cuni.cz/czeng/czeng16' +#alternative: use the prepared data but detokenize it? +wmt18_cs_et_en_manual_downloads = [ +#for cs, need to register and download; Register and download CzEng 1.6. +#Better results can be obtained by using a subset of sentences, released under a new version name CzEng 1.7. + # ((f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', + # f'data-plaintext-format.{i}.tar'), czeng_instruction) + # for i in range(10) +] + +wmt18_cs_et_en = DLDataset( + name='wmt18_cs_et_en', + train_urls=[ + 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', + 'http://data.statmt.org/wmt18/translation-task/training-parallel-ep-v8.tgz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-cs.zipporah0-dedup-clean.tgz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-et.zipporah0-dedup-clean.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz', + ('http://data.statmt.org/wmt18/translation-task/rapid2016.tgz', 'wmt18_rapid2016.tgz'), + # (tuple( + # (f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', + # f'data-plaintext-format.{i}.tar') + # for i in range(10) + # ), + # 'czeng16_data_plaintext.gz.tar'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt18/translation-task/dev.tgz', 'wmt18_dev.tgz'), + ], + test_urls=[ + ('http://data.statmt.org/wmt18/translation-task/test.tgz', 'wmt18_test.tgz'), + ], + train_files_patterns=[ + # ('*/*europarl-v7.{src}-{tgt}.{lang}', ['cs-en']), + ('*/*europarl-v8.{src}-{tgt}.{lang}', ['et-en']), + # ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['cs-en', 'et-en']), + ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['et-en']), + # ('*commoncrawl.{src}-{tgt}.{lang}', ['cs-en']), + # ('*/news-commentary-v13.{src}-{tgt}.{lang}', ['cs-en']), + # ('data.plaintext-format/*train.{lang}', ['cs-en']), + ('rapid2016.{tgt}-{src}.{lang}', ['et-en']), + ] , + valid_files_patterns=[ + ('dev/newsdev2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['et-en']), + # ('dev/newstest2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['cs-en']) + ], + test_files_patterns=[ + ('test/newstest2018-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + # ['cs-en', 'et-en']), + ['et-en']), + ] +) + +ru_en_yandex_instruction = 'Yandex Corpus download instruction at: https://translate.yandex.ru/corpus?lang=en' +wmt19_ru_gu_kk_lt_manual_downloads = [ + (('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), ru_en_yandex_instruction) +] +wmt19_ru_gu_kk_lt = DLDataset( + name='wmt19_ru_gu_kk_lt', + train_urls=[ + 'http://www.statmt.org/europarl/v9/training/europarl-v9.lt-en.tsv.gz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-lt.bicleaner07.tmx.gz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14-wmt19.en-kk.tsv.gz', + 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.en-ru.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.ru-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.lt-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.gu-en.tsv.gz', + (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00', + 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01', + 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02',), + 'wmt19_UNv1.0.en-ru.tar.gz'), + 'https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-lt.tmx.zip', + ('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt19/translation-task/dev.tgz', 'wmt19_dev.tgz'), + ], + test_urls=[ + ('http://data.statmt.org/wmt19/translation-task/test.tgz', 'wmt19_test.tgz'), + ], + train_files_patterns=[ + ('*europarl-v9.{src}-{tgt}.tsv.{lang}', ['lt-en']), + #paracrawl + ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['ru-en']), + ('bitext.{lang}', ['lt-en',]), + ('*commoncrawl.{src}-{tgt}.{lang}', ['ru-en',]), + ('*news-commentary-v14-wmt19.{tgt}-{src}.tsv.{lang}', ['kk-en', ]), + ('*news-commentary-v14.{tgt}-{src}.tsv.{lang}', ['ru-en']), + #yandex + ('corpus.{tgt}_{src}.1m.{lang}', ['ru-en']), + ('wikititles_v1_wikititles-v1.{src}-{tgt}.tsv.{lang}', ['ru-en', 'kk-en', 'lt-en', 'gu-en']), + ('*/UNv1.0.{tgt}-{src}.{lang}', ['ru-en']), + #rapid + ('bitext.{lang}', ['lt-en']) + ], + valid_files_patterns=[ + ('dev/newsdev2019*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['gu-en', 'kk-en', 'lt-en']), + ('dev/newstest2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['ru-en']), + ], + test_files_patterns=[ + ('sgm/newstest2019-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + ['ru-en', 'gu-en', 'kk-en', 'lt-en', 'en-ru', 'en-gu', 'en-kk', 'en-lt']), + ] +) + + +######### + +if __name__ == "__main__": + # speed up the downloads with multiple processing + dl_folder = f'{to_data_path}/downloads' + extract_folder = f'{to_data_path}/extracted' + + urls = [ + url + for dataset in [wmt13_es_en, wmt14_de_fr_en, wmt16_ro_en, wmt18_cs_et_en, wmt19_ru_gu_kk_lt] + for urls in [dataset.train_urls, dataset.valid_urls, dataset.test_urls] + for url in urls + ] + urls = set(urls) + download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=True) + + # check manually downlaods + to_manually_download_urls = ( + wmt17_fi_lv_tr_zh_en_manual_downloads + wmt18_cs_et_en_manual_downloads + wmt19_ru_gu_kk_lt_manual_downloads + ) + to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls) + if len(to_be_manually_dowloaded) > 0: + print('Missing files that need to be downloaded manually; stop the process now.') + exit(-1) + + completed_urls = {} + completed_extraction = {} + def work_on_wmt(directions, wmt_data): + download_and_extract( + to_data_path, + directions, + wmt_data, + to_manually_download_urls=to_manually_download_urls, + completed_urls=completed_urls, completed_extraction=completed_extraction, debug=True) + + work_on_wmt( + ['es_XX-en_XX'], + wmt13_es_en,) + work_on_wmt( + [ + 'fr_XX-en_XX', 'en_XX-fr_XX', + # 'en_XX-de_DE', 'de_DE-en_XX', + ], + wmt14_de_fr_en,) + work_on_wmt( + ['ro_RO-en_XX', 'en_XX-ro_XX'], + wmt16_ro_en,) + work_on_wmt( + [ + # 'zh_CN-en_XX', + 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX', + #in case the reversed directions have different train/valid/test data + # 'en_XX-zh_CN', + 'en_XX-lv_LV', 'en_XX-fi_FI', 'en_XX-tr_TR', + ], + wmt17_fi_lv_tr_zh_en, ) + # czeng17_script_path = download_czeng17_script(download_to, extract_to, debug=False) + # cz_username = None + work_on_wmt( + [ + # 'cs_CZ-en_XX', + 'et_EE-en_XX'], + wmt18_cs_et_en,) + work_on_wmt( + [ + # 'ru_RU-en_XX', 'en_XX-ru_RU', + 'gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX', + #in case the reversed directions have different train/valid/test data + 'en_XX-gu_IN', 'en_XX-kk_KZ', 'en_XX-lt_LT' + ], + wmt19_ru_gu_kk_lt,) + + not_matching = check_wmt_test_bleu( + f'{to_data_path}/raw', + [ + ('wmt13', ['es_XX-en_XX']), + ('wmt14/full', ['fr_XX-en_XX',]), + ('wmt16', ['ro_RO-en_XX',]), + # ('wmt17/improved', ['zh_CN-en_XX']), + ('wmt17', [ 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX']), + ('wmt18', ['cs_CZ-en_XX', 'et_EE-en_XX']), + ('wmt19', ['gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX']), + #'ru_RU-en_XX', + ] + ) + if len(not_matching) > 0: + print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching)) + diff --git a/fairseq/examples/multilingual/data_scripts/download_wmt20.sh b/fairseq/examples/multilingual/data_scripts/download_wmt20.sh new file mode 100644 index 0000000..31cd5c7 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/download_wmt20.sh @@ -0,0 +1,547 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + + +set -x -e + +# TODO update the workdir and dest dir name +# put fasttext model +WORKDIR=$WORKDIR_ROOT +# put intermediate files +TMP_DIR=$WORKDIR_ROOT/tmp/tmp_wmt20_lowres_download +# output {train,valid,test} files to dest +DEST=$WORKDIR_ROOT/ML50/raw + +UTILS=$PWD/utils + +# per dataset locations +COMMONCRAWL_DIR=$TMP_DIR/commoncrawl +YANDEX_CORPUS=$WORKDIR_ROOT/wmt20/official/ru/yandex/1mcorpus.zip +# unzipped +CZENG_CORPUS=$WORKDIR_ROOT/wmt20/official/cs/czeng/czeng20-train +CCMT_DIR=$WORKDIR_ROOT/wmt20/official/zh/ccmt/parallel + +download_and_select() { + SUBFOLDER=$1 + URL=$2 + UNCOMPRESS_CMD=$3 + LANG=$4 + INPUT_FILEPATH=$5 + if [[ $# -gt 5 ]]; then + LANG_COL=$6 + EN_COL=$7 + fi + + mkdir -p $SUBFOLDER + cd $SUBFOLDER + wget -nc --content-disposition $URL + $UNCOMPRESS_CMD + + if [[ $# -gt 5 ]]; then + cut -f$LANG_COL $INPUT_FILEPATH > $INPUT_FILEPATH.$LANG + cut -f$EN_COL $INPUT_FILEPATH > $INPUT_FILEPATH.en + fi + cd .. + + ln -sf $SUBFOLDER/$INPUT_FILEPATH.$LANG $SUBFOLDER.$LANG + ln -sf $SUBFOLDER/$INPUT_FILEPATH.en $SUBFOLDER.en +} + +prepare_lid() { + pip install fasttext + + # TODO specify global workdir + MODEL=$WORKDIR/fasttext/lid.176.bin + LID_MULTI=$UTILS/fasttext_multi_filter.py + + if [ ! -f "$MODEL" ]; then + echo "downloading fasttext lid model..." + mkdir -p $WORKDIR/fasttext + wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O $MODEL + fi +} + +prepare_moses() { + pushd $UTILS + echo 'Cloning Moses github repository (for tokenization scripts)...' + git clone https://github.com/moses-smt/mosesdecoder.git + popd +} + +lid_filter() { + # TODO specify global workdir + MODEL=$WORKDIR/fasttext/lid.176.bin + LID_MULTI=$UTILS/fasttext_multi_filter.py + + prepare_lid + + SRC=$1 + SRC_FILE=$2 + SRC_OUTPUT=$3 + TGT=$4 + TGT_FILE=$5 + TGT_OUTPUT=$6 + python $LID_MULTI --model $MODEL --inputs $SRC_FILE $TGT_FILE --langs $SRC $TGT --outputs $SRC_OUTPUT $TGT_OUTPUT +} + +prepare_ja_ted() { + mkdir -p ted + cd ted + + wget -nc https://wit3.fbk.eu/archive/2017-01-trnted//texts/en/ja/en-ja.tgz + tar -zxvf en-ja.tgz + cat en-ja/train.tags.en-ja.en | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.en + cat en-ja/train.tags.en-ja.ja | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.ja + + cd .. + ln -sf ted/en-ja/train.en-ja.ja ted.ja + ln -sf ted/en-ja/train.en-ja.en ted.en +} + +prepare_ja() { + OUTPUT_DIR=$TMP_DIR/ja + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/release/2.0/bitext/en-ja.tar.gz" "tar -zxvf en-ja.tar.gz" ja en-ja/en-ja.bicleaner05.txt 4 3 & + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ja.tsv.gz" "gunzip -f news-commentary-v15.en-ja.tsv.gz" ja news-commentary-v15.en-ja.tsv 2 1 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ja-en.tsv.gz" "gunzip -f wikititles-v2.ja-en.tsv.gz" ja wikititles-v2.ja-en.tsv 1 2 & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ja.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ja.langid.tsv.gz" ja WikiMatrix.v1.en-ja.langid.tsv 3 2 & + download_and_select subtitle "https://nlp.stanford.edu/projects/jesc/data/split.tar.gz" "tar -zxvf split.tar.gz" ja split/train 2 1 & + download_and_select kftt "http://www.phontron.com/kftt/download/kftt-data-1.0.tar.gz" "tar -zxvf kftt-data-1.0.tar.gz" ja kftt-data-1.0/data/orig/kyoto-train & + + prepare_ja_ted & + + # ted data needs to + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ja" | sort -V | xargs cat > all.ja + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ja all.ja $DEST/train.ja_XX-en_XX.ja_XX en all.en $DEST/train.ja_XX-en_XX.en_XX +} + +prepare_ta() { + OUTPUT_DIR=$TMP_DIR/ta + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ta-en.tsv.gz" "gunzip -f wikititles-v2.ta-en.tsv.gz" ta wikititles-v2.ta-en.tsv 1 2 & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ta.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ta.langid.tsv.gz" ta WikiMatrix.v1.en-ta.langid.tsv 3 2 & + download_and_select pmindia "http://data.statmt.org/pmindia/v1/parallel/pmindia.v1.ta-en.tsv" "" ta pmindia.v1.ta-en.tsv 2 1 & + download_and_select tanzil "https://object.pouta.csc.fi/OPUS-Tanzil/v1/moses/en-ta.txt.zip" "unzip en-ta.txt.zip" ta Tanzil.en-ta & + download_and_select pib "http://preon.iiit.ac.in/~jerin/resources/datasets/pib-v0.tar" "tar -xvf pib-v0.tar" ta pib/en-ta/train & + download_and_select mkb "http://preon.iiit.ac.in/~jerin/resources/datasets/mkb-v0.tar" "tar -xvf mkb-v0.tar" ta mkb/en-ta/mkb & + download_and_select ufal "http://ufal.mff.cuni.cz/~ramasamy/parallel/data/v2/en-ta-parallel-v2.tar.gz" "tar -zxvf en-ta-parallel-v2.tar.gz" ta en-ta-parallel-v2/corpus.bcn.train & + + wait + + # need special handling for nlpc + mkdir -p nlpc + cd nlpc + wget -nc https://raw.githubusercontent.com/nlpc-uom/English-Tamil-Parallel-Corpus/master/En-Ta%20Corpus/En-Ta%20English.txt + wget -nc https://github.com/nlpc-uom/English-Tamil-Parallel-Corpus/raw/master/En-Ta%20Corpus/En-Ta%20Tamil.txt + tail -n +4 "En-Ta English.txt" > en-ta.en + tail -n +4 "En-Ta Tamil.txt" > en-ta.ta + cd .. + ln -sf nlpc/en-ta.en nlpc.en + ln -sf nlpc/en-ta.ta nlpc.ta + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ta" | sort -V | xargs cat > all.ta + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ta all.ta $DEST/train.ta_IN-en_XX.ta_IN en all.en $DEST/train.ta_IN-en_XX.en_XX +} + +prepare_iu() { + OUTPUT_DIR=$TMP_DIR/iu + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select nh "https://nrc-digital-repository.canada.ca/eng/view/dataset/?id=c7e34fa7-7629-43c2-bd6d-19b32bf64f60" "tar -zxvf Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0.1.tgz" iu Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/NunavutHansard > /dev/null & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.iu-en.tsv.gz" "gunzip -f wikititles-v2.iu-en.tsv.gz" iu wikititles-v2.iu-en.tsv 1 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.iu" | sort -V | xargs cat | nh/Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/scripts/normalize-iu-spelling.pl > all.iu + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + paste all.iu all.en | awk -F $'\t' '$1!=""&&$2!=""' > all.iuen + cut -f1 all.iuen > $DEST/train.iu_CA-en_XX.iu_CA + cut -f2 all.iuen > $DEST/train.iu_CA-en_XX.en_XX +} + +prepare_km() { + OUTPUT_DIR=$TMP_DIR/km + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-km.xz" "unxz wmt20-sent.en-km.zx" km wmt20-sent.en-km 2 1 & + + # km-parallel has multiple sets, concat all of them together + mkdir -p opus + cd opus + wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/km-parallel.tgz" + tar -zxvf km-parallel.tgz + find ./km-parallel -maxdepth 1 -name "*.km" | sort -V | xargs cat > opus.km + find ./km-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en + cd .. + ln -sf opus/opus.km . + ln -sf opus/opus.en . + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.km" | sort -V | xargs cat > all.km + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter km all.km $DEST/train.km_KH-en_XX.km_KH en all.en $DEST/train.km_KH-en_XX.en_XX +} + +prepare_ps() { + OUTPUT_DIR=$TMP_DIR/ps + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-ps.xz" "unxz wmt20-sent.en-ps.xz" ps wmt20-sent.en-ps 2 1 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ps-en.tsv.gz" "gunzip -f wikititles-v2.ps-en.tsv.gz" ps wikititles-v2.ps-en.tsv 1 2 & + # ps-parallel has multiple sets, concat all of them together + mkdir -p opus + cd opus + wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/ps-parallel.tgz" + tar -zxvf ps-parallel.tgz + find ./ps-parallel -maxdepth 1 -name "*.ps" | sort -V | xargs cat > opus.ps + find ./ps-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en + cd .. + ln -sf opus/opus.ps opus.ps + ln -sf opus/opus.en opus.en + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ps" | sort -V | xargs cat > all.ps + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ps all.ps $DEST/train.ps_AF-en_XX.ps_AF en all.en $DEST/train.ps_AF-en_XX.en_XX +} + +download_commoncrawl() { + mkdir -p $COMMONCRAWL_DIR + cd $COMMONCRAWL_DIR + + wget -nc "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz" + tar -zxvf training-parallel-commoncrawl.tgz +} +link_commoncrawl() { + LANG=$1 + ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.en commoncrawl.en + ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.$LANG commoncrawl.$LANG +} + +strip_xlf() { + INPUT_FILE=$1 + SRC=$2 + TGT=$3 + grep '<source xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$SRC + grep '<target xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$TGT +} + +download_and_process_tilde() { + URL=$1 + UNCOMPRESS_CMD=$2 + FILENAME=$3 + LANG=$4 + PROCESS_CMD=$5 + + mkdir -p tilde + cd tilde + wget -nc $URL + $UNCOMPRESS_CMD + echo "executing cmd" + echo $PROCESS_CMD + $PROCESS_CMD + cd .. + ln -sf tilde/$FILENAME.$LANG tilde.$LANG + ln -sf tilde/$FILENAME.en tilde.en +} + +prepare_cs() { + OUTPUT_DIR=$TMP_DIR/cs + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + #download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.cs-en.tsv.gz" "gunzip europarl-v10.cs-en.tsv.gz" cs europarl-v10.cs-en.tsv 1 2 & + #download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-cs.txt.gz" "gunzip en-cs.txt.gz" cs en-cs.txt 2 1 & + #link_commoncrawl cs + #download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.cs-en.tsv.gz" "gunzip news-commentary-v15.cs-en.tsv.gz" cs news-commentary-v15.cs-en.tsv 1 2 & + #download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.cs-en.tsv.gz" "gunzip wikititles-v2.cs-en.tsv.gz" cs wikititles-v2.cs-en.tsv 1 2 & + #download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.cs-en.xlf.gz" "gunzip RAPID_2019.cs-en.xlf.gz" RAPID_2019.cs-en.xlf cs "strip_xlf RAPID_2019.cs-en.xlf cs en" & + #download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.cs-en.langid.tsv.gz" "gunzip WikiMatrix.v1.cs-en.langid.tsv.gz" cs WikiMatrix.v1.cs-en.langid.tsv 2 3 & + + #wait + + # remove previous results + #rm -f all.?? + #find ./ -maxdepth 1 -name "*.cs" | sort -V | xargs cat > all.cs + #find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + if [ -z $CZENG_CORPUS ] ; + then + echo "Please download CZENG_CORPUS manually and place them at $CZENG_CORPUS. Exitting..." + exit + fi + cat $CZENG_CORPUS | sed '/^$/d' | cut -f5 > all.cs + cat $CZENG_CORPUS | sed '/^$/d' | cut -f6 > all.en + + lid_filter cs all.cs $DEST/train.cs_CZ-en_XX.cs_CZ en all.en $DEST/train.cs_CZ-en_XX.en_XX +} + +prepare_de() { + OUTPUT_DIR=$TMP_DIR/de + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz" "gunzip europarl-v10.de-en.tsv.gz" de europarl-v10.de-en.tsv 1 2 & + download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-de.txt.gz" "gunzip en-de.txt.gz" de en-de.txt 2 1 & + link_commoncrawl de + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz" "gunzip news-commentary-v15.de-en.tsv.gz" de news-commentary-v15.de-en.tsv 1 2 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz" "gunzip wikititles-v2.de-en.tsv.gz" de wikititles-v2.de-en.tsv 1 2 & + download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.de-en.xlf.gz" "gunzip RAPID_2019.de-en.xlf.gz" RAPID_2019.de-en.xlf de "strip_xlf RAPID_2019.de-en.xlf de en" & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz" "gunzip WikiMatrix.v1.de-en.langid.tsv.gz" de WikiMatrix.v1.de-en.langid.tsv 2 3 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.de" | sort -V | xargs cat > all.de + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter de all.de $DEST/train.de_DE-en_XX.de_DE en all.en $DEST/train.de_DE-en_XX.en_XX +} + +prepare_tmx() { + TMX_FILE=$1 + git clone https://github.com/amake/TMX2Corpus $UTILS/tmx2corpus + pip install tinysegmenter + + python $UTILS/tmx2corpus/tmx2corpus.py $TMX_FILE +} + +prepare_pl() { + OUTPUT_DIR=$TMP_DIR/pl + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + # download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.pl-en.tsv.gz" "gunzip europarl-v10.pl-en.tsv.gz" pl europarl-v10.pl-en.tsv 1 2 & + # download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-pl.txt.gz" "gunzip en-pl.txt.gz" pl en-pl.txt 2 1 & + # download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.pl-en.tsv.gz" "gunzip wikititles-v2.pl-en.tsv.gz" pl wikititles-v2.pl-en.tsv 1 2 & + download_and_select tilde "https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.en-pl.tmx.zip" "gunzip rapid2019.en-pl.tmx.zip" bitext pl "prepare_tmx RAPID_2019.UNIQUE.en-pl.tmx" & + # download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-pl.langid.tsv.gz" "gunzip WikiMatrix.v1.en-pl.langid.tsv.gz" pl WikiMatrix.v1.en-pl.langid.tsv 3 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.pl" | sort -V | xargs cat > all.pl + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter pl all.pl $DEST/train.pl_PL-en_XX.pl_PL en all.en $DEST/train.pl_PL-en_XX.en_XX +} + +prepare_uncorpus() { + $URLS=$1 + $FILES=$2 + + mkdir -p uncorpus + cd uncorpus + + for URL in $URLS; do + wget -nc $URL + done + cat $FILES > uncorpus.tar.gz + tar -zxvf uncorpus.tar.gz + + cd .. + ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.$LANG uncorpus.$LANG + ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.en uncorpus.en +} + +prepare_yandex() { + mkdir -p yandex + cd yandex + unzip $YANDEX_CORPUS ./ + cd .. + ln -s yandex/corpus.en_ru.1m.en yandex.en + ln -s yandex/corpus.en_ru.1m.ru yandex.ru +} + +prepare_ru() { + OUTPUT_DIR=$TMP_DIR/ru + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" "tar -zxvf paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" ru paracrawl-release1.en-ru.zipporah0-dedup-clean & + link_commoncrawl ru + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ru.tsv.gz" "gunzip news-commentary-v15.en-ru.tsv.gz" ru news-commentary-v15.en-ru.tsv 2 1 & + prepare_yandex & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ru-en.tsv.gz" "gunzip wikititles-v2.ru-en.tsv.gz" ru wikititles-v2.ru-en.tsv 1 2 & + prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02" "UNv1.0.en-ru.tar.gz.00 UNv1.0.en-ru.tar.gz.01 UNv1.0.en-ru.tar.gz.02" & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ru.langid.tsv.gz" "gunzip WikiMatrix.v1.en-ru.langid.tsv.gz" ru WikiMatrix.v1.en-ru.langid.tsv 3 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ru" | sort -V | xargs cat > all.ru + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ru all.ru $DEST/train.ru_RU-en_XX.ru_RU en all.en $DEST/train.ru_RU-en_XX.en_XX +} + +prepare_ccmt() { + mkdir -p ccmt + cd ccmt + # assume ccmt data is already unzipped under CCMT_DIR folder + cat $CCMT_DIR/datum2017/Book*_cn.txt | sed 's/ //g' > datum2017.detok.zh + cat $CCMT_DIR/datum2017/Book*_en.txt > datum2017.detok.en + cat $CCMT_DIR/casict2011/casict-A_ch.txt $CCMT_DIR/casict2011/casict-B_ch.txt $CCMT_DIR/casict2015/casict2015_ch.txt $CCMT_DIR/datum2015/datum_ch.txt $CCMT_DIR/neu2017/NEU_cn.txt datum2017.detok.zh > ccmt.zh + cat $CCMT_DIR/casict2011/casict-A_en.txt $CCMT_DIR/casict2011/casict-B_en.txt $CCMT_DIR/casict2015/casict2015_en.txt $CCMT_DIR/datum2015/datum_en.txt $CCMT_DIR/neu2017/NEU_en.txt datum2017.detok.en > ccmt.en + cd .. + ln -sf ccmt/ccmt.zh ccmt.zh + ln -sf ccmt/ccmt.en ccmt.en +} + +prepare_zh() { + OUTPUT_DIR=$TMP_DIR/zh + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-zh.tsv.gz" "gunzip news-commentary-v15.en-zh.tsv.gz" zh news-commentary-v15.en-zh.tsv 2 1 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.zh-en.tsv.gz" "gunzip wikititles-v2.zh-en.tsv.gz" zh wikititles-v2.zh-en.tsv 1 2 & + prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01" "UNv1.0.en-zh.tar.gz.00 UNv1.0.en-zh.tar.gz.01" & + prepare_ccmt & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-zh.langid.tsv.gz" "gunzip WikiMatrix.v1.en-zh.langid.tsv.gz" zh WikiMatrix.v1.en-zh.langid.tsv 3 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.zh" | sort -V | xargs cat > all.zh + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter zh all.zh $DEST/train.zh_CN-en_XX.zh_CN en all.en $DEST/train.zh_CN-en_XX.en_XX +} + +prepare_tests() { + OUTPUT_DIR=$TMP_DIR + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + wget -nc http://data.statmt.org/wmt20/translation-task/dev.tgz + tar -zxvf dev.tgz + cd dev + + cat newsdev2020-jaen-src.ja.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.ja + cat newsdev2020-jaen-ref.en.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.en + split newsdev2020-jaen.ja -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.ja_XX + split newsdev2020-jaen.en -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.en_XX + split newsdev2020-jaen.ja -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.ja_XX + split newsdev2020-jaen.en -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.en_XX + + cat newsdev2020-iuen-src.iu.sgm | strip_sgm.sh > newsdev2020-iuen.iu + cat newsdev2020-iuen-ref.en.sgm | strip_sgm.sh > newsdev2020-iuen.en + split newsdev2020-iuen.iu -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.iu_CA + split newsdev2020-iuen.en -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.en_XX + split newsdev2020-iuen.iu -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.iu_CA + split newsdev2020-iuen.en -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.en_XX + + cat newsdev2020-taen-src.ta.sgm | strip_sgm.sh > newsdev2020-taen.ta + cat newsdev2020-taen-ref.en.sgm | strip_sgm.sh > newsdev2020-taen.en + split newsdev2020-taen.ta -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.ta_IN + split newsdev2020-taen.en -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.en_XX + split newsdev2020-taen.ta -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.ta_IN + split newsdev2020-taen.en -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.en_XX + + cp wikipedia.dev.km-en.km $DEST/valid.km_KH-en_XX.km_KH + cp wikipedia.dev.km-en.en $DEST/valid.km_KH-en_XX.en_XX + cp wikipedia.devtest.km-en.km $DEST/test.km_KH-en_XX.km_KH + cp wikipedia.devtest.km-en.en $DEST/test.km_KH-en_XX.en_XX + + cp wikipedia.dev.ps-en.ps $DEST/valid.ps_AF-en_XX.ps_AF + cp wikipedia.dev.ps-en.en $DEST/valid.ps_AF-en_XX.en_XX + cp wikipedia.devtest.ps-en.ps $DEST/test.ps_AF-en_XX.ps_AF + cp wikipedia.devtest.ps-en.en $DEST/test.ps_AF-en_XX.en_XX + + cat newsdev2020-plen-src.pl.sgm | strip_sgm.sh > newsdev2020-plen.pl + cat newsdev2020-plen-ref.en.sgm | strip_sgm.sh > newsdev2020-plen.en + split newsdev2020-plen.pl -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.pl_PL + split newsdev2020-plen.en -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.en_XX + split newsdev2020-plen.pl -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.pl_PL + split newsdev2020-plen.en -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.en_XX + + cat newstest2018-encs-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.en_XX + cat newstest2018-encs-ref.cs.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.cs_CZ + cat newstest2019-encs-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.en_XX + cat newstest2019-encs-ref.cs.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.cs_CZ + + cat newstest2018-deen-src.de.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.de_DE + cat newstest2018-deen-ref.en.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.en_XX + cat newstest2018-ende-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.en_XX + cat newstest2018-ende-ref.de.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.de_DE + cat newstest2019-deen-src.de.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.de_DE + cat newstest2019-deen-ref.en.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.en_XX + cat newstest2019-ende-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.en_XX + cat newstest2019-ende-ref.de.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.de_DE + + cat newstest2018-ruen-src.ru.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.ru_RU + cat newstest2018-ruen-ref.en.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.en_XX + cat newstest2018-enru-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.en_XX + cat newstest2018-enru-ref.ru.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.ru_RU + cat newstest2019-ruen-src.ru.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.ru_RU + cat newstest2019-ruen-ref.en.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.en_XX + cat newstest2019-enru-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.en_XX + cat newstest2019-enru-ref.ru.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.ru_RU + + cat newstest2018-zhen-src.zh.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.zh_CN + cat newstest2018-zhen-ref.en.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.en_XX + cat newstest2018-enzh-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.en_XX + cat newstest2018-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.zh_CN + cat newstest2019-zhen-src.zh.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.zh_CN + cat newstest2019-zhen-ref.en.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.en_XX + cat newstest2019-enzh-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.en_XX + cat newstest2019-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.zh_CN +} + +mkdir -p $DEST + +prepare_lid +prepare_moses +download_commoncrawl + +prepare_ja & +prepare_ta & +prepare_km & +prepare_ps & +prepare_iu & +prepare_cs & +prepare_de & +prepare_pl & +prepare_ru & +prepare_zh & + +# prepare valid/test set +prepare_tests & + +# wait + +# TODO remove intermediate files +# rm -rf $TMP_DIR diff --git a/fairseq/examples/multilingual/data_scripts/preprocess_ML50_v1.sh b/fairseq/examples/multilingual/data_scripts/preprocess_ML50_v1.sh new file mode 100644 index 0000000..4655936 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/preprocess_ML50_v1.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + +if [ -z $SPM_PATH ] ; +then + echo "Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting..." + exit +fi + +ML50=${WORKDIR_ROOT}/ML50 + +mkdir -p $ML50/dedup +mkdir -p $ML50/cleaned_dedup + +python ./dedup_all.py --from-folder $ML50/raw --to-folder $ML50/dedup +python ./remove_valid_test_in_train.py --from-folder $ML50/dedup --to-folder $ML50/clean +python ./binarize.py --raw-folder $ML50/clean \ No newline at end of file diff --git a/fairseq/examples/multilingual/data_scripts/remove_valid_test_in_train.py b/fairseq/examples/multilingual/data_scripts/remove_valid_test_in_train.py new file mode 100644 index 0000000..ef618ad --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/remove_valid_test_in_train.py @@ -0,0 +1,290 @@ +import os, sys +import glob, itertools +import pandas as pd + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +def load_langs(path): + with open(path) as fr: + langs = [l.strip() for l in fr] + return langs + + + +def load_sentences(raw_data, split, direction): + src, tgt = direction.split('-') + src_path = f"{raw_data}/{split}.{direction}.{src}" + tgt_path = f"{raw_data}/{split}.{direction}.{tgt}" + if os.path.exists(src_path) and os.path.exists(tgt_path): + return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())] + else: + return [] + +def swap_direction(d): + src, tgt = d.split('-') + return f'{tgt}-{src}' + +def get_all_test_data(raw_data, directions, split='test'): + test_data = [ + x + for dd in directions + for d in [dd, swap_direction(dd)] + for x in load_sentences(raw_data, split, d) + ] + # all_test_data = {s for _, d in test_data for s in d} + all_test_data = {} + for lang, d in test_data: + for s in d: + s = s.strip() + lgs = all_test_data.get(s, set()) + lgs.add(lang) + all_test_data[s] = lgs + return all_test_data, test_data + +def check_train_sentences(raw_data, direction, all_test_data, mess_up_train={}): + src, tgt = direction.split('-') + tgt_path = f"{raw_data}/train.{direction}.{tgt}" + src_path = f"{raw_data}/train.{direction}.{src}" + print(f'check training data in {raw_data}/train.{direction}') + size = 0 + if not os.path.exists(tgt_path) or not os.path.exists(src_path): + return mess_up_train, size + with open(src_path) as f, open(tgt_path) as g: + for src_line, tgt_line in zip(f, g): + s = src_line.strip() + t = tgt_line.strip() + size += 1 + if s in all_test_data: + langs = mess_up_train.get(s, set()) + langs.add(direction) + mess_up_train[s] = langs + if t in all_test_data: + langs = mess_up_train.get(t, set()) + langs.add(direction) + mess_up_train[t] = langs + return mess_up_train, size + +def check_train_all(raw_data, directions, all_test_data): + mess_up_train = {} + data_sizes = {} + for direction in directions: + _, size = check_train_sentences(raw_data, direction, all_test_data, mess_up_train) + data_sizes[direction] = size + return mess_up_train, data_sizes + +def count_train_in_other_set(mess_up_train): + train_in_others = [(direction, s) for s, directions in mess_up_train.items() for direction in directions] + counts = {} + for direction, s in train_in_others: + counts[direction] = counts.get(direction, 0) + 1 + return counts + +def train_size_if_remove_in_otherset(data_sizes, mess_up_train): + counts_in_other = count_train_in_other_set(mess_up_train) + remain_sizes = [] + for direction, count in counts_in_other.items(): + remain_sizes.append((direction, data_sizes[direction] - count, data_sizes[direction], count, 100 * count / data_sizes[direction] )) + return remain_sizes + + +def remove_messed_up_sentences(raw_data, direction, mess_up_train, mess_up_train_pairs, corrected_langs): + split = 'train' + src_lang, tgt_lang = direction.split('-') + + tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" + src = f"{raw_data}/{split}.{direction}.{src_lang}" + print(f'working on {direction}: ', src, tgt) + if not os.path.exists(tgt) or not os.path.exists(src) : + return + + corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" + corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" + line_num = 0 + keep_num = 0 + with open(src, encoding='utf8',) as fsrc, \ + open(tgt, encoding='utf8',) as ftgt, \ + open(corrected_src, 'w', encoding='utf8') as fsrc_corrected, \ + open(corrected_tgt, 'w', encoding='utf8') as ftgt_corrected: + for s, t in zip(fsrc, ftgt): + s = s.strip() + t = t.strip() + if t not in mess_up_train \ + and s not in mess_up_train \ + and (s, t) not in mess_up_train_pairs \ + and (t, s) not in mess_up_train_pairs: + corrected_langs.add(direction) + print(s, file=fsrc_corrected) + print(t, file=ftgt_corrected) + keep_num += 1 + line_num += 1 + if line_num % 1000 == 0: + print(f'completed {line_num} lines', end='\r') + return line_num, keep_num + +########## + + +def merge_valid_test_messup(mess_up_train_valid, mess_up_train_test): + merged_mess = [] + for s in set(list(mess_up_train_valid.keys()) + list(mess_up_train_test.keys())): + if not s: + continue + valid = mess_up_train_valid.get(s, set()) + test = mess_up_train_test.get(s, set()) + merged_mess.append((s, valid | test)) + return dict(merged_mess) + + + +######### +def check_train_pairs(raw_data, direction, all_test_data, mess_up_train={}): + src, tgt = direction.split('-') + #a hack; TODO: check the reversed directions + path1 = f"{raw_data}/train.{src}-{tgt}.{src}" + path2 = f"{raw_data}/train.{src}-{tgt}.{tgt}" + if not os.path.exists(path1) or not os.path.exists(path2) : + return + + with open(path1) as f1, open(path2) as f2: + for src_line, tgt_line in zip(f1, f2): + s = src_line.strip() + t = tgt_line.strip() + if (s, t) in all_test_data or (t, s) in all_test_data: + langs = mess_up_train.get( (s, t), set()) + langs.add(src) + langs.add(tgt) + mess_up_train[(s, t)] = langs + + +def load_pairs(raw_data, split, direction): + src, tgt = direction.split('-') + src_f = f"{raw_data}/{split}.{direction}.{src}" + tgt_f = f"{raw_data}/{split}.{direction}.{tgt}" + if tgt != 'en_XX': + src_f, tgt_f = tgt_f, src_f + if os.path.exists(src_f) and os.path.exists(tgt_f): + return list(zip(open(src_f).read().splitlines(), + open(tgt_f).read().splitlines(), + )) + else: + return [] + +# skip_langs = ['cs_CZ', 'en_XX', 'tl_XX', 'tr_TR'] +def get_messed_up_test_pairs(split, directions): + test_pairs = [ + (d, load_pairs(raw_data, split, d)) + for d in directions + ] + # all_test_data = {s for _, d in test_data for s in d} + all_test_pairs = {} + for direction, d in test_pairs: + src, tgt = direction.split('-') + for s in d: + langs = all_test_pairs.get(s, set()) + langs.add(src) + langs.add(tgt) + all_test_pairs[s] = langs + mess_up_train_pairs = {} + for direction in directions: + check_train_pairs(raw_data, direction, all_test_pairs, mess_up_train_pairs) + return all_test_pairs, mess_up_train_pairs + + + +if __name__ == "__main__": + ####### + import argparse + parser = argparse.ArgumentParser() + parser.add_argument( + '--from-folder', + required=True, + type=str) + parser.add_argument( + '--to-folder', + required=True, + type=str) + parser.add_argument( + '--directions', + default=None, + type=str) + + + args = parser.parse_args() + raw_data = args.from_folder + to_folder = args.to_folder + os.makedirs(to_folder, exist_ok=True) + + if args.directions: + directions = args.directions.split(',') + else: + raw_files = itertools.chain( + glob.glob(f'{raw_data}/train*'), + glob.glob(f'{raw_data}/valid*'), + glob.glob(f'{raw_data}/test*'), + ) + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + print('working on directions: ', directions) + + ########## + + + + all_test_data, test_data = get_all_test_data(raw_data, directions, 'test') + print('==loaded test data==') + all_valid_data, valid_data = get_all_test_data(raw_data, directions, 'valid') + print('==loaded valid data==') + all_valid_test_data = merge_valid_test_messup(all_test_data, all_valid_data) + mess_up_train, data_sizes = check_train_all(raw_data, directions, all_valid_test_data) + print('training messing up with valid, test data:', len(mess_up_train)) + data_situation = train_size_if_remove_in_otherset(data_sizes, mess_up_train) + df = pd.DataFrame(data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) + df.sort_values('remove_percent', ascending=False) + df.to_csv(f'{raw_data}/clean_summary.tsv', sep='\t') + print(f'projected data clean summary in: {raw_data}/clean_summary.tsv') + + # correct the dataset: + all_test_pairs, mess_up_test_train_pairs = get_messed_up_test_pairs('test', directions) + all_valid_pairs, mess_up_valid_train_pairs = get_messed_up_test_pairs('valid', directions) + + all_messed_pairs = set(mess_up_test_train_pairs.keys()).union(set(mess_up_valid_train_pairs.keys())) + corrected_directions = set() + + real_data_situation = [] + for direction in directions: + org_size, new_size = remove_messed_up_sentences(raw_data, direction, mess_up_train, all_messed_pairs, corrected_directions) + if org_size == 0: + print(f"{direction} has size 0") + continue + real_data_situation.append( + (direction, new_size, org_size, org_size - new_size, (org_size - new_size) / org_size * 100) + ) + print('corrected directions: ', corrected_directions) + df = pd.DataFrame(real_data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) + df.sort_values('remove_percent', ascending=False) + df.to_csv(f'{raw_data}/actual_clean_summary.tsv', sep='\t') + print(f'actual data clean summary (which can be different from the projected one because of duplications) in: {raw_data}/actual_clean_summary.tsv') + + import shutil + for direction in directions: + src_lang, tgt_lang = direction.split('-') + for split in ['train', 'valid', 'test']: + # copying valid, test and uncorrected train + if direction in corrected_directions and split == 'train': + continue + tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" + src = f"{raw_data}/{split}.{direction}.{src_lang}" + if not (os.path.exists(src) and os.path.exists(tgt)): + continue + corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" + corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" + print(f'copying {src} to {corrected_src}') + shutil.copyfile(src, corrected_src) + print(f'copying {tgt} to {corrected_tgt}') + shutil.copyfile(tgt, corrected_tgt) + + print('completed') \ No newline at end of file diff --git a/fairseq/examples/multilingual/data_scripts/requirement.txt b/fairseq/examples/multilingual/data_scripts/requirement.txt new file mode 100644 index 0000000..e85d7d5 --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/requirement.txt @@ -0,0 +1,2 @@ +wget +pandas \ No newline at end of file diff --git a/fairseq/examples/multilingual/data_scripts/utils/dedup.py b/fairseq/examples/multilingual/data_scripts/utils/dedup.py new file mode 100644 index 0000000..d6fed8c --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/utils/dedup.py @@ -0,0 +1,41 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import argparse + +def deup(src_file, tgt_file, src_file_out, tgt_file_out): + seen = set() + dup_count = 0 + with open(src_file, encoding='utf-8') as fsrc, \ + open(tgt_file, encoding='utf-8') as ftgt, \ + open(src_file_out, 'w', encoding='utf-8') as fsrc_out, \ + open(tgt_file_out, 'w', encoding='utf-8') as ftgt_out: + for s, t in zip(fsrc, ftgt): + if (s, t) not in seen: + fsrc_out.write(s) + ftgt_out.write(t) + seen.add((s, t)) + else: + dup_count += 1 + print(f'number of duplication: {dup_count}') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--src-file", type=str, required=True, + help="src file") + parser.add_argument("--tgt-file", type=str, required=True, + help="tgt file") + parser.add_argument("--src-file-out", type=str, required=True, + help="src ouptut file") + parser.add_argument("--tgt-file-out", type=str, required=True, + help="tgt ouput file") + args = parser.parse_args() + deup(args.src_file, args.tgt_file, args.src_file_out, args.tgt_file_out) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py b/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py new file mode 100644 index 0000000..41b38ba --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py @@ -0,0 +1,63 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +#!/bin/python + +import fasttext +from multiprocessing import Pool +import contextlib +import sys +import argparse +from functools import partial +import io + +model = None +def init(model_path): + global model + model = fasttext.load_model(model_path) + +def pred(lines): + return lines, [model.predict(line.strip())[0][0][9:] for line in lines] + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=True, + help="model to load") + parser.add_argument("--inputs", nargs="+", default=['-'], + help="input files to filter") + parser.add_argument("--langs", nargs="+", required=True, + help="lang ids of each input file") + parser.add_argument("--outputs", nargs="+", default=['-'], + help="path to save lid filtered outputs") + parser.add_argument("--num-workers", type=int, metavar="N", default=10, + help="number of processes in parallel") + args = parser.parse_args() + + assert len(args.inputs) == len(args.langs) and len(args.inputs) == len(args.outputs) + + with contextlib.ExitStack() as stack: + inputs = [ + stack.enter_context(open(input, "r", encoding="utf-8", newline="\n", errors="replace")) + if input != "-" else io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8', errors="replace") + for input in args.inputs + ] + outputs = [ + stack.enter_context(open(output, "w", encoding="utf-8", newline="\n")) + if output != "-" else sys.stdout + for output in args.outputs + ] + with Pool(args.num_workers, initializer=partial(init, args.model)) as p: + skip_cnt = 0 + for lines, preds in p.imap(pred, list(zip(*inputs)), chunksize=500): + if not all(a == b for a, b in zip(preds, args.langs)): + skip_cnt += 1 + continue + for line, output_h in zip(lines, outputs): + print(line.strip(), file=output_h) + print(f"Skipped {skip_cnt} lines.") + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/multilingual/data_scripts/utils/strip_sgm.sh b/fairseq/examples/multilingual/data_scripts/utils/strip_sgm.sh new file mode 100644 index 0000000..7f4f61d --- /dev/null +++ b/fairseq/examples/multilingual/data_scripts/utils/strip_sgm.sh @@ -0,0 +1 @@ +grep "seg id" | sed 's/<seg id="[0-9]\+">//g' | sed 's/<\/seg>//g' diff --git a/fairseq/examples/multilingual/finetune_multilingual_model.sh b/fairseq/examples/multilingual/finetune_multilingual_model.sh new file mode 100644 index 0000000..25960c5 --- /dev/null +++ b/fairseq/examples/multilingual/finetune_multilingual_model.sh @@ -0,0 +1,32 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +path_2_data=$1 # <path to data> which contains binarized data for each directions +lang_list=$2 # <path to a file which contains a list of languages separted by new lines> +lang_pairs=$3 #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en" +# pretrained can be an mBART pretrained model as well +pretrained_model=$4 #<path to a pretrained model> + + +fairseq-train "$path_2_data" \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --finetune-from-model "$pretrained_model" \ + --sampling-method "temperature" \ + --sampling-temperature "1.5" \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 diff --git a/fairseq/examples/multilingual/multilingual_fairseq_gen.sh b/fairseq/examples/multilingual/multilingual_fairseq_gen.sh new file mode 100644 index 0000000..65aa322 --- /dev/null +++ b/fairseq/examples/multilingual/multilingual_fairseq_gen.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +lang_pairs="en-fr,en-cs,fr-en,cs-en" +path_2_data=$1 # <path to data> +lang_list=$2 # <path to a file which contains list of languages separted by new lines> +model=$3 # <path to a trained model> +source_lang=cs +target_lang=en + +fairseq-generate "$path_2_data" \ + --path "$model" \ + --task translation_multi_simple_epoch \ + --gen-subset test \ + --source-lang "$source_lang" \ + --target-lang "$target_lang" \ + --sacrebleu --remove-bpe 'sentencepiece'\ + --batch-size 32 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" diff --git a/fairseq/examples/multilingual/train_multilingual_model.sh b/fairseq/examples/multilingual/train_multilingual_model.sh new file mode 100644 index 0000000..cc050bd --- /dev/null +++ b/fairseq/examples/multilingual/train_multilingual_model.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +path_2_data=$1 # <path to data> which contains binarized data for each directions +lang_list=$2 # <path to a file which contains a list of languages separted by new lines> +lang_pairs=$3 #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en" + +fairseq-train "$path_2_data" \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --sampling-method "temperature" \ + --sampling-temperature 1.5 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 diff --git a/fairseq/examples/noisychannel/README.md b/fairseq/examples/noisychannel/README.md new file mode 100644 index 0000000..9d101aa --- /dev/null +++ b/fairseq/examples/noisychannel/README.md @@ -0,0 +1,72 @@ +# Simple and Effective Noisy Channel Modeling for Neural Machine Translation (Yee et al., 2019) +This page contains pointers to pre-trained models as well as instructions on how to run the reranking scripts. + +## Citation: +```bibtex +@inproceedings{yee2019simple, + title = {Simple and Effective Noisy Channel Modeling for Neural Machine Translation}, + author = {Kyra Yee and Yann Dauphin and Michael Auli}, + booktitle = {Conference on Empirical Methods in Natural Language Processing}, + year = {2019}, +} +``` + +## Pre-trained Models: + +Model | Description | Download +---|---|--- +`transformer.noisychannel.de-en` | De->En Forward Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/forward_de2en.tar.bz2) +`transformer.noisychannel.en-de` | En->De Channel Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/backward_en2de.tar.bz2) +`transformer_lm.noisychannel.en` | En Language model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/reranking_en_lm.tar.bz2) + +Test Data: [newstest_wmt17](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/wmt17test.tar.bz2) + +## Example usage + +``` +mkdir rerank_example +curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/forward_de2en.tar.bz2 | tar xvjf - -C rerank_example +curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/backward_en2de.tar.bz2 | tar xvjf - -C rerank_example +curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/reranking_en_lm.tar.bz2 | tar xvjf - -C rerank_example +curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/wmt17test.tar.bz2 | tar xvjf - -C rerank_example + +beam=50 +num_trials=1000 +fw_name=fw_model_ex +bw_name=bw_model_ex +lm_name=lm_ex +data_dir=rerank_example/hyphen-splitting-mixed-case-wmt17test-wmt14bpe +data_dir_name=wmt17 +lm=rerank_example/lm/checkpoint_best.pt +lm_bpe_code=rerank_example/lm/bpe32k.code +lm_dict=rerank_example/lm/dict.txt +batch_size=32 +bw=rerank_example/backward_en2de.pt +fw=rerank_example/forward_de2en.pt + +# reranking with P(T|S) P(S|T) and P(T) +python examples/noisychannel/rerank_tune.py $data_dir --tune-param lenpen weight1 weight3 \ + --lower-bound 0 0 0 --upper-bound 3 3 3 --data-dir-name $data_dir_name \ + --num-trials $num_trials --source-lang de --target-lang en --gen-model $fw \ + -n $beam --batch-size $batch_size --score-model2 $fw --score-model1 $bw \ + --backwards1 --weight2 1 \ + -lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ + --model2-name $fw_name --model1-name $bw_name --gen-model-name $fw_name + +# reranking with P(T|S) and P(T) +python examples/noisychannel/rerank_tune.py $data_dir --tune-param lenpen weight3 \ + --lower-bound 0 0 --upper-bound 3 3 --data-dir-name $data_dir_name \ + --num-trials $num_trials --source-lang de --target-lang en --gen-model $fw \ + -n $beam --batch-size $batch_size --score-model1 $fw \ + -lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ + --model1-name $fw_name --gen-model-name $fw_name + +# to run with a preconfigured set of hyperparameters for the lenpen and model weights, using rerank.py instead. +python examples/noisychannel/rerank.py $data_dir \ + --lenpen 0.269 --weight1 1 --weight2 0.929 --weight3 0.831 \ + --data-dir-name $data_dir_name --source-lang de --target-lang en --gen-model $fw \ + -n $beam --batch-size $batch_size --score-model2 $fw --score-model1 $bw --backwards1 \ + -lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ + --model2-name $fw_name --model1-name $bw_name --gen-model-name $fw_name +``` + diff --git a/fairseq/examples/noisychannel/__init__.py b/fairseq/examples/noisychannel/__init__.py new file mode 100644 index 0000000..89f1aef --- /dev/null +++ b/fairseq/examples/noisychannel/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .rerank_options import * # noqa diff --git a/fairseq/examples/noisychannel/rerank.py b/fairseq/examples/noisychannel/rerank.py new file mode 100644 index 0000000..bb80d11 --- /dev/null +++ b/fairseq/examples/noisychannel/rerank.py @@ -0,0 +1,428 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from multiprocessing import Pool + +import numpy as np +from fairseq import options +from fairseq.data import dictionary +from fairseq.scoring import bleu + +from examples.noisychannel import ( + rerank_generate, + rerank_options, + rerank_score_bw, + rerank_score_lm, + rerank_utils, +) + + +def score_target_hypo( + args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize +): + + print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c) + gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args) + dict = dictionary.Dictionary() + scorer = scorer = bleu.Scorer( + bleu.BleuConfig( + pad=dict.pad(), + eos=dict.eos(), + unk=dict.unk(), + ) + ) + + ordered_hypos = {} + ordered_targets = {} + + for shard_id in range(len(bitext1_lst)): + bitext1 = bitext1_lst[shard_id] + bitext2 = bitext2_lst[shard_id] + gen_output = gen_output_lst[shard_id] + lm_res = lm_res_lst[shard_id] + + total = len(bitext1.rescore_source.keys()) + source_lst = [] + hypo_lst = [] + score_lst = [] + reference_lst = [] + j = 1 + best_score = -math.inf + + for i in range(total): + # length is measured in terms of words, not bpe tokens, since models may not share the same bpe + target_len = len(bitext1.rescore_hypo[i].split()) + + if lm_res is not None: + lm_score = lm_res.score[i] + else: + lm_score = 0 + + if bitext2 is not None: + bitext2_score = bitext2.rescore_score[i] + bitext2_backwards = bitext2.backwards + else: + bitext2_score = None + bitext2_backwards = None + + score = rerank_utils.get_score( + a, + b, + c, + target_len, + bitext1.rescore_score[i], + bitext2_score, + lm_score=lm_score, + lenpen=lenpen, + src_len=bitext1.source_lengths[i], + tgt_len=bitext1.target_lengths[i], + bitext1_backwards=bitext1.backwards, + bitext2_backwards=bitext2_backwards, + normalize=normalize, + ) + + if score > best_score: + best_score = score + best_hypo = bitext1.rescore_hypo[i] + + if j == gen_output.num_hypos[i] or j == args.num_rescore: + j = 1 + hypo_lst.append(best_hypo) + score_lst.append(best_score) + source_lst.append(bitext1.rescore_source[i]) + reference_lst.append(bitext1.rescore_target[i]) + + best_score = -math.inf + best_hypo = "" + else: + j += 1 + + gen_keys = list(sorted(gen_output.no_bpe_target.keys())) + + for key in range(len(gen_keys)): + if args.prefix_len is None: + assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( + "pred and rescore hypo mismatch: i: " + + str(key) + + ", " + + str(hypo_lst[key]) + + str(gen_keys[key]) + + str(gen_output.no_bpe_hypo[key]) + ) + sys_tok = dict.encode_line(hypo_lst[key]) + ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]]) + scorer.add(ref_tok, sys_tok) + + else: + full_hypo = rerank_utils.get_full_from_prefix( + hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] + ) + sys_tok = dict.encode_line(full_hypo) + ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]]) + scorer.add(ref_tok, sys_tok) + + # if only one set of hyper parameters is provided, write the predictions to a file + if write_hypos: + # recover the orinal ids from n best list generation + for key in range(len(gen_output.no_bpe_target)): + if args.prefix_len is None: + assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( + "pred and rescore hypo mismatch:" + + "i:" + + str(key) + + str(hypo_lst[key]) + + str(gen_output.no_bpe_hypo[key]) + ) + ordered_hypos[gen_keys[key]] = hypo_lst[key] + ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ + gen_keys[key] + ] + + else: + full_hypo = rerank_utils.get_full_from_prefix( + hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] + ) + ordered_hypos[gen_keys[key]] = full_hypo + ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ + gen_keys[key] + ] + + # write the hypos in the original order from nbest list generation + if args.num_shards == (len(bitext1_lst)): + with open(target_outfile, "w") as t: + with open(hypo_outfile, "w") as h: + for key in range(len(ordered_hypos)): + t.write(ordered_targets[key]) + h.write(ordered_hypos[key]) + + res = scorer.result_string(4) + if write_hypos: + print(res) + score = rerank_utils.parse_bleu_scoring(res) + return score + + +def match_target_hypo(args, target_outfile, hypo_outfile): + """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file""" + if len(args.weight1) == 1: + res = score_target_hypo( + args, + args.weight1[0], + args.weight2[0], + args.weight3[0], + args.lenpen[0], + target_outfile, + hypo_outfile, + True, + args.normalize, + ) + rerank_scores = [res] + else: + print("launching pool") + with Pool(32) as p: + rerank_scores = p.starmap( + score_target_hypo, + [ + ( + args, + args.weight1[i], + args.weight2[i], + args.weight3[i], + args.lenpen[i], + target_outfile, + hypo_outfile, + False, + args.normalize, + ) + for i in range(len(args.weight1)) + ], + ) + + if len(rerank_scores) > 1: + best_index = np.argmax(rerank_scores) + best_score = rerank_scores[best_index] + print("best score", best_score) + print("best lenpen", args.lenpen[best_index]) + print("best weight1", args.weight1[best_index]) + print("best weight2", args.weight2[best_index]) + print("best weight3", args.weight3[best_index]) + return ( + args.lenpen[best_index], + args.weight1[best_index], + args.weight2[best_index], + args.weight3[best_index], + best_score, + ) + + else: + return ( + args.lenpen[0], + args.weight1[0], + args.weight2[0], + args.weight3[0], + rerank_scores[0], + ) + + +def load_score_files(args): + if args.all_shards: + shard_ids = list(range(args.num_shards)) + else: + shard_ids = [args.shard_id] + + gen_output_lst = [] + bitext1_lst = [] + bitext2_lst = [] + lm_res1_lst = [] + + for shard_id in shard_ids: + using_nbest = args.nbest_list is not None + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + rerank1_is_gen = ( + args.gen_model == args.score_model1 and args.source_prefix_frac is None + ) + rerank2_is_gen = ( + args.gen_model == args.score_model2 and args.source_prefix_frac is None + ) + + score1_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model1_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards1, + ) + if args.score_model2 is not None: + score2_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model2_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards2, + ) + if args.language_model is not None: + lm_score_file = rerank_utils.rescore_file_name( + pre_gen, args.prefix_len, args.lm_name, lm_file=True + ) + + # get gen output + predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" + if using_nbest: + print("Using predefined n-best list from interactive.py") + predictions_bpe_file = args.nbest_list + gen_output = rerank_utils.BitextOutputFromGen( + predictions_bpe_file, + bpe_symbol=args.post_process, + nbest=using_nbest, + prefix_len=args.prefix_len, + target_prefix_frac=args.target_prefix_frac, + ) + + if rerank1_is_gen: + bitext1 = gen_output + else: + bitext1 = rerank_utils.BitextOutput( + score1_file, + args.backwards1, + args.right_to_left1, + args.post_process, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + if args.score_model2 is not None or args.nbest_list is not None: + if rerank2_is_gen: + bitext2 = gen_output + else: + bitext2 = rerank_utils.BitextOutput( + score2_file, + args.backwards2, + args.right_to_left2, + args.post_process, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + assert ( + bitext2.source_lengths == bitext1.source_lengths + ), "source lengths for rescoring models do not match" + assert ( + bitext2.target_lengths == bitext1.target_lengths + ), "target lengths for rescoring models do not match" + else: + if args.diff_bpe: + assert args.score_model2 is None + bitext2 = gen_output + else: + bitext2 = None + + if args.language_model is not None: + lm_res1 = rerank_utils.LMOutput( + lm_score_file, + args.lm_dict, + args.prefix_len, + args.post_process, + args.target_prefix_frac, + ) + else: + lm_res1 = None + + gen_output_lst.append(gen_output) + bitext1_lst.append(bitext1) + bitext2_lst.append(bitext2) + lm_res1_lst.append(lm_res1) + return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst + + +def rerank(args): + if type(args.lenpen) is not list: + args.lenpen = [args.lenpen] + if type(args.weight1) is not list: + args.weight1 = [args.weight1] + if type(args.weight2) is not list: + args.weight2 = [args.weight2] + if type(args.weight3) is not list: + args.weight3 = [args.weight3] + if args.all_shards: + shard_ids = list(range(args.num_shards)) + else: + shard_ids = [args.shard_id] + + for shard_id in shard_ids: + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + rerank_generate.gen_and_reprocess_nbest(args) + rerank_score_bw.score_bw(args) + rerank_score_lm.score_lm(args) + + if args.write_hypos is None: + write_targets = pre_gen + "/matched_targets" + write_hypos = pre_gen + "/matched_hypos" + else: + write_targets = args.write_hypos + "_targets" + args.gen_subset + write_hypos = args.write_hypos + "_hypos" + args.gen_subset + + if args.all_shards: + write_targets += "_all_shards" + write_hypos += "_all_shards" + + ( + best_lenpen, + best_weight1, + best_weight2, + best_weight3, + best_score, + ) = match_target_hypo(args, write_targets, write_hypos) + + return best_lenpen, best_weight1, best_weight2, best_weight3, best_score + + +def cli_main(): + parser = rerank_options.get_reranking_parser() + args = options.parse_args_and_arch(parser) + rerank(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/noisychannel/rerank_generate.py b/fairseq/examples/noisychannel/rerank_generate.py new file mode 100644 index 0000000..daeeae0 --- /dev/null +++ b/fairseq/examples/noisychannel/rerank_generate.py @@ -0,0 +1,397 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Generate n-best translations using a trained model. +""" + +import os +import subprocess +from contextlib import redirect_stdout + +from fairseq import options +from fairseq_cli import generate, preprocess + +from examples.noisychannel import rerank_options, rerank_utils + + +def gen_and_reprocess_nbest(args): + if args.score_dict_dir is None: + args.score_dict_dir = args.data + if args.prefix_len is not None: + assert ( + args.right_to_left1 is False + ), "prefix length not compatible with right to left models" + assert ( + args.right_to_left2 is False + ), "prefix length not compatible with right to left models" + + if args.nbest_list is not None: + assert args.score_model2 is None + + if args.backwards1: + scorer1_src = args.target_lang + scorer1_tgt = args.source_lang + else: + scorer1_src = args.source_lang + scorer1_tgt = args.target_lang + + store_data = ( + os.path.join(os.path.dirname(__file__)) + "/rerank_data/" + args.data_dir_name + ) + if not os.path.exists(store_data): + os.makedirs(store_data) + + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + args.shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + assert not ( + args.right_to_left1 and args.backwards1 + ), "backwards right to left not supported" + assert not ( + args.right_to_left2 and args.backwards2 + ), "backwards right to left not supported" + assert not ( + args.prefix_len is not None and args.target_prefix_frac is not None + ), "target prefix frac and target prefix len incompatible" + + # make directory to store generation results + if not os.path.exists(pre_gen): + os.makedirs(pre_gen) + + rerank1_is_gen = ( + args.gen_model == args.score_model1 and args.source_prefix_frac is None + ) + rerank2_is_gen = ( + args.gen_model == args.score_model2 and args.source_prefix_frac is None + ) + + if args.nbest_list is not None: + rerank2_is_gen = True + + # make directories to store preprossed nbest list for reranking + if not os.path.exists(left_to_right_preprocessed_dir): + os.makedirs(left_to_right_preprocessed_dir) + if not os.path.exists(right_to_left_preprocessed_dir): + os.makedirs(right_to_left_preprocessed_dir) + if not os.path.exists(lm_preprocessed_dir): + os.makedirs(lm_preprocessed_dir) + if not os.path.exists(backwards_preprocessed_dir): + os.makedirs(backwards_preprocessed_dir) + + score1_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model1_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards1, + ) + if args.score_model2 is not None: + score2_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model2_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards2, + ) + + predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" + + using_nbest = args.nbest_list is not None + + if using_nbest: + print("Using predefined n-best list from interactive.py") + predictions_bpe_file = args.nbest_list + + else: + if not os.path.isfile(predictions_bpe_file): + print("STEP 1: generate predictions using the p(T|S) model with bpe") + print(args.data) + param1 = [ + args.data, + "--path", + args.gen_model, + "--shard-id", + str(args.shard_id), + "--num-shards", + str(args.num_shards), + "--nbest", + str(args.num_rescore), + "--batch-size", + str(args.batch_size), + "--beam", + str(args.num_rescore), + "--batch-size", + str(args.num_rescore), + "--gen-subset", + args.gen_subset, + "--source-lang", + args.source_lang, + "--target-lang", + args.target_lang, + ] + if args.sampling: + param1 += ["--sampling"] + + gen_parser = options.get_generation_parser() + input_args = options.parse_args_and_arch(gen_parser, param1) + + print(input_args) + with open(predictions_bpe_file, "w") as f: + with redirect_stdout(f): + generate.main(input_args) + + gen_output = rerank_utils.BitextOutputFromGen( + predictions_bpe_file, + bpe_symbol=args.post_process, + nbest=using_nbest, + prefix_len=args.prefix_len, + target_prefix_frac=args.target_prefix_frac, + ) + + if args.diff_bpe: + rerank_utils.write_reprocessed( + gen_output.no_bpe_source, + gen_output.no_bpe_hypo, + gen_output.no_bpe_target, + pre_gen + "/source_gen_bpe." + args.source_lang, + pre_gen + "/target_gen_bpe." + args.target_lang, + pre_gen + "/reference_gen_bpe." + args.target_lang, + ) + bitext_bpe = args.rescore_bpe_code + bpe_src_param = [ + "-c", + bitext_bpe, + "--input", + pre_gen + "/source_gen_bpe." + args.source_lang, + "--output", + pre_gen + "/rescore_data." + args.source_lang, + ] + bpe_tgt_param = [ + "-c", + bitext_bpe, + "--input", + pre_gen + "/target_gen_bpe." + args.target_lang, + "--output", + pre_gen + "/rescore_data." + args.target_lang, + ] + + subprocess.call( + [ + "python", + os.path.join( + os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" + ), + ] + + bpe_src_param, + shell=False, + ) + + subprocess.call( + [ + "python", + os.path.join( + os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" + ), + ] + + bpe_tgt_param, + shell=False, + ) + + if (not os.path.isfile(score1_file) and not rerank1_is_gen) or ( + args.score_model2 is not None + and not os.path.isfile(score2_file) + and not rerank2_is_gen + ): + print( + "STEP 2: process the output of generate.py so we have clean text files with the translations" + ) + + rescore_file = "/rescore_data" + if args.prefix_len is not None: + prefix_len_rescore_file = rescore_file + "prefix" + str(args.prefix_len) + if args.target_prefix_frac is not None: + target_prefix_frac_rescore_file = ( + rescore_file + "target_prefix_frac" + str(args.target_prefix_frac) + ) + if args.source_prefix_frac is not None: + source_prefix_frac_rescore_file = ( + rescore_file + "source_prefix_frac" + str(args.source_prefix_frac) + ) + + if not args.right_to_left1 or not args.right_to_left2: + if not args.diff_bpe: + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + rescore_file + "." + args.source_lang, + pre_gen + rescore_file + "." + args.target_lang, + pre_gen + "/reference_file", + bpe_symbol=args.post_process, + ) + if args.prefix_len is not None: + bw_rescore_file = prefix_len_rescore_file + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + prefix_len_rescore_file + "." + args.source_lang, + pre_gen + prefix_len_rescore_file + "." + args.target_lang, + pre_gen + "/reference_file", + prefix_len=args.prefix_len, + bpe_symbol=args.post_process, + ) + elif args.target_prefix_frac is not None: + bw_rescore_file = target_prefix_frac_rescore_file + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + + target_prefix_frac_rescore_file + + "." + + args.source_lang, + pre_gen + + target_prefix_frac_rescore_file + + "." + + args.target_lang, + pre_gen + "/reference_file", + bpe_symbol=args.post_process, + target_prefix_frac=args.target_prefix_frac, + ) + else: + bw_rescore_file = rescore_file + + if args.source_prefix_frac is not None: + fw_rescore_file = source_prefix_frac_rescore_file + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + + source_prefix_frac_rescore_file + + "." + + args.source_lang, + pre_gen + + source_prefix_frac_rescore_file + + "." + + args.target_lang, + pre_gen + "/reference_file", + bpe_symbol=args.post_process, + source_prefix_frac=args.source_prefix_frac, + ) + else: + fw_rescore_file = rescore_file + + if args.right_to_left1 or args.right_to_left2: + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + "/right_to_left_rescore_data." + args.source_lang, + pre_gen + "/right_to_left_rescore_data." + args.target_lang, + pre_gen + "/right_to_left_reference_file", + right_to_left=True, + bpe_symbol=args.post_process, + ) + + print("STEP 3: binarize the translations") + if ( + not args.right_to_left1 + or args.score_model2 is not None + and not args.right_to_left2 + or not rerank1_is_gen + ): + + if args.backwards1 or args.backwards2: + if args.backwards_score_dict_dir is not None: + bw_dict = args.backwards_score_dict_dir + else: + bw_dict = args.score_dict_dir + bw_preprocess_param = [ + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + "--trainpref", + pre_gen + bw_rescore_file, + "--srcdict", + bw_dict + "/dict." + scorer1_src + ".txt", + "--tgtdict", + bw_dict + "/dict." + scorer1_tgt + ".txt", + "--destdir", + backwards_preprocessed_dir, + ] + preprocess_parser = options.get_preprocessing_parser() + input_args = preprocess_parser.parse_args(bw_preprocess_param) + preprocess.main(input_args) + + preprocess_param = [ + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + "--trainpref", + pre_gen + fw_rescore_file, + "--srcdict", + args.score_dict_dir + "/dict." + scorer1_src + ".txt", + "--tgtdict", + args.score_dict_dir + "/dict." + scorer1_tgt + ".txt", + "--destdir", + left_to_right_preprocessed_dir, + ] + preprocess_parser = options.get_preprocessing_parser() + input_args = preprocess_parser.parse_args(preprocess_param) + preprocess.main(input_args) + + if args.right_to_left1 or args.right_to_left2: + preprocess_param = [ + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + "--trainpref", + pre_gen + "/right_to_left_rescore_data", + "--srcdict", + args.score_dict_dir + "/dict." + scorer1_src + ".txt", + "--tgtdict", + args.score_dict_dir + "/dict." + scorer1_tgt + ".txt", + "--destdir", + right_to_left_preprocessed_dir, + ] + preprocess_parser = options.get_preprocessing_parser() + input_args = preprocess_parser.parse_args(preprocess_param) + preprocess.main(input_args) + + return gen_output + + +def cli_main(): + parser = rerank_options.get_reranking_parser() + args = options.parse_args_and_arch(parser) + gen_and_reprocess_nbest(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/noisychannel/rerank_options.py b/fairseq/examples/noisychannel/rerank_options.py new file mode 100644 index 0000000..de91939 --- /dev/null +++ b/fairseq/examples/noisychannel/rerank_options.py @@ -0,0 +1,149 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq import options + + +def get_reranking_parser(default_task="translation"): + parser = options.get_parser("Generation and reranking", default_task) + add_reranking_args(parser) + return parser + + +def get_tuning_parser(default_task="translation"): + parser = options.get_parser("Reranking tuning", default_task) + add_reranking_args(parser) + add_tuning_args(parser) + return parser + + +def add_reranking_args(parser): + group = parser.add_argument_group("Reranking") + # fmt: off + group.add_argument('--score-model1', '-s1', type=str, metavar='FILE', required=True, + help='path to first model or ensemble of models for rescoring') + group.add_argument('--score-model2', '-s2', type=str, metavar='FILE', required=False, + help='path to second model or ensemble of models for rescoring') + group.add_argument('--num-rescore', '-n', type=int, metavar='N', default=10, + help='the number of candidate hypothesis to rescore') + group.add_argument('-bz', '--batch-size', type=int, metavar='N', default=128, + help='batch size for generating the nbest list') + group.add_argument('--gen-subset', default='test', metavar='SET', choices=['test', 'train', 'valid'], + help='data subset to generate (train, valid, test)') + group.add_argument('--gen-model', default=None, metavar='FILE', + help='the model to generate translations') + group.add_argument('-b1', '--backwards1', action='store_true', + help='whether or not the first model group is backwards') + group.add_argument('-b2', '--backwards2', action='store_true', + help='whether or not the second model group is backwards') + group.add_argument('-a', '--weight1', default=1, nargs='+', type=float, + help='the weight(s) of the first model') + group.add_argument('-b', '--weight2', default=1, nargs='+', type=float, + help='the weight(s) of the second model, or the gen model if using nbest from interactive.py') + group.add_argument('-c', '--weight3', default=1, nargs='+', type=float, + help='the weight(s) of the third model') + + # lm arguments + group.add_argument('-lm', '--language-model', default=None, metavar='FILE', + help='language model for target language to rescore translations') + group.add_argument('--lm-dict', default=None, metavar='FILE', + help='the dict of the language model for the target language') + group.add_argument('--lm-name', default=None, + help='the name of the language model for the target language') + group.add_argument('--lm-bpe-code', default=None, metavar='FILE', + help='the bpe code for the language model for the target language') + group.add_argument('--data-dir-name', default=None, + help='name of data directory') + group.add_argument('--lenpen', default=1, nargs='+', type=float, + help='length penalty: <1.0 favors shorter, >1.0 favors longer sentences') + group.add_argument('--score-dict-dir', default=None, + help='the directory with dictionaries for the scoring models') + group.add_argument('--right-to-left1', action='store_true', + help='whether the first model group is a right to left model') + group.add_argument('--right-to-left2', action='store_true', + help='whether the second model group is a right to left model') + group.add_argument('--post-process', '--remove-bpe', default='@@ ', + help='the bpe symbol, used for the bitext and LM') + group.add_argument('--prefix-len', default=None, type=int, + help='the length of the target prefix to use in rescoring (in terms of words wo bpe)') + group.add_argument('--sampling', action='store_true', + help='use sampling instead of beam search for generating n best list') + group.add_argument('--diff-bpe', action='store_true', + help='bpe for rescoring and nbest list not the same') + group.add_argument('--rescore-bpe-code', default=None, + help='bpe code for rescoring models') + group.add_argument('--nbest-list', default=None, + help='use predefined nbest list in interactive.py format') + group.add_argument('--write-hypos', default=None, + help='filename prefix to write hypos to') + group.add_argument('--ref-translation', default=None, + help='reference translation to use with nbest list from interactive.py') + group.add_argument('--backwards-score-dict-dir', default=None, + help='the directory with dictionaries for the backwards model,' + 'if None then it is assumed the fw and backwards models share dictionaries') + + # extra scaling args + group.add_argument('--gen-model-name', default=None, + help='the name of the models that generated the nbest list') + group.add_argument('--model1-name', default=None, + help='the name of the set for model1 group ') + group.add_argument('--model2-name', default=None, + help='the name of the set for model2 group') + group.add_argument('--shard-id', default=0, type=int, + help='the id of the shard to generate') + group.add_argument('--num-shards', default=1, type=int, + help='the number of shards to generate across') + group.add_argument('--all-shards', action='store_true', + help='use all shards') + group.add_argument('--target-prefix-frac', default=None, type=float, + help='the fraction of the target prefix to use in rescoring (in terms of words wo bpe)') + group.add_argument('--source-prefix-frac', default=None, type=float, + help='the fraction of the source prefix to use in rescoring (in terms of words wo bpe)') + group.add_argument('--normalize', action='store_true', + help='whether to normalize by src and target len') + # fmt: on + return group + + +def add_tuning_args(parser): + group = parser.add_argument_group("Tuning") + + group.add_argument( + "--lower-bound", + default=[-0.7], + nargs="+", + type=float, + help="lower bound of search space", + ) + group.add_argument( + "--upper-bound", + default=[3], + nargs="+", + type=float, + help="upper bound of search space", + ) + group.add_argument( + "--tune-param", + default=["lenpen"], + nargs="+", + choices=["lenpen", "weight1", "weight2", "weight3"], + help="the parameter(s) to tune", + ) + group.add_argument( + "--tune-subset", + default="valid", + choices=["valid", "test", "train"], + help="the subset to tune on ", + ) + group.add_argument( + "--num-trials", + default=1000, + type=int, + help="number of trials to do for random search", + ) + group.add_argument( + "--share-weights", action="store_true", help="share weight2 and weight 3" + ) + return group diff --git a/fairseq/examples/noisychannel/rerank_score_bw.py b/fairseq/examples/noisychannel/rerank_score_bw.py new file mode 100644 index 0000000..b0bc913 --- /dev/null +++ b/fairseq/examples/noisychannel/rerank_score_bw.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +from contextlib import redirect_stdout + +from fairseq import options +from fairseq_cli import generate + +from examples.noisychannel import rerank_options, rerank_utils + + +def score_bw(args): + if args.backwards1: + scorer1_src = args.target_lang + scorer1_tgt = args.source_lang + else: + scorer1_src = args.source_lang + scorer1_tgt = args.target_lang + + if args.score_model2 is not None: + if args.backwards2: + scorer2_src = args.target_lang + scorer2_tgt = args.source_lang + else: + scorer2_src = args.source_lang + scorer2_tgt = args.target_lang + + rerank1_is_gen = ( + args.gen_model == args.score_model1 and args.source_prefix_frac is None + ) + rerank2_is_gen = ( + args.gen_model == args.score_model2 and args.source_prefix_frac is None + ) + + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + args.shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + score1_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model1_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards1, + ) + + if args.score_model2 is not None: + score2_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model2_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards2, + ) + + if args.right_to_left1: + rerank_data1 = right_to_left_preprocessed_dir + elif args.backwards1: + rerank_data1 = backwards_preprocessed_dir + else: + rerank_data1 = left_to_right_preprocessed_dir + + gen_param = ["--batch-size", str(128), "--score-reference", "--gen-subset", "train"] + if not rerank1_is_gen and not os.path.isfile(score1_file): + print("STEP 4: score the translations for model 1") + + model_param1 = [ + "--path", + args.score_model1, + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + ] + gen_model1_param = [rerank_data1] + gen_param + model_param1 + + gen_parser = options.get_generation_parser() + input_args = options.parse_args_and_arch(gen_parser, gen_model1_param) + + with open(score1_file, "w") as f: + with redirect_stdout(f): + generate.main(input_args) + + if ( + args.score_model2 is not None + and not os.path.isfile(score2_file) + and not rerank2_is_gen + ): + print("STEP 4: score the translations for model 2") + + if args.right_to_left2: + rerank_data2 = right_to_left_preprocessed_dir + elif args.backwards2: + rerank_data2 = backwards_preprocessed_dir + else: + rerank_data2 = left_to_right_preprocessed_dir + + model_param2 = [ + "--path", + args.score_model2, + "--source-lang", + scorer2_src, + "--target-lang", + scorer2_tgt, + ] + gen_model2_param = [rerank_data2] + gen_param + model_param2 + + gen_parser = options.get_generation_parser() + input_args = options.parse_args_and_arch(gen_parser, gen_model2_param) + + with open(score2_file, "w") as f: + with redirect_stdout(f): + generate.main(input_args) + + +def cli_main(): + parser = rerank_options.get_reranking_parser() + args = options.parse_args_and_arch(parser) + score_bw(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/noisychannel/rerank_score_lm.py b/fairseq/examples/noisychannel/rerank_score_lm.py new file mode 100644 index 0000000..e80948d --- /dev/null +++ b/fairseq/examples/noisychannel/rerank_score_lm.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os + +from fairseq import options + +from examples.noisychannel import rerank_options, rerank_utils + + +def score_lm(args): + using_nbest = args.nbest_list is not None + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + args.shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" + if using_nbest: + print("Using predefined n-best list from interactive.py") + predictions_bpe_file = args.nbest_list + + gen_output = rerank_utils.BitextOutputFromGen( + predictions_bpe_file, bpe_symbol=args.post_process, nbest=using_nbest + ) + + if args.language_model is not None: + lm_score_file = rerank_utils.rescore_file_name( + pre_gen, args.prefix_len, args.lm_name, lm_file=True + ) + + if args.language_model is not None and not os.path.isfile(lm_score_file): + print("STEP 4.5: language modeling for P(T)") + if args.lm_bpe_code is None: + bpe_status = "no bpe" + elif args.lm_bpe_code == "shared": + bpe_status = "shared" + else: + bpe_status = "different" + + rerank_utils.lm_scoring( + lm_preprocessed_dir, + bpe_status, + gen_output, + pre_gen, + args.lm_dict, + args.lm_name, + args.language_model, + args.lm_bpe_code, + 128, + lm_score_file, + args.target_lang, + args.source_lang, + prefix_len=args.prefix_len, + ) + + +def cli_main(): + parser = rerank_options.get_reranking_parser() + args = options.parse_args_and_arch(parser) + score_lm(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/noisychannel/rerank_tune.py b/fairseq/examples/noisychannel/rerank_tune.py new file mode 100644 index 0000000..b2e8b75 --- /dev/null +++ b/fairseq/examples/noisychannel/rerank_tune.py @@ -0,0 +1,102 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import random + +import numpy as np +from fairseq import options + +from examples.noisychannel import rerank, rerank_options + + +def random_search(args): + param_values = [] + tuneable_parameters = ["lenpen", "weight1", "weight2", "weight3"] + initial_params = [args.lenpen, args.weight1, args.weight2, args.weight3] + for i, elem in enumerate(initial_params): + if type(elem) is not list: + initial_params[i] = [elem] + else: + initial_params[i] = elem + + tune_parameters = args.tune_param.copy() + for i in range(len(args.tune_param)): + assert args.upper_bound[i] >= args.lower_bound[i] + index = tuneable_parameters.index(args.tune_param[i]) + del tuneable_parameters[index] + del initial_params[index] + + tune_parameters += tuneable_parameters + param_values += initial_params + random.seed(args.seed) + + random_params = np.array( + [ + [ + random.uniform(args.lower_bound[i], args.upper_bound[i]) + for i in range(len(args.tune_param)) + ] + for k in range(args.num_trials) + ] + ) + set_params = np.array( + [ + [initial_params[i][0] for i in range(len(tuneable_parameters))] + for k in range(args.num_trials) + ] + ) + random_params = np.concatenate((random_params, set_params), 1) + + rerank_args = vars(args).copy() + if args.nbest_list: + rerank_args["gen_subset"] = "test" + else: + rerank_args["gen_subset"] = args.tune_subset + + for k in range(len(tune_parameters)): + rerank_args[tune_parameters[k]] = list(random_params[:, k]) + + if args.share_weights: + k = tune_parameters.index("weight2") + rerank_args["weight3"] = list(random_params[:, k]) + + rerank_args = argparse.Namespace(**rerank_args) + best_lenpen, best_weight1, best_weight2, best_weight3, best_score = rerank.rerank( + rerank_args + ) + rerank_args = vars(args).copy() + rerank_args["lenpen"] = [best_lenpen] + rerank_args["weight1"] = [best_weight1] + rerank_args["weight2"] = [best_weight2] + rerank_args["weight3"] = [best_weight3] + + # write the hypothesis from the valid set from the best trial + + if args.gen_subset != "valid": + rerank_args["gen_subset"] = "valid" + rerank_args = argparse.Namespace(**rerank_args) + rerank.rerank(rerank_args) + + # test with the best hyperparameters on gen subset + rerank_args = vars(args).copy() + rerank_args["gen_subset"] = args.gen_subset + rerank_args["lenpen"] = [best_lenpen] + rerank_args["weight1"] = [best_weight1] + rerank_args["weight2"] = [best_weight2] + rerank_args["weight3"] = [best_weight3] + rerank_args = argparse.Namespace(**rerank_args) + rerank.rerank(rerank_args) + + +def cli_main(): + parser = rerank_options.get_tuning_parser() + args = options.parse_args_and_arch(parser) + + random_search(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/noisychannel/rerank_utils.py b/fairseq/examples/noisychannel/rerank_utils.py new file mode 100644 index 0000000..2c6bf1b --- /dev/null +++ b/fairseq/examples/noisychannel/rerank_utils.py @@ -0,0 +1,850 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import os +import re +import subprocess +from contextlib import redirect_stdout + +from fairseq import options +from fairseq_cli import eval_lm, preprocess + + +def reprocess(fle): + # takes in a file of generate.py translation generate_output + # returns a source dict and hypothesis dict, where keys are the ID num (as a string) + # and values and the corresponding source and translation. There may be several translations + # per source, so the values for hypothesis_dict are lists. + # parses output of generate.py + + with open(fle, "r") as f: + txt = f.read() + + """reprocess generate.py output""" + p = re.compile(r"[STHP][-]\d+\s*") + hp = re.compile(r"(\s*[-]?\d+[.]?\d+\s*)|(\s*(-inf)\s*)") + source_dict = {} + hypothesis_dict = {} + score_dict = {} + target_dict = {} + pos_score_dict = {} + lines = txt.split("\n") + + for line in lines: + line += "\n" + prefix = re.search(p, line) + if prefix is not None: + assert len(prefix.group()) > 2, "prefix id not found" + _, j = prefix.span() + id_num = prefix.group()[2:] + id_num = int(id_num) + line_type = prefix.group()[0] + if line_type == "H": + h_txt = line[j:] + hypo = re.search(hp, h_txt) + assert ( + hypo is not None + ), "regular expression failed to find the hypothesis scoring" + _, i = hypo.span() + score = hypo.group() + if id_num in hypothesis_dict: + hypothesis_dict[id_num].append(h_txt[i:]) + score_dict[id_num].append(float(score)) + else: + hypothesis_dict[id_num] = [h_txt[i:]] + score_dict[id_num] = [float(score)] + + elif line_type == "S": + source_dict[id_num] = line[j:] + elif line_type == "T": + target_dict[id_num] = line[j:] + elif line_type == "P": + pos_scores = (line[j:]).split() + pos_scores = [float(x) for x in pos_scores] + if id_num in pos_score_dict: + pos_score_dict[id_num].append(pos_scores) + else: + pos_score_dict[id_num] = [pos_scores] + + return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict + + +def reprocess_nbest(fle): + """reprocess interactive.py output""" + with open(fle, "r") as f: + txt = f.read() + + source_dict = {} + hypothesis_dict = {} + score_dict = {} + target_dict = {} + pos_score_dict = {} + lines = txt.split("\n") + + hp = re.compile(r"[-]?\d+[.]?\d+") + j = -1 + + for _i, line in enumerate(lines): + line += "\n" + line_type = line[0] + + if line_type == "H": + hypo = re.search(hp, line) + _, start_index = hypo.span() + score = hypo.group() + if j in score_dict: + score_dict[j].append(float(score)) + hypothesis_dict[j].append(line[start_index:].strip("\t")) + else: + score_dict[j] = [float(score)] + hypothesis_dict[j] = [line[start_index:].strip("\t")] + elif line_type == "O": + j += 1 + source_dict[j] = line[2:] + # we don't have the targets for interactive.py + target_dict[j] = "filler" + + elif line_type == "P": + pos_scores = [float(pos_score) for pos_score in line.split()[1:]] + if j in pos_score_dict: + pos_score_dict[j].append(pos_scores) + else: + pos_score_dict[j] = [pos_scores] + + assert source_dict.keys() == hypothesis_dict.keys() + assert source_dict.keys() == pos_score_dict.keys() + assert source_dict.keys() == score_dict.keys() + + return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict + + +def write_reprocessed( + sources, + hypos, + targets, + source_outfile, + hypo_outfile, + target_outfile, + right_to_left=False, + prefix_len=None, + bpe_symbol=None, + target_prefix_frac=None, + source_prefix_frac=None, +): + + """writes nbest hypothesis for rescoring""" + assert not ( + prefix_len is not None and target_prefix_frac is not None + ), "in writing reprocessed, only one type of prefix may be used" + assert not ( + prefix_len is not None and source_prefix_frac is not None + ), "in writing reprocessed, only one type of prefix may be used" + assert not ( + target_prefix_frac is not None and source_prefix_frac is not None + ), "in writing reprocessed, only one type of prefix may be used" + + with open(source_outfile, "w") as source_file, open( + hypo_outfile, "w" + ) as hypo_file, open(target_outfile, "w") as target_file: + + assert len(sources) == len(hypos), "sources and hypos list length mismatch" + if right_to_left: + for i in range(len(sources)): + for j in range(len(hypos[i])): + if prefix_len is None: + hypo_file.write(make_right_to_left(hypos[i][j]) + "\n") + else: + raise NotImplementedError() + source_file.write(make_right_to_left(sources[i]) + "\n") + target_file.write(make_right_to_left(targets[i]) + "\n") + else: + for i in sorted(sources.keys()): + for j in range(len(hypos[i])): + if prefix_len is not None: + shortened = ( + get_prefix_no_bpe(hypos[i][j], bpe_symbol, prefix_len) + + "\n" + ) + hypo_file.write(shortened) + source_file.write(sources[i]) + target_file.write(targets[i]) + elif target_prefix_frac is not None: + num_words, shortened, num_bpe_tokens = calc_length_from_frac( + hypos[i][j], target_prefix_frac, bpe_symbol + ) + shortened += "\n" + hypo_file.write(shortened) + source_file.write(sources[i]) + target_file.write(targets[i]) + elif source_prefix_frac is not None: + num_words, shortened, num_bpe_tokensn = calc_length_from_frac( + sources[i], source_prefix_frac, bpe_symbol + ) + shortened += "\n" + hypo_file.write(hypos[i][j]) + source_file.write(shortened) + target_file.write(targets[i]) + else: + hypo_file.write(hypos[i][j]) + source_file.write(sources[i]) + target_file.write(targets[i]) + + +def calc_length_from_frac(bpe_sentence, prefix_frac, bpe_symbol): + # return number of words, (not bpe tokens) that we want + no_bpe_sen = remove_bpe(bpe_sentence, bpe_symbol) + len_sen = len(no_bpe_sen.split()) + + num_words = math.ceil(len_sen * prefix_frac) + prefix = get_prefix_no_bpe(bpe_sentence, bpe_symbol, num_words) + num_bpe_tokens = len(prefix.split()) + return num_words, prefix, num_bpe_tokens + + +def get_prefix(sentence, prefix_len): + """assuming no bpe, gets the prefix of the sentence with prefix_len words""" + tokens = sentence.strip("\n").split() + if prefix_len >= len(tokens): + return sentence.strip("\n") + else: + return " ".join(tokens[:prefix_len]) + + +def get_prefix_no_bpe(sentence, bpe_symbol, prefix_len): + if bpe_symbol is None: + return get_prefix(sentence, prefix_len) + else: + return " ".join(get_prefix_from_len(sentence.split(), bpe_symbol, prefix_len)) + + +def get_prefix_from_len(sentence, bpe_symbol, prefix_len): + """get the prefix of sentence with bpe, with prefix len in terms of words, not bpe tokens""" + bpe_count = sum([bpe_symbol.strip(" ") in t for t in sentence[:prefix_len]]) + if bpe_count == 0: + return sentence[:prefix_len] + else: + return sentence[:prefix_len] + get_prefix_from_len( + sentence[prefix_len:], bpe_symbol, bpe_count + ) + + +def get_num_bpe_tokens_from_len(sentence, bpe_symbol, prefix_len): + """given a prefix length in terms of words, return the number of bpe tokens""" + prefix = get_prefix_no_bpe(sentence, bpe_symbol, prefix_len) + assert len(remove_bpe(prefix, bpe_symbol).split()) <= prefix_len + return len(prefix.split(" ")) + + +def make_right_to_left(line): + tokens = line.split() + tokens.reverse() + new_line = " ".join(tokens) + return new_line + + +def remove_bpe(line, bpe_symbol): + line = line.replace("\n", "") + line = (line + " ").replace(bpe_symbol, "").rstrip() + return line + ("\n") + + +def remove_bpe_dict(pred_dict, bpe_symbol): + new_dict = {} + for i in pred_dict: + if type(pred_dict[i]) == list: + new_list = [remove_bpe(elem, bpe_symbol) for elem in pred_dict[i]] + new_dict[i] = new_list + else: + new_dict[i] = remove_bpe(pred_dict[i], bpe_symbol) + return new_dict + + +def parse_bleu_scoring(line): + p = re.compile(r"(BLEU4 = )\d+[.]\d+") + res = re.search(p, line) + assert res is not None, line + return float(res.group()[8:]) + + +def get_full_from_prefix(hypo_prefix, hypos): + """given a hypo prefix, recover the first hypo from the list of complete hypos beginning with that prefix""" + for hypo in hypos: + hypo_prefix = hypo_prefix.strip("\n") + len_prefix = len(hypo_prefix) + if hypo[:len_prefix] == hypo_prefix: + return hypo + # no match found + raise Exception() + + +def get_score( + a, + b, + c, + target_len, + bitext_score1, + bitext_score2=None, + lm_score=None, + lenpen=None, + src_len=None, + tgt_len=None, + bitext1_backwards=False, + bitext2_backwards=False, + normalize=False, +): + if bitext1_backwards: + bitext1_norm = src_len + else: + bitext1_norm = tgt_len + if bitext_score2 is not None: + if bitext2_backwards: + bitext2_norm = src_len + else: + bitext2_norm = tgt_len + else: + bitext2_norm = 1 + bitext_score2 = 0 + if normalize: + score = ( + a * bitext_score1 / bitext1_norm + + b * bitext_score2 / bitext2_norm + + c * lm_score / src_len + ) + else: + score = a * bitext_score1 + b * bitext_score2 + c * lm_score + + if lenpen is not None: + score /= (target_len) ** float(lenpen) + + return score + + +class BitextOutput(object): + def __init__( + self, + output_file, + backwards, + right_to_left, + bpe_symbol, + prefix_len=None, + target_prefix_frac=None, + source_prefix_frac=None, + ): + """process output from rescoring""" + source, hypo, score, target, pos_score = reprocess(output_file) + if backwards: + self.hypo_fracs = source_prefix_frac + else: + self.hypo_fracs = target_prefix_frac + + # remove length penalty so we can use raw scores + score, num_bpe_tokens = get_score_from_pos( + pos_score, prefix_len, hypo, bpe_symbol, self.hypo_fracs, backwards + ) + source_lengths = {} + target_lengths = {} + + assert hypo.keys() == source.keys(), "key mismatch" + if backwards: + tmp = hypo + hypo = source + source = tmp + for i in source: + # since we are reranking, there should only be one hypo per source sentence + if backwards: + len_src = len(source[i][0].split()) + # record length without <eos> + if len_src == num_bpe_tokens[i][0] - 1: + source_lengths[i] = num_bpe_tokens[i][0] - 1 + else: + source_lengths[i] = num_bpe_tokens[i][0] + + target_lengths[i] = len(hypo[i].split()) + + source[i] = remove_bpe(source[i][0], bpe_symbol) + target[i] = remove_bpe(target[i], bpe_symbol) + hypo[i] = remove_bpe(hypo[i], bpe_symbol) + + score[i] = float(score[i][0]) + pos_score[i] = pos_score[i][0] + + else: + len_tgt = len(hypo[i][0].split()) + # record length without <eos> + if len_tgt == num_bpe_tokens[i][0] - 1: + target_lengths[i] = num_bpe_tokens[i][0] - 1 + else: + target_lengths[i] = num_bpe_tokens[i][0] + + source_lengths[i] = len(source[i].split()) + + if right_to_left: + source[i] = remove_bpe(make_right_to_left(source[i]), bpe_symbol) + target[i] = remove_bpe(make_right_to_left(target[i]), bpe_symbol) + hypo[i] = remove_bpe(make_right_to_left(hypo[i][0]), bpe_symbol) + score[i] = float(score[i][0]) + pos_score[i] = pos_score[i][0] + else: + assert ( + len(hypo[i]) == 1 + ), "expected only one hypothesis per source sentence" + source[i] = remove_bpe(source[i], bpe_symbol) + target[i] = remove_bpe(target[i], bpe_symbol) + hypo[i] = remove_bpe(hypo[i][0], bpe_symbol) + score[i] = float(score[i][0]) + pos_score[i] = pos_score[i][0] + + self.rescore_source = source + self.rescore_hypo = hypo + self.rescore_score = score + self.rescore_target = target + self.rescore_pos_score = pos_score + self.backwards = backwards + self.right_to_left = right_to_left + self.target_lengths = target_lengths + self.source_lengths = source_lengths + + +class BitextOutputFromGen(object): + def __init__( + self, + predictions_bpe_file, + bpe_symbol=None, + nbest=False, + prefix_len=None, + target_prefix_frac=None, + ): + if nbest: + ( + pred_source, + pred_hypo, + pred_score, + pred_target, + pred_pos_score, + ) = reprocess_nbest(predictions_bpe_file) + else: + pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess( + predictions_bpe_file + ) + + assert len(pred_source) == len(pred_hypo) + assert len(pred_source) == len(pred_score) + assert len(pred_source) == len(pred_target) + assert len(pred_source) == len(pred_pos_score) + + # remove length penalty so we can use raw scores + pred_score, num_bpe_tokens = get_score_from_pos( + pred_pos_score, prefix_len, pred_hypo, bpe_symbol, target_prefix_frac, False + ) + + self.source = pred_source + self.target = pred_target + self.score = pred_score + self.pos_score = pred_pos_score + self.hypo = pred_hypo + self.target_lengths = {} + self.source_lengths = {} + + self.no_bpe_source = remove_bpe_dict(pred_source.copy(), bpe_symbol) + self.no_bpe_hypo = remove_bpe_dict(pred_hypo.copy(), bpe_symbol) + self.no_bpe_target = remove_bpe_dict(pred_target.copy(), bpe_symbol) + + # indexes to match those from the rescoring models + self.rescore_source = {} + self.rescore_target = {} + self.rescore_pos_score = {} + self.rescore_hypo = {} + self.rescore_score = {} + self.num_hypos = {} + self.backwards = False + self.right_to_left = False + + index = 0 + + for i in sorted(pred_source.keys()): + for j in range(len(pred_hypo[i])): + + self.target_lengths[index] = len(self.hypo[i][j].split()) + self.source_lengths[index] = len(self.source[i].split()) + + self.rescore_source[index] = self.no_bpe_source[i] + self.rescore_target[index] = self.no_bpe_target[i] + self.rescore_hypo[index] = self.no_bpe_hypo[i][j] + self.rescore_score[index] = float(pred_score[i][j]) + self.rescore_pos_score[index] = pred_pos_score[i][j] + self.num_hypos[index] = len(pred_hypo[i]) + index += 1 + + +def get_score_from_pos( + pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_frac, backwards +): + score_dict = {} + num_bpe_tokens_dict = {} + assert prefix_len is None or hypo_frac is None + for key in pos_score_dict: + score_dict[key] = [] + num_bpe_tokens_dict[key] = [] + for i in range(len(pos_score_dict[key])): + if prefix_len is not None and not backwards: + num_bpe_tokens = get_num_bpe_tokens_from_len( + hypo_dict[key][i], bpe_symbol, prefix_len + ) + score_dict[key].append(sum(pos_score_dict[key][i][:num_bpe_tokens])) + num_bpe_tokens_dict[key].append(num_bpe_tokens) + elif hypo_frac is not None: + num_words, shortened, hypo_prefix_len = calc_length_from_frac( + hypo_dict[key][i], hypo_frac, bpe_symbol + ) + score_dict[key].append(sum(pos_score_dict[key][i][:hypo_prefix_len])) + num_bpe_tokens_dict[key].append(hypo_prefix_len) + else: + score_dict[key].append(sum(pos_score_dict[key][i])) + num_bpe_tokens_dict[key].append(len(pos_score_dict[key][i])) + return score_dict, num_bpe_tokens_dict + + +class LMOutput(object): + def __init__( + self, + lm_score_file, + lm_dict=None, + prefix_len=None, + bpe_symbol=None, + target_prefix_frac=None, + ): + ( + lm_sentences, + lm_sen_scores, + lm_sen_pos_scores, + lm_no_bpe_sentences, + lm_bpe_tokens, + ) = parse_lm( + lm_score_file, + prefix_len=prefix_len, + bpe_symbol=bpe_symbol, + target_prefix_frac=target_prefix_frac, + ) + + self.sentences = lm_sentences + self.score = lm_sen_scores + self.pos_score = lm_sen_pos_scores + self.lm_dict = lm_dict + self.no_bpe_sentences = lm_no_bpe_sentences + self.bpe_tokens = lm_bpe_tokens + + +def parse_lm(input_file, prefix_len=None, bpe_symbol=None, target_prefix_frac=None): + """parse output of eval_lm""" + with open(input_file, "r") as f: + text = f.readlines() + text = text[7:] + cleaned_text = text[:-2] + + sentences = {} + sen_scores = {} + sen_pos_scores = {} + no_bpe_sentences = {} + num_bpe_tokens_dict = {} + for _i, line in enumerate(cleaned_text): + tokens = line.split() + if tokens[0].isdigit(): + line_id = int(tokens[0]) + scores = [float(x[1:-1]) for x in tokens[2::2]] + sentences[line_id] = " ".join(tokens[1::2][:-1]) + "\n" + if bpe_symbol is not None: + # exclude <eos> symbol to match output from generate.py + bpe_sen = " ".join(tokens[1::2][:-1]) + "\n" + no_bpe_sen = remove_bpe(bpe_sen, bpe_symbol) + no_bpe_sentences[line_id] = no_bpe_sen + + if prefix_len is not None: + num_bpe_tokens = get_num_bpe_tokens_from_len( + bpe_sen, bpe_symbol, prefix_len + ) + sen_scores[line_id] = sum(scores[:num_bpe_tokens]) + num_bpe_tokens_dict[line_id] = num_bpe_tokens + elif target_prefix_frac is not None: + num_words, shortened, target_prefix_len = calc_length_from_frac( + bpe_sen, target_prefix_frac, bpe_symbol + ) + sen_scores[line_id] = sum(scores[:target_prefix_len]) + num_bpe_tokens_dict[line_id] = target_prefix_len + else: + sen_scores[line_id] = sum(scores) + num_bpe_tokens_dict[line_id] = len(scores) + + sen_pos_scores[line_id] = scores + + return sentences, sen_scores, sen_pos_scores, no_bpe_sentences, num_bpe_tokens_dict + + +def get_directories( + data_dir_name, + num_rescore, + gen_subset, + fw_name, + shard_id, + num_shards, + sampling=False, + prefix_len=None, + target_prefix_frac=None, + source_prefix_frac=None, +): + nbest_file_id = ( + "nbest_" + + str(num_rescore) + + "_subset_" + + gen_subset + + "_fw_name_" + + fw_name + + "_shard_" + + str(shard_id) + + "_of_" + + str(num_shards) + ) + + if sampling: + nbest_file_id += "_sampling" + + # the directory containing all information for this nbest list + pre_gen = ( + os.path.join(os.path.dirname(__file__)) + + "/rerank_data/" + + data_dir_name + + "/" + + nbest_file_id + ) + # the directory to store the preprocessed nbest list, for left to right rescoring + left_to_right_preprocessed_dir = pre_gen + "/left_to_right_preprocessed" + if source_prefix_frac is not None: + left_to_right_preprocessed_dir = ( + left_to_right_preprocessed_dir + "/prefix_frac" + str(source_prefix_frac) + ) + # the directory to store the preprocessed nbest list, for right to left rescoring + right_to_left_preprocessed_dir = pre_gen + "/right_to_left_preprocessed" + # the directory to store the preprocessed nbest list, for backwards rescoring + backwards_preprocessed_dir = pre_gen + "/backwards" + if target_prefix_frac is not None: + backwards_preprocessed_dir = ( + backwards_preprocessed_dir + "/prefix_frac" + str(target_prefix_frac) + ) + elif prefix_len is not None: + backwards_preprocessed_dir = ( + backwards_preprocessed_dir + "/prefix_" + str(prefix_len) + ) + + # the directory to store the preprocessed nbest list, for rescoring with P(T) + lm_preprocessed_dir = pre_gen + "/lm_preprocessed" + + return ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) + + +def lm_scoring( + preprocess_directory, + bpe_status, + gen_output, + pre_gen, + cur_lm_dict, + cur_lm_name, + cur_language_model, + cur_lm_bpe_code, + batch_size, + lm_score_file, + target_lang, + source_lang, + prefix_len=None, +): + if prefix_len is not None: + assert ( + bpe_status == "different" + ), "bpe status must be different to use prefix len" + if bpe_status == "no bpe": + # run lm on output without bpe + write_reprocessed( + gen_output.no_bpe_source, + gen_output.no_bpe_hypo, + gen_output.no_bpe_target, + pre_gen + "/rescore_data_no_bpe.de", + pre_gen + "/rescore_data_no_bpe.en", + pre_gen + "/reference_file_no_bpe", + ) + + preprocess_lm_param = [ + "--only-source", + "--trainpref", + pre_gen + "/rescore_data_no_bpe." + target_lang, + "--srcdict", + cur_lm_dict, + "--destdir", + preprocess_directory, + ] + preprocess_parser = options.get_preprocessing_parser() + input_args = preprocess_parser.parse_args(preprocess_lm_param) + preprocess.main(input_args) + + eval_lm_param = [ + preprocess_directory, + "--path", + cur_language_model, + "--output-word-probs", + "--batch-size", + str(batch_size), + "--max-tokens", + "1024", + "--sample-break-mode", + "eos", + "--gen-subset", + "train", + ] + + eval_lm_parser = options.get_eval_lm_parser() + input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) + + with open(lm_score_file, "w") as f: + with redirect_stdout(f): + eval_lm.main(input_args) + + elif bpe_status == "shared": + preprocess_lm_param = [ + "--only-source", + "--trainpref", + pre_gen + "/rescore_data." + target_lang, + "--srcdict", + cur_lm_dict, + "--destdir", + preprocess_directory, + ] + preprocess_parser = options.get_preprocessing_parser() + input_args = preprocess_parser.parse_args(preprocess_lm_param) + preprocess.main(input_args) + + eval_lm_param = [ + preprocess_directory, + "--path", + cur_language_model, + "--output-word-probs", + "--batch-size", + str(batch_size), + "--sample-break-mode", + "eos", + "--gen-subset", + "train", + ] + + eval_lm_parser = options.get_eval_lm_parser() + input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) + + with open(lm_score_file, "w") as f: + with redirect_stdout(f): + eval_lm.main(input_args) + + elif bpe_status == "different": + rescore_file = pre_gen + "/rescore_data_no_bpe" + rescore_bpe = pre_gen + "/rescore_data_new_bpe" + + rescore_file += "." + rescore_bpe += "." + + write_reprocessed( + gen_output.no_bpe_source, + gen_output.no_bpe_hypo, + gen_output.no_bpe_target, + rescore_file + source_lang, + rescore_file + target_lang, + pre_gen + "/reference_file_no_bpe", + bpe_symbol=None, + ) + + # apply LM bpe to nbest list + bpe_src_param = [ + "-c", + cur_lm_bpe_code, + "--input", + rescore_file + target_lang, + "--output", + rescore_bpe + target_lang, + ] + subprocess.call( + [ + "python", + os.path.join( + os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" + ), + ] + + bpe_src_param, + shell=False, + ) + # uncomment to use fastbpe instead of subword-nmt bpe + # bpe_src_param = [rescore_bpe+target_lang, rescore_file+target_lang, cur_lm_bpe_code] + # subprocess.call(["/private/home/edunov/fastBPE/fast", "applybpe"] + bpe_src_param, shell=False) + + preprocess_dir = preprocess_directory + + preprocess_lm_param = [ + "--only-source", + "--trainpref", + rescore_bpe + target_lang, + "--srcdict", + cur_lm_dict, + "--destdir", + preprocess_dir, + ] + preprocess_parser = options.get_preprocessing_parser() + input_args = preprocess_parser.parse_args(preprocess_lm_param) + preprocess.main(input_args) + + eval_lm_param = [ + preprocess_dir, + "--path", + cur_language_model, + "--output-word-probs", + "--batch-size", + str(batch_size), + "--max-tokens", + "1024", + "--sample-break-mode", + "eos", + "--gen-subset", + "train", + ] + + eval_lm_parser = options.get_eval_lm_parser() + input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) + + with open(lm_score_file, "w") as f: + with redirect_stdout(f): + eval_lm.main(input_args) + + +def rescore_file_name( + nbest_dir, + prefix_len, + scorer_name, + lm_file=False, + target_prefix_frac=None, + source_prefix_frac=None, + backwards=None, +): + if lm_file: + score_file = nbest_dir + "/lm_score_translations_model_" + scorer_name + ".txt" + else: + score_file = nbest_dir + "/" + scorer_name + "_score_translations.txt" + if backwards: + if prefix_len is not None: + score_file += "prefix_len" + str(prefix_len) + elif target_prefix_frac is not None: + score_file += "target_prefix_frac" + str(target_prefix_frac) + else: + if source_prefix_frac is not None: + score_file += "source_prefix_frac" + str(source_prefix_frac) + return score_file diff --git a/fairseq/examples/nonautoregressive_translation/README.md b/fairseq/examples/nonautoregressive_translation/README.md new file mode 100644 index 0000000..8793e22 --- /dev/null +++ b/fairseq/examples/nonautoregressive_translation/README.md @@ -0,0 +1,146 @@ +# Non-autoregressive Neural Machine Translation (NAT) + +This page mainly includes instructions for reproducing results from the following papers +* [Levenshtein Transformer (Gu et al., 2019)](https://arxiv.org/abs/1905.11006). +* [Understanding Knowledge Distillation in Non-autoregressive Machine Translation (Zhou et al., 2019)](https://arxiv.org/abs/1911.02727). + +We also provided our own implementations for several popular non-autoregressive-based models as reference:<br> +* [Non-Autoregressive Neural Machine Translation (Gu et al., 2017)](https://arxiv.org/abs/1711.02281)<br> +* [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al., 2018)](https://arxiv.org/abs/1802.06901)<br> +* [Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al., 2019)](https://arxiv.org/abs/1902.03249)<br> +* [Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)](https://arxiv.org/abs/1904.09324v2)<br> +* [Fast Structured Decoding for Sequence Models (Sun et al., 2019)](https://arxiv.org/abs/1910.11555) + +## Dataset + +First, follow the [instructions to download and preprocess the WMT'14 En-De dataset](../translation#wmt14-english-to-german-convolutional). +Make sure to learn a joint vocabulary by passing the `--joined-dictionary` option to `fairseq-preprocess`. + +### Knowledge Distillation +Following [Gu et al. 2019](https://arxiv.org/abs/1905.11006), [knowledge distillation](https://arxiv.org/abs/1606.07947) from an autoregressive model can effectively simplify the training data distribution, which is sometimes essential for NAT-based models to learn good translations. +The easiest way of performing distillation is to follow the [instructions of training a standard transformer model](../translation) on the same data, and then decode the training set to produce a distillation dataset for NAT. + +### Download +We also provided the preprocessed [original](http://dl.fbaipublicfiles.com/nat/original_dataset.zip) and [distillation](http://dl.fbaipublicfiles.com/nat/distill_dataset.zip) datasets. Please build the binarized dataset on your own. + + +## Train a model + +Then we can train a nonautoregressive model using the `translation_lev` task and a new criterion `nat_loss`. +Use the `--noise` flag to specify the input noise used on the target sentences. +In default, we run the task for *Levenshtein Transformer*, with `--noise='random_delete'`. Full scripts to run other models can also be found [here](./scripts.md). + +The following command will train a *Levenshtein Transformer* on the binarized dataset. + +```bash +fairseq-train \ + data-bin/wmt14_en_de_distill \ + --save-dir checkpoints \ + --ddp-backend=legacy_ddp \ + --task translation_lev \ + --criterion nat_loss \ + --arch levenshtein_transformer \ + --noise random_delete \ + --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9,0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ + --warmup-init-lr '1e-07' --label-smoothing 0.1 \ + --dropout 0.3 --weight-decay 0.01 \ + --decoder-learned-pos \ + --encoder-learned-pos \ + --apply-bert-init \ + --log-format 'simple' --log-interval 100 \ + --fixed-validation-seed 7 \ + --max-tokens 8000 \ + --save-interval-updates 10000 \ + --max-update 300000 +``` + +## Translate + +Once a model is trained, we can generate translations using an `iterative_refinement_generator` which will based on the model's initial output and iteratively read and greedily refine the translation until (1) the model predicts the same translations for two consecutive iterations; or (2) the generator reaches the maximum iterations (`--iter-decode-max-iter`). Use `--print-step` to check the actual # of iteration for each sentence. + +For *Levenshtein Transformer*, it sometimes helps to apply a `--iter-decode-eos-penalty` (typically, 0~3) to penalize the model finishing generation too early and generating too short translations. + +For example, to generate with `--iter-decode-max-iter=9`: +```bash +fairseq-generate \ + data-bin/wmt14_en_de_distill \ + --gen-subset test \ + --task translation_lev \ + --path checkpoints/checkpoint_best.pt \ + --iter-decode-max-iter 9 \ + --iter-decode-eos-penalty 0 \ + --beam 1 --remove-bpe \ + --print-step \ + --batch-size 400 +``` +In the end of the generation, we can see the tokenized BLEU score for the translation. + +## Advanced Decoding Methods +### Ensemble +The NAT models use special implementations of [ensembling](https://github.com/fairinternal/fairseq-py/blob/b98d88da52f2f21f1b169bab8c70c1c4ca19a768/fairseq/sequence_generator.py#L522) to support iterative refinement and a variety of parallel operations in different models, while it shares the same API as standard autoregressive models as follows: +```bash +fairseq-generate \ + data-bin/wmt14_en_de_distill \ + --gen-subset test \ + --task translation_lev \ + --path checkpoint_1.pt:checkpoint_2.pt:checkpoint_3.pt \ + --iter-decode-max-iter 9 \ + --iter-decode-eos-penalty 0 \ + --beam 1 --remove-bpe \ + --print-step \ + --batch-size 400 +``` +We use ``:`` to split multiple models. Note that, not all NAT models support ensembling for now. + + +### Length-beam +For models that predict lengths before decoding (e.g. the vanilla NAT, Mask-Predict, etc), it is possible to improve the translation quality by varying the target lengths around the predicted value, and translating the same example multiple times in parallel. We can select the best translation with the highest scores defined by your model's output. + +Note that, not all models support length beams. For models which dynamically change the lengths (e.g. *Insertion Transformer*, *Levenshtein Transformer*), the same trick does not apply. + +### Re-ranking +If the model generates multiple translations with length beam, we can also introduce an autoregressive model to rerank the translations considering scoring from an autoregressive model is much faster than decoding from that. + +For example, to generate translations with length beam and reranking, +```bash +fairseq-generate \ + data-bin/wmt14_en_de_distill \ + --gen-subset test \ + --task translation_lev \ + --path checkpoints/checkpoint_best.pt:at_checkpoints/checkpoint_best.pt \ + --iter-decode-max-iter 9 \ + --iter-decode-eos-penalty 0 \ + --iter-decode-with-beam 9 \ + --iter-decode-with-external-reranker \ + --beam 1 --remove-bpe \ + --print-step \ + --batch-size 100 +``` +Note that we need to make sure the autoregressive model shares the same vocabulary as our target non-autoregressive model. + + +## Citation + +```bibtex +@incollection{NIPS2019_9297, + title = {Levenshtein Transformer}, + author = {Gu, Jiatao and Wang, Changhan and Zhao, Junbo}, + booktitle = {Advances in Neural Information Processing Systems 32}, + editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, + pages = {11179--11189}, + year = {2019}, + publisher = {Curran Associates, Inc.}, + url = {http://papers.nips.cc/paper/9297-levenshtein-transformer.pdf} +} +``` +```bibtex +@article{zhou2019understanding, + title={Understanding Knowledge Distillation in Non-autoregressive Machine Translation}, + author={Zhou, Chunting and Neubig, Graham and Gu, Jiatao}, + journal={arXiv preprint arXiv:1911.02727}, + year={2019} +} +``` diff --git a/fairseq/examples/nonautoregressive_translation/scripts.md b/fairseq/examples/nonautoregressive_translation/scripts.md new file mode 100644 index 0000000..9d3d7b6 --- /dev/null +++ b/fairseq/examples/nonautoregressive_translation/scripts.md @@ -0,0 +1,179 @@ +# Examples of Training scripts for Non-autoregressive Machine Translation models + +### Non-autoregressive Transformer (NAT, Gu et al., 2017) +Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence. +```bash +fairseq-train \ + data-bin/wmt14_en_de_distill \ + --save-dir checkpoints \ + --ddp-backend=legacy_ddp \ + --task translation_lev \ + --criterion nat_loss \ + --arch nonautoregressive_transformer \ + --noise full_mask \ + --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9,0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ + --warmup-init-lr '1e-07' --label-smoothing 0.1 \ + --dropout 0.3 --weight-decay 0.01 \ + --decoder-learned-pos \ + --encoder-learned-pos \ + --pred-length-offset \ + --length-loss-factor 0.1 \ + --apply-bert-init \ + --log-format 'simple' --log-interval 100 \ + --fixed-validation-seed 7 \ + --max-tokens 8000 \ + --save-interval-updates 10000 \ + --max-update 300000 +``` + +### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019) +Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model. +```bash +fairseq-train \ + data-bin/wmt14_en_de_distill \ + --save-dir checkpoints \ + --ddp-backend=legacy_ddp \ + --task translation_lev \ + --criterion nat_loss \ + --arch nacrf_transformer \ + --noise full_mask \ + --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9,0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ + --warmup-init-lr '1e-07' --label-smoothing 0.1 \ + --dropout 0.3 --weight-decay 0.01 \ + --decoder-learned-pos \ + --encoder-learned-pos \ + --pred-length-offset \ + --length-loss-factor 0.1 \ + --word-ins-loss-factor 0.5 \ + --crf-lowrank-approx 32 \ + --crf-beam-approx 64 \ + --apply-bert-init \ + --log-format 'simple' --log-interval 100 \ + --fixed-validation-seed 7 \ + --max-tokens 8000 \ + --save-interval-updates 10000 \ + --max-update 300000 +``` + + +### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018) +Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper. +```bash +fairseq-train \ + data-bin/wmt14_en_de_distill \ + --save-dir checkpoints \ + --ddp-backend=legacy_ddp \ + --task translation_lev \ + --criterion nat_loss \ + --arch iterative_nonautoregressive_transformer \ + --noise full_mask \ + --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9,0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ + --warmup-init-lr '1e-07' --label-smoothing 0.1 \ + --dropout 0.3 --weight-decay 0.01 \ + --decoder-learned-pos \ + --encoder-learned-pos \ + --pred-length-offset \ + --length-loss-factor 0.1 \ + --train-step 4 \ + --dae-ratio 0.5 \ + --stochastic-approx \ + --apply-bert-init \ + --log-format 'simple' --log-interval 100 \ + --fixed-validation-seed 7 \ + --max-tokens 8000 \ + --save-interval-updates 10000 \ + --max-update 300000 +``` + +### Insertion Transformer (InsT, Stern et al., 2019) +Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature. + +```bash +fairseq-train \ + data-bin/wmt14_en_de_distill \ + --save-dir checkpoints \ + --ddp-backend=legacy_ddp \ + --task translation_lev \ + --criterion nat_loss \ + --arch insertion_transformer \ + --noise random_delete \ + --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9,0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ + --warmup-init-lr '1e-07' --label-smoothing 0.1 \ + --dropout 0.3 --weight-decay 0.01 \ + --decoder-learned-pos \ + --encoder-learned-pos \ + --apply-bert-init \ + --log-format 'simple' --log-interval 100 \ + --fixed-validation-seed 7 \ + --max-tokens 8000 \ + --save-interval-updates 10000 \ + --max-update 300000 +``` + + +### Mask Predict (CMLM, Ghazvininejad et al., 2019) +```bash +fairseq-train \ + data-bin/wmt14_en_de_distill \ + --save-dir checkpoints \ + --ddp-backend=legacy_ddp \ + --task translation_lev \ + --criterion nat_loss \ + --arch cmlm_transformer \ + --noise random_mask \ + --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9,0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ + --warmup-init-lr '1e-07' --label-smoothing 0.1 \ + --dropout 0.3 --weight-decay 0.01 \ + --decoder-learned-pos \ + --encoder-learned-pos \ + --apply-bert-init \ + --log-format 'simple' --log-interval 100 \ + --fixed-validation-seed 7 \ + --max-tokens 8000 \ + --save-interval-updates 10000 \ + --max-update 300000 +``` + + + + +### Levenshtein Transformer (LevT, Gu et al., 2019) +```bash +fairseq-train \ + data-bin/wmt14_en_de_distill \ + --save-dir checkpoints \ + --ddp-backend=legacy_ddp \ + --task translation_lev \ + --criterion nat_loss \ + --arch levenshtein_transformer \ + --noise random_delete \ + --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9,0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ + --warmup-init-lr '1e-07' --label-smoothing 0.1 \ + --dropout 0.3 --weight-decay 0.01 \ + --decoder-learned-pos \ + --encoder-learned-pos \ + --apply-bert-init \ + --log-format 'simple' --log-interval 100 \ + --fixed-validation-seed 7 \ + --max-tokens 8000 \ + --save-interval-updates 10000 \ + --max-update 300000 +``` diff --git a/fairseq/examples/normformer/README.md b/fairseq/examples/normformer/README.md new file mode 100644 index 0000000..037b453 --- /dev/null +++ b/fairseq/examples/normformer/README.md @@ -0,0 +1,70 @@ +### NormFormer +This is the code for the ["NormFormer: Improved Transformer Pretraining with Extra Normalization"](https://arxiv.org/abs/2110.09456) +- 2021-10-19: Commands for CLM Experiments +- Coming soon: Commands for MLM experiments + +If you have any issues or questions please post a github issue and tag `@sshleifer`. + + +### Data +- To preprocess language modeling data, see [here](https://github.com/pytorch/fairseq/blob/d0fbcb0baef6f6ff3425ded62d8daea0e8b12114/examples/language_model/README.md#1-preprocess-the-data). +- The replication commands below expect `$DATA` to be the path to the binarized data directory. +- Note that NormFormer results in Table 2 use a much larger private dataset, and to get good results you should adapt the pre-processing instructions to your dataset and compare to a baseline on the same data, rather than Table 2. +- The code uses `FSDP`, which requires `pip install fairscale>=0.4.0`. + + +### Modify existing Command +To modify an existing `fairseq-train` command to use NormFormer, simply add the following flags: +```bash +fairseq-train ... \ + --scale-attn --scale-fc --scale-heads +``` +- you probably also want to increase your learning rate +- if your model is small, you may want to add `--scale-resids` + +### Exact Training Commands + +- Note that NormFormer results in Table 2 use a much larger private dataset, and to get good results you should adapt the pre-processing instructions to your dataset. +The full commands are functions defined here, so to run them you must `source examples/normformer/train_lm.sh`. +- We default `--distributed-world-size 8`. You should adjust `--update-freq` and `--batch-size` and such that the effective batch size is (1024x1024x0.5) tokens for 125M and 355M, + and (1024x1024) for 1.3B parameter and above. For small models, `--update-freq`=256/`global_bs`. For large models, `--update-freq`=512/`global_bs`, where `global_bs` = `--batch-size` * `--distributed-world-size` +- The small models will all train on as few as 8 GPUs. + +```bash +train_125M --lr 6e-4 # GPT-3 Replicated +train_125M --lr 1e-3 # stronger high-lr baseline +train_125M --lr 3e-3 --scale-attn --scale-fc --scale-heads # No scale-resids +train_125M --lr 3e-3 --scale-attn --scale-fc --scale-heads --scale-resids # Best command +``` + +```bash +train_355M --lr 6e-4 # GPT-3 Replicated +train_355M --lr 1e-3 # stronger high-lr baseline +train_355M --lr 1e-3 --scale-attn --scale-fc --scale-heads # No scale-resids +train_355M --lr 1e-3 --scale-attn --scale-fc --scale-heads --scale-resids # Slightly better +``` + +```bash +train_1.3B --lr 2e-4 # GPT-3 Replicated +train_1.3B --lr 6e-4 # stronger high-lr baseline +train_1.3B --lr 6e-4 --scale-attn --scale-fc --scale-heads # NormFormer +``` + +```bash +train_2.7B --lr 1.6e-4 # GPT-3 Replicated +train_2.7B --lr 1.6e-4 --activation-fn relu_squared # stronger Relu^2 baseline +train_2.7B --lr 6e-4 --activation-fn relu_squared --scale-attn --scale-fc --scale-heads # NormFormer 2.7B +``` + + +### Citation +```bibtex +@misc{shleifer2021normformer, + title={NormFormer: Improved Transformer Pretraining with Extra Normalization}, + author={Sam Shleifer and Jason Weston and Myle Ott}, + year={2021}, + eprint={2110.09456}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/fairseq/examples/normformer/train_lm.sh b/fairseq/examples/normformer/train_lm.sh new file mode 100644 index 0000000..b081f2d --- /dev/null +++ b/fairseq/examples/normformer/train_lm.sh @@ -0,0 +1,78 @@ +#!/usr/bin/env bash +train_common () { + fairseq-train "$DATA" \ + --combine-val \ + --train-subset train \ + --num-workers 2 \ + --validate-interval-updates 1000 \ + --save-interval-updates 1000 \ + --no-epoch-checkpoints \ + --ddp-backend fully_sharded \ + --memory-efficient-fp16 \ + --fp16-init-scale 4 \ + --checkpoint-activations \ + --arch transformer_lm_gpt \ + --activation-fn gelu \ + --share-decoder-input-output-embed \ + --task language_modeling \ + --sample-break-mode none \ + --tokens-per-sample 2048 \ + --optimizer adam --adam-betas "(0.9, 0.98)" \ + --adam-eps 1e-08 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay \ + --warmup-updates 750 \ + --dropout 0.1 \ + --attention-dropout 0.1 \ + --weight-decay 0.01 \ + --batch-size 16 \ + --update-freq 2 \ + --required-batch-size-multiple 1 \ + --total-num-update 572204 \ + --max-update 572204 \ + --seed 1 \ + --log-format json --log-interval 1 \ + --distributed-world-size 8 --distributed-port 13177 \ + "$@" +} + +train_125M () { + train_common --decoder-layers 12 \ + --decoder-embed-dim 768 \ + --decoder-ffn-embed-dim 3072 \ + --decoder-attention-heads 12 "$@" +} + +train_355M () { + train_common --decoder-layers 24 \ + --decoder-embed-dim 1024\ + --decoder-ffn-embed-dim 4096 \ + --decoder-attention-heads 16 \ + --dropout 0.0 \ + --attention-dropout 0.0 \ + "$@" +} + +train_1.3B () { + train_common --decoder-layers 24 \ + --decoder-embed-dim 2048 \ + --decoder-ffn-embed-dim 8192 \ + --decoder-attention-heads 32 \ + --batch-size 4 \ + --update-freq 16 \ + --total-num-update 286102 \ + --max-update 286102 \ + "$@" +} + +train_2.7B () { + train_common --decoder-layers 32 \ + --decoder-embed-dim 2560 \ + --decoder-ffn-embed-dim 10240 \ + --decoder-attention-heads 32 \ + --batch-size 4 \ + --update-freq 16 \ + --total-num-update 286102 \ + --max-update 286102 \ + "$@" +} diff --git a/fairseq/examples/operators/alignment_train_cpu.cpp b/fairseq/examples/operators/alignment_train_cpu.cpp new file mode 100644 index 0000000..13c0153 --- /dev/null +++ b/fairseq/examples/operators/alignment_train_cpu.cpp @@ -0,0 +1,166 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <torch/extension.h> // @manual=//caffe2:torch_extension +#include <algorithm> + +namespace { + +template <typename T> +void exclusiveCumprod( + const T* p_choose, + T* cumprod_1mp, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len) { + // cumprod_1mp = 1 - p_choose + for (uint32_t b = 0; b < bsz; b++) { + for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { + for (uint32_t src = 0; src < src_len; src++) { + uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; + cumprod_1mp[idx] = 1 - p_choose[idx]; + } + } + } + + // Implementing exclusive cumprod in the innermost dimension + // cumprod_1mp = cumprod(1 - p_choose) + // There is cumprod in pytorch, however there is no exclusive mode. + // cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] + // exclusive means + // cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] + for (uint32_t b = 0; b < bsz; b++) { + for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { + uint32_t idx_offset = b * tgt_len * src_len + tgt * src_len; + T prev = cumprod_1mp[idx_offset]; + // index [b][tgt][0] + cumprod_1mp[idx_offset] = (T)1.0; + T curr; + for (uint32_t src = 1; src < src_len; src++) { + uint32_t idx = idx_offset + src; + curr = cumprod_1mp[idx]; + cumprod_1mp[idx] = cumprod_1mp[idx - 1] * prev; + prev = curr; + } + } + } +} + +template <typename T> +void clamp( + const T* cumprod_1mp, + T* cumprod_1mp_clamp, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len, + T min_val, + T max_val) { + for (uint32_t b = 0; b < bsz; b++) { + for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { + for (uint32_t src = 0; src < src_len; src++) { + uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; + if (cumprod_1mp[idx] < min_val) { + cumprod_1mp_clamp[idx] = min_val; + } else if (cumprod_1mp[idx] > max_val) { + cumprod_1mp_clamp[idx] = max_val; + } else { + cumprod_1mp_clamp[idx] = cumprod_1mp[idx]; + } + } + } + } +} + +template <typename T> +void alignmentTrainCPUImpl( + const T* p_choose, + T* alpha, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len, + float eps) { + // p_choose: bsz , tgt_len, src_len + // cumprod_1mp: bsz , tgt_len, src_len + // cumprod_1mp_clamp : bsz, tgt_len, src_len + // alpha: bsz + 1, tgt_len, src_len + + uint32_t elements = bsz * tgt_len * src_len; + T* cumprod_1mp = new T[elements]; + T* cumprod_1mp_clamp = new T[elements]; + + exclusiveCumprod<T>(p_choose, cumprod_1mp, bsz, tgt_len, src_len); + clamp<T>( + cumprod_1mp, cumprod_1mp_clamp, bsz, tgt_len, src_len, (T)eps, (T)1.0); + + // ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi)) + + // Initialize alpha [:, 0, 0] + for (uint32_t b = 0; b < bsz; b++) { + alpha[b * tgt_len * src_len] = 1.0; + } + + for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { + for (uint32_t b = 0; b < bsz; b++) { + uint32_t alpha_idx, inout_idx; + T prev_scan = 0, curr_scan, out; + for (uint32_t src = 0; src < src_len; src++) { + // Apply scan/cumsum + if (tgt == 0) { + // alpha index is [b][tgt][src] + alpha_idx = b * tgt_len * src_len + src; + } else { + // alpha index is [b][tgt-1][src] + alpha_idx = b * tgt_len * src_len + (tgt - 1) * src_len + src; + } + // input index is [b][tgt][src] + inout_idx = b * tgt_len * src_len + tgt * src_len + src; + curr_scan = prev_scan + alpha[alpha_idx] / cumprod_1mp_clamp[inout_idx]; + + out = curr_scan * p_choose[inout_idx] * cumprod_1mp[inout_idx]; + alpha[inout_idx] = std::min<T>(std::max<T>(out, 0), 1.0); + prev_scan = curr_scan; + } + } + } + + free(cumprod_1mp); + free(cumprod_1mp_clamp); +} + +void alignmentTrainCPU( + const torch::Tensor& p_choose, + torch::Tensor& alpha, + float eps) { + uint32_t bsz = p_choose.size(0); + uint32_t tgt_len = p_choose.size(1); + uint32_t src_len = p_choose.size(2); + + AT_DISPATCH_FLOATING_TYPES_AND2( + torch::ScalarType::Half, + torch::ScalarType::BFloat16, + p_choose.scalar_type(), + "alignmentCPUImpl", + [&]() { + alignmentTrainCPUImpl<scalar_t>( + p_choose.data_ptr<scalar_t>(), + alpha.data_ptr<scalar_t>(), + bsz, + tgt_len, + src_len, + eps); + }); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def( + "alignment_train_cpu", + &alignmentTrainCPU, + "expected_alignment_from_p_choose (CPU)"); +} + +} // namespace diff --git a/fairseq/examples/operators/alignment_train_cuda.cpp b/fairseq/examples/operators/alignment_train_cuda.cpp new file mode 100644 index 0000000..430e048 --- /dev/null +++ b/fairseq/examples/operators/alignment_train_cuda.cpp @@ -0,0 +1,31 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "alignment_train_cuda.h" +#include "utils.h" + +namespace { + +void alignmentTrainCUDA( + const torch::Tensor& p_choose, + torch::Tensor& alpha, + float eps) { + CHECK_INPUT(p_choose); + CHECK_INPUT(alpha); + + alignmentTrainCUDAWrapper(p_choose, alpha, eps); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def( + "alignment_train_cuda", + &alignmentTrainCUDA, + "expected_alignment_from_p_choose (CUDA)"); +} + +} // namespace diff --git a/fairseq/examples/operators/alignment_train_cuda.h b/fairseq/examples/operators/alignment_train_cuda.h new file mode 100644 index 0000000..8289d1a --- /dev/null +++ b/fairseq/examples/operators/alignment_train_cuda.h @@ -0,0 +1,16 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#pragma once + +#include <torch/extension.h> // @manual=//caffe2:torch_extension + +void alignmentTrainCUDAWrapper( + const torch::Tensor& p_choose, + torch::Tensor& alpha, + float eps); diff --git a/fairseq/examples/operators/alignment_train_kernel.cu b/fairseq/examples/operators/alignment_train_kernel.cu new file mode 100644 index 0000000..efae7cc --- /dev/null +++ b/fairseq/examples/operators/alignment_train_kernel.cu @@ -0,0 +1,354 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <ATen/ATen.h> +#include <ATen/cuda/CUDAContext.h> // @manual=//caffe2/aten:ATen-cu +#include <cuda_runtime.h> +#include <algorithm> // std::min/max +#include <cub/cub.cuh> + +#include "alignment_train_cuda.h" +#include "utils.h" + +namespace { + +// The thread block length in threads along the X dimension +constexpr int BLOCK_DIM_X = 128; +// The thread block length in threads along the Y dimension +constexpr int BLOCK_DIM_Y = 8; +// The thread block length in threads for scan operation +constexpr int SCAN_BLOCK = 512; + +#define gpuErrchk(ans) \ + { gpuAssert((ans), __FILE__, __LINE__); } + +inline void +gpuAssert(cudaError_t code, const char* file, int line, bool abort = true) { + if (code != cudaSuccess) { + fprintf( + stderr, + "\nGPUassert: %s %s %d\n", + cudaGetErrorString(code), + file, + line); + if (abort) + exit(code); + } +} + +template <typename T> +struct Prod { + /// prod operator, returns <tt>a * b</tt> + __host__ __device__ __forceinline__ T + operator()(const T& a, const T& b) const { + return a * b; + } +}; + +template <typename T> +struct BlockPrefixProdCallbackOp { + // Running prefix + T running_total; + + // Constructor + __device__ BlockPrefixProdCallbackOp(T running_total) + : running_total(running_total) {} + + // Callback operator to be entered by the first warp of threads in the block. + // Thread-0 is responsible for returning a value for seeding the block-wide + // scan. + __device__ T operator()(const T block_aggregate) { + T old_prefix = running_total; + running_total *= block_aggregate; + return old_prefix; + } +}; + +template <typename T> +struct BlockPrefixSumCallbackOp { + // Running prefix + T running_total; + + // Constructor + __device__ BlockPrefixSumCallbackOp(T running_total) + : running_total(running_total) {} + + // Callback operator to be entered by the first warp of threads in the block. + // Thread-0 is responsible for returning a value for seeding the block-wide + // scan. + __device__ T operator()(const T block_aggregate) { + T old_prefix = running_total; + running_total += block_aggregate; + return old_prefix; + } +}; + +template <typename T> +__global__ void oneMinusPKernel( + const T* __restrict__ p_choose, + T* __restrict__ cumprod_1mp, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len) { + for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { + for (uint32_t tgt = threadIdx.y; tgt < tgt_len; tgt += blockDim.y) { + for (uint32_t src = threadIdx.x; src < src_len; src += blockDim.x) { + uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; + cumprod_1mp[idx] = 1 - p_choose[idx]; + } + } + } +} + +template <typename T, int TPB> +__global__ void innermostScanKernel( + T* __restrict__ cumprod_1mp, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len) { + for (uint32_t b = blockIdx.y; b < bsz; b += gridDim.y) { + for (uint32_t tgt = blockIdx.x; tgt < tgt_len; tgt += gridDim.x) { + // Specialize BlockScan for a 1D block of TPB threads on type T + typedef cub::BlockScan<T, TPB> BlockScan; + // Allocate shared memory for BlockScan + __shared__ typename BlockScan::TempStorage temp_storage; + // Initialize running total + BlockPrefixProdCallbackOp<T> prefix_op(1); + + const uint32_t tid = threadIdx.x; + for (uint32_t block_src = 0; block_src < src_len; + block_src += blockDim.x) { + uint32_t src = block_src + tid; + uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; + T thread_data = (src < src_len) ? cumprod_1mp[idx] : (T)0; + + // Collectively compute the block-wide inclusive prefix sum + BlockScan(temp_storage) + .ExclusiveScan(thread_data, thread_data, Prod<T>(), prefix_op); + __syncthreads(); + + // write the scanned value to output + if (src < src_len) { + cumprod_1mp[idx] = thread_data; + } + } + } + } +} + +template <typename T> +__global__ void clampKernel( + const T* __restrict__ cumprod_1mp, + T* __restrict__ cumprod_1mp_clamp, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len, + T min_val, + T max_val) { + for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { + for (uint32_t tgt = threadIdx.y; tgt < tgt_len; tgt += blockDim.y) { + for (uint32_t src = threadIdx.x; src < src_len; src += blockDim.x) { + uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; + if (cumprod_1mp[idx] < min_val) { + cumprod_1mp_clamp[idx] = min_val; + } else if (cumprod_1mp[idx] > max_val) { + cumprod_1mp_clamp[idx] = max_val; + } else { + cumprod_1mp_clamp[idx] = cumprod_1mp[idx]; + } + } + } + } +} + +template <typename T> +__global__ void initAlphaCUDAKernel( + T* alpha, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len) { + // alpha[:, 0, 0] = 1.0 + for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { + alpha[b * tgt_len * src_len] = (T)1.0; + } +} + +template <typename T, int TPB> +__global__ void alignmentTrainCUDAKernel( + const T* __restrict__ p_choose, + const T* __restrict__ cumprod_1mp, + const T* __restrict__ cumprod_1mp_clamp, + T* __restrict__ alpha, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len, + uint32_t tgt) { + for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { + // Specialize BlockScan for a 1D block of TPB threads on type T + typedef cub::BlockScan<T, TPB> BlockScan; + + // Allocate shared memory for BlockScan + __shared__ typename BlockScan::TempStorage temp_storage; + // Initialize running total + BlockPrefixSumCallbackOp<T> prefix_op(0); + + uint32_t b_offset = b * tgt_len * src_len; + const uint32_t tid = threadIdx.x; + for (uint32_t block_src = 0; block_src < src_len; block_src += blockDim.x) { + uint32_t src = block_src + tid; + // Obtain a segment of consecutive items that are blocked across threads + uint32_t inout_idx, alpha_idx; + if (tgt == 0) { + // both alpha and other input index is [b][0][src] + alpha_idx = b_offset + src; + } else { + // alpha index is [b][tgt-1][src] + alpha_idx = b_offset + (tgt - 1) * src_len + src; + } + inout_idx = b_offset + tgt * src_len + src; + T thread_data = (T)0; + if (src < src_len) { + thread_data = alpha[alpha_idx] / cumprod_1mp_clamp[inout_idx]; + } + + // Collectively compute the block-wide inclusive prefix sum + BlockScan(temp_storage).InclusiveSum(thread_data, thread_data, prefix_op); + __syncthreads(); + + if (src < src_len) { + T out = thread_data * p_choose[inout_idx] * cumprod_1mp[inout_idx]; + // Clamps all elements into the range [ 0, 1.0 ] + alpha[inout_idx] = std::min<T>(std::max<T>(out, 0), (T)1.0); + } + } + } +} + +template <typename T> +void exclusiveCumprod( + const T* p_choose, + T* cumprod_1mp, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len, + uint32_t max_grid_x, + uint32_t max_grid_y, + cudaStream_t& stream) { + // cumprod_1mp = 1 - p_choose + dim3 grid(std::min<T>(max_grid_x, bsz), 1, 1); + dim3 block(BLOCK_DIM_X, BLOCK_DIM_Y, 1); + oneMinusPKernel<T><<<grid, block, 0, stream>>>( + p_choose, cumprod_1mp, bsz, tgt_len, src_len); + gpuErrchk(cudaGetLastError()); + + // scan on the innermost dimension of cumprod_1mp + // cumprod_1mp = cumprod(cumprod_1mp) + dim3 grid_scan( + std::min<T>(max_grid_x, tgt_len), std::min<T>(max_grid_y, bsz), 1); + innermostScanKernel<T, SCAN_BLOCK><<<grid_scan, SCAN_BLOCK, 0, stream>>>( + cumprod_1mp, bsz, tgt_len, src_len); + gpuErrchk(cudaGetLastError()); +} + +template <typename T> +void alignmentTrainCUDAImpl( + const T* p_choose, + T* alpha, + uint32_t bsz, + uint32_t tgt_len, + uint32_t src_len, + float eps) { + // p_choose: bsz , tgt_len, src_len + // cumprod_1mp: bsz , tgt_len, src_len + // cumprod_1mp_clamp : bsz, tgt_len, src_len + // alpha: bsz, tgt_len, src_len + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + uint32_t max_grid_x = at::cuda::getCurrentDeviceProperties()->maxGridSize[0]; + uint32_t max_grid_y = at::cuda::getCurrentDeviceProperties()->maxGridSize[1]; + + // Implementing exclusive cumprod. + // cumprod_1mp = cumprod(1 - p_choose) + // There is cumprod in pytorch, however there is no exclusive mode. + // cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] + // exclusive means + // cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] + uint32_t elements = bsz * tgt_len * src_len; + T* cumprod_1mp; + gpuErrchk(cudaMalloc(&cumprod_1mp, elements * sizeof(T))); + exclusiveCumprod<T>( + p_choose, + cumprod_1mp, + bsz, + tgt_len, + src_len, + max_grid_x, + max_grid_y, + stream); + + // clamp cumprod_1mp to the range [eps, 1.0] + T* cumprod_1mp_clamp; + gpuErrchk(cudaMalloc(&cumprod_1mp_clamp, elements * sizeof(T))); + dim3 grid_clamp(std::min<T>(max_grid_x, bsz), 1, 1); + dim3 block_clamp(BLOCK_DIM_X, BLOCK_DIM_Y, 1); + clampKernel<T><<<grid_clamp, block_clamp, 0, stream>>>( + cumprod_1mp, cumprod_1mp_clamp, bsz, tgt_len, src_len, (T)eps, (T)1.0); + gpuErrchk(cudaGetLastError()); + + // ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi)) + dim3 grid_init(std::min<int>(max_grid_x, bsz), 1, 1); + initAlphaCUDAKernel<T> + <<<grid_init, 1, 0, stream>>>(alpha, bsz, tgt_len, src_len); + gpuErrchk(cudaGetLastError()); + + const int grid = std::min(bsz, max_grid_x); + + for (uint32_t i = 0; i < tgt_len; i++) { + alignmentTrainCUDAKernel<T, SCAN_BLOCK><<<grid, SCAN_BLOCK, 0, stream>>>( + p_choose, + cumprod_1mp, + cumprod_1mp_clamp, + alpha, + bsz, + tgt_len, + src_len, + i); + gpuErrchk(cudaGetLastError()); + } + + gpuErrchk(cudaFree(cumprod_1mp)); + gpuErrchk(cudaFree(cumprod_1mp_clamp)); +} + +} // namespace + +void alignmentTrainCUDAWrapper( + const torch::Tensor& p_choose, + torch::Tensor& alpha, + float eps) { + // p_choose dimension: bsz, tgt_len, src_len + uint32_t bsz = p_choose.size(0); + uint32_t tgt_len = p_choose.size(1); + uint32_t src_len = p_choose.size(2); + + cudaSetDevice(p_choose.get_device()); + + AT_DISPATCH_FLOATING_TYPES_AND2( + torch::ScalarType::Half, + torch::ScalarType::BFloat16, + p_choose.scalar_type(), + "alignmentTrainCUDAImpl", + [&]() { + alignmentTrainCUDAImpl<scalar_t>( + p_choose.data_ptr<scalar_t>(), + alpha.data_ptr<scalar_t>(), + bsz, + tgt_len, + src_len, + eps); + }); +} diff --git a/fairseq/examples/operators/utils.h b/fairseq/examples/operators/utils.h new file mode 100644 index 0000000..0ef5b43 --- /dev/null +++ b/fairseq/examples/operators/utils.h @@ -0,0 +1,19 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#pragma once + +#include <torch/extension.h> // @manual=//caffe2:torch_extension + +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) diff --git a/fairseq/examples/paraphraser/README.md b/fairseq/examples/paraphraser/README.md new file mode 100644 index 0000000..3810311 --- /dev/null +++ b/fairseq/examples/paraphraser/README.md @@ -0,0 +1,46 @@ +# Paraphrasing with round-trip translation and mixture of experts + +Machine translation models can be used to paraphrase text by translating it to +an intermediate language and back (round-trip translation). + +This example shows how to paraphrase text by first passing it to an +English-French translation model, followed by a French-English [mixture of +experts translation model](/examples/translation_moe). + +##### 0. Setup + +Clone fairseq from source and install necessary dependencies: +```bash +git clone https://github.com/pytorch/fairseq.git +cd fairseq +pip install --editable . +pip install sacremoses sentencepiece +``` + +##### 1. Download models +```bash +wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.en-fr.tar.gz +wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.fr-en.hMoEup.tar.gz +tar -xzvf paraphraser.en-fr.tar.gz +tar -xzvf paraphraser.fr-en.hMoEup.tar.gz +``` + +##### 2. Paraphrase +```bash +python examples/paraphraser/paraphrase.py \ + --en2fr paraphraser.en-fr \ + --fr2en paraphraser.fr-en.hMoEup +# Example input: +# The new date for the Games, postponed for a year in response to the coronavirus pandemic, gives athletes time to recalibrate their training schedules. +# Example outputs: +# Delayed one year in response to the coronavirus pandemic, the new date of the Games gives athletes time to rebalance their training schedule. +# The new date of the Games, which was rescheduled one year in response to the coronavirus (CV) pandemic, gives athletes time to rebalance their training schedule. +# The new date of the Games, postponed one year in response to the coronavirus pandemic, provides athletes with time to rebalance their training schedule. +# The Games' new date, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule. +# The new Games date, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule. +# The new date of the Games, which was postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule. +# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule. +# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to re-balance their training schedule. +# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their schedule of training. +# The new date of the Games, postponed one year in response to the pandemic of coronavirus, gives the athletes time to rebalance their training schedule. +``` diff --git a/fairseq/examples/paraphraser/paraphrase.py b/fairseq/examples/paraphraser/paraphrase.py new file mode 100644 index 0000000..d3422fb --- /dev/null +++ b/fairseq/examples/paraphraser/paraphrase.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 -u + +import argparse +import fileinput +import logging +import os +import sys + +from fairseq.models.transformer import TransformerModel + + +logging.getLogger().setLevel(logging.INFO) + + +def main(): + parser = argparse.ArgumentParser(description="") + parser.add_argument("--en2fr", required=True, help="path to en2fr model") + parser.add_argument( + "--fr2en", required=True, help="path to fr2en mixture of experts model" + ) + parser.add_argument( + "--user-dir", help="path to fairseq examples/translation_moe/src directory" + ) + parser.add_argument( + "--num-experts", + type=int, + default=10, + help="(keep at 10 unless using a different model)", + ) + parser.add_argument( + "files", + nargs="*", + default=["-"], + help='input files to paraphrase; "-" for stdin', + ) + args = parser.parse_args() + + if args.user_dir is None: + args.user_dir = os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), # examples/ + "translation_moe", + "src", + ) + if os.path.exists(args.user_dir): + logging.info("found user_dir:" + args.user_dir) + else: + raise RuntimeError( + "cannot find fairseq examples/translation_moe/src " + "(tried looking here: {})".format(args.user_dir) + ) + + logging.info("loading en2fr model from:" + args.en2fr) + en2fr = TransformerModel.from_pretrained( + model_name_or_path=args.en2fr, + tokenizer="moses", + bpe="sentencepiece", + ).eval() + + logging.info("loading fr2en model from:" + args.fr2en) + fr2en = TransformerModel.from_pretrained( + model_name_or_path=args.fr2en, + tokenizer="moses", + bpe="sentencepiece", + user_dir=args.user_dir, + task="translation_moe", + ).eval() + + def gen_paraphrases(en): + fr = en2fr.translate(en) + return [ + fr2en.translate(fr, inference_step_args={"expert": i}) + for i in range(args.num_experts) + ] + + logging.info("Type the input sentence and press return:") + for line in fileinput.input(args.files): + line = line.strip() + if len(line) == 0: + continue + for paraphrase in gen_paraphrases(line): + print(paraphrase) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/pay_less_attention_paper/README.md b/fairseq/examples/pay_less_attention_paper/README.md new file mode 100644 index 0000000..5adab11 --- /dev/null +++ b/fairseq/examples/pay_less_attention_paper/README.md @@ -0,0 +1,176 @@ +# Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019) + +This page contains pointers to pre-trained models as well as instructions on how to train new models for [our paper](https://arxiv.org/abs/1901.10430). + +## Citation: +```bibtex +@inproceedings{wu2018pay, + title = {Pay Less Attention with Lightweight and Dynamic Convolutions}, + author = {Felix Wu and Angela Fan and Alexei Baevski and Yann Dauphin and Michael Auli}, + booktitle = {International Conference on Learning Representations}, + year = {2019}, + url = {https://arxiv.org/abs/1901.10430}, +} +``` + +## Translation + +### Pre-trained models +For some datasets we release models without GLUs which are faster at inference. + +Model | Description | Dataset | Download +---|---|---|--- +`lightconv.no_glu.iwslt14.de-en` | LightConv (without GLUs) | [IWSLT14 German-English](https://wit3.fbk.eu/archive/2014-01/texts/de/en/de-en.tgz) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.lightconv.tar.gz) <br> IWSLT14 test: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/iwslt14.de-en.test.tar.bz2) +`dynamicconv.no_glu.iwslt14.de-en` | DynamicConv (without GLUs) | [IWSLT14 German-English](https://wit3.fbk.eu/archive/2014-01/texts/de/en/de-en.tgz) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.dynamicconv.tar.gz) <br> IWSLT14 test: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/iwslt14.de-en.test.tar.bz2) +`lightconv.no_glu.wmt16.en-de` | LightConv (without GLUs) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) +`dynamicconv.no_glu.wmt16.en-de` | DynamicConv (without GLUs) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) +`lightconv.glu.wmt16.en-de` | LightConv | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) +`dynamicconv.glu.wmt16.en-de` | DynamicConv | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) +`lightconv.glu.wmt14.en-fr` | LightConv | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.lightconv-glu.tar.gz) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) +`dynamicconv.glu.wmt14.en-fr` | DynamicConv | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.dynamicconv-glu.tar.gz) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) +`lightconv.glu.wmt17.zh-en` | LightConv | [WMT17 Chinese-English](http://statmt.org/wmt17/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.lightconv-glu.tar.gz) <br> newstest2017: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.zh-en.newstest2017.tar.bz2) +`dynamicconv.glu.wmt17.zh-en` | DynamicConv | [WMT17 Chinese-English](http://statmt.org/wmt17/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.dynamicconv-glu.tar.gz) <br> newstest2017: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.zh-en.newstest2017.tar.bz2) + +### Memory-Efficient CUDA Kernels + +Since the PyTorch implementations of Light/Dynamic conv are quite memory intensive, we have developed CUDA kernels that implement the light and dynamic convolution operator in a memory-efficient and performant manner. For large sequence lengths, these kernels save about 50% memory compared to the PyTorch equivalent. + +To install the kernels, use the commands below. Once installed, they will automatically be used in place of the PyTorch implementations whenever a light or dynamic convolution is used. + +```sh +# to install lightconv +cd fairseq/modules/lightconv_layer +python cuda_function_gen.py +python setup.py install + +# to install dynamicconv +cd fairseq/modules/dynamicconv_layer +python cuda_function_gen.py +python setup.py install +``` + +### Example usage (torch.hub) + +We require a few additional Python dependencies for preprocessing: +```bash +pip install sacremoses subword_nmt +``` + +Interactive translation via PyTorch Hub: +```python +import torch + +# List available models +torch.hub.list('pytorch/fairseq') # [..., 'lightconv.glu.wmt17.zh-en', ... ] + +# Load a transformer trained on WMT'16 En-De +zh2en = torch.hub.load('pytorch/fairseq', 'lightconv.glu.wmt17.zh-en', tokenizer='moses', bpe='subword_nmt') + +# The underlying model is available under the *models* attribute +assert isinstance(zh2en.models[0], fairseq.models.lightconv.LightConvModel) + +# Translate a sentence +zh2en.translate('你好 世界') +# 'Hello World' +``` + +Loading custom models: +```python +from fairseq.models.lightconv import LightConvModel +en2fr = LightConvModel.from_pretrained( + '/path/to/checkpoints', + checkpoint_file='checkpoint_best.pt', + data_name_or_path='data-bin/wmt14_en_fr', + bpe='subword_nmt', + bpe_codes='data-bin/wmt14_en_fr/en.code' +) +en2fr.translate('Hello world!') +# 'Bonjour le monde' +``` + +### Preprocessing the training datasets + +Please follow the instructions in [`examples/translation/README.md`](../translation/README.md) to preprocess the data. + +### Training and evaluation options: +To use the model without GLU, please set `--encoder-glu 0 --decoder-glu 0`. +For LightConv, please use `--encoder-conv-type lightweight --decoder-conv-type lightweight`, otherwise the default is DynamicConv. +For best BLEU results, lenpen may need to be manually tuned. + +To use the CUDA kernels, first install the PyTorch modules using the commands +above. Once the CUDA modules are installed, they will automatically be used +instead of the PyTorch modules. + +### IWSLT14 De-En +Training and evaluating DynamicConv (without GLU) on a GPU: +```sh +# Training +SAVE="save/dynamic_conv_iwslt" +mkdir -p $SAVE +CUDA_VISIBLE_DEVICES=0 $(which fairseq-train) data-bin/iwslt14.tokenized.de-en \ + --clip-norm 0 --optimizer adam --lr 0.0005 \ + --source-lang de --target-lang en --max-tokens 4000 --no-progress-bar \ + --log-interval 100 --stop-min-lr '1e-09' --weight-decay 0.0001 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --lr-scheduler inverse_sqrt \ + --ddp-backend=legacy_ddp \ + --max-update 50000 --warmup-updates 4000 --warmup-init-lr '1e-07' \ + --adam-betas '(0.9, 0.98)' --keep-last-epochs 10 \ + -a lightconv_iwslt_de_en --save-dir $SAVE \ + --dropout 0.3 --attention-dropout 0.1 --weight-dropout 0.1 \ + --encoder-glu 0 --decoder-glu 0 +python scripts/average_checkpoints.py --inputs $SAVE \ + --num-epoch-checkpoints 10 --output "${SAVE}/checkpoint_last10_avg.pt" + +# Evaluation +CUDA_VISIBLE_DEVICES=0 fairseq-generate data-bin/iwslt14.tokenized.de-en --path "${SAVE}/checkpoint_last10_avg.pt" --batch-size 128 --beam 4 --remove-bpe --lenpen 1 --gen-subset test --quiet +``` + +### WMT16 En-De +Training and evaluating DynamicConv (with GLU) on WMT16 En-De using cosine scheduler on one machine with 8 V100 GPUs: +```sh +# Training +SAVE="save/dynamic_conv_wmt16en2de" +mkdir -p $SAVE +python -m torch.distributed.launch --nproc_per_node 8 $(which fairseq-train) \ + data-bin/wmt16_en_de_bpe32k --fp16 --log-interval 100 --no-progress-bar \ + --max-update 30000 --share-all-embeddings --optimizer adam \ + --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --stop-min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ + --ddp-backend=legacy_ddp --max-tokens 3584 \ + --lr-scheduler cosine --warmup-init-lr 1e-7 --warmup-updates 10000 \ + --lr-shrink 1 --lr 0.001 --min-lr 1e-7 --warmup-init-lr 1e-07 \ + --t-mult 1 --lr-period-updates 20000 \ + --arch lightconv_wmt_en_de_big --save-dir $SAVE \ + --dropout 0.3 --attention-dropout 0.1 --weight-dropout 0.1 \ + --encoder-glu 1 --decoder-glu 1 + +# Evaluation +CUDA_VISIBLE_DEVICES=0 fairseq-generate data-bin/wmt16.en-de.joined-dict.newstest2014 --path "${SAVE}/checkpoint_best.pt" --batch-size 128 --beam 5 --remove-bpe --lenpen 0.5 --gen-subset test > wmt16_gen.txt +bash scripts/compound_split_bleu.sh wmt16_gen.txt +``` + +### WMT14 En-Fr +Training DynamicConv (with GLU) on WMT14 En-Fr using cosine scheduler on one machine with 8 V100 GPUs: +```sh +# Training +SAVE="save/dynamic_conv_wmt14en2fr" +mkdir -p $SAVE +python -m torch.distributed.launch --nproc_per_node 8 $(which fairseq-train) \ + data-bin/wmt14_en_fr --fp16 --log-interval 100 --no-progress-bar \ + --max-update 30000 --share-all-embeddings --optimizer adam \ + --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --stop-min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ + --ddp-backend=legacy_ddp --max-tokens 3584 \ + --lr-scheduler cosine --warmup-init-lr 1e-7 --warmup-updates 10000 \ + --lr-shrink 1 --lr 0.001 --min-lr 1e-7 --warmup-init-lr 1e-07 \ + --t-mult 1 --lr-period-updates 70000 \ + --arch lightconv_wmt_en_fr_big --save-dir $SAVE \ + --dropout 0.1 --attention-dropout 0.1 --weight-dropout 0.1 \ + --encoder-glu 1 --decoder-glu 1 + +# Evaluation +CUDA_VISIBLE_DEVICES=0 fairseq-generate data-bin/wmt14.en-fr.joined-dict.newstest2014 --path "${SAVE}/checkpoint_best.pt" --batch-size 128 --beam 5 --remove-bpe --lenpen 0.9 --gen-subset test +``` diff --git a/fairseq/examples/pointer_generator/README.md b/fairseq/examples/pointer_generator/README.md new file mode 100644 index 0000000..6096570 --- /dev/null +++ b/fairseq/examples/pointer_generator/README.md @@ -0,0 +1,82 @@ +# Transformer with Pointer-Generator Network + +This page describes the `transformer_pointer_generator` model that incorporates +a pointing mechanism in the Transformer model that facilitates copying of input +words to the output. This architecture is described in [Enarvi et al. (2020)](https://www.aclweb.org/anthology/2020.nlpmc-1.4/). + +## Background + +The pointer-generator network was introduced in [See et al. (2017)](https://arxiv.org/abs/1704.04368) +for RNN encoder-decoder attention models. A similar mechanism can be +incorporated in a Transformer model by reusing one of the many attention +distributions for pointing. The attention distribution over the input words is +interpolated with the normal output distribution over the vocabulary words. This +allows the model to generate words that appear in the input, even if they don't +appear in the vocabulary, helping especially with small vocabularies. + +## Implementation + +The mechanism for copying out-of-vocabulary words from the input has been +implemented differently to See et al. In their [implementation](https://github.com/abisee/pointer-generator) +they convey the word identities through the model in order to be able to produce +words that appear in the input sequence but not in the vocabulary. A different +approach was taken in the Fairseq implementation to keep it self-contained in +the model file, avoiding any changes to the rest of the code base. Copying +out-of-vocabulary words is possible by pre-processing the input and +post-processing the output. This is described in detail in the next section. + +## Usage + +The training and evaluation procedure is outlined below. You can also find a +more detailed example for the XSum dataset on [this page](README.xsum.md). + +##### 1. Create a vocabulary and extend it with source position markers + +The pointing mechanism is especially helpful with small vocabularies, if we are +able to recover the identities of any out-of-vocabulary words that are copied +from the input. For this purpose, the model allows extending the vocabulary with +special tokens that can be used in place of `<unk>` tokens to identify different +input positions. For example, the user may add `<unk-0>`, `<unk-1>`, `<unk-2>`, +etc. to the end of the vocabulary, after the normal words. Below is an example +of how to create a vocabulary of 10000 most common words and add 1000 input +position markers. + +```bash +vocab_size=10000 +position_markers=1000 +export LC_ALL=C +cat train.src train.tgt | + tr -s '[:space:]' '\n' | + sort | + uniq -c | + sort -k1,1bnr -k2 | + head -n "$((vocab_size - 4))" | + awk '{ print $2 " " $1 }' >dict.pg.txt +python3 -c "[print('<unk-{}> 0'.format(n)) for n in range($position_markers)]" >>dict.pg.txt +``` + +##### 2. Preprocess the text data + +The idea is that any `<unk>` tokens in the text are replaced with `<unk-0>` if +it appears in the first input position, `<unk-1>` if it appears in the second +input position, and so on. This can be achieved using the `preprocess.py` script +that is provided in this directory. + +##### 3. Train a model + +The number of these special tokens is given to the model with the +`--source-position-markers` argument—the model simply maps all of these to the +same word embedding as `<unk>`. + +The attention distribution that is used for pointing is selected using the +`--alignment-heads` and `--alignment-layer` command-line arguments in the same +way as with the `transformer_align` model. + +##### 4. Generate text and postprocess it + +When using the model to generate text, you want to preprocess the input text in +the same way that training data was processed, replacing out-of-vocabulary words +with `<unk-N>` tokens. If any of these tokens are copied to the output, the +actual words can be retrieved from the unprocessed input text. Any `<unk-N>` +token should be replaced with the word at position N in the original input +sequence. This can be achieved using the `postprocess.py` script. diff --git a/fairseq/examples/pointer_generator/README.xsum.md b/fairseq/examples/pointer_generator/README.xsum.md new file mode 100644 index 0000000..ac3a8c3 --- /dev/null +++ b/fairseq/examples/pointer_generator/README.xsum.md @@ -0,0 +1,180 @@ +## Training a pointer-generator model on the Extreme Summarization dataset + +##### 1. Download the Extreme Summarization data and preprocess it + +Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to obtain +the original Extreme Summarization dataset. You should have six files, +{train,validation,test}.{document,summary}. + +##### 2. Create a vocabulary and extend it with source position markers + +```bash +vocab_size=10000 +position_markers=1000 +export LC_ALL=C +cat train.document train.summary | + tr -s '[:space:]' '\n' | + sort | + uniq -c | + sort -k1,1bnr -k2 | + head -n "$((vocab_size - 4))" | + awk '{ print $2 " " $1 }' >dict.pg.txt +python3 -c "[print('<unk-{}> 0'.format(n)) for n in range($position_markers)]" >>dict.pg.txt +``` + +This creates the file dict.pg.txt that contains the 10k most frequent words, +followed by 1k source position markers: + +``` +the 4954867 +. 4157552 +, 3439668 +to 2212159 +a 1916857 +of 1916820 +and 1823350 +... +<unk-0> 0 +<unk-1> 0 +<unk-2> 0 +<unk-3> 0 +<unk-4> 0 +... +``` + +##### 2. Preprocess the text data + +```bash +./preprocess.py --source train.document --target train.summary --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out train.pg.src --target-out train.pg.tgt +./preprocess.py --source validation.document --target validation.summary --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out valid.pg.src --target-out valid.pg.tgt +./preprocess.py --source test.document --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out test.pg.src +``` + +The data should now contain `<unk-N>` tokens in place of out-of-vocabulary words. + +##### 3. Binarize the dataset: + +```bash +fairseq-preprocess \ + --source-lang src \ + --target-lang tgt \ + --trainpref train.pg \ + --validpref valid.pg \ + --destdir bin \ + --workers 60 \ + --srcdict dict.pg.txt \ + --joined-dictionary +``` + +##### 3. Train a model + +```bash +total_updates=20000 +warmup_updates=500 +lr=0.001 +max_tokens=4096 +update_freq=4 +pointer_layer=-2 + +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train bin \ + --user-dir examples/pointer_generator/pointer_generator_src \ + --max-tokens "$max_tokens" \ + --task translation \ + --source-lang src --target-lang tgt \ + --truncate-source \ + --layernorm-embedding \ + --share-all-embeddings \ + --encoder-normalize-before \ + --decoder-normalize-before \ + --required-batch-size-multiple 1 \ + --arch transformer_pointer_generator \ + --alignment-layer "$pointer_layer" \ + --alignment-heads 1 \ + --source-position-markers 1000 \ + --criterion label_smoothed_cross_entropy \ + --label-smoothing 0.1 \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \ + --clip-norm 0.1 \ + --lr-scheduler inverse_sqrt --lr "$lr" --max-update "$total_updates" --warmup-updates "$warmup_updates" \ + --update-freq "$update_freq" \ + --skip-invalid-size-inputs-valid-test +``` + +Above we specify that our dictionary contains 1000 source position markers, and +that we want to use one attention head from the penultimate decoder layer for +pointing. It should run in 5.5 hours on one node with eight 32GB V100 GPUs. The +logged messages confirm that dictionary indices above 10000 will be mapped to +the `<unk>` embedding: + +``` +2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | [src] dictionary: 11000 types +2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | [tgt] dictionary: 11000 types +2020-09-24 20:43:53 | INFO | fairseq.data.data_utils | loaded 11332 examples from: bin/valid.src-tgt.src +2020-09-24 20:43:53 | INFO | fairseq.data.data_utils | loaded 11332 examples from: bin/valid.src-tgt.tgt +2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | bin valid src-tgt 11332 examples +2020-09-24 20:43:53 | INFO | fairseq.models.transformer_pg | dictionary indices from 10000 to 10999 will be mapped to 3 +``` + +##### 4. Summarize the test sequences + +```bash +batch_size=32 +beam_size=6 +max_length=60 +length_penalty=1.0 + +fairseq-interactive bin \ + --user-dir examples/pointer_generator/pointer_generator_src \ + --batch-size "$batch_size" \ + --task translation \ + --source-lang src --target-lang tgt \ + --path checkpoints/checkpoint_last.pt \ + --input test.pg.src \ + --buffer-size 200 \ + --max-len-a 0 \ + --max-len-b "$max_length" \ + --lenpen "$length_penalty" \ + --beam "$beam_size" \ + --skip-invalid-size-inputs-valid-test | + tee generate.out +grep ^H generate.out | cut -f 3- >generate.hyp +``` + +Now you should have the generated sequences in `generate.hyp`. They contain +`<unk-N>` tokens that the model has copied from the source sequence. In order to +retrieve the original words, we need the unprocessed source sequences from +`test.document`. + +##### 5. Process the generated output + +Since we skipped too long inputs when producing `generate.hyp`, we also have to +skip too long sequences now that we read `test.document`. + +```bash +./postprocess.py \ + --source <(awk 'NF<1024' test.document) \ + --target generate.hyp \ + --target-out generate.hyp.processed +``` + +Now you'll find the final sequences from `generate.hyp.processed`, with +`<unk-N>` replaced with the original word from the source sequence. + +##### An example of a summarized sequence + +The original source document in `test.document`: + +> de roon moved to teesside in june 2016 for an initial # 8.8 m fee and played 33 premier league games last term . the netherlands international , 26 , scored five goals in 36 league and cup games during his spell at boro . meanwhile , manager garry monk confirmed the championship club 's interest in signing chelsea midfielder lewis baker . `` he 's a target and one of many that we 've had throughout the summer months , '' said monk . find all the latest football transfers on our dedicated page . + +The preprocessed source document in `test.src.pg`: + +> de \<unk-1> moved to \<unk-4> in june 2016 for an initial # \<unk-12> m fee and played 33 premier league games last term . the netherlands international , 26 , scored five goals in 36 league and cup games during his spell at boro . meanwhile , manager garry monk confirmed the championship club 's interest in signing chelsea midfielder lewis baker . `` he 's a target and one of many that we 've had throughout the summer months , '' said monk . find all the latest football transfers on our dedicated page . + +The generated summary in `generate.hyp`: + +> middlesbrough striker \<unk> de \<unk-1> has joined spanish side \<unk> on a season-long loan . + +The generated summary after postprocessing in `generate.hyp.processed`: + +> middlesbrough striker \<unk> de roon has joined spanish side \<unk> on a season-long loan . diff --git a/fairseq/examples/pointer_generator/pointer_generator_src/__init__.py b/fairseq/examples/pointer_generator/pointer_generator_src/__init__.py new file mode 100644 index 0000000..c361ff6 --- /dev/null +++ b/fairseq/examples/pointer_generator/pointer_generator_src/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import transformer_pg # noqa diff --git a/fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py b/fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py new file mode 100644 index 0000000..4ccf30f --- /dev/null +++ b/fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py @@ -0,0 +1,518 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import Any, Dict, Optional, List, Tuple + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer import ( + DEFAULT_MAX_SOURCE_POSITIONS, + DEFAULT_MAX_TARGET_POSITIONS, + TransformerDecoder, + TransformerEncoder, + TransformerModel, + base_architecture, +) +from torch import Tensor + + +logger = logging.getLogger(__name__) + + +@register_model("transformer_pointer_generator") +class TransformerPointerGeneratorModel(TransformerModel): + """ + Transformer model from `"Attention Is All You Need" (Vaswani et al, 2017) + <https://arxiv.org/abs/1706.03762>`_, augmented with a pointer-generator + network from `"Get To The Point: Summarization with Pointer-Generator + Networks" (See et al, 2017) <https://arxiv.org/abs/1704.04368>`_. + + Args: + encoder (TransformerPointerGeneratorEncoder): the encoder + decoder (TransformerPointerGeneratorDecoder): the decoder + + The Transformer pointer-generator model provides the following named + architectures and command-line arguments: + + .. argparse:: + :ref: fairseq.models.transformer_pointer_generator_parser + :prog: + """ + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + TransformerModel.add_args(parser) + parser.add_argument('--alignment-heads', type=int, metavar='N', + help='number of attention heads to be used for ' + 'pointing') + parser.add_argument('--alignment-layer', type=int, metavar='I', + help='layer number to be used for pointing (0 ' + 'corresponding to the bottommost layer)') + parser.add_argument('--source-position-markers', type=int, metavar='N', + help='dictionary includes N additional items that ' + 'represent an OOV token at a particular input ' + 'position') + parser.add_argument('--force-generation', type=float, metavar='P', + default=None, + help='set the vocabulary distribution weight to P, ' + 'instead of predicting it from the input (1.0 ' + 'corresponding to generation, 0.0 to pointing)') + # fmt: on + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if args.encoder_layers_to_keep: + args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) + if args.decoder_layers_to_keep: + args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) + + if getattr(args, "max_source_positions", None) is None: + args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS + if getattr(args, "source_position_markers", None) is None: + args.source_position_markers = args.max_source_positions + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + if src_dict != tgt_dict: + raise ValueError("Pointer-generator requires a joined dictionary") + + def build_embedding(dictionary, embed_dim, path=None): + # The dictionary may include additional items that can be used in + # place of the normal OOV token and that all map to the same + # embedding. Using a different token for each input position allows + # one to restore the word identities from the original source text. + num_embeddings = len(dictionary) - args.source_position_markers + padding_idx = dictionary.pad() + unk_idx = dictionary.unk() + logger.info( + "dictionary indices from {0} to {1} will be mapped to {2}".format( + num_embeddings, len(dictionary) - 1, unk_idx + ) + ) + emb = Embedding(num_embeddings, embed_dim, padding_idx, unk_idx) + # if provided, load from preloaded dictionaries + if path: + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + return emb + + if args.share_all_embeddings: + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + encoder_embed_tokens = build_embedding( + src_dict, args.encoder_embed_dim, args.encoder_embed_path + ) + decoder_embed_tokens = encoder_embed_tokens + args.share_decoder_input_output_embed = True + else: + encoder_embed_tokens = build_embedding( + src_dict, args.encoder_embed_dim, args.encoder_embed_path + ) + decoder_embed_tokens = build_embedding( + tgt_dict, args.decoder_embed_dim, args.decoder_embed_path + ) + + encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens) + decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens) + return cls(args, encoder, decoder) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return TransformerPointerGeneratorEncoder(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return TransformerPointerGeneratorDecoder(args, tgt_dict, embed_tokens) + + +class TransformerPointerGeneratorEncoder(TransformerEncoder): + """ + Transformer encoder consisting of *args.encoder_layers* layers. Each layer + is a :class:`TransformerEncoderLayer`. The pointer-generator variant adds + the source tokens to the encoder output as these are otherwise not passed + to the decoder. + """ + + def forward( + self, + src_tokens, + src_lengths: Optional[Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[Tensor] = None + ): + """ + Runs the `forward()` method of the parent Transformer class. Then adds + the source tokens into the encoder output tuple. + + While it might be more elegant that the model would pass the source + tokens to the `forward()` method of the decoder too, this would require + changes to `SequenceGenerator`. + + Args: + src_tokens (torch.LongTensor): tokens in the source language of + shape `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + namedtuple: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + - **src_tokens** (Tensor): input token ids of shape + `(batch, src_len)` + """ + encoder_out = self.forward_scriptable(src_tokens, + src_lengths, + return_all_hiddens, + token_embeddings) + + # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in + # `forward` so we use a dictionary instead. + # TorchScript does not support mixed values so the values are all lists. + # The empty list is equivalent to None. + return { + "encoder_out": encoder_out["encoder_out"], # T x B x C + "encoder_padding_mask": encoder_out["encoder_padding_mask"], # B x T + "encoder_embedding": encoder_out["encoder_embedding"], # B x T x C + "encoder_states": encoder_out["encoder_states"], # List[T x B x C] + "src_tokens": [src_tokens], # B x T + "src_lengths": [], + } + + +class TransformerPointerGeneratorDecoder(TransformerDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. The pointer-generator variant mixes + the output probabilities with an attention distribution in the output layer. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + """ + + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens, no_encoder_attn=False) + + # In the pointer-generator model these arguments define the decoder + # layer and the number of attention heads that will be averaged to + # create the alignment for pointing. + self.alignment_heads = args.alignment_heads + self.alignment_layer = args.alignment_layer + + input_embed_dim = embed_tokens.embedding_dim + + # Generation probabilities / interpolation coefficients are predicted + # from the current decoder input embedding and the decoder output, which + # is the size of output_embed_dim. + p_gen_input_size = input_embed_dim + self.output_embed_dim + self.project_p_gens = nn.Linear(p_gen_input_size, 1) + nn.init.zeros_(self.project_p_gens.bias) + + # The dictionary may include a separate entry for an OOV token in each + # input position, so that their identity can be restored from the + # original source text. + self.num_types = len(dictionary) + self.num_oov_types = args.source_position_markers + self.num_embeddings = self.num_types - self.num_oov_types + self.force_p_gen = args.force_generation + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + alignment_layer: Optional[int] = 0, + alignment_heads: Optional[int] = 1, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict, optional): dictionary used for storing + state during :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False) + alignment_layer (int, optional): 0-based index of the layer to be + used for pointing (default: 0) + alignment_heads (int, optional): number of attention heads to be + used for pointing (default: 1) + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + # The normal Transformer model doesn't pass the alignment_layer and + # alignment_heads parameters correctly. We use our local variables. + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + alignment_layer=self.alignment_layer, + alignment_heads=self.alignment_heads, + ) + if not features_only: + # Embedding the tokens again for generation probability prediction, + # so that we don't have to reimplement the whole extract_features() + # method. + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + prev_output_embed = self.embed_tokens(prev_output_tokens) + prev_output_embed *= self.embed_scale + predictors = torch.cat((prev_output_embed, x), 2) + p_gens = self.project_p_gens(predictors) + p_gens = torch.sigmoid(p_gens.float()) + # Torchscript complains if encoder_out or attn are None because + # `output_layer()` signature expects tensors instead + attn: Optional[Tensor] = extra["attn"][0] + assert encoder_out is not None + assert attn is not None + x = self.output_layer(x, attn, encoder_out["src_tokens"][0], p_gens) + return x, extra + + def output_layer( + self, + features: Tensor, + attn: Tensor, + src_tokens: Tensor, + p_gens: Tensor + ) -> Tensor: + """ + Project features to the vocabulary size and mix with the attention + distributions. + """ + if self.force_p_gen is not None: + p_gens = self.force_p_gen + + # project back to size of vocabulary + if self.adaptive_softmax is None: + logits = self.output_projection(features) + else: + logits = features + + batch_size = logits.shape[0] + output_length = logits.shape[1] + assert logits.shape[2] == self.num_embeddings + assert src_tokens.shape[0] == batch_size + src_length = src_tokens.shape[1] + + # The final output distribution will be a mixture of the normal output + # distribution (softmax of logits) and attention weights. + gen_dists = self.get_normalized_probs_scriptable( + (logits, None), log_probs=False, sample=None + ) + gen_dists = torch.mul(gen_dists, p_gens) + padding_size = (batch_size, output_length, self.num_oov_types) + padding = gen_dists.new_zeros(padding_size) + gen_dists = torch.cat((gen_dists, padding), 2) + assert gen_dists.shape[2] == self.num_types + + # Scatter attention distributions to distributions over the extended + # vocabulary in a tensor of shape [batch_size, output_length, + # vocab_size]. Each attention weight will be written into a location + # that is for other dimensions the same as in the index tensor, but for + # the third dimension it's the value of the index tensor (the token ID). + attn = torch.mul(attn.float(), 1 - p_gens) + index = src_tokens[:, None, :] + index = index.expand(batch_size, output_length, src_length) + attn_dists_size = (batch_size, output_length, self.num_types) + attn_dists = attn.new_zeros(attn_dists_size) + attn_dists.scatter_add_(2, index, attn.float()) + + # Final distributions, [batch_size, output_length, num_types]. + return gen_dists + attn_dists + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """ + Get normalized probabilities (or log probs) from a net's output. + Pointer-generator network output is already normalized. + """ + probs = net_output[0] + # Make sure the probabilities are greater than zero when returning log + # probabilities. + return probs.clamp(1e-10, 1.0).log() if log_probs else probs + + +class Embedding(nn.Embedding): + r"""A simple lookup table that stores embeddings of a fixed dictionary and size. + This module is often used to store word embeddings and retrieve them using indices. + The input to the module is a list of indices, and the output is the corresponding + word embeddings. This subclass differs from the standard PyTorch Embedding class by + allowing additional vocabulary entries that will be mapped to the unknown token + embedding. + Args: + num_embeddings (int): size of the dictionary of embeddings + embedding_dim (int): the size of each embedding vector + padding_idx (int): Pads the output with the embedding vector at :attr:`padding_idx` + (initialized to zeros) whenever it encounters the index. + unk_idx (int): Maps all token indices that are greater than or equal to + num_embeddings to this index. + Attributes: + weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim) + initialized from :math:`\mathcal{N}(0, 1)` + Shape: + - Input: :math:`(*)`, LongTensor of arbitrary shape containing the indices to extract + - Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}` + .. note:: + Keep in mind that only a limited number of optimizers support + sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`), + :class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`) + .. note:: + With :attr:`padding_idx` set, the embedding vector at + :attr:`padding_idx` is initialized to all zeros. However, note that this + vector can be modified afterwards, e.g., using a customized + initialization method, and thus changing the vector used to pad the + output. The gradient for this vector from :class:`~torch.nn.Embedding` + is always zero. + """ + __constants__ = ["unk_idx"] + + # Torchscript: Inheriting from Embedding class produces an error when exporting to Torchscript + # -> RuntimeError: Unable to cast Python instance to C++ type (compile in debug mode for details + # It's happening because max_norm attribute from nn.Embedding is None by default and it cannot be + # cast to a C++ type + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int], + unk_idx: int, + max_norm: Optional[float] = float("inf"), + ): + super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx, max_norm=max_norm) + self.unk_idx = unk_idx + nn.init.normal_(self.weight, mean=0, std=embedding_dim ** -0.5) + nn.init.constant_(self.weight[padding_idx], 0) + + def forward(self, input): + input = torch.where( + input >= self.num_embeddings, torch.ones_like(input) * self.unk_idx, input + ) + return nn.functional.embedding( + input, self.weight, self.padding_idx, self.max_norm, + self.norm_type, self.scale_grad_by_freq, self.sparse + ) + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator" +) +def transformer_pointer_generator(args): + args.alignment_heads = getattr(args, "alignment_heads", 1) + args.alignment_layer = getattr(args, "alignment_layer", -1) + base_architecture(args) + if args.alignment_layer < 0: + args.alignment_layer = args.decoder_layers + args.alignment_layer + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_iwslt_de_en" +) +def transformer_pointer_generator_iwslt_de_en(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) + transformer_pointer_generator(args) + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de" +) +def transformer_pointer_generator_wmt_en_de(args): + transformer_pointer_generator(args) + + +# Transformer pointer-generator with the base Transformer parameters as used in +# the "Attention Is All You Need" paper (Vaswani et al., 2017) +@register_model_architecture( + "transformer_pointer_generator", + "transformer_pointer_generator_vaswani_wmt_en_de_big", +) +def transformer_pointer_generator_vaswani_wmt_en_de_big(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) + transformer_pointer_generator(args) + + +@register_model_architecture( + "transformer_pointer_generator", + "transformer_pointer_generator_vaswani_wmt_en_fr_big", +) +def transformer_pointer_generator_vaswani_wmt_en_fr_big(args): + args.dropout = getattr(args, "dropout", 0.1) + transformer_pointer_generator_vaswani_wmt_en_de_big(args) + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big" +) +def transformer_pointer_generator_wmt_en_de_big(args): + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + transformer_pointer_generator_vaswani_wmt_en_de_big(args) + + +# default parameters used in tensor2tensor implementation +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big_t2t" +) +def transformer_pointer_generator_wmt_en_de_big_t2t(args): + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_dropout = getattr(args, "activation_dropout", 0.1) + transformer_pointer_generator_vaswani_wmt_en_de_big(args) diff --git a/fairseq/examples/pointer_generator/postprocess.py b/fairseq/examples/pointer_generator/postprocess.py new file mode 100644 index 0000000..b213aed --- /dev/null +++ b/fairseq/examples/pointer_generator/postprocess.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import re +import sys + + +class OOVIndexError(IndexError): + def __init__(self, pos, source_seq, target_seq): + super(OOVIndexError, self).__init__( + "A <unk-N> tag in the target sequence refers to a position that is " + "outside the source sequence. Most likely there was a mismatch in " + "provided source and target sequences. Otherwise this would mean that " + "the pointing mechanism somehow attended to a position that is past " + "the actual sequence end." + ) + self.source_pos = pos + self.source_seq = source_seq + self.target_seq = target_seq + + +def replace_oovs(source_in, target_in, target_out): + """Replaces <unk-N> tokens in the target text with the corresponding word in + the source text. + """ + + oov_re = re.compile("^<unk-([0-9]+)>$") + + for source_seq, target_seq in zip(source_in, target_in): + target_seq_out = [] + + pos_to_word = source_seq.strip().split() + for token in target_seq.strip().split(): + m = oov_re.match(token) + if m: + pos = int(m.group(1)) + if pos >= len(pos_to_word): + raise OOVIndexError(pos, source_seq, target_seq) + token_out = pos_to_word[pos] + else: + token_out = token + target_seq_out.append(token_out) + target_out.write(" ".join(target_seq_out) + "\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Replaces <unk-N> tokens in target sequences with words from " + "the corresponding position in the source sequence." + ) + parser.add_argument( + "--source", type=str, help="text file with source sequences", required=True + ) + parser.add_argument( + "--target", type=str, help="text file with target sequences", required=True + ) + parser.add_argument( + "--target-out", + type=str, + help="where to write target sequences without <unk-N> " "entries", + required=True, + ) + args = parser.parse_args() + + target_in = ( + open(args.target, "r", encoding="utf-8") if args.target is not None else None + ) + target_out = ( + open(args.target_out, "w", encoding="utf-8") + if args.target_out is not None + else None + ) + with open(args.source, "r", encoding="utf-8") as source_in, open( + args.target, "r", encoding="utf-8" + ) as target_in, open(args.target_out, "w", encoding="utf-8") as target_out: + replace_oovs(source_in, target_in, target_out) + + +if __name__ == "__main__": + try: + main() + except OOVIndexError as e: + print(e, file=sys.stderr) + print("Source sequence:", e.source_seq.strip(), file=sys.stderr) + print("Target sequence:", e.target_seq.strip(), file=sys.stderr) + print( + "Source sequence length:", + len(e.source_seq.strip().split()), + file=sys.stderr, + ) + print("The offending tag points to:", e.source_pos) + sys.exit(2) diff --git a/fairseq/examples/pointer_generator/preprocess.py b/fairseq/examples/pointer_generator/preprocess.py new file mode 100644 index 0000000..f72ca7d --- /dev/null +++ b/fairseq/examples/pointer_generator/preprocess.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from itertools import zip_longest + + +def replace_oovs(source_in, target_in, vocabulary, source_out, target_out): + """Replaces out-of-vocabulary words in source and target text with <unk-N>, + where N in is the position of the word in the source sequence. + """ + + def format_unk(pos): + return "<unk-{}>".format(pos) + + if target_in is None: + target_in = [] + + for seq_num, (source_seq, target_seq) in enumerate( + zip_longest(source_in, target_in) + ): + source_seq_out = [] + target_seq_out = [] + + word_to_pos = dict() + for position, token in enumerate(source_seq.strip().split()): + if token in vocabulary: + token_out = token + else: + if token in word_to_pos: + oov_pos = word_to_pos[token] + else: + word_to_pos[token] = position + oov_pos = position + token_out = format_unk(oov_pos) + source_seq_out.append(token_out) + source_out.write(" ".join(source_seq_out) + "\n") + + if target_seq is not None: + for token in target_seq.strip().split(): + if token in word_to_pos: + token_out = format_unk(word_to_pos[token]) + else: + token_out = token + target_seq_out.append(token_out) + if target_out is not None: + target_out.write(" ".join(target_seq_out) + "\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Replaces out-of-vocabulary words in both source and target " + "sequences with tokens that indicate the position of the word " + "in the source sequence." + ) + parser.add_argument( + "--source", type=str, help="text file with source sequences", required=True + ) + parser.add_argument( + "--target", type=str, help="text file with target sequences", default=None + ) + parser.add_argument("--vocab", type=str, help="vocabulary file", required=True) + parser.add_argument( + "--source-out", + type=str, + help="where to write source sequences with <unk-N> entries", + required=True, + ) + parser.add_argument( + "--target-out", + type=str, + help="where to write target sequences with <unk-N> entries", + default=None, + ) + args = parser.parse_args() + + with open(args.vocab, encoding="utf-8") as vocab: + vocabulary = vocab.read().splitlines() + + target_in = ( + open(args.target, "r", encoding="utf-8") if args.target is not None else None + ) + target_out = ( + open(args.target_out, "w", encoding="utf-8") + if args.target_out is not None + else None + ) + with open(args.source, "r", encoding="utf-8") as source_in, open( + args.source_out, "w", encoding="utf-8" + ) as source_out: + replace_oovs(source_in, target_in, vocabulary, source_out, target_out) + if target_in is not None: + target_in.close() + if target_out is not None: + target_out.close() + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/quant_noise/README.md b/fairseq/examples/quant_noise/README.md new file mode 100644 index 0000000..a04d7e4 --- /dev/null +++ b/fairseq/examples/quant_noise/README.md @@ -0,0 +1,298 @@ +# Training with Quantization Noise for Extreme Model Compression ({Fan\*, Stock\*} *et al.*, 2020) +This page contains information for how to train and quantize models with Quantization Noise, for both scalar quantization like `int8` and Iterative Product Quantization. +Check out our paper [here](https://arxiv.org/abs/2004.07320). + +Looking for pretrained models? They will be added shortly. +Looking for code to train vision models? We are working on open sourcing our code as part of ClassyVision. Please check back, but note that both the Scalar and Iterative Product Quantization counterparts of the `nn.Conv2d` module are already included in this release. + +**Contents**: +- [Walk through of code](#walk-through-the-code) +- [Reproduce NLP Results](#looking-to-reproduce-the-nlp-results-in-the-paper) +- [Reproduce Vision Results](#looking-to-reproduce-the-vision-results-in-the-paper) + + +## Citation +```bibtex +@article{fan2020training, + title={Training with Quantization Noise for Extreme Model Compression}, + author={Angela Fan* and Pierre Stock* and and Benjamin Graham and Edouard Grave and Remi Gribonval and Herve Jegou and Armand Joulin}, + year={2020}, + eprint={2004.07320}, + archivePrefix={arXiv}, + primaryClass={cs.ML} +} +``` + +## Walk through the code + +Training a model with Quant-Noise improves the performance in subsequent inference-time quantization by training models to be robust to quantization. This technique is useful for both scalar and product quantization methods, as well as multiple domains. We detail below our approach to train, quantize models and integrate our code to quantize your favorite models. + +### Scalar Quantization + +Unlike the section [Iterative Product Quantization](#iterative-product-quantization) which gives state-of-the-art compression, this section showcases the usefulness of our approach for simple scalar quantization baselines such as int8 using on-GPU Fake Quantization. + +#### Training + +Scalar quantization with Quant-Noise consists in randomly quantizing a proportion `p` of the weights during training. Scalar quantization is implemented [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/scalar) under the form of Fake Quantization, meaning that we emulate int8 on GPU by quantizing and de-quantizing both the weights and the activations. We rely on PyTorch's [quantization primitives](https://github.com/pytorch/pytorch/tree/master/torch/quantization). + +To train a model with Quant-Noise, add the following flag: +``` +--quant-noise-scalar 0.5 +``` +Large values of noise make the network easier to quantize but may result in higher non-quantized test and validation perplexities. + +#### Quantization + +When evaluating a network, all quantized modules and activation hooks automatically switch to `p=1` so the validation accuracy reported by Fairseq is actually the quantized one, nothing more to do. + + +#### Integration with your own code + +Looking to quantize your own models with Quant-Noise + Scalar Quantization? +- Use the function `quantize_model_` implemented [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/scalar/utils.py) to (1) replace all your modules by their quantized counterparts and (2) add hooks to those modules to quantize the activations. +- Then, perform your training as usual. Note that in `eval()` mode, the network is always fully quantized (weights and activations) by default (`p=1`). + + + +### Iterative Product Quantization + + +Iterative Product Quantization with Quant-Noise proceeds in two steps. First, a model must be trained uncompressed with Quant-Noise. Second, the model must be quantized with iPQ. Note that we implement here the simplest form of noise, which consists in randomly dropping a proportion `p` of blocks, and that worked as well as assigning those blocks to their current centroid. + +#### Training + +To train a model with Quant-Noise, add the following flags: +``` +--quant-noise-pq 0.1 --quant-noise-pq-block-size 8 +``` +`quant-noise-pq` controls how much dropout is applied to the blocks of the weight matrix. `quant-noise-pq-block-size` controls the size of the weight matrix blocks. +We recommend training with 0.05 to 0.2 Quant-Noise, a value that worked well in our experiments. For the block-size, we recommend training with block-size of 8. Note that the block size must be a multiple of `input_features`, see the size checks [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py). Large block sizes result in higher compression ratio but may induce a loss in accuracy. + +We currently support training Transformer based models, such as sequence-to-sequence, language models, and BERT architectures. The `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py) wraps a module. It splits a weight matrix into blocks and applies random dropout to these blocks. +In the Transformer architectures, quant-noise is applied to the input and output embeddings, the attention, and the FFN. + +Quant-Noise can also be combined with **LayerDrop** (see [here](https://github.com/pytorch/fairseq/tree/main/examples/layerdrop)) to add its pruning effect to the quantized model and make the model even smaller. We recommend training with LayerDrop 0.1 or 0.2. + +#### Quantization + +We implement an improved version of product quantization from Stock et al, **iPQ**, described [here](https://arxiv.org/abs/1907.05686), see code with old API [here](https://github.com/facebookresearch/kill-the-bits). Note that we improved the iPQ API in terms of both compute speed and usability as described below. + +For the particular case of PQ, quantization is made sequentially. We recommend first quantizing the FFNs, then the EMBs, and finally the ATTNs. Quantization is done in two sub-steps: +- First, perform `n` steps of Product Quantization (generally `n=20` is enough). +- Then, finetune the obtained centroids. + +#### Integration with your own code + +Looking to quantize your own models with Quant-Noise + iPQ? +- First wrap your modules with the `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py), which is module-agnostic and train your favorite model. +- Then, quantize your trained model using the code [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/pq). This can be done *without any changes to your training loop*. Below is an example code for integration. +Note that we tried our approach only on Transformers and various Convolutional Models such as EfficientNets. + +```python +from fairseq.modules.quantization.pq import quantize_model_, SizeTracker + +# get configuration parameters +n_centroids_config = config["n_centroids"] +block_sizes_config = config["block_sizes"] +layers_to_quantize = config["layers_to_quantize"] + +# size tracker for keeping track of assignments, centroids and non-compressed sizes +size_tracker = SizeTracker(model) + +# Quantize model by stages +for step in range(len(layers_to_quantize)): + + # quantize model in-place + quantized_layers = quantize_model_( + model, + size_tracker, + layers_to_quantize, + block_sizes_config, + n_centroids_config, + step=step, + ) + logger.info(f"Finetuning stage {step}, quantized layers: {quantized_layers}") + logger.info(f"{size_tracker}") + + # Don't forget to re-create/update trainer/optimizer since model parameters have changed + optimizer = ... + + # Finetune the centroids with your usual training loop for a few epochs + trainer.train_epoch() +``` + + +## Looking to reproduce the NLP results in the paper? + +We detail below how to reproduce the state-of-the-art results in reported in the paper for Quant-Noise + Iterative Product Quantization. + +### Training with Quant-Noise + +To **train** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta). +The following command can be used to train a RoBERTa Base + QuantNoise model: + +```bash +TOTAL_UPDATES=125000 +WARMUP_UPDATES=10000 +PEAK_LR=0.0005 +TOKENS_PER_SAMPLE=512 +MAX_POSITIONS=512 +MAX_SENTENCES=16 +UPDATE_FREQ=2 +DATA_DIR=/path/to/data/here + +fairseq-train $DATA_DIR \ + --task masked_lm --criterion masked_lm --arch roberta_base \ + --sample-break-mode complete \ + --tokens-per-sample $TOKENS_PER_SAMPLE --max-positions $MAX_POSITIONS \ + --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-6 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $PEAK_LR \ + --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_UPDATES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.01 \ + --batch-size $MAX_SENTENCES \ + --update-freq $UPDATE_FREQ --max-update $TOTAL_UPDATES \ + --save-dir checkpoint/roberta \ + --ddp-backend legacy_ddp --encoder-layerdrop 0.2 \ + --quant-noise-pq 0.2 --quant-noise-pq-block-size 8 --untie-weights-roberta +``` + +To **finetune** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.glue.md). +The following command can be used to finetune a RoBERTa Base + QuantNoise model on the RTE dataset: + +```bash +TOTAL_NUM_UPDATES=2036 +WARMUP_UPDATES=122 +LR=2e-05 +NUM_CLASSES=2 +MAX_SENTENCES=16 +ROBERTA_PATH=/path/to/roberta_quantnoise/model.pt + +fairseq-train /path/to/rte/data/ \ + --restore-file $ROBERTA_PATH \ + --max-positions 512 \ + --batch-size $MAX_SENTENCES \ + --max-tokens 4400 \ + --task sentence_prediction \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --init-token 0 --separator-token 2 \ + --arch roberta_large \ + --criterion sentence_prediction \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --max-epoch 10 \ + --find-unused-parameters \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --ddp-backend legacy_ddp \ + --quant-noise-pq 0.2 --quant-noise-pq-block-size 8 +``` + +To **train** Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model). +The following command can be used to train a Transformer + QuantNoise model on Wikitext-103: + +```bash +fairseq-train --task language_modeling /path/to/wikitext-103/data \ + --save-dir checkpoints/transformer_wikitext-103 \ + --adaptive-input --adaptive-input-cutoff 20000,60000 --adaptive-input-factor 4 \ + --adaptive-softmax-cutoff 20000,60000 --adaptive-softmax-dropout 0.2 --adaptive-softmax-factor 4.0 \ + --tie-adaptive-proj --tie-adaptive-weights \ + --arch transformer_lm_gbw \ + --attention-dropout 0.1 --dropout 0.2 --relu-dropout 0.1 \ + --clip-norm 0.1 --criterion adaptive_loss \ + --ddp-backend legacy_ddp \ + --decoder-attention-heads 8 --decoder-embed-dim 1024 --decoder-ffn-embed-dim 4096 --decoder-input-dim 1024 \ + --decoder-layers 16 --decoder-normalize-before --decoder-output-dim 1024 \ + --min-lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --lr 1.0 --t-mult 2.0 \ + --max-tokens 3072 --tokens-per-sample 3072 --momentum 0.99 --optimizer nag \ + --sample-break-mode none --update-freq 3 \ + --warmup-init-lr 1e-07 --warmup-updates 16000 \ + --weight-decay 0 --seed 1 --stop-min-lr 1e-09 \ + --quant-noise-pq 0.05 --quant-noise-pq-block-size 8 +``` + +To **evaluate** this model, note you need to use the `eval.py` script. The following command can be used to evaluate: + +```bash +fairseq-eval-lm /path/to/wikitext-103/data --path /path/to/model/checkpoint \ + --sample-break-mode complete \ + --max-tokens 3072 \ + --context-window 2560 \ + --softmax-batch 1024 \ + --gen-subset valid +``` +and change the `--gen-subset` to `test` if you would like to evaluate on the test set instead. + + +### Iterative Product Quantization + +To quantize the finetuned RoBERTa model, we use this command on 1 GPU. This should run in a day. +```bash +TOTAL_NUM_UPDATES=6108 # 2036 updates for each iteration +WARMUP_UPDATES=122 +LR=2e-05 +NUM_CLASSES=2 +MAX_SENTENCES=16 +fairseq-train --task sentence_prediction /path/to/data/ \ + --restore-file $ROBERTA_PATH \ + --save-dir checkpoints/roberta_finetuned \ + --max-positions 512 \ + --batch-size $MAX_SENTENCES \ + --max-tokens 4400 \ + --init-token 0 --separator-token 2 \ + --arch roberta_large \ + --criterion sentence_prediction \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --clip-norm 0.0 --lr-scheduler polynomial_decay \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --no-progress-bar --skip-invalid-size-inputs-valid-test --ddp-backend legacy_ddp \ + --quantization-config-path /path/to/config/yaml +``` + +To quantize the trained Language Model, we use this command on 8 V100 23GB GPUs. This should run in a couple of hours. +```bash +fairseq-train --task language_modeling /path/to/wikitext-103/data \ + --save-dir checkpoints/transformer_wikitext-103 \ + --adaptive-input --adaptive-input-cutoff 20000,60000 --adaptive-input-factor 4 \ + --adaptive-softmax-cutoff 20000,60000 --adaptive-softmax-dropout 0.2 --adaptive-softmax-factor 4.0 \ + --arch transformer_lm_gbw \ + --attention-dropout 0.1 --dropout 0.2 --relu-dropout 0.1 \ + --bucket-cap-mb 25 --char-embedder-highway-layers 2 --character-embedding-dim 4 \ + --clip-norm 0.1 --criterion adaptive_loss \ + --ddp-backend legacy_ddp \ + --decoder-attention-heads 8 --decoder-embed-dim 1024 --decoder-ffn-embed-dim 4096 --decoder-input-dim 1024 --decoder-layers 16 --decoder-normalize-before --decoder-output-dim 1024 \ + --fp16 --keep-last-epochs -1 \ + --min-lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --lr 0.05 --stop-min-lr 1e-09 \ + --max-tokens 2944 --tokens-per-sample 2944\ + --momentum 0.99 --no-epoch-checkpoints --no-progress-bar --optimizer nag --required-batch-size-multiple 8 \ + --sample-break-mode none --t-mult 2.0 --skip-invalid-size-inputs-valid-test \ + --tie-adaptive-proj --tie-adaptive-weights --update-freq 3 --weight-decay 0 --seed 1 \ + --log-interval 100 --no-progress-bar --skip-invalid-size-inputs-valid-test \ + --restore-file path/to/trained/lm/with/quant/noise \ + --max-update 13500 --quantization-config-path /path/to/config/yaml +``` +If you have less capacity or if your distributed training freezes, try reducing `--max-tokens` and `--tokens-per-sample` (this may reduce the quantized accuracy a bit). + +### Remarks + +We try to keep the open-sourced code as readable and as easy-to-plug as possible. Therefore, we did not test it for the following cases: +- Scalar quantization with RoBERTa. +- Quantization with iPQ and `int8` combined. + +If you have trouble adapting it, we will be more than happy to help! + +## Looking to reproduce the Vision results in the paper? + +We are working on open sourcing our code as part of ClassyVision. Please check back. + + +## Having an issue or have a question? + +Please open an issue in this repository with the details of your question. Thanks! diff --git a/fairseq/examples/quant_noise/transformer_quantization_config.yaml b/fairseq/examples/quant_noise/transformer_quantization_config.yaml new file mode 100644 index 0000000..d4be14a --- /dev/null +++ b/fairseq/examples/quant_noise/transformer_quantization_config.yaml @@ -0,0 +1,33 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This file defines example configuration arguments for quantizing +# a transformer model with product quantization + +# Number of Centroids for Product Quantization, by default 256 (byte-aligned) +n_centroids: + Linear: + key: in_features + value: {"*": 256} + Embedding: + key: embedding_dim + value: {"*": 256} + +# Block Sizes for Product Quantization +# We suggest: 8 for FFN, 4 for ATTN, 4 for embedding projections, 8 for embeddings +block_sizes: + Linear: + key: fuzzy_name + value: {fc: 8, attn: 4, emb: 4} + Embedding: + key: fuzzy_name + value: {emb: 8} + +# Layers to Quantize Sequentially +# We suggest: first FFN, then EMB, then ATTN +layers_to_quantize: + - decoder\\.layers\\.\d+\\.fc[12] + - decoder\\.embed_tokens\\.embeddings\\.[012]\\.[01] + - decoder\\.layers\\.\d+\\.self_attn\\.(k_proj|v_proj|q_proj|out_proj) diff --git a/fairseq/examples/roberta/README.custom_classification.md b/fairseq/examples/roberta/README.custom_classification.md new file mode 100644 index 0000000..7254bb7 --- /dev/null +++ b/fairseq/examples/roberta/README.custom_classification.md @@ -0,0 +1,168 @@ +# Finetuning RoBERTa on a custom classification task + +This example shows how to finetune RoBERTa on the IMDB dataset, but should illustrate the process for most classification tasks. + +### 1) Get the data + +```bash +wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz +tar zxvf aclImdb_v1.tar.gz +``` + + +### 2) Format data + +`IMDB` data has one data-sample in each file, below python code-snippet converts it one file for train and valid each for ease of processing. +```python +import argparse +import os +import random +from glob import glob + +random.seed(0) + +def main(args): + for split in ['train', 'test']: + samples = [] + for class_label in ['pos', 'neg']: + fnames = glob(os.path.join(args.datadir, split, class_label) + '/*.txt') + for fname in fnames: + with open(fname) as fin: + line = fin.readline() + samples.append((line, 1 if class_label == 'pos' else 0)) + random.shuffle(samples) + out_fname = 'train' if split == 'train' else 'dev' + f1 = open(os.path.join(args.datadir, out_fname + '.input0'), 'w') + f2 = open(os.path.join(args.datadir, out_fname + '.label'), 'w') + for sample in samples: + f1.write(sample[0] + '\n') + f2.write(str(sample[1]) + '\n') + f1.close() + f2.close() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--datadir', default='aclImdb') + args = parser.parse_args() + main(args) +``` + + +### 3) BPE encode + +Run `multiprocessing_bpe_encoder`, you can also do this in previous step for each sample but that might be slower. +```bash +# Download encoder.json and vocab.bpe +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' + +for SPLIT in train dev; do + python -m examples.roberta.multiprocessing_bpe_encoder \ + --encoder-json encoder.json \ + --vocab-bpe vocab.bpe \ + --inputs "aclImdb/$SPLIT.input0" \ + --outputs "aclImdb/$SPLIT.input0.bpe" \ + --workers 60 \ + --keep-empty +done +``` + + +### 4) Preprocess data + +```bash +# Download fairseq dictionary. +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' + +fairseq-preprocess \ + --only-source \ + --trainpref "aclImdb/train.input0.bpe" \ + --validpref "aclImdb/dev.input0.bpe" \ + --destdir "IMDB-bin/input0" \ + --workers 60 \ + --srcdict dict.txt + +fairseq-preprocess \ + --only-source \ + --trainpref "aclImdb/train.label" \ + --validpref "aclImdb/dev.label" \ + --destdir "IMDB-bin/label" \ + --workers 60 + +``` + + +### 5) Run training + +```bash +TOTAL_NUM_UPDATES=7812 # 10 epochs through IMDB for bsz 32 +WARMUP_UPDATES=469 # 6 percent of the number of updates +LR=1e-05 # Peak LR for polynomial LR scheduler. +HEAD_NAME=imdb_head # Custom name for the classification head. +NUM_CLASSES=2 # Number of classes for the classification task. +MAX_SENTENCES=8 # Batch size. +ROBERTA_PATH=/path/to/roberta.large/model.pt + +CUDA_VISIBLE_DEVICES=0 fairseq-train IMDB-bin/ \ + --restore-file $ROBERTA_PATH \ + --max-positions 512 \ + --batch-size $MAX_SENTENCES \ + --max-tokens 4400 \ + --task sentence_prediction \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --init-token 0 --separator-token 2 \ + --arch roberta_large \ + --criterion sentence_prediction \ + --classification-head-name $HEAD_NAME \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --max-epoch 10 \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --shorten-method "truncate" \ + --find-unused-parameters \ + --update-freq 4 +``` + +The above command will finetune RoBERTa-large with an effective batch-size of 32 +sentences (`--batch-size=8 --update-freq=4`). The expected +`best-validation-accuracy` after 10 epochs is ~96.5%. + +If you run out of GPU memory, try decreasing `--batch-size` and increase +`--update-freq` to compensate. + + +### 6) Load model using hub interface + +Now we can load the trained model checkpoint using the RoBERTa hub interface. + +Assuming your checkpoints are stored in `checkpoints/`: +```python +from fairseq.models.roberta import RobertaModel +roberta = RobertaModel.from_pretrained( + 'checkpoints', + checkpoint_file='checkpoint_best.pt', + data_name_or_path='IMDB-bin' +) +roberta.eval() # disable dropout +``` + +Finally you can make predictions using the `imdb_head` (or whatever you set +`--classification-head-name` to during training): +```python +label_fn = lambda label: roberta.task.label_dictionary.string( + [label + roberta.task.label_dictionary.nspecial] +) + +tokens = roberta.encode('Best movie this year') +pred = label_fn(roberta.predict('imdb_head', tokens).argmax().item()) +assert pred == '1' # positive + +tokens = roberta.encode('Worst movie ever') +pred = label_fn(roberta.predict('imdb_head', tokens).argmax().item()) +assert pred == '0' # negative +``` diff --git a/fairseq/examples/roberta/README.glue.md b/fairseq/examples/roberta/README.glue.md new file mode 100644 index 0000000..4f596d5 --- /dev/null +++ b/fairseq/examples/roberta/README.glue.md @@ -0,0 +1,64 @@ +# Finetuning RoBERTa on GLUE tasks + +### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands: +```bash +wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py +python download_glue_data.py --data_dir glue_data --tasks all +``` + +### 2) Preprocess GLUE task data: +```bash +./examples/roberta/preprocess_GLUE_tasks.sh glue_data <glue_task_name> +``` +`glue_task_name` is one of the following: +`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}` +Use `ALL` for preprocessing all the glue tasks. + +### 3) Fine-tuning on GLUE task: +Example fine-tuning cmd for `RTE` task +```bash +ROBERTA_PATH=/path/to/roberta/model.pt + +CUDA_VISIBLE_DEVICES=0 fairseq-hydra-train -config-dir examples/roberta/config/finetuning --config-name rte \ +task.data=RTE-bin checkpoint.restore_file=$ROBERTA_PATH +``` + +There are additional config files for each of the GLUE tasks in the examples/roberta/config/finetuning directory. + +**Note:** + +a) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. + +b) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. + +### Inference on GLUE task +After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet: + +```python +from fairseq.models.roberta import RobertaModel + +roberta = RobertaModel.from_pretrained( + 'checkpoints/', + checkpoint_file='checkpoint_best.pt', + data_name_or_path='RTE-bin' +) + +label_fn = lambda label: roberta.task.label_dictionary.string( + [label + roberta.task.label_dictionary.nspecial] +) +ncorrect, nsamples = 0, 0 +roberta.cuda() +roberta.eval() +with open('glue_data/RTE/dev.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[1], tokens[2], tokens[3] + tokens = roberta.encode(sent1, sent2) + prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() + prediction_label = label_fn(prediction) + ncorrect += int(prediction_label == target) + nsamples += 1 +print('| Accuracy: ', float(ncorrect)/float(nsamples)) + +``` diff --git a/fairseq/examples/roberta/README.md b/fairseq/examples/roberta/README.md new file mode 100644 index 0000000..ed4d5df --- /dev/null +++ b/fairseq/examples/roberta/README.md @@ -0,0 +1,296 @@ +# RoBERTa: A Robustly Optimized BERT Pretraining Approach + +https://arxiv.org/abs/1907.11692 + +## Introduction + +RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. See the associated paper for more details. + +### What's New: + +- December 2020: German model (GottBERT) is available: [GottBERT](https://github.com/pytorch/fairseq/tree/main/examples/gottbert). +- January 2020: Italian model (UmBERTo) is available from Musixmatch Research: [UmBERTo](https://github.com/musixmatchresearch/umberto). +- November 2019: French model (CamemBERT) is available: [CamemBERT](https://github.com/pytorch/fairseq/tree/main/examples/camembert). +- November 2019: Multilingual encoder (XLM-RoBERTa) is available: [XLM-R](https://github.com/pytorch/fairseq/tree/main/examples/xlmr). +- September 2019: TensorFlow and TPU support via the [transformers library](https://github.com/huggingface/transformers). +- August 2019: RoBERTa is now supported in the [pytorch-transformers library](https://github.com/huggingface/pytorch-transformers). +- August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/main/examples/roberta/wsc#roberta-training-on-winogrande-dataset). +- August 2019: Added [tutorial for pretraining RoBERTa using your own data](README.pretraining.md). + +## Pre-trained models + +Model | Description | # params | Download +---|---|---|--- +`roberta.base` | RoBERTa using the BERT-base architecture | 125M | [roberta.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz) +`roberta.large` | RoBERTa using the BERT-large architecture | 355M | [roberta.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz) +`roberta.large.mnli` | `roberta.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | 355M | [roberta.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz) +`roberta.large.wsc` | `roberta.large` finetuned on [WSC](wsc/README.md) | 355M | [roberta.large.wsc.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz) + +## Results + +**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)** +_(dev set, single model, single-task finetuning)_ + +Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B +---|---|---|---|---|---|---|---|--- +`roberta.base` | 87.6 | 92.8 | 91.9 | 78.7 | 94.8 | 90.2 | 63.6 | 91.2 +`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4 +`roberta.large.mnli` | 90.2 | - | - | - | - | - | - | - + +**[SuperGLUE (Wang et al., 2019)](https://super.gluebenchmark.com/)** +_(dev set, single model, single-task finetuning)_ + +Model | BoolQ | CB | COPA | MultiRC | RTE | WiC | WSC +---|---|---|---|---|---|---|--- +`roberta.large` | 86.9 | 98.2 | 94.0 | 85.7 | 89.5 | 75.6 | - +`roberta.large.wsc` | - | - | - | - | - | - | 91.3 + +**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)** +_(dev set, no additional data used)_ + +Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1 +---|---|--- +`roberta.large` | 88.9/94.6 | 86.5/89.4 + +**[RACE (Lai et al., 2017)](http://www.qizhexie.com/data/RACE_leaderboard.html)** +_(test set)_ + +Model | Accuracy | Middle | High +---|---|---|--- +`roberta.large` | 83.2 | 86.5 | 81.3 + +**[HellaSwag (Zellers et al., 2019)](https://rowanzellers.com/hellaswag/)** +_(test set)_ + +Model | Overall | In-domain | Zero-shot | ActivityNet | WikiHow +---|---|---|---|---|--- +`roberta.large` | 85.2 | 87.3 | 83.1 | 74.6 | 90.9 + +**[Commonsense QA (Talmor et al., 2019)](https://www.tau-nlp.org/commonsenseqa)** +_(test set)_ + +Model | Accuracy +---|--- +`roberta.large` (single model) | 72.1 +`roberta.large` (ensemble) | 72.5 + +**[Winogrande (Sakaguchi et al., 2019)](https://arxiv.org/abs/1907.10641)** +_(test set)_ + +Model | Accuracy +---|--- +`roberta.large` | 78.1 + +**[XNLI (Conneau et al., 2018)](https://arxiv.org/abs/1809.05053)** +_(TRANSLATE-TEST)_ + +Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur +---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|--- +`roberta.large.mnli` | 91.3 | 82.91 | 84.27 | 81.24 | 81.74 | 83.13 | 78.28 | 76.79 | 76.64 | 74.17 | 74.05 | 77.5 | 70.9 | 66.65 | 66.81 + +## Example usage + +##### Load RoBERTa from torch.hub (PyTorch >= 1.1): +```python +import torch +roberta = torch.hub.load('pytorch/fairseq', 'roberta.large') +roberta.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Load RoBERTa (for PyTorch 1.0 or custom models): +```python +# Download roberta.large model +wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz +tar -xzvf roberta.large.tar.gz + +# Load the model in fairseq +from fairseq.models.roberta import RobertaModel +roberta = RobertaModel.from_pretrained('/path/to/roberta.large', checkpoint_file='model.pt') +roberta.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Apply Byte-Pair Encoding (BPE) to input text: +```python +tokens = roberta.encode('Hello world!') +assert tokens.tolist() == [0, 31414, 232, 328, 2] +roberta.decode(tokens) # 'Hello world!' +``` + +##### Extract features from RoBERTa: +```python +# Extract the last layer's features +last_layer_features = roberta.extract_features(tokens) +assert last_layer_features.size() == torch.Size([1, 5, 1024]) + +# Extract all layer's features (layer 0 is the embedding layer) +all_layers = roberta.extract_features(tokens, return_all_hiddens=True) +assert len(all_layers) == 25 +assert torch.all(all_layers[-1] == last_layer_features) +``` + +##### Use RoBERTa for sentence-pair classification tasks: +```python +# Download RoBERTa already finetuned for MNLI +roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli') +roberta.eval() # disable dropout for evaluation + +# Encode a pair of sentences and make a prediction +tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.') +roberta.predict('mnli', tokens).argmax() # 0: contradiction + +# Encode another pair of sentences +tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.') +roberta.predict('mnli', tokens).argmax() # 2: entailment +``` + +##### Register a new (randomly initialized) classification head: +```python +roberta.register_classification_head('new_task', num_classes=3) +logprobs = roberta.predict('new_task', tokens) # tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>) +``` + +##### Batched prediction: +```python +import torch +from fairseq.data.data_utils import collate_tokens + +roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli') +roberta.eval() + +batch_of_pairs = [ + ['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'], + ['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'], + ['potatoes are awesome.', 'I like to run.'], + ['Mars is very far from earth.', 'Mars is very close.'], +] + +batch = collate_tokens( + [roberta.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1 +) + +logprobs = roberta.predict('mnli', batch) +print(logprobs.argmax(dim=1)) +# tensor([0, 2, 1, 0]) +``` + +##### Using the GPU: +```python +roberta.cuda() +roberta.predict('new_task', tokens) # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>) +``` + +## Advanced usage + +#### Filling masks: + +RoBERTa can be used to fill `<mask>` tokens in the input. Some examples from the +[Natural Questions dataset](https://ai.google.com/research/NaturalQuestions/): +```python +roberta.fill_mask('The first Star wars movie came out in <mask>', topk=3) +# [('The first Star wars movie came out in 1977', 0.9504708051681519, ' 1977'), ('The first Star wars movie came out in 1978', 0.009986862540245056, ' 1978'), ('The first Star wars movie came out in 1979', 0.009574787691235542, ' 1979')] + +roberta.fill_mask('Vikram samvat calender is official in <mask>', topk=3) +# [('Vikram samvat calender is official in India', 0.21878819167613983, ' India'), ('Vikram samvat calender is official in Delhi', 0.08547237515449524, ' Delhi'), ('Vikram samvat calender is official in Gujarat', 0.07556215673685074, ' Gujarat')] + +roberta.fill_mask('<mask> is the common currency of the European Union', topk=3) +# [('Euro is the common currency of the European Union', 0.9456493854522705, 'Euro'), ('euro is the common currency of the European Union', 0.025748178362846375, 'euro'), ('€ is the common currency of the European Union', 0.011183084920048714, '€')] +``` + +#### Pronoun disambiguation (Winograd Schema Challenge): + +RoBERTa can be used to disambiguate pronouns. First install spaCy and download the English-language model: +```bash +pip install spacy +python -m spacy download en_core_web_lg +``` + +Next load the `roberta.large.wsc` model and call the `disambiguate_pronoun` +function. The pronoun should be surrounded by square brackets (`[]`) and the +query referent surrounded by underscores (`_`), or left blank to return the +predicted candidate text directly: +```python +roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.wsc', user_dir='examples/roberta/wsc') +roberta.cuda() # use the GPU (optional) + +roberta.disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.') +# True +roberta.disambiguate_pronoun('The trophy would not fit in the brown _suitcase_ because [it] was too big.') +# False + +roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] feared violence.') +# 'The city councilmen' +roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] advocated violence.') +# 'demonstrators' +``` + +See the [RoBERTA Winograd Schema Challenge (WSC) README](wsc/README.md) for more details on how to train this model. + +#### Extract features aligned to words: + +By default RoBERTa outputs one feature vector per BPE token. You can instead +realign the features to match [spaCy's word-level tokenization](https://spacy.io/usage/linguistic-features#tokenization) +with the `extract_features_aligned_to_words` method. This will compute a +weighted average of the BPE-level features for each word and expose them in +spaCy's `Token.vector` attribute: +```python +doc = roberta.extract_features_aligned_to_words('I said, "hello RoBERTa."') +assert len(doc) == 10 +for tok in doc: + print('{:10}{} (...)'.format(str(tok), tok.vector[:5])) +# <s> tensor([-0.1316, -0.0386, -0.0832, -0.0477, 0.1943], grad_fn=<SliceBackward>) (...) +# I tensor([ 0.0559, 0.1541, -0.4832, 0.0880, 0.0120], grad_fn=<SliceBackward>) (...) +# said tensor([-0.1565, -0.0069, -0.8915, 0.0501, -0.0647], grad_fn=<SliceBackward>) (...) +# , tensor([-0.1318, -0.0387, -0.0834, -0.0477, 0.1944], grad_fn=<SliceBackward>) (...) +# " tensor([-0.0486, 0.1818, -0.3946, -0.0553, 0.0981], grad_fn=<SliceBackward>) (...) +# hello tensor([ 0.0079, 0.1799, -0.6204, -0.0777, -0.0923], grad_fn=<SliceBackward>) (...) +# RoBERTa tensor([-0.2339, -0.1184, -0.7343, -0.0492, 0.5829], grad_fn=<SliceBackward>) (...) +# . tensor([-0.1341, -0.1203, -0.1012, -0.0621, 0.1892], grad_fn=<SliceBackward>) (...) +# " tensor([-0.1341, -0.1203, -0.1012, -0.0621, 0.1892], grad_fn=<SliceBackward>) (...) +# </s> tensor([-0.0930, -0.0392, -0.0821, 0.0158, 0.0649], grad_fn=<SliceBackward>) (...) +``` + +#### Evaluating the `roberta.large.mnli` model: + +Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set. +```python +label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'} +ncorrect, nsamples = 0, 0 +roberta.cuda() +roberta.eval() +with open('glue_data/MNLI/dev_matched.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[8], tokens[9], tokens[-1] + tokens = roberta.encode(sent1, sent2) + prediction = roberta.predict('mnli', tokens).argmax().item() + prediction_label = label_map[prediction] + ncorrect += int(prediction_label == target) + nsamples += 1 +print('| Accuracy: ', float(ncorrect)/float(nsamples)) +# Expected output: 0.9060 +``` + +## Finetuning + +- [Finetuning on GLUE](README.glue.md) +- [Finetuning on custom classification tasks (e.g., IMDB)](README.custom_classification.md) +- [Finetuning on Winograd Schema Challenge (WSC)](wsc/README.md) +- [Finetuning on Commonsense QA (CQA)](commonsense_qa/README.md) + +## Pretraining using your own data + +See the [tutorial for pretraining RoBERTa using your own data](README.pretraining.md). + +## Citation + +```bibtex +@article{liu2019roberta, + title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach}, + author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and + Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and + Luke Zettlemoyer and Veselin Stoyanov}, + journal={arXiv preprint arXiv:1907.11692}, + year = {2019}, +} +``` diff --git a/fairseq/examples/roberta/README.pretraining.md b/fairseq/examples/roberta/README.pretraining.md new file mode 100644 index 0000000..a4e7453 --- /dev/null +++ b/fairseq/examples/roberta/README.pretraining.md @@ -0,0 +1,84 @@ +# Pretraining RoBERTa using your own data + +This tutorial will walk you through pretraining RoBERTa over your own data. + +### 1) Preprocess the data + +Data should be preprocessed following the [language modeling format](/examples/language_model), i.e. each document should be separated by an empty line (only useful with `--sample-break-mode complete_doc`). Lines will be concatenated as a 1D text stream during training. + +We'll use the [WikiText-103 dataset](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/) +to demonstrate how to preprocess raw text data with the GPT-2 BPE. Of course +this dataset is quite small, so the resulting pretrained model will perform +poorly, but it gives the general idea. + +First download the dataset: +```bash +wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip +unzip wikitext-103-raw-v1.zip +``` + +Next encode it with the GPT-2 BPE: +```bash +mkdir -p gpt2_bpe +wget -O gpt2_bpe/encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json +wget -O gpt2_bpe/vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe +for SPLIT in train valid test; do \ + python -m examples.roberta.multiprocessing_bpe_encoder \ + --encoder-json gpt2_bpe/encoder.json \ + --vocab-bpe gpt2_bpe/vocab.bpe \ + --inputs wikitext-103-raw/wiki.${SPLIT}.raw \ + --outputs wikitext-103-raw/wiki.${SPLIT}.bpe \ + --keep-empty \ + --workers 60; \ +done +``` + +Finally preprocess/binarize the data using the GPT-2 fairseq dictionary: +```bash +wget -O gpt2_bpe/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt +fairseq-preprocess \ + --only-source \ + --srcdict gpt2_bpe/dict.txt \ + --trainpref wikitext-103-raw/wiki.train.bpe \ + --validpref wikitext-103-raw/wiki.valid.bpe \ + --testpref wikitext-103-raw/wiki.test.bpe \ + --destdir data-bin/wikitext-103 \ + --workers 60 +``` + +### 2) Train RoBERTa base +```bash +DATA_DIR=data-bin/wikitext-103 + +fairseq-hydra-train -m --config-dir examples/roberta/config/pretraining \ +--config-name base task.data=$DATA_DIR +``` + +**Note:** You can optionally resume training the released RoBERTa base model by +adding `checkpoint.restore_file=/path/to/roberta.base/model.pt`. + +**Note:** The above command assumes training on 8x32GB V100 GPUs. Each GPU uses +a batch size of 16 sequences (`dataset.batch_size`) and accumulates gradients to +further increase the batch size by 16x (`optimization.update_freq`), for a total batch size +of 2048 sequences. If you have fewer GPUs or GPUs with less memory you may need +to reduce `dataset.batch_size` and increase dataset.update_freq to compensate. +Alternatively if you have more GPUs you can decrease `dataset.update_freq` accordingly +to increase training speed. + +**Note:** The learning rate and batch size are tightly connected and need to be +adjusted together. We generally recommend increasing the learning rate as you +increase the batch size according to the following table (although it's also +dataset dependent, so don't rely on the following values too closely): + +batch size | peak learning rate +---|--- +256 | 0.0001 +2048 | 0.0005 +8192 | 0.0007 + +### 3) Load your pretrained model +```python +from fairseq.models.roberta import RobertaModel +roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'path/to/data') +assert isinstance(roberta.model, torch.nn.Module) +``` diff --git a/fairseq/examples/roberta/README.race.md b/fairseq/examples/roberta/README.race.md new file mode 100644 index 0000000..13c917e --- /dev/null +++ b/fairseq/examples/roberta/README.race.md @@ -0,0 +1,68 @@ +# Finetuning RoBERTa on RACE tasks + +### 1) Download the data from RACE website (http://www.cs.cmu.edu/~glai1/data/race/) + +### 2) Preprocess RACE data: +```bash +python ./examples/roberta/preprocess_RACE.py --input-dir <input-dir> --output-dir <extracted-data-dir> +./examples/roberta/preprocess_RACE.sh <extracted-data-dir> <output-dir> +``` + +### 3) Fine-tuning on RACE: + +```bash +MAX_EPOCH=5 # Number of training epochs. +LR=1e-05 # Peak LR for fixed LR scheduler. +NUM_CLASSES=4 +MAX_SENTENCES=1 # Batch size per GPU. +UPDATE_FREQ=8 # Accumulate gradients to simulate training on 8 GPUs. +DATA_DIR=/path/to/race-output-dir +ROBERTA_PATH=/path/to/roberta/model.pt + +CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=legacy_ddp \ + --restore-file $ROBERTA_PATH \ + --reset-optimizer --reset-dataloader --reset-meters \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --task sentence_ranking \ + --num-classes $NUM_CLASSES \ + --init-token 0 --separator-token 2 \ + --max-option-length 128 \ + --max-positions 512 \ + --shorten-method "truncate" \ + --arch roberta_large \ + --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ + --criterion sentence_ranking \ + --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler fixed --lr $LR \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --batch-size $MAX_SENTENCES \ + --required-batch-size-multiple 1 \ + --update-freq $UPDATE_FREQ \ + --max-epoch $MAX_EPOCH +``` + +**Note:** + +a) As contexts in RACE are relatively long, we are using smaller batch size per GPU while increasing update-freq to achieve larger effective batch size. + +b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. + +c) The setting in above command is based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. + +### 4) Evaluation: + +``` +DATA_DIR=/path/to/race-output-dir # data directory used during training +MODEL_PATH=/path/to/checkpoint_best.pt # path to the finetuned model checkpoint +PREDS_OUT=preds.tsv # output file path to save prediction +TEST_SPLIT=test # can be test (Middle) or test1 (High) +fairseq-validate \ + $DATA_DIR \ + --valid-subset $TEST_SPLIT \ + --path $MODEL_PATH \ + --batch-size 1 \ + --task sentence_ranking \ + --criterion sentence_ranking \ + --save-predictions $PREDS_OUT +``` diff --git a/fairseq/examples/roberta/commonsense_qa/README.md b/fairseq/examples/roberta/commonsense_qa/README.md new file mode 100644 index 0000000..7f386de --- /dev/null +++ b/fairseq/examples/roberta/commonsense_qa/README.md @@ -0,0 +1,99 @@ +# Finetuning RoBERTa on Commonsense QA + +We follow a similar approach to [finetuning RACE](../README.race.md). Specifically +for each question we construct five inputs, one for each of the five candidate +answer choices. Each input is constructed by concatenating the question and +candidate answer. We then encode each input and pass the resulting "[CLS]" +representations through a fully-connected layer to predict the correct answer. +We train with a standard cross-entropy loss. + +We also found it helpful to prepend a prefix of `Q:` to the question and `A:` to +the answer. The complete input format is: +``` +<s> Q: Where would I not want a fox? </s> A: hen house </s> +``` + +Our final submission is based on a hyperparameter search over the learning rate +(1e-5, 2e-5, 3e-5), batch size (8, 16), number of training steps (2000, 3000, +4000) and random seed. We selected the model with the best performance on the +development set after 100 trials. + +### 1) Download data from the Commonsense QA website (https://www.tau-nlp.org/commonsenseqa) +```bash +bash examples/roberta/commonsense_qa/download_cqa_data.sh +``` + +### 2) Finetune + +```bash +MAX_UPDATES=3000 # Number of training steps. +WARMUP_UPDATES=150 # Linearly increase LR over this many steps. +LR=1e-05 # Peak LR for polynomial LR scheduler. +MAX_SENTENCES=16 # Batch size. +SEED=1 # Random seed. +ROBERTA_PATH=/path/to/roberta/model.pt +DATA_DIR=data/CommonsenseQA + +# we use the --user-dir option to load the task from +# the examples/roberta/commonsense_qa directory: +FAIRSEQ_PATH=/path/to/fairseq +FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/commonsense_qa + +CUDA_VISIBLE_DEVICES=0 fairseq-train --fp16 --ddp-backend=legacy_ddp \ + $DATA_DIR \ + --user-dir $FAIRSEQ_USER_DIR \ + --restore-file $ROBERTA_PATH \ + --reset-optimizer --reset-dataloader --reset-meters \ + --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --task commonsense_qa --init-token 0 --bpe gpt2 \ + --arch roberta_large --max-positions 512 \ + --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ + --criterion sentence_ranking --num-classes 5 \ + --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR \ + --warmup-updates $WARMUP_UPDATES --total-num-update $MAX_UPDATES \ + --batch-size $MAX_SENTENCES \ + --max-update $MAX_UPDATES \ + --log-format simple --log-interval 25 \ + --seed $SEED +``` + +The above command assumes training on 1 GPU with 32GB of RAM. For GPUs with +less memory, decrease `--batch-size` and increase `--update-freq` +accordingly to compensate. + +### 3) Evaluate +```python +import json +import torch +from fairseq.models.roberta import RobertaModel +from examples.roberta import commonsense_qa # load the Commonsense QA task +roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'data/CommonsenseQA') +roberta.eval() # disable dropout +roberta.cuda() # use the GPU (optional) +nsamples, ncorrect = 0, 0 +with open('data/CommonsenseQA/valid.jsonl') as h: + for line in h: + example = json.loads(line) + scores = [] + for choice in example['question']['choices']: + input = roberta.encode( + 'Q: ' + example['question']['stem'], + 'A: ' + choice['text'], + no_separator=True + ) + score = roberta.predict('sentence_classification_head', input, return_logits=True) + scores.append(score) + pred = torch.cat(scores).argmax() + answer = ord(example['answerKey']) - ord('A') + nsamples += 1 + if pred == answer: + ncorrect += 1 + +print('Accuracy: ' + str(ncorrect / float(nsamples))) +# Accuracy: 0.7846027846027847 +``` + +The above snippet is not batched, which makes it quite slow. See [instructions +for batched prediction with RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta#batched-prediction). diff --git a/fairseq/examples/roberta/commonsense_qa/__init__.py b/fairseq/examples/roberta/commonsense_qa/__init__.py new file mode 100644 index 0000000..42d21f3 --- /dev/null +++ b/fairseq/examples/roberta/commonsense_qa/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import commonsense_qa_task # noqa diff --git a/fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py b/fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py new file mode 100644 index 0000000..7d8f813 --- /dev/null +++ b/fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py @@ -0,0 +1,190 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os + +import numpy as np +import torch +from fairseq.data import ( + Dictionary, + IdDataset, + ListDataset, + NestedDictionaryDataset, + NumelDataset, + NumSamplesDataset, + RawLabelDataset, + RightPadDataset, + SortDataset, + data_utils, + encoders, +) +from fairseq.tasks import LegacyFairseqTask, register_task + + +@register_task("commonsense_qa") +class CommonsenseQATask(LegacyFairseqTask): + """Task to finetune RoBERTa for Commonsense QA.""" + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument( + "data", metavar="DIR", help="path to data directory; we load <split>.jsonl" + ) + parser.add_argument( + "--init-token", + type=int, + default=None, + help="add token at the beginning of each batch item", + ) + parser.add_argument("--num-classes", type=int, default=5) + + def __init__(self, args, vocab): + super().__init__(args) + self.vocab = vocab + self.mask = vocab.add_symbol("<mask>") + + self.bpe = encoders.build_bpe(args) + + @classmethod + def load_dictionary(cls, filename): + """Load the dictionary from the filename + + Args: + filename (str): the filename + """ + dictionary = Dictionary.load(filename) + dictionary.add_symbol("<mask>") + return dictionary + + @classmethod + def setup_task(cls, args, **kwargs): + assert ( + args.criterion == "sentence_ranking" + ), "Must set --criterion=sentence_ranking" + + # load data and label dictionaries + vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) + print("| dictionary: {} types".format(len(vocab))) + + return cls(args, vocab) + + def load_dataset( + self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs + ): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + + def binarize(s, append_bos=False): + if self.bpe is not None: + s = self.bpe.encode(s) + tokens = self.vocab.encode_line( + s, + append_eos=True, + add_if_not_exist=False, + ).long() + if append_bos and self.args.init_token is not None: + tokens = torch.cat([tokens.new([self.args.init_token]), tokens]) + return tokens + + if data_path is None: + data_path = os.path.join(self.args.data, split + ".jsonl") + if not os.path.exists(data_path): + raise FileNotFoundError("Cannot find data: {}".format(data_path)) + + src_tokens = [[] for i in range(self.args.num_classes)] + src_lengths = [[] for i in range(self.args.num_classes)] + labels = [] + + with open(data_path) as h: + for line in h: + example = json.loads(line.strip()) + if "answerKey" in example: + label = ord(example["answerKey"]) - ord("A") + labels.append(label) + question = example["question"]["stem"] + assert len(example["question"]["choices"]) == self.args.num_classes + # format: `<s> Q: Where would I not want a fox? </s> A: hen house </s>` + question = "Q: " + question + question_toks = binarize(question, append_bos=True) + for i, choice in enumerate(example["question"]["choices"]): + src = "A: " + choice["text"] + src_bin = torch.cat([question_toks, binarize(src)]) + src_tokens[i].append(src_bin) + src_lengths[i].append(len(src_bin)) + assert all( + len(src_tokens[0]) == len(src_tokens[i]) + for i in range(self.args.num_classes) + ) + assert len(src_tokens[0]) == len(src_lengths[0]) + assert len(labels) == 0 or len(labels) == len(src_tokens[0]) + + for i in range(self.args.num_classes): + src_lengths[i] = np.array(src_lengths[i]) + src_tokens[i] = ListDataset(src_tokens[i], src_lengths[i]) + src_lengths[i] = ListDataset(src_lengths[i]) + + dataset = { + "id": IdDataset(), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens[0], reduce=True), + } + + for i in range(self.args.num_classes): + dataset.update( + { + "net_input{}".format(i + 1): { + "src_tokens": RightPadDataset( + src_tokens[i], + pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": src_lengths[i], + } + } + ) + + if len(labels) > 0: + dataset.update({"target": RawLabelDataset(labels)}) + + dataset = NestedDictionaryDataset( + dataset, + sizes=[np.maximum.reduce([src_token.sizes for src_token in src_tokens])], + ) + + with data_utils.numpy_seed(self.args.seed): + dataset = SortDataset( + dataset, + # shuffle + sort_order=[np.random.permutation(len(dataset))], + ) + + print("| Loaded {} with {} samples".format(split, len(dataset))) + + self.datasets[split] = dataset + return self.datasets[split] + + def build_model(self, args, from_checkpoint=False): + from fairseq import models + + model = models.build_model(args, self) + + model.register_classification_head( + "sentence_classification_head", + num_classes=1, + ) + + return model + + @property + def source_dictionary(self): + return self.vocab + + @property + def target_dictionary(self): + return self.vocab diff --git a/fairseq/examples/roberta/commonsense_qa/download_cqa_data.sh b/fairseq/examples/roberta/commonsense_qa/download_cqa_data.sh new file mode 100644 index 0000000..5f30009 --- /dev/null +++ b/fairseq/examples/roberta/commonsense_qa/download_cqa_data.sh @@ -0,0 +1,14 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +OUTDIR=data/CommonsenseQA + +mkdir -p $OUTDIR + +wget -O $OUTDIR/train.jsonl https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl +wget -O $OUTDIR/valid.jsonl https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl +wget -O $OUTDIR/test.jsonl https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl +wget -O $OUTDIR/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt diff --git a/fairseq/examples/roberta/config/finetuning/cola.yaml b/fairseq/examples/roberta/config/finetuning/cola.yaml new file mode 100644 index 0000000..ac76611 --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/cola.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 320 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 5336 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/finetuning/mnli.yaml b/fairseq/examples/roberta/config/finetuning/mnli.yaml new file mode 100644 index 0000000..5be10c3 --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/mnli.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 3 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 7432 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 123873 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/finetuning/mrpc.yaml b/fairseq/examples/roberta/config/finetuning/mrpc.yaml new file mode 100644 index 0000000..aa8b7db --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/mrpc.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 137 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 2296 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/finetuning/qnli.yaml b/fairseq/examples/roberta/config/finetuning/qnli.yaml new file mode 100644 index 0000000..b4595b0 --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/qnli.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 1986 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 33112 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/finetuning/qqp.yaml b/fairseq/examples/roberta/config/finetuning/qqp.yaml new file mode 100644 index 0000000..5a2b2ed --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/qqp.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 28318 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 113272 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/finetuning/rte.yaml b/fairseq/examples/roberta/config/finetuning/rte.yaml new file mode 100644 index 0000000..7318465 --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/rte.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 122 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 2036 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/finetuning/run_config/local.yaml b/fairseq/examples/roberta/config/finetuning/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/roberta/config/finetuning/run_config/slurm_1g.yaml b/fairseq/examples/roberta/config/finetuning/run_config/slurm_1g.yaml new file mode 100644 index 0000000..8bc2185 --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/run_config/slurm_1g.yaml @@ -0,0 +1,28 @@ + +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: '_' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/roberta_ft/${env:PREFIX}/${hydra.job.config_name}/${env:SUFFIX} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir}/submitit + timeout_min: 1000 + cpus_per_task: 8 + gpus_per_node: 1 + tasks_per_node: 1 + mem_gb: 60 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 + exclude: learnfair1381,learnfair5192,learnfair2304 diff --git a/fairseq/examples/roberta/config/finetuning/run_config/slurm_1g_aws.yaml b/fairseq/examples/roberta/config/finetuning/run_config/slurm_1g_aws.yaml new file mode 100644 index 0000000..085391c --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/run_config/slurm_1g_aws.yaml @@ -0,0 +1,25 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: '_' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /fsx-wav2vec/${env:USER}/roberta_ft/${env:PREFIX}/${hydra.job.config_name}/${env:SUFFIX} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir}/submitit + timeout_min: 1000 + cpus_per_task: 8 + gpus_per_node: 1 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: learnfair,wav2vec + max_num_timeout: 30 diff --git a/fairseq/examples/roberta/config/finetuning/sst_2.yaml b/fairseq/examples/roberta/config/finetuning/sst_2.yaml new file mode 100644 index 0000000..a93ad2f --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/sst_2.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 1256 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 20935 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/finetuning/sts_b.yaml b/fairseq/examples/roberta/config/finetuning/sts_b.yaml new file mode 100644 index 0000000..2d49522 --- /dev/null +++ b/fairseq/examples/roberta/config/finetuning/sts_b.yaml @@ -0,0 +1,58 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 1 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + regression_target: true + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 214 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 3598 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/pretraining/base.yaml b/fairseq/examples/roberta/config/pretraining/base.yaml new file mode 100644 index 0000000..9782990 --- /dev/null +++ b/fairseq/examples/roberta/config/pretraining/base.yaml @@ -0,0 +1,42 @@ +# @package _group_ +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + +task: + _name: masked_lm + data: ??? + sample_break_mode: complete + tokens_per_sample: 512 + +criterion: masked_lm + +dataset: + batch_size: 16 + ignore_unused_valid_subsets: true + +optimizer: + _name: adam + weight_decay: 0.01 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 10000 + +optimization: + clip_norm: 0 + lr: [0.0005] + max_update: 125000 + update_freq: [16] + +model: + _name: roberta + max_positions: 512 + dropout: 0.1 + attention_dropout: 0.1 diff --git a/fairseq/examples/roberta/config/pretraining/run_config/local.yaml b/fairseq/examples/roberta/config/pretraining/run_config/local.yaml new file mode 100644 index 0000000..45595f9 --- /dev/null +++ b/fairseq/examples/roberta/config/pretraining/run_config/local.yaml @@ -0,0 +1,15 @@ +# @package _global_ +hydra: + sweep: + dir: ${env:PWD}/tmp_dbg/${now:%H-%M-%S} + +distributed_training: + distributed_world_size: 1 + nprocs_per_node: 1 + distributed_port: -1 + +common: + log_interval: 1 + +dataset: + num_workers: 0 diff --git a/fairseq/examples/roberta/config/pretraining/run_config/slurm_2.yaml b/fairseq/examples/roberta/config/pretraining/run_config/slurm_2.yaml new file mode 100644 index 0000000..006a0f2 --- /dev/null +++ b/fairseq/examples/roberta/config/pretraining/run_config/slurm_2.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/roberta/config/pretraining/run_config/slurm_2_aws.yaml b/fairseq/examples/roberta/config/pretraining/run_config/slurm_2_aws.yaml new file mode 100644 index 0000000..a5937ea --- /dev/null +++ b/fairseq/examples/roberta/config/pretraining/run_config/slurm_2_aws.yaml @@ -0,0 +1,39 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.local_cache_path + - task.data + - task.post_save_script + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + - model.model_path + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 0 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec + max_num_timeout: 30 diff --git a/fairseq/examples/roberta/config/pretraining/run_config/slurm_3.yaml b/fairseq/examples/roberta/config/pretraining/run_config/slurm_3.yaml new file mode 100644 index 0000000..0e1555d --- /dev/null +++ b/fairseq/examples/roberta/config/pretraining/run_config/slurm_3.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 3 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/roberta/config/pretraining/run_config/slurm_4.yaml b/fairseq/examples/roberta/config/pretraining/run_config/slurm_4.yaml new file mode 100644 index 0000000..c54d735 --- /dev/null +++ b/fairseq/examples/roberta/config/pretraining/run_config/slurm_4.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 4 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb,ib4 + max_num_timeout: 30 diff --git a/fairseq/examples/roberta/fb_multilingual/README.multilingual.pretraining.md b/fairseq/examples/roberta/fb_multilingual/README.multilingual.pretraining.md new file mode 100644 index 0000000..234fd74 --- /dev/null +++ b/fairseq/examples/roberta/fb_multilingual/README.multilingual.pretraining.md @@ -0,0 +1,26 @@ +# Multilingual pretraining RoBERTa + +This tutorial will walk you through pretraining multilingual RoBERTa. + +### 1) Preprocess the data + +```bash +DICTIONARY="/private/home/namangoyal/dataset/XLM/wiki/17/175k/vocab" +DATA_LOCATION="/private/home/namangoyal/dataset/XLM/wiki/17/175k" + +for LANG in en es it +do + fairseq-preprocess \ + --only-source \ + --srcdict $DICTIONARY \ + --trainpref "$DATA_LOCATION/train.$LANG" \ + --validpref "$DATA_LOCATION/valid.$LANG" \ + --testpref "$DATA_LOCATION/test.$LANG" \ + --destdir "wiki_17-bin/$LANG" \ + --workers 60; +done +``` + +### 2) Train RoBERTa base + +[COMING UP...] diff --git a/fairseq/examples/roberta/multiprocessing_bpe_encoder.py b/fairseq/examples/roberta/multiprocessing_bpe_encoder.py new file mode 100644 index 0000000..43fe045 --- /dev/null +++ b/fairseq/examples/roberta/multiprocessing_bpe_encoder.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import contextlib +import sys +from collections import Counter +from multiprocessing import Pool + +from fairseq.data.encoders.gpt2_bpe import get_encoder + + +def main(): + """ + Helper script to encode raw text with the GPT-2 BPE using multiple processes. + + The encoder.json and vocab.bpe files can be obtained here: + - https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json + - https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "--encoder-json", + help="path to encoder.json", + ) + parser.add_argument( + "--vocab-bpe", + type=str, + help="path to vocab.bpe", + ) + parser.add_argument( + "--inputs", + nargs="+", + default=["-"], + help="input files to filter/encode", + ) + parser.add_argument( + "--outputs", + nargs="+", + default=["-"], + help="path to save encoded outputs", + ) + parser.add_argument( + "--keep-empty", + action="store_true", + help="keep empty lines", + ) + parser.add_argument("--workers", type=int, default=20) + args = parser.parse_args() + + assert len(args.inputs) == len( + args.outputs + ), "number of input and output paths should match" + + with contextlib.ExitStack() as stack: + inputs = [ + stack.enter_context(open(input, "r", encoding="utf-8")) + if input != "-" + else sys.stdin + for input in args.inputs + ] + outputs = [ + stack.enter_context(open(output, "w", encoding="utf-8")) + if output != "-" + else sys.stdout + for output in args.outputs + ] + + encoder = MultiprocessingEncoder(args) + pool = Pool(args.workers, initializer=encoder.initializer) + encoded_lines = pool.imap(encoder.encode_lines, zip(*inputs), 100) + + stats = Counter() + for i, (filt, enc_lines) in enumerate(encoded_lines, start=1): + if filt == "PASS": + for enc_line, output_h in zip(enc_lines, outputs): + print(enc_line, file=output_h) + else: + stats["num_filtered_" + filt] += 1 + if i % 10000 == 0: + print("processed {} lines".format(i), file=sys.stderr) + + for k, v in stats.most_common(): + print("[{}] filtered {} lines".format(k, v), file=sys.stderr) + + +class MultiprocessingEncoder(object): + def __init__(self, args): + self.args = args + + def initializer(self): + global bpe + bpe = get_encoder(self.args.encoder_json, self.args.vocab_bpe) + + def encode(self, line): + global bpe + ids = bpe.encode(line) + return list(map(str, ids)) + + def decode(self, tokens): + global bpe + return bpe.decode(tokens) + + def encode_lines(self, lines): + """ + Encode a set of lines. All lines will be encoded together. + """ + enc_lines = [] + for line in lines: + line = line.strip() + if len(line) == 0 and not self.args.keep_empty: + return ["EMPTY", None] + tokens = self.encode(line) + enc_lines.append(" ".join(tokens)) + return ["PASS", enc_lines] + + def decode_lines(self, lines): + dec_lines = [] + for line in lines: + tokens = map(int, line.strip().split()) + dec_lines.append(self.decode(tokens)) + return ["PASS", dec_lines] + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/roberta/preprocess_GLUE_tasks.sh b/fairseq/examples/roberta/preprocess_GLUE_tasks.sh new file mode 100644 index 0000000..7f215a3 --- /dev/null +++ b/fairseq/examples/roberta/preprocess_GLUE_tasks.sh @@ -0,0 +1,185 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +# raw glue data as downloaded by glue download script (https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) +if [[ $# -ne 2 ]]; then + echo "Run as following:" + echo "./examples/roberta/preprocess_GLUE_tasks.sh <glud_data_folder> <task_name>" + exit 1 +fi + +GLUE_DATA_FOLDER=$1 + +# download bpe encoder.json, vocabulary and fairseq dictionary +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' + +TASKS=$2 # QQP + +if [ "$TASKS" = "ALL" ] +then + TASKS="QQP MNLI QNLI MRPC RTE STS-B SST-2 CoLA" +fi + +for TASK in $TASKS +do + echo "Preprocessing $TASK" + + TASK_DATA_FOLDER="$GLUE_DATA_FOLDER/$TASK" + echo "Raw data as downloaded from glue website: $TASK_DATA_FOLDER" + + SPLITS="train dev test" + INPUT_COUNT=2 + if [ "$TASK" = "QQP" ] + then + INPUT_COLUMNS=( 4 5 ) + TEST_INPUT_COLUMNS=( 2 3 ) + LABEL_COLUMN=6 + elif [ "$TASK" = "MNLI" ] + then + SPLITS="train dev_matched dev_mismatched test_matched test_mismatched" + INPUT_COLUMNS=( 9 10 ) + TEST_INPUT_COLUMNS=( 9 10 ) + DEV_LABEL_COLUMN=16 + LABEL_COLUMN=12 + elif [ "$TASK" = "QNLI" ] + then + INPUT_COLUMNS=( 2 3 ) + TEST_INPUT_COLUMNS=( 2 3 ) + LABEL_COLUMN=4 + elif [ "$TASK" = "MRPC" ] + then + INPUT_COLUMNS=( 4 5 ) + TEST_INPUT_COLUMNS=( 4 5 ) + LABEL_COLUMN=1 + elif [ "$TASK" = "RTE" ] + then + INPUT_COLUMNS=( 2 3 ) + TEST_INPUT_COLUMNS=( 2 3 ) + LABEL_COLUMN=4 + elif [ "$TASK" = "STS-B" ] + then + INPUT_COLUMNS=( 8 9 ) + TEST_INPUT_COLUMNS=( 8 9 ) + LABEL_COLUMN=10 + # Following are single sentence tasks. + elif [ "$TASK" = "SST-2" ] + then + INPUT_COLUMNS=( 1 ) + TEST_INPUT_COLUMNS=( 2 ) + LABEL_COLUMN=2 + INPUT_COUNT=1 + elif [ "$TASK" = "CoLA" ] + then + INPUT_COLUMNS=( 4 ) + TEST_INPUT_COLUMNS=( 2 ) + LABEL_COLUMN=2 + INPUT_COUNT=1 + fi + + # Strip out header and filter lines that don't have expected number of fields. + rm -rf "$TASK_DATA_FOLDER/processed" + mkdir -p "$TASK_DATA_FOLDER/processed" + for SPLIT in $SPLITS + do + # CoLA train and dev doesn't have header. + if [[ ( "$TASK" = "CoLA") && ( "$SPLIT" != "test" ) ]] + then + cp "$TASK_DATA_FOLDER/$SPLIT.tsv" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; + else + tail -n +2 "$TASK_DATA_FOLDER/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; + fi + + # Remove unformatted lines from train and dev files for QQP dataset. + if [[ ( "$TASK" = "QQP") && ( "$SPLIT" != "test" ) ]] + then + awk -F '\t' -v NUM_FIELDS=6 'NF==NUM_FIELDS{print}{}' "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp" > "$TASK_DATA_FOLDER/processed/$SPLIT.tsv"; + else + cp "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv"; + fi + rm "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; + done + + # Split into input0, input1 and label + for SPLIT in $SPLITS + do + for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) + do + if [[ "$SPLIT" != test* ]] + then + COLUMN_NUMBER=${INPUT_COLUMNS[$INPUT_TYPE]} + else + COLUMN_NUMBER=${TEST_INPUT_COLUMNS[$INPUT_TYPE]} + fi + cut -f"$COLUMN_NUMBER" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.raw.input$INPUT_TYPE"; + done + + if [[ "$SPLIT" != test* ]] + then + if [ "$TASK" = "MNLI" ] && [ "$SPLIT" != "train" ] + then + cut -f"$DEV_LABEL_COLUMN" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.label"; + else + cut -f"$LABEL_COLUMN" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.label"; + fi + fi + + # BPE encode. + for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) + do + LANG="input$INPUT_TYPE" + echo "BPE encoding $SPLIT/$LANG" + python -m examples.roberta.multiprocessing_bpe_encoder \ + --encoder-json encoder.json \ + --vocab-bpe vocab.bpe \ + --inputs "$TASK_DATA_FOLDER/processed/$SPLIT.raw.$LANG" \ + --outputs "$TASK_DATA_FOLDER/processed/$SPLIT.$LANG" \ + --workers 60 \ + --keep-empty; + done + done + + # Remove output directory. + rm -rf "$TASK-bin" + + DEVPREF="$TASK_DATA_FOLDER/processed/dev.LANG" + TESTPREF="$TASK_DATA_FOLDER/processed/test.LANG" + if [ "$TASK" = "MNLI" ] + then + DEVPREF="$TASK_DATA_FOLDER/processed/dev_matched.LANG,$TASK_DATA_FOLDER/processed/dev_mismatched.LANG" + TESTPREF="$TASK_DATA_FOLDER/processed/test_matched.LANG,$TASK_DATA_FOLDER/processed/test_mismatched.LANG" + fi + + # Run fairseq preprocessing: + for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) + do + LANG="input$INPUT_TYPE" + fairseq-preprocess \ + --only-source \ + --trainpref "$TASK_DATA_FOLDER/processed/train.$LANG" \ + --validpref "${DEVPREF//LANG/$LANG}" \ + --testpref "${TESTPREF//LANG/$LANG}" \ + --destdir "$TASK-bin/$LANG" \ + --workers 60 \ + --srcdict dict.txt; + done + if [[ "$TASK" != "STS-B" ]] + then + fairseq-preprocess \ + --only-source \ + --trainpref "$TASK_DATA_FOLDER/processed/train.label" \ + --validpref "${DEVPREF//LANG/label}" \ + --destdir "$TASK-bin/label" \ + --workers 60; + else + # For STS-B output range is converted to be between: [0.0, 1.0] + mkdir -p "$TASK-bin/label" + awk '{print $1 / 5.0 }' "$TASK_DATA_FOLDER/processed/train.label" > "$TASK-bin/label/train.label" + awk '{print $1 / 5.0 }' "$TASK_DATA_FOLDER/processed/dev.label" > "$TASK-bin/label/valid.label" + fi +done diff --git a/fairseq/examples/roberta/preprocess_RACE.py b/fairseq/examples/roberta/preprocess_RACE.py new file mode 100644 index 0000000..cdd6607 --- /dev/null +++ b/fairseq/examples/roberta/preprocess_RACE.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import json +import os +import re + + +class InputExample: + def __init__(self, paragraph, qa_list, label): + self.paragraph = paragraph + self.qa_list = qa_list + self.label = label + + +def get_examples(data_dir, set_type): + """ + Extract paragraph and question-answer list from each json file + """ + examples = [] + + levels = ["middle", "high"] + set_type_c = set_type.split("-") + if len(set_type_c) == 2: + levels = [set_type_c[1]] + set_type = set_type_c[0] + for level in levels: + cur_dir = os.path.join(data_dir, set_type, level) + for filename in os.listdir(cur_dir): + cur_path = os.path.join(cur_dir, filename) + with open(cur_path, "r") as f: + cur_data = json.load(f) + answers = cur_data["answers"] + options = cur_data["options"] + questions = cur_data["questions"] + context = cur_data["article"].replace("\n", " ") + context = re.sub(r"\s+", " ", context) + for i in range(len(answers)): + label = ord(answers[i]) - ord("A") + qa_list = [] + question = questions[i] + for j in range(4): + option = options[i][j] + if "_" in question: + qa_cat = question.replace("_", option) + else: + qa_cat = " ".join([question, option]) + qa_cat = re.sub(r"\s+", " ", qa_cat) + qa_list.append(qa_cat) + examples.append(InputExample(context, qa_list, label)) + + return examples + + +def main(): + """ + Helper script to extract paragraphs questions and answers from RACE datasets. + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "--input-dir", + help="input directory for downloaded RACE dataset", + ) + parser.add_argument( + "--output-dir", + help="output directory for extracted data", + ) + args = parser.parse_args() + + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir, exist_ok=True) + + for set_type in ["train", "dev", "test-middle", "test-high"]: + examples = get_examples(args.input_dir, set_type) + qa_file_paths = [ + os.path.join(args.output_dir, set_type + ".input" + str(i + 1)) + for i in range(4) + ] + qa_files = [open(qa_file_path, "w") for qa_file_path in qa_file_paths] + outf_context_path = os.path.join(args.output_dir, set_type + ".input0") + outf_label_path = os.path.join(args.output_dir, set_type + ".label") + outf_context = open(outf_context_path, "w") + outf_label = open(outf_label_path, "w") + for example in examples: + outf_context.write(example.paragraph + "\n") + for i in range(4): + qa_files[i].write(example.qa_list[i] + "\n") + outf_label.write(str(example.label) + "\n") + + for f in qa_files: + f.close() + outf_label.close() + outf_context.close() + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/roberta/preprocess_RACE.sh b/fairseq/examples/roberta/preprocess_RACE.sh new file mode 100644 index 0000000..932d2ab --- /dev/null +++ b/fairseq/examples/roberta/preprocess_RACE.sh @@ -0,0 +1,59 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +# data should be downloaded and processed with reprocess_RACE.py +if [[ $# -ne 2 ]]; then + echo "Run as following:" + echo "./examples/roberta/preprocess_RACE.sh <race_data_folder> <output_folder>" + exit 1 +fi + +RACE_DATA_FOLDER=$1 +OUT_DATA_FOLDER=$2 + +# download bpe encoder.json, vocabulary and fairseq dictionary +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' + +SPLITS="train dev test-middle test-high" +INPUT_TYPES="input0 input1 input2 input3 input4" +for INPUT_TYPE in $INPUT_TYPES +do + for SPLIT in $SPLITS + do + echo "BPE encoding $SPLIT/$INPUT_TYPE" + python -m examples.roberta.multiprocessing_bpe_encoder \ + --encoder-json encoder.json \ + --vocab-bpe vocab.bpe \ + --inputs "$RACE_DATA_FOLDER/$SPLIT.$INPUT_TYPE" \ + --outputs "$RACE_DATA_FOLDER/$SPLIT.$INPUT_TYPE.bpe" \ + --workers 10 \ + --keep-empty; + + done +done + +for INPUT_TYPE in $INPUT_TYPES + do + LANG="input$INPUT_TYPE" + fairseq-preprocess \ + --only-source \ + --trainpref "$RACE_DATA_FOLDER/train.$INPUT_TYPE.bpe" \ + --validpref "$RACE_DATA_FOLDER/dev.$INPUT_TYPE.bpe" \ + --testpref "$RACE_DATA_FOLDER/test-middle.$INPUT_TYPE.bpe,$RACE_DATA_FOLDER/test-high.$INPUT_TYPE.bpe" \ + --destdir "$OUT_DATA_FOLDER/$INPUT_TYPE" \ + --workers 10 \ + --srcdict dict.txt; +done + +rm -rf "$OUT_DATA_FOLDER/label" +mkdir -p "$OUT_DATA_FOLDER/label" +cp "$RACE_DATA_FOLDER/train.label" "$OUT_DATA_FOLDER/label/" +cp "$RACE_DATA_FOLDER/dev.label" "$OUT_DATA_FOLDER/label/valid.label" +cp "$RACE_DATA_FOLDER/test-middle.label" "$OUT_DATA_FOLDER/label/test.label" +cp "$RACE_DATA_FOLDER/test-high.label" "$OUT_DATA_FOLDER/label/test1.label" diff --git a/fairseq/examples/roberta/wsc/README.md b/fairseq/examples/roberta/wsc/README.md new file mode 100644 index 0000000..21a045d --- /dev/null +++ b/fairseq/examples/roberta/wsc/README.md @@ -0,0 +1,125 @@ +# Finetuning RoBERTa on Winograd Schema Challenge (WSC) data + +The following instructions can be used to finetune RoBERTa on the WSC training +data provided by [SuperGLUE](https://super.gluebenchmark.com/). + +Note that there is high variance in the results. For our GLUE/SuperGLUE +submission we swept over the learning rate (1e-5, 2e-5, 3e-5), batch size (16, +32, 64) and total number of updates (500, 1000, 2000, 3000), as well as the +random seed. Out of ~100 runs we chose the best 7 models and ensembled them. + +**Approach:** The instructions below use a slightly different loss function than +what's described in the original RoBERTa arXiv paper. In particular, +[Kocijan et al. (2019)](https://arxiv.org/abs/1905.06290) introduce a margin +ranking loss between `(query, candidate)` pairs with tunable hyperparameters +alpha and beta. This is supported in our code as well with the `--wsc-alpha` and +`--wsc-beta` arguments. However, we achieved slightly better (and more robust) +results on the development set by instead using a single cross entropy loss term +over the log-probabilities for the query and all mined candidates. **The +candidates are mined using spaCy from each input sentence in isolation, so the +approach remains strictly pointwise.** This reduces the number of +hyperparameters and our best model achieved 92.3% development set accuracy, +compared to ~90% accuracy for the margin loss. Later versions of the RoBERTa +arXiv paper will describe this updated formulation. + +### 1) Download the WSC data from the SuperGLUE website: +```bash +wget https://dl.fbaipublicfiles.com/glue/superglue/data/v2/WSC.zip +unzip WSC.zip + +# we also need to copy the RoBERTa dictionary into the same directory +wget -O WSC/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt +``` + +### 2) Finetune over the provided training data: +```bash +TOTAL_NUM_UPDATES=2000 # Total number of training steps. +WARMUP_UPDATES=250 # Linearly increase LR over this many steps. +LR=2e-05 # Peak LR for polynomial LR scheduler. +MAX_SENTENCES=16 # Batch size per GPU. +SEED=1 # Random seed. +ROBERTA_PATH=/path/to/roberta/model.pt + +# we use the --user-dir option to load the task and criterion +# from the examples/roberta/wsc directory: +FAIRSEQ_PATH=/path/to/fairseq +FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc + +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train WSC/ \ + --restore-file $ROBERTA_PATH \ + --reset-optimizer --reset-dataloader --reset-meters \ + --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --valid-subset val \ + --fp16 --ddp-backend legacy_ddp \ + --user-dir $FAIRSEQ_USER_DIR \ + --task wsc --criterion wsc --wsc-cross-entropy \ + --arch roberta_large --bpe gpt2 --max-positions 512 \ + --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ + --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ + --lr-scheduler polynomial_decay --lr $LR \ + --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \ + --batch-size $MAX_SENTENCES \ + --max-update $TOTAL_NUM_UPDATES \ + --log-format simple --log-interval 100 \ + --seed $SEED +``` + +The above command assumes training on 4 GPUs, but you can achieve the same +results on a single GPU by adding `--update-freq=4`. + +### 3) Evaluate +```python +from fairseq.models.roberta import RobertaModel +from examples.roberta.wsc import wsc_utils # also loads WSC task and criterion +roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'WSC/') +roberta.cuda() +nsamples, ncorrect = 0, 0 +for sentence, label in wsc_utils.jsonl_iterator('WSC/val.jsonl', eval=True): + pred = roberta.disambiguate_pronoun(sentence) + nsamples += 1 + if pred == label: + ncorrect += 1 +print('Accuracy: ' + str(ncorrect / float(nsamples))) +# Accuracy: 0.9230769230769231 +``` + +## RoBERTa training on WinoGrande dataset +We have also provided `winogrande` task and criterion for finetuning on the +[WinoGrande](https://mosaic.allenai.org/projects/winogrande) like datasets +where there are always two candidates and one is correct. +It's more efficient implementation for such subcases. + +```bash +TOTAL_NUM_UPDATES=23750 # Total number of training steps. +WARMUP_UPDATES=2375 # Linearly increase LR over this many steps. +LR=1e-05 # Peak LR for polynomial LR scheduler. +MAX_SENTENCES=32 # Batch size per GPU. +SEED=1 # Random seed. +ROBERTA_PATH=/path/to/roberta/model.pt + +# we use the --user-dir option to load the task and criterion +# from the examples/roberta/wsc directory: +FAIRSEQ_PATH=/path/to/fairseq +FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc + +cd fairseq +CUDA_VISIBLE_DEVICES=0 fairseq-train winogrande_1.0/ \ + --restore-file $ROBERTA_PATH \ + --reset-optimizer --reset-dataloader --reset-meters \ + --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --valid-subset val \ + --fp16 --ddp-backend legacy_ddp \ + --user-dir $FAIRSEQ_USER_DIR \ + --task winogrande --criterion winogrande \ + --wsc-margin-alpha 5.0 --wsc-margin-beta 0.4 \ + --arch roberta_large --bpe gpt2 --max-positions 512 \ + --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ + --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ + --lr-scheduler polynomial_decay --lr $LR \ + --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \ + --batch-size $MAX_SENTENCES \ + --max-update $TOTAL_NUM_UPDATES \ + --log-format simple --log-interval 100 +``` diff --git a/fairseq/examples/roberta/wsc/__init__.py b/fairseq/examples/roberta/wsc/__init__.py new file mode 100644 index 0000000..78afa47 --- /dev/null +++ b/fairseq/examples/roberta/wsc/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import wsc_criterion # noqa +from . import wsc_task # noqa diff --git a/fairseq/examples/roberta/wsc/wsc_criterion.py b/fairseq/examples/roberta/wsc/wsc_criterion.py new file mode 100644 index 0000000..ed0251f --- /dev/null +++ b/fairseq/examples/roberta/wsc/wsc_criterion.py @@ -0,0 +1,167 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.criterions import LegacyFairseqCriterion, register_criterion +from fairseq.data import encoders + + +@register_criterion("wsc") +class WSCCriterion(LegacyFairseqCriterion): + def __init__(self, args, task): + super().__init__(args, task) + if self.args.save_predictions is not None: + self.prediction_h = open(self.args.save_predictions, "w") + else: + self.prediction_h = None + self.bpe = encoders.build_bpe(args.bpe) + self.tokenizer = encoders.build_tokenizer(args.tokenizer) + + def __del__(self): + if self.prediction_h is not None: + self.prediction_h.close() + + @staticmethod + def add_args(parser): + """Add criterion-specific arguments to the parser.""" + parser.add_argument("--wsc-margin-alpha", type=float, metavar="A", default=1.0) + parser.add_argument("--wsc-margin-beta", type=float, metavar="B", default=0.0) + parser.add_argument( + "--wsc-cross-entropy", + action="store_true", + help="use cross entropy formulation instead of margin loss", + ) + parser.add_argument( + "--save-predictions", metavar="FILE", help="file to save predictions to" + ) + + def get_masked_input(self, tokens, mask): + masked_tokens = tokens.clone() + masked_tokens[mask] = self.task.mask + return masked_tokens + + def get_lprobs(self, model, tokens, mask): + logits, _ = model(src_tokens=self.get_masked_input(tokens, mask)) + lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float) + scores = lprobs.gather(2, tokens.unsqueeze(-1)).squeeze(-1) + mask = mask.type_as(scores) + scores = (scores * mask).sum(dim=-1) / mask.sum(dim=-1) + return scores + + def get_loss(self, query_lprobs, cand_lprobs): + if self.args.wsc_cross_entropy: + return F.cross_entropy( + torch.cat([query_lprobs, cand_lprobs]).unsqueeze(0), + query_lprobs.new([0]).long(), + ) + else: + return ( + -query_lprobs + + self.args.wsc_margin_alpha + * (cand_lprobs - query_lprobs + self.args.wsc_margin_beta).clamp(min=0) + ).sum() + + def forward(self, model, sample, reduce=True): + # compute loss and accuracy + loss, nloss = 0.0, 0 + ncorrect, nqueries = 0, 0 + + for i, label in enumerate(sample["labels"]): + query_lprobs = self.get_lprobs( + model, + sample["query_tokens"][i].unsqueeze(0), + sample["query_masks"][i].unsqueeze(0), + ) + cand_lprobs = self.get_lprobs( + model, + sample["candidate_tokens"][i], + sample["candidate_masks"][i], + ) + + pred = (query_lprobs >= cand_lprobs).all().item() + + if label is not None: + label = 1 if label else 0 + ncorrect += 1 if pred == label else 0 + nqueries += 1 + + if label: + # only compute a loss for positive instances + nloss += 1 + loss += self.get_loss(query_lprobs, cand_lprobs) + + id = sample["id"][i].item() + if self.prediction_h is not None: + print("{}\t{}\t{}".format(id, pred, label), file=self.prediction_h) + + if nloss == 0: + loss = torch.tensor(0.0, requires_grad=True) + + sample_size = nqueries if nqueries > 0 else 1 + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "ncorrect": ncorrect, + "nqueries": nqueries, + } + return loss, sample_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + agg_output = { + "loss": loss_sum / sample_size / math.log(2), + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + nqueries = sum(log.get("nqueries", 0) for log in logging_outputs) + if nqueries > 0: + agg_output["accuracy"] = ncorrect / float(nqueries) + + return agg_output + + +@register_criterion("winogrande") +class WinograndeCriterion(WSCCriterion): + def forward(self, model, sample, reduce=True): + # compute loss and accuracy + query_lprobs = self.get_lprobs( + model, + sample["query_tokens"], + sample["query_masks"], + ) + cand_lprobs = self.get_lprobs( + model, + sample["candidate_tokens"], + sample["candidate_masks"], + ) + pred = query_lprobs >= cand_lprobs + loss = self.get_loss(query_lprobs, cand_lprobs) + + sample_size = sample["query_tokens"].size(0) + ncorrect = pred.sum().item() + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "ncorrect": ncorrect, + "nqueries": sample_size, + } + return loss, sample_size, logging_output diff --git a/fairseq/examples/roberta/wsc/wsc_task.py b/fairseq/examples/roberta/wsc/wsc_task.py new file mode 100644 index 0000000..602ea73 --- /dev/null +++ b/fairseq/examples/roberta/wsc/wsc_task.py @@ -0,0 +1,401 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import tempfile + +import numpy as np +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.data import ( + Dictionary, + IdDataset, + ListDataset, + NestedDictionaryDataset, + NumelDataset, + NumSamplesDataset, + PadDataset, + SortDataset, + data_utils, + encoders, +) +from fairseq.tasks import LegacyFairseqTask, register_task + +from . import wsc_utils + + +@register_task("wsc") +class WSCTask(LegacyFairseqTask): + """Task to finetune RoBERTa for Winograd Schemas.""" + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument( + "data", metavar="DIR", help="path to data directory; we load <split>.jsonl" + ) + parser.add_argument( + "--init-token", + type=int, + default=None, + help="add token at the beginning of each batch item", + ) + + def __init__(self, args, vocab): + super().__init__(args) + self.vocab = vocab + self.mask = vocab.add_symbol("<mask>") + + self.bpe = encoders.build_bpe(args) + self.tokenizer = encoders.build_tokenizer(args) + + # hack to handle GPT-2 BPE, which includes leading spaces + if args.bpe == "gpt2": + self.leading_space = True + self.trailing_space = False + else: + self.leading_space = False + self.trailing_space = True + + @classmethod + def load_dictionary(cls, filename): + """Load the dictionary from the filename + + Args: + filename (str): the filename + """ + dictionary = Dictionary.load(filename) + dictionary.add_symbol("<mask>") + return dictionary + + @classmethod + def setup_task(cls, args, **kwargs): + assert args.criterion == "wsc", "Must set --criterion=wsc" + + # load data and label dictionaries + vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) + print("| dictionary: {} types".format(len(vocab))) + + return cls(args, vocab) + + def binarize(self, s: str, append_eos: bool = False): + if self.tokenizer is not None: + s = self.tokenizer.encode(s) + if self.bpe is not None: + s = self.bpe.encode(s) + tokens = self.vocab.encode_line( + s, + append_eos=append_eos, + add_if_not_exist=False, + ).long() + if self.args.init_token is not None: + tokens = torch.cat([tokens.new([self.args.init_token]), tokens]) + return tokens + + def binarize_with_mask(self, txt, prefix, suffix, leading_space, trailing_space): + toks = self.binarize( + prefix + leading_space + txt + trailing_space + suffix, + append_eos=True, + ) + mask = torch.zeros_like(toks, dtype=torch.bool) + mask_start = len(self.binarize(prefix)) + mask_size = len(self.binarize(leading_space + txt)) + mask[mask_start : mask_start + mask_size] = 1 + return toks, mask + + def load_dataset( + self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs + ): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if data_path is None: + data_path = os.path.join(self.args.data, split + ".jsonl") + if not os.path.exists(data_path): + raise FileNotFoundError("Cannot find data: {}".format(data_path)) + + query_tokens = [] + query_masks = [] + query_lengths = [] + candidate_tokens = [] + candidate_masks = [] + candidate_lengths = [] + labels = [] + + for sentence, pronoun_span, query, label in wsc_utils.jsonl_iterator(data_path): + prefix = sentence[: pronoun_span.start].text + suffix = sentence[pronoun_span.end :].text_with_ws + + # spaCy spans include trailing spaces, but we need to know about + # leading spaces for the GPT-2 BPE + leading_space = ( + " " if sentence[: pronoun_span.start].text_with_ws.endswith(" ") else "" + ) + trailing_space = " " if pronoun_span.text_with_ws.endswith(" ") else "" + + # get noun phrases, excluding pronouns and anything overlapping with the query + cand_spans = wsc_utils.filter_noun_chunks( + wsc_utils.extended_noun_chunks(sentence), + exclude_pronouns=True, + exclude_query=query, + exact_match=False, + ) + + if query is not None: + query_toks, query_mask = self.binarize_with_mask( + query, prefix, suffix, leading_space, trailing_space + ) + query_len = len(query_toks) + else: + query_toks, query_mask, query_len = None, None, 0 + + query_tokens.append(query_toks) + query_masks.append(query_mask) + query_lengths.append(query_len) + + cand_toks, cand_masks = [], [] + for cand_span in cand_spans: + toks, mask = self.binarize_with_mask( + cand_span.text, + prefix, + suffix, + leading_space, + trailing_space, + ) + cand_toks.append(toks) + cand_masks.append(mask) + + # collate candidates + cand_toks = data_utils.collate_tokens(cand_toks, pad_idx=self.vocab.pad()) + cand_masks = data_utils.collate_tokens(cand_masks, pad_idx=0) + assert cand_toks.size() == cand_masks.size() + + candidate_tokens.append(cand_toks) + candidate_masks.append(cand_masks) + candidate_lengths.append(cand_toks.size(1)) + + labels.append(label) + + query_lengths = np.array(query_lengths) + query_tokens = ListDataset(query_tokens, query_lengths) + query_masks = ListDataset(query_masks, query_lengths) + + candidate_lengths = np.array(candidate_lengths) + candidate_tokens = ListDataset(candidate_tokens, candidate_lengths) + candidate_masks = ListDataset(candidate_masks, candidate_lengths) + + labels = ListDataset(labels, [1] * len(labels)) + + dataset = { + "id": IdDataset(), + "query_tokens": query_tokens, + "query_masks": query_masks, + "candidate_tokens": candidate_tokens, + "candidate_masks": candidate_masks, + "labels": labels, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(query_tokens, reduce=True), + } + + nested_dataset = NestedDictionaryDataset( + dataset, + sizes=[query_lengths], + ) + + with data_utils.numpy_seed(self.args.seed): + shuffle = np.random.permutation(len(query_tokens)) + dataset = SortDataset( + nested_dataset, + # shuffle + sort_order=[shuffle], + ) + + if return_only: + return dataset + + self.datasets[split] = dataset + return self.datasets[split] + + def build_dataset_for_inference(self, sample_json): + with tempfile.NamedTemporaryFile(buffering=0) as h: + h.write((json.dumps(sample_json) + "\n").encode("utf-8")) + dataset = self.load_dataset( + "disambiguate_pronoun", + data_path=h.name, + return_only=True, + ) + return dataset + + def disambiguate_pronoun(self, model, sentence, use_cuda=False): + sample_json = wsc_utils.convert_sentence_to_json(sentence) + dataset = self.build_dataset_for_inference(sample_json) + sample = dataset.collater([dataset[0]]) + if use_cuda: + sample = utils.move_to_cuda(sample) + + def get_masked_input(tokens, mask): + masked_tokens = tokens.clone() + masked_tokens[mask.bool()] = self.mask + return masked_tokens + + def get_lprobs(tokens, mask): + logits, _ = model(src_tokens=get_masked_input(tokens, mask)) + lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float) + scores = lprobs.gather(2, tokens.unsqueeze(-1)).squeeze(-1) + mask = mask.type_as(scores) + scores = (scores * mask).sum(dim=-1) / mask.sum(dim=-1) + return scores + + cand_lprobs = get_lprobs( + sample["candidate_tokens"][0], + sample["candidate_masks"][0], + ) + if sample["query_tokens"][0] is not None: + query_lprobs = get_lprobs( + sample["query_tokens"][0].unsqueeze(0), + sample["query_masks"][0].unsqueeze(0), + ) + return (query_lprobs >= cand_lprobs).all().item() == 1 + else: + best_idx = cand_lprobs.argmax().item() + full_cand = sample["candidate_tokens"][0][best_idx] + mask = sample["candidate_masks"][0][best_idx] + toks = full_cand[mask.bool()] + return self.bpe.decode(self.source_dictionary.string(toks)).strip() + + @property + def source_dictionary(self): + return self.vocab + + @property + def target_dictionary(self): + return self.vocab + + +@register_task("winogrande") +class WinograndeTask(WSCTask): + """ + Task for WinoGrande dataset. Efficient implementation for Winograd schema + tasks with exactly two candidates, one of which is correct. + """ + + @classmethod + def setup_task(cls, args, **kwargs): + assert args.criterion == "winogrande", "Must set --criterion=winogrande" + + # load data and label dictionaries + vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) + print("| dictionary: {} types".format(len(vocab))) + + return cls(args, vocab) + + def load_dataset( + self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs + ): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if data_path is None: + data_path = os.path.join(self.args.data, split + ".jsonl") + if not os.path.exists(data_path): + raise FileNotFoundError("Cannot find data: {}".format(data_path)) + + query_tokens = [] + query_masks = [] + query_lengths = [] + candidate_tokens = [] + candidate_masks = [] + candidate_lengths = [] + + itr = wsc_utils.winogrande_jsonl_iterator(data_path, eval=(split == "test")) + + for sample in itr: + sentence, pronoun_span, query, cand_text = sample + prefix = sentence[: pronoun_span[0]].rstrip() + suffix = sentence[pronoun_span[1] :] + + leading_space = " " if sentence[: pronoun_span[0]].endswith(" ") else "" + trailing_space = "" + + if query is not None: + query_toks, query_mask = self.binarize_with_mask( + query, + prefix, + suffix, + leading_space, + trailing_space, + ) + query_len = len(query_toks) + else: + query_toks, query_mask, query_len = None, None, 0 + + query_tokens.append(query_toks) + query_masks.append(query_mask) + query_lengths.append(query_len) + + cand_toks, cand_mask = self.binarize_with_mask( + cand_text, + prefix, + suffix, + leading_space, + trailing_space, + ) + + candidate_tokens.append(cand_toks) + candidate_masks.append(cand_mask) + candidate_lengths.append(cand_toks.size(0)) + + query_lengths = np.array(query_lengths) + + def get_pad_dataset_fn(tokens, length, pad_idx): + return PadDataset( + ListDataset(tokens, length), + pad_idx=pad_idx, + left_pad=False, + ) + + query_tokens = get_pad_dataset_fn(query_tokens, query_lengths, self.vocab.pad()) + query_masks = get_pad_dataset_fn(query_masks, query_lengths, 0) + + candidate_lengths = np.array(candidate_lengths) + candidate_tokens = get_pad_dataset_fn( + candidate_tokens, candidate_lengths, self.vocab.pad() + ) + candidate_masks = get_pad_dataset_fn(candidate_masks, candidate_lengths, 0) + + dataset = { + "id": IdDataset(), + "query_tokens": query_tokens, + "query_masks": query_masks, + "candidate_tokens": candidate_tokens, + "candidate_masks": candidate_masks, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(query_tokens, reduce=True), + } + + nested_dataset = NestedDictionaryDataset( + dataset, + sizes=[query_lengths], + ) + + with data_utils.numpy_seed(self.args.seed): + shuffle = np.random.permutation(len(query_tokens)) + dataset = SortDataset( + nested_dataset, + # shuffle + sort_order=[shuffle], + ) + + if return_only: + return dataset + + self.datasets[split] = dataset + return self.datasets[split] diff --git a/fairseq/examples/roberta/wsc/wsc_utils.py b/fairseq/examples/roberta/wsc/wsc_utils.py new file mode 100644 index 0000000..da6ba74 --- /dev/null +++ b/fairseq/examples/roberta/wsc/wsc_utils.py @@ -0,0 +1,241 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +from functools import lru_cache + + +def convert_sentence_to_json(sentence): + if "_" in sentence: + prefix, rest = sentence.split("_", 1) + query, rest = rest.split("_", 1) + query_index = len(prefix.rstrip().split(" ")) + else: + query, query_index = None, None + + prefix, rest = sentence.split("[", 1) + pronoun, rest = rest.split("]", 1) + pronoun_index = len(prefix.rstrip().split(" ")) + + sentence = sentence.replace("_", "").replace("[", "").replace("]", "") + + return { + "idx": 0, + "text": sentence, + "target": { + "span1_index": query_index, + "span1_text": query, + "span2_index": pronoun_index, + "span2_text": pronoun, + }, + } + + +def extended_noun_chunks(sentence): + noun_chunks = {(np.start, np.end) for np in sentence.noun_chunks} + np_start, cur_np = 0, "NONE" + for i, token in enumerate(sentence): + np_type = token.pos_ if token.pos_ in {"NOUN", "PROPN"} else "NONE" + if np_type != cur_np: + if cur_np != "NONE": + noun_chunks.add((np_start, i)) + if np_type != "NONE": + np_start = i + cur_np = np_type + if cur_np != "NONE": + noun_chunks.add((np_start, len(sentence))) + return [sentence[s:e] for (s, e) in sorted(noun_chunks)] + + +def find_token(sentence, start_pos): + found_tok = None + for tok in sentence: + if tok.idx == start_pos: + found_tok = tok + break + return found_tok + + +def find_span(sentence, search_text, start=0): + search_text = search_text.lower() + for tok in sentence[start:]: + remainder = sentence[tok.i :].text.lower() + if remainder.startswith(search_text): + len_to_consume = len(search_text) + start_idx = tok.idx + for next_tok in sentence[tok.i :]: + end_idx = next_tok.idx + len(next_tok.text) + if end_idx - start_idx == len_to_consume: + span = sentence[tok.i : next_tok.i + 1] + return span + return None + + +@lru_cache(maxsize=1) +def get_detokenizer(): + from sacremoses import MosesDetokenizer + + detok = MosesDetokenizer(lang="en") + return detok + + +@lru_cache(maxsize=1) +def get_spacy_nlp(): + import en_core_web_lg + + nlp = en_core_web_lg.load() + return nlp + + +def jsonl_iterator(input_fname, positive_only=False, ngram_order=3, eval=False): + detok = get_detokenizer() + nlp = get_spacy_nlp() + + with open(input_fname) as fin: + for line in fin: + sample = json.loads(line.strip()) + + if positive_only and "label" in sample and not sample["label"]: + # only consider examples where the query is correct + continue + + target = sample["target"] + + # clean up the query + query = target["span1_text"] + if query is not None: + if "\n" in query: + continue + if query.endswith(".") or query.endswith(","): + query = query[:-1] + + # split tokens + tokens = sample["text"].split(" ") + + def strip_pronoun(x): + return x.rstrip('.,"') + + # find the pronoun + pronoun_idx = target["span2_index"] + pronoun = strip_pronoun(target["span2_text"]) + if strip_pronoun(tokens[pronoun_idx]) != pronoun: + # hack: sometimes the index is misaligned + if strip_pronoun(tokens[pronoun_idx + 1]) == pronoun: + pronoun_idx += 1 + else: + raise Exception("Misaligned pronoun!") + assert strip_pronoun(tokens[pronoun_idx]) == pronoun + + # split tokens before and after the pronoun + before = tokens[:pronoun_idx] + after = tokens[pronoun_idx + 1 :] + + # the GPT BPE attaches leading spaces to tokens, so we keep track + # of whether we need spaces before or after the pronoun + leading_space = " " if pronoun_idx > 0 else "" + trailing_space = " " if len(after) > 0 else "" + + # detokenize + before = detok.detokenize(before, return_str=True) + pronoun = detok.detokenize([pronoun], return_str=True) + after = detok.detokenize(after, return_str=True) + + # hack: when the pronoun ends in a period (or comma), move the + # punctuation to the "after" part + if pronoun.endswith(".") or pronoun.endswith(","): + after = pronoun[-1] + trailing_space + after + pronoun = pronoun[:-1] + + # hack: when the "after" part begins with a comma or period, remove + # the trailing space + if after.startswith(".") or after.startswith(","): + trailing_space = "" + + # parse sentence with spacy + sentence = nlp(before + leading_space + pronoun + trailing_space + after) + + # find pronoun span + start = len(before + leading_space) + first_pronoun_tok = find_token(sentence, start_pos=start) + pronoun_span = find_span(sentence, pronoun, start=first_pronoun_tok.i) + assert pronoun_span.text == pronoun + + if eval: + # convert to format where pronoun is surrounded by "[]" and + # query is surrounded by "_" + query_span = find_span(sentence, query) + query_with_ws = "_{}_{}".format( + query_span.text, + (" " if query_span.text_with_ws.endswith(" ") else ""), + ) + pronoun_with_ws = "[{}]{}".format( + pronoun_span.text, + (" " if pronoun_span.text_with_ws.endswith(" ") else ""), + ) + if query_span.start < pronoun_span.start: + first = (query_span, query_with_ws) + second = (pronoun_span, pronoun_with_ws) + else: + first = (pronoun_span, pronoun_with_ws) + second = (query_span, query_with_ws) + sentence = ( + sentence[: first[0].start].text_with_ws + + first[1] + + sentence[first[0].end : second[0].start].text_with_ws + + second[1] + + sentence[second[0].end :].text + ) + yield sentence, sample.get("label", None) + else: + yield sentence, pronoun_span, query, sample.get("label", None) + + +def winogrande_jsonl_iterator(input_fname, eval=False): + with open(input_fname) as fin: + for line in fin: + sample = json.loads(line.strip()) + sentence, option1, option2 = ( + sample["sentence"], + sample["option1"], + sample["option2"], + ) + + pronoun_span = (sentence.index("_"), sentence.index("_") + 1) + + if eval: + query, cand = option1, option2 + else: + query = option1 if sample["answer"] == "1" else option2 + cand = option2 if sample["answer"] == "1" else option1 + yield sentence, pronoun_span, query, cand + + +def filter_noun_chunks( + chunks, exclude_pronouns=False, exclude_query=None, exact_match=False +): + if exclude_pronouns: + chunks = [ + np + for np in chunks + if (np.lemma_ != "-PRON-" and not all(tok.pos_ == "PRON" for tok in np)) + ] + + if exclude_query is not None: + excl_txt = [exclude_query.lower()] + filtered_chunks = [] + for chunk in chunks: + lower_chunk = chunk.text.lower() + found = False + for excl in excl_txt: + if ( + not exact_match and (lower_chunk in excl or excl in lower_chunk) + ) or lower_chunk == excl: + found = True + break + if not found: + filtered_chunks.append(chunk) + chunks = filtered_chunks + + return chunks diff --git a/fairseq/examples/rxf/README.md b/fairseq/examples/rxf/README.md new file mode 100644 index 0000000..22a1cc4 --- /dev/null +++ b/fairseq/examples/rxf/README.md @@ -0,0 +1,52 @@ +[Better Fine-Tuning by Reducing Representational Collapse](https://arxiv.org/abs/2008.03156) +===================== +This repo contains the code to replicate all experiments from the _Better Fine-Tuning by Reducing Representational Collapse_ paper excluding the probing results. + +The R3F sentence prediction criterion is registered as `sentence_prediction_r3f` while the label smoothing version of it is implemented as `label_smoothed_cross_entropy_r3f`. The R4F version of the sentence prediction criterion can be achieved by applying spectral norm to the classification head via the `--spectral-norm-classification-head` parameter. + +## Hyper-parameters +Our methods introduce 3 new hyper-parameters; `--eps` which sets the standard deviation or range of the distribution we're sampling from, `--r3f-lambda` which controls the combining of logistic loss and noisy KL loss and `--noise-type` which controls which parametric distribution we use ('normal', 'uniform'). + +For example to run R3F on RTE from GLUE + +``` +TOTAL_NUM_UPDATES=3120 +WARMUP_UPDATES=187 +LR=1e-05 +NUM_CLASSES=2 +MAX_SENTENCES=8 # Batch size. +ROBERTA_PATH=/path/to/roberta/model.pt + +CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin \ + --restore-file $ROBERTA_PATH \ + --max-positions 512 \ + --max-sentences $MAX_SENTENCES \ + --max-tokens 4400 \ + --task sentence_prediction \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --init-token 0 --separator-token 2 \ + --arch roberta_large \ + --criterion sentence_prediction_r3f \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --max-epoch 10 \ + --find-unused-parameters \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --noise-type uniform --r3f-lambda 0.7 \ + --user-dir examples/rxf/rxf_src +``` + +## Citation +```bibtex +@article{aghajanyan2020better, + title={Better Fine-Tuning by Reducing Representational Collapse}, + author={Aghajanyan, Armen and Shrivastava, Akshat and Gupta, Anchit and Goyal, Naman and Zettlemoyer, Luke and Gupta, Sonal}, + journal={arXiv preprint arXiv:2008.03156}, + year={2020} +} +``` diff --git a/fairseq/examples/rxf/__init__.py b/fairseq/examples/rxf/__init__.py new file mode 100644 index 0000000..b24cb6b --- /dev/null +++ b/fairseq/examples/rxf/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import rxf_src # noqa diff --git a/fairseq/examples/rxf/rxf_src/__init__.py b/fairseq/examples/rxf/rxf_src/__init__.py new file mode 100644 index 0000000..306e232 --- /dev/null +++ b/fairseq/examples/rxf/rxf_src/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import label_smoothed_cross_entropy_r3f, sentence_prediction_r3f # noqa diff --git a/fairseq/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py b/fairseq/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py new file mode 100644 index 0000000..6191fd5 --- /dev/null +++ b/fairseq/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py @@ -0,0 +1,158 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss + + +@register_criterion("label_smoothed_cross_entropy_r3f") +class LabelSmoothedCrossEntropyR3FCriterion(FairseqCriterion): + def __init__( + self, task, sentence_avg, label_smoothing, eps, r3f_lambda, noise_type + ): + super().__init__(task) + self.sentence_avg = sentence_avg + self.label_smoothing = label_smoothing + self.eps = eps + self.r3f_lambda = r3f_lambda + self.noise_type = noise_type + if self.noise_type in {"normal"}: + self.noise_sampler = torch.distributions.normal.Normal( + loc=0.0, scale=self.eps + ) + elif self.noise_type == "uniform": + self.noise_sampler = torch.distributions.uniform.Uniform( + low=-self.eps, high=self.eps + ) + else: + raise Exception(f"unrecognized noise type {self.noise_type}") + + @staticmethod + def add_args(parser): + """Add criterion-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', + help='epsilon for label smoothing, 0 means no label smoothing') + parser.add_argument('--eps', type=float, default=1e-5, + help='noise eps') + parser.add_argument('--r3f-lambda', type=float, default=1.0, + help='lambda for combining logistic loss and noisy KL loss') + parser.add_argument('--noise-type', type=str, default='normal', + choices=['normal', 'uniform'], + help='type of noises') + # fmt: on + + def _get_symm_kl(self, noised_logits, input_logits): + return ( + F.kl_div( + F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), + F.softmax(input_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + + F.kl_div( + F.log_softmax(input_logits, dim=-1, dtype=torch.float32), + F.softmax(noised_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + ) / noised_logits.size(0) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + token_embeddings = model.encoder.embed_tokens(sample["net_input"]["src_tokens"]) + input_logits, extra = model(**sample["net_input"]) + loss, nll_loss = self.compute_loss( + model, (input_logits, extra), sample, reduce=reduce + ) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + + if model.training: + noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( + token_embeddings + ) + noised_embeddings = token_embeddings.clone() + noise + + noised_logits, _ = model( + **sample["net_input"], token_embeddings=noised_embeddings + ) + symm_kl = self._get_symm_kl(noised_logits, input_logits) + + if model.training: + symm_kl = symm_kl * sample_size + loss = loss + self.r3f_lambda * symm_kl + + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + + if model.training: + logging_output.update( + symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data + ) + + return loss, sample_size, logging_output + + def compute_loss(self, model, net_output, sample, reduce=True): + lprobs = model.get_normalized_probs(net_output, log_probs=True) + lprobs = lprobs.view(-1, lprobs.size(-1)) + target = model.get_targets(sample, net_output).view(-1, 1) + loss, nll_loss = label_smoothed_nll_loss( + lprobs, + target, + self.label_smoothing, + ignore_index=self.padding_idx, + reduce=reduce, + ) + return loss, nll_loss + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) + + metrics.log_scalar("symm_kl", symm_kl_sum / sample_size, sample_size, round=3) + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py b/fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py new file mode 100644 index 0000000..6ecffd6 --- /dev/null +++ b/fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py @@ -0,0 +1,171 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.criterions import FairseqCriterion, register_criterion + + +@register_criterion("sentence_prediction_r3f") +class SentencePredictionR3F(FairseqCriterion): + def __init__( + self, + task, + eps, + r3f_lambda, + noise_type, + classification_head_name, + regression_target, + ): + super().__init__(task) + self.eps = eps + self.r3f_lambda = r3f_lambda + self.noise_type = noise_type + self.classification_head_name = classification_head_name + self.regression_target = regression_target + if self.noise_type in {"normal"}: + self.noise_sampler = torch.distributions.normal.Normal( + loc=0.0, scale=self.eps + ) + elif self.noise_type == "uniform": + self.noise_sampler = torch.distributions.uniform.Uniform( + low=-self.eps, high=self.eps + ) + else: + raise Exception(f"unrecognized noise type {self.noise_type}") + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--eps', type=float, default=1e-5, + help='noise eps') + parser.add_argument('--r3f-lambda', type=float, default=1.0, + help='lambda for combining logistic loss and noisy KL loss') + parser.add_argument('--noise-type', type=str, default='uniform', + choices=['normal', 'uniform'], + help='type of noises for RXF methods') + parser.add_argument('--classification-head-name', + default='sentence_classification_head', + help='name of the classification head to use') + parser.add_argument('--regression-target', action='store_true') + # fmt: on + + def _get_symm_kl(self, noised_logits, input_logits): + return ( + F.kl_div( + F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), + F.softmax(input_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + + F.kl_div( + F.log_softmax(input_logits, dim=-1, dtype=torch.float32), + F.softmax(noised_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + ) / noised_logits.size(0) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + assert ( + hasattr(model, "classification_heads") + and self.classification_head_name in model.classification_heads + ), "model must provide sentence classification head for --criterion=sentence_prediction" + + token_embeddings = model.encoder.sentence_encoder.embed_tokens( + sample["net_input"]["src_tokens"] + ) + input_logits, _ = model( + **sample["net_input"], + features_only=True, + classification_head_name=self.classification_head_name, + token_embeddings=token_embeddings, + ) + if model.training and self.noise_sampler: + noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( + token_embeddings + ) + noised_embeddings = token_embeddings.detach().clone() + noise + + noised_logits, _ = model( + **sample["net_input"], + features_only=True, + classification_head_name=self.classification_head_name, + token_embeddings=noised_embeddings, + ) + symm_kl = self._get_symm_kl(noised_logits, input_logits) + else: + symm_kl = 0 + + targets = model.get_targets(sample, [input_logits]).view(-1) + sample_size = targets.numel() + + if not self.regression_target: + loss = F.nll_loss( + F.log_softmax(input_logits, dim=-1, dtype=torch.float32), + targets, + reduction="sum", + ) + if model.training: + symm_kl = symm_kl * sample_size + loss = loss + self.r3f_lambda * symm_kl + else: + logits = input_logits.squeeze().float() + targets = targets.float() + loss = F.mse_loss(logits, targets, reduction="sum") + + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample_size, + "sample_size": sample_size, + } + + if not self.regression_target: + preds = input_logits.max(dim=1)[1] + logging_output.update(ncorrect=(preds == targets).sum().item()) + + if model.training and self.noise_sampler: + logging_output.update( + symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data + ) + return loss, sample_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + agg_output = { + "loss": loss_sum / sample_size / math.log(2), + "symm_kl": symm_kl_sum / sample_size, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + + if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + agg_output.update(accuracy=ncorrect / nsentences) + + if sample_size != ntokens: + agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) + return agg_output diff --git a/fairseq/examples/scaling_nmt/README.md b/fairseq/examples/scaling_nmt/README.md new file mode 100644 index 0000000..0cc3360 --- /dev/null +++ b/fairseq/examples/scaling_nmt/README.md @@ -0,0 +1,114 @@ +# Scaling Neural Machine Translation (Ott et al., 2018) + +This page includes instructions for reproducing results from the paper [Scaling Neural Machine Translation (Ott et al., 2018)](https://arxiv.org/abs/1806.00187). + +## Pre-trained models + +Model | Description | Dataset | Download +---|---|---|--- +`transformer.wmt14.en-fr` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) +`transformer.wmt16.en-de` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) + +## Training a new model on WMT'16 En-De + +First download the [preprocessed WMT'16 En-De data provided by Google](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8). + +Then: + +##### 1. Extract the WMT'16 En-De data +```bash +TEXT=wmt16_en_de_bpe32k +mkdir -p $TEXT +tar -xzvf wmt16_en_de.tar.gz -C $TEXT +``` + +##### 2. Preprocess the dataset with a joined dictionary +```bash +fairseq-preprocess \ + --source-lang en --target-lang de \ + --trainpref $TEXT/train.tok.clean.bpe.32000 \ + --validpref $TEXT/newstest2013.tok.bpe.32000 \ + --testpref $TEXT/newstest2014.tok.bpe.32000 \ + --destdir data-bin/wmt16_en_de_bpe32k \ + --nwordssrc 32768 --nwordstgt 32768 \ + --joined-dictionary \ + --workers 20 +``` + +##### 3. Train a model +```bash +fairseq-train \ + data-bin/wmt16_en_de_bpe32k \ + --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ + --dropout 0.3 --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --max-tokens 3584 \ + --fp16 +``` + +Note that the `--fp16` flag requires you have CUDA 9.1 or greater and a Volta GPU or newer. + +***IMPORTANT:*** You will get better performance by training with big batches and +increasing the learning rate. If you want to train the above model with big batches +(assuming your machine has 8 GPUs): +- add `--update-freq 16` to simulate training on 8x16=128 GPUs +- increase the learning rate; 0.001 works well for big batches + +##### 4. Evaluate + +Now we can evaluate our trained model. + +Note that the original [Attention Is All You Need](https://arxiv.org/abs/1706.03762) +paper used a couple tricks to achieve better BLEU scores. We use these same tricks in +the Scaling NMT paper, so it's important to apply them when reproducing our results. + +First, use the [average_checkpoints.py](/scripts/average_checkpoints.py) script to +average the last few checkpoints. Averaging the last 5-10 checkpoints is usually +good, but you may need to adjust this depending on how long you've trained: +```bash +python scripts/average_checkpoints \ + --inputs /path/to/checkpoints \ + --num-epoch-checkpoints 10 \ + --output checkpoint.avg10.pt +``` + +Next, generate translations using a beam width of 4 and length penalty of 0.6: +```bash +fairseq-generate \ + data-bin/wmt16_en_de_bpe32k \ + --path checkpoint.avg10.pt \ + --beam 4 --lenpen 0.6 --remove-bpe > gen.out +``` + +Finally, we apply the ["compound splitting" script](/scripts/compound_split_bleu.sh) to +add spaces around dashes. For example "Café-Liebhaber" would become three tokens: +"Café - Liebhaber". This typically results in larger BLEU scores, but it is not +appropriate to compare these inflated scores to work which does not include this trick. +This trick was used in the [original AIAYN code](https://github.com/tensorflow/tensor2tensor/blob/fc9335c0203685cbbfe2b30c92db4352d8f60779/tensor2tensor/utils/get_ende_bleu.sh), +so we used it in the Scaling NMT paper as well. That said, it's strongly advised to +report [sacrebleu](https://github.com/mjpost/sacrebleu) scores instead. + +To compute "compound split" tokenized BLEU (not recommended!): +```bash +bash scripts/compound_split_bleu.sh gen.out +# BLEU4 = 29.29, 60.3/35.0/22.8/15.3 (BP=1.000, ratio=1.004, syslen=64763, reflen=64496) +``` + +To compute detokenized BLEU with sacrebleu (preferred): +```bash +bash scripts/sacrebleu.sh wmt14/full en de gen.out +# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt14/full+tok.13a+version.1.4.3 = 28.6 59.3/34.3/22.1/14.9 (BP = 1.000 ratio = 1.016 hyp_len = 63666 ref_len = 62688) +``` + +## Citation + +```bibtex +@inproceedings{ott2018scaling, + title = {Scaling Neural Machine Translation}, + author = {Ott, Myle and Edunov, Sergey and Grangier, David and Auli, Michael}, + booktitle = {Proceedings of the Third Conference on Machine Translation (WMT)}, + year = 2018, +} +``` diff --git a/fairseq/examples/shuffled_word_order/README.finetuning.md b/fairseq/examples/shuffled_word_order/README.finetuning.md new file mode 100644 index 0000000..ecbcb65 --- /dev/null +++ b/fairseq/examples/shuffled_word_order/README.finetuning.md @@ -0,0 +1,135 @@ +# Fine-tuning details + +For each task (GLUE and PAWS), we perform hyperparam search for each model, and report the mean and standard deviation across 5 seeds of the best model. First, get the datasets following the instructions in [RoBERTa fine-tuning README](../roberta/README.glue.md). Alternatively, you can use [huggingface datasets](https://huggingface.co/docs/datasets/) to get the task data: + +```python +from datasets import load_dataset +import pandas as pd +from pathlib import Path + +key2file = { +"paws": { + "loc": "paws_data", + "columns": ["id", "sentence1", "sentence2", "label"], + "train": "train.tsv", + "validation": "dev.tsv", + "test": "test.tsv" + } +} + +task_data = load_dataset("paws", "labeled_final") +task_config = key2file["paws"] +save_path = Path(task_config["loc"]) +save_path.mkdir(exist_ok=True, parents=True) +for key, fl in task_config.items(): + if key in ["loc", "columns"]: + continue + print(f"Reading {key}") + columns = task_config["columns"] + df = pd.DataFrame(task_data[key]) + print(df.columns) + df = df[columns] + print(f"Got {len(df)} records") + save_loc = save_path / fl + print(f"Saving to : {save_loc}") + df.to_csv(save_loc, sep="\t", header=None, index=None) + +``` + +- Preprocess using RoBERTa GLUE preprocessing script, while keeping in mind the column numbers for `sentence1`, `sentence2` and `label` (which is 0,1,2 if you save the data according to the above example.) +- Then, fine-tuning is performed similarly to RoBERTa (for example, in case of RTE): + +```bash +TOTAL_NUM_UPDATES=30875 # 10 epochs through RTE for bsz 16 +WARMUP_UPDATES=1852 # 6 percent of the number of updates +LR=2e-05 # Peak LR for polynomial LR scheduler. +NUM_CLASSES=2 +MAX_SENTENCES=16 # Batch size. +SHUFFLED_ROBERTA_PATH=/path/to/shuffled_roberta/model.pt + +CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin/ \ + --restore-file $SHUFFLED_ROBERTA_PATH \ + --max-positions 512 \ + --batch-size $MAX_SENTENCES \ + --max-tokens 4400 \ + --task sentence_prediction \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --init-token 0 --separator-token 2 \ + --arch roberta_large \ + --criterion sentence_prediction \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --max-epoch 10 \ + --find-unused-parameters \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric; +``` + +- `TOTAL_NUM_UPDATES` is computed based on the `--batch_size` value and the dataset size. +- `WARMUP_UPDATES` is computed as 6% of `TOTAL_NUM_UPDATES` +- Best hyperparam of `--lr` and `--batch_size` is reported below: + +## `--lr` + +| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | +| --: | :----------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | +| 0 | original | 2e-05 | 2e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | +| 1 | n_1 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | +| 2 | n_2 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 3e-05 | +| 3 | n_3 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 3e-05 | 1e-05 | 1e-05 | 2e-05 | +| 4 | n_4 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | +| 5 | r512 | 1e-05 | 3e-05 | 2e-05 | 2e-05 | 3e-05 | 2e-05 | 3e-05 | 2e-05 | +| 6 | rand_corpus | 2e-05 | 1e-05 | 3e-05 | 1e-05 | 3e-05 | 3e-05 | 3e-05 | 2e-05 | +| 7 | rand_uniform | 2e-05 | 1e-05 | 3e-05 | 2e-05 | 3e-05 | 3e-05 | 3e-05 | 1e-05 | +| 8 | rand_init | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | +| 9 | no_pos | 1e-05 | 3e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | + +## `--batch_size` + +| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | +| --: | :----------- | --: | ---: | ----: | ---: | --: | ---: | ---: | ---: | +| 0 | orig | 16 | 16 | 32 | 16 | 16 | 32 | 32 | 16 | +| 1 | n_1 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 16 | +| 2 | n_2 | 32 | 16 | 32 | 16 | 32 | 32 | 16 | 32 | +| 3 | n_3 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 32 | +| 4 | n_4 | 32 | 16 | 32 | 16 | 32 | 32 | 32 | 32 | +| 5 | r512 | 32 | 16 | 16 | 32 | 32 | 16 | 16 | 16 | +| 6 | rand_corpus | 16 | 16 | 16 | 16 | 32 | 16 | 16 | 32 | +| 7 | rand_uniform | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | +| 8 | rand_init | 16 | 16 | 32 | 16 | 16 | 16 | 32 | 16 | +| 9 | no_pos | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | + +- Perform inference similar to RoBERTa as well: + +```python +from fairseq.models.roberta import RobertaModel + +roberta = RobertaModel.from_pretrained( + 'checkpoints/', + checkpoint_file='checkpoint_best.pt', + data_name_or_path='PAWS-bin' +) + +label_fn = lambda label: roberta.task.label_dictionary.string( + [label + roberta.task.label_dictionary.nspecial] +) +ncorrect, nsamples = 0, 0 +roberta.cuda() +roberta.eval() +with open('paws_data/dev.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[0], tokens[1], tokens[2] + tokens = roberta.encode(sent1, sent2) + prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() + prediction_label = label_fn(prediction) + ncorrect += int(prediction_label == target) + nsamples += 1 +print('| Accuracy: ', float(ncorrect)/float(nsamples)) + +``` diff --git a/fairseq/examples/shuffled_word_order/README.md b/fairseq/examples/shuffled_word_order/README.md new file mode 100644 index 0000000..6ce0b39 --- /dev/null +++ b/fairseq/examples/shuffled_word_order/README.md @@ -0,0 +1,94 @@ +# Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little + +[https://arxiv.org/abs/2104.06644](https://arxiv.org/abs/2104.06644) + +## Introduction + +In this work, we pre-train [RoBERTa](../roberta) base on various word shuffled variants of BookWiki corpus (16GB). We observe that a word shuffled pre-trained model achieves surprisingly good scores on GLUE, PAWS and several parametric probing tasks. Please read our paper for more details on the experiments. + +## Pre-trained models + +| Model | Description | Download | +| ------------------------------------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | +| `roberta.base.orig` | RoBERTa (base) trained on natural corpus | [roberta.base.orig.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.orig.tar.gz) | +| `roberta.base.shuffle.n1` | RoBERTa (base) trained on n=1 gram sentence word shuffled data | [roberta.base.shuffle.n1.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.tar.gz) | +| `roberta.base.shuffle.n2` | RoBERTa (base) trained on n=2 gram sentence word shuffled data | [roberta.base.shuffle.n2.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n2.tar.gz) | +| `roberta.base.shuffle.n3` | RoBERTa (base) trained on n=3 gram sentence word shuffled data | [roberta.base.shuffle.n3.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n3.tar.gz) | +| `roberta.base.shuffle.n4` | RoBERTa (base) trained on n=4 gram sentence word shuffled data | [roberta.base.shuffle.n4.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n4.tar.gz) | +| `roberta.base.shuffle.512` | RoBERTa (base) trained on unigram 512 word block shuffled data | [roberta.base.shuffle.512.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.512.tar.gz) | +| `roberta.base.shuffle.corpus` | RoBERTa (base) trained on unigram corpus word shuffled data | [roberta.base.shuffle.corpus.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus.tar.gz) | +| `roberta.base.shuffle.corpus_uniform` | RoBERTa (base) trained on unigram corpus word shuffled data, where all words are uniformly sampled | [roberta.base.shuffle.corpus_uniform.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus_uniform.tar.gz) | +| `roberta.base.nopos` | RoBERTa (base) without positional embeddings, trained on natural corpus | [roberta.base.nopos.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.nopos.tar.gz) | + +## Results + +[GLUE (Wang et al, 2019)](https://gluebenchmark.com/) & [PAWS (Zhang et al, 2019)](https://github.com/google-research-datasets/paws) _(dev set, single model, single-task fine-tuning, median of 5 seeds)_ + +| name | CoLA | MNLI | MRPC | PAWS | QNLI | QQP | RTE | SST-2 | +| :----------------------------------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | +| `roberta.base.orig` | 61.4 | 86.11 | 89.19 | 94.46 | 92.53 | 91.26 | 74.64 | 93.92 | +| `roberta.base.shuffle.n1` | 35.15 | 82.64 | 86 | 89.97 | 89.02 | 91.01 | 69.02 | 90.47 | +| `roberta.base.shuffle.n2` | 54.37 | 83.43 | 86.24 | 93.46 | 90.44 | 91.36 | 70.83 | 91.79 | +| `roberta.base.shuffle.n3` | 48.72 | 83.85 | 86.36 | 94.05 | 91.69 | 91.24 | 70.65 | 92.02 | +| `roberta.base.shuffle.n4` | 58.64 | 83.77 | 86.98 | 94.32 | 91.69 | 91.4 | 70.83 | 92.48 | +| `roberta.base.shuffle.512` | 12.76 | 77.52 | 79.61 | 84.77 | 85.19 | 90.2 | 56.52 | 86.34 | +| `roberta.base.shuffle.corpus` | 0 | 71.9 | 70.52 | 58.52 | 71.11 | 85.52 | 53.99 | 83.35 | +| `roberta.base.shuffle.corpus_random` | 9.19 | 72.33 | 70.76 | 58.42 | 77.76 | 85.93 | 53.99 | 84.04 | +| `roberta.base.nopos` | 0 | 63.5 | 72.73 | 57.08 | 77.72 | 87.87 | 54.35 | 83.24 | + +For more results on probing tasks, please refer to [our paper](https://arxiv.org/abs/2104.06644). + +## Example Usage + +Follow the same usage as in [RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta) to load and test your models: + +```python +# Download roberta.base.shuffle.n1 model +wget https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.tar.gz +tar -xzvf roberta.base.shuffle.n1.tar.gz +# Copy the dictionary files +cd roberta.base.shuffle.n1.tar.gz +wget -O dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt && wget -O encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json && wget -O vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe +cd .. + +# Load the model in fairseq +from fairseq.models.roberta import RobertaModel +roberta = RobertaModel.from_pretrained('/path/to/roberta.base.shuffle.n1', checkpoint_file='model.pt') +roberta.eval() # disable dropout (or leave in train mode to finetune) +``` + +We have also provided a [Google Colab](https://colab.research.google.com/drive/1IJDVfNVWdvRfLjphQKBGzmob84t-OXpm) notebook to demonstrate the loading of the model. The models were trained on top of Fairseq from the following commit: [62cff008ebeeed855093837507d5e6bf52065ee6](https://github.com/pytorch/fairseq/commit/62cff008ebeeed855093837507d5e6bf52065ee6). + +**Note**: The model trained without positional embeddings (`roberta.base.nopos`) is a modified `RoBERTa` model, where the positional embeddings are not used. Thus, the typical `from_pretrained` method on fairseq version of RoBERTa will not be able to load the above model weights. To do so, construct a new `RoBERTaModel` object by setting the flag `use_positional_embeddings` to `False` (or [in the latest code](https://github.com/pytorch/fairseq/blob/main/fairseq/models/roberta/model.py#L543), set `no_token_positional_embeddings` to `True`), and then load the individual weights. + +## Fine-tuning Evaluation + +We provide the trained fine-tuned models on MNLI here for each model above for quick evaluation (1 seed for each model). Please refer to [finetuning details](README.finetuning.md) for the parameters of these models. Follow [RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta) instructions to evaluate these models. + +| Model | MNLI M Dev Accuracy | Link | +| :----------------------------------------- | :------------------ | :--------------------------------------------------------------------------------------------------------------- | +| `roberta.base.orig.mnli` | 86.14 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.orig.mnli.tar.gz) | +| `roberta.base.shuffle.n1.mnli` | 82.55 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.mnli.tar.gz) | +| `roberta.base.shuffle.n2.mnli` | 83.21 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n2.mnli.tar.gz) | +| `roberta.base.shuffle.n3.mnli` | 83.89 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n3.mnli.tar.gz) | +| `roberta.base.shuffle.n4.mnli` | 84.00 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n4.mnli.tar.gz) | +| `roberta.base.shuffle.512.mnli` | 77.22 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.512.mnli.tar.gz) | +| `roberta.base.shuffle.corpus.mnli` | 71.88 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus.mnli.tar.gz) | +| `roberta.base.shuffle.corpus_uniform.mnli` | 72.46 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus_uniform.mnli.tar.gz) | + +## Citation + +```bibtex +@misc{sinha2021masked, + title={Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little}, + author={Koustuv Sinha and Robin Jia and Dieuwke Hupkes and Joelle Pineau and Adina Williams and Douwe Kiela}, + year={2021}, + eprint={2104.06644}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + +## Contact + +For questions and comments, please reach out to Koustuv Sinha (koustuv.sinha@mail.mcgill.ca). diff --git a/fairseq/examples/simultaneous_translation/README.md b/fairseq/examples/simultaneous_translation/README.md new file mode 100644 index 0000000..62a005e --- /dev/null +++ b/fairseq/examples/simultaneous_translation/README.md @@ -0,0 +1,5 @@ +# Simultaneous Translation +Examples of simultaneous translation in fairseq +- [English-to-Japanese text-to-text wait-k model](docs/enja-waitk.md) +- [English-to-Germen text-to-text monotonic multihead attention model](docs/ende-mma.md) +- [English-to-Germen speech-to-text simultaneous translation model](../speech_to_text/docs/simulst_mustc_example.md) diff --git a/fairseq/examples/simultaneous_translation/__init__.py b/fairseq/examples/simultaneous_translation/__init__.py new file mode 100644 index 0000000..5835316 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import models # noqa diff --git a/fairseq/examples/simultaneous_translation/docs/ende-mma.md b/fairseq/examples/simultaneous_translation/docs/ende-mma.md new file mode 100644 index 0000000..241d604 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/docs/ende-mma.md @@ -0,0 +1,74 @@ +# Simultaneous Machine Translation + +This directory contains the code for the paper [Monotonic Multihead Attention](https://openreview.net/forum?id=Hyg96gBKPS) + +## Prepare Data + +[Please follow the instructions to download and preprocess the WMT'15 En-De dataset.](https://github.com/pytorch/fairseq/tree/simulastsharedtask/examples/translation#prepare-wmt14en2desh) + +Another example of training an English to Japanese model can be found [here](docs/enja.md) + +## Training + +- MMA-IL + +```shell +fairseq-train \ + data-bin/wmt15_en_de_32k \ + --simul-type infinite_lookback \ + --user-dir $FAIRSEQ/example/simultaneous_translation \ + --mass-preservation \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --latency-weight-avg 0.1 \ + --max-update 50000 \ + --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr-scheduler 'inverse_sqrt' \ + --warmup-init-lr 1e-7 --warmup-updates 4000 \ + --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ + --dropout 0.3 \ + --label-smoothing 0.1\ + --max-tokens 3584 +``` + +- MMA-H + +```shell +fairseq-train \ + data-bin/wmt15_en_de_32k \ + --simul-type hard_aligned \ + --user-dir $FAIRSEQ/example/simultaneous_translation \ + --mass-preservation \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --latency-weight-var 0.1 \ + --max-update 50000 \ + --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr-scheduler 'inverse_sqrt' \ + --warmup-init-lr 1e-7 --warmup-updates 4000 \ + --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ + --dropout 0.3 \ + --label-smoothing 0.1\ + --max-tokens 3584 +``` + +- wait-k + +```shell +fairseq-train \ + data-bin/wmt15_en_de_32k \ + --simul-type wait-k \ + --waitk-lagging 3 \ + --user-dir $FAIRSEQ/example/simultaneous_translation \ + --mass-preservation \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --max-update 50000 \ + --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr-scheduler 'inverse_sqrt' \ + --warmup-init-lr 1e-7 --warmup-updates 4000 \ + --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ + --dropout 0.3 \ + --label-smoothing 0.1\ + --max-tokens 3584 +``` diff --git a/fairseq/examples/simultaneous_translation/docs/enja-waitk.md b/fairseq/examples/simultaneous_translation/docs/enja-waitk.md new file mode 100644 index 0000000..fb9d825 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/docs/enja-waitk.md @@ -0,0 +1,106 @@ +# An example of English to Japaneses Simultaneous Translation System + +This is an example of training and evaluating a transformer *wait-k* English to Japanese simultaneous text-to-text translation model. + +## Data Preparation +This section introduces the data preparation for training and evaluation. +If you only want to evaluate the model, please jump to [Inference & Evaluation](#inference-&-evaluation) + +For illustration, we only use the following subsets of the available data from [WMT20 news translation task](http://www.statmt.org/wmt20/translation-task.html), which results in 7,815,391 sentence pairs. +- News Commentary v16 +- Wiki Titles v3 +- WikiMatrix V1 +- Japanese-English Subtitle Corpus +- The Kyoto Free Translation Task Corpus + +We use WMT20 development data as development set. Training `transformer_vaswani_wmt_en_de_big` model on such amount of data will result in 17.3 BLEU with greedy search and 19.7 with beam (10) search. Notice that a better performance can be achieved with the full WMT training data. + +We use [sentencepiece](https://github.com/google/sentencepiece) toolkit to tokenize the data with a vocabulary size of 32000. +Additionally, we filtered out the sentences longer than 200 words after tokenization. +Assuming the tokenized text data is saved at `${DATA_DIR}`, +we prepare the data binary with the following command. + +```bash +fairseq-preprocess \ + --source-lang en --target-lang ja \ + --trainpref ${DATA_DIR}/train \ + --validpref ${DATA_DIR}/dev \ + --testpref ${DATA_DIR}/test \ + --destdir ${WMT20_ENJA_DATA_BIN} \ + --nwordstgt 32000 --nwordssrc 32000 \ + --workers 20 +``` + +## Simultaneous Translation Model Training +To train a wait-k `(k=10)` model. +```bash +fairseq-train ${WMT20_ENJA_DATA_BIN} \ + --save-dir ${SAVEDIR} + --simul-type waitk \ + --waitk-lagging 10 \ + --max-epoch 70 \ + --arch transformer_monotonic_vaswani_wmt_en_de_big \ + --optimizer adam \ + --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt \ + --warmup-init-lr 1e-07 \ + --warmup-updates 4000 \ + --lr 0.0005 \ + --stop-min-lr 1e-09 \ + --clip-norm 10.0 \ + --dropout 0.3 \ + --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy \ + --label-smoothing 0.1 \ + --max-tokens 3584 +``` +This command is for training on 8 GPUs. Equivalently, the model can be trained on one GPU with `--update-freq 8`. + +## Inference & Evaluation +First of all, install [SimulEval](https://github.com/facebookresearch/SimulEval) for evaluation. + +```bash +git clone https://github.com/facebookresearch/SimulEval.git +cd SimulEval +pip install -e . +``` + +The following command is for the evaluation. +Assuming the source and reference files are `${SRC_FILE}` and `${REF_FILE}`, the sentencepiece model file for English is saved at `${SRC_SPM_PATH}` + + +```bash +simuleval \ + --source ${SRC_FILE} \ + --target ${TGT_FILE} \ + --data-bin ${WMT20_ENJA_DATA_BIN} \ + --sacrebleu-tokenizer ja-mecab \ + --eval-latency-unit char \ + --no-space \ + --src-splitter-type sentencepiecemodel \ + --src-splitter-path ${SRC_SPM_PATH} \ + --agent ${FAIRSEQ}/examples/simultaneous_translation/agents/simul_trans_text_agent_enja.py \ + --model-path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --output ${OUTPUT} \ + --scores +``` + +The `--data-bin` should be the same in previous sections if you prepare the data from the scratch. +If only for evaluation, a prepared data directory can be found [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_databin.tgz) and a pretrained checkpoint (wait-k=10 model) can be downloaded from [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_wait10_ckpt.pt). + +The output should look like this: +```bash +{ + "Quality": { + "BLEU": 11.442253287568398 + }, + "Latency": { + "AL": 8.6587861866951, + "AP": 0.7863304776251316, + "DAL": 9.477850951194764 + } +} +``` +The latency is evaluated by characters (`--eval-latency-unit`) on the target side. The latency is evaluated with `sacrebleu` with `MeCab` tokenizer `--sacrebleu-tokenizer ja-mecab`. `--no-space` indicates that do not add space when merging the predicted words. + +If `--output ${OUTPUT}` option is used, the detailed log and scores will be stored under the `${OUTPUT}` directory. diff --git a/fairseq/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py b/fairseq/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py new file mode 100644 index 0000000..8f3c870 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py @@ -0,0 +1,226 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os + +from fairseq import checkpoint_utils, tasks +import sentencepiece as spm +import torch + +try: + from simuleval import READ_ACTION, WRITE_ACTION, DEFAULT_EOS + from simuleval.agents import TextAgent +except ImportError: + print("Please install simuleval 'pip install simuleval'") + + +BOS_PREFIX = "\u2581" + + +class SimulTransTextAgentJA(TextAgent): + """ + Simultaneous Translation + Text agent for Japanese + """ + def __init__(self, args): + + # Whether use gpu + self.gpu = getattr(args, "gpu", False) + + # Max len + self.max_len = args.max_len + + # Load Model + self.load_model_vocab(args) + + # build word splitter + self.build_word_splitter(args) + + self.eos = DEFAULT_EOS + + def initialize_states(self, states): + states.incremental_states = dict() + states.incremental_states["online"] = dict() + + def to_device(self, tensor): + if self.gpu: + return tensor.cuda() + else: + return tensor.cpu() + + def load_model_vocab(self, args): + + filename = args.model_path + if not os.path.exists(filename): + raise IOError("Model file not found: {}".format(filename)) + + state = checkpoint_utils.load_checkpoint_to_cpu(filename) + + task_args = state["cfg"]["task"] + task_args.data = args.data_bin + + task = tasks.setup_task(task_args) + + # build model for ensemble + state["cfg"]["model"].load_pretrained_encoder_from = None + state["cfg"]["model"].load_pretrained_decoder_from = None + + self.model = task.build_model(state["cfg"]["model"]) + self.model.load_state_dict(state["model"], strict=True) + self.model.eval() + self.model.share_memory() + + if self.gpu: + self.model.cuda() + + # Set dictionary + self.dict = {} + self.dict["tgt"] = task.target_dictionary + self.dict["src"] = task.source_dictionary + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--model-path', type=str, required=True, + help='path to your pretrained model.') + parser.add_argument("--data-bin", type=str, required=True, + help="Path of data binary") + parser.add_argument("--max-len", type=int, default=100, + help="Max length of translation") + parser.add_argument("--tgt-splitter-type", type=str, default="SentencePiece", + help="Subword splitter type for target text.") + parser.add_argument("--tgt-splitter-path", type=str, default=None, + help="Subword splitter model path for target text.") + parser.add_argument("--src-splitter-type", type=str, default="SentencePiece", + help="Subword splitter type for source text.") + parser.add_argument("--src-splitter-path", type=str, default=None, + help="Subword splitter model path for source text.") + # fmt: on + return parser + + def build_word_splitter(self, args): + self.spm = {} + for lang in ['src', 'tgt']: + if getattr(args, f'{lang}_splitter_type', None): + path = getattr(args, f'{lang}_splitter_path', None) + if path: + self.spm[lang] = spm.SentencePieceProcessor() + self.spm[lang].Load(path) + + def segment_to_units(self, segment, states): + # Split a full word (segment) into subwords (units) + return self.spm['src'].EncodeAsPieces(segment) + + def update_model_encoder(self, states): + if len(states.units.source) == 0: + return + + src_indices = [ + self.dict['src'].index(x) + for x in states.units.source.value + ] + + if states.finish_read(): + # Append the eos index when the prediction is over + src_indices += [self.dict["tgt"].eos_index] + + src_indices = self.to_device( + torch.LongTensor(src_indices).unsqueeze(0) + ) + src_lengths = self.to_device( + torch.LongTensor([src_indices.size(1)]) + ) + + states.encoder_states = self.model.encoder(src_indices, src_lengths) + + torch.cuda.empty_cache() + + def update_states_read(self, states): + # Happens after a read action. + self.update_model_encoder(states) + + def units_to_segment(self, units, states): + # Merge sub words (units) to full word (segment). + # For Japanese, we can directly send + # the untokenized token to server except the BOS token + # with following option + # --sacrebleu-tokenizer MeCab + # --eval-latency-unit char + # --no-space + token = units.value.pop() + + if ( + token == self.dict["tgt"].eos_word + or len(states.segments.target) > self.max_len + ): + return DEFAULT_EOS + + if BOS_PREFIX == token: + return None + if token[0] == BOS_PREFIX: + return token[1:] + else: + return token + + def policy(self, states): + + if not getattr(states, "encoder_states", None): + # No encoder states, read a token first + return READ_ACTION + + # encode previous predicted target tokens + tgt_indices = self.to_device( + torch.LongTensor( + [self.model.decoder.dictionary.eos()] + + [ + self.dict['tgt'].index(x) + for x in states.units.target.value + if x is not None + ] + ).unsqueeze(0) + ) + + # Current steps + states.incremental_states["steps"] = { + "src": states.encoder_states["encoder_out"][0].size(0), + "tgt": 1 + len(states.units.target), + } + + # Online only means the reading is not finished + states.incremental_states["online"]["only"] = ( + torch.BoolTensor([not states.finish_read()]) + ) + + x, outputs = self.model.decoder.forward( + prev_output_tokens=tgt_indices, + encoder_out=states.encoder_states, + incremental_state=states.incremental_states, + ) + + states.decoder_out = x + + torch.cuda.empty_cache() + + if outputs.action == 0: + return READ_ACTION + else: + return WRITE_ACTION + + def predict(self, states): + # Predict target token from decoder states + decoder_states = states.decoder_out + + lprobs = self.model.get_normalized_probs( + [decoder_states[:, -1:]], log_probs=True + ) + + index = lprobs.argmax(dim=-1)[0, 0].item() + + if index != self.dict['tgt'].eos_index: + token = self.dict['tgt'].string([index]) + else: + token = self.dict['tgt'].eos_word + + return token diff --git a/fairseq/examples/simultaneous_translation/models/__init__.py b/fairseq/examples/simultaneous_translation/models/__init__.py new file mode 100644 index 0000000..257a965 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/models/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + model_name = file[: file.find(".py")] + importlib.import_module( + "examples.simultaneous_translation.models." + model_name + ) diff --git a/fairseq/examples/simultaneous_translation/models/convtransformer_simul_trans.py b/fairseq/examples/simultaneous_translation/models/convtransformer_simul_trans.py new file mode 100644 index 0000000..4a26422 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/models/convtransformer_simul_trans.py @@ -0,0 +1,204 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +from fairseq import checkpoint_utils +from fairseq.models import ( + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_text import ( + ConvTransformerModel, + convtransformer_espnet, + ConvTransformerEncoder, +) +from fairseq.models.speech_to_text.modules.augmented_memory_attention import ( + augmented_memory, + SequenceEncoder, + AugmentedMemoryConvTransformerEncoder, +) + +from torch import nn, Tensor +from typing import Dict, List +from fairseq.models.speech_to_text.modules.emformer import NoSegAugmentedMemoryTransformerEncoderLayer + +@register_model("convtransformer_simul_trans") +class SimulConvTransformerModel(ConvTransformerModel): + """ + Implementation of the paper: + + SimulMT to SimulST: Adapting Simultaneous Text Translation to + End-to-End Simultaneous Speech Translation + + https://www.aclweb.org/anthology/2020.aacl-main.58.pdf + """ + + @staticmethod + def add_args(parser): + super(SimulConvTransformerModel, SimulConvTransformerModel).add_args(parser) + parser.add_argument( + "--train-monotonic-only", + action="store_true", + default=False, + help="Only train monotonic attention", + ) + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + tgt_dict = task.tgt_dict + + from examples.simultaneous_translation.models.transformer_monotonic_attention import ( + TransformerMonotonicDecoder, + ) + + decoder = TransformerMonotonicDecoder(args, tgt_dict, embed_tokens) + + if getattr(args, "load_pretrained_decoder_from", None): + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_decoder_from + ) + return decoder + + +@register_model_architecture( + "convtransformer_simul_trans", "convtransformer_simul_trans_espnet" +) +def convtransformer_simul_trans_espnet(args): + convtransformer_espnet(args) + + +@register_model("convtransformer_augmented_memory") +@augmented_memory +class AugmentedMemoryConvTransformerModel(SimulConvTransformerModel): + @classmethod + def build_encoder(cls, args): + encoder = SequenceEncoder(args, AugmentedMemoryConvTransformerEncoder(args)) + + if getattr(args, "load_pretrained_encoder_from", None) is not None: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + + return encoder + + +@register_model_architecture( + "convtransformer_augmented_memory", "convtransformer_augmented_memory" +) +def augmented_memory_convtransformer_espnet(args): + convtransformer_espnet(args) + + +# ============================================================================ # +# Convtransformer +# with monotonic attention decoder +# with emformer encoder +# ============================================================================ # + + +class ConvTransformerEmformerEncoder(ConvTransformerEncoder): + def __init__(self, args): + super().__init__(args) + stride = self.conv_layer_stride(args) + trf_left_context = args.segment_left_context // stride + trf_right_context = args.segment_right_context // stride + context_config = [trf_left_context, trf_right_context] + self.transformer_layers = nn.ModuleList( + [ + NoSegAugmentedMemoryTransformerEncoderLayer( + input_dim=args.encoder_embed_dim, + num_heads=args.encoder_attention_heads, + ffn_dim=args.encoder_ffn_embed_dim, + num_layers=args.encoder_layers, + dropout_in_attn=args.dropout, + dropout_on_attn=args.dropout, + dropout_on_fc1=args.dropout, + dropout_on_fc2=args.dropout, + activation_fn=args.activation_fn, + context_config=context_config, + segment_size=args.segment_length, + max_memory_size=args.max_memory_size, + scaled_init=True, # TODO: use constant for now. + tanh_on_mem=args.amtrf_tanh_on_mem, + ) + ] + ) + self.conv_transformer_encoder = ConvTransformerEncoder(args) + + def forward(self, src_tokens, src_lengths): + encoder_out: Dict[str, List[Tensor]] = self.conv_transformer_encoder(src_tokens, src_lengths.to(src_tokens.device)) + output = encoder_out["encoder_out"][0] + encoder_padding_masks = encoder_out["encoder_padding_mask"] + + return { + "encoder_out": [output], + # This is because that in the original implementation + # the output didn't consider the last segment as right context. + "encoder_padding_mask": [encoder_padding_masks[0][:, : output.size(0)]] if len(encoder_padding_masks) > 0 + else [], + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + @staticmethod + def conv_layer_stride(args): + # TODO: make it configurable from the args + return 4 + + +@register_model("convtransformer_emformer") +class ConvtransformerEmformer(SimulConvTransformerModel): + @staticmethod + def add_args(parser): + super(ConvtransformerEmformer, ConvtransformerEmformer).add_args(parser) + + parser.add_argument( + "--segment-length", + type=int, + metavar="N", + help="length of each segment (not including left context / right context)", + ) + parser.add_argument( + "--segment-left-context", + type=int, + help="length of left context in a segment", + ) + parser.add_argument( + "--segment-right-context", + type=int, + help="length of right context in a segment", + ) + parser.add_argument( + "--max-memory-size", + type=int, + default=-1, + help="Right context for the segment.", + ) + parser.add_argument( + "--amtrf-tanh-on-mem", + default=False, + action="store_true", + help="whether to use tanh on memory vector", + ) + + @classmethod + def build_encoder(cls, args): + encoder = ConvTransformerEmformerEncoder(args) + if getattr(args, "load_pretrained_encoder_from", None): + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + return encoder + + +@register_model_architecture( + "convtransformer_emformer", + "convtransformer_emformer", +) +def convtransformer_emformer_base(args): + convtransformer_espnet(args) diff --git a/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py b/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py new file mode 100644 index 0000000..7b9414b --- /dev/null +++ b/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py @@ -0,0 +1,302 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, NamedTuple, Optional + +import torch +import torch.nn as nn +from examples.simultaneous_translation.modules.monotonic_transformer_layer import ( + TransformerMonotonicDecoderLayer, + TransformerMonotonicEncoderLayer, +) +from fairseq.models import ( + register_model, + register_model_architecture, +) +from fairseq.models.transformer import ( + TransformerModel, + TransformerEncoder, + TransformerDecoder, + base_architecture, + transformer_iwslt_de_en, + transformer_vaswani_wmt_en_de_big, + tiny_architecture +) +from torch import Tensor + +DEFAULT_MAX_SOURCE_POSITIONS = 1024 +DEFAULT_MAX_TARGET_POSITIONS = 1024 +READ_ACTION = 0 +WRITE_ACTION = 1 + +TransformerMonotonicDecoderOut = NamedTuple( + "TransformerMonotonicDecoderOut", + [ + ("action", int), + ("p_choose", Optional[Tensor]), + ("attn_list", Optional[List[Optional[Dict[str, Tensor]]]]), + ("encoder_out", Optional[Dict[str, List[Tensor]]]), + ("encoder_padding_mask", Optional[Tensor]), + ], +) + + +@register_model("transformer_unidirectional") +class TransformerUnidirectionalModel(TransformerModel): + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return TransformerMonotonicEncoder(args, src_dict, embed_tokens) + + +@register_model("transformer_monotonic") +class TransformerModelSimulTrans(TransformerModel): + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return TransformerMonotonicEncoder(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return TransformerMonotonicDecoder(args, tgt_dict, embed_tokens) + + +class TransformerMonotonicEncoder(TransformerEncoder): + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens) + + self.dictionary = dictionary + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + TransformerMonotonicEncoderLayer(args) + for i in range(args.encoder_layers) + ] + ) + + +class TransformerMonotonicDecoder(TransformerDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): + super().__init__(args, dictionary, embed_tokens, no_encoder_attn=False) + + self.dictionary = dictionary + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + TransformerMonotonicDecoderLayer(args) + for _ in range(args.decoder_layers) + ] + ) + self.policy_criterion = getattr(args, "policy_criterion", "any") + self.num_updates = None + + def set_num_updates(self, num_updates): + self.num_updates = num_updates + + def pre_attention( + self, + prev_output_tokens, + encoder_out_dict: Dict[str, List[Tensor]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + positions = ( + self.embed_positions( + prev_output_tokens, + incremental_state=incremental_state, + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + encoder_out = encoder_out_dict["encoder_out"][0] + + if "encoder_padding_mask" in encoder_out_dict: + encoder_padding_mask = ( + encoder_out_dict["encoder_padding_mask"][0] + if encoder_out_dict["encoder_padding_mask"] + and len(encoder_out_dict["encoder_padding_mask"]) > 0 + else None + ) + else: + encoder_padding_mask = None + + return x, encoder_out, encoder_padding_mask + + def post_attention(self, x): + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x + + def clean_cache( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + end_id: Optional[int] = None, + ): + """ + Clean cache in the monotonic layers. + The cache is generated because of a forward pass of decoder has run but no prediction, + so that the self attention key value in decoder is written in the incremental state. + end_id is the last idx of the layers + """ + if end_id is None: + end_id = len(self.layers) + + for index, layer in enumerate(self.layers): + if index < end_id: + layer.prune_incremental_state(incremental_state) + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, # unused + alignment_layer: Optional[int] = None, # unused + alignment_heads: Optional[int] = None, # unsed + ): + """ + Similar to *forward* but only return features. + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + # incremental_state = None + assert encoder_out is not None + (x, encoder_outs, encoder_padding_mask) = self.pre_attention( + prev_output_tokens, encoder_out, incremental_state + ) + attn = None + inner_states = [x] + attn_list: List[Optional[Dict[str, Tensor]]] = [] + + p_choose = torch.tensor([1.0]) + + for i, layer in enumerate(self.layers): + + x, attn, _ = layer( + x=x, + encoder_out=encoder_outs, + encoder_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + self_attn_mask=self.buffered_future_mask(x) + if incremental_state is None + else None, + ) + + inner_states.append(x) + attn_list.append(attn) + + if incremental_state is not None: + if_online = incremental_state["online"]["only"] + assert if_online is not None + if if_online.to(torch.bool): + # Online indicates that the encoder states are still changing + assert attn is not None + if self.policy_criterion == "any": + # Any head decide to read than read + head_read = layer.encoder_attn._get_monotonic_buffer(incremental_state)["head_read"] + assert head_read is not None + if head_read.any(): + # We need to prune the last self_attn saved_state + # if model decide not to read + # otherwise there will be duplicated saved_state + self.clean_cache(incremental_state, i + 1) + + return x, TransformerMonotonicDecoderOut( + action=0, + p_choose=p_choose, + attn_list=None, + encoder_out=None, + encoder_padding_mask=None, + ) + + x = self.post_attention(x) + + return x, TransformerMonotonicDecoderOut( + action=1, + p_choose=p_choose, + attn_list=attn_list, + encoder_out=encoder_out, + encoder_padding_mask=encoder_padding_mask, + ) + + +@register_model_architecture("transformer_monotonic", "transformer_monotonic") +def base_monotonic_architecture(args): + base_architecture(args) + args.encoder_unidirectional = getattr(args, "encoder_unidirectional", False) + + +@register_model_architecture( + "transformer_monotonic", "transformer_monotonic_iwslt_de_en" +) +def transformer_monotonic_iwslt_de_en(args): + transformer_iwslt_de_en(args) + base_monotonic_architecture(args) + + +# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) +@register_model_architecture( + "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_de_big" +) +def transformer_monotonic_vaswani_wmt_en_de_big(args): + transformer_vaswani_wmt_en_de_big(args) + + +@register_model_architecture( + "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_fr_big" +) +def transformer_monotonic_vaswani_wmt_en_fr_big(args): + transformer_monotonic_vaswani_wmt_en_fr_big(args) + + +@register_model_architecture( + "transformer_unidirectional", "transformer_unidirectional_iwslt_de_en" +) +def transformer_unidirectional_iwslt_de_en(args): + transformer_iwslt_de_en(args) + + +@register_model_architecture("transformer_monotonic", "transformer_monotonic_tiny") +def monotonic_tiny_architecture(args): + tiny_architecture(args) + base_monotonic_architecture(args) diff --git a/fairseq/examples/simultaneous_translation/modules/__init__.py b/fairseq/examples/simultaneous_translation/modules/__init__.py new file mode 100644 index 0000000..f5ea180 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/modules/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import importlib +from fairseq import registry + +( + build_monotonic_attention, + register_monotonic_attention, + MONOTONIC_ATTENTION_REGISTRY, + _, +) = registry.setup_registry("--simul-type") + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + model_name = file[: file.find(".py")] + importlib.import_module( + "examples.simultaneous_translation.modules." + model_name + ) diff --git a/fairseq/examples/simultaneous_translation/modules/fixed_pre_decision.py b/fairseq/examples/simultaneous_translation/modules/fixed_pre_decision.py new file mode 100644 index 0000000..3991414 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/modules/fixed_pre_decision.py @@ -0,0 +1,190 @@ +from functools import partial + +import torch +from torch import Tensor +import math +import torch.nn.functional as F + +from . import register_monotonic_attention +from .monotonic_multihead_attention import ( + MonotonicAttention, + MonotonicInfiniteLookbackAttention, + WaitKAttention +) +from typing import Dict, Optional + + +def fixed_pooling_monotonic_attention(monotonic_attention): + def create_model(monotonic_attention, klass): + class FixedStrideMonotonicAttention(monotonic_attention): + def __init__(self, args): + self.waitk_lagging = 0 + self.num_heads = 0 + self.noise_mean = 0.0 + self.noise_var = 0.0 + super().__init__(args) + self.pre_decision_type = args.fixed_pre_decision_type + self.pre_decision_ratio = args.fixed_pre_decision_ratio + self.pre_decision_pad_threshold = args.fixed_pre_decision_pad_threshold + assert self.pre_decision_ratio > 1 + + if args.fixed_pre_decision_type == "average": + self.pooling_layer = torch.nn.AvgPool1d( + kernel_size=self.pre_decision_ratio, + stride=self.pre_decision_ratio, + ceil_mode=True, + ) + elif args.fixed_pre_decision_type == "last": + + def last(key): + if key.size(2) < self.pre_decision_ratio: + return key + else: + k = key[ + :, + :, + self.pre_decision_ratio - 1:: self.pre_decision_ratio, + ].contiguous() + if key.size(-1) % self.pre_decision_ratio != 0: + k = torch.cat([k, key[:, :, -1:]], dim=-1).contiguous() + return k + + self.pooling_layer = last + else: + raise NotImplementedError + + @staticmethod + def add_args(parser): + super( + FixedStrideMonotonicAttention, FixedStrideMonotonicAttention + ).add_args(parser) + parser.add_argument( + "--fixed-pre-decision-ratio", + type=int, + required=True, + help=( + "Ratio for the fixed pre-decision," + "indicating how many encoder steps will start" + "simultaneous decision making process." + ), + ) + parser.add_argument( + "--fixed-pre-decision-type", + default="average", + choices=["average", "last"], + help="Pooling type", + ) + parser.add_argument( + "--fixed-pre-decision-pad-threshold", + type=float, + default=0.3, + help="If a part of the sequence has pad" + ",the threshold the pooled part is a pad.", + ) + + def insert_zeros(self, x): + bsz_num_heads, tgt_len, src_len = x.size() + stride = self.pre_decision_ratio + weight = F.pad(torch.ones(1, 1, 1).to(x), (stride - 1, 0)) + x_upsample = F.conv_transpose1d( + x.view(-1, src_len).unsqueeze(1), + weight, + stride=stride, + padding=0, + ) + return x_upsample.squeeze(1).view(bsz_num_heads, tgt_len, -1) + + def p_choose( + self, + query: Optional[Tensor], + key: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + assert key is not None + assert query is not None + src_len = key.size(0) + tgt_len = query.size(0) + batch_size = query.size(1) + + key_pool = self.pooling_layer(key.transpose(0, 2)).transpose(0, 2) + + if key_padding_mask is not None: + key_padding_mask_pool = ( + self.pooling_layer(key_padding_mask.unsqueeze(0).float()) + .squeeze(0) + .gt(self.pre_decision_pad_threshold) + ) + # Make sure at least one element is not pad + key_padding_mask_pool[:, 0] = 0 + else: + key_padding_mask_pool = None + + if incremental_state is not None: + # The floor instead of ceil is used for inference + # But make sure the length key_pool at least 1 + if ( + max(1, math.floor(key.size(0) / self.pre_decision_ratio)) + ) < key_pool.size(0): + key_pool = key_pool[:-1] + if key_padding_mask_pool is not None: + key_padding_mask_pool = key_padding_mask_pool[:-1] + + p_choose_pooled = self.p_choose_from_qk( + query, + key_pool, + key_padding_mask_pool, + incremental_state=incremental_state, + ) + + # Upsample, interpolate zeros + p_choose = self.insert_zeros(p_choose_pooled) + + if p_choose.size(-1) < src_len: + # Append zeros if the upsampled p_choose is shorter than src_len + p_choose = torch.cat( + [ + p_choose, + torch.zeros( + p_choose.size(0), + tgt_len, + src_len - p_choose.size(-1) + ).to(p_choose) + ], + dim=2 + ) + else: + # can be larger than src_len because we used ceil before + p_choose = p_choose[:, :, :src_len] + p_choose[:, :, -1] = p_choose_pooled[:, :, -1] + + assert list(p_choose.size()) == [ + batch_size * self.num_heads, + tgt_len, + src_len, + ] + + return p_choose + + FixedStrideMonotonicAttention.__name__ = klass.__name__ + return FixedStrideMonotonicAttention + + return partial(create_model, monotonic_attention) + + +@register_monotonic_attention("waitk_fixed_pre_decision") +@fixed_pooling_monotonic_attention(WaitKAttention) +class WaitKAttentionFixedStride: + pass + + +@register_monotonic_attention("hard_aligned_fixed_pre_decision") +@fixed_pooling_monotonic_attention(MonotonicAttention) +class MonotonicAttentionFixedStride: + pass + + +@register_monotonic_attention("infinite_lookback_fixed_pre_decision") +@fixed_pooling_monotonic_attention(MonotonicInfiniteLookbackAttention) +class MonotonicInfiniteLookbackAttentionFixedStride: + pass diff --git a/fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py b/fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py new file mode 100644 index 0000000..06d20d8 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/modules/monotonic_multihead_attention.py @@ -0,0 +1,520 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from torch import Tensor +import torch.nn as nn + +from examples.simultaneous_translation.utils.p_choose_strategy import ( + learnable_p_choose, + waitk_p_choose +) + +from examples.simultaneous_translation.utils.monotonic_attention import ( + expected_alignment_from_p_choose, + expected_soft_attention, + mass_preservation, +) +from fairseq.modules import MultiheadAttention + +from . import register_monotonic_attention +from typing import Dict, Optional + + +@register_monotonic_attention("hard_aligned") +class MonotonicAttention(MultiheadAttention): + """ + Abstract class of monotonic attentions + """ + k_in_proj: Dict[str, nn.Linear] + q_in_proj: Dict[str, nn.Linear] + + def __init__(self, args): + super().__init__( + embed_dim=args.decoder_embed_dim, + num_heads=args.decoder_attention_heads, + kdim=getattr(args, "encoder_embed_dim", None), + vdim=getattr(args, "encoder_embed_dim", None), + dropout=args.attention_dropout, + encoder_decoder_attention=True, + ) + + self.soft_attention = False + + self.eps = getattr(args, "attention_eps", True) + self.mass_preservation = getattr(args, "mass_preservation", True) + + self.noise_type = args.noise_type + self.noise_mean = args.noise_mean + self.noise_var = args.noise_var + + self.energy_bias_init = args.energy_bias_init + self.energy_bias = ( + nn.Parameter(self.energy_bias_init * torch.ones([1])) + if args.energy_bias is True + else 0 + ) + + self.k_in_proj = {"monotonic": self.k_proj} + self.q_in_proj = {"monotonic": self.q_proj} + self.chunk_size = None + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--no-mass-preservation', action="store_false", + dest="mass_preservation", + help='Do not stay on the last token when decoding') + parser.add_argument('--mass-preservation', action="store_true", + dest="mass_preservation", + help='Stay on the last token when decoding') + parser.set_defaults(mass_preservation=True) + parser.add_argument('--noise-var', type=float, default=1.0, + help='Variance of discretness noise') + parser.add_argument('--noise-mean', type=float, default=0.0, + help='Mean of discretness noise') + parser.add_argument('--noise-type', type=str, default="flat", + help='Type of discretness noise') + parser.add_argument('--energy-bias', action="store_true", + default=False, + help='Bias for energy') + parser.add_argument('--energy-bias-init', type=float, default=-2.0, + help='Initial value of the bias for energy') + parser.add_argument('--attention-eps', type=float, default=1e-6, + help='Epsilon when calculating expected attention') + + def energy_from_qk( + self, + query: Tensor, + key: Tensor, + energy_type: str, + key_padding_mask: Optional[Tensor] = None, + bias: int = 0 + ): + """ + Compute energy from query and key + q_func_value is a tuple looks like + (q_proj_func, q_tensor) + q_tensor size: bsz, tgt_len, emb_dim + k_tensor size: bsz, src_len, emb_dim + key_padding_mask size: bsz, src_len + attn_mask: bsz, src_len + """ + + length, bsz, _ = query.size() + q = self.q_in_proj[energy_type].forward(query) + q = ( + q.contiguous() + .view(length, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + q = q * self.scaling + length, bsz, _ = key.size() + k = self.k_in_proj[energy_type].forward(key) + k = ( + k.contiguous() + .view(length, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + energy = torch.bmm(q, k.transpose(1, 2)) + bias + + if key_padding_mask is not None: + energy = energy.masked_fill( + key_padding_mask.unsqueeze(1).to(torch.bool), + - float("inf") + ) + + return energy + + def p_choose_from_qk(self, query, key, key_padding_mask, incremental_states=None): + monotonic_energy = self.energy_from_qk( + query, + key, + "monotonic", + key_padding_mask=key_padding_mask, + bias=self.energy_bias, + ) + + p_choose = learnable_p_choose( + monotonic_energy, + self.noise_mean, + self.noise_var, + self.training + ) + return p_choose + + def p_choose(self, query, key, key_padding_mask, incremental_states=None): + return self.p_choose_from_qk(self, query, key, key_padding_mask) + + def monotonic_attention_process_infer( + self, + query: Optional[Tensor], + key: Optional[Tensor], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + ): + """ + Monotonic attention at inference time + Notice that this function is designed for simuleval not sequence_generator + """ + assert query is not None + assert key is not None + + if query.size(1) != 1: + raise RuntimeError( + "Simultaneous translation models don't support batch decoding." + ) + # 1. compute stepwise probability + p_choose = self.p_choose( + query, key, None, incremental_state + ).squeeze(1) + + # 2. Compute the alpha + src_len = key.size(0) + # Maximum steps allows in this iteration + max_steps = src_len - 1 if self.mass_preservation else src_len + monotonic_cache = self._get_monotonic_buffer(incremental_state) + # Step for each head + monotonic_step = monotonic_cache.get( + 'head_step', + p_choose.new_zeros(1, self.num_heads).long() + ) + assert monotonic_step is not None + finish_read = monotonic_step.eq(max_steps) + p_choose_i = torch.tensor(1) + + while finish_read.sum().item() < self.num_heads: + # p_choose: self.num_heads, src_len + # only choose the p at monotonic steps + # p_choose_i: 1, self.num_heads + p_choose_i = ( + p_choose.gather( + 1, + monotonic_step + .clamp(0, src_len - 1), + ) + ) + + read_one_step = ( + (p_choose_i < 0.5) + .type_as(monotonic_step) + .masked_fill(finish_read, 0) + ) + # 1 x bsz + # sample actions on unfinished seq + # 0 means stay, finish reading + # 1 means leave, continue reading + + monotonic_step += read_one_step + + finish_read = monotonic_step.eq(max_steps) | (read_one_step == 0) + + # p_choose at last steps + p_choose_i = ( + p_choose.gather( + 1, + monotonic_step + .clamp(0, src_len - 1), + ) + ) + + monotonic_cache["head_step"] = monotonic_step + # Whether a head is looking for new input + monotonic_cache["head_read"] = ( + monotonic_step.eq(max_steps) & (p_choose_i < 0.5) + ) + self._set_monotonic_buffer(incremental_state, monotonic_cache) + + # 2. Update alpha + alpha = ( + p_choose + .new_zeros([self.num_heads, src_len]) + .scatter( + 1, + (monotonic_step) + .view(self.num_heads, 1).clamp(0, src_len - 1), + 1 + ) + ) + + if not self.mass_preservation: + alpha = alpha.masked_fill( + (monotonic_step == max_steps) + .view(self.num_heads, 1), + 0 + ) + + # 4. Compute Beta + if self.soft_attention: + monotonic_step = monotonic_step.t() + beta_mask = torch.arange(src_len).expand_as(alpha).gt(monotonic_step).unsqueeze(1) + # If it's soft attention just do softmax on current context + soft_energy = self.energy_from_qk( + query, + key, + "soft" + ) + beta = torch.nn.functional.softmax( + soft_energy.masked_fill(beta_mask, -float("inf")), dim=-1 + ) + # It could happen that a head doesn't move at all + beta = beta.masked_fill(monotonic_step.eq(0).unsqueeze(1), 0) + else: + # If it's hard attention just select the last state + beta = alpha + + return p_choose, alpha, beta + + def monotonic_attention_process_train( + self, + query: Optional[Tensor], + key: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + ): + """ + Calculating monotonic attention process for training + Including: + stepwise probability: p_choose + expected hard alignment: alpha + expected soft attention: beta + """ + assert query is not None + assert key is not None + + # 1. compute stepwise probability + p_choose = self.p_choose_from_qk(query, key, key_padding_mask) + + # 2. compute expected_alignment + alpha = expected_alignment_from_p_choose( + p_choose, + key_padding_mask, + eps=self.eps, + ) + + if self.mass_preservation: + alpha = mass_preservation( + alpha, key_padding_mask + ) + + # 3. compute expected soft attention (soft aligned model only) + if self.soft_attention: + soft_energy = self.energy_from_qk( + query, + key, + "soft", + key_padding_mask=None, + ) + + beta = expected_soft_attention( + alpha, + soft_energy, + padding_mask=key_padding_mask, + chunk_size=self.chunk_size, + eps=self.eps, + ) + else: + beta = alpha + soft_energy = alpha + + return p_choose, alpha, beta, soft_energy + + def forward( + self, + query: Optional[Tensor], + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, static_kv: bool = False, need_head_weights: bool = False, + ): + """ + query: tgt_len, bsz, embed_dim + key: src_len, bsz, embed_dim + value: src_len, bsz, embed_dim + """ + + assert attn_mask is None + assert query is not None + assert key is not None + assert value is not None + + tgt_len, bsz, embed_dim = query.size() + src_len = value.size(0) + + if key_padding_mask is not None: + assert not key_padding_mask[:, 0].any(), ( + "Only right padding is supported." + ) + key_padding_mask = ( + key_padding_mask + .unsqueeze(1) + .expand([bsz, self.num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) + + if incremental_state is not None: + # Inference + ( + p_choose, alpha, beta + ) = self.monotonic_attention_process_infer( + query, key, incremental_state + ) + soft_energy = beta + else: + # Train + ( + p_choose, alpha, beta, soft_energy + ) = self.monotonic_attention_process_train( + query, key, key_padding_mask + ) + + v = self.v_proj(value) + length, bsz, _ = v.size() + v = ( + v.contiguous() + .view(length, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + attn = torch.bmm(beta.type_as(v), v) + + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + + attn = self.out_proj(attn) + + p_choose = p_choose.view(bsz, self.num_heads, tgt_len, src_len) + alpha = alpha.view(bsz, self.num_heads, tgt_len, src_len) + beta = beta.view(bsz, self.num_heads, tgt_len, src_len) + + return attn, { + "p_choose": p_choose, + "alpha": alpha, + "beta": beta, + "soft_energy": soft_energy, + } + + def _get_monotonic_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]): + maybe_incremental_state = self.get_incremental_state( + incremental_state, + 'monotonic', + ) + if maybe_incremental_state is None: + typed_empty_dict: Dict[str, Optional[Tensor]] = {} + return typed_empty_dict + else: + return maybe_incremental_state + + def _set_monotonic_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], buffer: Dict[str, Optional[Tensor]]): + self.set_incremental_state( + incremental_state, + 'monotonic', + buffer, + ) + + +@register_monotonic_attention("infinite_lookback") +class MonotonicInfiniteLookbackAttention( + MonotonicAttention +): + def __init__(self, args): + super().__init__(args) + self.soft_attention = True + self.init_soft_attention() + + def init_soft_attention(self): + self.k_proj_soft = nn.Linear(self.kdim, self.embed_dim, bias=True) + self.q_proj_soft = nn.Linear(self.embed_dim, self.embed_dim, bias=True) + self.k_in_proj["soft"] = self.k_proj_soft + self.q_in_proj["soft"] = self.q_proj_soft + + if self.qkv_same_dim: + # Empirically observed the convergence to be much better with + # the scaled initialization + nn.init.xavier_uniform_( + self.k_in_proj["soft"].weight, gain=1 / math.sqrt(2) + ) + nn.init.xavier_uniform_( + self.q_in_proj["soft"].weight, gain=1 / math.sqrt(2) + ) + else: + nn.init.xavier_uniform_(self.k_in_proj["soft"].weight) + nn.init.xavier_uniform_(self.q_in_proj["soft"].weight) + + +@register_monotonic_attention("waitk") +class WaitKAttention( + MonotonicInfiniteLookbackAttention +): + """ + STACL: Simultaneous Translation with Implicit Anticipation and + Controllable Latency using Prefix-to-Prefix Framework + https://www.aclweb.org/anthology/P19-1289/ + """ + def __init__(self, args): + super().__init__(args) + self.q_in_proj["soft"] = self.q_in_proj["monotonic"] + self.k_in_proj["soft"] = self.k_in_proj["monotonic"] + + self.waitk_lagging = args.waitk_lagging + assert self.waitk_lagging > 0, ( + f"Lagging has to been larger than 0, get {self.waitk_lagging}." + ) + + @staticmethod + def add_args(parser): + super( + MonotonicInfiniteLookbackAttention, + MonotonicInfiniteLookbackAttention + ).add_args(parser) + + parser.add_argument( + "--waitk-lagging", type=int, required=True, help="Wait K lagging" + ) + + def p_choose_from_qk( + self, + query: Optional[Tensor], + key: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + assert query is not None + assert key is not None + + p_choose = waitk_p_choose( + tgt_len=query.size(0), + src_len=key.size(0), + bsz=query.size(1) * self.num_heads, + waitk_lagging=self.waitk_lagging, + key_padding_mask=key_padding_mask, + incremental_state=incremental_state, + ) + + return p_choose.to(query) + + +@register_monotonic_attention("chunkwise") +class ChunkwiseAttention( + MonotonicInfiniteLookbackAttention +): + def __init__(self, args): + super().__init__(args) + self.chunk_size = args.mocha_chunk_size + assert self.chunk_size > 1 + + @staticmethod + def add_args(parser): + super( + MonotonicInfiniteLookbackAttention + ).add_args(parser) + + parser.add_argument( + "--mocha-chunk-size", type=int, + required=True, help="Mocha chunk size" + ) diff --git a/fairseq/examples/simultaneous_translation/modules/monotonic_transformer_layer.py b/fairseq/examples/simultaneous_translation/modules/monotonic_transformer_layer.py new file mode 100644 index 0000000..94bd71f --- /dev/null +++ b/fairseq/examples/simultaneous_translation/modules/monotonic_transformer_layer.py @@ -0,0 +1,182 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer + +from . import build_monotonic_attention + +from typing import Dict, Optional, List + +from torch import Tensor +import torch + + +class TransformerMonotonicEncoderLayer(TransformerEncoderLayer): + def forward(self, x, encoder_padding_mask): + seq_len, _, _ = x.size() + attn_mask = x.new_ones([seq_len, seq_len]).triu(1) + attn_mask = attn_mask.masked_fill(attn_mask.bool(), float("-inf")) + return super().forward(x, encoder_padding_mask, attn_mask) + + +class TransformerMonotonicDecoderLayer(TransformerDecoderLayer): + def __init__(self, args): + super().__init__(args) + + assert args.simul_type is not None, "A --simul-type is needed." + self.encoder_attn = build_monotonic_attention(args) + + def prune_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ): + input_buffer = self.self_attn._get_input_buffer(incremental_state) + for key in ["prev_key", "prev_value"]: + input_buffer_key = input_buffer[key] + assert input_buffer_key is not None + if input_buffer_key.size(2) > 1: + input_buffer[key] = input_buffer_key[:, :, :-1, :] + else: + typed_empty_dict: Dict[str, Optional[Tensor]] = {} + input_buffer = typed_empty_dict + break + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state, input_buffer) + + def forward( + self, + x, + encoder_out: Optional[Tensor] = None, + encoder_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + prev_self_attn_state: Optional[List[Tensor]] = None, + prev_attn_state: Optional[List[Tensor]] = None, + self_attn_mask: Optional[Tensor] = None, + self_attn_padding_mask: Optional[Tensor] = None, + need_attn: bool = False, + need_head_weights: bool = False, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor, optional): binary + ByteTensor of shape `(batch, src_len)` where padding + elements are indicated by ``1``. + need_attn (bool, optional): return attention weights + need_head_weights (bool, optional): return attention weights + for each head (default: return average over heads). + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + if need_head_weights: + need_attn = True + + residual = x + if self.normalize_before: + x = self.self_attn_layer_norm(x) + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[2] + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state, saved_state) + _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state) + if self.cross_self_attention and not ( + incremental_state is not None + and _self_attn_input_buffer is not None + and "prev_key" in _self_attn_input_buffer + ): + if self_attn_mask is not None: + assert encoder_out is not None + self_attn_mask = torch.cat( + (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1 + ) + if self_attn_padding_mask is not None: + if encoder_padding_mask is None: + assert encoder_out is not None + encoder_padding_mask = self_attn_padding_mask.new_zeros( + encoder_out.size(1), encoder_out.size(0) + ) + self_attn_padding_mask = torch.cat( + (encoder_padding_mask, self_attn_padding_mask), dim=1 + ) + assert encoder_out is not None + y = torch.cat((encoder_out, x), dim=0) + else: + y = x + + x, attn = self.self_attn( + query=x, + key=y, + value=y, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + need_weights=False, + attn_mask=self_attn_mask, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + assert self.encoder_attn is not None + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.final_layer_norm(x) + if self.onnx_trace and incremental_state is not None: + saved_state = self.self_attn._get_input_buffer(incremental_state) + assert saved_state is not None + if self_attn_padding_mask is not None: + self_attn_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + saved_state["prev_key_padding_mask"], + ] + else: + self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]] + return x, attn, self_attn_state + return x, attn, None diff --git a/fairseq/examples/simultaneous_translation/tests/test_alignment_train.py b/fairseq/examples/simultaneous_translation/tests/test_alignment_train.py new file mode 100644 index 0000000..2ad4ef1 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/tests/test_alignment_train.py @@ -0,0 +1,88 @@ +import unittest + +import numpy as np +import torch + +import hypothesis.strategies as st +from hypothesis import assume, given, settings +from torch.testing._internal.common_utils import TestCase +from examples.simultaneous_translation.utils.functions import exclusive_cumprod + + +TEST_CUDA = torch.cuda.is_available() + + +class AlignmentTrainTest(TestCase): + def _test_custom_alignment_train_ref(self, p_choose, eps): + cumprod_1mp = exclusive_cumprod(1 - p_choose, dim=2, eps=eps) + cumprod_1mp_clamp = torch.clamp(cumprod_1mp, eps, 1.0) + + bsz = p_choose.size(0) + tgt_len = p_choose.size(1) + src_len = p_choose.size(2) + + alpha_0 = p_choose.new_zeros([bsz, 1, src_len]) + alpha_0[:, :, 0] = 1.0 + + previous_alpha = [alpha_0] + + for i in range(tgt_len): + # p_choose: bsz , tgt_len, src_len + # cumprod_1mp_clamp : bsz, tgt_len, src_len + # previous_alpha[i]: bsz, 1, src_len + # alpha_i: bsz, src_len + alpha_i = ( + p_choose[:, i] + * cumprod_1mp[:, i] + * torch.cumsum( + previous_alpha[i][:, 0] / cumprod_1mp_clamp[:, i], dim=1 + ) + ).clamp(0, 1.0) + + previous_alpha.append(alpha_i.unsqueeze(1)) + + # alpha: bsz * num_heads, tgt_len, src_len + alpha = torch.cat(previous_alpha[1:], dim=1) + return alpha + + def _test_custom_alignment_train_impl(self, p_choose, alpha, eps): + if p_choose.is_cuda: + from alignment_train_cuda_binding import alignment_train_cuda # @manual=//deeplearning/projects/fairseq-py:alignment_train_cuda_binding + alignment_train_cuda(p_choose, alpha, eps) + else: + from alignment_train_cpu_binding import alignment_train_cpu # @manual=//deeplearning/projects/fairseq-py:alignment_train_cpu_binding + alignment_train_cpu(p_choose, alpha, eps) + + @settings(deadline=None) + @given( + bsz=st.integers(1, 100), + tgt_len=st.integers(1, 100), + src_len=st.integers(1, 550), + device=st.sampled_from(["cpu", "cuda"]), + ) + def test_alignment_train(self, bsz, tgt_len, src_len, device): + eps = 1e-6 + + assume(device == "cpu" or TEST_CUDA) + p_choose = torch.rand(bsz, tgt_len, src_len, device=device) + + # run the alignment with the custom operator + alpha_act = p_choose.new_zeros([bsz, tgt_len, src_len]) + self._test_custom_alignment_train_impl(p_choose, alpha_act, eps) + + # runu the alignment with the ref implementation + alpha_ref = self._test_custom_alignment_train_ref(p_choose, eps) + + # verify the results + alpha_act = alpha_act.cpu().detach().numpy() + alpha_ref = alpha_ref.cpu().detach().numpy() + np.testing.assert_allclose( + alpha_act, + alpha_ref, + atol=1e-3, + rtol=1e-3, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/examples/simultaneous_translation/tests/test_text_models.py b/fairseq/examples/simultaneous_translation/tests/test_text_models.py new file mode 100644 index 0000000..19d6356 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/tests/test_text_models.py @@ -0,0 +1,407 @@ +import argparse +import unittest +from typing import Any, Dict + +import torch +from examples.simultaneous_translation.models import ( + transformer_monotonic_attention +) + + +from tests.test_roberta import FakeTask + + +DEFAULT_CONFIG = { + "attention_eps": 1e-6, + "mass_preservation": True, + "noise_type": "flat", + "noise_mean": 0.0, + "noise_var": 1.0, + "energy_bias_init": -2, + "energy_bias": True +} + + +PAD_INDEX = 1 + + +def generate_config(overrides_kv): + new_dict = {key: value for key, value in DEFAULT_CONFIG.items()} + for key, value in overrides_kv.items(): + new_dict[key] = value + return new_dict + + +def make_sample_with_padding(longer_src=False) -> Dict[str, Any]: + tokens_1 = torch.LongTensor( + [ + [2, 10, 11, 12, 13, 14, 15, 10, 11, 12, 13, 14, 15, 2], + [ + 2, 11, 12, 14, 15, 10, 11, 12, 13, 14, 15, 2, + PAD_INDEX, PAD_INDEX + ], + ] + ) + tokens_2 = torch.LongTensor( + [ + [2, 11, 12, 13, 14, 2, PAD_INDEX, PAD_INDEX], + [2, 11, 22, 33, 2, PAD_INDEX, PAD_INDEX, PAD_INDEX] + ] + ) + if longer_src: + src_tokens = tokens_1[:, 1:] + prev_output_tokens = tokens_2 + else: + src_tokens = tokens_2[:, 1:8] + prev_output_tokens = tokens_1 + + src_lengths = src_tokens.ne(PAD_INDEX).sum(dim=1).long() + + sample = { + "net_input": { + "src_tokens": src_tokens, + "prev_output_tokens": prev_output_tokens, + "src_lengths": src_lengths, + }, + "target": prev_output_tokens[:, 1:], + } + return sample + + +def build_transformer_monotonic_attention(**extra_args: Any): + overrides = { + # Use characteristics dimensions + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "decoder_embed_dim": 12, + "decoder_ffn_embed_dim": 14, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + } + overrides.update(extra_args) + # Overrides the defaults from the parser + args = argparse.Namespace(**overrides) + transformer_monotonic_attention.monotonic_tiny_architecture(args) + + torch.manual_seed(0) + task = FakeTask(args) + return ( + transformer_monotonic_attention + .TransformerModelSimulTrans + .build_model(args, task) + ) + + +def expected_alignment_formula( + p_choose, + mass_perservation=True, + padding_mask=None +): + # Online and Linear-Time Attention by Enforcing Monotonic Alignments + # https://arxiv.org/pdf/1704.00784.pdf + # Eq 18, 19 + bsz, tgt_len, src_len = p_choose.size() + alpha = torch.zeros_like(p_choose) + + if padding_mask is not None: + bsz_pad = padding_mask.size(0) + num_heads = int(bsz / bsz_pad) + padding_mask = ( + padding_mask + .unsqueeze(1) + .expand([bsz_pad, num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) + + p_choose = p_choose.masked_fill(padding_mask.unsqueeze(1), 0) + + for bsz_i in range(bsz): + for i in range(tgt_len): + for j in range(src_len): + if i == 0: + if j == 0: + # First source token + alpha[bsz_i, i, j] = p_choose[bsz_i, i, j] + else: + # First target token + alpha[bsz_i, i, j] = ( + p_choose[bsz_i, i, j] + * torch.prod( + 1 - p_choose[bsz_i, i, :j] + ) + ) + else: + alpha[bsz_i, i, j] = alpha[bsz_i, i - 1, j] + for k in range(j): + alpha[bsz_i, i, j] += ( + alpha[bsz_i, i - 1, k] + * torch.prod( + 1 - p_choose[bsz_i, i, k:j] + ) + ) + alpha[bsz_i, i, j] *= p_choose[bsz_i, i, j] + + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0) + + if mass_perservation: + alpha = mass_perservation_formula(alpha, False, padding_mask) + + return alpha + + +def mass_perservation_formula(alpha, left_padding=False, padding_mask=None): + if padding_mask is None or alpha.size(-1) == 1: + if alpha.size(-1) > 1: + alpha[:, :, -1] = 1 - alpha[:, :, :-1].sum(dim=-1) + return alpha + + src_lens = (padding_mask.logical_not()).sum(dim=1).long() + + bsz, tgt_len, src_len = alpha.size() + + assert ( + not left_padding + or (left_padding and (not padding_mask[:, 0].any())) + ) + + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0) + + for bsz_i in range(bsz): + if left_padding: + alpha[bsz_i, :, -1] = ( + 1 - alpha[bsz_i, :, :-1].sum(dim=-1) + ) + else: + alpha[bsz_i, :, src_lens[bsz_i] - 1] = ( + 1 - alpha[bsz_i, :, :src_lens[bsz_i] - 1].sum(dim=-1) + ) + + return alpha + + +def expected_soft_attention_formula( + alpha, + soft_energy, + padding_mask=None, + chunksize=1e10, +): + # Monotonic Infinite Lookback Attention for Simultaneous Machine Translation + # https://arxiv.org/pdf/1906.05218.pdf + # Eq 14 + + # Monotonic Chunkwise Attention + # https://arxiv.org/abs/1712.05382 + # Eq 17 + bsz, tgt_len, src_len = alpha.size() + beta = torch.zeros_like(alpha) + + if padding_mask is not None: + bsz_pad = padding_mask.size(0) + num_heads = int(bsz / bsz_pad) + # Expanding for potential head dimension + padding_mask = ( + padding_mask + .unsqueeze(1) + .expand([bsz_pad, num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) + soft_energy = soft_energy.masked_fill(padding_mask.unsqueeze(1), float('-inf')) + + for bsz_i in range(bsz): + for i in range(tgt_len): + for j in range(src_len): + for k in range(j, min([src_len, j + chunksize])): + if not padding_mask[bsz_i, j]: + beta[bsz_i, i, j] += ( + alpha[bsz_i, i, k] * torch.exp(soft_energy[bsz_i, i, j]) + / torch.sum(torch.exp(soft_energy[bsz_i, i, max([0, k - chunksize + 1]):k + 1])) + ) + return beta + + +class MonotonicAttentionTestAbstractClass(object): + def test_forward(self): + sample = make_sample_with_padding() + out, _ = self.model.forward(**sample["net_input"]) + loss = out.sum() + loss.backward() + + def test_p_choose(self): + sample = make_sample_with_padding() + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + self.assertTrue(p_choose.le(1.0).all()) + self.assertTrue(p_choose.ge(0.0).all()) + + def test_expected_alignment(self): + for longer_src in [True, False]: + sample = make_sample_with_padding(longer_src) + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + alpha_system = item["alpha"] + self.assertTrue(p_choose.size() == alpha_system.size()) + bsz, num_head, tgt_len, src_len = alpha_system.size() + alpha_system = alpha_system.view(-1, tgt_len, src_len) + p_choose = p_choose.view(-1, tgt_len, src_len) + + alpha_real = expected_alignment_formula( + p_choose, + self.model.decoder.layers[0].encoder_attn.mass_preservation, + sample["net_input"]["src_tokens"].eq(PAD_INDEX) + ) + + self.assertTrue( + torch.abs(alpha_system - alpha_real).le(5e-5).all(), + ) + + +class HardMonotonicAttentionTestCase( + unittest.TestCase, + MonotonicAttentionTestAbstractClass +): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config({"simul_type": "hard_aligned"}) + ) + + +class InfiniteLookbackTestCase( + unittest.TestCase, + MonotonicAttentionTestAbstractClass +): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "infinite_lookback" + } + ) + ) + self.model.train() + + def test_fp16_for_long_input(self): + sample = { + "net_input": { + "src_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0), + "prev_output_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0), + "src_lengths": torch.LongTensor([1000]).cuda(), + }, + "target": torch.LongTensor([2] + [7] * 1000).unsqueeze(0).cuda() + } + self.model.cuda().half() + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + for key in ["p_choose", "alpha", "beta", "soft_energy"]: + self.assertFalse(torch.isnan(item[key]).any()) + + def test_expected_attention(self): + for longer_src in [True, False]: + sample = make_sample_with_padding(longer_src) + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + alpha_system = item["alpha"] + beta_system = item["beta"] + soft_energy_system = item["soft_energy"] + self.assertTrue(beta_system.size() == alpha_system.size()) + self.assertTrue(p_choose.size() == alpha_system.size()) + + bsz, num_head, tgt_len, src_len = alpha_system.size() + + alpha_system = alpha_system.view(-1, tgt_len, src_len) + beta_system = beta_system.view(-1, tgt_len, src_len) + p_choose = p_choose.view(-1, tgt_len, src_len) + soft_energy_system = soft_energy_system.view(-1, tgt_len, src_len) + + alpha_real = expected_alignment_formula( + p_choose, + self.model.decoder.layers[0].encoder_attn.mass_preservation, + sample["net_input"]["src_tokens"].eq(PAD_INDEX) + ) + + beta_real = expected_soft_attention_formula( + alpha_real, + soft_energy_system, + sample["net_input"]["src_tokens"].eq(PAD_INDEX), + chunksize=getattr( + self.model.decoder.layers[0].encoder_attn, + "chunk_size", + int(1e10) + ) or int(1e10) + ) + + self.assertTrue( + torch.abs(beta_system - beta_real).le(1e-5).all(), + ) + + +class ChunkwiswTestCase( + InfiniteLookbackTestCase +): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "chunkwise", + "mocha_chunk_size": 3 + } + ) + ) + + +class WaitkTestCase(InfiniteLookbackTestCase): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "waitk", + "waitk_lagging": 3, + } + ) + ) + + def check_waitk(self, p_choose, lagging, padding_mask): + bsz, tgt_len, src_len = p_choose.size() + for bsz_i in range(bsz): + for i in range(tgt_len): + for j in range(src_len): + if not padding_mask[bsz_i, j]: + if j - i == lagging - 1: + self.assertTrue(p_choose[bsz_i, i, j] == 1) + else: + self.assertTrue(p_choose[bsz_i, i, j] == 0) + + def test_waitk_p_choose(self): + for longer_src in [True, False]: + for k in [1, 3, 10, 20, 100]: + sample = make_sample_with_padding(longer_src) + model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "waitk", + "waitk_lagging": k, + } + ) + ) + model.train() + _, extra_out = model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + bsz, num_heads, tgt_len, src_len = p_choose.size() + padding_mask = sample["net_input"]["src_tokens"].eq(PAD_INDEX) + padding_mask = ( + padding_mask + .unsqueeze(1) + .expand([bsz, num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) + p_choose = p_choose.view(bsz * num_heads, tgt_len, src_len) + self.check_waitk(p_choose, k, padding_mask) diff --git a/fairseq/examples/simultaneous_translation/utils/__init__.py b/fairseq/examples/simultaneous_translation/utils/__init__.py new file mode 100644 index 0000000..1e9ce84 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/utils/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + + +# automatically import any Python files in the criterions/ directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + module = file[: file.find(".py")] + importlib.import_module("examples.simultaneous_translation.utils." + module) diff --git a/fairseq/examples/simultaneous_translation/utils/functions.py b/fairseq/examples/simultaneous_translation/utils/functions.py new file mode 100644 index 0000000..590a6c1 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/utils/functions.py @@ -0,0 +1,125 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + + +def prob_check(tensor, eps=1e-10): + assert not torch.isnan(tensor).any(), ( + "Nan in a probability tensor." + ) + # Add the eps here to prevent errors introduced by precision + assert tensor.le(1.0 + eps).all() and tensor.ge(0.0 - eps).all(), ( + "Incorrect values in a probability tensor" + ", 0.0 <= tensor <= 1.0" + ) + + +def exclusive_cumprod(tensor, dim: int, eps: float = 1e-10): + """ + Implementing exclusive cumprod. + There is cumprod in pytorch, however there is no exclusive mode. + cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] + exclusive means + cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] + """ + tensor_size = list(tensor.size()) + tensor_size[dim] = 1 + return_tensor = safe_cumprod( + torch.cat([torch.ones(tensor_size).type_as(tensor), tensor], dim=dim), + dim=dim, + eps=eps, + ) + + if dim == 0: + return return_tensor[:-1] + elif dim == 1: + return return_tensor[:, :-1] + elif dim == 2: + return return_tensor[:, :, :-1] + else: + raise RuntimeError( + "Cumprod on dimension 3 and more is not implemented" + ) + + +def safe_cumprod(tensor, dim: int, eps: float = 1e-10): + """ + An implementation of cumprod to prevent precision issue. + cumprod(x) + = [x1, x1x2, x1x2x3, ....] + = [exp(log(x1)), exp(log(x1) + log(x2)), exp(log(x1) + log(x2) + log(x3)), ...] + = exp(cumsum(log(x))) + """ + + if (tensor + eps < 0).any().item(): + raise RuntimeError( + "Safe cumprod can only take non-negative tensors as input." + "Consider use torch.cumprod if you want to calculate negative values." + ) + + log_tensor = torch.log(tensor + eps) + cumsum_log_tensor = torch.cumsum(log_tensor, dim) + exp_cumsum_log_tensor = torch.exp(cumsum_log_tensor) + return exp_cumsum_log_tensor + + +def moving_sum(x, start_idx: int, end_idx: int): + """ + From MONOTONIC CHUNKWISE ATTENTION + https://arxiv.org/pdf/1712.05382.pdf + Equation (18) + + x = [x_1, x_2, ..., x_N] + MovingSum(x, start_idx, end_idx)_n = Sigma_{m=n−(start_idx−1)}^{n+end_idx-1} x_m + for n in {1, 2, 3, ..., N} + + x : src_len, batch_size + start_idx : start idx + end_idx : end idx + + Example + src_len = 5 + batch_size = 3 + x = + [[ 0, 5, 10], + [ 1, 6, 11], + [ 2, 7, 12], + [ 3, 8, 13], + [ 4, 9, 14]] + + MovingSum(x, 3, 1) = + [[ 0, 5, 10], + [ 1, 11, 21], + [ 3, 18, 33], + [ 6, 21, 36], + [ 9, 24, 39]] + + MovingSum(x, 1, 3) = + [[ 3, 18, 33], + [ 6, 21, 36], + [ 9, 24, 39], + [ 7, 17, 27], + [ 4, 9, 14]] + """ + # TODO: Make dimension configurable + assert start_idx > 0 and end_idx > 0 + batch_size, tgt_len, src_len = x.size() + x = x.view(-1, src_len).unsqueeze(1) + # batch_size, 1, src_len + moving_sum_weight = torch.ones([1, 1, end_idx + start_idx - 1]).type_as(x) + + moving_sum = torch.nn.functional.conv1d( + x, moving_sum_weight, padding=start_idx + end_idx - 1 + ).squeeze(1) + + moving_sum = moving_sum[:, end_idx:-start_idx] + + assert src_len == moving_sum.size(1) + assert batch_size * tgt_len == moving_sum.size(0) + + moving_sum = moving_sum.view(batch_size, tgt_len, src_len) + + return moving_sum diff --git a/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py b/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py new file mode 100644 index 0000000..3b8e0a8 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py @@ -0,0 +1,180 @@ +from typing import Optional +import torch +from torch import Tensor + +from examples.simultaneous_translation.utils.functions import ( + exclusive_cumprod, + prob_check, + moving_sum, +) + + +def expected_alignment_from_p_choose( + p_choose: Tensor, + padding_mask: Optional[Tensor] = None, + eps: float = 1e-6 +): + """ + Calculating expected alignment for from stepwise probability + + Reference: + Online and Linear-Time Attention by Enforcing Monotonic Alignments + https://arxiv.org/pdf/1704.00784.pdf + + q_ij = (1 − p_{ij−1})q_{ij−1} + a+{i−1j} + a_ij = p_ij q_ij + + Parallel solution: + ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi)) + + ============================================================ + Expected input size + p_choose: bsz, tgt_len, src_len + """ + prob_check(p_choose) + + # p_choose: bsz, tgt_len, src_len + bsz, tgt_len, src_len = p_choose.size() + dtype = p_choose.dtype + + p_choose = p_choose.float() + + if padding_mask is not None: + p_choose = p_choose.masked_fill(padding_mask.unsqueeze(1), 0.0) + + if p_choose.is_cuda: + p_choose = p_choose.contiguous() + from alignment_train_cuda_binding import alignment_train_cuda as alignment_train + else: + from alignment_train_cpu_binding import alignment_train_cpu as alignment_train + + alpha = p_choose.new_zeros([bsz, tgt_len, src_len]) + alignment_train(p_choose, alpha, eps) + + # Mix precision to prevent overflow for fp16 + alpha = alpha.type(dtype) + + prob_check(alpha) + + return alpha + + +def expected_soft_attention( + alpha: Tensor, + soft_energy: Tensor, + padding_mask: Optional[Tensor] = None, + chunk_size: Optional[int] = None, + eps: float = 1e-10 +): + """ + Function to compute expected soft attention for + monotonic infinite lookback attention from + expected alignment and soft energy. + + Reference: + Monotonic Chunkwise Attention + https://arxiv.org/abs/1712.05382 + + Monotonic Infinite Lookback Attention for Simultaneous Machine Translation + https://arxiv.org/abs/1906.05218 + + alpha: bsz, tgt_len, src_len + soft_energy: bsz, tgt_len, src_len + padding_mask: bsz, src_len + left_padding: bool + """ + if padding_mask is not None: + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0.0) + soft_energy = soft_energy.masked_fill( + padding_mask.unsqueeze(1), -float("inf") + ) + + prob_check(alpha) + + dtype = alpha.dtype + + alpha = alpha.float() + soft_energy = soft_energy.float() + + soft_energy = soft_energy - soft_energy.max(dim=2, keepdim=True)[0] + exp_soft_energy = torch.exp(soft_energy) + eps + + if chunk_size is not None: + # Chunkwise + beta = ( + exp_soft_energy + * moving_sum( + alpha / (eps + moving_sum(exp_soft_energy, chunk_size, 1)), + 1, chunk_size + ) + ) + else: + # Infinite lookback + # Notice that infinite lookback is a special case of chunkwise + # where chunksize = inf + inner_items = alpha / (eps + torch.cumsum(exp_soft_energy, dim=2)) + + beta = ( + exp_soft_energy + * torch.cumsum(inner_items.flip(dims=[2]), dim=2) + .flip(dims=[2]) + ) + + if padding_mask is not None: + beta = beta.masked_fill( + padding_mask.unsqueeze(1).to(torch.bool), 0.0) + + # Mix precision to prevent overflow for fp16 + beta = beta.type(dtype) + + beta = beta.clamp(0, 1) + + prob_check(beta) + + return beta + + +def mass_preservation( + alpha: Tensor, + padding_mask: Optional[Tensor] = None, + left_padding: bool = False +): + """ + Function to compute the mass perservation for alpha. + This means that the residual weights of alpha will be assigned + to the last token. + + Reference: + Monotonic Infinite Lookback Attention for Simultaneous Machine Translation + https://arxiv.org/abs/1906.05218 + + alpha: bsz, tgt_len, src_len + padding_mask: bsz, src_len + left_padding: bool + """ + + prob_check(alpha) + + if padding_mask is not None: + if not left_padding: + assert not padding_mask[:, 0].any(), ( + "Find padding on the beginning of the sequence." + ) + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0.0) + + if left_padding or padding_mask is None: + residuals = 1 - alpha[:, :, :-1].sum(dim=-1).clamp(0, 1) + alpha[:, :, -1] = residuals + else: + # right padding + _, tgt_len, src_len = alpha.size() + residuals = 1 - alpha.sum(dim=-1, keepdim=True).clamp(0, 1) + src_lens = src_len - padding_mask.sum(dim=1, keepdim=True) + src_lens = src_lens.expand(-1, tgt_len).contiguous() + # add back the last value + residuals += alpha.gather(2, src_lens.unsqueeze(2) - 1) + alpha = alpha.scatter(2, src_lens.unsqueeze(2) - 1, residuals) + + prob_check(alpha) + + return alpha diff --git a/fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py b/fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py new file mode 100644 index 0000000..724c691 --- /dev/null +++ b/fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py @@ -0,0 +1,126 @@ +from typing import Optional, Dict +from torch import Tensor +import torch + + +def waitk_p_choose( + tgt_len: int, + src_len: int, + bsz: int, + waitk_lagging: int, + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None +): + + max_src_len = src_len + if incremental_state is not None: + # Retrieve target length from incremental states + # For inference the length of query is always 1 + max_tgt_len = incremental_state["steps"]["tgt"] + assert max_tgt_len is not None + max_tgt_len = int(max_tgt_len) + else: + max_tgt_len = tgt_len + + if max_src_len < waitk_lagging: + if incremental_state is not None: + max_tgt_len = 1 + return torch.zeros( + bsz, max_tgt_len, max_src_len + ) + + # Assuming the p_choose looks like this for wait k=3 + # src_len = 6, max_tgt_len = 5 + # [0, 0, 1, 0, 0, 0, 0] + # [0, 0, 0, 1, 0, 0, 0] + # [0, 0, 0, 0, 1, 0, 0] + # [0, 0, 0, 0, 0, 1, 0] + # [0, 0, 0, 0, 0, 0, 1] + # linearize the p_choose matrix: + # [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0...] + # The indices of linearized matrix that equals 1 is + # 2 + 6 * 0 + # 3 + 6 * 1 + # ... + # n + src_len * n + k - 1 = n * (src_len + 1) + k - 1 + # n from 0 to max_tgt_len - 1 + # + # First, generate the indices (activate_indices_offset: bsz, max_tgt_len) + # Second, scatter a zeros tensor (bsz, max_tgt_len * src_len) + # with activate_indices_offset + # Third, resize the tensor to (bsz, max_tgt_len, src_len) + + activate_indices_offset = ( + ( + torch.arange(max_tgt_len) * (max_src_len + 1) + + waitk_lagging - 1 + ) + .unsqueeze(0) + .expand(bsz, max_tgt_len) + .long() + ) + + if key_padding_mask is not None: + if key_padding_mask[:, 0].any(): + # Left padding + activate_indices_offset += ( + key_padding_mask.sum(dim=1, keepdim=True) + ) + + # Need to clamp the indices that are too large + activate_indices_offset = ( + activate_indices_offset + .clamp( + 0, + min( + [ + max_tgt_len, + max_src_len - waitk_lagging + 1 + ] + ) * max_src_len - 1 + ) + ) + + p_choose = torch.zeros(bsz, max_tgt_len * max_src_len) + + p_choose = p_choose.scatter( + 1, + activate_indices_offset, + 1.0 + ).view(bsz, max_tgt_len, max_src_len) + + if key_padding_mask is not None: + p_choose = p_choose.to(key_padding_mask) + p_choose = p_choose.masked_fill(key_padding_mask.unsqueeze(1), 0) + + if incremental_state is not None: + p_choose = p_choose[:, -1:] + + return p_choose.float() + + +def learnable_p_choose( + energy, + noise_mean: float = 0.0, + noise_var: float = 0.0, + training: bool = True +): + """ + Calculating step wise prob for reading and writing + 1 to read, 0 to write + energy: bsz, tgt_len, src_len + """ + + noise = 0 + if training: + # add noise here to encourage discretness + noise = ( + torch.normal(noise_mean, noise_var, energy.size()) + .type_as(energy) + .to(energy.device) + ) + + p_choose = torch.sigmoid(energy + noise) + + # p_choose: bsz * self.num_heads, tgt_len, src_len + return p_choose diff --git a/fairseq/examples/speech_recognition/README.md b/fairseq/examples/speech_recognition/README.md new file mode 100644 index 0000000..5f9b278 --- /dev/null +++ b/fairseq/examples/speech_recognition/README.md @@ -0,0 +1,87 @@ +### 2021 Update: We are merging this example into the [S2T framework](../speech_to_text), which supports more generic speech-to-text tasks (e.g. speech translation) and more flexible data processing pipelines. Please stay tuned. + +# Speech Recognition +`examples/speech_recognition` is implementing ASR task in Fairseq, along with needed features, datasets, models and loss functions to train and infer model described in [Transformers with convolutional context for ASR (Abdelrahman Mohamed et al., 2019)](https://arxiv.org/abs/1904.11660). + + +## Additional dependencies +On top of main fairseq dependencies there are couple more additional requirements. + +1) Please follow the instructions to install [torchaudio](https://github.com/pytorch/audio). This is required to compute audio fbank features. +2) [Sclite](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htm#sclite_name_0) is used to measure WER. Sclite can be downloaded and installed from source from sctk package [here](http://www.openslr.org/4/). Training and inference doesn't require Sclite dependency. +3) [sentencepiece](https://github.com/google/sentencepiece) is required in order to create dataset with word-piece targets. + +## Preparing librispeech data +``` +./examples/speech_recognition/datasets/prepare-librispeech.sh $DIR_TO_SAVE_RAW_DATA $DIR_FOR_PREPROCESSED_DATA +``` + +## Training librispeech data +``` +python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 80 --task speech_recognition --arch vggtransformer_2 --optimizer adadelta --lr 1.0 --adadelta-eps 1e-8 --adadelta-rho 0.95 --clip-norm 10.0 --max-tokens 5000 --log-format json --log-interval 1 --criterion cross_entropy_acc --user-dir examples/speech_recognition/ +``` + +## Inference for librispeech +`$SET` can be `test_clean` or `test_other` +Any checkpoint in `$MODEL_PATH` can be selected. In this example we are working with `checkpoint_last.pt` +``` +python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --max-tokens 25000 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --beam 20 --results-path $RES_DIR --batch-size 40 --gen-subset $SET --user-dir examples/speech_recognition/ +``` + +## Inference for librispeech +``` +sclite -r ${RES_DIR}/ref.word-checkpoint_last.pt-${SET}.txt -h ${RES_DIR}/hypo.word-checkpoint_last.pt-${SET}.txt -i rm -o all stdout > $RES_REPORT +``` +`Sum/Avg` row from first table of the report has WER + +## Using flashlight (previously called [wav2letter](https://github.com/facebookresearch/wav2letter)) components +[flashlight](https://github.com/facebookresearch/flashlight) now has integration with fairseq. Currently this includes: + +* AutoSegmentationCriterion (ASG) +* flashlight-style Conv/GLU model +* flashlight's beam search decoder + +To use these, follow the instructions on [this page](https://github.com/flashlight/flashlight/tree/e16682fa32df30cbf675c8fe010f929c61e3b833/bindings/python) to install python bindings. **Flashlight v0.3.2** must be used to install the bindings. Running: +``` +git clone --branch v0.3.2 https://github.com/flashlight/flashlight +``` +will properly clone and check out this version. + +## Training librispeech data (flashlight style, Conv/GLU + ASG loss) +Training command: +``` +python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 100 --task speech_recognition --arch w2l_conv_glu_enc --batch-size 4 --optimizer sgd --lr 0.3,0.8 --momentum 0.8 --clip-norm 0.2 --max-tokens 50000 --log-format json --log-interval 100 --num-workers 0 --sentence-avg --criterion asg_loss --asg-transitions-init 5 --max-replabel 2 --linseg-updates 8789 --user-dir examples/speech_recognition +``` + +Note that ASG loss currently doesn't do well with word-pieces. You should prepare a dataset with character targets by setting `nbpe=31` in `prepare-librispeech.sh`. + +## Inference for librispeech (flashlight decoder, n-gram LM) +Inference command: +``` +python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder kenlm --kenlm-model $KENLM_MODEL_PATH --lexicon $LEXICON_PATH --beam 200 --beam-threshold 15 --lm-weight 1.5 --word-score 1.5 --sil-weight -0.3 --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition +``` + +`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a flashlight-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels): +``` +doorbell D O 1 R B E L 1 ▁ +``` +For CTC inference with word-pieces, repetition labels are not used and the lexicon should have most common spellings for each word (one can use sentencepiece's `NBestEncodeAsPieces` for this): +``` +doorbell ▁DOOR BE LL +doorbell ▁DOOR B E LL +doorbell ▁DO OR BE LL +doorbell ▁DOOR B EL L +doorbell ▁DOOR BE L L +doorbell ▁DO OR B E LL +doorbell ▁DOOR B E L L +doorbell ▁DO OR B EL L +doorbell ▁DO O R BE LL +doorbell ▁DO OR BE L L +``` +Lowercase vs. uppercase matters: the *word* should match the case of the n-gram language model (i.e. `$KENLM_MODEL_PATH`), while the *spelling* should match the case of the token dictionary (i.e. `$DIR_FOR_PREPROCESSED_DATA/dict.txt`). + +## Inference for librispeech (flashlight decoder, viterbi only) +Inference command: +``` +python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder viterbi --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition +``` diff --git a/fairseq/examples/speech_recognition/__init__.py b/fairseq/examples/speech_recognition/__init__.py new file mode 100644 index 0000000..0278f6a --- /dev/null +++ b/fairseq/examples/speech_recognition/__init__.py @@ -0,0 +1 @@ +from . import criterions, models, tasks # noqa diff --git a/fairseq/examples/speech_recognition/criterions/ASG_loss.py b/fairseq/examples/speech_recognition/criterions/ASG_loss.py new file mode 100644 index 0000000..41f50bb --- /dev/null +++ b/fairseq/examples/speech_recognition/criterions/ASG_loss.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from examples.speech_recognition.data.replabels import pack_replabels +from fairseq import utils +from fairseq.criterions import FairseqCriterion, register_criterion + + +@register_criterion("asg_loss") +class ASGCriterion(FairseqCriterion): + @staticmethod + def add_args(parser): + group = parser.add_argument_group("ASG Loss") + group.add_argument( + "--asg-transitions-init", + help="initial diagonal value of transition matrix", + type=float, + default=0.0, + ) + group.add_argument( + "--max-replabel", help="maximum # of replabels", type=int, default=2 + ) + group.add_argument( + "--linseg-updates", + help="# of training updates to use LinSeg initialization", + type=int, + default=0, + ) + group.add_argument( + "--hide-linseg-messages", + help="hide messages about LinSeg initialization", + action="store_true", + ) + + def __init__( + self, + task, + silence_token, + asg_transitions_init, + max_replabel, + linseg_updates, + hide_linseg_messages, + ): + from flashlight.lib.sequence.criterion import ASGLoss, CriterionScaleMode + + super().__init__(task) + self.tgt_dict = task.target_dictionary + self.eos = self.tgt_dict.eos() + self.silence = ( + self.tgt_dict.index(silence_token) + if silence_token in self.tgt_dict + else None + ) + self.max_replabel = max_replabel + + num_labels = len(self.tgt_dict) + self.asg = ASGLoss(num_labels, scale_mode=CriterionScaleMode.TARGET_SZ_SQRT) + self.asg.trans = torch.nn.Parameter( + asg_transitions_init * torch.eye(num_labels), requires_grad=True + ) + + self.linseg_progress = torch.nn.Parameter( + torch.tensor([0], dtype=torch.int), requires_grad=False + ) + self.linseg_maximum = linseg_updates + self.linseg_message_state = "none" if hide_linseg_messages else "start" + + @classmethod + def build_criterion(cls, args, task): + return cls( + task, + args.silence_token, + args.asg_transitions_init, + args.max_replabel, + args.linseg_updates, + args.hide_linseg_messages, + ) + + def linseg_step(self): + if not self.training: + return False + if self.linseg_progress.item() < self.linseg_maximum: + if self.linseg_message_state == "start": + print("| using LinSeg to initialize ASG") + self.linseg_message_state = "finish" + self.linseg_progress.add_(1) + return True + elif self.linseg_message_state == "finish": + print("| finished LinSeg initialization") + self.linseg_message_state = "none" + return False + + def replace_eos_with_silence(self, tgt): + if tgt[-1] != self.eos: + return tgt + elif self.silence is None or (len(tgt) > 1 and tgt[-2] == self.silence): + return tgt[:-1] + else: + return tgt[:-1] + [self.silence] + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + + net_output = model(**sample["net_input"]) + emissions = net_output["encoder_out"].transpose(0, 1).contiguous() + B = emissions.size(0) + T = emissions.size(1) + device = emissions.device + + target = torch.IntTensor(B, T) + target_size = torch.IntTensor(B) + using_linseg = self.linseg_step() + + for b in range(B): + initial_target_size = sample["target_lengths"][b].item() + if initial_target_size == 0: + raise ValueError("target size cannot be zero") + + tgt = sample["target"][b, :initial_target_size].tolist() + tgt = self.replace_eos_with_silence(tgt) + tgt = pack_replabels(tgt, self.tgt_dict, self.max_replabel) + tgt = tgt[:T] + + if using_linseg: + tgt = [tgt[t * len(tgt) // T] for t in range(T)] + + target[b][: len(tgt)] = torch.IntTensor(tgt) + target_size[b] = len(tgt) + + loss = self.asg.forward(emissions, target.to(device), target_size.to(device)) + + if reduce: + loss = torch.sum(loss) + + sample_size = ( + sample["target"].size(0) if self.args.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + return loss, sample_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + agg_output = { + "loss": loss_sum / nsentences, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + return agg_output diff --git a/fairseq/examples/speech_recognition/criterions/__init__.py b/fairseq/examples/speech_recognition/criterions/__init__.py new file mode 100644 index 0000000..579abd2 --- /dev/null +++ b/fairseq/examples/speech_recognition/criterions/__init__.py @@ -0,0 +1,17 @@ +import importlib +import os + + +# ASG loss requires flashlight bindings +files_to_skip = set() +try: + import flashlight.lib.sequence.criterion +except ImportError: + files_to_skip.add("ASG_loss.py") + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_") and file not in files_to_skip: + criterion_name = file[: file.find(".py")] + importlib.import_module( + "examples.speech_recognition.criterions." + criterion_name + ) diff --git a/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py b/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py new file mode 100644 index 0000000..7c4d8ba --- /dev/null +++ b/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py @@ -0,0 +1,130 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import logging +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.criterions import FairseqCriterion, register_criterion + + +@register_criterion("cross_entropy_acc") +class CrossEntropyWithAccCriterion(FairseqCriterion): + def __init__(self, task, sentence_avg): + super().__init__(task) + self.sentence_avg = sentence_avg + + def compute_loss(self, model, net_output, target, reduction, log_probs): + # N, T -> N * T + target = target.view(-1) + lprobs = model.get_normalized_probs(net_output, log_probs=log_probs) + if not hasattr(lprobs, "batch_first"): + logging.warning( + "ERROR: we need to know whether " + "batch first for the net output; " + "you need to set batch_first attribute for the return value of " + "model.get_normalized_probs. Now, we assume this is true, but " + "in the future, we will raise exception instead. " + ) + batch_first = getattr(lprobs, "batch_first", True) + if not batch_first: + lprobs = lprobs.transpose(0, 1) + + # N, T, D -> N * T, D + lprobs = lprobs.view(-1, lprobs.size(-1)) + loss = F.nll_loss( + lprobs, target, ignore_index=self.padding_idx, reduction=reduction + ) + return lprobs, loss + + def get_logging_output(self, sample, target, lprobs, loss): + target = target.view(-1) + mask = target != self.padding_idx + correct = torch.sum( + lprobs.argmax(1).masked_select(mask) == target.masked_select(mask) + ) + total = torch.sum(mask) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + + logging_output = { + "loss": utils.item(loss.data), # * sample['ntokens'], + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + "correct": utils.item(correct.data), + "total": utils.item(total.data), + "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(), + } + + return sample_size, logging_output + + def forward(self, model, sample, reduction="sum", log_probs=True): + """Computes the cross entropy with accuracy metric for the given sample. + + This is similar to CrossEntropyCriterion in fairseq, but also + computes accuracy metrics as part of logging + + Args: + logprobs (Torch.tensor) of shape N, T, D i.e. + batchsize, timesteps, dimensions + targets (Torch.tensor) of shape N, T i.e batchsize, timesteps + + Returns: + tuple: With three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + + TODO: + * Currently this Criterion will only work with LSTMEncoderModels or + FairseqModels which have decoder, or Models which return TorchTensor + as net_output. + We need to make a change to support all FairseqEncoder models. + """ + net_output = model(**sample["net_input"]) + target = model.get_targets(sample, net_output) + lprobs, loss = self.compute_loss( + model, net_output, target, reduction, log_probs + ) + sample_size, logging_output = self.get_logging_output( + sample, target, lprobs, loss + ) + return loss, sample_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + correct_sum = sum(log.get("correct", 0) for log in logging_outputs) + total_sum = sum(log.get("total", 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + nframes = sum(log.get("nframes", 0) for log in logging_outputs) + agg_output = { + "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, + # if args.sentence_avg, then sample_size is nsentences, then loss + # is per-sentence loss; else sample_size is ntokens, the loss + # becomes per-output token loss + "ntokens": ntokens, + "nsentences": nsentences, + "nframes": nframes, + "sample_size": sample_size, + "acc": correct_sum * 100.0 / total_sum if total_sum > 0 else 0.0, + "correct": correct_sum, + "total": total_sum, + # total is the number of validate tokens + } + if sample_size != ntokens: + agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) + # loss: per output token loss + # nll_loss: per sentence loss + return agg_output diff --git a/fairseq/examples/speech_recognition/datasets/asr_prep_json.py b/fairseq/examples/speech_recognition/datasets/asr_prep_json.py new file mode 100644 index 0000000..b8db8ff --- /dev/null +++ b/fairseq/examples/speech_recognition/datasets/asr_prep_json.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import argparse +import concurrent.futures +import json +import multiprocessing +import os +from collections import namedtuple +from itertools import chain + +import sentencepiece as spm +from fairseq.data import Dictionary + + +MILLISECONDS_TO_SECONDS = 0.001 + + +def process_sample(aud_path, lable, utt_id, sp, tgt_dict): + import torchaudio + + input = {} + output = {} + si, ei = torchaudio.info(aud_path) + input["length_ms"] = int( + si.length / si.channels / si.rate / MILLISECONDS_TO_SECONDS + ) + input["path"] = aud_path + + token = " ".join(sp.EncodeAsPieces(lable)) + ids = tgt_dict.encode_line(token, append_eos=False) + output["text"] = lable + output["token"] = token + output["tokenid"] = ", ".join(map(str, [t.tolist() for t in ids])) + return {utt_id: {"input": input, "output": output}} + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--audio-dirs", + nargs="+", + default=["-"], + required=True, + help="input directories with audio files", + ) + parser.add_argument( + "--labels", + required=True, + help="aggregated input labels with format <ID LABEL> per line", + type=argparse.FileType("r", encoding="UTF-8"), + ) + parser.add_argument( + "--spm-model", + required=True, + help="sentencepiece model to use for encoding", + type=argparse.FileType("r", encoding="UTF-8"), + ) + parser.add_argument( + "--dictionary", + required=True, + help="file to load fairseq dictionary from", + type=argparse.FileType("r", encoding="UTF-8"), + ) + parser.add_argument("--audio-format", choices=["flac", "wav"], default="wav") + parser.add_argument( + "--output", + required=True, + type=argparse.FileType("w"), + help="path to save json output", + ) + args = parser.parse_args() + + sp = spm.SentencePieceProcessor() + sp.Load(args.spm_model.name) + + tgt_dict = Dictionary.load(args.dictionary) + + labels = {} + for line in args.labels: + (utt_id, label) = line.split(" ", 1) + labels[utt_id] = label + if len(labels) == 0: + raise Exception("No labels found in ", args.labels_path) + + Sample = namedtuple("Sample", "aud_path utt_id") + samples = [] + for path, _, files in chain.from_iterable( + os.walk(path) for path in args.audio_dirs + ): + for f in files: + if f.endswith(args.audio_format): + if len(os.path.splitext(f)) != 2: + raise Exception("Expect <utt_id.extension> file name. Got: ", f) + utt_id = os.path.splitext(f)[0] + if utt_id not in labels: + continue + samples.append(Sample(os.path.join(path, f), utt_id)) + + utts = {} + num_cpu = multiprocessing.cpu_count() + with concurrent.futures.ThreadPoolExecutor(max_workers=num_cpu) as executor: + future_to_sample = { + executor.submit( + process_sample, s.aud_path, labels[s.utt_id], s.utt_id, sp, tgt_dict + ): s + for s in samples + } + for future in concurrent.futures.as_completed(future_to_sample): + try: + data = future.result() + except Exception as exc: + print("generated an exception: ", exc) + else: + utts.update(data) + json.dump({"utts": utts}, args.output, indent=4) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh b/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh new file mode 100644 index 0000000..9e9297f --- /dev/null +++ b/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh @@ -0,0 +1,88 @@ +#!/usr/bin/env bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Prepare librispeech dataset + +base_url=www.openslr.org/resources/12 +train_dir=train_960 + +if [ "$#" -ne 2 ]; then + echo "Usage: $0 <download_dir> <out_dir>" + echo "e.g.: $0 /tmp/librispeech_raw/ ~/data/librispeech_final" + exit 1 +fi + +download_dir=${1%/} +out_dir=${2%/} + +fairseq_root=~/fairseq-py/ +mkdir -p ${out_dir} +cd ${out_dir} || exit + +nbpe=5000 +bpemode=unigram + +if [ ! -d "$fairseq_root" ]; then + echo "$0: Please set correct fairseq_root" + exit 1 +fi + +echo "Data Download" +for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do + url=$base_url/$part.tar.gz + if ! wget -P $download_dir $url; then + echo "$0: wget failed for $url" + exit 1 + fi + if ! tar -C $download_dir -xvzf $download_dir/$part.tar.gz; then + echo "$0: error un-tarring archive $download_dir/$part.tar.gz" + exit 1 + fi +done + +echo "Merge all train packs into one" +mkdir -p ${download_dir}/LibriSpeech/${train_dir}/ +for part in train-clean-100 train-clean-360 train-other-500; do + mv ${download_dir}/LibriSpeech/${part}/* $download_dir/LibriSpeech/${train_dir}/ +done +echo "Merge train text" +find ${download_dir}/LibriSpeech/${train_dir}/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/${train_dir}/text + +# Use combined dev-clean and dev-other as validation set +find ${download_dir}/LibriSpeech/dev-clean/ ${download_dir}/LibriSpeech/dev-other/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/valid_text +find ${download_dir}/LibriSpeech/test-clean/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/test-clean/text +find ${download_dir}/LibriSpeech/test-other/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/test-other/text + + +dict=data/lang_char/${train_dir}_${bpemode}${nbpe}_units.txt +encoded=data/lang_char/${train_dir}_${bpemode}${nbpe}_encoded.txt +fairseq_dict=data/lang_char/${train_dir}_${bpemode}${nbpe}_fairseq_dict.txt +bpemodel=data/lang_char/${train_dir}_${bpemode}${nbpe} +echo "dictionary: ${dict}" +echo "Dictionary preparation" +mkdir -p data/lang_char/ +echo "<unk> 3" > ${dict} +echo "</s> 2" >> ${dict} +echo "<pad> 1" >> ${dict} +cut -f 2- -d" " ${download_dir}/LibriSpeech/${train_dir}/text > data/lang_char/input.txt +spm_train --input=data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000 --unk_id=3 --eos_id=2 --pad_id=1 --bos_id=-1 --character_coverage=1 +spm_encode --model=${bpemodel}.model --output_format=piece < data/lang_char/input.txt > ${encoded} +cat ${encoded} | tr ' ' '\n' | sort | uniq | awk '{print $0 " " NR+3}' >> ${dict} +cat ${encoded} | tr ' ' '\n' | sort | uniq -c | awk '{print $2 " " $1}' > ${fairseq_dict} +wc -l ${dict} + +echo "Prepare train and test jsons" +for part in train_960 test-other test-clean; do + python ${fairseq_root}/examples/speech_recognition/datasets/asr_prep_json.py --audio-dirs ${download_dir}/LibriSpeech/${part} --labels ${download_dir}/LibriSpeech/${part}/text --spm-model ${bpemodel}.model --audio-format flac --dictionary ${fairseq_dict} --output ${part}.json +done +# fairseq expects to find train.json and valid.json during training +mv train_960.json train.json + +echo "Prepare valid json" +python ${fairseq_root}/examples/speech_recognition/datasets/asr_prep_json.py --audio-dirs ${download_dir}/LibriSpeech/dev-clean ${download_dir}/LibriSpeech/dev-other --labels ${download_dir}/LibriSpeech/valid_text --spm-model ${bpemodel}.model --audio-format flac --dictionary ${fairseq_dict} --output valid.json + +cp ${fairseq_dict} ./dict.txt +cp ${bpemodel}.model ./spm.model diff --git a/fairseq/examples/speech_recognition/infer.py b/fairseq/examples/speech_recognition/infer.py new file mode 100644 index 0000000..ce16bf4 --- /dev/null +++ b/fairseq/examples/speech_recognition/infer.py @@ -0,0 +1,436 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Run inference for pre-processed data with a trained model. +""" + +import ast +import logging +import math +import os +import sys + +import editdistance +import numpy as np +import torch +from fairseq import checkpoint_utils, options, progress_bar, tasks, utils +from fairseq.data.data_utils import post_process +from fairseq.logging.meters import StopwatchMeter, TimeMeter + + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def add_asr_eval_argument(parser): + parser.add_argument("--kspmodel", default=None, help="sentence piece model") + parser.add_argument( + "--wfstlm", default=None, help="wfstlm on dictonary output units" + ) + parser.add_argument( + "--rnnt_decoding_type", + default="greedy", + help="wfstlm on dictonary\ +output units", + ) + try: + parser.add_argument( + "--lm-weight", + "--lm_weight", + type=float, + default=0.2, + help="weight for lm while interpolating with neural score", + ) + except: + pass + parser.add_argument( + "--rnnt_len_penalty", default=-0.5, help="rnnt length penalty on word level" + ) + parser.add_argument( + "--w2l-decoder", + choices=["viterbi", "kenlm", "fairseqlm"], + help="use a w2l decoder", + ) + parser.add_argument("--lexicon", help="lexicon for w2l decoder") + parser.add_argument("--unit-lm", action="store_true", help="if using a unit lm") + parser.add_argument("--kenlm-model", "--lm-model", help="lm model for w2l decoder") + parser.add_argument("--beam-threshold", type=float, default=25.0) + parser.add_argument("--beam-size-token", type=float, default=100) + parser.add_argument("--word-score", type=float, default=1.0) + parser.add_argument("--unk-weight", type=float, default=-math.inf) + parser.add_argument("--sil-weight", type=float, default=0.0) + parser.add_argument( + "--dump-emissions", + type=str, + default=None, + help="if present, dumps emissions into this file and exits", + ) + parser.add_argument( + "--dump-features", + type=str, + default=None, + help="if present, dumps features into this file and exits", + ) + parser.add_argument( + "--load-emissions", + type=str, + default=None, + help="if present, loads emissions from this file", + ) + return parser + + +def check_args(args): + # assert args.path is not None, "--path required for generation!" + # assert args.results_path is not None, "--results_path required for generation!" + assert ( + not args.sampling or args.nbest == args.beam + ), "--sampling requires --nbest to be equal to --beam" + assert ( + args.replace_unk is None or args.raw_text + ), "--replace-unk requires a raw text dataset (--raw-text)" + + +def get_dataset_itr(args, task, models): + return task.get_batch_iterator( + dataset=task.dataset(args.gen_subset), + max_tokens=args.max_tokens, + max_sentences=args.batch_size, + max_positions=(sys.maxsize, sys.maxsize), + ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=args.required_batch_size_multiple, + num_shards=args.num_shards, + shard_id=args.shard_id, + num_workers=args.num_workers, + data_buffer_size=args.data_buffer_size, + ).next_epoch_itr(shuffle=False) + + +def process_predictions( + args, hypos, sp, tgt_dict, target_tokens, res_files, speaker, id +): + for hypo in hypos[: min(len(hypos), args.nbest)]: + hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu()) + + if "words" in hypo: + hyp_words = " ".join(hypo["words"]) + else: + hyp_words = post_process(hyp_pieces, args.post_process) + + if res_files is not None: + print( + "{} ({}-{})".format(hyp_pieces, speaker, id), + file=res_files["hypo.units"], + ) + print( + "{} ({}-{})".format(hyp_words, speaker, id), + file=res_files["hypo.words"], + ) + + tgt_pieces = tgt_dict.string(target_tokens) + tgt_words = post_process(tgt_pieces, args.post_process) + + if res_files is not None: + print( + "{} ({}-{})".format(tgt_pieces, speaker, id), + file=res_files["ref.units"], + ) + print( + "{} ({}-{})".format(tgt_words, speaker, id), file=res_files["ref.words"] + ) + + if not args.quiet: + logger.info("HYPO:" + hyp_words) + logger.info("TARGET:" + tgt_words) + logger.info("___________________") + + hyp_words = hyp_words.split() + tgt_words = tgt_words.split() + return editdistance.eval(hyp_words, tgt_words), len(tgt_words) + + +def prepare_result_files(args): + def get_res_file(file_prefix): + if args.num_shards > 1: + file_prefix = f"{args.shard_id}_{file_prefix}" + path = os.path.join( + args.results_path, + "{}-{}-{}.txt".format( + file_prefix, os.path.basename(args.path), args.gen_subset + ), + ) + return open(path, "w", buffering=1) + + if not args.results_path: + return None + + return { + "hypo.words": get_res_file("hypo.word"), + "hypo.units": get_res_file("hypo.units"), + "ref.words": get_res_file("ref.word"), + "ref.units": get_res_file("ref.units"), + } + + +def optimize_models(args, use_cuda, models): + """Optimize ensemble for generation""" + for model in models: + model.make_generation_fast_( + beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, + need_attn=args.print_alignment, + ) + if args.fp16: + model.half() + if use_cuda: + model.cuda() + + +def apply_half(t): + if t.dtype is torch.float32: + return t.to(dtype=torch.half) + return t + + +class ExistingEmissionsDecoder(object): + def __init__(self, decoder, emissions): + self.decoder = decoder + self.emissions = emissions + + def generate(self, models, sample, **unused): + ids = sample["id"].cpu().numpy() + try: + emissions = np.stack(self.emissions[ids]) + except: + print([x.shape for x in self.emissions[ids]]) + raise Exception("invalid sizes") + emissions = torch.from_numpy(emissions) + return self.decoder.decode(emissions) + + +def main(args, task=None, model_state=None): + check_args(args) + + use_fp16 = args.fp16 + if args.max_tokens is None and args.batch_size is None: + args.max_tokens = 4000000 + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + + logger.info("| decoding with criterion {}".format(args.criterion)) + + task = tasks.setup_task(args) + + # Load ensemble + if args.load_emissions: + models, criterions = [], [] + task.load_dataset(args.gen_subset) + else: + logger.info("| loading model(s) from {}".format(args.path)) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + utils.split_paths(args.path, separator="\\"), + arg_overrides=ast.literal_eval(args.model_overrides), + task=task, + suffix=args.checkpoint_suffix, + strict=(args.checkpoint_shard_count == 1), + num_shards=args.checkpoint_shard_count, + state=model_state, + ) + optimize_models(args, use_cuda, models) + task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task) + + + # Set dictionary + tgt_dict = task.target_dictionary + + logger.info( + "| {} {} {} examples".format( + args.data, args.gen_subset, len(task.dataset(args.gen_subset)) + ) + ) + + # hack to pass transitions to W2lDecoder + if args.criterion == "asg_loss": + raise NotImplementedError("asg_loss is currently not supported") + # trans = criterions[0].asg.trans.data + # args.asg_transitions = torch.flatten(trans).tolist() + + # Load dataset (possibly sharded) + itr = get_dataset_itr(args, task, models) + + # Initialize generator + gen_timer = StopwatchMeter() + + def build_generator(args): + w2l_decoder = getattr(args, "w2l_decoder", None) + if w2l_decoder == "viterbi": + from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder + + return W2lViterbiDecoder(args, task.target_dictionary) + elif w2l_decoder == "kenlm": + from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder + + return W2lKenLMDecoder(args, task.target_dictionary) + elif w2l_decoder == "fairseqlm": + from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder + + return W2lFairseqLMDecoder(args, task.target_dictionary) + else: + print( + "only flashlight decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment" + ) + + # please do not touch this unless you test both generate.py and infer.py with audio_pretraining task + generator = build_generator(args) + + if args.load_emissions: + generator = ExistingEmissionsDecoder( + generator, np.load(args.load_emissions, allow_pickle=True) + ) + logger.info("loaded emissions from " + args.load_emissions) + + num_sentences = 0 + + if args.results_path is not None and not os.path.exists(args.results_path): + os.makedirs(args.results_path) + + max_source_pos = ( + utils.resolve_max_positions( + task.max_positions(), *[model.max_positions() for model in models] + ), + ) + + if max_source_pos is not None: + max_source_pos = max_source_pos[0] + if max_source_pos is not None: + max_source_pos = max_source_pos[0] - 1 + + if args.dump_emissions: + emissions = {} + if args.dump_features: + features = {} + models[0].bert.proj = None + else: + res_files = prepare_result_files(args) + errs_t = 0 + lengths_t = 0 + with progress_bar.build_progress_bar(args, itr) as t: + wps_meter = TimeMeter() + for sample in t: + sample = utils.move_to_cuda(sample) if use_cuda else sample + if use_fp16: + sample = utils.apply_to_sample(apply_half, sample) + if "net_input" not in sample: + continue + + prefix_tokens = None + if args.prefix_size > 0: + prefix_tokens = sample["target"][:, : args.prefix_size] + + gen_timer.start() + if args.dump_emissions: + with torch.no_grad(): + encoder_out = models[0](**sample["net_input"]) + emm = models[0].get_normalized_probs(encoder_out, log_probs=True) + emm = emm.transpose(0, 1).cpu().numpy() + for i, id in enumerate(sample["id"]): + emissions[id.item()] = emm[i] + continue + elif args.dump_features: + with torch.no_grad(): + encoder_out = models[0](**sample["net_input"]) + feat = encoder_out["encoder_out"].transpose(0, 1).cpu().numpy() + for i, id in enumerate(sample["id"]): + padding = ( + encoder_out["encoder_padding_mask"][i].cpu().numpy() + if encoder_out["encoder_padding_mask"] is not None + else None + ) + features[id.item()] = (feat[i], padding) + continue + hypos = task.inference_step(generator, models, sample, prefix_tokens) + num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) + gen_timer.stop(num_generated_tokens) + + for i, sample_id in enumerate(sample["id"].tolist()): + speaker = None + # id = task.dataset(args.gen_subset).ids[int(sample_id)] + id = sample_id + toks = ( + sample["target"][i, :] + if "target_label" not in sample + else sample["target_label"][i, :] + ) + target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu() + # Process top predictions + errs, length = process_predictions( + args, + hypos[i], + None, + tgt_dict, + target_tokens, + res_files, + speaker, + id, + ) + errs_t += errs + lengths_t += length + + wps_meter.update(num_generated_tokens) + t.log({"wps": round(wps_meter.avg)}) + num_sentences += ( + sample["nsentences"] if "nsentences" in sample else sample["id"].numel() + ) + + wer = None + if args.dump_emissions: + emm_arr = [] + for i in range(len(emissions)): + emm_arr.append(emissions[i]) + np.save(args.dump_emissions, emm_arr) + logger.info(f"saved {len(emissions)} emissions to {args.dump_emissions}") + elif args.dump_features: + feat_arr = [] + for i in range(len(features)): + feat_arr.append(features[i]) + np.save(args.dump_features, feat_arr) + logger.info(f"saved {len(features)} emissions to {args.dump_features}") + else: + if lengths_t > 0: + wer = errs_t * 100.0 / lengths_t + logger.info(f"WER: {wer}") + + logger.info( + "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}" + "sentences/s, {:.2f} tokens/s)".format( + num_sentences, + gen_timer.n, + gen_timer.sum, + num_sentences / gen_timer.sum, + 1.0 / gen_timer.avg, + ) + ) + logger.info("| Generate {} with beam={}".format(args.gen_subset, args.beam)) + return task, wer + + +def make_parser(): + parser = options.get_generation_parser() + parser = add_asr_eval_argument(parser) + return parser + + +def cli_main(): + parser = make_parser() + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/speech_recognition/kaldi/__init__.py b/fairseq/examples/speech_recognition/kaldi/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/speech_recognition/kaldi/add-self-loop-simple.cc b/fairseq/examples/speech_recognition/kaldi/add-self-loop-simple.cc new file mode 100644 index 0000000..e18fb62 --- /dev/null +++ b/fairseq/examples/speech_recognition/kaldi/add-self-loop-simple.cc @@ -0,0 +1,94 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <iostream> +#include "fstext/fstext-lib.h" // @manual +#include "util/common-utils.h" // @manual + +/* + * This program is to modify a FST without self-loop by: + * for each incoming arc with non-eps input symbol, add a self-loop arc + * with that non-eps symbol as input and eps as output. + * + * This is to make sure the resultant FST can do deduplication for repeated + * symbols, which is very common in acoustic model + * + */ +namespace { +int32 AddSelfLoopsSimple(fst::StdVectorFst* fst) { + typedef fst::MutableArcIterator<fst::StdVectorFst> IterType; + + int32 num_states_before = fst->NumStates(); + fst::MakePrecedingInputSymbolsSame(false, fst); + int32 num_states_after = fst->NumStates(); + KALDI_LOG << "There are " << num_states_before + << " states in the original FST; " + << " after MakePrecedingInputSymbolsSame, there are " + << num_states_after << " states " << std::endl; + + auto weight_one = fst::StdArc::Weight::One(); + + int32 num_arc_added = 0; + + fst::StdArc self_loop_arc; + self_loop_arc.weight = weight_one; + + int32 num_states = fst->NumStates(); + std::vector<std::set<int32>> incoming_non_eps_label_per_state(num_states); + + for (int32 state = 0; state < num_states; state++) { + for (IterType aiter(fst, state); !aiter.Done(); aiter.Next()) { + fst::StdArc arc(aiter.Value()); + if (arc.ilabel != 0) { + incoming_non_eps_label_per_state[arc.nextstate].insert(arc.ilabel); + } + } + } + + for (int32 state = 0; state < num_states; state++) { + if (!incoming_non_eps_label_per_state[state].empty()) { + auto& ilabel_set = incoming_non_eps_label_per_state[state]; + for (auto it = ilabel_set.begin(); it != ilabel_set.end(); it++) { + self_loop_arc.ilabel = *it; + self_loop_arc.olabel = 0; + self_loop_arc.nextstate = state; + fst->AddArc(state, self_loop_arc); + num_arc_added++; + } + } + } + return num_arc_added; +} + +void print_usage() { + std::cout << "add-self-loop-simple usage:\n" + "\tadd-self-loop-simple <in-fst> <out-fst> \n"; +} +} // namespace + +int main(int argc, char** argv) { + if (argc != 3) { + print_usage(); + exit(1); + } + + auto input = argv[1]; + auto output = argv[2]; + + auto fst = fst::ReadFstKaldi(input); + auto num_states = fst->NumStates(); + KALDI_LOG << "Loading FST from " << input << " with " << num_states + << " states." << std::endl; + + int32 num_arc_added = AddSelfLoopsSimple(fst); + KALDI_LOG << "Adding " << num_arc_added << " self-loop arcs " << std::endl; + + fst::WriteFstKaldi(*fst, std::string(output)); + KALDI_LOG << "Writing FST to " << output << std::endl; + + delete fst; +} diff --git a/fairseq/examples/speech_recognition/kaldi/config/kaldi_initializer.yaml b/fairseq/examples/speech_recognition/kaldi/config/kaldi_initializer.yaml new file mode 100644 index 0000000..be9ba98 --- /dev/null +++ b/fairseq/examples/speech_recognition/kaldi/config/kaldi_initializer.yaml @@ -0,0 +1,8 @@ +# @package _group_ + +data_dir: ??? +fst_dir: ??? +in_labels: ??? +kaldi_root: ??? +lm_arpa: ??? +blank_symbol: <s> diff --git a/fairseq/examples/speech_recognition/kaldi/kaldi_decoder.py b/fairseq/examples/speech_recognition/kaldi/kaldi_decoder.py new file mode 100644 index 0000000..5f62cc5 --- /dev/null +++ b/fairseq/examples/speech_recognition/kaldi/kaldi_decoder.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from concurrent.futures import ThreadPoolExecutor +import logging +from omegaconf import MISSING +import os +import torch +from typing import Optional +import warnings + + +from dataclasses import dataclass +from fairseq.dataclass import FairseqDataclass +from .kaldi_initializer import KaldiInitializerConfig, initalize_kaldi + + +logger = logging.getLogger(__name__) + + +@dataclass +class KaldiDecoderConfig(FairseqDataclass): + hlg_graph_path: Optional[str] = None + output_dict: str = MISSING + + kaldi_initializer_config: Optional[KaldiInitializerConfig] = None + + acoustic_scale: float = 0.5 + max_active: int = 10000 + beam_delta: float = 0.5 + hash_ratio: float = 2.0 + + is_lattice: bool = False + lattice_beam: float = 10.0 + prune_interval: int = 25 + determinize_lattice: bool = True + prune_scale: float = 0.1 + max_mem: int = 0 + phone_determinize: bool = True + word_determinize: bool = True + minimize: bool = True + + num_threads: int = 1 + + +class KaldiDecoder(object): + def __init__( + self, + cfg: KaldiDecoderConfig, + beam: int, + nbest: int = 1, + ): + try: + from kaldi.asr import FasterRecognizer, LatticeFasterRecognizer + from kaldi.base import set_verbose_level + from kaldi.decoder import ( + FasterDecoder, + FasterDecoderOptions, + LatticeFasterDecoder, + LatticeFasterDecoderOptions, + ) + from kaldi.lat.functions import DeterminizeLatticePhonePrunedOptions + from kaldi.fstext import read_fst_kaldi, SymbolTable + except: + warnings.warn( + "pykaldi is required for this functionality. Please install from https://github.com/pykaldi/pykaldi" + ) + + # set_verbose_level(2) + + self.acoustic_scale = cfg.acoustic_scale + self.nbest = nbest + + if cfg.hlg_graph_path is None: + assert ( + cfg.kaldi_initializer_config is not None + ), "Must provide hlg graph path or kaldi initializer config" + cfg.hlg_graph_path = initalize_kaldi(cfg.kaldi_initializer_config) + + assert os.path.exists(cfg.hlg_graph_path), cfg.hlg_graph_path + + if cfg.is_lattice: + self.dec_cls = LatticeFasterDecoder + opt_cls = LatticeFasterDecoderOptions + self.rec_cls = LatticeFasterRecognizer + else: + assert self.nbest == 1, "nbest > 1 requires lattice decoder" + self.dec_cls = FasterDecoder + opt_cls = FasterDecoderOptions + self.rec_cls = FasterRecognizer + + self.decoder_options = opt_cls() + self.decoder_options.beam = beam + self.decoder_options.max_active = cfg.max_active + self.decoder_options.beam_delta = cfg.beam_delta + self.decoder_options.hash_ratio = cfg.hash_ratio + + if cfg.is_lattice: + self.decoder_options.lattice_beam = cfg.lattice_beam + self.decoder_options.prune_interval = cfg.prune_interval + self.decoder_options.determinize_lattice = cfg.determinize_lattice + self.decoder_options.prune_scale = cfg.prune_scale + det_opts = DeterminizeLatticePhonePrunedOptions() + det_opts.max_mem = cfg.max_mem + det_opts.phone_determinize = cfg.phone_determinize + det_opts.word_determinize = cfg.word_determinize + det_opts.minimize = cfg.minimize + self.decoder_options.det_opts = det_opts + + self.output_symbols = {} + with open(cfg.output_dict, "r") as f: + for line in f: + items = line.rstrip().split() + assert len(items) == 2 + self.output_symbols[int(items[1])] = items[0] + + logger.info(f"Loading FST from {cfg.hlg_graph_path}") + self.fst = read_fst_kaldi(cfg.hlg_graph_path) + self.symbol_table = SymbolTable.read_text(cfg.output_dict) + + self.executor = ThreadPoolExecutor(max_workers=cfg.num_threads) + + def generate(self, models, sample, **unused): + """Generate a batch of inferences.""" + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" + } + emissions, padding = self.get_emissions(models, encoder_input) + return self.decode(emissions, padding) + + def get_emissions(self, models, encoder_input): + """Run encoder and normalize emissions""" + model = models[0] + + all_encoder_out = [m(**encoder_input) for m in models] + + if len(all_encoder_out) > 1: + + if "encoder_out" in all_encoder_out[0]: + encoder_out = { + "encoder_out": sum(e["encoder_out"] for e in all_encoder_out) + / len(all_encoder_out), + "encoder_padding_mask": all_encoder_out[0]["encoder_padding_mask"], + } + padding = encoder_out["encoder_padding_mask"] + else: + encoder_out = { + "logits": sum(e["logits"] for e in all_encoder_out) + / len(all_encoder_out), + "padding_mask": all_encoder_out[0]["padding_mask"], + } + padding = encoder_out["padding_mask"] + else: + encoder_out = all_encoder_out[0] + padding = ( + encoder_out["padding_mask"] + if "padding_mask" in encoder_out + else encoder_out["encoder_padding_mask"] + ) + + if hasattr(model, "get_logits"): + emissions = model.get_logits(encoder_out, normalize=True) + else: + emissions = model.get_normalized_probs(encoder_out, log_probs=True) + + return ( + emissions.cpu().float().transpose(0, 1), + padding.cpu() if padding is not None and padding.any() else None, + ) + + def decode_one(self, logits, padding): + from kaldi.matrix import Matrix + + decoder = self.dec_cls(self.fst, self.decoder_options) + asr = self.rec_cls( + decoder, self.symbol_table, acoustic_scale=self.acoustic_scale + ) + + if padding is not None: + logits = logits[~padding] + + mat = Matrix(logits.numpy()) + + out = asr.decode(mat) + + if self.nbest > 1: + from kaldi.fstext import shortestpath + from kaldi.fstext.utils import ( + convert_compact_lattice_to_lattice, + convert_lattice_to_std, + convert_nbest_to_list, + get_linear_symbol_sequence, + ) + + lat = out["lattice"] + + sp = shortestpath(lat, nshortest=self.nbest) + + sp = convert_compact_lattice_to_lattice(sp) + sp = convert_lattice_to_std(sp) + seq = convert_nbest_to_list(sp) + + results = [] + for s in seq: + _, o, w = get_linear_symbol_sequence(s) + words = list(self.output_symbols[z] for z in o) + results.append( + { + "tokens": words, + "words": words, + "score": w.value, + "emissions": logits, + } + ) + return results + else: + words = out["text"].split() + return [ + { + "tokens": words, + "words": words, + "score": out["likelihood"], + "emissions": logits, + } + ] + + def decode(self, emissions, padding): + if padding is None: + padding = [None] * len(emissions) + + ret = list( + map( + lambda e, p: self.executor.submit(self.decode_one, e, p), + emissions, + padding, + ) + ) + return ret diff --git a/fairseq/examples/speech_recognition/kaldi/kaldi_initializer.py b/fairseq/examples/speech_recognition/kaldi/kaldi_initializer.py new file mode 100644 index 0000000..6d2a2a4 --- /dev/null +++ b/fairseq/examples/speech_recognition/kaldi/kaldi_initializer.py @@ -0,0 +1,698 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass +import hydra +from hydra.core.config_store import ConfigStore +import logging +from omegaconf import MISSING, OmegaConf +import os +import os.path as osp +from pathlib import Path +import subprocess +from typing import Optional + +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass import FairseqDataclass + +script_dir = Path(__file__).resolve().parent +config_path = script_dir / "config" + + +logger = logging.getLogger(__name__) + + +@dataclass +class KaldiInitializerConfig(FairseqDataclass): + data_dir: str = MISSING + fst_dir: Optional[str] = None + in_labels: str = MISSING + out_labels: Optional[str] = None + wav2letter_lexicon: Optional[str] = None + lm_arpa: str = MISSING + kaldi_root: str = MISSING + blank_symbol: str = "<s>" + silence_symbol: Optional[str] = None + + +def create_units(fst_dir: Path, in_labels: str, vocab: Dictionary) -> Path: + in_units_file = fst_dir / f"kaldi_dict.{in_labels}.txt" + if not in_units_file.exists(): + + logger.info(f"Creating {in_units_file}") + + with open(in_units_file, "w") as f: + print("<eps> 0", file=f) + i = 1 + for symb in vocab.symbols[vocab.nspecial :]: + if not symb.startswith("madeupword"): + print(f"{symb} {i}", file=f) + i += 1 + return in_units_file + + +def create_lexicon( + cfg: KaldiInitializerConfig, + fst_dir: Path, + unique_label: str, + in_units_file: Path, + out_words_file: Path, +) -> (Path, Path): + + disambig_in_units_file = fst_dir / f"kaldi_dict.{cfg.in_labels}_disambig.txt" + lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}.txt" + disambig_lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}_disambig.txt" + if ( + not lexicon_file.exists() + or not disambig_lexicon_file.exists() + or not disambig_in_units_file.exists() + ): + logger.info(f"Creating {lexicon_file} (in units file: {in_units_file})") + + assert cfg.wav2letter_lexicon is not None or cfg.in_labels == cfg.out_labels + + if cfg.wav2letter_lexicon is not None: + lm_words = set() + with open(out_words_file, "r") as lm_dict_f: + for line in lm_dict_f: + lm_words.add(line.split()[0]) + + num_skipped = 0 + total = 0 + with open(cfg.wav2letter_lexicon, "r") as w2l_lex_f, open( + lexicon_file, "w" + ) as out_f: + for line in w2l_lex_f: + items = line.rstrip().split("\t") + assert len(items) == 2, items + if items[0] in lm_words: + print(items[0], items[1], file=out_f) + else: + num_skipped += 1 + logger.debug( + f"Skipping word {items[0]} as it was not found in LM" + ) + total += 1 + if num_skipped > 0: + logger.warning( + f"Skipped {num_skipped} out of {total} words as they were not found in LM" + ) + else: + with open(in_units_file, "r") as in_f, open(lexicon_file, "w") as out_f: + for line in in_f: + symb = line.split()[0] + if symb != "<eps>" and symb != "<ctc_blank>" and symb != "<SIL>": + print(symb, symb, file=out_f) + + lex_disambig_path = ( + Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_lex_disambig.pl" + ) + res = subprocess.run( + [lex_disambig_path, lexicon_file, disambig_lexicon_file], + check=True, + capture_output=True, + ) + ndisambig = int(res.stdout) + disamib_path = Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_disambig.pl" + res = subprocess.run( + [disamib_path, "--include-zero", in_units_file, str(ndisambig)], + check=True, + capture_output=True, + ) + with open(disambig_in_units_file, "wb") as f: + f.write(res.stdout) + + return disambig_lexicon_file, disambig_in_units_file + + +def create_G( + kaldi_root: Path, fst_dir: Path, lm_arpa: Path, arpa_base: str +) -> (Path, Path): + + out_words_file = fst_dir / f"kaldi_dict.{arpa_base}.txt" + grammar_graph = fst_dir / f"G_{arpa_base}.fst" + if not grammar_graph.exists() or not out_words_file.exists(): + logger.info(f"Creating {grammar_graph}") + arpa2fst = kaldi_root / "src/lmbin/arpa2fst" + subprocess.run( + [ + arpa2fst, + "--disambig-symbol=#0", + f"--write-symbol-table={out_words_file}", + lm_arpa, + grammar_graph, + ], + check=True, + ) + return grammar_graph, out_words_file + + +def create_L( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + lexicon_file: Path, + in_units_file: Path, + out_words_file: Path, +) -> Path: + lexicon_graph = fst_dir / f"L.{unique_label}.fst" + + if not lexicon_graph.exists(): + logger.info(f"Creating {lexicon_graph} (in units: {in_units_file})") + make_lex = kaldi_root / "egs/wsj/s5/utils/make_lexicon_fst.pl" + fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile" + fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops" + fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" + + def write_disambig_symbol(file): + with open(file, "r") as f: + for line in f: + items = line.rstrip().split() + if items[0] == "#0": + out_path = str(file) + "_disamig" + with open(out_path, "w") as out_f: + print(items[1], file=out_f) + return out_path + + return None + + in_disambig_sym = write_disambig_symbol(in_units_file) + assert in_disambig_sym is not None + out_disambig_sym = write_disambig_symbol(out_words_file) + assert out_disambig_sym is not None + + try: + with open(lexicon_graph, "wb") as out_f: + res = subprocess.run( + [make_lex, lexicon_file], capture_output=True, check=True + ) + assert len(res.stderr) == 0, res.stderr.decode("utf-8") + res = subprocess.run( + [ + fstcompile, + f"--isymbols={in_units_file}", + f"--osymbols={out_words_file}", + "--keep_isymbols=false", + "--keep_osymbols=false", + ], + input=res.stdout, + capture_output=True, + ) + assert len(res.stderr) == 0, res.stderr.decode("utf-8") + res = subprocess.run( + [fstaddselfloops, in_disambig_sym, out_disambig_sym], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstarcsort, "--sort_type=olabel"], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(lexicon_graph) + raise + except AssertionError: + os.remove(lexicon_graph) + raise + + return lexicon_graph + + +def create_LG( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + lexicon_graph: Path, + grammar_graph: Path, +) -> Path: + lg_graph = fst_dir / f"LG.{unique_label}.fst" + + if not lg_graph.exists(): + logger.info(f"Creating {lg_graph}") + + fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" + fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" + fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" + fstpushspecial = kaldi_root / "src/fstbin/fstpushspecial" + fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" + + try: + with open(lg_graph, "wb") as out_f: + res = subprocess.run( + [fsttablecompose, lexicon_graph, grammar_graph], + capture_output=True, + check=True, + ) + res = subprocess.run( + [ + fstdeterminizestar, + "--use-log=true", + ], + input=res.stdout, + capture_output=True, + ) + res = subprocess.run( + [fstminimizeencoded], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstpushspecial], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstarcsort, "--sort_type=ilabel"], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(lg_graph) + raise + + return lg_graph + + +def create_H( + kaldi_root: Path, + fst_dir: Path, + disambig_out_units_file: Path, + in_labels: str, + vocab: Dictionary, + blk_sym: str, + silence_symbol: Optional[str], +) -> (Path, Path, Path): + h_graph = ( + fst_dir / f"H.{in_labels}{'_' + silence_symbol if silence_symbol else ''}.fst" + ) + h_out_units_file = fst_dir / f"kaldi_dict.h_out.{in_labels}.txt" + disambig_in_units_file_int = Path(str(h_graph) + "isym_disambig.int") + disambig_out_units_file_int = Path(str(disambig_out_units_file) + ".int") + if ( + not h_graph.exists() + or not h_out_units_file.exists() + or not disambig_in_units_file_int.exists() + ): + logger.info(f"Creating {h_graph}") + eps_sym = "<eps>" + + num_disambig = 0 + osymbols = [] + + with open(disambig_out_units_file, "r") as f, open( + disambig_out_units_file_int, "w" + ) as out_f: + for line in f: + symb, id = line.rstrip().split() + if line.startswith("#"): + num_disambig += 1 + print(id, file=out_f) + else: + if len(osymbols) == 0: + assert symb == eps_sym, symb + osymbols.append((symb, id)) + + i_idx = 0 + isymbols = [(eps_sym, 0)] + + imap = {} + + for i, s in enumerate(vocab.symbols): + i_idx += 1 + isymbols.append((s, i_idx)) + imap[s] = i_idx + + fst_str = [] + + node_idx = 0 + root_node = node_idx + + special_symbols = [blk_sym] + if silence_symbol is not None: + special_symbols.append(silence_symbol) + + for ss in special_symbols: + fst_str.append("{} {} {} {}".format(root_node, root_node, ss, eps_sym)) + + for symbol, _ in osymbols: + if symbol == eps_sym or symbol.startswith("#"): + continue + + node_idx += 1 + # 1. from root to emitting state + fst_str.append("{} {} {} {}".format(root_node, node_idx, symbol, symbol)) + # 2. from emitting state back to root + fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym)) + # 3. from emitting state to optional blank state + pre_node = node_idx + node_idx += 1 + for ss in special_symbols: + fst_str.append("{} {} {} {}".format(pre_node, node_idx, ss, eps_sym)) + # 4. from blank state back to root + fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym)) + + fst_str.append("{}".format(root_node)) + + fst_str = "\n".join(fst_str) + h_str = str(h_graph) + isym_file = h_str + ".isym" + + with open(isym_file, "w") as f: + for sym, id in isymbols: + f.write("{} {}\n".format(sym, id)) + + with open(h_out_units_file, "w") as f: + for sym, id in osymbols: + f.write("{} {}\n".format(sym, id)) + + with open(disambig_in_units_file_int, "w") as f: + disam_sym_id = len(isymbols) + for _ in range(num_disambig): + f.write("{}\n".format(disam_sym_id)) + disam_sym_id += 1 + + fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile" + fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops" + fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" + + try: + with open(h_graph, "wb") as out_f: + res = subprocess.run( + [ + fstcompile, + f"--isymbols={isym_file}", + f"--osymbols={h_out_units_file}", + "--keep_isymbols=false", + "--keep_osymbols=false", + ], + input=str.encode(fst_str), + capture_output=True, + check=True, + ) + res = subprocess.run( + [ + fstaddselfloops, + disambig_in_units_file_int, + disambig_out_units_file_int, + ], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstarcsort, "--sort_type=olabel"], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(h_graph) + raise + return h_graph, h_out_units_file, disambig_in_units_file_int + + +def create_HLGa( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + h_graph: Path, + lg_graph: Path, + disambig_in_words_file_int: Path, +) -> Path: + hlga_graph = fst_dir / f"HLGa.{unique_label}.fst" + + if not hlga_graph.exists(): + logger.info(f"Creating {hlga_graph}") + + fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" + fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" + fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols" + fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal" + fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" + + try: + with open(hlga_graph, "wb") as out_f: + res = subprocess.run( + [ + fsttablecompose, + h_graph, + lg_graph, + ], + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstdeterminizestar, "--use-log=true"], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmsymbols, disambig_in_words_file_int], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmepslocal], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstminimizeencoded], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(hlga_graph) + raise + + return hlga_graph + + +def create_HLa( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + h_graph: Path, + l_graph: Path, + disambig_in_words_file_int: Path, +) -> Path: + hla_graph = fst_dir / f"HLa.{unique_label}.fst" + + if not hla_graph.exists(): + logger.info(f"Creating {hla_graph}") + + fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" + fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" + fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols" + fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal" + fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" + + try: + with open(hla_graph, "wb") as out_f: + res = subprocess.run( + [ + fsttablecompose, + h_graph, + l_graph, + ], + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstdeterminizestar, "--use-log=true"], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmsymbols, disambig_in_words_file_int], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmepslocal], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstminimizeencoded], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(hla_graph) + raise + + return hla_graph + + +def create_HLG( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + hlga_graph: Path, + prefix: str = "HLG", +) -> Path: + hlg_graph = fst_dir / f"{prefix}.{unique_label}.fst" + + if not hlg_graph.exists(): + logger.info(f"Creating {hlg_graph}") + + add_self_loop = script_dir / "add-self-loop-simple" + kaldi_src = kaldi_root / "src" + kaldi_lib = kaldi_src / "lib" + + try: + if not add_self_loop.exists(): + fst_include = kaldi_root / "tools/openfst-1.6.7/include" + add_self_loop_src = script_dir / "add-self-loop-simple.cc" + + subprocess.run( + [ + "c++", + f"-I{kaldi_src}", + f"-I{fst_include}", + f"-L{kaldi_lib}", + add_self_loop_src, + "-lkaldi-base", + "-lkaldi-fstext", + "-o", + add_self_loop, + ], + check=True, + ) + + my_env = os.environ.copy() + my_env["LD_LIBRARY_PATH"] = f"{kaldi_lib}:{my_env['LD_LIBRARY_PATH']}" + + subprocess.run( + [ + add_self_loop, + hlga_graph, + hlg_graph, + ], + check=True, + capture_output=True, + env=my_env, + ) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + raise + + return hlg_graph + + +def initalize_kaldi(cfg: KaldiInitializerConfig) -> Path: + if cfg.fst_dir is None: + cfg.fst_dir = osp.join(cfg.data_dir, "kaldi") + if cfg.out_labels is None: + cfg.out_labels = cfg.in_labels + + kaldi_root = Path(cfg.kaldi_root) + data_dir = Path(cfg.data_dir) + fst_dir = Path(cfg.fst_dir) + fst_dir.mkdir(parents=True, exist_ok=True) + + arpa_base = osp.splitext(osp.basename(cfg.lm_arpa))[0] + unique_label = f"{cfg.in_labels}.{arpa_base}" + + with open(data_dir / f"dict.{cfg.in_labels}.txt", "r") as f: + vocab = Dictionary.load(f) + + in_units_file = create_units(fst_dir, cfg.in_labels, vocab) + + grammar_graph, out_words_file = create_G( + kaldi_root, fst_dir, Path(cfg.lm_arpa), arpa_base + ) + + disambig_lexicon_file, disambig_L_in_units_file = create_lexicon( + cfg, fst_dir, unique_label, in_units_file, out_words_file + ) + + h_graph, h_out_units_file, disambig_in_units_file_int = create_H( + kaldi_root, + fst_dir, + disambig_L_in_units_file, + cfg.in_labels, + vocab, + cfg.blank_symbol, + cfg.silence_symbol, + ) + lexicon_graph = create_L( + kaldi_root, + fst_dir, + unique_label, + disambig_lexicon_file, + disambig_L_in_units_file, + out_words_file, + ) + lg_graph = create_LG( + kaldi_root, fst_dir, unique_label, lexicon_graph, grammar_graph + ) + hlga_graph = create_HLGa( + kaldi_root, fst_dir, unique_label, h_graph, lg_graph, disambig_in_units_file_int + ) + hlg_graph = create_HLG(kaldi_root, fst_dir, unique_label, hlga_graph) + + # for debugging + # hla_graph = create_HLa(kaldi_root, fst_dir, unique_label, h_graph, lexicon_graph, disambig_in_units_file_int) + # hl_graph = create_HLG(kaldi_root, fst_dir, unique_label, hla_graph, prefix="HL_looped") + # create_HLG(kaldi_root, fst_dir, "phnc", h_graph, prefix="H_looped") + + return hlg_graph + + +@hydra.main(config_path=config_path, config_name="kaldi_initializer") +def cli_main(cfg: KaldiInitializerConfig) -> None: + container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) + cfg = OmegaConf.create(container) + OmegaConf.set_struct(cfg, True) + initalize_kaldi(cfg) + + +if __name__ == "__main__": + + logging.root.setLevel(logging.INFO) + logging.basicConfig(level=logging.INFO) + + try: + from hydra._internal.utils import ( + get_args, + ) # pylint: disable=import-outside-toplevel + + cfg_name = get_args().config_name or "kaldi_initializer" + except ImportError: + logger.warning("Failed to get config name from hydra args") + cfg_name = "kaldi_initializer" + + cs = ConfigStore.instance() + cs.store(name=cfg_name, node=KaldiInitializerConfig) + + cli_main() diff --git a/fairseq/examples/speech_recognition/models/__init__.py b/fairseq/examples/speech_recognition/models/__init__.py new file mode 100644 index 0000000..54b5a1c --- /dev/null +++ b/fairseq/examples/speech_recognition/models/__init__.py @@ -0,0 +1,8 @@ +import importlib +import os + + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + model_name = file[: file.find(".py")] + importlib.import_module("examples.speech_recognition.models." + model_name) diff --git a/fairseq/examples/speech_recognition/models/vggtransformer.py b/fairseq/examples/speech_recognition/models/vggtransformer.py new file mode 100644 index 0000000..bca0ae5 --- /dev/null +++ b/fairseq/examples/speech_recognition/models/vggtransformer.py @@ -0,0 +1,1020 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import math +from collections.abc import Iterable + +import torch +import torch.nn as nn +from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqEncoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) +from fairseq.modules import ( + LinearizedConvolution, + TransformerDecoderLayer, + TransformerEncoderLayer, + VGGBlock, +) + + +@register_model("asr_vggtransformer") +class VGGTransformerModel(FairseqEncoderDecoderModel): + """ + Transformers with convolutional context for ASR + https://arxiv.org/abs/1904.11660 + """ + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--input-feat-per-channel", + type=int, + metavar="N", + help="encoder input dimension per input channel", + ) + parser.add_argument( + "--vggblock-enc-config", + type=str, + metavar="EXPR", + help=""" + an array of tuples each containing the configuration of one vggblock: + [(out_channels, + conv_kernel_size, + pooling_kernel_size, + num_conv_layers, + use_layer_norm), ...]) + """, + ) + parser.add_argument( + "--transformer-enc-config", + type=str, + metavar="EXPR", + help="""" + a tuple containing the configuration of the encoder transformer layers + configurations: + [(input_dim, + num_heads, + ffn_dim, + normalize_before, + dropout, + attention_dropout, + relu_dropout), ...]') + """, + ) + parser.add_argument( + "--enc-output-dim", + type=int, + metavar="N", + help=""" + encoder output dimension, can be None. If specified, projecting the + transformer output to the specified dimension""", + ) + parser.add_argument( + "--in-channels", + type=int, + metavar="N", + help="number of encoder input channels", + ) + parser.add_argument( + "--tgt-embed-dim", + type=int, + metavar="N", + help="embedding dimension of the decoder target tokens", + ) + parser.add_argument( + "--transformer-dec-config", + type=str, + metavar="EXPR", + help=""" + a tuple containing the configuration of the decoder transformer layers + configurations: + [(input_dim, + num_heads, + ffn_dim, + normalize_before, + dropout, + attention_dropout, + relu_dropout), ...] + """, + ) + parser.add_argument( + "--conv-dec-config", + type=str, + metavar="EXPR", + help=""" + an array of tuples for the decoder 1-D convolution config + [(out_channels, conv_kernel_size, use_layer_norm), ...]""", + ) + + @classmethod + def build_encoder(cls, args, task): + return VGGTransformerEncoder( + input_feat_per_channel=args.input_feat_per_channel, + vggblock_config=eval(args.vggblock_enc_config), + transformer_config=eval(args.transformer_enc_config), + encoder_output_dim=args.enc_output_dim, + in_channels=args.in_channels, + ) + + @classmethod + def build_decoder(cls, args, task): + return TransformerDecoder( + dictionary=task.target_dictionary, + embed_dim=args.tgt_embed_dim, + transformer_config=eval(args.transformer_dec_config), + conv_config=eval(args.conv_dec_config), + encoder_output_dim=args.enc_output_dim, + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted + # (in case there are any new ones) + base_architecture(args) + + encoder = cls.build_encoder(args, task) + decoder = cls.build_decoder(args, task) + return cls(encoder, decoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = super().get_normalized_probs(net_output, log_probs, sample) + lprobs.batch_first = True + return lprobs + + +DEFAULT_ENC_VGGBLOCK_CONFIG = ((32, 3, 2, 2, False),) * 2 +DEFAULT_ENC_TRANSFORMER_CONFIG = ((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2 +# 256: embedding dimension +# 4: number of heads +# 1024: FFN +# True: apply layerNorm before (dropout + resiaul) instead of after +# 0.2 (dropout): dropout after MultiheadAttention and second FC +# 0.2 (attention_dropout): dropout in MultiheadAttention +# 0.2 (relu_dropout): dropout after ReLu +DEFAULT_DEC_TRANSFORMER_CONFIG = ((256, 2, 1024, True, 0.2, 0.2, 0.2),) * 2 +DEFAULT_DEC_CONV_CONFIG = ((256, 3, True),) * 2 + + +# TODO: repace transformer encoder config from one liner +# to explicit args to get rid of this transformation +def prepare_transformer_encoder_params( + input_dim, + num_heads, + ffn_dim, + normalize_before, + dropout, + attention_dropout, + relu_dropout, +): + args = argparse.Namespace() + args.encoder_embed_dim = input_dim + args.encoder_attention_heads = num_heads + args.attention_dropout = attention_dropout + args.dropout = dropout + args.activation_dropout = relu_dropout + args.encoder_normalize_before = normalize_before + args.encoder_ffn_embed_dim = ffn_dim + return args + + +def prepare_transformer_decoder_params( + input_dim, + num_heads, + ffn_dim, + normalize_before, + dropout, + attention_dropout, + relu_dropout, +): + args = argparse.Namespace() + args.encoder_embed_dim = None + args.decoder_embed_dim = input_dim + args.decoder_attention_heads = num_heads + args.attention_dropout = attention_dropout + args.dropout = dropout + args.activation_dropout = relu_dropout + args.decoder_normalize_before = normalize_before + args.decoder_ffn_embed_dim = ffn_dim + return args + + +class VGGTransformerEncoder(FairseqEncoder): + """VGG + Transformer encoder""" + + def __init__( + self, + input_feat_per_channel, + vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG, + transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG, + encoder_output_dim=512, + in_channels=1, + transformer_context=None, + transformer_sampling=None, + ): + """constructor for VGGTransformerEncoder + + Args: + - input_feat_per_channel: feature dim (not including stacked, + just base feature) + - in_channel: # input channels (e.g., if stack 8 feature vector + together, this is 8) + - vggblock_config: configuration of vggblock, see comments on + DEFAULT_ENC_VGGBLOCK_CONFIG + - transformer_config: configuration of transformer layer, see comments + on DEFAULT_ENC_TRANSFORMER_CONFIG + - encoder_output_dim: final transformer output embedding dimension + - transformer_context: (left, right) if set, self-attention will be focused + on (t-left, t+right) + - transformer_sampling: an iterable of int, must match with + len(transformer_config), transformer_sampling[i] indicates sampling + factor for i-th transformer layer, after multihead att and feedfoward + part + """ + super().__init__(None) + + self.num_vggblocks = 0 + if vggblock_config is not None: + if not isinstance(vggblock_config, Iterable): + raise ValueError("vggblock_config is not iterable") + self.num_vggblocks = len(vggblock_config) + + self.conv_layers = nn.ModuleList() + self.in_channels = in_channels + self.input_dim = input_feat_per_channel + self.pooling_kernel_sizes = [] + + if vggblock_config is not None: + for _, config in enumerate(vggblock_config): + ( + out_channels, + conv_kernel_size, + pooling_kernel_size, + num_conv_layers, + layer_norm, + ) = config + self.conv_layers.append( + VGGBlock( + in_channels, + out_channels, + conv_kernel_size, + pooling_kernel_size, + num_conv_layers, + input_dim=input_feat_per_channel, + layer_norm=layer_norm, + ) + ) + self.pooling_kernel_sizes.append(pooling_kernel_size) + in_channels = out_channels + input_feat_per_channel = self.conv_layers[-1].output_dim + + transformer_input_dim = self.infer_conv_output_dim( + self.in_channels, self.input_dim + ) + # transformer_input_dim is the output dimension of VGG part + + self.validate_transformer_config(transformer_config) + self.transformer_context = self.parse_transformer_context(transformer_context) + self.transformer_sampling = self.parse_transformer_sampling( + transformer_sampling, len(transformer_config) + ) + + self.transformer_layers = nn.ModuleList() + + if transformer_input_dim != transformer_config[0][0]: + self.transformer_layers.append( + Linear(transformer_input_dim, transformer_config[0][0]) + ) + self.transformer_layers.append( + TransformerEncoderLayer( + prepare_transformer_encoder_params(*transformer_config[0]) + ) + ) + + for i in range(1, len(transformer_config)): + if transformer_config[i - 1][0] != transformer_config[i][0]: + self.transformer_layers.append( + Linear(transformer_config[i - 1][0], transformer_config[i][0]) + ) + self.transformer_layers.append( + TransformerEncoderLayer( + prepare_transformer_encoder_params(*transformer_config[i]) + ) + ) + + self.encoder_output_dim = encoder_output_dim + self.transformer_layers.extend( + [ + Linear(transformer_config[-1][0], encoder_output_dim), + LayerNorm(encoder_output_dim), + ] + ) + + def forward(self, src_tokens, src_lengths, **kwargs): + """ + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (B,) + """ + bsz, max_seq_len, _ = src_tokens.size() + x = src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) + x = x.transpose(1, 2).contiguous() + # (B, C, T, feat) + + for layer_idx in range(len(self.conv_layers)): + x = self.conv_layers[layer_idx](x) + + bsz, _, output_seq_len, _ = x.size() + + # (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) -> (T, B, C * feat) + x = x.transpose(1, 2).transpose(0, 1) + x = x.contiguous().view(output_seq_len, bsz, -1) + + input_lengths = src_lengths.clone() + for s in self.pooling_kernel_sizes: + input_lengths = (input_lengths.float() / s).ceil().long() + + encoder_padding_mask, _ = lengths_to_encoder_padding_mask( + input_lengths, batch_first=True + ) + if not encoder_padding_mask.any(): + encoder_padding_mask = None + + subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5) + attn_mask = self.lengths_to_attn_mask(input_lengths, subsampling_factor) + + transformer_layer_idx = 0 + + for layer_idx in range(len(self.transformer_layers)): + + if isinstance(self.transformer_layers[layer_idx], TransformerEncoderLayer): + x = self.transformer_layers[layer_idx]( + x, encoder_padding_mask, attn_mask + ) + + if self.transformer_sampling[transformer_layer_idx] != 1: + sampling_factor = self.transformer_sampling[transformer_layer_idx] + x, encoder_padding_mask, attn_mask = self.slice( + x, encoder_padding_mask, attn_mask, sampling_factor + ) + + transformer_layer_idx += 1 + + else: + x = self.transformer_layers[layer_idx](x) + + # encoder_padding_maks is a (T x B) tensor, its [t, b] elements indicate + # whether encoder_output[t, b] is valid or not (valid=0, invalid=1) + + return { + "encoder_out": x, # (T, B, C) + "encoder_padding_mask": encoder_padding_mask.t() + if encoder_padding_mask is not None + else None, + # (B, T) --> (T, B) + } + + def infer_conv_output_dim(self, in_channels, input_dim): + sample_seq_len = 200 + sample_bsz = 10 + x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim) + for i, _ in enumerate(self.conv_layers): + x = self.conv_layers[i](x) + x = x.transpose(1, 2) + mb, seq = x.size()[:2] + return x.contiguous().view(mb, seq, -1).size(-1) + + def validate_transformer_config(self, transformer_config): + for config in transformer_config: + input_dim, num_heads = config[:2] + if input_dim % num_heads != 0: + msg = ( + "ERROR in transformer config {}: ".format(config) + + "input dimension {} ".format(input_dim) + + "not dividable by number of heads {}".format(num_heads) + ) + raise ValueError(msg) + + def parse_transformer_context(self, transformer_context): + """ + transformer_context can be the following: + - None; indicates no context is used, i.e., + transformer can access full context + - a tuple/list of two int; indicates left and right context, + any number <0 indicates infinite context + * e.g., (5, 6) indicates that for query at x_t, transformer can + access [t-5, t+6] (inclusive) + * e.g., (-1, 6) indicates that for query at x_t, transformer can + access [0, t+6] (inclusive) + """ + if transformer_context is None: + return None + + if not isinstance(transformer_context, Iterable): + raise ValueError("transformer context must be Iterable if it is not None") + + if len(transformer_context) != 2: + raise ValueError("transformer context must have length 2") + + left_context = transformer_context[0] + if left_context < 0: + left_context = None + + right_context = transformer_context[1] + if right_context < 0: + right_context = None + + if left_context is None and right_context is None: + return None + + return (left_context, right_context) + + def parse_transformer_sampling(self, transformer_sampling, num_layers): + """ + parsing transformer sampling configuration + + Args: + - transformer_sampling, accepted input: + * None, indicating no sampling + * an Iterable with int (>0) as element + - num_layers, expected number of transformer layers, must match with + the length of transformer_sampling if it is not None + + Returns: + - A tuple with length num_layers + """ + if transformer_sampling is None: + return (1,) * num_layers + + if not isinstance(transformer_sampling, Iterable): + raise ValueError( + "transformer_sampling must be an iterable if it is not None" + ) + + if len(transformer_sampling) != num_layers: + raise ValueError( + "transformer_sampling {} does not match with the number " + "of layers {}".format(transformer_sampling, num_layers) + ) + + for layer, value in enumerate(transformer_sampling): + if not isinstance(value, int): + raise ValueError("Invalid value in transformer_sampling: ") + if value < 1: + raise ValueError( + "{} layer's subsampling is {}.".format(layer, value) + + " This is not allowed! " + ) + return transformer_sampling + + def slice(self, embedding, padding_mask, attn_mask, sampling_factor): + """ + embedding is a (T, B, D) tensor + padding_mask is a (B, T) tensor or None + attn_mask is a (T, T) tensor or None + """ + embedding = embedding[::sampling_factor, :, :] + if padding_mask is not None: + padding_mask = padding_mask[:, ::sampling_factor] + if attn_mask is not None: + attn_mask = attn_mask[::sampling_factor, ::sampling_factor] + + return embedding, padding_mask, attn_mask + + def lengths_to_attn_mask(self, input_lengths, subsampling_factor=1): + """ + create attention mask according to sequence lengths and transformer + context + + Args: + - input_lengths: (B, )-shape Int/Long tensor; input_lengths[b] is + the length of b-th sequence + - subsampling_factor: int + * Note that the left_context and right_context is specified in + the input frame-level while input to transformer may already + go through subsampling (e.g., the use of striding in vggblock) + we use subsampling_factor to scale the left/right context + + Return: + - a (T, T) binary tensor or None, where T is max(input_lengths) + * if self.transformer_context is None, None + * if left_context is None, + * attn_mask[t, t + right_context + 1:] = 1 + * others = 0 + * if right_context is None, + * attn_mask[t, 0:t - left_context] = 1 + * others = 0 + * elsif + * attn_mask[t, t - left_context: t + right_context + 1] = 0 + * others = 1 + """ + if self.transformer_context is None: + return None + + maxT = torch.max(input_lengths).item() + attn_mask = torch.zeros(maxT, maxT) + + left_context = self.transformer_context[0] + right_context = self.transformer_context[1] + if left_context is not None: + left_context = math.ceil(self.transformer_context[0] / subsampling_factor) + if right_context is not None: + right_context = math.ceil(self.transformer_context[1] / subsampling_factor) + + for t in range(maxT): + if left_context is not None: + st = 0 + en = max(st, t - left_context) + attn_mask[t, st:en] = 1 + if right_context is not None: + st = t + right_context + 1 + st = min(st, maxT - 1) + attn_mask[t, st:] = 1 + + return attn_mask.to(input_lengths.device) + + def reorder_encoder_out(self, encoder_out, new_order): + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(1, new_order) + return encoder_out + + +class TransformerDecoder(FairseqIncrementalDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs. + Default: ``False`` + left_pad (bool, optional): whether the input is left-padded. Default: + ``False`` + """ + + def __init__( + self, + dictionary, + embed_dim=512, + transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG, + conv_config=DEFAULT_DEC_CONV_CONFIG, + encoder_output_dim=512, + ): + + super().__init__(dictionary) + vocab_size = len(dictionary) + self.padding_idx = dictionary.pad() + self.embed_tokens = Embedding(vocab_size, embed_dim, self.padding_idx) + + self.conv_layers = nn.ModuleList() + for i in range(len(conv_config)): + out_channels, kernel_size, layer_norm = conv_config[i] + if i == 0: + conv_layer = LinearizedConv1d( + embed_dim, out_channels, kernel_size, padding=kernel_size - 1 + ) + else: + conv_layer = LinearizedConv1d( + conv_config[i - 1][0], + out_channels, + kernel_size, + padding=kernel_size - 1, + ) + self.conv_layers.append(conv_layer) + if layer_norm: + self.conv_layers.append(nn.LayerNorm(out_channels)) + self.conv_layers.append(nn.ReLU()) + + self.layers = nn.ModuleList() + if conv_config[-1][0] != transformer_config[0][0]: + self.layers.append(Linear(conv_config[-1][0], transformer_config[0][0])) + self.layers.append( + TransformerDecoderLayer( + prepare_transformer_decoder_params(*transformer_config[0]) + ) + ) + + for i in range(1, len(transformer_config)): + if transformer_config[i - 1][0] != transformer_config[i][0]: + self.layers.append( + Linear(transformer_config[i - 1][0], transformer_config[i][0]) + ) + self.layers.append( + TransformerDecoderLayer( + prepare_transformer_decoder_params(*transformer_config[i]) + ) + ) + self.fc_out = Linear(transformer_config[-1][0], vocab_size) + + def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for input feeding/teacher forcing + encoder_out (Tensor, optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + Returns: + tuple: + - the last decoder layer's output of shape `(batch, tgt_len, + vocab)` + - the last decoder layer's attention weights of shape `(batch, + tgt_len, src_len)` + """ + target_padding_mask = ( + (prev_output_tokens == self.padding_idx).to(prev_output_tokens.device) + if incremental_state is None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + + # embed tokens + x = self.embed_tokens(prev_output_tokens) + + # B x T x C -> T x B x C + x = self._transpose_if_training(x, incremental_state) + + for layer in self.conv_layers: + if isinstance(layer, LinearizedConvolution): + x = layer(x, incremental_state) + else: + x = layer(x) + + # B x T x C -> T x B x C + x = self._transpose_if_inference(x, incremental_state) + + # decoder layers + for layer in self.layers: + if isinstance(layer, TransformerDecoderLayer): + x, *_ = layer( + x, + (encoder_out["encoder_out"] if encoder_out is not None else None), + ( + encoder_out["encoder_padding_mask"].t() + if encoder_out["encoder_padding_mask"] is not None + else None + ), + incremental_state, + self_attn_mask=( + self.buffered_future_mask(x) + if incremental_state is None + else None + ), + self_attn_padding_mask=( + target_padding_mask if incremental_state is None else None + ), + ) + else: + x = layer(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + x = self.fc_out(x) + + return x, None + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + if self._future_mask.size(0) < dim: + self._future_mask = torch.triu( + utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def _transpose_if_training(self, x, incremental_state): + if incremental_state is None: + x = x.transpose(0, 1) + return x + + def _transpose_if_inference(self, x, incremental_state): + if incremental_state: + x = x.transpose(0, 1) + return x + + +@register_model("asr_vggtransformer_encoder") +class VGGTransformerEncoderModel(FairseqEncoderModel): + def __init__(self, encoder): + super().__init__(encoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--input-feat-per-channel", + type=int, + metavar="N", + help="encoder input dimension per input channel", + ) + parser.add_argument( + "--vggblock-enc-config", + type=str, + metavar="EXPR", + help=""" + an array of tuples each containing the configuration of one vggblock + [(out_channels, conv_kernel_size, pooling_kernel_size,num_conv_layers), ...] + """, + ) + parser.add_argument( + "--transformer-enc-config", + type=str, + metavar="EXPR", + help=""" + a tuple containing the configuration of the Transformer layers + configurations: + [(input_dim, + num_heads, + ffn_dim, + normalize_before, + dropout, + attention_dropout, + relu_dropout), ]""", + ) + parser.add_argument( + "--enc-output-dim", + type=int, + metavar="N", + help="encoder output dimension, projecting the LSTM output", + ) + parser.add_argument( + "--in-channels", + type=int, + metavar="N", + help="number of encoder input channels", + ) + parser.add_argument( + "--transformer-context", + type=str, + metavar="EXPR", + help=""" + either None or a tuple of two ints, indicating left/right context a + transformer can have access to""", + ) + parser.add_argument( + "--transformer-sampling", + type=str, + metavar="EXPR", + help=""" + either None or a tuple of ints, indicating sampling factor in each layer""", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + base_architecture_enconly(args) + encoder = VGGTransformerEncoderOnly( + vocab_size=len(task.target_dictionary), + input_feat_per_channel=args.input_feat_per_channel, + vggblock_config=eval(args.vggblock_enc_config), + transformer_config=eval(args.transformer_enc_config), + encoder_output_dim=args.enc_output_dim, + in_channels=args.in_channels, + transformer_context=eval(args.transformer_context), + transformer_sampling=eval(args.transformer_sampling), + ) + return cls(encoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + # net_output['encoder_out'] is a (T, B, D) tensor + lprobs = super().get_normalized_probs(net_output, log_probs, sample) + # lprobs is a (T, B, D) tensor + # we need to transoose to get (B, T, D) tensor + lprobs = lprobs.transpose(0, 1).contiguous() + lprobs.batch_first = True + return lprobs + + +class VGGTransformerEncoderOnly(VGGTransformerEncoder): + def __init__( + self, + vocab_size, + input_feat_per_channel, + vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG, + transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG, + encoder_output_dim=512, + in_channels=1, + transformer_context=None, + transformer_sampling=None, + ): + super().__init__( + input_feat_per_channel=input_feat_per_channel, + vggblock_config=vggblock_config, + transformer_config=transformer_config, + encoder_output_dim=encoder_output_dim, + in_channels=in_channels, + transformer_context=transformer_context, + transformer_sampling=transformer_sampling, + ) + self.fc_out = Linear(self.encoder_output_dim, vocab_size) + + def forward(self, src_tokens, src_lengths, **kwargs): + """ + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (B,) + """ + + enc_out = super().forward(src_tokens, src_lengths) + x = self.fc_out(enc_out["encoder_out"]) + # x = F.log_softmax(x, dim=-1) + # Note: no need this line, because model.get_normalized_prob will call + # log_softmax + return { + "encoder_out": x, # (T, B, C) + "encoder_padding_mask": enc_out["encoder_padding_mask"], # (T, B) + } + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return (1e6, 1e6) # an arbitrary large number + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + # nn.init.uniform_(m.weight, -0.1, 0.1) + # nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True, dropout=0): + """Linear layer (input: N x T x C)""" + m = nn.Linear(in_features, out_features, bias=bias) + # m.weight.data.uniform_(-0.1, 0.1) + # if bias: + # m.bias.data.uniform_(-0.1, 0.1) + return m + + +def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs): + """Weight-normalized Conv1d layer optimized for decoding""" + m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) + std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) + nn.init.normal_(m.weight, mean=0, std=std) + nn.init.constant_(m.bias, 0) + return nn.utils.weight_norm(m, dim=2) + + +def LayerNorm(embedding_dim): + m = nn.LayerNorm(embedding_dim) + return m + + +# seq2seq models +def base_architecture(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40) + args.vggblock_enc_config = getattr( + args, "vggblock_enc_config", DEFAULT_ENC_VGGBLOCK_CONFIG + ) + args.transformer_enc_config = getattr( + args, "transformer_enc_config", DEFAULT_ENC_TRANSFORMER_CONFIG + ) + args.enc_output_dim = getattr(args, "enc_output_dim", 512) + args.in_channels = getattr(args, "in_channels", 1) + args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128) + args.transformer_dec_config = getattr( + args, "transformer_dec_config", DEFAULT_ENC_TRANSFORMER_CONFIG + ) + args.conv_dec_config = getattr(args, "conv_dec_config", DEFAULT_DEC_CONV_CONFIG) + args.transformer_context = getattr(args, "transformer_context", "None") + + +@register_model_architecture("asr_vggtransformer", "vggtransformer_1") +def vggtransformer_1(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.vggblock_enc_config = getattr( + args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]" + ) + args.transformer_enc_config = getattr( + args, + "transformer_enc_config", + "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 14", + ) + args.enc_output_dim = getattr(args, "enc_output_dim", 1024) + args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128) + args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4") + args.transformer_dec_config = getattr( + args, + "transformer_dec_config", + "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 4", + ) + + +@register_model_architecture("asr_vggtransformer", "vggtransformer_2") +def vggtransformer_2(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.vggblock_enc_config = getattr( + args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]" + ) + args.transformer_enc_config = getattr( + args, + "transformer_enc_config", + "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16", + ) + args.enc_output_dim = getattr(args, "enc_output_dim", 1024) + args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512) + args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4") + args.transformer_dec_config = getattr( + args, + "transformer_dec_config", + "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 6", + ) + + +@register_model_architecture("asr_vggtransformer", "vggtransformer_base") +def vggtransformer_base(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.vggblock_enc_config = getattr( + args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]" + ) + args.transformer_enc_config = getattr( + args, "transformer_enc_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 12" + ) + + args.enc_output_dim = getattr(args, "enc_output_dim", 512) + args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512) + args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4") + args.transformer_dec_config = getattr( + args, "transformer_dec_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 6" + ) + # Size estimations: + # Encoder: + # - vggblock param: 64*1*3*3 + 64*64*3*3 + 128*64*3*3 + 128*128*3 = 258K + # Transformer: + # - input dimension adapter: 2560 x 512 -> 1.31M + # - transformer_layers (x12) --> 37.74M + # * MultiheadAttention: 512*512*3 (in_proj) + 512*512 (out_proj) = 1.048M + # * FFN weight: 512*2048*2 = 2.097M + # - output dimension adapter: 512 x 512 -> 0.26 M + # Decoder: + # - LinearizedConv1d: 512 * 256 * 3 + 256 * 256 * 3 * 3 + # - transformer_layer: (x6) --> 25.16M + # * MultiheadAttention (self-attention): 512*512*3 + 512*512 = 1.048M + # * MultiheadAttention (encoder-attention): 512*512*3 + 512*512 = 1.048M + # * FFN: 512*2048*2 = 2.097M + # Final FC: + # - FC: 512*5000 = 256K (assuming vocab size 5K) + # In total: + # ~65 M + + +# CTC models +def base_architecture_enconly(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40) + args.vggblock_enc_config = getattr( + args, "vggblock_enc_config", "[(32, 3, 2, 2, True)] * 2" + ) + args.transformer_enc_config = getattr( + args, "transformer_enc_config", "((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2" + ) + args.enc_output_dim = getattr(args, "enc_output_dim", 512) + args.in_channels = getattr(args, "in_channels", 1) + args.transformer_context = getattr(args, "transformer_context", "None") + args.transformer_sampling = getattr(args, "transformer_sampling", "None") + + +@register_model_architecture("asr_vggtransformer_encoder", "vggtransformer_enc_1") +def vggtransformer_enc_1(args): + # vggtransformer_1 is the same as vggtransformer_enc_big, except the number + # of layers is increased to 16 + # keep it here for backward compatiablity purpose + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.vggblock_enc_config = getattr( + args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]" + ) + args.transformer_enc_config = getattr( + args, + "transformer_enc_config", + "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16", + ) + args.enc_output_dim = getattr(args, "enc_output_dim", 1024) diff --git a/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py b/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py new file mode 100644 index 0000000..655a9b0 --- /dev/null +++ b/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderModel, + register_model, + register_model_architecture, +) +from fairseq.modules.fairseq_dropout import FairseqDropout + + +default_conv_enc_config = """[ + (400, 13, 170, 0.2), + (440, 14, 0, 0.214), + (484, 15, 0, 0.22898), + (532, 16, 0, 0.2450086), + (584, 17, 0, 0.262159202), + (642, 18, 0, 0.28051034614), + (706, 19, 0, 0.30014607037), + (776, 20, 0, 0.321156295296), + (852, 21, 0, 0.343637235966), + (936, 22, 0, 0.367691842484), + (1028, 23, 0, 0.393430271458), + (1130, 24, 0, 0.42097039046), + (1242, 25, 0, 0.450438317792), + (1366, 26, 0, 0.481969000038), + (1502, 27, 0, 0.51570683004), + (1652, 28, 0, 0.551806308143), + (1816, 29, 0, 0.590432749713), +]""" + + +@register_model("asr_w2l_conv_glu_encoder") +class W2lConvGluEncoderModel(FairseqEncoderModel): + def __init__(self, encoder): + super().__init__(encoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--input-feat-per-channel", + type=int, + metavar="N", + help="encoder input dimension per input channel", + ) + parser.add_argument( + "--in-channels", + type=int, + metavar="N", + help="number of encoder input channels", + ) + parser.add_argument( + "--conv-enc-config", + type=str, + metavar="EXPR", + help=""" + an array of tuples each containing the configuration of one conv layer + [(out_channels, kernel_size, padding, dropout), ...] + """, + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config) + encoder = W2lConvGluEncoder( + vocab_size=len(task.target_dictionary), + input_feat_per_channel=args.input_feat_per_channel, + in_channels=args.in_channels, + conv_enc_config=eval(conv_enc_config), + ) + return cls(encoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + lprobs = super().get_normalized_probs(net_output, log_probs, sample) + lprobs.batch_first = False + return lprobs + + +class W2lConvGluEncoder(FairseqEncoder): + def __init__( + self, vocab_size, input_feat_per_channel, in_channels, conv_enc_config + ): + super().__init__(None) + + self.input_dim = input_feat_per_channel + if in_channels != 1: + raise ValueError("only 1 input channel is currently supported") + + self.conv_layers = nn.ModuleList() + self.linear_layers = nn.ModuleList() + self.dropouts = [] + cur_channels = input_feat_per_channel + + for out_channels, kernel_size, padding, dropout in conv_enc_config: + layer = nn.Conv1d(cur_channels, out_channels, kernel_size, padding=padding) + layer.weight.data.mul_(math.sqrt(3)) # match wav2letter init + self.conv_layers.append(nn.utils.weight_norm(layer)) + self.dropouts.append( + FairseqDropout(dropout, module_name=self.__class__.__name__) + ) + if out_channels % 2 != 0: + raise ValueError("odd # of out_channels is incompatible with GLU") + cur_channels = out_channels // 2 # halved by GLU + + for out_channels in [2 * cur_channels, vocab_size]: + layer = nn.Linear(cur_channels, out_channels) + layer.weight.data.mul_(math.sqrt(3)) + self.linear_layers.append(nn.utils.weight_norm(layer)) + cur_channels = out_channels // 2 + + def forward(self, src_tokens, src_lengths, **kwargs): + + """ + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (B,) + """ + B, T, _ = src_tokens.size() + x = src_tokens.transpose(1, 2).contiguous() # (B, feat, T) assuming C == 1 + + for layer_idx in range(len(self.conv_layers)): + x = self.conv_layers[layer_idx](x) + x = F.glu(x, dim=1) + x = self.dropouts[layer_idx](x) + + x = x.transpose(1, 2).contiguous() # (B, T, 908) + x = self.linear_layers[0](x) + x = F.glu(x, dim=2) + x = self.dropouts[-1](x) + x = self.linear_layers[1](x) + + assert x.size(0) == B + assert x.size(1) == T + + encoder_out = x.transpose(0, 1) # (T, B, vocab_size) + + # need to debug this -- find a simpler/elegant way in pytorch APIs + encoder_padding_mask = ( + torch.arange(T).view(1, T).expand(B, -1).to(x.device) + >= src_lengths.view(B, 1).expand(-1, T) + ).t() # (B x T) -> (T x B) + + return { + "encoder_out": encoder_out, # (T, B, vocab_size) + "encoder_padding_mask": encoder_padding_mask, # (T, B) + } + + def reorder_encoder_out(self, encoder_out, new_order): + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(1, new_order) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return (1e6, 1e6) # an arbitrary large number + + +@register_model_architecture("asr_w2l_conv_glu_encoder", "w2l_conv_glu_enc") +def w2l_conv_glu_enc(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.in_channels = getattr(args, "in_channels", 1) + args.conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config) diff --git a/fairseq/examples/speech_recognition/new/README.md b/fairseq/examples/speech_recognition/new/README.md new file mode 100644 index 0000000..5fa0e97 --- /dev/null +++ b/fairseq/examples/speech_recognition/new/README.md @@ -0,0 +1,43 @@ +# Flashlight Decoder + +This script runs decoding for pre-trained speech recognition models. + +## Usage + +Assuming a few variables: + +```bash +checkpoint=<path-to-checkpoint> +data=<path-to-data-directory> +lm_model=<path-to-language-model> +lexicon=<path-to-lexicon> +``` + +Example usage for decoding a fine-tuned Wav2Vec model: + +```bash +python $FAIRSEQ_ROOT/examples/speech_recognition/new/infer.py --multirun \ + task=audio_pretraining \ + task.data=$data \ + task.labels=ltr \ + common_eval.path=$checkpoint \ + decoding.type=kenlm \ + decoding.lexicon=$lexicon \ + decoding.lmpath=$lm_model \ + dataset.gen_subset=dev_clean,dev_other,test_clean,test_other +``` + +Example usage for using Ax to sweep WER parameters (requires `pip install hydra-ax-sweeper`): + +```bash +python $FAIRSEQ_ROOT/examples/speech_recognition/new/infer.py --multirun \ + hydra/sweeper=ax \ + task=audio_pretraining \ + task.data=$data \ + task.labels=ltr \ + common_eval.path=$checkpoint \ + decoding.type=kenlm \ + decoding.lexicon=$lexicon \ + decoding.lmpath=$lm_model \ + dataset.gen_subset=dev_other +``` diff --git a/fairseq/examples/speech_recognition/new/__init__.py b/fairseq/examples/speech_recognition/new/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/speech_recognition/new/conf/hydra/sweeper/ax.yaml b/fairseq/examples/speech_recognition/new/conf/hydra/sweeper/ax.yaml new file mode 100644 index 0000000..38e9c22 --- /dev/null +++ b/fairseq/examples/speech_recognition/new/conf/hydra/sweeper/ax.yaml @@ -0,0 +1,29 @@ +# @package hydra.sweeper +_target_: hydra_plugins.hydra_ax_sweeper.ax_sweeper.AxSweeper +max_batch_size: null +ax_config: + max_trials: 128 + early_stop: + minimize: true + max_epochs_without_improvement: 10 + epsilon: 0.025 + experiment: + name: ${dataset.gen_subset} + objective_name: wer + minimize: true + parameter_constraints: null + outcome_constraints: null + status_quo: null + client: + verbose_logging: false + random_seed: null + params: + decoding.lmweight: + type: range + bounds: [0.0, 5.0] + decoding.wordscore: + type: range + bounds: [-5.0, 5.0] + decoding.silweight: + type: range + bounds: [ -8.0, 0.0 ] diff --git a/fairseq/examples/speech_recognition/new/conf/hydra/sweeper/ax_sil.yaml b/fairseq/examples/speech_recognition/new/conf/hydra/sweeper/ax_sil.yaml new file mode 100644 index 0000000..eaaebcf --- /dev/null +++ b/fairseq/examples/speech_recognition/new/conf/hydra/sweeper/ax_sil.yaml @@ -0,0 +1,29 @@ +# @package hydra.sweeper +_target_: hydra_plugins.hydra_ax_sweeper.ax_sweeper.AxSweeper +max_batch_size: null +ax_config: + max_trials: 64 + early_stop: + minimize: true + max_epochs_without_improvement: 10 + epsilon: 0.025 + experiment: + name: ${dataset.gen_subset} + objective_name: wer + minimize: true + parameter_constraints: null + outcome_constraints: null + status_quo: null + client: + verbose_logging: false + random_seed: null + params: + decoding.lmweight: + type: range + bounds: [0.0, 10.0] + decoding.wordscore: + type: range + bounds: [-10.0, 10.0] + decoding.silweight: + type: range + bounds: [ -10.0, 0.0 ] diff --git a/fairseq/examples/speech_recognition/new/conf/infer.yaml b/fairseq/examples/speech_recognition/new/conf/infer.yaml new file mode 100644 index 0000000..2d168d0 --- /dev/null +++ b/fairseq/examples/speech_recognition/new/conf/infer.yaml @@ -0,0 +1,27 @@ +# @package _group_ + +defaults: + - task: null + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/${dataset.gen_subset} + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${common_eval.results_path} + subdir: ${dataset.gen_subset} +common: + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +common_eval: + results_path: null + path: null + post_process: letter + quiet: true +dataset: + max_tokens: 3000000 + gen_subset: test +distributed_training: + distributed_world_size: 1 +decoding: + beam: 5 + type: viterbi diff --git a/fairseq/examples/speech_recognition/new/conf/run_config/fb_slurm_1.yaml b/fairseq/examples/speech_recognition/new/conf/run_config/fb_slurm_1.yaml new file mode 100644 index 0000000..d0a9b0e --- /dev/null +++ b/fairseq/examples/speech_recognition/new/conf/run_config/fb_slurm_1.yaml @@ -0,0 +1,28 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - common_eval.path + sweep: + dir: /checkpoint/abaevski/asr/d2v2/decoding/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} +# subdir: ${hydra.job.override_dirname} + launcher: + cpus_per_task: 16 + gpus_per_node: 1 + tasks_per_node: 1 + nodes: 1 + partition: devlab,learnlab + mem_gb: 100 + timeout_min: 2000 + max_num_timeout: 10 + name: ${env:PREFIX}_${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/%j + constraint: volta32gb + exclude: learnfair7598 \ No newline at end of file diff --git a/fairseq/examples/speech_recognition/new/conf/run_config/fb_slurm_2g.yaml b/fairseq/examples/speech_recognition/new/conf/run_config/fb_slurm_2g.yaml new file mode 100644 index 0000000..c0c442f --- /dev/null +++ b/fairseq/examples/speech_recognition/new/conf/run_config/fb_slurm_2g.yaml @@ -0,0 +1,27 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - common_eval.path + sweep: + dir: /checkpoint/abaevski/asr/d2v2/decoding/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} +# subdir: ${hydra.job.override_dirname} + launcher: + cpus_per_task: 16 + gpus_per_node: 2 + tasks_per_node: 2 + nodes: 1 + partition: devlab,learnlab + mem_gb: 100 + timeout_min: 2000 + max_num_timeout: 10 + name: ${env:PREFIX}_${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/%j + constraint: volta32gb \ No newline at end of file diff --git a/fairseq/examples/speech_recognition/new/decoders/__init__.py b/fairseq/examples/speech_recognition/new/decoders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/speech_recognition/new/decoders/base_decoder.py b/fairseq/examples/speech_recognition/new/decoders/base_decoder.py new file mode 100644 index 0000000..a097969 --- /dev/null +++ b/fairseq/examples/speech_recognition/new/decoders/base_decoder.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools as it +from typing import Any, Dict, List + +import torch +from fairseq.data.dictionary import Dictionary +from fairseq.models.fairseq_model import FairseqModel + + +class BaseDecoder: + def __init__(self, tgt_dict: Dictionary) -> None: + self.tgt_dict = tgt_dict + self.vocab_size = len(tgt_dict) + + self.blank = ( + tgt_dict.index("<ctc_blank>") + if "<ctc_blank>" in tgt_dict.indices + else tgt_dict.bos() + ) + if "<sep>" in tgt_dict.indices: + self.silence = tgt_dict.index("<sep>") + elif "|" in tgt_dict.indices: + self.silence = tgt_dict.index("|") + else: + self.silence = tgt_dict.eos() + + def generate( + self, models: List[FairseqModel], sample: Dict[str, Any], **unused + ) -> List[List[Dict[str, torch.LongTensor]]]: + encoder_input = { + k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" + } + emissions = self.get_emissions(models, encoder_input) + return self.decode(emissions) + + def get_emissions( + self, + models: List[FairseqModel], + encoder_input: Dict[str, Any], + ) -> torch.FloatTensor: + model = models[0] + encoder_out = model(**encoder_input) + if hasattr(model, "get_logits"): + emissions = model.get_logits(encoder_out) + else: + emissions = model.get_normalized_probs(encoder_out, log_probs=True) + return emissions.transpose(0, 1).float().cpu().contiguous() + + def get_tokens(self, idxs: torch.IntTensor) -> torch.LongTensor: + idxs = (g[0] for g in it.groupby(idxs)) + idxs = filter(lambda x: x != self.blank, idxs) + return torch.LongTensor(list(idxs)) + + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + raise NotImplementedError diff --git a/fairseq/examples/speech_recognition/new/decoders/decoder.py b/fairseq/examples/speech_recognition/new/decoders/decoder.py new file mode 100644 index 0000000..b5bec8c --- /dev/null +++ b/fairseq/examples/speech_recognition/new/decoders/decoder.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Union + +from fairseq.data.dictionary import Dictionary + +from .decoder_config import DecoderConfig, FlashlightDecoderConfig +from .base_decoder import BaseDecoder + + +def Decoder( + cfg: Union[DecoderConfig, FlashlightDecoderConfig], tgt_dict: Dictionary +) -> BaseDecoder: + + if cfg.type == "viterbi": + from .viterbi_decoder import ViterbiDecoder + + return ViterbiDecoder(tgt_dict) + if cfg.type == "kenlm": + from .flashlight_decoder import KenLMDecoder + + return KenLMDecoder(cfg, tgt_dict) + if cfg.type == "fairseqlm": + from .flashlight_decoder import FairseqLMDecoder + + return FairseqLMDecoder(cfg, tgt_dict) + raise NotImplementedError(f"Invalid decoder name: {cfg.name}") diff --git a/fairseq/examples/speech_recognition/new/decoders/decoder_config.py b/fairseq/examples/speech_recognition/new/decoders/decoder_config.py new file mode 100644 index 0000000..659eb94 --- /dev/null +++ b/fairseq/examples/speech_recognition/new/decoders/decoder_config.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import Optional + +from fairseq.dataclass.configs import FairseqDataclass +from fairseq.dataclass.constants import ChoiceEnum +from omegaconf import MISSING + + +DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"]) + + +@dataclass +class DecoderConfig(FairseqDataclass): + type: DECODER_CHOICES = field( + default="viterbi", + metadata={"help": "The type of decoder to use"}, + ) + + +@dataclass +class FlashlightDecoderConfig(FairseqDataclass): + nbest: int = field( + default=1, + metadata={"help": "Number of decodings to return"}, + ) + unitlm: bool = field( + default=False, + metadata={"help": "If set, use unit language model"}, + ) + lmpath: str = field( + default=MISSING, + metadata={"help": "Language model for KenLM decoder"}, + ) + lexicon: Optional[str] = field( + default=None, + metadata={"help": "Lexicon for Flashlight decoder"}, + ) + beam: int = field( + default=50, + metadata={"help": "Number of beams to use for decoding"}, + ) + beamthreshold: float = field( + default=50.0, + metadata={"help": "Threshold for beam search decoding"}, + ) + beamsizetoken: Optional[int] = field( + default=None, metadata={"help": "Beam size to use"} + ) + wordscore: float = field( + default=-1, + metadata={"help": "Word score for KenLM decoder"}, + ) + unkweight: float = field( + default=-math.inf, + metadata={"help": "Unknown weight for KenLM decoder"}, + ) + silweight: float = field( + default=0, + metadata={"help": "Silence weight for KenLM decoder"}, + ) + lmweight: float = field( + default=2, + metadata={"help": "Weight for LM while interpolating score"}, + ) diff --git a/fairseq/examples/speech_recognition/new/decoders/flashlight_decoder.py b/fairseq/examples/speech_recognition/new/decoders/flashlight_decoder.py new file mode 100644 index 0000000..7790fcd --- /dev/null +++ b/fairseq/examples/speech_recognition/new/decoders/flashlight_decoder.py @@ -0,0 +1,433 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import gc +import os.path as osp +import warnings +from collections import deque, namedtuple +from typing import Any, Dict, Tuple + +import numpy as np +import torch +from fairseq import tasks +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models.fairseq_model import FairseqModel +from fairseq.utils import apply_to_sample +from omegaconf import open_dict, OmegaConf + +from typing import List + +from .decoder_config import FlashlightDecoderConfig +from .base_decoder import BaseDecoder + +try: + from flashlight.lib.text.decoder import ( + LM, + CriterionType, + DecodeResult, + KenLM, + LexiconDecoder, + LexiconDecoderOptions, + LexiconFreeDecoder, + LexiconFreeDecoderOptions, + LMState, + SmearingMode, + Trie, + ) + from flashlight.lib.text.dictionary import create_word_dict, load_words + from flashlight.lib.text.dictionary import Dictionary as flDictionary +except ImportError: + warnings.warn( + "flashlight python bindings are required to use this functionality. " + "Please install from " + "https://github.com/facebookresearch/flashlight/tree/master/bindings/python" + ) + LM = object + LMState = object + + +class KenLMDecoder(BaseDecoder): + def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None: + super().__init__(tgt_dict) + + self.nbest = cfg.nbest + self.unitlm = cfg.unitlm + + if cfg.lexicon: + self.lexicon = load_words(cfg.lexicon) + self.word_dict = create_word_dict(self.lexicon) + self.unk_word = self.word_dict.get_index("<unk>") + + self.lm = KenLM(cfg.lmpath, self.word_dict) + self.trie = Trie(self.vocab_size, self.silence) + + start_state = self.lm.start(False) + for word, spellings in self.lexicon.items(): + word_idx = self.word_dict.get_index(word) + _, score = self.lm.score(start_state, word_idx) + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{word} {spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + word_score=cfg.wordscore, + unk_score=cfg.unkweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + [], + self.unitlm, + ) + else: + assert self.unitlm, "Lexicon-free decoding requires unit LM" + + self.word_dict = flDictionary() + for sym in tgt_dict.symbols: + self.word_dict.add_entry(sym, tgt_dict.index(sym)) + self.lm = KenLM(cfg.lmpath, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def get_timesteps(self, token_idxs: List[int]) -> List[int]: + """Returns frame numbers corresponding to every non-blank token. + + Parameters + ---------- + token_idxs : List[int] + IDs of decoded tokens. + + Returns + ------- + List[int] + Frame numbers corresponding to every non-blank token. + """ + timesteps = [] + for i, token_idx in enumerate(token_idxs): + if token_idx == self.blank: + continue + if i == 0 or token_idx != token_idxs[i-1]: + timesteps.append(i) + return timesteps + + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + B, T, N = emissions.size() + hypos = [] + for b in range(B): + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] + hypos.append( + [ + { + "tokens": self.get_tokens(result.tokens), + "score": result.score, + "timesteps": self.get_timesteps(result.tokens), + "words": [ + self.word_dict.get_entry(x) for x in result.words if x >= 0 + ], + } + for result in nbest_results + ] + ) + return hypos + + +FairseqLMState = namedtuple( + "FairseqLMState", + [ + "prefix", + "incremental_state", + "probs", + ], +) + + +class FairseqLM(LM): + def __init__(self, dictionary: Dictionary, model: FairseqModel) -> None: + super().__init__() + + self.dictionary = dictionary + self.model = model + self.unk = self.dictionary.unk() + + self.save_incremental = False # this currently does not work properly + self.max_cache = 20_000 + + if torch.cuda.is_available(): + model.cuda() + model.eval() + model.make_generation_fast_() + + self.states = {} + self.stateq = deque() + + def start(self, start_with_nothing: bool) -> LMState: + state = LMState() + prefix = torch.LongTensor([[self.dictionary.eos()]]) + incremental_state = {} if self.save_incremental else None + with torch.no_grad(): + res = self.model(prefix.cuda(), incremental_state=incremental_state) + probs = self.model.get_normalized_probs(res, log_probs=True, sample=None) + + if incremental_state is not None: + incremental_state = apply_to_sample(lambda x: x.cpu(), incremental_state) + self.states[state] = FairseqLMState( + prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy() + ) + self.stateq.append(state) + + return state + + def score( + self, + state: LMState, + token_index: int, + no_cache: bool = False, + ) -> Tuple[LMState, int]: + """ + Evaluate language model based on the current lm state and new word + Parameters: + ----------- + state: current lm state + token_index: index of the word + (can be lexicon index then you should store inside LM the + mapping between indices of lexicon and lm, or lm index of a word) + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + curr_state = self.states[state] + + def trim_cache(targ_size: int) -> None: + while len(self.stateq) > targ_size: + rem_k = self.stateq.popleft() + rem_st = self.states[rem_k] + rem_st = FairseqLMState(rem_st.prefix, None, None) + self.states[rem_k] = rem_st + + if curr_state.probs is None: + new_incremental_state = ( + curr_state.incremental_state.copy() + if curr_state.incremental_state is not None + else None + ) + with torch.no_grad(): + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cuda(), new_incremental_state + ) + elif self.save_incremental: + new_incremental_state = {} + + res = self.model( + torch.from_numpy(curr_state.prefix).cuda(), + incremental_state=new_incremental_state, + ) + probs = self.model.get_normalized_probs( + res, log_probs=True, sample=None + ) + + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cpu(), new_incremental_state + ) + + curr_state = FairseqLMState( + curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy() + ) + + if not no_cache: + self.states[state] = curr_state + self.stateq.append(state) + + score = curr_state.probs[token_index].item() + + trim_cache(self.max_cache) + + outstate = state.child(token_index) + if outstate not in self.states and not no_cache: + prefix = np.concatenate( + [curr_state.prefix, torch.LongTensor([[token_index]])], -1 + ) + incr_state = curr_state.incremental_state + + self.states[outstate] = FairseqLMState(prefix, incr_state, None) + + if token_index == self.unk: + score = float("-inf") + + return outstate, score + + def finish(self, state: LMState) -> Tuple[LMState, int]: + """ + Evaluate eos for language model based on the current lm state + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + return self.score(state, self.dictionary.eos()) + + def empty_cache(self) -> None: + self.states = {} + self.stateq = deque() + gc.collect() + + +class FairseqLMDecoder(BaseDecoder): + def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None: + super().__init__(tgt_dict) + + self.nbest = cfg.nbest + self.unitlm = cfg.unitlm + + self.lexicon = load_words(cfg.lexicon) if cfg.lexicon else None + self.idx_to_wrd = {} + + checkpoint = torch.load(cfg.lmpath, map_location="cpu") + + if "cfg" in checkpoint and checkpoint["cfg"] is not None: + lm_args = checkpoint["cfg"] + else: + lm_args = convert_namespace_to_omegaconf(checkpoint["args"]) + + if not OmegaConf.is_dict(lm_args): + lm_args = OmegaConf.create(lm_args) + + with open_dict(lm_args.task): + lm_args.task.data = osp.dirname(cfg.lmpath) + + task = tasks.setup_task(lm_args.task) + model = task.build_model(lm_args.model) + model.load_state_dict(checkpoint["model"], strict=False) + + self.trie = Trie(self.vocab_size, self.silence) + + self.word_dict = task.dictionary + self.unk_word = self.word_dict.unk() + self.lm = FairseqLM(self.word_dict, model) + + if self.lexicon: + start_state = self.lm.start(False) + for i, (word, spellings) in enumerate(self.lexicon.items()): + if self.unitlm: + word_idx = i + self.idx_to_wrd[i] = word + score = 0 + else: + word_idx = self.word_dict.index(word) + _, score = self.lm.score(start_state, word_idx, no_cache=True) + + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + word_score=cfg.wordscore, + unk_score=cfg.unkweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + [], + self.unitlm, + ) + else: + assert self.unitlm, "Lexicon-free decoding requires unit LM" + + d = {w: [[w]] for w in tgt_dict.symbols} + self.word_dict = create_word_dict(d) + self.lm = KenLM(cfg.lmpath, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + B, T, N = emissions.size() + hypos = [] + + def make_hypo(result: DecodeResult) -> Dict[str, Any]: + hypo = { + "tokens": self.get_tokens(result.tokens), + "score": result.score, + } + if self.lexicon: + hypo["words"] = [ + self.idx_to_wrd[x] if self.unitlm else self.word_dict[x] + for x in result.words + if x >= 0 + ] + return hypo + + for b in range(B): + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] + hypos.append([make_hypo(result) for result in nbest_results]) + self.lm.empty_cache() + + return hypos diff --git a/fairseq/examples/speech_recognition/new/decoders/viterbi_decoder.py b/fairseq/examples/speech_recognition/new/decoders/viterbi_decoder.py new file mode 100644 index 0000000..a35d95e --- /dev/null +++ b/fairseq/examples/speech_recognition/new/decoders/viterbi_decoder.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from typing import List, Dict + +from .base_decoder import BaseDecoder + + +class ViterbiDecoder(BaseDecoder): + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + def get_pred(e): + score = e.log_softmax(dim=-1).max(dim=-1)[0].sum() + toks = e.argmax(dim=-1).unique_consecutive() + return {"tokens":toks[toks != self.blank], "score":score} + return [[get_pred(x)] for x in emissions] diff --git a/fairseq/examples/speech_recognition/new/infer.py b/fairseq/examples/speech_recognition/new/infer.py new file mode 100644 index 0000000..ca5cea4 --- /dev/null +++ b/fairseq/examples/speech_recognition/new/infer.py @@ -0,0 +1,502 @@ +#!/usr/bin/env python -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import hashlib +import logging +import os +import shutil +import sys +import re +from dataclasses import dataclass, field, is_dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union + +import editdistance +import torch +import torch.distributed as dist +from examples.speech_recognition.new.decoders.decoder_config import ( + DecoderConfig, + FlashlightDecoderConfig, +) +from examples.speech_recognition.new.decoders.decoder import Decoder +from fairseq import checkpoint_utils, distributed_utils, progress_bar, tasks, utils +from fairseq.data.data_utils import post_process +from fairseq.dataclass.configs import ( + CheckpointConfig, + CommonConfig, + CommonEvalConfig, + DatasetConfig, + DistributedTrainingConfig, + FairseqDataclass, +) +from fairseq.logging.meters import StopwatchMeter, TimeMeter +from fairseq.logging.progress_bar import BaseProgressBar +from fairseq.models.fairseq_model import FairseqModel +from omegaconf import OmegaConf + +import hydra +from hydra.core.config_store import ConfigStore + +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +config_path = Path(__file__).resolve().parent / "conf" + + +@dataclass +class DecodingConfig(DecoderConfig, FlashlightDecoderConfig): + unique_wer_file: bool = field( + default=False, + metadata={"help": "If set, use a unique file for storing WER"}, + ) + results_path: Optional[str] = field( + default=None, + metadata={ + "help": "If set, write hypothesis and reference sentences into this directory" + }, + ) + + +@dataclass +class InferConfig(FairseqDataclass): + task: Any = None + decoding: DecodingConfig = DecodingConfig() + common: CommonConfig = CommonConfig() + common_eval: CommonEvalConfig = CommonEvalConfig() + checkpoint: CheckpointConfig = CheckpointConfig() + distributed_training: DistributedTrainingConfig = DistributedTrainingConfig() + dataset: DatasetConfig = DatasetConfig() + is_ax: bool = field( + default=False, + metadata={ + "help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume" + }, + ) + + +def reset_logging(): + root = logging.getLogger() + for handler in root.handlers: + root.removeHandler(handler) + root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper()) + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter( + logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + ) + root.addHandler(handler) + + +class InferenceProcessor: + cfg: InferConfig + + def __init__(self, cfg: InferConfig) -> None: + self.cfg = cfg + self.task = tasks.setup_task(cfg.task) + + models, saved_cfg = self.load_model_ensemble() + + ### LOAD ADAPTER #### + ckpt_obj = checkpoint_utils.load_checkpoint_to_cpu(self.cfg.common_eval.path) + if "adapter" in ckpt_obj: + target_lang = self.cfg.dataset.gen_subset.split(":")[0] + assert target_lang in ckpt_obj["adapter"] + + logger.info(f">>> LOADING ADAPTER: {target_lang}") + ft_obj = ckpt_obj["adapter"][target_lang] + ft_model = ft_obj["model"] + cdevice = models[0].w2v_encoder.proj.weight.device + cdtype = models[0].w2v_encoder.proj.weight.dtype + ft_proj_out, ft_proj_in = ft_model["w2v_encoder.proj.weight"].shape + ft_proj = torch.nn.Linear(ft_proj_in, ft_proj_out, bias=True) + ft_proj.to(device=cdevice, dtype=cdtype) + models[0].w2v_encoder.proj = ft_proj + with torch.no_grad(): + for kk, vv in models[0].named_parameters(): + if kk in ft_model: + vv.copy_(ft_model[kk]) + self.task.load_state_dict(ft_obj["task_state"]) + # overwrite gen_subset with master config + self.cfg.dataset.gen_subset = re.sub('^[\w-]+:', saved_cfg['task']['multi_corpus_keys']+":", self.cfg.dataset.gen_subset) + self.models = models + self.saved_cfg = saved_cfg + self.tgt_dict = self.task.target_dictionary + + self.task.load_dataset( + self.cfg.dataset.gen_subset, + task_cfg=saved_cfg.task, + ) + self.generator = Decoder(cfg.decoding, self.tgt_dict) + self.gen_timer = StopwatchMeter() + self.wps_meter = TimeMeter() + self.num_sentences = 0 + self.total_errors = 0 + self.total_length = 0 + + self.hypo_words_file = None + self.hypo_units_file = None + self.ref_words_file = None + self.ref_units_file = None + self.score_file = None + + self.progress_bar = self.build_progress_bar() + + def __enter__(self) -> "InferenceProcessor": + if self.cfg.decoding.results_path is not None: + self.hypo_words_file = self.get_res_file("hypo.word") + self.hypo_units_file = self.get_res_file("hypo.units") + self.ref_words_file = self.get_res_file("ref.word") + self.ref_units_file = self.get_res_file("ref.units") + self.score_file = self.get_res_file("asr_score") + return self + + def __exit__(self, *exc) -> bool: + if self.cfg.decoding.results_path is not None: + self.hypo_words_file.close() + self.hypo_units_file.close() + self.ref_words_file.close() + self.ref_units_file.close() + self.score_file.close() + return False + + def __iter__(self) -> Any: + for sample in self.progress_bar: + if not self.cfg.common.cpu: + sample = utils.move_to_cuda(sample) + + # Happens on the last batch. + if "net_input" not in sample: + continue + yield sample + + def log(self, *args, **kwargs): + self.progress_bar.log(*args, **kwargs) + + def print(self, *args, **kwargs): + self.progress_bar.print(*args, **kwargs) + + def get_res_file(self, fname: str) -> None: + fname = os.path.join(self.cfg.decoding.results_path, fname) + if self.data_parallel_world_size > 1: + fname = f"{fname}.{self.data_parallel_rank}" + return open(fname, "w", buffering=1) + + def merge_shards(self) -> None: + """Merges all shard files into shard 0, then removes shard suffix.""" + + shard_id = self.data_parallel_rank + num_shards = self.data_parallel_world_size + + if self.data_parallel_world_size > 1: + + def merge_shards_with_root(fname: str) -> None: + fname = os.path.join(self.cfg.decoding.results_path, fname) + logger.info("Merging %s on shard %d", fname, shard_id) + base_fpath = Path(f"{fname}.0") + with open(base_fpath, "a") as out_file: + for s in range(1, num_shards): + shard_fpath = Path(f"{fname}.{s}") + with open(shard_fpath, "r") as in_file: + for line in in_file: + out_file.write(line) + shard_fpath.unlink() + shutil.move(f"{fname}.0", fname) + + dist.barrier() # ensure all shards finished writing + if shard_id == (0 % num_shards): + merge_shards_with_root("hypo.word") + if shard_id == (1 % num_shards): + merge_shards_with_root("hypo.units") + if shard_id == (2 % num_shards): + merge_shards_with_root("ref.word") + if shard_id == (3 % num_shards): + merge_shards_with_root("ref.units") + dist.barrier() + + def optimize_model(self, model: FairseqModel) -> None: + model.make_generation_fast_() + if self.cfg.common.fp16: + model.half() + if not self.cfg.common.cpu: + model.cuda() + + def load_model_ensemble(self) -> Tuple[List[FairseqModel], FairseqDataclass]: + arg_overrides = ast.literal_eval(self.cfg.common_eval.model_overrides) + models, saved_cfg = checkpoint_utils.load_model_ensemble( + utils.split_paths(self.cfg.common_eval.path, separator="\\"), + arg_overrides=arg_overrides, + task=self.task, + suffix=self.cfg.checkpoint.checkpoint_suffix, + strict=(self.cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=self.cfg.checkpoint.checkpoint_shard_count, + ) + for model in models: + self.optimize_model(model) + return models, saved_cfg + + def get_dataset_itr(self, disable_iterator_cache: bool = False) -> None: + return self.task.get_batch_iterator( + dataset=self.task.dataset(self.cfg.dataset.gen_subset), + max_tokens=self.cfg.dataset.max_tokens, + max_sentences=self.cfg.dataset.batch_size, + max_positions=(sys.maxsize, sys.maxsize), + ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, + seed=self.cfg.common.seed, + num_shards=self.data_parallel_world_size, + shard_id=self.data_parallel_rank, + num_workers=self.cfg.dataset.num_workers, + data_buffer_size=self.cfg.dataset.data_buffer_size, + disable_iterator_cache=disable_iterator_cache, + ).next_epoch_itr(shuffle=False) + + def build_progress_bar( + self, + epoch: Optional[int] = None, + prefix: Optional[str] = None, + default_log_format: str = "tqdm", + ) -> BaseProgressBar: + return progress_bar.progress_bar( + iterator=self.get_dataset_itr(), + log_format=self.cfg.common.log_format, + log_interval=self.cfg.common.log_interval, + epoch=epoch, + prefix=prefix, + tensorboard_logdir=self.cfg.common.tensorboard_logdir, + default_log_format=default_log_format, + ) + + @property + def data_parallel_world_size(self): + if self.cfg.distributed_training.distributed_world_size == 1: + return 1 + return distributed_utils.get_data_parallel_world_size() + + @property + def data_parallel_rank(self): + if self.cfg.distributed_training.distributed_world_size == 1: + return 0 + return distributed_utils.get_data_parallel_rank() + + def process_sentence( + self, + sample: Dict[str, Any], + hypo: Dict[str, Any], + sid: int, + batch_id: int, + ) -> Tuple[int, int]: + speaker = None # Speaker can't be parsed from dataset. + if "target_label" in sample: + toks = sample["target_label"] + else: + toks = sample["target"] + toks = toks[batch_id, :] + + # Processes hypothesis. + hyp_pieces = self.tgt_dict.string(hypo["tokens"].int().cpu()) + if "words" in hypo: + hyp_words = " ".join(hypo["words"]) + else: + hyp_words = post_process(hyp_pieces, self.cfg.common_eval.post_process) + + # Processes target. + target_tokens = utils.strip_pad(toks, self.tgt_dict.pad()) + tgt_pieces = self.tgt_dict.string(target_tokens.int().cpu()) + tgt_words = post_process(tgt_pieces, self.cfg.common_eval.post_process) + + if self.cfg.decoding.results_path is not None: + print(f"{hyp_pieces} ({speaker}-{sid})", file=self.hypo_units_file) + print(f"{hyp_words} ({speaker}-{sid})", file=self.hypo_words_file) + print(f"{tgt_pieces} ({speaker}-{sid})", file=self.ref_units_file) + print(f"{tgt_words} ({speaker}-{sid})", file=self.ref_words_file) + print(f"{hypo['score'].item()} ({speaker}-{sid})", file=self.score_file) + + if not self.cfg.common_eval.quiet: + logger.info(f"HYPO: {hyp_words}") + logger.info(f"REF: {tgt_words}") + logger.info("---------------------") + + hyp_words, tgt_words = hyp_words.split(), tgt_words.split() + + return editdistance.eval(hyp_words, tgt_words), len(tgt_words) + + def process_sample(self, sample: Dict[str, Any]) -> None: + self.gen_timer.start() + hypos = self.task.inference_step( + generator=self.generator, + models=self.models, + sample=sample, + ) + num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) + self.gen_timer.stop(num_generated_tokens) + self.wps_meter.update(num_generated_tokens) + + for batch_id, sample_id in enumerate(sample["id"].tolist()): + errs, length = self.process_sentence( + sample=sample, + sid=sample_id, + batch_id=batch_id, + hypo=hypos[batch_id][0], + ) + self.total_errors += errs + self.total_length += length + + self.log({"wps": round(self.wps_meter.avg)}) + if "nsentences" in sample: + self.num_sentences += sample["nsentences"] + else: + self.num_sentences += sample["id"].numel() + + def log_generation_time(self) -> None: + logger.info( + "Processed %d sentences (%d tokens) in %.1fs %.2f " + "sentences per second, %.2f tokens per second)", + self.num_sentences, + self.gen_timer.n, + self.gen_timer.sum, + self.num_sentences / (self.gen_timer.sum + 1e-6), + 1.0 / (self.gen_timer.avg + 1e-6), + ) + + +def parse_wer(wer_file: Path) -> float: + with open(wer_file, "r") as f: + return float(f.readline().strip().split(" ")[1]) + + +def get_wer_file(cfg: InferConfig) -> Path: + """Hashes the decoding parameters to a unique file ID.""" + base_path = "wer" + if cfg.decoding.results_path is not None: + base_path = os.path.join(cfg.decoding.results_path, base_path) + + if cfg.decoding.unique_wer_file: + yaml_str = OmegaConf.to_yaml(cfg.decoding) + fid = int(hashlib.md5(yaml_str.encode("utf-8")).hexdigest(), 16) + return Path(f"{base_path}.{fid % 1000000}") + else: + return Path(base_path) + + +def main(cfg: InferConfig) -> float: + """Entry point for main processing logic. + + Args: + cfg: The inferance configuration to use. + wer: Optional shared memory pointer for returning the WER. If not None, + the final WER value will be written here instead of being returned. + + Returns: + The final WER if `wer` is None, otherwise None. + """ + + yaml_str, wer_file = OmegaConf.to_yaml(cfg.decoding), get_wer_file(cfg) + + # Validates the provided configuration. + if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: + cfg.dataset.max_tokens = 4000000 + if not cfg.common.cpu and not torch.cuda.is_available(): + raise ValueError("CUDA not found; set `cpu=True` to run without CUDA") + + logger.info(cfg.common_eval.path) + + with InferenceProcessor(cfg) as processor: + for sample in processor: + processor.process_sample(sample) + + processor.log_generation_time() + + if cfg.decoding.results_path is not None: + processor.merge_shards() + + errs_t, leng_t = processor.total_errors, processor.total_length + + if cfg.common.cpu: + logger.warning("Merging WER requires CUDA.") + elif processor.data_parallel_world_size > 1: + stats = torch.LongTensor([errs_t, leng_t]).cuda() + dist.all_reduce(stats, op=dist.ReduceOp.SUM) + errs_t, leng_t = stats[0].item(), stats[1].item() + + wer = errs_t * 100.0 / leng_t + + if distributed_utils.is_master(cfg.distributed_training): + with open(wer_file, "w") as f: + f.write( + ( + f"WER: {wer}\n" + f"err / num_ref_words = {errs_t} / {leng_t}\n\n" + f"{yaml_str}" + ) + ) + + return wer + + +@hydra.main(config_path=config_path, config_name="infer") +def hydra_main(cfg: InferConfig) -> Union[float, Tuple[float, Optional[float]]]: + container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) + cfg = OmegaConf.create(container) + OmegaConf.set_struct(cfg, True) + + if cfg.common.reset_logging: + reset_logging() + + utils.import_user_module(cfg.common) + + # logger.info("Config:\n%s", OmegaConf.to_yaml(cfg)) + wer = float("inf") + + try: + if cfg.common.profile: + with torch.cuda.profiler.profile(): + with torch.autograd.profiler.emit_nvtx(): + distributed_utils.call_main(cfg, main) + else: + distributed_utils.call_main(cfg, main) + + wer = parse_wer(get_wer_file(cfg)) + except BaseException as e: # pylint: disable=broad-except + if not cfg.common.suppress_crashes: + raise + else: + logger.error("Crashed! %s", str(e)) + + logger.info("Word error rate: %.4f", wer) + if cfg.is_ax: + return wer, None + + return wer + + +def cli_main() -> None: + try: + from hydra._internal.utils import ( + get_args, + ) # pylint: disable=import-outside-toplevel + + cfg_name = get_args().config_name or "infer" + except ImportError: + logger.warning("Failed to get config name from hydra args") + cfg_name = "infer" + + cs = ConfigStore.instance() + cs.store(name=cfg_name, node=InferConfig) + + for k in InferConfig.__dataclass_fields__: + if is_dataclass(InferConfig.__dataclass_fields__[k].type): + v = InferConfig.__dataclass_fields__[k].default + cs.store(name=k, node=v) + + hydra_main() # pylint: disable=no-value-for-parameter + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/speech_recognition/tasks/__init__.py b/fairseq/examples/speech_recognition/tasks/__init__.py new file mode 100644 index 0000000..7ac3b8d --- /dev/null +++ b/fairseq/examples/speech_recognition/tasks/__init__.py @@ -0,0 +1,8 @@ +import importlib +import os + + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + task_name = file[: file.find(".py")] + importlib.import_module("examples.speech_recognition.tasks." + task_name) diff --git a/fairseq/examples/speech_recognition/tasks/speech_recognition.py b/fairseq/examples/speech_recognition/tasks/speech_recognition.py new file mode 100644 index 0000000..d9f011d --- /dev/null +++ b/fairseq/examples/speech_recognition/tasks/speech_recognition.py @@ -0,0 +1,157 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import re +import sys + +import torch +from examples.speech_recognition.data import AsrDataset +from examples.speech_recognition.data.replabels import replabel_symbol +from fairseq.data import Dictionary +from fairseq.tasks import LegacyFairseqTask, register_task + + +def get_asr_dataset_from_json(data_json_path, tgt_dict): + """ + Parse data json and create dataset. + See scripts/asr_prep_json.py which pack json from raw files + + Json example: + { + "utts": { + "4771-29403-0025": { + "input": { + "length_ms": 170, + "path": "/tmp/file1.flac" + }, + "output": { + "text": "HELLO \n", + "token": "HE LLO", + "tokenid": "4815, 861" + } + }, + "1564-142299-0096": { + ... + } + } + """ + if not os.path.isfile(data_json_path): + raise FileNotFoundError("Dataset not found: {}".format(data_json_path)) + with open(data_json_path, "rb") as f: + data_samples = json.load(f)["utts"] + assert len(data_samples) != 0 + sorted_samples = sorted( + data_samples.items(), + key=lambda sample: int(sample[1]["input"]["length_ms"]), + reverse=True, + ) + aud_paths = [s[1]["input"]["path"] for s in sorted_samples] + ids = [s[0] for s in sorted_samples] + speakers = [] + for s in sorted_samples: + m = re.search("(.+?)-(.+?)-(.+?)", s[0]) + speakers.append(m.group(1) + "_" + m.group(2)) + frame_sizes = [s[1]["input"]["length_ms"] for s in sorted_samples] + tgt = [ + [int(i) for i in s[1]["output"]["tokenid"].split(", ")] + for s in sorted_samples + ] + # append eos + tgt = [[*t, tgt_dict.eos()] for t in tgt] + return AsrDataset(aud_paths, frame_sizes, tgt, tgt_dict, ids, speakers) + + +@register_task("speech_recognition") +class SpeechRecognitionTask(LegacyFairseqTask): + """ + Task for training speech recognition model. + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument("data", help="path to data directory") + parser.add_argument( + "--silence-token", default="\u2581", help="token for silence (used by w2l)" + ) + parser.add_argument( + "--max-source-positions", + default=sys.maxsize, + type=int, + metavar="N", + help="max number of frames in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + + def __init__(self, args, tgt_dict): + super().__init__(args) + self.tgt_dict = tgt_dict + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries).""" + dict_path = os.path.join(args.data, "dict.txt") + if not os.path.isfile(dict_path): + raise FileNotFoundError("Dict not found: {}".format(dict_path)) + tgt_dict = Dictionary.load(dict_path) + + if args.criterion == "ctc_loss": + tgt_dict.add_symbol("<ctc_blank>") + elif args.criterion == "asg_loss": + for i in range(1, args.max_replabel + 1): + tgt_dict.add_symbol(replabel_symbol(i)) + + print("| dictionary: {} types".format(len(tgt_dict))) + return cls(args, tgt_dict) + + def load_dataset(self, split, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + data_json_path = os.path.join(self.args.data, "{}.json".format(split)) + self.datasets[split] = get_asr_dataset_from_json(data_json_path, self.tgt_dict) + + def build_generator(self, models, args, **unused): + w2l_decoder = getattr(args, "w2l_decoder", None) + if w2l_decoder == "viterbi": + from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder + + return W2lViterbiDecoder(args, self.target_dictionary) + elif w2l_decoder == "kenlm": + from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder + + return W2lKenLMDecoder(args, self.target_dictionary) + elif w2l_decoder == "fairseqlm": + from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder + + return W2lFairseqLMDecoder(args, self.target_dictionary) + else: + return super().build_generator(models, args) + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.tgt_dict + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary` (if applicable + for this task).""" + return None + + def max_positions(self): + """Return the max speech and sentence length allowed by the task.""" + return (self.args.max_source_positions, self.args.max_target_positions) diff --git a/fairseq/examples/speech_recognition/utils/wer_utils.py b/fairseq/examples/speech_recognition/utils/wer_utils.py new file mode 100644 index 0000000..cf6f3d0 --- /dev/null +++ b/fairseq/examples/speech_recognition/utils/wer_utils.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import re +from collections import deque +from enum import Enum + +import numpy as np + + +""" + Utility modules for computation of Word Error Rate, + Alignments, as well as more granular metrics like + deletion, insersion and substitutions. +""" + + +class Code(Enum): + match = 1 + substitution = 2 + insertion = 3 + deletion = 4 + + +class Token(object): + def __init__(self, lbl="", st=np.nan, en=np.nan): + if np.isnan(st): + self.label, self.start, self.end = "", 0.0, 0.0 + else: + self.label, self.start, self.end = lbl, st, en + + +class AlignmentResult(object): + def __init__(self, refs, hyps, codes, score): + self.refs = refs # std::deque<int> + self.hyps = hyps # std::deque<int> + self.codes = codes # std::deque<Code> + self.score = score # float + + +def coordinate_to_offset(row, col, ncols): + return int(row * ncols + col) + + +def offset_to_row(offset, ncols): + return int(offset / ncols) + + +def offset_to_col(offset, ncols): + return int(offset % ncols) + + +def trimWhitespace(str): + return re.sub(" +", " ", re.sub(" *$", "", re.sub("^ *", "", str))) + + +def str2toks(str): + pieces = trimWhitespace(str).split(" ") + toks = [] + for p in pieces: + toks.append(Token(p, 0.0, 0.0)) + return toks + + +class EditDistance(object): + def __init__(self, time_mediated): + self.time_mediated_ = time_mediated + self.scores_ = np.nan # Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> + self.backtraces_ = ( + np.nan + ) # Eigen::Matrix<size_t, Eigen::Dynamic, Eigen::Dynamic> backtraces_; + self.confusion_pairs_ = {} + + def cost(self, ref, hyp, code): + if self.time_mediated_: + if code == Code.match: + return abs(ref.start - hyp.start) + abs(ref.end - hyp.end) + elif code == Code.insertion: + return hyp.end - hyp.start + elif code == Code.deletion: + return ref.end - ref.start + else: # substitution + return abs(ref.start - hyp.start) + abs(ref.end - hyp.end) + 0.1 + else: + if code == Code.match: + return 0 + elif code == Code.insertion or code == Code.deletion: + return 3 + else: # substitution + return 4 + + def get_result(self, refs, hyps): + res = AlignmentResult(refs=deque(), hyps=deque(), codes=deque(), score=np.nan) + + num_rows, num_cols = self.scores_.shape + res.score = self.scores_[num_rows - 1, num_cols - 1] + + curr_offset = coordinate_to_offset(num_rows - 1, num_cols - 1, num_cols) + + while curr_offset != 0: + curr_row = offset_to_row(curr_offset, num_cols) + curr_col = offset_to_col(curr_offset, num_cols) + + prev_offset = self.backtraces_[curr_row, curr_col] + + prev_row = offset_to_row(prev_offset, num_cols) + prev_col = offset_to_col(prev_offset, num_cols) + + res.refs.appendleft(curr_row - 1) # Note: this was .push_front() in C++ + res.hyps.appendleft(curr_col - 1) + if curr_row - 1 == prev_row and curr_col == prev_col: + res.codes.appendleft(Code.deletion) + elif curr_row == prev_row and curr_col - 1 == prev_col: + res.codes.appendleft(Code.insertion) + else: + # assert(curr_row - 1 == prev_row and curr_col - 1 == prev_col) + ref_str = refs[res.refs[0]].label + hyp_str = hyps[res.hyps[0]].label + + if ref_str == hyp_str: + res.codes.appendleft(Code.match) + else: + res.codes.appendleft(Code.substitution) + + confusion_pair = "%s -> %s" % (ref_str, hyp_str) + if confusion_pair not in self.confusion_pairs_: + self.confusion_pairs_[confusion_pair] = 1 + else: + self.confusion_pairs_[confusion_pair] += 1 + + curr_offset = prev_offset + + return res + + def align(self, refs, hyps): + if len(refs) == 0 and len(hyps) == 0: + return np.nan + + # NOTE: we're not resetting the values in these matrices because every value + # will be overridden in the loop below. If this assumption doesn't hold, + # be sure to set all entries in self.scores_ and self.backtraces_ to 0. + self.scores_ = np.zeros((len(refs) + 1, len(hyps) + 1)) + self.backtraces_ = np.zeros((len(refs) + 1, len(hyps) + 1)) + + num_rows, num_cols = self.scores_.shape + + for i in range(num_rows): + for j in range(num_cols): + if i == 0 and j == 0: + self.scores_[i, j] = 0.0 + self.backtraces_[i, j] = 0 + continue + + if i == 0: + self.scores_[i, j] = self.scores_[i, j - 1] + self.cost( + None, hyps[j - 1], Code.insertion + ) + self.backtraces_[i, j] = coordinate_to_offset(i, j - 1, num_cols) + continue + + if j == 0: + self.scores_[i, j] = self.scores_[i - 1, j] + self.cost( + refs[i - 1], None, Code.deletion + ) + self.backtraces_[i, j] = coordinate_to_offset(i - 1, j, num_cols) + continue + + # Below here both i and j are greater than 0 + ref = refs[i - 1] + hyp = hyps[j - 1] + best_score = self.scores_[i - 1, j - 1] + ( + self.cost(ref, hyp, Code.match) + if (ref.label == hyp.label) + else self.cost(ref, hyp, Code.substitution) + ) + + prev_row = i - 1 + prev_col = j - 1 + ins = self.scores_[i, j - 1] + self.cost(None, hyp, Code.insertion) + if ins < best_score: + best_score = ins + prev_row = i + prev_col = j - 1 + + delt = self.scores_[i - 1, j] + self.cost(ref, None, Code.deletion) + if delt < best_score: + best_score = delt + prev_row = i - 1 + prev_col = j + + self.scores_[i, j] = best_score + self.backtraces_[i, j] = coordinate_to_offset( + prev_row, prev_col, num_cols + ) + + return self.get_result(refs, hyps) + + +class WERTransformer(object): + def __init__(self, hyp_str, ref_str, verbose=True): + self.ed_ = EditDistance(False) + self.id2oracle_errs_ = {} + self.utts_ = 0 + self.words_ = 0 + self.insertions_ = 0 + self.deletions_ = 0 + self.substitutions_ = 0 + + self.process(["dummy_str", hyp_str, ref_str]) + + if verbose: + print("'%s' vs '%s'" % (hyp_str, ref_str)) + self.report_result() + + def process(self, input): # std::vector<std::string>&& input + if len(input) < 3: + print( + "Input must be of the form <id> ... <hypo> <ref> , got ", + len(input), + " inputs:", + ) + return None + + # Align + # std::vector<Token> hyps; + # std::vector<Token> refs; + + hyps = str2toks(input[-2]) + refs = str2toks(input[-1]) + + alignment = self.ed_.align(refs, hyps) + if alignment is None: + print("Alignment is null") + return np.nan + + # Tally errors + ins = 0 + dels = 0 + subs = 0 + for code in alignment.codes: + if code == Code.substitution: + subs += 1 + elif code == Code.insertion: + ins += 1 + elif code == Code.deletion: + dels += 1 + + # Output + row = input + row.append(str(len(refs))) + row.append(str(ins)) + row.append(str(dels)) + row.append(str(subs)) + # print(row) + + # Accumulate + kIdIndex = 0 + kNBestSep = "/" + + pieces = input[kIdIndex].split(kNBestSep) + + if len(pieces) == 0: + print( + "Error splitting ", + input[kIdIndex], + " on '", + kNBestSep, + "', got empty list", + ) + return np.nan + + id = pieces[0] + if id not in self.id2oracle_errs_: + self.utts_ += 1 + self.words_ += len(refs) + self.insertions_ += ins + self.deletions_ += dels + self.substitutions_ += subs + self.id2oracle_errs_[id] = [ins, dels, subs] + else: + curr_err = ins + dels + subs + prev_err = np.sum(self.id2oracle_errs_[id]) + if curr_err < prev_err: + self.id2oracle_errs_[id] = [ins, dels, subs] + + return 0 + + def report_result(self): + # print("---------- Summary ---------------") + if self.words_ == 0: + print("No words counted") + return + + # 1-best + best_wer = ( + 100.0 + * (self.insertions_ + self.deletions_ + self.substitutions_) + / self.words_ + ) + + print( + "\tWER = %0.2f%% (%i utts, %i words, %0.2f%% ins, " + "%0.2f%% dels, %0.2f%% subs)" + % ( + best_wer, + self.utts_, + self.words_, + 100.0 * self.insertions_ / self.words_, + 100.0 * self.deletions_ / self.words_, + 100.0 * self.substitutions_ / self.words_, + ) + ) + + def wer(self): + if self.words_ == 0: + wer = np.nan + else: + wer = ( + 100.0 + * (self.insertions_ + self.deletions_ + self.substitutions_) + / self.words_ + ) + return wer + + def stats(self): + if self.words_ == 0: + stats = {} + else: + wer = ( + 100.0 + * (self.insertions_ + self.deletions_ + self.substitutions_) + / self.words_ + ) + stats = dict( + { + "wer": wer, + "utts": self.utts_, + "numwords": self.words_, + "ins": self.insertions_, + "dels": self.deletions_, + "subs": self.substitutions_, + "confusion_pairs": self.ed_.confusion_pairs_, + } + ) + return stats + + +def calc_wer(hyp_str, ref_str): + t = WERTransformer(hyp_str, ref_str, verbose=0) + return t.wer() + + +def calc_wer_stats(hyp_str, ref_str): + t = WERTransformer(hyp_str, ref_str, verbose=0) + return t.stats() + + +def get_wer_alignment_codes(hyp_str, ref_str): + """ + INPUT: hypothesis string, reference string + OUTPUT: List of alignment codes (intermediate results from WER computation) + """ + t = WERTransformer(hyp_str, ref_str, verbose=0) + return t.ed_.align(str2toks(ref_str), str2toks(hyp_str)).codes + + +def merge_counts(x, y): + # Merge two hashes which have 'counts' as their values + # This can be used for example to merge confusion pair counts + # conf_pairs = merge_counts(conf_pairs, stats['confusion_pairs']) + for k, v in y.items(): + if k not in x: + x[k] = 0 + x[k] += v + return x diff --git a/fairseq/examples/speech_recognition/w2l_decoder.py b/fairseq/examples/speech_recognition/w2l_decoder.py new file mode 100644 index 0000000..fbf2d35 --- /dev/null +++ b/fairseq/examples/speech_recognition/w2l_decoder.py @@ -0,0 +1,486 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Flashlight decoders. +""" + +import gc +import itertools as it +import os.path as osp +from typing import List +import warnings +from collections import deque, namedtuple + +import numpy as np +import torch +from examples.speech_recognition.data.replabels import unpack_replabels +from fairseq import tasks +from fairseq.utils import apply_to_sample +from omegaconf import open_dict +from fairseq.dataclass.utils import convert_namespace_to_omegaconf + + +try: + from flashlight.lib.text.dictionary import create_word_dict, load_words + from flashlight.lib.sequence.criterion import CpuViterbiPath, get_data_ptr_as_bytes + from flashlight.lib.text.decoder import ( + CriterionType, + LexiconDecoderOptions, + KenLM, + LM, + LMState, + SmearingMode, + Trie, + LexiconDecoder, + ) +except: + warnings.warn( + "flashlight python bindings are required to use this functionality. Please install from https://github.com/facebookresearch/flashlight/tree/master/bindings/python" + ) + LM = object + LMState = object + + +class W2lDecoder(object): + def __init__(self, args, tgt_dict): + self.tgt_dict = tgt_dict + self.vocab_size = len(tgt_dict) + self.nbest = args.nbest + + # criterion-specific init + self.criterion_type = CriterionType.CTC + self.blank = ( + tgt_dict.index("<ctc_blank>") + if "<ctc_blank>" in tgt_dict.indices + else tgt_dict.bos() + ) + if "<sep>" in tgt_dict.indices: + self.silence = tgt_dict.index("<sep>") + elif "|" in tgt_dict.indices: + self.silence = tgt_dict.index("|") + else: + self.silence = tgt_dict.eos() + self.asg_transitions = None + + def generate(self, models, sample, **unused): + """Generate a batch of inferences.""" + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" + } + emissions = self.get_emissions(models, encoder_input) + return self.decode(emissions) + + def get_emissions(self, models, encoder_input): + """Run encoder and normalize emissions""" + model = models[0] + encoder_out = model(**encoder_input) + if hasattr(model, "get_logits"): + emissions = model.get_logits(encoder_out) # no need to normalize emissions + else: + emissions = model.get_normalized_probs(encoder_out, log_probs=True) + return emissions.transpose(0, 1).float().cpu().contiguous() + + def get_tokens(self, idxs): + """Normalize tokens by handling CTC blank, ASG replabels, etc.""" + idxs = (g[0] for g in it.groupby(idxs)) + idxs = filter(lambda x: x != self.blank, idxs) + return torch.LongTensor(list(idxs)) + + +class W2lViterbiDecoder(W2lDecoder): + def __init__(self, args, tgt_dict): + super().__init__(args, tgt_dict) + + def decode(self, emissions): + B, T, N = emissions.size() + hypos = [] + if self.asg_transitions is None: + transitions = torch.FloatTensor(N, N).zero_() + else: + transitions = torch.FloatTensor(self.asg_transitions).view(N, N) + viterbi_path = torch.IntTensor(B, T) + workspace = torch.ByteTensor(CpuViterbiPath.get_workspace_size(B, T, N)) + CpuViterbiPath.compute( + B, + T, + N, + get_data_ptr_as_bytes(emissions), + get_data_ptr_as_bytes(transitions), + get_data_ptr_as_bytes(viterbi_path), + get_data_ptr_as_bytes(workspace), + ) + return [ + [{"tokens": self.get_tokens(viterbi_path[b].tolist()), "score": 0}] + for b in range(B) + ] + + +class W2lKenLMDecoder(W2lDecoder): + def __init__(self, args, tgt_dict): + super().__init__(args, tgt_dict) + + self.unit_lm = getattr(args, "unit_lm", False) + + if args.lexicon: + self.lexicon = load_words(args.lexicon) + self.word_dict = create_word_dict(self.lexicon) + self.unk_word = self.word_dict.get_index("<unk>") + + self.lm = KenLM(args.kenlm_model, self.word_dict) + self.trie = Trie(self.vocab_size, self.silence) + + start_state = self.lm.start(False) + for i, (word, spellings) in enumerate(self.lexicon.items()): + word_idx = self.word_dict.get_index(word) + _, score = self.lm.score(start_state, word_idx) + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + word_score=args.word_score, + unk_score=args.unk_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + + if self.asg_transitions is None: + N = 768 + # self.asg_transitions = torch.FloatTensor(N, N).zero_() + self.asg_transitions = [] + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + self.asg_transitions, + self.unit_lm, + ) + else: + assert args.unit_lm, "lexicon free decoding can only be done with a unit language model" + from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions + + d = {w: [[w]] for w in tgt_dict.symbols} + self.word_dict = create_word_dict(d) + self.lm = KenLM(args.kenlm_model, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def get_timesteps(self, token_idxs: List[int]) -> List[int]: + """Returns frame numbers corresponding to every non-blank token. + + Parameters + ---------- + token_idxs : List[int] + IDs of decoded tokens. + + Returns + ------- + List[int] + Frame numbers corresponding to every non-blank token. + """ + timesteps = [] + for i, token_idx in enumerate(token_idxs): + if token_idx == self.blank: + continue + if i == 0 or token_idx != token_idxs[i-1]: + timesteps.append(i) + return timesteps + + def decode(self, emissions): + B, T, N = emissions.size() + hypos = [] + for b in range(B): + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] + hypos.append( + [ + { + "tokens": self.get_tokens(result.tokens), + "score": result.score, + "timesteps": self.get_timesteps(result.tokens), + "words": [ + self.word_dict.get_entry(x) for x in result.words if x >= 0 + ], + } + for result in nbest_results + ] + ) + return hypos + + +FairseqLMState = namedtuple("FairseqLMState", ["prefix", "incremental_state", "probs"]) + + +class FairseqLM(LM): + def __init__(self, dictionary, model): + LM.__init__(self) + self.dictionary = dictionary + self.model = model + self.unk = self.dictionary.unk() + + self.save_incremental = False # this currently does not work properly + self.max_cache = 20_000 + + model.cuda() + model.eval() + model.make_generation_fast_() + + self.states = {} + self.stateq = deque() + + def start(self, start_with_nothing): + state = LMState() + prefix = torch.LongTensor([[self.dictionary.eos()]]) + incremental_state = {} if self.save_incremental else None + with torch.no_grad(): + res = self.model(prefix.cuda(), incremental_state=incremental_state) + probs = self.model.get_normalized_probs(res, log_probs=True, sample=None) + + if incremental_state is not None: + incremental_state = apply_to_sample(lambda x: x.cpu(), incremental_state) + self.states[state] = FairseqLMState( + prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy() + ) + self.stateq.append(state) + + return state + + def score(self, state: LMState, token_index: int, no_cache: bool = False): + """ + Evaluate language model based on the current lm state and new word + Parameters: + ----------- + state: current lm state + token_index: index of the word + (can be lexicon index then you should store inside LM the + mapping between indices of lexicon and lm, or lm index of a word) + + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + curr_state = self.states[state] + + def trim_cache(targ_size): + while len(self.stateq) > targ_size: + rem_k = self.stateq.popleft() + rem_st = self.states[rem_k] + rem_st = FairseqLMState(rem_st.prefix, None, None) + self.states[rem_k] = rem_st + + if curr_state.probs is None: + new_incremental_state = ( + curr_state.incremental_state.copy() + if curr_state.incremental_state is not None + else None + ) + with torch.no_grad(): + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cuda(), new_incremental_state + ) + elif self.save_incremental: + new_incremental_state = {} + + res = self.model( + torch.from_numpy(curr_state.prefix).cuda(), + incremental_state=new_incremental_state, + ) + probs = self.model.get_normalized_probs( + res, log_probs=True, sample=None + ) + + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cpu(), new_incremental_state + ) + + curr_state = FairseqLMState( + curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy() + ) + + if not no_cache: + self.states[state] = curr_state + self.stateq.append(state) + + score = curr_state.probs[token_index].item() + + trim_cache(self.max_cache) + + outstate = state.child(token_index) + if outstate not in self.states and not no_cache: + prefix = np.concatenate( + [curr_state.prefix, torch.LongTensor([[token_index]])], -1 + ) + incr_state = curr_state.incremental_state + + self.states[outstate] = FairseqLMState(prefix, incr_state, None) + + if token_index == self.unk: + score = float("-inf") + + return outstate, score + + def finish(self, state: LMState): + """ + Evaluate eos for language model based on the current lm state + + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + return self.score(state, self.dictionary.eos()) + + def empty_cache(self): + self.states = {} + self.stateq = deque() + gc.collect() + + +class W2lFairseqLMDecoder(W2lDecoder): + def __init__(self, args, tgt_dict): + super().__init__(args, tgt_dict) + + self.unit_lm = getattr(args, "unit_lm", False) + + self.lexicon = load_words(args.lexicon) if args.lexicon else None + self.idx_to_wrd = {} + + checkpoint = torch.load(args.kenlm_model, map_location="cpu") + + if "cfg" in checkpoint and checkpoint["cfg"] is not None: + lm_args = checkpoint["cfg"] + else: + lm_args = convert_namespace_to_omegaconf(checkpoint["args"]) + + with open_dict(lm_args.task): + lm_args.task.data = osp.dirname(args.kenlm_model) + + task = tasks.setup_task(lm_args.task) + model = task.build_model(lm_args.model) + model.load_state_dict(checkpoint["model"], strict=False) + + self.trie = Trie(self.vocab_size, self.silence) + + self.word_dict = task.dictionary + self.unk_word = self.word_dict.unk() + self.lm = FairseqLM(self.word_dict, model) + + if self.lexicon: + start_state = self.lm.start(False) + for i, (word, spellings) in enumerate(self.lexicon.items()): + if self.unit_lm: + word_idx = i + self.idx_to_wrd[i] = word + score = 0 + else: + word_idx = self.word_dict.index(word) + _, score = self.lm.score(start_state, word_idx, no_cache=True) + + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + word_score=args.word_score, + unk_score=args.unk_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + [], + self.unit_lm, + ) + else: + assert args.unit_lm, "lexicon free decoding can only be done with a unit language model" + from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions + + d = {w: [[w]] for w in tgt_dict.symbols} + self.word_dict = create_word_dict(d) + self.lm = KenLM(args.kenlm_model, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def decode(self, emissions): + B, T, N = emissions.size() + hypos = [] + + def idx_to_word(idx): + if self.unit_lm: + return self.idx_to_wrd[idx] + else: + return self.word_dict[idx] + + def make_hypo(result): + hypo = {"tokens": self.get_tokens(result.tokens), "score": result.score} + if self.lexicon: + hypo["words"] = [idx_to_word(x) for x in result.words if x >= 0] + return hypo + + for b in range(B): + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] + hypos.append([make_hypo(result) for result in nbest_results]) + self.lm.empty_cache() + + return hypos diff --git a/fairseq/examples/speech_synthesis/README.md b/fairseq/examples/speech_synthesis/README.md new file mode 100644 index 0000000..a31e7f6 --- /dev/null +++ b/fairseq/examples/speech_synthesis/README.md @@ -0,0 +1,38 @@ +Speech Synthesis (S^2) +=== +[https://arxiv.org/abs/2109.06912](https://arxiv.org/abs/2109.06912) + +Speech synthesis with fairseq. + +## Features + +- Autoregressive and non-autoregressive models +- Multi-speaker synthesis +- Audio preprocessing (denoising, VAD, etc.) for less curated data +- Automatic metrics for model development +- Similar data configuration as [S2T](../speech_to_text/README.md) + + +## Examples +- [Single-speaker synthesis on LJSpeech](docs/ljspeech_example.md) +- [Multi-speaker synthesis on VCTK](docs/vctk_example.md) +- [Multi-speaker synthesis on Common Voice](docs/common_voice_example.md) + + +## Citation +Please cite as: +``` +@article{wang2021fairseqs2, + title={fairseq S\^{} 2: A Scalable and Integrable Speech Synthesis Toolkit}, + author={Wang, Changhan and Hsu, Wei-Ning and Adi, Yossi and Polyak, Adam and Lee, Ann and Chen, Peng-Jen and Gu, Jiatao and Pino, Juan}, + journal={arXiv preprint arXiv:2109.06912}, + year={2021} +} + +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` diff --git a/fairseq/examples/speech_synthesis/__init__.py b/fairseq/examples/speech_synthesis/__init__.py new file mode 100644 index 0000000..6264236 --- /dev/null +++ b/fairseq/examples/speech_synthesis/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/examples/speech_synthesis/data_utils.py b/fairseq/examples/speech_synthesis/data_utils.py new file mode 100644 index 0000000..3b2d079 --- /dev/null +++ b/fairseq/examples/speech_synthesis/data_utils.py @@ -0,0 +1,344 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import io +import os +from pathlib import Path +from typing import Optional, List, Dict +import zipfile +import tempfile +from dataclasses import dataclass +from itertools import groupby + +import torch +import torch.nn.functional as F +import numpy as np +from tqdm import tqdm + +from examples.speech_to_text.data_utils import load_tsv_to_dicts +from fairseq.data.audio.audio_utils import ( + TTSSpectrogram, TTSMelScale, parse_path, read_from_stored_zip, is_npy_data +) + + +def trim_or_pad_to_target_length( + data_1d_or_2d: np.ndarray, target_length: int +) -> np.ndarray: + assert len(data_1d_or_2d.shape) in {1, 2} + delta = data_1d_or_2d.shape[0] - target_length + if delta >= 0: # trim if being longer + data_1d_or_2d = data_1d_or_2d[: target_length] + else: # pad if being shorter + if len(data_1d_or_2d.shape) == 1: + data_1d_or_2d = np.concatenate( + [data_1d_or_2d, np.zeros(-delta)], axis=0 + ) + else: + data_1d_or_2d = np.concatenate( + [data_1d_or_2d, np.zeros((-delta, data_1d_or_2d.shape[1]))], + axis=0 + ) + return data_1d_or_2d + + +def extract_logmel_spectrogram( + waveform: torch.Tensor, sample_rate: int, + output_path: Optional[Path] = None, win_length: int = 1024, + hop_length: int = 256, n_fft: int = 1024, + win_fn: callable = torch.hann_window, n_mels: int = 80, + f_min: float = 0., f_max: float = 8000, eps: float = 1e-5, + overwrite: bool = False, target_length: Optional[int] = None +): + if output_path is not None and output_path.is_file() and not overwrite: + return + + spectrogram_transform = TTSSpectrogram( + n_fft=n_fft, win_length=win_length, hop_length=hop_length, + window_fn=win_fn + ) + mel_scale_transform = TTSMelScale( + n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max, + n_stft=n_fft // 2 + 1 + ) + spectrogram = spectrogram_transform(waveform) + mel_spec = mel_scale_transform(spectrogram) + logmel_spec = torch.clamp(mel_spec, min=eps).log() + assert len(logmel_spec.shape) == 3 and logmel_spec.shape[0] == 1 + logmel_spec = logmel_spec.squeeze().t() # D x T -> T x D + if target_length is not None: + logmel_spec = trim_or_pad_to_target_length(logmel_spec, target_length) + + if output_path is not None: + np.save(output_path.as_posix(), logmel_spec) + else: + return logmel_spec + + +def extract_pitch( + waveform: torch.Tensor, sample_rate: int, + output_path: Optional[Path] = None, hop_length: int = 256, + log_scale: bool = True, phoneme_durations: Optional[List[int]] = None +): + if output_path is not None and output_path.is_file(): + return + + try: + import pyworld + except ImportError: + raise ImportError("Please install PyWORLD: pip install pyworld") + + _waveform = waveform.squeeze(0).double().numpy() + pitch, t = pyworld.dio( + _waveform, sample_rate, frame_period=hop_length / sample_rate * 1000 + ) + pitch = pyworld.stonemask(_waveform, pitch, t, sample_rate) + + if phoneme_durations is not None: + pitch = trim_or_pad_to_target_length(pitch, sum(phoneme_durations)) + try: + from scipy.interpolate import interp1d + except ImportError: + raise ImportError("Please install SciPy: pip install scipy") + nonzero_ids = np.where(pitch != 0)[0] + if len(nonzero_ids) == 0: + print((f"{output_path} has all empty values in the pitch contour")) + return + elif len(nonzero_ids) == 1: + print((f"{output_path} has only one non-zero values in the pitch contour")) + return + else: + interp_fn = interp1d( + nonzero_ids, + pitch[nonzero_ids], + fill_value=(pitch[nonzero_ids[0]], pitch[nonzero_ids[-1]]), + bounds_error=False, + ) + pitch = interp_fn(np.arange(0, len(pitch))) + d_cumsum = np.cumsum(np.concatenate([np.array([0]), phoneme_durations])) + pitch = np.array( + [ + np.mean(pitch[d_cumsum[i-1]: d_cumsum[i]]) + for i in range(1, len(d_cumsum)) + ] + ) + assert len(pitch) == len(phoneme_durations) + + if log_scale: + pitch = np.log(pitch + 1) + + if output_path is not None: + np.save(output_path.as_posix(), pitch) + else: + return pitch + + +def extract_energy( + waveform: torch.Tensor, output_path: Optional[Path] = None, + hop_length: int = 256, n_fft: int = 1024, log_scale: bool = True, + phoneme_durations: Optional[List[int]] = None +): + if output_path is not None and output_path.is_file(): + return + + assert len(waveform.shape) == 2 and waveform.shape[0] == 1 + waveform = waveform.view(1, 1, waveform.shape[1]) + waveform = F.pad( + waveform.unsqueeze(1), [n_fft // 2, n_fft // 2, 0, 0], + mode="reflect" + ) + waveform = waveform.squeeze(1) + + fourier_basis = np.fft.fft(np.eye(n_fft)) + cutoff = int((n_fft / 2 + 1)) + fourier_basis = np.vstack( + [np.real(fourier_basis[:cutoff, :]), + np.imag(fourier_basis[:cutoff, :])] + ) + + forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) + forward_transform = F.conv1d( + waveform, forward_basis, stride=hop_length, padding=0 + ) + + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2) + energy = torch.norm(magnitude, dim=1).squeeze(0).numpy() + + if phoneme_durations is not None: + energy = trim_or_pad_to_target_length(energy, sum(phoneme_durations)) + d_cumsum = np.cumsum(np.concatenate([np.array([0]), phoneme_durations])) + energy = np.array( + [ + np.mean(energy[d_cumsum[i - 1]: d_cumsum[i]]) + for i in range(1, len(d_cumsum)) + ] + ) + assert len(energy) == len(phoneme_durations) + + if log_scale: + energy = np.log(energy + 1) + + if output_path is not None: + np.save(output_path.as_posix(), energy) + else: + return energy + + +def get_global_cmvn(feature_root: Path, output_path: Optional[Path] = None): + mean_x, mean_x2, n_frames = None, None, 0 + feature_paths = feature_root.glob("*.npy") + for p in tqdm(feature_paths): + with open(p, 'rb') as f: + frames = np.load(f).squeeze() + + n_frames += frames.shape[0] + + cur_mean_x = frames.sum(axis=0) + if mean_x is None: + mean_x = cur_mean_x + else: + mean_x += cur_mean_x + + cur_mean_x2 = (frames ** 2).sum(axis=0) + if mean_x2 is None: + mean_x2 = cur_mean_x2 + else: + mean_x2 += cur_mean_x2 + + mean_x /= n_frames + mean_x2 /= n_frames + var_x = mean_x2 - mean_x ** 2 + std_x = np.sqrt(np.maximum(var_x, 1e-10)) + + if output_path is not None: + with open(output_path, 'wb') as f: + np.savez(f, mean=mean_x, std=std_x) + else: + return {"mean": mean_x, "std": std_x} + + +def ipa_phonemize(text, lang="en-us", use_g2p=False): + if use_g2p: + assert lang == "en-us", "g2pE phonemizer only works for en-us" + try: + from g2p_en import G2p + g2p = G2p() + return " ".join("|" if p == " " else p for p in g2p(text)) + except ImportError: + raise ImportError( + "Please install phonemizer: pip install g2p_en" + ) + else: + try: + from phonemizer import phonemize + from phonemizer.separator import Separator + return phonemize( + text, backend='espeak', language=lang, + separator=Separator(word="| ", phone=" ") + ) + except ImportError: + raise ImportError( + "Please install phonemizer: pip install phonemizer" + ) + + +@dataclass +class ForceAlignmentInfo(object): + tokens: List[str] + frame_durations: List[int] + start_sec: Optional[float] + end_sec: Optional[float] + + +def get_mfa_alignment_by_sample_id( + textgrid_zip_path: str, sample_id: str, sample_rate: int, + hop_length: int, silence_phones: List[str] = ("sil", "sp", "spn") +) -> ForceAlignmentInfo: + try: + import tgt + except ImportError: + raise ImportError("Please install TextGridTools: pip install tgt") + + filename = f"{sample_id}.TextGrid" + out_root = Path(tempfile.gettempdir()) + tgt_path = out_root / filename + with zipfile.ZipFile(textgrid_zip_path) as f_zip: + f_zip.extract(filename, path=out_root) + textgrid = tgt.io.read_textgrid(tgt_path.as_posix()) + os.remove(tgt_path) + + phones, frame_durations = [], [] + start_sec, end_sec, end_idx = 0, 0, 0 + for t in textgrid.get_tier_by_name("phones")._objects: + s, e, p = t.start_time, t.end_time, t.text + # Trim leading silences + if len(phones) == 0: + if p in silence_phones: + continue + else: + start_sec = s + phones.append(p) + if p not in silence_phones: + end_sec = e + end_idx = len(phones) + r = sample_rate / hop_length + frame_durations.append(int(np.round(e * r) - np.round(s * r))) + # Trim tailing silences + phones = phones[:end_idx] + frame_durations = frame_durations[:end_idx] + + return ForceAlignmentInfo( + tokens=phones, frame_durations=frame_durations, start_sec=start_sec, + end_sec=end_sec + ) + + +def get_mfa_alignment( + textgrid_zip_path: str, sample_ids: List[str], sample_rate: int, + hop_length: int +) -> Dict[str, ForceAlignmentInfo]: + return { + i: get_mfa_alignment_by_sample_id( + textgrid_zip_path, i, sample_rate, hop_length + ) for i in tqdm(sample_ids) + } + + +def get_unit_alignment( + id_to_unit_tsv_path: str, sample_ids: List[str] +) -> Dict[str, ForceAlignmentInfo]: + id_to_units = { + e["id"]: e["units"] for e in load_tsv_to_dicts(id_to_unit_tsv_path) + } + id_to_units = {i: id_to_units[i].split() for i in sample_ids} + id_to_units_collapsed = { + i: [uu for uu, _ in groupby(u)] for i, u in id_to_units.items() + } + id_to_durations = { + i: [len(list(g)) for _, g in groupby(u)] for i, u in id_to_units.items() + } + + return { + i: ForceAlignmentInfo( + tokens=id_to_units_collapsed[i], frame_durations=id_to_durations[i], + start_sec=None, end_sec=None + ) + for i in sample_ids + } + + +def get_feature_value_min_max(feature_paths: List[str]): + v_min, v_max = 1e-8, -1e-8 + for p in tqdm(feature_paths): + _path, slice_ptr = parse_path(p) + assert len(slice_ptr) == 2 + byte_data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1]) + assert is_npy_data(byte_data) + path_or_fp = io.BytesIO(byte_data) + features = np.load(path_or_fp).squeeze() + v_min = min(v_min, features.min().item()) + v_max = max(v_max, features.max().item()) + return v_min, v_max diff --git a/fairseq/examples/speech_synthesis/docs/common_voice_example.md b/fairseq/examples/speech_synthesis/docs/common_voice_example.md new file mode 100644 index 0000000..1c0eef6 --- /dev/null +++ b/fairseq/examples/speech_synthesis/docs/common_voice_example.md @@ -0,0 +1,67 @@ +[[Back]](..) + +# Common Voice + +[Common Voice](https://commonvoice.mozilla.org/en/datasets) is a public domain speech corpus with 11.2K hours of read +speech in 76 languages (the latest version 7.0). We provide examples for building +[Transformer](https://arxiv.org/abs/1809.08895) models on this dataset. + + +## Data preparation +[Download](https://commonvoice.mozilla.org/en/datasets) and unpack Common Voice v4 to a path `${DATA_ROOT}/${LANG_ID}`. +Create splits and generate audio manifests with +```bash +python -m examples.speech_synthesis.preprocessing.get_common_voice_audio_manifest \ + --data-root ${DATA_ROOT} \ + --lang ${LANG_ID} \ + --output-manifest-root ${AUDIO_MANIFEST_ROOT} --convert-to-wav +``` + +To denoise audio and trim leading/trailing silence using signal processing based VAD, run +```bash +for SPLIT in dev test train; do + python -m examples.speech_synthesis.preprocessing.denoise_and_vad_audio \ + --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \ + --output-dir ${PROCESSED_DATA_ROOT} \ + --denoise --vad --vad-agg-level 2 +done +``` + +which generates a new audio TSV manifest under `${PROCESSED_DATA_ROOT}` with updated path to the processed audio and +a new column for SNR. + +To do filtering by CER, follow the [Automatic Evaluation](../docs/ljspeech_example.md#automatic-evaluation) section to +run ASR model (add `--eval-target` to `get_eval_manifest` for evaluation on the reference audio; add `--err-unit char` +to `eval_asr` to compute CER instead of WER). The example-level CER is saved to +`${EVAL_OUTPUT_ROOT}/uer_cer.${SPLIT}.tsv`. + +Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with +```bash +python -m examples.speech_synthesis.preprocessing.get_feature_manifest \ + --audio-manifest-root ${AUDIO_MANIFEST_ROOT} \ + --output-root ${FEATURE_MANIFEST_ROOT} \ + --ipa-vocab --lang ${LANG_ID} \ + --snr-threshold 15 \ + --cer-threshold 0.1 --cer-tsv-path ${EVAL_OUTPUT_ROOT}/uer_cer.${SPLIT}.tsv +``` +where we use phoneme inputs (`--ipa-vocab`) as example. For sample filtering, we set the SNR and CER threshold +to 15 and 10%, respectively. + + +## Training +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#transformer).) + + +## Inference +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#inference).) + +## Automatic Evaluation +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#automatic-evaluation).) + +## Results + +| Language | Speakers | --arch | Params | Test MCD | Model | +|---|---|---|---|---|---| +| English | 200 | tts_transformer | 54M | 3.8 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/cv4_en200_transformer_phn.tar) | + +[[Back]](..) diff --git a/fairseq/examples/speech_synthesis/docs/ljspeech_example.md b/fairseq/examples/speech_synthesis/docs/ljspeech_example.md new file mode 100644 index 0000000..836c30d --- /dev/null +++ b/fairseq/examples/speech_synthesis/docs/ljspeech_example.md @@ -0,0 +1,137 @@ +[[Back]](..) + +# LJSpeech + +[LJSpeech](https://keithito.com/LJ-Speech-Dataset) is a public domain TTS +corpus with around 24 hours of English speech sampled at 22.05kHz. We provide examples for building +[Transformer](https://arxiv.org/abs/1809.08895) and [FastSpeech 2](https://arxiv.org/abs/2006.04558) +models on this dataset. + + +## Data preparation + +Download data, create splits and generate audio manifests with +```bash +python -m examples.speech_synthesis.preprocessing.get_ljspeech_audio_manifest \ + --output-data-root ${AUDIO_DATA_ROOT} \ + --output-manifest-root ${AUDIO_MANIFEST_ROOT} +``` + +Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with +```bash +python -m examples.speech_synthesis.preprocessing.get_feature_manifest \ + --audio-manifest-root ${AUDIO_MANIFEST_ROOT} \ + --output-root ${FEATURE_MANIFEST_ROOT} \ + --ipa-vocab --use-g2p +``` +where we use phoneme inputs (`--ipa-vocab --use-g2p`) as example. + +FastSpeech 2 additionally requires frame durations, pitch and energy as auxiliary training targets. +Add `--add-fastspeech-targets` to include these fields in the feature manifests. We get frame durations either from +phoneme-level force-alignment or frame-level pseudo-text unit sequence. They should be pre-computed and specified via: +- `--textgrid-zip ${TEXT_GRID_ZIP_PATH}` for a ZIP file, inside which there is one + [TextGrid](https://www.fon.hum.uva.nl/praat/manual/TextGrid.html) file per sample to provide force-alignment info. +- `--id-to-units-tsv ${ID_TO_UNIT_TSV}` for a TSV file, where there are 2 columns for sample ID and + space-delimited pseudo-text unit sequence, respectively. + +For your convenience, we provide pre-computed +[force-alignment](https://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_mfa.zip) from +[Montreal Forced Aligner](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) and +[pseudo-text units](s3://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_hubert.tsv) from +[HuBERT](https://github.com/pytorch/fairseq/tree/main/examples/hubert). You can also generate them by yourself using +a different software or model. + + +## Training +#### Transformer +```bash +fairseq-train ${FEATURE_MANIFEST_ROOT} --save-dir ${SAVE_DIR} \ + --config-yaml config.yaml --train-subset train --valid-subset dev \ + --num-workers 4 --max-tokens 30000 --max-update 200000 \ + --task text_to_speech --criterion tacotron2 --arch tts_transformer \ + --clip-norm 5.0 --n-frames-per-step 4 --bce-pos-weight 5.0 \ + --dropout 0.1 --attention-dropout 0.1 --activation-dropout 0.1 \ + --encoder-normalize-before --decoder-normalize-before \ + --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --seed 1 --update-freq 8 --eval-inference --best-checkpoint-metric mcd_loss +``` +where `SAVE_DIR` is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to +update it accordingly when using more than 1 GPU. + +#### FastSpeech2 +```bash +fairseq-train ${FEATURE_MANIFEST_ROOT} --save-dir ${SAVE_DIR} \ + --config-yaml config.yaml --train-subset train --valid-subset dev \ + --num-workers 4 --max-sentences 6 --max-update 200000 \ + --task text_to_speech --criterion fastspeech2 --arch fastspeech2 \ + --clip-norm 5.0 --n-frames-per-step 1 \ + --dropout 0.1 --attention-dropout 0.1 \ + --optimizer adam --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --seed 1 --update-freq 8 --eval-inference --best-checkpoint-metric mcd_loss +``` + + +## Inference +Average the last 5 checkpoints, generate the test split spectrogram and waveform using the default Griffin-Lim vocoder: +```bash +SPLIT=test +CHECKPOINT_NAME=avg_last_5 +CHECKPOINT_PATH=${SAVE_DIR}/checkpoint_${CHECKPOINT_NAME}.pt +python scripts/average_checkpoints.py --inputs ${SAVE_DIR} \ + --num-epoch-checkpoints 5 \ + --output ${CHECKPOINT_PATH} + +python -m examples.speech_synthesis.generate_waveform ${FEATURE_MANIFEST_ROOT} \ + --config-yaml config.yaml --gen-subset ${SPLIT} --task text_to_speech \ + --path ${CHECKPOINT_PATH} --max-tokens 50000 --spec-bwd-max-iter 32 \ + --dump-waveforms +``` +which dumps files (waveform, feature, attention plot, etc.) to `${SAVE_DIR}/generate-${CHECKPOINT_NAME}-${SPLIT}`. To +re-synthesize target waveforms for automatic evaluation, add `--dump-target`. + +## Automatic Evaluation +To start with, generate the manifest for synthetic speech, which will be taken as inputs by evaluation scripts. +```bash +python -m examples.speech_synthesis.evaluation.get_eval_manifest \ + --generation-root ${SAVE_DIR}/generate-${CHECKPOINT_NAME}-${SPLIT} \ + --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \ + --output-path ${EVAL_OUTPUT_ROOT}/eval.tsv \ + --vocoder griffin_lim --sample-rate 22050 --audio-format flac \ + --use-resynthesized-target +``` +Speech recognition (ASR) models usually operate at lower sample rates (e.g. 16kHz). For the WER/CER metric, +you may need to resample the audios accordingly --- add `--output-sample-rate 16000` for `generate_waveform.py` and +use `--sample-rate 16000` for `get_eval_manifest.py`. + + +#### WER/CER metric +We use wav2vec 2.0 ASR model as example. [Download](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) +the model checkpoint and dictionary, then compute WER/CER with +```bash +python -m examples.speech_synthesis.evaluation.eval_asr \ + --audio-header syn --text-header text --err-unit char --split ${SPLIT} \ + --w2v-ckpt ${WAV2VEC2_CHECKPOINT_PATH} --w2v-dict-dir ${WAV2VEC2_DICT_DIR} \ + --raw-manifest ${EVAL_OUTPUT_ROOT}/eval_16khz.tsv --asr-dir ${EVAL_OUTPUT_ROOT}/asr +``` + +#### MCD/MSD metric +```bash +python -m examples.speech_synthesis.evaluation.eval_sp \ + ${EVAL_OUTPUT_ROOT}/eval.tsv --mcd --msd +``` + +#### F0 metrics +```bash +python -m examples.speech_synthesis.evaluation.eval_f0 \ + ${EVAL_OUTPUT_ROOT}/eval.tsv --gpe --vde --ffe +``` + + +## Results + +| --arch | Params | Test MCD | Model | +|---|---|---|---| +| tts_transformer | 54M | 3.8 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_transformer_phn.tar) | +| fastspeech2 | 41M | 3.8 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_fastspeech2_phn.tar) | + +[[Back]](..) diff --git a/fairseq/examples/speech_synthesis/docs/vctk_example.md b/fairseq/examples/speech_synthesis/docs/vctk_example.md new file mode 100644 index 0000000..6808256 --- /dev/null +++ b/fairseq/examples/speech_synthesis/docs/vctk_example.md @@ -0,0 +1,61 @@ +[[Back]](..) + +# VCTK + +[VCTK](https://datashare.ed.ac.uk/handle/10283/3443) is an open English speech corpus. We provide examples +for building [Transformer](https://arxiv.org/abs/1809.08895) models on this dataset. + + +## Data preparation +Download data, create splits and generate audio manifests with +```bash +python -m examples.speech_synthesis.preprocessing.get_vctk_audio_manifest \ + --output-data-root ${AUDIO_DATA_ROOT} \ + --output-manifest-root ${AUDIO_MANIFEST_ROOT} +``` + +To denoise audio and trim leading/trailing silence using signal processing based VAD, run +```bash +for SPLIT in dev test train; do + python -m examples.speech_synthesis.preprocessing.denoise_and_vad_audio \ + --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \ + --output-dir ${PROCESSED_DATA_ROOT} \ + --denoise --vad --vad-agg-level 3 +done +``` +which generates a new audio TSV manifest under `${PROCESSED_DATA_ROOT}` with updated path to the processed audio and +a new column for SNR. + +To do filtering by CER, follow the [Automatic Evaluation](../docs/ljspeech_example.md#automatic-evaluation) section to +run ASR model (add `--eval-target` to `get_eval_manifest` for evaluation on the reference audio; add `--err-unit char` +to `eval_asr` to compute CER instead of WER). The example-level CER is saved to +`${EVAL_OUTPUT_ROOT}/uer_cer.${SPLIT}.tsv`. + +Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with +```bash +python -m examples.speech_synthesis.preprocessing.get_feature_manifest \ + --audio-manifest-root ${PROCESSED_DATA_ROOT} \ + --output-root ${FEATURE_MANIFEST_ROOT} \ + --ipa-vocab --use-g2p \ + --snr-threshold 15 \ + --cer-threshold 0.1 --cer-tsv-path ${EVAL_OUTPUT_ROOT}/uer_cer.${SPLIT}.tsv +``` +where we use phoneme inputs (`--ipa-vocab --use-g2p`) as example. For sample filtering, we set the SNR and CER threshold +to 15 and 10%, respectively. + +## Training +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#transformer).) + +## Inference +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#inference).) + +## Automatic Evaluation +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#automatic-evaluation).) + +## Results + +| --arch | Params | Test MCD | Model | +|---|---|---|---| +| tts_transformer | 54M | 3.4 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/vctk_transformer_phn.tar) | + +[[Back]](..) diff --git a/fairseq/examples/speech_synthesis/evaluation/__init__.py b/fairseq/examples/speech_synthesis/evaluation/__init__.py new file mode 100644 index 0000000..6264236 --- /dev/null +++ b/fairseq/examples/speech_synthesis/evaluation/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/examples/speech_synthesis/evaluation/eval_asr.py b/fairseq/examples/speech_synthesis/evaluation/eval_asr.py new file mode 100644 index 0000000..005a11b --- /dev/null +++ b/fairseq/examples/speech_synthesis/evaluation/eval_asr.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import editdistance +import re +import shutil +import soundfile as sf +import subprocess +from pathlib import Path + +from examples.speech_to_text.data_utils import load_tsv_to_dicts + + +def preprocess_text(text): + text = "|".join(re.sub(r"[^A-Z' ]", " ", text.upper()).split()) + text = " ".join(text) + return text + + +def prepare_w2v_data( + dict_dir, sample_rate, label, audio_paths, texts, split, data_dir +): + data_dir.mkdir(parents=True, exist_ok=True) + shutil.copyfile( + dict_dir / f"dict.{label}.txt", + data_dir / f"dict.{label}.txt" + ) + with open(data_dir / f"{split}.tsv", "w") as f: + f.write("/\n") + for audio_path in audio_paths: + wav, sr = sf.read(audio_path) + assert sr == sample_rate, f"{sr} != sample_rate" + nsample = len(wav) + f.write(f"{audio_path}\t{nsample}\n") + with open(data_dir / f"{split}.{label}", "w") as f: + for text in texts: + text = preprocess_text(text) + f.write(f"{text}\n") + + +def run_asr(asr_dir, split, w2v_ckpt, w2v_label, res_dir): + """ + results will be saved at + {res_dir}/{ref,hypo}.word-{w2v_ckpt.filename}-{split}.txt + """ + cmd = ["python", "-m", "examples.speech_recognition.infer"] + cmd += [str(asr_dir.resolve())] + cmd += ["--task", "audio_finetuning", "--nbest", "1", "--quiet"] + cmd += ["--w2l-decoder", "viterbi", "--criterion", "ctc"] + cmd += ["--post-process", "letter", "--max-tokens", "4000000"] + cmd += ["--path", str(w2v_ckpt.resolve()), "--labels", w2v_label] + cmd += ["--gen-subset", split, "--results-path", str(res_dir.resolve())] + + print(f"running cmd:\n{' '.join(cmd)}") + subprocess.run(cmd, check=True) + + +def compute_error_rate(hyp_wrd_path, ref_wrd_path, unit="word"): + """each line is "<text> (None-<index>)" """ + tokenize_line = { + "word": lambda x: re.sub(r" \(.*\)$", "", x.rstrip()).split(), + "char": lambda x: list(re.sub(r" \(.*\)$", "", x.rstrip())) + }.get(unit) + if tokenize_line is None: + raise ValueError(f"{unit} not supported") + + inds = [int(re.sub(r"\D*(\d*)\D*", r"\1", line)) + for line in open(hyp_wrd_path)] + hyps = [tokenize_line(line) for line in open(hyp_wrd_path)] + refs = [tokenize_line(line) for line in open(ref_wrd_path)] + assert(len(hyps) == len(refs)) + err_rates = [ + editdistance.eval(hyp, ref) / len(ref) for hyp, ref in zip(hyps, refs) + ] + ind_to_err_rates = {i: e for i, e in zip(inds, err_rates)} + return ind_to_err_rates + + +def main(args): + samples = load_tsv_to_dicts(args.raw_manifest) + ids = [ + sample[args.id_header] if args.id_header else "" for sample in samples + ] + audio_paths = [sample[args.audio_header] for sample in samples] + texts = [sample[args.text_header] for sample in samples] + + prepare_w2v_data( + args.w2v_dict_dir, + args.w2v_sample_rate, + args.w2v_label, + audio_paths, + texts, + args.split, + args.asr_dir + ) + run_asr(args.asr_dir, args.split, args.w2v_ckpt, args.w2v_label, args.asr_dir) + ind_to_err_rates = compute_error_rate( + args.asr_dir / f"hypo.word-{args.w2v_ckpt.name}-{args.split}.txt", + args.asr_dir / f"ref.word-{args.w2v_ckpt.name}-{args.split}.txt", + args.err_unit, + ) + + uer_path = args.asr_dir / f"uer_{args.err_unit}.{args.split}.tsv" + with open(uer_path, "w") as f: + f.write("id\taudio\tuer\n") + for ind, (id_, audio_path) in enumerate(zip(ids, audio_paths)): + f.write(f"{id_}\t{audio_path}\t{ind_to_err_rates[ind]:.4f}\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--raw-manifest", required=True, type=Path) + parser.add_argument("--asr-dir", required=True, type=Path) + parser.add_argument("--id-header", default="id", type=str) + parser.add_argument("--audio-header", default="audio", type=str) + parser.add_argument("--text-header", default="src_text", type=str) + parser.add_argument("--split", default="raw", type=str) + parser.add_argument("--w2v-ckpt", required=True, type=Path) + parser.add_argument("--w2v-dict-dir", required=True, type=Path) + parser.add_argument("--w2v-sample-rate", default=16000, type=int) + parser.add_argument("--w2v-label", default="ltr", type=str) + parser.add_argument("--err-unit", default="word", type=str) + args = parser.parse_args() + + main(args) diff --git a/fairseq/examples/speech_synthesis/evaluation/eval_f0.py b/fairseq/examples/speech_synthesis/evaluation/eval_f0.py new file mode 100644 index 0000000..df721d6 --- /dev/null +++ b/fairseq/examples/speech_synthesis/evaluation/eval_f0.py @@ -0,0 +1,266 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Signal processing-based evaluation using waveforms +""" +import numpy as np +import os.path as op + +import torchaudio +import tqdm +from tabulate import tabulate + +from examples.speech_synthesis.utils import ( + gross_pitch_error, voicing_decision_error, f0_frame_error +) +from examples.speech_synthesis.evaluation.eval_sp import load_eval_spec + + +def difference_function(x, n, tau_max): + """ + Compute difference function of data x. This solution is implemented directly + with Numpy fft. + + + :param x: audio data + :param n: length of data + :param tau_max: integration window size + :return: difference function + :rtype: list + """ + + x = np.array(x, np.float64) + w = x.size + tau_max = min(tau_max, w) + x_cumsum = np.concatenate((np.array([0.]), (x * x).cumsum())) + size = w + tau_max + p2 = (size // 32).bit_length() + nice_numbers = (16, 18, 20, 24, 25, 27, 30, 32) + size_pad = min(x * 2 ** p2 for x in nice_numbers if x * 2 ** p2 >= size) + fc = np.fft.rfft(x, size_pad) + conv = np.fft.irfft(fc * fc.conjugate())[:tau_max] + return x_cumsum[w:w - tau_max:-1] + x_cumsum[w] - x_cumsum[:tau_max] - \ + 2 * conv + + +def cumulative_mean_normalized_difference_function(df, n): + """ + Compute cumulative mean normalized difference function (CMND). + + :param df: Difference function + :param n: length of data + :return: cumulative mean normalized difference function + :rtype: list + """ + + # scipy method + cmn_df = df[1:] * range(1, n) / np.cumsum(df[1:]).astype(float) + return np.insert(cmn_df, 0, 1) + + +def get_pitch(cmdf, tau_min, tau_max, harmo_th=0.1): + """ + Return fundamental period of a frame based on CMND function. + + :param cmdf: Cumulative Mean Normalized Difference function + :param tau_min: minimum period for speech + :param tau_max: maximum period for speech + :param harmo_th: harmonicity threshold to determine if it is necessary to + compute pitch frequency + :return: fundamental period if there is values under threshold, 0 otherwise + :rtype: float + """ + tau = tau_min + while tau < tau_max: + if cmdf[tau] < harmo_th: + while tau + 1 < tau_max and cmdf[tau + 1] < cmdf[tau]: + tau += 1 + return tau + tau += 1 + + return 0 # if unvoiced + + +def compute_yin(sig, sr, w_len=512, w_step=256, f0_min=100, f0_max=500, + harmo_thresh=0.1): + """ + + Compute the Yin Algorithm. Return fundamental frequency and harmonic rate. + + https://github.com/NVIDIA/mellotron adaption of + https://github.com/patriceguyot/Yin + + :param sig: Audio signal (list of float) + :param sr: sampling rate (int) + :param w_len: size of the analysis window (samples) + :param w_step: size of the lag between two consecutives windows (samples) + :param f0_min: Minimum fundamental frequency that can be detected (hertz) + :param f0_max: Maximum fundamental frequency that can be detected (hertz) + :param harmo_thresh: Threshold of detection. The yalgorithmù return the + first minimum of the CMND function below this threshold. + + :returns: + + * pitches: list of fundamental frequencies, + * harmonic_rates: list of harmonic rate values for each fundamental + frequency value (= confidence value) + * argmins: minimums of the Cumulative Mean Normalized DifferenceFunction + * times: list of time of each estimation + :rtype: tuple + """ + + tau_min = int(sr / f0_max) + tau_max = int(sr / f0_min) + + # time values for each analysis window + time_scale = range(0, len(sig) - w_len, w_step) + times = [t/float(sr) for t in time_scale] + frames = [sig[t:t + w_len] for t in time_scale] + + pitches = [0.0] * len(time_scale) + harmonic_rates = [0.0] * len(time_scale) + argmins = [0.0] * len(time_scale) + + for i, frame in enumerate(frames): + # Compute YIN + df = difference_function(frame, w_len, tau_max) + cm_df = cumulative_mean_normalized_difference_function(df, tau_max) + p = get_pitch(cm_df, tau_min, tau_max, harmo_thresh) + + # Get results + if np.argmin(cm_df) > tau_min: + argmins[i] = float(sr / np.argmin(cm_df)) + if p != 0: # A pitch was found + pitches[i] = float(sr / p) + harmonic_rates[i] = cm_df[p] + else: # No pitch, but we compute a value of the harmonic rate + harmonic_rates[i] = min(cm_df) + + return pitches, harmonic_rates, argmins, times + + +def extract_f0(samples): + f0_samples = [] + for sample in tqdm.tqdm(samples): + if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]): + f0_samples.append(None) + continue + + # assume single channel + yref, sr = torchaudio.load(sample["ref"]) + ysyn, _sr = torchaudio.load(sample["syn"]) + yref, ysyn = yref[0], ysyn[0] + assert sr == _sr, f"{sr} != {_sr}" + + yref_f0 = compute_yin(yref, sr) + ysyn_f0 = compute_yin(ysyn, sr) + + f0_samples += [ + { + "ref": yref_f0, + "syn": ysyn_f0 + } + ] + + return f0_samples + + +def eval_f0_error(samples, distortion_fn): + results = [] + for sample in tqdm.tqdm(samples): + if sample is None: + results.append(None) + continue + # assume single channel + yref_f, _, _, yref_t = sample["ref"] + ysyn_f, _, _, ysyn_t = sample["syn"] + + yref_f = np.array(yref_f) + yref_t = np.array(yref_t) + ysyn_f = np.array(ysyn_f) + ysyn_t = np.array(ysyn_t) + + distortion = distortion_fn(yref_t, yref_f, ysyn_t, ysyn_f) + results.append((distortion.item(), + len(yref_f), + len(ysyn_f) + )) + return results + + +def eval_gross_pitch_error(samples): + return eval_f0_error(samples, gross_pitch_error) + + +def eval_voicing_decision_error(samples): + return eval_f0_error(samples, voicing_decision_error) + + +def eval_f0_frame_error(samples): + return eval_f0_error(samples, f0_frame_error) + + +def print_results(results, show_bin): + results = np.array(list(filter(lambda x: x is not None, results))) + + np.set_printoptions(precision=3) + + def _print_result(results): + res = { + "nutt": len(results), + "error": results[:, 0].mean(), + "std": results[:, 0].std(), + "dur_ref": int(results[:, 1].sum()), + "dur_syn": int(results[:, 2].sum()), + } + print(tabulate([res.values()], res.keys(), floatfmt=".4f")) + + print(">>>> ALL") + _print_result(results) + + if show_bin: + edges = [0, 200, 400, 600, 800, 1000, 2000, 4000] + for i in range(1, len(edges)): + mask = np.logical_and(results[:, 1] >= edges[i-1], + results[:, 1] < edges[i]) + if not mask.any(): + continue + bin_results = results[mask] + print(f">>>> ({edges[i-1]}, {edges[i]})") + _print_result(bin_results) + + +def main(eval_f0, gpe, vde, ffe, show_bin): + samples = load_eval_spec(eval_f0) + if gpe or vde or ffe: + f0_samples = extract_f0(samples) + + if gpe: + print("===== Evaluate Gross Pitch Error =====") + results = eval_gross_pitch_error(f0_samples) + print_results(results, show_bin) + if vde: + print("===== Evaluate Voicing Decision Error =====") + results = eval_voicing_decision_error(f0_samples) + print_results(results, show_bin) + if ffe: + print("===== Evaluate F0 Frame Error =====") + results = eval_f0_frame_error(f0_samples) + print_results(results, show_bin) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("eval_f0") + parser.add_argument("--gpe", action="store_true") + parser.add_argument("--vde", action="store_true") + parser.add_argument("--ffe", action="store_true") + parser.add_argument("--show-bin", action="store_true") + args = parser.parse_args() + + main(args.eval_f0, args.gpe, args.vde, args.ffe, args.show_bin) diff --git a/fairseq/examples/speech_synthesis/evaluation/eval_sp.py b/fairseq/examples/speech_synthesis/evaluation/eval_sp.py new file mode 100644 index 0000000..702c498 --- /dev/null +++ b/fairseq/examples/speech_synthesis/evaluation/eval_sp.py @@ -0,0 +1,131 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +""" +Signal processing-based evaluation using waveforms +""" + +import csv +import numpy as np +import os.path as op + +import torch +import tqdm +from tabulate import tabulate +import torchaudio + +from examples.speech_synthesis.utils import batch_mel_spectral_distortion +from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion + + +def load_eval_spec(path): + with open(path) as f: + reader = csv.DictReader(f, delimiter='\t') + samples = list(reader) + return samples + + +def eval_distortion(samples, distortion_fn, device="cuda"): + nmiss = 0 + results = [] + for sample in tqdm.tqdm(samples): + if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]): + nmiss += 1 + results.append(None) + continue + # assume single channel + yref, sr = torchaudio.load(sample["ref"]) + ysyn, _sr = torchaudio.load(sample["syn"]) + yref, ysyn = yref[0].to(device), ysyn[0].to(device) + assert sr == _sr, f"{sr} != {_sr}" + + distortion, extra = distortion_fn([yref], [ysyn], sr, None)[0] + _, _, _, _, _, pathmap = extra + nins = torch.sum(pathmap.sum(dim=1) - 1) # extra frames in syn + ndel = torch.sum(pathmap.sum(dim=0) - 1) # missing frames from syn + results.append( + (distortion.item(), # path distortion + pathmap.size(0), # yref num frames + pathmap.size(1), # ysyn num frames + pathmap.sum().item(), # path length + nins.item(), # insertion + ndel.item(), # deletion + ) + ) + return results + + +def eval_mel_cepstral_distortion(samples, device="cuda"): + return eval_distortion(samples, batch_mel_cepstral_distortion, device) + + +def eval_mel_spectral_distortion(samples, device="cuda"): + return eval_distortion(samples, batch_mel_spectral_distortion, device) + + +def print_results(results, show_bin): + results = np.array(list(filter(lambda x: x is not None, results))) + + np.set_printoptions(precision=3) + + def _print_result(results): + dist, dur_ref, dur_syn, dur_ali, nins, ndel = results.sum(axis=0) + res = { + "nutt": len(results), + "dist": dist, + "dur_ref": int(dur_ref), + "dur_syn": int(dur_syn), + "dur_ali": int(dur_ali), + "dist_per_ref_frm": dist/dur_ref, + "dist_per_syn_frm": dist/dur_syn, + "dist_per_ali_frm": dist/dur_ali, + "ins": nins/dur_ref, + "del": ndel/dur_ref, + } + print(tabulate( + [res.values()], + res.keys(), + floatfmt=".4f" + )) + + print(">>>> ALL") + _print_result(results) + + if show_bin: + edges = [0, 200, 400, 600, 800, 1000, 2000, 4000] + for i in range(1, len(edges)): + mask = np.logical_and(results[:, 1] >= edges[i-1], + results[:, 1] < edges[i]) + if not mask.any(): + continue + bin_results = results[mask] + print(f">>>> ({edges[i-1]}, {edges[i]})") + _print_result(bin_results) + + +def main(eval_spec, mcd, msd, show_bin): + samples = load_eval_spec(eval_spec) + device = "cpu" + if mcd: + print("===== Evaluate Mean Cepstral Distortion =====") + results = eval_mel_cepstral_distortion(samples, device) + print_results(results, show_bin) + if msd: + print("===== Evaluate Mean Spectral Distortion =====") + results = eval_mel_spectral_distortion(samples, device) + print_results(results, show_bin) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("eval_spec") + parser.add_argument("--mcd", action="store_true") + parser.add_argument("--msd", action="store_true") + parser.add_argument("--show-bin", action="store_true") + args = parser.parse_args() + + main(args.eval_spec, args.mcd, args.msd, args.show_bin) diff --git a/fairseq/examples/speech_synthesis/evaluation/get_eval_manifest.py b/fairseq/examples/speech_synthesis/evaluation/get_eval_manifest.py new file mode 100644 index 0000000..44b3685 --- /dev/null +++ b/fairseq/examples/speech_synthesis/evaluation/get_eval_manifest.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import csv +from pathlib import Path + + +def main(args): + """ + `uid syn ref text` + """ + in_root = Path(args.generation_root).resolve() + ext = args.audio_format + with open(args.audio_manifest) as f, open(args.output_path, "w") as f_out: + reader = csv.DictReader( + f, delimiter="\t", quotechar=None, doublequote=False, + lineterminator="\n", quoting=csv.QUOTE_NONE + ) + header = ["id", "syn", "ref", "text", "speaker"] + f_out.write("\t".join(header) + "\n") + for row in reader: + dir_name = f"{ext}_{args.sample_rate}hz_{args.vocoder}" + id_ = row["id"] + syn = (in_root / dir_name / f"{id_}.{ext}").as_posix() + ref = row["audio"] + if args.use_resynthesized_target: + ref = (in_root / f"{dir_name}_tgt" / f"{id_}.{ext}").as_posix() + if args.eval_target: + syn = row["audio"] + sample = [id_, syn, ref, row["tgt_text"], row["speaker"]] + f_out.write("\t".join(sample) + "\n") + print(f"wrote evaluation file to {args.output_path}") + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument( + "--generation-root", help="output directory for generate_waveform.py" + ) + parser.add_argument( + "--audio-manifest", + help="used to determine the original utterance ID and text" + ) + parser.add_argument( + "--output-path", help="path to output evaluation spec file" + ) + parser.add_argument( + "--use-resynthesized-target", action="store_true", + help="use resynthesized reference instead of the original audio" + ) + parser.add_argument( + "--eval-target", action="store_true", + help="evaluate reference instead of model prediction" + ) + parser.add_argument("--vocoder", type=str, default="griffin_lim") + parser.add_argument("--sample-rate", type=int, default=22_050) + parser.add_argument("--audio-format", type=str, default="wav") + args = parser.parse_args() + + main(args) diff --git a/fairseq/examples/speech_synthesis/generate_waveform.py b/fairseq/examples/speech_synthesis/generate_waveform.py new file mode 100644 index 0000000..3b56190 --- /dev/null +++ b/fairseq/examples/speech_synthesis/generate_waveform.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import logging +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import soundfile as sf +import sys +import torch +import torchaudio + +from fairseq import checkpoint_utils, options, tasks, utils +from fairseq.logging import progress_bar +from fairseq.tasks.text_to_speech import plot_tts_output +from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDataset + + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def make_parser(): + parser = options.get_speech_generation_parser() + parser.add_argument("--dump-features", action="store_true") + parser.add_argument("--dump-waveforms", action="store_true") + parser.add_argument("--dump-attentions", action="store_true") + parser.add_argument("--dump-eos-probs", action="store_true") + parser.add_argument("--dump-plots", action="store_true") + parser.add_argument("--dump-target", action="store_true") + parser.add_argument("--output-sample-rate", default=22050, type=int) + parser.add_argument("--teacher-forcing", action="store_true") + parser.add_argument( + "--audio-format", type=str, default="wav", choices=["wav", "flac"] + ) + return parser + + +def postprocess_results( + dataset: TextToSpeechDataset, sample, hypos, resample_fn, dump_target +): + def to_np(x): + return None if x is None else x.detach().cpu().numpy() + + sample_ids = [dataset.ids[i] for i in sample["id"].tolist()] + texts = sample["src_texts"] if "src_texts" in sample else [""] * len(hypos) + attns = [to_np(hypo["attn"]) for hypo in hypos] + eos_probs = [to_np(hypo.get("eos_prob", None)) for hypo in hypos] + feat_preds = [to_np(hypo["feature"]) for hypo in hypos] + wave_preds = [to_np(resample_fn(h["waveform"])) for h in hypos] + if dump_target: + feat_targs = [to_np(hypo["targ_feature"]) for hypo in hypos] + wave_targs = [to_np(resample_fn(h["targ_waveform"])) for h in hypos] + else: + feat_targs = [None for _ in hypos] + wave_targs = [None for _ in hypos] + + return zip(sample_ids, texts, attns, eos_probs, feat_preds, wave_preds, + feat_targs, wave_targs) + + +def dump_result( + is_na_model, + args, + vocoder, + sample_id, + text, + attn, + eos_prob, + feat_pred, + wave_pred, + feat_targ, + wave_targ, +): + sample_rate = args.output_sample_rate + out_root = Path(args.results_path) + if args.dump_features: + feat_dir = out_root / "feat" + feat_dir.mkdir(exist_ok=True, parents=True) + np.save(feat_dir / f"{sample_id}.npy", feat_pred) + if args.dump_target: + feat_tgt_dir = out_root / "feat_tgt" + feat_tgt_dir.mkdir(exist_ok=True, parents=True) + np.save(feat_tgt_dir / f"{sample_id}.npy", feat_targ) + if args.dump_attentions: + attn_dir = out_root / "attn" + attn_dir.mkdir(exist_ok=True, parents=True) + np.save(attn_dir / f"{sample_id}.npy", attn.numpy()) + if args.dump_eos_probs and not is_na_model: + eos_dir = out_root / "eos" + eos_dir.mkdir(exist_ok=True, parents=True) + np.save(eos_dir / f"{sample_id}.npy", eos_prob) + + if args.dump_plots: + images = [feat_pred.T] if is_na_model else [feat_pred.T, attn] + names = ["output"] if is_na_model else ["output", "alignment"] + if feat_targ is not None: + images = [feat_targ.T] + images + names = [f"target (idx={sample_id})"] + names + if is_na_model: + plot_tts_output(images, names, attn, "alignment", suptitle=text) + else: + plot_tts_output(images, names, eos_prob, "eos prob", suptitle=text) + plot_dir = out_root / "plot" + plot_dir.mkdir(exist_ok=True, parents=True) + plt.savefig(plot_dir / f"{sample_id}.png") + plt.close() + + if args.dump_waveforms: + ext = args.audio_format + if wave_pred is not None: + wav_dir = out_root / f"{ext}_{sample_rate}hz_{vocoder}" + wav_dir.mkdir(exist_ok=True, parents=True) + sf.write(wav_dir / f"{sample_id}.{ext}", wave_pred, sample_rate) + if args.dump_target and wave_targ is not None: + wav_tgt_dir = out_root / f"{ext}_{sample_rate}hz_{vocoder}_tgt" + wav_tgt_dir.mkdir(exist_ok=True, parents=True) + sf.write(wav_tgt_dir / f"{sample_id}.{ext}", wave_targ, sample_rate) + + +def main(args): + assert(args.dump_features or args.dump_waveforms or args.dump_attentions + or args.dump_eos_probs or args.dump_plots) + if args.max_tokens is None and args.batch_size is None: + args.max_tokens = 8000 + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + task = tasks.setup_task(args) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [args.path], + task=task, + arg_overrides=ast.literal_eval(args.model_overrides), + ) + model = models[0].cuda() if use_cuda else models[0] + # use the original n_frames_per_step + task.args.n_frames_per_step = saved_cfg.task.n_frames_per_step + task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task) + + data_cfg = task.data_cfg + sample_rate = data_cfg.config.get("features", {}).get("sample_rate", 22050) + resample_fn = { + False: lambda x: x, + True: lambda x: torchaudio.sox_effects.apply_effects_tensor( + x.detach().cpu().unsqueeze(0), sample_rate, + [['rate', str(args.output_sample_rate)]] + )[0].squeeze(0) + }.get(args.output_sample_rate != sample_rate) + if args.output_sample_rate != sample_rate: + logger.info(f"resampling to {args.output_sample_rate}Hz") + + generator = task.build_generator([model], args) + itr = task.get_batch_iterator( + dataset=task.dataset(args.gen_subset), + max_tokens=args.max_tokens, + max_sentences=args.batch_size, + max_positions=(sys.maxsize, sys.maxsize), + ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=args.required_batch_size_multiple, + num_shards=args.num_shards, + shard_id=args.shard_id, + num_workers=args.num_workers, + data_buffer_size=args.data_buffer_size, + ).next_epoch_itr(shuffle=False) + + Path(args.results_path).mkdir(exist_ok=True, parents=True) + is_na_model = getattr(model, "NON_AUTOREGRESSIVE", False) + dataset = task.dataset(args.gen_subset) + vocoder = task.args.vocoder + with progress_bar.build_progress_bar(args, itr) as t: + for sample in t: + sample = utils.move_to_cuda(sample) if use_cuda else sample + hypos = generator.generate(model, sample, has_targ=args.dump_target) + for result in postprocess_results( + dataset, sample, hypos, resample_fn, args.dump_target + ): + dump_result(is_na_model, args, vocoder, *result) + + +def cli_main(): + parser = make_parser() + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/speech_synthesis/preprocessing/__init__.py b/fairseq/examples/speech_synthesis/preprocessing/__init__.py new file mode 100644 index 0000000..6264236 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py b/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py new file mode 100644 index 0000000..4e13b38 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py @@ -0,0 +1,204 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +import csv +import tempfile +from collections import defaultdict +from pathlib import Path + +import torchaudio +try: + import webrtcvad +except ImportError: + raise ImportError("Please install py-webrtcvad: pip install webrtcvad") +import pandas as pd +from tqdm import tqdm + +from examples.speech_synthesis.preprocessing.denoiser.pretrained import master64 +import examples.speech_synthesis.preprocessing.denoiser.utils as utils +from examples.speech_synthesis.preprocessing.vad import ( + frame_generator, vad_collector, read_wave, write_wave, FS_MS, THRESHOLD, + SCALE +) +from examples.speech_to_text.data_utils import save_df_to_tsv + + +log = logging.getLogger(__name__) + +PATHS = ["after_denoise", "after_vad"] +MIN_T = 0.05 + + +def generate_tmp_filename(extension="txt"): + return tempfile._get_default_tempdir() + "/" + \ + next(tempfile._get_candidate_names()) + "." + extension + + +def convert_sr(inpath, sr, output_path=None): + if not output_path: + output_path = generate_tmp_filename("wav") + cmd = f"sox {inpath} -r {sr} {output_path}" + os.system(cmd) + return output_path + + +def apply_vad(vad, inpath): + audio, sample_rate = read_wave(inpath) + frames = frame_generator(FS_MS, audio, sample_rate) + frames = list(frames) + segments = vad_collector(sample_rate, FS_MS, 300, vad, frames) + merge_segments = list() + timestamp_start = 0.0 + timestamp_end = 0.0 + # removing start, end, and long sequences of sils + for i, segment in enumerate(segments): + merge_segments.append(segment[0]) + if i and timestamp_start: + sil_duration = segment[1] - timestamp_end + if sil_duration > THRESHOLD: + merge_segments.append(int(THRESHOLD / SCALE) * (b'\x00')) + else: + merge_segments.append(int((sil_duration / SCALE)) * (b'\x00')) + timestamp_start = segment[1] + timestamp_end = segment[2] + segment = b''.join(merge_segments) + return segment, sample_rate + + +def write(wav, filename, sr=16_000): + # Normalize audio if it prevents clipping + wav = wav / max(wav.abs().max().item(), 1) + torchaudio.save(filename, wav.cpu(), sr, encoding="PCM_S", + bits_per_sample=16) + + +def process(args): + # making sure we are requested either denoise or vad + if not args.denoise and not args.vad: + log.error("No denoise or vad is requested.") + return + + log.info("Creating out directories...") + if args.denoise: + out_denoise = Path(args.output_dir).absolute().joinpath(PATHS[0]) + out_denoise.mkdir(parents=True, exist_ok=True) + if args.vad: + out_vad = Path(args.output_dir).absolute().joinpath(PATHS[1]) + out_vad.mkdir(parents=True, exist_ok=True) + + log.info("Loading pre-trained speech enhancement model...") + model = master64().to(args.device) + + log.info("Building the VAD model...") + vad = webrtcvad.Vad(int(args.vad_agg_level)) + + # preparing the output dict + output_dict = defaultdict(list) + + log.info(f"Parsing input manifest: {args.audio_manifest}") + with open(args.audio_manifest, "r") as f: + manifest_dict = csv.DictReader(f, delimiter="\t") + for row in tqdm(manifest_dict): + filename = str(row["audio"]) + + final_output = filename + keep_sample = True + n_frames = row["n_frames"] + snr = -1 + if args.denoise: + output_path_denoise = out_denoise.joinpath(Path(filename).name) + # convert to 16khz in case we use a differet sr + tmp_path = convert_sr(final_output, 16000) + + # loading audio file and generating the enhanced version + out, sr = torchaudio.load(tmp_path) + out = out.to(args.device) + estimate = model(out) + estimate = (1 - args.dry_wet) * estimate + args.dry_wet * out + write(estimate[0], str(output_path_denoise), sr) + + snr = utils.cal_snr(out, estimate) + snr = snr.cpu().detach().numpy()[0][0] + final_output = str(output_path_denoise) + + if args.vad: + output_path_vad = out_vad.joinpath(Path(filename).name) + sr = torchaudio.info(final_output).sample_rate + if sr in [16000, 32000, 48000]: + tmp_path = final_output + elif sr < 16000: + tmp_path = convert_sr(final_output, 16000) + elif sr < 32000: + tmp_path = convert_sr(final_output, 32000) + else: + tmp_path = convert_sr(final_output, 48000) + # apply VAD + segment, sample_rate = apply_vad(vad, tmp_path) + if len(segment) < sample_rate * MIN_T: + keep_sample = False + print(( + f"WARNING: skip {filename} because it is too short " + f"after VAD ({len(segment) / sample_rate} < {MIN_T})" + )) + else: + if sample_rate != sr: + tmp_path = generate_tmp_filename("wav") + write_wave(tmp_path, segment, sample_rate) + convert_sr(tmp_path, sr, + output_path=str(output_path_vad)) + else: + write_wave(str(output_path_vad), segment, sample_rate) + final_output = str(output_path_vad) + segment, _ = torchaudio.load(final_output) + n_frames = segment.size(1) + + if keep_sample: + output_dict["id"].append(row["id"]) + output_dict["audio"].append(final_output) + output_dict["n_frames"].append(n_frames) + output_dict["tgt_text"].append(row["tgt_text"]) + output_dict["speaker"].append(row["speaker"]) + output_dict["src_text"].append(row["src_text"]) + output_dict["snr"].append(snr) + + out_tsv_path = Path(args.output_dir) / Path(args.audio_manifest).name + log.info(f"Saving manifest to {out_tsv_path.as_posix()}") + save_df_to_tsv(pd.DataFrame.from_dict(output_dict), out_tsv_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio-manifest", "-i", required=True, + type=str, help="path to the input manifest.") + parser.add_argument( + "--output-dir", "-o", required=True, type=str, + help="path to the output dir. it will contain files after denoising and" + " vad" + ) + parser.add_argument("--vad-agg-level", "-a", type=int, default=2, + help="the aggresive level of the vad [0-3].") + parser.add_argument( + "--dry-wet", "-dw", type=float, default=0.01, + help="the level of linear interpolation between noisy and enhanced " + "files." + ) + parser.add_argument( + "--device", "-d", type=str, default="cpu", + help="the device to be used for the speech enhancement model: " + "cpu | cuda." + ) + parser.add_argument("--denoise", action="store_true", + help="apply a denoising") + parser.add_argument("--vad", action="store_true", help="apply a VAD") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_synthesis/preprocessing/denoiser/__init__.py b/fairseq/examples/speech_synthesis/preprocessing/denoiser/__init__.py new file mode 100644 index 0000000..6264236 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/denoiser/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py b/fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py new file mode 100644 index 0000000..3f70e73 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/denoiser/demucs.py @@ -0,0 +1,473 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import math +import time + +import torch as th +from torch import nn +from torch.nn import functional as F + +from .resample import downsample2, upsample2 +from .utils import capture_init + + +class BLSTM(nn.Module): + def __init__(self, dim, layers=2, bi=True): + super().__init__() + klass = nn.LSTM + self.lstm = klass( + bidirectional=bi, num_layers=layers, hidden_size=dim, input_size=dim + ) + self.linear = None + if bi: + self.linear = nn.Linear(2 * dim, dim) + + def forward(self, x, hidden=None): + x, hidden = self.lstm(x, hidden) + if self.linear: + x = self.linear(x) + return x, hidden + + +def rescale_conv(conv, reference): + std = conv.weight.std().detach() + scale = (std / reference)**0.5 + conv.weight.data /= scale + if conv.bias is not None: + conv.bias.data /= scale + + +def rescale_module(module, reference): + for sub in module.modules(): + if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)): + rescale_conv(sub, reference) + + +class Demucs(nn.Module): + """ + Demucs speech enhancement model. + Args: + - chin (int): number of input channels. + - chout (int): number of output channels. + - hidden (int): number of initial hidden channels. + - depth (int): number of layers. + - kernel_size (int): kernel size for each layer. + - stride (int): stride for each layer. + - causal (bool): if false, uses BiLSTM instead of LSTM. + - resample (int): amount of resampling to apply to the input/output. + Can be one of 1, 2 or 4. + - growth (float): number of channels is multiplied by this for every layer. + - max_hidden (int): maximum number of channels. Can be useful to + control the size/speed of the model. + - normalize (bool): if true, normalize the input. + - glu (bool): if true uses GLU instead of ReLU in 1x1 convolutions. + - rescale (float): controls custom weight initialization. + See https://arxiv.org/abs/1911.13254. + - floor (float): stability flooring when normalizing. + + """ + @capture_init + def __init__(self, + chin=1, + chout=1, + hidden=48, + depth=5, + kernel_size=8, + stride=4, + causal=True, + resample=4, + growth=2, + max_hidden=10_000, + normalize=True, + glu=True, + rescale=0.1, + floor=1e-3): + + super().__init__() + if resample not in [1, 2, 4]: + raise ValueError("Resample should be 1, 2 or 4.") + + self.chin = chin + self.chout = chout + self.hidden = hidden + self.depth = depth + self.kernel_size = kernel_size + self.stride = stride + self.causal = causal + self.floor = floor + self.resample = resample + self.normalize = normalize + + self.encoder = nn.ModuleList() + self.decoder = nn.ModuleList() + activation = nn.GLU(1) if glu else nn.ReLU() + ch_scale = 2 if glu else 1 + + for index in range(depth): + encode = [] + encode += [ + nn.Conv1d(chin, hidden, kernel_size, stride), + nn.ReLU(), + nn.Conv1d(hidden, hidden * ch_scale, 1), activation, + ] + self.encoder.append(nn.Sequential(*encode)) + + decode = [] + decode += [ + nn.Conv1d(hidden, ch_scale * hidden, 1), activation, + nn.ConvTranspose1d(hidden, chout, kernel_size, stride), + ] + if index > 0: + decode.append(nn.ReLU()) + self.decoder.insert(0, nn.Sequential(*decode)) + chout = hidden + chin = hidden + hidden = min(int(growth * hidden), max_hidden) + + self.lstm = BLSTM(chin, bi=not causal) + if rescale: + rescale_module(self, reference=rescale) + + def valid_length(self, length): + """ + Return the nearest valid length to use with the model so that + there is no time steps left over in a convolutions, e.g. for all + layers, size of the input - kernel_size % stride = 0. + + If the mixture has a valid length, the estimated sources + will have exactly the same length. + """ + length = math.ceil(length * self.resample) + for _ in range(self.depth): + length = math.ceil((length - self.kernel_size) / self.stride) + 1 + length = max(length, 1) + for _ in range(self.depth): + length = (length - 1) * self.stride + self.kernel_size + length = int(math.ceil(length / self.resample)) + return int(length) + + @property + def total_stride(self): + return self.stride ** self.depth // self.resample + + def forward(self, mix): + if mix.dim() == 2: + mix = mix.unsqueeze(1) + + if self.normalize: + mono = mix.mean(dim=1, keepdim=True) + std = mono.std(dim=-1, keepdim=True) + mix = mix / (self.floor + std) + else: + std = 1 + length = mix.shape[-1] + x = mix + x = F.pad(x, (0, self.valid_length(length) - length)) + if self.resample == 2: + x = upsample2(x) + elif self.resample == 4: + x = upsample2(x) + x = upsample2(x) + skips = [] + for encode in self.encoder: + x = encode(x) + skips.append(x) + x = x.permute(2, 0, 1) + x, _ = self.lstm(x) + x = x.permute(1, 2, 0) + for decode in self.decoder: + skip = skips.pop(-1) + x = x + skip[..., :x.shape[-1]] + x = decode(x) + if self.resample == 2: + x = downsample2(x) + elif self.resample == 4: + x = downsample2(x) + x = downsample2(x) + + x = x[..., :length] + return std * x + + +def fast_conv(conv, x): + """ + Faster convolution evaluation if either kernel size is 1 + or length of sequence is 1. + """ + batch, chin, length = x.shape + chout, chin, kernel = conv.weight.shape + assert batch == 1 + if kernel == 1: + x = x.view(chin, length) + out = th.addmm(conv.bias.view(-1, 1), + conv.weight.view(chout, chin), x) + elif length == kernel: + x = x.view(chin * kernel, 1) + out = th.addmm(conv.bias.view(-1, 1), + conv.weight.view(chout, chin * kernel), x) + else: + out = conv(x) + return out.view(batch, chout, -1) + + +class DemucsStreamer: + """ + Streaming implementation for Demucs. It supports being fed with any amount + of audio at a time. You will get back as much audio as possible at that + point. + + Args: + - demucs (Demucs): Demucs model. + - dry (float): amount of dry (e.g. input) signal to keep. 0 is maximum + noise removal, 1 just returns the input signal. Small values > 0 + allows to limit distortions. + - num_frames (int): number of frames to process at once. Higher values + will increase overall latency but improve the real time factor. + - resample_lookahead (int): extra lookahead used for the resampling. + - resample_buffer (int): size of the buffer of previous inputs/outputs + kept for resampling. + """ + def __init__(self, demucs, + dry=0, + num_frames=1, + resample_lookahead=64, + resample_buffer=256): + device = next(iter(demucs.parameters())).device + self.demucs = demucs + self.lstm_state = None + self.conv_state = None + self.dry = dry + self.resample_lookahead = resample_lookahead + resample_buffer = min(demucs.total_stride, resample_buffer) + self.resample_buffer = resample_buffer + self.frame_length = demucs.valid_length(1) + \ + demucs.total_stride * (num_frames - 1) + self.total_length = self.frame_length + self.resample_lookahead + self.stride = demucs.total_stride * num_frames + self.resample_in = th.zeros(demucs.chin, resample_buffer, device=device) + self.resample_out = th.zeros( + demucs.chin, resample_buffer, device=device + ) + + self.frames = 0 + self.total_time = 0 + self.variance = 0 + self.pending = th.zeros(demucs.chin, 0, device=device) + + bias = demucs.decoder[0][2].bias + weight = demucs.decoder[0][2].weight + chin, chout, kernel = weight.shape + self._bias = bias.view(-1, 1).repeat(1, kernel).view(-1, 1) + self._weight = weight.permute(1, 2, 0).contiguous() + + def reset_time_per_frame(self): + self.total_time = 0 + self.frames = 0 + + @property + def time_per_frame(self): + return self.total_time / self.frames + + def flush(self): + """ + Flush remaining audio by padding it with zero. Call this + when you have no more input and want to get back the last chunk of audio. + """ + pending_length = self.pending.shape[1] + padding = th.zeros( + self.demucs.chin, self.total_length, device=self.pending.device + ) + out = self.feed(padding) + return out[:, :pending_length] + + def feed(self, wav): + """ + Apply the model to mix using true real time evaluation. + Normalization is done online as is the resampling. + """ + begin = time.time() + demucs = self.demucs + resample_buffer = self.resample_buffer + stride = self.stride + resample = demucs.resample + + if wav.dim() != 2: + raise ValueError("input wav should be two dimensional.") + chin, _ = wav.shape + if chin != demucs.chin: + raise ValueError(f"Expected {demucs.chin} channels, got {chin}") + + self.pending = th.cat([self.pending, wav], dim=1) + outs = [] + while self.pending.shape[1] >= self.total_length: + self.frames += 1 + frame = self.pending[:, :self.total_length] + dry_signal = frame[:, :stride] + if demucs.normalize: + mono = frame.mean(0) + variance = (mono**2).mean() + self.variance = variance / self.frames + \ + (1 - 1 / self.frames) * self.variance + frame = frame / (demucs.floor + math.sqrt(self.variance)) + frame = th.cat([self.resample_in, frame], dim=-1) + self.resample_in[:] = frame[:, stride - resample_buffer:stride] + + if resample == 4: + frame = upsample2(upsample2(frame)) + elif resample == 2: + frame = upsample2(frame) + # remove pre sampling buffer + frame = frame[:, resample * resample_buffer:] + # remove extra samples after window + frame = frame[:, :resample * self.frame_length] + + out, extra = self._separate_frame(frame) + padded_out = th.cat([self.resample_out, out, extra], 1) + self.resample_out[:] = out[:, -resample_buffer:] + if resample == 4: + out = downsample2(downsample2(padded_out)) + elif resample == 2: + out = downsample2(padded_out) + else: + out = padded_out + + out = out[:, resample_buffer // resample:] + out = out[:, :stride] + + if demucs.normalize: + out *= math.sqrt(self.variance) + out = self.dry * dry_signal + (1 - self.dry) * out + outs.append(out) + self.pending = self.pending[:, stride:] + + self.total_time += time.time() - begin + if outs: + out = th.cat(outs, 1) + else: + out = th.zeros(chin, 0, device=wav.device) + return out + + def _separate_frame(self, frame): + demucs = self.demucs + skips = [] + next_state = [] + first = self.conv_state is None + stride = self.stride * demucs.resample + x = frame[None] + for idx, encode in enumerate(demucs.encoder): + stride //= demucs.stride + length = x.shape[2] + if idx == demucs.depth - 1: + # This is sligthly faster for the last conv + x = fast_conv(encode[0], x) + x = encode[1](x) + x = fast_conv(encode[2], x) + x = encode[3](x) + else: + if not first: + prev = self.conv_state.pop(0) + prev = prev[..., stride:] + tgt = (length - demucs.kernel_size) // demucs.stride + 1 + missing = tgt - prev.shape[-1] + offset = length - demucs.kernel_size - \ + demucs.stride * (missing - 1) + x = x[..., offset:] + x = encode[1](encode[0](x)) + x = fast_conv(encode[2], x) + x = encode[3](x) + if not first: + x = th.cat([prev, x], -1) + next_state.append(x) + skips.append(x) + + x = x.permute(2, 0, 1) + x, self.lstm_state = demucs.lstm(x, self.lstm_state) + x = x.permute(1, 2, 0) + # In the following, x contains only correct samples, i.e. the one + # for which each time position is covered by two window of the upper + # layer. extra contains extra samples to the right, and is used only as + # a better padding for the online resampling. + extra = None + for idx, decode in enumerate(demucs.decoder): + skip = skips.pop(-1) + x += skip[..., :x.shape[-1]] + x = fast_conv(decode[0], x) + x = decode[1](x) + + if extra is not None: + skip = skip[..., x.shape[-1]:] + extra += skip[..., :extra.shape[-1]] + extra = decode[2](decode[1](decode[0](extra))) + x = decode[2](x) + next_state.append( + x[..., -demucs.stride:] - decode[2].bias.view(-1, 1) + ) + if extra is None: + extra = x[..., -demucs.stride:] + else: + extra[..., :demucs.stride] += next_state[-1] + x = x[..., :-demucs.stride] + + if not first: + prev = self.conv_state.pop(0) + x[..., :demucs.stride] += prev + if idx != demucs.depth - 1: + x = decode[3](x) + extra = decode[3](extra) + self.conv_state = next_state + return x[0], extra[0] + + +def test(): + import argparse + parser = argparse.ArgumentParser( + "denoiser.demucs", + description="Benchmark the streaming Demucs implementation, as well as " + "checking the delta with the offline implementation.") + parser.add_argument("--depth", default=5, type=int) + parser.add_argument("--resample", default=4, type=int) + parser.add_argument("--hidden", default=48, type=int) + parser.add_argument("--sample_rate", default=16000, type=float) + parser.add_argument("--device", default="cpu") + parser.add_argument("-t", "--num_threads", type=int) + parser.add_argument("-f", "--num_frames", type=int, default=1) + args = parser.parse_args() + if args.num_threads: + th.set_num_threads(args.num_threads) + sr = args.sample_rate + sr_ms = sr / 1000 + demucs = Demucs( + depth=args.depth, hidden=args.hidden, resample=args.resample + ).to(args.device) + x = th.randn(1, int(sr * 4)).to(args.device) + out = demucs(x[None])[0] + streamer = DemucsStreamer(demucs, num_frames=args.num_frames) + out_rt = [] + frame_size = streamer.total_length + with th.no_grad(): + while x.shape[1] > 0: + out_rt.append(streamer.feed(x[:, :frame_size])) + x = x[:, frame_size:] + frame_size = streamer.demucs.total_stride + out_rt.append(streamer.flush()) + out_rt = th.cat(out_rt, 1) + model_size = sum(p.numel() for p in demucs.parameters()) * 4 / 2**20 + initial_lag = streamer.total_length / sr_ms + tpf = 1000 * streamer.time_per_frame + print(f"model size: {model_size:.1f}MB, ", end='') + print(f"delta batch/streaming: {th.norm(out - out_rt) / th.norm(out):.2%}") + print(f"initial lag: {initial_lag:.1f}ms, ", end='') + print(f"stride: {streamer.stride * args.num_frames / sr_ms:.1f}ms") + print(f"time per frame: {tpf:.1f}ms, ", end='') + rtf = (1000 * streamer.time_per_frame) / (streamer.stride / sr_ms) + print(f"RTF: {rtf:.2f}") + print(f"Total lag with computation: {initial_lag + tpf:.1f}ms") + + +if __name__ == "__main__": + test() diff --git a/fairseq/examples/speech_synthesis/preprocessing/denoiser/pretrained.py b/fairseq/examples/speech_synthesis/preprocessing/denoiser/pretrained.py new file mode 100644 index 0000000..2fa8460 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/denoiser/pretrained.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import logging + +import torch.hub + +from .demucs import Demucs +from .utils import deserialize_model + +logger = logging.getLogger(__name__) +ROOT = "https://dl.fbaipublicfiles.com/adiyoss/denoiser/" +DNS_48_URL = ROOT + "dns48-11decc9d8e3f0998.th" +DNS_64_URL = ROOT + "dns64-a7761ff99a7d5bb6.th" +MASTER_64_URL = ROOT + "master64-8a5dfb4bb92753dd.th" + + +def _demucs(pretrained, url, **kwargs): + model = Demucs(**kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu') + model.load_state_dict(state_dict) + return model + + +def dns48(pretrained=True): + return _demucs(pretrained, DNS_48_URL, hidden=48) + + +def dns64(pretrained=True): + return _demucs(pretrained, DNS_64_URL, hidden=64) + + +def master64(pretrained=True): + return _demucs(pretrained, MASTER_64_URL, hidden=64) + + +def add_model_flags(parser): + group = parser.add_mutually_exclusive_group(required=False) + group.add_argument( + "-m", "--model_path", help="Path to local trained model." + ) + group.add_argument( + "--dns48", action="store_true", + help="Use pre-trained real time H=48 model trained on DNS." + ) + group.add_argument( + "--dns64", action="store_true", + help="Use pre-trained real time H=64 model trained on DNS." + ) + group.add_argument( + "--master64", action="store_true", + help="Use pre-trained real time H=64 model trained on DNS and Valentini." + ) + + +def get_model(args): + """ + Load local model package or torchhub pre-trained model. + """ + if args.model_path: + logger.info("Loading model from %s", args.model_path) + pkg = torch.load(args.model_path) + model = deserialize_model(pkg) + elif args.dns64: + logger.info("Loading pre-trained real time H=64 model trained on DNS.") + model = dns64() + elif args.master64: + logger.info( + "Loading pre-trained real time H=64 model trained on DNS and Valentini." + ) + model = master64() + else: + logger.info("Loading pre-trained real time H=48 model trained on DNS.") + model = dns48() + logger.debug(model) + return model diff --git a/fairseq/examples/speech_synthesis/preprocessing/denoiser/resample.py b/fairseq/examples/speech_synthesis/preprocessing/denoiser/resample.py new file mode 100644 index 0000000..1222add --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/denoiser/resample.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import math + +import torch as th +from torch.nn import functional as F + + +def sinc(t): + """sinc. + + :param t: the input tensor + """ + return th.where(t == 0, th.tensor(1., device=t.device, dtype=t.dtype), + th.sin(t) / t) + + +def kernel_upsample2(zeros=56): + """kernel_upsample2. + + """ + win = th.hann_window(4 * zeros + 1, periodic=False) + winodd = win[1::2] + t = th.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros) + t *= math.pi + kernel = (sinc(t) * winodd).view(1, 1, -1) + return kernel + + +def upsample2(x, zeros=56): + """ + Upsampling the input by 2 using sinc interpolation. + Smith, Julius, and Phil Gossett. "A flexible sampling-rate conversion method." + ICASSP'84. IEEE International Conference on Acoustics, Speech, and Signal Processing. + Vol. 9. IEEE, 1984. + """ + *other, time = x.shape + kernel = kernel_upsample2(zeros).to(x) + out = F.conv1d(x.view(-1, 1, time), kernel, padding=zeros)[..., 1:].view( + *other, time + ) + y = th.stack([x, out], dim=-1) + return y.view(*other, -1) + + +def kernel_downsample2(zeros=56): + """kernel_downsample2. + + """ + win = th.hann_window(4 * zeros + 1, periodic=False) + winodd = win[1::2] + t = th.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros) + t.mul_(math.pi) + kernel = (sinc(t) * winodd).view(1, 1, -1) + return kernel + + +def downsample2(x, zeros=56): + """ + Downsampling the input by 2 using sinc interpolation. + Smith, Julius, and Phil Gossett. "A flexible sampling-rate conversion method." + ICASSP'84. IEEE International Conference on Acoustics, Speech, and Signal Processing. + Vol. 9. IEEE, 1984. + """ + if x.shape[-1] % 2 != 0: + x = F.pad(x, (0, 1)) + xeven = x[..., ::2] + xodd = x[..., 1::2] + *other, time = xodd.shape + kernel = kernel_downsample2(zeros).to(x) + out = xeven + F.conv1d( + xodd.view(-1, 1, time), kernel, padding=zeros + )[..., :-1].view(*other, time) + return out.view(*other, -1).mul(0.5) diff --git a/fairseq/examples/speech_synthesis/preprocessing/denoiser/utils.py b/fairseq/examples/speech_synthesis/preprocessing/denoiser/utils.py new file mode 100644 index 0000000..734d047 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/denoiser/utils.py @@ -0,0 +1,176 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import functools +import logging +from contextlib import contextmanager +import inspect +import time + +logger = logging.getLogger(__name__) + +EPS = 1e-8 + + +def capture_init(init): + """capture_init. + + Decorate `__init__` with this, and you can then + recover the *args and **kwargs passed to it in `self._init_args_kwargs` + """ + @functools.wraps(init) + def __init__(self, *args, **kwargs): + self._init_args_kwargs = (args, kwargs) + init(self, *args, **kwargs) + + return __init__ + + +def deserialize_model(package, strict=False): + """deserialize_model. + + """ + klass = package['class'] + if strict: + model = klass(*package['args'], **package['kwargs']) + else: + sig = inspect.signature(klass) + kw = package['kwargs'] + for key in list(kw): + if key not in sig.parameters: + logger.warning("Dropping inexistant parameter %s", key) + del kw[key] + model = klass(*package['args'], **kw) + model.load_state_dict(package['state']) + return model + + +def copy_state(state): + return {k: v.cpu().clone() for k, v in state.items()} + + +def serialize_model(model): + args, kwargs = model._init_args_kwargs + state = copy_state(model.state_dict()) + return {"class": model.__class__, "args": args, "kwargs": kwargs, "state": state} + + +@contextmanager +def swap_state(model, state): + """ + Context manager that swaps the state of a model, e.g: + + # model is in old state + with swap_state(model, new_state): + # model in new state + # model back to old state + """ + old_state = copy_state(model.state_dict()) + model.load_state_dict(state) + try: + yield + finally: + model.load_state_dict(old_state) + + +def pull_metric(history, name): + out = [] + for metrics in history: + if name in metrics: + out.append(metrics[name]) + return out + + +class LogProgress: + """ + Sort of like tqdm but using log lines and not as real time. + Args: + - logger: logger obtained from `logging.getLogger`, + - iterable: iterable object to wrap + - updates (int): number of lines that will be printed, e.g. + if `updates=5`, log every 1/5th of the total length. + - total (int): length of the iterable, in case it does not support + `len`. + - name (str): prefix to use in the log. + - level: logging level (like `logging.INFO`). + """ + def __init__(self, + logger, + iterable, + updates=5, + total=None, + name="LogProgress", + level=logging.INFO): + self.iterable = iterable + self.total = total or len(iterable) + self.updates = updates + self.name = name + self.logger = logger + self.level = level + + def update(self, **infos): + self._infos = infos + + def __iter__(self): + self._iterator = iter(self.iterable) + self._index = -1 + self._infos = {} + self._begin = time.time() + return self + + def __next__(self): + self._index += 1 + try: + value = next(self._iterator) + except StopIteration: + raise + else: + return value + finally: + log_every = max(1, self.total // self.updates) + # logging is delayed by 1 it, in order to have the metrics from update + if self._index >= 1 and self._index % log_every == 0: + self._log() + + def _log(self): + self._speed = (1 + self._index) / (time.time() - self._begin) + infos = " | ".join(f"{k.capitalize()} {v}" for k, v in self._infos.items()) + if self._speed < 1e-4: + speed = "oo sec/it" + elif self._speed < 0.1: + speed = f"{1/self._speed:.1f} sec/it" + else: + speed = f"{self._speed:.1f} it/sec" + out = f"{self.name} | {self._index}/{self.total} | {speed}" + if infos: + out += " | " + infos + self.logger.log(self.level, out) + + +def colorize(text, color): + """ + Display text with some ANSI color in the terminal. + """ + code = f"\033[{color}m" + restore = "\033[0m" + return "".join([code, text, restore]) + + +def bold(text): + """ + Display text in bold in the terminal. + """ + return colorize(text, "1") + + +def cal_snr(lbl, est): + import torch + y = 10.0 * torch.log10( + torch.sum(lbl**2, dim=-1) / (torch.sum((est-lbl)**2, dim=-1) + EPS) + + EPS + ) + return y diff --git a/fairseq/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py b/fairseq/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py new file mode 100644 index 0000000..a302546 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py @@ -0,0 +1,140 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +from collections import defaultdict +from typing import List, Dict, Tuple + +import pandas as pd +import numpy as np +import torchaudio +from tqdm import tqdm + +from examples.speech_to_text.data_utils import load_df_from_tsv, save_df_to_tsv + + +log = logging.getLogger(__name__) + +SPLITS = ["train", "dev", "test"] + + +def get_top_n( + root: Path, n_speakers: int = 10, min_n_tokens: int = 5 +) -> pd.DataFrame: + df = load_df_from_tsv(root / "validated.tsv") + df["n_tokens"] = [len(s.split()) for s in df["sentence"]] + df = df[df["n_tokens"] >= min_n_tokens] + df["n_frames"] = [ + torchaudio.info((root / "clips" / p).as_posix()).num_frames + for p in tqdm(df["path"]) + ] + df["id"] = [Path(p).stem for p in df["path"]] + total_duration_ms = df.groupby("client_id")["n_frames"].agg(["sum"]) + total_duration_ms = total_duration_ms.sort_values("sum", ascending=False) + + top_n_total_duration_ms = total_duration_ms.head(n_speakers) + top_n_client_ids = set(top_n_total_duration_ms.index.tolist()) + df_top_n = df[df["client_id"].isin(top_n_client_ids)] + return df_top_n + + +def get_splits( + df, train_split_ratio=0.99, speaker_in_all_splits=False, rand_seed=0 +) -> Tuple[Dict[str, str], List[str]]: + np.random.seed(rand_seed) + dev_split_ratio = (1. - train_split_ratio) / 3 + grouped = list(df.groupby("client_id")) + id_to_split = {} + for _, cur_df in tqdm(grouped): + cur_n_examples = len(cur_df) + if speaker_in_all_splits and cur_n_examples < 3: + continue + cur_n_train = int(cur_n_examples * train_split_ratio) + cur_n_dev = int(cur_n_examples * dev_split_ratio) + cur_n_test = cur_n_examples - cur_n_dev - cur_n_train + if speaker_in_all_splits and cur_n_dev * cur_n_test == 0: + cur_n_dev, cur_n_test = 1, 1 + cur_n_train = cur_n_examples - cur_n_dev - cur_n_test + cur_indices = cur_df.index.tolist() + cur_shuffled_indices = np.random.permutation(cur_n_examples) + cur_shuffled_indices = [cur_indices[i] for i in cur_shuffled_indices] + cur_indices_by_split = { + "train": cur_shuffled_indices[:cur_n_train], + "dev": cur_shuffled_indices[cur_n_train: cur_n_train + cur_n_dev], + "test": cur_shuffled_indices[cur_n_train + cur_n_dev:] + } + for split in SPLITS: + for i in cur_indices_by_split[split]: + id_ = df["id"].loc[i] + id_to_split[id_] = split + return id_to_split, sorted(df["client_id"].unique()) + + +def convert_to_wav(root: Path, filenames: List[str], target_sr=16_000): + out_root = root / "wav" + out_root.mkdir(exist_ok=True, parents=True) + print("Converting to WAV...") + for n in tqdm(filenames): + in_path = (root / "clips" / n).as_posix() + waveform, sr = torchaudio.load(in_path) + converted, converted_sr = torchaudio.sox_effects.apply_effects_tensor( + waveform, sr, [["rate", str(target_sr)], ["channels", "1"]] + ) + out_path = (out_root / Path(n).with_suffix(".wav").name).as_posix() + torchaudio.save(out_path, converted, converted_sr, encoding="PCM_S", + bits_per_sample=16) + + +def process(args): + data_root = Path(args.data_root).absolute() / args.lang + + # Generate TSV manifest + print("Generating manifest...") + + df_top_n = get_top_n(data_root) + id_to_split, speakers = get_splits(df_top_n) + + if args.convert_to_wav: + convert_to_wav(data_root, df_top_n["path"].tolist()) + + manifest_by_split = {split: defaultdict(list) for split in SPLITS} + for sample in tqdm(df_top_n.to_dict(orient="index").values()): + sample_id = sample["id"] + split = id_to_split[sample_id] + manifest_by_split[split]["id"].append(sample_id) + if args.convert_to_wav: + audio_path = data_root / "wav" / f"{sample_id}.wav" + else: + audio_path = data_root / "clips" / f"{sample_id}.mp3" + manifest_by_split[split]["audio"].append(audio_path.as_posix()) + manifest_by_split[split]["n_frames"].append(sample["n_frames"]) + manifest_by_split[split]["tgt_text"].append(sample["sentence"]) + manifest_by_split[split]["speaker"].append(sample["client_id"]) + manifest_by_split[split]["src_text"].append(sample["sentence"]) + + output_root = Path(args.output_manifest_root).absolute() + output_root.mkdir(parents=True, exist_ok=True) + for split in SPLITS: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[split]), + output_root / f"{split}.audio.tsv" + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument("--output-manifest-root", "-m", required=True, type=str) + parser.add_argument("--lang", "-l", required=True, type=str) + parser.add_argument("--convert-to-wav", action="store_true") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_synthesis/preprocessing/get_feature_manifest.py b/fairseq/examples/speech_synthesis/preprocessing/get_feature_manifest.py new file mode 100644 index 0000000..4a1e119 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/get_feature_manifest.py @@ -0,0 +1,262 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import shutil +from tempfile import NamedTemporaryFile +from collections import Counter, defaultdict + +import pandas as pd +import torchaudio +from tqdm import tqdm + +from fairseq.data.audio.audio_utils import convert_waveform +from examples.speech_to_text.data_utils import ( + create_zip, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_tsv_to_dicts, + save_df_to_tsv +) +from examples.speech_synthesis.data_utils import ( + extract_logmel_spectrogram, extract_pitch, extract_energy, get_global_cmvn, + ipa_phonemize, get_mfa_alignment, get_unit_alignment, + get_feature_value_min_max +) + + +log = logging.getLogger(__name__) + + +def process(args): + assert "train" in args.splits + out_root = Path(args.output_root).absolute() + out_root.mkdir(exist_ok=True) + + print("Fetching data...") + audio_manifest_root = Path(args.audio_manifest_root).absolute() + samples = [] + for s in args.splits: + for e in load_tsv_to_dicts(audio_manifest_root / f"{s}.audio.tsv"): + e["split"] = s + samples.append(e) + sample_ids = [s["id"] for s in samples] + + # Get alignment info + id_to_alignment = None + if args.textgrid_zip is not None: + assert args.id_to_units_tsv is None + id_to_alignment = get_mfa_alignment( + args.textgrid_zip, sample_ids, args.sample_rate, args.hop_length + ) + elif args.id_to_units_tsv is not None: + # assume identical hop length on the unit sequence + id_to_alignment = get_unit_alignment(args.id_to_units_tsv, sample_ids) + + # Extract features and pack features into ZIP + feature_name = "logmelspec80" + zip_path = out_root / f"{feature_name}.zip" + pitch_zip_path = out_root / "pitch.zip" + energy_zip_path = out_root / "energy.zip" + gcmvn_npz_path = out_root / "gcmvn_stats.npz" + if zip_path.exists() and gcmvn_npz_path.exists(): + print(f"{zip_path} and {gcmvn_npz_path} exist.") + else: + feature_root = out_root / feature_name + feature_root.mkdir(exist_ok=True) + pitch_root = out_root / "pitch" + energy_root = out_root / "energy" + if args.add_fastspeech_targets: + pitch_root.mkdir(exist_ok=True) + energy_root.mkdir(exist_ok=True) + print("Extracting Mel spectrogram features...") + for sample in tqdm(samples): + waveform, sample_rate = torchaudio.load(sample["audio"]) + waveform, sample_rate = convert_waveform( + waveform, sample_rate, normalize_volume=args.normalize_volume, + to_sample_rate=args.sample_rate + ) + sample_id = sample["id"] + target_length = None + if id_to_alignment is not None: + a = id_to_alignment[sample_id] + target_length = sum(a.frame_durations) + if a.start_sec is not None and a.end_sec is not None: + start_frame = int(a.start_sec * sample_rate) + end_frame = int(a.end_sec * sample_rate) + waveform = waveform[:, start_frame: end_frame] + extract_logmel_spectrogram( + waveform, sample_rate, feature_root / f"{sample_id}.npy", + win_length=args.win_length, hop_length=args.hop_length, + n_fft=args.n_fft, n_mels=args.n_mels, f_min=args.f_min, + f_max=args.f_max, target_length=target_length + ) + if args.add_fastspeech_targets: + assert id_to_alignment is not None + extract_pitch( + waveform, sample_rate, pitch_root / f"{sample_id}.npy", + hop_length=args.hop_length, log_scale=True, + phoneme_durations=id_to_alignment[sample_id].frame_durations + ) + extract_energy( + waveform, energy_root / f"{sample_id}.npy", + hop_length=args.hop_length, n_fft=args.n_fft, + log_scale=True, + phoneme_durations=id_to_alignment[sample_id].frame_durations + ) + print("ZIPing features...") + create_zip(feature_root, zip_path) + get_global_cmvn(feature_root, gcmvn_npz_path) + shutil.rmtree(feature_root) + if args.add_fastspeech_targets: + create_zip(pitch_root, pitch_zip_path) + shutil.rmtree(pitch_root) + create_zip(energy_root, energy_zip_path) + shutil.rmtree(energy_root) + + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + pitch_paths, pitch_lengths, energy_paths, energy_lengths = [None] * 4 + if args.add_fastspeech_targets: + pitch_paths, pitch_lengths = get_zip_manifest(pitch_zip_path) + energy_paths, energy_lengths = get_zip_manifest(energy_zip_path) + # Generate TSV manifest + print("Generating manifest...") + id_to_cer = None + if args.cer_threshold is not None: + assert Path(args.cer_tsv_path).is_file() + id_to_cer = { + x["id"]: x["uer"] for x in load_tsv_to_dicts(args.cer_tsv_path) + } + manifest_by_split = {split: defaultdict(list) for split in args.splits} + for sample in tqdm(samples): + sample_id, split = sample["id"], sample["split"] + + if args.snr_threshold is not None and "snr" in sample \ + and sample["snr"] < args.snr_threshold: + continue + if args.cer_threshold is not None \ + and id_to_cer[sample_id] > args.cer_threhold: + continue + + normalized_utt = sample["tgt_text"] + if id_to_alignment is not None: + normalized_utt = " ".join(id_to_alignment[sample_id].tokens) + elif args.ipa_vocab: + normalized_utt = ipa_phonemize( + normalized_utt, lang=args.lang, use_g2p=args.use_g2p + ) + manifest_by_split[split]["id"].append(sample_id) + manifest_by_split[split]["audio"].append(audio_paths[sample_id]) + manifest_by_split[split]["n_frames"].append(audio_lengths[sample_id]) + manifest_by_split[split]["tgt_text"].append(normalized_utt) + manifest_by_split[split]["speaker"].append(sample["speaker"]) + manifest_by_split[split]["src_text"].append(sample["src_text"]) + if args.add_fastspeech_targets: + assert id_to_alignment is not None + duration = " ".join( + str(d) for d in id_to_alignment[sample_id].frame_durations + ) + manifest_by_split[split]["duration"].append(duration) + manifest_by_split[split]["pitch"].append(pitch_paths[sample_id]) + manifest_by_split[split]["energy"].append(energy_paths[sample_id]) + for split in args.splits: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[split]), + out_root / f"{split}.tsv" + ) + # Generate vocab + vocab_name, spm_filename = None, None + if id_to_alignment is not None or args.ipa_vocab: + vocab = Counter() + for t in manifest_by_split["train"]["tgt_text"]: + vocab.update(t.split(" ")) + vocab_name = "vocab.txt" + with open(out_root / vocab_name, "w") as f: + for s, c in vocab.most_common(): + f.write(f"{s} {c}\n") + else: + spm_filename_prefix = "spm_char" + spm_filename = f"{spm_filename_prefix}.model" + with NamedTemporaryFile(mode="w") as f: + for t in manifest_by_split["train"]["tgt_text"]: + f.write(t + "\n") + f.flush() # needed to ensure gen_vocab sees dumped text + gen_vocab(Path(f.name), out_root / spm_filename_prefix, "char") + # Generate speaker list + speakers = sorted({sample["speaker"] for sample in samples}) + speakers_path = out_root / "speakers.txt" + with open(speakers_path, "w") as f: + for speaker in speakers: + f.write(f"{speaker}\n") + # Generate config YAML + win_len_t = args.win_length / args.sample_rate + hop_len_t = args.hop_length / args.sample_rate + extra = { + "sample_rate": args.sample_rate, + "features": { + "type": "spectrogram+melscale+log", + "eps": 1e-5, "n_mels": args.n_mels, "n_fft": args.n_fft, + "window_fn": "hann", "win_length": args.win_length, + "hop_length": args.hop_length, "sample_rate": args.sample_rate, + "win_len_t": win_len_t, "hop_len_t": hop_len_t, + "f_min": args.f_min, "f_max": args.f_max, + "n_stft": args.n_fft // 2 + 1 + } + } + if len(speakers) > 1: + extra["speaker_set_filename"] = "speakers.txt" + if args.add_fastspeech_targets: + pitch_min, pitch_max = get_feature_value_min_max( + [(out_root / n).as_posix() for n in pitch_paths.values()] + ) + energy_min, energy_max = get_feature_value_min_max( + [(out_root / n).as_posix() for n in energy_paths.values()] + ) + extra["features"]["pitch_min"] = pitch_min + extra["features"]["pitch_max"] = pitch_max + extra["features"]["energy_min"] = energy_min + extra["features"]["energy_max"] = energy_max + gen_config_yaml( + out_root, spm_filename=spm_filename, vocab_name=vocab_name, + audio_root=out_root.as_posix(), input_channels=None, + input_feat_per_channel=None, specaugment_policy=None, + cmvn_type="global", gcmvn_path=gcmvn_npz_path, extra=extra + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio-manifest-root", "-m", required=True, type=str) + parser.add_argument("--output-root", "-o", required=True, type=str) + parser.add_argument("--splits", "-s", type=str, nargs="+", + default=["train", "dev", "test"]) + parser.add_argument("--ipa-vocab", action="store_true") + parser.add_argument("--use-g2p", action="store_true") + parser.add_argument("--lang", type=str, default="en-us") + parser.add_argument("--win-length", type=int, default=1024) + parser.add_argument("--hop-length", type=int, default=256) + parser.add_argument("--n-fft", type=int, default=1024) + parser.add_argument("--n-mels", type=int, default=80) + parser.add_argument("--f-min", type=int, default=20) + parser.add_argument("--f-max", type=int, default=8000) + parser.add_argument("--sample-rate", type=int, default=22050) + parser.add_argument("--normalize-volume", "-n", action="store_true") + parser.add_argument("--textgrid-zip", type=str, default=None) + parser.add_argument("--id-to-units-tsv", type=str, default=None) + parser.add_argument("--add-fastspeech-targets", action="store_true") + parser.add_argument("--snr-threshold", type=float, default=None) + parser.add_argument("--cer-threshold", type=float, default=None) + parser.add_argument("--cer-tsv-path", type=str, default="") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py b/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py new file mode 100644 index 0000000..7ec1fb7 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +from collections import defaultdict + +import pandas as pd +from torchaudio.datasets import LJSPEECH +from tqdm import tqdm + +from examples.speech_to_text.data_utils import save_df_to_tsv + + +log = logging.getLogger(__name__) + +SPLITS = ["train", "dev", "test"] + + +def process(args): + out_root = Path(args.output_data_root).absolute() + out_root.mkdir(parents=True, exist_ok=True) + + # Generate TSV manifest + print("Generating manifest...") + # following FastSpeech's splits + dataset = LJSPEECH(out_root.as_posix(), download=True) + id_to_split = {} + for x in dataset._flist: + id_ = x[0] + speaker = id_.split("-")[0] + id_to_split[id_] = { + "LJ001": "test", "LJ002": "test", "LJ003": "dev" + }.get(speaker, "train") + manifest_by_split = {split: defaultdict(list) for split in SPLITS} + progress = tqdm(enumerate(dataset), total=len(dataset)) + for i, (waveform, _, utt, normalized_utt) in progress: + sample_id = dataset._flist[i][0] + split = id_to_split[sample_id] + manifest_by_split[split]["id"].append(sample_id) + audio_path = f"{dataset._path}/{sample_id}.wav" + manifest_by_split[split]["audio"].append(audio_path) + manifest_by_split[split]["n_frames"].append(len(waveform[0])) + manifest_by_split[split]["tgt_text"].append(normalized_utt) + manifest_by_split[split]["speaker"].append("ljspeech") + manifest_by_split[split]["src_text"].append(utt) + + manifest_root = Path(args.output_manifest_root).absolute() + manifest_root.mkdir(parents=True, exist_ok=True) + for split in SPLITS: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[split]), + manifest_root / f"{split}.audio.tsv" + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output-data-root", "-d", required=True, type=str) + parser.add_argument("--output-manifest-root", "-m", required=True, type=str) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_synthesis/preprocessing/get_speaker_embedding.py b/fairseq/examples/speech_synthesis/preprocessing/get_speaker_embedding.py new file mode 100644 index 0000000..0e3e4c5 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/get_speaker_embedding.py @@ -0,0 +1,89 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import argparse +from collections import defaultdict +from itertools import chain +from pathlib import Path + +import numpy as np +import torchaudio +import torchaudio.sox_effects as ta_sox +import yaml +from tqdm import tqdm + +from examples.speech_to_text.data_utils import load_tsv_to_dicts +from examples.speech_synthesis.preprocessing.speaker_embedder import SpkrEmbedder + + +def extract_embedding(audio_path, embedder): + wav, sr = torchaudio.load(audio_path) # 2D + if sr != embedder.RATE: + wav, sr = ta_sox.apply_effects_tensor( + wav, sr, [["rate", str(embedder.RATE)]] + ) + try: + emb = embedder([wav[0].cuda().float()]).cpu().numpy() + except RuntimeError: + emb = None + return emb + + +def process(args): + print("Fetching data...") + raw_manifest_root = Path(args.raw_manifest_root).absolute() + samples = [load_tsv_to_dicts(raw_manifest_root / (s + ".tsv")) + for s in args.splits] + samples = list(chain(*samples)) + with open(args.config, "r") as f: + config = yaml.load(f, Loader=yaml.FullLoader) + with open(f"{config['audio_root']}/{config['speaker_set_filename']}") as f: + speaker_to_id = {r.strip(): i for i, r in enumerate(f)} + + embedder = SpkrEmbedder(args.ckpt).cuda() + speaker_to_cnt = defaultdict(float) + speaker_to_emb = defaultdict(float) + for sample in tqdm(samples, desc="extract emb"): + emb = extract_embedding(sample["audio"], embedder) + if emb is not None: + speaker_to_cnt[sample["speaker"]] += 1 + speaker_to_emb[sample["speaker"]] += emb + if len(speaker_to_emb) != len(speaker_to_id): + missed = set(speaker_to_id) - set(speaker_to_emb.keys()) + print( + f"WARNING: missing embeddings for {len(missed)} speaker:\n{missed}" + ) + speaker_emb_mat = np.zeros((len(speaker_to_id), len(emb)), float) + for speaker in speaker_to_emb: + idx = speaker_to_id[speaker] + emb = speaker_to_emb[speaker] + cnt = speaker_to_cnt[speaker] + speaker_emb_mat[idx, :] = emb / cnt + speaker_emb_name = "speaker_emb.npy" + speaker_emb_path = f"{config['audio_root']}/{speaker_emb_name}" + np.save(speaker_emb_path, speaker_emb_mat) + config["speaker_emb_filename"] = speaker_emb_name + + with open(args.new_config, "w") as f: + yaml.dump(config, f) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--raw-manifest-root", "-m", required=True, type=str) + parser.add_argument("--splits", "-s", type=str, nargs="+", + default=["train"]) + parser.add_argument("--config", "-c", required=True, type=str) + parser.add_argument("--new-config", "-n", required=True, type=str) + parser.add_argument("--ckpt", required=True, type=str, + help="speaker embedder checkpoint") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py b/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py new file mode 100644 index 0000000..7afa40f --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import numpy as np +import re +from pathlib import Path +from collections import defaultdict + +import pandas as pd +from torchaudio.datasets import VCTK +from tqdm import tqdm + +from examples.speech_to_text.data_utils import save_df_to_tsv + + +log = logging.getLogger(__name__) + +SPLITS = ["train", "dev", "test"] + + +def normalize_text(text): + return re.sub(r"[^a-zA-Z.?!,'\- ]", '', text) + + +def process(args): + out_root = Path(args.output_data_root).absolute() + out_root.mkdir(parents=True, exist_ok=True) + + # Generate TSV manifest + print("Generating manifest...") + dataset = VCTK(out_root.as_posix(), download=False) + ids = list(dataset._walker) + np.random.seed(args.seed) + np.random.shuffle(ids) + n_train = len(ids) - args.n_dev - args.n_test + _split = ["train"] * n_train + ["dev"] * args.n_dev + ["test"] * args.n_test + id_to_split = dict(zip(ids, _split)) + manifest_by_split = {split: defaultdict(list) for split in SPLITS} + progress = tqdm(enumerate(dataset), total=len(dataset)) + for i, (waveform, _, text, speaker_id, _) in progress: + sample_id = dataset._walker[i] + _split = id_to_split[sample_id] + audio_dir = Path(dataset._path) / dataset._folder_audio / speaker_id + audio_path = audio_dir / f"{sample_id}.wav" + text = normalize_text(text) + manifest_by_split[_split]["id"].append(sample_id) + manifest_by_split[_split]["audio"].append(audio_path.as_posix()) + manifest_by_split[_split]["n_frames"].append(len(waveform[0])) + manifest_by_split[_split]["tgt_text"].append(text) + manifest_by_split[_split]["speaker"].append(speaker_id) + manifest_by_split[_split]["src_text"].append(text) + + manifest_root = Path(args.output_manifest_root).absolute() + manifest_root.mkdir(parents=True, exist_ok=True) + for _split in SPLITS: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[_split]), + manifest_root / f"{_split}.audio.tsv" + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output-data-root", "-d", required=True, type=str) + parser.add_argument("--output-manifest-root", "-m", required=True, type=str) + parser.add_argument("--n-dev", default=50, type=int) + parser.add_argument("--n-test", default=100, type=int) + parser.add_argument("--seed", "-s", default=1234, type=int) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py b/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py new file mode 100644 index 0000000..3b17867 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py @@ -0,0 +1,135 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import librosa +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data +import torchaudio + + +EMBEDDER_PARAMS = { + 'num_mels': 40, + 'n_fft': 512, + 'emb_dim': 256, + 'lstm_hidden': 768, + 'lstm_layers': 3, + 'window': 80, + 'stride': 40, +} + + +def set_requires_grad(nets, requires_grad=False): + """Set requies_grad=Fasle for all the networks to avoid unnecessary + computations + Parameters: + nets (network list) -- a list of networks + requires_grad (bool) -- whether the networks require gradients or not + """ + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad + + +class LinearNorm(nn.Module): + def __init__(self, hp): + super(LinearNorm, self).__init__() + self.linear_layer = nn.Linear(hp["lstm_hidden"], hp["emb_dim"]) + + def forward(self, x): + return self.linear_layer(x) + + +class SpeechEmbedder(nn.Module): + def __init__(self, hp): + super(SpeechEmbedder, self).__init__() + self.lstm = nn.LSTM(hp["num_mels"], + hp["lstm_hidden"], + num_layers=hp["lstm_layers"], + batch_first=True) + self.proj = LinearNorm(hp) + self.hp = hp + + def forward(self, mel): + # (num_mels, T) -> (num_mels, T', window) + mels = mel.unfold(1, self.hp["window"], self.hp["stride"]) + mels = mels.permute(1, 2, 0) # (T', window, num_mels) + x, _ = self.lstm(mels) # (T', window, lstm_hidden) + x = x[:, -1, :] # (T', lstm_hidden), use last frame only + x = self.proj(x) # (T', emb_dim) + x = x / torch.norm(x, p=2, dim=1, keepdim=True) # (T', emb_dim) + + x = x.mean(dim=0) + if x.norm(p=2) != 0: + x = x / x.norm(p=2) + return x + + +class SpkrEmbedder(nn.Module): + RATE = 16000 + + def __init__( + self, + embedder_path, + embedder_params=EMBEDDER_PARAMS, + rate=16000, + hop_length=160, + win_length=400, + pad=False, + ): + super(SpkrEmbedder, self).__init__() + embedder_pt = torch.load(embedder_path, map_location="cpu") + self.embedder = SpeechEmbedder(embedder_params) + self.embedder.load_state_dict(embedder_pt) + self.embedder.eval() + set_requires_grad(self.embedder, requires_grad=False) + self.embedder_params = embedder_params + + self.register_buffer('mel_basis', torch.from_numpy( + librosa.filters.mel( + sr=self.RATE, + n_fft=self.embedder_params["n_fft"], + n_mels=self.embedder_params["num_mels"]) + ) + ) + + self.resample = None + if rate != self.RATE: + self.resample = torchaudio.transforms.Resample(rate, self.RATE) + self.hop_length = hop_length + self.win_length = win_length + self.pad = pad + + def get_mel(self, y): + if self.pad and y.shape[-1] < 14000: + y = F.pad(y, (0, 14000 - y.shape[-1])) + + window = torch.hann_window(self.win_length).to(y) + y = torch.stft(y, n_fft=self.embedder_params["n_fft"], + hop_length=self.hop_length, + win_length=self.win_length, + window=window) + magnitudes = torch.norm(y, dim=-1, p=2) ** 2 + mel = torch.log10(self.mel_basis @ magnitudes + 1e-6) + return mel + + def forward(self, inputs): + dvecs = [] + for wav in inputs: + mel = self.get_mel(wav) + if mel.dim() == 3: + mel = mel.squeeze(0) + dvecs += [self.embedder(mel)] + dvecs = torch.stack(dvecs) + + dvec = torch.mean(dvecs, dim=0) + dvec = dvec / torch.norm(dvec) + + return dvec diff --git a/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py b/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py new file mode 100644 index 0000000..9cf1210 --- /dev/null +++ b/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import collections +import contextlib +import wave + +try: + import webrtcvad +except ImportError: + raise ImportError("Please install py-webrtcvad: pip install webrtcvad") +import argparse +import os +import logging +from tqdm import tqdm + +AUDIO_SUFFIX = '.wav' +FS_MS = 30 +SCALE = 6e-5 +THRESHOLD = 0.3 + + +def read_wave(path): + """Reads a .wav file. + Takes the path, and returns (PCM audio data, sample rate). + """ + with contextlib.closing(wave.open(path, 'rb')) as wf: + num_channels = wf.getnchannels() + assert num_channels == 1 + sample_width = wf.getsampwidth() + assert sample_width == 2 + sample_rate = wf.getframerate() + assert sample_rate in (8000, 16000, 32000, 48000) + pcm_data = wf.readframes(wf.getnframes()) + return pcm_data, sample_rate + + +def write_wave(path, audio, sample_rate): + """Writes a .wav file. + Takes path, PCM audio data, and sample rate. + """ + with contextlib.closing(wave.open(path, 'wb')) as wf: + wf.setnchannels(1) + wf.setsampwidth(2) + wf.setframerate(sample_rate) + wf.writeframes(audio) + + +class Frame(object): + """Represents a "frame" of audio data.""" + def __init__(self, bytes, timestamp, duration): + self.bytes = bytes + self.timestamp = timestamp + self.duration = duration + + +def frame_generator(frame_duration_ms, audio, sample_rate): + """Generates audio frames from PCM audio data. + Takes the desired frame duration in milliseconds, the PCM data, and + the sample rate. + Yields Frames of the requested duration. + """ + n = int(sample_rate * (frame_duration_ms / 1000.0) * 2) + offset = 0 + timestamp = 0.0 + duration = (float(n) / sample_rate) / 2.0 + while offset + n < len(audio): + yield Frame(audio[offset:offset + n], timestamp, duration) + timestamp += duration + offset += n + + +def vad_collector(sample_rate, frame_duration_ms, + padding_duration_ms, vad, frames): + """Filters out non-voiced audio frames. + Given a webrtcvad.Vad and a source of audio frames, yields only + the voiced audio. + Uses a padded, sliding window algorithm over the audio frames. + When more than 90% of the frames in the window are voiced (as + reported by the VAD), the collector triggers and begins yielding + audio frames. Then the collector waits until 90% of the frames in + the window are unvoiced to detrigger. + The window is padded at the front and back to provide a small + amount of silence or the beginnings/endings of speech around the + voiced frames. + Arguments: + sample_rate - The audio sample rate, in Hz. + frame_duration_ms - The frame duration in milliseconds. + padding_duration_ms - The amount to pad the window, in milliseconds. + vad - An instance of webrtcvad.Vad. + frames - a source of audio frames (sequence or generator). + Returns: A generator that yields PCM audio data. + """ + num_padding_frames = int(padding_duration_ms / frame_duration_ms) + # We use a deque for our sliding window/ring buffer. + ring_buffer = collections.deque(maxlen=num_padding_frames) + # We have two states: TRIGGERED and NOTTRIGGERED. We start in the + # NOTTRIGGERED state. + triggered = False + + voiced_frames = [] + for frame in frames: + is_speech = vad.is_speech(frame.bytes, sample_rate) + + # sys.stdout.write('1' if is_speech else '0') + if not triggered: + ring_buffer.append((frame, is_speech)) + num_voiced = len([f for f, speech in ring_buffer if speech]) + # If we're NOTTRIGGERED and more than 90% of the frames in + # the ring buffer are voiced frames, then enter the + # TRIGGERED state. + if num_voiced > 0.9 * ring_buffer.maxlen: + triggered = True + # We want to yield all the audio we see from now until + # we are NOTTRIGGERED, but we have to start with the + # audio that's already in the ring buffer. + for f, _ in ring_buffer: + voiced_frames.append(f) + ring_buffer.clear() + else: + # We're in the TRIGGERED state, so collect the audio data + # and add it to the ring buffer. + voiced_frames.append(frame) + ring_buffer.append((frame, is_speech)) + num_unvoiced = len([f for f, speech in ring_buffer if not speech]) + # If more than 90% of the frames in the ring buffer are + # unvoiced, then enter NOTTRIGGERED and yield whatever + # audio we've collected. + if num_unvoiced > 0.9 * ring_buffer.maxlen: + triggered = False + yield [b''.join([f.bytes for f in voiced_frames]), + voiced_frames[0].timestamp, voiced_frames[-1].timestamp] + ring_buffer.clear() + voiced_frames = [] + # If we have any leftover voiced audio when we run out of input, + # yield it. + if voiced_frames: + yield [b''.join([f.bytes for f in voiced_frames]), + voiced_frames[0].timestamp, voiced_frames[-1].timestamp] + + +def main(args): + # create output folder + try: + cmd = f"mkdir -p {args.out_path}" + os.system(cmd) + except Exception: + logging.error("Can not create output folder") + exit(-1) + + # build vad object + vad = webrtcvad.Vad(int(args.agg)) + # iterating over wavs in dir + for file in tqdm(os.listdir(args.in_path)): + if file.endswith(AUDIO_SUFFIX): + audio_inpath = os.path.join(args.in_path, file) + audio_outpath = os.path.join(args.out_path, file) + audio, sample_rate = read_wave(audio_inpath) + frames = frame_generator(FS_MS, audio, sample_rate) + frames = list(frames) + segments = vad_collector(sample_rate, FS_MS, 300, vad, frames) + merge_segments = list() + timestamp_start = 0.0 + timestamp_end = 0.0 + # removing start, end, and long sequences of sils + for i, segment in enumerate(segments): + merge_segments.append(segment[0]) + if i and timestamp_start: + sil_duration = segment[1] - timestamp_end + if sil_duration > THRESHOLD: + merge_segments.append(int(THRESHOLD / SCALE)*(b'\x00')) + else: + merge_segments.append(int((sil_duration / SCALE))*(b'\x00')) + timestamp_start = segment[1] + timestamp_end = segment[2] + segment = b''.join(merge_segments) + write_wave(audio_outpath, segment, sample_rate) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Apply vad to a file of fils.') + parser.add_argument('in_path', type=str, help='Path to the input files') + parser.add_argument('out_path', type=str, + help='Path to save the processed files') + parser.add_argument('--agg', type=int, default=3, + help='The level of aggressiveness of the VAD: [0-3]') + args = parser.parse_args() + + main(args) diff --git a/fairseq/examples/speech_synthesis/utils.py b/fairseq/examples/speech_synthesis/utils.py new file mode 100644 index 0000000..2c7b037 --- /dev/null +++ b/fairseq/examples/speech_synthesis/utils.py @@ -0,0 +1,101 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from scipy.interpolate import interp1d +import torchaudio + +from fairseq.tasks.text_to_speech import ( + batch_compute_distortion, compute_rms_dist +) + + +def batch_mel_spectral_distortion( + y1, y2, sr, normalize_type="path", mel_fn=None +): + """ + https://arxiv.org/pdf/2011.03568.pdf + + Same as Mel Cepstral Distortion, but computed on log-mel spectrograms. + """ + if mel_fn is None or mel_fn.sample_rate != sr: + mel_fn = torchaudio.transforms.MelSpectrogram( + sr, n_fft=int(0.05 * sr), win_length=int(0.05 * sr), + hop_length=int(0.0125 * sr), f_min=20, n_mels=80, + window_fn=torch.hann_window + ).to(y1[0].device) + offset = 1e-6 + return batch_compute_distortion( + y1, y2, sr, lambda y: torch.log(mel_fn(y) + offset).transpose(-1, -2), + compute_rms_dist, normalize_type + ) + + +# This code is based on +# "https://github.com/bastibe/MAPS-Scripts/blob/master/helper.py" +def _same_t_in_true_and_est(func): + def new_func(true_t, true_f, est_t, est_f): + assert type(true_t) is np.ndarray + assert type(true_f) is np.ndarray + assert type(est_t) is np.ndarray + assert type(est_f) is np.ndarray + + interpolated_f = interp1d( + est_t, est_f, bounds_error=False, kind='nearest', fill_value=0 + )(true_t) + return func(true_t, true_f, true_t, interpolated_f) + + return new_func + + +@_same_t_in_true_and_est +def gross_pitch_error(true_t, true_f, est_t, est_f): + """The relative frequency in percent of pitch estimates that are + outside a threshold around the true pitch. Only frames that are + considered pitched by both the ground truth and the estimator (if + applicable) are considered. + """ + + correct_frames = _true_voiced_frames(true_t, true_f, est_t, est_f) + gross_pitch_error_frames = _gross_pitch_error_frames( + true_t, true_f, est_t, est_f + ) + return np.sum(gross_pitch_error_frames) / np.sum(correct_frames) + + +def _gross_pitch_error_frames(true_t, true_f, est_t, est_f, eps=1e-8): + voiced_frames = _true_voiced_frames(true_t, true_f, est_t, est_f) + true_f_p_eps = [x + eps for x in true_f] + pitch_error_frames = np.abs(est_f / true_f_p_eps - 1) > 0.2 + return voiced_frames & pitch_error_frames + + +def _true_voiced_frames(true_t, true_f, est_t, est_f): + return (est_f != 0) & (true_f != 0) + + +def _voicing_decision_error_frames(true_t, true_f, est_t, est_f): + return (est_f != 0) != (true_f != 0) + + +@_same_t_in_true_and_est +def f0_frame_error(true_t, true_f, est_t, est_f): + gross_pitch_error_frames = _gross_pitch_error_frames( + true_t, true_f, est_t, est_f + ) + voicing_decision_error_frames = _voicing_decision_error_frames( + true_t, true_f, est_t, est_f + ) + return (np.sum(gross_pitch_error_frames) + + np.sum(voicing_decision_error_frames)) / (len(true_t)) + + +@_same_t_in_true_and_est +def voicing_decision_error(true_t, true_f, est_t, est_f): + voicing_decision_error_frames = _voicing_decision_error_frames( + true_t, true_f, est_t, est_f + ) + return np.sum(voicing_decision_error_frames) / (len(true_t)) diff --git a/fairseq/examples/speech_text_joint_to_text/README.md b/fairseq/examples/speech_text_joint_to_text/README.md new file mode 100644 index 0000000..c1aa119 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/README.md @@ -0,0 +1,51 @@ +# Joint Speech Text training in Fairseq +An extension of Fairseq s2t project with the speech to text task enhanced by the co-trained text to text mapping task. More details about Fairseq s2t can be found [here](../speech_to_text/README.md) + +## Examples +Examples of speech text joint training in fairseq +- [English-to-German MuST-C model](docs/ende-mustc.md) +- [IWSLT 2021 Multilingual Speech Translation](docs/iwslt2021.md) +- [Speech Text Joint Pre-training ](docs/pre-training.md) +## Citation +Please cite as: +``` +@inproceedings{Tang2022UnifiedSP, + title={Unified Speech-Text Pre-training for Speech Translation and Recognition}, + author={Yun Tang and Hongyu Gong and Ning Dong and Changhan Wang and Wei-Ning Hsu and Jiatao Gu and Alexei Baevski and Xian Li and Abdelrahman Mohamed and Michael Auli and Juan Miguel Pino}, + booktitle={ACL}, + year={2022} +} +@inproceedings{Tang2021IST, + title = {Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task}, + author = {Yun Tang and Juan Pino and Xian Li and Changhan Wang and Dmitriy Genzel}, + booktitle = {ACL}, + year = {2021}, +} + +@inproceedings{Tang2021FST, + title = {FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task}, + author = {Yun Tang and Hongyu Gong and Xian Li and Changhan Wang and Juan Pino and Holger Schwenk and Naman Goyal}, + booktitle = {IWSLT}, + year = {2021}, +} +@inproceedings{Tang2021AGM, + title={A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks}, + author={Yun Tang and J. Pino and Changhan Wang and Xutai Ma and Dmitriy Genzel}, + booktitle={ICASSP}, + year={2021} +} + +@inproceedings{wang2020fairseqs2t, + title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, + author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, + booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, + year = {2020}, +} + +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` diff --git a/fairseq/examples/speech_text_joint_to_text/__init__.py b/fairseq/examples/speech_text_joint_to_text/__init__.py new file mode 100644 index 0000000..239d2e6 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import tasks, criterions, models # noqa diff --git a/fairseq/examples/speech_text_joint_to_text/configs/mustc_noise.list b/fairseq/examples/speech_text_joint_to_text/configs/mustc_noise.list new file mode 100644 index 0000000..02eeac4 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/configs/mustc_noise.list @@ -0,0 +1,49 @@ +"(Applause) NOISE +"(Laughter) VOICE +"(Laughter)" VOICE +(Applause) NOISE +(Applause). NOISE +(Audience) VOICE +(Audio) NOISE +(Beat) NOISE +(Beatboxing) VOICE +(Beep) NOISE +(Beeps) NOISE +(Cheering) VOICE +(Cheers) VOICE +(Claps) NOISE +(Clicking) NOISE +(Clunk) NOISE +(Coughs) NOISE +(Drums) NOISE +(Explosion) NOISE +(Gasps) VOICE +(Guitar) NOISE +(Honk) NOISE +(Laugher) VOICE +(Laughing) VOICE +(Laughs) VOICE +(Laughter) VOICE +(Laughter). VOICE +(Laughter)... VOICE +(Mumbling) VOICE +(Music) NOISE +(Noise) NOISE +(Recording) VOICE +(Ringing) NOISE +(Shouts) VOICE +(Sigh) VOICE +(Sighs) VOICE +(Silence) NOISE +(Singing) VOICE +(Sings) VOICE +(Spanish) VOICE +(Static) NOISE +(Tones) NOISE +(Trumpet) NOISE +(Video) NOISE +(Video): NOISE +(Voice-over) NOISE +(Whistle) NOISE +(Whistling) NOISE +(video): NOISE diff --git a/fairseq/examples/speech_text_joint_to_text/criterions/__init__.py b/fairseq/examples/speech_text_joint_to_text/criterions/__init__.py new file mode 100644 index 0000000..7faae73 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/criterions/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + + +for file in os.listdir(os.path.dirname(__file__)): + if file.endswith(".py") and not file.startswith("_"): + criterion_name = file[: file.find(".py")] + importlib.import_module( + "examples.speech_text_joint_to_text.criterions." + criterion_name + ) diff --git a/fairseq/examples/speech_text_joint_to_text/criterions/multi_modality_compound.py b/fairseq/examples/speech_text_joint_to_text/criterions/multi_modality_compound.py new file mode 100644 index 0000000..b3a5506 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/criterions/multi_modality_compound.py @@ -0,0 +1,181 @@ +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import logging +import math +from dataclasses import dataclass, field + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.criterions.ctc import CtcCriterion, CtcCriterionConfig +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterionConfig, +) +from fairseq.logging.meters import safe_round + +from .multi_modality_cross_entropy import SpeechTextPreTrainCrossEntCriterion + +logger = logging.getLogger(__name__) + + +@dataclass +class SpeechTextPreTrainCompoundCriterionConfig( + LabelSmoothedCrossEntropyCriterionConfig +): + zero_infinity: bool = field( + default=False, + metadata={"help": "zero inf loss when source length <= target length"}, + ) + post_process: str = field( + default="none", + metadata={ + "help": "how to post process predictions into words. can be letter, " + "wordpiece, BPE symbols, etc. " + "See fairseq.data.data_utils.post_process() for full list of options" + }, + ) + + +@register_criterion( + "speech_text_pretrain_compound", dataclass=SpeechTextPreTrainCompoundCriterionConfig +) +class SpeechTextPreTrainCompoundCriterion(FairseqCriterion): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + report_accuracy=False, + zero_infinity=False, + post_process=None, + ): + super().__init__(task) + self.xent = SpeechTextPreTrainCrossEntCriterion( + task, sentence_avg, label_smoothing, report_accuracy + ) + cfg_dict = { + "zero_infinity": zero_infinity, + "sentence_avg": sentence_avg, + "post_process": post_process, + } + cfg_ctc = CtcCriterionConfig(**cfg_dict) + self.ctc = CtcCriterion(cfg_ctc, task) + + def forward(self, model, sample, reduce=True): + mode = sample["net_input"]["mode"] + if mode == "sup_speech_ctc": # CTC + sample["net_input"][ + "src_lengths" + ] = None # get downsampled src_lengths from padding_mask + loss, sample_size, logging_output = self.ctc(model, sample, reduce) + logging_output["mode"] = SpeechTextPreTrainCompoundCriterion.mode2value( + "CTC" + ) + else: + loss, sample_size, logging_output = self.xent(model, sample, reduce) + logging_output["mode"] = SpeechTextPreTrainCompoundCriterion.mode2value( + "xent" + ) + + return loss, sample_size, logging_output + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True + + @staticmethod + def mode2value(mode): # make the logging_outputs_can_be_summed = True + if mode == "CTC": + return 907 # prime number + if mode == "xent": + return 887 # prime number + return 0 + + @staticmethod + def value2mode(value): + if value % 907 == 0: + return "CTC" + if value % 887 == 0: + return "xent" + raise ValueError("Unknow mode") + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + + def _get_mode(logging_outputs): + mds = [ + SpeechTextPreTrainCompoundCriterion.value2mode(log["mode"]) + for log in logging_outputs + ] + if sum([1 if l != mds[0] else 0 for l in mds]) > 0: + raise ValueError("mode in one mini-batch is expected to be the same!") + return mds[0] + + log_mode = _get_mode(logging_outputs) + if log_mode == "xent": + return SpeechTextPreTrainCrossEntCriterion.reduce_metrics(logging_outputs) + + # ctc loss + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + nsentences = utils.item( + sum(log.get("nsentences", 0) for log in logging_outputs) + ) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "ctc_loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar("ctc_ntokens", ntokens) + metrics.log_scalar("ctc_nsentences", nsentences) + if sample_size != ntokens: + metrics.log_scalar( + "ctc_nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + + c_errors = sum(log.get("c_errors", 0) for log in logging_outputs) + metrics.log_scalar("_c_errors", c_errors) + c_total = sum(log.get("c_total", 0) for log in logging_outputs) + metrics.log_scalar("_c_total", c_total) + w_errors = sum(log.get("w_errors", 0) for log in logging_outputs) + metrics.log_scalar("_w_errors", w_errors) + wv_errors = sum(log.get("wv_errors", 0) for log in logging_outputs) + metrics.log_scalar("_wv_errors", wv_errors) + w_total = sum(log.get("w_total", 0) for log in logging_outputs) + metrics.log_scalar("_w_total", w_total) + + if c_total > 0: + metrics.log_derived( + "uer", + lambda meters: safe_round( + meters["_c_errors"].sum * 100.0 / meters["_c_total"].sum, 3 + ) + if meters["_c_total"].sum > 0 + else float("nan"), + ) + if w_total > 0: + metrics.log_derived( + "wer", + lambda meters: safe_round( + meters["_w_errors"].sum * 100.0 / meters["_w_total"].sum, 3 + ) + if meters["_w_total"].sum > 0 + else float("nan"), + ) + metrics.log_derived( + "raw_wer", + lambda meters: safe_round( + meters["_wv_errors"].sum * 100.0 / meters["_w_total"].sum, 3 + ) + if meters["_w_total"].sum > 0 + else float("nan"), + ) diff --git a/fairseq/examples/speech_text_joint_to_text/criterions/multi_modality_cross_entropy.py b/fairseq/examples/speech_text_joint_to_text/criterions/multi_modality_cross_entropy.py new file mode 100644 index 0000000..6c9cb0f --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/criterions/multi_modality_cross_entropy.py @@ -0,0 +1,101 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import torch + +from fairseq import utils +from fairseq.criterions import register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, + LabelSmoothedCrossEntropyCriterionConfig, + label_smoothed_nll_loss, +) + + +@register_criterion( + "speech_text_pretrain_cross_entropy", + dataclass=LabelSmoothedCrossEntropyCriterionConfig, +) +class SpeechTextPreTrainCrossEntCriterion(LabelSmoothedCrossEntropyCriterion): + def __init__(self, task, sentence_avg, label_smoothing, report_accuracy=False): + super().__init__( + task, sentence_avg, label_smoothing, report_accuracy=report_accuracy + ) + + def forward(self, model, sample, reduce=True): + net_output = model(**sample["net_input"]) + loss, nll_loss, nsentences, ntokens, n_correct = self.compute_loss( + model, net_output, sample, reduce=reduce + ) + sample_size = nsentences if self.sentence_avg else ntokens + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + if self.report_accuracy: + logging_output["n_correct"] = utils.item(n_correct) + logging_output["total"] = utils.item(ntokens) + return loss, sample_size, logging_output + + def get_lprobs_and_target(self, model, net_output, sample): + lprobs = model.get_normalized_probs(net_output, log_probs=True) + target = model.get_targets(sample, net_output) + assert self.ignore_prefix_size == 0 + if self.ignore_prefix_size > 0: + if getattr(lprobs, "batch_first", False): + lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous() + target = target[:, self.ignore_prefix_size :].contiguous() + else: + lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous() + target = target[self.ignore_prefix_size :, :].contiguous() + return lprobs, target + + def compute_loss(self, model, net_output, sample, reduce=True): + lprobs, target = self.get_lprobs_and_target(model, net_output, sample) + n_correct = 0 + if isinstance(target, dict): + t_lprobs = target["target_logprobs"] + + if not lprobs.batch_first: + lprobs = lprobs.transpose(0, 1) + t_lprobs = t_lprobs.transpose(0, 1) + nsentences, seq_len = lprobs.size()[:2] + ntokens = nsentences * seq_len + t_probs = t_lprobs.exp() + mask_indices = ( + net_output[1]["mask_indices"][0] + if len(net_output[1]["mask_indices"]) > 0 + else None + ) + + # mask_indices is True for those masking frames + if mask_indices is not None: # B X T + t_probs = t_probs.masked_fill(mask_indices.eq(False).unsqueeze(-1), 0) + ntokens = mask_indices.int().sum() + t_probs = t_probs.detach() + t_lprobs = t_lprobs.detach() + loss = ( + -(t_probs * (lprobs - t_lprobs)).sum() + if reduce + else -(t_probs * (lprobs - t_lprobs)).sum(-1, keepdim=True) + ) + nll_loss = loss + else: + nsentences = target.size(0) + mask = target.ne(self.padding_idx) + loss, nll_loss = label_smoothed_nll_loss( + lprobs.view(-1, lprobs.size(-1)), + target.view(-1), + self.eps, + ignore_index=self.padding_idx, + reduce=reduce, + ) + n_correct = torch.sum( + lprobs.argmax(-1).masked_select(mask).eq(target.masked_select(mask)) + ) + ntokens = torch.sum(mask) + return loss, nll_loss, nsentences, ntokens, n_correct diff --git a/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py b/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py new file mode 100644 index 0000000..fd6ff15 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py @@ -0,0 +1,224 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss +from fairseq.logging import metrics + + +@register_criterion("guided_label_smoothed_cross_entropy_with_accuracy") +class GuidedCrossEntAccCriterion(FairseqCriterion): + def __init__( + self, + task, + sentence_avg, + guide_alpha, + text_input_cost_ratio, + label_smoothing, + disable_text_guide_update_num=0, + attentive_cost_regularization=0, + ): + """ + guide_alpha: alpha to inteplate nll and kd loss + text_input_cost_ratio: loss ratio for text only input data + label_smoothing: label smoothing ratio + disable_text_guide_update_num: only use nll loss for the first N updates + attentive_cost_regularization: ratio fo attentive cost + """ + super().__init__(task) + self.alpha = guide_alpha + self.attn_beta = attentive_cost_regularization + self.sentence_avg = sentence_avg + self.eps = label_smoothing + self.text_input_cost_ratio = text_input_cost_ratio + self.disable_update_num = disable_text_guide_update_num + assert self.alpha >= 0 and self.alpha <= 1.0 + + @staticmethod + def add_args(parser): + """Add criterion-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', + help='epsilon for label smoothing, 0 means no label smoothing') + # fmt: off + parser.add_argument('--guide-alpha', default=0., type=float, metavar='D', + help='alpha to merge kd cost from text to speech input with ce loss') + # fmt: off + parser.add_argument('--disable-text-guide-update-num', default=0, type=int, metavar='D', + help='disable guided target from text for the first N updates.') + parser.add_argument("--attentive-cost-regularization", default=0.0, type=float, metavar='D', + help="use encoder attentive loss regularization with cost ratio D") + parser.add_argument("--attentive-cost-without-normalize", action='store_true', + help="Don't do normalization during attentive cost computation") + + def forward(self, model, sample, reduce=True): + reduction = 'sum' if reduce else 'none' + net_input = sample["net_input"] + net_output = model(**net_input) + attn_cost = None + lprobs = model.get_normalized_probs(net_output, log_probs=True) + is_dual_input = True if net_input['src_tokens'] is not None and net_input.get('src_txt_tokens') is not None else False + target = model.get_targets(sample, net_output) + src_token_num = 0 + if is_dual_input: + # lprobs_spch from speech encoder and lprobs_text from text encoder + lprobs_spch, lprobs_text = torch.chunk(lprobs, 2) + lprobs_spch.batch_first = lprobs.batch_first + lprobs_text.batch_first = lprobs.batch_first + + speech_loss, speech_nll_loss, speech_correct, speech_total = \ + self.guide_loss_and_acc(model, lprobs_spch, lprobs_text, target, reduce=(reduction == 'sum')) + text_loss, text_nll_loss, text_correct, text_total = self.compute_loss_and_acc(model, lprobs_text, target, reduction=reduction) + loss = (speech_loss + text_loss) + nll_loss = (speech_nll_loss + text_nll_loss) + correct = speech_correct + text_correct + total = speech_total + text_total + + attn_cost = net_output[1].get('attn_cost') + if attn_cost is not None: + # attn_cost is batch_first and padding tokens have been masked already + src_token_num = attn_cost.ne(0).sum() + attn_cost = attn_cost.sum() + loss = loss + attn_cost * self.attn_beta + else: + attn_cost = 0 + else: + loss, nll_loss, correct, total = self.compute_loss_and_acc(model, lprobs, target, reduction=reduction) + if sample["net_input"]['src_tokens'] is None: # text input only + loss = loss * self.text_input_cost_ratio + speech_loss = None + speech_nll_loss = None + + sample_size, logging_output = self.get_logging_output( + sample, loss, nll_loss, correct, total, src_token_num, speech_loss, speech_nll_loss, attn_cost, is_dual_input + ) + return loss, sample_size, logging_output + + def compute_loss_and_acc(self, model, lprobs, target, reduction='sum'): + if not lprobs.batch_first: + lprobs = lprobs.transpose(0, 1) + lprobs = lprobs.view(-1, lprobs.size(-1)) # -> (B x T) x C + target = target.view(-1) + loss, nll_loss = label_smoothed_nll_loss( + lprobs, target, self.eps, ignore_index=self.padding_idx, reduce=(reduction == 'sum'), + ) + + mask = target.ne(self.padding_idx) + correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))) + total = torch.sum(mask) + return loss, nll_loss, correct, total + + def guide_loss_and_acc(self, model, lprobs, lprobs_teacher, target, reduce=True): + """ lprobs_teacher is used as guide for lprobs """ + if self.alpha == 0.0 or model.num_updates < self.disable_update_num: + return self.compute_loss_and_acc(model, lprobs, target, reduction=('sum' if reduce else 'none')) + if not lprobs.batch_first: + lprobs = lprobs.transpose(0, 1) + lprobs_teacher = lprobs_teacher.transpose(0, 1) + + lprobs = lprobs.view(-1, lprobs.size(-1)).float() # -> (B x T) x C + lprobs_teacher = lprobs_teacher.view(-1, lprobs_teacher.size(-1)).float() # -> (B x T) x C + target = target.view(-1) + loss = F.nll_loss(lprobs, target, ignore_index=self.padding_idx, reduction='sum' if reduce else 'none') + nll_loss = loss + probs_teacher = lprobs_teacher.exp().masked_fill_(target.unsqueeze(-1).eq(self.padding_idx), 0) + probs_teacher = probs_teacher.detach() + guide_loss = -(probs_teacher*lprobs).sum() if reduce else -(probs_teacher*lprobs).sum(-1, keepdim=True) + loss = self.alpha*guide_loss + (1.0 - self.alpha)*loss + + mask = target.ne(self.padding_idx) + correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))) + total = torch.sum(mask) + return loss, nll_loss, correct, total + + def get_logging_output( + self, + sample, + loss, + nll_loss, + correct, + total, + src_token_num=0, + speech_loss=None, + speech_nll_loss=None, + attn_cost=None, + is_dual_input=False, + ): + + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + mul_size = 2 if is_dual_input else 1 + + logging_output = { + "loss": utils.item(loss.data), # * sample['ntokens'], + "nll_loss": utils.item(nll_loss.data), # * sample['ntokens'], + "ntokens": sample["ntokens"]*mul_size, + "nsentences": sample["target"].size(0)*mul_size, + "sample_size": sample_size*mul_size, + "correct": utils.item(correct.data), + "total": utils.item(total.data), + "src_token_num": utils.item(src_token_num.data) if src_token_num > 0 else 0, + "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(), + } + + if speech_loss is not None: + logging_output["speech_loss"] = utils.item(speech_loss.data) + logging_output["speech_nll_loss"] = utils.item(speech_nll_loss.data) + logging_output["sample_size_speech_cost"] = sample_size + logging_output["speech_attn_loss"] = attn_cost + + return sample_size*mul_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + correct_sum = sum(log.get("correct", 0) for log in logging_outputs) + total_sum = sum(log.get("total", 0) for log in logging_outputs) + src_token_sum = sum(log.get("src_token_num", 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + nframes = sum(log.get("nframes", 0) for log in logging_outputs) + speech_loss_sum = sum(log.get("speech_loss", 0) for log in logging_outputs) + speech_nll_loss_sum = sum(log.get("speech_nll_loss", 0) for log in logging_outputs) + speech_attn_loss_sum = sum(log.get("speech_attn_loss", 0) for log in logging_outputs) + sample_size_speech = sum(log.get("sample_size_speech_cost", 0) for log in logging_outputs) + + agg_output = { + "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, + "nll_loss": nll_loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, + # if args.sentence_avg, then sample_size is nsentences, and loss + # is per-sentence loss; else sample_size is ntokens, and the loss + # becomes per-output token loss + "speech_loss": speech_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0, + "speech_nll_loss": speech_nll_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0, + "speech_attn_loss": speech_attn_loss_sum / src_token_sum / math.log(2) if src_token_sum > 0 else 0.0, + "ntokens": ntokens, + "nsentences": nsentences, + "nframes": nframes, + "sample_size": sample_size, + "acc": correct_sum * 100.0 / total_sum if total_sum > 0 else 0.0, + "correct": correct_sum, + "total": total_sum, + "src_token_num": src_token_sum, + # total is the number of validate tokens + } + return agg_output + + @classmethod + def reduce_metrics(cls, logging_outputs): + """Aggregate logging outputs from data parallel training.""" + agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs) + for k, v in agg_logging_outputs.items(): + if k in {'nsentences', 'ntokens', 'sample_size'}: + continue + metrics.log_scalar(k, v, round=3) diff --git a/fairseq/examples/speech_text_joint_to_text/docs/ende-mustc.md b/fairseq/examples/speech_text_joint_to_text/docs/ende-mustc.md new file mode 100644 index 0000000..1acf6e0 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/docs/ende-mustc.md @@ -0,0 +1,118 @@ +[[Back]](..) + +# Joint Speech Text Training for the MuST-C English to German Speech Translation task + +Joint Training Baseline: it is based on paper ["A general multi-task learning framework to leverage text data for speech to text tasks"](https://arxiv.org/pdf/2010.11338.pdf) + +Enhanced Joint Training: the joint training is enhanced with pre-trained models, cross attentive regularization and online knowledge distillation based on paper ["Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task"](https://research.fb.com/publications/improving-speech-translation-by-understanding-and-learning-from-the-auxiliary-text-translation-task) + +## Prepare Data +#### Download files +- Sentence piece model [spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/spm.model) +- Dictionary [dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/dict.txt) +- config [config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/config.yaml) +#### Prepare MuST-C data set +- Please follow the data preparation in the [S2T example](https://github.com/pytorch/fairseq/blob/main/examples/speech_to_text/docs/mustc_example.md) +- Convert source text under the "src_text" column in the tsv file into phoneme representation. +```bash + python examples/speech_text_joint_to_text/scripts/g2p_encode.py \ + --lower-case --do-filter --use-word-start --no-punc \ + --reserve-word examples/speech_text_joint_to_text/configs/mustc_noise.list \ + --data-path ${must_c_en_de_src_text} \ + --out-path ${must_c_en_de_src_text_pho} +``` +- Replace the source text under the "src_text" column in the tsv file with the corresponding phoneme reprentation generated in the step above. +Below is the snapshot for the MuST-C en-de dev tsv +``` +id audio n_frames tgt_text src_text speaker +ted_767_0 en-de/flac.zip:10071514743:48445 56160 Heute spreche ich zu Ihnen über Energie und Klima. ▁AY1 M ▁G OW1 IH0 NG ▁T UW1 ▁T AO1 K ▁T AH0 D EY1 ▁AH0 B AW1 T ▁EH1 N ER0 JH IY0 ▁AH0 N D ▁K L AY1 M AH0 T spk.767_ +ted_767_1 en-de/flac.zip:1214217978:205678 226080 Und das überrascht vielleicht etwas, weil sich meine Vollzeitbeschäftigung bei der Stiftung hauptsächlich um Impfstoffe und Saatgut dreht, um die Dinge, die wir erfinden und liefern müssen um den ärmsten 2 Milliarden ein besseres Leben zu ermöglichen. ▁AH0 N D ▁DH AE1 T ▁M AY1 T ▁S IY1 M ▁AH0 ▁B IH1 T ▁S ER0 P R AY1 Z IH0 NG ▁B IH0 K AO1 Z ▁M AY1 ▁F UH1 L ▁T AY1 M ▁W ER1 K ▁AE1 T ▁DH AH0 ▁F AW0 N D EY1 SH AH0 N ▁IH1 Z ▁M OW1 S T L IY0 ▁AH0 B AW1 T ▁V AE2 K S IY1 N Z ▁AH0 N D ▁S IY1 D Z ▁AH0 B AW1 T ▁DH AH0 ▁TH IH1 NG Z ▁DH AE1 T ▁W IY1 ▁N IY1 D ▁T UW1 ▁IH0 N V EH1 N T ▁AH0 N D ▁D IH0 L IH1 V ER0 ▁T UW1 ▁HH EH1 L P ▁DH AH0 ▁P UH1 R IH0 S T ▁T UW1 ▁B IH1 L Y AH0 N ▁L AY1 V ▁B EH1 T ER0 ▁L IH1 V Z spk.767_ +``` +- Prepare phoneme dictionary and save to $MANIFEST_ROOT as [src_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/src_dict.txt) +#### Prepare WMT text data +- [Download wmt data](https://github.com/pytorch/fairseq/blob/main/examples/translation/prepare-wmt14en2de.sh) +- Convert source text (English) into phoneme representation as above +- Generate binary parallel files with "fairseq-preprocess" from fairseq for training and validation. The source input is English phoneme representation and the target input is German sentencepiece token . The output is saved under $parallel_text_data + +## Training +The model is trained with 8 v100 GPUs. + +#### Download pretrained models +- [pretrain_encoder](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_joint_asr_transformer_m.pt) +- [pretrain_nmt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/checkpoint_mt.pt) + +#### Training scripts +- Jointly trained model from scratch +```bash +python train.py ${MANIFEST_ROOT} \ + --save-dir ${save_dir} \ + --num-workers 8 \ + --task speech_text_joint_to_text \ + --arch dualinputs2ttransformer_s \ + --user-dir examples/speech_text_joint_to_text \ + --max-epoch 100 --update-mix-data \ + --optimizer adam --lr-scheduler inverse_sqrt \ + --lr 0.001 --update-freq 4 --clip-norm 10.0 \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy \ + --label-smoothing 0.1 --max-tokens 10000 --max-tokens-text 10000 \ + --max-positions-text 400 --seed 2 --speech-encoder-layers 12 \ + --text-encoder-layers 6 --encoder-shared-layers 6 --decoder-layers 6 \ + --dropout 0.1 --warmup-updates 20000 \ + --text-sample-ratio 0.25 --parallel-text-data ${parallel_text_data} \ + --text-input-cost-ratio 0.5 --enc-grad-mult 2.0 --add-speech-eos \ + --log-format json --langpairs en-de --noise-token '"'"'▁NOISE'"'"' \ + --mask-text-ratio 0.0 --max-tokens-valid 20000 --ddp-backend no_c10d \ + --log-interval 100 --data-buffer-size 50 --config-yaml config.yaml \ + --keep-last-epochs 10 +``` +- Jointly trained model with good initialization, cross attentive loss and online knowledge distillation +```bash +python train.py ${MANIFEST_ROOT} \ + --save-dir ${save_dir} \ + --num-workers 8 \ + --task speech_text_joint_to_text \ + --arch dualinputs2ttransformer_m \ + --user-dir examples/speech_text_joint_to_text \ + --max-epoch 100 --update-mix-data \ + --optimizer adam --lr-scheduler inverse_sqrt \ + --lr 0.002 --update-freq 4 --clip-norm 10.0 \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy \ + --guide-alpha 0.8 --disable-text-guide-update-num 5000 \ + --label-smoothing 0.1 --max-tokens 10000 --max-tokens-text 10000 \ + --max-positions-text 400 --seed 2 --speech-encoder-layers 12 \ + --text-encoder-layers 6 --encoder-shared-layers 6 --decoder-layers 6 \ + --dropout 0.1 --warmup-updates 20000 --attentive-cost-regularization 0.02 \ + --text-sample-ratio 0.25 --parallel-text-data ${parallel_text_data} \ + --text-input-cost-ratio 0.5 --enc-grad-mult 2.0 --add-speech-eos \ + --log-format json --langpairs en-de --noise-token '"'"'▁NOISE'"'"' \ + --mask-text-ratio 0.0 --max-tokens-valid 20000 --ddp-backend no_c10d \ + --log-interval 100 --data-buffer-size 50 --config-yaml config.yaml \ + --load-pretrain-speech-encoder ${pretrain_encoder} \ + --load-pretrain-decoder ${pretrain_nmt} \ + --load-pretrain-text-encoder-last ${pretrain_nmt} \ + --keep-last-epochs 10 +``` + +## Evaluation +```bash +python ./fairseq_cli/generate.py \ + ${MANIFEST_ROOT} \ + --task speech_text_joint_to_text \ + --max-tokens 25000 \ + --nbest 1 \ + --results-path ${infer_results} \ + --batch-size 512 \ + --path ${model} \ + --gen-subset tst-COMMON_st \ + --config-yaml config.yaml \ + --scoring sacrebleu \ + --beam 5 --lenpen 1.0 \ + --user-dir examples/speech_text_joint_to_text \ + --load-speech-only +``` + +## Results (Joint training with initialization + CAR + online KD) +|Direction|En-De | En-Es | En-Fr | +|---|---|---|---| +|BLEU|27.4| 31.2 | 37.6 | +|checkpoint | [link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/checkpoint_ave_10.pt) |[link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_es/checkpoint_ave_10.pt)|[link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_fr/checkpoint_ave_10.pt)| diff --git a/fairseq/examples/speech_text_joint_to_text/docs/iwslt2021.md b/fairseq/examples/speech_text_joint_to_text/docs/iwslt2021.md new file mode 100644 index 0000000..0af0fbf --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/docs/iwslt2021.md @@ -0,0 +1,76 @@ +[[Back]](..) + +# Joint Speech Text Training for the 2021 IWSLT multilingual speech translation + +This directory contains the code from paper ["FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task"](https://arxiv.org/pdf/2107.06959.pdf). + +## Prepare Data +#### Download files +- Sentence piece model [spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/spm.model) +- Dictionary [tgt_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/dict.txt) +- Config [config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/config.yaml) + +#### Prepare +- Please follow the data preparation in [speech-to-text](https://github.com/pytorch/fairseq/blob/main/examples/speech_to_text/docs/mtedx_example.md) with option "--use-audio-input" for raw audio tsv files. +- Prepare tsv files with phoneme based source text (under column 'src_text') as [MuST-C](ende-mustc.md) example. + + +## Training + +#### Download pretrained models +- [Pretrained mbart model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/mbart.pt) +- [Pretrained w2v model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/xlsr_53_56k.pt) + + +#### Training scripts + +```bash +python train.py ${MANIFEST_ROOT} \ + --save-dir ${save_dir} \ + --user-dir examples/speech_text_joint_to_text \ + --train-subset train_es_en_tedx,train_es_es_tedx,train_fr_en_tedx,train_fr_es_tedx,train_fr_fr_tedx,train_it_it_tedx,train_pt_en_tedx,train_pt_pt_tedx \ + --valid-subset valid_es_en_tedx,valid_es_es_tedx,valid_es_fr_tedx,valid_es_it_tedx,valid_es_pt_tedx,valid_fr_en_tedx,valid_fr_es_tedx,valid_fr_fr_tedx,valid_fr_pt_tedx,valid_it_en_tedx,valid_it_es_tedx,valid_it_it_tedx,valid_pt_en_tedx,valid_pt_es_tedx,valid_pt_pt_tedx \ + --config-yaml config.yaml --ddp-backend no_c10d \ + --num-workers 2 --task speech_text_joint_to_text \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy \ + --label-smoothing 0.3 --guide-alpha 0.8 \ + --disable-text-guide-update-num 5000 --arch dualinputxmtransformer_base \ + --max-tokens 500000 --max-sentences 3 --max-tokens-valid 800000 \ + --max-source-positions 800000 --enc-grad-mult 2.0 \ + --attentive-cost-regularization 0.02 --optimizer adam \ + --clip-norm 1.0 --log-format simple --log-interval 200 \ + --keep-last-epochs 5 --seed 1 \ + --w2v-path ${w2v_path} \ + --load-pretrained-mbart-from ${mbart_path} \ + --max-update 1000000 --update-freq 4 \ + --skip-invalid-size-inputs-valid-test \ + --skip-encoder-projection --save-interval 1 \ + --attention-dropout 0.3 --mbart-dropout 0.3 \ + --finetune-w2v-params all --finetune-mbart-decoder-params all \ + --finetune-mbart-encoder-params all --stack-w2v-mbart-encoder \ + --drop-w2v-layers 12 --normalize \ + --lr 5e-05 --lr-scheduler inverse_sqrt --warmup-updates 5000 +``` + +## Evaluation +```bash +python ./fairseq_cli/generate.py + ${MANIFEST_ROOT} \ + --task speech_text_joint_to_text \ + --user-dir ./examples/speech_text_joint_to_text \ + --load-speech-only --gen-subset test_es_en_tedx \ + --path ${model} \ + --max-source-positions 800000 \ + --skip-invalid-size-inputs-valid-test \ + --config-yaml config.yaml \ + --infer-target-lang en \ + --max-tokens 800000 \ + --beam 5 \ + --results-path ${RESULTS_DIR} \ + --scoring sacrebleu +``` +The trained model can be downloaded [here](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/checkpoint17.pt) + +|direction|es_en|fr_en|pt_en|it_en|fr_es|pt_es|it_es|es_es|fr_fr|pt_pt|it_it| +|---|---|---|---|---|---|---|---|---|---|---|---| +|BLEU|31.62|36.93|35.07|27.12|38.87|35.57|34.13|74.59|74.64|70.84|69.76| diff --git a/fairseq/examples/speech_text_joint_to_text/docs/pre-training.md b/fairseq/examples/speech_text_joint_to_text/docs/pre-training.md new file mode 100644 index 0000000..6d9e2cb --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/docs/pre-training.md @@ -0,0 +1,192 @@ +[[Back]](..) + +# Unified Speech-Text Pre-training for Speech Translation and Recognition + +This directory contains the pre-training recipes from paper ["Unified Speech-Text Pre-training for Speech Translation and Recognition"](https://arxiv.org/abs/2204.05409). + +## Librispeech ASR Pre-training +### Prepare Data +#### Download files +#### Prepare pre-training data +- Text to text task (T2T): prepare the binary data following the similar steps in [EN_DE Joint training](./ende-mustc.md). The source data is presented as phomeme token sequence and the target data is coded as subword tokens via SentencePiece. The text data is downloaded from [openslr](https://www.openslr.org/12) +- Self-supervised speech learning task (SSL): The data is prepared as [wav2vec 2.0](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec/README.md) +- Speech to phoneme classification task (S2P): The tsv file contains 5 fields: "id", "audio", "n_frames", "tgt_text", and "align". The tgt_text field is corresponding to the phoneme based representation of the speech data. "align" field contains the alignment information. The phoneme level forced alignment for the labelled speech data (i.e. Librispeech) can be obtained via [kaldi](http://kaldi-asr.org) or [MFA](https://montrealcorpustools.github.io/Montreal-Forced-Aligner/). The segmentation information is normalized to 0$\sim$1 for the whole utterance. The snapshot of the tsv file is below: +``` +id audio n_frames tgt_text align +116-288045-0000 /librispeech/dev-other/116/288045/116-288045-0000.flac 170400 <sil> ▁AE1 Z AY1 ▁AH0 P R OW1 CH T ▁DH AH1 ▁S IH1 T IY0 <sil> AY1 ▁HH ER1 D ▁B EH1 L Z ▁R IH1 NG IH0 NG <sil> ▁AE1 N D AH0 ▁L IH1 T AH0 L ▁L EY1 T ER0 AY1 ▁F AW1 N D ▁DH AH0 ▁S T R IY1 T S ▁AH0 S T IH1 R ▁W IH0 TH ▁TH R AO1 NG Z ▁AH0 V ▁W EH1 L ▁D R EH1 S T ▁P IY1 P AH0 L ▁IH1 N ▁F AE1 M L IY0 ▁G R UW1 P S <sil> ▁W EH1 N D IH0 NG ▁DH EH1 R ▁W EY1 <sil> ▁HH IH1 DH ER0 ▁AH0 N D ▁TH IH1 DH ER0 <sil> 0.047977 0.056444 0.064911 0.075259 0.081844 0.089370 0.095014 0.104421 0.109125 0.111947 0.115710 0.120414 0.134525 0.141110 0.143932 0.174036 0.176858 0.190028 0.199436 0.207902 0.218250 0.224835 0.231421 0.242709 0.251176 0.257761 0.263405 0.268109 0.270931 0.290687 0.342427 0.349953 0.353716 0.356538 0.360301 0.363123 0.365945 0.368768 0.371590 0.376294 0.384760 0.394167 0.401693 0.409219 0.419567 0.430856 0.441204 0.444026 0.446849 0.449671 0.456256 0.463782 0.471308 0.477893 0.486359 0.491063 0.494826 0.501411 0.512700 0.517404 0.520226 0.534337 0.540922 0.545626 0.550329 0.559737 0.568203 0.583255 0.592662 0.600188 0.603951 0.611477 0.619003 0.624647 0.634055 0.639699 0.646284 0.653810 0.659454 0.664158 0.670743 0.682032 0.687676 0.692380 0.708373 0.713076 0.719661 0.729069 0.740357 0.744120 0.748824 0.752587 0.761994 0.770461 0.781750 0.790216 0.805268 0.808090 0.823142 0.832549 0.836312 0.840075 0.843838 0.851364 0.854186 0.857008 0.862653 0.878645 0.898401 0.901223 0.906867 0.913452 0.920038 0.926623 0.934149 0.939793 0.942615 0.945437 0.952023 0.957667 0.977422 1.000000 + +``` +- Speech to text task (S2T): The data preparation follow the steps in [EN_DE Joint training](./ende-mustc.md). + +#### Prepare fine-tuning data: +We re-use the data from T2T and S2T tasks in the fine-tuning stage. + +### Model Build +#### Pre-training +``` +python train.py $T2T_DATA \ + --save-dir $SAVE_PRE_PATH --user-dir examples/speech_text_joint_to_text --task speech_text_joint_denoising \ + --criterion speech_text_pretrain_cross_entropy --optimizer adam --weight-decay 0.01 --config-yaml config_s2p.yaml --config-s2s-yaml config.yaml --ddp-backend no_c10d \ + --lang-pairs pho-wrd --num-workers 4 --log-interval 500 --save-interval-updates 5000 --keep-interval-updates 1 --no-emb-update-unsup --report-accuracy --lr 0.001 --end-learning-rate 1e-06 \ + --lr-scheduler polynomial_decay --warmup-updates 10000 --total-num-update 800000 --update-freq 6 --validate-interval-updates 10000 --train-subset train \ + --valid-subset valid,valid_sup_speech,valid_sup_speech_s2s,valid_unsup_speech --dataset-impl mmap \ + --sup-speech-data $S2P_DATA_PATH --sup-speech-train-subset train_960.ali --sup-speech-valid-subset dev-clean.ali --sup-speech-s2s-data $S2T_DATA_PATH \ + --sup-speech-s2s-train-subset train --sup-speech-s2s-valid-subset dev-clean --unsup-speech-train-data $SSL_DATA_PATH/train.tsv --unsup-speech-valid-data $SSL_DATA_PATH/valid.tsv \ + --batch-size 200 --batch-size-valid 150 --max-source-positions 1024 --max-target-positions 1024 --max-text-tokens 3072 --max-speech-positions 600000 \ + --max-sample-size 750000 --min-sample-size 64000 --max-speech-tokens 750000 --max-tokens-valid 750000 --skip-invalid-size-inputs-valid-test \ + --unsupervised-speech-sample-ratio 3.0 --supervised-speech-sample-ratio 5 --supervised-speech-s2s-sample-ratio 5 --text-sample-ratio 1.0 --mask 0.3 --mask-random 0.1 \ + --mask-length span-poisson --speech-sup-mask-prob 0.3 --speech-unsup-mask-prob 0.7 --use-mask-whole-words --arch speech_text_pretrain_bart_base_stack \ + --no-scale-feature --activation-fn gelu --speech-extractor-mode default --stacked-encoder all --encoder-normalize-before --decoder-normalize-before \ + --encoder-learned-pos --decoder-learned-pos --dropout 0.1 --load-pretrained-mbart-encoder-from $BART --load-pretrained-mbart-decoder-from $BART +``` +The current implementation also supports model pre-training without the forced alignment supervised data. In this case, CTC is used to optimize the S2P task. We need to do following changes for the setting: +1. options to be added +``` +--use-sup-speech-ctc --criterion speech_text_pretrain_compound +``` +2. options to be deleted +``` +--same-data-update --criterion speech_text_pretrain_cross_entropy +``` +However, we find the CTC based pre-training is still worse than the forced alignment based setting. It could be partially due to the inferior pre-training setting that we re-use the forced alignment based pre-training setting for the CTC based pre-training. + +#### Fine-tuning +``` +python train.py $S2T_DATA_PATH \ + --save-dir $SAVE_FT_PATH --num-workers 8 --task speech_text_joint_to_text --arch dualinputs2twavtransformer_base_stack \ + --user-dir examples/speech_text_joint_to_text --max-update 100000 --optimizer adam --lr-scheduler inverse_sqrt --lr 0.0003 --update-freq 3 --clip-norm 10.0 \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy --guide-alpha 0.8 --label-smoothing 0.1 --warmup-updates 20000 --attentive-cost-regularization 0.02 \ + --enc-grad-mult 2.0 --max-tokens 800000 --max-source-positions 800000 --max-tokens-text 10000 --max-positions-text 1024 --max-target-positions 1024 --no-scale-feature \ + --activation-fn gelu --load-pretrained-speech-text-encoder $SAVE_PRE_PATH/checkpoint_last.pt --load-pretrained-speech-text-decoder $SAVE_PRE_PATH/checkpoint_last.pt \ + --encoder-normalize-before --decoder-normalize-before --speech-extractor-mode default --speech-mask-channel-length 64 --speech-mask-channel-prob 0.5 \ + --speech-mask-length 10 --speech-mask-prob 0.65 --text-sample-ratio 0.25 --mask-text-ratio 0.3 --mask-text-type random --parallel-text-data text_bin \ + --text-input-cost-ratio 0.5 --langpairs pho-wrd --update-mix-data --log-format json --max-tokens-valid 800000 --ddp-backend no_c10d --log-interval 500 \ + --config-yaml config.yaml --skip-invalid-size-inputs-valid-test --keep-last-epochs 50 --layernorm-embedding --encoder-learned-pos --decoder-learned-pos +``` + +### Evaluation +The last 10 epoch models from fine-tuning is conducted model average to get $FINAL_MODEL +``` +python ./fairseq_cli/generate.py \ + $S2T_DATA_PATH \ + --task speech_text_joint_to_text \ + --max-tokens 800000 \ + --max-source-positions 800000 \ + --nbest 1 \ + --results-path $RESULTS_LOG \ + --batch-size 512 \ + --path $FINAL_MODEL \ + --gen-subset $SUBSET \ + --config-yaml config.yaml \ + --scoring wer \ + --beam 10 --lenpen 1.0 examples/speech_text_joint_to_text \ + --user-dir examples/speech_text_joint_to_text --load-speech-only \ + --model-overrides {'load_pretrained_speech_text_decoder':'','load_pretrained_speech_text_encoder':''} +``` + +### Results and models +| | dev-clean | dev-other | test-clean | test-other | +|---|---|---|---|---| +| WER| 2.0 | 4.4 | 2.1 |4.6 | + +**Model Links**: +- [config_s2p.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/pretrain/config_s2p.yaml): Config for S2P +- [spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned/spm.model): Sentence Piece model +- [src_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned/src_dict.txt): Source Phoneme Dictionary +- [tgt_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned/tgt_dict.txt): Target Sentence Piece Dictionary +- [config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned/config.yaml): Config for S2T +- [BART](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/pretrain/bart.pt): trained from Librispeech text data +- [Joint Pre-trained model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/pretrain/checkpoint6.pt): model pre-trained with 960 hours Librispeech data (S2P, S2T) Librispeech text training data (T2T) and Librilight data (SSL) +- [Fine-tuned model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned/checkpoint_ave_10.pt): the pre-trained model is fined one 960 hours Librispeech speech and text data. (S2T + T2T) + +## MuST-C +### Prepare Data +Compared with the ASR Librispeech ASR recipe, the differences are below: +- Replace the speech data with corresponding MuST-C data +- Parallel text data from WMT is replaced the Librispeech text data + +### Model Build +#### Pre-training +EN-DE is used as an example +``` +python train.py $TXT_DATA \ + --save-dir $SAVE_PRE_PATH --user-dir examples/speech_text_joint_to_text --task speech_text_joint_denoising --criterion speech_text_pretrain_cross_entropy --optimizer adam --weight-decay 0.01 \ + --config-yaml config_s2p.yaml --config-s2s-yaml config.yaml --ddp-backend no_c10d --lang-pairs-bitext en-fr --num-workers 4 --log-interval 500 --save-interval-updates 5000 --keep-interval-updates 1 \ + --no-emb-update-unsup --use-decoder-output-proj --report-accuracy --lr 0.001 --end-learning-rate 1e-06 --lr-scheduler polynomial_decay --warmup-updates 10000 --total-num-update 800000 \ + --update-freq 8 --validate-interval-updates 10000 --train-subset train --valid-subset valid_sup_speech,valid_sup_speech_s2s,valid_unsup_speech --dataset-impl mmap \ + --sup-speech-data $S2P_DATA_PATH --sup-speech-train-subset train --sup-speech-valid-subset dev --sup-speech-s2s-data $S2T_DATA_PATH --sup-speech-s2s-train-subset train \ + --sup-speech-s2s-valid-subset dev --unsup-speech-train-data $SSL_DATA_PATH/train.tsv --unsup-speech-valid-data $SSL_DATA_PATH/valid.tsv --batch-size 200 --batch-size-valid 100 \ + --max-source-positions 1024 --max-target-positions 1024 --max-text-tokens 2048 --max-speech-positions 600000 --max-sample-size 600000 --min-sample-size 64000 \ + --max-speech-tokens 600000 --max-tokens-valid 600000 --skip-invalid-size-inputs-valid-test --unsupervised-speech-sample-ratio 1.2 --supervised-speech-sample-ratio 10 \ + --supervised-speech-s2s-sample-ratio 10 --bitext-sample-ratio 0.5 --mask 0.3 --mask-random 0.1 --mask-length span-poisson --speech-sup-mask-prob 0.3 \ + --speech-unsup-mask-prob 0.7 --use-mask-whole-words --arch speech_text_pretrain_bart_base_stack --no-scale-feature --activation-fn gelu --speech-extractor-mode default \ + --stacked-encoder s2s --encoder-normalize-before --decoder-normalize-before --encoder-learned-pos --decoder-learned-pos --dropout 0.1 \ + --load-pretrained-mbart-encoder-from $EN_FR_NMT --load-pretrained-mbart-decoder-from $EN_FR_NMT +``` +#### Fine-tuning +``` +python train.py $S2T_DATA_PATH \ + --save-dir $SAVE_FT_PATH --num-workers 8 --task speech_text_joint_to_text --arch dualinputs2twavtransformer_base_stack --user-dir examples/speech_text_joint_to_text \ + --max-epoch 25 --update-mix-data --optimizer adam --lr-scheduler inverse_sqrt --lr 0.0003 --update-freq 4 --clip-norm 10.0 --warmup-updates 20000 \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy --guide-alpha 0.8 --attentive-cost-regularization 0.02 --enc-grad-mult 2.0 --label-smoothing 0.1 \ + --max-tokens 800000 --max-source-positions 800000 --max-tokens-text 10000 --max-positions-text 1024 --load-pretrained-speech-text-encoder $SAVE_PRE_PATH/checkpoint_last.pt \ + --load-pretrained-speech-text-decoder $SAVE_PRE_PATH/checkpoint_last.pt --speech-mask-channel-length 64 --speech-mask-channel-prob 0.5 --speech-mask-length 10 \ + --speech-mask-prob 0.65 --text-sample-ratio 0.05 --mask-text-ratio 0.3 --mask-text-type random --parallel-text-data data-bin-wt --text-input-cost-ratio 0.5 \ + --langpairs en-fr --log-format json --max-tokens-valid 800000 --ddp-backend no_c10d --log-interval 100 --config-yaml config.yaml --skip-invalid-size-inputs-valid-test \ + --noise-token '▁NOISE' --keep-last-epochs 40 --layernorm-embedding --encoder-learned-pos --decoder-learned-pos --activation-fn gelu \ + --speech-extractor-mode default --max-target-positions 1024 --encoder-normalize-before --decoder-normalize-before +``` + +### Evaluation +The last 10 epoch models from fine-tuning is conducted model average to get $FINAL_MODEL +``` +python fairseq_cli/generate.py \ + $S2T_DATA_PATH \ + --task speech_text_joint_to_text \ + --nbest 1 \ + --max-tokens 800000 \ + --max-source-positions 800000 \ + --results-path $RESULTS_LOG \ + --batch-size 512 \ + --path $FINAL_MODEL \ + --gen-subset $SUBSET \ + --config-yaml config.yaml \ + --scoring sacrebleu \ + --beam 10 --lenpen 1.0 examples/speech_text_joint_to_text \ + --user-dir examples/speech_text_joint_to_text --load-speech-only \ + --model-overrides {'load_pretrained_speech_text_decoder':'','load_pretrained_speech_text_encoder':''} +``` + + +### Results and models +| | en-fr | en-es | en-de | +|---|---|---|---| +| BLEU| 39.7 | 33.2 |29.2 | + + +**Model Links**: +1. DE + - [de config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/de/config.yaml) + - [de src_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/de/src_dict.txt) + - [de tgt_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/de/tgt_dict.txt) + - [de spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/de/spm.model) + - [de pre-trained nmt model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/de/nmt.pt) + - [de pre-trained model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/de/checkpoint_pretraing.pt) + - [de fine-tuned model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/de/checkpoint_finetune_ave10.pt) +2. ES + - [es config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/es/config.yaml) + - [es src_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/es/src_dict.txt) + - [es tgt_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/es/tgt_dict.txt) + - [es spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/es/spm.model) + - [es pre-trained nmt model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/es/nmt.pt) + - [es pre-trained model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/es/checkpoint_pretraing.pt) + - [es fine-tuned model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/es/checkpoint_finetune_ave10.pt) +3. FR + - [fr config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/fr/config.yaml) + - [fr src_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/fr/src_dict.txt) + - [fr tgt_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/fr/tgt_dict.txt) + - [fr spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/fr/spm.model) + - [fr pre-trained nmt model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/fr/nmt.pt) + - [fr pre-trained model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/fr/checkpoint_pretraing.pt) + - [fr fine-tuned model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/fr/checkpoint_finetune_ave10.pt) +4. [config_s2p.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/must_c/config_s2p.yaml) diff --git a/fairseq/examples/speech_text_joint_to_text/models/__init__.py b/fairseq/examples/speech_text_joint_to_text/models/__init__.py new file mode 100644 index 0000000..5fc5d9e --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/models/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + diff --git a/fairseq/examples/speech_text_joint_to_text/models/joint_speech_text_pretrain_transformer.py b/fairseq/examples/speech_text_joint_to_text/models/joint_speech_text_pretrain_transformer.py new file mode 100644 index 0000000..6f91739 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/models/joint_speech_text_pretrain_transformer.py @@ -0,0 +1,698 @@ +#!/usr/bin/env python3 + +import logging +from collections import OrderedDict, namedtuple +from typing import Dict, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from fairseq import checkpoint_utils, utils +from fairseq.file_io import PathManager +from fairseq.models import ( + FairseqDecoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_text import ( + MultiInputDecoder, + MultiModalityEncoder, + SpeechWavTransformerEncoder, + StackedSpeechWavTransformerEncoder, +) +from fairseq.models.transformer import ( + TransformerDecoder, + TransformerEncoder, + TransformerModel, +) + +logger = logging.getLogger(__name__) + + +class SpeechTextPreTrainEncoder(MultiModalityEncoder): + def __init__( + self, + dictionary, + sup_speech_encoder, + sup_s2s_speech_encoder, + unsup_speech_encoder, + text_encoder, + ): + super().__init__(dictionary) + self.sup_speech_encoder = sup_speech_encoder + self.sup_s2s_speech_encoder = sup_s2s_speech_encoder + self.unsup_speech_encoder = unsup_speech_encoder + self.text_encoder = text_encoder + + @classmethod + def update_transformer_encoder_cfg(cls, args, update_dict): + cfg = dict(args._get_kwargs()) + for fkey in update_dict.keys(): + cfg[fkey] = update_dict[fkey] + cfg.pop("_name", None) # remove keys start with _ + model_args = namedtuple("args", cfg.keys())(*cfg.values()) + return model_args + + @classmethod + def build_text_encoder(cls, args, src_dictionary): + enc_emb = nn.Embedding( + len(src_dictionary), args.encoder_embed_dim, src_dictionary.pad() + ) + model_args = cls.update_transformer_encoder_cfg( + args, {"encoder_layers": args.text_encoder_layers} + ) + text_encoder = TransformerEncoder(model_args, src_dictionary, enc_emb) + return text_encoder + + @classmethod + def build_speech_encoder(cls, args): + model_args = cls.update_transformer_encoder_cfg( + args, + { + "encoder_layers": args.speech_encoder_layers, + "speech_mask_prob": args.speech_sup_mask_prob, + }, + ) + speech_encoder = SpeechWavTransformerEncoder(model_args) + return speech_encoder + + @classmethod + def share_layers(cls, src_layers, tgt_layers): # share layer but not dropout + # share parameters in src_layers with tgt_layers + assert len(src_layers) == len(tgt_layers) + for i, ly in enumerate(src_layers): + tly = tgt_layers[i] + tly.self_attn = ly.self_attn + tly.self_attn_layer_norm = ly.self_attn_layer_norm + tly.activation_fn = ly.activation_fn + tly.normalize_before = ly.normalize_before + tly.fc1 = ly.fc1 + tly.fc2 = ly.fc2 + tly.final_layer_norm = ly.final_layer_norm + if hasattr(tly, "encoder_attn"): + tly.encoder_attn = ly.encoder_attn + tly.encoder_attn_layer_norm = ly.encoder_attn_layer_norm + return tgt_layers + + @classmethod + def build_unsup_speech_encoder(cls, args, sup_speech_encoder): + model_args = cls.update_transformer_encoder_cfg( + args, + { + "encoder_layers": args.speech_encoder_layers, + "speech_mask_prob": args.speech_unsup_mask_prob, + "encoder_layerdrop": 0.0, + "decoder_layerdrop": 0.0, + "dropout": args.speech_unsup_dropout, + "activation_dropout": args.speech_unsup_dropout, + "attention_dropout": 0.0, + "dropout_features": args.speech_unsup_feature_dropout, + "dropout_input": args.speech_unsup_feature_dropout, + }, + ) + + unsup_speech_encoder = SpeechWavTransformerEncoder(model_args, alway_mask=True) + unsup_speech_encoder.layer_norm = sup_speech_encoder.layer_norm + unsup_speech_encoder.layers = cls.share_layers( + sup_speech_encoder.layers, unsup_speech_encoder.layers + ) + unsup_speech_encoder.mask_emb = sup_speech_encoder.mask_emb + unsup_speech_encoder.embed_positions = sup_speech_encoder.embed_positions + unsup_speech_encoder.feat_layer_norm = sup_speech_encoder.feat_layer_norm + unsup_speech_encoder.feat_proj = sup_speech_encoder.feat_proj + unsup_speech_encoder.subsample = sup_speech_encoder.subsample + return unsup_speech_encoder + + @classmethod + def build_encoder(cls, args, dictionary): + text_encoder = cls.build_text_encoder(args, dictionary) + if getattr(args, "load_pretrained_mbart_encoder_from", None): + text_encoder = checkpoint_utils.load_pretrained_component_from_model( + component=text_encoder, + checkpoint=args.load_pretrained_mbart_encoder_from, + ) + speech_encoder = cls.build_speech_encoder(args) + if getattr(args, "load_pretrained_feature_extractor_from", None): + + def load_feature_extractor(component, checkpoint): + if not PathManager.exists(checkpoint): + raise IOError("Model file not found: {}".format(checkpoint)) + state = checkpoint_utils.load_checkpoint_to_cpu(checkpoint) + component_state_dict = OrderedDict() + + component_prefix = "feature_extractor" + for key in state["model"].keys(): + if key.startswith(component_prefix): + component_subkey = key[len(component_prefix) + 1 :] + component_state_dict[component_subkey] = state["model"][key] + component.load_state_dict(component_state_dict, strict=True) + return component + + speech_encoder.subsample = load_feature_extractor( + speech_encoder.subsample, args.load_pretrained_feature_extractor_from + ) + speech_s2s_encoder = speech_encoder + unsup_speech_encoder = cls.build_unsup_speech_encoder(args, speech_encoder) + if getattr(args, "stacked_encoder", "none") != "none": + if args.encoder_shared_text_layers_from_begin > 0: + raise ValueError( + "We can not stack encoders and share encoders at the same time!" + ) + speech_s2s_encoder = StackedSpeechWavTransformerEncoder( + speech_encoder, text_encoder.layers, text_encoder.layer_norm + ) + if args.stacked_encoder == "all": + speech_encoder = speech_s2s_encoder + unsup_speech_encoder = StackedSpeechWavTransformerEncoder( + unsup_speech_encoder, text_encoder.layers, text_encoder.layer_norm + ) + else: + cls.share_speech_text_encoder( + speech_encoder, text_encoder, args.encoder_shared_text_layers_from_begin + ) + return SpeechTextPreTrainEncoder( + dictionary, + speech_encoder, + speech_s2s_encoder, + unsup_speech_encoder, + text_encoder, + ) + + @classmethod + def share_speech_text_encoder( + cls, speech_encoder, text_encoder, shared_layers_from_begin + ): + if shared_layers_from_begin > 0: + num_text_encoder_layers = len(text_encoder.layers) + assert len(speech_encoder.layers) >= shared_layers_from_begin + assert num_text_encoder_layers >= shared_layers_from_begin + assert len(speech_encoder.layers) >= num_text_encoder_layers + for i, ly in enumerate( + speech_encoder.layers[ + -num_text_encoder_layers : -num_text_encoder_layers + + shared_layers_from_begin + ] + ): + assert isinstance(text_encoder.layers[i], type(ly)) + text_encoder.layers[i] = ly + + def select_encoder(self, mode, **kwargs): + if mode in ("speech", "sup_speech_ctc", "sup_speech_ali", "sup_speech_s2s"): + kwargs["features_only"] = True + if mode == "sup_speech_s2s": + return self.sup_s2s_speech_encoder, kwargs + return self.sup_speech_encoder, kwargs + elif mode == "unsup_speech": + kwargs["features_only"] = False + return self.unsup_speech_encoder, kwargs + elif mode in ("text", "bitext"): + return self.text_encoder, kwargs + else: + raise NotImplementedError(f"{mode} is not supported") + return None, kwargs + + def forward(self, src_tokens, src_lengths=None, mode="", alignment=None, **kwargs): + return super().forward(src_tokens, src_lengths, mode, **kwargs) + + +# SpeechDummyDecoder works as an extension of encoder, so we could fit encoder only training into seq2seq training +class SpeechDummyDecoder(FairseqDecoder): + def __init__( + self, + dictionary, + output_embedding, + no_emb_update_unsup=False, + use_output_proj=False, + ): + super().__init__(dictionary) + self.output_embedding = output_embedding + num_embedding, num_dim = self.output_embedding.weight.size() + self.out_proj = ( + None if use_output_proj is False else nn.Linear(num_dim, num_dim) + ) + self.no_emb_update_unsup = no_emb_update_unsup + + def extend_alignment(self, alignment, src_lengths, prev_output_tokens): + # alignment: B X N + # src_lengths: B X T + # prev_output_tokens: B X (N + 1) + tgt_tokens = prev_output_tokens[ + :, 1: + ] # remove the leading start of sentence token + ext_alignment = ( + torch.ones(len(src_lengths), src_lengths.max(), device=src_lengths.device) + .long() + .fill_(self.dictionary.pad()) + ) + for bs in range(src_lengths.size(0)): + tgt_length = tgt_tokens[bs].ne(self.dictionary.pad()).sum().item() + assert tgt_length == sum(alignment[bs].ne(1)) + 1 + src_st = 0 + for i in range(tgt_length): + tok = tgt_tokens[bs][i] + src_ed = (alignment[bs][i] * src_lengths[bs]).int().item() + ext_alignment[bs][src_st:src_ed].fill_(tok) + src_st = src_ed + return ext_alignment + + def forward( + self, + prev_output_tokens, + encoder_out, + incremental_state=None, + mode="speech", + alignment=None, + **kwargs, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + + Returns: + sup_speech_ctc: + dictionary{"logits": logits, "padding_mask": padding_mask} + sup_speech_ali and unsup_speech: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + emb_weight = self.output_embedding.weight + if ( + mode == "unsup_speech" and self.no_emb_update_unsup + ): # no gradient for embedding here + emb_weight = emb_weight.detach() + enc_out = ( + encoder_out["encoder_out"][0] + if self.out_proj is None + else self.out_proj(encoder_out["encoder_out"][0]) + ) + logits = F.linear(enc_out, emb_weight, None).transpose(0, 1) # B X T X C + others = None + if mode in ( + "speech", + "sup_speech_ctc", + ): # speech data with label, do forcealignment + if len(encoder_out["encoder_padding_mask"]) > 0: + padding_mask = encoder_out["encoder_padding_mask"][0] + logits = logits.masked_fill(padding_mask, float("-inf")) + else: + seq_len, bsz = encoder_out["encoder_out"][0].size()[:2] + padding_mask = torch.zeros( + bsz, seq_len, device=encoder_out["encoder_out"][0].device + ).bool() + return {"x": logits, "padding_mask": padding_mask} + elif mode == "sup_speech_ali": + src_lengths = None + if len(encoder_out["encoder_padding_mask"]) > 0: + src_lengths = (1 - encoder_out["encoder_padding_mask"][0].long()).sum( + -1 + ) + else: + seq_len, bsz = encoder_out["encoder_out"][0].size()[:2] + src_lengths = ( + torch.ones(bsz, device=encoder_out["encoder_out"][0].device).long() + * seq_len + ) + assert alignment is not None + alignment = self.extend_alignment( + alignment, src_lengths, prev_output_tokens + ) + others = {"pseudo_target_tokens": alignment} + elif mode == "unsup_speech": + enc_out_ori = ( + encoder_out["encoder_unmasked_out"][0] + if self.out_proj is None + else self.out_proj(encoder_out["encoder_unmasked_out"][0]) + ) + logits_ori = F.linear(enc_out_ori, emb_weight, None).transpose(0, 1) + if len(encoder_out["encoder_padding_mask"]) > 0: + encoder_padding_mask = encoder_out["encoder_padding_mask"][0] + logits_ori = logits_ori.masked_fill(encoder_padding_mask, float("-inf")) + pseudo_labels = utils.log_softmax(logits_ori, dim=-1) + others = { + "pseudo_target_logprobs": pseudo_labels, + "padding_mask": encoder_out["encoder_padding_mask"], # B X T + "mask_indices": encoder_out[ + "mask_indices" + ], # True for masked frames B X T + } + return logits, others + + def get_normalized_probs( + self, + net_output: Dict[str, Tensor], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + return self.get_normalized_probs_scriptable( + (net_output["x"], None), log_probs, sample + ) + + +class SpeechTextPreTrainDecoder(MultiInputDecoder): + def __init__(self, dictionary, speech_decoder, text_decoder): + super().__init__(dictionary) + self.speech_decoder = speech_decoder + self.text_decoder = text_decoder + + def select_decoder(self, mode, **kwargs): + if mode == "unsup_speech": + kwargs["mode"] = mode + return self.speech_decoder, kwargs + if mode in ("text", "bitext"): + return self.text_decoder, kwargs + if mode in ("speech", "sup_speech_ctc", "sup_speech_ali"): + kwargs["mode"] = mode + return self.speech_decoder, kwargs + if mode in ("speech", "sup_speech_s2s"): + if "alignment" in kwargs: + del kwargs["alignment"] + return self.text_decoder, kwargs + + raise NotImplementedError(f"{mode} is not supported") + return None, kwargs + + def get_normalized_probs( + self, + net_output, + log_probs, + sample=None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" + if isinstance(net_output, dict): + return self.speech_decoder.get_normalized_probs( + net_output, log_probs, sample + ) + return self.text_decoder.get_normalized_probs(net_output, log_probs, sample) + + @classmethod + def build_text_decoder(cls, args, tgt_dictionary, dec_emb_share=None): + dec_emb = ( + nn.Embedding( + len(tgt_dictionary), args.decoder_embed_dim, tgt_dictionary.pad() + ) + if dec_emb_share is None + else dec_emb_share + ) + text_decoder = TransformerDecoder(args, tgt_dictionary, dec_emb) + return text_decoder + + @classmethod + def build_dummy_speech_decoder(cls, args, dictionary, dec_emb_share=None): + dec_emb = ( + nn.Embedding(len(dictionary), args.decoder_embed_dim, dictionary.pad()) + if dec_emb_share is None + else dec_emb_share + ) + speech_decoder = SpeechDummyDecoder( + dictionary, + dec_emb, + no_emb_update_unsup=getattr(args, "no_emb_update_unsup", False), + use_output_proj=getattr(args, "use_decoder_output_proj", False), + ) + return speech_decoder + + @classmethod + def build_decoder( + cls, args, text_dictionary, speech_dictionary, speech_output_embedding + ): + text_decoder = cls.build_text_decoder(args, text_dictionary) + speech_decoder = cls.build_dummy_speech_decoder( + args, speech_dictionary, speech_output_embedding + ) + if getattr(args, "load_pretrained_mbart_decoder_from", None): + text_decoder = checkpoint_utils.load_pretrained_component_from_model( + component=text_decoder, + checkpoint=args.load_pretrained_mbart_decoder_from, + ) + return SpeechTextPreTrainDecoder(text_dictionary, speech_decoder, text_decoder) + + +@register_model("speech_text_pretrain_bart") +class SpeechTextPreTrainModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + self.num_updates = 0 + + def forward( + self, src_tokens, src_lengths, prev_output_tokens, src_lang_ids=None, **kwargs + ): + if src_lang_ids is not None: + encoder_out = self.encoder( + src_tokens, src_lengths=src_lengths, src_lang_ids=src_lang_ids, **kwargs + ) + else: + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + decoder_out = self.decoder( + prev_output_tokens, encoder_out=encoder_out, **kwargs + ) + return decoder_out + + def max_positions(self): + return None # it is provided in task + + def get_targets(self, sample, net_output): + mode = sample["net_input"]["mode"] + if mode == "unsup_speech": + return {"target_logprobs": net_output[1]["pseudo_target_logprobs"]} + if mode == "sup_speech_ali": + return net_output[1]["pseudo_target_tokens"] + return sample["target"] + + def get_normalized_probs( + self, + net_output, + log_probs, + sample=None, + ): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample) + lprobs.batch_first = True + return lprobs + + @staticmethod + def add_args(parser): + TransformerModel.add_args(parser) + SpeechWavTransformerEncoder.add_args(parser) + parser.add_argument( + "--speech-sup-mask-prob", + type=float, + help="probability of replacing a token with mask (sup-speech)", + ) + parser.add_argument( + "--speech-unsup-mask-prob", + type=float, + help="probability of replacing a token with mask (unsup-speech)", + ) + parser.add_argument( + "--load-pretrained-mbart-encoder-from", + type=str, + metavar="STR", + help="model to take text encoder weights from (for initialization)", + ) + + parser.add_argument( + "--load-pretrained-mbart-decoder-from", + type=str, + metavar="STR", + help="model to take text decoder weights from (for initialization)", + ) + + parser.add_argument( + "--load-pretrained-feature-extractor-from", + type=str, + metavar="STR", + help="model to take feature extractor weights from (for initialization)", + ) + + parser.add_argument( + "--speech-unsup-dropout", + type=float, + default=0, + help="dropout for unsupervised speech encoder", + ) + + parser.add_argument( + "--speech-unsup-feature-dropout", + type=float, + default=0, + help="dropout for unsupervised speech feature encoder", + ) + + parser.add_argument( + "--encoder-shared-text-layers-from-begin", + type=int, + help="number of text encoder layers shared with speech encoder (from first layer)", + ) + + parser.add_argument( + "--stacked-encoder", + default="none", + choices=["none", "s2s", "all"], + help="stack speech and text encoders", + ) + + parser.add_argument("--use-decoder-output-proj", action="store_true") + + @classmethod + def build_model(cls, args, task): + encoder = SpeechTextPreTrainEncoder.build_encoder(args, task.src_dict) + decoder = SpeechTextPreTrainDecoder.build_decoder( + args, task.tgt_dict, task.src_dict, encoder.text_encoder.embed_tokens + ) + model = SpeechTextPreTrainModel(encoder, decoder) + return model + + def upgrade_state_dict(self, state_dict): + """Upgrade old state dicts to work with newer code.""" + if "decoder.speech_decoder.output_projection.weight" in state_dict: + del state_dict["decoder.speech_decoder.output_projection.weight"] + self.upgrade_state_dict_named(state_dict, "") + + +@register_model_architecture( + "speech_text_pretrain_bart", "speech_text_pretrain_bart_base" +) +def speech_text_pretrain_bart_base(args): + # speech masking + args.dropout_input = getattr(args, "dropout_input", 0) + args.dropout_features = getattr(args, "dropout_features", 0) + args.speech_mask_length = getattr(args, "speech_mask_length", 10) + args.speech_mask_prob = getattr(args, "speech_mask_prob", 0.65) + args.speech_sup_mask_prob = getattr(args, "speech_sup_mask_prob", 0.3) + args.speech_unsup_mask_prob = getattr( + args, "speech_unsup_mask_prob", args.speech_mask_prob + ) + args.speech_mask_selection = getattr(args, "speech_mask_selection", "static") + args.speech_mask_other = getattr(args, "speech_mask_other", 0) + args.speech_mask_min_space = getattr(args, "speech_mask_min_space", 1) + args.speech_no_mask_overlap = getattr(args, "speech_no_mask_overlap", False) + + args.speech_mask_channel_length = getattr(args, "speech_mask_channel_length", 10) + args.speech_mask_channel_prob = getattr(args, "speech_mask_channel_prob", 0.0) + args.speech_mask_channel_selection = getattr( + args, "speech_mask_channel_selection", "static" + ) + args.speech_mask_channel_other = getattr(args, "speech_mask_channel_other", 0) + args.speech_mask_channel_min_space = getattr( + args, "speech_mask_channel_min_space", 1 + ) + args.speech_no_mask_channel_overlap = getattr( + args, "speech_no_mask_channel_overlap", False + ) + args.no_scale_feature = getattr(args, "", False) + args.feature_grad_mult = getattr(args, "feature_grad_mult", 1.0) # 0.1 + + # Transformer + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = getattr( + args, "encoder_ffn_embed_dim", args.encoder_embed_dim * 4 + ) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.speech_conv_bias = getattr(args, "speech_conv_bias", False) + + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_attention_heads = getattr( + args, "decoder_attention_heads", args.encoder_attention_heads + ) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", args.dropout) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") # gelu? + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + + args.speech_unsup_dropout = getattr(args, "speech_unsup_dropout", 0) + args.speech_unsup_feature_dropout = getattr(args, "speech_unsup_feature_dropout", 0) + + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.encoder_shared_text_layers_from_begin = getattr( + args, "encoder_shared_text_layers_from_begin", 6 + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + + args.no_emb_update_unsup = getattr(args, "no_emb_update_unsup", False) + + +@register_model_architecture( + "speech_text_pretrain_bart", "speech_text_pretrain_bart_base_stack" +) +def speech_text_pretrain_bart_base_stack(args): + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 6) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.encoder_shared_text_layers_from_begin = getattr( + args, "encoder_shared_text_layers_from_begin", 0 + ) + args.stacked_encoder = getattr(args, "stacked_encoder", "all") + args.layernorm_embedding = getattr(args, "layernorm_embedding", True) + speech_text_pretrain_bart_base(args) + + +@register_model_architecture( + "speech_text_pretrain_bart", "speech_text_pretrain_bart_large" +) +def speech_text_pretrain_bart_large(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 24) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 12) + args.encoder_shared_text_layers_from_begin = getattr( + args, "encoder_shared_text_layers_from_begin", 12 + ) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.dropout = getattr(args, "dropout", 0.3) + speech_text_pretrain_bart_base(args) + + +@register_model_architecture( + "speech_text_pretrain_bart", "speech_text_pretrain_bart_large_stack" +) +def speech_text_pretrain_bart_large_stack(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 6) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 12) + args.encoder_shared_text_layers_from_begin = getattr( + args, "encoder_shared_text_layers_from_begin", 0 + ) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.stacked_encoder = getattr(args, "stacked_encoder", "s2s") + args.layernorm_embedding = getattr(args, "layernorm_embedding", True) + speech_text_pretrain_bart_base(args) diff --git a/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py b/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py new file mode 100644 index 0000000..c4ec41b --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py @@ -0,0 +1,1093 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from collections import namedtuple + +import torch +import torch.nn as nn +from fairseq import checkpoint_utils +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqDecoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.fairseq_encoder import EncoderOut +from fairseq.models.speech_to_text import ( + TransformerDecoder, + S2TTransformerEncoder, +) +from fairseq.models.transformer import TransformerEncoder +from fairseq.modules import ( + TransformerEncoderLayer, + GradMultiply, + LayerNorm, +) + +logger = logging.getLogger(__name__) + + +class SpeechEoSEncoder(FairseqEncoder): + def __init__(self, encoder, eos_num, feat_dim, adapter_type="None", adapter_dim=0): + super().__init__(None) + self.encoder = encoder + self.eos_num = eos_num # downsampling rate for speech input feature + self.eos_emb = ( + nn.Parameter(torch.zeros(1, feat_dim), requires_grad=True) + if eos_num > 0 + else None + ) + self.adapter = self.add_adapter(adapter_type, adapter_dim) + + def add_adapter(self, adapter_type, adapter_dim): + def _make_identity(linear, eps=1e-5): + assert isinstance(linear, nn.Linear) + linear.weight.data.mul_(eps) + linear.weight.data.fill_diagonal_(1.0) + if linear.bias is not None: + linear.bias.data.mul_(eps) + + adapter = None + if adapter_type == "Linear": + assert adapter_dim > 0 + adapter = nn.Sequential( + nn.Linear(adapter_dim, adapter_dim), LayerNorm(adapter_dim) + ) + # initialize the adapter as identity matrix first + _make_identity(adapter[0]) + + elif adapter_type == "MLP": + assert adapter_dim > 0 + # assume the model is pre-norm model + adapter = nn.Sequential( + nn.Linear(adapter_dim, 2 * adapter_dim), + nn.ReLU(), + nn.Linear(2 * adapter_dim, adapter_dim), + LayerNorm(adapter_dim), + ) + _make_identity(adapter[0]) + _make_identity(adapter[2]) + return adapter + + def add_eos(self, src_tokens, src_lengths): + bsz, max_seq_len, fdim = src_tokens.size() + if self.eos_num > 0: + src_token_eos = torch.zeros( + [bsz, max_seq_len + self.eos_num, fdim], + dtype=src_tokens.dtype, + device=src_tokens.device, + ) + src_token_eos[:, :max_seq_len] = src_tokens + for bi in range(bsz): + src_token_eos[bi][ + src_lengths[bi] : src_lengths[bi] + self.eos_num + ] = self.eos_emb.expand(self.eos_num, fdim) + src_lengths = src_lengths + self.eos_num + src_tokens = src_token_eos + return src_tokens, src_lengths + + def apply_adapter(self, enc_out): + if self.adapter is None: + return enc_out + rst = self.adapter(enc_out.encoder_out) + if enc_out.encoder_padding_mask is not None: + rst.masked_fill_( + enc_out.encoder_padding_mask.transpose(0, 1).unsqueeze(-1), 0 + ) + return EncoderOut( + encoder_out=rst, + encoder_padding_mask=enc_out.encoder_padding_mask, + encoder_embedding=enc_out.encoder_embedding, + encoder_states=enc_out.encoder_states, + src_tokens=enc_out.src_tokens, + src_lengths=enc_out.src_lengths, + ) + + def forward(self, src_tokens, src_lengths=None, return_all_hiddens=False, **kwargs): + """ + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (B,) + """ + src_tokens, src_lengths = self.add_eos(src_tokens, src_lengths) + enc_out = self.encoder(src_tokens, src_lengths, return_all_hiddens) + enc_out = self.apply_adapter(enc_out) + return enc_out + + def reorder_encoder_out(self, encoder_out, new_order): + return self.encoder.reorder_encoder_out(encoder_out, new_order) + + +class DualInputEncoder(FairseqEncoder): + def __init__( + self, + args, + spch_encoder, + text_encoder, + dictionary, + cross_attentive_loss_before_last_layer=-1, + ): + super().__init__(dictionary) + + self.spch_encoder = spch_encoder + self.text_encoder = text_encoder + self.enc_grad_mult = args.enc_grad_mult + self.cross_attentive_loss_before_last_layer = ( + cross_attentive_loss_before_last_layer + ) + self.use_cross_attentive_loss = ( + False if cross_attentive_loss_before_last_layer <= -1 else True + ) + self.enc2_along_grad_mult = args.enc2_along_grad_mult + + @classmethod + def set_shared_layer(cls, share_level, src_layer, tgt_layer): + """ + share parameters from tgt_layer to src_layer + share_level: + 0: share everything + 1: share everything but different model + 2: share weight but not bias, layernorm + """ + if share_level == 0: + return tgt_layer + if isinstance(src_layer, nn.Linear): + return tgt_layer + if isinstance(src_layer, TransformerEncoderLayer): + assert src_layer.embed_dim == tgt_layer.embed_dim + assert src_layer.normalize_before == tgt_layer.normalize_before + if share_level == 1: + src_layer.fc1 = tgt_layer.fc1 + src_layer.fc2 = tgt_layer.fc2 + src_layer.self_attn = tgt_layer.self_attn + src_layer.final_layer_norm = tgt_layer.final_layer_norm + src_layer.self_attn_layer_norm = tgt_layer.self_attn_layer_norm + src_layer.layernorm_embedding = tgt_layer.layernorm_embedding + else: + src_layer.fc1.weight = tgt_layer.fc1.weight + src_layer.fc2.weight = tgt_layer.fc2.weight + src_layer.self_attn.k_proj.weight = tgt_layer.self_attn.k_proj.weight + src_layer.self_attn.v_proj.weight = tgt_layer.self_attn.v_proj.weight + src_layer.self_attn.q_proj.weight = tgt_layer.self_attn.q_proj.weight + src_layer.self_attn.out_proj.weight = ( + tgt_layer.self_attn.out_proj.weight + ) + else: + if share_level == 1: + return tgt_layer + return src_layer + + @classmethod + def build_spch_encoder(cls, args): + cfg = { + "input_feat_per_channel": args.input_feat_per_channel, + "input_channels": args.input_channels, + "conv_kernel_sizes": args.conv_kernel_sizes, + "conv_channels": args.conv_channels, + "encoder_embed_dim": args.encoder_embed_dim, + "encoder_ffn_embed_dim": args.encoder_ffn_embed_dim, + "encoder_layers": args.speech_encoder_layers, + "encoder_layerdrop": args.encoder_layerdrop, + "encoder_attention_heads": args.encoder_attention_heads, + "max_source_positions": args.max_source_positions, + "dropout": args.dropout, + "encoder_normalize_before": args.encoder_normalize_before, + "activation_dropout": args.activation_dropout, + "attention_dropout": args.attention_dropout, + "activation_fn": args.activation_fn, + "layernorm_embedding": args.layernorm_embedding, + "no_token_positional_embeddings": args.no_token_positional_embeddings, + "no_scale_embedding": args.no_scale_embedding, + "quant_noise_pq": args.quant_noise_pq, + "encoder_freezing_updates": 0, + } + model_args = namedtuple("args", cfg.keys())(*cfg.values()) + spch_encoder = S2TTransformerEncoder(model_args) + if args.add_speech_eos: + spch_encoder = SpeechEoSEncoder( + spch_encoder, + 2 * len(args.conv_kernel_sizes.split(",")), + args.input_feat_per_channel, + adapter_type=getattr(args, "speech_encoder_adapter_type", "None"), + adapter_dim=args.encoder_embed_dim, + ) + return spch_encoder + + @classmethod + def build_text_encoder(cls, args, src_dictionary, spch_encoder): + if args.encoder_shared_layers > 0: + mx_shared_layers = ( + args.speech_encoder_layers + if args.speech_encoder_layers < args.text_encoder_layers + else args.text_encoder_layers + ) + args.encoder_shared_layers = ( + args.encoder_shared_layers + if args.encoder_shared_layers <= mx_shared_layers + else mx_shared_layers + ) + cfg = { + "encoder_embed_dim": args.encoder_text_embed_dim, + "encoder_ffn_embed_dim": args.encoder_ffn_embed_dim, + "encoder_layers": args.text_encoder_layers, + "encoder_layerdrop": args.encoder_layerdrop, + "encoder_attention_heads": args.encoder_attention_heads, + "encoder_learned_pos": args.encoder_learned_pos, + "max_source_positions": args.max_source_positions, + "dropout": args.dropout, + "encoder_normalize_before": args.encoder_normalize_before, + "activation_dropout": args.activation_dropout, + "attention_dropout": args.attention_dropout, + "activation_fn": args.activation_fn, + "adaptive_input": args.adaptive_input, + "no_token_positional_embeddings": args.no_token_positional_embeddings, + "no_scale_embedding": args.no_scale_embedding, + "quant_noise_pq": args.quant_noise_pq, + } + model_args = namedtuple("args", cfg.keys())(*cfg.values()) + enc_emb = nn.Embedding( + len(src_dictionary), model_args.encoder_embed_dim, src_dictionary.pad() + ) + text_encoder = TransformerEncoder(model_args, src_dictionary, enc_emb) + if args.add_speech_eos: + spch_encoder = spch_encoder.encoder + if args.encoder_shared_layers > 0: + text_encoder.layer_norm = cls.set_shared_layer( + args.encoder_shared_layer_level, + text_encoder.layer_norm, + spch_encoder.layer_norm, + ) + for i, ly in enumerate( + spch_encoder.transformer_layers[-args.encoder_shared_layers :] + ): + ly_id = i + args.text_encoder_layers - args.encoder_shared_layers + if not isinstance(text_encoder.layers[ly_id], type(ly)): + if text_encoder.layers[ly_id]._get_name() not in ('TransformerEncoderLayerBase', 'TransformerEncoderLayer'): + raise ValueError("The shared layers are expected from the same class") + text_encoder.layers[ly_id] = cls.set_shared_layer( + args.encoder_shared_layer_level, + text_encoder.layers[ly_id], + ly, + ) + return text_encoder + + def mult_rst_grad(self, rst, ratio): + assert isinstance(rst, dict) # instead of EncoderOut + assert len(rst["encoder_out"]) == 1 + rst["encoder_out"][0] = GradMultiply.apply(rst["encoder_out"][0], ratio) + return rst + + def process_attentive_loss_states(self, rst, interstates): + assert isinstance(rst, dict) # instead of EncoderOut + rst["encoder_states"] = interstates + return rst + + def forward( + self, + src_tokens, + src_lengths=None, + src_txt_tokens=None, + src_txt_lengths=None, + **kwargs + ): + """ + Args: + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (speech) (B,) + src_txt_tokens: padded tensor (B, T) + src_txt_lengths: tensor of original lengths of input utterances (text) (B,) + """ + # src_tokens only: inference + # src_tokens, src_lengths: speech only training + # src_txt_tokens, src_txt_lengths: text only training + # all valid: speech + text training + + if src_tokens is None and src_txt_tokens is None: + raise ValueError( + "src_tokens and src_txt_tokens cannot be None at the same time" + ) + ret1 = None + ret2 = None + return_all_hiddens = False + if src_tokens is not None: + if ( + self.use_cross_attentive_loss and src_txt_tokens is not None + ): # remove self.training so we can get attn score during validation step + return_all_hiddens = True + ret1 = self.spch_encoder( + src_tokens, src_lengths, return_all_hiddens=return_all_hiddens + ) + + if self.use_cross_attentive_loss and src_txt_tokens is not None: + assert self.cross_attentive_loss_before_last_layer < len( + ret1["encoder_states"] + ) + ret1 = self.process_attentive_loss_states( + ret1, + ret1["encoder_states"][ + -self.cross_attentive_loss_before_last_layer - 1 + ], + ) + + if src_txt_tokens is not None: + ret2 = self.text_encoder( + src_txt_tokens, src_txt_lengths, return_all_hiddens=return_all_hiddens + ) + if return_all_hiddens: + if self.cross_attentive_loss_before_last_layer == len( + self.text_encoder.layers + ): + text_embedding, _ = self.text_encoder.forward_embedding( + src_txt_tokens + ) + text_embedding = text_embedding.transpose(0, 1) + ret2 = self.process_attentive_loss_states(ret2, text_embedding) + else: + assert self.cross_attentive_loss_before_last_layer < len( + self.text_encoder.layers + ) + ret2 = self.process_attentive_loss_states( + ret2, + ret2["encoder_states"][ + -self.cross_attentive_loss_before_last_layer - 1 + ], + ) + + def merge_output(rst1, rst2): + if rst1 is None: + if not (self.enc2_along_grad_mult == 1.0 or self.training): + rst2 = self.mult_rst_grad(rst2, self.enc2_along_grad_mult) + return rst2 + if rst2 is None: + return rst1 + if self.enc_grad_mult != 1.0 and self.training: + rst1 = self.mult_rst_grad(rst1, self.enc_grad_mult) + rst2 = self.mult_rst_grad(rst2, self.enc_grad_mult) + rst = (rst1, rst2) + return rst + + return merge_output(ret1, ret2) + + def reorder_encoder_out(self, encoder_out, new_order): + assert self.training is False # used for inference only + return self.spch_encoder.reorder_encoder_out(encoder_out, new_order) + + +# TransformerMultiInputDecoder: take one or two encoder inputs +class TransformerMultiInputDecoder(FairseqDecoder): + def __init__( + self, + dictionary, + spch_decoder, + text_decoder, + compute_cross_attentive_loss=False, + cross_attentive_loss_with_norm=True, + cross_attentive_loss_reverse=False, + ): + + super().__init__(dictionary) + self.spch_decoder = spch_decoder + self.text_decoder = text_decoder + self.compute_cross_attentive_loss = compute_cross_attentive_loss + self.cross_attentive_loss_with_norm = cross_attentive_loss_with_norm + self.cross_attentive_loss_reverse = cross_attentive_loss_reverse + + @classmethod + def share_spchdecoder(cls, task_args, text_decoder, spch_decoder): + if task_args.decoder_shared_layer_level == 0: + return text_decoder + assert text_decoder.embed_tokens == spch_decoder.embed_tokens + spch_decoder.project_in_dim = text_decoder.project_in_dim + spch_decoder.embed_positions = text_decoder.embed_positions + spch_decoder.layernorm_embedding = text_decoder.layernorm_embedding + spch_decoder.project_out_dim = text_decoder.project_out_dim + spch_decoder.adaptive_softmax = text_decoder.adaptive_softmax + if task_args.decoder_shared_layer_level == 1: + spch_decoder.output_projection = text_decoder.output_projection + spch_decoder.layer_norm = text_decoder.layer_norm + else: # 2 + spch_decoder.output_projection.weight = ( + text_decoder.output_projection.weight + ) + for i, ly in enumerate(text_decoder.layers): + sly = spch_decoder.layers[i] + sly.self_attn = ly.self_attn + sly.self_attn_layer_norm = ly.self_attn_layer_norm + # sly.encoder_attn = ly.encoder_attn + if ( + task_args.decoder_shared_layer_level == 1 + ): # share everything, but under different models + sly.encoder_attn = ly.encoder_attn + sly.encoder_attn_layer_norm = ly.encoder_attn_layer_norm + sly.fc1 = ly.fc1 + sly.fc2 = ly.fc2 + sly.final_layer_norm = ly.final_layer_norm + else: # task_args.decoder_shared_layer_level == 2: #separated encoder_attn_layer_norm and bias + sly.encoder_attn.k_proj.weight = ly.encoder_attn.k_proj.weight + sly.encoder_attn.v_proj.weight = ly.encoder_attn.v_proj.weight + sly.encoder_attn.q_proj.weight = ly.encoder_attn.q_proj.weight + sly.encoder_attn.out_proj.weight = ly.encoder_attn.out_proj.weight + sly.fc1.weight = ly.fc1.weight + sly.fc2.weight = ly.fc2.weight + + return spch_decoder + + def cross_attentive_loss( + self, teacher_states, student_states, teacher_masking, student_masking, eps=1e-6 + ): + x = teacher_states.transpose(0, 1) # from T X B X D to B X T X D + y = student_states.transpose(0, 1) + if self.cross_attentive_loss_with_norm: + x = x / (x.norm(dim=2, keepdim=True) + eps) + y = y / (y.norm(dim=2, keepdim=True) + eps) + dim = x.size(-1) + # lengths: batch X seqLen + sim_scores_xy = torch.bmm(x, y.transpose(1, 2)) # batch X lenx X leny ] + if y.dtype == torch.float16: + sim_scores_xy = sim_scores_xy.float() + y = y.float() + x = x.float() + if teacher_masking != []: + assert len(teacher_masking) == 1 + sim_scores_xy = sim_scores_xy.masked_fill( + teacher_masking[0].unsqueeze(-1), float("-inf") + ) + if student_masking != []: + sim_scores_xy = sim_scores_xy.masked_fill( + student_masking[0].unsqueeze(1), float("-inf") + ) + # do masking + y_weights = utils.softmax(sim_scores_xy, dim=-1) + if teacher_masking != []: + y_weights = y_weights.masked_fill(teacher_masking[0].unsqueeze(-1), 0) + x_reconstruct_from_y = torch.bmm(y_weights, y) + + sim_scores_xx = torch.bmm(x, x.transpose(1, 2)) # batch X lenx X lenx ] + x_weights = utils.softmax(sim_scores_xx, dim=-1) + if teacher_masking != []: + x_weights = x_weights.masked_fill(teacher_masking[0].unsqueeze(-1), 0) + + # no gradient for teacher state + x_reconstruct_from_x = torch.bmm(x_weights, x).detach() + cost = (x_reconstruct_from_x - x_reconstruct_from_y).norm(dim=2) + if teacher_masking != []: + cost = cost.masked_fill(teacher_masking[0], 0) + + if not self.cross_attentive_loss_with_norm: + cost = cost / dim + return cost + + def forward( + self, + prev_output_tokens, + encoder_out, + incremental_state=None, + has_txt_input=False, + **kwargs + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for input feeding/teacher forcing. If there are + two or more input during training, they will share the same prev_output_tokens + encoder_out (tuple[Tensor]): output from the encoder, used for + encoder-side attention. It will be tuple if there are more inputs, but a tensor + if only one input + incremental_state ([dict]): dictionary used for storing state during + :ref:`Incremental decoding`. It is only valid for inference, only from single + input + Returns: + tuple: + - the last decoder layer's output of shape `(batch, tgt_len, + vocab)`. If there are N inputs, batch will be N bigger than a single input + - the last decoder layer's attention weights of shape `(batch, + tgt_len, src_len)` + """ + assert not isinstance(encoder_out, EncoderOut) + if isinstance(encoder_out, tuple): # training with mulitple input + rst = [] + assert len(encoder_out) == 2 + for i, eo in enumerate(encoder_out): + assert incremental_state is None + if i == 0: + rst.append( + self.spch_decoder(prev_output_tokens, eo, incremental_state) + ) + else: + rst.append( + self.text_decoder(prev_output_tokens, eo, incremental_state) + ) + dec_out = torch.cat([r[0] for r in rst], dim=0) + attn_cost = None + if self.compute_cross_attentive_loss: + assert isinstance(encoder_out[0], dict) + if self.cross_attentive_loss_reverse: + attn_cost = self.cross_attentive_loss( + teacher_states=encoder_out[1]["encoder_states"], # text_states + student_states=encoder_out[0]["encoder_states"], # spch_states + teacher_masking=encoder_out[1]["encoder_padding_mask"], + student_masking=encoder_out[0]["encoder_padding_mask"], + ) + else: + attn_cost = self.cross_attentive_loss( + teacher_states=encoder_out[0]["encoder_states"], # spch_states + student_states=encoder_out[1]["encoder_states"], # text_states + teacher_masking=encoder_out[0]["encoder_padding_mask"], + student_masking=encoder_out[1]["encoder_padding_mask"], + ) + + return (dec_out, {"attn_cost": attn_cost}) + else: # inference or training with one input + if has_txt_input: + return self.text_decoder( + prev_output_tokens, encoder_out, incremental_state + ) + return self.spch_decoder(prev_output_tokens, encoder_out, incremental_state) + + +# Note: +# dual input transformer: +# encoder: S2TTransformerEncoder for speech + TransformerEncoder for text +# decoder: TransformerDecoder for text +@register_model("dual_input_s2t_transformer") +class DualInputS2TTransformerModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + self.num_updates = 0 + + def max_positions(self): + return None # it is provided in task + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # encoder 1: S2TTransformerEncoder for speech + parser.add_argument( + "--conv-kernel-sizes", + type=str, + metavar="N", + help="kernel sizes of Conv1d subsampling layers", + ) + parser.add_argument( + "--conv-channels", + type=int, + metavar="N", + help="# of channels in Conv1d subsampling layers", + ) + parser.add_argument( + "--enc-output-dim", + type=int, + metavar="N", + help=""" + encoder output dimension, can be None. If specified, projecting the + transformer output to the specified dimension""", + ) + # standard Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-text-embed-dim", + type=int, + metavar="N", + help="encoder text embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + # non-standard transformer parameters + parser.add_argument( + "--speech-encoder-layers", + type=int, + metavar="N", + help="num speech encoder layers", + ) + parser.add_argument( + "--text-encoder-layers", + type=int, + metavar="N", + help="num text encoder layers", + ) + parser.add_argument( + "--encoder-shared-layers", + type=int, + metavar="N", + help="num shared encoder layers", + ) + parser.add_argument( + "--encoder-shared-layer-level", + type=int, + metavar="N", + default=0, + choices=[0, 1, 2], + help="share layer level 0: all share 1: all share with separate model 2: share weight but not bias and layernorm", + ) + + parser.add_argument( + "--decoder-shared-layer-level", + default=0, + choices=[0, 1, 2], + type=int, + metavar="N", + help="0: share everything; 1: share everything with different model 2: no share layer_norm and bias", + ) + ### + parser.add_argument( + "--text-input-cost-ratio", + type=float, + default=1.0, + metavar="V", + help="text input cost ratio relative to speech input cost", + ) + parser.add_argument( + "--init-scale", + type=float, + default=1.0, + metavar="V", + help="scale the initial weight by given factor", + ) + parser.add_argument( + "--enc-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc1 and enc2 gradient by V", + ) + parser.add_argument( + "--enc2-along-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc2 gradient by V if only enc2 is used", + ) + parser.add_argument( + "--load-pretrain-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained encoder """, + ) + parser.add_argument( + "--load-pretrain-speech-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained speech encoder """, + ) + parser.add_argument( + "--load-pretrain-text-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained text encoder """, + ) + parser.add_argument( + "--load-pretrain-text-encoder-last", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained text encoder """, + ) + parser.add_argument( + "--load-pretrain-decoder", + type=str, + metavar="EXPR", + default="", + help=""" path to the pretrained encoder """, + ) + parser.add_argument( + "--add-speech-eos", + action="store_true", + help="add eos token at the end of input feature", + ) + parser.add_argument( + "--speech-encoder-adapter-type", + type=str, + metavar="EXPR", + default="None", + choices=["None", "Linear", "MLP"], + help="add speech encoder adapter", + ) + + @classmethod + def build_encoder(cls, args, task): + spch_encoder = DualInputEncoder.build_spch_encoder(args) + text_encoder = DualInputEncoder.build_text_encoder( + args, task.src_dict, spch_encoder + ) + cross_attentive_loss_before_last_layer = ( + 0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1 + ) + encoder = DualInputEncoder( + args, + spch_encoder, + text_encoder, + task.src_dict, + cross_attentive_loss_before_last_layer, + ) + if args.init_scale != 1.0: + with torch.no_grad(): + for param in encoder.parameters(): + param.data.mul_(args.init_scale) + if args.load_pretrain_text_encoder != "": + checkpoint_utils.load_pretrained_component_from_model( + text_encoder, args.load_pretrain_text_encoder + ) + if args.load_pretrain_speech_encoder != "": + if hasattr(spch_encoder, "encoder"): + checkpoint_utils.load_pretrained_component_from_model( + spch_encoder.encoder, args.load_pretrain_speech_encoder + ) + else: + checkpoint_utils.load_pretrained_component_from_model( + spch_encoder, args.load_pretrain_speech_encoder + ) + if ( + args.load_pretrain_text_encoder_last != "" + ): # if share encoder, speech encoder parameters will be used. + # It provides a chance to use pre-trained mt encoder instead + checkpoint_utils.load_pretrained_component_from_model( + text_encoder, args.load_pretrain_text_encoder_last + ) + + if args.load_pretrain_encoder != "": + checkpoint_utils.load_pretrained_component_from_model( + encoder, args.load_pretrain_encoder + ) + return encoder + + @classmethod + def build_decoder(cls, args, task): + dec_cfg = { + "decoder_layerdrop": args.decoder_layerdrop, + "share_decoder_input_output_embed": args.share_decoder_input_output_embed, + "decoder_embed_dim": args.decoder_embed_dim, + "max_target_positions": args.max_target_positions, + "dropout": args.dropout, + "encoder_learned_pos": args.encoder_learned_pos, + "decoder_learned_pos": args.decoder_learned_pos, + "layernorm_embedding": args.layernorm_embedding, + "decoder_normalize_before": args.decoder_normalize_before, + "activation_dropout": args.activation_dropout, + "attention_dropout": args.attention_dropout, + "decoder_ffn_embed_dim": args.decoder_ffn_embed_dim, + "decoder_layers": args.decoder_layers, + "decoder_attention_heads": args.decoder_attention_heads, + "decoder_output_dim": args.decoder_embed_dim, + "no_scale_embedding": args.no_scale_embedding, + "adaptive_input": args.adaptive_input, + "quant_noise_pq": args.quant_noise_pq, + "adaptive_softmax_cutoff": args.adaptive_softmax_cutoff, + "tie_adaptive_weights": args.tie_adaptive_weights, + "no_token_positional_embeddings": args.no_token_positional_embeddings, + "encoder": {"embed_dim":args.encoder_embed_dim} + } + dec_cfg = namedtuple("args", dec_cfg.keys())(*dec_cfg.values()) + dec_emb = nn.Embedding( + len(task.target_dictionary), + args.decoder_embed_dim, + task.target_dictionary.pad(), + ) + compute_cross_attentive_loss = ( + True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False + ) + cross_attentive_loss_without_norm = getattr( + args, "attentive_cost_without_normalize", False + ) + cross_attentive_loss_reverse = ( + False # getattr(args, "attentive_cost_reverse", False) + ) + + text_decoder = TransformerDecoder(dec_cfg, task.target_dictionary, dec_emb) + spch_decoder = TransformerDecoder(dec_cfg, task.target_dictionary, dec_emb) + spch_decoder = TransformerMultiInputDecoder.share_spchdecoder( + args, text_decoder, spch_decoder + ) + decoder = TransformerMultiInputDecoder( + dictionary=task.target_dictionary, + spch_decoder=spch_decoder, + text_decoder=text_decoder, + compute_cross_attentive_loss=compute_cross_attentive_loss, + cross_attentive_loss_with_norm=True + if not cross_attentive_loss_without_norm + else False, + cross_attentive_loss_reverse=cross_attentive_loss_reverse, + ) + if args.init_scale != 1.0: + with torch.no_grad(): + for param in decoder.parameters(): + param.data.mul_(args.init_scale) + if args.load_pretrain_decoder != "": + try: + checkpoint_utils.load_pretrained_component_from_model( + decoder, args.load_pretrain_decoder + ) + except RuntimeError: + checkpoint_utils.load_pretrained_component_from_model( + decoder.text_decoder, args.load_pretrain_decoder + ) + if args.decoder_shared_layer_level > 0: + checkpoint_utils.load_pretrained_component_from_model( + decoder.spch_decoder, args.load_pretrain_decoder + ) + + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted + # (in case there are any new ones) + dualinputs2ttransformer_base(args) + + encoder = cls.build_encoder(args, task) + decoder = cls.build_decoder(args, task) + return cls(encoder, decoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = super().get_normalized_probs(net_output, log_probs, sample) + lprobs.batch_first = True + return lprobs + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + use_encoder_outputs=False, + src_txt_tokens=None, + src_txt_lengths=None, + mode="sup_speech", + **kwargs + ): + """ + Run the forward pass for an encoder-decoder model. + + First feed a batch of source tokens through the encoder. Then, feed the + encoder output and previous decoder outputs (i.e., teacher forcing) to + the decoder to produce the next outputs:: + + encoder_out = self.encoder(src_tokens, src_lengths) + return self.decoder(prev_output_tokens, encoder_out) + + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (LongTensor): source sentence lengths of shape `(batch)` + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + mode = 'sup_speech' or 'text' + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + if mode == "text": + assert src_txt_tokens is None + src_txt_tokens = src_tokens + src_txt_lengths = src_lengths + src_tokens = None + src_lengths = None + encoder_out = self.encoder( + src_tokens, + src_lengths=src_lengths, + src_txt_tokens=src_txt_tokens, + src_txt_lengths=src_txt_lengths, + **kwargs + ) + has_txt_input = True if src_txt_tokens is not None else False + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + has_txt_input=has_txt_input, + **kwargs + ) + if use_encoder_outputs: + return decoder_out, encoder_out + return decoder_out + + +@register_model_architecture( + "dual_input_s2t_transformer", "dualinputs2ttransformer_base" +) +def dualinputs2ttransformer_base(args): + args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0) + # Convolutional subsampler + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5") + args.conv_channels = getattr(args, "conv_channels", 1024) + # Transformer + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_text_embed_dim = getattr( + args, "encoder_text_embed_dim", args.encoder_embed_dim + ) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", args.dropout) + args.activation_dropout = getattr(args, "activation_dropout", args.dropout) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 10) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.encoder_shared_layers = getattr(args, "encoder_shared_layers", 0) + args.decoder_layers = getattr(args, "decoder_layers", 6) + + args.add_speech_eos = getattr(args, "add_speech_eos", False) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_s") +def dualinputs2ttransformer_s(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 7) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 7) + args.decoder_layers = getattr(args, "decoder_layers", 7) + dualinputs2ttransformer_base(args) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_m") +def dualinputs2ttransformer_m(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.dropout = getattr(args, "dropout", 0.15) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 10) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 6) + dualinputs2ttransformer_base(args) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_b") +def dualinputs2ttransformer_b(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 768 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) + args.dropout = getattr(args, "dropout", 0.15) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 6) + dualinputs2ttransformer_base(args) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_l") +def dualinputs2ttransformer_l(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.2) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 6) + dualinputs2ttransformer_base(args) diff --git a/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputwavtransformer.py b/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputwavtransformer.py new file mode 100644 index 0000000..66e4b3f --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputwavtransformer.py @@ -0,0 +1,526 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from collections import OrderedDict, namedtuple + +import torch.nn as nn + +from fairseq import checkpoint_utils, utils +from fairseq.checkpoint_utils import load_checkpoint_to_cpu +from fairseq.file_io import PathManager +from fairseq.models import register_model, register_model_architecture +from fairseq.models.speech_to_text import ( + SpeechWavTransformerEncoder, + StackedSpeechWavTransformerEncoder, + TransformerDecoder, +) +from fairseq.models.transformer import TransformerEncoder + +from .s2t_dualinputtransformer import ( + DualInputEncoder, + DualInputS2TTransformerModel, + TransformerMultiInputDecoder, +) + +logger = logging.getLogger(__name__) + + +@register_model("dual_input_wav_transformer") +class DualInputWavTransformerModel(DualInputS2TTransformerModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + def add_transformer_args(parser): + # We can't use TransformerModel.add_args(parser), since it defines max-source-positions which is duplicated with tasks/speech_to_text.py + # Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + + parser.add_argument( + "--encoder-learned-pos", + action="store_true", + help="use learned positional embeddings", + ) + parser.add_argument( + "--decoder-learned-pos", + action="store_true", + help="use learned positional embeddings", + ) + + add_transformer_args(parser) + SpeechWavTransformerEncoder.add_args(parser) + parser.add_argument( + "--load-pretrained-speech-text-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained speech text encoder from SpeechTextPreTrainModel """, + ) + parser.add_argument( + "--load-pretrained-wav2vec-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained speech text encoder from wav2vec """, + ) + + parser.add_argument( + "--load-pretrained-speech-text-decoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained speech text decoder from SpeechTextPreTrainModel """, + ) + parser.add_argument( + "--load-pretrained-text-decoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained text decoder """, + ) + parser.add_argument( + "--load-init-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to load seed encoder model """, + ) + parser.add_argument( + "--load-init-decoder", + type=str, + default="", + metavar="EXPR", + help=""" path to load seed decoder model """, + ) + + parser.add_argument( + "--text-input-cost-ratio", + type=float, + default=1.0, + metavar="V", + help="text input cost ratio relative to speech input cost", + ) + parser.add_argument( + "--enc-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc1 and enc2 gradient by V", + ) + parser.add_argument( + "--enc2-along-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc2 gradient by V if only enc2 is used", + ) + parser.add_argument( + "--no-strict-check-pretrain-model", + action="store_true", + help="Don't apply strict model check for the pretrained model", + ) + + parser.add_argument( + "--stacked-encoder", + action="store_true", + help="stack speech and text encoders", + ) + + @classmethod + def update_transformer_encoder_cfg(cls, args, update_dict): + cfg = dict(args._get_kwargs()) + for fkey in update_dict.keys(): + cfg[fkey] = update_dict[fkey] + cfg.pop("_name", None) # remove keys start with _ + model_args = namedtuple("args", cfg.keys())(*cfg.values()) + return model_args + + @classmethod + def build_text_encoder(cls, args, src_dictionary): + enc_emb = nn.Embedding( + len(src_dictionary), args.encoder_embed_dim, src_dictionary.pad() + ) + model_args = cls.update_transformer_encoder_cfg( + args, + { + "encoder_layers": args.text_encoder_layers, + "max_source_positions": args.max_positions_text, + }, + ) + text_encoder = TransformerEncoder(model_args, src_dictionary, enc_emb) + return text_encoder + + @classmethod + def build_speech_encoder(cls, args): + model_args = cls.update_transformer_encoder_cfg( + args, {"encoder_layers": args.speech_encoder_layers} + ) + speech_encoder = SpeechWavTransformerEncoder(model_args) + return speech_encoder + + @classmethod + def check_args(cls, condition, is_strict, msg): + if condition: + return + if is_strict: + raise ValueError(msg) + logger.warn(msg) + + @classmethod + def build_encoder(cls, args, task): + # text_encoder = cls.build_text_encoder(args, task.source_dictionary ) + text_encoder = cls.build_text_encoder(args, task.src_dict) + speech_encoder = cls.build_speech_encoder(args) + if args.load_pretrained_wav2vec_encoder: + component_pairs = ( + ("feature_extractor", speech_encoder.subsample), + ("post_extract_proj", speech_encoder.feat_proj), + ("layer_norm", speech_encoder.feat_layer_norm), + ("encoder.pos_conv", speech_encoder.embed_positions), + ("encoder.layers", speech_encoder.layers), + ("encoder.layer_norm", speech_encoder.layer_norm), + ("mask_emb", speech_encoder.mask_emb), + ) + state = cls.load_pretrained_speech_text_components( + args.load_pretrained_wav2vec_encoder, component_pairs + ) + cls.check_args( + args.encoder_normalize_before + == state["cfg"]["model"]["layer_norm_first"], + not args.no_strict_check_pretrain_model, + f"encoder_normalize_before {args.encoder_normalize_before} doesn't match with the pretrained model", + ) + cls.check_args( + args.activation_fn == state["cfg"]["model"]["activation_fn"], + not args.no_strict_check_pretrain_model, + f"activation_fn {args.activation_fn} doesn't match with the pretrained model", + ) + + if getattr(args, "stacked_encoder", False): + if args.encoder_shared_text_layers_from_begin > 0: + raise ValueError( + "We can not stack encoders and share encoders at the same time!" + ) + speech_encoder = StackedSpeechWavTransformerEncoder( + speech_encoder, text_encoder.layers, text_encoder.layer_norm + ) + else: + cls.share_speech_text_encoder( + speech_encoder, text_encoder, args.encoder_shared_text_layers_from_begin + ) + + cross_attentive_loss_before_last_layer = ( + 0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1 + ) + encoder = DualInputEncoder( + args, + speech_encoder, + text_encoder, + task.src_dict, + cross_attentive_loss_before_last_layer, + ) + if args.load_pretrained_speech_text_encoder: + component_pairs = ( + ("encoder.sup_s2s_speech_encoder", encoder.spch_encoder), + ("encoder.text_encoder", encoder.text_encoder), + ) + cls.load_pretrained_speech_text_components( + args.load_pretrained_speech_text_encoder, component_pairs + ) + if getattr(args, "load_init_encoder", "") != "": + checkpoint_utils.load_pretrained_component_from_model( + encoder, args.load_init_encoder + ) + return encoder + + @classmethod + def build_text_decoder(cls, args, tgt_dictionary, dec_emb_share=None): + dec_emb = ( + nn.Embedding( + len(tgt_dictionary), args.decoder_embed_dim, tgt_dictionary.pad() + ) + if dec_emb_share is None + else dec_emb_share + ) + text_decoder = TransformerDecoder(args, tgt_dictionary, dec_emb) + return text_decoder + + @classmethod + def build_decoder(cls, args, task): + text_decoder = cls.build_text_decoder(args, task.target_dictionary) + compute_cross_attentive_loss = ( + True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False + ) + cross_attentive_loss_without_norm = getattr( + args, "attentive_cost_without_normalize", False + ) + cross_attentive_loss_reverse = ( + False # getattr(args, "attentive_cost_reverse", False) + ) + if getattr(args, "load_pretrained_text_decoder", "") != "": + checkpoint_utils.load_pretrained_component_from_model( + text_decoder, args.load_pretrained_text_decoder + ) + + if args.load_pretrained_speech_text_decoder: + component_pairs = (("decoder.text_decoder", text_decoder),) + cls.load_pretrained_speech_text_components( + args.load_pretrained_speech_text_decoder, component_pairs + ) + + decoder = TransformerMultiInputDecoder( + dictionary=task.target_dictionary, + spch_decoder=text_decoder, + text_decoder=text_decoder, + compute_cross_attentive_loss=compute_cross_attentive_loss, + cross_attentive_loss_with_norm=True + if not cross_attentive_loss_without_norm + else False, + cross_attentive_loss_reverse=cross_attentive_loss_reverse, + ) + if getattr(args, "load_init_decoder", "") != "": + checkpoint_utils.load_pretrained_component_from_model( + decoder, args.load_init_decoder + ) + return decoder + + @classmethod + def load_pretrained_speech_text_components(cls, checkpoint, component_pairs): + if not PathManager.exists(checkpoint): + raise IOError("Model file not found: {}".format(checkpoint)) + state = load_checkpoint_to_cpu(checkpoint) + for component_type, component in component_pairs: + if isinstance(component, nn.parameter.Parameter): + component.data.copy_(state["model"][component_type]) + else: + component_state_dict = OrderedDict() + for key in state["model"].keys(): + if key.startswith(component_type): + component_subkey = key[len(component_type) + 1 :] + component_state_dict[component_subkey] = state["model"][key] + component.load_state_dict(component_state_dict, strict=True) + return state + + @classmethod + def share_speech_text_encoder( + cls, speech_encoder, text_encoder, shared_layers_from_begin + ): + if shared_layers_from_begin > 0: + num_text_encoder_layers = len(text_encoder.layers) + assert len(speech_encoder.layers) >= shared_layers_from_begin + assert num_text_encoder_layers >= shared_layers_from_begin + assert len(speech_encoder.layers) >= num_text_encoder_layers + for i, ly in enumerate( + speech_encoder.layers[ + -num_text_encoder_layers : -num_text_encoder_layers + + shared_layers_from_begin + ] + ): + assert isinstance(text_encoder.layers[i], type(ly)) + text_encoder.layers[i] = ly + + +@register_model_architecture( + "dual_input_wav_transformer", "dualinputs2twavtransformer_base" +) +def dualinputs2twavtransformer_base(args): + # speech masking + args.dropout_input = getattr(args, "dropout_input", 0) + args.dropout_features = getattr(args, "dropout_features", 0) + args.speech_mask_length = getattr(args, "speech_mask_length", 10) + args.speech_mask_prob = getattr(args, "speech_mask_prob", 0.65) + args.speech_mask_selection = getattr(args, "speech_mask_selection", "static") + args.speech_mask_other = getattr(args, "speech_mask_other", 0) + args.speech_mask_min_space = getattr(args, "speech_mask_min_space", 1) + args.speech_no_mask_overlap = getattr(args, "speech_no_mask_overlap", False) + args.speech_conv_bias = getattr(args, "speech_conv_bias", False) + args.speech_extractor_mode = getattr(args, "speech_extractor_mode", "default") + args.no_strict_check_pretrain_model = getattr( + args, "no_strict_check_pretrain_model", False + ) + + args.speech_mask_channel_length = getattr(args, "speech_mask_channel_length", 10) + args.speech_mask_channel_prob = getattr(args, "speech_mask_channel_prob", 0.0) + args.speech_mask_channel_selection = getattr( + args, "speech_mask_channel_selection", "static" + ) + args.speech_mask_channel_other = getattr(args, "speech_mask_channel_other", 0) + args.speech_mask_channel_min_space = getattr( + args, "speech_mask_channel_min_space", 1 + ) + args.speech_no_mask_channel_overlap = getattr( + args, "speech_no_mask_channel_overlap", False + ) + args.no_scale_feature = getattr(args, "", False) + args.feature_grad_mult = getattr(args, "feature_grad_mult", 0.0) # 0.1 + + # Transformer + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = getattr( + args, "encoder_ffn_embed_dim", args.encoder_embed_dim * 4 + ) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0.1) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_attention_heads = getattr( + args, "decoder_attention_heads", args.encoder_attention_heads + ) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0) + args.activation_dropout = getattr(args, "activation_dropout", args.dropout) + args.activation_fn = getattr(args, "activation_fn", "relu") # gelu? + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.encoder_shared_text_layers_from_begin = getattr( + args, "encoder_shared_text_layers_from_begin", 6 + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + + +@register_model_architecture( + "dual_input_wav_transformer", "dualinputs2twavtransformer_base_stack" +) +def dualinputs2twavtransformer_base_stack(args): + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 6) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.encoder_shared_text_layers_from_begin = getattr( + args, "encoder_shared_text_layers_from_begin", 0 + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.stacked_encoder = getattr(args, "stacked_encoder", True) + args.layernorm_embedding = getattr(args, "layernorm_embedding", True) + dualinputs2twavtransformer_base(args) + + +@register_model_architecture( + "dual_input_wav_transformer", "dualinputs2twavtransformer_large" +) +def dualinputs2twavtransformer_large(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 24) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 12) + args.encoder_shared_text_layers_from_begin = getattr( + args, "encoder_shared_text_layers_from_begin", 12 + ) + args.decoder_layers = getattr(args, "decoder_layers", 12) + dualinputs2twavtransformer_base(args) diff --git a/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py b/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py new file mode 100644 index 0000000..7b4cbb0 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py @@ -0,0 +1,584 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy + +import torch.nn as nn +from fairseq import checkpoint_utils +from fairseq import utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + register_model, + register_model_architecture, + FairseqEncoder, +) +from fairseq.models.speech_to_text import Wav2VecEncoderWithAdaptor +from fairseq.models.speech_to_text.xm_transformer import ( + set_default_adaptor_args, + set_default_w2v_encoder_args, + need_finetuning +) +from fairseq.models.transformer import TransformerEncoder, TransformerDecoder +from fairseq.models.wav2vec import TransformerSentenceEncoderLayer +from fairseq.utils import safe_hasattr + +from .s2t_dualinputtransformer import ( + DualInputS2TTransformerModel, + TransformerMultiInputDecoder, + DualInputEncoder, +) + + +class TransformerSentenceEncoderLayerStd(TransformerSentenceEncoderLayer): + def __init__(self, sent_enc_layer): + super(TransformerSentenceEncoderLayer, self).__init__() + self.embedding_dim = sent_enc_layer.embedding_dim + self.dropout = sent_enc_layer.dropout + self.activation_dropout = sent_enc_layer.activation_dropout + + # Initialize blocks + self.activation_fn = sent_enc_layer.activation_fn + self.self_attn = sent_enc_layer.self_attn + + self.dropout1 = sent_enc_layer.dropout1 + self.dropout2 = sent_enc_layer.dropout2 + self.dropout3 = sent_enc_layer.dropout3 + + self.layer_norm_first = sent_enc_layer.layer_norm_first + + # layer norm associated with the self attention layer + self.self_attn_layer_norm = sent_enc_layer.self_attn_layer_norm + self.fc1 = sent_enc_layer.fc1 + self.fc2 = sent_enc_layer.fc2 + + # layer norm associated with the position wise feed-forward NN + self.final_layer_norm = sent_enc_layer.final_layer_norm + + def forward( + self, + x, + self_attn_mask=None, + self_attn_padding_mask=None, + need_weights=None, + att_args=None, + ): + x, attn = super().forward( + x, self_attn_mask, self_attn_padding_mask, need_weights, att_args + ) + return x + + +# TODO retire SharedEncoder +class SharedEncoder(FairseqEncoder): + def __init__(self, wav2vec_enc, mbart_enc, adaptor, shared_layers): + super().__init__(None) + self.w2v_encoder = wav2vec_enc + self.shared_layers = self.w2v_encoder.w2v_model.encoder.layers[-shared_layers:] + self.w2v_encoder.w2v_model.encoder.layers = ( + self.w2v_encoder.w2v_model.encoder.layers[:-shared_layers] + ) + self.adaptor = adaptor + if self.shared_layers[-1].layer_norm_first: + self.final_layer_norm = mbart_enc.layer_norm + else: + mbart_enc.layer_norm = None + self.final_layer_norm = None + shared_layer_from = len(mbart_enc.layers) - shared_layers + if shared_layer_from < 0: + shared_layer_from = 0 + for layer_id, layer in enumerate(self.shared_layers): + mbart_enc.layers[ + shared_layer_from + layer_id + ] = TransformerSentenceEncoderLayerStd(layer) + + def forward(self, src_tokens, src_lengths=None, **kwargs): + padding_mask = lengths_to_padding_mask(src_lengths) + if not padding_mask.any(): + padding_mask = None + + out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True) + x = out["encoder_out"] + enc_padding_mask = None + if out["encoder_padding_mask"] is not None: + enc_padding_mask = out["encoder_padding_mask"].transpose( + 0, 1 + ) # T X B --> B X T + + x, enc_padding_mask = self.adaptor(x, enc_padding_mask) + for layer in self.shared_layers: + x, _ = layer(x, enc_padding_mask) + if self.final_layer_norm is not None: + x = self.final_layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [enc_padding_mask] + if enc_padding_mask is not None + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": [], # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + +class StackedWav2VecEncoderWithAdaptor(FairseqEncoder): + def __init__( + self, + wav2vec_enc, + mbart_enc_layers, + mbart_layer_norm, + adaptor, + drop_w2v_layers=0, + ): + super().__init__(None) + self.w2v_encoder = wav2vec_enc + self.adaptor = adaptor + self.mbart_encoder_layers = mbart_enc_layers + self.final_layer_norm = mbart_layer_norm + if drop_w2v_layers > 0: + self.w2v_encoder.w2v_model.encoder.layers = ( + self.w2v_encoder.w2v_model.encoder.layers[:-drop_w2v_layers] + ) + + def forward(self, src_tokens, src_lengths=None, return_all_hiddens=False, **kwargs): + padding_mask = lengths_to_padding_mask(src_lengths) + if not padding_mask.any(): + padding_mask = None + + out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True) + x = out["encoder_out"] + enc_padding_mask = None + if out["padding_mask"] is not None: + enc_padding_mask = out["padding_mask"] # B X T + + x, enc_padding_mask = self.adaptor(x, enc_padding_mask) + encoder_states = [] + for layer in self.mbart_encoder_layers: + x = layer(x, enc_padding_mask) + if return_all_hiddens: + encoder_states.append(x) + if self.final_layer_norm is not None: + x = self.final_layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [enc_padding_mask] + if enc_padding_mask is not None + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] + if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] + if len(encoder_out["encoder_padding_mask"]) == 0 + else [ + x.index_select(0, new_order) + for x in encoder_out["encoder_padding_mask"] + ] + ) + + new_encoder_embedding = ( + [] + if len(encoder_out["encoder_embedding"]) == 0 + else [ + x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] + ] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } + + +# Note: +# dual input transformer: +# encoder: wav2vec for speech + mbart encoder for text +# decoder: mbart decoder for text +@register_model("dual_input_xm_transformer") +class DualInputXMTransformerModel(DualInputS2TTransformerModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # wav2vec encoder + Wav2VecEncoderWithAdaptor.add_args(parser) + # add_decoder_args(parser) + # mbart Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + + parser.add_argument( + "--mbart-dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--mbart-attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--mbart-activation-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-mbart-from", + type=str, + metavar="STR", + help="model to take text encoder decoder weights from (for initialization)", + ) + # parser.add_argument("--finetune-w2v-params", type=str, metavar="STR", + # help="comma-separated param strings to finetune.") + parser.add_argument( + "--finetune-mbart-decoder-params", + type=str, + metavar="STR", + help="comma-separated param strings to finetune.", + ) + parser.add_argument( + "--finetune-mbart-encoder-params", + type=str, + metavar="STR", + help="comma-separated param strings to finetune.", + ) + parser.add_argument( + "--skip-encoder-projection", + action="store_true", + help="skip the projection layer in encoder", + ) + + parser.add_argument( + "--enc-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc1 and enc2 gradient by V", + ) + parser.add_argument( + "--enc2-along-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc2 gradient by V if only enc2 is used", + ) + parser.add_argument( + "--text-input-cost-ratio", + type=float, + default=1.0, + metavar="V", + help="text input cost ratio relative to speech input cost", + ) + parser.add_argument( + "--stack-w2v-mbart-encoder", + action="store_true", + help="stack w2v and mbart encoder", + ) + parser.add_argument( + "--stack-w2v-mbart-nonorm-encoder", + action="store_true", + help="stack w2v and mbart encoder", + ) + parser.add_argument( + "--no-final-norm-decoder", action="store_true", help="no layer norm" + ) + parser.add_argument( + "--drop-w2v-layers", + type=int, + default=0, + metavar="N", + help="drop w2v encoder layers", + ) + + parser.add_argument( + "--share-w2v-text-encoder", + action="store_true", + help="share w2v encoder layers with text encoder", + ) + parser.add_argument( + "--shared-w2v-layers", + type=int, + default=0, + metavar="N", + help="shared encoder layers from w2v encoder", + ) + + @classmethod + def build_encoder(cls, args, task): + _args = copy.deepcopy(args) + _args.dropout = args.mbart_dropout + _args.attention_dropout = args.mbart_attention_dropout + _args.activation_dropout = args.mbart_activation_dropout + _args.max_source_positions = 1024 + enc_emb = nn.Embedding( + len(task.src_dict), _args.encoder_embed_dim, task.src_dict.pad() + ) + text_encoder = TransformerEncoder(_args, task.src_dict, enc_emb) + spch_encoder = Wav2VecEncoderWithAdaptor(args) + if getattr(args, "load_pretrained_mbart_from", None): + text_encoder = checkpoint_utils.load_pretrained_component_from_model( + component=text_encoder, checkpoint=args.load_pretrained_mbart_from + ) + if getattr(args, "stack_w2v_mbart_encoder", False): + assert getattr(args, "share_w2v_text_encoder", False) is False + spch_encoder = StackedWav2VecEncoderWithAdaptor( + spch_encoder.w2v_encoder, + text_encoder.layers, + text_encoder.layer_norm, + spch_encoder.adaptor, + args.drop_w2v_layers, + ) + elif getattr(args, "stack_w2v_mbart_nonorm_encoder", False): + text_encoder.layer_norm = None + spch_encoder = StackedWav2VecEncoderWithAdaptor( + spch_encoder.w2v_encoder, + text_encoder.layers, + text_encoder.layer_norm, + spch_encoder.adaptor, + args.drop_w2v_layers, + ) + elif getattr(args, "share_w2v_text_encoder", False): + spch_encoder = SharedEncoder( + spch_encoder.w2v_encoder, + text_encoder, + spch_encoder.adaptor, + args.shared_w2v_layers, + ) + + for k, p in spch_encoder.named_parameters(): + # Freeze pretrained models by default + if safe_hasattr( + args, "finetune_w2v_params" + ) and need_finetuning(args.finetune_w2v_params, k): + p.requires_grad = True + else: + p.requires_grad = False + for k, p in text_encoder.named_parameters(): + # Freeze pretrained models by default + if safe_hasattr( + args, "finetune_mbart_encoder_params" + ) and need_finetuning( + args.finetune_mbart_encoder_params, k + ): + p.requires_grad = True + else: + p.requires_grad = False + cross_attentive_loss_before_last_layer = ( + 0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1 + ) + encoder = DualInputEncoder( + args, + spch_encoder, + text_encoder, + task.src_dict, + cross_attentive_loss_before_last_layer, + ) + return encoder + + @classmethod + def build_decoder(cls, args, task): + _args = copy.deepcopy(args) + _args.dropout = args.mbart_dropout + _args.attention_dropout = args.mbart_attention_dropout + _args.activation_dropout = args.mbart_activation_dropout + _args.max_target_positions = 1024 + dec_emb = nn.Embedding( + len(task.tgt_dict), _args.encoder_embed_dim, task.tgt_dict.pad() + ) + decoder = TransformerDecoder(_args, task.tgt_dict, dec_emb) + if getattr(args, "load_pretrained_mbart_from", None): + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_mbart_from + ) + if getattr(args, "no_final_norm_decoder", False): + decoder.layer_norm = None + for k, p in decoder.named_parameters(): + # Freeze pretrained models by default + if safe_hasattr( + args, "finetune_mbart_decoder_params" + ) and need_finetuning( + args.finetune_mbart_decoder_params, k + ): + p.requires_grad = True + else: + p.requires_grad = False + + compute_cross_attentive_loss = ( + True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False + ) + cross_attentive_loss_without_norm = getattr( + args, "attentive_cost_without_normalize", False + ) + cross_attentive_loss_reverse = ( + False # getattr(args, "attentive_cost_reverse", False) + ) + decoder = TransformerMultiInputDecoder( + dictionary=task.target_dictionary, + spch_decoder=decoder, + text_decoder=decoder, + compute_cross_attentive_loss=compute_cross_attentive_loss, + cross_attentive_loss_with_norm=True + if not cross_attentive_loss_without_norm + else False, + cross_attentive_loss_reverse=cross_attentive_loss_reverse, + ) + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted + # (in case there are any new ones) + dualinputxmtransformer_base(args) + + encoder = cls.build_encoder(args, task) + decoder = cls.build_decoder(args, task) + return cls(encoder, decoder) + + +@register_model_architecture("dual_input_xm_transformer", "dualinputxmtransformer_base") +def dualinputxmtransformer_base(args): + # wav2vec encoder + set_default_w2v_encoder_args(args) + set_default_adaptor_args(args) + + # mbart model + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr( + args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim + ) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * 1024) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + + args.adaptive_input = getattr(args, "adaptive_input", False) + + args.mbart_attention_dropout = getattr(args, "mbart_attention_dropout", 0.0) + args.mbart_activation_dropout = getattr(args, "mbart_activation_dropout", 0.0) + args.mbart_dropout = getattr(args, "mbart_dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", True + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.layernorm_embedding = getattr(args, "layernorm_embedding", True) + + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) diff --git a/fairseq/examples/speech_text_joint_to_text/scripts/convert_model.py b/fairseq/examples/speech_text_joint_to_text/scripts/convert_model.py new file mode 100644 index 0000000..4923af1 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/scripts/convert_model.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import re +from collections import OrderedDict + +import torch + +from fairseq.file_io import PathManager + + +def is_update(param_name, module_name): + if module_name in param_name: + return True + return False + + +def load_checkpoint(src_cpt): + + with PathManager.open(src_cpt, "rb") as f: + state_src = torch.load( + f, + map_location=( + lambda s, _: torch.serialization.default_restore_location(s, "cpu") + ), + ) + + return state_src + + +def save_checkpoint(tgt_cpt, states): + + with PathManager.open(tgt_cpt, "wb") as f: + torch.save( + states, + f, + ) + + +# convert the pre-trained model into bart model +def main(): + parser = argparse.ArgumentParser() + # fmt: off + parser.add_argument('--input-model', required=True, + help='Input checkpoint file path.') + parser.add_argument('--output-model', required=True, + help='output checkpoint file path.') + # fmt: on + args = parser.parse_args() + print(args) + + states = load_checkpoint(args.input_model) + model = states["model"] + new_model = OrderedDict() + for key in model.keys(): + if re.search("^encoder.text_encoder", key): + new_key = re.sub("encoder.text_encoder", "encoder", key) + new_model[new_key] = model[key] + elif re.search("^decoder.text_decoder", key): + new_key = re.sub("decoder.text_decoder", "decoder", key) + new_model[new_key] = model[key] + states["model"] = new_model + save_checkpoint(args.output_model, states) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py b/fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py new file mode 100644 index 0000000..9db7793 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/scripts/g2p_encode.py @@ -0,0 +1,191 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import itertools +import logging +import re +import time + +from g2p_en import G2p + +logger = logging.getLogger(__name__) + +FAIL_SENT = "FAILED_SENTENCE" + + +def parse(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-path", type=str, required=True) + parser.add_argument("--out-path", type=str, required=True) + parser.add_argument("--lower-case", action="store_true") + parser.add_argument("--do-filter", action="store_true") + parser.add_argument("--use-word-start", action="store_true") + parser.add_argument("--dup-vowel", default=1, type=int) + parser.add_argument("--dup-consonant", default=1, type=int) + parser.add_argument("--no-punc", action="store_true") + parser.add_argument("--reserve-word", type=str, default="") + parser.add_argument( + "--reserve-first-column", + action="store_true", + help="first column is sentence id", + ) + ### + parser.add_argument("--parallel-process-num", default=1, type=int) + parser.add_argument("--logdir", default="") + args = parser.parse_args() + return args + + +def process_sent(sent, g2p, res_wrds, args): + sents = pre_process_sent(sent, args.do_filter, args.lower_case, res_wrds) + pho_seqs = [do_g2p(g2p, s, res_wrds, i == 0) for i, s in enumerate(sents)] + pho_seq = ( + [FAIL_SENT] + if [FAIL_SENT] in pho_seqs + else list(itertools.chain.from_iterable(pho_seqs)) + ) + if args.no_punc: + pho_seq = remove_punc(pho_seq) + if args.dup_vowel > 1 or args.dup_consonant > 1: + pho_seq = dup_pho(pho_seq, args.dup_vowel, args.dup_consonant) + if args.use_word_start: + pho_seq = add_word_start(pho_seq) + return " ".join(pho_seq) + + +def remove_punc(sent): + ns = [] + regex = re.compile("[^a-zA-Z0-9 ]") + for p in sent: + if (not regex.search(p)) or p == FAIL_SENT: + if p == " " and (len(ns) == 0 or ns[-1] == " "): + continue + ns.append(p) + return ns + + +def do_g2p(g2p, sent, res_wrds, is_first_sent): + if sent in res_wrds: + pho_seq = [res_wrds[sent]] + else: + pho_seq = g2p(sent) + if not is_first_sent: + pho_seq = [" "] + pho_seq # add space to separate + return pho_seq + + +def pre_process_sent(sent, do_filter, lower_case, res_wrds): + if do_filter: + sent = re.sub("-", " ", sent) + sent = re.sub("—", " ", sent) + if len(res_wrds) > 0: + wrds = sent.split() + wrds = ["SPLIT_ME " + w + " SPLIT_ME" if w in res_wrds else w for w in wrds] + sents = [x.strip() for x in " ".join(wrds).split("SPLIT_ME") if x.strip() != ""] + else: + sents = [sent] + if lower_case: + sents = [s.lower() if s not in res_wrds else s for s in sents] + return sents + + +def dup_pho(sent, dup_v_num, dup_c_num): + """ + duplicate phoneme defined as cmudict + http://www.speech.cs.cmu.edu/cgi-bin/cmudict + """ + if dup_v_num == 1 and dup_c_num == 1: + return sent + ns = [] + for p in sent: + ns.append(p) + if re.search(r"\d$", p): + for i in range(1, dup_v_num): + ns.append(f"{p}-{i}P") + elif re.search(r"\w", p): + for i in range(1, dup_c_num): + ns.append(f"{p}-{i}P") + return ns + + +def add_word_start(sent): + ns = [] + do_add = True + ws = "▁" + for p in sent: + if do_add: + p = ws + p + do_add = False + if p == " ": + do_add = True + else: + ns.append(p) + return ns + + +def load_reserve_word(reserve_word): + if reserve_word == "": + return [] + with open(reserve_word, "r") as fp: + res_wrds = [x.strip().split() for x in fp.readlines() if x.strip() != ""] + assert sum([0 if len(x) == 2 else 1 for x in res_wrds]) == 0 + res_wrds = dict(res_wrds) + return res_wrds + + +def process_sents(sents, args): + g2p = G2p() + out_sents = [] + res_wrds = load_reserve_word(args.reserve_word) + for sent in sents: + col1 = "" + if args.reserve_first_column: + col1, sent = sent.split(None, 1) + sent = process_sent(sent, g2p, res_wrds, args) + if args.reserve_first_column and col1 != "": + sent = f"{col1} {sent}" + out_sents.append(sent) + return out_sents + + +def main(): + args = parse() + out_sents = [] + with open(args.data_path, "r") as fp: + sent_list = [x.strip() for x in fp.readlines()] + if args.parallel_process_num > 1: + try: + import submitit + except ImportError: + logger.warn( + "submitit is not found and only one job is used to process the data" + ) + submitit = None + + if args.parallel_process_num == 1 or submitit is None: + out_sents = process_sents(sent_list, args) + else: + # process sentences with parallel computation + lsize = len(sent_list) // args.parallel_process_num + 1 + executor = submitit.AutoExecutor(folder=args.logdir) + executor.update_parameters(timeout_min=1000, cpus_per_task=4) + jobs = [] + for i in range(args.parallel_process_num): + job = executor.submit( + process_sents, sent_list[lsize * i : lsize * (i + 1)], args + ) + jobs.append(job) + is_running = True + while is_running: + time.sleep(5) + is_running = sum([job.done() for job in jobs]) < len(jobs) + out_sents = list(itertools.chain.from_iterable([job.result() for job in jobs])) + with open(args.out_path, "w") as fp: + fp.write("\n".join(out_sents) + "\n") + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py b/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py new file mode 100644 index 0000000..5fc5d9e --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + diff --git a/fairseq/examples/speech_text_joint_to_text/tasks/pair_denoising.py b/fairseq/examples/speech_text_joint_to_text/tasks/pair_denoising.py new file mode 100644 index 0000000..b13b1e5 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/tasks/pair_denoising.py @@ -0,0 +1,447 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import logging +import os +import re + +import numpy as np +import torch + +from examples.speech_text_joint_to_text.data.pair_denoising_dataset import ( + LanguagePairDenoisingDataset, +) +from fairseq import utils +from fairseq.data import ( + ConcatDataset, + Dictionary, + LanguagePairDataset, + ResamplingDataset, + TransformEosConcatLangPairDataset, + TransformEosLangPairDataset, + data_utils, + indexed_dataset, +) +from fairseq.data.encoders.utils import get_whole_word_mask +from fairseq.tasks import register_task +from fairseq.tasks.translation import TranslationTask + +logger = logging.getLogger(__name__) + + +def gen_whole_word_mask(args, dictionary): + def is_beginning_of_word(i): + if i < dictionary.nspecial: + # special elements are always considered beginnings + return True + tok = dictionary[i] + if tok.startswith("madeupword"): + return True + + if tok in ["<unk>", "<s>", "</s>", "<pad>"]: + return True + return tok.startswith("\u2581") + + if args.use_mask_whole_words: + mask_whole_words = torch.ByteTensor( + list(map(is_beginning_of_word, range(len(dictionary)))) + ) + else: + # it will mask every token as word leading token, since no bpe model is loaded for phoneme tokens + return get_whole_word_mask(args, dictionary) + return mask_whole_words + + +@register_task("paired_denoising") +class PairedDenoisingTask(TranslationTask): + + LANG_TAG_TEMPLATE = "<lang:{}>" # Tag for language (target) + + @staticmethod + def add_args(parser): + TranslationTask.add_args(parser) + # bart setting + parser.add_argument( + "--mask", + default=0.0, + type=float, + help="fraction of words/subwords that will be masked", + ) + parser.add_argument( + "--mask-random", + default=0.0, + type=float, + help="instead of using [MASK], use random token this often", + ) + parser.add_argument( + "--insert", + default=0.0, + type=float, + help="insert this percentage of additional random tokens", + ) + parser.add_argument( + "--poisson-lambda", + default=3.0, + type=float, + help="randomly shuffle sentences for this proportion of inputs", + ) + parser.add_argument( + "--mask-length", + default="span-poisson", + type=str, + choices=["subword", "word", "span-poisson"], + help="mask length to choose", + ) + parser.add_argument( + "--replace-length", + default=1, + type=int, + help="when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)", + ) + + # multi-lingual + parser.add_argument( + "--multilang-sampling-alpha", + type=float, + default=1.0, + help="smoothing alpha for sample ratios across multiple datasets", + ) + parser.add_argument( + "--lang-pairs", + default="", + metavar="PAIRS", + help="comma-separated list of language pairs (in training order): phnen-en,phnfr-fr,phnit-it. Do masking", + ) + parser.add_argument( + "--lang-pairs-bitext", + default="", + metavar="PAIRS", + help="comma-separated list of language pairs (in training order): en-de,en-fr,de-fr. No masking", + ) + parser.add_argument("--add-src-lang-token", default=False, action="store_true") + parser.add_argument("--add-tgt-lang-token", default=False, action="store_true") + parser.add_argument( + "--no-whole-word-mask-langs", + type=str, + default="", + metavar="N", + help="languages without spacing between words dont support whole word masking", + ) + parser.add_argument( + "--use-mask-whole-words", default=False, action="store_true" + ) + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task.""" + paths = args.data.split(":") + assert len(paths) > 0 + src_dict = Dictionary.load( + os.path.join(paths[0], "src_dict.txt") + ) # assume all languages share a source dictionary + tgt_dict = Dictionary.load( + os.path.join(paths[0], "tgt_dict.txt") + ) # assume all languages share a target dictionary + + lang_pairs = args.lang_pairs + "," + args.lang_pairs_bitext + lang_pairs = re.sub(",$", "", re.sub("^,", "", lang_pairs)) + src_langs = [lp.split("-")[0] for lp in lang_pairs.split(",")] + tgt_langs = [lp.split("-")[1] for lp in lang_pairs.split(",")] + + if args.add_src_lang_token: + for lang in src_langs: + assert ( + src_dict.index(PairedDenoisingTask.LANG_TAG_TEMPLATE.format(lang)) + != src_dict.unk() + ) + if args.add_tgt_lang_token: + for lang in tgt_langs: + assert ( + tgt_dict.index(PairedDenoisingTask.LANG_TAG_TEMPLATE.format(lang)) + != tgt_dict.unk() + ) + + logger.info("source dictionary: {} types".format(len(src_dict))) + logger.info("target dictionary: {} types".format(len(tgt_dict))) + if not hasattr(args, "shuffle_instance"): + args.shuffle_instance = False + return cls(args, src_dict, tgt_dict) + + def __init__(self, args, src_dict, tgt_dict): + super().__init__(args, src_dict, tgt_dict) + # check mask token + self.mask_idx = self.src_dict.index("<mask>") + assert self.mask_idx != self.src_dict.unk() + self.lang_pairs = args.lang_pairs + self.lang_pairs_bitext = args.lang_pairs_bitext + self.args = args + + @classmethod + def language_pair_denoising_dataset( + cls, + data_path, + do_mask, + split, + src, + src_dict, + tgt, + tgt_dict, + mask_idx, + mask_whole_words, + seed, + args, + dataset_impl, + combine=False, + left_pad_source=True, + left_pad_target=False, + max_source_positions=1024, + max_target_positions=1024, + shuffle=True, + src_lang_id=None, + tgt_lang_id=None, + ): + def split_exists(split, src, tgt, lang, data_path): + filename = os.path.join( + data_path, "{}.{}-{}.{}".format(split, src, tgt, lang) + ) + return indexed_dataset.dataset_exists(filename, impl=dataset_impl) + + src_datasets = [] + tgt_datasets = [] + + for k in itertools.count(): + split_k = split + (str(k) if k > 0 else "") + + # infer langcode + if split_exists(split_k, src, tgt, src, data_path): + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, src, tgt)) + elif split_exists(split_k, tgt, src, src, data_path): + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, tgt, src)) + else: + if k > 0: + break + else: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) + + src_dataset = data_utils.load_indexed_dataset( + prefix + src, src_dict, dataset_impl + ) + src_datasets.append(src_dataset) + + tgt_dataset = data_utils.load_indexed_dataset( + prefix + tgt, tgt_dict, dataset_impl + ) + if tgt_dataset is not None: + tgt_datasets.append(tgt_dataset) + + logger.info( + "{} {} {}-{} {} examples".format( + data_path, split_k, src, tgt, len(src_datasets[-1]) + ) + ) + + if not combine: + break + + assert len(src_datasets) == len(tgt_datasets) or len(tgt_datasets) == 0 + + if len(src_datasets) == 1: + src_dataset = src_datasets[0] + tgt_dataset = tgt_datasets[0] if len(tgt_datasets) > 0 else None + else: + sample_ratios = [1] * len(src_datasets) + src_dataset = ConcatDataset(src_datasets, sample_ratios) + if len(tgt_datasets) > 0: + tgt_dataset = ConcatDataset(tgt_datasets, sample_ratios) + else: + tgt_dataset = None + + eos = None + + tgt_dataset_sizes = tgt_dataset.sizes if tgt_dataset is not None else None + if not do_mask: + return LanguagePairDataset( + src_dataset, + src_dataset.sizes, + src_dict, + tgt_dataset, + tgt_dataset_sizes, + tgt_dict, + left_pad_source=left_pad_source, + left_pad_target=left_pad_target, + eos=eos, + shuffle=shuffle, + src_lang_id=src_lang_id, + tgt_lang_id=tgt_lang_id, + ) + + return LanguagePairDenoisingDataset( + src_dataset, + src_dataset.sizes, + src_dict, + tgt_dataset, + tgt_dataset_sizes, + tgt_dict, + mask_idx, + mask_whole_words, + seed, + args, + left_pad_source=left_pad_source, + left_pad_target=left_pad_target, + eos=eos, + shuffle=shuffle, + src_lang_id=src_lang_id, + tgt_lang_id=tgt_lang_id, + ) + + def _get_sample_prob(self, dataset_lens): + """ + Get smoothed sampling porbability by languages. This helps low resource + languages by upsampling them. + """ + prob = dataset_lens / dataset_lens.sum() + smoothed_prob = prob ** self.args.multilang_sampling_alpha + smoothed_prob = smoothed_prob / smoothed_prob.sum() + return smoothed_prob + + def resample_datasets(self, lang_datasets, lang_pairs_all, epoch): + # For train subset, additionally up or down sample languages. + if self.args.multilang_sampling_alpha == 1.0: + return lang_datasets + + dataset_lengths = np.array( + [len(d) for d in lang_datasets], + dtype=float, + ) + sample_probs = self._get_sample_prob(dataset_lengths) + logger.info( + "Sample probability by language pair: {}".format( + { + lp: "{0:.4f}".format(sample_probs[id]) + for id, lp in enumerate(lang_pairs_all) + } + ) + ) + size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths + logger.info( + "Up/Down Sampling ratio by language: {}".format( + { + lp: "{0:.2f}".format(size_ratio[id]) + for id, lp in enumerate(lang_pairs_all) + } + ) + ) + + resampled_lang_datasets = [ + ResamplingDataset( + lang_datasets[i], + size_ratio=size_ratio[i], + seed=self.args.seed, + epoch=epoch, + replace=size_ratio[i] >= 1.0, + ) + for i, d in enumerate(lang_datasets) + ] + return resampled_lang_datasets + + def load_dataset_only( + self, split, lang_pairs, do_mask=True, epoch=1, combine=False + ): + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + # TODO unk token will be considered as first word too, though it might be an unknown phoneme within a word + # get_whole_word_mask returns a tensor (size V by 1 ) to indicate if a token is a word start token + mask_whole_src_words = gen_whole_word_mask(self.args, self.src_dict) + language_without_segmentations = self.args.no_whole_word_mask_langs.split(",") + lang_datasets = [] + eos_bos = [] + lang_pairs = lang_pairs.split(",") if lang_pairs != "" else [] + assert len(lang_pairs) > 0 + for lp in lang_pairs: + src, tgt = lp.split("-") + lang_mask_whole_src_words = ( + mask_whole_src_words + if src not in language_without_segmentations + else None + ) + + end_token = ( + self.source_dictionary.index( + PairedDenoisingTask.LANG_TAG_TEMPLATE.format(src) + ) + if self.args.add_src_lang_token + else None + ) + bos_token = ( + self.target_dictionary.index( + PairedDenoisingTask.LANG_TAG_TEMPLATE.format(tgt) + ) + if self.args.add_tgt_lang_token + else None + ) + src_lang_id = None + + if self.args.add_src_lang_token or self.args.add_tgt_lang_token: + eos_bos.append((end_token, bos_token)) + + dataset = PairedDenoisingTask.language_pair_denoising_dataset( + data_path, + do_mask, + split, + src, + self.source_dictionary, + tgt, + self.target_dictionary, + self.mask_idx, + lang_mask_whole_src_words, + self.args.seed, + self.args, + self.args.dataset_impl, + combine=combine, + left_pad_source=utils.eval_bool(self.args.left_pad_source), + left_pad_target=utils.eval_bool(self.args.left_pad_target), + max_source_positions=self.args.max_source_positions, + max_target_positions=self.args.max_target_positions, + src_lang_id=src_lang_id, + ) + + lang_datasets.append(dataset) + + if len(lang_datasets) == 0: + return + elif len(lang_datasets) == 1: + dataset = lang_datasets[0] + if self.args.add_src_lang_token or self.args.add_tgt_lang_token: + end_token, bos_token = eos_bos[0] + dataset = TransformEosLangPairDataset( + dataset, + src_eos=self.source_dictionary.eos(), + new_src_eos=end_token, + tgt_bos=self.target_dictionary.eos(), + new_tgt_bos=bos_token, + ) + else: + end_tokens = [item[0] for item in eos_bos if item[0] is not None] + bos_tokens = [item[1] for item in eos_bos if item[1] is not None] + lang_datasets = self.resample_datasets(lang_datasets, lang_pairs, epoch) + dataset = TransformEosConcatLangPairDataset( + lang_datasets, + self.source_dictionary.eos(), + self.target_dictionary.eos(), + new_src_eos=end_tokens, + new_tgt_bos=bos_tokens, + ) + return dataset + + # split in (train, valid, test, ...) + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + self.datasets[split] = self.load_dataset_only( + split, self.lang_pairs, epoch=epoch, combine=combine + ) diff --git a/fairseq/examples/speech_text_joint_to_text/tasks/speech_text_denoise_pretrain.py b/fairseq/examples/speech_text_joint_to_text/tasks/speech_text_denoise_pretrain.py new file mode 100644 index 0000000..3ad8e1c --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/tasks/speech_text_denoise_pretrain.py @@ -0,0 +1,654 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import logging +import os +import re +from argparse import Namespace +from pathlib import Path + +from fairseq.data import ConcatDataset, Dictionary, encoders +from fairseq.data.audio.multi_modality_dataset import ( + FileAudioDatasetWrapper, + ModalityDatasetItem, + MultiModalityDataset, +) +from fairseq.data.audio.speech_to_text_joint_dataset import ( + S2TJointDataConfig, + SpeechToTextJointDatasetCreator, +) +from fairseq.data.iterators import GroupedEpochBatchIterator +from fairseq.tasks import register_task + +from .pair_denoising import PairedDenoisingTask + +logger = logging.getLogger(__name__) + + +@register_task("speech_text_joint_denoising") +class SpeechTextJointDenoisingPreTask(PairedDenoisingTask): + """ + Joint denoising training task for speech and text. + """ + + SIL_TOKEN = "sil" + + @classmethod + def add_args(cls, parser): + PairedDenoisingTask.add_args(parser) + # set max tokens and position + parser.add_argument( + "--max-text-tokens", + type=int, + metavar="N", + default=1024, + help="maximum samples for encoder text input ", + ) + parser.add_argument( + "--max-speech-tokens", + type=int, + metavar="N", + default=50000, + help="maximum samples for encoder speech input ", + ) + parser.add_argument( + "--max-speech-positions", + type=int, + metavar="N", + default=400, + help="maximum tokens for per encoder text input ", + ) + + parser.add_argument( + "--max-sample-size", + type=int, + metavar="N", + default=32000, + help="max sample size to crop to for batching (unsupervised speech) ", + ) + parser.add_argument( + "--min-sample-size", + type=int, + metavar="N", + default=4000, + help="min sample size to crop to for batching (unsupervised speech) ", + ) + + # set mini-batch ratio for different modalities/subtasks + # s2p + parser.add_argument( + "--supervised-speech-sample-ratio", + default="1", + type=str, + metavar="N", + help="Multiple Ratio for speech dataset with transcripts ", + ) + # s2t + parser.add_argument( + "--supervised-speech-s2s-sample-ratio", + default="1", + type=str, + metavar="N", + help="Multiple Ratio for speech dataset with transcripts ", + ) + # ssl + parser.add_argument( + "--unsupervised-speech-sample-ratio", + default="1", + type=str, + metavar="N", + help="Multiple Ratio for speech dataset without transcripts ", + ) + # t2t with monolingual data (masking) + parser.add_argument( + "--text-sample-ratio", + default="1", + type=str, + metavar="N", + help="Multiple Ratio for text set ", + ) + # t2t with parallel data (no masking) + parser.add_argument( + "--bitext-sample-ratio", + default="1", + type=str, + metavar="N", + help="Multiple Ratio for text set (bitext) ", + ) + # train_subset = "train", 'valid' or so + # parallel data is loaded according to string lang_pairs and lang_pairs_no_mask from args.data + # (un)supervised speech is loaded from args.(un)sup_speech_{train,valid}_subset + parser.add_argument( + "--sup-speech-data", default="", help="path to supervised speech data" + ) + parser.add_argument( + "--sup-speech-train-subset", + default="", + help="supervised speech training subsets", + ) + parser.add_argument( + "--sup-speech-valid-subset", + default="", + help="supervised speech validation subsets", + ) + parser.add_argument( + "--config-yaml", + default="config.yaml", + help="supervised speech configuration yaml file", + ) + parser.add_argument( + "--sup-speech-s2s-data", default="", help="path to supervised speech data" + ) + parser.add_argument( + "--sup-speech-s2s-train-subset", + default="", + help="supervised speech training subsets", + ) + parser.add_argument( + "--sup-speech-s2s-valid-subset", + default="", + help="supervised speech validation subsets", + ) + parser.add_argument( + "--config-s2s-yaml", + default="config.yaml", + help="supervised speech configuration yaml file", + ) + parser.add_argument( + "--unsup-speech-train-data", + default="", + help="path to unsupervised speech training data (tsv)", + ) + parser.add_argument( + "--unsup-speech-valid-data", + default="", + help="path to unsupervised speech valid data (tsv)", + ) + parser.add_argument( + "--sample-rate", + type=int, + metavar="N", + default=16000, + help="input audio sampling rate", + ) + parser.add_argument( + "--no-emb-update-unsup", + default=False, + action="store_true", + help="no update for output embedding during unsupervised_speech mode", + ) + parser.add_argument("--same-data-update", default=False, action="store_true") + + # used for sup_speech_ali + parser.add_argument( + "--use-sup-speech-ctc", + default=False, + action="store_true", + help="use speech_sup_ctc instead of speech_sup_ali", + ) + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task.""" + paths = args.data.split(":") + assert len(paths) > 0 + src_dict = Dictionary.load( + os.path.join(paths[0], "src_dict.txt") + ) # assume all languages share a source dictionary + tgt_dict = Dictionary.load( + os.path.join(paths[0], "tgt_dict.txt") + ) # assume all languages share a target dictionary + + lang_pairs = args.lang_pairs + "," + args.lang_pairs_bitext + lang_pairs = re.sub(",$", "", re.sub("^,", "", lang_pairs)) + if lang_pairs != "": + src_langs = [lp.split("-")[0] for lp in lang_pairs.split(",")] + tgt_langs = [lp.split("-")[1] for lp in lang_pairs.split(",")] + else: + src_langs = [] + tgt_langs = [] + + if args.add_src_lang_token: + for lang in src_langs: + assert ( + src_dict.index(PairedDenoisingTask.LANG_TAG_TEMPLATE.format(lang)) + != src_dict.unk() + ) + if args.add_tgt_lang_token: + for lang in tgt_langs: + assert ( + tgt_dict.index(PairedDenoisingTask.LANG_TAG_TEMPLATE.format(lang)) + != tgt_dict.unk() + ) + + logger.info("source dictionary: {} types".format(len(src_dict))) + logger.info("target dictionary: {} types".format(len(tgt_dict))) + if not hasattr(args, "shuffle_instance"): + args.shuffle_instance = False + return cls(args, src_dict, tgt_dict) + + def __init__(self, args, src_dict, tgt_dict): + super().__init__(args, src_dict, tgt_dict) + self.data_cfg = S2TJointDataConfig( + Path(args.sup_speech_data) / args.config_yaml + ) + logger.info( + f"load supervised speech data configure from {Path(args.sup_speech_data) / args.config_yaml}" + ) + self.data_s2s_cfg = ( + S2TJointDataConfig(Path(args.sup_speech_s2s_data) / args.config_s2s_yaml) + if args.sup_speech_s2s_train_subset != "" + else None + ) + if self.data_s2s_cfg is not None: + logger.info( + f"load supervised sequece to sequence speech data configure from {Path(args.sup_speech_s2s_data) / args.config_yaml}" + ) + + def parse_data_ratio(sample_ratio): + ratios = sample_ratio.split(",") + if len(ratios) == 1: + return [float(ratios[0])] + epoch_ratios = [] + for item in ratios: + ep, r = item.split(":") + ep = int(ep) + r = float(r) + assert ep > 0 # epoch is 1 based + assert ep >= len(epoch_ratios) + + if len(epoch_ratios) == 0: + epoch_ratios.append( + r + ) # epoch_ratios[0] is not used, but we still set it to the first value to make thing simple. + while len(epoch_ratios) < ep: + epoch_ratios.append(epoch_ratios[-1]) + epoch_ratios.append(r) + return epoch_ratios + + self.sup_ratio = parse_data_ratio(args.supervised_speech_sample_ratio) + self.sup_s2s_ratio = parse_data_ratio(args.supervised_speech_s2s_sample_ratio) + self.text_ratio = parse_data_ratio(args.text_sample_ratio) + self.bitext_ratio = parse_data_ratio(args.bitext_sample_ratio) + self.unsup_ratio = parse_data_ratio(args.unsupervised_speech_sample_ratio) + self.sample_mode = None + + def build_model(self, args): + args.input_feat_per_channel = self.data_cfg.input_feat_per_channel + args.input_channels = self.data_cfg.input_channels + return super().build_model(args) + + def build_tokenizer(self, data_cfg, msg=""): + logger.info(f"pre-tokenizer {msg}: {data_cfg.pre_tokenizer}") + return encoders.build_tokenizer(Namespace(**data_cfg.pre_tokenizer)) + + def build_bpe(self, data_cfg, msg=""): + logger.info(f"tokenizer {msg}: {data_cfg.bpe_tokenizer}") + return encoders.build_bpe(Namespace(**data_cfg.bpe_tokenizer)) + + @classmethod + def resolve_data_type(cls, split, use_sup_speech_ctc): + if len(split.split("_")) == 1: + # default case, train or valid + is_train = split + dtype = "text" + else: + is_train, dtype = split.split("_", 1) + is_train = True if is_train == "train" else False + if dtype == "sup_speech": + dtype = "sup_speech_ctc" if use_sup_speech_ctc else "sup_speech_ali" + assert dtype in ( + "text", + "bitext", + "sup_speech_ali", + "sup_speech_s2s", + "unsup_speech", + "sup_speech_ctc", + ), f"failed resolving {split} (it resulted into: {dtype} ; is_train={is_train})" + return is_train, dtype + + def create_modalitydatasetitem(self, dtype, dataset): + dsitem = None + if dtype in ("text", "bitext"): + dsitem = ModalityDatasetItem( + dtype, + dataset, + (self.args.max_source_positions, self.args.max_target_positions), + self.args.max_text_tokens, + self.args.batch_size, + ) + elif dtype in ("sup_speech_ctc", "sup_speech_ali", "sup_speech_s2s"): + dsitem = ModalityDatasetItem( + dtype, + dataset, + (self.args.max_speech_positions, self.args.max_target_positions), + self.args.max_speech_tokens, + self.args.batch_size, + ) + elif dtype == "unsup_speech": + dsitem = ModalityDatasetItem( + dtype, dataset, 1e8, self.args.max_speech_tokens, self.args.batch_size + ) + else: + raise ValueError(f"{dtype} is not supported") + return dsitem + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + def _get_sup_src_tgt_dict(src_dict, tgt_dict, use_s2s_sup_decoder): + if use_s2s_sup_decoder: + return None, tgt_dict + # use src_dict as tgt_dict here, since we use source dictionary as target for forcealignment + return None, src_dict + + is_train, dtype = self.resolve_data_type(split, self.args.use_sup_speech_ctc) + + # Note we use --add-tgt-lang-token instead of data_cfg.prepend_tgt_lang_tag_no_change to set target language tag in the text dataset + # Verify add_tgt_lang_token and prepend_tgt_lang_tag_no_change are same + + # Note we use --multilang-sampling-alpha instead of data_cfg.sampling_text_alpha to set text data sampling + if is_train: + msets = [] + # train split, load everything into one + if self.lang_pairs != "": + text_dataset = self.load_dataset_only( + "train", self.lang_pairs, epoch=epoch, combine=combine + ) + dsitem = self.create_modalitydatasetitem("text", text_dataset) + msets.append(dsitem) + if self.lang_pairs_bitext != "": # load bitext + bitext_dataset = self.load_dataset_only( + "train_bitext", + self.lang_pairs_bitext, + do_mask=False, + epoch=epoch, + combine=combine, + ) + dsitem = self.create_modalitydatasetitem("bitext", bitext_dataset) + msets.append(dsitem) + if self.args.sup_speech_train_subset != "": + pre_tokenizer = self.build_tokenizer(self.data_cfg) + bpe_tokenizer = self.build_bpe(self.data_cfg) + + append_eos = True + sup_speech_type = "sup_speech_ali" + if self.args.use_sup_speech_ctc: + # CTC mode + sup_speech_type = "sup_speech_ctc" + append_eos = False # CTC doesn't need eos in the target + + src_dict, tgt_dict = _get_sup_src_tgt_dict( + self.src_dict, self.tgt_dict, False + ) + sup_speech_dataset = SpeechToTextJointDatasetCreator.from_tsv( + self.args.sup_speech_data, + self.data_cfg, + self.args.sup_speech_train_subset, + tgt_dict=tgt_dict, + src_dict=src_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + src_pre_tokenizer=None, + src_bpe_tokenizer=None, + is_train_split=is_train, + epoch=epoch, + seed=self.args.seed, + append_eos=append_eos, + ) + dsitem = self.create_modalitydatasetitem( + sup_speech_type, sup_speech_dataset + ) + msets.append(dsitem) + + if self.args.sup_speech_s2s_train_subset != "": + pre_tokenizer = self.build_tokenizer(self.data_s2s_cfg, msg="(s2s)") + bpe_tokenizer = self.build_bpe(self.data_s2s_cfg, msg="(s2s)") + + # make sure self.data_cfg.prepend_tgt_lang_tag_no_change == self.args.add_tgt_lang_token + src_dict, tgt_dict = _get_sup_src_tgt_dict( + self.src_dict, self.tgt_dict, True + ) + sup_speech_s2s_dataset = SpeechToTextJointDatasetCreator.from_tsv( + self.args.sup_speech_s2s_data, + self.data_s2s_cfg, + self.args.sup_speech_s2s_train_subset, + tgt_dict=tgt_dict, + src_dict=src_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + src_pre_tokenizer=None, + src_bpe_tokenizer=None, + is_train_split=is_train, + epoch=epoch, + seed=self.args.seed, + ) + dsitem = self.create_modalitydatasetitem( + "sup_speech_s2s", sup_speech_s2s_dataset + ) + msets.append(dsitem) + if self.args.unsup_speech_train_data != "": + unsup_speech_dataset = FileAudioDatasetWrapper( + self.args.unsup_speech_train_data, + self.args.sample_rate, + max_sample_size=self.args.max_sample_size, + min_sample_size=self.args.min_sample_size, + normalize=False, + ) + dsitem = self.create_modalitydatasetitem( + "unsup_speech", unsup_speech_dataset + ) + msets.append(dsitem) + + pre_train_dataset = MultiModalityDataset(msets) + self.datasets[split] = pre_train_dataset + else: # validation split, load them for each type of data + if dtype == "text": + text_dataset = self.load_dataset_only( + split, self.lang_pairs, epoch=epoch, combine=combine + ) + dsitem = self.create_modalitydatasetitem("text", text_dataset) + self.datasets[split] = MultiModalityDataset([dsitem]) + elif dtype == "bitext": + bitext_dataset = self.load_dataset_only( + split, + self.lang_pairs_bitext, + do_mask=False, + epoch=epoch, + combine=combine, + ) + dsitem = self.create_modalitydatasetitem("bitext", bitext_dataset) + self.datasets[split] = MultiModalityDataset([dsitem]) + + elif dtype in ("sup_speech_ctc", "sup_speech_ali"): + assert self.args.sup_speech_valid_subset != "" + pre_tokenizer = self.build_tokenizer(self.data_cfg) + bpe_tokenizer = self.build_bpe(self.data_cfg) + append_eos = True + if dtype == "sup_speech_ctc": + # CTC mode + append_eos = False # CTC doesn't need eos + assert self.args.use_sup_speech_ctc + + datasets = [] + for split_name in self.args.sup_speech_valid_subset.split(","): + src_dict, tgt_dict = _get_sup_src_tgt_dict( + self.src_dict, self.tgt_dict, False + ) + datasets.append( + SpeechToTextJointDatasetCreator.from_tsv( + self.args.sup_speech_data, + self.data_cfg, + split_name, + tgt_dict=tgt_dict, + src_dict=src_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + src_pre_tokenizer=None, + src_bpe_tokenizer=None, + is_train_split=is_train, + epoch=epoch, + seed=self.args.seed, + append_eos=append_eos, + ) + ) + + dset = datasets[0] if len(datasets) == 1 else ConcatDataset(datasets) + dsitem = self.create_modalitydatasetitem(dtype, dset) + self.datasets[split] = MultiModalityDataset([dsitem]) + + elif dtype == "sup_speech_s2s": + assert self.args.sup_speech_s2s_valid_subset != "" + pre_tokenizer = self.build_tokenizer(self.data_s2s_cfg) + bpe_tokenizer = self.build_bpe(self.data_s2s_cfg) + datasets = [] + for split_name in self.args.sup_speech_s2s_valid_subset.split(","): + src_dict, tgt_dict = _get_sup_src_tgt_dict( + self.src_dict, self.tgt_dict, True + ) + datasets.append( + SpeechToTextJointDatasetCreator.from_tsv( + self.args.sup_speech_s2s_data, + self.data_s2s_cfg, + split_name, + tgt_dict=tgt_dict, + src_dict=src_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + src_pre_tokenizer=None, + src_bpe_tokenizer=None, + is_train_split=is_train, + epoch=epoch, + seed=self.args.seed, + ) + ) + + dset = datasets[0] if len(datasets) == 1 else ConcatDataset(datasets) + dsitem = self.create_modalitydatasetitem("sup_speech_s2s", dset) + self.datasets[split] = MultiModalityDataset([dsitem]) + elif dtype == "unsup_speech": + assert self.args.unsup_speech_valid_data != "" + unsup_speech_dataset = FileAudioDatasetWrapper( + self.args.unsup_speech_valid_data, + self.args.sample_rate, + max_sample_size=self.args.max_sample_size, + min_sample_size=self.args.min_sample_size, + normalize=False, + ) + dsitem = self.create_modalitydatasetitem( + "unsup_speech", unsup_speech_dataset + ) + self.datasets[split] = MultiModalityDataset([dsitem]) + else: + raise ValueError(f"Unsupported type {dtype}") + + def get_sample_ratio(self, epoch): + sup_ratio = ( + self.sup_ratio[epoch] if len(self.sup_ratio) > epoch else self.sup_ratio[-1] + ) + sup_s2s_ratio = ( + self.sup_s2s_ratio[epoch] + if len(self.sup_s2s_ratio) > epoch + else self.sup_s2s_ratio[-1] + ) + unsup_ratio = ( + self.unsup_ratio[epoch] + if len(self.unsup_ratio) > epoch + else self.unsup_ratio[-1] + ) + text_ratio = ( + self.text_ratio[epoch] + if len(self.text_ratio) > epoch + else self.text_ratio[-1] + ) + bitext_ratio = ( + self.bitext_ratio[epoch] + if len(self.bitext_ratio) > epoch + else self.bitext_ratio[-1] + ) + return text_ratio, bitext_ratio, sup_ratio, sup_s2s_ratio, unsup_ratio + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=0, + data_buffer_size=0, + disable_iterator_cache=False, + skip_remainder_batch=False, + grouped_shuffling=False, + update_epoch_batch_itr=False, + ): + + assert isinstance(dataset, MultiModalityDataset) + if len(dataset.id_to_mode) == 1: + max_positions = dataset.max_positions[0] + max_tokens = dataset.max_tokens[0] + max_sentences = dataset.max_sentences[0] + return super().get_batch_iterator( + dataset, + max_tokens, + max_sentences, + max_positions, + ignore_invalid_inputs, + required_batch_size_multiple, + seed, + num_shards, + shard_id, + num_workers, + epoch, + data_buffer_size, + disable_iterator_cache, + skip_remainder_batch=skip_remainder_batch, + ) + + mult_ratio = [] + ( + text_ratio, + bitext_ratio, + sup_ratio, + sup_s2s_ratio, + unsup_ratio, + ) = self.get_sample_ratio(epoch) + for mode in dataset.id_to_mode: + if mode in ("sup_speech_ctc", "sup_speech_ali"): + mult_ratio.append(sup_ratio) + elif mode == "sup_speech_s2s": + mult_ratio.append(sup_s2s_ratio) + elif mode == "text": + mult_ratio.append(text_ratio) + elif mode == "bitext": + mult_ratio.append(bitext_ratio) + elif mode == "unsup_speech": + mult_ratio.append(unsup_ratio) + + # initialize the dataset with the correct starting epoch + dataset.set_epoch(epoch) + + batch_samplers = dataset.get_batch_samplers( + mult_ratio, required_batch_size_multiple, seed + ) + + # return a reusable, sharded iterator + epoch_iter = GroupedEpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_samplers=batch_samplers, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + mult_rate=max(self.args.update_freq) if self.args.same_data_update else 1, + buffer_size=data_buffer_size, + skip_remainder_batch=skip_remainder_batch, + ) + self.dataset_to_epoch_iter[dataset] = {} # refresh it every epoch + return epoch_iter diff --git a/fairseq/examples/speech_text_joint_to_text/tasks/speech_text_joint.py b/fairseq/examples/speech_text_joint_to_text/tasks/speech_text_joint.py new file mode 100644 index 0000000..bb04f14 --- /dev/null +++ b/fairseq/examples/speech_text_joint_to_text/tasks/speech_text_joint.py @@ -0,0 +1,377 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import logging +import os +from argparse import Namespace +from pathlib import Path + +import torch +from fairseq.data import ( + encoders, + Dictionary, + ResamplingDataset, + TransformEosLangPairDataset, + ConcatDataset, +) +from fairseq.data.iterators import GroupedEpochBatchIterator +from fairseq.data.audio.multi_modality_dataset import ( + MultiModalityDataset, + LangPairMaskDataset, + ModalityDatasetItem, +) +from fairseq.data.audio.speech_to_text_dataset import ( + SpeechToTextDataset, + SpeechToTextDatasetCreator, +) +from fairseq.data.audio.speech_to_text_joint_dataset import ( + S2TJointDataConfig, + SpeechToTextJointDatasetCreator, +) +from fairseq.tasks import register_task +from fairseq.tasks.speech_to_text import SpeechToTextTask +from fairseq.tasks.translation import load_langpair_dataset + +logger = logging.getLogger(__name__) +LANG_TAG_TEMPLATE = "<lang:{}>" + + +@register_task("speech_text_joint_to_text") +class SpeechTextJointToTextTask(SpeechToTextTask): + """ + Task for joint training speech and text to text. + """ + + @classmethod + def add_args(cls, parser): + """Add task-specific arguments to the parser.""" + super(SpeechTextJointToTextTask, cls).add_args(parser) + ### + parser.add_argument( + "--parallel-text-data", + default="", + help="path to parallel text data directory", + ) + parser.add_argument( + "--max-tokens-text", + type=int, + metavar="N", + help="maximum tokens for encoder text input ", + ) + parser.add_argument( + "--max-positions-text", + type=int, + metavar="N", + default=400, + help="maximum tokens for per encoder text input ", + ) + parser.add_argument( + "--langpairs", + default=None, + metavar="S", + help='language pairs for text training, separated with ","', + ) + parser.add_argument( + "--speech-sample-ratio", + default=1, + type=float, + metavar="N", + help="Multiple Ratio for speech dataset with transcripts ", + ) + parser.add_argument( + "--text-sample-ratio", + default=1, + type=float, + metavar="N", + help="Multiple Ratio for text set ", + ) + parser.add_argument( + "--update-mix-data", + action="store_true", + help="use mixed data in one update when update-freq > 1", + ) + parser.add_argument( + "--load-speech-only", action="store_true", help="load speech data only", + ) + parser.add_argument( + "--mask-text-ratio", + type=float, + metavar="V", + default=0.0, + help="mask V source tokens for text only mode", + ) + parser.add_argument( + "--mask-text-type", + default="random", + choices=["random", "tail"], + help="mask text typed", + ) + parser.add_argument( + "--noise-token", + default="", + help="noise token for masking src text tokens if mask-text-ratio > 0", + ) + parser.add_argument( + "--infer-target-lang", + default="", + metavar="S", + help="target language for inference", + ) + + def __init__(self, args, src_dict, tgt_dict, infer_tgt_lang_id=None): + super().__init__(args, tgt_dict) + self.src_dict = src_dict + self.data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml) + assert self.tgt_dict.pad() == self.src_dict.pad() + assert self.tgt_dict.eos() == self.src_dict.eos() + self.speech_only = args.load_speech_only + self._infer_tgt_lang_id = infer_tgt_lang_id + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries).""" + data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml) + tgt_dict_path = Path(args.data) / data_cfg.vocab_filename + src_dict_path = Path(args.data) / data_cfg.src_vocab_filename + if (not os.path.isfile(src_dict_path)) or (not os.path.isfile(tgt_dict_path)): + raise FileNotFoundError("Dict not found: {}".format(args.data)) + src_dict = Dictionary.load(src_dict_path.as_posix()) + tgt_dict = Dictionary.load(tgt_dict_path.as_posix()) + + print("| src dictionary: {} types".format(len(src_dict))) + print("| tgt dictionary: {} types".format(len(tgt_dict))) + + if args.parallel_text_data != "": + if not os.path.isabs(args.parallel_text_data): + args.parallel_text_data = os.path.join( + args.data, args.parallel_text_data + ) + + if args.langpairs is None: + raise Exception( + "Could not infer language pair, please provide it explicitly" + ) + infer_tgt_lang_id = None + if args.infer_target_lang != "" and data_cfg.prepend_tgt_lang_tag_no_change: + tgt_lang_tag = SpeechToTextDataset.LANG_TAG_TEMPLATE.format( + args.infer_target_lang + ) + infer_tgt_lang_id = tgt_dict.index(tgt_lang_tag) + assert infer_tgt_lang_id != tgt_dict.unk() + return cls(args, src_dict, tgt_dict, infer_tgt_lang_id=infer_tgt_lang_id) + + def load_langpair_dataset( + self, prepend_tgt_lang_tag=False, sampling_alpha=1.0, epoch=0 + ): + lang_pairs = [] + text_dataset = None + split = "train" + for lp in self.args.langpairs.split(","): + src, tgt = lp.split("-") + text_dataset = load_langpair_dataset( + self.args.parallel_text_data, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=True, + dataset_impl=None, + upsample_primary=1, + left_pad_source=False, + left_pad_target=False, + max_source_positions=self.args.max_positions_text, + max_target_positions=self.args.max_target_positions, + load_alignments=False, + truncate_source=False, + ) + if prepend_tgt_lang_tag: + # TODO + text_dataset = TransformEosLangPairDataset( + text_dataset, + src_eos=self.src_dict.eos(), + tgt_bos=self.tgt_dict.eos(), # 'prev_output_tokens' starts with eos + new_tgt_bos=self.tgt_dict.index(LANG_TAG_TEMPLATE.format(tgt)), + ) + lang_pairs.append(text_dataset) + if len(lang_pairs) > 1: + if sampling_alpha != 1.0: + size_ratios = SpeechToTextDatasetCreator.get_size_ratios( + self.args.langpairs.split(","), + [len(s) for s in lang_pairs], + alpha=sampling_alpha, + ) + lang_pairs = [ + ResamplingDataset(d, size_ratio=r, epoch=epoch, replace=(r >= 1.0)) + for d, r in zip(lang_pairs, size_ratios) + ] + return ConcatDataset(lang_pairs) + return text_dataset + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + bos_token=self._infer_tgt_lang_id, + ) + + def build_src_tokenizer(self, args): + logger.info(f"src-pre-tokenizer: {self.data_cfg.src_pre_tokenizer}") + return encoders.build_tokenizer(Namespace(**self.data_cfg.src_pre_tokenizer)) + + def build_src_bpe(self, args): + logger.info(f"tokenizer: {self.data_cfg.src_bpe_tokenizer}") + return encoders.build_bpe(Namespace(**self.data_cfg.src_bpe_tokenizer)) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + src_pre_tokenizer = self.build_src_tokenizer(self.args) + src_bpe_tokenizer = self.build_src_bpe(self.args) + ast_dataset = SpeechToTextJointDatasetCreator.from_tsv( + self.args.data, + self.data_cfg, + split, + self.tgt_dict, + src_dict=None if self.speech_only else self.src_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + src_pre_tokenizer=src_pre_tokenizer, + src_bpe_tokenizer=src_bpe_tokenizer, + is_train_split=is_train_split, + epoch=epoch, + seed=self.args.seed, + ) + noise_token_id = -1 + text_dataset = None + if self.args.parallel_text_data != "" and is_train_split: + text_dataset = self.load_langpair_dataset( + self.data_cfg.prepend_tgt_lang_tag_no_change, 1.0, epoch=epoch, + ) + if self.args.mask_text_ratio > 0: + # add mask + noise_token_id = ( + self.src_dict.unk() + if self.args.noise_token == "" + else self.src_dict.index(self.args.noise_token) + ) + text_dataset = LangPairMaskDataset( + text_dataset, + src_bos=self.src_dict.bos(), + src_eos=self.src_dict.eos(), + noise_id=noise_token_id, + mask_ratio=self.args.mask_text_ratio, + mask_type=self.args.mask_text_type, + ) + + if text_dataset is not None: + mdsets = [ + ModalityDatasetItem( + "sup_speech", + ast_dataset, + (self.args.max_source_positions, self.args.max_target_positions), + self.args.max_tokens, + self.args.batch_size, + ), + ModalityDatasetItem( + "text", + text_dataset, + (self.args.max_positions_text, self.args.max_target_positions), + self.args.max_tokens_text + if self.args.max_tokens_text is not None + else self.args.max_tokens, + self.args.batch_size, + ), + ] + ast_dataset = MultiModalityDataset(mdsets) + self.datasets[split] = ast_dataset + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.tgt_dict + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary` (if applicable + for this task).""" + return None if self.speech_only else self.src_dict + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=0, + data_buffer_size=0, + disable_iterator_cache=False, + skip_remainder_batch=False, + grouped_shuffling=False, + update_epoch_batch_itr=False, + ): + + if not isinstance(dataset, MultiModalityDataset): + return super(SpeechTextJointToTextTask, self).get_batch_iterator( + dataset, + max_tokens, + max_sentences, + max_positions, + ignore_invalid_inputs, + required_batch_size_multiple, + seed, + num_shards, + shard_id, + num_workers, + epoch, + data_buffer_size, + disable_iterator_cache, + skip_remainder_batch=skip_remainder_batch, + update_epoch_batch_itr=update_epoch_batch_itr, + ) + + mult_ratio = [self.args.speech_sample_ratio, self.args.text_sample_ratio] + assert len(dataset.datasets) == 2 + + # initialize the dataset with the correct starting epoch + dataset.set_epoch(epoch) + + batch_samplers = dataset.get_batch_samplers( + mult_ratio, required_batch_size_multiple, seed + ) + + # return a reusable, sharded iterator + epoch_iter = GroupedEpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_samplers=batch_samplers, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + mult_rate=1 if self.args.update_mix_data else max(self.args.update_freq), + buffer_size=data_buffer_size, + skip_remainder_batch=skip_remainder_batch, + ) + self.dataset_to_epoch_iter[dataset] = {} # refresh it every epoch + return epoch_iter diff --git a/fairseq/examples/speech_to_speech/README.md b/fairseq/examples/speech_to_speech/README.md new file mode 100644 index 0000000..f03f6a3 --- /dev/null +++ b/fairseq/examples/speech_to_speech/README.md @@ -0,0 +1,7 @@ +# Speech to speech translation (S2ST) + +We provide the implementation and resources for the following work on speech-to-speech translation (S2ST): + +* [Direct speech-to-speech translation with discrete units (Lee et al. 2021)](docs/direct_s2st_discrete_units.md) +* [Textless Speech-to-Speech Translation on Real Data (Lee et al. 2021)](docs/textless_s2st_real_data.md) +* [Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation](docs/enhanced_direct_s2st_discrete_units.md) diff --git a/fairseq/examples/speech_to_speech/__init__.py b/fairseq/examples/speech_to_speech/__init__.py new file mode 100644 index 0000000..812b3c3 --- /dev/null +++ b/fairseq/examples/speech_to_speech/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import unity # noqa diff --git a/fairseq/examples/speech_to_speech/asr_bleu/README.md b/fairseq/examples/speech_to_speech/asr_bleu/README.md new file mode 100644 index 0000000..6a7ea7f --- /dev/null +++ b/fairseq/examples/speech_to_speech/asr_bleu/README.md @@ -0,0 +1,34 @@ +# ASR-BLEU evaluation toolkit + +This toolkit provides a set of public ASR models used for evaluation of different speech-to-speech translation systems at FAIR. It enables easier score comparisons between different system's outputs. + +The ASRGenerator wraps different CTC-based ASR models from HuggingFace and fairseq code bases. Torchaudio CTC decoder is built on top of it to decode given audio files. + +Please see `asr_model_cfgs.json` for a list of languages covered currently. + +The high-level pipeline is simple by design: given a lang tag, script loads the ASR model, transcribes model's predicted audio, and computes the BLEU score against provided reference translations using sacrebleu. + +# Dependencies + +Please see `requirements.txt`. + +# Usage examples + +This toolkit have been used with: + +* Speechmatrix project: https://github.com/facebookresearch/fairseq/tree/ust/examples/speech_matrix. + +* Hokkien speech-to-speech translation project: https://github.com/facebookresearch/fairseq/tree/ust/examples/hokkien. + +# Standalone run example + +High-level example, please substitute arguments per your case: + +```bash +python compute_asr_bleu.py --lang <LANG> \ +--audio_dirpath <PATH_TO_AUDIO_DIR> \ +--reference_path <PATH_TO_REFERENCES_FILE> \ +--reference_format txt +``` + +For more details about arguments please see the script argparser help. diff --git a/fairseq/examples/speech_to_speech/asr_bleu/__init__.py b/fairseq/examples/speech_to_speech/asr_bleu/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/speech_to_speech/asr_bleu/asr_model_cfgs.json b/fairseq/examples/speech_to_speech/asr_bleu/asr_model_cfgs.json new file mode 100644 index 0000000..d0a5f3e --- /dev/null +++ b/fairseq/examples/speech_to_speech/asr_bleu/asr_model_cfgs.json @@ -0,0 +1,198 @@ +{ + "en": { + "oct22": { + "desc": "Wav2Vec 2.0 Large (LV-60) + Self Training from https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec#pre-trained-models", + "ckpt_path": "https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_960h_pl.pt", + "dict_path": "https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt", + "model_type": "fairseq", + "lang": "en", + "post_process": "collapse" + } + }, + "hok": { + "oct22": { + "desc": "Hokkien ASR model, for details check [TODO add paper link]", + "ckpt_path": "https://dl.fbaipublicfiles.com/ust_asr/hok/checkpoint_best.pt", + "dict_path": "https://dl.fbaipublicfiles.com/ust_asr/hok/dict.ltr.txt", + "model_type": "fairseq", + "lang": "hok", + "post_process": "none" + } + }, + "es": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-large-xlsr-53-spanish", + "model_type": "hf", + "lang": "es", + "post_process": "collapse" + } + }, + "fr": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-large-fr-voxpopuli-french", + "model_type": "hf", + "lang": "fr", + "post_process": "collapse" + } + }, + "zh": { + "oct22": { + "model_path": "ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt", + "model_type": "hf", + "lang": "zh", + "post_process": "collapse" + } + }, + "tr": { + "oct22": { + "model_path": "cahya/wav2vec2-large-xlsr-turkish-artificial-cv", + "model_type": "hf", + "lang": "tr", + "post_process": "collapse" + } + }, + "ar": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic", + "model_type": "hf", + "lang": "ar", + "post_process": "collapse" + } + }, + "vi": { + "oct22": { + "model_path": "not-tanh/wav2vec2-large-xlsr-53-vietnamese", + "model_type": "hf", + "lang": "vi", + "post_process": "collapse" + } + }, + "de": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-xls-r-1b-german", + "model_type": "hf", + "lang": "de", + "post_process": "collapse" + } + }, + "pl": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-xls-r-1b-polish", + "model_type": "hf", + "lang": "pl", + "post_process": "collapse" + } + }, + "it": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-large-xlsr-53-italian", + "model_type": "hf", + "lang": "it", + "post_process": "collapse" + } + }, + "pt": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-xls-r-1b-portuguese", + "model_type": "hf", + "lang": "pt", + "post_process": "collapse" + } + }, + "ro": { + "oct22": { + "model_path": "gigant/romanian-wav2vec2", + "model_type": "hf", + "lang": "ro", + "post_process": "collapse" + } + }, + "cs": { + "oct22": { + "model_path": "comodoro/wav2vec2-xls-r-300m-cs-250", + "model_type": "hf", + "lang": "cs", + "post_process": "collapse" + } + }, + "sk": { + "oct22": { + "model_path": "anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm", + "model_type": "hf", + "lang": "sk", + "post_process": "collapse" + } + }, + "sl": { + "oct22": { + "model_path": "anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm", + "model_type": "hf", + "lang": "sl", + "post_process": "collapse" + } + }, + "fi": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-large-xlsr-53-finnish", + "model_type": "hf", + "lang": "fi", + "post_process": "collapse" + } + }, + "hu": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian", + "model_type": "hf", + "lang": "hu", + "post_process": "collapse" + } + }, + "et": { + "oct22": { + "model_path": "RASMUS/wav2vec2-xlsr-1b-et", + "model_type": "hf", + "lang": "et", + "post_process": "collapse" + } + }, + "lt": { + "oct22": { + "model_path": "sammy786/wav2vec2-xlsr-lithuanian", + "model_type": "hf", + "lang": "lt", + "post_process": "collapse" + } + }, + "nl": { + "oct22": { + "model_path": "jonatasgrosman/wav2vec2-xls-r-1b-dutch", + "model_type": "hf", + "lang": "nl", + "post_process": "collapse" + } + }, + "lv": { + "oct22": { + "model_path": "reach-vb/wav2vec2-large-xls-r-1B-common_voice7-lv-ft", + "model_type": "hf", + "lang": "lv", + "post_process": "collapse" + } + }, + "sv": { + "oct22": { + "model_path": "marinone94/xls-r-300m-sv-robust", + "model_type": "hf", + "lang": "sv", + "post_process": "collapse" + } + }, + "hr": { + "oct22": { + "model_path": "classla/wav2vec2-xls-r-parlaspeech-hr", + "model_type": "hf", + "lang": "hr", + "post_process": "collapse" + } + } +} diff --git a/fairseq/examples/speech_to_speech/asr_bleu/compute_asr_bleu.py b/fairseq/examples/speech_to_speech/asr_bleu/compute_asr_bleu.py new file mode 100644 index 0000000..d592619 --- /dev/null +++ b/fairseq/examples/speech_to_speech/asr_bleu/compute_asr_bleu.py @@ -0,0 +1,244 @@ +import os +from typing import Dict, List +import sacrebleu +import pandas as pd +from glob import glob +from pathlib import Path +from utils import retrieve_asr_config, ASRGenerator +from tqdm import tqdm +from argparse import ArgumentParser + + +def merge_tailo_init_final(text): + """ + Hokkien ASR hypothesis post-processing. + """ + sps = text.strip().split() + results = [] + last_syllable = "" + for sp in sps: + if sp == "NULLINIT" or sp == "nullinit": + continue + last_syllable += sp + if sp[-1].isnumeric(): + results.append(last_syllable) + last_syllable = "" + if last_syllable != "": + results.append(last_syllable) + return " ".join(results) + + +def remove_tone(text): + """ + Used for tone-less evaluation of Hokkien + """ + return " ".join([t[:-1] for t in text.split()]) + + +def extract_audio_for_eval(audio_dirpath: str, audio_format: str): + if audio_format == "n_pred.wav": + """ + The assumption here is that 0_pred.wav corresponds to the reference at line position 0 from the reference manifest + """ + audio_list = [] + audio_fp_list = glob((Path(audio_dirpath) / "*_pred.wav").as_posix()) + audio_fp_list = sorted( + audio_fp_list, key=lambda x: int(os.path.basename(x).split("_")[0]) + ) + for i in range(len(audio_fp_list)): + try: + audio_fp = (Path(audio_dirpath) / f"{i}_pred.wav").as_posix() + assert ( + audio_fp in audio_fp_list + ), f"{Path(audio_fp).name} does not exist in {audio_dirpath}" + except AssertionError: + # check the audio with random speaker + audio_fp = Path(audio_dirpath) / f"{i}_spk*_pred.wav" + audio_fp = glob( + audio_fp.as_posix() + ) # resolve audio filepath with random speaker + assert len(audio_fp) == 1 + audio_fp = audio_fp[0] + + audio_list.append(audio_fp) + else: + raise NotImplementedError + + return audio_list + + +def extract_text_for_eval( + references_filepath: str, reference_format: str, reference_tsv_column: str = None +): + if reference_format == "txt": + reference_sentences = open(references_filepath, "r").readlines() + reference_sentences = [l.strip() for l in reference_sentences] + elif reference_format == "tsv": + tsv_df = pd.read_csv(references_filepath, sep="\t", quoting=3) + reference_sentences = tsv_df[reference_tsv_column].to_list() + reference_sentences = [l.strip() for l in reference_sentences] + else: + raise NotImplementedError + + return reference_sentences + + +def compose_eval_data( + audio_dirpath: str, + audio_format: str, + references_filepath: str, + reference_format: str, + reference_tsv_column: str = None, + save_manifest_filepath=None, +): + """ + Speech matrix decoding pipeline produces audio with the following mask "N_pred.wav" where N is the order of the corresponding input sample + """ + + reference_sentences = extract_text_for_eval( + references_filepath, reference_format, reference_tsv_column + ) + predicted_audio_fp_list = extract_audio_for_eval(audio_dirpath, audio_format) + assert len(predicted_audio_fp_list) == len(reference_sentences) + + audio_text_pairs = [ + (audio, reference) + for audio, reference in zip(predicted_audio_fp_list, reference_sentences) + ] + + tsv_manifest = pd.DataFrame(audio_text_pairs, columns=["prediction", "reference"]) + + if save_manifest_filepath is not None: + tsv_manifest.to_csv(save_manifest_filepath, sep="\t", quoting=3) + + return tsv_manifest + + +def load_eval_data_from_tsv(eval_data_filepath: str): + """ + We may load the result of `compose_eval_data` directly if needed + """ + eval_df = pd.from_csv(eval_data_filepath, sep="\t") + + return eval_df + + +def run_asr_bleu(args): + + asr_config = retrieve_asr_config( + args.lang, args.asr_version, json_path="./asr_model_cfgs.json" + ) + asr_model = ASRGenerator(asr_config) + + eval_manifest = compose_eval_data( + audio_dirpath=args.audio_dirpath, + audio_format=args.audio_format, + references_filepath=args.reference_path, + reference_format=args.reference_format, + reference_tsv_column=args.reference_tsv_column, + save_manifest_filepath=None, + ) + + prediction_transcripts = [] + for _, eval_pair in tqdm( + eval_manifest.iterrows(), + desc="Transcribing predictions", + total=len(eval_manifest), + ): + transcription = asr_model.transcribe_audiofile(eval_pair.prediction) + prediction_transcripts.append(transcription.lower()) + + if args.lang == "hok": + prediction_transcripts = [ + merge_tailo_init_final(text) for text in prediction_transcripts + ] + + references = eval_manifest["reference"].tolist() + bleu_score = sacrebleu.corpus_bleu(prediction_transcripts, [references]) + + print(bleu_score) + + return prediction_transcripts, bleu_score + + +def main(): + parser = ArgumentParser( + description="This script computes the ASR-BLEU metric between model's generated audio and the text reference sequences." + ) + + parser.add_argument( + "--lang", + help="The target language used to initialize ASR model, see asr_model_cfgs.json for available languages", + type=str, + ) + parser.add_argument( + "--asr_version", + type=str, + default="oct22", + help="For future support we add and extra layer of asr versions. The current most recent version is oct22 meaning October 2022", + ) + parser.add_argument( + "--audio_dirpath", + type=str, + help="Path to the directory containing the audio predictions from the translation model", + ) + parser.add_argument( + "--reference_path", + type=str, + help="Path to the file containing reference translations in the form of normalized text (to be compared to ASR predictions", + ) + parser.add_argument( + "--reference_format", + choices=["txt", "tsv"], + help="Format of reference file. Txt means plain text format where each line represents single reference sequence", + ) + parser.add_argument( + "--reference_tsv_column", + default=None, + type=str, + help="If format is tsv, then specify the column name which contains reference sequence", + ) + parser.add_argument( + "--audio_format", + default="n_pred.wav", + choices=["n_pred.wav"], + help="Audio format n_pred.wav corresponds to names like 94_pred.wav or 94_spk7_pred.wav where spk7 is the speaker id", + ) + parser.add_argument( + "--results_dirpath", + default=None, + type=str, + help="If specified, the resulting BLEU score will be written to this file path as txt file", + ) + parser.add_argument( + "--transcripts_path", + default=None, + type=str, + help="If specified, the predicted transcripts will be written to this path as a txt file.", + ) + + args = parser.parse_args() + + prediction_transcripts, bleu_score = run_asr_bleu(args) + result_filename = f"{args.reference_format}_{args.lang}_bleu.txt" + if args.results_dirpath is not None: + if not Path(args.results_dirpath).exists(): + Path(args.results_dirpath).mkdir(parents=True) + with open(Path(args.results_dirpath) / result_filename, "w") as f: + f.write(bleu_score.format(width=2)) + + if args.transcripts_path is not None: + with open(args.transcripts_path, "w") as f: + for transcript in prediction_transcripts: + f.write(transcript + "\n") + + +if __name__ == "__main__": + main() + + +""" +Example to load Sl audio and references, compute BLEU: + +export lang=fi; split=vp && python compute_asr_bleu.py --lang $lang --audio_dirpath /checkpoint/hygong/S2S/speech_matrix_release_ckpts/generated_waveform_release/en-$lang/test_$split/checkpoint.pt --audio_format n_pred.wav --reference_path /large_experiments/ust/hygong/S2S/SpeechEncoder/manifests/vp-vp/en-$lang/test_$split.$lang --reference_format txt --results_dirpath ./ +""" diff --git a/fairseq/examples/speech_to_speech/asr_bleu/requirements.txt b/fairseq/examples/speech_to_speech/asr_bleu/requirements.txt new file mode 100644 index 0000000..cfa90f6 --- /dev/null +++ b/fairseq/examples/speech_to_speech/asr_bleu/requirements.txt @@ -0,0 +1,7 @@ +fairseq==0.12.2 +pandas==1.4.3 +sacrebleu==2.2.0 +torch==1.12.1 +torchaudio==0.12.1 +tqdm==4.64.0 +transformers==4.21.1 diff --git a/fairseq/examples/speech_to_speech/asr_bleu/utils.py b/fairseq/examples/speech_to_speech/asr_bleu/utils.py new file mode 100644 index 0000000..0fed55a --- /dev/null +++ b/fairseq/examples/speech_to_speech/asr_bleu/utils.py @@ -0,0 +1,306 @@ +import json +import re +import urllib.request +from pathlib import Path + +import fairseq +import torch +from fairseq.data.data_utils import lengths_to_padding_mask +from tqdm import tqdm + +try: + import torchaudio + from torchaudio.models.decoder import ctc_decoder +except ImportError: + raise ImportError("Upgrade torchaudio to 0.12 to enable CTC decoding") + + +class DownloadProgressBar(tqdm): + """A class to represent a download progress bar""" + + def update_to(self, b=1, bsize=1, tsize=None) -> None: + """ + Update the download progress + """ + if tsize is not None: + self.total = tsize + self.update(b * bsize - self.n) + + +def retrieve_asr_config(lang_key: str, asr_version: str, json_path: str) -> dict: + """ + Retrieve the asr model configs + + Args: + lang_key: the lanuage type as the key name + json_path: the path of the config json file + + Returns: + Dict of all the configs in the json file + """ + + with open(json_path, "r") as f: + asr_model_cfgs = json.load(f) + return asr_model_cfgs[lang_key][asr_version] + + +class ASRGenerator(object): + """A class to represent a ASR generator""" + + def __init__( + self, + model_cfg: dict, + cache_dirpath: str = (Path.home() / ".cache" / "ust_asr").as_posix(), + ) -> None: + """ + Construct all the necessary attributes of the ASRGenerator class + + Args: + model_cfg: the dict of the asr model config + cache_dirpath: the default cache path is "Path.home()/.cache/ust_asr" + """ + + self.cache_dirpath = Path(cache_dirpath) / model_cfg["lang"] + self.model_cfg = model_cfg + + self.use_cuda = torch.cuda.is_available() + + torchaudio.set_audio_backend("sox_io") + + if self.model_cfg["model_type"] == "hf": + self.prepare_hf_model(self.model_cfg) + elif self.model_cfg["model_type"] == "fairseq": + self.prepare_fairseq_model(self.model_cfg) + else: + raise NotImplementedError( + f"Model type {self.model_cfg['model_type']} is not supported" + ) + + if self.model_cfg["post_process"] == "collapse": + self.post_process_fn = lambda hypo: "".join(hypo).replace( + self.sil_token, " " + ) + elif self.model_cfg["post_process"] == "none": + self.post_process_fn = lambda hypo: " ".join(hypo).replace( + self.sil_token, " " + ) + else: + raise NotImplementedError + + if self.use_cuda: + self.model.cuda() + self.model.eval() + + self.decoder = ctc_decoder( + lexicon=None, + tokens=self.tokens, + lm=None, + nbest=1, + beam_size=1, + beam_size_token=None, + lm_weight=0.0, + word_score=0.0, + unk_score=float("-inf"), + sil_token=self.sil_token, + sil_score=0.0, + log_add=False, + blank_token=self.blank_token, + ) + + def prepare_hf_model(self, model_cfg: dict) -> None: + """ + Prepare the huggingface asr model + + Args: + model_cfg: dict with the relevant ASR config + """ + + def infer_silence_token(vocab: list): + """ + Different HF checkpoints have different notion of silence token + such as | or " " (space) + Important: when adding new HF asr model in, check what silence token it uses + """ + if "|" in vocab: + return "|" + elif " " in vocab: + return " " + else: + raise RuntimeError("Silence token is not found in the vocabulary") + + try: + from transformers import (AutoFeatureExtractor, AutoTokenizer, + Wav2Vec2ForCTC, Wav2Vec2Processor) + except ImportError: + raise ImportError("Install transformers to load HF wav2vec model") + + model_path = model_cfg["model_path"] + self.model = Wav2Vec2ForCTC.from_pretrained(model_path) + self.tokenizer = AutoTokenizer.from_pretrained(model_path) + self.preprocessor = AutoFeatureExtractor.from_pretrained(model_path) + self.processor = Wav2Vec2Processor.from_pretrained(model_path) + + # extra unk tokens are there to make some models work e.g. Finnish ASR has some vocab issue + vocab_list = [ + self.tokenizer.decoder.get(i, f"{self.tokenizer.unk_token}1") + for i in range(self.tokenizer.vocab_size) + ] + + self.sampling_rate = self.preprocessor.sampling_rate + self.normalize_input = self.preprocessor.do_normalize + self.tokens = vocab_list + self.sil_token = infer_silence_token(vocab_list) + self.blank_token = self.tokenizer.pad_token + + def prepare_fairseq_model(self, model_cfg: dict) -> None: + """ + Prepare the fairseq asr model + + Args: + model_cfg: the specific model config dict must have: (1) ckpt_path, (2) dict_path + """ + + def download_file(url: str, cache_dir: Path): + download_path = cache_dir / url.split("/")[-1] + if not (cache_dir / url.split("/")[-1]).exists(): + with DownloadProgressBar( + unit="B", unit_scale=True, miniters=1, desc=url.split("/")[-1] + ) as t: + cache_dir.mkdir(parents=True, exist_ok=True) + urllib.request.urlretrieve( + url, filename=download_path.as_posix(), reporthook=t.update_to + ) + else: + print(f"'{url}' exists in {cache_dir}") + + return download_path.as_posix() + + try: + ckpt_path = model_cfg["ckpt_path"] + dict_path = model_cfg["dict_path"] + except KeyError: + raise KeyError( + "Fairseq model cfg must provide (1) ckpt_path, (2) dict_path" + ) + + if re.search("^https", ckpt_path): + ckpt_path = download_file(ckpt_path, self.cache_dirpath) + if re.search("^https", dict_path): + dict_path = download_file(dict_path, self.cache_dirpath) + + model, saved_cfg, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [ckpt_path], + arg_overrides={ + "task": "audio_finetuning", + "data": self.cache_dirpath.as_posix(), + }, # data must have dict in it + ) + + dict_lines = open(dict_path, "r").readlines() + tokens = [l.split()[0] for l in dict_lines] + # adding default fairseq special tokens + tokens = ["<s>", "<pad>", "</s>", "<unk>"] + tokens + + self.model = model[0] + self.tokens = tokens + + if "|" in tokens: + self.sil_token = "|" + else: + self.sil_token = tokens[ + 2 + ] # use eos as silence token if | not presented e.g., Hok ASR model + print(f"Inferring silence token from the dict: {self.sil_token}") + self.blank_token = self.tokens[0] + + self.sampling_rate = saved_cfg.task.sample_rate + self.normalize_input = saved_cfg.task.normalize + + @torch.inference_mode() + def load_audiofile(self, audio_path: str) -> torch.Tensor: + """ + Load the audio files and apply resampling and normalizaion + + Args: + audio_path: the audio file path + + Returns: + audio_waveform: the audio waveform as a torch.Tensor object + """ + + audio_waveform, sampling_rate = torchaudio.load(audio_path) + if audio_waveform.dim == 2: + audio_waveform = audio_waveform.mean(-1) + if self.sampling_rate != sampling_rate: + audio_waveform = torchaudio.functional.resample( + audio_waveform, sampling_rate, self.sampling_rate + ) + if self.normalize_input: + # following fairseq raw audio dataset + audio_waveform = torch.nn.functional.layer_norm( + audio_waveform, audio_waveform.shape + ) + + return audio_waveform + + @torch.inference_mode() + def compute_emissions(self, audio_input: torch.Tensor) -> torch.Tensor: + """ + Compute the emissions for either fairseq or huggingface asr model + + Args: + audio_path: the input audio waveform + + Returns: + emissions: the logits of the encoded prediction. + """ + + if self.use_cuda: + audio_input = audio_input.to("cuda") + if isinstance(self.model, fairseq.models.wav2vec.wav2vec2_asr.Wav2VecCtc): + padding_mask = lengths_to_padding_mask(torch.tensor([audio_input.numel()])) + emissions = self.model.w2v_encoder(audio_input, padding_mask)[ + "encoder_out" + ].transpose(0, 1) + else: + emissions = self.model(audio_input).logits + + return emissions + + def decode_emissions(self, emissions: torch.Tensor) -> str: + """ + Decode the emissions and apply post process functions + + Args: + emissions: the input Tensor object + + Returns: + hypo: the str as the decoded transcriptions + """ + + emissions = emissions.cpu() + results = self.decoder(emissions) + + # assuming the lexicon-free decoder and working with tokens + hypo = self.decoder.idxs_to_tokens(results[0][0].tokens) + hypo = self.post_process_fn(hypo) + + return hypo + + def transcribe_audiofile(self, audio_path: str, lower=True) -> str: + """ + Transcribe the audio into string + + Args: + audio_path: the input audio waveform + lower: the case of the transcriptions with lowercase as the default + + Returns: + hypo: the transcription result + """ + + asr_input = self.load_audiofile(audio_path) + emissions = self.compute_emissions(asr_input) + hypo = self.decode_emissions(emissions) + + return hypo.strip().lower() if lower else hypo.strip() diff --git a/fairseq/examples/speech_to_speech/benchmarking/README.md b/fairseq/examples/speech_to_speech/benchmarking/README.md new file mode 100644 index 0000000..c62fe12 --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/README.md @@ -0,0 +1,31 @@ +# Benchmarking + +## Overview + +The goal of this framework is to support benchmarking various speech to speech translation(S2ST) models in terms of runtime, max-memory consumption and total number of floating point operations(FLOPS). It is a generic framework and can be easily extended to support any fairseq models. To accurately benchmark the performance, core inference modules are re-implemented based on fairseq_cli/generate.py (core.py/Processing) and examples/speech_to_text/generate_waveform.py(core.py/SpeechGeneration. To ensure that the end to end models and cascaded models are compared fairly, for cascaded models we only consider the performance metrics for model inference at all stages ignoring any intermediate data and io processing consumption. We run all the benchmarking runs on CPU as it is generally used in production environment and also due to lack of good benchmarking library support for GPUs. + +1. Runtime: Average time in seconds to run model inference on an example from a given dataset. We use [timeit](https://docs.python.org/3/library/timeit.html) library to measure the runtime. +2. Max memory: Maximum memory in MiB averaged over by running the model inference on all examples from the given dataset. We use [memory_profiler](https://pypi.org/project/memory-profiler/) library to gather memory footprints for a code snippet and find the maximum to get the max memory used by the code. For cascaded models, we find the max of all stages to get the overall max_memory footprint. +3. FLOPS: We compute the average number of floating point operations needed to run model inference for an example from the given dataset. We use [PAPI library](http://www.bnikolic.co.uk/blog/python/flops/2019/10/01/pytorch-count-flops.html) to benchmark the number of flops. + +## CLI Commands + +```{python} +CUBLAS_WORKSPACE_CONFIG=:4096:8 python examples/speech_to_speech/benchmarking/get_metrics.py ‘’ --config $config +``` + + +## Note: + +1. The npy dataset is a list of samples saved as a .npy file. Each sample is a dictionary with id, net_input. +2. The raw dataset is a list of raw audio paths similar to wav2vec2 input tsv file + +```{python} +sample: { + "id": xx, + "net_input": { + "src_tokens": torch.tensor([]), + "src_lengths": torch.tensor([]) + } +} +``` diff --git a/fairseq/examples/speech_to_speech/benchmarking/configs/2StageS2ST.yaml b/fairseq/examples/speech_to_speech/benchmarking/configs/2StageS2ST.yaml new file mode 100644 index 0000000..11deb42 --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/configs/2StageS2ST.yaml @@ -0,0 +1,19 @@ +general: + dataset_path: $npy_dataset + cpu: True + model_type: 2StageS2ST + dataset_size: 1 + +stage1: + data: $data_bin_stage1 + task: speech_to_text + path: $checkpoint_stage1 + config_yaml: config.yaml + max_len_a: 2 + max_len_b: 500 + +stage2: + data: $data_bin_stage2 + task: text_to_speech + path: $checkpoint_stage2 + config_yaml: config.yaml diff --git a/fairseq/examples/speech_to_speech/benchmarking/configs/3StageS2ST.yaml b/fairseq/examples/speech_to_speech/benchmarking/configs/3StageS2ST.yaml new file mode 100644 index 0000000..9638136 --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/configs/3StageS2ST.yaml @@ -0,0 +1,28 @@ +general: + dataset_path: $npy_dataset + cpu: True + model_type: 3StageS2ST + max_len_a: 2 + max_len_b: 500 + dataset_size: 1 + +stage1: + data: $data_bin_stage1 + task: speech_to_text + path: $checkpoint_stage1 + config_yaml: config.yaml + max_len_a: 2 + max_len_b: 500 + +stage2: + data: $data_bin_stage2 + task: translation + path: $checkpoint_stage2 + config_yaml: config.yaml + + +stage2: + data: $data_bin_stage3 + task: text_to_speech + path: $checkpoint_stage3 + config_yaml: config.yaml diff --git a/fairseq/examples/speech_to_speech/benchmarking/configs/DirectS2U.yaml b/fairseq/examples/speech_to_speech/benchmarking/configs/DirectS2U.yaml new file mode 100644 index 0000000..96264ce --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/configs/DirectS2U.yaml @@ -0,0 +1,22 @@ +general: + dataset_path: $npy_dataset_path + cpu: True + model_type: S2UT + dataset_size: 5 + dump_speech_waveforms_dir: $dump_waveforms_dir_path + +stage1: + data: $data_bin + task: speech_to_speech + path: $checkpoint + config_yaml: config.yaml + max_len_b: 100000 + beam: 10 + target_is_code: True + max_target_positions: 3000 + target_code_size: 100 + +stage2: + vocoder: $vocoder_path + vocoder_cfg: $vocoder_cfg_json + dur_prediction: True diff --git a/fairseq/examples/speech_to_speech/benchmarking/configs/S2T.yaml b/fairseq/examples/speech_to_speech/benchmarking/configs/S2T.yaml new file mode 100644 index 0000000..3a106a0 --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/configs/S2T.yaml @@ -0,0 +1,13 @@ +general: + dataset_path: $npy_dataset + cpu: True + model_type: S2T + dataset_size: 1 + +stage1: + data: $data_bin + task: speech_to_text + path: $checkpoint + config_yaml: config.yaml + max_len_a: 2 + max_len_b: 500 diff --git a/fairseq/examples/speech_to_speech/benchmarking/core.py b/fairseq/examples/speech_to_speech/benchmarking/core.py new file mode 100644 index 0000000..da22a34 --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/core.py @@ -0,0 +1,487 @@ +import timeit +import logging +import torch +from pypapi import events, papi_high as high +from memory_profiler import memory_usage +from torch import nn +from argparse import Namespace +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.data import data_utils as fairseq_data_utils +from fairseq import checkpoint_utils, tasks, utils +from fairseq.models.text_to_speech.vocoder import CodeHiFiGANVocoder +from examples.hubert.simple_kmeans.dump_hubert_feature import HubertFeatureReader +from examples.hubert.simple_kmeans.dump_km_label import ApplyKmeans +from fairseq_cli.generate import get_symbols_to_strip_from_output +import soundfile as sf +import ast +import json + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +torch.manual_seed(1) +torch.set_deterministic(True) + + +class BenchmarkingBase(nn.Module): + def __init__(self): + nn.Module.__init__(self) + self.s2x_task = None + + def warm_up(self, sample, repeat): + """Warm up the model""" + for _i in range(repeat): + self.forward(sample) + logger.info(f"Model warmed up by running inference {repeat} times") + + def benchmark_run_time(self, dataset, repeat): + """Benchmark average runtime for the model by calling benchmark_run_time_single_sample function""" + logger.info("Starting run time benchmarking") + time_elapsed = 0 + for i, sample in enumerate(dataset): + time_elapsed += self.benchmark_run_time_single_sample(sample, repeat=repeat) + if i % 100 == 0: + logger.info(f"Benchmarked run time for {i}/{len(dataset)} samples") + total_time_elapsed = time_elapsed / len(dataset) + return total_time_elapsed + + def benchmark_run_time_single_sample(self, sample, repeat): + """Benchmark average runtime for a single sample using timeit library. Units are seconds""" + timer = timeit.Timer(lambda: self.forward(sample)) + time_elapsed = timer.timeit(repeat) + return time_elapsed / repeat + + def count_flops( + self, + dataset, + repeat, + ): + """Use PYPAPI library to count average flops for model inference. + Note: It only works if the model is being run on cpu""" + logger.info("Starting flop counter") + high.start_counters([events.PAPI_DP_OPS]) + for i, sample in enumerate(dataset): + for _r in range(repeat): + self.forward(sample) + if i % 100 == 0: + logger.info(f"Counted flops for {i}/{len(dataset)} samples") + flops = high.stop_counters() + flops = round(flops[0] / (repeat * len(dataset))) + return flops + + def max_memory(self, dataset, repeat): + """Compute average max memory consumed by model inference. Units are MiB""" + logger.info("Starting memory benchmarking") + total_memory = 0 + for i, sample in enumerate(dataset): + for _r in range(repeat): + total_memory += max(memory_usage((self.forward, (sample,), {}))) + if i % 100 == 0: + logger.info(f"Benchmarked memory for {i}/{len(dataset)} samples") + total_memory = total_memory / (repeat * len(dataset)) + return total_memory + + def gather_all_metrics(self, dataset, repeat): + run_time = self.benchmark_run_time(dataset, repeat) + max_memory = self.max_memory(dataset, repeat) + flops = self.count_flops(dataset, repeat) + + return run_time, max_memory, flops + + def dump_final_speech_output( + self, dataset, output_dir, resample_fn, sample_rate, prefix=None + ): + + for i, sample in enumerate(dataset): + hypo = self.forward(sample)[0] + + def to_np(x): + return x.detach().cpu().numpy() + + try: + wave_preds = to_np(resample_fn(hypo["waveform"])) + sf.write( + f"{output_dir}/{prefix}_{i}_pred.wav", + wave_preds, + sample_rate, + ) + except Exception as e: + raise Exception( + f" Encountered {e} - Invalid waveform. Make sure the model outputs a waveform" + ) + + +class Processing(BenchmarkingBase): + """Class similar to fairseq_cli/generate.py. Supports ASR, MT and ST model inference""" + + def __init__(self, args): + super().__init__() + self.use_cuda = not getattr(args, "cpu", False) + self.setUp(args) + self.training = False + self.s2x_task = self.task + + def setUp(self, cfg): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + self.task = tasks.setup_task(cfg.task) + self.tgt_dict = self.task.target_dictionary + + # Load ensemble + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, _ = checkpoint_utils.load_model_ensemble( + utils.split_paths(cfg.common_eval.path), + arg_overrides={}, + task=self.task, + suffix=cfg.checkpoint.checkpoint_suffix, + strict=False, + num_shards=cfg.checkpoint.checkpoint_shard_count, + ) + if len(models) > 1: + raise Exception("Currently loading multiple models is not supported") + self.model = models[0] + + # Optimize model for generation + if cfg.common.fp16: + self.model.half() + if self.use_cuda: + self.model.cuda() + self.model.prepare_for_inference_(cfg) + + self.generator = self.task.build_generator( + [self.model], + cfg.generation, + extra_gen_cls_kwargs={}, + ) + # Handle tokenization and BPE + self.tokenizer = self.task.build_tokenizer(cfg.tokenizer) + self.bpe = self.task.build_bpe(cfg.bpe) + self.remove_bpe = cfg.common_eval.post_process + + def encode_source(self, src): + """Method to generate source tokens from a string""" + if self.tokenizer is not None: + src = self.tokenizer.encode(src) + if self.bpe is not None: + src = self.bpe.encode(src) + src_tokens = self.task.source_dictionary.encode_line(src).long() + src_lens = src_tokens.size(0) + return { + "net_input": { + "src_tokens": src_tokens.view(1, src_lens), + "src_lengths": torch.tensor([src_lens]), + } + } + + def decode_target(self, hypos): + """Method to decode target string from tokens""" + hypo_str = self.tgt_dict.string( + hypos[0][0]["tokens"].int().cpu(), + self.remove_bpe, + get_symbols_to_strip_from_output(self.generator), + ) + if self.bpe is not None: + hypo_str = self.bpe.decode(hypo_str) + if self.tokenizer is not None: + hypo_str = self.tokenizer.decode(hypo_str) + return hypo_str + + def forward(self, sample): + hypos = self.task.inference_step( + self.generator, + [self.model], + sample, + prefix_tokens=None, + constraints=None, + ) + return hypos + + +class GenerateWaveformFromCode(BenchmarkingBase): + """Class to support waveform generation from code. Currently, vocoder only supports single speaker""" + + def __init__(self, args): + super().__init__() + with open(args.vocoder_cfg) as f: + vocoder_cfg = json.load(f) + self.dur_prediction = args.dur_prediction + self.vocoder = CodeHiFiGANVocoder(args.vocoder, vocoder_cfg) + + def format_units(self, input): + code = torch.LongTensor(list(map(int, input.strip().split()))).view(1, -1) + return {"code": code} + + def generate_vocoder_input(self, dataset): + return [self.format_units(sample) for sample in dataset] + + def forward(self, sample): + return [{"waveform": self.vocoder(sample, self.dur_prediction)}] + + +class HubertUnitExtractor(BenchmarkingBase): + def __init__(self, args): + self.feature_reader = HubertFeatureReader( + args.hubert_ckpt_path, args.hubert_layer + ) + self.kmeans = ApplyKmeans(args.hubert_km_path) + + def forward(self, sample): + with torch.no_grad(): + feat = [] + for start in range(0, sample.size(1), self.feature_reader.max_chunk): + x_chunk = sample[:, start : start + self.max_chunk] + feat_chunk, _ = self.feature_reader.model.extract_features( + source=x_chunk, + padding_mask=None, + mask=False, + output_layer=self.layer, + ) + feat.append(feat_chunk) + torch.cat(feat, 1).squeeze(0) + return self.kmeans(feat).tolist() + + +class SpeechGeneration(BenchmarkingBase): + """Class similar to examples/text_to_speech/generate_waveform.py. + Supports models with speech generation as end goal (TTS, Direct S2ST models etc)""" + + def __init__(self, args): + super().__init__() + self.use_cuda = not getattr(args, "cpu", False) + self.setUp(args) + self.s2x_task = self.task + + def setUp(self, args): + if args.task == "speech_to_speech": + args.normalize_waveform = False + self.task = tasks.setup_task(args) + self.pre_tokenizer = self.task.build_tokenizer(args) + self.bpe_tokenizer = self.task.build_bpe(args) + try: + self.src_dict = self.task.src_dict + except Exception: + self.src_dict = None + ensemble, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [args.path], + arg_overrides=ast.literal_eval(args.model_overrides), + task=self.task, + strict=False, + ) + self.model = ensemble[0] + if self.use_cuda: + self.model.cuda() + # criterion.cuda() + self.model.eval() + self.generator = self.task.build_generator( + [self.model], + args, + ) + + def processTextInput(self, text): + """Generate source tokens from text input""" + if self.pre_tokenizer is not None: + text = self.pre_tokenizer.encode(text) + if self.bpe_tokenizer is not None: + text = self.bpe_tokenizer.encode(text) + target = self.src_dict.encode_line( + text, add_if_not_exist=False, append_eos=True + ).long() + target = fairseq_data_utils.collate_tokens( + [target], + self.src_dict.pad(), + self.src_dict.eos(), + left_pad=False, + move_eos_to_beginning=False, + ) + src_lengths = torch.tensor([target.size(1)], dtype=torch.long) + prev_output_tokens = None + sample = { + "net_input": { + "src_tokens": target, + "src_lengths": src_lengths, + "prev_output_tokens": prev_output_tokens, + } + } + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + return sample + + def forward(self, sample): + sample["speaker"] = None + output = self.generator.generate(self.model, sample) # , has_targ=False + return output + + +class S2UT(BenchmarkingBase): + """Class to support S2UT models. Also supports generating waveforms from the units predicted""" + + def __init__(self, s2u_args, vocoder_args=None): + super().__init__() + self.s2u = Processing(s2u_args) + self.vocoder = None + if vocoder_args: + self.vocoder = GenerateWaveformFromCode(vocoder_args) + self.vocoder_input = None + + def forward(self, sample): + s2u_hypos = self.s2u(sample) + s2u_output = self.s2u.decode_target(s2u_hypos) + if not self.vocoder: + return s2u_output + units = self.vocoder.format_units(s2u_output) + vocoder_output = self.vocoder(units) + return vocoder_output + + def generate_s2u_outputs(self, dataset): + return [self.s2u.decode_target(self.s2u(sample)) for sample in dataset] + + def compute_metrics(self, metric_type, dataset, repeat=None): + """Generic function to compute metrics ignoring the io processing time""" + if self.vocoder and not self.vocoder_input: + self.s2u_output = self.generate_s2u_outputs(dataset) + self.vocoder_input = self.vocoder.generate_vocoder_input(self.s2u_output) + + s2u_metrics = getattr(self.s2u, metric_type)( + dataset, + repeat, + ) + vocoder_metrics = 0 + if self.vocoder: + vocoder_metrics = getattr(self.vocoder, metric_type)( + self.vocoder_input, + repeat, + ) + print( + f"metric_type = {metric_type} s2u_metrics = {s2u_metrics} \t vocoder_metrics = {vocoder_metrics}" + ) + if metric_type == "max_memory": + return max(s2u_metrics, vocoder_metrics) + else: + return s2u_metrics + vocoder_metrics + + def benchmark_run_time(self, dataset, repeat): + return self.compute_metrics("benchmark_run_time", dataset, repeat) + + def count_flops(self, dataset, repeat): + return self.compute_metrics("count_flops", dataset, repeat) + + def max_memory(self, dataset, repeat): + return self.compute_metrics("max_memory", dataset, repeat) + + +class Cascaded2StageS2ST(BenchmarkingBase): + """ST + TTS""" + + def __init__(self, s2t_args, tts_args): + super().__init__() + self.s2t = Processing(s2t_args) + self.s2x_task = self.s2t.task + self.tts = SpeechGeneration(tts_args) if tts_args else None + self.training = False + self.tts_inputs = None + + def forward(self, sample): + if not self.tts: + raise Exception( + "Forward function is not callable without tts. Reinitialize the class with tts_args" + ) + s2t_hypos = self.s2t(sample) + s2t_output = self.s2t.decode_target(s2t_hypos) + tts_input = self.tts.processTextInput(s2t_output) + tts_output = self.tts(tts_input) + return tts_output + + def generate_s2t_outputs(self, dataset): + """Process dataset and generate s2t outputs""" + return [self.s2t.decode_target(self.s2t(sample)) for sample in dataset] + + def generate_tts_inputs(self, dataset): + """Process dataset and generate tts inputs""" + return [self.tts.processTextInput(sample) for sample in dataset] + + def compute_metrics(self, metric_type, dataset, repeat=None): + """Generic function to compute metrics ignoring the io processing time""" + if not self.tts_inputs: + s2t_outputs = self.generate_s2t_outputs(dataset) + self.tts_inputs = self.generate_tts_inputs(s2t_outputs) + + s2t_metrics = getattr(self.s2t, metric_type)( + dataset, + repeat, + ) + + tts_metrics = getattr(self.tts, metric_type)( + self.tts_inputs, + repeat, + ) + print( + f"metric_type = {metric_type} s2t_metrics = {s2t_metrics} \t tts_metrics = {tts_metrics}" + ) + if metric_type == "max_memory": + return max(s2t_metrics, tts_metrics) + else: + return s2t_metrics + tts_metrics + + def benchmark_run_time(self, dataset, repeat): + return self.compute_metrics("benchmark_run_time", dataset, repeat) + + def count_flops(self, dataset, repeat): + return self.compute_metrics("count_flops", dataset, repeat) + + def max_memory(self, dataset, repeat): + return self.compute_metrics("max_memory", dataset, repeat) + + +class Cascaded3StageS2ST(Cascaded2StageS2ST): + """ASR + MT + TTS""" + + def __init__(self, s2t_args, tts_args, mt_args): + super().__init__(s2t_args, tts_args) + self.mt = Processing(mt_args) + self.mt_inputs = [] + + def forward(self, sample): + s2t_hypos = self.s2t(sample) + s2t_output = self.s2t.decode_target(s2t_hypos) + mt_input = self.mt.encode_source(s2t_output) + mt_hypos = self.mt(mt_input) + mt_output = self.mt.decode_target(mt_hypos) + tts_input = self.tts.processTextInput(mt_output) + tts_output = self.tts(tts_input) + return tts_output + + def generate_mt_inputs(self, dataset): + """Process dataset to generate mt model inputs""" + return [self.mt.encode_source(sample) for sample in dataset] + + def generate_mt_outputs(self, dataset): + """Process dataset to generate mt model outputs""" + return [self.mt.decode_target(self.mt(sample)) for sample in dataset] + + def compute_metrics(self, metric_type, dataset, repeat=None): + """Generic function to compute metrics ignoring the io processing time""" + if not self.tts_inputs: + s2t_outputs = self.generate_s2t_outputs(dataset) + self.mt_inputs = self.generate_mt_inputs(s2t_outputs) + mt_outputs = self.generate_mt_outputs(self.mt_inputs) + self.tts_inputs = self.generate_tts_inputs(mt_outputs) + + s2t_metrics = getattr(self.s2t, metric_type)( + dataset, + repeat, + ) + mt_metrics = getattr(self.mt, metric_type)(self.mt_inputs, repeat) + tts_metrics = getattr(self.tts, metric_type)( + self.tts_inputs, + repeat, + ) + print( + f"metric_type = {metric_type} s2t_metrics = {s2t_metrics} \t mt_metrics = {mt_metrics} \t tts_metrics = {tts_metrics}" + ) + if metric_type == "max_memory": + return max(s2t_metrics, mt_metrics, tts_metrics) + else: + return s2t_metrics + mt_metrics + tts_metrics diff --git a/fairseq/examples/speech_to_speech/benchmarking/data_utils.py b/fairseq/examples/speech_to_speech/benchmarking/data_utils.py new file mode 100644 index 0000000..c73a599 --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/data_utils.py @@ -0,0 +1,264 @@ +from fairseq import tasks +import numpy as np +import logging +import random +from fairseq import options +import torch +import os +import soundfile as sf + +from fairseq.data.audio.audio_utils import ( + get_waveform, + parse_path, +) + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +random.seed(1) +np.random.seed(1) +random_number_generator = np.random.RandomState(30) + + +def generate_random_data_sample(T, B=1, D=80): + """Generate random data sample given the T, B, D values""" + net_input = { + "src_tokens": torch.tensor(random_number_generator.randn(B, T, D)).float(), + "src_lengths": torch.tensor([T]), + } + return {"net_input": net_input} + + +def generate_random_dataset(T_range_min, T_range_max, B=1, D=80, dataset_size=100): + """Generate random dataset with T values within a given range, B, D""" + T_values = [random.randint(T_range_min, T_range_max) for i in range(dataset_size)] + dataset = [] + for t in T_values: + dataset.append(generate_random_data_sample(t, B, D)) + return dataset, sum(T_values) / dataset_size + + +def load_dataset_npy(file_name, dataset_size=None): + """Load dataset from a .npy file.""" + data = np.load(file_name, allow_pickle=True) + if dataset_size: + data = data[:dataset_size] + return data + + +def load_dataset_raw_to_waveforms( + file_name, + dataset_size=None, + need_waveform=True, + sample_rate=16000, + read_using_soundfile=False, +): + """Load raw dataset from w2v tsv file. Optionally get waveforms""" + data = [] + with open(file_name, "r") as fp: + lines = fp.readlines() + data = [ + os.path.join(lines[0].strip(), line.strip().split("\t")[0]) + for line in lines[1:] + ] + + if dataset_size: + data = data[:dataset_size] + + if not need_waveform: + return data + + features = [] + if read_using_soundfile: + for _i, d in enumerate(data): + wav = sf.read(d)[0] + if wav.ndim == 2: + wav = wav.mean(-1) + features.append(torch.from_numpy(wav).float().view(1, -1)) + else: + for i, d in enumerate(data): + _path, slice_ptr = parse_path(d) + if len(slice_ptr) == 0: + feat = get_waveform( + _path, always_2d=True, output_sample_rate=sample_rate + )[0] + features.append( + { + "id": i, + "net_input": { + "src_tokens": torch.tensor(feat), + "src_lengths": torch.tensor([feat.shape[1]]), + }, + } + ) + else: + raise Exception("Currently unsupported data format") + return features + + +def load_dataset_task( + args, + batch_size=1, + limit_size=None, + ref_dataset=None, +): + """Loads dataset based on args by creating a task""" + if not args.data or not args.subset or not args.task: + raise Exception( + "Please provide necessary arguments to load the dataset - data, subset and task" + ) + task = tasks.setup_task(args) + + task.load_dataset(args.subset) + if not limit_size: + limit_size = len(task.dataset(args.subset)) + + iter = task.get_batch_iterator( + dataset=task.dataset(args.subset), max_sentences=batch_size + ).next_epoch_itr(shuffle=False) + dataset = [] + for i, sample in enumerate(iter): + sample = { + "id": task.datasets[args.subset].ids[sample["id"].item()], + "net_input": { + "src_tokens": sample["net_input"]["src_tokens"], + "src_lengths": sample["net_input"]["src_lengths"], + }, + } + dataset.append(sample) + if i == limit_size - 1: + break + + if ref_dataset: + try: + ids = get_ids_from_dataset(ref_dataset) + except Exception as e: + raise Exception(f"{e} - Cannot extract ids from reference dataset") + + filtered_dataset = [] + for sample in dataset: + if ( + sample["id"] in ids + or sample["id"][5:] in ids + or f"dev_{sample['id']}" in ids + ): + filtered_dataset.append(sample) + dataset = filtered_dataset + + max_len, min_len, avg_len = get_dataset_stats(dataset) + print( + f"{args.subset} dataset stats : num_samples={len(dataset)} max_len = {max_len} min_len = {min_len} avg_len = {avg_len}" + ) + + return dataset + + +def randomly_sample_subset(dataset, size=500): + """Randomly sample subset from a dataset""" + random_indices = [random.randint(0, len(dataset) - 1) for i in range(size)] + return [dataset[i] for i in random_indices] + + +def get_short_data_subset(dataset, size=500): + """Get a subset of desired size by sorting based on src_lengths""" + return sort_dataset(dataset)[:size] + + +def get_long_data_subset(dataset, size=500): + """Get a subset of desired size by sorting based on src_lengths descending""" + return sort_dataset(dataset, reverse=True)[:size] + + +def sort_dataset(dataset, reverse=False): + return sorted( + dataset, key=lambda x: x["net_input"]["src_lengths"].item(), reverse=reverse + ) + + +def save_dataset_npy(dataset, file_name): + """Save a dataset as .npy file""" + np.save(file_name, dataset) + + +def get_dataset_stats(dataset): + """Get stats about dataset based on src_lengths of samples""" + max_len = 0 + min_len = 100000 + avg_len = 0 + for d in dataset: + max_len = max(max_len, d["net_input"]["src_lengths"].item()) + min_len = min(min_len, d["net_input"]["src_lengths"].item()) + avg_len += d["net_input"]["src_lengths"].item() + + return max_len, min_len, avg_len / len(dataset) + + +def make_parser(): + """ + Additional args: + 1. Provide the dataset dir path using --data. + 2. Loading the dataset doesn't require config, provide --config-yaml to apply additional feature transforms + """ + parser = options.get_speech_generation_parser() + parser.add_argument( + "--subset", + default=None, + type=str, + required=True, + help="Subset to use for dataset generation", + ) + parser.add_argument( + "--dataset-save-dir", + default=None, + type=str, + required=False, + help="Dir path in which the datasets are to be saved", + ) + parser.add_argument( + "--ref-dataset", + default=None, + type=str, + required=False, + help="If provided, the ids in the reference dataset will be used to filter the new dataset generated.", + ) + parser.add_argument("--dataset-save-token", default="", type=str, required=False) + + options.add_generation_args(parser) + return parser + + +def get_ids_from_dataset(dataset): + return {sample["id"]: 1 for sample in dataset} + + +def cli_main(): + parser = make_parser() + args = options.parse_args_and_arch(parser) + dataset = load_dataset_task(args) + + random_dataset = randomly_sample_subset(dataset) + short_dataset = get_short_data_subset(dataset) + long_dataset = get_long_data_subset(dataset) + + if args.dataset_save_token: + args.dataset_save_token = f"_{args.dataset_save_token}_" + + if args.dataset_save_dir: + save_dataset_npy( + random_dataset, + f"{args.dataset_save_dir}/random_dataset{args.dataset_save_token}w_ids.npy", + ) + save_dataset_npy( + short_dataset, + f"{args.dataset_save_dir}/short_dataset{args.dataset_save_token}w_ids.npy", + ) + save_dataset_npy( + long_dataset, + f"{args.dataset_save_dir}/long_dataset{args.dataset_save_token}w_ids.npy", + ) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/speech_to_speech/benchmarking/get_metrics.py b/fairseq/examples/speech_to_speech/benchmarking/get_metrics.py new file mode 100644 index 0000000..773257f --- /dev/null +++ b/fairseq/examples/speech_to_speech/benchmarking/get_metrics.py @@ -0,0 +1,162 @@ +import copy +import torch +import logging +from argparse import Namespace +import yaml +from fairseq import options +from examples.speech_to_speech.benchmarking.core import ( + Processing, + SpeechGeneration, + Cascaded2StageS2ST, + Cascaded3StageS2ST, + S2UT, +) +from examples.speech_to_speech.benchmarking.data_utils import ( + load_dataset_npy, + load_dataset_raw_to_waveforms, +) + + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +torch.manual_seed(1) +torch.set_deterministic(True) + + +def make_parser(): + """Note: As the names indicate use s2x_args(ex:ST, ASR etc) for models with speech input, + x2s_args for models with speech output(ex:TTS) and mt_args for translation models (ex: mt, T2U etc). + For direct S2ST models, use x2s_args to provide model details. + """ + parser = options.get_speech_generation_parser() + parser.add_argument("--target-is-code", action="store_true", default=False) + parser.add_argument("--config", type=str) + parser.add_argument( + "--model-type", + default="S2U", + choices=["S2S", "TTS", "S2UT", "MT", "S2T", "2StageS2ST", "3StageS2ST"], + help="Choose one of the models. For model inference implementation, refer to core.py", + ) + parser.add_argument( + "--dataset-path", + type=str, + help="""File to load dataset from. Assumes dataset is a list of samples. + Each sample is a dict of format {'net_input':{'src_tokens':torch.tenor(),'src_lengths':torch.tensor()}}""", + ) + parser.add_argument( + "--dataset-type", + type=str, + default="npy", + choices=["npy", "raw"], + help="""Type of input dataset file""", + ) + parser.add_argument( + "--read-using-sf", + type=str, + default=False, + help="""If sound file should be used to read the raw dataset""", + ) + parser.add_argument( + "--dataset-size", + default=None, + type=int, + help="Dataset size to use for benchmarking", + ) + parser.add_argument( + "--dump-speech-waveforms-dir", + default=None, + type=str, + help="Directory to dump the speech waveforms computed on the dataset.", + ) + parser.add_argument( + "--dump-waveform-file-prefix", + default="", + type=str, + help="File name prefix for the saved speech waveforms", + ) + parser.add_argument( + "--feat-dim", default=80, type=int, help="Input feature dimension" + ) + parser.add_argument( + "--target-sr", + default=16000, + type=int, + help="Target sample rate for dumping waveforms", + ) + + options.add_generation_args(parser) + options.get_interactive_generation_parser(parser) + return parser + + +def cli_main(): + parser = make_parser() + args = options.parse_args_and_arch(parser) + + with open( + args.config, + "r", + ) as f: + config = yaml.load(f, Loader=yaml.FullLoader) + dict_args = vars(args) + dict_args.update(config["general"]) + args = Namespace(**dict_args) + + i = 1 + stage_args = [] + while i <= 3: + var = f"stage{i}" + tmp_args = copy.deepcopy(dict_args) + if var in config: + tmp_args.update(config[var]) + stage_args.append(Namespace(**tmp_args)) + i += 1 + else: + break + + if args.model_type == "S2S" or args.model_type == "TTS": + model = SpeechGeneration(stage_args[0]) + elif args.model_type == "S2UT": + model = S2UT(stage_args[0], stage_args[1] if len(stage_args) > 1 else None) + elif args.model_type == "MT" or args.model_type == "S2T": + model = Processing(stage_args[0]) + elif args.model_type == "2StageS2ST": + model = Cascaded2StageS2ST(stage_args[0], stage_args[1]) + elif args.model_type == "3StageS2ST": + model = Cascaded3StageS2ST(stage_args[0], stage_args[2], stage_args[1]) + else: + raise Exception(f"Currently unsupported model type {args.model_type}") + + print(f"Evaluating on dataset - {args.dataset_path}\n") + + if args.dataset_type == "npy": + dataset = load_dataset_npy(args.dataset_path, dataset_size=args.dataset_size) + elif args.dataset_type == "raw": + dataset = load_dataset_raw_to_waveforms( + args.dataset_path, + dataset_size=args.dataset_size, + read_using_soundfile=args.read_using_sf, + ) + else: + raise Exception(f"Invalid dataset type {args.dataset_type}") + + model.warm_up(sample=dataset[0], repeat=2) + + run_time, memory, flops = model.gather_all_metrics(dataset, repeat=1) + print(f"run_time = {run_time}sec \tmemory = {memory}MiB \tflops = {flops}") + + if args.dump_speech_waveforms_dir: + model.dump_final_speech_output( + dataset, + args.dump_speech_waveforms_dir, + lambda x: x, + args.target_sr, + prefix=args.dump_waveform_file_prefix, + ) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/speech_to_speech/docs/data_augmentation.md b/fairseq/examples/speech_to_speech/docs/data_augmentation.md new file mode 100644 index 0000000..c0c17ff --- /dev/null +++ b/fairseq/examples/speech_to_speech/docs/data_augmentation.md @@ -0,0 +1,435 @@ +# Noise and audio augmentation techniques + +The noise and data augmentation techniques were written in an effort to understand how augmenatation can affect model robustness and performance in both clean and noisy settings. + +All transforms discussed in this section are subclasses of `AudioFeatureTransform`, `AudioWaveformTransform`, or `AudioDatasetTransform`. Each `Audio*Transform` has unique interaction with the data. If interested in implemented one's own transforms, it is highly advisable to review the differences (see [Adding your own transforms](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/data_augmentation.md#adding-your-own-transforms)). If only applying the in-built transforms, then one only needs to be mindful that the correct kind of transform is listed in the config (see [Using transforms](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/data_augmentation.md#using-transforms)). These transforms can be applied to instances of `SpeechToTextDataset`. + +### Contents +[In-built transforms](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/data_augmentation.md#in-built-transforms) + +[Benchmark studies](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/data_augmentation.md#benchmark-studies) + +[Using transforms](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/data_augmentation.md#using-transforms) + +[Adding your own transforms](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/data_augmentation.md#adding-your-own-transforms) + + +## In-built transforms +### 1. Utterance concatenation +Utterance concatenation is a data augmenation technique introduced as ConcatAug in [Translatotron 2: High-quality direct speech-to-speech translation +with voice preservation](https://arxiv.org/pdf/2107.08661.pdf). +With some parameterized probability, samples are concatenated with one other randomly chosen sample from the whole dataset. In the positive (concatenation) case, accessing `dataset[i]` will return a `SpeechToTextDatasetItem` where `source=source[i]+source[j]` and `target=target[i]+target[j]`. In the negative (skip concatenation) case, accessing `dataset[i]` will return a `SpeechToTextDatasetItem` where `source=source[i]` and `target=target[i]` as usual. + +**Usage**: `concataugment` is an `AudioDatasetTransform` and has three configurable hyperparameters: +- `rate`: probability that any single access will result in the positive (concatenation) case. Defaults to 0.25. +- `max_tokens`: maximum number of tokens allowed for concatenated source sequences. This parameter is meant to limit the length of concatenated samples to avoid out-of-memory errors. Defaults to 300. +- `attempts`: maximum number of invalid concatenation attempts before defaulting to the negative (skip concatenation) case. This parameter aims to limit excessive time spent trying to find candidate samples that are short enough to concatenate with. Defaults to 5. + +Please be wary of OOMs while using this augmentation technique; we used smaller batch sizes as a workaround to avoid OOMs. Batch size is determined by update frequency, batch size hyperparameter, and the number of GPU, so you may want to alter these to this end. + +### 2. Noise augmentation suite + +The four noise augmentation methods in this suite adhere to the following principle: with some parameterized probability, samples are overlayed with a noise track. The content of the noise track is specific to the method. Signal-to-noise ratio with which the noise track is overlayed is determined by choosing a value from a random uniform distribution with parameterized endpoints. The first three methods are based off data augmentation methods suggested in Section 3.3 of [X-Vectors: Robust DNN Embeddings for Speaker Recognition](https://danielpovey.com/files/2018_icassp_xvectors.pdf). + +#### 2.1. Music augmentation +For music augmentation, the noise track consists of one file uniformly randomly selected from a corpus of music files. The music file is cut to size, including being repeated to fill the original sample length if necessary. + +**Usage**: `musicaugment` is an `AudioWaveformTransform` and has four configurable hyperparameters: +- `samples_path`: path where background music files are saved as audios (.wav files). No default. +- `rate`: probability that any single access will result in the positive (background music) case. Defaults to 0.25. +- `snr_min`: lower endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 5. +- `snr_max`: higher endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 15. + +#### 2.2. Babble augmentation +For babble augmentation, the noise track consists of multiple audios uniformly randomly selected from a corpus of speech files. The number of speech audios in the background track is chosen randomly with equal probability between 3 and 7 audios. + +**Usage**: `babbleaugment` is an `AudioWaveformTransform` and has four configurable hyperparameters: +- `samples_path`: path where background speech files are saved as audios (.wav files). No default. +- `rate`: probability that any single access will result in the positive (background speech) case. Defaults to 0.25. +- `snr_min`: lower endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 5. +- `snr_max`: higher endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 15. + +#### 2.3. Sporadic noise augmentation +For sporadic noise augmentation, the noise track is mostly silent except for intermittent short clips of noise which are added at roughly a parameterized frequency. These clips are randomly chosen and cut from a corpus of noise files to lengths according to a parameterized Gaussian distribution. + +**Usage**: `sporadicnoiseaugment` is an `AudioWaveformTransform` and has seven configurable hyperparameters: +- `samples_path`: path where background noise files are saved as audios (.wav files). No default. +- `rate`: probability that any single access will result in the positive (add a sporadic noise track) case. Defaults to 0.25. +- `snr_min`: lower endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 5. +- `snr_max`: higher endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 15. +- `noise_rate`: rate in noises per second at which noise clip will be added to the original sample +- `noise_len_mean`: mean of Gaussian normal distribution from which length of noise clip is chosen +- `noise_len_std`: standard deviation of Gaussian normal distribution from which length of noise clip is chosen + +#### 2.4. Background noise augmentation +For background noise augmentation, the noise track is a single track uniformly randomly selected from a corpus of noise files. The noise file is cut to size, including being repeated to fill the original sample length if necessary. + +**Usage**: `backgroundnoiseaugment` is an `AudioWaveformTransform` and has four configurable hyperparameters: +- `samples_path`: path where background noise files are saved as audios (.wav files). No default. +- `rate`: probability that any single access will result in the positive (background noise) case. Defaults to 0.25. +- `snr_min`: lower endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 5. +- `snr_max`: higher endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 15. + +### 3. Mixed babble and background noise augmentation with recognizable source speaker + +This augmentation technique is based on Algorithm 1 in [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) and is similar to the noise augmentation suite techniques in that it has a background noise track. The noise track consists of either (1) another audio sample from the batch or (2) a background noise track. A key difference is the length of the noise track is chosen from a uniform random distribution between 0 and half of the original sample length. + +**Usage**: `noisyoverlapaugment` is an `AudioDatasetTransform` and has seven configurable hyperparameters: +- `noises_path`: path where background noise files are saved as audios (.wav files). No default. +- `rate`: probability that any single access will result in the positive (background noise) case. Defaults to 0.25. +- `mixing_noise_rate`: probability that in a positive (background noise) case, the noise track will consist of background noise (rather than babble from the batch). Defaults to 0.1. +- `noise_snr_min`: lower endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to -5. +- `noise_snr_max`: higher endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add background noise to the original source. Defaults to 5. +- `utterance_snr_min`: lower endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add **another audio from the batch** to the original source. Defaults to -5. +- `utterance_snr_max`: higher endpoint of the range from which a signal-to-noise ratio is uniformly randomly chosen with which to add **another audio from the batch** to the original source. Defaults to 5. + +## Benchmark studies +### Evaluation on clean data +Augmentation in training data|Hyperparameters|Training loss|BLEU (covost)|BLEU (epst)|BLEU (mtedx) +---|---|---|---|---|--- +None||3.954|24.984|23.962|24.448 +ConcatAugment|rate = 0.25, max_tokens = 3000, attempts = 5|3.940|25.322|26.124|26.19 +BabbleAugment|rate = 0.25, MUSAN speech, snr_min = (-5), snr_max = 5|3.957|24.226|23.186|22.368| +BackgroundNoiseAugment|rate = 0.1, MUSAN noises, snr_min = (-10), snr_max = 10|3.955|24.745|23.513|23.819 +MusicAugment|rate = 0.25, MUSAN music, snr_min = 0, snr_max = 20|3.954|25.096|24.301|23.341| +SporadicNoiseAugment|rate = 0.1, noise_rate = 0.25, MUSAN noises, snr_min = 10, snr_max = 35|3.954|24.924|23.951|23.484| +MusicAugment + BabbleAugment + BackgroundNoiseAugment + SporadicNoiseAugment|as above, except limited rates to sum to 0.25: music (0.074), background (0.029), babble (0.074), sporadic (0.029)|3.953|24.874|23.675|24.249| +NoisyOverlapAugment|rate = 0.25, mixing_noise_rate = 0.5, MUSAN noises, utterance_snr_min = (-10), utterance_snr_max = 0, noise_snr_min = (-5), noise_snr_max = 20|3.954|24.949|24.015|23.768| + +### Evaluation on data with music noise added at SNR = (-5) - 5 +Augmentation in training data|Training loss|BLEU (covost)|BLEU (epst)|BLEU (mtedx) +---|---|---|---|--- +None|3.954|15.785|21.105|16.944 +ConcatAugment|3.940|17.186|23.255|18.24 +BabbleAugment|3.957|19.158|22.064|17.116 +BackgroundNoiseAugment|3.955|17.777|22.0|17.535| +MusicAugment|3.954|20.345|23.126|19.433| +SporadicNoiseAugment|3.954|15.927|21.382|14.736| +MusicAugment + BabbleAugment + BackgroundNoiseAugment + SporadicNoiseAugment|3.953|19.724|22.659|17.852| +NoisyOverlapAugment|3.954|17.49|22.142|17.207| + +### Evaluation on data with babble noise added at SNR = (-5) - 5 +Augmentation in training data|Training loss|BLEU (covost)|BLEU (epst)|BLEU (mtedx) +---|---|---|---|--- +None|3.954|4.092|13.514|5.13 +ConcatAugment|3.940|5.493|15.835|6.893 +BabbleAugment|3.957|16.12|21.097|13.996 +BackgroundNoiseAugment|3.955|4.691|15.784|5.982 +MusicAugment|3.954|8.06|17.764|9.008 +SporadicNoiseAugment|3.954|4.009|13.935|4.814 +MusicAugment + BabbleAugment + BackgroundNoiseAugment + SporadicNoiseAugment|3.953|14.692|20.882|14.45 +NoisyOverlapAugment|3.954|4.032|16.434|7.284 + +### Evaluation on data with sporadic noise added at SNR = (-5) - 5 +Augmentation in training data|Training loss|BLEU (covost)|BLEU (epst)|BLEU (mtedx) +---|---|---|---|--- +None|3.954|23.778|23.745|22.748 +ConcatAugment|3.940|24.239|25.907|25.723 +BabbleAugment|3.957|23.42|23.048|21.076 +BackgroundNoiseAugment|3.955|23.998|23.467|22.494 +MusicAugment|3.954|24.142|24.181|19.143 +SporadicNoiseAugment|3.954|23.97|23.894|22.61 +MusicAugment + BabbleAugment + BackgroundNoiseAugment + SporadicNoiseAugment|3.953|24.118|23.59|23.717 +NoisyOverlapAugment|3.954|24.265|24.103|23.167 + +### Evaluation on data with background noise added at SNR = (-5) - 5 +Augmentation in training data|Training loss|BLEU (covost)|BLEU (epst)|BLEU (mtedx) +---|---|---|---|--- +None|3.954|20.201|22.525|19.66 +ConcatAugment|3.940|20.904|24.706|21.353 +BabbleAugment|3.957|20.687|22.374|18.907 +BackgroundNoiseAugment|3.955|21.574|22.998|20.043 +MusicAugment|3.954|21.65|23.529|19.87 +SporadicNoiseAugment|3.954|20.578|22.577|19.096 +MusicAugment + BabbleAugment + BackgroundNoiseAugment + SporadicNoiseAugment|3.953|21.811|23.144|20.986 +NoisyOverlapAugment|3.954|21.312|23.153|20.302 + +### Evaluation on data with all four types of noises added at SNR = (-5) - 5, each applied with prob 0.5 +Augmentation in training data|Training loss|BLEU (covost)|BLEU (epst)|BLEU (mtedx) +---|---|---|---|--- +None|3.954|10.895|19.319|12.748 +ConcatAugment|3.940|13.517|21.658|15.428 +BabbleAugment|3.957|18.09|21.384|16.018 +BackgroundNoiseAugment|3.955|12.837|20.719|13.933 +MusicAugment|3.954|16.589|21.823|15.927 +SporadicNoiseAugment|3.954|11.238|19.91|13.31 +MusicAugment + BabbleAugment + BackgroundNoiseAugment + SporadicNoiseAugment|3.953|18.636|21.935|17.845 +NoisyOverlapAugment|3.954|12.829|20.856|15.048 + +### Evaluation on data with noisy overlap augment +Augmentation in training data|Training loss|BLEU (covost)|BLEU (epst)|BLEU (mtedx) +---|---|---|---|--- +None|3.954|21.245|22.24|20.994 +ConcatAugment|3.940|21.611|24.247|23.068 +BabbleAugment|3.957|21.867|21.987|20.099| +BackgroundNoiseAugment|3.955|21.533|21.806|19.717| +MusicAugment|3.954|21.823|22.643|20.847| +SporadicNoiseAugment|3.954|21.373|22.381|20.672| +MusicAugment + BabbleAugment + BackgroundNoiseAugment + SporadicNoiseAugment|3.953|22.206|22.414|21.375| +NoisyOverlapAugment|3.954|23.371|23.396|22.627| + +## Using transforms +Transforms are configurable. + +1. Please pay careful attention to the type of transform you are applying. + - `concataugment` and `noisyoverlapaugment` are instances of `AudioDatasetTransform` and should be listed in the config under `dataset_transforms`. + - `musicaugment`, `babbleaugment`, `sporadicnoiseaugment`, and `backgroundnoiseaugment` are instances of `AudioWaveformTransform` and should be listed under `waveform_transforms`. + - Instances of `AudioFeatureTransform` should be listed under `feature_transforms`. +2. Feel free to apply these augmentations in different contexts, e.g., you may use a `_train` or `_eval` flag to specify when the transform will be applied. If the dataset at hand contains `train` in its name, those transforms under the `_train` flag will be applied; else, the remaining transforms will be applied. + +For example, you would add this to your config to apply the musicaugment transform to a training dataset: +```yaml +musicaugment: + samples_path: ${MUSIC_PATH} + snr_min: 10 + snr_max: 15 + rate: 0.25 +waveform_transforms: + _train: + - musicaugment +``` +or add this to apply the concataugment transform: +```yaml +concataugment: + rate: 0.25 + max_tokens: 3000 + attempts: 5 +dataset_transforms: + _train: + - concataugment + ``` +You may also want to add multiple of one type of transform; here, we add multiple `AudioWaveformTransform`s: +```yaml +musicaugment: + samples_path: ${MUSIC_PATH} + snr_min: 5 + snr_max: 20 + rate: 0.25 +backgroundnoiseaugment: + samples_path: ${NOISES_PATH} + snr_min: 10 + snr_max: 20 + rate: 0.1 +sporadicnoiseaugment: + samples_path: ${NOISES_PATH} + snr_min: 5 + snr_max: 15 + rate: 0.1 + noise_rate: 0.25 +waveform_transforms: + _train: + - musicaugment + - backgroundnoiseaugment + - sporadicnoiseaugment +``` + +## Adding your own transforms +Note: We store transform implementations in `fairseq/data/audio/*_transforms` directories. You may refer to these as examples while implementing your own transform. + +### Step 1. Picking the right class for your transform +The integration into SpeechToTextDataset is quite different for each kind of transform, so it is important to understand which one is best suited to your purposes. + +**Feature transforms** +`AudioFeatureTransform` is a base class which allows **some transform to be applied to audio spectrograms** in the data loading step. One thing to note is that the source data is either saved as `np.ndarrays` or as audio files, and is to be returned either as features (spectrogram) or waveform. If and only if the data is to be returned as a spectrogram, then `AudioFeatureTransform`s will be applied. + +**Waveform transforms** +`AudioWaveformTransform` is a base class which allows some **transform to be applied to waveforms** in the data loading step. As mentioned above, there are two source and return types to data loading for this dataset. If and only if the data is saved in audio file format, then `AudioWaveformTransform`s will be applied, whichever return type is used. + +**Dataset transforms** +`AudioDatasetTransform` is a base class for transforms **based on more than one item in a dataset**, ex. concatenation of two random samples in a dataset. Rather than being applied in a consistent way, i.e., to all features or to all waveforms, the integration of a dataset transform is entirely specific. Adding a dataset transform requires actually editing the `fairseq/data/audio/speech_to_text_dataset.py` file. + +### Step 2. Setting up your transform (generic to all types of transforms) +Now that you know which kind of transform you would like to use, we are ready to implement it. This step is generic for all transform types, i.e., `TRANSFORM_TYPE` may be any of `feature`, `waveform`, or `dataset`. We will show how to build utterance concatenation (an `AudioDatasetTransform`) as an example. + +Import the base class and registration function for your transform. +```python +from fairseq.data.audio.dataset_transforms import ( + AudioDatasetTransform, + register_audio_dataset_transform +) +``` + +Define the class and register the transform. The name passed into the registration function is how your transform should be named in the config. +```python +@register_audio_dataset_transform("concataugment") +class ConcatAugment(AudioDatasetTransform): +``` + +We are now ready to add the basic important functions to our new class. In this example, `_DEFAULTS` refers to a dictionary with the default hyperparameter values that we defined. `from_config_dict` is called to instantiate the transform given hyperparameters from the config. +```python + @classmethod + def from_config_dict(cls, config=None): + _config = {} if config is None else config + return ConcatAugment( + _config.get("rate", _DEFAULTS["rate"]), + _config.get("max_tokens", _DEFAULTS["max_tokens"]), + _config.get("attempts", _DEFAULTS["attempts"]), + ) +``` +We edit the instantiation function `__init__` to track hyperparameters and do any setup work. +```python + def __init__( + self, + rate=_DEFAULTS["rate"], + max_tokens=_DEFAULTS["max_tokens"], + attempts=_DEFAULTS["attempts"], + ): + self.rate, self.max_tokens, self.attempts = rate, max_tokens, attempts +``` +Lastly `__repr__` gives how the transform will be reported in an output log. +```python + def __repr__(self): + return ( + self.__class__.__name__ + + "(" + + ", ".join( + [ + f"rate={self.rate}", + f"max_tokens={self.max_tokens}", + f"attempts={self.attempts}", + ] + ) + + ")" + ) +``` + +### Step 3. Adding the transform logic +At this point, we are ready to implement the actual transform logic. The flow from here is different for each of the three transforms, so follow the path that is relevant to you. +### ...for feature transforms +The final step is implementing the `__call__` function, which applies the transform logic and **returns** the spectrogram with transform applied. This supports and should take exactly **two arguments**: +- `self` +- `x` (np.ndarray): the spectrogram for one source sample. (This is a positional argument, so you can use another parameter name like `spectrogram` instead of `x`.) + +For example, this is the `__call__` function for GlobalCMVN (cepstral mean and variance normalization). +```python + def __call__(self, x): + x = np.subtract(x, self.mean) + x = np.divide(x, self.std) + return x + +``` +### ...for waveform transforms +The final step is implementing the `__call__` function, which applies the transform logic. This supports and should take exactly **three arguments**: +- `self` +- `source` (numpy.ndarray or torch.Tensor): source audio 2d waveform (channels x length) +- `sample_rate` (optional, defaults to None): sample rate of `source` + +`__call__` **returns**: +- transformed audio waveform +- sample rate of transformed audio waveform + +For example, this is the `__call__` function for augmentations in the Noise Augmentation Suite. +```python + def __call__(self, source, sample_rate=None): + if np.random.random() > self.rate: + return source + + noise = self._get_noise( + source.shape, always_2d=True, use_sample_rate=sample_rate + ) + return self._mix(source, noise, rand_uniform(self.snr_min, self.snr_max)), sample_rate +``` + +### ...for dataset transforms +Dataset transforms are extremely flexible, and implementation involves directly integrating them into `fairseq/data/audio/speech_to_text_dataset.py` in transform-specific ways. +There are two basic components: (1) check whether or not this transform is part of this dataset instance using `self.dataset_transforms.has_transform(TRANSFORM_CLS)`, and (2) if so, get the transform using `self.dataset_transforms.get_transform(TRANSFORM_CLS)` & apply it. +Due to the case-by-case specificity, it is easier to demonstrate this by examples. + +#### Example: NoisyOverlapAugment +This transform requires access to multiple items within the same batch at once. + +**Logic**: We still use the transform classes to keep away the transform logic. For example, `__call__` of `NoisyOverlapAugment` class takes a list of source tokens for items in a mini-batch, applies noise/utterance as dictated by the transform, and returns the list of transformed source tokens for items in the mini-batch. + +```python + def __call__(self, sources): + for i, source in enumerate(sources): + if np.random.random() > self.rate: + continue + + pri = source.numpy() + + # ... some transform code omitted + + pri[s_source : s_source + l] = np.add( + pri[s_source : s_source + l], np.multiply(scl, sec[s_sec : s_sec + l]) + ) + sources[i] = torch.from_numpy(pri).float() + + return sources +``` + +**Integration**: The `collater` function for `SpeechToTextDataset` is responsible for preparing a mini-batch for training, so we integrate NOAug through adding a few lines to the top of this function: +```python +def collater( + self, samples: List[SpeechToTextDatasetItem], return_order: bool = False +) -> Dict: + if len(samples) == 0: + return {} + indices = torch.tensor([x.index for x in samples], dtype=torch.long) + + sources = [x.source for x in samples] + + # NOAUG INTEGRATION BLOCK + # (1) Check whether or not this transform is part of this dataset instance + has_NOAug = self.dataset_transforms.has_transform(NoisyOverlapAugment) + # (2) If so, get & apply the transform + if has_NOAug and self.cfg.use_audio_input: + NOAug = self.dataset_transforms.get_transform(NoisyOverlapAugment) + sources = NOAug(sources) + + frames = _collate_frames(sources, self.cfg.use_audio_input) + # sort samples by descending number of frames + n_frames = torch.tensor([x.size(0) for x in sources], dtype=torch.long) + n_frames, order = n_frames.sort(descending=True) + indices = indices.index_select(0, order) + frames = frames.index_select(0, order) + + # ... rest of function +``` + +#### Example: ConcatAugment +This transform requires access to another item within the dataset at once. + +**Logic**: We abstract the logic for picking indices to concatenate by adding a `find_indices` function to the `ConcatAugment` class, which takes one index in the dataset and finds a compatible second index to concatenate source and target tokens. +```python + def find_indices(self, index: int, n_frames: List[int], n_samples: int): + # skip conditions: application rate, max_tokens limit exceeded + if np.random.random() > self.rate: + return [index] + if self.max_tokens and n_frames[index] > self.max_tokens: + return [index] + + # pick second sample to concatenate + for _ in range(self.attempts): + index2 = np.random.randint(0, n_samples) + if index2 != index and ( + not self.max_tokens + or n_frames[index] + n_frames[index2] < self.max_tokens + ): + return [index, index2] + + return [index] +``` + +**Integration**: `SpeechToTextDataset` uses a custom `__getitem__(self, index)` function (called in the background when you write `dataset[i]`). We edited this function (as well as `_get_source_audio` and `get_tokenized_tgt_text`) to achieve the desired transform effect where accessing `dataset[i]` will return a `SpeechToTextDatasetItem` where `source=source[i]+source[j]` and `target=target[i]+target[j]`. +```python +def __getitem__(self, index: int) -> SpeechToTextDatasetItem: + + # CONCATAUGMENT INTEGRATION BLOCK + # (1) Check whether or not this transform is part of this dataset instance + has_concat = self.dataset_transforms.has_transform(ConcatAugment) + # (2) If so, get & apply the transform + if has_concat: + concat = self.dataset_transforms.get_transform(ConcatAugment) + indices = concat.find_indices(index, self.n_frames, self.n_samples) + + source = self._get_source_audio(indices if has_concat else index) + source = self.pack_frames(source) + + target = None + if self.tgt_texts is not None: + tokenized = self.get_tokenized_tgt_text(indices if has_concat else index) + target = self.tgt_dict.encode_line( + + # ... rest of function +``` diff --git a/fairseq/examples/speech_to_speech/docs/direct_s2st_discrete_units.md b/fairseq/examples/speech_to_speech/docs/direct_s2st_discrete_units.md new file mode 100644 index 0000000..0c63ffe --- /dev/null +++ b/fairseq/examples/speech_to_speech/docs/direct_s2st_discrete_units.md @@ -0,0 +1,181 @@ +# Direct speech-to-speech translation with discrete units + +We provide the implementation for speech-to-unit translation (S2UT) proposed in "[Direct speech-to-speech translation with discrete units (Lee et al. 2021)](https://arxiv.org/abs/2107.05604)" and also the transformer-based implementation of the speech-to-spectrogram translation (S2SPECT, or transformer-based [Translatotron](https://arxiv.org/abs/1904.06037)) baseline in the paper. + +## Pretrained Models + +### Unit-based HiFi-GAN Vocoder +Unit config | Unit size | Vocoder dataset | Model +|---|---|---|--- +[HuBERT Base, Librispeech](https://github.com/fairinternal/fairseq-py/tree/main/examples/hubert), layer 6 | 100 | [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/hubert_base_100_lj/g_00500000), [config](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/hubert_base_100_lj/config.json) + + +## Data preparation +### Target speech +0. (optional) To prepare S2S data from a speech-to-text translation (ST) dataset, see [fairseq-S^2](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis) for pre-trained TTS models and instructions on how to train and decode TTS models. +1. Prepare two folders, `$SRC_AUDIO` and `$TGT_AUDIO`, with `${SPLIT}/${SAMPLE_ID}.wav` for source and target speech under each folder, separately. Note that for S2UT experiments, target audio sampling rate should be in 16,000 Hz, and for S2SPECT experiments, target audio sampling rate is recommended to be in 22,050 Hz. +2. To prepare target discrete units for S2UT model training, see [Generative Spoken Language Modeling (speech2unit)](https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/gslm/speech2unit) for pre-trained k-means models, checkpoints, and instructions on how to decode units from speech. Set the output target unit files (`--out_quantized_file_path`) as `${TGT_AUDIO}/${SPLIT}.txt`. In [Lee et al. 2021](https://arxiv.org/abs/2107.05604), we use 100 units from the sixth layer (`--layer 6`) of the HuBERT Base model. + +### Formatting data +**Speech-to-speech data** + +_S2UT_ + * Set `--reduce-unit` for training S2UT _reduced_ model + * Pre-trained vocoder and config (`$VOCODER_CKPT`, `$VOCODER_CFG`) can be downloaded from the **Pretrained Models** section. They are not required if `--eval-inference` is not going to be set during model training. +``` +# $SPLIT1, $SPLIT2, etc. are split names such as train, dev, test, etc. + +python examples/speech_to_speech/preprocessing/prep_s2ut_data.py \ + --source-dir $SRC_AUDIO --target-dir $TGT_AUDIO --data-split $SPLIT1 $SPLIT2 \ + --output-root $DATA_ROOT --reduce-unit \ + --vocoder-checkpoint $VOCODER_CKPT --vocoder-cfg $VOCODER_CFG +``` + +_S2SPECT_ +``` +# $SPLIT1, $SPLIT2, etc. are split names such as train, dev, test, etc. + +python examples/speech_to_speech/preprocessing/prep_s2spect_data.py \ + --source-dir $SRC_AUDIO --target-dir $TGT_AUDIO --data-split $SPLIT1 $SPLIT2 \ + --output-root $DATA_ROOT +``` + +**Multitask data** + * For each multitask `$TASK_NAME`, prepare `${DATA_ROOT}/${TASK_NAME}/${SPLIT}.tsv` files for each split following the format below: (Two tab separated columns. The sample_ids should match with the sample_ids for the speech-to-speech data in `${DATA_ROOT}/${SPLIT}.tsv`.) +``` +id tgt_text +sample_id_0 token1 token2 token3 ... +sample_id_1 token1 token2 token3 ... +... +``` + * For each multitask `$TASK_NAME`, prepare `${DATA_ROOT}/${TASK_NAME}/dict.txt`, a dictionary in fairseq format with all tokens for the targets for `$TASK_NAME`. + * Create `config_multitask.yaml`. Below is an example of the config used for S2UT _reduced_ with Fisher experiments including two encoder multitasks (`source_letter`, `target_letter`) and one decoder CTC task (`decoder_target_ctc`). +``` +source_letter: # $TASK_NAME + decoder_type: transformer + dict: ${DATA_ROOT}/source_letter/dict.txt + data: ${DATA_ROOT}/source_letter + encoder_layer: 6 + loss_weight: 8.0 +target_letter: + decoder_type: transformer + dict: ${DATA_ROOT}/target_letter/dict.txt + data: ${DATA_ROOT}/target_letter + encoder_layer: 8 + loss_weight: 8.0 +decoder_target_ctc: + decoder_type: ctc + dict: ${DATA_ROOT}/decoder_target_ctc/dict.txt + data: ${DATA_ROOT}/decoder_target_ctc + decoder_layer: 3 + loss_weight: 1.6 +``` + + +## Training + +**Speech-to-unit translation (S2UT)** + +Here's an example for training Fisher S2UT models with 100 discrete units as target: +``` +fairseq-train $DATA_ROOT \ + --config-yaml config.yaml --multitask-config-yaml config_multitask.yaml \ + --task speech_to_speech --target-is-code --target-code-size 100 --vocoder code_hifigan \ + --criterion speech_to_unit --label-smoothing 0.2 \ + --arch s2ut_transformer_fisher --share-decoder-input-output-embed \ + --dropout 0.1 --attention-dropout 0.1 --relu-dropout 0.1 \ + --train-subset train --valid-subset dev \ + --save-dir ${MODEL_DIR} \ + --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-init-lr 1e-7 --warmup-updates 10000 \ + --optimizer adam --adam-betas "(0.9,0.98)" --clip-norm 10.0 \ + --max-update 400000 --max-tokens 20000 --max-target-positions 3000 --update-freq 4 \ + --seed 1 --fp16 --num-workers 8 +``` +* Adjust `--update-freq` accordingly for different #GPUs. In the above we set `--update-freq 4` to simulate training with 4 GPUs. +* Set `--n-frames-per-step 5` to train an S2UT _stacked_ system with reduction ratio r=5. (Use `$DATA_ROOT` prepared without `--reduce-unit`.) +* (optional) one can turn on tracking MCD loss during training for checkpoint selection by setting `--eval-inference --eval-args '{"beam": 1, "max_len_a": 1}' --best-checkpoint-metric mcd_loss`. It is recommended to sample a smaller subset as the validation set as MCD loss computation is time-consuming. + +**Speech-to-spectrogram translation (S2SPECT)** + +Here's an example for training Fisher S2SPECT models with reduction ratio r=5: +``` +fairseq-train $DATA_ROOT \ + --config-yaml config.yaml --multitask-config-yaml config_multitask.yaml \ + --task speech_to_speech --n-frames-per-step 5 \ + --criterion speech_to_spectrogram \ + --arch s2spect_transformer_fisher --decoder-normalize-before \ + --dropout 0.1 --attention-dropout 0.1 --relu-dropout 0.1 \ + --train-subset train --valid-subset dev \ + --save-dir ${MODEL_DIR} \ + --eval-inference --best-checkpoint-metric mcd_loss \ + --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-init-lr 1e-7 --warmup-updates 10000 \ + --optimizer adam --adam-betas "(0.9,0.98)" --clip-norm 10.0 --weight-decay 1e-6 \ + --max-update 400000 --max-tokens 80000 --max-tokens-valid 30000 --required-batch-size-multiple 1 \ + --max-target-positions 3000 --update-freq 16 \ + --seed 1 --fp16 --num-workers 8 +``` +* Adjust `--update-freq` accordingly for different #GPUs. In the above we set `--update-freq 16` to simulate training with 16 GPUs. +* We recommend turning on MCD loss during training for the best checkpoint selection. + +**Unit-based HiFi-GAN vocoder** + +The vocoder is trained with the [speech-resynthesis repo](https://github.com/facebookresearch/speech-resynthesis). See [here](https://github.com/facebookresearch/speech-resynthesis/tree/main/examples/speech_to_speech_translation) for instructions on how to train the unit-based HiFi-GAN vocoder with duration prediction. The same vocoder can support waveform generation for both _reduced_ unit sequences (with `--dur-prediction` set during inference) and original unit sequences. + +## Inference + +**Speech-to-unit translation (S2UT)** + +1. Follow the same inference process as in [fairseq-S2T](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text) to generate unit sequences (`${RESULTS_PATH}/generate-${GEN_SUBSET}.txt`). +``` +fairseq-generate $DATA_ROOT \ + --config-yaml config.yaml --multitask-config-yaml config_multitask.yaml \ + --task speech_to_speech --target-is-code --target-code-size 100 --vocoder code_hifigan \ + --path $MODEL_DIR/checkpoint_best.pt --gen-subset $GEN_SUBSET \ + --max-tokens 50000 \ + --beam 10 --max-len-a 1 \ + --results-path ${RESULTS_PATH} +``` + * Set `--beam 1 --n-frames-per-step $r` for decoding with S2UT _stacked_ models. + +2. Convert unit sequences to waveform. +``` +grep "^D\-" ${RESULTS_PATH}/generate-${GEN_SUBSET}.txt | \ + sed 's/^D-//ig' | sort -nk1 | cut -f3 \ + > ${RESULTS_PATH}/generate-${GEN_SUBSET}.unit + +python examples/speech_to_speech/generate_waveform_from_code.py \ + --in-code-file ${RESULTS_PATH}/generate-${GEN_SUBSET}.unit \ + --vocoder $VOCODER_CKPT --vocoder-cfg $VOCODER_CFG \ + --results-path ${RESULTS_PATH} --dur-prediction +``` + * Set `--dur-prediction` for generating audio for S2UT _reduced_ models. + + +**Speech-to-spectrogram translation (S2SPECT)** + +Follow the same inference process as in [fairseq-S^2](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis) to generate waveform. + +``` +# assume using a default Griffin-Lim vocoder + +python examples/speech_synthesis/generate_waveform.py $DATA_ROOT \ + --config-yaml config.yaml --multitask-config-yaml config_multitask.yaml \ + --task speech_to_speech --n-frames-per-step 5 \ + --path $MODEL_DIR/checkpoint_best.pt --gen-subset $GEN_SUBSET \ + --max-tokens 50000 \ + --results-path ${RESULTS_PATH} --dump-waveforms --output-sample-rate 16000 +``` + +In addition to using the default Griffin-Lim vocoder, one can also finetune a HiFi-GAN vocoder for the S2SPECT model by following the instructions in the [HiFi-GAN repo](https://github.com/jik876/hifi-gan). + +**Multitask decoding** + +Coming soon. + +## Evaluation + +To evaluate speech translation output, we first apply ASR on the speech output and then compute BLEU score betweent the ASR decoded text and the references using sacreBLEU. + +**En** +* ASR: We use the "[Wav2Vec 2.0 Large (LV-60) + Self Training / 960 hours / Libri-Light + Librispeech](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_960h_pl.pt)" En ASR model open-sourced by the [wav2vec](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) project. See [instructions](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#evaluating-a-ctc-model) on how to run inference with a wav2vec-based ASR model. The model is also available on [Hugging Face](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self). +* Text normalization: We use the text cleaner at [https://github.com/keithito/tacotron](https://github.com/keithito/tacotron) for pre-processing reference English text for ASR BLEU evaluation. diff --git a/fairseq/examples/speech_to_speech/docs/enhanced_direct_s2st_discrete_units.md b/fairseq/examples/speech_to_speech/docs/enhanced_direct_s2st_discrete_units.md new file mode 100644 index 0000000..fbfa5dd --- /dev/null +++ b/fairseq/examples/speech_to_speech/docs/enhanced_direct_s2st_discrete_units.md @@ -0,0 +1,125 @@ +# Speech to speech translation (S2ST) + +We provide the implementation for speech-to-unit translation (S2UT) proposed in [Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation (Popuri et al. 2022)](https://arxiv.org/abs/2204.02967) and the various pretrained models used. + +## Pretrained Models + +### Unit extraction + +We used the multilingual HuBERT model open sourced in [Textless S2ST with Real Data](textless_s2st_real_data.md) + +### Wav2vec 2.0 + +Language | Block type | Model size | Dataset | Model | +--- | --- | --- | --- | --- | +Es | Transformer | BASE | Voxpopuli | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/w2v2/es/transformer_B.pt) | +Es | Transformer | LARGE | Voxpopuli | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/w2v2/es/transformer_L.pt) | +Es | Conformer | LARGE | Voxpopuli | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/w2v2/es/conformer_L.pt) | +En | Transformer | BASE | Librilight| [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/w2v2/en/transformer_B.pt) | +En | Conformer | LARGE | Librilight | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/w2v2/en/conformer_L.pt) | + +### Unit mBART + +Unit size | Dataset | Unit config | Model | +--- | --- | --- | --- | +1000 | [Voxpopuli](https://aclanthology.org/2021.acl-long.80) En, Es unlabelled speech | [mbart_large](https://github.com/pytorch/fairseq/blob/f591cc94caa85098ccf125a4782f91125b6a086d/fairseq/models/bart/model.py#L368) |[ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/unit_mBART/checkpoint.pt) | + +## Data preparation + +1. To prepare data for S2UT finetuning, follow the steps from [Direct S2ST with Discrete Units](./direct_s2st_discrete_units.md) and format the data in the _S2UT_ format. Note that we use 1000 units from the eleventh layer (`--layer 11`) of the multilingual hubert model linked above instead +2. Run + +``` +var="id\taudio\tn_frames\ttgt_text\ttgt_n_frames" +sed -i "1s/.*/$var/" ${SPLIT}.tsv +``` + +## Training + +**Speech-to-unit translation (S2UT)** + +Here's an example for finetuning S2UT models with 1000 discrete units as target. You can download the sample [config](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/config.yaml) file and [vocabulary](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/dict.txt) for Es-En from here: + +``` +fairseq-train $DATA_ROOT \ + --config-yaml config.yaml \ + --task speech_to_text --arch xm_transformer\ + --criterion l --label-smoothing 0.2 \ + --share-decoder-input-output-embed --adaptor-n-layers 1 --normalize\ + --dropout 0.1 --attention-dropout 0.1 --relu-dropout 0.1 \ + --train-subset train --valid-subset dev \ + --load-pretrained-decoder-from ${unit_mBART} --w2v-path ${wav2vec2.0} \ + --mask-prob 0.3 --mask-channel-length 32 --mask-channel-prob 0.25\ + --save-dir ${MODEL_DIR} --checkpoint-activations --encoder-proj \ + --lr 0.0005 --dropout 0.1 --attention-dropout 0.1 --lr-scheduler inverse_sqrt\ + --warmup-init-lr 1e-7 --warmup-updates 10000 \ + --optimizer adam --adam-betas "(0.9,0.98)" --clip-norm 10.0 \ + --max-update 20000 --max-tokens 4000 --max-tokens-valid 4000 --max-source-positions 4000 \ + --max-target-positions 4000 --update-freq 120 \ + --seed 1 --fp16 --num-workers 1 +``` + +* Adjust `--update-freq` accordingly for different #GPUs. In the above we set `--update-freq 15` to simulate training with 120 GPUs. +* In the above setting we finetune the model end to end, corresponding to the full setup in the paper. +* To apply LNA-E partial finetuning, add `--finetune-w2v-params layer_norm,self_attn` +* For LNA-D partial finetuning add `--finetune-decoder-params encoder_attn,layer_norm,self_attn`. To optionally freeze the encoder by k updates, use `--freeze-finetune-updates ${K}` +* For LNA-E,D partial finetuning add both the above options. + +**Unit-based HiFi-GAN vocoder** + +We apply the open-sourced unit-based HiFi-GAN vocoders to convert the predicted unit sequences to waveform. They are open sourced in [Textless S2ST with Real Data](textless_s2st_real_data.md) + +## Inference + +**Speech-to-unit translation (S2UT)** + +1. Follow the same inference process as in [fairseq-S2T](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text) to generate unit sequences (`${RESULTS_PATH}/generate-${GEN_SUBSET}.txt`). + +``` +fairseq-generate $DATA_ROOT \ + --config-yaml config.yaml \ + --task speech_to_text \ + --path $MODEL_DIR/checkpoint_best.pt --gen-subset $GEN_SUBSET \ + --max-tokens 10000 --max-source-positions 10000 --max-target-positions 10000\ + --beam 10 --max-len-a 1 --max-len-b 200 \ + --results-path ${RESULTS_PATH} +``` + +2. Convert unit sequences to waveform. + +``` +grep "^D\-" ${RESULTS_PATH}/generate-${GEN_SUBSET}.txt | \ + sed 's/^D-//ig' | sort -nk1 | cut -f3 \ + > ${RESULTS_PATH}/generate-${GEN_SUBSET}.unit + +python examples/speech_to_speech/generate_waveform_from_code.py \ + --in-code-file ${RESULTS_PATH}/generate-${GEN_SUBSET}.unit \ + --vocoder $VOCODER_CKPT --vocoder-cfg $VOCODER_CFG \ + --results-path ${RESULTS_PATH} --dur-prediction +``` + +## Evaluation + +To evaluate speech translation output, we first apply ASR on the speech output and then compute BLEU score betweent the ASR decoded text and the references using sacreBLEU. + +* Text normalization: We use the text cleaner at [https://github.com/keithito/tacotron](https://github.com/keithito/tacotron) for pre-processing reference English text for ASR BLEU evaluation. The text cleaner used for Spanish text normalization will be updated here shortly. +* En ASR: We use the "[Wav2Vec 2.0 Large (LV-60) + Self Training / 960 hours / Libri-Light + Librispeech](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_960h_pl.pt)" En ASR model open-sourced by the [wav2vec](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) project. The model is also available on [Hugging Face](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self). +* Es ASR: We use the [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) finetuned on spanish Common Voice Es ASR model open-sourced by Jonatasgrosman(<https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish>) on [Hugging Face](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish). +* See [instructions](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#evaluating-a-ctc-model) on how to run inference with a wav2vec-based ASR model. + + +## Finetuned Model Checkpoints + +ID | En - Es | Es - En | +| --- | --- | --- | +**S2UT systems without pre-training** +S2UT with multitask | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/en_es//S2UT_w_multitask.pt) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/es_en//S2UT_w_multitask.pt) | +**S2UT systems with model pre-training** +w2v2-L | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/en_es//w2v2_only.pt ) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/es_en//w2v2_only.pt) | +w2v2-L + mBART (LNA-E) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/en_es//w2v2_mbart_LNE.pt) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/es_en//w2v2_mbart_LNE.pt) | +w2v2-L + mBART (LNA-D) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/en_es//w2v2_mbart_LND.pt) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/es_en//w2v2_mbart_LND.pt) | +w2v2-L + mBART (LNA-E,D) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/en_es//w2v2_mbart_LNED.pt) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/es_en//w2v2_mbart_LNED.pt) | +**S2UT systems with model pre-training and data augmentation** +w2v2-L + mBART (LNA-D) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/en_es//w2v2_mbart_LND_w_ASR.pt) | [checkpoint](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/s2st_finetuning/es_en//w2v2_mbart_LND_w_ASR.pt) | + +Note: Some of the tasks use speech_to_text_sharded task which is yet to be open sourced. So make sure to override the task to speech_to_text to use those models. diff --git a/fairseq/examples/speech_to_speech/docs/textless_s2st_real_data.md b/fairseq/examples/speech_to_speech/docs/textless_s2st_real_data.md new file mode 100644 index 0000000..ca6044b --- /dev/null +++ b/fairseq/examples/speech_to_speech/docs/textless_s2st_real_data.md @@ -0,0 +1,89 @@ +# Textless Speech-to-Speech Translation (S2ST) on Real Data + +We provide instructions and pre-trained models for the work "[Textless Speech-to-Speech Translation on Real Data (Lee et al. 2021)](https://arxiv.org/abs/2112.08352)". + +## Pre-trained Models + +### HuBERT +Model | Pretraining Data | Model | Quantizer +|---|---|---|--- +mHuBERT Base | [VoxPopuli](https://github.com/facebookresearch/voxpopuli) En, Es, Fr speech from the 100k subset | [download](https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3.pt) | [L11 km1000](https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin) + + +### Unit-based HiFi-GAN vocoder +Unit config | Unit size | Vocoder language | Dataset | Model +|---|---|---|---|--- +mHuBERT, layer 11 | 1000 | En | [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000), [config](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/config.json) +mHuBERT, layer 11 | 1000 | Es | [CSS10](https://github.com/Kyubyong/css10) | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_es_css10/g_00500000), [config](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_es_css10/config.json) +mHuBERT, layer 11 | 1000 | Fr | [CSS10](https://github.com/Kyubyong/css10) | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10/g_00500000), [config](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10/config.json) + + +### Speech normalizer +Language | Training data | Target unit config | Model +|---|---|---|--- +En | 10 mins | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/en/en_10min.tar.gz) +En | 1 hr | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/en/en_1h.tar.gz) +En | 10 hrs | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/en/en_10h.tar.gz) +Es | 10 mins | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/es/es_10min.tar.gz) +Es | 1 hr | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/es/es_1h.tar.gz) +Es | 10 hrs | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/es/es_10h.tar.gz) +Fr | 10 mins | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/fr/fr_10min.tar.gz) +Fr | 1 hr | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/fr/fr_1h.tar.gz) +Fr | 10 hrs | mHuBERT, layer 11, km1000 | [download](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/speech_normalizer/fr/fr_10h.tar.gz) + +* Refer to the paper for the details of the training data. + +## Inference with Pre-trained Models + +### Speech normalizer +1. Download the pre-trained models, including the dictionary, to `DATA_DIR`. +2. Format the audio data. +```bash +# AUDIO_EXT: audio extension, e.g. wav, flac, etc. +# Assume all audio files are at ${AUDIO_DIR}/*.${AUDIO_EXT} + +python examples/speech_to_speech/preprocessing/prep_sn_data.py \ + --audio-dir ${AUDIO_DIR} --ext ${AUIDO_EXT} \ + --data-name ${GEN_SUBSET} --output-dir ${DATA_DIR} \ + --for-inference +``` + +3. Run the speech normalizer and post-process the output. +```bash +mkdir -p ${RESULTS_PATH} + +python examples/speech_recognition/new/infer.py \ + --config-dir examples/hubert/config/decode/ \ + --config-name infer_viterbi \ + task.data=${DATA_DIR} \ + task.normalize=false \ + common_eval.results_path=${RESULTS_PATH}/log \ + common_eval.path=${DATA_DIR}/checkpoint_best.pt \ + dataset.gen_subset=${GEN_SUBSET} \ + '+task.labels=["unit"]' \ + +decoding.results_path=${RESULTS_PATH} \ + common_eval.post_process=none \ + +dataset.batch_size=1 \ + common_eval.quiet=True + +# Post-process and generate output at ${RESULTS_PATH}/${GEN_SUBSET}.txt +python examples/speech_to_speech/preprocessing/prep_sn_output_data.py \ + --in-unit ${RESULTS_PATH}/hypo.units \ + --in-audio ${DATA_DIR}/${GEN_SUBSET}.tsv \ + --output-root ${RESULTS_PATH} +``` + + +### Unit-to-waveform conversion with unit vocoder +The pre-trained vocoders can support generating audio for both full unit sequences and reduced unit sequences (i.e. duplicating consecutive units removed). Set `--dur-prediction` for generating audio with reduced unit sequences. +```bash +# IN_CODE_FILE contains one unit sequence per line. Units are separated by space. + +python examples/speech_to_speech/generate_waveform_from_code.py \ + --in-code-file ${IN_CODE_FILE} \ + --vocoder ${VOCODER_CKPT} --vocoder-cfg ${VOCODER_CFG} \ + --results-path ${RESULTS_PATH} --dur-prediction +``` + +## Training new models +To be updated. diff --git a/fairseq/examples/speech_to_speech/generate_waveform_from_code.py b/fairseq/examples/speech_to_speech/generate_waveform_from_code.py new file mode 100644 index 0000000..82aa7ac --- /dev/null +++ b/fairseq/examples/speech_to_speech/generate_waveform_from_code.py @@ -0,0 +1,116 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import json +import logging +from pathlib import Path +import random +import soundfile as sf +import torch + +from tqdm import tqdm + +from fairseq import utils +from fairseq.models.text_to_speech.vocoder import CodeHiFiGANVocoder + + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def dump_result(args, sample_id, pred_wav, suffix=""): + sf.write( + f"{args.results_path}/{sample_id}{suffix}_pred.wav", + pred_wav.detach().cpu().numpy(), + 16000, + ) + + +def load_code(in_file): + with open(in_file) as f: + out = [list(map(int, line.strip().split())) for line in f] + return out + + +def main(args): + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + + with open(args.vocoder_cfg) as f: + vocoder_cfg = json.load(f) + vocoder = CodeHiFiGANVocoder(args.vocoder, vocoder_cfg) + if use_cuda: + vocoder = vocoder.cuda() + + multispkr = vocoder.model.multispkr + if multispkr: + logger.info("multi-speaker vocoder") + num_speakers = vocoder_cfg.get( + "num_speakers", 200 + ) # following the default in codehifigan to set to 200 + assert ( + args.speaker_id < num_speakers + ), f"invalid --speaker-id ({args.speaker_id}) with total #speakers = {num_speakers}" + + data = load_code(args.in_code_file) + Path(args.results_path).mkdir(exist_ok=True, parents=True) + for i, d in tqdm(enumerate(data), total=len(data)): + x = { + "code": torch.LongTensor(d).view(1, -1), + } + suffix = "" + if multispkr: + spk = ( + random.randint(0, num_speakers - 1) + if args.speaker_id == -1 + else args.speaker_id + ) + suffix = f"_spk{spk}" + x["spkr"] = torch.LongTensor([spk]).view(1, 1) + + x = utils.move_to_cuda(x) if use_cuda else x + wav = vocoder(x, args.dur_prediction) + dump_result(args, i, wav, suffix=suffix) + + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-code-file", type=str, required=True, help="one unit sequence per line" + ) + parser.add_argument( + "--vocoder", type=str, required=True, help="path to the CodeHiFiGAN vocoder" + ) + parser.add_argument( + "--vocoder-cfg", + type=str, + required=True, + help="path to the CodeHiFiGAN vocoder config", + ) + parser.add_argument("--results-path", type=str, required=True) + parser.add_argument( + "--dur-prediction", + action="store_true", + help="enable duration prediction (for reduced/unique code sequences)", + ) + parser.add_argument( + "--speaker-id", + type=int, + default=-1, + help="Speaker id (for vocoder that supports multispeaker). Set to -1 to randomly sample speakers.", + ) + parser.add_argument("--cpu", action="store_true", help="run on CPU") + + args = parser.parse_args() + + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/speech_to_speech/preprocessing/__init__.py b/fairseq/examples/speech_to_speech/preprocessing/__init__.py new file mode 100644 index 0000000..6264236 --- /dev/null +++ b/fairseq/examples/speech_to_speech/preprocessing/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/examples/speech_to_speech/preprocessing/data_utils.py b/fairseq/examples/speech_to_speech/preprocessing/data_utils.py new file mode 100644 index 0000000..a83a67f --- /dev/null +++ b/fairseq/examples/speech_to_speech/preprocessing/data_utils.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from pathlib import Path +from typing import List, Optional + +from examples.speech_to_text.data_utils import S2TDataConfigWriter + + +def gen_config_yaml( + manifest_root: Path, + yaml_filename: str = "config.yaml", + specaugment_policy: Optional[str] = "lb", + feature_transform: Optional[List[str]] = None, + input_channels: Optional[int] = 1, + input_feat_per_channel: Optional[int] = 80, + audio_root: str = "", + vocoder_type: Optional[str] = None, + vocoder_checkpoint: Optional[str] = None, + vocoder_cfg: Optional[str] = None, + extra=None, +): + manifest_root = manifest_root.absolute() + writer = S2TDataConfigWriter(manifest_root / yaml_filename) + + if input_channels is not None: + writer.set_input_channels(input_channels) + if input_feat_per_channel is not None: + writer.set_input_feat_per_channel(input_feat_per_channel) + specaugment_setters = { + "lb": writer.set_specaugment_lb_policy, + "ld": writer.set_specaugment_ld_policy, + "sm": writer.set_specaugment_sm_policy, + "ss": writer.set_specaugment_ss_policy, + } + specaugment_setter = specaugment_setters.get(specaugment_policy, None) + if specaugment_setter is not None: + specaugment_setter() + + if feature_transform is None: + feature_transform = [] + else: + writer.set_feature_transforms("*", feature_transform) + + if specaugment_policy is not None: + writer.set_feature_transforms("_train", feature_transform + ["specaugment"]) + + if len(audio_root) > 0: + writer.set_audio_root(audio_root) + + if ( + vocoder_type is not None + and vocoder_checkpoint is not None + and vocoder_cfg is not None + ): + writer.set_extra( + { + "vocoder": { + "type": vocoder_type, + "config": vocoder_cfg, + "checkpoint": vocoder_checkpoint, + } + } + ) + + if extra is not None: + writer.set_extra(extra) + writer.flush() + + +def load_units(in_file): + out = {} + with open(in_file) as f: + for line in f: + sample_id, units = line.strip().split("|", 1) + out[sample_id] = units.split() + + return out + + +def process_units(units, reduce=False): + if not reduce: + return units + + out = [u for i, u in enumerate(units) if i == 0 or u != units[i - 1]] + return out diff --git a/fairseq/examples/speech_to_speech/preprocessing/prep_s2spect_data.py b/fairseq/examples/speech_to_speech/preprocessing/prep_s2spect_data.py new file mode 100644 index 0000000..2748b37 --- /dev/null +++ b/fairseq/examples/speech_to_speech/preprocessing/prep_s2spect_data.py @@ -0,0 +1,169 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +from pathlib import Path +import shutil +import torchaudio + +import soundfile as sf +from tqdm import tqdm +import pandas as pd + +from examples.speech_synthesis.data_utils import extract_logmel_spectrogram +from examples.speech_to_speech.preprocessing.data_utils import gen_config_yaml +from examples.speech_to_text.data_utils import create_zip, get_zip_manifest, save_df_to_tsv +from fairseq.data.audio.audio_utils import convert_waveform + + +logger = logging.getLogger(__name__) + +MANIFEST_COLUMNS = ["id", "src_audio", "src_n_frames", "tgt_audio", "tgt_n_frames"] + + +def prepare_target_data(args, tgt_audios): + feature_name = "logmelspec80" + zip_path = args.output_root / f"{feature_name}.zip" + if zip_path.exists(): + print(f"{zip_path} exists.") + return zip_path + + feature_root = args.output_root / feature_name + feature_root.mkdir(exist_ok=True) + + print("Extracting Mel spectrogram features...") + for tgt_audio in tqdm(tgt_audios): + sample_id = tgt_audio.stem + waveform, sample_rate = torchaudio.load(tgt_audio.as_posix()) + waveform, sample_rate = convert_waveform( + waveform, sample_rate, normalize_volume=args.normalize_volume, + to_sample_rate=args.sample_rate + ) + extract_logmel_spectrogram( + waveform, sample_rate, feature_root / f"{sample_id}.npy", + win_length=args.win_length, hop_length=args.hop_length, + n_fft=args.n_fft, n_mels=args.n_mels, f_min=args.f_min, + f_max=args.f_max + ) + print("ZIPing features...") + create_zip(feature_root, zip_path) + shutil.rmtree(feature_root) + + return zip_path + + +def process(args): + os.makedirs(args.output_root, exist_ok=True) + + manifest = {} + tgt_audios = [] + for split in args.data_split: + print(f"Processing {split}...") + + manifest[split] = {c: [] for c in MANIFEST_COLUMNS} + missing_tgt_audios = [] + src_audios = list(args.source_dir.glob(f"{split}/*.wav")) + for src_audio in tqdm(src_audios): + sample_id = src_audio.stem + + tgt_audio = args.target_dir / split / f"{sample_id}.wav" + if not tgt_audio.is_file(): + missing_tgt_audios.append(sample_id) + continue + + tgt_audios.append(tgt_audio) + + src_n_frames = sf.info(src_audio.as_posix()).frames + manifest[split]["id"].append(sample_id) + manifest[split]["src_audio"].append(src_audio.as_posix()) + manifest[split]["src_n_frames"].append( + src_n_frames // 160 + ) # estimation of 10-ms frame for 16kHz audio + + print(f"Processed {len(manifest[split]['id'])} samples") + if len(missing_tgt_audios) > 0: + print( + f"{len(missing_tgt_audios)} with missing target data (first 3 examples: {', '.join(missing_tgt_audios[:3])})" + ) + + # Extract features and pack features into ZIP + zip_path = prepare_target_data(args, tgt_audios) + + print("Fetching ZIP manifest...") + tgt_audio_paths, tgt_audio_lengths = get_zip_manifest(zip_path) + + print("Generating manifest...") + for split in args.data_split: + print(f"Processing {split}...") + + for sample_id in tqdm(manifest[split]["id"]): + manifest[split]["tgt_audio"].append(tgt_audio_paths[sample_id]) + manifest[split]["tgt_n_frames"].append(tgt_audio_lengths[sample_id]) + + out_manifest = args.output_root / f"{split}.tsv" + print(f"Writing manifest to {out_manifest}...") + save_df_to_tsv(pd.DataFrame.from_dict(manifest[split]), out_manifest) + + # Generate config YAML + win_len_t = args.win_length / args.sample_rate + hop_len_t = args.hop_length / args.sample_rate + extra = { + "features": { + "type": "spectrogram+melscale+log", + "sample_rate": args.sample_rate, + "eps": 1e-5, "n_mels": args.n_mels, "n_fft": args.n_fft, + "window_fn": "hann", "win_length": args.win_length, + "hop_length": args.hop_length, + "win_len_t": win_len_t, "hop_len_t": hop_len_t, + "f_min": args.f_min, "f_max": args.f_max, + "n_stft": args.n_fft // 2 + 1 + } + } + gen_config_yaml( + args.output_root, + audio_root=args.output_root.as_posix(), + specaugment_policy="lb", + feature_transform=["utterance_cmvn", "delta_deltas"], + extra=extra, + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--source-dir", required=True, type=Path, help="source audio directory" + ) + parser.add_argument( + "--target-dir", required=True, type=Path, help="target audio directory" + ) + parser.add_argument( + "--data-split", + default=["train", "valid", "test"], + nargs="+", + help="data split names", + ) + parser.add_argument( + "--output-root", required=True, type=Path, help="output directory" + ) + # target feature related + parser.add_argument("--win-length", type=int, default=1024) + parser.add_argument("--hop-length", type=int, default=256) + parser.add_argument("--n-fft", type=int, default=1024) + parser.add_argument("--n-mels", type=int, default=80) + parser.add_argument("--f-min", type=int, default=20) + parser.add_argument("--f-max", type=int, default=8000) + parser.add_argument("--sample-rate", type=int, default=22050) + parser.add_argument("--normalize-volume", "-n", action="store_true") + + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_speech/preprocessing/prep_s2ut_data.py b/fairseq/examples/speech_to_speech/preprocessing/prep_s2ut_data.py new file mode 100644 index 0000000..c97c0fe --- /dev/null +++ b/fairseq/examples/speech_to_speech/preprocessing/prep_s2ut_data.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path + +import soundfile as sf +from tqdm import tqdm +import pandas as pd + +from examples.speech_to_speech.preprocessing.data_utils import ( + gen_config_yaml, + load_units, + process_units, +) +from examples.speech_to_text.data_utils import save_df_to_tsv + +logger = logging.getLogger(__name__) + +MANIFEST_COLUMNS = ["id", "src_audio", "src_n_frames", "tgt_audio", "tgt_n_frames"] + + +def process(args): + args.output_root.mkdir(exist_ok=True) + + print("Generating manifest...") + for split in args.data_split: + print(f"Processing {split}") + + # load target units + target_unit_data = load_units(args.target_dir / f"{split}.txt") + + manifest = {c: [] for c in MANIFEST_COLUMNS} + missing_tgt_audios = [] + src_audios = list(args.source_dir.glob(f"{split}/*.wav")) + for src_audio in tqdm(src_audios): + sample_id = src_audio.stem + + if sample_id not in target_unit_data: + missing_tgt_audios.append(sample_id) + continue + + src_n_frames = sf.info(src_audio.as_posix()).frames + manifest["id"].append(sample_id) + manifest["src_audio"].append(src_audio.as_posix()) + manifest["src_n_frames"].append( + src_n_frames // 160 + ) # estimation of 10-ms frame for 16kHz audio + + target_units = process_units(target_unit_data[sample_id], args.reduce_unit) + manifest["tgt_audio"].append(" ".join(target_units)) + manifest["tgt_n_frames"].append(len(target_units)) + + print(f"Processed {len(manifest['id'])} samples") + if len(missing_tgt_audios) > 0: + print( + f"{len(missing_tgt_audios)} with missing target data (first 3 examples: {', '.join(missing_tgt_audios[:3])})" + ) + + out_manifest = args.output_root / f"{split}.tsv" + print(f"Writing manifest to {out_manifest}...") + save_df_to_tsv(pd.DataFrame.from_dict(manifest), out_manifest) + + # Generate config YAML + gen_config_yaml( + args.output_root, + specaugment_policy="lb", + feature_transform=["utterance_cmvn"], + vocoder_type="code_hifigan", + vocoder_checkpoint=args.vocoder_checkpoint, + vocoder_cfg=args.vocoder_cfg, + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--source-dir", required=True, type=Path, help="source audio directory" + ) + parser.add_argument( + "--target-dir", required=True, type=Path, help="target audio directory" + ) + parser.add_argument( + "--data-split", + default=["train", "valid", "test"], + nargs="+", + help="data split names", + ) + parser.add_argument( + "--output-root", required=True, type=Path, help="output directory" + ) + parser.add_argument( + "--reduce-unit", + action="store_true", + help="reduce a target unit sequence to a unique unit sequence, i.e. '1 1 1 2 2' -> '1 2'", + ) + parser.add_argument( + "--vocoder-checkpoint", default=None, type=str, help="vocoder checkpoint" + ) + parser.add_argument( + "--vocoder-cfg", default=None, type=str, help="vocoder config file" + ) + + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_speech/preprocessing/prep_sn_data.py b/fairseq/examples/speech_to_speech/preprocessing/prep_sn_data.py new file mode 100644 index 0000000..ea94175 --- /dev/null +++ b/fairseq/examples/speech_to_speech/preprocessing/prep_sn_data.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# +# Adapted from examples/wav2vec/wav2vec_manifest.py +""" +Data preparation for the speech normalizer +""" + +import argparse +import glob +import os + +import soundfile + +from examples.speech_to_speech.preprocessing.data_utils import load_units, process_units + + +def process(args): + assert ( + args.for_inference or args.target_unit is not None + ), "missing --target-unit or --for-inference" + + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + + dir_path = os.path.realpath(args.audio_dir) + search_path = os.path.join(dir_path, "**/*." + args.ext) + + if args.target_unit: + unit_data = load_units(args.target_unit) + + with open(os.path.join(args.output_dir, f"{args.data_name}.tsv"), "w") as o_t, open( + os.path.join(args.output_dir, f"{args.data_name}.unit"), "w" + ) as o_u: + print(dir_path, file=o_t) + for fname in glob.iglob(search_path, recursive=True): + file_path = os.path.realpath(fname) + frames = soundfile.info(fname).frames + print( + "{}\t{}".format(os.path.relpath(file_path, dir_path), frames), file=o_t + ) + + if args.for_inference: + print("0", file=o_u) + else: + sample_id = os.path.basename(file_path)[: -len(args.ext) - 1] + assert ( + sample_id in unit_data + ), f'{fname} does not have unit data in {args.target_unit}. Expecting sample_id "{sample_id}".' + target_units = process_units(unit_data[sample_id], reduce=True) + print(" ".join(target_units), file=o_u) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio-dir", required=True, type=str, help="audio directory") + parser.add_argument("--ext", default="flac", type=str, help="audio extension") + parser.add_argument( + "--data-name", + required=True, + type=str, + help="dataset name", + ) + parser.add_argument( + "--output-dir", required=True, type=str, help="output directory" + ) + parser.add_argument( + "--for-inference", + action="store_true", + help="set this if preparing data for running inference with a speech normalizer", + ) + parser.add_argument( + "--target-unit", + default=None, + type=str, + help="a file containing unit sequences in the format: sample_id|u1 u2 ...", + ) + + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_speech/preprocessing/prep_sn_output_data.py b/fairseq/examples/speech_to_speech/preprocessing/prep_sn_output_data.py new file mode 100644 index 0000000..0699134 --- /dev/null +++ b/fairseq/examples/speech_to_speech/preprocessing/prep_sn_output_data.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from pathlib import Path + +from tqdm import tqdm + + +def process(args): + args.output_root.mkdir(exist_ok=True) + + # load units + units = {} + with open(args.in_unit) as f: + for line in f: + unit_seq, utt_id = line.strip().rsplit(" ", 1) + utt_id = int(utt_id[6:-1]) # remove "(None-" + units[utt_id] = unit_seq + + with open(args.in_audio) as f, open( + args.output_root / f"{args.in_audio.stem}.txt", "w" + ) as o: + f.readline() + for i, line in enumerate(tqdm(f.readlines())): + audio, _ = line.strip().split("\t", 1) + sample_id = Path(audio).stem + o.write(f"{sample_id}|{units[i]}\n") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-unit", + required=True, + type=Path, + help="unit file (output from the speech normalizer)", + ) + parser.add_argument( + "--in-audio", + required=True, + type=Path, + help="tsv file (input to the normalizer)", + ) + parser.add_argument( + "--output-root", required=True, type=Path, help="output directory" + ) + + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_speech/unity/__init__.py b/fairseq/examples/speech_to_speech/unity/__init__.py new file mode 100644 index 0000000..349db7c --- /dev/null +++ b/fairseq/examples/speech_to_speech/unity/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import sequence_generator # noqa +from . import sequence_generator_multi_decoder # noqa diff --git a/fairseq/examples/speech_to_speech/unity/sequence_generator.py b/fairseq/examples/speech_to_speech/unity/sequence_generator.py new file mode 100644 index 0000000..c482098 --- /dev/null +++ b/fairseq/examples/speech_to_speech/unity/sequence_generator.py @@ -0,0 +1,626 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import sys +from typing import Dict, List, Optional + +import torch +from torch import Tensor + +from fairseq.sequence_generator import EnsembleModel as EnsembleModelBase +from fairseq.sequence_generator import SequenceGenerator as SequenceGeneratorBase + + +class SequenceGenerator(SequenceGeneratorBase): + def __init__( + self, + models, + tgt_dict, + beam_size=1, + max_len_a=0, + max_len_b=200, + max_len=0, + min_len=1, + normalize_scores=True, + len_penalty=1.0, + unk_penalty=0.0, + temperature=1.0, + match_source_len=False, + no_repeat_ngram_size=0, + search_strategy=None, + eos=None, + symbols_to_strip_from_output=None, + lm_model=None, + lm_weight=1.0, + tokens_to_suppress=(), + ): + """Generates translations of a given source sentence. + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models, + currently support fairseq.models.TransformerModel for scripting + beam_size (int, optional): beam width (default: 1) + max_len_a/b (int, optional): generate sequences of maximum length + ax + b, where x is the source length + max_len (int, optional): the maximum length of the generated output + (not including end-of-sentence) + min_len (int, optional): the minimum length of the generated output + (not including end-of-sentence) + normalize_scores (bool, optional): normalize scores by the length + of the output (default: True) + len_penalty (float, optional): length penalty, where <1.0 favors + shorter, >1.0 favors longer sentences (default: 1.0) + unk_penalty (float, optional): unknown word penalty, where <0 + produces more unks, >0 produces fewer (default: 0.0) + temperature (float, optional): temperature, where values + >1.0 produce more uniform samples and values <1.0 produce + sharper samples (default: 1.0) + match_source_len (bool, optional): outputs should match the source + length (default: False) + """ + super().__init__( + models=models, + tgt_dict=tgt_dict, + beam_size=beam_size, + max_len_a=max_len_a, + max_len_b=max_len_b, + max_len=max_len, + min_len=min_len, + normalize_scores=normalize_scores, + len_penalty=len_penalty, + unk_penalty=unk_penalty, + temperature=temperature, + match_source_len=match_source_len, + no_repeat_ngram_size=no_repeat_ngram_size, + search_strategy=search_strategy, + eos=eos, + symbols_to_strip_from_output=symbols_to_strip_from_output, + lm_model=lm_model, + lm_weight=lm_weight, + tokens_to_suppress=tokens_to_suppress, + ) + + if isinstance(models, EnsembleModel): + self.model = models + else: + self.model = EnsembleModel(models) + + self.model.set_decoder_beam_size(self.beam_size) + self.model.eval() + + def _generate( + self, + sample: Dict[str, Dict[str, Tensor]], + prefix_tokens: Optional[Tensor] = None, + constraints: Optional[Tensor] = None, + bos_token: Optional[int] = None, + ): + net_input = sample["net_input"] + + if "src_tokens" in net_input: + src_tokens = net_input["src_tokens"] + # length of the source text being the character length except EndOfSentence and pad + # if src_lengths exists in net_input (speech_to_text dataset case), then use it + if "src_lengths" in net_input: + src_lengths = net_input["src_lengths"] + else: + src_lengths = ( + (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)) + .long() + .sum(dim=1) + ) + elif "source" in net_input: + src_tokens = net_input["source"] + src_lengths = ( + net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) + if net_input["padding_mask"] is not None + else torch.tensor(src_tokens.size(-1)).to(src_tokens) + ) + elif "features" in net_input: + src_tokens = net_input["features"] + src_lengths = ( + net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) + if net_input["padding_mask"] is not None + else torch.tensor(src_tokens.size(-1)).to(src_tokens) + ) + else: + raise Exception( + "expected src_tokens or source in net input. input keys: " + + str(net_input.keys()) + ) + + if constraints is not None and not self.search.supports_constraints: + raise NotImplementedError( + "Target-side constraints were provided, but search method doesn't support them" + ) + + # Initialize constraints, when active + self.search.init_constraints(constraints, self.beam_size) + + # compute the encoder output for each beam + with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"): + encoder_outs = self.model.forward_encoder(net_input) + + finalized = self.generate_decoder( + encoder_outs, + src_tokens, + src_lengths, + sample, + prefix_tokens, + constraints, + bos_token, + ) + return finalized + + def generate_decoder( + self, + encoder_outs, + src_tokens, + src_lengths, + sample: Dict[str, Dict[str, Tensor]], + prefix_tokens: Optional[Tensor] = None, + constraints: Optional[Tensor] = None, + bos_token: Optional[int] = None, + aux_task_name="", + encoder_outs_aug: Optional[ + Tensor + ] = None, # an additional/augmented encoder_outs + ): + incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(self.model.models_size) + ], + ) + + # bsz: total number of sentences in beam + # Note that src_tokens may have more than 2 dimensions (i.e. audio features) + bsz, src_len = src_tokens.size()[:2] + beam_size = self.beam_size + + decoder_name = f"{aux_task_name}_decoder" if aux_task_name else "decoder" + + max_len: int = -1 + if self.match_source_len: + max_len = src_lengths.max().item() + else: + max_len = min( + int(self.max_len_a * src_len + self.max_len_b), + self.max_len - 1, + ) + assert ( + self.min_len <= max_len + ), "min_len cannot be larger than max_len, please adjust these!" + + # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores + new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) + new_order = new_order.to(src_tokens.device).long() + encoder_outs = self.model.reorder_encoder_out(encoder_outs, new_order) + # ensure encoder_outs is a List. + assert encoder_outs is not None + if encoder_outs_aug is not None: + encoder_outs_aug = self.model.reorder_encoder_out( + encoder_outs_aug, new_order + ) + + # initialize buffers + scores = ( + torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() + ) # +1 for eos; pad is never chosen for scoring + tokens = ( + torch.zeros(bsz * beam_size, max_len + 2) + .to(src_tokens) + .long() + .fill_(self.pad) + ) # +2 for eos and pad + tokens[:, 0] = self.eos if bos_token is None else bos_token + attn: Optional[Tensor] = None + + # A list that indicates candidates that should be ignored. + # For example, suppose we're sampling and have already finalized 2/5 + # samples. Then cands_to_ignore would mark 2 positions as being ignored, + # so that we only finalize the remaining 3 samples. + cands_to_ignore = ( + torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) + ) # forward and backward-compatible False mask + + # list of completed sentences + finalized = torch.jit.annotate( + List[List[Dict[str, Tensor]]], + [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], + ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step + + # a boolean array indicating if the sentence at the index is finished or not + finished = [False for i in range(bsz)] + num_remaining_sent = bsz # number of sentences remaining + + # number of candidate hypos per step + cand_size = 2 * beam_size # 2 x beam size in case half are EOS + + # offset arrays for converting between different indexing schemes + bbsz_offsets = ( + (torch.arange(0, bsz) * beam_size) + .unsqueeze(1) + .type_as(tokens) + .to(src_tokens.device) + ) + cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) + + reorder_state: Optional[Tensor] = None + batch_idxs: Optional[Tensor] = None + + original_batch_idxs: Optional[Tensor] = None + if "id" in sample and isinstance(sample["id"], Tensor): + original_batch_idxs = sample["id"] + else: + original_batch_idxs = torch.arange(0, bsz).type_as(tokens) + + for step in range(max_len + 1): # one extra step for EOS marker + # reorder decoder internal states based on the prev choice of beams + if reorder_state is not None: + if batch_idxs is not None: + # update beam indices to take into account removed sentences + corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( + batch_idxs + ) + reorder_state.view(-1, beam_size).add_( + corr.unsqueeze(-1) * beam_size + ) + original_batch_idxs = original_batch_idxs[batch_idxs] + self.model.reorder_incremental_state( + incremental_states, reorder_state, decoder_name + ) + encoder_outs = self.model.reorder_encoder_out( + encoder_outs, reorder_state + ) + if encoder_outs_aug is not None: + encoder_outs_aug = self.model.reorder_encoder_out( + encoder_outs_aug, reorder_state + ) + with torch.autograd.profiler.record_function( + "EnsembleModel: forward_decoder" + ): + lprobs, avg_attn_scores = self.model.forward_decoder( + tokens[:, : step + 1], + encoder_outs, + incremental_states, + self.temperature, + decoder_name=decoder_name, + encoder_outs_aug=encoder_outs_aug, + ) + + if self.lm_model is not None and not aux_task_name: + lm_out = self.lm_model(tokens[:, : step + 1]) + probs = self.lm_model.get_normalized_probs( + lm_out, log_probs=True, sample=None + ) + probs = probs[:, -1, :] * self.lm_weight + lprobs += probs + + lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) + + lprobs[:, self.pad] = -math.inf # never select pad + lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty + + # handle max length constraint + if step >= max_len: + lprobs[:, : self.eos] = -math.inf + lprobs[:, self.eos + 1 :] = -math.inf + + # handle prefix tokens (possibly with different lengths) + if ( + prefix_tokens is not None + and step < prefix_tokens.size(1) + and step < max_len + ): + lprobs, tokens, scores = self._prefix_tokens( + step, lprobs, scores, tokens, prefix_tokens, beam_size + ) + else: + if step < self.min_len: + # minimum length constraint (does not apply if using prefix_tokens) + lprobs[:, self.eos] = -math.inf + + if self.token_indices_to_suppress is not None: + lprobs[:, self.token_indices_to_suppress] = -math.inf + + # Record attention scores, only support avg_attn_scores is a Tensor + if avg_attn_scores is not None: + if attn is None: + attn = torch.empty( + bsz * beam_size, avg_attn_scores.size(1), max_len + 2 + ).to(scores) + attn[:, :, step + 1].copy_(avg_attn_scores) + + scores = scores.type_as(lprobs) + eos_bbsz_idx = torch.empty(0).to( + tokens + ) # indices of hypothesis ending with eos (finished sentences) + eos_scores = torch.empty(0).to( + scores + ) # scores of hypothesis ending with eos (finished sentences) + + if self.should_set_src_lengths: + self.search.set_src_lengths(src_lengths) + + if self.repeat_ngram_blocker is not None: + lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step) + + # Shape: (batch, cand_size) + cand_scores, cand_indices, cand_beams = self.search.step( + step, + lprobs.view(bsz, -1, self.vocab_size), + scores.view(bsz, beam_size, -1)[:, :, :step], + tokens[:, : step + 1], + original_batch_idxs, + ) + + # cand_bbsz_idx contains beam indices for the top candidate + # hypotheses, with a range of values: [0, bsz*beam_size), + # and dimensions: [bsz, cand_size] + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + + # finalize hypotheses that end in eos + # Shape of eos_mask: (batch size, beam size) + eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) + eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) + + # only consider eos when it's among the top beam_size indices + # Now we know what beam item(s) to finish + # Shape: 1d list of absolute-numbered + eos_bbsz_idx = torch.masked_select( + cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + + finalized_sents: List[int] = [] + if eos_bbsz_idx.numel() > 0: + eos_scores = torch.masked_select( + cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + + finalized_sents = self.finalize_hypos( + step, + eos_bbsz_idx, + eos_scores, + tokens, + scores, + finalized, + finished, + beam_size, + attn, + src_lengths, + max_len, + ) + num_remaining_sent -= len(finalized_sents) + + assert num_remaining_sent >= 0 + if num_remaining_sent == 0: + break + if self.search.stop_on_max_len and step >= max_len: + break + assert step < max_len, f"{step} < {max_len}" + + # Remove finalized sentences (ones for which {beam_size} + # finished hypotheses have been generated) from the batch. + if len(finalized_sents) > 0: + new_bsz = bsz - len(finalized_sents) + + # construct batch_idxs which holds indices of batches to keep for the next pass + batch_mask = torch.ones( + bsz, dtype=torch.bool, device=cand_indices.device + ) + batch_mask[finalized_sents] = False + # TODO replace `nonzero(as_tuple=False)` after TorchScript supports it + batch_idxs = torch.arange( + bsz, device=cand_indices.device + ).masked_select(batch_mask) + + # Choose the subset of the hypothesized constraints that will continue + self.search.prune_sentences(batch_idxs) + + eos_mask = eos_mask[batch_idxs] + cand_beams = cand_beams[batch_idxs] + bbsz_offsets.resize_(new_bsz, 1) + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + cand_scores = cand_scores[batch_idxs] + cand_indices = cand_indices[batch_idxs] + + if prefix_tokens is not None: + prefix_tokens = prefix_tokens[batch_idxs] + src_lengths = src_lengths[batch_idxs] + cands_to_ignore = cands_to_ignore[batch_idxs] + + scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + if attn is not None: + attn = attn.view(bsz, -1)[batch_idxs].view( + new_bsz * beam_size, attn.size(1), -1 + ) + bsz = new_bsz + else: + batch_idxs = None + + # Set active_mask so that values > cand_size indicate eos hypos + # and values < cand_size indicate candidate active hypos. + # After, the min values per row are the top candidate active hypos + + # Rewrite the operator since the element wise or is not supported in torchscript. + + eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) + active_mask = torch.add( + eos_mask.type_as(cand_offsets) * cand_size, + cand_offsets[: eos_mask.size(1)], + ) + + # get the top beam_size active hypotheses, which are just + # the hypos with the smallest values in active_mask. + # {active_hypos} indicates which {beam_size} hypotheses + # from the list of {2 * beam_size} candidates were + # selected. Shapes: (batch size, beam size) + new_cands_to_ignore, active_hypos = torch.topk( + active_mask, k=beam_size, dim=1, largest=False + ) + + # update cands_to_ignore to ignore any finalized hypos. + cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] + # Make sure there is at least one active item for each sentence in the batch. + assert (~cands_to_ignore).any(dim=1).all() + + # update cands_to_ignore to ignore any finalized hypos + + # {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam + # can be selected more than once). + active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) + active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) + + active_bbsz_idx = active_bbsz_idx.view(-1) + active_scores = active_scores.view(-1) + + # copy tokens and scores for active hypotheses + + # Set the tokens for each beam (can select the same row more than once) + tokens[:, : step + 1] = torch.index_select( + tokens[:, : step + 1], dim=0, index=active_bbsz_idx + ) + # Select the next token for each of them + tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( + cand_indices, dim=1, index=active_hypos + ) + if step > 0: + scores[:, :step] = torch.index_select( + scores[:, :step], dim=0, index=active_bbsz_idx + ) + scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( + cand_scores, dim=1, index=active_hypos + ) + + # Update constraints based on which candidates were selected for the next beam + self.search.update_constraints(active_hypos) + + # copy attention for active hypotheses + if attn is not None: + attn[:, :, : step + 2] = torch.index_select( + attn[:, :, : step + 2], dim=0, index=active_bbsz_idx + ) + + # reorder incremental state in decoder + reorder_state = active_bbsz_idx + + # sort by score descending + for sent in range(len(finalized)): + scores = torch.tensor( + [float(elem["score"].item()) for elem in finalized[sent]] + ) + _, sorted_scores_indices = torch.sort(scores, descending=True) + finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] + finalized[sent] = torch.jit.annotate( + List[Dict[str, Tensor]], finalized[sent] + ) + return finalized + + +class EnsembleModel(EnsembleModelBase): + """A wrapper around an ensemble of models.""" + + def __init__(self, models): + super().__init__(models) + + @torch.jit.export + def forward_decoder( + self, + tokens, + encoder_outs: List[Dict[str, List[Tensor]]], + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + temperature: float = 1.0, + decoder_name="decoder", + encoder_outs_aug: List[Dict[str, List[Tensor]]] = None, + ): + log_probs = [] + avg_attn: Optional[Tensor] = None + encoder_out: Optional[Dict[str, List[Tensor]]] = None + encoder_out_aug: Optional[Dict[str, List[Tensor]]] = None + for i, model in enumerate(self.models): + if self.has_encoder(): + encoder_out = encoder_outs[i] + if encoder_outs_aug is not None: + encoder_out_aug = encoder_outs_aug[i] + # decode each model + if self.has_incremental_states(): + if encoder_out_aug is not None: + decoder_out = getattr(model, decoder_name).forward( + tokens, + encoder_out=encoder_out, + encoder_out_aug=encoder_out_aug, + incremental_state=incremental_states[i], + ) + else: + decoder_out = getattr(model, decoder_name).forward( + tokens, + encoder_out=encoder_out, + incremental_state=incremental_states[i], + ) + else: + if hasattr(model, decoder_name): + decoder_out = getattr(model, decoder_name).forward( + tokens, encoder_out=encoder_out + ) + else: + decoder_out = model.forward(tokens) + + attn: Optional[Tensor] = None + decoder_len = len(decoder_out) + if decoder_len > 1 and decoder_out[1] is not None: + if isinstance(decoder_out[1], Tensor): + attn = decoder_out[1] + else: + attn_holder = decoder_out[1]["attn"] + if isinstance(attn_holder, Tensor): + attn = attn_holder + elif attn_holder is not None: + attn = attn_holder[0] + if attn is not None: + attn = attn[:, -1, :] + + decoder_out_tuple = ( + decoder_out[0][:, -1:, :].div_(temperature), + None if decoder_len <= 1 else decoder_out[1], + ) + probs = getattr(model, decoder_name).get_normalized_probs( + decoder_out_tuple, log_probs=True, sample=None + ) + probs = probs[:, -1, :] + if self.models_size == 1: + return probs, attn + + log_probs.append(probs) + if attn is not None: + if avg_attn is None: + avg_attn = attn + else: + avg_attn.add_(attn) + + avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( + self.models_size + ) + + if avg_attn is not None: + avg_attn.div_(self.models_size) + return avg_probs, avg_attn + + @torch.jit.export + def reorder_incremental_state( + self, + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + new_order, + decoder_name="decoder", + ): + if not self.has_incremental_states(): + return + for i, model in enumerate(self.models): + getattr(model, decoder_name).reorder_incremental_state_scripting( + incremental_states[i], new_order + ) diff --git a/fairseq/examples/speech_to_speech/unity/sequence_generator_multi_decoder.py b/fairseq/examples/speech_to_speech/unity/sequence_generator_multi_decoder.py new file mode 100644 index 0000000..af99a96 --- /dev/null +++ b/fairseq/examples/speech_to_speech/unity/sequence_generator_multi_decoder.py @@ -0,0 +1,267 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import search + + +class MultiDecoderSequenceGenerator(nn.Module): + def __init__( + self, + models, + tgt_dict, + tgt_dict_mt, + beam_size=1, + beam_size_mt=1, + max_len_a=0, + max_len_b=200, + max_len_a_mt=0, + max_len_b_mt=200, + max_len=0, + min_len=1, + normalize_scores=True, + len_penalty=1.0, + len_penalty_mt=1.0, + unk_penalty=0.0, + temperature=1.0, + match_source_len=False, + no_repeat_ngram_size=0, + eos=None, + eos_mt=None, + symbols_to_strip_from_output=None, + lm_model=None, + lm_weight=1.0, + ): + """Generates translations of a given source sentence. + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models, + currently support fairseq.models.TransformerModel for scripting + beam_size (int, optional): beam width (default: 1) + max_len_a/b (int, optional): generate sequences of maximum length + ax + b, where x is the source length for the second pass + max_len_a_mt/b_mt (int, optional): generate sequences of maximum length + ax + b, where x is the source length for the first pass + max_len (int, optional): the maximum length of the generated output + (not including end-of-sentence) + min_len (int, optional): the minimum length of the generated output + (not including end-of-sentence) + normalize_scores (bool, optional): normalize scores by the length + of the output (default: True) + len_penalty (float, optional): length penalty in the second pass, where <1.0 favors + shorter, >1.0 favors longer sentences (default: 1.0) + len_penalty (float, optional): length penalty in the first pass, where <1.0 favors + shorter, >1.0 favors longer sentences (default: 1.0) + unk_penalty (float, optional): unknown word penalty, where <0 + produces more unks, >0 produces fewer (default: 0.0) + temperature (float, optional): temperature, where values + >1.0 produce more uniform samples and values <1.0 produce + sharper samples (default: 1.0) + match_source_len (bool, optional): outputs should match the source + length (default: False) + """ + super().__init__() + + from examples.speech_to_speech.unity.sequence_generator import SequenceGenerator + + self.generator = SequenceGenerator( + models, + tgt_dict, + beam_size=beam_size, + max_len_a=max_len_a, + max_len_b=max_len_b, + max_len=max_len, + min_len=min_len, + normalize_scores=normalize_scores, + len_penalty=len_penalty, + unk_penalty=unk_penalty, + temperature=temperature, + match_source_len=match_source_len, + no_repeat_ngram_size=no_repeat_ngram_size, + search_strategy=search.BeamSearch(tgt_dict), + eos=eos, + symbols_to_strip_from_output=symbols_to_strip_from_output, + lm_model=lm_model, + lm_weight=lm_weight, + ) + self.eos = self.generator.eos + + self.generator_mt = SequenceGenerator( + models, + tgt_dict_mt, + beam_size=beam_size_mt, + max_len_a=max_len_a_mt, + max_len_b=max_len_b_mt, + max_len=max_len, + min_len=min_len, + normalize_scores=normalize_scores, + len_penalty=len_penalty_mt, + unk_penalty=unk_penalty, + temperature=temperature, + match_source_len=match_source_len, + no_repeat_ngram_size=no_repeat_ngram_size, + search_strategy=search.BeamSearch(tgt_dict_mt), + eos=eos_mt, + symbols_to_strip_from_output=symbols_to_strip_from_output, + ) + + @torch.no_grad() + def generate( + self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs + ) -> List[List[Dict[str, Tensor]]]: + """Generate translations. Match the api of other fairseq generators. + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + sample (dict): batch + prefix_tokens (torch.LongTensor, optional): force decoder to begin + with these tokens + constraints (torch.LongTensor, optional): force decoder to include + the list of constraints + bos_token (int, optional): beginning of sentence token + (default: self.eos) + """ + return self._generate(sample, **kwargs) + + def _generate( + self, + sample: Dict[str, Dict[str, Tensor]], + prefix_tokens: Optional[Tensor] = None, + constraints: Optional[Tensor] = None, + bos_token: Optional[int] = None, + ): + net_input = sample["net_input"] + + if "src_tokens" in net_input: + src_tokens = net_input["src_tokens"] + # length of the source text being the character length except EndOfSentence and pad + # if src_lengths exists in net_input (speech_to_text dataset case), then use it + if "src_lengths" in net_input: + src_lengths = net_input["src_lengths"] + else: + src_lengths = ( + ( + src_tokens.ne(self.generator.eos) + & src_tokens.ne(self.generator.pad) + ) + .long() + .sum(dim=1) + ) + else: + raise Exception( + "expected src_tokens or source in net input. input keys: " + + str(net_input.keys()) + ) + + if constraints is not None and not self.generator.search.supports_constraints: + raise NotImplementedError( + "Target-side constraints were provided, but search method doesn't support them" + ) + + # Initialize constraints, when active + self.generator.search.init_constraints(constraints, self.generator.beam_size) + self.generator_mt.search.init_constraints( + constraints, self.generator_mt.beam_size + ) + + # compute the encoder output for each beam + with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"): + encoder_outs = self.generator.model.forward_encoder(net_input) + + single_model = self.generator.model.single_model + mt_decoder = getattr(single_model, f"{single_model.mt_task_name}_decoder") + + # 1. MT decoder + finalized_mt = self.generator_mt.generate_decoder( + encoder_outs, + src_tokens, + src_lengths, + sample, + prefix_tokens, + constraints, + bos_token, + aux_task_name=single_model.mt_task_name, + ) + + # extract decoder output corresponding to the best hypothesis + max_tgt_len = max([len(hypo[0]["tokens"]) for hypo in finalized_mt]) + prev_output_tokens_mt = ( + src_tokens.new_zeros(src_tokens.shape[0], max_tgt_len) + .fill_(mt_decoder.padding_idx) + .int() + ) # B x T + for i, hypo in enumerate(finalized_mt): + i_beam = 0 + tmp = hypo[i_beam]["tokens"].int() # hyp + eos + prev_output_tokens_mt[i, 0] = self.generator_mt.eos + if tmp[-1] == self.generator_mt.eos: + tmp = tmp[:-1] + prev_output_tokens_mt[i, 1 : len(tmp) + 1] = tmp + + text = "".join([self.generator_mt.tgt_dict[c] for c in tmp]) + text = text.replace("_", " ") + text = text.replace("▁", " ") + text = text.replace("<unk>", " ") + text = text.replace("<s>", "") + text = text.replace("</s>", "") + if len(text) > 0 and text[0] == " ": + text = text[1:] + sample_id = sample["id"].tolist()[i] + print("{} (None-{})".format(text, sample_id)) + + x = mt_decoder( + prev_output_tokens_mt, + encoder_out=encoder_outs[0], + features_only=True, + )[0].transpose(0, 1) + + if getattr(single_model, "proj", None) is not None: + x = single_model.proj(x) + + mt_decoder_padding_mask = None + if prev_output_tokens_mt.eq(mt_decoder.padding_idx).any(): + mt_decoder_padding_mask = prev_output_tokens_mt.eq(mt_decoder.padding_idx) + + # 2. T2U encoder + if getattr(single_model, "synthesizer_encoder", None) is not None: + t2u_encoder_out = single_model.synthesizer_encoder( + x, + mt_decoder_padding_mask, + ) + else: + t2u_encoder_out = { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [mt_decoder_padding_mask] + if mt_decoder_padding_mask is not None + else [], # B x T + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + if getattr(single_model, "t2u_augmented_cross_attn", False): + encoder_outs_aug = [t2u_encoder_out] + else: + encoder_outs = [t2u_encoder_out] + encoder_outs_aug = None + + # 3. T2U decoder + finalized = self.generator.generate_decoder( + encoder_outs, + src_tokens, + src_lengths, + sample, + prefix_tokens, + constraints, + bos_token, + encoder_outs_aug=encoder_outs_aug, + ) + return finalized diff --git a/fairseq/examples/speech_to_text/README.md b/fairseq/examples/speech_to_text/README.md new file mode 100644 index 0000000..f639d30 --- /dev/null +++ b/fairseq/examples/speech_to_text/README.md @@ -0,0 +1,77 @@ +# Speech-to-Text (S2T) Modeling + +[https://www.aclweb.org/anthology/2020.aacl-demo.6](https://www.aclweb.org/anthology/2020.aacl-demo.6.pdf) + +Speech recognition (ASR) and speech-to-text translation (ST) with fairseq. + +## Data Preparation +S2T modeling data consists of source speech features, target text and other optional information +(source text, speaker id, etc.). Fairseq S2T uses per-dataset-split TSV manifest files +to store these information. Each data field is represented by a column in the TSV file. + +Unlike text token embeddings, speech features (e.g. log mel-scale filter banks) are usually fixed +during model training and can be pre-computed. The manifest file contains the path to +either the feature file in NumPy format or the WAV/FLAC audio file. For the latter, +features will be extracted on-the-fly by fairseq S2T. Optionally, feature/audio files can be packed +into uncompressed ZIP files (then accessed via byte offset and length) to improve I/O performance. + +Fairseq S2T also employs a YAML file for data related configurations: tokenizer type and dictionary path +for the target text, feature transforms such as CMVN (cepstral mean and variance normalization) and SpecAugment, +temperature-based resampling, etc. + +## Model Training +Fairseq S2T uses the unified `fairseq-train` interface for model training. It requires arguments `--task speech_to_text`, + `--arch <model architecture in fairseq.models.speech_to_text.*>` and `--config-yaml <config YAML filename>`. + +## Inference & Evaluation +Fairseq S2T uses the unified `fairseq-generate`/`fairseq-interactive` interface for inference and evaluation. It +requires arguments `--task speech_to_text` and `--config-yaml <config YAML filename>`. The interactive console takes +audio paths (one per line) as inputs. + + +## Examples +- [Speech Recognition (ASR) on LibriSpeech](docs/librispeech_example.md) + +- [Speech-to-Text Translation (ST) on MuST-C](docs/mustc_example.md) + +- [Speech-to-Text Translation (ST) on CoVoST 2](docs/covost_example.md) + +- [Speech-to-Text Translation (ST) on Multilingual TEDx](docs/mtedx_example.md) +- [Simultaneous Speech-to-Text Translation (SimulST) on MuST-C](docs/simulst_mustc_example.md) + +## Updates +- 02/04/2021: Added interactive decoding (`fairseq-interactive`) support. Examples: + [ASR (LibriSpeech)](docs/librispeech_example.md#interactive-decoding) + and [ST (CoVoST 2)](docs/covost_example.md#interactive-decoding). +- 01/08/2021: Several fixes for S2T Transformer model, inference-time de-tokenization, scorer configuration and data + preparation scripts. We also add pre-trained models to the examples and revise the instructions. + Breaking changes: the data preparation scripts now extract filterbank features without CMVN. CMVN is instead applied + on-the-fly (defined in the config YAML). + +## What's Next +- We are migrating the old fairseq [ASR example](../speech_recognition) into this S2T framework and + merging the features from both sides. +- The following papers also base their experiments on fairseq S2T. We are adding more examples for replication. + - [Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation (Wang et al., 2020)](https://arxiv.org/abs/2006.05474) + - [Self-Supervised Representations Improve End-to-End Speech Translation (Wu et al., 2020)](https://arxiv.org/abs/2006.12124) + - [Self-Training for End-to-End Speech Translation (Pino et al., 2020)](https://arxiv.org/abs/2006.02490) + - [CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus (Wang et al., 2020)](https://arxiv.org/abs/2002.01320) + - [Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade (Pino et al., 2019)](https://arxiv.org/abs/1909.06515) + +## Citation +Please cite as: +``` +@inproceedings{wang2020fairseqs2t, + title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, + author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, + booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, + year = {2020}, +} + +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` diff --git a/fairseq/examples/speech_to_text/data_utils.py b/fairseq/examples/speech_to_text/data_utils.py new file mode 100644 index 0000000..b8648cb --- /dev/null +++ b/fairseq/examples/speech_to_text/data_utils.py @@ -0,0 +1,383 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import csv +from pathlib import Path +import zipfile +from functools import reduce +from multiprocessing import cpu_count +from typing import Any, Dict, List, Optional, Union +import io + +import numpy as np +import pandas as pd +import sentencepiece as sp +from fairseq.data.audio.audio_utils import ( + convert_waveform, _get_kaldi_fbank, _get_torchaudio_fbank, is_npy_data, + is_sf_audio_data +) +import torch +import soundfile as sf +from tqdm import tqdm + + +UNK_TOKEN, UNK_TOKEN_ID = "<unk>", 3 +BOS_TOKEN, BOS_TOKEN_ID = "<s>", 0 +EOS_TOKEN, EOS_TOKEN_ID = "</s>", 2 +PAD_TOKEN, PAD_TOKEN_ID = "<pad>", 1 + + +def gen_vocab( + input_path: Path, output_path_prefix: Path, model_type="bpe", + vocab_size=1000, special_symbols: Optional[List[str]] = None +): + # Train SentencePiece Model + arguments = [ + f"--input={input_path.as_posix()}", + f"--model_prefix={output_path_prefix.as_posix()}", + f"--model_type={model_type}", + f"--vocab_size={vocab_size}", + "--character_coverage=1.0", + f"--num_threads={cpu_count()}", + f"--unk_id={UNK_TOKEN_ID}", + f"--bos_id={BOS_TOKEN_ID}", + f"--eos_id={EOS_TOKEN_ID}", + f"--pad_id={PAD_TOKEN_ID}", + ] + if special_symbols is not None: + _special_symbols = ",".join(special_symbols) + arguments.append(f"--user_defined_symbols={_special_symbols}") + sp.SentencePieceTrainer.Train(" ".join(arguments)) + # Export fairseq dictionary + spm = sp.SentencePieceProcessor() + spm.Load(output_path_prefix.as_posix() + ".model") + vocab = {i: spm.IdToPiece(i) for i in range(spm.GetPieceSize())} + assert ( + vocab.get(UNK_TOKEN_ID) == UNK_TOKEN + and vocab.get(PAD_TOKEN_ID) == PAD_TOKEN + and vocab.get(BOS_TOKEN_ID) == BOS_TOKEN + and vocab.get(EOS_TOKEN_ID) == EOS_TOKEN + ) + vocab = { + i: s + for i, s in vocab.items() + if s not in {UNK_TOKEN, BOS_TOKEN, EOS_TOKEN, PAD_TOKEN} + } + with open(output_path_prefix.as_posix() + ".txt", "w") as f_out: + for _, s in sorted(vocab.items(), key=lambda x: x[0]): + f_out.write(f"{s} 1\n") + + +def extract_fbank_features( + waveform: torch.FloatTensor, + sample_rate: int, + output_path: Optional[Path] = None, + n_mel_bins: int = 80, + overwrite: bool = False, +): + if output_path is not None and output_path.is_file() and not overwrite: + return + + _waveform, _ = convert_waveform(waveform, sample_rate, to_mono=True) + # Kaldi compliance: 16-bit signed integers + _waveform = _waveform * (2 ** 15) + _waveform = _waveform.numpy() + + features = _get_kaldi_fbank(_waveform, sample_rate, n_mel_bins) + if features is None: + features = _get_torchaudio_fbank(_waveform, sample_rate, n_mel_bins) + if features is None: + raise ImportError( + "Please install pyKaldi or torchaudio to enable fbank feature extraction" + ) + + if output_path is not None: + np.save(output_path.as_posix(), features) + return features + + +def create_zip(data_root: Path, zip_path: Path): + paths = list(data_root.glob("*.npy")) + paths.extend(data_root.glob("*.flac")) + with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_STORED) as f: + for path in tqdm(paths): + f.write(path, arcname=path.name) + + +def get_zip_manifest( + zip_path: Path, zip_root: Optional[Path] = None, is_audio=False +): + _zip_path = Path.joinpath(zip_root or Path(""), zip_path) + with zipfile.ZipFile(_zip_path, mode="r") as f: + info = f.infolist() + paths, lengths = {}, {} + for i in tqdm(info): + utt_id = Path(i.filename).stem + offset, file_size = i.header_offset + 30 + len(i.filename), i.file_size + paths[utt_id] = f"{zip_path.as_posix()}:{offset}:{file_size}" + with open(_zip_path, "rb") as f: + f.seek(offset) + byte_data = f.read(file_size) + assert len(byte_data) > 1 + if is_audio: + assert is_sf_audio_data(byte_data), i + else: + assert is_npy_data(byte_data), i + byte_data_fp = io.BytesIO(byte_data) + if is_audio: + lengths[utt_id] = sf.info(byte_data_fp).frames + else: + lengths[utt_id] = np.load(byte_data_fp).shape[0] + return paths, lengths + + +def gen_config_yaml( + manifest_root: Path, + spm_filename: Optional[str] = None, + vocab_name: Optional[str] = None, + yaml_filename: str = "config.yaml", + specaugment_policy: Optional[str] = "lb", + prepend_tgt_lang_tag: bool = False, + sampling_alpha: Optional[float] = None, + input_channels: Optional[int] = 1, + input_feat_per_channel: Optional[int] = 80, + audio_root: str = "", + cmvn_type: str = "utterance", + gcmvn_path: Optional[Path] = None, + extra=None +): + manifest_root = manifest_root.absolute() + writer = S2TDataConfigWriter(manifest_root / yaml_filename) + assert spm_filename is not None or vocab_name is not None + vocab_name = spm_filename.replace(".model", ".txt") if vocab_name is None \ + else vocab_name + writer.set_vocab_filename(vocab_name) + if input_channels is not None: + writer.set_input_channels(input_channels) + if input_feat_per_channel is not None: + writer.set_input_feat_per_channel(input_feat_per_channel) + specaugment_setters = { + "lb": writer.set_specaugment_lb_policy, + "ld": writer.set_specaugment_ld_policy, + "sm": writer.set_specaugment_sm_policy, + "ss": writer.set_specaugment_ss_policy, + } + specaugment_setter = specaugment_setters.get(specaugment_policy, None) + if specaugment_setter is not None: + specaugment_setter() + if spm_filename is not None: + writer.set_bpe_tokenizer( + { + "bpe": "sentencepiece", + "sentencepiece_model": (manifest_root / spm_filename).as_posix(), + } + ) + if prepend_tgt_lang_tag: + writer.set_prepend_tgt_lang_tag(True) + if sampling_alpha is not None: + writer.set_sampling_alpha(sampling_alpha) + + if cmvn_type not in ["global", "utterance"]: + raise NotImplementedError + + if specaugment_policy is not None: + writer.set_feature_transforms( + "_train", [f"{cmvn_type}_cmvn", "specaugment"] + ) + writer.set_feature_transforms("*", [f"{cmvn_type}_cmvn"]) + + if cmvn_type == "global": + if gcmvn_path is None: + raise ValueError("Please provide path of global cmvn file.") + else: + writer.set_global_cmvn(gcmvn_path.as_posix()) + + if len(audio_root) > 0: + writer.set_audio_root(audio_root) + + if extra is not None: + writer.set_extra(extra) + writer.flush() + + +def load_df_from_tsv(path: Union[str, Path]) -> pd.DataFrame: + _path = path if isinstance(path, str) else path.as_posix() + return pd.read_csv( + _path, + sep="\t", + header=0, + encoding="utf-8", + escapechar="\\", + quoting=csv.QUOTE_NONE, + na_filter=False, + ) + + +def save_df_to_tsv(dataframe, path: Union[str, Path]): + _path = path if isinstance(path, str) else path.as_posix() + dataframe.to_csv( + _path, + sep="\t", + header=True, + index=False, + encoding="utf-8", + escapechar="\\", + quoting=csv.QUOTE_NONE, + ) + + +def load_tsv_to_dicts(path: Union[str, Path]) -> List[dict]: + with open(path, "r") as f: + reader = csv.DictReader( + f, + delimiter="\t", + quotechar=None, + doublequote=False, + lineterminator="\n", + quoting=csv.QUOTE_NONE, + ) + rows = [dict(e) for e in reader] + return rows + + +def filter_manifest_df( + df, is_train_split=False, extra_filters=None, min_n_frames=5, max_n_frames=3000 +): + filters = { + "no speech": df["audio"] == "", + f"short speech (<{min_n_frames} frames)": df["n_frames"] < min_n_frames, + "empty sentence": df["tgt_text"] == "", + } + if is_train_split: + filters[f"long speech (>{max_n_frames} frames)"] = df["n_frames"] > max_n_frames + if extra_filters is not None: + filters.update(extra_filters) + invalid = reduce(lambda x, y: x | y, filters.values()) + valid = ~invalid + print( + "| " + + ", ".join(f"{n}: {f.sum()}" for n, f in filters.items()) + + f", total {invalid.sum()} filtered, {valid.sum()} remained." + ) + return df[valid] + + +def cal_gcmvn_stats(features_list): + features = np.concatenate(features_list) + square_sums = (features ** 2).sum(axis=0) + mean = features.mean(axis=0) + features = np.subtract(features, mean) + var = square_sums / features.shape[0] - mean ** 2 + std = np.sqrt(np.maximum(var, 1e-8)) + return {"mean": mean.astype("float32"), "std": std.astype("float32")} + + +class S2TDataConfigWriter(object): + DEFAULT_VOCAB_FILENAME = "dict.txt" + DEFAULT_INPUT_FEAT_PER_CHANNEL = 80 + DEFAULT_INPUT_CHANNELS = 1 + + def __init__(self, yaml_path: Path): + try: + import yaml + except ImportError: + print("Please install PyYAML for S2T data config YAML files") + self.yaml = yaml + self.yaml_path = yaml_path + self.config = {} + + def flush(self): + with open(self.yaml_path, "w") as f: + self.yaml.dump(self.config, f) + + def set_audio_root(self, audio_root=""): + self.config["audio_root"] = audio_root + + def set_vocab_filename(self, vocab_filename: str = "dict.txt"): + self.config["vocab_filename"] = vocab_filename + + def set_specaugment( + self, + time_wrap_w: int, + freq_mask_n: int, + freq_mask_f: int, + time_mask_n: int, + time_mask_t: int, + time_mask_p: float, + ): + self.config["specaugment"] = { + "time_wrap_W": time_wrap_w, + "freq_mask_N": freq_mask_n, + "freq_mask_F": freq_mask_f, + "time_mask_N": time_mask_n, + "time_mask_T": time_mask_t, + "time_mask_p": time_mask_p, + } + + def set_specaugment_lb_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=1, + freq_mask_f=27, + time_mask_n=1, + time_mask_t=100, + time_mask_p=1.0, + ) + + def set_specaugment_ld_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=2, + freq_mask_f=27, + time_mask_n=2, + time_mask_t=100, + time_mask_p=1.0, + ) + + def set_specaugment_sm_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=2, + freq_mask_f=15, + time_mask_n=2, + time_mask_t=70, + time_mask_p=0.2, + ) + + def set_specaugment_ss_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=2, + freq_mask_f=27, + time_mask_n=2, + time_mask_t=70, + time_mask_p=0.2, + ) + + def set_input_channels(self, input_channels: int = 1): + self.config["input_channels"] = input_channels + + def set_input_feat_per_channel(self, input_feat_per_channel: int = 80): + self.config["input_feat_per_channel"] = input_feat_per_channel + + def set_bpe_tokenizer(self, bpe_tokenizer: Dict[str, Any]): + self.config["bpe_tokenizer"] = bpe_tokenizer + + def set_global_cmvn(self, stats_npz_path: str): + self.config["global_cmvn"] = {"stats_npz_path": stats_npz_path} + + def set_feature_transforms(self, split: str, transforms: List[str]): + if "transforms" not in self.config: + self.config["transforms"] = {} + self.config["transforms"][split] = transforms + + def set_prepend_tgt_lang_tag(self, flag: bool = True): + self.config["prepend_tgt_lang_tag"] = flag + + def set_sampling_alpha(self, sampling_alpha: float = 1.0): + self.config["sampling_alpha"] = sampling_alpha + + def set_extra(self, data): + self.config.update(data) diff --git a/fairseq/examples/speech_to_text/docs/covost_example.md b/fairseq/examples/speech_to_text/docs/covost_example.md new file mode 100644 index 0000000..6282428 --- /dev/null +++ b/fairseq/examples/speech_to_text/docs/covost_example.md @@ -0,0 +1,140 @@ +[[Back]](..) + +# S2T Example: ST on CoVoST + +We replicate the experiments in +[CoVoST 2 and Massively Multilingual Speech-to-Text Translation (Wang et al., 2020)](https://arxiv.org/abs/2007.10310). + +## Data Preparation + +[Download](https://commonvoice.mozilla.org/en/datasets) and unpack Common Voice v4 to a path +`${COVOST_ROOT}/${SOURCE_LANG_ID}`, then preprocess it with + +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio sentencepiece + +# En ASR +python examples/speech_to_text/prep_covost_data.py \ + --data-root ${COVOST_ROOT} --vocab-type char --src-lang en +# ST +python examples/speech_to_text/prep_covost_data.py \ + --data-root ${COVOST_ROOT} --vocab-type char \ + --src-lang fr --tgt-lang en +``` + +The generated files (manifest, features, vocabulary and data configuration) will be added to +`${COVOST_ROOT}/${SOURCE_LANG_ID}`. + +Download our vocabulary files if you want to use our pre-trained models: + +- ASR: [En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_asr_vocab_char.zip) +- ST: [Fr-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_fr_en_st_vocab_char.zip), [De-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_de_en_st_vocab_char.zip), [Es-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_es_en_st_vocab_char.zip), [Ca-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_ca_en_st_vocab_char.zip), [En-De](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_de_st_vocab_char.zip), [En-Ca](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_ca_st_vocab_char.zip), [En-Fa](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_fa_st_vocab_char.zip), [En-Et](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_et_st_vocab_char.zip) + +## ASR + +#### Training + +We train an En ASR model for encoder pre-training some of the ST models. + +```bash +fairseq-train ${COVOST_ROOT}/en \ + --config-yaml config_asr_en.yaml --train-subset train_asr_en --valid-subset dev_asr_en \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 50000 --max-update 60000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --report-accuracy --arch s2t_transformer_s --dropout 0.15 --optimizer adam --lr 2e-3 \ + --lr-scheduler inverse_sqrt --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \ + --attn-type None --pos-enc-type ${POS_ENC_TYPE} +``` + +where `ASR_SAVE_DIR` is the checkpoint root path and `POS_ENC_TYPE` refers to positional encoding to be used in the conformer encoder. +Set it to `abs`, `rope` or `rel_pos` to use the absolute positional encoding, rotary positional encoding or relative positional encoding in the conformer layer respectively. +Transformer encoder only supports absolute positional encoding and by default, the transformer encoder will be used. +To switch to conformer, set `--attn-type espnet` and `--POS_ENC_TYPE`. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly when using more than 1 GPU. + +#### Inference & Evaluation + +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${COVOST_ROOT}/en \ + --config-yaml config_asr_en.yaml --gen-subset test_asr_en --task speech_to_text \ + --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct +``` + +#### Results + +| --arch | --pos-enc-type | Params | En | Model | +|---|---|---|---|---| +| s2t_transformer_s | - | 31M | 25.6 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_asr_transformer_s.pt) | +| s2t_conformer | rel_pos | 42.9M | 23.18| [Download](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_asr/rel_pos_asr_checkpoint_best.pt) | +| s2t_conformer | rope | 42.1M | 23.8| [Download](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_asr/rope_pos_asr_checkpoint_best.pt) | +| s2t_conformer | abs | 42.1M | 23.8| [Download](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_asr/abs_asr_checkpoint_best.pt) | + +## ST + +#### Training + +Fr-En as example: + +```bash +fairseq-train ${COVOST_ROOT}/fr \ + --config-yaml config_st_fr_en.yaml --train-subset train_st_fr_en --valid-subset dev_st_fr_en \ + --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-update 30000 --max-tokens 40000 \ # --max-tokens 50000 for en-* + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --encoder-freezing-updates 1000 --optimizer adam --lr 2e-3 \ + --lr-scheduler inverse_sqrt --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \ + --attn-type None --pos-enc-type ${POS_ENC_TYPE} \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} +``` + +where `ST_SAVE_DIR` is the checkpoint root path and `POS_ENC_TYPE` refers to positional encoding to be used in the conformer encoder. +Set it to `abs`, `rope` or `rel_pos` to use the absolute positional encoding, rotary positional encoding or relative positional encoding in the conformer layer respectively. +Transformer encoder only supports absolute positional encoding and by default, the transformer encoder will be used. +To switch to conformer, set `--attn-type espnet` and `--POS_ENC_TYPE`. Optionally load the pre-trained En ASR encoder for faster training and better +performance: `--load-pretrained-encoder-from <ASR checkpoint path>`. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. +You may want to update it accordingly when using more than 1 GPU. + +#### Inference & Evaluation + +Average the last 10 checkpoints and evaluate on test split: + +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${COVOST_ROOT}/fr \ + --config-yaml config_st_fr_en.yaml --gen-subset test_st_fr_en --task speech_to_text \ + --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu +``` + +## Interactive Decoding + +Launch the interactive console via + +```bash +fairseq-interactive ${COVOST_ROOT}/fr --config-yaml config_st_fr_en.yaml \ + --task speech_to_text --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 +``` + +Type in WAV/FLAC/OGG audio paths (one per line) after the prompt. + +#### Results + +| --arch | --pos-enc-type | Params | ASR PT | Fr-En | De-En | Es-En | Ca-En | En-De | En-Ca | En-Fa | En-Et | Model | +|---|---|---|---|---|---|---|---|---|---|---|---|---| +| s2t_transformer | - | 31M | Yes | [27.2](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_fr_en_st_transformer_s.pt) | [17.7](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_de_en_st_transformer_s.pt) | [23.1](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_es_en_st_transformer_s.pt) | [19.3](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_ca_en_st_transformer_s.pt) | [16.1](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_de_st_transformer_s.pt) | [21.6](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_ca_st_transformer_s.pt) | [12.9](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_fa_st_transformer_s.pt) | [12.8](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_et_st_transformer_s.pt) | (<-Download) | +| s2t_conformer | rel_pos | 42.9M | No | [28.32](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/fr_en/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | [18.21](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/de_en/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | [25.98](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/es_en/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | [21.13](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/ca_en/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | [20.37](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_de/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | [25.89](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_ca/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | [15.59](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_fa/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | [14.49](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_et/rel_pos_from_scratch_avg_last_10_checkpoint.pt) | (<-Download) | +| s2t_conformer | rel_pos | 42.9M | Yes| [27.15](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/fr_en/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | [18.22](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/de_en/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | [25.14](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/es_en/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | [21.68](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/ca_en/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | [20.35](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_de/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | [25.92](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_ca/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | [15.76](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_fa/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | [16.52](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_et/rel_pos_asr_pt_avg_last_10_checkpoint.pt) | (<-Download) | +| s2t_conformer | rope | 42.1M | No | [27.61](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/fr_en/rope_from_scratch_avg_last_10_checkpoint.pt) | [17.6](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/de_en/rope_from_scratch_avg_last_10_checkpoint.pt) | [24.91](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/es_en/rope_from_scratch_avg_last_10_checkpoint.pt) | [20.78](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/ca_en/rope_from_scratch_avg_last_10_checkpoint.pt) | [19.7](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_de/rope_from_scratch_avg_last_10_checkpoint.pt) | [25.13](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_ca/rope_from_scratch_avg_last_10_checkpoint.pt) | [15.22](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_fa/rope_from_scratch_avg_last_10_checkpoint.pt) | [15.87](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_et/rope_from_scratch_avg_last_10_checkpoint.pt) | (<-Download) | +| s2t_conformer | rope | 42.1M | Yes | [26.99](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/fr_en/rope_asr_pt_avg_last_10_checkpoint.pt) | [17.71](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/de_en/rope_asr_pt_avg_last_10_checkpoint.pt) | [24.24](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/es_en/rope_asr_pt_avg_last_10_checkpoint.pt) | [21.24](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/ca_en/rope_asr_pt_avg_last_10_checkpoint.pt) | [19.9](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_de/rope_asr_pt_avg_last_10_checkpoint.pt) | [25.25](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_ca/rope_asr_pt_avg_last_10_checkpoint.pt) | [15.58](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_fa/rope_asr_pt_avg_last_10_checkpoint.pt) | [15.97](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_et/rope_asr_pt_avg_last_10_checkpoint.pt) | (<-Download) | +| s2t_conformer | abs | 42.1M | No | [27.45](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/fr_en/abs_from_scratch_avg_last_10_checkpoint.pt) | [17.25](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/de_en/abs_from_scratch_avg_last_10_checkpoint.pt) | [25.01](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/es_en/abs_from_scratch_avg_last_10_checkpoint.pt) | [20.26](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/ca_en/abs_from_scratch_avg_last_10_checkpoint.pt) | [19.86](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_de/abs_from_scratch_avg_last_10_checkpoint.pt) | [25.25](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_ca/abs_from_scratch_avg_last_10_checkpoint.pt) | [15.46](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_fa/abs_from_scratch_avg_last_10_checkpoint.pt) | [15.81](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_et/abs_from_scratch_avg_last_10_checkpoint.pt) | (<-Download) | +| s2t_conforme | abs | 42.1M | Yes| [26.52](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/fr_en/abs_asr_pt_avg_last_10_checkpoint.pt) | [17.37](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/de_en/abs_asr_pt_avg_last_10_checkpoint.pt) | [25.40](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/es_en/abs_asr_pt_avg_last_10_checkpoint.pt) | [20.45](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/ca_en/abs_asr_pt_avg_last_10_checkpoint.pt) | [19.57](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_de/abs_asr_pt_avg_last_10_checkpoint.pt) | [25.40](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_ca/abs_asr_pt_avg_last_10_checkpoint.pt) | [15.17](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_fa/abs_asr_pt_avg_last_10_checkpoint.pt) | [15.83](https://dl.fbaipublicfiles.com/fairseq/conformer/covost2/en_et/abs_asr_pt_avg_last_10_checkpoint.pt) | (<-Download) | + +[[Back]](..) diff --git a/fairseq/examples/speech_to_text/docs/librispeech_example.md b/fairseq/examples/speech_to_text/docs/librispeech_example.md new file mode 100644 index 0000000..4040fda --- /dev/null +++ b/fairseq/examples/speech_to_text/docs/librispeech_example.md @@ -0,0 +1,69 @@ +[[Back]](..) + +# S2T Example: Speech Recognition (ASR) on LibriSpeech +[LibriSpeech](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) is a de-facto standard English ASR +benchmark. We provide competitive +vanilla [Transformer](https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) baselines. + +## Data preparation +Download and preprocess LibriSpeech data with +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio sentencepiece + +python examples/speech_to_text/prep_librispeech_data.py \ + --output-root ${LS_ROOT} --vocab-type unigram --vocab-size 10000 +``` +where `LS_ROOT` is the root path for downloaded data as well as generated files (manifest, features, vocabulary and +data configuration). + +[Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_vocab_unigram10000.zip) our vocabulary files +if you want to use our pre-trained models. + +## Training +```bash +fairseq-train ${LS_ROOT} --save-dir ${SAVE_DIR} \ + --config-yaml config.yaml --train-subset train-clean-100,train-clean-360,train-other-500 --valid-subset dev-clean,dev-other \ + --num-workers 4 --max-tokens 40000 --max-update 300000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --share-decoder-input-output-embed \ + --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt --warmup-updates 10000 \ + --clip-norm 10.0 --seed 1 --update-freq 8 +``` +where `SAVE_DIR` is the checkpoint root path. Here we use `--arch s2t_transformer_s` (31M parameters) as example. +For better performance, you may switch to `s2t_transformer_m` (71M, with `--lr 1e-3`) or `s2t_transformer_l` +(268M, with `--lr 5e-4`). We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly +when using more than 1 GPU. + +## Inference & Evaluation +Average the last 10 checkpoints and evaluate on the 4 splits +(`dev-clean`, `dev-other`, `test-clean` and `test-other`): +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py --inputs ${SAVE_DIR} \ + --num-epoch-checkpoints 10 \ + --output "${SAVE_DIR}/${CHECKPOINT_FILENAME}" +for SUBSET in dev-clean dev-other test-clean test-other; do + fairseq-generate ${LS_ROOT} --config-yaml config.yaml --gen-subset ${SUBSET} \ + --task speech_to_text --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring wer +done +``` + +## Interactive Decoding +Launch the interactive console via +```bash +fairseq-interactive ${LS_ROOT} --config-yaml config.yaml --task speech_to_text \ + --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 +``` +Type in WAV/FLAC/OGG audio paths (one per line) after the prompt. + +## Results + +| --arch | Params | dev-clean | dev-other | test-clean | test-other | Model | +|---|---|---|---|---|---|---| +| s2t_transformer_s | 30M | 3.8 | 8.9 | 4.4 | 9.0 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_s.pt) | +| s2t_transformer_m | 71M | 3.2 | 8.0 | 3.4 | 7.9 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_m.pt) | +| s2t_transformer_l | 268M | 3.0 | 7.5 | 3.2 | 7.5 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_l.pt) | + +[[Back]](..) diff --git a/fairseq/examples/speech_to_text/docs/mtedx_example.md b/fairseq/examples/speech_to_text/docs/mtedx_example.md new file mode 100644 index 0000000..7e3d759 --- /dev/null +++ b/fairseq/examples/speech_to_text/docs/mtedx_example.md @@ -0,0 +1,201 @@ +[[Back]](..) + +# S2T Example: Speech Translation (ST) on Multilingual TEDx + +[Multilingual TEDx](https://arxiv.org/abs/2102.01757) is multilingual corpus for speech recognition and +speech translation. The data is derived from TEDx talks in 8 source languages +with translations to a subset of 5 target languages. + +## Data Preparation +[Download](http://openslr.org/100/) and unpack Multilingual TEDx data to a path +`${MTEDX_ROOT}/${LANG_PAIR}`, then preprocess it with +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio soundfile sentencepiece + +# Generate TSV manifests, features, vocabulary +# and configuration for each language +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task asr \ + --vocab-type unigram --vocab-size 1000 +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task st \ + --vocab-type unigram --vocab-size 1000 + +# Add vocabulary and configuration for joint data +# (based on the manifests and features generated above) +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task asr --joint \ + --vocab-type unigram --vocab-size 8000 +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task st --joint \ + --vocab-type unigram --vocab-size 8000 +``` +The generated files (manifest, features, vocabulary and data configuration) will be added to +`${MTEDX_ROOT}/${LANG_PAIR}` (per-language data) and `MTEDX_ROOT` (joint data). + + +## ASR +#### Training +Spanish as example: +```bash +fairseq-train ${MTEDX_ROOT}/es-es \ + --config-yaml config_asr.yaml --train-subset train_asr --valid-subset valid_asr \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_xs --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --load-pretrained-encoder-from ${PRETRAINED_ENCODER} \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 +``` +For joint model (using ASR data from all 8 languages): +```bash +fairseq-train ${MTEDX_ROOT} \ + --config-yaml config_asr.yaml \ + --train-subset train_es-es_asr,train_fr-fr_asr,train_pt-pt_asr,train_it-it_asr,train_ru-ru_asr,train_el-el_asr,train_ar-ar_asr,train_de-de_asr \ + --valid-subset valid_es-es_asr,valid_fr-fr_asr,valid_pt-pt_asr,valid_it-it_asr,valid_ru-ru_asr,valid_el-el_asr,valid_ar-ar_asr,valid_de-de_asr \ + --save-dir ${MULTILINGUAL_ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 \ + --ignore-prefix-size 1 +``` +where `MULTILINGUAL_ASR_SAVE_DIR` is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs +with 1 GPU. You may want to update it accordingly when using more than 1 GPU. +For multilingual models, we prepend target language ID token as target BOS, which should be excluded from +the training loss via `--ignore-prefix-size 1`. + +#### Inference & Evaluation +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +fairseq-generate ${MTEDX_ROOT}/es-es \ + --config-yaml config_asr.yaml --gen-subset test --task speech_to_text \ + --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct --remove-bpe + +# For models trained on joint data +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${MULTILINGUAL_ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${MULTILINGUAL_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +for LANG in es fr pt it ru el ar de; do + fairseq-generate ${MTEDX_ROOT} \ + --config-yaml config_asr.yaml --gen-subset test_${LANG}-${LANG}_asr --task speech_to_text \ + --prefix-size 1 --path ${MULTILINGUAL_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 40000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct --remove-bpe +done +``` +#### Results +| Data | --arch | Params | Es | Fr | Pt | It | Ru | El | Ar | De | +|--------------|--------------------|--------|------|------|------|------|------|-------|-------|-------| +| Monolingual | s2t_transformer_xs | 10M | 46.4 | 45.6 | 54.8 | 48.0 | 74.7 | 109.5 | 104.4 | 111.1 | + + +## ST +#### Training +Es-En as example: +```bash +fairseq-train ${MTEDX_ROOT}/es-en \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset valid_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_xs --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --load-pretrained-encoder-from ${PRETRAINED_ENCODER} \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 +``` +For multilingual model (all 12 directions): +```bash +fairseq-train ${MTEDX_ROOT} \ + --config-yaml config_st.yaml \ + --train-subset train_el-en_st,train_es-en_st,train_es-fr_st,train_es-it_st,train_es-pt_st,train_fr-en_st,train_fr-es_st,train_fr-pt_st,train_it-en_st,train_it-es_st,train_pt-en_st,train_pt-es_st,train_ru-en_st \ + --valid-subset valid_el-en_st,valid_es-en_st,valid_es-fr_st,valid_es-it_st,valid_es-pt_st,valid_fr-en_st,valid_fr-es_st,valid_fr-pt_st,valid_it-en_st,valid_it-es_st,valid_pt-en_st,valid_pt-es_st,valid_ru-en_st \ + --save-dir ${MULTILINGUAL_ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 \ + --ignore-prefix-size 1 \ + --load-pretrained-encoder-from ${PRETRAINED_ENCODER} +``` +where `ST_SAVE_DIR` (`MULTILINGUAL_ST_SAVE_DIR`) is the checkpoint root path. The ST encoder is pre-trained by ASR +for faster training and better performance: `--load-pretrained-encoder-from <(JOINT_)ASR checkpoint path>`. We set +`--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly when using more than 1 GPU. +For multilingual models, we prepend target language ID token as target BOS, which should be excluded from +the training loss via `--ignore-prefix-size 1`. + +#### Inference & Evaluation +Average the last 10 checkpoints and evaluate on the `test` split: +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +fairseq-generate ${MTEDX_ROOT}/es-en \ + --config-yaml config_st.yaml --gen-subset test --task speech_to_text \ + --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu --remove-bpe + +# For multilingual models +python scripts/average_checkpoints.py \ + --inputs ${MULTILINGUAL_ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +for LANGPAIR in es-en es-fr es-pt fr-en fr-es fr-pt pt-en pt-es it-en it-es ru-en el-en; do + fairseq-generate ${MTEDX_ROOT} \ + --config-yaml config_st.yaml --gen-subset test_${LANGPAIR}_st --task speech_to_text \ + --prefix-size 1 --path ${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 40000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --scoring sacrebleu --remove-bpe +done +``` +For multilingual models, we force decoding from the target language ID token (as BOS) via `--prefix-size 1`. + +#### Results +| Data | --arch | Params | Es-En | Es-Pt | Es-Fr | Fr-En | Fr-Es | Fr-Pt | Pt-En | Pt-Es | It-En | It-Es | Ru-En | El-En | +|--------------|--------------------|-----|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------| +| Bilingual | s2t_transformer_xs | 10M | 7.0 | 12.2 | 1.7 | 8.9 | 10.6 | 7.9 | 8.1 | 8.7 | 6.4 | 1.0 | 0.7 | 0.6 | +| Multilingual | s2t_transformer_s | 31M | 12.3 | 17.4 | 6.1 | 12.0 | 13.6 | 13.2 | 12.0 | 13.7 | 10.7 | 13.1 | 0.6 | 0.8 | + + +## Citation +Please cite as: +``` +@inproceedings{salesky2021mtedx, + title={Multilingual TEDx Corpus for Speech Recognition and Translation}, + author={Elizabeth Salesky and Matthew Wiesner and Jacob Bremerman and Roldano Cattoni and Matteo Negri and Marco Turchi and Douglas W. Oard and Matt Post}, + booktitle={Proceedings of Interspeech}, + year={2021}, +} + +@inproceedings{wang2020fairseqs2t, + title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, + author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, + booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, + year = {2020}, +} + +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` + +[[Back]](..) diff --git a/fairseq/examples/speech_to_text/docs/mustc_example.md b/fairseq/examples/speech_to_text/docs/mustc_example.md new file mode 100644 index 0000000..c95ef3e --- /dev/null +++ b/fairseq/examples/speech_to_text/docs/mustc_example.md @@ -0,0 +1,155 @@ +[[Back]](..) + +# S2T Example: Speech Translation (ST) on MuST-C + +[MuST-C](https://www.aclweb.org/anthology/N19-1202) is multilingual speech-to-text translation corpus with +8-language translations on English TED talks. We match the state-of-the-art performance in +[ESPNet-ST](https://arxiv.org/pdf/2004.10234.pdf) with a simpler model training pipeline. + +## Data Preparation +[Download](https://ict.fbk.eu/must-c) and unpack MuST-C data to a path +`${MUSTC_ROOT}/en-${TARGET_LANG_ID}`, then preprocess it with +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio soundfile sentencepiece + +# Generate TSV manifests, features, vocabulary +# and configuration for each language +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task asr \ + --vocab-type unigram --vocab-size 5000 +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task st \ + --vocab-type unigram --vocab-size 8000 + +# Add vocabulary and configuration for joint data +# (based on the manifests and features generated above) +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task asr --joint \ + --vocab-type unigram --vocab-size 10000 +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task st --joint \ + --vocab-type unigram --vocab-size 10000 +``` +The generated files (manifest, features, vocabulary and data configuration) will be added to +`${MUSTC_ROOT}/en-${TARGET_LANG_ID}` (per-language data) and `MUSTC_ROOT` (joint data). + +Download our vocabulary files if you want to use our pre-trained models: +- ASR: [En-De](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_asr_vocab_unigram5000.zip), [En-Nl](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_asr_vocab_unigram5000.zip), [En-Es](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_asr_vocab_unigram5000.zip), [En-Fr](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_asr_vocab_unigram5000.zip), [En-It](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_asr_vocab_unigram5000.zip), [En-Pt](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_asr_vocab_unigram5000.zip), [En-Ro](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_asr_vocab_unigram5000.zip), [En-Ru](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_asr_vocab_unigram5000.zip), [Joint](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_joint_asr_vocab_unigram10000.zip) +- ST: [En-De](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_st_vocab_unigram8000.zip), [En-Nl](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_st_vocab_unigram8000.zip), [En-Es](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_st_vocab_unigram8000.zip), [En-Fr](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_st_vocab_unigram8000.zip), [En-It](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_st_vocab_unigram8000.zip), [En-Pt](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_st_vocab_unigram8000.zip), [En-Ro](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_st_vocab_unigram8000.zip), [En-Ru](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_st_vocab_unigram8000.zip), [Multilingual](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_multilingual_st_vocab_unigram10000.zip) + +## ASR +#### Training +En-De as example: +```bash +fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_asr.yaml --train-subset train_asr --valid-subset dev_asr \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 1e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 +``` +For joint model (using ASR data from all 8 directions): +```bash +fairseq-train ${MUSTC_ROOT} \ + --config-yaml config_asr.yaml \ + --train-subset train_de_asr,train_nl_asr,train_es_asr,train_fr_asr,train_it_asr,train_pt_asr,train_ro_asr,train_ru_asr \ + --valid-subset dev_de_asr,dev_nl_asr,dev_es_asr,dev_fr_asr,dev_it_asr,dev_pt_asr,dev_ro_asr,dev_ru_asr \ + --save-dir ${JOINT_ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 1e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 +``` +where `ASR_SAVE_DIR` (`JOINT_ASR_SAVE_DIR`) is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs +with 1 GPU. You may want to update it accordingly when using more than 1 GPU. + +#### Inference & Evaluation +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${MUSTC_ROOT}/en-de \ + --config-yaml config_asr.yaml --gen-subset tst-COMMON_asr --task speech_to_text \ + --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct + +# For models trained on joint data +python scripts/average_checkpoints.py \ + --inputs ${JOINT_ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" +for LANG in de nl es fr it pt ro ru; do + fairseq-generate ${MUSTC_ROOT} \ + --config-yaml config_asr.yaml --gen-subset tst-COMMON_${LANG}_asr --task speech_to_text \ + --path ${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct +done +``` +#### Results +| Data | --arch | Params | En-De | En-Nl | En-Es | En-Fr | En-It | En-Pt | En-Ro | En-Ru | Model | +|---|---|---|---|---|---|---|---|---|---|---|---| +| Single | s2t_transformer_s | 31M | [18.2](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_asr_transformer_s.pt) | [17.6](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_asr_transformer_s.pt) | [17.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_asr_transformer_s.pt) | [17.2](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_asr_transformer_s.pt) | [17.9](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_asr_transformer_s.pt) | [19.1](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_asr_transformer_s.pt) | [18.1](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_asr_transformer_s.pt) | [17.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_asr_transformer_s.pt) | (<-Download) | +| Joint | s2t_transformer_m | 76M | 16.8 | 16.7 | 16.9 | 16.9 | 17.0 | 17.4 | 17.0 | 16.9 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_joint_asr_transformer_m.pt) | + +## ST +#### Training +En-De as example: +```bash +fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset dev_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} +``` +For multilingual model (all 8 directions): +```bash +fairseq-train ${MUSTC_ROOT} \ + --config-yaml config_st.yaml \ + --train-subset train_de_st,train_nl_st,train_es_st,train_fr_st,train_it_st,train_pt_st,train_ro_st,train_ru_st \ + --valid-subset dev_de_st,dev_nl_st,dev_es_st,dev_fr_st,dev_it_st,dev_pt_st,dev_ro_st,dev_ru_st \ + --save-dir ${MULTILINGUAL_ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --ignore-prefix-size 1 --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \ + --load-pretrained-encoder-from ${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} +``` +where `ST_SAVE_DIR` (`MULTILINGUAL_ST_SAVE_DIR`) is the checkpoint root path. The ST encoder is pre-trained by ASR +for faster training and better performance: `--load-pretrained-encoder-from <(JOINT_)ASR checkpoint path>`. We set +`--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly when using more than 1 GPU. +For multilingual models, we prepend target language ID token as target BOS, which should be excluded from +the training loss via `--ignore-prefix-size 1`. + +#### Inference & Evaluation +Average the last 10 checkpoints and evaluate on the `tst-COMMON` split: +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --gen-subset tst-COMMON_st --task speech_to_text \ + --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu + +# For multilingual models +python scripts/average_checkpoints.py \ + --inputs ${MULTILINGUAL_ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" +for LANG in de nl es fr it pt ro ru; do + fairseq-generate ${MUSTC_ROOT} \ + --config-yaml config_st.yaml --gen-subset tst-COMMON_${LANG}_st --task speech_to_text \ + --prefix-size 1 --path ${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu +done +``` +For multilingual models, we force decoding from the target language ID token (as BOS) via `--prefix-size 1`. + +#### Results +| Data | --arch | Params | En-De | En-Nl | En-Es | En-Fr | En-It | En-Pt | En-Ro | En-Ru | Model | +|---|---|---|---|---|---|---|---|---|---|---|---| +| Bilingual | s2t_transformer_s | 31M | [22.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_st_transformer_s.pt) | [27.3](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_st_transformer_s.pt) | [27.2](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_st_transformer_s.pt) | [32.9](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_st_transformer_s.pt) | [22.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_st_transformer_s.pt) | [28.1](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_st_transformer_s.pt) | [21.9](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_st_transformer_s.pt) | [15.3](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_st_transformer_s.pt) | (<-Download) | +| Multilingual | s2t_transformer_m | 76M | 24.5 | 28.6 | 28.2 | 34.9 | 24.6 | 31.1 | 23.8 | 16.0 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_multilingual_st_transformer_m.pt) | + +[[Back]](..) diff --git a/fairseq/examples/speech_to_text/docs/simulst_mustc_example.md b/fairseq/examples/speech_to_text/docs/simulst_mustc_example.md new file mode 100644 index 0000000..f3b5a41 --- /dev/null +++ b/fairseq/examples/speech_to_text/docs/simulst_mustc_example.md @@ -0,0 +1,190 @@ +# Simultaneous Speech Translation (SimulST) on MuST-C + +This is a tutorial of training and evaluating a transformer *wait-k* simultaneous model on MUST-C English-Germen Dataset, from [SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation](https://www.aclweb.org/anthology/2020.aacl-main.58.pdf). + +[MuST-C](https://www.aclweb.org/anthology/N19-1202) is multilingual speech-to-text translation corpus with 8-language translations on English TED talks. + +## Data Preparation +This section introduces the data preparation for training and evaluation. +If you only want to evaluate the model, please jump to [Inference & Evaluation](#inference--evaluation) + +[Download](https://ict.fbk.eu/must-c) and unpack MuST-C data to a path +`${MUSTC_ROOT}/en-${TARGET_LANG_ID}`, then preprocess it with +```bash +# Additional Python packages for S2T data processing/model training +pip install pandas torchaudio sentencepiece + +# Generate TSV manifests, features, vocabulary, +# global cepstral and mean estimation, +# and configuration for each language +cd fairseq + +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task asr \ + --vocab-type unigram --vocab-size 10000 \ + --cmvn-type global + +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task st \ + --vocab-type unigram --vocab-size 10000 \ + --cmvn-type global +``` + +## ASR Pretraining +We need a pretrained offline ASR model. Assuming the save directory of the ASR model is `${ASR_SAVE_DIR}`. +The following command (and the subsequent training commands in this tutorial) assume training on 1 GPU (you can also train on 8 GPUs and remove the `--update-freq 8` option). +``` +fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_asr.yaml --train-subset train_asr --valid-subset dev_asr \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch convtransformer_espnet --optimizer adam --lr 0.0005 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 +``` +A pretrained ASR checkpoint can be downloaded [here](https://dl.fbaipublicfiles.com/simultaneous_translation/must_c_v1_en_de_pretrained_asr) + +## Simultaneous Speech Translation Training + +### Wait-K with fixed pre-decision module +Fixed pre-decision indicates that the model operate simultaneous policy on the boundaries of fixed chunks. +Here is a example of fixed pre-decision ratio 7 (the simultaneous decision is made every 7 encoder states) and +a wait-3 policy model. Assuming the save directory is `${ST_SAVE_DIR}` +```bash + fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset dev_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 8 \ + --optimizer adam --lr 0.0001 --lr-scheduler inverse_sqrt --clip-norm 10.0 \ + --criterion label_smoothed_cross_entropy \ + --warmup-updates 4000 --max-update 100000 --max-tokens 40000 --seed 2 \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/checkpoint_best.pt \ + --task speech_to_text \ + --arch convtransformer_simul_trans_espnet \ + --simul-type waitk_fixed_pre_decision \ + --waitk-lagging 3 \ + --fixed-pre-decision-ratio 7 \ + --update-freq 8 + +``` +### Monotonic multihead attention with fixed pre-decision module +``` + fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset dev_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 8 \ + --optimizer adam --lr 0.0001 --lr-scheduler inverse_sqrt --clip-norm 10.0 \ + --warmup-updates 4000 --max-update 100000 --max-tokens 40000 --seed 2 \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --task speech_to_text \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --latency-weight-avg 0.1 \ + --arch convtransformer_simul_trans_espnet \ + --simul-type infinite_lookback_fixed_pre_decision \ + --fixed-pre-decision-ratio 7 \ + --update-freq 8 +``` +## Inference & Evaluation +[SimulEval](https://github.com/facebookresearch/SimulEval) is used for evaluation. +The following command is for evaluation. + +``` +git clone https://github.com/facebookresearch/SimulEval.git +cd SimulEval +pip install -e . + +simuleval \ + --agent ${FAIRSEQ}/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py + --source ${SRC_LIST_OF_AUDIO} + --target ${TGT_FILE} + --data-bin ${MUSTC_ROOT}/en-de \ + --config config_st.yaml \ + --model-path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --output ${OUTPUT} \ + --scores +``` + +The source file `${SRC_LIST_OF_AUDIO}` is a list of paths of audio files. Assuming your audio files stored at `/home/user/data`, +it should look like this + +```bash +/home/user/data/audio-1.wav +/home/user/data/audio-2.wav +``` + +Each line of target file `${TGT_FILE}` is the translation for each audio file input. +```bash +Translation_1 +Translation_2 +``` +The evaluation runs on the original MUSTC segmentation. +The following command will generate the wav list and text file for a evaluation set `${SPLIT}` (chose from `dev`, `tst-COMMON` and `tst-HE`) in MUSTC to `${EVAL_DATA}`. +```bash +python ${FAIRSEQ}/examples/speech_to_text/seg_mustc_data.py \ + --data-root ${MUSTC_ROOT} --lang de \ + --split ${SPLIT} --task st \ + --output ${EVAL_DATA} +``` + +The `--data-bin` and `--config` should be the same in previous section if you prepare the data from the scratch. +If only for evaluation, a prepared data directory can be found [here](https://dl.fbaipublicfiles.com/simultaneous_translation/must_c_v1.0_en_de_databin.tgz). It contains +- `spm_unigram10000_st.model`: a sentencepiece model binary. +- `spm_unigram10000_st.txt`: the dictionary file generated by the sentencepiece model. +- `gcmvn.npz`: the binary for global cepstral mean and variance. +- `config_st.yaml`: the config yaml file. It looks like this. +You will need to set the absolute paths for `sentencepiece_model` and `stats_npz_path` if the data directory is downloaded. +```yaml +bpe_tokenizer: + bpe: sentencepiece + sentencepiece_model: ABS_PATH_TO_SENTENCEPIECE_MODEL +global_cmvn: + stats_npz_path: ABS_PATH_TO_GCMVN_FILE +input_channels: 1 +input_feat_per_channel: 80 +sampling_alpha: 1.0 +specaugment: + freq_mask_F: 27 + freq_mask_N: 1 + time_mask_N: 1 + time_mask_T: 100 + time_mask_p: 1.0 + time_wrap_W: 0 +transforms: + '*': + - global_cmvn + _train: + - global_cmvn + - specaugment +vocab_filename: spm_unigram10000_st.txt +``` + +Notice that once a `--data-bin` is set, the `--config` is the base name of the config yaml, not the full path. + +Set `--model-path` to the model checkpoint. +A pretrained checkpoint can be downloaded from [here](https://dl.fbaipublicfiles.com/simultaneous_translation/convtransformer_wait5_pre7), which is a wait-5 model with a pre-decision of 280 ms. + +The result of this model on `tst-COMMON` is: +```bash +{ + "Quality": { + "BLEU": 13.94974229366959 + }, + "Latency": { + "AL": 1751.8031870037803, + "AL_CA": 2338.5911762796536, + "AP": 0.7931395378788959, + "AP_CA": 0.9405103863210942, + "DAL": 1987.7811616943081, + "DAL_CA": 2425.2751560926167 + } +} +``` + +If `--output ${OUTPUT}` option is used, the detailed log and scores will be stored under the `${OUTPUT}` directory. + + +The quality is measured by detokenized BLEU. So make sure that the predicted words sent to the server are detokenized. + +The latency metrics are +* Average Proportion +* Average Lagging +* Differentiable Average Lagging + +Again they will also be evaluated on detokenized text. diff --git a/fairseq/examples/speech_to_text/prep_covost_data.py b/fairseq/examples/speech_to_text/prep_covost_data.py new file mode 100644 index 0000000..411e9b5 --- /dev/null +++ b/fairseq/examples/speech_to_text/prep_covost_data.py @@ -0,0 +1,279 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import shutil +from tempfile import NamedTemporaryFile +from typing import Optional, Tuple + +import pandas as pd +import torchaudio +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + filter_manifest_df, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_df_from_tsv, + save_df_to_tsv, +) +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import download_url, extract_archive +from tqdm import tqdm + + +log = logging.getLogger(__name__) + + +MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"] + + +class CoVoST(Dataset): + """Create a Dataset for CoVoST (https://github.com/facebookresearch/covost). + + Args: + root (str): root path to the dataset and generated manifests/features + source_language (str): source (audio) language + target_language (str, optional): target (text) language, + None for no translation (default: None) + version (int, optional): CoVoST version. (default: 2) + download (bool, optional): Whether to download the dataset if it is not + found at root path. (default: ``False``). + """ + + COVOST_URL_TEMPLATE = ( + "https://dl.fbaipublicfiles.com/covost/" + "covost_v2.{src_lang}_{tgt_lang}.tsv.tar.gz" + ) + + VERSIONS = {2} + SPLITS = ["train", "dev", "test"] + + XX_EN_LANGUAGES = { + 1: ["fr", "de", "nl", "ru", "es", "it", "tr", "fa", "sv-SE", "mn", "zh-CN"], + 2: [ + "fr", + "de", + "es", + "ca", + "it", + "ru", + "zh-CN", + "pt", + "fa", + "et", + "mn", + "nl", + "tr", + "ar", + "sv-SE", + "lv", + "sl", + "ta", + "ja", + "id", + "cy", + ], + } + EN_XX_LANGUAGES = { + 1: [], + 2: [ + "de", + "tr", + "fa", + "sv-SE", + "mn", + "zh-CN", + "cy", + "ca", + "sl", + "et", + "id", + "ar", + "ta", + "lv", + "ja", + ], + } + + def __init__( + self, + root: str, + split: str, + source_language: str, + target_language: Optional[str] = None, + version: int = 2, + ) -> None: + assert version in self.VERSIONS and split in self.SPLITS + assert source_language is not None + self.no_translation = target_language is None + if not self.no_translation: + assert "en" in {source_language, target_language} + if source_language == "en": + assert target_language in self.EN_XX_LANGUAGES[version] + else: + assert source_language in self.XX_EN_LANGUAGES[version] + else: + # Hack here so that we can get "split" column from CoVoST TSV. + # Note that we use CoVoST train split for ASR which is an extension + # to Common Voice train split. + target_language = "de" if source_language == "en" else "en" + + self.root: Path = Path(root) + + cv_tsv_path = self.root / "validated.tsv" + assert cv_tsv_path.is_file() + + covost_url = self.COVOST_URL_TEMPLATE.format( + src_lang=source_language, tgt_lang=target_language + ) + covost_archive = self.root / Path(covost_url).name + if not covost_archive.is_file(): + download_url(covost_url, self.root.as_posix(), hash_value=None) + extract_archive(covost_archive.as_posix()) + + cv_tsv = load_df_from_tsv(cv_tsv_path) + covost_tsv = load_df_from_tsv( + self.root / Path(covost_url).name.replace(".tar.gz", "") + ) + df = pd.merge( + left=cv_tsv[["path", "sentence", "client_id"]], + right=covost_tsv[["path", "translation", "split"]], + how="inner", + on="path", + ) + if split == "train": + df = df[(df["split"] == split) | (df["split"] == f"{split}_covost")] + else: + df = df[df["split"] == split] + data = df.to_dict(orient="index").items() + data = [v for k, v in sorted(data, key=lambda x: x[0])] + self.data = [] + for e in data: + try: + path = self.root / "clips" / e["path"] + _ = torchaudio.info(path.as_posix()) + self.data.append(e) + except RuntimeError: + pass + + def __getitem__( + self, n: int + ) -> Tuple[Tensor, int, str, str, Optional[str], str, str]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, sentence, translation, speaker_id, + sample_id)`` + """ + data = self.data[n] + path = self.root / "clips" / data["path"] + waveform, sample_rate = torchaudio.load(path) + sentence = data["sentence"] + translation = None if self.no_translation else data["translation"] + speaker_id = data["client_id"] + _id = data["path"].replace(".mp3", "") + return waveform, sample_rate, sentence, translation, speaker_id, _id + + def __len__(self) -> int: + return len(self.data) + + +def process(args): + root = Path(args.data_root).absolute() / args.src_lang + if not root.is_dir(): + raise NotADirectoryError(f"{root} does not exist") + # Extract features + feature_root = root / "fbank80" + feature_root.mkdir(exist_ok=True) + for split in CoVoST.SPLITS: + print(f"Fetching split {split}...") + dataset = CoVoST(root, split, args.src_lang, args.tgt_lang) + print("Extracting log mel filter bank features...") + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + extract_fbank_features( + waveform, sample_rate, feature_root / f"{utt_id}.npy" + ) + # Pack features into ZIP + zip_path = root / "fbank80.zip" + print("ZIPing features...") + create_zip(feature_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + task = f"asr_{args.src_lang}" + if args.tgt_lang is not None: + task = f"st_{args.src_lang}_{args.tgt_lang}" + for split in CoVoST.SPLITS: + manifest = {c: [] for c in MANIFEST_COLUMNS} + dataset = CoVoST(root, split, args.src_lang, args.tgt_lang) + for _, _, src_utt, tgt_utt, speaker_id, utt_id in tqdm(dataset): + manifest["id"].append(utt_id) + manifest["audio"].append(audio_paths[utt_id]) + manifest["n_frames"].append(audio_lengths[utt_id]) + manifest["tgt_text"].append(src_utt if args.tgt_lang is None else tgt_utt) + manifest["speaker"].append(speaker_id) + is_train_split = split.startswith("train") + if is_train_split: + train_text.extend(manifest["tgt_text"]) + df = pd.DataFrame.from_dict(manifest) + df = filter_manifest_df(df, is_train_split=is_train_split) + save_df_to_tsv(df, root / f"{split}_{task}.tsv") + # Generate vocab + vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{task}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + root / spm_filename_prefix, + args.vocab_type, + args.vocab_size + ) + # Generate config YAML + gen_config_yaml( + root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{task}.yaml", + specaugment_policy="lb", + ) + # Clean up + shutil.rmtree(feature_root) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--data-root", "-d", required=True, type=str, + help="data root with sub-folders for each language <root>/<src_lang>" + ) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=1000, type=int) + parser.add_argument("--src-lang", "-s", required=True, type=str) + parser.add_argument("--tgt-lang", "-t", type=str) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_text/prep_librispeech_data.py b/fairseq/examples/speech_to_text/prep_librispeech_data.py new file mode 100644 index 0000000..f379fa7 --- /dev/null +++ b/fairseq/examples/speech_to_text/prep_librispeech_data.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import shutil +from tempfile import NamedTemporaryFile + +import pandas as pd +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + save_df_to_tsv, +) +from torchaudio.datasets import LIBRISPEECH +from tqdm import tqdm + + +log = logging.getLogger(__name__) + +SPLITS = [ + "train-clean-100", + "train-clean-360", + "train-other-500", + "dev-clean", + "dev-other", + "test-clean", + "test-other", +] + +MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"] + + +def process(args): + out_root = Path(args.output_root).absolute() + out_root.mkdir(exist_ok=True) + # Extract features + feature_root = out_root / "fbank80" + feature_root.mkdir(exist_ok=True) + for split in SPLITS: + print(f"Fetching split {split}...") + dataset = LIBRISPEECH(out_root.as_posix(), url=split, download=True) + print("Extracting log mel filter bank features...") + for wav, sample_rate, _, spk_id, chapter_no, utt_no in tqdm(dataset): + sample_id = f"{spk_id}-{chapter_no}-{utt_no}" + extract_fbank_features( + wav, sample_rate, feature_root / f"{sample_id}.npy" + ) + # Pack features into ZIP + zip_path = out_root / "fbank80.zip" + print("ZIPing features...") + create_zip(feature_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + for split in SPLITS: + manifest = {c: [] for c in MANIFEST_COLUMNS} + dataset = LIBRISPEECH(out_root.as_posix(), url=split) + for _, _, utt, spk_id, chapter_no, utt_no in tqdm(dataset): + sample_id = f"{spk_id}-{chapter_no}-{utt_no}" + manifest["id"].append(sample_id) + manifest["audio"].append(audio_paths[sample_id]) + manifest["n_frames"].append(audio_lengths[sample_id]) + manifest["tgt_text"].append(utt.lower()) + manifest["speaker"].append(spk_id) + save_df_to_tsv( + pd.DataFrame.from_dict(manifest), out_root / f"{split}.tsv" + ) + if split.startswith("train"): + train_text.extend(manifest["tgt_text"]) + # Generate vocab + vocab_size = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + out_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + ) + # Generate config YAML + gen_config_yaml( + out_root, + spm_filename=spm_filename_prefix + ".model", + specaugment_policy="ld" + ) + # Clean up + shutil.rmtree(feature_root) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output-root", "-o", required=True, type=str) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=10000, type=int) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_text/prep_mtedx_data.py b/fairseq/examples/speech_to_text/prep_mtedx_data.py new file mode 100644 index 0000000..2dfd631 --- /dev/null +++ b/fairseq/examples/speech_to_text/prep_mtedx_data.py @@ -0,0 +1,271 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +from pathlib import Path +import shutil +from itertools import groupby +from tempfile import NamedTemporaryFile +from typing import Tuple + +import pandas as pd +import soundfile as sf +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + filter_manifest_df, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_df_from_tsv, + save_df_to_tsv, +) +import torch +from torch.utils.data import Dataset +from tqdm import tqdm + +from fairseq.data.audio.audio_utils import get_waveform, convert_waveform + + +log = logging.getLogger(__name__) + + +MANIFEST_COLUMNS = [ + "id", "audio", "n_frames", "tgt_text", "speaker", "tgt_lang" +] + + +class mTEDx(Dataset): + """ + Create a Dataset for Multilingual TEDx. + Each item is a tuple of the form: waveform, sample_rate, source utterance, + target utterance, speaker_id, utterance_id + """ + + SPLITS = ["train", "valid", "test"] + LANGPAIRS = ["es-es", "fr-fr", "pt-pt", "it-it", "ru-ru", "el-el", "ar-ar", + "de-de", "es-en", "es-fr", "es-pt", "es-it", "fr-en", "fr-es", + "fr-pt", "pt-en", "pt-es", "it-en", "it-es", "ru-en", "el-en"] + + def __init__(self, root: str, lang: str, split: str) -> None: + assert split in self.SPLITS and lang in self.LANGPAIRS + _root = Path(root) / f"{lang}" / "data" / split + wav_root, txt_root = _root / "wav", _root / "txt" + assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir() + # Load audio segments + try: + import yaml + except ImportError: + print( + "Please install PyYAML to load the Multilingual TEDx YAML files" + ) + with open(txt_root / f"{split}.yaml") as f: + segments = yaml.load(f, Loader=yaml.BaseLoader) + # Load source and target utterances + src, tgt = lang.split("-") + for _lang in [src, tgt]: + with open(txt_root / f"{split}.{_lang}") as f: + utterances = [r.strip() for r in f] + assert len(segments) == len(utterances) + for i, u in enumerate(utterances): + segments[i][_lang] = u + # Gather info + self.data = [] + for wav_filename, _seg_group in groupby(segments, lambda x: x["wav"]): + wav_filename = wav_filename.replace(".wav", ".flac") + wav_path = wav_root / wav_filename + sample_rate = sf.info(wav_path.as_posix()).samplerate + seg_group = sorted(_seg_group, key=lambda x: float(x["offset"])) + for i, segment in enumerate(seg_group): + offset = int(float(segment["offset"]) * sample_rate) + n_frames = int(float(segment["duration"]) * sample_rate) + _id = f"{wav_path.stem}_{i}" + self.data.append( + ( + wav_path.as_posix(), + offset, + n_frames, + sample_rate, + segment[src], + segment[tgt], + segment["speaker_id"], + tgt, + _id, + ) + ) + + def __getitem__( + self, n: int + ) -> Tuple[torch.Tensor, int, str, str, str, str, str]: + wav_path, offset, n_frames, sr, src_utt, tgt_utt, spk_id, tgt_lang, \ + utt_id = self.data[n] + waveform, _ = get_waveform(wav_path, frames=n_frames, start=offset) + waveform = torch.from_numpy(waveform) + return waveform, sr, src_utt, tgt_utt, spk_id, tgt_lang, utt_id + + def __len__(self) -> int: + return len(self.data) + + +def process(args): + root = Path(args.data_root).absolute() + for lang in mTEDx.LANGPAIRS: + cur_root = root / f"{lang}" + if not cur_root.is_dir(): + print(f"{cur_root.as_posix()} does not exist. Skipped.") + continue + # Extract features + audio_root = cur_root / ("flac" if args.use_audio_input else "fbank80") + audio_root.mkdir(exist_ok=True) + for split in mTEDx.SPLITS: + print(f"Fetching split {split}...") + dataset = mTEDx(root.as_posix(), lang, split) + if args.use_audio_input: + print("Converting audios...") + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + tgt_sample_rate = 16_000 + _wavform, _ = convert_waveform( + waveform, sample_rate, to_mono=True, + to_sample_rate=tgt_sample_rate + ) + sf.write( + (audio_root / f"{utt_id}.flac").as_posix(), + _wavform.numpy(), tgt_sample_rate + ) + else: + print("Extracting log mel filter bank features...") + for waveform, sample_rate, _, _, _, _, utt_id in tqdm(dataset): + extract_fbank_features( + waveform, sample_rate, audio_root / f"{utt_id}.npy" + ) + # Pack features into ZIP + zip_path = cur_root / f"{audio_root.name}.zip" + print("ZIPing audios/features...") + create_zip(audio_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + for split in mTEDx.SPLITS: + is_train_split = split.startswith("train") + manifest = {c: [] for c in MANIFEST_COLUMNS} + ds = mTEDx(args.data_root, lang, split) + for _, _, src_utt, tgt_utt, spk_id, tgt_lang, utt_id in tqdm(ds): + manifest["id"].append(utt_id) + manifest["audio"].append(audio_paths[utt_id]) + manifest["n_frames"].append(audio_lengths[utt_id]) + manifest["tgt_text"].append( + src_utt if args.task == "asr" else tgt_utt + ) + manifest["speaker"].append(spk_id) + manifest["tgt_lang"].append(tgt_lang) + if is_train_split: + train_text.extend(manifest["tgt_text"]) + df = pd.DataFrame.from_dict(manifest) + df = filter_manifest_df(df, is_train_split=is_train_split) + save_df_to_tsv(df, cur_root / f"{split}_{args.task}.tsv") + # Generate vocab + v_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{v_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + ) + # Generate config YAML + if args.use_audio_input: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy=None, + extra={"use_audio_input": True} + ) + else: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="lb", + ) + # Clean up + shutil.rmtree(audio_root) + + +def process_joint(args): + cur_root = Path(args.data_root) + assert all((cur_root / f"{lang}").is_dir() for lang in mTEDx.LANGPAIRS), \ + "do not have downloaded data available for all languages" + # Generate vocab + vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for lang in mTEDx.LANGPAIRS: + tsv_path = cur_root / f"{lang}" / f"train_{args.task}.tsv" + df = load_df_from_tsv(tsv_path) + for t in df["tgt_text"]: + f.write(t + "\n") + special_symbols = None + if args.joint: + # Add tgt_lang tags to dict + special_symbols = list( + {f'<lang:{lang.split("-")[1]}>' for lang in mTEDx.LANGPAIRS} + ) + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + special_symbols=special_symbols + ) + # Generate config YAML + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="ld", + prepend_tgt_lang_tag=(args.joint), + ) + # Make symbolic links to manifests + for lang in mTEDx.LANGPAIRS: + for split in mTEDx.SPLITS: + src_path = cur_root / f"{lang}" / f"{split}_{args.task}.tsv" + desc_path = cur_root / f"{split}_{lang}_{args.task}.tsv" + if not desc_path.is_symlink(): + os.symlink(src_path, desc_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=8000, type=int) + parser.add_argument("--task", type=str, choices=["asr", "st"]) + parser.add_argument("--joint", action="store_true", help="") + parser.add_argument("--use-audio-input", action="store_true") + args = parser.parse_args() + + if args.joint: + process_joint(args) + else: + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_text/prep_mustc_data.py b/fairseq/examples/speech_to_text/prep_mustc_data.py new file mode 100644 index 0000000..c2362f7 --- /dev/null +++ b/fairseq/examples/speech_to_text/prep_mustc_data.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +from pathlib import Path +import shutil +from itertools import groupby +from tempfile import NamedTemporaryFile +from typing import Tuple + +import numpy as np +import pandas as pd +import soundfile as sf +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + filter_manifest_df, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_df_from_tsv, + save_df_to_tsv, + cal_gcmvn_stats, +) +import torch +from torch.utils.data import Dataset +from tqdm import tqdm + +from fairseq.data.audio.audio_utils import get_waveform, convert_waveform + + +log = logging.getLogger(__name__) + + +MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"] + + +class MUSTC(Dataset): + """ + Create a Dataset for MuST-C. Each item is a tuple of the form: + waveform, sample_rate, source utterance, target utterance, speaker_id, + utterance_id + """ + + SPLITS = ["train", "dev", "tst-COMMON", "tst-HE"] + LANGUAGES = ["de", "es", "fr", "it", "nl", "pt", "ro", "ru"] + + def __init__(self, root: str, lang: str, split: str) -> None: + assert split in self.SPLITS and lang in self.LANGUAGES + _root = Path(root) / f"en-{lang}" / "data" / split + wav_root, txt_root = _root / "wav", _root / "txt" + assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir() + # Load audio segments + try: + import yaml + except ImportError: + print("Please install PyYAML to load the MuST-C YAML files") + with open(txt_root / f"{split}.yaml") as f: + segments = yaml.load(f, Loader=yaml.BaseLoader) + # Load source and target utterances + for _lang in ["en", lang]: + with open(txt_root / f"{split}.{_lang}") as f: + utterances = [r.strip() for r in f] + assert len(segments) == len(utterances) + for i, u in enumerate(utterances): + segments[i][_lang] = u + # Gather info + self.data = [] + for wav_filename, _seg_group in groupby(segments, lambda x: x["wav"]): + wav_path = wav_root / wav_filename + sample_rate = sf.info(wav_path.as_posix()).samplerate + seg_group = sorted(_seg_group, key=lambda x: x["offset"]) + for i, segment in enumerate(seg_group): + offset = int(float(segment["offset"]) * sample_rate) + n_frames = int(float(segment["duration"]) * sample_rate) + _id = f"{wav_path.stem}_{i}" + self.data.append( + ( + wav_path.as_posix(), + offset, + n_frames, + sample_rate, + segment["en"], + segment[lang], + segment["speaker_id"], + _id, + ) + ) + + def __getitem__( + self, n: int + ) -> Tuple[torch.Tensor, int, str, str, str, str]: + wav_path, offset, n_frames, sr, src_utt, tgt_utt, spk_id, \ + utt_id = self.data[n] + waveform, _ = get_waveform(wav_path, frames=n_frames, start=offset) + waveform = torch.from_numpy(waveform) + return waveform, sr, src_utt, tgt_utt, spk_id, utt_id + + def __len__(self) -> int: + return len(self.data) + + +def process(args): + root = Path(args.data_root).absolute() + for lang in MUSTC.LANGUAGES: + cur_root = root / f"en-{lang}" + if not cur_root.is_dir(): + print(f"{cur_root.as_posix()} does not exist. Skipped.") + continue + # Extract features + audio_root = cur_root / ("flac" if args.use_audio_input else "fbank80") + audio_root.mkdir(exist_ok=True) + + for split in MUSTC.SPLITS: + print(f"Fetching split {split}...") + dataset = MUSTC(root.as_posix(), lang, split) + if args.use_audio_input: + print("Converting audios...") + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + tgt_sample_rate = 16_000 + _wavform, _ = convert_waveform( + waveform, sample_rate, to_mono=True, + to_sample_rate=tgt_sample_rate + ) + sf.write( + (audio_root / f"{utt_id}.flac").as_posix(), + _wavform.T.numpy(), tgt_sample_rate + ) + else: + print("Extracting log mel filter bank features...") + gcmvn_feature_list = [] + if split == 'train' and args.cmvn_type == "global": + print("And estimating cepstral mean and variance stats...") + + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + features = extract_fbank_features( + waveform, sample_rate, audio_root / f"{utt_id}.npy" + ) + if split == 'train' and args.cmvn_type == "global": + if len(gcmvn_feature_list) < args.gcmvn_max_num: + gcmvn_feature_list.append(features) + + if split == 'train' and args.cmvn_type == "global": + # Estimate and save cmv + stats = cal_gcmvn_stats(gcmvn_feature_list) + with open(cur_root / "gcmvn.npz", "wb") as f: + np.savez(f, mean=stats["mean"], std=stats["std"]) + + # Pack features into ZIP + zip_path = cur_root / f"{audio_root.name}.zip" + print("ZIPing audios/features...") + create_zip(audio_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest( + zip_path, + is_audio=args.use_audio_input, + ) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + for split in MUSTC.SPLITS: + is_train_split = split.startswith("train") + manifest = {c: [] for c in MANIFEST_COLUMNS} + dataset = MUSTC(args.data_root, lang, split) + for _, _, src_utt, tgt_utt, speaker_id, utt_id in tqdm(dataset): + manifest["id"].append(utt_id) + manifest["audio"].append(audio_paths[utt_id]) + manifest["n_frames"].append(audio_lengths[utt_id]) + manifest["tgt_text"].append( + src_utt if args.task == "asr" else tgt_utt + ) + manifest["speaker"].append(speaker_id) + if is_train_split: + train_text.extend(manifest["tgt_text"]) + df = pd.DataFrame.from_dict(manifest) + df = filter_manifest_df(df, is_train_split=is_train_split) + save_df_to_tsv(df, cur_root / f"{split}_{args.task}.tsv") + # Generate vocab + v_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{v_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + ) + # Generate config YAML + if args.use_audio_input: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy=None, + extra={"use_audio_input": True} + ) + else: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="lb", + cmvn_type=args.cmvn_type, + gcmvn_path=( + cur_root / "gcmvn.npz" if args.cmvn_type == "global" + else None + ), + ) + # Clean up + shutil.rmtree(audio_root) + + +def process_joint(args): + cur_root = Path(args.data_root) + assert all( + (cur_root / f"en-{lang}").is_dir() for lang in MUSTC.LANGUAGES + ), "do not have downloaded data available for all 8 languages" + # Generate vocab + vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for lang in MUSTC.LANGUAGES: + tsv_path = cur_root / f"en-{lang}" / f"train_{args.task}.tsv" + df = load_df_from_tsv(tsv_path) + for t in df["tgt_text"]: + f.write(t + "\n") + special_symbols = None + if args.task == 'st': + special_symbols = [f'<lang:{lang}>' for lang in MUSTC.LANGUAGES] + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + special_symbols=special_symbols + ) + # Generate config YAML + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="ld", + prepend_tgt_lang_tag=(args.task == "st"), + ) + # Make symbolic links to manifests + for lang in MUSTC.LANGUAGES: + for split in MUSTC.SPLITS: + src_path = cur_root / f"en-{lang}" / f"{split}_{args.task}.tsv" + desc_path = cur_root / f"{split}_{lang}_{args.task}.tsv" + if not desc_path.is_symlink(): + os.symlink(src_path, desc_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=8000, type=int) + parser.add_argument("--task", type=str, choices=["asr", "st"]) + parser.add_argument("--joint", action="store_true", help="") + parser.add_argument( + "--cmvn-type", default="utterance", + choices=["global", "utterance"], + help="The type of cepstral mean and variance normalization" + ) + parser.add_argument( + "--gcmvn-max-num", default=150000, type=int, + help="Maximum number of sentences to use to estimate global mean and " + "variance" + ) + parser.add_argument("--use-audio-input", action="store_true") + args = parser.parse_args() + + if args.joint: + process_joint(args) + else: + process(args) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/speech_to_text/seg_mustc_data.py b/fairseq/examples/speech_to_text/seg_mustc_data.py new file mode 100644 index 0000000..1ee665d --- /dev/null +++ b/fairseq/examples/speech_to_text/seg_mustc_data.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import soundfile as sf +from examples.speech_to_text.prep_mustc_data import ( + MUSTC +) + +from tqdm import tqdm + +log = logging.getLogger(__name__) + + +def main(args): + root = Path(args.data_root).absolute() + lang = args.lang + split = args.split + + cur_root = root / f"en-{lang}" + assert cur_root.is_dir(), ( + f"{cur_root.as_posix()} does not exist. Skipped." + ) + + dataset = MUSTC(root.as_posix(), lang, split) + output = Path(args.output).absolute() + output.mkdir(exist_ok=True) + f_text = open(output / f"{split}.{lang}", "w") + f_wav_list = open(output / f"{split}.wav_list", "w") + for waveform, sample_rate, _, text, _, utt_id in tqdm(dataset): + sf.write( + output / f"{utt_id}.wav", + waveform.squeeze(0).numpy(), + samplerate=int(sample_rate) + ) + f_text.write(text + "\n") + f_wav_list.write(str(output / f"{utt_id}.wav") + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument("--task", required=True, type=str, choices=["asr", "st"]) + parser.add_argument("--lang", required=True, type=str) + parser.add_argument("--output", required=True, type=str) + parser.add_argument("--split", required=True, choices=MUSTC.SPLITS) + args = parser.parse_args() + + main(args) diff --git a/fairseq/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py b/fairseq/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py new file mode 100644 index 0000000..61617a1 --- /dev/null +++ b/fairseq/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py @@ -0,0 +1,363 @@ +import math +import os +import json +import numpy as np +import torch +import torchaudio.compliance.kaldi as kaldi +import yaml +from fairseq import checkpoint_utils, tasks +from fairseq.file_io import PathManager + +try: + from simuleval import READ_ACTION, WRITE_ACTION, DEFAULT_EOS + from simuleval.agents import SpeechAgent + from simuleval.states import ListEntry, SpeechStates +except ImportError: + print("Please install simuleval 'pip install simuleval'") + +SHIFT_SIZE = 10 +WINDOW_SIZE = 25 +SAMPLE_RATE = 16000 +FEATURE_DIM = 80 +BOW_PREFIX = "\u2581" + + +class OnlineFeatureExtractor: + """ + Extract speech feature on the fly. + """ + + def __init__(self, args): + self.shift_size = args.shift_size + self.window_size = args.window_size + assert self.window_size >= self.shift_size + + self.sample_rate = args.sample_rate + self.feature_dim = args.feature_dim + self.num_samples_per_shift = int(self.shift_size * self.sample_rate / 1000) + self.num_samples_per_window = int(self.window_size * self.sample_rate / 1000) + self.len_ms_to_samples = lambda x: x * self.sample_rate / 1000 + self.previous_residual_samples = [] + self.global_cmvn = args.global_cmvn + + def clear_cache(self): + self.previous_residual_samples = [] + + def __call__(self, new_samples): + samples = self.previous_residual_samples + new_samples + if len(samples) < self.num_samples_per_window: + self.previous_residual_samples = samples + return + + # num_frames is the number of frames from the new segment + num_frames = math.floor( + (len(samples) - self.len_ms_to_samples(self.window_size - self.shift_size)) + / self.num_samples_per_shift + ) + + # the number of frames used for feature extraction + # including some part of thte previous segment + effective_num_samples = int( + num_frames * self.len_ms_to_samples(self.shift_size) + + self.len_ms_to_samples(self.window_size - self.shift_size) + ) + + input_samples = samples[:effective_num_samples] + self.previous_residual_samples = samples[ + num_frames * self.num_samples_per_shift: + ] + + torch.manual_seed(1) + output = kaldi.fbank( + torch.FloatTensor(input_samples).unsqueeze(0), + num_mel_bins=self.feature_dim, + frame_length=self.window_size, + frame_shift=self.shift_size, + ).numpy() + + output = self.transform(output) + + return torch.from_numpy(output) + + def transform(self, input): + if self.global_cmvn is None: + return input + + mean = self.global_cmvn["mean"] + std = self.global_cmvn["std"] + + x = np.subtract(input, mean) + x = np.divide(x, std) + return x + + +class TensorListEntry(ListEntry): + """ + Data structure to store a list of tensor. + """ + + def append(self, value): + + if len(self.value) == 0: + self.value = value + return + + self.value = torch.cat([self.value] + [value], dim=0) + + def info(self): + return { + "type": str(self.new_value_type), + "length": self.__len__(), + "value": "" if type(self.value) is list else self.value.size(), + } + + +class FairseqSimulSTAgent(SpeechAgent): + + speech_segment_size = 40 # in ms, 4 pooling ratio * 10 ms step size + + def __init__(self, args): + super().__init__(args) + + self.eos = DEFAULT_EOS + + self.gpu = getattr(args, "gpu", False) + + self.args = args + + self.load_model_vocab(args) + + if getattr( + self.model.decoder.layers[0].encoder_attn, + 'pre_decision_ratio', + None + ) is not None: + self.speech_segment_size *= ( + self.model.decoder.layers[0].encoder_attn.pre_decision_ratio + ) + + args.global_cmvn = None + if args.config: + with open(os.path.join(args.data_bin, args.config), "r") as f: + config = yaml.load(f, Loader=yaml.BaseLoader) + + if "global_cmvn" in config: + args.global_cmvn = np.load(config["global_cmvn"]["stats_npz_path"]) + + if args.global_stats: + with PathManager.open(args.global_stats, "r") as f: + global_cmvn = json.loads(f.read()) + self.global_cmvn = {"mean": global_cmvn["mean"], "std": global_cmvn["stddev"]} + + self.feature_extractor = OnlineFeatureExtractor(args) + + self.max_len = args.max_len + + self.force_finish = args.force_finish + + torch.set_grad_enabled(False) + + def build_states(self, args, client, sentence_id): + # Initialize states here, for example add customized entry to states + # This function will be called at beginning of every new sentence + states = SpeechStates(args, client, sentence_id, self) + self.initialize_states(states) + return states + + def to_device(self, tensor): + if self.gpu: + return tensor.cuda() + else: + return tensor.cpu() + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--model-path', type=str, required=True, + help='path to your pretrained model.') + parser.add_argument("--data-bin", type=str, required=True, + help="Path of data binary") + parser.add_argument("--config", type=str, default=None, + help="Path to config yaml file") + parser.add_argument("--global-stats", type=str, default=None, + help="Path to json file containing cmvn stats") + parser.add_argument("--tgt-splitter-type", type=str, default="SentencePiece", + help="Subword splitter type for target text") + parser.add_argument("--tgt-splitter-path", type=str, default=None, + help="Subword splitter model path for target text") + parser.add_argument("--user-dir", type=str, default="examples/simultaneous_translation", + help="User directory for simultaneous translation") + parser.add_argument("--max-len", type=int, default=200, + help="Max length of translation") + parser.add_argument("--force-finish", default=False, action="store_true", + help="Force the model to finish the hypothsis if the source is not finished") + parser.add_argument("--shift-size", type=int, default=SHIFT_SIZE, + help="Shift size of feature extraction window.") + parser.add_argument("--window-size", type=int, default=WINDOW_SIZE, + help="Window size of feature extraction window.") + parser.add_argument("--sample-rate", type=int, default=SAMPLE_RATE, + help="Sample rate") + parser.add_argument("--feature-dim", type=int, default=FEATURE_DIM, + help="Acoustic feature dimension.") + + # fmt: on + return parser + + def load_model_vocab(self, args): + + filename = args.model_path + if not os.path.exists(filename): + raise IOError("Model file not found: {}".format(filename)) + + state = checkpoint_utils.load_checkpoint_to_cpu(filename) + + task_args = state["cfg"]["task"] + task_args.data = args.data_bin + + if args.config is not None: + task_args.config_yaml = args.config + + task = tasks.setup_task(task_args) + + # build model for ensemble + state["cfg"]["model"].load_pretrained_encoder_from = None + state["cfg"]["model"].load_pretrained_decoder_from = None + self.model = task.build_model(state["cfg"]["model"]) + self.model.load_state_dict(state["model"], strict=True) + self.model.eval() + self.model.share_memory() + + if self.gpu: + self.model.cuda() + + # Set dictionary + self.dict = {} + self.dict["tgt"] = task.target_dictionary + + def initialize_states(self, states): + self.feature_extractor.clear_cache() + states.units.source = TensorListEntry() + states.units.target = ListEntry() + states.incremental_states = dict() + + def segment_to_units(self, segment, states): + # Convert speech samples to features + features = self.feature_extractor(segment) + if features is not None: + return [features] + else: + return [] + + def units_to_segment(self, units, states): + # Merge sub word to full word. + if self.model.decoder.dictionary.eos() == units[0]: + return DEFAULT_EOS + + segment = [] + if None in units.value: + units.value.remove(None) + + for index in units: + if index is None: + units.pop() + token = self.model.decoder.dictionary.string([index]) + if token.startswith(BOW_PREFIX): + if len(segment) == 0: + segment += [token.replace(BOW_PREFIX, "")] + else: + for j in range(len(segment)): + units.pop() + + string_to_return = ["".join(segment)] + + if self.model.decoder.dictionary.eos() == units[0]: + string_to_return += [DEFAULT_EOS] + + return string_to_return + else: + segment += [token.replace(BOW_PREFIX, "")] + + if ( + len(units) > 0 + and self.model.decoder.dictionary.eos() == units[-1] + or len(states.units.target) > self.max_len + ): + tokens = [self.model.decoder.dictionary.string([unit]) for unit in units] + return ["".join(tokens).replace(BOW_PREFIX, "")] + [DEFAULT_EOS] + + return None + + def update_model_encoder(self, states): + if len(states.units.source) == 0: + return + src_indices = self.to_device( + states.units.source.value.unsqueeze(0) + ) + src_lengths = self.to_device( + torch.LongTensor([states.units.source.value.size(0)]) + ) + + states.encoder_states = self.model.encoder(src_indices, src_lengths) + torch.cuda.empty_cache() + + def update_states_read(self, states): + # Happens after a read action. + self.update_model_encoder(states) + + def policy(self, states): + if not getattr(states, "encoder_states", None): + return READ_ACTION + + tgt_indices = self.to_device( + torch.LongTensor( + [self.model.decoder.dictionary.eos()] + + [x for x in states.units.target.value if x is not None] + ).unsqueeze(0) + ) + + states.incremental_states["steps"] = { + "src": states.encoder_states["encoder_out"][0].size(0), + "tgt": 1 + len(states.units.target), + } + + states.incremental_states["online"] = {"only": torch.tensor(not states.finish_read())} + + x, outputs = self.model.decoder.forward( + prev_output_tokens=tgt_indices, + encoder_out=states.encoder_states, + incremental_state=states.incremental_states, + ) + + states.decoder_out = x + + states.decoder_out_extra = outputs + + torch.cuda.empty_cache() + + if outputs.action == 0: + return READ_ACTION + else: + return WRITE_ACTION + + def predict(self, states): + decoder_states = states.decoder_out + + lprobs = self.model.get_normalized_probs( + [decoder_states[:, -1:]], log_probs=True + ) + + index = lprobs.argmax(dim=-1) + + index = index[0, 0].item() + + if ( + self.force_finish + and index == self.model.decoder.dictionary.eos() + and not states.finish_read() + ): + # If we want to force finish the translation + # (don't stop before finish reading), return a None + # self.model.decoder.clear_cache(states.incremental_states) + index = None + + return index diff --git a/fairseq/examples/stories/README.md b/fairseq/examples/stories/README.md new file mode 100644 index 0000000..588941e --- /dev/null +++ b/fairseq/examples/stories/README.md @@ -0,0 +1,66 @@ +# Hierarchical Neural Story Generation (Fan et al., 2018) + +The following commands provide an example of pre-processing data, training a model, and generating text for story generation with the WritingPrompts dataset. + +## Pre-trained models + +Description | Dataset | Model | Test set(s) +---|---|---|--- +Stories with Convolutional Model <br> ([Fan et al., 2018](https://arxiv.org/abs/1805.04833)) | [WritingPrompts](https://dl.fbaipublicfiles.com/fairseq/data/writingPrompts.tar.gz) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.bz2) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2) + +We provide sample stories generated by the [convolutional seq2seq model](https://dl.fbaipublicfiles.com/fairseq/data/seq2seq_stories.txt) and [fusion model](https://dl.fbaipublicfiles.com/fairseq/data/fusion_stories.txt) from [Fan et al., 2018](https://arxiv.org/abs/1805.04833). The corresponding prompts for the fusion model can be found [here](https://dl.fbaipublicfiles.com/fairseq/data/fusion_prompts.txt). Note that there are unk in the file, as we modeled a small full vocabulary (no BPE or pre-training). We did not use these unk prompts for human evaluation. + +## Dataset + +The dataset can be downloaded like this: + +```bash +cd examples/stories +curl https://dl.fbaipublicfiles.com/fairseq/data/writingPrompts.tar.gz | tar xvzf - +``` + +and contains a train, test, and valid split. The dataset is described here: https://arxiv.org/abs/1805.04833. We model only the first 1000 words of each story, including one newLine token. + +## Example usage + +First we will preprocess the dataset. Note that the dataset release is the full data, but the paper models the first 1000 words of each story. Here is example code that trims the dataset to the first 1000 words of each story: +```python +data = ["train", "test", "valid"] +for name in data: + with open(name + ".wp_target") as f: + stories = f.readlines() + stories = [" ".join(i.split()[0:1000]) for i in stories] + with open(name + ".wp_target", "w") as o: + for line in stories: + o.write(line.strip() + "\n") +``` + +Once we've trimmed the data we can binarize it and train our model: +```bash +# Binarize the dataset: +export TEXT=examples/stories/writingPrompts +fairseq-preprocess --source-lang wp_source --target-lang wp_target \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/writingPrompts --padding-factor 1 --thresholdtgt 10 --thresholdsrc 10 + +# Train the model: +fairseq-train data-bin/writingPrompts -a fconv_self_att_wp --lr 0.25 --optimizer nag --clip-norm 0.1 --max-tokens 1500 --lr-scheduler reduce_lr_on_plateau --decoder-attention True --encoder-attention False --criterion label_smoothed_cross_entropy --weight-decay .0000001 --label-smoothing 0 --source-lang wp_source --target-lang wp_target --gated-attention True --self-attention True --project-input True --pretrained False + +# Train a fusion model: +# add the arguments: --pretrained True --pretrained-checkpoint path/to/checkpoint + +# Generate: +# Note: to load the pretrained model at generation time, you need to pass in a model-override argument to communicate to the fusion model at generation time where you have placed the pretrained checkpoint. By default, it will load the exact path of the fusion model's pretrained model from training time. You should use model-override if you have moved the pretrained model (or are using our provided models). If you are generating from a non-fusion model, the model-override argument is not necessary. + +fairseq-generate data-bin/writingPrompts --path /path/to/trained/model/checkpoint_best.pt --batch-size 32 --beam 1 --sampling --sampling-topk 10 --temperature 0.8 --nbest 1 --model-overrides "{'pretrained_checkpoint':'/path/to/pretrained/model/checkpoint'}" +``` + +## Citation +```bibtex +@inproceedings{fan2018hierarchical, + title = {Hierarchical Neural Story Generation}, + author = {Fan, Angela and Lewis, Mike and Dauphin, Yann}, + booktitle = {Conference of the Association for Computational Linguistics (ACL)}, + year = 2018, +} +``` diff --git a/fairseq/examples/textless_nlp/dgslm/README.md b/fairseq/examples/textless_nlp/dgslm/README.md new file mode 100644 index 0000000..917dbb2 --- /dev/null +++ b/fairseq/examples/textless_nlp/dgslm/README.md @@ -0,0 +1,183 @@ +# Generative Spoken Dialogue Language Modeling +[[paper]](https://arxiv.org/abs/2203.16502) [[demo samples]](https://speechbot.github.io/dgslm/index.html) [[blog]](https://ai.facebook.com/blog/generating-chit-chat-including-laughs-yawns-ums-and-other-nonverbal-cues-from-raw-audio/) + +This repo contains the code and pre-trained models for the paper _Generative Spoken Dialogue Language Modeling_. +<details> + <summary>Paper abstract </summary> + +> We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled with a dual-tower transformer architecture with cross-attention trained on 2000 hours of two-channel raw conversational audio (Fisher dataset) without any text or labels. We show that our model is able to generate speech, laughter and other paralinguistic signals in the two channels simultaneously and reproduces more naturalistic and fluid turn taking compared to a text-based cascaded model. + +</details> + +## [Speech-to-Unit Encoder for dGSLM: The Fisher HuBERT model](hubert_fisher/) +The [hubert_fisher](hubert_fisher/) repository contains the pre-trained models and recipies to produce discrete units for the dGSLM model. + +## [Unit-to-Speech Decoder for dGSLM](vocoder_hifigan/) +The [vocoder_hifigan](vocoder_hifigan/) repo contains the vocoder and recipies to synthesize the waveform from the discrete units. + +## Spoken Dialogue Transformer Language Model (SpeechDLM) +### Pre-trained model +We share the pre-trained model checkpoint for the best configuration in the paper (DLM-5 model, with Edge Unit Prediction & Delayed Duration Prediction objectives), dubbed as `SpeechDLM`, trained on the 2000 hours of Fisher dataset : +| Pre-trained SpeechDLM model trained on Fisher dataset | +|-----------------------------------------------| +|[model checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/dgslm/checkpoints/speech_dlm/speech_dlm_base.pt) - [dictionary 1](https://dl.fbaipublicfiles.com/textless_nlp/dgslm/checkpoints/speech_dlm/dict.unitA.txt) - [dictionary 2](https://dl.fbaipublicfiles.com/textless_nlp/dgslm/checkpoints/speech_dlm/dict.unitB.txt)| +the two dictionary files correspond to the two channels, and actually have the same content. + +### Sample from a trained model +You can sample from a trained SpeechDLM model interactively : +```python +from fairseq.models.speech_dlm import SpeechDLM + +# Load SpeechDLM model +speech_dlm = SpeechDLM.from_pretrained( + model_name_or_path='/path/to/model/dir', + checkpoint_file='speech_dlm_base.pt', + data_name_or_path='/path/to/data/dir' + ) +# Disable dropout +speech_dlm.eval() +# Move model to GPU +speech_dlm.cuda() + +# Define the input sequences +input_sequences = [{ + 'unitA': '7 376 376 133 178 486 486 486 486 486 486 486 486 2 486', + 'unitB': '7 499 415 177 7 7 7 7 7 7 136 136 289 289 408' + }] + +# Sample from the SpeechDLM model +generated_units = speech_dlm.sample( + input_sequences, + max_len_a = 0, + max_len_b = 500, + sampling=True, + beam=5, + ) +# >> {'unitA': '7 376 376 133 178 486 486 486 486 486 486 486 486 2 486 486 178 486 486 2 2 376 376 486 486 486 376 376 387 387 ...', +# >> 'unitB': '7 499 415 177 7 7 7 7 7 7 136 136 289 289 408 32 428 95 356 141 331 439 350 350 192 331 445 202 104 104 ...'} +``` + +Or using the `sample_speech_dlm.py` script : +```bash +python sample_speech_dlm.py \ + --in-file $INPUT_CODE_FILE --out-file $OUTPUT_FILE \ + --ckpt $CHECKPOINT_PATH --data $DATA_DIR +``` +where each line of INPUT_CODE_FILE is a dictionary with keys `'audio', 'unitA', 'unitB'` as follows : +``` +{'audio': 'file_1', 'unitA': '8 8 ... 352 352', 'unitB': '217 8 ... 8 8'} +{'audio': 'file_2', 'unitA': '5 5 ... 65 65', 'unitB': '6 35 ... 8 9'} +... +``` +This code file can be created with the script `create_input_code.py` (using the outputs of `quantize_with_kmeans.py` [here](hubert_fisher/#encode-audio-to-discrete-units)) : +```bash +python examples/textless_nlp/dgslm/vocoder_hifigan/create_input_code.py \ + $CHANNEL1_UNITS $CHANNEL2_UNITS $OUTPUT_CODE_FILE +``` + +### Training a SpeechDLM model +#### 1) Data preparation +First, you need to prepare the raw dataset. For each `split` (train, valid), you need two files corresponding to two channels (namely `unitA` and `unitB` for example) containing the units from each channel separately. Make sure that 2 files have the same number of lines and each corresponding line has the same number of units. + +Here is an example of `.unitA` file : +``` +7 376 376 133 178 +486 486 486 +486 376 +``` +and the corresponding `.unitB` file : +``` +7 499 415 177 7 +7 7 136 +331 445 +``` +These two files can be obtained using the [example command](hubert_fisher/#encode-audio-to-discrete-units) of hubert fisher, with the `--hide-fname` option added. + +The raw dataset directory should contain the following files : +``` +train.unitA valid.unitA +train.unitB valid.unitB +``` + +Next preprocess/binarize the data with `fairseq-preprocess`, but make sure to preprocess each channel separately, and **rename** the preprocessed files under the following format `${split}.${channel}.{bin, idx}`. Each channel also needs a separate dictionary file under the name `dict.${channel}.txt` . + +Here is an example pre-processing code : + +```bash +# Preprocess the first channel (unitA) +fairseq-preprocess --source-lang unitA \ + --only-source \ + --trainpref $RAW_DATA_DIR/train \ + --validpref $RAW_DATA_DIR/valid \ + --destdir $BIN_DATA_DIR \ + --workers 20 + +# Preprocess the second channel (unitB) and reuse the dictionary from the first channel +fairseq-preprocess --source-lang unitB \ + --srcdict $BIN_DATA_DIR/dict.unitA.txt \ + --only-source \ + --trainpref $RAW_DATA_DIR/train \ + --validpref $RAW_DATA_DIR/valid \ + --destdir $BIN_DATA_DIR \ + --workers 20 + +# Rename the bin & index files +for channel in unitA unitB; do + for split in train valid; do + mv $BIN_DATA_DIR/${split}.${channel}-None.${channel}.bin $BIN_DATA_DIR/${split}.${channel}.bin + mv $BIN_DATA_DIR/${split}.${channel}-None.${channel}.idx $BIN_DATA_DIR/${split}.${channel}.idx + done +done +``` +Finally, the preprocessed (bin) dataset directory should contain the following files : +``` +dict.unitA.txt train.unitA.idx train.unitA.bin valid.unitA.idx valid.unitA.bin +dict.unitB.txt train.unitB.idx train.unitB.bin valid.unitB.idx valid.unitB.bin +``` + +#### 2) Train the model +To train the SpeechDLM (with the configuration as the pre-trained model) on 2 GPUs : +```bash +fairseq-train $BIN_DATA_DIR \ + --save-dir $CHECKPOINT_DIR \ + --tensorboard-logdir $CHECKPOINT_DIR \ + --task speech_dlm_task --channels unitA,unitB \ + --next-unit-prediction "False" --edge-unit-prediction "True" \ + --duration-prediction "True" --delayed-duration-target "True" \ + --criterion speech_dlm_criterion \ + --arch speech_dlm --decoder-cross-layers 4 \ + --share-decoder-input-output-embed \ + --dropout 0.1 --attention-dropout 0.1 \ + --optimizer adam --adam-betas "(0.9, 0.98)" --clip-norm 1.0 \ + --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 \ + --max-tokens 18432 --tokens-per-sample 6144 --sample-break-mode none \ + --update-freq 16 --num-workers 4 --skip-invalid-size-inputs-valid-test \ + --max-update 250000 --warmup-updates 20000 \ + --save-interval-updates 10000 --keep-last-epochs 1 --no-epoch-checkpoints \ + --log-interval 50 --seed 100501 \ + --fp16 --checkpoint-activations +``` + +#### 3) Validate +The model can be validated via the `fairseq-validate` command : +```bash +fairseq-validate $BIN_DATA_DIR \ + --task speech_dlm_task \ + --path $CHECKPOINT_PATH \ + --max-tokens 6144 +``` + +## Reference + +If you find our work useful in your research, please consider citing our paper: + +```bibtex +@article{nguyen2022dgslm, + title = {Generative Spoken Dialogue Language Modeling}, + author = {Nguyen, Tu Anh and Kharitonov, Eugene and Copet, Jade and Adi, Yossi and Hsu, Wei-Ning and Elkahky, Ali and Tomasello, Paden and Algayres, Robin and Sagot, Benoit and Mohamed, Abdelrahman and Dupoux, Emmanuel}, + eprint={2203.16502}, + archivePrefix={arXiv}, + primaryClass={cs.CL}, + year={2022} +} +``` diff --git a/fairseq/examples/textless_nlp/dgslm/create_code_file.py b/fairseq/examples/textless_nlp/dgslm/create_code_file.py new file mode 100644 index 0000000..d10f948 --- /dev/null +++ b/fairseq/examples/textless_nlp/dgslm/create_code_file.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse + + +def main(): + """ + Create code file with the following format: + {'audio': 'file1', 'unitA': 'file1_chnl1_units', 'unitB': 'file1_chnl2_units'} + {'audio': 'file2', 'unitA': 'file2_chnl1_units', 'unitB': 'file2_chnl2_units'} + ... + + Given the input units files + - channel1_units_file: + file1|file1_chnl1_units + file2|file2_chnl1_units + ... + - channel2_units_file: + file1|file1_chnl2_units + file2|file2_chnl2_units + ... + """ + + parser = argparse.ArgumentParser() + parser.add_argument( + "channel1_units_file", + type=str, + help="Units of the first channel.", + ) + parser.add_argument( + "channel2_units_file", + type=str, + help="Units of the second channel.", + ) + parser.add_argument( + "output_file", + type=str, + help="Output file.", + ) + parser.add_argument( + "--channels", + type=str, + default='unitA,unitB', + help="Comma-separated list of the channel names to create in the code" + "(Default: 'unitA,unitB').", + ) + + args = parser.parse_args() + + channel_names = args.channels.split(',') + + with open(args.channel1_units_file) as funit1, \ + open(args.channel2_units_file) as funit2, \ + open(args.output_file, 'w') as fout: + for line1, line2 in zip(funit1, funit2): + fname1, units1 = line1.strip().split('|') + fname2, units2 = line2.strip().split('|') + assert len(units1.split()) == len(units2.split()), \ + f"Mismatch units length ({len(units1.split())} vs {len(units2.split())})" + base_fname1 = fname1[:-9] + base_fname2 = fname2[:-9] + assert base_fname1 == base_fname2, \ + f"Mismatch filenames ({base_fname1} vs {base_fname2}). " \ + f"Expected $filename-channel1 and $filename-channel2 in two files" + code = { + "audio" : base_fname1, + channel_names[0] : units1, + channel_names[1] : units2, + } + fout.write(str(code)) + fout.write("\n") + print(f"Codes written to {args.output_file}") + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/textless_nlp/dgslm/dgslm_utils.py b/fairseq/examples/textless_nlp/dgslm/dgslm_utils.py new file mode 100644 index 0000000..8049d49 --- /dev/null +++ b/fairseq/examples/textless_nlp/dgslm/dgslm_utils.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +import json + +from fairseq import utils +from fairseq.models.text_to_speech.vocoder import CodeHiFiGANVocoder + +# from examples.hubert.simple_kmeans.dump_hubert_feature import HubertFeatureReader +from examples.textless_nlp.gslm.speech2unit.pretrained.hubert_feature_reader import HubertFeatureReader +from examples.hubert.simple_kmeans.dump_km_label import ApplyKmeans + + +# Hubert tokenizer +class HubertTokenizer: + def __init__( + self, + hubert_path, + hubert_layer, + km_path, + use_cuda=True, + ): + self.feature_extractor = HubertFeatureReader(hubert_path, hubert_layer, use_cuda=use_cuda) + self.quantizer = ApplyKmeans(km_path) + if not use_cuda: + self.quantizer.C = self.quantizer.C.cpu() + self.quantizer.Cnorm = self.quantizer.Cnorm.cpu() + + def wav2code(self, path, channel_id=1): + feat = self.feature_extractor.get_feats(path, channel_id=channel_id) + code = self.quantizer(feat) + return ' '.join(map(str, code)) + + def wav2codes(self, path): + codes = [ + self.wav2code(path, channel_id=1), + self.wav2code(path, channel_id=2) + ] + return codes + + +# Vocoder +class HifiganVocoder: + def __init__( + self, + vocoder_path, + vocoder_cfg_path, + use_cuda=True, + ): + with open(vocoder_cfg_path) as f: + cfg = json.load(f) + self.vocoder = CodeHiFiGANVocoder(vocoder_path, cfg).eval() + self.use_cuda = use_cuda + if self.use_cuda: + self.vocoder.cuda() + + def code2wav(self, code, speaker_id=0, pred_dur=False): + if isinstance(code, str): + code = list(map(int, code.split())) + inp = {"code": torch.LongTensor(code).view(1, -1)} + if self.vocoder.model.multispkr: + inp["spkr"] = torch.LongTensor([speaker_id]).view(1, 1) + if self.use_cuda: + inp = utils.move_to_cuda(inp) + return self.vocoder(inp, pred_dur).detach().cpu().numpy() + + def codes2wav(self, codes, speaker_ids=[0, 4], pred_dur=False): + if isinstance(codes, dict): + codes = list(codes.values()) + assert len(codes) == 2 + wav1 = self.code2wav(codes[0], speaker_ids[0], pred_dur) + wav2 = self.code2wav(codes[1], speaker_ids[1], pred_dur) + wav = np.stack([wav1, wav2]) + return wav diff --git a/fairseq/examples/textless_nlp/dgslm/hubert_fisher/README.md b/fairseq/examples/textless_nlp/dgslm/hubert_fisher/README.md new file mode 100644 index 0000000..52c528f --- /dev/null +++ b/fairseq/examples/textless_nlp/dgslm/hubert_fisher/README.md @@ -0,0 +1,47 @@ +# Dialogue Speech-to-Unit Encoder for dGSLM: The Fisher HuBERT model +For the speech2unit encoder, we train a [HuBERT model](https://arxiv.org/pdf/2106.07447.pdf) on the [Fisher dataset](http://www.lrec-conf.org/proceedings/lrec2004/pdf/767.pdf) for 3 iterations (see [our paper](https://arxiv.org/pdf/2203.16502.pdf) for more details) and train a k-means model with 500 units on the layer 12 features of the HuBERT model. + +## Model checkpoints +The pre-trained HuBERT and k-means model checkpoints can be found here: + +| Fisher HuBERT model | k-means model | +|---------------------|---------------| +|[download](https://dl.fbaipublicfiles.com/textless_nlp/dgslm/checkpoints/hubert/hubert_fisher.pt)|[download](https://dl.fbaipublicfiles.com/textless_nlp/dgslm/checkpoints/hubert/hubert_fisher_km_500.bin)| + + +## Encode audio to discrete units +Below is an example command to encode a stereo dataset to discrete units using the pre-trained model checkpoints : +```bash +for CHANNEL_ID in 1 2; do + python examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py \ + --feature_type hubert \ + --kmeans_model_path path/to/hubert_fisher_km_500.bin \ + --acoustic_model_path path/to/hubert_fisher.pt \ + --layer 12 \ + --manifest_path $MANIFEST_FILE \ + --out_quantized_file_path ${OUTPUT_FILE}-channel${CHANNEL_ID} \ + --extension $EXTENSION \ + --channel_id $CHANNEL_ID +done +``` +where MANIFEST_FILE is the output of [wav2vec manifest script](https://github.com/facebookresearch/fairseq/blob/main/examples/wav2vec/wav2vec_manifest.py), which can be obtained through the following command : +``` +python examples/wav2vec/wav2vec_manifest.py --valid-percent=0.0 $AUDIO_DIR --dest=$OUTPUT_DIR --ext=$EXTENSION +``` + +Otherwise, you can encode an audio file in python interactively with the HubertTokenizer class : +```python +# Load the Hubert tokenizer +from examples.textless_nlp.dgslm.dgslm_utils import HubertTokenizer +encoder = HubertTokenizer( + hubert_path = "/path/to/hubert_ckpt.pt", + hubert_layer = 12, + km_path = "path/to/km.bin" +) + +# Encode the audio to units +path = "/path/to/stereo/audio.wav" +codes = encoder.wav2codes(path) +# > ['7 376 376 133 178 486 486 486 486 486 486 486 486 2 486', +# > '7 499 415 177 7 7 7 7 7 7 136 136 289 289 408'] +``` \ No newline at end of file diff --git a/fairseq/examples/textless_nlp/dgslm/sample_speech_dlm.py b/fairseq/examples/textless_nlp/dgslm/sample_speech_dlm.py new file mode 100644 index 0000000..484cbab --- /dev/null +++ b/fairseq/examples/textless_nlp/dgslm/sample_speech_dlm.py @@ -0,0 +1,202 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import ast +import argparse +import logging +import torch + +from fairseq import utils +from fairseq.models.speech_dlm import SpeechDLM + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def load_data(in_file): + with open(in_file) as f: + data = [ast.literal_eval(line.strip()) for line in f] + return data + + +def write_data(out_file, data): + with open(out_file, 'w') as f: + for d in data: + f.write(str(d)) + f.write('\n') + + +def limit(codes, n): + new_codes = {} + for k, v in codes.items(): + new_codes[k] = ' '.join(v.split()[:n]) + return new_codes + + +def main(args): + logger.info(args) + + use_cuda = torch.cuda.is_available() + + # Load the data + data = load_data(args.in_file) + channels = args.channels.split(',') + unit_sequences = [{ + channels[0]: d[channels[0]], + channels[1]: d[channels[1]], + } for d in data] + fnames = [d['audio'] for d in data] + print(f"Found {len(data)} sequences from {args.in_file}") + + # Limit the prefix size + if args.prefix_size is not None: + print(f"Limit the prefix size to {args.prefix_size}") + unit_sequences = [limit(codes, args.prefix_size) for codes in unit_sequences] + + # Load model from ckpt + print(f"Loading the SpeechDLM model from {args.ckpt}") + model = SpeechDLM.from_pretrained( + model_name_or_path=os.path.dirname(args.ckpt), + checkpoint_file=os.path.basename(args.ckpt), + data_name_or_path=args.data + ) + model.eval() + if use_cuda: + model.cuda() + + # Set batch sizes + model.cfg.dataset.max_tokens = args.batch_max_tokens + model.max_positions = args.batch_max_positions + if args.batch_max_sentences is not None: + model.cfg.dataset.batch_size = args.batch_max_sentences + + # Set seed (if needed) + if args.seed is not None: + utils.set_torch_seed(args.seed) + + # Sample from the SpeechDLM model + print(f"Generating {len(unit_sequences)} sequences with SpeechDLM model...\n" + f"Generation args: sampling={(not args.beam_search)}, " + f"sampling_topk={args.sampling_topk}, sampling_topp={args.sampling_topp}, " + f"beam={args.beam_size}, min_len={args.min_len}, " + f"max_len_a={args.max_len_a}, max_len_b={args.max_len_b}, " + f"temperature={args.temperature}, dur_temperature={args.dur_temperature}, " + f"seed={args.seed}") + generated_units = model.sample( + unit_sequences, + sampling=(not args.beam_search), + sampling_topk=args.sampling_topk, + sampling_topp=args.sampling_topp, + beam=args.beam_size, + max_len_a=args.max_len_a, + max_len_b=args.max_len_b, + min_len=args.min_len, + temperature=args.temperature, + duration_temperature=args.dur_temperature, + verbose=args.verbose, + skip_invalid_size_inputs=args.skip_invalid_size_batch, + ) + + # Create the generated sequences + generated_data = [] + for fname, gen_units in zip(fnames, generated_units): + d = { + "audio" : fname+'-generated', + **gen_units + } + generated_data.append(d) + + # Write the generated sequences + print(f"Write the generated units to {args.out_file}") + if args.out_file: + os.makedirs(os.path.dirname(args.out_file), exist_ok=True) + write_data(args.out_file, generated_data) + + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-file", + type=str, + required=True, + help="Input file following the same format of the output from create_input.py", + ) + parser.add_argument( + "--ckpt", + type=str, + required=True, + help="Path to the model checkpoint." + ) + parser.add_argument( + "--data", + type=str, + required=True, + help="path to the model data dir (containing dict files)", + ) + parser.add_argument( + "--out-file", + type=str, + required=True, + help="Path of the output file.", + ) + parser.add_argument( + "--channels", + type=str, + default='unitA,unitB', + help="Comma-separated list of the channel names" + "(Default: 'unitA,unitB').", + ) + parser.add_argument("--prefix-size", type=int, default=None, + help='Limit the prefix size') + + # Batch sizes + parser.add_argument("--batch-max-tokens", type=int, default=9216, + help='maximum number of tokens considered in a batch') + parser.add_argument("--batch-max-positions", type=int, default=6144, + help='maximum number of tokens allowed for a sentence in a batch') + parser.add_argument("--batch-max-sentences", type=int, default=None, + help='maximum number of sentences considered in a batch') + parser.add_argument("--skip-invalid-size-batch", action='store_true', + help='skip sentences with more tokens than --batch-max-positions') + + # Generation args + parser.add_argument("--beam-search", action='store_true', + help='perform beam search instead of sampling') + parser.add_argument("--beam-size", type=int, default=5, + help="beam width (used in both sampling and beam search mode) " + "(default: 5)") + parser.add_argument("--sampling-topk", type=int, default=-1, + help="only sample from top-k candidates (default: -1, non applied)") + parser.add_argument("--sampling-topp", type=float, default=-1.0, + help="only sample among the smallest set of elements whose cumulative " + "probability mass exceeds p (default: -1.0, non applied)") + parser.add_argument("--max-len-a", type=int, default=0, + help="generate sequences of maximum length ax + b, " + "where x is the source length (default: 0)") + parser.add_argument("--max-len-b", type=int, default=500, + help="generate sequences of maximum length ax + b, " + "where x is the source length (default: 500 ~ 10s)") + parser.add_argument("--min-len", type=int, default=1, + help="generate sequences of maximum length ax + b, " + "where x is the source length (default: 1)") + parser.add_argument("--temperature", type=float, default=1.0, + help="temperature when generating unit tokens (default: 1.0)") + parser.add_argument("--dur-temperature", type=float, default=1.0, + help="temperature when generating duration tokens (default: 1.0)") + parser.add_argument("--verbose", action='store_true', + help="print the scores given by the model to generated sequences") + parser.add_argument("--seed", type=int, default=123, + help="seed of the generation model") + + args = parser.parse_args() + + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/textless_nlp/dgslm/vocoder_hifigan/README.md b/fairseq/examples/textless_nlp/dgslm/vocoder_hifigan/README.md new file mode 100644 index 0000000..5d4a59a --- /dev/null +++ b/fairseq/examples/textless_nlp/dgslm/vocoder_hifigan/README.md @@ -0,0 +1,47 @@ +# Dialogue Unit-to-Speech Decoder for dGSLM +For the unit2speech decoder, we train a [discrete unit-based HiFi-GAN vocoder](https://arxiv.org/pdf/2104.00355.pdf) on the [Fisher dataset](http://www.lrec-conf.org/proceedings/lrec2004/pdf/767.pdf). + +## Model checkpoint +The pre-trained model checkpoint can be found here : + +| HiFi-GAN vocoder based on HuBERT Fisher Units | +|-----------------------------------------------| +|[model checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/dgslm/checkpoints/hifigan/hifigan_vocoder) - [config](https://dl.fbaipublicfiles.com/textless_nlp/dgslm/checkpoints/hifigan/config.json) | + +## Decode discrete units to audio +To create waveform from discrete units, use the script `generate_stereo_waveform.py` : +```bash +python examples/textless_nlp/dgslm/vocoder_hifigan/generate_stereo_waveform.py \ + --in-file $INPUT_CODE_FILE \ + --vocoder $VOCODER_PATH \ + --vocoder-cfg $VOCODER_CONFIG \ + --results-path $OUTPUT_DIR +``` +where INPUT_CODE_FILE is expected to have the following format : +``` +{'audio': 'file_1', 'unitA': '8 8 ... 352 352', 'unitB': '217 8 ... 8 8'} +{'audio': 'file_2', 'unitA': '5 5 ... 65 65', 'unitB': '6 35 ... 8 9'} +... +``` + +You can also use the HifiganVocoder class to generate waveform from the codes interactively : +```python +# Load the Hifigan vocoder +from examples.textless_nlp.dgslm.dgslm_utils import HifiganVocoder +decoder = HifiganVocoder( + vocoder_path = "/path/to/hifigan_vocoder", + vocoder_cfg_path = "/path/to/config.json", +) + +# Decode the units to waveform +codes = [ + '7 376 376 133 178 486 486 486 486 486 486 486 486 2 486', + '7 499 415 177 7 7 7 7 7 7 136 136 289 289 408', +] +wav = decoder.codes2wav(codes) +# > array of shape (2, 4800) + +# Play the waveform +import IPython.display as ipd +ipd.Audio(wav, rate=16_000) +``` diff --git a/fairseq/examples/textless_nlp/dgslm/vocoder_hifigan/generate_stereo_waveform.py b/fairseq/examples/textless_nlp/dgslm/vocoder_hifigan/generate_stereo_waveform.py new file mode 100644 index 0000000..1e15f43 --- /dev/null +++ b/fairseq/examples/textless_nlp/dgslm/vocoder_hifigan/generate_stereo_waveform.py @@ -0,0 +1,137 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import argparse +import json +import logging +from pathlib import Path +import soundfile as sf +import torch + +from tqdm import tqdm + +from fairseq import utils +from fairseq.models.text_to_speech.vocoder import CodeHiFiGANVocoder + + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def dump_result(args, data, sample_id, pred_wav): + assert "audio" in data or args.results_path is not None + if args.results_path: + fname = Path(data["audio"]).stem + ".wav" if "audio" in data else f"{sample_id}_pred.wav" + out_file = Path(args.results_path) / fname + + sf.write( + out_file.as_posix(), + pred_wav.detach().cpu().numpy(), + args.sample_rate, + ) + + +def load_data(in_file): + with open(in_file) as f: + data = [ast.literal_eval(line.strip()) for line in f] + + return data + + +def load_vocoder(vocoder_path, vocoder_cfg_path, use_cuda=True): + with open(vocoder_cfg_path) as f: + cfg = json.load(f) + vocoder = CodeHiFiGANVocoder(vocoder_path, cfg).eval() + if use_cuda: + vocoder = vocoder.cuda() + return vocoder + + +def code2wav(vocoder, code, speaker_id, use_cuda=True): + if isinstance(code, str): + code = list(map(int, code.split())) + inp = dict() + inp["code"] = torch.LongTensor(code).view(1, -1) + if vocoder.model.multispkr: + inp["spkr"] = torch.LongTensor([speaker_id]).view(1, 1) + if use_cuda: + inp = utils.move_to_cuda(inp) + return vocoder(inp) + + +def main(args): + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + + vocoder = load_vocoder(args.vocoder, args.vocoder_cfg, use_cuda) + + data = load_data(args.in_file) + + if args.results_path: + Path(args.results_path).mkdir(exist_ok=True, parents=True) + + channels = args.channels.split(',') + speakers = [args.channel1_spk, args.channel2_spk] + + for i, d in tqdm(enumerate(data), total=len(data)): + wavs = [] + for key, speaker_id in zip(channels, speakers): + wav = code2wav(vocoder, d[key], speaker_id, use_cuda=use_cuda) + wavs.append(wav) + + wav = torch.stack(wavs, dim=-1) + if args.mix: + wav = torch.mean(wav, dim=-1) + + dump_result(args, d, i, wav) + + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-file", + type=str, + required=True, + help="Input file following the same format of the output from create_input.py", + ) + parser.add_argument( + "--vocoder", type=str, required=True, help="path to the vocoder" + ) + parser.add_argument( + "--vocoder-cfg", + type=str, + required=True, + help="path to the vocoder config", + ) + parser.add_argument( + "--channels", + type=str, + default='unitA,unitB', + help="Comma-separated list of the channel names" + "(Default: 'unitA,unitB').", + ) + parser.add_argument("--sample-rate", type=int, default=16_000) + parser.add_argument( + "--results-path", + type=str, + default=None, + help="Output directory. If not set, the audios will be stored following the 'audio' field specified in the input file", + ) + parser.add_argument("--channel1-spk", type=int, default=0, help="Speaker of the first channel",) + parser.add_argument("--channel2-spk", type=int, default=4, help="Speaker of the second channel",) + parser.add_argument("--mix", action="store_true", help="Mix the two channels to create output mono files") + parser.add_argument("--cpu", action="store_true", help="run on CPU") + + args = parser.parse_args() + + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/textless_nlp/gslm/README.md b/fairseq/examples/textless_nlp/gslm/README.md new file mode 100644 index 0000000..7a76ffd --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/README.md @@ -0,0 +1,21 @@ +# Generative Spoken Language Modeling + +* [Paper](https://arxiv.org/abs/2102.01192) +* [Demo](https://speechbot.github.io/gslm/index.html) + +We build and evaluate generative speech2speech systems using [Log Mel Filtebank](https://pytorch.org/audio/stable/compliance.kaldi.html#fbank), [Modified CPC](https://github.com/facebookresearch/CPC_audio), [HuBERT Base](https://github.com/pytorch/fairseq/tree/main/examples/hubert) and [Wav2Vec 2.0 Large](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec). Our system is composed of three components, namely, *speech2unit*, *ulm* and *unit2speech*. We explain about models and usage of these components in their respective sub-directories. See the links below. + +## Speech to Unit Model (speech2unit) +Speech to unit model is used for quantizing raw speech into learned discrete speech units. [More details](speech2unit) + +## Unit Language Model (ulm) +Unit Language Model is a generative language model trained on discrete speech units. [More details](ulm) + +## Unit to Speech Model (unit2speech) +Unit to speech model is used for synthesizing speech from discrete speech units. [More details](unit2speech) + +## Metrics +We show how to compute ASR based metrics as well as zero-shot metrics proposed in our paper [here](metrics). + +## Tools +We share two tools to resynthesize a given spoken utterance, and generate novel spoken language given a spoken prompt. [More detail](tools) diff --git a/fairseq/examples/textless_nlp/gslm/metrics/README.md b/fairseq/examples/textless_nlp/gslm/metrics/README.md new file mode 100644 index 0000000..0a63e2f --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/README.md @@ -0,0 +1,10 @@ +# GSLM Metrics + +## ASR Metrics +The suite of metrics here uses an ASR model to transcribe the synthesized speech into text, and then uses text-based metrics. We also use word error rate from ASR transcription itself as one of the metrics. [More details](asr_metrics) + +## ABX Metrics +We use [ABX](https://www.semanticscholar.org/paper/ABX-Discriminability-Measures-and-Applications-Schatz/13d3537228f728c1063cc83743cb118bba3367a0) to evaluate how well-separated phonetic categories are with quantized representations. [More details](abx_metrics) + +## sWUGGY and sBLIMP +We refer to [ZeroSpeech challenge](https://www.zerospeech.com/2021/track_s.html#scoring-based-metrics) for details on the sWUGGY and sBLIMP metrics. diff --git a/fairseq/examples/textless_nlp/gslm/metrics/abx_metrics/README.md b/fairseq/examples/textless_nlp/gslm/metrics/abx_metrics/README.md new file mode 100644 index 0000000..aa2560f --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/abx_metrics/README.md @@ -0,0 +1,77 @@ +# ABX-based evaluation + +ABX is used to evaluate the quality of the obtained discrete units. + +The life cycle of the ABX-based evaluation for the Speech-to-Unit contains the following steps: +1. Training an acoustic model (or use an existing acoustic model) ([description](./../..)) +2. Perform quantization of speech by learning a K-means clustering model ([description](./../..)) +3. Compute discrete features for ABX computation using the learned clusters +4. Compute the ABX score over the discrete features taking advantage of [libri-light's ABX evaluation script][ll-abx] + +Here we assume that you already went throught the first two steps and focus solely on extracting features and computing ABX scores. + +## Libri-light setup + +Follow [libri-light's instructions][ll-instructions] for installation and [ABX evaluation setup][ll-abx] (including the download of the data items required for ABX computation). + +## Computing ABX + +### Dumping quantized features + +The first step for the ABX computation is to dump the quantized representations corresponding to the test files. + +```shell +TYPE="hubert" +LAYER=6 +CKPT_PATH="<PATH_TO_HUBERT_MODEL_CHECKPOINT_FILE>" +KM_MODEL_PATH="<PATH_TO_PRETRAINED_KM_MODEL_FILE>" + +SUBSET="dev-clean" +MANIFEST="<PATH_TO_MANIFEST_FOR_LS_DEV-CLEAN>" +DATA_DIR="<PATH_TO_DIR_TO_STORE_FEATURES>/$SUBSET" + +PYTHONPATH=. python examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py \ + --feature_type $TYPE \ + --kmeans_model_path $KM_MODEL_PATH \ + --checkpoint_path $CKPT_PATH \ + --layer $LAYER \ + --manifest_path $MANIFEST \ + --out_dir_path $DATA_DIR \ + --extension ".flac" +``` + +Again the manifest file follows the same structure than elsewhere in the codebase. + +### Compute ABX with Libri-light + +Use libri-light's `eval_ABX.py` script (within the appropriate environment set up) as followed: + +```shell +LIBRILIGHT_ROOT="<PATH_TO_LIBRILIGHT>" + +SUBSET="dev-clean" +DATA_DIR="<PATH_TO_DIR_TO_STORE_FEATURES>/$SUBSET" +ITEM_FILE_PATH="$LIBRILIGHT_ROOT/eval/ABX_data/$SUBSET.item" +OUT_DIR="<PATH_TO_DIR_TO_STORE_ABX_SCORES>/$SUBSET" + +FILE_EXTENSION=".npy" +FEATURE_SIZE=0.02 # depends on the model used + +PYTHONPATH=$LIBRILIGHT_ROOT \ + python $LIBRILIGHT_ROOT/eval/eval_ABX.py \ + $DATA_DIR \ + $ITEM_FILE_PATH \ + --file_extension $FILE_EXTENSION \ + --feature_size $FEATURE_SIZE \ + --out $OUT_DIR \ + --mode "all" +``` + +Note that `FEATURE_SIZE` will depend on the model type you are using to extract the acoustic features: +* For HuBERT and Wav2Vec2.0, use `FEATURE_SIZE=0.02` +* For CPC and Log Mel, use `FEATURE_SIZE=0.01` + +If you have a gpu available, make sure you add the `--cuda` flag for faster computation. + +[ll-instructions]: https://github.com/facebookresearch/libri-light +[ll-abx]: https://github.com/facebookresearch/libri-light/tree/master/eval#abx diff --git a/fairseq/examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py b/fairseq/examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py new file mode 100644 index 0000000..41cf558 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os + +import joblib +import numpy as np + +from examples.textless_nlp.gslm.speech2unit.clustering.utils import get_audio_files +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import get_features + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + +def get_parser(): + parser = argparse.ArgumentParser( + description="Quantize using K-means clustering over acoustic features." + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + required=True, + help="Acoustic feature type", + ) + parser.add_argument( + "--kmeans_model_path", + type=str, + required=True, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + help="Pretrained model checkpoint", + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--out_dir_path", + required=True, + type=str, + help="File path of quantized output.", + ) + parser.add_argument( + "--extension", type=str, default=".flac", help="Features file path" + ) + return parser + + +def one_hot(feat, n_clusters): + return np.eye(n_clusters)[feat] + +def main(args, logger): + # Feature extraction + logger.info(f"Extracting {args.feature_type} acoustic features...") + features_batch = get_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=1.0, + flatten=False, + ) + logger.info(f"Features extracted for {len(features_batch)} utterances.\n") + logger.info(f"Dimensionality of representation = {features_batch[0].shape[1]}") + + logger.info(f"Loading K-means model from {args.kmeans_model_path} ...") + kmeans_model = joblib.load(open(args.kmeans_model_path, "rb")) + kmeans_model.verbose = False + + _, fnames, _ = get_audio_files(args.manifest_path) + + os.makedirs(args.out_dir_path, exist_ok=True) + logger.info(f"Writing quantized features to {args.out_dir_path}") + for i, feats in enumerate(features_batch): + pred = kmeans_model.predict(feats) + emb = one_hot(pred, kmeans_model.n_clusters) + base_fname = os.path.basename(fnames[i]).rstrip(args.extension) + output_path = os.path.join(args.out_dir_path, f"{base_fname}.npy") + with open(output_path, "wb") as f: + np.save(f, emb) + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md new file mode 100644 index 0000000..90741f4 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md @@ -0,0 +1,87 @@ +# ASR-based evaluation + +Overall, the life cycle of the ASR-based evaluation for an ULM contains the following steps: + 1. Training an ULM and sampling from it [[description]](./../../ulm) + 2. Running UTS on the sampled unit sequences [[description]](./../../unit2speech) + 3. Pre-processing for the ASR (down-sampling to 16 KHz, aligning length of the generated audio with ground-truth utterances) + 4. Running ASR + 5. Calculation of the post-ASR evaluation metrics + +Here we assume that you have already went throught the first two steps and focus on the rest. + +## Preprocessing +### Down-sampling to 16KHz +The bulk conversion can be done by running +```bash + python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py $UTS_OUTPUT $UTS_OUTPUT_DOWNSAMPLE + ``` + where `$UTS_OUTPUT` specifies the directory with the generated audio and `$UTS_OUTPUT_DOWNSAMPLE` is the directory where downsampled audio would be saved. + + ### Matching by length +This step is somewhat optional. However, if you want to compare the fluency and diversity of a generated speech utterance to that of the ground-truth speech with the same prefix, it is a good idea to force them to be of the same length. +```bash +python $FAIRSEQ_ROOT/examples/textless_nlp/asr_metrics/cut_as.py \ + --samples_dir=$UTS_OUTPUT_DOWNSAMPLE --out_dir=$UTS_OUTPUT_DOWNSAMPLE_CUT \ + --prompts_description=data/ground_truth_continuation_dev.json +``` + +Here `ground_truth_continuation_dev.json` is a json file with ground-truth text from LibriSpeech dev-clean, associated with some meta-data (assuming the evaluation is done on dev-clean). This file can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_dev.json). A similar file for the test-clean is [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_test.json). These files are used for the evaluation and contain texts for audio sequences that are at least 6s long. + +## Running ASR +We use a pre-trained wav2vec model to run the ASR step. We firstly need to prepare manifest files which, roughly, tell the ASR system which files we want to transcribe. You can find more details and download the `960h_scratch.pt` checkpoint +[[here]](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md)). To run ASR, you would also need to +install KenLM, Flashlight decoder, and download the KenLM 4-gram English language model. + +```bash + python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py \ + $UTS_OUTPUT_DOWNSAMPLE_CUT --valid-percent 0.0 --dest $MANIFEST_DIR --ext wav +``` +where `$UTS_OUTPUT_DOWNSAMPLE_CUT` speficies the directory with the preprocessed UTS outputs and `$MANIFEST_DIR` is the output directory. + +We will be running an out-of-the-box evaluation script which requires ground-truth transcripts to measure quality metrics. We are only +interested in the transcripts (and we don't have ground-truth outputs for when our ULM generated!), hence we will just generate +some dummy transcripts instead: +```bash +cp $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dict.ltr.txt $MANIFEST_DIR +python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dummy_asr_data.py --tsv=$MANIFEST_DIR/train.tsv \ + --output-dir=$MANIFEST_DIR +``` + +Now we are ready for running ASR: +``` +mkdir -p asr +python $FAIRSEQ_ROOT/examples/speech_recognition/infer.py \ + $MANIFEST_DIR \ + --task audio_pretraining --nbest 1 --path 960h_scratch.pt \ + --gen-subset=train --results-path $PATH_TO_ASR_OUTPUT \ + --w2l-decoder kenlm --lm-model 4-gram.bin \ + --lexicon librispeech/lexicon_ltr.lst --word-score -1 \ + --sil-weight 0 --lm-weight 2 --criterion ctc --labels ltr --max-tokens 300000 --remove-bpe letter +``` +where `lexicon_ltr.lst` is the LibriSpeech lexicon and `$PATH_TO_ASR_OUTPUT` is the output directory (can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/lexicon_ltr.lst)). + +## Evaluation metrics +We run evaluation on the 1_000 shortest sequences that are at least 6s long. To filter those from the ASR transcript, we additionally provide each metric script with the paths to the manifest and `ground_truth_continuation_*` files. + +### Perplexity (PPX) +To get a PPX metric estimate on an ASR transcript, you need to run the following command: +```bash +python ppx.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt --cut-tail\ + --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json +``` +where `--cut-tail` tells the script to ignore the last token on each line (ASR puts the sequence ID there). + +### Self- and Auto-BLEU +```bash +python self_bleu.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt --cut-tail \ + --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json +``` + +### Continuation-BLEU +```bash +python continuation_eval.py --asr-transcript $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt \ + --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json +``` + +### AUC +Based on the metrics calculated above, we can estimate the AUC of the perplexity/diversity trade-off. We provide an illustration in a [Colab notebook](https://colab.research.google.com/drive/1pVPfOVax_PU3MkYdHRSsa-SI8GBUldNt?usp=sharing). diff --git a/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py new file mode 100644 index 0000000..72b92a3 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py @@ -0,0 +1,99 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from collections import defaultdict +import numpy as np +from misc.bleu_utils import sentence_bleu +import json +import warnings + + +def get_args(): + import argparse + + parser = argparse.ArgumentParser("Tool to calculate Continuation-BLEU2") + parser.add_argument('--asr-transcript', type=str, + help='Path to the transcript file.') + parser.add_argument('--prompts-description', type=str, + help='Path to the ground-truth continuation') + parser.add_argument('--manifest', type=str, required=True) + parser.add_argument('--take-shortest', type=int, default=1000) + + args = parser.parse_args() + + return args + + +def main(): + # NLTK produces warnings + warnings.filterwarnings("ignore") + + args = get_args() + + with open(args.prompts_description, 'r') as fin: + original_continuations = json.loads(fin.read()) + + sequence2length = [(k, v[0]) for k, v in original_continuations.items()] + assert all(float(v) >= 6.0 for (_, v) in sequence2length) # 6 seconds + + sequence2length.sort(key=lambda x: x[1]) + to_take = set(v[0] for v in sequence2length[:args.take_shortest]) + + with open(args.manifest, 'r') as fin: + fin.readline() + + linenum2file = dict([ + (i, l.split("__")[0]) for (i, l) in enumerate(fin) + ]) + + max_files = max(linenum2file.keys()) + continuations = defaultdict(list) + + mean_length_after = 0 + n_examples = 0 + + with open(args.asr_transcript, 'r') as fin: + for line in fin: + n_examples += 1 + line = line.split() + sequence_id = int(line[-1].split('-')[1][:-1]) + + assert sequence_id <= max_files + + sequence_name = linenum2file[sequence_id] + + continuations[sequence_name].append(line[:-1]) + mean_length_after += len(line) + + mean_length_after /= n_examples + print(f'Mean length of continuations, in words: {mean_length_after}') + metric_values = [] + + mean_ground_truth_words = 0 + n_examples = 0 + n_candidates = 0 + + for k, candidates in continuations.items(): + if k not in to_take: + continue + + n_examples += 1 + + ground_truth = original_continuations[k][1].split() + n_candidates += len(candidates) + bleu = sentence_bleu(candidates, ground_truth, weights=( + 0.5, 0.5), no_length_penalty=True, averaging_mode="geometric") + mean_ground_truth_words += len(ground_truth) + + metric_values.append(bleu) + + n = len(metric_values) + print( + f'Median BLEU over {n} examples: {np.median(metric_values)} +- {np.std(metric_values) / np.sqrt(n)}') + + +if __name__ == '__main__': + main() diff --git a/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py new file mode 100644 index 0000000..75cc527 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py @@ -0,0 +1,166 @@ +""" + +TODO: the code is take from Apache-2 Licensed NLTK: make sure we do this properly! + + +Copied over from nltk.tranlate.bleu_score. This code has two major changes: + - allows to turn off length/brevity penalty --- it has no sense for self-bleu, + - allows to use arithmetic instead of geometric mean +""" + +import math +import sys +from fractions import Fraction +import warnings +from collections import Counter +from nltk.translate.bleu_score import modified_precision, closest_ref_length, brevity_penalty, SmoothingFunction + + +def corpus_bleu( + list_of_references, + hypotheses, + weights=(0.25, 0.25, 0.25, 0.25), + smoothing_function=None, + auto_reweigh=False, + averaging_mode="geometric", + no_length_penalty=False +): + """ + Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all + the hypotheses and their respective references. + + Instead of averaging the sentence level BLEU scores (i.e. marco-average + precision), the original BLEU metric (Papineni et al. 2002) accounts for + the micro-average precision (i.e. summing the numerators and denominators + for each hypothesis-reference(s) pairs before the division). + + >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', + ... 'ensures', 'that', 'the', 'military', 'always', + ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] + >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', + ... 'ensures', 'that', 'the', 'military', 'will', 'forever', + ... 'heed', 'Party', 'commands'] + >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which', + ... 'guarantees', 'the', 'military', 'forces', 'always', + ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party'] + >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', + ... 'army', 'always', 'to', 'heed', 'the', 'directions', + ... 'of', 'the', 'party'] + + >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', + ... 'interested', 'in', 'world', 'history'] + >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', + ... 'because', 'he', 'read', 'the', 'book'] + + >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] + >>> hypotheses = [hyp1, hyp2] + >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS + 0.5920... + + The example below show that corpus_bleu() is different from averaging + sentence_bleu() for hypotheses + + >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1) + >>> score2 = sentence_bleu([ref2a], hyp2) + >>> (score1 + score2) / 2 # doctest: +ELLIPSIS + 0.6223... + + :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses + :type list_of_references: list(list(list(str))) + :param hypotheses: a list of hypothesis sentences + :type hypotheses: list(list(str)) + :param weights: weights for unigrams, bigrams, trigrams and so on + :type weights: list(float) + :param smoothing_function: + :type smoothing_function: SmoothingFunction + :param auto_reweigh: Option to re-normalize the weights uniformly. + :type auto_reweigh: bool + :return: The corpus-level BLEU score. + :rtype: float + """ + # Before proceeding to compute BLEU, perform sanity checks. + + p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches. + p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref. + hyp_lengths, ref_lengths = 0, 0 + + assert len(list_of_references) == len(hypotheses), ( + "The number of hypotheses and their reference(s) should be the " "same " + ) + + # Iterate through each hypothesis and their corresponding references. + for references, hypothesis in zip(list_of_references, hypotheses): + # For each order of ngram, calculate the numerator and + # denominator for the corpus-level modified precision. + for i, _ in enumerate(weights, start=1): + p_i = modified_precision(references, hypothesis, i) + p_numerators[i] += p_i.numerator + p_denominators[i] += p_i.denominator + + # Calculate the hypothesis length and the closest reference length. + # Adds them to the corpus-level hypothesis and reference counts. + hyp_len = len(hypothesis) + hyp_lengths += hyp_len + ref_lengths += closest_ref_length(references, hyp_len) + + # Calculate corpus-level brevity penalty. + if no_length_penalty and averaging_mode == 'geometric': + bp = 1.0 + elif no_length_penalty and averaging_mode == 'arithmetic': + bp = 0.0 + else: + assert not no_length_penalty + assert averaging_mode != 'arithmetic', 'Not sure how to apply length penalty when aurithmetic mode' + bp = brevity_penalty(ref_lengths, hyp_lengths) + + # Uniformly re-weighting based on maximum hypothesis lengths if largest + # order of n-grams < 4 and weights is set at default. + if auto_reweigh: + if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25): + weights = (1 / hyp_lengths,) * hyp_lengths + + # Collects the various precision values for the different ngram orders. + p_n = [ + Fraction(p_numerators[i], p_denominators[i], _normalize=False) + for i, _ in enumerate(weights, start=1) + ] + + # Returns 0 if there's no matching n-grams + # We only need to check for p_numerators[1] == 0, since if there's + # no unigrams, there won't be any higher order ngrams. + if p_numerators[1] == 0: + return 0 + + # If there's no smoothing, set use method0 from SmoothinFunction class. + if not smoothing_function: + smoothing_function = SmoothingFunction().method0 + # Smoothen the modified precision. + # Note: smoothing_function() may convert values into floats; + # it tries to retain the Fraction object as much as the + # smoothing method allows. + p_n = smoothing_function( + p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths + ) + + if averaging_mode == "geometric": + s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n)) + s = bp * math.exp(math.fsum(s)) + elif averaging_mode == "arithmetic": + s = (w_i * p_i for w_i, p_i in zip(weights, p_n)) + s = math.fsum(s) + + return s + + +def sentence_bleu( + references, + hypothesis, + weights=(0.25, 0.25, 0.25, 0.25), + smoothing_function=None, + auto_reweigh=False, + averaging_mode="geometric", + no_length_penalty=False +): + return corpus_bleu( + [references], [hypothesis], weights, smoothing_function, auto_reweigh, averaging_mode, no_length_penalty + ) \ No newline at end of file diff --git a/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py new file mode 100644 index 0000000..5b7e1e9 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py @@ -0,0 +1,69 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torchaudio +import argparse +import json +import pathlib + + +def get_args(): + parser = argparse.ArgumentParser( + "Assuring generated audio have the same length as ground-truth audio") + parser.add_argument('--samples_dir', required=True, type=str) + parser.add_argument('--out_dir', required=True, type=str) + parser.add_argument('--prompts_description', required=True, type=str) + return parser.parse_args() + + +def cut(src, tgt, l): + x, sr = torchaudio.load(str(src)) + assert sr == 16_000 + + x = x.squeeze() + target_frames = int(l * sr) + + flag = 0 + if target_frames <= x.size(0): + x = x[:target_frames] + flag = 1 + else: + flag = 0 + torchaudio.save(str(tgt), x.unsqueeze(0), sr) + return flag + + +def main(): + args = get_args() + tgt_dir = pathlib.Path(args.out_dir) + tgt_dir.mkdir(exist_ok=True, parents=True) + + total_files, sufficiently_long = 0, 0 + + with open(args.prompts_description, 'r') as f: + description = json.loads(f.read()) + + for src_f in pathlib.Path(args.samples_dir).glob('*.wav'): + name_prompt = src_f.with_suffix('').name.split('__')[0] + + assert name_prompt in description, f'Cannot find {name_prompt}!' + + target_length = description[name_prompt][0] + tgt_f = tgt_dir / (src_f.name) + + is_long_enough = cut(src_f, tgt_f, target_length) + sufficiently_long += is_long_enough + if not is_long_enough: + print(f'{src_f} is not long enough') + + total_files += 1 + + print( + f'Total files: {total_files}; sufficiently long: {sufficiently_long}') + + +if __name__ == '__main__': + main() diff --git a/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/dict.ltr.txt b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/dict.ltr.txt new file mode 100644 index 0000000..69929e1 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/dict.ltr.txt @@ -0,0 +1,28 @@ +| 94802 +E 51860 +T 38431 +A 33152 +O 31495 +N 28855 +I 28794 +H 27187 +S 26071 +R 23546 +D 18289 +L 16308 +U 12400 +M 10685 +W 10317 +C 9844 +F 9062 +G 8924 +Y 8226 +P 6890 +B 6339 +V 3936 +K 3456 +' 1023 +X 636 +J 598 +Q 437 +Z 213 diff --git a/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/ppx.py b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/ppx.py new file mode 100644 index 0000000..d6a40e4 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/ppx.py @@ -0,0 +1,122 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import numpy as np +import warnings + + +def get_target_sequences(manifest, ground_truth, to_take=1000): + import json + import pathlib + + with open(ground_truth, 'r') as fin: + original_continuations = json.loads(fin.read()) + + sequence2length = [(k, v[0]) for k, v in original_continuations.items()] + assert all(float(v) >= 6.0 for (_, v) in sequence2length) # 6 seconds + + sequence2length.sort(key=lambda x: x[1]) + to_take_sequences = set(v[0] for v in sequence2length[:to_take]) + to_take_ids = [] + + with open(manifest, 'r') as f: + f.readline() + + for i, line in enumerate(f.readlines()): + seq_id = line.split()[0] + seq_id = pathlib.Path(seq_id).name.split('__')[0] + + if seq_id in to_take_sequences: + to_take_ids.append(i) + + print(f'Took {len(to_take_ids)} ids') + return set(to_take_ids) + + +def get_args(): + import argparse + + parser = argparse.ArgumentParser("Evaluate PPX metric of a transcript.") + parser.add_argument('--asr-transcript', type=str, + help='Path to the transcript file.') + parser.add_argument('--cut-id', action='store_true', + help='Whether cut the first token (typically a seq id)') + parser.add_argument('--cut-tail', action='store_true', + help='Whether cut the last token (typically a speaker id)') + + parser.add_argument('--manifest', type=str, default=None) + parser.add_argument('--prompts-description', type=str, default=None) + + args = parser.parse_args() + + return args + + +def main(): + args = get_args() + + lm = torch.hub.load( + 'pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe') + + lm.eval().cuda() # disable dropout + + if args.manifest is None and args.prompts_description is None: + target_ids = None + else: + target_ids = get_target_sequences( + args.manifest, args.prompts_description) + + with open(args.asr_transcript, 'r') as fin: + lines = fin.readlines() + + if target_ids is not None: + filtered = [] + for line in lines: + line_id = line.split()[-1] + line_id = int(line_id.split('-')[1][:-1]) + if line_id in target_ids: + filtered.append(line) + lines = filtered + else: + pass + + if args.cut_id: + lines = [' '.join(x.split()[1:]) for x in lines] + if args.cut_tail: + lines = [' '.join(x.split()[:-1]) for x in lines] + lines = [x.strip().lower() for x in lines] + + def get_logprob(sent): return \ + lm.score(sent)['positional_scores'].mean().neg().item() + + logprobs = [get_logprob(l) for l in lines] + + filtered = [x for x in logprobs if not np.isnan(x)] + if len(filtered) != len(logprobs): + warnings.warn("NaNs detected!") + logprobs = filtered + + perplexities = [np.exp(l) for l in logprobs] + + for name, stats in [('logprob', logprobs), ('perplexity', perplexities)]: + mean = np.mean(stats) + sem = np.std(stats) / np.sqrt(len(stats)) + + median = np.median(stats) + interval = list(np.percentile(stats, [10, 90])) + + mean, sem, median, percentile10, percentile90 = [ + round(x, 2) for x in [mean, sem, median] + interval] + + print(name) + print(f"\tMean {mean} +- {sem}") + print( + f"\tMedian {median}, 90% confidence interval {percentile10}...{percentile90}") + + +if __name__ == '__main__': + main() diff --git a/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py new file mode 100644 index 0000000..062bb82 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py @@ -0,0 +1,201 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import nltk +from misc.bleu_utils import sentence_bleu +import warnings + + +def get_target_sequences(manifest, ground_truth, to_take=1000): + import json + import pathlib + + with open(ground_truth, 'r') as fin: + original_continuations = json.loads(fin.read()) + + sequence2length = [(k, v[0]) for k, v in original_continuations.items()] + assert all(float(v) >= 6.0 for (_, v) in sequence2length) # 6 seconds + + sequence2length.sort(key=lambda x: x[1]) + to_take_sequences = set(v[0] for v in sequence2length[:to_take]) + to_take_ids = [] + + with open(manifest, 'r') as f: + f.readline() + + for i, line in enumerate(f.readlines()): + seq_id = line.split()[0] + seq_id = pathlib.Path(seq_id).name.split('__')[0] + + if seq_id in to_take_sequences: + to_take_ids.append(i) + + print(f'Took {len(to_take_ids)} ids') + return set(to_take_ids) + + +def get_args(): + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument('--asr-transcript', type=str, + help='Path to the transcript file.') + + parser.add_argument('--manifest', required=True) + parser.add_argument('--prompts-description', required=True) + + parser.add_argument('--cut-id', action='store_true', + help='Whether cut the first token (typically a seq id)') + parser.add_argument('--cut-tail', action='store_true', + help='Whether cut the last token (typically a speaker id)') + parser.add_argument('--debug', action='store_true') + + args = parser.parse_args() + + return args + + +def get_self_bleu(utterances, averaging_mode, weights): + self_bleu = [] + + for i in range(len(utterances)): + hypo = utterances[i] + rest = utterances[:i] + utterances[i+1:] + + self_bleu.append(sentence_bleu(rest, hypo, weights, + no_length_penalty=True, averaging_mode=averaging_mode)) + + return self_bleu + + +def get_self_bleu2_arithmetic(utterances): + weights = (0.5, 0.5) # equal weight for unigrams and bigrams + return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights) + + +def get_self_bleu2_geometric(utterances): + weights = (0.5, 0.5) + return get_self_bleu(utterances, averaging_mode='geometric', weights=weights) + + +def get_auto_bleu2_arithmetic(utterances): + weights = (0.5, 0.5) + return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances] + + +def get_auto_bleu2_geometric(utterances): + weights = (0.5, 0.5) + return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances] + + +def get_auto_bleu3_geometric(utterances): + weights = (1./3, 1./3, 1./3) + return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances] + + +def get_auto_bleu3_arithmetic(utterances): + weights = (1./3, 1./3, 1./3) + return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances] + + +def get_self_bleu3_arithmetic(utterances): + weights = (1./3, 1./3, 1./3) + return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights) + + +def get_self_bleu3_geometric(utterances): + weights = (1./3, 1./3, 1./3) + return get_self_bleu(utterances, averaging_mode='geometric', weights=weights) + + +def auto_bleu(sentence, weights, mean_mode='arithmetic'): + if len(sentence) <= 1: + return 0 + + N = len(weights) + + bleu_n = np.zeros([N]) + for n in range(N): + targ_ngrams = list(nltk.ngrams(sentence, n+1)) + for p in range(len(targ_ngrams)): + left = sentence[:p] + right = sentence[(p+n+1):] + rest_ngrams = list(nltk.ngrams(left, n+1)) + \ + list(nltk.ngrams(right, n+1)) + # compute the nb of matching ngrams + bleu_n[n] += targ_ngrams[p] in rest_ngrams + bleu_n[n] /= len(targ_ngrams) # average them to get a proportion + + weights = np.array(weights) + if mean_mode == 'arithmetic': + return (bleu_n * weights).sum() + elif mean_mode == 'geometric': + return (bleu_n ** weights).prod() + else: + raise ValueError(f'Unknown agggregation mode {mean_mode}') + + +def main(): + from multiprocessing import Pool + + args = get_args() + target_ids = get_target_sequences(args.manifest, args.prompts_description) + + with open(args.asr_transcript, 'r') as fin: + lines = fin.readlines() + + terms = [x.strip().split() for x in lines] + filtered = [] + for term in terms: + line_id = int(term[-1].split('-')[1][:-1]) + if line_id in target_ids: + filtered.append(term) + terms = filtered + + if args.cut_id: + terms = [x[1:] for x in terms] + if args.cut_tail: + terms = [x[:-1] for x in terms] + + if args.debug: + terms = terms[:10] + + tasks = [ + ('Self-BLEU2-arithmetic', get_self_bleu2_arithmetic), + ('Self-BLEU2-geometric', get_self_bleu2_geometric), + ('Auto-BLEU2-arithmetic', get_auto_bleu2_arithmetic), + ('Auto-BLEU2-geometric', get_auto_bleu2_geometric), + + ('Self-BLEU3-arithmetic', get_self_bleu3_arithmetic), + ('Self-BLEU3-geometric', get_self_bleu3_geometric), + ('Auto-BLEU3-arithmetic', get_auto_bleu3_arithmetic), + ('Auto-BLEU3-geometric', get_auto_bleu3_geometric), + ] + + n_processes = min(16, len(tasks)) + with Pool(n_processes) as pool: + metrics = pool.map(run_f, [(t[1], terms) for t in tasks]) + + for (metric_name, _), metric in zip(tasks, metrics): + metric, sem = np.mean(metric), np.std(metric) / np.sqrt(len(metric)) + + metric, sem = [ + round(100 * x, 2) for x in [metric, sem] + ] + + print(f'{metric_name} {metric} +- {sem}') + + +def run_f(task_params): + f, terms = task_params + return f(terms) + + +if __name__ == '__main__': + # NLTK produces warnings + warnings.filterwarnings("ignore") + + main() diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/README.md b/fairseq/examples/textless_nlp/gslm/speech2unit/README.md new file mode 100644 index 0000000..9dff9d3 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/README.md @@ -0,0 +1,68 @@ +# Speech to Unit Model (speech2unit) + +## Acoustic Model +For quantizing speech we learn a K-means clustering over acoustic representations for which we either use Log-Mel Filterbank or pretrained acoustic representation models. For using pretrained models, please download from their respective locations linked below. +* [Modified CPC](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/cpc_big_ll6kh_top_ctc.pt) +* [HuBERT-Base](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) +* [Wav2Vec 2.0-Base](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt) + +## Quantization Model +You can download pretrained quantized model from the list below. + +K-Means Model | Download Link +|-|- +Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/km50/km.bin) +Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/km100/km.bin) +Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/km200/km.bin) +Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/km50/km.bin) +Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/km100/km.bin) +Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/km200/km.bin) +HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km50/km.bin) +HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km100/km.bin) +HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km200/km.bin) +wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/km50/km.bin) +wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/km100/km.bin) +wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/km200/km.bin) + +### Quantization +For quantizing speech with a given acoustic representation, please follow the steps below. +1. Learn K-means clustering model +``` +N_CLUSTERS=<number_of_clusters_used_for_kmeans> +TYPE=<one_of_logmel/cpc/hubert/w2v2> +CKPT_PATH=<path_of_pretrained_acoustic_model> +LAYER=<layer_of_acoustic_model_to_extract_features_from> +MANIFEST=<tab_separated_manifest_of_audio_files_for_training_kmeans> +KM_MODEL_PATH=<output_path_of_the_kmeans_model> + +PYTHONPATH=. python examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py \ + --num_clusters $N_CLUSTERS \ + --feature_type $TYPE \ + --checkpoint_path $CKPT_PATH \ + --layer $LAYER \ + --manifest_path $MANIFEST \ + --out_kmeans_model_path $KM_MODEL_PATH +``` +2. Quantize using the learned clusters +``` +MANIFEST=<tab_separated_manifest_of_audio_files_to_quantize> +OUT_QUANTIZED_FILE=<output_quantized_audio_file_path> + +python examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py \ + --feature_type $TYPE \ + --kmeans_model_path $KM_MODEL_PATH \ + --acoustic_model_path $CKPT_PATH \ + --layer $LAYER \ + --manifest_path $MANIFEST \ + --out_quantized_file_path $OUT_QUANTIZED_FILE \ + --extension ".flac" +``` + +Note about the manifest file is a file with paths and length of input audio files. The format of the file is as follows: +``` +<path_of_root_directory_containing_audio_files> +<relative_path_of_audio_file_1>\t<number_of_frames_1> +<relative_path_of_audio_file_2>\t<number_of_frames_1> +... +``` + diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/__init__.py b/fairseq/examples/textless_nlp/gslm/speech2unit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/__init__.py b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py new file mode 100644 index 0000000..7cf844a --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py @@ -0,0 +1,212 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +import time + +import numpy as np +from sklearn.cluster import MiniBatchKMeans + +import joblib +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( + get_and_dump_features, + get_features, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Learn K-means clustering over acoustic features." + ) + + # Features arguments + parser.add_argument( + "--in_features_path", type=str, default=None, help="Features file path" + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + help="Acoustic feature type", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--out_features_path", + type=str, + default=None, + help="Features file path to write to", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + help="Pretrained acoustic model checkpoint", + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--sample_pct", + type=float, + help="Percent data to use for K-means training", + default=0.1, + ) + + # K-means arguments + parser.add_argument( + "--num_clusters", type=int, help="Nubmer of clusters", default=50 + ) + parser.add_argument("--init", default="k-means++") + parser.add_argument( + "--max_iter", + type=int, + help="Maximum number of iterations for K-means training", + default=150, + ) + parser.add_argument( + "--batch_size", + type=int, + help="Batch size for K-means training", + default=10000, + ) + parser.add_argument("--tol", default=0.0, type=float) + parser.add_argument("--max_no_improvement", default=100, type=int) + parser.add_argument("--n_init", default=20, type=int) + parser.add_argument("--reassignment_ratio", default=0.5, type=float) + parser.add_argument( + "--out_kmeans_model_path", + type=str, + required=True, + help="Path to save K-means model", + ) + + # Leftovers + parser.add_argument( + "--seed", + type=int, + help="Random seed to use for K-means training", + default=1369, + ) + + return parser + + +def get_kmeans_model( + n_clusters, + init, + max_iter, + batch_size, + tol, + max_no_improvement, + n_init, + reassignment_ratio, + random_state, +): + return MiniBatchKMeans( + n_clusters=n_clusters, + init=init, + max_iter=max_iter, + batch_size=batch_size, + tol=tol, + max_no_improvement=max_no_improvement, + n_init=n_init, + reassignment_ratio=reassignment_ratio, + random_state=random_state, + verbose=1, + compute_labels=True, + init_size=None, + ) + + +def train_kmeans(kmeans_model, features_batch): + start_time = time.time() + kmeans_model.fit(features_batch) + time_taken = round((time.time() - start_time) // 60, 2) + return kmeans_model, time_taken + + +def main(args, logger): + # Features loading/extraction for K-means + if args.in_features_path: + # Feature loading + logger.info(f"Loading features from {args.in_features_path}...") + features_batch = np.load(args.in_features_path, allow_pickle=True) + else: + # Feature extraction + logger.info(f"Extracting {args.feature_type} acoustic features...") + features_batch = ( + get_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=args.sample_pct, + flatten=True, + ) + if not args.out_features_path + else get_and_dump_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=args.sample_pct, + flatten=True, + out_features_path=args.out_features_path, + ) + ) + if args.out_features_path: + logger.info( + f"Saved extracted features at {args.out_features_path}" + ) + logger.info(f"Features shape = {features_batch.shape}\n") + + # Learn and save K-means model + kmeans_model = get_kmeans_model( + n_clusters=args.num_clusters, + init=args.init, + max_iter=args.max_iter, + batch_size=args.batch_size, + tol=args.tol, + max_no_improvement=args.max_no_improvement, + n_init=args.n_init, + reassignment_ratio=args.reassignment_ratio, + random_state=args.seed, + ) + logger.info("Starting k-means training...") + kmeans_model, time_taken = train_kmeans( + kmeans_model=kmeans_model, features_batch=features_batch + ) + logger.info(f"...done k-means training in {time_taken} minutes") + inertia = -kmeans_model.score(features_batch) / len(features_batch) + logger.info(f"Total intertia: {round(inertia, 2)}\n") + + logger.info(f"Saving k-means model to {args.out_kmeans_model_path}") + os.makedirs(os.path.dirname(args.out_kmeans_model_path), exist_ok=True) + joblib.dump(kmeans_model, open(args.out_kmeans_model_path, "wb")) + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py new file mode 100644 index 0000000..031567c --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging + +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( + get_and_dump_features, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Compute and dump log mel fbank features." + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + help="Acoustic feature type", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--out_features_path", + type=str, + default=None, + help="Features file path to write to", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + help="Pretrained acoustic model checkpoint", + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--sample_pct", + type=float, + help="Percent data to use for K-means training", + default=0.1, + ) + parser.add_argument( + "--out_features_path", + type=str, + help="Path to save log mel fbank features", + ) + return parser + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +if __name__ == "__main__": + """ + Example command: + python ~/speechbot/clustering/dump_logmelfank_feats.py \ + --manifest_path /checkpoint/kushall/data/LJSpeech-1.1/asr_input_wavs_16k/train.tsv + --out_features_path /checkpoint/kushall/experiments/speechbot/logmelfbank/features/ljspeech/train.npy + """ + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + + logger.info(f"Extracting {args.feature_type} acoustic features...") + get_and_dump_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=args.sample_pct, + flatten=True, + out_features_path=args.out_features_path, + ) + logger.info(f"Saved extracted features at {args.out_features_path}") diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py new file mode 100644 index 0000000..dd95105 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py @@ -0,0 +1,141 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os + +import numpy as np + +import joblib +from examples.textless_nlp.gslm.speech2unit.clustering.utils import ( + get_audio_files, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( + get_features, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Quantize using K-means clustering over acoustic features." + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + required=True, + help="Acoustic feature type", + ) + parser.add_argument( + "--acoustic_model_path", + type=str, + help="Pretrained acoustic model checkpoint" + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--kmeans_model_path", + type=str, + required=True, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--features_path", + type=str, + default=None, + help="Features file path. You don't need to enter acoustic model details if you have dumped features", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--out_quantized_file_path", + required=True, + type=str, + help="File path of quantized output.", + ) + parser.add_argument( + "--extension", type=str, default=".flac", help="Features file path" + ) + parser.add_argument( + "--channel_id", + choices=['1', '2'], + help="The audio channel to extract the units in case of stereo file.", + default=None, + ) + parser.add_argument( + "--hide-fname", action='store_true', + help="Hide file names in the output file." + ) + return parser + + +def main(args, logger): + # Feature extraction + if args.features_path is not None: + logger.info(f"Loading acoustic features from {args.features_path}...") + features_batch = np.load(args.features_path) + else: + logger.info(f"Extracting {args.feature_type} acoustic features...") + features_batch = get_features( + feature_type=args.feature_type, + checkpoint_path=args.acoustic_model_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=1.0, + flatten=False, + channel_id=int(args.channel_id) if args.channel_id else None, + ) + logger.info( + f"Features extracted for {len(features_batch)} utterances.\n" + ) + logger.info( + f"Dimensionality of representation = {features_batch[0].shape[1]}" + ) + + # K-means model + logger.info(f"Loading K-means model from {args.kmeans_model_path} ...") + kmeans_model = joblib.load(open(args.kmeans_model_path, "rb")) + kmeans_model.verbose = False + + _, fnames, _ = get_audio_files(args.manifest_path) + + os.makedirs(os.path.dirname(args.out_quantized_file_path), exist_ok=True) + print(f"Writing quantized predictions to {args.out_quantized_file_path}") + with open(args.out_quantized_file_path, "w") as fout: + for i, feats in enumerate(features_batch): + pred = kmeans_model.predict(feats) + pred_str = " ".join(str(p) for p in pred) + base_fname = os.path.basename(fnames[i]).rstrip('.'+args.extension.lstrip('.')) + if args.channel_id is not None: + base_fname = base_fname+f'-channel{args.channel_id}' + if not args.hide_fname: + fout.write(f"{base_fname}|{pred_str}\n") + else: + fout.write(f"{pred_str}\n") + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/utils.py b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/utils.py new file mode 100644 index 0000000..cf08d1f --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/utils.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Tuple + + +def get_audio_files(manifest_path: str) -> Tuple[str, List[str], List[int]]: + fnames, sizes = [], [] + with open(manifest_path, "r") as f: + root_dir = f.readline().strip() + for line in f: + items = line.strip().split("\t") + assert ( + len(items) == 2 + ), f"File must have two columns separated by tab. Got {line}" + fnames.append(items[0]) + sizes.append(int(items[1])) + return root_dir, fnames, sizes diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py new file mode 100644 index 0000000..2ea3890 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py @@ -0,0 +1,204 @@ +import soundfile as sf +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class CpcFeatureReader: + """ + Wrapper class to run inference on CPC model. + Helps extract features for a given audio file. + """ + + def __init__( + self, + checkpoint_path, + layer, + use_encoder_layer=False, + norm_features=False, + sample_rate=16000, + max_chunk=64000, + use_cuda=True, + ): + self.model = load_cpc_model(checkpoint_path, layer).eval() + self.sample_rate = sample_rate + self.max_chunk = max_chunk + self.norm_features = norm_features + self.use_encoder_layer = use_encoder_layer + self.use_cuda = use_cuda + if self.use_cuda: + self.model.cuda() + + def read_audio(self, path, ref_len=None, channel_id=None): + wav, sr = sf.read(path) + if channel_id is not None: + assert wav.ndim == 2, \ + f"Expected stereo input when channel_id is given ({path})" + assert channel_id in [1, 2], \ + "channel_id is expected to be in [1, 2]" + wav = wav[:, channel_id-1] + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + assert sr == self.sample_rate, sr + if ref_len is not None and abs(ref_len - len(wav)) > 160: + print(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, file_path, ref_len=None, channel_id=None): + x = self.read_audio(file_path, ref_len, channel_id) + # Inspired from CPC_audio feature_loader.py + with torch.no_grad(): + x = torch.from_numpy(x).float() + if self.use_cuda: + x = x.cuda() + x = x.view(1, 1, -1) + size = x.size(2) + feat = [] + start = 0 + while start < size: + if start + self.max_chunk > size: + break + x_chunk = x[..., start : start + self.max_chunk] + feat_chunk = self.model.extract_features( + source=x_chunk, + get_encoded=self.use_encoder_layer, + norm_output=self.norm_features, + ) + feat.append(feat_chunk) + start += self.max_chunk + + if start < size: + x_chunk = x[:, -self.max_chunk :] + feat_chunk = self.model.extract_features( + source=x_chunk, + get_encoded=self.use_encoder_layer, + norm_output=self.norm_features, + ) + df = x_chunk.size(2) // feat_chunk.size(1) + delta = (size - start) // df + feat.append(feat_chunk[:, -delta:]) + return torch.cat(feat, 1).squeeze(0) + + +def load_cpc_model(checkpoint_path, layer=None): + state_dict = torch.load(checkpoint_path) + weights = state_dict["weights"] + config = state_dict["config"] + if layer is not None: + config["nLevelsGRU"] = layer + + encoder = CPCEncoder(config["hiddenEncoder"]) + ar_net = CPCAR( + config["hiddenEncoder"], config["hiddenGar"], False, config["nLevelsGRU"] + ) + + model = CPCModel(encoder, ar_net) + model.load_state_dict(weights, strict=False) + model.config = config + + return model + + +class ChannelNorm(nn.Module): + def __init__(self, num_features, epsilon=1e-05, affine=True): + super(ChannelNorm, self).__init__() + if affine: + self.weight = nn.parameter.Parameter(torch.Tensor(1, num_features, 1)) + self.bias = nn.parameter.Parameter(torch.Tensor(1, num_features, 1)) + else: + self.weight = None + self.bias = None + self.epsilon = epsilon + self.p = 0 + self.affine = affine + self.reset_parameters() + + def reset_parameters(self): + if self.affine: + torch.nn.init.ones_(self.weight) + torch.nn.init.zeros_(self.bias) + + def forward(self, x): + cum_mean = x.mean(dim=1, keepdim=True) + cum_var = x.var(dim=1, keepdim=True) + x = (x - cum_mean) * torch.rsqrt(cum_var + self.epsilon) + if self.weight is not None: + x = x * self.weight + self.bias + return x + + +class CPCEncoder(nn.Module): + def __init__(self, hidden_dim=512): + super(CPCEncoder, self).__init__() + self.conv0 = nn.Conv1d(1, hidden_dim, 10, stride=5, padding=3) + self.batchNorm0 = ChannelNorm(hidden_dim) + self.conv1 = nn.Conv1d(hidden_dim, hidden_dim, 8, stride=4, padding=2) + self.batchNorm1 = ChannelNorm(hidden_dim) + self.conv2 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) + self.batchNorm2 = ChannelNorm(hidden_dim) + self.conv3 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) + self.batchNorm3 = ChannelNorm(hidden_dim) + self.conv4 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) + self.batchNorm4 = ChannelNorm(hidden_dim) + self.DOWNSAMPLING = 160 + + def get_output_dim(self): + return self.conv4.out_channels + + def forward(self, x): + x = F.relu(self.batchNorm0(self.conv0(x))) + x = F.relu(self.batchNorm1(self.conv1(x))) + x = F.relu(self.batchNorm2(self.conv2(x))) + x = F.relu(self.batchNorm3(self.conv3(x))) + x = F.relu(self.batchNorm4(self.conv4(x))) + return x + + +class CPCAR(nn.Module): + def __init__(self, dim_encoded, dim_output, keep_hidden, num_layers): + super(CPCAR, self).__init__() + self.baseNet = nn.LSTM( + dim_encoded, dim_output, num_layers=num_layers, batch_first=True + ) + self.hidden = None + self.keep_hidden = keep_hidden + + def get_output_dim(self): + return self.baseNet.hidden_size + + def forward(self, x): + try: + self.baseNet.flatten_parameters() + except RuntimeError: + pass + x, h = self.baseNet(x, self.hidden) + if self.keep_hidden: + if isinstance(h, tuple): + self.hidden = tuple(x.detach() for x in h) + else: + self.hidden = h.detach() + return x + + +class CPCModel(nn.Module): + def __init__(self, encoder, ar_net): + super(CPCModel, self).__init__() + self.gEncoder = encoder + self.gAR = ar_net + self.config = None + + def forward(self, x, label): + encoded = self.gEncoder(x).permute(0, 2, 1) + cpc_feature = self.gAR(encoded) + return cpc_feature, encoded, label + + def extract_features(self, source, get_encoded=False, norm_output=False): + cpc_feature, encoded, _ = self.forward(source, None) + if get_encoded: + cpc_feature = encoded + if norm_output: + mean = cpc_feature.mean(dim=1, keepdim=True) + var = cpc_feature.var(dim=1, keepdim=True) + cpc_feature = (cpc_feature - mean) / torch.sqrt(var + 1e-08) + return cpc_feature diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py new file mode 100644 index 0000000..4fef859 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import fairseq +import soundfile as sf +import torch.nn.functional as F + + +class HubertFeatureReader: + """ + Wrapper class to run inference on HuBERT model. + Helps extract features for a given audio file. + """ + + def __init__(self, checkpoint_path, layer, max_chunk=1600000, use_cuda=True): + ( + model, + cfg, + task, + ) = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [checkpoint_path] + ) + self.model = model[0].eval() + self.task = task + self.layer = layer + self.max_chunk = max_chunk + self.use_cuda = use_cuda + if self.use_cuda: + self.model.cuda() + + def read_audio(self, path, ref_len=None, channel_id=None): + wav, sr = sf.read(path) + if channel_id is not None: + assert wav.ndim == 2, \ + f"Expected stereo input when channel_id is given ({path})" + assert channel_id in [1, 2], \ + "channel_id is expected to be in [1, 2]" + wav = wav[:, channel_id-1] + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + assert sr == self.task.cfg.sample_rate, sr + if ref_len is not None and abs(ref_len - len(wav)) > 160: + print(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, file_path, ref_len=None, channel_id=None): + x = self.read_audio(file_path, ref_len, channel_id) + with torch.no_grad(): + x = torch.from_numpy(x).float() + if self.use_cuda: + x = x.cuda() + if self.task.cfg.normalize: + x = F.layer_norm(x, x.shape) + x = x.view(1, -1) + + feat = [] + for start in range(0, x.size(1), self.max_chunk): + x_chunk = x[:, start: start + self.max_chunk] + feat_chunk, _ = self.model.extract_features( + source=x_chunk, + padding_mask=None, + mask=False, + output_layer=self.layer, + ) + feat.append(feat_chunk) + return torch.cat(feat, 1).squeeze(0) diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/logmel_feature_reader.py b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/logmel_feature_reader.py new file mode 100644 index 0000000..5879da7 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/logmel_feature_reader.py @@ -0,0 +1,34 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import soundfile as sf +import torch +import torchaudio.compliance.kaldi as kaldi + + +class LogMelFeatureReader: + """ + Wrapper class to run inference on HuBERT model. + Helps extract features for a given audio file. + """ + + def __init__(self, *args, **kwargs): + self.num_mel_bins = kwargs.get("num_mel_bins", 80) + self.frame_length = kwargs.get("frame_length", 25.0) + + def get_feats(self, file_path, channel_id=None): + wav, sr = sf.read(file_path) + if channel_id is not None: + assert wav.ndim == 2, \ + f"Expected stereo input when channel_id is given ({file_path})" + wav = wav[:, channel_id-1] + feats = torch.from_numpy(wav).float() + feats = kaldi.fbank( + feats.unsqueeze(0), + num_mel_bins=self.num_mel_bins, + frame_length=self.frame_length, + sample_frequency=sr, + ) + return feats diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py new file mode 100644 index 0000000..2eca68e --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py @@ -0,0 +1,127 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import gc +import os +import random +import shutil +import numpy as np + +import torch +import tqdm +from examples.textless_nlp.gslm.speech2unit.pretrained.cpc_feature_reader import ( + CpcFeatureReader, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.hubert_feature_reader import ( + HubertFeatureReader, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.logmel_feature_reader import ( + LogMelFeatureReader, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.w2v2_feature_reader import ( + Wav2VecFeatureReader, +) + + +def get_feature_reader(feature_type): + if feature_type == "logmel": + return LogMelFeatureReader + elif feature_type == "hubert": + return HubertFeatureReader + elif feature_type == "w2v2": + return Wav2VecFeatureReader + elif feature_type == "cpc": + return CpcFeatureReader + else: + raise NotImplementedError(f"{feature_type} is not supported.") + + +def get_feature_iterator( + feature_type, checkpoint_path, layer, manifest_path, sample_pct, channel_id +): + feature_reader_cls = get_feature_reader(feature_type) + with open(manifest_path, "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + file_path_list = [ + os.path.join(root, line.split("\t")[0]) + for line in lines + if len(line) > 0 + ] + if sample_pct < 1.0: + file_path_list = random.sample( + file_path_list, int(sample_pct * len(file_path_list)) + ) + num_files = len(file_path_list) + reader = feature_reader_cls( + checkpoint_path=checkpoint_path, layer=layer + ) + + def iterate(): + for file_path in file_path_list: + feats = reader.get_feats(file_path, channel_id=channel_id) + yield feats.cpu().numpy() + + return iterate, num_files + + +def get_features( + feature_type, checkpoint_path, layer, manifest_path, sample_pct, flatten, channel_id +): + generator, num_files = get_feature_iterator( + feature_type=feature_type, + checkpoint_path=checkpoint_path, + layer=layer, + manifest_path=manifest_path, + sample_pct=sample_pct, + channel_id=channel_id + ) + iterator = generator() + + features_list = [] + for features in tqdm.tqdm(iterator, total=num_files): + features_list.append(features) + + # Explicit clean up + del iterator + del generator + gc.collect() + torch.cuda.empty_cache() + + if flatten: + return np.concatenate(features_list) + + return features_list + + +def get_and_dump_features( + feature_type, + checkpoint_path, + layer, + manifest_path, + sample_pct, + flatten, + out_features_path, +): + # Feature extraction + features_batch = get_features( + feature_type=feature_type, + checkpoint_path=checkpoint_path, + layer=layer, + manifest_path=manifest_path, + sample_pct=sample_pct, + flatten=flatten, + ) + + # Save features + out_dir_path = os.path.dirname(out_features_path) + os.makedirs(out_dir_path, exist_ok=True) + shutil.copyfile( + manifest_path, + os.path.join(out_dir_path, os.path.basename(manifest_path)), + ) + np.save(out_features_path, features_batch) + + return features_batch diff --git a/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/w2v2_feature_reader.py b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/w2v2_feature_reader.py new file mode 100644 index 0000000..9f9da6c --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/w2v2_feature_reader.py @@ -0,0 +1,56 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import fairseq +import soundfile as sf + + +class Wav2VecFeatureReader: + """ + Wrapper class to run inference on Wav2Vec 2.0 model. + Helps extract features for a given audio file. + """ + + def __init__(self, checkpoint_path, layer, use_cuda=True): + state = fairseq.checkpoint_utils.load_checkpoint_to_cpu( + checkpoint_path + ) + + w2v_args = state["args"] + self.task = fairseq.tasks.setup_task(w2v_args) + model = self.task.build_model(w2v_args) + model.load_state_dict(state["model"], strict=True) + model.eval() + self.model = model + self.layer = layer + self.use_cuda = use_cuda + if self.use_cuda: + self.model.cuda() + + def read_audio(self, fname, channel_id=None): + wav, sr = sf.read(fname) + if channel_id is not None: + assert wav.ndim == 2, \ + f"Expected stereo input when channel_id is given ({fname})" + assert channel_id in [1, 2], \ + "channel_id is expected to be in [1, 2]" + wav = wav[:, channel_id-1] + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + assert sr == self.task.cfg.sample_rate, sr + return wav + + def get_feats(self, file_path, channel_id=None): + x = self.read_audio(file_path, channel_id) + with torch.no_grad(): + source = torch.from_numpy(x).view(1, -1).float() + if self.use_cuda: + source = source.cuda() + res = self.model( + source=source, mask=False, features_only=True, layer=self.layer + ) + return res["layer_results"][self.layer][0].squeeze(1) diff --git a/fairseq/examples/textless_nlp/gslm/tools/README.md b/fairseq/examples/textless_nlp/gslm/tools/README.md new file mode 100644 index 0000000..3858348 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/tools/README.md @@ -0,0 +1,25 @@ +# GSLM Tools + +## Resynthesis +You can use the command line tool below to input an audio file and get the resynthesized audio. This tool implements the unsupervised method for resynthesis described in the paper. The way to invoke the command line tool is shown below. +``` +FAIRSEQ_ROOT=<path_to_your_fairseq_repo_root> +TYPE=<one_of_logmel/cpc/hubert/w2v2> +ACOUSTIC_MODEL_PATH=<path_of_pretrained_acoustic_model> +LAYER=<layer_of_acoustic_model_to_extract_features_from> +KM_MODEL_PATH=<output_path_of_the_kmeans_model> +TTS_MODEL_PATH=<unit2speech_model_file_path> +# A text file containing the codes, one per line +CODE_DICT_PATH=<unit2speech_code_dict_path> +WAVEGLOW_PATH=<path_where_you_have_downloaded_waveglow_checkpoint> + +PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/tools/resynthesize_speech.py \ + --feature_type $TYPE \ + --acoustic_model_path $ACOUSTIC_MODEL_PATH \ + --layer $LAYER \ + --kmeans_model_path $KM_MODEL_PATH \ + --tts_model_path $TTS_MODEL_PATH \ + --code_dict_path $CODE_DICT_PATH \ + --waveglow_path $WAVEGLOW_PATH \ + --max_decoder_steps 2000 +``` \ No newline at end of file diff --git a/fairseq/examples/textless_nlp/gslm/tools/resynthesize_speech.py b/fairseq/examples/textless_nlp/gslm/tools/resynthesize_speech.py new file mode 100644 index 0000000..3098772 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/tools/resynthesize_speech.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import gc +import logging +import os + +import joblib +import soundfile as sf +import torch +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import get_feature_reader +from examples.textless_nlp.gslm.unit2speech.tts_data import TacotronInputDataset +from examples.textless_nlp.gslm.unit2speech.utils import ( + load_tacotron, + load_waveglow, + synthesize_audio, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser(description="GSLM U2S tool") + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + required=True, + help="Acoustic feature type", + ) + parser.add_argument( + "--acoustic_model_path", + type=str, + help="Pretrained acoustic model checkpoint", + ) + parser.add_argument("--layer", type=int, help="Layer of acoustic model") + parser.add_argument( + "--kmeans_model_path", + type=str, + required=True, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--tts_model_path", + type=str, + help="TTS model file path to use for inference", + ) + parser.add_argument( + "--code_dict_path", + type=str, + help="Code dict file path to use for inference", + ) + parser.add_argument( + "--waveglow_path", + type=str, + help="Waveglow (vocoder) model file path to use for inference", + ) + parser.add_argument("--max_decoder_steps", type=int, default=2000) + parser.add_argument("--denoiser_strength", type=float, default=0.1) + return parser + + +################################################ +def main(args, logger): + # Acoustic Model + logger.info(f"Loading acoustic model from {args.tts_model_path}...") + feature_reader_cls = get_feature_reader(args.feature_type) + reader = feature_reader_cls( + checkpoint_path=args.acoustic_model_path, layer=args.layer + ) + + # K-means Model + logger.info(f"Loading K-means model from {args.kmeans_model_path} ...") + kmeans_model = joblib.load(open(args.kmeans_model_path, "rb")) + kmeans_model.verbose = False + + # TTS Model + logger.info(f"Loading TTS model from {args.tts_model_path}...") + tacotron_model, sample_rate, hparams = load_tacotron( + tacotron_model_path=args.tts_model_path, + max_decoder_steps=args.max_decoder_steps, + ) + + # Waveglow Model + logger.info(f"Loading Waveglow model from {args.waveglow_path}...") + waveglow, denoiser = load_waveglow(waveglow_path=args.waveglow_path) + + # Dataset + if not os.path.exists(hparams.code_dict): + hparams.code_dict = args.code_dict_path + tts_dataset = TacotronInputDataset(hparams) + + iters = 0 + while True: + in_file_path = input("Input: Enter the full file path of audio file...\n") + out_file_path = input("Output: Enter the full file path of audio file...\n") + feats = reader.get_feats(in_file_path).cpu().numpy() + iters += 1 + if iters == 1000: + gc.collect() + torch.cuda.empty_cache() + + quantized_units = kmeans_model.predict(feats) + quantized_units_str = " ".join(map(str, quantized_units)) + + tts_input = tts_dataset.get_tensor(quantized_units_str) + mel, aud, aud_dn, has_eos = synthesize_audio( + tacotron_model, + waveglow, + denoiser, + tts_input.unsqueeze(0), + strength=args.denoiser_strength, + ) + sf.write(f"{out_file_path}", aud_dn[0].cpu().float().numpy(), sample_rate) + logger.info("Resynthesis done!\n") + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/fairseq/examples/textless_nlp/gslm/ulm/README.md b/fairseq/examples/textless_nlp/gslm/ulm/README.md new file mode 100644 index 0000000..0145912 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/ulm/README.md @@ -0,0 +1,72 @@ +# Unit Language Model (ULM) + +Here you can find links to the pre-trained ULMs and instructions on training new models using fairseq. At the end of the page, we also share how to run sampling for those models and provide pointers to the transcribed prompts we used. + +## Pre-trained models + +Using the links below, you can download pre-trained models for various unit types and vocabulary sizes: + +| | 50 | 100 | 200 +|-|-|-|- +| LogMel Filterbank | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km50/logmel50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km100/logmel100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km200/logmel200_lm.tgz) +| Modified CPC | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km50/cpc50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km100/cpc100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km200/cpc200_lm.tgz) +| HuBERT | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km50/hubert50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km100/hubert100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km200/hubert200_lm.tgz) +| Wav2Vec 2.0 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km50/w2v2_50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km100/w2v2_100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km200/w2v2_200_lm.tgz) + + +## Preprocessing data +Assuming that unit-transcribed train, valid, and test sets are located in `data/train.txt`, `data/valid.txt`, and `data/test.txt`, respectively, +we run the following command to get a preprocessed version of the datast in `data-bin`: + +```bash +fairseq-preprocess --only-source \ + --trainpref data/train.txt --validpref data/valid.txt --testpref data/test.txt \ + --destdir data-bin/ --workers 40 +``` +As a result, the `data-bin` directory should appear. + +## Fitting a Unit Language Model (ULM) +As an ULM, we train a standard fairseq Transformer LM. Assuming 8 GPUs used for training, a good starting point for an ULM training would be: +```bash + fairseq-train data-bin/ \ + --task=language_modeling \ + --arch=transformer_lm_big \ + --share-decoder-input-output-embed \ + --dropout=0.1 \ + --attention-dropout=0.1 \ + --optimizer=adam \ + --adam-betas='(0.9, 0.98)' \ + --clip-norm=1.0 \ + --lr=0.0005 \ + --lr-scheduler=inverse_sqrt \ + --warmup-updates=4000 \ + --warmup-init-lr=1e-07 \ + --tokens-per-sample=3072 \ + --update-freq=16 \ + --max-tokens=4096 \ + --num-workers=4 \ + --skip-invalid-size-inputs-valid-test \ + --max-update=500000 \ + --log-interval=10 \ + --seed=100501 \ + --fp16 \ + --sample-break-mode=eos +``` +This command will train a Transformer-large model (12 layers). You can train other standard LM models provided by fairseq, e.g. specify `--arch=transformer_lm` to train a smaller (6-layer) Transformer model. When training with a different number of GPUs, it might be a good idea to adjust the `update-freq` parameter. To save the GPU memory at an expense of additional computation, it can be useful to enable activation checkpointing with `--checkpoint-activations`. + +## Sampling from an ULM +Once an ULM was trained, we can use it for generating new utterances. Suppose, that the prompts are given in a file named `prompts.txt`. Then we can sample continuations by running the following command: + +```bash + python sample.py data-bin/ \ + --path=checkpoints/checkpoint_best.pt --task=language_modeling --sampling --temperature=0.7 \ + --seed=1 --prompts=prompts.txt --output=samples.txt --max-len-a=0 --max-len-b=500 \ + --prefix-size=-1 --batch-size=16 --fp16 --samples-per-prompt=10 +``` +Here, `--prefix-size` controls the number of tokens that are used to prime the ULM. When set to a positive value, the sampling script will take first `prefix-size` tokens to prompt the ULM; with `0` it runs unconditional sampling and with `-1` the entire prompt is used. +`--samples-per-prompt` specifies how many utterances are generated with every prompt which can be useful when generating multiple prompt continuations. In this command, `--max-len-a` and `--max-len-b` control the number of generated tokens. + +When using a pretrained model from above, `data-bin` should point to the unpacked directory (with `dict.txt` file). + +Evaluation-time, to generate prompts, we used utterances from LibriSpeech dev-clean and test-clean that are longer than 6s. We took first 3s from an utterance as a prompt. Unit transcripts of those prompts can be downloaded here: [[dev]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/dev_prompts.tgz) [[test]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/test_prompts.tgz) + diff --git a/fairseq/examples/textless_nlp/gslm/ulm/sample.py b/fairseq/examples/textless_nlp/gslm/ulm/sample.py new file mode 100644 index 0000000..77302a6 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/ulm/sample.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Sample from a trained LM; hacked fairseq-interactive +""" +from collections import namedtuple +import os +import ast +import numpy as np + +from fairseq import checkpoint_utils, options, tasks, utils + +import tqdm + +Batch = namedtuple('Batch', 'ids src_tokens src_lengths') +Translation = namedtuple('Translation', 'src_str hypos pos_scores alignments') + + +def make_batches(lines, args, task, max_positions): + tokens = [ + task.source_dictionary.encode_line( + src_str, add_if_not_exist=False + ).long() + for src_str in lines + ] + lengths = [t.numel() for t in tokens] + itr = task.get_batch_iterator( + dataset=task.build_dataset_for_inference(tokens, lengths), + max_tokens=args.dataset.max_tokens, + max_sentences=args.dataset.batch_size, + max_positions=max_positions, + ignore_invalid_inputs=args.dataset.skip_invalid_size_inputs_valid_test + ).next_epoch_itr(shuffle=False) + for batch in itr: + yield Batch( + ids=batch['id'], + src_tokens=batch['net_input']['src_tokens'], src_lengths=batch['net_input']['src_lengths'], + ) + + +def main(args): + arg_prompts = args.prompts + arg_output = args.output + arg_debug = args.debug + arg_sample_size = args.samples_per_prompt + + try: + from fairseq.dataclass.utils import convert_namespace_to_omegaconf + args = convert_namespace_to_omegaconf(args) + except: + pass + + # if args.max_tokens is None and args.max_sentences is None: + if args.common.seed is not None: + np.random.seed(args.common.seed) + utils.set_torch_seed(args.common.seed) + + if args.generation.sampling: + args.generation.nbest = args.generation.beam = arg_sample_size + + task = tasks.setup_task(args.task) + + overrides = ast.literal_eval(args.common_eval.model_overrides) + + models, _model_args = checkpoint_utils.load_model_ensemble( + args.common_eval.path.split(os.pathsep), + arg_overrides=overrides, + task=task, + suffix=getattr(args, "checkpoint_suffix", ""), + ) + + # Set dictionaries + src_dict = task.source_dictionary + tgt_dict = task.target_dictionary + + # Optimize ensemble for generation + for model in models: + model.prepare_for_inference_(args) + model.cuda() + + # Load alignment dictionary for unknown word replacement + # (None if no unknown word replacement, empty if no path to align dictionary) + align_dict = utils.load_align_dict(args.generation.replace_unk) + + max_positions = utils.resolve_max_positions( + task.max_positions(), + *[model.max_positions() for model in models] + ) + + output_file = open(arg_output, 'w') + + with open(arg_prompts, 'r') as fin: + lines = fin.readlines() + + split = [x.split('|', 1) for x in lines] + seq_id = [x[0] for x in split] + prompts = [x[1] for x in split] + + if args.generation.prefix_size >= 0: + prompts = [' '.join(l.split()[:args.generation.prefix_size]) + for l in prompts] + + if arg_debug: + prompts = prompts[:10] + + generator = task.build_generator(models, args.generation) + + start_id = 0 + pbar = tqdm.tqdm(total=len(prompts)) + for batch in make_batches(prompts, args, task, max_positions): + src_tokens = batch.src_tokens + src_lengths = batch.src_lengths + src_tokens = src_tokens.cuda() + src_lengths = src_lengths.cuda() + + sample = { + 'net_input': { + 'src_tokens': src_tokens, + 'src_lengths': src_lengths, + }, + } + + results = [] + translations = task.inference_step(generator, models, sample) + for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)): + src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad()) + results.append((i + start_id, src_tokens_i, hypos)) + + # sort output to match input order + for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]): + if src_dict is not None: + src_str = src_dict.string( + src_tokens, args.common_eval.post_process) + + # Process top predictions + for hypo_id, hypo in enumerate(hypos): + _hypo_tokens, hypo_str, _alignment = utils.post_process_prediction( + hypo_tokens=hypo['tokens'].int().cpu(), + src_str=src_str, + alignment=hypo['alignment'], + align_dict=align_dict, + tgt_dict=tgt_dict, + remove_bpe=args.common_eval.post_process, + ) + + detok_hypo_str = hypo_str + utterance = detok_hypo_str + print(f'{seq_id[id]}__{hypo_id}|{utterance}', file=output_file) + pbar.update(1) + start_id += len(results) + + # output_file.close() + + +def cli_main(): + parser = options.get_interactive_generation_parser() + parser.add_argument('--prompts', type=str, default=None, required=True) + parser.add_argument('--output', type=str, default=None, required=True) + parser.add_argument('--debug', action='store_true') + parser.add_argument('--samples-per-prompt', type=int, default=1) + + args = options.parse_args_and_arch(parser) + + np.random.seed(args.seed) + utils.set_torch_seed(args.seed) + + main(args) + + +if __name__ == '__main__': + cli_main() diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/fairseq/examples/textless_nlp/gslm/unit2speech/README.md new file mode 100644 index 0000000..e616013 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/README.md @@ -0,0 +1,40 @@ +# Unit to Speech Model (unit2speech) + +Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). + +Upstream Units | Download Links | model md5 +|-|-|- +Log Mel Filterbank + KM50 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/code_dict) | 932b3b8527c0125f5f964b57762eba49 +Log Mel Filterbank + KM100 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/code_dict) | cde0b0d278a39011d0acbd5df27abdf4 +Log Mel Filterbank + KM200 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/code_dict) | dba0f1d4de64bc7976718834010b23e7 +Modified CPC + KM50 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/code_dict) | a585e8dd8890ea56164f17635dd8e613 +Modified CPC + KM100 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/code_dict) | 5c0ee2869b4f483d17f37f1a41a548e0 +Modified CPC + KM200 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/code_dict) | 2f0c9951cf37020d9464514bff48bc5d +HuBERT Base + KM50 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/code_dict) | 85ffce8baec5aa90035ab696fe676fce +HuBERT Base + KM100 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/code_dict) | df4a9c6ffd1bb00c91405432c234aba3 +HuBERT Base + KM200 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/code_dict) | ac72f2c0c563589819bec116c7f8d274 +wav2vec 2.0 Large + KM50 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/code_dict) | e3503d0ad822b2c24b89f68b857fedff +wav2vec 2.0 Large + KM100 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/code_dict) | eb3666e456ae4c96bf2a1eec825c13ed +wav2vec 2.0 Large + KM200 | [model](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt) - [code_dict](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/code_dict) | 777d343e963c4d64f04d78eef032f4e8 + +## Run inference using a unit2speech model +* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect` +* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder. + +Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with. +``` +FAIRSEQ_ROOT=<path_to_your_fairseq_repo_root> +TTS_MODEL_PATH=<unit2speech_model_file_path> +QUANTIZED_UNIT_PATH=<quantized_audio_file_path> +OUT_DIR=<dir_to_dump_synthesized_audio_files> +WAVEGLOW_PATH=<path_where_you_have_downloaded_waveglow_checkpoint> +CODE_DICT_PATH=<unit2speech_code_dict_path> + +PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \ + --tts_model_path $TTS_MODEL_PATH \ + --quantized_unit_path $QUANTIZED_UNIT_PATH \ + --out_audio_dir $OUT_DIR \ + --waveglow_path $WAVEGLOW_PATH \ + --code_dict_path $CODE_DICT_PATH \ + --max_decoder_steps 2000 +``` diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py b/fairseq/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py new file mode 100644 index 0000000..2be848f --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py @@ -0,0 +1,56 @@ +import os +import shlex +import subprocess +import progressbar +from time import time +from pathlib import Path + +def find_all_files(path_dir, extension): + out = [] + for root, dirs, filenames in os.walk(path_dir): + for f in filenames: + if f.endswith(extension): + out.append(((str(Path(f).stem)), os.path.join(root, f))) + return out + +def convert16k(inputfile, outputfile16k): + command = ('sox -c 1 -b 16 {} -t wav {} rate 16k'.format(inputfile, outputfile16k)) + subprocess.call(shlex.split(command)) + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description='Convert to wav 16k audio using sox.') + parser.add_argument('input_dir', type=str, + help='Path to the input dir.') + parser.add_argument('output_dir', type=str, + help='Path to the output dir.') + parser.add_argument('--extension', type=str, default='wav', + help='Audio file extension in the input. Default: mp3') + args = parser.parse_args() + + # Find all sequences + print(f"Finding all audio files with extension '{args.extension}' from {args.input_dir}...") + audio_files = find_all_files(args.input_dir, args.extension) + print(f"Done! Found {len(audio_files)} files.") + + # Convert to relative path + audio_files = [os.path.relpath(file[-1], start=args.input_dir) for file in audio_files] + + # Create all the directories needed + rel_dirs_set = set([os.path.dirname(file) for file in audio_files]) + for rel_dir in rel_dirs_set: + Path(os.path.join(args.output_dir, rel_dir)).mkdir(parents=True, exist_ok=True) + + # Converting wavs files + print("Converting the audio to wav files...") + bar = progressbar.ProgressBar(maxval=len(audio_files)) + bar.start() + start_time = time() + for index, file in enumerate(audio_files): + bar.update(index) + input_file = os.path.join(args.input_dir, file) + output_file = os.path.join(args.output_dir, os.path.splitext(file)[0]+".wav") + convert16k(input_file, output_file) + bar.finish() + print(f"...done {len(audio_files)} files in {time()-start_time} seconds.") \ No newline at end of file diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/glow.py b/fairseq/examples/textless_nlp/gslm/unit2speech/glow.py new file mode 100644 index 0000000..41fd437 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/glow.py @@ -0,0 +1,312 @@ +# ***************************************************************************** +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of the NVIDIA CORPORATION nor the +# names of its contributors may be used to endorse or promote products +# derived from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY +# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +# ***************************************************************************** +import copy +import torch +from torch.autograd import Variable +import torch.nn.functional as F + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a+input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +class WaveGlowLoss(torch.nn.Module): + def __init__(self, sigma=1.0): + super(WaveGlowLoss, self).__init__() + self.sigma = sigma + + def forward(self, model_output): + z, log_s_list, log_det_W_list = model_output + for i, log_s in enumerate(log_s_list): + if i == 0: + log_s_total = torch.sum(log_s) + log_det_W_total = log_det_W_list[i] + else: + log_s_total = log_s_total + torch.sum(log_s) + log_det_W_total += log_det_W_list[i] + + loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total + return loss/(z.size(0)*z.size(1)*z.size(2)) + + +class Invertible1x1Conv(torch.nn.Module): + """ + The layer outputs both the convolution, and the log determinant + of its weight matrix. If reverse=True it does convolution with + inverse + """ + def __init__(self, c): + super(Invertible1x1Conv, self).__init__() + self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0, + bias=False) + + # Sample a random orthonormal matrix to initialize weights + _qr = torch.linalg.qr if torch.__version__ >= "1.8" else torch.qr + W = _qr(torch.FloatTensor(c, c).normal_())[0] + + # Ensure determinant is 1.0 not -1.0 + if torch.det(W) < 0: + W[:,0] = -1*W[:,0] + W = W.view(c, c, 1) + self.conv.weight.data = W + + def forward(self, z, reverse=False): + # shape + batch_size, group_size, n_of_groups = z.size() + + W = self.conv.weight.squeeze() + + if reverse: + if not hasattr(self, 'W_inverse'): + # Reverse computation + W_inverse = W.float().inverse() + W_inverse = Variable(W_inverse[..., None]) + if z.type() == 'torch.cuda.HalfTensor': + W_inverse = W_inverse.half() + self.W_inverse = W_inverse + z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0) + return z + else: + # Forward computation + log_det_W = batch_size * n_of_groups * torch.logdet(W) + z = self.conv(z) + return z, log_det_W + + +class WN(torch.nn.Module): + """ + This is the WaveNet like layer for the affine coupling. The primary difference + from WaveNet is the convolutions need not be causal. There is also no dilation + size reset. The dilation only doubles on each layer + """ + def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels, + kernel_size): + super(WN, self).__init__() + assert(kernel_size % 2 == 1) + assert(n_channels % 2 == 0) + self.n_layers = n_layers + self.n_channels = n_channels + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + + start = torch.nn.Conv1d(n_in_channels, n_channels, 1) + start = torch.nn.utils.weight_norm(start, name='weight') + self.start = start + + # Initializing last layer to 0 makes the affine coupling layers + # do nothing at first. This helps with training stability + end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = end + + cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + + for i in range(n_layers): + dilation = 2 ** i + padding = int((kernel_size*dilation - dilation)/2) + in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size, + dilation=dilation, padding=padding) + in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') + self.in_layers.append(in_layer) + + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2*n_channels + else: + res_skip_channels = n_channels + res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') + self.res_skip_layers.append(res_skip_layer) + + def forward(self, forward_input): + audio, spect = forward_input + audio = self.start(audio) + output = torch.zeros_like(audio) + n_channels_tensor = torch.IntTensor([self.n_channels]) + + spect = self.cond_layer(spect) + + for i in range(self.n_layers): + spect_offset = i*2*self.n_channels + acts = fused_add_tanh_sigmoid_multiply( + self.in_layers[i](audio), + spect[:,spect_offset:spect_offset+2*self.n_channels,:], + n_channels_tensor) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + audio = audio + res_skip_acts[:,:self.n_channels,:] + output = output + res_skip_acts[:,self.n_channels:,:] + else: + output = output + res_skip_acts + + return self.end(output) + + +class WaveGlow(torch.nn.Module): + def __init__(self, n_mel_channels, n_flows, n_group, n_early_every, + n_early_size, WN_config): + super(WaveGlow, self).__init__() + + self.upsample = torch.nn.ConvTranspose1d(n_mel_channels, + n_mel_channels, + 1024, stride=256) + assert(n_group % 2 == 0) + self.n_flows = n_flows + self.n_group = n_group + self.n_early_every = n_early_every + self.n_early_size = n_early_size + self.WN = torch.nn.ModuleList() + self.convinv = torch.nn.ModuleList() + + n_half = int(n_group/2) + + # Set up layers with the right sizes based on how many dimensions + # have been output already + n_remaining_channels = n_group + for k in range(n_flows): + if k % self.n_early_every == 0 and k > 0: + n_half = n_half - int(self.n_early_size/2) + n_remaining_channels = n_remaining_channels - self.n_early_size + self.convinv.append(Invertible1x1Conv(n_remaining_channels)) + self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config)) + self.n_remaining_channels = n_remaining_channels # Useful during inference + + def forward(self, forward_input): + """ + forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames + forward_input[1] = audio: batch x time + """ + spect, audio = forward_input + + # Upsample spectrogram to size of audio + spect = self.upsample(spect) + assert(spect.size(2) >= audio.size(1)) + if spect.size(2) > audio.size(1): + spect = spect[:, :, :audio.size(1)] + + spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) + spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) + + audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1) + output_audio = [] + log_s_list = [] + log_det_W_list = [] + + for k in range(self.n_flows): + if k % self.n_early_every == 0 and k > 0: + output_audio.append(audio[:,:self.n_early_size,:]) + audio = audio[:,self.n_early_size:,:] + + audio, log_det_W = self.convinv[k](audio) + log_det_W_list.append(log_det_W) + + n_half = int(audio.size(1)/2) + audio_0 = audio[:,:n_half,:] + audio_1 = audio[:,n_half:,:] + + output = self.WN[k]((audio_0, spect)) + log_s = output[:, n_half:, :] + b = output[:, :n_half, :] + audio_1 = torch.exp(log_s)*audio_1 + b + log_s_list.append(log_s) + + audio = torch.cat([audio_0, audio_1],1) + + output_audio.append(audio) + return torch.cat(output_audio,1), log_s_list, log_det_W_list + + def infer(self, spect, sigma=1.0): + spect = self.upsample(spect) + # trim conv artifacts. maybe pad spec to kernel multiple + time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0] + spect = spect[:, :, :-time_cutoff] + + spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) + spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) + + if spect.type() == 'torch.cuda.HalfTensor': + audio = torch.cuda.HalfTensor(spect.size(0), + self.n_remaining_channels, + spect.size(2)).normal_() + else: + audio = torch.cuda.FloatTensor(spect.size(0), + self.n_remaining_channels, + spect.size(2)).normal_() + + audio = torch.autograd.Variable(sigma*audio) + + for k in reversed(range(self.n_flows)): + n_half = int(audio.size(1)/2) + audio_0 = audio[:,:n_half,:] + audio_1 = audio[:,n_half:,:] + + output = self.WN[k]((audio_0, spect)) + + s = output[:, n_half:, :] + b = output[:, :n_half, :] + audio_1 = (audio_1 - b)/torch.exp(s) + audio = torch.cat([audio_0, audio_1],1) + + audio = self.convinv[k](audio, reverse=True) + + if k % self.n_early_every == 0 and k > 0: + if spect.type() == 'torch.cuda.HalfTensor': + z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_() + else: + z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_() + audio = torch.cat((sigma*z, audio),1) + + audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data + return audio + + @staticmethod + def remove_weightnorm(model): + waveglow = model + for WN in waveglow.WN: + WN.start = torch.nn.utils.remove_weight_norm(WN.start) + WN.in_layers = remove(WN.in_layers) + WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer) + WN.res_skip_layers = remove(WN.res_skip_layers) + return waveglow + + +def remove(conv_list): + new_conv_list = torch.nn.ModuleList() + for old_conv in conv_list: + old_conv = torch.nn.utils.remove_weight_norm(old_conv) + new_conv_list.append(old_conv) + return new_conv_list diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/multiproc.py b/fairseq/examples/textless_nlp/gslm/unit2speech/multiproc.py new file mode 100644 index 0000000..2a287a4 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/multiproc.py @@ -0,0 +1,27 @@ +import os +import time +import torch +import sys +import subprocess + +argslist = list(sys.argv)[1:] +log_dir = argslist[-1] +num_gpus = torch.cuda.device_count() +argslist.append('--n_gpus={}'.format(num_gpus)) +workers = [] +job_id = time.strftime("%Y_%m_%d-%H%M%S") +argslist.append("--group_name=group_{}".format(job_id)) + +print("GPU log directory is {}".format(log_dir)) +os.makedirs(log_dir, exist_ok=True) +for i in range(num_gpus): + argslist.append('--rank={}'.format(i)) + stdout = None if i == 0 else open("{}/{}_GPU_{}.log".format(log_dir, job_id, i), + "w") + print(argslist) + p = subprocess.Popen([str(sys.executable)]+argslist, stdout=stdout) + workers.append(p) + argslist = argslist[:-1] + +for p in workers: + p.wait() diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py b/fairseq/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py new file mode 100644 index 0000000..8073084 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py @@ -0,0 +1,105 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os + +import soundfile as sf +from examples.textless_nlp.gslm.unit2speech.tts_data import ( + TacotronInputDataset, +) +from examples.textless_nlp.gslm.unit2speech.utils import ( + load_quantized_audio_from_file, + load_tacotron, + load_waveglow, + synthesize_audio, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Wav2Vec 2.0 speech generator." + ) + parser.add_argument( + "--quantized_unit_path", + type=str, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--tts_model_path", + type=str, + help="TTS model file path to use for inference", + ) + parser.add_argument( + "--waveglow_path", + type=str, + help="Path to the waveglow checkpoint (vocoder).", + ) + parser.add_argument( + "--code_dict_path", + type=str, + help="Code dict file path to use for inference", + ) + parser.add_argument("--max_decoder_steps", type=int, default=2000) + parser.add_argument("--denoiser_strength", type=float, default=0.1) + parser.add_argument( + "--out_audio_dir", + type=str, + help="Output directory to dump audio files", + ) + + return parser + + +def main(args, logger): + # Load quantized audio + logger.info(f"Loading quantized audio from {args.quantized_unit_path}...") + names_batch, quantized_units_batch = load_quantized_audio_from_file( + file_path=args.quantized_unit_path + ) + + logger.info(f"Loading TTS model from {args.tts_model_path}...") + tacotron_model, sample_rate, hparams = load_tacotron( + tacotron_model_path=args.tts_model_path, + max_decoder_steps=args.max_decoder_steps, + ) + + logger.info(f"Loading Waveglow model from {args.waveglow_path}...") + waveglow, denoiser = load_waveglow(waveglow_path=args.waveglow_path) + + if not os.path.exists(hparams.code_dict): + hparams.code_dict = args.code_dict_path + tts_dataset = TacotronInputDataset(hparams) + + for name, quantized_units in zip(names_batch, quantized_units_batch): + quantized_units_str = " ".join(map(str, quantized_units)) + tts_input = tts_dataset.get_tensor(quantized_units_str) + mel, aud, aud_dn, has_eos = synthesize_audio( + tacotron_model, + waveglow, + denoiser, + tts_input.unsqueeze(0), + strength=args.denoiser_strength, + ) + out_file_path = os.path.join(args.out_audio_dir, f"{name}.wav") + sf.write( + f"{out_file_path}", aud_dn[0].cpu().float().numpy(), sample_rate + ) + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/__init__.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py new file mode 100644 index 0000000..b5af7f7 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py @@ -0,0 +1,93 @@ +import torch +import numpy as np +from scipy.signal import get_window +import librosa.util as librosa_util + + +def window_sumsquare(window, n_frames, hop_length=200, win_length=800, + n_fft=800, dtype=np.float32, norm=None): + """ + # from librosa 0.6 + Compute the sum-square envelope of a window function at a given hop length. + + This is used to estimate modulation effects induced by windowing + observations in short-time fourier transforms. + + Parameters + ---------- + window : string, tuple, number, callable, or list-like + Window specification, as in `get_window` + + n_frames : int > 0 + The number of analysis frames + + hop_length : int > 0 + The number of samples to advance between frames + + win_length : [optional] + The length of the window function. By default, this matches `n_fft`. + + n_fft : int > 0 + The length of each analysis frame. + + dtype : np.dtype + The data type of the output + + Returns + ------- + wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` + The sum-squared envelope of the window function + """ + if win_length is None: + win_length = n_fft + + n = n_fft + hop_length * (n_frames - 1) + x = np.zeros(n, dtype=dtype) + + # Compute the squared window at the desired length + win_sq = get_window(window, win_length, fftbins=True) + win_sq = librosa_util.normalize(win_sq, norm=norm)**2 + win_sq = librosa_util.pad_center(win_sq, n_fft) + + # Fill the envelope + for i in range(n_frames): + sample = i * hop_length + x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))] + return x + + +def griffin_lim(magnitudes, stft_fn, n_iters=30): + """ + PARAMS + ------ + magnitudes: spectrogram magnitudes + stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods + """ + + angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size()))) + angles = angles.astype(np.float32) + angles = torch.autograd.Variable(torch.from_numpy(angles)) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + + for i in range(n_iters): + _, angles = stft_fn.transform(signal) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + return signal + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + """ + PARAMS + ------ + C: compression factor + """ + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression(x, C=1): + """ + PARAMS + ------ + C: compression factor used to compress + """ + return torch.exp(x) / C diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py new file mode 100644 index 0000000..e2e35c1 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py @@ -0,0 +1,90 @@ +""" from https://github.com/keithito/tacotron """ + +''' +Cleaners are transformations that run over the input text at both training and eval time. + +Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" +hyperparameter. Some cleaners are English-specific. You'll typically want to use: + 1. "english_cleaners" for English text + 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using + the Unidecode library (https://pypi.python.org/pypi/Unidecode) + 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update + the symbols in symbols.py to match your data). +''' + +import re +from unidecode import unidecode +from .numbers import normalize_numbers + + +# Regular expression matching whitespace: +_whitespace_re = re.compile(r'\s+') + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ + ('mrs', 'misess'), + ('mr', 'mister'), + ('dr', 'doctor'), + ('st', 'saint'), + ('co', 'company'), + ('jr', 'junior'), + ('maj', 'major'), + ('gen', 'general'), + ('drs', 'doctors'), + ('rev', 'reverend'), + ('lt', 'lieutenant'), + ('hon', 'honorable'), + ('sgt', 'sergeant'), + ('capt', 'captain'), + ('esq', 'esquire'), + ('ltd', 'limited'), + ('col', 'colonel'), + ('ft', 'fort'), +]] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def expand_numbers(text): + return normalize_numbers(text) + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, ' ', text) + + +def convert_to_ascii(text): + return unidecode(text) + + +def basic_cleaners(text): + '''Basic pipeline that lowercases and collapses whitespace without transliteration.''' + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def transliteration_cleaners(text): + '''Pipeline for non-English text that transliterates to ASCII.''' + text = convert_to_ascii(text) + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def english_cleaners(text): + '''Pipeline for English text, including number and abbreviation expansion.''' + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_numbers(text) + text = expand_abbreviations(text) + text = collapse_whitespace(text) + return text diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py new file mode 100644 index 0000000..62bfef7 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py @@ -0,0 +1,65 @@ +""" from https://github.com/keithito/tacotron """ + +import re + + +valid_symbols = [ + 'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2', + 'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2', + 'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY', + 'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1', + 'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0', + 'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW', + 'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH' +] + +_valid_symbol_set = set(valid_symbols) + + +class CMUDict: + '''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict''' + def __init__(self, file_or_path, keep_ambiguous=True): + if isinstance(file_or_path, str): + with open(file_or_path, encoding='latin-1') as f: + entries = _parse_cmudict(f) + else: + entries = _parse_cmudict(file_or_path) + if not keep_ambiguous: + entries = {word: pron for word, pron in entries.items() if len(pron) == 1} + self._entries = entries + + + def __len__(self): + return len(self._entries) + + + def lookup(self, word): + '''Returns list of ARPAbet pronunciations of the given word.''' + return self._entries.get(word.upper()) + + + +_alt_re = re.compile(r'\([0-9]+\)') + + +def _parse_cmudict(file): + cmudict = {} + for line in file: + if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"): + parts = line.split(' ') + word = re.sub(_alt_re, '', parts[0]) + pronunciation = _get_pronunciation(parts[1]) + if pronunciation: + if word in cmudict: + cmudict[word].append(pronunciation) + else: + cmudict[word] = [pronunciation] + return cmudict + + +def _get_pronunciation(s): + parts = s.strip().split(' ') + for part in parts: + if part not in _valid_symbol_set: + return None + return ' '.join(parts) diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py new file mode 100644 index 0000000..f10d557 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py @@ -0,0 +1,103 @@ +import torch +from librosa.filters import mel as librosa_mel_fn +from .audio_processing import dynamic_range_compression +from .audio_processing import dynamic_range_decompression +from .stft import STFT +from .utils import get_mask_from_lengths + + +class LinearNorm(torch.nn.Module): + def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): + super(LinearNorm, self).__init__() + self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) + + torch.nn.init.xavier_uniform_( + self.linear_layer.weight, + gain=torch.nn.init.calculate_gain(w_init_gain)) + + def forward(self, x): + return self.linear_layer(x) + + +class ConvNorm(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, + padding=None, dilation=1, bias=True, w_init_gain='linear'): + super(ConvNorm, self).__init__() + if padding is None: + assert(kernel_size % 2 == 1) + padding = int(dilation * (kernel_size - 1) / 2) + + self.conv = torch.nn.Conv1d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, + padding=padding, dilation=dilation, + bias=bias) + + torch.nn.init.xavier_uniform_( + self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) + + def forward(self, signal): + conv_signal = self.conv(signal) + return conv_signal + + +class GlobalAvgPool(torch.nn.Module): + def __init__(self): + super(GlobalAvgPool, self).__init__() + + def forward(self, x, lengths=None): + """Average pooling across time steps (dim=1) with optionally lengths. + Args: + x: torch.Tensor of shape (N, T, ...) + lengths: None or torch.Tensor of shape (N,) + dim: dimension to pool + """ + if lengths is None: + return x.mean(dim=1, keepdim=False) + else: + mask = get_mask_from_lengths(lengths).type(x.type()).to(x.device) + mask_shape = list(mask.size()) + [1 for _ in range(x.ndimension()-2)] + mask = mask.reshape(*mask_shape) + numer = (x * mask).sum(dim=1, keepdim=False) + denom = mask.sum(dim=1, keepdim=False) + return numer / denom + + +class TacotronSTFT(torch.nn.Module): + def __init__(self, filter_length=1024, hop_length=256, win_length=1024, + n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0, + mel_fmax=8000.0): + super(TacotronSTFT, self).__init__() + self.n_mel_channels = n_mel_channels + self.sampling_rate = sampling_rate + self.stft_fn = STFT(filter_length, hop_length, win_length) + mel_basis = librosa_mel_fn( + sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax) + mel_basis = torch.from_numpy(mel_basis).float() + self.register_buffer('mel_basis', mel_basis) + + def spectral_normalize(self, magnitudes): + output = dynamic_range_compression(magnitudes) + return output + + def spectral_de_normalize(self, magnitudes): + output = dynamic_range_decompression(magnitudes) + return output + + def mel_spectrogram(self, y): + """Computes mel-spectrograms from a batch of waves + PARAMS + ------ + y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] + + RETURNS + ------- + mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) + """ + assert(torch.min(y.data) >= -1) + assert(torch.max(y.data) <= 1) + + magnitudes, phases = self.stft_fn.transform(y) + magnitudes = magnitudes.data + mel_output = torch.matmul(self.mel_basis, magnitudes) + mel_output = self.spectral_normalize(mel_output) + return mel_output diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py new file mode 100644 index 0000000..ccf132b --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py @@ -0,0 +1,669 @@ +from math import sqrt +import torch +import torch.distributions as distr +from torch.autograd import Variable +from torch import nn +from torch.nn import functional as F +from .layers import ConvNorm, LinearNorm, GlobalAvgPool +from .utils import to_gpu, get_mask_from_lengths + + +class LocationLayer(nn.Module): + def __init__(self, attention_n_filters, attention_kernel_size, + attention_dim): + super(LocationLayer, self).__init__() + padding = int((attention_kernel_size - 1) / 2) + self.location_conv = ConvNorm(2, attention_n_filters, + kernel_size=attention_kernel_size, + padding=padding, bias=False, stride=1, + dilation=1) + self.location_dense = LinearNorm(attention_n_filters, attention_dim, + bias=False, w_init_gain='tanh') + + def forward(self, attention_weights_cat): + processed_attention = self.location_conv(attention_weights_cat) + processed_attention = processed_attention.transpose(1, 2) + processed_attention = self.location_dense(processed_attention) + return processed_attention + + +class Attention(nn.Module): + def __init__(self, attention_rnn_dim, embedding_dim, attention_dim, + attention_location_n_filters, attention_location_kernel_size): + super(Attention, self).__init__() + self.query_layer = LinearNorm(attention_rnn_dim, attention_dim, + bias=False, w_init_gain='tanh') + self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False, + w_init_gain='tanh') + self.v = LinearNorm(attention_dim, 1, bias=False) + self.location_layer = LocationLayer(attention_location_n_filters, + attention_location_kernel_size, + attention_dim) + self.score_mask_value = -float("inf") + + def get_alignment_energies(self, query, processed_memory, + attention_weights_cat): + """ + PARAMS + ------ + query: decoder output (batch, n_mel_channels * n_frames_per_step) + processed_memory: processed encoder outputs (B, T_in, attention_dim) + attention_weights_cat: cumulative and prev. att weights (B, 2, max_time) + + RETURNS + ------- + alignment (batch, max_time) + """ + + processed_query = self.query_layer(query.unsqueeze(1)) + processed_attention_weights = self.location_layer(attention_weights_cat) + energies = self.v(torch.tanh( + processed_query + processed_attention_weights + processed_memory)) + + energies = energies.squeeze(-1) + return energies + + def forward(self, attention_hidden_state, memory, processed_memory, + attention_weights_cat, mask): + """ + PARAMS + ------ + attention_hidden_state: attention rnn last output + memory: encoder outputs + processed_memory: processed encoder outputs + attention_weights_cat: previous and cummulative attention weights + mask: binary mask for padded data + """ + alignment = self.get_alignment_energies( + attention_hidden_state, processed_memory, attention_weights_cat) + + if mask is not None: + alignment.data.masked_fill_(mask, self.score_mask_value) + + attention_weights = F.softmax(alignment, dim=1) + attention_context = torch.bmm(attention_weights.unsqueeze(1), memory) + attention_context = attention_context.squeeze(1) + + return attention_context, attention_weights + + +class Prenet(nn.Module): + def __init__(self, in_dim, sizes): + super(Prenet, self).__init__() + in_sizes = [in_dim] + sizes[:-1] + self.layers = nn.ModuleList( + [LinearNorm(in_size, out_size, bias=False) + for (in_size, out_size) in zip(in_sizes, sizes)]) + + def forward(self, x): + for linear in self.layers: + x = F.dropout(F.relu(linear(x)), p=0.5, training=True) + return x + + +class Postnet(nn.Module): + """Postnet + - Five 1-d convolution with 512 channels and kernel size 5 + """ + + def __init__(self, hparams): + super(Postnet, self).__init__() + self.convolutions = nn.ModuleList() + + self.convolutions.append( + nn.Sequential( + ConvNorm(hparams.n_mel_channels, hparams.postnet_embedding_dim, + kernel_size=hparams.postnet_kernel_size, stride=1, + padding=int((hparams.postnet_kernel_size - 1) / 2), + dilation=1, w_init_gain='tanh'), + nn.BatchNorm1d(hparams.postnet_embedding_dim)) + ) + + for i in range(1, hparams.postnet_n_convolutions - 1): + self.convolutions.append( + nn.Sequential( + ConvNorm(hparams.postnet_embedding_dim, + hparams.postnet_embedding_dim, + kernel_size=hparams.postnet_kernel_size, stride=1, + padding=int((hparams.postnet_kernel_size - 1) / 2), + dilation=1, w_init_gain='tanh'), + nn.BatchNorm1d(hparams.postnet_embedding_dim)) + ) + + self.convolutions.append( + nn.Sequential( + ConvNorm(hparams.postnet_embedding_dim, hparams.n_mel_channels, + kernel_size=hparams.postnet_kernel_size, stride=1, + padding=int((hparams.postnet_kernel_size - 1) / 2), + dilation=1, w_init_gain='linear'), + nn.BatchNorm1d(hparams.n_mel_channels)) + ) + + def forward(self, x): + for i in range(len(self.convolutions) - 1): + x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training) + x = F.dropout(self.convolutions[-1](x), 0.5, self.training) + + return x + + +class Encoder(nn.Module): + """Encoder module: + - Three 1-d convolution banks + - Bidirectional LSTM + """ + def __init__(self, hparams): + super(Encoder, self).__init__() + + convolutions = [] + for _ in range(hparams.encoder_n_convolutions): + conv_layer = nn.Sequential( + ConvNorm(hparams.encoder_embedding_dim, + hparams.encoder_embedding_dim, + kernel_size=hparams.encoder_kernel_size, stride=1, + padding=int((hparams.encoder_kernel_size - 1) / 2), + dilation=1, w_init_gain='relu'), + nn.BatchNorm1d(hparams.encoder_embedding_dim)) + convolutions.append(conv_layer) + self.convolutions = nn.ModuleList(convolutions) + + self.lstm = nn.LSTM(hparams.encoder_embedding_dim, + int(hparams.encoder_embedding_dim / 2), 1, + batch_first=True, bidirectional=True) + + def forward(self, x, input_lengths): + for conv in self.convolutions: + x = F.dropout(F.relu(conv(x)), 0.5, self.training) + + x = x.transpose(1, 2) + + # pytorch tensor are not reversible, hence the conversion + input_lengths = input_lengths.cpu().numpy() + x = nn.utils.rnn.pack_padded_sequence( + x, input_lengths, batch_first=True) + + self.lstm.flatten_parameters() + outputs, _ = self.lstm(x) + + outputs, _ = nn.utils.rnn.pad_packed_sequence( + outputs, batch_first=True) + + return outputs + + def inference(self, x): + for conv in self.convolutions: + x = F.dropout(F.relu(conv(x)), 0.5, self.training) + + x = x.transpose(1, 2) + + self.lstm.flatten_parameters() + outputs, _ = self.lstm(x) + + return outputs + + +class AudioEncoder(nn.Module): + def __init__(self, hparams): + super(AudioEncoder, self).__init__() + + assert hparams.lat_dim > 0 + + convolutions = [] + inp_dim = hparams.n_mel_channels + for _ in range(hparams.lat_n_convolutions): + conv_layer = nn.Sequential( + ConvNorm(inp_dim, hparams.lat_n_filters, + kernel_size=hparams.lat_kernel_size, stride=1, + padding=int((hparams.lat_kernel_size - 1) / 2), + dilation=1, w_init_gain='tanh'), + nn.BatchNorm1d(hparams.lat_n_filters)) + inp_dim = hparams.lat_n_filters + convolutions.append(conv_layer) + self.convolutions = nn.ModuleList(convolutions) + + self.lstm = nn.LSTM(hparams.lat_n_filters, + int(hparams.lat_n_filters / 2), + hparams.lat_n_blstms, batch_first=True, + bidirectional=True) + self.pool = GlobalAvgPool() + + self.mu_proj = LinearNorm(hparams.lat_n_filters, hparams.lat_dim) + self.logvar_proj = LinearNorm(hparams.lat_n_filters, hparams.lat_dim) + self.lat_dim = hparams.lat_dim + + def forward(self, x, lengths): + """ + Args: + x (torch.Tensor): (B, F, T) + """ + + for conv in self.convolutions: + x = F.dropout(F.tanh(conv(x)), 0.5, self.training) + + x = x.transpose(1, 2) # (B, T, D) + + # x may not be sorted by length. Sort->process->unsort + max_len = x.size(1) + assert max_len == torch.max(lengths).item() + + lengths, perm_idx = lengths.sort(0, descending=True) + x = x[perm_idx] + x = nn.utils.rnn.pack_padded_sequence(x, lengths, batch_first=True) + + self.lstm.flatten_parameters() + outputs, _ = self.lstm(x) + outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True) + + _, unperm_idx = perm_idx.sort(0) + outputs = outputs[unperm_idx] # (B, T, D) + lengths = lengths[unperm_idx] # (B, T, D) + + outputs = self.pool(outputs, lengths) # (B, D) + + mu = self.mu_proj(outputs) + logvar = self.logvar_proj(outputs) + z = distr.Normal(mu, logvar).rsample() + return z, mu, logvar + + +class Decoder(nn.Module): + def __init__(self, hparams): + super(Decoder, self).__init__() + self.n_mel_channels = hparams.n_mel_channels + self.n_frames_per_step = hparams.n_frames_per_step + self.encoder_embedding_dim = hparams.encoder_embedding_dim + self.obs_dim = hparams.obs_dim + self.lat_dim = hparams.lat_dim + self.attention_rnn_dim = hparams.attention_rnn_dim + self.decoder_rnn_dim = hparams.decoder_rnn_dim + self.prenet_dim = hparams.prenet_dim + self.max_decoder_steps = hparams.max_decoder_steps + self.gate_threshold = hparams.gate_threshold + self.p_attention_dropout = hparams.p_attention_dropout + self.p_decoder_dropout = hparams.p_decoder_dropout + + self.prenet = Prenet( + hparams.n_mel_channels * hparams.n_frames_per_step, + [hparams.prenet_dim, hparams.prenet_dim]) + + self.attention_rnn = nn.LSTMCell( + hparams.prenet_dim + hparams.encoder_embedding_dim, + hparams.attention_rnn_dim) + + self.attention_layer = Attention( + hparams.attention_rnn_dim, hparams.encoder_embedding_dim, + hparams.attention_dim, hparams.attention_location_n_filters, + hparams.attention_location_kernel_size) + + encoder_tot_dim = (hparams.encoder_embedding_dim + \ + hparams.lat_dim + hparams.obs_dim) + self.decoder_rnn = nn.LSTMCell( + hparams.attention_rnn_dim + encoder_tot_dim, + hparams.decoder_rnn_dim, 1) + + self.linear_projection = LinearNorm( + hparams.decoder_rnn_dim + encoder_tot_dim, + hparams.n_mel_channels * hparams.n_frames_per_step) + + self.gate_layer = LinearNorm( + hparams.decoder_rnn_dim + encoder_tot_dim, 1, + bias=True, w_init_gain='sigmoid') + + def get_go_frame(self, memory): + """ Gets all zeros frames to use as first decoder input + PARAMS + ------ + memory: decoder outputs + + RETURNS + ------- + decoder_input: all zeros frames + """ + B = memory.size(0) + decoder_input = Variable(memory.data.new( + B, self.n_mel_channels * self.n_frames_per_step).zero_()) + return decoder_input + + def initialize_decoder_states(self, memory, obs_and_lat, mask): + """ Initializes attention rnn states, decoder rnn states, attention + weights, attention cumulative weights, attention context, stores memory + and stores processed memory + PARAMS + ------ + memory: Encoder outputs + obs_and_lat: Observed and latent attribute embeddings + mask: Mask for padded data if training, expects None for inference + """ + B = memory.size(0) + MAX_TIME = memory.size(1) + + self.attention_hidden = Variable(memory.data.new( + B, self.attention_rnn_dim).zero_()) + self.attention_cell = Variable(memory.data.new( + B, self.attention_rnn_dim).zero_()) + + self.decoder_hidden = Variable(memory.data.new( + B, self.decoder_rnn_dim).zero_()) + self.decoder_cell = Variable(memory.data.new( + B, self.decoder_rnn_dim).zero_()) + + self.attention_weights = Variable(memory.data.new( + B, MAX_TIME).zero_()) + self.attention_weights_cum = Variable(memory.data.new( + B, MAX_TIME).zero_()) + self.attention_context = Variable(memory.data.new( + B, self.encoder_embedding_dim).zero_()) + + self.memory = memory + self.processed_memory = self.attention_layer.memory_layer(memory) + self.obs_and_lat = obs_and_lat + self.mask = mask + + def parse_decoder_inputs(self, decoder_inputs): + """ Prepares decoder inputs, i.e. mel outputs + PARAMS + ------ + decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs + + RETURNS + ------- + inputs: processed decoder inputs + + """ + # (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels) + decoder_inputs = decoder_inputs.transpose(1, 2) + decoder_inputs = decoder_inputs.view( + decoder_inputs.size(0), + int(decoder_inputs.size(1)/self.n_frames_per_step), -1) + # (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels) + decoder_inputs = decoder_inputs.transpose(0, 1) + return decoder_inputs + + def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments): + """ Prepares decoder outputs for output + PARAMS + ------ + mel_outputs: + gate_outputs: gate output energies + alignments: + + RETURNS + ------- + mel_outputs: + gate_outpust: gate output energies + alignments: + """ + # (T_out, B) -> (B, T_out) + alignments = torch.stack(alignments).transpose(0, 1) + # (T_out, B) -> (B, T_out) + gate_outputs = torch.stack(gate_outputs).transpose(0, 1) + gate_outputs = gate_outputs.contiguous() + # (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels) + mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous() + # decouple frames per step + mel_outputs = mel_outputs.view( + mel_outputs.size(0), -1, self.n_mel_channels) + # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out) + mel_outputs = mel_outputs.transpose(1, 2) + + return mel_outputs, gate_outputs, alignments + + def decode(self, decoder_input): + """ Decoder step using stored states, attention and memory + PARAMS + ------ + decoder_input: previous mel output + + RETURNS + ------- + mel_output: + gate_output: gate output energies + attention_weights: + """ + cell_input = torch.cat((decoder_input, self.attention_context), -1) + self.attention_hidden, self.attention_cell = self.attention_rnn( + cell_input, (self.attention_hidden, self.attention_cell)) + self.attention_hidden = F.dropout( + self.attention_hidden, self.p_attention_dropout, self.training) + + attention_weights_cat = torch.cat( + (self.attention_weights.unsqueeze(1), + self.attention_weights_cum.unsqueeze(1)), dim=1) + self.attention_context, self.attention_weights = self.attention_layer( + self.attention_hidden, self.memory, self.processed_memory, + attention_weights_cat, self.mask) + + self.attention_weights_cum += self.attention_weights + decoder_input = torch.cat( + (self.attention_hidden, self.attention_context), -1) + if self.obs_and_lat is not None: + decoder_input = torch.cat((decoder_input, self.obs_and_lat), -1) + self.decoder_hidden, self.decoder_cell = self.decoder_rnn( + decoder_input, (self.decoder_hidden, self.decoder_cell)) + self.decoder_hidden = F.dropout( + self.decoder_hidden, self.p_decoder_dropout, self.training) + + decoder_hidden_attention_context = torch.cat( + (self.decoder_hidden, self.attention_context), dim=1) + if self.obs_and_lat is not None: + decoder_hidden_attention_context = torch.cat( + (decoder_hidden_attention_context, self.obs_and_lat), dim=1) + decoder_output = self.linear_projection( + decoder_hidden_attention_context) + + gate_prediction = self.gate_layer(decoder_hidden_attention_context) + return decoder_output, gate_prediction, self.attention_weights + + def forward(self, memory, obs_and_lat, decoder_inputs, memory_lengths): + """ Decoder forward pass for training + PARAMS + ------ + memory: Encoder outputs + obs_and_lat: Observed and latent attribute embeddings + decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs + memory_lengths: Encoder output lengths for attention masking. + + RETURNS + ------- + mel_outputs: mel outputs from the decoder + gate_outputs: gate outputs from the decoder + alignments: sequence of attention weights from the decoder + """ + + decoder_input = self.get_go_frame(memory).unsqueeze(0) + decoder_inputs = self.parse_decoder_inputs(decoder_inputs) + decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0) + decoder_inputs = self.prenet(decoder_inputs) + + self.initialize_decoder_states( + memory, obs_and_lat, mask=~get_mask_from_lengths(memory_lengths)) + + mel_outputs, gate_outputs, alignments = [], [], [] + while len(mel_outputs) < decoder_inputs.size(0) - 1: + decoder_input = decoder_inputs[len(mel_outputs)] + mel_output, gate_output, attention_weights = self.decode( + decoder_input) + mel_outputs += [mel_output.squeeze(1)] + gate_outputs += [gate_output.squeeze()] + alignments += [attention_weights] + + mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs( + mel_outputs, gate_outputs, alignments) + + return mel_outputs, gate_outputs, alignments + + def inference(self, memory, obs_and_lat, ret_has_eos=False): + """ Decoder inference + PARAMS + ------ + memory: Encoder outputs + obs_and_lat: Observed and latent attribute embeddings + + RETURNS + ------- + mel_outputs: mel outputs from the decoder + gate_outputs: gate outputs from the decoder + alignments: sequence of attention weights from the decoder + """ + decoder_input = self.get_go_frame(memory) + + self.initialize_decoder_states(memory, obs_and_lat, mask=None) + + mel_outputs, gate_outputs, alignments = [], [], [] + has_eos = False + while True: + decoder_input = self.prenet(decoder_input) + mel_output, gate_output, alignment = self.decode(decoder_input) + + mel_outputs += [mel_output.squeeze(1)] + gate_outputs += [gate_output] + alignments += [alignment] + + if torch.sigmoid(gate_output.data) > self.gate_threshold: + has_eos = True + break + elif len(mel_outputs) == self.max_decoder_steps: + # print("Warning! Reached max decoder steps") + break + + decoder_input = mel_output + + mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs( + mel_outputs, gate_outputs, alignments) + + if ret_has_eos: + return mel_outputs, gate_outputs, alignments, has_eos + else: + return mel_outputs, gate_outputs, alignments + + +class Tacotron2(nn.Module): + def __init__(self, hparams): + super(Tacotron2, self).__init__() + self.mask_padding = hparams.mask_padding + self.fp16_run = hparams.fp16_run + self.n_mel_channels = hparams.n_mel_channels + self.n_frames_per_step = hparams.n_frames_per_step + + # initialize text encoder embedding + self.embedding = nn.Embedding( + hparams.n_symbols, hparams.symbols_embedding_dim) + std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim)) + val = sqrt(3.0) * std # uniform bounds for std + self.embedding.weight.data.uniform_(-val, val) + + # initialize observed attribute embedding + self.obs_embedding = None + if hparams.obs_dim > 0: + self.obs_embedding = nn.Embedding( + hparams.obs_n_class, hparams.obs_dim) + std = sqrt(2.0 / (hparams.obs_n_class + hparams.obs_dim)) + val = sqrt(3.0) * std # uniform bounds for std + self.obs_embedding.weight.data.uniform_(-val, val) + + self.encoder = Encoder(hparams) + self.decoder = Decoder(hparams) + self.postnet = Postnet(hparams) + + self.lat_encoder = None + if hparams.lat_dim > 0: + self.lat_encoder = AudioEncoder(hparams) + + def parse_batch(self, batch): + (text_padded, input_lengths, obs_labels, + mel_padded, gate_padded, output_lengths) = batch + text_padded = to_gpu(text_padded).long() + input_lengths = to_gpu(input_lengths).long() + obs_labels = to_gpu(obs_labels).long() + max_len = torch.max(input_lengths.data).item() + mel_padded = to_gpu(mel_padded).float() + gate_padded = to_gpu(gate_padded).float() + output_lengths = to_gpu(output_lengths).long() + + return ( + (text_padded, input_lengths, obs_labels, + mel_padded, max_len, output_lengths), + (mel_padded, gate_padded)) + + def parse_output(self, outputs, output_lengths=None): + if self.mask_padding and output_lengths is not None: + mask = ~get_mask_from_lengths(output_lengths) + mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1)) + mask = mask.permute(1, 0, 2) + + outputs[0].data.masked_fill_(mask, 0.0) + outputs[1].data.masked_fill_(mask, 0.0) + outputs[2].data.masked_fill_(mask[:, 0, :], 1e3) # gate energies + + return outputs + + def forward(self, inputs): + (text_inputs, text_lengths, obs_labels, + mels, max_len, output_lengths) = inputs + text_lengths, output_lengths = text_lengths.data, output_lengths.data + + embedded_inputs = self.embedding(text_inputs).transpose(1, 2) + + encoder_outputs = self.encoder(embedded_inputs, text_lengths) + + obs = None + if self.obs_embedding is not None: + obs = self.obs_embedding(obs_labels) + + lat, lat_mu, lat_logvar = None, None, None + if self.lat_encoder is not None: + (lat, lat_mu, lat_logvar) = self.lat_encoder(mels, output_lengths) + + obs_and_lat = [x for x in [obs, lat] if x is not None] + if bool(obs_and_lat): + obs_and_lat = torch.cat(obs_and_lat, dim=-1) + else: + obs_and_lat = None + + mel_outputs, gate_outputs, alignments = self.decoder( + encoder_outputs, obs_and_lat, mels, memory_lengths=text_lengths) + + mel_outputs_postnet = self.postnet(mel_outputs) + mel_outputs_postnet = mel_outputs + mel_outputs_postnet + + return self.parse_output( + [mel_outputs, mel_outputs_postnet, gate_outputs, alignments, + lat_mu, lat_logvar], + output_lengths) + + def inference(self, inputs, obs_labels=None, lat=None, ret_has_eos=False): + embedded_inputs = self.embedding(inputs).transpose(1, 2) + encoder_outputs = self.encoder.inference(embedded_inputs) + + if obs_labels is None: + obs_labels = torch.LongTensor(len(inputs)) + obs_labels = obs_labels.to(inputs.device).zero_() + + obs = None + if self.obs_embedding is not None: + obs = self.obs_embedding(obs_labels) + + if self.lat_encoder is not None: + if lat is None: + lat = torch.FloatTensor(len(inputs), self.lat_encoder.lat_dim) + lat = lat.to(inputs.device).zero_().type(encoder_outputs.type()) + + obs_and_lat = [x for x in [obs, lat] if x is not None] + if bool(obs_and_lat): + obs_and_lat = torch.cat(obs_and_lat, dim=-1) + else: + obs_and_lat = None + + mel_outputs, gate_outputs, alignments, has_eos = self.decoder.inference( + encoder_outputs, obs_and_lat, ret_has_eos=True) + + mel_outputs_postnet = self.postnet(mel_outputs) + mel_outputs_postnet = mel_outputs + mel_outputs_postnet + + outputs = self.parse_output( + [mel_outputs, mel_outputs_postnet, gate_outputs, alignments]) + + if ret_has_eos: + return outputs + [has_eos] + else: + return outputs diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/numbers.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/numbers.py new file mode 100644 index 0000000..0d5f7fa --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/numbers.py @@ -0,0 +1,71 @@ +""" from https://github.com/keithito/tacotron """ + +import inflect +import re + + +_inflect = inflect.engine() +_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') +_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') +_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') +_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') +_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') +_number_re = re.compile(r'[0-9]+') + + +def _remove_commas(m): + return m.group(1).replace(',', '') + + +def _expand_decimal_point(m): + return m.group(1).replace('.', ' point ') + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split('.') + if len(parts) > 2: + return match + ' dollars' # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + return '%s %s' % (dollars, dollar_unit) + elif cents: + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s' % (cents, cent_unit) + else: + return 'zero dollars' + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return 'two thousand' + elif num > 2000 and num < 2010: + return 'two thousand ' + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + ' hundred' + else: + return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') + else: + return _inflect.number_to_words(num, andword='') + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r'\1 pounds', text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py new file mode 100644 index 0000000..63fcd43 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py @@ -0,0 +1,141 @@ +""" +BSD 3-Clause License + +Copyright (c) 2017, Prem Seetharaman +All rights reserved. + +* Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +""" + +import torch +import numpy as np +import torch.nn.functional as F +from torch.autograd import Variable +from scipy.signal import get_window +from librosa.util import pad_center, tiny +from .audio_processing import window_sumsquare + + +class STFT(torch.nn.Module): + """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" + def __init__(self, filter_length=800, hop_length=200, win_length=800, + window='hann'): + super(STFT, self).__init__() + self.filter_length = filter_length + self.hop_length = hop_length + self.win_length = win_length + self.window = window + self.forward_transform = None + scale = self.filter_length / self.hop_length + fourier_basis = np.fft.fft(np.eye(self.filter_length)) + + cutoff = int((self.filter_length / 2 + 1)) + fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]), + np.imag(fourier_basis[:cutoff, :])]) + + forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) + inverse_basis = torch.FloatTensor( + np.linalg.pinv(scale * fourier_basis).T[:, None, :]) + + if window is not None: + assert(filter_length >= win_length) + # get window and zero center pad it to filter_length + fft_window = get_window(window, win_length, fftbins=True) + fft_window = pad_center(fft_window, filter_length) + fft_window = torch.from_numpy(fft_window).float() + + # window the bases + forward_basis *= fft_window + inverse_basis *= fft_window + + self.register_buffer('forward_basis', forward_basis.float()) + self.register_buffer('inverse_basis', inverse_basis.float()) + + def transform(self, input_data): + num_batches = input_data.size(0) + num_samples = input_data.size(1) + + self.num_samples = num_samples + + # similar to librosa, reflect-pad the input + input_data = input_data.view(num_batches, 1, num_samples) + input_data = F.pad( + input_data.unsqueeze(1), + (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), + mode='reflect') + input_data = input_data.squeeze(1) + + forward_transform = F.conv1d( + input_data, + Variable(self.forward_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + cutoff = int((self.filter_length / 2) + 1) + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + + magnitude = torch.sqrt(real_part**2 + imag_part**2) + phase = torch.autograd.Variable( + torch.atan2(imag_part.data, real_part.data)) + + return magnitude, phase + + def inverse(self, magnitude, phase): + recombine_magnitude_phase = torch.cat( + [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1) + + inverse_transform = F.conv_transpose1d( + recombine_magnitude_phase, + Variable(self.inverse_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + if self.window is not None: + window_sum = window_sumsquare( + self.window, magnitude.size(-1), hop_length=self.hop_length, + win_length=self.win_length, n_fft=self.filter_length, + dtype=np.float32) + # remove modulation effects + approx_nonzero_indices = torch.from_numpy( + np.where(window_sum > tiny(window_sum))[0]) + window_sum = torch.autograd.Variable( + torch.from_numpy(window_sum), requires_grad=False) + window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum + inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices] + + # scale by hop ratio + inverse_transform *= float(self.filter_length) / self.hop_length + + inverse_transform = inverse_transform[:, :, int(self.filter_length/2):] + inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):] + + return inverse_transform + + def forward(self, input_data): + self.magnitude, self.phase = self.transform(input_data) + reconstruction = self.inverse(self.magnitude, self.phase) + return reconstruction diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py new file mode 100644 index 0000000..5f0d70f --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py @@ -0,0 +1,18 @@ +""" from https://github.com/keithito/tacotron """ + +''' +Defines the set of symbols used in text input to the model. + +The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. ''' +from . import cmudict + +_pad = '_' +_punctuation = '!\'(),.:;? ' +_special = '-' +_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' + +# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters): +_arpabet = ['@' + s for s in cmudict.valid_symbols] + +# Export all symbols: +symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py new file mode 100644 index 0000000..49e2ca4 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py @@ -0,0 +1,107 @@ +""" from https://github.com/keithito/tacotron """ +import numpy as np +import re +from . import cleaners +from .symbols import symbols + + +# Mappings from symbol to numeric ID and vice versa: +_symbol_to_id = {s: i for i, s in enumerate(symbols)} +_id_to_symbol = {i: s for i, s in enumerate(symbols)} + +# Regular expression matching text enclosed in curly braces: +_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)') + +# Special symbols +SOS_TOK = '<s>' +EOS_TOK = '</s>' + +def text_to_sequence(text, cleaner_names): + '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + + The text can optionally have ARPAbet sequences enclosed in curly braces embedded + in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street." + + Args: + text: string to convert to a sequence + cleaner_names: names of the cleaner functions to run the text through + + Returns: + List of integers corresponding to the symbols in the text + ''' + sequence = [] + + # Check for curly braces and treat their contents as ARPAbet: + while len(text): + m = _curly_re.match(text) + if not m: + sequence += _symbols_to_sequence(_clean_text(text, cleaner_names)) + break + sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names)) + sequence += _arpabet_to_sequence(m.group(2)) + text = m.group(3) + + return sequence + + +def sample_code_chunk(code, size): + assert(size > 0 and size <= len(code)) + start = np.random.randint(len(code) - size + 1) + end = start + size + return code[start:end], start, end + + +def code_to_sequence(code, code_dict, collapse_code): + if collapse_code: + prev_c = None + sequence = [] + for c in code: + if c in code_dict and c != prev_c: + sequence.append(code_dict[c]) + prev_c = c + else: + sequence = [code_dict[c] for c in code if c in code_dict] + if len(sequence) < 0.95 * len(code): + print('WARNING : over 5%% codes are OOV') + + return sequence + + +def sequence_to_text(sequence): + '''Converts a sequence of IDs back to a string''' + result = '' + for symbol_id in sequence: + if symbol_id in _id_to_symbol: + s = _id_to_symbol[symbol_id] + # Enclose ARPAbet back in curly braces: + if len(s) > 1 and s[0] == '@': + s = '{%s}' % s[1:] + result += s + return result.replace('}{', ' ') + + +def sequence_to_code(sequence, code_dict): + '''Analogous to sequence_to_text''' + id_to_code = {i: c for c, i in code_dict.items()} + return ' '.join([id_to_code[i] for i in sequence]) + + +def _clean_text(text, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise Exception('Unknown cleaner: %s' % name) + text = cleaner(text) + return text + + +def _symbols_to_sequence(symbols): + return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] + + +def _arpabet_to_sequence(text): + return _symbols_to_sequence(['@' + s for s in text.split()]) + + +def _should_keep_symbol(s): + return s in _symbol_to_id and s != '_' and s != '~' diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py new file mode 100644 index 0000000..b72ae0e --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py @@ -0,0 +1,171 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import collections +import io +import json +import librosa +import numpy as np +import soundfile as sf +import time +import torch +from scipy.io.wavfile import read +from .text import SOS_TOK, EOS_TOK + + +def get_mask_from_lengths(lengths): + max_len = torch.max(lengths).item() + ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len)) + mask = (ids < lengths.unsqueeze(1)) + return mask + + +def load_wav_to_torch(full_path, sr=None): + data, sr = librosa.load(full_path, sr=sr) + data = np.clip(data, -1, 1) # potentially out of [-1, 1] due to resampling + data = data * 32768.0 # match values loaded by scipy + return torch.FloatTensor(data.astype(np.float32)), sr + + +def read_binary_audio(bin_data, tar_sr=None): + """ + read binary audio (`bytes` or `uint8` `numpy.ndarray`) to `float32` + `numpy.ndarray` + + RETURNS: + data (np.ndarray) : audio of shape (n,) or (2, n) + tar_sr (int) : sample rate + """ + data, ori_sr = sf.read(io.BytesIO(bin_data), dtype='float32') + data = data.T + if (tar_sr is not None) and (ori_sr != tar_sr): + data = librosa.resample(data, ori_sr, tar_sr) + else: + tar_sr = ori_sr + data = np.clip(data, -1, 1) + data = data * 32768.0 + return torch.FloatTensor(data.astype(np.float32)), tar_sr + + +def load_filepaths_and_text(filename): + with open(filename, encoding='utf-8') as f: + data = [json.loads(line.rstrip()) for line in f] + return data + + +def to_gpu(x): + x = x.contiguous() + + if torch.cuda.is_available(): + x = x.cuda(non_blocking=True) + return torch.autograd.Variable(x) + + +def load_code_dict(path, add_sos=False, add_eos=False): + if not path: + return {} + + with open(path, 'r') as f: + codes = ['_'] + [line.rstrip() for line in f] # '_' for pad + code_dict = {c: i for i, c in enumerate(codes)} + + if add_sos: + code_dict[SOS_TOK] = len(code_dict) + if add_eos: + code_dict[EOS_TOK] = len(code_dict) + assert(set(code_dict.values()) == set(range(len(code_dict)))) + + return code_dict + + +def load_obs_label_dict(path): + if not path: + return {} + with open(path, 'r') as f: + obs_labels = [line.rstrip() for line in f] + return {c: i for i, c in enumerate(obs_labels)} + + +# A simple timer class inspired from `tnt.TimeMeter` +class CudaTimer: + def __init__(self, keys): + self.keys = keys + self.reset() + + def start(self, key): + s = torch.cuda.Event(enable_timing=True) + s.record() + self.start_events[key].append(s) + return self + + def stop(self, key): + e = torch.cuda.Event(enable_timing=True) + e.record() + self.end_events[key].append(e) + return self + + def reset(self): + self.start_events = collections.defaultdict(list) + self.end_events = collections.defaultdict(list) + self.running_times = collections.defaultdict(float) + self.n = collections.defaultdict(int) + return self + + def value(self): + self._synchronize() + return {k: self.running_times[k] / self.n[k] for k in self.keys} + + def _synchronize(self): + torch.cuda.synchronize() + for k in self.keys: + starts = self.start_events[k] + ends = self.end_events[k] + if len(starts) == 0: + raise ValueError("Trying to divide by zero in TimeMeter") + if len(ends) != len(starts): + raise ValueError("Call stop before checking value!") + time = 0 + for start, end in zip(starts, ends): + time += start.elapsed_time(end) + self.running_times[k] += time * 1e-3 + self.n[k] += len(starts) + self.start_events = collections.defaultdict(list) + self.end_events = collections.defaultdict(list) + + +# Used to measure the time taken for multiple events +class Timer: + def __init__(self, keys): + self.keys = keys + self.n = {} + self.running_time = {} + self.total_time = {} + self.reset() + + def start(self, key): + self.running_time[key] = time.time() + return self + + def stop(self, key): + self.total_time[key] = time.time() - self.running_time[key] + self.n[key] += 1 + self.running_time[key] = None + return self + + def reset(self): + for k in self.keys: + self.total_time[k] = 0 + self.running_time[k] = None + self.n[k] = 0 + return self + + def value(self): + vals = {} + for k in self.keys: + if self.n[k] == 0: + raise ValueError("Trying to divide by zero in TimeMeter") + else: + vals[k] = self.total_time[k] / self.n[k] + return vals diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py new file mode 100644 index 0000000..6a6585e --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py @@ -0,0 +1,40 @@ +# import sys +# sys.path.append('tacotron2') +import torch +from .layers import STFT + + +class Denoiser(torch.nn.Module): + """ Removes model bias from audio produced with waveglow """ + + def __init__(self, waveglow, filter_length=1024, n_overlap=4, + win_length=1024, mode='zeros'): + super(Denoiser, self).__init__() + self.stft = STFT(filter_length=filter_length, + hop_length=int(filter_length/n_overlap), + win_length=win_length).cuda() + if mode == 'zeros': + mel_input = torch.zeros( + (1, 80, 88), + dtype=waveglow.upsample.weight.dtype, + device=waveglow.upsample.weight.device) + elif mode == 'normal': + mel_input = torch.randn( + (1, 80, 88), + dtype=waveglow.upsample.weight.dtype, + device=waveglow.upsample.weight.device) + else: + raise Exception("Mode {} if not supported".format(mode)) + + with torch.no_grad(): + bias_audio = waveglow.infer(mel_input, sigma=0.0).float() + bias_spec, _ = self.stft.transform(bias_audio) + + self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None]) + + def forward(self, audio, strength=0.1): + audio_spec, audio_angles = self.stft.transform(audio.cuda().float()) + audio_spec_denoised = audio_spec - self.bias_spec * strength + audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) + audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles) + return audio_denoised diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/tts_data.py b/fairseq/examples/textless_nlp/gslm/unit2speech/tts_data.py new file mode 100644 index 0000000..d2b04c0 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/tts_data.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import numpy as np +from examples.textless_nlp.gslm.unit2speech.tacotron2.text import ( + EOS_TOK, + SOS_TOK, + code_to_sequence, + text_to_sequence, +) +from examples.textless_nlp.gslm.unit2speech.tacotron2.utils import ( + load_code_dict, +) + + +class TacotronInputDataset: + def __init__(self, hparams, append_str=""): + self.is_text = getattr(hparams, "text_or_code", "text") == "text" + if not self.is_text: + self.code_dict = load_code_dict( + hparams.code_dict, hparams.add_sos, hparams.add_eos + ) + self.code_key = hparams.code_key + self.add_sos = hparams.add_sos + self.add_eos = hparams.add_eos + self.collapse_code = hparams.collapse_code + self.append_str = append_str + + def process_code(self, inp_str): + inp_toks = inp_str.split() + if self.add_sos: + inp_toks = [SOS_TOK] + inp_toks + if self.add_eos: + inp_toks = inp_toks + [EOS_TOK] + return code_to_sequence(inp_toks, self.code_dict, self.collapse_code) + + def process_text(self, inp_str): + return text_to_sequence(inp_str, ["english_cleaners"]) + + def get_tensor(self, inp_str): + # uid, txt, inp_str = self._get_data(idx) + inp_str = inp_str + self.append_str + if self.is_text: + inp_toks = self.process_text(inp_str) + else: + inp_toks = self.process_code(inp_str) + return torch.from_numpy(np.array(inp_toks)).long() + + def __len__(self): + return len(self.data) diff --git a/fairseq/examples/textless_nlp/gslm/unit2speech/utils.py b/fairseq/examples/textless_nlp/gslm/unit2speech/utils.py new file mode 100644 index 0000000..7aced08 --- /dev/null +++ b/fairseq/examples/textless_nlp/gslm/unit2speech/utils.py @@ -0,0 +1,55 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +from examples.textless_nlp.gslm.unit2speech.tacotron2.model import Tacotron2 +from examples.textless_nlp.gslm.unit2speech.tacotron2.waveglow_denoiser import ( + Denoiser, +) + + +def load_quantized_audio_from_file(file_path): + base_fname_batch, quantized_units_batch = [], [] + with open(file_path) as f: + for line in f: + base_fname, quantized_units_str = line.rstrip().split("|") + quantized_units = [int(q) for q in quantized_units_str.split(" ")] + base_fname_batch.append(base_fname) + quantized_units_batch.append(quantized_units) + return base_fname_batch, quantized_units_batch + + +def synthesize_audio(model, waveglow, denoiser, inp, lab=None, strength=0.0): + assert inp.size(0) == 1 + inp = inp.cuda() + if lab is not None: + lab = torch.LongTensor(1).cuda().fill_(lab) + + with torch.no_grad(): + _, mel, _, ali, has_eos = model.inference(inp, lab, ret_has_eos=True) + aud = waveglow.infer(mel, sigma=0.666) + aud_dn = denoiser(aud, strength=strength).squeeze(1) + return mel, aud, aud_dn, has_eos + + +def load_tacotron(tacotron_model_path, max_decoder_steps): + ckpt_dict = torch.load(tacotron_model_path) + hparams = ckpt_dict["hparams"] + hparams.max_decoder_steps = max_decoder_steps + sr = hparams.sampling_rate + model = Tacotron2(hparams) + model.load_state_dict(ckpt_dict["model_dict"]) + model = model.cuda().eval().half() + return model, sr, hparams + + +def load_waveglow(waveglow_path): + waveglow = torch.load(waveglow_path)["model"] + waveglow = waveglow.cuda().eval().half() + for k in waveglow.convinv: + k.float() + denoiser = Denoiser(waveglow) + return waveglow, denoiser diff --git a/fairseq/examples/textless_nlp/pgslm/README.md b/fairseq/examples/textless_nlp/pgslm/README.md new file mode 100644 index 0000000..596467f --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/README.md @@ -0,0 +1,318 @@ +# Text-Free Prosody-Aware Generative Spoken Language Modeling + +This folder contains code and recipes to reproduce results reported in a paper _Text-Free Prosody-Aware Generative Spoken Language Modeling_, +Eugene Kharitonov*, Ann Lee*, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu-Anh Nguyen, Morgane Rivière, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu, 2021. arxiv/2109.03264 [[arxiv]](https://arxiv.org/abs/2109.03264). + +`*` denotes equal contribution. + +You can find demo samples [[here]](https://speechbot.github.io/pgslm/index.html). + +<details> + <summary>If you find this code useful, please consider citing our work using this bibtex </summary> + +``` + @misc{Kharitonov2021, + title={Text-Free Prosody-Aware Generative Spoken Language Modeling}, + author={Eugene Kharitonov and Ann Lee and Adam Polyak and Yossi Adi and Jade Copet and Kushal Lakhotia and Tu-Anh Nguyen and Morgane Rivière and Abdelrahman Mohamed and Emmanuel Dupoux and Wei-Ning Hsu}, + year={2021}, + eprint={2109.03264}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` +</details> + + +## Additional requirements +Three packages are required in addition to fairseq, they are installable with pip: +```bash +pip install AMFM-decompy SoundFile scipy sklearn torchaudio npy-append-array +``` + +## Data preprocessing + +### Prepare unit pseudo-text transcriptions of the audio +To get unit trascripts of the speech data we rely on the preprocessing steps of [GSLM](https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/gslm/speech2unit/) work. + +Firstly, we will need to prepare manifest files for the dataset we want to preprocess +``` +mkdir manifests/ +python examples/wav2vec/wav2vec_manifest.py --valid-percent=0.0 $DATA_PATH --dest=manifests/train/ +``` +Next, we need a pre-trained HuBERT-base-ls960 model [[download]](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) and a corresponding kmeans-100 quantizer [[download]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km100/km.bin). Having those we can quantize the dataset: +``` +python examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py \ + --feature_type hubert \ + --kmeans_model_path km.bin \ + --acoustic_model_path hubert_base_ls960.pt \ + --layer 6 \ + --manifest_path manifests/train/train.tsv \ + --out_quantized_file_path manifests/train/units +``` + +Finally, by running +``` +python examples/textless_nlp/pgslm/scripts/join_units_manifest.py --manifest=manifests/train/train.tsv --units=manifests/train/units --output=train.txt +``` +We will get the training data description `train.txt` in the format that pGSLM expects. The above steps have to be repeated for +dev/test sets. Importantly, we rely on an assumption that the directories are structured as in LibriSpeech, i.e. the file paths follow the +`<spk_id>/<session_id>/<sample_id>.wav` format. + +### Preprocess data for pGSLM +The very first step is to obtain the F0 quantization bins. +Assume the vocoder training manifest is `vocoder_train.txt` (in pGSLM data format prepared with the same process above). +We prepare the quantized F0 from the vocoder training data by running +```sh +bash examples/textless_nlp/pgslm/scripts/prepare_f0_quantization.sh \ + vocoder_train.txt <sample_rate> 32 <preprocessed_dir> <output_prefix> # we use 32 bins in the paper +``` +- `<sample_rate>`: sampling rate of the audio files in the manifest +- `<preprocessed_dir>`: where to output the output files +- `<output_prefix>`: prefix of the output files + +The script will generate +- `<output_prefix>.f0_stat.pt`: the speaker-level F0 statistics, which can be used in vocoder training +- `<output_prefix>_mean_norm_log_f0_bin.th`: the quantized F0, which should be used in `prepare_data.sh` below + +**Note:** See "Pre-trained models" for the pre-computed speaker-level F0 statistics and quantized F0 bins. We suggest using the pre-computed statistics for the data preparation below in order to take advantage of the pre-trained vocoder for waveform generation. + +Next prepare the pGSLM data. +Assume train/valid/test manifests are `{train,valid,test}.txt`. +Here is an example of how to preprocess data: + +```sh +bash examples/textless_nlp/pgslm/scripts/prepare_data.sh \ + train.txt valid.txt test.txt <n_unit> <hop_size> <sample_rate> \ + <preprocessed_dir>/<output_prefix>_mean_norm_log_f0_bin.th <preprocessed_dir> +``` +- `<n_unit>`: discrete unit vocabulary size (we used a kmeans quantizer with the number of units equal to 100 in the example above) +- `<hop_size>`: downsampling rate relative to the waveform (e.g., 320 for HuBERT units) +- `<sample_rate>`: sampling rate of the audio files in the manifest +- `<preprocessed_dir>`: where to output the preprocessed files + +This will create the dataset json config used for the next section at +`<preprocessed_dir>/data_config.json`. + +Note that the example script uses only one thread to compute F0, which can take +_very long_ for preprocessing large datasets. It is suggested to distribute +jobs over multiple nodes/processes with `--nshards=x` and `--rank=z` (where z is +in [1, x]) in `preprocess_f0.py`, and set `--nshards_list=x` in +`prepare_data.py` correspondingly to collect sharded F0 data. + +Now, everything is ready for training a model. + +## Training Multi-Stream Transformer Unit Language Model (MS-TLM) + +Below is an example command that trains Multi-Stream Transformer Language Model (MS-TLM) on a prepared dataset: +```bash +DATASET=data_config.json + +fairseq-train $DATASET \ + --task=speech_unit_modeling \ + --arch="transformer_ulm_tiny" \ + --criterion=speech_unit_lm_criterion \ + --share-decoder-input-output-embed \ + --dropout=0.1 \ + --attention-dropout=0.1 \ + --optimizer="adam" \ + --adam-betas="(0.9, 0.98)" \ + --clip-norm=1.0 \ + --lr=0.0005 \ + --lr-scheduler="inverse_sqrt" \ + --warmup-updates=4000 \ + --warmup-init-lr=1e-07 \ + --tokens-per-sample=3072 \ + --max-tokens=3072 \ + --update-freq=4 \ + --max-epoch=70 \ + --num-workers=0 \ + --skip-invalid-size-inputs-valid-test \ + --loss-weights="1.0;0.5;0.0" \ + --ignore-f0-input \ + --checkpoint-activations \ + --fp16 \ + --max-target-positions=4096 \ + --stream-shifts="1,1" \ + --log-f0 --normalize-f0-mean --interpolate-f0 \ + --ignore-unused-valid-subsets \ + --discrete-duration --discrete-f0 +``` + +Some of the important parameters that are specific to MS-TLM: + * `arch`: specifies the Transformer architecture used. Supported options are: + * `transformer_ulm_tiny` - a tiny model that can be used for debugging; it has 2 layers, 1 attention head, FFN and embedding dimensions of 64, + * `transformer_ulm` - a base model with 6 layers, 8 heads, embedding dimension 512, and FFN dimensionality of 2048, + * `transformer_ulm_big` - the largest model we experiment with in the paper: 12-layer/16 heads, 1024/4096 embedding and FFN dimensions; + * `loss-weights`: this parameter sets importance weights (must be non-negative) for the components of the loss that correspond to unit, duration, and F0 streams. To turn off a component of the loss, its weight has to be set to 0. For instance, to predict only unit stream the parameter should be set to "1;0;0"; + * `stream-shifts`: specifies relative shifts of the two prosodic streams w.r.t. the unit stream (duration and F0, respectively). No shift corresponds to "0,0"; + * `ignore-duration-input`/`ignore-f0-input`: setting these flags would zero-out correpsonding input streams; + * `max-token-duration`: duration values would be max-capped by the specified value; + * `discrete-duration`/`discrete-f0`: whether duration and F0 streams should be quantized; + * `log_f0`, `normalize-f0-mean`, `normalize-f0-std`, `interpolate-f0`: configure how F0 stream is treated. `log_f0` sets up modelling in the log-space, `normalize-f0-mean`/`normalize-f0-std` control per-speaker normalization, and `interpolate-f0` enables F0 interpolation for unvoiced regions where F0 was set to 0, + * `mask-dur-prob`, `mask-f0-prob`, `mask-dur-seg-prob`, `mask-f0-seg-prob`, `mask-unit-seg-prob`, `mask-unit-seg-leng`: this family of parameters sets the probababilities of masking individual steps and spans on each stream as well as lengths of the maked spans. + + +## Pre-trained models +### MS-TLM +Below you can find checkpoints for four best-performing models from the paper (IDs 9..12 in Table 1). These models are trained on Hubert-100 transcripts of the LibriLight-6K dataset. They have the prosody streams shifted by 1 w.r.t. the unit stream. All models predict all three streams (units, duration, and F0), but two +of them only have unit steam in their input. + +| | Continuous prosody | Quantized prosody | +|-------------------|--------------------|-------------------| +| No prosody input | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/ulm_checkpoints/continuous_no_prosody_shift_1_1.pt) | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/ulm_checkpoints/discrete_no_prosody_shift_1_1.pt) | +| Has prosody input | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/ulm_checkpoints/continuous_prosody_shift_1_1.pt) | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/ulm_checkpoints/discrete_prosody_shift_1_1.pt)| + +The optimal per-stream sampling temperatures/scaling parameters that we have identified for these models, in the (`T-token, T-duration, T-f0`) format: + +| | Continuous prosody | Quantized prosody | +|-------------------|--------------------|-------------------| +| No prosody input | 0.7, 0.125, 0.0003125| 0.7, 0.25, 0.5 | +| Has prosody input | 0.7, 0.125, 0.00125 | 0.7, 0.25, 0.7 | + +## Vocoder +| Units | Prosody | F0 stats | Checkpoint | Config | +|-------------------|---------|--------------|------------|--------| +| HuBERT-base-ls960, kmeans-100 | [[Quantized 32 bins]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/mean_norm_log_f0_seg_bin.th) | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/f0_stats.pt) | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/naive_quant_32_norm_log_seg_hubert/checkpoint.pt) | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/naive_quant_32_norm_log_seg_hubert/config.json) | +| HuBERT-base-ls960, kmeans-100 | Continuous | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/f0_stats.pt) | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/mean_norm_log_f0_hubert/checkpoint.pt) | [[download]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/mean_norm_log_f0_hubert/config.json) | + + +## Evaluating a trained model +Evaluation is done with the `eval/cont_metrics.py` scripts. As described in the paper, there are several metrics used. + +**Teacher-forced metrics** +```bash +SET=valid +CHECKPOINT_PATH=discrete_prosody_shift_1_1.pt +DATA=data_config.json + +python examples/textless_nlp/pgslm/eval/cont_metrics.py $DATA \ + --metric=teacher_force_everything \ + --path=$CHECKPOINT_PATH \ + --batch-size=16 \ + --fp16 \ + --seed=111 \ + --eval-subset=$SET \ + --f0-discretization-bounds=mean_norm_log_f0_seg_bin.th --dequantize-prosody +``` +(Using this command, our provided `discrete_prosody_shift_1_1.pt` checkpoint should produce `{'token_loss': 1.408..., 'duration_loss': 0.5424..., 'f0_loss': 0.0474...}` on LibriSpeech dev-clean). + +The parameters `--f0-discretization-bounds=mean_norm_log_f0_seg_bin.th --dequantize-prosody` are specific for quantized-prosody models. They signal that the prosody streams must be decoded into the continuous domain before calculating correlation. It is the same `*_mean_norm_log_f0_bin.th` file as we prepared before. +The `mean_norm_log_f0_seg_bin.th` file we used with the pre-trained models can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/pgslm/vocoder/blizzard2013/mean_norm_log_f0_seg_bin.th). + + +**Consistency (aka Correlation) metrics** + +The following command estimates correlation between mean values of the F0 stream in the prompt and in the generated continuation (unit and duration steams are fixed). + +```bash +T_F0=0.7 +EXPLOSION=20 +SET=test +CHECKPOINT_PATH=discrete_prosody_shift_1_1.pt +DATA=data_config.json + +python examples/textless_nlp/pgslm/eval/cont_metrics.py $DATA \ + --prefix-length=150 \ + --metric=correlation \ + --path=$CHECKPOINT_PATH \ + --batch-size=16 \ + --fp16 \ + --seed=111 \ + --teacher-force-tokens \ + --teacher-force-duration \ + --min-length=300 \ + --batch-explosion-rate=$EXPLOSION \ + --T-f0=$T_F0 \ + --eval-subset=$SET \ + --f0-discretization-bounds=mean_norm_log_f0_seg_bin.th \ + --dequantize-prosody --n-workers=8 +``` +(Using this command, our provided `discrete_prosody_shift_1_1.pt` checkpoint should produce `{...'F0 corr': 0.315 ..}` on LibriSpeech test-clean). + + * By using flags `--teacher-force-tokens, --teacher-force-duration, --teacher-force-f0` one can calculate correlations along each stream while having other two streams fixed to ground-truth values (or freeze all three streams to get ground-truth correlation values); + * The parameters `T-f0`, `T-duration`, and `T-token` specify per-stream temperatures and, in the case of continuous-valued prosody, scaling parameter of the corresponding Laplace distribution (setting a temperature to 0 will enforce greedy sampling); + * `min-length` filters out sequences that are shorter then 300 duration units (i.e. 6s in the case of Hubert units); + * `prefix-length` specifies that we want to use first 150 duration units are prompt (i.e. 3s in the case of Hubert units) + + +**Correctness (aka Continuation) and Expressiveness (aka Std) metrics** + +By running the following command, we can get minMAE and Std for the log-F0 stream for the model with quantized prosody. +```bash +DATA=data_config.json +EXPLOSION=20 +SET=test +CHECKPOINT_PATH=discrete_prosody_shift_1_1.pt +T_F0=0.7 + +python examples/textless_nlp/pgslm/eval/cont_metrics.py $DATA \ + --prefix-length=150 \ + --metric=continuation \ + --path=$CHECKPOINT_PATH \ + --batch-size=16 \ + --fp16 \ + --seed=111 \ + --batch-explosion-rate=$EXPLOSION \ + --teacher-force-tokens \ + --teacher-force-duration \ + --T-f0=$T_F0 \ + --eval-subset=$SET \ + --f0-discretization-bounds=mean_norm_log_f0_seg_bin.th --dequantize-prosody +``` +(Using this command, our provided `discrete_prosody_shift_1_1.pt` checkpoint should produce `{...'F0 MAE': 0.0772, 'F0 Std': 0.1489...}` on LibriSpeech test-clean). + +Again, by setting `--teacher-force-tokens, --teacher-force-duration, --teacher-force-f0` we can calculate Token BLEU for the token stream (when `--teacher-force-duration` & `--teacher-force-f0` are on) and per-stream min MAE for each prosody stream individually. + +Finally, `cont_metrics.py` allows to specify the number of workers (e.g., `n-workers=8`) which allows to speed up the computation by spreading multiple worker processes +over the available GPUs. + +**Cont Word BLEU** + +We used the code and the evaluation protocol of [(Lakhotia et al., 2021)](https://arxiv.org/abs/2102.01192). + +## Sampling from a trained model + +To get (prompted or not) samples from a trained model it is enough to run `sample.py`: +```bash +CHECKPOINT_PATH=checkpoints/checkpoint_best.pt +DATASET=examples/textless_nlp/pgslm/repro/dataset/data_config.json +python examples/textless_nlp/pgslm/sample/sample.py $DATASET \ + --output=$SAMPLES \ + --path=$CHECKPOINT_PATH \ + --sampling \ + --T-token=0.7 \ + --T-duration=0.25 \ + --T-f0=0.7 \ + --max-length=500 \ + --prefix-length=150 \ + --subset=valid \ + --seed=1 \ + --match-duration \ + --code-type=hubert \ + --batch-explosion-rate=2 +``` + +Some useful parameters: + * `T-token`, `T-duration`, `T-f0` specify sampling temperature for the three streams. Setting a temperature to `0` switches sample to the greedy (argmax) one; + * `prefix-length`: length of the prompt, measured in timesteps (e.g. for Hubert (CPC) each timestep is 20 (10) ms); + * `subset`: which subset of the dataset to use as prompts (can be `train`, `valid`, `test`); + * `teacher-force-tokens`, `teacher-force-duration`, `teacher-force-f0`: if set, at each autoregressive step, ground-truth values replace the produced one; + * `short-curcuit`: replace sampling by ground-truth inputs; + * `match-duration`: forces the produced sample to have the same duration (in time), as the entire sequence (beyond the prompt if there is any); + * `batch-explosion-rate`: number of samples per prompt; + * `f0-discretization-bounds`: path to a file with quantization boundaries. If it is set, F0 values are de-quantized back to the continuous domain + (the model must be a quanized one); + * `max-length` sets the maximal number of segment steps to be produced. + +Note that `sample.py` automatically uses all available GPUs, to avoid that please use environment variable `CUDA_VISIBLE_DEVICES`. + +## Vocoding samples +To generate audios for output from `sample.py` (`$IN_FILE`): +```bash +python examples/textless_nlp/pgslm/generate_waveform.py \ + --in-file=$IN_FILE \ + --vocoder=$VODOER \ + --vocoder-cfg=$VOCODER_CFG \ + --results-path=$RESULTS_PATH +``` +See "Pre-trained model" for `$VOCODER` and `VOCODER_CFG`. diff --git a/fairseq/examples/textless_nlp/pgslm/data_utils.py b/fairseq/examples/textless_nlp/pgslm/data_utils.py new file mode 100644 index 0000000..2033697 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/data_utils.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch + +from tqdm import tqdm + + +class Stat: + def __init__(self, keep_raw=False): + self.x = 0.0 + self.x2 = 0.0 + self.z = 0.0 # z = logx + self.z2 = 0.0 + self.n = 0.0 + self.u = 0.0 + self.keep_raw = keep_raw + self.raw = [] + + def update(self, new_x): + new_z = new_x.log() + + self.x += new_x.sum() + self.x2 += (new_x**2).sum() + self.z += new_z.sum() + self.z2 += (new_z**2).sum() + self.n += len(new_x) + self.u += 1 + + if self.keep_raw: + self.raw.append(new_x) + + @property + def mean(self): + return self.x / self.n + + @property + def std(self): + return (self.x2 / self.n - self.mean**2) ** 0.5 + + @property + def mean_log(self): + return self.z / self.n + + @property + def std_log(self): + return (self.z2 / self.n - self.mean_log**2) ** 0.5 + + @property + def n_frms(self): + return self.n + + @property + def n_utts(self): + return self.u + + @property + def raw_data(self): + assert self.keep_raw, "does not support storing raw data!" + return torch.cat(self.raw) + + +class F0Stat(Stat): + def update(self, new_x): + # assume unvoiced frames are 0 and consider only voiced frames + if new_x is not None: + super().update(new_x[new_x != 0]) + + +def dump_speaker_f0_stat(speaker_to_f0_stat, out_prefix): + path = f"{out_prefix}.f0_stat.pt" + assert not os.path.exists(path) + + d = { + speaker: { + "f0_mean": speaker_to_f0_stat[speaker].mean, + "f0_std": speaker_to_f0_stat[speaker].std, + "logf0_mean": speaker_to_f0_stat[speaker].mean_log, + "logf0_std": speaker_to_f0_stat[speaker].std_log, + } + for speaker in speaker_to_f0_stat + } + torch.save(d, path) + + return d + + +def load_audio_path(path): + audio_paths = [] + with open(path) as f: + for line in f.readlines(): + sample = eval(line.strip()) + audio_paths.append(sample["audio"]) + + return audio_paths + + +def load_f0(f0_dir, nshards): + path_to_f0 = {} + for rank in tqdm(range(1, nshards + 1), desc=f"load f0"): + f0_shard_path = f"{f0_dir}/f0_{rank}_{nshards}.pt" + shard_path_to_f0 = torch.load(f0_shard_path) + path_to_f0.update(shard_path_to_f0) + return path_to_f0 diff --git a/fairseq/examples/textless_nlp/pgslm/eval/__init__.py b/fairseq/examples/textless_nlp/pgslm/eval/__init__.py new file mode 100644 index 0000000..0e028c2 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/eval/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/examples/textless_nlp/pgslm/eval/cont_metrics.py b/fairseq/examples/textless_nlp/pgslm/eval/cont_metrics.py new file mode 100644 index 0000000..e98abad --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/eval/cont_metrics.py @@ -0,0 +1,730 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import numpy as np +import scipy + +import torch +import torch.multiprocessing as mp +from fairseq import checkpoint_utils, options +from fairseq.data.codedataset import CodeDataset, ExpressiveCodeDataConfig +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from torch.utils.data import DataLoader, DistributedSampler +from fairseq.utils import move_to_cuda +from fairseq import utils +from fairseq.criterions.speech_ulm_criterion import nll_loss, mae_loss + +import time +from types import SimpleNamespace + +import sys, pathlib + +sys.path.append(str(pathlib.Path(__file__).parent.parent.resolve())) + +from naive_decoder import Naive_F0_Decoder +from inference_dataset import InferenceDataset, explode_batch +from sample.sample import do_sampling, TemperatureDecoder, FilterNamesDataset + +try: + from nltk.translate.bleu_score import sentence_bleu +except ImportError: + print("Please install nltk: `pip install --user -U nltk`") + raise + + +@torch.no_grad() +def teacher_force_everything( + args, dataset, model, criterion, tgt_dict, rank, world_size +): + prefix = args.prefix_length + + f0_decoder = None + if args.dequantize_prosody: + assert dataset.discrete_f0 + print("Reporting MAE for a discrete model") + f0_decoder = Naive_F0_Decoder( + args.f0_discretization_bounds, dataset.config.f0_vq_n_units + ).cuda() + + dataset = InferenceDataset( + dataset, + prefix=args.prefix_length, + only_prefix=False, + filter_short=True, + presort_by_length=True, + ) + sampler = ( + None + if world_size == 1 + else DistributedSampler( + dataset, num_replicas=world_size, rank=rank, shuffle=False + ) + ) + dataloader = DataLoader( + dataset, + args.batch_size, + shuffle=False, + collate_fn=dataset.collater, + sampler=sampler, + ) + + total_token_loss, total_duration_loss, total_f0_loss, total_tokens = ( + 0.0, + 0.0, + 0.0, + 0.0, + ) + + i = 0 + for batch in dataloader: + i += 1 + batch = move_to_cuda(batch) + output = model(**batch["net_input"]) + + tokens, durations, f0 = output["token"], output["duration"], output["f0"] + durations, f0 = durations.squeeze(), f0.squeeze() + + token_loss = nll_loss( + tokens[:, prefix - 1 :], + batch["target"][:, prefix - 1 :].contiguous(), + batch["mask"][:, prefix - 1 :].contiguous(), + reduce=True, + ) + + if args.dequantize_prosody: + durations = durations.argmax(dim=-1) + duration_loss = mae_loss( + durations[:, prefix - 1 :].contiguous().float(), + batch["dur_target"][:, prefix - 1 :].contiguous().float(), + batch["dur_mask"][:, prefix - 1 :].contiguous(), + reduce=True, + ) + else: + duration_loss = criterion.dur_loss_fn( + durations[:, prefix - 1 :].contiguous(), + batch["dur_target"][:, prefix - 1 :].contiguous(), + batch["dur_mask"][:, prefix - 1 :].contiguous(), + reduce=True, + ) + + if f0_decoder: + f0 = f0.argmax(dim=-1) + f0 = f0_decoder(f0).squeeze(-1) + + f0_target = batch["raw_f0"] + f0_loss = mae_loss( + f0[:, prefix - 1 :].contiguous(), + f0_target[:, prefix - 1 :].contiguous(), + batch["f0_mask"][:, prefix - 1 :].contiguous(), + reduce=True, + ) + else: + f0_loss = criterion.f0_loss_fn( + f0[:, prefix - 1 :].contiguous(), + batch["f0_target"][:, prefix - 1 :].contiguous(), + batch["f0_mask"][:, prefix - 1 :].contiguous(), + reduce=True, + ) + + n_tokens = (~batch["dur_mask"])[:, prefix - 1 :].sum() + + total_token_loss += token_loss.item() + total_duration_loss += duration_loss.item() + total_f0_loss += f0_loss.item() + + total_tokens += n_tokens.item() + if args.debug and i > 5: + break + + values = torch.tensor([total_token_loss, total_duration_loss, total_f0_loss]) + normalizers = torch.tensor([total_tokens for _ in range(3)]) + + return values, normalizers + + +def get_bleu(produced_tokens, target_tokens, tgt_dict): + assert target_tokens.ndim == 1 + assert produced_tokens.size(1) == target_tokens.size(0) + + # we can have padding due to shifted channels + shift = 0 + for token in reversed(target_tokens.cpu().tolist()): + if token in [tgt_dict.pad(), tgt_dict.eos()]: + shift += 1 + else: + break + target_tokens = target_tokens[:-shift] + produced_tokens = produced_tokens[:, :-shift] + + string_target = tgt_dict.string(target_tokens).split() + string_candidates = [ + tgt_dict.string(produced_tokens[i, :]).split() + for i in range(produced_tokens.size(0)) + ] + + bleu3 = sentence_bleu( + references=string_candidates, + hypothesis=string_target, + weights=(1.0 / 3, 1.0 / 3, 1.0 / 3), + ) + return bleu3 + + +@torch.no_grad() +def continuation(args, dataset, model, criterion, tgt_dict, rank, world_size): + is_discrete_duration = dataset.discrete_dur + is_discrete_f0 = dataset.discrete_f0 + + f0_decoder = None + if args.dequantize_prosody: + assert dataset.discrete_f0 + print("Reporting MAE F0 for a discrete model") + f0_decoder = Naive_F0_Decoder( + args.f0_discretization_bounds, dataset.config.f0_vq_n_units + ).cuda() + + dataset = InferenceDataset( + dataset, args.prefix_length, filter_short=True, presort_by_length=True + ) + sampler = ( + None + if world_size == 1 + else DistributedSampler( + dataset, num_replicas=world_size, rank=rank, shuffle=False + ) + ) + dataloader = DataLoader( + dataset, + batch_size=1, + shuffle=False, + collate_fn=dataset.collater, + sampler=sampler, + ) + + Ts = args.T_token, args.T_duration, args.T_f0 + decoder = TemperatureDecoder( + Ts, discrete_dur=is_discrete_duration, discrete_f0=is_discrete_f0 + ) + + running_stats = SimpleNamespace( + token_bleu=0.0, + duration_nll=0.0, + duration_mae=0.0, + f0_nll=0.0, + f0_mae=0.0, + n_tokens=0.0, + n_sentences=0.0, + f0_sum=0.0, + f0_sum_sq=0.0, + dur_sum=0.0, + dur_sum_sq=0.0, + ) + + for i, batch in enumerate(dataloader): + batch = explode_batch(batch, args.batch_explosion_rate) + bsz = batch["target"].size(0) + + batch = move_to_cuda(batch) + prefix = batch["prefix"][0] + + max_length_to_unroll = batch["target"].size(1) + prefix_length = batch["net_input"]["src_tokens"].size(1) + steps = max_length_to_unroll - prefix_length + 1 + + assert steps > 0 + produced_tokens, produced_durations, produced_f0, outputs = do_sampling( + model, + batch, + tgt_dict.eos(), + decoder, + autoregressive_steps=steps, + teacher_force_tokens=args.teacher_force_tokens, + teacher_force_duration=args.teacher_force_duration, + teacher_force_f0=args.teacher_force_f0, + ) + + if args.teacher_force_tokens: + assert (produced_tokens[:, 1:] == batch["target"]).all() + if args.teacher_force_duration: + assert (produced_durations[:, 1:] == batch["dur_target"]).all() + if args.teacher_force_f0: + assert (produced_f0[:, 1:] == batch["f0_target"]).all() + + dur_target = batch["dur_target"][:, prefix - 1 :].contiguous() + f0_target = batch["f0_target"][:, prefix - 1 :].contiguous() + + f0_mask = batch["f0_mask"][:, prefix - 1 :].contiguous() + dur_mask = batch["dur_mask"][:, prefix - 1 :].contiguous() + + duration_mae = mae_loss( + produced_durations[:, prefix:].float(), + dur_target.float(), + dur_mask, + reduce=False, + ) + min_duration_mae = duration_mae.view(bsz, -1).sum(dim=-1).min(dim=0)[0] + running_stats.duration_mae += min_duration_mae + + running_stats.dur_sum += ( + produced_durations[:, prefix:].float() * (~dur_mask) + ).sum() / args.batch_explosion_rate + running_stats.dur_sum_sq += ( + produced_durations[:, prefix:].float() * (~dur_mask) + ).pow(2.0).sum() / args.batch_explosion_rate + + if is_discrete_duration: + duration_loss = criterion.dur_loss_fn( + torch.stack([x[1] for x in outputs], dim=1), + dur_target, + dur_mask, + reduce=False, + ) + min_duration_loss = duration_loss.view(bsz, -1).sum(dim=-1).min(dim=0)[0] + running_stats.duration_nll += min_duration_loss + + if f0_decoder: # can only exist for discrete F0 models + decoded_produced_f0 = f0_decoder(produced_f0[:, prefix:]) + decoded_f0_target = batch["raw_f0"][:, prefix - 1 :].contiguous() + + if produced_f0.ndim == 3: + decoded_produced_f0 = decoded_produced_f0.squeeze(2) + decoded_f0_target = decoded_f0_target.squeeze(2) + + f0_mae = mae_loss( + decoded_produced_f0, decoded_f0_target, f0_mask, reduce=False + ) + f0_mae = f0_mae.view(bsz, -1).sum(dim=-1).min(dim=0)[0] + running_stats.f0_mae += f0_mae + + f0_loss = criterion.f0_loss_fn( + torch.stack([x[2] for x in outputs], dim=1), + f0_target.long(), + f0_mask, + reduce=False, + ) + f0_loss = f0_loss.view(bsz, -1).sum(dim=-1).min(dim=0)[0] + running_stats.f0_nll += f0_loss + + running_stats.f0_sum += ( + decoded_produced_f0 * (~f0_mask) + ).sum() / args.batch_explosion_rate + running_stats.f0_sum_sq += (decoded_produced_f0 * (~f0_mask)).pow( + 2.0 + ).sum() / args.batch_explosion_rate + + else: + assert not is_discrete_duration + + f0_loss = mae_loss( + produced_f0[:, prefix:], f0_target, f0_mask, reduce=False + ) + f0_loss = f0_loss.view(bsz, -1).sum(dim=-1).min(dim=0)[0] + running_stats.f0_mae += f0_loss + + running_stats.f0_sum += ( + produced_f0[:, prefix:].sum() / args.batch_explosion_rate + ) + running_stats.f0_sum_sq += ( + produced_f0[:, prefix:].pow(2.0).sum() / args.batch_explosion_rate + ) + + running_stats.n_tokens += (~dur_mask)[0, ...].sum() + + token_loss = get_bleu( + produced_tokens[:, prefix:], batch["target"][0, prefix - 1 :], tgt_dict + ) + running_stats.token_bleu += token_loss + running_stats.n_sentences += 1 + + if args.debug: + break + + values = torch.tensor( + [ + running_stats.token_bleu, + running_stats.duration_nll, + running_stats.duration_mae, + running_stats.f0_nll, + running_stats.f0_mae, + running_stats.f0_sum, + running_stats.f0_sum_sq, + running_stats.dur_sum, + running_stats.dur_sum_sq, + ] + ) + normalizers = torch.tensor( + [running_stats.n_sentences] + [running_stats.n_tokens] * 8 + ) + + return values, normalizers + + +@torch.no_grad() +def correlation(args, dataset, model, criterion, tgt_dict, rank, world_size): + is_discrete_duration = dataset.discrete_dur + is_discrete_f0 = dataset.discrete_f0 + + f0_decoder = None + if is_discrete_f0: + assert dataset.discrete_f0 + f0_decoder = Naive_F0_Decoder( + args.f0_discretization_bounds, dataset.config.f0_vq_n_units + ).cuda() + + if is_discrete_f0: + assert f0_decoder # correlation on tokens is meaningless + + dataset = InferenceDataset( + dataset, + args.prefix_length, + filter_short=True, + presort_by_length=True, + min_length=args.min_length, + ) + sampler = ( + None + if world_size == 1 + else DistributedSampler( + dataset, num_replicas=world_size, rank=rank, shuffle=False + ) + ) + dataloader = DataLoader( + dataset, + batch_size=1, + shuffle=False, + collate_fn=dataset.collater, + sampler=sampler, + ) + + Ts = args.T_token, args.T_duration, args.T_f0 + decoder = TemperatureDecoder( + Ts, discrete_dur=is_discrete_duration, discrete_f0=is_discrete_f0 + ) + + mean_dur_prefix, mean_dur_cont = [], [] + mean_f0_prefix, mean_f0_cont = [], [] + + for batch in dataloader: + batch = explode_batch(batch, args.batch_explosion_rate) + batch = move_to_cuda(batch) + + assert len(batch["prefix"]) == 1 + + if args.teacher_force_tokens: + autoregressive_steps = batch["target"].size(1) - args.prefix_length - 1 + else: + autoregressive_steps = args.max_length - args.prefix_length # + max_shift? + + if args.copy_target: + produced_durations, produced_f0 = batch["dur_target"], batch["f0_target"] + else: + _, produced_durations, produced_f0, outputs = do_sampling( + model, + batch, + tgt_dict.eos(), + decoder, + autoregressive_steps=autoregressive_steps, + teacher_force_tokens=args.teacher_force_tokens, + teacher_force_duration=args.teacher_force_duration, + teacher_force_f0=args.teacher_force_f0, + ) + + # first tokens actually correspond to BOS + produced_durations = produced_durations[:, 1:] + produced_f0 = produced_f0[:, 1:] + + dur_target = batch["dur_target"] + if is_discrete_duration: + produced_durations = produced_durations.float() + dur_target = dur_target.float() + + if is_discrete_f0: + produced_f0 = f0_decoder(produced_f0).squeeze(-1) + f0_target = batch["raw_f0"] + else: + f0_target = batch["f0_target"] + + # prefix values + prefix = batch["prefix"][0] + dur_prefix_mean = dur_target[:, :prefix].sum(dim=-1) / ( + (~batch["dur_mask"][:, :prefix]).sum(dim=-1) + ) + + non_voiced = f0_target[:, :prefix] == 0.0 + f0_mask = batch["f0_mask"][:, :prefix].logical_or(non_voiced) + f0_prefix_mean = f0_target[:, :prefix].sum(dim=-1) / ((~f0_mask).sum(dim=-1)) + + # continuation values + dur_cont_mean = produced_durations[:, prefix:].sum(dim=-1) / ( + (~batch["dur_mask"][:, prefix:]).sum(dim=-1) + ) + + non_voiced = produced_f0[:, prefix:] == 0.0 + f0_mask = non_voiced + f0_cont_mean = produced_f0[:, prefix:].sum(dim=-1) / ((~f0_mask).sum(dim=-1)) + + assert not f0_cont_mean.isnan().any() + + mean_dur_prefix.append(dur_prefix_mean.cpu()) + mean_dur_cont.append(dur_cont_mean.cpu()) + + mean_f0_prefix.append(f0_prefix_mean.cpu()) + mean_f0_cont.append(f0_cont_mean.cpu()) + + if args.debug and len(mean_dur_prefix) > 10: + break + + mean_dur_prefix, mean_dur_cont = torch.cat(mean_dur_prefix), torch.cat( + mean_dur_cont + ) + mean_f0_prefix, mean_f0_cont = torch.cat(mean_f0_prefix), torch.cat(mean_f0_cont) + + return mean_dur_prefix, mean_dur_cont, mean_f0_prefix, mean_f0_cont + + +def main(rank, world_size, args): + start = time.time() + + if world_size > 1: + torch.distributed.init_process_group( + backend="gloo", init_method="env://", world_size=world_size, rank=rank + ) + torch.cuda.set_device(rank % torch.cuda.device_count()) + + raw_args = args + + args = convert_namespace_to_omegaconf(args) + if args.common.seed is not None: + np.random.seed(args.common.seed) + utils.set_torch_seed(args.common.seed) + + models, model_args, task = checkpoint_utils.load_model_ensemble_and_task( + [raw_args.path], arg_overrides={"data": args.task.data} + ) + + tgt_dict = task.target_dictionary + + for model in models: + model.prepare_for_inference_(args) + model.cuda().eval() + if raw_args.fp16: + model = model.half() + model = models[0] + + config = ExpressiveCodeDataConfig(args.task.data) + + dataset = CodeDataset( + manifest=config.manifests[raw_args.eval_subset], + dictionary=task.source_dictionary, + dur_dictionary=task.source_duration_dictionary, + f0_dictionary=task.source_f0_dictionary, + config=config, + discrete_dur=task.cfg.discrete_duration, + discrete_f0=task.cfg.discrete_f0, + log_f0=task.cfg.log_f0, + normalize_f0_mean=task.cfg.normalize_f0_mean, + normalize_f0_std=task.cfg.normalize_f0_std, + interpolate_f0=task.cfg.interpolate_f0, + shifts=task.cfg.stream_shifts, + return_filename=True, + strip_filename=False, + return_continuous_f0=raw_args.dequantize_prosody, + ) + + if raw_args.filter_names: + dataset = FilterNamesDataset(dataset, raw_args.filter_names) + + criterion = task.build_criterion(model_args.criterion) + + name2metric = { + "continuation": continuation, + "teacher_force_everything": teacher_force_everything, + "correlation": correlation, + } + + name2keys = { + "continuation": ( + "Token BLEU3", + "Duration NLL", + "Duration MAE", + "F0 NLL", + "F0 MAE", + "F0 sum", + "F0 sum_sq", + "Dur sum", + "Dur sum_sq", + ), + "teacher_force_everything": ("token_loss", "duration_loss", "f0_loss"), + "correlation": ("Duration corr", "F0 corr"), + } + metric_name = raw_args.metric + + metric = name2metric[metric_name] + results = metric(raw_args, dataset, model, criterion, tgt_dict, rank, world_size) + + values = None + + if metric_name not in [ + "correlation", + ]: + values, normalizers = results + values = maybe_aggregate_normalize(values, normalizers, world_size) + elif metric_name == "correlation": + values = maybe_aggregate_correlations(results, world_size) + else: + assert False + + assert values is not None + summary = dict(zip(name2keys[raw_args.metric], values.tolist())) + if metric_name == "continuation": + summary["F0 Std"] = np.sqrt(-summary["F0 sum"] ** 2 + summary["F0 sum_sq"]) + summary["Dur Std"] = np.sqrt(-summary["Dur sum"] ** 2 + summary["Dur sum_sq"]) + del summary["F0 sum"] + del summary["F0 sum_sq"] + del summary["Dur sum"] + del summary["Dur sum_sq"] + + summary["metric"] = metric_name + + if rank == 0: + print(summary) + if raw_args.wandb: + wandb_results(summary, raw_args) + print("# finished in ", time.time() - start, "seconds") + + +def wandb_results(summary, raw_args): + import wandb + + run = wandb.init( + project=raw_args.wandb_project_name, tags=raw_args.wandb_tags.split(",") + ) + run.config.metric = raw_args.metric + run.config.model = raw_args.path + run.config.data = raw_args.data + + if raw_args.wandb_run_name: + run.name = raw_args.wandb_run_name + run.save() + + wandb.log(summary) + wandb.finish() + + +def maybe_aggregate_normalize(values, normalizers, world_size): + if world_size > 1: + torch.distributed.barrier() + + torch.distributed.all_reduce_multigpu([values]) + torch.distributed.all_reduce_multigpu([normalizers]) + + return values / normalizers + + +def maybe_aggregate_correlations(results, world_size): + if world_size > 1: + output = [None for _ in range(world_size)] + torch.distributed.all_gather_object(output, results) + mean_dur_prefix, mean_dur_cont, mean_f0_prefix, mean_f0_cont = [ + torch.cat([x[i] for x in output]) for i in range(4) + ] + else: + mean_dur_prefix, mean_dur_cont, mean_f0_prefix, mean_f0_cont = results + + corr_dur = scipy.stats.pearsonr(mean_dur_prefix.numpy(), mean_dur_cont.numpy())[0] + corr_f0 = scipy.stats.pearsonr(mean_f0_prefix.numpy(), mean_f0_cont.numpy())[0] + values = torch.tensor([corr_dur, corr_f0]) + + return values + + +def cli_main(): + parser = options.get_interactive_generation_parser() + parser.add_argument( + "--prefix-length", + type=int, + default=1, + help="Prompt prefix length (including <s>)", + ) + parser.add_argument( + "--duration-scale", + type=float, + default=1, + help="Multiply durations by the given scaler", + ) + parser.add_argument( + "--debug", action="store_true", help="Process only the first batch" + ) + parser.add_argument("--n_hypotheses", type=int, default=1) + parser.add_argument("--filter-names", type=str, default=None) + parser.add_argument( + "--max-length", type=int, default=200, help="Maximal produced length" + ) + + parser.add_argument("--teacher-force-tokens", action="store_true", default=False) + parser.add_argument("--teacher-force-duration", action="store_true", default=False) + parser.add_argument("--teacher-force-f0", action="store_true", default=False) + + parser.add_argument("--copy-target", action="store_true", default=False) + parser.add_argument("--min-length", type=int, default=None) + parser.add_argument("--f0-discretization-bounds", type=str, default=None) + parser.add_argument("--dequantize-prosody", action="store_true") + parser.add_argument("--batch-explosion-rate", type=int, default=1) + + parser.add_argument( + "--metric", + choices=["continuation", "teacher_force_everything", "correlation"], + required=True, + ) + + parser.add_argument("--wandb", action="store_true") + parser.add_argument("--wandb-project-name", type=str, default="eslm") + parser.add_argument("--wandb-tags", type=str, default="") + parser.add_argument("--wandb-run-name", type=str, default="") + + parser.add_argument("--T-token", type=float, default=1.0) + parser.add_argument("--T-duration", type=float, default=1.0) + parser.add_argument("--T-f0", type=float, default=1.0) + + parser.add_argument("--n-workers", type=int, default=1) + + parser.add_argument( + "--eval-subset", type=str, default="valid", choices=["valid", "test"] + ) + + args = options.parse_args_and_arch(parser) + + assert ( + args.prefix_length >= 1 + ), "Prefix length includes bos token <s>, hence the minimum is 1." + assert args.temperature >= 0.0, "T must be non-negative!" + + if args.dequantize_prosody: + assert args.f0_discretization_bounds + + world_size = args.n_workers or torch.cuda.device_count() + if world_size > 1: + import random + + mp.set_start_method("spawn", force=True) + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(random.randint(10_000, 50_000)) + + mp.spawn( + main, + nprocs=world_size, + args=( + world_size, + args, + ), + join=True, + ) + else: + main(rank=0, world_size=world_size, args=args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/textless_nlp/pgslm/generate_waveform.py b/fairseq/examples/textless_nlp/pgslm/generate_waveform.py new file mode 100644 index 0000000..a6f348b --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/generate_waveform.py @@ -0,0 +1,120 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import argparse +import json +import logging +from pathlib import Path +import soundfile as sf +import torch + +from tqdm import tqdm + +from fairseq import utils +from fairseq.models.text_to_speech.vocoder import CodeHiFiGANVocoder + + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def dump_result(args, data, sample_id, pred_wav): + assert "audio" in data or args.results_path is not None + if args.results_path: + fname = Path(data["audio"]).name if "audio" in data else f"{sample_id}_pred.wav" + out_file = Path(args.results_path) / fname + + sf.write( + out_file.as_posix(), + pred_wav.detach().cpu().numpy(), + args.sample_rate, + ) + + +def load_data(in_file): + with open(in_file) as f: + data = [ast.literal_eval(line.strip()) for line in f] + + return data + + +def get_f0_upsample_ratio(code_hop_size, f_hop_size): + ratio = (code_hop_size // 160) // (f_hop_size // 256) * 2 + return ratio + + +def main(args): + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + + with open(args.vocoder_cfg) as f: + vocoder_cfg = json.load(f) + vocoder = CodeHiFiGANVocoder(args.vocoder, vocoder_cfg) + if use_cuda: + vocoder = vocoder.cuda() + + data = load_data(args.in_file) + + if args.results_path: + Path(args.results_path).mkdir(exist_ok=True, parents=True) + + for i, d in tqdm(enumerate(data), total=len(data)): + code_key = "cpc_km100" if "cpc_km100" in d else "hubert" + code = list(map(int, d[code_key].split())) + + x = { + "code": torch.LongTensor(code).view(1, -1), + "f0": torch.Tensor(d["f0"]).view(1, -1), + } + + f0_up_ratio = get_f0_upsample_ratio( + vocoder_cfg["code_hop_size"], vocoder_cfg["hop_size"] + ) + if f0_up_ratio > 1: + bsz, cond_length = x["f0"].size() + x["f0"] = x["f0"].unsqueeze(2).repeat(1, 1, f0_up_ratio).view(bsz, -1) + + x = utils.move_to_cuda(x) if use_cuda else x + wav = vocoder(x) + dump_result(args, d, i, wav) + + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-file", + type=str, + required=True, + help="Input file following the same format of the output from sample.py ('f0' and 'cpc_km100/hubert' are required fields)", + ) + parser.add_argument( + "--vocoder", type=str, required=True, help="path to the vocoder" + ) + parser.add_argument( + "--vocoder-cfg", + type=str, + required=True, + help="path to the vocoder config", + ) + parser.add_argument("--sample-rate", type=int, default=16_000) + parser.add_argument( + "--results-path", + type=str, + default=None, + help="Output directory. If not set, the audios will be stored following the 'audio' field specified in the input file.", + ) + parser.add_argument("--cpu", action="store_true", help="run on CPU") + + args = parser.parse_args() + + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/textless_nlp/pgslm/inference_dataset.py b/fairseq/examples/textless_nlp/pgslm/inference_dataset.py new file mode 100644 index 0000000..9f7cfa5 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/inference_dataset.py @@ -0,0 +1,103 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch + + +class InferenceDataset: + def __init__( + self, + dataset, + prefix, + only_prefix=True, + presort_by_length=True, + filter_short=False, + min_length=None, + ): + self.dataset = dataset + self.collater = self.dataset.collater + self.prefix = prefix + self.only_prefix = only_prefix + self.filter_short = filter_short + + self.remapping = list(range(len(self.dataset))) + if min_length: + assert min_length >= prefix + 1 + + length_thr = prefix + 1 if not min_length else min_length + + if filter_short: + self.remapping = list( + filter( + lambda i: self.dataset[i]["dur_source"].sum() > length_thr, + self.remapping, + ) + ) + print( + f"# the initial dataset of {len(self.dataset)} examples became {len(self.remapping)} after filtering" + f" examples shorter than {length_thr} (in duration units)" + ) + + if presort_by_length: + lengths = {index: dataset.size(index) for index in self.remapping} + self.remapping.sort(key=lambda i: lengths[i]) + + @property + def pads(self): + return self.dataset.pads + + def __len__(self): + return len(self.remapping) + + def original_size(self, k): + k = self.remapping[k] + return self.dataset.size(k) + + def __getitem__(self, k): + k = self.remapping[k] + channels = self.dataset[k] + + if self.prefix and self.only_prefix: + dur_channel = channels["dur_source"] + assert dur_channel.sum() >= self.prefix + + token_times = dur_channel.cumsum(dim=-1) + cut_after = torch.searchsorted(token_times, torch.tensor(self.prefix)) + + r = {} + for channel_name, value in channels.items(): + if isinstance(value, torch.Tensor) and "source" in channel_name: + # if self.filter_short: assert value.size(0) >= self.prefix + r[channel_name] = value[: cut_after + 1] + else: + r[channel_name] = value + + r["prefix"] = cut_after + 1 + else: + r = channels + + return r + + +def explode_batch(batch, times): + if times == 1: + return batch + + new_batch = {} + + for key, value in batch.items(): + if isinstance(value, torch.Tensor): + assert value.size(0) == 1 + new_batch[key] = torch.cat([value] * times) + elif key in ["ntokens", "nsentences"]: + new_batch[key] = value * times + elif key in ["prefix", "filename"]: + new_batch[key] = value + elif key == "net_input": + new_batch[key] = explode_batch(value, times) + else: + assert False, key + return new_batch diff --git a/fairseq/examples/textless_nlp/pgslm/naive_decoder.py b/fairseq/examples/textless_nlp/pgslm/naive_decoder.py new file mode 100644 index 0000000..5132889 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/naive_decoder.py @@ -0,0 +1,40 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import warnings + + +class Naive_F0_Decoder(torch.nn.Module): + def __init__(self, bounds_path, n_units=32): + super().__init__() + + bounds = torch.load(bounds_path) + bounds = torch.from_numpy(bounds[n_units]) + assert bounds.ndim == 1 + + pad = torch.tensor([-5.0, -5.0]) # bos, eos, pad are in the dictionary + centers = torch.cat( + [bounds[0:1], 0.5 * (bounds[1:] + bounds[:-1]), bounds[-1:], pad[:]] + ) + + self.embedding = torch.nn.Embedding.from_pretrained( + centers.unsqueeze(-1), freeze=True + ) + self.max_n = self.embedding.weight.numel() + + def forward(self, discrete_f0: torch.Tensor): + in_bounds = (0 <= discrete_f0).all() and (discrete_f0 < self.max_n).all() + if not in_bounds: + warnings.warn( + f"F0 contains some weird outputs: discrete_f0.max().item()={discrete_f0.max().item()} discrete_f0.min().item()={discrete_f0.min().item()}; " + f"while we have embeddings for {self.max_n} values. " + "Assuming this is a no-prosody model -- but be careful!" + ) + + mask = discrete_f0 >= self.max_n + discrete_f0 = discrete_f0.masked_fill(mask, self.max_n - 1) + + return self.embedding(discrete_f0).squeeze(-1) diff --git a/fairseq/examples/textless_nlp/pgslm/prepare_dataset.py b/fairseq/examples/textless_nlp/pgslm/prepare_dataset.py new file mode 100644 index 0000000..3d5edaa --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/prepare_dataset.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from multiprocessing import Pool + +import os +from collections import defaultdict +from itertools import starmap + +import torch +from npy_append_array import NpyAppendArray +from tqdm import tqdm + +from data_utils import dump_speaker_f0_stat, F0Stat, load_f0 +from fairseq.data.codedataset import ( + ExpressiveCodeDataConfig, + parse_manifest, + F0_FRAME_SPACE, + align_f0_to_durations, +) +from fairseq.tasks.speech_ulm_task import UnitDictionary + + +def load_meta(meta_path, split): + config = ExpressiveCodeDataConfig(meta_path) + manifest_path = config.manifests[split] + dictionary = UnitDictionary(n_units=config.n_units) + audio_paths, codes, durs, speakers = parse_manifest(manifest_path, dictionary) + return config, audio_paths, codes, durs, speakers + + +def _align_f0(f0, dur, ratio, frm_tol=5): + if f0 is None: + seg_f0 = torch.zeros_like(dur, dtype=torch.float) + else: + seg_f0 = align_f0_to_durations(f0, dur, ratio, tol=frm_tol * ratio) + return seg_f0.numpy() # try a hacky stuff + + +def align_f0(path_to_f0, audio_paths, durs, ratio, mp=False): + chunk_size = 2000 + num_procs = 40 + iterable = ((path_to_f0[p], d, ratio) for p, d in zip(audio_paths, durs)) + + seg_f0s = [] + if mp: + with Pool(num_procs) as pool: + iterator = tqdm( + pool.istarmap(_align_f0, iterable, chunk_size), + desc="align f0", + total=len(durs), + ) + for seg_f0 in iterator: + seg_f0s.append(torch.from_numpy(seg_f0).float()) + else: + iterator = tqdm(starmap(_align_f0, iterable), desc="align f0", total=len(durs)) + for seg_f0 in iterator: + seg_f0s.append(torch.from_numpy(seg_f0).float()) + + return seg_f0s + + +def prepare_seg_data(config, audio_paths, codes, durs, speakers, path_to_f0): + ratio = config.code_hop_size / (config.sampling_rate * F0_FRAME_SPACE) + seg_f0s = align_f0(path_to_f0, audio_paths, durs, ratio) + data = { + "codes": codes, + "duration": durs, + "f0": seg_f0s, + "speaker": speakers, + "path": audio_paths, + } + return data + + +def dump_seg_data(data, out_prefix): + key_targs = { + "codes": f"{out_prefix}.code.npy", + "duration": f"{out_prefix}.dur.npy", + "f0": f"{out_prefix}.f0.npy", + } + for key, targ in key_targs.items(): + assert not os.path.exists(targ) + npaa = NpyAppendArray(targ) + for utt_data in tqdm(data[key], desc=f"dumping {key}"): + npaa.append(utt_data.numpy()) + + assert not os.path.exists(f"{out_prefix}.path.txt") + with open(f"{out_prefix}.path.txt", "w") as f: + for x in data["path"]: + f.write(f"{str(x)}\n") + + assert not os.path.exists(f"{out_prefix}.leng.txt") + with open(f"{out_prefix}.leng.txt", "w") as f: + for x in data["codes"]: + f.write(f"{len(x)}\n") + + assert not os.path.exists(f"{out_prefix}.speaker.txt") + with open(f"{out_prefix}.speaker.txt", "w") as f: + for x in data["speaker"]: + f.write(f"{str(x)}\n") + + print(f"wrote to files with prefix {out_prefix}") + + +def main(meta_path, f0_dir, splits, nshards_list): + speaker_to_stat = defaultdict(F0Stat) + if len(nshards_list) == 1: + nshards_list = nshards_list * len(splits) + else: + assert len(nshards_list) == len(splits) + + for split, nshards in zip(splits, nshards_list): + config, audio_paths, codes, durs, speakers = load_meta(meta_path, split) + path_to_f0 = load_f0(f"{f0_dir}/{split}", nshards) + + # segment-level data + data = prepare_seg_data(config, audio_paths, codes, durs, speakers, path_to_f0) + dump_seg_data(data, config.manifests[split]) + + # speaker f0 + for audio_path, speaker in tqdm(zip(audio_paths, speakers)): + f0 = path_to_f0[audio_path] + speaker_to_stat[speaker].update(f0) + dump_speaker_f0_stat(speaker_to_stat, config.manifests[split]) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("meta_path") + parser.add_argument("f0_dir", help="out_dir from preprocess_f0") + parser.add_argument("--splits", nargs="+", default=["train", "valid"]) + parser.add_argument( + "--nshards_list", type=int, nargs="+", default=[20], help="number of f0 shards" + ) + args = parser.parse_args() + print(args) + + main(**vars(args)) diff --git a/fairseq/examples/textless_nlp/pgslm/preprocess_f0.py b/fairseq/examples/textless_nlp/pgslm/preprocess_f0.py new file mode 100644 index 0000000..afe899c --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/preprocess_f0.py @@ -0,0 +1,65 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +from tqdm import tqdm +from data_utils import load_audio_path +from fairseq.data.codedataset import get_f0_by_filename + + +def process_one(path, sr): + """ + Args: + path: audio file path + sr: sampling rate + """ + try: + # YAAPT throws errors in some rare cases + f0 = get_f0_by_filename(path, sr) + except Exception as e: + print( + f"WARNING: error when processing {path}. set f0 to zero. original error message:\n{e}" + ) + f0 = None + return f0 + + +def main(file_path, out_dir, nshards, rank, sampling_rate): + # load data + audio_paths = load_audio_path(file_path) + + # shard + assert nshards <= len(audio_paths) and nshards > 0 + shard_size = len(audio_paths) / nshards + s = int(round((rank - 1) * shard_size)) + e = int(round(rank * shard_size)) + audio_paths = audio_paths[s:e] + + # process + path_to_f0 = {} + for i, audio_path in enumerate(tqdm(audio_paths)): + f0 = process_one(audio_path, sampling_rate) + path_to_f0[audio_path] = f0 + print(f"finished processing {len(path_to_f0)} utterances ({s}-{e})") + + f0_path = f"{out_dir}/f0_{rank}_{nshards}.pt" + os.makedirs(out_dir, exist_ok=True) + torch.save(path_to_f0, f0_path) + print(f"saved to {f0_path}") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("file_path") + parser.add_argument("out_dir") + parser.add_argument("--nshards", type=int, default=20) + parser.add_argument("--rank", type=int, default=1) + parser.add_argument("--sampling_rate", type=int, default=16000) + args = parser.parse_args() + + main(**vars(args)) diff --git a/fairseq/examples/textless_nlp/pgslm/quantize_f0.py b/fairseq/examples/textless_nlp/pgslm/quantize_f0.py new file mode 100644 index 0000000..d9e3df2 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/quantize_f0.py @@ -0,0 +1,94 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections import defaultdict +from functools import partial + +import numpy as np +import torch +from tqdm import tqdm + +from data_utils import dump_speaker_f0_stat, F0Stat, load_audio_path, load_f0 + + +def load_speaker(path): + speakers = [] + with open(path) as f: + for line in f.readlines(): + sample = eval(line.strip()) + assert "speaker" in sample + speakers.append(sample["speaker"]) + return speakers + + +def quantize_f0(speaker_to_f0, f0_stats, nbins, normalize, log): + f0_all = [] + for speaker, f0 in speaker_to_f0.items(): + f0 = f0.raw_data + if log: + f0 = f0.log() + mean = f0_stats[speaker]["logf0_mean"] if log else f0_stats[speaker]["f0_mean"] + std = f0_stats[speaker]["logf0_std"] if log else f0_stats[speaker]["f0_std"] + if normalize == "mean": + f0 = f0 - mean + elif normalize == "meanstd": + f0 = (f0 - mean) / std + f0_all.extend(f0.tolist()) + + hist, bin_x = np.histogram(f0_all, 100000) + cum_hist = np.cumsum(hist) / len(f0_all) * 100 + + f0_bin = {} + for num_bin in nbins: + bin_offset = [] + bin_size = 100 / num_bin + threshold = bin_size + for i in range(num_bin - 1): + index = (np.abs(cum_hist - threshold)).argmin() + bin_offset.append(bin_x[index]) + threshold += bin_size + f0_bin[num_bin] = np.array(bin_offset) + + return f0_bin + + +def main(file_path, f0_dir, out_dir, out_prefix, nbins, nshards, normalize, log): + audio_paths = load_audio_path(file_path) + path_to_f0 = load_f0(f0_dir, nshards) + + speakers = load_speaker(file_path) + speaker_to_f0 = defaultdict(partial(F0Stat, True)) + + # speaker f0 stats + for audio_path, speaker in tqdm(zip(audio_paths, speakers)): + f0 = path_to_f0[audio_path] + speaker_to_f0[speaker].update(f0) + f0_stats = dump_speaker_f0_stat(speaker_to_f0, f"{out_dir}/{out_prefix}") + + # quantize + f0_bin = quantize_f0(speaker_to_f0, f0_stats, nbins, normalize, log) + log_suffix = "_log" if log else "" + f0_bin_out_file = f"{out_dir}/{out_prefix}_{normalize}_norm{log_suffix}_f0_bin.th" + torch.save(f0_bin, f0_bin_out_file) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("file_path") + parser.add_argument("f0_dir", help="out_dir from preprocess_f0") + parser.add_argument("out_dir") + parser.add_argument("out_prefix") + parser.add_argument("--nbins", nargs="+", type=int, default=[32]) + parser.add_argument("--nshards", type=int, default=20, help="number of f0 shards") + parser.add_argument( + "--normalize", type=str, choices=["meanstd", "mean", "none"], default="mean" + ) + parser.add_argument("--log", action="store_true") + args = parser.parse_args() + print(args) + + main(**vars(args)) diff --git a/fairseq/examples/textless_nlp/pgslm/sample/__init__.py b/fairseq/examples/textless_nlp/pgslm/sample/__init__.py new file mode 100644 index 0000000..0e028c2 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/sample/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/examples/textless_nlp/pgslm/sample/sample.py b/fairseq/examples/textless_nlp/pgslm/sample/sample.py new file mode 100644 index 0000000..55ec7a9 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/sample/sample.py @@ -0,0 +1,612 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch.multiprocessing as mp +import numpy as np +import json + +import torch +from torch.distributions.categorical import Categorical + +from fairseq import checkpoint_utils, options, utils +from fairseq.data.codedataset import CodeDataset, ExpressiveCodeDataConfig +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from torch.utils.data import DataLoader, DistributedSampler +from fairseq.utils import move_to_cuda + +import tqdm +import random +import pathlib + +import sys, pathlib + +sys.path.append(str(pathlib.Path(__file__).parent.parent)) +from inference_dataset import InferenceDataset, explode_batch +from naive_decoder import Naive_F0_Decoder +from truncated_laplace import truncated_laplace + +CODETYPE_TO_FRAMETIME = {"cpc_km100": 0.01, "hubert": 0.02} # 10ms # 20ms + + +class TemperatureDecoder: + def __init__(self, Ts, discrete_dur=False, discrete_f0=False): + self.T_token, self.T_dur, self.T_f0 = Ts + self.discrete_dur = discrete_dur + self.discrete_f0 = discrete_f0 + + def __call__(self, output): + def sample_multinomial(key, T): + logits = output[key][:, -1, :].float() + return Categorical(logits=logits / T).sample().unsqueeze(-1) + + def sample_laplace(key, T, truncate_at_zero): + mean = output[key][:, -1, :].float() + return truncated_laplace(mean=mean, T=T, truncate_by_zero=truncate_at_zero) + + if self.T_token > 0: + new_tokens = sample_multinomial("token", self.T_token) + else: + new_tokens = output["token"][:, -1, :].argmax(dim=-1, keepdim=True) + + if not self.discrete_dur and self.T_dur == 0: + new_durations = output["duration"][:, -1].round().int() + elif not self.discrete_dur and self.T_dur > 0: + new_durations = ( + sample_laplace("duration", self.T_dur, truncate_at_zero=True) + .round() + .int() + ) + elif self.discrete_dur and self.T_dur > 0: + new_durations = sample_multinomial("duration", self.T_dur) + elif self.discrete_dur and self.T_dur == 0: + new_durations = output["duration"][:, -1, :].argmax(dim=-1, keepdim=True) + else: + assert False + + if not self.discrete_f0 and self.T_f0 == 0: + new_f0 = output["f0"][:, -1] + elif not self.discrete_f0 and self.T_f0 > 0: + new_f0 = sample_laplace("f0", self.T_f0, truncate_at_zero=False) + elif self.discrete_f0 and self.T_f0 > 0: + new_f0 = sample_multinomial("f0", self.T_f0) + elif self.discrete_f0 and self.T_f0 == 0: + new_f0 = output["f0"][:, -1, :].argmax(dim=-1, keepdim=True) + else: + assert False + + return new_tokens, new_durations, new_f0 + + +class FilterNamesDataset: + def __init__(self, dataset, fnames_path): + self.dataset = dataset + + with open(fnames_path, "r") as fin: + fnames = set((eval(line)["audio"] for line in fin)) + print(f"# will retrict the dataset for {len(fnames)} files") + + self.indexes = [] + + for i, datapoint in enumerate(dataset): + if datapoint["filename"] in fnames: + self.indexes.append(i) + assert len(self.indexes) == len(fnames), f"{len(self.indexes)} {len(fnames)}" + + self.collater = self.dataset.collater + self.discrete_dur = self.dataset.discrete_dur + self.discrete_f0 = self.dataset.discrete_f0 + + def __len__(self): + return len(self.indexes) + + def __getitem__(self, k): + k = self.indexes[k] + return self.dataset[k] + + def size(self, k): + k = self.indexes[k] + return self.dataset.size(k) + + +@torch.no_grad() +def do_sampling( + model, + batch, + eos_token, + decoder, + autoregressive_steps=100, + teacher_force_tokens=False, + teacher_force_duration=False, + teacher_force_f0=False, + match_duration=False, +): + def autoregressive_step_(output, autoregressive_steps): + new_tokens, new_durations, new_f0 = decoder(output) + + n = output["token"].size(1) if output["token"].ndim == 3 else 1 + + if teacher_force_tokens: + new_tokens = batch["target"][:, n - 1].unsqueeze(-1) + if teacher_force_duration: + new_durations = batch["dur_target"][:, n - 1].unsqueeze(-1) + if teacher_force_f0: + new_f0 = batch["f0_target"][:, n - 1].unsqueeze(-1) + + batch["net_input"]["src_tokens"] = torch.cat( + [batch["net_input"]["src_tokens"], new_tokens], dim=1 + ) + batch["net_input"]["dur_src"] = torch.cat( + [batch["net_input"]["dur_src"], new_durations], dim=1 + ) + batch["net_input"]["f0_src"] = torch.cat( + [batch["net_input"]["f0_src"], new_f0], dim=1 + ) + + outputs = [] + + if teacher_force_tokens or teacher_force_duration or teacher_force_f0: + max_time = batch["target"].size(1) + prefix_time = batch["net_input"]["src_tokens"].size(1) + + autoregressive_steps = max_time - prefix_time + 1 # should be 0 + + for _ in range(autoregressive_steps): + output = model(**batch["net_input"]) + + last_steps = ( + output["token"][:, -1, ...], + output["duration"][:, -1, ...], + output["f0"][:, -1, ...], + ) + outputs.append(last_steps) + + autoregressive_step_(output, autoregressive_steps) + tokens, duration, f0 = ( + batch["net_input"]["src_tokens"], + batch["net_input"]["dur_src"], + batch["net_input"]["f0_src"], + ) + + if ( + match_duration + and (batch["dur_target"].sum(dim=-1) < duration.sum(dim=-1)).all() + ): + break + + return tokens, duration, f0, outputs + + +def unroll_duration(token_stream, duration_stream): + assert len(token_stream) == len( + duration_stream + ), f"{len(token_stream)} != {len(duration_stream)}" + non_positive_durations = sum(d <= 0 for d in duration_stream) + if non_positive_durations > 0: + print( + f"# {non_positive_durations} durations are non-positive, they will be capped to 1" + ) + + result = [] + + duration_stream_rounded_capped = [max(1, int(round(x))) for x in duration_stream] + for t, d in zip(token_stream, duration_stream_rounded_capped): + result.extend([t] * d) + + return result + + +def realign_shifted_streams(tokens, durations, F0s, shifts): + """ + Durations are shifted by 1, F0 by 2 + >>> tokens = ["<s>", "t1", "t2", "t3", "</s>", "x", "x"] + >>> durations = ["<0>", "<0>", "d1", "d2", "d3", "<0>", "x"] + >>> F0s = ["<0>", "<0>", "<0>", "f1", "f2", "f3", "<0>"] + >>> shifts = [1,2] + >>> realign_shifted_streams(tokens, durations, F0s, shifts) + (['<s>', 't1', 't2', 't3', '</s>'], ['<0>', 'd1', 'd2', 'd3', '<0>'], ['<0>', 'f1', 'f2', 'f3', '<0>']) + """ + max_shift = max(shifts) + if max_shift > 0: + shift_durations, shift_F0s = shifts + + tokens = tokens[:-max_shift] + durations = durations[shift_durations:] + if shift_durations < max_shift: + durations = durations[: -(max_shift - shift_durations)] + + if F0s is not None: + F0s = F0s[shift_F0s:] + if shift_F0s < max_shift: + F0s = F0s[: -(max_shift - shift_F0s)] + + assert len(tokens) == len(durations), f"{len(tokens)} =! {len(durations)}" + if F0s is not None: + assert len(tokens) == len(F0s), f"{len(tokens)} =! {len(F0s)}" + + return tokens, durations, F0s + + +def maybe_cut_eos(produced_tokens, produced_duration, produced_f0, eos_idx): + if eos_idx in produced_tokens: + eos_index = produced_tokens.index(eos_idx) + produced_tokens = produced_tokens[:eos_index] + produced_duration = produced_duration[:eos_index] + produced_f0 = produced_f0[:eos_index] + return produced_tokens, produced_duration, produced_f0 + + +def maybe_filter_pad(produced_tokens, produced_duration, produced_f0, pad_idx): + if pad_idx not in produced_tokens: + return produced_tokens, produced_duration, produced_f0 + + assert len(produced_tokens) == len(produced_duration) == len(produced_f0) + + print("<pad> is detected in the output!") + filtered_tokens, filtered_duration, filtered_f0 = [], [], [] + + for t, d, f in zip(produced_tokens, produced_duration, produced_f0): + if t != pad_idx: + filtered_tokens.append(t) + filtered_duration.append(d) + filtered_f0.append(f) + return filtered_tokens, filtered_duration, filtered_f0 + + +def match_duration(produced_tokens, produced_duration, produced_f0, target_duration): + """ + >>> tokens = ['t'] * 4 + >>> F0s = ['f0'] * 4 + >>> produced_duration = [1, 10, 10, 10] + >>> match_duration(tokens, produced_duration, F0s, target_duration=100) + (['t', 't', 't', 't'], [1, 10, 10, 10], ['f0', 'f0', 'f0', 'f0']) + >>> match_duration(tokens, produced_duration, F0s, target_duration=5) + (['t', 't'], [1, 4], ['f0', 'f0']) + """ + if sum(produced_duration) <= target_duration: + return produced_tokens, produced_duration, produced_f0 + + running_duration = 0 + filtered_duration = [] + + for next_tok_duration in produced_duration: + if running_duration + next_tok_duration < target_duration: + filtered_duration.append(next_tok_duration) + running_duration += next_tok_duration + else: + to_add = target_duration - running_duration + assert to_add <= next_tok_duration + filtered_duration.append(to_add) + break + + produced_duration = filtered_duration + assert sum(produced_duration) == target_duration + + n_tok = len(filtered_duration) + + return produced_tokens[:n_tok], produced_duration, produced_f0[:n_tok] + + +def main(rank, world_size, args): + if world_size > 1: + torch.distributed.init_process_group( + backend="gloo", init_method="env://", world_size=world_size, rank=rank + ) + torch.cuda.set_device(rank) + + raw_args = args + args = convert_namespace_to_omegaconf(args) + if args.common.seed is not None: + random.seed(args.common.seed) + np.random.seed(args.common.seed) + utils.set_torch_seed(args.common.seed) + + models, model_args, task = checkpoint_utils.load_model_ensemble_and_task( + [raw_args.path], arg_overrides={"data": args.task.data} + ) + tgt_dict = task.target_dictionary + + for model in models: + model.prepare_for_inference_(args) + model.cuda().eval() + if raw_args.fp16: + model = model.half() + model = models[0] + + config = ExpressiveCodeDataConfig(args.task.data) + + dataset = CodeDataset( + manifest=config.manifests[raw_args.subset], + dictionary=task.source_dictionary, + dur_dictionary=task.source_duration_dictionary, + f0_dictionary=task.source_f0_dictionary, + config=config, + discrete_dur=task.cfg.discrete_duration, + discrete_f0=task.cfg.discrete_f0, + log_f0=task.cfg.log_f0, + normalize_f0_mean=task.cfg.normalize_f0_mean, + normalize_f0_std=task.cfg.normalize_f0_std, + interpolate_f0=task.cfg.interpolate_f0, + shifts=task.cfg.stream_shifts, + return_filename=True, + strip_filename=False, + ) + tgt_dict = task.target_dictionary + shifts = dataset.shifts.dur, dataset.shifts.f0 + max_shift = max(shifts) + + fname = raw_args.output + if world_size > 1: + fname += f"_{rank}" + output_file = open(fname, "w") + + if raw_args.filter_names: + dataset = FilterNamesDataset(dataset, raw_args.filter_names) + + dataset = InferenceDataset(dataset, raw_args.prefix_length, filter_short=True) + print(f"Dataset size {len(dataset)}") + sampler = ( + None + if world_size == 1 + else DistributedSampler( + dataset, num_replicas=world_size, rank=rank, shuffle=False + ) + ) + dataloader = DataLoader( + dataset, + batch_size=1, + shuffle=False, + collate_fn=dataset.collater, + sampler=sampler, + ) + + Ts = raw_args.T_token, raw_args.T_duration, raw_args.T_f0 + decoder = TemperatureDecoder( + Ts, discrete_dur=task.cfg.discrete_duration, discrete_f0=task.cfg.discrete_f0 + ) + + dataset_size = len(dataset) + + f0_decoder = None + if raw_args.f0_discretization_bounds: + assert task.cfg.discrete_f0 + f0_decoder = Naive_F0_Decoder(raw_args.f0_discretization_bounds).cuda() + + pbar = ( + tqdm.tqdm( + total=dataset_size + if raw_args.max_samples is None + else min(raw_args.max_samples, dataset_size) + ) + if world_size == 1 + else None + ) + + samples_produced = 0 + + for batch in dataloader: + if ( + raw_args.max_samples is not None + and samples_produced >= raw_args.max_samples + ): + break + + prefix = batch["prefix"][0] + + batch = explode_batch(batch, raw_args.batch_explosion_rate) + batch = move_to_cuda(batch) + + if not raw_args.short_curcuit: + produced_tokens, produced_durations, produced_f0, _ = do_sampling( + models[0], + batch, + tgt_dict.eos(), + decoder, + autoregressive_steps=raw_args.max_length - prefix + max_shift, + teacher_force_tokens=raw_args.teacher_force_tokens, + match_duration=raw_args.match_duration, + teacher_force_duration=raw_args.teacher_force_duration, + teacher_force_f0=raw_args.teacher_force_f0, + ) + + # stip entries corresponding to <s> + produced_tokens = produced_tokens[:, 1:] + produced_durations = produced_durations[:, 1:] + produced_f0 = produced_f0[:, 1:] + + else: + max_length = raw_args.max_length + max_shift + produced_tokens, produced_durations, produced_f0 = ( + batch["target"][:, :max_length], + batch["dur_target"][:, :max_length], + batch["f0_target"][:, :max_length], + ) + + if f0_decoder is not None: + produced_f0 = f0_decoder(produced_f0) + + produced_tokens, produced_durations, produced_f0 = ( + produced_tokens.cpu().tolist(), + produced_durations.cpu().tolist(), + produced_f0.cpu().tolist(), + ) + + bsz = batch["target"].size(0) + assert bsz == raw_args.batch_explosion_rate + + for i in range(bsz): + if ( + raw_args.max_samples is not None + and samples_produced >= raw_args.max_samples + ): + break + + produced_tokens_i = produced_tokens[i] + produced_durations_i = produced_durations[i] + produced_f0_i = produced_f0[i] + + ( + produced_tokens_i, + produced_durations_i, + produced_f0_i, + ) = realign_shifted_streams( + produced_tokens_i, produced_durations_i, produced_f0_i, shifts + ) + + produced_tokens_i, produced_durations_i, produced_f0_i = maybe_cut_eos( + produced_tokens_i, produced_durations_i, produced_f0_i, tgt_dict.eos() + ) + + produced_tokens_i, produced_durations_i, produced_f0_i = maybe_filter_pad( + produced_tokens_i, produced_durations_i, produced_f0_i, tgt_dict.pad() + ) + + if raw_args.match_duration: + # NB: here we cheat a bit and use that padding has duration 0 + # so no need to re-align and remove padding + dur_target_i = batch["dur_target"][i, :].sum().item() + produced_tokens_i, produced_durations_i, produced_f0_i = match_duration( + produced_tokens_i, produced_durations_i, produced_f0_i, dur_target_i + ) + + if raw_args.cut_prompt: + produced_tokens_i, produced_durations_i, produced_f0_i = ( + produced_tokens_i[prefix:], + produced_durations_i[prefix:], + produced_f0_i[prefix:], + ) + + prompt_fname = batch["filename"][0] + fname = str(pathlib.Path(prompt_fname).with_suffix("")) + f"__{i}.wav" + + token_stream = unroll_duration(produced_tokens_i, produced_durations_i) + f0_stream = unroll_duration(produced_f0_i, produced_durations_i) + output_line = json.dumps( + { + "audio": fname, + "prompt": prompt_fname, + raw_args.code_type: " ".join(map(str, token_stream)), + "duration": round( + sum(produced_durations_i) + * CODETYPE_TO_FRAMETIME[raw_args.code_type], + 3, + ), + "raw_duration": produced_durations_i, + "raw_f0": produced_f0_i, + "f0": [round(f0, 3) for f0 in f0_stream], + } + ) + print(output_line, file=output_file) + + if pbar: + pbar.update(1) + samples_produced += 1 + + if raw_args.debug: + break + + output_file.close() + + if world_size > 1: + # important that everything is flushed before aggregating + torch.distributed.barrier() + + if world_size > 1 and rank == 0: + with open(raw_args.output, "w") as fout: + for i in range(world_size): + f = raw_args.output + f"_{i}" + with open(f, "r") as fin: + fout.write(fin.read()) + os.remove(f) + + +def cli_main(): + parser = options.get_interactive_generation_parser() + parser.add_argument( + "--prefix-length", + type=int, + default=1, + help="Prompt prefix length (including <s>)", + ) + parser.add_argument("--output", type=str, default=None, required=True) + parser.add_argument( + "--debug", action="store_true", help="Process only the first batch" + ) + parser.add_argument( + "--ignore-durations", + action="store_true", + help="If set, the duration stream is ignored", + ) + parser.add_argument( + "--max-length", type=int, default=200, help="Maximal produced length" + ) + parser.add_argument( + "--code-type", choices=["cpc_km100", "hubert"], default="cpc_km100" + ) + parser.add_argument("--max-samples", type=int, default=None) + parser.add_argument("--prompt-duration-scaler", type=float, default=1.0) + parser.add_argument("--teacher-force-tokens", action="store_true", default=False) + parser.add_argument("--teacher-force-duration", action="store_true", default=False) + parser.add_argument("--teacher-force-f0", action="store_true", default=False) + parser.add_argument("--filter-names", type=str, default=None) + parser.add_argument( + "--match-duration", + action="store_true", + help="Do not produce sequences longer that ground-truth", + ) + parser.add_argument( + "--cut-prompt", + action="store_true", + help="Remove prompt from the produced audio", + ) + parser.add_argument( + "--short-curcuit", action="store_true", help="Use 'target' as a sample" + ) + parser.add_argument("--f0-discretization-bounds", type=str, default=None) + + parser.add_argument("--batch-explosion-rate", type=int, default=1) + + parser.add_argument("--T-token", type=float, default=1.0) + parser.add_argument("--T-duration", type=float, default=1.0) + parser.add_argument("--T-f0", type=float, default=1.0) + + parser.add_argument( + "--subset", type=str, default="valid", choices=["test", "valid"] + ) + + args = options.parse_args_and_arch(parser) + + assert ( + args.prefix_length >= 1 + ), "Prefix length includes bos token <s>, hence the minimum is 1." + assert all( + t >= 0 for t in [args.T_token, args.T_f0, args.T_duration] + ), "T must be non-negative!" + + world_size = torch.cuda.device_count() + if world_size > 1: + import random + + mp.set_start_method("spawn", force=True) + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(random.randint(10_000, 50_000)) + + print(f"Using {world_size} devices, master port {os.environ['MASTER_PORT']}") + + mp.spawn( + main, + nprocs=world_size, + args=( + world_size, + args, + ), + join=True, + ) + else: + main(rank=0, world_size=world_size, args=args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/textless_nlp/pgslm/scripts/join_units_manifest.py b/fairseq/examples/textless_nlp/pgslm/scripts/join_units_manifest.py new file mode 100644 index 0000000..ed14fc5 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/scripts/join_units_manifest.py @@ -0,0 +1,48 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import argparse +import pathlib + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--manifest", required=True) + parser.add_argument("--units", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--sample_rate", type=int, default=16_000) + + args = parser.parse_args() + + with open(args.manifest, "r") as manifest, open(args.units, "r") as units, open( + args.output, "w" + ) as outp: + root = manifest.readline().strip() + root = pathlib.Path(root) + + for manifest_line, unit_line in zip(manifest.readlines(), units.readlines()): + path, frames = manifest_line.split() + duration = int(frames) / float(args.sample_rate) + fname = root / path + speaker = fname.parent.parent.name + + units = unit_line.split("|")[1] + + print( + json.dumps( + dict( + audio=str(root / path), + duration=duration, + hubert_km100=units.strip(), + speaker=speaker, + ) + ), + file=outp, + ) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/textless_nlp/pgslm/scripts/prepare_data.sh b/fairseq/examples/textless_nlp/pgslm/scripts/prepare_data.sh new file mode 100644 index 0000000..ec892e5 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/scripts/prepare_data.sh @@ -0,0 +1,57 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +set -eu + +train_json=$1 +valid_json=$2 +test_json=$3 +n_units=$4 +hop_size=$5 +sr=$6 +f0_quantizer=$7 +out_dir=$8 + +meta_path="$out_dir/data_config.json" +f0_dir="$out_dir/f0" + +mkdir -p $out_dir +ln -sf $train_json $out_dir/train.txt +ln -sf $valid_json $out_dir/valid.txt +ln -sf $test_json $out_dir/test.txt + +cat <<EOF >$meta_path +{ + "manifests": { + "train": "$out_dir/train.txt", + "valid": "$out_dir/valid.txt", + "test": "$out_dir/test.txt" + }, + "n_units": $n_units, + "code_hop_size": $hop_size, + "sampling_rate": $sr, + "multispkr": "parent_parent_name", + + "f0_vq_type": "naive", + "f0_vq_naive_quantizer": { + "log_mean_norm": "$f0_quantizer" + }, + "f0_vq_n_units": 32 +} +EOF + +for split in train valid test; do + python examples/textless_nlp/pgslm/preprocess_f0.py \ + $out_dir/$split.txt $f0_dir/$split --nshards=1 --rank=1 --sampling_rate=$sr + + #NSHARDS=16 + #seq 1 $NSHARDS | parallel -j $NSHARDS python examples/textless_nlp/pgslm/preprocess_f0.py \ + # $out_dir/$split.txt $f0_dir/$split --nshards=$NSHARDS --sampling_rate=$sr --rank +done + +# Please make sure that the number of shards (--nshards_list) is consistent across commands +python examples/textless_nlp/pgslm/prepare_dataset.py \ + $meta_path $f0_dir --splits test valid train --nshards_list 1 diff --git a/fairseq/examples/textless_nlp/pgslm/scripts/prepare_f0_quantization.sh b/fairseq/examples/textless_nlp/pgslm/scripts/prepare_f0_quantization.sh new file mode 100644 index 0000000..3a285a3 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/scripts/prepare_f0_quantization.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +set -eu + +train_json=$1 +sr=$2 +nbins=$3 +out_dir=$4 +out_prefix=$5 + +f0_dir="$out_dir/f0" + +python examples/textless_nlp/pgslm/preprocess_f0.py \ + $train_json $f0_dir/${out_prefix}_f0_quant --nshards 1 --rank 1 --sampling_rate $sr + +# NB: one can use parallel here: +# NSHARDS=16 +# +#seq 1 $NSHARDS | parallel -j $NSHARDS python examples/textless_nlp/pgslm/preprocess_f0.py \ +# $train_json $f0_dir/${out_prefix}_f0_quant --nshards $NSHARDS --sampling_rate $sr --rank + +python examples/textless_nlp/pgslm/quantize_f0.py \ + $train_json $f0_dir/${out_prefix}_f0_quant $out_dir $out_prefix --nbins $nbins --nshards 1 --normalize mean --log diff --git a/fairseq/examples/textless_nlp/pgslm/truncated_laplace.py b/fairseq/examples/textless_nlp/pgslm/truncated_laplace.py new file mode 100644 index 0000000..089f8a8 --- /dev/null +++ b/fairseq/examples/textless_nlp/pgslm/truncated_laplace.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import warnings + + +def truncated_laplace(mean, T, truncate_by_zero=False): + """Generating a sample from a Laplace distribution, possible left-truncated at zero. + A bit of explanation here https://stats.stackexchange.com/a/357598 . + """ + assert isinstance(mean, torch.Tensor) + + if not truncate_by_zero: + percentile = 0.0 + else: + if not (mean >= 0.0).all(): + warnings.warn(f"means are supposed to be non-negative, but got {mean}") + mean = torch.clamp_min(mean, 0.0) + + lower_bound = mean.new_tensor([0.0]) + percentile = 0.5 + 0.5 * torch.sign(lower_bound - mean) * ( + 1.0 - torch.exp(-1.0 / T * torch.abs(mean - lower_bound)) + ) + + p = torch.empty_like(mean).uniform_() * (1.0 - percentile) + percentile + return mean - T * torch.sign(p - 0.5) * torch.log(1 - 2 * torch.abs(p - 0.5)) diff --git a/fairseq/examples/textless_nlp/speech-resynth/README.md b/fairseq/examples/textless_nlp/speech-resynth/README.md new file mode 100644 index 0000000..a099682 --- /dev/null +++ b/fairseq/examples/textless_nlp/speech-resynth/README.md @@ -0,0 +1,28 @@ + +# Speech Resynthesis from Discrete Disentangled Self-Supervised Representations +Landing page with usfull resources for the [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://arxiv.org/abs/2104.00355) paper. + +<p align="center"><img width="70%" src="img/fig.png" /></p> + +__Abstract__: We propose using self-supervised discrete representations for the task of speech resynthesis. To generate disentangled representation, we separately extract low-bitrate representations for speech content, prosodic information, and speaker identity. This allows to synthesize speech in a controllable manner. We analyze various state-of-the-art, self-supervised representation learning methods and shed light on the advantages of each method while considering reconstruction quality and disentanglement properties. Specifically, we evaluate the F0 reconstruction, speaker identification performance (for both resynthesis and voice conversion), recordings' intelligibility, and overall quality using subjective human evaluation. Lastly, we demonstrate how these representations can be used for an ultra-lightweight speech codec. Using the obtained representations, we can get to a rate of 365 bits per second while providing better speech quality than the baseline methods. + + +## Quick Links +- [Paper](https://arxiv.org/pdf/2104.00355.pdf) +- [Samples](https://speechbot.github.io/resynthesis/index.html) +- [Code](https://github.com/facebookresearch/speech-resynthesis) + +The codebase for the [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://arxiv.org/abs/2104.00355) paper can be found under the following [repository](https://github.com/facebookresearch/speech-resynthesis). + + +## Citation +``` +@inproceedings{polyak21_interspeech, + author={Adam Polyak and Yossi Adi and Jade Copet and + Eugene Kharitonov and Kushal Lakhotia and + Wei-Ning Hsu and Abdelrahman Mohamed and Emmanuel Dupoux}, + title={{Speech Resynthesis from Discrete Disentangled Self-Supervised Representations}}, + year=2021, + booktitle={Proc. Interspeech 2021}, +} +``` diff --git a/fairseq/examples/translation/README.md b/fairseq/examples/translation/README.md new file mode 100644 index 0000000..2941f5e --- /dev/null +++ b/fairseq/examples/translation/README.md @@ -0,0 +1,301 @@ +# Neural Machine Translation + +This README contains instructions for [using pretrained translation models](#example-usage-torchhub) +as well as [training new models](#training-a-new-model). + +## Pre-trained models + +Model | Description | Dataset | Download +---|---|---|--- +`conv.wmt14.en-fr` | Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2) +`conv.wmt14.en-de` | Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2) +`conv.wmt17.en-de` | Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2) +`transformer.wmt14.en-fr` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) +`transformer.wmt16.en-de` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) +`transformer.wmt18.en-de` | Transformer <br> ([Edunov et al., 2018](https://arxiv.org/abs/1808.09381)) <br> WMT'18 winner | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz) <br> See NOTE in the archive +`transformer.wmt19.en-de` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 English-German](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz) +`transformer.wmt19.de-en` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 German-English](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz) +`transformer.wmt19.en-ru` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 English-Russian](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz) +`transformer.wmt19.ru-en` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 Russian-English](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz) + +## Example usage (torch.hub) + +We require a few additional Python dependencies for preprocessing: +```bash +pip install fastBPE sacremoses subword_nmt +``` + +Interactive translation via PyTorch Hub: +```python +import torch + +# List available models +torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt16.en-de', ... ] + +# Load a transformer trained on WMT'16 En-De +# Note: WMT'19 models use fastBPE instead of subword_nmt, see instructions below +en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de', + tokenizer='moses', bpe='subword_nmt') +en2de.eval() # disable dropout + +# The underlying model is available under the *models* attribute +assert isinstance(en2de.models[0], fairseq.models.transformer.TransformerModel) + +# Move model to GPU for faster translation +en2de.cuda() + +# Translate a sentence +en2de.translate('Hello world!') +# 'Hallo Welt!' + +# Batched translation +en2de.translate(['Hello world!', 'The cat sat on the mat.']) +# ['Hallo Welt!', 'Die Katze saß auf der Matte.'] +``` + +Loading custom models: +```python +from fairseq.models.transformer import TransformerModel +zh2en = TransformerModel.from_pretrained( + '/path/to/checkpoints', + checkpoint_file='checkpoint_best.pt', + data_name_or_path='data-bin/wmt17_zh_en_full', + bpe='subword_nmt', + bpe_codes='data-bin/wmt17_zh_en_full/zh.code' +) +zh2en.translate('你好 世界') +# 'Hello World' +``` + +If you are using a `transformer.wmt19` models, you will need to set the `bpe` +argument to `'fastbpe'` and (optionally) load the 4-model ensemble: +```python +en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de', + checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt', + tokenizer='moses', bpe='fastbpe') +en2de.eval() # disable dropout +``` + +## Example usage (CLI tools) + +Generation with the binarized test sets can be run in batch mode as follows, e.g. for WMT 2014 English-French on a GTX-1080ti: +```bash +mkdir -p data-bin +curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf - -C data-bin +curl https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2 | tar xvjf - -C data-bin +fairseq-generate data-bin/wmt14.en-fr.newstest2014 \ + --path data-bin/wmt14.en-fr.fconv-py/model.pt \ + --beam 5 --batch-size 128 --remove-bpe | tee /tmp/gen.out +# ... +# | Translated 3003 sentences (96311 tokens) in 166.0s (580.04 tokens/s) +# | Generate test with beam=5: BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787) + +# Compute BLEU score +grep ^H /tmp/gen.out | cut -f3- > /tmp/gen.out.sys +grep ^T /tmp/gen.out | cut -f2- > /tmp/gen.out.ref +fairseq-score --sys /tmp/gen.out.sys --ref /tmp/gen.out.ref +# BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787) +``` + +## Training a new model + +### IWSLT'14 German to English (Transformer) + +The following instructions can be used to train a Transformer model on the [IWSLT'14 German to English dataset](http://workshop2014.iwslt.org/downloads/proceeding.pdf). + +First download and preprocess the data: +```bash +# Download and prepare the data +cd examples/translation/ +bash prepare-iwslt14.sh +cd ../.. + +# Preprocess/binarize the data +TEXT=examples/translation/iwslt14.tokenized.de-en +fairseq-preprocess --source-lang de --target-lang en \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/iwslt14.tokenized.de-en \ + --workers 20 +``` + +Next we'll train a Transformer translation model over this data: +```bash +CUDA_VISIBLE_DEVICES=0 fairseq-train \ + data-bin/iwslt14.tokenized.de-en \ + --arch transformer_iwslt_de_en --share-decoder-input-output-embed \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --dropout 0.3 --weight-decay 0.0001 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --max-tokens 4096 \ + --eval-bleu \ + --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \ + --eval-bleu-detok moses \ + --eval-bleu-remove-bpe \ + --eval-bleu-print-samples \ + --best-checkpoint-metric bleu --maximize-best-checkpoint-metric +``` + +Finally we can evaluate our trained model: +```bash +fairseq-generate data-bin/iwslt14.tokenized.de-en \ + --path checkpoints/checkpoint_best.pt \ + --batch-size 128 --beam 5 --remove-bpe +``` + +### WMT'14 English to German (Convolutional) + +The following instructions can be used to train a Convolutional translation model on the WMT English to German dataset. +See the [Scaling NMT README](../scaling_nmt/README.md) for instructions to train a Transformer translation model on this data. + +The WMT English to German dataset can be preprocessed using the `prepare-wmt14en2de.sh` script. +By default it will produce a dataset that was modeled after [Attention Is All You Need (Vaswani et al., 2017)](https://arxiv.org/abs/1706.03762), but with additional news-commentary-v12 data from WMT'17. + +To use only data available in WMT'14 or to replicate results obtained in the original [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](https://arxiv.org/abs/1705.03122) paper, please use the `--icml17` option. + +```bash +# Download and prepare the data +cd examples/translation/ +# WMT'17 data: +bash prepare-wmt14en2de.sh +# or to use WMT'14 data: +# bash prepare-wmt14en2de.sh --icml17 +cd ../.. + +# Binarize the dataset +TEXT=examples/translation/wmt17_en_de +fairseq-preprocess \ + --source-lang en --target-lang de \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/wmt17_en_de --thresholdtgt 0 --thresholdsrc 0 \ + --workers 20 + +# Train the model +mkdir -p checkpoints/fconv_wmt_en_de +fairseq-train \ + data-bin/wmt17_en_de \ + --arch fconv_wmt_en_de \ + --dropout 0.2 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --optimizer nag --clip-norm 0.1 \ + --lr 0.5 --lr-scheduler fixed --force-anneal 50 \ + --max-tokens 4000 \ + --save-dir checkpoints/fconv_wmt_en_de + +# Evaluate +fairseq-generate data-bin/wmt17_en_de \ + --path checkpoints/fconv_wmt_en_de/checkpoint_best.pt \ + --beam 5 --remove-bpe +``` + +### WMT'14 English to French +```bash +# Download and prepare the data +cd examples/translation/ +bash prepare-wmt14en2fr.sh +cd ../.. + +# Binarize the dataset +TEXT=examples/translation/wmt14_en_fr +fairseq-preprocess \ + --source-lang en --target-lang fr \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/wmt14_en_fr --thresholdtgt 0 --thresholdsrc 0 \ + --workers 60 + +# Train the model +mkdir -p checkpoints/fconv_wmt_en_fr +fairseq-train \ + data-bin/wmt14_en_fr \ + --arch fconv_wmt_en_fr \ + --dropout 0.1 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --optimizer nag --clip-norm 0.1 \ + --lr 0.5 --lr-scheduler fixed --force-anneal 50 \ + --max-tokens 3000 \ + --save-dir checkpoints/fconv_wmt_en_fr + +# Evaluate +fairseq-generate \ + data-bin/fconv_wmt_en_fr \ + --path checkpoints/fconv_wmt_en_fr/checkpoint_best.pt \ + --beam 5 --remove-bpe +``` + +## Multilingual Translation + +We also support training multilingual translation models. In this example we'll +train a multilingual `{de,fr}-en` translation model using the IWSLT'17 datasets. + +Note that we use slightly different preprocessing here than for the IWSLT'14 +En-De data above. In particular we learn a joint BPE code for all three +languages and use fairseq-interactive and sacrebleu for scoring the test set. + +```bash +# First install sacrebleu and sentencepiece +pip install sacrebleu sentencepiece + +# Then download and preprocess the data +cd examples/translation/ +bash prepare-iwslt17-multilingual.sh +cd ../.. + +# Binarize the de-en dataset +TEXT=examples/translation/iwslt17.de_fr.en.bpe16k +fairseq-preprocess --source-lang de --target-lang en \ + --trainpref $TEXT/train.bpe.de-en \ + --validpref $TEXT/valid0.bpe.de-en,$TEXT/valid1.bpe.de-en,$TEXT/valid2.bpe.de-en,$TEXT/valid3.bpe.de-en,$TEXT/valid4.bpe.de-en,$TEXT/valid5.bpe.de-en \ + --destdir data-bin/iwslt17.de_fr.en.bpe16k \ + --workers 10 + +# Binarize the fr-en dataset +# NOTE: it's important to reuse the en dictionary from the previous step +fairseq-preprocess --source-lang fr --target-lang en \ + --trainpref $TEXT/train.bpe.fr-en \ + --validpref $TEXT/valid0.bpe.fr-en,$TEXT/valid1.bpe.fr-en,$TEXT/valid2.bpe.fr-en,$TEXT/valid3.bpe.fr-en,$TEXT/valid4.bpe.fr-en,$TEXT/valid5.bpe.fr-en \ + --tgtdict data-bin/iwslt17.de_fr.en.bpe16k/dict.en.txt \ + --destdir data-bin/iwslt17.de_fr.en.bpe16k \ + --workers 10 + +# Train a multilingual transformer model +# NOTE: the command below assumes 1 GPU, but accumulates gradients from +# 8 fwd/bwd passes to simulate training on 8 GPUs +mkdir -p checkpoints/multilingual_transformer +CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt17.de_fr.en.bpe16k/ \ + --max-epoch 50 \ + --ddp-backend=legacy_ddp \ + --task multilingual_translation --lang-pairs de-en,fr-en \ + --arch multilingual_transformer_iwslt_de_en \ + --share-decoders --share-decoder-input-output-embed \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ + --warmup-updates 4000 --warmup-init-lr '1e-07' \ + --label-smoothing 0.1 --criterion label_smoothed_cross_entropy \ + --dropout 0.3 --weight-decay 0.0001 \ + --save-dir checkpoints/multilingual_transformer \ + --max-tokens 4000 \ + --update-freq 8 + +# Generate and score the test set with sacrebleu +SRC=de +sacrebleu --test-set iwslt17 --language-pair ${SRC}-en --echo src \ + | python scripts/spm_encode.py --model examples/translation/iwslt17.de_fr.en.bpe16k/sentencepiece.bpe.model \ + > iwslt17.test.${SRC}-en.${SRC}.bpe +cat iwslt17.test.${SRC}-en.${SRC}.bpe \ + | fairseq-interactive data-bin/iwslt17.de_fr.en.bpe16k/ \ + --task multilingual_translation --lang-pairs de-en,fr-en \ + --source-lang ${SRC} --target-lang en \ + --path checkpoints/multilingual_transformer/checkpoint_best.pt \ + --buffer-size 2000 --batch-size 128 \ + --beam 5 --remove-bpe=sentencepiece \ + > iwslt17.test.${SRC}-en.en.sys +grep ^H iwslt17.test.${SRC}-en.en.sys | cut -f3 \ + | sacrebleu --test-set iwslt17 --language-pair ${SRC}-en +``` + +##### Argument format during inference + +During inference it is required to specify a single `--source-lang` and +`--target-lang`, which indicates the inference langauge direction. +`--lang-pairs`, `--encoder-langtok`, `--decoder-langtok` have to be set to +the same value as training. diff --git a/fairseq/examples/translation/prepare-iwslt14.sh b/fairseq/examples/translation/prepare-iwslt14.sh new file mode 100644 index 0000000..2fb6643 --- /dev/null +++ b/fairseq/examples/translation/prepare-iwslt14.sh @@ -0,0 +1,115 @@ +#!/usr/bin/env bash +# +# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh + +echo 'Cloning Moses github repository (for tokenization scripts)...' +git clone https://github.com/moses-smt/mosesdecoder.git + +echo 'Cloning Subword NMT repository (for BPE pre-processing)...' +git clone https://github.com/rsennrich/subword-nmt.git + +SCRIPTS=mosesdecoder/scripts +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +LC=$SCRIPTS/tokenizer/lowercase.perl +CLEAN=$SCRIPTS/training/clean-corpus-n.perl +BPEROOT=subword-nmt/subword_nmt +BPE_TOKENS=10000 + +URL="http://dl.fbaipublicfiles.com/fairseq/data/iwslt14/de-en.tgz" +GZ=de-en.tgz + +if [ ! -d "$SCRIPTS" ]; then + echo "Please set SCRIPTS variable correctly to point to Moses scripts." + exit +fi + +src=de +tgt=en +lang=de-en +prep=iwslt14.tokenized.de-en +tmp=$prep/tmp +orig=orig + +mkdir -p $orig $tmp $prep + +echo "Downloading data from ${URL}..." +cd $orig +wget "$URL" + +if [ -f $GZ ]; then + echo "Data successfully downloaded." +else + echo "Data not successfully downloaded." + exit +fi + +tar zxvf $GZ +cd .. + +echo "pre-processing train data..." +for l in $src $tgt; do + f=train.tags.$lang.$l + tok=train.tags.$lang.tok.$l + + cat $orig/$lang/$f | \ + grep -v '<url>' | \ + grep -v '<talkid>' | \ + grep -v '<keywords>' | \ + sed -e 's/<title>//g' | \ + sed -e 's/<\/title>//g' | \ + sed -e 's/<description>//g' | \ + sed -e 's/<\/description>//g' | \ + perl $TOKENIZER -threads 8 -l $l > $tmp/$tok + echo "" +done +perl $CLEAN -ratio 1.5 $tmp/train.tags.$lang.tok $src $tgt $tmp/train.tags.$lang.clean 1 175 +for l in $src $tgt; do + perl $LC < $tmp/train.tags.$lang.clean.$l > $tmp/train.tags.$lang.$l +done + +echo "pre-processing valid/test data..." +for l in $src $tgt; do + for o in `ls $orig/$lang/IWSLT14.TED*.$l.xml`; do + fname=${o##*/} + f=$tmp/${fname%.*} + echo $o $f + grep '<seg id' $o | \ + sed -e 's/<seg id="[0-9]*">\s*//g' | \ + sed -e 's/\s*<\/seg>\s*//g' | \ + sed -e "s/\’/\'/g" | \ + perl $TOKENIZER -threads 8 -l $l | \ + perl $LC > $f + echo "" + done +done + + +echo "creating train, valid, test..." +for l in $src $tgt; do + awk '{if (NR%23 == 0) print $0; }' $tmp/train.tags.de-en.$l > $tmp/valid.$l + awk '{if (NR%23 != 0) print $0; }' $tmp/train.tags.de-en.$l > $tmp/train.$l + + cat $tmp/IWSLT14.TED.dev2010.de-en.$l \ + $tmp/IWSLT14.TEDX.dev2012.de-en.$l \ + $tmp/IWSLT14.TED.tst2010.de-en.$l \ + $tmp/IWSLT14.TED.tst2011.de-en.$l \ + $tmp/IWSLT14.TED.tst2012.de-en.$l \ + > $tmp/test.$l +done + +TRAIN=$tmp/train.en-de +BPE_CODE=$prep/code +rm -f $TRAIN +for l in $src $tgt; do + cat $tmp/train.$l >> $TRAIN +done + +echo "learn_bpe.py on ${TRAIN}..." +python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE + +for L in $src $tgt; do + for f in train.$L valid.$L test.$L; do + echo "apply_bpe.py to ${f}..." + python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $prep/$f + done +done diff --git a/fairseq/examples/translation/prepare-iwslt17-multilingual.sh b/fairseq/examples/translation/prepare-iwslt17-multilingual.sh new file mode 100644 index 0000000..23be875 --- /dev/null +++ b/fairseq/examples/translation/prepare-iwslt17-multilingual.sh @@ -0,0 +1,133 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +SRCS=( + "de" + "fr" +) +TGT=en + +ROOT=$(dirname "$0") +SCRIPTS=$ROOT/../../scripts +SPM_TRAIN=$SCRIPTS/spm_train.py +SPM_ENCODE=$SCRIPTS/spm_encode.py + +BPESIZE=16384 +ORIG=$ROOT/iwslt17_orig +DATA=$ROOT/iwslt17.de_fr.en.bpe16k +mkdir -p "$ORIG" "$DATA" + +TRAIN_MINLEN=1 # remove sentences with <1 BPE token +TRAIN_MAXLEN=250 # remove sentences with >250 BPE tokens + +URLS=( + "https://wit3.fbk.eu/archive/2017-01-trnted/texts/de/en/de-en.tgz" + "https://wit3.fbk.eu/archive/2017-01-trnted/texts/fr/en/fr-en.tgz" +) +ARCHIVES=( + "de-en.tgz" + "fr-en.tgz" +) +VALID_SETS=( + "IWSLT17.TED.dev2010.de-en IWSLT17.TED.tst2010.de-en IWSLT17.TED.tst2011.de-en IWSLT17.TED.tst2012.de-en IWSLT17.TED.tst2013.de-en IWSLT17.TED.tst2014.de-en IWSLT17.TED.tst2015.de-en" + "IWSLT17.TED.dev2010.fr-en IWSLT17.TED.tst2010.fr-en IWSLT17.TED.tst2011.fr-en IWSLT17.TED.tst2012.fr-en IWSLT17.TED.tst2013.fr-en IWSLT17.TED.tst2014.fr-en IWSLT17.TED.tst2015.fr-en" +) + +# download and extract data +for ((i=0;i<${#URLS[@]};++i)); do + ARCHIVE=$ORIG/${ARCHIVES[i]} + if [ -f "$ARCHIVE" ]; then + echo "$ARCHIVE already exists, skipping download" + else + URL=${URLS[i]} + wget -P "$ORIG" "$URL" + if [ -f "$ARCHIVE" ]; then + echo "$URL successfully downloaded." + else + echo "$URL not successfully downloaded." + exit 1 + fi + fi + FILE=${ARCHIVE: -4} + if [ -e "$FILE" ]; then + echo "$FILE already exists, skipping extraction" + else + tar -C "$ORIG" -xzvf "$ARCHIVE" + fi +done + +echo "pre-processing train data..." +for SRC in "${SRCS[@]}"; do + for LANG in "${SRC}" "${TGT}"; do + cat "$ORIG/${SRC}-${TGT}/train.tags.${SRC}-${TGT}.${LANG}" \ + | grep -v '<url>' \ + | grep -v '<talkid>' \ + | grep -v '<keywords>' \ + | grep -v '<speaker>' \ + | grep -v '<reviewer' \ + | grep -v '<translator' \ + | grep -v '<doc' \ + | grep -v '</doc>' \ + | sed -e 's/<title>//g' \ + | sed -e 's/<\/title>//g' \ + | sed -e 's/<description>//g' \ + | sed -e 's/<\/description>//g' \ + | sed 's/^\s*//g' \ + | sed 's/\s*$//g' \ + > "$DATA/train.${SRC}-${TGT}.${LANG}" + done +done + +echo "pre-processing valid data..." +for ((i=0;i<${#SRCS[@]};++i)); do + SRC=${SRCS[i]} + VALID_SET=(${VALID_SETS[i]}) + for ((j=0;j<${#VALID_SET[@]};++j)); do + FILE=${VALID_SET[j]} + for LANG in "$SRC" "$TGT"; do + grep '<seg id' "$ORIG/${SRC}-${TGT}/${FILE}.${LANG}.xml" \ + | sed -e 's/<seg id="[0-9]*">\s*//g' \ + | sed -e 's/\s*<\/seg>\s*//g' \ + | sed -e "s/\’/\'/g" \ + > "$DATA/valid${j}.${SRC}-${TGT}.${LANG}" + done + done +done + +# learn BPE with sentencepiece +TRAIN_FILES=$(for SRC in "${SRCS[@]}"; do echo $DATA/train.${SRC}-${TGT}.${SRC}; echo $DATA/train.${SRC}-${TGT}.${TGT}; done | tr "\n" ",") +echo "learning joint BPE over ${TRAIN_FILES}..." +python "$SPM_TRAIN" \ + --input=$TRAIN_FILES \ + --model_prefix=$DATA/sentencepiece.bpe \ + --vocab_size=$BPESIZE \ + --character_coverage=1.0 \ + --model_type=bpe + +# encode train/valid +echo "encoding train with learned BPE..." +for SRC in "${SRCS[@]}"; do + python "$SPM_ENCODE" \ + --model "$DATA/sentencepiece.bpe.model" \ + --output_format=piece \ + --inputs $DATA/train.${SRC}-${TGT}.${SRC} $DATA/train.${SRC}-${TGT}.${TGT} \ + --outputs $DATA/train.bpe.${SRC}-${TGT}.${SRC} $DATA/train.bpe.${SRC}-${TGT}.${TGT} \ + --min-len $TRAIN_MINLEN --max-len $TRAIN_MAXLEN +done + +echo "encoding valid with learned BPE..." +for ((i=0;i<${#SRCS[@]};++i)); do + SRC=${SRCS[i]} + VALID_SET=(${VALID_SETS[i]}) + for ((j=0;j<${#VALID_SET[@]};++j)); do + python "$SPM_ENCODE" \ + --model "$DATA/sentencepiece.bpe.model" \ + --output_format=piece \ + --inputs $DATA/valid${j}.${SRC}-${TGT}.${SRC} $DATA/valid${j}.${SRC}-${TGT}.${TGT} \ + --outputs $DATA/valid${j}.bpe.${SRC}-${TGT}.${SRC} $DATA/valid${j}.bpe.${SRC}-${TGT}.${TGT} + done +done diff --git a/fairseq/examples/translation/prepare-wmt14en2de.sh b/fairseq/examples/translation/prepare-wmt14en2de.sh new file mode 100644 index 0000000..6702c88 --- /dev/null +++ b/fairseq/examples/translation/prepare-wmt14en2de.sh @@ -0,0 +1,142 @@ +#!/bin/bash +# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh + +echo 'Cloning Moses github repository (for tokenization scripts)...' +git clone https://github.com/moses-smt/mosesdecoder.git + +echo 'Cloning Subword NMT repository (for BPE pre-processing)...' +git clone https://github.com/rsennrich/subword-nmt.git + +SCRIPTS=mosesdecoder/scripts +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +CLEAN=$SCRIPTS/training/clean-corpus-n.perl +NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl +BPEROOT=subword-nmt/subword_nmt +BPE_TOKENS=40000 + +URLS=( + "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz" + "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz" + "http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz" + "http://data.statmt.org/wmt17/translation-task/dev.tgz" + "http://statmt.org/wmt14/test-full.tgz" +) +FILES=( + "training-parallel-europarl-v7.tgz" + "training-parallel-commoncrawl.tgz" + "training-parallel-nc-v12.tgz" + "dev.tgz" + "test-full.tgz" +) +CORPORA=( + "training/europarl-v7.de-en" + "commoncrawl.de-en" + "training/news-commentary-v12.de-en" +) + +# This will make the dataset compatible to the one used in "Convolutional Sequence to Sequence Learning" +# https://arxiv.org/abs/1705.03122 +if [ "$1" == "--icml17" ]; then + URLS[2]="http://statmt.org/wmt14/training-parallel-nc-v9.tgz" + FILES[2]="training-parallel-nc-v9.tgz" + CORPORA[2]="training/news-commentary-v9.de-en" + OUTDIR=wmt14_en_de +else + OUTDIR=wmt17_en_de +fi + +if [ ! -d "$SCRIPTS" ]; then + echo "Please set SCRIPTS variable correctly to point to Moses scripts." + exit +fi + +src=en +tgt=de +lang=en-de +prep=$OUTDIR +tmp=$prep/tmp +orig=orig +dev=dev/newstest2013 + +mkdir -p $orig $tmp $prep + +cd $orig + +for ((i=0;i<${#URLS[@]};++i)); do + file=${FILES[i]} + if [ -f $file ]; then + echo "$file already exists, skipping download" + else + url=${URLS[i]} + wget "$url" + if [ -f $file ]; then + echo "$url successfully downloaded." + else + echo "$url not successfully downloaded." + exit -1 + fi + if [ ${file: -4} == ".tgz" ]; then + tar zxvf $file + elif [ ${file: -4} == ".tar" ]; then + tar xvf $file + fi + fi +done +cd .. + +echo "pre-processing train data..." +for l in $src $tgt; do + rm $tmp/train.tags.$lang.tok.$l + for f in "${CORPORA[@]}"; do + cat $orig/$f.$l | \ + perl $NORM_PUNC $l | \ + perl $REM_NON_PRINT_CHAR | \ + perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l + done +done + +echo "pre-processing test data..." +for l in $src $tgt; do + if [ "$l" == "$src" ]; then + t="src" + else + t="ref" + fi + grep '<seg id' $orig/test-full/newstest2014-deen-$t.$l.sgm | \ + sed -e 's/<seg id="[0-9]*">\s*//g' | \ + sed -e 's/\s*<\/seg>\s*//g' | \ + sed -e "s/\’/\'/g" | \ + perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l + echo "" +done + +echo "splitting train and valid..." +for l in $src $tgt; do + awk '{if (NR%100 == 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l + awk '{if (NR%100 != 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l +done + +TRAIN=$tmp/train.de-en +BPE_CODE=$prep/code +rm -f $TRAIN +for l in $src $tgt; do + cat $tmp/train.$l >> $TRAIN +done + +echo "learn_bpe.py on ${TRAIN}..." +python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE + +for L in $src $tgt; do + for f in train.$L valid.$L test.$L; do + echo "apply_bpe.py to ${f}..." + python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f + done +done + +perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250 +perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250 + +for L in $src $tgt; do + cp $tmp/bpe.test.$L $prep/test.$L +done diff --git a/fairseq/examples/translation/prepare-wmt14en2fr.sh b/fairseq/examples/translation/prepare-wmt14en2fr.sh new file mode 100644 index 0000000..2ac97a5 --- /dev/null +++ b/fairseq/examples/translation/prepare-wmt14en2fr.sh @@ -0,0 +1,136 @@ +#!/bin/bash +# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh + +echo 'Cloning Moses github repository (for tokenization scripts)...' +git clone https://github.com/moses-smt/mosesdecoder.git + +echo 'Cloning Subword NMT repository (for BPE pre-processing)...' +git clone https://github.com/rsennrich/subword-nmt.git + +SCRIPTS=mosesdecoder/scripts +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +CLEAN=$SCRIPTS/training/clean-corpus-n.perl +NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl +BPEROOT=subword-nmt/subword_nmt +BPE_TOKENS=40000 + +URLS=( + "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz" + "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz" + "http://statmt.org/wmt13/training-parallel-un.tgz" + "http://statmt.org/wmt14/training-parallel-nc-v9.tgz" + "http://statmt.org/wmt10/training-giga-fren.tar" + "http://statmt.org/wmt14/test-full.tgz" +) +FILES=( + "training-parallel-europarl-v7.tgz" + "training-parallel-commoncrawl.tgz" + "training-parallel-un.tgz" + "training-parallel-nc-v9.tgz" + "training-giga-fren.tar" + "test-full.tgz" +) +CORPORA=( + "training/europarl-v7.fr-en" + "commoncrawl.fr-en" + "un/undoc.2000.fr-en" + "training/news-commentary-v9.fr-en" + "giga-fren.release2.fixed" +) + +if [ ! -d "$SCRIPTS" ]; then + echo "Please set SCRIPTS variable correctly to point to Moses scripts." + exit +fi + +src=en +tgt=fr +lang=en-fr +prep=wmt14_en_fr +tmp=$prep/tmp +orig=orig + +mkdir -p $orig $tmp $prep + +cd $orig + +for ((i=0;i<${#URLS[@]};++i)); do + file=${FILES[i]} + if [ -f $file ]; then + echo "$file already exists, skipping download" + else + url=${URLS[i]} + wget "$url" + if [ -f $file ]; then + echo "$url successfully downloaded." + else + echo "$url not successfully downloaded." + exit -1 + fi + if [ ${file: -4} == ".tgz" ]; then + tar zxvf $file + elif [ ${file: -4} == ".tar" ]; then + tar xvf $file + fi + fi +done + +gunzip giga-fren.release2.fixed.*.gz +cd .. + +echo "pre-processing train data..." +for l in $src $tgt; do + rm $tmp/train.tags.$lang.tok.$l + for f in "${CORPORA[@]}"; do + cat $orig/$f.$l | \ + perl $NORM_PUNC $l | \ + perl $REM_NON_PRINT_CHAR | \ + perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l + done +done + +echo "pre-processing test data..." +for l in $src $tgt; do + if [ "$l" == "$src" ]; then + t="src" + else + t="ref" + fi + grep '<seg id' $orig/test-full/newstest2014-fren-$t.$l.sgm | \ + sed -e 's/<seg id="[0-9]*">\s*//g' | \ + sed -e 's/\s*<\/seg>\s*//g' | \ + sed -e "s/\’/\'/g" | \ + perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l + echo "" +done + +echo "splitting train and valid..." +for l in $src $tgt; do + awk '{if (NR%1333 == 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l + awk '{if (NR%1333 != 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l +done + +TRAIN=$tmp/train.fr-en +BPE_CODE=$prep/code +rm -f $TRAIN +for l in $src $tgt; do + cat $tmp/train.$l >> $TRAIN +done + +echo "learn_bpe.py on ${TRAIN}..." +python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE + +for L in $src $tgt; do + for f in train.$L valid.$L test.$L; do + echo "apply_bpe.py to ${f}..." + python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f + done +done + +perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250 +perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250 + +for L in $src $tgt; do + cp $tmp/bpe.test.$L $prep/test.$L +done diff --git a/fairseq/examples/translation_moe/README.md b/fairseq/examples/translation_moe/README.md new file mode 100644 index 0000000..2e5c8af --- /dev/null +++ b/fairseq/examples/translation_moe/README.md @@ -0,0 +1,89 @@ +# Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019) + +This page includes instructions for reproducing results from the paper [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](https://arxiv.org/abs/1902.07816). + +## Download data + +First, follow the [instructions to download and preprocess the WMT'17 En-De dataset](../translation#prepare-wmt14en2desh). +Make sure to learn a joint vocabulary by passing the `--joined-dictionary` option to `fairseq-preprocess`. + +## Train a model + +Then we can train a mixture of experts model using the `translation_moe` task. +Use the `--method` flag to choose the MoE variant; we support hard mixtures with a learned or uniform prior (`--method hMoElp` and `hMoEup`, respectively) and soft mixures (`--method sMoElp` and `sMoEup`). +The model is trained with online responsibility assignment and shared parameterization. + +The following command will train a `hMoElp` model with `3` experts: +```bash +fairseq-train --ddp-backend='legacy_ddp' \ + data-bin/wmt17_en_de \ + --max-update 100000 \ + --task translation_moe --user-dir examples/translation_moe/translation_moe_src \ + --method hMoElp --mean-pool-gating-network \ + --num-experts 3 \ + --arch transformer_wmt_en_de --share-all-embeddings \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \ + --lr 0.0007 \ + --dropout 0.1 --weight-decay 0.0 --criterion cross_entropy \ + --max-tokens 3584 +``` + +## Translate + +Once a model is trained, we can generate translations from different experts using the `--gen-expert` option. +For example, to generate from expert 0: +```bash +fairseq-generate data-bin/wmt17_en_de \ + --path checkpoints/checkpoint_best.pt \ + --beam 1 --remove-bpe \ + --task translation_moe --user-dir examples/translation_moe/translation_moe_src \ + --method hMoElp --mean-pool-gating-network \ + --num-experts 3 \ + --gen-expert 0 +``` + +## Evaluate + +First download a tokenized version of the WMT'14 En-De test set with multiple references: +```bash +wget dl.fbaipublicfiles.com/fairseq/data/wmt14-en-de.extra_refs.tok +``` + +Next apply BPE on the fly and run generation for each expert: +```bash +BPE_CODE=examples/translation/wmt17_en_de/code +for EXPERT in $(seq 0 2); do \ + cat wmt14-en-de.extra_refs.tok \ + | grep ^S | cut -f 2 \ + | fairseq-interactive data-bin/wmt17_en_de \ + --path checkpoints/checkpoint_best.pt \ + --beam 1 \ + --bpe subword_nmt --bpe-codes $BPE_CODE \ + --buffer-size 500 --max-tokens 6000 \ + --task translation_moe --user-dir examples/translation_moe/translation_moe_src \ + --method hMoElp --mean-pool-gating-network \ + --num-experts 3 \ + --gen-expert $EXPERT ; \ +done > wmt14-en-de.extra_refs.tok.gen.3experts +``` + +Finally use `score_moe.py` to compute pairwise BLUE and average oracle BLEU: +```bash +python examples/translation_moe/score.py --sys wmt14-en-de.extra_refs.tok.gen.3experts --ref wmt14-en-de.extra_refs.tok +# pairwise BLEU: 48.26 +# #refs covered: 2.11 +# multi-reference BLEU (leave-one-out): 59.46 +``` +This matches row 3 from Table 7 in the paper. + +## Citation + +```bibtex +@article{shen2019mixture, + title = {Mixture Models for Diverse Machine Translation: Tricks of the Trade}, + author = {Tianxiao Shen and Myle Ott and Michael Auli and Marc'Aurelio Ranzato}, + journal = {International Conference on Machine Learning}, + year = 2019, +} +``` diff --git a/fairseq/examples/translation_moe/score.py b/fairseq/examples/translation_moe/score.py new file mode 100644 index 0000000..e45b2cb --- /dev/null +++ b/fairseq/examples/translation_moe/score.py @@ -0,0 +1,197 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Scoring script for computing pairwise BLEU and multi-ref BLEU over a set of +candidate hypotheses. + +See `"Mixture Models for Diverse Machine Translation: Tricks of the Trade" +(Shen et al., 2019) <https://arxiv.org/abs/1902.07816>`_. +""" + +import argparse +import random +import sys +from itertools import chain + +import numpy as np +import sacrebleu +from sacrebleu import corpus_bleu as _corpus_bleu + +def main(): + parser = argparse.ArgumentParser(sys.argv[0]) + parser.add_argument( + "--sys", nargs="*", default="", metavar="FILE", help="path to system output" + ) + parser.add_argument("--ref", default="", metavar="FILE", help="path to references") + parser.add_argument( + "--output", + default="", + metavar="FILE", + help="print outputs into a pretty format", + ) + args = parser.parse_args() + + if args.sys: + src, tgt, hypos, log_probs = load_sys(args.sys) + print("pairwise BLEU: %.2f" % pairwise(hypos)) + if args.output: + merge(src, tgt, hypos, log_probs, args.output) + + if args.ref: + _, _, refs = load_ref(args.ref) + if args.sys: + multi_ref(refs, hypos) + else: + intra_ref(refs) + + +def dictolist(d): + a = sorted(d.items(), key=lambda i: i[0]) + return [i[1] for i in a] + + +def load_sys(paths): + src, tgt, hypos, log_probs = {}, {}, {}, {} + for path in paths: + with open(path) as f: + for line in f: + line = line.rstrip() + # S: source + # T: target + # D: detokenized system output + if line.startswith(("S-", "T-", "D-")): + i = int(line[line.find("-") + 1 : line.find("\t")]) + if line.startswith("S-"): + src[i] = line.split("\t")[1] + if line.startswith("T-"): + tgt[i] = line.split("\t")[1] + if line.startswith("D-"): + if i not in hypos: + hypos[i] = [] + log_probs[i] = [] + hypos[i].append(line.split("\t")[2]) + log_probs[i].append(float(line.split("\t")[1])) + return dictolist(src), dictolist(tgt), dictolist(hypos), dictolist(log_probs) + + +def load_ref(path): + with open(path) as f: + lines = f.readlines() + src, tgt, refs = [], [], [] + i = 0 + while i < len(lines): + if lines[i].startswith("S-"): + src.append(lines[i].split("\t")[1].rstrip()) + i += 1 + elif lines[i].startswith("T-"): + tgt.append(lines[i].split("\t")[1].rstrip()) + i += 1 + else: + a = [] + while i < len(lines) and lines[i].startswith("R"): + a.append(lines[i].split("\t")[1].rstrip()) + i += 1 + refs.append(a) + return src, tgt, refs + + +def merge(src, tgt, hypos, log_probs, path): + with open(path, "w") as f: + for s, t, hs, lps in zip(src, tgt, hypos, log_probs): + f.write(s + "\n") + f.write(t + "\n") + f.write("\n") + for h, lp in zip(hs, lps): + f.write("\t%f\t%s\n" % (lp, h.strip())) + f.write("------------------------------------------------------\n") + + +def corpus_bleu(sys_stream, ref_streams): + bleu = _corpus_bleu(sys_stream, ref_streams, tokenize="none") + return bleu.score + + +def sentence_bleu(hypothesis, reference): + bleu = _corpus_bleu(hypothesis, reference) + for i in range(1, 4): + bleu.counts[i] += 1 + bleu.totals[i] += 1 + bleu = sacrebleu.BLEU.compute_bleu( + bleu.counts, + bleu.totals, + bleu.sys_len, + bleu.ref_len, + smooth_method="exp", + ) + return bleu.score + + +def pairwise(sents): + _ref, _hypo = [], [] + for s in sents: + for i in range(len(s)): + for j in range(len(s)): + if i != j: + _ref.append(s[i]) + _hypo.append(s[j]) + return corpus_bleu(_hypo, [_ref]) + + +def multi_ref(refs, hypos): + _ref, _hypo = [], [] + ref_cnt = 0 + assert len(refs) == len(hypos) + + # count number of refs covered + for rs, hs in zip(refs, hypos): + a = set() + for h in hs: + s = [sentence_bleu(h, r) for r in rs] + j = np.argmax(s) + _ref.append(rs[j]) + _hypo.append(h) + best = [k for k in range(len(rs)) if s[k] == s[j]] + a.add(random.choice(best)) + ref_cnt += len(a) + print("#refs covered: %.2f" % (ref_cnt / len(refs))) + + # transpose refs and hypos + refs = list(zip(*refs)) + hypos = list(zip(*hypos)) + + # compute multi-ref corpus BLEU (leave-one-out to be comparable to intra_ref) + k = len(hypos) + m = len(refs) + flat_hypos = [hypos[j][i] for i in range(len(hypos[0])) for j in range(k)] + duplicated_refs = [[ref for ref in refs_i for _ in range(k)] for refs_i in refs] + loo_bleus = [] + for held_out_ref in range(m): + remaining_refs = ( + duplicated_refs[:held_out_ref] + duplicated_refs[held_out_ref + 1 :] + ) + assert len(remaining_refs) == m - 1 + loo_bleus.append(corpus_bleu(flat_hypos, remaining_refs)) + print("average multi-reference BLEU (leave-one-out): %.2f" % np.mean(loo_bleus)) + + +def intra_ref(refs): + print("ref pairwise BLEU: %.2f" % pairwise(refs)) + refs = list(zip(*refs)) + m = len(refs) + concat_h = [] + concat_rest = [[] for j in range(m - 1)] + for i, h in enumerate(refs): + rest = refs[:i] + refs[i + 1 :] + concat_h.append(h) + for j in range(m - 1): + concat_rest[j].extend(rest[j]) + concat_h = list(chain.from_iterable(concat_h)) + bleu = corpus_bleu(concat_h, concat_rest) + print("multi-reference BLEU (leave-one-out): %.2f" % bleu) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/translation_moe/translation_moe_src/__init__.py b/fairseq/examples/translation_moe/translation_moe_src/__init__.py new file mode 100644 index 0000000..c0abe53 --- /dev/null +++ b/fairseq/examples/translation_moe/translation_moe_src/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import translation_moe # noqa diff --git a/fairseq/examples/translation_moe/translation_moe_src/logsumexp_moe.py b/fairseq/examples/translation_moe/translation_moe_src/logsumexp_moe.py new file mode 100644 index 0000000..fb299da --- /dev/null +++ b/fairseq/examples/translation_moe/translation_moe_src/logsumexp_moe.py @@ -0,0 +1,26 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + + +class LogSumExpMoE(torch.autograd.Function): + """Standard LogSumExp forward pass, but use *posterior* for the backward. + + See `"Mixture Models for Diverse Machine Translation: Tricks of the Trade" + (Shen et al., 2019) <https://arxiv.org/abs/1902.07816>`_. + """ + + @staticmethod + def forward(ctx, logp, posterior, dim=-1): + ctx.save_for_backward(posterior) + ctx.dim = dim + return torch.logsumexp(logp, dim=dim) + + @staticmethod + def backward(ctx, grad_output): + (posterior,) = ctx.saved_tensors + grad_logp = grad_output.unsqueeze(ctx.dim) * posterior + return grad_logp, None, None diff --git a/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py b/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py new file mode 100644 index 0000000..efc7ae4 --- /dev/null +++ b/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py @@ -0,0 +1,50 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F + + +class MeanPoolGatingNetwork(torch.nn.Module): + """A simple mean-pooling gating network for selecting experts. + + This module applies mean pooling over an encoder's output and returns + reponsibilities for each expert. The encoder format is expected to match + :class:`fairseq.models.transformer.TransformerEncoder`. + """ + + def __init__(self, embed_dim, num_experts, dropout=None): + super().__init__() + self.embed_dim = embed_dim + self.num_experts = num_experts + + self.fc1 = torch.nn.Linear(embed_dim, embed_dim) + self.dropout = torch.nn.Dropout(dropout) if dropout is not None else None + self.fc2 = torch.nn.Linear(embed_dim, num_experts) + + def forward(self, encoder_out): + if not ( + "encoder_out" in encoder_out + and "encoder_padding_mask" in encoder_out + and encoder_out["encoder_out"][0].size(2) == self.embed_dim + ): + raise ValueError("Unexpected format for encoder_out") + + # mean pooling over time + encoder_padding_mask = encoder_out["encoder_padding_mask"][0] # B x T + encoder_out = encoder_out["encoder_out"][0].transpose(0, 1) # B x T x C + if encoder_padding_mask is not None: + encoder_out = encoder_out.clone() # required because of transpose above + encoder_out[encoder_padding_mask] = 0 + ntokens = torch.sum(~encoder_padding_mask, dim=1, keepdim=True) + x = torch.sum(encoder_out, dim=1) / ntokens.type_as(encoder_out) + else: + x = torch.mean(encoder_out, dim=1) + + x = torch.tanh(self.fc1(x)) + if self.dropout is not None: + x = self.dropout(x) + x = self.fc2(x) + return F.log_softmax(x, dim=-1, dtype=torch.float32).type_as(x) diff --git a/fairseq/examples/translation_moe/translation_moe_src/translation_moe.py b/fairseq/examples/translation_moe/translation_moe_src/translation_moe.py new file mode 100644 index 0000000..a829bf7 --- /dev/null +++ b/fairseq/examples/translation_moe/translation_moe_src/translation_moe.py @@ -0,0 +1,259 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +import torch +from omegaconf import II + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.dataclass import ChoiceEnum +from fairseq.tasks import register_task +from fairseq.tasks.translation import TranslationConfig, TranslationTask + +from .logsumexp_moe import LogSumExpMoE +from .mean_pool_gating_network import MeanPoolGatingNetwork + + +METHOD_CHOICES = ChoiceEnum(["sMoElp", "sMoEup", "hMoElp", "hMoEup"]) + + +@dataclass +class TranslationMoEConfig(TranslationConfig): + method: METHOD_CHOICES = field( + default="hMoEup", + metadata={"help": "MoE method"}, + ) + num_experts: int = field( + default=3, + metadata={"help": "number of experts"}, + ) + mean_pool_gating_network: bool = field( + default=False, + metadata={"help": "use a simple mean-pooling gating network"}, + ) + mean_pool_gating_network_dropout: float = field( + default=0, + metadata={"help": "dropout for mean-pooling gating network"}, + ) + mean_pool_gating_network_encoder_dim: int = field( + default=0, + metadata={"help": "encoder output dim for mean-pooling gating network"}, + ) + gen_expert: int = field( + default=0, + metadata={"help": "which expert to use for generation"}, + ) + sentence_avg: bool = II("optimization.sentence_avg") + + +@register_task("translation_moe", dataclass=TranslationMoEConfig) +class TranslationMoETask(TranslationTask): + """ + Translation task for Mixture of Experts (MoE) models. + + See `"Mixture Models for Diverse Machine Translation: Tricks of the Trade" + (Shen et al., 2019) <https://arxiv.org/abs/1902.07816>`_. + + Args: + src_dict (~fairseq.data.Dictionary): dictionary for the source language + tgt_dict (~fairseq.data.Dictionary): dictionary for the target language + + .. note:: + + The translation task is compatible with :mod:`fairseq-train`, + :mod:`fairseq-generate` and :mod:`fairseq-interactive`. + + The translation task provides the following additional command-line + arguments: + + .. argparse:: + :ref: fairseq.tasks.translation_parser + :prog: + """ + + cfg: TranslationMoEConfig + + def __init__(self, cfg: TranslationMoEConfig, src_dict, tgt_dict): + if cfg.method == "sMoElp": + # soft MoE with learned prior + self.uniform_prior = False + self.hard_selection = False + elif cfg.method == "sMoEup": + # soft MoE with uniform prior + self.uniform_prior = True + self.hard_selection = False + elif cfg.method == "hMoElp": + # hard MoE with learned prior + self.uniform_prior = False + self.hard_selection = True + elif cfg.method == "hMoEup": + # hard MoE with uniform prior + self.uniform_prior = True + self.hard_selection = True + + # add indicator tokens for each expert + for i in range(cfg.num_experts): + # add to both dictionaries in case we're sharing embeddings + src_dict.add_symbol("<expert_{}>".format(i)) + tgt_dict.add_symbol("<expert_{}>".format(i)) + + super().__init__(cfg, src_dict, tgt_dict) + + def build_model(self, cfg, from_checkpoint=False): + from fairseq import models + + model = models.build_model(cfg, self) + if not self.uniform_prior and not hasattr(model, "gating_network"): + if self.cfg.mean_pool_gating_network: + if self.cfg.mean_pool_gating_network_encoder_dim > 0: + encoder_dim = self.cfg.mean_pool_gating_network_encoder_dim + elif getattr(cfg, "encoder_embed_dim", None): + # assume that encoder_embed_dim is the encoder's output dimension + encoder_dim = cfg.encoder_embed_dim + else: + raise ValueError( + "Must specify --mean-pool-gating-network-encoder-dim" + ) + + if self.cfg.mean_pool_gating_network_dropout > 0: + dropout = self.cfg.mean_pool_gating_network_dropout + elif getattr(cfg, "dropout", None): + dropout = cfg.dropout + else: + raise ValueError("Must specify task.mean_pool_gating_network_dropout") + + model.gating_network = MeanPoolGatingNetwork( + encoder_dim, + self.cfg.num_experts, + dropout, + ) + else: + raise ValueError( + "translation_moe task with learned prior requires the model to " + "have a gating network; try using --mean-pool-gating-network" + ) + return model + + def expert_index(self, i): + return i + self.tgt_dict.index("<expert_0>") + + def _get_loss(self, sample, model, criterion): + assert hasattr( + criterion, "compute_loss" + ), "translation_moe task requires the criterion to implement the compute_loss() method" + + k = self.cfg.num_experts + bsz = sample["target"].size(0) + + def get_lprob_y(encoder_out, prev_output_tokens_k): + net_output = model.decoder( + prev_output_tokens=prev_output_tokens_k, + encoder_out=encoder_out, + ) + loss, _ = criterion.compute_loss(model, net_output, sample, reduce=False) + loss = loss.view(bsz, -1) + return -loss.sum(dim=1, keepdim=True) # -> B x 1 + + def get_lprob_yz(winners=None): + encoder_out = model.encoder( + src_tokens=sample["net_input"]["src_tokens"], + src_lengths=sample["net_input"]["src_lengths"], + ) + + if winners is None: + lprob_y = [] + for i in range(k): + prev_output_tokens_k = sample["net_input"][ + "prev_output_tokens" + ].clone() + assert not prev_output_tokens_k.requires_grad + prev_output_tokens_k[:, 0] = self.expert_index(i) + lprob_y.append(get_lprob_y(encoder_out, prev_output_tokens_k)) + lprob_y = torch.cat(lprob_y, dim=1) # -> B x K + else: + prev_output_tokens_k = sample["net_input"]["prev_output_tokens"].clone() + prev_output_tokens_k[:, 0] = self.expert_index(winners) + lprob_y = get_lprob_y(encoder_out, prev_output_tokens_k) # -> B + + if self.uniform_prior: + lprob_yz = lprob_y + else: + lprob_z = model.gating_network(encoder_out) # B x K + if winners is not None: + lprob_z = lprob_z.gather(dim=1, index=winners.unsqueeze(-1)) + lprob_yz = lprob_y + lprob_z.type_as(lprob_y) # B x K + + return lprob_yz + + # compute responsibilities without dropout + with utils.model_eval(model): # disable dropout + with torch.no_grad(): # disable autograd + lprob_yz = get_lprob_yz() # B x K + prob_z_xy = torch.nn.functional.softmax(lprob_yz, dim=1) + assert not prob_z_xy.requires_grad + + # compute loss with dropout + if self.hard_selection: + winners = prob_z_xy.max(dim=1)[1] + loss = -get_lprob_yz(winners) + else: + lprob_yz = get_lprob_yz() # B x K + loss = -LogSumExpMoE.apply(lprob_yz, prob_z_xy, 1) + + loss = loss.sum() + sample_size = ( + sample["target"].size(0) if self.cfg.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": bsz, + "sample_size": sample_size, + "posterior": prob_z_xy.float().sum(dim=0).cpu(), + } + return loss, sample_size, logging_output + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + model.train() + loss, sample_size, logging_output = self._get_loss(sample, model, criterion) + if ignore_grad: + loss *= 0 + optimizer.backward(loss) + return loss, sample_size, logging_output + + def valid_step(self, sample, model, criterion): + model.eval() + with torch.no_grad(): + loss, sample_size, logging_output = self._get_loss(sample, model, criterion) + return loss, sample_size, logging_output + + def inference_step( + self, + generator, + models, + sample, + prefix_tokens=None, + expert=None, + constraints=None, + ): + expert = expert or self.cfg.gen_expert + with torch.no_grad(): + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + bos_token=self.expert_index(expert), + ) + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + metrics.log_scalar( + "posterior", + sum(log["posterior"] for log in logging_outputs if "posterior" in log), + ) diff --git a/fairseq/examples/truncated_bptt/README.md b/fairseq/examples/truncated_bptt/README.md new file mode 100644 index 0000000..86518c9 --- /dev/null +++ b/fairseq/examples/truncated_bptt/README.md @@ -0,0 +1,70 @@ +# Truncated Backpropagation Through Time (BPTT) + +Truncated BPTT is a useful technique for training language models on very long +sequences. Typically a long sequences is split into chunks and a language model +is trained over the chunks sequentially. The LM may condition on previous +chunks, but gradients only flow through the current chunk. This technique was +the basis for the paper: [Transformer-XL: Attentive Language Models Beyond a +Fixed-Length Context](https://arxiv.org/abs/1901.02860), which achieved +state-of-the-art language modeling results at the time of publication. + +It is slightly tricky to implement Truncated BPTT efficiently in fairseq, since +we need to iterate over the data sequentially and disable any batch shuffling +logic. The code provided in this example illustrates how to implement Truncated +BPTT in fairseq by overriding ``FairseqTask::get_batch_iterator`` to iterate +over the data sequentially. Crucially, this example supports batching and +multi-GPU (data parallel) training. + +##### 0. Setup + +First, see the general [language modeling README](README.md) for instructions on +preprocessing the WikiText-103 data. + +##### 1. Train a Transformer-XL model on WikiText-103 + +We will train a 16-layer Transformer-XL model following the [hyperparameters +used in the original +paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_wt103_base.sh). + +The following command assumes 4 GPUs, so that the total batch size is 60 +sequences (15 x 4). Training should take ~24 hours on 4 V100 GPUs: +```bash +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ + --user-dir examples/truncated_bptt \ + data-bin/wikitext-103/ \ + --task truncated_bptt_lm --tokens-per-sample 150 \ + --batch-size 15 --max-update 200000 \ + --arch transformer_xl --n-layer 16 --d-model 410 --n-head 10 \ + --d-head 41 --d-inner 2100 --dropout 0.1 --dropatt 0.0 --mem-len 150 \ + --optimizer adam --clip-norm 0.25 \ + --lr-scheduler cosine --warmup-updates 0 --min-lr 0.0 --lr 0.00025 \ + --log-format json --log-interval 25 \ + --fp16 +``` + +If training on a single GPU, set `--update-freq=4` to accumulate 4x gradients +and simulate training on 4 GPUs. + +##### 2. Evaluate + +```bash +fairseq-eval-lm data-bin/wikitext-103/ \ + --path checkpoints/checkpoint_best.pt \ + --user-dir examples/truncated_bptt/ \ + --task truncated_bptt_lm \ + --batch-size 1 --required-batch-size-multiple 1 \ + --model-overrides '{"mem_len":640,"clamp_len":400,"same_length":True}' \ + --tokens-per-sample 64 +# ... | INFO | fairseq_cli.eval_lm | num. model params: 151123537 +# ... | INFO | fairseq_cli.eval_lm | Evaluated 245569 tokens in 83.1s (2956.82 tokens/s) +# ... | INFO | fairseq_cli.eval_lm | Loss (base 2): 4.5668, Perplexity: 23.70 +# Compare to 24.0 test perplexity from the paper +``` + +*Note:* During training the model saw 150 tokens of context +(``--tokens-per-sample=150``) and 150 extra memory tokens (``--mem-len=150``). +During evaluation we measure perplexity on sequences of 64 tokens +(``--tokens-per-sample=64``) and increase the memory length +(``--model-overrides='{"mem_len":640}'``). These settings match the evaluation +settings from [the original +paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_wt103_base.sh). diff --git a/fairseq/examples/truncated_bptt/__init__.py b/fairseq/examples/truncated_bptt/__init__.py new file mode 100644 index 0000000..eee484d --- /dev/null +++ b/fairseq/examples/truncated_bptt/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import transformer_xl_model, truncated_bptt_lm_task # noqa diff --git a/fairseq/examples/truncated_bptt/transformer_xl_model.py b/fairseq/examples/truncated_bptt/transformer_xl_model.py new file mode 100644 index 0000000..58c0f6a --- /dev/null +++ b/fairseq/examples/truncated_bptt/transformer_xl_model.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Dict, List, Optional + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.models import ( + FairseqIncrementalDecoder, + FairseqLanguageModel, + register_model, +) +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from omegaconf import II + + +logger = logging.getLogger(__name__) + + +@dataclass +class TransformerXLConfig(FairseqDataclass): + # defaults come from the original Transformer-XL code + cutoffs: List[int] = field(default_factory=lambda: [20000, 40000, 200000]) + d_model: int = 500 + n_head: int = 10 + d_head: int = 50 + d_inner: int = 1000 + div_val: int = 1 + n_layer: int = 12 + mem_len: int = 0 + clamp_len: int = -1 + same_length: bool = False + dropout: float = 0.0 + dropatt: float = 0.0 + checkpoint_activations: bool = False + offload_activations: bool = False + max_target_positions: int = II("task.max_target_positions") + + +@register_model("transformer_xl", dataclass=TransformerXLConfig) +class TransformerXLLanguageModel(FairseqLanguageModel): + @classmethod + def build_model(cls, cfg: TransformerXLConfig, task): + return cls(TransformerXLDecoder(cfg, task)) + + +class TransformerXLDecoder(FairseqIncrementalDecoder): + def __init__(self, cfg, task): + try: + from transformers.models.transfo_xl import ( + TransfoXLConfig, + TransfoXLLMHeadModel, + ) + except ImportError: + from transformers.configuration_transfo_xl import TransfoXLConfig + from transformers.modeling_transfo_xl import TransfoXLLMHeadModel + + super().__init__(task.target_dictionary) + self.cfg = cfg + + # remove any cutoffs larger than the vocab size + cutoffs = [ + cutoff for cutoff in cfg.cutoffs if cutoff < len(task.target_dictionary) + ] + + config = TransfoXLConfig( + vocab_size=len(task.target_dictionary), + cutoffs=cutoffs, + d_model=cfg.d_model, + d_embed=cfg.d_model, + n_head=cfg.n_head, + d_head=cfg.d_head, + d_inner=cfg.d_inner, + div_val=cfg.div_val, + n_layer=cfg.n_layer, + mem_len=cfg.mem_len, + clamp_len=cfg.clamp_len, + same_length=cfg.same_length, + dropout=cfg.dropout, + dropatt=cfg.dropatt, + ) + logger.info(config) + self.model = TransfoXLLMHeadModel(config) + + if cfg.checkpoint_activations or cfg.offload_activations: + for i in range(len(self.model.transformer.layers)): + self.model.transformer.layers[i] = checkpoint_wrapper( + self.model.transformer.layers[i], + offload_to_cpu=cfg.offload_activations, + ) + # TODO: may save mem to wrap(layer.pos_ff.CoreNet[3]) + + self._mems = None + + def forward( + self, + src_tokens, + src_lengths=None, # unused + incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, + encoder_out=None, + ): + if incremental_state is not None: # used during inference + mems = self.get_incremental_state(incremental_state, "mems") + src_tokens = src_tokens[:, -1:] # only keep the most recent token + else: + mems = self._mems + + output = self.model( + input_ids=src_tokens, + mems=mems, + return_dict=False, + ) + + if len(output) >= 2: + if incremental_state is not None: + self.set_incremental_state(incremental_state, "mems", output[1]) + else: + self._mems = output[1] + + return (output[0],) + + def max_positions(self): + return self.cfg.max_target_positions + + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]], + new_order: torch.Tensor, + ): + """Reorder incremental state. + + This will be called when the order of the input has changed from the + previous time step. A typical use case is beam search, where the input + order changes between time steps based on the selection of beams. + """ + mems = self.get_incremental_state(incremental_state, "mems") + if mems is not None: + new_mems = [mems_i.index_select(1, new_order) for mems_i in mems] + self.set_incremental_state(incremental_state, "mems", new_mems) diff --git a/fairseq/examples/truncated_bptt/truncated_bptt_lm_task.py b/fairseq/examples/truncated_bptt/truncated_bptt_lm_task.py new file mode 100644 index 0000000..9978481 --- /dev/null +++ b/fairseq/examples/truncated_bptt/truncated_bptt_lm_task.py @@ -0,0 +1,285 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field +from typing import List, Optional, Tuple + +import torch +from fairseq import utils +from fairseq.data import ( + Dictionary, + TokenBlockDataset, + data_utils, + iterators, +) +from fairseq.dataclass import FairseqDataclass +from fairseq.distributed import utils as dist_utils +from fairseq.tasks import FairseqTask, register_task +from omegaconf import II + + +logger = logging.getLogger(__name__) + + +@dataclass +class TruncatedBPTTLMConfig(FairseqDataclass): + data: str = field(default="???", metadata={"help": "path to data directory"}) + tokens_per_sample: int = field( + default=1024, metadata={"help": "max number of tokens per sequence"}, + ) + batch_size: int = II("dataset.batch_size") + # Some models use *max_target_positions* to know how many positional + # embeddings to learn. We use II(...) to make it default to + # *tokens_per_sample*, but in principle there could be more positional + # embeddings than tokens in a single batch. This may also be irrelevant for + # custom model implementations. + max_target_positions: int = II("task.tokens_per_sample") + # these will be populated automatically if not provided + data_parallel_rank: Optional[int] = None + data_parallel_size: Optional[int] = None + + +@register_task("truncated_bptt_lm", dataclass=TruncatedBPTTLMConfig) +class TruncatedBPTTLMTask(FairseqTask): + def __init__(self, cfg: TruncatedBPTTLMConfig): + super().__init__(cfg) + + if cfg.data_parallel_rank is None or cfg.data_parallel_size is None: + if torch.distributed.is_initialized(): + cfg.data_parallel_rank = dist_utils.get_data_parallel_rank() + cfg.data_parallel_size = dist_utils.get_data_parallel_world_size() + else: + cfg.data_parallel_rank = 0 + cfg.data_parallel_size = 1 + + # load the dictionary + paths = utils.split_paths(cfg.data) + assert len(paths) > 0 + self.dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(self.dictionary))) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split (e.g., train, valid, test)""" + + # support sharded datasets + paths = utils.split_paths(self.cfg.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + split_path = os.path.join(data_path, split) + + # each element of *data* will be a tensorized line from the original + # text dataset, similar to ``open(split_path).readlines()`` + data = data_utils.load_indexed_dataset( + split_path, self.dictionary, combine=combine + ) + if data is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + # this is similar to ``data.view(-1).split(tokens_per_sample)`` + data = TokenBlockDataset( + data, + data.sizes, + block_size=self.cfg.tokens_per_sample, + pad=None, # unused + eos=None, # unused + break_mode="none", + ) + + self.datasets[split] = TruncatedBPTTDataset( + data=data, + bsz_per_shard=self.cfg.batch_size, + shard_id=self.cfg.data_parallel_rank, + num_shards=self.cfg.data_parallel_size, + ) + + def dataset(self, split): + return self.datasets[split] + + def get_batch_iterator( + self, + dataset, + num_workers=0, + epoch=1, + data_buffer_size=0, + skip_remainder_batch=False, + **kwargs + ): + return iterators.EpochBatchIterator( + dataset=dataset, + collate_fn=self._collate_fn, + num_workers=num_workers, + epoch=epoch, + buffer_size=data_buffer_size, + # we don't use the batching functionality from EpochBatchIterator; + # instead every item in *dataset* is a whole batch + batch_sampler=[[i] for i in range(len(dataset))], + disable_shuffling=True, + skip_remainder_batch=skip_remainder_batch, + ) + + def _collate_fn(self, items: List[List[torch.Tensor]]): + # we don't use fairseq's batching functionality, so we expect a single + # Tensor of type List[torch.Tensor] + assert len(items) == 1 + + # item will have shape B x T (the last batch may have length < T) + id, item = items[0] + item = data_utils.collate_tokens(item, pad_idx=self.source_dictionary.pad()) + B, T = item.size() + + # shift item one position over and append a padding token for the target + target = torch.nn.functional.pad( + item[:, 1:], (0, 1, 0, 0), value=self.target_dictionary.pad() + ) + + # fairseq expects batches to have the following structure + return { + "id": torch.tensor([id] * item.size(0)), + "net_input": {"src_tokens": item,}, + "target": target, + "nsentences": item.size(0), + "ntokens": item.numel(), + } + + def build_dataset_for_inference( + self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs + ) -> torch.utils.data.Dataset: + eos = self.source_dictionary.eos() + dataset = TokenBlockDataset( + src_tokens, + src_lengths, + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=eos, + break_mode="eos", + ) + + class Dataset(torch.utils.data.Dataset): + def __getitem__(self, i): + item = dataset[i] + if item[-1] == eos: + # remove eos to support generating with a prefix + item = item[:-1] + return (i, [item]) + + def __len__(self): + return len(dataset) + + return Dataset() + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + if constraints is not None: + raise NotImplementedError + + # SequenceGenerator doesn't use *src_tokens* directly, we need to + # pass the *prefix_tokens* argument instead. + if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement(): + prefix_tokens = sample["net_input"]["src_tokens"] + + # begin generation with the end-of-sentence token + bos_token = self.source_dictionary.eos() + + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token + ) + + def eval_lm_dataloader( + self, + dataset, + max_tokens: Optional[int] = 36000, + batch_size: Optional[int] = None, + max_positions: Optional[int] = None, + num_shards: int = 1, + shard_id: int = 0, + num_workers: int = 1, + data_buffer_size: int = 10, + context_window: int = 0, + ): + if context_window > 0: + raise NotImplementedError( + "Transformer-XL doesn't need --context-window, try " + "--model-overrides '{\"mem_len\":42}' instead " + ) + return self.get_batch_iterator( + dataset=dataset, + max_tokens=max_tokens, + max_sentences=batch_size, + max_positions=max_positions, + ignore_invalid_inputs=True, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + data_buffer_size=data_buffer_size, + ).next_epoch_itr(shuffle=False) + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + +class TruncatedBPTTDataset(torch.utils.data.Dataset): + def __init__( + self, + data: List[torch.Tensor], # ordered list of items + bsz_per_shard, # number of items processed per GPUs per forward + shard_id, # current GPU ID + num_shards, # number of GPUs + ): + super().__init__() + self.data = data + + def batchify(data, bsz): + # Work out how cleanly we can divide the dataset into bsz parts. + nbatch = data.size(0) // bsz + # Trim off any extra elements that wouldn't cleanly fit (remainders). + data = data.narrow(0, 0, nbatch * bsz) + # Evenly divide the data across the bsz batches. + data = data.view(bsz, -1).contiguous() + return data + + # total number of sequences processed by all GPUs in each forward pass + global_batch_size = bsz_per_shard * num_shards + + """ + With a 16 item dataset, bsz_per_shard=2 and num_shards=3, + *indices* might look like: + + indices = [[0, 1], + [2, 3], + [4, 5], + [6, 7], + [8, 9], + [10, 11]] + + The size of the TruncatedBPTTDataset instance will be 2, + and shard 1 will see items: + + [(0, [data[4], data[6]]), + (1, [data[5], data[7]])] + """ + indices = batchify(torch.arange(len(data)), global_batch_size) + assert indices.size(0) == global_batch_size + + self.my_indices = indices[ + shard_id * bsz_per_shard : (shard_id + 1) * bsz_per_shard + ] + assert self.my_indices.size(0) == bsz_per_shard + + def __len__(self): + return self.my_indices.size(1) + + def __getitem__(self, i) -> Tuple[int, List[torch.Tensor]]: + return (i, [self.data[idx] for idx in self.my_indices[:, i]]) diff --git a/fairseq/examples/unsupervised_quality_estimation/README.md b/fairseq/examples/unsupervised_quality_estimation/README.md new file mode 100644 index 0000000..e86a0d1 --- /dev/null +++ b/fairseq/examples/unsupervised_quality_estimation/README.md @@ -0,0 +1,126 @@ +# Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020) + +This page includes instructions for reproducing results from the paper [Unsupervised Quality Estimation for Neural +Machine Translation (Fomicheva et al., 2020)](https://arxiv.org/abs/2005.10608) + +## Requirements: + +* mosesdecoder: https://github.com/moses-smt/mosesdecoder +* subword-nmt: https://github.com/rsennrich/subword-nmt +* flores: https://github.com/facebookresearch/flores + +## Download Models and Test Data + +Download translation models and test data from [MLQE dataset repository](https://github.com/facebookresearch/mlqe). + +## Set up: + +Given a testset consisting of source sentences and reference translations: + +* `SRC_LANG`: source language +* `TGT_LANG`: target language +* `INPUT`: input prefix, such that the file `$INPUT.$SRC_LANG` contains source sentences and `$INPUT.$TGT_LANG` +contains the reference sentences +* `OUTPUT_DIR`: output path to store results +* `MOSES_DECODER`: path to mosesdecoder installation +* `BPE_ROOT`: path to subword-nmt installation +* `BPE`: path to BPE model +* `MODEL_DIR`: directory containing the NMT model `.pt` file as well as the source and target vocabularies. +* `TMP`: directory for intermediate temporary files +* `GPU`: if translating with GPU, id of the GPU to use for inference +* `DROPOUT_N`: number of stochastic forward passes + +`$DROPOUT_N` is set to 30 in the experiments reported in the paper. However, we observed that increasing it beyond 10 +does not bring substantial improvements. + +## Translate the data using standard decoding + +Preprocess the input data: +``` +for LANG in $SRC_LANG $TGT_LANG; do + perl $MOSES_DECODER/scripts/tokenizer/tokenizer.perl -threads 80 -a -l $LANG < $INPUT.$LANG > $TMP/preprocessed.tok.$LANG + python $BPE_ROOT/apply_bpe.py -c ${BPE} < $TMP/preprocessed.tok.$LANG > $TMP/preprocessed.tok.bpe.$LANG +done +``` + +Binarize the data for faster translation: + +``` +fairseq-preprocess --srcdict $MODEL_DIR/dict.$SRC_LANG.txt --tgtdict $MODEL_DIR/dict.$TGT_LANG.txt +--source-lang ${SRC_LANG} --target-lang ${TGT_LANG} --testpref $TMP/preprocessed.tok.bpe --destdir $TMP/bin --workers 4 +``` + +Translate + +``` +CUDA_VISIBLE_DEVICES=$GPU fairseq-generate $TMP/bin --path ${MODEL_DIR}/${SRC_LANG}-${TGT_LANG}.pt --beam 5 +--source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --unkpen 5 > $TMP/fairseq.out +grep ^H $TMP/fairseq.out | cut -d- -f2- | sort -n | cut -f3- > $TMP/mt.out +``` + +Post-process + +``` +sed -r 's/(@@ )| (@@ ?$)//g' < $TMP/mt.out | perl $MOSES_DECODER/scripts/tokenizer/detokenizer.perl +-l $TGT_LANG > $OUTPUT_DIR/mt.out +``` + +## Produce uncertainty estimates + +### Scoring + +Make temporary files to store the translations repeated N times. + +``` +python ${SCRIPTS}/scripts/uncertainty/repeat_lines.py -i $TMP/preprocessed.tok.bpe.$SRC_LANG -n $DROPOUT_N +-o $TMP/repeated.$SRC_LANG +python ${SCRIPTS}/scripts/uncertainty/repeat_lines.py -i $TMP/mt.out -n $DROPOUT_N -o $TMP/repeated.$TGT_LANG + +fairseq-preprocess --srcdict ${MODEL_DIR}/dict.${SRC_LANG}.txt $TGT_DIC --source-lang ${SRC_LANG} +--target-lang ${TGT_LANG} --testpref ${TMP}/repeated --destdir ${TMP}/bin-repeated +``` + +Produce model scores for the generated translations using `--retain-dropout` option to apply dropout at inference time: + +``` +CUDA_VISIBLE_DEVICES=${GPU} fairseq-generate ${TMP}/bin-repeated --path ${MODEL_DIR}/${LP}.pt --beam 5 + --source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --unkpen 5 --score-reference --retain-dropout + --retain-dropout-modules '["TransformerModel","TransformerEncoder","TransformerDecoder","TransformerEncoderLayer"]' + TransformerDecoderLayer --seed 46 > $TMP/dropout.scoring.out + +grep ^H $TMP/dropout.scoring.out | cut -d- -f2- | sort -n | cut -f2 > $TMP/dropout.scores + +``` + +Use `--retain-dropout-modules` to specify the modules. By default, dropout is applied in the same places +as for training. + +Compute the mean of the resulting output distribution: + +``` +python $SCRIPTS/scripts/uncertainty/aggregate_scores.py -i $TMP/dropout.scores -o $OUTPUT_DIR/dropout.scores.mean +-n $DROPOUT_N +``` + +### Generation + +Produce multiple translation hypotheses for the same source using `--retain-dropout` option: + +``` +CUDA_VISIBLE_DEVICES=${GPU} fairseq-generate ${TMP}/bin-repeated --path ${MODEL_DIR}/${LP}.pt + --beam 5 --source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --retain-dropout + --unkpen 5 --retain-dropout-modules TransformerModel TransformerEncoder TransformerDecoder +TransformerEncoderLayer TransformerDecoderLayer --seed 46 > $TMP/dropout.generation.out + +grep ^H $TMP/dropout.generation.out | cut -d- -f2- | sort -n | cut -f3- > $TMP/dropout.hypotheses_ + +sed -r 's/(@@ )| (@@ ?$)//g' < $TMP/dropout.hypotheses_ | perl $MOSES_DECODER/scripts/tokenizer/detokenizer.perl +-l $TGT_LANG > $TMP/dropout.hypotheses +``` + +Compute similarity between multiple hypotheses corresponding to the same source sentence using Meteor +evaluation metric: +``` +python meteor.py -i $TMP/dropout.hypotheses -m <path_to_meteor_installation> -n $DROPOUT_N -o +$OUTPUT_DIR/dropout.gen.sim.meteor +``` diff --git a/fairseq/examples/unsupervised_quality_estimation/aggregate_scores.py b/fairseq/examples/unsupervised_quality_estimation/aggregate_scores.py new file mode 100644 index 0000000..66d50d0 --- /dev/null +++ b/fairseq/examples/unsupervised_quality_estimation/aggregate_scores.py @@ -0,0 +1,41 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +import numpy as np + + +aggregate_funcs = { + "std": np.std, + "var": np.var, + "median": np.median, + "mean": np.mean, + "min": np.min, + "max": np.max, +} + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--input_file", required=True, type=str) + parser.add_argument("-n", "--repeat_times", required=True, type=int) + parser.add_argument("-o", "--output_file", required=False) + parser.add_argument("-f", "--func", required=False, default="mean") + args = parser.parse_args() + + stream = open(args.output_file, "w") if args.output_file else sys.stdout + + segment_scores = [] + for line in open(args.input_file): + segment_scores.append(float(line.strip())) + if len(segment_scores) == args.repeat_times: + stream.write("{}\n".format(aggregate_funcs[args.func](segment_scores))) + segment_scores = [] + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/unsupervised_quality_estimation/meteor.py b/fairseq/examples/unsupervised_quality_estimation/meteor.py new file mode 100644 index 0000000..2ee0448 --- /dev/null +++ b/fairseq/examples/unsupervised_quality_estimation/meteor.py @@ -0,0 +1,109 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import math +import os +import subprocess +import sys +import tempfile +from collections import defaultdict +from itertools import combinations + + +def read_translations(path, n_repeats): + segment_counter = 0 + segment_translations = [] + translations = defaultdict(list) + for line in open(path): + segment_translations.append(" ".join(line.split())) + if len(segment_translations) == n_repeats: + translations[segment_counter] = segment_translations + segment_translations = [] + segment_counter += 1 + return translations + + +def generate_input(translations, n_repeats): + _, ref_path = tempfile.mkstemp() + _, mt_path = tempfile.mkstemp() + ref_fh = open(ref_path, "w") + mt_fh = open(mt_path, "w") + for segid in sorted(translations.keys()): + assert len(translations[segid]) == n_repeats + indexes = combinations(range(n_repeats), 2) + for idx1, idx2 in indexes: + mt_fh.write(translations[segid][idx1].strip() + "\n") + ref_fh.write(translations[segid][idx2].strip() + "\n") + sys.stderr.write("\nSaved translations to %s and %s" % (ref_path, mt_path)) + return ref_path, mt_path + + +def run_meteor(ref_path, mt_path, metric_path, lang="en"): + _, out_path = tempfile.mkstemp() + subprocess.call( + [ + "java", + "-Xmx2G", + "-jar", + metric_path, + mt_path, + ref_path, + "-p", + "0.5 0.2 0.6 0.75", # default parameters, only changed alpha to give equal weight to P and R + "-norm", + "-l", + lang, + ], + stdout=open(out_path, "w"), + ) + os.remove(ref_path) + os.remove(mt_path) + sys.stderr.write("\nSaved Meteor output to %s" % out_path) + return out_path + + +def read_output(meteor_output_path, n_repeats): + n_combinations = math.factorial(n_repeats) / ( + math.factorial(2) * math.factorial(n_repeats - 2) + ) + raw_scores = [] + average_scores = [] + for line in open(meteor_output_path): + if not line.startswith("Segment "): + continue + score = float(line.strip().split("\t")[1]) + raw_scores.append(score) + if len(raw_scores) == n_combinations: + average_scores.append(sum(raw_scores) / n_combinations) + raw_scores = [] + os.remove(meteor_output_path) + return average_scores + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--infile") + parser.add_argument("-n", "--repeat_times", type=int) + parser.add_argument("-m", "--meteor") + parser.add_argument("-o", "--output") + args = parser.parse_args() + + translations = read_translations(args.infile, args.repeat_times) + sys.stderr.write("\nGenerating input for Meteor...") + ref_path, mt_path = generate_input(translations, args.repeat_times) + sys.stderr.write("\nRunning Meteor...") + out_path = run_meteor(ref_path, mt_path, args.meteor) + sys.stderr.write("\nReading output...") + scores = read_output(out_path, args.repeat_times) + sys.stderr.write("\nWriting results...") + with open(args.output, "w") as o: + for scr in scores: + o.write("{}\n".format(scr)) + o.close() + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/unsupervised_quality_estimation/repeat_lines.py b/fairseq/examples/unsupervised_quality_estimation/repeat_lines.py new file mode 100644 index 0000000..5a04851 --- /dev/null +++ b/fairseq/examples/unsupervised_quality_estimation/repeat_lines.py @@ -0,0 +1,28 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + + +def _normalize_spaces(line): + return " ".join(line.split()) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--input_file", required=True, type=str) + parser.add_argument("-n", "--repeat_times", required=True, type=int) + parser.add_argument("-o", "--output_file", required=False, type=str) + args = parser.parse_args() + stream = open(args.output_file, "w") if args.output_file else sys.stdout + + for line in open(args.input_file): + for _ in range(args.repeat_times): + stream.write(_normalize_spaces(line) + "\n") + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/README.md b/fairseq/examples/wav2vec/README.md new file mode 100644 index 0000000..e979733 --- /dev/null +++ b/fairseq/examples/wav2vec/README.md @@ -0,0 +1,426 @@ +# wav2vec 2.0 + +wav2vec 2.0 learns speech representations on unlabeled data as described in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](https://arxiv.org/abs/2006.11477). + +We learned speech representations in multiple languages as well in [Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)](https://arxiv.org/abs/2006.13979). + +We also combined wav2vec 2.0 with self-training in [Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020)](https://arxiv.org/abs/2010.11430). + +We combined speech data from multiple domains in [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)](https://arxiv.org/abs/2104.01027). + +We finetuned XLSR-53 on multiple languages to transcribe unseen languages in [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021)](https://arxiv.org/abs/2109.11680). + +## Pre-trained models + +Model | Finetuning split | Dataset | Model +|---|---|---|--- +Wav2Vec 2.0 Base | No finetuning | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt) +Wav2Vec 2.0 Base | 10 minutes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_10m.pt) +Wav2Vec 2.0 Base | 100 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_100h.pt) +Wav2Vec 2.0 Base | 960 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_960h.pt) +Wav2Vec 2.0 Large | No finetuning | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/libri960_big.pt) +Wav2Vec 2.0 Large | 10 minutes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_big_10m.pt) +Wav2Vec 2.0 Large | 100 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_big_100h.pt) +Wav2Vec 2.0 Large | 960 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_big_960h.pt) +Wav2Vec 2.0 Large (LV-60)* | No finetuning | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt) +Wav2Vec 2.0 Large conformer - rel_pos (LV-60)* | No finetuning | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](s3://dl.fbaipublicfiles.com/fairseq/conformer/wav2vec2/librilight/LL_relpos_PT_no_FT) +Wav2Vec 2.0 Large conformer - rope (LV-60)* | No finetuning | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](s3://dl.fbaipublicfiles.com/fairseq/conformer/wav2vec2/librilight/LL_rope_PT_no_FT) +Wav2Vec 2.0 Large (LV-60)* | 10 minutes | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_10m_new.pt) +Wav2Vec 2.0 Large (LV-60)* | 100 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_100h_new.pt) +Wav2Vec 2.0 Large conformer - rel_pos (LV-60)* | 100 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](s3://dl.fbaipublicfiles.com/fairseq/conformer/wav2vec2/librilight/LL_relpos_PT_100h_FT.pt) +Wav2Vec 2.0 Large conformer - rope (LV-60)* | 100 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](s3://dl.fbaipublicfiles.com/fairseq/conformer/wav2vec2/librilight/LL_rope_PT_100h_FT.pt) +Wav2Vec 2.0 Large (LV-60)* | 960 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec2_vox_960h_new.pt) +Wav2Vec 2.0 Large conformer - rel_pos (LV-60)* | 960 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](s3://dl.fbaipublicfiles.com/fairseq/conformer/wav2vec2/librilight/LL_relpos_PT_960h_FT.pt) +Wav2Vec 2.0 Large conformer - rope (LV-60)* | 960 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](s3://dl.fbaipublicfiles.com/fairseq/conformer/wav2vec2/librilight/LL_rope_PT_960h_FT.pt) +Wav2Vec 2.0 Large (LV-60) + Self Training * | 10 minutes | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_10m_pl.pt) +Wav2Vec 2.0 Large (LV-60) + Self Training * | 100 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_100h_pl.pt) +Wav2Vec 2.0 Large (LV-60) + Self Training * | 960 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_960h_pl.pt) +Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | No finetuning | [Libri-Light](https://github.com/facebookresearch/libri-light) + [CommonVoice](https://commonvoice.mozilla.org/en/languages) + [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62) + [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/w2v_large_lv_fsh_swbd_cv.pt) +Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | 960 hours Librispeech | [Libri-Light](https://github.com/facebookresearch/libri-light) + [CommonVoice](https://commonvoice.mozilla.org/en/languages) + [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62) + [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/w2v_large_lv_fsh_swbd_cv_ftls960_updated.pt) +Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | 300 hours Switchboard | [Libri-Light](https://github.com/facebookresearch/libri-light) + [CommonVoice](https://commonvoice.mozilla.org/en/languages) + [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62) + [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/w2v_large_lv_fsh_swbd_cv_ftsb300_updated.pt) + +\* updated (Oct. 24, 2020)\ +** updated (Nov. 13, 2021) + +We also release multilingual pre-trained wav2vec 2.0 (XLSR) models: + +Model | Architecture | Hours | Languages | Datasets | Model +|---|---|---|---|---|--- +XLSR-53 | Large | 56k | 53 | MLS, CommonVoice, BABEL | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr_53_56k.pt) + +The XLSR model uses the following datasets for multilingual pretraining: + +* **[MLS: Multilingual LibriSpeech](https://indico2.conference4me.psnc.pl/event/35/contributions/3585/attachments/1060/1101/Wed-2-6-10.pdf)** (8 languages, 50.7k hours): *Dutch, English, French, German, Italian, Polish, Portuguese, Spanish* + +* **[CommonVoice](https://commonvoice.mozilla.org/en/languages)** (36 languages, 3.6k hours): *Arabic, Basque, Breton, Chinese (CN), Chinese (HK), Chinese (TW), Chuvash, Dhivehi, Dutch, English, Esperanto, Estonian, French, German, Hakh-Chin, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Mongolian, Persian, Portuguese, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Welsh* (see also [finetuning splits]([https://dl.fbaipublicfiles.com/cpc_audio/common_voices_splits.tar.gz]) from [this paper](https://arxiv.org/abs/2002.02848)). + +* **[Babel](https://catalog.ldc.upenn.edu/byyear)** (17 languages, 1.7k hours): *Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu* + +We also finetuned several models on languages from [CommonVoice](https://commonvoice.mozilla.org/en/languages) (version 6.1) and [Babel](https://catalog.ldc.upenn.edu/byyear). Please refer to [our paper](https://arxiv.org/abs/2109.11680) for details about which languages are used. + +Pretrained Model | Fintune Dataset | # Languages | Phonemizer | Model | Dictionary +|---|---|---|---|---|--- +LV-60 | CommonVoice | 26 | [Espeak](https://github.com/espeak-ng/espeak-ng/blob/master/docs/languages.md) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/espeak_en_26lang_m10.pt) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/espeak_dict.txt) +XLSR-53 | CommonVoice | 26 | [Espeak](https://github.com/espeak-ng/espeak-ng/blob/master/docs/languages.md) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/espeak_26lang_m10.pt) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/espeak_dict.txt) +XLSR-53 | CommonVoice | 21 | [Phonetisaurus](https://github.com/AdolfVonKleist/Phonetisaurus) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/phonetisaurus_21lang_m10.pt) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/phonetisaurus_dict.txt) +XLSR-53 | CommonVoice, BABEL | 21, 19 | [Phonetisaurus](https://github.com/AdolfVonKleist/Phonetisaurus) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/phonetisaurus_40lang_m10.pt) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/zero_shot/phonetisaurus_40lang.dict.txt) + +We release 2 models that are finetuned on data from 2 different phonemizers. Although the phonemes are all [IPA](https://en.wikipedia.org/wiki/International_Phonetic_Alphabet) symbols, there are still subtle differences between the phonemized transcriptions from the 2 phonemizers. Thus, it's better to use the corresponding model, if your data is phonemized by either phonemizer above. + +## Training a new model with the CLI tools + +Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length) + +### Prepare training data manifest + +First, install the `soundfile` library: + +```shell script +pip install soundfile +``` + +Next, run: + +```shell script +python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext $ext --valid-percent $valid +``` + +$ext should be set to flac, wav, or whatever format your dataset happens to use that soundfile can read. + +$valid should be set to some reasonable percentage (like 0.01) of training data to use for validation. +To use a pre-defined validation set (like dev-other from librispeech), set to it 0 and then overwrite valid.tsv with a +separately pre-processed manifest file. + +### Train a wav2vec 2.0 base model + +This configuration was used for the base model trained on the Librispeech dataset in the wav2vec 2.0 paper + +Note that the input is expected to be single channel, sampled at 16 kHz + +```shell script +$ fairseq-hydra-train \ + task.data=/path/to/data \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_base_librispeech +``` + +Note: you can simulate 64 GPUs by using k GPUs and adding command line parameters (before `--config-dir`) +`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 64/k + +### Train a wav2vec 2.0 large model + +This configuration was used for the large model trained on the Libri-light dataset in the wav2vec 2.0 paper + +```shell script +$ fairseq-hydra-train \ + task.data=/path/to/data \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_large_librivox +``` + +Note: you can simulate 128 GPUs by using k GPUs and adding command line parameters (before `--config-dir`) +`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 128/k + +### Train a wav2vec 2.0 model with conformer backbone + +To replace the transformer layers in the encoder with the conformer layers, set `--layer-type conformer --attn-type espnet --pos-enc-type ${POS_ENC_TYPE}`. `POS_ENC_TYPE` refers to positional encoding to be used in the conformer encoder. +Set it to `abs`, `rope` or `rel_pos` to use the absolute positional encoding, rotary positional encoding or relative positional encoding in the conformer layer respectively. + +To train a base model with conformer: + +```shell script +$ fairseq-hydra-train \ + task.data=/path/to/data \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_conformer_base_librispeech \ + --attn-type espnet --pos-enc-type ${POS_ENC_TYPE} +``` + +To train a large model with conformer: + +```shell script +$ fairseq-hydra-train \ + task.data=/path/to/data \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_conformer_large_librivox + --attn-type espnet --pos-enc-type ${POS_ENC_TYPE} + +``` + +### Fine-tune a pre-trained model with CTC + +Fine-tuning a model requires parallel audio and labels file, as well as a vocabulary file in fairseq format. +A letter vocabulary can be downloaded [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). +An example [script](libri_labels.py) that generates labels for the Librispeech dataset from the tsv file produced by wav2vec_manifest.py can be used as follows: + +```shell script +split=train +$ python libri_labels.py /path/to/tsv --output-dir /output/dir --output-name $split +``` + +Fine-tuning on 100h of Librispeech with letter targets: + +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_port=$PORT \ + task.data=/path/to/data \ + model.w2v_path=/path/to/model.pt \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/finetuning \ + --config-name base_100h +``` + +There are other config files in the config/finetuning directory that can be used to fine-tune on other splits. +You can specify the right config via the `--config-name` parameter. + +Note: you can simulate 24 GPUs by using k GPUs and adding command line parameters (before `--config-dir`) +`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 24/k + +Decoding with a language model during training requires flashlight [python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter). +If you want to use a language model, add `+criterion.wer_args='[/path/to/kenlm, /path/to/lexicon, 2, -1]'` to the command line. + +### Evaluating a CTC model + +Evaluating a CTC model with a language model requires [flashlight python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter) to be installed. + +Fairseq transformer language model used in the wav2vec 2.0 paper can be obtained from the [wav2letter model repository](https://github.com/facebookresearch/wav2letter/tree/master/recipes/sota/2019). +Be sure to upper-case the language model vocab after downloading it. + +Letter dictionary for pre-trained models can be found [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). + +Next, run the evaluation command: + +```shell script +$subset=dev_other +python examples/speech_recognition/infer.py /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw --task audio_finetuning \ +--nbest 1 --path /path/to/model --gen-subset $subset --results-path /path/to/save/results/for/sclite --w2l-decoder kenlm \ +--lm-model /path/to/kenlm.bin --lm-weight 2 --word-score -1 --sil-weight 0 --criterion ctc --labels ltr --max-tokens 4000000 \ +--post-process letter +``` + +To get raw numbers, use --w2l-decoder viterbi and omit the lexicon. To use the transformer language model, use --w2l-decoder fairseqlm. + +## Use wav2vec 2.0 with 🤗Transformers + +Wav2Vec2 is also available in the [🤗Transformers library](https://github.com/huggingface/transformers) since version 4.4. + +Pretrained Models can be found on the [hub](https://huggingface.co/models?filter=wav2vec2) +and documentation can be found [here](https://huggingface.co/transformers/master/model_doc/wav2vec2.html). + +Usage example: + +```python +# !pip install transformers +# !pip install datasets +import soundfile as sf +import torch +from datasets import load_dataset +from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor + +# load pretrained model +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") +model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") + + +librispeech_samples_ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") + +# load audio +audio_input, sample_rate = sf.read(librispeech_samples_ds[0]["file"]) + +# pad input values and return pt tensor +input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values + +# INFERENCE + +# retrieve logits & take argmax +logits = model(input_values).logits +predicted_ids = torch.argmax(logits, dim=-1) + +# transcribe +transcription = processor.decode(predicted_ids[0]) + +# FINE-TUNE + +target_transcription = "A MAN SAID TO THE UNIVERSE I EXIST" + +# encode labels +with processor.as_target_processor(): + labels = processor(target_transcription, return_tensors="pt").input_ids + +# compute loss by passing labels +loss = model(input_values, labels=labels).loss +loss.backward() +``` + +# wav2vec + +Example to train a wav2vec model as described in [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](https://arxiv.org/abs/1904.05862). + +## Pre-trained models + +Description | Dataset | Model +---|---|--- +Wav2Vec large | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_large.pt) + +#### Example usage + +```python +import torch +import fairseq + +cp_path = '/path/to/wav2vec.pt' +model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp_path]) +model = model[0] +model.eval() + +wav_input_16khz = torch.randn(1,10000) +z = model.feature_extractor(wav_input_16khz) +c = model.feature_aggregator(z) +``` + +## Training a new model with the CLI tools + +Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate files 10 to 30 seconds in length) + +### Prepare training data manifest + +``` +python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext wav +``` + +### Train a wav2vec model + +``` +$ python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \ +--arch wav2vec --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \ +--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \ +--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ +--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \ +--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test +``` + +### Run wav2vec2 pre-training on Google Cloud TPUs + +Wav2Vec2 is now supported on TPUs! It's currently pre-training only. + +#### Using hydra on a v3-8 + +``` +$ OMP_NUM_THREADS=1 fairseq-hydra-train \ + task.data=/manifest/path \ + --config-dir /PATH/TO/FAIRSEQ/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_large_librivox_tpu.yaml +``` + +#### Using command line arguments on a v3-8 + +Note: Commandline arguments way of execution has a [known-problem](https://github.com/pytorch/fairseq/issues/3741) currently. + +``` +$ OMP_NUM_THREADS=1 python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \ +--arch wav2vec2 --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \ +--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \ +--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ +--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \ +--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test \ +--tpu --distributed-world-size 8 --num-batch-buckets 3 --enable-padding \ +--encoder-layerdrop 0 --mask-channel-prob 0.1 +``` + +#### Using hydra on a pod slice (v3-N with N > 8) + +``` +$ OMP_NUM_THREADS=1 fairseq-hydra-train \ + task.data=/manifest/path \ + --config-dir /PATH/TO/FAIRSEQ/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_large_librivox_tpu-pod.yaml # edit distributed-world-size accordingly +``` + +#### Using command line arguments on a pod slice (v3-N with N > 8) + +Note: Commandline arguments way of execution has a [known-problem](https://github.com/pytorch/fairseq/issues/3741) currently. + +``` +$ python -m torch_xla.distributed.xla_dist \ + --tpu ${TPUNAME} --conda-env=torch-xla-${TORCH_XLA_VERSION} --env OMP_NUM_THREADS=1 \ + -- \ +python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \ +--arch wav2vec2 --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \ +--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \ +--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ +--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \ +--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test \ +--tpu --distributed-world-size ${WORLD_SIZE} --num-batch-buckets 3 --enable-padding \ +--encoder-layerdrop 0 --mask-channel-prob 0.1 +``` + +### Extract embeddings from the downstream task data + +``` +$ PYTHONPATH=/path/to/fairseq python examples/wav2vec/wav2vec_featurize.py --input /path/to/task/waves --output /path/to/output \ +--model /model/path/checkpoint_best.pt --split train valid test +``` + +# vq-wav2vec + +Example to train a vq-wav2vec model as described in [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations (Baevski et al., 2019)](https://arxiv.org/abs/1910.05453). + +These models are also used in [Effectiveness of self-supervised pre-training for speech recognition (Baevski et al., 2019)](https://arxiv.org/abs/1911.03912). + +## Pre-trained models + +Description | Dataset | Model +---|---|--- +vq-wav2vec Gumbel | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/vq-wav2vec.pt) +vq-wav2vec K-means | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/vq-wav2vec_kmeans.pt) +Roberta on K-means codes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/bert_kmeans.tar) + +#### Example usage + +```python +import torch +import fairseq + +cp = torch.load('/path/to/vq-wav2vec.pt') +model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp]) +model = model[0] +model.eval() + +wav_input_16khz = torch.randn(1,10000) +z = model.feature_extractor(wav_input_16khz) +_, idxs = model.vector_quantizer.forward_idx(z) +print(idxs.shape) # output: torch.Size([1, 60, 2]), 60 timesteps with 2 indexes corresponding to 2 groups in the model +``` + +## Training a new model with the CLI tools + +Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length) + +### Prepare training data manifest + +``` +python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext wav +``` + +### Train a gumbel vq-wav2vec model + +``` +$ python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 \ +--save-interval 1 --no-epoch-checkpoints --arch wav2vec --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 \ +--optimizer adam --lr 1e-05 --lr-scheduler cosine \ +--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1), (512, 1, 1)] \ +--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ +--activation gelu --offset auto --skip-connections-agg --residual-scale 0.5 \ +--log-keys ["prob_perplexity","code_perplexity","temp"] --vq-type gumbel --vq-groups 2 --vq-depth 2 \ +--combine-groups --vq-vars 320 --vq-temp (2,0.5,0.999995) --prediction-steps 12 --warmup-updates 1000 \ +--warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 --max-sample-size 150000 \ +--max-tokens 300000 --cross-sample-negatives 0 --update-freq 1 --seed 2 --skip-invalid-size-inputs-valid-test +``` + +for k-means training, set vq-type with "kmeans" and add --loss-weights [1] argument. Pre-trained models were trained on 16 GPUs. + +### Tokenize audio data (e.g. for BERT training) + +``` +$ PYTHONPATH=/path/to/fairseq python examples/wav2vec/vq-wav2vec_featurize.py --data-dir /manifest/path --output-dir /path/to/output \ +--checkpoint /model/path/checkpoint_best.pt --split train valid test --extension tsv +``` diff --git a/fairseq/examples/wav2vec/__init__.py b/fairseq/examples/wav2vec/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/wav2vec/config/finetuning/base_100h.yaml b/fairseq/examples/wav2vec/config/finetuning/base_100h.yaml new file mode 100644 index 0000000..153b5df --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/base_100h.yaml @@ -0,0 +1,58 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 80000 + lr: [0.00003] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.5 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 0 diff --git a/fairseq/examples/wav2vec/config/finetuning/base_10h.yaml b/fairseq/examples/wav2vec/config/finetuning/base_10h.yaml new file mode 100644 index 0000000..5044518 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/base_10h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 50 + save_interval_updates: 10000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 50 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 20000 + lr: [0.00005] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.5 + mask_channel_length: 64 + layerdrop: 0.05 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/base_10m.yaml b/fairseq/examples/wav2vec/config/finetuning/base_10m.yaml new file mode 100644 index 0000000..14abc01 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/base_10m.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 1000 + save_interval_updates: 50 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.00005] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/base_1h.yaml b/fairseq/examples/wav2vec/config/finetuning/base_1h.yaml new file mode 100644 index 0000000..a0af1cf --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/base_1h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 50 + save_interval_updates: 1000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.00005] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/base_960h.yaml b/fairseq/examples/wav2vec/config/finetuning/base_960h.yaml new file mode 100644 index 0000000..3eadc36 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/base_960h.yaml @@ -0,0 +1,57 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 320000 + lr: [0.0001] + sentence_avg: true + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.5 + mask_channel_prob: 0.1 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 0 diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1.yaml new file mode 100644 index 0000000..4a84843 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 \ No newline at end of file diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_16.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_16.yaml new file mode 100644 index 0000000..041843a --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_16.yaml @@ -0,0 +1,27 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 16 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 + exclude: learnfair1381,learnfair5192,learnfair2304 \ No newline at end of file diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1_aws.yaml new file mode 100644 index 0000000..b9335df --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.local_cache_path + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1_old.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1_old.yaml new file mode 100644 index 0000000..a8d2363 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_1_old.yaml @@ -0,0 +1,27 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 450 + nodes: 1 + name: ${env:PREFIX}_wav2vec3_small_librispeech + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 + exclude: learnfair1381 \ No newline at end of file diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2.yaml new file mode 100644 index 0000000..65ec489 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2.yaml @@ -0,0 +1,27 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 + exclude: learnfair7491,learnfair7477,learnfair7487 \ No newline at end of file diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2_aws.yaml new file mode 100644 index 0000000..e7590ef --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.local_cache_path + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 8 + tasks_per_node: 1 + mem_gb: 0 + nodes: 2 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2g.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2g.yaml new file mode 100644 index 0000000..aaa20eb --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_2g.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 2 + tasks_per_node: 2 + mem_gb: 200 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_3.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_3.yaml new file mode 100644 index 0000000..9614ece --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_3.yaml @@ -0,0 +1,27 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 450 + nodes: 3 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 + exclude: learnfair7491,learnfair7477,learnfair7487 \ No newline at end of file diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_4g.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_4g.yaml new file mode 100644 index 0000000..c0c9f60 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_4g.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 200 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_4g_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_4g_aws.yaml new file mode 100644 index 0000000..6bbbf3b --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_4g_aws.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '/' + exclude_keys: + - run_config + - distributed_training.distributed_port + - distributed_training.distributed_world_size + - model.pretrained_model_path + - model.target_network_path + - next_script + - task.cache_in_scratch + - task.local_cache_path + - task.data + - checkpoint.save_interval_updates + - checkpoint.keep_interval_updates + - checkpoint.save_on_overflow + - common.log_interval + - common.user_dir + sweep: + dir: /fsx-wav2vec/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: '' + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 80 + gpus_per_node: 4 + tasks_per_node: 1 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab,learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_8.yaml b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_8.yaml new file mode 100644 index 0000000..984f218 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/run_config/slurm_8.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 4320 + cpus_per_task: 10 + gpus_per_node: 8 + tasks_per_node: 8 + mem_gb: 400 + nodes: 8 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_100h.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_100h.yaml new file mode 100644 index 0000000..b8f81e5 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_100h.yaml @@ -0,0 +1,58 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 80000 + lr: [0.00003] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.5 + mask_channel_prob: 0.5 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_100h_2.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_100h_2.yaml new file mode 100644 index 0000000..9bf588f --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_100h_2.yaml @@ -0,0 +1,106 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/1h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 1 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: 0 + wer_sil_weight: -2 + +optimization: + max_update: 100000 + lr: [1e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: null + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_100h_2_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_100h_2_aws.yaml new file mode 100644 index 0000000..3a0d517 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_100h_2_aws.yaml @@ -0,0 +1,82 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /data/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx-wav2vec/abaevski/data/libri/100h/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 1 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /fsx-wav2vec/abaevski/data/libri/4-gram.bin + wer_lexicon: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: 0 + wer_sil_weight: -2 + +optimization: + max_update: 100000 + lr: [1e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: null + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 82000 + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 7 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_100h_3.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_100h_3.yaml new file mode 100644 index 0000000..4677866 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_100h_3.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/1h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 1 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 + +optimization: + max_update: 100000 + lr: [1e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: cosine + warmup_updates: 8000 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10h.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10h.yaml new file mode 100644 index 0000000..8f1ca71 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 50 + save_interval_updates: 10000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 50 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 20000 + lr: [0.0001] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.75 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10h_2.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10h_2.yaml new file mode 100644 index 0000000..05ee76f --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10h_2.yaml @@ -0,0 +1,102 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 10 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + keep_interval_updates: 1 + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 10 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 + +optimization: + max_update: 60000 + lr: [2e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: cosine + warmup_updates: 8000 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.5 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10h_2_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10h_2_aws.yaml new file mode 100644 index 0000000..a0afc9c --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10h_2_aws.yaml @@ -0,0 +1,81 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /data/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 10 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 10 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /fsx-wav2vec/abaevski/data/libri/4-gram.bin + wer_lexicon: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: 4 + wer_sil_weight: -5 + +optimization: + max_update: 60000 + lr: [1e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: null + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.75 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10h_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10h_aws.yaml new file mode 100644 index 0000000..c754373 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10h_aws.yaml @@ -0,0 +1,104 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /data/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 10 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 10 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter +# wer_kenlm_model: /fsx-wav2vec/abaevski/data/libri/4-gram.bin +# wer_lexicon: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst +# wer_lm_weight: 2.0 +# wer_word_score: -1.0 + +optimization: + max_update: 60000 + lr: [2e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: null + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: wav2vec,learnlab + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10h_aws_v100.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10h_aws_v100.yaml new file mode 100644 index 0000000..58ad2ac --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10h_aws_v100.yaml @@ -0,0 +1,102 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 +# tensorboard_logdir: tb + +checkpoint: + save_interval: 10 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx/abaevski/data/libri/10h/wav2vec/raw + labels: ltr + cache_in_scratch: true + + +dataset: + num_workers: 10 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 10 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_lexicon: /fsx/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 + +optimization: + max_update: 60000 + lr: [2e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: null + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.6 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /fsx/${env:USER}/w2v_ft/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 0 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: learnfair + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10m.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10m.yaml new file mode 100644 index 0000000..07e327f --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10m.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 1000 + save_interval_updates: 50 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.0001] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10m_2.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10m_2.yaml new file mode 100644 index 0000000..1ac7c12 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10m_2.yaml @@ -0,0 +1,114 @@ +# @package _group_ + +common: + fp16: true + fp16_no_flatten_grads: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 500 + save_interval_updates: 500 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/10m/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 500 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 5 + wer_word_score: 2 + wer_sil_weight: -2 + +optimization: + max_update: 10000 + lr: [2e-6] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [4] # base 10h we -> 2/4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 2e-6 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + lr_scheduler: + _name: cosine + warmup_updates: 1000 + +lr_scheduler: pass_through + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 3 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + freeze_finetune_updates: 100 + + zero_mask: true + feature_grad_mult: 0.0 + activation_dropout: 0.1 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + update_alibi: false + +#hydra: +# job: +# config: +# override_dirname: +# kv_sep: ':' +# item_sep: '__' +# exclude_keys: +# - run_config +# - distributed_training.distributed_port +# sweep: +# dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} +# subdir: ${hydra.job.num} +# launcher: +# submitit_folder: ${hydra.sweep.dir} +# timeout_min: 3000 +# cpus_per_task: 10 +# gpus_per_node: 4 +# tasks_per_node: 4 +# mem_gb: 250 +# nodes: 1 +# name: ${env:PREFIX}_${hydra.job.config_name} +# partition: devlab,learnlab,learnfair,scavenge +# constraint: volta32gb +# max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10m_2_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10m_2_aws.yaml new file mode 100644 index 0000000..a9c2708 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10m_2_aws.yaml @@ -0,0 +1,114 @@ +# @package _group_ + +common: + fp16: true + fp16_no_flatten_grads: true + log_format: json + log_interval: 200 + user_dir: /data/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 500 + save_interval_updates: 500 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx-wav2vec/abaevski/data/libri/10m/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 500 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /fsx-wav2vec/abaevski/data/libri/4-gram.bin + wer_lexicon: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 5 + wer_word_score: 2 + wer_sil_weight: -2 + +optimization: + max_update: 10000 + lr: [2e-6] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [4] # base 10h we -> 2/4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 2e-6 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + lr_scheduler: + _name: cosine + warmup_updates: 1000 + +lr_scheduler: pass_through + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 3 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + freeze_finetune_updates: 100 + + zero_mask: true + feature_grad_mult: 0.0 + activation_dropout: 0.1 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + update_alibi: false + +#hydra: +# job: +# config: +# override_dirname: +# kv_sep: ':' +# item_sep: '__' +# exclude_keys: +# - run_config +# - distributed_training.distributed_port +# sweep: +# dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} +# subdir: ${hydra.job.num} +# launcher: +# submitit_folder: ${hydra.sweep.dir} +# timeout_min: 3000 +# cpus_per_task: 10 +# gpus_per_node: 4 +# tasks_per_node: 4 +# mem_gb: 250 +# nodes: 1 +# name: ${env:PREFIX}_${hydra.job.config_name} +# partition: devlab,learnlab,learnfair,scavenge +# constraint: volta32gb +# max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_10m_3.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_10m_3.yaml new file mode 100644 index 0000000..b680412 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_10m_3.yaml @@ -0,0 +1,105 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 1000 + save_interval_updates: 100 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/10m/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 500 + valid_subset: dev_other + required_batch_size_multiple: 8 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 8 + wer_word_score: 5.8 + wer_sil_weight: -8 + +optimization: + max_update: 13000 + lr: [2e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [5] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_length: 10 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_1h.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_1h.yaml new file mode 100644 index 0000000..fac1bbb --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_1h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 1000 + save_interval_updates: 50 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.0003] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.75 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_1h_2.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_1h_2.yaml new file mode 100644 index 0000000..75f4aaf --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_1h_2.yaml @@ -0,0 +1,104 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 100 + save_interval_updates: 500 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/1h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 100 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 6 + wer_word_score: -0.1 + wer_sil_weight: -4.7 + +optimization: + max_update: 60000 + lr: [1e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: cosine + warmup_updates: 4000 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_1h_2_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_1h_2_aws.yaml new file mode 100644 index 0000000..cc4d511 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_1h_2_aws.yaml @@ -0,0 +1,114 @@ +# @package _group_ + +common: + fp16: true + fp16_no_flatten_grads: true + log_format: json + log_interval: 200 + user_dir: /data/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 100 + save_interval_updates: 500 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx-wav2vec/abaevski/data/libri/1h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 500 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /fsx-wav2vec/abaevski/data/libri/4-gram.bin + wer_lexicon: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 5 + wer_word_score: 0 + wer_sil_weight: -4 + +optimization: + max_update: 10000 + lr: [2e-6] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [4] # base 10h we -> 2/4 + +optimizer: + _name: composite + dynamic_groups: true + groups: + default: + lr_float: 2e-6 + optimizer: + _name: adam + adam_betas: [0.9,0.95] + lr_scheduler: + _name: cosine + warmup_updates: 1000 + +lr_scheduler: pass_through + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 3 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + freeze_finetune_updates: 100 + + zero_mask: true + feature_grad_mult: 0.0 + activation_dropout: 0.1 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + update_alibi: false + +#hydra: +# job: +# config: +# override_dirname: +# kv_sep: ':' +# item_sep: '__' +# exclude_keys: +# - run_config +# - distributed_training.distributed_port +# sweep: +# dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} +# subdir: ${hydra.job.num} +# launcher: +# submitit_folder: ${hydra.sweep.dir} +# timeout_min: 3000 +# cpus_per_task: 10 +# gpus_per_node: 4 +# tasks_per_node: 4 +# mem_gb: 250 +# nodes: 1 +# name: ${env:PREFIX}_${hydra.job.config_name} +# partition: devlab,learnlab,learnfair,scavenge +# constraint: volta32gb +# max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_1h_3.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_1h_3.yaml new file mode 100644 index 0000000..842c897 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_1h_3.yaml @@ -0,0 +1,104 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 100 + save_interval_updates: 500 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/1h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 640000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 100 + valid_subset: dev_other + required_batch_size_multiple: 8 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 6 + wer_word_score: -0.1 + wer_sil_weight: -4.7 + +optimization: + max_update: 13000 + lr: [6e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [5] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: cosine + warmup_updates: 4000 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.3 + mask_length: 3 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_1h_4.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_1h_4.yaml new file mode 100644 index 0000000..698ed8c --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_1h_4.yaml @@ -0,0 +1,104 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 100 + save_interval_updates: 1000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/1h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 640000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 100 + valid_subset: dev_other + required_batch_size_multiple: 8 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 + +optimization: + max_update: 13000 + lr: [6e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [5] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_length: 10 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_1h_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_1h_aws.yaml new file mode 100644 index 0000000..aa67004 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_1h_aws.yaml @@ -0,0 +1,80 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /data/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 100 + save_interval_updates: 500 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx-wav2vec/abaevski/data/libri/10m/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 100 + valid_subset: dev_other + required_batch_size_multiple: 8 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /fsx-wav2vec/abaevski/data/libri/4-gram.bin + wer_lexicon: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 5 + wer_word_score: -0.1 + wer_sil_weight: -4.7 + +optimization: + max_update: 13000 + lr: [6e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [5] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: cosine + warmup_updates: 4000 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.3 + mask_length: 3 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.25 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + update_alibi: false diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_960h.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_960h.yaml new file mode 100644 index 0000000..9d72404 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_960h.yaml @@ -0,0 +1,57 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 24 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 320000 + lr: [0.00003] + sentence_avg: true + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.5 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_960h_2.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_960h_2.yaml new file mode 100644 index 0000000..d96e232 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_960h_2.yaml @@ -0,0 +1,105 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/960h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 1 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 16 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 + +optimization: + max_update: 200000 + lr: [1e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: null + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 200000 + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_960h_2_aws.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_960h_2_aws.yaml new file mode 100644 index 0000000..41d2b38 --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_960h_2_aws.yaml @@ -0,0 +1,82 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /data/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /fsx-wav2vec/abaevski/data/librispeech + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 1 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 16 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /fsx-wav2vec/abaevski/data/libri/4-gram.bin + wer_lexicon: /fsx-wav2vec/abaevski/data/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 1.5 + wer_word_score: 0 + wer_sil_weight: -1 + +optimization: + max_update: 200000 + lr: [2e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: null + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 192000 + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.3 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + diff --git a/fairseq/examples/wav2vec/config/finetuning/vox_960h_3.yaml b/fairseq/examples/wav2vec/config/finetuning/vox_960h_3.yaml new file mode 100644 index 0000000..ef6597a --- /dev/null +++ b/fairseq/examples/wav2vec/config/finetuning/vox_960h_3.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + user_dir: /private/home/abaevski/fairseq-py/examples/data2vec +# tensorboard_logdir: tb + +checkpoint: + save_interval: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: /checkpoint/abaevski/data/speech/libri/1h/wav2vec/raw + labels: ltr + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1000000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 100 + validate_interval: 1 + valid_subset: dev_other + required_batch_size_multiple: 1 + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 16 + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + wer_kenlm_model: /checkpoint/abaevski/data/speech/libri/4-gram.bin + wer_lexicon: /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw/lexicon_ltr2.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 + +optimization: + max_update: 200000 + lr: [1e-5] +# lr: [1e-5] # base 10h wer + sentence_avg: true + update_freq: [1] # base 10h we -> 2/4 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: cosine + warmup_updates: 8000 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.4 + mask_length: 5 +# mask_prob: 0.65 # base 10h wer + mask_channel_prob: 0.1 +# mask_channel_prob: 0.6 # base 10h wer + mask_channel_length: 64 + layerdrop: 0.1 +# layerdrop: 0.05 # base 10h wer + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 100 + dropout: 0 + final_dropout: 0 + attention_dropout: 0 + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 4 + tasks_per_node: 4 + mem_gb: 250 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml new file mode 100644 index 0000000..b686e21 --- /dev/null +++ b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml @@ -0,0 +1,57 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 250000 + min_sample_size: 32000 + normalize: false + +dataset: + num_workers: 6 + max_tokens: 1400000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 64 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 10] + +optimization: + max_update: 400000 + lr: [0.0005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + final_dim: 256 + encoder_layerdrop: 0.05 + dropout_input: 0.1 + dropout_features: 0.1 + feature_grad_mult: 0.1 + encoder_embed_dim: 768 diff --git a/fairseq/examples/wav2vec/config/pretraining/wav2vec2_conformer_base_librispeech.yaml b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_conformer_base_librispeech.yaml new file mode 100644 index 0000000..912ac15 --- /dev/null +++ b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_conformer_base_librispeech.yaml @@ -0,0 +1,60 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 250000 + min_sample_size: 32000 + normalize: false + +dataset: + num_workers: 6 + max_tokens: 1400000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 64 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 10] + +optimization: + max_update: 400000 + lr: [0.0005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + final_dim: 256 + encoder_layerdrop: 0.05 + dropout_input: 0.1 + dropout_features: 0.1 + feature_grad_mult: 0.1 + encoder_embed_dim: 768 + layer_type: conformer + attn_type: espnet + pos_enc_type: rel_pos diff --git a/fairseq/examples/wav2vec/config/pretraining/wav2vec2_conformer_large_librivox.yaml b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_conformer_large_librivox.yaml new file mode 100644 index 0000000..676166b --- /dev/null +++ b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_conformer_large_librivox.yaml @@ -0,0 +1,72 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 320000 + min_sample_size: 32000 + normalize: true + +dataset: + num_workers: 6 + max_tokens: 1200000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 128 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 0] + +optimization: + max_update: 1000000 + lr: [0.005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + extractor_mode: layer_norm + layer_norm_first: true + final_dim: 768 + latent_temp: [2.0,0.1,0.999995] + encoder_layerdrop: 0.00 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + conv_bias: true + + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + + feature_grad_mult: 1.0 + + layer_type: conformer + attn_type: espnet + pos_enc_type: rel_pos diff --git a/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml new file mode 100644 index 0000000..3192ce4 --- /dev/null +++ b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml @@ -0,0 +1,70 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 320000 + min_sample_size: 32000 + normalize: true + +dataset: + batch_size: 4 + num_workers: 6 + max_tokens: 1200000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 128 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 0] + +optimization: + max_update: 1000000 + lr: [0.005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + extractor_mode: layer_norm + layer_norm_first: true + final_dim: 768 + latent_temp: [2.0,0.1,0.999995] + encoder_layerdrop: 0.00 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + conv_bias: true + + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + + feature_grad_mult: 1.0 + diff --git a/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu-pod.yaml b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu-pod.yaml new file mode 100644 index 0000000..ff35a95 --- /dev/null +++ b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu-pod.yaml @@ -0,0 +1,72 @@ +# @package _group_ + +common: + tpu: true + fp16: false + log_format: json + log_interval: 10 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 250000 + min_sample_size: 32000 + normalize: true + num_batch_buckets: 3 + precompute_mask_indices: true + enable_padding: true + +dataset: + num_workers: 6 + max_tokens: 1200000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 128 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 0] + +optimization: + max_update: 1000000 + lr: [0.005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + extractor_mode: layer_norm + layer_norm_first: true + final_dim: 768 + latent_temp: [2.0,0.1,0.999995] + encoder_layerdrop: 0.00 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + conv_bias: true + + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + + feature_grad_mult: 1.0 diff --git a/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu.yaml b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu.yaml new file mode 100644 index 0000000..ee55bda --- /dev/null +++ b/fairseq/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu.yaml @@ -0,0 +1,77 @@ +# @package _group_ + +common: + tpu: true + fp16: false + log_format: json + log_interval: 10 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 250000 + min_sample_size: 32000 + normalize: true + num_batch_buckets: 3 + precompute_mask_indices: true + enable_padding: true + inferred_w2v_config: + mask_prob: 0.65 + mask_selection: 'static' + mask_other: 0 + mask_channel_prob: 0.1 + +dataset: + num_workers: 6 + max_tokens: 1200000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 8 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 0] + +optimization: + max_update: 1000000 + lr: [0.005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + extractor_mode: layer_norm + layer_norm_first: true + final_dim: 768 + latent_temp: [2.0,0.1,0.999995] + encoder_layerdrop: 0.00 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + conv_bias: true + + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + + feature_grad_mult: 1.0 diff --git a/fairseq/examples/wav2vec/libri_labels.py b/fairseq/examples/wav2vec/libri_labels.py new file mode 100644 index 0000000..694a202 --- /dev/null +++ b/fairseq/examples/wav2vec/libri_labels.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset +""" + +import argparse +import os + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("tsv") + parser.add_argument("--output-dir", required=True) + parser.add_argument("--output-name", required=True) + args = parser.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) + + transcriptions = {} + + with open(args.tsv, "r") as tsv, open( + os.path.join(args.output_dir, args.output_name + ".ltr"), "w" + ) as ltr_out, open( + os.path.join(args.output_dir, args.output_name + ".wrd"), "w" + ) as wrd_out: + root = next(tsv).strip() + for line in tsv: + line = line.strip() + dir = os.path.dirname(line) + if dir not in transcriptions: + parts = dir.split(os.path.sep) + trans_path = f"{parts[-2]}-{parts[-1]}.trans.txt" + path = os.path.join(root, dir, trans_path) + assert os.path.exists(path) + texts = {} + with open(path, "r") as trans_f: + for tline in trans_f: + items = tline.strip().split() + texts[items[0]] = " ".join(items[1:]) + transcriptions[dir] = texts + part = os.path.basename(line).split(".")[0] + assert part in transcriptions[dir] + print(transcriptions[dir][part], file=wrd_out) + print( + " ".join(list(transcriptions[dir][part].replace(" ", "|"))) + " |", + file=ltr_out, + ) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/scripts/binarize_manifest.sh b/fairseq/examples/wav2vec/scripts/binarize_manifest.sh new file mode 100644 index 0000000..6f201bd --- /dev/null +++ b/fairseq/examples/wav2vec/scripts/binarize_manifest.sh @@ -0,0 +1,33 @@ +#!/usr/bin/env bash + +# usage: bash binarize_manifest <dest_dir> <train_split> <valid_split> + +DEST_DIR=$1 +TRAIN_SPLIT=$2 +VALID_SPLIT=$3 +FAIRSEQ_ROOT=$4 + +mkdir -p $DEST_DIR + +# split file path and lengths into separate files +cut -f1 $TRAIN_SPLIT.tsv > $DEST_DIR/train_fnames.txt +cut -f1 $VALID_SPLIT.tsv > $DEST_DIR/valid_fnames.txt +cut -f2 $TRAIN_SPLIT.tsv > $DEST_DIR/train.lengths +cut -f2 $VALID_SPLIT.tsv > $DEST_DIR/valid.lengths + +# copy root directory +head -1 $TRAIN_SPLIT.tsv > $DEST_DIR/train.root +head -1 $VALID_SPLIT.tsv > $DEST_DIR/valid.root + +# remove root directory +sed -i '1d' $DEST_DIR/train_fnames.txt +sed -i '1d' $DEST_DIR/valid_fnames.txt +sed -i '1d' $DEST_DIR/train.lengths +sed -i '1d' $DEST_DIR/valid.lengths + +# insert spaces between characters +sed -i -e 's/\(.\)/\1 /g' $DEST_DIR/train_fnames.txt +sed -i -e 's/\(.\)/\1 /g' $DEST_DIR/valid_fnames.txt + +# run preprocessor +PYTHONPATH=$FAIRSEQ_ROOT python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $DEST_DIR/train_fnames.txt --validpref $DEST_DIR/valid_fnames.txt --workers 60 --only-source --destdir $DEST_DIR diff --git a/fairseq/examples/wav2vec/unsupervised/README.md b/fairseq/examples/wav2vec/unsupervised/README.md new file mode 100644 index 0000000..b9d6f67 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/README.md @@ -0,0 +1,119 @@ +# wav2vec Unsupervised (wav2vec-U) + +Wav2vec Unsupervised (wav2vec-U) and the 2.0 version are frameworks for building speech recognition systems without any labeled training data as described in [Unsupervised Speech Recognition (Baevski et al., 2021)](https://ai.facebook.com/research/publications/unsupervised-speech-recognition) and [Towards End-to-end Unsupervised Speech Recognition (Liu, et al., 2022)](https://arxiv.org/abs/2204.02492). The model takes as input wav2vec 2.0 or XLSR representations (see [pretrained models](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec)) as well as unlabeled speech and text data. + + The training procedure consists of three consecutive main steps: +* Preparation of speech representations and text data +* Generative adversarial training (GAN) +* Iterative self-training + Kaldi LM-decoding + +## Preparation of speech and text data +Similar to [wav2vec 2.0](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md), data folders contain {train,valid,test}.{tsv,wrd,phn} files, where audio paths are stored in tsv files, and word, letter or phoneme transcriptions are stored in .{wrd,ltr,phn}. + +In **/path/to/data/with_silence** you need a *train.tsv* file as well as (optionally) *{valid,test}.{tsv,wrd,phn}*. It is nice to have *10h.{tsv,phn}* files there too for reproducing the ablation study on layer selection. In **/path/to/data/without_silence** you have the same files, except *.tsv* files contain audios with silences removed using rVAD. + +Pre-requisites: +* set FAIRSEQ_ROOT environmental variable to your fairseq installation +* set RVAD_ROOT environmental variable to a checkout of [rVADfast](https://github.com/zhenghuatan/rVADfast) +* set KENLM_ROOT environmental variable to the location of [KenLM](https://github.com/kpu/kenlm) binaries +* install [PyKaldi](https://github.com/pykaldi/pykaldi) and set KALDI_ROOT environmental variable to the location of your kaldi installation. To use the version bundled with PyKaldi, you can use /path/to/pykaldi/tools/kaldi + +Create new audio files without silences: +```shell +# create a manifest file for the set original of audio files +python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py /dir/to/save/audio/files --ext wav --dest /path/to/new/train.tsv --valid-percent 0 + +python scripts/vads.py -r $RVAD_ROOT < /path/to/train.tsv > train.vads + +python scripts/remove_silence.py --tsv /path/to/train.tsv --vads train.vads --out /dir/to/save/audio/files + +python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py /dir/to/save/audio/files --ext wav --dest /path/to/new/train.tsv --valid-percent 0.01 +``` + +Next, we need to preprocess the audio data to better match phonemized text data: + +```shell +# wav2vec-U +zsh scripts/prepare_audio.sh /dir/with/{train,test,valid}.tsv /output/dir /path/to/wav2vec2/model.pt 512 14 +# wav2vec-U 2.0 +zsh scripts/prepare_audio_v2.sh /dir/with/{train,test,valid}.tsv /output/dir /path/to/wav2vec2/model.pt 64 14 +``` +Note that if you have splits different than train/valid/test, you will need to modify this script. The thrid argument is the PCA dimensionality for wav2vec-U and the number of MFCC clusters for wav2vec-U 2.0. The last argument is the 0-based index of the layer from which to extract representations. + +Now we need to prepare text data: +```shell +zsh scripts/prepare_text.sh language /path/to/text/file /output/dir 1000 espeak /path/to/fasttext/lid/model sil_prob +``` + +The fourth argument is minimum number observations of phones to keep. If your text corpus is small, you might want to reduce this number. + +The fifth argument is which phonemizer to use. Supported values are [espeak](http://espeak.sourceforge.net/), [espeak-ng](https://github.com/espeak-ng/espeak-ng), and [G2P](https://github.com/Kyubyong/g2p) (english only). + +Pre-trained fasttext LID models can be downloaded [here](https://fasttext.cc/docs/en/language-identification.html). + +The last argument is the probability to introduce silence (`<SIL>`) between the word boundaries. We found the value `0.25`/`0.5` works in general for wav2vec-U and the 2.0 version respectively, but you might want to vary for languages that are never tested. + +### Prepare TIMIT data +TIMIT transcripts include silence. Therefore VAD is not used for audio preprocessing, and we do not wrap transcripts with silences or insert random silence in between words. + +To prepare TIMIT data for both the matched an unmatched setup: +```shell +bash scripts/prepare_timit.sh /dir/to/timit/raw/data /output/dir /path/to/wav2vec2/model.pt +``` + +Note that we assume the TIMIT distribution with capitalized directories and filenames are used (e.g., `TRAIN/DR1/FCJF0/SA1.PHN`). + +## Generative adversarial training (GAN) + +We then use a GAN model to build a first unsupervised ASR model. The data preparation above of both speech features and text data is a necessary procedure that enables the generator to match speech to text in an unsupervised way. + +Launching GAN training on top of preprocessed features, with default hyperparameters can be done with: + +``` +PREFIX=w2v_unsup_gan_xp + +# For wav2vec-U, audio features are pre-segmented +CONFIG_NAME=w2vu +TASK_DATA=/path/to/features/precompute_unfiltered_pca512_cls128_mean_pooled + +# For wav2vec-U 2.0, use raw audio features +CONFIG_NAME=w2vu2 +TASK_DATA=/path/to/features/ + +# Unpaired text input +TEXT_DATA=/path/to/data/phones # path to fairseq-preprocessed GAN data (phones dir) +KENLM_PATH=/path/to/data/phones/kenlm.phn.o4.bin # KenLM 4-gram phoneme language model (LM data = GAN data here) + +PYTHONPATH=$FAIRSEQ_ROOT PREFIX=$PREFIX fairseq-hydra-train \ + -m --config-dir config/gan \ + --config-name $CONFIG_NAME \ + task.data=${TASK_DATA} \ + task.text_data=${TEXT_DATA} \ + task.kenlm_path=${KENLM_PATH} \ + common.user_dir=${FAIRSEQ_ROOT}/examples/wav2vec/unsupervised \ + model.code_penalty=2,4 model.gradient_penalty=1.5,2.0 \ + model.smoothness_weight=0.5,0.75,1.0 'common.seed=range(0,5)' +``` + + +Once we find the best checkpoint (chosen using unsupervised metric that combined language model perplexity and vocabulary usage), we can use it to generate phone labels (or word labels with an appropriate kaldi WFST): + +```shell +python w2vu_generate.py --config-dir config/generate --config-name viterbi \ +fairseq.common.user_dir=${FAIRSEQ_ROOT}/examples/wav2vec/unsupervised \ +fairseq.task.data=/path/to/dir/with/features \ +fairseq.common_eval.path=/path/to/gan/checkpoint \ +fairseq.dataset.gen_subset=valid results_path=/where/to/save/transcriptions +``` + +The decoding without LM works best on the same adjacent-mean-pooled features that the gan was trained on, while decoding with LM works better on features before the adjacent timestep mean-pooling step (without the "_pooled" suffix). + +While the generator of wav2vec-U 2.0 is trained with an output frequency of 16hz, we found decoding at a higher frequency produces better results. This can be done by adding `decode_stride=1` or `2` to the argument. + +## Iterative self-training + Kaldi LM-decoding +After the GAN training provides a first unsupervised model, we can then progressively refine the quality of transcriptions using several iterations of semi-supervised learning. We perform two iterations: first, pseudo-label the training data with the unsupervised GAN model and train an HMM on the pseudo-labels. Second, we relabel the training data with the HMM and then fine-tune the original wav2vec 2.0 model using the HMM pseudo-labels with a CTC loss. Note that HMM models use phonemes as output, while wav2vec 2.0 use letter. Both are decoded using WFST decoders into words. + + +Please see [this README](kaldi_self_train/README.md) for more instructions on how to do iterative self-training + Kaldi LM-decoding. + +*** Note: these instructions are a work in progress and will be updated over the next few days diff --git a/fairseq/examples/wav2vec/unsupervised/__init__.py b/fairseq/examples/wav2vec/unsupervised/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/examples/wav2vec/unsupervised/config/finetuning/w2v_finetune.yaml b/fairseq/examples/wav2vec/unsupervised/config/finetuning/w2v_finetune.yaml new file mode 100644 index 0000000..19a3ef3 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/config/finetuning/w2v_finetune.yaml @@ -0,0 +1,62 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + no_epoch_checkpoints: true + save_interval_updates: 20000 + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 800000 + skip_invalid_size_inputs_valid_test: true + train_subset: train + valid_subset: valid + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + find_unused_parameters: True + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + +optimization: + max_update: 80000 + lr: [0.00003] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.25 + mask_channel_prob: 0.1 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 0 diff --git a/fairseq/examples/wav2vec/unsupervised/config/gan/w2vu.yaml b/fairseq/examples/wav2vec/unsupervised/config/gan/w2vu.yaml new file mode 100644 index 0000000..74f1829 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/config/gan/w2vu.yaml @@ -0,0 +1,115 @@ +# @package _group_ + +common: + fp16: false + fp16_no_flatten_grads: true + log_format: json + log_interval: 100 + tensorboard_logdir: tb + reset_logging: false + suppress_crashes: false + +checkpoint: + save_interval: 1000 + save_interval_updates: 1000 + no_epoch_checkpoints: true + best_checkpoint_metric: weighted_lm_ppl + save_dir: . + +distributed_training: + distributed_world_size: 1 + +task: + _name: unpaired_audio_text + data: ??? + text_data: ??? + labels: phn + sort_by_length: false + unfiltered: false + max_length: null + append_eos: false + kenlm_path: ??? + +dataset: + num_workers: 6 + batch_size: 160 + skip_invalid_size_inputs_valid_test: true + valid_subset: valid + validate_interval: 1000 + validate_interval_updates: 1000 + +criterion: + _name: model + log_keys: + - accuracy_dense + - accuracy_token + - temp + - code_ppl + +optimization: + max_update: 150000 + clip_norm: 5.0 + lr: [0] + +optimizer: + _name: composite + groups: + generator: + lr: [0.0004] + lr_float: null + optimizer: + _name: adam + adam_betas: [0.5,0.98] + adam_eps: 1e-06 + weight_decay: 0 + amsgrad: false + lr_scheduler: + _name: fixed + warmup_updates: 0 + discriminator: + lr: [ 0.0005 ] + lr_float: null + optimizer: + _name: adam + adam_betas: [0.5,0.98] + adam_eps: 1e-06 + weight_decay: 0.0001 + amsgrad: false + lr_scheduler: + _name: fixed + warmup_updates: 0 + +lr_scheduler: pass_through + +model: + _name: wav2vec_u + + discriminator_dim: 384 + discriminator_depth: 2 + discriminator_kernel: 6 + discriminator_linear_emb: false + discriminator_causal: true + discriminator_max_pool: false + discriminator_act_after_linear: false + discriminator_dropout: 0.0 + discriminator_weight_norm: false + + generator_stride: 1 + generator_kernel: 4 + generator_bias: false + generator_dropout: 0.1 + + smoothness_weight: 0.5 + smoothing: 0 + smoothing_one_sided: false + gumbel: false + hard_gumbel: false + gradient_penalty: 1.5 + code_penalty: 4.0 + temp: [ 2,0.1,0.99995 ] + input_dim: 512 + + segmentation: + type: JOIN + mean_pool_join: false + remove_zeros: false diff --git a/fairseq/examples/wav2vec/unsupervised/config/gan/w2vu2.yaml b/fairseq/examples/wav2vec/unsupervised/config/gan/w2vu2.yaml new file mode 100644 index 0000000..5201422 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/config/gan/w2vu2.yaml @@ -0,0 +1,154 @@ +# @package _group_ + +common: + fp16: false + fp16_no_flatten_grads: true + log_format: json + log_interval: 100 + tensorboard_logdir: tb + reset_logging: false + suppress_crashes: false + +checkpoint: + save_interval: 1000 + save_interval_updates: 1000 + no_epoch_checkpoints: true + best_checkpoint_metric: weighted_lm_ppl + save_dir: . + +distributed_training: + distributed_world_size: 1 + +task: + _name: unpaired_audio_text + data: ??? + text_data: ??? + labels: phn + sort_by_length: false + unfiltered: false + max_length: null + append_eos: false + kenlm_path: ??? + aux_target_postfix: km + +dataset: + num_workers: 6 + batch_size: 160 + skip_invalid_size_inputs_valid_test: true + valid_subset: valid + validate_interval: 1000 + validate_interval_updates: 1000 + +criterion: + _name: model + log_keys: + - accuracy_dense + - accuracy_token + - temp + - code_ppl + +optimization: + max_update: 150000 + clip_norm: 5.0 + lr: [0] + +optimizer: + _name: composite + groups: + generator: + lr: [0.00005] + lr_float: null + optimizer: + _name: adam + adam_betas: [0.5,0.98] + adam_eps: 1e-06 + weight_decay: 0 + amsgrad: false + lr_scheduler: + _name: fixed + warmup_updates: 0 + discriminator: + lr: [ 0.0003 ] + lr_float: null + optimizer: + _name: adam + adam_betas: [0.5,0.98] + adam_eps: 1e-06 + weight_decay: 0.0001 + amsgrad: false + lr_scheduler: + _name: fixed + warmup_updates: 0 + +lr_scheduler: pass_through + +model: + _name: wav2vec_u + + discriminator_dim: 384 + discriminator_depth: 2 + discriminator_kernel: 8 + discriminator_linear_emb: false + discriminator_causal: true + discriminator_max_pool: false + discriminator_act_after_linear: false + discriminator_dropout: 0.0 + discriminator_weight_norm: false + + generator_stride: 3 + generator_kernel: 9 + generator_bias: false + generator_dropout: 0.1 + generator_batch_norm: 30 + generator_residual: true + + smoothness_weight: 1.5 + smoothing: 0 + smoothing_one_sided: false + gumbel: false + hard_gumbel: false + gradient_penalty: 1.0 + code_penalty: 3.0 + temp: [ 2,0.1,0.99995 ] + input_dim: 1024 + mmi_weight: 0.5 + target_dim: 64 + + segmentation: + type: JOIN + mean_pool_join: false + remove_zeros: false + + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - run_config + - distributed_training.distributed_port + - common.user_dir + - task.data + - task.kenlm_path + - task.text_data + - model.generator_layers + - task.labels + - task.force_model_seed + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}/${hydra.job.override_dirname} + subdir: ${hydra.job.num} + launcher: + submitit_folder: ${hydra.sweep.dir} + timeout_min: 3000 + cpus_per_task: 10 + gpus_per_node: 1 + tasks_per_node: 1 + mem_gb: 120 + nodes: 1 + name: ${env:PREFIX}_${hydra.job.config_name} + partition: devlab,learnlab,learnfair,scavenge + comment: intern_endding_soon + constraint: volta32gb + max_num_timeout: 30 diff --git a/fairseq/examples/wav2vec/unsupervised/config/generate/viterbi.yaml b/fairseq/examples/wav2vec/unsupervised/config/generate/viterbi.yaml new file mode 100644 index 0000000..9c88bee --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/config/generate/viterbi.yaml @@ -0,0 +1,21 @@ +# @package _group_ + +fairseq: + task: + _name: unpaired_audio_text + labels: phn + data: ??? + sort_by_length: false + shuffle: false + text_data: '' + + common_eval: + path: ??? + quiet: true + + dataset: + gen_subset: valid + batch_size: 1 + +w2l_decoder: VITERBI +post_process: silence diff --git a/fairseq/examples/wav2vec/unsupervised/config/timit_matched/test.uid b/fairseq/examples/wav2vec/unsupervised/config/timit_matched/test.uid new file mode 100644 index 0000000..4010082 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/config/timit_matched/test.uid @@ -0,0 +1,192 @@ +FDHC0_SI1559 +FDHC0_SI2189 +FDHC0_SI929 +FDHC0_SX119 +FDHC0_SX209 +FDHC0_SX29 +FDHC0_SX299 +FDHC0_SX389 +FELC0_SI1386 +FELC0_SI2016 +FELC0_SI756 +FELC0_SX126 +FELC0_SX216 +FELC0_SX306 +FELC0_SX36 +FELC0_SX396 +FJLM0_SI1043 +FJLM0_SI1673 +FJLM0_SI2303 +FJLM0_SX143 +FJLM0_SX233 +FJLM0_SX323 +FJLM0_SX413 +FJLM0_SX53 +FMGD0_SI1564 +FMGD0_SI2194 +FMGD0_SI934 +FMGD0_SX124 +FMGD0_SX214 +FMGD0_SX304 +FMGD0_SX34 +FMGD0_SX394 +FMLD0_SI2185 +FMLD0_SI822 +FMLD0_SI925 +FMLD0_SX115 +FMLD0_SX205 +FMLD0_SX25 +FMLD0_SX295 +FMLD0_SX385 +FNLP0_SI1308 +FNLP0_SI1938 +FNLP0_SI678 +FNLP0_SX138 +FNLP0_SX228 +FNLP0_SX318 +FNLP0_SX408 +FNLP0_SX48 +FPAS0_SI1272 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+MWAR0_SI1045 +MWDK0_SI1436 +MWEM0_SX420 +MWRE0_SA2 +MWRE0_SI1057 +MWRE0_SX67 +MWRP0_SI1443 +MWSB0_SI996 +MWSH0_SI1426 +MWSH0_SI796 +MWSH0_SX166 diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/README.md b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/README.md new file mode 100644 index 0000000..314984f --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/README.md @@ -0,0 +1,56 @@ +# Self-Training with Kaldi HMM Models +This folder contains recipes for self-training on pseudo phone transcripts and +decoding into phones or words with [kaldi](https://github.com/kaldi-asr/kaldi). + +To start, download and install kaldi follow its instruction, and place this +folder in `path/to/kaldi/egs`. + +## Training +Assuming the following has been prepared: +- `w2v_dir`: contains features `{train,valid}.{npy,lengths}`, real transcripts `{train,valid}.${label}`, and dict `dict.${label}.txt` +- `lab_dir`: contains pseudo labels `{train,valid}.txt` +- `arpa_lm`: Arpa-format n-gram phone LM for decoding +- `arpa_lm_bin`: Arpa-format n-gram phone LM for unsupervised model selection to be used with KenLM + +Set these variables in `train.sh`, as well as `out_dir`, the output directory, +and then run it. + +The output will be: +``` +==== WER w.r.t. real transcript (select based on unsupervised metric) +INFO:root:./out/exp/mono/decode_valid/scoring/14.0.0.tra.txt: score 0.9178 wer 28.71% lm_ppl 24.4500 gt_wer 25.57% +INFO:root:./out/exp/tri1/decode_valid/scoring/17.1.0.tra.txt: score 0.9257 wer 26.99% lm_ppl 30.8494 gt_wer 21.90% +INFO:root:./out/exp/tri2b/decode_valid/scoring/8.0.0.tra.txt: score 0.7506 wer 23.15% lm_ppl 25.5944 gt_wer 15.78% +``` +where `wer` is the word eror rate with respect to the pseudo label, `gt_wer` to +the ground truth label, `lm_ppl` the language model perplexity of HMM prediced +transcripts, and `score` is the unsupervised metric for model selection. We +choose the model and the LM parameter of the one with the lowest score. In the +example above, it is `tri2b`, `8.0.0`. + + +## Decoding into Phones +In `decode_phone.sh`, set `out_dir` the same as used in `train.sh`, set +`dec_exp` and `dec_lmparam` to the selected model and LM parameter (e.g. +`tri2b` and `8.0.0` in the above example). `dec_script` needs to be set +according to `dec_exp`: for mono/tri1/tri2b, use `decode.sh`; for tri3b, use +`decode_fmllr.sh`. + +The output will be saved at `out_dir/dec_data` + + +## Decoding into Words +`decode_word_step1.sh` prepares WFSTs for word decoding. Besides the variables +mentioned above, set +- `wrd_arpa_lm`: Arpa-format n-gram word LM for decoding +- `wrd_arpa_lm_bin`: Arpa-format n-gram word LM for unsupervised model selection + +`decode_word_step1.sh` decodes the `train` and `valid` split into word and runs +unsupervised model selection using the `valid` split. The output is like: +``` +INFO:root:./out/exp/tri2b/decodeword_valid/scoring/17.0.0.tra.txt: score 1.8693 wer 24.97% lm_ppl 1785.5333 gt_wer 31.45% +``` + +After determining the LM parameter (`17.0.0` in the example above), set it in +`decode_word_step2.sh` and run it. The output will be saved at +`out_dir/dec_data_word`. diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh new file mode 100644 index 0000000..e749531 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh @@ -0,0 +1,15 @@ +# you can change cmd.sh depending on what type of queue you are using. +# If you have no queueing system and want to run on a local machine, you +# can change all instances 'queue.pl' to run.pl (but be careful and run +# commands one by one: most recipes will exhaust the memory on your +# machine). queue.pl works with GridEngine (qsub). slurm.pl works +# with slurm. Different queues are configured differently, with different +# queue names and different ways of specifying things like memory; +# to account for these differences you can create and edit the file +# conf/queue.conf to match your queue's configuration. Search for +# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information, +# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl. + +export train_cmd="run.pl --mem 2G" +export decode_cmd="run.pl --mem 4G" +export mkgraph_cmd="run.pl --mem 8G" diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_phone.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_phone.sh new file mode 100644 index 0000000..947342a --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_phone.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# decode into phones (and prepare a new data directory for HMM outputs) + +. ./path.sh + +set -eu + +out_dir= # same as in train.sh +dec_lmparam= # LM hyperparameters (e.g., 7.0.0) +dec_exp= +dec_script= +dec_splits="train valid" +dec_data_dir=$out_dir/dec_data # where to write HMM output + +data_dir=${out_dir}/data + +local/decode.sh --nj 40 --graph_name graph \ + --val_sets "$dec_splits" --decode_script $dec_script \ + $out_dir/exp/$dec_exp $data_dir $data_dir/lang_test + +if [ ! -z $dec_lmparam ]; then + for x in $dec_splits; do + mkdir -p $dec_data_dir/$x + cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $dec_data_dir/$x/ + + tra=$out_dir/exp/$dec_exp/decode_${x}/scoring/${dec_lmparam}.tra + cat $tra | utils/int2sym.pl -f 2- $data_dir/lang/words.txt | \ + sed 's:<UNK>::g' | sed 's:<SIL>::g' > $dec_data_dir/${x}/text + utils/fix_data_dir.sh $dec_data_dir/${x} + echo "WER on ${x} is" $(compute-wer ark:$data_dir/${x}_gt/text ark:$dec_data_dir/$x/text | cut -d" " -f2-) + done +fi diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh new file mode 100644 index 0000000..c1276bb --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# prepare word WFSTs, reference data, and decode + +set -eu + +w2v_dir= # same as in train.sh +out_dir= # same as in train.sh +lexicon= # word to phone mapping +wrd_arpa_lm= # word LM +wrd_arpa_lm_bin= # word LM for KenLM, used in unsupervised selection + +dec_exp= # what HMM stage to decode (e.g., tri3b) +dec_script= # what decoding script to use (e.g., steps/decode_fmllr.sh) +phn_label=phnc +wrd_label=wrd +dec_suffix=word +dec_splits="train valid" +valid_split="valid" + +data_dir=$out_dir/data +wrd_data_dir=$out_dir/data_word + +lexicon_clean=$(mktemp) +cat $lexicon | sort | uniq > $lexicon_clean +local/prepare_lang_word.sh $w2v_dir/dict.${phn_label}.txt $data_dir $lexicon_clean && rm $lexicon_clean +local/prepare_lm.sh --langdir $data_dir/lang_word --lmdir $data_dir/lang_test_word $wrd_arpa_lm $data_dir + +for x in $dec_splits; do + x_gt=${x}_gt + mkdir -p $wrd_data_dir/$x_gt + cp $data_dir/$x_gt/{feats.scp,cmvn.scp,utt2spk,spk2utt} $wrd_data_dir/$x_gt/ + python local/copy_aligned_text.py < $w2v_dir/$x.$wrd_label > $wrd_data_dir/$x_gt/text +done + +local/decode.sh --nj 40 --graph_name graph${dec_suffix} --decode_suffix $dec_suffix \ + --val_sets "$dec_splits" --decode_script $dec_script \ + $out_dir/exp/$dec_exp $data_dir $data_dir/lang_test_word + +local/unsup_select_decode_word.sh \ + --split $valid_split --kenlm_path $wrd_arpa_lm_bin \ + --ref_txt $wrd_data_dir/${valid_split}_gt/text \ + --psd_txt $data_dir/${valid_split}/text \ + --dec_name decode${dec_suffix} --graph_name graph${dec_suffix} \ + --phonemize_lexicon $data_dir/local/dict_word/lexicon.txt \ + $out_dir/exp diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh new file mode 100644 index 0000000..59a6cbb --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +# prepare a new data directory of HMM word output + +. ./path.sh + +set -eu + +out_dir= # same as in train.sh +dec_lmparam= # LM hyperparameters (e.g., 7.0.0) + +dec_exp=tri3b # what HMM stage to decode (e.g., tri3b) +dec_suffix=word +dec_splits="train valid" +dec_data_dir=$out_dir/dec_data_word # where to write HMM output + +data_dir=$out_dir/data +wrd_data_dir=$out_dir/data_word + +for x in $dec_splits; do + mkdir -p $dec_data_dir/$x + cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $dec_data_dir/$x/ + + tra=$out_dir/exp/$dec_exp/decode${dec_suffix}_${x}/scoring/${dec_lmparam}.tra + cat $tra | utils/int2sym.pl -f 2- $data_dir/lang_word/words.txt | \ + sed 's:<UNK>::g' | sed 's:<SIL>::g' > $dec_data_dir/$x/text + utils/fix_data_dir.sh $dec_data_dir/$x + echo "WER on $x is" $(compute-wer ark:$wrd_data_dir/${x}_gt/text ark:$dec_data_dir/$x/text | cut -d" " -f2-) +done + diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py new file mode 100644 index 0000000..5f4faa9 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py @@ -0,0 +1,4 @@ +import sys + +for idx, line in enumerate(sys.stdin): + print(f"utt{idx:010d} {line}", end='') \ No newline at end of file diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh new file mode 100644 index 0000000..811cb63 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +set -u + +val_sets="dev_other" +graph_name=graph +decode_suffix="" +decode_script="steps/decode_fmllr.sh" +decode_args="" +nj=60 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +set -x +exp_dir=$1 +data_root=$2 +lang_test=$3 + +graph=$exp_dir/$graph_name + +if [ ! -d $graph ]; then + utils/mkgraph.sh $lang_test $exp_dir $graph +fi + +for part in $val_sets; do + dec_dir=$exp_dir/decode${decode_suffix}_${part} + if [ ! -d $dec_dir ]; then + echo "decoding $part for $exp_dir" + $decode_script --nj $nj --cmd "$decode_cmd" $decode_args \ + $graph $data_root/$part $dec_dir & + else + echo "$dec_dir exists. skip" + fi +done + +wait diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_data_from_w2v.py b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_data_from_w2v.py new file mode 100644 index 0000000..66954ea --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_data_from_w2v.py @@ -0,0 +1,56 @@ +import kaldi_io +import numpy as np +import os + + +def get_parser(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("w2v_dir", help="wav2vec feature and text directory") + parser.add_argument("tar_root", help="output data directory in kaldi's format") + parser.add_argument("split", help="name of the subset") + parser.add_argument("--label", default="", help="if specified, copy labels too") + return parser + +def main(): + parser = get_parser() + args = parser.parse_args() + + tar_dir = os.path.join(args.tar_root, args.split) + os.makedirs(tar_dir, exist_ok=True) + + lengths_path = os.path.join(args.w2v_dir, f"{args.split}.lengths") + with open(lengths_path) as f: + lengths = [int(line.rstrip()) for line in f] + offsets = [0] + np.cumsum(lengths[:-1]).tolist() + feats = np.load( + os.path.join(args.w2v_dir, f"{args.split}.npy"), + mmap_mode="r" + ) + assert feats.shape[0] == sum(lengths), \ + f"lengths mismatch {feats.shape[0]} != {sum(lengths)}" + + ark_path = os.path.join(tar_dir, "feats.ark") + scp_path = os.path.join(tar_dir, "feats.scp") + wspec = f"ark:| copy-feats --compress=true ark:- ark,scp:{ark_path},{scp_path}" + with kaldi_io.open_or_fd(wspec, "wb") as f: + for idx, (offset, length) in enumerate(zip(offsets, lengths)): + feat = feats[offset:offset+length] + kaldi_io.write_mat(f, feat, key=f"utt{idx:010d}") + + u2s_path = os.path.join(tar_dir, "utt2spk") + s2u_path = os.path.join(tar_dir, "spk2utt") + with open(u2s_path, "w") as f_u2s, open(s2u_path, "w") as f_s2u: + for idx in range(len(lengths)): + f_u2s.write(f"utt{idx:010d} utt{idx:010d}\n") + f_s2u.write(f"utt{idx:010d} utt{idx:010d}\n") + + if bool(args.label): + lab_path = os.path.join(args.w2v_dir, f"{args.split}.{args.label}") + txt_path = os.path.join(tar_dir, "text") + with open(lab_path) as f_lab, open(txt_path, "w") as f_txt: + for idx, line in enumerate(f_lab): + f_txt.write(f"utt{idx:010d} {line}") + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh new file mode 100644 index 0000000..e9a8000 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +sil_prob=0.5 +num_sil_states=3 +num_nonsil_states=1 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +set -eux + +dict=$1 +data_dir=$2 + +dict_dir=$data_dir/local/dict +tmplm_dir=$data_dir/local/lang_tmp +lm_dir=$data_dir/lang + +mkdir -p $dict_dir $tmplm_dir $lm_dir + +# prepare dict +echo "SIL" > $dict_dir/silence_phones.txt +echo "SIL" > $dict_dir/optional_silence.txt +awk '{print $1}' $dict > $dict_dir/nonsilence_phones.txt + +echo "SIL SIL" > $dict_dir/lexicon.txt +echo "<UNK> SIL" >> $dict_dir/lexicon.txt +awk '{print $1" "$1}' $dict >> $dict_dir/lexicon.txt + +echo "SIL" > $dict_dir/extra_questions.txt +awk '{printf $1" "} END {printf "\n"}' $dict >> $dict_dir/extra_questions.txt + +# prepare lang +utils/prepare_lang.sh --sil-prob $sil_prob --position-dependent-phones false \ + --num_sil_states $num_sil_states --num_nonsil_states $num_nonsil_states \ + $dict_dir "<UNK>" $tmplm_dir $lm_dir diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang_word.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang_word.sh new file mode 100644 index 0000000..a7ea387 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang_word.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +num_sil_states=3 +num_nonsil_states=1 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +set -eux + +dict=$1 +data_dir=$2 +lexicon=$3 + +dict_dir=$data_dir/local/dict_word +tmplm_dir=$data_dir/local/lang_tmp_word +lm_dir=$data_dir/lang_word + +mkdir -p $dict_dir $tmplm_dir $lm_dir + +# prepare dict +echo "SIL" > $dict_dir/silence_phones.txt +echo "SIL" > $dict_dir/optional_silence.txt +awk '{print $1}' $dict > $dict_dir/nonsilence_phones.txt + +(echo "!SIL SIL"; echo "<UNK> SIL";) | cat - $lexicon > $dict_dir/lexicon.txt + +echo "SIL" > $dict_dir/extra_questions.txt +awk '{printf $1" "} END {printf "\n"}' $dict >> $dict_dir/extra_questions.txt + +# prepare lang +utils/prepare_lang.sh --position-dependent-phones false \ + --num_sil_states $num_sil_states --num_nonsil_states $num_nonsil_states \ + $dict_dir "<UNK>" $tmplm_dir $lm_dir diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh new file mode 100644 index 0000000..c2edcef --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash + +langdir="" +lmdir="" + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +arpa_lm=$1 +data=$2 + +if [ -z $langdir ]; then + langdir=$data/lang +fi +if [ -z $lmdir ]; then + lmdir=$data/lang_test +fi + +if [ ! -d $langdir ]; then + echo "$langdir not found. run local/prepare_lang.sh first" && exit 1 +fi + +mkdir -p $lmdir +cp -r $langdir/* $lmdir + +if [[ "$arpa_lm" == *.gz ]]; then + gunzip -c $arpa_lm | arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt - $lmdir/G.fst +else + arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt $arpa_lm $lmdir/G.fst +fi +fstisstochastic $lmdir/G.fst +utils/validate_lang.pl $lmdir || exit 1 + +echo "done preparing lm ($lmdir)" diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh new file mode 100644 index 0000000..cb5bbb7 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh @@ -0,0 +1,63 @@ +#!/usr/bin/env bash +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey) +# 2014 Guoguo Chen +# Apache 2.0 + +[ -f ./path.sh ] && . ./path.sh + +# begin configuration section. +cmd=run.pl +stage=0 +decode_mbr=true +word_ins_penalty=0.0,0.5,1.0 +min_lmwt=7 +max_lmwt=17 +iter=final +#end configuration section. + +[ -f ./path.sh ] && . ./path.sh +. parse_options.sh || exit 1; + +if [ $# -ne 3 ]; then + echo "Usage: local/score.sh [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>" + echo " Options:" + echo " --cmd (run.pl|queue.pl...) # specify how to run the sub-processes." + echo " --stage (0|1|2) # start scoring script from part-way through." + echo " --decode_mbr (true/false) # maximum bayes risk decoding (confusion network)." + echo " --min_lmwt <int> # minumum LM-weight for lattice rescoring " + echo " --max_lmwt <int> # maximum LM-weight for lattice rescoring " + exit 1; +fi + +data=$1 +lang_or_graph=$2 +dir=$3 + +symtab=$lang_or_graph/words.txt + +for f in $symtab $dir/lat.1.gz $data/text; do + [ ! -f $f ] && echo "score.sh: no such file $f" && exit 1; +done + +mkdir -p $dir/scoring/log + +cat $data/text | sed 's:<NOISE>::g' | sed 's:<SPOKEN_NOISE>::g' > $dir/scoring/test_filt.txt + +for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do + $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/best_path.LMWT.$wip.log \ + lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \ + lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \ + lattice-best-path --word-symbol-table=$symtab \ + ark:- ark,t:$dir/scoring/LMWT.$wip.tra || exit 1; +done + +# Note: the double level of quoting for the sed command +for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do + $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/score.LMWT.$wip.log \ + cat $dir/scoring/LMWT.$wip.tra \| \ + utils/int2sym.pl -f 2- $symtab \| sed 's:\<UNK\>::g' \| \ + compute-wer --text --mode=present \ + ark:$dir/scoring/test_filt.txt ark,p:- ">&" $dir/wer_LMWT_$wip || exit 1; +done + +exit 0; diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh new file mode 100644 index 0000000..9ecf169 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh @@ -0,0 +1,52 @@ +#!/bin/bash + +split="dev_other" +ref_data="" +get_best_wer=true +dec_name="decode" +graph_name="graph" + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +exp_root=$1 + +set -eu + +echo "==== WER w.r.t. pseudo transcript" +for x in $exp_root/*/${dec_name}_${split}*; do grep WER $x/wer_* 2>/dev/null | utils/best_wer.sh; done + + +if [ ! -z $ref_data ]; then + echo "==== WER w.r.t. real transcript (select based on pseudo WER)" + ref_txt=$ref_data/$split/text + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + lmwt=$( + grep WER $x/wer_* 2>/dev/null | utils/best_wer.sh | + sed 's/.*wer_\(.*\)$/\1/g' | sed 's/_/./g' + ) + tra=$x/scoring/$lmwt.tra + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:<UNK>::g' | sed 's:<SIL>::g' | \ + compute-wer --text --mode=present \ + ark:$ref_txt ark,p:- 2> /dev/null | grep WER | xargs -I{} echo {} $tra + done +fi + +if [ ! -z $ref_data ] && $get_best_wer; then + echo "==== WER w.r.t. real transcript (select based on true WER)" + ref_txt=$ref_data/$split/text + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + for tra in $x/scoring/*.tra; do + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:<UNK>::g' | sed 's:<SIL>::g' | \ + compute-wer --text --mode=present \ + ark:$ref_txt ark,p:- 2> /dev/null | grep WER | xargs -I{} echo {} $tra + done | sort -k2n | head -n1 + done +fi + +exit 0; diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/train_subset_lgbeam.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/train_subset_lgbeam.sh new file mode 100644 index 0000000..913c1d8 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/train_subset_lgbeam.sh @@ -0,0 +1,129 @@ +#!/usr/bin/env bash + +out_root=/tmp +out_name=train_${RANDOM} +num_nonsil_states=1 + +valid="dev_other" +train="train" +mono_size="-1" # 2000 +tri1_size="-1" # 5000 +tri2b_size="-1" # 10000 +tri3b_size="-1" # 10000 + +# Acoustic model parameters +numLeavesTri1=2000 +numGaussTri1=10000 +numLeavesMLLT=2500 +numGaussMLLT=15000 +numLeavesSAT=2500 +numGaussSAT=15000 + +stage=1 +max_stage=1 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +data=$1 +lang=$2 +lang_test=$3 + +exp_root=$out_root/$out_name + +# you might not want to do this for interactive shells. +set -e + + +if [ $stage -le 1 ] && [ $max_stage -ge 1 ]; then + # train a monophone system + if [ ! $mono_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $mono_size $data/${train}_${mono_size} + mono_train=${train}_${mono_size} + else + mono_train=${train} + fi + + steps/train_mono.sh --boost-silence 1.25 --nj 20 --cmd "$train_cmd" \ + --initial-beam 40 --regular-beam 60 --retry-beam 120 \ + $data/$mono_train $lang $exp_root/mono + + utils/mkgraph.sh $lang_test $exp_root/mono $exp_root/mono/graph + steps/decode.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/mono/graph $data/$valid $exp_root/mono/decode_$valid & +fi + + +if [ $stage -le 2 ] && [ $max_stage -ge 2 ]; then + # train a first delta + delta-delta triphone system on a subset of 5000 utterances + if [ ! $tri1_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $tri1_size $data/${train}_${tri1_size} + tri1_train=${train}_${tri1_size} + else + tri1_train=${train} + fi + + steps/align_si.sh --boost-silence 1.25 --nj 10 --cmd "$train_cmd" \ + $data/$tri1_train $lang \ + $exp_root/mono $exp_root/mono_ali_${tri1_train} + + steps_gan/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \ + --num_nonsil_states $num_nonsil_states $numLeavesTri1 $numGaussTri1 \ + $data/$tri1_train $lang \ + $exp_root/mono_ali_${tri1_train} $exp_root/tri1 + + utils/mkgraph.sh $lang_test $exp_root/tri1 $exp_root/tri1/graph + steps/decode.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/tri1/graph $data/$valid $exp_root/tri1/decode_$valid & +fi + +if [ $stage -le 3 ] && [ $max_stage -ge 3 ]; then + # train an LDA+MLLT system. + if [ ! $tri2b_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $tri2b_size $data/${train}_${tri2b_size} + tri2b_train=${train}_${tri2b_size} + else + tri2b_train=${train} + fi + + steps/align_si.sh --nj 10 --cmd "$train_cmd" \ + $data/$tri2b_train $lang \ + $exp_root/tri1 $exp_root/tri1_ali_${tri2b_train} + + steps_gan/train_lda_mllt.sh --cmd "$train_cmd" \ + --num_nonsil_states $num_nonsil_states \ + --splice-opts "--left-context=3 --right-context=3" $numLeavesMLLT $numGaussMLLT \ + $data/$tri2b_train $lang \ + $exp_root/tri1_ali_${tri2b_train} $exp_root/tri2b + + utils/mkgraph.sh $lang_test $exp_root/tri2b $exp_root/tri2b/graph + steps/decode.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/tri2b/graph $data/$valid $exp_root/tri2b/decode_$valid & +fi + + +if [ $stage -le 4 ] && [ $max_stage -ge 4 ]; then + # Train tri3b, which is LDA+MLLT+SAT on 10k utts + if [ ! $tri3b_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $tri3b_size $data/${train}_${tri3b_size} + tri3b_train=${train}_${tri3b_size} + else + tri3b_train=${train} + fi + + steps/align_si.sh --nj 10 --cmd "$train_cmd" --use-graphs true \ + $data/$tri3b_train $lang \ + $exp_root/tri2b $exp_root/tri2b_ali_${tri2b_train} + + steps_gan/train_sat.sh --cmd "$train_cmd" \ + --num_nonsil_states $num_nonsil_states $numLeavesSAT $numGaussSAT \ + $data/$tri3b_train $lang \ + $exp_root/tri2b_ali_${tri2b_train} $exp_root/tri3b + + utils/mkgraph.sh $lang_test $exp_root/tri3b $exp_root/tri3b/graph + steps/decode_fmllr.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/tri3b/graph $data/$valid $exp_root/tri3b/decode_$valid & +fi + +wait diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py new file mode 100644 index 0000000..1122c88 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py @@ -0,0 +1,135 @@ +""" +Implement unsupervised metric for decoding hyperparameter selection: + $$ alpha * LM_PPL + ViterbitUER(%) * 100 $$ +""" +import argparse +import logging +import math +import sys + +import kenlm +import editdistance +from g2p_en import G2p + +logging.root.setLevel(logging.INFO) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) +logger = logging.getLogger(__name__) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("ref_tra", help="reference pseudo labels") + parser.add_argument("hyp_tra", help="decoded pseudo labels to be assess") + parser.add_argument("--kenlm_path", default="/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o5.bin", help="") + parser.add_argument("--uppercase", action="store_true", help="") + parser.add_argument("--skipwords", default="", help="") + parser.add_argument("--gt_tra", default="", help="ground truth pseudo labels for computing oracle WER") + parser.add_argument("--min_vt_uer", default=0.0, type=float) + parser.add_argument("--phonemize", action="store_true", help="phonemize word hypotheses, used when reference is phone transcript") + parser.add_argument("--phonemize_lexicon", default="", type=str, help="use a lexicon for phonemizing") + return parser + +def load_tra(tra_path): + with open(tra_path, "r") as f: + uid_to_tra = {} + for line in f: + toks = line.rstrip().split() + uid, tra = toks[0], " ".join(toks[1:]) + uid_to_tra[uid] = tra + logger.debug(f"loaded {len(uid_to_tra)} utterances from {tra_path}") + return uid_to_tra + +def load_lex(lex_path): + with open(lex_path, "r") as f: + w2p = {} + for line in f: + w, p = line.rstrip().split(None, 1) + w2p[w] = p.split() + return w2p + +def compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p, g2p_dict): + d_cnt = 0 + w_cnt = 0 + w_cnt_h = 0 + for uid in hyp_uid_to_tra: + ref = ref_uid_to_tra[uid].split() + if g2p_dict is not None: + hyp = [] + for word in hyp_uid_to_tra[uid].split(): + if word in g2p_dict: + hyp = hyp + g2p_dict[word] + else: + logger.warning(f"{word} not in g2p_dict") + elif g2p is not None: + hyp = g2p(hyp_uid_to_tra[uid]) + hyp = [p for p in hyp if p != "'" and p != " "] + hyp = [p[:-1] if p[-1].isnumeric() else p for p in hyp] + else: + hyp = hyp_uid_to_tra[uid].split() + logger.debug(( + f"======================\n" + f"HYP: {' '.join(hyp)}\n" + f"REF: {' '.join(ref)}" + )) + d_cnt += editdistance.eval(ref, hyp) + w_cnt += len(ref) + w_cnt_h += len(hyp) + wer = float(d_cnt) / w_cnt + logger.debug(( + f"wer = {wer*100:.2f}%; num. of ref words = {w_cnt}; " + f"num. of hyp words = {w_cnt_h}; num. of sentences = {len(ref_uid_to_tra)}" + )) + return wer + +def compute_lm_ppl(hyp_uid_to_tra, score_fn): + lm_score = 0. + w_cnt = 0 + for hyp in hyp_uid_to_tra.values(): + cur_score = score_fn(hyp) + cur_cnt = len(hyp.split()) + 1 # plus one for </s> + lm_score += cur_score + w_cnt += cur_cnt + logger.debug(( + f"======================\n" + f"score sum/avg = {cur_score:.2f}/{cur_score/cur_cnt:.2f}\n" + f"hyp = {hyp}" + )) + lm_ppl = math.pow(10, -lm_score / w_cnt) + logger.debug(f"lm ppl = {lm_ppl:.2f}; num. of words = {w_cnt}") + return lm_ppl + +def main(): + args = get_parser().parse_args() + logger.debug(f"Args: {args}") + + ref_uid_to_tra = load_tra(args.ref_tra) + hyp_uid_to_tra = load_tra(args.hyp_tra) + assert not bool(set(hyp_uid_to_tra.keys()) - set(ref_uid_to_tra.keys())) + + lm = kenlm.Model(args.kenlm_path) + skipwords = set(args.skipwords.split(",")) + def compute_lm_score(s): + s = " ".join(w for w in s.split() if w not in skipwords) + s = s.upper() if args.uppercase else s + return lm.score(s) + + g2p, g2p_dict = None, None + if args.phonemize: + if args.phonemize_lexicon: + g2p_dict = load_lex(args.phonemize_lexicon) + else: + g2p = G2p() + + wer = compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p, g2p_dict) + lm_ppl = compute_lm_ppl(hyp_uid_to_tra, compute_lm_score) + + gt_wer = -math.inf + if args.gt_tra: + gt_uid_to_tra = load_tra(args.gt_tra) + gt_wer = compute_wer(gt_uid_to_tra, hyp_uid_to_tra, None, None) + + score = math.log(lm_ppl) * max(wer, args.min_vt_uer) + logging.info(f"{args.hyp_tra}: score={score:.4f}; wer={wer*100:.2f}%; lm_ppl={lm_ppl:.4f}; gt_wer={gt_wer*100:.2f}%") + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode.sh new file mode 100644 index 0000000..b34c5b6 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +split="dev_other" +ref_txt="" # ground truth transcript path +psd_txt="" # pseudo transcript path +get_best_wer=true +dec_name="decode" +graph_name="graph" +kenlm_path=/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o6.bin + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +exp_root=$1 +unsup_args="" +if [ $# -ge 2 ]; then + unsup_args=$2 +fi + +set -eu + +if [ ! -z $ref_txt ] && $get_best_wer; then + echo "==== WER w.r.t. real transcript (select based on unsupervised metric)" + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + ( + for tra in $x/scoring/*.tra; do + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:<UNK>::g' | sed 's:<SIL>::g' > $tra.txt + python local/unsup_select.py $psd_txt $tra.txt --kenlm_path $kenlm_path --gt_tra $ref_txt $unsup_args + done 2>/dev/null | grep "score=" | sed 's/=/ /g' | sed 's/;//g' | sort -k3n | head -n1 + ) & + done +fi +wait + diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh new file mode 100644 index 0000000..c10a6b8 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +split="dev_other" +ref_txt="" # ground truth transcript path +psd_txt="" # pseudo transcript path +get_best_wer=true +dec_name="decode" +graph_name="graph" +kenlm_path=/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o6.bin +phonemize_lexicon="" + +. ./cmd.sh +. ./path.sh +. parse_options.sh +. /private/home/wnhsu/unsup_asr/fairseq-py-unsup/env.sh + +exp_root=$1 + +set -eu + +if [ ! -z $ref_txt ] && $get_best_wer; then + echo "==== WER w.r.t. real transcript (select based on unsupervised metric)" + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + for tra in $x/scoring/*.tra; do + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:\<UNK\>::g' > $tra.txt + python local/unsup_select.py $psd_txt $tra.txt \ + --kenlm_path $kenlm_path --gt_tra $ref_txt --phonemize \ + --phonemize_lexicon "$phonemize_lexicon" + done | grep "score=" | sed 's/=/ /g' | sed 's/;//g' | sort -k3n | head -n1 + done +fi + + diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/path.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/path.sh new file mode 100644 index 0000000..1a6fb5f --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/path.sh @@ -0,0 +1,5 @@ +export KALDI_ROOT=`pwd`/../../.. +export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH +[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1 +. $KALDI_ROOT/tools/config/common_path.sh +export LC_ALL=C diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps new file mode 100644 index 0000000..6e99bf5 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps @@ -0,0 +1 @@ +../../wsj/s5/steps \ No newline at end of file diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh new file mode 100644 index 0000000..af68715 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh @@ -0,0 +1,175 @@ +#!/usr/bin/env bash + +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey) +# Apache 2.0 + +# Begin configuration. +stage=-4 # This allows restarting after partway, when something when wrong. +config= +cmd=run.pl +scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" +realign_iters="10 20 30"; +num_iters=35 # Number of iterations of training +max_iter_inc=25 # Last iter to increase #Gauss on. +beam=10 +careful=false +retry_beam=40 +boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment +power=0.25 # Exponent for number of gaussians according to occurrence counts +cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves +norm_vars=false # deprecated. Prefer --cmvn-opts "--norm-vars=true" + # use the option --cmvn-opts "--norm-means=false" +cmvn_opts= +delta_opts= +context_opts= # use"--context-width=5 --central-position=2" for quinphone +num_nonsil_states=3 +# End configuration. + +echo "$0 $@" # Print the command line for logging + +[ -f path.sh ] && . ./path.sh; +. parse_options.sh || exit 1; + +if [ $# != 6 ]; then + echo "Usage: steps/train_deltas.sh <num-leaves> <tot-gauss> <data-dir> <lang-dir> <alignment-dir> <exp-dir>" + echo "e.g.: steps/train_deltas.sh 2000 10000 data/train_si84_half data/lang exp/mono_ali exp/tri1" + echo "main options (for others, see top of script file)" + echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs." + echo " --config <config-file> # config containing options" + echo " --stage <stage> # stage to do partial re-run from." + exit 1; +fi + +numleaves=$1 +totgauss=$2 +data=$3 +lang=$4 +alidir=$5 +dir=$6 + +for f in $alidir/final.mdl $alidir/ali.1.gz $data/feats.scp $lang/phones.txt; do + [ ! -f $f ] && echo "train_deltas.sh: no such file $f" && exit 1; +done + +numgauss=$numleaves +incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter increment for #Gauss +oov=`cat $lang/oov.int` || exit 1; +ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1; +nj=`cat $alidir/num_jobs` || exit 1; +mkdir -p $dir/log +echo $nj > $dir/num_jobs + +utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1; +cp $lang/phones.txt $dir || exit 1; + +sdata=$data/split$nj; +split_data.sh $data $nj || exit 1; + + +[ $(cat $alidir/cmvn_opts 2>/dev/null | wc -c) -gt 1 ] && [ -z "$cmvn_opts" ] && \ + echo "$0: warning: ignoring CMVN options from source directory $alidir" +$norm_vars && cmvn_opts="--norm-vars=true $cmvn_opts" +echo $cmvn_opts > $dir/cmvn_opts # keep track of options to CMVN. +[ ! -z $delta_opts ] && echo $delta_opts > $dir/delta_opts + +feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |" + +rm $dir/.error 2>/dev/null + +if [ $stage -le -3 ]; then + echo "$0: accumulating tree stats" + $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \ + acc-tree-stats $context_opts \ + --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \ + "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1; + sum-tree-stats $dir/treeacc $dir/*.treeacc 2>$dir/log/sum_tree_acc.log || exit 1; + rm $dir/*.treeacc +fi + +if [ $stage -le -2 ]; then + echo "$0: getting questions for tree-building, via clustering" + # preparing questions, roots file... + cluster-phones --pdf-class-list=$(($num_nonsil_states / 2)) $context_opts \ + $dir/treeacc $lang/phones/sets.int \ + $dir/questions.int 2> $dir/log/questions.log || exit 1; + cat $lang/phones/extra_questions.int >> $dir/questions.int + compile-questions $context_opts $lang/topo $dir/questions.int \ + $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1; + + echo "$0: building the tree" + $cmd $dir/log/build_tree.log \ + build-tree $context_opts --verbose=1 --max-leaves=$numleaves \ + --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \ + $dir/questions.qst $lang/topo $dir/tree || exit 1; + + $cmd $dir/log/init_model.log \ + gmm-init-model --write-occs=$dir/1.occs \ + $dir/tree $dir/treeacc $lang/topo $dir/1.mdl || exit 1; + if grep 'no stats' $dir/log/init_model.log; then + echo "** The warnings above about 'no stats' generally mean you have phones **" + echo "** (or groups of phones) in your phone set that had no corresponding data. **" + echo "** You should probably figure out whether something went wrong, **" + echo "** or whether your data just doesn't happen to have examples of those **" + echo "** phones. **" + fi + + gmm-mixup --mix-up=$numgauss $dir/1.mdl $dir/1.occs $dir/1.mdl 2>$dir/log/mixup.log || exit 1; + rm $dir/treeacc +fi + +if [ $stage -le -1 ]; then + # Convert the alignments. + echo "$0: converting alignments from $alidir to use current tree" + $cmd JOB=1:$nj $dir/log/convert.JOB.log \ + convert-ali $alidir/final.mdl $dir/1.mdl $dir/tree \ + "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; +fi + +if [ $stage -le 0 ]; then + echo "$0: compiling graphs of transcripts" + $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \ + compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $dir/tree $dir/1.mdl $lang/L.fst \ + "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $sdata/JOB/text |" \ + "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; +fi + +x=1 +while [ $x -lt $num_iters ]; do + echo "$0: training pass $x" + if [ $stage -le $x ]; then + if echo $realign_iters | grep -w $x >/dev/null; then + echo "$0: aligning data" + mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |" + $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \ + gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \ + "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \ + "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; + fi + $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ + gmm-acc-stats-ali $dir/$x.mdl "$feats" \ + "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1; + $cmd $dir/log/update.$x.log \ + gmm-est --mix-up=$numgauss --power=$power \ + --write-occs=$dir/$[$x+1].occs $dir/$x.mdl \ + "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1; + rm $dir/$x.mdl $dir/$x.*.acc + rm $dir/$x.occs + fi + [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss]; + x=$[$x+1]; +done + +rm $dir/final.mdl $dir/final.occs 2>/dev/null +ln -s $x.mdl $dir/final.mdl +ln -s $x.occs $dir/final.occs + +steps/diagnostic/analyze_alignments.sh --cmd "$cmd" $lang $dir + +# Summarize warning messages... +utils/summarize_warnings.pl $dir/log + +steps/info/gmm_dir_info.pl $dir + +echo "$0: Done training system with delta+delta-delta features in $dir" + +exit 0 diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_lda_mllt.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_lda_mllt.sh new file mode 100644 index 0000000..9d8c319 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_lda_mllt.sh @@ -0,0 +1,239 @@ +#!/usr/bin/env bash + +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey) +# +# LDA+MLLT refers to the way we transform the features after computing +# the MFCCs: we splice across several frames, reduce the dimension (to 40 +# by default) using Linear Discriminant Analysis), and then later estimate, +# over multiple iterations, a diagonalizing transform known as MLLT or STC. +# See http://kaldi-asr.org/doc/transform.html for more explanation. +# +# Apache 2.0. + +# Begin configuration. +cmd=run.pl +config= +stage=-5 +scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" +realign_iters="10 20 30"; +mllt_iters="2 4 6 12"; +num_iters=35 # Number of iterations of training +max_iter_inc=25 # Last iter to increase #Gauss on. +dim=40 +beam=10 +retry_beam=40 +careful=false +boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment +power=0.25 # Exponent for number of gaussians according to occurrence counts +randprune=4.0 # This is approximately the ratio by which we will speed up the + # LDA and MLLT calculations via randomized pruning. +splice_opts= +cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves +norm_vars=false # deprecated. Prefer --cmvn-opts "--norm-vars=false" +cmvn_opts= +context_opts= # use "--context-width=5 --central-position=2" for quinphone. +# End configuration. +train_tree=true # if false, don't actually train the tree. +use_lda_mat= # If supplied, use this LDA[+MLLT] matrix. +num_nonsil_states=3 + +echo "$0 $@" # Print the command line for logging + +[ -f path.sh ] && . ./path.sh +. parse_options.sh || exit 1; + +if [ $# != 6 ]; then + echo "Usage: steps/train_lda_mllt.sh [options] <#leaves> <#gauss> <data> <lang> <alignments> <dir>" + echo " e.g.: steps/train_lda_mllt.sh 2500 15000 data/train_si84 data/lang exp/tri1_ali_si84 exp/tri2b" + echo "Main options (for others, see top of script file)" + echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs." + echo " --config <config-file> # config containing options" + echo " --stage <stage> # stage to do partial re-run from." + exit 1; +fi + +numleaves=$1 +totgauss=$2 +data=$3 +lang=$4 +alidir=$5 +dir=$6 + +for f in $alidir/final.mdl $alidir/ali.1.gz $data/feats.scp $lang/phones.txt; do + [ ! -f $f ] && echo "train_lda_mllt.sh: no such file $f" && exit 1; +done + +numgauss=$numleaves +incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter #gauss increment +oov=`cat $lang/oov.int` || exit 1; +nj=`cat $alidir/num_jobs` || exit 1; +silphonelist=`cat $lang/phones/silence.csl` || exit 1; +ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1; + +mkdir -p $dir/log + +utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1; +cp $lang/phones.txt $dir || exit 1; + +echo $nj >$dir/num_jobs +echo "$splice_opts" >$dir/splice_opts # keep track of frame-splicing options + # so that later stages of system building can know what they were. + + +[ $(cat $alidir/cmvn_opts 2>/dev/null | wc -c) -gt 1 ] && [ -z "$cmvn_opts" ] && \ + echo "$0: warning: ignoring CMVN options from source directory $alidir" +$norm_vars && cmvn_opts="--norm-vars=true $cmvn_opts" +echo $cmvn_opts > $dir/cmvn_opts # keep track of options to CMVN. + +sdata=$data/split$nj; +split_data.sh $data $nj || exit 1; + +splicedfeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- |" +# Note: $feats gets overwritten later in the script. +feats="$splicedfeats transform-feats $dir/0.mat ark:- ark:- |" + + + +if [ $stage -le -5 ]; then + if [ -z "$use_lda_mat" ]; then + echo "$0: Accumulating LDA statistics." + rm $dir/lda.*.acc 2>/dev/null + $cmd JOB=1:$nj $dir/log/lda_acc.JOB.log \ + ali-to-post "ark:gunzip -c $alidir/ali.JOB.gz|" ark:- \| \ + weight-silence-post 0.0 $silphonelist $alidir/final.mdl ark:- ark:- \| \ + acc-lda --rand-prune=$randprune $alidir/final.mdl "$splicedfeats" ark,s,cs:- \ + $dir/lda.JOB.acc || exit 1; + est-lda --write-full-matrix=$dir/full.mat --dim=$dim $dir/0.mat $dir/lda.*.acc \ + 2>$dir/log/lda_est.log || exit 1; + rm $dir/lda.*.acc + else + echo "$0: Using supplied LDA matrix $use_lda_mat" + cp $use_lda_mat $dir/0.mat || exit 1; + [ ! -z "$mllt_iters" ] && \ + echo "$0: Warning: using supplied LDA matrix $use_lda_mat but we will do MLLT," && \ + echo " which you might not want; to disable MLLT, specify --mllt-iters ''" && \ + sleep 5 + fi +fi + +cur_lda_iter=0 + +if [ $stage -le -4 ] && $train_tree; then + echo "$0: Accumulating tree stats" + $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \ + acc-tree-stats $context_opts \ + --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \ + "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1; + [ `ls $dir/*.treeacc | wc -w` -ne "$nj" ] && echo "$0: Wrong #tree-accs" && exit 1; + $cmd $dir/log/sum_tree_acc.log \ + sum-tree-stats $dir/treeacc $dir/*.treeacc || exit 1; + rm $dir/*.treeacc +fi + + +if [ $stage -le -3 ] && $train_tree; then + echo "$0: Getting questions for tree clustering." + # preparing questions, roots file... + cluster-phones --pdf-class-list=$(($num_nonsil_states / 2)) $context_opts $dir/treeacc $lang/phones/sets.int \ + $dir/questions.int 2> $dir/log/questions.log || exit 1; + cat $lang/phones/extra_questions.int >> $dir/questions.int + compile-questions $context_opts $lang/topo $dir/questions.int \ + $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1; + + echo "$0: Building the tree" + $cmd $dir/log/build_tree.log \ + build-tree $context_opts --verbose=1 --max-leaves=$numleaves \ + --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \ + $dir/questions.qst $lang/topo $dir/tree || exit 1; +fi + +if [ $stage -le -2 ]; then + echo "$0: Initializing the model" + if $train_tree; then + gmm-init-model --write-occs=$dir/1.occs \ + $dir/tree $dir/treeacc $lang/topo $dir/1.mdl 2> $dir/log/init_model.log || exit 1; + grep 'no stats' $dir/log/init_model.log && echo "This is a bad warning."; + rm $dir/treeacc + else + cp $alidir/tree $dir/ || exit 1; + $cmd JOB=1 $dir/log/init_model.log \ + gmm-init-model-flat $dir/tree $lang/topo $dir/1.mdl \ + "$feats subset-feats ark:- ark:-|" || exit 1; + fi +fi + + +if [ $stage -le -1 ]; then + # Convert the alignments. + echo "$0: Converting alignments from $alidir to use current tree" + $cmd JOB=1:$nj $dir/log/convert.JOB.log \ + convert-ali $alidir/final.mdl $dir/1.mdl $dir/tree \ + "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; +fi + +if [ $stage -le 0 ] && [ "$realign_iters" != "" ]; then + echo "$0: Compiling graphs of transcripts" + $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \ + compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $dir/tree $dir/1.mdl $lang/L.fst \ + "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $data/split$nj/JOB/text |" \ + "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; +fi + + +x=1 +while [ $x -lt $num_iters ]; do + echo Training pass $x + if echo $realign_iters | grep -w $x >/dev/null && [ $stage -le $x ]; then + echo Aligning data + mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |" + $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \ + gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \ + "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \ + "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; + fi + if echo $mllt_iters | grep -w $x >/dev/null; then + if [ $stage -le $x ]; then + echo "$0: Estimating MLLT" + $cmd JOB=1:$nj $dir/log/macc.$x.JOB.log \ + ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ + weight-silence-post 0.0 $silphonelist $dir/$x.mdl ark:- ark:- \| \ + gmm-acc-mllt --rand-prune=$randprune $dir/$x.mdl "$feats" ark:- $dir/$x.JOB.macc \ + || exit 1; + est-mllt $dir/$x.mat.new $dir/$x.*.macc 2> $dir/log/mupdate.$x.log || exit 1; + gmm-transform-means $dir/$x.mat.new $dir/$x.mdl $dir/$x.mdl \ + 2> $dir/log/transform_means.$x.log || exit 1; + compose-transforms --print-args=false $dir/$x.mat.new $dir/$cur_lda_iter.mat $dir/$x.mat || exit 1; + rm $dir/$x.*.macc + fi + feats="$splicedfeats transform-feats $dir/$x.mat ark:- ark:- |" + cur_lda_iter=$x + fi + + if [ $stage -le $x ]; then + $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ + gmm-acc-stats-ali $dir/$x.mdl "$feats" \ + "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1; + $cmd $dir/log/update.$x.log \ + gmm-est --write-occs=$dir/$[$x+1].occs --mix-up=$numgauss --power=$power \ + $dir/$x.mdl "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1; + rm $dir/$x.mdl $dir/$x.*.acc $dir/$x.occs + fi + [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss]; + x=$[$x+1]; +done + +rm $dir/final.{mdl,mat,occs} 2>/dev/null +ln -s $x.mdl $dir/final.mdl +ln -s $x.occs $dir/final.occs +ln -s $cur_lda_iter.mat $dir/final.mat + +steps/diagnostic/analyze_alignments.sh --cmd "$cmd" $lang $dir + +# Summarize warning messages... +utils/summarize_warnings.pl $dir/log + +steps/info/gmm_dir_info.pl $dir + +echo "$0: Done training system with LDA+MLLT features in $dir" + +exit 0 diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_sat.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_sat.sh new file mode 100644 index 0000000..f75afaf --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_sat.sh @@ -0,0 +1,281 @@ +#!/usr/bin/env bash +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0. + + +# This does Speaker Adapted Training (SAT), i.e. train on +# fMLLR-adapted features. It can be done on top of either LDA+MLLT, or +# delta and delta-delta features. If there are no transforms supplied +# in the alignment directory, it will estimate transforms itself before +# building the tree (and in any case, it estimates transforms a number +# of times during training). + + +# Begin configuration section. +stage=-5 +exit_stage=-100 # you can use this to require it to exit at the + # beginning of a specific stage. Not all values are + # supported. +fmllr_update_type=full +cmd=run.pl +scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" +beam=10 +retry_beam=40 +careful=false +boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment +context_opts= # e.g. set this to "--context-width 5 --central-position 2" for quinphone. +realign_iters="10 20 30"; +fmllr_iters="2 4 6 12"; +silence_weight=0.0 # Weight on silence in fMLLR estimation. +num_iters=35 # Number of iterations of training +max_iter_inc=25 # Last iter to increase #Gauss on. +power=0.2 # Exponent for number of gaussians according to occurrence counts +cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves +phone_map= +train_tree=true +tree_stats_opts= +cluster_phones_opts= +compile_questions_opts= +# End configuration section. +num_nonsil_states=3 + +echo "$0 $@" # Print the command line for logging + +[ -f path.sh ] && . ./path.sh +. parse_options.sh || exit 1; + +if [ $# != 6 ]; then + echo "Usage: steps/train_sat.sh <#leaves> <#gauss> <data> <lang> <ali-dir> <exp-dir>" + echo " e.g.: steps/train_sat.sh 2500 15000 data/train_si84 data/lang exp/tri2b_ali_si84 exp/tri3b" + echo "Main options (for others, see top of script file)" + echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs." + echo " --config <config-file> # config containing options" + echo " --stage <stage> # stage to do partial re-run from." + exit 1; +fi + +numleaves=$1 +totgauss=$2 +data=$3 +lang=$4 +alidir=$5 +dir=$6 + +for f in $data/feats.scp $lang/phones.txt $alidir/final.mdl $alidir/ali.1.gz; do + [ ! -f $f ] && echo "train_sat.sh: no such file $f" && exit 1; +done + +numgauss=$numleaves +incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter #gauss increment +oov=`cat $lang/oov.int` +nj=`cat $alidir/num_jobs` || exit 1; +silphonelist=`cat $lang/phones/silence.csl` +ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1; +sdata=$data/split$nj; +splice_opts=`cat $alidir/splice_opts 2>/dev/null` # frame-splicing options. +cmvn_opts=`cat $alidir/cmvn_opts 2>/dev/null` +delta_opts=`cat $alidir/delta_opts 2>/dev/null` +phone_map_opt= +[ ! -z "$phone_map" ] && phone_map_opt="--phone-map='$phone_map'" + +mkdir -p $dir/log +cp $alidir/splice_opts $dir 2>/dev/null # frame-splicing options. +cp $alidir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option. +cp $alidir/delta_opts $dir 2>/dev/null # delta option. + +utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1; +cp $lang/phones.txt $dir || exit 1; + +echo $nj >$dir/num_jobs +[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1; + +# Set up features. + +if [ -f $alidir/final.mat ]; then feat_type=lda; else feat_type=delta; fi +echo "$0: feature type is $feat_type" + +## Set up speaker-independent features. +case $feat_type in + delta) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";; + lda) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $alidir/final.mat ark:- ark:- |" + cp $alidir/final.mat $dir + cp $alidir/full.mat $dir 2>/dev/null + ;; + *) echo "$0: invalid feature type $feat_type" && exit 1; +esac + +## Get initial fMLLR transforms (possibly from alignment dir) +if [ -f $alidir/trans.1 ]; then + echo "$0: Using transforms from $alidir" + feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$alidir/trans.JOB ark:- ark:- |" + cur_trans_dir=$alidir +else + if [ $stage -le -5 ]; then + echo "$0: obtaining initial fMLLR transforms since not present in $alidir" + # The next line is necessary because of $silphonelist otherwise being incorrect; would require + # old $lang dir which would require another option. Not needed anyway. + [ ! -z "$phone_map" ] && \ + echo "$0: error: you must provide transforms if you use the --phone-map option." && exit 1; + $cmd JOB=1:$nj $dir/log/fmllr.0.JOB.log \ + ali-to-post "ark:gunzip -c $alidir/ali.JOB.gz|" ark:- \| \ + weight-silence-post $silence_weight $silphonelist $alidir/final.mdl ark:- ark:- \| \ + gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \ + --spk2utt=ark:$sdata/JOB/spk2utt $alidir/final.mdl "$sifeats" \ + ark:- ark:$dir/trans.JOB || exit 1; + fi + feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |" + cur_trans_dir=$dir +fi + +if [ $stage -le -4 ] && $train_tree; then + # Get tree stats. + echo "$0: Accumulating tree stats" + $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \ + acc-tree-stats $context_opts $tree_stats_opts $phone_map_opt --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \ + "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1; + [ "`ls $dir/*.treeacc | wc -w`" -ne "$nj" ] && echo "$0: Wrong #tree-accs" && exit 1; + $cmd $dir/log/sum_tree_acc.log \ + sum-tree-stats $dir/treeacc $dir/*.treeacc || exit 1; + rm $dir/*.treeacc +fi + +if [ $stage -le -3 ] && $train_tree; then + echo "$0: Getting questions for tree clustering." + # preparing questions, roots file... + cluster-phones --pdf-class-list=$(($num_nonsil_states / 2)) \ + $cluster_phones_opts $context_opts \ + $dir/treeacc $lang/phones/sets.int $dir/questions.int 2>$dir/log/questions.log || exit 1; + cat $lang/phones/extra_questions.int >> $dir/questions.int + compile-questions $context_opts $compile_questions_opts $lang/topo $dir/questions.int $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1; + + echo "$0: Building the tree" + $cmd $dir/log/build_tree.log \ + build-tree $context_opts --verbose=1 --max-leaves=$numleaves \ + --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \ + $dir/questions.qst $lang/topo $dir/tree || exit 1; +fi + +if [ $stage -le -2 ]; then + echo "$0: Initializing the model" + if $train_tree; then + gmm-init-model --write-occs=$dir/1.occs \ + $dir/tree $dir/treeacc $lang/topo $dir/1.mdl 2> $dir/log/init_model.log || exit 1; + grep 'no stats' $dir/log/init_model.log && echo "This is a bad warning."; + rm $dir/treeacc + else + cp $alidir/tree $dir/ || exit 1; + $cmd JOB=1 $dir/log/init_model.log \ + gmm-init-model-flat $dir/tree $lang/topo $dir/1.mdl \ + "$feats subset-feats ark:- ark:-|" || exit 1; + fi +fi + +if [ $stage -le -1 ]; then + # Convert the alignments. + echo "$0: Converting alignments from $alidir to use current tree" + $cmd JOB=1:$nj $dir/log/convert.JOB.log \ + convert-ali $phone_map_opt $alidir/final.mdl $dir/1.mdl $dir/tree \ + "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; +fi + +[ "$exit_stage" -eq 0 ] && echo "$0: Exiting early: --exit-stage $exit_stage" && exit 0; + +if [ $stage -le 0 ] && [ "$realign_iters" != "" ]; then + echo "$0: Compiling graphs of transcripts" + $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \ + compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $dir/tree $dir/1.mdl $lang/L.fst \ + "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $sdata/JOB/text |" \ + "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; +fi + +x=1 +while [ $x -lt $num_iters ]; do + echo Pass $x + if echo $realign_iters | grep -w $x >/dev/null && [ $stage -le $x ]; then + echo Aligning data + mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |" + $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \ + gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \ + "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \ + "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; + fi + + if echo $fmllr_iters | grep -w $x >/dev/null; then + if [ $stage -le $x ]; then + echo Estimating fMLLR transforms + # We estimate a transform that's additional to the previous transform; + # we'll compose them. + $cmd JOB=1:$nj $dir/log/fmllr.$x.JOB.log \ + ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ + weight-silence-post $silence_weight $silphonelist $dir/$x.mdl ark:- ark:- \| \ + gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \ + --spk2utt=ark:$sdata/JOB/spk2utt $dir/$x.mdl \ + "$feats" ark:- ark:$dir/tmp_trans.JOB || exit 1; + for n in `seq $nj`; do + ! ( compose-transforms --b-is-affine=true \ + ark:$dir/tmp_trans.$n ark:$cur_trans_dir/trans.$n ark:$dir/composed_trans.$n \ + && mv $dir/composed_trans.$n $dir/trans.$n && \ + rm $dir/tmp_trans.$n ) 2>$dir/log/compose_transforms.$x.log \ + && echo "$0: Error composing transforms" && exit 1; + done + fi + feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |" + cur_trans_dir=$dir + fi + + if [ $stage -le $x ]; then + $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ + gmm-acc-stats-ali $dir/$x.mdl "$feats" \ + "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1; + [ `ls $dir/$x.*.acc | wc -w` -ne "$nj" ] && echo "$0: Wrong #accs" && exit 1; + $cmd $dir/log/update.$x.log \ + gmm-est --power=$power --write-occs=$dir/$[$x+1].occs --mix-up=$numgauss $dir/$x.mdl \ + "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1; + rm $dir/$x.mdl $dir/$x.*.acc + rm $dir/$x.occs + fi + [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss]; + x=$[$x+1]; +done + + +if [ $stage -le $x ]; then + # Accumulate stats for "alignment model"-- this model is + # computed with the speaker-independent features, but matches Gaussian-for-Gaussian + # with the final speaker-adapted model. + $cmd JOB=1:$nj $dir/log/acc_alimdl.JOB.log \ + ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ + gmm-acc-stats-twofeats $dir/$x.mdl "$feats" "$sifeats" \ + ark,s,cs:- $dir/$x.JOB.acc || exit 1; + [ `ls $dir/$x.*.acc | wc -w` -ne "$nj" ] && echo "$0: Wrong #accs" && exit 1; + # Update model. + $cmd $dir/log/est_alimdl.log \ + gmm-est --power=$power --remove-low-count-gaussians=false $dir/$x.mdl \ + "gmm-sum-accs - $dir/$x.*.acc|" $dir/$x.alimdl || exit 1; + rm $dir/$x.*.acc +fi + +rm $dir/final.{mdl,alimdl,occs} 2>/dev/null +ln -s $x.mdl $dir/final.mdl +ln -s $x.occs $dir/final.occs +ln -s $x.alimdl $dir/final.alimdl + + +steps/diagnostic/analyze_alignments.sh --cmd "$cmd" $lang $dir + +utils/summarize_warnings.pl $dir/log +( + echo "$0: Likelihood evolution:" + for x in `seq $[$num_iters-1]`; do + tail -n 30 $dir/log/acc.$x.*.log | awk '/Overall avg like/{l += $(NF-3)*$(NF-1); t += $(NF-1); } + /Overall average logdet/{d += $(NF-3)*$(NF-1); t2 += $(NF-1);} + END{ d /= t2; l /= t; printf("%s ", d+l); } ' + done + echo +) | tee $dir/log/summary.log + + +steps/info/gmm_dir_info.pl $dir + +echo "$0: done training SAT system in $dir" + +exit 0 diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh new file mode 100644 index 0000000..f3a3d3f --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +set -eu + +w2v_dir= # contains features `{train,valid}.{npy,lengths}`, real transcripts `{train,valid}.${label}`, and dict `dict.${label}.txt` +lab_dir= # contains pseudo labels `{train,valid}.txt` +out_dir= # output root +arpa_lm= # phone LM +arpa_lm_bin= # (binary) phone LM for KenLM, used in unsupervised selection + +label=phnc +train_name="train" +valid_name="valid" +data_dir=${out_dir}/data + +mkdir -p ${out_dir}/exp +local/prepare_lang.sh $w2v_dir/dict.${label}.txt $data_dir +local/prepare_lm.sh $arpa_lm $data_dir + +for x in $train_name $valid_name; do + x_gt=${x}_gt + + # prepare pseudo data + python local/prepare_data_from_w2v.py $w2v_dir $data_dir $x + steps/compute_cmvn_stats.sh $data_dir/$x $out_dir/exp/make_feat/$x $out_dir/feats/$x + python local/copy_aligned_text.py < $lab_dir/$x.txt > $data_dir/$x/text + + # prepare ground truth data + mkdir $data_dir/$x_gt + cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $data_dir/$x_gt/ + python local/copy_aligned_text.py < $w2v_dir/$x.$label > $data_dir/$x_gt/text +done + +local/train_subset_lgbeam.sh \ + --out_root ${out_dir} --out_name exp --train $train_name --valid $valid_name \ + --mono_size 2000 --tri1_size 5000 --tri2b_size -1 --tri3b_size -1 \ + --stage 1 --max_stage 3 $data_dir $data_dir/lang $data_dir/lang_test + +local/unsup_select_decode.sh \ + --split $valid_name --kenlm_path $arpa_lm_bin \ + --ref_txt $data_dir/${valid_name}_gt/text \ + --psd_txt $data_dir/${valid_name}/text \ + $out_dir/exp diff --git a/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/utils b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/utils new file mode 100644 index 0000000..b240885 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/utils @@ -0,0 +1 @@ +../../wsj/s5/utils \ No newline at end of file diff --git a/fairseq/examples/wav2vec/unsupervised/models/__init__.py b/fairseq/examples/wav2vec/unsupervised/models/__init__.py new file mode 100644 index 0000000..3e3039b --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/models/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .wav2vec_u import Wav2vec_U + + +__all__ = [ + "Wav2vec_U", +] diff --git a/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py b/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py new file mode 100644 index 0000000..8a1e905 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py @@ -0,0 +1,687 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass +from enum import Enum, auto +import math +import numpy as np +from typing import Tuple, List, Optional, Dict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import autograd + +from fairseq import checkpoint_utils, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.modules import ( + SamePad, + TransposeLast, +) + + +class SegmentationType(Enum): + NONE = auto() + RANDOM = auto() + UNIFORM_RANDOM = auto() + UNIFORM_RANDOM_JOIN = auto() + JOIN = auto() + + +@dataclass +class SegmentationConfig(FairseqDataclass): + type: SegmentationType = SegmentationType.NONE + subsample_rate: float = 0.25 + mean_pool: bool = True + mean_pool_join: bool = False + remove_zeros: bool = False + + +@dataclass +class Wav2vec_UConfig(FairseqDataclass): + discriminator_kernel: int = 3 + discriminator_dilation: int = 1 + discriminator_dim: int = 256 + discriminator_causal: bool = True + discriminator_linear_emb: bool = False + discriminator_depth: int = 1 + discriminator_max_pool: bool = False + discriminator_act_after_linear: bool = False + discriminator_dropout: float = 0.0 + discriminator_spectral_norm: bool = False + discriminator_weight_norm: bool = False + + generator_kernel: int = 4 + generator_dilation: int = 1 + generator_stride: int = 1 + generator_pad: int = -1 + generator_bias: bool = False + generator_dropout: float = 0.0 + generator_batch_norm: int = 0 + generator_residual: bool = False + + blank_weight: float = 0 + blank_mode: str = "add" + blank_is_sil: bool = False + no_softmax: bool = False + + smoothness_weight: float = 0.0 + smoothing: float = 0.0 + smoothing_one_sided: bool = False + gradient_penalty: float = 0.0 + probabilistic_grad_penalty_slicing: bool = False + code_penalty: float = 0.0 + mmi_weight: float = 0.0 + target_dim: int = 64 + target_downsample_rate: int = 2 + gumbel: bool = False + hard_gumbel: bool = True + temp: Tuple[float, float, float] = (2, 0.1, 0.99995) + input_dim: int = 128 + + segmentation: SegmentationConfig = SegmentationConfig() + + +class Segmenter(nn.Module): + cfg: SegmentationConfig + + def __init__(self, cfg: SegmentationConfig): + super().__init__() + self.cfg = cfg + self.subsample_rate = cfg.subsample_rate + + def pre_segment(self, dense_x, dense_padding_mask): + return dense_x, dense_padding_mask + + def logit_segment(self, logits, padding_mask): + return logits, padding_mask + + +class RandomSegmenter(Segmenter): + def pre_segment(self, dense_x, dense_padding_mask): + target_num = math.ceil(dense_x.size(1) * self.subsample_rate) + ones = torch.ones(dense_x.shape[:-1], device=dense_x.device) + indices, _ = ones.multinomial(target_num).sort(dim=-1) + indices_ld = indices.unsqueeze(-1).expand(-1, -1, dense_x.size(-1)) + dense_x = dense_x.gather(1, indices_ld) + dense_padding_mask = dense_padding_mask.gather(1, index=indices) + return dense_x, dense_padding_mask + + +class UniformRandomSegmenter(Segmenter): + def pre_segment(self, dense_x, dense_padding_mask): + bsz, tsz, fsz = dense_x.shape + + target_num = math.ceil(tsz * self.subsample_rate) + + rem = tsz % target_num + + if rem > 0: + dense_x = F.pad(dense_x, [0, 0, 0, target_num - rem]) + dense_padding_mask = F.pad( + dense_padding_mask, [0, target_num - rem], value=True + ) + + dense_x = dense_x.view(bsz, target_num, -1, fsz) + dense_padding_mask = dense_padding_mask.view(bsz, target_num, -1) + + if self.cfg.mean_pool: + dense_x = dense_x.mean(dim=-2) + dense_padding_mask = dense_padding_mask.all(dim=-1) + else: + ones = torch.ones((bsz, dense_x.size(2)), device=dense_x.device) + indices = ones.multinomial(1) + indices = indices.unsqueeze(-1).expand(-1, target_num, -1) + indices_ld = indices.unsqueeze(-1).expand(-1, -1, -1, fsz) + dense_x = dense_x.gather(2, indices_ld).reshape(bsz, -1, fsz) + dense_padding_mask = dense_padding_mask.gather(2, index=indices).reshape( + bsz, -1 + ) + return dense_x, dense_padding_mask + + +class JoinSegmenter(Segmenter): + def logit_segment(self, logits, padding_mask): + preds = logits.argmax(dim=-1) + + if padding_mask.any(): + preds[padding_mask] = -1 # mark pad + uniques = [] + + bsz, tsz, csz = logits.shape + + for p in preds: + uniques.append( + p.cpu().unique_consecutive(return_inverse=True, return_counts=True) + ) + + new_tsz = max(u[0].numel() for u in uniques) + new_logits = logits.new_zeros(bsz, new_tsz, csz) + new_pad = padding_mask.new_zeros(bsz, new_tsz) + + for b in range(bsz): + u, idx, c = uniques[b] + keep = u != -1 + + if self.cfg.remove_zeros: + keep.logical_and_(u != 0) + + if self.training and not self.cfg.mean_pool_join: + u[0] = 0 + u[1:] = c.cumsum(0)[:-1] + m = c > 1 + r = torch.rand(m.sum()) + o = (c[m] * r).long() + u[m] += o + new_logits[b, : u.numel()] = logits[b, u] + else: + new_logits[b].index_add_( + dim=0, index=idx.to(new_logits.device), source=logits[b] + ) + new_logits[b, : c.numel()] /= c.unsqueeze(-1).to(new_logits.device) + + new_sz = keep.sum() + if not keep.all(): + kept_logits = new_logits[b, : c.numel()][keep] + new_logits[b, :new_sz] = kept_logits + + if new_sz < new_tsz: + pad = new_tsz - new_sz + new_logits[b, -pad:] = 0 + new_pad[b, -pad:] = True + + return new_logits, new_pad + + +class UniformRandomJoinSegmenter(UniformRandomSegmenter, JoinSegmenter): + pass + + +SEGMENT_FACTORY = { + SegmentationType.NONE: Segmenter, + SegmentationType.RANDOM: RandomSegmenter, + SegmentationType.UNIFORM_RANDOM: UniformRandomSegmenter, + SegmentationType.UNIFORM_RANDOM_JOIN: UniformRandomJoinSegmenter, + SegmentationType.JOIN: JoinSegmenter, +} + + +class Discriminator(nn.Module): + def __init__(self, dim, cfg: Wav2vec_UConfig): + super().__init__() + + inner_dim = cfg.discriminator_dim + kernel = cfg.discriminator_kernel + dilation = cfg.discriminator_dilation + self.max_pool = cfg.discriminator_max_pool + + if cfg.discriminator_causal: + padding = kernel - 1 + else: + padding = kernel // 2 + + def make_conv(in_d, out_d, k, p=0, has_dilation=True): + conv = nn.Conv1d( + in_d, + out_d, + kernel_size=k, + padding=p, + dilation=dilation if has_dilation else 1, + ) + if cfg.discriminator_spectral_norm: + conv = nn.utils.spectral_norm(conv) + elif cfg.discriminator_weight_norm: + conv = nn.utils.weight_norm(conv) + return conv + + inner_net = [ + nn.Sequential( + make_conv(inner_dim, inner_dim, kernel, padding), + SamePad(kernel_size=kernel, causal=cfg.discriminator_causal), + nn.Dropout(cfg.discriminator_dropout), + nn.GELU(), + ) + for _ in range(cfg.discriminator_depth - 1) + ] + [ + make_conv(inner_dim, 1, kernel, padding, has_dilation=False), + SamePad(kernel_size=kernel, causal=cfg.discriminator_causal), + ] + + if cfg.discriminator_linear_emb: + emb_net = [make_conv(dim, inner_dim, 1)] + else: + emb_net = [ + make_conv(dim, inner_dim, kernel, padding), + SamePad(kernel_size=kernel, causal=cfg.discriminator_causal), + ] + + if cfg.discriminator_act_after_linear: + emb_net.append(nn.GELU()) + + self.net = nn.Sequential( + *emb_net, + nn.Dropout(cfg.discriminator_dropout), + *inner_net, + ) + + def forward(self, x, padding_mask): + x = x.transpose(1, 2) # BTC -> BCT + x = self.net(x) + x = x.transpose(1, 2) + x_sz = x.size(1) + if padding_mask is not None and padding_mask.any() and padding_mask.dim() > 1: + padding_mask = padding_mask[:, : x.size(1)] + x[padding_mask] = float("-inf") if self.max_pool else 0 + x_sz = x_sz - padding_mask.sum(dim=-1) + x = x.squeeze(-1) + if self.max_pool: + x, _ = x.max(dim=-1) + else: + x = x.sum(dim=-1) + x = x / x_sz + return x + + +class Generator(nn.Module): + def __init__(self, input_dim, output_dim, cfg: Wav2vec_UConfig): + super().__init__() + + self.cfg = cfg + self.output_dim = output_dim + self.stride = cfg.generator_stride + self.dropout = nn.Dropout(cfg.generator_dropout) + self.batch_norm = cfg.generator_batch_norm != 0 + self.residual = cfg.generator_residual + + padding = ( + cfg.generator_kernel // 2 if cfg.generator_pad < 0 else cfg.generator_pad + ) + self.proj = nn.Sequential( + TransposeLast(), + nn.Conv1d( + input_dim, + output_dim, + kernel_size=cfg.generator_kernel, + stride=cfg.generator_stride, + dilation=cfg.generator_dilation, + padding=padding, + bias=cfg.generator_bias, + ), + TransposeLast(), + ) + + if self.batch_norm: + self.bn = nn.BatchNorm1d(input_dim) + self.bn.weight.data.fill_(cfg.generator_batch_norm) + if self.residual: + self.in_proj = nn.Linear(input_dim, input_dim) + + def forward(self, dense_x, tokens, dense_padding_mask): + result = {} + + if self.batch_norm: + dense_x = self.bn_padded_data(dense_x, dense_padding_mask) + if self.residual: + inter_x = self.in_proj(self.dropout(dense_x)) + dense_x = dense_x + inter_x + result["inter_x"] = inter_x + + dense_x = self.dropout(dense_x) + + dense_x = self.proj(dense_x) + if self.stride > 1: + dense_padding_mask = dense_padding_mask[:, :: self.stride] + + if dense_padding_mask.size(1) != dense_x.size(1): + new_padding = dense_padding_mask.new_zeros(dense_x.shape[:-1]) + diff = new_padding.size(1) - dense_padding_mask.size(1) + + if diff > 0: + new_padding[:, diff:] = dense_padding_mask + else: + assert diff < 0 + new_padding = dense_padding_mask[:, :diff] + + dense_padding_mask = new_padding + + token_x = None + if tokens is not None: + token_x = dense_x.new_zeros(tokens.numel(), self.output_dim) + token_x.scatter_(1, tokens.view(-1, 1).long(), 1) + token_x = token_x.view(tokens.shape + (self.output_dim,)) + + result["dense_x"] = dense_x + result["token_x"] = token_x + result["dense_padding_mask"] = dense_padding_mask + + return result + + def bn_padded_data(self, feature, padding_mask): + normed_feature = feature.clone() + normed_feature[~padding_mask] = self.bn( + feature[~padding_mask].unsqueeze(-1) + ).squeeze(-1) + return normed_feature + + +@register_model("wav2vec_u", dataclass=Wav2vec_UConfig) +class Wav2vec_U(BaseFairseqModel): + def calc_gradient_penalty(self, real_data, fake_data): + + b_size = min(real_data.size(0), fake_data.size(0)) + t_size = min(real_data.size(1), fake_data.size(1)) + + if self.cfg.probabilistic_grad_penalty_slicing: + + def get_slice(data, dim, target_size): + + size = data.size(dim) + diff = size - target_size + if diff <= 0: + return data + + start = np.random.randint(0, diff + 1) + return data.narrow(dim=dim, start=start, length=target_size) + + real_data = get_slice(real_data, 0, b_size) + real_data = get_slice(real_data, 1, t_size) + fake_data = get_slice(fake_data, 0, b_size) + fake_data = get_slice(fake_data, 1, t_size) + + else: + real_data = real_data[:b_size, :t_size] + fake_data = fake_data[:b_size, :t_size] + + alpha = torch.rand(real_data.size(0), 1, 1) + alpha = alpha.expand(real_data.size()) + alpha = alpha.to(real_data.device) + + interpolates = alpha * real_data + ((1 - alpha) * fake_data) + + disc_interpolates = self.discriminator(interpolates, None) + + gradients = autograd.grad( + outputs=disc_interpolates, + inputs=interpolates, + grad_outputs=torch.ones(disc_interpolates.size(), device=real_data.device), + create_graph=True, + retain_graph=True, + only_inputs=True, + )[0] + + gradient_penalty = (gradients.norm(2, dim=1) - 1) ** 2 + return gradient_penalty + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self.update_num = num_updates + self.curr_temp = max( + self.max_temp * self.temp_decay ** num_updates, self.min_temp + ) + + def discrim_step(self, num_updates): + return num_updates % 2 == 1 + + def get_groups_for_update(self, num_updates): + return "discriminator" if self.discrim_step(num_updates) else "generator" + + def __init__(self, cfg: Wav2vec_UConfig, target_dict): + super().__init__() + + self.cfg = cfg + self.zero_index = target_dict.index("<SIL>") if "<SIL>" in target_dict else 0 + self.smoothness_weight = cfg.smoothness_weight + + output_size = len(target_dict) + self.pad = target_dict.pad() + self.eos = target_dict.eos() + self.smoothing = cfg.smoothing + self.smoothing_one_sided = cfg.smoothing_one_sided + self.no_softmax = cfg.no_softmax + self.gumbel = cfg.gumbel + self.hard_gumbel = cfg.hard_gumbel + self.last_acc = None + + self.gradient_penalty = cfg.gradient_penalty + self.code_penalty = cfg.code_penalty + self.mmi_weight = cfg.mmi_weight + self.blank_weight = cfg.blank_weight + self.blank_mode = cfg.blank_mode + self.blank_index = target_dict.index("<SIL>") if cfg.blank_is_sil else 0 + assert self.blank_index != target_dict.unk() + + self.discriminator = Discriminator(output_size, cfg) + for p in self.discriminator.parameters(): + p.param_group = "discriminator" + + self.pca_A = self.pca_b = None + d = cfg.input_dim + + self.segmenter = SEGMENT_FACTORY[cfg.segmentation.type](cfg.segmentation) + + self.generator = Generator(d, output_size, cfg) + + for p in self.generator.parameters(): + p.param_group = "generator" + + for p in self.segmenter.parameters(): + p.param_group = "generator" + + self.max_temp, self.min_temp, self.temp_decay = cfg.temp + self.curr_temp = self.max_temp + self.update_num = 0 + + if self.mmi_weight > 0: + self.target_downsample_rate = cfg.target_downsample_rate + self.decoder = nn.Linear(d, cfg.target_dim) + for p in self.decoder.parameters(): + p.param_group = "generator" + + @classmethod + def build_model(cls, cfg, task): + return cls(cfg, task.target_dictionary) + + def get_logits( + self, + net_output: Optional[Dict[str, List[Optional[torch.Tensor]]]], + normalize: bool = False, + ): + logits = net_output["logits"] + + if self.blank_weight != 0: + if self.blank_mode == "add": + logits[..., self.blank_index] += self.blank_weight + elif self.blank_mode == "set": + logits[..., self.blank_index] = self.blank_weight + else: + raise Exception(f"invalid blank mode {self.blank_mode}") + + padding = net_output["padding_mask"] + if padding.any(): + logits[padding] = float("-inf") + logits[padding][..., self.blank_index] = float("inf") + + if normalize: + logits = utils.log_softmax(logits.float(), dim=-1) + + return logits.transpose(0, 1) + + def get_normalized_probs( + self, + net_output: Tuple[ + torch.Tensor, Optional[Dict[str, List[Optional[torch.Tensor]]]] + ], + log_probs: bool, + sample: Optional[Dict[str, torch.Tensor]] = None, + ): + logits = self.get_logits(net_output) + + probs = super().get_normalized_probs(logits, log_probs, sample) + # BTC -> TBC for ctc + probs = probs.transpose(0, 1) + return probs + + def normalize(self, dense_x): + + bsz, tsz, csz = dense_x.shape + + if dense_x.numel() == 0: + raise Exception(dense_x.shape) + _, k = dense_x.max(-1) + hard_x = ( + dense_x.new_zeros(bsz * tsz, csz) + .scatter_(-1, k.view(-1, 1), 1.0) + .view(-1, csz) + ) + hard_probs = torch.mean(hard_x.float(), dim=0) + code_perplexity = torch.exp( + -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) + ) + + avg_probs = torch.softmax(dense_x.reshape(-1, csz).float(), dim=-1).mean(dim=0) + prob_perplexity = torch.exp( + -torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1) + ) + + if not self.no_softmax: + if self.training and self.gumbel: + dense_x = F.gumbel_softmax( + dense_x.float(), tau=self.curr_temp, hard=self.hard_gumbel + ).type_as(dense_x) + else: + dense_x = dense_x.softmax(-1) + + return dense_x, code_perplexity, prob_perplexity + + def forward( + self, + features, + padding_mask, + random_label=None, + dense_x_only=False, + segment=True, + aux_target=None, + ): + if segment: + features, padding_mask = self.segmenter.pre_segment(features, padding_mask) + + orig_size = features.size(0) * features.size(1) - padding_mask.sum() + + gen_result = self.generator(features, random_label, padding_mask) + + orig_dense_x, token_x = gen_result["dense_x"], gen_result["token_x"] + orig_dense_padding_mask = gen_result["dense_padding_mask"] + + if segment: + dense_x, dense_padding_mask = self.segmenter.logit_segment( + orig_dense_x, orig_dense_padding_mask + ) + else: + dense_x = orig_dense_x + dense_padding_mask = orig_dense_padding_mask + + dense_logits = dense_x + prob_perplexity = None + code_perplexity = None + + if not (self.no_softmax and dense_x_only): + dense_x, code_perplexity, prob_perplexity = self.normalize(dense_logits) + + if dense_x_only or self.discriminator is None: + return { + "logits": dense_x, + "padding_mask": dense_padding_mask, + } + + token_padding_mask = random_label == self.pad + + dense_y = self.discriminator(dense_x, dense_padding_mask) + token_y = self.discriminator(token_x, token_padding_mask) + + sample_size = features.size(0) + + d_step = self.discrim_step(self.update_num) + + fake_smooth = self.smoothing + real_smooth = self.smoothing + if self.smoothing_one_sided: + fake_smooth = 0 + + zero_loss = None + smoothness_loss = None + code_pen = None + mmi_loss = None + + if d_step: + loss_dense = F.binary_cross_entropy_with_logits( + dense_y, + dense_y.new_ones(dense_y.shape) - fake_smooth, + reduction="sum", + ) + loss_token = F.binary_cross_entropy_with_logits( + token_y, + token_y.new_zeros(token_y.shape) + real_smooth, + reduction="sum", + ) + if self.training and self.gradient_penalty > 0: + grad_pen = self.calc_gradient_penalty(token_x, dense_x) + grad_pen = grad_pen.sum() * self.gradient_penalty + else: + grad_pen = None + else: + grad_pen = None + loss_token = None + loss_dense = F.binary_cross_entropy_with_logits( + dense_y, + dense_y.new_zeros(dense_y.shape) + fake_smooth, + reduction="sum", + ) + num_vars = dense_x.size(-1) + if prob_perplexity is not None: + code_pen = (num_vars - prob_perplexity) / num_vars + code_pen = code_pen * sample_size * self.code_penalty + + if self.smoothness_weight > 0: + smoothness_loss = F.mse_loss( + dense_logits[:, :-1], dense_logits[:, 1:], reduction="none" + ) + smoothness_loss[dense_padding_mask[:, 1:]] = 0 + smoothness_loss = ( + smoothness_loss.mean() * sample_size * self.smoothness_weight + ) + + if (self.mmi_weight > 0) and (aux_target is not None): + inter_x = self.decoder(gen_result["inter_x"]) + if self.target_downsample_rate > 1: + aux_target = aux_target[:, :: self.target_downsample_rate] + max_t_len = min(aux_target.shape[1], inter_x.shape[1]) + mmi_loss = F.cross_entropy( + inter_x[:, :max_t_len].transpose(1, 2), + aux_target[:, :max_t_len], + ignore_index=-1, + reduction="none", + ) + mmi_loss = mmi_loss.mean() * mmi_loss.shape[0] * self.mmi_weight + + result = { + "losses": { + "grad_pen": grad_pen, + "code_pen": code_pen, + "smoothness": smoothness_loss, + "mmi": mmi_loss, + }, + "temp": self.curr_temp, + "code_ppl": code_perplexity, + "prob_ppl": prob_perplexity, + "d_steps": int(d_step), + "sample_size": sample_size, + } + + suff = "_d" if d_step else "_g" + result["losses"]["dense" + suff] = loss_dense + result["losses"]["token" + suff] = loss_token + + return result diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/apply_pca.py b/fairseq/examples/wav2vec/unsupervised/scripts/apply_pca.py new file mode 100644 index 0000000..10ad6ce --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/apply_pca.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import math +import numpy as np +import tqdm +import torch +from shutil import copyfile + +from npy_append_array import NpyAppendArray + + +def get_parser(): + parser = argparse.ArgumentParser( + description="transforms features via a given pca and stored them in target dir" + ) + # fmt: off + parser.add_argument('source', help='directory with features') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--pca-path', type=str, help='pca location. will append _A.npy and _b.npy', required=True) + parser.add_argument('--batch-size', type=int, default=2048000, help='batch size') + parser.add_argument('--unfiltered', action='store_true', help='process the unfiltered version') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + source_path = osp.join(args.source, args.split) + data_poth = source_path + "_unfiltered" if args.unfiltered else source_path + + print(f"data path: {data_poth}") + + features = np.load(data_poth + ".npy", mmap_mode="r") + pca_A = torch.from_numpy(np.load(args.pca_path + "_A.npy")).cuda() + pca_b = torch.from_numpy(np.load(args.pca_path + "_b.npy")).cuda() + + os.makedirs(args.save_dir, exist_ok=True) + save_path = osp.join(args.save_dir, args.split) + + copyfile(source_path + ".tsv", save_path + ".tsv") + copyfile(data_poth + ".lengths", save_path + ".lengths") + + if osp.exists(source_path + ".phn"): + copyfile(source_path + ".phn", save_path + ".phn") + + if osp.exists(source_path + ".wrd"): + copyfile(source_path + ".wrd", save_path + ".wrd") + + if osp.exists(save_path + ".npy"): + os.remove(save_path + ".npy") + npaa = NpyAppendArray(save_path + ".npy") + + batches = math.ceil(features.shape[0] / args.batch_size) + + with torch.no_grad(): + for b in tqdm.trange(batches): + start = b * args.batch_size + end = start + args.batch_size + x = torch.from_numpy(features[start:end]).cuda() + x = torch.matmul(x, pca_A) + pca_b + npaa.append(x.cpu().numpy()) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py b/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py new file mode 100644 index 0000000..9898683 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +for idx, line in enumerate(sys.stdin): + print(f"utt{idx:010d} {line}", end="") diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py b/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py new file mode 100644 index 0000000..5bf3e51 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +from fairseq.data import Dictionary + + +def get_parser(): + parser = argparse.ArgumentParser( + description="filters a lexicon given a unit dictionary" + ) + parser.add_argument("-d", "--unit-dict", help="unit dictionary", required=True) + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + d = Dictionary.load(args.unit_dict) + symbols = set(d.symbols) + + for line in sys.stdin: + items = line.rstrip().split() + skip = len(items) < 2 + for x in items[1:]: + if x not in symbols: + skip = True + break + if not skip: + print(line, end="") + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/filter_tsv.py b/fairseq/examples/wav2vec/unsupervised/scripts/filter_tsv.py new file mode 100644 index 0000000..a09d79a --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/filter_tsv.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import argparse +import sys + + +parser = argparse.ArgumentParser() +parser.add_argument("--tsv", required=True, type=str) +parser.add_argument("--no-skip", action="store_true") +parser.add_argument("--keep", action="store_true") +params = parser.parse_args() + + +def get_fname(line): + p = os.path.basename(line.split("\t")[0]) + p = os.path.splitext(p)[0] + return p + + +# filenames to exclude +seen = set() +with open(params.tsv) as f: + if not params.no_skip: + root = next(f).rstrip() + for line in f: + seen.add(get_fname(line)) + +for i, line in enumerate(sys.stdin): + exists = get_fname(line) in seen + keep = (exists and params.keep) or (not exists and not params.keep) + if i == 0 or keep: + print(line, end="") diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py b/fairseq/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py new file mode 100644 index 0000000..2e31c30 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +from g2p_en import G2p + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--compact", + action="store_true", + help="if set, compacts phones", + ) + args = parser.parse_args() + + compact = args.compact + + wrd_to_phn = {} + g2p = G2p() + for line in sys.stdin: + words = line.strip().split() + phones = [] + for w in words: + if w not in wrd_to_phn: + wrd_to_phn[w] = g2p(w) + if compact: + wrd_to_phn[w] = [ + p[:-1] if p[-1].isnumeric() else p for p in wrd_to_phn[w] + ] + phones.extend(wrd_to_phn[w]) + try: + print(" ".join(phones)) + except: + print(wrd_to_phn, words, phones, file=sys.stderr) + raise + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py b/fairseq/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py new file mode 100644 index 0000000..36c85d1 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + + +def main(): + for line in sys.stdin: + print(line.replace(" ", "").replace("|", " ").strip()) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/mean_pool.py b/fairseq/examples/wav2vec/unsupervised/scripts/mean_pool.py new file mode 100644 index 0000000..4eea048 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/mean_pool.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import math +import numpy as np +import tqdm +import torch +import torch.nn.functional as F +from shutil import copyfile + +from npy_append_array import NpyAppendArray + + +def get_parser(): + parser = argparse.ArgumentParser( + description="mean pools representations by compressing uniform splits of the data" + ) + # fmt: off + parser.add_argument('source', help='directory with features') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--subsample-rate', type=float, default=0.5, help='size to subsample data to') + + parser.add_argument('--remove-extra', action='store_true', help='if true, removes extra states that cant be pooled, otherwise pads with 0s') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + source_path = osp.join(args.source, args.split) + + print(f"data path: {source_path}") + + features = np.load(source_path + ".npy", mmap_mode="r") + + os.makedirs(args.save_dir, exist_ok=True) + save_path = osp.join(args.save_dir, args.split) + + copyfile(source_path + ".tsv", save_path + ".tsv") + + if os.path.exists(source_path + ".phn"): + copyfile(source_path + ".phn", save_path + ".phn") + if os.path.exists(source_path + ".wrd"): + copyfile(source_path + ".wrd", save_path + ".wrd") + + if os.path.exists(osp.join(args.source, "dict.phn.txt")): + copyfile( + osp.join(args.source, "dict.phn.txt"), + osp.join(args.save_dir, "dict.phn.txt"), + ) + + if osp.exists(save_path + ".npy"): + os.remove(save_path + ".npy") + npaa = NpyAppendArray(save_path + ".npy") + + with open(source_path + ".lengths", "r") as lf: + lengths = lf.readlines() + + fsz = features.shape[-1] + start = 0 + with torch.no_grad(): + with open(save_path + ".lengths", "w") as lengths_out: + for length in tqdm.tqdm(lengths): + length = int(length) + end = start + length + feats = features[start:end] + start += length + x = torch.from_numpy(feats).cuda() + target_num = math.ceil(length * args.subsample_rate) + rem = length % target_num + + if rem > 0: + if args.remove_extra: + to_rem = target_num - rem + target_num -= 1 + x = x[:-to_rem] + else: + to_add = target_num - rem + x = F.pad(x, [0, 0, 0, to_add]) + x[-to_add:] = x[-to_add - 1] + + x = x.view(target_num, -1, fsz) + x = x.mean(dim=-2) + print(target_num, file=lengths_out) + npaa.append(x.cpu().numpy()) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/merge_clusters.py b/fairseq/examples/wav2vec/unsupervised/scripts/merge_clusters.py new file mode 100644 index 0000000..2780f9d --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/merge_clusters.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import numpy as np +import tqdm +import torch +import random +from shutil import copyfile + +from npy_append_array import NpyAppendArray + + +def get_parser(): + parser = argparse.ArgumentParser( + description="transforms features via a given pca and stored them in target dir" + ) + # fmt: off + parser.add_argument('source', help='directory with features') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--cluster-dir', help='where the clusters are') + parser.add_argument('--pooling', type=str, default='mean', choices=['mean', 'sample'], help='how to pool') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + source_path = osp.join(args.source, args.split) + cluster_path = osp.join(args.cluster_dir, args.split + ".src") + print(f"data path: {source_path}") + + features = np.load(source_path + ".npy", mmap_mode="r") + sizes = [] + offsets = [] + offset = 0 + with open(source_path + ".lengths", "r") as len_f: + for line in len_f: + length = int(line.rstrip()) + sizes.append(length) + offsets.append(offset) + offset += length + + clusters = [] + with open(cluster_path, "r") as cf: + for line in cf: + line = line.rstrip() + items = line.split() + items = list(map(int, items)) + clusters.append(items) + + os.makedirs(args.save_dir, exist_ok=True) + save_path = osp.join(args.save_dir, args.split) + + copyfile(source_path + ".tsv", save_path + ".tsv") + + if os.path.exists(source_path + ".phn"): + copyfile(source_path + ".phn", save_path + ".phn") + if os.path.exists(osp.join(args.source, "dict.phn.txt")): + copyfile( + osp.join(args.source, "dict.phn.txt"), + osp.join(args.save_dir, "dict.phn.txt"), + ) + if os.path.exists(source_path + ".wrd"): + copyfile(source_path + ".wrd", save_path + ".wrd") + + if osp.exists(save_path + ".npy"): + os.remove(save_path + ".npy") + npaa = NpyAppendArray(save_path + ".npy") + + def merge(feats, clust): + feats = torch.from_numpy(feats.copy()) + clust = torch.LongTensor(clust) + _, counts = clust.unique_consecutive(return_counts=True) + curr = 0 + + merged = [] + for c in counts: + c = c.item() + start = curr + end = curr + c + curr += c + if args.pooling == "mean": + new_x = feats[start:end].mean(dim=0) + elif args.pooling == "sample": + new_x = feats[start + int(random.random() * c)] + else: + raise NotImplementedError() + merged.append(new_x) + + return torch.stack(merged, dim=0).numpy() + + with open(save_path + ".lengths", "w") as l_f: + for size, offset, clust in tqdm.tqdm( + zip(sizes, offsets, clusters), total=len(sizes) + ): + end = size + offset + feats = features[offset:end] + feats = merge(feats, clust) + print(len(feats), file=l_f) + npaa.append(feats) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py b/fairseq/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py new file mode 100644 index 0000000..c2bd16e --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import fasttext as ft +import os +import regex +import sys + + +def get_parser(): + parser = argparse.ArgumentParser( + description="reads text from stdin and outputs normalized, lid-filtered version to stdout" + ) + parser.add_argument( + "--fasttext-model", + help="path to fasttext model", + default="lid.187.bin", + ) + parser.add_argument("--lang", help="language id", required=True) + parser.add_argument( + "--lid-threshold", + type=float, + help="threshold for this lang id probability", + default=0.4, + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + filter_r = regex.compile(r"[^\p{L}\p{N}\p{M}\' \-]") + + lg = args.lang.lower() + lg_label = f"__label__{lg}" + thresh = args.lid_threshold + + if os.path.exists(args.fasttext_model): + model = ft.load_model(args.fasttext_model) + else: + print( + f"fasttext language id model {args.fasttext_model} not found. Proceeding without language filtering. " + f"To enable language filtering, please download the latest language id model " + f"from https://fasttext.cc/docs/en/language-identification.html", + file=sys.stderr, + ) + model = None + + for line in sys.stdin: + line = line.strip() + line = filter_r.sub(" ", line) + line = " ".join(line.split()) + + if model is not None: + lid, prob = model.predict(line, k=100) + try: + target_idx = lid.index(lg_label) + except ValueError: + continue + if target_idx == 0 or prob[target_idx] >= thresh: + print(line) + else: + print(line) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/normalize_text.py b/fairseq/examples/wav2vec/unsupervised/scripts/normalize_text.py new file mode 100644 index 0000000..9d0ffeb --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/normalize_text.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import regex +import sys + + +def main(): + filter_r = regex.compile(r"[^\p{L}\p{N}\p{M}\' \-]") + + for line in sys.stdin: + line = line.strip() + line = filter_r.sub(" ", line) + line = " ".join(line.split()) + print(line) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/pca.py b/fairseq/examples/wav2vec/unsupervised/scripts/pca.py new file mode 100644 index 0000000..948cf53 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/pca.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import numpy as np + +import faiss + + + +def get_parser(): + parser = argparse.ArgumentParser( + description="compute a pca matrix given an array of numpy features" + ) + # fmt: off + parser.add_argument('data', help='numpy file containing features') + parser.add_argument('--output', help='where to save the pca matrix', required=True) + parser.add_argument('--dim', type=int, help='dim for pca reduction', required=True) + parser.add_argument('--eigen-power', type=float, default=0, help='eigen power, -0.5 for whitening') + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + print("Reading features") + x = np.load(args.data, mmap_mode="r") + + print("Computing PCA") + pca = faiss.PCAMatrix(x.shape[-1], args.dim, args.eigen_power) + pca.train(x) + b = faiss.vector_to_array(pca.b) + A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in) + + os.makedirs(args.output, exist_ok=True) + + prefix = str(args.dim) + if args.eigen_power != 0: + prefix += f"_{args.eigen_power}" + + np.save(osp.join(args.output, f"{prefix}_pca_A"), A.T) + np.save(osp.join(args.output, f"{prefix}_pca_b"), b) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py b/fairseq/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py new file mode 100644 index 0000000..c6512d7 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import numpy as np +import sys + + +def get_parser(): + parser = argparse.ArgumentParser( + description="converts words to phones adding optional silences around in between words" + ) + parser.add_argument( + "--sil-prob", + "-s", + type=float, + default=0, + help="probability of inserting silence between each word", + ) + parser.add_argument( + "--surround", + action="store_true", + help="if set, surrounds each example with silence", + ) + parser.add_argument( + "--lexicon", + help="lexicon to convert to phones", + required=True, + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + sil_prob = args.sil_prob + surround = args.surround + sil = "<SIL>" + + wrd_to_phn = {} + + with open(args.lexicon, "r") as lf: + for line in lf: + items = line.rstrip().split() + assert len(items) > 1, line + assert items[0] not in wrd_to_phn, items + wrd_to_phn[items[0]] = items[1:] + + for line in sys.stdin: + words = line.strip().split() + + if not all(w in wrd_to_phn for w in words): + continue + + phones = [] + if surround: + phones.append(sil) + + sample_sil_probs = None + if sil_prob > 0 and len(words) > 1: + sample_sil_probs = np.random.random(len(words) - 1) + + for i, w in enumerate(words): + phones.extend(wrd_to_phn[w]) + if ( + sample_sil_probs is not None + and i < len(sample_sil_probs) + and sample_sil_probs[i] < sil_prob + ): + phones.append(sil) + + if surround: + phones.append(sil) + print(" ".join(phones)) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh new file mode 100644 index 0000000..013f7a9 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh @@ -0,0 +1,78 @@ +#!/usr/bin/env zsh +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +source_dir=$1 +tgt_dir=$2 +model=$3 + +if [ -z "$4" ] + then + dim=512 + else + dim=$4 +fi + +echo "using $dim dim for PCA" + +if [ -z "$5" ] + then + layer=14 + else + layer=$5 +fi + +echo "extracting from layer $layer" + +train_split=train +valid_split=valid +test_split=test + +all_splits=($train_split) + +if [[ -f "$source_dir/valid.tsv" ]]; then + all_splits+=('valid') +fi + +if [[ -f "$source_dir/test.tsv" ]]; then + all_splits+=('test') +fi + +echo "processing splits: $all_splits" + +mkdir -p $tgt_dir + +cp $source_dir/*.tsv $tgt_dir +cp $source_dir/*.wrd $tgt_dir +cp $source_dir/*.ltr $tgt_dir +cp $source_dir/*.phn $tgt_dir +cp $source_dir/dict* $tgt_dir + +setopt shwordsplit + +for split in $all_splits; do + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py $source_dir --split $split \ + --save-dir $tgt_dir --checkpoint $model --layer $layer +done + +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py $tgt_dir/${train_split}.tsv \ +--checkpoint $model --save-dir $tgt_dir -f "CLUS128" --sample-pct 1.0 + +for split in $all_splits; do + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py $tgt_dir \ + --checkpoint $model --path $tgt_dir/CLUS128 --split $split +done + +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/pca.py $tgt_dir/${train_split}.npy --output $tgt_dir/pca --dim $dim + +for split in $all_splits; do + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/apply_pca.py $tgt_dir --split $split --save-dir $tgt_dir/precompute_pca$dim --pca-path $tgt_dir/pca/${dim}_pca --batch-size 1048000 + + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/merge_clusters.py $tgt_dir/precompute_pca$dim --cluster-dir $tgt_dir/CLUS128 \ + --split $split --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean --pooling mean + + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/mean_pool.py $tgt_dir/precompute_pca${dim}_cls128_mean \ + --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean_pooled --split $split +done diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio_v2.sh b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio_v2.sh new file mode 100644 index 0000000..96a52c5 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio_v2.sh @@ -0,0 +1,68 @@ +#!/usr/bin/env zsh +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +source_dir=$1 +tgt_dir=$2 +model=$3 + +if [ -z "$4" ] + then + dim=64 + else + dim=$4 +fi + +echo "using $dim clusters for auxilary target" + +if [ -z "$5" ] + then + layer=14 + else + layer=$5 +fi + +echo "extracting from layer $layer" + +train_split=train +valid_split=valid +test_split=test + +all_splits=($train_split) + +if [[ -f "$source_dir/valid.tsv" ]]; then + all_splits+=('valid') +fi + +if [[ -f "$source_dir/test.tsv" ]]; then + all_splits+=('test') +fi + +echo "processing splits: $all_splits" + +mkdir -p $tgt_dir + +cp $source_dir/*.tsv $tgt_dir +cp $source_dir/*.wrd $tgt_dir +cp $source_dir/*.ltr $tgt_dir +cp $source_dir/*.phn $tgt_dir +cp $source_dir/dict* $tgt_dir + +setopt shwordsplit + +for split in $all_splits; do + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py $source_dir --split $split \ + --save-dir $tgt_dir --checkpoint $model --layer $layer +done + + +mkdir -p $tgt_dir/mfcc + +# Consider spliting corpus into chuncks for large corpus, see HuBERT preprocessing for more details +python $FAIRSEQ_ROOT/examples/hubert/simple_kmeans/dump_mfcc_feature.py \ + $tgt_dir $train_split 1 0 $tgt_dir/mfcc +python $FAIRSEQ_ROOT/examples/hubert/simple_kmeans/dump_km_label.py \ + $tgt_dir/mfcc $train_split $tgt_dir/mfcc/cls$dim 1 0 $tgt_dir/mfcc/cls${dim}_idx +cp $tgt_dir/mfcc/cls${dim}_idx/${train_split}_0_1.km $tgt_dir/$train_split.km diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/prepare_text.sh b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_text.sh new file mode 100644 index 0000000..dbd17a2 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_text.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env zsh +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +lg=$1 +text_path=$2 +target_dir=$3 +min_phones=$4 +phonemizer=$5 +lid_path=$6 +sil_prob=$7 + +if [ -z "$lid_path" ]; then + lid_path="lid.187.bin" +fi + +ph_lg=${lg:l} +if test "$lg" = 'fr'; then + ph_lg='fr-fr' +elif test "$lg" = 'en'; then + ph_lg='en-us' +elif test "$lg" = 'pt'; then + ph_lg='pt-br' +fi + +ESPEAK_PATH='' +if test "$phonemizer" = 'espeak'; then + ESPEAK_PATH=$(which espeak) +elif test "$phonemizer" = 'espeak-ng'; then + ESPEAK_PATH=$(which espeak-ng) +elif test "$phonemizer" = 'G2P'; then + ESPEAK_PATH='' +else + echo "Unknown phonemizer $phonemizer. Valid options are espeak, espean-ng and G2P" + exit 1 +fi + +echo $lg +echo $ph_lg +echo $text_path +echo $target_dir +echo "min phone seen threshold is $min_phones" + +mkdir -p $target_dir +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py --lang $lg --fasttext-model $lid_path < $text_path | grep -v '\-\-\-' >! $target_dir/lm.upper.lid.txt +python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/lm.upper.lid.txt --only-source --destdir $target_dir --thresholdsrc 2 --padding-factor 1 --dict-only +cut -f1 -d' ' $target_dir/dict.txt | grep -v -x '[[:punct:]]*' | grep -Pv '\d\d\d\d\d+' >! $target_dir/words.txt + + +if [ -z "$ESPEAK_PATH" ]; then + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py --compact < $target_dir/words.txt > $target_dir/phones.txt +else + # echoing 1 into corpus will prevent the mismatch lines between lexicon and phones in case the phonemizer fails + one=$(echo "1" | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -p ' ' -w '' -l $ph_lg --language-switch remove-flags) + sed 's/$/ 1/' $target_dir/words.txt | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -o $target_dir/phones.txt -p ' ' -w '' -l $ph_lg -j 70 --language-switch remove-flags + echo "one is ${one}" + sed -i "s/${one}$//" $target_dir/phones.txt +fi + +paste $target_dir/words.txt $target_dir/phones.txt >! $target_dir/lexicon.lst + +python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones.txt --only-source --destdir $target_dir/phones --thresholdsrc $min_phones --padding-factor 1 --dict-only + +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/filter_lexicon.py -d $target_dir/phones/dict.txt < $target_dir/lexicon.lst >! $target_dir/lexicon_filtered.lst +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py -s $sil_prob --surround --lexicon $target_dir/lexicon_filtered.lst < $target_dir/lm.upper.lid.txt >! $target_dir/phones/lm.phones.filtered.txt +cp $target_dir/phones/dict.txt $target_dir/phones/dict.phn.txt +echo "<SIL> 0" >> $target_dir/phones/dict.phn.txt +python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones/lm.phones.filtered.txt --workers 70 --only-source --destdir $target_dir/phones --srcdict $target_dir/phones/dict.phn.txt + +$KENLM_ROOT/lmplz -o 4 < $target_dir/lm.upper.lid.txt --discount_fallback --prune 0 0 0 3 >! $target_dir/kenlm.wrd.o40003.arpa +$KENLM_ROOT/build_binary $target_dir/kenlm.wrd.o40003.arpa $target_dir/kenlm.wrd.o40003.bin + +lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words_sil lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn "blank_symbol='<SIL>'" +lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn + +$KENLM_ROOT/lmplz -o 4 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.04.arpa +$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.04.arpa $target_dir/phones/lm.phones.filtered.04.bin +$KENLM_ROOT/lmplz -o 6 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.06.arpa +$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.06.arpa $target_dir/phones/lm.phones.filtered.06.bin + +lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_phn_sil lm_arpa=$target_dir/phones/lm.phones.filtered.06.arpa data_dir=$target_dir/phones in_labels=phn "blank_symbol='<SIL>'" diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/prepare_timit.sh b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_timit.sh new file mode 100644 index 0000000..d8f5d59 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/prepare_timit.sh @@ -0,0 +1,79 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +timit_root=$1 # assume it is the upper-cased version +tgt_dir=$2 +model=$3 + +set -eu + +setups="matched unmatched" +splits="test valid train train_text" + +tgt_dir=$(realpath $tgt_dir) +sph2wav=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe +wav_dir=$tgt_dir/wav + + +mkdir -p $tgt_dir $wav_dir +find $timit_root/{TRAIN,TEST} -iname "*.WAV" > $tgt_dir/all_sph.flist +cat $tgt_dir/all_sph.flist | sed -e 's#//*#/#g' -e 's#.*/\([^/]*\)/\([^/]*\).WAV#\1_\2#g' > $tgt_dir/all.uid +paste -d' ' $tgt_dir/{all_sph.flist,all.uid} | \ + awk -v sph2wav=$sph2wav -v wav_dir=$wav_dir '{print sph2wav " -f wav " $1 " > " wav_dir "/" $2 ".wav"}' \ + > $tgt_dir/sph2wav.sh +bash $tgt_dir/sph2wav.sh +cat $tgt_dir/all.uid | awk -v wav_dir=$(pwd)/$wav_dir '{print $1" "wav_dir"/"$1".wav"}' | sort > $tgt_dir/all_wav.scp +cut -d' ' -f2 $tgt_dir/all_wav.scp | xargs -I{} soxi -s {} > $tgt_dir/all.dur +paste -d' ' $tgt_dir/{all_wav.scp,all.dur} > $tgt_dir/all_wav_dur.scp +rm $tgt_dir/{all.uid,all_sph.flist,sph2wav.sh} + +find $timit_root/{TRAIN,TEST} -iname "*.PHN" > $tgt_dir/all_phn60.flist +while read line; do + if [ ! -f $line ]; then + >&2 echo "Cannot find transcription file '$line'" && exit 1; + fi + cut -f3 -d' ' "$line" | tr '\n' ' ' | perl -ape 's: *$:\n:;' +done < $tgt_dir/all_phn60.flist > $tgt_dir/all.phn60 +cat $tgt_dir/all_phn60.flist | sed -e 's#//*#/#g' -e 's#.*/\([^/]*\)/\([^/]*\).PHN#\1_\2#g' | \ + paste -d' ' - $tgt_dir/all.phn60 | \ + $KALDI_ROOT/egs/timit/s5/local/timit_norm_trans.pl -i - -m $KALDI_ROOT/egs/timit/s5/conf/phones.60-48-39.map -to 39 | \ + sort > $tgt_dir/all.phn +echo "done preparing wav and 39-phone transcripts" + + +for s in $setups; do + mkdir -p $tgt_dir/$s + for x in $splits; do + uid_path=config/timit_${s}/${x}.uid + grep -w -f $uid_path $tgt_dir/all.phn | cut -d' ' -f2- > $tgt_dir/$s/$x.phn + ln -sf $(realpath $tgt_dir/$s/$x.phn) $tgt_dir/$s/$x.wrd + + echo "/" > $tgt_dir/$s/$x.tsv && grep -w -f $uid_path $tgt_dir/all_wav_dur.scp | cut -d' ' -f2- | sed 's# #\t#' >> $tgt_dir/$s/$x.tsv + done + + for x in $splits; do + cat $tgt_dir/$s/$x.phn + done | tr ' ' '\n' | sort -u | awk '{print $1" "1}' > $tgt_dir/$s/dict.phn.txt + ln -sf $(realpath $tgt_dir/$s/dict.phn.txt) $tgt_dir/$s/dict.wrd.txt +done +echo "done preparing unmatched and matched setups for TIMIT" + + +for s in $setups; do + zsh scripts/prepare_audio.sh $tgt_dir/$s $tgt_dir/$s/feat $model + + lm_dir=$tgt_dir/$s/phones + fst_dir=$tgt_dir/$s/fst/phn_to_phn + + python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $tgt_dir/$s/train_text.phn --workers 10 --only-source --destdir $lm_dir --srcdict $tgt_dir/$s/dict.phn.txt + $KENLM_ROOT/lmplz -o 3 < $tgt_dir/$s/train_text.phn --discount_fallback >$lm_dir/train_text_phn.03.arpa + $KENLM_ROOT/build_binary $lm_dir/train_text_phn.03.arpa $lm_dir/train_text_phn.03.bin + $KENLM_ROOT/lmplz -o 4 < $tgt_dir/$s/train_text.phn --discount_fallback >$lm_dir/train_text_phn.04.arpa + $KENLM_ROOT/build_binary $lm_dir/train_text_phn.04.arpa $lm_dir/train_text_phn.04.bin + + python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$fst_dir lm_arpa=$lm_dir/train_text_phn.03.arpa data_dir=$tgt_dir/$s in_labels=phn +done +echo "done preprocessing audio and text for wav2vec-U" diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/remove_silence.py b/fairseq/examples/wav2vec/unsupervised/scripts/remove_silence.py new file mode 100644 index 0000000..fac88b9 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/remove_silence.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +get intervals from .vads file, specify output data, and this script removes silences and saves the audio data in out path folder +paths=shards/train.tsv +vads=shards/train.vads +python remove_silence.py --paths $paths --vads $vads +""" + +import os +import argparse +import torch +import torchaudio +import tqdm + + +parser = argparse.ArgumentParser() +parser.add_argument("--tsv", default="", type=str) +parser.add_argument("--vads", default="", type=str) +parser.add_argument("--out", type=str) +params = parser.parse_args() + +# load paths +paths = [] +with open(params.tsv) as f: + root = next(f).rstrip() + for line in f: + paths.append(os.path.join(root, line.rstrip().split("\t")[0])) + +# load vads +list_intervals = [] +with open(params.vads) as f: + for line in f: + interval = [ + [int(w.split(":")[0]), int(w.split(":")[1])] for w in line.rstrip().split() + ] + list_intervals.append(interval) + + +# load audio and keep only intervals (i.e. remove silences) +for i in tqdm.trange(len(paths)): + data, _ = torchaudio.load(paths[i]) + if len(list_intervals[i]) > 0: + data_filtered = torch.cat( + [data[0][int(it[0]) : int(it[1])] for it in list_intervals[i]] + ).unsqueeze(0) + else: + data_filtered = data + + # YOU MAY NEED TO MODIFY THIS TO GET THE RIGHT SUBPATH + # outpath = params.out + '/'.join(paths[i].split('/')[-1]) + outpath = params.out + "/" + "/".join(paths[i].split("/")[-2:]) + + if not os.path.isdir("/".join(outpath.split("/")[:-1])): + os.makedirs("/".join(outpath.split("/")[:-1])) + if not os.path.exists(outpath): + torchaudio.save(outpath, data_filtered, sample_rate=16000) + else: + print(outpath, "exists!") diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/vads.py b/fairseq/examples/wav2vec/unsupervised/scripts/vads.py new file mode 100644 index 0000000..2398da9 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/vads.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +from copy import deepcopy +from scipy.signal import lfilter + +import numpy as np +from tqdm import tqdm +import soundfile as sf +import os.path as osp + + +def get_parser(): + parser = argparse.ArgumentParser(description="compute vad segments") + parser.add_argument( + "--rvad-home", + "-r", + help="path to rvad home (see https://github.com/zhenghuatan/rVADfast)", + required=True, + ) + + return parser + + +def rvad(speechproc, path): + winlen, ovrlen, pre_coef, nfilter, nftt = 0.025, 0.01, 0.97, 20, 512 + ftThres = 0.5 + vadThres = 0.4 + opts = 1 + + data, fs = sf.read(path) + assert fs == 16_000, "sample rate must be 16khz" + ft, flen, fsh10, nfr10 = speechproc.sflux(data, fs, winlen, ovrlen, nftt) + + # --spectral flatness -- + pv01 = np.zeros(ft.shape[0]) + pv01[np.less_equal(ft, ftThres)] = 1 + pitch = deepcopy(ft) + + pvblk = speechproc.pitchblockdetect(pv01, pitch, nfr10, opts) + + # --filtering-- + ENERGYFLOOR = np.exp(-50) + b = np.array([0.9770, -0.9770]) + a = np.array([1.0000, -0.9540]) + fdata = lfilter(b, a, data, axis=0) + + # --pass 1-- + noise_samp, noise_seg, n_noise_samp = speechproc.snre_highenergy( + fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk + ) + + # sets noisy segments to zero + for j in range(n_noise_samp): + fdata[range(int(noise_samp[j, 0]), int(noise_samp[j, 1]) + 1)] = 0 + + vad_seg = speechproc.snre_vad( + fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk, vadThres + ) + return vad_seg, data + + +def main(): + parser = get_parser() + args = parser.parse_args() + + sys.path.append(args.rvad_home) + import speechproc + + stride = 160 + lines = sys.stdin.readlines() + root = lines[0].rstrip() + for fpath in tqdm(lines[1:]): + path = osp.join(root, fpath.split()[0]) + vads, wav = rvad(speechproc, path) + + start = None + vad_segs = [] + for i, v in enumerate(vads): + if start is None and v == 1: + start = i * stride + elif start is not None and v == 0: + vad_segs.append((start, i * stride)) + start = None + if start is not None: + vad_segs.append((start, len(wav))) + + print(" ".join(f"{v[0]}:{v[1]}" for v in vad_segs)) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py b/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py new file mode 100644 index 0000000..a5dd7ae --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import numpy as np +import tqdm +import torch +import sys + +import faiss +import torch.nn.functional as F + +from wav2vec_cluster_faiss import parse_faiss_specs, Wav2VecFeatureReader + + +def get_parser(): + parser = argparse.ArgumentParser(description="apply clusters") + # fmt: off + parser.add_argument('data', help='location of tsv files') + parser.add_argument('--split', help='split to process', required=True) + parser.add_argument('--labels', help='split to process', default="phn") + parser.add_argument('--path', help='path to pca and centroids', required=True) + parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec model (if using wav2vec features)', required=True) + parser.add_argument('--layer', '-l', type=int, help='which layer to read', default=14) + parser.add_argument('--max-tsz', type=int, help='batch kmeans up to this much', default=14) + # fmt: on + + return parser + + +def get_iterator(args): + label_path = osp.join(args.data, f"{args.split}.{args.labels}") + if osp.exists(label_path): + lp = open(label_path, "r") + else: + lp = None + + with open(osp.join(args.data, f"{args.split}.tsv"), "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + files = [line.rstrip() for line in lines if len(line) > 0] + + if lp is not None: + lbls = [line.rstrip() for line in lp] + else: + lbls = [None] * len(files) + + num = len(files) + reader = Wav2VecFeatureReader(args.checkpoint, args.layer) + + def iterate(): + for fname, lbl in zip(files, lbls): + file = osp.join(root, fname.split("\t")[0]) + feats = reader.get_feats(file) + yield feats.data, fname, lbl + + return iterate, num, root + + +def main(): + parser = get_parser() + args = parser.parse_args() + + spec = osp.basename(args.path) + + try: + faiss_spec = parse_faiss_specs(spec.rstrip("/"))[0] + except: + print(spec) + raise + + print("Faiss Spec:", faiss_spec, file=sys.stderr) + + if faiss_spec.pca: + A = torch.from_numpy(np.load(osp.join(args.path, "pca_A.npy"))).cuda() + b = torch.from_numpy(np.load(osp.join(args.path, "pca_b.npy"))).cuda() + print("Loaded PCA", file=sys.stderr) + + centroids = np.load(osp.join(args.path, "centroids.npy")) + print("Loaded centroids", centroids.shape, file=sys.stderr) + + res = faiss.StandardGpuResources() + index_flat = ( + faiss.IndexFlatL2(centroids.shape[1]) + if not faiss_spec.sphere + else faiss.IndexFlatIP(centroids.shape[1]) + ) + faiss_index = faiss.index_cpu_to_gpu(res, 0, index_flat) + faiss_index.add(centroids) + + generator, num, root = get_iterator(args) + iterator = generator() + + had_labels = False + label_path = osp.join(args.path, f"{args.split}.{args.labels}") + + with torch.no_grad(): + with open(osp.join(args.path, f"{args.split}.src"), "w") as fp, open( + osp.join(args.path, f"{args.split}.tsv"), "w" + ) as pp, open(label_path, "w") as lp: + print(root, file=pp) + for f, fname, lbl in tqdm.tqdm(iterator, total=num): + if faiss_spec.pca: + f = torch.mm(f, A) + b + if faiss_spec.norm: + f = F.normalize(f, p=2, dim=-1) + + f = f.cpu().numpy() + + _, z = faiss_index.search(f, 1) + + print(" ".join(str(x.item()) for x in z), file=fp) + print(fname, file=pp) + + if lbl is not None: + print(lbl, file=lp) + had_labels = True + if not had_labels: + os.remove(label_path) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py b/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py new file mode 100644 index 0000000..632a69e --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py @@ -0,0 +1,210 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import gc +import os +import os.path as osp +import random +import numpy as np +import tqdm +import torch + +from collections import namedtuple + +import faiss + +import fairseq +import soundfile as sf + + +def get_parser(): + parser = argparse.ArgumentParser( + description="compute kmeans codebook from kaldi-computed feats" + ) + # fmt: off + parser.add_argument('data', help='location of tsv files') + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec model (if using wav2vec features)', required=True) + parser.add_argument('--sample-pct', '-r', type=float, help='percentage of timesteps to sample', default=0) + parser.add_argument('--layer', '-l', type=int, help='which layer to read', default=14) + parser.add_argument('--faiss-specs', '-f', type=str, + help='faiss index specs; separated by space ' + 'format is: PCAx_NORM_CLUSx_SPHERICAL -> ' + 'PCAx if exists first apply PCA ' + 'NORM if exists, normalize the vector by L2 norm ' + 'CLUSx must exist, cluster to x clusters ' + 'SPEHRICAL if exists, apply spherical kmeans', + default='l2') + # fmt: on + + return parser + + +faiss_spec = namedtuple("faiss_spec", ["pca", "norm", "n_clus", "sphere", "spec_str"]) + + +def parse_faiss_specs(specs_str): + specs = [] + for ss in specs_str.split(): + comps = ss.split("_") + pca = 0 + norm = False + n_clus = 0 + sphere = False + for c in comps: + if c.startswith("PCA"): + pca = int(c[3:]) + elif c == "NORM": + norm = True + elif c.startswith("CLUS"): + n_clus = int(c[4:]) + elif c == "SPHERICAL": + sphere = True + assert n_clus > 0 + specs.append( + faiss_spec(pca=pca, norm=norm, n_clus=n_clus, sphere=sphere, spec_str=ss) + ) + return specs + + +class Wav2VecFeatureReader(object): + def __init__(self, cp_file, layer): + state = fairseq.checkpoint_utils.load_checkpoint_to_cpu(cp_file) + + self.layer = layer + + if "cfg" in state: + w2v_args = state["cfg"] + task = fairseq.tasks.setup_task(w2v_args.task) + model = task.build_model(w2v_args.model) + else: + w2v_args = state["args"] + task = fairseq.tasks.setup_task(w2v_args) + model = task.build_model(w2v_args) + model.load_state_dict(state["model"], strict=True) + model.eval() + model.cuda() + self.model = model + + def read_audio(self, fname): + """Load an audio file and return PCM along with the sample rate""" + wav, sr = sf.read(fname) + assert sr == 16e3 + + return wav + + def get_feats(self, loc): + x = self.read_audio(loc) + with torch.no_grad(): + source = torch.from_numpy(x).view(1, -1).float().cuda() + res = self.model( + source=source, mask=False, features_only=True, layer=self.layer + ) + return res["layer_results"][self.layer][0].squeeze(1) + + +def get_iterator(args): + with open(args.data, "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + files = [osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0] + + if getattr(args, "sample_pct", 0) > 0: + files = random.sample(files, int(args.sample_pct * len(files))) + num = len(files) + reader = Wav2VecFeatureReader(args.checkpoint, args.layer) + + def iterate(): + for fname in files: + feats = reader.get_feats(fname) + yield feats.cpu().numpy() + + return iterate, num + + +def main(): + parser = get_parser() + args = parser.parse_args() + + faiss_specs = parse_faiss_specs(args.faiss_specs) + print("Faiss Specs:", faiss_specs) + + feat_path = osp.join(args.save_dir, "features") + if osp.exists(feat_path + ".npy"): + feats = np.load(feat_path + ".npy") + else: + generator, num = get_iterator(args) + iterator = generator() + + feats = [] + for f in tqdm.tqdm(iterator, total=num): + feats.append(f) + + del iterator + del generator + + feats = np.concatenate(feats) + + print(feats.shape) + + os.makedirs(args.save_dir, exist_ok=True) + # np.save(feat_path, feats) + + gc.collect() + torch.cuda.empty_cache() + + reload = False + for spec in faiss_specs: + print("Processing spec", spec) + + if reload: + print("Reloading...") + del feats + gc.collect() + feats = np.load(feat_path + ".npy") + + save_path = osp.join(args.save_dir, spec.spec_str) + os.makedirs(save_path, exist_ok=True) + d = feats.shape[-1] + x = feats + if spec.pca > 0: + print("Computing PCA") + pca = faiss.PCAMatrix(d, spec.pca) + pca.train(x) + d = spec.pca + b = faiss.vector_to_array(pca.b) + A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in) + np.save(osp.join(save_path, "pca_A"), A.T) + np.save(osp.join(save_path, "pca_b"), b) + print("Applying PCA") + x = pca.apply_py(x) + + if spec.norm: + reload = spec.pca <= 0 + print("Normalizing") + faiss.normalize_L2(x) + + print("Computing kmeans") + kmeans = faiss.Kmeans( + d, + spec.n_clus, + niter=50, + verbose=True, + spherical=spec.sphere, + max_points_per_centroid=feats.shape[0], + gpu=True, + nredo=3, + ) + kmeans.train(x) + np.save(osp.join(save_path, "centroids"), kmeans.centroids) + del kmeans + del x + gc.collect() + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py b/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py new file mode 100644 index 0000000..b07e274 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import tqdm +import torch +import torch.nn.functional as F +from shutil import copyfile + +from npy_append_array import NpyAppendArray + +import fairseq +import soundfile as sf + + +def get_parser(): + parser = argparse.ArgumentParser( + description="compute kmeans codebook from kaldi-computed feats" + ) + # fmt: off + parser.add_argument('data', help='location of tsv files') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec ctc model', required=True) + parser.add_argument('--layer', type=int, default=14, help='which layer to use') + # fmt: on + + return parser + + +class Wav2VecFeatureReader(object): + def __init__(self, cp_file, layer): + model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [cp_file] + ) + model = model[0] + model.eval() + model.cuda() + self.model = model + self.task = task + self.layer = layer + + def read_audio(self, fname): + """Load an audio file and return PCM along with the sample rate""" + wav, sr = sf.read(fname) + assert sr == 16e3 + + return wav + + def get_feats(self, loc): + x = self.read_audio(loc) + with torch.no_grad(): + source = torch.from_numpy(x).float().cuda() + if self.task.cfg.normalize: + assert source.dim() == 1, source.dim() + with torch.no_grad(): + source = F.layer_norm(source, source.shape) + source = source.view(1, -1) + + m_res = self.model(source=source, mask=False, features_only=True, layer=self.layer) + return m_res["x"].squeeze(0).cpu() + + +def get_iterator(args): + with open(osp.join(args.data, args.split) + ".tsv", "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + files = [osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0] + + num = len(files) + reader = Wav2VecFeatureReader(args.checkpoint, args.layer) + + def iterate(): + for fname in files: + w2v_feats = reader.get_feats(fname) + yield w2v_feats + + return iterate, num + + +def main(): + parser = get_parser() + args = parser.parse_args() + + os.makedirs(args.save_dir, exist_ok=True) + + def create_files(dest): + copyfile(osp.join(args.data, args.split) + ".tsv", dest + ".tsv") + if osp.exists(osp.join(args.data, args.split) + ".wrd"): + copyfile(osp.join(args.data, args.split) + ".wrd", dest + ".wrd") + if osp.exists(osp.join(args.data, args.split) + ".phn"): + copyfile(osp.join(args.data, args.split) + ".phn", dest + ".phn") + + if osp.exists(dest + ".npy"): + os.remove(dest + ".npy") + npaa = NpyAppendArray(dest + ".npy") + return npaa + + save_path = osp.join(args.save_dir, args.split) + npaa = create_files(save_path) + + generator, num = get_iterator(args) + iterator = generator() + + with open(save_path + ".lengths", "w") as l_f: + for w2v_feats in tqdm.tqdm(iterator, total=num): + print(len(w2v_feats), file=l_f) + + if len(w2v_feats) > 0: + npaa.append(w2v_feats.numpy()) + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/wer.py b/fairseq/examples/wav2vec/unsupervised/scripts/wer.py new file mode 100644 index 0000000..613ab50 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/wer.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Implement unsupervised metric for decoding hyperparameter selection: + $$ alpha * LM_PPL + ViterbitUER(%) * 100 $$ +""" +import argparse +import logging +import sys + +import editdistance + +logging.root.setLevel(logging.INFO) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) +logger = logging.getLogger(__name__) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--hypo", help="hypo transcription", required=True) + parser.add_argument( + "-r", "--reference", help="reference transcription", required=True + ) + return parser + + +def compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p): + d_cnt = 0 + w_cnt = 0 + w_cnt_h = 0 + for uid in hyp_uid_to_tra: + ref = ref_uid_to_tra[uid].split() + if g2p is not None: + hyp = g2p(hyp_uid_to_tra[uid]) + hyp = [p for p in hyp if p != "'" and p != " "] + hyp = [p[:-1] if p[-1].isnumeric() else p for p in hyp] + else: + hyp = hyp_uid_to_tra[uid].split() + d_cnt += editdistance.eval(ref, hyp) + w_cnt += len(ref) + w_cnt_h += len(hyp) + wer = float(d_cnt) / w_cnt + logger.debug( + ( + f"wer = {wer * 100:.2f}%; num. of ref words = {w_cnt}; " + f"num. of hyp words = {w_cnt_h}; num. of sentences = {len(ref_uid_to_tra)}" + ) + ) + return wer + + +def main(): + args = get_parser().parse_args() + + errs = 0 + count = 0 + with open(args.hypo, "r") as hf, open(args.reference, "r") as rf: + for h, r in zip(hf, rf): + h = h.rstrip().split() + r = r.rstrip().split() + errs += editdistance.eval(r, h) + count += len(r) + + logger.info(f"UER: {errs / count * 100:.2f}%") + + +if __name__ == "__main__": + main() + + +def load_tra(tra_path): + with open(tra_path, "r") as f: + uid_to_tra = {} + for line in f: + uid, tra = line.split(None, 1) + uid_to_tra[uid] = tra + logger.debug(f"loaded {len(uid_to_tra)} utterances from {tra_path}") + return uid_to_tra diff --git a/fairseq/examples/wav2vec/unsupervised/scripts/wrd_to_ltr.py b/fairseq/examples/wav2vec/unsupervised/scripts/wrd_to_ltr.py new file mode 100644 index 0000000..f834714 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/scripts/wrd_to_ltr.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + + +def main(): + for line in sys.stdin: + print(" ".join(list(line.strip().replace(" ", "|"))) + " |") + + +if __name__ == "__main__": + main() diff --git a/fairseq/examples/wav2vec/unsupervised/tasks/__init__.py b/fairseq/examples/wav2vec/unsupervised/tasks/__init__.py new file mode 100644 index 0000000..6d7dd62 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/tasks/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .unpaired_audio_text import UnpairedAudioText + + +__all__ = [ + "UnpairedAudioText", +] diff --git a/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py b/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py new file mode 100644 index 0000000..b6b65d5 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py @@ -0,0 +1,452 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +from dataclasses import dataclass, field +import logging +import math +import os +from typing import Optional +import torch + +from fairseq.logging import metrics +from fairseq.tasks import FairseqTask, register_task +from ..data import ExtractedFeaturesDataset, RandomInputDataset + +from fairseq.data import ( + Dictionary, + data_utils, + StripTokenDataset, +) +from fairseq.dataclass import FairseqDataclass +from fairseq.distributed.utils import get_data_parallel_world_size +from omegaconf import MISSING + +from examples.speech_recognition.kaldi.kaldi_decoder import ( + KaldiDecoder, + KaldiDecoderConfig, +) + + +logger = logging.getLogger(__name__) + + +@dataclass +class DecodingConfig(FairseqDataclass): + kenlm_path: Optional[str] = None + lm_weight: float = 0 + blank_weight: float = 0 + + +@dataclass +class UnpairedAudioTextConfig(FairseqDataclass): + data: str = field( + default=MISSING, metadata={"help": "path to data directory containing audio"} + ) + text_data: str = field( + default=MISSING, metadata={"help": "path to data directory containing text"} + ) + max_length: Optional[int] = None + labels: Optional[str] = field( + default=None, + metadata={"help": "extension of the label file to load, used for fine-tuning"}, + ) + aux_target_postfix: Optional[str] = field( + default=None, + metadata={"help": "auxaliry target filename extension"}, + ) + unfiltered: bool = field( + default=False, metadata={"help": "load data with _unfiltered suffix"} + ) + ctc_eval: bool = field( + default=False, metadata={"help": "eval UER as if computed by CTC"} + ) + sort_by_length: bool = field( + default=True, metadata={"help": "sort examples by length of audio timesteps"} + ) + shuffle: bool = field(default=True, metadata={"help": "shuffle examples"}) + append_eos: bool = field(default=False, metadata={"help": "append eos"}) + uppercase: Optional[bool] = field( + default=False, metadata={"help": "uppercase for LM score computation"} + ) + skipwords: Optional[str] = field( + default="", + metadata={ + "help": "comma-separated words to be removed for LM score computation" + }, + ) + kenlm_path: Optional[str] = None + vocab_usage_power: float = 2 + + word_decoder_config: Optional[KaldiDecoderConfig] = None + word_kenlm_path: Optional[str] = None + + decoding_config: DecodingConfig = DecodingConfig() + + +@register_task("unpaired_audio_text", dataclass=UnpairedAudioTextConfig) +class UnpairedAudioText(FairseqTask): + """ """ + + cfg: UnpairedAudioTextConfig + + def __init__( + self, + cfg: UnpairedAudioTextConfig, + source_dictionary=None, + target_dictionary=None, + ): + super().__init__(cfg) + + self._target_dictionary = target_dictionary + self._source_dictionary = source_dictionary + self.num_symbols = ( + len([s for s in target_dictionary.symbols if not s.startswith("madeup")]) + - target_dictionary.nspecial + ) + self.sil_id = ( + target_dictionary.index("<SIL>") if "<SIL>" in target_dictionary else -1 + ) + self.kenlm = None + if cfg.kenlm_path is not None: + import kenlm + + self.kenlm = kenlm.Model(cfg.kenlm_path) + + self.word_kenlm = None + if cfg.word_kenlm_path is not None: + import kenlm + + self.word_kenlm = kenlm.Model(cfg.word_kenlm_path) + + self.uppercase = cfg.uppercase + self.skipwords = set(cfg.skipwords.split(",")) + + def str_postprocess(s): + s = " ".join(w for w in s.split() if w not in self.skipwords) + s = s.upper() if self.uppercase else s + return s + + self.str_postprocess = str_postprocess + self.compute_lm_score = lambda s: self.kenlm.score(self.str_postprocess(s)) + + self.compute_word_score = None + if cfg.word_decoder_config is not None: + self.kaldi_decoder = KaldiDecoder(cfg.word_decoder_config, beam=10) + + def compute_word_score(logits, padding): + res = self.kaldi_decoder.decode(logits, padding) + for r in res: + r = r.result() + assert len(r) == 1 + r = r[0] + yield r["score"], r["words"] + + self.compute_word_score = compute_word_score + + @classmethod + def setup_task(cls, cfg: UnpairedAudioTextConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (AudioPretrainingConfig): configuration of this task + """ + + dict_path = os.path.join(cfg.text_data, "dict.txt") + if os.path.exists(dict_path): + target_dictionary = Dictionary.load(dict_path) + else: + dict_path = os.path.join(cfg.data, f"dict.{cfg.labels}.txt") + target_dictionary = Dictionary.load(dict_path) + + return cls(cfg, target_dictionary=target_dictionary) + + def optimizer_step(self, optimizer, model, update_num): + if hasattr(model, "get_groups_for_update"): + groups = model.get_groups_for_update(update_num) + optimizer.step(groups={groups}) + else: + optimizer.step() + + def valid_step(self, sample, model, criterion): + res = model( + **sample["net_input"], + dense_x_only=True, + ) + + dense_x = res["logits"] + padding_mask = res["padding_mask"] + + word_scores = None + if self.compute_word_score is not None: + word_scores = self.compute_word_score(dense_x.cpu(), padding_mask.cpu()) + + z = dense_x.argmax(-1) + z[padding_mask] = self.target_dictionary.pad() + + vocab_seen = torch.zeros(self.num_symbols, dtype=torch.bool) + + import editdistance + + c_err = 0 + c_len = 0 + pred_c_len = 0 + lm_score_sum = 0 + for i, (x, t, id) in enumerate( + zip( + z, + sample["target"] if "target" in sample else [None] * len(z), + sample["id"], + ) + ): + + if t is not None: + t = t[(t >= self.target_dictionary.nspecial)] + x = x[ + (x >= self.target_dictionary.nspecial) + & (x < (self.num_symbols + self.target_dictionary.nspecial)) + ] + if self.sil_id >= 0: + x = x[x != self.sil_id] + + vocab_seen[x - self.target_dictionary.nspecial] = True + + pred_units_arr = x + if self.cfg.ctc_eval: + pred_units_arr = pred_units_arr.unique_consecutive() + pred_units_arr = pred_units_arr[pred_units_arr != 0] + + if id == 0: + if t is not None: + logger.info(f"REF: {self.target_dictionary.string(t)}") + logger.info(f"HYP: {self.target_dictionary.string(pred_units_arr)}") + + if self.kenlm is not None: + if t is not None: + ref_lm_s = self.compute_lm_score( + self.target_dictionary.string(t) + ) + logger.info( + f"LM [REF]: {ref_lm_s}, {math.pow(10, -ref_lm_s / (len(t) + 1))}" + ) + + hyp_lm_s = self.compute_lm_score( + self.target_dictionary.string(pred_units_arr) + ) + logger.info( + f"LM [HYP]: {hyp_lm_s}, {math.pow(10, -hyp_lm_s / (len(pred_units_arr) + 1))}" + ) + + pred_units_arr = pred_units_arr.tolist() + + pred_c_len += len(pred_units_arr) + + if t is not None: + t = t.tolist() + c_err += editdistance.eval(pred_units_arr, t) + c_len += len(t) + else: + c_len = pred_c_len + + if self.kenlm is not None: + pred_str = self.target_dictionary.string(pred_units_arr) + lm_score = self.compute_lm_score(pred_str) + lm_score_sum += lm_score + + kaldi_score_sum = 0 + word_lm_sum = 0 + num_words = 0 + if word_scores is not None: + for score, words in word_scores: + kaldi_score_sum += score + num_words += len(words) + if self.word_kenlm is not None: + word_lm_sum += self.kenlm.score(" ".join(words)) + + try: + world_size = get_data_parallel_world_size() + except: + world_size = 1 + + logging_output = { + "loss": c_err, + "_num_char_errors": c_err, + "_num_chars": c_len, + "_num_pred_chars": pred_c_len, + "ntokens": c_len, + "nsentences": z.size(0), + "sample_size": c_len, + "_world_size": world_size, + "_lm_score_sum": lm_score_sum, + "_kaldi_score_sum": kaldi_score_sum, + "_word_lm_sum": word_lm_sum, + "_num_words": num_words, + "_vocab_seen": vocab_seen, + } + + return c_err, c_len, logging_output + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + task_cfg = task_cfg or self.cfg + + has_unpaired_text = os.path.exists( + os.path.join(self.cfg.text_data, f"{split}.idx") + ) + + self.datasets[split] = ExtractedFeaturesDataset( + path=data_path, + split=split, + min_length=3, + max_length=task_cfg.max_length, + labels=None if has_unpaired_text else task_cfg.labels, + label_dict=self.target_dictionary, + shuffle=getattr(task_cfg, "shuffle", True), + sort_by_length=task_cfg.sort_by_length, + aux_target_postfix=task_cfg.aux_target_postfix, + ) + + logger.info(f"split {split} has unpaired text? {has_unpaired_text}") + if has_unpaired_text: + text_dataset = data_utils.load_indexed_dataset( + os.path.join(self.cfg.text_data, split), self.target_dictionary + ) + text_dataset = StripTokenDataset(text_dataset, self.target_dictionary.eos()) + self.datasets[split] = RandomInputDataset( + self.datasets[split], + text_dataset, + ["random_label"], + add_to_input=True, + pad_idx=self.target_dictionary.pad(), + ) + + @property + def source_dictionary(self): + return self._source_dictionary + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self._target_dictionary + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return None + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + zero = torch.scalar_tensor(0.0) + num_char_errors = sum( + log.get("_num_char_errors", zero) for log in logging_outputs + ) + num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) + num_word_errors = sum( + log.get("_num_word_errors", zero) for log in logging_outputs + ) + num_words = sum(log.get("_num_words", zero) for log in logging_outputs) + num_pred_chars = sum( + log.get("_num_pred_chars", zero) for log in logging_outputs + ) + + lm_score_sum = sum(log.get("_lm_score_sum", zero) for log in logging_outputs) + vocab_seen = ( + sum(log.get("_vocab_seen", zero) for log in logging_outputs) + .bool() + .sum() + .item() + ) + kaldi_score_sum = sum( + log.get("_kaldi_score_sum", zero) for log in logging_outputs + ) + word_lm_sum = sum(log.get("_word_lm_sum", zero) for log in logging_outputs) + + metrics.log_scalar_sum("_num_char_errors", num_char_errors) + metrics.log_scalar_sum("_num_chars", num_chars) + metrics.log_scalar_sum("_num_word_errors", num_word_errors) + metrics.log_scalar_sum("_num_words", num_words) + + metrics.log_scalar_sum("lm_score_sum", lm_score_sum) + metrics.log_scalar_sum("num_pred_chars", num_pred_chars) + + if self.cfg.word_kenlm_path is not None: + metrics.log_scalar_sum("kaldi_score_sum", kaldi_score_sum) + metrics.log_scalar_sum("word_lm_sum", word_lm_sum) + + if num_chars > 0: + metrics.log_derived( + "uer", + lambda meters: meters["_num_char_errors"].sum + * 100.0 + / meters["_num_chars"].sum + if meters["_num_chars"].sum > 0 + else float("nan"), + ) + + if lm_score_sum < 0 and vocab_seen > 0: + metrics.log_scalar("vocab_seen_pct", vocab_seen / self.num_symbols) + + metrics.log_derived( + "weighted_lm_ppl", + lambda meters: math.pow( + 10, + -meters["lm_score_sum"].sum + / ( + meters["num_pred_chars"].sum + meters["nsentences"].sum + ), # account for </s> + ) + / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, + ) + + metrics.log_derived( + "lm_ppl", + lambda meters: math.pow( + 10, + -meters["lm_score_sum"].sum + / ( + meters["num_pred_chars"].sum + meters["nsentences"].sum + ), # account for </s> + ), + ) + else: + metrics.log_derived("weighted_lm_ppl", lambda meters: float("inf")) + + if num_words > 0: + if word_lm_sum != 0: + metrics.log_derived( + "word_lm_ppl", + lambda meters: math.pow( + 10, + -meters["word_lm_sum"].sum + / ( + meters["_num_words"].sum + meters["nsentences"].sum + ), # account for </s> + ), + ) + metrics.log_derived( + "weighted_word_lm_ppl", + lambda meters: math.pow( + 10, + -meters["word_lm_sum"].sum + / ( + meters["_num_words"].sum + meters["nsentences"].sum + ), # account for </s> + ) + / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, + ) + + if self.cfg.word_kenlm_path is not None: + metrics.log_derived( + "kaldi_score", + lambda meters: meters["kaldi_score_sum"].sum + / meters["nsentences"].sum, + ) + + def build_model(self, cfg: FairseqDataclass, from_checkpoint=False): + model = super().build_model(cfg) + + return model diff --git a/fairseq/examples/wav2vec/unsupervised/w2vu_generate.py b/fairseq/examples/wav2vec/unsupervised/w2vu_generate.py new file mode 100644 index 0000000..0611297 --- /dev/null +++ b/fairseq/examples/wav2vec/unsupervised/w2vu_generate.py @@ -0,0 +1,714 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Run inference for pre-processed data with a trained model. +""" + +import ast +from collections import namedtuple +from dataclasses import dataclass, field +from enum import Enum, auto +import hydra +from hydra.core.config_store import ConfigStore +import logging +import math +import os +from omegaconf import OmegaConf +from typing import Optional +import sys + +import editdistance +import torch + +from hydra.core.hydra_config import HydraConfig + +from fairseq import checkpoint_utils, progress_bar, tasks, utils +from fairseq.data.data_utils import post_process +from fairseq.dataclass.configs import FairseqDataclass, FairseqConfig +from fairseq.logging.meters import StopwatchMeter +from omegaconf import open_dict + +from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoderConfig + +logging.root.setLevel(logging.INFO) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) +logger = logging.getLogger(__name__) + + +class DecoderType(Enum): + VITERBI = auto() + KENLM = auto() + FAIRSEQ = auto() + KALDI = auto() + + +@dataclass +class UnsupGenerateConfig(FairseqDataclass): + fairseq: FairseqConfig = FairseqConfig() + lm_weight: float = field( + default=2.0, + metadata={"help": "language model weight"}, + ) + w2l_decoder: DecoderType = field( + default=DecoderType.VITERBI, + metadata={"help": "type of decoder to use"}, + ) + kaldi_decoder_config: Optional[KaldiDecoderConfig] = None + lexicon: Optional[str] = field( + default=None, + metadata={ + "help": "path to lexicon. This is also used to 'phonemize' for unsupvised param tuning" + }, + ) + lm_model: Optional[str] = field( + default=None, + metadata={"help": "path to language model (kenlm or fairseq)"}, + ) + decode_stride: Optional[float] = field( + default=None, + metadata={"help": "changing the decoding frequency of the generator"}, + ) + unit_lm: bool = field( + default=False, + metadata={"help": "whether to use unit lm"}, + ) + beam_threshold: float = field( + default=50.0, + metadata={"help": "beam score threshold"}, + ) + beam_size_token: float = field( + default=100.0, + metadata={"help": "max tokens per beam"}, + ) + beam: int = field( + default=5, + metadata={"help": "decoder beam size"}, + ) + nbest: int = field( + default=1, + metadata={"help": "number of results to return"}, + ) + word_score: float = field( + default=1.0, + metadata={"help": "word score to add at end of word"}, + ) + unk_weight: float = field( + default=-math.inf, + metadata={"help": "unknown token weight"}, + ) + sil_weight: float = field( + default=0.0, + metadata={"help": "silence token weight"}, + ) + targets: Optional[str] = field( + default=None, + metadata={"help": "extension of ground truth labels to compute UER"}, + ) + results_path: Optional[str] = field( + default=None, + metadata={"help": "where to store results"}, + ) + post_process: Optional[str] = field( + default=None, + metadata={"help": "how to post process results"}, + ) + vocab_usage_power: float = field( + default=2, + metadata={"help": "for unsupervised param tuning"}, + ) + + viterbi_transcript: Optional[str] = field( + default=None, + metadata={"help": "for unsupervised param tuning"}, + ) + min_lm_ppl: float = field( + default=0, + metadata={"help": "for unsupervised param tuning"}, + ) + min_vt_uer: float = field( + default=0, + metadata={"help": "for unsupervised param tuning"}, + ) + + blank_weight: float = field( + default=0, + metadata={"help": "value to add or set for blank emission"}, + ) + blank_mode: str = field( + default="set", + metadata={ + "help": "can be add or set, how to modify blank emission with blank weight" + }, + ) + sil_is_blank: bool = field( + default=False, + metadata={"help": "if true, <SIL> token is same as blank token"}, + ) + + unsupervised_tuning: bool = field( + default=False, + metadata={ + "help": "if true, returns a score based on unsupervised param selection metric instead of UER" + }, + ) + is_ax: bool = field( + default=False, + metadata={ + "help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume" + }, + ) + + +def get_dataset_itr(cfg, task): + return task.get_batch_iterator( + dataset=task.dataset(cfg.fairseq.dataset.gen_subset), + max_tokens=cfg.fairseq.dataset.max_tokens, + max_sentences=cfg.fairseq.dataset.batch_size, + max_positions=(sys.maxsize, sys.maxsize), + ignore_invalid_inputs=cfg.fairseq.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=cfg.fairseq.dataset.required_batch_size_multiple, + num_shards=cfg.fairseq.dataset.num_shards, + shard_id=cfg.fairseq.dataset.shard_id, + num_workers=cfg.fairseq.dataset.num_workers, + data_buffer_size=cfg.fairseq.dataset.data_buffer_size, + ).next_epoch_itr(shuffle=False) + + +def process_predictions( + cfg: UnsupGenerateConfig, + hypos, + tgt_dict, + target_tokens, + res_files, +): + retval = [] + word_preds = [] + transcriptions = [] + dec_scores = [] + + for i, hypo in enumerate(hypos[: min(len(hypos), cfg.nbest)]): + if torch.is_tensor(hypo["tokens"]): + tokens = hypo["tokens"].int().cpu() + tokens = tokens[tokens >= tgt_dict.nspecial] + hyp_pieces = tgt_dict.string(tokens) + else: + hyp_pieces = " ".join(hypo["tokens"]) + + if "words" in hypo and len(hypo["words"]) > 0: + hyp_words = " ".join(hypo["words"]) + else: + hyp_words = post_process(hyp_pieces, cfg.post_process) + + to_write = {} + if res_files is not None: + to_write[res_files["hypo.units"]] = hyp_pieces + to_write[res_files["hypo.words"]] = hyp_words + + tgt_words = "" + if target_tokens is not None: + if isinstance(target_tokens, str): + tgt_pieces = tgt_words = target_tokens + else: + tgt_pieces = tgt_dict.string(target_tokens) + tgt_words = post_process(tgt_pieces, cfg.post_process) + + if res_files is not None: + to_write[res_files["ref.units"]] = tgt_pieces + to_write[res_files["ref.words"]] = tgt_words + + if not cfg.fairseq.common_eval.quiet: + logger.info(f"HYPO {i}:" + hyp_words) + if tgt_words: + logger.info("TARGET:" + tgt_words) + + if "am_score" in hypo and "lm_score" in hypo: + logger.info( + f"DECODER AM SCORE: {hypo['am_score']}, DECODER LM SCORE: {hypo['lm_score']}, DECODER SCORE: {hypo['score']}" + ) + elif "score" in hypo: + logger.info(f"DECODER SCORE: {hypo['score']}") + + logger.info("___________________") + + hyp_words_arr = hyp_words.split() + tgt_words_arr = tgt_words.split() + + retval.append( + ( + editdistance.eval(hyp_words_arr, tgt_words_arr), + len(hyp_words_arr), + len(tgt_words_arr), + hyp_pieces, + hyp_words, + ) + ) + word_preds.append(hyp_words_arr) + transcriptions.append(to_write) + dec_scores.append(-hypo.get("score", 0)) # negate cuz kaldi returns NLL + + if len(retval) > 1: + best = None + for r, t in zip(retval, transcriptions): + if best is None or r[0] < best[0][0]: + best = r, t + for dest, tran in best[1].items(): + print(tran, file=dest) + dest.flush() + return best[0] + + assert len(transcriptions) == 1 + for dest, tran in transcriptions[0].items(): + print(tran, file=dest) + + return retval[0] + + +def prepare_result_files(cfg: UnsupGenerateConfig): + def get_res_file(file_prefix): + if cfg.fairseq.dataset.num_shards > 1: + file_prefix = f"{cfg.fairseq.dataset.shard_id}_{file_prefix}" + path = os.path.join( + cfg.results_path, + "{}{}.txt".format( + cfg.fairseq.dataset.gen_subset, + file_prefix, + ), + ) + return open(path, "w", buffering=1) + + if not cfg.results_path: + return None + + return { + "hypo.words": get_res_file(""), + "hypo.units": get_res_file("_units"), + "ref.words": get_res_file("_ref"), + "ref.units": get_res_file("_ref_units"), + "hypo.nbest.words": get_res_file("_nbest_words"), + } + + +def optimize_models(cfg: UnsupGenerateConfig, use_cuda, models): + """Optimize ensemble for generation""" + for model in models: + model.eval() + if cfg.fairseq.common.fp16: + model.half() + if use_cuda: + model.cuda() + + +GenResult = namedtuple( + "GenResult", + [ + "count", + "errs_t", + "gen_timer", + "lengths_hyp_unit_t", + "lengths_hyp_t", + "lengths_t", + "lm_score_t", + "num_feats", + "num_sentences", + "num_symbols", + "vt_err_t", + "vt_length_t", + ], +) + + +def generate(cfg: UnsupGenerateConfig, models, saved_cfg, use_cuda): + task = tasks.setup_task(cfg.fairseq.task) + saved_cfg.task.labels = cfg.fairseq.task.labels + task.load_dataset(cfg.fairseq.dataset.gen_subset, task_cfg=saved_cfg.task) + # Set dictionary + tgt_dict = task.target_dictionary + logger.info( + "| {} {} {} examples".format( + cfg.fairseq.task.data, + cfg.fairseq.dataset.gen_subset, + len(task.dataset(cfg.fairseq.dataset.gen_subset)), + ) + ) + # Load dataset (possibly sharded) + itr = get_dataset_itr(cfg, task) + # Initialize generator + gen_timer = StopwatchMeter() + + def build_generator(cfg: UnsupGenerateConfig): + w2l_decoder = cfg.w2l_decoder + if w2l_decoder == DecoderType.VITERBI: + from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder + + return W2lViterbiDecoder(cfg, task.target_dictionary) + elif w2l_decoder == DecoderType.KENLM: + from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder + + return W2lKenLMDecoder(cfg, task.target_dictionary) + elif w2l_decoder == DecoderType.FAIRSEQ: + from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder + + return W2lFairseqLMDecoder(cfg, task.target_dictionary) + elif w2l_decoder == DecoderType.KALDI: + from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoder + + assert cfg.kaldi_decoder_config is not None + + return KaldiDecoder( + cfg.kaldi_decoder_config, + cfg.beam, + ) + else: + raise NotImplementedError( + "only wav2letter decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment but found " + + str(w2l_decoder) + ) + + generator = build_generator(cfg) + + kenlm = None + fairseq_lm = None + if cfg.lm_model is not None: + import kenlm + + kenlm = kenlm.Model(cfg.lm_model) + + num_sentences = 0 + if cfg.results_path is not None and not os.path.exists(cfg.results_path): + os.makedirs(cfg.results_path) + + res_files = prepare_result_files(cfg) + errs_t = 0 + lengths_hyp_t = 0 + lengths_hyp_unit_t = 0 + lengths_t = 0 + count = 0 + num_feats = 0 + all_hyp_pieces = [] + all_hyp_words = [] + + num_symbols = ( + len([s for s in tgt_dict.symbols if not s.startswith("madeup")]) + - tgt_dict.nspecial + ) + targets = None + if cfg.targets is not None: + tgt_path = os.path.join( + cfg.fairseq.task.data, cfg.fairseq.dataset.gen_subset + "." + cfg.targets + ) + if os.path.exists(tgt_path): + with open(tgt_path, "r") as f: + targets = f.read().splitlines() + viterbi_transcript = None + if cfg.viterbi_transcript is not None and len(cfg.viterbi_transcript) > 0: + logger.info(f"loading viterbi transcript from {cfg.viterbi_transcript}") + with open(cfg.viterbi_transcript, "r") as vf: + viterbi_transcript = vf.readlines() + viterbi_transcript = [v.rstrip().split() for v in viterbi_transcript] + + gen_timer.start() + + start = 0 + end = len(itr) + + hypo_futures = None + if cfg.w2l_decoder == DecoderType.KALDI: + logger.info("Extracting features") + hypo_futures = [] + samples = [] + with progress_bar.build_progress_bar(cfg.fairseq.common, itr) as t: + for i, sample in enumerate(t): + if "net_input" not in sample or i < start or i >= end: + continue + if "padding_mask" not in sample["net_input"]: + sample["net_input"]["padding_mask"] = None + + hypos, num_feats = gen_hypos( + generator, models, num_feats, sample, task, use_cuda + ) + hypo_futures.append(hypos) + samples.append(sample) + itr = list(zip(hypo_futures, samples)) + start = 0 + end = len(itr) + logger.info("Finished extracting features") + + with progress_bar.build_progress_bar(cfg.fairseq.common, itr) as t: + for i, sample in enumerate(t): + if i < start or i >= end: + continue + + if hypo_futures is not None: + hypos, sample = sample + hypos = [h.result() for h in hypos] + else: + if "net_input" not in sample: + continue + + hypos, num_feats = gen_hypos( + generator, models, num_feats, sample, task, use_cuda + ) + + for i, sample_id in enumerate(sample["id"].tolist()): + if targets is not None: + target_tokens = targets[sample_id] + elif "target" in sample or "target_label" in sample: + toks = ( + sample["target"][i, :] + if "target_label" not in sample + else sample["target_label"][i, :] + ) + + target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu() + else: + target_tokens = None + + # Process top predictions + ( + errs, + length_hyp, + length, + hyp_pieces, + hyp_words, + ) = process_predictions( + cfg, + hypos[i], + tgt_dict, + target_tokens, + res_files, + ) + errs_t += errs + lengths_hyp_t += length_hyp + lengths_hyp_unit_t += ( + len(hyp_pieces) if len(hyp_pieces) > 0 else len(hyp_words) + ) + lengths_t += length + count += 1 + all_hyp_pieces.append(hyp_pieces) + all_hyp_words.append(hyp_words) + + num_sentences += ( + sample["nsentences"] if "nsentences" in sample else sample["id"].numel() + ) + + lm_score_sum = 0 + if kenlm is not None: + + if cfg.unit_lm: + lm_score_sum = sum(kenlm.score(w) for w in all_hyp_pieces) + else: + lm_score_sum = sum(kenlm.score(w) for w in all_hyp_words) + elif fairseq_lm is not None: + lm_score_sum = sum(fairseq_lm.score([h.split() for h in all_hyp_words])[0]) + + vt_err_t = 0 + vt_length_t = 0 + if viterbi_transcript is not None: + unit_hyps = [] + if cfg.targets is not None and cfg.lexicon is not None: + lex = {} + with open(cfg.lexicon, "r") as lf: + for line in lf: + items = line.rstrip().split() + lex[items[0]] = items[1:] + for h in all_hyp_pieces: + hyp_ws = [] + for w in h.split(): + assert w in lex, w + hyp_ws.extend(lex[w]) + unit_hyps.append(hyp_ws) + + else: + unit_hyps.extend([h.split() for h in all_hyp_words]) + + vt_err_t = sum( + editdistance.eval(vt, h) for vt, h in zip(viterbi_transcript, unit_hyps) + ) + + vt_length_t = sum(len(h) for h in viterbi_transcript) + + if res_files is not None: + for r in res_files.values(): + r.close() + + gen_timer.stop(lengths_hyp_t) + + return GenResult( + count, + errs_t, + gen_timer, + lengths_hyp_unit_t, + lengths_hyp_t, + lengths_t, + lm_score_sum, + num_feats, + num_sentences, + num_symbols, + vt_err_t, + vt_length_t, + ) + + +def gen_hypos(generator, models, num_feats, sample, task, use_cuda): + sample = utils.move_to_cuda(sample) if use_cuda else sample + + if "features" in sample["net_input"]: + sample["net_input"]["dense_x_only"] = True + num_feats += ( + sample["net_input"]["features"].shape[0] + * sample["net_input"]["features"].shape[1] + ) + hypos = task.inference_step(generator, models, sample, None) + return hypos, num_feats + + +def main(cfg: UnsupGenerateConfig, model=None): + if ( + cfg.fairseq.dataset.max_tokens is None + and cfg.fairseq.dataset.batch_size is None + ): + cfg.fairseq.dataset.max_tokens = 1024000 + + use_cuda = torch.cuda.is_available() and not cfg.fairseq.common.cpu + + task = tasks.setup_task(cfg.fairseq.task) + + overrides = ast.literal_eval(cfg.fairseq.common_eval.model_overrides) + + if cfg.fairseq.task._name == "unpaired_audio_text": + overrides["model"] = { + "blank_weight": cfg.blank_weight, + "blank_mode": cfg.blank_mode, + "blank_is_sil": cfg.sil_is_blank, + "no_softmax": True, + "segmentation": { + "type": "NONE", + }, + } + else: + overrides["model"] = { + "blank_weight": cfg.blank_weight, + "blank_mode": cfg.blank_mode, + } + + if cfg.decode_stride: + overrides["model"]["generator_stride"] = cfg.decode_stride + + if model is None: + # Load ensemble + logger.info("| loading model(s) from {}".format(cfg.fairseq.common_eval.path)) + models, saved_cfg = checkpoint_utils.load_model_ensemble( + cfg.fairseq.common_eval.path.split("\\"), + arg_overrides=overrides, + task=task, + suffix=cfg.fairseq.checkpoint.checkpoint_suffix, + strict=(cfg.fairseq.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.fairseq.checkpoint.checkpoint_shard_count, + ) + optimize_models(cfg, use_cuda, models) + else: + models = [model] + saved_cfg = cfg.fairseq + + with open_dict(saved_cfg.task): + saved_cfg.task.shuffle = False + saved_cfg.task.sort_by_length = False + + gen_result = generate(cfg, models, saved_cfg, use_cuda) + + wer = None + if gen_result.lengths_t > 0: + wer = gen_result.errs_t * 100.0 / gen_result.lengths_t + logger.info(f"WER: {wer}") + + lm_ppl = float("inf") + + if gen_result.lm_score_t != 0 and gen_result.lengths_hyp_t > 0: + hyp_len = gen_result.lengths_hyp_t + lm_ppl = math.pow( + 10, -gen_result.lm_score_t / (hyp_len + gen_result.num_sentences) + ) + logger.info(f"LM PPL: {lm_ppl}") + + logger.info( + "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}" + " sentences/s, {:.2f} tokens/s)".format( + gen_result.num_sentences, + gen_result.gen_timer.n, + gen_result.gen_timer.sum, + gen_result.num_sentences / gen_result.gen_timer.sum, + 1.0 / gen_result.gen_timer.avg, + ) + ) + + vt_diff = None + if gen_result.vt_length_t > 0: + vt_diff = gen_result.vt_err_t / gen_result.vt_length_t + vt_diff = max(cfg.min_vt_uer, vt_diff) + + lm_ppl = max(cfg.min_lm_ppl, lm_ppl) + + if not cfg.unsupervised_tuning: + weighted_score = wer + else: + weighted_score = math.log(lm_ppl) * (vt_diff or 1.0) + + res = ( + f"| Generate {cfg.fairseq.dataset.gen_subset} with beam={cfg.beam}, " + f"lm_weight={cfg.kaldi_decoder_config.acoustic_scale if cfg.kaldi_decoder_config else cfg.lm_weight}, " + f"word_score={cfg.word_score}, sil_weight={cfg.sil_weight}, blank_weight={cfg.blank_weight}, " + f"WER: {wer}, LM_PPL: {lm_ppl}, num feats: {gen_result.num_feats}, " + f"length: {gen_result.lengths_hyp_t}, UER to viterbi: {(vt_diff or 0) * 100}, score: {weighted_score}" + ) + + logger.info(res) + # print(res) + + return task, weighted_score + + +@hydra.main( + config_path=os.path.join("../../..", "fairseq", "config"), config_name="config" +) +def hydra_main(cfg): + with open_dict(cfg): + # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) + cfg.job_logging_cfg = OmegaConf.to_container( + HydraConfig.get().job_logging, resolve=True + ) + + cfg = OmegaConf.create( + OmegaConf.to_container(cfg, resolve=False, enum_to_str=False) + ) + OmegaConf.set_struct(cfg, True) + logger.info(cfg) + + utils.import_user_module(cfg.fairseq.common) + + _, score = main(cfg) + + if cfg.is_ax: + return score, None + return score + + +def cli_main(): + try: + from hydra._internal.utils import get_args + + cfg_name = get_args().config_name or "config" + except: + logger.warning("Failed to get config name from hydra args") + cfg_name = "config" + + cs = ConfigStore.instance() + cs.store(name=cfg_name, node=UnsupGenerateConfig) + hydra_main() + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/examples/wav2vec/vq-wav2vec_featurize.py b/fairseq/examples/wav2vec/vq-wav2vec_featurize.py new file mode 100644 index 0000000..627072e --- /dev/null +++ b/fairseq/examples/wav2vec/vq-wav2vec_featurize.py @@ -0,0 +1,250 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset +""" + +import argparse +import glob +import os +import os.path as osp +import pprint + +import soundfile as sf +import torch +import fairseq +from torch import nn +from torch.utils.data import DataLoader + + +try: + import tqdm +except: + print("Install tqdm to use --log-format=tqdm") + + +class FilesDataset: + def __init__(self, files, labels): + self.files = files + if labels and osp.exists(labels): + with open(labels, "r") as lbl_f: + self.labels = [line.rstrip() for line in lbl_f] + else: + self.labels = labels + + def __len__(self): + return len(self.files) + + def __getitem__(self, index): + fname = self.files[index] + + wav, sr = sf.read(fname) + assert sr == 16000 + + wav = torch.from_numpy(wav).float() + lbls = None + if self.labels: + if isinstance(self.labels, str): + lbl_file = osp.splitext(fname)[0] + "." + self.labels + with open(lbl_file, "r") as lblf: + lbls = lblf.readline() + assert lbls is not None + else: + lbls = self.labels[index] + return wav, lbls + + def collate(self, batch): + return batch + + +class ArgTypes: + @staticmethod + def existing_path(arg): + arg = str(arg) + assert osp.exists(arg), f"File {arg} does not exist" + return arg + + @staticmethod + def mkdir(arg): + arg = str(arg) + os.makedirs(arg, exist_ok=True) + return arg + + +class DatasetWriter: + def __init__(self): + + self.args = self.load_config() + pprint.pprint(self.args.__dict__) + + self.model = self.load_model() + + def __getattr__(self, attr): + return getattr(self.args, attr) + + def read_manifest(self, fname): + + with open(fname, "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + fnames = [ + osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0 + ] + + return fnames + + def process_splits(self): + + if self.args.shard is not None or self.args.num_shards is not None: + assert self.args.shard is not None and self.args.num_shards is not None + + for split in self.splits: + print(split) + + if self.extension == "tsv": + datadir = osp.join(self.data_dir, f"{split}.{self.extension}") + print("Reading manifest file: ", datadir) + files = self.read_manifest(datadir) + else: + datadir = osp.join(self.data_dir, split, f"**/*.{self.extension}") + files = glob.glob(datadir, recursive=True) + + assert len(files) > 0 + + if self.args.shard is not None: + files = files[self.args.shard :: self.args.num_shards] + + lbls = [] + with open(self.data_file(split), "w") as srcf: + for line, lbl in self.iterate(files): + print(line, file=srcf) + if self.args.labels: + lbls.append(lbl + "\n") + + if self.args.labels: + assert all(a is not None for a in lbls) + with open(self.lbl_file(split), "w") as lblf: + lblf.writelines(lbls) + + def iterate(self, files): + + data = self.load_data(files) + for samples in tqdm.tqdm(data, total=len(files) // 32): + + for wav, lbl in samples: + x = wav.unsqueeze(0).float().cuda() + + div = 1 + while x.size(-1) // div > self.args.max_size: + div += 1 + + xs = x.chunk(div, dim=-1) + + result = [] + for x in xs: + torch.cuda.empty_cache() + x = self.model.feature_extractor(x) + if self.quantize_location == "encoder": + with torch.no_grad(): + _, idx = self.model.vector_quantizer.forward_idx(x) + idx = idx.squeeze(0).cpu() + else: + with torch.no_grad(): + z = self.model.feature_aggregator(x) + _, idx = self.model.vector_quantizer.forward_idx(z) + idx = idx.squeeze(0).cpu() + result.append(idx) + + idx = torch.cat(result, dim=0) + yield " ".join("-".join(map(str, a.tolist())) for a in idx), lbl + + def lbl_file(self, name): + shard_part = "" if self.args.shard is None else f".{self.args.shard}" + return osp.join(self.output_dir, f"{name}.lbl{shard_part}") + + def data_file(self, name): + shard_part = "" if self.args.shard is None else f".{self.args.shard}" + return osp.join(self.output_dir, f"{name}.src{shard_part}") + + def var_file(self): + return osp.join(self.output_dir, f"vars.pt") + + def load_config(self): + + parser = argparse.ArgumentParser("Vector Quantized wav2vec features") + + # Model Arguments + parser.add_argument("--checkpoint", type=ArgTypes.existing_path, required=True) + parser.add_argument("--data-parallel", action="store_true") + + # Output Arguments + parser.add_argument("--output-dir", type=ArgTypes.mkdir, required=True) + + # Data Arguments + parser.add_argument("--data-dir", type=ArgTypes.existing_path, required=True) + parser.add_argument("--splits", type=str, nargs="+", required=True) + parser.add_argument("--extension", type=str, required=True) + parser.add_argument("--labels", type=str, required=False) + + parser.add_argument("--shard", type=int, default=None) + parser.add_argument("--num-shards", type=int, default=None) + parser.add_argument("--max-size", type=int, default=1300000) + + # Logger Arguments + parser.add_argument( + "--log-format", type=str, choices=["none", "simple", "tqdm"] + ) + + return parser.parse_args() + + def load_data(self, fnames): + + dataset = FilesDataset(fnames, self.args.labels) + loader = DataLoader( + dataset, batch_size=32, collate_fn=dataset.collate, num_workers=8 + ) + return loader + + def load_model(self): + model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([self.checkpoint]) + model = model[0] + + self.quantize_location = getattr(cfg.model, "vq", "encoder") + + model.eval().float() + model.cuda() + + if self.data_parallel: + model = nn.DataParallel(model) + + return model + + def __call__(self): + + self.process_splits() + + if hasattr(self.model.feature_extractor, "vars") and ( + self.args.shard is None or self.args.shard == 0 + ): + vars = ( + self.model.feature_extractor.vars.view( + self.model.feature_extractor.banks, + self.model.feature_extractor.num_vars, + -1, + ) + .cpu() + .detach() + ) + print("writing learned latent variable embeddings: ", vars.shape) + torch.save(vars, self.var_file()) + + +if __name__ == "__main__": + write_data = DatasetWriter() + + write_data() + print("Done.") diff --git a/fairseq/examples/wav2vec/wav2vec_featurize.py b/fairseq/examples/wav2vec/wav2vec_featurize.py new file mode 100644 index 0000000..588268b --- /dev/null +++ b/fairseq/examples/wav2vec/wav2vec_featurize.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset +""" + +import argparse +import glob +import os +from shutil import copy + +import h5py +import numpy as np +import soundfile as sf +import torch +import tqdm +import fairseq +from torch import nn + + +def read_audio(fname): + """ Load an audio file and return PCM along with the sample rate """ + + wav, sr = sf.read(fname) + assert sr == 16e3 + + return wav, 16e3 + + +class PretrainedWav2VecModel(nn.Module): + def __init__(self, fname): + super().__init__() + + model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([fname]) + model = model[0] + model.eval() + + self.model = model + + def forward(self, x): + with torch.no_grad(): + z = self.model.feature_extractor(x) + if isinstance(z, tuple): + z = z[0] + c = self.model.feature_aggregator(z) + return z, c + + +class EmbeddingWriterConfig(argparse.ArgumentParser): + def __init__(self): + super().__init__("Pre-compute embeddings for flashlight datasets") + + kwargs = {"action": "store", "type": str, "required": True} + + self.add_argument("--input", "-i", help="Input Directory", **kwargs) + self.add_argument("--output", "-o", help="Output Directory", **kwargs) + self.add_argument("--model", help="Path to model checkpoint", **kwargs) + self.add_argument("--split", help="Dataset Splits", nargs="+", **kwargs) + self.add_argument( + "--ext", default="wav", required=False, help="Audio file extension" + ) + + self.add_argument( + "--no-copy-labels", + action="store_true", + help="Do not copy label files. Useful for large datasets, use --targetdir in flashlight then.", + ) + self.add_argument( + "--use-feat", + action="store_true", + help="Use the feature vector ('z') instead of context vector ('c') for features", + ) + self.add_argument("--gpu", help="GPU to use", default=0, type=int) + + +class Prediction: + """ Lightweight wrapper around a fairspeech embedding model """ + + def __init__(self, fname, gpu=0): + self.gpu = gpu + self.model = PretrainedWav2VecModel(fname).cuda(gpu) + + def __call__(self, x): + x = torch.from_numpy(x).float().cuda(self.gpu) + with torch.no_grad(): + z, c = self.model(x.unsqueeze(0)) + + return z.squeeze(0).cpu().numpy(), c.squeeze(0).cpu().numpy() + + +class H5Writer: + """ Write features as hdf5 file in flashlight compatible format """ + + def __init__(self, fname): + self.fname = fname + os.makedirs(os.path.dirname(self.fname), exist_ok=True) + + def write(self, data): + channel, T = data.shape + + with h5py.File(self.fname, "w") as out_ds: + data = data.T.flatten() + out_ds["features"] = data + out_ds["info"] = np.array([16e3 // 160, T, channel]) + + +class EmbeddingDatasetWriter(object): + """Given a model and a flashlight dataset, pre-compute and store embeddings + + Args: + input_root, str : + Path to the flashlight dataset + output_root, str : + Desired output directory. Will be created if non-existent + split, str : + Dataset split + """ + + def __init__( + self, + input_root, + output_root, + split, + model_fname, + extension="wav", + gpu=0, + verbose=False, + use_feat=False, + ): + + assert os.path.exists(model_fname) + + self.model_fname = model_fname + self.model = Prediction(self.model_fname, gpu) + + self.input_root = input_root + self.output_root = output_root + self.split = split + self.verbose = verbose + self.extension = extension + self.use_feat = use_feat + + assert os.path.exists(self.input_path), "Input path '{}' does not exist".format( + self.input_path + ) + + def _progress(self, iterable, **kwargs): + if self.verbose: + return tqdm.tqdm(iterable, **kwargs) + return iterable + + def require_output_path(self, fname=None): + path = self.get_output_path(fname) + os.makedirs(path, exist_ok=True) + + @property + def input_path(self): + return self.get_input_path() + + @property + def output_path(self): + return self.get_output_path() + + def get_input_path(self, fname=None): + if fname is None: + return os.path.join(self.input_root, self.split) + return os.path.join(self.get_input_path(), fname) + + def get_output_path(self, fname=None): + if fname is None: + return os.path.join(self.output_root, self.split) + return os.path.join(self.get_output_path(), fname) + + def copy_labels(self): + self.require_output_path() + + labels = list( + filter( + lambda x: self.extension not in x, glob.glob(self.get_input_path("*")) + ) + ) + for fname in tqdm.tqdm(labels): + copy(fname, self.output_path) + + @property + def input_fnames(self): + return sorted(glob.glob(self.get_input_path("*.{}".format(self.extension)))) + + def __len__(self): + return len(self.input_fnames) + + def write_features(self): + + paths = self.input_fnames + + fnames_context = map( + lambda x: os.path.join( + self.output_path, x.replace("." + self.extension, ".h5context") + ), + map(os.path.basename, paths), + ) + + for name, target_fname in self._progress( + zip(paths, fnames_context), total=len(self) + ): + wav, sr = read_audio(name) + z, c = self.model(wav) + feat = z if self.use_feat else c + writer = H5Writer(target_fname) + writer.write(feat) + + def __repr__(self): + + return "EmbeddingDatasetWriter ({n_files} files)\n\tinput:\t{input_root}\n\toutput:\t{output_root}\n\tsplit:\t{split})".format( + n_files=len(self), **self.__dict__ + ) + + +if __name__ == "__main__": + + args = EmbeddingWriterConfig().parse_args() + + for split in args.split: + + writer = EmbeddingDatasetWriter( + input_root=args.input, + output_root=args.output, + split=split, + model_fname=args.model, + gpu=args.gpu, + extension=args.ext, + use_feat=args.use_feat, + ) + + print(writer) + writer.require_output_path() + + print("Writing Features...") + writer.write_features() + print("Done.") + + if not args.no_copy_labels: + print("Copying label data...") + writer.copy_labels() + print("Done.") diff --git a/fairseq/examples/wav2vec/wav2vec_manifest.py b/fairseq/examples/wav2vec/wav2vec_manifest.py new file mode 100644 index 0000000..9b8aa18 --- /dev/null +++ b/fairseq/examples/wav2vec/wav2vec_manifest.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Data pre-processing: build vocabularies and binarize training data. +""" + +import argparse +import glob +import os +import random + +import soundfile + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument( + "root", metavar="DIR", help="root directory containing flac files to index" + ) + parser.add_argument( + "--valid-percent", + default=0.01, + type=float, + metavar="D", + help="percentage of data to use as validation set (between 0 and 1)", + ) + parser.add_argument( + "--dest", default=".", type=str, metavar="DIR", help="output directory" + ) + parser.add_argument( + "--ext", default="flac", type=str, metavar="EXT", help="extension to look for" + ) + parser.add_argument("--seed", default=42, type=int, metavar="N", help="random seed") + parser.add_argument( + "--path-must-contain", + default=None, + type=str, + metavar="FRAG", + help="if set, path must contain this substring for a file to be included in the manifest", + ) + return parser + + +def main(args): + assert args.valid_percent >= 0 and args.valid_percent <= 1.0 + + if not os.path.exists(args.dest): + os.makedirs(args.dest) + + dir_path = os.path.realpath(args.root) + search_path = os.path.join(dir_path, "**/*." + args.ext) + rand = random.Random(args.seed) + + valid_f = ( + open(os.path.join(args.dest, "valid.tsv"), "w") + if args.valid_percent > 0 + else None + ) + + with open(os.path.join(args.dest, "train.tsv"), "w") as train_f: + print(dir_path, file=train_f) + + if valid_f is not None: + print(dir_path, file=valid_f) + + for fname in glob.iglob(search_path, recursive=True): + file_path = os.path.realpath(fname) + + if args.path_must_contain and args.path_must_contain not in file_path: + continue + + frames = soundfile.info(fname).frames + dest = train_f if rand.random() > args.valid_percent else valid_f + print( + "{}\t{}".format(os.path.relpath(file_path, dir_path), frames), file=dest + ) + if valid_f is not None: + valid_f.close() + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + main(args) diff --git a/fairseq/examples/wav2vec/xlsr/README.md b/fairseq/examples/wav2vec/xlsr/README.md new file mode 100644 index 0000000..e0a7c4e --- /dev/null +++ b/fairseq/examples/wav2vec/xlsr/README.md @@ -0,0 +1,95 @@ +# XLS-R + +XLS-R is a set of large-scale models for self-supervised cross-lingual speech representation learning based on wav2vec 2.0. It was pretrained on 128 languages and approximately 436K hours of unlabeled speech data. With finetuning, these models achieve state of the art performance in speech translation, speech recognition and language identification. We evaluate the model across multiple benchmarks such as CoVoST-2 for speech translation, BABEL / MLS / CommonVoice / VoxPopuli for automatic speech recognition, and VoxLingua107 for language identification as we llas VoxCeleb1 for speaker identification. More details about this work can be found in our [paper](https://arxiv.org/pdf/2111.09296.pdf) and download links can be found below. + +Model | Link +|------|------ +XLS-R 300M | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr2_300m.pt) +XLS-R 1B | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr2_960m_1000k.pt) +XLS-R 2B | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr2_2B_1000k.pt) + +You can also download these models [here](https://huggingface.co/models?other=xls_r) and read more about it in the [blogpost](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) from Hugging Face. + +## Speech Translation Finetuned Models + +We multilingually finetune XLS-R models on [CoVoST 2](https://github.com/facebookresearch/covost), which has 21 +into-English and 15 out-of-English directions. + +Model | Directions | Link +|------|------|------ +XLS-R 300M | 21 langs → En | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xls_r_300m_21_en.pt) +XLS-R 300M | En → 15 langs | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xls_r_300m_en_15.pt) +XLS-R 1B | 21 langs → En | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xls_r_1b_21_en.pt) +XLS-R 1B | En → 15 langs | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xls_r_1b_en_15.pt) +XLS-R 2B | 21 langs → En | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xls_r_2b_21_en.pt) +XLS-R 2B | En → 15 langs | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xls_r_2b_en_15.pt) +XLS-R 2B | 21 langs → En + En → 15 langs | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xls_r_2b_22_16.pt) + +## ASR Finetuning + +You can refer the original wav2vec documentation on detailed instructions about how to finetune a pretrained model with CTC [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#fine-tune-a-pre-trained-model-with-ctc). Below is an example command and you can find the values for different hyperparameters to reproduce the results in our paper. + +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_port=$PORT \ + task.data=/path/to/data \ + model.w2v_path=/path/to/model.pt \ + --config-dir /path/to/fairseq-py/examples/wav2vec/xlsr/config \ + --config-name finetune +``` + +For finetuning the 300M as well as 1B model, we use the same hyperparameter setting defined in `finetune.yaml`. We vary `optimization.max_update` as described in the below table and the `optimization.lr` is picked from the interval [2e-5, 3e-4] based on dev word error rate. + +Benchmark | Total Number of Updates +|------|------ +Babel | 26000 +Common Voice | 13000 +VoxPopuli | 50000 +MLS 10h | 20000 + +For finetuning the 2B model, we make some additional changes for `finetune.yaml` . We use the fully_sharded `distributed_training.ddp_backend` provided by the [fairscale](https://github.com/facebookresearch/fairscale) library and and set `model.activation_checkpoint` to true. We also increase `dataset.max_tokens` to 2560000 and use a total effective batch size of 2560000*24. We sweep for the best `optimization.lr` within the interval [3e−6,3e−5] using dev error rate. For common voice dataset, we pick the `model.mask_prob` for different languages among {0.30, 0.40} based on best dev error rate. + +## LID Inference + +Model | Link +|------|------ +XLS-R 300M + ft Voxlingua107 | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr_300m_voxlingua107_ft.pt) + +How to run inference & calculate accuracy (step-by-step): +1. Download the Voxlingua107 checkpoint from the table above. +1. Use this python script to extract logit/embedding from the XLSR model: https://github.com/fairinternal/fairseq-py/blob/xlsr2/examples/wav2vec/gen_audio_embedding.py +```shell command +CUDA_VISIBLE_DEVICES=0 PYTHONPATH=. python3 examples/wav2vec/gen_audio_embedding.py \ + /fsx/data/VoxLingua107/manifest --path "/path/to/checkpoint.pt" \ + --task audio_classification --batch-size 90 --gen-subset test \ + --infer-manifest /fsx/data/VoxLingua107/manifest/test.tsv \ + --infer-xtimes 10 --infer-max-sample-size 160000 --output-path /tmp/tmp_voxling_infer.npz +``` + +2. Calculate the overall accuracy, 0-5 seconds and 5-20 seconds: +```shell command +PYTHONPATH='.' python examples/wav2vec/eval_speaker_clf_task.py \ + --task cls --merge mean_logit --data /tmp/tmp_voxling_infer.npz + +Output: +| run classification evaluation +| acc = 94.34% -- err = 5.66% -- correct=1518 total=1609 +| acc 0to5 = 90.91% -- err = 9.09% -- c_5=230.0 t_5=253 +| acc 5to20 = 94.99% -- err = 5.01% -- c_20=1288.0 t_20=1356 +``` + +## Citation + +Please cite as: + +``` bibtex +@article{babu2021xlsr, + title={XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale}, + author={Arun Babu and Changhan Wang and Andros Tjandra and Kushal Lakhotia and Qiantong Xu and Naman Goyal and Kritika Singh and Patrick von Platen and Yatharth Saraf and Juan Pino and Alexei Baevski and Alexis Conneau and Michael Auli}, + year={2021}, + volume={abs/2111.09296}, + journal={arXiv}, +} +``` + + diff --git a/fairseq/examples/wav2vec/xlsr/config/finetune.yaml b/fairseq/examples/wav2vec/xlsr/config/finetune.yaml new file mode 100644 index 0000000..8736e10 --- /dev/null +++ b/fairseq/examples/wav2vec/xlsr/config/finetune.yaml @@ -0,0 +1,66 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + save_interval: 1000 + save_interval_updates: 1000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval_updates: 1000 + valid_subset: valid + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: ??? + lr: [0.0003] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.75 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + + checkpoint_activations: false diff --git a/fairseq/examples/wav2vec/xlsr/scripts/eval_speaker_clf_task.py b/fairseq/examples/wav2vec/xlsr/scripts/eval_speaker_clf_task.py new file mode 100644 index 0000000..16d0751 --- /dev/null +++ b/fairseq/examples/wav2vec/xlsr/scripts/eval_speaker_clf_task.py @@ -0,0 +1,173 @@ +""" +Usage: + This scripts it to evaluate the classification accuracy/error rate from the embedding extracted + by gen_audio_embedding.py + Example (LID classification) + + PYTHONPATH='.' python examples/wav2vec/eval_speaker_clf_task.py \ + --data /fsx/androstj/exps/lid_voxlingua/infer/atj_xlsr2_100pct_300M_mean_fast_upd_100k_new.npz \ + --task cls --merge mean_logit +""" +import numpy as np +import sklearn +from sklearn.metrics.pairwise import cosine_similarity +from sklearn.preprocessing import StandardScaler +from tqdm import tqdm +import ipdb +import logging +import argparse +from scipy.special import softmax + +log=logging.getLogger(__name__) +log.setLevel(logging.INFO) + +def calculate_eer(y_label, y_score): + # y denotes groundtruth scores, + # y_score denotes the prediction scores. + from scipy.optimize import brentq + from sklearn.metrics import roc_curve + from scipy.interpolate import interp1d + + fpr, tpr, thresholds = roc_curve(y_label, y_score, pos_label=1) + eer = brentq(lambda x : 1. - x - interp1d(fpr, tpr)(x), 0., 1.) + optimal_threshold = interp1d(fpr, thresholds)(eer) + return eer, optimal_threshold + +def calculate_minDCF(y_label, y_score, p_target=0.01, c_miss=1, c_fa=1): + # https://github.com/kaldi-asr/kaldi/blob/master/egs/sre08/v1/sid/compute_min_dcf.py + from sklearn.metrics import det_curve + fpr, fnr, thresholds = det_curve(y_label, y_score, pos_label=1) + min_c_det = float("inf") + min_c_det_threshold = thresholds[0] + for i in range(0, len(fpr)): + # See Equation (2). it is a weighted sum of false negative + # and false positive errors. + c_det = c_miss * fnr[i] * p_target + c_fa * fpr[i] * (1 - p_target) + if c_det < min_c_det: + min_c_det = c_det + min_c_det_threshold = thresholds[i] + # See Equations (3) and (4). Now we normalize the cost. + c_def = min(c_miss * p_target, c_fa * (1 - p_target)) + min_dcf = min_c_det / c_def + return min_dcf, min_c_det_threshold + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', help='npz contains name & latent file') + parser.add_argument('--task', choices=['cls', 'veri', 'cls_voxlingua']) + parser.add_argument('--merge', choices=['mean_logit', 'first_logit', 'mean_latent_sim', 'first_latent_sim', 'mean_logit_sim', 'first_logit_sim']) + parser.add_argument('--veri-pair', help='verification file contains 1/0 utt_x utt_y') + parser.add_argument('--scaler', type=str, choices=['mean_var']) + parser.add_argument('--compress-method', choices=['pca']) + parser.add_argument('--compress-dim', type=int) + args = parser.parse_args() + + if args.task in ['cls', 'cls_voxlingua']: + print('| run classification evaluation') + data = np.load(args.data) + data_logit = data['logit'] + data_target = data['target'] + data_src_len = data['src_len'] + assert data_logit.shape[0] == data_target.shape[0] + B = data_logit.shape[0] + correct = 0 + total = 0 + data_prob = softmax(data_logit, axis=2) + correct_vs_len = np.empty((B, 2)) + for ii in range(B): + _target = data_target[ii] + if args.merge == 'mean_logit': + _prob = np.mean(data_prob[ii], axis=0) + top_1 = np.argmax(_prob) + elif args.merge == 'first_logit': + _prob = data_prob[ii][0] + top_1 = np.argmax(_prob) + else : + raise ValueError() + is_top_1 = (1 if top_1 == _target else 0) + correct += is_top_1 + total += 1 + _src_len = data_src_len[ii] / 16000 + correct_vs_len[ii] = [is_top_1, _src_len] + + acc = correct / total * 100 + t_5 = correct_vs_len[:, 1] <= 5 + t_20 = correct_vs_len[:, 1] > 5 + c_5 = correct_vs_len[t_5, 0].sum() + c_20 = correct_vs_len[t_20, 0].sum() + t_5 = t_5.sum() + t_20 = t_20.sum() + acc_5 = c_5 / t_5 * 100 + acc_20 = c_20 / t_20 * 100 + print(f'| acc = {acc:.2f}% -- err = {100-acc:.2f}% -- {correct=} {total=}') + print(f'| acc 0to5 = {acc_5:.2f}% -- err = {100-acc_5:.2f}% -- {c_5=} {t_5=}') + print(f'| acc 5to20 = {acc_20:.2f}% -- err = {100-acc_20:.2f}% -- {c_20=} {t_20=}') + + + + if args.task == 'veri': + print('| run verification evaluation') + veri_pairs = [] + with open(args.veri_pair) as ff: + for fi in ff: + a,b,c = fi.split() + a = int(a) + veri_pairs.append([a,b,c]) + + data = np.load(args.data) + if 'logit' in args.merge: + data_latent = data['logit'] + elif 'latent' in args.merge: + data_latent = data['latent'] + else : + raise ValueError() + + data_name = data['name'] + assert len(data_name) == len(data_latent) + map_name_latent = {} + + from sklearn.pipeline import make_pipeline + pipe = [] + if args.scaler == 'mean_var': + print(f'| apply StandardScaler') + pipe.append(StandardScaler()) + + if args.compress_method == 'pca': + n_comp = args.compress_dim + print(f'| apply PCA with {n_comp=}') + from sklearn.decomposition import PCA + pipe.append(PCA(n_components=n_comp)) + if len(pipe) > 0 : + pipe = make_pipeline(*pipe) + data_latent_2d = data_latent.reshape(-1, data_latent.shape[-1]) + pipe.fit(data_latent_2d) + data_latent_2d = pipe.transform(data_latent_2d) + data_latent = data_latent_2d.reshape(data_latent.shape[0], data_latent.shape[1], -1) + + for ii in range(len(data_name)): + map_name_latent[data_name[ii]] = data_latent[ii] + labels = [] + scores = [] + for lbl, pair_a, pair_b in tqdm(veri_pairs): + labels.append(lbl) + pair_a = map_name_latent[pair_a] + pair_b = map_name_latent[pair_b] + assert pair_a.ndim == pair_b.ndim == 2 + score = cosine_similarity(pair_a, pair_b) + if args.merge.startswith('mean'): + score = np.mean(score) + elif args.merge.startswith('first'): + score = score[0, 0] + else : + raise ValueError() + scores.append(score) + labels = np.array(labels) + scores = np.array(scores) + eer, eer_threshold = calculate_eer(labels, scores) + minDCF, minDCF_threshold = calculate_minDCF(labels, scores) + print('='*40) + print(f'| EER = {eer*100:.2f}%\tthreshold = {eer_threshold:.2f}') + print(f'| minDCF = {minDCF:.2f}\tthreshold = {minDCF_threshold:.2f}') + + diff --git a/fairseq/examples/wav2vec/xlsr/scripts/gen_audio_embedding.py b/fairseq/examples/wav2vec/xlsr/scripts/gen_audio_embedding.py new file mode 100644 index 0000000..e5de1d5 --- /dev/null +++ b/fairseq/examples/wav2vec/xlsr/scripts/gen_audio_embedding.py @@ -0,0 +1,222 @@ +""" +Usage: + This script is used to extract the embedding / logit for speech classification task. + 1. Set fdir into your model checkpoint directory + 2. Run the following command (preferrably on GPU machine to speed up the inference process) + + CUDA_VISIBLE_DEVICES=0 python3 examples/wav2vec/gen_audio_embedding.py /fsx/data/VoxLingua107/manifest --path ${fdir} \ + --task audio_classification --batch-size 90 --gen-subset test \ + --infer-manifest /fsx/data/VoxLingua107/manifest/test.tsv \ + --infer-xtimes 10 --infer-max-sample-size 160000 --output-path $odir + + Example: + Case: LID logit extraction + fdir='/fsx/androstj/exps/voxlingua_lid_train_all/ckpt_100pct_300m_voxling-act_linear-pool_mean_fast-lr_1e-4-phase_0.1_0.4_0.5-maxupd_100000-ufreq_1-mprob_0.5-fz_0-cr_softmax/0/checkpoints/checkpoint_best.pt' + python3 examples/wav2vec/gen_audio_embedding.py /fsx/data/VoxLingua107/manifest --path ${fdir} \ + --task audio_classification --batch-size 90 --gen-subset test \ + --infer-manifest /fsx/data/VoxLingua107/manifest/test.tsv \ + --infer-xtimes 10 --infer-max-sample-size 160000 --output-path $odir + +""" +import torch +from fairseq import checkpoint_utils, distributed_utils, options, utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.logging import metrics, progress_bar +from fairseq import checkpoint_utils, data, options, tasks +from fairseq.data import FileAudioDataset, AddTargetDataset, Dictionary +from fairseq.tasks.audio_classification import LabelEncoder +import ipdb +import copy +import sys +from tqdm import tqdm +import tempfile +import numpy as np +import sklearn + +def subset_manifest(infer_manifest, veri_pair): + with open(infer_manifest) as ff, open(veri_pair) as gg, \ + tempfile.NamedTemporaryFile('w', delete=False) as ww: + fnames = ff.read().strip().split("\n") + basedir = fnames[0] + needed_fname = [] + for gi in gg.read().strip().split('\n'): + _, x1, x2 = gi.split() + needed_fname.append(x1) + needed_fname.append(x2) + needed_fname = set(needed_fname) + + ww.write(basedir+'\n') + for ii in range(1, len(fnames)): + x1,x2 = fnames[ii].split() + if x1 in needed_fname: + ww.write(fnames[ii]+'\n') + print(f'| subset manifest for verification: {ww.name}') + return ww.name + +def wrap_target_dataset(infer_manifest, dataset, task): + label_path = infer_manifest.replace(".tsv", ".label") + with open(label_path, "r") as f: + labels = f.read().strip().split("\n") + assert len(labels) == len(dataset) + process_label = LabelEncoder(task.target_dictionary) + dataset = AddTargetDataset(dataset, labels, + pad=task.target_dictionary.pad(), + eos=task.target_dictionary.eos(), + batch_targets=True, + process_label=process_label, + add_to_input=False) + return dataset + +def resample_data(source, padding_mask, n_sample, max_sample_len): + # source: BxT + # padding_mask: BxT + B = source.shape[0] + T = source.shape[1] + sources = [] + padding_masks = [] + seq_len = (~padding_mask).sum(1) + for jj in range(n_sample): + new_source = source.new_zeros(B, max_sample_len) + new_padding_mask = padding_mask.new_zeros(B, max_sample_len) + for ii in range(B): + if seq_len[ii] > max_sample_len: + start = np.random.randint(0, seq_len[ii]-max_sample_len+1) + end = start + max_sample_len + else : + start = 0 + end = seq_len[ii] + new_source[ii, 0:end-start] = source[ii, start:end] + new_padding_mask[ii, end-start+1:] = True + sources.append(new_source) + padding_masks.append(new_padding_mask) + return sources, padding_masks + +def resample_sample(sample, n_sample, max_sample_len): + new_sources, new_padding_masks = resample_data(sample['net_input']['source'], sample['net_input']['padding_mask'], n_sample, max_sample_len) + new_samples = [] + for ii in range(n_sample): + new_sample = copy.deepcopy(sample) + new_sample['net_input']['source'] = new_sources[ii] + new_sample['net_input']['padding_mask'] = new_padding_masks[ii] + new_samples.append(new_sample) + return new_samples + +if __name__ == '__main__': + np.random.seed(123) + # Parse command-line arguments for generation + parser = options.get_generation_parser(default_task='audio_classification') + # parser.add_argument('--infer-merge', type=str, default='mean') + parser.add_argument('--infer-xtimes', type=int, default=1) + parser.add_argument('--infer-max-sample-size', type=int, default=5*16000) # 5 secs + parser.add_argument('--infer-manifest', type=str) + parser.add_argument('--verification-pair', type=str, required=False, + help=''' + a file that contains pairs of utts to evaluated if they are from same speaker or not + format: (following voxceleb) + 1/0 <wav_pair_a> <wav_pair_b> + ''') + parser.add_argument('--output-path', type=str) + # parser.add_argument('--infer-xtimes', type=int, default=1) + + args = options.parse_args_and_arch(parser) + # Setup task + # task = tasks.setup_task(args) + use_cuda = not args.cpu + + # Load model & task + print('| loading model from {}'.format(args.path)) + arg_overrides = { + 'data': args.data, + # 'mask_prob': 0 + #'max_sample_size': sys.maxsize, + #'min_sample_size': 0, + } + state = checkpoint_utils.load_checkpoint_to_cpu(args.path) + # move to AWS + state['cfg']['model']['w2v_path'] = state['cfg']['model']['w2v_path'].replace('/checkpoint/arbabu/XLSR2/model_versions/', '/fsx/data/model_versions/').replace('/checkpoint/kushall/final_model_checkpoints/wav2vec2/', '/fsx/data/wav2vec_ckpt/') + state['cfg']['task']['data'] = state['cfg']['task']['data'].replace('/checkpoint/kushall/data/', '/fsx/data/') + + models, _model_args, task = checkpoint_utils.load_model_ensemble_and_task([args.path], + arg_overrides=arg_overrides, + task=None, + state=state) + model = models[0] + model.eval() + if use_cuda: + model.cuda() + + + # Load dataset + task.load_dataset(args.gen_subset) + dataset = task.dataset(args.gen_subset) + infer_manifest = args.infer_manifest + # only decode needed utts + # infer_manifest = subset_manifest(infer_manifest, + # args.verification_pair) + infer_dataset = FileAudioDataset(infer_manifest, + sample_rate=task.cfg.sample_rate, + max_sample_size=10**10, #task.cfg.max_sample_size, + min_sample_size=1, #task.cfg.min_sample_size, + pad=True, + normalize=task.cfg.normalize) + # add target (if needed) + infer_dataset = wrap_target_dataset(infer_manifest, infer_dataset, task) + itr = task.get_batch_iterator( + dataset=infer_dataset, + max_sentences=args.batch_size, + ).next_epoch_itr(shuffle=False) + + + # correct = 0 + # total = 0 + list_uttname = [] + list_latent = [] + list_logit = [] + list_target = [] + list_src_len = [] + with torch.no_grad(): + for _, sample in tqdm(enumerate(itr)): + # resample if needed + samples = resample_sample(sample, args.infer_xtimes, args.infer_max_sample_size) + list_uttname.extend(sample['name']) + list_target.extend(sample['target'][:, 0].cpu().numpy()) + list_src_len.extend((~sample['net_input']['padding_mask']).sum(1).cpu().numpy()) + latents = [] + logits = [] + for sample in samples: + sample = utils.move_to_cuda(sample) if use_cuda else sample + try: + latent = model.forward_latent(**sample['net_input']) + latents.append(latent.detach().cpu().numpy()) + except: + latent = None + logit = model.forward(**sample['net_input']) + logits.append(logit.detach().cpu().numpy()) + + if len(latents) > 0: + latents = np.stack(latents, 1) # B,X,D + logits = np.stack(logits, 1) # B,X,Cls + list_latent.extend(latents) + list_logit.extend(logits) + + # create big npz + list_uttname = np.array(list_uttname) + list_latent = np.array(list_latent) + list_target = np.array(list_target) + list_logit = np.array(list_logit) + list_src_len = np.array(list_src_len) + # save to npz + output_path = args.output_path + if (output_path is None): + output_path = tempfile.NamedTemporaryFile('wb', delete=False).name + + with open(output_path, 'wb') as ww: + np.savez(ww, name=list_uttname, + latent=list_latent, + target=list_target, + logit=list_logit, + src_len=list_src_len) + + print("="*10 + " REPORT " + "="*10) + print(f'| latent saved in {output_path}') + print(f'| {list_uttname.shape=}, {list_latent.shape=}, {list_target.shape=}, {list_logit.shape=}, {list_src_len.shape=}') diff --git a/fairseq/examples/wmt19/README.md b/fairseq/examples/wmt19/README.md new file mode 100644 index 0000000..5c90d0e --- /dev/null +++ b/fairseq/examples/wmt19/README.md @@ -0,0 +1,85 @@ +# WMT 19 + +This page provides pointers to the models of Facebook-FAIR's WMT'19 news translation task submission [(Ng et al., 2019)](https://arxiv.org/abs/1907.06616). + +## Pre-trained models + +Model | Description | Download +---|---|--- +`transformer.wmt19.en-de` | En->De Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz) +`transformer.wmt19.de-en` | De->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz) +`transformer.wmt19.en-ru` | En->Ru Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz) +`transformer.wmt19.ru-en` | Ru->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz) +`transformer_lm.wmt19.en` | En Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz) +`transformer_lm.wmt19.de` | De Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz) +`transformer_lm.wmt19.ru` | Ru Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz) + +## Pre-trained single models before finetuning + +Model | Description | Download +---|---|--- +`transformer.wmt19.en-de` | En->De Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.ffn8192.tar.gz) +`transformer.wmt19.de-en` | De->En Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.ffn8192.tar.gz) +`transformer.wmt19.en-ru` | En->Ru Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ffn8192.tar.gz) +`transformer.wmt19.ru-en` | Ru->En Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ffn8192.tar.gz) + +## Example usage (torch.hub) + +#### Requirements + +We require a few additional Python dependencies for preprocessing: +```bash +pip install fastBPE sacremoses +``` + +#### Translation + +```python +import torch + +# English to German translation +en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt', + tokenizer='moses', bpe='fastbpe') +en2de.translate("Machine learning is great!") # 'Maschinelles Lernen ist großartig!' + +# German to English translation +de2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.de-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt', + tokenizer='moses', bpe='fastbpe') +de2en.translate("Maschinelles Lernen ist großartig!") # 'Machine learning is great!' + +# English to Russian translation +en2ru = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-ru', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt', + tokenizer='moses', bpe='fastbpe') +en2ru.translate("Machine learning is great!") # 'Машинное обучение - это здорово!' + +# Russian to English translation +ru2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.ru-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt', + tokenizer='moses', bpe='fastbpe') +ru2en.translate("Машинное обучение - это здорово!") # 'Machine learning is great!' +``` + +#### Language Modeling + +```python +# Sample from the English LM +en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe') +en_lm.sample("Machine learning is") # 'Machine learning is the future of computing, says Microsoft boss Satya Nadella ...' + +# Sample from the German LM +de_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.de', tokenizer='moses', bpe='fastbpe') +de_lm.sample("Maschinelles lernen ist") # 'Maschinelles lernen ist das A und O (neues-deutschland.de) Die Arbeitsbedingungen für Lehrerinnen und Lehrer sind seit Jahren verbesserungswürdig ...' + +# Sample from the Russian LM +ru_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.ru', tokenizer='moses', bpe='fastbpe') +ru_lm.sample("машинное обучение это") # 'машинное обучение это то, что мы называем "искусственным интеллектом".' +``` + +## Citation +```bibtex +@inproceedings{ng2019facebook}, + title = {Facebook FAIR's WMT19 News Translation Task Submission}, + author = {Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}, + booktitle = {Proc. of WMT}, + year = 2019, +} +``` diff --git a/fairseq/examples/wmt20/README.md b/fairseq/examples/wmt20/README.md new file mode 100644 index 0000000..b4f2874 --- /dev/null +++ b/fairseq/examples/wmt20/README.md @@ -0,0 +1,72 @@ +# WMT 20 + +This page provides pointers to the models of Facebook-FAIR's WMT'20 news translation task submission [(Chen et al., 2020)](https://arxiv.org/abs/2011.08298). + +## Single best MT models (after finetuning on part of WMT20 news dev set) + +Model | Description | Download +---|---|--- +`transformer.wmt20.ta-en` | Ta->En | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta-en.single.tar.gz) +`transformer.wmt20.en-ta` | En->Ta | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-ta.single.tar.gz) +`transformer.wmt20.iu-en.news` | Iu->En (News domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.news.single.tar.gz) +`transformer.wmt20.en-iu.news` | En->Iu (News domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.news.single.tar.gz) +`transformer.wmt20.iu-en.nh` | Iu->En (Nunavut Hansard domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.nh.single.tar.gz) +`transformer.wmt20.en-iu.nh` | En->Iu (Nunavut Hansard domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.nh.single.tar.gz) + +## Language models +Model | Description | Download +---|---|--- +`transformer_lm.wmt20.en` | En Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en.tar.gz) +`transformer_lm.wmt20.ta` | Ta Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta.tar.gz) +`transformer_lm.wmt20.iu.news` | Iu Language Model (News domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu.news.tar.gz) +`transformer_lm.wmt20.iu.nh` | Iu Language Model (Nunavut Hansard domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu.nh.tar.gz) + +## Example usage (torch.hub) + +#### Translation + +```python +import torch + +# English to Tamil translation +en2ta = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.en-ta') +en2ta.translate("Machine learning is great!") # 'இயந்திரக் கற்றல் அருமை!' + +# Tamil to English translation +ta2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.ta-en') +ta2en.translate("இயந்திரக் கற்றல் அருமை!") # 'Machine learning is great!' + +# English to Inuktitut translation +en2iu = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.en-iu.news') +en2iu.translate("machine learning is great!") # 'ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ ᐱᐅᔪᒻᒪᕆᒃ!' + +# Inuktitut to English translation +iu2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.iu-en.news') +iu2en.translate("ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ ᐱᐅᔪᒻᒪᕆᒃ!") # 'Machine learning excellence!' +``` + +#### Language Modeling + +```python +# Sample from the English LM +en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt20.en') +en_lm.sample("Machine learning is") # 'Machine learning is a type of artificial intelligence that uses machine learning to learn from data and make predictions.' + +# Sample from the Tamil LM +ta_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt20.ta') +ta_lm.sample("இயந்திரக் கற்றல் என்பது செயற்கை நுண்ணறிவின்") # 'இயந்திரக் கற்றல் என்பது செயற்கை நுண்ணறிவின் ஒரு பகுதியாகும்.' + +# Sample from the Inuktitut LM +iu_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt20.iu.news') +iu_lm.sample("ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ") # 'ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ, ᐊᒻᒪᓗ ᓯᓚᐅᑉ ᐊᓯᙳᖅᐸᓪᓕᐊᓂᖓᓄᑦ ᖃᓄᐃᓕᐅᕈᑎᒃᓴᑦ, ᐃᓚᖃᖅᖢᑎᒃ ᐅᑯᓂᖓ:' +``` + +## Citation +```bibtex +@inproceedings{chen2020facebook + title={Facebook AI's WMT20 News Translation Task Submission}, + author={Peng-Jen Chen and Ann Lee and Changhan Wang and Naman Goyal and Angela Fan and Mary Williamson and Jiatao Gu}, + booktitle={Proc. of WMT}, + year={2020}, +} +``` diff --git a/fairseq/examples/wmt21/README.md b/fairseq/examples/wmt21/README.md new file mode 100644 index 0000000..524fffb --- /dev/null +++ b/fairseq/examples/wmt21/README.md @@ -0,0 +1,25 @@ +# WMT 21 + +This page provides pointers to the models of Facebook AI's WMT'21 news translation task submission [(Tran et al., 2021)](https://arxiv.org/abs/2108.03265). + +## Single best dense models + +Model | Description | Download +---|---|--- +`wmt21.dense-24-wide.X-En` | X-En | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt21.dense-24-wide.X-En.tar.gz) +`wmt21.dense-24-wide.En-X` | En-X | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt21.dense-24-wide.En-X.tar.gz) + +## Example usage + +See eval.sh + + +## Citation +```bibtex +@inproceedings{tran2021facebook + title={Facebook AI’s WMT21 News Translation Task Submission}, + author={Chau Tran and Shruti Bhosale and James Cross and Philipp Koehn and Sergey Edunov and Angela Fan}, + booktitle={Proc. of WMT}, + year={2021}, +} +``` diff --git a/fairseq/examples/wmt21/eval.sh b/fairseq/examples/wmt21/eval.sh new file mode 100644 index 0000000..b36d934 --- /dev/null +++ b/fairseq/examples/wmt21/eval.sh @@ -0,0 +1,49 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +SRC=en +TGT=is +MODEL_NAME=wmt21.dense-24-wide.En-X + +PATH_TO_FAIRSEQ_PY=. +TMP_DIR=generation_tmp +mkdir -p $TMP_DIR + +REPLACE_UNICODE_PUNCT=$PATH_TO_FAIRSEQ_PY/examples/wmt21/scripts/replace-unicode-punctuation.perl +NORM_PUNCT=$PATH_TO_FAIRSEQ_PY/examples/wmt21/scripts/normalize-punctuation.perl +if [ ! -d "${TMP_DIR}/${MODEL_NAME}" ]; then + wget https://dl.fbaipublicfiles.com/fairseq/models/${MODEL_NAME}.tar.gz -P $TMP_DIR/ + tar -xvf $TMP_DIR/${MODEL_NAME}.tar.gz -C $TMP_DIR +fi +MODEL_DIR=$TMP_DIR/${MODEL_NAME} +if [ ! -d "${TMP_DIR}/wmt21-news-systems" ]; then + git clone https://github.com/wmt-conference/wmt21-news-systems $TMP_DIR/wmt21-news-systems +fi + +DOMAIN_TAG="wmtdata newsdomain" +INPUT_FILE=$TMP_DIR/wmt21-news-systems/txt/sources/newstest2021.${SRC}-${TGT}.src.${SRC} +REF_FILE=$TMP_DIR/wmt21-news-systems/txt/references/newstest2021.${SRC}-${TGT}.ref.A.${TGT} + +# Translate +cat ${INPUT_FILE} | sed "s/^/${DOMAIN_TAG} /" | $REPLACE_UNICODE_PUNCT | $NORM_PUNCT -l ${SRC} | python $PATH_TO_FAIRSEQ_PY/fairseq_cli/interactive.py $MODEL_DIR \ + --path ${MODEL_DIR}/checkpoint.pt \ + --task translation_multi_simple_epoch \ + --langs "en,ha,is,ja,cs,ru,zh,de" \ + --lang-pairs $SRC-$TGT \ + --bpe "sentencepiece" \ + --sentencepiece-model ${MODEL_DIR}/sentencepiece.model \ + --buffer-size 1024 \ + --batch-size 10 -s $SRC -t $TGT \ + --decoder-langtok \ + --encoder-langtok src \ + --beam 5 \ + --lenpen 1.0 \ + --fp16 > $TMP_DIR/${SRC}-${TGT}.gen_log + +cat $TMP_DIR/$SRC-$TGT.gen_log | grep -P "^D-" | cut -f3 > $TMP_DIR/$SRC-$TGT.hyp + +# Calculate BLEU score +sacrebleu -l $SRC-$TGT $REF_FILE < $TMP_DIR/$SRC-$TGT.hyp diff --git a/fairseq/examples/wmt21/scripts/normalize-punctuation.perl b/fairseq/examples/wmt21/scripts/normalize-punctuation.perl new file mode 100644 index 0000000..a7c0750 --- /dev/null +++ b/fairseq/examples/wmt21/scripts/normalize-punctuation.perl @@ -0,0 +1,90 @@ +#!/usr/bin/env perl +# +# This file is part of moses. Its use is licensed under the GNU Lesser General +# Public License version 2.1 or, at your option, any later version. + +use warnings; +use strict; + +my $language = "en"; +my $PENN = 0; + +while (@ARGV) { + $_ = shift; + /^-b$/ && ($| = 1, next); # not buffered (flush each line) + /^-l$/ && ($language = shift, next); + /^[^\-]/ && ($language = $_, next); + /^-penn$/ && ($PENN = 1, next); +} + +while(<STDIN>) { + s/\r//g; + # remove extra spaces + s/\(/ \(/g; + s/\)/\) /g; s/ +/ /g; + s/\) ([\.\!\:\?\;\,])/\)$1/g; + s/\( /\(/g; + s/ \)/\)/g; + s/(\d) \%/$1\%/g; + s/ :/:/g; + s/ ;/;/g; + # normalize unicode punctuation + if ($PENN == 0) { + s/\`/\'/g; + s/\'\'/ \" /g; + } + + s/„/\"/g; + s/“/\"/g; + s/”/\"/g; + s/–/-/g; + s/—/ - /g; s/ +/ /g; + s/´/\'/g; + s/([a-z])‘([a-z])/$1\'$2/gi; + s/([a-z])’([a-z])/$1\'$2/gi; + s/‘/\'/g; + s/‚/\'/g; + s/’/\"/g; + s/''/\"/g; + s/´´/\"/g; + s/…/.../g; + # French quotes + s/ « / \"/g; + s/« /\"/g; + s/«/\"/g; + s/ » /\" /g; + s/ »/\"/g; + s/»/\"/g; + # handle pseudo-spaces + s/ \%/\%/g; + s/nº /nº /g; + s/ :/:/g; + s/ ºC/ ºC/g; + s/ cm/ cm/g; + s/ \?/\?/g; + s/ \!/\!/g; + s/ ;/;/g; + s/, /, /g; s/ +/ /g; + + # English "quotation," followed by comma, style + if ($language eq "en") { + s/\"([,\.]+)/$1\"/g; + } + # Czech is confused + elsif ($language eq "cs" || $language eq "cz") { + } + # German/Spanish/French "quotation", followed by comma, style + else { + s/,\"/\",/g; + s/(\.+)\"(\s*[^<])/\"$1$2/g; # don't fix period at end of sentence + } + + + if ($language eq "de" || $language eq "es" || $language eq "cz" || $language eq "cs" || $language eq "fr") { + s/(\d) (\d)/$1,$2/g; + } + else { + s/(\d) (\d)/$1.$2/g; + } + print $_; +} diff --git a/fairseq/examples/wmt21/scripts/replace-unicode-punctuation.perl b/fairseq/examples/wmt21/scripts/replace-unicode-punctuation.perl new file mode 100644 index 0000000..faed2cd --- /dev/null +++ b/fairseq/examples/wmt21/scripts/replace-unicode-punctuation.perl @@ -0,0 +1,55 @@ +#!/usr/bin/env perl +# +# This file is part of moses. Its use is licensed under the GNU Lesser General +# Public License version 2.1 or, at your option, any later version. + +use warnings; +use strict; + +while (@ARGV) { + $_ = shift; + /^-b$/ && ($| = 1, next); # not buffered (flush each line) +} + +#binmode(STDIN, ":utf8"); +#binmode(STDOUT, ":utf8"); + +while(<STDIN>) { + s/，/,/g; + s/。 */. /g; + s/、/,/g; + s/”/"/g; + s/“/"/g; + s/∶/:/g; + s/：/:/g; + s/？/\?/g; + s/《/"/g; + s/》/"/g; + s/）/\)/g; + s/！/\!/g; + s/（/\(/g; + s/；/;/g; + s/１/1/g; + s/」/"/g; + s/「/"/g; + s/０/0/g; + s/３/3/g; + s/２/2/g; + s/５/5/g; + s/６/6/g; + s/９/9/g; + s/７/7/g; + s/８/8/g; + s/４/4/g; + s/． */. /g; + s/～/\~/g; + s/’/\'/g; + s/…/\.\.\./g; + s/━/\-/g; + s/〈/\</g; + s/〉/\>/g; + s/【/\[/g; + s/】/\]/g; + s/％/\%/g; + print $_; +} diff --git a/fairseq/examples/womens_bios/README.md b/fairseq/examples/womens_bios/README.md new file mode 100644 index 0000000..07d0646 --- /dev/null +++ b/fairseq/examples/womens_bios/README.md @@ -0,0 +1,81 @@ +# Wikipedia Biographies of Women + + +## Training: + +The training dataset is created based on WikiSum, a dataset created from the paper [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/pdf/1801.10198.pdf). The dataset needs to be generated following the instructions in this [Github Repository](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikisum). + +### How is the WikiSum dataset structured? + +Overall, the task in WikiSum was to generate the entire Wikipedia article based on the contents of the top 10 Google Search Results. The authors provide a way for people to recreate their work. In the WikiSum Github, there are two options for the dataset recreation --- the first is to use CommonCrawl (a static, open source crawl of the web) and the second to do Live Web Fetches. The second has higher coverage, but the content is subject to change and difficult to fetch. We used the static, Commoncrawl version. This can be downloaded following the Github repo instructions, though note it will require usage of Google Cloud. + +Note: in our experience, it also requires requesting that the resource limit of the Google Cloud instance be raised, which requires emailing. + +Note: Having higher coverage in the training dataset would be expected to improve the model quality. There are many instances in the dataset where the training input (web evidence) does not contain sufficient content for producing the desired Wikipedia article. This may harm the model's ability to learn to retrieve, look at the input evidence, and overall could contribute to increased challenges in generating verifiable Wikipedia biographies. + +### How do you go from WikiSum dataset to Biography dataset? + +The WikiSum dataset is for Wikipedia in general, not just biographies. We do this by querying WikiData to see if the Wikipedia article has an occupation, with the thought that all articles with occupations are probably biographies. + + +## Evaluation: + +You can download the dataset and baseline model with the following command: + +``` +wget -N 'https://dl.fbaipublicfiles.com/fairseq/womenbios_dataset.zip' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' +wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' +``` + +We provide the full text Wikipedia articles split into four categories: +- Women in Africa +- Women in Asia +- Women in Science +- Women +We note that these are not exhaustive intersectional categories and mainly stem from personal interest. + +We also provide the URL of the Wikipedia article. Note that Wikipedia articles are constantly being improved, edited, and changed. Thus, it's completely possible that the Wikipedia article on Wikipedia has been lovingly improved by other Wikipedia editors. + +To get the occupations of each biographical subject, we use WikiData. We provide a sample script to do this. We also provide the raw output of this query. + +The final part of the evaluation dataset is to query web evidence for each of the biographical subjects. This is the part of the evaluation dataset that requires the most improvement. As we discuss in our paper, one of the major reasons why it is difficult to write biographies for sometimes very well qualified women is that there is not information online about them. Further, the search engine may not find it. We encourage others to improve upon this part of the data, as even re-querying again on the internet may find new, updated sources of information as the web is constantly evolving. + +We use the search engine from [Internet-Augmented Dialogue Generation](https://arxiv.org/abs/2107.07566), see [project URL](https://parl.ai/projects/sea/) to do the search queries. Note: we remove wikipedia site sources from our query (or we'd query the data itself). However, it's possible Wikipedia information can be copied around in multiple forms on the web, linked with edits, etc. + + +## Section by Section Generation: + +Wikipedia articles are split into sections, which are usually separated by headings. These headings can be separated in the article text by looking for these equal signs (==), where the number of equal signs usually signals if you are looking at a toplevel heading or a subheading, etc. An example regex that you can use is: + +` +section_header_re = re.compile(r"(?<!=)==([^=]+)==(?!=)") +` + + +## List of Notes: +- People can have multiple occupations, and we keep all occupations that we query from WikiData + + +## List of Possible Improvement Areas: +Using a larger generative pre-trained model, larger-scale retrieval, a retrieval encoder specialized to Wikipedia (or biographies), tuning all of the training & generation parameters exhaustively --- and the like --- would most likely be very useful. Overall, we hope that this is a starting point for others who might be interested in focusing on how we can help address the gender gap on Wikipedia. + + +## Interested in Wikipedia and Gender Gap? +You might want to check out: +- https://humaniki.wmcloud.org/ +- https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Women_in_Red and https://wikimediafoundation.org/news/2018/10/18/women-in-red-wikiproject/ +- https://meta.wikimedia.org/wiki/Whose_Knowledge%3F/VisibleWikiWomen +- https://www.ted.com/talks/jess_wade_a_voice_for_diversity_in_science + +and thanks again to all of the Wikipedia editors and the entire community that is already working so hard to write amazing articles for diverse groups of people. + + +# LICENSE +This is licensed under CC-BY-NC, however portions of the dataset are available under separate license terms: text sourced from Wikipedia is licensed under CC-BY-SA. + + + + + diff --git a/fairseq/examples/womens_bios/query_occupations_from_wikidata.py b/fairseq/examples/womens_bios/query_occupations_from_wikidata.py new file mode 100644 index 0000000..8028c6e --- /dev/null +++ b/fairseq/examples/womens_bios/query_occupations_from_wikidata.py @@ -0,0 +1,34 @@ +import sys +from SPARQLWrapper import SPARQLWrapper, JSON + +endpoint_url = "https://query.wikidata.org/sparql" + +with open("/your/urls/here") as f: + data = f.readlines() +urls = [i.strip() for i in data] + +def get_results(endpoint_url, URL): + query = f"""SELECT ?uriLabel ?occupation ?occupationLabel ?dob ?dobLabel WHERE {{ + <{URL}> schema:about ?uri . + ?uri wdt:P106 ?occupation . + SERVICE wikibase:label {{ bd:serviceParam wikibase:language "en" }} + }}""" + user_agent = "WDQS-example Python/%s.%s" % (sys.version_info[0], sys.version_info[1]) + sparql = SPARQLWrapper(endpoint_url, agent=user_agent) + sparql.setQuery(query) + sparql.setReturnFormat(JSON) + return sparql.query().convert() + +all_occupations = [] +for URL in urls: + results = get_results(endpoint_url, URL) + occupations = [] + for result in results["results"]["bindings"]: + occupations.append(result['occupationLabel']['value']) + all_occupations.append(result['uriLabel']['value'] + ", " + ", ".join(occupations)) + +assert(len(all_occupations) == len(urls)) + +with open("/your/file/output/here", "w") as o: + for line in all_occupations: + o.write(line.strip() + "\n") \ No newline at end of file diff --git a/fairseq/examples/xformers/README.md b/fairseq/examples/xformers/README.md new file mode 100644 index 0000000..400a74d --- /dev/null +++ b/fairseq/examples/xformers/README.md @@ -0,0 +1,43 @@ +# Using xFormers with FairSeq + +[xFormers](https://github.com/facebookresearch/xformers) is a xFormers is a modular library for flexibly generating transformer architectures with interoperable and optimized building blocks. +The current integration allows for FairSeq users to use an attention variant available in the xFormers repository. + +In order to enable xFormers, all that needs to be passed in is a string representing an [xFormers attention config](https://github.com/facebookresearch/xformers/blob/5f754129bfb1ea53747b1ab2077261ea762faa47/xformers/components/attention/base.py#L18). + +The various attention variants can be found [here](https://github.com/facebookresearch/xformers/tree/main/xformers/components/attention). +These include sparse attention and blocksparse attention. + +For example, you could pass in the following args: + ```python +decoder_xformers_att_config = '{"name": "scaled_dot_product"}' + +encoder_xformers_att_config = '{"name": "linformer", "seq_len": "256"}' + ``` + +In order to use blocksparse attention you would have to additionally pass in a blocksparse layout and blocksize. For example: + + ```python + + xformers_att_config = '{"name": "scaled_dot_product"}' + xformers_blocksparse_blocksize = 16 + xformers_blocksparse_layout = torch.ones( + seq_len // xformers_blocksparse_blocksize, + seq_len // xformers_blocksparse_blocksize, + ) + + xf_blocksparse_mha = ( + MultiheadAttention( + embedding, + num_heads, + dropout=0.0, + add_zero_attn=add_zero_attn, + xformers_att_config=xformers_att_config, + xformers_blocksparse_layout=xformers_blocksparse_layout, + xformers_blocksparse_blocksize=xformers_blocksparse_blocksize, + ) + + ``` + +The xFormers repository currenlty has benchmarks on the [runtime](https://github.com/facebookresearch/xformers/blob/main/docs/plots/runtime_vs_attention.png) +and [memory usage](https://github.com/facebookresearch/xformers/blob/main/docs/plots/memory_vs_attention.png) of the various attentions. diff --git a/fairseq/examples/xglm/README.md b/fairseq/examples/xglm/README.md new file mode 100644 index 0000000..914e297 --- /dev/null +++ b/fairseq/examples/xglm/README.md @@ -0,0 +1,195 @@ +# Few-shot Learning with Multilingual Language Models + +## Introduction + +In this work, we train a family of multilingual generative language models, dubbed XGLM, on a balanced corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters sets new state of the art in few-shot learning on more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (+7.4 accuracy points for 0-shot, +9.4 for 4-shot) and natural language inference (+5.4 for 0-shot, +5.4 for 4-shot). We have included a [model card](model_card.md) of XGLM for transparency and accountability. + +## Data and Languages +XGLM models are trained on a new multilingual corpus extracted from CommonCrawl (CC100-XL), a significantly larger multilingual dataset covering 68 Common Crawl (CC) snapshots (from [Summer 2013](http://commoncrawl.org/2013/11/new-crawl-data-available/) to [March/April 2020](https://commoncrawl.org/2020/04/march-april-2020-crawl-archive-now-available/) consisting of 134 languages. The detailed languages and data statistics are reported in the paper (Table A.1). + +## Pre-trained models + +Model | Layers | Model Dim | FFN Dim | Languages | Download +---|---|---|---|---|--- +`XGLM 564M` | 24 | 1024 | 4096 | trained on 30 languages| [xglm.564M.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xglm/xglm.564M.tar.gz) +`XGLM 1.7B` | 24 | 2048 | 8192 | trained on 30 languages| [xglm.1.7B.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xglm/xglm.1.7B.tar.gz) +`XGLM 2.9B` | 48 | 2048 | 8192 | trained on 30 languages| [xglm.2.9B.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xglm/xglm.2.9B.tar.gz) +`XGLM 7.5B` | 32 | 4096 | 16384 | trained on 30 languages| [xglm.7.5B.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xglm/xglm.7.5B.tar.gz) +`XGLM 4.5B` | 48 | 2048 | 16384 | trained on 134 languages| [xglm.4.5B.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xglm/xglm.4.5B.tar.gz) + +## Pre-training Data Format +Our models were pre-trained with data in the following format (i.e. paragraphs are separated with new lines and documents were separated with double new lines). +``` +<doc0,para0,tok0> ... <doc0,para0,tokX0> # X0: number of tokens in para0 of doc0 +<doc0,para1,tok0> ... <doc0,para1,tokY0> # Y0: number of tokens in para1 of doc0 + +<doc1,para0,tok0> ... <doc1,para0,tokX1> # X1: number of tokens in para0 of doc1 +<doc1,para1,tok0> ... <doc1,para1,tokY1> # Y1: number of tokens in para1 of doc1 + +... +``` +Fairseq's preprocessing replaces newlines with the end-of-sentence symbol (`</s>`). As a result, the models never saw newline characters during pretraining and the same preprocessing should be run prior to few-shot inference to maximize performance. For example, our language model scoring function has `replace_newlines_with_eos` argument to trigger this preprocessing: +```python +from fairseq.models.transformer_lm import TransformerLanguageModel + +model_dir = 'path_to_decompressed_tar_gz_dir' +lm = TransformerLanguageModel.from_pretrained(model_dir, bpe='sentencepiece') + +text = """First paragraph of the first document. +Second paragraph of the first document. + +First paragraph of the second document. +""" +tokens = lm.score(text, replace_newlines_with_eos=True)['tokens'] +assert '\n' not in lm.decode(tokens) # no newlines were encoded +``` + +## Evaluation + +### Example (COPA) + +The following snippet show how to evaluate our models on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi. + +```python +data_samples = { + 'en': [ + { + "premise": "I wanted to conserve energy.", + "choice1": "I swept the floor in the unoccupied room.", + "choice2": "I shut off the light in the unoccupied room.", + "question": "effect", + "label": "1" + }, + { + "premise": "The flame on the candle went out.", + "choice1": "I blew on the wick.", + "choice2": "I put a match to the wick.", + "question": "cause", + "label": "0" + } + ], + 'zh': [ + { + "premise": "我想节约能源。", + "choice1": "我在空着的房间里扫了地板。", + "choice2": "我把空房间里的灯关了。", + "question": "effect", + "label": "1" + }, + { + "premise": "蜡烛上的火焰熄灭了。", + "choice1": "我吹灭了灯芯。", + "choice2": "我把一根火柴放在灯芯上。", + "question": "cause", + "label": "0" + } + ], + 'hi': [ + { + "premise": "M te vle konsève enèji.", + "choice1": "Mwen te fin baleye chanm lib la.", + "choice2": "Mwen te femen limyè nan chanm lib la.", + "question": "effect", + "label": "1" + }, + { + "premise": "Flam bouji a te etenn.", + "choice1": "Mwen te soufle bouji a.", + "choice2": "Mwen te limen mèch bouji a.", + "question": "cause", + "label": "0" + } + ] +} +``` +In this example, we format the examples use the non-verbal prompts `{premise}\n{choice1}` and `{premise}\n{choice2}`, which are shared by all three languages. +```python +from fairseq.models.transformer_lm import TransformerLanguageModel + +model_dir = 'path_to_decompressed_tar_gz_dir' +lm = TransformerLanguageModel.from_pretrained(model_dir, bpe='sentencepiece') +lm = lm.eval() +lm = lm.half() +lm = lm.cuda() + +def get_logprobs(prompt): + import re + prompt = re.sub('\n+' , '\n', prompt) # collapse repeated newlines, which indicate separate documents + return lm.score(prompt, replace_newlines_with_eos=True)['positional_scores'] + +# Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task. +# A return value of 0 indicates that the first alternative is more plausible, +# while 1 indicates that the second alternative is more plausible. +def COPA_eval(prompt, alternative1, alternative2): + lprob1 = get_logprobs(prompt + "\n" + alternative1).sum() + lprob2 = get_logprobs(prompt + "\n" + alternative2).sum() + return 0 if lprob1 > lprob2 else 1 + +for lang in ['en', 'zh', 'hi']: + for idx, example in enumerate(data_samples[lang]): + predict = COPA_eval(example["premise"], example["choice1"], example["choice2"]) + print(f'{lang}-{idx}', predict, example['label']) + +# en-0 1 1 +# en-1 0 0 +# zh-0 1 1 +# zh-1 0 0 +# hi-0 1 1 +# hi-1 0 0 +``` + +## XStoryCloze + +We release XStoryCloze, a new multilingual dataset intended for few-shot evaluation, alongside this paper. XStoryCloze consists of professional translation of the validation split of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 other languages. It is opensourced under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode), the same license as the English StoryCloze. + +You can download the dataset via [this link](https://dl.fbaipublicfiles.com/xstorycloze.zip). + +Language | ar | es | eu | hi | id | my | ru | sw | te | zh +---|---|---|---|---|---|---|---|---|---|--- +Train size | 360 | 360 | 360 | 360 | 360 | 360 | 360 | 360 | 360 | 360 +Eval size | 1511 | 1511 | 1511 | 1511 | 1511 | 1511 | 1511 | 1511 | 1511 | 1511 + +Please refer to [the dataset doc](XStoryCloze.md) for more information. + + +## Publication +[Few-shot Learning with Multilingual Generative Language Models](https://arxiv.org/abs/2112.10668). +Xi Victoria Lin*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li* (* Equal Contribution). +EMNLP 2022. + +## Citation +``` +@article{DBLP:journals/corr/abs-2112-10668, + author = {Xi Victoria Lin and + Todor Mihaylov and + Mikel Artetxe and + Tianlu Wang and + Shuohui Chen and + Daniel Simig and + Myle Ott and + Naman Goyal and + Shruti Bhosale and + Jingfei Du and + Ramakanth Pasunuru and + Sam Shleifer and + Punit Singh Koura and + Vishrav Chaudhary and + Brian O'Horo and + Jeff Wang and + Luke Zettlemoyer and + Zornitsa Kozareva and + Mona T. Diab and + Veselin Stoyanov and + Xian Li}, + title = {Few-shot Learning with Multilingual Language Models}, + journal = {CoRR}, + volume = {abs/2112.10668}, + year = {2021}, + url = {https://arxiv.org/abs/2112.10668}, + eprinttype = {arXiv}, + eprint = {2112.10668}, + timestamp = {Tue, 04 Jan 2022 15:59:27 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` diff --git a/fairseq/examples/xglm/XStoryCloze.md b/fairseq/examples/xglm/XStoryCloze.md new file mode 100644 index 0000000..9b0fce0 --- /dev/null +++ b/fairseq/examples/xglm/XStoryCloze.md @@ -0,0 +1,57 @@ +XStoryCloze consists of professional translation of the validation split of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 other languages. This dataset is released by FAIR (Fundamental Artificial Intelligence Research) alongside the paper [Few-shot Learning with Multilingual Generative Language Models. EMNLP 2022](https://arxiv.org/abs/2112.10668). + +# Languages +ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my. + +# Data Splits +This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment. + +# Access English StoryCloze +Please request the original English StoryCloze dataset through the [official website](https://cs.rochester.edu/nlp/rocstories/). You can create a split of the en data following our data split scheme using the following commands: +``` +head -361 spring2016.val.tsv > spring2016.val.en.tsv.split_20_80_train.tsv + +head -1 spring2016.val.tsv > spring2016.val.en.tsv.split_20_80_eval.tsv # TSV header +tail -1511 spring2016.val.tsv >> spring2016.val.en.tsv.split_20_80_eval.tsv +``` + +# Licence +XStoryCloze is opensourced under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode), the same license as the original English StoryCloze. + +# Citation +We hope this dataset is helpful for the research and wider NLP community. If you use XStoryCloze in your work, please cite +``` +@article{DBLP:journals/corr/abs-2112-10668, + author = {Xi Victoria Lin and + Todor Mihaylov and + Mikel Artetxe and + Tianlu Wang and + Shuohui Chen and + Daniel Simig and + Myle Ott and + Naman Goyal and + Shruti Bhosale and + Jingfei Du and + Ramakanth Pasunuru and + Sam Shleifer and + Punit Singh Koura and + Vishrav Chaudhary and + Brian O'Horo and + Jeff Wang and + Luke Zettlemoyer and + Zornitsa Kozareva and + Mona T. Diab and + Veselin Stoyanov and + Xian Li}, + title = {Few-shot Learning with Multilingual Language Models}, + journal = {CoRR}, + volume = {abs/2112.10668}, + year = {2021}, + url = {https://arxiv.org/abs/2112.10668}, + eprinttype = {arXiv}, + eprint = {2112.10668}, + timestamp = {Tue, 04 Jan 2022 15:59:27 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` diff --git a/fairseq/examples/xglm/model_card.md b/fairseq/examples/xglm/model_card.md new file mode 100644 index 0000000..2656ec5 --- /dev/null +++ b/fairseq/examples/xglm/model_card.md @@ -0,0 +1,152 @@ +# XGLM multilingual model +## Version 1.0.0 + +### Model developer +FAIR (Fundamental Artificial Intelligence Research) + +### Model type +A family of multilingual autoregressive language models (ranging from 564 million to 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages. The language model can learn tasks from natural language descriptions and a few examples. + +### Model Feedback Channel +https://github.com/pytorch/fairseq + +## Intended use +### Primary intended use +For research purposes only, e.g. reproducing model evaluation results. Generation is only used in a limited capacity for explanation/justification or for prompting/probing/priming for class labels. + +### Out of scope uses +The primary purpose of the model is not to generate language, although the model is capable of doing that. + +## Potential risks +This section lists the potential risks associated with using the model. + +### Relevant factors +Based on known problems with NLP technology, potential relevant factors include output correctness, robustness, bias (gender, profession, race and religion), etc. + +### Evaluation factors +The model was evaluated on hate speech detection and occupation identification. +* Hate speech detection (Huang et al. (2020)) - A safety task to test language models’ ability to identify hateful and offensive text. +* Occupation identification (De-Arteaga et al., 2019), (Zhao et al., 2020) - A bias task to study language models’ performance divergence between different gender groups on the task of occupation identification. + +## Metrics +### Model performance measures +The XGLM model was primarily evaluated on +1. Zero shot and few shot learning by looking at per-language performance on tasks spanning commonsense reasoning (XCOPA, XWinograd), natural language inference (XNLI) and paraphrasing (PAWS-X). The model is also evaluated on XStoryCloze, a new dataset created by FAIR (Fundamental Artificial Intelligence Research). +2. Cross lingual transfer through templates and few-shot examples. +3. Knowledge probing - Evaluate to what extent the XGLM model can effectively store factual knowledge in different languages using the mLAMA benchmark. +4. Translation - We report machine translation results on WMT benchmarks and a subset of FLORES-101 in the main paper. + +The model was also evaluated on hate speech datasets introduced by Huang et al. (2020) and an occupation identification dataset by De-Arteaga et al. 2019 to identify bias in the model. + +### Approaches to handle uncertainty +Report confidence intervals, variance metrics for the model performance metrics. Few-shot evaluation was conducted with different sampling with 5 seeds. We reported statistical significance. + +## Evaluation data +## Zero Shot and Few Shot evaluation + +### XNLI (Conneau et al., 2018) +#### Description +The Cross-lingual Natural Language Inference (XNLI) corpus is the extension of the Multi-Genre NLI (MultiNLI) corpus to 15 languages. The dataset was created by manually translating the validation and test sets of MultiNLI into each of those 15 languages. + +### XStoryCloze +#### Description +A new dataset created by FAIR along side this work by translating the validation split of the English StoryCloze dataset (Mostafazadeh et al., 2016) (Spring 2016 version) to 10 other typologically diverse languages (ru, zh Simplified, es Latin America, ar, hi, id, te, sw, eu, my). + +### XCOPA (Ponti et al., 2020) +#### Description +The Cross-lingual Choice of Plausible Alternatives (XCOPA) dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. + +### XWinograd (Tikhonov and Ryabinin, 2021) +#### Description +XWinograd is a multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities. + +### PAWS-X (Yang et al., 2019) +#### Description +PAWS-X contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki. + +## Responsible AI (RAI) evaluation +### Hate speech (Huang et al. 2020) +This is a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. + +### Bias dataset (De-Arteaga et al. 2019) +The aim of this dataset is to study the gender bias of models that identify a person’s occupation from their bios. + +---- + +## Training data +### CC100-XL +#### Description +Following the recent success of multilingual self-supervised pre-training (Devlin et al., 2019; Lample and Conneau, 2019; Con; Xue et al., 2020; Goyal et al., 2021a; Liu et al., 2020), we train our language models on a mixture of monolingual text of different languages. We extended the pipeline used for mining the CC100 corpus to generate CC100-XL, a significantly larger multilingual dataset covering 68 Common Crawl snapshots (from Summer 2013 to March/April 2020) and 134 languages. + +More details on the CC100-XL dataset can be found in the Appendix section of the paper. + +## RAI Dimensions +### Fairness (Bias and inclusion) +The XGLM model was evaluated on Hate speech and bias identification datasets. For hate speech, we observe that across the 5 languages in the dataset, in context learning results are only slightly better than random (50%). Another interesting observation is that most few shot results are worse than zero-shot, which indicates that the model is not able to utilize examples using the templates described in the paper. For bias identification, the XGLM (6.7B) English only model achieves the best performance on English and Spanish, while the GPT-3 model of comparable size (6.7B) model achieves the best in French. On certain occupations (e.g. model and teacher), XGLM 6.7B En only model and GPT-3 (6.7B) have very significant bias while XGLM 7.5B is much less biased. + +### Privacy and security +The XGLM model did not have any special Privacy and Security considerations. The training data and evaluation data were both public and went through standard Meta privacy and licensing procedures. + +### Transparency and control +In the spirit of transparency and accountability we have created this model card and a data card for the CC100-XL which can be found in the Appendix section of the paper. + +### Efficiency (Green AI) +From an engineering perspective, XGLM pertains to a family of models that represent single unified models catering to many languages which have wide application across many applications. Such a unified single model saves on carbon footprint as well as energy consumption (comparing to the alternative: separate models for different languages) leading to more energy efficiency. A single model, despite having the risk of being a single point of failure, has the powerful incentive of being easier to maintain, access, distribute, and track. + +## References +Edoardo Maria Ponti, Goran Glavas, Olga Majewska, Qianchu Liu, Ivan Vulic, and Anna Korhonen. 2020. XCOPA: A multilingual dataset for causal commonsense reasoning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 2362–2376. Association for Computational Linguistics. +XCOPA Dataset | Papers With Code + +Alexey Tikhonov and Max Ryabinin. 2021. It’s all in the heads: Using attention heads as a baseline for cross-lingual transfer in commonsense reasoning. In Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, August 1-6, 2021, volume ACL/IJCNLP 2021 of Findings of ACL, pages 3534–3546. Association for Computational Linguistics. +XWINO Dataset | Papers With Code (XWinograd) + +Yinfei Yang, Yuan Zhang, Chris Tar, and Jason Baldridge. 2019. PAWS-X: A cross-lingual adversarial dataset for paraphrase identification. CoRR, abs/1908.11828. +PAWS-X Dataset | Papers With Code + +Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov. 2018. XNLI: evaluating cross-lingual sentence representations. CoRR, abs/1809.05053. +XNLI Dataset | Papers With Code + +Xiaolei Huang, Linzi Xing, Franck Dernoncourt, and Michael Paul. 2020. Multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1440–1448. + +Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019. Bias in bios: A case study of semantic representation bias in a high-stakes setting. In proceedings of the Conference on Fairness, Accountability, and Transparency, pages 120–128. + +Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, James F. Allen. A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories. CoRR abs/1604.01696. + +Jieyu Zhao, Subhabrata Mukherjee, Saghar Hosseini, Kai-Wei Chang, and Ahmed Hassan Awadallah. 2020. Gender bias in multilingual embeddings and crosslingual transfer. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2896–2907. + +## Citation details +``` +@article{DBLP:journals/corr/abs-2112-10668, + author = {Xi Victoria Lin and + Todor Mihaylov and + Mikel Artetxe and + Tianlu Wang and + Shuohui Chen and + Daniel Simig and + Myle Ott and + Naman Goyal and + Shruti Bhosale and + Jingfei Du and + Ramakanth Pasunuru and + Sam Shleifer and + Punit Singh Koura and + Vishrav Chaudhary and + Brian O'Horo and + Jeff Wang and + Luke Zettlemoyer and + Zornitsa Kozareva and + Mona T. Diab and + Veselin Stoyanov and + Xian Li}, + title = {Few-shot Learning with Multilingual Language Models}, + journal = {CoRR}, + volume = {abs/2112.10668}, + year = {2021}, + url = {https://arxiv.org/abs/2112.10668}, + eprinttype = {arXiv}, + eprint = {2112.10668}, + timestamp = {Tue, 04 Jan 2022 15:59:27 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` diff --git a/fairseq/examples/xlmr/README.md b/fairseq/examples/xlmr/README.md new file mode 100644 index 0000000..bba7910 --- /dev/null +++ b/fairseq/examples/xlmr/README.md @@ -0,0 +1,144 @@ +# Unsupervised Cross-lingual Representation Learning at Scale (XLM-RoBERTa) +https://arxiv.org/pdf/1911.02116.pdf + +# Larger-Scale Transformers for Multilingual Masked Language Modeling +https://arxiv.org/pdf/2105.00572.pdf + + +## What's New: +- June 2021: `XLMR-XL` AND `XLMR-XXL` models released. + +## Introduction + +`XLM-R` (`XLM-RoBERTa`) is a generic cross lingual sentence encoder that obtains state-of-the-art results on many cross-lingual understanding (XLU) benchmarks. It is trained on `2.5T` of filtered CommonCrawl data in 100 languages (list below). + + Language | Language|Language |Language | Language +---|---|---|---|--- +Afrikaans | Albanian | Amharic | Arabic | Armenian +Assamese | Azerbaijani | Basque | Belarusian | Bengali +Bengali Romanize | Bosnian | Breton | Bulgarian | Burmese +Burmese zawgyi font | Catalan | Chinese (Simplified) | Chinese (Traditional) | Croatian +Czech | Danish | Dutch | English | Esperanto +Estonian | Filipino | Finnish | French | Galician +Georgian | German | Greek | Gujarati | Hausa +Hebrew | Hindi | Hindi Romanize | Hungarian | Icelandic +Indonesian | Irish | Italian | Japanese | Javanese +Kannada | Kazakh | Khmer | Korean | Kurdish (Kurmanji) +Kyrgyz | Lao | Latin | Latvian | Lithuanian +Macedonian | Malagasy | Malay | Malayalam | Marathi +Mongolian | Nepali | Norwegian | Oriya | Oromo +Pashto | Persian | Polish | Portuguese | Punjabi +Romanian | Russian | Sanskrit | Scottish Gaelic | Serbian +Sindhi | Sinhala | Slovak | Slovenian | Somali +Spanish | Sundanese | Swahili | Swedish | Tamil +Tamil Romanize | Telugu | Telugu Romanize | Thai | Turkish +Ukrainian | Urdu | Urdu Romanize | Uyghur | Uzbek +Vietnamese | Welsh | Western Frisian | Xhosa | Yiddish + +## Pre-trained models + +Model | Description | #params | vocab size | Download +---|---|---|---|--- +`xlmr.base` | XLM-R using the BERT-base architecture | 250M | 250k | [xlm.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz) +`xlmr.large` | XLM-R using the BERT-large architecture | 560M | 250k | [xlm.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz) +`xlmr.xl` | XLM-R (`layers=36, model_dim=2560`) | 3.5B | 250k | [xlm.xl.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xl.tar.gz) +`xlmr.xxl` | XLM-R (`layers=48, model_dim=4096`) | 10.7B | 250k | [xlm.xxl.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xxl.tar.gz) + +## Results + +**[XNLI (Conneau et al., 2018)](https://arxiv.org/abs/1809.05053)** + +Model | average | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur +---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|--- +`roberta.large.mnli` _(TRANSLATE-TEST)_ | 77.8 | 91.3 | 82.9 | 84.3 | 81.2 | 81.7 | 83.1 | 78.3 | 76.8 | 76.6 | 74.2 | 74.1 | 77.5 | 70.9 | 66.7 | 66.8 +`xlmr.large` _(TRANSLATE-TRAIN-ALL)_ | 83.6 | 89.1 | 85.1 | 86.6 | 85.7 | 85.3 | 85.9 | 83.5 | 83.2 | 83.1 | 83.7 | 81.5 | 83.7 | 81.6 | 78.0 | 78.1 +`xlmr.xl` _(TRANSLATE-TRAIN-ALL)_ | 85.4 | 91.1 | 87.2 | 88.1 | 87.0 | 87.4 | 87.8 | 85.3 | 85.2 | 85.3 | 86.2 | 83.8 | 85.3 | 83.1 | 79.8 | 78.2 | 85.4 +`xlmr.xxl` _(TRANSLATE-TRAIN-ALL)_ | 86.0 | 91.5 | 87.6 | 88.7 | 87.8 | 87.4 | 88.2 | 85.6 | 85.1 | 85.8 | 86.3 | 83.9 | 85.6 | 84.6 | 81.7 | 80.6 + +**[MLQA (Lewis et al., 2018)](https://arxiv.org/abs/1910.07475)** + +Model | average | en | es | de | ar | hi | vi | zh +---|---|---|---|---|---|---|---|--- +`BERT-large` | - | 80.2/67.4 | - | - | - | - | - | - +`mBERT` | 57.7 / 41.6 | 77.7 / 65.2 | 64.3 / 46.6 | 57.9 / 44.3 | 45.7 / 29.8| 43.8 / 29.7 | 57.1 / 38.6 | 57.5 / 37.3 +`xlmr.large` | 70.7 / 52.7 | 80.6 / 67.8 | 74.1 / 56.0 | 68.5 / 53.6 | 63.1 / 43.5 | 69.2 / 51.6 | 71.3 / 50.9 | 68.0 / 45.4 +`xlmr.xl` | 73.4 / 55.3 | 85.1 / 72.6 | 66.7 / 46.2 | 70.5 / 55.5 | 74.3 / 56.9 | 72.2 / 54.7 | 74.4 / 52.9 | 70.9 / 48.5 +`xlmr.xxl` | 74.8 / 56.6 | 85.5 / 72.4 | 68.6 / 48.4 | 72.7 / 57.8 | 75.4 / 57.6 | 73.7 / 55.8 | 76.0 / 55.0 | 71.7 / 48.9 + + +## Example usage + +##### Load XLM-R from torch.hub (PyTorch >= 1.1): +```python +import torch +xlmr = torch.hub.load('pytorch/fairseq:main', 'xlmr.large') +xlmr.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Load XLM-R (for PyTorch 1.0 or custom models): +```python +# Download xlmr.large model +wget https://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz +tar -xzvf xlmr.large.tar.gz + +# Load the model in fairseq +from fairseq.models.roberta import XLMRModel +xlmr = XLMRModel.from_pretrained('/path/to/xlmr.large', checkpoint_file='model.pt') +xlmr.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Apply sentence-piece-model (SPM) encoding to input text: +```python +en_tokens = xlmr.encode('Hello world!') +assert en_tokens.tolist() == [0, 35378, 8999, 38, 2] +xlmr.decode(en_tokens) # 'Hello world!' + +zh_tokens = xlmr.encode('你好，世界') +assert zh_tokens.tolist() == [0, 6, 124084, 4, 3221, 2] +xlmr.decode(zh_tokens) # '你好，世界' + +hi_tokens = xlmr.encode('नमस्ते दुनिया') +assert hi_tokens.tolist() == [0, 68700, 97883, 29405, 2] +xlmr.decode(hi_tokens) # 'नमस्ते दुनिया' + +ar_tokens = xlmr.encode('مرحبا بالعالم') +assert ar_tokens.tolist() == [0, 665, 193478, 258, 1705, 77796, 2] +xlmr.decode(ar_tokens) # 'مرحبا بالعالم' + +fr_tokens = xlmr.encode('Bonjour le monde') +assert fr_tokens.tolist() == [0, 84602, 95, 11146, 2] +xlmr.decode(fr_tokens) # 'Bonjour le monde' +``` + +##### Extract features from XLM-R: +```python +# Extract the last layer's features +last_layer_features = xlmr.extract_features(zh_tokens) +assert last_layer_features.size() == torch.Size([1, 6, 1024]) + +# Extract all layer's features (layer 0 is the embedding layer) +all_layers = xlmr.extract_features(zh_tokens, return_all_hiddens=True) +assert len(all_layers) == 25 +assert torch.all(all_layers[-1] == last_layer_features) +``` + +## Citation + +```bibtex +@article{conneau2019unsupervised, + title={Unsupervised Cross-lingual Representation Learning at Scale}, + author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, + journal={arXiv preprint arXiv:1911.02116}, + year={2019} +} +``` + + +```bibtex +@article{goyal2021larger, + title={Larger-Scale Transformers for Multilingual Masked Language Modeling}, + author={Goyal, Naman and Du, Jingfei and Ott, Myle and Anantharaman, Giri and Conneau, Alexis}, + journal={arXiv preprint arXiv:2105.00572}, + year={2021} +} +``` diff --git a/fairseq/examples/xmod/README.md b/fairseq/examples/xmod/README.md new file mode 100644 index 0000000..46958b8 --- /dev/null +++ b/fairseq/examples/xmod/README.md @@ -0,0 +1,151 @@ +# X-MOD: Lifting the Curse of Multilinguality by Pre-training Modular Transformers + +https://arxiv.org/abs/2205.06266 + + +## Introduction + +X-MOD extends multilingual masked language models like XLM-R to include language-specific modular components, introduced at each transformer layer. Each module is only used by one language. For fine-tuning, the modular components are frozen, and replaced with the target language in cross-lingual transfer settings. + + +## Pre-trained models + +Model | Size | # train steps | # langs | Download +---|---|---|---|--- +`xmod.base.13.125k` | BERT-base | 125k | 13 | [xmod.base.13.125k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.13.125k.tar.gz) +`xmod.base.30.125k` | BERT-base | 125k | 30 | [xmod.base.30.125k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.30.125k.tar.gz) +`xmod.base.30.195k` | BERT-base | 195k | 30 | [xmod.base.30.195k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.30.195k.tar.gz) +`xmod.base.60.125k` | BERT-base | 125k | 60 | [xmod.base.60.125k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.60.125k.tar.gz) +`xmod.base.60.265k` | BERT-base | 265k | 60 | [xmod.base.60.265k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.60.265k.tar.gz) +`xmod.base.75.125k` | BERT-base | 125k | 75 | [xmod.base.75.125k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.75.125k.tar.gz) +`xmod.base.75.269k` | BERT-base | 269k | 75 | [xmod.base.75.269k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.75.269k.tar.gz) +`xmod.base` | BERT-base | 1M | 81 | [xmod.base.81.1M.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.81.1M.tar.gz) +`xmod.large.prenorm` | BERT-large | 500k | 81 | [xmod.large.prenorm.81.500k.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.large.prenorm.81.500k.tar.gz) + + +## Fine-tuning on NLI + +We next provide an example of how to fine-tune the pre-trained models above on Natural Language Inference (NLI). We use MNLI for training in English, and show how to run inference in other languages. + +### 1) Download a pre-trained model + +```bash +MODEL=xmod.base.81.1M +wget https://dl.fbaipublicfiles.com/fairseq/models/xmod/$MODEL.tar.gz +tar -xzf $MODEL.tar.gz +``` + +### 2) Download and preprocess [MNLI](https://cims.nyu.edu/~sbowman/multinli/) +```bash +wget https://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip +unzip multinli_1.0.zip +python ./examples/xmod/preprocess_nli.py \ + --sentencepiece-model $MODEL/sentencepiece.bpe.model \ + --train multinli_1.0/multinli_1.0_train.jsonl \ + --valid multinli_1.0/multinli_1.0_dev_matched.jsonl \ + --destdir multinli_1.0/fairseq +``` + +### 3) Fine-tune on MNLI: + +```bash +MAX_EPOCH=5 +LR=1e-05 +BATCH_SIZE=32 +DATA_DIR=multinli_1.0/fairseq/bin + +CUDA_VISIBLE_DEVICES=0 fairseq-train $DATA_DIR \ + --restore-file $MODEL/model.pt \ + --save-dir $MODEL/nli \ + --reset-optimizer \ + --reset-dataloader \ + --reset-meters \ + --best-checkpoint-metric accuracy \ + --maximize-best-checkpoint-metric \ + --task sentence_prediction_adapters \ + --num-classes 3 \ + --init-token 0 \ + --separator-token 2 \ + --max-positions 512 \ + --shorten-method "truncate" \ + --arch xmod_base \ + --dropout 0.1 \ + --attention-dropout 0.1 \ + --weight-decay 0.01 \ + --criterion sentence_prediction_adapters \ + --optimizer adam \ + --adam-betas '(0.9, 0.98)' \ + --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler fixed \ + --lr $LR \ + --fp16 \ + --fp16-init-scale 4 \ + --threshold-loss-scale 1 \ + --fp16-scale-window 128 \ + --batch-size $BATCH_SIZE \ + --required-batch-size-multiple 1 \ + --update-freq 1 \ + --max-epoch $MAX_EPOCH +``` + +### 4) Run inference + +After training the model, we can load it and run inference in our target language. The default language is set to English, which is why we were not required to pass a language ID to the model during fine-tuning. To run inference in a non-English language, we need to tell the model that the module of the target language should be used instead: + +```python +from fairseq.models.xmod import XMODModel + +MODEL='xmod.base.81.1M/nli' +DATA='multinli_1.0/fairseq/bin' + +# Load model +model = XMODModel.from_pretrained( + model_name_or_path=MODEL, + checkpoint_file='checkpoint_best.pt', + data_name_or_path=DATA, + suffix='', + criterion='cross_entropy', + bpe='sentencepiece', + sentencepiece_model=DATA+'/input0/sentencepiece.bpe.model') +model = model.eval(); # disable dropout +model = model.half(); # use FP16 +model = model.cuda(); # move to GPU + +def predict(premise, hypothesis, lang): + tokens = model.encode(premise, hypothesis) + idx = model.predict('sentence_classification_head', tokens, lang_id=[lang]).argmax().item() + dictionary = model.task.label_dictionary + return dictionary[idx + dictionary.nspecial] + +predict( + premise='X-Mod hat spezifische Module die für jede Sprache existieren.', + hypothesis='X-Mod hat Module.', + lang='de_DE' +) # entailment + +predict( + premise='Londres es la capital del Reino Unido.', + hypothesis='Londres está en Francia.', + lang='es_XX', +) # contradiction + +predict( + premise='Patxik gogoko ditu babarrunak.', + hypothesis='Patxik babarrunak bazkaldu zituen.', + lang='eu_ES', +) # neutral +``` + + +## Citation + +```bibtex +@misc{pfeiffer2022xmod, + doi = {10.48550/ARXIV.2205.06266}, + url = {https://arxiv.org/abs/2205.06266}, + title = {Lifting the Curse of Multilinguality by Pre-training Modular Transformers}, + publisher = {arXiv}, + year = {2022}, +} +``` diff --git a/fairseq/examples/xmod/preprocess_nli.py b/fairseq/examples/xmod/preprocess_nli.py new file mode 100644 index 0000000..e1fb91c --- /dev/null +++ b/fairseq/examples/xmod/preprocess_nli.py @@ -0,0 +1,168 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import collections +import argparse +import shutil +import subprocess +import sys +import tempfile +from multiprocessing import Pool +import sentencepiece as spm + + +def preprocess(spm_model_path, train_path, valid_path, test_path, dest_dir, remove_empty=False, output_format='piece', workers=20): + with tempfile.TemporaryDirectory() as tmp: + # Tokenize with SentencePiece + for split, path in ('train', train_path), ('valid', valid_path), ('test', test_path): + if path is None: + continue + if path == '-': + path = sys.stdin.fileno() + with open(path, encoding='utf-8', errors='surrogateescape') as fin: + with open(f'{tmp}/{split}', mode='w', encoding='utf-8', errors='surrogateescape') as fout: + encoder = MultiprocessingEncoder(model=spm_model_path, remove_empty=remove_empty, output_format=output_format) + pool = Pool(workers, initializer=encoder.initializer) + encoded_lines = pool.imap(encoder.encode, fin, 10000) + for i, line in enumerate(encoded_lines, start=1): + if line is not None: + print(line, file=fout) + if i % 10000 == 0: + print("tokenized {} lines".format(i), file=sys.stderr) + + # Generate dictionary + sp = spm.SentencePieceProcessor(model_file=spm_model_path) + if output_format == 'piece': + vocab = [sp.id_to_piece(i) for i in range(3, sp.vocab_size())] + else: + vocab = map(str, range(sp.vocab_size())) + with open(f'{tmp}/dict.txt', mode='w', encoding='utf-8', errors='surrogateescape') as f: + for word in vocab: + print(word, 1, file=f) + + # Binarize + command = [ + 'python3', '-m', 'fairseq_cli.preprocess', + '--only-source', + '--thresholdsrc', '0', + '--destdir', dest_dir, + '--srcdict', f'{tmp}/dict.txt', + '--workers', '20', + ] + for split, path in ('train', train_path), ('valid', valid_path), ('test', test_path): + if path is not None: + command += [f'--{split}pref', f'{tmp}/{split}'] + subprocess.run(command) + + # Copy SentencePiece model + shutil.copyfile(spm_model_path, f'{dest_dir}/sentencepiece.bpe.model') + + +class MultiprocessingEncoder(object): + def __init__(self, model, remove_empty, output_format): + self.model = model + self.remove_empty = remove_empty + self.output_format = output_format + + def initializer(self): + global sp + sp = spm.SentencePieceProcessor(model_file=self.model) + + def encode(self, line): + global sp + line = line.strip() + if len(line) == 0 and self.remove_empty: + return None + + if self.output_format == 'piece': + return ' '.join(sp.encode_as_pieces(line)) + else: + return ' '.join(map(str, sp.encode(line))) + + +def write_lines(lines, path): + with open(path, mode='x', encoding='utf-8') as f: + for line in lines: + print(line, file=f) + + +def read_jsonl(path): + with open(path, encoding='utf-8') as f: + return [json.loads(line) for line in f.read().splitlines()] + + +def read_nli(path, langs=None): + data = read_jsonl(path) + + if langs is not None: + data = [sample for sample in data if sample.get('language') in langs] + + lang2count = collections.defaultdict(int) + for sample in data: + lang2count[sample.get('language')] += 1 + + if langs: + assert set(lang2count.keys()) == set(langs) + + nlangs = len(lang2count) + assert nlangs > 0 + lens = list(lang2count.values()) + assert all([lens[0] == length for length in lens]) + + print(f'Loaded {lens[0]} samples in {nlangs} languages from {path}', file=sys.stderr) + return data + + +def main(): + parser = argparse.ArgumentParser(description='Tokenize and binarize NLI data') + parser.add_argument('--sentencepiece-model', required=True) + parser.add_argument('--train', required=True, help='Training data in jsonl format') + parser.add_argument('--valid', required=True, help='Validation data in jsonl format') + parser.add_argument('--destdir', required=True) + + args = parser.parse_args() + + os.makedirs(args.destdir + '/raw',) + os.makedirs(args.destdir + '/bin', ) + + # Extract input/labels + for split, path in ('train', args.train), ('valid', args.valid): + data = read_nli(path, langs=None) + original_size = len(data) + data = [sample for sample in data if sample['gold_label'] != '-'] + assert all(sample['gold_label'] in ('contradiction', 'entailment', 'neutral') for sample in data) + filtered_size = len(data) + if filtered_size != original_size: + print(f'Filtered {filtered_size}/{original_size} samples from {path}', file=sys.stderr) + for name, field in ('input0', 'sentence1'), ('input1', 'sentence2'), ('label', 'gold_label'): + write_lines([sample[field] for sample in data], f'{args.destdir}/raw/{split}.{name}.txt') + + # Tokenize and binarize input + for field in 'input0', 'input1': + preprocess( + spm_model_path=args.sentencepiece_model, + train_path=f'{args.destdir}/raw/train.{field}.txt', + valid_path=f'{args.destdir}/raw/valid.{field}.txt', + test_path=None, + dest_dir=f'{args.destdir}/bin/{field}', + workers=20, + ) + + # Binarize labels + subprocess.run([ + 'python3', '-m', 'fairseq_cli.preprocess', + '--trainpref', f'{args.destdir}/raw/train.label.txt', + '--validpref', f'{args.destdir}/raw/valid.label.txt', + '--only-source', + '--thresholdsrc', '0', + '--destdir', f'{args.destdir}/bin/label', + '--workers', '20', + ]) + + +if __name__ == '__main__': + main() diff --git a/fairseq/fairseq/__init__.py b/fairseq/fairseq/__init__.py new file mode 100644 index 0000000..080c988 --- /dev/null +++ b/fairseq/fairseq/__init__.py @@ -0,0 +1,45 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import os +import sys + +try: + from .version import __version__ # noqa +except ImportError: + version_txt = os.path.join(os.path.dirname(__file__), "version.txt") + with open(version_txt) as f: + __version__ = f.read().strip() + +__all__ = ["pdb"] + +# backwards compatibility to support `from fairseq.X import Y` +from fairseq.distributed import utils as distributed_utils +from fairseq.logging import meters, metrics, progress_bar # noqa + +sys.modules["fairseq.distributed_utils"] = distributed_utils +sys.modules["fairseq.meters"] = meters +sys.modules["fairseq.metrics"] = metrics +sys.modules["fairseq.progress_bar"] = progress_bar + +# initialize hydra +from fairseq.dataclass.initialize import hydra_init + +hydra_init() + +import fairseq.criterions # noqa +import fairseq.distributed # noqa +import fairseq.models # noqa +import fairseq.modules # noqa +import fairseq.optim # noqa +import fairseq.optim.lr_scheduler # noqa +import fairseq.pdb # noqa +import fairseq.scoring # noqa +import fairseq.tasks # noqa +import fairseq.token_generation_constraints # noqa + +import fairseq.benchmark # noqa +import fairseq.model_parallel # noqa diff --git a/fairseq/fairseq/benchmark/__init__.py b/fairseq/fairseq/benchmark/__init__.py new file mode 100644 index 0000000..0317d5c --- /dev/null +++ b/fairseq/fairseq/benchmark/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# import models/tasks to register them +from . import dummy_dataset, dummy_lm, dummy_masked_lm, dummy_model, dummy_mt # noqa diff --git a/fairseq/fairseq/benchmark/benchmark_multihead_attention.py b/fairseq/fairseq/benchmark/benchmark_multihead_attention.py new file mode 100644 index 0000000..a44847f --- /dev/null +++ b/fairseq/fairseq/benchmark/benchmark_multihead_attention.py @@ -0,0 +1,172 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import random + +import torch +from torch.utils import benchmark + +from fairseq.modules.multihead_attention import MultiheadAttention + +BATCH = [20, 41, 97] +SEQ = 64 +EMB = 48 +HEADS = 4 +DROP = 0.1 +DEVICE = torch.device("cuda") +ATTN_MASK_DTYPE = [torch.uint8, torch.bool, torch.float] +KEY_PADDING_MASK_DTYPE = [torch.uint8, torch.bool] + + +def _reset_seeds(): + torch.manual_seed(0) + random.seed(0) + + +def _get_mask(to_dtype: torch.dtype, dim0: int, dim1: int): + if to_dtype == torch.float: + mask = torch.randint(0, 2, (dim0, dim1)).to(dtype=torch.bool) + return mask.to(dtype=to_dtype).masked_fill(mask, -float("inf")) + return torch.randint(0, 2, (dim0, dim1)).to(dtype=to_dtype) + + +def benchmark_multihead_attention( + label="", + attn_dtype=torch.uint8, + key_padding_dtype=torch.uint8, + add_bias_kv=False, + add_zero_attn=False, + static_kv=False, + batch_size=20, + embedding=EMB, + seq_len=SEQ, + num_heads=HEADS, +): + + results = [] + # device = torch.device("cuda") + + xformers_att_config = '{"name": "scaled_dot_product"}' + + attn_mask = _get_mask(to_dtype=attn_dtype, dim0=seq_len, dim1=seq_len) + key_padding_mask = _get_mask( + to_dtype=key_padding_dtype, dim0=batch_size, dim1=seq_len + ) + + q = torch.rand(seq_len, batch_size, embedding, requires_grad=True) + k = torch.rand(seq_len, batch_size, embedding, requires_grad=True) + v = torch.rand(seq_len, batch_size, embedding, requires_grad=True) + + _reset_seeds() + + original_mha = MultiheadAttention( + embedding, + num_heads, + dropout=0.0, + xformers_att_config=None, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + + xformers_mha = MultiheadAttention( + embedding, + num_heads, + dropout=0.0, + xformers_att_config=xformers_att_config, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + + def original_bench_fw(q, k, v, key_padding_mask, attn_mask, static_kv): + original_mha( + query=q, + key=k, + value=v, + key_padding_mask=key_padding_mask, + attn_mask=attn_mask, + static_kv=static_kv, + ) + + def xformers_bench_fw(q, k, v, key_padding_mask, attn_mask, static_kv): + xformers_mha( + query=q, + key=k, + value=v, + key_padding_mask=key_padding_mask, + attn_mask=attn_mask, + static_kv=static_kv, + ) + + def original_bench_fw_bw(q, k, v, key_padding_mask, attn_mask, static_kv): + output, _ = original_mha( + query=q, + key=k, + value=v, + key_padding_mask=key_padding_mask, + attn_mask=attn_mask, + static_kv=static_kv, + ) + loss = torch.norm(output) + loss.backward() + + def xformers_bench_fw_bw(q, k, v, key_padding_mask, attn_mask, static_kv): + output, _ = xformers_mha( + query=q, + key=k, + value=v, + key_padding_mask=key_padding_mask, + attn_mask=attn_mask, + static_kv=static_kv, + ) + loss = torch.norm(output) + loss.backward() + + fns = [ + original_bench_fw, + xformers_bench_fw, + original_bench_fw_bw, + xformers_bench_fw_bw, + ] + + for fn in fns: + results.append( + benchmark.Timer( + stmt="fn(q, k, v, key_padding_mask, attn_mask, static_kv)", + globals={ + "q": q, + "k": k, + "v": v, + "key_padding_mask": key_padding_mask, + "attn_mask": attn_mask, + "static_kv": static_kv, + "fn": fn, + }, + label="multihead fw + bw", + sub_label=f"{fn.__name__}", + description=label, + ).blocked_autorange(min_run_time=1) + ) + + compare = benchmark.Compare(results) + compare.print() + + +def run_benchmarks(): + for attn_dtype, key_padding_dtype, add_bias_kv, add_zero_attn in itertools.product( + ATTN_MASK_DTYPE, KEY_PADDING_MASK_DTYPE, [True, False], [True, False] + ): + label = f"attn_dtype {attn_dtype}, key_padding_dtype {key_padding_dtype}, \ + add_bias_kv {add_bias_kv}, add_zero_attn {add_zero_attn}" + benchmark_multihead_attention( + label=label, + attn_dtype=attn_dtype, + key_padding_dtype=key_padding_dtype, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + + +run_benchmarks() diff --git a/fairseq/fairseq/benchmark/dummy_dataset.py b/fairseq/fairseq/benchmark/dummy_dataset.py new file mode 100644 index 0000000..2f05175 --- /dev/null +++ b/fairseq/fairseq/benchmark/dummy_dataset.py @@ -0,0 +1,36 @@ +import numpy as np +from fairseq.data import FairseqDataset + + +class DummyDataset(FairseqDataset): + def __init__(self, batch, num_items, item_size): + super().__init__() + self.batch = batch + self.num_items = num_items + self.item_size = item_size + + def __getitem__(self, index): + return index + + def __len__(self): + return self.num_items + + def collater(self, samples): + return self.batch + + @property + def sizes(self): + return np.array([self.item_size] * self.num_items) + + def num_tokens(self, index): + return self.item_size + + def size(self, index): + return self.item_size + + def ordered_indices(self): + return np.arange(self.num_items) + + @property + def supports_prefetch(self): + return False diff --git a/fairseq/fairseq/benchmark/dummy_lm.py b/fairseq/fairseq/benchmark/dummy_lm.py new file mode 100644 index 0000000..c6246a0 --- /dev/null +++ b/fairseq/fairseq/benchmark/dummy_lm.py @@ -0,0 +1,83 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Optional + +import torch +from .dummy_dataset import DummyDataset +from fairseq.data import Dictionary +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task +from omegaconf import II + + +logger = logging.getLogger(__name__) + + +@dataclass +class DummyLMConfig(FairseqDataclass): + dict_size: int = 49996 + dataset_size: int = 100000 + tokens_per_sample: int = field( + default=512, metadata={"help": "max sequence length"} + ) + add_bos_token: bool = False + batch_size: Optional[int] = II("dataset.batch_size") + max_tokens: Optional[int] = II("dataset.max_tokens") + max_target_positions: int = II("task.tokens_per_sample") + + +@register_task("dummy_lm", dataclass=DummyLMConfig) +class DummyLMTask(FairseqTask): + def __init__(self, cfg: DummyLMConfig): + super().__init__(cfg) + + # load dictionary + self.dictionary = Dictionary() + for i in range(cfg.dict_size): + self.dictionary.add_symbol("word{}".format(i)) + self.dictionary.pad_to_multiple_(8) # often faster if divisible by 8 + logger.info("dictionary: {} types".format(len(self.dictionary))) + + seq = torch.arange(cfg.tokens_per_sample + 1) + self.dictionary.pad() + 1 + + self.dummy_src = seq[:-1] + self.dummy_tgt = seq[1:] + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if self.cfg.batch_size is not None: + bsz = self.cfg.batch_size + else: + bsz = max(1, self.cfg.max_tokens // self.cfg.tokens_per_sample) + self.datasets[split] = DummyDataset( + { + "id": 1, + "net_input": { + "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]), + "src_lengths": torch.full( + (bsz,), self.cfg.tokens_per_sample, dtype=torch.long + ), + }, + "target": torch.stack([self.dummy_tgt for _ in range(bsz)]), + "nsentences": bsz, + "ntokens": bsz * self.cfg.tokens_per_sample, + }, + num_items=self.cfg.dataset_size, + item_size=self.cfg.tokens_per_sample, + ) + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary diff --git a/fairseq/fairseq/benchmark/dummy_masked_lm.py b/fairseq/fairseq/benchmark/dummy_masked_lm.py new file mode 100644 index 0000000..12b9c5d --- /dev/null +++ b/fairseq/fairseq/benchmark/dummy_masked_lm.py @@ -0,0 +1,94 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Optional + +import torch +from omegaconf import II + +from .dummy_dataset import DummyDataset +from fairseq.data import Dictionary +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + +logger = logging.getLogger(__name__) + + +@dataclass +class DummyMaskedLMConfig(FairseqDataclass): + dict_size: int = 49996 + dataset_size: int = 100000 + tokens_per_sample: int = field( + default=512, + metadata={ + "help": "max number of total tokens over all" + " segments per sample for BERT dataset" + }, + ) + batch_size: Optional[int] = II("dataset.batch_size") + max_tokens: Optional[int] = II("dataset.max_tokens") + max_target_positions: int = II("task.tokens_per_sample") + + +@register_task("dummy_masked_lm", dataclass=DummyMaskedLMConfig) +class DummyMaskedLMTask(FairseqTask): + def __init__(self, cfg: DummyMaskedLMConfig): + super().__init__(cfg) + + self.dictionary = Dictionary() + for i in range(cfg.dict_size): + self.dictionary.add_symbol("word{}".format(i)) + logger.info("dictionary: {} types".format(len(self.dictionary))) + # add mask token + self.mask_idx = self.dictionary.add_symbol("<mask>") + self.dictionary.pad_to_multiple_(8) # often faster if divisible by 8 + + mask_idx = 0 + pad_idx = 1 + seq = torch.arange(cfg.tokens_per_sample) + pad_idx + 1 + mask = torch.arange(2, cfg.tokens_per_sample, 7) # ~15% + src = seq.clone() + src[mask] = mask_idx + tgt = torch.full_like(seq, pad_idx) + tgt[mask] = seq[mask] + + self.dummy_src = src + self.dummy_tgt = tgt + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if self.cfg.batch_size is not None: + bsz = self.cfg.batch_size + else: + bsz = max(1, self.cfg.max_tokens // self.cfg.tokens_per_sample) + self.datasets[split] = DummyDataset( + { + "id": 1, + "net_input": { + "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]), + "src_lengths": torch.full( + (bsz,), self.cfg.tokens_per_sample, dtype=torch.long + ), + }, + "target": torch.stack([self.dummy_tgt for _ in range(bsz)]), + "nsentences": bsz, + "ntokens": bsz * self.cfg.tokens_per_sample, + }, + num_items=self.cfg.dataset_size, + item_size=self.cfg.tokens_per_sample, + ) + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary diff --git a/fairseq/fairseq/benchmark/dummy_model.py b/fairseq/fairseq/benchmark/dummy_model.py new file mode 100644 index 0000000..ff26e4f --- /dev/null +++ b/fairseq/fairseq/benchmark/dummy_model.py @@ -0,0 +1,96 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch.nn.functional as F +from fairseq.data import Dictionary +from fairseq.models import ( + FairseqDecoder, + FairseqLanguageModel, + register_model, + register_model_architecture, +) + + +@register_model("dummy_model") +class DummyModel(FairseqLanguageModel): + def __init__(self, args, encoder): + super().__init__(encoder) + self.args = args + + @staticmethod + def add_args(parser): + parser.add_argument("--num-layers", type=int, default=24) + parser.add_argument("--embed-dim", type=int, default=1024) + + @classmethod + def build_model(cls, args, task): + encoder = DummyEncoder( + num_embed=len(task.target_dictionary), + embed_dim=args.embed_dim, + num_layers=args.num_layers, + ) + return cls(args, encoder) + + def forward(self, src_tokens, masked_tokens=None, **kwargs): + return self.decoder(src_tokens, masked_tokens=masked_tokens) + + +class DummyEncoder(FairseqDecoder): + def __init__(self, num_embed=50000, embed_dim=1024, num_layers=24): + super().__init__(Dictionary()) + self.embed = nn.Embedding( + num_embeddings=num_embed, embedding_dim=embed_dim, padding_idx=0 + ) + self.layers_a = nn.ModuleList( + [ + nn.Sequential( + nn.LayerNorm(embed_dim), + nn.Linear(embed_dim, 3 * embed_dim), # q, k, v input projection + nn.Linear(3 * embed_dim, embed_dim), # skip self-attention + nn.Linear(embed_dim, embed_dim), # output projection + nn.Dropout(), + ) + for i in range(num_layers) + ] + ) + self.layers_b = nn.ModuleList( + [ + nn.Sequential( + nn.LayerNorm(embed_dim), + nn.Linear(embed_dim, 4 * embed_dim), # FFN + nn.ReLU(), + nn.Linear(4 * embed_dim, embed_dim), # FFN + nn.Dropout(0.1), + ) + for i in range(num_layers) + ] + ) + self.out_proj = nn.Linear(embed_dim, num_embed) + + def forward(self, tokens, masked_tokens=None): + x = self.embed(tokens) + for layer_a, layer_b in zip(self.layers_a, self.layers_b): + x = x + layer_a(x) + x = x + layer_b(x) + x = self.out_proj(x) + if masked_tokens is not None: + x = x[masked_tokens] + return (x,) + + def max_positions(self): + return 1024 + + def get_normalized_probs(self, net_output, log_probs, sample=None): + logits = net_output[0].float() + if log_probs: + return F.log_softmax(logits, dim=-1) + else: + return F.softmax(logits, dim=-1) + + +@register_model_architecture("dummy_model", "dummy_model") +def base_architecture(args): + pass diff --git a/fairseq/fairseq/benchmark/dummy_mt.py b/fairseq/fairseq/benchmark/dummy_mt.py new file mode 100644 index 0000000..28d78cf --- /dev/null +++ b/fairseq/fairseq/benchmark/dummy_mt.py @@ -0,0 +1,119 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import numpy as np +import torch + +from fairseq.data import Dictionary, FairseqDataset +from fairseq.tasks import LegacyFairseqTask, register_task + +logger = logging.getLogger(__name__) + + +@register_task("dummy_mt") +class DummyMTTask(LegacyFairseqTask): + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument("--dict-size", default=49996, type=int) + parser.add_argument("--dataset-size", default=100000, type=int) + parser.add_argument("--src-len", default=30, type=int) + parser.add_argument("--tgt-len", default=30, type=int) + + def __init__(self, args, dictionary): + super().__init__(args) + self.dictionary = dictionary + self.seed = args.seed + + dictionary.pad_to_multiple_(8) # often faster if divisible by 8 + + self.dummy_src = torch.arange(args.src_len + 1) + dictionary.pad() + 1 + self.dummy_tgt = torch.arange(args.tgt_len + 1) + dictionary.pad() + 1 + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task.""" + dictionary = Dictionary() + for i in range(args.dict_size): + dictionary.add_symbol("word{}".format(i)) + logger.info("dictionary: {} types".format(len(dictionary))) + + args.max_source_positions = args.src_len + dictionary.pad() + 2 + args.max_target_positions = args.tgt_len + dictionary.pad() + 2 + + return cls(args, dictionary) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + Args: + split (str): name of the split (e.g., train, valid, test) + """ + item_size = max(self.args.src_len, self.args.tgt_len) + if self.args.batch_size is not None: + bsz = self.args.batch_size + else: + bsz = max(1, self.args.max_tokens // item_size) + tgt = torch.stack([self.dummy_tgt for _ in range(bsz)]) + self.datasets[split] = DummyDataset( + { + "id": 1, + "net_input": { + "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]), + "src_lengths": torch.full( + (bsz,), self.args.src_len, dtype=torch.long + ), + "prev_output_tokens": tgt.clone(), + }, + "target": tgt, + "nsentences": bsz, + "ntokens": bsz * self.args.tgt_len, + }, + num_items=self.args.dataset_size, + item_size=item_size, + ) + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + +class DummyDataset(FairseqDataset): + def __init__(self, batch, num_items, item_size): + super().__init__() + self.batch = batch + self.num_items = num_items + self.item_size = item_size + + def __getitem__(self, index): + return index + + def __len__(self): + return self.num_items + + def collater(self, samples): + return self.batch + + @property + def sizes(self): + return np.array([self.item_size] * self.num_items) + + def num_tokens(self, index): + return self.item_size + + def size(self, index): + return self.item_size + + def ordered_indices(self): + return np.arange(self.num_items) + + @property + def supports_prefetch(self): + return False diff --git a/fairseq/fairseq/binarizer.py b/fairseq/fairseq/binarizer.py new file mode 100644 index 0000000..6f03d7a --- /dev/null +++ b/fairseq/fairseq/binarizer.py @@ -0,0 +1,381 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import typing as tp +from abc import ABC, abstractmethod +from collections import Counter +from dataclasses import dataclass +from multiprocessing import Pool + +import torch + +from fairseq.data import Dictionary, indexed_dataset +from fairseq.file_chunker_utils import Chunker, find_offsets +from fairseq.file_io import PathManager +from fairseq.tokenizer import tokenize_line + +logger = logging.getLogger("binarizer") + + +@dataclass +class BinarizeSummary: + """ + Keep track of what's going on in the binarizer + """ + + num_seq: int = 0 + replaced: tp.Optional[Counter] = None + num_tok: int = 0 + + @property + def num_replaced(self) -> int: + if self.replaced is None: + return 0 + return sum(self.replaced.values()) + + @property + def replaced_percent(self) -> float: + return 100 * self.num_replaced / self.num_tok + + def __str__(self) -> str: + base = f"{self.num_seq} sents, {self.num_tok} tokens" + if self.replaced is None: + return base + + return f"{base}, {self.replaced_percent:.3}% replaced" + + def merge(self, other: "BinarizeSummary"): + replaced = None + if self.replaced is not None: + replaced = self.replaced + if other.replaced is not None: + if replaced is None: + replaced = other.replaced + else: + replaced += other.replaced + self.replaced = replaced + self.num_seq += other.num_seq + self.num_tok += other.num_tok + + +class Binarizer(ABC): + """ + a binarizer describes how to take a string and build a tensor out of it + """ + + @abstractmethod + def binarize_line( + self, + line: str, + summary: BinarizeSummary, + ) -> torch.IntTensor: + ... + + +def _worker_prefix(output_prefix: str, worker_id: int): + return f"{output_prefix}.pt{worker_id}" + + +class FileBinarizer: + """ + An file binarizer can take a file, tokenize it, and binarize each line to a tensor + """ + + @classmethod + def multiprocess_dataset( + cls, + input_file: str, + dataset_impl: str, + binarizer: Binarizer, + output_prefix: str, + vocab_size=None, + num_workers=1, + ) -> BinarizeSummary: + final_summary = BinarizeSummary() + + offsets = find_offsets(input_file, num_workers) + # find_offsets returns a list of position [pos1, pos2, pos3, pos4] but we would want pairs: + # [(pos1, pos2), (pos2, pos3), (pos3, pos4)] to process the chunks with start/end info + # we zip the list with itself shifted by one to get all the pairs. + (first_chunk, *more_chunks) = zip(offsets, offsets[1:]) + pool = None + if num_workers > 1: + pool = Pool(processes=num_workers - 1) + worker_results = [ + pool.apply_async( + cls._binarize_chunk_and_finalize, + args=( + binarizer, + input_file, + start_offset, + end_offset, + _worker_prefix( + output_prefix, + worker_id, + ), + dataset_impl, + ), + kwds={ + "vocab_size": vocab_size, + } + if vocab_size is not None + else {}, + ) + for worker_id, (start_offset, end_offset) in enumerate( + more_chunks, start=1 + ) + ] + + pool.close() + pool.join() + for r in worker_results: + summ = r.get() + final_summary.merge(summ) + + # do not close the bin file as we need to merge the worker results in + final_ds, summ = cls._binarize_file_chunk( + binarizer, + input_file, + offset_start=first_chunk[0], + offset_end=first_chunk[1], + output_prefix=output_prefix, + dataset_impl=dataset_impl, + vocab_size=vocab_size if vocab_size is not None else None, + ) + final_summary.merge(summ) + + if num_workers > 1: + for worker_id in range(1, num_workers): + # merge the worker outputs + worker_output_prefix = _worker_prefix( + output_prefix, + worker_id, + ) + final_ds.merge_file_(worker_output_prefix) + try: + os.remove(indexed_dataset.data_file_path(worker_output_prefix)) + os.remove(indexed_dataset.index_file_path(worker_output_prefix)) + except Exception as e: + logger.error( + f"couldn't remove {worker_output_prefix}.*", exc_info=e + ) + + # now we can close the file + idx_file = indexed_dataset.index_file_path(output_prefix) + final_ds.finalize(idx_file) + return final_summary + + @staticmethod + def _binarize_file_chunk( + binarizer: Binarizer, + filename: str, + offset_start: int, + offset_end: int, + output_prefix: str, + dataset_impl: str, + vocab_size=None, + ) -> tp.Tuple[tp.Any, BinarizeSummary]: # (dataset builder, BinarizeSummary) + """ + creates a dataset builder and append binarized items to it. This function does not + finalize the builder, this is useful if you want to do other things with your bin file + like appending/merging other files + """ + bin_file = indexed_dataset.data_file_path(output_prefix) + ds = indexed_dataset.make_builder( + bin_file, + impl=dataset_impl, + vocab_size=vocab_size, + ) + summary = BinarizeSummary() + + with Chunker( + PathManager.get_local_path(filename), offset_start, offset_end + ) as line_iterator: + for line in line_iterator: + ds.add_item(binarizer.binarize_line(line, summary)) + + return ds, summary + + @classmethod + def _binarize_chunk_and_finalize( + cls, + binarizer: Binarizer, + filename: str, + offset_start: int, + offset_end: int, + output_prefix: str, + dataset_impl: str, + vocab_size=None, + ): + """ + same as above, but also finalizes the builder + """ + ds, summ = cls._binarize_file_chunk( + binarizer, + filename, + offset_start, + offset_end, + output_prefix, + dataset_impl, + vocab_size=vocab_size, + ) + + idx_file = indexed_dataset.index_file_path(output_prefix) + ds.finalize(idx_file) + + return summ + + +class VocabularyDatasetBinarizer(Binarizer): + """ + Takes a Dictionary/Vocabulary, assign ids to each + token using the dictionary encode_line function. + """ + + def __init__( + self, + dict: Dictionary, + tokenize: tp.Callable[[str], tp.List[str]] = tokenize_line, + append_eos: bool = True, + reverse_order: bool = False, + already_numberized: bool = False, + ) -> None: + self.dict = dict + self.tokenize = tokenize + self.append_eos = append_eos + self.reverse_order = reverse_order + self.already_numberized = already_numberized + super().__init__() + + def binarize_line( + self, + line: str, + summary: BinarizeSummary, + ): + if summary.replaced is None: + summary.replaced = Counter() + + def replaced_consumer(word, idx): + if idx == self.dict.unk_index and word != self.dict.unk_word: + summary.replaced.update([word]) + + if self.already_numberized: + id_strings = line.strip().split() + id_list = [int(id_string) for id_string in id_strings] + if self.reverse_order: + id_list.reverse() + if self.append_eos: + id_list.append(self.dict.eos()) + ids = torch.IntTensor(id_list) + else: + ids = self.dict.encode_line( + line=line, + line_tokenizer=self.tokenize, + add_if_not_exist=False, + consumer=replaced_consumer, + append_eos=self.append_eos, + reverse_order=self.reverse_order, + ) + + summary.num_seq += 1 + summary.num_tok += len(ids) + return ids + + +class AlignmentDatasetBinarizer(Binarizer): + """ + binarize by parsing a set of alignments and packing + them in a tensor (see utils.parse_alignment) + """ + + def __init__( + self, + alignment_parser: tp.Callable[[str], torch.IntTensor], + ) -> None: + super().__init__() + self.alignment_parser = alignment_parser + + def binarize_line( + self, + line: str, + summary: BinarizeSummary, + ): + ids = self.alignment_parser(line) + summary.num_seq += 1 + summary.num_tok += len(ids) + return ids + + +class LegacyBinarizer: + @classmethod + def binarize( + cls, + filename: str, + dico: Dictionary, + consumer: tp.Callable[[torch.IntTensor], None], + tokenize: tp.Callable[[str], tp.List[str]] = tokenize_line, + append_eos: bool = True, + reverse_order: bool = False, + offset: int = 0, + end: int = -1, + already_numberized: bool = False, + ) -> tp.Dict[str, int]: + binarizer = VocabularyDatasetBinarizer( + dict=dico, + tokenize=tokenize, + append_eos=append_eos, + reverse_order=reverse_order, + already_numberized=already_numberized, + ) + return cls._consume_file( + filename, + binarizer, + consumer, + offset_start=offset, + offset_end=end, + ) + + @classmethod + def binarize_alignments( + cls, + filename: str, + alignment_parser: tp.Callable[[str], torch.IntTensor], + consumer: tp.Callable[[torch.IntTensor], None], + offset: int = 0, + end: int = -1, + ) -> tp.Dict[str, int]: + binarizer = AlignmentDatasetBinarizer(alignment_parser) + return cls._consume_file( + filename, + binarizer, + consumer, + offset_start=offset, + offset_end=end, + ) + + @staticmethod + def _consume_file( + filename: str, + binarizer: Binarizer, + consumer: tp.Callable[[torch.IntTensor], None], + offset_start: int, + offset_end: int, + ) -> tp.Dict[str, int]: + summary = BinarizeSummary() + + with Chunker( + PathManager.get_local_path(filename), offset_start, offset_end + ) as line_iterator: + for line in line_iterator: + consumer(binarizer.binarize_line(line, summary)) + + return { + "nseq": summary.num_seq, + "nunk": summary.num_replaced, + "ntok": summary.num_tok, + "replaced": summary.replaced, + } diff --git a/fairseq/fairseq/checkpoint_utils.py b/fairseq/fairseq/checkpoint_utils.py new file mode 100644 index 0000000..8dd2c54 --- /dev/null +++ b/fairseq/fairseq/checkpoint_utils.py @@ -0,0 +1,937 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import collections +import contextlib +import inspect +import logging +import os +import re +import time +import traceback +from collections import OrderedDict +from pathlib import Path +from typing import Any, Dict, Optional, Union + +import numpy as np +import torch +from fairseq.data import data_utils +from fairseq.dataclass.configs import CheckpointConfig +from fairseq.dataclass.utils import ( + convert_namespace_to_omegaconf, + overwrite_args_by_name, +) +from fairseq.distributed.fully_sharded_data_parallel import FSDP, has_FSDP +from fairseq.file_io import PathManager +from fairseq.models import FairseqDecoder, FairseqEncoder +from omegaconf import DictConfig, OmegaConf, open_dict + +logger = logging.getLogger(__name__) + + +def save_checkpoint(cfg: CheckpointConfig, trainer, epoch_itr, val_loss): + from fairseq import meters + + # only one worker should attempt to create the required dir + if trainer.data_parallel_rank == 0: + os.makedirs(cfg.save_dir, exist_ok=True) + + prev_best = getattr(save_checkpoint, "best", val_loss) + if val_loss is not None: + best_function = max if cfg.maximize_best_checkpoint_metric else min + save_checkpoint.best = best_function(val_loss, prev_best) + + if cfg.no_save: + return None + + trainer.consolidate_optimizer() # TODO(SS): do we need this if no_save_optimizer_state + + if not trainer.should_save_checkpoint_on_current_rank: + if trainer.always_call_state_dict_during_save_checkpoint: + trainer.state_dict() + return None + + write_timer = meters.StopwatchMeter() + write_timer.start() + + epoch = epoch_itr.epoch + end_of_epoch = epoch_itr.end_of_epoch() + updates = trainer.get_num_updates() + + logger.info(f"Preparing to save checkpoint for epoch {epoch} @ {updates} updates") + + def is_better(a, b): + return a >= b if cfg.maximize_best_checkpoint_metric else a <= b + + suffix = trainer.checkpoint_suffix + checkpoint_conds = collections.OrderedDict() + checkpoint_conds["checkpoint{}{}.pt".format(epoch, suffix)] = ( + end_of_epoch and not cfg.no_epoch_checkpoints and epoch % cfg.save_interval == 0 + ) + checkpoint_conds["checkpoint_{}_{}{}.pt".format(epoch, updates, suffix)] = ( + not end_of_epoch + and cfg.save_interval_updates > 0 + and updates % cfg.save_interval_updates == 0 + ) + checkpoint_conds["checkpoint_best{}.pt".format(suffix)] = val_loss is not None and ( + not hasattr(save_checkpoint, "best") + or is_better(val_loss, save_checkpoint.best) + ) + if val_loss is not None and cfg.keep_best_checkpoints > 0: + worst_best = getattr(save_checkpoint, "best", None) + chkpts = checkpoint_paths( + cfg.save_dir, + pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format( + cfg.best_checkpoint_metric, suffix + ), + ) + if len(chkpts) > 0: + p = chkpts[-1] if cfg.maximize_best_checkpoint_metric else chkpts[0] + worst_best = float(p.rsplit("_")[-1].replace("{}.pt".format(suffix), "")) + # add random digits to resolve ties + with data_utils.numpy_seed(epoch, updates, val_loss): + rand_sfx = np.random.randint(0, cfg.keep_best_checkpoints) + + checkpoint_conds[ + "checkpoint.best_{}_{:.3f}{}{}.pt".format( + cfg.best_checkpoint_metric, val_loss, rand_sfx, suffix + ) + ] = worst_best is None or is_better(val_loss, worst_best) + checkpoint_conds[ + "checkpoint_last{}.pt".format(suffix) + ] = not cfg.no_last_checkpoints + + extra_state = { + "train_iterator": epoch_itr.state_dict(), + "val_loss": val_loss, + } + + # Going forward, different tasks could expose an API like this to dump all + # the checkpoint worthy attributes in a dictionary which then will be + # merged with the parent dictionary to create the "extra_state". This + # allows for an extensible yet simple design to checkpoint task level + # attributes + if hasattr(trainer.task, "get_checkpoint_dict"): + extra_state = {**extra_state, **trainer.task.get_checkpoint_dict()} + logger.info(f"State of {trainer.task.__class__.__name__} is ready to be persisted with the checkpoint") + + if hasattr(save_checkpoint, "best"): + extra_state.update({"best": save_checkpoint.best}) + + checkpoints = [ + os.path.join(cfg.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond + ] + saved_cp = None + if len(checkpoints) > 0 and trainer.should_save_checkpoint_on_current_rank: + saved_cp = trainer.save_checkpoint(checkpoints[0], extra_state) + for cp in checkpoints[1:]: + if cfg.write_checkpoints_asynchronously: + # TODO[ioPath]: Need to implement a delayed asynchronous + # file copying/moving feature. + logger.warning( + f"ioPath is not copying {checkpoints[0]} to {cp} " + "since async write mode is on." + ) + else: + assert PathManager.copy( + checkpoints[0], cp, overwrite=True + ), f"Failed to copy {checkpoints[0]} to {cp}" + + write_timer.stop() + logger.info( + "Saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format( + checkpoints[0], epoch, updates, val_loss, write_timer.sum + ) + ) + + if ( + not end_of_epoch + and cfg.keep_interval_updates > 0 + and trainer.should_save_checkpoint_on_current_rank + ): + # remove old checkpoints; checkpoints are sorted in descending order + if cfg.keep_interval_updates_pattern == -1: + checkpoints = checkpoint_paths( + cfg.save_dir, pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix) + ) + else: + checkpoints = checkpoint_paths( + cfg.save_dir, + pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix), + keep_match=True, + ) + checkpoints = [ + x[0] + for x in checkpoints + if x[1] % cfg.keep_interval_updates_pattern != 0 + ] + + for old_chk in checkpoints[cfg.keep_interval_updates :]: + if os.path.lexists(old_chk): + os.remove(old_chk) + elif PathManager.exists(old_chk): + PathManager.rm(old_chk) + + if cfg.keep_last_epochs > 0 and trainer.should_save_checkpoint_on_current_rank: + # remove old epoch checkpoints; checkpoints are sorted in descending order + checkpoints = checkpoint_paths( + cfg.save_dir, pattern=r"checkpoint(\d+){}\.pt".format(suffix) + ) + for old_chk in checkpoints[cfg.keep_last_epochs :]: + if os.path.lexists(old_chk): + os.remove(old_chk) + elif PathManager.exists(old_chk): + PathManager.rm(old_chk) + + if cfg.keep_best_checkpoints > 0 and trainer.should_save_checkpoint_on_current_rank: + # only keep the best N checkpoints according to validation metric + checkpoints = checkpoint_paths( + cfg.save_dir, + pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format( + cfg.best_checkpoint_metric, suffix + ), + ) + if not cfg.maximize_best_checkpoint_metric: + checkpoints = checkpoints[::-1] + for old_chk in checkpoints[cfg.keep_best_checkpoints :]: + if os.path.lexists(old_chk): + os.remove(old_chk) + elif PathManager.exists(old_chk): + PathManager.rm(old_chk) + + return saved_cp + + +def load_checkpoint(cfg: CheckpointConfig, trainer, **passthrough_args): + """ + Load a checkpoint and restore the training iterator. + + *passthrough_args* will be passed through to + ``trainer.get_train_iterator``. + """ + + reset_optimizer = cfg.reset_optimizer + reset_lr_scheduler = cfg.reset_lr_scheduler + optimizer_overrides = ast.literal_eval(cfg.optimizer_overrides) + reset_meters = cfg.reset_meters + reset_dataloader = cfg.reset_dataloader + + if cfg.finetune_from_model is not None and ( + reset_optimizer or reset_lr_scheduler or reset_meters or reset_dataloader + ): + raise ValueError( + "--finetune-from-model can not be set together with either --reset-optimizer" + " or reset_lr_scheduler or reset_meters or reset_dataloader" + ) + + suffix = trainer.checkpoint_suffix + if ( + cfg.restore_file == "checkpoint_last.pt" + ): # default value of restore_file is 'checkpoint_last.pt' + checkpoint_path = os.path.join( + cfg.save_dir, "checkpoint_last{}.pt".format(suffix) + ) + first_launch = not PathManager.exists(checkpoint_path) + if first_launch and getattr(cfg, "continue_once", None) is not None: + checkpoint_path = cfg.continue_once + elif cfg.finetune_from_model is not None and first_launch: + # if there is no last checkpoint to restore, start the finetune from pretrained model + # else just use usual logic to load checkpoint, e.g. restart from last checkpoint and etc. + if PathManager.exists(cfg.finetune_from_model): + checkpoint_path = cfg.finetune_from_model + reset_optimizer = True + reset_lr_scheduler = True + reset_meters = True + reset_dataloader = True + logger.info( + f"loading pretrained model from {checkpoint_path}: " + "optimizer, lr scheduler, meters, dataloader will be reset" + ) + else: + raise ValueError( + f"--finetune-from-model {cfg.finetune_from_model} does not exist" + ) + elif suffix is not None: + checkpoint_path = cfg.restore_file.replace(".pt", suffix + ".pt") + else: + checkpoint_path = cfg.restore_file + + if cfg.restore_file != "checkpoint_last.pt" and cfg.finetune_from_model: + raise ValueError( + "--finetune-from-model and --restore-file (non-default value) " + "can not be specified together: " + str(cfg) + ) + + extra_state = trainer.load_checkpoint( + checkpoint_path, + reset_optimizer, + reset_lr_scheduler, + optimizer_overrides, + reset_meters=reset_meters, + ) + + if ( + extra_state is not None + and "best" in extra_state + and not reset_optimizer + and not reset_meters + ): + save_checkpoint.best = extra_state["best"] + + if extra_state is not None and not reset_dataloader: + # restore iterator from checkpoint + itr_state = extra_state["train_iterator"] + epoch_itr = trainer.get_train_iterator( + epoch=itr_state["epoch"], load_dataset=True, **passthrough_args + ) + epoch_itr.load_state_dict(itr_state) + + # Preload the checkpoint for the task + task_cp_dict = extra_state.get(trainer.task.__class__.__name__, {}) + if task_cp_dict and hasattr(trainer.task, "set_checkpoint_dict"): + trainer.task.set_checkpoint_dict(task_cp_dict) + else: + epoch_itr = trainer.get_train_iterator( + epoch=1, load_dataset=True, **passthrough_args + ) + + trainer.lr_step(epoch_itr.epoch) + + return extra_state, epoch_itr + + +def load_checkpoint_to_cpu(path, arg_overrides=None, load_on_all_ranks=False): + """Loads a checkpoint to CPU (with upgrading for backward compatibility). + + If doing single-GPU training or if the checkpoint is only being loaded by at + most one process on each node (current default behavior is for only rank 0 + to read the checkpoint from disk), load_on_all_ranks should be False to + avoid errors from torch.distributed not having been initialized or + torch.distributed.barrier() hanging. + + If all processes on each node may be loading the checkpoint + simultaneously, load_on_all_ranks should be set to True to avoid I/O + conflicts. + + There's currently no support for > 1 but < all processes loading the + checkpoint on each node. + """ + local_path = PathManager.get_local_path(path) + # The locally cached file returned by get_local_path() may be stale for + # remote files that are periodically updated/overwritten (ex: + # checkpoint_last.pt) - so we remove the local copy, sync across processes + # (if needed), and then download a fresh copy. + if local_path != path and PathManager.path_requires_pathmanager(path): + try: + os.remove(local_path) + except FileNotFoundError: + # With potentially multiple processes removing the same file, the + # file being missing is benign (missing_ok isn't available until + # Python 3.8). + pass + if load_on_all_ranks: + torch.distributed.barrier() + local_path = PathManager.get_local_path(path) + + with open(local_path, "rb") as f: + state = torch.load(f, map_location=torch.device("cpu"), weights_only=False) + + if "args" in state and state["args"] is not None and arg_overrides is not None: + args = state["args"] + for arg_name, arg_val in arg_overrides.items(): + setattr(args, arg_name, arg_val) + + if "cfg" in state and state["cfg"] is not None: + + # hack to be able to set Namespace in dict config. this should be removed when we update to newer + # omegaconf version that supports object flags, or when we migrate all existing models + from omegaconf import __version__ as oc_version + from omegaconf import _utils + + if oc_version < "2.2": + old_primitive = _utils.is_primitive_type + _utils.is_primitive_type = lambda _: True + + state["cfg"] = OmegaConf.create(state["cfg"]) + + _utils.is_primitive_type = old_primitive + OmegaConf.set_struct(state["cfg"], True) + else: + state["cfg"] = OmegaConf.create(state["cfg"], flags={"allow_objects": True}) + + if arg_overrides is not None: + overwrite_args_by_name(state["cfg"], arg_overrides) + + state = _upgrade_state_dict(state) + return state + + +def load_model_ensemble( + filenames, + arg_overrides: Optional[Dict[str, Any]] = None, + task=None, + strict=True, + suffix="", + num_shards=1, + state=None, +): + """Loads an ensemble of models. + + Args: + filenames (List[str]): checkpoint files to load + arg_overrides (Dict[str,Any], optional): override model args that + were used during model training + task (fairseq.tasks.FairseqTask, optional): task to use for loading + """ + assert not ( + strict and num_shards > 1 + ), "Cannot load state dict with strict=True and checkpoint shards > 1" + ensemble, args, _task = load_model_ensemble_and_task( + filenames, + arg_overrides, + task, + strict, + suffix, + num_shards, + state, + ) + return ensemble, args + + +def get_maybe_sharded_checkpoint_filename( + filename: str, suffix: str, shard_idx: int, num_shards: int +) -> str: + orig_filename = filename + filename = filename.replace(".pt", suffix + ".pt") + fsdp_filename = filename[:-3] + f"-shard{shard_idx}.pt" + model_parallel_filename = orig_filename[:-3] + f"_part{shard_idx}.pt" + if PathManager.exists(fsdp_filename): + return fsdp_filename + elif num_shards > 1: + return model_parallel_filename + else: + return filename + + +def load_model_ensemble_and_task( + filenames, + arg_overrides: Optional[Dict[str, Any]] = None, + task=None, + strict=True, + suffix="", + num_shards=1, + state=None, +): + assert state is None or len(filenames) == 1 + + from fairseq import tasks + + assert not ( + strict and num_shards > 1 + ), "Cannot load state dict with strict=True and checkpoint shards > 1" + ensemble = [] + cfg = None + for filename in filenames: + orig_filename = filename + model_shard_state = {"shard_weights": [], "shard_metadata": []} + assert num_shards > 0 + st = time.time() + for shard_idx in range(num_shards): + filename = get_maybe_sharded_checkpoint_filename( + orig_filename, suffix, shard_idx, num_shards + ) + + if not PathManager.exists(filename): + raise IOError("Model file not found: {}".format(filename)) + if state is None: + state = load_checkpoint_to_cpu(filename, arg_overrides) + if "args" in state and state["args"] is not None: + cfg = convert_namespace_to_omegaconf(state["args"]) + elif "cfg" in state and state["cfg"] is not None: + cfg = state["cfg"] + else: + raise RuntimeError( + f"Neither args nor cfg exist in state keys = {state.keys()}" + ) + + if task is None: + task = tasks.setup_task(cfg.task, from_checkpoint=True) + + if "task_state" in state: + task.load_state_dict(state["task_state"]) + + argspec = inspect.getfullargspec(task.build_model) + + if "fsdp_metadata" in state and num_shards > 1: + model_shard_state["shard_weights"].append(state["model"]) + model_shard_state["shard_metadata"].append(state["fsdp_metadata"]) + # check FSDP import before the code goes too far + if not has_FSDP: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + if shard_idx == num_shards - 1: + consolidated_model_state = FSDP.consolidate_shard_weights( + shard_weights=model_shard_state["shard_weights"], + shard_metadata=model_shard_state["shard_metadata"], + ) + if "from_checkpoint" in argspec.args: + model = task.build_model(cfg.model, from_checkpoint=True) + else: + model = task.build_model(cfg.model) + if ( + "optimizer_history" in state + and len(state["optimizer_history"]) > 0 + and "num_updates" in state["optimizer_history"][-1] + ): + model.set_num_updates( + state["optimizer_history"][-1]["num_updates"] + ) + model.load_state_dict( + consolidated_model_state, strict=strict, model_cfg=cfg.model + ) + else: + # model parallel checkpoint or unsharded checkpoint + # support old external tasks + + if "from_checkpoint" in argspec.args: + model = task.build_model(cfg.model, from_checkpoint=True) + else: + model = task.build_model(cfg.model) + if ( + "optimizer_history" in state + and len(state["optimizer_history"]) > 0 + and "num_updates" in state["optimizer_history"][-1] + ): + model.set_num_updates(state["optimizer_history"][-1]["num_updates"]) + model.load_state_dict( + state["model"], strict=strict, model_cfg=cfg.model + ) + + # reset state so it gets loaded for the next model in ensemble + state = None + if shard_idx % 10 == 0 and shard_idx > 0: + elapsed = time.time() - st + logger.info( + f"Loaded {shard_idx} shards in {elapsed:.2f}s, {elapsed / (shard_idx+1):.2f}s/shard" + ) + + # build model for ensemble + ensemble.append(model) + return ensemble, cfg, task + + +def load_model_ensemble_and_task_from_hf_hub( + model_id, + cache_dir: Optional[str] = None, + arg_overrides: Optional[Dict[str, Any]] = None, + **kwargs: Any, +): + try: + from huggingface_hub import snapshot_download + except ImportError: + raise ImportError( + "You need to install huggingface_hub to use `load_from_hf_hub`. " + "See https://pypi.org/project/huggingface-hub/ for installation." + ) + + library_name = "fairseq" + cache_dir = cache_dir or (Path.home() / ".cache" / library_name).as_posix() + cache_dir = snapshot_download( + model_id, cache_dir=cache_dir, library_name=library_name, **kwargs + ) + + _arg_overrides = arg_overrides or {} + _arg_overrides["data"] = cache_dir + return load_model_ensemble_and_task( + [p.as_posix() for p in Path(cache_dir).glob("*.pt")], + arg_overrides=_arg_overrides, + ) + + +def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt", keep_match=False): + """Retrieves all checkpoints found in `path` directory. + + Checkpoints are identified by matching filename to the specified pattern. If + the pattern contains groups, the result will be sorted by the first group in + descending order. + """ + pt_regexp = re.compile(pattern) + files = PathManager.ls(path) + + entries = [] + for i, f in enumerate(files): + m = pt_regexp.fullmatch(f) + if m is not None: + idx = float(m.group(1)) if len(m.groups()) > 0 else i + entries.append((idx, m.group(0))) + if keep_match: + return [(os.path.join(path, x[1]), x[0]) for x in sorted(entries, reverse=True)] + else: + return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)] + + +def torch_persistent_save(obj, filename, async_write: bool = False): + if async_write: + with PathManager.opena(filename, "wb") as f: + _torch_persistent_save(obj, f) + else: + if PathManager.supports_rename(filename): + # do atomic save + with PathManager.open(filename + ".tmp", "wb") as f: + _torch_persistent_save(obj, f) + PathManager.rename(filename + ".tmp", filename) + else: + # fallback to non-atomic save + with PathManager.open(filename, "wb") as f: + _torch_persistent_save(obj, f) + + +def _torch_persistent_save(obj, f): + if isinstance(f, str): + with PathManager.open(f, "wb") as h: + torch_persistent_save(obj, h) + return + for i in range(3): + try: + return torch.save(obj, f) + except Exception: + if i == 2: + logger.error(traceback.format_exc()) + raise + else: + time.sleep(2.5) + + +def _upgrade_state_dict(state): + """Helper for upgrading old model checkpoints.""" + + # add optimizer_history + if "optimizer_history" not in state: + state["optimizer_history"] = [ + {"criterion_name": "CrossEntropyCriterion", "best_loss": state["best_loss"]} + ] + state["last_optimizer_state"] = state["optimizer"] + del state["optimizer"] + del state["best_loss"] + # move extra_state into sub-dictionary + if "epoch" in state and "extra_state" not in state: + state["extra_state"] = { + "epoch": state["epoch"], + "batch_offset": state["batch_offset"], + "val_loss": state["val_loss"], + } + del state["epoch"] + del state["batch_offset"] + del state["val_loss"] + # reduce optimizer history's memory usage (only keep the last state) + if "optimizer" in state["optimizer_history"][-1]: + state["last_optimizer_state"] = state["optimizer_history"][-1]["optimizer"] + for optim_hist in state["optimizer_history"]: + del optim_hist["optimizer"] + # record the optimizer class name + if "optimizer_name" not in state["optimizer_history"][-1]: + state["optimizer_history"][-1]["optimizer_name"] = "FairseqNAG" + # move best_loss into lr_scheduler_state + if "lr_scheduler_state" not in state["optimizer_history"][-1]: + state["optimizer_history"][-1]["lr_scheduler_state"] = { + "best": state["optimizer_history"][-1]["best_loss"] + } + del state["optimizer_history"][-1]["best_loss"] + # keep track of number of updates + if "num_updates" not in state["optimizer_history"][-1]: + state["optimizer_history"][-1]["num_updates"] = 0 + # use stateful training data iterator + if "train_iterator" not in state["extra_state"]: + state["extra_state"]["train_iterator"] = { + "epoch": state["extra_state"].get("epoch", 0), + "iterations_in_epoch": state["extra_state"].get("batch_offset", 0), + } + + # backward compatibility, cfg updates + if "args" in state and state["args"] is not None: + # old model checkpoints may not have separate source/target positions + if hasattr(state["args"], "max_positions") and not hasattr( + state["args"], "max_source_positions" + ): + state["args"].max_source_positions = state["args"].max_positions + state["args"].max_target_positions = state["args"].max_positions + # default to translation task + if not hasattr(state["args"], "task"): + state["args"].task = "translation" + # --raw-text and --lazy-load are deprecated + if getattr(state["args"], "raw_text", False): + state["args"].dataset_impl = "raw" + elif getattr(state["args"], "lazy_load", False): + state["args"].dataset_impl = "lazy" + # epochs start at 1 + if state["extra_state"]["train_iterator"] is not None: + state["extra_state"]["train_iterator"]["epoch"] = max( + state["extra_state"]["train_iterator"].get("epoch", 1), 1 + ) + # --remove-bpe ==> --postprocess + if hasattr(state["args"], "remove_bpe"): + state["args"].post_process = state["args"].remove_bpe + # --min-lr ==> --stop-min-lr + if hasattr(state["args"], "min_lr"): + state["args"].stop_min_lr = state["args"].min_lr + del state["args"].min_lr + # binary_cross_entropy / kd_binary_cross_entropy => wav2vec criterion + if hasattr(state["args"], "criterion") and state["args"].criterion in [ + "binary_cross_entropy", + "kd_binary_cross_entropy", + ]: + state["args"].criterion = "wav2vec" + # remove log_keys if it's None (criteria will supply a default value of []) + if hasattr(state["args"], "log_keys") and state["args"].log_keys is None: + delattr(state["args"], "log_keys") + # speech_pretraining => audio pretraining + if ( + hasattr(state["args"], "task") + and state["args"].task == "speech_pretraining" + ): + state["args"].task = "audio_pretraining" + # audio_cpc => wav2vec + if hasattr(state["args"], "arch") and state["args"].arch == "audio_cpc": + state["args"].arch = "wav2vec" + # convert legacy float learning rate to List[float] + if hasattr(state["args"], "lr") and isinstance(state["args"].lr, float): + state["args"].lr = [state["args"].lr] + # convert task data arg to a string instead of List[string] + if ( + hasattr(state["args"], "data") + and isinstance(state["args"].data, list) + and len(state["args"].data) > 0 + ): + state["args"].data = state["args"].data[0] + + state["cfg"] = convert_namespace_to_omegaconf(state["args"]) + + if "cfg" in state and state["cfg"] is not None: + cfg = state["cfg"] + with open_dict(cfg): + # any upgrades for Hydra-based configs + if ( + "task" in cfg + and "eval_wer_config" in cfg.task + and isinstance(cfg.task.eval_wer_config.print_alignment, bool) + ): + cfg.task.eval_wer_config.print_alignment = "hard" + if "generation" in cfg and isinstance(cfg.generation.print_alignment, bool): + cfg.generation.print_alignment = ( + "hard" if cfg.generation.print_alignment else None + ) + if ( + "model" in cfg + and "w2v_args" in cfg.model + and cfg.model.w2v_args is not None + and ( + hasattr(cfg.model.w2v_args, "task") or "task" in cfg.model.w2v_args + ) + and hasattr(cfg.model.w2v_args.task, "eval_wer_config") + and cfg.model.w2v_args.task.eval_wer_config is not None + and isinstance( + cfg.model.w2v_args.task.eval_wer_config.print_alignment, bool + ) + ): + cfg.model.w2v_args.task.eval_wer_config.print_alignment = "hard" + + return state + + +def prune_state_dict(state_dict, model_cfg: Optional[DictConfig]): + """Prune the given state_dict if desired for LayerDrop + (https://arxiv.org/abs/1909.11556). + + Training with LayerDrop allows models to be robust to pruning at inference + time. This function prunes state_dict to allow smaller models to be loaded + from a larger model and re-maps the existing state_dict for this to occur. + + It's called by functions that load models from checkpoints and does not + need to be called directly. + """ + arch = None + if model_cfg is not None: + arch = ( + model_cfg._name + if isinstance(model_cfg, DictConfig) + else getattr(model_cfg, "arch", None) + ) + + if not model_cfg or arch is None or arch == "ptt_transformer": + # args should not be none, but don't crash if it is. + return state_dict + + encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None) + decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None) + + if not encoder_layers_to_keep and not decoder_layers_to_keep: + return state_dict + + # apply pruning + logger.info( + "Pruning model to specified layer configuration - this works best if the model was trained with LayerDrop" + ) + + def create_pruning_pass(layers_to_keep, layer_name): + keep_layers = sorted( + int(layer_string) for layer_string in layers_to_keep.split(",") + ) + mapping_dict = {} + for i in range(len(keep_layers)): + mapping_dict[str(keep_layers[i])] = str(i) + + regex = re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name)) + return {"substitution_regex": regex, "mapping_dict": mapping_dict} + + pruning_passes = [] + if encoder_layers_to_keep: + pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder")) + if decoder_layers_to_keep: + pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder")) + + new_state_dict = {} + for layer_name in state_dict.keys(): + match = re.search(r"\.layers\.(\d+)\.", layer_name) + # if layer has no number in it, it is a supporting layer, such as an + # embedding + if not match: + new_state_dict[layer_name] = state_dict[layer_name] + continue + + # otherwise, layer should be pruned. + original_layer_number = match.group(1) + # figure out which mapping dict to replace from + for pruning_pass in pruning_passes: + if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass[ + "substitution_regex" + ].search(layer_name): + new_layer_number = pruning_pass["mapping_dict"][original_layer_number] + substitution_match = pruning_pass["substitution_regex"].search( + layer_name + ) + new_state_key = ( + layer_name[: substitution_match.start(1)] + + new_layer_number + + layer_name[substitution_match.end(1) :] + ) + new_state_dict[new_state_key] = state_dict[layer_name] + + # Since layers are now pruned, *_layers_to_keep are no longer needed. + # This is more of "It would make it work fix" rather than a proper fix. + if isinstance(model_cfg, DictConfig): + context = open_dict(model_cfg) + else: + context = contextlib.ExitStack() + with context: + if hasattr(model_cfg, "encoder_layers_to_keep"): + model_cfg.encoder_layers_to_keep = None + if hasattr(model_cfg, "decoder_layers_to_keep"): + model_cfg.decoder_layers_to_keep = None + + return new_state_dict + + +def load_pretrained_component_from_model( + component: Union[FairseqEncoder, FairseqDecoder], + checkpoint: str, + strict: bool = True, +): + """ + Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the + provided `component` object. If state_dict fails to load, there may be a + mismatch in the architecture of the corresponding `component` found in the + `checkpoint` file. + """ + if not PathManager.exists(checkpoint): + raise IOError("Model file not found: {}".format(checkpoint)) + state = load_checkpoint_to_cpu(checkpoint) + if isinstance(component, FairseqEncoder): + component_type = "encoder" + elif isinstance(component, FairseqDecoder): + component_type = "decoder" + else: + raise ValueError( + "component to load must be either a FairseqEncoder or " + "FairseqDecoder. Loading other component types are not supported." + ) + component_state_dict = OrderedDict() + for key in state["model"].keys(): + if key.startswith(component_type): + # encoder.input_layers.0.0.weight --> input_layers.0.0.weight + component_subkey = key[len(component_type) + 1 :] + component_state_dict[component_subkey] = state["model"][key] + component.load_state_dict(component_state_dict, strict=strict) + return component + + +def verify_checkpoint_directory(save_dir: str) -> None: + if not os.path.exists(save_dir): + os.makedirs(save_dir, exist_ok=True) + temp_file_path = os.path.join(save_dir, "dummy") + try: + with open(temp_file_path, "w"): + pass + except OSError as e: + logger.warning( + "Unable to access checkpoint save directory: {}".format(save_dir) + ) + raise e + else: + os.remove(temp_file_path) + + +def save_ema_as_checkpoint(src_path, dst_path): + state = load_ema_from_checkpoint(src_path) + torch_persistent_save(state, dst_path) + + +def load_ema_from_checkpoint(fpath): + """Loads exponential moving averaged (EMA) checkpoint from input and + returns a model with ema weights. + + Args: + fpath: A string path of checkpoint to load from. + + Returns: + A dict of string keys mapping to various values. The 'model' key + from the returned dict should correspond to an OrderedDict mapping + string parameter names to torch Tensors. + """ + params_dict = collections.OrderedDict() + new_state = None + + with PathManager.open(fpath, "rb") as f: + new_state = torch.load( + f, + map_location=( + lambda s, _: torch.serialization.default_restore_location(s, "cpu") + ), + weights_only=False, + ) + + # EMA model is stored in a separate "extra state" + model_params = new_state["extra_state"]["ema"] + + for key in list(model_params.keys()): + p = model_params[key] + if isinstance(p, torch.HalfTensor): + p = p.float() + if key not in params_dict: + params_dict[key] = p.clone() + # NOTE: clone() is needed in case of p is a shared parameter + else: + raise ValueError("Key {} is repeated in EMA model params.".format(key)) + + if len(params_dict) == 0: + raise ValueError( + f"Input checkpoint path '{fpath}' does not contain " + "ema model weights, is this model trained with EMA?" + ) + + new_state["model"] = params_dict + return new_state diff --git a/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp b/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp new file mode 100644 index 0000000..7072191 --- /dev/null +++ b/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp @@ -0,0 +1,55 @@ +/* +Copyright (c) Microsoft Corporation. +Licensed under the MIT License. +*/ + +#include <torch/extension.h> +#include <vector> + +/* +CPP Binding for CUDA OP +*/ + +// CUDA forward declarations +torch::Tensor ngram_repeat_block_cuda_forward( + torch::Tensor tokens, + torch::Tensor lprobs, + int bsz, + int step, + int beam_size, + int no_repeat_ngram_size); + +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +// Input check and call to CUDA OP +// Backward method not required +torch::Tensor ngram_repeat_block_forward( + torch::Tensor tokens, + torch::Tensor lprobs, + int bsz, + int step, + int beam_size, + int no_repeat_ngram_size) { + CHECK_INPUT(tokens); + CHECK_INPUT(lprobs); + assert(bsz > 0); + assert(step >= 0); + assert(beam_size > 0); + assert(no_repeat_ngram_size > 0); + + return ngram_repeat_block_cuda_forward( + tokens, lprobs, bsz, step, beam_size, no_repeat_ngram_size); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def( + "forward", + &ngram_repeat_block_forward, + "No Repeat Ngram Block forward (CUDA)"); +} diff --git a/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu b/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu new file mode 100644 index 0000000..bd6106c --- /dev/null +++ b/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu @@ -0,0 +1,82 @@ +/* +Copyright (c) Microsoft Corporation. +Licensed under the MIT License. +*/ + +/* +Kernel implementation for blocking repeated n-grams. +*/ + +#include <cuda.h> +#include <cuda_runtime.h> +#include <math.h> +#include <torch/extension.h> +#include <vector> + +// Ban repeated ngrams of length = 'no_repeat_ngram_size' +__global__ void banRepeatedTokens( + long* __restrict__ tokens, + float* __restrict__ lprobs, + int max_predict_len, + int vocab_size, + int no_repeat_ngram_size) { + auto row = blockIdx.x; + auto col = threadIdx.x; + auto start = row * (max_predict_len) + col; + // Each thread compares ngram starting from + // thread index with final ngram starting from + // step - no_repeat_ngram_size +2 + auto check_start_pos = blockDim.x; + auto lprob_start = row * vocab_size; + bool is_banned = true; + extern __shared__ long tokens_shm[]; + tokens_shm[col] = tokens[start]; + if (col == blockDim.x - 1) { + for (int i = 1; i < no_repeat_ngram_size; i++) { + if (col + i < max_predict_len) { + tokens_shm[col + i] = tokens[start + i]; + } + } + } + __syncthreads(); + + for (int k = 0; k < no_repeat_ngram_size - 1; k++) { + if (tokens_shm[col + k] != tokens_shm[check_start_pos + k]) { + is_banned = false; + } + } + if (is_banned == true) { + auto token_to_be_banned = tokens_shm[col + no_repeat_ngram_size - 1]; + lprobs[lprob_start + token_to_be_banned] = -INFINITY; + } +} + +// Allocate blocks and threads based on +// batch size and sequence length and launch +// kernel +torch::Tensor ngram_repeat_block_cuda_forward( + const torch::Tensor tokens, + torch::Tensor lprobs, + int bsz, + int step, + int beam_size, + int no_repeat_ngram_size) { + int threads = step - no_repeat_ngram_size + 2; + if (threads <= 0) + return lprobs; + int max_predict_len = tokens.size(1); + int vocab_size = lprobs.size(1); + auto token_ptr = tokens.data_ptr<long>(); + auto lprob_ptr = lprobs.data_ptr<float>(); + int blocks = bsz * beam_size; + int shared_mem_size = (step + 1) * sizeof(long); + + // Launching N blocks where N is number of samples in a batch (beams*bsz) + // Launching T threads where T is number of previous ngrams in a sample + // Allocating shared mem per block for fastser access of input tokens since + // each token will be accessed N times to compare with current Ngram where + // N is Ngram size. + banRepeatedTokens<<<blocks, threads, shared_mem_size>>>( + token_ptr, lprob_ptr, max_predict_len, vocab_size, no_repeat_ngram_size); + return lprobs; +} diff --git a/fairseq/fairseq/clib/libbase/balanced_assignment.cpp b/fairseq/fairseq/clib/libbase/balanced_assignment.cpp new file mode 100644 index 0000000..1a5a106 --- /dev/null +++ b/fairseq/fairseq/clib/libbase/balanced_assignment.cpp @@ -0,0 +1,109 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +/* +C++ code for solving the linear assignment problem. +Based on the Auction Algorithm from +https://dspace.mit.edu/bitstream/handle/1721.1/3265/P-2108-26912652.pdf and the +implementation from: https://github.com/bkj/auction-lap Adapted to be more +efficient when each worker is looking for k jobs instead of 1. +*/ +#include <torch/extension.h> +#include <iostream> +using namespace torch::indexing; +torch::Tensor balanced_assignment(torch::Tensor job_and_worker_to_score) { + int max_iterations = 100; + torch::Tensor epsilon = + (job_and_worker_to_score.max() - job_and_worker_to_score.min()) / 50; + epsilon.clamp_min_(1e-04); + torch::Tensor worker_and_job_to_score = + job_and_worker_to_score.detach().transpose(0, 1).contiguous(); + int num_workers = worker_and_job_to_score.size(0); + int num_jobs = worker_and_job_to_score.size(1); + auto device = worker_and_job_to_score.device(); + int jobs_per_worker = num_jobs / num_workers; + torch::Tensor value = worker_and_job_to_score.clone(); + int counter = 0; + torch::Tensor max_value = worker_and_job_to_score.max(); + + torch::Tensor bid_indices; + torch::Tensor cost = worker_and_job_to_score.new_zeros({1, num_jobs}); + torch::Tensor bids = + worker_and_job_to_score.new_empty({num_workers, num_jobs}); + torch::Tensor bid_increments = + worker_and_job_to_score.new_empty({num_workers, jobs_per_worker}); + torch::Tensor top_values = + worker_and_job_to_score.new_empty({num_workers, jobs_per_worker + 1}); + torch::Tensor high_bids = worker_and_job_to_score.new_empty({num_jobs}); + + torch::Tensor top_index = top_values.to(torch::kLong); + torch::Tensor high_bidders = top_index.new_empty({num_jobs}); + torch::Tensor have_bids = high_bidders.to(torch::kBool); + torch::Tensor jobs_indices = + torch::arange({num_jobs}, torch::dtype(torch::kLong).device(device)); + torch::Tensor true_tensor = + torch::ones({1}, torch::dtype(torch::kBool).device(device)); + + while (true) { + bids.zero_(); + torch::topk_out(top_values, top_index, value, jobs_per_worker + 1, 1); + + // Each worker bids the difference in value between that job and the k+1th + // job + torch::sub_out( + bid_increments, + top_values.index({Slice(None, None), Slice(0, jobs_per_worker)}), + top_values.index({Slice(None, None), jobs_per_worker}).unsqueeze(1)); + + bid_increments.add_(epsilon); + bids.scatter_( + 1, + top_index.index({Slice(None, None), Slice(0, jobs_per_worker)}), + bid_increments); + + if (counter < max_iterations && counter > 0) { + // Put in a minimal bid to retain items from the last round if no-one else + // bids for them this round + bids.view(-1).index_put_({bid_indices}, epsilon); + } + + // Find the highest bidding worker per job + torch::max_out(high_bids, high_bidders, bids, 0); + torch::gt_out(have_bids, high_bids, 0); + + if (have_bids.all().item<bool>()) { + // All jobs were bid for + break; + } + + // Make popular items more expensive + cost.add_(high_bids); + torch::sub_out(value, worker_and_job_to_score, cost); + + bid_indices = ((high_bidders * num_jobs) + jobs_indices).index({have_bids}); + + if (counter < max_iterations) { + // Make sure that this item will be in the winning worker's top-k next + // time. + value.view(-1).index_put_({bid_indices}, max_value); + } else { + // Suboptimal approximation that converges quickly from current solution + value.view(-1).index_put_( + {bid_indices}, worker_and_job_to_score.view(-1).index({bid_indices})); + } + + counter += 1; + } + + return top_index.index({Slice(None, None), Slice(0, jobs_per_worker)}) + .reshape(-1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("balanced_assignment", &balanced_assignment, "Balanced Assignment"); +} diff --git a/fairseq/fairseq/clib/libbleu/libbleu.cpp b/fairseq/fairseq/clib/libbleu/libbleu.cpp new file mode 100644 index 0000000..939d9e1 --- /dev/null +++ b/fairseq/fairseq/clib/libbleu/libbleu.cpp @@ -0,0 +1,157 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <array> +#include <cstdio> +#include <cstring> +#include <map> + +// NOLINTNEXTLINE +typedef struct { + size_t reflen; + size_t predlen; + size_t match1; + size_t count1; + size_t match2; + size_t count2; + size_t match3; + size_t count3; + size_t match4; + size_t count4; +} bleu_stat; + +// left trim (remove pad) +void bleu_ltrim(size_t* len, int** sent, int pad) { + size_t start = 0; + while (start < *len) { + if (*(*sent + start) != pad) { + break; + } + start++; + } + *sent += start; + *len -= start; +} + +// right trim remove (eos) +void bleu_rtrim(size_t* len, int** sent, int pad, int eos) { + size_t end = *len - 1; + while (end > 0) { + if (*(*sent + end) != eos && *(*sent + end) != pad) { + break; + } + end--; + } + *len = end + 1; +} + +// left and right trim +void bleu_trim(size_t* len, int** sent, int pad, int eos) { + bleu_ltrim(len, sent, pad); + bleu_rtrim(len, sent, pad, eos); +} + +size_t bleu_hash(int len, int* data) { + size_t h = 14695981039346656037ul; + size_t prime = 0x100000001b3; + char* b = (char*)data; + size_t blen = sizeof(int) * len; + + while (blen-- > 0) { + h ^= *b++; + h *= prime; + } + + return h; +} + +void bleu_addngram( + size_t* ntotal, + size_t* nmatch, + size_t n, + size_t reflen, + int* ref, + size_t predlen, + int* pred) { + if (predlen < n) { + return; + } + + predlen = predlen - n + 1; + (*ntotal) += predlen; + + if (reflen < n) { + return; + } + + reflen = reflen - n + 1; + + std::map<size_t, size_t> count; + while (predlen > 0) { + size_t w = bleu_hash(n, pred++); + count[w]++; + predlen--; + } + + while (reflen > 0) { + size_t w = bleu_hash(n, ref++); + if (count[w] > 0) { + (*nmatch)++; + count[w] -= 1; + } + reflen--; + } +} + +extern "C" { + +#ifdef _WIN64 +__declspec(dllexport) +#endif + void bleu_zero_init(bleu_stat* stat) { + std::memset(stat, 0, sizeof(bleu_stat)); +} + +#ifdef _WIN64 +__declspec(dllexport) +#endif + void bleu_one_init(bleu_stat* stat) { + bleu_zero_init(stat); + stat->count1 = 0; + stat->count2 = 1; + stat->count3 = 1; + stat->count4 = 1; + stat->match1 = 0; + stat->match2 = 1; + stat->match3 = 1; + stat->match4 = 1; +} + +#ifdef _WIN64 +__declspec(dllexport) +#endif + void bleu_add( + bleu_stat* stat, + size_t reflen, + int* ref, + size_t predlen, + int* pred, + int pad, + int eos) { + + bleu_trim(&reflen, &ref, pad, eos); + bleu_trim(&predlen, &pred, pad, eos); + stat->reflen += reflen; + stat->predlen += predlen; + + bleu_addngram(&stat->count1, &stat->match1, 1, reflen, ref, predlen, pred); + bleu_addngram(&stat->count2, &stat->match2, 2, reflen, ref, predlen, pred); + bleu_addngram(&stat->count3, &stat->match3, 3, reflen, ref, predlen, pred); + bleu_addngram(&stat->count4, &stat->match4, 4, reflen, ref, predlen, pred); +} +} diff --git a/fairseq/fairseq/clib/libbleu/module.cpp b/fairseq/fairseq/clib/libbleu/module.cpp new file mode 100644 index 0000000..35288b3 --- /dev/null +++ b/fairseq/fairseq/clib/libbleu/module.cpp @@ -0,0 +1,33 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <Python.h> + +static PyMethodDef method_def[] = {{NULL, NULL, 0, NULL}}; // NOLINT + +static struct PyModuleDef module_def = { + PyModuleDef_HEAD_INIT, + "libbleu", /* name of module */ + // NOLINTNEXTLINE + NULL, /* module documentation, may be NULL */ + -1, /* size of per-interpreter state of the module, + or -1 if the module keeps state in global variables. */ + method_def}; // NOLINT + +#if PY_MAJOR_VERSION == 2 +PyMODINIT_FUNC init_libbleu() +#else +PyMODINIT_FUNC PyInit_libbleu() +#endif +{ + PyObject* m = PyModule_Create(&module_def); + if (!m) { + return NULL; + } + return m; +} diff --git a/fairseq/fairseq/clib/libnat/edit_dist.cpp b/fairseq/fairseq/clib/libnat/edit_dist.cpp new file mode 100644 index 0000000..9ffb605 --- /dev/null +++ b/fairseq/fairseq/clib/libnat/edit_dist.cpp @@ -0,0 +1,231 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <pybind11/detail/common.h> +#include <pybind11/pybind11.h> +#include <torch/torch.h> // @manual=//caffe2:torch_extension +#include <algorithm> +#include <cstdint> +#include <iosfwd> +#include <memory> +#include <new> +#include <string> +#include <utility> +#include <vector> + +using namespace ::std; + +vector<vector<uint32_t>> edit_distance2_with_dp( + vector<uint32_t>& x, + vector<uint32_t>& y) { + uint32_t lx = x.size(); + uint32_t ly = y.size(); + vector<vector<uint32_t>> d(lx + 1, vector<uint32_t>(ly + 1)); + for (uint32_t i = 0; i < lx + 1; i++) { + d[i][0] = i; + } + for (uint32_t j = 0; j < ly + 1; j++) { + d[0][j] = j; + } + for (uint32_t i = 1; i < lx + 1; i++) { + for (uint32_t j = 1; j < ly + 1; j++) { + d[i][j] = + min(min(d[i - 1][j], d[i][j - 1]) + 1, + d[i - 1][j - 1] + 2 * (x.at(i - 1) == y.at(j - 1) ? 0 : 1)); + } + } + return d; +} + +vector<vector<uint32_t>> edit_distance2_backtracking( + vector<vector<uint32_t>>& d, + vector<uint32_t>& x, + vector<uint32_t>& y, + uint32_t terminal_symbol) { + vector<uint32_t> seq; + vector<vector<uint32_t>> edit_seqs(x.size() + 2, vector<uint32_t>()); + /* + edit_seqs: + 0~x.size() cell is the insertion sequences + last cell is the delete sequence + */ + + if (x.size() == 0) { + edit_seqs.at(0) = y; + return edit_seqs; + } + + uint32_t i = d.size() - 1; + uint32_t j = d.at(0).size() - 1; + + while ((i >= 0) && (j >= 0)) { + if ((i == 0) && (j == 0)) { + break; + } + + if ((j > 0) && (d.at(i).at(j - 1) < d.at(i).at(j))) { + seq.push_back(1); // insert + seq.push_back(y.at(j - 1)); + j--; + } else if ((i > 0) && (d.at(i - 1).at(j) < d.at(i).at(j))) { + seq.push_back(2); // delete + seq.push_back(x.at(i - 1)); + i--; + } else { + seq.push_back(3); // keep + seq.push_back(x.at(i - 1)); + i--; + j--; + } + } + + uint32_t prev_op, op, s, word; + prev_op = 0, s = 0; + for (uint32_t k = 0; k < seq.size() / 2; k++) { + op = seq.at(seq.size() - 2 * k - 2); + word = seq.at(seq.size() - 2 * k - 1); + if (prev_op != 1) { + s++; + } + if (op == 1) // insert + { + edit_seqs.at(s - 1).push_back(word); + } else if (op == 2) // delete + { + edit_seqs.at(x.size() + 1).push_back(1); + } else { + edit_seqs.at(x.size() + 1).push_back(0); + } + + prev_op = op; + } + + for (uint32_t k = 0; k < edit_seqs.size(); k++) { + if (edit_seqs[k].size() == 0) { + edit_seqs[k].push_back(terminal_symbol); + } + } + return edit_seqs; +} + +vector<vector<uint32_t>> edit_distance2_backtracking_with_delete( + vector<vector<uint32_t>>& d, + vector<uint32_t>& x, + vector<uint32_t>& y, + uint32_t terminal_symbol, + uint32_t deletion_symbol) { + vector<uint32_t> seq; + vector<vector<uint32_t>> edit_seqs(x.size() + 1, vector<uint32_t>()); + /* + edit_seqs: + 0~x.size() cell is the insertion sequences + last cell is the delete sequence + */ + + if (x.size() == 0) { + edit_seqs.at(0) = y; + return edit_seqs; + } + + uint32_t i = d.size() - 1; + uint32_t j = d.at(0).size() - 1; + + while ((i >= 0) && (j >= 0)) { + if ((i == 0) && (j == 0)) { + break; + } + + if ((j > 0) && (d.at(i).at(j - 1) < d.at(i).at(j))) { + seq.push_back(1); // insert + seq.push_back(y.at(j - 1)); + j--; + } else if ((i > 0) && (d.at(i - 1).at(j) < d.at(i).at(j))) { + seq.push_back(2); // delete + seq.push_back(x.at(i - 1)); + i--; + } else { + seq.push_back(3); // keep + seq.push_back(x.at(i - 1)); + i--; + j--; + } + } + + uint32_t prev_op, op, s, word; + prev_op = 0, s = 0; + for (uint32_t k = 0; k < seq.size() / 2; k++) { + op = seq.at(seq.size() - 2 * k - 2); + word = seq.at(seq.size() - 2 * k - 1); + if (prev_op != 1) { + s++; + } + if (op == 1) // insert + { + edit_seqs.at(s - 1).push_back(word); + } else if (op == 2) // delete + { + edit_seqs.at(s - 1).push_back(deletion_symbol); + } + + prev_op = op; + } + + for (uint32_t k = 0; k < edit_seqs.size(); k++) { + if (edit_seqs.at(k).size() == 0) { + edit_seqs.at(k).push_back(terminal_symbol); + } + } + return edit_seqs; +} + +vector<uint32_t> compute_ed2( + vector<vector<uint32_t>>& xs, + vector<vector<uint32_t>>& ys) { + vector<uint32_t> distances(xs.size()); + for (uint32_t i = 0; i < xs.size(); i++) { + vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i)); + distances.at(i) = d.at(xs.at(i).size()).at(ys.at(i).size()); + } + return distances; +} + +vector<vector<vector<uint32_t>>> suggested_ed2_path( + vector<vector<uint32_t>>& xs, + vector<vector<uint32_t>>& ys, + uint32_t terminal_symbol) { + vector<vector<vector<uint32_t>>> seq(xs.size()); + for (uint32_t i = 0; i < xs.size(); i++) { + vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i)); + seq.at(i) = + edit_distance2_backtracking(d, xs.at(i), ys.at(i), terminal_symbol); + } + return seq; +} + +vector<vector<vector<uint32_t>>> suggested_ed2_path_with_delete( + vector<vector<uint32_t>>& xs, + vector<vector<uint32_t>>& ys, + uint32_t terminal_symbol, + uint32_t deletion_symbol) { + vector<vector<vector<uint32_t>>> seq(xs.size()); + for (uint32_t i = 0; i < xs.size(); i++) { + vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i)); + seq.at(i) = edit_distance2_backtracking_with_delete( + d, xs.at(i), ys.at(i), terminal_symbol, deletion_symbol); + } + return seq; +} + +PYBIND11_MODULE(libnat, m) { + m.def("compute_ed2", &compute_ed2, "compute_ed2"); + m.def("suggested_ed2_path", &suggested_ed2_path, "suggested_ed2_path"); + m.def( + "suggested_ed2_path_with_delete", + &suggested_ed2_path_with_delete, + "suggested_ed2_path_with_delete"); +} diff --git a/fairseq/fairseq/clib/libnat_cuda/binding.cpp b/fairseq/fairseq/clib/libnat_cuda/binding.cpp new file mode 100644 index 0000000..ced91c0 --- /dev/null +++ b/fairseq/fairseq/clib/libnat_cuda/binding.cpp @@ -0,0 +1,67 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +/* + This code is partially adpoted from + https://github.com/1ytic/pytorch-edit-distance + */ + +#include <torch/types.h> +#include "edit_dist.h" + +#ifndef TORCH_CHECK +#define TORCH_CHECK AT_CHECK +#endif + +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +torch::Tensor LevenshteinDistance( + torch::Tensor source, + torch::Tensor target, + torch::Tensor source_length, + torch::Tensor target_length) { + CHECK_INPUT(source); + CHECK_INPUT(target); + CHECK_INPUT(source_length); + CHECK_INPUT(target_length); + return LevenshteinDistanceCuda(source, target, source_length, target_length); +} + +torch::Tensor GenerateDeletionLabel( + torch::Tensor source, + torch::Tensor operations) { + CHECK_INPUT(source); + CHECK_INPUT(operations); + return GenerateDeletionLabelCuda(source, operations); +} + +std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabel( + torch::Tensor target, + torch::Tensor operations) { + CHECK_INPUT(target); + CHECK_INPUT(operations); + return GenerateInsertionLabelCuda(target, operations); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("levenshtein_distance", &LevenshteinDistance, "Levenshtein distance"); + m.def( + "generate_deletion_labels", + &GenerateDeletionLabel, + "Generate Deletion Label"); + m.def( + "generate_insertion_labels", + &GenerateInsertionLabel, + "Generate Insertion Label"); +} diff --git a/fairseq/fairseq/clib/libnat_cuda/edit_dist.cu b/fairseq/fairseq/clib/libnat_cuda/edit_dist.cu new file mode 100644 index 0000000..1ea5ec7 --- /dev/null +++ b/fairseq/fairseq/clib/libnat_cuda/edit_dist.cu @@ -0,0 +1,344 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "edit_dist.h" + +#include <c10/cuda/CUDAStream.h> +#include <cuda.h> +#include <cuda_runtime.h> +#include <device_launch_parameters.h> +#include <utility> // std::pair + +template <typename scalar_t> +__global__ void generate_deletion_label_kernel( + const scalar_t* __restrict__ source, + const size_t source_size, + const size_t operation_size, + int* __restrict__ operations, + int* __restrict__ labels) { + const int index = blockIdx.x; + const int offset = index * operation_size; + const int offset_label = index * source_size; + + for (int i = 0; i < source_size; i++) { + labels[offset_label + i] = 0; + } + + int k = 0; + for (int i = 0; i < operation_size; i++) { + if (operations[offset + i] == 0) { + break; + } else if (operations[offset + i] == 1) { + continue; + } else { + labels[offset_label + k] = 3 - operations[offset + i]; + k++; + } + } +} + +template <typename scalar_t> +__global__ void generate_insertion_label_kernel( + const scalar_t* __restrict__ target, + const size_t target_size, + const size_t operation_size, + int* __restrict__ operations, + int* __restrict__ labels, + int* __restrict__ masks) { + const int index = blockIdx.x; + const int offset = index * operation_size; + const int offset_label = index * target_size; + + int k = 0; + int u = 0; + int m = 0; + + for (int i = 0; i < target_size; i++) { + labels[offset_label + i] = 0; + masks[offset_label + i] = 0; + } + + for (int i = 0; i < operation_size - 1; i++) { + if (operations[offset + i] == 0) { + break; + } else if (operations[offset + i] == 2) { + continue; + } else if (operations[offset + i] == 1) { + masks[offset_label + m] = 1; + u++; + m++; + } else { + labels[offset_label + k] = u; + masks[offset_label + m] = 0; + k++; + m++; + u = 0; + } + } +} + +template <typename scalar_t> +__global__ void levenshtein_distance_kernel( + const scalar_t* __restrict__ source, + const scalar_t* __restrict__ target, + const int* __restrict__ source_length, + const int* __restrict__ target_length, + const size_t source_size, + const size_t target_size, + int* __restrict__ operations, + int* __restrict__ errors_curr) { + const int index = blockIdx.x; + const int offset = index * (source_size + target_size); + const int d = index * (source_size + 1) * (target_size + 1); + const int t = target_size + 1; + + auto err_idx = [d, t](int i, int j) { return d + i * t + j; }; + auto opt_idx = [offset](int k) { return offset + k; }; + + const int hyp_len = source_length[index]; + const int ref_len = target_length[index]; + const scalar_t* hyp_begin = source + index * source_size; + const scalar_t* ref_begin = target + index * target_size; + + // dynamic programming + for (int i = 0; i <= hyp_len; i++) { + errors_curr[err_idx(i, 0)] = i; + } + for (int j = 0; j <= ref_len; j++) { + errors_curr[err_idx(0, j)] = j; + } + for (int i = 1; i <= hyp_len; i++) { + for (int j = 1; j <= ref_len; j++) { + errors_curr[err_idx(i, j)] = min( + min(errors_curr[err_idx(i - 1, j)], errors_curr[err_idx(i, j - 1)]) + + 1, + errors_curr[err_idx(i - 1, j - 1)] + + 2 * (*(hyp_begin + i - 1) == *(ref_begin + j - 1) ? 0 : 1)); + } + } + + // back-tracing + int i = hyp_len; + int j = ref_len; + int o = hyp_len + ref_len; + + for (int k = 0; k < source_size + target_size; k++) { + operations[opt_idx(k)] = 0; + } + + while ((i >= 0) && (j >= 0)) { + if ((i == 0) && (j == 0)) { + break; + } + + if ((j > 0) && + (errors_curr[err_idx(i, j - 1)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 1; + j--; // insertion + } else if ( + (i > 0) && + (errors_curr[err_idx(i - 1, j)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 2; + i--; // deletion + } else { + o--; + operations[opt_idx(o)] = 3; + i--; + j--; // do nothing + } + } + + // moving to the left + for (int k = 0; k < hyp_len + ref_len; k++) { + if (k + o < hyp_len + ref_len) { + operations[opt_idx(k)] = operations[opt_idx(k + o)]; + } else { + operations[opt_idx(k)] = 0; // padding + } + } +} + +template <typename scalar_t> +__global__ void faster_levenshtein_distance_kernel( + const scalar_t* __restrict__ source, + const scalar_t* __restrict__ target, + const int* __restrict__ source_length, + const int* __restrict__ target_length, + const size_t source_size, + const size_t target_size, + int* __restrict__ operations) { + extern __shared__ short errors[]; + auto errors_curr = errors; + + const int index = blockIdx.x; + const int offset = index * (source_size + target_size); + const int t = target_size + 1; + + auto err_idx = [t](int i, int j) { return i * t + j; }; + auto opt_idx = [offset](int k) { return offset + k; }; + + const int hyp_len = source_length[index]; + const int ref_len = target_length[index]; + const scalar_t* hyp_begin = source + index * source_size; + const scalar_t* ref_begin = target + index * target_size; + + // dynamic programming + for (int i = 0; i <= hyp_len; i++) { + errors_curr[err_idx(i, 0)] = i; + } + for (int j = 0; j <= ref_len; j++) { + errors_curr[err_idx(0, j)] = j; + } + for (int i = 1; i <= hyp_len; i++) { + for (int j = 1; j <= ref_len; j++) { + errors_curr[err_idx(i, j)] = min( + min(errors_curr[err_idx(i - 1, j)], errors_curr[err_idx(i, j - 1)]) + + 1, + errors_curr[err_idx(i - 1, j - 1)] + + 2 * (*(hyp_begin + i - 1) == *(ref_begin + j - 1) ? 0 : 1)); + } + } + + // back-tracing + int i = hyp_len; + int j = ref_len; + int o = hyp_len + ref_len; + + for (int k = 0; k < source_size + target_size; k++) { + operations[opt_idx(k)] = 0; + } + + while ((i >= 0) && (j >= 0)) { + if ((i == 0) && (j == 0)) { + break; + } + + if ((j > 0) && + (errors_curr[err_idx(i, j - 1)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 1; + j--; // insertion + } else if ( + (i > 0) && + (errors_curr[err_idx(i - 1, j)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 2; + i--; // deletion + } else { + o--; + operations[opt_idx(o)] = 3; + i--; + j--; // do nothing + } + } + + // moving to the left + for (int k = 0; k < hyp_len + ref_len; k++) { + if (k + o < hyp_len + ref_len) { + operations[opt_idx(k)] = operations[opt_idx(k + o)]; + } else { + operations[opt_idx(k)] = 0; // padding + } + } +} + +torch::Tensor GenerateDeletionLabelCuda( + torch::Tensor source, + torch::Tensor operations) { + const auto batch_size = source.size(0); + at::TensorOptions options(source.device()); + options = options.dtype(at::ScalarType::Int); + auto labels = torch::empty({batch_size, source.size(1)}, options); + auto stream = at::cuda::getCurrentCUDAStream(source.device().index()); + + AT_DISPATCH_ALL_TYPES(source.scalar_type(), "generate_deletion_labels", ([&] { + generate_deletion_label_kernel<scalar_t> + <<<batch_size, 1, 0, stream>>>( + source.data_ptr<scalar_t>(), + source.size(1), + operations.size(1), + operations.data_ptr<int>(), + labels.data_ptr<int>()); + })); + + return labels; +} + +std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabelCuda( + torch::Tensor target, + torch::Tensor operations) { + const auto batch_size = target.size(0); + at::TensorOptions options(target.device()); + options = options.dtype(at::ScalarType::Int); + auto labels = torch::empty({batch_size, target.size(1)}, options); + auto masks = torch::empty({batch_size, target.size(1)}, options); + auto stream = at::cuda::getCurrentCUDAStream(target.device().index()); + + AT_DISPATCH_ALL_TYPES( + target.scalar_type(), "generate_insertion_labels", ([&] { + generate_insertion_label_kernel<scalar_t><<<batch_size, 1, 0, stream>>>( + target.data_ptr<scalar_t>(), + target.size(1), + operations.size(1), + operations.data_ptr<int>(), + labels.data_ptr<int>(), + masks.data_ptr<int>()); + })); + + return std::make_pair(labels, masks); +} + +torch::Tensor LevenshteinDistanceCuda( + torch::Tensor source, + torch::Tensor target, + torch::Tensor source_length, + torch::Tensor target_length) { + const auto batch_size = source.size(0); + const auto shared_size = + (source.size(1) + 1) * (target.size(1) + 1) * sizeof(short); + + at::TensorOptions options(source.device()); + options = options.dtype(at::ScalarType::Int); + auto operations = + torch::empty({batch_size, source.size(1) + target.size(1)}, options); + auto stream = at::cuda::getCurrentCUDAStream(source.device().index()); + + if (shared_size > 40000) { + auto distances = torch::empty( + {batch_size, (source.size(1) + 1) * (target.size(1) + 1)}, options); + AT_DISPATCH_ALL_TYPES(source.scalar_type(), "levenshtein_distance", ([&] { + levenshtein_distance_kernel<scalar_t> + <<<batch_size, 1, 0, stream>>>( + source.data_ptr<scalar_t>(), + target.data_ptr<scalar_t>(), + source_length.data_ptr<int>(), + target_length.data_ptr<int>(), + source.size(1), + target.size(1), + operations.data_ptr<int>(), + distances.data_ptr<int>()); + })); + } else { + AT_DISPATCH_ALL_TYPES( + source.scalar_type(), "faster_levenshtein_distance", ([&] { + faster_levenshtein_distance_kernel<scalar_t> + <<<batch_size, 1, shared_size, stream>>>( + source.data_ptr<scalar_t>(), + target.data_ptr<scalar_t>(), + source_length.data_ptr<int>(), + target_length.data_ptr<int>(), + source.size(1), + target.size(1), + operations.data_ptr<int>()); + })); + } + + return operations; +} diff --git a/fairseq/fairseq/clib/libnat_cuda/edit_dist.h b/fairseq/fairseq/clib/libnat_cuda/edit_dist.h new file mode 100644 index 0000000..5220c52 --- /dev/null +++ b/fairseq/fairseq/clib/libnat_cuda/edit_dist.h @@ -0,0 +1,25 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +#pragma once + +#include <torch/extension.h> + +torch::Tensor LevenshteinDistanceCuda( + torch::Tensor source, + torch::Tensor target, + torch::Tensor source_length, + torch::Tensor target_length); + +torch::Tensor GenerateDeletionLabelCuda( + torch::Tensor source, + torch::Tensor operations); + +std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabelCuda( + torch::Tensor source, + torch::Tensor operations); diff --git a/fairseq/fairseq/config/__init__.py b/fairseq/fairseq/config/__init__.py new file mode 100644 index 0000000..6264236 --- /dev/null +++ b/fairseq/fairseq/config/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/fairseq/config/config.yaml b/fairseq/fairseq/config/config.yaml new file mode 100644 index 0000000..2ed7168 --- /dev/null +++ b/fairseq/fairseq/config/config.yaml @@ -0,0 +1,19 @@ +# @package _group_ + +hydra: + run: + dir: . + +defaults: + - _self_ + - task: null + - model: null + - criterion: cross_entropy + - optimizer: null + - lr_scheduler: fixed + - bpe: null + - tokenizer: null + - scoring: null + - generation: null + - common_eval: null + - eval_lm: null diff --git a/fairseq/fairseq/config/fb_run_config/slurm.yaml b/fairseq/fairseq/config/fb_run_config/slurm.yaml new file mode 100644 index 0000000..20cf8f5 --- /dev/null +++ b/fairseq/fairseq/config/fb_run_config/slurm.yaml @@ -0,0 +1,29 @@ +# @package _global_ + +hydra: + job: + config: + override_dirname: + kv_sep: ':' + item_sep: '__' + exclude_keys: + - fb_run_config + - distributed_training.distributed_port + sweep: + dir: /checkpoint/${env:USER}/${env:PREFIX}/${hydra.job.config_name}_${hydra.launcher.gpus_per_node}/${hydra.job.override_dirname} + launcher: + cpus_per_task: 60 + gpus_per_node: ??? + tasks_per_node: 1 + nodes: 1 + partition: learnfair + mem_gb: 400 + timeout_min: 4320 + max_num_timeout: 10 + name: ${env:PREFIX}_${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir} + +distributed_training: + ddp_backend: c10d + distributed_world_size: ??? + distributed_port: ??? diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml new file mode 100644 index 0000000..30b1a4f --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 512 +decoder_output_dim: 512 +decoder_input_dim: 512 +decoder_ffn_embed_dim: 4096 +decoder_layers: 12 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml new file mode 100644 index 0000000..1154cfa --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.3 +attention_dropout: 0.1 +activation_dropout: 0.1 +relu_dropout: 0.1 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 16 +decoder_attention_heads: 8 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: "20000,60000" +adaptive_softmax_dropout: 0.2 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: true +adaptive_input_factor: 4 +adaptive_input_cutoff: "20000,60000" +tie_adaptive_weights: true +tie_adaptive_proj: true +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_big.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_big.yaml new file mode 100644 index 0000000..3095753 --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_big.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.1 +attention_dropout: 0.0 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 12 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml new file mode 100644 index 0000000..30b1a4f --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 512 +decoder_output_dim: 512 +decoder_input_dim: 512 +decoder_ffn_embed_dim: 4096 +decoder_layers: 12 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml new file mode 100644 index 0000000..2c6cb7b --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 768 +decoder_output_dim: 768 +decoder_input_dim: 768 +decoder_ffn_embed_dim: 3072 +decoder_layers: 12 +decoder_attention_heads: 12 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml new file mode 100644 index 0000000..a08769a --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1600 +decoder_output_dim: 1600 +decoder_input_dim: 1600 +decoder_ffn_embed_dim: 6400 +decoder_layers: 48 +decoder_attention_heads: 25 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml new file mode 100644 index 0000000..64261d7 --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1280 +decoder_output_dim: 1280 +decoder_input_dim: 1280 +decoder_ffn_embed_dim: 5120 +decoder_layers: 36 +decoder_attention_heads: 20 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml new file mode 100644 index 0000000..702e81f --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 24 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml new file mode 100644 index 0000000..1154cfa --- /dev/null +++ b/fairseq/fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.3 +attention_dropout: 0.1 +activation_dropout: 0.1 +relu_dropout: 0.1 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 16 +decoder_attention_heads: 8 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: "20000,60000" +adaptive_softmax_dropout: 0.2 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: true +adaptive_input_factor: 4 +adaptive_input_cutoff: "20000,60000" +tie_adaptive_weights: true +tie_adaptive_proj: true +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml b/fairseq/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml new file mode 100644 index 0000000..ee1329b --- /dev/null +++ b/fairseq/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml @@ -0,0 +1,5 @@ +# @package _group_ +activation: gelu +vq_type: gumbel +vq_depth: 2 +combine_groups: true diff --git a/fairseq/fairseq/config/model/wav2vec2/wav2vec2_base.yaml b/fairseq/fairseq/config/model/wav2vec2/wav2vec2_base.yaml new file mode 100644 index 0000000..ce65499 --- /dev/null +++ b/fairseq/fairseq/config/model/wav2vec2/wav2vec2_base.yaml @@ -0,0 +1,8 @@ +# @package _group_ + +quantize_targets: true +final_dim: 256 +encoder_layerdrop: 0.05 +dropout_input: 0.1 +dropout_features: 0.1 +feature_grad_mult: 0.1 diff --git a/fairseq/fairseq/config/model/wav2vec2/wav2vec2_large.yaml b/fairseq/fairseq/config/model/wav2vec2/wav2vec2_large.yaml new file mode 100644 index 0000000..5846f75 --- /dev/null +++ b/fairseq/fairseq/config/model/wav2vec2/wav2vec2_large.yaml @@ -0,0 +1,20 @@ +# @package _group_ + +quantize_targets: true +extractor_mode: layer_norm +layer_norm_first: true +final_dim: 768 +latent_temp: [2.0,0.1,0.999995] +encoder_layerdrop: 0.0 +dropout_input: 0.0 +dropout_features: 0.0 +dropout: 0.0 +attention_dropout: 0.0 +conv_bias: true + +encoder_layers: 24 +encoder_embed_dim: 1024 +encoder_ffn_embed_dim: 4096 +encoder_attention_heads: 16 + +feature_grad_mult: 1.0 diff --git a/fairseq/fairseq/criterions/__init__.py b/fairseq/fairseq/criterions/__init__.py new file mode 100644 index 0000000..ecd65d3 --- /dev/null +++ b/fairseq/fairseq/criterions/__init__.py @@ -0,0 +1,36 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import importlib +import os + +from fairseq import registry +from fairseq.criterions.fairseq_criterion import ( # noqa + FairseqCriterion, + LegacyFairseqCriterion, +) +from omegaconf import DictConfig + + +( + build_criterion_, + register_criterion, + CRITERION_REGISTRY, + CRITERION_DATACLASS_REGISTRY, +) = registry.setup_registry( + "--criterion", base_class=FairseqCriterion, default="cross_entropy" +) + + +def build_criterion(cfg: DictConfig, task, from_checkpoint=False): + return build_criterion_(cfg, task, from_checkpoint=from_checkpoint) + + +# automatically import any Python files in the criterions/ directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.criterions." + file_name) diff --git a/fairseq/fairseq/criterions/adaptive_loss.py b/fairseq/fairseq/criterions/adaptive_loss.py new file mode 100644 index 0000000..fc1ac85 --- /dev/null +++ b/fairseq/fairseq/criterions/adaptive_loss.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass + +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.constants import DDP_BACKEND_CHOICES +from omegaconf import II + + +@dataclass +class AdaptiveLossConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + ddp_backend: DDP_BACKEND_CHOICES = II("distributed_training.ddp_backend") + + +@register_criterion("adaptive_loss", dataclass=AdaptiveLossConfig) +class AdaptiveLoss(FairseqCriterion): + """This is an implementation of the loss function accompanying the adaptive softmax approximation for + graphical processing units (GPU), described in the paper "Efficient softmax approximation for GPUs" + (http://arxiv.org/abs/1609.04309).""" + + def __init__(self, task, sentence_avg): + super().__init__(task) + self.sentence_avg = sentence_avg + + @classmethod + def build_criterion(cls, cfg: AdaptiveLossConfig, task): + if cfg.ddp_backend in {"c10d", "pytorch_ddp"}: + raise Exception( + "AdaptiveLoss is not compatible with the PyTorch " + "version of DistributedDataParallel. Please use " + "`--ddp-backend=legacy_ddp` instead." + ) + return cls(task, cfg.sentence_avg) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + + assert ( + hasattr(model.decoder, "adaptive_softmax") + and model.decoder.adaptive_softmax is not None + ) + adaptive_softmax = model.decoder.adaptive_softmax + + net_output = model(**sample["net_input"]) + orig_target = model.get_targets(sample, net_output) + + nsentences = orig_target.size(0) + orig_target = orig_target.view(-1) + + bsz = orig_target.size(0) + + logits, target = adaptive_softmax(net_output[0], orig_target) + assert len(target) == len(logits) + + loss = net_output[0].new(1 if reduce else bsz).zero_() + + for i in range(len(target)): + if target[i] is not None: + assert target[i].min() >= 0 and target[i].max() <= logits[i].size(1) + loss += F.cross_entropy( + logits[i], + target[i], + ignore_index=self.padding_idx, + reduction="sum" if reduce else "none", + ) + + orig = utils.strip_pad(orig_target, self.padding_idx) + ntokens = orig.numel() + sample_size = sample["target"].size(0) if self.sentence_avg else ntokens + logging_output = { + "loss": loss.data, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + else: + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/composite_loss.py b/fairseq/fairseq/criterions/composite_loss.py new file mode 100644 index 0000000..98e835f --- /dev/null +++ b/fairseq/fairseq/criterions/composite_loss.py @@ -0,0 +1,100 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq import utils +from fairseq.criterions import LegacyFairseqCriterion, register_criterion +from torch import nn + + +@register_criterion("composite_loss") +class CompositeLoss(LegacyFairseqCriterion): + """This is a composite loss that, given a list of model outputs and a list of targets, + computes an average of losses for each output-target pair""" + + def __init__(self, args, task): + super().__init__(args, task) + self.underlying_criterion = args.underlying_criterion + + @staticmethod + def add_args(parser): + """Add criterion-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--underlying-criterion', type=str, metavar='VAL', required=True, + help='underlying criterion to use for the composite loss') + # fmt: on + + @staticmethod + def build_underlying_criterion(args, task): + saved_criterion = args.criterion + args.criterion = args.underlying_criterion + assert saved_criterion != args.underlying_criterion + underlying_criterion = task.build_criterion(args) + args.criterion = saved_criterion + return underlying_criterion + + @classmethod + def build_criterion(cls, args, task): + underlying_criterion = CompositeLoss.build_underlying_criterion(args, task) + + class FakeModel(nn.Module): + def __init__(self, model, net_out, target): + super().__init__() + self.model = model + self.net_out = net_out + self.target = target + + def forward(self, **unused): + return self.net_out + + def get_normalized_probs(self, net_output, log_probs, sample=None): + return self.model.get_normalized_probs( + net_output, log_probs, sample=sample + ) + + def get_targets(self, *unused): + return self.target + + @property + def decoder(self): + return self.model.decoder + + class _CompositeLoss(LegacyFairseqCriterion): + def __init__(self, args, task, underlying_criterion): + super().__init__(args, task) + self.underlying_criterion = underlying_criterion + + def forward(self, model, sample, reduce=True): + net_outputs = model(**sample["net_input"]) + targets = sample["target"] + + bsz = targets[0].size(0) + loss = net_outputs[0][0].new(1 if reduce else bsz).float().zero_() + + sample_size = 0 + logging_output = {} + for o, t in zip(net_outputs[0], targets): + m = FakeModel(model, (o, net_outputs[1]), t) + sample["target"] = t + l, ss, logging_output = self.underlying_criterion(m, sample, reduce) + loss += l + sample_size += ss + + loss.div_(len(targets)) + sample_size /= len(targets) + + logging_output["loss"] = utils.item(loss.data) if reduce else loss.data + return loss, sample_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + return underlying_criterion.__class__.aggregate_logging_outputs( + logging_outputs + ) + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + underlying_criterion.__class__.reduce_metrics(logging_outputs) + + return _CompositeLoss(args, task, underlying_criterion) diff --git a/fairseq/fairseq/criterions/cross_entropy.py b/fairseq/fairseq/criterions/cross_entropy.py new file mode 100644 index 0000000..24d6bcd --- /dev/null +++ b/fairseq/fairseq/criterions/cross_entropy.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass + +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II + + +@dataclass +class CrossEntropyCriterionConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + + +@register_criterion("cross_entropy", dataclass=CrossEntropyCriterionConfig) +class CrossEntropyCriterion(FairseqCriterion): + def __init__(self, task, sentence_avg): + super().__init__(task) + self.sentence_avg = sentence_avg + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + loss, _ = self.compute_loss(model, net_output, sample, reduce=reduce) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + return loss, sample_size, logging_output + + def compute_loss(self, model, net_output, sample, reduce=True): + lprobs = model.get_normalized_probs(net_output, log_probs=True) + lprobs = lprobs.view(-1, lprobs.size(-1)) + target = model.get_targets(sample, net_output).view(-1) + loss = F.nll_loss( + lprobs, + target, + ignore_index=self.padding_idx, + reduction="sum" if reduce else "none", + ) + return loss, loss + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + # we divide by log(2) to convert the loss from base e to base 2 + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + else: + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/ctc.py b/fairseq/fairseq/criterions/ctc.py new file mode 100644 index 0000000..368213c --- /dev/null +++ b/fairseq/fairseq/criterions/ctc.py @@ -0,0 +1,325 @@ +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import math +from argparse import Namespace +from dataclasses import dataclass, field +from omegaconf import II +from typing import Optional + +import torch +import torch.nn.functional as F + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.data.data_utils import post_process +from fairseq.tasks import FairseqTask +from fairseq.logging.meters import safe_round + + +@dataclass +class CtcCriterionConfig(FairseqDataclass): + zero_infinity: bool = field( + default=False, + metadata={"help": "zero inf loss when source length <= target length"}, + ) + sentence_avg: bool = II("optimization.sentence_avg") + post_process: str = field( + default="letter", + metadata={ + "help": "how to post process predictions into words. can be letter, " + "wordpiece, BPE symbols, etc. " + "See fairseq.data.data_utils.post_process() for full list of options" + }, + ) + wer_kenlm_model: Optional[str] = field( + default=None, + metadata={ + "help": "if this is provided, use kenlm to compute wer (along with other wer_* args)" + }, + ) + wer_lexicon: Optional[str] = field( + default=None, + metadata={"help": "lexicon to use with wer_kenlm_model"}, + ) + wer_lm_weight: float = field( + default=2.0, + metadata={"help": "lm weight to use with wer_kenlm_model"}, + ) + wer_word_score: float = field( + default=-1.0, + metadata={"help": "lm word score to use with wer_kenlm_model"}, + ) + wer_sil_weight: float = field( + default=0, + metadata={"help": "lm word score to use with wer_kenlm_model"}, + ) + + wer_args: Optional[str] = field( + default=None, + metadata={ + "help": "DEPRECATED: tuple of (wer_kenlm_model, wer_lexicon, wer_lm_weight, wer_word_score)" + }, + ) + + +@register_criterion("ctc", dataclass=CtcCriterionConfig) +class CtcCriterion(FairseqCriterion): + def __init__( + self, cfg: CtcCriterionConfig, task: FairseqTask, rdrop_alpha: int = 0.0 + ): + super().__init__(task) + self.blank_idx = ( + task.target_dictionary.index(task.blank_symbol) + if hasattr(task, "blank_symbol") + else 0 + ) + self.pad_idx = task.target_dictionary.pad() + self.eos_idx = task.target_dictionary.eos() + self.post_process = cfg.post_process + + self.rdrop_alpha = rdrop_alpha + + if cfg.wer_args is not None: + ( + cfg.wer_kenlm_model, + cfg.wer_lexicon, + cfg.wer_lm_weight, + cfg.wer_word_score, + ) = eval(cfg.wer_args) + + if cfg.wer_kenlm_model is not None and cfg.wer_kenlm_model != "": + from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder + + dec_args = Namespace() + dec_args.nbest = 1 + dec_args.criterion = "ctc" + dec_args.kenlm_model = cfg.wer_kenlm_model + dec_args.lexicon = cfg.wer_lexicon + dec_args.beam = 50 + dec_args.beam_size_token = min(50, len(task.target_dictionary)) + dec_args.beam_threshold = min(50, len(task.target_dictionary)) + dec_args.lm_weight = cfg.wer_lm_weight + dec_args.word_score = cfg.wer_word_score + dec_args.sil_weight = cfg.wer_sil_weight + dec_args.unk_weight = -math.inf + dec_args.sil_weight = 0 + + self.w2l_decoder = W2lKenLMDecoder(dec_args, task.target_dictionary) + else: + self.w2l_decoder = None + + self.zero_infinity = cfg.zero_infinity + self.sentence_avg = cfg.sentence_avg + + def forward(self, model, sample, reduce=True, **kwargs): + net_output = model(**sample["net_input"]) + lprobs = model.get_normalized_probs( + net_output, log_probs=True + ).contiguous() # (T, B, C) from the encoder + + # CTC loss is calculated over duplicated inputs + # sample is already duplicated for R-Drop + if self.rdrop_alpha > 0: + for k, v in sample.items(): + if k in ["target", "target_lengths"]: + sample[k] = torch.cat([v, v.clone()], dim=0) + elif k == "net_input": + if sample[k]["src_tokens"].size(1) != sample[k]["src_lengths"].size( + 0 + ): + # for decoder CTC loss + sample[k]["src_lengths"] = torch.cat( + [ + sample[k]["src_lengths"], + sample[k]["src_lengths"].clone(), + ], + dim=0, + ) + + if "src_lengths" in sample["net_input"]: + input_lengths = sample["net_input"]["src_lengths"] + else: + if net_output["padding_mask"] is not None: + non_padding_mask = ~net_output["padding_mask"] + input_lengths = non_padding_mask.long().sum(-1) + else: + input_lengths = lprobs.new_full( + (lprobs.size(1),), lprobs.size(0), dtype=torch.long + ) + + pad_mask = (sample["target"] != self.pad_idx) & ( + sample["target"] != self.eos_idx + ) + targets_flat = sample["target"].masked_select(pad_mask) + if "target_lengths" in sample: + target_lengths = sample["target_lengths"] + else: + target_lengths = pad_mask.sum(-1) + + with torch.backends.cudnn.flags(enabled=False): + loss = F.ctc_loss( + lprobs, + targets_flat, + input_lengths, + target_lengths, + blank=self.blank_idx, + reduction="sum", + zero_infinity=self.zero_infinity, + ) + + ntokens = ( + sample["ntokens"] if "ntokens" in sample else target_lengths.sum().item() + ) + + sample_size = sample["target"].size(0) if self.sentence_avg else ntokens + logging_output = { + "loss": utils.item(loss.data), # * sample['ntokens'], + "ntokens": ntokens, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + } + + if not model.training: + import editdistance + + with torch.no_grad(): + lprobs_t = lprobs.transpose(0, 1).float().contiguous().cpu() + + c_err = 0 + c_len = 0 + w_errs = 0 + w_len = 0 + wv_errs = 0 + for lp, t, inp_l in zip( + lprobs_t, + sample["target_label"] + if "target_label" in sample + else sample["target"], + input_lengths, + ): + lp = lp[:inp_l].unsqueeze(0) + + decoded = None + if self.w2l_decoder is not None: + decoded = self.w2l_decoder.decode(lp) + if len(decoded) < 1: + decoded = None + else: + decoded = decoded[0] + if len(decoded) < 1: + decoded = None + else: + decoded = decoded[0] + + p = (t != self.task.target_dictionary.pad()) & ( + t != self.task.target_dictionary.eos() + ) + targ = t[p] + targ_units = self.task.target_dictionary.string(targ) + targ_units_arr = targ.tolist() + + toks = lp.argmax(dim=-1).unique_consecutive() + pred_units_arr = toks[toks != self.blank_idx].tolist() + + c_err += editdistance.eval(pred_units_arr, targ_units_arr) + c_len += len(targ_units_arr) + + targ_words = post_process(targ_units, self.post_process).split() + + pred_units = self.task.target_dictionary.string(pred_units_arr) + pred_words_raw = post_process(pred_units, self.post_process).split() + + if decoded is not None and "words" in decoded: + pred_words = decoded["words"] + w_errs += editdistance.eval(pred_words, targ_words) + wv_errs += editdistance.eval(pred_words_raw, targ_words) + else: + dist = editdistance.eval(pred_words_raw, targ_words) + w_errs += dist + wv_errs += dist + + w_len += len(targ_words) + + logging_output["wv_errors"] = wv_errs + logging_output["w_errors"] = w_errs + logging_output["w_total"] = w_len + logging_output["c_errors"] = c_err + logging_output["c_total"] = c_len + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + nsentences = utils.item( + sum(log.get("nsentences", 0) for log in logging_outputs) + ) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar("ntokens", ntokens) + metrics.log_scalar("nsentences", nsentences) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + + c_errors = sum(log.get("c_errors", 0) for log in logging_outputs) + metrics.log_scalar("_c_errors", c_errors) + c_total = sum(log.get("c_total", 0) for log in logging_outputs) + metrics.log_scalar("_c_total", c_total) + w_errors = sum(log.get("w_errors", 0) for log in logging_outputs) + metrics.log_scalar("_w_errors", w_errors) + wv_errors = sum(log.get("wv_errors", 0) for log in logging_outputs) + metrics.log_scalar("_wv_errors", wv_errors) + w_total = sum(log.get("w_total", 0) for log in logging_outputs) + metrics.log_scalar("_w_total", w_total) + + if c_total > 0: + metrics.log_derived( + "uer", + lambda meters: safe_round( + meters["_c_errors"].sum * 100.0 / meters["_c_total"].sum, 3 + ) + if meters["_c_total"].sum > 0 + else float("nan"), + ) + if w_total > 0: + metrics.log_derived( + "wer", + lambda meters: safe_round( + meters["_w_errors"].sum * 100.0 / meters["_w_total"].sum, 3 + ) + if meters["_w_total"].sum > 0 + else float("nan"), + ) + metrics.log_derived( + "raw_wer", + lambda meters: safe_round( + meters["_wv_errors"].sum * 100.0 / meters["_w_total"].sum, 3 + ) + if meters["_w_total"].sum > 0 + else float("nan"), + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/fairseq_criterion.py b/fairseq/fairseq/criterions/fairseq_criterion.py new file mode 100644 index 0000000..0b1e64a --- /dev/null +++ b/fairseq/fairseq/criterions/fairseq_criterion.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import inspect +from typing import Any, Dict, List + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import gen_parser_from_dataclass +from torch.nn.modules.loss import _Loss + + +class FairseqCriterion(_Loss): + def __init__(self, task): + super().__init__() + self.task = task + if hasattr(task, "target_dictionary"): + tgt_dict = task.target_dictionary + self.padding_idx = tgt_dict.pad() if tgt_dict is not None else -100 + + @classmethod + def add_args(cls, parser): + """Add criterion-specific arguments to the parser.""" + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) + + @classmethod + def build_criterion(cls, cfg: FairseqDataclass, task): + """Construct a criterion from command-line args.""" + # arguments in the __init__. + init_args = {} + for p in inspect.signature(cls).parameters.values(): + if ( + p.kind == p.POSITIONAL_ONLY + or p.kind == p.VAR_POSITIONAL + or p.kind == p.VAR_KEYWORD + ): + # we haven't implemented inference for these argument types, + # but PRs welcome :) + raise NotImplementedError("{} not supported".format(p.kind)) + + assert p.kind in {p.POSITIONAL_OR_KEYWORD, p.KEYWORD_ONLY} + + if p.name == "task": + init_args["task"] = task + elif p.name == "cfg": + init_args["cfg"] = cfg + elif hasattr(cfg, p.name): + init_args[p.name] = getattr(cfg, p.name) + elif p.default != p.empty: + pass # we'll use the default value + else: + raise NotImplementedError( + "Unable to infer Criterion arguments, please implement " + "{}.build_criterion".format(cls.__name__) + ) + return cls(**init_args) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + raise NotImplementedError + + @staticmethod + def aggregate_logging_outputs( + logging_outputs: List[Dict[str, Any]] + ) -> Dict[str, Any]: + """Aggregate logging outputs from data parallel training.""" + utils.deprecation_warning( + "The aggregate_logging_outputs API is deprecated. " + "Please use the reduce_metrics API instead." + ) + raise NotImplementedError + + @classmethod + def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None: + """Aggregate logging outputs from data parallel training.""" + utils.deprecation_warning( + "Criterions should implement the reduce_metrics API. " + "Falling back to deprecated aggregate_logging_outputs API." + ) + agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs) + for k, v in agg_logging_outputs.items(): + if k in {"nsentences", "ntokens", "sample_size"}: + continue + metrics.log_scalar(k, v) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return False + + +class LegacyFairseqCriterion(FairseqCriterion): + def __init__(self, args, task): + super().__init__(task=task) + self.args = args + + utils.deprecation_warning( + "Criterions should take explicit arguments instead of an " + "argparse.Namespace object, please update your criterion by " + "extending FairseqCriterion instead of LegacyFairseqCriterion." + ) + + @classmethod + def build_criterion(cls, args, task): + """Construct a criterion from command-line args.""" + return cls(args, task) diff --git a/fairseq/fairseq/criterions/fastspeech2_loss.py b/fairseq/fairseq/criterions/fastspeech2_loss.py new file mode 100644 index 0000000..ab7cd08 --- /dev/null +++ b/fairseq/fairseq/criterions/fastspeech2_loss.py @@ -0,0 +1,137 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +from typing import List, Dict, Any +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.data.data_utils import lengths_to_mask +from fairseq.models.fairseq_model import FairseqEncoderModel + + +@dataclass +class FastSpeech2CriterionConfig(FairseqDataclass): + ctc_weight: float = field(default=0.0, metadata={"help": "weight for CTC loss"}) + + +@register_criterion("fastspeech2", dataclass=FastSpeech2CriterionConfig) +class FastSpeech2Loss(FairseqCriterion): + def __init__(self, task, ctc_weight): + super().__init__(task) + self.ctc_weight = ctc_weight + + def forward(self, model: FairseqEncoderModel, sample, reduction="mean"): + src_tokens = sample["net_input"]["src_tokens"] + src_lens = sample["net_input"]["src_lengths"] + tgt_lens = sample["target_lengths"] + _feat_out, _feat_out_post, _, log_dur_out, pitch_out, energy_out = model( + src_tokens=src_tokens, + src_lengths=src_lens, + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + incremental_state=None, + target_lengths=tgt_lens, + speaker=sample["speaker"], + durations=sample["durations"], + pitches=sample["pitches"], + energies=sample["energies"], + ) + + src_mask = lengths_to_mask(sample["net_input"]["src_lengths"]) + tgt_mask = lengths_to_mask(sample["target_lengths"]) + + pitches, energies = sample["pitches"], sample["energies"] + pitch_out, pitches = pitch_out[src_mask], pitches[src_mask] + energy_out, energies = energy_out[src_mask], energies[src_mask] + + feat_out, feat = _feat_out[tgt_mask], sample["target"][tgt_mask] + l1_loss = F.l1_loss(feat_out, feat, reduction=reduction) + if _feat_out_post is not None: + l1_loss += F.l1_loss(_feat_out_post[tgt_mask], feat, reduction=reduction) + + pitch_loss = F.mse_loss(pitch_out, pitches, reduction=reduction) + energy_loss = F.mse_loss(energy_out, energies, reduction=reduction) + + log_dur_out = log_dur_out[src_mask] + dur = sample["durations"].float() + dur = dur.half() if log_dur_out.type().endswith(".HalfTensor") else dur + log_dur = torch.log(dur + 1)[src_mask] + dur_loss = F.mse_loss(log_dur_out, log_dur, reduction=reduction) + + ctc_loss = torch.tensor(0.0).type_as(l1_loss) + if self.ctc_weight > 0.0: + lprobs = model.get_normalized_probs((_feat_out,), log_probs=True) + lprobs = lprobs.transpose(0, 1) # T x B x C + src_mask = lengths_to_mask(src_lens) + src_tokens_flat = src_tokens.masked_select(src_mask) + ctc_loss = ( + F.ctc_loss( + lprobs, + src_tokens_flat, + tgt_lens, + src_lens, + reduction=reduction, + zero_infinity=True, + ) + * self.ctc_weight + ) + + loss = l1_loss + dur_loss + pitch_loss + energy_loss + ctc_loss + + sample_size = sample["nsentences"] + logging_output = { + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "l1_loss": utils.item(l1_loss.data), + "dur_loss": utils.item(dur_loss.data), + "pitch_loss": utils.item(pitch_loss.data), + "energy_loss": utils.item(energy_loss.data), + "ctc_loss": utils.item(ctc_loss.data), + } + return loss, sample_size, logging_output + + @classmethod + def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None: + ns = [log.get("sample_size", 0) for log in logging_outputs] + ntot = sum(ns) + ws = [n / (ntot + 1e-8) for n in ns] + for key in [ + "loss", + "l1_loss", + "dur_loss", + "pitch_loss", + "energy_loss", + "ctc_loss", + ]: + vals = [log.get(key, 0) for log in logging_outputs] + val = sum(val * w for val, w in zip(vals, ws)) + metrics.log_scalar(key, val, ntot, round=3) + metrics.log_scalar("sample_size", ntot, len(logging_outputs)) + + # inference metrics + if "targ_frames" not in logging_outputs[0]: + return + n = sum(log.get("targ_frames", 0) for log in logging_outputs) + for key, new_key in [ + ("mcd_loss", "mcd_loss"), + ("pred_frames", "pred_ratio"), + ("nins", "ins_rate"), + ("ndel", "del_rate"), + ]: + val = sum(log.get(key, 0) for log in logging_outputs) + metrics.log_scalar(new_key, val / n, n, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + return False diff --git a/fairseq/fairseq/criterions/hubert_criterion.py b/fairseq/fairseq/criterions/hubert_criterion.py new file mode 100644 index 0000000..262874b --- /dev/null +++ b/fairseq/fairseq/criterions/hubert_criterion.py @@ -0,0 +1,195 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import re +from dataclasses import dataclass, field +from typing import List, Optional + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass + + +@dataclass +class HubertCriterionConfig(FairseqDataclass): + pred_masked_weight: float = field( + default=1.0, + metadata={"help": "weight for predictive loss for masked frames"}, + ) + pred_nomask_weight: float = field( + default=0.0, + metadata={"help": "weight for predictive loss for unmasked frames"}, + ) + loss_weights: Optional[List[float]] = field( + default=None, + metadata={"help": "weights for additional loss terms (not first one)"}, + ) + log_keys: List[str] = field( + default_factory=lambda: [], + metadata={"help": "output keys to log"}, + ) + + +@register_criterion("hubert", dataclass=HubertCriterionConfig) +class HubertCriterion(FairseqCriterion): + def __init__( + self, + task, + pred_masked_weight, + pred_nomask_weight, + loss_weights=None, + log_keys=None, + ): + super().__init__(task) + self.pred_masked_weight = pred_masked_weight + self.pred_nomask_weight = pred_nomask_weight + self.loss_weights = loss_weights + self.log_keys = [] if log_keys is None else log_keys + + def forward(self, model, sample, reduce=True, log_pred=False): + """Compute the loss for the given sample. + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(target_list=sample["target_list"], **sample["net_input"]) + loss = 0.0 + sample_size = 0 + logging_output = {} + reduction = "sum" if reduce else "none" + + loss_m_list = [] + logp_m_list = model.get_logits(net_output, True) + targ_m_list = model.get_targets(net_output, True) + assert self.pred_masked_weight == 0 or len(logp_m_list) > 0 + for i, (logp_m, targ_m) in enumerate(zip(logp_m_list, targ_m_list)): + loss_m = F.cross_entropy(logp_m, targ_m, reduction=reduction) + loss_m_list.append(loss_m) + logging_output[f"loss_m_{i}"] = loss_m.detach().item() + if self.pred_masked_weight > 0: + loss += self.pred_masked_weight * sum(loss_m_list) + sample_size += targ_m_list[0].numel() + + loss_u_list = [] + logp_u_list = model.get_logits(net_output, False) + targ_u_list = model.get_targets(net_output, False) + assert self.pred_nomask_weight == 0 or len(logp_u_list) > 0 + for i, (logp_u, targ_u) in enumerate(zip(logp_u_list, targ_u_list)): + loss_u = F.cross_entropy(logp_u, targ_u, reduction=reduction) + loss_u_list.append(loss_u) + logging_output[f"loss_u_{i}"] = loss_u.detach().item() + if self.pred_nomask_weight > 0: + loss += self.pred_nomask_weight * sum(loss_u_list) + sample_size += targ_u_list[0].numel() + + if self.loss_weights is not None: + assert hasattr(model, "get_extra_losses") + extra_losses, names = model.get_extra_losses(net_output) + if torch.is_tensor(extra_losses): + extra_losses = [extra_losses] + names = [names] + if len(self.loss_weights) == 1 and len(extra_losses) != 1: + self.loss_weights = [self.loss_weights[0]] * len(extra_losses) + assert len(extra_losses) == len( + self.loss_weights + ), f"{len(extra_losses)}, {len(self.loss_weights)}" + for p, n, coef in zip(extra_losses, names, self.loss_weights): + if coef != 0 and p is not None: + p = coef * p.float() * sample_size + loss += p + logging_output[f"loss_{n}"] = p.item() + + logging_output = { + "loss": loss.item() if reduce else loss, + "ntokens": sample_size, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + **logging_output, + } + + for lk in self.log_keys: + if lk in net_output: + logging_output[lk] = float((net_output[lk])) + + def compute_correct(logits): + if logits.numel() == 0: + return 0, 0 + else: + assert logits.dim() > 1, logits.shape + max = logits.argmax(-1) == 0 + min = logits.argmin(-1) == 0 + both = max & min + corr = max.long().sum().item() - both.long().sum().item() + count = max.numel() + return corr, count + + with torch.no_grad(): + for i, logp_m in enumerate(logp_m_list): + corr_m, count_m = compute_correct(logp_m) + logging_output[f"correct_m_{i}"] = corr_m + logging_output[f"count_m_{i}"] = count_m + + for i, logp_u in enumerate(logp_u_list): + corr_u, count_u = compute_correct(logp_u) + logging_output[f"correct_u_{i}"] = corr_u + logging_output[f"count_u_{i}"] = count_u + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training (copied from normal cross entropy).""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + else: + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + counts = {} + for lk in logging_outputs[0].keys(): + if lk.startswith("count_"): + val = sum(log[lk] for log in logging_outputs) + metrics.log_scalar(lk, val) + counts[lk] = val + + for lk in logging_outputs[0].keys(): + if lk.startswith("loss_"): + val = sum(log[lk] for log in logging_outputs) + metrics.log_scalar(lk, val / sample_size / math.log(2), round=3) + elif lk.startswith("correct_"): + val = sum(log[lk] for log in logging_outputs) + metrics.log_scalar(lk, val / counts[re.sub("correct", "count", lk)]) + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + raise NotImplementedError() + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return False diff --git a/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py new file mode 100644 index 0000000..325679b --- /dev/null +++ b/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py @@ -0,0 +1,168 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field + +import torch +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II + + +@dataclass +class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass): + label_smoothing: float = field( + default=0.0, + metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"}, + ) + report_accuracy: bool = field( + default=False, + metadata={"help": "report accuracy metric"}, + ) + ignore_prefix_size: int = field( + default=0, + metadata={"help": "Ignore first N tokens"}, + ) + sentence_avg: bool = II("optimization.sentence_avg") + + +def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True): + if target.dim() == lprobs.dim() - 1: + target = target.unsqueeze(-1) + nll_loss = -lprobs.gather(dim=-1, index=target) + smooth_loss = -lprobs.sum(dim=-1, keepdim=True) + if ignore_index is not None: + pad_mask = target.eq(ignore_index) + nll_loss.masked_fill_(pad_mask, 0.0) + smooth_loss.masked_fill_(pad_mask, 0.0) + else: + nll_loss = nll_loss.squeeze(-1) + smooth_loss = smooth_loss.squeeze(-1) + if reduce: + nll_loss = nll_loss.sum() + smooth_loss = smooth_loss.sum() + eps_i = epsilon / (lprobs.size(-1) - 1) + loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss + return loss, nll_loss + + +@register_criterion( + "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig +) +class LabelSmoothedCrossEntropyCriterion(FairseqCriterion): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size=0, + report_accuracy=False, + ): + super().__init__(task) + self.sentence_avg = sentence_avg + self.eps = label_smoothing + self.ignore_prefix_size = ignore_prefix_size + self.report_accuracy = report_accuracy + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, net_output, sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) + return loss, sample_size, logging_output + + def get_lprobs_and_target(self, model, net_output, sample): + lprobs = model.get_normalized_probs(net_output, log_probs=True) + target = model.get_targets(sample, net_output) + if self.ignore_prefix_size > 0: + # lprobs: B x T x C + lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous() + target = target[:, self.ignore_prefix_size :].contiguous() + return lprobs.view(-1, lprobs.size(-1)), target.view(-1) + + def compute_loss(self, model, net_output, sample, reduce=True): + lprobs, target = self.get_lprobs_and_target(model, net_output, sample) + loss, nll_loss = label_smoothed_nll_loss( + lprobs, + target, + self.eps, + ignore_index=self.padding_idx, + reduce=reduce, + ) + return loss, nll_loss + + def compute_accuracy(self, model, net_output, sample): + lprobs, target = self.get_lprobs_and_target(model, net_output, sample) + mask = target.ne(self.padding_idx) + n_correct = torch.sum( + lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask)) + ) + total = torch.sum(mask) + return n_correct, total + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + + total = utils.item(sum(log.get("total", 0) for log in logging_outputs)) + if total > 0: + metrics.log_scalar("total", total) + n_correct = utils.item( + sum(log.get("n_correct", 0) for log in logging_outputs) + ) + metrics.log_scalar("n_correct", n_correct) + metrics.log_derived( + "accuracy", + lambda meters: round( + meters["n_correct"].sum * 100.0 / meters["total"].sum, 3 + ) + if meters["total"].sum > 0 + else float("nan"), + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py new file mode 100644 index 0000000..6eaedab --- /dev/null +++ b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py @@ -0,0 +1,221 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +import torch +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, + LabelSmoothedCrossEntropyCriterionConfig, +) + +try: + from simuleval.metrics.latency import ( + AverageLagging, + AverageProportion, + DifferentiableAverageLagging, + ) + + LATENCY_METRICS = { + "average_lagging": AverageLagging, + "average_proportion": AverageProportion, + "differentiable_average_lagging": DifferentiableAverageLagging, + } +except ImportError: + LATENCY_METRICS = None + + +@dataclass +class LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig( + LabelSmoothedCrossEntropyCriterionConfig +): + latency_avg_weight: float = field( + default=0.0, + metadata={"help": "weight fot average latency loss."}, + ) + latency_var_weight: float = field( + default=0.0, + metadata={"help": "weight fot variance latency loss."}, + ) + latency_avg_type: str = field( + default="differentiable_average_lagging", + metadata={"help": "latency type for average loss"}, + ) + latency_var_type: str = field( + default="variance_delay", + metadata={"help": "latency typ for variance loss"}, + ) + latency_gather_method: str = field( + default="weighted_average", + metadata={"help": "method to gather latency loss for all heads"}, + ) + latency_update_after: int = field( + default=0, + metadata={"help": "Add latency loss after certain steps"}, + ) + + +@register_criterion( + "latency_augmented_label_smoothed_cross_entropy", + dataclass=LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig, +) +class LatencyAugmentedLabelSmoothedCrossEntropyCriterion( + LabelSmoothedCrossEntropyCriterion +): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size, + report_accuracy, + latency_avg_weight, + latency_var_weight, + latency_avg_type, + latency_var_type, + latency_gather_method, + latency_update_after, + ): + super().__init__( + task, sentence_avg, label_smoothing, ignore_prefix_size, report_accuracy + ) + assert LATENCY_METRICS is not None, "Please make sure SimulEval is installed." + + self.latency_avg_weight = latency_avg_weight + self.latency_var_weight = latency_var_weight + self.latency_avg_type = latency_avg_type + self.latency_var_type = latency_var_type + self.latency_gather_method = latency_gather_method + self.latency_update_after = latency_update_after + + def forward(self, model, sample, reduce=True): + net_output = model(**sample["net_input"]) + # 1. Compute cross entropy loss + loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) + + # 2. Compute cross latency loss + latency_loss, expected_latency, expected_delays_var = self.compute_latency_loss( + model, sample, net_output + ) + + if self.latency_update_after > 0: + num_updates = getattr(model.decoder, "num_updates", None) + assert ( + num_updates is not None + ), "model.decoder doesn't have attribute 'num_updates'" + if num_updates <= self.latency_update_after: + latency_loss = 0 + + loss += latency_loss + + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + "latency": expected_latency, + "delays_var": expected_delays_var, + "latency_loss": latency_loss, + } + + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, net_output, sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) + return loss, sample_size, logging_output + + def compute_latency_loss(self, model, sample, net_output): + assert ( + net_output[-1].encoder_padding_mask is None + or not net_output[-1].encoder_padding_mask[:, 0].any() + ), "Only right padding on source is supported." + # 1. Obtain the expected alignment + alpha_list = [item["alpha"] for item in net_output[1].attn_list] + num_layers = len(alpha_list) + bsz, num_heads, tgt_len, src_len = alpha_list[0].size() + + # bsz * num_layers * num_heads, tgt_len, src_len + alpha_all = torch.cat(alpha_list, dim=1).view(-1, tgt_len, src_len) + + # 2 compute expected delays + # bsz * num_heads * num_layers, tgt_len, src_len for MMA + steps = ( + torch.arange(1, 1 + src_len) + .unsqueeze(0) + .unsqueeze(1) + .expand_as(alpha_all) + .type_as(alpha_all) + ) + + expected_delays = torch.sum(steps * alpha_all, dim=-1) + + target_padding_mask = ( + model.get_targets(sample, net_output) + .eq(self.padding_idx) + .unsqueeze(1) + .expand(bsz, num_layers * num_heads, tgt_len) + .contiguous() + .view(-1, tgt_len) + ) + + src_lengths = ( + sample["net_input"]["src_lengths"] + .unsqueeze(1) + .expand(bsz, num_layers * num_heads) + .contiguous() + .view(-1) + ) + expected_latency = LATENCY_METRICS[self.latency_avg_type]( + expected_delays, src_lengths, None, target_padding_mask=target_padding_mask + ) + + # 2.1 average expected latency of heads + # bsz, num_layers * num_heads + expected_latency = expected_latency.view(bsz, -1) + if self.latency_gather_method == "average": + # bsz * tgt_len + expected_latency = expected_delays.mean(dim=1) + elif self.latency_gather_method == "weighted_average": + weights = torch.nn.functional.softmax(expected_latency, dim=1) + expected_latency = torch.sum(expected_latency * weights, dim=1) + elif self.latency_gather_method == "max": + expected_latency = expected_latency.max(dim=1)[0] + else: + raise NotImplementedError + + expected_latency = expected_latency.sum() + avg_loss = self.latency_avg_weight * expected_latency + + # 2.2 variance of expected delays + expected_delays_var = ( + expected_delays.view(bsz, -1, tgt_len).var(dim=1).mean(dim=1) + ) + expected_delays_var = expected_delays_var.sum() + var_loss = self.latency_avg_weight * expected_delays_var + + # 3. Final loss + latency_loss = avg_loss + var_loss + + return latency_loss, expected_latency, expected_delays_var + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + super().reduce_metrics(logging_outputs) + latency = sum(log.get("latency", 0) for log in logging_outputs) + delays_var = sum(log.get("delays_var", 0) for log in logging_outputs) + latency_loss = sum(log.get("latency_loss", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + metrics.log_scalar("latency", latency.float() / nsentences, nsentences, round=3) + metrics.log_scalar("delays_var", delays_var / nsentences, nsentences, round=3) + metrics.log_scalar( + "latency_loss", latency_loss / nsentences, nsentences, round=3 + ) diff --git a/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py new file mode 100644 index 0000000..b55f65e --- /dev/null +++ b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py @@ -0,0 +1,131 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import register_criterion + +from .label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, + LabelSmoothedCrossEntropyCriterionConfig, +) + +from dataclasses import dataclass, field + + +@dataclass +class LabelSmoothedCrossEntropyCriterionWithAlignmentConfig( + LabelSmoothedCrossEntropyCriterionConfig +): + alignment_lambda: float = field( + default=0.05, metadata={"help": "weight for the alignment loss"} + ) + + +@register_criterion( + "label_smoothed_cross_entropy_with_alignment", + dataclass=LabelSmoothedCrossEntropyCriterionWithAlignmentConfig, +) +class LabelSmoothedCrossEntropyCriterionWithAlignment( + LabelSmoothedCrossEntropyCriterion +): + def __init__(self, task, sentence_avg, label_smoothing, alignment_lambda): + super().__init__(task, sentence_avg, label_smoothing) + self.alignment_lambda = alignment_lambda + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "nll_loss": utils.item(nll_loss.data) if reduce else nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + + alignment_loss = None + + # Compute alignment loss only for training set and non dummy batches. + if "alignments" in sample and sample["alignments"] is not None: + alignment_loss = self.compute_alignment_loss(sample, net_output) + + if alignment_loss is not None: + logging_output["alignment_loss"] = utils.item(alignment_loss.data) + loss += self.alignment_lambda * alignment_loss + + return loss, sample_size, logging_output + + def compute_alignment_loss(self, sample, net_output): + attn_prob = net_output[1]["attn"][0] + bsz, tgt_sz, src_sz = attn_prob.shape + attn = attn_prob.view(bsz * tgt_sz, src_sz) + + align = sample["alignments"] + align_weights = sample["align_weights"].float() + + if len(align) > 0: + # Alignment loss computation. align (shape [:, 2]) contains the src-tgt index pairs corresponding to + # the alignments. align_weights (shape [:]) contains the 1 / frequency of a tgt index for normalizing. + loss = -( + (attn[align[:, 1][:, None], align[:, 0][:, None]]).log() + * align_weights[:, None] + ).sum() + else: + return None + + return loss + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + nll_loss_sum = utils.item( + sum(log.get("nll_loss", 0) for log in logging_outputs) + ) + alignment_loss_sum = utils.item( + sum(log.get("alignment_loss", 0) for log in logging_outputs) + ) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_scalar( + "alignment_loss", + alignment_loss_sum / sample_size / math.log(2), + sample_size, + round=3, + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py new file mode 100644 index 0000000..f2e8cdf --- /dev/null +++ b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py @@ -0,0 +1,97 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, + LabelSmoothedCrossEntropyCriterionConfig, +) +from fairseq.data.data_utils import lengths_to_mask + + +@dataclass +class LabelSmoothedCrossEntropyWithCtcCriterionConfig( + LabelSmoothedCrossEntropyCriterionConfig +): + ctc_weight: float = field(default=1.0, metadata={"help": "weight for CTC loss"}) + + +@register_criterion( + "label_smoothed_cross_entropy_with_ctc", + dataclass=LabelSmoothedCrossEntropyWithCtcCriterionConfig, +) +class LabelSmoothedCrossEntropyWithCtcCriterion(LabelSmoothedCrossEntropyCriterion): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size, + report_accuracy, + ctc_weight, + ): + super().__init__( + task, sentence_avg, label_smoothing, ignore_prefix_size, report_accuracy + ) + self.ctc_weight = ctc_weight + + def forward(self, model, sample, reduce=True): + net_output = model(**sample["net_input"]) + loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) + + ctc_loss = torch.tensor(0.0).type_as(loss) + if self.ctc_weight > 0.0: + ctc_lprobs, ctc_lens = model.get_ctc_output(net_output, sample) + ctc_tgt, ctc_tgt_lens = model.get_ctc_target(sample) + ctc_tgt_mask = lengths_to_mask(ctc_tgt_lens) + ctc_tgt_flat = ctc_tgt.masked_select(ctc_tgt_mask) + reduction = "sum" if reduce else "none" + ctc_loss = ( + F.ctc_loss( + ctc_lprobs, + ctc_tgt_flat, + ctc_lens, + ctc_tgt_lens, + reduction=reduction, + zero_infinity=True, + ) + * self.ctc_weight + ) + loss += ctc_loss + + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": utils.item(loss.data), + "nll_loss": utils.item(nll_loss.data), + "ctc_loss": utils.item(ctc_loss.data), + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, net_output, sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) + return loss, sample_size, logging_output + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + super().reduce_metrics(logging_outputs) + loss_sum = sum(log.get("ctc_loss", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "ctc_loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) diff --git a/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_rdrop.py b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_rdrop.py new file mode 100644 index 0000000..47ee263 --- /dev/null +++ b/fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_rdrop.py @@ -0,0 +1,177 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field + +import torch + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, + LabelSmoothedCrossEntropyCriterionConfig, + label_smoothed_nll_loss, +) + + +@dataclass +class RdropLabelSmoothedCrossEntropyCriterionConfig( + LabelSmoothedCrossEntropyCriterionConfig +): + rdrop_alpha: float = field( + default=0.0, + metadata={"help": "alpha for r-drop, 0 means no r-drop"}, + ) + + +@register_criterion( + "label_smoothed_cross_entropy_with_rdrop", + dataclass=RdropLabelSmoothedCrossEntropyCriterionConfig, +) +class RdropLabelSmoothedCrossEntropyCriterion(LabelSmoothedCrossEntropyCriterion): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size=0, + report_accuracy=False, + rdrop_alpha=0.0, + ): + super().__init__( + task, + sentence_avg, + label_smoothing, + ignore_prefix_size=ignore_prefix_size, + report_accuracy=report_accuracy, + ) + self.sentence_avg = sentence_avg + self.eps = label_smoothing + self.ignore_prefix_size = ignore_prefix_size + self.report_accuracy = report_accuracy + self.rdrop_alpha = rdrop_alpha + + def forward(self, model, sample, reduce=True, net_output=None): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + if net_output is None: + if self.rdrop_alpha > 0 and sample["net_input"]["src_tokens"].size( + 0 + ) == sample["target"].size(0): + sample = duplicate_input(sample) + net_output = model(**sample["net_input"]) + loss, nll_loss, rdrop_kl_loss = self.compute_loss( + model, net_output, sample, reduce=reduce + ) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, net_output, sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) + if self.rdrop_alpha > 0: + logging_output["rdrop_kl_loss"] = utils.item(rdrop_kl_loss.data) + return loss, sample_size, logging_output + + def get_lprobs_and_target(self, model, net_output, sample): + lprobs = model.get_normalized_probs(net_output, log_probs=True) + target = model.get_targets(sample, net_output) + if self.rdrop_alpha > 0 or target.size(0) != lprobs.size(0): + target = torch.cat([target, target.clone()], dim=0) + + if self.ignore_prefix_size > 0: + # lprobs: B x T x C + lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous() + target = target[:, self.ignore_prefix_size :].contiguous() + return lprobs.view(-1, lprobs.size(-1)), target.view(-1) + + def compute_loss(self, model, net_output, sample, reduce=True): + lprobs, target = self.get_lprobs_and_target(model, net_output, sample) + loss, nll_loss = label_smoothed_nll_loss( + lprobs, + target, + self.eps, + ignore_index=self.padding_idx, + reduce=reduce, + ) + + if self.rdrop_alpha > 0: + pad_mask = target[: target.size(0) // 2].unsqueeze(-1).eq(self.padding_idx) + rdrop_kl_loss = compute_kl_loss(model, net_output, pad_mask) + loss += self.rdrop_alpha * rdrop_kl_loss + else: + rdrop_kl_loss = loss.new_zeros(1) + return loss, nll_loss, rdrop_kl_loss + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + super().reduce_metrics(logging_outputs) + + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + rdrop_kl_loss = utils.item( + sum(log.get("rdrop_kl_loss", 0) for log in logging_outputs) + / sample_size + / math.log(2) + ) + if rdrop_kl_loss > 0: + metrics.log_scalar("rdrop_kl_loss", rdrop_kl_loss) + + +def duplicate_input(sample): + if "net_input" in sample.keys(): + sample_input = sample["net_input"] + else: + sample_input = sample + + for k, v in sample_input.items(): + if isinstance(v, torch.Tensor): + sample_input[k] = torch.cat([v, v.clone()], dim=0) + if "net_input" in sample.keys(): + sample["net_input"] = sample_input + else: + sample = sample_input + return sample + + +def compute_kl_loss(model, net_output, pad_mask=None, reduce=True): + net_prob = model.get_normalized_probs(net_output, log_probs=True) + net_prob_tec = model.get_normalized_probs(net_output, log_probs=False) + + net_prob = net_prob.view(-1, net_prob.size(-1)) + net_prob_tec = net_prob_tec.view(-1, net_prob_tec.size(-1)) + + p, q = torch.split(net_prob, net_prob.size(0) // 2, dim=0) + p_tec, q_tec = torch.split(net_prob_tec, net_prob_tec.size(0) // 2, dim=0) + + p_loss = torch.nn.functional.kl_div(p, q_tec, reduction="none") + q_loss = torch.nn.functional.kl_div(q, p_tec, reduction="none") + + if pad_mask is not None: + p_loss.masked_fill_(pad_mask, 0.0) + q_loss.masked_fill_(pad_mask, 0.0) + + if reduce: + p_loss = p_loss.sum() + q_loss = q_loss.sum() + + loss = (p_loss + q_loss) / 2 + return loss diff --git a/fairseq/fairseq/criterions/legacy_masked_lm.py b/fairseq/fairseq/criterions/legacy_masked_lm.py new file mode 100644 index 0000000..5cf70df --- /dev/null +++ b/fairseq/fairseq/criterions/legacy_masked_lm.py @@ -0,0 +1,178 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion + + +def compute_cross_entropy_loss(logits, targets, ignore_index=-100): + """ + Function to compute the cross entropy loss. The default value of + ignore_index is the same as the default value for F.cross_entropy in + pytorch. + """ + assert logits.size(0) == targets.size( + -1 + ), "Logits and Targets tensor shapes don't match up" + + loss = F.nll_loss( + F.log_softmax(logits, -1, dtype=torch.float32), + targets, + reduction="sum", + ignore_index=ignore_index, + ) + return loss + + +@register_criterion("legacy_masked_lm_loss") +class LegacyMaskedLmLoss(FairseqCriterion): + """ + Implementation for the loss used in masked language model (MLM) training. + This optionally also computes the next sentence prediction (NSP) loss and + adds it to the overall loss based on the specified args. There are three + cases to consider: + 1) Generic MLM training without NSP loss. In this case sentence_targets + and sentence_logits are both None. + 2) BERT training without NSP loss. In this case sentence_targets is + not None but sentence_logits is None and we should not be computing + a sentence level loss. + 3) BERT training with NSP loss. In this case both sentence_targets and + sentence_logits are not None and we should be computing a sentence + level loss. The weight of the sentence level loss is specified as + an argument. + """ + + def __init__(self, task, masked_lm_only, nsp_loss_weight): + super().__init__(task) + self.masked_lm_only = masked_lm_only + self.nsp_loss_weight = nsp_loss_weight + + @staticmethod + def add_args(parser): + """Args for MaskedLM Loss""" + # Default for masked_lm_only is False so as to not break BERT training + parser.add_argument( + "--masked-lm-only", + default=False, + action="store_true", + help="compute MLM loss only", + ) + parser.add_argument( + "--nsp-loss-weight", + default=1.0, + type=float, + help="weight for next sentence prediction" " loss (default 1)", + ) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + lm_logits, output_metadata = model(**sample["net_input"]) + + # reshape lm_logits from (N,T,C) to (N*T,C) + lm_logits = lm_logits.view(-1, lm_logits.size(-1)) + lm_targets = sample["lm_target"].view(-1) + lm_loss = compute_cross_entropy_loss(lm_logits, lm_targets, self.padding_idx) + + # compute the number of tokens for which loss is computed. This is used + # to normalize the loss + ntokens = utils.strip_pad(lm_targets, self.padding_idx).numel() + loss = lm_loss / ntokens + nsentences = sample["nsentences"] + # nsentences = 0 + + # Compute sentence loss if masked_lm_only is False + sentence_loss = None + if not self.masked_lm_only: + sentence_logits = output_metadata["sentence_logits"] + sentence_targets = sample["sentence_target"].view(-1) + # This needs to be recomputed due to some differences between + # TokenBlock and BlockPair dataset. This can be resolved with a + # refactor of BERTModel which we will do in the future. + # TODO: Remove this after refactor of BERTModel + nsentences = sentence_targets.size(0) + + # Check for logits being none which can happen when remove_heads + # is set to true in the BERT model. Ideally we should set + # masked_lm_only to true in this case, but that requires some + # refactor in the BERT model. + if sentence_logits is not None: + sentence_loss = compute_cross_entropy_loss( + sentence_logits, sentence_targets + ) + + loss += self.nsp_loss_weight * (sentence_loss / nsentences) + + # NOTE: as we are summing up per token mlm loss and per sentence nsp loss + # we don't need to use sample_size as denominator for the gradient + # here sample_size is just used for logging + sample_size = 1 + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "lm_loss": utils.item(lm_loss.data) if reduce else lm_loss.data, + # sentence loss is not always computed + "sentence_loss": ( + (utils.item(sentence_loss.data) if reduce else sentence_loss.data) + if sentence_loss is not None + else 0.0 + ), + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + lm_loss_sum = sum(log.get("lm_loss", 0) for log in logging_outputs) + sentence_loss_sum = sum(log.get("sentence_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + agg_loss = sum(log.get("loss", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", + agg_loss / sample_size / math.log(2) if sample_size > 0 else 0.0, + sample_size, + round=3, + ) + metrics.log_scalar( + "lm_loss", + lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0, + ntokens, + round=3, + ) + metrics.log_scalar( + "sentence_loss", + sentence_loss_sum / nsentences / math.log(2) if nsentences > 0 else 0.0, + nsentences, + round=3, + ) + metrics.log_scalar( + "nll_loss", + lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0, + ntokens, + round=3, + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/masked_lm.py b/fairseq/fairseq/criterions/masked_lm.py new file mode 100644 index 0000000..09ddd9f --- /dev/null +++ b/fairseq/fairseq/criterions/masked_lm.py @@ -0,0 +1,99 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass +import math +from omegaconf import II + +import torch +from fairseq import modules, utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass + + +@dataclass +class MaskedLmConfig(FairseqDataclass): + tpu: bool = II("common.tpu") + + +@register_criterion("masked_lm", dataclass=MaskedLmConfig) +class MaskedLmLoss(FairseqCriterion): + """ + Implementation for the loss used in masked language model (MLM) training. + """ + + def __init__(self, cfg: MaskedLmConfig, task): + super().__init__(task) + self.tpu = cfg.tpu + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + masked_tokens = sample["target"].ne(self.padding_idx) + sample_size = masked_tokens.int().sum() + + # Rare: when all tokens are masked, project all tokens. + # We use torch.where to avoid device-to-host transfers, + # except on CPU where torch.where is not well supported + # (see github.com/pytorch/pytorch/issues/26247). + if self.tpu: + masked_tokens = None # always project all tokens on TPU + elif masked_tokens.device == torch.device("cpu"): + if not masked_tokens.any(): + masked_tokens = None + else: + masked_tokens = torch.where( + masked_tokens.any(), + masked_tokens, + masked_tokens.new([True]), + ) + + logits = model(**sample["net_input"], masked_tokens=masked_tokens)[0] + targets = model.get_targets(sample, [logits]) + if masked_tokens is not None: + targets = targets[masked_tokens] + + loss = modules.cross_entropy( + logits.view(-1, logits.size(-1)), + targets.view(-1), + reduction="sum", + ignore_index=self.padding_idx, + ) + + logging_output = { + "loss": loss if self.tpu else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + } + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/model_criterion.py b/fairseq/fairseq/criterions/model_criterion.py new file mode 100644 index 0000000..4c020dd --- /dev/null +++ b/fairseq/fairseq/criterions/model_criterion.py @@ -0,0 +1,177 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Dict, List + +import torch + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.logging.meters import safe_round + + +logger = logging.getLogger(__name__) + + +@dataclass +class ModelCriterionConfig(FairseqDataclass): + loss_weights: Dict[str, float] = field( + default_factory=dict, + metadata={"help": "weights for the loss terms"}, + ) + log_keys: List[str] = field( + default_factory=list, + metadata={"help": "additional output keys to log"}, + ) + can_sum: bool = True + + +@register_criterion("model", dataclass=ModelCriterionConfig) +class ModelCriterion(FairseqCriterion): + """ + This criterion relies on the model to supply losses. + The losses should be a dictionary of name -> scalar returned by + the model either by including it in the net_output dict or by + implementing a get_losses(net_output, sample) method. The final loss is + a scaled sum of all losses according to weights in loss_weights. + If no weights are provided, then all losses are scaled by 1.0. + + The losses will be automatically logged. Additional keys from + net_output dict can be logged via the log_keys parameter. + """ + + def __init__(self, task, loss_weights=None, log_keys=None, can_sum=True): + super().__init__(task) + self.loss_weights = loss_weights + self.log_keys = log_keys + self.can_sum = can_sum + + def forward(self, model, sample, reduce=True): + net_output = model(**sample["net_input"]) + + scaled_losses = {} + + if hasattr(model, "get_losses"): + losses = model.get_losses(net_output, sample) + elif isinstance(net_output, dict) and "losses" in net_output: + losses = net_output["losses"] + else: + raise Exception("Could not retrieve losses") + + for lk, p in losses.items(): + try: + coef = 1.0 if len(self.loss_weights) == 0 else self.loss_weights[lk] + except KeyError: + logger.error( + f"weight for loss {lk} is not in loss_weights ({self.loss_weights})" + ) + raise + if coef != 0 and p is not None: + scaled_losses[lk] = coef * p.float().sum() + + loss = sum(scaled_losses.values()) + + if "sample_size" in net_output: + sample_size = net_output["sample_size"] + else: + sample_size = loss.numel() + + if reduce and loss.numel() > 1: + loss = loss.sum() + + logging_output = { + "loss": loss.data, + "ntokens": sample_size, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + "_world_size": 1, + } + + for lk in self.log_keys: + if lk in net_output and net_output[lk] is not None: + if not torch.is_tensor(net_output[lk]) or net_output[lk].numel() == 1: + logging_output[lk] = float(net_output[lk]) + elif lk.startswith("_"): + logging_output[lk] = net_output[lk] + else: + for i, v in enumerate(net_output[lk]): + logging_output[f"{lk}_{i}"] = float(v) + + if len(scaled_losses) > 1: + for lk, l in scaled_losses.items(): + if l.numel() > 1: + l = l.sum() + logging_output[f"loss_{lk}"] = l.item() + + if "logs" in net_output: + for lgw in net_output["logs"]: + logging_output[lgw] = net_output["logs"][lgw] + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + nsentences = utils.item( + sum(log.get("nsentences", 0) for log in logging_outputs) + ) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3) + metrics.log_scalar("ntokens", ntokens) + metrics.log_scalar("nsentences", nsentences) + metrics.log_scalar("sample_size", sample_size) + + builtin_keys = { + "loss", + "ntokens", + "nsentences", + "sample_size", + "_world_size", + } + + world_size = utils.item( + sum(log.get("_world_size", 0) for log in logging_outputs) + ) + + for k in logging_outputs[0]: + if k not in builtin_keys and not k.startswith("_"): + val = sum(log.get(k, 0) for log in logging_outputs) + if k.startswith("loss_"): + metrics.log_scalar(k, val / sample_size, sample_size, round=3) + else: + metrics.log_scalar(k, val / world_size, round=3) + + correct = sum(log.get("correct", 0) for log in logging_outputs) + total = sum(log.get("count", 0) for log in logging_outputs) + + if total > 0: + metrics.log_scalar("_correct", correct) + metrics.log_scalar("_total", total) + + metrics.log_derived( + "accuracy", + lambda meters: safe_round( + meters["_correct"].sum / meters["_total"].sum, 5 + ) + if meters["_total"].sum > 0 + else float("nan"), + ) + + def logging_outputs_can_be_summed(self) -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return self.can_sum diff --git a/fairseq/fairseq/criterions/nat_loss.py b/fairseq/fairseq/criterions/nat_loss.py new file mode 100644 index 0000000..fc0bdaf --- /dev/null +++ b/fairseq/fairseq/criterions/nat_loss.py @@ -0,0 +1,181 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from torch import Tensor + +from dataclasses import dataclass, field + + +@dataclass +class LabelSmoothedDualImitationCriterionConfig(FairseqDataclass): + label_smoothing: float = field( + default=0.0, + metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"}, + ) + + +@register_criterion("nat_loss", dataclass=LabelSmoothedDualImitationCriterionConfig) +class LabelSmoothedDualImitationCriterion(FairseqCriterion): + def __init__(self, task, label_smoothing): + super().__init__(task) + self.label_smoothing = label_smoothing + + def _compute_loss( + self, outputs, targets, masks=None, label_smoothing=0.0, name="loss", factor=1.0 + ): + """ + outputs: batch x len x d_model + targets: batch x len + masks: batch x len + + policy_logprob: if there is some policy + depends on the likelihood score as rewards. + """ + + def mean_ds(x: Tensor, dim=None) -> Tensor: + return ( + x.float().mean().type_as(x) + if dim is None + else x.float().mean(dim).type_as(x) + ) + + if masks is not None: + outputs, targets = outputs[masks], targets[masks] + + if masks is not None and not masks.any(): + nll_loss = torch.tensor(0) + loss = nll_loss + else: + logits = F.log_softmax(outputs, dim=-1) + if targets.dim() == 1: + losses = F.nll_loss(logits, targets.to(logits.device), reduction="none") + + else: # soft-labels + losses = F.kl_div(logits, targets.to(logits.device), reduction="none") + losses = losses.sum(-1) + + nll_loss = mean_ds(losses) + if label_smoothing > 0: + loss = ( + nll_loss * (1 - label_smoothing) - mean_ds(logits) * label_smoothing + ) + else: + loss = nll_loss + + loss = loss * factor + return {"name": name, "loss": loss, "nll_loss": nll_loss, "factor": factor} + + def _custom_loss(self, loss, name="loss", factor=1.0): + return {"name": name, "loss": loss, "factor": factor} + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + nsentences, ntokens = sample["nsentences"], sample["ntokens"] + + # B x T + src_tokens, src_lengths = ( + sample["net_input"]["src_tokens"], + sample["net_input"]["src_lengths"], + ) + tgt_tokens, prev_output_tokens = sample["target"], sample["prev_target"] + + outputs = model(src_tokens, src_lengths, prev_output_tokens, tgt_tokens) + losses, nll_loss = [], [] + + for obj in outputs: + if outputs[obj].get("loss", None) is None: + _losses = self._compute_loss( + outputs[obj].get("out"), + outputs[obj].get("tgt"), + outputs[obj].get("mask", None), + outputs[obj].get("ls", 0.0), + name=obj + "-loss", + factor=outputs[obj].get("factor", 1.0), + ) + else: + _losses = self._custom_loss( + outputs[obj].get("loss"), + name=obj + "-loss", + factor=outputs[obj].get("factor", 1.0), + ) + + losses += [_losses] + if outputs[obj].get("nll_loss", False): + nll_loss += [_losses.get("nll_loss", 0.0)] + + loss = sum(l["loss"] for l in losses) + nll_loss = sum(l for l in nll_loss) if len(nll_loss) > 0 else loss.new_tensor(0) + + # NOTE: + # we don't need to use sample_size as denominator for the gradient + # here sample_size is just used for logging + sample_size = 1 + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + + for l in losses: + logging_output[l["name"]] = ( + utils.item(l["loss"].data / l["factor"]) + if reduce + else l[["loss"]].data / l["factor"] + ) + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + loss = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + nll_loss = utils.item(sum(log.get("nll_loss", 0) for log in logging_outputs)) + + metrics.log_scalar( + "loss", loss / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + for key in logging_outputs[0]: + if key[-5:] == "-loss": + val = sum(log.get(key, 0) for log in logging_outputs) + metrics.log_scalar( + key[:-5], + val / sample_size / math.log(2) if sample_size > 0 else 0.0, + sample_size, + round=3, + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/sentence_prediction.py b/fairseq/fairseq/criterions/sentence_prediction.py new file mode 100644 index 0000000..298b805 --- /dev/null +++ b/fairseq/fairseq/criterions/sentence_prediction.py @@ -0,0 +1,288 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from itertools import chain + +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.metrics import f1_score +from sklearn.metrics import matthews_corrcoef as _matthews_corrcoef +from scipy.stats import pearsonr, spearmanr + +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.logging.meters import safe_round + + +def simple_accuracy(preds, labels): + return (preds == labels).mean() + + +def acc_and_f1(preds, labels): + acc = simple_accuracy(preds, labels) + f1 = f1_score(y_true=labels, y_pred=preds) + return { + "acc": acc, + "f1": f1, + "acc_and_f1": (acc + f1) / 2, + } + + +def pearson_and_spearman(preds, labels): + pearson_corr = pearsonr(preds, labels)[0] + spearman_corr = spearmanr(preds, labels)[0] + return { + "pearson": pearson_corr, + "spearmanr": spearman_corr, + "corr": (pearson_corr + spearman_corr) / 2, + } + + +def matthews_corrcoef(preds, labels): + # make it consistent with other metrics taking (preds, labels) as input + mcc = _matthews_corrcoef(labels, preds) + return mcc + + +@dataclass +class SentencePredictionConfig(FairseqDataclass): + classification_head_name: str = field( + default="sentence_classification_head", + metadata={"help": "name of the classification head to use"}, + ) + regression_target: bool = field( + default=False, + ) + report_mcc: bool = False + report_acc_and_f1: bool = False + report_pearson_and_spearman: bool = False + + +@register_criterion("sentence_prediction", dataclass=SentencePredictionConfig) +class SentencePredictionCriterion(FairseqCriterion): + def __init__(self, cfg: SentencePredictionConfig, task): + super().__init__(task) + self.classification_head_name = cfg.classification_head_name + self.regression_target = cfg.regression_target + self.keep_pred_and_targ = ( + cfg.report_mcc or cfg.report_acc_and_f1 or cfg.report_pearson_and_spearman + ) + self.report_mcc = cfg.report_mcc + self.report_acc_and_f1 = cfg.report_acc_and_f1 + self.report_pearson_and_spearman = cfg.report_pearson_and_spearman + self.label_dict = task.label_dictionary + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + assert ( + hasattr(model, "classification_heads") + and self.classification_head_name in model.classification_heads + ), "model must provide sentence classification head for --criterion=sentence_prediction" + + logits, _ = model( + **sample["net_input"], + features_only=True, + classification_head_name=self.classification_head_name, + ) + targets = model.get_targets(sample, [logits]).view(-1) + sample_size = targets.numel() + + if not self.regression_target: + lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) + task_loss = F.nll_loss(lprobs, targets, reduction="sum") + else: + logits = logits.view(-1).float() + targets = targets.float() + task_loss = F.mse_loss(logits, targets, reduction="sum") + + logging_output = {} + loss = task_loss + # mha & ffn regularization update + if ( + hasattr(model, "args") + and hasattr(model.args, "mha_reg_scale_factor") + and model.args.mha_reg_scale_factor != 0.0 + ): + mha_reg_loss = model._get_adaptive_head_loss() + loss += mha_reg_loss + logging_output.update({"mha_reg_loss": mha_reg_loss}) + if ( + hasattr(model, "args") + and hasattr(model.args, "ffn_reg_scale_factor") + and model.args.ffn_reg_scale_factor != 0.0 + ): + ffn_reg_loss = model._get_adaptive_ffn_loss() + loss += ffn_reg_loss + logging_output.update({"ffn_reg_loss": ffn_reg_loss}) + + logging_output.update( + { + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample_size, + "sample_size": sample_size, + } + ) + if not self.regression_target: + preds = logits.argmax(dim=1) + logging_output["ncorrect"] = (preds == targets).sum() + if self.keep_pred_and_targ and not model.training: + if self.regression_target: + logging_output["pred"] = logits.detach().cpu().tolist() + logging_output["targ"] = targets.detach().cpu().tolist() + else: + # remove offset `self.label_dict.nspecial` from OffsetTokensDataset + preds = self.label_dict.string(preds + self.label_dict.nspecial).split() + targets = self.label_dict.string( + targets + self.label_dict.nspecial + ).split() + logging_output["pred"] = list(map(int, preds)) + logging_output["targ"] = list(map(int, targets)) + + if self.report_mcc: + logging_output["report_mcc"] = True + if self.report_acc_and_f1: + logging_output["report_acc_and_f1"] = True + if self.report_pearson_and_spearman: + logging_output["report_pearson_and_spearman"] = True + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + mha_reg_loss_sum = sum(log.get("mha_reg_loss", 0) for log in logging_outputs) + ffn_reg_loss_sum = sum(log.get("ffn_reg_loss", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + if mha_reg_loss_sum: + metrics.log_scalar( + "mha_reg_loss", + mha_reg_loss_sum / sample_size / math.log(2), + sample_size, + round=3, + ) + if ffn_reg_loss_sum: + metrics.log_scalar( + "ffn_reg_loss", + ffn_reg_loss_sum / sample_size / math.log(2), + sample_size, + round=3, + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + + if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + metrics.log_scalar( + "accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1 + ) + + # Metrics used by GLUE + pred = np.array( + list(chain.from_iterable(log.get("pred", []) for log in logging_outputs)) + ) + targ = np.array( + list(chain.from_iterable(log.get("targ", []) for log in logging_outputs)) + ) + if len(pred): + metrics.log_concat_tensor("pred", torch.from_numpy(pred), dim=0) + metrics.log_concat_tensor("targ", torch.from_numpy(targ), dim=0) + if any("report_mcc" in log for log in logging_outputs): + metrics.log_derived( + "mcc", + lambda meters: safe_round( + matthews_corrcoef( + meters["pred"].tensor.numpy(), + meters["targ"].tensor.numpy(), + ) + * 100, + 1, + ), + ) + if any("report_acc_and_f1" in log for log in logging_outputs): + metrics.log_derived( + "acc_and_f1", + lambda meters: safe_round( + acc_and_f1( + meters["pred"].tensor.numpy(), + meters["targ"].tensor.numpy(), + )["acc_and_f1"] + * 100, + 1, + ), + ) + metrics.log_derived( + "f1", + lambda meters: safe_round( + acc_and_f1( + meters["pred"].tensor.numpy(), + meters["targ"].tensor.numpy(), + )["f1"] + * 100, + 1, + ), + ) + if any("report_pearson_and_spearman" in log for log in logging_outputs): + metrics.log_derived( + "pearson_and_spearman", + lambda meters: safe_round( + pearson_and_spearman( + meters["pred"].tensor.numpy(), + meters["targ"].tensor.numpy(), + )["corr"] + * 100, + 1, + ), + ) + metrics.log_derived( + "pearson", + lambda meters: safe_round( + pearson_and_spearman( + meters["pred"].tensor.numpy(), + meters["targ"].tensor.numpy(), + )["pearson"] + * 100, + 1, + ), + ) + metrics.log_derived( + "spearman", + lambda meters: safe_round( + pearson_and_spearman( + meters["pred"].tensor.numpy(), + meters["targ"].tensor.numpy(), + )["spearmanr"] + * 100, + 1, + ), + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/sentence_prediction_adapters.py b/fairseq/fairseq/criterions/sentence_prediction_adapters.py new file mode 100644 index 0000000..8a873a4 --- /dev/null +++ b/fairseq/fairseq/criterions/sentence_prediction_adapters.py @@ -0,0 +1,63 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from fairseq.criterions import register_criterion +from fairseq.criterions.sentence_prediction import ( + SentencePredictionCriterion, + SentencePredictionConfig, +) + + +@register_criterion("sentence_prediction_adapters", dataclass=SentencePredictionConfig) +class SentencePredictionCriterionAdapters(SentencePredictionCriterion): + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + assert ( + hasattr(model, "classification_heads") + and self.classification_head_name in model.classification_heads + ), "model must provide sentence classification head for --criterion=sentence_prediction" + + if not hasattr(sample, "lang_id"): + # If no language ID is given, we fall back to English + lang_id = ["en_XX"] * sample["nsentences"] + else: + lang_id = sample["lang_id"] + + logits, _ = model( + **sample["net_input"], + features_only=True, + classification_head_name=self.classification_head_name, + lang_id=lang_id, + ) + targets = model.get_targets(sample, [logits]).view(-1) + sample_size = targets.numel() + + if not self.regression_target: + lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) + loss = F.nll_loss(lprobs, targets, reduction="sum") + else: + logits = logits.view(-1).float() + targets = targets.float() + loss = F.mse_loss(logits, targets, reduction="sum") + + logging_output = { + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample_size, + "sample_size": sample_size, + } + if not self.regression_target: + preds = logits.argmax(dim=1) + logging_output["ncorrect"] = (preds == targets).sum() + + return loss, sample_size, logging_output diff --git a/fairseq/fairseq/criterions/sentence_ranking.py b/fairseq/fairseq/criterions/sentence_ranking.py new file mode 100644 index 0000000..bfb9f05 --- /dev/null +++ b/fairseq/fairseq/criterions/sentence_ranking.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion + + +@register_criterion("sentence_ranking") +class SentenceRankingCriterion(FairseqCriterion): + def __init__(self, task, ranking_head_name, save_predictions, num_classes): + super().__init__(task) + self.ranking_head_name = ranking_head_name + if save_predictions is not None: + self.prediction_h = open(save_predictions, "w") + else: + self.prediction_h = None + self.num_classes = num_classes + + def __del__(self): + if self.prediction_h is not None: + self.prediction_h.close() + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--save-predictions', metavar='FILE', + help='file to save predictions to') + parser.add_argument('--ranking-head-name', + default='sentence_classification_head', + help='name of the ranking head to use') + # fmt: on + + def forward(self, model, sample, reduce=True): + """Compute ranking loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + assert ( + hasattr(model, "classification_heads") + and self.ranking_head_name in model.classification_heads + ), "model must provide sentence ranking head for --criterion=sentence_ranking" + + scores = [] + for idx in range(self.num_classes): + score, _ = model( + **sample["net_input{idx}".format(idx=idx + 1)], + classification_head_name=self.ranking_head_name, + ) + scores.append(score) + + logits = torch.cat(scores, dim=1) + sample_size = logits.size(0) + + if "target" in sample: + targets = model.get_targets(sample, [logits]).view(-1) + lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) + loss = F.nll_loss(lprobs, targets, reduction="sum") + else: + targets = None + loss = torch.tensor(0.0, requires_grad=True) + + if self.prediction_h is not None: + preds = logits.argmax(dim=1) + for i, (id, pred) in enumerate(zip(sample["id"].tolist(), preds.tolist())): + if targets is not None: + label = targets[i].item() + print("{}\t{}\t{}".format(id, pred, label), file=self.prediction_h) + else: + print("{}\t{}".format(id, pred), file=self.prediction_h) + + logging_output = { + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample_size, + "sample_size": sample_size, + } + if targets is not None: + logging_output["ncorrect"] = (logits.argmax(dim=1) == targets).sum() + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + + if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + metrics.log_scalar( + "accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1 + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/speech_dlm_criterion.py b/fairseq/fairseq/criterions/speech_dlm_criterion.py new file mode 100644 index 0000000..8888180 --- /dev/null +++ b/fairseq/fairseq/criterions/speech_dlm_criterion.py @@ -0,0 +1,335 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import Optional + +import torch.nn.functional as F +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II + + +@dataclass +class SpeechDLMCriterionConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + main_and_cross_weights: Optional[str] = field( + default="1,0", + metadata={ + "help": "Comma-separated list of weights of Main-channel vs Cross-channel Prediction Losses" + "(default: 1,0)" + }, + ) + general_unit_loss_weight: float = field( + default=0, + metadata={ + "help": "The weight of the General Prediction Loss (Next-step Unit Prediction Loss)" + "(default: 0)" + }, + ) + edge_unit_loss_weight: float = field( + default=1, + metadata={"help": "The weight of the Edge Unit Prediction Loss" "(default: 1)"}, + ) + duration_loss_weight: float = field( + default=1, + metadata={ + "help": "The weight of the Edge Unit Duration Prediction Loss" + "(default: 1)" + }, + ) + + +@register_criterion("speech_dlm_criterion", dataclass=SpeechDLMCriterionConfig) +class SpeechDLMCriterion(FairseqCriterion): + """Criteron for the SpeechDLM model as described in the paper: + https://arxiv.org/pdf/2203.16502.pdf + + There are 3 possible losses depending on the targets of the model: + - general_unit_loss : The next unit prediction loss, corresponding to + 'next' target + - edge_unit_loss : The edge unit prediction loss, corresponding to + 'edge' target + - duration_loss : The duration prediction loss, corresponding to + 'duration' target + """ + + def __init__( + self, + task, + sentence_avg, + main_and_cross_weights, + general_unit_loss_weight, + edge_unit_loss_weight, + duration_loss_weight, + ): + super().__init__(task) + self.sentence_avg = sentence_avg + + self.channels = task.channels + self.targets = task.targets + self.delayed_duration_target = task.delayed_duration_target + + self.main_channel_weight = float(main_and_cross_weights.split(",")[0]) + self.cross_channel_weight = float(main_and_cross_weights.split(",")[1]) + assert self.main_channel_weight >= 0 and self.cross_channel_weight >= 0 + + self.channel_weights = { + channel: weight + for channel, weight in zip(self.channels, task.channel_weights) + } + + self.target_weights = {} + for t in self.targets: + if t == "next": + self.target_weights[t] = general_unit_loss_weight + assert ( + general_unit_loss_weight > 0 + ), "Expect a positive --general-unit-loss-weight for next unit prediction" + elif t == "edge": + self.target_weights[t] = edge_unit_loss_weight + assert ( + edge_unit_loss_weight > 0 + ), "Expect a positive --edge-unit-loss-weight for edge unit prediction" + elif t == "duration": + self.target_weights[t] = duration_loss_weight + assert ( + duration_loss_weight > 0 + ), "Expect a positive --duration-loss-weight for duration prediction" + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + loss_dict, stats_dict = self.compute_loss( + model, net_output, sample, reduce=reduce + ) + nsentences = sample["net_input"]["src_tokens"][self.channels[0]].size(0) + + logging_output = { + "nsentences": nsentences, + } + logging_output["nsentences"] = nsentences + + loss_all = {t: 0 for t in self.targets} + correct_all = {t: 0 for t in self.targets} + count_all = {t: 0 for t in self.targets} + ntokens_all = 0 + sample_size_all = 0 + for channel in loss_dict: + for pred_channel in loss_dict[channel]: + # Get ntokens & sample_size + ntokens = sample["net_input"]["src_tokens"][channel].numel() + sample_size = nsentences if self.sentence_avg else ntokens + prefix = "[{}-{}]".format(channel, pred_channel) + log_keys = { + "next": "general_token", + "edge": "edge_token", + "duration": "edge_duration", + } + + # Log & Update the sizes + logging_output["{}ntokens".format(prefix)] = ntokens + logging_output["{}sample_size".format(prefix)] = sample_size + ntokens_all += ntokens + sample_size_all += sample_size + + for t in self.targets: + log_key = log_keys[t] + loss = loss_dict[channel][pred_channel][t] + correct, count = stats_dict[channel][pred_channel][t] + + # Log the statistics + logging_output["{}{}_loss".format(prefix, log_key)] = loss.data + logging_output["{}{}_correct".format(prefix, log_key)] = correct + logging_output["{}{}_count".format(prefix, log_key)] = count + + # Scale the training loss by weights + target_loss = loss * self.channel_weights[channel] + if pred_channel == channel: + target_loss = target_loss * self.main_channel_weight + else: + target_loss = target_loss * self.cross_channel_weight + # Normalize the losses in the training by the number of edges + if t in ["edge", "duration"]: + target_loss = target_loss / count * sample_size + + # Update the statistics + loss_all[t] += target_loss + correct_all[t] += correct + count_all[t] += count + + # Logging the average statistics + logging_output["ntokens"] = ntokens_all + logging_output["sample_size"] = sample_size_all + for t in self.targets: + log_key = { + "next": "general_token", + "edge": "edge_token", + "duration": "edge_duration", + }[t] + logging_output["{}_loss".format(log_key)] = loss_all[t].data + logging_output["{}_correct".format(log_key)] = correct_all[t] + logging_output["{}_count".format(log_key)] = count_all[t] + + # Define the training loss + training_loss = 0 + for t in self.targets: + training_loss += loss_all[t] * self.target_weights[t] + logging_output["loss"] = training_loss.data + + return training_loss, sample_size_all, logging_output + + def compute_loss(self, model, net_output, sample, reduce=True): + # Get the model outputs and target + lprobs_dict = model.get_normalized_probs(net_output, log_probs=True) + target_dict = model.get_targets(sample, net_output) + + # Init the dictionaries + loss_dict, stats_dict = {}, {} + + for channel in lprobs_dict: + # Init the dictionaries + loss_dict[channel], stats_dict[channel] = {}, {} + + for pred_channel in lprobs_dict[channel]: + # Init the dictionaries + loss_dict[channel][pred_channel] = {} + stats_dict[channel][pred_channel] = {} + + # Get token & duration predictions + outputs = lprobs_dict[channel][pred_channel] + if not isinstance(outputs, dict): + token_lprobs = outputs + else: + token_lprobs = outputs["pred_token"] + dur_preds = outputs["pred_duration"] + dur_preds = dur_preds.view(-1) + token_lprobs = token_lprobs.view(-1, token_lprobs.size(-1)) + token_preds = token_lprobs.argmax(dim=-1) + + # Get edge indices + if "edge" in self.targets or "duration" in self.targets: + edge_indices = target_dict["edge_indices"][pred_channel] + + # Compute loss and statistics + for t in self.targets: + if t in ["next", "edge"]: + if t == "next": + target = target_dict["next"][pred_channel].view(-1) + lprobs = token_lprobs + preds = token_preds + elif t == "edge": + target = target_dict["edge"][pred_channel] + lprobs = token_lprobs[edge_indices] + preds = token_preds[edge_indices] + + loss = F.nll_loss( + lprobs, + target, + ignore_index=self.padding_idx, + reduction="sum" if reduce else "none", + ) + elif t == "duration": + target = target_dict["duration"][pred_channel] + if self.delayed_duration_target: + duration_indices = edge_indices + 1 + if duration_indices[-1] == len(dur_preds): + duration_indices = duration_indices[:-1] + target = target[:-1] + else: + duration_indices = edge_indices + preds = dur_preds[duration_indices] + + loss = F.l1_loss( + preds, + target, + reduction="sum" if reduce else "none", + ) + preds = preds.round() + + correct = (preds == target).sum().float().cpu().item() + count = float(target.size(0)) + + loss_dict[channel][pred_channel][t] = loss + stats_dict[channel][pred_channel][t] = (correct, count) + + return loss_dict, stats_dict + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + logging_keys = next(iter(logging_outputs)).keys() + channels = [item[:-7] for item in logging_keys if item.endswith("ntokens")] + target_prefixes = set( + [ + item[:-5].split("]")[-1] + for item in logging_keys + if item.endswith("_loss") + ] + ) + for channel_prefix in channels: + for target_prefix in target_prefixes: + prefix = "{}{}".format(channel_prefix, target_prefix) + count_sum = sum( + log.get("{}_count".format(prefix), 0) for log in logging_outputs + ) + correct_sum = sum( + log.get("{}_correct".format(prefix), 0) for log in logging_outputs + ) + loss_sum = sum( + log.get("{}_loss".format(prefix), 0) for log in logging_outputs + ) + + if "duration" not in target_prefix: + # we divide by log(2) to convert the loss from base e to base 2 + metrics.log_scalar( + "{}_loss".format(prefix), + loss_sum / count_sum / math.log(2), + count_sum, + round=3, + ) + metrics.log_derived( + "{}_ppl".format(prefix), + lambda meters, prefix=prefix: utils.get_perplexity( + meters["{}_loss".format(prefix)].avg + ), + ) + else: + # for duration we don't need to divide by log(2) + metrics.log_scalar( + "{}_loss".format(prefix), + loss_sum / count_sum, + count_sum, + round=3, + ) + + accuracy = 100 * correct_sum / count_sum + metrics.log_scalar("{}_pred_acc".format(prefix), accuracy, round=3) + + # Logging training loss + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + + # we divide by log(2) to convert the loss from base e to base 2 + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/criterions/speech_to_speech_criterion.py b/fairseq/fairseq/criterions/speech_to_speech_criterion.py new file mode 100644 index 0000000..06a8252 --- /dev/null +++ b/fairseq/fairseq/criterions/speech_to_speech_criterion.py @@ -0,0 +1,517 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from collections import OrderedDict + +import torch + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import register_criterion +from fairseq.criterions.ctc import CtcCriterion +from fairseq.criterions.label_smoothed_cross_entropy_with_rdrop import ( + RdropLabelSmoothedCrossEntropyCriterion, + RdropLabelSmoothedCrossEntropyCriterionConfig, + duplicate_input, +) +from fairseq.criterions.tacotron2_loss import ( + Tacotron2Criterion, + Tacotron2CriterionConfig, +) + +logger = logging.getLogger(__name__) + + +class MultitaskCriterion: + def __init__(self, multitask_tasks, rdrop_alpha=0.0): + self.rdrop_alpha = rdrop_alpha + self.rdrop_alpha_mtl = rdrop_alpha + + self.multitask_criterion = OrderedDict() + self.multitask_loss_weight = OrderedDict() + for task_name, task_obj in multitask_tasks.items(): + if task_obj.args.get_loss_weight(0) == 0: + logger.info(f"Skip {task_name} loss criterion") + continue + + rdrop_alpha_task = task_obj.args.rdrop_alpha + if rdrop_alpha_task is None: + rdrop_alpha_task = rdrop_alpha + self.rdrop_alpha_mtl = rdrop_alpha_task + logger.info(f"rdrop_alpha is set to {rdrop_alpha_task} for {task_name}") + + if task_obj.args.decoder_type == "ctc": + self.multitask_criterion[task_name] = CtcCriterion( + task_obj.args.criterion_cfg, + task_obj, + rdrop_alpha=rdrop_alpha_task, + ) + else: + self.multitask_criterion[ + task_name + ] = RdropLabelSmoothedCrossEntropyCriterion( + task_obj, + task_obj.args.criterion_cfg.sentence_avg, + label_smoothing=task_obj.args.criterion_cfg.label_smoothing, + rdrop_alpha=rdrop_alpha_task, + ) + + def set_multitask_loss_weight(self, task_name, weight=0.0): + self.multitask_loss_weight[task_name] = weight + + def get_multitask_loss(self, model, sample, model_out): + logging_output = {} + loss = 0.0 + for task_name, task_criterion in self.multitask_criterion.items(): + layer_id = task_criterion.task.args.input_layer + if isinstance(task_criterion, CtcCriterion): + if task_criterion.task.args.input_from == "encoder": + if len(model_out["encoder_padding_mask"]) > 0: + non_padding_mask = ~model_out["encoder_padding_mask"][0] + input_lengths = non_padding_mask.long().sum(-1) + else: + out = model_out["encoder_states"][layer_id] + input_lengths = out.new_full( + (out.shape[1],), out.shape[0] + ).long() + + task_sample = { + "net_input": { + "src_tokens": model_out["encoder_states"][ + layer_id + ], # check batch idx + "src_lengths": input_lengths, + }, + "id": sample["id"], + } + else: + task_sample = { + "net_input": { + "src_tokens": model_out["inner_states"][layer_id], + "src_lengths": sample["target_lengths"], + }, + "id": sample["id"], + } + else: + task_sample = { + "net_input": { + "src_tokens": sample["multitask"][task_name]["net_input"][ + "prev_output_tokens" + ], + "encoder_out": { + "encoder_out": [model_out["encoder_states"][layer_id]], + "encoder_padding_mask": model_out["encoder_padding_mask"], + }, + } + } + + for key in ["target", "target_lengths", "ntokens"]: + task_sample[key] = sample["multitask"][task_name][key] + + if task_name == getattr(model, "mt_task_name", None): + decoder_out = model_out["mt_decoder_out"] + else: + decoder_out = None + task_loss, task_sample_size, task_logging_output = task_criterion( + model.multitask_decoders[task_name], task_sample, net_output=decoder_out + ) + + loss = loss + self.multitask_loss_weight[task_name] * task_loss + task_logging_output["loss_weight"] = self.multitask_loss_weight[task_name] + logging_output[task_name] = task_logging_output + return loss, logging_output + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + for task_name in logging_outputs[0]["multitask"].keys(): + # different criterion may return different logging + # currently only reduce on loss, the most common one + # ideally the way that losses are reduced should also depend on the task type + loss_sum = sum( + log["multitask"][task_name].get("loss", 0) for log in logging_outputs + ) + sample_size = sum( + log["multitask"][task_name].get("sample_size", 0) + for log in logging_outputs + ) + + metrics.log_scalar( + f"multitask_{task_name}_loss", + loss_sum / sample_size / math.log(2), + sample_size, + round=3, + ) + + loss_weight = logging_outputs[0]["multitask"][task_name].get( + "loss_weight", 0 + ) + metrics.log_scalar( + f"multitask_{task_name}_loss_weight", + loss_weight, + weight=0, + priority=250, + ) + + +@register_criterion( + "speech_to_unit", dataclass=RdropLabelSmoothedCrossEntropyCriterionConfig +) +class SpeechToUnitMultitaskTaskCriterion( + RdropLabelSmoothedCrossEntropyCriterion, MultitaskCriterion +): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size=0, + report_accuracy=False, + rdrop_alpha=0.0, + ): + super().__init__( + task, + sentence_avg, + label_smoothing, + ignore_prefix_size, + report_accuracy, + rdrop_alpha, + ) + MultitaskCriterion.__init__(self, task.multitask_tasks, rdrop_alpha) + + def forward(self, model, sample, reduce=True): + net_input_concat = { + "src_tokens": sample["net_input"]["src_tokens"], + "src_lengths": sample["net_input"]["src_lengths"], + "prev_output_tokens": sample["net_input"]["prev_output_tokens"], + "tgt_speaker": sample["net_input"].get("tgt_speaker", None), + "return_all_hiddens": True, + } + + if self.rdrop_alpha > 0 or self.rdrop_alpha_mtl > 0: + net_input_concat = duplicate_input(net_input_concat) + + net_output, extra = model(**net_input_concat) + loss, nll_loss, rdrop_kl_loss = self.compute_loss( + model, [net_output], sample, reduce=reduce + ) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, [net_output], sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) + if self.rdrop_alpha > 0: + logging_output["rdrop_kl_loss"] = utils.item(rdrop_kl_loss.data) + + if len(self.multitask_criterion) == 0: + return loss, sample_size, logging_output + + # multitask + multitask_loss, multitask_log = self.get_multitask_loss(model, sample, extra) + loss += multitask_loss + logging_output["multitask"] = multitask_log + + return loss, sample_size, logging_output + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + super().reduce_metrics(logging_outputs) + + # inference metrics + if "targ_frames" in logging_outputs[0]: + n = sum(log.get("norm_frames", 0) for log in logging_outputs) + for key, new_key in [ + ("mcd_loss", "mcd_loss"), + ("pred_frames", "pred_ratio"), + ("nins", "ins_rate"), + ("ndel", "del_rate"), + ]: + val = sum(log.get(key, 0) for log in logging_outputs) + metrics.log_scalar(new_key, val / n, n, round=3) + + if "multitask" not in logging_outputs[0]: + return + + MultitaskCriterion.reduce_metrics(logging_outputs) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return False + + +@register_criterion( + "speech_to_unit_2pass", dataclass=RdropLabelSmoothedCrossEntropyCriterionConfig +) +class SpeechToUnit2passMultitaskTaskCriterion(SpeechToUnitMultitaskTaskCriterion): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size=0, + report_accuracy=False, + rdrop_alpha=0.0, + ): + super().__init__( + task, + sentence_avg, + label_smoothing, + ignore_prefix_size, + report_accuracy, + rdrop_alpha, + ) + + def forward(self, model, sample, reduce=True): + net_input_concat = { + "src_tokens": sample["net_input"]["src_tokens"], + "src_lengths": sample["net_input"]["src_lengths"], + "prev_output_tokens": sample["net_input"]["prev_output_tokens"], + "prev_output_tokens_mt": sample["multitask"][model.mt_task_name][ + "net_input" + ]["prev_output_tokens"], + "tgt_speaker": sample["net_input"].get("tgt_speaker", None), + "return_all_hiddens": True, + } + if getattr(model, "asr_task_name", None) is not None: + net_input_concat["prev_output_tokens_asr"] = sample["multitask"][ + model.asr_task_name + ]["net_input"]["prev_output_tokens"] + + if self.rdrop_alpha > 0 or self.rdrop_alpha_mtl > 0: + net_input_concat = duplicate_input(net_input_concat) + + net_output, extra = model(**net_input_concat) + loss, nll_loss, rdrop_kl_loss = self.compute_loss( + model, [net_output], sample, reduce=reduce + ) + + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, [net_output], sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) + if self.rdrop_alpha > 0: + logging_output["rdrop_kl_loss"] = utils.item(rdrop_kl_loss.data) + + if len(self.multitask_criterion) == 0: + return loss, sample_size, logging_output + + # multitask + multitask_loss, multitask_log = self.get_multitask_loss(model, sample, extra) + loss += multitask_loss + logging_output["multitask"] = multitask_log + + return loss, sample_size, logging_output + + +@register_criterion("speech_to_spectrogram", dataclass=Tacotron2CriterionConfig) +class SpeechToSpectrogramMultitaskTaskCriterion(Tacotron2Criterion, MultitaskCriterion): + def __init__( + self, + task, + sentence_avg, + use_guided_attention_loss, + guided_attention_loss_sigma, + bce_pos_weight, + ctc_weight, + ): + super().__init__( + task, + sentence_avg, + use_guided_attention_loss, + guided_attention_loss_sigma, + bce_pos_weight, + ctc_weight, + ) + MultitaskCriterion.__init__(self, task.multitask_tasks) + + def forward(self, model, sample, reduction="mean"): + bsz, max_len, _ = sample["target"].size() + feat_tgt = sample["target"] + feat_len = sample["target_lengths"].view(bsz, 1).expand(-1, max_len) + eos_tgt = torch.arange(max_len).to(sample["target"].device) + eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1) + eos_tgt = (eos_tgt == (feat_len - 1)).float() + + feat_out, eos_out, extra = model( + src_tokens=sample["net_input"]["src_tokens"], + src_lengths=sample["net_input"]["src_lengths"], + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + tgt_speaker=sample["net_input"]["tgt_speaker"], + target_lengths=sample["target_lengths"], + return_all_hiddens=True, + ) + + l1_loss, mse_loss, eos_loss = self.compute_loss( + extra["feature_out"], + feat_out, + eos_out, + feat_tgt, + eos_tgt, + sample["target_lengths"], + reduction, + ) + attn_loss = torch.tensor(0.0).type_as(l1_loss) + if self.guided_attn is not None: + attn_loss = self.guided_attn( + extra["attn"], + sample["net_input"]["src_lengths"], + sample["target_lengths"], + reduction, + ) + loss = ( + l1_loss + mse_loss + eos_loss + attn_loss + ) # do not include ctc loss as there's no text target + + sample_size = sample["nsentences"] if self.sentence_avg else sample["ntokens"] + logging_output = { + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "l1_loss": utils.item(l1_loss.data), + "mse_loss": utils.item(mse_loss.data), + "eos_loss": utils.item(eos_loss.data), + "attn_loss": utils.item(attn_loss.data), + } + + if len(self.multitask_criterion) == 0: + return loss, sample_size, logging_output + + # multitask + multitask_loss, multitask_log = self.get_multitask_loss(model, sample, extra) + loss += multitask_loss + logging_output["multitask"] = multitask_log + return loss, sample_size, logging_output + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + super().reduce_metrics(logging_outputs) + + # inference metrics + if "targ_frames" in logging_outputs[0]: + n = sum(log.get("norm_frames", 0) for log in logging_outputs) + for key, new_key in [ + ("mcd_loss", "mcd_loss"), + ("pred_frames", "pred_ratio"), + ("nins", "ins_rate"), + ("ndel", "del_rate"), + ]: + val = sum(log.get(key, 0) for log in logging_outputs) + metrics.log_scalar(new_key, val / n, n, round=3) + + if "multitask" not in logging_outputs[0]: + return + + MultitaskCriterion.reduce_metrics(logging_outputs) + + +@register_criterion("speech_to_spectrogram_2pass", dataclass=Tacotron2CriterionConfig) +class SpeechToSpectrogram2passMultitaskTaskCriterion( + SpeechToSpectrogramMultitaskTaskCriterion +): + def __init__( + self, + task, + sentence_avg, + use_guided_attention_loss, + guided_attention_loss_sigma, + bce_pos_weight, + ctc_weight, + ): + super().__init__( + task, + sentence_avg, + use_guided_attention_loss, + guided_attention_loss_sigma, + bce_pos_weight, + ctc_weight, + ) + + def forward(self, model, sample, reduction="mean"): + bsz, max_len, _ = sample["target"].size() + feat_tgt = sample["target"] + feat_len = sample["target_lengths"].view(bsz, 1).expand(-1, max_len) + eos_tgt = torch.arange(max_len).to(sample["target"].device) + eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1) + eos_tgt = (eos_tgt == (feat_len - 1)).float() + + feat_out, eos_out, extra = model( + src_tokens=sample["net_input"]["src_tokens"], + src_lengths=sample["net_input"]["src_lengths"], + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + prev_output_tokens_mt=sample["multitask"][model.mt_task_name]["net_input"][ + "prev_output_tokens" + ], + tgt_speaker=sample["net_input"]["tgt_speaker"], + target_lengths=sample["target_lengths"], + return_all_hiddens=True, + ) + + l1_loss, mse_loss, eos_loss = self.compute_loss( + extra["feature_out"], + feat_out, + eos_out, + feat_tgt, + eos_tgt, + sample["target_lengths"], + reduction, + ) + attn_loss = torch.tensor(0.0).type_as(l1_loss) + if self.guided_attn is not None: + attn_loss = self.guided_attn( + extra["attn"], + sample["net_input"]["src_lengths"], + sample["target_lengths"], + reduction, + ) + loss = ( + l1_loss + mse_loss + eos_loss + attn_loss + ) # do not include ctc loss as there's no text target + + sample_size = sample["nsentences"] if self.sentence_avg else sample["ntokens"] + logging_output = { + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "l1_loss": utils.item(l1_loss.data), + "mse_loss": utils.item(mse_loss.data), + "eos_loss": utils.item(eos_loss.data), + "attn_loss": utils.item(attn_loss.data), + } + + if len(self.multitask_criterion) == 0: + return loss, sample_size, logging_output + + # multitask + multitask_loss, multitask_log = self.get_multitask_loss(model, sample, extra) + loss += multitask_loss + logging_output["multitask"] = multitask_log + return loss, sample_size, logging_output diff --git a/fairseq/fairseq/criterions/speech_ulm_criterion.py b/fairseq/fairseq/criterions/speech_ulm_criterion.py new file mode 100644 index 0000000..eea74ba --- /dev/null +++ b/fairseq/fairseq/criterions/speech_ulm_criterion.py @@ -0,0 +1,126 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from dataclasses import dataclass, field + +import torch.nn.functional as F +from fairseq.logging import metrics +from fairseq.tasks import FairseqTask +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II + + +@dataclass +class SpeechUnitLmCriterionConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + loss_weights: str = field( + default="1.;0.0;0.0", + metadata={ + "help": "Weights of the losses that correspond to token, duration, and F0 streams" + }, + ) + discrete_duration: bool = II("task.discrete_duration") + discrete_f0: bool = II("task.discrete_f0") + + +def mae_loss(pred, targ, mask, reduce=True): + if pred.ndim == 3: + pred = pred.squeeze(2) + else: + assert pred.ndim == 2 + loss = (pred.float() - targ.float()).abs() * (~mask).float() + loss = loss.sum() if reduce else loss.view(-1) + return loss + + +def nll_loss(pred, targ, mask, reduce=True): + lprob = F.log_softmax(pred, dim=-1) + loss = F.nll_loss(lprob.view(-1, lprob.size(-1)), targ.view(-1), reduction="none") + loss = loss * (~mask).float().view(-1) + loss = loss.sum() if reduce else loss.view(-1) + return loss + + +@register_criterion("speech_unit_lm_criterion", dataclass=SpeechUnitLmCriterionConfig) +class SpeechUnitLmCriterion(FairseqCriterion): + def __init__(self, cfg: SpeechUnitLmCriterionConfig, task: FairseqTask): + super().__init__(task) + self.sentence_avg = cfg.sentence_avg + self.weights = torch.tensor([float(w) for w in cfg.loss_weights.split(";")]) + assert self.weights.size(0) == 3 + assert (self.weights >= 0.0).all() + + self.dur_loss_fn = nll_loss if cfg.discrete_duration else mae_loss + self.f0_loss_fn = nll_loss if cfg.discrete_f0 else mae_loss + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + + token_loss = nll_loss( + net_output["token"], sample["target"], sample["mask"], reduce + ) + dur_loss = self.dur_loss_fn( + net_output["duration"], + sample["dur_target"], + sample["dur_mask"], + reduce, + ) + f0_loss = self.f0_loss_fn( + net_output["f0"], + sample["f0_target"], + sample["f0_mask"], + reduce, + ) + loss = self.weights.to(token_loss.device) * torch.stack( + [token_loss, dur_loss, f0_loss], dim=-1 + ) + loss = loss.sum() if reduce else loss.sum(-1) + + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": loss.detach().sum().item(), + "token_loss": token_loss.detach().sum().item(), + "dur_loss": dur_loss.detach().sum().item(), + "f0_loss": f0_loss.detach().sum().item(), + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + token_loss_sum = sum(log.get("token_loss", 0) for log in logging_outputs) + dur_loss_sum = sum(log.get("dur_loss", 0) for log in logging_outputs) + f0_loss_sum = sum(log.get("f0_loss", 0) for log in logging_outputs) + + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3) + + metrics.log_scalar( + "token_loss", token_loss_sum / sample_size, sample_size, round=3 + ) + + metrics.log_scalar("dur_loss", dur_loss_sum / sample_size, sample_size, round=3) + + metrics.log_scalar("f0_loss", f0_loss_sum / sample_size, sample_size, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + return True diff --git a/fairseq/fairseq/criterions/tacotron2_loss.py b/fairseq/fairseq/criterions/tacotron2_loss.py new file mode 100644 index 0000000..4113fdc --- /dev/null +++ b/fairseq/fairseq/criterions/tacotron2_loss.py @@ -0,0 +1,227 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +from dataclasses import dataclass, field +from functools import lru_cache +from typing import Any, Dict, List + +import torch +import torch.nn.functional as F +from omegaconf import II + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.data.data_utils import lengths_to_mask +from fairseq.dataclass import FairseqDataclass + +logger = logging.getLogger(__name__) + + +@dataclass +class Tacotron2CriterionConfig(FairseqDataclass): + bce_pos_weight: float = field( + default=1.0, + metadata={"help": "weight of positive examples for BCE loss"}, + ) + use_guided_attention_loss: bool = field( + default=False, + metadata={"help": "use guided attention loss"}, + ) + guided_attention_loss_sigma: float = field( + default=0.4, + metadata={"help": "weight of positive examples for BCE loss"}, + ) + ctc_weight: float = field(default=0.0, metadata={"help": "weight for CTC loss"}) + sentence_avg: bool = II("optimization.sentence_avg") + + +class GuidedAttentionLoss(torch.nn.Module): + """ + Efficiently Trainable Text-to-Speech System Based on Deep Convolutional + Networks with Guided Attention (https://arxiv.org/abs/1710.08969) + """ + + def __init__(self, sigma): + super().__init__() + self.sigma = sigma + + @staticmethod + @lru_cache(maxsize=8) + def _get_weight(s_len, t_len, sigma): + grid_x, grid_y = torch.meshgrid(torch.arange(t_len), torch.arange(s_len)) + grid_x = grid_x.to(s_len.device) + grid_y = grid_y.to(s_len.device) + w = (grid_y.float() / s_len - grid_x.float() / t_len) ** 2 + return 1.0 - torch.exp(-w / (2 * (sigma**2))) + + def _get_weights(self, src_lens, tgt_lens): + bsz, max_s_len, max_t_len = len(src_lens), max(src_lens), max(tgt_lens) + weights = torch.zeros((bsz, max_t_len, max_s_len)) + for i, (s_len, t_len) in enumerate(zip(src_lens, tgt_lens)): + weights[i, :t_len, :s_len] = self._get_weight(s_len, t_len, self.sigma) + return weights + + @staticmethod + def _get_masks(src_lens, tgt_lens): + in_masks = lengths_to_mask(src_lens) + out_masks = lengths_to_mask(tgt_lens) + return out_masks.unsqueeze(2) & in_masks.unsqueeze(1) + + def forward(self, attn, src_lens, tgt_lens, reduction="mean"): + weights = self._get_weights(src_lens, tgt_lens).to(attn.device) + masks = self._get_masks(src_lens, tgt_lens).to(attn.device) + loss = (weights * attn.transpose(1, 2)).masked_select(masks) + loss = torch.sum(loss) if reduction == "sum" else torch.mean(loss) + return loss + + +@register_criterion("tacotron2", dataclass=Tacotron2CriterionConfig) +class Tacotron2Criterion(FairseqCriterion): + def __init__( + self, + task, + sentence_avg, + use_guided_attention_loss, + guided_attention_loss_sigma, + bce_pos_weight, + ctc_weight, + ): + super().__init__(task) + self.sentence_avg = sentence_avg + self.bce_pos_weight = bce_pos_weight + + self.guided_attn = None + if use_guided_attention_loss: + self.guided_attn = GuidedAttentionLoss(guided_attention_loss_sigma) + self.ctc_weight = ctc_weight + + def forward(self, model, sample, reduction="mean"): + bsz, max_len, _ = sample["target"].size() + feat_tgt = sample["target"] + feat_len = sample["target_lengths"].view(bsz, 1).expand(-1, max_len) + eos_tgt = torch.arange(max_len).to(sample["target"].device) + eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1) + eos_tgt = (eos_tgt == (feat_len - 1)).float() + src_tokens = sample["net_input"]["src_tokens"] + src_lens = sample["net_input"]["src_lengths"] + tgt_lens = sample["target_lengths"] + + feat_out, eos_out, extra = model( + src_tokens=src_tokens, + src_lengths=src_lens, + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + incremental_state=None, + target_lengths=tgt_lens, + speaker=sample["speaker"], + ) + + l1_loss, mse_loss, eos_loss = self.compute_loss( + extra["feature_out"], + feat_out, + eos_out, + feat_tgt, + eos_tgt, + tgt_lens, + reduction, + ) + attn_loss = torch.tensor(0.0).type_as(l1_loss) + if self.guided_attn is not None: + attn_loss = self.guided_attn(extra["attn"], src_lens, tgt_lens, reduction) + ctc_loss = torch.tensor(0.0).type_as(l1_loss) + if self.ctc_weight > 0.0: + net_output = (feat_out, eos_out, extra) + lprobs = model.get_normalized_probs(net_output, log_probs=True) + lprobs = lprobs.transpose(0, 1) # T x B x C + src_mask = lengths_to_mask(src_lens) + src_tokens_flat = src_tokens.masked_select(src_mask) + ctc_loss = ( + F.ctc_loss( + lprobs, + src_tokens_flat, + tgt_lens, + src_lens, + reduction=reduction, + zero_infinity=True, + ) + * self.ctc_weight + ) + loss = l1_loss + mse_loss + eos_loss + attn_loss + ctc_loss + + sample_size = sample["nsentences"] if self.sentence_avg else sample["ntokens"] + logging_output = { + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "l1_loss": utils.item(l1_loss.data), + "mse_loss": utils.item(mse_loss.data), + "eos_loss": utils.item(eos_loss.data), + "attn_loss": utils.item(attn_loss.data), + "ctc_loss": utils.item(ctc_loss.data), + } + return loss, sample_size, logging_output + + def compute_loss( + self, + feat_out, + feat_out_post, + eos_out, + feat_tgt, + eos_tgt, + tgt_lens, + reduction="mean", + ): + mask = lengths_to_mask(tgt_lens) + _eos_out = eos_out[mask].squeeze() + _eos_tgt = eos_tgt[mask] + _feat_tgt = feat_tgt[mask] + _feat_out = feat_out[mask] + _feat_out_post = feat_out_post[mask] + + l1_loss = F.l1_loss(_feat_out, _feat_tgt, reduction=reduction) + F.l1_loss( + _feat_out_post, _feat_tgt, reduction=reduction + ) + mse_loss = F.mse_loss(_feat_out, _feat_tgt, reduction=reduction) + F.mse_loss( + _feat_out_post, _feat_tgt, reduction=reduction + ) + eos_loss = F.binary_cross_entropy_with_logits( + _eos_out, + _eos_tgt, + pos_weight=torch.tensor(self.bce_pos_weight), + reduction=reduction, + ) + return l1_loss, mse_loss, eos_loss + + @classmethod + def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None: + ns = [log.get("sample_size", 0) for log in logging_outputs] + ntot = sum(ns) + ws = [n / (ntot + 1e-8) for n in ns] + for key in ["loss", "l1_loss", "mse_loss", "eos_loss", "attn_loss", "ctc_loss"]: + vals = [log.get(key, 0) for log in logging_outputs] + val = sum(val * w for val, w in zip(vals, ws)) + metrics.log_scalar(key, val, ntot, round=3) + metrics.log_scalar("sample_size", ntot, len(logging_outputs)) + + # inference metrics + if "targ_frames" not in logging_outputs[0]: + return + n = sum(log.get("targ_frames", 0) for log in logging_outputs) + for key, new_key in [ + ("mcd_loss", "mcd_loss"), + ("pred_frames", "pred_ratio"), + ("nins", "ins_rate"), + ("ndel", "del_rate"), + ]: + val = sum(log.get(key, 0) for log in logging_outputs) + metrics.log_scalar(new_key, val / n, n, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + return False diff --git a/fairseq/fairseq/criterions/wav2vec_criterion.py b/fairseq/fairseq/criterions/wav2vec_criterion.py new file mode 100644 index 0000000..3975468 --- /dev/null +++ b/fairseq/fairseq/criterions/wav2vec_criterion.py @@ -0,0 +1,231 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import List, Optional + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.logging.meters import safe_round +from fairseq.utils import is_xla_tensor + + +@dataclass +class Wav2VecCriterionConfig(FairseqDataclass): + infonce: bool = field( + default=False, + metadata={ + "help": "if set, uses cross entropy instead of binary cross entropy (i.e. InfoNCE loss)" + }, + ) + loss_weights: Optional[List[float]] = field( + default=None, + metadata={"help": "weights for additional loss terms (not first one)"}, + ) + log_keys: List[str] = field( + default_factory=lambda: [], + metadata={"help": "output keys to log"}, + ) + + +@register_criterion("wav2vec", dataclass=Wav2VecCriterionConfig) +class Wav2vecCriterion(FairseqCriterion): + def __init__(self, task, infonce=False, loss_weights=None, log_keys=None): + super().__init__(task) + self.infonce = infonce + self.loss_weights = loss_weights + self.log_keys = [] if log_keys is None else log_keys + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + logits = model.get_logits(net_output).float() + target = model.get_targets(sample, net_output) + self.xla = is_xla_tensor(logits) + + # XXX: handle weights on xla. + weights = None + if hasattr(model, "get_target_weights") and not self.infonce: + weights = model.get_target_weights(target, net_output) + if torch.is_tensor(weights): + weights = weights.float() + + losses = [] + + reduction = "none" if ((not reduce) or self.xla) else "sum" + if self.infonce: + loss = F.cross_entropy(logits, target, reduction=reduction) + else: + loss = F.binary_cross_entropy_with_logits( + logits, target.float(), weights, reduction=reduction + ) + + if self.xla: + # tpu-comment: since dynamic shapes lead to recompilations on xla, + # we don't shrink tensors using mask_indices. + # Instead, we use mask indices to adjust loss. + mi = ( + sample["net_input"]["mask_indices"] + .transpose(0, 1) # logits are transposed in `model.get_logits` + .reshape(logits.size(0)) + ) + loss = (loss * mi).sum() if reduce else (loss * mi) + + if "sample_size" in sample: + sample_size = sample["sample_size"] + elif "mask_indices" in sample["net_input"]: + sample_size = sample["net_input"]["mask_indices"].sum() + else: + sample_size = target.numel() if self.infonce else target.long().sum().item() + losses.append(loss.detach().clone()) + + if self.loss_weights is not None: + assert hasattr(model, "get_extra_losses") + extra_losses = model.get_extra_losses(net_output) + if torch.is_tensor(extra_losses): + extra_losses = [extra_losses] + if len(self.loss_weights) == 1 and len(extra_losses) != 1: + self.loss_weights = [self.loss_weights[0]] * len(extra_losses) + assert len(extra_losses) == len( + self.loss_weights + ), f"{len(extra_losses)}, {len(self.loss_weights)}" + for p, coef in zip(extra_losses, self.loss_weights): + if coef != 0 and p is not None: + p = coef * p.float() * sample_size + loss += p + losses.append(p) + + logging_output = { + "loss": loss.item() if (reduce and not self.xla) else loss.detach(), + "ntokens": sample_size, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + } + + for lk in self.log_keys: + # Only store "logits" and "target" for computing MAP and MAUC + # during validation + if lk == "logits": + if not self.training: + logging_output["logits"] = logits.cpu().numpy() + elif lk == "target": + if not self.training: + # If the targets have been mixed with the predictions of + # teacher models, find the original targets + if hasattr(model, "get_original_targets"): + original_target = model.get_original_targets(sample, net_output) + else: + original_target = target + logging_output["target"] = original_target.cpu().numpy() + elif lk in net_output: + value = net_output[lk] + if not is_xla_tensor(value): + value = float(value) + logging_output[lk] = value + + if len(losses) > 1: + for i, l in enumerate(losses): + logging_output[f"loss_{i}"] = l.item() if not self.xla else l.detach() + + if self.infonce: + with torch.no_grad(): + if logits.numel() == 0: + corr = 0 + count = 0 + else: + assert logits.dim() > 1, logits.shape + max = logits.argmax(-1) == 0 + min = logits.argmin(-1) == 0 + if is_xla_tensor(logits): + max, min = max * mi, min * mi + both = max & min + corr = max.long().sum() - both.long().sum() + count = mi.sum() + else: + both = max & min + corr = max.long().sum().item() - both.long().sum().item() + count = float(max.numel()) + + logging_output["correct"] = corr + logging_output["count"] = count + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + nsentences = utils.item( + sum(log.get("nsentences", 0) for log in logging_outputs) + ) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / (sample_size or 1) / math.log(2), sample_size, round=3 + ) + metrics.log_scalar("ntokens", ntokens) + metrics.log_scalar("nsentences", nsentences) + + correct = sum(log.get("correct", 0) for log in logging_outputs) + metrics.log_scalar("_correct", correct) + + total = sum(log.get("count", 0) for log in logging_outputs) + metrics.log_scalar("_total", total) + + if total > 0: + metrics.log_derived( + "accuracy", + lambda meters: safe_round( + meters["_correct"].sum / meters["_total"].sum, 5 + ) + if meters["_total"].sum > 0 + else float("nan"), + ) + + builtin_keys = { + "loss", + "ntokens", + "nsentences", + "sample_size", + "correct", + "count", + } + + for k in logging_outputs[0]: + if k not in builtin_keys: + val = sum(log.get(k, 0) for log in logging_outputs) + if k.startswith("loss"): + metrics.log_scalar( + k, val / (sample_size or 1) / math.log(2), sample_size, round=3 + ) + else: + metrics.log_scalar(k, val / len(logging_outputs), round=3) + + # FIXME: revert when gather based xla reduction is implemented + # @staticmethod + # def logging_outputs_can_be_summed() -> bool: + def logging_outputs_can_be_summed(self) -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + # XXX: Gather based reduction not implemented for xla yet. + # So we fall to sum based reduction for xla. + return self.xla diff --git a/fairseq/fairseq/dataclass/__init__.py b/fairseq/fairseq/dataclass/__init__.py new file mode 100644 index 0000000..25408d2 --- /dev/null +++ b/fairseq/fairseq/dataclass/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .configs import FairseqDataclass +from .constants import ChoiceEnum + + +__all__ = [ + "FairseqDataclass", + "ChoiceEnum", +] diff --git a/fairseq/fairseq/dataclass/configs.py b/fairseq/fairseq/dataclass/configs.py new file mode 100644 index 0000000..af957fe --- /dev/null +++ b/fairseq/fairseq/dataclass/configs.py @@ -0,0 +1,1147 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import sys +from dataclasses import _MISSING_TYPE, dataclass, field +from typing import Any, List, Optional + +import torch +from omegaconf import II, MISSING + +from fairseq.dataclass.constants import ( + DATASET_IMPL_CHOICES, + DDP_BACKEND_CHOICES, + DDP_COMM_HOOK_CHOICES, + GENERATION_CONSTRAINTS_CHOICES, + GENERATION_DECODING_FORMAT_CHOICES, + LOG_FORMAT_CHOICES, + PIPELINE_CHECKPOINT_CHOICES, + PRINT_ALIGNMENT_CHOICES, + ZERO_SHARDING_CHOICES, +) + + +@dataclass +class FairseqDataclass: + """fairseq base dataclass that supported fetching attributes and metas""" + + _name: Optional[str] = None + + @staticmethod + def name(): + return None + + def _get_all_attributes(self) -> List[str]: + return [k for k in self.__dataclass_fields__.keys()] + + def _get_meta( + self, attribute_name: str, meta: str, default: Optional[Any] = None + ) -> Any: + return self.__dataclass_fields__[attribute_name].metadata.get(meta, default) + + def _get_name(self, attribute_name: str) -> str: + return self.__dataclass_fields__[attribute_name].name + + def _get_default(self, attribute_name: str) -> Any: + if hasattr(self, attribute_name): + if str(getattr(self, attribute_name)).startswith("${"): + return str(getattr(self, attribute_name)) + elif str(self.__dataclass_fields__[attribute_name].default).startswith( + "${" + ): + return str(self.__dataclass_fields__[attribute_name].default) + elif ( + getattr(self, attribute_name) + != self.__dataclass_fields__[attribute_name].default + ): + return getattr(self, attribute_name) + + f = self.__dataclass_fields__[attribute_name] + if not isinstance(f.default_factory, _MISSING_TYPE): + return f.default_factory() + return f.default + + def _get_type(self, attribute_name: str) -> Any: + return self.__dataclass_fields__[attribute_name].type + + def _get_help(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "help") + + def _get_argparse_const(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "argparse_const") + + def _get_argparse_alias(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "argparse_alias") + + def _get_choices(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "choices") + + @classmethod + def from_namespace(cls, args): + if isinstance(args, cls): + return args + else: + config = cls() + for k in config.__dataclass_fields__.keys(): + if k.startswith("_"): + # private member, skip + continue + if hasattr(args, k): + setattr(config, k, getattr(args, k)) + + return config + + +@dataclass +class CommonConfig(FairseqDataclass): + # This is the core dataclass including common parameters shared by all different jobs. Please append your params to other dataclasses if they were + # used for a particular purpose or task, such as those dedicated for `distributed training`, `optimization`, etc. + no_progress_bar: bool = field( + default=False, metadata={"help": "disable progress bar"} + ) + log_interval: int = field( + default=100, + metadata={ + "help": "log progress every N batches (when progress bar is disabled)" + }, + ) + log_format: Optional[LOG_FORMAT_CHOICES] = field( + default=None, metadata={"help": "log format to use"} + ) + log_file: Optional[str] = field( + default=None, metadata={"help": "log file to copy metrics to."} + ) + aim_repo: Optional[str] = field( + default=None, + metadata={"help": "path to Aim repository"}, + ) + aim_run_hash: Optional[str] = field( + default=None, + metadata={ + "help": "Aim run hash. If skipped, creates or continues run " + "based on save_dir" + }, + ) + tensorboard_logdir: Optional[str] = field( + default=None, + metadata={ + "help": "path to save logs for tensorboard, should match --logdir " + "of running tensorboard (default: no tensorboard logging)" + }, + ) + wandb_project: Optional[str] = field( + default=None, + metadata={"help": "Weights and Biases project name to use for logging"}, + ) + azureml_logging: Optional[bool] = field( + default=False, + metadata={"help": "Log scalars to AzureML context"}, + ) + seed: int = field( + default=1, metadata={"help": "pseudo random number generator seed"} + ) + cpu: bool = field(default=False, metadata={"help": "use CPU instead of CUDA"}) + tpu: bool = field(default=False, metadata={"help": "use TPU instead of CUDA"}) + bf16: bool = field(default=False, metadata={"help": "use bfloat16; implies --tpu"}) + memory_efficient_bf16: bool = field( + default=False, + metadata={ + "help": "use a memory-efficient version of BF16 training; implies --bf16" + }, + ) + fp16: bool = field(default=False, metadata={"help": "use FP16"}) + memory_efficient_fp16: bool = field( + default=False, + metadata={ + "help": "use a memory-efficient version of FP16 training; implies --fp16" + }, + ) + fp16_no_flatten_grads: bool = field( + default=False, metadata={"help": "don't flatten FP16 grads tensor"} + ) + fp16_init_scale: int = field( + default=2**7, metadata={"help": "default FP16 loss scale"} + ) + fp16_scale_window: Optional[int] = field( + default=None, + metadata={"help": "number of updates before increasing loss scale"}, + ) + fp16_scale_tolerance: float = field( + default=0.0, + metadata={ + "help": "pct of updates that can overflow before decreasing the loss scale" + }, + ) + on_cpu_convert_precision: bool = field( + default=False, + metadata={ + "help": "if set, the floating point conversion to fp16/bf16 runs on CPU. " + "This reduces bus transfer time and GPU memory usage." + }, + ) + min_loss_scale: float = field( + default=1e-4, + metadata={ + "help": "minimum FP16/AMP loss scale, after which training is stopped" + }, + ) + threshold_loss_scale: Optional[float] = field( + default=None, metadata={"help": "threshold FP16 loss scale from below"} + ) + amp: bool = field(default=False, metadata={"help": "use automatic mixed precision"}) + amp_batch_retries: int = field( + default=2, + metadata={ + "help": "number of retries of same batch after reducing loss scale with AMP" + }, + ) + amp_init_scale: int = field( + default=2**7, metadata={"help": "default AMP loss scale"} + ) + amp_scale_window: Optional[int] = field( + default=None, + metadata={"help": "number of updates before increasing AMP loss scale"}, + ) + user_dir: Optional[str] = field( + default=None, + metadata={ + "help": "path to a python module containing custom extensions (tasks and/or architectures)" + }, + ) + empty_cache_freq: int = field( + default=0, + metadata={"help": "how often to clear the PyTorch CUDA cache (0 to disable)"}, + ) + all_gather_list_size: int = field( + default=16384, + metadata={"help": "number of bytes reserved for gathering stats from workers"}, + ) + model_parallel_size: int = field( + default=1, metadata={"help": "total number of GPUs to parallelize model over"} + ) + quantization_config_path: Optional[str] = field( + default=None, metadata={"help": "path to quantization config file"} + ) + profile: bool = field( + default=False, metadata={"help": "enable autograd profiler emit_nvtx"} + ) + reset_logging: bool = field( + default=False, + metadata={ + "help": "when using Hydra, reset the logging at the beginning of training" + }, + ) + suppress_crashes: bool = field( + default=False, + metadata={ + "help": "suppress crashes when training with the hydra_train entry point so that the " + "main method can return a value (useful for sweeps)" + }, + ) + use_plasma_view: bool = field( + default=False, metadata={"help": "Store indices and sizes in shared memory"} + ) + plasma_path: Optional[str] = field( + default="/tmp/plasma", + metadata={ + "help": "path to run plasma_store, defaults to /tmp/plasma. Paths outside /tmp tend to fail." + }, + ) + + +@dataclass +class DistributedTrainingConfig(FairseqDataclass): + distributed_world_size: int = field( + default=max(1, torch.cuda.device_count()), + metadata={ + "help": "total number of GPUs across all nodes (default: all visible GPUs)" + }, + ) + distributed_num_procs: Optional[int] = field( + default=max(1, torch.cuda.device_count()), + metadata={ + "help": "total number of processes to fork (default: all visible GPUs)" + }, + ) + distributed_rank: Optional[int] = field( + default=0, metadata={"help": "rank of the current worker"} + ) + distributed_backend: str = field( + default="nccl", metadata={"help": "distributed backend"} + ) + distributed_init_method: Optional[str] = field( + default=None, + metadata={ + "help": "typically tcp://hostname:port that will be used to " + "establish initial connetion" + }, + ) + distributed_port: int = field( + default=-1, + metadata={ + "help": "port number (not required if using --distributed-init-method)" + }, + ) + device_id: int = field( + default=os.getenv("LOCAL_RANK", 0), + metadata={ + "help": "which GPU to use (by default looks for $LOCAL_RANK, usually configured automatically)", + "argparse_alias": "--local_rank", + }, + ) + distributed_no_spawn: bool = field( + default=False, + metadata={ + "help": "do not spawn multiple processes even if multiple GPUs are visible" + }, + ) + ddp_backend: DDP_BACKEND_CHOICES = field( + default="pytorch_ddp", metadata={"help": "DistributedDataParallel backend"} + ) + ddp_comm_hook: DDP_COMM_HOOK_CHOICES = field( + default="none", metadata={"help": "communication hook"} + ) + bucket_cap_mb: int = field( + default=25, metadata={"help": "bucket size for reduction"} + ) + fix_batches_to_gpus: bool = field( + default=False, + metadata={ + "help": "don't shuffle batches between GPUs; this reduces overall " + "randomness and may affect precision but avoids the cost of re-reading the data" + }, + ) + find_unused_parameters: bool = field( + default=False, + metadata={ + "help": "disable unused parameter detection (not applicable to " + "--ddp-backend=legacy_ddp)" + }, + ) + gradient_as_bucket_view: bool = field( + default=False, + metadata={ + "help": "when set to True, gradients will be views pointing to different offsets of allreduce communication buckets. This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. " + "--gradient-as-bucket-view=gradient_as_bucket_view)" + }, + ) + fast_stat_sync: bool = field( + default=False, + metadata={"help": "[deprecated] this is now defined per Criterion"}, + ) + heartbeat_timeout: int = field( + default=-1, + metadata={ + "help": "kill the job if no progress is made in N seconds; " + "set to -1 to disable" + }, + ) + broadcast_buffers: bool = field( + default=False, + metadata={ + "help": "Copy non-trainable parameters between GPUs, such as " + "batchnorm population statistics" + }, + ) + slowmo_momentum: Optional[float] = field( + default=None, + metadata={ + "help": "SlowMo momentum term; by default use 0.0 for 16 GPUs, " + "0.2 for 32 GPUs; 0.5 for 64 GPUs, 0.6 for > 64 GPUs" + }, + ) + slowmo_base_algorithm: str = field( + default="localsgd", + metadata={ + "help": "Base algorithm. Either 'localsgd' or 'sgp'. Please refer " + "to the documentation of 'slowmo_base_algorithm' parameter in " + "https://fairscale.readthedocs.io/en/latest/api/experimental/nn/slowmo_ddp.html " + "for more details" + }, + ) + localsgd_frequency: int = field( + default=3, metadata={"help": "Local SGD allreduce frequency"} + ) + nprocs_per_node: int = field( + default=max(1, torch.cuda.device_count()), + metadata={ + "help": "number of GPUs in each node. An allreduce operation across GPUs in " + "a node is very fast. Hence, we do allreduce across GPUs in a node, " + "and gossip across different nodes" + }, + ) + pipeline_model_parallel: bool = field( + default=False, + metadata={"help": "if set, use pipeline model parallelism across GPUs"}, + ) + pipeline_balance: Optional[str] = field( + default=None, + metadata={ + "help": "partition the model into N_K pieces, where each piece " + "contains N_i layers. The sum(args.pipeline_balance) " + "should equal the total number of layers in the model" + }, + ) + pipeline_devices: Optional[str] = field( + default=None, + metadata={ + "help": "a list of device indices indicating which device to place " + "each of the N_K partitions. The length of this list should " + "equal the length of the --pipeline-balance argument" + }, + ) + pipeline_chunks: Optional[int] = field( + default=0, metadata={"help": "microbatch count for pipeline model parallelism"} + ) + pipeline_encoder_balance: Optional[str] = field( + default=None, + metadata={ + "help": "partition the pipeline parallel encoder into N_K pieces, where each piece " + "contains N_i layers. The sum(args.pipeline_encoder_balance) " + "should equal the total number of encoder layers in the model" + }, + ) + pipeline_encoder_devices: Optional[str] = field( + default=None, + metadata={ + "help": "a list of device indices indicating which device to place " + "each of the N_K partitions. The length of this list should " + "equal the length of the --pipeline-encoder-balance argument" + }, + ) + pipeline_decoder_balance: Optional[str] = field( + default=None, + metadata={ + "help": "partition the pipeline parallel decoder into N_K pieces, where each piece " + "contains N_i layers. The sum(args.pipeline_decoder_balance) " + "should equal the total number of decoder layers in the model" + }, + ) + pipeline_decoder_devices: Optional[str] = field( + default=None, + metadata={ + "help": "a list of device indices indicating which device to place " + "each of the N_K partitions. The length of this list should " + "equal the length of the --pipeline-decoder-balance argument" + }, + ) + pipeline_checkpoint: PIPELINE_CHECKPOINT_CHOICES = field( + default="never", + metadata={"help": "checkpointing mode for pipeline model parallelism"}, + ) + zero_sharding: ZERO_SHARDING_CHOICES = field( + default="none", metadata={"help": "ZeRO sharding"} + ) + fp16: bool = II("common.fp16") + memory_efficient_fp16: bool = II("common.memory_efficient_fp16") + tpu: bool = II("common.tpu") + # configuration for --ddp-backend=fully_sharded + no_reshard_after_forward: bool = field( + default=False, + metadata={"help": "don't reshard parameters after forward pass"}, + ) + fp32_reduce_scatter: bool = field( + default=False, + metadata={"help": "reduce-scatter grads in FP32"}, + ) + cpu_offload: bool = field( + default=False, metadata={"help": "offload FP32 params to CPU"} + ) + use_sharded_state: bool = field( + default=False, + metadata={"help": "use sharded checkpoint files"}, + ) + not_fsdp_flatten_parameters: bool = field( + default=False, + metadata={"help": "not flatten parameter param for fsdp"}, + ) + + +@dataclass +class DatasetConfig(FairseqDataclass): + num_workers: int = field( + default=1, metadata={"help": "how many subprocesses to use for data loading"} + ) + skip_invalid_size_inputs_valid_test: bool = field( + default=False, + metadata={"help": "ignore too long or too short lines in valid and test set"}, + ) + max_tokens: Optional[int] = field( + default=None, metadata={"help": "maximum number of tokens in a batch"} + ) + batch_size: Optional[int] = field( + default=None, + metadata={ + "help": "number of examples in a batch", + "argparse_alias": "--max-sentences", + }, + ) + required_batch_size_multiple: int = field( + default=8, metadata={"help": "batch size will be a multiplier of this value"} + ) + required_seq_len_multiple: int = field( + default=1, + metadata={ + "help": "maximum sequence length in batch will be a multiplier of this value" + }, + ) + dataset_impl: Optional[DATASET_IMPL_CHOICES] = field( + default=None, metadata={"help": "output dataset implementation"} + ) + data_buffer_size: int = field( + default=10, metadata={"help": "Number of batches to preload"} + ) + train_subset: str = field( + default="train", + metadata={"help": "data subset to use for training (e.g. train, valid, test)"}, + ) + valid_subset: str = field( + default="valid", + metadata={ + "help": "comma separated list of data subsets to use for validation" + " (e.g. train, valid, test)" + }, + ) + combine_valid_subsets: Optional[bool] = field( + default=None, + metadata={ + "help": "comma separated list of data subsets to use for validation" + " (e.g. train, valid, test)", + "argparse_alias": "--combine-val", + }, + ) + ignore_unused_valid_subsets: Optional[bool] = field( + default=False, + metadata={"help": "do not raise error if valid subsets are ignored"}, + ) + + validate_interval: int = field( + default=1, metadata={"help": "validate every N epochs"} + ) + validate_interval_updates: int = field( + default=0, metadata={"help": "validate every N updates"} + ) + validate_after_updates: int = field( + default=0, metadata={"help": "dont validate until reaching this many updates"} + ) + fixed_validation_seed: Optional[int] = field( + default=None, metadata={"help": "specified random seed for validation"} + ) + disable_validation: bool = field( + default=False, metadata={"help": "disable validation"} + ) + max_tokens_valid: Optional[int] = field( + default=II("dataset.max_tokens"), + metadata={ + "help": "maximum number of tokens in a validation batch" + " (defaults to --max-tokens)" + }, + ) + batch_size_valid: Optional[int] = field( + default=II("dataset.batch_size"), + metadata={ + "help": "batch size of the validation batch (defaults to --batch-size)", + "argparse_alias": "--max-sentences-valid", + }, + ) + max_valid_steps: Optional[int] = field( + default=None, + metadata={"help": "How many batches to evaluate", "argparse_alias": "--nval"}, + ) + curriculum: int = field( + default=0, metadata={"help": "don't shuffle batches for first N epochs"} + ) + gen_subset: str = field( + default="test", + metadata={"help": "data subset to generate (train, valid, test)"}, + ) + num_shards: int = field( + default=1, metadata={"help": "shard generation over N shards"} + ) + shard_id: int = field( + default=0, metadata={"help": "id of the shard to generate (id < num_shards)"} + ) + grouped_shuffling: bool = field( + default=False, + metadata={ + "help": "shuffle batches in groups of num_shards to enable similar sequence lengths on each GPU worker when batches are sorted by length", + }, + ) + update_epoch_batch_itr: bool = field( + default=II("dataset.grouped_shuffling"), + metadata={ + "help": "if true then prevents the reuse the epoch batch iterator by setting can_reuse_epoch_itr to false, defaults to --grouped-shuffling )", + }, + ) + update_ordered_indices_seed: bool = field( + default=False, + metadata={ + "help": "if true then increment seed with epoch for getting batch iterators, defautls to False.", + }, + ) + + +@dataclass +class OptimizationConfig(FairseqDataclass): + max_epoch: int = field( + default=0, metadata={"help": "force stop training at specified epoch"} + ) + max_update: int = field( + default=0, metadata={"help": "force stop training at specified update"} + ) + stop_time_hours: float = field( + default=0, + metadata={ + "help": "force stop training after specified cumulative time (if >0)" + }, + ) + clip_norm: float = field( + default=0.0, metadata={"help": "clip threshold of gradients"} + ) + sentence_avg: bool = field( + default=False, + metadata={ + "help": "normalize gradients by the number of sentences in a batch" + " (default is to normalize by number of tokens)" + }, + ) + update_freq: List[int] = field( + default_factory=lambda: [1], + metadata={"help": "update parameters every N_i batches, when in epoch i"}, + ) + lr: List[float] = field( + default_factory=lambda: [0.25], + metadata={ + "help": "learning rate for the first N epochs; all epochs >N using LR_N" + " (note: this may be interpreted differently depending on --lr-scheduler)" + }, + ) + stop_min_lr: float = field( + default=-1.0, + metadata={"help": "stop training when the learning rate reaches this minimum"}, + ) + use_bmuf: bool = field( + default=False, + metadata={ + "help": "specify global optimizer for syncing models on different GPUs/shards" + }, + ) + skip_remainder_batch: Optional[bool] = field( + default=False, + metadata={ + "help": "if set, include the last (partial) batch of each epoch in training" + " (default is to skip it)." + }, + ) + debug_param_names: bool = False + + +@dataclass +class CheckpointConfig(FairseqDataclass): + save_dir: str = field( + default="checkpoints", metadata={"help": "path to save checkpoints"} + ) + restore_file: str = field( + default="checkpoint_last.pt", + metadata={ + "help": "filename from which to load checkpoint " + "(default: <save-dir>/checkpoint_last.pt" + }, + ) + continue_once: Optional[str] = field( + default=None, + metadata={ + "help": "continues from this checkpoint, unless a checkpoint indicated in 'restore_file' option is present" + }, + ) + finetune_from_model: Optional[str] = field( + default=None, + metadata={ + "help": "finetune from a pretrained model; note that meters and lr scheduler will be reset" + }, + ) + reset_dataloader: bool = field( + default=False, + metadata={ + "help": "if set, does not reload dataloader state from the checkpoint" + }, + ) + reset_lr_scheduler: bool = field( + default=False, + metadata={ + "help": "if set, does not load lr scheduler state from the checkpoint" + }, + ) + reset_meters: bool = field( + default=False, + metadata={"help": "if set, does not load meters from the checkpoint"}, + ) + reset_optimizer: bool = field( + default=False, + metadata={"help": "if set, does not load optimizer state from the checkpoint"}, + ) + optimizer_overrides: str = field( + default="{}", + metadata={ + "help": "a dictionary used to override optimizer args when loading a checkpoint" + }, + ) + save_interval: int = field( + default=1, metadata={"help": "save a checkpoint every N epochs"} + ) + save_interval_updates: int = field( + default=0, metadata={"help": "save a checkpoint (and validate) every N updates"} + ) + keep_interval_updates: int = field( + default=-1, + metadata={ + "help": "keep the last N checkpoints saved with --save-interval-updates" + }, + ) + keep_interval_updates_pattern: int = field( + default=-1, + metadata={ + "help": "when used with --keep-interval-updates, skips deleting " + "any checkpoints with update X where " + "X %% keep_interval_updates_pattern == 0" + }, + ) + keep_last_epochs: int = field( + default=-1, metadata={"help": "keep last N epoch checkpoints"} + ) + keep_best_checkpoints: int = field( + default=-1, metadata={"help": "keep best N checkpoints based on scores"} + ) + no_save: bool = field( + default=False, metadata={"help": "don't save models or checkpoints"} + ) + no_epoch_checkpoints: bool = field( + default=False, metadata={"help": "only store last and best checkpoints"} + ) + no_last_checkpoints: bool = field( + default=False, metadata={"help": "don't store last checkpoints"} + ) + no_save_optimizer_state: bool = field( + default=False, + metadata={"help": "don't save optimizer-state as part of checkpoint"}, + ) + best_checkpoint_metric: str = field( + default="loss", metadata={"help": 'metric to use for saving "best" checkpoints'} + ) + maximize_best_checkpoint_metric: bool = field( + default=False, + metadata={ + "help": 'select the largest metric value for saving "best" checkpoints' + }, + ) + patience: int = field( + default=-1, + metadata={ + "help": ( + "early stop training if valid performance doesn't " + "improve for N consecutive validation runs; note " + "that this is influenced by --validate-interval" + ) + }, + ) + checkpoint_suffix: str = field( + default="", metadata={"help": "suffix to add to the checkpoint file name"} + ) + checkpoint_shard_count: int = field( + default=1, + metadata={ + "help": "Number of shards containing the checkpoint - " + "if the checkpoint is over 300GB, it is preferable " + "to split it into shards to prevent OOM on CPU while loading " + "the checkpoint" + }, + ) + load_checkpoint_on_all_dp_ranks: bool = field( + default=False, + metadata={ + "help": "load checkpoints on all data parallel devices " + "(default: only load on rank 0 and broadcast to other devices)" + }, + ) + write_checkpoints_asynchronously: bool = field( + default=False, + metadata={ + "help": ( + "Write checkpoints asynchronously in a separate " + "thread. NOTE: This feature is currently being tested." + ), + "argparse_alias": "--save-async", + }, + ) + model_parallel_size: int = II("common.model_parallel_size") + + +@dataclass +class FairseqBMUFConfig(FairseqDataclass): + block_lr: float = field( + default=1, metadata={"help": "block learning rate for bmuf"} + ) + block_momentum: float = field( + default=0.875, metadata={"help": "block momentum for bmuf"} + ) + global_sync_iter: int = field( + default=50, metadata={"help": "Iteration for syncing global model"} + ) + warmup_iterations: int = field( + default=500, metadata={"help": "warmup iterations for model to broadcast"} + ) + use_nbm: bool = field( + default=False, + metadata={"help": "Specify whether you want to use classical BM / Nesterov BM"}, + ) + average_sync: bool = field( + default=False, + metadata={ + "help": "Specify whether you want to average the local momentum after each sync" + }, + ) + distributed_world_size: int = II("distributed_training.distributed_world_size") + + +@dataclass +class GenerationConfig(FairseqDataclass): + beam: int = field( + default=5, + metadata={"help": "beam size"}, + ) + beam_mt: int = field( + default=0, + metadata={"help": "beam size for the first-pass decoder"}, + ) + nbest: int = field( + default=1, + metadata={"help": "number of hypotheses to output"}, + ) + max_len_a: float = field( + default=0, + metadata={ + "help": "generate sequences of maximum length ax + b, where x is the source length" + }, + ) + max_len_b: int = field( + default=200, + metadata={ + "help": "generate sequences of maximum length ax + b, where x is the source length" + }, + ) + max_len_a_mt: float = field( + default=0, + metadata={ + "help": "generate sequences of maximum length ax + b, where x is the source length for the first-pass decoder" + }, + ) + max_len_b_mt: int = field( + default=200, + metadata={ + "help": "generate sequences of maximum length ax + b, where x is the source length for the first-pass decoder" + }, + ) + min_len: int = field( + default=1, + metadata={"help": "minimum generation length"}, + ) + match_source_len: bool = field( + default=False, + metadata={"help": "generations should match the source length"}, + ) + unnormalized: bool = field( + default=False, + metadata={"help": "compare unnormalized hypothesis scores"}, + ) + no_early_stop: bool = field( + default=False, + metadata={"help": "deprecated"}, + ) + no_beamable_mm: bool = field( + default=False, + metadata={"help": "don't use BeamableMM in attention layers"}, + ) + lenpen: float = field( + default=1, + metadata={ + "help": "length penalty: <1.0 favors shorter, >1.0 favors longer sentences" + }, + ) + lenpen_mt: float = field( + default=1, + metadata={ + "help": "length penalty for the first-pass decoder: <1.0 favors shorter, >1.0 favors longer sentences" + }, + ) + unkpen: float = field( + default=0, + metadata={ + "help": "unknown word penalty: <0 produces more unks, >0 produces fewer" + }, + ) + replace_unk: Optional[str] = field( + default=None, + metadata={ + "help": "perform unknown replacement (optionally with alignment dictionary)", + "argparse_const": "@@ ", + }, + ) + sacrebleu: bool = field( + default=False, + metadata={"help": "score with sacrebleu"}, + ) + score_reference: bool = field( + default=False, + metadata={"help": "just score the reference translation"}, + ) + prefix_size: int = field( + default=0, + metadata={"help": "initialize generation by target prefix of given length"}, + ) + no_repeat_ngram_size: int = field( + default=0, + metadata={ + "help": "ngram blocking such that this size ngram cannot be repeated in the generation" + }, + ) + sampling: bool = field( + default=False, + metadata={"help": "sample hypotheses instead of using beam search"}, + ) + sampling_topk: int = field( + default=-1, + metadata={"help": "sample from top K likely next words instead of all words"}, + ) + sampling_topp: float = field( + default=-1.0, + metadata={ + "help": "sample from the smallest set whose cumulative probability mass exceeds p for next words" + }, + ) + constraints: Optional[GENERATION_CONSTRAINTS_CHOICES] = field( + default=None, + metadata={ + "help": "enables lexically constrained decoding", + "argparse_const": "ordered", + }, + ) + temperature: float = field( + default=1.0, + metadata={"help": "temperature for generation"}, + ) + diverse_beam_groups: int = field( + default=-1, + metadata={"help": "number of groups for Diverse Beam Search"}, + ) + diverse_beam_strength: float = field( + default=0.5, + metadata={"help": "strength of diversity penalty for Diverse Beam Search"}, + ) + diversity_rate: float = field( + default=-1.0, + metadata={"help": "strength of diversity penalty for Diverse Siblings Search"}, + ) + print_alignment: Optional[PRINT_ALIGNMENT_CHOICES] = field( + default=None, + metadata={ + "help": "if set, uses attention feedback to compute and print alignment to source tokens " + "(valid options are: hard, soft, otherwise treated as hard alignment)", + "argparse_const": "hard", + }, + ) + print_step: bool = field( + default=False, + metadata={"help": "print steps"}, + ) + lm_path: Optional[str] = field( + default=None, + metadata={"help": "path to lm checkpoint for lm fusion"}, + ) + lm_weight: float = field( + default=0.0, + metadata={"help": "weight for lm probs for lm fusion"}, + ) + + # arguments for iterative refinement generator + iter_decode_eos_penalty: float = field( + default=0.0, + metadata={"help": "if > 0.0, it penalized early-stopping in decoding."}, + ) + iter_decode_max_iter: int = field( + default=10, + metadata={"help": "maximum iterations for iterative refinement."}, + ) + iter_decode_force_max_iter: bool = field( + default=False, + metadata={ + "help": "if set, run exact the maximum number of iterations without early stop" + }, + ) + iter_decode_with_beam: int = field( + default=1, + metadata={ + "help": "if > 1, model will generate translations varying by the lengths." + }, + ) + iter_decode_with_external_reranker: bool = field( + default=False, + metadata={ + "help": "if set, the last checkpoint are assumed to be a reranker to rescore the translations" + }, + ) + retain_iter_history: bool = field( + default=False, + metadata={ + "help": "if set, decoding returns the whole history of iterative refinement" + }, + ) + retain_dropout: bool = field( + default=False, + metadata={"help": "Use dropout at inference time"}, + ) + # temporarily set to Any until https://github.com/facebookresearch/hydra/issues/1117 is fixed + # retain_dropout_modules: Optional[List[str]] = field( + retain_dropout_modules: Any = field( + default=None, + metadata={ + "help": "if set, only retain dropout for the specified modules; " + "if not set, then dropout will be retained for all modules" + }, + ) + # special decoding format for advanced decoding. + decoding_format: Optional[GENERATION_DECODING_FORMAT_CHOICES] = field( + default=None, + metadata={"help": "special decoding format for advanced decoding."}, + ) + no_seed_provided: bool = field( + default=False, + metadata={"help": "if set, dont use seed for initializing random generators"}, + ) + eos_token: Optional[str] = field( + default=None, + metadata={"help": "EOS token"}, + ) + + +@dataclass +class CommonEvalConfig(FairseqDataclass): + path: Optional[str] = field( + default=None, + metadata={"help": "path(s) to model file(s), colon separated"}, + ) + post_process: Optional[str] = field( + default=None, + metadata={ + "help": ( + "post-process text by removing BPE, letter segmentation, etc. " + "Valid options can be found in fairseq.data.utils.post_process." + ), + "argparse_const": "subword_nmt", + "argparse_alias": "--remove-bpe", + }, + ) + quiet: bool = field(default=False, metadata={"help": "only print final scores"}) + model_overrides: str = field( + default="{}", + metadata={ + "help": "a dictionary used to override model args at generation that were used during model training" + }, + ) + results_path: Optional[str] = field( + default=None, metadata={"help": "path to save eval results (optional)"} + ) + + +@dataclass +class EvalLMConfig(FairseqDataclass): + output_word_probs: bool = field( + default=False, + metadata={ + "help": "if set, outputs words and their predicted log probabilities to standard output" + }, + ) + output_word_stats: bool = field( + default=False, + metadata={ + "help": "if set, outputs word statistics such as word count, average probability, etc" + }, + ) + context_window: int = field( + default=0, + metadata={ + "help": "ensures that every evaluated token has access to a context of at least this size, if possible" + }, + ) + softmax_batch: int = field( + default=sys.maxsize, + metadata={ + "help": "if BxT is more than this, will batch the softmax over vocab to this amount of tokens, in order to fit into GPU memory" + }, + ) + + +@dataclass +class InteractiveConfig(FairseqDataclass): + buffer_size: int = field( + default=0, + metadata={ + "help": "read this many sentences into a buffer before processing them" + }, + ) + input: str = field( + default="-", + metadata={"help": "file to read from; use - for stdin"}, + ) + + +@dataclass +class EMAConfig(FairseqDataclass): + store_ema: bool = field( + default=False, metadata={help: "store exponential moving average shadow model"} + ) + ema_decay: float = field( + default=0.9999, metadata={"help": "decay for exponential moving average model"} + ) + ema_start_update: int = field( + default=0, metadata={"help": "start EMA update after this many model updates"} + ) + ema_seed_model: Optional[str] = field( + default=None, + metadata={ + "help": "Seed to load EMA model from. " + "Used to load EMA model separately from the actual model." + }, + ) + ema_update_freq: int = field( + default=1, metadata={"help": "Do EMA update every this many model updates"} + ) + ema_fp32: bool = field( + default=False, + metadata={"help": "If true, store EMA model in fp32 even if model is in fp16"}, + ) + + +@dataclass +class FairseqConfig(FairseqDataclass): + common: CommonConfig = CommonConfig() + common_eval: CommonEvalConfig = CommonEvalConfig() + distributed_training: DistributedTrainingConfig = DistributedTrainingConfig() + dataset: DatasetConfig = DatasetConfig() + optimization: OptimizationConfig = OptimizationConfig() + checkpoint: CheckpointConfig = CheckpointConfig() + bmuf: FairseqBMUFConfig = FairseqBMUFConfig() + generation: GenerationConfig = GenerationConfig() + eval_lm: EvalLMConfig = EvalLMConfig() + interactive: InteractiveConfig = InteractiveConfig() + model: Any = MISSING + task: Any = None + criterion: Any = None + optimizer: Any = None + lr_scheduler: Any = None + scoring: Any = None + bpe: Any = None + tokenizer: Any = None + ema: EMAConfig = EMAConfig() diff --git a/fairseq/fairseq/dataclass/constants.py b/fairseq/fairseq/dataclass/constants.py new file mode 100644 index 0000000..5af92f2 --- /dev/null +++ b/fairseq/fairseq/dataclass/constants.py @@ -0,0 +1,56 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from enum import Enum, EnumMeta +from typing import List + + +class StrEnumMeta(EnumMeta): + # this is workaround for submitit pickling leading to instance checks failing in hydra for StrEnum, see + # https://github.com/facebookresearch/hydra/issues/1156 + @classmethod + def __instancecheck__(cls, other): + return "enum" in str(type(other)) + + +class StrEnum(Enum, metaclass=StrEnumMeta): + def __str__(self): + return self.value + + def __eq__(self, other: str): + return self.value == other + + def __repr__(self): + return self.value + + def __hash__(self): + return hash(str(self)) + + +def ChoiceEnum(choices: List[str]): + """return the Enum class used to enforce list of choices""" + return StrEnum("Choices", {k: k for k in choices}) + + +LOG_FORMAT_CHOICES = ChoiceEnum(["json", "none", "simple", "tqdm"]) +DDP_BACKEND_CHOICES = ChoiceEnum( + [ + "c10d", # alias for pytorch_ddp + "fully_sharded", # FullyShardedDataParallel from fairscale + "legacy_ddp", + "no_c10d", # alias for legacy_ddp + "pytorch_ddp", + "slowmo", + ] +) +DDP_COMM_HOOK_CHOICES = ChoiceEnum(["none", "fp16"]) +DATASET_IMPL_CHOICES = ChoiceEnum(["raw", "lazy", "cached", "mmap", "fasta", "huffman"]) +GENERATION_CONSTRAINTS_CHOICES = ChoiceEnum(["ordered", "unordered"]) +GENERATION_DECODING_FORMAT_CHOICES = ChoiceEnum( + ["unigram", "ensemble", "vote", "dp", "bs"] +) +ZERO_SHARDING_CHOICES = ChoiceEnum(["none", "os"]) +PIPELINE_CHECKPOINT_CHOICES = ChoiceEnum(["always", "never", "except_last"]) +PRINT_ALIGNMENT_CHOICES = ChoiceEnum(["hard", "soft"]) diff --git a/fairseq/fairseq/dataclass/initialize.py b/fairseq/fairseq/dataclass/initialize.py new file mode 100644 index 0000000..5a7784b --- /dev/null +++ b/fairseq/fairseq/dataclass/initialize.py @@ -0,0 +1,61 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import logging +from hydra.core.config_store import ConfigStore +from fairseq.dataclass.configs import FairseqConfig +from omegaconf import DictConfig, OmegaConf + + +logger = logging.getLogger(__name__) + + +def hydra_init(cfg_name="config") -> None: + + cs = ConfigStore.instance() + cs.store(name=f"{cfg_name}", node=FairseqConfig) + + for k in FairseqConfig.__dataclass_fields__: + v = FairseqConfig.__dataclass_fields__[k].default + try: + cs.store(name=k, node=v) + except BaseException: + logger.error(f"{k} - {v}") + raise + + +def add_defaults(cfg: DictConfig) -> None: + """This function adds default values that are stored in dataclasses that hydra doesn't know about""" + + from fairseq.registry import REGISTRIES + from fairseq.tasks import TASK_DATACLASS_REGISTRY + from fairseq.models import ARCH_MODEL_NAME_REGISTRY, MODEL_DATACLASS_REGISTRY + from fairseq.dataclass.utils import merge_with_parent + from typing import Any + + OmegaConf.set_struct(cfg, False) + + for k, v in FairseqConfig.__dataclass_fields__.items(): + field_cfg = cfg.get(k) + if field_cfg is not None and v.type == Any: + dc = None + + if isinstance(field_cfg, str): + field_cfg = DictConfig({"_name": field_cfg}) + field_cfg.__dict__["_parent"] = field_cfg.__dict__["_parent"] + + name = getattr(field_cfg, "_name", None) + + if k == "task": + dc = TASK_DATACLASS_REGISTRY.get(name) + elif k == "model": + name = ARCH_MODEL_NAME_REGISTRY.get(name, name) + dc = MODEL_DATACLASS_REGISTRY.get(name) + elif k in REGISTRIES: + dc = REGISTRIES[k]["dataclass_registry"].get(name) + + if dc is not None: + cfg[k] = merge_with_parent(dc, field_cfg) diff --git a/fairseq/fairseq/dataclass/utils.py b/fairseq/fairseq/dataclass/utils.py new file mode 100644 index 0000000..f6467d5 --- /dev/null +++ b/fairseq/fairseq/dataclass/utils.py @@ -0,0 +1,510 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import inspect +import logging +import os +import re +from argparse import ArgumentError, ArgumentParser, Namespace +from dataclasses import _MISSING_TYPE, MISSING, is_dataclass +from enum import Enum +from typing import Any, Dict, List, Optional, Tuple, Type + +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.configs import FairseqConfig +from hydra.core.global_hydra import GlobalHydra +from hydra.experimental import compose, initialize +from omegaconf import DictConfig, OmegaConf, open_dict, _utils + +logger = logging.getLogger(__name__) + + +def eval_str_list(x, x_type=float): + if x is None: + return None + if isinstance(x, str): + if len(x) == 0: + return [] + x = ast.literal_eval(x) + try: + return list(map(x_type, x)) + except TypeError: + return [x_type(x)] + + +def interpret_dc_type(field_type): + if isinstance(field_type, str): + raise RuntimeError("field should be a type") + + if field_type == Any: + return str + + typestring = str(field_type) + if re.match( + r"(typing.|^)Union\[(.*), NoneType\]$", typestring + ) or typestring.startswith("typing.Optional"): + return field_type.__args__[0] + return field_type + + +def gen_parser_from_dataclass( + parser: ArgumentParser, + dataclass_instance: FairseqDataclass, + delete_default: bool = False, + with_prefix: Optional[str] = None, +) -> None: + """ + convert a dataclass instance to tailing parser arguments. + + If `with_prefix` is provided, prefix all the keys in the resulting parser with it. It means that we are + building a flat namespace from a structured dataclass (see transformer_config.py for example). + """ + + def argparse_name(name: str): + if name == "data" and (with_prefix is None or with_prefix == ""): + # normally data is positional args, so we don't add the -- nor the prefix + return name + if name == "_name": + # private member, skip + return None + full_name = "--" + name.replace("_", "-") + if with_prefix is not None and with_prefix != "": + # if a prefix is specified, construct the prefixed arg name + full_name = with_prefix + "-" + full_name[2:] # strip -- when composing + return full_name + + def get_kwargs_from_dc( + dataclass_instance: FairseqDataclass, k: str + ) -> Dict[str, Any]: + """k: dataclass attributes""" + + kwargs = {} + + field_type = dataclass_instance._get_type(k) + inter_type = interpret_dc_type(field_type) + + field_default = dataclass_instance._get_default(k) + + if isinstance(inter_type, type) and issubclass(inter_type, Enum): + field_choices = [t.value for t in list(inter_type)] + else: + field_choices = None + + field_help = dataclass_instance._get_help(k) + field_const = dataclass_instance._get_argparse_const(k) + + if isinstance(field_default, str) and field_default.startswith("${"): + kwargs["default"] = field_default + else: + if field_default is MISSING: + kwargs["required"] = True + if field_choices is not None: + kwargs["choices"] = field_choices + if ( + isinstance(inter_type, type) + and (issubclass(inter_type, List) or issubclass(inter_type, Tuple)) + ) or ("List" in str(inter_type) or "Tuple" in str(inter_type)): + if "int" in str(inter_type): + kwargs["type"] = lambda x: eval_str_list(x, int) + elif "float" in str(inter_type): + kwargs["type"] = lambda x: eval_str_list(x, float) + elif "str" in str(inter_type): + kwargs["type"] = lambda x: eval_str_list(x, str) + else: + raise NotImplementedError( + "parsing of type " + str(inter_type) + " is not implemented" + ) + if field_default is not MISSING: + kwargs["default"] = ( + ",".join(map(str, field_default)) + if field_default is not None + else None + ) + elif ( + isinstance(inter_type, type) and issubclass(inter_type, Enum) + ) or "Enum" in str(inter_type): + kwargs["type"] = str + if field_default is not MISSING: + if isinstance(field_default, Enum): + kwargs["default"] = field_default.value + else: + kwargs["default"] = field_default + elif inter_type is bool: + kwargs["action"] = ( + "store_false" if field_default is True else "store_true" + ) + kwargs["default"] = field_default + else: + kwargs["type"] = inter_type + if field_default is not MISSING: + kwargs["default"] = field_default + + # build the help with the hierarchical prefix + if with_prefix is not None and with_prefix != "" and field_help is not None: + field_help = with_prefix[2:] + ": " + field_help + + kwargs["help"] = field_help + if field_const is not None: + kwargs["const"] = field_const + kwargs["nargs"] = "?" + + return kwargs + + for k in dataclass_instance._get_all_attributes(): + field_name = argparse_name(dataclass_instance._get_name(k)) + field_type = dataclass_instance._get_type(k) + if field_name is None: + continue + elif inspect.isclass(field_type) and issubclass(field_type, FairseqDataclass): + # for fields that are of type FairseqDataclass, we can recursively + # add their fields to the namespace (so we add the args from model, task, etc. to the root namespace) + prefix = None + if with_prefix is not None: + # if a prefix is specified, then we don't want to copy the subfields directly to the root namespace + # but we prefix them with the name of the current field. + prefix = field_name + gen_parser_from_dataclass(parser, field_type(), delete_default, prefix) + continue + + kwargs = get_kwargs_from_dc(dataclass_instance, k) + + field_args = [field_name] + alias = dataclass_instance._get_argparse_alias(k) + if alias is not None: + field_args.append(alias) + + if "default" in kwargs: + if isinstance(kwargs["default"], str) and kwargs["default"].startswith( + "${" + ): + if kwargs["help"] is None: + # this is a field with a name that will be added elsewhere + continue + else: + del kwargs["default"] + if delete_default and "default" in kwargs: + del kwargs["default"] + try: + parser.add_argument(*field_args, **kwargs) + except ArgumentError: + pass + + +def _set_legacy_defaults(args, cls): + """Helper to set default arguments based on *add_args*.""" + if not hasattr(cls, "add_args"): + return + + import argparse + + parser = argparse.ArgumentParser( + argument_default=argparse.SUPPRESS, allow_abbrev=False + ) + cls.add_args(parser) + # copied from argparse.py: + defaults = argparse.Namespace() + for action in parser._actions: + if action.dest is not argparse.SUPPRESS: + if not hasattr(defaults, action.dest): + if action.default is not argparse.SUPPRESS: + setattr(defaults, action.dest, action.default) + for key, default_value in vars(defaults).items(): + if not hasattr(args, key): + setattr(args, key, default_value) + + +def _override_attr( + sub_node: str, data_class: Type[FairseqDataclass], args: Namespace +) -> List[str]: + overrides = [] + + if not inspect.isclass(data_class) or not issubclass(data_class, FairseqDataclass): + return overrides + + def get_default(f): + if not isinstance(f.default_factory, _MISSING_TYPE): + return f.default_factory() + return f.default + + for k, v in data_class.__dataclass_fields__.items(): + if k.startswith("_"): + # private member, skip + continue + + val = get_default(v) if not hasattr(args, k) else getattr(args, k) + + field_type = interpret_dc_type(v.type) + if ( + isinstance(val, str) + and not val.startswith("${") # not interpolation + and field_type != str + and ( + not inspect.isclass(field_type) or not issubclass(field_type, Enum) + ) # not choices enum + ): + # upgrade old models that stored complex parameters as string + val = ast.literal_eval(val) + + if isinstance(val, tuple): + val = list(val) + + v_type = getattr(v.type, "__origin__", None) + if ( + (v_type is List or v_type is list or v_type is Optional) + # skip interpolation + and not (isinstance(val, str) and val.startswith("${")) + ): + # if type is int but val is float, then we will crash later - try to convert here + if hasattr(v.type, "__args__"): + t_args = v.type.__args__ + if len(t_args) == 1 and (t_args[0] is float or t_args[0] is int): + val = list(map(t_args[0], val)) + elif val is not None and ( + field_type is int or field_type is bool or field_type is float + ): + try: + val = field_type(val) + except: + pass # ignore errors here, they are often from interpolation args + + if val is None: + overrides.append("{}.{}=null".format(sub_node, k)) + elif val == "": + overrides.append("{}.{}=''".format(sub_node, k)) + elif isinstance(val, str): + val = val.replace("'", r"\'") + overrides.append("{}.{}='{}'".format(sub_node, k, val)) + elif isinstance(val, FairseqDataclass): + overrides += _override_attr(f"{sub_node}.{k}", type(val), args) + elif isinstance(val, Namespace): + sub_overrides, _ = override_module_args(val) + for so in sub_overrides: + overrides.append(f"{sub_node}.{k}.{so}") + else: + overrides.append("{}.{}={}".format(sub_node, k, val)) + + return overrides + + +def migrate_registry( + name, value, registry, args, overrides, deletes, use_name_as_val=False +): + if value in registry: + overrides.append("{}={}".format(name, value)) + overrides.append("{}._name={}".format(name, value)) + overrides.extend(_override_attr(name, registry[value], args)) + elif use_name_as_val and value is not None: + overrides.append("{}={}".format(name, value)) + else: + deletes.append(name) + + +def override_module_args(args: Namespace) -> Tuple[List[str], List[str]]: + """use the field in args to overrides those in cfg""" + overrides = [] + deletes = [] + + for k in FairseqConfig.__dataclass_fields__.keys(): + overrides.extend( + _override_attr(k, FairseqConfig.__dataclass_fields__[k].type, args) + ) + + if args is not None: + if hasattr(args, "task"): + from fairseq.tasks import TASK_DATACLASS_REGISTRY + + migrate_registry( + "task", args.task, TASK_DATACLASS_REGISTRY, args, overrides, deletes + ) + else: + deletes.append("task") + + # these options will be set to "None" if they have not yet been migrated + # so we can populate them with the entire flat args + CORE_REGISTRIES = {"criterion", "optimizer", "lr_scheduler"} + + from fairseq.registry import REGISTRIES + + for k, v in REGISTRIES.items(): + if hasattr(args, k): + migrate_registry( + k, + getattr(args, k), + v["dataclass_registry"], + args, + overrides, + deletes, + use_name_as_val=k not in CORE_REGISTRIES, + ) + else: + deletes.append(k) + + no_dc = True + if hasattr(args, "arch"): + from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_MODEL_NAME_REGISTRY + + if args.arch in ARCH_MODEL_REGISTRY: + m_cls = ARCH_MODEL_REGISTRY[args.arch] + dc = getattr(m_cls, "__dataclass", None) + if dc is not None: + m_name = ARCH_MODEL_NAME_REGISTRY[args.arch] + overrides.append("model={}".format(m_name)) + overrides.append("model._name={}".format(args.arch)) + # override model params with those exist in args + overrides.extend(_override_attr("model", dc, args)) + no_dc = False + if no_dc: + deletes.append("model") + + return overrides, deletes + + +class omegaconf_no_object_check: + def __init__(self): + # Changed in https://github.com/omry/omegaconf/pull/911 - both are kept for back compat. + if hasattr(_utils, "is_primitive_type"): + self.old_is_primitive = _utils.is_primitive_type + else: + self.old_is_primitive = _utils.is_primitive_type_annotation + + def __enter__(self): + if hasattr(_utils, "is_primitive_type"): + _utils.is_primitive_type = lambda _: True + else: + _utils.is_primitive_type_annotation = lambda _: True + + def __exit__(self, type, value, traceback): + if hasattr(_utils, "is_primitive_type"): + _utils.is_primitive_type = self.old_is_primitive + else: + _utils.is_primitive_type_annotation = self.old_is_primitive + + +def convert_namespace_to_omegaconf(args: Namespace) -> DictConfig: + """Convert a flat argparse.Namespace to a structured DictConfig.""" + + # Here we are using field values provided in args to override counterparts inside config object + overrides, deletes = override_module_args(args) + + # configs will be in fairseq/config after installation + config_path = os.path.join("..", "config") + + GlobalHydra.instance().clear() + + with initialize(config_path=config_path): + try: + composed_cfg = compose("config", overrides=overrides, strict=False) + except: + logger.error("Error when composing. Overrides: " + str(overrides)) + raise + + for k in deletes: + composed_cfg[k] = None + + cfg = OmegaConf.create( + OmegaConf.to_container(composed_cfg, resolve=True, enum_to_str=True) + ) + + # hack to be able to set Namespace in dict config. this should be removed when we update to newer + # omegaconf version that supports object flags, or when we migrate all existing models + from omegaconf import _utils + + with omegaconf_no_object_check(): + if cfg.task is None and getattr(args, "task", None): + cfg.task = Namespace(**vars(args)) + from fairseq.tasks import TASK_REGISTRY + + _set_legacy_defaults(cfg.task, TASK_REGISTRY[args.task]) + cfg.task._name = args.task + if cfg.model is None and getattr(args, "arch", None): + cfg.model = Namespace(**vars(args)) + from fairseq.models import ARCH_MODEL_REGISTRY + + _set_legacy_defaults(cfg.model, ARCH_MODEL_REGISTRY[args.arch]) + cfg.model._name = args.arch + if cfg.optimizer is None and getattr(args, "optimizer", None): + cfg.optimizer = Namespace(**vars(args)) + from fairseq.optim import OPTIMIZER_REGISTRY + + _set_legacy_defaults(cfg.optimizer, OPTIMIZER_REGISTRY[args.optimizer]) + cfg.optimizer._name = args.optimizer + if cfg.lr_scheduler is None and getattr(args, "lr_scheduler", None): + cfg.lr_scheduler = Namespace(**vars(args)) + from fairseq.optim.lr_scheduler import LR_SCHEDULER_REGISTRY + + _set_legacy_defaults( + cfg.lr_scheduler, LR_SCHEDULER_REGISTRY[args.lr_scheduler] + ) + cfg.lr_scheduler._name = args.lr_scheduler + if cfg.criterion is None and getattr(args, "criterion", None): + cfg.criterion = Namespace(**vars(args)) + from fairseq.criterions import CRITERION_REGISTRY + + _set_legacy_defaults(cfg.criterion, CRITERION_REGISTRY[args.criterion]) + cfg.criterion._name = args.criterion + + OmegaConf.set_struct(cfg, True) + return cfg + + +def overwrite_args_by_name(cfg: DictConfig, overrides: Dict[str, any]): + # this will be deprecated when we get rid of argparse and model_overrides logic + + from fairseq.registry import REGISTRIES + + with open_dict(cfg): + for k in cfg.keys(): + # "k in cfg" will return false if its a "mandatory value (e.g. ???)" + if k in cfg and isinstance(cfg[k], DictConfig): + if k in overrides and isinstance(overrides[k], dict): + for ok, ov in overrides[k].items(): + if isinstance(ov, dict) and cfg[k][ok] is not None: + overwrite_args_by_name(cfg[k][ok], ov) + else: + cfg[k][ok] = ov + else: + overwrite_args_by_name(cfg[k], overrides) + elif k in cfg and isinstance(cfg[k], Namespace): + for override_key, val in overrides.items(): + setattr(cfg[k], override_key, val) + elif k in overrides: + if ( + k in REGISTRIES + and overrides[k] in REGISTRIES[k]["dataclass_registry"] + ): + cfg[k] = DictConfig( + REGISTRIES[k]["dataclass_registry"][overrides[k]] + ) + overwrite_args_by_name(cfg[k], overrides) + cfg[k]._name = overrides[k] + else: + cfg[k] = overrides[k] + + +def merge_with_parent(dc: FairseqDataclass, cfg: DictConfig, remove_missing=False): + if remove_missing: + + def remove_missing_rec(src_keys, target_cfg): + if is_dataclass(target_cfg): + target_keys = set(target_cfg.__dataclass_fields__.keys()) + else: + target_keys = set(target_cfg.keys()) + + for k in list(src_keys.keys()): + if k not in target_keys: + del src_keys[k] + elif OmegaConf.is_config(src_keys[k]): + tgt = getattr(target_cfg, k) + if tgt is not None and (is_dataclass(tgt) or hasattr(tgt, "keys")): + remove_missing_rec(src_keys[k], tgt) + + with open_dict(cfg): + remove_missing_rec(cfg, dc) + + merged_cfg = OmegaConf.merge(dc, cfg) + merged_cfg.__dict__["_parent"] = cfg.__dict__["_parent"] + OmegaConf.set_struct(merged_cfg, True) + return merged_cfg diff --git a/fairseq/fairseq/distributed/__init__.py b/fairseq/fairseq/distributed/__init__.py new file mode 100644 index 0000000..9130db8 --- /dev/null +++ b/fairseq/fairseq/distributed/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .distributed_timeout_wrapper import DistributedTimeoutWrapper +from .fully_sharded_data_parallel import ( + fsdp_enable_wrap, + fsdp_wrap, + FullyShardedDataParallel, +) +from .legacy_distributed_data_parallel import LegacyDistributedDataParallel +from .module_proxy_wrapper import ModuleProxyWrapper +from .tpu_distributed_data_parallel import TPUDistributedDataParallel + + +__all__ = [ + "DistributedTimeoutWrapper", + "fsdp_enable_wrap", + "fsdp_wrap", + "FullyShardedDataParallel", + "LegacyDistributedDataParallel", + "ModuleProxyWrapper", + "TPUDistributedDataParallel", +] diff --git a/fairseq/fairseq/distributed/distributed_timeout_wrapper.py b/fairseq/fairseq/distributed/distributed_timeout_wrapper.py new file mode 100644 index 0000000..6e06b4b --- /dev/null +++ b/fairseq/fairseq/distributed/distributed_timeout_wrapper.py @@ -0,0 +1,97 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import signal +import threading + +from torch import nn + + +logger = logging.getLogger(__name__) + + +class DistributedTimeoutWrapper(nn.Module): + """ + A wrapper that kills the process if no progress is made within a given + *timeout*. The timer is reset every time :func:`forward` is called. + + Usage:: + + module = DistributedTimeoutWrapper(module, timeout=30) + x = module(input) + time.sleep(20) # safe + x = module(input) + time.sleep(45) # job will be killed before this returns + + Args: + module (nn.Module): module to wrap + timeout (int): number of seconds before killing the process + (set to a value <= 0 to disable the timeout) + signal (Optional): signal to send once timeout is triggered + """ + + def __init__(self, module: nn.Module, timeout: int, signal=signal.SIGINT): + super().__init__() + self.module = module + self.timeout = timeout + self.signal = signal + + if timeout > 0: + self._heartbeat = threading.Event() + self._heartbeat_thread = threading.Thread( + target=self._check_heartbeat, + args=(os.getpid(),), + daemon=True, + ) + self._heartbeat_thread.start() + self._terminated = False + else: + self._heartbeat = None + self._heartbeat_thread = None + + def __del__(self): + self.stop_timeout() + + def __getattr__(self, name): + """Forward missing attributes to wrapped module.""" + try: + return super().__getattr__(name) # defer to nn.Module's logic + except AttributeError: + return getattr(self.module, name) + + def stop_timeout(self): + if self._heartbeat_thread is not None: + self._terminated = True + self._heartbeat_thread.join() + + def state_dict(self, *args, **kwargs): + return self.module.state_dict(*args, **kwargs) + + def load_state_dict(self, *args, **kwargs): + return self.module.load_state_dict(*args, **kwargs) + + def forward(self, *args, **kwargs): + if self._heartbeat is not None: + self._heartbeat.set() + return self.module(*args, **kwargs) + + def _check_heartbeat(self, parent_pid): + self._heartbeat.wait() # wait for the first forward pass + while True: + self._heartbeat.clear() + success = self._heartbeat.wait(timeout=self.timeout) + if self._terminated: + break + elif not success: + logger.error( + ( + "Killing job for not making progress in {} seconds. " + "Set --heartbeat-timeout=-1 to disable this timeout." + ).format(int(self.timeout)) + ) + os.kill(parent_pid, self.signal) + return diff --git a/fairseq/fairseq/distributed/fully_sharded_data_parallel.py b/fairseq/fairseq/distributed/fully_sharded_data_parallel.py new file mode 100644 index 0000000..1c508b0 --- /dev/null +++ b/fairseq/fairseq/distributed/fully_sharded_data_parallel.py @@ -0,0 +1,145 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +from typing import Optional + +import torch +from fairseq.dataclass.configs import DistributedTrainingConfig +from fairseq.distributed import utils as dist_utils + + +try: + from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP + + has_FSDP = True +except ImportError: + FSDP = torch.nn.Module + has_FSDP = False + + +class FullyShardedDataParallel(FSDP): + """ + A small wrapper around fairscale's FullyShardedDataParallel (FSDP) with some + fairseq-specific checkpoint saving/loading logic. + + Args: + use_sharded_state (bool): if True, then ``state_dict`` will return + ``FSDP.local_state_dict`` and ``load_state_dict`` will call + ``FSDP.load_local_state_dict``. Otherwise, ``state_dict`` will + return the full model weights on data parallel rank 0 (empty on + other ranks) and ``load_state_dict`` will broadcast model weights + from rank 0 to other ranks. + """ + + def __init__(self, *args, use_sharded_state: bool = False, **kwargs): + if not has_FSDP: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + super().__init__(*args, **kwargs) + self.use_sharded_state = use_sharded_state + + @property + def unwrapped_module(self) -> torch.nn.Module: + if self.flatten_parameters: + return self.module.module + else: + return self.module + + def state_dict(self, destination=None, prefix="", keep_vars=False): + if self.use_sharded_state: + return super().local_state_dict( + destination=destination, prefix=prefix, keep_vars=keep_vars + ) + else: + if self.rank == 0: + return super().state_dict( + destination=destination, prefix=prefix, keep_vars=keep_vars + ) + else: + # We must call state_dict() due to use of communication + # primitives. But we don't use the result. + super().state_dict() + return destination or {} + + def load_state_dict(self, state_dict, strict=True, model_cfg=None): + if self.use_sharded_state: + return super().load_local_state_dict(state_dict, strict=strict) + else: + state_dict = dist_utils.broadcast_object( + state_dict, src_rank=0, group=self.process_group + ) + return super().load_state_dict(state_dict, strict=strict) + + +class DummyProcessGroup: + def __init__(self, rank: int, size: int): + self._rank = rank + self._size = size + + def rank(self) -> int: + return self._rank + + def size(self) -> int: + return self._size + + +@contextlib.contextmanager +def fsdp_enable_wrap(cfg: DistributedTrainingConfig): + try: + from fairscale.nn import enable_wrap + except ImportError: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + if cfg.memory_efficient_fp16: + assert cfg.fp16 # memory_efficient_fp16 should imply fp16 + group = dist_utils.get_data_parallel_group() + if group is None and cfg.distributed_world_size == 1: + group = DummyProcessGroup(rank=0, size=1) + fsdp_config = { + "process_group": group, + "reshard_after_forward": not cfg.no_reshard_after_forward, + "mixed_precision": cfg.fp16 and not cfg.memory_efficient_fp16, + "fp32_reduce_scatter": cfg.fp32_reduce_scatter, + "flatten_parameters": not cfg.not_fsdp_flatten_parameters, + "cpu_offload": cfg.cpu_offload, + "compute_dtype": torch.float16 if cfg.fp16 else torch.float32, + "bucket_cap_mb": cfg.bucket_cap_mb, + "state_dict_device": torch.device("cpu"), # reduce GPU mem usage + } + with enable_wrap( + wrapper_cls=FullyShardedDataParallel, + use_sharded_state=cfg.use_sharded_state, + **fsdp_config, + ): + yield + + +def fsdp_wrap(module, min_num_params: Optional[int] = None, **kwargs): + """ + Helper to wrap layers/modules in FSDP. This falls back to a no-op if + fairscale is not available. + + Args: + module (nn.Module): module to (maybe) wrap + min_num_params (int, Optional): minimum number of layer params to wrap + """ + try: + from fairscale.nn import wrap + + if min_num_params is not None: + num_params = sum(p.numel() for p in module.parameters()) + if num_params >= min_num_params: + return wrap(module, **kwargs) + else: + return module + else: + return wrap(module, **kwargs) + except ImportError: + return module diff --git a/fairseq/fairseq/distributed/legacy_distributed_data_parallel.py b/fairseq/fairseq/distributed/legacy_distributed_data_parallel.py new file mode 100644 index 0000000..cd434c7 --- /dev/null +++ b/fairseq/fairseq/distributed/legacy_distributed_data_parallel.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +A modified version of the legacy DistributedDataParallel module that uses c10d +communication primitives. This version is simpler than the latest PyTorch +version and is useful for debugging. Notably it does not overlap gradient +communication with the backward pass, which makes it slower but more robust +than the PyTorch version. + +This version also supports the *no_sync* context manager, which allows faster +training with `--update-freq`. +""" + +from collections import OrderedDict +from contextlib import contextmanager + +import torch +from torch import nn + +from fairseq.distributed import utils + + +class LegacyDistributedDataParallel(nn.Module): + """Implements distributed data parallelism at the module level. + + A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`. + This version uses a c10d process group for communication and does not + broadcast buffers. + + Args: + module (~torch.nn.Module): module to be parallelized + process_group: the c10d process group to be used for distributed data + parallel all-reduction. + buffer_size (int, optional): number of elements to buffer before + performing all-reduce (default: 256M). + """ + + def __init__(self, module, process_group, buffer_size=2**28): + super().__init__() + + self.module = module + self.process_group = process_group + self.world_size = utils.get_world_size(self.process_group) + + # Never use a bigger buffer than the number of model params + self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters())) + self.buffer = None + + # We can also forcibly accumulate grads locally and only do the + # all-reduce at some later time + self.accumulate_grads = False + + # make per-device lists of parameters + paramlists = OrderedDict() + for param in self.module.parameters(): + device = param.device + if paramlists.get(device) is None: + paramlists[device] = [] + paramlists[device] += [param] + self.per_device_params = list(paramlists.values()) + + @contextmanager + def no_sync(self): + """A context manager to disable gradient synchronization.""" + old_accumulate_grads = self.accumulate_grads + self.accumulate_grads = True + yield + self.accumulate_grads = old_accumulate_grads + + def forward(self, *inputs, **kwargs): + return self.module(*inputs, **kwargs) + + def all_reduce_grads(self): + """ + This function must be called explicitly after backward to reduce + gradients. There is no automatic hook like c10d. + """ + + def all_reduce_params(params): + buffer = self.buffer + nonzero_buffer = False + if len(params) > 1: + offset = 0 + for p in params: + sz = p.numel() + if p.grad is not None: + buffer[offset : offset + sz].copy_(p.grad.data.view(-1)) + nonzero_buffer = True + else: + buffer[offset : offset + sz].zero_() + offset += sz + else: + # we only have a single grad to all-reduce + p = params[0] + if p.grad is not None: + buffer = p.grad.data + nonzero_buffer = True + elif p.numel() <= self.buffer.numel(): + buffer = buffer[: p.numel()] + buffer.zero_() + else: + buffer = torch.zeros_like(p) + + if nonzero_buffer: + buffer.div_(self.world_size) + + utils.all_reduce(buffer, self.process_group) + + # copy all-reduced grads back into their original place + offset = 0 + for p in params: + sz = p.numel() + if p.grad is not None: + p.grad.data.copy_(buffer[offset : offset + sz].view_as(p)) + else: + p.grad = buffer[offset : offset + sz].view_as(p).clone() + offset += sz + + def reduction_fn(): + # This function only needs to be called once + if self.accumulate_grads: + return + + if self.buffer is None: + self.buffer = next(self.module.parameters()).new(self.buffer_size) + + for params in self.per_device_params: + # All-reduce the gradients in buckets + offset = 0 + buffered_params = [] + for param in params: + if not param.requires_grad: + continue + if param.grad is None: + param.grad = torch.zeros_like(param) + + if hasattr(param, "expert"): + # Skip gradient sync for unshared parameters + continue + + if param.grad.requires_grad: + raise RuntimeError( + "DistributedDataParallel only works " + "with gradients that don't require " + "grad" + ) + sz = param.numel() + if sz > self.buffer.numel(): + # all-reduce big params directly + all_reduce_params([param]) + else: + if offset + sz > self.buffer.numel(): + all_reduce_params(buffered_params) + offset = 0 + buffered_params.clear() + buffered_params.append(param) + offset += sz + + if len(buffered_params) > 0: + all_reduce_params(buffered_params) + + reduction_fn() diff --git a/fairseq/fairseq/distributed/module_proxy_wrapper.py b/fairseq/fairseq/distributed/module_proxy_wrapper.py new file mode 100644 index 0000000..904dc0c --- /dev/null +++ b/fairseq/fairseq/distributed/module_proxy_wrapper.py @@ -0,0 +1,56 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from torch import nn + + +class ModuleProxyWrapper(nn.Module): + """ + Wrap a DistributedDataParallel module and forward requests for missing + attributes to the module wrapped by DDP (the twice-wrapped module). + Also forward calls to :func:`state_dict` and :func:`load_state_dict`. + + Usage:: + + module.xyz = "hello world" + wrapped_module = DistributedDataParallel(module, **ddp_args) + wrapped_module = ModuleProxyWrapper(wrapped_module) + assert wrapped_module.xyz == "hello world" + assert wrapped_module.state_dict().keys() == module.state_dict().keys() + + Args: + module (nn.Module): module to wrap + """ + + def __init__(self, module: nn.Module): + super().__init__() + assert hasattr( + module, "module" + ), "ModuleProxyWrapper expects input to wrap another module" + self.module = module + + def __getattr__(self, name): + """Forward missing attributes to twice-wrapped module.""" + try: + # defer to nn.Module's logic + return super().__getattr__(name) + except AttributeError: + try: + # forward to the once-wrapped module + return getattr(self.module, name) + except AttributeError: + # forward to the twice-wrapped module + return getattr(self.module.module, name) + + def state_dict(self, *args, **kwargs): + """Forward to the twice-wrapped module.""" + return self.module.module.state_dict(*args, **kwargs) + + def load_state_dict(self, *args, **kwargs): + """Forward to the twice-wrapped module.""" + return self.module.module.load_state_dict(*args, **kwargs) + + def forward(self, *args, **kwargs): + return self.module(*args, **kwargs) diff --git a/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py b/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py new file mode 100644 index 0000000..3b9e103 --- /dev/null +++ b/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py @@ -0,0 +1,43 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn + +from fairseq.distributed import utils + + +class TPUDistributedDataParallel(nn.Module): + def __init__(self, module, process_group): + super().__init__() + self.module = module + self.process_group = process_group + self.world_size = utils.get_world_size(self.process_group) + + def forward(self, *inputs, **kwargs): + return self.module(*inputs, **kwargs) + + def all_reduce_grads(self): + gradients = [] + for p in self.parameters(): + if not p.requires_grad: + continue + if p.grad is None: + p.grad = torch.zeros_like(p) + if p.grad.requires_grad: + raise RuntimeError( + "TPUDistributedDataParallel only works with gradients that don't " + "require grad" + ) + gradients.append(p.grad) + + import torch_xla.core.xla_model as xm + + xm.all_reduce( + "sum", + gradients, + scale=1.0 / self.world_size, + groups=self.process_group[1], + ) diff --git a/fairseq/fairseq/distributed/utils.py b/fairseq/fairseq/distributed/utils.py new file mode 100644 index 0000000..968830d --- /dev/null +++ b/fairseq/fairseq/distributed/utils.py @@ -0,0 +1,843 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import io +import logging +import os +import pickle +import random +import socket +import struct +import subprocess +import warnings +from argparse import Namespace +from collections import OrderedDict +from dataclasses import dataclass +from typing import Any, Dict, List, Mapping, Optional + +import torch +import torch.distributed as dist +from fairseq.dataclass.configs import DistributedTrainingConfig, FairseqConfig +from omegaconf import open_dict + +try: + import torch_xla.core.xla_model as xm +except ImportError: + xm = None + + +# Flag to indicate if we're using Megatron +# NOTE: this is a temporary hack until we move away from Megatron's model parallel init +_USE_MEGATRON = False + +# Whether to use XLA ops (e.g., on TPUs) instead of CUDA ops. +_USE_XLA = False + + +logger = logging.getLogger(__name__) + + +def is_master(cfg: DistributedTrainingConfig): + return cfg.distributed_rank == 0 + + +def infer_init_method(cfg: DistributedTrainingConfig, force_distributed=False): + if cfg.distributed_init_method is not None or cfg.tpu: + return + + num_pipelines_per_node = None + if cfg.pipeline_model_parallel: + num_pipeline_devices, num_pipelines_per_node = _pipeline_parallel_pre_init(cfg) + + if cfg.distributed_world_size == 1: + return + if all( + key in os.environ + for key in ["MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK"] + ): + # support torch.distributed.launch + _infer_torch_distributed_launch_init(cfg) + else: + # we can determine the init method automatically for Slurm + if not _infer_slurm_init(cfg, num_pipelines_per_node): + if cfg.distributed_port <= 0 or force_distributed: + _infer_single_node_init(cfg) + elif cfg.distributed_port <= 0: + _infer_single_node_init(cfg) + + if cfg.pipeline_model_parallel: + _pipeline_parallel_post_init(cfg, num_pipeline_devices, num_pipelines_per_node) + elif not cfg.distributed_no_spawn: + with open_dict(cfg): + cfg.distributed_num_procs = min( + torch.cuda.device_count(), cfg.distributed_world_size + ) + else: + if cfg.device_id > 0: + logger.info( + "setting CUDA device={} on rank {}".format( + cfg.device_id, cfg.distributed_rank + ) + ) + torch.cuda.set_device(cfg.device_id) + + +def _infer_torch_distributed_launch_init(cfg: DistributedTrainingConfig): + cfg.distributed_init_method = "env://" + cfg.distributed_world_size = int(os.environ["WORLD_SIZE"]) + cfg.distributed_rank = int(os.environ["RANK"]) + cfg.device_id = cfg.distributed_rank % torch.cuda.device_count() + # processes are created by torch.distributed.launch + cfg.distributed_no_spawn = True + + +def _infer_slurm_init(cfg: DistributedTrainingConfig, num_pipelines_per_node): + node_list = os.environ.get("SLURM_STEP_NODELIST") + if node_list is None: + node_list = os.environ.get("SLURM_JOB_NODELIST") + if node_list is not None: + try: + hostnames = subprocess.check_output( + ["scontrol", "show", "hostnames", node_list] + ) + cfg.distributed_init_method = "tcp://{host}:{port}".format( + host=hostnames.split()[0].decode("utf-8"), + port=cfg.distributed_port, + ) + nnodes = int(os.environ.get("SLURM_NNODES")) + ntasks_per_node = os.environ.get("SLURM_NTASKS_PER_NODE") + if ntasks_per_node is not None: + ntasks_per_node = int(ntasks_per_node) + else: + ntasks = int(os.environ.get("SLURM_NTASKS")) + nnodes = int(os.environ.get("SLURM_NNODES")) + assert ntasks % nnodes == 0 + ntasks_per_node = int(ntasks / nnodes) + if ntasks_per_node == 1: + gpus_per_node = torch.cuda.device_count() + node_id = int(os.environ.get("SLURM_NODEID")) + cfg.distributed_rank = node_id * gpus_per_node + cfg.distributed_world_size = nnodes * gpus_per_node + elif cfg.pipeline_model_parallel: + assert ntasks_per_node == num_pipelines_per_node, ( + "SLURM --ntasks-per-node must match number of pipelines per " + "node (={})".format(num_pipelines_per_node) + ) + cfg.distributed_no_spawn = True + # For 4-way MP on nodes with 8 GPUs, ranks will be [0, 1] on + # the first node, [1, 2] on the second node, etc. This + # matches torch.distributed.launch. + node_id = int(os.environ.get("SLURM_NODEID")) + local_id = int(os.environ.get("SLURM_LOCALID")) + cfg.distributed_rank = node_id * num_pipelines_per_node + local_id + # In the above example, device_id will always be in [0, 1], + # which also matches torch.distributed.launch. + cfg.device_id = local_id + # We also want to set distributed_world_size to be the total + # number of pipelines across all nodes. + cfg.distributed_world_size = nnodes * num_pipelines_per_node + else: + assert ( + ntasks_per_node == cfg.distributed_world_size // nnodes + ), f"{ntasks_per_node}, {cfg.distributed_world_size}, {nnodes}" + cfg.distributed_no_spawn = True + cfg.distributed_rank = int(os.environ.get("SLURM_PROCID")) + cfg.device_id = int(os.environ.get("SLURM_LOCALID")) + logger.info(f"Rank {cfg.distributed_rank}, device_id: {cfg.device_id}") + return True + except subprocess.CalledProcessError as e: # scontrol failed + raise e + except FileNotFoundError: # Slurm is not installed + pass + + return False + + +def _infer_single_node_init(cfg: DistributedTrainingConfig): + assert ( + cfg.distributed_world_size <= torch.cuda.device_count() + ), f"world size is {cfg.distributed_world_size} but have {torch.cuda.device_count()} available devices" + + if cfg.distributed_port <= 0: + jobid = os.environ.get("SLURM_JOB_ID") + task_id = os.environ.get("SLURM_ARRAY_TASK_ID") + + if jobid is not None: + if task_id is not None: + jobid += str(task_id) + jobid = int(jobid) + rng = random.Random(jobid) + port = rng.randint(10000, 60000) + else: + port = random.randint(10000, 60000) + + cfg.distributed_port = port + cfg.distributed_init_method = "tcp://localhost:{port}".format( + port=cfg.distributed_port + ) + + +def _pipeline_parallel_pre_init(cfg: DistributedTrainingConfig): + from fairseq import utils + + balance_exists = ( + cfg.pipeline_balance is not None + or cfg.pipeline_encoder_balance is not None + or cfg.pipeline_decoder_balance is not None + ) + devices_exist = ( + cfg.pipeline_devices is not None + or cfg.pipeline_encoder_devices is not None + or cfg.pipeline_decoder_devices is not None + ) + if not balance_exists: + raise ValueError( + "--pipeline-balance is currently required for pipeline model parallelism" + ) + if not devices_exist: + raise ValueError( + "--pipeline-devices is currently required for pipeline model parallelism" + ) + + cfg.pipeline_balance = utils.eval_str_list(cfg.pipeline_balance, type=int) + if cfg.pipeline_devices is not None: + cfg.pipeline_devices = utils.eval_str_list(cfg.pipeline_devices, type=int) + num_pipeline_devices = len(set(cfg.pipeline_devices)) + else: + cfg.pipeline_encoder_devices = utils.eval_str_list( + cfg.pipeline_encoder_devices, type=int + ) + cfg.pipeline_decoder_devices = utils.eval_str_list( + cfg.pipeline_decoder_devices, type=int + ) + num_pipeline_devices = len( + set(cfg.pipeline_encoder_devices + cfg.pipeline_decoder_devices) + ) + gpus_per_node = torch.cuda.device_count() + assert ( + gpus_per_node >= num_pipeline_devices + and gpus_per_node % num_pipeline_devices == 0 + ), ( + "the number of unique device IDs in --pipeline-devices must evenly divide " + "the number of GPUs per node (multi-node pipelining is not yet supported)" + ) + num_pipelines_per_node = gpus_per_node // num_pipeline_devices + return num_pipeline_devices, num_pipelines_per_node + + +def _pipeline_parallel_post_init( + cfg: DistributedTrainingConfig, num_pipeline_devices, num_pipelines_per_node +): + if not cfg.distributed_no_spawn: + # When distributed_no_spawn is False, we expect distributed_rank and + # distributed_world_size to be based on the total number of GPUs, so + # we need to correct them to be based on the number of pipelines. + assert cfg.distributed_world_size % num_pipeline_devices == 0 + cfg.distributed_world_size = cfg.distributed_world_size // num_pipeline_devices + # In the case of 4-way MP on nodes with 8 GPUs, we want + # distributed_rank to be the starting GPU index for each pipeline + # i.e., 0, 2, ... + gpus_per_node = torch.cuda.device_count() + assert cfg.distributed_rank % gpus_per_node == 0 + assert cfg.distributed_rank % num_pipeline_devices == 0 + + with open_dict(cfg): + cfg.distributed_rank = cfg.distributed_rank // num_pipeline_devices + # launch one process per pipeline + cfg.distributed_num_procs = num_pipelines_per_node + + # if we have 4-way MP on a node with 8 GPUs, we want device_ids to be 0 + # and 4, indicating the starting device IDs for each pipeline + cfg.device_id *= num_pipeline_devices + + if cfg.device_id > 0: + # if there's multiple pipelines on a node (e.g., 4-way MP on an 8 + # GPU node), we need to adjust pipeline_devices accordingly + logger.debug( + "setting CUDA device={} on rank {}".format( + cfg.device_id, cfg.distributed_rank + ) + ) + torch.cuda.set_device(cfg.device_id) + with open_dict(cfg): + cfg.pipeline_devices = [cfg.device_id + d for d in cfg.pipeline_devices] + logger.info( + "setting pipeline_devices={} on rank {}".format( + cfg.pipeline_devices, cfg.distributed_rank + ) + ) + + +def distributed_init(cfg: FairseqConfig): + if isinstance(cfg, Namespace): + from fairseq.dataclass.utils import convert_namespace_to_omegaconf + + cfg = convert_namespace_to_omegaconf(cfg) + + if not cfg.common.tpu: + if torch.distributed.is_available() and torch.distributed.is_initialized(): + warnings.warn( + "Distributed is already initialized, cannot initialize twice!" + ) + else: + logger.info( + "distributed init (rank {}): {}".format( + cfg.distributed_training.distributed_rank, + cfg.distributed_training.distributed_init_method, + ) + ) + dist.init_process_group( + backend=cfg.distributed_training.distributed_backend, + init_method=cfg.distributed_training.distributed_init_method, + world_size=cfg.distributed_training.distributed_world_size, + rank=cfg.distributed_training.distributed_rank, + ) + logger.info( + "initialized host {} as rank {}".format( + socket.gethostname(), + cfg.distributed_training.distributed_rank, + ) + ) + + # perform a dummy all-reduce to initialize the NCCL communicator + if torch.cuda.is_available(): + dist.all_reduce(torch.zeros(1).cuda()) + + cfg.distributed_training.distributed_rank = torch.distributed.get_rank() + else: + assert xm.xrt_world_size() == cfg.distributed_training.distributed_world_size + global _USE_XLA + _USE_XLA = True + cfg.distributed_training.device_id = xm.get_local_ordinal() + cfg.distributed_training.distributed_rank = xm.get_ordinal() + xm.rendezvous("distributed_init") # wait for all workers + + if is_master(cfg.distributed_training): + logging.getLogger().setLevel(logging.INFO) + else: + logging.getLogger().setLevel(logging.WARNING) + + if cfg.common.model_parallel_size > 1: + try: + from fairseq.model_parallel.megatron.mpu import ( + initialize_model_parallel, + model_parallel_cuda_manual_seed, + ) + except ImportError: + raise ImportError( + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" + ) + global _USE_MEGATRON + _USE_MEGATRON = True + initialize_model_parallel(cfg.common.model_parallel_size) + model_parallel_cuda_manual_seed(cfg.common.seed) + model_part_number = get_model_parallel_rank() + cfg.checkpoint.checkpoint_suffix += "-model_part-{0}".format(model_part_number) + + if hasattr(cfg, "model") and getattr(cfg.model, "base_layers", 0) > 0: + cfg.checkpoint.checkpoint_suffix = ( + f"-rank-{cfg.distributed_training.distributed_rank}" + ) + + return cfg.distributed_training.distributed_rank + + +def distributed_main(i, main, cfg: FairseqConfig, kwargs): + cfg.distributed_training.device_id = i + if torch.cuda.is_available() and not cfg.common.cpu and not cfg.common.tpu: + torch.cuda.set_device(cfg.distributed_training.device_id) + if cfg.distributed_training.distributed_rank is None: # torch.multiprocessing.spawn + cfg.distributed_training.distributed_rank = kwargs.pop("start_rank", 0) + i + + cfg.distributed_training.distributed_rank = distributed_init(cfg) + + after_distributed_init_fn = kwargs.pop("after_distributed_init_fn", None) + if after_distributed_init_fn: + cfg = after_distributed_init_fn(cfg) + + main(cfg, **kwargs) + + if torch.distributed.is_initialized(): + torch.distributed.barrier(get_global_group()) + + +def call_main(cfg: FairseqConfig, main, **kwargs): + if cfg.distributed_training.distributed_init_method is None: + infer_init_method(cfg.distributed_training) + + if cfg.distributed_training.distributed_init_method is not None: + # distributed training + if not cfg.distributed_training.distributed_no_spawn: + start_rank = cfg.distributed_training.distributed_rank + cfg.distributed_training.distributed_rank = None # assign automatically + kwargs["start_rank"] = start_rank + + torch.multiprocessing.spawn( + fn=distributed_main, + args=(main, cfg, kwargs), + nprocs=min( + torch.cuda.device_count(), + cfg.distributed_training.distributed_world_size, + ), + join=True, + ) + else: + distributed_main(cfg.distributed_training.device_id, main, cfg, kwargs) + elif cfg.common.tpu and cfg.distributed_training.distributed_world_size > 1: + import torch_xla.distributed.xla_multiprocessing as xmp + + torch.multiprocessing.set_sharing_strategy("file_system") + xmp.spawn( + fn=distributed_main, + args=(main, cfg, kwargs), + # tpu-comment: + # 8 devices in one TPU VM, is the max processes to be spawned. + # The rest is driven by xm.distributed.xla_dist + nprocs=min(cfg.distributed_training.distributed_world_size, 8), + ) + else: + # single GPU main + main(cfg, **kwargs) + + +def use_xla(): + global _USE_XLA + return _USE_XLA + + +def new_groups(grouped_ranks: List[List[int]]): + if use_xla(): + return ("tpu", grouped_ranks) + else: + groups = [dist.new_group(g) for g in grouped_ranks] + my_group_idx = _find_my_group_index(grouped_ranks) + return groups[my_group_idx] + + +def _find_my_group_index(grouped_ranks): + my_rank = get_global_rank() + for i, group in enumerate(grouped_ranks): + if my_rank in group: + return i + raise RuntimeError + + +def _find_my_group(grouped_ranks): + index = _find_my_group_index(grouped_ranks) + return grouped_ranks[index] + + +def get_rank(group): + if use_xla(): + assert group[0] == "tpu" + my_group = _find_my_group(group[1]) + return my_group.index(get_global_rank()) + else: + return dist.get_rank(group=group) + + +def get_world_size(group): + if use_xla(): + assert group[0] == "tpu" + my_group = _find_my_group(group[1]) + return len(my_group) + elif torch.distributed.is_initialized(): + return dist.get_world_size(group=group) + else: + return 1 + + +def get_global_group(): + if use_xla(): + return new_groups([list(range(get_global_world_size()))]) + elif torch.distributed.is_initialized(): + if not hasattr(get_global_group, "_global_group"): + # ideally we could use torch.distributed.group.WORLD, but it seems + # to cause random NCCL hangs in some cases + get_global_group._global_group = dist.new_group() + return get_global_group._global_group + else: + return None + + +def get_global_rank(): + if use_xla(): + return xm.get_ordinal() + elif torch.distributed.is_initialized(): + return torch.distributed.get_rank() + else: + return 0 + + +def get_global_world_size(): + if use_xla(): + return xm.xrt_world_size() + elif torch.distributed.is_initialized(): + return torch.distributed.get_world_size() + else: + return 1 + + +def get_data_parallel_group(): + """Get the data parallel group the caller rank belongs to.""" + global _USE_MEGATRON + if _USE_MEGATRON: + from fairseq.model_parallel.megatron import mpu + + return mpu.get_data_parallel_group() + else: + return get_global_group() + + +def get_data_parallel_rank(): + """Return my rank for the data parallel group.""" + return get_rank(get_data_parallel_group()) + + +def get_data_parallel_world_size(): + """Return world size for the data parallel group.""" + return get_world_size(get_data_parallel_group()) + + +def get_model_parallel_group(): + global _USE_MEGATRON + if _USE_MEGATRON: + from fairseq.model_parallel.megatron import mpu + + return mpu.get_model_parallel_group() + else: + return None + + +def get_model_parallel_rank(): + """Return my rank for the model parallel group.""" + return get_rank(get_model_parallel_group()) + + +def get_model_parallel_world_size(): + """Return world size for the model parallel group.""" + return get_world_size(get_model_parallel_group()) + + +def all_reduce(tensor, group, op="sum"): + if use_xla(): + assert isinstance(group, tuple) and group[0] == "tpu" + tensor = [tensor] # wrap in a list to make xm.all_reduce in-place + return xm.all_reduce(op, tensor, groups=group[1])[0] + else: + if op == "sum": + op = dist.ReduceOp.SUM + elif op == "max": + op = dist.ReduceOp.MAX + else: + raise NotImplementedError + dist.all_reduce(tensor, op=op, group=group) + return tensor + + +def broadcast(tensor, src, group): + if use_xla(): + # XLA doesn't support broadcast, hack it with all_reduce + if get_rank(group) != src: + tensor.zero_() + all_reduce(tensor, group) + else: + dist.broadcast(tensor, src=src, group=group) + + +def all_to_all(tensor, group): + """Perform an all-to-all operation on a 1D Tensor.""" + assert tensor.dim() == 1 + split_count = get_world_size(group=group) + assert tensor.numel() % split_count == 0 + if use_xla(): + assert isinstance(group, tuple) and group[0] == "tpu" + return xm.all_to_all( + tensor, + split_dimension=0, + concat_dimension=0, + split_count=split_count, + groups=group[1], + ) + else: + output = torch.zeros_like(tensor) + dist.all_to_all_single(output, tensor, group=group) + return output + + +def all_gather(tensor, group, return_tensor=False): + """Perform an all-gather operation.""" + if use_xla(): + result = xm.all_gather(tensor, groups=group[1]) + world_size = get_world_size(group=group) + result = result.view(world_size, *tensor.size()) + if return_tensor: + return result + else: + return [result[i] for i in range(world_size)] + else: + world_size = get_world_size(group=group) + rank = get_rank(group=group) + tensor_list = [ + tensor if i == rank else torch.empty_like(tensor) for i in range(world_size) + ] + dist.all_gather(tensor_list, tensor, group=group) + if return_tensor: + return torch.stack(tensor_list, dim=0) + else: + return tensor_list + + +def all_gather_list(data, group=None, max_size=16384): + """Gathers arbitrary data from all nodes into a list. + + Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python + data. Note that *data* must be picklable and any CUDA tensors will be moved + to CPU and returned on CPU as well. + + Args: + data (Any): data from the local worker to be gathered on other workers + group: group of the collective + max_size (int, optional): maximum size of the data to be gathered + across workers + """ + from fairseq import utils + + if group is None: + group = get_global_group() + rank = get_rank(group=group) + world_size = get_world_size(group=group) + + buffer_size = max_size * world_size + if ( + not hasattr(all_gather_list, "_buffer") + or all_gather_list._buffer.numel() < buffer_size + ): + all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size) + all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory() + buffer = all_gather_list._buffer + buffer.zero_() + cpu_buffer = all_gather_list._cpu_buffer + + data = utils.move_to_cpu(data) + enc = pickle.dumps(data) + enc_size = len(enc) + header_size = 4 # size of header that contains the length of the encoded data + size = header_size + enc_size + if size > max_size: + raise ValueError( + "encoded data size ({}) exceeds max_size ({})".format(size, max_size) + ) + + header = struct.pack(">I", enc_size) + cpu_buffer[:size] = torch.ByteTensor(list(header + enc)) + start = rank * max_size + buffer[start : start + size].copy_(cpu_buffer[:size]) + + all_reduce(buffer, group=group) + + buffer = buffer.cpu() + try: + result = [] + for i in range(world_size): + out_buffer = buffer[i * max_size : (i + 1) * max_size] + (enc_size,) = struct.unpack(">I", bytes(out_buffer[:header_size].tolist())) + if enc_size > 0: + result.append( + pickle.loads( + bytes(out_buffer[header_size : header_size + enc_size].tolist()) + ) + ) + return result + except pickle.UnpicklingError: + raise Exception( + "Unable to unpickle data from other workers. all_gather_list requires all " + "workers to enter the function together, so this error usually indicates " + "that the workers have fallen out of sync somehow. Workers can fall out of " + "sync if one of them runs out of memory, or if there are other conditions " + "in your training script that can cause one worker to finish an epoch " + "while other workers are still iterating over their portions of the data. " + "Try rerunning with --ddp-backend=legacy_ddp and see if that helps." + ) + + +def all_reduce_dict(data: Mapping[str, Any], device, group) -> Dict[str, Any]: + """ + AllReduce a dictionary of values across workers. We separately + reduce items that are already on the device and items on CPU for + better performance. + + Args: + data (Mapping[str, Any]): dictionary of data to all-reduce, but + cannot be a nested dictionary + device (torch.device): device for the reduction + group: group of the collective + """ + data_keys = list(data.keys()) + + # We want to separately reduce items that are already on the + # device and items on CPU for performance reasons. + cpu_data = OrderedDict() + device_data = OrderedDict() + for k in data_keys: + t = data[k] + if not torch.is_tensor(t): + cpu_data[k] = torch.tensor(t, dtype=torch.double) + elif t.device.type != device.type: + cpu_data[k] = t.to(dtype=torch.double) + else: + device_data[k] = t.to(dtype=torch.double) + + def _all_reduce_dict(data: OrderedDict): + if len(data) == 0: + return data + buf = torch.cat([t.view(-1) for t in data.values()]).to(device=device) + all_reduce(buf, group=group) + split_buf = torch.split(buf.clone(), [t.numel() for t in data.values()]) + reduced_data = [t.view_as(orig) for t, orig in zip(split_buf, data.values())] + return OrderedDict(zip(data.keys(), reduced_data)) + + cpu_data = _all_reduce_dict(cpu_data) + device_data = _all_reduce_dict(device_data) + + def get_from_stack(key): + if key in cpu_data: + return cpu_data[key] + elif key in device_data: + return device_data[key] + raise KeyError + + return OrderedDict([(key, get_from_stack(key)) for key in data_keys]) + + +def broadcast_tensors( + tensors: Optional[List[torch.Tensor]], + src_rank: int, + group: object, + dist_device: Optional[torch.device] = None, +) -> List[torch.Tensor]: + """ + Broadcasts a list of tensors without other (non-src) ranks needing to know + the dtypes/shapes of the tensors. + """ + if dist_device is None: + if torch.distributed.get_backend(group) == "nccl": + dist_device = torch.device("cuda") + else: + dist_device = torch.device("cpu") + + # share metadata first to simplify transfer + is_src_rank = get_rank(group) == src_rank + if is_src_rank: + metadata = [ + {"size": t.size(), "dtype": t.dtype, "device": t.device} for t in tensors + ] + metadata = _broadcast_object_slow(metadata, src_rank, group, dist_device) + else: + metadata = _broadcast_object_slow(None, src_rank, group, dist_device) + + out_tensors = [] + for i, meta in enumerate(metadata): + if is_src_rank: + tensor = tensors[i] + broadcast(tensors[i].to(dist_device), src=src_rank, group=group) + else: + tensor = torch.zeros( + [meta["size"].numel()], dtype=meta["dtype"], device=dist_device + ) + broadcast(tensor, src=src_rank, group=group) + tensor = tensor.view(meta["size"]).to(meta["device"]) + out_tensors.append(tensor) + return out_tensors + + +def broadcast_object( + obj: Any, + src_rank: int, + group: object, + dist_device: Optional[torch.device] = None, +) -> Any: + """Broadcast an arbitrary Python object to other workers.""" + if dist_device is None: + if torch.distributed.get_backend(group) == "nccl": + dist_device = torch.device("cuda") + else: + dist_device = torch.device("cpu") + + if get_rank(group) == src_rank: + # split the tensors from the non-tensors so we can broadcast them + # directly, avoiding unnecessary serialization/deserialization + tensors = [] + obj = _split_tensors_from_obj(obj, tensors) + obj = _broadcast_object_slow(obj, src_rank, group, dist_device) + tensors = broadcast_tensors(tensors, src_rank, group, dist_device) + else: + obj = _broadcast_object_slow(None, src_rank, group, dist_device) + tensors = broadcast_tensors(None, src_rank, group, dist_device) + return _put_tensors_in_obj(obj, tensors) + + +def _broadcast_object_slow( + obj: Any, + src_rank: int, + group: object, + dist_device: torch.device, +) -> Any: + if get_rank(group) == src_rank: + # Emit data + buffer = io.BytesIO() + torch.save(obj, buffer) + buffer = torch.ByteTensor(buffer.getbuffer()).to(dist_device) + length = torch.LongTensor([len(buffer)]).to(dist_device) + broadcast(length, src=src_rank, group=group) + broadcast(buffer, src=src_rank, group=group) + else: + # Fetch from the source + length = torch.LongTensor([0]).to(dist_device) + broadcast(length, src=src_rank, group=group) + buffer = torch.ByteTensor(int(length.item())).to(dist_device) + broadcast(buffer, src=src_rank, group=group) + buffer = io.BytesIO(buffer.cpu().numpy()) + obj = torch.load(buffer, map_location="cpu") + return obj + + +@dataclass(frozen=True) +class _TensorPlaceholder: + index: int + + +def _split_tensors_from_obj(obj: Any, tensors: List[torch.Tensor]) -> Any: + if torch.is_tensor(obj): + placeholder = _TensorPlaceholder(index=len(tensors)) + tensors.append(obj) + return placeholder + elif isinstance(obj, dict): + return {k: _split_tensors_from_obj(v, tensors) for k, v in obj.items()} + elif isinstance(obj, list): + return [_split_tensors_from_obj(v, tensors) for v in obj] + elif isinstance(obj, tuple): + return tuple(_split_tensors_from_obj(v, tensors) for v in obj) + elif isinstance(obj, set): + return {_split_tensors_from_obj(v, tensors) for v in obj} + else: + return obj + + +def _put_tensors_in_obj(obj: Any, tensors: List[torch.Tensor]) -> Any: + if isinstance(obj, _TensorPlaceholder): + return tensors[obj.index] + elif isinstance(obj, dict): + return {k: _put_tensors_in_obj(v, tensors) for k, v in obj.items()} + elif isinstance(obj, list): + return [_put_tensors_in_obj(v, tensors) for v in obj] + elif isinstance(obj, tuple): + return tuple(_put_tensors_in_obj(v, tensors) for v in obj) + elif isinstance(obj, set): + return {_put_tensors_in_obj(v, tensors) for v in obj} + else: + return obj diff --git a/fairseq/fairseq/file_chunker_utils.py b/fairseq/fairseq/file_chunker_utils.py new file mode 100644 index 0000000..3f27549 --- /dev/null +++ b/fairseq/fairseq/file_chunker_utils.py @@ -0,0 +1,84 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import typing as tp + + +def _safe_readline(fd) -> str: + pos = fd.tell() + while True: + try: + return fd.readline() + except UnicodeDecodeError: + pos -= 1 + fd.seek(pos) # search where this character begins + + +def find_offsets(filename: str, num_chunks: int) -> tp.List[int]: + """ + given a file and a number of chuncks, find the offsets in the file + to be able to chunk around full lines. + """ + with open(filename, "r", encoding="utf-8") as f: + size = os.fstat(f.fileno()).st_size + chunk_size = size // num_chunks + offsets = [0 for _ in range(num_chunks + 1)] + for i in range(1, num_chunks): + f.seek(chunk_size * i) + _safe_readline(f) + offsets[i] = f.tell() + offsets[-1] = size + return offsets + + +class ChunkLineIterator: + """ + Iterator to properly iterate over lines of a file chunck. + """ + + def __init__(self, fd, start_offset: int, end_offset: int): + self._fd = fd + self._start_offset = start_offset + self._end_offset = end_offset + + def __iter__(self) -> tp.Iterable[str]: + self._fd.seek(self._start_offset) + # next(f) breaks f.tell(), hence readline() must be used + line = _safe_readline(self._fd) + while line: + pos = self._fd.tell() + # f.tell() does not always give the byte position in the file + # sometimes it skips to a very large number + # it is unlikely that through a normal read we go from + # end bytes to end + 2**32 bytes (4 GB) and this makes it unlikely + # that the procedure breaks by the undeterministic behavior of + # f.tell() + if ( + self._end_offset > 0 + and pos > self._end_offset + and pos < self._end_offset + 2**32 + ): + break + yield line + line = self._fd.readline() + + +class Chunker: + """ + contextmanager to read a chunck of a file line by line. + """ + + def __init__(self, path: str, start_offset: int, end_offset: int): + self.path = path + self.start_offset = start_offset + self.end_offset = end_offset + + def __enter__(self) -> ChunkLineIterator: + self.fd = open(self.path, "r", encoding="utf-8") + return ChunkLineIterator(self.fd, self.start_offset, self.end_offset) + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + self.fd.close() diff --git a/fairseq/fairseq/file_io.py b/fairseq/fairseq/file_io.py new file mode 100644 index 0000000..8eca70a --- /dev/null +++ b/fairseq/fairseq/file_io.py @@ -0,0 +1,196 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import shutil +from typing import List, Optional + + +logger = logging.getLogger(__file__) + + +try: + from iopath.common.file_io import g_pathmgr as IOPathManager + + try: + # [FB only - for now] AWS PathHandler for PathManager + from .fb_pathhandlers import S3PathHandler + + IOPathManager.register_handler(S3PathHandler()) + except KeyError: + logging.warning("S3PathHandler already registered.") + except ImportError: + logging.debug( + "S3PathHandler couldn't be imported. Either missing fb-only files, or boto3 module." + ) + +except ImportError: + IOPathManager = None + + +class PathManager: + """ + Wrapper for insulating OSS I/O (using Python builtin operations) from + iopath's PathManager abstraction (for transparently handling various + internal backends). + """ + + @staticmethod + def open( + path: str, + mode: str = "r", + buffering: int = -1, + encoding: Optional[str] = None, + errors: Optional[str] = None, + newline: Optional[str] = None, + ): + if IOPathManager: + return IOPathManager.open( + path=path, + mode=mode, + buffering=buffering, + encoding=encoding, + errors=errors, + newline=newline, + ) + return open( + path, + mode=mode, + buffering=buffering, + encoding=encoding, + errors=errors, + newline=newline, + ) + + @staticmethod + def copy(src_path: str, dst_path: str, overwrite: bool = False) -> bool: + if IOPathManager: + return IOPathManager.copy( + src_path=src_path, dst_path=dst_path, overwrite=overwrite + ) + return shutil.copyfile(src_path, dst_path) + + @staticmethod + def get_local_path(path: str, **kwargs) -> str: + if IOPathManager: + return IOPathManager.get_local_path(path, **kwargs) + return path + + @staticmethod + def exists(path: str) -> bool: + if IOPathManager: + return IOPathManager.exists(path) + return os.path.exists(path) + + @staticmethod + def isfile(path: str) -> bool: + if IOPathManager: + return IOPathManager.isfile(path) + return os.path.isfile(path) + + @staticmethod + def ls(path: str) -> List[str]: + if IOPathManager: + return IOPathManager.ls(path) + return os.listdir(path) + + @staticmethod + def mkdirs(path: str) -> None: + if IOPathManager: + return IOPathManager.mkdirs(path) + os.makedirs(path, exist_ok=True) + + @staticmethod + def rm(path: str) -> None: + if IOPathManager: + return IOPathManager.rm(path) + os.remove(path) + + @staticmethod + def chmod(path: str, mode: int) -> None: + if not PathManager.path_requires_pathmanager(path): + os.chmod(path, mode) + + @staticmethod + def register_handler(handler) -> None: + if IOPathManager: + return IOPathManager.register_handler(handler=handler) + + @staticmethod + def copy_from_local( + local_path: str, dst_path: str, overwrite: bool = False, **kwargs + ) -> None: + if IOPathManager: + return IOPathManager.copy_from_local( + local_path=local_path, dst_path=dst_path, overwrite=overwrite, **kwargs + ) + return shutil.copyfile(local_path, dst_path) + + @staticmethod + def path_requires_pathmanager(path: str) -> bool: + """Do we require PathManager to access given path?""" + if IOPathManager: + for p in IOPathManager._path_handlers.keys(): + if path.startswith(p): + return True + return False + + @staticmethod + def supports_rename(path: str) -> bool: + # PathManager doesn't yet support renames + return not PathManager.path_requires_pathmanager(path) + + @staticmethod + def rename(src: str, dst: str): + os.rename(src, dst) + + """ + ioPath async PathManager methods: + """ + + @staticmethod + def opena( + path: str, + mode: str = "r", + buffering: int = -1, + encoding: Optional[str] = None, + errors: Optional[str] = None, + newline: Optional[str] = None, + ): + """ + Return file descriptor with asynchronous write operations. + """ + global IOPathManager + if not IOPathManager: + logging.info("ioPath is initializing PathManager.") + try: + from iopath.common.file_io import PathManager + + IOPathManager = PathManager() + except Exception: + logging.exception("Failed to initialize ioPath PathManager object.") + return IOPathManager.opena( + path=path, + mode=mode, + buffering=buffering, + encoding=encoding, + errors=errors, + newline=newline, + ) + + @staticmethod + def async_close() -> bool: + """ + Wait for files to be written and clean up asynchronous PathManager. + NOTE: `PathManager.async_close()` must be called at the end of any + script that uses `PathManager.opena(...)`. + """ + global IOPathManager + if IOPathManager: + return IOPathManager.async_close() + return False diff --git a/fairseq/fairseq/file_utils.py b/fairseq/fairseq/file_utils.py new file mode 100644 index 0000000..b99da2e --- /dev/null +++ b/fairseq/fairseq/file_utils.py @@ -0,0 +1,370 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Utilities for working with the local dataset cache. +This file is adapted from `AllenNLP <https://github.com/allenai/allennlp>`_. +and `huggingface <https://github.com/huggingface>`_. +""" + +import fnmatch +import json +import logging +import os +import shutil +import tarfile +import tempfile +from functools import partial, wraps +from hashlib import sha256 +from io import open + + +try: + from torch.hub import _get_torch_home + + torch_cache_home = _get_torch_home() +except ImportError: + torch_cache_home = os.path.expanduser( + os.getenv( + "TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch") + ) + ) +default_cache_path = os.path.join(torch_cache_home, "pytorch_fairseq") + +try: + from urllib.parse import urlparse +except ImportError: + from urlparse import urlparse + +try: + from pathlib import Path + + PYTORCH_FAIRSEQ_CACHE = Path(os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path)) +except (AttributeError, ImportError): + PYTORCH_FAIRSEQ_CACHE = os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path) + +CONFIG_NAME = "config.json" +WEIGHTS_NAME = "pytorch_model.bin" + +logger = logging.getLogger(__name__) # pylint: disable=invalid-name + + +def load_archive_file(archive_file): + # redirect to the cache, if necessary + try: + resolved_archive_file = cached_path(archive_file, cache_dir=None) + except EnvironmentError: + logger.info( + "Archive name '{}' was not found in archive name list. " + "We assumed '{}' was a path or URL but couldn't find any file " + "associated to this path or URL.".format( + archive_file, + archive_file, + ) + ) + return None + + if resolved_archive_file == archive_file: + logger.info("loading archive file {}".format(archive_file)) + else: + logger.info( + "loading archive file {} from cache at {}".format( + archive_file, resolved_archive_file + ) + ) + + # Extract archive to temp dir and replace .tar.bz2 if necessary + tempdir = None + if not os.path.isdir(resolved_archive_file): + tempdir = tempfile.mkdtemp() + logger.info( + "extracting archive file {} to temp dir {}".format( + resolved_archive_file, tempdir + ) + ) + ext = os.path.splitext(archive_file)[1][1:] + with tarfile.open(resolved_archive_file, "r:" + ext) as archive: + top_dir = os.path.commonprefix(archive.getnames()) + archive.extractall(tempdir) + os.remove(resolved_archive_file) + shutil.move(os.path.join(tempdir, top_dir), resolved_archive_file) + shutil.rmtree(tempdir) + + return resolved_archive_file + + +def url_to_filename(url, etag=None): + """ + Convert `url` into a hashed filename in a repeatable way. + If `etag` is specified, append its hash to the URL's, delimited + by a period. + """ + url_bytes = url.encode("utf-8") + url_hash = sha256(url_bytes) + filename = url_hash.hexdigest() + + if etag: + etag_bytes = etag.encode("utf-8") + etag_hash = sha256(etag_bytes) + filename += "." + etag_hash.hexdigest() + + return filename + + +def filename_to_url(filename, cache_dir=None): + """ + Return the url and etag (which may be ``None``) stored for `filename`. + Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. + """ + if cache_dir is None: + cache_dir = PYTORCH_FAIRSEQ_CACHE + if isinstance(cache_dir, Path): + cache_dir = str(cache_dir) + + cache_path = os.path.join(cache_dir, filename) + if not os.path.exists(cache_path): + raise EnvironmentError("file {} not found".format(cache_path)) + + meta_path = cache_path + ".json" + if not os.path.exists(meta_path): + raise EnvironmentError("file {} not found".format(meta_path)) + + with open(meta_path, encoding="utf-8") as meta_file: + metadata = json.load(meta_file) + url = metadata["url"] + etag = metadata["etag"] + + return url, etag + + +def cached_path_from_pm(url_or_filename): + """ + Tries to cache the specified URL using PathManager class. + Returns the cached path if success otherwise failure. + """ + try: + from fairseq.file_io import PathManager + + local_path = PathManager.get_local_path(url_or_filename) + return local_path + except Exception: + return None + + +def cached_path(url_or_filename, cache_dir=None): + """ + Given something that might be a URL (or might be a local path), + determine which. If it's a URL, download the file and cache it, and + return the path to the cached file. If it's already a local path, + make sure the file exists and then return the path. + """ + if cache_dir is None: + cache_dir = PYTORCH_FAIRSEQ_CACHE + if isinstance(url_or_filename, Path): + url_or_filename = str(url_or_filename) + if isinstance(cache_dir, Path): + cache_dir = str(cache_dir) + + parsed = urlparse(url_or_filename) + + if parsed.scheme in ("http", "https", "s3"): + # URL, so get it from the cache (downloading if necessary) + return get_from_cache(url_or_filename, cache_dir) + elif os.path.exists(url_or_filename): + # File, and it exists. + return url_or_filename + elif parsed.scheme == "": + # File, but it doesn't exist. + raise EnvironmentError("file {} not found".format(url_or_filename)) + else: + cached_path = cached_path_from_pm(url_or_filename) + if cached_path: + return cached_path + # Something unknown + raise ValueError( + "unable to parse {} as a URL or as a local path".format(url_or_filename) + ) + + +def split_s3_path(url): + """Split a full s3 path into the bucket name and path.""" + parsed = urlparse(url) + if not parsed.netloc or not parsed.path: + raise ValueError("bad s3 path {}".format(url)) + bucket_name = parsed.netloc + s3_path = parsed.path + # Remove '/' at beginning of path. + if s3_path.startswith("/"): + s3_path = s3_path[1:] + return bucket_name, s3_path + + +def s3_request(func): + """ + Wrapper function for s3 requests in order to create more helpful error + messages. + """ + + @wraps(func) + def wrapper(url, *args, **kwargs): + from botocore.exceptions import ClientError + + try: + return func(url, *args, **kwargs) + except ClientError as exc: + if int(exc.response["Error"]["Code"]) == 404: + raise EnvironmentError("file {} not found".format(url)) + else: + raise + + return wrapper + + +@s3_request +def s3_etag(url): + """Check ETag on S3 object.""" + import boto3 + + s3_resource = boto3.resource("s3") + bucket_name, s3_path = split_s3_path(url) + s3_object = s3_resource.Object(bucket_name, s3_path) + return s3_object.e_tag + + +@s3_request +def s3_get(url, temp_file): + """Pull a file directly from S3.""" + import boto3 + + s3_resource = boto3.resource("s3") + bucket_name, s3_path = split_s3_path(url) + s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) + + +def request_wrap_timeout(func, url): + import requests + + for attempt, timeout in enumerate([10, 20, 40, 60, 60]): + try: + return func(timeout=timeout) + except requests.exceptions.Timeout as e: + logger.warning( + "Request for %s timed-out (attempt %d). Retrying with a timeout of %d secs", + url, + attempt, + timeout, + exc_info=e, + ) + continue + raise RuntimeError(f"Unable to fetch file {url}") + + +def http_get(url, temp_file): + import requests + from tqdm import tqdm + + req = request_wrap_timeout(partial(requests.get, url, stream=True), url) + content_length = req.headers.get("Content-Length") + total = int(content_length) if content_length is not None else None + progress = tqdm(unit="B", total=total) + for chunk in req.iter_content(chunk_size=1024): + if chunk: # filter out keep-alive new chunks + progress.update(len(chunk)) + temp_file.write(chunk) + progress.close() + + +def get_from_cache(url, cache_dir=None): + """ + Given a URL, look for the corresponding dataset in the local cache. + If it's not there, download it. Then return the path to the cached file. + """ + if cache_dir is None: + cache_dir = PYTORCH_FAIRSEQ_CACHE + if isinstance(cache_dir, Path): + cache_dir = str(cache_dir) + + if not os.path.exists(cache_dir): + os.makedirs(cache_dir) + + # Get eTag to add to filename, if it exists. + if url.startswith("s3://"): + etag = s3_etag(url) + else: + try: + import requests + + response = request_wrap_timeout( + partial(requests.head, url, allow_redirects=True), url + ) + if response.status_code != 200: + etag = None + else: + etag = response.headers.get("ETag") + except RuntimeError: + etag = None + + filename = url_to_filename(url, etag) + + # get cache path to put the file + cache_path = os.path.join(cache_dir, filename) + + # If we don't have a connection (etag is None) and can't identify the file + # try to get the last downloaded one + if not os.path.exists(cache_path) and etag is None: + matching_files = fnmatch.filter(os.listdir(cache_dir), filename + ".*") + matching_files = list(filter(lambda s: not s.endswith(".json"), matching_files)) + if matching_files: + cache_path = os.path.join(cache_dir, matching_files[-1]) + + if not os.path.exists(cache_path): + # Download to temporary file, then copy to cache dir once finished. + # Otherwise you get corrupt cache entries if the download gets interrupted. + with tempfile.NamedTemporaryFile() as temp_file: + logger.info("%s not found in cache, downloading to %s", url, temp_file.name) + + # GET file object + if url.startswith("s3://"): + s3_get(url, temp_file) + else: + http_get(url, temp_file) + + # we are copying the file before closing it, so flush to avoid truncation + temp_file.flush() + # shutil.copyfileobj() starts at the current position, so go to the start + temp_file.seek(0) + + logger.info("copying %s to cache at %s", temp_file.name, cache_path) + with open(cache_path, "wb") as cache_file: + shutil.copyfileobj(temp_file, cache_file) + + logger.info("creating metadata file for %s", cache_path) + meta = {"url": url, "etag": etag} + meta_path = cache_path + ".json" + with open(meta_path, "w") as meta_file: + output_string = json.dumps(meta) + meta_file.write(output_string) + + logger.info("removing temp file %s", temp_file.name) + + return cache_path + + +def read_set_from_file(filename): + """ + Extract a de-duped collection (set) of text from a file. + Expected file format is one item per line. + """ + collection = set() + with open(filename, "r", encoding="utf-8") as file_: + for line in file_: + collection.add(line.rstrip()) + return collection + + +def get_file_extension(path, dot=True, lower=True): + ext = os.path.splitext(path)[1] + ext = ext if dot else ext[1:] + return ext.lower() if lower else ext diff --git a/fairseq/fairseq/hub_utils.py b/fairseq/fairseq/hub_utils.py new file mode 100644 index 0000000..b0c2da1 --- /dev/null +++ b/fairseq/fairseq/hub_utils.py @@ -0,0 +1,326 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import copy +import logging +import os +from typing import Any, Dict, Iterator, List + +import torch +from omegaconf import open_dict +from torch import nn + +from fairseq import utils +from fairseq.data import encoders + +logger = logging.getLogger(__name__) + + +def from_pretrained( + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + archive_map=None, + **kwargs +): + from fairseq import checkpoint_utils, file_utils + + if archive_map is not None: + if model_name_or_path in archive_map: + model_name_or_path = archive_map[model_name_or_path] + if data_name_or_path is not None and data_name_or_path in archive_map: + data_name_or_path = archive_map[data_name_or_path] + + # allow archive_map to set default arg_overrides (e.g., tokenizer, bpe) + # for each model + if isinstance(model_name_or_path, dict): + for k, v in model_name_or_path.items(): + if k == "checkpoint_file": + checkpoint_file = v + elif ( + k != "path" + # only set kwargs that don't already have overrides + and k not in kwargs + ): + kwargs[k] = v + model_name_or_path = model_name_or_path["path"] + + model_path = file_utils.load_archive_file(model_name_or_path) + + # convenience hack for loading data and BPE codes from model archive + if data_name_or_path.startswith("."): + kwargs["data"] = os.path.abspath(os.path.join(model_path, data_name_or_path)) + else: + kwargs["data"] = file_utils.load_archive_file(data_name_or_path) + for file, arg in { + "code": "bpe_codes", + "bpecodes": "bpe_codes", + "sentencepiece.bpe.model": "sentencepiece_model", + "merges.txt": "bpe_merges", + "vocab.json": "bpe_vocab", + }.items(): + path = os.path.join(model_path, file) + if os.path.exists(path): + kwargs[arg] = path + + if "user_dir" in kwargs: + utils.import_user_module(argparse.Namespace(user_dir=kwargs["user_dir"])) + + model_path = [ + os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep) + ] + + if "is_vocoder" in kwargs: + args = {"data": kwargs["data"], "model_path": model_path} + task = None + models = None + else: + models, args, task = checkpoint_utils.load_model_ensemble_and_task( + model_path, + arg_overrides=kwargs, + ) + if "generation_args" in kwargs and kwargs["generation_args"]: + for key in kwargs["generation_args"]: + setattr(args["generation"], key, kwargs["generation_args"][key]) + + return { + "args": args, + "task": task, + "models": models, + } + + +class GeneratorHubInterface(nn.Module): + """ + PyTorch Hub interface for generating sequences from a pre-trained + translation or language model. + """ + + def __init__(self, cfg, task, models): + super().__init__() + self.cfg = cfg + self.task = task + self.models = nn.ModuleList(models) + self.src_dict = task.source_dictionary + self.tgt_dict = task.target_dictionary + + # optimize model for generation + for model in self.models: + model.prepare_for_inference_(cfg) + + # Load alignment dictionary for unknown word replacement + # (None if no unknown word replacement, empty if no path to align dictionary) + self.align_dict = utils.load_align_dict(cfg.generation.replace_unk) + + self.tokenizer = encoders.build_tokenizer(cfg.tokenizer) + self.bpe = encoders.build_bpe(cfg.bpe) + + self.max_positions = utils.resolve_max_positions( + self.task.max_positions(), *[model.max_positions() for model in models] + ) + + # this is useful for determining the device + self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float)) + + @property + def device(self): + return self._float_tensor.device + + def translate( + self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs + ) -> List[str]: + return self.sample(sentences, beam, verbose, **kwargs) + + def sample( + self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs + ) -> List[str]: + if isinstance(sentences, str): + return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0] + tokenized_sentences = [self.encode(sentence) for sentence in sentences] + batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs) + return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos] + + def score( + self, sentences: List[str], replace_newline_with_eos: bool = False, **kwargs + ): + if isinstance(sentences, str): + return self.score( + [sentences], replace_newline_with_eos=replace_newline_with_eos, **kwargs + )[0] + + def encode(sentence): + if replace_newline_with_eos: + return torch.cat([self.encode(line) for line in sentence.splitlines()]) + else: + return self.encode(sentence) + + # NOTE: this doesn't support translation tasks currently + tokenized_sentences = [encode(sentence) for sentence in sentences] + return [ + hypos[0] + for hypos in self.generate( + tokenized_sentences, score_reference=True, **kwargs + ) + ] + + def generate( + self, + tokenized_sentences: List[torch.LongTensor], + beam: int = 5, + verbose: bool = False, + skip_invalid_size_inputs=False, + inference_step_args=None, + prefix_allowed_tokens_fn=None, + **kwargs + ) -> List[List[Dict[str, torch.Tensor]]]: + if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1: + return self.generate( + tokenized_sentences.unsqueeze(0), beam=beam, verbose=verbose, **kwargs + )[0] + + # build generator using current args as well as any kwargs + gen_args = copy.deepcopy(self.cfg.generation) + with open_dict(gen_args): + gen_args.beam = beam + for k, v in kwargs.items(): + setattr(gen_args, k, v) + generator = self.task.build_generator( + self.models, + gen_args, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + ) + + inference_step_args = inference_step_args or {} + results = [] + for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs): + batch = utils.apply_to_sample(lambda t: t.to(self.device), batch) + translations = self.task.inference_step( + generator, self.models, batch, **inference_step_args + ) + for id, hypos in zip(batch["id"].tolist(), translations): + results.append((id, hypos)) + + # sort output to match input order + outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])] + + if verbose: + + def getarg(name, default): + return getattr(gen_args, name, getattr(self.cfg, name, default)) + + for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs): + src_str_with_unk = self.string(source_tokens) + logger.info("S\t{}".format(src_str_with_unk)) + for hypo in target_hypotheses: + hypo_str = self.decode(hypo["tokens"]) + logger.info("H\t{}\t{}".format(hypo["score"], hypo_str)) + logger.info( + "P\t{}".format( + " ".join( + map( + lambda x: "{:.4f}".format(x), + hypo["positional_scores"].tolist(), + ) + ) + ) + ) + if hypo["alignment"] is not None and getarg( + "print_alignment", False + ): + logger.info( + "A\t{}".format( + " ".join( + [ + "{}-{}".format(src_idx, tgt_idx) + for src_idx, tgt_idx in hypo["alignment"] + ] + ) + ) + ) + return outputs + + def encode(self, sentence: str) -> torch.LongTensor: + sentence = self.tokenize(sentence) + sentence = self.apply_bpe(sentence) + return self.binarize(sentence) + + def decode(self, tokens: torch.LongTensor) -> str: + sentence = self.string(tokens) + sentence = self.remove_bpe(sentence) + return self.detokenize(sentence) + + def tokenize(self, sentence: str) -> str: + if self.tokenizer is not None: + sentence = self.tokenizer.encode(sentence) + return sentence + + def detokenize(self, sentence: str) -> str: + if self.tokenizer is not None: + sentence = self.tokenizer.decode(sentence) + return sentence + + def apply_bpe(self, sentence: str) -> str: + if self.bpe is not None: + sentence = self.bpe.encode(sentence) + return sentence + + def remove_bpe(self, sentence: str) -> str: + if self.bpe is not None: + sentence = self.bpe.decode(sentence) + return sentence + + def binarize(self, sentence: str) -> torch.LongTensor: + return self.src_dict.encode_line(sentence, add_if_not_exist=False).long() + + def string(self, tokens: torch.LongTensor) -> str: + return self.tgt_dict.string(tokens) + + def _build_batches( + self, tokens: List[List[int]], skip_invalid_size_inputs: bool + ) -> Iterator[Dict[str, Any]]: + lengths = torch.LongTensor([t.numel() for t in tokens]) + batch_iterator = self.task.get_batch_iterator( + dataset=self.task.build_dataset_for_inference(tokens, lengths), + max_tokens=self.cfg.dataset.max_tokens, + max_sentences=self.cfg.dataset.batch_size, + max_positions=self.max_positions, + ignore_invalid_inputs=skip_invalid_size_inputs, + disable_iterator_cache=True, + ).next_epoch_itr(shuffle=False) + return batch_iterator + + +class BPEHubInterface(object): + """PyTorch Hub interface for Byte-Pair Encoding (BPE).""" + + def __init__(self, bpe, **kwargs): + super().__init__() + args = argparse.Namespace(bpe=bpe, **kwargs) + self.bpe = encoders.build_bpe(args) + assert self.bpe is not None + + def encode(self, sentence: str) -> str: + return self.bpe.encode(sentence) + + def decode(self, sentence: str) -> str: + return self.bpe.decode(sentence) + + +class TokenizerHubInterface(object): + """PyTorch Hub interface for tokenization.""" + + def __init__(self, tokenizer, **kwargs): + super().__init__() + args = argparse.Namespace(tokenizer=tokenizer, **kwargs) + self.tokenizer = encoders.build_tokenizer(args) + assert self.tokenizer is not None + + def encode(self, sentence: str) -> str: + return self.tokenizer.encode(sentence) + + def decode(self, sentence: str) -> str: + return self.tokenizer.decode(sentence) diff --git a/fairseq/fairseq/incremental_decoding_utils.py b/fairseq/fairseq/incremental_decoding_utils.py new file mode 100644 index 0000000..b26e6cd --- /dev/null +++ b/fairseq/fairseq/incremental_decoding_utils.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import uuid +from typing import Dict, Optional + +from torch import Tensor + + +class FairseqIncrementalState(object): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.init_incremental_state() + + def init_incremental_state(self): + self._incremental_state_id = str(uuid.uuid4()) + + def _get_full_incremental_state_key(self, key: str) -> str: + return "{}.{}".format(self._incremental_state_id, key) + + def get_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + key: str, + ) -> Optional[Dict[str, Optional[Tensor]]]: + """Helper for getting incremental state for an nn.Module.""" + full_key = self._get_full_incremental_state_key(key) + if incremental_state is None or full_key not in incremental_state: + return None + return incremental_state[full_key] + + def set_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + key: str, + value: Dict[str, Optional[Tensor]], + ) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]: + """Helper for setting incremental state for an nn.Module.""" + if incremental_state is not None: + full_key = self._get_full_incremental_state_key(key) + incremental_state[full_key] = value + return incremental_state + + +def with_incremental_state(cls): + cls.__bases__ = (FairseqIncrementalState,) + tuple( + b for b in cls.__bases__ if b != FairseqIncrementalState + ) + return cls diff --git a/fairseq/fairseq/iterative_refinement_generator.py b/fairseq/fairseq/iterative_refinement_generator.py new file mode 100644 index 0000000..3d32c6b --- /dev/null +++ b/fairseq/fairseq/iterative_refinement_generator.py @@ -0,0 +1,359 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections import namedtuple + +import numpy as np +import torch +from fairseq import utils + + +DecoderOut = namedtuple( + "IterativeRefinementDecoderOut", + ["output_tokens", "output_scores", "attn", "step", "max_step", "history"], +) + + +class IterativeRefinementGenerator(object): + def __init__( + self, + tgt_dict, + models=None, + eos_penalty=0.0, + max_iter=10, + max_ratio=2, + beam_size=1, + decoding_format=None, + retain_dropout=False, + adaptive=True, + retain_history=False, + reranking=False, + ): + """ + Generates translations based on iterative refinement. + + Args: + tgt_dict: target dictionary + eos_penalty: if > 0.0, it penalized early-stopping in decoding + max_iter: maximum number of refinement iterations + max_ratio: generate sequences of maximum length ax, where x is the source length + decoding_format: decoding mode in {'unigram', 'ensemble', 'vote', 'dp', 'bs'} + retain_dropout: retaining dropout in the inference + adaptive: decoding with early stop + """ + self.bos = tgt_dict.bos() + self.pad = tgt_dict.pad() + self.unk = tgt_dict.unk() + self.eos = tgt_dict.eos() + self.vocab_size = len(tgt_dict) + self.eos_penalty = eos_penalty + self.max_iter = max_iter + self.max_ratio = max_ratio + self.beam_size = beam_size + self.reranking = reranking + self.decoding_format = decoding_format + self.retain_dropout = retain_dropout + self.retain_history = retain_history + self.adaptive = adaptive + self.models = models + + def generate_batched_itr( + self, + data_itr, + maxlen_a=None, + maxlen_b=None, + cuda=False, + timer=None, + prefix_size=0, + ): + """Iterate over a batched dataset and yield individual translations. + + Args: + maxlen_a/b: generate sequences of maximum length ax + b, + where x is the source sentence length. + cuda: use GPU for generation + timer: StopwatchMeter for timing generations. + """ + + for sample in data_itr: + if "net_input" not in sample: + continue + if timer is not None: + timer.start() + with torch.no_grad(): + hypos = self.generate( + self.models, + sample, + prefix_tokens=sample["target"][:, :prefix_size] + if prefix_size > 0 + else None, + ) + if timer is not None: + timer.stop(sample["ntokens"]) + for i, id in enumerate(sample["id"]): + # remove padding + src = utils.strip_pad(sample["net_input"]["src_tokens"][i, :], self.pad) + ref = utils.strip_pad(sample["target"][i, :], self.pad) + yield id, src, ref, hypos[i] + + @torch.no_grad() + def generate(self, models, sample, prefix_tokens=None, constraints=None): + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the IterativeRefinementGenerator is not supported" + ) + + # TODO: iterative refinement generator does not support ensemble for now. + if not self.retain_dropout: + for model in models: + model.eval() + + model, reranker = models[0], None + if self.reranking: + assert len(models) > 1, "Assuming the last checkpoint is the reranker" + assert ( + self.beam_size > 1 + ), "Reranking requires multiple translation for each example" + + reranker = models[-1] + models = models[:-1] + + if len(models) > 1 and hasattr(model, "enable_ensemble"): + assert model.allow_ensemble, "{} does not support ensembling".format( + model.__class__.__name__ + ) + model.enable_ensemble(models) + + # TODO: better encoder inputs? + src_tokens = sample["net_input"]["src_tokens"] + src_lengths = sample["net_input"]["src_lengths"] + bsz, src_len = src_tokens.size() + + # initialize + encoder_out = model.forward_encoder([src_tokens, src_lengths]) + prev_decoder_out = model.initialize_output_tokens(encoder_out, src_tokens) + + if self.beam_size > 1: + assert ( + model.allow_length_beam + ), "{} does not support decoding with length beam.".format( + model.__class__.__name__ + ) + + # regenerate data based on length-beam + length_beam_order = ( + utils.new_arange(src_tokens, self.beam_size, bsz).t().reshape(-1) + ) + encoder_out = model.encoder.reorder_encoder_out( + encoder_out, length_beam_order + ) + prev_decoder_out = model.regenerate_length_beam( + prev_decoder_out, self.beam_size + ) + bsz = bsz * self.beam_size + + sent_idxs = torch.arange(bsz) + prev_output_tokens = prev_decoder_out.output_tokens.clone() + + if self.retain_history: + prev_decoder_out = prev_decoder_out._replace(history=[prev_output_tokens]) + + finalized = [[] for _ in range(bsz)] + + def is_a_loop(x, y, s, a): + b, l_x, l_y = x.size(0), x.size(1), y.size(1) + if l_x > l_y: + y = torch.cat([y, x.new_zeros(b, l_x - l_y).fill_(self.pad)], 1) + s = torch.cat([s, s.new_zeros(b, l_x - l_y)], 1) + if a is not None: + a = torch.cat([a, a.new_zeros(b, l_x - l_y, a.size(2))], 1) + elif l_x < l_y: + x = torch.cat([x, y.new_zeros(b, l_y - l_x).fill_(self.pad)], 1) + return (x == y).all(1), y, s, a + + def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn): + cutoff = prev_out_token.ne(self.pad) + tokens = prev_out_token[cutoff] + if prev_out_score is None: + scores, score = None, None + else: + scores = prev_out_score[cutoff] + score = scores.mean() + + if prev_out_attn is None: + hypo_attn, alignment = None, None + else: + hypo_attn = prev_out_attn[cutoff] + alignment = hypo_attn.max(dim=1)[1] + return { + "steps": step, + "tokens": tokens, + "positional_scores": scores, + "score": score, + "hypo_attn": hypo_attn, + "alignment": alignment, + } + + for step in range(self.max_iter + 1): + + decoder_options = { + "eos_penalty": self.eos_penalty, + "max_ratio": self.max_ratio, + "decoding_format": self.decoding_format, + } + prev_decoder_out = prev_decoder_out._replace( + step=step, + max_step=self.max_iter + 1, + ) + + decoder_out = model.forward_decoder( + prev_decoder_out, encoder_out, **decoder_options + ) + + if self.adaptive: + # terminate if there is a loop + terminated, out_tokens, out_scores, out_attn = is_a_loop( + prev_output_tokens, + decoder_out.output_tokens, + decoder_out.output_scores, + decoder_out.attn, + ) + decoder_out = decoder_out._replace( + output_tokens=out_tokens, + output_scores=out_scores, + attn=out_attn, + ) + + else: + terminated = decoder_out.output_tokens.new_zeros( + decoder_out.output_tokens.size(0) + ).bool() + + if step == self.max_iter: # reach last iteration, terminate + terminated.fill_(1) + + # collect finalized sentences + finalized_idxs = sent_idxs[terminated.to(sent_idxs.device)] + finalized_tokens = decoder_out.output_tokens[terminated] + finalized_scores = decoder_out.output_scores[terminated] + finalized_attn = ( + None + if (decoder_out.attn is None or decoder_out.attn.size(0) == 0) + else decoder_out.attn[terminated] + ) + + if self.retain_history: + finalized_history_tokens = [h[terminated] for h in decoder_out.history] + + for i in range(finalized_idxs.size(0)): + finalized[finalized_idxs[i]] = [ + finalized_hypos( + step, + finalized_tokens[i], + finalized_scores[i], + None if finalized_attn is None else finalized_attn[i], + ) + ] + + if self.retain_history: + finalized[finalized_idxs[i]][0]["history"] = [] + for j in range(len(finalized_history_tokens)): + finalized[finalized_idxs[i]][0]["history"].append( + finalized_hypos( + step, finalized_history_tokens[j][i], None, None + ) + ) + + # check if all terminated + if terminated.sum() == terminated.size(0): + break + + # for next step + not_terminated = ~terminated + prev_decoder_out = decoder_out._replace( + output_tokens=decoder_out.output_tokens[not_terminated], + output_scores=decoder_out.output_scores[not_terminated], + attn=decoder_out.attn[not_terminated] + if (decoder_out.attn is not None and decoder_out.attn.size(0) > 0) + else None, + history=[h[not_terminated] for h in decoder_out.history] + if decoder_out.history is not None + else None, + ) + encoder_out = model.encoder.reorder_encoder_out( + encoder_out, not_terminated.nonzero(as_tuple=False).squeeze() + ) + sent_idxs = sent_idxs[not_terminated.to(sent_idxs.device)] + prev_output_tokens = prev_decoder_out.output_tokens.clone() + + if self.beam_size > 1: + if reranker is not None: + finalized = self.rerank( + reranker, finalized, [src_tokens, src_lengths], self.beam_size + ) + + # aggregate information from length beam + finalized = [ + finalized[ + np.argmax( + [ + finalized[self.beam_size * i + j][0]["score"] + for j in range(self.beam_size) + ] + ) + + self.beam_size * i + ] + for i in range(len(finalized) // self.beam_size) + ] + + return finalized + + def rerank(self, reranker, finalized, encoder_input, beam_size): + def rebuild_batch(finalized): + finalized_tokens = [f[0]["tokens"] for f in finalized] + finalized_maxlen = max(f.size(0) for f in finalized_tokens) + final_output_tokens = ( + finalized_tokens[0] + .new_zeros(len(finalized_tokens), finalized_maxlen) + .fill_(self.pad) + ) + for i, f in enumerate(finalized_tokens): + final_output_tokens[i, : f.size(0)] = f + return final_output_tokens + + final_output_tokens = rebuild_batch(finalized) + final_output_tokens[ + :, 0 + ] = self.eos # autoregressive model assumes starting with EOS + + reranker_encoder_out = reranker.encoder(*encoder_input) + length_beam_order = ( + utils.new_arange( + final_output_tokens, beam_size, reranker_encoder_out.encoder_out.size(1) + ) + .t() + .reshape(-1) + ) + reranker_encoder_out = reranker.encoder.reorder_encoder_out( + reranker_encoder_out, length_beam_order + ) + reranking_scores = reranker.get_normalized_probs( + reranker.decoder(final_output_tokens[:, :-1], reranker_encoder_out), + True, + None, + ) + reranking_scores = reranking_scores.gather(2, final_output_tokens[:, 1:, None]) + reranking_masks = final_output_tokens[:, 1:].ne(self.pad) + reranking_scores = ( + reranking_scores[:, :, 0].masked_fill_(~reranking_masks, 0).sum(1) + ) + reranking_scores = reranking_scores / reranking_masks.sum(1).type_as( + reranking_scores + ) + + for i in range(len(finalized)): + finalized[i][0]["score"] = reranking_scores[i] + + return finalized diff --git a/fairseq/fairseq/logging/__init__.py b/fairseq/fairseq/logging/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/fairseq/logging/meters.py b/fairseq/fairseq/logging/meters.py new file mode 100644 index 0000000..495bd08 --- /dev/null +++ b/fairseq/fairseq/logging/meters.py @@ -0,0 +1,351 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import bisect +import time +from collections import OrderedDict +from typing import Dict, Optional + +try: + import torch + + def type_as(a, b): + if torch.is_tensor(a) and torch.is_tensor(b): + return a.to(b) + else: + return a + +except ImportError: + torch = None + + def type_as(a, b): + return a + + +try: + import numpy as np +except ImportError: + np = None + + +class Meter(object): + """Base class for Meters.""" + + def __init__(self): + pass + + def state_dict(self): + return {} + + def load_state_dict(self, state_dict): + pass + + def reset(self): + raise NotImplementedError + + @property + def smoothed_value(self) -> float: + """Smoothed value used for logging.""" + raise NotImplementedError + + +def safe_round(number, ndigits): + if hasattr(number, "__round__"): + return round(number, ndigits) + elif torch is not None and torch.is_tensor(number) and number.numel() == 1: + return safe_round(number.item(), ndigits) + elif np is not None and np.ndim(number) == 0 and hasattr(number, "item"): + return safe_round(number.item(), ndigits) + else: + return number + + +class AverageMeter(Meter): + """Computes and stores the average and current value""" + + def __init__(self, round: Optional[int] = None): + self.round = round + self.reset() + + def reset(self): + self.val = None # most recent update + self.sum = 0 # sum from all updates + self.count = 0 # total n from all updates + + def update(self, val, n=1): + if val is not None: + self.val = val + if n > 0: + self.sum = type_as(self.sum, val) + (val * n) + self.count = type_as(self.count, n) + n + + def state_dict(self): + return { + "val": self.val, + "sum": self.sum, + "count": self.count, + "round": self.round, + } + + def load_state_dict(self, state_dict): + self.val = state_dict["val"] + self.sum = state_dict["sum"] + self.count = state_dict["count"] + self.round = state_dict.get("round", None) + + @property + def avg(self): + return self.sum / self.count if self.count > 0 else self.val + + @property + def smoothed_value(self) -> float: + val = self.avg + if self.round is not None and val is not None: + val = safe_round(val, self.round) + return val + + +class SumMeter(Meter): + """Computes and stores the sum""" + + def __init__(self, round: Optional[int] = None): + self.round = round + self.reset() + + def reset(self): + self.sum = 0 # sum from all updates + + def update(self, val): + if val is not None: + self.sum = type_as(self.sum, val) + val + + def state_dict(self): + return { + "sum": self.sum, + "round": self.round, + } + + def load_state_dict(self, state_dict): + self.sum = state_dict["sum"] + self.round = state_dict.get("round", None) + + @property + def smoothed_value(self) -> float: + val = self.sum + if self.round is not None and val is not None: + val = safe_round(val, self.round) + return val + + +class ConcatTensorMeter(Meter): + """Concatenates tensors""" + + def __init__(self, dim=0): + super().__init__() + self.reset() + self.dim = dim + + def reset(self): + self.tensor = None + + def update(self, val): + if self.tensor is None: + self.tensor = val + else: + self.tensor = torch.cat([self.tensor, val], dim=self.dim) + + def state_dict(self): + return { + "tensor": self.tensor, + } + + def load_state_dict(self, state_dict): + self.tensor = state_dict["tensor"] + + @property + def smoothed_value(self) -> float: + return [] # return a dummy value + + +class TimeMeter(Meter): + """Computes the average occurrence of some event per second""" + + def __init__( + self, + init: int = 0, + n: int = 0, + round: Optional[int] = None, + ): + self.round = round + self.reset(init, n) + + def reset(self, init=0, n=0): + self.init = init + self.start = time.perf_counter() + self.n = n + self.i = 0 + + def update(self, val=1): + self.n = type_as(self.n, val) + val + self.i += 1 + + def state_dict(self): + return { + "init": self.elapsed_time, + "n": self.n, + "round": self.round, + } + + def load_state_dict(self, state_dict): + if "start" in state_dict: + # backwards compatibility for old state_dicts + self.reset(init=state_dict["init"]) + else: + self.reset(init=state_dict["init"], n=state_dict["n"]) + self.round = state_dict.get("round", None) + + @property + def avg(self): + return self.n / self.elapsed_time + + @property + def elapsed_time(self): + return self.init + (time.perf_counter() - self.start) + + @property + def smoothed_value(self) -> float: + val = self.avg + if self.round is not None and val is not None: + val = safe_round(val, self.round) + return val + + +class StopwatchMeter(Meter): + """Computes the sum/avg duration of some event in seconds""" + + def __init__(self, round: Optional[int] = None): + self.round = round + self.sum = 0 + self.n = 0 + self.start_time = None + + def start(self): + self.start_time = time.perf_counter() + + def stop(self, n=1, prehook=None): + if self.start_time is not None: + if prehook is not None: + prehook() + delta = time.perf_counter() - self.start_time + self.sum = self.sum + delta + self.n = type_as(self.n, n) + n + + def reset(self): + self.sum = 0 # cumulative time during which stopwatch was active + self.n = 0 # total n across all start/stop + self.start() + + def state_dict(self): + return { + "sum": self.sum, + "n": self.n, + "round": self.round, + } + + def load_state_dict(self, state_dict): + self.sum = state_dict["sum"] + self.n = state_dict["n"] + self.start_time = None + self.round = state_dict.get("round", None) + + @property + def avg(self): + return self.sum / self.n if self.n > 0 else self.sum + + @property + def elapsed_time(self): + if self.start_time is None: + return 0.0 + return time.perf_counter() - self.start_time + + @property + def smoothed_value(self) -> float: + val = self.avg if self.sum > 0 else self.elapsed_time + if self.round is not None and val is not None: + val = safe_round(val, self.round) + return val + + +class MetersDict(OrderedDict): + """A sorted dictionary of :class:`Meters`. + + Meters are sorted according to a priority that is given when the + meter is first added to the dictionary. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.priorities = [] + + def __setitem__(self, key, value): + assert key not in self, "MetersDict doesn't support reassignment" + priority, value = value + bisect.insort(self.priorities, (priority, len(self.priorities), key)) + super().__setitem__(key, value) + for _, _, key in self.priorities: # reorder dict to match priorities + self.move_to_end(key) + + def add_meter(self, key, meter, priority): + self.__setitem__(key, (priority, meter)) + + def state_dict(self): + return [ + (pri, key, self[key].__class__.__name__, self[key].state_dict()) + for pri, _, key in self.priorities + # can't serialize DerivedMeter instances + if not isinstance(self[key], MetersDict._DerivedMeter) + ] + + def load_state_dict(self, state_dict): + self.clear() + self.priorities.clear() + for pri, key, meter_cls, meter_state in state_dict: + meter = globals()[meter_cls]() + meter.load_state_dict(meter_state) + self.add_meter(key, meter, pri) + + def get_smoothed_value(self, key: str) -> float: + """Get a single smoothed value.""" + meter = self[key] + if isinstance(meter, MetersDict._DerivedMeter): + return meter.fn(self) + else: + return meter.smoothed_value + + def get_smoothed_values(self) -> Dict[str, float]: + """Get all smoothed values.""" + return OrderedDict( + [ + (key, self.get_smoothed_value(key)) + for key in self.keys() + if not key.startswith("_") + ] + ) + + def reset(self): + """Reset Meter instances.""" + for meter in self.values(): + if isinstance(meter, MetersDict._DerivedMeter): + continue + meter.reset() + + class _DerivedMeter(Meter): + """A Meter whose values are derived from other Meters.""" + + def __init__(self, fn): + self.fn = fn + + def reset(self): + pass diff --git a/fairseq/fairseq/logging/metrics.py b/fairseq/fairseq/logging/metrics.py new file mode 100644 index 0000000..49301f2 --- /dev/null +++ b/fairseq/fairseq/logging/metrics.py @@ -0,0 +1,336 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +A standalone module for aggregating metrics. + +Metrics can be logged from anywhere using the `log_*` functions defined +in this module. The logged values will be aggregated dynamically based +on the aggregation context in which the logging occurs. See the +:func:`aggregate` context manager for more details. +""" + +import contextlib +import uuid +from collections import defaultdict +from typing import Callable, List, Optional + +from .meters import * + + +# Aggregation contexts are considered "active" when inside the scope +# created by the :func:`aggregate` context manager. +_aggregators = OrderedDict() +_active_aggregators = OrderedDict() +_active_aggregators_cnt = defaultdict(lambda: 0) + + +def reset() -> None: + """Reset all metrics aggregators.""" + _aggregators.clear() + _active_aggregators.clear() + _active_aggregators_cnt.clear() + + # The "default" aggregator observes all logged values. + _aggregators["default"] = MetersDict() + _active_aggregators["default"] = _aggregators["default"] + _active_aggregators_cnt["default"] = 1 + + +reset() + + +@contextlib.contextmanager +def aggregate(name: Optional[str] = None, new_root: bool = False): + """Context manager to aggregate metrics under a given name. + + Aggregations can be nested. If *new_root* is ``False``, then logged + metrics will be recorded along the entire stack of nested + aggregators, including a global "default" aggregator. If *new_root* + is ``True``, then this aggregator will be the root of a new + aggregation stack, thus bypassing any parent aggregators. + + Note that aggregation contexts are uniquely identified by their + *name* (e.g., train, valid). Creating a context with an existing + name will reuse the corresponding :class:`MetersDict` instance. + If no name is given, then a temporary aggregator will be created. + + Usage:: + + with metrics.aggregate("train"): + for step, batch in enumerate(epoch): + with metrics.aggregate("train_inner") as agg: + metrics.log_scalar("loss", get_loss(batch)) + if step % log_interval == 0: + print(agg.get_smoothed_value("loss")) + agg.reset() + print(metrics.get_smoothed_values("train")["loss"]) + + Args: + name (str): name of the aggregation. Defaults to a + random/temporary name if not given explicitly. + new_root (bool): make this aggregation the root of a new + aggregation stack. + """ + if name is None: + # generate a temporary name + name = str(uuid.uuid4()) + assert name not in _aggregators + agg = MetersDict() + else: + assert name != "default" + agg = _aggregators.setdefault(name, MetersDict()) + + if new_root: + backup_aggregators = _active_aggregators.copy() + _active_aggregators.clear() + backup_aggregators_cnt = _active_aggregators_cnt.copy() + _active_aggregators_cnt.clear() + + _active_aggregators[name] = agg + _active_aggregators_cnt[name] += 1 + + yield agg + + _active_aggregators_cnt[name] -= 1 + if _active_aggregators_cnt[name] == 0 and name in _active_aggregators: + del _active_aggregators[name] + + if new_root: + _active_aggregators.clear() + _active_aggregators.update(backup_aggregators) + _active_aggregators_cnt.clear() + _active_aggregators_cnt.update(backup_aggregators_cnt) + + +def get_active_aggregators() -> List[MetersDict]: + return list(_active_aggregators.values()) + + +def log_scalar( + key: str, + value: float, + weight: float = 1, + priority: int = 10, + round: Optional[int] = None, +): + """Log a scalar value. + + Args: + key (str): name of the field to log + value (float): value to log + weight (float): weight that this value contributes to the average. + A weight of 0 will always log the latest value. + priority (int): smaller values are logged earlier in the output + round (Optional[int]): number of digits to round to when displaying + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, AverageMeter(round=round), priority) + agg[key].update(value, weight) + + +def log_scalar_sum( + key: str, + value: float, + priority: int = 10, + round: Optional[int] = None, +): + """Log a scalar value that is summed for reporting. + + Args: + key (str): name of the field to log + value (float): value to log + priority (int): smaller values are logged earlier in the output + round (Optional[int]): number of digits to round to when displaying + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, SumMeter(round=round), priority) + agg[key].update(value) + + +def log_concat_tensor( + key: str, + value: torch.Tensor, + priority: int = 10, + dim: int = 0, +): + """Log a scalar value that is summed for reporting. + + Args: + key (str): name of the field to log + value (float): value to log + priority (int): smaller values are logged earlier in the output + round (Optional[int]): number of digits to round to when displaying + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, ConcatTensorMeter(dim=dim), priority) + agg[key].update(value) + + +def log_derived(key: str, fn: Callable[[MetersDict], float], priority: int = 20): + """Log a scalar value derived from other meters. + + Args: + key (str): name of the field to log + fn (Callable[[MetersDict], float]): function that takes a single + argument *meters* and returns the derived value + priority (int): smaller values are logged earlier in the output + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, MetersDict._DerivedMeter(fn), priority) + + +def log_speed( + key: str, + value: float, + priority: int = 30, + round: Optional[int] = None, +): + """Log the rate of some quantity per second. + + Args: + key (str): name of the field to log + value (float): value to log + priority (int): smaller values are logged earlier in the output + round (Optional[int]): number of digits to round to when displaying + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, TimeMeter(round=round), priority) + agg[key].reset() # reset meter on the first call + else: + agg[key].update(value) + + +def log_start_time(key: str, priority: int = 40, round: Optional[int] = None): + """Log the duration of some event in seconds. + + The duration will be computed once :func:`log_stop_time` is called. + + Args: + key (str): name of the field to log + priority (int): smaller values are logged earlier in the output + round (Optional[int]): number of digits to round to when displaying + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, StopwatchMeter(round=round), priority) + agg[key].start() + + +def log_stop_time(key: str, weight: float = 0.0, prehook=None): + """Log the duration of some event in seconds. + + The duration will be computed since :func:`log_start_time` was called. + Set weight > 0 to report the average time instead of the sum. + + Args: + key (str): name of the field to log + weight (float): weight that this time contributes to the average + prehook (function, no arguments): will be called before the timer + is stopped. For example, use prehook=torch.cuda.synchronize to + make sure all gpu operations are done before timer is stopped. + """ + for agg in get_active_aggregators(): + if key in agg: + agg[key].stop(weight, prehook) + + +def log_custom( + new_meter_fn: Callable[[], Meter], + key: str, + *args, + priority: int = 50, + **kwargs, +): + """Log using a custom Meter. + + Any extra *args* or *kwargs* will be passed through to the Meter's + *update* method. + + Args: + new_meter_fn (Callable[[], Meter]): function that returns a new + Meter instance + key (str): name of the field to log + priority (int): smaller values are logged earlier in the output + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, new_meter_fn(), priority) + agg[key].update(*args, **kwargs) + + +def reset_meter(name: str, key: str) -> None: + """Reset Meter instance aggregated under a given *name* and *key*.""" + meter = get_meter(name, key) + if meter is not None: + meter.reset() + + +def reset_meters(name: str) -> None: + """Reset Meter instances aggregated under a given *name*.""" + meters = get_meters(name) + if meters is not None: + meters.reset() + + +def get_meter(name: str, key: str) -> Meter: + """Get a single Meter instance aggregated under *name* and *key*. + + Returns: + Meter or None if no metrics have been logged under *name* and *key*. + """ + if name not in _aggregators: + return None + return _aggregators[name].get(key, None) + + +def get_meters(name: str) -> MetersDict: + """Get Meter instances aggregated under a given *name*. + + Returns: + MetersDict or None if no metrics have been logged under *name*. + """ + return _aggregators.get(name, None) + + +def get_smoothed_value(name: str, key: str) -> float: + """Get a single smoothed value. + + Raises: + KeyError: if no metrics have been logged under *name* and *key*. + """ + return _aggregators[name].get_smoothed_value(key) + + +def get_smoothed_values(name: str) -> Dict[str, float]: + """Get smoothed values aggregated under a given *name*. + + Raises: + KeyError: if no metrics have been logged under *name*. + """ + return _aggregators[name].get_smoothed_values() + + +def state_dict(): + return OrderedDict([(name, agg.state_dict()) for name, agg in _aggregators.items()]) + + +def load_state_dict(state_dict): + for name, agg_state in state_dict.items(): + _aggregators[name] = MetersDict() + _aggregators[name].load_state_dict(agg_state) + + +def xla_metrics_report(): + try: + import torch_xla.debug.metrics as met + + print(met.metrics_report()) + except ImportError: + return diff --git a/fairseq/fairseq/logging/progress_bar.py b/fairseq/fairseq/logging/progress_bar.py new file mode 100644 index 0000000..4c64b61 --- /dev/null +++ b/fairseq/fairseq/logging/progress_bar.py @@ -0,0 +1,582 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Wrapper around various loggers and progress bars (e.g., tqdm). +""" + +import atexit +import json +import logging +import os +import sys +from collections import OrderedDict +from contextlib import contextmanager +from numbers import Number +from typing import Optional + +import torch + +from .meters import AverageMeter, StopwatchMeter, TimeMeter + +logger = logging.getLogger(__name__) + + +def progress_bar( + iterator, + log_format: Optional[str] = None, + log_interval: int = 100, + log_file: Optional[str] = None, + epoch: Optional[int] = None, + prefix: Optional[str] = None, + aim_repo: Optional[str] = None, + aim_run_hash: Optional[str] = None, + aim_param_checkpoint_dir: Optional[str] = None, + tensorboard_logdir: Optional[str] = None, + default_log_format: str = "tqdm", + wandb_project: Optional[str] = None, + wandb_run_name: Optional[str] = None, + azureml_logging: Optional[bool] = False, +): + if log_format is None: + log_format = default_log_format + if log_file is not None: + handler = logging.FileHandler(filename=log_file) + logger.addHandler(handler) + + if log_format == "tqdm" and not sys.stderr.isatty(): + log_format = "simple" + + if log_format == "json": + bar = JsonProgressBar(iterator, epoch, prefix, log_interval) + elif log_format == "none": + bar = NoopProgressBar(iterator, epoch, prefix) + elif log_format == "simple": + bar = SimpleProgressBar(iterator, epoch, prefix, log_interval) + elif log_format == "tqdm": + bar = TqdmProgressBar(iterator, epoch, prefix) + else: + raise ValueError("Unknown log format: {}".format(log_format)) + + if aim_repo: + bar = AimProgressBarWrapper( + bar, + aim_repo=aim_repo, + aim_run_hash=aim_run_hash, + aim_param_checkpoint_dir=aim_param_checkpoint_dir, + ) + + if tensorboard_logdir: + try: + # [FB only] custom wrapper for TensorBoard + import palaas # noqa + + from .fb_tbmf_wrapper import FbTbmfWrapper + + bar = FbTbmfWrapper(bar, log_interval) + except ImportError: + bar = TensorboardProgressBarWrapper(bar, tensorboard_logdir) + + if wandb_project: + bar = WandBProgressBarWrapper(bar, wandb_project, run_name=wandb_run_name) + + if azureml_logging: + bar = AzureMLProgressBarWrapper(bar) + + return bar + + +def build_progress_bar( + args, + iterator, + epoch: Optional[int] = None, + prefix: Optional[str] = None, + default: str = "tqdm", + no_progress_bar: str = "none", +): + """Legacy wrapper that takes an argparse.Namespace.""" + if getattr(args, "no_progress_bar", False): + default = no_progress_bar + if getattr(args, "distributed_rank", 0) == 0: + tensorboard_logdir = getattr(args, "tensorboard_logdir", None) + else: + tensorboard_logdir = None + return progress_bar( + iterator, + log_format=args.log_format, + log_interval=args.log_interval, + epoch=epoch, + prefix=prefix, + tensorboard_logdir=tensorboard_logdir, + default_log_format=default, + ) + + +def format_stat(stat): + if isinstance(stat, Number): + stat = "{:g}".format(stat) + elif isinstance(stat, AverageMeter): + stat = "{:.3f}".format(stat.avg) + elif isinstance(stat, TimeMeter): + stat = "{:g}".format(round(stat.avg)) + elif isinstance(stat, StopwatchMeter): + stat = "{:g}".format(round(stat.sum)) + elif torch.is_tensor(stat): + stat = stat.tolist() + return stat + + +class BaseProgressBar(object): + """Abstract class for progress bars.""" + + def __init__(self, iterable, epoch=None, prefix=None): + self.iterable = iterable + self.n = getattr(iterable, "n", 0) + self.epoch = epoch + self.prefix = "" + if epoch is not None: + self.prefix += "epoch {:03d}".format(epoch) + if prefix is not None: + self.prefix += (" | " if self.prefix != "" else "") + prefix + + def __len__(self): + return len(self.iterable) + + def __enter__(self): + return self + + def __exit__(self, *exc): + return False + + def __iter__(self): + raise NotImplementedError + + def log(self, stats, tag=None, step=None): + """Log intermediate stats according to log_interval.""" + raise NotImplementedError + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + raise NotImplementedError + + def update_config(self, config): + """Log latest configuration.""" + pass + + def _str_commas(self, stats): + return ", ".join(key + "=" + stats[key].strip() for key in stats.keys()) + + def _str_pipes(self, stats): + return " | ".join(key + " " + stats[key].strip() for key in stats.keys()) + + def _format_stats(self, stats): + postfix = OrderedDict(stats) + # Preprocess stats according to datatype + for key in postfix.keys(): + postfix[key] = str(format_stat(postfix[key])) + return postfix + + +@contextmanager +def rename_logger(logger, new_name): + old_name = logger.name + if new_name is not None: + logger.name = new_name + yield logger + logger.name = old_name + + +class JsonProgressBar(BaseProgressBar): + """Log output in JSON format.""" + + def __init__(self, iterable, epoch=None, prefix=None, log_interval=1000): + super().__init__(iterable, epoch, prefix) + self.log_interval = log_interval + self.i = None + self.size = None + + def __iter__(self): + self.size = len(self.iterable) + for i, obj in enumerate(self.iterable, start=self.n): + self.i = i + yield obj + + def log(self, stats, tag=None, step=None): + """Log intermediate stats according to log_interval.""" + step = step or self.i or 0 + if step > 0 and self.log_interval is not None and step % self.log_interval == 0: + update = ( + self.epoch - 1 + (self.i + 1) / float(self.size) + if self.epoch is not None + else None + ) + stats = self._format_stats(stats, epoch=self.epoch, update=update) + with rename_logger(logger, tag): + logger.info(json.dumps(stats)) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + self.stats = stats + if tag is not None: + self.stats = OrderedDict( + [(tag + "_" + k, v) for k, v in self.stats.items()] + ) + stats = self._format_stats(self.stats, epoch=self.epoch) + with rename_logger(logger, tag): + logger.info(json.dumps(stats)) + + def _format_stats(self, stats, epoch=None, update=None): + postfix = OrderedDict() + if epoch is not None: + postfix["epoch"] = epoch + if update is not None: + postfix["update"] = round(update, 3) + # Preprocess stats according to datatype + for key in stats.keys(): + postfix[key] = format_stat(stats[key]) + return postfix + + +class NoopProgressBar(BaseProgressBar): + """No logging.""" + + def __init__(self, iterable, epoch=None, prefix=None): + super().__init__(iterable, epoch, prefix) + + def __iter__(self): + for obj in self.iterable: + yield obj + + def log(self, stats, tag=None, step=None): + """Log intermediate stats according to log_interval.""" + pass + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + pass + + +class SimpleProgressBar(BaseProgressBar): + """A minimal logger for non-TTY environments.""" + + def __init__(self, iterable, epoch=None, prefix=None, log_interval=1000): + super().__init__(iterable, epoch, prefix) + self.log_interval = log_interval + self.i = None + self.size = None + + def __iter__(self): + self.size = len(self.iterable) + for i, obj in enumerate(self.iterable, start=self.n): + self.i = i + yield obj + + def log(self, stats, tag=None, step=None): + """Log intermediate stats according to log_interval.""" + step = step or self.i or 0 + if step > 0 and self.log_interval is not None and step % self.log_interval == 0: + stats = self._format_stats(stats) + postfix = self._str_commas(stats) + with rename_logger(logger, tag): + logger.info( + "{}: {:5d} / {:d} {}".format( + self.prefix, self.i + 1, self.size, postfix + ) + ) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + postfix = self._str_pipes(self._format_stats(stats)) + with rename_logger(logger, tag): + logger.info("{} | {}".format(self.prefix, postfix)) + + +class TqdmProgressBar(BaseProgressBar): + """Log to tqdm.""" + + def __init__(self, iterable, epoch=None, prefix=None): + super().__init__(iterable, epoch, prefix) + from tqdm import tqdm + + self.tqdm = tqdm( + iterable, + self.prefix, + leave=False, + disable=(logger.getEffectiveLevel() > logging.INFO), + ) + + def __iter__(self): + return iter(self.tqdm) + + def log(self, stats, tag=None, step=None): + """Log intermediate stats according to log_interval.""" + self.tqdm.set_postfix(self._format_stats(stats), refresh=False) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + postfix = self._str_pipes(self._format_stats(stats)) + with rename_logger(logger, tag): + logger.info("{} | {}".format(self.prefix, postfix)) + + +try: + import functools + + from aim import Repo as AimRepo + + @functools.lru_cache() + def get_aim_run(repo, run_hash): + from aim import Run + + return Run(run_hash=run_hash, repo=repo) + +except ImportError: + get_aim_run = None + AimRepo = None + + +class AimProgressBarWrapper(BaseProgressBar): + """Log to Aim.""" + + def __init__(self, wrapped_bar, aim_repo, aim_run_hash, aim_param_checkpoint_dir): + self.wrapped_bar = wrapped_bar + + if get_aim_run is None: + self.run = None + logger.warning("Aim not found, please install with: pip install aim") + else: + logger.info(f"Storing logs at Aim repo: {aim_repo}") + + if not aim_run_hash: + # Find run based on save_dir parameter + query = f"run.checkpoint.save_dir == '{aim_param_checkpoint_dir}'" + try: + runs_generator = AimRepo(aim_repo).query_runs(query) + run = next(runs_generator.iter_runs()) + aim_run_hash = run.run.hash + except Exception: + pass + + if aim_run_hash: + logger.info(f"Appending to run: {aim_run_hash}") + + self.run = get_aim_run(aim_repo, aim_run_hash) + + def __iter__(self): + return iter(self.wrapped_bar) + + def log(self, stats, tag=None, step=None): + """Log intermediate stats to Aim.""" + self._log_to_aim(stats, tag, step) + self.wrapped_bar.log(stats, tag=tag, step=step) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + self._log_to_aim(stats, tag, step) + self.wrapped_bar.print(stats, tag=tag, step=step) + + def update_config(self, config): + """Log latest configuration.""" + if self.run is not None: + for key in config: + self.run.set(key, config[key], strict=False) + self.wrapped_bar.update_config(config) + + def _log_to_aim(self, stats, tag=None, step=None): + if self.run is None: + return + + if step is None: + step = stats["num_updates"] + + if "train" in tag: + context = {"tag": tag, "subset": "train"} + elif "val" in tag: + context = {"tag": tag, "subset": "val"} + else: + context = {"tag": tag} + + for key in stats.keys() - {"num_updates"}: + self.run.track(stats[key], name=key, step=step, context=context) + + +try: + _tensorboard_writers = {} + from torch.utils.tensorboard import SummaryWriter +except ImportError: + try: + from tensorboardX import SummaryWriter + except ImportError: + SummaryWriter = None + + +def _close_writers(): + for w in _tensorboard_writers.values(): + w.close() + + +atexit.register(_close_writers) + + +class TensorboardProgressBarWrapper(BaseProgressBar): + """Log to tensorboard.""" + + def __init__(self, wrapped_bar, tensorboard_logdir): + self.wrapped_bar = wrapped_bar + self.tensorboard_logdir = tensorboard_logdir + + if SummaryWriter is None: + logger.warning( + "tensorboard not found, please install with: pip install tensorboard" + ) + + def _writer(self, key): + if SummaryWriter is None: + return None + _writers = _tensorboard_writers + if key not in _writers: + _writers[key] = SummaryWriter(os.path.join(self.tensorboard_logdir, key)) + _writers[key].add_text("sys.argv", " ".join(sys.argv)) + return _writers[key] + + def __iter__(self): + return iter(self.wrapped_bar) + + def log(self, stats, tag=None, step=None): + """Log intermediate stats to tensorboard.""" + self._log_to_tensorboard(stats, tag, step) + self.wrapped_bar.log(stats, tag=tag, step=step) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + self._log_to_tensorboard(stats, tag, step) + self.wrapped_bar.print(stats, tag=tag, step=step) + + def update_config(self, config): + """Log latest configuration.""" + # TODO add hparams to Tensorboard + self.wrapped_bar.update_config(config) + + def _log_to_tensorboard(self, stats, tag=None, step=None): + writer = self._writer(tag or "") + if writer is None: + return + if step is None: + step = stats["num_updates"] + for key in stats.keys() - {"num_updates"}: + if isinstance(stats[key], AverageMeter): + writer.add_scalar(key, stats[key].val, step) + elif isinstance(stats[key], Number): + writer.add_scalar(key, stats[key], step) + elif torch.is_tensor(stats[key]) and stats[key].numel() == 1: + writer.add_scalar(key, stats[key].item(), step) + writer.flush() + + +try: + import wandb +except ImportError: + wandb = None + + +class WandBProgressBarWrapper(BaseProgressBar): + """Log to Weights & Biases.""" + + def __init__(self, wrapped_bar, wandb_project, run_name=None): + self.wrapped_bar = wrapped_bar + if wandb is None: + logger.warning("wandb not found, pip install wandb") + return + + # reinit=False to ensure if wandb.init() is called multiple times + # within one process it still references the same run + wandb.init(project=wandb_project, reinit=False, name=run_name) + + def __iter__(self): + return iter(self.wrapped_bar) + + def log(self, stats, tag=None, step=None): + """Log intermediate stats to tensorboard.""" + self._log_to_wandb(stats, tag, step) + self.wrapped_bar.log(stats, tag=tag, step=step) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + self._log_to_wandb(stats, tag, step) + self.wrapped_bar.print(stats, tag=tag, step=step) + + def update_config(self, config): + """Log latest configuration.""" + if wandb is not None: + wandb.config.update(config) + self.wrapped_bar.update_config(config) + + def _log_to_wandb(self, stats, tag=None, step=None): + if wandb is None: + return + if step is None: + step = stats["num_updates"] + + prefix = "" if tag is None else tag + "/" + + for key in stats.keys() - {"num_updates"}: + if isinstance(stats[key], AverageMeter): + wandb.log({prefix + key: stats[key].val}, step=step) + elif isinstance(stats[key], Number): + wandb.log({prefix + key: stats[key]}, step=step) + + +try: + from azureml.core import Run +except ImportError: + Run = None + + +class AzureMLProgressBarWrapper(BaseProgressBar): + """Log to Azure ML""" + + def __init__(self, wrapped_bar): + self.wrapped_bar = wrapped_bar + if Run is None: + logger.warning("azureml.core not found, pip install azureml-core") + return + self.run = Run.get_context() + + def __exit__(self, *exc): + if Run is not None: + self.run.complete() + return False + + def __iter__(self): + return iter(self.wrapped_bar) + + def log(self, stats, tag=None, step=None): + """Log intermediate stats to AzureML""" + self._log_to_azureml(stats, tag, step) + self.wrapped_bar.log(stats, tag=tag, step=step) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats""" + self._log_to_azureml(stats, tag, step) + self.wrapped_bar.print(stats, tag=tag, step=step) + + def update_config(self, config): + """Log latest configuration.""" + self.wrapped_bar.update_config(config) + + def _log_to_azureml(self, stats, tag=None, step=None): + if Run is None: + return + if step is None: + step = stats["num_updates"] + + prefix = "" if tag is None else tag + "/" + + for key in stats.keys() - {"num_updates"}: + name = prefix + key + if isinstance(stats[key], AverageMeter): + self.run.log_row(name=name, **{"step": step, key: stats[key].val}) + elif isinstance(stats[key], Number): + self.run.log_row(name=name, **{"step": step, key: stats[key]}) diff --git a/fairseq/fairseq/model_parallel/__init__.py b/fairseq/fairseq/model_parallel/__init__.py new file mode 100644 index 0000000..69f2168 --- /dev/null +++ b/fairseq/fairseq/model_parallel/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import criterions, models, modules # noqa diff --git a/fairseq/fairseq/model_parallel/criterions/__init__.py b/fairseq/fairseq/model_parallel/criterions/__init__.py new file mode 100644 index 0000000..5fae7bd --- /dev/null +++ b/fairseq/fairseq/model_parallel/criterions/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + + +# automatically import any Python files in the criterions/ directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + module = file[: file.find(".py")] + importlib.import_module("fairseq.model_parallel.criterions." + module) diff --git a/fairseq/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py b/fairseq/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py new file mode 100644 index 0000000..5ffbaa8 --- /dev/null +++ b/fairseq/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +from fairseq import utils +from fairseq.logging import metrics +from fairseq.criterions import FairseqCriterion, register_criterion + + +try: + from fairseq.model_parallel.megatron.mpu.cross_entropy import ( + vocab_parallel_cross_entropy, + ) + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + + +@register_criterion("vocab_parallel_cross_entropy") +class VocabParallelCrossEntropyCriterion(FairseqCriterion): + def __init__(self, task, sentence_avg): + super().__init__(task) + self.sentence_avg = sentence_avg + if not has_megatron_submodule: + raise ImportError( + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" + ) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + target = sample["target"] + + loss = vocab_parallel_cross_entropy(net_output[0].float(), target) + loss = (loss * (target != self.padding_idx)).sum() + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + else: + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/fairseq/model_parallel/megatron_trainer.py b/fairseq/fairseq/model_parallel/megatron_trainer.py new file mode 100644 index 0000000..aedf608 --- /dev/null +++ b/fairseq/fairseq/model_parallel/megatron_trainer.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Train a network across multiple GPUs. +""" + +from fairseq.dataclass.configs import FairseqConfig +from fairseq.distributed import utils as distributed_utils +from fairseq.trainer import Trainer + +try: + from fairseq.model_parallel.megatron.mpu import ( + get_data_parallel_rank, + get_data_parallel_world_size, + get_model_parallel_src_rank, + get_cuda_rng_tracker, + ) + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + + +class MegatronTrainer(Trainer): + """Main class for model parallel with data parallel training.""" + + def __init__(self, cfg: FairseqConfig, task, model, criterion, **kwargs): + if not has_megatron_submodule: + raise ImportError( + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" + ) + super().__init__(cfg, task, model, criterion, **kwargs) + + def clip_grad_norm(self, clip_norm): + def _aggregate_model_parallel_grad_norm(total_norm): + total_norm = total_norm**2 + distributed_utils.all_reduce( + total_norm, group=distributed_utils.get_model_parallel_group() + ) + total_norm = total_norm**0.5 + return total_norm + + return self.optimizer.clip_grad_norm( + clip_norm, + aggregate_norm_fn=_aggregate_model_parallel_grad_norm, + ) + + def save_checkpoint(self, filename, extra_state): + """Save all training state in a checkpoint file.""" + extra_state["rng_tracker_states"] = get_cuda_rng_tracker().get_states() + super().save_checkpoint(filename, extra_state) + + def load_checkpoint( + self, + filename, + reset_optimizer=False, + reset_lr_scheduler=False, + optimizer_overrides=None, + reset_meters=False, + ): + extra_state = super().load_checkpoint( + filename, + reset_optimizer=reset_optimizer, + reset_lr_scheduler=reset_lr_scheduler, + optimizer_overrides=optimizer_overrides, + reset_meters=reset_meters, + ) + if extra_state is not None and "rng_tracker_states" in extra_state: + get_cuda_rng_tracker().set_states(extra_state["rng_tracker_states"]) + return extra_state diff --git a/fairseq/fairseq/model_parallel/models/__init__.py b/fairseq/fairseq/model_parallel/models/__init__.py new file mode 100644 index 0000000..3532479 --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + + +# automatically import any Python files in the models/ directory +models_dir = os.path.dirname(__file__) +for file in os.listdir(models_dir): + path = os.path.join(models_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + model_name = file[: file.find(".py")] if file.endswith(".py") else file + module = importlib.import_module("fairseq.model_parallel.models." + model_name) diff --git a/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/__init__.py b/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/__init__.py new file mode 100644 index 0000000..117827c --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .model import * # noqa diff --git a/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py b/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py new file mode 100644 index 0000000..85dbd44 --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py @@ -0,0 +1,600 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from collections import namedtuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import options, utils +from fairseq.modules import ( + AdaptiveSoftmax, + LayerNorm, + MultiheadAttention, + PositionalEmbedding, +) + +EncoderOut = namedtuple( + "TransformerEncoderOut", + [ + "encoder_out", # T x B x C + "encoder_padding_mask", # B x T + "encoder_embedding", # B x T x C + "encoder_states", # List[T x B x C] + ], +) + + +class TransformerEncoderEmbedding(nn.Module): + """Encoder Embedding + Positional Embedding""" + + def __init__(self, args, embed_tokens): + super().__init__() + self.dropout = args.dropout + self.max_source_positions = args.max_source_positions + self.embed_tokens = embed_tokens + if isinstance(embed_tokens, nn.ModuleList): + self.padding_idx = embed_tokens[0].padding_idx + embed_dim = sum(e.embedding_dim for e in embed_tokens) + else: + self.padding_idx = embed_tokens.padding_idx + embed_dim = embed_tokens.embedding_dim + self.embed_scale = math.sqrt(embed_dim) + self.embed_positions = ( + PositionalEmbedding( + args.max_source_positions, + embed_dim, + self.padding_idx, + learned=args.encoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) + if getattr(args, "layernorm_embedding", False): + self.layernorm_embedding = LayerNorm(embed_dim) + else: + self.layernorm_embedding = None + + def forward(self, input): + # embed tokens and positions + src_tokens = input[0] + prev_output_tokens = input[2] + if isinstance(self.embed_tokens, nn.ModuleList): + x_embed_list = [] + for embed_tokens_part in self.embed_tokens: + x_embed_list.append(embed_tokens_part(src_tokens)) + + embedded = torch.cat(x_embed_list, dim=-1) + else: + embedded = self.embed_tokens(src_tokens) + x = embed = self.embed_scale * embedded + if self.embed_positions is not None: + x = embed + self.embed_positions(src_tokens) + if self.layernorm_embedding: + x = self.layernorm_embedding(x) + x = F.dropout(x, p=self.dropout, training=self.training) + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # compute padding mask + encoder_padding_mask = src_tokens.eq(self.padding_idx) + return (x, encoder_padding_mask, prev_output_tokens) + + +class TransformerEncoderLayerNorm(nn.Module): + """ + Layer norm at the the end of all encoder layers if + args.encoder_enormalize_before = True + """ + + def __init__(self, args, embed_dim): + super().__init__() + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward(self, input): + x = input[0] + encoder_padding_mask = input[1] + prev_output_tokens = input[2] + if self.layer_norm: + x = self.layer_norm(x) + # keeping track of the incremental_state is not supported yet + return (x, encoder_padding_mask, prev_output_tokens) + + +class TransformerDecoderEmbedding(nn.Module): + """Decoder Embedding + Positional Embedding""" + + def __init__(self, args, embed_tokens): + super().__init__() + self.dropout = args.dropout + self.share_input_output_embed = args.share_decoder_input_output_embed + input_embed_dim = ( + sum(e.embedding_dim for e in embed_tokens) + if isinstance(embed_tokens, nn.ModuleList) + else embed_tokens.embedding_dim + ) + embed_dim = args.decoder_embed_dim + self.output_embed_dim = args.decoder_output_dim + + padding_idx = ( + embed_tokens[0].padding_idx + if isinstance(embed_tokens, nn.ModuleList) + else embed_tokens.padding_idx + ) + self.max_target_positions = args.max_target_positions + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + args.max_target_positions, + embed_dim, + padding_idx, + learned=args.decoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) + + def forward(self, input): + mt_task = False + if isinstance(input, tuple): + if len(input) == 3: + encoder_out = input[0] + encoder_padding_mask = input[1] + prev_output_tokens = input[2] + incremental_state = None # Hardcoding to avoid passing of None objects + mt_task = True + else: + # HACK for now, need to fix (TODO sidgoyal) + prev_output_tokens = input[0] + # discard "src_lengths" + encoder_out = None + encoder_padding_mask = None + incremental_state = None + + else: + prev_output_tokens = input + encoder_out = None + encoder_padding_mask = None + incremental_state = None + + positions = ( + self.embed_positions( + prev_output_tokens, + incremental_state=incremental_state, + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + + if isinstance(self.embed_tokens, nn.ModuleList): + x_embed_list = [] + for embed_tokens_part in self.embed_tokens: + x_embed_list.append(embed_tokens_part(prev_output_tokens)) + + x = self.embed_scale * torch.cat(x_embed_list, dim=-1) + else: + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + if mt_task: + return (x, encoder_out, encoder_padding_mask) + return x + + +class TransformerDecoderOutputLayer(nn.Module): + def __init__(self, args, embed_tokens, dictionary): + super().__init__() + self.share_input_output_embed = args.share_decoder_input_output_embed + self.embed_tokens = embed_tokens + self.output_embed_dim = args.decoder_output_dim + embed_dim = args.decoder_embed_dim + + self.project_out_dim = ( + Linear(embed_dim, self.output_embed_dim, bias=False) + if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights + else None + ) + self.adaptive_softmax = None + if args.adaptive_softmax_cutoff is not None: + assert not isinstance(embed_tokens, nn.ModuleList) + self.adaptive_softmax = AdaptiveSoftmax( + len(dictionary), + self.output_embed_dim, + options.eval_str_list(args.adaptive_softmax_cutoff, type=int), + dropout=args.adaptive_softmax_dropout, + adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None, + factor=args.adaptive_softmax_factor, + tie_proj=args.tie_adaptive_proj, + ) + elif not self.share_input_output_embed: + self.embed_tokens = nn.Parameter( + torch.Tensor(len(dictionary), self.output_embed_dim) + ) + nn.init.normal_( + self.embed_tokens, mean=0, std=self.output_embed_dim**-0.5 + ) + + if args.decoder_normalize_before and not getattr( + args, "no_decoder_final_norm", False + ): + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward(self, input, apply_final_proj=True): + if isinstance(input, tuple): + x = input[0] + else: + x = input + + if self.layer_norm: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + if apply_final_proj: + x = self.output_layer(x) + return x + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + if self.adaptive_softmax is None: + # project back to size of vocabulary + if self.share_input_output_embed: + if isinstance(self.embed_tokens, nn.ModuleList): + output = None + for i, emb in enumerate(self.embed_tokens): + sidx = i * emb.embedding_dim + eidx = (i + 1) * emb.embedding_dim + if output is None: + output = F.linear(features[:, :, sidx:eidx], emb.weight) + else: + output += F.linear(features[:, :, sidx:eidx], emb.weight) + + return output + else: + return F.linear(features, self.embed_tokens.weight) + else: + return F.linear(features, self.embed_tokens) + else: + return features + + +class TransformerEncoderLayer(nn.Module): + """Encoder layer block. + In the original paper each operation (multi-head attention or FFN) is + postprocessed with: `dropout -> add residual -> layernorm`. In the + tensor2tensor code they suggest that learning is more robust when + preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *args.encoder_normalize_before* to ``True``. + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + + def __init__(self, args): + super().__init__() + self.embed_dim = args.encoder_embed_dim + self.self_attn = MultiheadAttention( + self.embed_dim, + args.encoder_attention_heads, + dropout=args.attention_dropout, + self_attention=True, + ) + self.self_attn_layer_norm = LayerNorm(self.embed_dim) + self.dropout = args.dropout + self.activation_fn = utils.get_activation_fn( + activation=getattr(args, "activation_fn", "relu") + ) + self.activation_dropout = getattr(args, "activation_dropout", 0) + if self.activation_dropout == 0: + # for backwards compatibility with models that use args.relu_dropout + self.activation_dropout = getattr(args, "relu_dropout", 0) + self.normalize_before = args.encoder_normalize_before + self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim) + self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim) + self.final_layer_norm = LayerNorm(self.embed_dim) + + def upgrade_state_dict_named(self, state_dict, name): + """ + Rename layer norm states from `...layer_norms.0.weight` to + `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to + `...final_layer_norm.weight` + """ + layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"} + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layer_norms.{}.{}".format(name, old, m) + if k in state_dict: + state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k] + del state_dict[k] + + def forward(self, input): + """ + Args: + input (Tuple): + input[0] (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + input[1] (ByteTensor/FloatTensor): encoder padding mask - + binary ByteTensor of shape `(batch, src_len)` where padding elements + are indicated by ``1``. + input[2] (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing) + Returns: + output (Tuple): + output[0] (Tensor): encoded output of shape `(batch, src_len, embed_dim)` + output[1] (ByteTensor/FloatTensor): encoder padding mask + output[2] (LongTensor): previous decoder outputs + """ + x = input[0] + encoder_padding_mask = input[1] + prev_output_tokens = input[2] + residual = x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True) + x, _ = self.self_attn( + query=x, key=x, value=x, key_padding_mask=encoder_padding_mask + ) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True) + + residual = x + x = self.maybe_layer_norm(self.final_layer_norm, x, before=True) + x = self.activation_fn(self.fc1(x)) + x = F.dropout(x, p=self.activation_dropout, training=self.training) + x = self.fc2(x) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.final_layer_norm, x, after=True) + return (x, encoder_padding_mask, prev_output_tokens) + + def maybe_layer_norm(self, layer_norm, x, before=False, after=False): + assert before ^ after + if after ^ self.normalize_before: + return layer_norm(x) + else: + return x + + +class TransformerDecoderLayer(nn.Module): + """Decoder layer block. + + In the original paper each operation (multi-head attention, encoder + attention or FFN) is postprocessed with: `dropout -> add residual -> + layernorm`. In the tensor2tensor code they suggest that learning is more + robust when preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *args.decoder_normalize_before* to ``True``. + + Args: + args (argparse.Namespace): parsed command-line arguments + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__() + self.embed_dim = args.decoder_embed_dim + self.self_attn = MultiheadAttention( + embed_dim=self.embed_dim, + num_heads=args.decoder_attention_heads, + dropout=args.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=True, + ) + self.dropout = args.dropout + self.activation_fn = utils.get_activation_fn( + activation=getattr(args, "activation_fn", "relu") + ) + self.activation_dropout = getattr(args, "activation_dropout", 0) + if self.activation_dropout == 0: + # for backwards compatibility with models that use args.relu_dropout + self.activation_dropout = getattr(args, "relu_dropout", 0) + self.normalize_before = args.decoder_normalize_before + + # use layerNorm rather than FusedLayerNorm for exporting. + # char_inputs can be used to determint this. + # TODO remove this once we update apex with the fix + export = getattr(args, "char_inputs", False) + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + if no_encoder_attn: + self.encoder_attn = None + self.encoder_attn_layer_norm = None + else: + self.encoder_attn = MultiheadAttention( + self.embed_dim, + args.decoder_attention_heads, + kdim=getattr(args, "encoder_embed_dim", None), + vdim=getattr(args, "encoder_embed_dim", None), + dropout=args.attention_dropout, + encoder_decoder_attention=True, + ) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim) + self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim) + + self.final_layer_norm = LayerNorm(self.embed_dim, export=export) + self.need_attn = True + + self.onnx_trace = False + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def forward(self, input): + """ + Args: + input (Tuple): + input[0] (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + input[1] (Tensor): encoder output of shape `(batch, src_len, embed_dim)` + input[2] (ByteTensor/FloatTensor): encoder padding mask - + binary ByteTensor of shape `(batch, src_len)` where padding elements + are indicated by ``1``. + Returns: + output (Tuple): + output[0] (Tensor): encoded output of shape `(batch, src_len, embed_dim)` + output[1] (ByteTensor/FloatTensor): encoder padding mask + output[2] (LongTensor): previous decoder outputs + """ + # Note: incremental state is not yet supported + mt_task = False + if isinstance(input, tuple): + x = input[0] + encoder_out = input[1] + encoder_padding_mask = input[2] + incremental_state = None + mt_task = True + else: + x = input + encoder_out = None + encoder_padding_mask = None + incremental_state = None + + if incremental_state is None: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + # TODO: add back prev_self_attn_state, prev_attn_state, + # self_attn_padding_mask + prev_self_attn_state = None + prev_attn_state = None + self_attn_padding_mask = None + + residual = x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True) + if prev_self_attn_state is not None: + if incremental_state is None: + incremental_state = {} + prev_key, prev_value = prev_self_attn_state + saved_state = {"prev_key": prev_key, "prev_value": prev_value} + self.self_attn._set_input_buffer(incremental_state, saved_state) + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + need_weights=False, + attn_mask=self_attn_mask, + ) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True) + + if self.encoder_attn is not None: + residual = x + x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, before=True) + if prev_attn_state is not None: + if incremental_state is None: + incremental_state = {} + prev_key, prev_value = prev_attn_state + saved_state = {"prev_key": prev_key, "prev_value": prev_value} + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=(not self.training and self.need_attn), + ) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True) + + residual = x + x = self.maybe_layer_norm(self.final_layer_norm, x, before=True) + x = self.activation_fn(self.fc1(x)) + x = F.dropout(x, p=self.activation_dropout, training=self.training) + x = self.fc2(x) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.final_layer_norm, x, after=True) + + if mt_task: + return (x, encoder_out, encoder_padding_mask) + return x + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + if self._future_mask.size(0) < dim: + self._future_mask = torch.triu( + utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def maybe_layer_norm(self, layer_norm, x, before=False, after=False): + assert before ^ after + if after ^ self.normalize_before: + return layer_norm(x) + else: + return x + + def make_generation_fast_(self, need_attn=False, **kwargs): + self.need_attn = need_attn + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m diff --git a/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/model.py b/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/model.py new file mode 100644 index 0000000..7873ac6 --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/model.py @@ -0,0 +1,779 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.model_parallel.models.pipeline_parallel_transformer.layers import ( + Embedding, + TransformerDecoderEmbedding, + TransformerDecoderLayer, + TransformerDecoderOutputLayer, + TransformerEncoderEmbedding, + TransformerEncoderLayer, + TransformerEncoderLayerNorm, +) +from fairseq.models import ( + BaseFairseqModel, + FairseqDecoder, + FairseqEncoder, + register_model, + register_model_architecture, +) +from fairseq.models.fairseq_encoder import EncoderOut +from fairseq.models.transformer import ( + base_architecture, + transformer_iwslt_de_en, + transformer_wmt_en_de_big, +) +from fairseq.modules import SinusoidalPositionalEmbedding + + +logger = logging.getLogger(__name__) + + +DEFAULT_MAX_SOURCE_POSITIONS = 1024 +DEFAULT_MAX_TARGET_POSITIONS = 1024 +TORCH_PIPE = False +RPC_INIT = False + + +def import_pipe(): + global TORCH_PIPE + global RPC_INIT + try: + from torch.distributed.pipeline.sync import Pipe # noqa + + global Pipe + from torch.distributed.pipeline.sync.utils import partition_model + + global partition_model + from torch.distributed import rpc + import tempfile + + TORCH_PIPE = True + # Initialize single process RPC agent since TORCH_PIPE requires + # RRef. RRef depends on RPC being initialized and as a result we initialize + # RPC with a single node. + tmpfile = tempfile.NamedTemporaryFile() + if not RPC_INIT: + rpc.init_rpc( + name="worker", + rank=0, + world_size=1, + rpc_backend_options=rpc.TensorPipeRpcBackendOptions( + init_method="file://{}".format(tmpfile.name), + ), + ) + RPC_INIT = True + logger.info("Using torch pipe") + except ImportError: + try: + from fairscale.nn import Pipe # noqa + + logger.info("Using fairscale pipe") + except ImportError: + raise ImportError("Please install fairscale with: pip install fairscale") + + +@register_model("pipeline_parallel_transformer") +class PipelineParallelTransformerModel(BaseFairseqModel): + def __init__(self, encoder, decoder, balance, devices, chunks, checkpoint): + import_pipe() + super().__init__() + assert isinstance(encoder, FairseqEncoder) + assert isinstance(decoder, FairseqDecoder) + encoder_module_list = ( + [encoder.embedding_layer] + + list(encoder.encoder_layers) + + [encoder.final_layer_norm] + ) + self.num_encoder_modules = len(encoder_module_list) + decoder_module_list = ( + [decoder.embedding_layer] + + list(decoder.decoder_layers) + + [decoder.decoder_output_layer] + ) + self.num_decoder_modules = len(decoder_module_list) + module_list = encoder_module_list + decoder_module_list + self.devices = devices + if TORCH_PIPE: + self.model = Pipe( + partition_model(nn.Sequential(*module_list), balance, devices), + chunks=chunks, + checkpoint=checkpoint, + ) + else: + self.model = Pipe( + nn.Sequential(*module_list), + balance=balance, + devices=devices, + chunks=chunks, + checkpoint=checkpoint, + ) + self.encoder_max_positions = self.max_positions_helper( + encoder.embedding_layer, "max_source_positions" + ) + self.decoder_max_positions = self.max_positions_helper( + decoder.embedding_layer, "max_target_positions" + ) + self.adaptive_softmax = getattr(decoder, "adaptive_softmax", None) + # Note: To be populated during inference + self.encoder = None + self.decoder = None + + def forward(self, src_tokens, src_lengths, prev_output_tokens): + if self.training: + input_lst = [src_tokens, src_lengths, prev_output_tokens] + input = tuple(i.to(self.devices[0], non_blocking=True) for i in input_lst) + if TORCH_PIPE: + return self.model(input).local_value() + else: + return self.model(input) + else: + assert self.encoder is not None and self.decoder is not None, ( + "encoder and decoder need to be initialized by " + + "calling the `prepare_for_inference_()` method" + ) + encoder_output_tuple = self.encoder(input) + return self.decoder(encoder_output_tuple) + + def prepare_for_inference_(self, cfg): + if self.encoder is not None and self.decoder is not None: + logger.info("Encoder and Decoder already initialized") + return + encoder_module_list = [] + decoder_module_list = [] + module_count = 0 + for partition in self.model.partitions: + for module in partition: + if module_count < self.num_encoder_modules: + encoder_module_list.append(module) + else: + decoder_module_list.append(module) + module_count += 1 + self.model = None + self.encoder = TransformerEncoder( + cfg.distributed_training, None, None, encoder_module_list + ) + self.decoder = TransformerDecoder( + cfg.distributed_training, + None, + None, + decoder_module_list=decoder_module_list, + ) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--activation-fn', + choices=utils.get_available_activation_fns(), + help='activation function to use') + parser.add_argument('--dropout', type=float, metavar='D', + help='dropout probability') + parser.add_argument('--attention-dropout', type=float, metavar='D', + help='dropout probability for attention weights') + parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D', + help='dropout probability after activation in FFN.') + parser.add_argument('--encoder-embed-path', type=str, metavar='STR', + help='path to pre-trained encoder embedding') + parser.add_argument('--encoder-embed-dim', type=int, metavar='N', + help='encoder embedding dimension') + parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N', + help='encoder embedding dimension for FFN') + parser.add_argument('--encoder-layers', type=int, metavar='N', + help='num encoder layers') + parser.add_argument('--encoder-attention-heads', type=int, metavar='N', + help='num encoder attention heads') + parser.add_argument('--encoder-normalize-before', action='store_true', + help='apply layernorm before each encoder block') + parser.add_argument('--encoder-learned-pos', action='store_true', + help='use learned positional embeddings in the encoder') + parser.add_argument('--decoder-embed-path', type=str, metavar='STR', + help='path to pre-trained decoder embedding') + parser.add_argument('--decoder-embed-dim', type=int, metavar='N', + help='decoder embedding dimension') + parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N', + help='decoder embedding dimension for FFN') + parser.add_argument('--decoder-layers', type=int, metavar='N', + help='num decoder layers') + parser.add_argument('--decoder-attention-heads', type=int, metavar='N', + help='num decoder attention heads') + parser.add_argument('--decoder-learned-pos', action='store_true', + help='use learned positional embeddings in the decoder') + parser.add_argument('--decoder-normalize-before', action='store_true', + help='apply layernorm before each decoder block') + parser.add_argument('--share-decoder-input-output-embed', action='store_true', + help='share decoder input and output embeddings') + parser.add_argument('--share-all-embeddings', action='store_true', + help='share encoder, decoder and output embeddings' + ' (requires shared dictionary and embed dim)') + parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true', + help='if set, disables positional embeddings (outside self attention)') + parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', + help='comma separated list of adaptive softmax cutoff points. ' + 'Must be used with adaptive_loss criterion'), + parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D', + help='sets adaptive softmax dropout for the tail projections') + parser.add_argument('--num-embedding-chunks', type=int, metavar='N', default=1, + help='Number of embedding layer chunks (enables more even distribution' + 'of optimizer states across data parallel nodes' + 'when using optimizer state sharding and' + 'a big embedding vocabulary)') + # fmt: on + + @classmethod + def build_model_base(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if not hasattr(args, "max_source_positions"): + args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS + if not hasattr(args, "max_target_positions"): + args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + def build_embedding(dictionary, embed_dim, path=None, num_embed_chunks=1): + assert embed_dim % num_embed_chunks == 0, ( + f"Number of embedding chunks = {num_embed_chunks} should be " + + f"divisible by the embedding dimension = {embed_dim}" + ) + assert path is None or num_embed_chunks == 1, ( + "Loading embedding from a path with number of embedding chunks > 1" + + " is not yet supported" + ) + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + # if provided, load from preloaded dictionaries + if path: + emb = Embedding(num_embeddings, embed_dim, padding_idx) + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + else: + embed_chunk_dim = embed_dim // num_embed_chunks + emb = nn.ModuleList() + for i in range(num_embed_chunks): + emb.append(Embedding(num_embeddings, embed_chunk_dim, padding_idx)) + return emb + + num_embed_chunks = args.num_embedding_chunks + if args.share_all_embeddings: + if src_dict != tgt_dict: + raise ValueError("--share-all-embeddings requires a joined dictionary") + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + encoder_embed_tokens = build_embedding( + src_dict, + args.encoder_embed_dim, + args.encoder_embed_path, + num_embed_chunks, + ) + decoder_embed_tokens = encoder_embed_tokens + args.share_decoder_input_output_embed = True + else: + assert args.share_decoder_input_output_embed or num_embed_chunks == 1, ( + "Not sharing decoder I/O embeddings is not yet supported with number of " + + "embedding chunks > 1" + ) + encoder_embed_tokens = build_embedding( + src_dict, + args.encoder_embed_dim, + args.encoder_embed_path, + num_embed_chunks, + ) + decoder_embed_tokens = build_embedding( + tgt_dict, + args.decoder_embed_dim, + args.decoder_embed_path, + num_embed_chunks, + ) + + encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens) + decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens) + return (encoder, decoder) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return TransformerEncoder(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return TransformerDecoder(args, tgt_dict, embed_tokens) + + @classmethod + def build_model(cls, args, task): + encoder, decoder = cls.build_model_base(args, task) + return PipelineParallelTransformerModel( + encoder=encoder, + decoder=decoder, + balance=utils.eval_str_list(args.pipeline_balance, type=int), + devices=utils.eval_str_list(args.pipeline_devices, type=int), + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + + def output_layer(self, features, **kwargs): + """Project features to the default output size (typically vocabulary size).""" + return self.decoder.output_layer(features, **kwargs) + + def max_positions(self): + """Maximum length supported by the model.""" + return (self.encoder_max_positions, self.decoder_max_positions) + + def max_positions_helper( + self, embedding_layer, max_positions_field="max_source_positions" + ): + """Maximum input length supported by the encoder or decoder.""" + if embedding_layer.embed_positions is None: + return getattr(embedding_layer, max_positions_field) + return min( + getattr(embedding_layer, max_positions_field), + embedding_layer.embed_positions.max_positions, + ) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + """Get normalized probabilities (or log probs) from a net's output.""" + + if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None: + if sample is not None: + assert "target" in sample + target = sample["target"] + else: + target = None + out = self.adaptive_softmax.get_log_prob(net_output, target=target) + return out.exp_() if not log_probs else out + + # A Pipe() module returns a tuple of tensors as the output. + # In this case, the tuple has one element - the output tensor of logits + logits = net_output if isinstance(net_output, torch.Tensor) else net_output[0] + if log_probs: + return utils.log_softmax(logits, dim=-1, onnx_trace=False) + else: + return utils.softmax(logits, dim=-1, onnx_trace=False) + + def max_decoder_positions(self): + """Maximum length supported by the decoder.""" + return self.decoder_max_positions + + def load_state_dict(self, state_dict, strict=True, model_cfg=None): + """Copies parameters and buffers from *state_dict* into this module and + its descendants. + + Overrides the method in :class:`nn.Module`. Compared with that method + this additionally "upgrades" *state_dicts* from old checkpoints. + """ + self.upgrade_state_dict(state_dict) + is_regular_transformer = not any("model.partitions" in k for k in state_dict) + if is_regular_transformer: + state_dict = self.convert_to_pipeline_parallel_state_dict(state_dict) + return super().load_state_dict(state_dict, strict) + + def convert_to_pipeline_parallel_state_dict(self, state_dict): + new_state_dict = self.state_dict() + encoder_layer_idx = 0 + decoder_layer_idx = 0 + encoder_key_suffixes = [ + "self_attn.k_proj.weight", + "self_attn.k_proj.bias", + "self_attn.v_proj.weight", + "self_attn.v_proj.bias", + "self_attn.q_proj.weight", + "self_attn.q_proj.bias", + "self_attn.out_proj.weight", + "self_attn.out_proj.bias", + "self_attn_layer_norm.weight", + "self_attn_layer_norm.bias", + "fc1.weight", + "fc1.bias", + "fc2.weight", + "fc2.bias", + "final_layer_norm.weight", + "final_layer_norm.bias", + ] + decoder_key_suffixes = [ + "self_attn.k_proj.weight", + "self_attn.k_proj.bias", + "self_attn.v_proj.weight", + "self_attn.v_proj.bias", + "self_attn.q_proj.weight", + "self_attn.q_proj.bias", + "self_attn.out_proj.weight", + "self_attn.out_proj.bias", + "self_attn_layer_norm.weight", + "self_attn_layer_norm.bias", + "encoder_attn.k_proj.weight", + "encoder_attn.k_proj.bias", + "encoder_attn.v_proj.weight", + "encoder_attn.v_proj.bias", + "encoder_attn.q_proj.weight", + "encoder_attn.q_proj.bias", + "encoder_attn.out_proj.weight", + "encoder_attn.out_proj.bias", + "encoder_attn_layer_norm.weight", + "encoder_attn_layer_norm.bias", + "fc1.weight", + "fc1.bias", + "fc2.weight", + "fc2.bias", + "final_layer_norm.weight", + "final_layer_norm.bias", + ] + for pid, partition in enumerate(self.model.partitions): + logger.info(f"Begin Partition {pid}") + for mid, module in enumerate(partition): + # fmt: off + if isinstance(module, TransformerEncoderEmbedding): + new_state_dict[f'model.partitions.{pid}.{mid}.embed_tokens.weight'] = state_dict['encoder.embed_tokens.weight'] + if isinstance(module, TransformerEncoderLayer): + for suffix in encoder_key_suffixes: + new_state_dict[f'model.partitions.{pid}.{mid}.{suffix}'] = state_dict[f'encoder.layers.{encoder_layer_idx}.{suffix}'] + encoder_layer_idx += 1 + if isinstance(module, TransformerDecoderLayer): + for suffix in decoder_key_suffixes: + new_state_dict[f'model.partitions.{pid}.{mid}.{suffix}'] = state_dict[f'decoder.layers.{decoder_layer_idx}.{suffix}'] + decoder_layer_idx += 1 + if isinstance(module, TransformerEncoderLayerNorm): + if 'encoder.layer_norm.weight' in state_dict: + new_state_dict[f'model.partitions.{pid}.{mid}.layer_norm.weight'] = state_dict['encoder.layer_norm.weight'] + new_state_dict[f'model.partitions.{pid}.{mid}.layer_norm.bias'] = state_dict['encoder.layer_norm.bias'] + if isinstance(module, TransformerDecoderEmbedding): + new_state_dict[f'model.partitions.{pid}.{mid}.embed_tokens.weight'] = state_dict['decoder.embed_tokens.weight'] + if isinstance(module, TransformerDecoderOutputLayer): + new_state_dict[f'model.partitions.{pid}.{mid}.output_projection.weight'] = state_dict['decoder.output_projection.weight'] + # fmt: on + return new_state_dict + + +class TransformerEncoder(FairseqEncoder): + """ + Transformer encoder consisting of *args.encoder_layers* layers. Each layer + is a :class:`TransformerEncoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): encoding dictionary + embed_tokens (torch.nn.Embedding): input embedding + """ + + def __init__(self, args, dictionary, embed_tokens, encoder_module_list=None): + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + import_pipe() + self.use_pipeline = encoder_module_list is not None + if not self.use_pipeline: + self.embedding_layer = TransformerEncoderEmbedding(args, embed_tokens) + self.encoder_layers = nn.Sequential( + *[TransformerEncoderLayer(args) for i in range(args.encoder_layers)] + ) + if isinstance(embed_tokens, nn.ModuleList): + emb_dim = sum(e.embedding_dim for e in embed_tokens) + else: + emb_dim = embed_tokens.embedding_dim + self.final_layer_norm = TransformerEncoderLayerNorm(args, emb_dim) + else: + encoder_balance = utils.eval_str_list( + args.pipeline_encoder_balance, type=int + ) + encoder_devices = utils.eval_str_list( + args.pipeline_encoder_devices, type=int + ) + assert sum(encoder_balance) == len(encoder_module_list), ( + f"Sum of encoder_balance={encoder_balance} is not equal " + + f"to num_encoder_modules={len(encoder_module_list)}" + ) + if TORCH_PIPE: + self.model = Pipe( + module=partition_model( + nn.Sequential(*encoder_module_list), + encoder_balance, + encoder_devices, + ), + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + else: + self.model = Pipe( + module=nn.Sequential(*encoder_module_list), + balance=encoder_balance, + devices=encoder_devices, + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + + def forward(self, src_tokens, src_lengths): + """ + Args: + input_tuple( + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + ) + + Returns: + output_tuple( + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - prev_output_tokens + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + ) + """ + dummy_prev_output_tokens = torch.zeros( + 1, dtype=src_tokens.dtype, device=src_tokens.device + ) + input_tuple = (src_tokens, src_lengths, dummy_prev_output_tokens) + if self.use_pipeline: + input_tuple = tuple(i.to(self.model.devices[0]) for i in input_tuple) + if TORCH_PIPE: + encoder_out = self.model(input_tuple).local_value() + else: + encoder_out = self.model(input_tuple) + else: + encoder_embed_output_tuple = self.embedding_layer(input_tuple) + encoder_layers_output = self.encoder_layers(encoder_embed_output_tuple) + encoder_out = self.final_layer_norm(encoder_layers_output) + # first element is the encoder output + # second element is the encoder padding mask + # the remaining elements of EncoderOut are not computed by + # the PipelineParallelTransformer + return EncoderOut(encoder_out[0], encoder_out[1], None, None, None, None) + + def reorder_encoder_out(self, encoder_out, new_order): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + if encoder_out.encoder_out is not None: + encoder_out = encoder_out._replace( + encoder_out=encoder_out.encoder_out.index_select(1, new_order) + ) + if encoder_out.encoder_padding_mask is not None: + encoder_out = encoder_out._replace( + encoder_padding_mask=encoder_out.encoder_padding_mask.index_select( + 0, new_order + ) + ) + if encoder_out.encoder_embedding is not None: + encoder_out = encoder_out._replace( + encoder_embedding=encoder_out.encoder_embedding.index_select( + 0, new_order + ) + ) + if encoder_out.encoder_states is not None: + for idx, state in enumerate(encoder_out.encoder_states): + encoder_out.encoder_states[idx] = state.index_select(1, new_order) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + if self.embedding_layer.embed_positions is None: + return self.embedding_layer.max_source_positions + return min( + self.embedding_layer.max_source_positions, + self.embedding_layer.embed_positions.max_positions, + ) + + +class TransformerDecoder(FairseqDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + args, + dictionary, + embed_tokens, + no_encoder_attn=False, + decoder_module_list=None, + ): + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + import_pipe() + self.use_pipeline = decoder_module_list is not None + if not self.use_pipeline: + self.embedding_layer = TransformerDecoderEmbedding(args, embed_tokens) + self.decoder_layers = nn.Sequential( + *[ + TransformerDecoderLayer(args, no_encoder_attn) + for _ in range(args.decoder_layers) + ] + ) + self.decoder_output_layer = TransformerDecoderOutputLayer( + args, embed_tokens, dictionary + ) + else: + decoder_balance = utils.eval_str_list( + args.pipeline_decoder_balance, type=int + ) + decoder_devices = utils.eval_str_list( + args.pipeline_decoder_devices, type=int + ) + assert sum(decoder_balance) == len(decoder_module_list), ( + f"Sum of decoder_balance={decoder_balance} is not equal " + + f"to num_decoder_modules={len(decoder_module_list)}" + ) + if TORCH_PIPE: + self.model = Pipe( + module=partition_model( + nn.Sequential(*decoder_module_list), + decoder_balance, + decoder_devices, + ), + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + else: + self.model = Pipe( + module=nn.Sequential(*decoder_module_list), + balance=decoder_balance, + devices=decoder_devices, + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + + def forward( + self, + prev_output_tokens, + encoder_out=None, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + input_tuple = ( + encoder_out.encoder_out, + encoder_out.encoder_padding_mask, + prev_output_tokens, + ) + if self.use_pipeline: + input_tuple = tuple(i.to(self.model.devices[0]) for i in input_tuple) + if TORCH_PIPE: + return (self.model(input_tuple).local_value(),) + else: + return (self.model(input_tuple),) + else: + embed_layer_output = self.embedding_layer(input_tuple) + state = self.decoder_layers(embed_layer_output) + return (self.decoder_output_layer(state),) + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + if self.adaptive_softmax is None: + # project back to size of vocabulary + if self.share_input_output_embed: + return F.linear(features, self.embed_tokens.weight) + else: + return F.linear(features, self.embed_out) + else: + return features + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embedding_layer.embed_positions is None: + return self.embedding_layer.max_target_positions + return min( + self.embedding_layer.max_target_positions, + self.embedding_layer.embed_positions.max_positions, + ) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + for i in range(len(self.layers)): + # update layer norms + layer_norm_map = { + "0": "self_attn_layer_norm", + "1": "encoder_attn_layer_norm", + "2": "final_layer_norm", + } + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m) + if k in state_dict: + state_dict[ + "{}.layers.{}.{}.{}".format(name, i, new, m) + ] = state_dict[k] + del state_dict[k] + + version_key = "{}.version".format(name) + if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2: + # earlier checkpoints did not normalize after the stack of layers + self.layer_norm = None + self.normalize = False + state_dict[version_key] = torch.Tensor([1]) + + return state_dict + + +@register_model_architecture( + "pipeline_parallel_transformer", "transformer_iwslt_de_en_pipeline_parallel" +) +def transformer_iwslt_de_en_dist(args): + transformer_iwslt_de_en(args) + + +@register_model_architecture( + "pipeline_parallel_transformer", "transformer_wmt_en_de_big_pipeline_parallel" +) +def transformer_wmt_en_de_big_dist(args): + transformer_wmt_en_de_big(args) diff --git a/fairseq/fairseq/model_parallel/models/roberta/__init__.py b/fairseq/fairseq/model_parallel/models/roberta/__init__.py new file mode 100644 index 0000000..117827c --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/roberta/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .model import * # noqa diff --git a/fairseq/fairseq/model_parallel/models/roberta/model.py b/fairseq/fairseq/model_parallel/models/roberta/model.py new file mode 100644 index 0000000..77a80ef --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/roberta/model.py @@ -0,0 +1,225 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +RoBERTa: A Robustly Optimized BERT Pretraining Approach. +""" + +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.model_parallel.models.transformer import ModelParallelTransformerEncoder +from fairseq.models import register_model, register_model_architecture +from fairseq.models.roberta import ( + roberta_base_architecture, + roberta_prenorm_architecture, + RobertaEncoder, + RobertaModel, +) +from fairseq.modules import LayerNorm + + +try: + from fairseq.model_parallel.megatron.mpu import ( + copy_to_model_parallel_region, + gather_from_model_parallel_region, + ColumnParallelLinear, + VocabParallelEmbedding, + ) + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + +logger = logging.getLogger(__name__) + + +@register_model("model_parallel_roberta") +class ModelParallelRobertaModel(RobertaModel): + def __init__(self, args, encoder): + super().__init__(args, encoder) + + self.classification_heads = nn.ModuleDict() + + @staticmethod + def add_args(parser): + RobertaModel.add_args(parser) + parser.add_argument( + "--no-final-layer-norm", + action="store_true", + help=( + "don't add final layernorm (only applicable when " + "--encoder-normalize-before=True" + ), + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present + base_architecture(args) + + task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + + if not hasattr(args, "max_positions"): + args.max_positions = args.tokens_per_sample + + if getattr(args, "untie_weights_roberta", False): + raise NotImplementedError( + "--untie-weights-roberta is not supported in model parallel mode" + ) + + encoder = ModelParallelRobertaEncoder(args, task.source_dictionary) + return cls(args, encoder) + + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + classification_head_name=None, + **kwargs + ): + if classification_head_name is not None: + features_only = True + + x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs) + + if classification_head_name is not None: + x = self.classification_heads[classification_head_name](x) + return x, extra + + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): + """Register a classification head.""" + if name in self.classification_heads: + prev_num_classes = self.classification_heads[name].out_proj.out_features + prev_inner_dim = self.classification_heads[name].dense.out_features + if num_classes != prev_num_classes or inner_dim != prev_inner_dim: + logger.warning( + 're-registering head "{}" with num_classes {} (prev: {}) ' + "and inner_dim {} (prev: {})".format( + name, num_classes, prev_num_classes, inner_dim, prev_inner_dim + ) + ) + self.classification_heads[name] = ModelParallelRobertaClassificationHead( + self.args.encoder_embed_dim, + inner_dim or self.args.encoder_embed_dim, + num_classes, + self.args.pooler_activation_fn, + self.args.pooler_dropout, + ) + + +class ModelParallelRobertaLMHead(nn.Module): + """Head for masked language modeling.""" + + def __init__(self, embed_dim, output_dim, activation_fn, weight=None): + super().__init__() + self.dense = ColumnParallelLinear(embed_dim, embed_dim, gather_output=True) + self.activation_fn = utils.get_activation_fn(activation_fn) + self.layer_norm = LayerNorm(embed_dim) + + if weight is None: + weight = nn.Linear(embed_dim, output_dim, bias=False).weight + self.weight = weight + self.bias = nn.Parameter(torch.zeros(output_dim)) + + def forward(self, features, masked_tokens=None, **kwargs): + # Only project the unmasked tokens while training, + # saves both memory and computation + if masked_tokens is not None: + features = features[masked_tokens, :] + + x = self.dense(features) + x = self.activation_fn(x) + x = self.layer_norm(x) + + x = copy_to_model_parallel_region(x) + # project back to size of vocabulary with bias + x = F.linear(x, self.weight) + x = gather_from_model_parallel_region(x).contiguous() + x = x + self.bias + return x + + +class ModelParallelRobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__( + self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout + ): + super().__init__() + self.dense = ColumnParallelLinear(input_dim, inner_dim, gather_output=True) + self.activation_fn = utils.get_activation_fn(activation_fn) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = nn.Linear(inner_dim, num_classes) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take <s> token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = self.activation_fn(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +class ModelParallelRobertaEncoder(RobertaEncoder): + """RoBERTa encoder.""" + + def __init__(self, args, dictionary): + super().__init__(args, dictionary) + assert not self.args.untie_weights_roberta + + def build_embedding(self, vocab_size, embedding_dim, padding_idx): + return VocabParallelEmbedding(vocab_size, embedding_dim, padding_idx) + + def build_encoder(self, args, dictionary, embed_tokens): + return ModelParallelTransformerEncoder(args, dictionary, embed_tokens) + + def build_lm_head(self, embed_dim, output_dim, activation_fn, weight): + return ModelParallelRobertaLMHead(embed_dim, output_dim, activation_fn, weight) + + +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta") +def base_architecture(args): + args.no_final_layer_norm = getattr(args, "no_final_layer_norm", False) + # model parallel RoBERTa defaults to "Pre-LN" formulation + roberta_prenorm_architecture(args) + + +# earlier versions of model parallel RoBERTa removed the final layer norm +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_v1") +def model_parallel_roberta_v1_architecture(args): + args.no_final_layer_norm = getattr(args, "no_final_layer_norm", True) + base_architecture(args) + + +@register_model_architecture( + "model_parallel_roberta", "model_parallel_roberta_postnorm" +) +def model_parallel_roberta_postnorm_architecture(args): + # the original BERT/RoBERTa uses the "Post-LN" formulation + roberta_base_architecture(args) + + +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_base") +def model_parallel_roberta_base_architecture(args): + base_architecture(args) + + +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_large") +def model_parallel_roberta_large_architecture(args): + args.encoder_layers = getattr(args, "encoder_layers", 24) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + base_architecture(args) diff --git a/fairseq/fairseq/model_parallel/models/transformer.py b/fairseq/fairseq/model_parallel/models/transformer.py new file mode 100644 index 0000000..cf3b2e8 --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/transformer.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch.nn as nn + +from fairseq.model_parallel.modules import ( + ModelParallelTransformerDecoderLayer, + ModelParallelTransformerEncoderLayer, +) +from fairseq.models import register_model +from fairseq.models.transformer import ( + TransformerDecoder, + TransformerEncoder, + TransformerModel, +) + +try: + from fairseq.model_parallel.megatron.mpu import ( + VocabParallelEmbedding, + copy_to_model_parallel_region, + gather_from_model_parallel_region, + ) + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + + +logger = logging.getLogger(__name__) + + +@register_model("model_parallel_transformer") +class ModelParallelTransformerModel(TransformerModel): + """ + Model parallel Transformer model. + """ + + @classmethod + def build_embedding(cls, args, dictionary, embed_dim, path=None): + if not has_megatron_submodule: + raise ImportError( + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" + ) + dictionary.pad_to_multiple_(args.model_parallel_size * 8) + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + + def _vocab_init(tensor, **kwargs): + nn.init.normal_(tensor, mean=0, std=num_embeddings**-0.5) + nn.init.constant_(tensor[1], 0) + + emb = VocabParallelEmbedding( + num_embeddings, embed_dim, padding_idx, init_method=_vocab_init + ) + # if provided, load from preloaded dictionaries + if path: + raise NotImplementedError( + "Loading of embedding from path is not supported for model parallel" + ) + return emb + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return ModelParallelTransformerEncoder(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return ModelParallelTransformerDecoder( + args, + tgt_dict, + embed_tokens, + no_encoder_attn=getattr(args, "no_cross_attention", False), + ) + + +class ModelParallelTransformerEncoder(TransformerEncoder): + """ + Model parallel Transformer encoder consisting of *args.encoder_layers* layers. Each layer + is a :class:`ModelParallelTransformerEncoderLayer`. + """ + + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens) + + if args.no_final_layer_norm: + self.layer_norm = None + + def build_encoder_layer(self, args): + return ModelParallelTransformerEncoderLayer(args) + + +class ModelParallelTransformerDecoder(TransformerDecoder): + """ + Model Parallel Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`ModelParallelTransformerDecoderLayer`. + """ + + def build_decoder_layer(self, args, no_encoder_attn=False): + return ModelParallelTransformerDecoderLayer(args, no_encoder_attn) + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + if not self.share_input_output_embed: + raise NotImplementedError( + "Model parallel training currently requires --share-decoder-input-output-embed" + ) + + features = copy_to_model_parallel_region(features) + + # project back to size of vocabulary + x = self.output_projection(features) + + if getattr(self.args, "criterion") != "vocab_parallel_cross_entropy": + x = gather_from_model_parallel_region(x).contiguous() + return x diff --git a/fairseq/fairseq/model_parallel/models/transformer_lm.py b/fairseq/fairseq/model_parallel/models/transformer_lm.py new file mode 100644 index 0000000..03e4dbe --- /dev/null +++ b/fairseq/fairseq/model_parallel/models/transformer_lm.py @@ -0,0 +1,169 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn + +from fairseq.model_parallel.models.transformer import ModelParallelTransformerDecoder +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer_lm import TransformerLanguageModel + +try: + from fairseq.model_parallel.megatron.mpu import VocabParallelEmbedding + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + + +DEFAULT_MAX_TARGET_POSITIONS = 1024 + + +@register_model("model_parallel_transformer_lm") +class ModelParallelTransformerLanguageModel(TransformerLanguageModel): + @staticmethod + def add_args(parser): + TransformerLanguageModel.add_args(parser) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + if not has_megatron_submodule: + raise ImportError( + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" + ) + + # make sure all arguments are present in older models + base_lm_architecture(args) + + task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + + if args.decoder_layers_to_keep: + args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) + + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) + + if args.character_embeddings: + raise NotImplementedError( + "Character embeddings is not supported for model parallel" + ) + elif args.adaptive_input: + raise NotImplementedError( + "Adaptive input is not supported for model parallel" + ) + else: + embed_tokens = cls.build_embedding( + args, task.source_dictionary, args.decoder_input_dim + ) + + decoder = ModelParallelTransformerDecoder( + args, + task.target_dictionary, + embed_tokens, + no_encoder_attn=True, + ) + return cls(decoder) + + @classmethod + def build_embedding(cls, args, dictionary, embed_dim, path=None): + def _vocab_init(tensor, **kwargs): + nn.init.normal_(tensor, mean=0, std=embed_dim**-0.5) + nn.init.constant_(tensor[1], 0) + + embed_tokens = VocabParallelEmbedding( + len(dictionary), embed_dim, dictionary.pad(), init_method=_vocab_init + ) + return embed_tokens + + +def base_lm_architecture(args): + # backward compatibility for older model checkpoints + if hasattr(args, "no_tie_adaptive_proj"): + # previous models defined --no-tie-adaptive-proj, so use the existence of + # that option to determine if this is an "old" model checkpoint + args.no_decoder_final_norm = True # old models always set this to True + if args.no_tie_adaptive_proj is False: + args.tie_adaptive_proj = True + if hasattr(args, "decoder_final_norm"): + args.no_decoder_final_norm = not args.decoder_final_norm + + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.relu_dropout = getattr(args, "relu_dropout", 0.0) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + # Model training is not stable without this + args.decoder_normalize_before = True + args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", False) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.character_embeddings = getattr(args, "character_embeddings", False) + args.character_filters = getattr( + args, + "character_filters", + "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", + ) + args.character_embedding_dim = getattr(args, "character_embedding_dim", 4) + args.char_embedder_highway_layers = getattr(args, "char_embedder_highway_layers", 2) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4) + args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0.0) + args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0.0) + args.add_bos_token = getattr(args, "add_bos_token", False) + + +@register_model_architecture("model_parallel_transformer_lm", "transformer_lm_megatron") +def transformer_lm_megatron(args): + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 4) + args.decoder_layers = getattr(args, "decoder_layers", 72) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture( + "model_parallel_transformer_lm", "transformer_lm_megatron_11b" +) +def transformer_lm_megatron_11b(args): + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 6) + args.decoder_layers = getattr(args, "decoder_layers", 72) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) diff --git a/fairseq/fairseq/model_parallel/modules/__init__.py b/fairseq/fairseq/model_parallel/modules/__init__.py new file mode 100644 index 0000000..1160321 --- /dev/null +++ b/fairseq/fairseq/model_parallel/modules/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +from .multihead_attention import ModelParallelMultiheadAttention +from .transformer_layer import ( + ModelParallelTransformerEncoderLayer, + ModelParallelTransformerDecoderLayer, +) + +__all__ = [ + "ModelParallelMultiheadAttention", + "ModelParallelTransformerEncoderLayer", + "ModelParallelTransformerDecoderLayer", +] diff --git a/fairseq/fairseq/model_parallel/modules/multihead_attention.py b/fairseq/fairseq/model_parallel/modules/multihead_attention.py new file mode 100644 index 0000000..bbea450 --- /dev/null +++ b/fairseq/fairseq/model_parallel/modules/multihead_attention.py @@ -0,0 +1,349 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from fairseq import utils +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout + +try: + from fairseq.model_parallel.megatron.mpu import ( + ColumnParallelLinear, + RowParallelLinear, + get_cuda_rng_tracker, + get_model_parallel_world_size, + ) + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + + +@with_incremental_state +class ModelParallelMultiheadAttention(nn.Module): + """Model parallel Multi-headed attention. + This performs the Multi-headed attention over multiple gpus. + + See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + self_attention=False, + encoder_decoder_attention=False, + ): + super().__init__() + if not has_megatron_submodule: + raise ImportError( + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" + ) + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.model_parallel_size = get_model_parallel_world_size() + + self.num_heads_partition = num_heads // self.model_parallel_size + assert ( + self.num_heads_partition * self.model_parallel_size == num_heads + ), "Number of heads must be divisible by model parallel size" + + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim**-0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert ( + not self.self_attention or self.qkv_same_dim + ), "Self-attention requires query, key and value to be of the same size" + + self.k_proj = ColumnParallelLinear( + self.kdim, embed_dim, bias=bias, gather_output=False + ) + self.v_proj = ColumnParallelLinear( + self.vdim, embed_dim, bias=bias, gather_output=False + ) + self.q_proj = ColumnParallelLinear( + embed_dim, embed_dim, bias=bias, gather_output=False + ) + self.out_proj = RowParallelLinear( + embed_dim, embed_dim, bias=bias, input_is_parallel=True + ) + + def forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + **unused_kwargs, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + """ + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + + is_tpu = query.device.type == "xla" + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + q = ( + q.contiguous() + .view(tgt_len, bsz * self.num_heads_partition, self.head_dim) + .transpose(0, 1) + ) + if k is not None: + k = ( + k.contiguous() + .view(-1, bsz * self.num_heads_partition, self.head_dim) + .transpose(0, 1) + ) + if v is not None: + v = ( + v.contiguous() + .view(-1, bsz * self.num_heads_partition, self.head_dim) + .transpose(0, 1) + ) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads_partition, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + prev_key = _prev_key.view( + bsz * self.num_heads_partition, -1, self.head_dim + ) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + prev_value = _prev_value.view( + bsz * self.num_heads_partition, -1, self.head_dim + ) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = ( + ModelParallelMultiheadAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + ) + + saved_state["prev_key"] = k.view( + bsz, self.num_heads_partition, -1, self.head_dim + ) + saved_state["prev_value"] = v.view( + bsz, self.num_heads_partition, -1, self.head_dim + ) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + src_len = k.size(1) + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.dim() == 0: + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + + assert list(attn_weights.size()) == [ + bsz * self.num_heads_partition, + tgt_len, + src_len, + ] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + attn_weights += attn_mask + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view( + bsz, self.num_heads_partition, tgt_len, src_len + ) + if not is_tpu: + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + else: + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.view( + bsz * self.num_heads_partition, tgt_len, src_len + ) + + attn_weights_float = utils.softmax(attn_weights, dim=-1) + attn_weights = attn_weights_float.type_as(attn_weights) + + with get_cuda_rng_tracker().fork(): + attn_probs = self.dropout_module(attn_weights) + + assert v is not None + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [ + bsz * self.num_heads_partition, + tgt_len, + self.head_dim, + ] + embed_dim_partition = embed_dim // self.model_parallel_size + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim_partition) + attn = self.out_proj(attn) + # return attn_weights None to keep the return type same as single gpu multihead attention + # This will be deprecated. + attn_weights: Optional[Tensor] = None + + return attn, attn_weights + + @staticmethod + def _append_prev_key_padding_mask( + key_padding_mask: Optional[Tensor], + prev_key_padding_mask: Optional[Tensor], + batch_size: int, + src_len: int, + static_kv: bool, + ) -> Optional[Tensor]: + # saved key padding masks have shape (bsz, seq_len) + if prev_key_padding_mask is not None and static_kv: + new_key_padding_mask = prev_key_padding_mask + elif prev_key_padding_mask is not None and key_padding_mask is not None: + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 + ) + # During incremental decoding, as the padding token enters and + # leaves the frame, there will be a time when prev or current + # is None + elif prev_key_padding_mask is not None: + + filler = torch.zeros(batch_size, src_len - prev_key_padding_mask.size(1)) + if prev_key_padding_mask.is_cuda: + filler = filler.cuda() + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + elif key_padding_mask is not None: + filler = torch.zeros(batch_size, src_len - key_padding_mask.size(1)) + if key_padding_mask.is_cuda: + filler = filler.cuda() + new_key_padding_mask = torch.cat( + [filler.float(), key_padding_mask.float()], dim=1 + ) + else: + new_key_padding_mask = prev_key_padding_mask + return new_key_padding_mask + + def reorder_incremental_state( + self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order + ): + """Reorder buffered internal state (for incremental generation).""" + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + for k in input_buffer.keys(): + if input_buffer[k] is not None: + input_buffer[k] = input_buffer[k].index_select(0, new_order) + incremental_state = self._set_input_buffer(incremental_state, input_buffer) + return incremental_state + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ) -> Dict[str, Optional[Tensor]]: + result = self.get_incremental_state(incremental_state, "attn_state") + if result is not None: + return result + else: + empty_result: Dict[str, Optional[Tensor]] = {} + return empty_result + + def _set_input_buffer( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + buffer: Dict[str, Optional[Tensor]], + ): + return self.set_incremental_state(incremental_state, "attn_state", buffer) diff --git a/fairseq/fairseq/model_parallel/modules/transformer_layer.py b/fairseq/fairseq/model_parallel/modules/transformer_layer.py new file mode 100644 index 0000000..7ab53c6 --- /dev/null +++ b/fairseq/fairseq/model_parallel/modules/transformer_layer.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.model_parallel.modules import ModelParallelMultiheadAttention +from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer + + +try: + from fairseq.model_parallel.megatron.mpu import ( + ColumnParallelLinear, + RowParallelLinear, + ) + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + + +class ModelParallelTransformerEncoderLayer(TransformerEncoderLayer): + """Encoder layer block over multiple gpus. + + See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. + """ + + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + if q_noise > 0: + raise NotImplementedError + return ColumnParallelLinear(input_dim, output_dim, gather_output=False) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + if q_noise > 0: + raise NotImplementedError + return RowParallelLinear(input_dim, output_dim, input_is_parallel=True) + + def build_self_attention(self, embed_dim, args, **unused_kwargs): + return ModelParallelMultiheadAttention( + embed_dim, + args.encoder_attention_heads, + dropout=args.attention_dropout, + self_attention=True, + ) + + +class ModelParallelTransformerDecoderLayer(TransformerDecoderLayer): + """Decoder layer block. + + See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. + """ + + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + if q_noise > 0: + raise NotImplementedError + return ColumnParallelLinear(input_dim, output_dim, gather_output=False) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + if q_noise > 0: + raise NotImplementedError + return RowParallelLinear(input_dim, output_dim, input_is_parallel=True) + + def build_self_attention(self, embed_dim, args, **unused_kwargs): + return ModelParallelMultiheadAttention( + embed_dim=embed_dim, + num_heads=args.decoder_attention_heads, + dropout=args.attention_dropout, + self_attention=not getattr(args, "cross_self_attention", False), + ) + + def build_encoder_attention(self, embed_dim, args, **unused_kwargs): + return ModelParallelMultiheadAttention( + embed_dim=embed_dim, + num_heads=args.decoder_attention_heads, + kdim=getattr(args, "encoder_embed_dim", None), + vdim=getattr(args, "encoder_embed_dim", None), + dropout=args.attention_dropout, + encoder_decoder_attention=True, + ) diff --git a/fairseq/fairseq/models/__init__.py b/fairseq/fairseq/models/__init__.py new file mode 100644 index 0000000..11cf6ee --- /dev/null +++ b/fairseq/fairseq/models/__init__.py @@ -0,0 +1,236 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import argparse +import importlib +import os + +from contextlib import ExitStack + +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import merge_with_parent +from hydra.core.config_store import ConfigStore +from omegaconf import open_dict, OmegaConf + +from .composite_encoder import CompositeEncoder +from .distributed_fairseq_model import DistributedFairseqModel +from .fairseq_decoder import FairseqDecoder +from .fairseq_encoder import FairseqEncoder +from .fairseq_incremental_decoder import FairseqIncrementalDecoder +from .fairseq_model import ( + BaseFairseqModel, + FairseqEncoderDecoderModel, + FairseqEncoderModel, + FairseqLanguageModel, + FairseqModel, + FairseqMultiModel, +) + + +MODEL_REGISTRY = {} +MODEL_DATACLASS_REGISTRY = {} +ARCH_MODEL_REGISTRY = {} +ARCH_MODEL_NAME_REGISTRY = {} +ARCH_MODEL_INV_REGISTRY = {} +ARCH_CONFIG_REGISTRY = {} + + +__all__ = [ + "BaseFairseqModel", + "CompositeEncoder", + "DistributedFairseqModel", + "FairseqDecoder", + "FairseqEncoder", + "FairseqEncoderDecoderModel", + "FairseqEncoderModel", + "FairseqIncrementalDecoder", + "FairseqLanguageModel", + "FairseqModel", + "FairseqMultiModel", +] + + +def build_model(cfg: FairseqDataclass, task, from_checkpoint=False): + + model = None + model_type = getattr(cfg, "_name", None) or getattr(cfg, "arch", None) + + if not model_type and len(cfg) == 1: + # this is hit if config object is nested in directory that is named after model type + + model_type = next(iter(cfg)) + if model_type in MODEL_DATACLASS_REGISTRY: + cfg = cfg[model_type] + else: + raise Exception( + "Could not infer model type from directory. Please add _name field to indicate model type. " + "Available models: " + + str(MODEL_DATACLASS_REGISTRY.keys()) + + " Requested model type: " + + model_type + ) + + if model_type in ARCH_MODEL_REGISTRY: + # case 1: legacy models + model = ARCH_MODEL_REGISTRY[model_type] + elif model_type in MODEL_DATACLASS_REGISTRY: + # case 2: config-driven models + model = MODEL_REGISTRY[model_type] + + if model_type in MODEL_DATACLASS_REGISTRY: + # set defaults from dataclass. note that arch name and model name can be the same + dc = MODEL_DATACLASS_REGISTRY[model_type] + + if isinstance(cfg, argparse.Namespace): + cfg = dc.from_namespace(cfg) + else: + cfg = merge_with_parent(dc(), cfg, from_checkpoint) + else: + if model_type in ARCH_CONFIG_REGISTRY: + with open_dict(cfg) if OmegaConf.is_config(cfg) else ExitStack(): + # this calls the different "arch" functions (like base_architecture()) that you indicate + # if you specify --arch on the command line. this is only applicable to the old argparse based models + # hydra models should expose different architectures via different config files + # it will modify the cfg object and default parameters according to the arch + ARCH_CONFIG_REGISTRY[model_type](cfg) + + assert model is not None, ( + f"Could not infer model type from {cfg}. " + "Available models: {}".format(MODEL_DATACLASS_REGISTRY.keys()) + + f" Requested model type: {model_type}" + ) + + return model.build_model(cfg, task) + + +def register_model(name, dataclass=None): + """ + New model types can be added to fairseq with the :func:`register_model` + function decorator. + + For example:: + + @register_model('lstm') + class LSTM(FairseqEncoderDecoderModel): + (...) + + .. note:: All models must implement the :class:`BaseFairseqModel` interface. + Typically you will extend :class:`FairseqEncoderDecoderModel` for + sequence-to-sequence tasks or :class:`FairseqLanguageModel` for + language modeling tasks. + + Args: + name (str): the name of the model + """ + + def register_model_cls(cls): + if name in MODEL_REGISTRY: + return MODEL_REGISTRY[name] + + if not issubclass(cls, BaseFairseqModel): + raise ValueError( + "Model ({}: {}) must extend BaseFairseqModel".format(name, cls.__name__) + ) + MODEL_REGISTRY[name] = cls + if dataclass is not None and not issubclass(dataclass, FairseqDataclass): + raise ValueError( + "Dataclass {} must extend FairseqDataclass".format(dataclass) + ) + + cls.__dataclass = dataclass + if dataclass is not None: + MODEL_DATACLASS_REGISTRY[name] = dataclass + + cs = ConfigStore.instance() + node = dataclass() + node._name = name + cs.store(name=name, group="model", node=node, provider="fairseq") + + @register_model_architecture(name, name) + def noop(_): + pass + + return cls + + return register_model_cls + + +def register_model_architecture(model_name, arch_name): + """ + New model architectures can be added to fairseq with the + :func:`register_model_architecture` function decorator. After registration, + model architectures can be selected with the ``--arch`` command-line + argument. + + For example:: + + @register_model_architecture('lstm', 'lstm_luong_wmt_en_de') + def lstm_luong_wmt_en_de(cfg): + args.encoder_embed_dim = getattr(cfg.model, 'encoder_embed_dim', 1000) + (...) + + The decorated function should take a single argument *cfg*, which is a + :class:`omegaconf.DictConfig`. The decorated function should modify these + arguments in-place to match the desired architecture. + + Args: + model_name (str): the name of the Model (Model must already be + registered) + arch_name (str): the name of the model architecture (``--arch``) + """ + + def register_model_arch_fn(fn): + if model_name not in MODEL_REGISTRY: + raise ValueError( + "Cannot register model architecture for unknown model type ({})".format( + model_name + ) + ) + if arch_name in ARCH_MODEL_REGISTRY: + raise ValueError( + "Cannot register duplicate model architecture ({})".format(arch_name) + ) + if not callable(fn): + raise ValueError( + "Model architecture must be callable ({})".format(arch_name) + ) + ARCH_MODEL_REGISTRY[arch_name] = MODEL_REGISTRY[model_name] + ARCH_MODEL_NAME_REGISTRY[arch_name] = model_name + ARCH_MODEL_INV_REGISTRY.setdefault(model_name, []).append(arch_name) + ARCH_CONFIG_REGISTRY[arch_name] = fn + return fn + + return register_model_arch_fn + + +def import_models(models_dir, namespace): + for file in os.listdir(models_dir): + path = os.path.join(models_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + model_name = file[: file.find(".py")] if file.endswith(".py") else file + importlib.import_module(namespace + "." + model_name) + + # extra `model_parser` for sphinx + if model_name in MODEL_REGISTRY: + parser = argparse.ArgumentParser(add_help=False) + group_archs = parser.add_argument_group("Named architectures") + group_archs.add_argument( + "--arch", choices=ARCH_MODEL_INV_REGISTRY[model_name] + ) + group_args = parser.add_argument_group( + "Additional command-line arguments" + ) + MODEL_REGISTRY[model_name].add_args(group_args) + globals()[model_name + "_parser"] = parser + + +# automatically import any Python files in the models/ directory +models_dir = os.path.dirname(__file__) +import_models(models_dir, "fairseq.models") diff --git a/fairseq/fairseq/models/bart/__init__.py b/fairseq/fairseq/models/bart/__init__.py new file mode 100644 index 0000000..a701923 --- /dev/null +++ b/fairseq/fairseq/models/bart/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .hub_interface import * # noqa +from .model import * # noqa diff --git a/fairseq/fairseq/models/bart/hub_interface.py b/fairseq/fairseq/models/bart/hub_interface.py new file mode 100644 index 0000000..6b647c9 --- /dev/null +++ b/fairseq/fairseq/models/bart/hub_interface.py @@ -0,0 +1,211 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import logging +from typing import Dict, List + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.data import encoders +from fairseq.hub_utils import GeneratorHubInterface +from omegaconf import open_dict + + +logger = logging.getLogger(__name__) + + +class BARTHubInterface(GeneratorHubInterface): + """A simple PyTorch Hub interface to BART. + + Usage: https://github.com/pytorch/fairseq/tree/main/examples/bart + """ + + def __init__(self, cfg, task, model): + super().__init__(cfg, task, [model]) + self.model = self.models[0] + + def encode( + self, sentence: str, *addl_sentences, no_separator=True + ) -> torch.LongTensor: + """ + BPE-encode a sentence (or multiple sentences). + + Every sequence begins with a beginning-of-sentence (`<s>`) symbol. + Every sentence ends with an end-of-sentence (`</s>`). + + Example (single sentence): `<s> a b c </s>` + Example (sentence pair): `<s> d e f </s> 1 2 3 </s>` + + The BPE encoding follows GPT-2. One subtle detail is that the GPT-2 BPE + requires leading spaces. For example:: + + >>> bart.encode('Hello world').tolist() + [0, 31414, 232, 2] + >>> bart.encode(' world').tolist() + [0, 232, 2] + >>> bart.encode('world').tolist() + [0, 8331, 2] + """ + tokens = self.bpe.encode(sentence) + if len(tokens.split(" ")) > min(self.max_positions) - 2: + tokens = " ".join(tokens.split(" ")[: min(self.max_positions) - 2]) + bpe_sentence = "<s> " + tokens + " </s>" + for s in addl_sentences: + bpe_sentence += " </s>" if not no_separator else "" + bpe_sentence += " " + self.bpe.encode(s) + " </s>" + tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=False) + return tokens.long() + + def decode(self, tokens: torch.LongTensor): + assert tokens.dim() == 1 + tokens = tokens.cpu().numpy() + if tokens[0] == self.task.source_dictionary.bos(): + tokens = tokens[1:] # remove <s> + eos_mask = tokens == self.task.source_dictionary.eos() + doc_mask = eos_mask[1:] & eos_mask[:-1] + sentences = np.split(tokens, doc_mask.nonzero()[0] + 1) + sentences = [ + self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences + ] + if len(sentences) == 1: + return sentences[0] + return sentences + + def _build_sample(self, src_tokens: List[torch.LongTensor]): + # assert torch.is_tensor(src_tokens) + dataset = self.task.build_dataset_for_inference( + src_tokens, + [x.numel() for x in src_tokens], + ) + sample = dataset.collater(dataset) + sample = utils.apply_to_sample(lambda tensor: tensor.to(self.device), sample) + return sample + + def generate( + self, + tokenized_sentences: List[torch.LongTensor], + *args, + inference_step_args=None, + skip_invalid_size_inputs=False, + **kwargs + ) -> List[List[Dict[str, torch.Tensor]]]: + inference_step_args = inference_step_args or {} + if "prefix_tokens" in inference_step_args: + raise NotImplementedError("prefix generation not implemented for BART") + res = [] + for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs): + src_tokens = batch["net_input"]["src_tokens"] + inference_step_args["prefix_tokens"] = src_tokens.new_full( + (src_tokens.size(0), 1), fill_value=self.task.source_dictionary.bos() + ).to(device=self.device) + results = super().generate( + src_tokens, + *args, + inference_step_args=inference_step_args, + skip_invalid_size_inputs=skip_invalid_size_inputs, + **kwargs + ) + for id, hypos in zip(batch["id"].tolist(), results): + res.append((id, hypos)) + res = [hypos for _, hypos in sorted(res, key=lambda x: x[0])] + return res + + def extract_features( + self, tokens: torch.LongTensor, return_all_hiddens: bool = False + ) -> torch.Tensor: + if tokens.dim() == 1: + tokens = tokens.unsqueeze(0) + if tokens.size(-1) > min(self.model.max_positions()): + raise ValueError( + "tokens exceeds maximum length: {} > {}".format( + tokens.size(-1), self.model.max_positions() + ) + ) + tokens.to(device=self.device), + prev_output_tokens = tokens.clone() + + prev_output_tokens[:, 0] = tokens.gather( + 1, + (tokens.ne(self.task.source_dictionary.pad()).sum(dim=1) - 1).unsqueeze(-1), + ).squeeze() + + prev_output_tokens[:, 1:] = tokens[:, :-1] + features, extra = self.model( + src_tokens=tokens, + src_lengths=None, + prev_output_tokens=prev_output_tokens, + features_only=True, + return_all_hiddens=return_all_hiddens, + ) + if return_all_hiddens: + # convert from T x B x C -> B x T x C + inner_states = extra["inner_states"] + return [inner_state.transpose(0, 1) for inner_state in inner_states] + else: + return features # just the last layer's features + + def register_classification_head( + self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs + ): + self.model.register_classification_head( + name, num_classes=num_classes, embedding_size=embedding_size, **kwargs + ) + + def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = False): + if tokens.dim() == 1: + tokens = tokens.unsqueeze(0) + features = self.extract_features(tokens.to(device=self.device)) + sentence_representation = features[ + tokens.eq(self.task.source_dictionary.eos()), : + ].view(features.size(0), -1, features.size(-1))[:, -1, :] + + logits = self.model.classification_heads[head](sentence_representation) + if return_logits: + return logits + return F.log_softmax(logits, dim=-1) + + def fill_mask( + self, + masked_inputs: List[str], + topk: int = 5, + match_source_len: bool = True, + **generate_kwargs + ): + masked_token = "<mask>" + batch_tokens = [] + for masked_input in masked_inputs: + assert ( + masked_token in masked_input + ), "please add one {} token for the input".format(masked_token) + + text_spans = masked_input.split(masked_token) + text_spans_bpe = ( + (" {0} ".format(masked_token)) + .join([self.bpe.encode(text_span.rstrip()) for text_span in text_spans]) + .strip() + ) + tokens = self.task.source_dictionary.encode_line( + "<s> " + text_spans_bpe + " </s>", + append_eos=False, + add_if_not_exist=False, + ).long() + batch_tokens.append(tokens) + + # ensure beam size is at least as big as topk + generate_kwargs["beam"] = max( + topk, + generate_kwargs.get("beam", -1), + ) + generate_kwargs["match_source_len"] = match_source_len + batch_hypos = self.generate(batch_tokens, **generate_kwargs) + + return [ + [(self.decode(hypo["tokens"]), hypo["score"]) for hypo in hypos[:topk]] + for hypos in batch_hypos + ] diff --git a/fairseq/fairseq/models/bart/model.py b/fairseq/fairseq/models/bart/model.py new file mode 100644 index 0000000..e3670c0 --- /dev/null +++ b/fairseq/fairseq/models/bart/model.py @@ -0,0 +1,394 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +BART: Denoising Sequence-to-Sequence Pre-training for +Natural Language Generation, Translation, and Comprehension +""" +import logging +from typing import Optional + +import torch +import torch.nn as nn + +from fairseq import utils +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer import TransformerModel +from fairseq.modules.transformer_sentence_encoder import init_bert_params + +from .hub_interface import BARTHubInterface + +logger = logging.getLogger(__name__) + + +@register_model("bart") +class BARTModel(TransformerModel): + __jit_unused_properties__ = ["supported_targets"] + + @classmethod + def hub_models(cls): + return { + "bart.base": "http://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz", + "bart.large": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz", + "bart.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz", + "bart.large.cnn": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz", + "bart.large.xsum": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz", + } + + def __init__(self, args, encoder, decoder): + super().__init__(args, encoder, decoder) + + # We follow BERT's random weight initialization + self.apply(init_bert_params) + + self.classification_heads = nn.ModuleDict() + if hasattr(self.encoder, "dictionary"): + self.eos: int = self.encoder.dictionary.eos() + + @staticmethod + def add_args(parser): + super(BARTModel, BARTModel).add_args(parser) + parser.add_argument( + "--pooler-dropout", + type=float, + metavar="D", + help="dropout probability in the masked_lm pooler layers", + ) + parser.add_argument( + "--pooler-activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use for pooler layer", + ) + parser.add_argument( + "--spectral-norm-classification-head", + action="store_true", + help="Apply spectral normalization on the classification head", + ) + + @property + def supported_targets(self): + return {"self"} + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + features_only: bool = False, + classification_head_name: Optional[str] = None, + token_embeddings: Optional[torch.Tensor] = None, + return_all_hiddens: bool = True, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + if classification_head_name is not None: + features_only = True + + encoder_out = self.encoder( + src_tokens, + src_lengths=src_lengths, + token_embeddings=token_embeddings, + return_all_hiddens=return_all_hiddens, + ) + x, extra = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + features_only=features_only, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + src_lengths=src_lengths, + return_all_hiddens=return_all_hiddens, + ) + eos: int = self.eos + if classification_head_name is not None: + sentence_representation = x[src_tokens.eq(eos), :].view( + x.size(0), -1, x.size(-1) + )[:, -1, :] + for k, head in self.classification_heads.items(): + # for torch script only supports iteration + if k == classification_head_name: + x = head(sentence_representation) + break + return x, extra + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="gpt2", + sample_break_mode="eos", + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + bpe=bpe, + load_checkpoint_heads=True, + sample_break_mode=sample_break_mode, + **kwargs, + ) + return BARTHubInterface(x["args"], x["task"], x["models"][0]) + + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): + """Register a classification head.""" + logger.info("Registering classification head: {0}".format(name)) + if name in self.classification_heads: + prev_num_classes = self.classification_heads[name].out_proj.out_features + prev_inner_dim = self.classification_heads[name].dense.out_features + if num_classes != prev_num_classes or inner_dim != prev_inner_dim: + logger.warning( + 're-registering head "{}" with num_classes {} (prev: {}) ' + "and inner_dim {} (prev: {})".format( + name, num_classes, prev_num_classes, inner_dim, prev_inner_dim + ) + ) + self.classification_heads[name] = BARTClassificationHead( + input_dim=self.args.encoder_embed_dim, + inner_dim=inner_dim or self.args.encoder_embed_dim, + num_classes=num_classes, + activation_fn=self.args.pooler_activation_fn, + pooler_dropout=self.args.pooler_dropout, + do_spectral_norm=getattr( + self.args, "spectral_norm_classification_head", False + ), + ) + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + + prefix = name + "." if name != "" else "" + current_head_names = ( + [] + if not hasattr(self, "classification_heads") + else self.classification_heads.keys() + ) + + # Handle new classification heads present in the state dict. + keys_to_delete = [] + for k in state_dict.keys(): + if not k.startswith(prefix + "classification_heads."): + continue + + head_name = k[len(prefix + "classification_heads.") :].split(".")[0] + num_classes = state_dict[ + prefix + "classification_heads." + head_name + ".out_proj.weight" + ].size(0) + inner_dim = state_dict[ + prefix + "classification_heads." + head_name + ".dense.weight" + ].size(0) + + if getattr(self.args, "load_checkpoint_heads", False): + if head_name not in current_head_names: + self.register_classification_head(head_name, num_classes, inner_dim) + else: + if head_name not in current_head_names: + logger.warning( + "deleting classification head ({}) from checkpoint " + "not present in current model: {}".format(head_name, k) + ) + keys_to_delete.append(k) + elif ( + num_classes + != self.classification_heads[head_name].out_proj.out_features + or inner_dim + != self.classification_heads[head_name].dense.out_features + ): + logger.warning( + "deleting classification head ({}) from checkpoint " + "with different dimensions than current model: {}".format( + head_name, k + ) + ) + keys_to_delete.append(k) + for k in keys_to_delete: + del state_dict[k] + + def truncate_emb(key): + if key in state_dict: + state_dict[key] = state_dict[key][:-1, :] + + # When finetuning on translation task, remove last row of + # embedding matrix that corresponds to mask_idx token. + loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0) + if ( + loaded_dict_size == len(self.encoder.dictionary) + 1 + and "<mask>" not in self.encoder.dictionary + ): + truncate_emb("encoder.embed_tokens.weight") + truncate_emb("decoder.embed_tokens.weight") + truncate_emb("encoder.output_projection.weight") + truncate_emb("decoder.output_projection.weight") + + # When continued pretraining on new set of languages for mbart, + # add extra lang embeddings at the end of embed_tokens. + # Note: newly added languages are assumed to have been added at the end. + if self.args.task == "multilingual_denoising" and loaded_dict_size < len( + self.encoder.dictionary + ): + logger.info( + "Adding extra language embeddings not found in pretrained model for " + "continued pretraining of MBART on new set of languages." + ) + loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][ + -1, : + ] + + num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size + embed_dim = state_dict["encoder.embed_tokens.weight"].size(1) + + new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim) + nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim**-0.5) + new_lang_embed_to_add = new_lang_embed_to_add.to( + dtype=state_dict["encoder.embed_tokens.weight"].dtype, + ) + + state_dict["encoder.embed_tokens.weight"] = torch.cat( + [ + state_dict["encoder.embed_tokens.weight"][ + : loaded_dict_size - 1, : + ], + new_lang_embed_to_add, + loaded_mask_token_embedding.unsqueeze(0), + ] + ) + state_dict["decoder.embed_tokens.weight"] = torch.cat( + [ + state_dict["decoder.embed_tokens.weight"][ + : loaded_dict_size - 1, : + ], + new_lang_embed_to_add, + loaded_mask_token_embedding.unsqueeze(0), + ] + ) + + # Copy any newly-added classification heads into the state dict + # with their current weights. + if hasattr(self, "classification_heads"): + cur_state = self.classification_heads.state_dict() + for k, v in cur_state.items(): + if prefix + "classification_heads." + k not in state_dict: + logger.info("Overwriting " + prefix + "classification_heads." + k) + state_dict[prefix + "classification_heads." + k] = v + + def set_beam_size(self, beam): + """Set beam size for efficient beamable enc-dec attention.""" + beamable = False + for layer in self.decoder.layers: + if layer.encoder_attn is not None: + if hasattr(layer.encoder_attn, "set_beam_size"): + layer.encoder_attn.set_beam_size(beam) + beamable = True + if beamable: + self.encoder.reorder_encoder_out = self.encoder._reorder_encoder_out + + +class BARTClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__( + self, + input_dim, + inner_dim, + num_classes, + activation_fn, + pooler_dropout, + do_spectral_norm=False, + ): + super().__init__() + self.dense = nn.Linear(input_dim, inner_dim) + self.activation_fn = utils.get_activation_fn(activation_fn) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = nn.Linear(inner_dim, num_classes) + + if do_spectral_norm: + self.out_proj = torch.nn.utils.spectral_norm(self.out_proj) + + def forward(self, features, **kwargs): + x = features + x = self.dropout(x) + x = self.dense(x) + x = self.activation_fn(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +@register_model_architecture("bart", "bart_large") +def bart_large_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.relu_dropout = getattr(args, "relu_dropout", 0.0) + args.dropout = getattr(args, "dropout", 0.1) + args.max_target_positions = getattr(args, "max_target_positions", 1024) + args.max_source_positions = getattr(args, "max_source_positions", 1024) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", True + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", True) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", True) + args.layernorm_embedding = getattr(args, "layernorm_embedding", True) + + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) + + +@register_model_architecture("bart", "bart_base") +def bart_base_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) + bart_large_architecture(args) + + +@register_model_architecture("bart", "mbart_large") +def mbart_large_architecture(args): + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + bart_large_architecture(args) + + +@register_model_architecture("bart", "mbart_base") +def mbart_base_architecture(args): + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + bart_base_architecture(args) + + +@register_model_architecture("bart", "mbart_base_wmt20") +def mbart_base_wmt20_architecture(args): + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + mbart_base_architecture(args) diff --git a/fairseq/fairseq/models/composite_encoder.py b/fairseq/fairseq/models/composite_encoder.py new file mode 100644 index 0000000..4e20fe3 --- /dev/null +++ b/fairseq/fairseq/models/composite_encoder.py @@ -0,0 +1,57 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .fairseq_encoder import FairseqEncoder + + +class CompositeEncoder(FairseqEncoder): + """ + A wrapper around a dictionary of :class:`FairseqEncoder` objects. + + We run forward on each encoder and return a dictionary of outputs. The first + encoder's dictionary is used for initialization. + + Args: + encoders (dict): a dictionary of :class:`FairseqEncoder` objects. + """ + + def __init__(self, encoders): + super().__init__(next(iter(encoders.values())).dictionary) + self.encoders = encoders + for key in self.encoders: + self.add_module(key, self.encoders[key]) + + def forward(self, src_tokens, src_lengths): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (LongTensor): lengths of each source sentence of shape + `(batch)` + + Returns: + dict: + the outputs from each Encoder + """ + encoder_out = {} + for key in self.encoders: + encoder_out[key] = self.encoders[key](src_tokens, src_lengths) + return encoder_out + + def reorder_encoder_out(self, encoder_out, new_order): + """Reorder encoder output according to new_order.""" + for key in self.encoders: + encoder_out[key] = self.encoders[key].reorder_encoder_out( + encoder_out[key], new_order + ) + return encoder_out + + def max_positions(self): + return min(self.encoders[key].max_positions() for key in self.encoders) + + def upgrade_state_dict(self, state_dict): + for key in self.encoders: + self.encoders[key].upgrade_state_dict(state_dict) + return state_dict diff --git a/fairseq/fairseq/models/distributed_fairseq_model.py b/fairseq/fairseq/models/distributed_fairseq_model.py new file mode 100644 index 0000000..fd76bcd --- /dev/null +++ b/fairseq/fairseq/models/distributed_fairseq_model.py @@ -0,0 +1,147 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import signal +import threading + +import torch +import torch.nn as nn +from torch.nn.parallel import DistributedDataParallel + +from fairseq.distributed import ( + DistributedTimeoutWrapper, + LegacyDistributedDataParallel, + ModuleProxyWrapper, + TPUDistributedDataParallel, +) + +logger = logging.getLogger(__name__) + + +_SLOWMO_DDP_DISABLED = False +try: + from fairscale.experimental.nn.data_parallel import ( + SlowMoBaseAlgorithm, + SlowMoDistributedDataParallel, + ) +except ImportError: + _SLOWMO_DDP_DISABLED = True + + +def DistributedFairseqModel(args, model, process_group, device): + """ + Wrap a *model* to support distributed data parallel training. + + This is similar to the built-in DistributedDataParallel, but allows + additional configuration of the DistributedDataParallel class to + use, and also provides easier access to the wrapped model by + forwarding requests for missing attributes to the wrapped model. + + Args: + args (argparse.Namespace): fairseq args + model (BaseFairseqModel): model to wrap + process_group: the c10d process group to be used for distributed data + parallel all-reduction. + device: device to move model to + """ + assert isinstance(model, nn.Module) + if args.tpu: + wrapped_model = TPUDistributedDataParallel( + module=model.to(device), + process_group=process_group, + ) + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend in {"c10d", "pytorch_ddp"}: + wrapped_model = DistributedDataParallel( + module=model.to(device), + device_ids=[args.device_id], + output_device=args.device_id, + broadcast_buffers=args.broadcast_buffers, + bucket_cap_mb=args.bucket_cap_mb, + process_group=process_group, + find_unused_parameters=args.find_unused_parameters, + gradient_as_bucket_view=args.gradient_as_bucket_view, + ) + if args.ddp_comm_hook == "fp16": + logger.info("enable fp16 communication hook in DDP") + try: + from torch.distributed.algorithms.ddp_comm_hooks import ( + DDPCommHookType, + register_ddp_comm_hook, + ) + except: + logger.error( + "Could not import from torch.distributed.algorithms.ddp_comm_hooks; you may need to update your pytorch version" + ) + raise + + register_ddp_comm_hook(DDPCommHookType.FP16_COMPRESS, wrapped_model) + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend in {"no_c10d", "legacy_ddp"}: + wrapped_model = LegacyDistributedDataParallel( + module=model.to(device), + buffer_size=2**28, + process_group=process_group, + ) + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend == "slowmo": + if _SLOWMO_DDP_DISABLED: + raise ImportError( + "Cannot find SlowMoDistributedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + + # The values of slowmo_momentum below were obtained by tuning on the + # En-De 16 dataset by training the transformer_wmt_en_de_large model + if args.slowmo_momentum is None: + if args.distributed_world_size <= 16: + args.slowmo_momentum = 0.0 + elif args.distributed_world_size <= 32: + args.slowmo_momentum = 0.2 + elif args.distributed_world_size <= 64: + args.slowmo_momentum = 0.5 + else: + args.slowmo_momentum = 0.6 + slowmo_base_algorithm = SlowMoBaseAlgorithm[args.slowmo_base_algorithm.upper()] + + wrapped_model = SlowMoDistributedDataParallel( + module=model.to(device), + broadcast_buffers=args.broadcast_buffers, + nprocs_per_node=args.nprocs_per_node, + slowmo_momentum=args.slowmo_momentum, + slowmo_base_algorithm=slowmo_base_algorithm, + localsgd_frequency=args.localsgd_frequency, + ) + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend == "fully_sharded": + try: + from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP + except ImportError: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + assert isinstance(model, FSDP), "expected model to already be wrapped in FSDP" + wrapped_model = model + if args.memory_efficient_fp16: + wrapped_model = wrapped_model.half() + if not args.cpu_offload: + wrapped_model = wrapped_model.to(device=device) + else: + raise ValueError("Unknown --ddp-backend: " + args.ddp_backend) + + # kill hung distributed jobs after a timeout + if getattr(args, "heartbeat_timeout", -1) > 0: + wrapped_model = DistributedTimeoutWrapper( + wrapped_model, timeout=getattr(args, "heartbeat_timeout", -1) + ) + + return wrapped_model diff --git a/fairseq/fairseq/models/ema/__init__.py b/fairseq/fairseq/models/ema/__init__.py new file mode 100644 index 0000000..503ceaa --- /dev/null +++ b/fairseq/fairseq/models/ema/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + +from .ema import EMA + + +def build_ema(model, cfg, device): + return EMA(model, cfg, device) + + +# automatically import any Python files in the models/ema/ directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.models.ema." + file_name) diff --git a/fairseq/fairseq/models/ema/ema.py b/fairseq/fairseq/models/ema/ema.py new file mode 100644 index 0000000..472d5d5 --- /dev/null +++ b/fairseq/fairseq/models/ema/ema.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python3 + +""" +This module has the EMA class used to store a copy of the exponentially decayed +model params. + +Typical usage of EMA class involves initializing an object using an existing +model (random or from a seed model) and setting the config like ema_decay, +ema_start_update which determine how the EMA model is updated. After every +update of the model i.e. at the end of the train_step, the EMA should be updated +by passing the new model to the EMA.step function. The EMA model state dict +can be stored in the extra state under the key of "ema" and dumped +into a checkpoint and loaded. The EMA object can be passed to tasks +by setting task.uses_ema property. +EMA is a smoothed/ensemble model which might have better performance +when used for inference or further fine-tuning. EMA class has a +reverse function to load the EMA params into a model and use it +like a regular model. + +This implementation is used for trainer-level ema tracking. For EMA tracking +inside the model, please use fairseq/modules/ema_module.py instead. +""" + +import copy +import logging + +import torch + +from fairseq import checkpoint_utils + + +class EMA(object): + """Exponential Moving Average of Fairseq Models + EMA keeps a copy of the exponentially decayed model params. + The set of params should include both gradient-descent and + non-gradient descent params, such as batch mean/var and buffers. + This is a modified implementation of + the open source code in https://github.com/zhawe01/fairseq-gec.git, + and internal source code in + fbcode/mobile-vision/projects/classification_pytorch/lib/utils/model_ema.py. + + Similar to TF EMA. + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage. + EMA provides a averaged and smoothed set of model weights, and has been shown to + improve vision models. EMA class does all necessary functions to update, reload, + or init EMA methods. + + EMA object is initialized from an arbitrary model. By default, it is stored in + the same device (unless device specified at initialization) and with the + same precision as the model (unless ema_fp32 is True). ema_fp32 is recommended. + This stores the EMA parameters in fp32 only for the EMA update step, and + is used at the default precision otherwise. + EMA is usually enabled using EMAConfig with store_ema=True. Some important + parameters to configure EMA are + 1) ema_decay - The decay of EMA + 2) ema_update_freq - EMA is updated every this many model updates. + 3) ema_start_update - Start EMA update after this many model updates [default 0] + + Key methods: + 1) step - One update of EMA using new model + 2) restore - Update EMA from a state dict + 3) reverse - Load EMA into a model + 4) get_decay, _set_decay - Used to get or set the decay. Note _set_decay is + called from step. + 5) build_fp32_params - Used to initialize or update the fp32 copy of EMA params. + Note this is enabled only when ema_fp32=True + """ + + def __init__(self, model, config, device=None, skip_keys=None): + """ + @param model model to initialize the EMA with + @param config EMAConfig object with configuration like + ema_decay, ema_update_freq, ema_fp32 + @param device If provided, copy EMA to this device (e.g. gpu). + Otherwise EMA is in the same device as the model. + """ + + self.decay = config.ema_decay + self.model = copy.deepcopy(model) + self.model.requires_grad_(False) + self.config = config + self.skip_keys = skip_keys or set() + self.fp32_params = {} + + if self.config.ema_seed_model is not None: + state = checkpoint_utils.load_ema_from_checkpoint( + self.config.ema_seed_model + ) + self.model.load_state_dict(state["model"], strict=True) + + if device is not None: + logging.info(f"Copying EMA model to device {device}") + self.model = self.model.to(device=device) + + if self.config.ema_fp32: + self.build_fp32_params() + + self.update_freq_counter = 0 + + def get_model(self): + return self.model + + def build_fp32_params(self, state_dict=None): + """ + Store a copy of the EMA params in fp32. + If state dict is passed, the EMA params is copied from + the provided state dict. Otherwise, it is copied from the + current EMA model parameters. + """ + if not self.config.ema_fp32: + raise RuntimeError( + "build_fp32_params should not be called if ema_fp32=False. " + "Use ema_fp32=True if this is really intended." + ) + + if state_dict is None: + state_dict = self.model.state_dict() + + def _to_float(t): + return t.float() if torch.is_floating_point(t) else t + + for param_key in state_dict: + if param_key in self.fp32_params: + self.fp32_params[param_key].copy_(state_dict[param_key]) + else: + self.fp32_params[param_key] = _to_float(state_dict[param_key]) + + def restore(self, state_dict, build_fp32_params=False): + """Load data from a model spec into EMA model""" + self.model.load_state_dict(state_dict, strict=False) + if build_fp32_params: + self.build_fp32_params(state_dict) + + def _set_decay(self, decay): + self.decay = decay + + def get_decay(self): + return self.decay + + def _step_internal(self, new_model, updates=None): + """One update of the EMA model based on new model weights""" + decay = self.decay + + ema_state_dict = {} + ema_params = ( + self.fp32_params if self.config.ema_fp32 else self.model.state_dict() + ) + for key, param in new_model.state_dict().items(): + if isinstance(param, dict): + continue + try: + ema_param = ema_params[key] + except KeyError: + ema_param = ( + param.float().clone() if param.ndim == 1 else copy.deepcopy(param) + ) + + if param.shape != ema_param.shape: + raise ValueError( + "incompatible tensor shapes between model param and ema param" + + "{} vs. {}".format(param.shape, ema_param.shape) + ) + + if "version" in key: + # Do not decay a model.version pytorch param + continue + + if key in self.skip_keys: + ema_param = param.to(dtype=ema_param.dtype).clone() + else: + ema_param.mul_(decay) + ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1 - decay) + ema_state_dict[key] = ema_param + self.restore(ema_state_dict, build_fp32_params=False) + + def step(self, new_model, updates=None): + """ + One update of EMA which is done every self.config.ema_update_freq + updates of the model. + + @param updates The current number of model updates done. + Decay is set of 0 if model updates < ema_start_update, which means + the model will be simply copied over to the EMA. + When model updates >= ema_start_updates, then EMA is updated with + a decay of self.config.ema_decay. + """ + if updates is not None: + self._set_decay( + 0 if updates < self.config.ema_start_update else self.config.ema_decay + ) + if self.config.ema_update_freq > 1: + self.update_freq_counter += 1 + if self.update_freq_counter >= self.config.ema_update_freq: + self._step_internal(new_model, updates) + self.update_freq_counter = 0 + else: + self._step_internal(new_model, updates) + + def reverse(self, model): + """ + Load the model parameters from EMA model. + Useful for inference or fine-tuning from the EMA model. + """ + d = self.model.state_dict() + if "_ema" in d: + del d["_ema"] + + model.load_state_dict(d, strict=False) + return model diff --git a/fairseq/fairseq/models/fairseq_decoder.py b/fairseq/fairseq/models/fairseq_decoder.py new file mode 100644 index 0000000..13b73d6 --- /dev/null +++ b/fairseq/fairseq/models/fairseq_decoder.py @@ -0,0 +1,104 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional, Tuple + +import torch.nn as nn +from fairseq import utils +from torch import Tensor + + +class FairseqDecoder(nn.Module): + """Base class for decoders.""" + + def __init__(self, dictionary): + super().__init__() + self.dictionary = dictionary + self.onnx_trace = False + self.adaptive_softmax = None + + def forward(self, prev_output_tokens, encoder_out=None, **kwargs): + """ + Args: + prev_output_tokens (LongTensor): shifted output tokens of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (dict, optional): output from the encoder, used for + encoder-side attention + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + x, extra = self.extract_features( + prev_output_tokens, encoder_out=encoder_out, **kwargs + ) + x = self.output_layer(x) + return x, extra + + def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs): + """ + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + raise NotImplementedError + + def output_layer(self, features, **kwargs): + """ + Project features to the default output size, e.g., vocabulary size. + + Args: + features (Tensor): features returned by *extract_features*. + """ + raise NotImplementedError + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" + return self.get_normalized_probs_scriptable(net_output, log_probs, sample) + + # TorchScript doesn't support super() method so that the scriptable Subclass + # can't access the base class model in Torchscript. + # Current workaround is to add a helper function with different name and + # call the helper function from scriptable Subclass. + def get_normalized_probs_scriptable( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" + + if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None: + if sample is not None: + assert "target" in sample + target = sample["target"] + else: + target = None + out = self.adaptive_softmax.get_log_prob(net_output[0], target=target) + return out.exp_() if not log_probs else out + + logits = net_output[0] + if log_probs: + return utils.log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace) + else: + return utils.softmax(logits, dim=-1, onnx_trace=self.onnx_trace) + + def max_positions(self): + """Maximum input length supported by the decoder.""" + return 1e6 # an arbitrary large number + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade old state dicts to work with newer code.""" + return state_dict + + def prepare_for_onnx_export_(self): + self.onnx_trace = True diff --git a/fairseq/fairseq/models/fairseq_encoder.py b/fairseq/fairseq/models/fairseq_encoder.py new file mode 100644 index 0000000..08cbde1 --- /dev/null +++ b/fairseq/fairseq/models/fairseq_encoder.py @@ -0,0 +1,92 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, NamedTuple, Optional + +import torch +import torch.nn as nn +from torch import Tensor + + +EncoderOut = NamedTuple( + "EncoderOut", + [ + ("encoder_out", Tensor), # T x B x C + ("encoder_padding_mask", Optional[Tensor]), # B x T + ("encoder_embedding", Optional[Tensor]), # B x T x C + ("encoder_states", Optional[List[Tensor]]), # List[T x B x C] + ("src_tokens", Optional[Tensor]), # B x T + ("src_lengths", Optional[Tensor]), # B x 1 + ], +) + + +class FairseqEncoder(nn.Module): + """Base class for encoders.""" + + def __init__(self, dictionary): + super().__init__() + self.dictionary = dictionary + + def forward(self, src_tokens, src_lengths=None, **kwargs): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (LongTensor): lengths of each source sentence of shape + `(batch)` + """ + raise NotImplementedError + + def forward_torchscript(self, net_input: Dict[str, Tensor]): + """A TorchScript-compatible version of forward. + + Encoders which use additional arguments may want to override + this method for TorchScript compatibility. + """ + if torch.jit.is_scripting(): + return self.forward( + src_tokens=net_input["src_tokens"], + src_lengths=net_input["src_lengths"], + ) + else: + return self.forward_non_torchscript(net_input) + + @torch.jit.unused + def forward_non_torchscript(self, net_input: Dict[str, Tensor]): + encoder_input = { + k: v for k, v in net_input.items() if k != "prev_output_tokens" + } + return self.forward(**encoder_input) + + def reorder_encoder_out(self, encoder_out, new_order): + """ + Reorder encoder output according to `new_order`. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + `encoder_out` rearranged according to `new_order` + """ + raise NotImplementedError + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return 1e6 # an arbitrary large number + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade old state dicts to work with newer code.""" + return state_dict + + def set_num_updates(self, num_updates): + """State from trainer to pass along to model at every update.""" + + def _apply(m): + if hasattr(m, "set_num_updates") and m != self: + m.set_num_updates(num_updates) + + self.apply(_apply) diff --git a/fairseq/fairseq/models/fairseq_incremental_decoder.py b/fairseq/fairseq/models/fairseq_incremental_decoder.py new file mode 100644 index 0000000..cc72a0f --- /dev/null +++ b/fairseq/fairseq/models/fairseq_incremental_decoder.py @@ -0,0 +1,118 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import Dict, Optional + +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.models import FairseqDecoder +from torch import Tensor + + +logger = logging.getLogger(__name__) + + +@with_incremental_state +class FairseqIncrementalDecoder(FairseqDecoder): + """Base class for incremental decoders. + + Incremental decoding is a special mode at inference time where the Model + only receives a single timestep of input corresponding to the previous + output token (for teacher forcing) and must produce the next output + *incrementally*. Thus the model must cache any long-term state that is + needed about the sequence, e.g., hidden states, convolutional states, etc. + + Compared to the standard :class:`FairseqDecoder` interface, the incremental + decoder interface allows :func:`forward` functions to take an extra keyword + argument (*incremental_state*) that can be used to cache state across + time-steps. + + The :class:`FairseqIncrementalDecoder` interface also defines the + :func:`reorder_incremental_state` method, which is used during beam search + to select and reorder the incremental state based on the selection of beams. + + To learn more about how incremental decoding works, refer to `this blog + <http://www.telesens.co/2019/04/21/understanding-incremental-decoding-in-fairseq/>`_. + """ + + def __init__(self, dictionary): + super().__init__(dictionary) + + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs + ): + """ + Args: + prev_output_tokens (LongTensor): shifted output tokens of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (dict, optional): output from the encoder, used for + encoder-side attention + incremental_state (dict, optional): dictionary used for storing + state during :ref:`Incremental decoding` + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + raise NotImplementedError + + def extract_features( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs + ): + """ + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + raise NotImplementedError + + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): + """Reorder incremental state. + + This will be called when the order of the input has changed from the + previous time step. A typical use case is beam search, where the input + order changes between time steps based on the selection of beams. + """ + pass + + def reorder_incremental_state_scripting( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): + """Main entry point for reordering the incremental state. + + Due to limitations in TorchScript, we call this function in + :class:`fairseq.sequence_generator.SequenceGenerator` instead of + calling :func:`reorder_incremental_state` directly. + """ + for module in self.modules(): + if hasattr(module, "reorder_incremental_state"): + result = module.reorder_incremental_state(incremental_state, new_order) + if result is not None: + incremental_state = result + + def set_beam_size(self, beam_size): + """Sets the beam size in the decoder and all children.""" + if getattr(self, "_beam_size", -1) != beam_size: + seen = set() + + def apply_set_beam_size(module): + if ( + module != self + and hasattr(module, "set_beam_size") + and module not in seen + ): + seen.add(module) + module.set_beam_size(beam_size) + + self.apply(apply_set_beam_size) + self._beam_size = beam_size diff --git a/fairseq/fairseq/models/fairseq_model.py b/fairseq/fairseq/models/fairseq_model.py new file mode 100644 index 0000000..65ead9d --- /dev/null +++ b/fairseq/fairseq/models/fairseq_model.py @@ -0,0 +1,579 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Base classes for various fairseq models. +""" + +import logging +from argparse import Namespace +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.data import Dictionary +from fairseq.dataclass.utils import ( + convert_namespace_to_omegaconf, + gen_parser_from_dataclass, +) +from fairseq.models import FairseqDecoder, FairseqEncoder +from omegaconf import DictConfig +from torch import Tensor + + +logger = logging.getLogger(__name__) + + +def check_type(module, expected_type): + if hasattr(module, "unwrapped_module"): + assert isinstance( + module.unwrapped_module, expected_type + ), f"{type(module.unwrapped_module)} != {expected_type}" + else: + assert isinstance(module, expected_type), f"{type(module)} != {expected_type}" + + +class BaseFairseqModel(nn.Module): + """Base class for fairseq models.""" + + def __init__(self): + super().__init__() + self._is_generation_fast = False + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + dc = getattr(cls, "__dataclass", None) + if dc is not None: + # do not set defaults so that settings defaults from various architectures still works + gen_parser_from_dataclass(parser, dc(), delete_default=True) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + raise NotImplementedError("Model must implement the build_model method") + + def get_targets(self, sample, net_output): + """Get targets from either the sample or the net's output.""" + return sample["target"] + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" + return self.get_normalized_probs_scriptable(net_output, log_probs, sample) + + # TorchScript doesn't support super() method so that the scriptable Subclass + # can't access the base class model in Torchscript. + # Current workaround is to add a helper function with different name and + # call the helper function from scriptable Subclass. + def get_normalized_probs_scriptable( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Scriptable helper function for get_normalized_probs in ~BaseFairseqModel""" + if hasattr(self, "decoder"): + return self.decoder.get_normalized_probs(net_output, log_probs, sample) + elif torch.is_tensor(net_output): + # syntactic sugar for simple models which don't have a decoder + # (e.g., the classification tutorial) + logits = net_output.float() + if log_probs: + return F.log_softmax(logits, dim=-1) + else: + return F.softmax(logits, dim=-1) + raise NotImplementedError + + def extract_features(self, *args, **kwargs): + """Similar to *forward* but only return features.""" + return self(*args, **kwargs) + + def max_positions(self): + """Maximum length supported by the model.""" + return None + + def load_state_dict( + self, + state_dict, + strict=True, + model_cfg: Optional[DictConfig] = None, + args: Optional[Namespace] = None, + ): + """Copies parameters and buffers from *state_dict* into this module and + its descendants. + + Overrides the method in :class:`nn.Module`. Compared with that method + this additionally "upgrades" *state_dicts* from old checkpoints. + """ + + if model_cfg is None and args is not None: + logger.warn( + "using 'args' is deprecated, please update your code to use dataclass config" + ) + model_cfg = convert_namespace_to_omegaconf(args).model + + self.upgrade_state_dict(state_dict) + + from fairseq.checkpoint_utils import prune_state_dict + + new_state_dict = prune_state_dict(state_dict, model_cfg) + return super().load_state_dict(new_state_dict, strict) + + def upgrade_state_dict(self, state_dict): + """Upgrade old state dicts to work with newer code.""" + self.upgrade_state_dict_named(state_dict, "") + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade old state dicts to work with newer code. + + Args: + state_dict (dict): state dictionary to upgrade, in place + name (str): the state dict key corresponding to the current module + """ + assert state_dict is not None + + def do_upgrade(m, prefix): + if len(prefix) > 0: + prefix += "." + + for n, c in m.named_children(): + name = prefix + n + if hasattr(c, "upgrade_state_dict_named"): + c.upgrade_state_dict_named(state_dict, name) + elif hasattr(c, "upgrade_state_dict"): + c.upgrade_state_dict(state_dict) + do_upgrade(c, name) + + do_upgrade(self, name) + + def set_num_updates(self, num_updates): + """State from trainer to pass along to model at every update.""" + for m in self.modules(): + if hasattr(m, "set_num_updates") and m != self: + m.set_num_updates(num_updates) + + def set_epoch(self, epoch): + for m in self.modules(): + if hasattr(m, "set_epoch") and m != self: + m.set_epoch(epoch) + + def prepare_for_inference_(self, cfg: DictConfig): + """Prepare model for inference.""" + kwargs = {} + kwargs["beamable_mm_beam_size"] = ( + None + if getattr(cfg.generation, "no_beamable_mm", False) + else getattr(cfg.generation, "beam", 5) + ) + kwargs["need_attn"] = getattr(cfg.generation, "print_alignment", False) + if getattr(cfg.generation, "retain_dropout", False): + kwargs["retain_dropout"] = cfg.generation.retain_dropout + kwargs["retain_dropout_modules"] = cfg.generation.retain_dropout_modules + self.make_generation_fast_(**kwargs) + + def make_generation_fast_(self, **kwargs): + """ + Legacy entry point to optimize model for faster generation. + Prefer prepare_for_inference_. + """ + if self._is_generation_fast: + return # only apply once + self._is_generation_fast = True + + # remove weight norm from all modules in the network + def apply_remove_weight_norm(module): + try: + nn.utils.remove_weight_norm(module) + except (AttributeError, ValueError): # this module didn't have weight norm + return + + self.apply(apply_remove_weight_norm) + + def apply_make_generation_fast_(module, prefix): + if len(prefix) > 0: + prefix += "." + + base_func = BaseFairseqModel.make_generation_fast_ + for n, m in module.named_modules(): + if ( + m != self + and hasattr(m, "make_generation_fast_") + # don't call this implementation again, e.g., if + # children modules also inherit from BaseFairseqModel + and m.make_generation_fast_.__func__ is not base_func + ): + name = prefix + n + m.make_generation_fast_(name=name, **kwargs) + + apply_make_generation_fast_(self, "") + + def train(mode=True): + if mode: + raise RuntimeError("cannot train after make_generation_fast") + + # this model should no longer be used for training + self.eval() + self.train = train + + def prepare_for_onnx_export_(self, **kwargs): + """Make model exportable via ONNX trace.""" + seen = set() + + def apply_prepare_for_onnx_export_(module): + if ( + module != self + and hasattr(module, "prepare_for_onnx_export_") + and module not in seen + ): + seen.add(module) + module.prepare_for_onnx_export_(**kwargs) + + self.apply(apply_prepare_for_onnx_export_) + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + **kwargs, + ): + """ + Load a :class:`~fairseq.models.FairseqModel` from a pre-trained model + file. Downloads and caches the pre-trained model file if needed. + + The base implementation returns a + :class:`~fairseq.hub_utils.GeneratorHubInterface`, which can be used to + generate translations or sample from language models. The underlying + :class:`~fairseq.models.FairseqModel` can be accessed via the + *generator.models* attribute. + + Other models may override this to implement custom hub interfaces. + + Args: + model_name_or_path (str): either the name of a pre-trained model to + load or a path/URL to a pre-trained model state dict + checkpoint_file (str, optional): colon-separated list of checkpoint + files in the model archive to ensemble (default: 'model.pt') + data_name_or_path (str, optional): point args.data to the archive + at the given path/URL. Can start with '.' or './' to reuse the + model archive path. + """ + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + **kwargs, + ) + logger.info(x["args"]) + return hub_utils.GeneratorHubInterface(x["args"], x["task"], x["models"]) + + @classmethod + def hub_models(cls): + return {} + + +class FairseqEncoderDecoderModel(BaseFairseqModel): + """Base class for encoder-decoder models. + + Args: + encoder (FairseqEncoder): the encoder + decoder (FairseqDecoder): the decoder + """ + + def __init__(self, encoder, decoder): + super().__init__() + + self.encoder = encoder + self.decoder = decoder + + check_type(self.encoder, FairseqEncoder) + check_type(self.decoder, FairseqDecoder) + + def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): + """ + Run the forward pass for an encoder-decoder model. + + First feed a batch of source tokens through the encoder. Then, feed the + encoder output and previous decoder outputs (i.e., teacher forcing) to + the decoder to produce the next outputs:: + + encoder_out = self.encoder(src_tokens, src_lengths) + return self.decoder(prev_output_tokens, encoder_out) + + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (LongTensor): source sentence lengths of shape `(batch)` + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + decoder_out = self.decoder( + prev_output_tokens, encoder_out=encoder_out, **kwargs + ) + return decoder_out + + def forward_decoder(self, prev_output_tokens, **kwargs): + return self.decoder(prev_output_tokens, **kwargs) + + def extract_features(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): + """ + Similar to *forward* but only return features. + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + features = self.decoder.extract_features( + prev_output_tokens, encoder_out=encoder_out, **kwargs + ) + return features + + def output_layer(self, features, **kwargs): + """Project features to the default output size (typically vocabulary size).""" + return self.decoder.output_layer(features, **kwargs) + + def max_positions(self): + """Maximum length supported by the model.""" + return (self.encoder.max_positions(), self.decoder.max_positions()) + + def max_decoder_positions(self): + """Maximum length supported by the decoder.""" + return self.decoder.max_positions() + + +class FairseqModel(FairseqEncoderDecoderModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + utils.deprecation_warning( + "FairseqModel is deprecated, please use FairseqEncoderDecoderModel " + "or BaseFairseqModel instead", + stacklevel=4, + ) + + +class FairseqMultiModel(BaseFairseqModel): + """Base class for combining multiple encoder-decoder models.""" + + def __init__(self, encoders, decoders): + super().__init__() + assert encoders.keys() == decoders.keys() + self.keys = list(encoders.keys()) + for key in self.keys: + check_type(encoders[key], FairseqEncoder) + check_type(decoders[key], FairseqDecoder) + + self.models = nn.ModuleDict( + { + key: FairseqEncoderDecoderModel(encoders[key], decoders[key]) + for key in self.keys + } + ) + + @staticmethod + def build_shared_embeddings( + dicts: Dict[str, Dictionary], + langs: List[str], + embed_dim: int, + build_embedding: callable, + pretrained_embed_path: Optional[str] = None, + ): + """ + Helper function to build shared embeddings for a set of languages after + checking that all dicts corresponding to those languages are equivalent. + + Args: + dicts: Dict of lang_id to its corresponding Dictionary + langs: languages that we want to share embeddings for + embed_dim: embedding dimension + build_embedding: callable function to actually build the embedding + pretrained_embed_path: Optional path to load pretrained embeddings + """ + shared_dict = dicts[langs[0]] + if any(dicts[lang] != shared_dict for lang in langs): + raise ValueError( + "--share-*-embeddings requires a joined dictionary: " + "--share-encoder-embeddings requires a joined source " + "dictionary, --share-decoder-embeddings requires a joined " + "target dictionary, and --share-all-embeddings requires a " + "joint source + target dictionary." + ) + return build_embedding(shared_dict, embed_dim, pretrained_embed_path) + + def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): + raise NotImplementedError + + def max_positions(self): + """Maximum length supported by the model.""" + return { + key: ( + self.models[key].encoder.max_positions(), + self.models[key].decoder.max_positions(), + ) + for key in self.keys + } + + def max_decoder_positions(self): + """Maximum length supported by the decoder.""" + return min(model.decoder.max_positions() for model in self.models.values()) + + @property + def encoder(self): + return self.models[self.keys[0]].encoder + + @property + def decoder(self): + return self.models[self.keys[0]].decoder + + def forward_decoder(self, prev_output_tokens, **kwargs): + return self.decoder(prev_output_tokens, **kwargs) + + def load_state_dict( + self, + state_dict, + strict=True, + model_cfg=None, + args: Optional[Namespace] = None, + ): + """Copies parameters and buffers from *state_dict* into this module and + its descendants. + + Overrides the method in :class:`nn.Module`. Compared with that method + this additionally "upgrades" *state_dicts* from old checkpoints. + """ + + if model_cfg is None and args is not None: + logger.warn( + "using 'args' is deprecated, please update your code to use dataclass config" + ) + model_cfg = convert_namespace_to_omegaconf(args).model + + self.upgrade_state_dict(state_dict) + + from fairseq.checkpoint_utils import prune_state_dict + + new_state_dict = prune_state_dict(state_dict, model_cfg) + return super().load_state_dict(new_state_dict, strict) + + +class FairseqLanguageModel(BaseFairseqModel): + """Base class for decoder-only models. + + Args: + decoder (FairseqDecoder): the decoder + """ + + def __init__(self, decoder): + super().__init__() + self.decoder = decoder + check_type(self.decoder, FairseqDecoder) + + def forward(self, src_tokens, **kwargs): + """ + Run the forward pass for a decoder-only model. + + Feeds a batch of tokens through the decoder to predict the next tokens. + + Args: + src_tokens (LongTensor): tokens on which to condition the decoder, + of shape `(batch, tgt_len)` + src_lengths (LongTensor): source sentence lengths of shape `(batch)` + + Returns: + tuple: + - the decoder's output of shape `(batch, seq_len, vocab)` + - a dictionary with any model-specific outputs + """ + return self.decoder(src_tokens, **kwargs) + + def forward_decoder(self, prev_output_tokens, **kwargs): + return self.decoder(prev_output_tokens, **kwargs) + + def extract_features(self, src_tokens, **kwargs): + """ + Similar to *forward* but only return features. + + Returns: + tuple: + - the decoder's features of shape `(batch, seq_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + return self.decoder.extract_features(src_tokens, **kwargs) + + def output_layer(self, features, **kwargs): + """Project features to the default output size (typically vocabulary size).""" + return self.decoder.output_layer(features, **kwargs) + + def max_positions(self): + """Maximum length supported by the model.""" + return self.decoder.max_positions() + + def max_decoder_positions(self): + """Maximum length supported by the decoder.""" + return self.decoder.max_positions() + + @property + def supported_targets(self): + return {"future"} + + +class FairseqEncoderModel(BaseFairseqModel): + """Base class for encoder-only models. + + Args: + encoder (FairseqEncoder): the encoder + """ + + def __init__(self, encoder): + super().__init__() + self.encoder = encoder + check_type(self.encoder, FairseqEncoder) + + def forward(self, src_tokens, src_lengths, **kwargs): + """ + Run the forward pass for a encoder-only model. + + Feeds a batch of tokens through the encoder to generate features. + + Args: + src_tokens (LongTensor): input tokens of shape `(batch, src_len)` + src_lengths (LongTensor): source sentence lengths of shape `(batch)` + + Returns: + the encoder's output, typically of shape `(batch, src_len, features)` + """ + return self.encoder(src_tokens, src_lengths, **kwargs) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + """Get normalized probabilities (or log probs) from a net's output.""" + encoder_out = net_output["encoder_out"] + if torch.is_tensor(encoder_out): + logits = encoder_out.float() + if log_probs: + return F.log_softmax(logits, dim=-1) + else: + return F.softmax(logits, dim=-1) + raise NotImplementedError + + def max_positions(self): + """Maximum length supported by the model.""" + return self.encoder.max_positions() diff --git a/fairseq/fairseq/models/fconv.py b/fairseq/fairseq/models/fconv.py new file mode 100644 index 0000000..c99a215 --- /dev/null +++ b/fairseq/fairseq/models/fconv.py @@ -0,0 +1,756 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) +from fairseq.modules import ( + AdaptiveSoftmax, + BeamableMM, + FairseqDropout, + GradMultiply, + LearnedPositionalEmbedding, + LinearizedConvolution, +) + + +@register_model("fconv") +class FConvModel(FairseqEncoderDecoderModel): + """ + A fully convolutional model, i.e. a convolutional encoder and a + convolutional decoder, as described in `"Convolutional Sequence to Sequence + Learning" (Gehring et al., 2017) <https://arxiv.org/abs/1705.03122>`_. + + Args: + encoder (FConvEncoder): the encoder + decoder (FConvDecoder): the decoder + + The Convolutional model provides the following named architectures and + command-line arguments: + + .. argparse:: + :ref: fairseq.models.fconv_parser + :prog: + """ + + @classmethod + def hub_models(cls): + def moses_subword(path): + return { + "path": path, + "tokenizer": "moses", + "bpe": "subword_nmt", + } + + return { + "conv.wmt14.en-fr": moses_subword( + "https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2" + ), + "conv.wmt14.en-de": moses_subword( + "https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2" + ), + "conv.wmt17.en-de": moses_subword( + "https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2" + ), + } + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + self.encoder.num_attention_layers = sum( + layer is not None for layer in decoder.attention + ) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--dropout', type=float, metavar='D', + help='dropout probability') + parser.add_argument('--encoder-embed-dim', type=int, metavar='N', + help='encoder embedding dimension') + parser.add_argument('--encoder-embed-path', type=str, metavar='STR', + help='path to pre-trained encoder embedding') + parser.add_argument('--encoder-layers', type=str, metavar='EXPR', + help='encoder layers [(dim, kernel_size), ...]') + parser.add_argument('--decoder-embed-dim', type=int, metavar='N', + help='decoder embedding dimension') + parser.add_argument('--decoder-embed-path', type=str, metavar='STR', + help='path to pre-trained decoder embedding') + parser.add_argument('--decoder-layers', type=str, metavar='EXPR', + help='decoder layers [(dim, kernel_size), ...]') + parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', + help='decoder output embedding dimension') + parser.add_argument('--decoder-attention', type=str, metavar='EXPR', + help='decoder attention [True, ...]') + parser.add_argument('--share-input-output-embed', action='store_true', + help='share input and output embeddings (requires' + ' --decoder-out-embed-dim and --decoder-embed-dim' + ' to be equal)') + # fmt: on + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted (in case there are any new ones) + base_architecture(args) + + encoder_embed_dict = None + if args.encoder_embed_path: + encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path) + utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary) + + decoder_embed_dict = None + if args.decoder_embed_path: + decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path) + utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary) + + encoder = FConvEncoder( + dictionary=task.source_dictionary, + embed_dim=args.encoder_embed_dim, + embed_dict=encoder_embed_dict, + convolutions=eval(args.encoder_layers), + dropout=args.dropout, + max_positions=args.max_source_positions, + ) + decoder = FConvDecoder( + dictionary=task.target_dictionary, + embed_dim=args.decoder_embed_dim, + embed_dict=decoder_embed_dict, + convolutions=eval(args.decoder_layers), + out_embed_dim=args.decoder_out_embed_dim, + attention=eval(args.decoder_attention), + dropout=args.dropout, + max_positions=args.max_target_positions, + share_embed=args.share_input_output_embed, + ) + return FConvModel(encoder, decoder) + + +class FConvEncoder(FairseqEncoder): + """ + Convolutional encoder consisting of `len(convolutions)` layers. + + Args: + dictionary (~fairseq.data.Dictionary): encoding dictionary + embed_dim (int, optional): embedding dimension + embed_dict (str, optional): filename from which to load pre-trained + embeddings + max_positions (int, optional): maximum supported input sequence length + convolutions (list, optional): the convolutional layer structure. Each + list item `i` corresponds to convolutional layer `i`. Layers are + given as ``(out_channels, kernel_width, [residual])``. Residual + connections are added between layers when ``residual=1`` (which is + the default behavior). + dropout (float, optional): dropout to be applied before each conv layer + """ + + def __init__( + self, + dictionary, + embed_dim=512, + embed_dict=None, + max_positions=1024, + convolutions=((512, 3),) * 20, + dropout=0.1, + ): + super().__init__(dictionary) + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.num_attention_layers = None + + num_embeddings = len(dictionary) + self.padding_idx = dictionary.pad() + self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) + if embed_dict: + self.embed_tokens = utils.load_embedding( + embed_dict, self.dictionary, self.embed_tokens + ) + + self.embed_positions = PositionalEmbedding( + max_positions, + embed_dim, + self.padding_idx, + ) + + convolutions = extend_conv_spec(convolutions) + in_channels = convolutions[0][0] + self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) + self.projections = nn.ModuleList() + self.convolutions = nn.ModuleList() + self.residuals = [] + + layer_in_channels = [in_channels] + for _, (out_channels, kernel_size, residual) in enumerate(convolutions): + if residual == 0: + residual_dim = out_channels + else: + residual_dim = layer_in_channels[-residual] + self.projections.append( + Linear(residual_dim, out_channels) + if residual_dim != out_channels + else None + ) + if kernel_size % 2 == 1: + padding = kernel_size // 2 + else: + padding = 0 + self.convolutions.append( + ConvTBC( + in_channels, + out_channels * 2, + kernel_size, + dropout=dropout, + padding=padding, + ) + ) + self.residuals.append(residual) + in_channels = out_channels + layer_in_channels.append(out_channels) + self.fc2 = Linear(in_channels, embed_dim) + + def forward(self, src_tokens, src_lengths): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (LongTensor): lengths of each source sentence of shape + `(batch)` + + Returns: + dict: + - **encoder_out** (tuple): a tuple with two elements, where the + first element is the last encoder layer's output and the + second element is the same quantity summed with the input + embedding (used for attention). The shape of both tensors is + `(batch, src_len, embed_dim)`. + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + """ + # embed tokens and positions + x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens) + x = self.dropout_module(x) + input_embedding = x + + # project to size of convolution + x = self.fc1(x) + + # used to mask padding in input + encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B + if not encoder_padding_mask.any(): + encoder_padding_mask = None + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + residuals = [x] + # temporal convolutions + for proj, conv, res_layer in zip( + self.projections, self.convolutions, self.residuals + ): + if res_layer > 0: + residual = residuals[-res_layer] + residual = residual if proj is None else proj(residual) + else: + residual = None + + if encoder_padding_mask is not None: + x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) + + x = self.dropout_module(x) + if conv.kernel_size[0] % 2 == 1: + # padding is implicit in the conv + x = conv(x) + else: + padding_l = (conv.kernel_size[0] - 1) // 2 + padding_r = conv.kernel_size[0] // 2 + x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r)) + x = conv(x) + x = F.glu(x, dim=2) + + if residual is not None: + x = (x + residual) * math.sqrt(0.5) + residuals.append(x) + + # T x B x C -> B x T x C + x = x.transpose(1, 0) + + # project back to size of embedding + x = self.fc2(x) + + if encoder_padding_mask is not None: + encoder_padding_mask = encoder_padding_mask.t() # -> B x T + x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) + + # scale gradients (this only affects backward, not forward) + x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers)) + + # add output to input embedding for attention + y = (x + input_embedding) * math.sqrt(0.5) + + return { + "encoder_out": (x, y), + "encoder_padding_mask": encoder_padding_mask, # B x T + } + + def reorder_encoder_out(self, encoder_out, new_order): + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = ( + encoder_out["encoder_out"][0].index_select(0, new_order), + encoder_out["encoder_out"][1].index_select(0, new_order), + ) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return self.embed_positions.max_positions + + +class AttentionLayer(nn.Module): + def __init__(self, conv_channels, embed_dim, bmm=None): + super().__init__() + # projects from output of convolution to embedding dimension + self.in_projection = Linear(conv_channels, embed_dim) + # projects from embedding dimension to convolution size + self.out_projection = Linear(embed_dim, conv_channels) + + self.bmm = bmm if bmm is not None else torch.bmm + + def forward(self, x, target_embedding, encoder_out, encoder_padding_mask): + residual = x + + # attention + x = (self.in_projection(x) + target_embedding) * math.sqrt(0.5) + x = self.bmm(x, encoder_out[0]) + + # don't attend over padding + if encoder_padding_mask is not None: + x = ( + x.float() + .masked_fill(encoder_padding_mask.unsqueeze(1), float("-inf")) + .type_as(x) + ) # FP16 support: cast to float and back + + # softmax over last dim + sz = x.size() + x = F.softmax(x.view(sz[0] * sz[1], sz[2]), dim=1) + x = x.view(sz) + attn_scores = x + + x = self.bmm(x, encoder_out[1]) + + # scale attention output (respecting potentially different lengths) + s = encoder_out[1].size(1) + if encoder_padding_mask is None: + x = x * (s * math.sqrt(1.0 / s)) + else: + s = s - encoder_padding_mask.type_as(x).sum( + dim=1, keepdim=True + ) # exclude padding + s = s.unsqueeze(-1) + x = x * (s * s.rsqrt()) + + # project back + x = (self.out_projection(x) + residual) * math.sqrt(0.5) + return x, attn_scores + + def make_generation_fast_(self, beamable_mm_beam_size=None, **kwargs): + """Replace torch.bmm with BeamableMM.""" + if beamable_mm_beam_size is not None: + del self.bmm + self.add_module("bmm", BeamableMM(beamable_mm_beam_size)) + + +class FConvDecoder(FairseqIncrementalDecoder): + """Convolutional decoder""" + + def __init__( + self, + dictionary, + embed_dim=512, + embed_dict=None, + out_embed_dim=256, + max_positions=1024, + convolutions=((512, 3),) * 20, + attention=True, + dropout=0.1, + share_embed=False, + positional_embeddings=True, + adaptive_softmax_cutoff=None, + adaptive_softmax_dropout=0.0, + ): + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([2])) + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.need_attn = True + + convolutions = extend_conv_spec(convolutions) + in_channels = convolutions[0][0] + if isinstance(attention, bool): + # expand True into [True, True, ...] and do the same with False + attention = [attention] * len(convolutions) + if not isinstance(attention, list) or len(attention) != len(convolutions): + raise ValueError( + "Attention is expected to be a list of booleans of " + "length equal to the number of layers." + ) + + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + if embed_dict: + self.embed_tokens = utils.load_embedding( + embed_dict, self.dictionary, self.embed_tokens + ) + + self.embed_positions = ( + PositionalEmbedding( + max_positions, + embed_dim, + padding_idx, + ) + if positional_embeddings + else None + ) + + self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) + self.projections = nn.ModuleList() + self.convolutions = nn.ModuleList() + self.attention = nn.ModuleList() + self.residuals = [] + + layer_in_channels = [in_channels] + for i, (out_channels, kernel_size, residual) in enumerate(convolutions): + if residual == 0: + residual_dim = out_channels + else: + residual_dim = layer_in_channels[-residual] + self.projections.append( + Linear(residual_dim, out_channels) + if residual_dim != out_channels + else None + ) + self.convolutions.append( + LinearizedConv1d( + in_channels, + out_channels * 2, + kernel_size, + padding=(kernel_size - 1), + dropout=dropout, + ) + ) + self.attention.append( + AttentionLayer(out_channels, embed_dim) if attention[i] else None + ) + self.residuals.append(residual) + in_channels = out_channels + layer_in_channels.append(out_channels) + + self.adaptive_softmax = None + self.fc2 = self.fc3 = None + + if adaptive_softmax_cutoff is not None: + assert not share_embed + self.adaptive_softmax = AdaptiveSoftmax( + num_embeddings, + in_channels, + adaptive_softmax_cutoff, + dropout=adaptive_softmax_dropout, + ) + else: + self.fc2 = Linear(in_channels, out_embed_dim) + if share_embed: + assert out_embed_dim == embed_dim, ( + "Shared embed weights implies same dimensions " + " out_embed_dim={} vs embed_dim={}".format(out_embed_dim, embed_dim) + ) + self.fc3 = nn.Linear(out_embed_dim, num_embeddings) + self.fc3.weight = self.embed_tokens.weight + else: + self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout) + + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): + if encoder_out is not None: + encoder_padding_mask = encoder_out["encoder_padding_mask"] + encoder_out = encoder_out["encoder_out"] + + # split and transpose encoder outputs + encoder_a, encoder_b = self._split_encoder_out( + encoder_out, incremental_state + ) + + if self.embed_positions is not None: + pos_embed = self.embed_positions(prev_output_tokens, incremental_state) + else: + pos_embed = 0 + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + x = self._embed_tokens(prev_output_tokens, incremental_state) + + # embed tokens and combine with positional embeddings + x += pos_embed + x = self.dropout_module(x) + target_embedding = x + + # project to size of convolution + x = self.fc1(x) + + # B x T x C -> T x B x C + x = self._transpose_if_training(x, incremental_state) + + # temporal convolutions + avg_attn_scores = None + num_attn_layers = len(self.attention) + residuals = [x] + for proj, conv, attention, res_layer in zip( + self.projections, self.convolutions, self.attention, self.residuals + ): + if res_layer > 0: + residual = residuals[-res_layer] + residual = residual if proj is None else proj(residual) + else: + residual = None + + x = self.dropout_module(x) + x = conv(x, incremental_state) + x = F.glu(x, dim=2) + + # attention + if attention is not None: + x = self._transpose_if_training(x, incremental_state) + + x, attn_scores = attention( + x, target_embedding, (encoder_a, encoder_b), encoder_padding_mask + ) + + if not self.training and self.need_attn: + attn_scores = attn_scores / num_attn_layers + if avg_attn_scores is None: + avg_attn_scores = attn_scores + else: + avg_attn_scores.add_(attn_scores) + + x = self._transpose_if_training(x, incremental_state) + + # residual + if residual is not None: + x = (x + residual) * math.sqrt(0.5) + residuals.append(x) + + # T x B x C -> B x T x C + x = self._transpose_if_training(x, incremental_state) + + # project back to size of vocabulary if not using adaptive softmax + if self.fc2 is not None and self.fc3 is not None: + x = self.fc2(x) + x = self.dropout_module(x) + x = self.fc3(x) + + return x, avg_attn_scores + + def reorder_incremental_state(self, incremental_state, new_order): + super().reorder_incremental_state(incremental_state, new_order) + encoder_out = utils.get_incremental_state( + self, incremental_state, "encoder_out" + ) + if encoder_out is not None: + encoder_out = tuple(eo.index_select(0, new_order) for eo in encoder_out) + utils.set_incremental_state( + self, incremental_state, "encoder_out", encoder_out + ) + + def max_positions(self): + """Maximum output length supported by the decoder.""" + return ( + self.embed_positions.max_positions + if self.embed_positions is not None + else float("inf") + ) + + def upgrade_state_dict(self, state_dict): + if utils.item(state_dict.get("decoder.version", torch.Tensor([1]))[0]) < 2: + # old models use incorrect weight norm dimension + for i, conv in enumerate(self.convolutions): + # reconfigure weight norm + nn.utils.remove_weight_norm(conv) + self.convolutions[i] = nn.utils.weight_norm(conv, dim=0) + state_dict["decoder.version"] = torch.Tensor([1]) + return state_dict + + def make_generation_fast_(self, need_attn=False, **kwargs): + self.need_attn = need_attn + + def _embed_tokens(self, tokens, incremental_state): + if incremental_state is not None: + # keep only the last token for incremental forward pass + tokens = tokens[:, -1:] + return self.embed_tokens(tokens) + + def _split_encoder_out(self, encoder_out, incremental_state): + """Split and transpose encoder outputs. + + This is cached when doing incremental inference. + """ + cached_result = utils.get_incremental_state( + self, incremental_state, "encoder_out" + ) + if cached_result is not None: + return cached_result + + # transpose only once to speed up attention layers + encoder_a, encoder_b = encoder_out + encoder_a = encoder_a.transpose(1, 2).contiguous() + result = (encoder_a, encoder_b) + + if incremental_state is not None: + utils.set_incremental_state(self, incremental_state, "encoder_out", result) + return result + + def _transpose_if_training(self, x, incremental_state): + if incremental_state is None: + x = x.transpose(0, 1) + return x + + +def extend_conv_spec(convolutions): + """ + Extends convolutional spec that is a list of tuples of 2 or 3 parameters + (kernel size, dim size and optionally how many layers behind to look for residual) + to default the residual propagation param if it is not specified + """ + extended = [] + for spec in convolutions: + if len(spec) == 3: + extended.append(spec) + elif len(spec) == 2: + extended.append(spec + (1,)) + else: + raise Exception( + "invalid number of parameters in convolution spec " + + str(spec) + + ". expected 2 or 3" + ) + return tuple(extended) + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, 0, 0.1) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx): + m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx) + nn.init.normal_(m.weight, 0, 0.1) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, dropout=0.0): + """Weight-normalized Linear layer (input: N x T x C)""" + m = nn.Linear(in_features, out_features) + nn.init.normal_(m.weight, mean=0, std=math.sqrt((1 - dropout) / in_features)) + nn.init.constant_(m.bias, 0) + return nn.utils.weight_norm(m) + + +def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): + """Weight-normalized Conv1d layer optimized for decoding""" + m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) + std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) + nn.init.normal_(m.weight, mean=0, std=std) + nn.init.constant_(m.bias, 0) + return nn.utils.weight_norm(m, dim=2) + + +def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): + """Weight-normalized Conv1d layer""" + from fairseq.modules import ConvTBC + + m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs) + std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) + nn.init.normal_(m.weight, mean=0, std=std) + nn.init.constant_(m.bias, 0) + return nn.utils.weight_norm(m, dim=2) + + +@register_model_architecture("fconv", "fconv") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 20") + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 20") + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.decoder_attention = getattr(args, "decoder_attention", "True") + args.share_input_output_embed = getattr(args, "share_input_output_embed", False) + + +@register_model_architecture("fconv", "fconv_iwslt_de_en") +def fconv_iwslt_de_en(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_layers = getattr(args, "encoder_layers", "[(256, 3)] * 4") + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_layers = getattr(args, "decoder_layers", "[(256, 3)] * 3") + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + base_architecture(args) + + +@register_model_architecture("fconv", "fconv_wmt_en_ro") +def fconv_wmt_en_ro(args): + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + base_architecture(args) + + +@register_model_architecture("fconv", "fconv_wmt_en_de") +def fconv_wmt_en_de(args): + convs = "[(512, 3)] * 9" # first 9 layers have 512 units + convs += " + [(1024, 3)] * 4" # next 4 layers have 1024 units + convs += " + [(2048, 1)] * 2" # final 2 layers use 1x1 convolutions + + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_layers = getattr(args, "encoder_layers", convs) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) + args.decoder_layers = getattr(args, "decoder_layers", convs) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + base_architecture(args) + + +@register_model_architecture("fconv", "fconv_wmt_en_fr") +def fconv_wmt_en_fr(args): + convs = "[(512, 3)] * 6" # first 6 layers have 512 units + convs += " + [(768, 3)] * 4" # next 4 layers have 768 units + convs += " + [(1024, 3)] * 3" # next 3 layers have 1024 units + convs += " + [(2048, 1)] * 1" # next 1 layer uses 1x1 convolutions + convs += " + [(4096, 1)] * 1" # final 1 layer uses 1x1 convolutions + + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_layers = getattr(args, "encoder_layers", convs) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) + args.decoder_layers = getattr(args, "decoder_layers", convs) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + base_architecture(args) diff --git a/fairseq/fairseq/models/fconv_lm.py b/fairseq/fairseq/models/fconv_lm.py new file mode 100644 index 0000000..4b243d6 --- /dev/null +++ b/fairseq/fairseq/models/fconv_lm.py @@ -0,0 +1,136 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq import utils +from fairseq.models import ( + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.fconv import FConvDecoder +from fairseq.utils import safe_hasattr + + +@register_model("fconv_lm") +class FConvLanguageModel(FairseqLanguageModel): + def __init__(self, decoder): + super().__init__(decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-layers", + type=str, + metavar="EXPR", + help="decoder layers [(dim, kernel_size), ...]", + ) + parser.add_argument( + "--decoder-out-embed-dim", + type=int, + metavar="N", + help="decoder output embedding dimension", + ) + parser.add_argument( + "--adaptive-softmax-cutoff", + metavar="EXPR", + help="comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion", + ) + parser.add_argument( + "--adaptive-softmax-dropout", + type=float, + metavar="D", + help="sets adaptive softmax dropout for the tail projections", + ) + parser.add_argument( + "--decoder-attention", + type=str, + metavar="EXPR", + help="decoder attention [True, ...]", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure all arguments are present in older models + base_lm_architecture(args) + + if safe_hasattr(args, "max_target_positions") and not safe_hasattr( + args, "tokens_per_sample" + ): + args.tokens_per_sample = args.max_target_positions + + decoder = FConvDecoder( + dictionary=task.target_dictionary, + embed_dim=args.decoder_embed_dim, + convolutions=eval(args.decoder_layers), + out_embed_dim=args.decoder_embed_dim, + attention=eval(args.decoder_attention), + dropout=args.dropout, + max_positions=args.tokens_per_sample, + share_embed=False, + positional_embeddings=False, + adaptive_softmax_cutoff=( + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) + if args.criterion == "adaptive_loss" + else None + ), + adaptive_softmax_dropout=args.adaptive_softmax_dropout, + ) + return FConvLanguageModel(decoder) + + +@register_model_architecture("fconv_lm", "fconv_lm") +def base_lm_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) + args.decoder_layers = getattr(args, "decoder_layers", "[(1268, 4)] * 13") + args.decoder_attention = getattr(args, "decoder_attention", "False") + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + + +@register_model_architecture("fconv_lm", "fconv_lm_dauphin_wikitext103") +def fconv_lm_dauphin_wikitext103(args): + layers = "[(850, 6)] * 3" + layers += " + [(850, 1)] * 1" + layers += " + [(850, 5)] * 4" + layers += " + [(850, 1)] * 1" + layers += " + [(850, 4)] * 3" + layers += " + [(1024, 4)] * 1" + layers += " + [(2048, 4)] * 1" + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 280) + args.decoder_layers = getattr(args, "decoder_layers", layers) + args.decoder_attention = getattr(args, "decoder_attention", "False") + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,20000,200000" + ) + base_lm_architecture(args) + + +@register_model_architecture("fconv_lm", "fconv_lm_dauphin_gbw") +def fconv_lm_dauphin_gbw(args): + layers = "[(512, 5)]" + layers += " + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3" + layers += " + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3" + layers += " + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6" + layers += " + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]" + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) + args.decoder_layers = getattr(args, "decoder_layers", layers) + args.decoder_attention = getattr(args, "decoder_attention", "False") + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,50000,200000" + ) + base_lm_architecture(args) diff --git a/fairseq/fairseq/models/fconv_self_att.py b/fairseq/fairseq/models/fconv_self_att.py new file mode 100644 index 0000000..8357ef7 --- /dev/null +++ b/fairseq/fairseq/models/fconv_self_att.py @@ -0,0 +1,674 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import checkpoint_utils +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.models import ( + CompositeEncoder, + FairseqDecoder, + FairseqEncoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.modules import ( + DownsampledMultiHeadAttention, + FairseqDropout, + GradMultiply, + LayerNorm, + LearnedPositionalEmbedding, + LinearizedConvolution, +) + + +logger = logging.getLogger(__name__) + + +@register_model("fconv_self_att") +class FConvModelSelfAtt(FairseqEncoderDecoderModel): + @classmethod + def hub_models(cls): + return { + "conv.stories.pretrained": { + "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz", + "checkpoint_file": "pretrained_checkpoint.pt", + "tokenizer": "nltk", + }, + "conv.stories": { + "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz", + "checkpoint_file": "fusion_checkpoint.pt", + "tokenizer": "nltk", + "pretrained": "True", + "pretrained_checkpoint": "./pretrained_checkpoint.pt", + }, + # Test set containing dictionaries + "data.stories": "https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2", + } + + def __init__(self, encoder, decoder, pretrained_encoder=None): + super().__init__(encoder, decoder) + self.encoder.num_attention_layers = sum( + layer is not None for layer in decoder.attention + ) + self.pretrained_encoder = pretrained_encoder + if self.pretrained_encoder is None: + encoders = {"encoder": encoder} + else: + encoders = {"encoder": encoder, "pretrained": self.pretrained_encoder} + # for fusion model, CompositeEncoder contains both pretrained and training encoders + # these are forwarded and then combined in the decoder + self.encoder = CompositeEncoder(encoders) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--dropout', type=float, metavar='D', + help='dropout probability') + parser.add_argument('--encoder-embed-dim', type=int, metavar='N', + help='encoder embedding dimension') + parser.add_argument('--encoder-layers', type=str, metavar='EXPR', + help='encoder layers [(dim, kernel_size), ...]') + parser.add_argument('--decoder-embed-dim', type=int, metavar='N', + help='decoder embedding dimension') + parser.add_argument('--decoder-layers', type=str, metavar='EXPR', + help='decoder layers [(dim, kernel_size), ...]') + parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', + help='decoder output embedding dimension') + parser.add_argument('--decoder-attention', type=str, metavar='EXPR', + help='decoder attention [True, ...]') + parser.add_argument('--self-attention', type=str, metavar='EXPR', + help='decoder self-attention layers, ex: [True] + [False]*5') + parser.add_argument('--multihead-attention-nheads', type=int, + help='Number of heads to use in attention') + parser.add_argument('--multihead-self-attention-nheads', type=int, + help='Number of heads to use in self-attention') + parser.add_argument('--encoder-attention', type=str, metavar='EXPR', + help='encoder attention [True, ...]') + parser.add_argument('--encoder-attention-nheads', type=int, + help='Number of heads to use in encoder attention') + parser.add_argument('--project-input', type=str, metavar='EXPR', + help='Use projections in self-attention [True, ...]') + parser.add_argument('--gated-attention', type=str, metavar='EXPR', + help='Use GLU layers in self-attention projections [True, ...]') + parser.add_argument('--downsample', type=str, metavar='EXPR', + help='Use downsampling in self-attention [True, ...]') + parser.add_argument('--pretrained-checkpoint', metavar='DIR', + help='path to load checkpoint from pretrained model') + parser.add_argument('--pretrained', type=str, metavar='EXPR', + help='use pretrained model when training [True, ...]') + # fmt: on + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + trained_encoder, trained_decoder = None, None + pretrained = eval(args.pretrained) + if pretrained: + logger.info("loading pretrained model") + if not os.path.exists(args.pretrained_checkpoint): + new_pretrained_checkpoint = os.path.join( + args.data, args.pretrained_checkpoint + ) + if os.path.exists(new_pretrained_checkpoint): + args.pretrained_checkpoint = new_pretrained_checkpoint + trained_model = checkpoint_utils.load_model_ensemble( + filenames=[args.pretrained_checkpoint], + task=task, + )[0][0] + trained_decoder = list(trained_model.children())[1] + trained_encoder = list(trained_model.children())[0] + + # freeze pretrained model + for param in trained_decoder.parameters(): + param.requires_grad = False + for param in trained_encoder.parameters(): + param.requires_grad = False + + encoder = FConvEncoder( + task.source_dictionary, + embed_dim=args.encoder_embed_dim, + convolutions=eval(args.encoder_layers), + dropout=args.dropout, + max_positions=args.max_source_positions, + attention=eval(args.encoder_attention), + attention_nheads=args.encoder_attention_nheads, + ) + + decoder = FConvDecoder( + task.target_dictionary, + embed_dim=args.decoder_embed_dim, + convolutions=eval(args.decoder_layers), + out_embed_dim=args.decoder_out_embed_dim, + attention=eval(args.decoder_attention), + dropout=args.dropout, + max_positions=args.max_target_positions, + selfattention=eval(args.self_attention), + attention_nheads=args.multihead_attention_nheads, + selfattention_nheads=args.multihead_self_attention_nheads, + project_input=eval(args.project_input), + gated_attention=eval(args.gated_attention), + downsample=eval(args.downsample), + pretrained=pretrained, + trained_decoder=trained_decoder, + ) + model = FConvModelSelfAtt(encoder, decoder, trained_encoder) + + return model + + @property + def pretrained(self): + return self.pretrained_encoder is not None + + +class FConvEncoder(FairseqEncoder): + """Convolutional encoder""" + + def __init__( + self, + dictionary, + embed_dim=512, + max_positions=1024, + convolutions=((512, 3),) * 20, + dropout=0.1, + attention=False, + attention_nheads=1, + ): + super().__init__(dictionary) + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.num_attention_layers = None + + num_embeddings = len(dictionary) + self.padding_idx = dictionary.pad() + self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) + self.embed_positions = PositionalEmbedding( + max_positions, + embed_dim, + self.padding_idx, + ) + + def expand_bool_array(val): + if isinstance(val, bool): + # expand True into [True, True, ...] and do the same with False + return [val] * len(convolutions) + return val + + attention = expand_bool_array(attention) + + in_channels = convolutions[0][0] + self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) + self.projections = nn.ModuleList() + self.convolutions = nn.ModuleList() + self.attention = nn.ModuleList() + self.attproj = nn.ModuleList() + for i, (out_channels, kernel_size) in enumerate(convolutions): + self.projections.append( + Linear(in_channels, out_channels) + if in_channels != out_channels + else None + ) + self.convolutions.append( + ConvTBC(in_channels, out_channels * 2, kernel_size, dropout=dropout) + ) + + self.attention.append( + SelfAttention(out_channels, embed_dim, attention_nheads) + if attention[i] + else None + ) + in_channels = out_channels + + self.fc2 = Linear(in_channels, embed_dim) + + def forward(self, src_tokens, src_lengths): + # embed tokens and positions + x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens) + x = self.dropout_module(x) + input_embedding = x.transpose(0, 1) + + # project to size of convolution + x = self.fc1(x) + + encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B + if not encoder_padding_mask.any(): + encoder_padding_mask = None + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # temporal convolutions + for proj, conv, attention in zip( + self.projections, self.convolutions, self.attention + ): + residual = x if proj is None else proj(x) + + if encoder_padding_mask is not None: + x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) + + x = self.dropout_module(x) + padding_l = (conv.kernel_size[0] - 1) // 2 + padding_r = conv.kernel_size[0] // 2 + x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r)) + x = conv(x) + x = F.glu(x, dim=2) + if attention is not None: + x = attention(x) + x = (x + residual) * math.sqrt(0.5) + + # T x B x C -> B x T x C + x = x.transpose(1, 0) + + # project back to size of embedding + x = self.fc2(x) + + if encoder_padding_mask is not None: + encoder_padding_mask = encoder_padding_mask.t() # -> B x T + x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) + + # scale gradients (this only affects backward, not forward) + x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers)) + + # add output to input embedding for attention + y = (x + input_embedding.transpose(0, 1)) * math.sqrt(0.5) + + return { + "encoder_out": (x, y), + "encoder_padding_mask": encoder_padding_mask, # B x T + } + + def reorder_encoder_out(self, encoder_out, new_order): + encoder_out["encoder_out"] = tuple( + eo.index_select(0, new_order) for eo in encoder_out["encoder_out"] + ) + + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) + + if "pretrained" in encoder_out: + encoder_out["pretrained"]["encoder_out"] = tuple( + eo.index_select(0, new_order) + for eo in encoder_out["pretrained"]["encoder_out"] + ) + + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return self.embed_positions.max_positions + + +@with_incremental_state +class FConvDecoder(FairseqDecoder): + """Convolutional decoder""" + + def __init__( + self, + dictionary, + embed_dim=512, + out_embed_dim=256, + max_positions=1024, + convolutions=((512, 3),) * 8, + attention=True, + dropout=0.1, + selfattention=False, + attention_nheads=1, + selfattention_nheads=1, + project_input=False, + gated_attention=False, + downsample=False, + pretrained=False, + trained_decoder=None, + ): + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([2])) + self.pretrained = pretrained + self.pretrained_decoder = trained_decoder + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.need_attn = True + in_channels = convolutions[0][0] + + def expand_bool_array(val): + if isinstance(val, bool): + # expand True into [True, True, ...] and do the same with False + return [val] * len(convolutions) + return val + + attention = expand_bool_array(attention) + selfattention = expand_bool_array(selfattention) + + if not isinstance(attention, list) or len(attention) != len(convolutions): + raise ValueError( + "Attention is expected to be a list of booleans of " + "length equal to the number of layers." + ) + + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + + self.embed_positions = PositionalEmbedding( + max_positions, + embed_dim, + padding_idx, + ) + + self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) + self.projections = nn.ModuleList() + self.convolutions = nn.ModuleList() + self.attention = nn.ModuleList() + self.selfattention = nn.ModuleList() + self.attproj = nn.ModuleList() + for i, (out_channels, kernel_size) in enumerate(convolutions): + self.projections.append( + Linear(in_channels, out_channels) + if in_channels != out_channels + else None + ) + self.convolutions.append( + LinearizedConv1d( + in_channels, + out_channels * 2, + kernel_size, + padding=(kernel_size - 1), + dropout=dropout, + ) + ) + + self.attention.append( + DownsampledMultiHeadAttention( + out_channels, + embed_dim, + attention_nheads, + project_input=project_input, + gated=False, + downsample=False, + ) + if attention[i] + else None + ) + + self.attproj.append( + Linear(out_channels, embed_dim, dropout=dropout) + if attention[i] + else None + ) + self.selfattention.append( + SelfAttention( + out_channels, + embed_dim, + selfattention_nheads, + project_input=project_input, + gated=gated_attention, + downsample=downsample, + ) + if selfattention[i] + else None + ) + in_channels = out_channels + + self.fc2 = Linear(in_channels, out_embed_dim) + self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout) + + # model fusion + if self.pretrained: + # independent gates are learned from the concatenated input + self.gate1 = nn.Sequential( + Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid() + ) + self.gate2 = nn.Sequential( + Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid() + ) + # pretrained and trained models are joined + self.joining = nn.Sequential( + Linear(out_embed_dim * 2, out_embed_dim * 2), + LayerNorm(out_embed_dim * 2), + nn.GLU(), + Linear(out_embed_dim, out_embed_dim * 2), + LayerNorm(out_embed_dim * 2), + nn.GLU(), + Linear(out_embed_dim, out_embed_dim), + LayerNorm(out_embed_dim), + ) + # pretrained model contains an output layer that is nhid -> vocab size + # but the models are combined in their hidden state + # the hook stores the output of the pretrained model forward + self.pretrained_outputs = {} + + def save_output(): + def hook(a, b, output): + self.pretrained_outputs["out"] = output + + return hook + + self.pretrained_decoder.fc2.register_forward_hook(save_output()) + + def forward(self, prev_output_tokens, encoder_out): + trained_encoder_out = encoder_out["pretrained"] if self.pretrained else None + encoder_out = encoder_out["encoder"]["encoder_out"] + + encoder_a, encoder_b = self._split_encoder_out(encoder_out) + + # embed positions + positions = self.embed_positions(prev_output_tokens) + + # embed tokens and positions + x = self.embed_tokens(prev_output_tokens) + positions + x = self.dropout_module(x) + target_embedding = x.transpose(0, 1) + + # project to size of convolution + x = self.fc1(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # temporal convolutions + avg_attn_scores = None + for proj, conv, attention, selfattention, attproj in zip( + self.projections, + self.convolutions, + self.attention, + self.selfattention, + self.attproj, + ): + residual = x if proj is None else proj(x) + + x = self.dropout_module(x) + x = conv(x) + x = F.glu(x, dim=2) + + # attention + if attention is not None: + r = x + x, attn_scores = attention( + attproj(x) + target_embedding, encoder_a, encoder_b + ) + x = x + r + if not self.training and self.need_attn: + if avg_attn_scores is None: + avg_attn_scores = attn_scores + else: + avg_attn_scores.add_(attn_scores) + + if selfattention is not None: + x = selfattention(x) + + x = (x + residual) * math.sqrt(0.5) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + # project back to size of vocabulary + x = self.fc2(x) + x = self.dropout_module(x) + if not self.pretrained: + x = self.fc3(x) + + # fusion gating + if self.pretrained: + trained_x, _ = self.pretrained_decoder.forward( + prev_output_tokens, trained_encoder_out + ) + y = torch.cat([x, self.pretrained_outputs["out"]], dim=-1) + gate1 = self.gate1(y) + gate2 = self.gate2(y) + gated_x1 = gate1 * x + gated_x2 = gate2 * self.pretrained_outputs["out"] + fusion = torch.cat([gated_x1, gated_x2], dim=-1) + fusion = self.joining(fusion) + fusion_output = self.fc3(fusion) + return fusion_output, avg_attn_scores + else: + return x, avg_attn_scores + + def max_positions(self): + """Maximum output length supported by the decoder.""" + return self.embed_positions.max_positions + + def make_generation_fast_(self, need_attn=False, **kwargs): + self.need_attn = need_attn + + def _split_encoder_out(self, encoder_out): + """Split and transpose encoder outputs.""" + # transpose only once to speed up attention layers + encoder_a, encoder_b = encoder_out + encoder_a = encoder_a.transpose(0, 1).contiguous() + encoder_b = encoder_b.transpose(0, 1).contiguous() + result = (encoder_a, encoder_b) + return result + + +class SelfAttention(nn.Module): + def __init__( + self, + out_channels, + embed_dim, + num_heads, + project_input=False, + gated=False, + downsample=False, + ): + super().__init__() + self.attention = DownsampledMultiHeadAttention( + out_channels, + embed_dim, + num_heads, + dropout=0, + bias=True, + project_input=project_input, + gated=gated, + downsample=downsample, + ) + self.in_proj_q = Linear(out_channels, embed_dim) + self.in_proj_k = Linear(out_channels, embed_dim) + self.in_proj_v = Linear(out_channels, embed_dim) + self.ln = LayerNorm(out_channels) + + def forward(self, x): + residual = x + query = self.in_proj_q(x) + key = self.in_proj_k(x) + value = self.in_proj_v(x) + x, _ = self.attention( + query, key, value, mask_future_timesteps=True, use_scalar_bias=True + ) + return self.ln(x + residual) + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + m.weight.data.normal_(0, 0.1) + return m + + +def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx): + m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx) + m.weight.data.normal_(0, 0.1) + return m + + +def Linear(in_features, out_features, dropout=0.0): + """Weight-normalized Linear layer (input: N x T x C)""" + m = nn.Linear(in_features, out_features) + m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features)) + m.bias.data.zero_() + return m + + +def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): + """Weight-normalized Conv1d layer optimized for decoding""" + m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) + std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) + m.weight.data.normal_(mean=0, std=std) + m.bias.data.zero_() + return m + + +def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): + """Weight-normalized Conv1d layer""" + from fairseq.modules import ConvTBC + + m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs) + std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) + m.weight.data.normal_(mean=0, std=std) + m.bias.data.zero_() + return m + + +@register_model_architecture("fconv_self_att", "fconv_self_att") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 3") + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 8") + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.decoder_attention = getattr(args, "decoder_attention", "True") + args.self_attention = getattr(args, "self_attention", "False") + args.encoder_attention = getattr(args, "encoder_attention", "False") + args.multihead_attention_nheads = getattr(args, "multihead_attention_nheads", 1) + args.multihead_self_attention_nheads = getattr( + args, "multihead_self_attention_nheads", 1 + ) + args.encoder_attention_nheads = getattr(args, "encoder_attention_nheads", 1) + args.project_input = getattr(args, "project_input", "False") + args.gated_attention = getattr(args, "gated_attention", "False") + args.downsample = getattr(args, "downsample", "False") + args.pretrained_checkpoint = getattr(args, "pretrained_checkpoint", "") + args.pretrained = getattr(args, "pretrained", "False") + + +@register_model_architecture("fconv_self_att", "fconv_self_att_wp") +def fconv_self_att_wp(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_layers = getattr( + args, "encoder_layers", "[(128, 3)] * 2 + [(512,3)] * 1" + ) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_layers = getattr( + args, "decoder_layers", "[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1" + ) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.self_attention = getattr(args, "self_attention", "True") + args.multihead_self_attention_nheads = getattr( + args, "multihead_self_attention_nheads", 4 + ) + args.project_input = getattr(args, "project_input", "True") + args.gated_attention = getattr(args, "gated_attention", "True") + args.downsample = getattr(args, "downsample", "True") + base_architecture(args) diff --git a/fairseq/fairseq/models/hubert/__init__.py b/fairseq/fairseq/models/hubert/__init__.py new file mode 100644 index 0000000..a1b0eab --- /dev/null +++ b/fairseq/fairseq/models/hubert/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .hubert import * # noqa +from .hubert_asr import * # noqa diff --git a/fairseq/fairseq/models/hubert/hubert.py b/fairseq/fairseq/models/hubert/hubert.py new file mode 100644 index 0000000..cc3b777 --- /dev/null +++ b/fairseq/fairseq/models/hubert/hubert.py @@ -0,0 +1,576 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +from omegaconf import II + +from fairseq import utils +from fairseq.data.data_utils import compute_mask_indices +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.wav2vec.wav2vec2 import ( + EXTRACTOR_MODE_CHOICES, + MASKING_DISTRIBUTION_CHOICES, + LAYER_TYPE_CHOICES, + ConvFeatureExtractionModel, + TransformerEncoder, +) +from fairseq.modules import GradMultiply, LayerNorm +from fairseq.tasks.hubert_pretraining import ( + HubertPretrainingConfig, + HubertPretrainingTask, +) + +logger = logging.getLogger(__name__) + + +@dataclass +class HubertConfig(FairseqDataclass): + label_rate: float = II("task.label_rate") + + extractor_mode: EXTRACTOR_MODE_CHOICES = field( + default="default", + metadata={ + "help": "mode for feature extractor. default has a single group " + "norm with d groups in the first conv block, whereas layer_norm " + "has layer norms in every block (meant to use with normalize=True)" + }, + ) + encoder_layers: int = field( + default=12, metadata={"help": "num encoder layers in the transformer"} + ) + encoder_embed_dim: int = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + encoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "encoder embedding dimension for FFN"} + ) + encoder_attention_heads: int = field( + default=12, metadata={"help": "num encoder attention heads"} + ) + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="gelu", metadata={"help": "activation function to use"} + ) + layer_type: LAYER_TYPE_CHOICES = field( + default="transformer", metadata={"help": "layer type in encoder"} + ) + + # dropouts + dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for the transformer"}, + ) + attention_dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for attention weights"}, + ) + activation_dropout: float = field( + default=0.0, + metadata={"help": "dropout probability after activation in FFN"}, + ) + encoder_layerdrop: float = field( + default=0.0, + metadata={"help": "probability of dropping a tarnsformer layer"}, + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + dropout_features: float = field( + default=0.0, + metadata={"help": "dropout to apply to the features (after feat extr)"}, + ) + + final_dim: int = field( + default=0, + metadata={ + "help": "project final representations and targets to this many " + "dimensions. set to encoder_embed_dim is <= 0" + }, + ) + untie_final_proj: bool = field( + default=False, + metadata={"help": "use separate projection for each target"}, + ) + layer_norm_first: bool = field( + default=False, + metadata={"help": "apply layernorm first in the transformer"}, + ) + conv_feature_layers: str = field( + default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", + metadata={ + "help": "string describing convolutional feature extraction " + "layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + conv_bias: bool = field( + default=False, metadata={"help": "include bias in conv encoder"} + ) + logit_temp: float = field( + default=0.1, metadata={"help": "temperature to divide logits by"} + ) + target_glu: bool = field( + default=False, metadata={"help": "adds projection + glu to targets"} + ) + feature_grad_mult: float = field( + default=1.0, + metadata={"help": "multiply feature extractor var grads by this"}, + ) + + # masking + mask_length: int = field(default=10, metadata={"help": "mask length"}) + mask_prob: float = field( + default=0.65, + metadata={"help": "probability of replacing a token with mask"}, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose mask length"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # channel masking + mask_channel_length: int = field( + default=10, + metadata={"help": "length of the mask for features (channels)"}, + ) + mask_channel_prob: float = field( + default=0.0, + metadata={"help": "probability of replacing a feature with 0"}, + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, + metadata={"help": "whether to allow channel masks to overlap"}, + ) + mask_channel_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # positional embeddings + conv_pos: int = field( + default=128, + metadata={"help": "number of filters for convolutional positional embeddings"}, + ) + conv_pos_groups: int = field( + default=16, + metadata={"help": "number of groups for convolutional positional embedding"}, + ) + conv_pos_batch_norm: bool = field( + default=False, + metadata={ + "help": "use batch norm instead of weight norm in conv_pos (for bf16 models)" + }, + ) + + latent_temp: Tuple[float, float, float] = field( + default=(2, 0.5, 0.999995), + metadata={"help": "legacy (to be removed)"}, + ) + + # loss computation + skip_masked: bool = field( + default=False, + metadata={"help": "skip computing losses over masked frames"}, + ) + skip_nomask: bool = field( + default=False, + metadata={"help": "skip computing losses over unmasked frames"}, + ) + + checkpoint_activations: bool = field( + default=False, + metadata={"help": "recompute activations and save memory for extra compute"}, + ) + + # FP16 optimization + required_seq_len_multiple: int = field( + default=2, + metadata={ + "help": "pad the input to encoder such that the sequence length is divisible by multiple" + }, + ) + + # Conformer + depthwise_conv_kernel_size: int = field( + default=31, + metadata={ + "help": "depthwise-conv-kernel-size for convolution in conformer layer" + }, + ) + attn_type: str = field( + default="", + metadata={"help": "if espnet use ESPNET MHA"}, + ) + pos_enc_type: str = field( + default="abs", + metadata={"help": "Positional encoding type to use in conformer"}, + ) + fp16: bool = field(default=False, metadata={"help": "If fp16 is being used"}) + + +@register_model("hubert", dataclass=HubertConfig) +class HubertModel(BaseFairseqModel): + def __init__( + self, + cfg: HubertConfig, + task_cfg: HubertPretrainingConfig, + dictionaries: List[Dictionary], + ) -> None: + super().__init__() + logger.info(f"HubertModel Config: {cfg}") + + feature_enc_layers = eval(cfg.conv_feature_layers) # noqa + self.embed = feature_enc_layers[-1][0] + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers]) + self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / task_cfg.sample_rate + + self.post_extract_proj = ( + nn.Linear(self.embed, cfg.encoder_embed_dim) + if self.embed != cfg.encoder_embed_dim + else None + ) + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + self.logit_temp = cfg.logit_temp + self.skip_masked = cfg.skip_masked + self.skip_nomask = cfg.skip_nomask + + final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + + self.encoder = TransformerEncoder(cfg) + self.layer_norm = LayerNorm(self.embed) + + self.target_glu = None + if cfg.target_glu: + self.target_glu = nn.Sequential( + nn.Linear(final_dim, final_dim * 2), nn.GLU() + ) + + self.untie_final_proj = cfg.untie_final_proj + if self.untie_final_proj: + self.final_proj = nn.Linear( + cfg.encoder_embed_dim, final_dim * len(dictionaries) + ) + else: + self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) + + # modules below are not needed during fine-tuning + if any([d is None for d in dictionaries]): + logger.info("cannot find dictionary. assume will be used for fine-tuning") + else: + self.num_classes = [len(d) for d in dictionaries] + self.label_embs_concat = nn.Parameter( + torch.FloatTensor(sum(self.num_classes), final_dim) + ) + nn.init.uniform_(self.label_embs_concat) + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: HubertConfig, task: HubertPretrainingTask): + """Build a new model instance.""" + + model = HubertModel(cfg, task.cfg, task.dictionaries) + return model + + def apply_mask(self, x, padding_mask, target_list): + B, T, C = x.shape + if self.mask_prob > 0: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb + else: + mask_indices = None + + if self.mask_channel_prob > 0: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def compute_nce(self, x, pos, negs): + neg_is_pos = (pos == negs).all(-1) + pos = pos.unsqueeze(0) + targets = torch.cat([pos, negs], dim=0) + + logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x) + logits /= self.logit_temp + if neg_is_pos.any(): + logits[1:][neg_is_pos] = float("-inf") + logits = logits.transpose(0, 1) # (num_x, num_cls+1) + return logits + + def forward_features(self, source: torch.Tensor) -> torch.Tensor: + if self.feature_grad_mult > 0: + features = self.feature_extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(source) + return features + + def forward_targets( + self, + features: torch.Tensor, + target_list: List[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Trim features to ensure labels exist and then get aligned labels + feat_tsz = features.size(2) + targ_tsz = min([t.size(1) for t in target_list]) + if self.feat2tar_ratio * feat_tsz > targ_tsz: + feat_tsz = int(targ_tsz / self.feat2tar_ratio) + features = features[..., :feat_tsz] + target_inds = torch.arange(feat_tsz).float() * self.feat2tar_ratio + target_list = [t[:, target_inds.long()] for t in target_list] + return features, target_list + + def forward_padding_mask( + self, + features: torch.Tensor, + padding_mask: torch.Tensor, + ) -> torch.Tensor: + extra = padding_mask.size(1) % features.size(1) + if extra > 0: + padding_mask = padding_mask[:, :-extra] + padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1) + padding_mask = padding_mask.all(-1) + return padding_mask + + def forward( + self, + source: torch.Tensor, + target_list: Optional[List[torch.Tensor]] = None, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = True, + features_only: bool = False, + output_layer: Optional[int] = None, + ) -> Dict[str, torch.Tensor]: + """output layer is 1-based""" + features = self.forward_features(source) + if target_list is not None: + features, target_list = self.forward_targets(features, target_list) + + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + unmasked_features = features.clone() + + if padding_mask is not None: + padding_mask = self.forward_padding_mask(features, padding_mask) + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + + if mask: + x, mask_indices = self.apply_mask(features, padding_mask, target_list) + else: + x = features + mask_indices = None + + # feature: (B, T, D), float + # target: (B, T), long + # x: (B, T, D), float + # padding_mask: (B, T), bool + # mask_indices: (B, T), bool + x, _ = self.encoder( + x, + padding_mask=padding_mask, + layer=None if output_layer is None else output_layer - 1, + ) + + if features_only: + return {"x": x, "padding_mask": padding_mask, "features": features} + + def compute_pred(proj_x, target, label_embs): + # compute logits for the i-th label set + y = torch.index_select(label_embs, 0, target.long()) + negs = label_embs.unsqueeze(1).expand(-1, proj_x.size(0), -1) + if self.target_glu: + y = self.target_glu(y) + negs = self.target_glu(negs) + # proj_x: (S, D) + # y: (S, D) + # negs: (Neg, S, D) + return self.compute_nce(proj_x, y, negs) + + label_embs_list = self.label_embs_concat.split(self.num_classes, 0) + + if not self.skip_masked: + masked_indices = torch.logical_and(~padding_mask, mask_indices) + proj_x_m = self.final_proj(x[masked_indices]) + if self.untie_final_proj: + proj_x_m_list = proj_x_m.chunk(len(target_list), dim=-1) + else: + proj_x_m_list = [proj_x_m for _ in range(len(target_list))] + logit_m_list = [ + compute_pred(proj_x_m, t[masked_indices], label_embs_list[i]) + for i, (proj_x_m, t) in enumerate(zip(proj_x_m_list, target_list)) + ] + else: + logit_m_list = [None for _ in target_list] + + if not self.skip_nomask: + nomask_indices = torch.logical_and(~padding_mask, ~mask_indices) + proj_x_u = self.final_proj(x[nomask_indices]) + if self.untie_final_proj: + proj_x_u_list = proj_x_u.chunk(len(target_list), dim=-1) + else: + proj_x_u_list = [proj_x_u for _ in range(len(target_list))] + + logit_u_list = [ + compute_pred(proj_x_u, t[nomask_indices], label_embs_list[i]) + for i, (proj_x_u, t) in enumerate(zip(proj_x_u_list, target_list)) + ] + else: + logit_u_list = [None for _ in target_list] + + result = { + "logit_m_list": logit_m_list, + "logit_u_list": logit_u_list, + "padding_mask": padding_mask, + "features_pen": features_pen, + } + return result + + def extract_features( + self, + source: torch.Tensor, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = False, + ret_conv: bool = False, + output_layer: Optional[int] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + res = self.forward( + source, + padding_mask=padding_mask, + mask=mask, + features_only=True, + output_layer=output_layer, + ) + feature = res["features"] if ret_conv else res["x"] + return feature, res["padding_mask"] + + def get_logits(self, net_output, is_masked=True): + if is_masked: + logits_list = net_output["logit_m_list"] + else: + logits_list = net_output["logit_u_list"] + logits_list = [x.float() for x in logits_list if x is not None] + return logits_list + + def get_targets(self, net_output, is_masked=True): + logits_list = self.get_logits(net_output, is_masked) + targets_list = [x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list] + return targets_list + + def get_extra_losses(self, net_output): + extra_losses = [] + names = [] + + if "features_pen" in net_output: + extra_losses.append(net_output["features_pen"]) + names.append("features_pen") + + return extra_losses, names + + def remove_pretraining_modules(self): + self.target_glu = None + self.final_proj = None diff --git a/fairseq/fairseq/models/hubert/hubert_asr.py b/fairseq/fairseq/models/hubert/hubert_asr.py new file mode 100644 index 0000000..11c85ce --- /dev/null +++ b/fairseq/fairseq/models/hubert/hubert_asr.py @@ -0,0 +1,675 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import copy +import logging +import math +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Any, Optional +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from omegaconf import II, MISSING, open_dict + +from fairseq import checkpoint_utils, tasks, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models import ( + BaseFairseqModel, + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, +) +from fairseq.models.hubert.hubert import MASKING_DISTRIBUTION_CHOICES +from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerDecoderLayer +from fairseq.tasks import FairseqTask + +logger = logging.getLogger(__name__) + + +@dataclass +class HubertAsrConfig(FairseqDataclass): + w2v_path: str = field(default=MISSING, metadata={"help": "path to hubert model"}) + no_pretrained_weights: bool = field( + default=False, + metadata={"help": "if true, does not load pretrained weights"}, + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + final_dropout: float = field( + default=0.0, + metadata={"help": "dropout after transformer and before final projection"}, + ) + dropout: float = field( + default=0.0, + metadata={"help": "dropout probability inside hubert model"}, + ) + attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights " "inside hubert model" + }, + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN " "inside hubert model" + }, + ) + encoder_embed_dim: Optional[int] = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + + # masking + apply_mask: bool = field( + default=False, metadata={"help": "apply masking during fine-tuning"} + ) + mask_length: int = field( + default=10, metadata={"help": "repeat the mask indices multiple times"} + ) + mask_prob: float = field( + default=0.5, + metadata={ + "help": "probability of replacing a token with mask " + "(normalized by length)" + }, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose masks"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + + # channel masking + mask_channel_length: int = field( + default=10, + metadata={"help": "length of the mask for features (channels)"}, + ) + mask_channel_prob: float = field( + default=0.0, + metadata={"help": "probability of replacing a feature with 0"}, + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, + metadata={"help": "whether to allow channel masks to overlap"}, + ) + freeze_finetune_updates: int = field( + default=0, + metadata={"help": "dont finetune hubert for this many updates"}, + ) + feature_grad_mult: float = field( + default=0.0, + metadata={"help": "reset feature grad mult in hubert to this"}, + ) + layerdrop: float = field( + default=0.0, + metadata={"help": "probability of dropping a layer in hubert"}, + ) + normalize: bool = II("task.normalize") + data: str = II("task.data") + + # this holds the loaded hubert args + w2v_args: Any = None + + +@dataclass +class HubertCtcConfig(HubertAsrConfig): + pass + + +@register_model("hubert_ctc", dataclass=HubertCtcConfig) +class HubertCtc(BaseFairseqModel): + def __init__(self, cfg: HubertCtcConfig, w2v_encoder: BaseFairseqModel): + super().__init__() + self.cfg = cfg + self.w2v_encoder = w2v_encoder + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: HubertCtcConfig, task: FairseqTask): + """Build a new model instance.""" + w2v_encoder = HubertEncoder(cfg, task) + return cls(cfg, w2v_encoder) + + def get_normalized_probs(self, net_output, log_probs): + """Get normalized probabilities (or log probs) from a net's output.""" + + logits = net_output["encoder_out"] + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def get_logits(self, net_output): + logits = net_output["encoder_out"] + padding = net_output["encoder_padding_mask"] + if padding is not None and padding.any(): + padding = padding.T + logits[padding][..., 0] = 0 + logits[padding][..., 1:] = float("-inf") + + return logits + + def forward(self, **kwargs): + x = self.w2v_encoder(**kwargs) + return x + + +@dataclass +class HubertSeq2SeqConfig(HubertAsrConfig): + decoder_embed_dim: int = field( + default=768, metadata={"help": "decoder embedding dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "decoder layerdrop chance"} + ) + decoder_attention_heads: int = field( + default=4, metadata={"help": "num decoder attention heads"} + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + decoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each decoder block"} + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings (outside self attention)" + }, + ) + decoder_dropout: float = field( + default=0.0, metadata={"help": "dropout probability in the decoder"} + ) + decoder_attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights inside the decoder" + }, + ) + decoder_activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN inside the decoder" + }, + ) + max_target_positions: int = field( + default=2048, metadata={"help": "max target positions"} + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + autoregressive: bool = II("task.autoregressive") + seq2seq_path: str = field( + default="", + metadata={"help": "reset_dict"}, + ) + reset_dict: bool = field( + default=False, + metadata={"help": "reset_dict"}, + ) + + +@register_model("hubert_seq2seq", dataclass=HubertSeq2SeqConfig) +class HubertSeq2SeqModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def build_model(cls, cfg: HubertSeq2SeqConfig, task: FairseqTask): + """Build a new model instance.""" + + assert ( + cfg.autoregressive + ), "Please set task.autoregressive=true for seq2seq asr models" + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + emb = Embedding(num_embeddings, embed_dim, padding_idx) + return emb + + decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim) + + encoder = cls.build_encoder(cfg, task) + decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) + + model = HubertSeq2SeqModel(encoder, decoder) + + if cfg["seq2seq_path"]: + state = checkpoint_utils.load_checkpoint_to_cpu(cfg.seq2seq_path) + state = state["model"] + if cfg["reset_dict"]: + del state["decoder.embed_out"] + del state["decoder.embed_tokens.weight"] + model.load_state_dict(state, strict=False) + return model + + @classmethod + def build_encoder(cls, cfg: HubertAsrConfig, task): + return HubertEncoder(cfg, task) + + @classmethod + def build_decoder(cls, cfg: HubertSeq2SeqConfig, tgt_dict, embed_tokens): + return TransformerDecoder(cfg, tgt_dict, embed_tokens) + + def forward(self, **kwargs): + encoder_out = self.encoder(**kwargs) + decoder_out = self.decoder(encoder_out=encoder_out, **kwargs) + return decoder_out + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + def load_state_dict( + self, + state_dict, + strict=True, + model_cfg=None, + args: Optional[Namespace] = None, + ): + if model_cfg.reset_dict: + logger.warn("Overriding loading strict state dict!") + del state_dict["decoder.embed_out"] + del state_dict["decoder.embed_tokens.weight"] + return super().load_state_dict(state_dict, False, model_cfg, args) + return super().load_state_dict(state_dict, strict, model_cfg, args) + + +class HubertEncoder(FairseqEncoder): + def __init__(self, cfg: HubertAsrConfig, task): + self.apply_mask = cfg.apply_mask + + arg_overrides = { + "dropout": cfg.dropout, + "activation_dropout": cfg.activation_dropout, + "dropout_input": cfg.dropout_input, + "attention_dropout": cfg.attention_dropout, + "mask_length": cfg.mask_length, + "mask_prob": cfg.mask_prob, + "mask_selection": cfg.mask_selection, + "mask_other": cfg.mask_other, + "no_mask_overlap": cfg.no_mask_overlap, + "mask_channel_length": cfg.mask_channel_length, + "mask_channel_prob": cfg.mask_channel_prob, + "mask_channel_selection": cfg.mask_channel_selection, + "mask_channel_other": cfg.mask_channel_other, + "no_mask_channel_overlap": cfg.no_mask_channel_overlap, + "encoder_layerdrop": cfg.layerdrop, + "feature_grad_mult": cfg.feature_grad_mult, + } + + if cfg.w2v_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) + w2v_args = state.get("cfg", None) + if w2v_args is None: + w2v_args = convert_namespace_to_omegaconf(state["args"]) + cfg.w2v_args = w2v_args + else: + state = None + w2v_args = cfg.w2v_args + if isinstance(w2v_args, Namespace): + cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args) + + assert cfg.normalize == w2v_args.task.normalize, ( + "Fine-tuning works best when data normalization is the same. " + "Please check that --normalize is set or unset for " + "both pre-training and here" + ) + + w2v_args.task.data = cfg.data + pretrain_task = tasks.setup_task(w2v_args.task) + if state is not None and "task_state" in state: + # This will load the stored "dictionaries" object + pretrain_task.load_state_dict(state["task_state"]) + else: + pretrain_task.load_state_dict(task.state_dict()) + + model = pretrain_task.build_model(w2v_args.model, from_checkpoint=True) + if state is not None and not cfg.no_pretrained_weights: + # set strict=False because we omit some modules + model.load_state_dict(state["model"], strict=False) + + model.remove_pretraining_modules() + + super().__init__(pretrain_task.source_dictionary) + + d = w2v_args.model.encoder_embed_dim + + self.w2v_model = model + + self.final_dropout = nn.Dropout(cfg.final_dropout) + self.freeze_finetune_updates = cfg.freeze_finetune_updates + self.num_updates = 0 + + if task.target_dictionary is not None and not cfg.autoregressive: + self.proj = Linear(d, len(task.target_dictionary)) + elif getattr(cfg, "decoder_embed_dim", d) != d: + self.proj = Linear(d, cfg.decoder_embed_dim) + else: + self.proj = None + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward(self, source, padding_mask, tbc=True, **kwargs): + + w2v_args = { + "source": source, + "padding_mask": padding_mask, + "mask": self.apply_mask and self.training, + } + + ft = self.freeze_finetune_updates <= self.num_updates + + with torch.no_grad() if not ft else contextlib.ExitStack(): + x, padding_mask = self.w2v_model.extract_features(**w2v_args) + + if tbc: + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + x = self.final_dropout(x) + + if self.proj: + x = self.proj(x) + + return { + "encoder_out": x, # T x B x C + "encoder_padding_mask": padding_mask, # B x T + "padding_mask": padding_mask, + } + + def reorder_encoder_out(self, encoder_out, new_order): + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) + if encoder_out["padding_mask"] is not None: + encoder_out["padding_mask"] = encoder_out["padding_mask"].index_select( + 0, new_order + ) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return None + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +class TransformerDecoder(FairseqIncrementalDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + cfg: HubertSeq2SeqConfig, + dictionary, + embed_tokens, + no_encoder_attn=False, + ): + super().__init__(dictionary) + + self.dropout = cfg.decoder_dropout + self.share_input_output_embed = cfg.share_decoder_input_output_embed + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = cfg.decoder_embed_dim + self.output_embed_dim = cfg.decoder_embed_dim + + self.layerdrop = cfg.decoder_layerdrop + + self.padding_idx = embed_tokens.padding_idx + self.max_target_positions = cfg.max_target_positions + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + cfg.max_target_positions, + embed_dim, + self.padding_idx, + learned=cfg.decoder_learned_pos, + ) + if not cfg.no_token_positional_embeddings + else None + ) + + # TODO: update this when transformer gets converted to dataclass configs + transformer_cfg = copy.deepcopy(cfg) + with open_dict(transformer_cfg): + transformer_cfg.dropout = transformer_cfg.decoder_dropout + transformer_cfg.attention_dropout = ( + transformer_cfg.decoder_attention_dropout + ) + transformer_cfg.activation_dropout = ( + transformer_cfg.decoder_activation_dropout + ) + + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + TransformerDecoderLayer(transformer_cfg, no_encoder_attn) + for _ in range(transformer_cfg.decoder_layers) + ] + ) + + if not self.share_input_output_embed: + self.embed_out = nn.Parameter( + torch.Tensor(len(dictionary), self.output_embed_dim) + ) + nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim**-0.5) + + if transformer_cfg.decoder_normalize_before: + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (Tensor, optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + if type(prev_output_tokens) == list: + max_len = max((len(x) for x in prev_output_tokens)) + tmp = torch.zeros( + [len(prev_output_tokens), max_len], device=prev_output_tokens[0].device + ) + for (i, p) in enumerate(prev_output_tokens): + tmp[i, : len(p)] = p + prev_output_tokens = tmp + prev_output_tokens = prev_output_tokens.long() + x, extra = self.extract_features( + prev_output_tokens, encoder_out, incremental_state + ) + x = self.output_layer(x) + return x, extra + + def extract_features( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): + """ + Similar to *forward* but only return features. + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + + # embed positions + positions = ( + self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + attn = None + + inner_states = [x] + + # decoder layers + self_attn_padding_mask = None + if prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + for layer in self.layers: + dropout_probability = np.random.random() + if not self.training or (dropout_probability > self.layerdrop): + x, attn, _ = layer( + x, + encoder_out["encoder_out"] if encoder_out is not None else None, + encoder_out["padding_mask"] if encoder_out is not None else None, + incremental_state, + self_attn_mask=self.buffered_future_mask(x) + if incremental_state is None + else None, + self_attn_padding_mask=self_attn_padding_mask, + ) + inner_states.append(x) + + if self.layer_norm: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, {"attn": attn, "inner_states": inner_states} + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + # project back to size of vocabulary + if self.share_input_output_embed: + return F.linear(features, self.embed_tokens.weight) + else: + return F.linear(features, self.embed_out) + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embed_positions is None: + return self.max_target_positions + return min(self.max_target_positions, self.embed_positions.max_positions) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m diff --git a/fairseq/fairseq/models/huggingface/__init__.py b/fairseq/fairseq/models/huggingface/__init__.py new file mode 100644 index 0000000..f7911c2 --- /dev/null +++ b/fairseq/fairseq/models/huggingface/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + + +# automatically import any Python files in the models/huggingface/ directory +models_dir = os.path.dirname(__file__) +for file in os.listdir(models_dir): + path = os.path.join(models_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + model_name = file[: file.find(".py")] if file.endswith(".py") else file + module = importlib.import_module("fairseq.models.huggingface." + model_name) diff --git a/fairseq/fairseq/models/huggingface/hf_gpt2.py b/fairseq/fairseq/models/huggingface/hf_gpt2.py new file mode 100644 index 0000000..3a8eb78 --- /dev/null +++ b/fairseq/fairseq/models/huggingface/hf_gpt2.py @@ -0,0 +1,168 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys +from typing import Dict, List, Optional + +import torch +from fairseq.models import ( + FairseqIncrementalDecoder, + FairseqLanguageModel, + register_model, + register_model_architecture, +) + + +logger = logging.getLogger(__name__) + + +DEFAULT_MAX_TARGET_POSITIONS = 1024 + + +@register_model("hf_gpt2") +class HuggingFaceGPT2LanguageModel(FairseqLanguageModel): + def __init__(self, decoder): + super().__init__(decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--embed-dim', type=int, metavar='N', + help='embedding dimension') + parser.add_argument('--num-attention-heads', type=int, metavar='N', + help='num attention heads') + parser.add_argument('--num-layers', type=int, metavar='N', + help='num layers') + parser.add_argument('--dropout', type=float, metavar='D', + help='dropout probability for all fully connected layers ' + 'in the embeddings, encoder, and pooler') + parser.add_argument('--attention-dropout', type=float, metavar='D', + help='dropout probability for attention weights') + # fmt: on + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + default_architecture(args) + return cls(HuggingFaceGPT2Decoder(args, task)) + + +class HuggingFaceGPT2Decoder(FairseqIncrementalDecoder): + def __init__(self, args, task): + try: + from transformers import GPT2Config, GPT2LMHeadModel + except ImportError: + raise ImportError( + "\n\nPlease install huggingface/transformers with:" + "\n\n pip install transformers" + ) + + super().__init__(task.target_dictionary) + + config = GPT2Config( + vocab_size=len(task.target_dictionary), + n_positions=args.max_target_positions + 1, + n_ctx=args.max_target_positions, + n_embd=args.embed_dim, + n_layer=args.num_layers, + n_head=args.num_attention_heads, + resid_pdrop=args.dropout, + embd_pdrop=args.dropout, + attn_pdrop=args.attention_dropout, + layer_norm_epsilon=1e-6, + ) + self.model = GPT2LMHeadModel(config) + + # set zero embedding for padding symbol + self.pad_idx = task.target_dictionary.pad() + self.model.transformer.wte.weight.data[self.pad_idx].zero_() + self.model.transformer.wpe.weight.data[0].zero_() + + def forward( + self, + prev_output_tokens, + src_lengths=None, + incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, + encoder_out=None, + ): + features = self.extract_features(prev_output_tokens, incremental_state) + lm_logits = self.model.lm_head(features) + return (lm_logits,) + + def extract_features( + self, + prev_output_tokens, + incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, + ): + if incremental_state: + past = self.get_incremental_state("past") + else: + past = None + + # don't attend to padding symbols + attention_mask = prev_output_tokens.ne(self.pad_idx).int() + + # set position ids to exclude padding symbols + position_ids = attention_mask * ( + torch.arange(1, 1 + prev_output_tokens.size(1)) + .to(prev_output_tokens) + .repeat(prev_output_tokens.size(0), 1) + ) + + outputs = self.model.transformer( + input_ids=prev_output_tokens, + past=past, + attention_mask=attention_mask, + position_ids=position_ids, + ) + last_hidden_states = outputs[0] + + if incremental_state: + self.set_incremental_state(incremental_state, "past", outputs[1]) + + return last_hidden_states + + def max_positions(self): + return self.model.config.n_positions - 1 + + +@register_model_architecture("hf_gpt2", "hf_gpt2") +def default_architecture(args): + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) + args.embed_dim = getattr(args, "embed_dim", 768) + args.num_attention_heads = getattr(args, "num_attention_heads", 12) + args.num_layers = getattr(args, "num_layers", 12) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + + +@register_model_architecture("hf_gpt2", "hf_gpt2_medium") +def hf_gpt2_medium(args): + args.embed_dim = getattr(args, "embed_dim", 1024) + args.num_attention_heads = getattr(args, "num_attention_heads", 16) + args.num_layers = getattr(args, "num_layers", 24) + default_architecture(args) + + +@register_model_architecture("hf_gpt2", "hf_gpt2_large") +def hf_gpt2_large(args): + args.embed_dim = getattr(args, "embed_dim", 1280) + args.num_attention_heads = getattr(args, "num_attention_heads", 20) + args.num_layers = getattr(args, "num_layers", 36) + default_architecture(args) + + +@register_model_architecture("hf_gpt2", "hf_gpt2_xl") +def hf_gpt2_xl(args): + args.embed_dim = getattr(args, "embed_dim", 1600) + args.num_attention_heads = getattr(args, "num_attention_heads", 25) + args.num_layers = getattr(args, "num_layers", 48) + default_architecture(args) diff --git a/fairseq/fairseq/models/lightconv.py b/fairseq/fairseq/models/lightconv.py new file mode 100644 index 0000000..7950280 --- /dev/null +++ b/fairseq/fairseq/models/lightconv.py @@ -0,0 +1,1119 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) +from fairseq.modules import ( + AdaptiveSoftmax, + DynamicConv_scripatable as DynamicConv, + FairseqDropout, + LayerNorm, + LightweightConv, + MultiheadAttention, + PositionalEmbedding, +) +from fairseq.utils import safe_hasattr +from torch import Tensor + + +@register_model("lightconv") +class LightConvModel(FairseqEncoderDecoderModel): + """ + LightConv and DynamicConv model from `"Pay Less Attention with Lightweight and Dynamic Convolutions" (Wu, et al, 2019) + <https://openreview.net/pdf?id=SkVhlh09tX>`_. + To use LightConv please set ``--encoder-conv-type lightweight --decoder-conv-type lightweight`` + To use DynamicConv please set ``--encoder-conv-type dynamic --decoder-conv-type dynamic`` + + Args: + encoder (LightConvEncoder): the encoder + decoder (LightConvDecoder): the decoder + + The LightConv model provides the following named architectures and + command-line arguments: + + .. argparse:: + :ref: fairseq.models.lightconv_parser + :prog: + """ + + @classmethod + def hub_models(cls): + # fmt: off + + def moses_subword(path): + return { + 'path': path, + 'tokenizer': 'moses', + 'bpe': 'subword_nmt', + } + + return { + 'lightconv.no_glu.iwslt14.de-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.lightconv.tar.gz'), + 'dynamicconv.no_glu.iwslt14.de-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.dynamicconv.tar.gz'), + 'lightconv.no_glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv.tar.gz'), + 'dynamicconv.no_glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv.tar.gz'), + 'lightconv.glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz'), + 'dynamicconv.glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz'), + 'lightconv.glu.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz'), + 'dynamicconv.glu.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz'), + 'lightconv.glu.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.lightconv-glu.tar.gz'), + 'dynamicconv.glu.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.dynamicconv-glu.tar.gz'), + 'lightconv.glu.wmt17.zh-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.lightconv-glu.tar.gz'), + 'dynamicconv.glu.wmt17.zh-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.dynamicconv-glu.tar.gz'), + } + # fmt: on + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after ReLU in FFN", + ) + parser.add_argument( + "--input-dropout", + type=float, + metavar="D", + help="dropout probability of the inputs", + ) + parser.add_argument( + "--encoder-embed-path", + type=str, + metavar="STR", + help="path to pre-trained encoder embedding", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-conv-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads or LightConv/DynamicConv heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--encoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the encoder", + ) + parser.add_argument( + "--decoder-embed-path", + type=str, + metavar="STR", + help="path to pre-trained decoder embedding", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-conv-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads or LightConv/DynamicConv heads", + ) + parser.add_argument( + "--decoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the decoder", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--share-all-embeddings", + action="store_true", + help="share encoder, decoder and output embeddings" + " (requires shared dictionary and embed dim)", + ) + parser.add_argument( + "--adaptive-softmax-cutoff", + metavar="EXPR", + help="comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion", + ), + parser.add_argument( + "--adaptive-softmax-dropout", + type=float, + metavar="D", + help="sets adaptive softmax dropout for the tail projections", + ) + + """LightConv and DynamicConv arguments""" + parser.add_argument( + "--encoder-kernel-size-list", + type=lambda x: utils.eval_str_list(x, int), + help='list of kernel size (default: "[3,7,15,31,31,31,31]")', + ) + parser.add_argument( + "--decoder-kernel-size-list", + type=lambda x: utils.eval_str_list(x, int), + help='list of kernel size (default: "[3,7,15,31,31,31]")', + ) + parser.add_argument( + "--encoder-glu", type=utils.eval_bool, help="glu after in proj" + ) + parser.add_argument( + "--decoder-glu", type=utils.eval_bool, help="glu after in proj" + ) + parser.add_argument( + "--encoder-conv-type", + default="dynamic", + type=str, + choices=["dynamic", "lightweight"], + help="type of convolution", + ) + parser.add_argument( + "--decoder-conv-type", + default="dynamic", + type=str, + choices=["dynamic", "lightweight"], + help="type of convolution", + ) + parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool) + parser.add_argument( + "--weight-dropout", + type=float, + metavar="D", + help="dropout probability for conv weights", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if not safe_hasattr(args, "max_source_positions"): + args.max_source_positions = 1024 + if not safe_hasattr(args, "max_target_positions"): + args.max_target_positions = 1024 + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + def build_embedding(dictionary, embed_dim, path=None): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + emb = Embedding(num_embeddings, embed_dim, padding_idx) + # if provided, load from preloaded dictionaries + if path: + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + return emb + + if args.share_all_embeddings: + if src_dict != tgt_dict: + raise RuntimeError( + "--share-all-embeddings requires a joined dictionary" + ) + if args.encoder_embed_dim != args.decoder_embed_dim: + raise RuntimeError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise RuntimeError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + encoder_embed_tokens = build_embedding( + src_dict, args.encoder_embed_dim, args.encoder_embed_path + ) + decoder_embed_tokens = encoder_embed_tokens + args.share_decoder_input_output_embed = True + else: + encoder_embed_tokens = build_embedding( + src_dict, args.encoder_embed_dim, args.encoder_embed_path + ) + decoder_embed_tokens = build_embedding( + tgt_dict, args.decoder_embed_dim, args.decoder_embed_path + ) + + encoder = LightConvEncoder(args, src_dict, encoder_embed_tokens) + decoder = LightConvDecoder(args, tgt_dict, decoder_embed_tokens) + return LightConvModel(encoder, decoder) + + def forward( + self, + src_tokens: Tensor, + src_lengths: Tensor, + prev_output_tokens: Tensor, + ): + """ + (The forward method inherited from the base class has a **kwargs + argument in its input, which is not supported in torchscript. This + method overwrites the forward method definition without **kwargs.) + + Run the forward pass for an encoder-decoder model. + + First feed a batch of source tokens through the encoder. Then, feed the + encoder output and previous decoder outputs (i.e., teacher forcing) to + the decoder to produce the next outputs:: + + encoder_out = self.encoder(src_tokens, src_lengths) + return self.decoder(prev_output_tokens, encoder_out) + + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (LongTensor): source sentence lengths of shape `(batch)` + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + encoder_out = self.encoder(src_tokens, src_lengths) + decoder_out = self.decoder(prev_output_tokens, encoder_out=encoder_out) + return decoder_out + + +class LightConvEncoder(FairseqEncoder): + """ + LightConv encoder consisting of *args.encoder_layers* layers. Each layer + is a :class:`LightConvEncoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): encoding dictionary + embed_tokens (torch.nn.Embedding): input embedding + """ + + def __init__(self, args, dictionary, embed_tokens): + super().__init__(dictionary) + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + + embed_dim = embed_tokens.embedding_dim + self.padding_idx = embed_tokens.padding_idx + self.max_source_positions = args.max_source_positions + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) + self.embed_positions = ( + PositionalEmbedding( + args.max_source_positions, + embed_dim, + self.padding_idx, + learned=args.encoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) + + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + LightConvEncoderLayer( + args, kernel_size=args.encoder_kernel_size_list[i] + ) + for i in range(args.encoder_layers) + ] + ) + self.register_buffer("version", torch.Tensor([2])) + self.normalize = args.encoder_normalize_before + if self.normalize: + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward( + self, src_tokens: Tensor, src_lengths: Optional[Tensor] = None + ) -> Dict[str, List[Tensor]]: + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + """ + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(src_tokens) + if self.embed_positions is not None: + x += self.embed_positions(src_tokens) + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # compute padding mask + encoder_padding_mask = src_tokens.eq(self.padding_idx) # B x T + if not encoder_padding_mask.any(): + encoder_mask = None + else: + encoder_mask = encoder_padding_mask + + # encoder layers + for layer in self.layers: + x = layer(x, encoder_mask) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + output_dict: Dict[str, List[Tensor]] = {} + if src_lengths is not None: + output_dict["src_lengths"] = [src_lengths] + output_dict["encoder_out"] = [x] # T x B x C + if encoder_mask is not None: + output_dict["encoder_padding_mask"] = [encoder_mask] # B x T + + return output_dict + + @torch.jit.export + def reorder_encoder_out( + self, encoder_out: Dict[str, List[Tensor]], new_order: Tensor + ): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + if len(encoder_out["encoder_out"]) == 0: + encoder = [] + else: + encoder = [encoder_out["encoder_out"][0].index_select(1, new_order)] + output_dict = {"encoder_out": encoder} + + if ("encoder_padding_mask" not in encoder_out) or ( + len(encoder_out["encoder_padding_mask"]) == 0 + ): + encoder_padding_mask = [] + else: + encoder_padding_mask = [ + encoder_out["encoder_padding_mask"][0].index_select(0, new_order) + ] + output_dict["encoder_padding_mask"] = encoder_padding_mask + return output_dict + + def max_positions(self): + """Maximum input length supported by the encoder.""" + if self.embed_positions is None: + return self.max_source_positions + return min(self.max_source_positions, self.embed_positions.max_positions) + + +class LightConvDecoder(FairseqIncrementalDecoder): + """ + LightConv decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`LightConvDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs. + Default: ``False`` + """ + + def __init__( + self, args, dictionary, embed_tokens, no_encoder_attn=False, final_norm=True + ): + super().__init__(dictionary) + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.share_input_output_embed = args.share_decoder_input_output_embed + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = args.decoder_embed_dim + output_embed_dim = args.decoder_output_dim + + padding_idx = embed_tokens.padding_idx + self.max_target_positions = args.max_target_positions + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + args.max_target_positions, + embed_dim, + padding_idx, + learned=args.decoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) + + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + LightConvDecoderLayer( + args, + no_encoder_attn, + kernel_size=args.decoder_kernel_size_list[i], + dictionary=dictionary, + ) + for i in range(args.decoder_layers) + ] + ) + + self.adaptive_softmax = None + self.output_projection = None + + self.project_out_dim = ( + Linear(embed_dim, output_embed_dim, bias=False) + if embed_dim != output_embed_dim and not args.tie_adaptive_weights + else None + ) + + if args.adaptive_softmax_cutoff is not None: + self.adaptive_softmax = AdaptiveSoftmax( + len(dictionary), + output_embed_dim, + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int), + dropout=args.adaptive_softmax_dropout, + adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None, + factor=args.adaptive_softmax_factor, + tie_proj=args.tie_adaptive_proj, + ) + elif self.share_input_output_embed: + self.output_projection = nn.Linear( + self.embed_tokens.weight.shape[1], + self.embed_tokens.weight.shape[0], + bias=False, + ) + self.output_projection.weight = self.embed_tokens.weight + + else: + self.output_projection = nn.Linear( + output_embed_dim, len(dictionary), bias=False + ) + nn.init.normal_( + self.output_projection.weight, mean=0, std=output_embed_dim**-0.5 + ) + self.register_buffer("version", torch.Tensor([2])) + self.normalize = args.decoder_normalize_before and final_norm + if self.normalize: + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward( + self, + prev_output_tokens: Tensor, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + src_lengths: Optional[Any] = None, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (Tensor, optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + + Returns: + tuple: + - the last decoder layer's output of shape `(batch, tgt_len, + vocab)` + - the last decoder layer's attention weights of shape `(batch, + tgt_len, src_len)` + """ + # embed positions + positions = ( + self.embed_positions( + prev_output_tokens, + incremental_state=incremental_state, + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens.contiguous()) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + attn = None + + inner_states: List[Optional[Tensor]] = [x] + + # decoder layers + attn: Optional[Tensor] = None + for layer in self.layers: + encoder: Optional[Tensor] = None + encoder_padding_mask: Optional[Tensor] = None + if encoder_out is not None: + if len(encoder_out["encoder_out"]) > 0: + encoder = encoder_out["encoder_out"][0] + if ( + "encoder_padding_mask" in encoder_out + and len(encoder_out["encoder_padding_mask"]) > 0 + ): + encoder_padding_mask = encoder_out["encoder_padding_mask"][0] + x, attn = layer( + x, + encoder, + encoder_padding_mask, + incremental_state, + ) + inner_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + if self.adaptive_softmax is None: + # project back to size of vocabulary + x = self.output_projection(x) + + return x, {"attn": [attn], "inner_states": inner_states} + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embed_positions is None: + return self.max_target_positions + return min(self.max_target_positions, self.embed_positions.max_positions) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + if self._future_mask.size(0) < dim: + self._future_mask = torch.triu( + utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + +class LightConvEncoderLayer(nn.Module): + """Encoder layer block. + + Args: + args (argparse.Namespace): parsed command-line arguments + kernel_size: kernel size of the convolution + """ + + def __init__(self, args, kernel_size=0): + super().__init__() + self.embed_dim = args.encoder_embed_dim + self.conv_dim = args.encoder_conv_dim + padding_l = ( + kernel_size // 2 + if kernel_size % 2 == 1 + else ((kernel_size - 1) // 2, kernel_size // 2) + ) + + if args.encoder_glu: + self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim) + self.act = nn.GLU() + else: + self.linear1 = Linear(self.embed_dim, self.conv_dim) + self.act = None + if args.encoder_conv_type == "lightweight": + self.conv = LightweightConv( + self.conv_dim, + kernel_size, + padding_l=padding_l, + weight_softmax=args.weight_softmax, + num_heads=args.encoder_attention_heads, + weight_dropout=args.weight_dropout, + ) + elif args.encoder_conv_type == "dynamic": + self.conv = DynamicConv( + self.conv_dim, + kernel_size, + padding_l=padding_l, + weight_softmax=args.weight_softmax, + num_heads=args.encoder_attention_heads, + weight_dropout=args.weight_dropout, + ) + else: + raise NotImplementedError + self.linear2 = Linear(self.conv_dim, self.embed_dim) + + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.relu_dropout_module = FairseqDropout( + args.relu_dropout, module_name=self.__class__.__name__ + ) + self.input_dropout_module = FairseqDropout( + args.input_dropout, module_name=self.__class__.__name__ + ) + self.normalize_before = args.encoder_normalize_before + self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim) + self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim) + self.layer_norm1 = LayerNorm(self.embed_dim) + self.layer_norm2 = LayerNorm(self.embed_dim) + + def forward(self, x, encoder_padding_mask: Optional[Tensor] = None) -> Tensor: + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor): binary ByteTensor of shape + `(batch, src_len)` where padding elements are indicated by ``1``. + + Returns: + encoded output of shape `(batch, src_len, embed_dim)` + """ + residual = x + normalize = self.maybe_layer_norm(before=True) + if normalize: + x = self.layer_norm1(x) + x = self.input_dropout_module(x) + x = self.linear1(x) + if self.act is not None: + x = self.act(x) + if encoder_padding_mask is not None: + x = x.masked_fill(encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0) + x = self.conv(x) + x = self.linear2(x) + x = self.dropout_module(x) + x = residual + x + normalize = self.maybe_layer_norm(after=True) + if normalize: + x = self.layer_norm1(x) + + residual = x + normalize = self.maybe_layer_norm(before=True) + if normalize: + x = self.layer_norm2(x) + x = F.relu(self.fc1(x)) + x = self.relu_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = residual + x + normalize = self.maybe_layer_norm(after=True) + if normalize: + x = self.layer_norm2(x) + return x + + def maybe_layer_norm(self, before: bool = False, after: bool = False): + assert before ^ after, "Incorrect arguments" + return after ^ self.normalize_before + + def extra_repr(self): + return ( + "dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format( + self.dropout_module.p, + self.relu_dropout_module.p, + self.input_dropout_module.p, + self.normalize_before, + ) + ) + + +class LightConvDecoderLayer(nn.Module): + """Decoder layer block. + + Args: + args (argparse.Namespace): parsed command-line arguments + no_encoder_attn (bool, optional): whether to attend to encoder outputs. + Default: ``False`` + kernel_size: kernel size of the convolution + """ + + def __init__(self, args, no_encoder_attn=False, kernel_size=0, dictionary=None): + super().__init__() + self.embed_dim = args.decoder_embed_dim + self.conv_dim = args.decoder_conv_dim + if args.decoder_glu: + self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim) + self.act = nn.GLU() + else: + self.linear1 = Linear(self.embed_dim, self.conv_dim) + self.act = None + if args.decoder_conv_type == "lightweight": + self.conv = LightweightConv( + self.conv_dim, + kernel_size, + padding_l=kernel_size - 1, + weight_softmax=args.weight_softmax, + num_heads=args.decoder_attention_heads, + weight_dropout=args.weight_dropout, + ) + elif args.decoder_conv_type == "dynamic": + self.conv = DynamicConv( + self.conv_dim, + kernel_size, + padding_l=kernel_size - 1, + weight_softmax=args.weight_softmax, + num_heads=args.decoder_attention_heads, + weight_dropout=args.weight_dropout, + ) + else: + raise NotImplementedError + self.linear2 = Linear(self.conv_dim, self.embed_dim) + + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.relu_dropout_module = FairseqDropout( + args.relu_dropout, module_name=self.__class__.__name__ + ) + self.input_dropout_module = FairseqDropout( + args.input_dropout, module_name=self.__class__.__name__ + ) + self.normalize_before = args.decoder_normalize_before + + self.conv_layer_norm = LayerNorm(self.embed_dim) + + if no_encoder_attn: + self.encoder_attn = None + self.encoder_attn_layer_norm = None + else: + self.encoder_attn = MultiheadAttention( + self.embed_dim, + args.decoder_attention_heads, + dropout=args.attention_dropout, + encoder_decoder_attention=True, + dictionary=dictionary, + ) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim) + + self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim) + self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim) + + self.final_layer_norm = LayerNorm(self.embed_dim) + self.need_attn = True + + def forward( + self, + x: Tensor, + encoder_out: Optional[Tensor], + encoder_padding_mask: Optional[Tensor], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + prev_conv_state: Optional[Tensor] = None, + prev_attn_state: Optional[Tuple[Tensor, Tensor]] = None, + conv_mask: Optional[Tensor] = None, + conv_padding_mask: Optional[Tensor] = None, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor): binary ByteTensor of shape + `(batch, src_len)` where padding elements are indicated by ``1``. + + Returns: + encoded output of shape `(batch, src_len, embed_dim)` + """ + residual = x + normalize = self.maybe_layer_norm(before=True) + if normalize: + x = self.conv_layer_norm(x) + if prev_conv_state is not None: + self.conv._set_input_buffer(incremental_state, prev_conv_state) + x = self.input_dropout_module(x) + x = self.linear1(x) + if self.act is not None: + x = self.act(x) + x = self.conv(x, incremental_state=incremental_state) + x = self.linear2(x) + x = self.dropout_module(x) + x = residual + x + normalize = self.maybe_layer_norm(after=True) + if normalize: + x = self.conv_layer_norm(x) + + attn: Optional[Tensor] = None + if self.encoder_attn is not None: + residual = x + normalize = self.maybe_layer_norm(before=True) + if normalize: + x = self.encoder_attn_layer_norm(x) + + if prev_attn_state is not None: + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_attn_state[0], + "prev_value": prev_attn_state[1], + } + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=(not self.training and self.need_attn), + ) + x = self.dropout_module(x) + x = residual + x + normalize = self.maybe_layer_norm(after=True) + if normalize: + x = self.encoder_attn_layer_norm(x) + + residual = x + normalize = self.maybe_layer_norm(before=True) + if normalize: + x = self.final_layer_norm(x) + x = F.relu(self.fc1(x)) + x = self.relu_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = residual + x + normalize = self.maybe_layer_norm(after=True) + if normalize: + x = self.final_layer_norm(x) + return x, attn + + def maybe_layer_norm(self, before: bool = False, after: bool = False): + assert before ^ after, "Incorrect usage" + return after ^ self.normalize_before + + def make_generation_fast_(self, need_attn: bool = False, **kwargs): + self.need_attn = need_attn + + def extra_repr(self): + return ( + "dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format( + self.dropout_module.p, + self.relu_dropout_module.p, + self.input_dropout_module.p, + self.normalize_before, + ) + ) + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m + + +@register_model_architecture("lightconv", "lightconv") +def base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 7) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.relu_dropout = getattr(args, "relu_dropout", 0.0) + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.encoder_conv_dim = getattr(args, "encoder_conv_dim", args.encoder_embed_dim) + args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim) + + args.encoder_kernel_size_list = getattr( + args, "encoder_kernel_size_list", [3, 7, 15, 31, 31, 31, 31] + ) + args.decoder_kernel_size_list = getattr( + args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31] + ) + if len(args.encoder_kernel_size_list) == 1: + args.encoder_kernel_size_list = ( + args.encoder_kernel_size_list * args.encoder_layers + ) + if len(args.decoder_kernel_size_list) == 1: + args.decoder_kernel_size_list = ( + args.decoder_kernel_size_list * args.decoder_layers + ) + assert ( + len(args.encoder_kernel_size_list) == args.encoder_layers + ), "encoder_kernel_size_list doesn't match encoder_layers" + assert ( + len(args.decoder_kernel_size_list) == args.decoder_layers + ), "decoder_kernel_size_list doesn't match decoder_layers" + args.encoder_glu = getattr(args, "encoder_glu", True) + args.decoder_glu = getattr(args, "decoder_glu", True) + args.input_dropout = getattr(args, "input_dropout", 0.1) + args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout) + + +@register_model_architecture("lightconv", "lightconv_iwslt_de_en") +def lightconv_iwslt_de_en(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 7) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.weight_dropout = getattr(args, "weight_dropout", 0.1) + args.encoder_glu = getattr(args, "encoder_glu", False) + args.decoder_glu = getattr(args, "decoder_glu", False) + args.input_dropout = getattr(args, "input_dropout", 0.0) + base_architecture(args) + + +@register_model_architecture("lightconv", "lightconv_wmt_en_de") +def lightconv_wmt_en_de(args): + base_architecture(args) + + +@register_model_architecture("lightconv", "lightconv_wmt_en_de_big") +def lightconv_wmt_en_de_big(args): + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) + base_architecture(args) + + +@register_model_architecture("lightconv", "lightconv_wmt_en_fr_big") +def lightconv_wmt_en_fr_big(args): + args.dropout = getattr(args, "dropout", 0.1) + lightconv_wmt_en_de_big(args) + + +@register_model_architecture("lightconv", "lightconv_wmt_zh_en_big") +def lightconv_wmt_zh_en_big(args): + args.dropout = getattr(args, "dropout", 0.2) + args.attention_dropout = getattr(args, "attention_dropout", 0.2) + args.weight_dropout = getattr(args, "weight_dropout", 0.2) + lightconv_wmt_en_de_big(args) diff --git a/fairseq/fairseq/models/lightconv_lm.py b/fairseq/fairseq/models/lightconv_lm.py new file mode 100644 index 0000000..1d9efc4 --- /dev/null +++ b/fairseq/fairseq/models/lightconv_lm.py @@ -0,0 +1,306 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq import utils +from fairseq.models import ( + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.lightconv import Embedding, LightConvDecoder +from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder + + +@register_model("lightconv_lm") +class LightConvLanguageModel(FairseqLanguageModel): + def __init__(self, decoder): + super().__init__(decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--dropout", + default=0.1, + type=float, + metavar="D", + help="dropout probability", + ) + parser.add_argument( + "--attention-dropout", + default=0.0, + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--relu-dropout", + default=0.0, + type=float, + metavar="D", + help="dropout probability after ReLU in FFN", + ) + parser.add_argument( + "--input-dropout", + type=float, + metavar="D", + help="dropout probability of the inputs", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-output-dim", + type=int, + metavar="N", + help="decoder output dimension", + ) + parser.add_argument( + "--decoder-input-dim", type=int, metavar="N", help="decoder input dimension" + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads or LightConv/DynamicConv heads", + ) + parser.add_argument( + "--decoder-normalize-before", + default=False, + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--adaptive-softmax-cutoff", + metavar="EXPR", + help="comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion", + ) + parser.add_argument( + "--adaptive-softmax-dropout", + type=float, + metavar="D", + help="sets adaptive softmax dropout for the tail projections", + ) + parser.add_argument( + "--adaptive-softmax-factor", + type=float, + metavar="N", + help="adaptive input factor", + ) + parser.add_argument( + "--no-token-positional-embeddings", + default=False, + action="store_true", + help="if set, disables positional embeddings (outside self attention)", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + default=False, + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--character-embeddings", + default=False, + action="store_true", + help="if set, uses character embedding convolutions to produce token embeddings", + ) + parser.add_argument( + "--character-filters", + type=str, + metavar="LIST", + default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", + help="size of character embeddings", + ) + parser.add_argument( + "--character-embedding-dim", + type=int, + metavar="N", + default=4, + help="size of character embeddings", + ) + parser.add_argument( + "--char-embedder-highway-layers", + type=int, + metavar="N", + default=2, + help="number of highway layers for character token embeddder", + ) + parser.add_argument( + "--adaptive-input", + default=False, + action="store_true", + help="if set, uses adaptive input", + ) + parser.add_argument( + "--adaptive-input-factor", + type=float, + metavar="N", + help="adaptive input factor", + ) + parser.add_argument( + "--adaptive-input-cutoff", + metavar="EXPR", + help="comma separated list of adaptive input cutoff points.", + ) + parser.add_argument( + "--tie-adaptive-weights", + action="store_true", + help="if set, ties the weights of adaptive softmax and adaptive input", + ) + parser.add_argument( + "--tie-adaptive-proj", + action="store_true", + help="if set, ties the projection weights of adaptive softmax and adaptive input", + ) + parser.add_argument( + "--decoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the decoder", + ) + + """LightConv and DynamicConv arguments""" + parser.add_argument( + "--decoder-kernel-size-list", + type=lambda x: utils.eval_str_list(x, int), + help='list of kernel size (default: "[3,7,15,31,31,31]")', + ) + parser.add_argument( + "--decoder-glu", type=utils.eval_bool, help="glu after in proj" + ) + parser.add_argument( + "--decoder-conv-type", + default="dynamic", + type=str, + choices=["dynamic", "lightweight"], + help="type of convolution", + ) + parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool) + parser.add_argument( + "--weight-dropout", + type=float, + metavar="D", + help="dropout probability for conv weights", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_lm_architecture(args) + + if getattr(args, "max_source_positions", None) is None: + args.max_source_positions = args.tokens_per_sample + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = args.tokens_per_sample + + if args.character_embeddings: + embed_tokens = CharacterTokenEmbedder( + task.dictionary, + eval(args.character_filters), + args.character_embedding_dim, + args.decoder_embed_dim, + args.char_embedder_highway_layers, + ) + elif args.adaptive_input: + embed_tokens = AdaptiveInput( + len(task.dictionary), + task.dictionary.pad(), + args.decoder_input_dim, + args.adaptive_input_factor, + args.decoder_embed_dim, + utils.eval_str_list(args.adaptive_input_cutoff, type=int), + ) + else: + embed_tokens = Embedding( + len(task.dictionary), args.decoder_input_dim, task.dictionary.pad() + ) + + if args.tie_adaptive_weights: + assert args.adaptive_input + assert args.adaptive_input_factor == args.adaptive_softmax_factor + assert ( + args.adaptive_softmax_cutoff == args.adaptive_input_cutoff + ), "{} != {}".format( + args.adaptive_softmax_cutoff, args.adaptive_input_cutoff + ) + assert args.decoder_input_dim == args.decoder_output_dim + + decoder = LightConvDecoder( + args, + task.output_dictionary, + embed_tokens, + no_encoder_attn=True, + final_norm=False, + ) + return LightConvLanguageModel(decoder) + + +@register_model_architecture("lightconv_lm", "lightconv_lm") +def base_lm_architecture(args): + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + + args.character_embeddings = getattr(args, "character_embeddings", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim) + + # The model training is not stable without this + args.decoder_normalize_before = True + + args.adaptive_input = getattr(args, "adaptive_input", False) + args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4) + args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None) + + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False) + + args.decoder_kernel_size_list = getattr( + args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31] + ) + if len(args.decoder_kernel_size_list) == 1: + args.decoder_kernel_size_list = ( + args.decoder_kernel_size_list * args.decoder_layers + ) + assert ( + len(args.decoder_kernel_size_list) == args.decoder_layers + ), "decoder_kernel_size_list doesn't match decoder_layers" + args.decoder_glu = getattr(args, "decoder_glu", True) + args.input_dropout = getattr(args, "input_dropout", 0.1) + args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout) + + +@register_model_architecture("lightconv_lm", "lightconv_lm_gbw") +def lightconv_lm_gbw(args): + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + base_lm_architecture(args) diff --git a/fairseq/fairseq/models/lstm.py b/fairseq/fairseq/models/lstm.py new file mode 100644 index 0000000..8a29156 --- /dev/null +++ b/fairseq/fairseq/models/lstm.py @@ -0,0 +1,755 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) +from fairseq.modules import AdaptiveSoftmax, FairseqDropout +from torch import Tensor + + +DEFAULT_MAX_SOURCE_POSITIONS = 1e5 +DEFAULT_MAX_TARGET_POSITIONS = 1e5 + + +@register_model("lstm") +class LSTMModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--dropout', type=float, metavar='D', + help='dropout probability') + parser.add_argument('--encoder-embed-dim', type=int, metavar='N', + help='encoder embedding dimension') + parser.add_argument('--encoder-embed-path', type=str, metavar='STR', + help='path to pre-trained encoder embedding') + parser.add_argument('--encoder-freeze-embed', action='store_true', + help='freeze encoder embeddings') + parser.add_argument('--encoder-hidden-size', type=int, metavar='N', + help='encoder hidden size') + parser.add_argument('--encoder-layers', type=int, metavar='N', + help='number of encoder layers') + parser.add_argument('--encoder-bidirectional', action='store_true', + help='make all layers of encoder bidirectional') + parser.add_argument('--decoder-embed-dim', type=int, metavar='N', + help='decoder embedding dimension') + parser.add_argument('--decoder-embed-path', type=str, metavar='STR', + help='path to pre-trained decoder embedding') + parser.add_argument('--decoder-freeze-embed', action='store_true', + help='freeze decoder embeddings') + parser.add_argument('--decoder-hidden-size', type=int, metavar='N', + help='decoder hidden size') + parser.add_argument('--decoder-layers', type=int, metavar='N', + help='number of decoder layers') + parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', + help='decoder output embedding dimension') + parser.add_argument('--decoder-attention', type=str, metavar='BOOL', + help='decoder attention') + parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', + help='comma separated list of adaptive softmax cutoff points. ' + 'Must be used with adaptive_loss criterion') + parser.add_argument('--share-decoder-input-output-embed', default=False, + action='store_true', + help='share decoder input and output embeddings') + parser.add_argument('--share-all-embeddings', default=False, action='store_true', + help='share encoder, decoder and output embeddings' + ' (requires shared dictionary and embed dim)') + + # Granular dropout settings (if not specified these default to --dropout) + parser.add_argument('--encoder-dropout-in', type=float, metavar='D', + help='dropout probability for encoder input embedding') + parser.add_argument('--encoder-dropout-out', type=float, metavar='D', + help='dropout probability for encoder output') + parser.add_argument('--decoder-dropout-in', type=float, metavar='D', + help='dropout probability for decoder input embedding') + parser.add_argument('--decoder-dropout-out', type=float, metavar='D', + help='dropout probability for decoder output') + # fmt: on + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted (in case there are any new ones) + base_architecture(args) + + if args.encoder_layers != args.decoder_layers: + raise ValueError("--encoder-layers must match --decoder-layers") + + max_source_positions = getattr( + args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS + ) + max_target_positions = getattr( + args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS + ) + + def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + embed_dict = utils.parse_embedding(embed_path) + utils.print_embed_overlap(embed_dict, dictionary) + return utils.load_embedding(embed_dict, dictionary, embed_tokens) + + if args.encoder_embed_path: + pretrained_encoder_embed = load_pretrained_embedding_from_file( + args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim + ) + else: + num_embeddings = len(task.source_dictionary) + pretrained_encoder_embed = Embedding( + num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad() + ) + + if args.share_all_embeddings: + # double check all parameters combinations are valid + if task.source_dictionary != task.target_dictionary: + raise ValueError("--share-all-embeddings requires a joint dictionary") + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embed not compatible with --decoder-embed-path" + ) + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to " + "match --decoder-embed-dim" + ) + pretrained_decoder_embed = pretrained_encoder_embed + args.share_decoder_input_output_embed = True + else: + # separate decoder input embeddings + pretrained_decoder_embed = None + if args.decoder_embed_path: + pretrained_decoder_embed = load_pretrained_embedding_from_file( + args.decoder_embed_path, + task.target_dictionary, + args.decoder_embed_dim, + ) + # one last double check of parameter combinations + if args.share_decoder_input_output_embed and ( + args.decoder_embed_dim != args.decoder_out_embed_dim + ): + raise ValueError( + "--share-decoder-input-output-embeddings requires " + "--decoder-embed-dim to match --decoder-out-embed-dim" + ) + + if args.encoder_freeze_embed: + pretrained_encoder_embed.weight.requires_grad = False + if args.decoder_freeze_embed: + pretrained_decoder_embed.weight.requires_grad = False + + encoder = LSTMEncoder( + dictionary=task.source_dictionary, + embed_dim=args.encoder_embed_dim, + hidden_size=args.encoder_hidden_size, + num_layers=args.encoder_layers, + dropout_in=args.encoder_dropout_in, + dropout_out=args.encoder_dropout_out, + bidirectional=args.encoder_bidirectional, + pretrained_embed=pretrained_encoder_embed, + max_source_positions=max_source_positions, + ) + decoder = LSTMDecoder( + dictionary=task.target_dictionary, + embed_dim=args.decoder_embed_dim, + hidden_size=args.decoder_hidden_size, + out_embed_dim=args.decoder_out_embed_dim, + num_layers=args.decoder_layers, + dropout_in=args.decoder_dropout_in, + dropout_out=args.decoder_dropout_out, + attention=utils.eval_bool(args.decoder_attention), + encoder_output_units=encoder.output_units, + pretrained_embed=pretrained_decoder_embed, + share_input_output_embed=args.share_decoder_input_output_embed, + adaptive_softmax_cutoff=( + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) + if args.criterion == "adaptive_loss" + else None + ), + max_target_positions=max_target_positions, + residuals=False, + ) + return cls(encoder, decoder) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths) + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + ) + return decoder_out + + +class LSTMEncoder(FairseqEncoder): + """LSTM encoder.""" + + def __init__( + self, + dictionary, + embed_dim=512, + hidden_size=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + bidirectional=False, + left_pad=True, + pretrained_embed=None, + padding_idx=None, + max_source_positions=DEFAULT_MAX_SOURCE_POSITIONS, + ): + super().__init__(dictionary) + self.num_layers = num_layers + self.dropout_in_module = FairseqDropout( + dropout_in * 1.0, module_name=self.__class__.__name__ + ) + self.dropout_out_module = FairseqDropout( + dropout_out * 1.0, module_name=self.__class__.__name__ + ) + self.bidirectional = bidirectional + self.hidden_size = hidden_size + self.max_source_positions = max_source_positions + + num_embeddings = len(dictionary) + self.padding_idx = padding_idx if padding_idx is not None else dictionary.pad() + if pretrained_embed is None: + self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) + else: + self.embed_tokens = pretrained_embed + + self.lstm = LSTM( + input_size=embed_dim, + hidden_size=hidden_size, + num_layers=num_layers, + dropout=self.dropout_out_module.p if num_layers > 1 else 0.0, + bidirectional=bidirectional, + ) + self.left_pad = left_pad + + self.output_units = hidden_size + if bidirectional: + self.output_units *= 2 + + def forward( + self, + src_tokens: Tensor, + src_lengths: Tensor, + enforce_sorted: bool = True, + ): + """ + Args: + src_tokens (LongTensor): tokens in the source language of + shape `(batch, src_len)` + src_lengths (LongTensor): lengths of each source sentence of + shape `(batch)` + enforce_sorted (bool, optional): if True, `src_tokens` is + expected to contain sequences sorted by length in a + decreasing order. If False, this condition is not + required. Default: True. + """ + if self.left_pad: + # nn.utils.rnn.pack_padded_sequence requires right-padding; + # convert left-padding to right-padding + src_tokens = utils.convert_padding_direction( + src_tokens, + torch.zeros_like(src_tokens).fill_(self.padding_idx), + left_to_right=True, + ) + + bsz, seqlen = src_tokens.size() + + # embed tokens + x = self.embed_tokens(src_tokens) + x = self.dropout_in_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # pack embedded source tokens into a PackedSequence + packed_x = nn.utils.rnn.pack_padded_sequence( + x, src_lengths.cpu(), enforce_sorted=enforce_sorted + ) + + # apply LSTM + if self.bidirectional: + state_size = 2 * self.num_layers, bsz, self.hidden_size + else: + state_size = self.num_layers, bsz, self.hidden_size + h0 = x.new_zeros(*state_size) + c0 = x.new_zeros(*state_size) + packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0)) + + # unpack outputs and apply dropout + x, _ = nn.utils.rnn.pad_packed_sequence( + packed_outs, padding_value=self.padding_idx * 1.0 + ) + x = self.dropout_out_module(x) + assert list(x.size()) == [seqlen, bsz, self.output_units] + + if self.bidirectional: + final_hiddens = self.combine_bidir(final_hiddens, bsz) + final_cells = self.combine_bidir(final_cells, bsz) + + encoder_padding_mask = src_tokens.eq(self.padding_idx).t() + + return tuple( + ( + x, # seq_len x batch x hidden + final_hiddens, # num_layers x batch x num_directions*hidden + final_cells, # num_layers x batch x num_directions*hidden + encoder_padding_mask, # seq_len x batch + ) + ) + + def combine_bidir(self, outs, bsz: int): + out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous() + return out.view(self.num_layers, bsz, -1) + + def reorder_encoder_out( + self, encoder_out: Tuple[Tensor, Tensor, Tensor, Tensor], new_order + ): + return tuple( + ( + encoder_out[0].index_select(1, new_order), + encoder_out[1].index_select(1, new_order), + encoder_out[2].index_select(1, new_order), + encoder_out[3].index_select(1, new_order), + ) + ) + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return self.max_source_positions + + +class AttentionLayer(nn.Module): + def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False): + super().__init__() + + self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias) + self.output_proj = Linear( + input_embed_dim + source_embed_dim, output_embed_dim, bias=bias + ) + + def forward(self, input, source_hids, encoder_padding_mask): + # input: bsz x input_embed_dim + # source_hids: srclen x bsz x source_embed_dim + + # x: bsz x source_embed_dim + x = self.input_proj(input) + + # compute attention + attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2) + + # don't attend over padding + if encoder_padding_mask is not None: + attn_scores = ( + attn_scores.float() + .masked_fill_(encoder_padding_mask, float("-inf")) + .type_as(attn_scores) + ) # FP16 support: cast to float and back + + attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz + + # sum weighted sources + x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0) + + x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1))) + return x, attn_scores + + +class LSTMDecoder(FairseqIncrementalDecoder): + """LSTM decoder.""" + + def __init__( + self, + dictionary, + embed_dim=512, + hidden_size=512, + out_embed_dim=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + attention=True, + encoder_output_units=512, + pretrained_embed=None, + share_input_output_embed=False, + adaptive_softmax_cutoff=None, + max_target_positions=DEFAULT_MAX_TARGET_POSITIONS, + residuals=False, + ): + super().__init__(dictionary) + self.dropout_in_module = FairseqDropout( + dropout_in * 1.0, module_name=self.__class__.__name__ + ) + self.dropout_out_module = FairseqDropout( + dropout_out * 1.0, module_name=self.__class__.__name__ + ) + self.hidden_size = hidden_size + self.share_input_output_embed = share_input_output_embed + self.need_attn = True + self.max_target_positions = max_target_positions + self.residuals = residuals + self.num_layers = num_layers + + self.adaptive_softmax = None + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + if pretrained_embed is None: + self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + else: + self.embed_tokens = pretrained_embed + + self.encoder_output_units = encoder_output_units + if encoder_output_units != hidden_size and encoder_output_units != 0: + self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size) + self.encoder_cell_proj = Linear(encoder_output_units, hidden_size) + else: + self.encoder_hidden_proj = self.encoder_cell_proj = None + + # disable input feeding if there is no encoder + # input feeding is described in arxiv.org/abs/1508.04025 + input_feed_size = 0 if encoder_output_units == 0 else hidden_size + self.layers = nn.ModuleList( + [ + LSTMCell( + input_size=input_feed_size + embed_dim + if layer == 0 + else hidden_size, + hidden_size=hidden_size, + ) + for layer in range(num_layers) + ] + ) + + if attention: + # TODO make bias configurable + self.attention = AttentionLayer( + hidden_size, encoder_output_units, hidden_size, bias=False + ) + else: + self.attention = None + + if hidden_size != out_embed_dim: + self.additional_fc = Linear(hidden_size, out_embed_dim) + + if adaptive_softmax_cutoff is not None: + # setting adaptive_softmax dropout to dropout_out for now but can be redefined + self.adaptive_softmax = AdaptiveSoftmax( + num_embeddings, + hidden_size, + adaptive_softmax_cutoff, + dropout=dropout_out, + ) + elif not self.share_input_output_embed: + self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out) + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + src_lengths: Optional[Tensor] = None, + ): + x, attn_scores = self.extract_features( + prev_output_tokens, encoder_out, incremental_state + ) + return self.output_layer(x), attn_scores + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + """ + Similar to *forward* but only return features. + """ + # get outputs from encoder + if encoder_out is not None: + encoder_outs = encoder_out[0] + encoder_hiddens = encoder_out[1] + encoder_cells = encoder_out[2] + encoder_padding_mask = encoder_out[3] + else: + encoder_outs = torch.empty(0) + encoder_hiddens = torch.empty(0) + encoder_cells = torch.empty(0) + encoder_padding_mask = torch.empty(0) + srclen = encoder_outs.size(0) + + if incremental_state is not None and len(incremental_state) > 0: + prev_output_tokens = prev_output_tokens[:, -1:] + + bsz, seqlen = prev_output_tokens.size() + + # embed tokens + x = self.embed_tokens(prev_output_tokens) + x = self.dropout_in_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # initialize previous states (or get from cache during incremental generation) + if incremental_state is not None and len(incremental_state) > 0: + prev_hiddens, prev_cells, input_feed = self.get_cached_state( + incremental_state + ) + elif encoder_out is not None: + # setup recurrent cells + prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)] + prev_cells = [encoder_cells[i] for i in range(self.num_layers)] + if self.encoder_hidden_proj is not None: + prev_hiddens = [self.encoder_hidden_proj(y) for y in prev_hiddens] + prev_cells = [self.encoder_cell_proj(y) for y in prev_cells] + input_feed = x.new_zeros(bsz, self.hidden_size) + else: + # setup zero cells, since there is no encoder + zero_state = x.new_zeros(bsz, self.hidden_size) + prev_hiddens = [zero_state for i in range(self.num_layers)] + prev_cells = [zero_state for i in range(self.num_layers)] + input_feed = None + + assert ( + srclen > 0 or self.attention is None + ), "attention is not supported if there are no encoder outputs" + attn_scores: Optional[Tensor] = ( + x.new_zeros(srclen, seqlen, bsz) if self.attention is not None else None + ) + outs = [] + for j in range(seqlen): + # input feeding: concatenate context vector from previous time step + if input_feed is not None: + input = torch.cat((x[j, :, :], input_feed), dim=1) + else: + input = x[j] + + for i, rnn in enumerate(self.layers): + # recurrent cell + hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i])) + + # hidden state becomes the input to the next layer + input = self.dropout_out_module(hidden) + if self.residuals: + input = input + prev_hiddens[i] + + # save state for next time step + prev_hiddens[i] = hidden + prev_cells[i] = cell + + # apply attention using the last layer's hidden state + if self.attention is not None: + assert attn_scores is not None + out, attn_scores[:, j, :] = self.attention( + hidden, encoder_outs, encoder_padding_mask + ) + else: + out = hidden + out = self.dropout_out_module(out) + + # input feeding + if input_feed is not None: + input_feed = out + + # save final output + outs.append(out) + + # Stack all the necessary tensors together and store + prev_hiddens_tensor = torch.stack(prev_hiddens) + prev_cells_tensor = torch.stack(prev_cells) + cache_state = torch.jit.annotate( + Dict[str, Optional[Tensor]], + { + "prev_hiddens": prev_hiddens_tensor, + "prev_cells": prev_cells_tensor, + "input_feed": input_feed, + }, + ) + self.set_incremental_state(incremental_state, "cached_state", cache_state) + + # collect outputs across time steps + x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) + + # T x B x C -> B x T x C + x = x.transpose(1, 0) + + if hasattr(self, "additional_fc") and self.adaptive_softmax is None: + x = self.additional_fc(x) + x = self.dropout_out_module(x) + # srclen x tgtlen x bsz -> bsz x tgtlen x srclen + if not self.training and self.need_attn and self.attention is not None: + assert attn_scores is not None + attn_scores = attn_scores.transpose(0, 2) + else: + attn_scores = None + return x, attn_scores + + def output_layer(self, x): + """Project features to the vocabulary size.""" + if self.adaptive_softmax is None: + if self.share_input_output_embed: + x = F.linear(x, self.embed_tokens.weight) + else: + x = self.fc_out(x) + return x + + def get_cached_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + ) -> Tuple[List[Tensor], List[Tensor], Optional[Tensor]]: + cached_state = self.get_incremental_state(incremental_state, "cached_state") + assert cached_state is not None + prev_hiddens_ = cached_state["prev_hiddens"] + assert prev_hiddens_ is not None + prev_cells_ = cached_state["prev_cells"] + assert prev_cells_ is not None + prev_hiddens = [prev_hiddens_[i] for i in range(self.num_layers)] + prev_cells = [prev_cells_[j] for j in range(self.num_layers)] + input_feed = cached_state[ + "input_feed" + ] # can be None for decoder-only language models + return prev_hiddens, prev_cells, input_feed + + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): + if incremental_state is None or len(incremental_state) == 0: + return + prev_hiddens, prev_cells, input_feed = self.get_cached_state(incremental_state) + prev_hiddens = [p.index_select(0, new_order) for p in prev_hiddens] + prev_cells = [p.index_select(0, new_order) for p in prev_cells] + if input_feed is not None: + input_feed = input_feed.index_select(0, new_order) + cached_state_new = torch.jit.annotate( + Dict[str, Optional[Tensor]], + { + "prev_hiddens": torch.stack(prev_hiddens), + "prev_cells": torch.stack(prev_cells), + "input_feed": input_feed, + }, + ) + self.set_incremental_state(incremental_state, "cached_state", cached_state_new), + return + + def max_positions(self): + """Maximum output length supported by the decoder.""" + return self.max_target_positions + + def make_generation_fast_(self, need_attn=False, **kwargs): + self.need_attn = need_attn + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.uniform_(m.weight, -0.1, 0.1) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def LSTM(input_size, hidden_size, **kwargs): + m = nn.LSTM(input_size, hidden_size, **kwargs) + for name, param in m.named_parameters(): + if "weight" in name or "bias" in name: + param.data.uniform_(-0.1, 0.1) + return m + + +def LSTMCell(input_size, hidden_size, **kwargs): + m = nn.LSTMCell(input_size, hidden_size, **kwargs) + for name, param in m.named_parameters(): + if "weight" in name or "bias" in name: + param.data.uniform_(-0.1, 0.1) + return m + + +def Linear(in_features, out_features, bias=True, dropout=0.0): + """Linear layer (input: N x T x C)""" + m = nn.Linear(in_features, out_features, bias=bias) + m.weight.data.uniform_(-0.1, 0.1) + if bias: + m.bias.data.uniform_(-0.1, 0.1) + return m + + +@register_model_architecture("lstm", "lstm") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_freeze_embed = getattr(args, "encoder_freeze_embed", False) + args.encoder_hidden_size = getattr( + args, "encoder_hidden_size", args.encoder_embed_dim + ) + args.encoder_layers = getattr(args, "encoder_layers", 1) + args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False) + args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_freeze_embed = getattr(args, "decoder_freeze_embed", False) + args.decoder_hidden_size = getattr( + args, "decoder_hidden_size", args.decoder_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 1) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + args.decoder_attention = getattr(args, "decoder_attention", "1") + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,50000,200000" + ) + + +@register_model_architecture("lstm", "lstm_wiseman_iwslt_de_en") +def lstm_wiseman_iwslt_de_en(args): + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_dropout_in = getattr(args, "encoder_dropout_in", 0) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", 0) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) + base_architecture(args) + + +@register_model_architecture("lstm", "lstm_luong_wmt_en_de") +def lstm_luong_wmt_en_de(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1000) + args.encoder_layers = getattr(args, "encoder_layers", 4) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1000) + args.decoder_layers = getattr(args, "decoder_layers", 4) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 1000) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", 0) + base_architecture(args) diff --git a/fairseq/fairseq/models/lstm_lm.py b/fairseq/fairseq/models/lstm_lm.py new file mode 100644 index 0000000..454f0ac --- /dev/null +++ b/fairseq/fairseq/models/lstm_lm.py @@ -0,0 +1,142 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq import utils +from fairseq.models import ( + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.lstm import Embedding, LSTMDecoder + + +DEFAULT_MAX_TARGET_POSITIONS = 1e5 + + +@register_model("lstm_lm") +class LSTMLanguageModel(FairseqLanguageModel): + def __init__(self, decoder): + super().__init__(decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--dropout', type=float, metavar='D', + help='dropout probability') + parser.add_argument('--decoder-embed-dim', type=int, metavar='N', + help='decoder embedding dimension') + parser.add_argument('--decoder-embed-path', type=str, metavar='STR', + help='path to pre-trained decoder embedding') + parser.add_argument('--decoder-hidden-size', type=int, metavar='N', + help='decoder hidden size') + parser.add_argument('--decoder-layers', type=int, metavar='N', + help='number of decoder layers') + parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', + help='decoder output embedding dimension') + parser.add_argument('--decoder-attention', type=str, metavar='BOOL', + help='decoder attention') + parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', + help='comma separated list of adaptive softmax cutoff points. ' + 'Must be used with adaptive_loss criterion') + parser.add_argument('--residuals', default=False, + action='store_true', + help='applying residuals between LSTM layers') + + # Granular dropout settings (if not specified these default to --dropout) + parser.add_argument('--decoder-dropout-in', type=float, metavar='D', + help='dropout probability for decoder input embedding') + parser.add_argument('--decoder-dropout-out', type=float, metavar='D', + help='dropout probability for decoder output') + parser.add_argument('--share-decoder-input-output-embed', default=False, + action='store_true', + help='share decoder input and output embeddings') + # fmt: on + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if getattr(args, "max_target_positions", None) is not None: + max_target_positions = args.max_target_positions + else: + max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) + + def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + embed_dict = utils.parse_embedding(embed_path) + utils.print_embed_overlap(embed_dict, dictionary) + return utils.load_embedding(embed_dict, dictionary, embed_tokens) + + pretrained_decoder_embed = None + if args.decoder_embed_path: + pretrained_decoder_embed = load_pretrained_embedding_from_file( + args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim + ) + + if args.share_decoder_input_output_embed: + # double check all parameters combinations are valid + if task.source_dictionary != task.target_dictionary: + raise ValueError( + "--share-decoder-input-output-embeddings requires a joint dictionary" + ) + + if args.decoder_embed_dim != args.decoder_out_embed_dim: + raise ValueError( + "--share-decoder-input-output-embeddings requires " + "--decoder-embed-dim to match --decoder-out-embed-dim" + ) + + decoder = LSTMDecoder( + dictionary=task.dictionary, + embed_dim=args.decoder_embed_dim, + hidden_size=args.decoder_hidden_size, + out_embed_dim=args.decoder_out_embed_dim, + num_layers=args.decoder_layers, + dropout_in=args.decoder_dropout_in, + dropout_out=args.decoder_dropout_out, + attention=False, # decoder-only language model doesn't support attention + encoder_output_units=0, + pretrained_embed=pretrained_decoder_embed, + share_input_output_embed=args.share_decoder_input_output_embed, + adaptive_softmax_cutoff=( + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) + if args.criterion == "adaptive_loss" + else None + ), + max_target_positions=max_target_positions, + residuals=args.residuals, + ) + + return cls(decoder) + + +@register_model_architecture("lstm_lm", "lstm_lm") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_hidden_size = getattr( + args, "decoder_hidden_size", args.decoder_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 1) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + args.decoder_attention = getattr(args, "decoder_attention", "0") + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,50000,200000" + ) + args.residuals = getattr(args, "residuals", False) diff --git a/fairseq/fairseq/models/masked_lm.py b/fairseq/fairseq/models/masked_lm.py new file mode 100644 index 0000000..b71254c --- /dev/null +++ b/fairseq/fairseq/models/masked_lm.py @@ -0,0 +1,398 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderModel, + register_model, + register_model_architecture, +) +from fairseq.modules import ( + LayerNorm, + SinusoidalPositionalEmbedding, + TransformerSentenceEncoder, +) +from fairseq.modules.transformer_sentence_encoder import init_bert_params +from fairseq.utils import safe_hasattr + + +logger = logging.getLogger(__name__) + + +@register_model("masked_lm") +class MaskedLMModel(FairseqEncoderModel): + """ + Class for training a Masked Language Model. It also supports an + additional sentence level prediction if the sent-loss argument is set. + """ + + def __init__(self, args, encoder): + super().__init__(encoder) + self.args = args + + # if specified then apply bert initialization on the model. We need + # to explictly call this to make sure that the output embeddings + # and projection layers are also correctly initialized + if getattr(args, "apply_bert_init", False): + self.apply(init_bert_params) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # Arguments related to dropout + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for" " attention weights", + ) + parser.add_argument( + "--act-dropout", + type=float, + metavar="D", + help="dropout probability after" " activation in FFN", + ) + + # Arguments related to hidden states and self-attention + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + + # Arguments related to input and output embeddings + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--share-encoder-input-output-embed", + action="store_true", + help="share encoder input" " and output embeddings", + ) + parser.add_argument( + "--encoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the encoder", + ) + parser.add_argument( + "--no-token-positional-embeddings", + action="store_true", + help="if set, disables positional embeddings" " (outside self attention)", + ) + parser.add_argument( + "--num-segment", type=int, metavar="N", help="num segment in the input" + ) + parser.add_argument( + "--max-positions", type=int, help="number of positional embeddings to learn" + ) + + # Arguments related to sentence level prediction + parser.add_argument( + "--sentence-class-num", + type=int, + metavar="N", + help="number of classes for sentence task", + ) + parser.add_argument( + "--sent-loss", + action="store_true", + help="if set," " calculate sentence level predictions", + ) + + # Arguments related to parameter initialization + parser.add_argument( + "--apply-bert-init", + action="store_true", + help="use custom param initialization for BERT", + ) + + # misc params + parser.add_argument( + "--activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--pooler-activation-fn", + choices=utils.get_available_activation_fns(), + help="Which activation function to use for pooler layer.", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + + def forward(self, src_tokens, segment_labels=None, **kwargs): + return self.encoder(src_tokens, segment_labels=segment_labels, **kwargs) + + def max_positions(self): + return self.encoder.max_positions + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure all arguments are present in older models + base_architecture(args) + + if not safe_hasattr(args, "max_positions"): + args.max_positions = args.tokens_per_sample + + logger.info(args) + + encoder = MaskedLMEncoder(args, task.dictionary) + return cls(args, encoder) + + +class MaskedLMEncoder(FairseqEncoder): + """ + Encoder for Masked Language Modelling. + """ + + def __init__(self, args, dictionary): + super().__init__(dictionary) + + self.padding_idx = dictionary.pad() + self.vocab_size = dictionary.__len__() + self.max_positions = args.max_positions + + self.sentence_encoder = TransformerSentenceEncoder( + padding_idx=self.padding_idx, + vocab_size=self.vocab_size, + num_encoder_layers=args.encoder_layers, + embedding_dim=args.encoder_embed_dim, + ffn_embedding_dim=args.encoder_ffn_embed_dim, + num_attention_heads=args.encoder_attention_heads, + dropout=args.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.act_dropout, + max_seq_len=self.max_positions, + num_segments=args.num_segment, + use_position_embeddings=not args.no_token_positional_embeddings, + encoder_normalize_before=args.encoder_normalize_before, + apply_bert_init=args.apply_bert_init, + activation_fn=args.activation_fn, + learned_pos_embedding=args.encoder_learned_pos, + ) + + self.share_input_output_embed = args.share_encoder_input_output_embed + self.embed_out = None + self.sentence_projection_layer = None + self.sentence_out_dim = args.sentence_class_num + self.lm_output_learned_bias = None + + # Remove head is set to true during fine-tuning + self.load_softmax = not getattr(args, "remove_head", False) + + self.masked_lm_pooler = nn.Linear( + args.encoder_embed_dim, args.encoder_embed_dim + ) + self.pooler_activation = utils.get_activation_fn(args.pooler_activation_fn) + + self.lm_head_transform_weight = nn.Linear( + args.encoder_embed_dim, args.encoder_embed_dim + ) + self.activation_fn = utils.get_activation_fn(args.activation_fn) + self.layer_norm = LayerNorm(args.encoder_embed_dim) + + self.lm_output_learned_bias = None + if self.load_softmax: + self.lm_output_learned_bias = nn.Parameter(torch.zeros(self.vocab_size)) + + if not self.share_input_output_embed: + self.embed_out = nn.Linear( + args.encoder_embed_dim, self.vocab_size, bias=False + ) + + if args.sent_loss: + self.sentence_projection_layer = nn.Linear( + args.encoder_embed_dim, self.sentence_out_dim, bias=False + ) + + def forward(self, src_tokens, segment_labels=None, masked_tokens=None, **unused): + """ + Forward pass for Masked LM encoder. This first computes the token + embedding using the token embedding matrix, position embeddings (if + specified) and segment embeddings (if specified). + + Here we assume that the sentence representation corresponds to the + output of the classification_token (see bert_task or cross_lingual_lm + task for more details). + Args: + - src_tokens: B x T matrix representing sentences + - segment_labels: B x T matrix representing segment label for tokens + Returns: + - a tuple of the following: + - logits for predictions in format B x T x C to be used in + softmax afterwards + - a dictionary of additional data, where 'pooled_output' contains + the representation for classification_token and 'inner_states' + is a list of internal model states used to compute the + predictions (similar in ELMO). 'sentence_logits' + is the prediction logit for NSP task and is only computed if + this is specified in the input arguments. + """ + + inner_states, sentence_rep = self.sentence_encoder( + src_tokens, + segment_labels=segment_labels, + ) + + x = inner_states[-1].transpose(0, 1) + # project masked tokens only + if masked_tokens is not None: + x = x[masked_tokens, :] + x = self.layer_norm(self.activation_fn(self.lm_head_transform_weight(x))) + + pooled_output = self.pooler_activation(self.masked_lm_pooler(sentence_rep)) + + # project back to size of vocabulary + if self.share_input_output_embed and hasattr( + self.sentence_encoder.embed_tokens, "weight" + ): + x = F.linear(x, self.sentence_encoder.embed_tokens.weight) + elif self.embed_out is not None: + x = self.embed_out(x) + if self.lm_output_learned_bias is not None: + x = x + self.lm_output_learned_bias + sentence_logits = None + if self.sentence_projection_layer: + sentence_logits = self.sentence_projection_layer(pooled_output) + + return x, { + "inner_states": inner_states, + "pooled_output": pooled_output, + "sentence_logits": sentence_logits, + } + + def max_positions(self): + """Maximum output length supported by the encoder.""" + return self.max_positions + + def upgrade_state_dict_named(self, state_dict, name): + if not self.load_softmax: + for k in list(state_dict.keys()): + if ( + "embed_out.weight" in k + or "sentence_projection_layer.weight" in k + or "lm_output_learned_bias" in k + ): + del state_dict[k] + return state_dict + + +@register_model_architecture("masked_lm", "masked_lm") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.act_dropout = getattr(args, "act_dropout", 0.0) + + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.share_encoder_input_output_embed = getattr( + args, "share_encoder_input_output_embed", False + ) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.num_segment = getattr(args, "num_segment", 2) + + args.sentence_class_num = getattr(args, "sentence_class_num", 2) + args.sent_loss = getattr(args, "sent_loss", False) + + args.apply_bert_init = getattr(args, "apply_bert_init", False) + + args.activation_fn = getattr(args, "activation_fn", "relu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + + +@register_model_architecture("masked_lm", "bert_base") +def bert_base_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.share_encoder_input_output_embed = getattr( + args, "share_encoder_input_output_embed", True + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + args.num_segment = getattr(args, "num_segment", 2) + + args.encoder_layers = getattr(args, "encoder_layers", 12) + + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 3072) + + args.sentence_class_num = getattr(args, "sentence_class_num", 2) + args.sent_loss = getattr(args, "sent_loss", True) + + args.apply_bert_init = getattr(args, "apply_bert_init", True) + + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + base_architecture(args) + + +@register_model_architecture("masked_lm", "bert_large") +def bert_large_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_layers = getattr(args, "encoder_layers", 24) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + bert_base_architecture(args) + + +@register_model_architecture("masked_lm", "xlm_base") +def xlm_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.share_encoder_input_output_embed = getattr( + args, "share_encoder_input_output_embed", True + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + args.num_segment = getattr(args, "num_segment", 1) + + args.encoder_layers = getattr(args, "encoder_layers", 6) + + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + + args.sent_loss = getattr(args, "sent_loss", False) + + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.apply_bert_init = getattr(args, "apply_bert_init", True) + base_architecture(args) diff --git a/fairseq/fairseq/models/model_utils.py b/fairseq/fairseq/models/model_utils.py new file mode 100644 index 0000000..732d66b --- /dev/null +++ b/fairseq/fairseq/models/model_utils.py @@ -0,0 +1,92 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Optional + +import torch +from torch import Tensor + + +@torch.jit.script +def script_skip_tensor_list(x: List[Tensor], mask): + res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x] + outputs = [] + for i, t in enumerate(res): + if t.numel() != 0: + outputs.append(t) + else: + outputs.append(x[i]) + return outputs + + +@torch.jit.script +def script_skip_tensor(x: Tensor, mask): + # None case + if x.size(0) == 0: + return x + res = x[mask] if x.size(0) == mask.size(0) else x[:, mask] + if res.numel() == 0: + return x + else: + return res + + +@torch.jit.script +def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int): + """ + Expand 2D/3D tensor on dim=1 + """ + if x is None: + return None + + assert x.dim() == 2 or x.dim() == 3 + assert trg_dim >= x.size(1), (trg_dim, x.size()) + if trg_dim == x.size(1): + return x + + dims = [x.size(0), trg_dim - x.size(1)] + if x.dim() == 3: + dims.append(x.size(2)) + x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1) + + return x + + +@torch.jit.script +def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor: + return x if x is not None else y + + +@torch.jit.script +def fill_tensors( + x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int +) -> Optional[Tensor]: + """ + Filling tensor x with y at masked positions (dim=0). + """ + if x is None or x.size()[0] == 0 or y is None: + return x + assert x.dim() == y.dim() and mask.size(0) == x.size(0) + assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2)) + + n_selected = mask.sum() + if n_selected == 0: + return x + assert n_selected == y.size(0) + if n_selected == x.size(0): + return y + + if x.size(1) < y.size(1): + x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx) + x[mask] = y + elif x.size(1) > y.size(1): + x[mask] = torch.tensor(padding_idx).type_as(x) + if x.dim() == 2: + x[mask, : y.size(1)] = y + else: + x[mask, : y.size(1), :] = y + else: + x[mask] = y + return x diff --git a/fairseq/fairseq/models/multilingual_transformer.py b/fairseq/fairseq/models/multilingual_transformer.py new file mode 100644 index 0000000..e722b64 --- /dev/null +++ b/fairseq/fairseq/models/multilingual_transformer.py @@ -0,0 +1,229 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections import OrderedDict + +from fairseq import utils +from fairseq.models import ( + FairseqMultiModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import ( + Embedding, + TransformerDecoder, + TransformerEncoder, + TransformerModel, + base_architecture, +) +from fairseq.utils import safe_hasattr + + +@register_model("multilingual_transformer") +class MultilingualTransformerModel(FairseqMultiModel): + """Train Transformer models for multiple language pairs simultaneously. + + Requires `--task multilingual_translation`. + + We inherit all arguments from TransformerModel and assume that all language + pairs use a single Transformer architecture. In addition, we provide several + options that are specific to the multilingual setting. + + Args: + --share-encoder-embeddings: share encoder embeddings across all source languages + --share-decoder-embeddings: share decoder embeddings across all target languages + --share-encoders: share all encoder params (incl. embeddings) across all source languages + --share-decoders: share all decoder params (incl. embeddings) across all target languages + """ + + def __init__(self, encoders, decoders): + super().__init__(encoders, decoders) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + TransformerModel.add_args(parser) + parser.add_argument( + "--share-encoder-embeddings", + action="store_true", + help="share encoder embeddings across languages", + ) + parser.add_argument( + "--share-decoder-embeddings", + action="store_true", + help="share decoder embeddings across languages", + ) + parser.add_argument( + "--share-encoders", + action="store_true", + help="share encoders across languages", + ) + parser.add_argument( + "--share-decoders", + action="store_true", + help="share decoders across languages", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + from fairseq.tasks.multilingual_translation import MultilingualTranslationTask + + assert isinstance(task, MultilingualTranslationTask) + + # make sure all arguments are present in older models + base_multilingual_architecture(args) + + if not safe_hasattr(args, "max_source_positions"): + args.max_source_positions = 1024 + if not safe_hasattr(args, "max_target_positions"): + args.max_target_positions = 1024 + + src_langs = [lang_pair.split("-")[0] for lang_pair in task.model_lang_pairs] + tgt_langs = [lang_pair.split("-")[1] for lang_pair in task.model_lang_pairs] + + if args.share_encoders: + args.share_encoder_embeddings = True + if args.share_decoders: + args.share_decoder_embeddings = True + + def build_embedding(dictionary, embed_dim, path=None): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + emb = Embedding(num_embeddings, embed_dim, padding_idx) + # if provided, load from preloaded dictionaries + if path: + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + return emb + + # build shared embeddings (if applicable) + shared_encoder_embed_tokens, shared_decoder_embed_tokens = None, None + if args.share_all_embeddings: + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=task.langs, + embed_dim=args.encoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.encoder_embed_path, + ) + shared_decoder_embed_tokens = shared_encoder_embed_tokens + args.share_decoder_input_output_embed = True + else: + if args.share_encoder_embeddings: + shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=src_langs, + embed_dim=args.encoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.encoder_embed_path, + ) + if args.share_decoder_embeddings: + shared_decoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=tgt_langs, + embed_dim=args.decoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.decoder_embed_path, + ) + + # encoders/decoders for each language + lang_encoders, lang_decoders = {}, {} + + def get_encoder(lang): + if lang not in lang_encoders: + if shared_encoder_embed_tokens is not None: + encoder_embed_tokens = shared_encoder_embed_tokens + else: + encoder_embed_tokens = build_embedding( + task.dicts[lang], + args.encoder_embed_dim, + args.encoder_embed_path, + ) + lang_encoders[lang] = cls._get_module_class( + True, args, task.dicts[lang], encoder_embed_tokens, src_langs + ) + return lang_encoders[lang] + + def get_decoder(lang): + if lang not in lang_decoders: + if shared_decoder_embed_tokens is not None: + decoder_embed_tokens = shared_decoder_embed_tokens + else: + decoder_embed_tokens = build_embedding( + task.dicts[lang], + args.decoder_embed_dim, + args.decoder_embed_path, + ) + lang_decoders[lang] = cls._get_module_class( + False, args, task.dicts[lang], decoder_embed_tokens, tgt_langs + ) + return lang_decoders[lang] + + # shared encoders/decoders (if applicable) + shared_encoder, shared_decoder = None, None + if args.share_encoders: + shared_encoder = get_encoder(src_langs[0]) + if args.share_decoders: + shared_decoder = get_decoder(tgt_langs[0]) + + encoders, decoders = OrderedDict(), OrderedDict() + for lang_pair, src, tgt in zip(task.model_lang_pairs, src_langs, tgt_langs): + encoders[lang_pair] = ( + shared_encoder if shared_encoder is not None else get_encoder(src) + ) + decoders[lang_pair] = ( + shared_decoder if shared_decoder is not None else get_decoder(tgt) + ) + + return MultilingualTransformerModel(encoders, decoders) + + @classmethod + def _get_module_class(cls, is_encoder, args, lang_dict, embed_tokens, langs): + module_class = TransformerEncoder if is_encoder else TransformerDecoder + return module_class(args, lang_dict, embed_tokens) + + def load_state_dict(self, state_dict, strict=True, model_cfg=None): + state_dict_subset = state_dict.copy() + for k, _ in state_dict.items(): + assert k.startswith("models.") + lang_pair = k.split(".")[1] + if lang_pair not in self.models: + del state_dict_subset[k] + super().load_state_dict(state_dict_subset, strict=strict, model_cfg=model_cfg) + + +@register_model_architecture("multilingual_transformer", "multilingual_transformer") +def base_multilingual_architecture(args): + base_architecture(args) + args.share_encoder_embeddings = getattr(args, "share_encoder_embeddings", False) + args.share_decoder_embeddings = getattr(args, "share_decoder_embeddings", False) + args.share_encoders = getattr(args, "share_encoders", False) + args.share_decoders = getattr(args, "share_decoders", False) + + +@register_model_architecture( + "multilingual_transformer", "multilingual_transformer_iwslt_de_en" +) +def multilingual_transformer_iwslt_de_en(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) + base_multilingual_architecture(args) diff --git a/fairseq/fairseq/models/multires_hubert/__init__.py b/fairseq/fairseq/models/multires_hubert/__init__.py new file mode 100644 index 0000000..ec36505 --- /dev/null +++ b/fairseq/fairseq/models/multires_hubert/__init__.py @@ -0,0 +1,2 @@ +from .multires_hubert import * # noqa +from .multires_hubert_asr import * # noqa diff --git a/fairseq/fairseq/models/multires_hubert/multires_hubert.py b/fairseq/fairseq/models/multires_hubert/multires_hubert.py new file mode 100644 index 0000000..eacb29e --- /dev/null +++ b/fairseq/fairseq/models/multires_hubert/multires_hubert.py @@ -0,0 +1,1231 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch +import math +import torch.nn as nn +from omegaconf import II +from fairseq.models.wav2vec.wav2vec import norm_block + +from fairseq import utils +from fairseq.data.data_utils import compute_mask_indices +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.wav2vec.wav2vec2 import ( + EXTRACTOR_MODE_CHOICES, + MASKING_DISTRIBUTION_CHOICES, + LAYER_TYPE_CHOICES, + ConvFeatureExtractionModel, + TransformerEncoder, +) +from omegaconf import II, MISSING, open_dict +from fairseq.modules import GradMultiply, LayerNorm +from fairseq.tasks.multires_hubert_pretraining import ( + MultiresHubertPretrainingConfig, + MultiresHubertPretrainingTask, +) + +logger = logging.getLogger(__name__) + + +@dataclass +class MultiresHubertConfig(FairseqDataclass): + label_rate: float = II("task.label_rate") + # label_rate: 1,2,2,5 + # (imply (1,2), (2,5)) + # if base label_rate = 50 + # (1,2), (2,5) --> label rates 50, 25, 10 + label_rate_ratios: List[int] = field( + default=MISSING, metadata={"help": "tuple for label rates e.g., [(1,2), (2,5)]"} + ) + + extractor_mode: EXTRACTOR_MODE_CHOICES = field( + default="default", + metadata={ + "help": "mode for feature extractor. default has a single group " + "norm with d groups in the first conv block, whereas layer_norm " + "has layer norms in every block (meant to use with normalize=True)" + }, + ) + # the blocks for each label rate + encoder_layers: int = field( + default="2", + metadata={ + "help": "num encoder layers in the each block (one sub module of the U-net)" + }, + ) + override_encoder_layers: str = field( + default="", + metadata={ + "help": "specific layer numbers for each block (one sub module of the U-net) for the training" + }, + ) + encoder_embed_dim: int = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + encoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "encoder embedding dimension for FFN"} + ) + encoder_attention_heads: int = field( + default=12, metadata={"help": "num encoder attention heads"} + ) + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="gelu", metadata={"help": "activation function to use"} + ) + layer_type: LAYER_TYPE_CHOICES = field( + default="transformer", metadata={"help": "layer type in encoder"} + ) + conv_adapator_kernal: int = field( + default=7, metadata={"help": "kernal size for conv adaptor"} + ) + use_plain_updownsample: bool = field( + default=False, metadata={"help": "whether to use plain up downsample"} + ) + + # dropouts + dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for the transformer"}, + ) + attention_dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for attention weights"}, + ) + activation_dropout: float = field( + default=0.0, + metadata={"help": "dropout probability after activation in FFN"}, + ) + encoder_layerdrop: float = field( + default=0.0, + metadata={"help": "probability of dropping a tarnsformer layer"}, + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + dropout_features: float = field( + default=0.0, + metadata={"help": "dropout to apply to the features (after feat extr)"}, + ) + + final_dim: int = field( + default=0, + metadata={ + "help": "project final representations and targets to this many " + "dimensions. set to encoder_embed_dim is <= 0" + }, + ) + untie_final_proj: bool = field( + default=True, + metadata={"help": "use separate projection for each target"}, + ) + layer_norm_first: bool = field( + default=False, + metadata={"help": "apply layernorm first in the transformer"}, + ) + conv_feature_layers: str = field( + default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", + metadata={ + "help": "string describing convolutional feature extraction " + "layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + conv_bias: bool = field( + default=False, metadata={"help": "include bias in conv encoder"} + ) + logit_temp: float = field( + default=0.1, metadata={"help": "temperature to divide logits by"} + ) + target_glu: bool = field( + default=False, metadata={"help": "adds projection + glu to targets"} + ) + feature_grad_mult: float = field( + default=1.0, + metadata={"help": "multiply feature extractor var grads by this"}, + ) + use_single_target: bool = field( + default=False, + metadata={ + "help": "whether to use single data (in that case, we will compute with the fixed label rate)" + }, + ) + use_single_prediction: bool = field( + default=False, + metadata={ + "help": "if true, we will not conduct mlm prediction in low resolution in the middle" + }, + ) + use_multi_stream: bool = field( + default=False, + metadata={ + "help": "whether to use multi-stream setting (in this setting, we have multiple streams with the same resolution)" + }, + ) + + # masking + mask_length: int = field(default=10, metadata={"help": "mask length"}) + mask_prob: float = field( + default=0.65, + metadata={"help": "probability of replacing a token with mask"}, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose mask length"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # channel masking + mask_channel_length: int = field( + default=10, + metadata={"help": "length of the mask for features (channels)"}, + ) + mask_channel_prob: float = field( + default=0.0, + metadata={"help": "probability of replacing a feature with 0"}, + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, + metadata={"help": "whether to allow channel masks to overlap"}, + ) + mask_channel_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # positional embeddings + conv_pos: int = field( + default=128, + metadata={"help": "number of filters for convolutional positional embeddings"}, + ) + conv_pos_groups: int = field( + default=16, + metadata={"help": "number of groups for convolutional positional embedding"}, + ) + + latent_temp: Tuple[float, float, float] = field( + default=(2, 0.5, 0.999995), + metadata={"help": "legacy (to be removed)"}, + ) + + # loss computation + skip_masked: bool = field( + default=False, + metadata={"help": "skip computing losses over masked frames"}, + ) + skip_nomask: bool = field( + default=False, + metadata={"help": "skip computing losses over unmasked frames"}, + ) + + checkpoint_activations: bool = field( + default=False, + metadata={"help": "recompute activations and save memory for extra compute"}, + ) + + # FP16 optimization + required_seq_len_multiple: int = field( + default=2, + metadata={ + "help": "pad the input to encoder such that the sequence length is divisible by multiple" + }, + ) + + # Conformer + depthwise_conv_kernel_size: int = field( + default=31, + metadata={ + "help": "depthwise-conv-kernel-size for convolution in conformer layer" + }, + ) + attn_type: str = field( + default="", + metadata={"help": "if espnet use ESPNET MHA"}, + ) + pos_enc_type: str = field( + default="abs", + metadata={"help": "Positional encoding type to use in conformer"}, + ) + fp16: bool = field(default=False, metadata={"help": "If fp16 is being used"}) + + +@register_model("multires_hubert", dataclass=MultiresHubertConfig) +class MultiresHubertModel(BaseFairseqModel): + def __init__( + self, + cfg: MultiresHubertConfig, + task_cfg: MultiresHubertPretrainingConfig, + dictionaries: List[Dictionary], + ) -> None: + super().__init__() + logger.info(f"MultiresHubertModel Config: {cfg}") + + feature_enc_layers = eval(cfg.conv_feature_layers) # noqa + self.embed = feature_enc_layers[-1][0] + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + self.post_extract_proj = ( + nn.Linear(self.embed, cfg.encoder_embed_dim) + if self.embed != cfg.encoder_embed_dim + else None + ) + + # Estimate label rates + assert ( + cfg.label_rate_ratios != "None" + ), "without ratios, the model is exactly as the Hubert model" + self.label_rate_ratios = [] + self.base_rate = cfg.label_rate + self.label_rates = [] + self.downsample_modules = nn.ModuleList() + self.upsample_modules = nn.ModuleList() + self.encoders = nn.ModuleList() + self.decoders = nn.ModuleList() + self.use_single_target = cfg.use_single_target + self.use_single_prediction = cfg.use_single_prediction + self.use_plain_updownsample = cfg.use_plain_updownsample + + # For decide the override encoder layers, so that the layer number is not equally distributed + if cfg.override_encoder_layers != "": + self.override_encoder_layers = eval(cfg.override_encoder_layers) + assert ( + len(self.override_encoder_layers) % 2 == 1 + ), "must be odd number of layers if specify detailed layers" + assert ( + len(self.override_encoder_layers) // 2 + == len(cfg.label_rate_ratios) // 2 + ), "number of override encoder layers must match the label rate ratios information" + self.len_encoder_modules = len(self.override_encoder_layers) + else: + self.override_encoder_layers = None + self.len_encoder_modules = None + + # use different layers instead of equally distributed ones + middle_override_encoder_layer = ( + self.override_encoder_layers[self.len_encoder_modules // 2] + if self.override_encoder_layers is not None + else None + ) + skip_middle_pos_conv = False if len(cfg.label_rate_ratios) < 2 else True + + self.middle_encoder = TransformerEncoder( + cfg, + skip_pos_conv=skip_middle_pos_conv, + override_encoder_layer=middle_override_encoder_layer, + ) + + first_pos_conv = False # only enable pos_conv for the first encoder + raw_label_rate_ratios = cfg.label_rate_ratios + for i in range(len(raw_label_rate_ratios) // 2): + # check if have override encoder layers + if self.override_encoder_layers is not None: + override_encoder_layer = self.override_encoder_layers[i] + override_decoder_layer = self.override_encoder_layers[ + self.len_encoder_modules - 1 - i + ] + else: + override_encoder_layer, override_decoder_layer = None, None + + self.label_rate_ratios.append( + (raw_label_rate_ratios[i * 2], raw_label_rate_ratios[i * 2 + 1]) + ) + if self.use_plain_updownsample: + self.downsample_modules.append( + ConvDownsampler( + k=cfg.conv_adapator_kernal, + label_rate=( + ( + raw_label_rate_ratios[i * 2], + raw_label_rate_ratios[i * 2 + 1], + ) + ), + dropout=0.0, + channels=cfg.encoder_embed_dim, + activation=nn.GELU(), + log_compression=False, + skip_connections=True, + highway=True, + residual_scale=0.4, + ) + ) + else: + self.downsample_modules.append( + ConvAdapter( + k=cfg.conv_adapator_kernal, + label_rate=( + ( + raw_label_rate_ratios[i * 2], + raw_label_rate_ratios[i * 2 + 1], + ) + ), + dropout=0.0, + channels=cfg.encoder_embed_dim, + activation=nn.GELU(), + log_compression=False, + skip_connections=True, + highway=True, + residual_scale=0.4, + ) + ) + if not first_pos_conv: + self.encoders.append( + TransformerEncoder( + cfg, override_encoder_layer=override_encoder_layer + ) + ) # TODO(jiatong): add conformer options + first_pos_conv = True + else: + self.encoders.append( + TransformerEncoder( + cfg, + skip_pos_conv=True, + override_encoder_layer=override_encoder_layer, + ) + ) + if self.use_plain_updownsample: + self.upsample_modules.append( + ConvUpsampler( + k=cfg.conv_adapator_kernal, + label_rate=( + ( + raw_label_rate_ratios[i * 2 + 1], + raw_label_rate_ratios[i * 2], + ) + ), + dropout=0.0, + channels=cfg.encoder_embed_dim, + activation=nn.GELU(), + log_compression=False, + skip_connections=True, + highway=True, + residual_scale=0.4, + ) + ) + else: + self.upsample_modules.append( + ConvAdapter( + k=cfg.conv_adapator_kernal, + label_rate=( + ( + raw_label_rate_ratios[i * 2 + 1], + raw_label_rate_ratios[i * 2], + ) + ), + dropout=0.0, + channels=cfg.encoder_embed_dim, + activation=nn.GELU(), + log_compression=False, + skip_connections=True, + highway=True, + residual_scale=0.4, + ) + ) + self.decoders.append( + TransformerEncoder( + cfg, + skip_pos_conv=True, + override_encoder_layer=override_decoder_layer, + ) + ) + + base_ds_rate = np.prod([s for _, _, s in feature_enc_layers]) + self.feature_ds_rates = [base_ds_rate] + running_rate = self.base_rate + + if cfg.use_single_target or cfg.use_multi_stream: + self.label_rates = self.base_rate + else: + self.label_rates.append(self.base_rate) + + for label_rate_ratio in self.label_rate_ratios: + upsample_rate, downsample_rate = label_rate_ratio + if (base_ds_rate * upsample_rate) % downsample_rate != 0: + logger.warning( + "base rate: {} cannot be ideally processed with downsample rate {}".format( + base_ds_rate, downsample_rate + ) + ) + + base_ds_rate = base_ds_rate * downsample_rate // upsample_rate + self.feature_ds_rates.append(base_ds_rate) + + if not cfg.use_single_target and not cfg.use_multi_stream: + running_rate = running_rate * upsample_rate // downsample_rate + self.label_rates.append(running_rate) + self.label_nums = len( + self.feature_ds_rates + ) # the number of labels for prediction (activate at iter 2) + + if type(self.label_rates) == float: + self.feat2tar_ratios = [ + self.feature_ds_rates[i] * self.label_rates / task_cfg.sample_rate + for i in range(len(self.feature_ds_rates)) + ] + else: + self.feat2tar_ratios = [ + self.feature_ds_rates[i] * self.label_rates[i] / task_cfg.sample_rate + for i in range(len(self.feature_ds_rates)) + ] + + # self.feat2tar_ratios = self.feat2tar_ratios[::-1] + + # An running example of the label rate: + # base_ds_rate = 320 + # self.label_rate_ratios = [(1, 2)] + # self.feature_ds_rates = [320, 640] + # self.label_rates = [50, 25] + # self.feat2tar_ratios = [1, 1] + + # Another running example of the label rate: + # base_ds_rate = 320 + # self.label_rate_ratios = [(1, 2)] + # self.feature_ds_rates = [320, 640] + # self.label_rates = 100 + # self.feat2tar_ratios = [4, 2] + # self.use_sinlge_target = True + + logging.info( + "ds_rates: {}, label_rates: {}, feat2tar_ratios: {}".format( + self.feature_ds_rates, self.label_rates, self.feat2tar_ratios + ) + ) + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + self.logit_temp = cfg.logit_temp + self.skip_masked = cfg.skip_masked + self.skip_nomask = cfg.skip_nomask + + # Note(jiatong): different from hubert, we just set the final dim as encoder_embed_dim + final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + + self.layer_norm = LayerNorm(self.embed) + + self.predictor_head_num = 1 if self.use_single_prediction else self.label_nums + + self.target_glu = None + if cfg.target_glu: + self.target_glus = nn.ModuleList() + for i in range(self.predictor_head_num): + self.target_glus.append( + nn.Sequential(nn.Linear(final_dim, final_dim * 2), nn.GLU()) + ) + + self.untie_final_proj = cfg.untie_final_proj + self.final_projs = nn.ModuleList() + + # Note(jiatong): we do not have untie cases for multires hubert + for i in range(self.predictor_head_num): + self.final_projs.append(nn.Linear(cfg.encoder_embed_dim, final_dim)) + + # modules below are not needed during fine-tuning + self.multires_classes = [] + self.label_embs_concat = nn.ParameterList() + + for i in range(self.predictor_head_num): + if self.use_single_target: + num_classes = len(dictionaries[0]) + else: + num_classes = len(dictionaries[i]) + self.multires_classes.append(num_classes) + self.label_embs_concat.append( + nn.Parameter(torch.FloatTensor(num_classes, final_dim)) + ) + nn.init.uniform_(self.label_embs_concat[i]) + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model( + cls, cfg: MultiresHubertConfig, task: MultiresHubertPretrainingTask + ): + """Build a new model instance.""" + + model = MultiresHubertModel(cfg, task.cfg, task.dictionaries) + return model + + def apply_mask(self, x, padding_mask, target_list): + B, T, C = x.shape + if self.mask_prob > 0: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb + else: + mask_indices = None + + if self.mask_channel_prob > 0: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def compute_nce(self, x, pos, negs): + neg_is_pos = (pos == negs).all(-1) + pos = pos.unsqueeze(0) + targets = torch.cat([pos, negs], dim=0) + + logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x) + logits /= self.logit_temp + if neg_is_pos.any(): + logits[1:][neg_is_pos] = float("-inf") + logits = logits.transpose(0, 1) # (num_x, num_cls+1) + return logits + + def forward_features(self, source: torch.Tensor) -> torch.Tensor: + if self.feature_grad_mult > 0: + features = self.feature_extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(source) + return features + + def forward_targets( + self, + features: torch.Tensor, + target: torch.Tensor, + feat2tar_ratio: float, + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Trim features to ensure labels exist and then get aligned labels + + feat_tsz = features.size(1) + + # skip if no target is provided + if target is None: + return features, None, None + targ_tsz = target.size(1) + if feat2tar_ratio * feat_tsz > targ_tsz: + feat_tsz = int(targ_tsz / feat2tar_ratio) + features = features[:, :feat_tsz] + target_inds = torch.arange(feat_tsz).float() * feat2tar_ratio + target = target[:, target_inds.long()] + return features, target + + def forward_padding_mask( + self, + features: torch.Tensor, + padding_mask: torch.Tensor, + ) -> torch.Tensor: + extra = padding_mask.size(1) % features.size(1) + if extra > 0: + padding_mask = padding_mask[:, :-extra] + padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1) + padding_mask = padding_mask.all(-1) + return padding_mask + + def forward( + self, + source: torch.Tensor, + target_list: Optional[List[torch.Tensor]] = None, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = True, + features_only: bool = False, + output_layer: Optional[int] = None, + ) -> Dict[str, torch.Tensor]: + """output layer is 1-based""" + features = self.forward_features(source) + + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + unmasked_features = features.clone() + + if padding_mask is not None: + padding_mask = self.forward_padding_mask(features, padding_mask) + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + + if mask: + x, mask_indices = self.apply_mask(features, padding_mask, target_list) + else: + x = features + mask_indices = None + + # feature: (B, T, D), float + # target: (B, T), long + # x: (B, T, D), float + # padding_mask: (B, T), bool + # mask_indices: (B, T), bool + + def align_size_sum(feat1, pad1, feat2): + assert ( + abs(feat1.size(1) - feat2.size(1)) < 10 + ), "misaligned results for feat1 and feat2 of size {} - {}".format( + feat1.size(1), feat2.size(1) + ) + common_size = min(feat1.size(1), feat2.size(1)) + + return ( + feat1[:, :common_size] + feat2[:, :common_size], + pad1[:, :common_size], + ) + + # process encoders + res_outputs = [] # final output for different resolution + multi_mask_indices = [] # mask indices for different resolution + residuals = [] # record the x in encoders + padding_masks = [] # final padding masks + # The encoder has (self.label_nums - 1) blocks + for i in range(self.label_nums - 1): + x, _ = self.encoders[i](x, padding_mask=padding_mask, layer=None) + residuals.append(x) + x, padding_mask, mask_indices = self.downsample_modules[i]( + x, padding=padding_mask, mask_indices=mask_indices + ) + + residual = self.middle_encoder(x, padding_mask=padding_mask, layer=None)[0] + x = x + residual + res_outputs.append(x) + + # process decoders + # The encoder has (self.label_nums - 1) blocks + padding_masks.append(padding_mask) + multi_mask_indices.append(mask_indices) + residuals.reverse() # NOTE(jiatong): reverse res_output to match corresponding input + for i in range(self.label_nums - 1): + x, padding_mask, mask_indices = self.upsample_modules[ + self.label_nums - 2 - i + ](x, padding=padding_mask, mask_indices=mask_indices) + x, _ = self.decoders[i](x, padding_mask=padding_mask, layer=None) + x, padding_mask = align_size_sum(x, padding_mask, residuals[i]) + res_outputs.append(x) + padding_masks.append(padding_mask) + multi_mask_indices.append(mask_indices) + + # NOTE(jiatong): need reverse of target list to allow matched target-representation + res_outputs.reverse() + padding_masks.reverse() + multi_mask_indices.reverse() + if target_list is not None: + new_target_list = [] + for i in range(self.label_nums): + if self.use_single_target: + res_outputs[i], reformat_target_list = self.forward_targets( + res_outputs[i], target_list[0], self.feat2tar_ratios[i] + ) + new_target_list.append(reformat_target_list) + else: + if target_list[i] is not None: + res_outputs[i], reformat_target_list = self.forward_targets( + res_outputs[i], target_list[i], self.feat2tar_ratios[i] + ) + new_target_list.append(reformat_target_list) + else: + # Append a None target list then it won't be used to calculate loss + new_target_list.append(None) + if padding_masks[i] is not None: + padding_masks[i] = self.forward_padding_mask( + res_outputs[i], padding_masks[i] + ) + if multi_mask_indices[i] is not None: + multi_mask_indices[i] = self.forward_padding_mask( + res_outputs[i], multi_mask_indices[i] + ) + + + if features_only: + # NOTE(jiatong): need to reverse back + res_outputs.reverse() + return { + "x": res_outputs, + "padding_mask": padding_masks[0], + "features": features, + } + + def compute_pred(proj_x, target, label_embs): + # compute logits for the i-th label set + y = torch.index_select(label_embs, 0, target.long()) + negs = label_embs.unsqueeze(1).expand(-1, proj_x.size(0), -1) + if self.target_glu: + y = self.target_glu(y) + negs = self.target_glu(negs) + # proj_x: (S, D) + # y: (S, D) + # negs: (Neg, S, D) + return self.compute_nce(proj_x, y, negs) + + logit_m_list, logit_u_list = [], [] + for j in range(self.label_nums): + if new_target_list[j] is None: + continue # skip empty targets + label_embs_list = self.label_embs_concat[j].split( + [self.multires_classes[j]], 0 + ) + # set the variables (after the set, the procedure is the same as hubert) + # all the elements are list with only one element (to simulate the normal hubert process) + x = res_outputs[j] + target = new_target_list[j] + padding_mask = padding_masks[j] + mask_indices = multi_mask_indices[j] + final_proj = self.final_projs[j] + + if not self.skip_masked: + masked_indices = torch.logical_and(~padding_mask, mask_indices) + proj_x_m = final_proj(x[masked_indices]) + logit_m_list.append( + compute_pred(proj_x_m, target[masked_indices], label_embs_list[0]) + ) + else: + logit_m_list.append(None) + + if not self.skip_nomask: + nomask_indices = torch.logical_and(~padding_mask, ~mask_indices) + proj_x_u = final_proj(x[nomask_indices]) + logit_u_list.append( + compute_pred(proj_x_u, target[nomask_indices], label_embs_list[0]) + ) + else: + logit_u_list.append(None) + + # if we only want one prediction, we can exit now + if self.predictor_head_num == 1: + break + + result = { + "logit_m_list": logit_m_list, + "logit_u_list": logit_u_list, + "padding_mask": padding_mask, + "features_pen": features_pen, + } + return result + + def extract_features( + self, + source: torch.Tensor, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = False, + ret_conv: bool = False, + output_layer: Optional[int] = None, + last_layer: Optional[bool] = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + res = self.forward( + source, + padding_mask=padding_mask, + mask=mask, + features_only=True, + output_layer=output_layer, + ) + feature = res["features"] if ret_conv else res["x"] + if last_layer: + feature = feature[-1] + return feature, res["padding_mask"] + + def get_logits(self, net_output, is_masked=True): + if is_masked: + logits_list = net_output["logit_m_list"] + else: + logits_list = net_output["logit_u_list"] + logits_list = [x.float() for x in logits_list if x is not None] + return logits_list + + def get_targets(self, net_output, is_masked=True): + logits_list = self.get_logits(net_output, is_masked) + targets_list = [x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list] + return targets_list + + def get_extra_losses(self, net_output): + extra_losses = [] + names = [] + + if "features_pen" in net_output: + extra_losses.append(net_output["features_pen"]) + names.append("features_pen") + + return extra_losses, names + + def remove_pretraining_modules(self): + self.target_glu = None + self.final_proj = None + + +class ConvAdapter(nn.Module): + """Conv adapter that combines two modules with different label rate with downsample or upsample. + To allow different ratios than integer, two convs are utilized with first to upsample (numerator) + and the second to downsample (denominator)""" + + def __init__( + self, + k, + label_rate, + dropout, + channels, + activation, + log_compression=False, + skip_connections=True, + highway=True, + residual_scale=0.4, + non_affine_group_norm=False, + ): + super().__init__() + + def downsample_block(channel, k, stride): + return nn.Sequential( + # with padding (k - 1) // 2 to keep the same size + nn.Conv1d( + channel, + channel, + k, + stride=stride, + bias=False, + padding=(k - 1) // 2, + ), + nn.Dropout(p=dropout), + norm_block( + is_layer_norm=False, dim=channel, affine=not non_affine_group_norm + ), + activation, + ) + + def upsample_block(channel, k, stride): + return nn.Sequential( + # with padding (k - 1) // 2 to keep the same size + nn.ConvTranspose1d( + channel, + channel, + k, + stride=stride, + bias=False, + padding=0, # padding=(k - 1) // 2, + output_padding=(stride - 1), + ), + nn.Dropout(p=dropout), + norm_block( + is_layer_norm=False, dim=channel, affine=not non_affine_group_norm + ), + activation, + ) + + assert len(label_rate) == 2, "label_rate should be sized two to apply fusion" + # Lout =(Lin~H~R1)~Wstride~H~R2~Wpadding+dilation~W(kernel_size~H~R1)+output_padding+1 + self.upsample_conv = upsample_block(channels, k, label_rate[0]) + self.downsample_conv = downsample_block(channels, k, label_rate[1]) + + self.upsample_rate, self.downsample_rate = label_rate + self.log_compression = log_compression + self.skip_connections = skip_connections + self.highway = highway + self.residual_scale = math.sqrt(residual_scale) + + def forward(self, x, padding=None, mask_indices=None): + # Assume x1 = (B, T, C) as input + x = x.permute(0, 2, 1) + residual_before_upsample = x + x = self.upsample_conv(x) + upsample_size = x.size(2) + + # conduct upsample + if self.skip_connections: + residual_upsample = torch.repeat_interleave( + residual_before_upsample, self.upsample_rate, dim=2 + ) + upsample_size = min(upsample_size, residual_upsample.size(2)) + x = ( + x[..., :upsample_size] + residual_upsample[..., :upsample_size] + ) * self.residual_scale + + residual_before_downsample = x + x = self.downsample_conv(x) + downsample_size = x.size(2) + + if self.skip_connections: + residual_downsample = residual_before_downsample[ + ..., :: self.downsample_rate + ] + downsample_size = min(x.size(2), residual_downsample.size(2)) + x = ( + x[..., :downsample_size] + residual_downsample[..., :downsample_size] + ) * self.residual_scale + + if self.highway: + residual_after_sample = residual_upsample[..., :: self.downsample_rate] + final_size = min(x.size(2), residual_after_sample.size(2)) + x = ( + x[..., :final_size] + residual_after_sample[..., :final_size] + ) * self.residual_scale + + if self.log_compression: + x = x.abs() + x = x + 1 + x = x.log() + + x = x.permute(0, 2, 1) + + # process padding + if padding is not None: + padding = torch.repeat_interleave(padding, self.upsample_rate, dim=1) + padding = padding[..., :: self.downsample_rate] + padding = padding[..., : x.size(1)] + + # process mask indices + if mask_indices is not None: + mask_indices = torch.repeat_interleave( + mask_indices, self.upsample_rate, dim=1 + ) + mask_indices = mask_indices[..., :: self.downsample_rate] + mask_indices = mask_indices[..., : x.size(1)] + return x, padding, mask_indices + + +class ConvDownsampler(nn.Module): + """Conv downsampler that combines two modules with different label rate with downsample or upsample. + To allow different ratios than integer, two convs are utilized with first to upsample (numerator) + and the second to downsample (denominator)""" + + def __init__( + self, + k, + label_rate, + dropout, + channels, + activation, + log_compression=False, + skip_connections=True, + highway=True, + residual_scale=0.4, + non_affine_group_norm=False, + ): + super().__init__() + + def downsample_block(channel, k, stride): + return nn.Sequential( + # with padding (k - 1) // 2 to keep the same size + nn.Conv1d( + channel, + channel, + k, + stride=stride, + bias=False, + padding=(k - 1) // 2, + ), + nn.Dropout(p=dropout), + norm_block( + is_layer_norm=False, dim=channel, affine=not non_affine_group_norm + ), + activation, + ) + + assert len(label_rate) == 2, "label_rate should be sized two to apply fusion" + self.downsample_conv = downsample_block(channels, k, label_rate[1]) + + upsample_rate, self.downsample_rate = label_rate + assert upsample_rate == 1, "must be 1 to perform downsample only" + self.log_compression = log_compression + self.skip_connections = skip_connections + self.highway = highway # Useless as placeholder + self.residual_scale = math.sqrt(residual_scale) + + def forward(self, x, padding=None, mask_indices=None): + # Assume x1 = (B, T, C) as input + x = x.permute(0, 2, 1) + + residual_before_downsample = x + x = self.downsample_conv(x) + downsample_size = x.size(2) + + if self.skip_connections: + residual_downsample = residual_before_downsample[ + ..., :: self.downsample_rate + ] + downsample_size = min(x.size(2), residual_downsample.size(2)) + x = ( + x[..., :downsample_size] + residual_downsample[..., :downsample_size] + ) * self.residual_scale + + if self.log_compression: + x = x.abs() + x = x + 1 + x = x.log() + + x = x.permute(0, 2, 1) + + # process padding + if padding is not None: + padding = padding[..., :: self.downsample_rate] + padding = padding[..., : x.size(1)] + + # process mask indices + if mask_indices is not None: + mask_indices = mask_indices[..., :: self.downsample_rate] + mask_indices = mask_indices[..., : x.size(1)] + return x, padding, mask_indices + + +class ConvUpsampler(nn.Module): + """Conv upsampler that combines two modules with different label rate with downsample or upsample. + To allow different ratios than integer, two convs are utilized with first to upsample (numerator) + and the second to downsample (denominator)""" + + def __init__( + self, + k, + label_rate, + dropout, + channels, + activation, + log_compression=False, + skip_connections=True, + highway=True, + residual_scale=0.4, + non_affine_group_norm=False, + ): + super().__init__() + + def upsample_block(channel, k, stride): + return nn.Sequential( + # with padding (k - 1) // 2 to keep the same size + nn.ConvTranspose1d( + channel, + channel, + k, + stride=stride, + bias=False, + padding=0, # padding=(k - 1) // 2, + output_padding=(stride - 1), + ), + nn.Dropout(p=dropout), + norm_block( + is_layer_norm=False, dim=channel, affine=not non_affine_group_norm + ), + activation, + ) + + assert len(label_rate) == 2, "label_rate should be sized two to apply fusion" + # Lout =(Lin~H~R1)~Wstride~H~R2~Wpadding+dilation~W(kernel_size~H~R1)+output_padding+1 + self.upsample_conv = upsample_block(channels, k, label_rate[0]) + + self.upsample_rate, downsample_rate = label_rate + assert downsample_rate == 1, "must be 1 to perform downsample only" + self.log_compression = log_compression + self.skip_connections = skip_connections + self.highway = highway # Useless + self.residual_scale = math.sqrt(residual_scale) + + def forward(self, x, padding=None, mask_indices=None): + # Assume x1 = (B, T, C) as input + x = x.permute(0, 2, 1) + residual_before_upsample = x + x = self.upsample_conv(x) + upsample_size = x.size(2) + + # conduct upsample + if self.skip_connections: + residual_upsample = torch.repeat_interleave( + residual_before_upsample, self.upsample_rate, dim=2 + ) + upsample_size = min(upsample_size, residual_upsample.size(2)) + x = ( + x[..., :upsample_size] + residual_upsample[..., :upsample_size] + ) * self.residual_scale + + if self.log_compression: + x = x.abs() + x = x + 1 + x = x.log() + + x = x.permute(0, 2, 1) + + # process padding + if padding is not None: + padding = torch.repeat_interleave(padding, self.upsample_rate, dim=1) + padding = padding[..., : x.size(1)] + + # process mask indices + if mask_indices is not None: + mask_indices = torch.repeat_interleave( + mask_indices, self.upsample_rate, dim=1 + ) + mask_indices = mask_indices[..., : x.size(1)] + return x, padding, mask_indices diff --git a/fairseq/fairseq/models/multires_hubert/multires_hubert_asr.py b/fairseq/fairseq/models/multires_hubert/multires_hubert_asr.py new file mode 100644 index 0000000..2e7ad99 --- /dev/null +++ b/fairseq/fairseq/models/multires_hubert/multires_hubert_asr.py @@ -0,0 +1,376 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Any + +import torch +import torch.nn as nn +from omegaconf import II, MISSING + +from fairseq import checkpoint_utils, tasks, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model +from fairseq.models.hubert.hubert import MASKING_DISTRIBUTION_CHOICES +from fairseq.tasks import FairseqTask + + +@dataclass +class MultiresHubertAsrConfig(FairseqDataclass): + multires_hubert_path: str = field( + default=MISSING, metadata={"help": "path to multires_hubert model"} + ) + no_pretrained_weights: bool = field( + default=False, + metadata={"help": "if true, does not load pretrained weights"}, + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + final_dropout: float = field( + default=0.0, + metadata={"help": "dropout after transformer and before final projection"}, + ) + dropout: float = field( + default=0.0, + metadata={"help": "dropout probability inside hubert model"}, + ) + attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights " "inside hubert model" + }, + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN " "inside hubert model" + }, + ) + + # masking + apply_mask: bool = field( + default=False, metadata={"help": "apply masking during fine-tuning"} + ) + mask_length: int = field( + default=10, metadata={"help": "repeat the mask indices multiple times"} + ) + mask_prob: float = field( + default=0.5, + metadata={ + "help": "probability of replacing a token with mask " + "(normalized by length)" + }, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose masks"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + + # channel masking + mask_channel_length: int = field( + default=10, + metadata={"help": "length of the mask for features (channels)"}, + ) + mask_channel_prob: float = field( + default=0.0, + metadata={"help": "probability of replacing a feature with 0"}, + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, + metadata={"help": "whether to allow channel masks to overlap"}, + ) + freeze_finetune_updates: int = field( + default=0, + metadata={"help": "dont finetune hubert for this many updates"}, + ) + feature_grad_mult: float = field( + default=0.0, + metadata={"help": "reset feature grad mult in hubert to this"}, + ) + layerdrop: float = field( + default=0.0, + metadata={"help": "probability of dropping a layer in hubert"}, + ) + normalize: bool = II("task.normalize") + data: str = II("task.data") + + # this holds the loaded hubert args + multires_hubert_args: Any = None + + +@dataclass +class MultiresHubertCtcConfig(MultiresHubertAsrConfig): + pass + + +@register_model("multires_hubert_ctc", dataclass=MultiresHubertAsrConfig) +class MultiresHubertCtc(BaseFairseqModel): + def __init__( + self, cfg: MultiresHubertAsrConfig, multireshubert_encoder: BaseFairseqModel + ): + super().__init__() + self.cfg = cfg + self.multireshubert_encoder = multireshubert_encoder + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: MultiresHubertAsrConfig, task: FairseqTask): + """Build a new model instance.""" + multireshubert_encoder = MultiresHubertEncoder(cfg, task) + return cls(cfg, multireshubert_encoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + """Get normalized probabilities (or log probs) from a net's output.""" + + logits = net_output["encoder_out"] + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def get_logits(self, net_output): + logits = net_output["encoder_out"] + padding = net_output["encoder_padding_mask"] + if padding is not None and padding.any(): + padding = padding.T + logits[padding][..., 0] = 0 + logits[padding][..., 1:] = float("-inf") + + return logits + + def forward(self, **kwargs): + x = self.multireshubert_encoder(**kwargs) + return x + + +@dataclass +class MultiresHubertSeq2SeqConfig(MultiresHubertAsrConfig): + decoder_embed_dim: int = field( + default=768, metadata={"help": "decoder embedding dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "decoder layerdrop chance"} + ) + decoder_attention_heads: int = field( + default=4, metadata={"help": "num decoder attention heads"} + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + decoder_normalize_before: bool = field( + default=False, + metadata={"help": "apply layernorm before each decoder block"}, + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings " "(outside self attention)" + }, + ) + decoder_dropout: float = field( + default=0.0, metadata={"help": "dropout probability in the decoder"} + ) + decoder_attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights " "inside the decoder" + }, + ) + decoder_activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN " "inside the decoder" + }, + ) + max_target_positions: int = field( + default=2048, metadata={"help": "max target positions"} + ) + share_decoder_input_output_embed: bool = field( + default=False, + metadata={"help": "share decoder input and output embeddings"}, + ) + + +class MultiresHubertEncoder(FairseqEncoder): + def __init__(self, cfg: MultiresHubertAsrConfig, task): + self.apply_mask = cfg.apply_mask + + arg_overrides = { + "dropout": cfg.dropout, + "activation_dropout": cfg.activation_dropout, + "dropout_input": cfg.dropout_input, + "attention_dropout": cfg.attention_dropout, + "mask_length": cfg.mask_length, + "mask_prob": cfg.mask_prob, + "mask_selection": cfg.mask_selection, + "mask_other": cfg.mask_other, + "no_mask_overlap": cfg.no_mask_overlap, + "mask_channel_length": cfg.mask_channel_length, + "mask_channel_prob": cfg.mask_channel_prob, + "mask_channel_selection": cfg.mask_channel_selection, + "mask_channel_other": cfg.mask_channel_other, + "no_mask_channel_overlap": cfg.no_mask_channel_overlap, + "encoder_layerdrop": cfg.layerdrop, + "feature_grad_mult": cfg.feature_grad_mult, + } + + if cfg.multires_hubert_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu( + cfg.multires_hubert_path, arg_overrides + ) + multires_hubert_args = state.get("cfg", None) + if multires_hubert_args is None: + multires_hubert_args = convert_namespace_to_omegaconf(state["args"]) + cfg.multires_hubert_args = multires_hubert_args + else: + state = None + multires_hubert_args = cfg.multires_hubert_args + if isinstance(multires_hubert_args, Namespace): + cfg.multires_hubert_args = ( + multires_hubert_args + ) = convert_namespace_to_omegaconf(multires_hubert_args) + + assert cfg.normalize == multires_hubert_args.task.normalize, ( + "Fine-tuning works best when data normalization is the same. " + "Please check that --normalize is set or unset for " + "both pre-training and here" + ) + + multires_hubert_args.task.data = cfg.data + pretrain_task = tasks.setup_task(multires_hubert_args.task) + if state is not None and "task_state" in state: + # This will load the stored "dictionaries" object + pretrain_task.load_state_dict(state["task_state"]) + else: + pretrain_task.load_state_dict(task.state_dict()) + + model = pretrain_task.build_model( + multires_hubert_args.model, from_checkpoint=True + ) + if state is not None and not cfg.no_pretrained_weights: + # set strict=False because we omit some modules + model.load_state_dict(state["model"], strict=False) + + model.remove_pretraining_modules() + + super().__init__(pretrain_task.source_dictionary) + + d = multires_hubert_args.model.encoder_embed_dim + + self.multires_hubert_model = model + + self.final_dropout = nn.Dropout(cfg.final_dropout) + self.freeze_finetune_updates = cfg.freeze_finetune_updates + self.num_updates = 0 + + if task.target_dictionary is not None: + self.proj = Linear(d, len(task.target_dictionary)) + elif getattr(cfg, "decoder_embed_dim", d) != d: + self.proj = Linear(d, cfg.decoder_embed_dim) + else: + self.proj = None + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward(self, source, padding_mask, tbc=True, **kwargs): + multires_hubert_args = { + "source": source, + "padding_mask": padding_mask, + "mask": self.apply_mask and self.training, + "last_layer": True, + } + + ft = self.freeze_finetune_updates <= self.num_updates + + with torch.no_grad() if not ft else contextlib.ExitStack(): + x, padding_mask = self.multires_hubert_model.extract_features( + **multires_hubert_args + ) + + if tbc: + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + x = self.final_dropout(x) + + if self.proj: + x = self.proj(x) + + return { + "encoder_out": x, # T x B x C + "encoder_padding_mask": padding_mask, # B x T + "padding_mask": padding_mask, + } + + def reorder_encoder_out(self, encoder_out, new_order): + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return None + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m diff --git a/fairseq/fairseq/models/nat/__init__.py b/fairseq/fairseq/models/nat/__init__.py new file mode 100644 index 0000000..05fe822 --- /dev/null +++ b/fairseq/fairseq/models/nat/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +from .fairseq_nat_model import * +from .nonautoregressive_transformer import * +from .nat_crf_transformer import * +from .iterative_nonautoregressive_transformer import * +from .cmlm_transformer import * +from .levenshtein_transformer import * +from .insertion_transformer import * diff --git a/fairseq/fairseq/models/nat/cmlm_transformer.py b/fairseq/fairseq/models/nat/cmlm_transformer.py new file mode 100644 index 0000000..c876e94 --- /dev/null +++ b/fairseq/fairseq/models/nat/cmlm_transformer.py @@ -0,0 +1,162 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +This file implements: +Ghazvininejad, Marjan, et al. +"Constant-time machine translation with conditional masked language models." +arXiv preprint arXiv:1904.09324 (2019). +""" + +from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import NATransformerModel +from fairseq.utils import new_arange + + +def _skeptical_unmasking(output_scores, output_masks, p): + sorted_index = output_scores.sort(-1)[1] + boundary_len = ( + (output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p + ).long() + skeptical_mask = new_arange(output_masks) < boundary_len + return skeptical_mask.scatter(1, sorted_index, skeptical_mask) + + +@register_model("cmlm_transformer") +class CMLMNATransformerModel(NATransformerModel): + @staticmethod + def add_args(parser): + NATransformerModel.add_args(parser) + + def forward( + self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs + ): + assert not self.decoder.src_embedding_copy, "do not support embedding copy." + + # encoding + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + # length prediction + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) + + # decoding + word_ins_out = self.decoder( + normalize=False, + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + ) + word_ins_mask = prev_output_tokens.eq(self.unk) + + return { + "word_ins": { + "out": word_ins_out, + "tgt": tgt_tokens, + "mask": word_ins_mask, + "ls": self.args.label_smoothing, + "nll_loss": True, + }, + "length": { + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, + } + + def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): + + step = decoder_out.step + max_step = decoder_out.max_step + + output_tokens = decoder_out.output_tokens + output_scores = decoder_out.output_scores + history = decoder_out.history + + # execute the decoder + output_masks = output_tokens.eq(self.unk) + _scores, _tokens = self.decoder( + normalize=True, + prev_output_tokens=output_tokens, + encoder_out=encoder_out, + ).max(-1) + output_tokens.masked_scatter_(output_masks, _tokens[output_masks]) + output_scores.masked_scatter_(output_masks, _scores[output_masks]) + + if history is not None: + history.append(output_tokens.clone()) + + # skeptical decoding (depend on the maximum decoding steps.) + if (step + 1) < max_step: + skeptical_mask = _skeptical_unmasking( + output_scores, output_tokens.ne(self.pad), 1 - (step + 1) / max_step + ) + + output_tokens.masked_fill_(skeptical_mask, self.unk) + output_scores.masked_fill_(skeptical_mask, 0.0) + + if history is not None: + history.append(output_tokens.clone()) + + return decoder_out._replace( + output_tokens=output_tokens, + output_scores=output_scores, + attn=None, + history=history, + ) + + +@register_model_architecture("cmlm_transformer", "cmlm_transformer") +def cmlm_base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", True) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.apply_bert_init = getattr(args, "apply_bert_init", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + # --- special arguments --- + args.sg_length_pred = getattr(args, "sg_length_pred", False) + args.pred_length_offset = getattr(args, "pred_length_offset", False) + args.length_loss_factor = getattr(args, "length_loss_factor", 0.1) + args.ngram_predictor = getattr(args, "ngram_predictor", 1) + args.src_embedding_copy = getattr(args, "src_embedding_copy", False) + + +@register_model_architecture("cmlm_transformer", "cmlm_transformer_wmt_en_de") +def cmlm_wmt_en_de(args): + cmlm_base_architecture(args) diff --git a/fairseq/fairseq/models/nat/fairseq_nat_model.py b/fairseq/fairseq/models/nat/fairseq_nat_model.py new file mode 100644 index 0000000..a5594a4 --- /dev/null +++ b/fairseq/fairseq/models/nat/fairseq_nat_model.py @@ -0,0 +1,172 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from fairseq.models.transformer import ( + TransformerDecoder, + TransformerEncoder, + TransformerModel, +) +from fairseq.modules.transformer_sentence_encoder import init_bert_params + + +def ensemble_encoder(func): + def wrapper(self, *args, **kwargs): + if self.ensemble_models is None or len(self.ensemble_models) == 1: + return func(self, *args, **kwargs) + encoder_outs = [ + func(model, *args, **kwargs, return_all_hiddens=True) + for model in self.ensemble_models + ] + _encoder_out = encoder_outs[0].copy() + + def stack(key): + outs = [e[key][0] for e in encoder_outs] + return [torch.stack(outs, -1) if outs[0] is not None else None] + + _encoder_out["encoder_out"] = stack("encoder_out") + _encoder_out["encoder_embedding"] = stack("encoder_embedding") + + num_layers = len(_encoder_out["encoder_states"]) + if num_layers > 0: + _encoder_out["encoder_states"] = [ + torch.stack([e["encoder_states"][i] for e in encoder_outs], -1) + for i in range(num_layers) + ] + return _encoder_out + + return wrapper + + +def ensemble_decoder(func): + def wrapper(self, normalize=False, encoder_out=None, *args, **kwargs): + if self.ensemble_models is None or len(self.ensemble_models) == 1: + return func( + self, normalize=normalize, encoder_out=encoder_out, *args, **kwargs + ) + + def _replace(encoder_out, new_val): + new_encoder_out = encoder_out.copy() + new_encoder_out["encoder_out"] = [new_val] + return new_encoder_out + + action_outs = [ + func( + model, + normalize=normalize, + encoder_out=_replace( + encoder_out, encoder_out["encoder_out"][0][:, :, :, i] + ), + *args, + **kwargs + ) + for i, model in enumerate(self.ensemble_models) + ] + + if not isinstance(action_outs[0], tuple): # return multiple values + action_outs = [[a] for a in action_outs] + else: + action_outs = [list(a) for a in action_outs] + + ensembled_outs = [] + for i in range(len(action_outs[0])): + if i == 0 and normalize: + ensembled_outs += [ + torch.logsumexp( + torch.stack([a[i] for a in action_outs], -1), dim=-1 + ) + - math.log(len(self.ensemble_models)) + ] + elif action_outs[0][i] is not None: + ensembled_outs += [torch.stack([a[i] for a in action_outs], -1)] + else: + ensembled_outs += [None] + + if len(ensembled_outs) == 1: + return ensembled_outs[0] + return tuple(ensembled_outs) + + return wrapper + + +class FairseqNATModel(TransformerModel): + """ + Abstract class for all nonautoregressive-based models + """ + + def __init__(self, args, encoder, decoder): + super().__init__(args, encoder, decoder) + self.tgt_dict = decoder.dictionary + self.bos = decoder.dictionary.bos() + self.eos = decoder.dictionary.eos() + self.pad = decoder.dictionary.pad() + self.unk = decoder.dictionary.unk() + + self.ensemble_models = None + + @property + def allow_length_beam(self): + return False + + @property + def allow_ensemble(self): + return True + + def enable_ensemble(self, models): + self.encoder.ensemble_models = [m.encoder for m in models] + self.decoder.ensemble_models = [m.decoder for m in models] + + @staticmethod + def add_args(parser): + TransformerModel.add_args(parser) + parser.add_argument( + "--apply-bert-init", + action="store_true", + help="use custom param initialization for BERT", + ) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + decoder = FairseqNATDecoder(args, tgt_dict, embed_tokens) + if getattr(args, "apply_bert_init", False): + decoder.apply(init_bert_params) + return decoder + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + encoder = FairseqNATEncoder(args, src_dict, embed_tokens) + if getattr(args, "apply_bert_init", False): + encoder.apply(init_bert_params) + return encoder + + def forward_encoder(self, encoder_inputs): + return self.encoder(*encoder_inputs) + + def forward_decoder(self, *args, **kwargs): + return NotImplementedError + + def initialize_output_tokens(self, *args, **kwargs): + return NotImplementedError + + def forward(self, *args, **kwargs): + return NotImplementedError + + +class FairseqNATEncoder(TransformerEncoder): + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens) + self.ensemble_models = None + + @ensemble_encoder + def forward(self, *args, **kwargs): + return super().forward(*args, **kwargs) + + +class FairseqNATDecoder(TransformerDecoder): + def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): + super().__init__(args, dictionary, embed_tokens, no_encoder_attn) + self.ensemble_models = None diff --git a/fairseq/fairseq/models/nat/insertion_transformer.py b/fairseq/fairseq/models/nat/insertion_transformer.py new file mode 100644 index 0000000..bc28000 --- /dev/null +++ b/fairseq/fairseq/models/nat/insertion_transformer.py @@ -0,0 +1,280 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +import torch.nn.functional as F +from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import ( + FairseqNATModel, + LevenshteinTransformerDecoder, + LevenshteinTransformerModel, + ensemble_decoder, +) +from fairseq.models.transformer import Linear +from fairseq.modules.transformer_sentence_encoder import init_bert_params +from fairseq.utils import new_arange + + +class NegativeDistanceScore(object): + def __init__(self): + + # pre-compute some values + self.scores = {} + + self.scores[0.5] = self.compute_score_full(50, 0.5) + self.scores[1.0] = self.compute_score_full(50, 1.0) + self.scores[2.0] = self.compute_score_full(50, 2.0) + + def __call__(self, i, L, tau): + if (tau is None) or (tau > 1000): + return 1 / L + + if tau in self.scores: + if L < self.scores[tau].shape[0]: + return self.scores[tau][L - 1, i] + return self.compute_score(L, tau)[i] + + def compute_score(self, L, tau): + s = np.array([-abs(L / 2 - i) / tau for i in range(L)]) + s = np.exp(s - s.max()) + return s / s.sum() + + def compute_score_full(self, L, tau): + s = -abs(np.arange(0, L - 1)[:, None] / 2 - np.arange(L)[None, :]) / tau + s = np.tril(s, 0) + np.triu(s - float("inf"), 1) + s = np.exp(s - s.max(1, keepdims=True)) + return s / s.sum(1, keepdims=True) + + +neg_scorer = NegativeDistanceScore() + + +def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx, vocab_size, tau=None): + try: + from fairseq import libnat + except ImportError as e: + import sys + + sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n") + raise e + + B = in_tokens.size(0) + T = in_tokens.size(1) + V = vocab_size + + with torch.cuda.device_of(in_tokens): + in_tokens_list = [ + [t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist()) + ] + out_tokens_list = [ + [t for t in s if t != padding_idx] + for i, s in enumerate(out_tokens.tolist()) + ] + + full_labels = libnat.suggested_ed2_path( + in_tokens_list, out_tokens_list, padding_idx + ) + insert_labels = [a[:-1] for a in full_labels] + + # numericalize1 + insert_label_tensors = in_tokens.new_zeros(B * (T - 1) * V).float() + insert_index, insert_labels = zip( + *[ + (w + (j + i * (T - 1)) * V, neg_scorer(k, len(label), tau)) + for i, labels in enumerate(insert_labels) + for j, label in enumerate(labels[1:-1]) + for k, w in enumerate(label) + ] + ) # HACK 1:-1 + insert_index, insert_labels = [ + torch.tensor(list(a), device=in_tokens.device) + for a in [insert_index, insert_labels] + ] + insert_label_tensors.scatter_(0, insert_index.long(), insert_labels) + insert_label_tensors = insert_label_tensors.view(B, T - 1, V) + + return insert_label_tensors + + +def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, padding_idx): + + padding_masks = in_tokens[:, 1:].eq(padding_idx) + word_ins_scores.masked_fill_(padding_masks, 0.0) + word_ins_pred.masked_fill_(padding_masks, padding_idx) + + in_coords = new_arange(in_tokens).type_as(in_scores) + + # shift all padding predictions to infinite + out_coords = (in_coords[:, 1:] - 0.5).masked_fill( + word_ins_pred.eq(padding_idx), float("inf") + ) + out_coords = torch.cat([in_coords, out_coords], 1).sort(-1)[1] + out_tokens = torch.cat([in_tokens, word_ins_pred], 1).gather(1, out_coords) + out_scores = torch.cat([in_scores, word_ins_scores], 1).gather(1, out_coords) + return out_tokens, out_scores + + +@register_model("insertion_transformer") +class InsertionTransformerModel(LevenshteinTransformerModel): + def __init__(self, args, encoder, decoder): + super().__init__(args, encoder, decoder) + + @staticmethod + def add_args(parser): + FairseqNATModel.add_args(parser) + parser.add_argument("--label-tau", default=None, type=float) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + decoder = InsertionTransformerDecoder(args, tgt_dict, embed_tokens) + if getattr(args, "apply_bert_init", False): + decoder.apply(init_bert_params) + return decoder + + def forward( + self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs + ): + + assert tgt_tokens is not None, "forward function only supports training." + + # encoding + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + + # generate training labels for insertion + word_ins_out = self.decoder.forward_word_ins( + normalize=False, + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + ) + + word_ins_tgt = _get_ins_targets( + prev_output_tokens, + tgt_tokens, + self.pad, + self.unk, + len(self.tgt_dict), + tau=self.decoder.label_tau, + ).type_as(word_ins_out) + word_ins_masks = prev_output_tokens[:, 1:].ne(self.pad) + + return { + "word_ins": { + "out": word_ins_out, + "tgt": word_ins_tgt, + "mask": word_ins_masks, + "ls": self.args.label_smoothing, + "nll_loss": True, + } + } + + def forward_decoder( + self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs + ): + + output_tokens = decoder_out.output_tokens + output_scores = decoder_out.output_scores + history = decoder_out.history + + # TODO: decoding for InsertionTransformer + word_ins_score = self.decoder.forward_word_ins( + normalize=True, prev_output_tokens=output_tokens, encoder_out=encoder_out + ) + + if eos_penalty > 0.0: + word_ins_score[:, :, self.pad] -= eos_penalty + word_ins_score, word_ins_pred = word_ins_score.max(-1) + output_tokens, output_scores = _apply_ins_words( + output_tokens, output_scores, word_ins_pred, word_ins_score, self.pad + ) + + # delete some unnecessary paddings + cut_off = output_tokens.ne(self.pad).sum(1).max() + output_tokens = output_tokens[:, :cut_off] + output_scores = output_scores[:, :cut_off] + + if history is not None: + history.append(output_tokens.clone()) + + return decoder_out._replace( + output_tokens=output_tokens, + output_scores=output_scores, + attn=None, + history=history, + ) + + +class InsertionTransformerDecoder(LevenshteinTransformerDecoder): + def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): + # use the TransformerDecoder's __init__ + super(LevenshteinTransformerDecoder, self).__init__( + args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn + ) + + self.dictionary = dictionary + self.bos = dictionary.bos() + self.unk = dictionary.unk() + self.eos = dictionary.eos() + self.pool_out = Linear(self.output_embed_dim * 2, self.output_embed_dim) + + self.label_tau = getattr(args, "label_tau", None) + + @ensemble_decoder + def forward_word_ins(self, normalize, encoder_out, prev_output_tokens): + features = self.extract_features(prev_output_tokens, encoder_out=encoder_out)[0] + features = self.pool_out( + torch.cat([features[:, :-1, :], features[:, 1:, :]], 2) + ) + decoder_out = self.output_layer(features) + return F.log_softmax(decoder_out, -1) if normalize else decoder_out + + def forward_mask_ins(self, *args, **kwargs): + raise NotImplementedError + + def forward_word_del(self, *args, **kwargs): + raise NotImplementedError + + +@register_model_architecture("insertion_transformer", "insertion_transformer") +def insertion_base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.apply_bert_init = getattr(args, "apply_bert_init", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + # special for insertion transformer + args.label_tau = getattr(args, "label_tau", None) diff --git a/fairseq/fairseq/models/nat/iterative_nonautoregressive_transformer.py b/fairseq/fairseq/models/nat/iterative_nonautoregressive_transformer.py new file mode 100644 index 0000000..bc39509 --- /dev/null +++ b/fairseq/fairseq/models/nat/iterative_nonautoregressive_transformer.py @@ -0,0 +1,228 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import NATransformerModel + + +def _sequential_poisoning(s, V, beta=0.33, bos=2, eos=3, pad=1): + # s: input batch + # V: vocabulary size + rand_words = torch.randint(low=4, high=V, size=s.size(), device=s.device) + choices = torch.rand(size=s.size(), device=s.device) + choices.masked_fill_((s == pad) | (s == bos) | (s == eos), 1) + + replace = choices < beta / 3 + repeat = (choices >= beta / 3) & (choices < beta * 2 / 3) + swap = (choices >= beta * 2 / 3) & (choices < beta) + safe = choices >= beta + + for i in range(s.size(1) - 1): + rand_word = rand_words[:, i] + next_word = s[:, i + 1] + self_word = s[:, i] + + replace_i = replace[:, i] + swap_i = swap[:, i] & (next_word != 3) + repeat_i = repeat[:, i] & (next_word != 3) + safe_i = safe[:, i] | ((next_word == 3) & (~replace_i)) + + s[:, i] = ( + self_word * (safe_i | repeat_i).long() + + next_word * swap_i.long() + + rand_word * replace_i.long() + ) + s[:, i + 1] = ( + next_word * (safe_i | replace_i).long() + + self_word * (swap_i | repeat_i).long() + ) + return s + + +def gumbel_noise(input, TINY=1e-8): + return ( + input.new_zeros(*input.size()) + .uniform_() + .add_(TINY) + .log_() + .neg_() + .add_(TINY) + .log_() + .neg_() + ) + + +@register_model("iterative_nonautoregressive_transformer") +class IterNATransformerModel(NATransformerModel): + @staticmethod + def add_args(parser): + NATransformerModel.add_args(parser) + parser.add_argument( + "--train-step", + type=int, + help="number of refinement iterations during training", + ) + parser.add_argument( + "--dae-ratio", + type=float, + help="the probability of switching to the denoising auto-encoder loss", + ) + parser.add_argument( + "--stochastic-approx", + action="store_true", + help="sampling from the decoder as the inputs for next iteration", + ) + + @classmethod + def build_model(cls, args, task): + model = super().build_model(args, task) + model.train_step = getattr(args, "train_step", 4) + model.dae_ratio = getattr(args, "dae_ratio", 0.5) + model.stochastic_approx = getattr(args, "stochastic_approx", False) + return model + + def forward( + self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs + ): + + B, T = prev_output_tokens.size() + + # encoding + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + + # length prediction + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) + + # decoding + word_ins_outs, word_ins_tgts, word_ins_masks = [], [], [] + for t in range(self.train_step): + word_ins_out = self.decoder( + normalize=False, + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + step=t, + ) + word_ins_tgt = tgt_tokens + word_ins_mask = word_ins_tgt.ne(self.pad) + + word_ins_outs.append(word_ins_out) + word_ins_tgts.append(word_ins_tgt) + word_ins_masks.append(word_ins_mask) + + if t < (self.train_step - 1): + # prediction for next iteration + if self.stochastic_approx: + word_ins_prediction = ( + word_ins_out + gumbel_noise(word_ins_out) + ).max(-1)[1] + else: + word_ins_prediction = word_ins_out.max(-1)[1] + + prev_output_tokens = prev_output_tokens.masked_scatter( + word_ins_mask, word_ins_prediction[word_ins_mask] + ) + + if self.dae_ratio > 0: + # we do not perform denoising for the first iteration + corrputed = ( + torch.rand(size=(B,), device=prev_output_tokens.device) + < self.dae_ratio + ) + corrputed_tokens = _sequential_poisoning( + tgt_tokens[corrputed], + len(self.tgt_dict), + 0.33, + self.bos, + self.eos, + self.pad, + ) + prev_output_tokens[corrputed] = corrputed_tokens + + # concat everything + word_ins_out = torch.cat(word_ins_outs, 0) + word_ins_tgt = torch.cat(word_ins_tgts, 0) + word_ins_mask = torch.cat(word_ins_masks, 0) + + return { + "word_ins": { + "out": word_ins_out, + "tgt": word_ins_tgt, + "mask": word_ins_mask, + "ls": self.args.label_smoothing, + "nll_loss": True, + }, + "length": { + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, + } + + +@register_model_architecture( + "iterative_nonautoregressive_transformer", "iterative_nonautoregressive_transformer" +) +def inat_base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.apply_bert_init = getattr(args, "apply_bert_init", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + # --- special arguments --- + args.sg_length_pred = getattr(args, "sg_length_pred", False) + args.pred_length_offset = getattr(args, "pred_length_offset", False) + args.length_loss_factor = getattr(args, "length_loss_factor", 0.1) + args.ngram_predictor = getattr(args, "ngram_predictor", 1) + args.src_embedding_copy = getattr(args, "src_embedding_copy", False) + + args.train_step = getattr(args, "train_step", 4) + args.dae_ratio = getattr(args, "dae_ratio", 0.5) + args.stochastic_approx = getattr(args, "stochastic_approx", False) + + +@register_model_architecture( + "iterative_nonautoregressive_transformer", + "iterative_nonautoregressive_transformer_wmt_en_de", +) +def iter_nat_wmt_en_de(args): + inat_base_architecture(args) diff --git a/fairseq/fairseq/models/nat/levenshtein_transformer.py b/fairseq/fairseq/models/nat/levenshtein_transformer.py new file mode 100644 index 0000000..d60d3c5 --- /dev/null +++ b/fairseq/fairseq/models/nat/levenshtein_transformer.py @@ -0,0 +1,510 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq.iterative_refinement_generator import DecoderOut +from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder +from fairseq.models.transformer import Embedding +from fairseq.modules import TransformerDecoderLayer +from fairseq.modules.transformer_sentence_encoder import init_bert_params + +from .levenshtein_utils import ( + _apply_del_words, + _apply_ins_masks, + _apply_ins_words, + _fill, + _get_del_targets, + _get_ins_targets, + _skip, + _skip_encoder_out, +) + + +@register_model("levenshtein_transformer") +class LevenshteinTransformerModel(FairseqNATModel): + @property + def allow_length_beam(self): + return False + + @staticmethod + def add_args(parser): + FairseqNATModel.add_args(parser) + parser.add_argument( + "--early-exit", + default="6,6,6", + type=str, + help="number of decoder layers before word_del, mask_ins, word_ins", + ) + parser.add_argument( + "--no-share-discriminator", + action="store_true", + help="separate parameters for discriminator", + ) + parser.add_argument( + "--no-share-maskpredictor", + action="store_true", + help="separate parameters for mask-predictor", + ) + parser.add_argument( + "--share-discriminator-maskpredictor", + action="store_true", + help="share the parameters for both mask-predictor and discriminator", + ) + parser.add_argument( + "--sampling-for-deletion", + action="store_true", + help="instead of argmax, use sampling to predict the tokens", + ) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + decoder = LevenshteinTransformerDecoder(args, tgt_dict, embed_tokens) + if getattr(args, "apply_bert_init", False): + decoder.apply(init_bert_params) + return decoder + + def forward( + self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs + ): + + assert tgt_tokens is not None, "forward function only supports training." + + # encoding + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + + # generate training labels for insertion + masked_tgt_masks, masked_tgt_tokens, mask_ins_targets = _get_ins_targets( + prev_output_tokens, tgt_tokens, self.pad, self.unk + ) + mask_ins_targets = mask_ins_targets.clamp(min=0, max=255) # for safe prediction + mask_ins_masks = prev_output_tokens[:, 1:].ne(self.pad) + + mask_ins_out, _ = self.decoder.forward_mask_ins( + normalize=False, + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + ) + word_ins_out, _ = self.decoder.forward_word_ins( + normalize=False, + prev_output_tokens=masked_tgt_tokens, + encoder_out=encoder_out, + ) + + # make online prediction + if self.decoder.sampling_for_deletion: + word_predictions = torch.multinomial( + F.softmax(word_ins_out, -1).view(-1, word_ins_out.size(-1)), 1 + ).view(word_ins_out.size(0), -1) + else: + word_predictions = F.log_softmax(word_ins_out, dim=-1).max(2)[1] + + word_predictions.masked_scatter_( + ~masked_tgt_masks, tgt_tokens[~masked_tgt_masks] + ) + + # generate training labels for deletion + word_del_targets = _get_del_targets(word_predictions, tgt_tokens, self.pad) + word_del_out, _ = self.decoder.forward_word_del( + normalize=False, + prev_output_tokens=word_predictions, + encoder_out=encoder_out, + ) + word_del_masks = word_predictions.ne(self.pad) + + return { + "mask_ins": { + "out": mask_ins_out, + "tgt": mask_ins_targets, + "mask": mask_ins_masks, + "ls": 0.01, + }, + "word_ins": { + "out": word_ins_out, + "tgt": tgt_tokens, + "mask": masked_tgt_masks, + "ls": self.args.label_smoothing, + "nll_loss": True, + }, + "word_del": { + "out": word_del_out, + "tgt": word_del_targets, + "mask": word_del_masks, + }, + } + + def forward_decoder( + self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs + ): + + output_tokens = decoder_out.output_tokens + output_scores = decoder_out.output_scores + attn = decoder_out.attn + history = decoder_out.history + + bsz = output_tokens.size(0) + if max_ratio is None: + max_lens = torch.zeros_like(output_tokens).fill_(255) + else: + if not encoder_out["encoder_padding_mask"]: + max_src_len = encoder_out["encoder_out"].size(0) + src_lens = encoder_out["encoder_out"].new(bsz).fill_(max_src_len) + else: + src_lens = (~encoder_out["encoder_padding_mask"][0]).sum(1) + max_lens = (src_lens * max_ratio).clamp(min=10).long() + + # delete words + # do not delete tokens if it is <s> </s> + can_del_word = output_tokens.ne(self.pad).sum(1) > 2 + if can_del_word.sum() != 0: # we cannot delete, skip + word_del_score, word_del_attn = self.decoder.forward_word_del( + normalize=True, + prev_output_tokens=_skip(output_tokens, can_del_word), + encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_del_word), + ) + word_del_pred = word_del_score.max(-1)[1].bool() + + _tokens, _scores, _attn = _apply_del_words( + output_tokens[can_del_word], + output_scores[can_del_word], + word_del_attn, + word_del_pred, + self.pad, + self.bos, + self.eos, + ) + output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad) + output_scores = _fill(output_scores, can_del_word, _scores, 0) + attn = _fill(attn, can_del_word, _attn, 0.0) + + if history is not None: + history.append(output_tokens.clone()) + + # insert placeholders + can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens + if can_ins_mask.sum() != 0: + mask_ins_score, _ = self.decoder.forward_mask_ins( + normalize=True, + prev_output_tokens=_skip(output_tokens, can_ins_mask), + encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_mask), + ) + if eos_penalty > 0.0: + mask_ins_score[:, :, 0] = mask_ins_score[:, :, 0] - eos_penalty + mask_ins_pred = mask_ins_score.max(-1)[1] + mask_ins_pred = torch.min( + mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred) + ) + + _tokens, _scores = _apply_ins_masks( + output_tokens[can_ins_mask], + output_scores[can_ins_mask], + mask_ins_pred, + self.pad, + self.unk, + self.eos, + ) + output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad) + output_scores = _fill(output_scores, can_ins_mask, _scores, 0) + + if history is not None: + history.append(output_tokens.clone()) + + # insert words + can_ins_word = output_tokens.eq(self.unk).sum(1) > 0 + if can_ins_word.sum() != 0: + word_ins_score, word_ins_attn = self.decoder.forward_word_ins( + normalize=True, + prev_output_tokens=_skip(output_tokens, can_ins_word), + encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_word), + ) + word_ins_score, word_ins_pred = word_ins_score.max(-1) + _tokens, _scores = _apply_ins_words( + output_tokens[can_ins_word], + output_scores[can_ins_word], + word_ins_pred, + word_ins_score, + self.unk, + ) + + output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad) + output_scores = _fill(output_scores, can_ins_word, _scores, 0) + attn = _fill(attn, can_ins_word, word_ins_attn, 0.0) + + if history is not None: + history.append(output_tokens.clone()) + + # delete some unnecessary paddings + cut_off = output_tokens.ne(self.pad).sum(1).max() + output_tokens = output_tokens[:, :cut_off] + output_scores = output_scores[:, :cut_off] + attn = None if attn is None else attn[:, :cut_off, :] + + return decoder_out._replace( + output_tokens=output_tokens, + output_scores=output_scores, + attn=attn, + history=history, + ) + + def initialize_output_tokens(self, encoder_out, src_tokens): + initial_output_tokens = src_tokens.new_zeros(src_tokens.size(0), 2) + initial_output_tokens[:, 0] = self.bos + initial_output_tokens[:, 1] = self.eos + + initial_output_scores = initial_output_tokens.new_zeros( + *initial_output_tokens.size() + ).type_as(encoder_out["encoder_out"][0]) + + return DecoderOut( + output_tokens=initial_output_tokens, + output_scores=initial_output_scores, + attn=None, + step=0, + max_step=0, + history=None, + ) + + +class LevenshteinTransformerDecoder(FairseqNATDecoder): + def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): + super().__init__( + args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn + ) + self.dictionary = dictionary + self.bos = dictionary.bos() + self.unk = dictionary.unk() + self.eos = dictionary.eos() + self.sampling_for_deletion = getattr(args, "sampling_for_deletion", False) + self.embed_mask_ins = Embedding(256, self.output_embed_dim * 2, None) + self.embed_word_del = Embedding(2, self.output_embed_dim, None) + + # del_word, ins_mask, ins_word + self.early_exit = [int(i) for i in args.early_exit.split(",")] + assert len(self.early_exit) == 3 + + # copy layers for mask-predict/deletion + self.layers_msk = None + if getattr(args, "no_share_maskpredictor", False): + self.layers_msk = nn.ModuleList( + [ + TransformerDecoderLayer(args, no_encoder_attn) + for _ in range(self.early_exit[1]) + ] + ) + self.layers_del = None + if getattr(args, "no_share_discriminator", False): + self.layers_del = nn.ModuleList( + [ + TransformerDecoderLayer(args, no_encoder_attn) + for _ in range(self.early_exit[0]) + ] + ) + + if getattr(args, "share_discriminator_maskpredictor", False): + assert getattr( + args, "no_share_discriminator", False + ), "must set saperate discriminator" + self.layers_msk = self.layers_del + + def extract_features( + self, + prev_output_tokens, + encoder_out=None, + early_exit=None, + layers=None, + **unused + ): + """ + Similar to *forward* but only return features. + Inputs: + prev_output_tokens: Tensor(B, T) + encoder_out: a dictionary of hidden states and masks + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + the LevenshteinTransformer decoder has full-attention to all generated tokens + """ + # embed positions + positions = ( + self.embed_positions(prev_output_tokens) + if self.embed_positions is not None + else None + ) + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + attn = None + inner_states = [x] + + # decoder layers + decoder_padding_mask = prev_output_tokens.eq(self.padding_idx) + layers = self.layers if layers is None else layers + early_exit = len(layers) if early_exit is None else early_exit + for _, layer in enumerate(layers[:early_exit]): + x, attn, _ = layer( + x, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, + self_attn_mask=None, + self_attn_padding_mask=decoder_padding_mask, + ) + inner_states.append(x) + + if self.layer_norm: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x, {"attn": attn, "inner_states": inner_states} + + @ensemble_decoder + def forward_mask_ins(self, normalize, encoder_out, prev_output_tokens, **unused): + features, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + early_exit=self.early_exit[1], + layers=self.layers_msk, + **unused + ) + features_cat = torch.cat([features[:, :-1, :], features[:, 1:, :]], 2) + decoder_out = F.linear(features_cat, self.embed_mask_ins.weight) + if normalize: + return F.log_softmax(decoder_out, -1), extra["attn"] + return decoder_out, extra["attn"] + + @ensemble_decoder + def forward_word_ins(self, normalize, encoder_out, prev_output_tokens, **unused): + features, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + early_exit=self.early_exit[2], + layers=self.layers, + **unused + ) + decoder_out = self.output_layer(features) + if normalize: + return F.log_softmax(decoder_out, -1), extra["attn"] + return decoder_out, extra["attn"] + + @ensemble_decoder + def forward_word_del(self, normalize, encoder_out, prev_output_tokens, **unused): + features, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + early_exit=self.early_exit[0], + layers=self.layers_del, + **unused + ) + decoder_out = F.linear(features, self.embed_word_del.weight) + if normalize: + return F.log_softmax(decoder_out, -1), extra["attn"] + return decoder_out, extra["attn"] + + +@register_model_architecture("levenshtein_transformer", "levenshtein_transformer") +def levenshtein_base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.apply_bert_init = getattr(args, "apply_bert_init", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.sampling_for_deletion = getattr(args, "sampling_for_deletion", False) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.early_exit = getattr(args, "early_exit", "6,6,6") + args.no_share_discriminator = getattr(args, "no_share_discriminator", False) + args.no_share_maskpredictor = getattr(args, "no_share_maskpredictor", False) + args.share_discriminator_maskpredictor = getattr( + args, "share_discriminator_maskpredictor", False + ) + args.no_share_last_layer = getattr(args, "no_share_last_layer", False) + + +@register_model_architecture( + "levenshtein_transformer", "levenshtein_transformer_wmt_en_de" +) +def levenshtein_transformer_wmt_en_de(args): + levenshtein_base_architecture(args) + + +# similar parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) +@register_model_architecture( + "levenshtein_transformer", "levenshtein_transformer_vaswani_wmt_en_de_big" +) +def levenshtein_transformer_vaswani_wmt_en_de_big(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) + levenshtein_base_architecture(args) + + +# default parameters used in tensor2tensor implementation +@register_model_architecture( + "levenshtein_transformer", "levenshtein_transformer_wmt_en_de_big" +) +def levenshtein_transformer_wmt_en_de_big_t2t(args): + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_dropout = getattr(args, "activation_dropout", 0.1) + levenshtein_transformer_vaswani_wmt_en_de_big(args) diff --git a/fairseq/fairseq/models/nat/levenshtein_utils.py b/fairseq/fairseq/models/nat/levenshtein_utils.py new file mode 100644 index 0000000..375a98c --- /dev/null +++ b/fairseq/fairseq/models/nat/levenshtein_utils.py @@ -0,0 +1,293 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq.utils import new_arange + + +# -------------- Helper Functions --------------------------------------------------- # + + +def load_libnat(): + try: + from fairseq import libnat_cuda + + return libnat_cuda, True + + except ImportError as e: + print(str(e) + "... fall back to CPU version") + + try: + from fairseq import libnat + + return libnat, False + + except ImportError as e: + import sys + + sys.stderr.write( + "ERROR: missing libnat_cuda. run `python setup.py build_ext --inplace`\n" + ) + raise e + + +def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx): + libnat, use_cuda = load_libnat() + + def _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx): + in_masks = in_tokens.ne(padding_idx) + out_masks = out_tokens.ne(padding_idx) + mask_ins_targets, masked_tgt_masks = libnat.generate_insertion_labels( + out_tokens.int(), + libnat.levenshtein_distance( + in_tokens.int(), + out_tokens.int(), + in_masks.sum(1).int(), + out_masks.sum(1).int(), + ), + ) + masked_tgt_masks = masked_tgt_masks.bool() & out_masks + mask_ins_targets = mask_ins_targets.type_as(in_tokens)[ + :, 1 : in_masks.size(1) + ].masked_fill_(~in_masks[:, 1:], 0) + masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx) + return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets + + def _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx): + in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1) + + in_tokens_list = [ + [t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist()) + ] + out_tokens_list = [ + [t for t in s if t != padding_idx] + for i, s in enumerate(out_tokens.tolist()) + ] + + full_labels = libnat.suggested_ed2_path( + in_tokens_list, out_tokens_list, padding_idx + ) + mask_inputs = [ + [len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels + ] + + # generate labels + masked_tgt_masks = [] + for mask_input in mask_inputs: + mask_label = [] + for beam_size in mask_input[1:-1]: # HACK 1:-1 + mask_label += [0] + [1 for _ in range(beam_size)] + masked_tgt_masks.append( + mask_label + [0 for _ in range(out_seq_len - len(mask_label))] + ) + mask_ins_targets = [ + mask_input[1:-1] + + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))] + for mask_input in mask_inputs + ] + + # transform to tensor + masked_tgt_masks = torch.tensor( + masked_tgt_masks, device=out_tokens.device + ).bool() + mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device) + masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx) + return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets + + if use_cuda: + return _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx) + return _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx) + + +def _get_del_targets(in_tokens, out_tokens, padding_idx): + libnat, use_cuda = load_libnat() + + def _get_del_targets_cuda(in_tokens, out_tokens, padding_idx): + in_masks = in_tokens.ne(padding_idx) + out_masks = out_tokens.ne(padding_idx) + + word_del_targets = libnat.generate_deletion_labels( + in_tokens.int(), + libnat.levenshtein_distance( + in_tokens.int(), + out_tokens.int(), + in_masks.sum(1).int(), + out_masks.sum(1).int(), + ), + ) + word_del_targets = word_del_targets.type_as(in_tokens).masked_fill_( + ~in_masks, 0 + ) + return word_del_targets + + def _get_del_targets_cpu(in_tokens, out_tokens, padding_idx): + out_seq_len = out_tokens.size(1) + with torch.cuda.device_of(in_tokens): + in_tokens_list = [ + [t for t in s if t != padding_idx] + for i, s in enumerate(in_tokens.tolist()) + ] + out_tokens_list = [ + [t for t in s if t != padding_idx] + for i, s in enumerate(out_tokens.tolist()) + ] + + full_labels = libnat.suggested_ed2_path( + in_tokens_list, out_tokens_list, padding_idx + ) + word_del_targets = [b[-1] for b in full_labels] + word_del_targets = [ + labels + [0 for _ in range(out_seq_len - len(labels))] + for labels in word_del_targets + ] + + # transform to tensor + word_del_targets = torch.tensor(word_del_targets, device=out_tokens.device) + return word_del_targets + + if use_cuda: + return _get_del_targets_cuda(in_tokens, out_tokens, padding_idx) + return _get_del_targets_cpu(in_tokens, out_tokens, padding_idx) + + +def _apply_ins_masks( + in_tokens, in_scores, mask_ins_pred, padding_idx, unk_idx, eos_idx +): + + in_masks = in_tokens.ne(padding_idx) + in_lengths = in_masks.sum(1) + + # HACK: hacky way to shift all the paddings to eos first. + in_tokens.masked_fill_(~in_masks, eos_idx) + mask_ins_pred.masked_fill_(~in_masks[:, 1:], 0) + + out_lengths = in_lengths + mask_ins_pred.sum(1) + out_max_len = out_lengths.max() + out_masks = new_arange(out_lengths, out_max_len)[None, :] < out_lengths[:, None] + + reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1) + out_tokens = ( + in_tokens.new_zeros(in_tokens.size(0), out_max_len) + .fill_(padding_idx) + .masked_fill_(out_masks, unk_idx) + ) + out_tokens[:, 0] = in_tokens[:, 0] + out_tokens.scatter_(1, reordering, in_tokens[:, 1:]) + + out_scores = None + if in_scores is not None: + in_scores.masked_fill_(~in_masks, 0) + out_scores = in_scores.new_zeros(*out_tokens.size()) + out_scores[:, 0] = in_scores[:, 0] + out_scores.scatter_(1, reordering, in_scores[:, 1:]) + + return out_tokens, out_scores + + +def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx): + word_ins_masks = in_tokens.eq(unk_idx) + out_tokens = in_tokens.masked_scatter(word_ins_masks, word_ins_pred[word_ins_masks]) + + if in_scores is not None: + out_scores = in_scores.masked_scatter( + word_ins_masks, word_ins_scores[word_ins_masks] + ) + else: + out_scores = None + + return out_tokens, out_scores + + +def _apply_del_words( + in_tokens, in_scores, in_attn, word_del_pred, padding_idx, bos_idx, eos_idx +): + # apply deletion to a tensor + in_masks = in_tokens.ne(padding_idx) + bos_eos_masks = in_tokens.eq(bos_idx) | in_tokens.eq(eos_idx) + + max_len = in_tokens.size(1) + word_del_pred.masked_fill_(~in_masks, 1) + word_del_pred.masked_fill_(bos_eos_masks, 0) + + reordering = new_arange(in_tokens).masked_fill_(word_del_pred, max_len).sort(1)[1] + + out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering) + + out_scores = None + if in_scores is not None: + out_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering) + + out_attn = None + if in_attn is not None: + _mask = word_del_pred[:, :, None].expand_as(in_attn) + _reordering = reordering[:, :, None].expand_as(in_attn) + out_attn = in_attn.masked_fill(_mask, 0.0).gather(1, _reordering) + + return out_tokens, out_scores, out_attn + + +def _skip(x, mask): + """ + Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors. + """ + if isinstance(x, int): + return x + + if x is None: + return None + + if isinstance(x, torch.Tensor): + if x.size(0) == mask.size(0): + return x[mask] + elif x.size(1) == mask.size(0): + return x[:, mask] + + if isinstance(x, list): + return [_skip(x_i, mask) for x_i in x] + + if isinstance(x, dict): + return {k: _skip(v, mask) for k, v in x.items()} + + raise NotImplementedError + + +def _skip_encoder_out(encoder, encoder_out, mask): + if not mask.any(): + return encoder_out + else: + return encoder.reorder_encoder_out( + encoder_out, mask.nonzero(as_tuple=False).squeeze() + ) + + +def _fill(x, mask, y, padding_idx): + """ + Filling tensor x with y at masked positions (dim=0). + """ + if x is None: + return y + assert x.dim() == y.dim() and mask.size(0) == x.size(0) + assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2)) + n_selected = mask.sum() + assert n_selected == y.size(0) + + if n_selected == x.size(0): + return y + + if x.size(1) < y.size(1): + dims = [x.size(0), y.size(1) - x.size(1)] + if x.dim() == 3: + dims.append(x.size(2)) + x = torch.cat([x, x.new_zeros(*dims).fill_(padding_idx)], 1) + x[mask] = y + elif x.size(1) > y.size(1): + x[mask] = padding_idx + if x.dim() == 2: + x[mask, : y.size(1)] = y + else: + x[mask, : y.size(1), :] = y + else: + x[mask] = y + return x diff --git a/fairseq/fairseq/models/nat/nat_crf_transformer.py b/fairseq/fairseq/models/nat/nat_crf_transformer.py new file mode 100644 index 0000000..d4b3cd9 --- /dev/null +++ b/fairseq/fairseq/models/nat/nat_crf_transformer.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import NATransformerModel, base_architecture +from fairseq.modules import DynamicCRF + + +@register_model("nacrf_transformer") +class NACRFTransformerModel(NATransformerModel): + def __init__(self, args, encoder, decoder): + super().__init__(args, encoder, decoder) + self.crf_layer = DynamicCRF( + num_embedding=len(self.tgt_dict), + low_rank=args.crf_lowrank_approx, + beam_size=args.crf_beam_approx, + ) + + @property + def allow_ensemble(self): + return False + + @staticmethod + def add_args(parser): + NATransformerModel.add_args(parser) + parser.add_argument( + "--crf-lowrank-approx", + type=int, + help="the dimension of low-rank approximation of transition", + ) + parser.add_argument( + "--crf-beam-approx", + type=int, + help="the beam size for apporixmating the normalizing factor", + ) + parser.add_argument( + "--word-ins-loss-factor", + type=float, + help="weights on NAT loss used to co-training with CRF loss.", + ) + + def forward( + self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs + ): + # encoding + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + + # length prediction + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) + + # decoding + word_ins_out = self.decoder( + normalize=False, + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + ) + word_ins_tgt, word_ins_mask = tgt_tokens, tgt_tokens.ne(self.pad) + + # compute the log-likelihood of CRF + crf_nll = -self.crf_layer(word_ins_out, word_ins_tgt, word_ins_mask) + crf_nll = (crf_nll / word_ins_mask.type_as(crf_nll).sum(-1)).mean() + + return { + "word_ins": { + "out": word_ins_out, + "tgt": word_ins_tgt, + "mask": word_ins_mask, + "ls": self.args.label_smoothing, + "nll_loss": True, + "factor": self.args.word_ins_loss_factor, + }, + "word_crf": {"loss": crf_nll}, + "length": { + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, + } + + def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): + output_tokens = decoder_out.output_tokens + output_scores = decoder_out.output_scores + history = decoder_out.history + + # execute the decoder and get emission scores + output_masks = output_tokens.ne(self.pad) + word_ins_out = self.decoder( + normalize=False, prev_output_tokens=output_tokens, encoder_out=encoder_out + ) + + # run viterbi decoding through CRF + _scores, _tokens = self.crf_layer.forward_decoder(word_ins_out, output_masks) + output_tokens.masked_scatter_(output_masks, _tokens[output_masks]) + output_scores.masked_scatter_(output_masks, _scores[output_masks]) + if history is not None: + history.append(output_tokens.clone()) + + return decoder_out._replace( + output_tokens=output_tokens, + output_scores=output_scores, + attn=None, + history=history, + ) + + +@register_model_architecture("nacrf_transformer", "nacrf_transformer") +def nacrf_base_architecture(args): + args.crf_lowrank_approx = getattr(args, "crf_lowrank_approx", 32) + args.crf_beam_approx = getattr(args, "crf_beam_approx", 64) + args.word_ins_loss_factor = getattr(args, "word_ins_loss_factor", 0.5) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + base_architecture(args) diff --git a/fairseq/fairseq/models/nat/nonautoregressive_ensembles.py b/fairseq/fairseq/models/nat/nonautoregressive_ensembles.py new file mode 100644 index 0000000..0a0221f --- /dev/null +++ b/fairseq/fairseq/models/nat/nonautoregressive_ensembles.py @@ -0,0 +1,254 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq.models.nat import ( + _apply_del_words, + _apply_ins_masks, + _apply_ins_words, + _fill, + _skip, + _skip_encoder_out, +) + + +class _EnsembleModelEncoder(object): + def __init__(self, models): + self.models = models + + def reorder_encoder_out(self, encoder_outs, new_order): + encoder_outs = [ + model.encoder.reorder_encoder_out(encoder_out, new_order) + for model, encoder_out in zip(self.models, encoder_outs) + ] + return encoder_outs + + +class BasicEnsembleModel(torch.nn.Module): + """A wrapper around an ensemble of models.""" + + def __init__(self, models): + super().__init__() + self.models = torch.nn.ModuleList(models) + self.bos = self.models[0].decoder.dictionary.bos() + self.eos = self.models[0].decoder.dictionary.eos() + self.pad = self.models[0].decoder.dictionary.pad() + self.unk = self.models[0].decoder.dictionary.unk() + self.encoder = _EnsembleModelEncoder(self.models) + + def has_encoder(self): + return hasattr(self.models[0], "encoder") + + def max_decoder_positions(self): + return min(m.max_decoder_positions() for m in self.models) + + @torch.no_grad() + def forward_encoder(self, encoder_input): + if not self.has_encoder(): + return None + return [model.forward_encoder(encoder_input) for model in self.models] + + @torch.no_grad() + def forward_decoder(self, *inputs): + raise NotImplementedError + + def initialize_output_tokens(self, *inputs): + raise NotImplementedError + + +class EnsembleLevT(BasicEnsembleModel): + """A wrapper around an ensemble of models.""" + + def __init__(self, models): + super().__init__(models) + + @torch.no_grad() + def forward_decoder( + self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio=None, **kwargs + ): + # LevT ensembling + # A pipeline of three steps: deletion, placeholder, and word insertion. + # We need to average scores in each step in a pipeline way because of dependence. + # deletion + output_tokens = decoder_out.output_tokens + output_scores = decoder_out.output_scores + attn = decoder_out.attn + + bsz = output_tokens.size(0) + if max_ratio is None: + max_lens = output_tokens.new().fill_(255) + else: + if not encoder_outs[0]["encoder_padding_mask"]: + src_lens = ( + encoder_outs[0]["encoder_out"][0] + .new(bsz) + .fill_(encoder_outs[0]["encoder_out"][0].size(1)) + ) + else: + src_lens = (~encoder_outs[0]["encoder_padding_mask"][0]).sum(1) + max_lens = (src_lens * max_ratio).clamp(min=10).long() + + # delete words + # do not delete tokens if it is <s> </s> + can_del_word = output_tokens.ne(self.pad).sum(1) > 2 + if can_del_word.sum() != 0: # we cannot delete, skip + output_tokens, output_scores, attn = self.forward_word_del( + encoder_outs, + output_tokens, + output_scores, + attn, + can_del_word, + ) + + # insert placeholders + can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens + if can_ins_mask.sum() != 0: + output_tokens, output_scores = self.forward_mask_ins( + encoder_outs, + output_tokens, + output_scores, + can_ins_mask, + eos_penalty, + max_lens, + ) + + # insert words + can_ins_word = output_tokens.eq(self.unk).sum(1) > 0 + if can_ins_word.sum() != 0: + output_tokens, output_scores, attn = self.forward_word_ins( + encoder_outs, + output_tokens, + output_scores, + attn, + can_ins_word, + ) + + # delete some unnecessary paddings + cut_off = output_tokens.ne(self.pad).sum(1).max() + output_tokens = output_tokens[:, :cut_off] + output_scores = output_scores[:, :cut_off] + attn = None if attn is None else attn[:, :cut_off, :] + return decoder_out._replace( + output_tokens=output_tokens, + output_scores=output_scores, + attn=attn, + history=None, + ) + + def forward_word_del( + self, encoder_outs, output_tokens, output_scores, attn, can_del_word + ): + word_del_score_avg = [] + word_del_attn_avg = [] + for model, encoder_out in zip(self.models, encoder_outs): + word_del_out, word_del_attn = model.decoder.forward_word_del( + _skip(output_tokens, can_del_word), + _skip_encoder_out(model.encoder, encoder_out, can_del_word), + ) + word_del_score = F.log_softmax(word_del_out, 2) + word_del_score_avg.append(word_del_score) + word_del_attn_avg.append(word_del_attn) + word_del_score_avg = torch.logsumexp( + torch.stack(word_del_score_avg, dim=0), dim=0 + ) - math.log(len(self.models)) + word_del_pred = word_del_score_avg.max(-1)[1].bool() + if word_del_attn_avg[0] is not None: + word_del_attn_avg = torch.stack(word_del_attn_avg, dim=0) / len(self.models) + else: + word_del_attn_avg = None + + _tokens, _scores, _attn = _apply_del_words( + output_tokens[can_del_word], + output_scores[can_del_word], + word_del_attn_avg, + word_del_pred, + self.pad, + self.bos, + self.eos, + ) + output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad) + output_scores = _fill(output_scores, can_del_word, _scores, 0) + attn = _fill(attn, can_del_word, _attn, 0.0) + return output_tokens, output_scores, attn + + def forward_mask_ins( + self, + encoder_outs, + output_tokens, + output_scores, + can_ins_mask, + eos_penalty, + max_lens, + ): + mask_ins_score_avg = [] + for model, encoder_out in zip(self.models, encoder_outs): + mask_ins_out, _ = model.decoder.forward_mask_ins( + _skip(output_tokens, can_ins_mask), + _skip_encoder_out(model.encoder, encoder_out, can_ins_mask), + ) + mask_ins_score = F.log_softmax(mask_ins_out, 2) + if eos_penalty > 0.0: + mask_ins_score[:, :, 0] -= eos_penalty + mask_ins_score_avg.append(mask_ins_score) + mask_ins_score_avg = torch.logsumexp( + torch.stack(mask_ins_score_avg, dim=0), dim=0 + ) - math.log(len(self.models)) + mask_ins_pred = mask_ins_score_avg.max(-1)[1] + mask_ins_pred = torch.min( + mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred) + ) + _tokens, _scores = _apply_ins_masks( + output_tokens[can_ins_mask], + output_scores[can_ins_mask], + mask_ins_pred, + self.pad, + self.unk, + self.eos, + ) + output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad) + output_scores = _fill(output_scores, can_ins_mask, _scores, 0) + return output_tokens, output_scores + + def forward_word_ins( + self, encoder_outs, output_tokens, output_scores, attn, can_ins_word + ): + word_ins_score_avg = [] + word_ins_attn_avg = [] + for model, encoder_out in zip(self.models, encoder_outs): + word_ins_out, word_ins_attn = model.decoder.forward_word_ins( + _skip(output_tokens, can_ins_word), + _skip_encoder_out(model.encoder, encoder_out, can_ins_word), + ) + word_ins_score = F.log_softmax(word_ins_out, 2) + word_ins_score_avg.append(word_ins_score) + word_ins_attn_avg.append(word_ins_attn) + word_ins_score_avg = torch.logsumexp( + torch.stack(word_ins_score_avg, dim=0), dim=0 + ) - math.log(len(self.models)) + if word_ins_attn_avg[0] is not None: + word_ins_attn_avg = torch.stack(word_ins_attn_avg, dim=0) / len(self.models) + else: + word_ins_attn_avg = None + word_ins_score_max, word_ins_pred = word_ins_score_avg.max(-1) + + _tokens, _scores = _apply_ins_words( + output_tokens[can_ins_word], + output_scores[can_ins_word], + word_ins_pred, + word_ins_score_max, + self.unk, + ) + + output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad) + output_scores = _fill(output_scores, can_ins_word, _scores, 0) + attn = _fill(attn, can_ins_word, word_ins_attn, 0.0) + return output_tokens, output_scores, attn + + def initialize_output_tokens(self, encoder_outs, src_tokens): + # LevT doesn't do length prediction. + return self.models[0].initialize_output_tokens(encoder_outs[0], src_tokens) diff --git a/fairseq/fairseq/models/nat/nonautoregressive_transformer.py b/fairseq/fairseq/models/nat/nonautoregressive_transformer.py new file mode 100644 index 0000000..d114202 --- /dev/null +++ b/fairseq/fairseq/models/nat/nonautoregressive_transformer.py @@ -0,0 +1,456 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.iterative_refinement_generator import DecoderOut +from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder +from fairseq.models.transformer import Embedding +from fairseq.modules.transformer_sentence_encoder import init_bert_params + + +def _mean_pooling(enc_feats, src_masks): + # enc_feats: T x B x C + # src_masks: B x T or None + if src_masks is None: + enc_feats = enc_feats.mean(0) + else: + src_masks = (~src_masks).transpose(0, 1).type_as(enc_feats) + enc_feats = ( + (enc_feats / src_masks.sum(0)[None, :, None]) * src_masks[:, :, None] + ).sum(0) + return enc_feats + + +def _argmax(x, dim): + return (x == x.max(dim, keepdim=True)[0]).type_as(x) + + +def _uniform_assignment(src_lens, trg_lens): + max_trg_len = trg_lens.max() + steps = (src_lens.float() - 1) / (trg_lens.float() - 1) # step-size + # max_trg_len + index_t = utils.new_arange(trg_lens, max_trg_len).float() + index_t = steps[:, None] * index_t[None, :] # batch_size X max_trg_len + index_t = torch.round(index_t).long().detach() + return index_t + + +@register_model("nonautoregressive_transformer") +class NATransformerModel(FairseqNATModel): + @property + def allow_length_beam(self): + return True + + @staticmethod + def add_args(parser): + FairseqNATModel.add_args(parser) + + # length prediction + parser.add_argument( + "--src-embedding-copy", + action="store_true", + help="copy encoder word embeddings as the initial input of the decoder", + ) + parser.add_argument( + "--pred-length-offset", + action="store_true", + help="predicting the length difference between the target and source sentences", + ) + parser.add_argument( + "--sg-length-pred", + action="store_true", + help="stop the gradients back-propagated from the length predictor", + ) + parser.add_argument( + "--length-loss-factor", + type=float, + help="weights on the length prediction loss", + ) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + decoder = NATransformerDecoder(args, tgt_dict, embed_tokens) + if getattr(args, "apply_bert_init", False): + decoder.apply(init_bert_params) + return decoder + + def forward( + self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs + ): + # encoding + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + + # length prediction + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) + + # decoding + word_ins_out = self.decoder( + normalize=False, + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + ) + + return { + "word_ins": { + "out": word_ins_out, + "tgt": tgt_tokens, + "mask": tgt_tokens.ne(self.pad), + "ls": self.args.label_smoothing, + "nll_loss": True, + }, + "length": { + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, + } + + def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): + step = decoder_out.step + output_tokens = decoder_out.output_tokens + output_scores = decoder_out.output_scores + history = decoder_out.history + + # execute the decoder + output_masks = output_tokens.ne(self.pad) + _scores, _tokens = self.decoder( + normalize=True, + prev_output_tokens=output_tokens, + encoder_out=encoder_out, + step=step, + ).max(-1) + + output_tokens.masked_scatter_(output_masks, _tokens[output_masks]) + output_scores.masked_scatter_(output_masks, _scores[output_masks]) + if history is not None: + history.append(output_tokens.clone()) + + return decoder_out._replace( + output_tokens=output_tokens, + output_scores=output_scores, + attn=None, + history=history, + ) + + def initialize_output_tokens(self, encoder_out, src_tokens): + # length prediction + length_tgt = self.decoder.forward_length_prediction( + self.decoder.forward_length(normalize=True, encoder_out=encoder_out), + encoder_out=encoder_out, + ) + + max_length = length_tgt.clamp_(min=2).max() + idx_length = utils.new_arange(src_tokens, max_length) + + initial_output_tokens = src_tokens.new_zeros( + src_tokens.size(0), max_length + ).fill_(self.pad) + initial_output_tokens.masked_fill_( + idx_length[None, :] < length_tgt[:, None], self.unk + ) + initial_output_tokens[:, 0] = self.bos + initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos) + + initial_output_scores = initial_output_tokens.new_zeros( + *initial_output_tokens.size() + ).type_as(encoder_out["encoder_out"][0]) + + return DecoderOut( + output_tokens=initial_output_tokens, + output_scores=initial_output_scores, + attn=None, + step=0, + max_step=0, + history=None, + ) + + def regenerate_length_beam(self, decoder_out, beam_size): + output_tokens = decoder_out.output_tokens + length_tgt = output_tokens.ne(self.pad).sum(1) + length_tgt = ( + length_tgt[:, None] + + utils.new_arange(length_tgt, 1, beam_size) + - beam_size // 2 + ) + length_tgt = length_tgt.view(-1).clamp_(min=2) + max_length = length_tgt.max() + idx_length = utils.new_arange(length_tgt, max_length) + + initial_output_tokens = output_tokens.new_zeros( + length_tgt.size(0), max_length + ).fill_(self.pad) + initial_output_tokens.masked_fill_( + idx_length[None, :] < length_tgt[:, None], self.unk + ) + initial_output_tokens[:, 0] = self.bos + initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos) + + initial_output_scores = initial_output_tokens.new_zeros( + *initial_output_tokens.size() + ).type_as(decoder_out.output_scores) + + return decoder_out._replace( + output_tokens=initial_output_tokens, output_scores=initial_output_scores + ) + + +class NATransformerDecoder(FairseqNATDecoder): + def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): + super().__init__( + args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn + ) + self.dictionary = dictionary + self.bos = dictionary.bos() + self.unk = dictionary.unk() + self.eos = dictionary.eos() + + self.encoder_embed_dim = args.encoder_embed_dim + self.sg_length_pred = getattr(args, "sg_length_pred", False) + self.pred_length_offset = getattr(args, "pred_length_offset", False) + self.length_loss_factor = getattr(args, "length_loss_factor", 0.1) + self.src_embedding_copy = getattr(args, "src_embedding_copy", False) + self.embed_length = Embedding(256, self.encoder_embed_dim, None) + + @ensemble_decoder + def forward(self, normalize, encoder_out, prev_output_tokens, step=0, **unused): + features, _ = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + embedding_copy=(step == 0) & self.src_embedding_copy, + ) + decoder_out = self.output_layer(features) + return F.log_softmax(decoder_out, -1) if normalize else decoder_out + + @ensemble_decoder + def forward_length(self, normalize, encoder_out): + enc_feats = encoder_out["encoder_out"][0] # T x B x C + if len(encoder_out["encoder_padding_mask"]) > 0: + src_masks = encoder_out["encoder_padding_mask"][0] # B x T + else: + src_masks = None + enc_feats = _mean_pooling(enc_feats, src_masks) + if self.sg_length_pred: + enc_feats = enc_feats.detach() + length_out = F.linear(enc_feats, self.embed_length.weight) + return F.log_softmax(length_out, -1) if normalize else length_out + + def extract_features( + self, + prev_output_tokens, + encoder_out=None, + early_exit=None, + embedding_copy=False, + **unused + ): + """ + Similar to *forward* but only return features. + + Inputs: + prev_output_tokens: Tensor(B, T) + encoder_out: a dictionary of hidden states and masks + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + the LevenshteinTransformer decoder has full-attention to all generated tokens + """ + # embedding + if embedding_copy: + src_embd = encoder_out["encoder_embedding"][0] + if len(encoder_out["encoder_padding_mask"]) > 0: + src_mask = encoder_out["encoder_padding_mask"][0] + else: + src_mask = None + src_mask = ( + ~src_mask + if src_mask is not None + else prev_output_tokens.new_ones(*src_embd.size()[:2]).bool() + ) + + x, decoder_padding_mask = self.forward_embedding( + prev_output_tokens, + self.forward_copying_source( + src_embd, src_mask, prev_output_tokens.ne(self.padding_idx) + ), + ) + + else: + + x, decoder_padding_mask = self.forward_embedding(prev_output_tokens) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + attn = None + inner_states = [x] + + # decoder layers + for i, layer in enumerate(self.layers): + + # early exit from the decoder. + if (early_exit is not None) and (i >= early_exit): + break + + x, attn, _ = layer( + x, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, + self_attn_mask=None, + self_attn_padding_mask=decoder_padding_mask, + ) + inner_states.append(x) + + if self.layer_norm: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x, {"attn": attn, "inner_states": inner_states} + + def forward_embedding(self, prev_output_tokens, states=None): + # embed positions + positions = ( + self.embed_positions(prev_output_tokens) + if self.embed_positions is not None + else None + ) + + # embed tokens and positions + if states is None: + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + if self.project_in_dim is not None: + x = self.project_in_dim(x) + else: + x = states + + if positions is not None: + x += positions + x = self.dropout_module(x) + decoder_padding_mask = prev_output_tokens.eq(self.padding_idx) + return x, decoder_padding_mask + + def forward_copying_source(self, src_embeds, src_masks, tgt_masks): + length_sources = src_masks.sum(1) + length_targets = tgt_masks.sum(1) + mapped_inputs = _uniform_assignment(length_sources, length_targets).masked_fill( + ~tgt_masks, 0 + ) + copied_embedding = torch.gather( + src_embeds, + 1, + mapped_inputs.unsqueeze(-1).expand( + *mapped_inputs.size(), src_embeds.size(-1) + ), + ) + return copied_embedding + + def forward_length_prediction(self, length_out, encoder_out, tgt_tokens=None): + enc_feats = encoder_out["encoder_out"][0] # T x B x C + if len(encoder_out["encoder_padding_mask"]) > 0: + src_masks = encoder_out["encoder_padding_mask"][0] # B x T + else: + src_masks = None + if self.pred_length_offset: + if src_masks is None: + src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_( + enc_feats.size(0) + ) + else: + src_lengs = (~src_masks).transpose(0, 1).type_as(enc_feats).sum(0) + src_lengs = src_lengs.long() + + if tgt_tokens is not None: + # obtain the length target + tgt_lengs = tgt_tokens.ne(self.padding_idx).sum(1).long() + if self.pred_length_offset: + length_tgt = tgt_lengs - src_lengs + 128 + else: + length_tgt = tgt_lengs + length_tgt = length_tgt.clamp(min=0, max=255) + + else: + # predict the length target (greedy for now) + # TODO: implementing length-beam + pred_lengs = length_out.max(-1)[1] + if self.pred_length_offset: + length_tgt = pred_lengs - 128 + src_lengs + else: + length_tgt = pred_lengs + + return length_tgt + + +@register_model_architecture( + "nonautoregressive_transformer", "nonautoregressive_transformer" +) +def base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.apply_bert_init = getattr(args, "apply_bert_init", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + # --- special arguments --- + args.sg_length_pred = getattr(args, "sg_length_pred", False) + args.pred_length_offset = getattr(args, "pred_length_offset", False) + args.length_loss_factor = getattr(args, "length_loss_factor", 0.1) + args.src_embedding_copy = getattr(args, "src_embedding_copy", False) + + +@register_model_architecture( + "nonautoregressive_transformer", "nonautoregressive_transformer_wmt_en_de" +) +def nonautoregressive_transformer_wmt_en_de(args): + base_architecture(args) diff --git a/fairseq/fairseq/models/roberta/__init__.py b/fairseq/fairseq/models/roberta/__init__.py new file mode 100644 index 0000000..4cd723a --- /dev/null +++ b/fairseq/fairseq/models/roberta/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .hub_interface import * # noqa +from .model import * # noqa +from .enc_dec import * # noqa +from .model_camembert import * # noqa +from .model_gottbert import * # noqa +from .model_xlmr import * # noqa diff --git a/fairseq/fairseq/models/roberta/alignment_utils.py b/fairseq/fairseq/models/roberta/alignment_utils.py new file mode 100644 index 0000000..ccc7f74 --- /dev/null +++ b/fairseq/fairseq/models/roberta/alignment_utils.py @@ -0,0 +1,118 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections import Counter +from typing import List + +import torch + + +def align_bpe_to_words(roberta, bpe_tokens: torch.LongTensor, other_tokens: List[str]): + """ + Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy). + + Args: + roberta (RobertaHubInterface): RoBERTa instance + bpe_tokens (torch.LongTensor): GPT-2 BPE tokens of shape `(T_bpe)` + other_tokens (List[str]): other tokens of shape `(T_words)` + + Returns: + List[str]: mapping from *other_tokens* to corresponding *bpe_tokens*. + """ + assert bpe_tokens.dim() == 1 + assert bpe_tokens[0] == 0 + + def clean(text): + return text.strip() + + # remove whitespaces to simplify alignment + bpe_tokens = [roberta.task.source_dictionary.string([x]) for x in bpe_tokens] + bpe_tokens = [ + clean(roberta.bpe.decode(x) if x not in {"<s>", ""} else x) for x in bpe_tokens + ] + other_tokens = [clean(str(o)) for o in other_tokens] + + # strip leading <s> + bpe_tokens = bpe_tokens[1:] + assert "".join(bpe_tokens) == "".join(other_tokens) + + # create alignment from every word to a list of BPE tokens + alignment = [] + bpe_toks = filter(lambda item: item[1] != "", enumerate(bpe_tokens, start=1)) + j, bpe_tok = next(bpe_toks) + for other_tok in other_tokens: + bpe_indices = [] + while True: + if other_tok.startswith(bpe_tok): + bpe_indices.append(j) + other_tok = other_tok[len(bpe_tok) :] + try: + j, bpe_tok = next(bpe_toks) + except StopIteration: + j, bpe_tok = None, None + elif bpe_tok.startswith(other_tok): + # other_tok spans multiple BPE tokens + bpe_indices.append(j) + bpe_tok = bpe_tok[len(other_tok) :] + other_tok = "" + else: + raise Exception('Cannot align "{}" and "{}"'.format(other_tok, bpe_tok)) + if other_tok == "": + break + assert len(bpe_indices) > 0 + alignment.append(bpe_indices) + assert len(alignment) == len(other_tokens) + + return alignment + + +def align_features_to_words(roberta, features, alignment): + """ + Align given features to words. + + Args: + roberta (RobertaHubInterface): RoBERTa instance + features (torch.Tensor): features to align of shape `(T_bpe x C)` + alignment: alignment between BPE tokens and words returned by + func:`align_bpe_to_words`. + """ + assert features.dim() == 2 + + bpe_counts = Counter(j for bpe_indices in alignment for j in bpe_indices) + assert bpe_counts[0] == 0 # <s> shouldn't be aligned + denom = features.new([bpe_counts.get(j, 1) for j in range(len(features))]) + weighted_features = features / denom.unsqueeze(-1) + + output = [weighted_features[0]] + largest_j = -1 + for bpe_indices in alignment: + output.append(weighted_features[bpe_indices].sum(dim=0)) + largest_j = max(largest_j, *bpe_indices) + for j in range(largest_j + 1, len(features)): + output.append(weighted_features[j]) + output = torch.stack(output) + assert torch.all(torch.abs(output.sum(dim=0) - features.sum(dim=0)) < 1e-4) + return output + + +def spacy_nlp(): + if getattr(spacy_nlp, "_nlp", None) is None: + try: + from spacy.lang.en import English + + spacy_nlp._nlp = English() + except ImportError: + raise ImportError("Please install spacy with: pip install spacy") + return spacy_nlp._nlp + + +def spacy_tokenizer(): + if getattr(spacy_tokenizer, "_tokenizer", None) is None: + try: + nlp = spacy_nlp() + spacy_tokenizer._tokenizer = nlp.Defaults.create_tokenizer(nlp) + except ImportError: + raise ImportError("Please install spacy with: pip install spacy") + return spacy_tokenizer._tokenizer diff --git a/fairseq/fairseq/models/roberta/enc_dec.py b/fairseq/fairseq/models/roberta/enc_dec.py new file mode 100644 index 0000000..e538dee --- /dev/null +++ b/fairseq/fairseq/models/roberta/enc_dec.py @@ -0,0 +1,192 @@ +import argparse +import logging + +import torch.nn as nn +import fairseq.checkpoint_utils +from fairseq.models import ( + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import TransformerDecoder +from fairseq.models.roberta import model as roberta + +logger = logging.getLogger(__name__) + + +@register_model("roberta_enc_dec") +class RobertaEncDecModel(FairseqEncoderDecoderModel): + @staticmethod + def add_args(parser): + parser.add_argument( + "--pretrained-mlm-checkpoint", + default=None, + type=str, + metavar="PRETRAINED", + help="path to pretrained mlm checkpoint", + ) + parser.add_argument( + "--pretrained-decoder", action="store_true", help="reload decoder" + ) + parser.add_argument( + "--hack-layernorm-embedding", + action="store_true", + help="hack to reload old models trained with encoder-normalize-before=False (no equivalent to encoder-normalize-before=False and layernorm_embedding=False", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--share-all-embeddings", + action="store_true", + help="share encoder, decoder and output embeddings" + " (requires shared dictionary and embed dim)", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present + base_enc_dec_architecture(args) + if args.pretrained_mlm_checkpoint: + arg_overrides = None + if args.hack_layernorm_embedding: + arg_overrides = {"layernorm_embedding": False} + loaded = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [args.pretrained_mlm_checkpoint], arg_overrides=arg_overrides + ) + ([roberta_enc], _cfg, _task) = loaded + else: + # Do we need to edit untie_weights here ? + share_in_out = ( + args.share_decoder_input_output_embed or args.share_all_embeddings + ) + args.untie_weights_roberta = not share_in_out + if args.hack_layernorm_embedding: + args.layernorm_embedding = False + args.encoder_normalize_before = False + roberta_enc = roberta.RobertaModel.build_model(args, task) + + return cls.from_roberta(roberta_enc, args, task.source_dictionary) + + @staticmethod + def from_roberta(roberta_enc: roberta.RobertaModel, args, dictionary): + encoder = roberta_enc.encoder.sentence_encoder + vocab_size, embed_dim = encoder.embed_tokens.weight.shape + + if args.share_all_embeddings: + lm_head = roberta_enc.encoder.lm_head + assert encoder.embed_tokens.weight is lm_head.weight, ( + "Can't use --share-all-embeddings with a model " + "that was pretraiend with --untie-weights-roberta_enc" + ) + else: + lm_head = roberta.RobertaLMHead( + embed_dim, vocab_size, roberta_enc.args.activation_fn + ) + + dec_embs = nn.Embedding(vocab_size, embed_dim, dictionary.pad()) + if args.share_all_embeddings or args.share_decoder_input_output_embed: + # Note: I wasn't able to use Embedding _weight parameter to achive this sharing. + dec_embs.weight = lm_head.weight + + decoder = TransformerDecoder( + RobertaEncDecModel.read_args_from_roberta(roberta_enc.args), + dictionary, + dec_embs, + no_encoder_attn=False, + output_projection=lm_head, + ) + if getattr(args, "pretrained_decoder", False): + decoder_dict = encoder.state_dict() + + # TODO: hide setting "encoder_attn" layers behind a flag. + for k, w in list(decoder_dict.items()): + if ".self_attn" in k: + k_enc_attn = k.replace(".self_attn", ".encoder_attn") + decoder_dict[k_enc_attn] = w.detach().clone() + + for k, w in lm_head.state_dict().items(): + decoder_dict["output_projection." + k] = w + + missing_keys, unexpected_keys = decoder.load_state_dict( + decoder_dict, strict=False + ) + # missing_keys = [m for m in missing_keys if ".encoder_attn" not in m] + assert not missing_keys and not unexpected_keys, ( + "Failed to load state dict. " + f"Missing keys: {missing_keys}. " + f"Unexpected keys: {unexpected_keys}." + ) + + if args.share_all_embeddings: + assert decoder.output_projection.weight is decoder.embed_tokens.weight + assert encoder.embed_tokens.weight is decoder.embed_tokens.weight + elif args.share_decoder_input_output_embed: + assert decoder.output_projection.weight is decoder.embed_tokens.weight + assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight + else: + assert decoder.output_projection.weight is not decoder.embed_tokens.weight + assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight + + return RobertaEncDecModel(encoder, decoder) + + @staticmethod + def read_args_from_roberta(roberta_args: argparse.Namespace): + # TODO: this would become easier if encoder/decoder where using a similar + # TransformerConfig object + args = argparse.Namespace(**vars(roberta_args)) + attr_map = [ + ("encoder_attention_heads", "decoder_attention_heads"), + ("encoder_embed_dim", "decoder_embed_dim"), + ("encoder_embed_dim", "decoder_output_dim"), + ("encoder_normalize_before", "decoder_normalize_before"), + ("encoder_layers_to_keep", "decoder_layers_to_keep"), + ("encoder_ffn_embed_dim", "decoder_ffn_embed_dim"), + ("encoder_layerdrop", "decoder_layerdrop"), + ("encoder_layers", "decoder_layers"), + ("encoder_learned_pos", "decoder_learned_pos"), + # should this be set from here ? + ("max_positions", "max_target_positions"), + ] + for k1, k2 in attr_map: + setattr(args, k2, getattr(roberta_args, k1)) + + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = not roberta_args.untie_weights_roberta + return args + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + super().upgrade_state_dict_named(state_dict, name) + old_keys = list(state_dict.keys()) + + # rename decoder -> encoder before upgrading children modules + for k in old_keys: + if k.startswith(prefix + "encoder.lm_head"): + state_dict.pop(k) + continue + new_k = k + new_k = new_k.replace(".sentence_encoder.", ".") + new_k = new_k.replace("decoder.lm_head.", "decoder.output_projection.") + if k == new_k: + continue + # print(k, "->", new_k) + state_dict[new_k] = state_dict.pop(k) + + +@register_model_architecture("roberta_enc_dec", "roberta_enc_dec") +def base_enc_dec_architecture(args): + args.hack_layernorm_embedding = getattr(args, "hack_layernorm_embedding", False) + args.pretrained_mlm_checkpoint = getattr(args, "pretrained_mlm_checkpoint", None) + args.pretrained_decoder = getattr(args, "pretrained_decoder", None) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + + roberta.base_architecture(args) diff --git a/fairseq/fairseq/models/roberta/hub_interface.py b/fairseq/fairseq/models/roberta/hub_interface.py new file mode 100644 index 0000000..ba298d6 --- /dev/null +++ b/fairseq/fairseq/models/roberta/hub_interface.py @@ -0,0 +1,235 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.data import encoders + + +class RobertaHubInterface(nn.Module): + """A simple PyTorch Hub interface to RoBERTa. + + Usage: https://github.com/pytorch/fairseq/tree/main/examples/roberta + """ + + def __init__(self, cfg, task, model): + super().__init__() + self.cfg = cfg + self.task = task + self.model = model + + self.bpe = encoders.build_bpe(cfg.bpe) + + # this is useful for determining the device + self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float)) + + @property + def device(self): + return self._float_tensor.device + + def encode( + self, sentence: str, *addl_sentences, no_separator=False + ) -> torch.LongTensor: + """ + BPE-encode a sentence (or multiple sentences). + + Every sequence begins with a beginning-of-sentence (`<s>`) symbol. + Every sentence ends with an end-of-sentence (`</s>`) and we use an + extra end-of-sentence (`</s>`) as a separator. + + Example (single sentence): `<s> a b c </s>` + Example (sentence pair): `<s> d e f </s> </s> 1 2 3 </s>` + + The BPE encoding follows GPT-2. One subtle detail is that the GPT-2 BPE + requires leading spaces. For example:: + + >>> roberta.encode('Hello world').tolist() + [0, 31414, 232, 2] + >>> roberta.encode(' world').tolist() + [0, 232, 2] + >>> roberta.encode('world').tolist() + [0, 8331, 2] + """ + bpe_sentence = "<s> " + self.bpe.encode(sentence) + " </s>" + for s in addl_sentences: + bpe_sentence += " </s>" if not no_separator else "" + bpe_sentence += " " + self.bpe.encode(s) + " </s>" + tokens = self.task.source_dictionary.encode_line( + bpe_sentence, append_eos=False, add_if_not_exist=False + ) + return tokens.long() + + def decode(self, tokens: torch.LongTensor): + assert tokens.dim() == 1 + tokens = tokens.numpy() + if tokens[0] == self.task.source_dictionary.bos(): + tokens = tokens[1:] # remove <s> + eos_mask = tokens == self.task.source_dictionary.eos() + doc_mask = eos_mask[1:] & eos_mask[:-1] + sentences = np.split(tokens, doc_mask.nonzero()[0] + 1) + sentences = [ + self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences + ] + if len(sentences) == 1: + return sentences[0] + return sentences + + def extract_features( + self, tokens: torch.LongTensor, return_all_hiddens: bool = False + ) -> torch.Tensor: + if tokens.dim() == 1: + tokens = tokens.unsqueeze(0) + if tokens.size(-1) > self.model.max_positions(): + raise ValueError( + "tokens exceeds maximum length: {} > {}".format( + tokens.size(-1), self.model.max_positions() + ) + ) + features, extra = self.model( + tokens.to(device=self.device), + features_only=True, + return_all_hiddens=return_all_hiddens, + ) + if return_all_hiddens: + # convert from T x B x C -> B x T x C + inner_states = extra["inner_states"] + return [inner_state.transpose(0, 1) for inner_state in inner_states] + else: + return features # just the last layer's features + + def register_classification_head( + self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs + ): + self.model.register_classification_head( + name, num_classes=num_classes, embedding_size=embedding_size, **kwargs + ) + + def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = False): + features = self.extract_features(tokens.to(device=self.device)) + logits = self.model.classification_heads[head](features) + if return_logits: + return logits + return F.log_softmax(logits, dim=-1) + + def extract_features_aligned_to_words( + self, sentence: str, return_all_hiddens: bool = False + ) -> torch.Tensor: + """Extract RoBERTa features, aligned to spaCy's word-level tokenizer.""" + from fairseq.models.roberta import alignment_utils + from spacy.tokens import Doc + + nlp = alignment_utils.spacy_nlp() + tokenizer = alignment_utils.spacy_tokenizer() + + # tokenize both with GPT-2 BPE and spaCy + bpe_toks = self.encode(sentence) + spacy_toks = tokenizer(sentence) + spacy_toks_ws = [t.text_with_ws for t in tokenizer(sentence)] + alignment = alignment_utils.align_bpe_to_words(self, bpe_toks, spacy_toks_ws) + + # extract features and align them + features = self.extract_features( + bpe_toks, return_all_hiddens=return_all_hiddens + ) + features = features.squeeze(0) + aligned_feats = alignment_utils.align_features_to_words( + self, features, alignment + ) + + # wrap in spaCy Doc + doc = Doc( + nlp.vocab, + words=["<s>"] + [x.text for x in spacy_toks] + ["</s>"], + spaces=[True] + + [x.endswith(" ") for x in spacy_toks_ws[:-1]] + + [True, False], + ) + assert len(doc) == aligned_feats.size(0) + doc.user_token_hooks["vector"] = lambda token: aligned_feats[token.i] + return doc + + def fill_mask(self, masked_input: str, topk: int = 5): + masked_token = "<mask>" + assert ( + masked_token in masked_input and masked_input.count(masked_token) == 1 + ), "Please add one {0} token for the input, eg: 'He is a {0} guy'".format( + masked_token + ) + + text_spans = masked_input.split(masked_token) + text_spans_bpe = ( + (" {0} ".format(masked_token)) + .join([self.bpe.encode(text_span.rstrip()) for text_span in text_spans]) + .strip() + ) + tokens = self.task.source_dictionary.encode_line( + "<s> " + text_spans_bpe + " </s>", + append_eos=False, + add_if_not_exist=False, + ) + + masked_index = (tokens == self.task.mask_idx).nonzero(as_tuple=False) + if tokens.dim() == 1: + tokens = tokens.unsqueeze(0) + + with utils.model_eval(self.model): + features, extra = self.model( + tokens.long().to(device=self.device), + features_only=False, + return_all_hiddens=False, + ) + logits = features[0, masked_index, :].squeeze() + prob = logits.softmax(dim=0) + values, index = prob.topk(k=topk, dim=0) + topk_predicted_token_bpe = self.task.source_dictionary.string(index) + + topk_filled_outputs = [] + for index, predicted_token_bpe in enumerate( + topk_predicted_token_bpe.split(" ") + ): + predicted_token = self.bpe.decode(predicted_token_bpe) + # Quick hack to fix https://github.com/pytorch/fairseq/issues/1306 + if predicted_token_bpe.startswith("\u2581"): + predicted_token = " " + predicted_token + if " {0}".format(masked_token) in masked_input: + topk_filled_outputs.append( + ( + masked_input.replace( + " {0}".format(masked_token), predicted_token + ), + values[index].item(), + predicted_token, + ) + ) + else: + topk_filled_outputs.append( + ( + masked_input.replace(masked_token, predicted_token), + values[index].item(), + predicted_token, + ) + ) + return topk_filled_outputs + + def disambiguate_pronoun(self, sentence: str) -> bool: + """ + Usage:: + + >>> disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.') + True + + >>> disambiguate_pronoun('The trophy would not fit in the brown suitcase because [it] was too big.') + 'The trophy' + """ + assert hasattr( + self.task, "disambiguate_pronoun" + ), "roberta.disambiguate_pronoun() requires a model trained with the WSC task." + with utils.model_eval(self.model): + return self.task.disambiguate_pronoun( + self.model, sentence, use_cuda=self.device.type == "cuda" + ) diff --git a/fairseq/fairseq/models/roberta/model.py b/fairseq/fairseq/models/roberta/model.py new file mode 100644 index 0000000..d7ced91 --- /dev/null +++ b/fairseq/fairseq/models/roberta/model.py @@ -0,0 +1,700 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +RoBERTa: A Robustly Optimized BERT Pretraining Approach. +""" + +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder +from fairseq.modules import LayerNorm +from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ +from fairseq.modules.transformer_sentence_encoder import init_bert_params +from fairseq.utils import safe_getattr, safe_hasattr + +from .hub_interface import RobertaHubInterface + +logger = logging.getLogger(__name__) + + +@register_model("roberta") +class RobertaModel(FairseqEncoderModel): + @classmethod + def hub_models(cls): + return { + "roberta.base": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz", + "roberta.large": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz", + "roberta.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz", + "roberta.large.wsc": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz", + } + + def __init__(self, args, encoder): + super().__init__(encoder) + self.args = args + + # We follow BERT's random weight initialization + self.apply(init_bert_params) + + self.classification_heads = nn.ModuleDict() + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--encoder-layers", type=int, metavar="L", help="num encoder layers" + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="H", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="F", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="A", + help="num encoder attention heads", + ) + parser.add_argument( + "--activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--pooler-activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use for pooler layer", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN", + ) + parser.add_argument( + "--pooler-dropout", + type=float, + metavar="D", + help="dropout probability in the masked_lm pooler layers", + ) + parser.add_argument( + "--max-positions", type=int, help="number of positional embeddings to learn" + ) + parser.add_argument( + "--load-checkpoint-heads", + action="store_true", + help="(re-)register and load heads when loading checkpoints", + ) + parser.add_argument( + "--untie-weights-roberta", + action="store_true", + help="Untie weights between embeddings and classifiers in RoBERTa", + ) + # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) + parser.add_argument( + "--encoder-layerdrop", + type=float, + metavar="D", + default=0, + help="LayerDrop probability for encoder", + ) + parser.add_argument( + "--encoder-layers-to-keep", + default=None, + help="which layers to *keep* when pruning as a comma-separated list", + ) + # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) + parser.add_argument( + "--quant-noise-pq", + type=float, + metavar="D", + default=0, + help="iterative PQ quantization noise at training time", + ) + parser.add_argument( + "--quant-noise-pq-block-size", + type=int, + metavar="D", + default=8, + help="block size of quantization noise at training time", + ) + parser.add_argument( + "--quant-noise-scalar", + type=float, + metavar="D", + default=0, + help="scalar quantization noise and scalar quantization at training time", + ) + # args for "Better Fine-Tuning by Reducing Representational Collapse" (Aghajanyan et al. 2020) + parser.add_argument( + "--spectral-norm-classification-head", + action="store_true", + default=False, + help="Apply spectral normalization on the classification head", + ) + # args for Fully Sharded Data Parallel (FSDP) training + parser.add_argument( + "--min-params-to-wrap", + type=int, + metavar="D", + default=DEFAULT_MIN_PARAMS_TO_WRAP, + help=( + "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + ), + ) + # args for AdaPruning + # In short, it adds regularizarion for the multihead attention module and feed forward neural nets + # For more details, please refer to the paper https://openreview.net/forum?id=_CMSV7FTzGI + parser.add_argument( + "--mha-reg-scale-factor", + type=float, + metavar="D", + default=0.0, + help="scaling factor for regularization term in adptive pruning, recommendation is 0.000375", + ) + parser.add_argument( + "--ffn-reg-scale-factor", + type=float, + metavar="D", + default=0.0, + help="scaling factor for regularization term in adptive pruning, recommendation is 0.000375", + ) + parser.add_argument( + "--mha-heads-to-keep", + type=int, + metavar="D", + default=-1, + help="number of heads to keep in each multi-head attention module, -1 means keeping all heads", + ) + parser.add_argument( + "--ffn-blocks-to-remove", + type=int, + metavar="D", + default=-1, + help="number of feedforward blocks to remove in each transformer layer, -1 means keeping all ffn blocks", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + from omegaconf import OmegaConf + + if OmegaConf.is_config(args): + OmegaConf.set_struct(args, False) + + # make sure all arguments are present + base_architecture(args) + + if not safe_hasattr(args, "max_positions"): + if not safe_hasattr(args, "tokens_per_sample"): + args.tokens_per_sample = task.max_positions() + args.max_positions = args.tokens_per_sample + + encoder = RobertaEncoder(args, task.source_dictionary) + + if OmegaConf.is_config(args): + OmegaConf.set_struct(args, True) + + return cls(args, encoder) + + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + classification_head_name=None, + **kwargs, + ): + if classification_head_name is not None: + features_only = True + + x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs) + + if classification_head_name is not None: + x = self.classification_heads[classification_head_name](x) + return x, extra + + def _get_adaptive_head_loss(self): + norm_loss = 0 + scaling = float(self.args.mha_reg_scale_factor) + for layer in self.encoder.sentence_encoder.layers: + norm_loss_layer = 0 + for i in range(layer.self_attn.num_heads): + start_idx = i * layer.self_attn.head_dim + end_idx = (i + 1) * layer.self_attn.head_dim + norm_loss_layer += scaling * ( + torch.sum( + torch.abs( + layer.self_attn.q_proj.weight[ + start_idx:end_idx, + ] + ) + ) + + torch.sum( + torch.abs(layer.self_attn.q_proj.bias[start_idx:end_idx]) + ) + ) + norm_loss_layer += scaling * ( + torch.sum( + torch.abs( + layer.self_attn.k_proj.weight[ + start_idx:end_idx, + ] + ) + ) + + torch.sum( + torch.abs(layer.self_attn.k_proj.bias[start_idx:end_idx]) + ) + ) + norm_loss_layer += scaling * ( + torch.sum( + torch.abs( + layer.self_attn.v_proj.weight[ + start_idx:end_idx, + ] + ) + ) + + torch.sum( + torch.abs(layer.self_attn.v_proj.bias[start_idx:end_idx]) + ) + ) + + norm_loss += norm_loss_layer + return norm_loss + + def _get_adaptive_ffn_loss(self): + ffn_scale_factor = float(self.args.ffn_reg_scale_factor) + filter_loss = 0 + for layer in self.encoder.sentence_encoder.layers: + filter_loss += torch.sum( + torch.abs(layer.fc1.weight * ffn_scale_factor) + ) + torch.sum(torch.abs(layer.fc2.weight * ffn_scale_factor)) + filter_loss += torch.sum( + torch.abs(layer.fc1.bias * ffn_scale_factor) + ) + torch.sum(torch.abs(layer.fc2.bias * ffn_scale_factor)) + return filter_loss + + def get_normalized_probs(self, net_output, log_probs, sample=None): + """Get normalized probabilities (or log probs) from a net's output.""" + logits = net_output[0].float() + if log_probs: + return F.log_softmax(logits, dim=-1) + else: + return F.softmax(logits, dim=-1) + + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): + """Register a classification head.""" + if name in self.classification_heads: + prev_num_classes = self.classification_heads[name].out_proj.out_features + prev_inner_dim = self.classification_heads[name].dense.out_features + if num_classes != prev_num_classes or inner_dim != prev_inner_dim: + logger.warning( + 're-registering head "{}" with num_classes {} (prev: {}) ' + "and inner_dim {} (prev: {})".format( + name, num_classes, prev_num_classes, inner_dim, prev_inner_dim + ) + ) + self.classification_heads[name] = RobertaClassificationHead( + input_dim=self.args.encoder_embed_dim, + inner_dim=inner_dim or self.args.encoder_embed_dim, + num_classes=num_classes, + activation_fn=self.args.pooler_activation_fn, + pooler_dropout=self.args.pooler_dropout, + q_noise=self.args.quant_noise_pq, + qn_block_size=self.args.quant_noise_pq_block_size, + do_spectral_norm=self.args.spectral_norm_classification_head, + ) + + @property + def supported_targets(self): + return {"self"} + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="gpt2", + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + bpe=bpe, + load_checkpoint_heads=True, + **kwargs, + ) + + logger.info(x["args"]) + return RobertaHubInterface(x["args"], x["task"], x["models"][0]) + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + + # rename decoder -> encoder before upgrading children modules + for k in list(state_dict.keys()): + if k.startswith(prefix + "decoder"): + new_k = prefix + "encoder" + k[len(prefix + "decoder") :] + state_dict[new_k] = state_dict[k] + del state_dict[k] + + # rename emb_layer_norm -> layernorm_embedding + for k in list(state_dict.keys()): + if ".emb_layer_norm." in k: + new_k = k.replace(".emb_layer_norm.", ".layernorm_embedding.") + state_dict[new_k] = state_dict[k] + del state_dict[k] + + # upgrade children modules + super().upgrade_state_dict_named(state_dict, name) + + # Handle new classification heads present in the state dict. + current_head_names = ( + [] + if not hasattr(self, "classification_heads") + else self.classification_heads.keys() + ) + keys_to_delete = [] + for k in state_dict.keys(): + if not k.startswith(prefix + "classification_heads."): + continue + + head_name = k[len(prefix + "classification_heads.") :].split(".")[0] + num_classes = state_dict[ + prefix + "classification_heads." + head_name + ".out_proj.weight" + ].size(0) + inner_dim = state_dict[ + prefix + "classification_heads." + head_name + ".dense.weight" + ].size(0) + + if getattr(self.args, "load_checkpoint_heads", False): + if head_name not in current_head_names: + self.register_classification_head(head_name, num_classes, inner_dim) + else: + if head_name not in current_head_names: + logger.warning( + "deleting classification head ({}) from checkpoint " + "not present in current model: {}".format(head_name, k) + ) + keys_to_delete.append(k) + elif ( + num_classes + != self.classification_heads[head_name].out_proj.out_features + or inner_dim + != self.classification_heads[head_name].dense.out_features + ): + logger.warning( + "deleting classification head ({}) from checkpoint " + "with different dimensions than current model: {}".format( + head_name, k + ) + ) + keys_to_delete.append(k) + for k in keys_to_delete: + del state_dict[k] + + # Copy any newly-added classification heads into the state dict + # with their current weights. + if hasattr(self, "classification_heads"): + cur_state = self.classification_heads.state_dict() + for k, v in cur_state.items(): + if prefix + "classification_heads." + k not in state_dict: + logger.info("Overwriting " + prefix + "classification_heads." + k) + state_dict[prefix + "classification_heads." + k] = v + + # adapt data2vec models + if ( + "encoder._ema" in state_dict + and "encoder.lm_head.weight" not in state_dict + ): + lm_state = self.encoder.lm_head.state_dict() + for k, v in lm_state.items(): + state_dict["encoder.lm_head." + k] = v + + for k in list(state_dict.keys()): + if k.startswith("encoder.regression_head") or k == "encoder._ema": + del state_dict[k] + + +class RobertaLMHead(nn.Module): + """Head for masked language modeling.""" + + def __init__(self, embed_dim, output_dim, activation_fn, weight=None): + super().__init__() + self.dense = nn.Linear(embed_dim, embed_dim) + self.activation_fn = utils.get_activation_fn(activation_fn) + self.layer_norm = LayerNorm(embed_dim) + + if weight is None: + weight = nn.Linear(embed_dim, output_dim, bias=False).weight + self.weight = weight + self.bias = nn.Parameter(torch.zeros(output_dim)) + + def forward(self, features, masked_tokens=None, **kwargs): + # Only project the masked tokens while training, + # saves both memory and computation + if masked_tokens is not None: + features = features[masked_tokens, :] + + x = self.dense(features) + x = self.activation_fn(x) + x = self.layer_norm(x) + # project back to size of vocabulary with bias + x = F.linear(x, self.weight) + self.bias + return x + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__( + self, + input_dim, + inner_dim, + num_classes, + activation_fn, + pooler_dropout, + q_noise=0, + qn_block_size=8, + do_spectral_norm=False, + ): + super().__init__() + self.dense = nn.Linear(input_dim, inner_dim) + self.activation_fn = utils.get_activation_fn(activation_fn) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = apply_quant_noise_( + nn.Linear(inner_dim, num_classes), q_noise, qn_block_size + ) + if do_spectral_norm: + if q_noise != 0: + raise NotImplementedError( + "Attempting to use Spectral Normalization with Quant Noise. This is not officially supported" + ) + self.out_proj = torch.nn.utils.spectral_norm(self.out_proj) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take <s> token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = self.activation_fn(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +class RobertaEncoder(FairseqEncoder): + """RoBERTa encoder.""" + + def __init__(self, args, dictionary): + super().__init__(dictionary) + + # set any missing default values + base_architecture(args) + self.args = args + + if args.encoder_layers_to_keep: + args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) + + embed_tokens = self.build_embedding( + len(dictionary), args.encoder_embed_dim, dictionary.pad() + ) + + self.sentence_encoder = self.build_encoder(args, dictionary, embed_tokens) + + self.lm_head = self.build_lm_head( + embed_dim=args.encoder_embed_dim, + output_dim=len(dictionary), + activation_fn=args.activation_fn, + weight=( + self.sentence_encoder.embed_tokens.weight + if not args.untie_weights_roberta + else None + ), + ) + + def build_embedding(self, vocab_size, embedding_dim, padding_idx): + return nn.Embedding(vocab_size, embedding_dim, padding_idx) + + def build_encoder(self, args, dictionary, embed_tokens): + encoder = TransformerEncoder(args, dictionary, embed_tokens) + encoder.apply(init_bert_params) + return encoder + + def build_lm_head(self, embed_dim, output_dim, activation_fn, weight): + return RobertaLMHead(embed_dim, output_dim, activation_fn, weight) + + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + masked_tokens=None, + **unused, + ): + """ + Args: + src_tokens (LongTensor): input tokens of shape `(batch, src_len)` + features_only (bool, optional): skip LM head and just return + features. If True, the output will be of shape + `(batch, src_len, embed_dim)`. + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + + Returns: + tuple: + - the LM output of shape `(batch, src_len, vocab)` + - a dictionary of additional data, where 'inner_states' + is a list of hidden states. Note that the hidden + states have shape `(src_len, batch, vocab)`. + """ + x, extra = self.extract_features( + src_tokens, return_all_hiddens=return_all_hiddens + ) + if not features_only: + x = self.output_layer(x, masked_tokens=masked_tokens) + return x, extra + + def extract_features(self, src_tokens, return_all_hiddens=False, **kwargs): + encoder_out = self.sentence_encoder( + src_tokens, + return_all_hiddens=return_all_hiddens, + token_embeddings=kwargs.get("token_embeddings", None), + ) + # T x B x C -> B x T x C + features = encoder_out["encoder_out"][0].transpose(0, 1) + inner_states = encoder_out["encoder_states"] if return_all_hiddens else None + return features, {"inner_states": inner_states} + + def output_layer(self, features, masked_tokens=None, **unused): + return self.lm_head(features, masked_tokens) + + def max_positions(self): + """Maximum output length supported by the encoder.""" + return self.args.max_positions + + +@register_model_architecture("roberta", "roberta") +def base_architecture(args): + args.encoder_layers = safe_getattr(args, "encoder_layers", 12) + args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 3072) + args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 12) + + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_dropout = safe_getattr(args, "activation_dropout", 0.0) + args.pooler_dropout = safe_getattr(args, "pooler_dropout", 0.0) + + args.max_source_positions = safe_getattr(args, "max_positions", 512) + args.no_token_positional_embeddings = safe_getattr( + args, "no_token_positional_embeddings", False + ) + + # BERT has a few structural differences compared to the original Transformer + args.encoder_learned_pos = safe_getattr(args, "encoder_learned_pos", True) + args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", True) + args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", True) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + args.encoder_normalize_before = safe_getattr( + args, "encoder_normalize_before", False + ) + args.pooler_activation_fn = safe_getattr(args, "pooler_activation_fn", "tanh") + args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False) + + # Adaptive input config + args.adaptive_input = safe_getattr(args, "adaptive_input", False) + + # LayerDrop config + args.encoder_layerdrop = safe_getattr(args, "encoder_layerdrop", 0.0) + args.encoder_layers_to_keep = safe_getattr(args, "encoder_layers_to_keep", None) + + # Quantization noise config + args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0) + args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0) + + # R4F config + args.spectral_norm_classification_head = safe_getattr( + args, "spectral_norm_classification_head", False + ) + + +@register_model_architecture("roberta", "roberta_prenorm") +def roberta_prenorm_architecture(args): + args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False) + args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", True) + base_architecture(args) + + +@register_model_architecture("roberta", "roberta_base") +def roberta_base_architecture(args): + base_architecture(args) + + +@register_model_architecture("roberta", "roberta_large") +def roberta_large_architecture(args): + args.encoder_layers = safe_getattr(args, "encoder_layers", 24) + args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16) + base_architecture(args) + + +@register_model_architecture("roberta", "xlm") +def xlm_architecture(args): + args.encoder_layers = safe_getattr(args, "encoder_layers", 16) + args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1280) + args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 1280 * 4) + args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16) + base_architecture(args) diff --git a/fairseq/fairseq/models/roberta/model_camembert.py b/fairseq/fairseq/models/roberta/model_camembert.py new file mode 100644 index 0000000..4644754 --- /dev/null +++ b/fairseq/fairseq/models/roberta/model_camembert.py @@ -0,0 +1,50 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +CamemBERT: a Tasty French Language Model +""" + +from fairseq.models import register_model + +from .hub_interface import RobertaHubInterface +from .model import RobertaModel + + +@register_model("camembert") +class CamembertModel(RobertaModel): + @classmethod + def hub_models(cls): + return { + "camembert": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", + "camembert.v0": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", + "camembert-base": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", + "camembert-large": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz", + "camembert-base-ccnet": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz", + "camembert-base-ccnet-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz", + "camembert-base-wikipedia-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz", + "camembert-base-oscar-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz", + } + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="sentencepiece", + **kwargs + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + bpe=bpe, + load_checkpoint_heads=True, + **kwargs, + ) + return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/fairseq/fairseq/models/roberta/model_gottbert.py b/fairseq/fairseq/models/roberta/model_gottbert.py new file mode 100644 index 0000000..dc7a019 --- /dev/null +++ b/fairseq/fairseq/models/roberta/model_gottbert.py @@ -0,0 +1,49 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +GottBERT: a pure German Language Model +""" + +from fairseq.models import register_model + +from .hub_interface import RobertaHubInterface +from .model import RobertaModel + + +@register_model("gottbert") +class GottbertModel(RobertaModel): + @classmethod + def hub_models(cls): + return { + "gottbert-base": "https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz", + } + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="hf_byte_bpe", + bpe_vocab="vocab.json", + bpe_merges="merges.txt", + bpe_add_prefix_space=False, + **kwargs + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + bpe=bpe, + load_checkpoint_heads=True, + bpe_vocab=bpe_vocab, + bpe_merges=bpe_merges, + bpe_add_prefix_space=bpe_add_prefix_space, + **kwargs, + ) + return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/fairseq/fairseq/models/roberta/model_xlmr.py b/fairseq/fairseq/models/roberta/model_xlmr.py new file mode 100644 index 0000000..cf6e354 --- /dev/null +++ b/fairseq/fairseq/models/roberta/model_xlmr.py @@ -0,0 +1,46 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Unsupervised Cross-lingual Representation Learning at Scale +""" + +from fairseq.models import register_model + +from .hub_interface import RobertaHubInterface +from .model import RobertaModel + + +@register_model("xlmr") +class XLMRModel(RobertaModel): + @classmethod + def hub_models(cls): + return { + "xlmr.base": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz", + "xlmr.large": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz", + "xlmr.xl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xl.tar.gz", + "xlmr.xxl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xxl.tar.gz", + } + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="sentencepiece", + **kwargs + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + bpe=bpe, + load_checkpoint_heads=True, + **kwargs, + ) + return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/fairseq/fairseq/models/speech_dlm/__init__.py b/fairseq/fairseq/models/speech_dlm/__init__.py new file mode 100644 index 0000000..6ea914d --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .speech_dlm import * # noqa +from .hub_interface import * # noqa diff --git a/fairseq/fairseq/models/speech_dlm/hub_interface.py b/fairseq/fairseq/models/speech_dlm/hub_interface.py new file mode 100644 index 0000000..11bc0f5 --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/hub_interface.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import logging +from typing import Any, Dict, Iterator, List + +import torch +from fairseq import utils +from omegaconf import open_dict +from torch import nn + +from tqdm import tqdm + +from fairseq.hub_utils import GeneratorHubInterface + + +logger = logging.getLogger(__name__) + + +class MultichannelGeneratorHubInterface(GeneratorHubInterface): + """Pytorch Hub interface for generating sequences from a pre-trained + multichannel language model. + """ + + def __init__(self, cfg, task, models): + super().__init__(cfg, task, models) + self.cfg = cfg + self.task = task + self.models = nn.ModuleList(models) + self.src_dicts = task.source_dictionaries + self.tgt_dicts = task.target_dictionaries + self.channels = task.channels + + # optimize model for generation + for model in self.models: + model.prepare_for_inference_(cfg) + + def sample( + self, + sentences: List[Dict[str, str]], + beam: int = 1, + verbose: bool = False, + **kwargs + ) -> List[str]: + if isinstance(sentences, dict): + return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0] + tokenized_sentences = [self.encode(sentence) for sentence in sentences] + batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs) + return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos] + + def score(self, sentences: List[Dict[str, str]], **kwargs): + raise NotImplementedError( + "MultichannelGeneratorHubInterface doesn't support score() method" + ) + + def generate( + self, + tokenized_sentences: List[Dict[str, torch.LongTensor]], + beam: int = 5, + verbose: bool = False, + skip_invalid_size_inputs=False, + inference_step_args=None, + **kwargs + ) -> List[List[Dict[str, torch.Tensor]]]: + if isinstance(tokenized_sentences, dict): + return self.generate( + [tokenized_sentences], beam=beam, verbose=verbose, **kwargs + )[0] + + # build generator using current args as well as any kwargs + gen_args = copy.deepcopy(self.cfg.generation) + with open_dict(gen_args): + gen_args.beam = beam + for k, v in kwargs.items(): + setattr(gen_args, k, v) + generator = self.task.build_generator(self.models, gen_args) + + inference_step_args = inference_step_args or {} + results = [] + for batch in tqdm( + self._build_batches(tokenized_sentences, skip_invalid_size_inputs) + ): + batch = utils.apply_to_sample(lambda t: t.to(self.device), batch) + translations = self.task.inference_step( + generator, self.models, batch, **inference_step_args + ) + for id, hypos in zip(batch["id"].tolist(), translations): + # The output of the generator is supposed to be a tensor of size (bsz x max_len x n_channels) + # So we need to convert it to dictionary form + for i in range(len(hypos)): + hypos[i]["tokens"] = { + channel: hypos[i]["tokens"][..., j] + for j, channel in enumerate(self.channels) + } + results.append((id, hypos)) + + # sort output to match input order + outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])] + + if verbose: + + def getarg(name, default): + return getattr(gen_args, name, getattr(self.cfg, name, default)) + + for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs): + src_str_with_unk = { + channel: self.string(source_tokens[channel], channel) + for channel in source_tokens + } + logger.info("S\t{}".format(src_str_with_unk)) + for hypo in target_hypotheses: + hypo_str = self.decode(hypo["tokens"]) + logger.info("H\t{}\t{}".format(hypo["score"], hypo_str)) + # hypo["positional_scores"]: T x n_channels + pos_scores = {} + for c, channel in enumerate(source_tokens): + pos_scores[channel] = " ".join( + map( + lambda x: "{:.4f}".format(x), + hypo["positional_scores"][:, c].tolist(), + ) + ) + logger.info("P\t{}".format(pos_scores)) + + return outputs + + def encode(self, sentence: Dict[str, str]) -> Dict[str, torch.LongTensor]: + assert isinstance( + sentence, dict + ), "Input sentence is expected to be a dictionary over channels" + assert set(sentence.keys()) == set( + self.channels + ), "Mismatch between input sentence keys and model channels ({} vs {})".format( + set(sentence.keys()), set(self.channels) + ) + encoded_sentence = {} + for channel in sentence: + sentence_channel = sentence[channel] + sentence_channel = self.tokenize(sentence_channel) + sentence_channel = self.apply_bpe(sentence_channel) + sentence_channel = self.binarize(sentence_channel, channel) + encoded_sentence[channel] = sentence_channel + sentence_size = encoded_sentence[self.channels[0]].size() + assert all( + encoded_sentence[channel].size() == sentence_size + for channel in encoded_sentence + ), "Input tensors are expected to have the same size in all channels" + return encoded_sentence + + def decode(self, tokens: Dict[str, torch.LongTensor]) -> Dict[str, str]: + assert isinstance( + tokens, dict + ), "Input tokens are expected to be a dictionary over channels" + assert set(tokens.keys()) == set( + self.channels + ), "Mismatch between input tokens keys and model channels ({} vs {})".format( + set(tokens.keys()), set(self.channels) + ) + decoded_sentence = {} + for channel in tokens: + tokens_channel = tokens[channel] + sentence_channel = self.string(tokens_channel, channel) + sentence_channel = self.remove_bpe(sentence_channel) + sentence_channel = self.detokenize(sentence_channel) + decoded_sentence[channel] = sentence_channel + return decoded_sentence + + def binarize(self, sentence: str, channel: str) -> torch.LongTensor: + return ( + self.src_dicts[channel].encode_line(sentence, add_if_not_exist=False).long() + ) + + def string(self, tokens: torch.LongTensor, channel: str) -> str: + return self.tgt_dicts[channel].string(tokens) + + def _build_batches( + self, tokens: List[Dict[str, List[int]]], skip_invalid_size_inputs: bool + ) -> Iterator[Dict[str, Any]]: + lengths = torch.LongTensor([next(iter(d.values())).numel() for d in tokens]) + batch_iterator = self.task.get_batch_iterator( + dataset=self.task.build_dataset_for_inference(tokens, lengths), + max_tokens=self.cfg.dataset.max_tokens, + max_sentences=self.cfg.dataset.batch_size, + max_positions=self.max_positions, + ignore_invalid_inputs=skip_invalid_size_inputs, + disable_iterator_cache=True, + ).next_epoch_itr(shuffle=False) + return batch_iterator diff --git a/fairseq/fairseq/models/speech_dlm/modules/__init__.py b/fairseq/fairseq/models/speech_dlm/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/fairseq/models/speech_dlm/modules/speech_dlm_decoder.py b/fairseq/fairseq/models/speech_dlm/modules/speech_dlm_decoder.py new file mode 100644 index 0000000..a14a1d6 --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/modules/speech_dlm_decoder.py @@ -0,0 +1,572 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.models import FairseqIncrementalDecoder +from fairseq.modules import ( + FairseqDropout, + LayerDropModuleList, + LayerNorm, + PositionalEmbedding, +) +from .speech_dlm_decoder_layer import ( + CrossChannelTransformerDecoderLayer, + StandardTransformerDecoderLayer, +) +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ +from torch import Tensor + + +class CrossChannelTransformerDecoder(FairseqIncrementalDecoder): + """ + Cross-channel Transformer Decoder Block for parallel spoken dialogue units + as described in the paper: https://arxiv.org/pdf/2203.16502.pdf; + consisting of *args.decoder_layers* layers. Each layer is a + :class:`StandardTransformerDecoderLayer` or + :class:`CrossChannelTransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + channels (list): list of channel names (string) + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__(self, args, dictionary, embed_tokens, channels, no_encoder_attn=False): + self.args = args + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + self._future_mask = torch.empty(0) + + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.decoder_layerdrop = args.decoder_layerdrop + self.share_input_output_embed = args.share_decoder_input_output_embed + self.channels = channels + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = args.decoder_embed_dim + self.embed_dim = embed_dim + self.output_embed_dim = args.decoder_output_dim + + self.padding_idx = embed_tokens.padding_idx + self.max_target_positions = args.max_target_positions + + self.embed_tokens = embed_tokens + + self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim) + + if args.quant_noise_pq > 0: + self.quant_noise = apply_quant_noise_( + nn.Linear(embed_dim, embed_dim, bias=False), + args.quant_noise_pq, + args.quant_noise_pq_block_size, + ) + else: + self.quant_noise = None + + self.project_in_dim = ( + nn.Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + self.embed_positions = ( + PositionalEmbedding( + self.max_target_positions, + embed_dim, + self.padding_idx, + learned=args.decoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) + + if getattr(args, "layernorm_embedding", False): + self.layernorm_embedding = LayerNorm(embed_dim) + else: + self.layernorm_embedding = None + + self.cross_self_attention = getattr(args, "cross_self_attention", False) + + assert 0 <= args.decoder_cross_layers <= args.decoder_layers, ( + "The number of cross-channel attention decoder layers must be non-negative" + f"and not exceeds the number of decoder layers (found {args.decoder_cross_layers})" + ) + + if self.decoder_layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.decoder_layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + self.build_decoder_layer(args, no_encoder_attn) + if i < args.decoder_layers - args.decoder_cross_layers + else self.build_cross_decoder_layer(args, no_encoder_attn) + for i in range(args.decoder_layers) + ] + ) + self.num_layers = len(self.layers) + self.non_cross_layers = args.decoder_layers - args.decoder_cross_layers + + if args.decoder_normalize_before and not getattr( + args, "no_decoder_final_norm", False + ): + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + self.project_out_dim = ( + nn.Linear(embed_dim, self.output_embed_dim, bias=False) + if embed_dim != self.output_embed_dim + else None + ) + + self.output_projection = None + self.is_cross_prediction = bool( + float(args.main_and_cross_weights.split(",")[1]) != 0 + ) + self.n_output_projections = ( + 1 if not self.is_cross_prediction else len(self.channels) + ) + + if self.share_input_output_embed: + # Output projection is a list of projections + # where the first proj is for the main-channel, + # then roll in a cicular way. + # For example: if the main channel has index i + # the second proj is for channel i+1 (mod N_channels), etc. + self.output_projection = nn.ModuleList( + [ + nn.Linear( + embed_tokens.weight.shape[1], # embed_dim + embed_tokens.weight.shape[0], # n_dictionaries + bias=False, + ) + for _ in range(self.n_output_projections) + ] + ) + # Only share the main-channel projection + self.output_projection[0].weight = embed_tokens.weight + for i in range(1, self.n_output_projections): + nn.init.normal_( + self.output_projection[i].weight, + mean=0, + std=embed_tokens.weight.shape[1] ** -0.5, + ) + else: + self.output_projection = nn.ModuleList( + [ + nn.Linear(self.output_embed_dim, len(dictionary), bias=False) + for _ in range(self.n_output_projections) + ] + ) + for i in range(self.n_output_projections): + nn.init.normal_( + self.output_projection[i].weight, + mean=0, + std=self.output_embed_dim**-0.5, + ) + self.output_duration_prediction = ( + None + if str(args.duration_prediction).lower() == "false" + else nn.ModuleList( + [ + nn.Linear(self.output_embed_dim, 1) + for _ in range(self.n_output_projections) + ] + ) + ) + + def build_decoder_layer(self, args, no_encoder_attn=False): + layer = StandardTransformerDecoderLayer(args, no_encoder_attn) + if getattr(args, "checkpoint_activations", False): + offload_to_cpu = getattr(args, "offload_activations", False) + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + return layer + + def build_cross_decoder_layer(self, args, no_encoder_attn=False): + layer = CrossChannelTransformerDecoderLayer(args, no_encoder_attn) + if getattr(args, "checkpoint_activations", False): + offload_to_cpu = getattr(args, "offload_activations", False) + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + return layer + + def forward( + self, + prev_output_tokens: Dict[str, Tensor], + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[ + List[Dict[str, Dict[str, Optional[Tensor]]]] + ] = None, + features_only: bool = False, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + # return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (dict[str, LongTensor]): previous decoder outputs, + dictionary over all channels with the values being the tensors + of shape `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): list of dictionaries used for storing state + during :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + + Returns: + tuple: + - the decoder's output, dict over channels of tensors + of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + full_context_alignment=full_context_alignment, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + ) + if not features_only: + x = self.output_layer(x) + return x, extra + + def extract_features( + self, + prev_output_tokens: Dict[str, Tensor], + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[ + List[Dict[str, Dict[str, Optional[Tensor]]]] + ] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + return self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + + """ + A scriptable subclass of this class has an extract_features method and calls + super().extract_features, but super() is not supported in torchscript. A copy of + this function is made to be used in the subclass instead. + """ + + def extract_features_scriptable( + self, + prev_output_tokens: Dict[str, Tensor], + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[ + List[Dict[str, Dict[str, Optional[Tensor]]]] + ] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + """ + The core function of *forward* but only return features. + + The input (prev_output_tokens) is a dictionary over all channels, + expected to have the following form: + { + 'channel1' : Tensor((batch x tgt_len)), + 'channel2' : Tensor((batch x tgt_len)), + } + + Args: + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + alignment_layer (int, optional): return mean alignment over + heads at this layer (default: last layer). + alignment_heads (int, optional): only average alignment over + this many heads (default: all heads). + + Returns: + tuple: + - the decoder's features, dict over channels of tensors + of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + if alignment_layer is None: + alignment_layer = self.num_layers - 1 + + x_list = [] + for i, channel in enumerate(self.channels): + # embed positions + positions = None + if self.embed_positions is not None: + positions = self.embed_positions( + prev_output_tokens[channel], + incremental_state=incremental_state[i] + if incremental_state is not None + else None, + ) + + if incremental_state is not None: + prev_output_tokens[channel] = prev_output_tokens[channel][:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_tokens(prev_output_tokens[channel]) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + x = self.embed_scale * x + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if positions is not None: + x += positions + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + x_list.append(x) + + self_attn_padding_mask: Optional[Tensor] = None + if ( + self.cross_self_attention + or prev_output_tokens[self.channels[0]].eq(self.padding_idx).any() + ): + self_attn_padding_mask = prev_output_tokens[self.channels[0]].eq( + self.padding_idx + ) + + # decoder layers + attn: Optional[Dict[Tensor]] = None + inner_states: List[Optional[Dict[str, Tensor]]] = [ + {channel: x_list[i] for i, channel in enumerate(self.channels)} + ] + for idx, layer in enumerate(self.layers): + if incremental_state is None and not full_context_alignment: + self_attn_mask = self.buffered_future_mask(x_list[0]) + else: + self_attn_mask = None + + # need to change to tensor for the checkpoint activation to work + if isinstance(x_list, list): + x_list = torch.stack(x_list) + x_list, layer_attn_list, _ = layer( + x_list, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + + inner_states.append( + {channel: x_list[i] for i, channel in enumerate(self.channels)} + ) + if idx == alignment_layer and all( + layer_attn is not None for layer_attn in layer_attn_list + ): + attn = { + channel: layer_attn_list[i].float().to(x_list[0]) + for i, channel in enumerate(self.channels) + } + # change back from tensor to list + if not isinstance(x_list, list): + x_list = list(torch.unbind(x_list)) + + if attn is not None: + for channel in attn: + if alignment_heads is not None: + attn[channel] = attn[channel][:alignment_heads] + + # average probabilities over heads + attn[channel] = attn[channel].mean(dim=0) + + for i, x in enumerate(x_list): + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + x_list[i] = x + + x = {channel: x_list[i] for i, channel in enumerate(self.channels)} + + return x, {"attn": [attn], "inner_states": inner_states} + + def output_layer(self, features): + """Project features to the vocabulary size. + Return a dictionary of the form: + { + 'input-channel': { + 'predicted-channel': token prediction tensor of shape `(batch, tgt_len, vocab)`, + } + } + + if duration_prediction is enabled + { + 'input-channel': { + 'predicted-channel': { + 'pred_token': token prediction tensor of shape `(batch, tgt_len, vocab)`, + 'pred_duration': duration prediction tensor + } + } + } + """ + # project back to size of vocabulary + if self.output_duration_prediction is None: + if self.is_cross_prediction: + return { + channel: { + pred_channel: self.output_projection[j - i](features[channel]) + for j, pred_channel in enumerate(self.channels) + } + for i, channel in enumerate(self.channels) + } + else: + return { + channel: {channel: self.output_projection[0](features[channel])} + for i, channel in enumerate(self.channels) + } + else: + if self.is_cross_prediction: + return { + channel: { + pred_channel: { + "pred_token": self.output_projection[j - i]( + features[channel] + ), + "pred_duration": self.output_duration_prediction[j - i]( + features[channel] + ), + } + for j, pred_channel in enumerate(self.channels) + } + for i, channel in enumerate(self.channels) + } + else: + return { + channel: { + channel: { + "pred_token": self.output_projection[0](features[channel]), + "pred_duration": self.output_duration_prediction[0]( + features[channel] + ), + } + } + for i, channel in enumerate(self.channels) + } + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embed_positions is None: + return self.max_target_positions + return min(self.max_target_positions, self.embed_positions.max_positions) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. + if ( + self._future_mask.size(0) == 0 + or (not self._future_mask.device == tensor.device) + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 + ) + self._future_mask = self._future_mask.to(tensor) + return self._future_mask[:dim, :dim] + + def get_normalized_probs_scriptable( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" + + logits_dict = net_output[0] + out_dict = {} + for channel in logits_dict: + out_dict[channel] = {} + for pred_channel in logits_dict[channel]: + if isinstance(logits_dict[channel][pred_channel], dict): + pred_token_logits = logits_dict[channel][pred_channel]["pred_token"] + else: + pred_token_logits = logits_dict[channel][pred_channel] + if log_probs: + out = utils.log_softmax( + pred_token_logits, dim=-1, onnx_trace=self.onnx_trace + ) + else: + out = utils.softmax( + pred_token_logits, dim=-1, onnx_trace=self.onnx_trace + ) + if isinstance(logits_dict[channel][pred_channel], dict): + out_dict[channel][pred_channel] = { + "pred_token": out, + "pred_duration": logits_dict[channel][pred_channel][ + "pred_duration" + ].float(), + } # move to float32 to avoid inf loss + else: + out_dict[channel][pred_channel] = out + return out_dict + + def reorder_incremental_state_scripting( + self, + incremental_state: List[Dict[str, Dict[str, Optional[Tensor]]]], + new_order: Tensor, + ): + """Main entry point for reordering the incremental state. + + Due to limitations in TorchScript, we call this function in + :class:`fairseq.sequence_generator.SequenceGenerator` instead of + calling :func:`reorder_incremental_state` directly. + """ + for module in self.modules(): + if hasattr(module, "reorder_incremental_state"): + for i, incremental_state_channel in enumerate(incremental_state): + result = module.reorder_incremental_state( + incremental_state_channel, new_order + ) + if result is not None: + incremental_state[i] = result diff --git a/fairseq/fairseq/models/speech_dlm/modules/speech_dlm_decoder_layer.py b/fairseq/fairseq/models/speech_dlm/modules/speech_dlm_decoder_layer.py new file mode 100644 index 0000000..fb65fdf --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/modules/speech_dlm_decoder_layer.py @@ -0,0 +1,717 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Tuple, Optional + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.modules import LayerNorm, MultiheadAttention +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.quant_noise import quant_noise +from torch import Tensor + + +class CrossChannelTransformerDecoderLayer(nn.Module): + """Cross-Attention Transformer Decoder Layer block as described + in the paper: https://arxiv.org/pdf/2203.16502.pdf + + Composed of a Multi-head Self Attention block followed by a + Multi-head Cross-Attention block which attends to the self-attention + outputs of the other channels. The weights of the attention blocks + in all channels are shared. + + Args: + args (argparse.Namespace): parsed command-line arguments + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__() + self.embed_dim = args.decoder_embed_dim + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.quant_noise = getattr(args, "quant_noise_pq", 0) + self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8) + + # This cross_self_attention is used for encoder-decoder systems, + # It's not the cross-channel attention (defined below as cross_channel_attn) + self.cross_self_attention = getattr(args, "cross_self_attention", False) + + self.self_attn = self.build_self_attention( + self.embed_dim, + args, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + self.cross_channel_attn = self.build_cross_channel_attention( + self.embed_dim, + args, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + + self.activation_fn = utils.get_activation_fn( + activation=str(args.activation_fn) + if getattr(args, "activation_fn", None) is not None + else "relu" + ) + activation_dropout_p = getattr(args, "activation_dropout", 0) or 0 + if activation_dropout_p == 0: + # for backwards compatibility with models that use args.relu_dropout + activation_dropout_p = getattr(args, "relu_dropout", 0) or 0 + self.activation_dropout_module = FairseqDropout( + float(activation_dropout_p), module_name=self.__class__.__name__ + ) + self.normalize_before = args.decoder_normalize_before + + # use layerNorm rather than FusedLayerNorm for exporting. + # char_inputs can be used to determint this. + # TODO remove this once we update apex with the fix + export = getattr(args, "char_inputs", False) + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + self.cross_channel_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + if no_encoder_attn: + self.encoder_attn = None + self.encoder_attn_layer_norm = None + else: + self.encoder_attn = self.build_encoder_attention(self.embed_dim, args) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + self.fc1 = self.build_fc1( + self.embed_dim, + args.decoder_ffn_embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + self.fc2 = self.build_fc2( + args.decoder_ffn_embed_dim, + self.embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + + self.final_layer_norm = LayerNorm(self.embed_dim, export=export) + self.need_attn = True + + self.onnx_trace = False + + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_self_attention( + self, embed_dim, args, add_bias_kv=False, add_zero_attn=False + ): + return MultiheadAttention( + embed_dim, + args.decoder_attention_heads, + dropout=args.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=not getattr(args, "cross_self_attention", False), + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + ) + + def build_cross_channel_attention( + self, embed_dim, args, add_bias_kv=False, add_zero_attn=False + ): + return MultiheadAttention( + embed_dim, + args.decoder_attention_heads, + dropout=args.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=False, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + ) + + def build_encoder_attention(self, embed_dim, args): + return MultiheadAttention( + embed_dim, + args.decoder_attention_heads, + kdim=getattr(args, "encoder_embed_dim", None), + vdim=getattr(args, "encoder_embed_dim", None), + dropout=args.attention_dropout, + encoder_decoder_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + ) + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def residual_connection(self, x, residual): + return residual + x + + def forward( + self, + x_list_tensor: List[torch.Tensor], + encoder_out: Optional[torch.Tensor] = None, + encoder_padding_mask: Optional[torch.Tensor] = None, + incremental_state: Optional[ + List[Dict[str, Dict[str, Optional[Tensor]]]] + ] = None, + prev_self_attn_state: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, + prev_attn_state: Optional[List[torch.Tensor]] = None, + self_attn_mask: Optional[torch.Tensor] = None, + self_attn_padding_mask: Optional[torch.Tensor] = None, + need_attn: bool = False, + need_head_weights: bool = False, + ): + """ + Args: + x_list_tensor (List[Tensor]): list of input tensors in different channels, + each tensor is of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor, optional): binary + ByteTensor of shape `(batch, src_len)` where padding + elements are indicated by ``1``. + incremental_state (optional): list of incremental_state dictionaries over + different channels (sequence generation mode) + prev_self_attn_state (List[Tuple[Tensor, Tensor]], optional): list of tuples + (self_attn_state, cross_channel_attn_state) over different channels + need_attn (bool, optional): return attention weights + need_head_weights (bool, optional): return attention weights + for each head (default: return average over heads). + + Returns: + list of encoded output of shape `(seq_len, batch, embed_dim)` + """ + n_channels = len(x_list_tensor) + if need_head_weights: + need_attn = True + + # incremental_state is a list of dictionaries over different channels + if incremental_state is not None: + assert isinstance(incremental_state, list) + assert len(incremental_state) == n_channels + + # prev_self_attn_state is a list of tuples (self_attn_state, cross_channel_attn_state) over different channels + if prev_self_attn_state is not None: + assert isinstance(prev_self_attn_state, list) + assert len(prev_self_attn_state) == n_channels + for prev_self_attn_state_channel in prev_self_attn_state: + assert isinstance(prev_self_attn_state_channel, tuple) + assert len(prev_self_attn_state_channel) == 2 + + # Backup for other channels & cross channel attention + self_attn_mask_orin = self_attn_mask + self_attn_padding_mask_orin = self_attn_padding_mask + + x_list = [] + attn_list = [] + for i, x in enumerate(x_list_tensor): + residual = x + + if self.normalize_before: + x = self.self_attn_layer_norm(x) + + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[i][0][:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state[i][0]) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[i][0][2] + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state[i], saved_state) + _self_attn_input_buffer = self.self_attn._get_input_buffer( + incremental_state[i] if incremental_state is not None else None + ) + if self.cross_self_attention and not ( + incremental_state is not None + and _self_attn_input_buffer is not None + and "prev_key" in _self_attn_input_buffer + ): + if self_attn_mask_orin is not None: + assert encoder_out is not None + self_attn_mask = torch.cat( + ( + x.new_zeros(x.size(0), encoder_out.size(0)), + self_attn_mask_orin, + ), + dim=1, + ) + if self_attn_padding_mask_orin is not None: + if encoder_padding_mask is None: + assert encoder_out is not None + encoder_padding_mask = self_attn_padding_mask_orin.new_zeros( + encoder_out.size(1), encoder_out.size(0) + ) + self_attn_padding_mask = torch.cat( + (encoder_padding_mask, self_attn_padding_mask_orin), dim=1 + ) + assert encoder_out is not None + y = torch.cat((encoder_out, x), dim=0) + else: + y = x + + x, attn = self.self_attn( + query=x, + key=y, + value=y, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state[i] + if incremental_state is not None + else None, + need_weights=False, + attn_mask=self_attn_mask, + ) + + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + if self.encoder_attn is not None and encoder_out is not None: + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer( + incremental_state[i], saved_state + ) + + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state[i] + if incremental_state is not None + else None, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm(x) + + x_list.append(x) + attn_list.append(attn) + + # Store attentions & new x(s) (bc the old x(s) are used in other channels) + x_list_new = [] + # Here comes the cross channel attention + for i, x in enumerate(x_list): + residual = x + if self.normalize_before: + x = self.cross_channel_attn_layer_norm(x) + + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[i][1][:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state[i][1]) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[i][1][2] + assert incremental_state is not None + self.cross_channel_attn._set_input_buffer( + incremental_state[i], saved_state + ) + + # The cross attention is computed with the concatenation of attentions from other channels + if len(x_list) > 1: + x_other = torch.cat( + [x_list[(i + j) % len(x_list)] for j in range(1, len(x_list))], + dim=0, + ) + else: + # Self-attention when having only one channel + x_other = x_list[i] + + x, attn = self.cross_channel_attn( + query=x, + key=x_other, + value=x_other, + key_padding_mask=self_attn_padding_mask_orin, + incremental_state=incremental_state[i] + if incremental_state is not None + else None, + need_weights=False, + attn_mask=self_attn_mask_orin, + ) + + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.cross_channel_attn_layer_norm(x) + + x_list_new.append(x) + x_list = x_list_new + + for i, x in enumerate(x_list): + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.final_layer_norm(x) + + x_list[i] = x + # Trick for the checkpoint activation + x_list_tensor = torch.stack(x_list) + if self.onnx_trace and incremental_state is not None: + self_and_cross_attn_state_list = [] + for i in range(n_channels): + self_and_cross_attn_state = [] + for self_attn_module in [self.self_attn, self.cross_channel_attn]: + saved_state = self_attn_module._get_input_buffer( + incremental_state[i] + ) + assert saved_state is not None + if self_attn_padding_mask is not None: + self_attn_module_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + saved_state["prev_key_padding_mask"], + ] + else: + self_attn_module_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + ] + self_and_cross_attn_state.append(self_attn_module_state) + self_and_cross_attn_state_list.append(tuple(self_and_cross_attn_state)) + return x_list_tensor, attn_list, self_and_cross_attn_state_list + return x_list_tensor, attn_list, None + + def make_generation_fast_(self, need_attn: bool = False, **kwargs): + self.need_attn = need_attn + + +# Rewrite fairseq.modules.TransformerDecoderLayer +# to be compatible with checkpoint_activations +# (avoid forwarding model multiple times) +class StandardTransformerDecoderLayer(nn.Module): + """Rewrite fairseq.modules.TransformerDecoderLayer to avoid forwarding + model multiple times and be compatible with checkpoint_activations. + + The input is expected to be a list of tensors from different channels, + each is forwarded to the same model (shared attention weights). + + In the original paper each operation (multi-head attention, encoder + attention or FFN) is postprocessed with: `dropout -> add residual -> + layernorm`. In the tensor2tensor code they suggest that learning is more + robust when preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *args.decoder_normalize_before* to ``True``. + + Args: + args (argparse.Namespace): parsed command-line arguments + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__() + self.embed_dim = args.decoder_embed_dim + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.quant_noise = getattr(args, "quant_noise_pq", 0) + self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8) + + self.cross_self_attention = getattr(args, "cross_self_attention", False) + + self.self_attn = self.build_self_attention( + self.embed_dim, + args, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + + self.activation_fn = utils.get_activation_fn( + activation=str(args.activation_fn) + if getattr(args, "activation_fn", None) is not None + else "relu" + ) + activation_dropout_p = getattr(args, "activation_dropout", 0) or 0 + if activation_dropout_p == 0: + # for backwards compatibility with models that use args.relu_dropout + activation_dropout_p = getattr(args, "relu_dropout", 0) or 0 + self.activation_dropout_module = FairseqDropout( + float(activation_dropout_p), module_name=self.__class__.__name__ + ) + self.normalize_before = args.decoder_normalize_before + + # use layerNorm rather than FusedLayerNorm for exporting. + # char_inputs can be used to determint this. + # TODO remove this once we update apex with the fix + export = getattr(args, "char_inputs", False) + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + if no_encoder_attn: + self.encoder_attn = None + self.encoder_attn_layer_norm = None + else: + self.encoder_attn = self.build_encoder_attention(self.embed_dim, args) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + self.fc1 = self.build_fc1( + self.embed_dim, + args.decoder_ffn_embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + self.fc2 = self.build_fc2( + args.decoder_ffn_embed_dim, + self.embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + + self.final_layer_norm = LayerNorm(self.embed_dim, export=export) + self.need_attn = True + + self.onnx_trace = False + + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_self_attention( + self, embed_dim, args, add_bias_kv=False, add_zero_attn=False + ): + return MultiheadAttention( + embed_dim, + args.decoder_attention_heads, + dropout=args.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=not getattr(args, "cross_self_attention", False), + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + ) + + def build_encoder_attention(self, embed_dim, args): + return MultiheadAttention( + embed_dim, + args.decoder_attention_heads, + kdim=getattr(args, "encoder_embed_dim", None), + vdim=getattr(args, "encoder_embed_dim", None), + dropout=args.attention_dropout, + encoder_decoder_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + ) + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def residual_connection(self, x, residual): + return residual + x + + def forward( + self, + x_list_tensor: List[torch.Tensor], + encoder_out: Optional[torch.Tensor] = None, + encoder_padding_mask: Optional[torch.Tensor] = None, + incremental_state: Optional[ + List[Dict[str, Dict[str, Optional[Tensor]]]] + ] = None, + prev_self_attn_state: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, + prev_attn_state: Optional[List[torch.Tensor]] = None, + self_attn_mask: Optional[torch.Tensor] = None, + self_attn_padding_mask: Optional[torch.Tensor] = None, + need_attn: bool = False, + need_head_weights: bool = False, + ): + """ + Args: + x_list_tensor (List[Tensor]): list of input tensors in different channels, + each tensor is of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor, optional): binary + ByteTensor of shape `(batch, src_len)` where padding + elements are indicated by ``1``. + incremental_state (optional): list of incremental_state dictionaries over + different channels (sequence generation mode) + prev_self_attn_state (List[Tuple[Tensor, Tensor]], optional): list of tuples + (self_attn_state, cross_channel_attn_state) over different channels + need_attn (bool, optional): return attention weights + need_head_weights (bool, optional): return attention weights + for each head (default: return average over heads). + + Returns: + list of encoded output of shape `(seq_len, batch, embed_dim)` + """ + n_channels = len(x_list_tensor) + if need_head_weights: + need_attn = True + + # incremental_state is a list of dictionaries over different channels + if incremental_state is not None: + assert isinstance(incremental_state, list) + assert len(incremental_state) == n_channels + + # prev_self_attn_state is a list of self_attn_state over different channels + if prev_self_attn_state is not None: + assert isinstance(prev_self_attn_state, list) + assert len(prev_self_attn_state) == n_channels + + x_list = [] + attn_list = [] + for i, x in enumerate(x_list_tensor): + residual = x + + if self.normalize_before: + x = self.self_attn_layer_norm(x) + + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[i][:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state[i]) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[2] + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state[i], saved_state) + _self_attn_input_buffer = self.self_attn._get_input_buffer( + incremental_state + ) + if self.cross_self_attention and not ( + incremental_state is not None + and _self_attn_input_buffer is not None + and "prev_key" in _self_attn_input_buffer + ): + if self_attn_mask is not None: + assert encoder_out is not None + self_attn_mask = torch.cat( + (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), + dim=1, + ) + if self_attn_padding_mask is not None: + if encoder_padding_mask is None: + assert encoder_out is not None + encoder_padding_mask = self_attn_padding_mask.new_zeros( + encoder_out.size(1), encoder_out.size(0) + ) + self_attn_padding_mask = torch.cat( + (encoder_padding_mask, self_attn_padding_mask), dim=1 + ) + assert encoder_out is not None + y = torch.cat((encoder_out, x), dim=0) + else: + y = x + + x, attn = self.self_attn( + query=x, + key=y, + value=y, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state[i] + if incremental_state is not None + else None, + need_weights=False, + attn_mask=self_attn_mask, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + if self.encoder_attn is not None and encoder_out is not None: + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state[i] + if incremental_state is not None + else None, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.final_layer_norm(x) + + x_list.append(x) + attn_list.append(attn) + + # Trick for the checkpoint activation + x_list_tensor = torch.stack(x_list) + if self.onnx_trace and incremental_state is not None: + self_attn_state_list = [] + for i in range(n_channels): + saved_state = self.self_attn._get_input_buffer(incremental_state[i]) + assert saved_state is not None + if self_attn_padding_mask is not None: + self_attn_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + saved_state["prev_key_padding_mask"], + ] + else: + self_attn_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + ] + self_attn_state_list.append(self_attn_state) + return x_list_tensor, attn_list, self_attn_state_list + return x_list_tensor, attn_list, None + + def make_generation_fast_(self, need_attn: bool = False, **kwargs): + self.need_attn = need_attn diff --git a/fairseq/fairseq/models/speech_dlm/sequence_generator/__init__.py b/fairseq/fairseq/models/speech_dlm/sequence_generator/__init__.py new file mode 100644 index 0000000..a88e144 --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/sequence_generator/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .multichannel_sequence_generator import * # noqa diff --git a/fairseq/fairseq/models/speech_dlm/sequence_generator/multichannel_search.py b/fairseq/fairseq/models/speech_dlm/sequence_generator/multichannel_search.py new file mode 100644 index 0000000..db4b77f --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/sequence_generator/multichannel_search.py @@ -0,0 +1,430 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, Optional + +import torch +import torch.nn as nn +from torch import Tensor + + +class MultichannelSearch(nn.Module): + def __init__(self, tgt_dicts): + super().__init__() + tgt_dict = list(tgt_dicts.values())[0] + self.pad = tgt_dict.pad() + self.unk = tgt_dict.unk() + self.eos = tgt_dict.eos() + for tgt_dict in tgt_dicts.values(): + assert self.pad == tgt_dict.pad() + assert self.unk == tgt_dict.unk() + assert self.eos == tgt_dict.eos() + self.vocab_sizes = {channel: len(tgt_dicts[channel]) for channel in tgt_dicts} + self.src_lengths = torch.tensor(-1) + self.supports_constraints = False + self.stop_on_max_len = False + + def step( + self, step, lprobs, scores, prev_output_tokens=None, original_batch_idxs=None + ): + """Take a single search step. + + Args: + step: the current search step, starting at 0 + lprobs: dictionary of channels {channel : (bsz x input_beam_size x vocab_size_channel)} + the model's log-probabilities over the vocabulary at the current step + scores: {channel : (bsz x input_beam_size x step)} + the historical model scores of each hypothesis up to this point + prev_output_tokens: {channel : (bsz x step)} + the previously generated oputput tokens + original_batch_idxs: (bsz) + the tensor with the batch indices, in the range [0, bsz) + this is useful in case there has been applied a re-ordering + and we need to know the orignal indices + + Return: A tuple of (scores, indices, beams) where: + scores: {channel : (bsz x output_beam_size)} + the scores of the chosen elements; output_beam_size can be + larger than input_beam_size, e.g., we may return + 2*input_beam_size to account for EOS + indices: {channel : (bsz x output_beam_size)} + the indices of the chosen elements + beams: (bsz x output_beam_size) + the hypothesis ids of the chosen elements, in the range [0, input_beam_size) + """ + raise NotImplementedError + + @torch.jit.export + def set_src_lengths(self, src_lengths): + self.src_lengths = src_lengths + + @torch.jit.export + def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int): + """Initialize constraint states for constrained decoding (if supported). + + Args: + batch_constraints: (torch.Tensor, optional) + the list of constraints, in packed form + beam_size: (int) + the beam size + Returns: + *encoder_out* rearranged according to *new_order* + """ + pass + + def prune_sentences(self, batch_idxs: Tensor): + """ + Removes constraint states for completed sentences (if supported). + This is called from sequence_generator._generate() when sentences are + deleted from the batch. + + Args: + batch_idxs: Indices of *sentences* whose constraint state should be *kept*. + """ + pass + + def update_constraints(self, active_hypos: Tensor): + """ + Updates the constraint states by selecting the beam items that are retained. + This is called at each time step of sequence_generator._generate() when + the set of 2 * {beam_size} candidate hypotheses are reduced to the beam size. + + Args: + active_hypos: (batch size, beam size) + list of integers denoting, for each sentence, which beam candidate items + should be kept. + """ + pass + + +def unravel_index(index, shape): + out = [] + for dim in reversed(shape): + out.append(index % dim) + index = index // dim + return torch.stack(tuple(reversed(out)), dim=-1) + + +def topk_sum(lprobs_list, k): + """ + lprobs_list = [lprobs_1,...,lprobs_n], where: + lprobs_1 : (batch_size x beam_size x vocab_1) + ... + lprobs_n : (batch_size x beam_size x vocab_n) + + Return: + - topk_values : (batch_size x k) + values of the topk sum of the form : + lprobs_1[bsz, beam_idx, vocab_1_idx] + ... + lprobs_n[bsz, beam_idx, vocab_n_idx] + - topk_idxs : (batch_size x k x n+1) + each (n+1)-tensor being [beam_idx, vocab_1_idx, ..., vocab_n_idx] + """ + # Reduce all lprobs to k candidates first to reduce later complexity + # We may assume that k << vocab + lprobs_topk_list = [] + lprobs_topk_indices_list = [] + for lprobs in lprobs_list: + k_i = min(k, lprobs.size(-1)) + topk_values, topk_indices = torch.topk(lprobs, k=k_i) + # topk_values : (batch_size x beam_size x k_i) + # topk_indices : (batch_size x beam_size x k_i) + lprobs_topk_list.append(topk_values) + lprobs_topk_indices_list.append(topk_indices) + + # Compute all possible sums + sum_lprobs_topk = lprobs_topk_list[0] + for i in range(1, len(lprobs_topk_list)): + unsqueezed_lprobs = lprobs_topk_list[i] + for _ in range(i): + unsqueezed_lprobs = unsqueezed_lprobs.unsqueeze(-2) + sum_lprobs_topk = sum_lprobs_topk.unsqueeze(-1) + unsqueezed_lprobs + # sum_lprobs : (batch_size x beam_size x k_1 x ... x k_n) + + # Get the top k sums and the (transformed indices) + topk_sum_values, topk_sum_indices = torch.topk( + sum_lprobs_topk.view(sum_lprobs_topk.size(0), -1), k=k + ) + # topk_sum_values : (batch_size x k) + # topk_sum_indices : (batch_size x k) + topk_sum_indices = unravel_index(topk_sum_indices, tuple(sum_lprobs_topk.shape[1:])) + # topk_sum_indices : (batch_size x k x n+1) + + # Convert the transformed indices to the true indices + for i_batch in range(topk_sum_indices.size(0)): + for i_cand in range(topk_sum_indices.size(1)): + i_beam, *transformed_vocab_indices = topk_sum_indices[i_batch, i_cand] + true_vocab_indices = [i_beam] + for j, transformed_vocab_j_idx in enumerate(transformed_vocab_indices): + true_vocab_j_idx = lprobs_topk_indices_list[j][ + i_batch, i_beam, transformed_vocab_j_idx + ] + true_vocab_indices.append(true_vocab_j_idx) + topk_sum_indices[i_batch, i_cand] = torch.tensor(true_vocab_indices) + + topk_sum_beams = topk_sum_indices[:, :, 0] + topk_sum_indices = topk_sum_indices[:, :, 1:] + + return topk_sum_values, topk_sum_indices, topk_sum_beams + + +class MultichannelBeamSearch(MultichannelSearch): + def __init__(self, tgt_dicts): + super().__init__(tgt_dicts) + self.constraint_states = None + + @torch.jit.export + def step( + self, + step: int, + lprobs, + scores: Optional[Dict[str, Tensor]], + prev_output_tokens: Optional[Dict[str, Tensor]] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + channels = list(lprobs.keys()) + bsz, beam_size, _ = lprobs[channels[0]].size() + + lprobs_list = [] + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + for channel in channels: + lprobs_list.append(lprobs[channel][:, ::beam_size, :].contiguous()) + else: + # make probs contain cumulative scores for each hypothesis + assert scores is not None + for channel in channels: + lprobs_list.append( + lprobs[channel] + scores[channel][:, :, step - 1].unsqueeze(-1) + ) + + topk_sum_values, topk_sum_indices, topk_sum_beams = topk_sum( + lprobs_list, k=beam_size * 2 + ) + + beams_buf = topk_sum_beams + scores_buf = {} + indices_buf = {} + for i, channel in enumerate(channels): + indices_buf[channel] = topk_sum_indices[:, :, i] + scores_buf[channel] = ( + torch.tensor( + [ + lprobs_list[i][i_batch, i_beam, i_index] + for i_batch in range(bsz) + for i_beam, i_index in zip( + beams_buf[i_batch], indices_buf[channel][i_batch] + ) + ] + ) + .view(bsz, -1) + .to(lprobs_list[i].device) + ) + + # At this point, beams_buf and indices_buf are single-dim and contain relative indices + return scores_buf, indices_buf, beams_buf + + +class ContiguousMultichannelBeamSearch(MultichannelSearch): + def __init__(self, tgt_dicts): + super().__init__(tgt_dicts) + self.constraint_states = None + + @torch.jit.export + def step( + self, + step: int, + lprobs, + scores: Optional[Tensor], + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + n_channels = len(lprobs) + bsz, beam_size, _ = lprobs[0].size() + + lprobs_list = [] + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + for i in range(n_channels): + lprobs_list.append(lprobs[i][:, ::beam_size, :].contiguous()) + else: + # make probs contain cumulative scores for each hypothesis + assert scores is not None + for i in range(n_channels): + lprobs_list.append(lprobs[i] + scores[:, :, step - 1, i].unsqueeze(-1)) + + topk_sum_values, topk_sum_indices, topk_sum_beams = topk_sum( + lprobs_list, k=beam_size * 2 + ) + + beams_buf = topk_sum_beams + indices_buf = topk_sum_indices + scores_buf = ( + torch.tensor( + [ + lprobs_list[i][i_batch, i_beam, i_index] + for i in range(len(lprobs_list)) + for i_batch in range(bsz) + for i_beam, i_index in zip( + beams_buf[i_batch], indices_buf[i_batch, :, i] + ) + ] + ) + .view(len(lprobs_list), bsz, -1) + .permute(1, 2, 0) + .to(lprobs_list[0].device) + ) + + # At this point, beams_buf and indices_buf are single-dim and contain relative indices + return scores_buf, indices_buf, beams_buf + + +class ContiguousMultichannelSampling(MultichannelSearch): + sampling_topk: int + sampling_topp: float + + def __init__(self, tgt_dicts, sampling_topk=-1, sampling_topp=-1.0): + super().__init__(tgt_dicts) + self.sampling_topk = sampling_topk + self.sampling_topp = sampling_topp + + def _sample_topp(self, lprobs): + """Sample among the smallest set of elements whose cumulative probability mass exceeds p. + + See `"The Curious Case of Neural Text Degeneration" + (Holtzman et al., 2019) <https://arxiv.org/abs/1904.09751>`_. + + Args: + lprobs: (bsz x input_beam_size x vocab_size) + the model's log-probabilities over the vocabulary at the current step + + Return: A tuple of (trimed_probs, truncated_indices) where: + trimed_probs: (bsz x input_beam_size x ?) + the model's probabilities over the elements selected to sample from. The + width of the third dimension is determined by top-P. + truncated_indices: (bsz x input_beam_size x ?) + the indices of the chosen elements. + """ + probs = lprobs.exp_() + + # sort the last dimension (vocab dimension) in descending order + sorted_probs, sorted_indices = probs.sort(descending=True) + + # compute a mask to indicate the words to be included in the top-P set. + cumsum_probs = sorted_probs.cumsum(dim=2) + mask = cumsum_probs.lt(self.sampling_topp) + + # note that mask was computed by 'lt'. One more word needs to be included + # so that the cumulative probability mass can exceed p. + cumsum_mask = mask.cumsum(dim=2) + last_included = cumsum_mask[:, :, -1:] + last_included.clamp_(0, mask.size()[2] - 1) + mask = mask.scatter_(2, last_included, 1) + + # truncate unnecessary dims. + max_dim = last_included.max() + truncated_mask = mask[:, :, : max_dim + 1] + truncated_probs = sorted_probs[:, :, : max_dim + 1] + truncated_indices = sorted_indices[:, :, : max_dim + 1] + + # trim the words that are not in top-P by setting their probabilities + # to 0, so that they would not be sampled later. + trim_mask = ~truncated_mask + trimed_probs = truncated_probs.masked_fill_(trim_mask, 0) + return trimed_probs, truncated_indices + + @torch.jit.export + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + n_channels = len(lprobs) + bsz, beam_size, vocab_size = lprobs[0].size() + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + for i in range(n_channels): + lprobs[i] = lprobs[i][:, ::beam_size, :].contiguous() + + probs = [] + top_indices = [] + for i in range(n_channels): + if self.sampling_topp > 0: + # only sample from the smallest set of words whose cumulative probability mass exceeds p + probs_i, top_indices_i = self._sample_topp(lprobs[i]) + elif self.sampling_topk > 0: + # only sample from top-k candidates + lprobs[i], top_indices_i = lprobs[i].topk( + min(self.sampling_topk, lprobs[i].size(-1)) + ) + probs_i = lprobs[i].exp_() + else: + probs_i = lprobs[i].exp_() + + # dummy data to be consistent with true branch for type check + top_indices_i = torch.empty(0).to(probs_i) + probs.append(probs_i) + top_indices.append(top_indices_i) + # sample + indices_buf = [] + for i in range(n_channels): + if step == 0: + indices_buf.append( + torch.multinomial( + probs[i].view(bsz, -1), + beam_size, + replacement=True, + ).view(bsz, beam_size) + ) + else: + indices_buf.append( + torch.multinomial( + probs[i].view(bsz * beam_size, -1), + 1, + replacement=True, + ).view(bsz, beam_size) + ) + + if step == 0: + for i in range(n_channels): + # expand to beam size + probs[i] = probs[i].expand(bsz, beam_size, -1) + + # gather scores + scores_buf = [] + for i in range(n_channels): + scores_buf.append( + torch.gather(probs[i], dim=2, index=indices_buf[i].unsqueeze(-1)) + ) + scores_buf[i] = scores_buf[i].log_().view(bsz, -1) + + # remap indices if using top-k or top-P sampling + if self.sampling_topk > 0 or self.sampling_topp > 0: + for i in range(n_channels): + indices_buf[i] = torch.gather( + top_indices[i].expand(bsz, beam_size, -1), + dim=2, + index=indices_buf[i].unsqueeze(-1), + ).squeeze(2) + + if step == 0: + beams_buf = indices_buf[0].new_zeros(bsz, beam_size) + else: + beams_buf = torch.arange(0, beam_size).to(indices_buf[0]).repeat(bsz, 1) + # make scores cumulative + for i in range(n_channels): + scores_buf[i].add_( + torch.gather(scores[:, :, step - 1, i], dim=1, index=beams_buf) + ) + scores_buf = torch.stack(scores_buf, dim=-1) + indices_buf = torch.stack(indices_buf, dim=-1) + + return scores_buf, indices_buf, beams_buf diff --git a/fairseq/fairseq/models/speech_dlm/sequence_generator/multichannel_sequence_generator.py b/fairseq/fairseq/models/speech_dlm/sequence_generator/multichannel_sequence_generator.py new file mode 100644 index 0000000..24807b8 --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/sequence_generator/multichannel_sequence_generator.py @@ -0,0 +1,1110 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, List, Optional + +from omegaconf.listconfig import ListConfig +from omegaconf.dictconfig import DictConfig + +import torch +import torch.nn as nn +from fairseq.models import FairseqIncrementalDecoder +from torch import Tensor +from fairseq.ngram_repeat_block import NGramRepeatBlock +from .multichannel_search import ContiguousMultichannelBeamSearch +from fairseq.models.speech_dlm import SpeechDLM + + +class MultichannelSequenceGenerator(nn.Module): + def __init__( + self, + models, + tgt_dicts, + beam_size=1, + max_len_a=0, + max_len_b=200, + min_len=1, + normalize_scores=True, + len_penalty=1.0, + unk_penalty=0.0, + temperature=1.0, + match_source_len=False, + no_repeat_ngram_size=0, + search_strategy=None, + eos=None, + symbols_to_strip_from_output=None, + lm_model=None, + lm_weight=1.0, + duration_temperature=1.0, + ): + """Generate multi-channel parallel units with the SpeechDLM model + as described in the paper: https://arxiv.org/pdf/2203.16502.pdf; + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models, + currently support fairseq.models.TransformerModel for scripting + beam_size (int, optional): beam width (default: 1) + max_len_a/b (int, optional): generate sequences of maximum length + ax + b, where x is the source length + min_len (int, optional): the minimum length of the generated output + (not including end-of-sentence) + normalize_scores (bool, optional): normalize scores by the length + of the output (default: True) + len_penalty (float, optional): length penalty, where <1.0 favors + shorter, >1.0 favors longer sentences (default: 1.0) + unk_penalty (float, optional): unknown word penalty, where <0 + produces more unks, >0 produces fewer (default: 0.0) + temperature (float, optional): temperature, where values + >1.0 produce more uniform samples and values <1.0 produce + sharper samples (default: 1.0) + match_source_len (bool, optional): outputs should match the source + length (default: False) + duration_temperature (float, optional): rate of the duration prediction, + higher rate induces a faster generated wav (default: 1.0) + """ + super().__init__() + if isinstance(models, MultichannelEnsembleModel): + self.model = models + else: + self.model = MultichannelEnsembleModel(models) + self.tgt_dicts = tgt_dicts + self.pad = list(tgt_dicts.values())[0].pad() + self.unk = list(tgt_dicts.values())[0].unk() + self.eos = list(tgt_dicts.values())[0].eos() if eos is None else eos + self.symbols_to_strip_from_output = ( + symbols_to_strip_from_output.union({self.eos}) + if symbols_to_strip_from_output is not None + else {self.eos} + ) + self.channels = list(tgt_dicts.keys()) + self.n_channels = len(self.channels) + self.vocab_sizes = [len(tgt_dicts[channel]) for channel in self.channels] + # the max beam size is the dictionary size - 1, since we never select pad + max_possible_beam_size = 1 + for i in self.vocab_sizes: + max_possible_beam_size *= i - 1 + self.beam_size = min(beam_size, max_possible_beam_size) + self.max_len_a = max_len_a + self.max_len_b = max_len_b + self.min_len = min_len + + self.normalize_scores = normalize_scores + self.len_penalty = len_penalty + self.unk_penalty = unk_penalty + if isinstance(temperature, (int, float)): + temperature = {channel: temperature for channel in self.channels} + elif isinstance(temperature, ListConfig) or isinstance(temperature, list): + temperature = { + channel: temperature[i] for i, channel in enumerate(self.channels) + } + assert isinstance(temperature, DictConfig) or isinstance( + temperature, dict + ), f"temperature: expected dict, but found {type(temperature)}" + self.temperature = temperature + self.match_source_len = match_source_len + + if no_repeat_ngram_size > 0: + self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size) + else: + self.repeat_ngram_blocker = None + + for channel in temperature: + assert temperature[channel] > 0, "--temperature must be greater than 0" + + if search_strategy is None: + self.search = ContiguousMultichannelBeamSearch(tgt_dicts) + else: + self.search = search_strategy + # We only need to set src_lengths in LengthConstrainedBeamSearch. + # As a module attribute, setting it would break in multithread + # settings when the model is shared. + self.should_set_src_lengths = ( + hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths + ) + + self.model.eval() + + self.lm_model = lm_model + self.lm_weight = lm_weight + if self.lm_model is not None: + self.lm_model.eval() + + self.duration_prediction = bool( + str(getattr(models[0].decoder.args, "duration_prediction", "false")).lower() + == "true" + ) + self.delayed_duration = bool( + str( + getattr(models[0].decoder.args, "delayed_duration_target", "false") + ).lower() + == "true" + ) + self.duration_temperature = duration_temperature + + def cuda(self): + self.model.cuda() + return self + + @torch.no_grad() + def forward( + self, + sample: Dict[str, Dict[str, Tensor]], # TODO: Modify this + prefix_tokens: Optional[Dict[str, Tensor]] = None, + bos_token: Optional[int] = None, + ): + """Generate a batch of translations. + + Args: + sample (dict): batch + prefix_tokens (dict of torch.LongTensor, optional): force decoder to begin + with these tokens + bos_token (int, optional): beginning of sentence token + (default: self.eos) + """ + return self._generate(sample, prefix_tokens, bos_token=bos_token) + + @torch.no_grad() + def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs): + """Generate translations. Match the api of other fairseq generators. + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + sample (dict): batch + prefix_tokens (dict of torch.LongTensor, optional): force decoder to begin + with these tokens + constraints (torch.LongTensor, optional): force decoder to include + the list of constraints + bos_token (int, optional): beginning of sentence token + (default: self.eos) + """ + return self._generate(sample, **kwargs) + + def _generate( + self, + sample: Dict[str, Dict[str, Tensor]], + prefix_tokens: Optional[Dict[str, Tensor]] = None, + constraints: Optional[Tensor] = None, + bos_token: Optional[int] = None, + ): + """ + Here sample is expected to have the following form + { + 'id': index, + 'net_input': { + 'src_tokens': { + 'channel1' : tensor((batch x src_length)), + 'channel2' : tensor((batch x src_length)), + }, + ... + }, + } + and prefix_tokens + { + 'channel1' : tensor((batch x prefix_length)), + 'channel2' : tensor((batch x prefix_length)), + } + """ + if self.model.is_speech_dlm: + incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [{} for _ in range(self.n_channels)], + ) + for i in range(self.model.models_size) + ], + ) + else: + incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(self.model.models_size) + ], + ) + net_input = sample["net_input"] + # Convert from dict to tensor form + # shape of src_tokens : (bsz x src_len x n_channels) + src_tokens = torch.stack( + [net_input["src_tokens"][channel] for channel in self.channels], dim=-1 + ) + prefix_tokens = torch.stack( + [prefix_tokens[channel] for channel in self.channels], dim=-1 + ) + # length of the source text being the character length except EndOfSentence and pad + src_lengths = ( + (src_tokens[..., 0].ne(self.eos) & src_tokens[..., 0].ne(self.pad)) + .long() + .sum(dim=1) + ) + + # bsz: total number of sentences in beam + # Note that src_tokens may have more than 2 dimensions (i.e. audio features) + bsz, src_len = src_tokens.size()[:2] + beam_size = self.beam_size + + if constraints is not None and not self.search.supports_constraints: + raise NotImplementedError( + "Target-side constraints were provided, but search method doesn't support them" + ) + + # Initialize constraints, when active + self.search.init_constraints(constraints, beam_size) + + max_len: int = -1 + if self.match_source_len: + max_len = src_lengths.max().item() + else: + max_len = min( + int(self.max_len_a * src_len + self.max_len_b), + # exclude the EOS marker + self.model.max_decoder_positions() - 1, + ) + assert ( + self.min_len <= max_len + ), "min_len cannot be larger than max_len, please adjust these!" + # compute the encoder output for each beam + encoder_outs = self.model.forward_encoder(net_input) + + # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores + new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) + new_order = new_order.to(src_tokens.device).long() + encoder_outs = self.model.reorder_encoder_out(encoder_outs, new_order) + # ensure encoder_outs is a List. + assert encoder_outs is not None + + # initialize buffers + # cumulative scores of hypotheses + scores = ( + torch.zeros(bsz * beam_size, max_len + 1, self.n_channels) + .to(src_tokens) + .float() + ) # +1 for eos; pad is never chosen for scoring + tokens = ( + torch.zeros(bsz * beam_size, max_len + 2, self.n_channels) + .to(src_tokens) + .long() + .fill_(self.pad) + ) # +2 for eos and pad + tokens[:, 0] = self.eos if bos_token is None else bos_token + attn: Optional[Tensor] = None + + # A list that indicates candidates that should be ignored. + # For example, suppose we're sampling and have already finalized 2/5 + # samples. Then cands_to_ignore would mark 2 positions as being ignored, + # so that we only finalize the remaining 3 samples. + cands_to_ignore = ( + torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) + ) # forward and backward-compatible False mask + + # list of completed sentences + finalized = torch.jit.annotate( + List[List[Dict[str, Tensor]]], + [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], + ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step + + finished = [ + False for i in range(bsz) + ] # a boolean array indicating if the sentence at the index is finished or not + num_remaining_sent = bsz # number of sentences remaining + + # number of candidate hypos per step + cand_size = 2 * beam_size # 2 x beam size in case half are EOS + + # offset arrays for converting between different indexing schemes + bbsz_offsets = ( + (torch.arange(0, bsz) * beam_size) + .unsqueeze(1) + .type_as(tokens) + .to(src_tokens.device) + ) + cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) + + reorder_state: Optional[Tensor] = None + batch_idxs: Optional[Tensor] = None + + original_batch_idxs: Optional[Tensor] = None + if "id" in sample and isinstance(sample["id"], Tensor): + original_batch_idxs = sample["id"] + else: + original_batch_idxs = torch.arange(0, bsz).type_as(tokens) + + if self.duration_prediction: + dur_counter = torch.ones(bsz * beam_size, self.n_channels).to(src_tokens) + # save the indice where the dur_counter just copied from dur_pred + dur_counter_jump_indices = None + + for step in range(max_len + 1): # one extra step for EOS marker + # reorder decoder internal states based on the prev choice of beams + if reorder_state is not None: + if batch_idxs is not None: + # update beam indices to take into account removed sentences + corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( + batch_idxs + ) + reorder_state.view(-1, beam_size).add_( + corr.unsqueeze(-1) * beam_size + ) + original_batch_idxs = original_batch_idxs[batch_idxs] + self.model.reorder_incremental_state(incremental_states, reorder_state) + encoder_outs = self.model.reorder_encoder_out( + encoder_outs, reorder_state + ) + + input_tokens = { + channel: tokens[:, : step + 1, i] + for i, channel in enumerate(self.channels) + } + + lprobs_dict, avg_attn_scores = self.model.forward_decoder( + input_tokens, + encoder_outs, + incremental_states, + self.temperature, + ) + + # Because the sizes of vocab is different, we cannot concat the lprobs to form a single tensor + if not self.duration_prediction: + lprobs_list = list(lprobs_dict.values()) + else: + lprobs_list = [ + net_output["pred_token"] for net_output in lprobs_dict.values() + ] + + # non-positive predicted durations + dur_preds = ( + torch.stack( + [ + net_output["pred_duration"] + for net_output in lprobs_dict.values() + ] + ) + .squeeze(-1) + .T + ) + dur_preds = dur_preds / self.duration_temperature + dur_preds = dur_preds.round().long() + dur_preds[dur_preds < 1] = 1 + + # dur_preds & dur_counter needs to be modified when there isn't an edge + if step > 0: + non_edge_indices = tokens[:, step, :] == tokens[:, step - 1, :] + if self.delayed_duration: + dur_preds[non_edge_indices] = 1 + else: + if dur_counter_jump_indices is not None: + dur_counter[dur_counter_jump_indices & non_edge_indices] = 2 + + # update dur_counter + if step > 0: + if self.delayed_duration: + dur_counter -= ( + (dur_counter == 1) + | (tokens[:, step, :] == tokens[:, step - 1, :]) + ).int() + dur_counter[dur_counter < 0] = 0 + else: + dur_counter -= ( + tokens[:, step, :] == tokens[:, step - 1, :] + ).int() + dur_counter[dur_counter < 1] = 1 + + # whether to copy previous token (ie. if the counter is still on) + # and get get the new duration + if self.delayed_duration: + dur_counter_jump_indices = dur_counter == 0 + dur_counter[dur_counter_jump_indices] = dur_preds[ + dur_counter_jump_indices + ] + + # whether to copy previous token in this step + copy_prev_token = dur_counter != 1 + if self.delayed_duration is False: + dur_counter_jump_indices = dur_counter == 1 + dur_counter[dur_counter_jump_indices] = dur_preds[ + dur_counter_jump_indices + ] + # else: + # dur_counter[dur_counter==0] = dur_preds[dur_counter==0] - 1 + # copy_prev_token = (dur_counter > 0) + + if self.lm_model is not None: + assert False, "Currently not supported in multichannelLM case" + + for i in range(self.n_channels): + lprobs_list[i][lprobs_list[i] != lprobs_list[i]] = torch.tensor( + -math.inf + ).to(lprobs_list[i]) + + lprobs_list[i][:, self.pad] = -math.inf # never select pad + lprobs_list[i][:, self.unk] -= self.unk_penalty # apply unk penalty + + # handle max length constraint + if step >= max_len: + lprobs_list[i][:, : self.eos] = -math.inf + lprobs_list[i][:, self.eos + 1 :] = -math.inf + else: + lprobs_list[i][ + :, self.eos + ] = -math.inf # quick fix for short generation + + # handle prefix tokens (possibly with different lengths) + if ( + prefix_tokens is not None + and step < prefix_tokens.size(1) + and step < max_len + ): + ( + lprobs_list[i], + tokens[..., i], + scores[..., i], + ) = self._prefix_tokens( + step, + lprobs_list[i], + scores[..., i], + tokens[..., i], + prefix_tokens[..., i], + beam_size, + ) + if self.duration_prediction: + # Can copy previous token if the prefix token is padding or unk (1-channel conditionned case) + can_copy_mask = ( + prefix_tokens[:, step, i].eq(self.pad) + | prefix_tokens[:, step, i].eq(self.unk) + ).repeat_interleave(beam_size) + copy_prev_token[:, i] &= can_copy_mask + elif step < self.min_len: + # minimum length constraint (does not apply if using prefix_tokens) + lprobs_list[i][:, self.eos] = -math.inf + + if self.duration_prediction: + if step < max_len: + for j in range(copy_prev_token.size(0)): + if copy_prev_token[j, i]: + prev_token = tokens[j, step, i] + lprobs_list[i][j, :prev_token] = -math.inf + lprobs_list[i][j, prev_token + 1 :] = -math.inf + # lprobs_list[i][j, prev_token] = 0. + # dur_counter[j,i] -= 1 + # else: + # prev_token = tokens[j, step, i] + # if not (lprobs_list[i][j,:].ne(-math.inf).nonzero() == prev_token).all(): + # lprobs_list[i][j, prev_token] = -math.inf + # dur_counter[j,i] = 0. + + # Record attention scores, only support avg_attn_scores is a Tensor + if avg_attn_scores is not None: + if attn is None: + attn = torch.empty( + bsz * beam_size, avg_attn_scores.size(1), max_len + 2 + ).to(scores) + attn[:, :, step + 1].copy_(avg_attn_scores) + + scores = scores.type_as(lprobs_list[0]) + eos_bbsz_idx = torch.empty(0).to( + tokens + ) # indices of hypothesis ending with eos (finished sentences) + eos_scores = torch.empty(0).to( + scores + ) # scores of hypothesis ending with eos (finished sentences) + + if self.should_set_src_lengths: + self.search.set_src_lengths(src_lengths) + + if self.repeat_ngram_blocker is not None: + for i in range(self.n_channels): + lprobs_list[i] = self.repeat_ngram_blocker( + tokens, lprobs_list[i], bsz, beam_size, step + ) + + # Shape: (batch, cand_size) + cand_scores, cand_indices, cand_beams = self.search.step( + step, + [ + lprobs_list[i].view(bsz, -1, self.vocab_sizes[i]) + for i in range(self.n_channels) + ], + scores.view(bsz, beam_size, -1, self.n_channels)[:, :, :step, :], + tokens[:, : step + 1], + original_batch_idxs, + ) + + # cand_bbsz_idx contains beam indices for the top candidate + # hypotheses, with a range of values: [0, bsz*beam_size), + # and dimensions: [bsz, cand_size] + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + + # finalize hypotheses that end in eos + # Shape of eos_mask: (batch size, beam size) + eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) + eos_mask = torch.any(eos_mask, dim=-1, keepdim=False) + eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) + + # only consider eos when it's among the top beam_size indices + # Now we know what beam item(s) to finish + # Shape: 1d list of absolute-numbered + eos_bbsz_idx = torch.masked_select( + cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + + finalized_sents: List[int] = [] + if eos_bbsz_idx.numel() > 0: + eos_scores = torch.stack( + [ + torch.masked_select( + cand_scores[:, :beam_size, i], mask=eos_mask[:, :beam_size] + ) + for i in range(self.n_channels) + ], + dim=-1, + ) + finalized_sents = self.finalize_hypos( + step, + eos_bbsz_idx, + eos_scores, + tokens, + scores, + finalized, + finished, + beam_size, + attn, + src_lengths, + max_len, + ) + num_remaining_sent -= len(finalized_sents) + + assert num_remaining_sent >= 0 + if num_remaining_sent == 0: + break + if self.search.stop_on_max_len and step >= max_len: + break + assert step < max_len, f"{step} < {max_len}" + + # Remove finalized sentences (ones for which {beam_size} + # finished hypotheses have been generated) from the batch. + if len(finalized_sents) > 0: + new_bsz = bsz - len(finalized_sents) + + # construct batch_idxs which holds indices of batches to keep for the next pass + batch_mask = torch.ones( + bsz, dtype=torch.bool, device=cand_indices.device + ) + batch_mask[finalized_sents] = False + # TODO replace `nonzero(as_tuple=False)` after TorchScript supports it + batch_idxs = torch.arange( + bsz, device=cand_indices.device + ).masked_select(batch_mask) + + # Choose the subset of the hypothesized constraints that will continue + self.search.prune_sentences(batch_idxs) + + eos_mask = eos_mask[batch_idxs] + cand_beams = cand_beams[batch_idxs] + bbsz_offsets.resize_(new_bsz, 1) + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + cand_scores = cand_scores[batch_idxs] + cand_indices = cand_indices[batch_idxs] + + if prefix_tokens is not None: + prefix_tokens = prefix_tokens[batch_idxs] + src_lengths = src_lengths[batch_idxs] + cands_to_ignore = cands_to_ignore[batch_idxs] + + scores = scores.view(bsz, -1)[batch_idxs].view( + new_bsz * beam_size, -1, self.n_channels + ) + tokens = tokens.view(bsz, -1)[batch_idxs].view( + new_bsz * beam_size, -1, self.n_channels + ) + if self.duration_prediction: + dur_counter = dur_counter.view(bsz, -1)[batch_idxs].view( + new_bsz * beam_size, self.n_channels + ) + if attn is not None: + attn = attn.view(bsz, -1)[batch_idxs].view( + new_bsz * beam_size, attn.size(1), -1 + ) + bsz = new_bsz + else: + batch_idxs = None + + # Set active_mask so that values > cand_size indicate eos hypos + # and values < cand_size indicate candidate active hypos. + # After, the min values per row are the top candidate active hypos + + # Rewrite the operator since the element wise or is not supported in torchscript. + + eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) + active_mask = torch.add( + eos_mask.type_as(cand_offsets) * cand_size, + cand_offsets[: eos_mask.size(1)], + ) + + # get the top beam_size active hypotheses, which are just + # the hypos with the smallest values in active_mask. + # {active_hypos} indicates which {beam_size} hypotheses + # from the list of {2 * beam_size} candidates were + # selected. Shapes: (batch size, beam size) + new_cands_to_ignore, active_hypos = torch.topk( + active_mask, k=beam_size, dim=1, largest=False + ) + + # update cands_to_ignore to ignore any finalized hypos. + cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] + # Make sure there is at least one active item for each sentence in the batch. + assert (~cands_to_ignore).any(dim=1).all() + + # update cands_to_ignore to ignore any finalized hypos + # {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam + # can be selected more than once). + active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) + active_bbsz_idx = active_bbsz_idx.view(-1) + + # active_scores = torch.stack([ + # torch.gather(cand_scores[...,0], dim=1, index=active_hypos) + # for i in range(self.n_channels) + # ], dim = -1) + # active_scores = active_scores.view(-1) + + # copy tokens and scores for active hypotheses + + # Set the tokens for each beam (can select the same row more than once) + tokens[:, : step + 1] = torch.index_select( + tokens[:, : step + 1], dim=0, index=active_bbsz_idx + ) + # Select the next token for each of them + for i in range(self.n_channels): + tokens.view(bsz, beam_size, -1, self.n_channels)[ + :, :, step + 1, i + ] = torch.gather(cand_indices[..., i], dim=1, index=active_hypos) + if step > 0: + scores[:, :step] = torch.index_select( + scores[:, :step], dim=0, index=active_bbsz_idx + ) + for i in range(self.n_channels): + scores.view(bsz, beam_size, -1, self.n_channels)[ + :, :, step, i + ] = torch.gather(cand_scores[..., i], dim=1, index=active_hypos) + + if self.duration_prediction: + dur_counter = torch.index_select( + dur_counter, dim=0, index=active_bbsz_idx + ) + + # Update constraints based on which candidates were selected for the next beam + self.search.update_constraints(active_hypos) + + # copy attention for active hypotheses + if attn is not None: + attn[:, :, : step + 2] = torch.index_select( + attn[:, :, : step + 2], dim=0, index=active_bbsz_idx + ) + + # reorder incremental state in decoder + reorder_state = active_bbsz_idx + + # sort by score descending + for sent in range(len(finalized)): + scores = torch.tensor( + [float(elem["score"].item()) for elem in finalized[sent]] + ) + _, sorted_scores_indices = torch.sort(scores, descending=True) + finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] + finalized[sent] = torch.jit.annotate( + List[Dict[str, Tensor]], finalized[sent] + ) + return finalized + + def _prefix_tokens( + self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int + ): + """Handle prefix tokens""" + prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) + prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + prefix_mask = prefix_toks.ne(self.pad) + # used for 1-channel generation, do not force the unk token (i.e. unk tokens are changed) + prefix_mask &= prefix_toks.ne(self.unk) + # zeroing the copying tokens + # if step > 0: + # copy_mask = (prefix_tokens[:, step] == prefix_tokens[:, step-1]).unsqueeze(-1).repeat(1, beam_size).view(-1) + # prefix_lprobs[copy_mask & prefix_mask] = 0. + lprobs[prefix_mask] = torch.tensor(-math.inf).to(lprobs) + lprobs[prefix_mask] = lprobs[prefix_mask].scatter( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] + ) + # shouldn't stop at unk token + unk_mask = prefix_toks.eq(self.unk) + if len(lprobs[unk_mask]) > 0: + # otherwise it won't assign to lprobs, + # see: https://discuss.pytorch.org/t/how-to-mask-and-assign-a-value-to-tensor/18437 + copy_lprobs = lprobs[unk_mask][:, :] + copy_lprobs[:, self.eos] = -math.inf + lprobs[unk_mask] = copy_lprobs + # if prefix includes eos, then we should make sure tokens and + # scores are the same across all beams + eos_mask = prefix_toks.eq(self.eos) + if eos_mask.any(): + # validate that the first beam matches the prefix + first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[ + :, 0, 1 : step + 1 + ] + eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] + target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] + assert (first_beam == target_prefix).all() + + # copy tokens, scores and lprobs from the first beam to all beams + tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size) + scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size) + lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size) + return lprobs, tokens, scores + + def replicate_first_beam(self, tensor, mask, beam_size: int): + tensor = tensor.view(-1, beam_size, tensor.size(-1)) + tensor[mask] = tensor[mask][:, :1, :] + return tensor.view(-1, tensor.size(-1)) + + def finalize_hypos( + self, + step: int, + bbsz_idx, + eos_scores, + tokens, + scores, + finalized: List[List[Dict[str, Tensor]]], + finished: List[bool], + beam_size: int, + attn: Optional[Tensor], + src_lengths, + max_len: int, + ): + """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. + A sentence is finalized when {beam_size} finished items have been collected for it. + + Returns number of sentences (not beam items) being finalized. + These will be removed from the batch and not processed further. + Args: + bbsz_idx (Tensor): + """ + assert bbsz_idx.numel() == eos_scores.size(0) + + # clone relevant token and attention tensors. + # tokens is (batch * beam, max_len). So the index_select + # gets the newly EOS rows, then selects cols 1..{step + 2} + tokens_clone = tokens.index_select(0, bbsz_idx)[ + :, 1 : step + 2 + ] # skip the first index, which is EOS + + tokens_clone[:, step] = self.eos + attn_clone = ( + attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] + if attn is not None + else None + ) + + # compute scores per token position + pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] + pos_scores[:, step, :] = eos_scores + # convert from cumulative to per-position scores + pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] + + # normalize sentence-level scores + if self.normalize_scores: + eos_scores /= (step + 1) ** self.len_penalty + + # cum_unfin records which sentences in the batch are finished. + # It helps match indexing between (a) the original sentences + # in the batch and (b) the current, possibly-reduced set of + # sentences. + cum_unfin: List[int] = [] + prev = 0 + for f in finished: + if f: + prev += 1 + else: + cum_unfin.append(prev) + + # The keys here are of the form "{sent}_{unfin_idx}", where + # "unfin_idx" is the index in the current (possibly reduced) + # list of sentences, and "sent" is the index in the original, + # unreduced batch + # set() is not supported in script export + sents_seen: Dict[str, Optional[Tensor]] = {} + + # For every finished beam item + for i in range(bbsz_idx.size()[0]): + idx = bbsz_idx[i] + score = eos_scores[i].sum() + # sentence index in the current (possibly reduced) batch + unfin_idx = idx // beam_size + # sentence index in the original (unreduced) batch + sent = unfin_idx + cum_unfin[unfin_idx] + # Cannot create dict for key type '(int, int)' in torchscript. + # The workaround is to cast int to string + seen = str(sent.item()) + "_" + str(unfin_idx.item()) + if seen not in sents_seen: + sents_seen[seen] = None + + if self.match_source_len and step > src_lengths[unfin_idx]: + score = torch.tensor(-math.inf).to(score) + + # An input sentence (among those in a batch) is finished when + # beam_size hypotheses have been collected for it + if len(finalized[sent]) < beam_size: + if attn_clone is not None: + # remove padding tokens from attn scores + hypo_attn = attn_clone[i] + else: + hypo_attn = torch.empty(0) + + finalized[sent].append( + { + "tokens": tokens_clone[i], + "score": score, + "attention": hypo_attn, # src_len x tgt_len + "alignment": torch.empty(0), + "positional_scores": pos_scores[i], + } + ) + + newly_finished: List[int] = [] + + for seen in sents_seen.keys(): + # check termination conditions for this sentence + sent: int = int(float(seen.split("_")[0])) + unfin_idx: int = int(float(seen.split("_")[1])) + + if not finished[sent] and self.is_finished( + step, unfin_idx, max_len, len(finalized[sent]), beam_size + ): + finished[sent] = True + newly_finished.append(unfin_idx) + + return newly_finished + + def is_finished( + self, + step: int, + unfin_idx: int, + max_len: int, + finalized_sent_len: int, + beam_size: int, + ): + """ + Check whether decoding for a sentence is finished, which + occurs when the list of finalized sentences has reached the + beam size, or when we reach the maximum length. + """ + assert finalized_sent_len <= beam_size + if finalized_sent_len == beam_size or step == max_len: + return True + return False + + +class MultichannelEnsembleModel(nn.Module): + """A wrapper around an ensemble of SpeechDLM models.""" + + def __init__(self, models): + super().__init__() + self.models_size = len(models) + # method '__len__' is not supported in ModuleList for torch script + self.single_model = models[0] + self.models = nn.ModuleList(models) + + self.has_incremental: bool = False + if all( + hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) + for m in models + ): + self.has_incremental = True + + if isinstance(models[0], SpeechDLM): + self.is_speech_dlm = True + # Otherwise it's a multi-channel language model (without cross-prediction outputs) + else: + self.is_speech_dlm = False + + if getattr(models[0].decoder.args, "duration_prediction", False): + self.is_duration_prediction = True + else: + self.is_duration_prediction = False + + def forward(self): + pass + + def has_encoder(self): + return hasattr(self.single_model, "encoder") + + def has_incremental_states(self): + return self.has_incremental + + def max_decoder_positions(self): + return min([m.max_decoder_positions() for m in self.models]) + + @torch.jit.export + def forward_encoder(self, net_input: Dict[str, Tensor]): + if not self.has_encoder(): + return None + return [model.encoder.forward_torchscript(net_input) for model in self.models] + + @torch.jit.export + def forward_decoder( + self, + tokens, + encoder_outs: List[Dict[str, List[Tensor]]], + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + temperature: Dict[str, float] = 1.0, + ): + if isinstance(temperature, (float, int)): + temperature = {channel: temperature for channel in tokens} + log_probs = {channel: [] for channel in tokens} + avg_attn: Optional[Tensor] = None + encoder_out: Optional[Dict[str, List[Tensor]]] = None + for i, model in enumerate(self.models): + if self.has_encoder(): + encoder_out = encoder_outs[i] + # decode each model + if self.has_incremental_states(): + decoder_out = model.decoder.forward( + tokens, + encoder_out=encoder_out, + incremental_state=incremental_states[i], + ) + else: + decoder_out = model.decoder.forward(tokens, encoder_out=encoder_out) + + attn: Optional[Tensor] = None + decoder_len = len(decoder_out) + if decoder_len > 1 and decoder_out[1] is not None: + if isinstance(decoder_out[1], Tensor): + attn = decoder_out[1] + else: + attn_holder = decoder_out[1]["attn"] + if isinstance(attn_holder, Tensor): + attn = attn_holder + elif attn_holder is not None: + attn = attn_holder[0] + if attn is not None: + attn = attn[:, -1, :] + + if self.is_speech_dlm: + if self.is_duration_prediction: + decoder_out_divided_by_temperature = { + channel_src: { + channel_pred: { + "pred_token": decoder_out[0][channel_src][channel_pred][ + "pred_token" + ][:, -1:, :].div_(temperature[channel_pred]), + "pred_duration": decoder_out[0][channel_src][ + channel_pred + ]["pred_duration"][:, -1:, :], + } + for channel_pred in decoder_out[0][channel_src] + } + for channel_src in decoder_out[0] + } + else: + decoder_out_divided_by_temperature = { + channel_src: { + channel_pred: decoder_out[0][channel_src][channel_pred][ + :, -1:, : + ].div_(temperature[channel_pred]) + for channel_pred in decoder_out[0][channel_src] + } + for channel_src in decoder_out[0] + } + else: + decoder_out_divided_by_temperature = { + channel: decoder_out[0][channel][:, -1:, :].div_( + temperature[channel] + ) + for channel in decoder_out[0] + } + decoder_out_tuple = ( + decoder_out_divided_by_temperature, + None if decoder_len <= 1 else decoder_out[1], + ) + + probs = model.get_normalized_probs( + decoder_out_tuple, log_probs=True, sample=None + ) + + if self.is_speech_dlm: + if self.is_duration_prediction: + probs = { + channel: { + "pred_token": probs[channel][channel]["pred_token"][ + :, -1, : + ], + "pred_duration": probs[channel][channel]["pred_duration"][ + :, -1, : + ], + } + for channel in probs + } + else: + probs = { + channel: probs[channel][channel][:, -1, :] for channel in probs + } + else: + probs = {channel: probs[channel][:, -1, :] for channel in probs} + if self.models_size == 1: + return probs, attn + + for channel in probs: + log_probs[channel].append(probs[channel]) + if attn is not None: + if avg_attn is None: + avg_attn = attn + else: + avg_attn.add_(attn) + + avg_probs = {} + for channel in log_probs: + avg_probs[channel] = torch.logsumexp( + torch.stack(log_probs[channel], dim=0), dim=0 + ) - math.log(self.models_size) + + if avg_attn is not None: + avg_attn.div_(self.models_size) + return avg_probs, avg_attn + + @torch.jit.export + def reorder_encoder_out( + self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order + ): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + new_outs: List[Dict[str, List[Tensor]]] = [] + if not self.has_encoder(): + return new_outs + for i, model in enumerate(self.models): + assert encoder_outs is not None + new_outs.append( + model.encoder.reorder_encoder_out(encoder_outs[i], new_order) + ) + return new_outs + + @torch.jit.export + def reorder_incremental_state( + self, + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + new_order, + ): + if not self.has_incremental_states(): + return + for i, model in enumerate(self.models): + model.decoder.reorder_incremental_state_scripting( + incremental_states[i], new_order + ) diff --git a/fairseq/fairseq/models/speech_dlm/speech_dlm.py b/fairseq/fairseq/models/speech_dlm/speech_dlm.py new file mode 100644 index 0000000..dc13f56 --- /dev/null +++ b/fairseq/fairseq/models/speech_dlm/speech_dlm.py @@ -0,0 +1,280 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Optional + +from fairseq import utils +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import ( + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import Embedding +from .modules.speech_dlm_decoder import CrossChannelTransformerDecoder +from omegaconf import II + + +DEFAULT_MAX_TARGET_POSITIONS = 1024 + +logger = logging.getLogger(__name__) + + +@dataclass +class SpeechDLMConfig(FairseqDataclass): + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="relu", metadata={"help": "activation function to use"} + ) + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + attention_dropout: float = field( + default=0.0, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN."} + ) + relu_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN."} + ) + decoder_embed_dim: int = field( + default=512, metadata={"help": "decoder embedding dimension"} + ) + decoder_output_dim: int = field( + default=512, metadata={"help": "decoder output dimension"} + ) + decoder_input_dim: int = field( + default=512, metadata={"help": "decoder input dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=2048, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field(default=6, metadata={"help": "num decoder layers"}) + decoder_cross_layers: int = field( + default=-1, metadata={"help": "num self cross attention decoder layers"} + ) + decoder_attention_heads: int = field( + default=8, metadata={"help": "num decoder attention heads"} + ) + decoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each decoder block"} + ) + no_decoder_final_norm: bool = field( + default=False, + metadata={"help": "don't add an extra layernorm after the last decoder block"}, + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings (outside self attention)" + }, + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "LayerDrop probability for decoder"} + ) + decoder_layers_to_keep: Optional[str] = field( + default=None, + metadata={ + "help": "which layers to *keep* when pruning as a comma-separated list" + }, + ) + layernorm_embedding: bool = field( + default=False, metadata={"help": "add layernorm to embedding"} + ) + no_scale_embedding: bool = field( + default=False, metadata={"help": "if True, dont scale embeddings"} + ) + checkpoint_activations: bool = field( + default=False, metadata={"help": "checkpoint activations at each layer"} + ) + offload_activations: bool = field( + default=False, + metadata={"help": "move checkpointed activations to CPU after they are used."}, + ) + quant_noise_pq: float = field( + default=0.0, + metadata={"help": "iterative PQ quantization noise at training time"}, + ) + quant_noise_pq_block_size: int = field( + default=8, + metadata={"help": "block size of quantization noise at training time"}, + ) + # TODO common var add to parent + quant_noise_scalar: float = field( + default=0.0, + metadata={ + "help": "scalar quantization noise and scalar quantization at training time" + }, + ) + add_bos_token: bool = II("task.add_bos_token") + tokens_per_sample: int = II("task.tokens_per_sample") + max_target_positions: Optional[int] = II("task.max_target_positions") + tpu: bool = II("common.tpu") + duration_prediction: str = II("task.duration_prediction") + delayed_duration_target: str = II("task.delayed_duration_target") + main_and_cross_weights: str = II("criterion.main_and_cross_weights") + + +@register_model("speech_dlm", dataclass=SpeechDLMConfig) +class SpeechDLM(FairseqLanguageModel): + """Spoken Unit-based Dialogue Language Model model (SpeechDLM) as described + in the paper: https://arxiv.org/pdf/2203.16502.pdf + """ + + def __init__(self, decoder): + super().__init__(decoder) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure all arguments are present in older models + base_lm_architecture(args) + + if args.decoder_layers_to_keep: + args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) + + if args.decoder_cross_layers < 0: + args.decoder_cross_layers = args.decoder_layers + + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) + + # Assert all dictionary to be the same + assert all( + task.source_dictionaries[channel] == task.source_dictionary + for channel in task.channels + ), "Source dictionaries of all channels are expected to be the same!!!" + assert all( + task.target_dictionaries[channel] == task.target_dictionary + for channel in task.channels + ), "Target dictionaries of all channels are expected to be the same!!!" + # Build the unit embeddings + embed_tokens = cls.build_embedding( + args, task.source_dictionary, args.decoder_input_dim + ) + + decoder = CrossChannelTransformerDecoder( + args, + task.target_dictionary, + embed_tokens, + channels=task.channels, + no_encoder_attn=True, + ) + return cls(decoder) + + @classmethod + def build_embedding(cls, args, dictionary, embed_dim, path=None): + embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad()) + return embed_tokens + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + **kwargs, + ): + """ + Load a :class:`~fairseq.models.FairseqModel` from a pre-trained model + file. Downloads and caches the pre-trained model file if needed. + + The base implementation returns a + :class:`~fairseq.hub_utils.GeneratorHubInterface`, which can be used to + generate translations or sample from language models. The underlying + :class:`~fairseq.models.FairseqModel` can be accessed via the + *generator.models* attribute. + + This function return a class:`MultichannelGeneratorHubInterface` object, + which allows generation in multiple channels with a multichannel model. + + Args: + model_name_or_path (str): either the name of a pre-trained model to + load or a path/URL to a pre-trained model state dict + checkpoint_file (str, optional): colon-separated list of checkpoint + files in the model archive to ensemble (default: 'model.pt') + data_name_or_path (str, optional): point args.data to the archive + at the given path/URL. Can start with '.' or './' to reuse the + model archive path. + """ + from fairseq import hub_utils + from .hub_interface import MultichannelGeneratorHubInterface + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + **kwargs, + ) + logger.info(x["args"]) + return MultichannelGeneratorHubInterface(x["args"], x["task"], x["models"]) + + @property + def supported_targets(self): + return {"next", "edge", "duration"} + + +def base_lm_architecture(args): + # backward compatibility for older model checkpoints + if hasattr(args, "decoder_final_norm"): + args.no_decoder_final_norm = not args.decoder_final_norm + + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_cross_layers = getattr(args, "decoder_cross_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0) + args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0) + + args.add_bos_token = getattr(args, "add_bos_token", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + # Model training is not stable without this + args.decoder_normalize_before = True + args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.checkpoint_activations = getattr(args, "checkpoint_activations", False) + args.offload_activations = getattr(args, "offload_activations", False) + if args.offload_activations: + args.checkpoint_activations = True + + +@register_model_architecture("speech_dlm", "speech_dlm_big") +def speech_dlm_big(args): + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_cross_layers = getattr(args, "decoder_cross_layers", 12) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + base_lm_architecture(args) diff --git a/fairseq/fairseq/models/speech_to_speech/__init__.py b/fairseq/fairseq/models/speech_to_speech/__init__.py new file mode 100644 index 0000000..f29215c --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .s2s_conformer import * # noqa +from .s2s_conformer_translatotron2 import * # noqa +from .s2s_conformer_unity import * # noqa +from .s2s_transformer import * # noqa diff --git a/fairseq/fairseq/models/speech_to_speech/modules/__init__.py b/fairseq/fairseq/models/speech_to_speech/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/fairseq/models/speech_to_speech/modules/ctc_decoder.py b/fairseq/fairseq/models/speech_to_speech/modules/ctc_decoder.py new file mode 100644 index 0000000..721efbf --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/modules/ctc_decoder.py @@ -0,0 +1,18 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from torch import nn + +from fairseq.models import FairseqEncoder + + +class CTCDecoder(FairseqEncoder): + def __init__(self, dictionary, in_dim): + super().__init__(dictionary) + self.proj = nn.Linear(in_dim, len(dictionary)) + + def forward(self, src_tokens, src_lengths=None, **kwargs): + encoder_out = self.proj(src_tokens) + return {"encoder_out": encoder_out} diff --git a/fairseq/fairseq/models/speech_to_speech/modules/stacked_embedding.py b/fairseq/fairseq/models/speech_to_speech/modules/stacked_embedding.py new file mode 100644 index 0000000..5955a08 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/modules/stacked_embedding.py @@ -0,0 +1,48 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn + +from fairseq.models.transformer import Linear + + +class StackedEmbedding(nn.Embedding): + """Embedding module that supports stacked units -> single embedding""" + + def __init__(self, num_embeddings, embed_dim, padding_idx, num_stacked=1): + super().__init__(num_embeddings, embed_dim, padding_idx) + # follow transformer.Embedding + nn.init.normal_(self.weight, mean=0, std=embed_dim**-0.5) + nn.init.constant_(self.weight[padding_idx], 0) + + self.offset = ( + 4 # skip <bos>, <pad>, <eos>, <unk>, specific to fairseq dictionary + ) + self.vocab_size = num_embeddings - self.offset + self.num_stacked = num_stacked + + if self.num_stacked > 1: + self.project_in_dim = Linear(embed_dim * num_stacked, embed_dim, bias=False) + + def forward(self, input): + if self.num_stacked == 1: + return super().forward(input) + + # expand input indices + mask = input >= self.offset + stacked_input = [] + cum_input = input.new_zeros(input.shape) + for i in range(1, self.num_stacked + 1): + div = pow(self.vocab_size, i) + next_input = torch.remainder(input - self.offset - cum_input, div) + cum_input += next_input + next_input = torch.floor_divide(next_input, div // self.vocab_size) + stacked_input.append((next_input + self.offset) * mask + input * ~mask) + + stacked_input = torch.stack(stacked_input[::-1], dim=2) + embed = super().forward(stacked_input).view(input.size(0), input.size(1), -1) + embed = self.project_in_dim(embed) + return embed diff --git a/fairseq/fairseq/models/speech_to_speech/modules/transformer_decoder_aug.py b/fairseq/fairseq/models/speech_to_speech/modules/transformer_decoder_aug.py new file mode 100644 index 0000000..68f42c2 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/modules/transformer_decoder_aug.py @@ -0,0 +1,108 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Any, Dict, List, Optional + +from torch import Tensor + +from fairseq.models.transformer import Linear +from fairseq.models.transformer.transformer_decoder_aug import AugTransformerDecoder + + +class AugTransformerUnitDecoder(AugTransformerDecoder): + """Based on Transformer decoder, with support to decoding stacked units""" + + def __init__( + self, + args, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=None, + ): + super().__init__( + args, dictionary, embed_tokens, no_encoder_attn, output_projection + ) + self.n_frames_per_step = args.n_frames_per_step + + self.out_proj_n_frames = ( + Linear( + self.output_embed_dim, + self.output_embed_dim * self.n_frames_per_step, + bias=False, + ) + if self.n_frames_per_step > 1 + else None + ) + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + encoder_out_aug: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention, should be of size T x B x C + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + encoder_out_aug=encoder_out_aug, + incremental_state=incremental_state, + full_context_alignment=full_context_alignment, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + ) + + if not features_only: + bsz, seq_len, d = x.size() + if self.out_proj_n_frames: + x = self.out_proj_n_frames(x) + x = self.output_layer(x.view(bsz, seq_len, self.n_frames_per_step, d)) + x = x.view(bsz, seq_len * self.n_frames_per_step, -1) + if ( + incremental_state is None and self.n_frames_per_step > 1 + ): # teacher-forcing mode in training + x = x[ + :, : -(self.n_frames_per_step - 1), : + ] # remove extra frames after <eos> + + return x, extra + + def upgrade_state_dict_named(self, state_dict, name): + if self.n_frames_per_step > 1: + move_keys = [ + ( + f"{name}.project_in_dim.weight", + f"{name}.embed_tokens.project_in_dim.weight", + ) + ] + for from_k, to_k in move_keys: + if from_k in state_dict and to_k not in state_dict: + state_dict[to_k] = state_dict[from_k] + del state_dict[from_k] diff --git a/fairseq/fairseq/models/speech_to_speech/modules/transformer_encoder.py b/fairseq/fairseq/models/speech_to_speech/modules/transformer_encoder.py new file mode 100644 index 0000000..fb1af43 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/modules/transformer_encoder.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn + +from fairseq.models import FairseqEncoder +from fairseq.modules import LayerNorm, TransformerEncoderLayer + + +class TransformerEncoderNoEmb(FairseqEncoder): + """Transformer encoder without token embeddings.""" + + def __init__(self, args): + super().__init__(None) + + self.layers = nn.ModuleList( + [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)] + ) + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(args.encoder_embed_dim) + else: + self.layer_norm = None + + def forward(self, x, encoder_padding_mask, return_all_hiddens=False): + + encoder_states = [] + + for layer in self.layers: + x = layer(x, encoder_padding_mask) + if return_all_hiddens: + encoder_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask] + if encoder_padding_mask is not None and encoder_padding_mask.any() + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] + if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] + if len(encoder_out["encoder_padding_mask"]) == 0 + else [ + x.index_select(0, new_order) + for x in encoder_out["encoder_padding_mask"] + ] + ) + + new_encoder_embedding = ( + [] + if len(encoder_out["encoder_embedding"]) == 0 + else [ + x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] + ] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } diff --git a/fairseq/fairseq/models/speech_to_speech/s2s_conformer.py b/fairseq/fairseq/models/speech_to_speech/s2s_conformer.py new file mode 100644 index 0000000..636396d --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/s2s_conformer.py @@ -0,0 +1,172 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from pathlib import Path + +import torch + +from fairseq import checkpoint_utils +from fairseq.models import register_model, register_model_architecture +from fairseq.models.speech_to_speech.s2s_transformer import ( + S2SpecTTransformerModel, + S2UTTransformerModel, + s2spect_architecture_base, + s2ut_architecture_base, +) +from fairseq.models.speech_to_text import S2TConformerEncoder +from fairseq.models.transformer import Linear + +logger = logging.getLogger(__name__) + + +def build_s2s_conformer_encoder(args): + encoder = S2SConformerEncoder(args) + pretraining_path = getattr(args, "load_pretrained_encoder_from", None) + if pretraining_path is not None: + if not Path(pretraining_path).exists(): + logger.warning( + f"skipped pretraining because {pretraining_path} does not exist" + ) + else: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=pretraining_path + ) + logger.info(f"loaded pretrained encoder from: {pretraining_path}") + return encoder + + +class S2SConformerEncoder(S2TConformerEncoder): + """Based on S2T transformer encoder, with support + to incorporate target speaker embedding.""" + + def __init__(self, args): + super().__init__(args) + + self.spk_emb_proj = None + if args.target_speaker_embed: + self.spk_emb_proj = Linear( + args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim + ) + + def forward( + self, src_tokens, src_lengths, tgt_speaker=None, return_all_hiddens=False + ): + out = super().forward(src_tokens, src_lengths, return_all_hiddens) + + if self.spk_emb_proj: + x = out["encoder_out"][0] + seq_len, bsz, _ = x.size() + tgt_speaker_emb = tgt_speaker.view(1, bsz, -1).expand(seq_len, bsz, -1) + x = self.spk_emb_proj(torch.cat([x, tgt_speaker_emb], dim=2)) + out["encoder_out"][0] = x + + return out + + +@register_model("s2ut_conformer") +class S2UTConformerModel(S2UTTransformerModel): + """ + Direct speech-to-speech translation model with Conformer encoder + Transformer discrete unit decoder + """ + + @staticmethod + def add_args(parser): + S2UTTransformerModel.add_args(parser) + parser.add_argument( + "--depthwise-conv-kernel-size", + type=int, + metavar="N", + help="kernel size of depthwise convolution layers", + ) + parser.add_argument( + "--attn-type", + type=str, + metavar="STR", + help="If not specified uses fairseq MHA. Other valid option is espnet for using conformer", + ) + parser.add_argument( + "--pos-enc-type", + type=str, + metavar="STR", + help="Must be specified in addition to attn-type=espnet for rel_pos and rope", + ) + + @classmethod + def build_encoder(cls, args): + return build_s2s_conformer_encoder(args) + + +@register_model("s2spect_conformer") +class S2SpecTConformerModel(S2SpecTTransformerModel): + """ + Direct speech-to-speech translation model with Conformer encoder + TTS Transformer decoder + """ + + @staticmethod + def add_args(parser): + S2SpecTTransformerModel.add_args(parser) + parser.add_argument("--depthwise-conv-kernel-size", type=int, default=31) + parser.add_argument( + "--attn-type", + type=str, + default=None, + help="If not specified uses fairseq MHA. Other valid option is espnet for using conformer", + ) + parser.add_argument( + "--pos-enc-type", + type=str, + default="abs", + help="Must be specified in addition to attn-type=espnet for rel_pos and rope", + ) + + @classmethod + def build_encoder(cls, args): + return build_s2s_conformer_encoder(args) + + +@register_model_architecture("s2ut_conformer", "s2ut_conformer") +def s2ut_conformer_architecture_base(args): + args.attn_type = getattr(args, "attn_type", None) + args.pos_enc_type = getattr(args, "pos_enc_type", "abs") + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.input_channels = getattr(args, "input_channels", 1) + args.max_source_positions = getattr(args, "max_source_positions", 6000) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_layers = getattr(args, "encoder_layers", 16) + args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31) + s2ut_architecture_base(args) + + +@register_model_architecture("s2spect_conformer", "s2spect_conformer") +def s2spect_conformer_architecture_base(args): + args.attn_type = getattr(args, "attn_type", None) + args.pos_enc_type = getattr(args, "pos_enc_type", "abs") + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.input_channels = getattr(args, "input_channels", 1) + args.max_source_positions = getattr(args, "max_source_positions", 6000) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_layers = getattr(args, "encoder_layers", 16) + args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31) + s2spect_architecture_base(args) + + +@register_model_architecture("s2spect_conformer", "s2spect_conformer_fisher") +def s2spect_architecture_fisher(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + + # decoder + args.prenet_dim = getattr(args, "prenet_dim", 32) + + s2spect_conformer_architecture_base(args) diff --git a/fairseq/fairseq/models/speech_to_speech/s2s_conformer_translatotron2.py b/fairseq/fairseq/models/speech_to_speech/s2s_conformer_translatotron2.py new file mode 100644 index 0000000..8016dae --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/s2s_conformer_translatotron2.py @@ -0,0 +1,262 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import logging + +from fairseq.models import ( + FairseqEncoderModel, + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder +from fairseq.models.speech_to_speech.modules.transformer_encoder import ( + TransformerEncoderNoEmb, +) +from fairseq.models.speech_to_speech.s2s_conformer import S2SpecTConformerModel +from fairseq.models.speech_to_speech.s2s_conformer_unity import ( + multitask_text_transformer_decoder_arch, +) +from fairseq.models.speech_to_speech.s2s_transformer import ( + base_multitask_text_transformer_decoder_arch, + s2spect_architecture_base, +) +from fairseq.models.text_to_speech import TTSTransformerDecoder +from fairseq.models.transformer import TransformerDecoder, TransformerModelBase + +logger = logging.getLogger(__name__) + + +@register_model("s2spect2_conformer") +class S2SpecT2ConformerModel(S2SpecTConformerModel): + """ + Direct speech-to-speech translation model with Conformer encoder + MT Transformer decoder + TTS Transformer decoder + """ + + @staticmethod + def add_args(parser): + S2SpecTConformerModel.add_args(parser) + parser.add_argument( + "--translation-decoder-layers", + type=int, + default=4, + metavar="N", + help="num decoder layers in the first-pass translation module", + ) + parser.add_argument( + "--synthesizer", + default="transformer", + choices=["transformer"], + help="", + ) + parser.add_argument( + "--synthesizer-encoder-layers", + type=int, + default=0, + metavar="N", + help="num encoder layers in the second-pass synthesizer module", + ) + + @classmethod + def build_multitask_decoder( + cls, + args, + tgt_dict, + in_dim, + is_mt_decoder, + decoder_layers, + decoder_embed_dim, + decoder_attention_heads, + ): + decoder_args = args.decoder_args + decoder_args.encoder_embed_dim = in_dim + if args.decoder_type == "transformer": + if is_mt_decoder: + multitask_text_transformer_decoder_arch( + decoder_args, + decoder_layers, + decoder_embed_dim, + decoder_attention_heads, + ) # 4L + else: + base_multitask_text_transformer_decoder_arch(decoder_args) # 2L + task_decoder = TransformerDecoder( + decoder_args, + tgt_dict, + embed_tokens=TransformerModelBase.build_embedding( + decoder_args, + tgt_dict, + decoder_args.decoder_embed_dim, + ), + ) + elif args.decoder_type == "ctc": + task_decoder = CTCDecoder( + dictionary=tgt_dict, + in_dim=in_dim, + ) + else: + raise NotImplementedError( + "currently only support multitask decoder_type 'transformer', 'ctc'" + ) + + return task_decoder + + @classmethod + def build_decoder(cls, args): + _args = copy.deepcopy(args) + _args.encoder_embed_dim = args.decoder_embed_dim + + if args.synthesizer == "transformer": + return TTSTransformerDecoder(_args, None, padding_idx=1) + else: + raise NotImplementedError(args.synthesizer) + + @classmethod + def build_model(cls, args, task): + encoder = cls.build_encoder(args) + decoder = cls.build_decoder(args) + base_model = cls(encoder, decoder) + + # set up multitask decoders + base_model.mt_task_name = None + base_model.multitask_decoders = {} + has_first_pass_decoder = False + for task_name, task_obj in task.multitask_tasks.items(): + if task_obj.is_first_pass_decoder: + has_first_pass_decoder = True + base_model.mt_task_name = task_name + + in_dim = ( + args.encoder_embed_dim + if task_obj.args.input_from == "encoder" + else args.decoder_embed_dim + ) + task_decoder = cls.build_multitask_decoder( + task_obj.args, + task_obj.target_dictionary, + in_dim, + task_obj.is_first_pass_decoder, + getattr(args, "translation_decoder_layers", 4), + getattr(args, "decoder_embed_dim", 256), + getattr(args, "decoder_attention_heads", 4), + ) + + setattr(base_model, f"{task_name}_decoder", task_decoder) + decoder_model_cls = ( + FairseqEncoderModel + if task_obj.args.decoder_type == "ctc" + else FairseqLanguageModel + ) + base_model.multitask_decoders[task_name] = decoder_model_cls( + getattr(base_model, f"{task_name}_decoder") + ) + + assert has_first_pass_decoder, "set at least one intermediate non-CTC decoder" + + # set up encoder on top of the auxiliary MT decoder + if getattr(args, "synthesizer_encoder_layers", 0) > 0: + base_model.synthesizer_encoder = cls.build_text_encoder(args) + else: + base_model.synthesizer_encoder = None + + return base_model + + @classmethod + def build_text_encoder(cls, args): + _args = copy.deepcopy(args) + _args.encoder_layers = args.synthesizer_encoder_layers + _args.encoder_embed_dim = args.decoder_embed_dim + _args.encoder_ffn_embed_dim = args.decoder_ffn_embed_dim + _args.encoder_attention_heads = args.decoder_attention_heads + _args.encoder_normalize_before = True + return TransformerEncoderNoEmb(_args) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + prev_output_tokens_mt, + tgt_speaker=None, + incremental_state=None, + target_lengths=None, + speaker=None, + return_all_hiddens=False, + ): + encoder_out = self.encoder( + src_tokens, + src_lengths=src_lengths, + tgt_speaker=tgt_speaker, + return_all_hiddens=return_all_hiddens, + ) + + # 1. MT decoder + mt_decoder = getattr(self, f"{self.mt_task_name}_decoder") + mt_decoder_out = mt_decoder( + prev_output_tokens_mt, + encoder_out=encoder_out, + ) + x = mt_decoder_out[1]["inner_states"][-1] + if mt_decoder.layer_norm is not None: + x = mt_decoder.layer_norm(x) + + mt_decoder_padding_mask = None + if prev_output_tokens_mt.eq(mt_decoder.padding_idx).any(): + mt_decoder_padding_mask = prev_output_tokens_mt.eq(mt_decoder.padding_idx) + + # 2. TTS encoder + if self.synthesizer_encoder is not None: + tts_encoder_out = self.synthesizer_encoder( + x, + mt_decoder_padding_mask, + return_all_hiddens=return_all_hiddens, + ) + else: + tts_encoder_out = { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [mt_decoder_padding_mask], # B x T + } + + # 3. TTS decoder + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=tts_encoder_out, + incremental_state=incremental_state, + target_lengths=target_lengths, + speaker=speaker, + ) + if return_all_hiddens: + decoder_out[-1]["encoder_states"] = encoder_out["encoder_states"] + decoder_out[-1]["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ] + decoder_out[-1]["mt_decoder_out"] = mt_decoder_out + return decoder_out + + +@register_model_architecture( + model_name="s2spect2_conformer", arch_name="s2spect2_conformer" +) +def s2spect2_conformer_architecture_base(args): + args.conv_version = getattr(args, "conv_version", "convtransformer") + args.attn_type = getattr(args, "attn_type", None) + args.pos_enc_type = getattr(args, "pos_enc_type", "abs") + args.max_source_positions = getattr(args, "max_source_positions", 6000) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_layers = getattr(args, "encoder_layers", 16) + args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31) + s2spect_architecture_base(args) + + +# for old naming +@register_model_architecture( + model_name="s2spect2_conformer", arch_name="s2spect_conformer_translatotron2" +) +def s2spect2_conformer_architecture_base_legacy(args): + s2spect2_conformer_architecture_base(args) diff --git a/fairseq/fairseq/models/speech_to_speech/s2s_conformer_unity.py b/fairseq/fairseq/models/speech_to_speech/s2s_conformer_unity.py new file mode 100644 index 0000000..64388d6 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/s2s_conformer_unity.py @@ -0,0 +1,298 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import logging + +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderModel, + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder +from fairseq.models.speech_to_speech.modules.stacked_embedding import StackedEmbedding +from fairseq.models.speech_to_speech.modules.transformer_decoder_aug import ( + AugTransformerUnitDecoder, +) +from fairseq.models.speech_to_speech.modules.transformer_encoder import ( + TransformerEncoderNoEmb, +) +from fairseq.models.speech_to_speech.s2s_conformer import S2UTConformerModel +from fairseq.models.speech_to_speech.s2s_transformer import ( + TransformerUnitDecoder, + base_multitask_text_transformer_decoder_arch, + s2ut_architecture_base, +) +from fairseq.models.transformer import TransformerDecoder, TransformerModelBase + +logger = logging.getLogger(__name__) + + +def multitask_text_transformer_decoder_arch( + args, decoder_layers, decoder_embed_dim=256, decoder_attention_heads=4 +): + args.decoder_layers = decoder_layers + args.decoder_embed_dim = decoder_embed_dim + args.decoder_attention_heads = decoder_attention_heads + base_multitask_text_transformer_decoder_arch(args) + + +@register_model("unity_conformer") +class UnityConformerModel(S2UTConformerModel): + """ + Direct speech-to-speech translation model with Conformer encoder + MT Transformer decoder + Transformer discrete unit decoder + """ + + @staticmethod + def add_args(parser): + S2UTConformerModel.add_args(parser) + parser.add_argument( + "--translation-decoder-layers", + type=int, + default=4, + metavar="N", + help="num decoder layers in the first-pass translation module", + ) + parser.add_argument( + "--synthesizer", + default="transformer", + choices=["transformer"], + help="", + ) + parser.add_argument( + "--synthesizer-encoder-layers", + type=int, + default=0, + metavar="N", + help="num encoder layers in the second-pass synthesizer module", + ) + parser.add_argument( + "--synthesizer-augmented-cross-attention", + action="store_true", + default=False, + help="augmented cross-attention over speech encoder output", + ) + + @classmethod + def build_multitask_decoder( + cls, + args, + tgt_dict, + in_dim, + is_first_pass_decoder, + decoder_layers, + decoder_embed_dim, + decoder_attention_heads, + ): + decoder_args = args.decoder_args + decoder_args.encoder_embed_dim = in_dim + if args.decoder_type == "transformer": + if is_first_pass_decoder: + multitask_text_transformer_decoder_arch( + decoder_args, + decoder_layers, + decoder_embed_dim, + decoder_attention_heads, + ) # 4L + else: + base_multitask_text_transformer_decoder_arch(decoder_args) # 2L + task_decoder = TransformerDecoder( + decoder_args, + tgt_dict, + embed_tokens=TransformerModelBase.build_embedding( + decoder_args, + tgt_dict, + decoder_args.decoder_embed_dim, + ), + ) + elif args.decoder_type == "ctc": + task_decoder = CTCDecoder( + dictionary=tgt_dict, + in_dim=in_dim, + ) + else: + raise NotImplementedError( + "currently only support multitask decoder_type 'transformer', 'ctc'" + ) + + return task_decoder + + @classmethod + def build_decoder(cls, args, tgt_dict, aug_attn=False): + num_embeddings = len(tgt_dict) + padding_idx = tgt_dict.pad() + embed_tokens = StackedEmbedding( + num_embeddings, + args.decoder_embed_dim, + padding_idx, + num_stacked=args.n_frames_per_step, + ) + + _args = copy.deepcopy(args) + _args.encoder_embed_dim = args.decoder_embed_dim + + decoder_cls = AugTransformerUnitDecoder if aug_attn else TransformerUnitDecoder + return decoder_cls( + _args, + tgt_dict, + embed_tokens, + ) + + @classmethod + def build_model(cls, args, task): + encoder = cls.build_encoder(args) + decoder = cls.build_decoder( + args, + task.target_dictionary, + aug_attn=getattr(args, "synthesizer_augmented_cross_attention", False), + ) + base_model = cls(encoder, decoder) + + base_model.t2u_augmented_cross_attn = getattr( + args, "synthesizer_augmented_cross_attention", False + ) + + # set up multitask decoders + base_model.mt_task_name = None + base_model.multitask_decoders = {} + has_first_pass_decoder = False + for task_name, task_obj in task.multitask_tasks.items(): + if task_obj.is_first_pass_decoder: + has_first_pass_decoder = True + base_model.mt_task_name = task_name + + in_dim = ( + args.encoder_embed_dim + if task_obj.args.input_from == "encoder" + else args.decoder_embed_dim + ) + task_decoder = cls.build_multitask_decoder( + task_obj.args, + task_obj.target_dictionary, + in_dim, + task_obj.is_first_pass_decoder, + getattr(args, "translation_decoder_layers", 4), + getattr(args, "decoder_embed_dim", 256), + getattr(args, "decoder_attention_heads", 4), + ) + + setattr(base_model, f"{task_name}_decoder", task_decoder) + decoder_model_cls = ( + FairseqEncoderModel + if task_obj.args.decoder_type == "ctc" + else FairseqLanguageModel + ) + base_model.multitask_decoders[task_name] = decoder_model_cls( + getattr(base_model, f"{task_name}_decoder") + ) + + assert has_first_pass_decoder, "set at least one intermediate non-CTC decoder" + + # set up encoder on top of the auxiliary MT decoder + if getattr(args, "synthesizer_encoder_layers", 0) > 0: + base_model.synthesizer_encoder = cls.build_text_encoder(args) + else: + base_model.synthesizer_encoder = None + + return base_model + + @classmethod + def build_text_encoder(cls, args): + _args = copy.deepcopy(args) + _args.encoder_layers = args.synthesizer_encoder_layers + _args.encoder_embed_dim = args.decoder_embed_dim + _args.encoder_ffn_embed_dim = args.decoder_ffn_embed_dim + _args.encoder_attention_heads = args.decoder_attention_heads + _args.encoder_normalize_before = True + return TransformerEncoderNoEmb(_args) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + prev_output_tokens_mt, + tgt_speaker=None, + return_all_hiddens=False, + ): + mt_decoder = getattr(self, f"{self.mt_task_name}_decoder") + + encoder_out = self.encoder( + src_tokens, + src_lengths=src_lengths, + tgt_speaker=tgt_speaker, + return_all_hiddens=return_all_hiddens, + ) + + # 1. MT decoder + mt_decoder_out = mt_decoder( + prev_output_tokens_mt, + encoder_out=encoder_out, + ) + x = mt_decoder_out[1]["inner_states"][-1] + if mt_decoder.layer_norm is not None: + x = mt_decoder.layer_norm(x) + + mt_decoder_padding_mask = None + if prev_output_tokens_mt.eq(mt_decoder.padding_idx).any(): + mt_decoder_padding_mask = prev_output_tokens_mt.eq(mt_decoder.padding_idx) + + # 2. T2U encoder + if self.synthesizer_encoder is not None: + t2u_encoder_out = self.synthesizer_encoder( + x, + mt_decoder_padding_mask, + return_all_hiddens=return_all_hiddens, + ) + else: + t2u_encoder_out = { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [mt_decoder_padding_mask], # B x T + } + + # 3. T2U decoder + if self.t2u_augmented_cross_attn: + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + encoder_out_aug=t2u_encoder_out, + ) + else: + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=t2u_encoder_out, + ) + if return_all_hiddens: + decoder_out[-1]["encoder_states"] = encoder_out["encoder_states"] + decoder_out[-1]["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ] + decoder_out[-1]["mt_decoder_out"] = mt_decoder_out + return decoder_out + + +@register_model_architecture(model_name="unity_conformer", arch_name="unity_conformer") +def unity_conformer_architecture_base(args): + args.conv_version = getattr(args, "conv_version", "convtransformer") + args.attn_type = getattr(args, "attn_type", None) + args.pos_enc_type = getattr(args, "pos_enc_type", "abs") + args.max_source_positions = getattr(args, "max_source_positions", 6000) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_layers = getattr(args, "encoder_layers", 16) + args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31) + s2ut_architecture_base(args) + + +# for old naming +@register_model_architecture( + model_name="unity_conformer", arch_name="s2ut_conformer_translatotron2" +) +def unity_conformer_architecture_base_legacy(args): + unity_conformer_architecture_base(args) diff --git a/fairseq/fairseq/models/speech_to_speech/s2s_transformer.py b/fairseq/fairseq/models/speech_to_speech/s2s_transformer.py new file mode 100644 index 0000000..07393d2 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_speech/s2s_transformer.py @@ -0,0 +1,722 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from pathlib import Path +from typing import Any, Dict, List, Optional + +import torch +from torch import Tensor + +from fairseq import checkpoint_utils, utils +from fairseq.models import ( + FairseqEncoderDecoderModel, + FairseqEncoderModel, + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder +from fairseq.models.speech_to_speech.modules.stacked_embedding import StackedEmbedding +from fairseq.models.speech_to_text import S2TTransformerEncoder +from fairseq.models.text_to_speech import TTSTransformerDecoder +from fairseq.models.transformer import Linear, TransformerDecoder, TransformerModelBase + +logger = logging.getLogger(__name__) + + +class S2STransformerEncoder(S2TTransformerEncoder): + """Based on S2T transformer encoder, with support + to incorporate target speaker embedding.""" + + def __init__(self, args): + super().__init__(args) + + self.spk_emb_proj = None + if args.target_speaker_embed: + self.spk_emb_proj = Linear( + args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim + ) + + def forward( + self, src_tokens, src_lengths, tgt_speaker=None, return_all_hiddens=False + ): + out = super().forward(src_tokens, src_lengths, return_all_hiddens) + + if self.spk_emb_proj: + x = out["encoder_out"][0] + seq_len, bsz, _ = x.size() + tgt_speaker_emb = tgt_speaker.view(1, bsz, -1).expand(seq_len, bsz, -1) + x = self.spk_emb_proj(torch.cat([x, tgt_speaker_emb], dim=2)) + out["encoder_out"][0] = x + + return out + + +class TransformerUnitDecoder(TransformerDecoder): + """Based on Transformer decoder, with support to decoding stacked units""" + + def __init__( + self, + args, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=None, + ): + super().__init__( + args, dictionary, embed_tokens, no_encoder_attn, output_projection + ) + self.n_frames_per_step = args.n_frames_per_step + + self.out_proj_n_frames = ( + Linear( + self.output_embed_dim, + self.output_embed_dim * self.n_frames_per_step, + bias=False, + ) + if self.n_frames_per_step > 1 + else None + ) + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention, should be of size T x B x C + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + full_context_alignment=full_context_alignment, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + ) + + if not features_only: + bsz, seq_len, d = x.size() + if self.out_proj_n_frames: + x = self.out_proj_n_frames(x) + x = self.output_layer(x.view(bsz, seq_len, self.n_frames_per_step, d)) + x = x.view(bsz, seq_len * self.n_frames_per_step, -1) + if ( + incremental_state is None and self.n_frames_per_step > 1 + ): # teacher-forcing mode in training + x = x[ + :, : -(self.n_frames_per_step - 1), : + ] # remove extra frames after <eos> + + return x, extra + + def upgrade_state_dict_named(self, state_dict, name): + if self.n_frames_per_step > 1: + move_keys = [ + ( + f"{name}.project_in_dim.weight", + f"{name}.embed_tokens.project_in_dim.weight", + ) + ] + for from_k, to_k in move_keys: + if from_k in state_dict and to_k not in state_dict: + state_dict[to_k] = state_dict[from_k] + del state_dict[from_k] + + +class S2STransformerMultitaskModelBase(FairseqEncoderDecoderModel): + @classmethod + def build_encoder(cls, args): + encoder = S2STransformerEncoder(args) + pretraining_path = getattr(args, "load_pretrained_encoder_from", None) + if pretraining_path is not None: + if not Path(pretraining_path).exists(): + logger.warning( + f"skipped pretraining because {pretraining_path} does not exist" + ) + else: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=pretraining_path + ) + logger.info(f"loaded pretrained encoder from: {pretraining_path}") + return encoder + + @classmethod + def build_multitask_decoder(cls, args, tgt_dict, in_dim): + decoder_args = args.decoder_args + decoder_args.encoder_embed_dim = in_dim + if args.decoder_type == "transformer": + base_multitask_text_transformer_decoder_arch(decoder_args) + task_decoder = TransformerDecoder( + decoder_args, + tgt_dict, + embed_tokens=TransformerModelBase.build_embedding( + decoder_args, + tgt_dict, + decoder_args.decoder_embed_dim, + ), + ) + elif args.decoder_type == "ctc": + task_decoder = CTCDecoder( + dictionary=tgt_dict, + in_dim=in_dim, + ) + else: + raise NotImplementedError( + "currently only support multitask decoder_type 'transformer', 'ctc'" + ) + + return task_decoder + + @classmethod + def build_model(cls, args, task): + encoder = cls.build_encoder(args) + decoder = ( + cls.build_decoder(args, task.target_dictionary) + if task.args.target_is_code + else cls.build_decoder(args) + ) + base_model = cls(encoder, decoder) + + # set up multitask decoders + base_model.multitask_decoders = {} + for task_name, task_obj in task.multitask_tasks.items(): + in_dim = ( + args.encoder_embed_dim + if task_obj.args.input_from == "encoder" + else args.decoder_embed_dim + ) + task_decoder = cls.build_multitask_decoder( + task_obj.args, task_obj.target_dictionary, in_dim + ) + + setattr(base_model, f"{task_name}_decoder", task_decoder) + decoder_model_cls = ( + FairseqEncoderModel + if task_obj.args.decoder_type == "ctc" + else FairseqLanguageModel + ) + base_model.multitask_decoders[task_name] = decoder_model_cls( + getattr(base_model, f"{task_name}_decoder") + ) + + return base_model + + def forward_encoder(self, src_tokens, src_lengths, speaker=None, **kwargs): + return self.encoder( + src_tokens, src_lengths=src_lengths, tgt_speaker=speaker, **kwargs + ) + + +@register_model("s2ut_transformer") +class S2UTTransformerModel(S2STransformerMultitaskModelBase): + """ + Direct speech-to-speech translation model with Transformer encoder + Transformer discrete unit decoder + https://arxiv.org/abs/2107.05604 + """ + + @staticmethod + def add_args(parser): + # input + parser.add_argument( + "--conv-kernel-sizes", + type=str, + metavar="STR", + help="kernel sizes of Conv1d (s2t_transformer) subsampling layers", + ) + parser.add_argument( + "--conv-channels", + type=int, + metavar="N", + help="# of channels in Conv1d (s2t_transformer) subsampling layers", + ) + parser.add_argument( + "--conv-out-channels", + type=int, + metavar="N", + help="# of channels in Conv2d (convtransformer) subsampling layers", + ) + parser.add_argument( + "--conv-version", + type=str, + default="s2t_transformer", + choices=["s2t_transformer", "convtransformer"], + help="version of frontend convolutional layers", + ) + # Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + "--encoder-freezing-updates", + type=int, + metavar="N", + help="freeze encoder for first N updates", + ) + # speaker + parser.add_argument( + "--speaker-embed-dim", + type=int, + metavar="N", + help="speaker embedding dimension", + ) + + @classmethod + def build_decoder(cls, args, tgt_dict): + num_embeddings = len(tgt_dict) + padding_idx = tgt_dict.pad() + embed_tokens = StackedEmbedding( + num_embeddings, + args.decoder_embed_dim, + padding_idx, + num_stacked=args.n_frames_per_step, + ) + + return TransformerUnitDecoder( + args, + tgt_dict, + embed_tokens, + ) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + tgt_speaker=None, + return_all_hiddens=False, + ): + encoder_out = self.encoder( + src_tokens, + src_lengths=src_lengths, + tgt_speaker=tgt_speaker, + return_all_hiddens=return_all_hiddens, + ) + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + ) + if return_all_hiddens: + decoder_out[-1]["encoder_states"] = encoder_out["encoder_states"] + decoder_out[-1]["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ] + return decoder_out + + +@register_model("s2spect_transformer") +class S2SpecTTransformerModel(S2STransformerMultitaskModelBase): + """ + Speech-to-spectrogram model with S2T Transformer encoder + TTS Transformer decoder + """ + + @staticmethod + def add_args(parser): + # input + parser.add_argument( + "--conv-kernel-sizes", + type=str, + metavar="STR", + help="kernel sizes of Conv1d (s2t_transformer) subsampling layers", + ) + parser.add_argument( + "--conv-channels", + type=int, + metavar="N", + help="# of channels in Conv1d (s2t_transformer) subsampling layers", + ) + parser.add_argument( + "--conv-version", + type=str, + default="s2t_transformer", + choices=["s2t_transformer", "convtransformer"], + help="version of frontend convolutional layers", + ) + # Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + "--encoder-freezing-updates", + type=int, + metavar="N", + help="freeze encoder for first N updates", + ) + # speaker + parser.add_argument( + "--speaker-embed-dim", + type=int, + metavar="N", + help="speaker embedding dimension", + ) + # decoder + parser.add_argument("--output-frame-dim", type=int) + # decoder prenet + parser.add_argument("--prenet-dropout", type=float) + parser.add_argument("--prenet-layers", type=int) + parser.add_argument("--prenet-dim", type=int) + # decoder postnet + parser.add_argument("--postnet-dropout", type=float) + parser.add_argument("--postnet-layers", type=int) + parser.add_argument("--postnet-conv-dim", type=int) + parser.add_argument("--postnet-conv-kernel-size", type=int) + # decoder transformer layers + parser.add_argument("--decoder-transformer-layers", type=int) + parser.add_argument("--decoder-embed-dim", type=int) + parser.add_argument("--decoder-ffn-embed-dim", type=int) + parser.add_argument("--decoder-normalize-before", action="store_true") + parser.add_argument("--decoder-attention-heads", type=int) + + @classmethod + def build_decoder(cls, args): + return TTSTransformerDecoder(args, None, padding_idx=1) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + tgt_speaker=None, + incremental_state=None, + target_lengths=None, + speaker=None, + return_all_hiddens=False, + ): + encoder_out = self.encoder( + src_tokens, + src_lengths=src_lengths, + tgt_speaker=tgt_speaker, + return_all_hiddens=return_all_hiddens, + ) + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + target_lengths=target_lengths, + speaker=speaker, + ) + if return_all_hiddens: + decoder_out[-1]["encoder_states"] = encoder_out["encoder_states"] + decoder_out[-1]["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ] + return decoder_out + + +def base_multitask_text_transformer_decoder_arch(args): + args.dropout = getattr(args, "dropout", 0.3) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", True + ) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.max_target_positions = getattr(args, "max_target_positions", 1024) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + + args.adaptive_input = getattr(args, "adaptive_input", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + + args.decoder_layers = getattr(args, "decoder_layers", 2) + + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + + # decoder layer + args.activation_dropout = getattr(args, "activation_dropout", args.dropout) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) + + args.attention_dropout = getattr(args, "attention_dropout", args.dropout) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + + +def base_s2st_transformer_encoder_architecture(args): + args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0) + + # Convolutional subsampler + args.input_channels = getattr(args, "input_channels", 1) + args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5") # for Conv1d + args.conv_channels = getattr(args, "conv_channels", 1024) # for Conv1d + args.conv_out_channels = getattr(args, "conv_out_channels", 256) # for Conv2d + args.conv_version = getattr(args, "conv_version", "s2t_transformer") + # Transformer + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", args.dropout) + args.activation_dropout = getattr(args, "activation_dropout", args.dropout) + args.activation_fn = getattr(args, "activation_fn", "relu") + + args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 256) + + +@register_model_architecture( + model_name="s2ut_transformer", arch_name="s2ut_transformer" +) +def s2ut_architecture_base(args): + base_s2st_transformer_encoder_architecture(args) + + # decoder + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + +@register_model_architecture("s2ut_transformer", "s2ut_transformer_fisher") +def s2ut_architecture_fisher(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + + s2ut_architecture_base(args) + + +@register_model_architecture( + model_name="s2spect_transformer", arch_name="s2spect_transformer" +) +def s2spect_architecture_base(args): + base_s2st_transformer_encoder_architecture(args) + + # decoder + args.output_frame_dim = getattr(args, "output_frame_dim", 80) + # decoder prenet + args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) + args.prenet_layers = getattr(args, "prenet_layers", 2) + args.prenet_dim = getattr(args, "prenet_dim", 256) + # decoder postnet + args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) + args.postnet_layers = getattr(args, "postnet_layers", 5) + args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) + args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) + # decoder transformer layers + args.decoder_transformer_layers = getattr(args, "decoder_transformer_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim + ) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + + +@register_model_architecture("s2spect_transformer", "s2spect_transformer_fisher") +def s2spect_architecture_fisher(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + + # decoder + args.prenet_dim = getattr(args, "prenet_dim", 32) + + s2spect_architecture_base(args) diff --git a/fairseq/fairseq/models/speech_to_text/__init__.py b/fairseq/fairseq/models/speech_to_text/__init__.py new file mode 100644 index 0000000..62ef663 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .berard import * # noqa +from .convtransformer import * # noqa +from .multi_modality_model import * # noqa +from .s2t_conformer import * # noqa +from .s2t_transformer import * # noqa +from .s2t_wav_transformer import * # noqa +from .xm_transformer import * # noqa +from .xm_transformer_unity import * # noqa diff --git a/fairseq/fairseq/models/speech_to_text/berard.py b/fairseq/fairseq/models/speech_to_text/berard.py new file mode 100644 index 0000000..107ac98 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/berard.py @@ -0,0 +1,607 @@ +#!/usr/bin/env python3 + +from ast import literal_eval +from typing import List, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import checkpoint_utils, utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) + + +@register_model("s2t_berard") +class BerardModel(FairseqEncoderDecoderModel): + """Implementation of a model similar to https://arxiv.org/abs/1802.04200 + + Paper title: End-to-End Automatic Speech Translation of Audiobooks + An implementation is available in tensorflow at + https://github.com/eske/seq2seq + Relevant files in this implementation are the config + (https://github.com/eske/seq2seq/blob/master/config/LibriSpeech/AST.yaml) + and the model code + (https://github.com/eske/seq2seq/blob/master/translate/models.py). + The encoder and decoder try to be close to the original implementation. + The attention is an MLP as in Bahdanau et al. + (https://arxiv.org/abs/1409.0473). + There is no state initialization by averaging the encoder outputs. + """ + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + parser.add_argument( + "--input-layers", + type=str, + metavar="EXPR", + help="List of linear layer dimensions. These " + "layers are applied to the input features and " + "are followed by tanh and possibly dropout.", + ) + parser.add_argument( + "--dropout", + type=float, + metavar="D", + help="Dropout probability to use in the encoder/decoder. " + "Note that this parameters control dropout in various places, " + "there is no fine-grained control for dropout for embeddings " + "vs LSTM layers for example.", + ) + parser.add_argument( + "--in-channels", + type=int, + metavar="N", + help="Number of encoder input channels. " "Typically value is 1.", + ) + parser.add_argument( + "--conv-layers", + type=str, + metavar="EXPR", + help="List of conv layers " "(format: (channels, kernel, stride)).", + ) + parser.add_argument( + "--num-blstm-layers", + type=int, + metavar="N", + help="Number of encoder bi-LSTM layers.", + ) + parser.add_argument( + "--lstm-size", type=int, metavar="N", help="LSTM hidden size." + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="Embedding dimension of the decoder target tokens.", + ) + parser.add_argument( + "--decoder-hidden-dim", + type=int, + metavar="N", + help="Decoder LSTM hidden dimension.", + ) + parser.add_argument( + "--decoder-num-layers", + type=int, + metavar="N", + help="Number of decoder LSTM layers.", + ) + parser.add_argument( + "--attention-dim", + type=int, + metavar="N", + help="Hidden layer dimension in MLP attention.", + ) + parser.add_argument( + "--output-layer-dim", + type=int, + metavar="N", + help="Hidden layer dim for linear layer prior to output projection.", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + "--load-pretrained-decoder-from", + type=str, + metavar="STR", + help="model to take decoder weights from (for initialization)", + ) + + @classmethod + def build_encoder(cls, args, task): + encoder = BerardEncoder( + input_layers=literal_eval(args.input_layers), + conv_layers=literal_eval(args.conv_layers), + in_channels=args.input_channels, + input_feat_per_channel=args.input_feat_per_channel, + num_blstm_layers=args.num_blstm_layers, + lstm_size=args.lstm_size, + dropout=args.dropout, + ) + if getattr(args, "load_pretrained_encoder_from", None) is not None: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + return encoder + + @classmethod + def build_decoder(cls, args, task): + decoder = LSTMDecoder( + dictionary=task.target_dictionary, + embed_dim=args.decoder_embed_dim, + num_layers=args.decoder_num_layers, + hidden_size=args.decoder_hidden_dim, + dropout=args.dropout, + encoder_output_dim=2 * args.lstm_size, # bidirectional + attention_dim=args.attention_dim, + output_layer_dim=args.output_layer_dim, + ) + if getattr(args, "load_pretrained_decoder_from", None) is not None: + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_decoder_from + ) + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + encoder = cls.build_encoder(args, task) + decoder = cls.build_decoder(args, task) + + return cls(encoder, decoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = super().get_normalized_probs(net_output, log_probs, sample) + # lprobs is a (B, T, D) tensor + lprobs.batch_first = True + return lprobs + + +class BerardEncoder(FairseqEncoder): + def __init__( + self, + input_layers: List[int], + conv_layers: List[Tuple[int]], + in_channels: int, + input_feat_per_channel: int, + num_blstm_layers: int, + lstm_size: int, + dropout: float, + ): + """ + Args: + input_layers: list of linear layer dimensions. These layers are + applied to the input features and are followed by tanh and + possibly dropout. + conv_layers: list of conv2d layer configurations. A configuration is + a tuple (out_channels, conv_kernel_size, stride). + in_channels: number of input channels. + input_feat_per_channel: number of input features per channel. These + are speech features, typically 40 or 80. + num_blstm_layers: number of bidirectional LSTM layers. + lstm_size: size of the LSTM hidden (and cell) size. + dropout: dropout probability. Dropout can be applied after the + linear layers and LSTM layers but not to the convolutional + layers. + """ + super().__init__(None) + + self.input_layers = nn.ModuleList() + in_features = input_feat_per_channel + for out_features in input_layers: + if dropout > 0: + self.input_layers.append( + nn.Sequential( + nn.Linear(in_features, out_features), nn.Dropout(p=dropout) + ) + ) + else: + self.input_layers.append(nn.Linear(in_features, out_features)) + in_features = out_features + + self.in_channels = in_channels + self.input_dim = input_feat_per_channel + self.conv_kernel_sizes_and_strides = [] + self.conv_layers = nn.ModuleList() + lstm_input_dim = input_layers[-1] + for conv_layer in conv_layers: + out_channels, conv_kernel_size, conv_stride = conv_layer + self.conv_layers.append( + nn.Conv2d( + in_channels, + out_channels, + conv_kernel_size, + stride=conv_stride, + padding=conv_kernel_size // 2, + ) + ) + self.conv_kernel_sizes_and_strides.append((conv_kernel_size, conv_stride)) + in_channels = out_channels + lstm_input_dim //= conv_stride + + lstm_input_dim *= conv_layers[-1][0] + self.lstm_size = lstm_size + self.num_blstm_layers = num_blstm_layers + self.lstm = nn.LSTM( + input_size=lstm_input_dim, + hidden_size=lstm_size, + num_layers=num_blstm_layers, + dropout=dropout, + bidirectional=True, + ) + self.output_dim = 2 * lstm_size # bidirectional + if dropout > 0: + self.dropout = nn.Dropout(p=dropout) + else: + self.dropout = None + + def forward(self, src_tokens, src_lengths=None, **kwargs): + """ + Args + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (B,) + """ + bsz, max_seq_len, _ = src_tokens.size() + # (B, C, T, feat) + x = ( + src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) + .transpose(1, 2) + .contiguous() + ) + + for input_layer in self.input_layers: + x = input_layer(x) + x = torch.tanh(x) + + for conv_layer in self.conv_layers: + x = conv_layer(x) + + bsz, _, output_seq_len, _ = x.size() + + # (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) -> + # (T, B, C * feat) + x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) + + input_lengths = src_lengths.clone() + for k, s in self.conv_kernel_sizes_and_strides: + p = k // 2 + input_lengths = (input_lengths.float() + 2 * p - k) / s + 1 + input_lengths = input_lengths.floor().long() + + packed_x = nn.utils.rnn.pack_padded_sequence(x, input_lengths) + + h0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_() + c0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_() + packed_outs, _ = self.lstm(packed_x, (h0, c0)) + + # unpack outputs and apply dropout + x, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_outs) + if self.dropout is not None: + x = self.dropout(x) + + encoder_padding_mask = ( + lengths_to_padding_mask(output_lengths).to(src_tokens.device).t() + ) + + return { + "encoder_out": x, # (T, B, C) + "encoder_padding_mask": encoder_padding_mask, # (T, B) + } + + def reorder_encoder_out(self, encoder_out, new_order): + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(1, new_order) + return encoder_out + + +class MLPAttention(nn.Module): + """The original attention from Badhanau et al. (2014) + + https://arxiv.org/abs/1409.0473, based on a Multi-Layer Perceptron. + The attention score between position i in the encoder and position j in the + decoder is: alpha_ij = V_a * tanh(W_ae * enc_i + W_ad * dec_j + b_a) + """ + + def __init__(self, decoder_hidden_state_dim, context_dim, attention_dim): + super().__init__() + + self.context_dim = context_dim + self.attention_dim = attention_dim + # W_ae and b_a + self.encoder_proj = nn.Linear(context_dim, self.attention_dim, bias=True) + # W_ad + self.decoder_proj = nn.Linear( + decoder_hidden_state_dim, self.attention_dim, bias=False + ) + # V_a + self.to_scores = nn.Linear(self.attention_dim, 1, bias=False) + + def forward(self, decoder_state, source_hids, encoder_padding_mask): + """The expected input dimensions are: + decoder_state: bsz x decoder_hidden_state_dim + source_hids: src_len x bsz x context_dim + encoder_padding_mask: src_len x bsz + """ + src_len, bsz, _ = source_hids.size() + # (src_len*bsz) x context_dim (to feed through linear) + flat_source_hids = source_hids.view(-1, self.context_dim) + # (src_len*bsz) x attention_dim + encoder_component = self.encoder_proj(flat_source_hids) + # src_len x bsz x attention_dim + encoder_component = encoder_component.view(src_len, bsz, self.attention_dim) + # 1 x bsz x attention_dim + decoder_component = self.decoder_proj(decoder_state).unsqueeze(0) + # Sum with broadcasting and apply the non linearity + # src_len x bsz x attention_dim + hidden_att = torch.tanh( + (decoder_component + encoder_component).view(-1, self.attention_dim) + ) + # Project onto the reals to get attentions scores (src_len x bsz) + attn_scores = self.to_scores(hidden_att).view(src_len, bsz) + + # Mask + softmax (src_len x bsz) + if encoder_padding_mask is not None: + attn_scores = ( + attn_scores.float() + .masked_fill_(encoder_padding_mask, float("-inf")) + .type_as(attn_scores) + ) # FP16 support: cast to float and back + # srclen x bsz + normalized_masked_attn_scores = F.softmax(attn_scores, dim=0) + + # Sum weighted sources (bsz x context_dim) + attn_weighted_context = ( + source_hids * normalized_masked_attn_scores.unsqueeze(2) + ).sum(dim=0) + + return attn_weighted_context, normalized_masked_attn_scores + + +class LSTMDecoder(FairseqIncrementalDecoder): + def __init__( + self, + dictionary, + embed_dim, + num_layers, + hidden_size, + dropout, + encoder_output_dim, + attention_dim, + output_layer_dim, + ): + """ + Args: + dictionary: target text dictionary. + embed_dim: embedding dimension for target tokens. + num_layers: number of LSTM layers. + hidden_size: hidden size for LSTM layers. + dropout: dropout probability. Dropout can be applied to the + embeddings, the LSTM layers, and the context vector. + encoder_output_dim: encoder output dimension (hidden size of + encoder LSTM). + attention_dim: attention dimension for MLP attention. + output_layer_dim: size of the linear layer prior to output + projection. + """ + super().__init__(dictionary) + self.num_layers = num_layers + self.hidden_size = hidden_size + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + self.embed_tokens = nn.Embedding(num_embeddings, embed_dim, padding_idx) + if dropout > 0: + self.dropout = nn.Dropout(p=dropout) + else: + self.dropout = None + + self.layers = nn.ModuleList() + for layer_id in range(num_layers): + input_size = embed_dim if layer_id == 0 else encoder_output_dim + self.layers.append( + nn.LSTMCell(input_size=input_size, hidden_size=hidden_size) + ) + + self.context_dim = encoder_output_dim + self.attention = MLPAttention( + decoder_hidden_state_dim=hidden_size, + context_dim=encoder_output_dim, + attention_dim=attention_dim, + ) + + self.deep_output_layer = nn.Linear( + hidden_size + encoder_output_dim + embed_dim, output_layer_dim + ) + self.output_projection = nn.Linear(output_layer_dim, num_embeddings) + + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs + ): + encoder_padding_mask = encoder_out["encoder_padding_mask"] + encoder_outs = encoder_out["encoder_out"] + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + bsz, seqlen = prev_output_tokens.size() + + srclen = encoder_outs.size(0) + + # embed tokens + embeddings = self.embed_tokens(prev_output_tokens) + x = embeddings + if self.dropout is not None: + x = self.dropout(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # initialize previous states (or get from cache during incremental + # generation) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is not None: + prev_hiddens, prev_cells = cached_state + else: + prev_hiddens = [encoder_out["encoder_out"].mean(dim=0)] * self.num_layers + prev_cells = [x.new_zeros(bsz, self.hidden_size)] * self.num_layers + + attn_scores = x.new_zeros(bsz, srclen) + attention_outs = [] + outs = [] + for j in range(seqlen): + input = x[j, :, :] + attention_out = None + for i, layer in enumerate(self.layers): + # the previous state is one layer below except for the bottom + # layer where the previous state is the state emitted by the + # top layer + hidden, cell = layer( + input, + ( + prev_hiddens[(i - 1) % self.num_layers], + prev_cells[(i - 1) % self.num_layers], + ), + ) + if self.dropout is not None: + hidden = self.dropout(hidden) + prev_hiddens[i] = hidden + prev_cells[i] = cell + if attention_out is None: + attention_out, attn_scores = self.attention( + hidden, encoder_outs, encoder_padding_mask + ) + if self.dropout is not None: + attention_out = self.dropout(attention_out) + attention_outs.append(attention_out) + input = attention_out + + # collect the output of the top layer + outs.append(hidden) + + # cache previous states (no-op except during incremental generation) + utils.set_incremental_state( + self, incremental_state, "cached_state", (prev_hiddens, prev_cells) + ) + + # collect outputs across time steps + x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) + attention_outs_concat = torch.cat(attention_outs, dim=0).view( + seqlen, bsz, self.context_dim + ) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + attention_outs_concat = attention_outs_concat.transpose(0, 1) + + # concat LSTM output, attention output and embedding + # before output projection + x = torch.cat((x, attention_outs_concat, embeddings), dim=2) + x = self.deep_output_layer(x) + x = torch.tanh(x) + if self.dropout is not None: + x = self.dropout(x) + # project back to size of vocabulary + x = self.output_projection(x) + + # to return the full attn_scores tensor, we need to fix the decoder + # to account for subsampling input frames + # return x, attn_scores + return x, None + + def reorder_incremental_state(self, incremental_state, new_order): + super().reorder_incremental_state(incremental_state, new_order) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is None: + return + + def reorder_state(state): + if isinstance(state, list): + return [reorder_state(state_i) for state_i in state] + return state.index_select(0, new_order) + + new_state = tuple(map(reorder_state, cached_state)) + utils.set_incremental_state(self, incremental_state, "cached_state", new_state) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard") +def berard(args): + """The original version: "End-to-End Automatic Speech Translation of + Audiobooks" (https://arxiv.org/abs/1802.04200) + """ + args.input_layers = getattr(args, "input_layers", "[256, 128]") + args.conv_layers = getattr(args, "conv_layers", "[(16, 3, 2), (16, 3, 2)]") + args.num_blstm_layers = getattr(args, "num_blstm_layers", 3) + args.lstm_size = getattr(args, "lstm_size", 256) + args.dropout = getattr(args, "dropout", 0.2) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) + args.decoder_num_layers = getattr(args, "decoder_num_layers", 2) + args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 512) + args.attention_dim = getattr(args, "attention_dim", 512) + args.output_layer_dim = getattr(args, "output_layer_dim", 128) + args.load_pretrained_encoder_from = getattr( + args, "load_pretrained_encoder_from", None + ) + args.load_pretrained_decoder_from = getattr( + args, "load_pretrained_decoder_from", None + ) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_256_3_3") +def berard_256_3_3(args): + """Used in + * "Harnessing Indirect Training Data for End-to-End Automatic Speech + Translation: Tricks of the Trade" (https://arxiv.org/abs/1909.06515) + * "CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus" + (https://arxiv.org/pdf/2002.01320.pdf) + * "Self-Supervised Representations Improve End-to-End Speech Translation" + (https://arxiv.org/abs/2006.12124) + """ + args.decoder_num_layers = getattr(args, "decoder_num_layers", 3) + berard(args) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_3_2") +def berard_512_3_2(args): + args.num_blstm_layers = getattr(args, "num_blstm_layers", 3) + args.lstm_size = getattr(args, "lstm_size", 512) + args.dropout = getattr(args, "dropout", 0.3) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_num_layers = getattr(args, "decoder_num_layers", 2) + args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024) + args.attention_dim = getattr(args, "attention_dim", 512) + args.output_layer_dim = getattr(args, "output_layer_dim", 256) + berard(args) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_5_3") +def berard_512_5_3(args): + args.num_blstm_layers = getattr(args, "num_blstm_layers", 5) + args.lstm_size = getattr(args, "lstm_size", 512) + args.dropout = getattr(args, "dropout", 0.3) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_num_layers = getattr(args, "decoder_num_layers", 3) + args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024) + args.attention_dim = getattr(args, "attention_dim", 512) + args.output_layer_dim = getattr(args, "output_layer_dim", 256) + berard(args) diff --git a/fairseq/fairseq/models/speech_to_text/convtransformer.py b/fairseq/fairseq/models/speech_to_text/convtransformer.py new file mode 100644 index 0000000..4d0fc02 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/convtransformer.py @@ -0,0 +1,443 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from fairseq import checkpoint_utils, utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_text.modules.convolution import infer_conv_output_dim +from fairseq.models.transformer import Embedding, TransformerDecoder +from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerEncoderLayer + +logger = logging.getLogger(__name__) + + +@register_model("convtransformer") +class ConvTransformerModel(FairseqEncoderDecoderModel): + """ + Transformer-based Speech translation model from ESPNet-ST + https://arxiv.org/abs/2004.10234 + """ + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--input-feat-per-channel", + type=int, + metavar="N", + help="encoder input dimension per input channel", + ) + parser.add_argument( + "--activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--decoder-output-dim", + type=int, + metavar="N", + help="decoder output dimension (extra linear layer if different from decoder embed dim)", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + "--load-pretrained-decoder-from", + type=str, + metavar="STR", + help="model to take decoder weights from (for initialization)", + ) + parser.add_argument( + "--conv-out-channels", + type=int, + metavar="INT", + help="the number of output channels of conv layer", + ) + + @classmethod + def build_encoder(cls, args): + encoder = ConvTransformerEncoder(args) + if getattr(args, "load_pretrained_encoder_from", None) is not None: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + return encoder + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + decoder = TransformerDecoderNoExtra(args, task.target_dictionary, embed_tokens) + if getattr(args, "load_pretrained_decoder_from", None) is not None: + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_decoder_from + ) + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + return Embedding(num_embeddings, embed_dim, padding_idx) + + decoder_embed_tokens = build_embedding( + task.target_dictionary, args.decoder_embed_dim + ) + encoder = cls.build_encoder(args) + decoder = cls.build_decoder(args, task, decoder_embed_tokens) + return cls(encoder, decoder) + + @staticmethod + @torch.jit.unused + def set_batch_first(lprobs): + lprobs.batch_first = True + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample) + if self.training: + self.set_batch_first(lprobs) + return lprobs + + def output_layout(self): + return "BTD" + + """ + The forward method inherited from the base class has a **kwargs argument in + its input, which is not supported in torchscript. This method overrites the forward + method definition without **kwargs. + """ + + def forward(self, src_tokens, src_lengths, prev_output_tokens): + encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths) + decoder_out = self.decoder( + prev_output_tokens=prev_output_tokens, encoder_out=encoder_out + ) + return decoder_out + + +class ConvTransformerEncoder(FairseqEncoder): + """Conv + Transformer encoder""" + + def __init__(self, args): + """Construct an Encoder object.""" + super().__init__(None) + + self.dropout = args.dropout + self.embed_scale = ( + 1.0 if args.no_scale_embedding else math.sqrt(args.encoder_embed_dim) + ) + self.padding_idx = 1 + self.in_channels = 1 + self.input_dim = args.input_feat_per_channel + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, args.conv_out_channels, 3, stride=2, padding=3 // 2), + torch.nn.ReLU(), + torch.nn.Conv2d( + args.conv_out_channels, + args.conv_out_channels, + 3, + stride=2, + padding=3 // 2, + ), + torch.nn.ReLU(), + ) + transformer_input_dim = infer_conv_output_dim( + self.in_channels, self.input_dim, args.conv_out_channels + ) + self.out = torch.nn.Linear(transformer_input_dim, args.encoder_embed_dim) + self.embed_positions = PositionalEmbedding( + args.max_source_positions, + args.encoder_embed_dim, + self.padding_idx, + learned=False, + ) + + self.transformer_layers = nn.ModuleList([]) + self.transformer_layers.extend( + [TransformerEncoderLayer(args) for i in range(args.encoder_layers)] + ) + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(args.encoder_embed_dim) + else: + self.layer_norm = None + + def pooling_ratio(self): + return 4 + + def forward(self, src_tokens, src_lengths): + """Encode input sequence. + :param torch.Tensor xs: input tensor + :param torch.Tensor masks: input mask + :return: position embedded tensor and mask + :rtype Tuple[torch.Tensor, torch.Tensor]: + """ + bsz, max_seq_len, _ = src_tokens.size() + x = ( + src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) + .transpose(1, 2) + .contiguous() + ) + x = self.conv(x) + bsz, _, output_seq_len, _ = x.size() + x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) + x = self.out(x) + x = self.embed_scale * x + + subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5) + input_len_0 = (src_lengths.float() / subsampling_factor).ceil().long() + input_len_1 = x.size(0) * torch.ones([src_lengths.size(0)]).long().to( + input_len_0.device + ) + input_lengths = torch.min(input_len_0, input_len_1) + + encoder_padding_mask = lengths_to_padding_mask(input_lengths) + + positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + for layer in self.transformer_layers: + x = layer(x, encoder_padding_mask) + + if not encoder_padding_mask.any(): + maybe_encoder_padding_mask = None + else: + maybe_encoder_padding_mask = encoder_padding_mask + + return { + "encoder_out": [x], + "encoder_padding_mask": [maybe_encoder_padding_mask] + if maybe_encoder_padding_mask is not None + else [], + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + @torch.jit.export + def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)] + if len(encoder_out["encoder_padding_mask"]) == 0: + new_encoder_padding_mask = [] + else: + new_encoder_padding_mask = [ + (encoder_out["encoder_padding_mask"][0]).index_select(0, new_order) + ] + if len(encoder_out["encoder_embedding"]) == 0: + new_encoder_embedding = [] + else: + new_encoder_embedding = [ + (encoder_out["encoder_embedding"][0]).index_select(0, new_order) + ] + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, + "encoder_padding_mask": new_encoder_padding_mask, + "encoder_embedding": new_encoder_embedding, + "encoder_states": encoder_states, + "src_tokens": [], + "src_lengths": [], + } + + +class TransformerDecoderNoExtra(TransformerDecoder): + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + # call scriptable method from parent class + x, _ = self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + return x, None + + +@register_model_architecture(model_name="convtransformer", arch_name="convtransformer") +def base_architecture(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.max_source_positions = getattr(args, "max_source_positions", 3000) + args.max_target_positions = getattr(args, "max_target_positions", 1024) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.conv_out_channels = getattr(args, "conv_out_channels", args.encoder_embed_dim) + + +@register_model_architecture("convtransformer", "convtransformer_espnet") +def convtransformer_espnet(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) diff --git a/fairseq/fairseq/models/speech_to_text/hub_interface.py b/fairseq/fairseq/models/speech_to_text/hub_interface.py new file mode 100644 index 0000000..d78427f --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/hub_interface.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from argparse import Namespace +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import fairseq.data.audio.feature_transforms.utterance_cmvn as utt_cmvn +from fairseq.data import encoders +from fairseq.data.audio.audio_utils import convert_waveform as convert_wav +from fairseq.data.audio.audio_utils import get_fbank +from fairseq.data.audio.audio_utils import get_waveform as get_wav +from fairseq.data.audio.speech_to_text_dataset import SpeechToTextDataset + +logger = logging.getLogger(__name__) + + +class S2THubInterface(nn.Module): + def __init__(self, cfg, task, model): + super().__init__() + self.cfg = cfg + self.task = task + self.model = model + self.model.eval() + self.generator = self.task.build_generator([self.model], self.cfg.generation) + + @classmethod + def get_model_input(cls, task, audio: Union[str, torch.Tensor]): + input_type = task.data_cfg.hub.get("input_type", "fbank80") + if input_type == "fbank80_w_utt_cmvn": + if isinstance(audio, str): + feat = utt_cmvn.UtteranceCMVN()(get_fbank(audio)) + feat = feat.unsqueeze(0) # T x D -> 1 x T x D + else: + import torchaudio.compliance.kaldi as kaldi + + feat = kaldi.fbank(audio, num_mel_bins=80).numpy() # 1 x T x D + elif input_type in {"waveform", "standardized_waveform"}: + if isinstance(audio, str): + feat, sr = get_wav(audio) # C x T + feat, _ = convert_wav( + feat, sr, to_sample_rate=16_000, to_mono=True + ) # C x T -> 1 x T + else: + feat = audio.numpy() + else: + raise ValueError(f"Unknown value: input_type = {input_type}") + + src_lengths = torch.Tensor([feat.shape[1]]).long() + src_tokens = torch.from_numpy(feat) # 1 x T (x D) + if input_type == "standardized_waveform": + with torch.no_grad(): + src_tokens = F.layer_norm(src_tokens, src_tokens.shape) + + return { + "net_input": { + "src_tokens": src_tokens, + "src_lengths": src_lengths, + "prev_output_tokens": None, + }, + "target_lengths": None, + "speaker": None, + } + + @classmethod + def detokenize(cls, task, tokens): + text = task.tgt_dict.string(tokens) + tkn_cfg = task.data_cfg.bpe_tokenizer + tokenizer = encoders.build_bpe(Namespace(**tkn_cfg)) + return text if tokenizer is None else tokenizer.decode(text) + + @classmethod + def get_prefix_token(cls, task, lang): + prefix_size = int(task.data_cfg.prepend_tgt_lang_tag) + prefix_tokens = None + if prefix_size > 0: + assert lang is not None + lang_tag = SpeechToTextDataset.get_lang_tag_idx(lang, task.tgt_dict) + prefix_tokens = torch.Tensor([lang_tag]).long().unsqueeze(0) + return prefix_tokens + + @classmethod + def get_prediction( + cls, task, model, generator, sample, tgt_lang=None, synthesize_speech=False + ) -> Union[str, Tuple[str, Tuple[torch.Tensor, int]]]: + _tgt_lang = tgt_lang or task.data_cfg.hub.get("tgt_lang", None) + prefix = cls.get_prefix_token(task, _tgt_lang) + pred_tokens = generator.generate([model], sample, prefix_tokens=prefix) + pred = cls.detokenize(task, pred_tokens[0][0]["tokens"]) + eos_token = task.data_cfg.config.get("eos_token", None) + if eos_token: + pred = " ".join(pred.split(" ")[:-1]) + + if synthesize_speech: + pfx = f"{_tgt_lang}_" if task.data_cfg.prepend_tgt_lang_tag else "" + tts_model_id = task.data_cfg.hub.get(f"{pfx}tts_model_id", None) + speaker = task.data_cfg.hub.get(f"{pfx}speaker", None) + if tts_model_id is None: + logger.warning("TTS model configuration not found") + else: + _repo, _id = tts_model_id.split(":") + tts_model = torch.hub.load(_repo, _id, verbose=False) + pred = (pred, tts_model.predict(pred, speaker=speaker)) + return pred + + def predict( + self, + audio: Union[str, torch.Tensor], + tgt_lang: Optional[str] = None, + synthesize_speech: bool = False, + ) -> Union[str, Tuple[str, Tuple[torch.Tensor, int]]]: + # `audio` is either a file path or a 1xT Tensor + # return either text or (text, synthetic speech) + sample = self.get_model_input(self.task, audio) + return self.get_prediction( + self.task, + self.model, + self.generator, + sample, + tgt_lang=tgt_lang, + synthesize_speech=synthesize_speech, + ) diff --git a/fairseq/fairseq/models/speech_to_text/modules/__init__.py b/fairseq/fairseq/models/speech_to_text/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/fairseq/models/speech_to_text/modules/augmented_memory_attention.py b/fairseq/fairseq/models/speech_to_text/modules/augmented_memory_attention.py new file mode 100644 index 0000000..2d330f9 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/modules/augmented_memory_attention.py @@ -0,0 +1,487 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from fairseq.models import FairseqEncoder +from fairseq.models.speech_to_text import ConvTransformerEncoder +from fairseq.models.speech_to_text.utils import ( + attention_suppression, + lengths_to_encoder_padding_mask, + segments_to_sequence, + sequence_to_segments, +) +from fairseq.modules import MultiheadAttention, TransformerEncoderLayer + +# ------------------------------------------------------------------------------ +# AugmentedMemoryConvTransformerEncoder +# ------------------------------------------------------------------------------ + + +class AugmentedMemoryConvTransformerEncoder(ConvTransformerEncoder): + def __init__(self, args): + super().__init__(args) + + args.encoder_stride = self.stride() + + self.left_context = args.left_context // args.encoder_stride + + self.right_context = args.right_context // args.encoder_stride + + self.left_context_after_stride = args.left_context // args.encoder_stride + self.right_context_after_stride = args.right_context // args.encoder_stride + + self.transformer_layers = nn.ModuleList([]) + self.transformer_layers.extend( + [ + AugmentedMemoryTransformerEncoderLayer(args) + for i in range(args.encoder_layers) + ] + ) + + def stride(self): + # Hard coded here. Should infer from convs in future + stride = 4 + return stride + + def forward(self, src_tokens, src_lengths, states=None): + """Encode input sequence. + :param torch.Tensor xs: input tensor + :param torch.Tensor masks: input mask + :return: position embedded tensor and mask + :rtype Tuple[torch.Tensor, torch.Tensor]: + """ + bsz, max_seq_len, _ = src_tokens.size() + x = ( + src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) + .transpose(1, 2) + .contiguous() + ) + x = self.conv(x) + bsz, _, output_seq_len, _ = x.size() + x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) + x = self.out(x) + x = self.embed_scale * x + + subsampling_factor = 1.0 * max_seq_len / output_seq_len + input_lengths = torch.max( + (src_lengths.float() / subsampling_factor).ceil().long(), + x.size(0) * src_lengths.new_ones([src_lengths.size(0)]).long(), + ) + + encoder_padding_mask, _ = lengths_to_encoder_padding_mask( + input_lengths, batch_first=True + ) + + # TODO: fix positional embedding + positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) + + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + # State to store memory banks etc. + if states is None: + states = [ + {"memory_banks": None, "encoder_states": None} + for i in range(len(self.transformer_layers)) + ] + + for i, layer in enumerate(self.transformer_layers): + # x size: + # (self.left_size + self.segment_size + self.right_size) + # / self.stride, num_heads, dim + # TODO: Consider mask here + x = layer(x, states[i]) + states[i]["encoder_states"] = x[ + self.left_context_after_stride : -self.right_context_after_stride + ] + + lengths = ( + ( + ~encoder_padding_mask[ + :, self.left_context_after_stride : -self.right_context_after_stride + ] + ) + .sum(dim=1, keepdim=True) + .long() + ) + + return states[-1]["encoder_states"], lengths, states + + +# ------------------------------------------------------------------------------ +# AugmentedMemoryTransformerEncoderLayer +# ------------------------------------------------------------------------------ +class AugmentedMemoryTransformerEncoderLayer(TransformerEncoderLayer): + def __init__(self, args): + super().__init__(args) + + self.left_context = args.left_context // args.encoder_stride + self.right_context = args.right_context // args.encoder_stride + + def forward(self, x, state): + + length, batch_size, x_dim = x.size() + + residual = x + + if self.normalize_before: + x = self.self_attn_layer_norm(x) + + # init_state + if state.get("memory_banks", None) is None: + state["memory_banks"] = [] + + # TODO reseach new sum_query method + seg_start = self.left_context + seg_end = length - self.right_context + if seg_start < seg_end: + summarization_query = torch.mean(x[seg_start:seg_end], keepdim=True, dim=0) + else: + summarization_query = x.new_zeros(1, batch_size, x_dim) + + x = torch.cat([x, summarization_query], dim=0) + + x = self.self_attn(input_and_summary=x, state=state) + + x = self.dropout_module(x) + x = residual + x + + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = residual + x + if not self.normalize_before: + x = self.final_layer_norm(x) + + return x + + def build_self_attention(self, embed_dim, args): + return AugmentedMemoryMultiheadAttention( + embed_dim=embed_dim, + num_heads=args.encoder_attention_heads, + dropout=args.attention_dropout, + self_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + tanh_on_mem=True, + max_memory_size=args.max_memory_size, + ) + + +# ------------------------------------------------------------------------------ +# AugmentedMemoryMultiheadAttention +# ------------------------------------------------------------------------------ +class AugmentedMemoryMultiheadAttention(MultiheadAttention): + """ + Augmented Memory Attention from + Streaming Transformer-based Acoustic Models + Using Self-attention with Augmented Memory + https://arxiv.org/abs/2005.08042 + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + q_noise=0.0, + qn_block_size=8, + tanh_on_mem=False, + memory_dim=None, + std_scale=0.5, # 0.5 based on https://arxiv.org/abs/2005.09137 + max_memory_size=-1, + disable_mem_on_mem_attn=True, + ): + super().__init__( + embed_dim, + num_heads, + kdim, + vdim, + dropout, + bias, + add_bias_kv, + add_zero_attn, + self_attention, + encoder_decoder_attention, + q_noise, + qn_block_size, + ) + + self.memory_dim = memory_dim if memory_dim is not None else embed_dim + self.std_scale = std_scale + self.disable_mem_on_mem_attn = disable_mem_on_mem_attn + + # This Operator was used for factorization in PySpeech + self.v2e = lambda x: x + + if tanh_on_mem: + self.squash_mem = torch.tanh + self.nonlinear_squash_mem = True + else: + self.squash_mem = lambda x: x + self.nonlinear_squash_mem = False + + self.max_memory_size = max_memory_size + + def forward(self, input_and_summary, state): + """ + input: Encoder states of current segment with left or right context, + plus one summarization query + + """ + + length, batch_size, _ = input_and_summary.shape + length = length - 1 # not include sum_query, last index + + memory = state["memory_banks"] + # TODO: positional embedding on memory + + if self.max_memory_size > -1 and len(memory) > self.max_memory_size: + # TODO: need to fix here + if self.max_memory_size == 0: + memory = memory.new_zeros(1, memory.size(1), self.memory_dim) + else: + memory = memory[-self.max_memory_size :] + + memory_and_input = torch.cat(memory + [input_and_summary[:-1]], dim=0) + input_and_sum_query = input_and_summary + + q = self.q_proj(self.v2e(input_and_sum_query)) + k = self.k_proj(self.v2e(memory_and_input)) + v = self.v_proj(self.v2e(memory_and_input)) + + q = ( + q.contiguous() + .view(-1, batch_size * self.num_heads, self.head_dim) + .transpose(0, 1) + * self.scaling + ) + k = ( + k.contiguous() + .view(-1, batch_size * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + v = ( + v.contiguous() + .view(-1, batch_size * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + attention_weights = torch.bmm(q, k.transpose(1, 2)) + + if self.disable_mem_on_mem_attn: + attention_weights = self.suppress_mem_on_mem_attention( + batch_size, self.num_heads, len(memory), attention_weights + ) + + if self.std_scale is not None: + attention_weights = attention_suppression(attention_weights, self.std_scale) + + assert list(attention_weights.shape) == [ + batch_size * self.num_heads, + length + 1, + length + len(memory), + ] + + attention_weights = torch.nn.functional.softmax( + attention_weights.float(), dim=-1 + ).type_as(attention_weights) + + attention_probs = self.dropout_module(attention_weights) + + # [T, T, B, n_head] + [T, B, n_head, d_head] -> [T, B, n_head, d_head] + attention = torch.bmm(attention_probs, v) + + assert list(attention.shape) == [ + batch_size * self.num_heads, + length + 1, + self.head_dim, + ] + + attention = ( + attention.transpose(0, 1) + .contiguous() + .view(length + 1, batch_size, self.embed_dim) + ) + + output_and_memory = self.out_proj(attention) + + next_m = output_and_memory[-1:] + next_m = self.squash_mem(next_m) + output = output_and_memory[:-1] + + state["memory_banks"].append(next_m) + + return output + + def suppress_mem_on_mem_attention( + self, B: int, num_heads: int, mem_size: int, attention_weight: Tensor + ): + """ + Arguments: + - B: batch size + - num_heads: number of attention heads + - mem_size: size of memory bank + - attention_weight: a [B*num_heads, T + 1, T + mem_size] vector + + Return: + modified attention_weight with [B*num_heads, -1, :mem_size] = -inf + """ + attention_weight[:, -1, :mem_size] = float("-inf") + return attention_weight + + +# ------------------------------------------------------------------------------ +# SequenceEncoder +# ------------------------------------------------------------------------------ +class SequenceEncoder(FairseqEncoder): + """ + SequenceEncoder encodes sequences. + + More specifically, `src_tokens` and `src_lengths` in `forward()` should + describe a batch of "complete" sequences rather than segments. + + Segment-by-segment inference can be triggered by `segment_size`: + 1) `segment_size` is None: + SequenceEncoder treats the input sequence as one single segment. + 2) `segment_size` is not None (some int instead): + SequenceEncoder does the following: + 1. breaks the input sequence into several segments + 2. inference on each segment and collect the outputs + 3. concatanete segment outputs into the output sequence. + Note that `segment_size` here shouldn't include additional left/right + contexts needed, for example if we wish to infer with LC-BLSTM where the + middle chunk size is 100 and right context is 20, `segment_size` should be + 100. + """ + + def __init__(self, args, module): + super().__init__(None) + + self.module = module + self.input_time_axis = 1 + self.output_time_axis = 0 + self.segment_size = args.segment_size + self.left_context = args.left_context + self.right_context = args.right_context + + def forward( + self, + src_tokens: Tensor, + src_lengths: Tensor, + states=None, + ): + + seg_src_tokens_lengths = sequence_to_segments( + sequence=src_tokens, + time_axis=self.input_time_axis, + lengths=src_lengths, + segment_size=self.segment_size, + extra_left_context=self.left_context, + extra_right_context=self.right_context, + ) + + seg_encoder_states_lengths: List[Tuple[Tensor, Tensor]] = [] + + for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths: + (seg_encoder_states, seg_enc_lengths, states) = self.module( + seg_src_tokens, + seg_src_lengths, + states=states, + ) + + seg_encoder_states_lengths.append((seg_encoder_states, seg_enc_lengths)) + + encoder_out, enc_lengths = segments_to_sequence( + segments=seg_encoder_states_lengths, time_axis=self.output_time_axis + ) + + encoder_padding_mask, _ = lengths_to_encoder_padding_mask( + enc_lengths, batch_first=True + ) + + if not encoder_padding_mask.any(): + encoder_padding_mask = None + + return { + "encoder_out": [encoder_out], + "encoder_padding_mask": [encoder_padding_mask], + "encoder_embedding": [], + "encoder_states": [states], + "src_tokens": [], + "src_lengths": [], + } + + def incremental_encode( + self, + seg_src_tokens: Tensor, + seg_src_lengths: Tensor, + states=None, + ): + """ + Different from forward function, this function takes segmented speech + as input, and append encoder states to previous states + """ + (seg_encoder_states, seg_enc_lengths, states) = self.module( + seg_src_tokens, + seg_src_lengths, + states=states, + ) + return seg_encoder_states, seg_enc_lengths, states + + +# ------------------------------------------------------------------------------ +# Augmented memory model decorator +# ------------------------------------------------------------------------------ +def augmented_memory(klass): + class StreamSeq2SeqModel(klass): + @staticmethod + def add_args(parser): + super(StreamSeq2SeqModel, StreamSeq2SeqModel).add_args(parser) + parser.add_argument( + "--segment-size", type=int, required=True, help="Length of the segment." + ) + parser.add_argument( + "--left-context", + type=int, + default=0, + help="Left context for the segment.", + ) + parser.add_argument( + "--right-context", + type=int, + default=0, + help="Right context for the segment.", + ) + parser.add_argument( + "--max-memory-size", + type=int, + default=-1, + help="Right context for the segment.", + ) + + StreamSeq2SeqModel.__name__ = klass.__name__ + return StreamSeq2SeqModel diff --git a/fairseq/fairseq/models/speech_to_text/modules/convolution.py b/fairseq/fairseq/models/speech_to_text/modules/convolution.py new file mode 100644 index 0000000..526d754 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/modules/convolution.py @@ -0,0 +1,126 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from typing import List + +import torch +import torch.nn as nn + + +class Conv1dSubsampler(nn.Module): + """Convolutional subsampler: a stack of 1D convolution (along temporal + dimension) followed by non-linear activation via gated linear units + (https://arxiv.org/abs/1911.08460) + + Args: + in_channels (int): the number of input channels + mid_channels (int): the number of intermediate channels + out_channels (int): the number of output channels + kernel_sizes (List[int]): the kernel size for each convolutional layer + """ + + def __init__( + self, + in_channels: int, + mid_channels: int, + out_channels: int, + kernel_sizes: List[int] = (3, 3), + ): + super(Conv1dSubsampler, self).__init__() + self.n_layers = len(kernel_sizes) + self.conv_layers = nn.ModuleList( + nn.Conv1d( + in_channels if i == 0 else mid_channels // 2, + mid_channels if i < self.n_layers - 1 else out_channels * 2, + k, + stride=2, + padding=k // 2, + ) + for i, k in enumerate(kernel_sizes) + ) + + def get_out_seq_lens_tensor(self, in_seq_lens_tensor): + out = in_seq_lens_tensor.clone() + for _ in range(self.n_layers): + out = ((out.float() - 1) / 2 + 1).floor().long() + return out + + def forward(self, src_tokens, src_lengths): + bsz, in_seq_len, _ = src_tokens.size() # B x T x (C x D) + x = src_tokens.transpose(1, 2).contiguous() # -> B x (C x D) x T + for conv in self.conv_layers: + x = conv(x) + x = nn.functional.glu(x, dim=1) + _, _, out_seq_len = x.size() + x = x.transpose(1, 2).transpose(0, 1).contiguous() # -> T x B x (C x D) + return x, self.get_out_seq_lens_tensor(src_lengths) + + +def infer_conv_output_dim(in_channels, input_dim, out_channels): + sample_seq_len = 200 + sample_bsz = 10 + x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim) + x = torch.nn.Conv2d(in_channels, out_channels, 3, stride=2, padding=3 // 2)(x) + x = torch.nn.Conv2d(out_channels, out_channels, 3, stride=2, padding=3 // 2)(x) + x = x.transpose(1, 2) + mb, seq = x.size()[:2] + return x.contiguous().view(mb, seq, -1).size(-1) + + +class Conv2dSubsampler(nn.Module): + """Convolutional subsampler: a stack of 2D convolution based on ESPnet implementation + (https://github.com/espnet/espnet) + + Args: + input_channels (int): the number of input channels + input_feat_per_channel (int): encoder input dimension per input channel + conv_out_channels (int): the number of output channels of conv layer + encoder_embed_dim (int): encoder dimentions + """ + + def __init__( + self, + input_channels: int, + input_feat_per_channel: int, + conv_out_channels: int, + encoder_embed_dim: int, + ): + super().__init__() + assert input_channels == 1, input_channels + self.conv = torch.nn.Sequential( + torch.nn.Conv2d( + input_channels, conv_out_channels, 3, stride=2, padding=3 // 2 + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_out_channels, + conv_out_channels, + 3, + stride=2, + padding=3 // 2, + ), + torch.nn.ReLU(), + ) + transformer_input_dim = infer_conv_output_dim( + input_channels, input_feat_per_channel, conv_out_channels + ) + self.out = torch.nn.Linear(transformer_input_dim, encoder_embed_dim) + + def forward(self, src_tokens, src_lengths): + B, T_i, C = src_tokens.size() + x = src_tokens.view(B, T_i, 1, C).transpose(1, 2).contiguous() + x = self.conv(x) + B, _, T_o, _ = x.size() + x = x.transpose(1, 2).transpose(0, 1).contiguous().view(T_o, B, -1) + x = self.out(x) + + subsampling_factor = int(T_i * 1.0 / T_o + 0.5) + input_len_0 = (src_lengths.float() / subsampling_factor).ceil().long() + input_len_1 = x.size(0) * torch.ones([src_lengths.size(0)]).long().to( + input_len_0.device + ) + input_lengths = torch.min(input_len_0, input_len_1) + return x, input_lengths diff --git a/fairseq/fairseq/models/speech_to_text/modules/emformer.py b/fairseq/fairseq/models/speech_to_text/modules/emformer.py new file mode 100644 index 0000000..935d593 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/modules/emformer.py @@ -0,0 +1,1844 @@ +#!/usr/bin/env python3 +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + + +import math +import re +from functools import partial +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from torch import Tensor +from torch import device as Device + +from fairseq.models import FairseqEncoder +from fairseq.models.speech_to_text.utils import ( + NoOp, + attention_suppression, + layer_norm_backward_hook, + lengths_to_padding_mask, + segments_to_sequence, +) + +try: + import torch.ao.quantization as quantization + from torch.ao.quantization.qconfig import ( + default_dynamic_qconfig, + per_channel_dynamic_qconfig, + ) +except ImportError: + import torch.quantization as quantization + from torch.quantization.qconfig import ( + default_dynamic_qconfig, + per_channel_dynamic_qconfig, + ) + + +class RelativePositionEmbedding(nn.Module): + """ + Implementation according to https://arxiv.org/abs/1803.02155 + """ + + def __init__(self, head_dim, max_position, norm_init=True): + super().__init__() + self.head_dim = head_dim + self.max_position = max_position + self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim)) + if norm_init: + nn.init.xavier_normal_(self.embeddings) + else: + nn.init.xavier_uniform_(self.embeddings) + + def forward(self, input: Tensor): + output = nn.functional.embedding(input.long(), self.embeddings) + return output + + +class Fp32LayerNorm(nn.Module): + def __init__( + self, + input_dim, + clamp_grad=True, + max_grad_value=256, + eps=1e-5, + elementwise_affine=True, + ): + super().__init__() + self.torch_module = torch.nn.LayerNorm( + input_dim, eps=eps, elementwise_affine=elementwise_affine + ) + if clamp_grad: + hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value) + self.torch_module.register_backward_hook(hook) + + def forward(self, input): + output = torch.nn.functional.layer_norm( + input.float(), + self.torch_module.normalized_shape, + self.torch_module.weight.float() + if self.torch_module.weight is not None + else None, + self.torch_module.bias.float() + if self.torch_module.bias is not None + else None, + self.torch_module.eps, + ).type_as(input) + return output + + +# ------------------------------------------------------------------------------ +# PositionwiseFF +# ------------------------------------------------------------------------------ + + +class PositionwiseFF(nn.Module): + """ + FFN layer in transformer. + + Args: + input_dim: input embedding dimension + ffn_dim: FFN layer inner dimension + dropout_on_fc1: dropout for first linear layer + dropout_on_fc2: dropout fr second linear layer + activation_fn: activation function used after first linear layer. \ + Only relu or gelu is supported. + + """ + + def __init__( + self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn + ): + super(PositionwiseFF, self).__init__() + + self.input_dim = input_dim + self.ffn_dim = ffn_dim + if activation_fn == "relu": + ac = nn.ReLU() + elif activation_fn == "gelu": + ac = nn.GELU() + else: + raise ValueError("Unsupported activation_fn = ({})".format(activation_fn)) + + # fc1 -> ac -> dropout -> fc2 -> dropout + self.module = nn.Sequential( + nn.Linear(input_dim, ffn_dim), + ac, + nn.Dropout(dropout_on_fc1), + nn.Linear(ffn_dim, input_dim), + nn.Dropout(dropout_on_fc2), + ) + + self.layer_norm = Fp32LayerNorm(input_dim) + + def forward(self, input): + module_out = self.module(self.layer_norm(input)) + output = module_out + input + + return output + + def quantize_(self, params=None): + if params and "per_channel" in params and params["per_channel"]: + qconfig = per_channel_dynamic_qconfig + else: + qconfig = default_dynamic_qconfig + quantization.quantize_dynamic( + self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True + ) + return self + + +# ------------------------------------------------------------------------------ +# SummarizationLayer +# ------------------------------------------------------------------------------ + + +class SummarizationLayer(nn.Module): + def __init__(self, method, segment_size, embedding_dim): + super(SummarizationLayer, self).__init__() + self.segment_size = segment_size + self.embedding_dim = embedding_dim + nonlin_match = re.match(r"nonlinear\((?P<act>[a-z]+),(?P<dim>[0-9]+)\)", method) + self.method = method + if method == "mean": + self.module = nn.AvgPool1d( + kernel_size=segment_size, + stride=segment_size, + ceil_mode=True, + ) + elif method == "max": + self.module = nn.MaxPool1d( + kernel_size=segment_size, + stride=segment_size, + ceil_mode=True, + ) + elif method == "linear": + self.module = nn.Linear(segment_size, 1) + elif nonlin_match: + nonlin_args = nonlin_match.groupdict() + act_type = nonlin_args["act"] + hid_dim = int(nonlin_args["dim"]) + if act_type == "relu": + act = nn.ReLU() + elif act_type == "gelu": + act = nn.GELU() + else: + raise ValueError("Unsupported activation_fn = ({})".format(act_type)) + self.module = nn.Sequential( + nn.Linear(segment_size, hid_dim), + act, + nn.Linear(hid_dim, 1), + ) + else: + raise ValueError("Unsupported summarization method = ({})".format(method)) + + def forward(self, input): + # T, B, D -> B, D, T + input = input.permute(1, 2, 0) + + if self.method == "mean" or self.method == "max": + output = self.module(input) + output = output.permute(2, 0, 1) + return output + + full_seg_length = input.size(2) // self.segment_size * self.segment_size + if full_seg_length > 0: + # at least one seg is full + B = input.size(0) + D = input.size(1) + input_todo = ( + input[:, :, :full_seg_length] + .contiguous() + .view(B, -1, self.segment_size) + ) + output = self.module(input_todo) + output = output.view(B, D, -1) + else: + output = input.new_zeros(input.size(0), input.size(1), 0) + left = input.size(2) - full_seg_length + if left > 0: + # when last seg is not full, use zeros as last memory placeholder + zeros = input.new_zeros(input.size(0), input.size(1), 1) + output = torch.cat([output, zeros], dim=2) + output = output.permute(2, 0, 1) + return output + + +# ------------------------------------------------------------------------------ +# NoSegAugmentedMemoryMultiheadAttentionBmm +# ------------------------------------------------------------------------------ + + +class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module): + """ + Whole utterance augmented memory multihead attention using BMM. + + Different with previous augmented memory multihead attention where + the utterance is chunked into segments. Here we use attention mask + achieve so. The input embedding [right_context, utterance, summary] + is a concatenation of right context, utterance and summary. + + Right context block is the concatenation of all the right context for + each segments. [right_context_0, right_context_1, ..., right_context_n] + For example, if we have utterance = [v0, v1, v2, ...., v20]. segment + size 8, right_context size 4. Then the right context blocks = + [v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10, + and v11 are the right context for first segment. v16, v17, v18 and v19 + are the right context for second segment. 0, 0, 0 and 0 are right context + for the last segment. + + utterance is corresponding to input embedding sequence + + summary is concatenation of average of each segments. [summary_0, + summary_1, ..., ]. + + In augmented memory multihead attention, the query is [right_context, + utterance, summary], key is [memory, right_context, utterance]. Different + with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from + previous attention layer. For the first attention layer, memory is average + of each segment. + + Memory is a concatenation of memory from each segments in previous attention + layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n]. + Each m_k is the output from seg_k in layer i-1. + + args: + input_dim: input embedding dimension + num_heads: number of heads in multihead self-attention + dropout: attention dropout + std_scale: if std_scale is not None. The weak attention suppression is + turned on. For std_scale = 0.5, all the attention smaller than + mean + 0.5 * std will be suppressed. + scaled_init: whether to use scaled init for linear weight + tanh_on_mem: whether to use tanh on memory output + use_mem: whether to use memory or not. When max_memory_size is 0, then + we don't have memory anymore. + layer_index: current self-attention layer index that is used in depth + initialization + max_relative_position: max relative position used in relative position + embedding + rpe_old_option: To be compatible with previous model. The previous model + was trained with attention += attention + rpe. The correct equation + should be attention = attention + rpe + + """ + + def __init__( + self, + input_dim, + num_heads, + dropout=0.0, + std_scale=None, + scaled_init=False, + tanh_on_mem=False, + use_mem=True, + mini_batches=False, + negative_inf="-inf", + layer_index=-1, + max_relative_position=0, + rpe_old_option=True, + ): + if input_dim % num_heads: + raise ValueError( + "input_dim ({}) must be divisible by num_heads ({})".format( + input_dim, num_heads + ) + ) + + super().__init__() + + embed_dim = input_dim + self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True) + self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True) + self.rpe_old_option = rpe_old_option + if max_relative_position > 0: + self.use_rpe = True + self.rpe_k = RelativePositionEmbedding( + head_dim=input_dim // num_heads, + max_position=max_relative_position, + ) + self.rpe_v = RelativePositionEmbedding( + head_dim=input_dim // num_heads, + max_position=max_relative_position, + ) + else: + self.use_rpe = False + self.rpe_k = None + self.rpe_v = None + if scaled_init: + if layer_index == -1: + gain = 1.0 / math.sqrt(2) + else: + # https://arxiv.org/abs/2005.09684 depthwise initialization + # stablize the training greatly. Use depthwise initialization to + # replace incremental loss. + gain = 1.0 / math.sqrt(layer_index + 1) + torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain) + torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain) + + self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True) + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + + self.head_dim = embed_dim // num_heads + self.scaling = self.head_dim**-0.5 + + self.std_scale = std_scale + self.use_mem = use_mem + self.mini_batches = mini_batches + self.negative_inf = negative_inf + + if tanh_on_mem: + self.squash_mem = torch.tanh + self.nonlinear_squash_mem = True + else: + self.squash_mem = NoOp() + self.nonlinear_squash_mem = False + + def prepare_qkv( + self, + input: Tensor, + mems: Tensor, + lengths: Tensor, + summary_length: int, + lc_length: int, + ): + # T: right_context length + utterance_length + summary_length + T, B, D = input.shape + mem_length = mems.size(0) + utterance_length = torch.max(lengths) + + right_context_blocks_length = T - utterance_length - summary_length + rc_block = input[:right_context_blocks_length, :, :] + utterance_block = input[right_context_blocks_length : T - summary_length, :, :] + + if B == 1: + padding_mask = None + else: + klengths = lengths + mem_length + right_context_blocks_length + lc_length + padding_mask = lengths_to_padding_mask(lengths=klengths) + + mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0) + + # In training lc_length = 0 + key_length = mem_rc_input.size(0) + lc_length + rc_input_sum = input + q = self.e2h_q(rc_input_sum) + kv = self.e2h_kv(mem_rc_input) + k, v = kv.chunk(chunks=2, dim=2) + result_qkv = (q, k, v) + input_shape = (T, B, D) + result_lengths_info = ( + mem_length, + utterance_length, + right_context_blocks_length, + key_length, + ) + if padding_mask is not None: + assert padding_mask.size(0) == B + assert padding_mask.size(1) == key_length + + return result_qkv, input_shape, result_lengths_info, padding_mask + + def prepare_attention_weights( + self, + q: Tensor, + new_k: Tensor, + new_v: Tensor, + input_shape: Tuple[int, int, int], + rpe: Optional[Tensor], + ) -> Tuple[Tensor, Tensor, Tensor]: + T, B, D = input_shape + q = ( + q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1) + * self.scaling + ) + + k = ( + new_k.contiguous() + .view(-1, B * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + v = ( + new_v.contiguous() + .view(-1, B * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + attention_weights = torch.bmm(q, k.transpose(1, 2)) + if self.use_rpe and rpe is not None and self.rpe_v is not None: + r_k = self.rpe_k(rpe) + # [q, B*h, d] * [q, k, d] -> [B*h, q, k] + attention_weights_rpe = torch.matmul( + q.transpose(0, 1), r_k.transpose(1, 2) + ).transpose(0, 1) + attention_weights = attention_weights + attention_weights_rpe + attention_weights_float = attention_weights.float() + + return attention_weights, attention_weights_float, v + + def prepare_attention_output( + self, + attention_weights: Tensor, + attention_weights_float: Tensor, + v: Tensor, + input_shape: Tuple[int, int, int], + key_length: int, + padding_mask: Optional[Tensor], + rpe: Optional[Tensor], + ) -> Tensor: + T, B, D = input_shape + if padding_mask is not None: + attention_weights_float = attention_weights_float.view( + B, self.num_heads, T, key_length + ) + attention_weights_float = attention_weights_float.masked_fill( + padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf") + ) + attention_weights_float = attention_weights_float.view( + B * self.num_heads, T, key_length + ) + + if self.std_scale is not None: + attention_weights_float = attention_suppression( + attention_weights_float, self.std_scale + ) + + attention_weights_float = torch.nn.functional.softmax( + attention_weights_float, dim=-1 + ) + attention_weights = attention_weights_float.type_as(attention_weights) + + attention_probs = torch.nn.functional.dropout( + attention_weights, p=self.dropout, training=self.training + ) + + # [T, key_length, B, n_head]+ [key_length, B, n_head, d_head] + # -> [T, B, n_head, d_head] + attention = torch.bmm(attention_probs, v) + if self.use_rpe and rpe is not None and self.rpe_v is not None: + r_v = self.rpe_v(rpe) + attention_rpe = torch.matmul( + attention_probs.transpose(0, 1), r_v + ).transpose(0, 1) + + if self.rpe_old_option: + attention += attention + attention_rpe + else: + attention = attention + attention_rpe + + assert list(attention.shape) == [B * self.num_heads, T, self.head_dim] + + attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim) + + rc_output_memory = self.out_proj(attention) + return rc_output_memory + + @torch.jit.unused + def forward( + self, + input: Tensor, + lengths: Tensor, + mems: Tensor, + attention_mask: Tensor, + pre_mems: Optional[Tensor] = None, + left_context_key: Optional[Tensor] = None, + left_context_val: Optional[Tensor] = None, + rpe: Optional[Tensor] = None, + ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + """ + forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training. + + args: + input: formed in the following way + [right_context_0, right_contex_1, ..., seg_0, seg_1, + ..., summary_0, summary_1,..] + lengths: the length of query which is [seg_0, seg_1, ....] + mems: [mem_0, mem_1, ...]. + attention_mask: attention mask for query = [right_context, query, summary] + key = [mem, right_context, query]. This is only used for traing. + + """ + if self.use_mem: + mem_length = mems.size(0) + summary_length = mem_length + 1 + if pre_mems is not None: + mems = torch.cat([pre_mems, mems], dim=0) + else: + mem_length = 0 + summary_length = 0 + + # In training, lc_length = 0 + if left_context_key is not None: + lc_length = left_context_key.size(0) + else: + lc_length = 0 + results = self.prepare_qkv( + input=input, + mems=mems, + lengths=lengths, + summary_length=summary_length, + lc_length=lc_length, + ) + result_qkv, input_shape, result_lengths_info, padding_mask = results + q, k, v = result_qkv + ( + mem_length, + utterance_length, + right_context_blocks_length, + key_length, + ) = result_lengths_info + + if left_context_key is not None: + # add the cache key and value + new_k = torch.cat( + [ + k[: mem_length + right_context_blocks_length, :, :], + left_context_key, + k[-utterance_length:, :, :], + ], + dim=0, + ) + new_v = torch.cat( + [ + v[: mem_length + right_context_blocks_length, :, :], + left_context_val, + v[-utterance_length:, :, :], + ], + dim=0, + ) + next_k = new_k[mem_length + right_context_blocks_length :, :, :] + next_v = new_v[mem_length + right_context_blocks_length :, :, :] + else: + new_k = k + new_v = v + next_k = None + next_v = None + + attention_weights, attention_weights_float, v = self.prepare_attention_weights( + q=q, + new_k=new_k, + new_v=new_v, + input_shape=input_shape, + rpe=rpe, + ) + + # mask attention + attention_mask = attention_mask.unsqueeze(0) + attention_weights_float = attention_weights_float.masked_fill( + attention_mask, float(self.negative_inf) + ) + + rc_output_memory = self.prepare_attention_output( + attention_weights=attention_weights, + attention_weights_float=attention_weights_float, + v=v, + input_shape=input_shape, + key_length=key_length, + padding_mask=padding_mask, + rpe=rpe, + ) + + if self.use_mem: + # next_m length equals to summary length - 1 + # last memory is ignored + if self.mini_batches: + next_m = rc_output_memory[-summary_length:] + else: + next_m = rc_output_memory[-summary_length:-1] + + next_m = self.squash_mem(next_m) + # rc and output + rc_output = rc_output_memory[:-summary_length] + if not self.nonlinear_squash_mem: + next_m = torch.clamp(next_m, min=-10, max=10) + else: + next_m = mems + rc_output = rc_output_memory + + return rc_output, next_m, next_k, next_v + + @torch.jit.export + def forward_jit( + self, + input: Tensor, + lengths: Tensor, + mems: Tensor, + left_context_key: Tensor, + left_context_val: Tensor, + rpe: Optional[Tensor], + ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + """ + forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding. + + args: + input: formed in the following way + [right_context_0, right_contex_1, ..., seg_0, seg_1, + ..., summary_0, summary_1,..] + lengths: the length of query which is [seg_0, seg_1, ....] + mems: [mem_0, mem_1, ...]. + left_context_key: left_context for key part. This is only used for online + decoding. In training, this is empty tensor + left_context_val: left_context for value part. This is only used for online + decoding. In training, this is empty tensor + + """ + lc_length = left_context_key.size(0) + + # In decoding, summary_length = 1 or 0 + if self.use_mem: + summary_length = 1 + else: + summary_length = 0 + + results = self.prepare_qkv( + input=input, + mems=mems, + lengths=lengths, + summary_length=summary_length, + lc_length=lc_length, + ) + result_qkv, input_shape, result_lengths_info, padding_mask = results + q, k, v = result_qkv + ( + mem_length, + utterance_length, + right_context_blocks_length, + key_length, + ) = result_lengths_info + + # add the cache key and value + new_k = torch.cat( + [ + k[: mem_length + right_context_blocks_length, :, :], + left_context_key, + k[-utterance_length:, :, :], + ], + dim=0, + ) + new_v = torch.cat( + [ + v[: mem_length + right_context_blocks_length, :, :], + left_context_val, + v[-utterance_length:, :, :], + ], + dim=0, + ) + next_k = new_k[mem_length + right_context_blocks_length :, :, :] + next_v = new_v[mem_length + right_context_blocks_length :, :, :] + + attention_weights, attention_weights_float, v = self.prepare_attention_weights( + q=q, + new_k=new_k, + new_v=new_v, + input_shape=input_shape, + rpe=rpe, + ) + # In online decoding, we don't have attention mask. But we still need + # to disable the attention from summary query to memory + attention_weights_float[:, -1, :mem_length] = float(self.negative_inf) + rc_output_memory = self.prepare_attention_output( + attention_weights=attention_weights, + attention_weights_float=attention_weights_float, + v=v, + input_shape=input_shape, + key_length=key_length, + padding_mask=padding_mask, + rpe=rpe, + ) + + # In decoding, summary length is 1 + if self.use_mem: + next_m = rc_output_memory[-1:] + next_m = self.squash_mem(next_m) + # rc and output + rc_output = rc_output_memory[:-1] + if not self.nonlinear_squash_mem: + next_m = torch.clamp(next_m, min=-10, max=10) + else: + rc_output = rc_output_memory + # empty tensor as input mems + next_m = mems + + return rc_output, next_m, next_k, next_v + + def quantize_(self, params=None): + if params and "per_channel" in params and params["per_channel"]: + qconfig = per_channel_dynamic_qconfig + else: + qconfig = default_dynamic_qconfig + quantization.quantize_dynamic( + self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True + ) + return self + + +class NoSegAugmentedMemoryTransformer(nn.Module): + """ + Whole utterance augmented memory transformer. + + This is not pyspeech nn layer. It is used as a module in a master layer where + multiple transformers is used. + """ + + def __init__( + self, + input_dim, + num_heads, + ffn_dim, + dropout_in_attn=0.0, + dropout_on_attn=None, + dropout_on_fc1=None, + dropout_on_fc2=None, + activation_fn="relu", + tanh_on_mem=False, + std_scale=None, + scaled_init=False, + segment_size=128, + use_mem=True, + mini_batches=False, + negative_inf="-inf", + layer_index=-1, + summarization_method="mean", + max_relative_position=0, + rpe_old_option=True, + ): + super(NoSegAugmentedMemoryTransformer, self).__init__() + + self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm( + input_dim=input_dim, + num_heads=num_heads, + dropout=dropout_in_attn, + scaled_init=scaled_init, + tanh_on_mem=tanh_on_mem, + std_scale=std_scale, + use_mem=use_mem, + mini_batches=mini_batches, + negative_inf=negative_inf, + layer_index=layer_index, + max_relative_position=max_relative_position, + ) + self.dropout = nn.Dropout(dropout_on_attn) + self.pos_ff = PositionwiseFF( + input_dim=input_dim, + ffn_dim=ffn_dim, + dropout_on_fc1=dropout_on_fc1, + dropout_on_fc2=dropout_on_fc2, + activation_fn=activation_fn, + ) + self.layer_norm_pre = Fp32LayerNorm(input_dim) + self.layer_norm = Fp32LayerNorm(input_dim) + self.segment_size = segment_size + self.use_mem = use_mem + + self.memory_op = SummarizationLayer( + summarization_method, segment_size, input_dim + ) + + def set_mini_batches(self, mini_batches): + self.attention.mini_batches = mini_batches + + def gen_summary_queries(self, input): + sum_input = self.memory_op(input) + return sum_input + + def pre_attention_ops(self, input, right_context_blocks): + rc_length = right_context_blocks.size(0) + input_length = input.size(0) + + rc_and_input = torch.cat([right_context_blocks, input], dim=0) + residual_input = rc_and_input + rc_and_input = self.layer_norm_pre(rc_and_input) + + query_input = rc_and_input[-input_length:, :, :] + return rc_length, input_length, residual_input, query_input, rc_and_input + + def after_attention_ops(self, attention_output, residual_input): + output = self.dropout(attention_output) + output = output + residual_input + output = self.pos_ff(output) + output = self.layer_norm(output) + return output + + @torch.jit.export + def forward_jit( + self, + input: Tensor, + lengths: Tensor, + mems: Tensor, + left_context_key: Tensor, + left_context_val: Tensor, + right_context_blocks: Tensor, + rpe: Optional[Tensor], + ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + + results = self.pre_attention_ops(input, right_context_blocks) + rc_length, input_length, residual_input, query_input, rc_and_input = results + + # In online decoding, the summary query size is always 1 or 0 + if self.use_mem: + summary_query = self.gen_summary_queries(query_input) + summary_query = summary_query[0:1, :, :] + rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0) + else: + rc_qu_su = rc_and_input + + rc_output, next_m, next_k, next_v = self.attention.forward_jit( + input=rc_qu_su, + lengths=lengths, + mems=mems, + left_context_key=left_context_key, + left_context_val=left_context_val, + rpe=rpe, + ) + rc_output = self.after_attention_ops(rc_output, residual_input) + results = ( + rc_output[-input_length:, :, :], + next_m, + rc_output[0:rc_length, :, :], + next_k, + next_v, + ) + return results + + @torch.jit.unused + def forward( + self, + input, + lengths, + mems, + right_context_blocks, + attention_mask, + pre_mems, + left_context_key, + left_context_val, + rpe, + ): + + results = self.pre_attention_ops(input, right_context_blocks) + rc_length, input_length, residual_input, query_input, rc_and_input = results + if self.use_mem: + summary_query = self.gen_summary_queries(query_input) + rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0) + else: + rc_qu_su = rc_and_input + + rc_output, next_m, next_k, next_v = self.attention( + input=rc_qu_su, + lengths=lengths, + mems=mems, + attention_mask=attention_mask, + pre_mems=pre_mems, + left_context_key=left_context_key, + left_context_val=left_context_val, + rpe=rpe, + ) + + # [TODO] Note memory did not go through pos_ff. What happen if we pass + # memory through the pos_ff as well? + rc_output = self.after_attention_ops(rc_output, residual_input) + results = ( + rc_output[-input_length:, :, :], + next_m, + rc_output[0:rc_length, :, :], + next_k, + next_v, + ) + + return results + + +class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder): + """ + Whole utterance augmented memory transformer encoder layer. This is a master layer + where we can define multiple augmented memory transformers. There are two reasons + to setup the master layer. + 1. We only need to define once about the attention mask. All the layers in the master + layer share the same mask. + 2. pyspeech nn layer has special input and output format. Defining one master layer is + easier to passing memory between different layes inside the master layer + + args: + input_dim: input embedding dimension + num_heads: number of heads in multihead self-attention + ffn_dim: ffn dimension in FFN layer + num_layers: number of augmented memory transformer layers + dropout_in_attn: dropout used in multi-head self-attention + dropout_on_attn: dropout used for output from te multihead self-attention + dropout_on_fc1: dropout used in FFN layer for the first linear layer + dropout_on_fc2: dropout used in FFN layer for the second linear layer + segment_size: segment size for each segment + context_config: (left_context_size, right_context_size) defines the surround context size + for each segment + max_memory_size: maximum memory size used for each segment + scaled_init: whether use scaled init for weight initialization in attention layer + std_scale: if std_scale is not None. The weak attention suppression is + turned on. For std_scale = 0.5, all the attention smaller than + mean + 0.5 * std will be suppressed. + activation_fn: activation function used in FFN layer. [ReLU, GELU] supported + tanh_on_mem: whether use tanh on memory + mini_batches: use mini-btach training + negative_inf: the negative infinity value used in attention masking. default is "-inf". + For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue. + summarization_method: method to generate segment summrization embedding + max_relative_position: max relatie position for relative position embedding + rpe_old_option: To be compatible with previous model. The previous model + was trained with attention += attention + rpe. The correct equation + should be attention = attention + rpe + [TODO]: remove the rpe_old_option by the end of 2021 Q1. + + """ + + def __init__( + self, + input_dim, + num_heads, + ffn_dim, + num_layers=1, + dropout_in_attn=0.0, + dropout_on_attn=0.0, + dropout_on_fc1=0.0, + dropout_on_fc2=0.0, + segment_size=128, + context_config=(0, 0), + max_memory_size=0, + scaled_init=True, + std_scale=None, + activation_fn="relu", + tanh_on_mem=False, + mini_batches=False, + negative_inf="-inf", + deep_init=True, + summarization_method="mean", + max_relative_position=0, + rpe_old_option=True, + ): + super().__init__(None) + if input_dim % num_heads: + raise ValueError( + "input_dim ({}) must be divisible by num_heads ({})".format( + input_dim, num_heads + ) + ) + + # we used to support growing memory size. However, it will cause + # cross stream batching failure. Now we need to have exact max memory size + if max_memory_size < 0: + raise ValueError("max_memory_size must be >= 0") + + # Only assign right_context. In decoding, left context will be cached. + # No need to let the online decoder to re-assign the left context + self.left_context, self.right_context = context_config + self.segment_size = segment_size + self.memory_dim = input_dim + self.max_memory_size = max_memory_size + self.mini_batches = mini_batches + if self.max_memory_size != 0: + self.use_mem = True + else: + self.use_mem = False + + self.memory_op = SummarizationLayer( + summarization_method, segment_size, input_dim + ) + + self.layers = torch.nn.ModuleList() + self.num_layers = num_layers + self.max_relative_position = max_relative_position + if self.max_relative_position > 0: + self.use_rpe = True + else: + self.use_rpe = False + for i in range(self.num_layers): + if deep_init: + layer_index = i + else: + layer_index = -1 + + self.layers.append( + NoSegAugmentedMemoryTransformer( + num_heads=num_heads, + input_dim=input_dim, + ffn_dim=ffn_dim, + dropout_in_attn=dropout_in_attn, + dropout_on_attn=dropout_on_attn, + dropout_on_fc1=dropout_on_fc1, + dropout_on_fc2=dropout_on_fc2, + segment_size=segment_size, + std_scale=std_scale, + activation_fn=activation_fn, + tanh_on_mem=tanh_on_mem, + scaled_init=scaled_init, + use_mem=self.use_mem, + mini_batches=mini_batches, + negative_inf=negative_inf, + layer_index=layer_index, + summarization_method=summarization_method, + max_relative_position=max_relative_position, + rpe_old_option=rpe_old_option, + ) + ) + + def set_mini_batches(self, mini_batches): + # handy function only used for unit test + self.mini_batches = mini_batches + for layer in self.layers: + layer.set_mini_batches(mini_batches) + + def _get_relative_position( + self, + input: Tensor, + max_relative_position: int, + left_context_length: int, + past_length: int, + is_decoding: bool, + ): + # For training, we copy the right context to the start of the utterance + # First dimension in distance is corresponding to query. + # [right context, utterance, summary vector] + # Second dimension in distance is corresponding to key. + # [Memory bank, right context, utterance] + # For summary vector in query part, the distance with + # all other position is 2*max_position. For memory bank in key, + # the distance with all other positions is 0. + + T, B, D = input.shape + num_segs = math.ceil((T - self.right_context) / self.segment_size) + + # utterance + u_st = past_length * self.segment_size + u_ed = u_st + T + utterance_ranges = torch.arange(u_st, u_ed - self.right_context) + + # left context. Only in minibatch or decoding + left_context_ranges = torch.arange(u_st - left_context_length, u_st) + + # Right context block + # right context + utterance + right_context_blocks = [] + for i in range(0, num_segs - 1): + st = (i + 1) * self.segment_size + u_st + ed = st + self.right_context + assert ed < u_ed + temp = torch.arange(st, ed) + right_context_blocks.append(temp) + right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed)) + right_context_ranges = torch.cat(right_context_blocks) + + if self.use_mem: + # Memory bank + # The position for memory -n, .., -1 + if is_decoding: + memory_size = min(past_length, self.max_memory_size) + else: + memory_size = num_segs + past_length - 1 + memory_bank_ranges = torch.arange( + -max_relative_position - 1, -max_relative_position - 1 - memory_size, -1 + ) + + # summary vector + # The position for summary vector as the T+max_relative_position+1. + # After the clamping, the relative position is max_relative_position + summary_pos_st = u_ed + max_relative_position + 1 + summary_vector_ranges = torch.arange( + summary_pos_st, summary_pos_st + num_segs + ) + + key_ranges = torch.cat( + [ + memory_bank_ranges, + right_context_ranges, + left_context_ranges, + utterance_ranges, + ] + ) + + query_ranges = torch.cat( + [right_context_ranges, utterance_ranges, summary_vector_ranges] + ) + else: + key_ranges = torch.cat( + [right_context_ranges, left_context_ranges, utterance_ranges] + ) + + query_ranges = torch.cat([right_context_ranges, utterance_ranges]) + + distance = key_ranges[None, :] - query_ranges[:, None] + distance_clamp = ( + torch.clamp(distance, -max_relative_position, max_relative_position) + + max_relative_position + ) + distance_clamp = distance_clamp.to(input.device).long().detach() + return distance_clamp + + def _get_attention_mask(self, input, past_length=0, left_context_cache=0): + # attention mask for each query contains three parts: + # 1. memory part + # 2. left_context + segment + # 3. right_context_block + # so for each segment and its correspoinding right context block, + # the attention matrix is formed by 9 parts: + # [0, m, 0, 0, right_context, 0, 0, seg, 0] + # [before memory, memory, after memory, before right context, right_context, + # after right context, before seg, seg, after seg] + # + # Query is formed in the way as [right_context_blocks, utterance, summary] + # + # Note: put m and right_context before segment is convenient + # for padding_mask operation. + # Key lengths = m_length + right_context_block_length + lengths + utterance_length, batch_size, _ = input.shape + summary_length = math.ceil(utterance_length / self.segment_size) + num_segs = summary_length + rc_length = self.right_context * num_segs + rc = self.right_context + lc = self.left_context + + # using mini-batches, there is left context cache available for current + # sequence. + lcc = left_context_cache + + # max_memory_size is 0 then we don't have memory and summary + # past_length is the memory carry from previous sequence + if self.use_mem: + mem_length = num_segs - 1 + past_length + else: + mem_length = 0 + rc_mask = [] + query_mask = [] + summary_mask = [] + for j in range(0, num_segs): + ssize = min(self.segment_size, utterance_length - j * self.segment_size) + + rc_size = rc + rc_mat = [] + q_mat = [] + s_mat = [] + m_start = max(j + past_length - self.max_memory_size, 0) + + # max_memory_size is 0, then we don't use memory + if self.use_mem: + # part 0: before memory + rc_mat.append(input.new_zeros(rc_size, m_start)) + q_mat.append(input.new_zeros(ssize, m_start)) + s_mat.append(input.new_zeros(1, m_start)) + + # part 1: memory + col_1 = j + past_length - m_start + rc_mat.append(torch.ones(rc_size, col_1, device=input.device)) + q_mat.append(torch.ones(ssize, col_1, device=input.device)) + # based on D22875746, disable summary query attention + # on memeory is better for long form utterance + s_mat.append(input.new_zeros(1, col_1)) + + # part 2: after memory + col_2 = mem_length - (j + past_length) + rc_mat.append(input.new_zeros(rc_size, col_2)) + q_mat.append(input.new_zeros(ssize, col_2)) + s_mat.append(input.new_zeros(1, col_2)) + + # part 3: before right context + rc_start = j * rc + rc_mat.append(input.new_zeros(rc_size, rc_start)) + q_mat.append(input.new_zeros(ssize, rc_start)) + s_mat.append(input.new_zeros(1, rc_start)) + + # part 4: right context + rc_end = rc_start + rc + col_4 = rc + rc_mat.append(torch.ones(rc_size, col_4, device=input.device)) + q_mat.append(torch.ones(ssize, col_4, device=input.device)) + s_mat.append(torch.ones(1, col_4, device=input.device)) + + # part 5: after right context + col_5 = rc_length - rc_end + rc_mat.append(input.new_zeros(rc_size, col_5)) + q_mat.append(input.new_zeros(ssize, col_5)) + s_mat.append(input.new_zeros(1, col_5)) + + # part 6: before query segment + seg_start = max(j * self.segment_size + lcc - lc, 0) + rc_mat.append(input.new_zeros(rc_size, seg_start)) + q_mat.append(input.new_zeros(ssize, seg_start)) + s_mat.append(input.new_zeros(1, seg_start)) + + # part 7: query segment + # note: right context is put in right context block + # here we only need to consider about left context + seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc) + col_7 = seg_end - seg_start + rc_mat.append(torch.ones(rc_size, col_7, device=input.device)) + q_mat.append(torch.ones(ssize, col_7, device=input.device)) + s_mat.append(torch.ones(1, col_7, device=input.device)) + + # part 8: after query segment + col_8 = utterance_length + lcc - seg_end + rc_mat.append(input.new_zeros(rc_size, col_8)) + q_mat.append(input.new_zeros(ssize, col_8)) + s_mat.append(input.new_zeros(1, col_8)) + + rc_mask.append(torch.cat(rc_mat, dim=1)) + query_mask.append(torch.cat(q_mat, dim=1)) + summary_mask.append(torch.cat(s_mat, dim=1)) + + # no memory, then we don't need summary either + if self.use_mem: + attention_mask = ( + 1 + - torch.cat( + [ + torch.cat(rc_mask, dim=0), + torch.cat(query_mask, dim=0), + torch.cat(summary_mask, dim=0), + ], + dim=0, + ) + ).to(torch.bool) + else: + attention_mask = ( + 1 + - torch.cat( + [torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0 + ) + ).to(torch.bool) + + return attention_mask + + @torch.jit.export + def init_state( + self, batch_size: int, device: Optional[Device] = None + ) -> List[Tensor]: + empty_memory = torch.zeros( + self.num_layers, + self.max_memory_size, + batch_size, + self.memory_dim, + device=device, + ) + left_context_key = torch.zeros( + self.num_layers, + self.left_context, + batch_size, + self.memory_dim, + device=device, + ) + left_context_val = torch.zeros( + self.num_layers, + self.left_context, + batch_size, + self.memory_dim, + device=device, + ) + past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device) + + return [empty_memory, left_context_key, left_context_val, past_length] + + @torch.jit.export + def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]: + if len(states) == 0: + return [] + batched_m = [] + batched_lc_key = [] + batched_lc_val = [] + batched_past_length = [] + for state in states: + if len(state) == 0: + continue + m, lc_key, lc_val, past_length = state + batched_m.append(m) + batched_lc_key.append(lc_key) + batched_lc_val.append(lc_val) + batched_past_length.append(past_length) + + if ( + (len(batched_m) == 0) + or (len(batched_lc_key) == 0) + or (len(batched_lc_val) == 0) + or (len(batched_past_length) == 0) + ): + return [ + torch.tensor([]), + torch.tensor([]), + torch.tensor([]), + torch.tensor([]), + ] + + batched_m = torch.cat(batched_m, dim=2) + batched_lc_key = torch.cat(batched_lc_key, dim=2) + batched_lc_val = torch.cat(batched_lc_val, dim=2) + batched_past_length = torch.cat(batched_past_length, dim=1) + return [batched_m, batched_lc_key, batched_lc_val, batched_past_length] + + @torch.jit.export + def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]: + if len(state) == 0: + return [] + m, lc_key, lc_val, past_length = state + indices = indices.to(device=m.device) + reord_m = torch.index_select(m, 2, indices) + reord_lc_key = torch.index_select(lc_key, 2, indices) + reord_lc_val = torch.index_select(lc_val, 2, indices) + reord_past_length = torch.index_select(past_length, 1, indices) + return [reord_m, reord_lc_key, reord_lc_val, reord_past_length] + + @torch.jit.export + def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]: + m, lc_key, lc_val, past_length = state + m = m.index_fill(dim=2, index=indices, value=0.0) + lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0) + lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0) + past_length = past_length.index_fill(dim=1, index=indices, value=0) + + return [m, lc_key, lc_val, past_length] + + @torch.jit.export + def state_size(self) -> int: + return 4 + + @torch.jit.export + def batch_size_in_state( + self, state: Optional[List[Tensor]], sloppy: bool = True + ) -> Optional[int]: + if state is None: + return None + return state[0].size(2) + + def gen_summary_queries(self, input): + sum_input = self.memory_op(input) + return sum_input + + def _gen_right_context_padded_input(self, input): + # This function deals with input that is already + # padded with right context (e.g. minibatch training) + right_context_blocks = [] + T, B, D = input.shape + num_segs = math.ceil((T - self.right_context) / self.segment_size) + for i in range(0, num_segs - 1): + st = (i + 1) * self.segment_size + ed = st + self.right_context + assert ed < T + temp = input[st:ed, :, :] + right_context_blocks.append(temp) + + # last segment right context is already available + right_context_blocks.append(input[T - self.right_context :, :, :]) + return torch.cat(right_context_blocks, dim=0) + + def _gen_segs_right_context(self, input, lengths): + segments = [] + T, B, D = input.size() + nT = T - self.right_context + + # assume input is right context padded + num_segs = math.ceil(nT / self.segment_size) + # pad zeros to the utterance to make sure each + # segment has the same right context. For the + for i in range(0, num_segs - 1): + st = i * self.segment_size + ed = min(T, st + self.segment_size + self.right_context) + temp = input[st:ed, :, :] + rest_lengths = torch.clamp( + lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size + ) + segments.append((temp, lengths - rest_lengths + self.right_context)) + lengths = rest_lengths + + last_seg = input[st + self.segment_size :, :, :] + segments.append((last_seg, rest_lengths + self.right_context)) + + return segments + + @torch.jit.unused + def forward( + self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]: + # Xutai: originally the second argument is lengths. + lengths = (~padding_masks).sum(dim=1).long() + # mini batch training. + if self.mini_batches: + return self.forward_mini_batches(input, lengths, state) + + # regular full sequence training. Note, assume the right context in provided + # in the input. + T, B, D = input.size() + right_context_blocks = self._gen_right_context_padded_input(input) + + # generate the relative positional embedding + if self.use_rpe: + rpe = self._get_relative_position( + input=input, + max_relative_position=self.max_relative_position, + left_context_length=0, + past_length=0, + is_decoding=False, + ) + else: + rpe = None + input = input[: T - self.right_context, :, :] + + attention_mask = self._get_attention_mask(input) + + # firt layer use each segment mean as memory + # ignore the last one seg average + if self.use_mem: + mems = self.gen_summary_queries(input)[:-1, :, :] + else: + mems = torch.zeros(0, input.size(1), input.size(2), device=input.device) + mems = mems.type_as(input) + + output = input + all_outputs = [] + + for layer in self.layers: + output, mems, right_context_blocks, _, _ = layer( + input=output, + lengths=lengths, + attention_mask=attention_mask, + mems=mems, + right_context_blocks=right_context_blocks, + pre_mems=None, + left_context_key=None, + left_context_val=None, + rpe=rpe, + ) + all_outputs.append(output) + return output, padding_masks, [], all_outputs + + def forward_jit_mini_batch_init( + self, + seg: Tensor, + state: Optional[List[Tensor]] = None, + is_decoding: bool = False, + ): + # Prepare state. In whole sequence training, state is ignored. + # For minibatch training, we need to prepare state + if state is None: + state = self.init_state(batch_size=seg.size(1), device=seg.device) + if seg.dtype == torch.half: + state = [state[0].half(), state[1].half(), state[2].half(), state[3]] + + if self.use_mem: + # note input average only on seg, not on right context + # first layer use each segmetn mean as memory. the last + # one segment average is used in state + full_mems = self.gen_summary_queries(seg) + if is_decoding: + mems = full_mems[0:1, :, :] + state_mems = torch.cat([state[0][0], mems], dim=0) + else: + mems = full_mems[:-1, :, :] + state_mems = torch.cat([state[0][0], full_mems], dim=0) + else: + mems = state[0][0] + state_mems = mems + + # track processed segment number or memory number + # the same batch as the same bumber of past length + past_length = state[3][0][0].item() + past_left_context = min(past_length * self.segment_size, self.left_context) + past_length = min(self.max_memory_size, past_length) + + return state, mems, state_mems, past_length, past_left_context + + def state_update_before( + self, layer: int, state: List[Tensor], past_length: int, past_left_context: int + ): + pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :] + lc_key = state[1][layer][self.left_context - past_left_context :, :, :] + lc_val = state[2][layer][self.left_context - past_left_context :, :, :] + return pre_mems, lc_key, lc_val + + def state_update_after( + self, + layer: int, + state: List[Tensor], + mems: Tensor, + next_key: Tensor, + next_val: Tensor, + mems_list: List[Tensor], + lc_key_list: List[Tensor], + lc_val_list: List[Tensor], + ): + # mems is used for next layer + if layer < self.num_layers - 1: + state_mems = torch.cat([state[0][layer + 1], mems], dim=0) + mems_list.append(state_mems[-self.max_memory_size :, :, :]) + + # when mems pass to next sequence, we need the last memory. when mems + # use for the next layer, we can ignore the last memory + mems = mems[:-1, :, :] + + # note state[1][i] and state[2][i] original length equals to self.left_context + new_k = torch.cat([state[1][layer], next_key], dim=0) + new_v = torch.cat([state[2][layer], next_val], dim=0) + lc_key_list.append(new_k[-self.left_context :, :, :]) + lc_val_list.append(new_v[-self.left_context :, :, :]) + return mems_list, lc_key_list, lc_val_list, mems + + def state_update_after_loop( + self, + state: List[Tensor], + mems_list: List[Tensor], + lc_key_list: List[Tensor], + lc_val_list: List[Tensor], + update_length: int, + ): + state[0] = torch.stack(mems_list, dim=0) + state[1] = torch.stack(lc_key_list, dim=0) + state[2] = torch.stack(lc_val_list, dim=0) + state[3] = state[3] + update_length + return state + + @torch.jit.unused + def forward_mini_batches( + self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]: + T, B, D = input.size() + + # input without right context + seg = input[: T - self.right_context, :, :] + + # get right context blocks + right_context_blocks = self._gen_right_context_padded_input(input) + + mems_list = [] + lc_key_list = [] + lc_val_list = [] + results = self.forward_jit_mini_batch_init(seg, state, False) + state, mems, state_mems, past_length, past_left_context = results + + # relative position embedding + if self.use_rpe: + rpe = self._get_relative_position( + input=input, + max_relative_position=self.max_relative_position, + left_context_length=past_left_context, + past_length=past_length, + is_decoding=False, + ) + else: + rpe = None + + # get attention mask based on seg (not include right context) and available + # left context + attention_mask = self._get_attention_mask(seg, past_length, past_left_context) + mems_list.append(state_mems[-self.max_memory_size :, :, :]) + output = seg + i = 0 + all_outputs = [] + for layer in self.layers: + # In order to make cross stream batching work, mem, left context key + # and left context value in the state should always be the same shape. + # We use the past length to track the processed segment number. In this + # way, we take out the essential memory, left context key and left + # context val from the state. After finish the forward for current segment + # we add the new memory, left context key and left context value into the + # staate and trim out the oldest part to keep the shape consistent. + pre_mems, lc_key, lc_val = self.state_update_before( + i, state, past_length, past_left_context + ) + + output, mems, right_context_blocks, next_key, next_val = layer.forward( + input=output, + lengths=lengths, + attention_mask=attention_mask, + mems=mems, + right_context_blocks=right_context_blocks, + pre_mems=pre_mems, + left_context_key=lc_key, + left_context_val=lc_val, + rpe=rpe, + ) + all_outputs.append(output) + mems_list, lc_key_list, lc_val_list, mems = self.state_update_after( + layer=i, + state=state, + mems=mems, + next_key=next_key, + next_val=next_val, + mems_list=mems_list, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + ) + + i += 1 + + # update state + update_length = math.ceil((T - self.right_context) / self.segment_size) + state = self.state_update_after_loop( + state=state, + mems_list=mems_list, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + update_length=update_length, + ) + + return output, lengths, state, all_outputs + + def forward_jit_test( + self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor]]: + """ + This one simulate sequence encoder forward jit. This is for unit test purpose. + It is not used in training or decoding. Note, extra_right_context is set in + the model. In unit test, input = [utterance, right_context], lengths = + [utterance_length]. + args: + input: input utterance + lengths: utterance input length + state: None here. input is whole utterance + """ + # [TODO] sequence_to_segment has bug in lengths. + seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths) + + seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = [] + state: Optional[List[Tensor]] = None + for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths: + seg_enc_tokens, seg_enc_lengths, state = self.forward_jit( + input=seg_src_tokens, lengths=seg_src_lengths, state=state + ) + seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths)) + + enc_tokens, enc_lengths = segments_to_sequence( + segments=seg_enc_tokens_lengths, time_axis=0 + ) + + state = [] # returns trivial state + + return enc_tokens, enc_lengths, state + + @torch.jit.export + def forward_jit( + self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor]]: + """ + Forward helper for online decoding. + + args: + input: [seg, right_context]. We assume in online we + always padding the right context to the preset right context size. + For the last segment, we may have short segment size, but right + context size is the same as other segments + lengths: utterance input length is the utterance segment length and + right context size + state: [memory, left_context_key, left_context_val]. To improve throughput, + in addition to memory, we also cache key and value for left_context in + multihead self-attention + """ + # In online decoding, input = [segment, right_context] + # Lengths = [segment_length, right_context_length] + # so we need strip right context in output + T, B, D = input.size() + rc_str = T - self.right_context + rc_end = T + right_context_blocks = input[rc_str:rc_end, :, :] + seg = input[:rc_str, :, :] + lengths = torch.clamp(lengths - self.right_context, min=0) + mems_list = [] + lc_key_list = [] + lc_val_list = [] + + results = self.forward_jit_mini_batch_init(seg, state, True) + state, mems, state_mems, past_length, past_left_context = results + + # relative position embedding + if self.use_rpe: + rpe = self._get_relative_position( + input=input, + max_relative_position=self.max_relative_position, + left_context_length=past_left_context, + past_length=past_length, + is_decoding=True, + ) + else: + rpe = None + + # memory for first layer. + mems_list.append(state_mems[-self.max_memory_size :, :, :]) + output = seg + i = 0 + for layer in self.layers: + # In order to make cross stream batching work, mem, left context key + # and left context value in the state should always be the same shape. + # We use the past length to track the processed segment number. In this + # way, we take out the essential memory, left context key and left + # context val from the state. After finish the forward for current segment + # we add the new memory, left context key and left context value into the + # staate and trim out the oldest part to keep the shape consistent. + true_mems, lc_key, lc_val = self.state_update_before( + layer=i, + state=state, + past_length=past_length, + past_left_context=past_left_context, + ) + + output, mems, right_context_blocks, next_key, next_val = layer.forward_jit( + input=output, + lengths=lengths, + mems=true_mems, + right_context_blocks=right_context_blocks, + left_context_key=lc_key, + left_context_val=lc_val, + rpe=rpe, + ) + # mems is used for next layer + mems_list, lc_key_list, lc_val_list, _ = self.state_update_after( + layer=i, + state=state, + mems_list=mems_list, + mems=mems, + next_key=next_key, + next_val=next_val, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + ) + i += 1 + + # update state + state = self.state_update_after_loop( + state=state, + mems_list=mems_list, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + update_length=1, + ) + + return output, lengths, state + + def quantize_(self, params=None): + if params and "per_channel" in params and params["per_channel"]: + qconfig = per_channel_dynamic_qconfig + else: + qconfig = default_dynamic_qconfig + quantization.quantize_dynamic( + self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True + ) + return self + + +# ------------------------------------------------------------------------------ +# Emformer encoder for seq2seq model +# This is a wrapper over the original emformer +# ------------------------------------------------------------------------------ +def emformer_encoder(klass): + class SpeechEncoder(klass): + def __init__(self, args): + super().__init__(args) + stride = SpeechEncoder.conv_layer_stride(args) + trf_left_context = args.segment_left_context // stride + trf_right_context = args.segment_right_context // stride + context_config = [trf_left_context, trf_right_context] + self.transformer_layers = nn.ModuleList( + [ + NoSegAugmentedMemoryTransformerEncoderLayer( + input_dim=args.encoder_embed_dim, + num_heads=args.encoder_attention_heads, + ffn_dim=args.encoder_ffn_embed_dim, + num_layers=args.encoder_layers, + dropout_in_attn=args.dropout, + dropout_on_attn=args.dropout, + dropout_on_fc1=args.dropout, + dropout_on_fc2=args.dropout, + activation_fn=args.activation_fn, + context_config=context_config, + segment_size=args.segment_length, + max_memory_size=args.max_memory_size, + scaled_init=True, # TODO: use constant for now. + tanh_on_mem=args.amtrf_tanh_on_mem, + ) + ] + ) + + def forward(self, src_tokens, src_lengths): + encoder_out = super().forward(src_tokens, src_lengths) + output = encoder_out["encoder_out"][0] + encoder_padding_masks = encoder_out["encoder_padding_mask"][0] + + # This is because that in the original implementation + # the output didn't consider the last segment as right context. + encoder_padding_masks = encoder_padding_masks[:, : output.size(0)] + + return { + "encoder_out": [output], + "encoder_padding_mask": [encoder_padding_masks], + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + @staticmethod + def conv_layer_stride(args): + # TODO: make it configurable from the args + return 4 + + SpeechEncoder.__name__ = klass.__name__ + return SpeechEncoder diff --git a/fairseq/fairseq/models/speech_to_text/multi_modality_model.py b/fairseq/fairseq/models/speech_to_text/multi_modality_model.py new file mode 100644 index 0000000..0464216 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/multi_modality_model.py @@ -0,0 +1,49 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.models import FairseqDecoder, FairseqEncoder + + +# a container for different encoders with training samples from different modality +# each time, only one encoder is selected +class MultiModalityEncoder(FairseqEncoder): + def __init__(self, dictionary): + super().__init__(dictionary) + + def select_encoder(self, mode, **kwargs): + raise NotImplementedError("Model must implement the select_encoder method") + return None, kwargs + + # def post_encoder(self, encoder_out, src_tokens, src_lengths, mode, **kwargs): + # # Default do nothing + # return encoder_out + + # get sample data from JointSpeechTextDataset + def forward(self, src_tokens, src_lengths=None, mode="", **kwargs): + encoder, kwargs = self.select_encoder(mode, **kwargs) + # return self.post_encoder(encoder(src_tokens, src_lengths, **kwargs), src_tokens, src_lengths, mode, **kwargs) + return encoder(src_tokens, src_lengths, **kwargs) + + +# a container for different decoders with training samples from different modality +# each time, only one decoder is selected +class MultiInputDecoder(FairseqDecoder): + def __init__(self, dictionary): + super().__init__(dictionary) + + def select_decoder(self, mode, **kwargs): + raise NotImplementedError("Model must implement the select_decoder method") + return None, kwargs + + def forward( + self, prev_output_tokens, encoder_out, incremental_state=None, mode="", **kwargs + ): + decoder, kwargs = self.select_decoder(mode, **kwargs) + return decoder( + prev_output_tokens, + encoder_out, + incremental_state=incremental_state, + **kwargs + ) diff --git a/fairseq/fairseq/models/speech_to_text/s2t_conformer.py b/fairseq/fairseq/models/speech_to_text/s2t_conformer.py new file mode 100644 index 0000000..79dbbec --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/s2t_conformer.py @@ -0,0 +1,234 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from pathlib import Path + +import torch + +from fairseq import checkpoint_utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import FairseqEncoder, register_model, register_model_architecture +from fairseq.models.speech_to_text.modules.convolution import ( + Conv1dSubsampler, + Conv2dSubsampler, +) +from fairseq.models.speech_to_text.s2t_transformer import ( + S2TTransformerEncoder, + S2TTransformerModel, +) +from fairseq.models.speech_to_text.s2t_transformer import ( + base_architecture as transformer_base_architecture, +) +from fairseq.modules import PositionalEmbedding, RelPositionalEncoding +from fairseq.modules.conformer_layer import ConformerEncoderLayer + +logger = logging.getLogger(__name__) + + +class S2TConformerEncoder(FairseqEncoder): + """Conformer Encoder for speech translation based on https://arxiv.org/abs/2005.08100""" + + def __init__(self, args): + super().__init__(None) + + self.encoder_freezing_updates = args.encoder_freezing_updates + self.num_updates = 0 + + self.embed_scale = math.sqrt(args.encoder_embed_dim) + if args.no_scale_embedding: + self.embed_scale = 1.0 + self.padding_idx = 1 + self.conv_version = args.conv_version + if self.conv_version == "s2t_transformer": + self.subsample = Conv1dSubsampler( + args.input_feat_per_channel * args.input_channels, + args.conv_channels, + args.encoder_embed_dim, + [int(k) for k in args.conv_kernel_sizes.split(",")], + ) + elif self.conv_version == "convtransformer": + self.subsample = Conv2dSubsampler( + args.input_channels, + args.input_feat_per_channel, + args.conv_out_channels, + args.encoder_embed_dim, + ) + self.pos_enc_type = args.pos_enc_type + if self.pos_enc_type == "rel_pos": + self.embed_positions = RelPositionalEncoding( + args.max_source_positions, args.encoder_embed_dim + ) + elif self.pos_enc_type == "rope": + self.embed_positions = None + else: # Use absolute positional embedding + self.pos_enc_type = "abs" + self.embed_positions = PositionalEmbedding( + args.max_source_positions, args.encoder_embed_dim, self.padding_idx + ) + + self.linear = torch.nn.Linear(args.encoder_embed_dim, args.encoder_embed_dim) + self.dropout = torch.nn.Dropout(args.dropout) + self.conformer_layers = torch.nn.ModuleList( + [ + ConformerEncoderLayer( + embed_dim=args.encoder_embed_dim, + ffn_embed_dim=args.encoder_ffn_embed_dim, + attention_heads=args.encoder_attention_heads, + dropout=args.dropout, + depthwise_conv_kernel_size=args.depthwise_conv_kernel_size, + attn_type=args.attn_type, + pos_enc_type=self.pos_enc_type, + use_fp16=args.fp16, + ) + for _ in range(args.encoder_layers) + ] + ) + + def _forward(self, src_tokens, src_lengths, return_all_hiddens=False): + """ + Args: + src_tokens: Input source tokens Tensor of shape B X T X C + src_lengths: Lengths Tensor corresponding to input source tokens + return_all_hiddens: If true will append the self attention states to the encoder states + Returns: + encoder_out: Tensor of shape B X T X C + encoder_padding_mask: Optional Tensor with mask + encoder_embedding: Optional Tensor. Always empty here + encoder_states: List of Optional Tensors wih self attention states + src_tokens: Optional Tensor. Always empty here + src_lengths: Optional Tensor. Always empty here + """ + x, input_lengths = self.subsample(src_tokens, src_lengths) # returns T X B X C + encoder_padding_mask = lengths_to_padding_mask(input_lengths) + x = self.embed_scale * x + if self.pos_enc_type == "rel_pos": + positions = self.embed_positions(x) + + elif self.pos_enc_type == "rope": + positions = None + + else: + positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) + x += positions + positions = None + + x = self.linear(x) + x = self.dropout(x) + encoder_states = [] + + # x is T X B X C + for layer in self.conformer_layers: + x, _ = layer(x, encoder_padding_mask, positions) + if return_all_hiddens: + encoder_states.append(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask] + if encoder_padding_mask.any() + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def forward(self, src_tokens, src_lengths, return_all_hiddens=False): + if self.num_updates < self.encoder_freezing_updates: + with torch.no_grad(): + x = self._forward( + src_tokens, + src_lengths, + return_all_hiddens=return_all_hiddens, + ) + else: + x = self._forward( + src_tokens, + src_lengths, + return_all_hiddens=return_all_hiddens, + ) + return x + + def reorder_encoder_out(self, encoder_out, new_order): + """Required method for a FairseqEncoder. Calls the method from the parent class""" + return S2TTransformerEncoder.reorder_encoder_out(self, encoder_out, new_order) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self.num_updates = num_updates + + +@register_model("s2t_conformer") +class S2TConformerModel(S2TTransformerModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + S2TTransformerModel.add_args(parser) + parser.add_argument( + "--input-feat-per-channel", + type=int, + metavar="N", + help="dimension of input features per channel", + ) + parser.add_argument( + "--input-channels", + type=int, + metavar="N", + help="number of chennels of input features", + ) + parser.add_argument( + "--depthwise-conv-kernel-size", + type=int, + metavar="N", + help="kernel size of depthwise convolution layers", + ) + parser.add_argument( + "--attn-type", + type=str, + metavar="STR", + help="If not specified uses fairseq MHA. Other valid option is espnet", + ) + parser.add_argument( + "--pos-enc-type", + type=str, + metavar="STR", + help="Must be specified in addition to attn-type=espnet for rel_pos and rope", + ) + + @classmethod + def build_encoder(cls, args): + encoder = S2TConformerEncoder(args) + pretraining_path = getattr(args, "load_pretrained_encoder_from", None) + if pretraining_path is not None: + if not Path(pretraining_path).exists(): + logger.warning( + f"skipped pretraining because {pretraining_path} does not exist" + ) + else: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=pretraining_path + ) + logger.info(f"loaded pretrained encoder from: {pretraining_path}") + return encoder + + +@register_model_architecture("s2t_conformer", "s2t_conformer") +def conformer_base_architecture(args): + args.attn_type = getattr(args, "attn_type", None) + args.pos_enc_type = getattr(args, "pos_enc_type", "abs") + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.input_channels = getattr(args, "input_channels", 1) + args.max_source_positions = getattr(args, "max_source_positions", 6000) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_layers = getattr(args, "encoder_layers", 16) + args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31) + transformer_base_architecture(args) diff --git a/fairseq/fairseq/models/speech_to_text/s2t_transformer.py b/fairseq/fairseq/models/speech_to_text/s2t_transformer.py new file mode 100644 index 0000000..50fae2f --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/s2t_transformer.py @@ -0,0 +1,552 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import checkpoint_utils, utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_text.hub_interface import S2THubInterface +from fairseq.models.speech_to_text.modules.convolution import ( + Conv1dSubsampler, + Conv2dSubsampler, +) +from fairseq.models.transformer import Embedding, TransformerDecoder +from fairseq.modules import ( + FairseqDropout, + LayerNorm, + PositionalEmbedding, + TransformerEncoderLayer, +) + +logger = logging.getLogger(__name__) + + +@register_model("s2t_transformer") +class S2TTransformerModel(FairseqEncoderDecoderModel): + """Adapted Transformer model (https://arxiv.org/abs/1706.03762) for + speech-to-text tasks. The Transformer encoder/decoder remains the same. + A trainable input subsampler is prepended to the Transformer encoder to + project inputs into the encoder dimension as well as downsample input + sequence for computational efficiency.""" + + @classmethod + def hub_models(cls): + base_url = "http://dl.fbaipublicfiles.com/fairseq/s2t" + model_ids = [ + "s2t_transformer_s-en-asr-librispeech", + "s2t_transformer_m-en-asr-librispeech", + "s2t_transformer_l-en-asr-librispeech", + ] + return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + config_yaml="config.yaml", + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + config_yaml=config_yaml, + **kwargs, + ) + return S2THubInterface(x["args"], x["task"], x["models"][0]) + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # input + parser.add_argument( + "--conv-kernel-sizes", + type=str, + metavar="STR", + help="kernel sizes of Conv1d (s2t_transformer) subsampling layers", + ) + parser.add_argument( + "--conv-channels", + type=int, + metavar="N", + help="# of channels in Conv1d (s2t_transformer) subsampling layers", + ) + parser.add_argument( + "--conv-out-channels", + type=int, + metavar="N", + help="# of channels in Conv2d (convtransformer) subsampling layers", + ) + parser.add_argument( + "--conv-version", + type=str, + default="s2t_transformer", + choices=["s2t_transformer", "convtransformer"], + help="version of frontend convolutional layers", + ) + # Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + "--encoder-freezing-updates", + type=int, + metavar="N", + help="freeze encoder for first N updates", + ) + + @classmethod + def build_encoder(cls, args): + encoder = S2TTransformerEncoder(args) + pretraining_path = getattr(args, "load_pretrained_encoder_from", None) + if pretraining_path is not None: + if not Path(pretraining_path).exists(): + logger.warning( + f"skipped pretraining because {pretraining_path} does not exist" + ) + else: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=pretraining_path + ) + logger.info(f"loaded pretrained encoder from: {pretraining_path}") + return encoder + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + return Embedding(num_embeddings, embed_dim, padding_idx) + + decoder_embed_tokens = build_embedding( + task.target_dictionary, args.decoder_embed_dim + ) + args.tgt_dict_size = len(task.target_dictionary) + encoder = cls.build_encoder(args) + decoder = cls.build_decoder(args, task, decoder_embed_tokens) + return cls(encoder, decoder) + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample) + lprobs.batch_first = True + return lprobs + + def get_ctc_target(self, sample: Optional[Dict[str, Tensor]]): + return sample["target"], sample["target_lengths"] + + def get_ctc_output( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + sample: Optional[Dict[str, Tensor]], + ): + encoder_out = net_output[1]["encoder_out"]["encoder_out"][0] + logits = self.encoder.ctc_proj(encoder_out) # T x B x C + out = utils.log_softmax(logits.float(), dim=-1) + padding_mask = net_output[1]["encoder_out"]["encoder_padding_mask"] + lens = out.new_full((out.shape[1],), out.shape[0]).long() + if len(padding_mask) > 0: + lens -= padding_mask[0].sum(dim=-1) + return out, lens + + def forward(self, src_tokens, src_lengths, prev_output_tokens): + """ + The forward method inherited from the base class has a **kwargs + argument in its input, which is not supported in torchscript. This + method overwrites the forward method definition without **kwargs. + """ + encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths) + decoder_out = self.decoder( + prev_output_tokens=prev_output_tokens, encoder_out=encoder_out + ) + return decoder_out + + +class S2TTransformerEncoder(FairseqEncoder): + """Speech-to-text Transformer encoder that consists of input subsampler and + Transformer encoder.""" + + def __init__(self, args): + super().__init__(None) + + self.encoder_freezing_updates = args.encoder_freezing_updates + self.num_updates = 0 + + self.dropout_module = FairseqDropout( + p=args.dropout, module_name=self.__class__.__name__ + ) + self.embed_scale = math.sqrt(args.encoder_embed_dim) + if args.no_scale_embedding: + self.embed_scale = 1.0 + self.padding_idx = 1 + + self.conv_version = args.conv_version + if self.conv_version == "s2t_transformer": + self.subsample = Conv1dSubsampler( + args.input_feat_per_channel * args.input_channels, + args.conv_channels, + args.encoder_embed_dim, + [int(k) for k in args.conv_kernel_sizes.split(",")], + ) + elif self.conv_version == "convtransformer": + self.subsample = Conv2dSubsampler( + args.input_channels, + args.input_feat_per_channel, + args.conv_out_channels, + args.encoder_embed_dim, + ) + + self.embed_positions = PositionalEmbedding( + args.max_source_positions, args.encoder_embed_dim, self.padding_idx + ) + + self.transformer_layers = nn.ModuleList( + [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)] + ) + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(args.encoder_embed_dim) + else: + self.layer_norm = None + + self.ctc_proj = None + if getattr(args, "ctc_weight", 0.0) > 0.0: + self.ctc_proj = nn.Linear(args.encoder_embed_dim, args.tgt_dict_size) + + def _forward(self, src_tokens, src_lengths, return_all_hiddens=False): + x, input_lengths = self.subsample(src_tokens, src_lengths) + x = self.embed_scale * x + + encoder_padding_mask = lengths_to_padding_mask(input_lengths) + positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) + x += positions + x = self.dropout_module(x) + + encoder_states = [] + + for layer in self.transformer_layers: + x = layer(x, encoder_padding_mask) + if return_all_hiddens: + encoder_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask] + if encoder_padding_mask.any() + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def forward(self, src_tokens, src_lengths, return_all_hiddens=False): + if self.num_updates < self.encoder_freezing_updates: + with torch.no_grad(): + x = self._forward( + src_tokens, src_lengths, return_all_hiddens=return_all_hiddens + ) + else: + x = self._forward( + src_tokens, src_lengths, return_all_hiddens=return_all_hiddens + ) + return x + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] + if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] + if len(encoder_out["encoder_padding_mask"]) == 0 + else [ + x.index_select(0, new_order) + for x in encoder_out["encoder_padding_mask"] + ] + ) + + new_encoder_embedding = ( + [] + if len(encoder_out["encoder_embedding"]) == 0 + else [ + x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] + ] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self.num_updates = num_updates + + +class TransformerDecoderScriptable(TransformerDecoder): + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + # call scriptable method from parent class + x, _ = self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + extra = {"encoder_out": encoder_out} if incremental_state is None else None + return x, extra + + +@register_model_architecture(model_name="s2t_transformer", arch_name="s2t_transformer") +def base_architecture(args): + args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0) + # Convolutional subsampler + args.input_channels = getattr(args, "input_channels", 1) + args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5") # for Conv1d + args.conv_channels = getattr(args, "conv_channels", 1024) # for Conv1d + args.conv_out_channels = getattr(args, "conv_out_channels", 256) # for Conv2d + args.conv_version = getattr(args, "conv_version", "s2t_transformer") + # Transformer + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", args.dropout) + args.activation_dropout = getattr(args, "activation_dropout", args.dropout) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_s") +def s2t_transformer_s(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + base_architecture(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_xs") +def s2t_transformer_xs(args): + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 3) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4) + args.dropout = getattr(args, "dropout", 0.3) + s2t_transformer_s(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_sp") +def s2t_transformer_sp(args): + args.encoder_layers = getattr(args, "encoder_layers", 16) + s2t_transformer_s(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_m") +def s2t_transformer_m(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.dropout = getattr(args, "dropout", 0.15) + base_architecture(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_mp") +def s2t_transformer_mp(args): + args.encoder_layers = getattr(args, "encoder_layers", 16) + s2t_transformer_m(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_l") +def s2t_transformer_l(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.2) + base_architecture(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_lp") +def s2t_transformer_lp(args): + args.encoder_layers = getattr(args, "encoder_layers", 16) + s2t_transformer_l(args) diff --git a/fairseq/fairseq/models/speech_to_text/s2t_wav_transformer.py b/fairseq/fairseq/models/speech_to_text/s2t_wav_transformer.py new file mode 100644 index 0000000..ad21aee --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/s2t_wav_transformer.py @@ -0,0 +1,504 @@ +#!/usr/bin/env python3 + +import math + +import torch +import torch.nn as nn + +from fairseq.data.data_utils import compute_mask_indices +from fairseq.models import FairseqEncoder +from fairseq.models.wav2vec import ConvFeatureExtractionModel +from fairseq.modules import GradMultiply, LayerNorm, SamePad, TransformerEncoderLayer + + +# Transformer encoder with wave input, it is adopted from wav2vec 2.0 Encoder. +# use wav input +# use trained position embedding so it is easier to match with text input +class SpeechWavTransformerEncoder(FairseqEncoder): + + # extra parameters for speech encoder besides those defined in transformermodel + @staticmethod + def add_args(parser): + parser.add_argument( + "--dropout-input", + type=float, + metavar="D", + help="dropout to apply to the input (after feat extr)", + ) + parser.add_argument( + "--dropout-features", + type=float, + metavar="D", + help="dropout to apply to the unmasked features (after feat extr)", + ) + parser.add_argument( + "--speech-extractor-mode", + type=str, + default="layer_norm", + choices=["default", "layer_norm"], + help="feature extractor norm", + ) + + parser.add_argument( + "--speech-conv-bias", + action="store_true", + help="include bias in speech conv encoder", + ) + + parser.add_argument( + "--conv-feature-layers", + default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", + help="string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]", + ) + + parser.add_argument( + "--speech-mask-length", + type=int, + help="repeat the mask indices multiple times", + ) + + parser.add_argument( + "--speech-mask-prob", + type=float, + help="probability of replacing a token with mask", + ) + + parser.add_argument( + "--speech-mask-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + help="how to choose masks", + ) + + parser.add_argument( + "--speech-mask-other", + type=float, + help="stdev of the mask length in case of 'normal' selection strategy", + ) + + parser.add_argument( + "--speech-no-mask-overlap", + action="store_true", + help="whether to allow masks to overlap", + ) + + parser.add_argument( + "--speech-mask-min-space", + type=int, + help="min space between spans (if no overlap is enabled)", + ) + + parser.add_argument( + "--speech-mask-channel-length", + type=int, + help="repeat the mask indices multiple times", + ) + + parser.add_argument( + "--speech-mask-channel-prob", + type=float, + help="probability of replacing a token with mask", + ) + + parser.add_argument( + "--speech-mask-channel-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + help="how to choose masks", + ) + + parser.add_argument( + "--speech-mask-channel-other", + type=float, + help="stdev of the mask length in case of 'normal' selection strategy", + ) + + parser.add_argument( + "--speech-no-mask-channel-overlap", + action="store_true", + help="whether to allow masks to overlap", + ) + + parser.add_argument( + "--no-scale-feature", + action="store_true", + help="no scale for the calculated features", + ) + + parser.add_argument( + "--speech-mask-channel-min-space", + type=int, + help="min space between spans (if no overlap is enabled)", + ) + + parser.add_argument( + "--feature-grad-mult", + type=float, + help="reset feature grad mult in wav2vec 2.0 to this", + ) + + # positional embeddings + parser.add_argument( + "--conv-pos", + type=int, + default=128, + help="number of filters for convolutional positional embeddings", + ) + + parser.add_argument( + "--conv-pos-groups", + type=int, + default=16, + help="number of groups for convolutional positional embedding", + ) + # model configures + parser.add_argument( + "--speech-encoder-layers", + type=int, + help="number of speech encoder layers", + ) + parser.add_argument( + "--text-encoder-layers", + type=int, + help="number of text encoder layers", + ) + + def __init__(self, args, alway_mask=False): + super().__init__(args) + self.args = args + self.dropout = args.dropout + self.embedding_dim = args.encoder_embed_dim + self.feat_scale = math.sqrt(args.encoder_embed_dim) + if args.no_scale_feature: + self.feat_scale = 1.0 + + subsample = ConvFeatureExtractionModel( + conv_layers=eval(args.conv_feature_layers), + dropout=0.0, + mode=args.speech_extractor_mode, # default, layer_norm + conv_bias=args.speech_conv_bias, + ) + self.feature_enc_layers = eval(args.conv_feature_layers) + self.subsample = subsample + self.feat_proj = ( + nn.Linear(self.feature_enc_layers[-1][0], self.embedding_dim) + if self.feature_enc_layers[-1][0] != self.embedding_dim + else None + ) + + self.feat_layer_norm = LayerNorm(self.feature_enc_layers[-1][0]) + + self.embed_positions = nn.Conv1d( + self.embedding_dim, + self.embedding_dim, + kernel_size=args.conv_pos, + padding=args.conv_pos // 2, + groups=args.conv_pos_groups, + ) + std = math.sqrt(4 / (args.conv_pos * self.embedding_dim)) + nn.init.normal_(self.embed_positions.weight, mean=0, std=std) + nn.init.constant_(self.embed_positions.bias, 0) + + self.embed_positions = nn.utils.weight_norm( + self.embed_positions, name="weight", dim=2 + ) + self.embed_positions = nn.Sequential( + self.embed_positions, SamePad(args.conv_pos), nn.GELU() + ) + + self.mask_prob = args.speech_mask_prob + self.mask_selection = args.speech_mask_selection + self.mask_other = args.speech_mask_other + self.mask_length = args.speech_mask_length + self.no_mask_overlap = args.speech_no_mask_overlap + self.mask_min_space = args.speech_mask_min_space + + self.mask_channel_prob = args.speech_mask_channel_prob + self.mask_channel_selection = args.speech_mask_channel_selection + self.mask_channel_other = args.speech_mask_channel_other + self.mask_channel_length = args.speech_mask_channel_length + self.no_mask_channel_overlap = args.speech_no_mask_channel_overlap + self.mask_channel_min_space = args.speech_mask_channel_min_space + + self.dropout_input = nn.Dropout(args.dropout_input) + self.dropout_features = nn.Dropout(args.dropout_features) + + self.feature_grad_mult = args.feature_grad_mult + + self.mask_emb = nn.Parameter( + torch.FloatTensor(args.encoder_embed_dim).uniform_() + ) + + self.layers = nn.ModuleList( + [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)] + ) + self.layer_norm = LayerNorm(args.encoder_embed_dim) + self.normalize_before = args.encoder_normalize_before + self.alway_mask = alway_mask + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + return torch.floor((input_length - kernel_size) / stride + 1) + + for i in range(len(self.feature_enc_layers)): + input_lengths = _conv_out_length( + input_lengths, + self.feature_enc_layers[i][1], + self.feature_enc_layers[i][2], + ) + + return input_lengths.to(torch.long) + + def apply_mask(self, x, padding_mask): + B, T, C = x.shape + if self.mask_prob > 0: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb + else: + mask_indices = None + + if self.mask_channel_prob > 0: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def forward( + self, + src_tokens, + src_lengths, + return_all_hiddens=False, + padding_mask=None, + features_only=True, + ): + mask = self.training or self.alway_mask + if self.feature_grad_mult > 0 and self.training: + features = self.subsample(src_tokens) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.subsample(src_tokens) + features = features.transpose(1, 2) + features = self.feat_layer_norm(features) + if self.feat_proj is not None: + features = self.feat_proj(features) + + if padding_mask is not None: + input_lengths = (1 - padding_mask.long()).sum(-1) + else: + input_lengths = src_lengths + # apply conv formula to get real output_lengths + output_lengths = self._get_feat_extract_output_lengths(input_lengths) + + padding_mask = torch.zeros( + features.shape[:2], dtype=features.dtype, device=features.device + ) + + # these two operations makes sure that all values + # before the output lengths indices are attended to + padding_mask[ + ( + torch.arange(padding_mask.shape[0], device=padding_mask.device), + output_lengths - 1, + ) + ] = 1 + padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool() + + features = self.feat_scale * features if self.feat_scale != 1.0 else features + unmasked_features = features.clone() + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + if mask: + x, mask_indices = self.apply_mask(features, padding_mask) + else: + x = features + mask_indices = None + + def cal_transformer_layers(x, encoder_padding_mask, return_all_hiddens=False): + # x: B x T x C + positions = self.embed_positions(x.transpose(1, 2)).transpose(1, 2) + x = x + positions + if not self.normalize_before: + x = self.layer_norm(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + encoder_states = [] + for layer in self.layers: + x = layer(x, encoder_padding_mask) + if return_all_hiddens: + encoder_states.append(x) + if self.normalize_before: + x = self.layer_norm(x) + return x, encoder_states + + x, encoder_states = cal_transformer_layers(x, padding_mask, return_all_hiddens) + if features_only: + return { + "encoder_out": [x], # [T x B x C] + "encoder_padding_mask": [padding_mask] + if padding_mask is not None + else [], # B x T + "encoder_embedding": [], # + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + "mask_indices": [mask_indices], + } + + x_unmasked = x + if self.mask_prob > 0 or self.mask_channel_prob > 0: + x_unmasked, _ = cal_transformer_layers(unmasked_features, padding_mask) + return { + "encoder_out": [x], # [T x B x C] + "encoder_unmasked_out": [x_unmasked], # [T x B x C] + "encoder_padding_mask": [padding_mask] + if padding_mask is not None + else [], # B x T + "encoder_embedding": [], # + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + "mask_indices": [mask_indices] if mask_indices is not None else [], # B X T + } + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] + if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] + if len(encoder_out["encoder_padding_mask"]) == 0 + else [ + x.index_select(0, new_order) + for x in encoder_out["encoder_padding_mask"] + ] + ) + + new_encoder_embedding = ( + [] + if len(encoder_out["encoder_embedding"]) == 0 + else [ + x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] + ] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } + + +class StackedSpeechWavTransformerEncoder(FairseqEncoder): + def __init__(self, speech_enc, text_enc_layers, text_layer_norm): + super().__init__(None) + self.speech_encoder = speech_enc + self.text_encoder_layers = text_enc_layers + self.final_layer_norm = text_layer_norm + + def forward( + self, + src_tokens, + src_lengths=None, + return_all_hiddens=False, + padding_mask=None, + features_only=True, + ): + + out = self.speech_encoder.forward( + src_tokens, + src_lengths, + return_all_hiddens, + padding_mask=padding_mask, + features_only=features_only, + ) + x = out["encoder_out"][0] + encoder_padding_mask = None + if len(out["encoder_padding_mask"]) > 0: + encoder_padding_mask = out["encoder_padding_mask"][0] + + def cal_text_layers(x, padding_mask, return_all_hiddens=False): + encoder_states = [] + for layer in self.text_encoder_layers: + x = layer(x, padding_mask) + if return_all_hiddens: + encoder_states.append(x) + if self.final_layer_norm is not None: + x = self.final_layer_norm(x) + return x, encoder_states + + x, encoder_states = cal_text_layers(x, encoder_padding_mask, return_all_hiddens) + if features_only: + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask] + if encoder_padding_mask is not None + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + x_u = out["encoder_unmasked_out"][0] + x_u, _ = cal_text_layers(x_u, encoder_padding_mask) + + return { + "encoder_out": [x], # [T x B x C] + "encoder_unmasked_out": [x_u], # [T x B x C] + "encoder_padding_mask": [encoder_padding_mask] + if encoder_padding_mask is not None + else [], # B x T + "encoder_embedding": [], # + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + "mask_indices": out["mask_indices"], # B X T + } + + def reorder_encoder_out(self, encoder_out, new_order): + return self.speech_encoder.reorder_encoder_out(encoder_out, new_order) diff --git a/fairseq/fairseq/models/speech_to_text/utils.py b/fairseq/fairseq/models/speech_to_text/utils.py new file mode 100644 index 0000000..3311744 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/utils.py @@ -0,0 +1,562 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + + +import logging +from collections.abc import Iterable +from itertools import repeat +from typing import List, Optional, Tuple + +import torch +from torch import Tensor + +# ------------------------------------------------------------------------------ +# assert_equal() +# ------------------------------------------------------------------------------ + + +def assert_equal(value1, value2, name1=None, name2=None): + """Asserts two values are equal otherwise raise an error.""" + + str_name1 = "" if name1 is None else "{} ".format(name1) + str_name2 = "" if name2 is None else "{} ".format(name2) + if value1 != value2: + str_value1 = "{}" if name1 is None else "({})" + str_value1 = str_value1.format(value1) + str_value2 = "{}" if name2 is None else "({})" + str_value2 = str_value2.format(value2) + raise ValueError( + "Expected {}{} == {}{}".format(str_name1, str_value1, str_name2, str_value2) + ) + + +def fill_config(config, key, value): + if value is not None: + if key not in config or config[key] is None: + config[key] = value + assert_equal(value, config[key], "value", f'config["{key}"]') + + +# ------------------------------------------------------------------------------ +# check_and_return_expected() +# ------------------------------------------------------------------------------ + + +def check_and_return_expected(value, undefined_value, expected_value, name=None): + """ + Return the expected value while checking if the given value is undefined or + equal to the expected value. + """ + if (undefined_value is None and value is None) or (undefined_value == value): + return expected_value + if value != expected_value: + str_name = "" if name is None else "{} ".format(name) + str_value = "{}" if name is None else "({})" + str_value = str_value.format(value) + raise ValueError( + "Expected {}{} == {}".format(str_name, str_value, expected_value) + ) + return expected_value + + +# ------------------------------------------------------------------------------ +# get_time_axis() +# ------------------------------------------------------------------------------ + + +def get_time_axis(layout): + """ + Extract the time axis from the layout, for example for breaking sequence into + segments. + """ + if layout in ["TB", "TBD"]: + return 0 + if layout in ["BT", "BTD"]: + return 1 + if layout in ["BCTD"]: + return 2 + raise ValueError("Unsupported layout = {}".format(layout)) + + +# ------------------------------------------------------------------------------ +# get_batch_axis() +# ------------------------------------------------------------------------------ + + +def get_batch_axis(layout): + """ + Extract the batch axis from the layout + """ + if layout in ["TB", "TBD"]: + return 1 + if layout in ["BT", "BTD", "BCTD"]: + return 0 + raise ValueError("Unsupported layout = {}".format(layout)) + + +# ------------------------------------------------------------------------------ +# monotonically_increasing_and_bounded() +# ------------------------------------------------------------------------------ + + +def monotonically_increasing_and_bounded(iterable, min=None, max=None): + """ + Check if the elements in the given iterable are monotonically increasing and + bounded by upper/lower bounds. + """ + if not isinstance(iterable, Iterable): + raise TypeError( + "Expected iterable to be of type Iterable, got ({})".format( + iterable.__class__.__name__ + ) + ) + for i in range(len(iterable)): + if min is not None and iterable[i] < min: + return False + if max is not None and iterable[i] > max: + return False + if i > 0 and iterable[i] <= iterable[i - 1]: + return False + return True + + +# ------------------------------------------------------------------------------ +# to_pair() +# ------------------------------------------------------------------------------ + + +def to_pair(value, name): + """Make a pair (of type tuple) of given value.""" + if isinstance(value, Iterable): + if len(value) != 2: + raise ValueError( + "Expected `{}` to have exactly 2 elements, got: ({})".format( + name, value + ) + ) + return value + return tuple(repeat(value, 2)) + + +# ------------------------------------------------------------------------------ +# infer_conv_output_attrs() +# ------------------------------------------------------------------------------ + + +# TODO(cfyeh): figure out if we can get `output_dim` without calling the module. +def infer_conv_output_attrs( + module, input_channels, input_dim, batch_size=1, max_length=8 +): + """Get output attributes of a module with input.""" + input = torch.randn(batch_size, input_channels, max_length, input_dim) + output = module(input) + output_channels = output.shape[1] + output_dim = output.shape[-1] + return output_channels, output_dim + + +# ------------------------------------------------------------------------------ +# NoOp +# ------------------------------------------------------------------------------ + + +class NoOp(torch.nn.Module): + """ + NoOp simply passes the input as the output. + """ + + def __init__(self): + super().__init__() + + def forward(self, input: Tensor) -> Tensor: + return input + + +# ------------------------------------------------------------------------------ +# Permute: a torch.nn.Module applies permutation on the input tensor. +# ------------------------------------------------------------------------------ + + +class Permute(torch.nn.Module): + def __init__(self, dims): + super().__init__() + self.dims = dims + + def forward(self, input: Tensor) -> Tensor: + return input.permute(self.dims).contiguous() + + +# ------------------------------------------------------------------------------ +# lengths_to_padding_mask() +# ------------------------------------------------------------------------------ + + +def lengths_to_padding_mask(lengths: Tensor) -> Tensor: + """Convert lengths of shape (B, ) to padding mask.""" + batch_size = lengths.shape[0] + max_length = int(torch.max(lengths).item()) + padding_mask = torch.arange( # [0, ..., T-1] + max_length, device=lengths.device, dtype=lengths.dtype + ).expand(batch_size, max_length) >= lengths.unsqueeze(1) + + return padding_mask + + +# ------------------------------------------------------------------------------ +# lengths_to_attention_mask() +# ------------------------------------------------------------------------------ + + +def lengths_to_attention_mask( + lengths: Tensor, + left_context: Optional[int] = None, + right_context: Optional[int] = None, +) -> Optional[Tensor]: + """ + Generate attention mask based on (lengths, left_context, right_context). + left_context is None means unlimited left context. + right_context is None means unlimited right context. + """ + + if left_context is None and right_context is None: + return None + + max_length = int(torch.max(lengths).item()) + + # For example, with `max_length` == 5, + # indices = tensor([ + # [ 0, 1, 2, 3, 4, 5], + # [-1, 0, 1, 2, 3, 4], + # [-2, -1, 0, 1, 2, 3], + # [-3, -2, -1, 0, 1, 2], + # [-4, -3, -2, -1, 0, 1], + # [-5, -4, -3, -2, -1, 0], + # ]) + + # In some cases the second torch.arange is created on cpu which causes a + # failure. Adding the device option to guard against it. + indices = torch.arange( + max_length, device=lengths.device, dtype=lengths.dtype + ).expand(max_length, max_length) - torch.arange( + max_length, device=lengths.device + ).view( + max_length, -1 + ) + + # For example, with `max_length` == 5, + # bool_mask = tensor([ + # [True, True, True, True, True], + # [True, True, True, True, True], + # [True, True, True, True, True], + # [True, True, True, True, True], + # [True, True, True, True, True], + # ]) + bool_mask = ( + torch.tensor([True]).to(device=lengths.device).expand(max_length, max_length) + ) + + # For example, with `max_length` == 5, left_context == 2 + # left_mask = tensor([ + # [ True, True, True, True, True], + # [ True, True, True, True, True], + # [ True, True, True, True, True], + # [False, True, True, True, True], + # [False, False, True, True, True], + # ]) + if left_context is not None: + left_mask = indices >= -left_context + bool_mask = bool_mask & left_mask + + # For example, with `max_length` == 5, right_context == 1 + # right_mask = tensor([ + # [True, True, False, False, False], + # [True, True, True, False, False], + # [True, True, True, True, False], + # [True, True, True, True, True], + # [True, True, True, True, True], + # ]) + if right_context is not None: + right_mask = indices <= right_context + bool_mask = bool_mask & right_mask + + bool_mask = (~bool_mask).to(device=lengths.device) + return bool_mask + + +# ------------------------------------------------------------------------------ +# infer_output_norm() +# ------------------------------------------------------------------------------ + + +def infer_output_norm(module, output_norm=None): + """ + Infer the output norm (string and module) needed on the module gvien desired + output normalization. + """ + if output_norm == module.output_norm(): + # output_norm already matches module.output_norm(). + return (None, NoOp()) + + if output_norm is None and module.output_norm() is not None: + logger = logging.getLogger("infer_output_norm()") + logger.warning( + "trying to set output_norm ({}) ".format(output_norm) + + "but got module.output_norm() ({}), ".format(module.output_norm()) + + "the combined output_norm() will be ({})".format(module.output_norm()) + ) + return (None, NoOp()) + + if output_norm == "log_softmax": + if module.output_norm() is not None: + raise ValueError( + "incompatible output_norm ({}) ".format(output_norm) + + "and module.output_norm() ({})".format(module.output_norm()) + ) + else: + return ("log_softmax", torch.nn.LogSoftmax(dim=-1)) + + if output_norm == "softmax": + if module.output_norm() is not None: + raise ValueError( + "incompatible output_norm ({}) ".format(output_norm) + + "and module.output_norm() ({})".format(module.output_norm()) + ) + else: + return ("softmax", torch.nn.Softmax(dim=-1)) + + raise ValueError( + "output_norm ({}) not in ".format(output_norm) + + "supported list = [None, softmax, log_softmax]" + ) + + +# ------------------------------------------------------------------------------ +# infer_channels_from_layout() +# ------------------------------------------------------------------------------ + + +def infer_channels_from_layout(layout, channels): + """Extract the number of channels from the layout.""" + if layout in ("TBD", "BTD"): + if channels is not None and channels != 1: + raise ValueError( + "Expected channels ({}) to be 1 for layout = {}".format( + channels, layout + ) + ) + if channels is None: + return 1 + return channels + + +# ------------------------------------------------------------------------------ +# pad_sequence() +# ------------------------------------------------------------------------------ + + +@torch.jit.export +def pad_sequence( + sequence: Tensor, + time_axis: int, + extra_left_context: int = 0, + extra_right_context: int = 0, +) -> Tensor: + """Pad extra left/right contexts to the sequence.""" + + if extra_left_context == 0 and extra_right_context == 0: + return sequence + + tensors_to_concat = [] + + if extra_left_context: + size = (extra_left_context,) + fill_value = 0 + indices = torch.full( + size=size, + fill_value=fill_value, + dtype=torch.long, + device=sequence.device, + ) + left_padding = torch.index_select(sequence, time_axis, indices) + tensors_to_concat.append(left_padding) + + tensors_to_concat.append(sequence) + + # NOTE(cfyeh): for efficiency reason we pad 0 instead of the last frame for + # extra right contexts. + if extra_right_context: + size = list(sequence.shape) + size[time_axis] = extra_right_context + right_padding = torch.zeros(size, dtype=sequence.dtype, device=sequence.device) + tensors_to_concat.append(right_padding) + + padded_sequence = torch.cat(tensors_to_concat, dim=time_axis) + return padded_sequence + + +# ------------------------------------------------------------------------------ +# sequence_to_segments() +# ------------------------------------------------------------------------------ + + +@torch.jit.export +def sequence_to_segments( + sequence: Tensor, + time_axis: int, + lengths: Tensor, + segment_size: Optional[int] = None, + extra_left_context: int = 0, + extra_right_context: int = 0, +) -> List[Tuple[Tensor, Tensor]]: + """Breaks sequence into segments.""" + + sequence = pad_sequence( + sequence=sequence, + time_axis=time_axis, + extra_left_context=extra_left_context, + extra_right_context=extra_right_context, + ) + + lengths = lengths + extra_left_context + extra_right_context + + segments: List[Tuple[Tensor, Tensor]] = [] + + if segment_size is None: + segments.append((sequence, lengths)) + return segments + + offset = 0 + end = sequence.shape[time_axis] + step = segment_size + size = extra_left_context + segment_size + extra_right_context + + while offset + extra_left_context + extra_right_context < end: + clamped_size = min(size, end - offset) + segment_lengths = torch.clamp(lengths - offset, min=0, max=clamped_size) + indices = torch.arange( + start=offset, + end=(offset + clamped_size), + step=1, + dtype=torch.long, + device=sequence.device, + ) + segment_tensor = torch.index_select(sequence, time_axis, indices) + segments.append((segment_tensor, segment_lengths)) + offset = offset + step + + return segments + + +# ------------------------------------------------------------------------------ +# segments_to_sequence() +# ------------------------------------------------------------------------------ + + +@torch.jit.export +def segments_to_sequence( + segments: List[Tuple[Tensor, Tensor]], time_axis: int +) -> Tuple[Tensor, Tensor]: + """Concatenate segments into a full sequence.""" + if len(segments) == 1: + return segments[0] + + tensors_to_concat: List[Tensor] = [] + lengths_to_stack: List[Tensor] = [] + + for tensor, lengths in segments: + tensors_to_concat.append(tensor) + lengths_to_stack.append(lengths) + + sequence = torch.cat(tensors_to_concat, dim=time_axis) + lengths = torch.stack(lengths_to_stack, dim=0) + lengths = torch.sum(lengths, dim=0) + + return sequence, lengths + + +def lengths_to_encoder_padding_mask(lengths, batch_first: bool = False): + """ + convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor + + Args: + lengths: a (B, )-shaped tensor + batch_first: whether to return a (B, T) tensor + + Return: + max_length: maximum length of B sequences + encoder_padding_mask: a (max_length, B) binary mask, where + [t, b] = False for t < lengths[b] and True otherwise + + TODO: + kernelize this function if benchmarking shows this function is slow + """ + max_lengths = torch.max(lengths).item() + bsz = lengths.size(0) + encoder_padding_mask = torch.arange( + max_lengths + ).to( # a (T, ) tensor with [0, ..., T-1] + lengths.device + ).view( # move to the right device + 1, max_lengths + ).expand( # reshape to (1, T)-shaped tensor + bsz, -1 + ) > lengths.view( # expand to (B, T)-shaped tensor + bsz, 1 + ).expand( + -1, max_lengths + ) + if not batch_first: + return encoder_padding_mask.t(), max_lengths + else: + return encoder_padding_mask, max_lengths + + +# ------------------------------------------------------------------------------ +# attention suppression +# ------------------------------------------------------------------------------ + + +def attention_suppression(attention_weights: Tensor, scale: float): + # B, H, qlen, klen -> B, H, qlen, 1 + attention_prob = torch.nn.functional.softmax(attention_weights.float(), dim=-1) + attention_nozeros = attention_prob.to(torch.bool) + nozeros_sum = torch.sum(attention_nozeros.to(torch.float), dim=-1, keepdim=True) + + # For very sparse situation, we need get round about 0s + key_sum = torch.sum(attention_prob, dim=-1, keepdim=True) + + # nozeros_sum should > 1 + key_mean = key_sum / (nozeros_sum + 1e-8) + + # std calculation + dis = (attention_prob - key_mean) * (attention_prob - key_mean) + + # if attention_prob[i] < threshold, then dis_masked[i] = 0; for all i + dis_masked = torch.where( + attention_nozeros, dis, attention_prob.new_zeros(attention_prob.size()) + ) + + key_var = torch.sum(dis_masked, dim=-1, keepdim=True) + key_var = key_var / (nozeros_sum - 1.0 + 1e-8) + key_std = torch.sqrt(key_var) + key_thread = key_mean - scale * key_std + + # if attention_prob[i] >= key_thread, then attention_prob[i] + # , otherwise "-inf" + inf_tensor = attention_prob.new_zeros(attention_prob.size()).detach() + inf_tensor[:] = float("-inf") + attention_weights_float = torch.where( + attention_prob < key_thread, + inf_tensor, + attention_weights.float(), + ) + + return attention_weights_float.type_as(attention_weights) + + +def layer_norm_backward_hook(module, grad_input, grad_output, clamp_value): + return tuple(torch.clamp(v, min=-clamp_value, max=clamp_value) for v in grad_input) diff --git a/fairseq/fairseq/models/speech_to_text/xm_transformer.py b/fairseq/fairseq/models/speech_to_text/xm_transformer.py new file mode 100644 index 0000000..7b4b234 --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/xm_transformer.py @@ -0,0 +1,855 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import logging +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import checkpoint_utils, utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqEncoderModel, + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder +from fairseq.models.speech_to_text.hub_interface import S2THubInterface +from fairseq.models.transformer import ( + Embedding, + TransformerDecoder, + TransformerModelBase, +) +from fairseq.models.wav2vec import Wav2VecEncoder +from fairseq.modules.layer_norm import LayerNorm + +logger = logging.getLogger(__name__) + + +def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + return Embedding(num_embeddings, embed_dim, padding_idx) + + +class Conv1dAdaptor(nn.Module): + def __init__( + self, + in_dim, + out_dim, + n_layers=3, + kernel_size=3, + stride=2, + layerdrop=0.0, + layernorm=False, + proj=False, + ): + super().__init__() + self.proj, self.proj_ln = None, None + self.post_proj, self.post_proj_ln = None, None + if proj: + self.proj = nn.Sequential( + nn.Linear(in_dim, in_dim * 4), nn.ReLU(), nn.Linear(in_dim * 4, in_dim) + ) + self.proj_ln = LayerNorm(in_dim) + self.post_proj = nn.Sequential( + nn.Linear(out_dim, out_dim * 4), + nn.ReLU(), + nn.Linear(out_dim * 4, out_dim), + ) + self.post_proj_ln = LayerNorm(out_dim) + + self.layers = nn.ModuleList( + nn.Conv1d( + in_dim if i == 0 else out_dim, + out_dim * 2, + kernel_size, + stride=stride, + padding=kernel_size // 2, + ) + for i in range(n_layers) + ) + self.stride = stride + self.layerdrop = layerdrop + self.layernorm = LayerNorm(in_dim) if layernorm else None + + @classmethod + def add_args(cls, parser): + parser.add_argument("--adaptor-n-layers", type=int) + parser.add_argument("--adaptor-kernel-size", type=int) + parser.add_argument("--adaptor-stride", type=int) + parser.add_argument("--adaptor-layerdrop", type=float) + parser.add_argument("--adaptor-layernorm", action="store_true") + parser.add_argument("--adaptor-proj", action="store_true") + + def forward(self, x, padding_mask: Optional[torch.Tensor]): + if self.layernorm is not None: + x = self.layernorm(x) + + if self.proj is not None: + x = x + 0.5 * self.proj(x) + x = self.proj_ln(x) + + if padding_mask is not None: + x = utils.index_put(x, padding_mask.T, 0) + + # T x B x C -> B x C x T + x = x.transpose(0, 1).transpose(1, 2) + out_lens = None + if padding_mask is not None: + out_lens = (~padding_mask).sum(1).float() + + for layer in self.layers: + layerdrop_prob = np.random.random() + if not self.training or (layerdrop_prob > self.layerdrop): + x = nn.functional.glu(layer(x), dim=1) + if padding_mask is not None: + out_lens = ((out_lens - 1) / self.stride + 1).floor() + # B x C x T -> T x B x C + x = x.transpose(1, 2).transpose(0, 1) + + if self.post_proj is not None: + x = x + 0.5 * self.post_proj(x) + x = self.post_proj_ln(x) + + out_padding_mask = None + if padding_mask is not None: + out_padding_mask = lengths_to_padding_mask(out_lens.long()) + x = utils.index_put(x, out_padding_mask.T, 0) + return x, out_padding_mask + + +def add_wav2vec_asr_args(parser): + parser.add_argument("--w2v-path", help="path to wav2vec 2.0 model") + parser.add_argument( + "--no-pretrained-weights", + action="store_true", + help="if true, does not load pretrained weights", + ) + parser.add_argument( + "--dropout-input", + type=float, + metavar="D", + help="dropout to apply to the input (after feat extr)", + ) + parser.add_argument( + "--final-dropout", + type=float, + metavar="D", + help="dropout after transformer and before final projection", + ) + parser.add_argument( + "--apply-mask", action="store_true", help="apply masking during fine-tuning" + ) + parser.add_argument( + "--dropout", + type=float, + metavar="D", + help="dropout probability inside wav2vec 2.0 model", + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights inside wav2vec 2.0 model", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN inside wav2vec 2.0 model", + ) + parser.add_argument( + "--mask-length", type=int, help="repeat the mask indices multiple times" + ) + parser.add_argument( + "--mask-prob", type=float, help="probability of replacing a token with mask" + ) + parser.add_argument( + "--mask-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + help="how to choose masks", + ) + parser.add_argument( + "--mask-other", + type=float, + help="stdev of the mask length in case of 'normal' selection strategy", + ) + parser.add_argument( + "--no-mask-overlap", + action="store_true", + help="whether to allow masks to overlap", + ) + parser.add_argument( + "--mask-channel-length", type=int, help="repeat the mask indices multiple times" + ) + parser.add_argument( + "--mask-channel-prob", + type=float, + help="probability of replacing a token with mask", + ) + parser.add_argument( + "--mask-channel-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + help="how to choose masks", + ) + parser.add_argument( + "--mask-channel-other", + type=float, + help="stdev of the mask length in case of 'normal' selection strategy", + ) + parser.add_argument( + "--no-mask-channel-overlap", + action="store_true", + help="whether to allow masks to overlap", + ) + parser.add_argument( + "--freeze-finetune-updates", + type=int, + metavar="N", + help="dont finetune wav2vec for this many updates", + ) + parser.add_argument( + "--feature-grad-mult", + type=float, + metavar="D", + help="reset feature grad mult in wav2vec 2.0 to this", + ) + parser.add_argument( + "--layerdrop", + type=float, + metavar="D", + help="probability of dropping a layer in wav2vec 2.0", + ) + parser.add_argument( + "--max-positions", + type=int, + metavar="N", + help="Max input positions to be used in the conformer encoder in wav2vec 2.0", + ) + parser.add_argument("--encoder-proj", action="store_true") + parser.add_argument("--w2v-args", default=None) + parser.add_argument( + "--remove-weight-norm", + action="store_true", + help="if set, then the weight-norm (in one pos_conv layer) is removed from the model", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension to be used when w2v_path is None and no encoder_proj is set", + ) + + +def need_finetuning(ft_params, param_name): + if ft_params == "all": + return True + ft_params_list = ft_params.split(",") + for ft_param in ft_params_list: + if ft_param in param_name: + return True + return False + + +class Wav2VecEncoderWithAdaptor(FairseqEncoder): + def build_adaptor(self, args): + adaptor = None + if args.adaptor_n_layers > 0: + adaptor = Conv1dAdaptor( + args.decoder_embed_dim, + args.decoder_embed_dim, + n_layers=args.adaptor_n_layers, + kernel_size=args.adaptor_kernel_size, + stride=args.adaptor_stride, + layerdrop=args.adaptor_layerdrop, + layernorm=args.adaptor_layernorm, + proj=args.adaptor_proj, + ) + return adaptor + + def __init__(self, args): + super().__init__(None) + self.w2v_encoder = Wav2VecEncoder(args) + self.is_v0_arch = not args.adaptor_proj + self.w2v_proj_ln = None + if not self.is_v0_arch and self.w2v_encoder.proj is not None: + self.w2v_proj_ln = LayerNorm(args.decoder_embed_dim) + self.adaptor = self.build_adaptor(args) + + self.num_updates = 0 + self.freezing_updates = args.w2v_freezing_updates + self.finetuning_params = args.finetune_w2v_params + for k, p in self.w2v_encoder.w2v_model.named_parameters(): + p.requires_grad = need_finetuning(self.finetuning_params, k) + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + add_wav2vec_asr_args(parser) + parser.add_argument( + "--normalize", + action="store_true", + help="if set, normalizes input to have 0 mean and unit variance", + ) + parser.add_argument( + "--finetune-w2v-params", + type=str, + metavar="STR", + help="comma-separated param strings to finetune.", + ) + parser.add_argument("--w2v-freezing-updates", type=int) + parser.add_argument("--load-pretrained-encoder-from", type=str, metavar="STR") + Conv1dAdaptor.add_args(parser) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward(self, src_tokens, src_lengths=None, **kwargs): + if ( + self.freezing_updates is not None + and self.num_updates > self.freezing_updates + ): + for p in self.w2v_encoder.w2v_model.parameters(): + p.requires_grad = True + + padding_mask = lengths_to_padding_mask(src_lengths) + out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True) + x, padding_mask = out["encoder_out"], out["padding_mask"] + if self.w2v_proj_ln is not None: + x = self.w2v_proj_ln(x) + + if self.adaptor is not None: + x, padding_mask = self.adaptor(x, padding_mask) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [] + if padding_mask is None + else [padding_mask], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": [], # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] + if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] + if len(encoder_out["encoder_padding_mask"]) == 0 + else [ + x.index_select(0, new_order) + for x in encoder_out["encoder_padding_mask"] + ] + ) + + new_encoder_embedding = ( + [] + if len(encoder_out["encoder_embedding"]) == 0 + else [ + x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] + ] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } + + +def add_decoder_args(parser): + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--decoder-dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--decoder-attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--decoder-activation-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--decoder-embed-dim", type=int, metavar="N", help="decoder embedding dimension" + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--layernorm-embedding", action="store_true", help="add layernorm to embedding" + ) + parser.add_argument( + "--decoder-layerdrop", + type=float, + metavar="D", + help="layerdrop probability for decoder", + ) + parser.add_argument( + "--decoder-learned-pos", + action="store_true", + help="learn positional embedding in decoder", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-decoder-from", + type=str, + metavar="STR", + help="model to take decoder weights from (for initialization)", + ) + parser.add_argument( + "--finetune-decoder-params", + type=str, + metavar="STR", + help="comma-separated param strings to finetune.", + ) + + +def remove_weight_norm_from_model(model): + from functools import reduce + + layers_with_wn = [] + for param_name, _ in model.named_parameters(): + if param_name.endswith("_g"): + # retrieve the module with this param_name + module_names = param_name.split(".")[ + :-1 + ] # exclude the actual parameter name + wn_module = reduce(getattr, module_names, model) + layers_with_wn.append(wn_module) + for wn_module in layers_with_wn: + torch.nn.utils.remove_weight_norm(wn_module) + logger.warning(f"Weight norm removed from module with {wn_module}\n") + + +@register_model("xm_transformer") +class XMTransformerModel(FairseqEncoderDecoderModel): + @classmethod + def hub_models(cls): + base_url = "http://dl.fbaipublicfiles.com/fairseq/s2t" + model_ids = [ + "xm_transformer_600m-es_en-multi_domain", + "xm_transformer_600m-ru_en-multi_domain", + "xm_transformer_600m-fr_en-multi_domain", + "xm_transformer_600m-en_es-multi_domain", + "xm_transformer_600m-en_ru-multi_domain", + "xm_transformer_600m-en_fr-multi_domain", + "xm_transformer_600m-en_zh-multi_domain", + "xm_transformer_600m-en_ar-multi_domain", + "xm_transformer_600m-en_tr-multi_domain", + "xm_transformer_600m-en_vi-multi_domain", + "xm_transformer-21_en-xls_r_300m", + "xm_transformer-en_15-xls_r_300m", + "xm_transformer-21_en-xls_r_1b", + "xm_transformer-en_15-xls_r_1b", + "xm_transformer-21_en-xls_r_2b", + "xm_transformer-en_15-xls_r_2b", + "xm_transformer-22_16-xls_r_2b", + "xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022", + "xm_transformer_s2ut_800m-en-es-st_plus_asr", + "xm_transformer_s2ut_800m-hk-en-h1_2022", + "xm_transformer_s2ut_800m-en-hk-h1_2022", + ] + return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + config_yaml="config.yaml", + task="speech_to_text", + generation_args=None, + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + config_yaml=config_yaml, + task=task, + generation_args=generation_args, + **kwargs, + ) + return S2THubInterface(x["args"], x["task"], x["models"][0]) + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + Wav2VecEncoderWithAdaptor.add_args(parser) + add_decoder_args(parser) + parser.add_argument("--checkpoint-activations", action="store_true") + parser.add_argument("--offload-activations", action="store_true") + parser.add_argument("--min-params-to-wrap", type=int, metavar="N") + + @classmethod + def maybe_load_pretrained(cls, component, checkpoint: Optional[str] = None): + if checkpoint is None: + return component + + _load = checkpoint_utils.load_pretrained_component_from_model + try: + return _load(component, checkpoint) + except RuntimeError as e: + logger.warning(e) + return _load(component, checkpoint, strict=False) + + @classmethod + def build_encoder(cls, args): + _args = copy.deepcopy(args) + if not args.adaptor_proj and not args.encoder_proj: # V0 arch + if args.w2v_path: + state = checkpoint_utils.load_checkpoint_to_cpu(args.w2v_path) + if state.get("cfg") is not None: + encoder_embed_dim = state["cfg"]._content["model"][ + "encoder_embed_dim" + ] + elif state.get("args") is not None: + encoder_embed_dim = state["args"].encoder_embed_dim + else: + raise ValueError(f"Invalid config in {args.w2v_path}") + _args.decoder_embed_dim = encoder_embed_dim + del state + else: + _args.decoder_embed_dim = args.encoder_embed_dim + + encoder = Wav2VecEncoderWithAdaptor(_args) + encoder = cls.maybe_load_pretrained( + encoder, getattr(args, "load_pretrained_encoder_from", None) + ) + if args.remove_weight_norm: + # remove the wn for EMA usage + logger.warning("Removing weight norm from wav2vec encoder") + remove_weight_norm_from_model(encoder) + + return encoder + + @classmethod + def get_decoder_args_from_checkpoint(cls, ckpt_args): + assert "model" in ckpt_args, "Model args not found in checkpoint cfg!" + decoder_args = {} + for k, v in ckpt_args["model"].__dict__.items(): + if "decoder" in k: + decoder_args[k] = v + + return decoder_args + + @classmethod + def override_decoder_args(cls, cli_args, decoder_args_dict): + for k, v in decoder_args_dict.items(): + if v != getattr(cli_args, k, None): + logger.warning( + f"Overriding decoder arg {k}: from {getattr(cli_args, k, None)} to {v}" + ) + setattr(cli_args, k, v) + + return cli_args + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + _args = copy.deepcopy(args) + if args.adaptor_proj or args.encoder_proj: # not V0 arch + _args.encoder_embed_dim = _args.decoder_embed_dim + _args.dropout = args.decoder_dropout + _args.attention_dropout = args.decoder_attention_dropout + _args.activation_dropout = args.decoder_activation_dropout + _args.layerdrop = _args.decoder_layerdrop + + decoder = TransformerDecoder(_args, task.target_dictionary, embed_tokens) + decoder = cls.maybe_load_pretrained( + decoder, getattr(args, "load_pretrained_decoder_from", None) + ) + + for k, p in decoder.named_parameters(): + p.requires_grad = need_finetuning(args.finetune_decoder_params, k) + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + if getattr(args, "load_pretrained_decoder_from", None) is not None: + ckpt = torch.load(getattr(args, "load_pretrained_decoder_from", None)) + decoder_args_dict = cls.get_decoder_args_from_checkpoint(ckpt["cfg"]) + args = cls.override_decoder_args(args, decoder_args_dict) + + decoder_embed_tokens = build_embedding( + task.target_dictionary, args.decoder_embed_dim + ) + + encoder = cls.build_encoder(args) + decoder = cls.build_decoder(args, task, decoder_embed_tokens) + base_model = cls(encoder, decoder) + + # set up multitask decoders + base_model.multitask_decoders = {} + for i, (task_name, task_obj) in enumerate(task.multitask_tasks.items()): + # dummy auxiliary decoder + if task_obj.args.get_loss_weight(0) == 0: + continue + + task_decoder = cls.build_multitask_decoder( + args, task_obj.args, task_obj.target_dictionary, args.decoder_embed_dim + ) + + setattr(base_model, f"{task_name}_decoder", task_decoder) + decoder_model_cls = ( + FairseqEncoderModel + if task_obj.args.decoder_type == "ctc" + else FairseqLanguageModel + ) + base_model.multitask_decoders[task_name] = decoder_model_cls( + getattr(base_model, f"{task_name}_decoder") + ) + return base_model + + @classmethod + def build_multitask_decoder( + cls, + args, + mtl_args, + tgt_dict, + in_dim, + is_first_pass_decoder=False, + ): + decoder_args = mtl_args.decoder_args + decoder_args.encoder_embed_dim = in_dim + if mtl_args.decoder_type == "transformer": + if is_first_pass_decoder: + task_decoder = cls.build_text_decoder(args, tgt_dict) + else: + from fairseq.models.speech_to_speech import ( + base_multitask_text_transformer_decoder_arch, + ) + + base_multitask_text_transformer_decoder_arch(decoder_args) # 2L + task_decoder = TransformerDecoder( + decoder_args, + tgt_dict, + embed_tokens=TransformerModelBase.build_embedding( + decoder_args, + tgt_dict, + decoder_args.decoder_embed_dim, + ), + ) + elif mtl_args.decoder_type == "ctc": + task_decoder = CTCDecoder( + dictionary=tgt_dict, + in_dim=in_dim, + ) + else: + raise NotImplementedError( + "currently only support multitask decoder_type 'transformer', 'ctc'" + ) + + return task_decoder + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + return self.get_normalized_probs_scriptable(net_output, log_probs, sample) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + return_all_hiddens=False, + **kwargs, + ): + """ + The forward method inherited from the base class has a **kwargs + argument in its input, which is not supported in torchscript. This + method overwrites the forward method definition without **kwargs. + """ + encoder_out = self.encoder( + src_tokens=src_tokens, src_lengths=src_lengths, **kwargs + ) + decoder_out = self.decoder( + prev_output_tokens=prev_output_tokens, encoder_out=encoder_out + ) + if return_all_hiddens: + decoder_out[-1]["encoder_states"] = encoder_out["encoder_out"] + # NOTE: from the top layer + decoder_out[-1]["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ] + return decoder_out + + def upgrade_state_dict(self, state_dict): + for k, _ in state_dict.items(): + if "adaptor.layers" in state_dict: + new = k.replace("adaptor.layers", "adaptor_layers") + state_dict[new] = state_dict[k] + del state_dict[k] + + +def set_default_w2v_encoder_args(args): + args.no_pretrained_weights = getattr(args, "no_pretrained_weights", False) + args.dropout_input = getattr(args, "dropout_input", 0) + args.final_dropout = getattr(args, "final_dropout", 0) + args.apply_mask = getattr(args, "apply_mask", False) + args.dropout = getattr(args, "dropout", 0) + args.attention_dropout = getattr(args, "attention_dropout", 0) + args.activation_dropout = getattr(args, "activation_dropout", 0) + args.encoder_proj = getattr(args, "encoder_proj", False) + args.remove_weight_norm = getattr(args, "remove_weight_norm", False) + + args.mask_length = getattr(args, "mask_length", 10) + args.mask_prob = getattr(args, "mask_prob", 0.5) + args.mask_selection = getattr(args, "mask_selection", "static") + args.mask_other = getattr(args, "mask_other", 0) + args.no_mask_overlap = getattr(args, "no_mask_overlap", False) + args.mask_channel_length = getattr(args, "mask_channel_length", 10) + args.mask_channel_prob = getattr(args, "mask_channel_prob", 0.5) + args.mask_channel_before = getattr(args, "mask_channel_before", False) + args.mask_channel_selection = getattr(args, "mask_channel_selection", "static") + args.mask_channel_other = getattr(args, "mask_channel_other", 0) + args.no_mask_channel_overlap = getattr(args, "no_mask_channel_overlap", False) + + args.freeze_finetune_updates = getattr(args, "freeze_finetune_updates", 0) + args.feature_grad_mult = 0.1 + args.layerdrop = getattr(args, "layerdrop", 0.0) + + args.normalize = getattr(args, "normalize", False) + args.finetune_w2v_params = getattr(args, "finetune_w2v_params", "all") + args.w2v_freezing_updates = getattr(args, "w2v_freezing_updates", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + + +def set_default_adaptor_args(args): + args.adaptor_n_layers = getattr(args, "adaptor_n_layers", 3) + args.adaptor_kernel_size = getattr(args, "adaptor_kernel_size", 3) + args.adaptor_stride = getattr(args, "adaptor_stride", 2) + args.adaptor_layerdrop = getattr(args, "adaptor_layerdrop", 0.0) + args.adaptor_layernorm = getattr(args, "adaptor_layernorm", False) + args.adaptor_proj = getattr(args, "adaptor_proj", False) + + +def set_default_transformer_decoder_args(args): + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * 1024) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_attention_dropout = getattr(args, "decoder_attention_dropout", 0.0) + args.decoder_activation_dropout = getattr(args, "decoder_activation_dropout", 0.0) + args.decoder_dropout = getattr(args, "decoder_dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) + + args.finetune_decoder_params = getattr(args, "finetune_decoder_params", "all") + + +def set_default_general_args(args): + args.checkpoint_activations = getattr(args, "checkpoint_activations", False) + args.offload_activations = getattr(args, "offload_activations", False) + args.min_params_to_wrap = getattr(args, "min_params_to_wrap", int(1e8)) + args.max_positions = getattr(args, "max_positions", 3000) + + +@register_model_architecture(model_name="xm_transformer", arch_name="xm_transformer") +def base_architecture(args): + set_default_general_args(args) + set_default_w2v_encoder_args(args) + set_default_adaptor_args(args) + set_default_transformer_decoder_args(args) diff --git a/fairseq/fairseq/models/speech_to_text/xm_transformer_unity.py b/fairseq/fairseq/models/speech_to_text/xm_transformer_unity.py new file mode 100644 index 0000000..f77ef4e --- /dev/null +++ b/fairseq/fairseq/models/speech_to_text/xm_transformer_unity.py @@ -0,0 +1,315 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import logging + +from fairseq.models import ( + FairseqEncoderModel, + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder +from fairseq.models.speech_to_speech.modules.transformer_encoder import ( + TransformerEncoderNoEmb, +) +from fairseq.models.speech_to_text.xm_transformer import XMTransformerModel +from fairseq.models.speech_to_text.xm_transformer import ( + base_architecture as xm_t_base_architecture, +) +from fairseq.models.speech_to_text.xm_transformer import ( + build_embedding, + need_finetuning, + set_default_adaptor_args, + set_default_general_args, + set_default_transformer_decoder_args, + set_default_w2v_encoder_args, +) +from fairseq.models.transformer import Linear, TransformerDecoder, TransformerModelBase +from fairseq.models.transformer.transformer_decoder_aug import AugTransformerDecoder + +logger = logging.getLogger(__name__) + + +def unit_transformer_decoder_arch_base( + args, decoder_layers=6, decoder_embed_dim=768, decoder_attention_heads=12 +): + args.encoder_layers = decoder_layers + args.decoder_layers = decoder_layers + args.decoder_embed_dim = decoder_embed_dim + args.decoder_ffn_embed_dim = decoder_embed_dim * 4 + args.decoder_attention_heads = decoder_attention_heads + args.encoder_embed_dim = args.decoder_embed_dim + args.decoder_output_dim = decoder_embed_dim + args.decoder_input_dim = decoder_embed_dim + + +def unit_transformer_decoder_arch_large( + args, decoder_layers=12, decoder_embed_dim=1024, decoder_attention_heads=16 +): + args.encoder_layers = decoder_layers + args.decoder_layers = decoder_layers + args.decoder_embed_dim = decoder_embed_dim + args.decoder_ffn_embed_dim = decoder_embed_dim * 4 + args.decoder_attention_heads = decoder_attention_heads + args.encoder_embed_dim = args.decoder_embed_dim + args.decoder_output_dim = decoder_embed_dim + args.decoder_input_dim = decoder_embed_dim + + +@register_model("unity_xm_transformer") +class XMTransformerModelUnitY(XMTransformerModel): + @classmethod + def hub_models(cls): + base_url = "http://dl.fbaipublicfiles.com/fairseq/s2t" + model_ids = [] + return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + XMTransformerModel.add_args(parser) + parser.add_argument( + "--translation-decoder-layers", + type=int, + default=4, + metavar="N", + help="num decoder layers in the first-pass translation module", + ) + parser.add_argument( + "--synthesizer-encoder-layers", + type=int, + default=0, + metavar="N", + help="num encoder layers in the second-pass synthesizer module", + ) + parser.add_argument( + "--synthesizer-augmented-cross-attention", + action="store_true", + default=False, + help="augmented cross-attention over speech encoder output", + ) + parser.add_argument( + "--load-pretrained-aux-decoder-from", + type=str, + metavar="STR", + help="model to take decoder weights from (for initialization)", + ) + + @classmethod + def build_text_decoder(cls, args, tgt_dict): + _args = copy.deepcopy(args) + + if args.adaptor_proj or args.encoder_proj: # not V0 arch + _args.encoder_embed_dim = _args.decoder_embed_dim + _args.dropout = args.decoder_dropout + _args.attention_dropout = args.decoder_attention_dropout + _args.activation_dropout = args.decoder_activation_dropout + _args.layerdrop = _args.decoder_layerdrop + _args.decoder_layers = _args.translation_decoder_layers + + embed_tokens = build_embedding(tgt_dict, _args.decoder_embed_dim) + decoder = TransformerDecoder(_args, tgt_dict, embed_tokens) + + if getattr(args, "load_pretrained_aux_decoder_from", None) is not None: + decoder = cls.maybe_load_pretrained( + decoder, getattr(args, "load_pretrained_aux_decoder_from", None) + ) + + for k, p in decoder.named_parameters(): + p.requires_grad = need_finetuning(args.finetune_decoder_params, k) + return decoder + + @classmethod + def build_decoder(cls, args, task, aug_attn=False): + _args = copy.deepcopy(args) + _args.layerdrop = 0.0 # turn off layerdrop for shallow layers + + _args.encoder_embed_dim = args.decoder_embed_dim + + proj = None + if args.decoder_embed_dim != _args.decoder_embed_dim: + proj = Linear(args.decoder_embed_dim, _args.decoder_embed_dim) + + embed_tokens = build_embedding(task.target_dictionary, _args.decoder_embed_dim) + decoder_cls = AugTransformerDecoder if aug_attn else TransformerDecoder + decoder = decoder_cls(_args, task.target_dictionary, embed_tokens) + + if getattr(args, "load_pretrained_decoder_from", None) is not None: + # load all layers first and then discard the bottom layers + embed_tokens = build_embedding( + task.target_dictionary, _args.decoder_embed_dim + ) + decoder_tmp = decoder_cls(_args, task.target_dictionary, embed_tokens) + decoder_tmp = cls.maybe_load_pretrained( + decoder_tmp, getattr(_args, "load_pretrained_decoder_from", None) + ) + state_dict = decoder_tmp.state_dict() + for k, p in decoder.named_parameters(): + p.data = state_dict[k].data + p.requires_grad = need_finetuning(_args.finetune_decoder_params, k) + decoder.layers = decoder.layers[-_args.decoder_layers :] + + return decoder, proj, _args + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + xm_t_base_architecture(args) + + encoder = cls.build_encoder(args) + decoder, proj, unit_args = cls.build_decoder( + args, + task, + aug_attn=getattr(args, "synthesizer_augmented_cross_attention", False), + ) + base_model = cls(encoder, decoder) + setattr(base_model, "proj", proj) + + base_model.t2u_augmented_cross_attn = getattr( + args, "synthesizer_augmented_cross_attention", False + ) + + # set up multitask decoders + base_model.mt_task_name = None + base_model.multitask_decoders = {} + has_first_pass_decoder = False + for task_name, task_obj in task.multitask_tasks.items(): + if task_obj.is_first_pass_decoder: + has_first_pass_decoder = True + base_model.mt_task_name = task_name + + task_decoder = cls.build_multitask_decoder( + args, + task_obj.args, + task_obj.target_dictionary, + args.decoder_embed_dim, + task_obj.is_first_pass_decoder, + ) + + setattr(base_model, f"{task_name}_decoder", task_decoder) + decoder_model_cls = ( + FairseqEncoderModel + if task_obj.args.decoder_type == "ctc" + else FairseqLanguageModel + ) + base_model.multitask_decoders[task_name] = decoder_model_cls( + getattr(base_model, f"{task_name}_decoder") + ) + + assert has_first_pass_decoder, "set at least one intermediate non-CTC decoder" + + # set up encoder on top of the auxiliary MT decoder + if getattr(args, "synthesizer_encoder_layers", 0) > 0: + base_model.synthesizer_encoder = cls.build_t2u_encoder(unit_args) + else: + base_model.synthesizer_encoder = None + + return base_model + + @classmethod + def build_t2u_encoder(cls, args): + _args = copy.deepcopy(args) + _args.encoder_layers = _args.synthesizer_encoder_layers + _args.encoder_embed_dim = args.decoder_embed_dim + _args.encoder_ffn_embed_dim = args.decoder_ffn_embed_dim + _args.encoder_attention_heads = args.decoder_attention_heads + _args.encoder_normalize_before = True + return TransformerEncoderNoEmb(_args) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + prev_output_tokens_mt, + return_all_hiddens=False, + tgt_speaker=None, + **kwargs, + ): + """ + The forward method inherited from the base class has a **kwargs + argument in its input, which is not supported in torchscript. This + method overwrites the forward method definition without **kwargs. + """ + encoder_out = self.encoder( + src_tokens=src_tokens, src_lengths=src_lengths, **kwargs + ) + + # 1. MT decoder + mt_decoder = getattr(self, f"{self.mt_task_name}_decoder") + mt_decoder_out = mt_decoder( + prev_output_tokens_mt, + encoder_out=encoder_out, + ) + x = mt_decoder_out[1]["inner_states"][-1] + if mt_decoder.layer_norm is not None: + x = mt_decoder.layer_norm(x) + if self.proj is not None: + x = self.proj(x) + + mt_decoder_padding_mask = None + if prev_output_tokens_mt.eq(mt_decoder.padding_idx).any(): + mt_decoder_padding_mask = prev_output_tokens_mt.eq(mt_decoder.padding_idx) + + # 2. T2U encoder + if self.synthesizer_encoder is not None: + t2u_encoder_out = self.synthesizer_encoder( + x, + mt_decoder_padding_mask, + ) + else: + t2u_encoder_out = { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [mt_decoder_padding_mask], # B x T + } + + # 3. T2U decoder + if self.t2u_augmented_cross_attn: + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + encoder_out_aug=t2u_encoder_out, + ) + else: + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=t2u_encoder_out, + ) + if return_all_hiddens: + decoder_out[-1]["encoder_states"] = encoder_out["encoder_out"] + # NOTE: from the top layer + decoder_out[-1]["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ] + decoder_out[-1]["mt_decoder_out"] = mt_decoder_out + return decoder_out + + +@register_model_architecture( + model_name="unity_xm_transformer", arch_name="unity_xm_transformer" +) +def base_architecture_unity(args): + set_default_general_args(args) + set_default_w2v_encoder_args(args) + set_default_adaptor_args(args) + set_default_transformer_decoder_args(args) + + args.layernorm_embedding = False + args.decoder_learned_pos = False + + +# for old models +@register_model_architecture( + model_name="unity_xm_transformer", arch_name="xm_transformer_t2" +) +def base_architecture_unity_legacy(args): + base_architecture_unity(args) diff --git a/fairseq/fairseq/models/text_to_speech/__init__.py b/fairseq/fairseq/models/text_to_speech/__init__.py new file mode 100644 index 0000000..c0dcd69 --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .tacotron2 import * # noqa +from .tts_transformer import * # noqa +from .fastspeech2 import * # noqa +from .vocoder import * # noqa diff --git a/fairseq/fairseq/models/text_to_speech/codehifigan.py b/fairseq/fairseq/models/text_to_speech/codehifigan.py new file mode 100644 index 0000000..d1574dd --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/codehifigan.py @@ -0,0 +1,95 @@ +from argparse import Namespace +import torch +import torch.nn as nn + +from fairseq.models.text_to_speech.fastspeech2 import VariancePredictor +from fairseq.models.text_to_speech.hifigan import Generator + + +class CodeGenerator(Generator): + def __init__(self, cfg): + super().__init__(cfg) + self.dict = nn.Embedding(cfg["num_embeddings"], cfg["embedding_dim"]) + self.multispkr = cfg.get("multispkr", None) + self.embedder = cfg.get("embedder_params", None) + + if self.multispkr and not self.embedder: + self.spkr = nn.Embedding(cfg.get("num_speakers", 200), cfg["embedding_dim"]) + elif self.embedder: + self.spkr = nn.Linear(cfg.get("embedder_dim", 256), cfg["embedding_dim"]) + + self.dur_predictor = None + if cfg.get("dur_predictor_params", None): + self.dur_predictor = VariancePredictor( + Namespace(**cfg["dur_predictor_params"]) + ) + + self.f0 = cfg.get("f0", None) + n_f0_bin = cfg.get("f0_quant_num_bin", 0) + self.f0_quant_embed = ( + None if n_f0_bin <= 0 else nn.Embedding(n_f0_bin, cfg["embedding_dim"]) + ) + + @staticmethod + def _upsample(signal, max_frames): + if signal.dim() == 3: + bsz, channels, cond_length = signal.size() + elif signal.dim() == 2: + signal = signal.unsqueeze(2) + bsz, channels, cond_length = signal.size() + else: + signal = signal.view(-1, 1, 1) + bsz, channels, cond_length = signal.size() + + signal = signal.unsqueeze(3).repeat(1, 1, 1, max_frames // cond_length) + + # pad zeros as needed (if signal's shape does not divide completely with max_frames) + reminder = (max_frames - signal.shape[2] * signal.shape[3]) // signal.shape[3] + if reminder > 0: + raise NotImplementedError( + "Padding condition signal - misalignment between condition features." + ) + + signal = signal.view(bsz, channels, max_frames) + return signal + + def forward(self, **kwargs): + x = self.dict(kwargs["code"]).transpose(1, 2) + + if self.dur_predictor and kwargs.get("dur_prediction", False): + assert x.size(0) == 1, "only support single sample" + log_dur_pred = self.dur_predictor(x.transpose(1, 2)) + dur_out = torch.clamp( + torch.round((torch.exp(log_dur_pred) - 1)).long(), min=1 + ) + # B x C x T + x = torch.repeat_interleave(x, dur_out.view(-1), dim=2) + + if self.f0: + if self.f0_quant_embed: + kwargs["f0"] = self.f0_quant_embed(kwargs["f0"].long()).transpose(1, 2) + else: + kwargs["f0"] = kwargs["f0"].unsqueeze(1) + + if x.shape[-1] < kwargs["f0"].shape[-1]: + x = self._upsample(x, kwargs["f0"].shape[-1]) + elif x.shape[-1] > kwargs["f0"].shape[-1]: + kwargs["f0"] = self._upsample(kwargs["f0"], x.shape[-1]) + x = torch.cat([x, kwargs["f0"]], dim=1) + + if self.multispkr: + assert ( + "spkr" in kwargs + ), 'require "spkr" input for multispeaker CodeHiFiGAN vocoder' + spkr = self.spkr(kwargs["spkr"]).transpose(1, 2) + spkr = self._upsample(spkr, x.shape[-1]) + x = torch.cat([x, spkr], dim=1) + + for k, feat in kwargs.items(): + if k in ["spkr", "code", "f0", "dur_prediction"]: + continue + + feat = self._upsample(feat, x.shape[-1]) + x = torch.cat([x, feat], dim=1) + + return super().forward(x) diff --git a/fairseq/fairseq/models/text_to_speech/fastspeech2.py b/fairseq/fairseq/models/text_to_speech/fastspeech2.py new file mode 100644 index 0000000..fb2d0df --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/fastspeech2.py @@ -0,0 +1,448 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +from torch import nn + +from fairseq import utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.text_to_speech.hub_interface import TTSHubInterface +from fairseq.models.text_to_speech.tacotron2 import Postnet +from fairseq.modules import ( + FairseqDropout, + LayerNorm, + MultiheadAttention, + PositionalEmbedding, +) + +logger = logging.getLogger(__name__) + + +def model_init(m): + if isinstance(m, nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) + + +def Embedding(num_embeddings, embedding_dim, padding_idx=None): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + return m + + +class PositionwiseFeedForward(nn.Module): + def __init__(self, in_dim, hidden_dim, kernel_size, dropout): + super().__init__() + self.ffn = nn.Sequential( + nn.Conv1d( + in_dim, + hidden_dim, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + ), + nn.ReLU(), + nn.Conv1d( + hidden_dim, + in_dim, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + ), + ) + self.layer_norm = LayerNorm(in_dim) + self.dropout = self.dropout_module = FairseqDropout( + p=dropout, module_name=self.__class__.__name__ + ) + + def forward(self, x): + # B x T x C + residual = x + x = self.ffn(x.transpose(1, 2)).transpose(1, 2) + x = self.dropout(x) + return self.layer_norm(x + residual) + + +class FFTLayer(torch.nn.Module): + def __init__( + self, embed_dim, n_heads, hidden_dim, kernel_size, dropout, attention_dropout + ): + super().__init__() + self.self_attn = MultiheadAttention( + embed_dim, n_heads, dropout=attention_dropout, self_attention=True + ) + self.layer_norm = LayerNorm(embed_dim) + self.ffn = PositionwiseFeedForward( + embed_dim, hidden_dim, kernel_size, dropout=dropout + ) + + def forward(self, x, padding_mask=None): + # B x T x C + residual = x + x = x.transpose(0, 1) + x, _ = self.self_attn( + query=x, key=x, value=x, key_padding_mask=padding_mask, need_weights=False + ) + x = x.transpose(0, 1) + x = self.layer_norm(x + residual) + return self.ffn(x) + + +class LengthRegulator(nn.Module): + def forward(self, x, durations): + # x: B x T x C + out_lens = durations.sum(dim=1) + max_len = out_lens.max() + bsz, seq_len, dim = x.size() + out = x.new_zeros((bsz, max_len, dim)) + + for b in range(bsz): + indices = [] + for t in range(seq_len): + indices.extend([t] * utils.item(durations[b, t])) + indices = torch.tensor(indices, dtype=torch.long).to(x.device) + out_len = utils.item(out_lens[b]) + out[b, :out_len] = x[b].index_select(0, indices) + + return out, out_lens + + +class VariancePredictor(nn.Module): + def __init__(self, args): + super().__init__() + self.conv1 = nn.Sequential( + nn.Conv1d( + args.encoder_embed_dim, + args.var_pred_hidden_dim, + kernel_size=args.var_pred_kernel_size, + padding=(args.var_pred_kernel_size - 1) // 2, + ), + nn.ReLU(), + ) + self.ln1 = nn.LayerNorm(args.var_pred_hidden_dim) + self.dropout_module = FairseqDropout( + p=args.var_pred_dropout, module_name=self.__class__.__name__ + ) + self.conv2 = nn.Sequential( + nn.Conv1d( + args.var_pred_hidden_dim, + args.var_pred_hidden_dim, + kernel_size=args.var_pred_kernel_size, + padding=1, + ), + nn.ReLU(), + ) + self.ln2 = nn.LayerNorm(args.var_pred_hidden_dim) + self.proj = nn.Linear(args.var_pred_hidden_dim, 1) + + def forward(self, x): + # Input: B x T x C; Output: B x T + x = self.conv1(x.transpose(1, 2)).transpose(1, 2) + x = self.dropout_module(self.ln1(x)) + x = self.conv2(x.transpose(1, 2)).transpose(1, 2) + x = self.dropout_module(self.ln2(x)) + return self.proj(x).squeeze(dim=2) + + +class VarianceAdaptor(nn.Module): + def __init__(self, args): + super().__init__() + self.args = args + self.length_regulator = LengthRegulator() + self.duration_predictor = VariancePredictor(args) + self.pitch_predictor = VariancePredictor(args) + self.energy_predictor = VariancePredictor(args) + + n_bins, steps = self.args.var_pred_n_bins, self.args.var_pred_n_bins - 1 + self.pitch_bins = torch.linspace(args.pitch_min, args.pitch_max, steps) + self.embed_pitch = Embedding(n_bins, args.encoder_embed_dim) + self.energy_bins = torch.linspace(args.energy_min, args.energy_max, steps) + self.embed_energy = Embedding(n_bins, args.encoder_embed_dim) + + def get_pitch_emb(self, x, tgt=None, factor=1.0): + out = self.pitch_predictor(x) + bins = self.pitch_bins.to(x.device) + if tgt is None: + out = out * factor + emb = self.embed_pitch(torch.bucketize(out, bins)) + else: + emb = self.embed_pitch(torch.bucketize(tgt, bins)) + return out, emb + + def get_energy_emb(self, x, tgt=None, factor=1.0): + out = self.energy_predictor(x) + bins = self.energy_bins.to(x.device) + if tgt is None: + out = out * factor + emb = self.embed_energy(torch.bucketize(out, bins)) + else: + emb = self.embed_energy(torch.bucketize(tgt, bins)) + return out, emb + + def forward( + self, + x, + padding_mask, + durations=None, + pitches=None, + energies=None, + d_factor=1.0, + p_factor=1.0, + e_factor=1.0, + ): + # x: B x T x C + log_dur_out = self.duration_predictor(x) + dur_out = torch.clamp( + torch.round((torch.exp(log_dur_out) - 1) * d_factor).long(), min=0 + ) + dur_out.masked_fill_(padding_mask, 0) + + pitch_out, pitch_emb = self.get_pitch_emb(x, pitches, p_factor) + x = x + pitch_emb + energy_out, energy_emb = self.get_energy_emb(x, energies, e_factor) + x = x + energy_emb + + x, out_lens = self.length_regulator( + x, dur_out if durations is None else durations + ) + + return x, out_lens, log_dur_out, pitch_out, energy_out + + +class FastSpeech2Encoder(FairseqEncoder): + def __init__(self, args, src_dict, embed_speaker): + super().__init__(src_dict) + self.args = args + self.padding_idx = src_dict.pad() + self.n_frames_per_step = args.n_frames_per_step + self.out_dim = args.output_frame_dim * args.n_frames_per_step + + self.embed_speaker = embed_speaker + self.spk_emb_proj = None + if embed_speaker is not None: + self.spk_emb_proj = nn.Linear( + args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim + ) + + self.dropout_module = FairseqDropout( + p=args.dropout, module_name=self.__class__.__name__ + ) + self.embed_tokens = Embedding( + len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx + ) + + self.embed_positions = PositionalEmbedding( + args.max_source_positions, args.encoder_embed_dim, self.padding_idx + ) + self.pos_emb_alpha = nn.Parameter(torch.ones(1)) + self.dec_pos_emb_alpha = nn.Parameter(torch.ones(1)) + + self.encoder_fft_layers = nn.ModuleList( + FFTLayer( + args.encoder_embed_dim, + args.encoder_attention_heads, + args.fft_hidden_dim, + args.fft_kernel_size, + dropout=args.dropout, + attention_dropout=args.attention_dropout, + ) + for _ in range(args.encoder_layers) + ) + + self.var_adaptor = VarianceAdaptor(args) + + self.decoder_fft_layers = nn.ModuleList( + FFTLayer( + args.decoder_embed_dim, + args.decoder_attention_heads, + args.fft_hidden_dim, + args.fft_kernel_size, + dropout=args.dropout, + attention_dropout=args.attention_dropout, + ) + for _ in range(args.decoder_layers) + ) + + self.out_proj = nn.Linear(args.decoder_embed_dim, self.out_dim) + + self.postnet = None + if args.add_postnet: + self.postnet = Postnet( + self.out_dim, + args.postnet_conv_dim, + args.postnet_conv_kernel_size, + args.postnet_layers, + args.postnet_dropout, + ) + + self.apply(model_init) + + def forward( + self, + src_tokens, + src_lengths=None, + speaker=None, + durations=None, + pitches=None, + energies=None, + **kwargs, + ): + x = self.embed_tokens(src_tokens) + + enc_padding_mask = src_tokens.eq(self.padding_idx) + x += self.pos_emb_alpha * self.embed_positions(enc_padding_mask) + x = self.dropout_module(x) + + for layer in self.encoder_fft_layers: + x = layer(x, enc_padding_mask) + + if self.embed_speaker is not None: + bsz, seq_len, _ = x.size() + emb = self.embed_speaker(speaker).expand(bsz, seq_len, -1) + x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) + + x, out_lens, log_dur_out, pitch_out, energy_out = self.var_adaptor( + x, enc_padding_mask, durations, pitches, energies + ) + + dec_padding_mask = lengths_to_padding_mask(out_lens) + x += self.dec_pos_emb_alpha * self.embed_positions(dec_padding_mask) + for layer in self.decoder_fft_layers: + x = layer(x, dec_padding_mask) + + x = self.out_proj(x) + x_post = None + if self.postnet is not None: + x_post = x + self.postnet(x) + return x, x_post, out_lens, log_dur_out, pitch_out, energy_out + + +@register_model("fastspeech2") +class FastSpeech2Model(FairseqEncoderModel): + """ + Implementation for https://arxiv.org/abs/2006.04558 + """ + + NON_AUTOREGRESSIVE = True + + @classmethod + def hub_models(cls): + base_url = "http://dl.fbaipublicfiles.com/fairseq/s2" + model_ids = [ + "fastspeech2-en-ljspeech", + "fastspeech2-en-200_speaker-cv4", + ] + return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + config_yaml="config.yaml", + vocoder: str = "griffin_lim", + fp16: bool = False, + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + config_yaml=config_yaml, + vocoder=vocoder, + fp16=fp16, + **kwargs, + ) + return TTSHubInterface(x["args"], x["task"], x["models"][0]) + + @staticmethod + def add_args(parser): + parser.add_argument("--dropout", type=float) + parser.add_argument("--output-frame-dim", type=int) + parser.add_argument("--speaker-embed-dim", type=int) + # FFT blocks + parser.add_argument("--fft-hidden-dim", type=int) + parser.add_argument("--fft-kernel-size", type=int) + parser.add_argument("--attention-dropout", type=float) + parser.add_argument("--encoder-layers", type=int) + parser.add_argument("--encoder-embed-dim", type=int) + parser.add_argument("--encoder-attention-heads", type=int) + parser.add_argument("--decoder-layers", type=int) + parser.add_argument("--decoder-embed-dim", type=int) + parser.add_argument("--decoder-attention-heads", type=int) + # variance predictor + parser.add_argument("--var-pred-n-bins", type=int) + parser.add_argument("--var-pred-hidden-dim", type=int) + parser.add_argument("--var-pred-kernel-size", type=int) + parser.add_argument("--var-pred-dropout", type=float) + # postnet + parser.add_argument("--add-postnet", action="store_true") + parser.add_argument("--postnet-dropout", type=float) + parser.add_argument("--postnet-layers", type=int) + parser.add_argument("--postnet-conv-dim", type=int) + parser.add_argument("--postnet-conv-kernel-size", type=int) + + def __init__(self, encoder, args, src_dict): + super().__init__(encoder) + self._num_updates = 0 + + out_dim = args.output_frame_dim * args.n_frames_per_step + self.ctc_proj = None + if getattr(args, "ctc_weight", 0.0) > 0.0: + self.ctc_proj = nn.Linear(out_dim, len(src_dict)) + + @classmethod + def build_model(cls, args, task): + embed_speaker = task.get_speaker_embeddings(args) + encoder = FastSpeech2Encoder(args, task.src_dict, embed_speaker) + return cls(encoder, args, task.src_dict) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self._num_updates = num_updates + + def get_normalized_probs(self, net_output, log_probs, sample=None): + logits = self.ctc_proj(net_output[0]) + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + +@register_model_architecture("fastspeech2", "fastspeech2") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.2) + args.output_frame_dim = getattr(args, "output_frame_dim", 80) + args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64) + # FFT blocks + args.fft_hidden_dim = getattr(args, "fft_hidden_dim", 1024) + args.fft_kernel_size = getattr(args, "fft_kernel_size", 9) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.encoder_layers = getattr(args, "encoder_layers", 4) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2) + args.decoder_layers = getattr(args, "decoder_layers", 4) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2) + # variance predictor + args.var_pred_n_bins = getattr(args, "var_pred_n_bins", 256) + args.var_pred_hidden_dim = getattr(args, "var_pred_hidden_dim", 256) + args.var_pred_kernel_size = getattr(args, "var_pred_kernel_size", 3) + args.var_pred_dropout = getattr(args, "var_pred_dropout", 0.5) + # postnet + args.add_postnet = getattr(args, "add_postnet", False) + args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) + args.postnet_layers = getattr(args, "postnet_layers", 5) + args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) + args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) diff --git a/fairseq/fairseq/models/text_to_speech/hifigan.py b/fairseq/fairseq/models/text_to_speech/hifigan.py new file mode 100644 index 0000000..a852bee --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/hifigan.py @@ -0,0 +1,179 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import remove_weight_norm, weight_norm + +LRELU_SLOPE = 0.1 + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def get_padding(kernel_size, dilation=1): + return (kernel_size * dilation - dilation) // 2 + + +class ResBlock(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock, self).__init__() + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for layer in self.convs1: + remove_weight_norm(layer) + for layer in self.convs2: + remove_weight_norm(layer) + + +class Generator(torch.nn.Module): + def __init__(self, cfg): + super(Generator, self).__init__() + self.num_kernels = len(cfg["resblock_kernel_sizes"]) + self.num_upsamples = len(cfg["upsample_rates"]) + self.conv_pre = weight_norm( + Conv1d( + cfg.get("model_in_dim", 80), + cfg["upsample_initial_channel"], + 7, + 1, + padding=3, + ) + ) + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate( + zip(cfg["upsample_rates"], cfg["upsample_kernel_sizes"]) + ): + self.ups.append( + weight_norm( + ConvTranspose1d( + cfg["upsample_initial_channel"] // (2**i), + cfg["upsample_initial_channel"] // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = cfg["upsample_initial_channel"] // (2 ** (i + 1)) + for k, d in zip( + cfg["resblock_kernel_sizes"], cfg["resblock_dilation_sizes"] + ): + self.resblocks.append(ResBlock(ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for layer in self.ups: + remove_weight_norm(layer) + for layer in self.resblocks: + layer.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) diff --git a/fairseq/fairseq/models/text_to_speech/hub_interface.py b/fairseq/fairseq/models/text_to_speech/hub_interface.py new file mode 100644 index 0000000..e251c65 --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/hub_interface.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import random +from pathlib import Path +from typing import Dict, Optional, Tuple + +import torch +import torch.nn as nn + +logger = logging.getLogger(__name__) + + +class TTSHubInterface(nn.Module): + def __init__(self, cfg, task, model): + super().__init__() + self.cfg = cfg + self.task = task + self.model = model + self.model.eval() + + self.update_cfg_with_data_cfg(self.cfg, self.task.data_cfg) + self.generator = self.task.build_generator([self.model], self.cfg) + + @classmethod + def phonemize( + cls, + text: str, + lang: Optional[str], + phonemizer: Optional[str] = None, + preserve_punct: bool = False, + to_simplified_zh: bool = False, + ): + if to_simplified_zh: + import hanziconv + + text = hanziconv.HanziConv.toSimplified(text) + + if phonemizer == "g2p": + import g2p_en + + g2p = g2p_en.G2p() + if preserve_punct: + return " ".join("|" if p == " " else p for p in g2p(text)) + else: + res = [{",": "sp", ";": "sp"}.get(p, p) for p in g2p(text)] + return " ".join(p for p in res if p.isalnum()) + if phonemizer == "g2pc": + import g2pc + + g2p = g2pc.G2pC() + return " ".join([w[3] for w in g2p(text)]) + elif phonemizer == "ipa": + assert lang is not None + import phonemizer + from phonemizer.separator import Separator + + lang_map = {"en": "en-us", "fr": "fr-fr"} + return phonemizer.phonemize( + text, + backend="espeak", + language=lang_map.get(lang, lang), + separator=Separator(word="| ", phone=" "), + ) + else: + return text + + @classmethod + def tokenize(cls, text: str, tkn_cfg: Dict[str, str]): + sentencepiece_model = tkn_cfg.get("sentencepiece_model", None) + if sentencepiece_model is not None: + assert Path(sentencepiece_model).exists() + import sentencepiece as sp + + spm = sp.SentencePieceProcessor() + spm.Load(sentencepiece_model) + return " ".join(spm.Encode(text, out_type=str)) + else: + return text + + @classmethod + def update_cfg_with_data_cfg(cls, cfg, data_cfg): + cfg["task"].vocoder = data_cfg.vocoder.get("type", "griffin_lim") + + @classmethod + def get_model_input( + cls, task, text: str, speaker: Optional[int] = None, verbose: bool = False + ): + phonemized = cls.phonemize( + text, + task.data_cfg.hub.get("lang", None), + task.data_cfg.hub.get("phonemizer", None), + task.data_cfg.hub.get("preserve_punct", False), + task.data_cfg.hub.get("to_simplified_zh", False), + ) + tkn_cfg = task.data_cfg.bpe_tokenizer + tokenized = cls.tokenize(phonemized, tkn_cfg) + if verbose: + logger.info(f"text: {text}") + logger.info(f"phonemized: {phonemized}") + logger.info(f"tokenized: {tokenized}") + + spk = task.data_cfg.hub.get("speaker", speaker) + n_speakers = len(task.speaker_to_id or {}) + if spk is None and n_speakers > 0: + spk = random.randint(0, n_speakers - 1) + if spk is not None: + spk = max(0, min(spk, n_speakers - 1)) + if verbose: + logger.info(f"speaker: {spk}") + spk = None if spk is None else torch.Tensor([[spk]]).long() + + src_tokens = task.src_dict.encode_line(tokenized, add_if_not_exist=False).view( + 1, -1 + ) + src_lengths = torch.Tensor([len(tokenized.split())]).long() + return { + "net_input": { + "src_tokens": src_tokens, + "src_lengths": src_lengths, + "prev_output_tokens": None, + }, + "target_lengths": None, + "speaker": spk, + } + + @classmethod + def get_prediction(cls, task, model, generator, sample) -> Tuple[torch.Tensor, int]: + prediction = generator.generate(model, sample) + return prediction[0]["waveform"], task.sr + + def predict( + self, text: str, speaker: Optional[int] = None, verbose: bool = False + ) -> Tuple[torch.Tensor, int]: + sample = self.get_model_input(self.task, text, speaker, verbose=verbose) + return self.get_prediction(self.task, self.model, self.generator, sample) + + +class VocoderHubInterface(nn.Module): + """Vocoder interface to run vocoder models through hub. Currently we only support unit vocoder""" + + def __init__(self, cfg, model): + super().__init__() + self.vocoder = model + self.vocoder.eval() + self.sr = 16000 + self.multispkr = self.vocoder.model.multispkr + if self.multispkr: + logger.info("multi-speaker vocoder") + self.num_speakers = cfg.get( + "num_speakers", + 200, + ) # following the default in codehifigan to set to 200 + + def get_model_input( + self, + text: str, + speaker: Optional[int] = -1, + ): + units = list(map(int, text.strip().split())) + x = { + "code": torch.LongTensor(units).view(1, -1), + } + if not speaker: + speaker = -1 + if self.multispkr: + assert ( + speaker < self.num_speakers + ), f"invalid --speaker-id ({speaker}) with total #speakers = {self.num_speakers}" + spk = random.randint(0, self.num_speakers - 1) if speaker == -1 else speaker + x["spkr"] = torch.LongTensor([spk]).view(1, 1) + return x + + def get_prediction(self, sample, dur_prediction: Optional[bool] = True): + wav = self.vocoder(sample, dur_prediction) + return wav, self.sr + + def predict( + self, + text: str, + speaker: Optional[int] = None, + dur_prediction: Optional[bool] = True, + ): + sample = self.get_model_input(text, speaker) + return self.get_prediction(sample, dur_prediction) diff --git a/fairseq/fairseq/models/text_to_speech/tacotron2.py b/fairseq/fairseq/models/text_to_speech/tacotron2.py new file mode 100644 index 0000000..4df4075 --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/tacotron2.py @@ -0,0 +1,380 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +from torch import nn +from torch.nn import functional as F + +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) +from fairseq.modules import LSTMCellWithZoneOut, LocationAttention + + +logger = logging.getLogger(__name__) + + +def encoder_init(m): + if isinstance(m, nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) + + +class Tacotron2Encoder(FairseqEncoder): + def __init__(self, args, src_dict, embed_speaker): + super().__init__(src_dict) + self.padding_idx = src_dict.pad() + self.embed_speaker = embed_speaker + self.spk_emb_proj = None + if embed_speaker is not None: + self.spk_emb_proj = nn.Linear( + args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim + ) + + self.embed_tokens = nn.Embedding( + len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx + ) + + assert args.encoder_conv_kernel_size % 2 == 1 + self.convolutions = nn.ModuleList( + nn.Sequential( + nn.Conv1d( + args.encoder_embed_dim, + args.encoder_embed_dim, + kernel_size=args.encoder_conv_kernel_size, + padding=((args.encoder_conv_kernel_size - 1) // 2), + ), + nn.BatchNorm1d(args.encoder_embed_dim), + nn.ReLU(), + nn.Dropout(args.encoder_dropout), + ) + for _ in range(args.encoder_conv_layers) + ) + + self.lstm = nn.LSTM( + args.encoder_embed_dim, + args.encoder_embed_dim // 2, + num_layers=args.encoder_lstm_layers, + batch_first=True, + bidirectional=True, + ) + + self.apply(encoder_init) + + def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs): + x = self.embed_tokens(src_tokens) + x = x.transpose(1, 2).contiguous() # B x T x C -> B x C x T + for conv in self.convolutions: + x = conv(x) + x = x.transpose(1, 2).contiguous() # B x C x T -> B x T x C + + src_lengths = src_lengths.cpu().long() + x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True) + x = self.lstm(x)[0] + x = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)[0] + + encoder_padding_mask = src_tokens.eq(self.padding_idx) + + if self.embed_speaker is not None: + seq_len, bsz, _ = x.size() + emb = self.embed_speaker(speaker).expand(seq_len, bsz, -1) + x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) + + return { + "encoder_out": [x], # B x T x C + "encoder_padding_mask": encoder_padding_mask, # B x T + } + + +class Prenet(nn.Module): + def __init__(self, in_dim, n_layers, n_units, dropout): + super().__init__() + self.layers = nn.ModuleList( + nn.Sequential(nn.Linear(in_dim if i == 0 else n_units, n_units), nn.ReLU()) + for i in range(n_layers) + ) + self.dropout = dropout + + def forward(self, x): + for layer in self.layers: + x = F.dropout(layer(x), p=self.dropout) # always applies dropout + return x + + +class Postnet(nn.Module): + def __init__(self, in_dim, n_channels, kernel_size, n_layers, dropout): + super(Postnet, self).__init__() + self.convolutions = nn.ModuleList() + assert kernel_size % 2 == 1 + for i in range(n_layers): + cur_layers = ( + [ + nn.Conv1d( + in_dim if i == 0 else n_channels, + n_channels if i < n_layers - 1 else in_dim, + kernel_size=kernel_size, + padding=((kernel_size - 1) // 2), + ), + nn.BatchNorm1d(n_channels if i < n_layers - 1 else in_dim), + ] + + ([nn.Tanh()] if i < n_layers - 1 else []) + + [nn.Dropout(dropout)] + ) + nn.init.xavier_uniform_( + cur_layers[0].weight, + torch.nn.init.calculate_gain("tanh" if i < n_layers - 1 else "linear"), + ) + self.convolutions.append(nn.Sequential(*cur_layers)) + + def forward(self, x): + x = x.transpose(1, 2) # B x T x C -> B x C x T + for conv in self.convolutions: + x = conv(x) + return x.transpose(1, 2) + + +def decoder_init(m): + if isinstance(m, torch.nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh")) + + +class Tacotron2Decoder(FairseqIncrementalDecoder): + def __init__(self, args, src_dict): + super().__init__(None) + self.args = args + self.n_frames_per_step = args.n_frames_per_step + self.out_dim = args.output_frame_dim * args.n_frames_per_step + + self.prenet = Prenet( + self.out_dim, args.prenet_layers, args.prenet_dim, args.prenet_dropout + ) + + # take prev_context, prev_frame, (speaker embedding) as input + self.attention_lstm = LSTMCellWithZoneOut( + args.zoneout, + args.prenet_dim + args.encoder_embed_dim, + args.decoder_lstm_dim, + ) + + # take attention_lstm output, attention_state, encoder_out as input + self.attention = LocationAttention( + args.attention_dim, + args.encoder_embed_dim, + args.decoder_lstm_dim, + (1 + int(args.attention_use_cumprob)), + args.attention_conv_dim, + args.attention_conv_kernel_size, + ) + + # take attention_lstm output, context, (gated_latent) as input + self.lstm = nn.ModuleList( + LSTMCellWithZoneOut( + args.zoneout, + args.encoder_embed_dim + args.decoder_lstm_dim, + args.decoder_lstm_dim, + ) + for i in range(args.decoder_lstm_layers) + ) + + proj_in_dim = args.encoder_embed_dim + args.decoder_lstm_dim + self.feat_proj = nn.Linear(proj_in_dim, self.out_dim) + self.eos_proj = nn.Linear(proj_in_dim, 1) + + self.postnet = Postnet( + self.out_dim, + args.postnet_conv_dim, + args.postnet_conv_kernel_size, + args.postnet_layers, + args.postnet_dropout, + ) + + self.ctc_proj = None + if getattr(args, "ctc_weight", 0.0) > 0.0: + self.ctc_proj = nn.Linear(self.out_dim, len(src_dict)) + + self.apply(decoder_init) + + def _get_states(self, incremental_state, enc_out): + bsz, in_len, _ = enc_out.size() + alstm_h = self.get_incremental_state(incremental_state, "alstm_h") + if alstm_h is None: + alstm_h = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + alstm_c = self.get_incremental_state(incremental_state, "alstm_c") + if alstm_c is None: + alstm_c = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + + lstm_h = self.get_incremental_state(incremental_state, "lstm_h") + if lstm_h is None: + lstm_h = [ + enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + for _ in range(self.args.decoder_lstm_layers) + ] + lstm_c = self.get_incremental_state(incremental_state, "lstm_c") + if lstm_c is None: + lstm_c = [ + enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + for _ in range(self.args.decoder_lstm_layers) + ] + + attn_w = self.get_incremental_state(incremental_state, "attn_w") + if attn_w is None: + attn_w = enc_out.new_zeros(bsz, in_len) + attn_w_cum = self.get_incremental_state(incremental_state, "attn_w_cum") + if attn_w_cum is None: + attn_w_cum = enc_out.new_zeros(bsz, in_len) + return alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum + + def _get_init_attn_c(self, enc_out, enc_mask): + bsz = enc_out.size(0) + if self.args.init_attn_c == "zero": + return enc_out.new_zeros(bsz, self.args.encoder_embed_dim) + elif self.args.init_attn_c == "avg": + enc_w = (~enc_mask).type(enc_out.type()) + enc_w = enc_w / enc_w.sum(dim=1, keepdim=True) + return torch.sum(enc_out * enc_w.unsqueeze(2), dim=1) + else: + raise ValueError(f"{self.args.init_attn_c} not supported") + + def forward( + self, + prev_output_tokens, + encoder_out=None, + incremental_state=None, + target_lengths=None, + **kwargs, + ): + enc_mask = encoder_out["encoder_padding_mask"] + enc_out = encoder_out["encoder_out"][0] + in_len = enc_out.size(1) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:, :] + bsz, out_len, _ = prev_output_tokens.size() + + prenet_out = self.prenet(prev_output_tokens) + (alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum) = self._get_states( + incremental_state, enc_out + ) + attn_ctx = self._get_init_attn_c(enc_out, enc_mask) + + attn_out = enc_out.new_zeros(bsz, in_len, out_len) + feat_out = enc_out.new_zeros(bsz, out_len, self.out_dim) + eos_out = enc_out.new_zeros(bsz, out_len) + for t in range(out_len): + alstm_in = torch.cat((attn_ctx, prenet_out[:, t, :]), dim=1) + alstm_h, alstm_c = self.attention_lstm(alstm_in, (alstm_h, alstm_c)) + + attn_state = attn_w.unsqueeze(1) + if self.args.attention_use_cumprob: + attn_state = torch.stack((attn_w, attn_w_cum), dim=1) + attn_ctx, attn_w = self.attention(enc_out, enc_mask, alstm_h, attn_state) + attn_w_cum = attn_w_cum + attn_w + attn_out[:, :, t] = attn_w + + for i, cur_lstm in enumerate(self.lstm): + if i == 0: + lstm_in = torch.cat((attn_ctx, alstm_h), dim=1) + else: + lstm_in = torch.cat((attn_ctx, lstm_h[i - 1]), dim=1) + lstm_h[i], lstm_c[i] = cur_lstm(lstm_in, (lstm_h[i], lstm_c[i])) + + proj_in = torch.cat((attn_ctx, lstm_h[-1]), dim=1) + feat_out[:, t, :] = self.feat_proj(proj_in) + eos_out[:, t] = self.eos_proj(proj_in).squeeze(1) + self.attention.clear_cache() + + self.set_incremental_state(incremental_state, "alstm_h", alstm_h) + self.set_incremental_state(incremental_state, "alstm_c", alstm_c) + self.set_incremental_state(incremental_state, "lstm_h", lstm_h) + self.set_incremental_state(incremental_state, "lstm_c", lstm_c) + self.set_incremental_state(incremental_state, "attn_w", attn_w) + self.set_incremental_state(incremental_state, "attn_w_cum", attn_w_cum) + + post_feat_out = feat_out + self.postnet(feat_out) + eos_out = eos_out.view(bsz, out_len, 1) + return post_feat_out, eos_out, {"attn": attn_out, "feature_out": feat_out} + + +@register_model("tacotron_2") +class Tacotron2Model(FairseqEncoderDecoderModel): + """ + Implementation for https://arxiv.org/pdf/1712.05884.pdf + """ + + @staticmethod + def add_args(parser): + # encoder + parser.add_argument("--encoder-dropout", type=float) + parser.add_argument("--encoder-embed-dim", type=int) + parser.add_argument("--encoder-conv-layers", type=int) + parser.add_argument("--encoder-conv-kernel-size", type=int) + parser.add_argument("--encoder-lstm-layers", type=int) + # decoder + parser.add_argument("--attention-dim", type=int) + parser.add_argument("--attention-conv-dim", type=int) + parser.add_argument("--attention-conv-kernel-size", type=int) + parser.add_argument("--prenet-dropout", type=float) + parser.add_argument("--prenet-layers", type=int) + parser.add_argument("--prenet-dim", type=int) + parser.add_argument("--postnet-dropout", type=float) + parser.add_argument("--postnet-layers", type=int) + parser.add_argument("--postnet-conv-dim", type=int) + parser.add_argument("--postnet-conv-kernel-size", type=int) + parser.add_argument("--init-attn-c", type=str) + parser.add_argument("--attention-use-cumprob", action="store_true") + parser.add_argument("--zoneout", type=float) + parser.add_argument("--decoder-lstm-layers", type=int) + parser.add_argument("--decoder-lstm-dim", type=int) + parser.add_argument("--output-frame-dim", type=int) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._num_updates = 0 + + @classmethod + def build_model(cls, args, task): + embed_speaker = task.get_speaker_embeddings(args) + encoder = Tacotron2Encoder(args, task.src_dict, embed_speaker) + decoder = Tacotron2Decoder(args, task.src_dict) + return cls(encoder, decoder) + + def forward_encoder(self, src_tokens, src_lengths, **kwargs): + return self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self._num_updates = num_updates + + +@register_model_architecture("tacotron_2", "tacotron_2") +def base_architecture(args): + # encoder + args.encoder_dropout = getattr(args, "encoder_dropout", 0.5) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3) + args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5) + args.encoder_lstm_layers = getattr(args, "encoder_lstm_layers", 1) + # decoder + args.attention_dim = getattr(args, "attention_dim", 128) + args.attention_conv_dim = getattr(args, "attention_conv_dim", 32) + args.attention_conv_kernel_size = getattr(args, "attention_conv_kernel_size", 15) + args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) + args.prenet_layers = getattr(args, "prenet_layers", 2) + args.prenet_dim = getattr(args, "prenet_dim", 256) + args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) + args.postnet_layers = getattr(args, "postnet_layers", 5) + args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) + args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) + args.init_attn_c = getattr(args, "init_attn_c", "zero") + args.attention_use_cumprob = getattr(args, "attention_use_cumprob", True) + args.zoneout = getattr(args, "zoneout", 0.1) + args.decoder_lstm_layers = getattr(args, "decoder_lstm_layers", 2) + args.decoder_lstm_dim = getattr(args, "decoder_lstm_dim", 1024) + args.output_frame_dim = getattr(args, "output_frame_dim", 80) diff --git a/fairseq/fairseq/models/text_to_speech/tts_transformer.py b/fairseq/fairseq/models/text_to_speech/tts_transformer.py new file mode 100644 index 0000000..19afc2b --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/tts_transformer.py @@ -0,0 +1,454 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import List, Optional + +import torch +from torch import nn + +from fairseq import utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) +from fairseq.models.text_to_speech.hub_interface import TTSHubInterface +from fairseq.models.text_to_speech.tacotron2 import Postnet, Prenet +from fairseq.modules import ( + FairseqDropout, + LayerNorm, + PositionalEmbedding, + TransformerDecoderLayer, + TransformerEncoderLayer, +) + +logger = logging.getLogger(__name__) + + +def encoder_init(m): + if isinstance(m, nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) + + +def Embedding(num_embeddings, embedding_dim): + m = nn.Embedding(num_embeddings, embedding_dim) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + return m + + +class TTSTransformerEncoder(FairseqEncoder): + def __init__(self, args, src_dict, embed_speaker): + super().__init__(src_dict) + self.padding_idx = src_dict.pad() + self.embed_speaker = embed_speaker + self.spk_emb_proj = None + if embed_speaker is not None: + self.spk_emb_proj = nn.Linear( + args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim + ) + + self.dropout_module = FairseqDropout( + p=args.dropout, module_name=self.__class__.__name__ + ) + self.embed_tokens = nn.Embedding( + len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx + ) + assert args.encoder_conv_kernel_size % 2 == 1 + self.prenet = nn.ModuleList( + nn.Sequential( + nn.Conv1d( + args.encoder_embed_dim, + args.encoder_embed_dim, + kernel_size=args.encoder_conv_kernel_size, + padding=((args.encoder_conv_kernel_size - 1) // 2), + ), + nn.BatchNorm1d(args.encoder_embed_dim), + nn.ReLU(), + nn.Dropout(args.encoder_dropout), + ) + for _ in range(args.encoder_conv_layers) + ) + self.prenet_proj = nn.Linear(args.encoder_embed_dim, args.encoder_embed_dim) + self.embed_positions = PositionalEmbedding( + args.max_source_positions, args.encoder_embed_dim, self.padding_idx + ) + self.pos_emb_alpha = nn.Parameter(torch.ones(1)) + + self.transformer_layers = nn.ModuleList( + TransformerEncoderLayer(args) + for _ in range(args.encoder_transformer_layers) + ) + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(args.encoder_embed_dim) + else: + self.layer_norm = None + + self.apply(encoder_init) + + def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs): + x = self.embed_tokens(src_tokens) + x = x.transpose(1, 2).contiguous() # B x T x C -> B x C x T + for conv in self.prenet: + x = conv(x) + x = x.transpose(1, 2).contiguous() # B x C x T -> B x T x C + x = self.prenet_proj(x) + + padding_mask = src_tokens.eq(self.padding_idx) + positions = self.embed_positions(padding_mask) + x += self.pos_emb_alpha * positions + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + for layer in self.transformer_layers: + x = layer(x, padding_mask) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + if self.embed_speaker is not None: + seq_len, bsz, _ = x.size() + emb = self.embed_speaker(speaker).transpose(0, 1) + emb = emb.expand(seq_len, bsz, -1) + x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [padding_mask] + if padding_mask.any() + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": [], # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + +def decoder_init(m): + if isinstance(m, torch.nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh")) + + +class TTSTransformerDecoder(FairseqIncrementalDecoder): + def __init__(self, args, src_dict, padding_idx=1): + super().__init__(None) + self._future_mask = torch.empty(0) + + self.args = args + self.padding_idx = src_dict.pad() if src_dict else padding_idx + self.n_frames_per_step = args.n_frames_per_step + self.out_dim = args.output_frame_dim * args.n_frames_per_step + + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.embed_positions = PositionalEmbedding( + args.max_target_positions, args.decoder_embed_dim, self.padding_idx + ) + self.pos_emb_alpha = nn.Parameter(torch.ones(1)) + self.prenet = nn.Sequential( + Prenet( + self.out_dim, args.prenet_layers, args.prenet_dim, args.prenet_dropout + ), + nn.Linear(args.prenet_dim, args.decoder_embed_dim), + ) + + self.n_transformer_layers = args.decoder_transformer_layers + self.transformer_layers = nn.ModuleList( + TransformerDecoderLayer(args) for _ in range(self.n_transformer_layers) + ) + if args.decoder_normalize_before: + self.layer_norm = LayerNorm(args.decoder_embed_dim) + else: + self.layer_norm = None + + self.feat_proj = nn.Linear(args.decoder_embed_dim, self.out_dim) + self.eos_proj = nn.Linear(args.decoder_embed_dim, 1) + + self.postnet = Postnet( + self.out_dim, + args.postnet_conv_dim, + args.postnet_conv_kernel_size, + args.postnet_layers, + args.postnet_dropout, + ) + + self.ctc_proj = None + if getattr(args, "ctc_weight", 0.0) > 0.0: + self.ctc_proj = nn.Linear(self.out_dim, len(src_dict)) + + self.apply(decoder_init) + + def extract_features( + self, + prev_outputs, + encoder_out=None, + incremental_state=None, + target_lengths=None, + speaker=None, + **kwargs, + ): + alignment_layer = self.n_transformer_layers - 1 + self_attn_padding_mask = lengths_to_padding_mask(target_lengths) + positions = self.embed_positions( + self_attn_padding_mask, incremental_state=incremental_state + ) + + if incremental_state is not None: + prev_outputs = prev_outputs[:, -1:, :] + self_attn_padding_mask = self_attn_padding_mask[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + x = self.prenet(prev_outputs) + x += self.pos_emb_alpha * positions + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + if not self_attn_padding_mask.any(): + self_attn_padding_mask = None + + attn: Optional[torch.Tensor] = None + inner_states: List[Optional[torch.Tensor]] = [x] + for idx, transformer_layer in enumerate(self.transformer_layers): + if incremental_state is None: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = transformer_layer( + x, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + + if attn is not None: + # average probabilities over heads, transpose to + # (B, src_len, tgt_len) + attn = attn.mean(dim=0).transpose(2, 1) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, {"attn": attn, "inner_states": inner_states} + + def forward( + self, + prev_output_tokens, + encoder_out=None, + incremental_state=None, + target_lengths=None, + speaker=None, + **kwargs, + ): + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + target_lengths=target_lengths, + speaker=speaker, + **kwargs, + ) + attn = extra["attn"] + feat_out = self.feat_proj(x) + bsz, seq_len, _ = x.size() + eos_out = self.eos_proj(x) + post_feat_out = feat_out + self.postnet(feat_out) + return ( + post_feat_out, + eos_out, + { + "attn": attn, + "feature_out": feat_out, + "inner_states": extra["inner_states"], + }, + ) + + def get_normalized_probs(self, net_output, log_probs, sample): + logits = self.ctc_proj(net_output[2]["feature_out"]) + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. + if ( + self._future_mask.size(0) == 0 + or (not self._future_mask.device == tensor.device) + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 + ) + self._future_mask = self._future_mask.to(tensor) + return self._future_mask[:dim, :dim] + + +@register_model("tts_transformer") +class TTSTransformerModel(FairseqEncoderDecoderModel): + """ + Implementation for https://arxiv.org/pdf/1809.08895.pdf + """ + + @classmethod + def hub_models(cls): + base_url = "http://dl.fbaipublicfiles.com/fairseq/s2" + model_ids = [ + "tts_transformer-en-ljspeech", + "tts_transformer-en-200_speaker-cv4", + "tts_transformer-es-css10", + "tts_transformer-fr-cv7_css10", + "tts_transformer-ru-cv7_css10", + "tts_transformer-zh-cv7_css10", + "tts_transformer-ar-cv7_css10", + "tts_transformer-tr-cv7_css10", + "tts_transformer-vi-cv7", + ] + return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + config_yaml="config.yaml", + vocoder: str = "griffin_lim", + fp16: bool = False, + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + config_yaml=config_yaml, + vocoder=vocoder, + fp16=fp16, + **kwargs, + ) + return TTSHubInterface(x["args"], x["task"], x["models"][0]) + + @staticmethod + def add_args(parser): + parser.add_argument("--dropout", type=float) + parser.add_argument("--output-frame-dim", type=int) + parser.add_argument("--speaker-embed-dim", type=int) + # encoder prenet + parser.add_argument("--encoder-dropout", type=float) + parser.add_argument("--encoder-conv-layers", type=int) + parser.add_argument("--encoder-conv-kernel-size", type=int) + # encoder transformer layers + parser.add_argument("--encoder-transformer-layers", type=int) + parser.add_argument("--encoder-embed-dim", type=int) + parser.add_argument("--encoder-ffn-embed-dim", type=int) + parser.add_argument("--encoder-normalize-before", action="store_true") + parser.add_argument("--encoder-attention-heads", type=int) + parser.add_argument("--attention-dropout", type=float) + parser.add_argument("--activation-dropout", "--relu-dropout", type=float) + parser.add_argument("--activation-fn", type=str, default="relu") + # decoder prenet + parser.add_argument("--prenet-dropout", type=float) + parser.add_argument("--prenet-layers", type=int) + parser.add_argument("--prenet-dim", type=int) + # decoder postnet + parser.add_argument("--postnet-dropout", type=float) + parser.add_argument("--postnet-layers", type=int) + parser.add_argument("--postnet-conv-dim", type=int) + parser.add_argument("--postnet-conv-kernel-size", type=int) + # decoder transformer layers + parser.add_argument("--decoder-transformer-layers", type=int) + parser.add_argument("--decoder-embed-dim", type=int) + parser.add_argument("--decoder-ffn-embed-dim", type=int) + parser.add_argument("--decoder-normalize-before", action="store_true") + parser.add_argument("--decoder-attention-heads", type=int) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._num_updates = 0 + + @classmethod + def build_model(cls, args, task): + embed_speaker = task.get_speaker_embeddings(args) + encoder = TTSTransformerEncoder(args, task.src_dict, embed_speaker) + decoder = TTSTransformerDecoder(args, task.src_dict) + return cls(encoder, decoder) + + def forward_encoder(self, src_tokens, src_lengths, speaker=None, **kwargs): + return self.encoder( + src_tokens, src_lengths=src_lengths, speaker=speaker, **kwargs + ) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self._num_updates = num_updates + + +@register_model_architecture("tts_transformer", "tts_transformer") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.output_frame_dim = getattr(args, "output_frame_dim", 80) + args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64) + # encoder prenet + args.encoder_dropout = getattr(args, "encoder_dropout", 0.5) + args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3) + args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5) + # encoder transformer layers + args.encoder_transformer_layers = getattr(args, "encoder_transformer_layers", 6) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr( + args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim + ) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + # decoder prenet + args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) + args.prenet_layers = getattr(args, "prenet_layers", 2) + args.prenet_dim = getattr(args, "prenet_dim", 256) + # decoder postnet + args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) + args.postnet_layers = getattr(args, "postnet_layers", 5) + args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) + args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) + # decoder transformer layers + args.decoder_transformer_layers = getattr(args, "decoder_transformer_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim + ) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) diff --git a/fairseq/fairseq/models/text_to_speech/vocoder.py b/fairseq/fairseq/models/text_to_speech/vocoder.py new file mode 100644 index 0000000..dbc02da --- /dev/null +++ b/fairseq/fairseq/models/text_to_speech/vocoder.py @@ -0,0 +1,305 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import logging +from typing import Dict + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from fairseq.data.audio.audio_utils import ( + TTSSpectrogram, + get_fourier_basis, + get_mel_filters, + get_window, +) +from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.text_to_speech.codehifigan import CodeGenerator as CodeHiFiGANModel +from fairseq.models.text_to_speech.hifigan import Generator as HiFiGANModel +from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface + +logger = logging.getLogger(__name__) + + +class PseudoInverseMelScale(torch.nn.Module): + def __init__(self, n_stft, n_mels, sample_rate, f_min, f_max) -> None: + super(PseudoInverseMelScale, self).__init__() + self.n_mels = n_mels + basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max) + basis = torch.pinverse(basis) # F x F_mel + self.register_buffer("basis", basis) + + def forward(self, melspec: torch.Tensor) -> torch.Tensor: + # pack batch + shape = melspec.shape # B_1 x ... x B_K x F_mel x T + n_mels, time = shape[-2], shape[-1] + melspec = melspec.view(-1, n_mels, time) + + freq, _ = self.basis.size() # F x F_mel + assert self.n_mels == n_mels, (self.n_mels, n_mels) + specgram = self.basis.matmul(melspec).clamp(min=0) + + # unpack batch + specgram = specgram.view(shape[:-2] + (freq, time)) + return specgram + + +class GriffinLim(torch.nn.Module): + def __init__( + self, + n_fft: int, + win_length: int, + hop_length: int, + n_iter: int, + window_fn=torch.hann_window, + ): + super(GriffinLim, self).__init__() + self.transform = TTSSpectrogram( + n_fft, win_length, hop_length, return_phase=True + ) + + basis = get_fourier_basis(n_fft) + basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :] + basis *= get_window(window_fn, n_fft, win_length) + self.register_buffer("basis", basis) + + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.n_iter = n_iter + + self.tiny = 1.1754944e-38 + + @classmethod + def get_window_sum_square( + cls, n_frames, hop_length, win_length, n_fft, window_fn=torch.hann_window + ) -> torch.Tensor: + w_sq = get_window(window_fn, n_fft, win_length) ** 2 + n = n_fft + hop_length * (n_frames - 1) + x = torch.zeros(n, dtype=torch.float32) + for i in range(n_frames): + ofst = i * hop_length + x[ofst : min(n, ofst + n_fft)] += w_sq[: max(0, min(n_fft, n - ofst))] + return x + + def inverse(self, magnitude: torch.Tensor, phase) -> torch.Tensor: + x = torch.cat( + [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 + ) + x = F.conv_transpose1d(x, self.basis, stride=self.hop_length) + win_sum_sq = self.get_window_sum_square( + magnitude.shape[-1], + hop_length=self.hop_length, + win_length=self.win_length, + n_fft=self.n_fft, + ).to(magnitude.device) + # remove modulation effects + approx_nonzero_indices = win_sum_sq > self.tiny + x[:, :, approx_nonzero_indices] /= win_sum_sq[approx_nonzero_indices] + x *= self.n_fft / self.hop_length + x = x[:, :, self.n_fft // 2 :] + x = x[:, :, : -self.n_fft // 2 :] + return x + + def forward(self, specgram: torch.Tensor) -> torch.Tensor: + angles = np.angle(np.exp(2j * np.pi * np.random.rand(*specgram.shape))) + angles = torch.from_numpy(angles).to(specgram) + _specgram = specgram.view(-1, specgram.shape[-2], specgram.shape[-1]) + waveform = self.inverse(_specgram, angles).squeeze(1) + for _ in range(self.n_iter): + _, angles = self.transform(waveform) + waveform = self.inverse(_specgram, angles).squeeze(1) + return waveform.squeeze(0) + + +class GriffinLimVocoder(nn.Module): + def __init__( + self, + sample_rate, + win_size, + hop_size, + n_fft, + n_mels, + f_min, + f_max, + window_fn, + spec_bwd_max_iter=32, + fp16=False, + ): + super().__init__() + self.inv_mel_transform = PseudoInverseMelScale( + n_stft=n_fft // 2 + 1, + n_mels=n_mels, + sample_rate=sample_rate, + f_min=f_min, + f_max=f_max, + ) + self.gl_transform = GriffinLim( + n_fft=n_fft, + win_length=win_size, + hop_length=hop_size, + window_fn=window_fn, + n_iter=spec_bwd_max_iter, + ) + if fp16: + self.half() + self.inv_mel_transform.half() + self.gl_transform.half() + else: + self.float() + self.inv_mel_transform.float() + self.gl_transform.float() + + def forward(self, x): + # x: (B x) T x D -> (B x) 1 x T + # NOTE: batched forward produces noisier waveform. recommend running + # one utterance at a time + self.eval() + x = x.exp().transpose(-1, -2) + x = self.inv_mel_transform(x) + x = self.gl_transform(x) + return x + + @classmethod + def from_data_cfg(cls, args, data_cfg: S2TDataConfig): + feat_cfg = data_cfg.config["features"] + window_fn = getattr(torch, feat_cfg["window_fn"] + "_window") + return cls( + sample_rate=feat_cfg["sample_rate"], + win_size=int(feat_cfg["win_len_t"] * feat_cfg["sample_rate"]), + hop_size=int(feat_cfg["hop_len_t"] * feat_cfg["sample_rate"]), + n_fft=feat_cfg["n_fft"], + n_mels=feat_cfg["n_mels"], + f_min=feat_cfg["f_min"], + f_max=feat_cfg["f_max"], + window_fn=window_fn, + spec_bwd_max_iter=args.spec_bwd_max_iter, + fp16=args.fp16, + ) + + +class HiFiGANVocoder(nn.Module): + def __init__( + self, checkpoint_path: str, model_cfg: Dict[str, str], fp16: bool = False + ) -> None: + super().__init__() + self.model = HiFiGANModel(model_cfg) + state_dict = torch.load(checkpoint_path) + self.model.load_state_dict(state_dict["generator"]) + if fp16: + self.model.half() + logger.info(f"loaded HiFiGAN checkpoint from {checkpoint_path}") + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # (B x) T x D -> (B x) 1 x T + model = self.model.eval() + if len(x.shape) == 2: + return model(x.unsqueeze(0).transpose(1, 2)).detach().squeeze(0) + else: + return model(x.transpose(-1, -2)).detach() + + @classmethod + def from_data_cfg(cls, args, data_cfg: S2TDataConfig): + vocoder_cfg = data_cfg.vocoder + assert vocoder_cfg.get("type", "griffin_lim") == "hifigan" + with open(vocoder_cfg["config"]) as f: + model_cfg = json.load(f) + return cls(vocoder_cfg["checkpoint"], model_cfg, fp16=args.fp16) + + +@register_model("CodeHiFiGANVocoder") +class CodeHiFiGANVocoder(BaseFairseqModel): + def __init__( + self, checkpoint_path: str, model_cfg: Dict[str, str], fp16: bool = False + ) -> None: + super().__init__() + self.model = CodeHiFiGANModel(model_cfg) + if torch.cuda.is_available(): + state_dict = torch.load(checkpoint_path) + else: + state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu")) + self.model.load_state_dict(state_dict["generator"]) + self.model.eval() + if fp16: + self.model.half() + self.model.remove_weight_norm() + logger.info(f"loaded CodeHiFiGAN checkpoint from {checkpoint_path}") + + def forward(self, x: Dict[str, torch.Tensor], dur_prediction=False) -> torch.Tensor: + assert "code" in x + x["dur_prediction"] = dur_prediction + + # remove invalid code + mask = x["code"] >= 0 + x["code"] = x["code"][mask].unsqueeze(dim=0) + if "f0" in x: + f0_up_ratio = x["f0"].size(1) // x["code"].size(1) + mask = mask.unsqueeze(2).repeat(1, 1, f0_up_ratio).view(-1, x["f0"].size(1)) + x["f0"] = x["f0"][mask].unsqueeze(dim=0) + + return self.model(**x).detach().squeeze() + + @classmethod + def from_data_cfg(cls, args, data_cfg): + vocoder_cfg = data_cfg.vocoder + assert vocoder_cfg is not None, "vocoder not specified in the data config" + with open(vocoder_cfg["config"]) as f: + model_cfg = json.load(f) + return cls(vocoder_cfg["checkpoint"], model_cfg, fp16=args.fp16) + + @classmethod + def hub_models(cls): + base_url = "http://dl.fbaipublicfiles.com/fairseq/vocoder" + model_ids = [ + "unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur", + "unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_es_css10_dur", + "unit_hifigan_HK_layer12.km2500_frame_TAT-TTS", + ] + return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + config="config.json", + fp16: bool = False, + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + config_yaml=config, + fp16=fp16, + is_vocoder=True, + **kwargs, + ) + + with open(f"{x['args']['data']}/{config}") as f: + vocoder_cfg = json.load(f) + assert len(x["args"]["model_path"]) == 1, "Too many vocoder models in the input" + + vocoder = CodeHiFiGANVocoder(x["args"]["model_path"][0], vocoder_cfg) + return VocoderHubInterface(vocoder_cfg, vocoder) + + +def get_vocoder(args, data_cfg: S2TDataConfig): + if args.vocoder == "griffin_lim": + return GriffinLimVocoder.from_data_cfg(args, data_cfg) + elif args.vocoder == "hifigan": + return HiFiGANVocoder.from_data_cfg(args, data_cfg) + elif args.vocoder == "code_hifigan": + return CodeHiFiGANVocoder.from_data_cfg(args, data_cfg) + else: + raise ValueError("Unknown vocoder") diff --git a/fairseq/fairseq/models/transformer/__init__.py b/fairseq/fairseq/models/transformer/__init__.py new file mode 100644 index 0000000..681fca3 --- /dev/null +++ b/fairseq/fairseq/models/transformer/__init__.py @@ -0,0 +1,50 @@ +# Copyright (c) Facebook Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +from .transformer_config import ( + TransformerConfig, + DEFAULT_MAX_SOURCE_POSITIONS, + DEFAULT_MAX_TARGET_POSITIONS, + DEFAULT_MIN_PARAMS_TO_WRAP, +) +from .transformer_decoder import TransformerDecoder, TransformerDecoderBase, Linear +from .transformer_encoder import TransformerEncoder, TransformerEncoderBase +from .transformer_legacy import ( + TransformerModel, + base_architecture, + tiny_architecture, + transformer_iwslt_de_en, + transformer_wmt_en_de, + transformer_vaswani_wmt_en_de_big, + transformer_vaswani_wmt_en_fr_big, + transformer_wmt_en_de_big, + transformer_wmt_en_de_big_t2t, +) +from .transformer_base import TransformerModelBase, Embedding + + +__all__ = [ + "TransformerModelBase", + "TransformerConfig", + "TransformerDecoder", + "TransformerDecoderBase", + "TransformerEncoder", + "TransformerEncoderBase", + "TransformerModel", + "Embedding", + "Linear", + "base_architecture", + "tiny_architecture", + "transformer_iwslt_de_en", + "transformer_wmt_en_de", + "transformer_vaswani_wmt_en_de_big", + "transformer_vaswani_wmt_en_fr_big", + "transformer_wmt_en_de_big", + "transformer_wmt_en_de_big_t2t", + "DEFAULT_MAX_SOURCE_POSITIONS", + "DEFAULT_MAX_TARGET_POSITIONS", + "DEFAULT_MIN_PARAMS_TO_WRAP", +] diff --git a/fairseq/fairseq/models/transformer/transformer_base.py b/fairseq/fairseq/models/transformer/transformer_base.py new file mode 100644 index 0000000..f9f097f --- /dev/null +++ b/fairseq/fairseq/models/transformer/transformer_base.py @@ -0,0 +1,193 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +from torch import Tensor + +import logging + +from fairseq import utils +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.distributed import fsdp_wrap +from fairseq.models import FairseqEncoderDecoderModel +from fairseq.models.transformer import ( + TransformerConfig, + TransformerDecoderBase, + TransformerEncoderBase, +) + + +logger = logging.getLogger(__name__) + + +class TransformerModelBase(FairseqEncoderDecoderModel): + """ + Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017) + <https://arxiv.org/abs/1706.03762>`_. + + Args: + encoder (TransformerEncoder): the encoder + decoder (TransformerDecoder): the decoder + + The Transformer model provides the following named architectures and + command-line arguments: + + .. argparse:: + :ref: fairseq.models.transformer_parser + :prog: + """ + + def __init__(self, cfg, encoder, decoder): + super().__init__(encoder, decoder) + self.cfg = cfg + self.supports_align_args = True + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + # we want to build the args recursively in this case. + gen_parser_from_dataclass( + parser, TransformerConfig(), delete_default=False, with_prefix="" + ) + + @classmethod + def build_model(cls, cfg, task): + """Build a new model instance.""" + + # -- TODO T96535332 + # bug caused by interaction between OmegaConf II and argparsing + cfg.decoder.input_dim = int(cfg.decoder.input_dim) + cfg.decoder.output_dim = int(cfg.decoder.output_dim) + # -- + + if cfg.encoder.layers_to_keep: + cfg.encoder.layers = len(cfg.encoder.layers_to_keep.split(",")) + if cfg.decoder.layers_to_keep: + cfg.decoder.layers = len(cfg.decoder.layers_to_keep.split(",")) + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + if cfg.share_all_embeddings: + if src_dict != tgt_dict: + raise ValueError("--share-all-embeddings requires a joined dictionary") + if cfg.encoder.embed_dim != cfg.decoder.embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if cfg.decoder.embed_path and ( + cfg.decoder.embed_path != cfg.encoder.embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + encoder_embed_tokens = cls.build_embedding( + cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path + ) + decoder_embed_tokens = encoder_embed_tokens + cfg.share_decoder_input_output_embed = True + elif cfg.merge_src_tgt_embed: + logger.info(f"source dict size: {len(src_dict)}") + logger.info(f"target dict size: {len(tgt_dict)}") + src_dict.update(tgt_dict) + task.src_dict = src_dict + task.tgt_dict = src_dict + logger.info(f"merged dict size: {len(src_dict)}") + encoder_embed_tokens = cls.build_embedding( + cfg, src_dict, cfg.encoder.embed_dim + ) + decoder_embed_tokens = encoder_embed_tokens + cfg.share_decoder_input_output_embed = True + else: + encoder_embed_tokens = cls.build_embedding( + cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path + ) + decoder_embed_tokens = cls.build_embedding( + cfg, tgt_dict, cfg.decoder.embed_dim, cfg.decoder.embed_path + ) + if cfg.offload_activations: + cfg.checkpoint_activations = True # offloading implies checkpointing + encoder = cls.build_encoder(cfg, src_dict, encoder_embed_tokens) + decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) + return cls(cfg, encoder, decoder) + + @classmethod + def build_embedding(cls, cfg, dictionary, embed_dim, path=None): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + + emb = Embedding(num_embeddings, embed_dim, padding_idx) + # if provided, load from preloaded dictionaries + if path: + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + return emb + + @classmethod + def build_encoder(cls, cfg, src_dict, embed_tokens): + return TransformerEncoderBase(cfg, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, cfg, tgt_dict, embed_tokens): + return TransformerDecoderBase( + cfg, + tgt_dict, + embed_tokens, + no_encoder_attn=cfg.no_cross_attention, + ) + + # TorchScript doesn't support optional arguments with variable length (**kwargs). + # Current workaround is to add union of all arguments in child classes. + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + return_all_hiddens: bool = True, + features_only: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + """ + Run the forward pass for an encoder-decoder model. + + Copied from the base class, but without ``**kwargs``, + which are not supported by TorchScript. + """ + encoder_out = self.encoder( + src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens + ) + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + features_only=features_only, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + src_lengths=src_lengths, + return_all_hiddens=return_all_hiddens, + ) + return decoder_out + + # Since get_normalized_probs is in the Fairseq Model which is not scriptable, + # I rewrite the get_normalized_probs from Base Class to call the + # helper function in the Base Class. + @torch.jit.export + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" + return self.get_normalized_probs_scriptable(net_output, log_probs, sample) + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m diff --git a/fairseq/fairseq/models/transformer/transformer_config.py b/fairseq/fairseq/models/transformer/transformer_config.py new file mode 100644 index 0000000..4650de2 --- /dev/null +++ b/fairseq/fairseq/models/transformer/transformer_config.py @@ -0,0 +1,341 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import re +from dataclasses import dataclass, field, fields +from typing import List, Optional + +from omegaconf import II + +from fairseq import utils +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.utils import safe_getattr, safe_hasattr + +DEFAULT_MAX_SOURCE_POSITIONS = 1024 +DEFAULT_MAX_TARGET_POSITIONS = 1024 + +DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8) + +_NAME_PARSER = r"(decoder|encoder|quant_noise)_(.*)" + + +@dataclass +class EncDecBaseConfig(FairseqDataclass): + embed_path: Optional[str] = field( + default=None, metadata={"help": "path to pre-trained embedding"} + ) + embed_dim: Optional[int] = field( + default=512, metadata={"help": "embedding dimension"} + ) + ffn_embed_dim: int = field( + default=2048, metadata={"help": "embedding dimension for FFN"} + ) + layers: int = field(default=6, metadata={"help": "number of layers"}) + attention_heads: int = field( + default=8, metadata={"help": "number of attention heads"} + ) + normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each block"} + ) + learned_pos: bool = field( + default=False, metadata={"help": "use learned positional embeddings"} + ) + # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) + layerdrop: float = field(default=0, metadata={"help": "LayerDrop probability"}) + layers_to_keep: Optional[List[int]] = field( + default=None, metadata={"help": "which layers to *keep* when pruning"} + ) + + xformers_att_config: Optional[str] = field( + default=None, + metadata={ + "help": "config for xFormers attention, defined in xformers.components.attention.AttentionConfig" + }, + ) + + +@dataclass +class DecoderConfig(EncDecBaseConfig): + input_dim: int = II("model.decoder.embed_dim") + output_dim: int = field( + default=II("model.decoder.embed_dim"), + metadata={ + "help": "decoder output dimension (extra linear layer if different from decoder embed dim)" + }, + ) + + def __post_init__(self): + # II doesn't work if we are just creating the object outside of hydra so fix that + if self.input_dim == II("model.decoder.embed_dim"): + self.input_dim = self.embed_dim + if self.output_dim == II("model.decoder.embed_dim"): + self.output_dim = self.embed_dim + + +@dataclass +class QuantNoiseConfig(FairseqDataclass): + pq: float = field( + default=0.0, + metadata={"help": "iterative PQ quantization noise at training time"}, + ) + pq_block_size: int = field( + default=8, + metadata={"help": "block size of quantization noise at training time"}, + ) + scalar: float = field( + default=0.0, + metadata={ + "help": "scalar quantization noise and scalar quantization at training time" + }, + ) + + +@dataclass +class TransformerConfig(FairseqDataclass): + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="relu", + metadata={"help": "activation function to use"}, + ) + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + attention_dropout: float = field( + default=0.0, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN.", + "alias": "--relu-dropout", + }, + ) + adaptive_input: bool = False + encoder: EncDecBaseConfig = EncDecBaseConfig() + # TODO should really be in the encoder config + max_source_positions: int = field( + default=DEFAULT_MAX_SOURCE_POSITIONS, + metadata={"help": "Maximum input length supported by the encoder"}, + ) + decoder: DecoderConfig = DecoderConfig() + # TODO should really be in the decoder config + max_target_positions: int = field( + default=DEFAULT_MAX_TARGET_POSITIONS, + metadata={"help": "Maximum output length supported by the decoder"}, + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + share_all_embeddings: bool = field( + default=False, + metadata={ + "help": "share encoder, decoder and output embeddings (requires shared dictionary and embed dim)" + }, + ) + merge_src_tgt_embed: bool = field( + default=False, + metadata={ + "help": "if true then the source and target embedding table is " + "merged into one table. This is going to make the model smaller but " + "it might hurt performance." + }, + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if True, disables positional embeddings (outside self attention)" + }, + ) + adaptive_softmax_cutoff: Optional[List[int]] = field( + default=None, + metadata={ + "help": "list of adaptive softmax cutoff points. Must be used with adaptive_loss criterion" + }, + ) + adaptive_softmax_dropout: float = field( + default=0.0, + metadata={"help": "sets adaptive softmax dropout for the tail projections"}, + ) + adaptive_softmax_factor: float = field( + default=4, metadata={"help": "adaptive input factor"} + ) + layernorm_embedding: bool = field( + default=False, metadata={"help": "add layernorm to embedding"} + ) + tie_adaptive_weights: bool = field( + default=False, + metadata={ + "help": "if set, ties the weights of adaptive softmax and adaptive input" + }, + ) + tie_adaptive_proj: bool = field( + default=False, + metadata={ + "help": "if set, ties the projection weights of adaptive softmax and adaptive input" + }, + ) + no_scale_embedding: bool = field( + default=False, metadata={"help": "if True, dont scale embeddings"} + ) + checkpoint_activations: bool = field( + default=False, + metadata={ + "help": "checkpoint activations at each layer, which saves GPU memory usage at the cost of some additional compute" + }, + ) + offload_activations: bool = field( + default=False, + metadata={ + "help": "checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations." + }, + ) + # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019) + no_cross_attention: bool = field( + default=False, metadata={"help": "do not perform cross-attention"} + ) + cross_self_attention: bool = field( + default=False, metadata={"help": "perform cross+self-attention"} + ) + # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) + quant_noise: QuantNoiseConfig = field(default=QuantNoiseConfig()) + min_params_to_wrap: int = field( + default=DEFAULT_MIN_PARAMS_TO_WRAP, + metadata={ + "help": "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + }, + ) + # DEPRECATED field, but some old checkpoints might have it + char_inputs: bool = field( + default=False, metadata={"help": "if set, model takes character ids as input"} + ) + relu_dropout: float = 0.0 + # config for "BASE Layers: Simplifying Training of Large, Sparse Models" + base_layers: Optional[int] = field( + default=0, metadata={"help": "number of BASE layers in total"} + ) + base_sublayers: Optional[int] = field( + default=1, metadata={"help": "number of sublayers in each BASE layer"} + ) + base_shuffle: Optional[int] = field( + default=1, + metadata={"help": "shuffle tokens between workers before computing assignment"}, + ) + + export: bool = field( + default=False, + metadata={"help": "make the layernorm exportable with torchscript."}, + ) + + # copied from transformer_lm but expected in transformer_decoder: + no_decoder_final_norm: bool = field( + default=False, + metadata={"help": "don't add an extra layernorm after the last decoder block"}, + ) + + # We need to make this hierarchical dataclass like the flat namespace + # __getattr__ and __setattr__ here allow backward compatibility + # for subclasses of Transformer(Legacy) that depend on read/write on + # the flat namespace. + + def __getattr__(self, name): + match = re.match(_NAME_PARSER, name) + if match: + sub = safe_getattr(self, match[1]) + return safe_getattr(sub, match[2]) + raise AttributeError(f"invalid argument {name}.") + + def __setattr__(self, name, value): + match = re.match(_NAME_PARSER, name) + if match: + sub = safe_getattr(self, match[1]) + setattr(sub, match[2], value) + else: + super().__setattr__(name, value) + + @staticmethod + def _copy_keys(args, cls, prefix, seen): + """ + copy the prefixed keys (decoder_embed_dim) to the DC fields: decoder.embed_dim + """ + cfg = cls() + for fld in fields(cls): + # for all the fields in the DC, find the fields (e.g. embed_dim) + # in the namespace with the prefix (e.g. decoder) + # and set it on the dc. + args_key = f"{prefix}_{fld.name}" + if safe_hasattr(args, args_key): + seen.add(args_key) + setattr(cfg, fld.name, safe_getattr(args, args_key)) + if safe_hasattr(args, fld.name): + seen.add(fld.name) + setattr(cfg, fld.name, safe_getattr(args, fld.name)) + return cfg + + @classmethod + def from_namespace(cls, args): + if args is None: + return None + if not isinstance(args, cls): + seen = set() + config = cls() + # currently, we can go generically from DC fields to args hierarchically + # but we can't easily deconstruct a flat namespace to a hierarchical + # DC. Mostly because we could have a sub-dc called `decoder-foo` that should not + # go to the sub struct called `decoder`. There are ways to go around this, but let's keep it simple + # for now. + for fld in fields(cls): + # concretelly, the transformer_config know what sub-dc it has, so we go through all the dc fields + # and if it's one that has a sub-dc, we build that sub-dc with `copy_keys()` + if fld.name == "decoder": + if safe_hasattr(args, "decoder"): + # in some cases, the args we receive is already structured (as DictConfigs), so let's just build the correct DC + seen.add("decoder") + config.decoder = DecoderConfig(**args.decoder) + else: + config.decoder = cls._copy_keys( + args, DecoderConfig, "decoder", seen + ) + elif fld.name == "encoder": + # same but for encoder + if safe_hasattr(args, "encoder"): + seen.add("encoder") + config.encoder = EncDecBaseConfig(**args.encoder) + else: + config.encoder = cls._copy_keys( + args, EncDecBaseConfig, "encoder", seen + ) + elif fld.name == "quant_noise": + # same but for quant_noise + if safe_hasattr(args, "quant_noise"): + seen.add("quant_noise") + config.quant_noise = QuantNoiseConfig(**args.quant_noise) + else: + config.quant_noise = cls._copy_keys( + args, QuantNoiseConfig, "quant_noise", seen + ) + elif safe_hasattr(args, fld.name): + # if it's not a structure field, it's just a normal field, copy it over + seen.add(fld.name) + setattr(config, fld.name, safe_getattr(args, fld.name)) + # we got all the fields defined in the dataclass, but + # the argparse namespace might have extra args for two reasons: + # - we are in a legacy class so all the args are not declared in the dataclass. Ideally once everyone has defined a dataclass for their model, we won't need this + # - some places expect args to be there but never define them + args_dict = ( + args._asdict() + if safe_hasattr(args, "_asdict") + else vars(args) + if safe_hasattr(args, "__dict__") + else {} + ) # namedtupled doesn't have __dict__ :-/ + for key, value in args_dict.items(): + if key not in seen: + setattr(config, key, value) + return config + else: + return args diff --git a/fairseq/fairseq/models/transformer/transformer_decoder.py b/fairseq/fairseq/models/transformer/transformer_decoder.py new file mode 100644 index 0000000..744c73f --- /dev/null +++ b/fairseq/fairseq/models/transformer/transformer_decoder.py @@ -0,0 +1,474 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import utils +from fairseq.distributed import fsdp_wrap +from fairseq.models import FairseqIncrementalDecoder +from fairseq.models.transformer import TransformerConfig +from fairseq.modules import ( + AdaptiveSoftmax, + BaseLayer, + FairseqDropout, + LayerDropModuleList, + LayerNorm, + PositionalEmbedding, + SinusoidalPositionalEmbedding, + transformer_layer, +) +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ + + +# rewrite name for backward compatibility in `make_generation_fast_` +def module_name_fordropout(module_name: str) -> str: + if module_name == "TransformerDecoderBase": + return "TransformerDecoder" + else: + return module_name + + +class TransformerDecoderBase(FairseqIncrementalDecoder): + """ + Transformer decoder consisting of *cfg.decoder.layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + cfg (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + cfg, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=None, + ): + self.cfg = cfg + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + self._future_mask = torch.empty(0) + + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__) + ) + self.decoder_layerdrop = cfg.decoder.layerdrop + self.share_input_output_embed = cfg.share_decoder_input_output_embed + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = cfg.decoder.embed_dim + self.embed_dim = embed_dim + self.output_embed_dim = cfg.decoder.output_dim + + self.padding_idx = embed_tokens.padding_idx + self.max_target_positions = cfg.max_target_positions + + self.embed_tokens = embed_tokens + + self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim) + + if not cfg.adaptive_input and cfg.quant_noise.pq > 0: + self.quant_noise = apply_quant_noise_( + nn.Linear(embed_dim, embed_dim, bias=False), + cfg.quant_noise.pq, + cfg.quant_noise.pq_block_size, + ) + else: + self.quant_noise = None + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + self.embed_positions = ( + PositionalEmbedding( + self.max_target_positions, + embed_dim, + self.padding_idx, + learned=cfg.decoder.learned_pos, + ) + if not cfg.no_token_positional_embeddings + else None + ) + if cfg.layernorm_embedding: + self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export) + else: + self.layernorm_embedding = None + + self.cross_self_attention = cfg.cross_self_attention + + if self.decoder_layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.decoder_layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + self.build_decoder_layer(cfg, no_encoder_attn) + for _ in range(cfg.decoder.layers) + ] + ) + self.num_layers = len(self.layers) + + if cfg.decoder.normalize_before and not cfg.no_decoder_final_norm: + self.layer_norm = LayerNorm(embed_dim, export=cfg.export) + else: + self.layer_norm = None + + self.project_out_dim = ( + Linear(embed_dim, self.output_embed_dim, bias=False) + if embed_dim != self.output_embed_dim and not cfg.tie_adaptive_weights + else None + ) + + self.adaptive_softmax = None + self.output_projection = output_projection + if self.output_projection is None: + self.build_output_projection(cfg, dictionary, embed_tokens) + + def build_output_projection(self, cfg, dictionary, embed_tokens): + if cfg.adaptive_softmax_cutoff is not None: + self.adaptive_softmax = AdaptiveSoftmax( + len(dictionary), + self.output_embed_dim, + utils.eval_str_list(cfg.adaptive_softmax_cutoff, type=int), + dropout=cfg.adaptive_softmax_dropout, + adaptive_inputs=embed_tokens if cfg.tie_adaptive_weights else None, + factor=cfg.adaptive_softmax_factor, + tie_proj=cfg.tie_adaptive_proj, + ) + elif self.share_input_output_embed: + self.output_projection = nn.Linear( + self.embed_tokens.weight.shape[1], + self.embed_tokens.weight.shape[0], + bias=False, + ) + self.output_projection.weight = self.embed_tokens.weight + else: + self.output_projection = nn.Linear( + self.output_embed_dim, len(dictionary), bias=False + ) + nn.init.normal_( + self.output_projection.weight, mean=0, std=self.output_embed_dim**-0.5 + ) + num_base_layers = cfg.base_layers + for i in range(num_base_layers): + self.layers.insert( + ((i + 1) * cfg.decoder.layers) // (num_base_layers + 1), + BaseLayer(cfg), + ) + + def build_decoder_layer(self, cfg, no_encoder_attn=False): + layer = transformer_layer.TransformerDecoderLayerBase(cfg, no_encoder_attn) + checkpoint = cfg.checkpoint_activations + if checkpoint: + offload_to_cpu = cfg.offload_activations + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + # if we are checkpointing, enforce that FSDP always wraps the + # checkpointed layer, regardless of layer size + min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 + layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) + return layer + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention, should be of size T x B x C + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + full_context_alignment=full_context_alignment, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + ) + + if not features_only: + x = self.output_layer(x) + return x, extra + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + return self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + + """ + A scriptable subclass of this class has an extract_features method and calls + super().extract_features, but super() is not supported in torchscript. A copy of + this function is made to be used in the subclass instead. + """ + + def extract_features_scriptable( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + """ + Similar to *forward* but only return features. + + Includes several features from "Jointly Learning to Align and + Translate with Transformer Models" (Garg et al., EMNLP 2019). + + Args: + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + alignment_layer (int, optional): return mean alignment over + heads at this layer (default: last layer). + alignment_heads (int, optional): only average alignment over + this many heads (default: all heads). + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + bs, slen = prev_output_tokens.size() + if alignment_layer is None: + alignment_layer = self.num_layers - 1 + + enc: Optional[Tensor] = None + padding_mask: Optional[Tensor] = None + if encoder_out is not None and len(encoder_out["encoder_out"]) > 0: + enc = encoder_out["encoder_out"][0] + if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0: + padding_mask = encoder_out["encoder_padding_mask"][0] + + # embed positions + positions = None + if self.embed_positions is not None: + positions = self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # Prevent torchscript exporting issue for dynamic quant embedding + prev_output_tokens = prev_output_tokens.contiguous() + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + self_attn_padding_mask: Optional[Tensor] = None + if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + + # decoder layers + attn: Optional[Tensor] = None + inner_states: List[Optional[Tensor]] = [x] + for idx, layer in enumerate(self.layers): + if incremental_state is None and not full_context_alignment: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = layer( + x, + enc, + padding_mask, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + + if attn is not None: + if alignment_heads is not None: + attn = attn[:alignment_heads] + + # average probabilities over heads + attn = attn.mean(dim=0) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x, {"attn": [attn], "inner_states": inner_states} + + def output_layer(self, features): + """Project features to the vocabulary size.""" + if self.adaptive_softmax is None: + # project back to size of vocabulary + return self.output_projection(features) + else: + return features + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embed_positions is None: + return self.max_target_positions + return min(self.max_target_positions, self.embed_positions.max_positions) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. + if ( + self._future_mask.size(0) == 0 + or (not self._future_mask.device == tensor.device) + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 + ) + self._future_mask = self._future_mask.to(tensor) + return self._future_mask[:dim, :dim] + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + if f"{name}.output_projection.weight" not in state_dict: + if self.share_input_output_embed: + embed_out_key = f"{name}.embed_tokens.weight" + else: + embed_out_key = f"{name}.embed_out" + if embed_out_key in state_dict: + state_dict[f"{name}.output_projection.weight"] = state_dict[ + embed_out_key + ] + if not self.share_input_output_embed: + del state_dict[embed_out_key] + + for i in range(self.num_layers): + # update layer norms + layer_norm_map = { + "0": "self_attn_layer_norm", + "1": "encoder_attn_layer_norm", + "2": "final_layer_norm", + } + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m) + if k in state_dict: + state_dict[ + "{}.layers.{}.{}.{}".format(name, i, new, m) + ] = state_dict[k] + del state_dict[k] + + version_key = "{}.version".format(name) + if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2: + # earlier checkpoints did not normalize after the stack of layers + self.layer_norm = None + self.normalize = False + state_dict[version_key] = torch.Tensor([1]) + + return state_dict + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m + + +class TransformerDecoder(TransformerDecoderBase): + def __init__( + self, + args, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=None, + ): + self.args = args + super().__init__( + TransformerConfig.from_namespace(args), + dictionary, + embed_tokens, + no_encoder_attn=no_encoder_attn, + output_projection=output_projection, + ) + + def build_output_projection(self, args, dictionary, embed_tokens): + super().build_output_projection( + TransformerConfig.from_namespace(args), dictionary, embed_tokens + ) + + def build_decoder_layer(self, args, no_encoder_attn=False): + return super().build_decoder_layer( + TransformerConfig.from_namespace(args), no_encoder_attn=no_encoder_attn + ) diff --git a/fairseq/fairseq/models/transformer/transformer_decoder_aug.py b/fairseq/fairseq/models/transformer/transformer_decoder_aug.py new file mode 100644 index 0000000..b73c06e --- /dev/null +++ b/fairseq/fairseq/models/transformer/transformer_decoder_aug.py @@ -0,0 +1,384 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import utils +from fairseq.distributed import fsdp_wrap +from fairseq.models.transformer import TransformerConfig +from fairseq.models.transformer.transformer_decoder import TransformerDecoderBase +from fairseq.modules import ( + LayerDropModuleList, + SinusoidalPositionalEmbedding, + transformer_layer_aug, +) +from fairseq.modules.checkpoint_activations import checkpoint_wrapper + + +class AugTransformerDecoderBase(TransformerDecoderBase): + """ + Transformer decoder augmented with an additional cross-attention. Each layer + is a :class:`AugTransformerDecoderLayerBase`. + + Args: + cfg (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + encoder_attn_merge_type (str, optional): the way to combine outputs from + two cross-attention modules. If "sequential" is set, two cross-attention + modules are stacked sequentially. If "parallel" is set, they are processed + in parallel and combined before feeding it to FFN (default: sequential). + dropnet_ratio (float, optional): a probability to drop each cross-attention + module during training (default: 0.0). + """ + + def __init__( + self, + cfg, + dictionary, + embed_tokens, + output_projection=None, + encoder_attn_merge_type="sequential", + dropnet_ratio=0.0, + ): + super().__init__( + cfg, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=output_projection, + ) + # assert cfg.cross_self_attention + self.cross_self_attention = cfg.cross_self_attention + + if self.decoder_layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.decoder_layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + self.build_decoder_layer(cfg, encoder_attn_merge_type, dropnet_ratio) + for _ in range(cfg.decoder.layers) + ] + ) + + def build_decoder_layer( + self, + cfg, + encoder_attn_merge_type="sequential", + dropnet_ratio=0, + ): + layer = transformer_layer_aug.AugTransformerDecoderLayerBase( + cfg, + no_encoder_attn=False, + encoder_attn_merge_type=encoder_attn_merge_type, + dropnet_ratio=dropnet_ratio, + ) + checkpoint = cfg.checkpoint_activations + if checkpoint: + offload_to_cpu = cfg.offload_activations + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + # if we are checkpointing, enforce that FSDP always wraps the + # checkpointed layer, regardless of layer size + min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 + layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) + return layer + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + encoder_out_aug: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention, should be of size T x B x C + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + encoder_out_aug=encoder_out_aug, + incremental_state=incremental_state, + full_context_alignment=full_context_alignment, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + ) + + if not features_only: + x = self.output_layer(x) + return x, extra + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + encoder_out_aug: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + return self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + encoder_out_aug, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + + """ + A scriptable subclass of this class has an extract_features method and calls + super().extract_features, but super() is not supported in torchscript. A copy of + this function is made to be used in the subclass instead. + """ + + def extract_features_scriptable( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + encoder_out_aug: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + """ + Similar to *forward* but only return features. + + Includes several features from "Jointly Learning to Align and + Translate with Transformer Models" (Garg et al., EMNLP 2019). + + Args: + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + alignment_layer (int, optional): return mean alignment over + heads at this layer (default: last layer). + alignment_heads (int, optional): only average alignment over + this many heads (default: all heads). + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + bs, slen = prev_output_tokens.size() + if alignment_layer is None: + alignment_layer = self.num_layers - 1 + + enc: Optional[Tensor] = None + padding_mask: Optional[Tensor] = None + if encoder_out is not None and len(encoder_out["encoder_out"]) > 0: + enc = encoder_out["encoder_out"][0] + if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0: + padding_mask = encoder_out["encoder_padding_mask"][0] + + enc_aug: Optional[Tensor] = None + padding_mask_aug: Optional[Tensor] = None + if encoder_out_aug is not None and len(encoder_out_aug["encoder_out"]) > 0: + enc_aug = encoder_out_aug["encoder_out"][0] + if ( + encoder_out_aug is not None + and len(encoder_out_aug["encoder_padding_mask"]) > 0 + ): + padding_mask_aug = encoder_out_aug["encoder_padding_mask"][0] + + # embed positions + positions = None + if self.embed_positions is not None: + positions = self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # Prevent torchscript exporting issue for dynamic quant embedding + prev_output_tokens = prev_output_tokens.contiguous() + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + self_attn_padding_mask: Optional[Tensor] = None + if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + + # decoder layers + attn: Optional[Tensor] = None + attn_aug: Optional[Tensor] = None + inner_states: List[Optional[Tensor]] = [x] + for idx, layer in enumerate(self.layers): + if incremental_state is None and not full_context_alignment: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, layer_attn_aug, _ = layer( + x, + enc, + padding_mask, + enc_aug, + padding_mask_aug, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + if layer_attn_aug is not None and idx == alignment_layer: + attn_aug = layer_attn_aug.float().to(x) + + if attn is not None: + if alignment_heads is not None: + attn = attn[:alignment_heads] + + # average probabilities over heads + attn = attn.mean(dim=0) + + if attn_aug is not None: + if alignment_heads is not None: + attn_aug = attn_aug[:alignment_heads] + + # average probabilities over heads + attn_aug = attn_aug.mean(dim=0) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x, {"attn": [attn], "attn_aug": [attn_aug], "inner_states": inner_states} + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + if f"{name}.output_projection.weight" not in state_dict: + if self.share_input_output_embed: + embed_out_key = f"{name}.embed_tokens.weight" + else: + embed_out_key = f"{name}.embed_out" + if embed_out_key in state_dict: + state_dict[f"{name}.output_projection.weight"] = state_dict[ + embed_out_key + ] + if not self.share_input_output_embed: + del state_dict[embed_out_key] + + for i in range(self.num_layers): + # update layer norms + layer_norm_map = { + "0": "self_attn_layer_norm", + "1": "encoder_attn_layer_norm", + "2": "encoder_attn_layer_norm2", + "3": "final_layer_norm", + } + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m) + if k in state_dict: + state_dict[ + "{}.layers.{}.{}.{}".format(name, i, new, m) + ] = state_dict[k] + del state_dict[k] + + version_key = "{}.version".format(name) + if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2: + # earlier checkpoints did not normalize after the stack of layers + self.layer_norm = None + self.normalize = False + state_dict[version_key] = torch.Tensor([1]) + + return state_dict + + +class AugTransformerDecoder(AugTransformerDecoderBase): + def __init__( + self, + args, + dictionary, + embed_tokens, + output_projection=None, + ): + self.args = args + super().__init__( + TransformerConfig.from_namespace(args), + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=output_projection, + encoder_attn_merge_type=getattr( + args, "synthesizer_augmented_cross_attention_merge_type", "sequential" + ), + dropnet_ratio=getattr(args, "dropnet_ratio", 0), + ) + + def build_output_projection(self, args, dictionary, embed_tokens): + super().build_output_projection( + TransformerConfig.from_namespace(args), dictionary, embed_tokens + ) + + def build_decoder_layer( + self, + args, + encoder_attn_merge_type="sequential", + dropnet_ratio=0, + ): + return super().build_decoder_layer( + TransformerConfig.from_namespace(args), + no_encoder_attn=False, + encoder_attn_merge_type=encoder_attn_merge_type, + dropnet_ratio=dropnet_ratio, + ) diff --git a/fairseq/fairseq/models/transformer/transformer_encoder.py b/fairseq/fairseq/models/transformer/transformer_encoder.py new file mode 100644 index 0000000..a684fcb --- /dev/null +++ b/fairseq/fairseq/models/transformer/transformer_encoder.py @@ -0,0 +1,362 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import utils +from fairseq.distributed import fsdp_wrap +from fairseq.models import FairseqEncoder +from fairseq.models.transformer import TransformerConfig +from fairseq.modules import ( + FairseqDropout, + LayerDropModuleList, + LayerNorm, + PositionalEmbedding, + SinusoidalPositionalEmbedding, + transformer_layer, +) +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ + + +# rewrite name for backward compatibility in `make_generation_fast_` +def module_name_fordropout(module_name: str) -> str: + if module_name == "TransformerEncoderBase": + return "TransformerEncoder" + else: + return module_name + + +class TransformerEncoderBase(FairseqEncoder): + """ + Transformer encoder consisting of *cfg.encoder.layers* layers. Each layer + is a :class:`TransformerEncoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): encoding dictionary + embed_tokens (torch.nn.Embedding): input embedding + """ + + def __init__(self, cfg, dictionary, embed_tokens, return_fc=False): + self.cfg = cfg + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__) + ) + self.encoder_layerdrop = cfg.encoder.layerdrop + self.return_fc = return_fc + + embed_dim = embed_tokens.embedding_dim + self.padding_idx = embed_tokens.padding_idx + self.max_source_positions = cfg.max_source_positions + + self.embed_tokens = embed_tokens + + self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim) + + self.embed_positions = ( + PositionalEmbedding( + cfg.max_source_positions, + embed_dim, + self.padding_idx, + learned=cfg.encoder.learned_pos, + ) + if not cfg.no_token_positional_embeddings + else None + ) + if cfg.layernorm_embedding: + self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export) + else: + self.layernorm_embedding = None + + if not cfg.adaptive_input and cfg.quant_noise.pq > 0: + self.quant_noise = apply_quant_noise_( + nn.Linear(embed_dim, embed_dim, bias=False), + cfg.quant_noise.pq, + cfg.quant_noise.pq_block_size, + ) + else: + self.quant_noise = None + + if self.encoder_layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.encoder_layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [self.build_encoder_layer(cfg) for i in range(cfg.encoder.layers)] + ) + self.num_layers = len(self.layers) + + if cfg.encoder.normalize_before: + self.layer_norm = LayerNorm(embed_dim, export=cfg.export) + else: + self.layer_norm = None + + def build_encoder_layer(self, cfg): + layer = transformer_layer.TransformerEncoderLayerBase( + cfg, return_fc=self.return_fc + ) + checkpoint = cfg.checkpoint_activations + if checkpoint: + offload_to_cpu = cfg.offload_activations + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + # if we are checkpointing, enforce that FSDP always wraps the + # checkpointed layer, regardless of layer size + min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 + layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) + return layer + + def forward_embedding( + self, src_tokens, token_embedding: Optional[torch.Tensor] = None + ): + # embed tokens and positions + if token_embedding is None: + token_embedding = self.embed_tokens(src_tokens) + x = embed = self.embed_scale * token_embedding + if self.embed_positions is not None: + x = embed + self.embed_positions(src_tokens) + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + x = self.dropout_module(x) + if self.quant_noise is not None: + x = self.quant_noise(x) + return x, embed + + def forward( + self, + src_tokens, + src_lengths: Optional[torch.Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[torch.Tensor] = None, + ): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + """ + return self.forward_scriptable( + src_tokens, src_lengths, return_all_hiddens, token_embeddings + ) + + # TorchScript doesn't support super() method so that the scriptable Subclass + # can't access the base class model in Torchscript. + # Current workaround is to add a helper function with different name and + # call the helper function from scriptable Subclass. + def forward_scriptable( + self, + src_tokens, + src_lengths: Optional[torch.Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[torch.Tensor] = None, + ): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + """ + # compute padding mask + encoder_padding_mask = src_tokens.eq(self.padding_idx) + has_pads = ( + torch.tensor(src_tokens.device.type == "xla") or encoder_padding_mask.any() + ) + # Torchscript doesn't handle bool Tensor correctly, so we need to work around. + if torch.jit.is_scripting(): + has_pads = torch.tensor(1) if has_pads else torch.tensor(0) + + x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings) + + # account for padding while computing the representation + x = x * ( + 1 - encoder_padding_mask.unsqueeze(-1).type_as(x) * has_pads.type_as(x) + ) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + encoder_states = [] + fc_results = [] + + if return_all_hiddens: + encoder_states.append(x) + + # encoder layers + for layer in self.layers: + lr = layer( + x, encoder_padding_mask=encoder_padding_mask if has_pads else None + ) + + if isinstance(lr, tuple) and len(lr) == 2: + x, fc_result = lr + else: + x = lr + fc_result = None + + if return_all_hiddens and not torch.jit.is_scripting(): + assert encoder_states is not None + encoder_states.append(x) + fc_results.append(fc_result) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in + # `forward` so we use a dictionary instead. + # TorchScript does not support mixed values so the values are all lists. + # The empty list is equivalent to None. + src_lengths = ( + src_tokens.ne(self.padding_idx) + .sum(dim=1, dtype=torch.int32) + .reshape(-1, 1) + .contiguous() + ) + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask], # B x T + "encoder_embedding": [encoder_embedding], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "fc_results": fc_results, # List[T x B x C] + "src_tokens": [], + "src_lengths": [src_lengths], + } + + @torch.jit.export + def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + if len(encoder_out["encoder_out"]) == 0: + new_encoder_out = [] + else: + new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)] + if len(encoder_out["encoder_padding_mask"]) == 0: + new_encoder_padding_mask = [] + else: + new_encoder_padding_mask = [ + encoder_out["encoder_padding_mask"][0].index_select(0, new_order) + ] + if len(encoder_out["encoder_embedding"]) == 0: + new_encoder_embedding = [] + else: + new_encoder_embedding = [ + encoder_out["encoder_embedding"][0].index_select(0, new_order) + ] + + if len(encoder_out["src_tokens"]) == 0: + src_tokens = [] + else: + src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)] + + if len(encoder_out["src_lengths"]) == 0: + src_lengths = [] + else: + src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)] + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": src_tokens, # B x T + "src_lengths": src_lengths, # B x 1 + } + + @torch.jit.export + def _reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): + """Dummy re-order function for beamable enc-dec attention""" + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + if self.embed_positions is None: + return self.max_source_positions + return min(self.max_source_positions, self.embed_positions.max_positions) + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + for i in range(self.num_layers): + # update layer norms + self.layers[i].upgrade_state_dict_named( + state_dict, "{}.layers.{}".format(name, i) + ) + + version_key = "{}.version".format(name) + if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2: + # earlier checkpoints did not normalize after the stack of layers + self.layer_norm = None + self.normalize = False + state_dict[version_key] = torch.Tensor([1]) + return state_dict + + +class TransformerEncoder(TransformerEncoderBase): + def __init__(self, args, dictionary, embed_tokens, return_fc=False): + self.args = args + super().__init__( + TransformerConfig.from_namespace(args), + dictionary, + embed_tokens, + return_fc=return_fc, + ) + + def build_encoder_layer(self, args): + return super().build_encoder_layer( + TransformerConfig.from_namespace(args), + ) diff --git a/fairseq/fairseq/models/transformer/transformer_legacy.py b/fairseq/fairseq/models/transformer/transformer_legacy.py new file mode 100644 index 0000000..00d14a7 --- /dev/null +++ b/fairseq/fairseq/models/transformer/transformer_legacy.py @@ -0,0 +1,277 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.models import ( + register_model, + register_model_architecture, +) +from fairseq.models.transformer.transformer_config import ( + TransformerConfig, + DEFAULT_MAX_SOURCE_POSITIONS, + DEFAULT_MAX_TARGET_POSITIONS, + DEFAULT_MIN_PARAMS_TO_WRAP, +) +from fairseq.models.transformer.transformer_base import ( + TransformerModelBase, +) + + +@register_model("transformer") +class TransformerModel(TransformerModelBase): + """ + This is the legacy implementation of the transformer model that + uses argparse for configuration. + """ + + @classmethod + def hub_models(cls): + # fmt: off + + def moses_subword(path): + return { + 'path': path, + 'tokenizer': 'moses', + 'bpe': 'subword_nmt', + } + + def moses_fastbpe(path): + return { + 'path': path, + 'tokenizer': 'moses', + 'bpe': 'fastbpe', + } + + def spm(path): + return { + 'path': path, + 'bpe': 'sentencepiece', + 'tokenizer': 'space', + } + + return { + 'transformer.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2'), + 'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2', + 'transformer.wmt18.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz'), + 'transformer.wmt19.en-de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz'), + 'transformer.wmt19.en-ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz'), + 'transformer.wmt19.de-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz'), + 'transformer.wmt19.ru-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz'), + 'transformer.wmt19.en-de.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz'), + 'transformer.wmt19.en-ru.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz'), + 'transformer.wmt19.de-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz'), + 'transformer.wmt19.ru-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz'), + 'transformer.wmt20.en-ta': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-ta.single.tar.gz'), + 'transformer.wmt20.en-iu.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.news.single.tar.gz'), + 'transformer.wmt20.en-iu.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.nh.single.tar.gz'), + 'transformer.wmt20.ta-en': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta-en.single.tar.gz'), + 'transformer.wmt20.iu-en.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.news.single.tar.gz'), + 'transformer.wmt20.iu-en.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.nh.single.tar.gz'), + 'transformer.flores101.mm100.615M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz'), + 'transformer.flores101.mm100.175M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz'), + } + # fmt: on + + def __init__(self, args, encoder, decoder): + cfg = TransformerConfig.from_namespace(args) + super().__init__(cfg, encoder, decoder) + self.args = args + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + # we want to build the args recursively in this case. + # do not set defaults so that settings defaults from various architectures still works + gen_parser_from_dataclass( + parser, TransformerConfig(), delete_default=True, with_prefix="" + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if args.encoder_layers_to_keep: + args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) + if args.decoder_layers_to_keep: + args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) + + if getattr(args, "max_source_positions", None) is None: + args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + if args.share_all_embeddings: + if src_dict != tgt_dict: + raise ValueError("--share-all-embeddings requires a joined dictionary") + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + args.share_decoder_input_output_embed = True + + if getattr(args, "offload_activations", False): + args.checkpoint_activations = True # offloading implies checkpointing + + if not args.share_all_embeddings: + args.min_params_to_wrap = getattr( + args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP + ) + cfg = TransformerConfig.from_namespace(args) + return super().build_model(cfg, task) + + @classmethod + def build_embedding(cls, args, dictionary, embed_dim, path=None): + return super().build_embedding( + TransformerConfig.from_namespace(args), dictionary, embed_dim, path + ) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return super().build_encoder( + TransformerConfig.from_namespace(args), src_dict, embed_tokens + ) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return super().build_decoder( + TransformerConfig.from_namespace(args), tgt_dict, embed_tokens + ) + + +# architectures + + +@register_model_architecture("transformer", "transformer_tiny") +def tiny_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 64) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 64) + args.encoder_layers = getattr(args, "encoder_layers", 2) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2) + args.decoder_layers = getattr(args, "decoder_layers", 2) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2) + return base_architecture(args) + + +@register_model_architecture("transformer", "transformer") +def base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.merge_src_tgt_embed = getattr(args, "merge_src_tgt_embed", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.no_cross_attention = getattr(args, "no_cross_attention", False) + args.cross_self_attention = getattr(args, "cross_self_attention", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.checkpoint_activations = getattr(args, "checkpoint_activations", False) + args.offload_activations = getattr(args, "offload_activations", False) + if args.offload_activations: + args.checkpoint_activations = True + args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None) + args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0) + + +@register_model_architecture("transformer", "transformer_iwslt_de_en") +def transformer_iwslt_de_en(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) + base_architecture(args) + + +@register_model_architecture("transformer", "transformer_wmt_en_de") +def transformer_wmt_en_de(args): + base_architecture(args) + + +# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) +@register_model_architecture("transformer", "transformer_vaswani_wmt_en_de_big") +def transformer_vaswani_wmt_en_de_big(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) + base_architecture(args) + + +@register_model_architecture("transformer", "transformer_vaswani_wmt_en_fr_big") +def transformer_vaswani_wmt_en_fr_big(args): + args.dropout = getattr(args, "dropout", 0.1) + transformer_vaswani_wmt_en_de_big(args) + + +@register_model_architecture("transformer", "transformer_wmt_en_de_big") +def transformer_wmt_en_de_big(args): + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + transformer_vaswani_wmt_en_de_big(args) + + +# default parameters used in tensor2tensor implementation +@register_model_architecture("transformer", "transformer_wmt_en_de_big_t2t") +def transformer_wmt_en_de_big_t2t(args): + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_dropout = getattr(args, "activation_dropout", 0.1) + transformer_vaswani_wmt_en_de_big(args) diff --git a/fairseq/fairseq/models/transformer_align.py b/fairseq/fairseq/models/transformer_align.py new file mode 100644 index 0000000..eaf585b --- /dev/null +++ b/fairseq/fairseq/models/transformer_align.py @@ -0,0 +1,93 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer import ( + TransformerModel, + base_architecture, + transformer_wmt_en_de_big, +) + + +@register_model("transformer_align") +class TransformerAlignModel(TransformerModel): + """ + See "Jointly Learning to Align and Translate with Transformer + Models" (Garg et al., EMNLP 2019). + """ + + def __init__(self, encoder, decoder, args): + super().__init__(args, encoder, decoder) + self.alignment_heads = args.alignment_heads + self.alignment_layer = args.alignment_layer + self.full_context_alignment = args.full_context_alignment + + @staticmethod + def add_args(parser): + # fmt: off + super(TransformerAlignModel, TransformerAlignModel).add_args(parser) + parser.add_argument('--alignment-heads', type=int, metavar='D', + help='Number of cross attention heads per layer to supervised with alignments') + parser.add_argument('--alignment-layer', type=int, metavar='D', + help='Layer number which has to be supervised. 0 corresponding to the bottommost layer.') + parser.add_argument('--full-context-alignment', action='store_true', + help='Whether or not alignment is supervised conditioned on the full target context.') + # fmt: on + + @classmethod + def build_model(cls, args, task): + # set any default arguments + transformer_align(args) + + transformer_model = TransformerModel.build_model(args, task) + return TransformerAlignModel( + transformer_model.encoder, transformer_model.decoder, args + ) + + def forward(self, src_tokens, src_lengths, prev_output_tokens): + encoder_out = self.encoder(src_tokens, src_lengths) + return self.forward_decoder(prev_output_tokens, encoder_out) + + def forward_decoder( + self, + prev_output_tokens, + encoder_out=None, + incremental_state=None, + features_only=False, + **extra_args, + ): + attn_args = { + "alignment_layer": self.alignment_layer, + "alignment_heads": self.alignment_heads, + } + decoder_out = self.decoder(prev_output_tokens, encoder_out, **attn_args) + + if self.full_context_alignment: + attn_args["full_context_alignment"] = self.full_context_alignment + _, alignment_out = self.decoder( + prev_output_tokens, + encoder_out, + features_only=True, + **attn_args, + **extra_args, + ) + decoder_out[1]["attn"] = alignment_out["attn"] + + return decoder_out + + +@register_model_architecture("transformer_align", "transformer_align") +def transformer_align(args): + args.alignment_heads = getattr(args, "alignment_heads", 1) + args.alignment_layer = getattr(args, "alignment_layer", 4) + args.full_context_alignment = getattr(args, "full_context_alignment", False) + base_architecture(args) + + +@register_model_architecture("transformer_align", "transformer_wmt_en_de_big_align") +def transformer_wmt_en_de_big_align(args): + args.alignment_heads = getattr(args, "alignment_heads", 1) + args.alignment_layer = getattr(args, "alignment_layer", 4) + transformer_wmt_en_de_big(args) diff --git a/fairseq/fairseq/models/transformer_from_pretrained_xlm.py b/fairseq/fairseq/models/transformer_from_pretrained_xlm.py new file mode 100644 index 0000000..236d994 --- /dev/null +++ b/fairseq/fairseq/models/transformer_from_pretrained_xlm.py @@ -0,0 +1,152 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +from typing import Any, Dict + +from fairseq import checkpoint_utils +from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer import ( + TransformerDecoder, + TransformerEncoder, + TransformerModel, + base_architecture as transformer_base_architecture, +) + + +@register_model("transformer_from_pretrained_xlm") +class TransformerFromPretrainedXLMModel(TransformerModel): + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + TransformerModel.add_args(parser) + parser.add_argument( + "--pretrained-xlm-checkpoint", + type=str, + metavar="STR", + help="XLM model to use for initializing transformer encoder and/or decoder", + ) + parser.add_argument( + "--init-encoder-only", + action="store_true", + help="if set, don't load the XLM weights and embeddings into decoder", + ) + parser.add_argument( + "--init-decoder-only", + action="store_true", + help="if set, don't load the XLM weights and embeddings into encoder", + ) + + @classmethod + def build_model(self, args, task, cls_dictionary=MaskedLMDictionary): + assert hasattr(args, "pretrained_xlm_checkpoint"), ( + "You must specify a path for --pretrained-xlm-checkpoint to use " + "--arch transformer_from_pretrained_xlm" + ) + assert isinstance(task.source_dictionary, cls_dictionary) and isinstance( + task.target_dictionary, cls_dictionary + ), ( + "You should use a MaskedLMDictionary when using --arch " + "transformer_from_pretrained_xlm because the pretrained XLM model " + "was trained using data binarized with MaskedLMDictionary. " + "For translation, you may want to use --task " + "translation_from_pretrained_xlm" + ) + assert not ( + getattr(args, "init_encoder_only", False) + and getattr(args, "init_decoder_only", False) + ), "Only one of --init-encoder-only and --init-decoder-only can be set." + return super().build_model(args, task) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return TransformerEncoderFromPretrainedXLM(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return TransformerDecoderFromPretrainedXLM(args, tgt_dict, embed_tokens) + + +def upgrade_state_dict_with_xlm_weights( + state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str +) -> Dict[str, Any]: + """ + Load XLM weights into a Transformer encoder or decoder model. + + Args: + state_dict: state dict for either TransformerEncoder or + TransformerDecoder + pretrained_xlm_checkpoint: checkpoint to load XLM weights from + + Raises: + AssertionError: If architecture (num layers, attention heads, etc.) + does not match between the current Transformer encoder or + decoder and the pretrained_xlm_checkpoint + """ + if not os.path.exists(pretrained_xlm_checkpoint): + raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint)) + + state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint) + xlm_state_dict = state["model"] + for key in xlm_state_dict.keys(): + + for search_key in ["embed_tokens", "embed_positions", "layers"]: + if search_key in key: + subkey = key[key.find(search_key) :] + assert subkey in state_dict, ( + "{} Transformer encoder / decoder " + "state_dict does not contain {}. Cannot " + "load {} from pretrained XLM checkpoint " + "{} into Transformer.".format( + str(state_dict.keys()), subkey, key, pretrained_xlm_checkpoint + ) + ) + + state_dict[subkey] = xlm_state_dict[key] + return state_dict + + +class TransformerEncoderFromPretrainedXLM(TransformerEncoder): + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens) + if getattr(args, "init_decoder_only", False): + # Don't load XLM weights for encoder if --init-decoder-only + return + + assert hasattr(args, "pretrained_xlm_checkpoint"), ( + "--pretrained-xlm-checkpoint must be specified to load Transformer " + "encoder from pretrained XLM" + ) + xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights( + state_dict=self.state_dict(), + pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint, + ) + self.load_state_dict(xlm_loaded_state_dict, strict=True) + + +class TransformerDecoderFromPretrainedXLM(TransformerDecoder): + def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): + super().__init__(args, dictionary, embed_tokens, no_encoder_attn) + if getattr(args, "init_encoder_only", False): + # Don't load XLM weights for decoder if --init-encoder-only + return + assert hasattr(args, "pretrained_xlm_checkpoint"), ( + "--pretrained-xlm-checkpoint must be specified to load Transformer " + "decoder from pretrained XLM" + ) + + xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights( + state_dict=self.state_dict(), + pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint, + ) + self.load_state_dict(xlm_loaded_state_dict, strict=True) + + +@register_model_architecture( + "transformer_from_pretrained_xlm", "transformer_from_pretrained_xlm" +) +def base_architecture(args): + transformer_base_architecture(args) diff --git a/fairseq/fairseq/models/transformer_lm.py b/fairseq/fairseq/models/transformer_lm.py new file mode 100644 index 0000000..1e3aa72 --- /dev/null +++ b/fairseq/fairseq/models/transformer_lm.py @@ -0,0 +1,607 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from dataclasses import dataclass, field +from typing import Optional + +from omegaconf import II + +from fairseq import options, utils +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import ( + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import ( + DEFAULT_MIN_PARAMS_TO_WRAP, + Embedding, + TransformerDecoder, +) +from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder +from fairseq.utils import safe_getattr, safe_hasattr + +DEFAULT_MAX_TARGET_POSITIONS = 1024 + + +@dataclass +class TransformerLanguageModelConfig(FairseqDataclass): + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="relu", metadata={"help": "activation function to use"} + ) + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + attention_dropout: float = field( + default=0.0, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN."} + ) + relu_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN."} + ) + decoder_embed_dim: int = field( + default=512, metadata={"help": "decoder embedding dimension"} + ) + decoder_output_dim: int = field( + default=512, metadata={"help": "decoder output dimension"} + ) + decoder_input_dim: int = field( + default=512, metadata={"help": "decoder input dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=2048, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field(default=6, metadata={"help": "num decoder layers"}) + decoder_attention_heads: int = field( + default=8, metadata={"help": "num decoder attention heads"} + ) + decoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each decoder block"} + ) + no_decoder_final_norm: bool = field( + default=False, + metadata={"help": "don't add an extra layernorm after the last decoder block"}, + ) + adaptive_softmax_cutoff: Optional[str] = field( + default=None, + metadata={ + "help": "comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion" + }, + ) + adaptive_softmax_dropout: float = field( + default=0, + metadata={"help": "sets adaptive softmax dropout for the tail projections"}, + ) + adaptive_softmax_factor: float = field( + default=4, metadata={"help": "adaptive input factor"} + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings (outside self attention)" + }, + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + character_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, uses character embedding convolutions to produce token embeddings" + }, + ) + character_filters: str = field( + default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", + metadata={"help": "size of character embeddings"}, + ) + character_embedding_dim: int = field( + default=4, metadata={"help": "size of character embeddings"} + ) + char_embedder_highway_layers: int = field( + default=2, + metadata={"help": "number of highway layers for character token embeddder"}, + ) + adaptive_input: bool = field( + default=False, metadata={"help": "if set, uses adaptive input"} + ) + adaptive_input_factor: float = field( + default=4, metadata={"help": "adaptive input factor"} + ) + adaptive_input_cutoff: Optional[str] = field( + default=None, + metadata={"help": "comma separated list of adaptive input cutoff points."}, + ) + tie_adaptive_weights: bool = field( + default=False, + metadata={ + "help": "if set, ties the weights of adaptive softmax and adaptive input" + }, + ) + tie_adaptive_proj: bool = field( + default=False, + metadata={ + "help": "if set, ties the projection weights of adaptive softmax and adaptive input" + }, + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + layernorm_embedding: bool = field( + default=False, metadata={"help": "add layernorm to embedding"} + ) + no_scale_embedding: bool = field( + default=False, metadata={"help": "if True, dont scale embeddings"} + ) + checkpoint_activations: bool = field( + default=False, metadata={"help": "checkpoint activations at each layer"} + ) + offload_activations: bool = field( + default=False, + metadata={"help": "move checkpointed activations to CPU after they are used."}, + ) + # config for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "LayerDrop probability for decoder"} + ) + decoder_layers_to_keep: Optional[str] = field( + default=None, + metadata={ + "help": "which layers to *keep* when pruning as a comma-separated list" + }, + ) + # config for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) + quant_noise_pq: float = field( + default=0.0, + metadata={"help": "iterative PQ quantization noise at training time"}, + ) + quant_noise_pq_block_size: int = field( + default=8, + metadata={"help": "block size of quantization noise at training time"}, + ) + quant_noise_scalar: float = field( + default=0.0, + metadata={ + "help": "scalar quantization noise and scalar quantization at training time" + }, + ) + # config for Fully Sharded Data Parallel (FSDP) training + min_params_to_wrap: int = field( + default=DEFAULT_MIN_PARAMS_TO_WRAP, + metadata={ + "help": ( + "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + ) + }, + ) + # config for "BASE Layers: Simplifying Training of Large, Sparse Models" + base_layers: Optional[int] = field( + default=0, metadata={"help": "number of BASE layers in total"} + ) + base_sublayers: Optional[int] = field( + default=1, metadata={"help": "number of sublayers in each BASE layer"} + ) + base_shuffle: Optional[int] = field( + default=1, + metadata={"help": "shuffle tokens between workers before computing assignment"}, + ) + # NormFormer + scale_fc: Optional[bool] = field( + default=False, + metadata={"help": "Insert LayerNorm between fully connected layers"}, + ) + scale_attn: Optional[bool] = field( + default=False, metadata={"help": "Insert LayerNorm after attention"} + ) + scale_heads: Optional[bool] = field( + default=False, + metadata={"help": "Learn a scale coefficient for each attention head"}, + ) + scale_resids: Optional[bool] = field( + default=False, + metadata={"help": "Learn a scale coefficient for each residual connection"}, + ) + + # xFormers arguments + decoder_xformers_att_config: Optional[str] = field( + default=None, + metadata={ + "help": "config for xFormers library attention, defined in xformers.components.attention.AttentionConfig", + }, + ) + + # options from other parts of the config + add_bos_token: bool = II("task.add_bos_token") + tokens_per_sample: int = II("task.tokens_per_sample") + max_target_positions: Optional[int] = II("task.max_target_positions") + tpu: bool = II("common.tpu") + + +@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig) +class TransformerLanguageModel(FairseqLanguageModel): + @classmethod + def hub_models(cls): + def moses_fastbpe(path): + return {"path": path, "tokenizer": "moses", "bpe": "fastbpe"} + + def spm(path): + return {"path": path, "tokenizer": "space", "bpe": "sentencepiece"} + + return { + "transformer_lm.gbw.adaptive_huge": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2", + "transformer_lm.wiki103.adaptive": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2", + "transformer_lm.wmt19.en": moses_fastbpe( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.bz2" + ), + "transformer_lm.wmt19.de": moses_fastbpe( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.bz2" + ), + "transformer_lm.wmt19.ru": moses_fastbpe( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.bz2" + ), + "transformer_lm.wmt20.en": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.en.tar.gz" + ), + "transformer_lm.wmt20.ta": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.ta.tar.gz" + ), + "transformer_lm.wmt20.iu.news": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.news.tar.gz" + ), + "transformer_lm.wmt20.iu.nh": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.nh.tar.gz" + ), + } + + def __init__(self, decoder): + super().__init__(decoder) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + if args.decoder_layers_to_keep: + args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) + + if safe_getattr(args, "max_target_positions", None) is None: + args.max_target_positions = safe_getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) + + if args.character_embeddings: + embed_tokens = CharacterTokenEmbedder( + task.source_dictionary, + eval(args.character_filters), + args.character_embedding_dim, + args.decoder_embed_dim, + args.char_embedder_highway_layers, + ) + elif args.adaptive_input: + embed_tokens = AdaptiveInput( + len(task.source_dictionary), + task.source_dictionary.pad(), + args.decoder_input_dim, + args.adaptive_input_factor, + args.decoder_embed_dim, + options.eval_str_list(args.adaptive_input_cutoff, type=int), + args.quant_noise_pq, + args.quant_noise_pq_block_size, + ) + else: + embed_tokens = cls.build_embedding( + args, task.source_dictionary, args.decoder_input_dim + ) + + if args.tie_adaptive_weights: + assert args.adaptive_input + assert args.adaptive_input_factor == args.adaptive_softmax_factor + assert ( + args.adaptive_softmax_cutoff == args.adaptive_input_cutoff + ), "{} != {}".format( + args.adaptive_softmax_cutoff, args.adaptive_input_cutoff + ) + assert args.decoder_input_dim == args.decoder_output_dim + + decoder = TransformerDecoder( + args, task.target_dictionary, embed_tokens, no_encoder_attn=True + ) + return cls(decoder) + + @classmethod + def build_embedding(cls, args, dictionary, embed_dim, path=None): + embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad()) + return embed_tokens + + +def base_lm_architecture(args): + # backward compatibility for older model checkpoints + if safe_hasattr(args, "no_tie_adaptive_proj"): + # previous models defined --no-tie-adaptive-proj, so use the existence of + # that option to determine if this is an "old" model checkpoint + args.no_decoder_final_norm = True # old models always set this to True + if args.no_tie_adaptive_proj is False: + args.tie_adaptive_proj = True + if safe_hasattr(args, "decoder_final_norm"): + args.no_decoder_final_norm = not args.decoder_final_norm + + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0) + + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 2048) + args.decoder_layers = safe_getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8) + args.adaptive_softmax_cutoff = safe_getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0) + args.adaptive_softmax_factor = safe_getattr(args, "adaptive_softmax_factor", 4) + args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", False) + args.activation_fn = safe_getattr(args, "activation_fn", "relu") + + args.decoder_layerdrop = safe_getattr(args, "decoder_layerdrop", 0) + args.decoder_layers_to_keep = safe_getattr(args, "decoder_layers_to_keep", None) + args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0) + args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0) + + args.base_layers = safe_getattr(args, "base_layers", 0) + args.base_sublayers = safe_getattr(args, "base_sublayers", 1) + args.base_shuffle = safe_getattr(args, "base_shuffle", False) + + args.add_bos_token = safe_getattr(args, "add_bos_token", False) + args.no_token_positional_embeddings = safe_getattr( + args, "no_token_positional_embeddings", False + ) + args.share_decoder_input_output_embed = safe_getattr( + args, "share_decoder_input_output_embed", False + ) + args.character_embeddings = safe_getattr(args, "character_embeddings", False) + + args.decoder_output_dim = safe_getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = safe_getattr( + args, "decoder_input_dim", args.decoder_embed_dim + ) + + # Model training is not stable without this + args.decoder_normalize_before = True + args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", False) + + args.adaptive_input = safe_getattr(args, "adaptive_input", False) + args.adaptive_input_factor = safe_getattr(args, "adaptive_input_factor", 4) + args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", None) + + args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", False) + args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", False) + + args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", False) + args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False) + args.checkpoint_activations = safe_getattr(args, "checkpoint_activations", False) + args.offload_activations = safe_getattr(args, "offload_activations", False) + args.scale_fc = safe_getattr(args, "scale_fc", False) + args.scale_attn = safe_getattr(args, "scale_attn", False) + args.scale_heads = safe_getattr(args, "scale_heads", False) + args.scale_resids = safe_getattr(args, "scale_resids", False) + if args.offload_activations: + args.checkpoint_activations = True + + +@register_model_architecture("transformer_lm", "transformer_lm_big") +def transformer_lm_big(args): + args.decoder_layers = safe_getattr(args, "decoder_layers", 12) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_wiki103") +@register_model_architecture("transformer_lm", "transformer_lm_baevski_wiki103") +def transformer_lm_baevski_wiki103(args): + args.decoder_layers = safe_getattr(args, "decoder_layers", 16) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8) + args.dropout = safe_getattr(args, "dropout", 0.3) + args.adaptive_input = safe_getattr(args, "adaptive_input", True) + args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", True) + args.adaptive_input_cutoff = safe_getattr( + args, "adaptive_input_cutoff", "20000,60000" + ) + args.adaptive_softmax_cutoff = safe_getattr( + args, "adaptive_softmax_cutoff", "20000,60000" + ) + args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0.2) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_dropout = safe_getattr(args, "activation_dropout", 0.1) + args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True) + args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", True) + transformer_lm_big(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gbw") +@register_model_architecture("transformer_lm", "transformer_lm_baevski_gbw") +def transformer_lm_baevski_gbw(args): + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512) + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True) + transformer_lm_big(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt") +def transformer_lm_gpt(args): + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768) + args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 3072) + args.decoder_layers = safe_getattr(args, "decoder_layers", 12) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12) + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_small") +def transformer_lm_gpt2_small(args): + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_layers = safe_getattr(args, "decoder_layers", 24) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_tiny") +def transformer_lm_gpt2_tiny(args): + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 64) + args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 64) + args.decoder_layers = safe_getattr(args, "decoder_layers", 2) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 1) + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_medium") +def transformer_lm_gpt2_medium(args): + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1280) + args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 5120) + args.decoder_layers = safe_getattr(args, "decoder_layers", 36) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 20) + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big") +def transformer_lm_gpt2_big(args): + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1600) + args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 6400) + args.decoder_layers = safe_getattr(args, "decoder_layers", 48) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 25) + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big_wide") +def transformer_lm_gpt2_big_wide(args): + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 2048) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 8192) + args.decoder_layers = getattr(args, "decoder_layers", 24) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_bigger") +def transformer_lm_gpt2_bigger(args): + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 2048) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 8192) + args.decoder_layers = getattr(args, "decoder_layers", 48) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +def base_gpt3_architecture(args): + args.decoder_input_dim = args.decoder_embed_dim + args.decoder_output_dim = args.decoder_embed_dim + args.decoder_ffn_embed_dim = safe_getattr( + args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4 + ) + # GPT-3 used learned positional embeddings, rather than sinusoidal + args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", True) + args.dropout = safe_getattr(args, "dropout", 0.0) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + args.share_decoder_input_output_embed = True + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_small") +def transformer_lm_gpt3_small(args): + # 125M params + args.decoder_layers = safe_getattr(args, "decoder_layers", 12) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_medium") +def transformer_lm_gpt3_medium(args): + # 350M params + args.decoder_layers = safe_getattr(args, "decoder_layers", 24) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_large") +def transformer_lm_gpt3_large(args): + # 760M params + args.decoder_layers = safe_getattr(args, "decoder_layers", 24) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1536) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_xl") +def transformer_lm_gpt3_xl(args): + # 1.3B params + args.decoder_layers = safe_getattr(args, "decoder_layers", 24) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2048) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_2_7") +def transformer_lm_gpt3_2_7(args): + # 2.7B params + args.decoder_layers = safe_getattr(args, "decoder_layers", 32) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2560) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_6_7") +def transformer_lm_gpt3_6_7(args): + # 6.7B params + args.decoder_layers = safe_getattr(args, "decoder_layers", 32) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 4096) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_13") +def transformer_lm_gpt3_13(args): + # 13B params + args.decoder_layers = safe_getattr(args, "decoder_layers", 40) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 5120) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 40) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_175") +def transformer_lm_gpt3_175(args): + # 175B params + args.decoder_layers = safe_getattr(args, "decoder_layers", 96) + args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 12288) + args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 96) + base_gpt3_architecture(args) diff --git a/fairseq/fairseq/models/transformer_ulm.py b/fairseq/fairseq/models/transformer_ulm.py new file mode 100644 index 0000000..0fc9ae4 --- /dev/null +++ b/fairseq/fairseq/models/transformer_ulm.py @@ -0,0 +1,408 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from dataclasses import dataclass, field +from fairseq.models.fairseq_decoder import FairseqDecoder +import numpy as np +from typing import Optional, Dict, Any, List +import torch +from torch import nn +from fairseq.data.data_utils import compute_mask_indices +from fairseq.dataclass import ChoiceEnum +from fairseq.models import ( + FairseqLanguageModel, + register_model, + register_model_architecture, +) +from fairseq.tasks.speech_ulm_task import SpeechUnitLanguageModelingTask +from fairseq.models.transformer import Embedding, TransformerDecoder, Linear +from fairseq.models.transformer_lm import TransformerLanguageModelConfig +from torch import Tensor + + +DEFAULT_MAX_TARGET_POSITIONS = 1024 +MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"]) + + +@dataclass +class SpeechUnitLanguageModelConfig(TransformerLanguageModelConfig): + mask_unit_seg_prob: float = field( + default=0.0, metadata={"help": "probability to mask a segment of unit sequence"} + ) + mask_unit_seg_leng: int = field( + default=5, metadata={"help": "length of unit segment mask"} + ) + mask_unit_seg_type: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose unit mask length"} + ) + + mask_dur_prob: float = field( + default=0.0, metadata={"help": "probability to mask entire duration sequence"} + ) + mask_dur_seg_prob: float = field( + default=0.0, + metadata={"help": "probability to mask a segment of duration sequence"}, + ) + mask_dur_seg_leng: int = field( + default=5, metadata={"help": "length of duration segment mask"} + ) + mask_dur_seg_type: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose duration mask length"} + ) + + mask_f0_prob: float = field( + default=0.0, metadata={"help": "probability to mask entire duration sequence"} + ) + mask_f0_seg_prob: float = field( + default=0.0, metadata={"help": "probability to mask a segment of f0 sequence"} + ) + mask_f0_seg_leng: int = field( + default=5, metadata={"help": "length of f0 segment mask"} + ) + mask_f0_seg_type: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose f0 mask length"} + ) + + +@register_model("transformer_ulm", dataclass=SpeechUnitLanguageModelConfig) +class TransformerUnitLanguageModel(FairseqLanguageModel): + def __init__( + self, + cfg: SpeechUnitLanguageModelConfig, + task: SpeechUnitLanguageModelingTask, + decoder: FairseqDecoder, + ): + super().__init__(decoder) + self.cfg = cfg + + self.channel_names = task.channel_names + self.channel_sizes = task.channel_sizes + + self.unit_mask_val = task.source_dictionary.unk() + self.dur_mask_val = ( + task.source_duration_dictionary.unk() if task.cfg.discrete_duration else 0 + ) + self.f0_mask_val = ( + task.source_f0_dictionary.unk() if task.cfg.discrete_f0 else 0 + ) + + self.ignore_duration_input = task.cfg.ignore_duration_input + self.ignore_f0_input = task.cfg.ignore_f0_input + + @classmethod + def build_model(cls, args, task): + base_ulm_architecture(args) + + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) + + embed_tokens = Embedding( + len(task.source_dictionary), + args.decoder_input_dim, + padding_idx=task.source_dictionary.pad(), + ) + embed_duration = None + if task.cfg.discrete_duration: + embed_duration = Embedding( + len(task.source_duration_dictionary), + args.decoder_input_dim, + padding_idx=0, # duration uses 0 for padding + ) + embed_f0 = None + if task.cfg.discrete_f0: + embed_f0 = Embedding( + len(task.source_f0_dictionary), + args.decoder_input_dim, + padding_idx=task.source_f0_dictionary.pad(), + ) + + decoder = MultiStreamTransformerDecoder( + args, + task.target_dictionary, + embed_tokens, + [embed_duration, embed_f0], + no_encoder_attn=True, + channel_sizes=task.channel_sizes, + ) + + return cls(args, task, decoder) + + def apply_seg_dropout(self, inp, mask_prob, mask_leng, mask_type, mask_val): + B, T = inp.size() + if mask_prob > 0: + mask_indices = compute_mask_indices( + (B, T), None, mask_prob, mask_leng, mask_type # may mask padding + ) + mask_indices = torch.from_numpy(mask_indices).to(inp.device) + inp[mask_indices] = mask_val + else: + mask_indices = torch.zeros_like(inp).bool() + return inp, mask_indices + + def apply_seq_dropout(self, inp, mask_prob, mask_val): + B, T = inp.size() + if mask_prob > 0: + mask_indices = np.random.uniform(0, 1, (B,)) < mask_prob + mask_indices = ( + torch.from_numpy(mask_indices).to(inp.device).unsqueeze(1).expand(-1, T) + ) + inp[mask_indices] = mask_val + else: + mask_indices = torch.zeros_like(inp).bool() + return inp, mask_indices + + def apply_dropout(self, src_tokens, dur_src, f0_src): + src_tokens, unit_mask = self.apply_seg_dropout( + src_tokens, + self.cfg.mask_unit_seg_prob, + self.cfg.mask_unit_seg_leng, + self.cfg.mask_unit_seg_type, + self.unit_mask_val, + ) + + dur_src, dur_mask = self.apply_seq_dropout( + dur_src, self.cfg.mask_dur_prob, self.dur_mask_val + ) + dur_src, _dur_mask = self.apply_seg_dropout( + dur_src, + self.cfg.mask_dur_seg_prob, + self.cfg.mask_dur_seg_leng, + self.cfg.mask_dur_seg_type, + self.dur_mask_val, + ) + dur_mask = dur_mask.logical_or(_dur_mask) + + f0_src, f0_mask = self.apply_seq_dropout( + f0_src, self.cfg.mask_f0_prob, self.f0_mask_val + ) + f0_src, _f0_mask = self.apply_seg_dropout( + f0_src, + self.cfg.mask_f0_seg_prob, + self.cfg.mask_f0_seg_leng, + self.cfg.mask_f0_seg_type, + self.f0_mask_val, + ) + f0_mask = f0_mask.logical_or(_f0_mask) + + return src_tokens, unit_mask, dur_src, dur_mask, f0_src, f0_mask + + def forward( + self, + src_tokens: torch.Tensor, + dur_src: torch.Tensor, + f0_src: torch.Tensor, + src_lengths: Optional[Any] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + if self.ignore_duration_input: + dur_src = torch.zeros_like(dur_src) + + if self.ignore_f0_input: + f0_src = torch.zeros_like(f0_src) + + if self.training: + ( + src_tokens, + unit_mask, + dur_src, + dur_mask, + f0_src, + f0_mask, + ) = self.apply_dropout(src_tokens, dur_src, f0_src) + else: + unit_masks = dur_mask = f0_mask = None + + prediction, _ = self.decoder( + prev_output_tokens=(src_tokens, dur_src, f0_src), + incremental_state=incremental_state, + src_lengths=src_lengths, + features_only=True, + ) + + result = dict(zip(self.channel_names, prediction)) + + return result + + +def base_ulm_architecture(args): + from .transformer_lm import base_lm_architecture + + base_lm_architecture(args) + + +@register_model_architecture("transformer_ulm", "transformer_ulm_big") +def transformer_ulm_big(args): + from .transformer_lm import transformer_lm_big + + transformer_lm_big(args) + base_ulm_architecture(args) + + +@register_model_architecture("transformer_ulm", "transformer_ulm_tiny") +def transformer_ulm_tiny(args): + from .transformer_lm import transformer_lm_gpt2_tiny + + transformer_lm_gpt2_tiny(args) + base_ulm_architecture(args) + + +class MultiStreamTransformerDecoder(TransformerDecoder): + def __init__( + self, + args, + dictionary, + embed_tokens, + embed_other_list, + no_encoder_attn, + channel_sizes, + ): + super().__init__( + args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn + ) + + # embed each channel and project if dimensions do not match + self.embed_other_list = torch.nn.ModuleList(embed_other_list) + self.proj_other_list = torch.nn.ModuleList() + dim = embed_tokens.embedding_dim + for embed_other in embed_other_list: + other_dim = 1 if embed_other is None else embed_other.embedding_dim + self.proj_other_list.append( + nn.Linear(other_dim, dim) if other_dim != dim else None + ) + + # tranformer output to prediction + self.channel_sizes = channel_sizes + self.project_out_dim = Linear( + embed_tokens.embedding_dim, sum(channel_sizes), bias=False + ) + + def extract_features_scriptable( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + if alignment_layer is None: + alignment_layer = self.num_layers - 1 + + # XXX: first multi-channel change start + prev_output_tokens, *other_channels = prev_output_tokens + # XXX: first multi-channel change end + + # embed positions + positions = None + if self.embed_positions is not None: + positions = self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + other_channels = [o[:, -1:] for o in other_channels] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + # XXX: second multi-channel change start + other_channels = [ + o.unsqueeze(-1).to(dtype=x.dtype) if emb is None else emb(o) + for o, emb in zip(other_channels, self.embed_other_list) + ] + other_channels = [ + o if proj_other is None else proj_other(o) + for o, proj_other in zip(other_channels, self.proj_other_list) + ] + for o in other_channels: + x = x + o + # XXX: second multi-channel change end + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + self_attn_padding_mask: Optional[Tensor] = None + if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + + # decoder layers + attn: Optional[Tensor] = None + inner_states: List[Optional[Tensor]] = [x] + for idx, layer in enumerate(self.layers): + if incremental_state is None and not full_context_alignment: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = layer( + x, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + + if attn is not None: + if alignment_heads is not None: + attn = attn[:alignment_heads] + + # average probabilities over heads + attn = attn.mean(dim=0) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + else: + assert False + + # XXX: the last change start + result = [] + start = 0 + for channel_size in self.channel_sizes: + end = start + channel_size + result.append(x[:, :, start:end]) + start = end + assert end == x.size(-1) + # XXX: the last change end + + return result, {"attn": [attn], "inner_states": inner_states} diff --git a/fairseq/fairseq/models/wav2vec/__init__.py b/fairseq/fairseq/models/wav2vec/__init__.py new file mode 100644 index 0000000..b756e45 --- /dev/null +++ b/fairseq/fairseq/models/wav2vec/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .wav2vec import * # noqa +from .wav2vec2 import * # noqa +from .wav2vec2_asr import * # noqa +from .wav2vec2_laser import * # noqa +from .wav2vec2_classification import * # noqa diff --git a/fairseq/fairseq/models/wav2vec/utils.py b/fairseq/fairseq/models/wav2vec/utils.py new file mode 100644 index 0000000..dd52d86 --- /dev/null +++ b/fairseq/fairseq/models/wav2vec/utils.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import torch.nn.functional as F + + +def pad_to_multiple(x, multiple, dim=-1, value=0): + # Inspired from https://github.com/lucidrains/local-attention/blob/master/local_attention/local_attention.py#L41 + if x is None: + return None, 0 + tsz = x.size(dim) + m = tsz / multiple + remainder = math.ceil(m) * multiple - tsz + if m.is_integer(): + return x, 0 + pad_offset = (0,) * (-1 - dim) * 2 + + return F.pad(x, (*pad_offset, 0, remainder), value=value), remainder diff --git a/fairseq/fairseq/models/wav2vec/wav2vec.py b/fairseq/fairseq/models/wav2vec/wav2vec.py new file mode 100644 index 0000000..af6604d --- /dev/null +++ b/fairseq/fairseq/models/wav2vec/wav2vec.py @@ -0,0 +1,630 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +import logging +import math +from typing import Optional, Tuple +from omegaconf import II +import sys + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.modules import ( + Fp32GroupNorm, + Fp32LayerNorm, + GumbelVectorQuantizer, + KmeansVectorQuantizer, + TransposeLast, +) +from fairseq.tasks import FairseqTask +from fairseq.utils import buffered_arange + + +logger = logging.getLogger(__name__) + + +AGGREGATOR_CHOICES = ChoiceEnum(["cnn", "gru"]) +PROJECT_FEATURES_CHOICES = ChoiceEnum(["none", "same", "new"]) +ACTIVATION_CHOICES = ChoiceEnum(["relu", "gelu"]) +VQ_TYPE_CHOICES = ChoiceEnum(["none", "gumbel", "kmeans"]) + + +@dataclass +class Wav2VecConfig(FairseqDataclass): + prediction_steps: int = field( + default=12, metadata={"help": "number of steps ahead to predict"} + ) + sample_distance: Optional[int] = field( + default=None, + metadata={ + "help": "sample distance from target. does not work properly with cross-sampling" + }, + ) + cross_sample_negatives: int = field( + default=0, metadata={"help": "num of cross sampled negatives"} + ) + num_negatives: int = field( + default=10, metadata={"help": "num of sampled negatives"} + ) + conv_feature_layers: str = field( + default="[(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1), (512, 1, 1)]", + metadata={ + "help": "convolutional feature extraction layers [(dim, kernel_size, stride), ...]" + }, + ) + conv_aggregator_layers: str = field( + default="[(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)]", + metadata={ + "help": "convolutional aggregator layers [(dim, kernel_size, stride), ...]" + }, + ) + dropout: float = field( + default=0.0, metadata={"help": "dropout to apply within the model"} + ) + dropout_features: float = field( + default=0.0, metadata={"help": "dropout to apply to the features"} + ) + dropout_agg: float = field( + default=0.0, metadata={"help": "dropout to apply after aggregation step"} + ) + aggregator: AGGREGATOR_CHOICES = field( + default="cnn", metadata={"help": "type of aggregator to use"} + ) + gru_dim: int = field(default=512, metadata={"help": "GRU dimensionality"}) + no_conv_bias: bool = field( + default=False, metadata={"help": "if set, does not learn bias for conv layers"} + ) + agg_zero_pad: bool = field( + default=False, + metadata={"help": "if set, zero pads in aggregator instead of repl pad"}, + ) + skip_connections_feat: bool = field( + default=False, + metadata={"help": "if set, adds skip connections to the feature extractor"}, + ) + skip_connections_agg: bool = field( + default=True, + metadata={"help": "if set, adds skip connections to the aggregator"}, + ) + residual_scale: float = field( + default=0.5, metadata={"help": "scales residual by sqrt(value)"} + ) + log_compression: bool = field( + default=True, + metadata={"help": "if set, adds a log compression to feature extractor"}, + ) + balanced_classes: bool = field( + default=False, + metadata={"help": "if set, loss is scaled to balance for number of negatives"}, + ) + project_features: PROJECT_FEATURES_CHOICES = field( + default="none", + metadata={ + "help": "if not none, features are projected using the (same or new) aggregator" + }, + ) + non_affine_group_norm: bool = field( + default=False, metadata={"help": "if set, group norm is not affine"} + ) + offset: str = field( + default="auto", + metadata={ + "help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value" + }, + ) + activation: ACTIVATION_CHOICES = field( + default="relu", + metadata={ + "help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value" + }, + ) + vq_type: VQ_TYPE_CHOICES = field( + default="none", metadata={"help": "which type of quantizer to use"} + ) + vq_vars: int = field( + default=320, + metadata={"help": "project to this many vector quantized variables per group"}, + ) + vq_groups: int = field( + default=2, metadata={"help": "number of groups of latent variables"} + ) + vq_dim: int = field( + default=0, + metadata={ + "help": "uses this dimensionality for quantized vectors. 0 to use model dim // groups" + }, + ) + vq_depth: int = field( + default=1, metadata={"help": "number of layers for vq weight projection"} + ) + combine_groups: bool = field( + default=False, metadata={"help": "if set, variables are shared among groups"} + ) + vq_temp: Tuple[float, float, float] = field( + default=(2.0, 0.5, 0.999995), + metadata={ + "help": "temperature for latent variable sampling with gumbel softmax. should be a tuple of 3 values (start, end, decay)" + }, + ) + vq_gamma: float = field( + default=0.25, + metadata={"help": "gamma parameter for kmeans style vector quantization"}, + ) + infonce: bool = II("criterion.infonce") + + +@register_model("wav2vec", dataclass=Wav2VecConfig) +class Wav2VecModel(BaseFairseqModel): + @classmethod + def build_model(cls, cfg: Wav2VecConfig, task: FairseqTask): + """Build a new model instance.""" + + model = Wav2VecModel(cfg) + logger.info(model) + return model + + def __init__(self, cfg: Wav2VecConfig): + super().__init__() + + self.prediction_steps = cfg.prediction_steps + offset = cfg.offset + + if cfg.activation == "relu": + activation = nn.ReLU() + elif cfg.activation == "gelu": + activation = nn.GELU() + else: + raise Exception("unknown activation " + cfg.activation) + + feature_enc_layers = eval(cfg.conv_feature_layers) + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + log_compression=cfg.log_compression, + skip_connections=cfg.skip_connections_feat, + residual_scale=cfg.residual_scale, + non_affine_group_norm=cfg.non_affine_group_norm, + activation=activation, + ) + embed = feature_enc_layers[-1][0] + + self.vector_quantizer = None + if cfg.vq_type == "gumbel": + self.vector_quantizer = GumbelVectorQuantizer( + dim=embed, + num_vars=cfg.vq_vars, + temp=cfg.vq_temp, + groups=cfg.vq_groups, + combine_groups=cfg.combine_groups, + vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed, + time_first=False, + activation=activation, + weight_proj_depth=cfg.vq_depth, + weight_proj_factor=2, + ) + elif cfg.vq_type == "kmeans": + self.vector_quantizer = KmeansVectorQuantizer( + dim=embed, + num_vars=cfg.vq_vars, + groups=cfg.vq_groups, + combine_groups=cfg.combine_groups, + vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed, + time_first=False, + gamma=cfg.vq_gamma, + ) + else: + assert ( + cfg.vq_type == "none" or cfg.vq_type is None + ), "Unknown quantizer type" + + if cfg.offset == "auto": + jin = 0 + rin = 0 + for _, k, stride in feature_enc_layers: + if rin == 0: + rin = k + rin = rin + (k - 1) * jin + if jin == 0: + jin = stride + else: + jin *= stride + offset = math.ceil(rin / jin) + + offset = int(offset) + + def make_aggregator(): + if cfg.aggregator == "cnn": + agg_layers = eval(cfg.conv_aggregator_layers) + agg_dim = agg_layers[-1][0] + feature_aggregator = ConvAggegator( + conv_layers=agg_layers, + embed=embed, + dropout=cfg.dropout, + skip_connections=cfg.skip_connections_agg, + residual_scale=cfg.residual_scale, + non_affine_group_norm=cfg.non_affine_group_norm, + conv_bias=not cfg.no_conv_bias, + zero_pad=cfg.agg_zero_pad, + activation=activation, + ) + elif cfg.aggregator == "gru": + agg_dim = cfg.gru_dim + feature_aggregator = nn.Sequential( + TransposeLast(), + nn.GRU( + input_size=embed, + hidden_size=agg_dim, + num_layers=1, + dropout=cfg.dropout, + ), + TransposeLast(deconstruct_idx=0), + ) + else: + raise Exception("unknown aggregator type " + cfg.aggregator) + + return feature_aggregator, agg_dim + + self.feature_aggregator, agg_dim = make_aggregator() + + self.wav2vec_predictions = Wav2VecPredictionsModel( + in_dim=agg_dim, + out_dim=embed, + prediction_steps=cfg.prediction_steps, + n_negatives=cfg.num_negatives, + cross_sample_negatives=cfg.cross_sample_negatives, + sample_distance=cfg.sample_distance, + dropout=cfg.dropout, + offset=offset, + balanced_classes=cfg.balanced_classes, + infonce=cfg.infonce, + ) + + self.dropout_feats = nn.Dropout(p=cfg.dropout_features) + self.dropout_agg = nn.Dropout(p=cfg.dropout_agg) + + if cfg.project_features == "none": + self.project_features = None + elif cfg.project_features == "same": + self.project_features = self.feature_aggregator + elif cfg.project_features == "new": + self.project_features, _ = make_aggregator() + + def forward(self, source): + result = {} + + features = self.feature_extractor(source) + if self.vector_quantizer: + q_res = self.vector_quantizer(features) + features = q_res["x"] + for k in q_res.keys(): + if k != "x": + result[k] = q_res[k] + + x = self.dropout_feats(features) + x = self.feature_aggregator(x) + x = self.dropout_agg(x) + + if self.project_features is not None: + features = self.project_features(features) + x, targets = self.wav2vec_predictions(x, features) + result["cpc_logits"] = x + result["cpc_targets"] = targets + + return result + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + + def max_positions(self): + """Maximum length supported by the model.""" + return sys.maxsize + + def get_logits(self, net_output): + logits = net_output["cpc_logits"] + return logits + + def get_targets(self, sample, net_output): + t = net_output["cpc_targets"] + if isinstance(t, tuple): + t = t[0] + return t.contiguous() + + def get_target_weights(self, targets, net_output): + targets = net_output["cpc_targets"] + if isinstance(targets, tuple) and targets[-1] is not None: + return targets[-1] + return None + + def get_extra_losses(self, net_output): + loss = None + if "prob_perplexity" in net_output: + loss = net_output["num_vars"] - net_output["prob_perplexity"] + elif "kmeans_loss" in net_output: + loss = net_output["kmeans_loss"] + + return loss + + +def norm_block(is_layer_norm, dim, affine=True): + if is_layer_norm: + mod = nn.Sequential( + TransposeLast(), + Fp32LayerNorm(dim, elementwise_affine=affine), + TransposeLast(), + ) + else: + mod = Fp32GroupNorm(1, dim, affine=affine) + + return mod + + +class ConvFeatureExtractionModel(nn.Module): + def __init__( + self, + conv_layers, + dropout, + log_compression, + skip_connections, + residual_scale, + non_affine_group_norm, + activation, + ): + super().__init__() + + def block(n_in, n_out, k, stride): + return nn.Sequential( + nn.Conv1d(n_in, n_out, k, stride=stride, bias=False), + nn.Dropout(p=dropout), + norm_block( + is_layer_norm=False, dim=n_out, affine=not non_affine_group_norm + ), + activation, + ) + + in_d = 1 + self.conv_layers = nn.ModuleList() + for dim, k, stride in conv_layers: + self.conv_layers.append(block(in_d, dim, k, stride)) + in_d = dim + + self.log_compression = log_compression + self.skip_connections = skip_connections + self.residual_scale = math.sqrt(residual_scale) + + def forward(self, x): + # BxT -> BxCxT + x = x.unsqueeze(1) + + for conv in self.conv_layers: + residual = x + x = conv(x) + if self.skip_connections and x.size(1) == residual.size(1): + tsz = x.size(2) + r_tsz = residual.size(2) + residual = residual[..., :: r_tsz // tsz][..., :tsz] + x = (x + residual) * self.residual_scale + + if self.log_compression: + x = x.abs() + x = x + 1 + x = x.log() + + return x + + +class ZeroPad1d(nn.Module): + def __init__(self, pad_left, pad_right): + super().__init__() + self.pad_left = pad_left + self.pad_right = pad_right + + def forward(self, x): + return F.pad(x, (self.pad_left, self.pad_right)) + + +class ConvAggegator(nn.Module): + def __init__( + self, + conv_layers, + embed, + dropout, + skip_connections, + residual_scale, + non_affine_group_norm, + conv_bias, + zero_pad, + activation, + ): + super().__init__() + + def block(n_in, n_out, k, stride): + # padding dims only really make sense for stride = 1 + ka = k // 2 + kb = ka - 1 if k % 2 == 0 else ka + + pad = ( + ZeroPad1d(ka + kb, 0) if zero_pad else nn.ReplicationPad1d((ka + kb, 0)) + ) + + return nn.Sequential( + pad, + nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias), + nn.Dropout(p=dropout), + norm_block(False, n_out, affine=not non_affine_group_norm), + activation, + ) + + in_d = embed + self.conv_layers = nn.ModuleList() + self.residual_proj = nn.ModuleList() + for dim, k, stride in conv_layers: + if in_d != dim and skip_connections: + self.residual_proj.append(nn.Conv1d(in_d, dim, 1, bias=False)) + else: + self.residual_proj.append(None) + + self.conv_layers.append(block(in_d, dim, k, stride)) + in_d = dim + self.conv_layers = nn.Sequential(*self.conv_layers) + self.skip_connections = skip_connections + self.residual_scale = math.sqrt(residual_scale) + + def forward(self, x): + for rproj, conv in zip(self.residual_proj, self.conv_layers): + residual = x + x = conv(x) + if self.skip_connections: + if rproj is not None: + residual = rproj(residual) + x = (x + residual) * self.residual_scale + return x + + +class Wav2VecPredictionsModel(nn.Module): + def __init__( + self, + in_dim, + out_dim, + prediction_steps, + n_negatives, + cross_sample_negatives, + sample_distance, + dropout, + offset, + balanced_classes, + infonce, + ): + super().__init__() + + self.n_negatives = n_negatives + self.cross_sample_negatives = cross_sample_negatives + self.sample_distance = sample_distance + self.project_to_steps = nn.ConvTranspose2d( + in_dim, out_dim, (1, prediction_steps) + ) + self.dropout = nn.Dropout(p=dropout) + self.offset = offset + self.balanced_classes = balanced_classes + self.infonce = infonce + + def sample_negatives(self, y): + bsz, fsz, tsz = y.shape + + y = y.transpose(0, 1) # BCT -> CBT + y = y.contiguous().view(fsz, -1) # CBT => C(BxT) + + cross_high = tsz * bsz + high = tsz if self.sample_distance is None else min(tsz, self.sample_distance) + assert high > 1 + + neg_idxs = torch.randint(low=0, high=high, size=(bsz, self.n_negatives * tsz)) + + with torch.no_grad(): + if self.n_negatives > 0: + tszs = ( + buffered_arange(tsz) + .unsqueeze(-1) + .expand(-1, self.n_negatives) + .flatten() + ) + + neg_idxs = torch.randint( + low=0, high=high - 1, size=(bsz, self.n_negatives * tsz) + ) + neg_idxs[neg_idxs >= tszs] += 1 + + if self.cross_sample_negatives > 0: + tszs = ( + buffered_arange(tsz) + .unsqueeze(-1) + .expand(-1, self.cross_sample_negatives) + .flatten() + ) + + cross_neg_idxs = torch.randint( + low=0, + high=cross_high - 1, + size=(bsz, self.cross_sample_negatives * tsz), + ) + cross_neg_idxs[cross_neg_idxs >= tszs] += 1 + + if self.n_negatives > 0: + for i in range(1, bsz): + neg_idxs[i] += i * high + else: + neg_idxs = cross_neg_idxs + + if self.cross_sample_negatives > 0 and self.n_negatives > 0: + neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1) + + negs = y[..., neg_idxs.view(-1)] + negs = negs.view( + fsz, bsz, self.n_negatives + self.cross_sample_negatives, tsz + ).permute( + 2, 1, 0, 3 + ) # to NxBxCxT + + return negs + + def forward(self, x, y): + + x = x.unsqueeze(-1) + x = self.project_to_steps(x) # BxCxTxS + x = self.dropout(x) + + negatives = self.sample_negatives(y) + y = y.unsqueeze(0) + targets = torch.cat([y, negatives], dim=0) # Copies x B x C x T + + copies = targets.size(0) + bsz, dim, tsz, steps = x.shape + steps = min(steps, tsz - self.offset) + + predictions = x.new( + bsz * copies * (tsz - self.offset + 1) * steps + - ((steps + 1) * steps // 2) * copies * bsz + ) + if self.infonce: + labels = predictions.new_full( + (predictions.shape[0] // copies,), 0, dtype=torch.long + ) + else: + labels = torch.zeros_like(predictions) + weights = ( + torch.full_like(labels, 1 / self.n_negatives) + if self.balanced_classes and not self.infonce + else None + ) + + start = end = 0 + for i in range(steps): + offset = i + self.offset + end = start + (tsz - offset) * bsz * copies + if self.infonce: + predictions[start:end] = torch.einsum( + "bct,nbct->tbn", x[..., :-offset, i], targets[..., offset:] + ).flatten() + else: + pos_num = (end - start) // copies + predictions[start:end] = torch.einsum( + "bct,nbct->nbt", x[..., :-offset, i], targets[..., offset:] + ).flatten() + labels[start : start + pos_num] = 1.0 + if weights is not None: + weights[start : start + pos_num] = 1.0 + start = end + assert end == predictions.numel(), "{} != {}".format(end, predictions.numel()) + + if self.infonce: + predictions = predictions.view(-1, copies) + else: + if weights is not None: + labels = (labels, weights) + + return predictions, labels diff --git a/fairseq/fairseq/models/wav2vec/wav2vec2.py b/fairseq/fairseq/models/wav2vec/wav2vec2.py new file mode 100644 index 0000000..0faba77 --- /dev/null +++ b/fairseq/fairseq/models/wav2vec/wav2vec2.py @@ -0,0 +1,1499 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import List, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import utils +from fairseq.data.data_utils import compute_mask_indices +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.distributed import fsdp_wrap +from fairseq.models import BaseFairseqModel, register_model +from fairseq.distributed.fully_sharded_data_parallel import FullyShardedDataParallel +from fairseq.modules import ( + Fp32GroupNorm, + Fp32LayerNorm, + GradMultiply, + GumbelVectorQuantizer, + LayerNorm, + MultiheadAttention, + RelPositionalEncoding, + SamePad, + TransposeLast, +) +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer +from fairseq.modules.transformer_sentence_encoder import init_bert_params +from fairseq.utils import buffered_arange, index_put, is_xla_tensor + +from .utils import pad_to_multiple + +EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"]) +MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"]) +LAYER_TYPE_CHOICES = ChoiceEnum(["transformer", "conformer", "trf_adp"]) + + +@dataclass +class Wav2Vec2Config(FairseqDataclass): + extractor_mode: EXTRACTOR_MODE_CHOICES = field( + default="default", + metadata={ + "help": "mode for feature extractor. default has a single group norm with d " + "groups in the first conv block, whereas layer_norm has layer norms in " + "every block (meant to use with normalize=True)" + }, + ) + encoder_layers: int = field( + default=12, metadata={"help": "num encoder layers in the transformer"} + ) + encoder_embed_dim: int = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + encoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "encoder embedding dimension for FFN"} + ) + encoder_attention_heads: int = field( + default=12, metadata={"help": "num encoder attention heads"} + ) + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="gelu", metadata={"help": "activation function to use"} + ) + layer_type: LAYER_TYPE_CHOICES = field( + default="transformer", metadata={"help": "layer type in encoder"} + ) + # dropouts + dropout: float = field( + default=0.1, metadata={"help": "dropout probability for the transformer"} + ) + attention_dropout: float = field( + default=0.1, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN"} + ) + encoder_layerdrop: float = field( + default=0.0, metadata={"help": "probability of dropping a tarnsformer layer"} + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + dropout_features: float = field( + default=0.0, + metadata={"help": "dropout to apply to the features (after feat extr)"}, + ) + + final_dim: int = field( + default=0, + metadata={ + "help": "project final representations and targets to this many dimensions." + "set to encoder_embed_dim is <= 0" + }, + ) + layer_norm_first: bool = field( + default=False, metadata={"help": "apply layernorm first in the transformer"} + ) + conv_feature_layers: str = field( + default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", + metadata={ + "help": "string describing convolutional feature extraction layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + conv_bias: bool = field( + default=False, metadata={"help": "include bias in conv encoder"} + ) + logit_temp: float = field( + default=0.1, metadata={"help": "temperature to divide logits by"} + ) + quantize_targets: bool = field( + default=False, metadata={"help": "use quantized targets"} + ) + quantize_input: bool = field( + default=False, metadata={"help": "use quantized inputs"} + ) + same_quantizer: bool = field( + default=False, metadata={"help": "use same quantizer for inputs and targets"} + ) + target_glu: bool = field( + default=False, metadata={"help": "adds projection + glu to targets"} + ) + feature_grad_mult: float = field( + default=1.0, metadata={"help": "multiply feature extractor var grads by this"} + ) + quantizer_depth: int = field( + default=1, + metadata={"help": "number of quantizer layers"}, + ) + quantizer_factor: int = field( + default=3, + metadata={ + "help": "dimensionality increase for inner quantizer layers (if depth > 1)" + }, + ) + latent_vars: int = field( + default=320, + metadata={"help": "number of latent variables V in each group of the codebook"}, + ) + latent_groups: int = field( + default=2, + metadata={"help": "number of groups G of latent variables in the codebook"}, + ) + latent_dim: int = field( + default=0, + metadata={ + "help": "if > 0, uses this dimensionality for latent variables. " + "otherwise uses final_dim / latent_groups" + }, + ) + + # masking + mask_length: int = field(default=10, metadata={"help": "mask length"}) + mask_prob: float = field( + default=0.65, metadata={"help": "probability of replacing a token with mask"} + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose mask length"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + require_same_masks: bool = field( + default=True, + metadata={ + "help": "whether to number of masked timesteps must be the same across all " + "examples in a batch" + }, + ) + mask_dropout: float = field( + default=0.0, + metadata={"help": "percent of masks to unmask for each sample"}, + ) + + # channel masking + mask_channel_length: int = field( + default=10, metadata={"help": "length of the mask for features (channels)"} + ) + mask_channel_prob: float = field( + default=0.0, metadata={"help": "probability of replacing a feature with 0"} + ) + mask_channel_before: bool = False + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, metadata={"help": "whether to allow channel masks to overlap"} + ) + mask_channel_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # negative selection + num_negatives: int = field( + default=100, + metadata={"help": "number of negative examples from the same sample"}, + ) + negatives_from_everywhere: bool = field( + default=False, + metadata={"help": "sample negatives from everywhere, not just masked states"}, + ) + cross_sample_negatives: int = field( + default=0, metadata={"help": "number of negative examples from the any sample"} + ) + codebook_negatives: int = field( + default=0, metadata={"help": "number of negative examples codebook"} + ) + + # positional embeddings + conv_pos: int = field( + default=128, + metadata={"help": "number of filters for convolutional positional embeddings"}, + ) + conv_pos_groups: int = field( + default=16, + metadata={"help": "number of groups for convolutional positional embedding"}, + ) + pos_conv_depth: int = field( + default=1, + metadata={"help": "depth of positional encoder network"}, + ) + + latent_temp: Tuple[float, float, float] = field( + default=(2, 0.5, 0.999995), + metadata={ + "help": "temperature for latent variable sampling. " + "can be tuple of 3 values (start, end, decay)" + }, + ) + max_positions: int = field(default=100000, metadata={"help": "Max positions"}) + checkpoint_activations: bool = field( + default=False, + metadata={"help": "recompute activations and save memory for extra compute"}, + ) + + # FP16 optimization + required_seq_len_multiple: int = field( + default=2, + metadata={ + "help": "pad the input to encoder such that the sequence length is divisible by multiple" + }, + ) + crop_seq_to_multiple: int = field( + default=1, + metadata={ + "help": "crop convolutional feature extractor output such that the sequence length is divisible by multiple" + }, + ) + + # Conformer + depthwise_conv_kernel_size: int = field( + default=31, + metadata={ + "help": "depthwise-conv-kernel-size for convolution in conformer layer" + }, + ) + attn_type: str = field( + default="", + metadata={"help": "if espnet use ESPNET MHA"}, + ) + pos_enc_type: str = field( + default="abs", + metadata={"help": "Positional encoding type to use in conformer"}, + ) + fp16: bool = field(default=False, metadata={"help": "If fp16 is being used"}) + + # Adapter num + adp_num: int = field( + default=-1 + ) + adp_dim: int = field( + default=64 + ) + adp_act_fn: str = field( + default="relu" + ) + adp_trf_idx: str = field( + default="all", + ) + + +@register_model("wav2vec2", dataclass=Wav2Vec2Config) +class Wav2Vec2Model(BaseFairseqModel): + def __init__(self, cfg: Wav2Vec2Config): + super().__init__() + self.cfg = cfg + + feature_enc_layers = eval(cfg.conv_feature_layers) + self.embed = feature_enc_layers[-1][0] + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + + self.post_extract_proj = ( + nn.Linear(self.embed, cfg.encoder_embed_dim) + if self.embed != cfg.encoder_embed_dim and not cfg.quantize_input + else None + ) + + self.crop_seq_to_multiple = cfg.crop_seq_to_multiple + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_before = cfg.mask_channel_before + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + + self.quantizer = None + self.input_quantizer = None + + self.n_negatives = cfg.num_negatives + self.cross_sample_negatives = cfg.cross_sample_negatives + self.codebook_negatives = cfg.codebook_negatives + self.negatives_from_everywhere = cfg.negatives_from_everywhere + + self.logit_temp = cfg.logit_temp + + final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim + + if cfg.quantize_targets: + vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else final_dim + self.quantizer = GumbelVectorQuantizer( + dim=self.embed, + num_vars=cfg.latent_vars, + temp=cfg.latent_temp, + groups=cfg.latent_groups, + combine_groups=False, + vq_dim=vq_dim, + time_first=True, + weight_proj_depth=cfg.quantizer_depth, + weight_proj_factor=cfg.quantizer_factor, + ) + self.project_q = nn.Linear(vq_dim, final_dim) + else: + self.project_q = nn.Linear(self.embed, final_dim) + + if cfg.quantize_input: + if cfg.same_quantizer and self.quantizer is not None: + vq_dim = final_dim + self.input_quantizer = self.quantizer + else: + vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else cfg.encoder_embed_dim + self.input_quantizer = GumbelVectorQuantizer( + dim=self.embed, + num_vars=cfg.latent_vars, + temp=cfg.latent_temp, + groups=cfg.latent_groups, + combine_groups=False, + vq_dim=vq_dim, + time_first=True, + weight_proj_depth=cfg.quantizer_depth, + weight_proj_factor=cfg.quantizer_factor, + ) + self.project_inp = nn.Linear(vq_dim, cfg.encoder_embed_dim) + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + encoder_cls = TransformerEncoder + if cfg.layer_type == "conformer" and cfg.pos_enc_type in ["rel_pos", "rope"]: + encoder_cls = ConformerEncoder + + self.encoder = encoder_cls(cfg) + self.layer_norm = LayerNorm(self.embed) + + self.target_glu = None + if cfg.target_glu: + self.target_glu = nn.Sequential( + nn.Linear(final_dim, final_dim * 2), nn.GLU() + ) + + self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + return state_dict + + @classmethod + def build_model(cls, cfg: Wav2Vec2Config, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def apply_mask( + self, + x, + padding_mask, + mask_indices=None, + mask_channel_indices=None, + ): + B, T, C = x.shape + + if self.mask_channel_prob > 0 and self.mask_channel_before: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + if self.mask_prob > 0: + if mask_indices is None: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + require_same_masks=self.cfg.require_same_masks, + mask_dropout=self.cfg.mask_dropout, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x = index_put(x, mask_indices, self.mask_emb) + else: + mask_indices = None + + if self.mask_channel_prob > 0 and not self.mask_channel_before: + if mask_channel_indices is None: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x = index_put(x, mask_channel_indices, 0) + + return x, mask_indices + + def sample_negatives(self, y, num, padding_count=None): + + if self.n_negatives == 0 and self.cross_sample_negatives == 0: + return y.new(0) + + bsz, tsz, fsz = y.shape + y = y.view(-1, fsz) # BTC => (BxT)C + + # FIXME: what happens if padding_count is specified? + cross_high = tsz * bsz + high = tsz - (padding_count or 0) + with torch.no_grad(): + assert high > 1, f"{bsz,tsz,fsz}" + + if self.n_negatives > 0: + tszs = ( + buffered_arange(num) + .unsqueeze(-1) + .expand(-1, self.n_negatives) + .flatten() + ) + + neg_idxs = torch.randint( + low=0, high=high - 1, size=(bsz, self.n_negatives * num) + ) + neg_idxs[neg_idxs >= tszs] += 1 + + if self.cross_sample_negatives > 0: + tszs = ( + buffered_arange(num) + .unsqueeze(-1) + .expand(-1, self.cross_sample_negatives) + .flatten() + ) + + cross_neg_idxs = torch.randint( + low=0, + high=cross_high - 1, + size=(bsz, self.cross_sample_negatives * num), + ) + cross_neg_idxs[cross_neg_idxs >= tszs] += 1 + + if self.n_negatives > 0: + neg_idxs = neg_idxs + (torch.arange(bsz).unsqueeze(1) * high) + else: + neg_idxs = cross_neg_idxs + + if self.cross_sample_negatives > 0 and self.n_negatives > 0: + neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1) + + negs = y[neg_idxs.view(-1)] + negs = negs.view( + bsz, num, self.n_negatives + self.cross_sample_negatives, fsz + ).permute( + 2, 0, 1, 3 + ) # to NxBxTxC + return negs, neg_idxs + + def compute_preds(self, x, y, negatives): + + neg_is_pos = (y == negatives).all(-1) + y = y.unsqueeze(0) + targets = torch.cat([y, negatives], dim=0) + + logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1) + logits = logits / self.logit_temp + logits = logits.type_as(x) + + if is_xla_tensor(logits) or neg_is_pos.any(): + if not hasattr(self, "_inftensor"): + fillval = -float(2**30) + self._inftensor = ( + torch.tensor(fillval).to(x.device) + if is_xla_tensor(logits) + else float("-inf") + ) + logits[1:] = index_put(logits[1:], neg_is_pos, self._inftensor) + + return logits + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + return torch.floor((input_length - kernel_size) / stride + 1) + + conv_cfg_list = eval(self.cfg.conv_feature_layers) + + for i in range(len(conv_cfg_list)): + input_lengths = _conv_out_length( + input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2] + ) + + return input_lengths.to(torch.long) + + def forward( + self, + source, + padding_mask=None, + mask=True, + features_only=False, + layer=None, + mask_indices=None, + mask_channel_indices=None, + padding_count=None, + corpus_key=None, + ): + + if self.feature_grad_mult > 0: + features = self.feature_extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(source) + + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + unmasked_features = features.clone() + + if padding_mask is not None and padding_mask.any(): + input_lengths = (1 - padding_mask.long()).sum(-1) + # apply conv formula to get real output_lengths + output_lengths = self._get_feat_extract_output_lengths(input_lengths) + + padding_mask = torch.zeros( + features.shape[:2], dtype=features.dtype, device=features.device + ) + + # these two operations makes sure that all values + # before the output lengths indices are attended to + padding_mask[ + ( + torch.arange(padding_mask.shape[0], device=padding_mask.device), + output_lengths - 1, + ) + ] = 1 + padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool() + else: + padding_mask = None + + time_steps_to_drop = features.size(1) % self.crop_seq_to_multiple + if time_steps_to_drop != 0: + features = features[:, :-time_steps_to_drop] + unmasked_features = unmasked_features[:, :-time_steps_to_drop] + if padding_mask is not None: + padding_mask = padding_mask[:, :-time_steps_to_drop] + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + + num_vars = None + code_ppl = None + prob_ppl = None + curr_temp = None + + if self.input_quantizer: + q = self.input_quantizer(features, produce_targets=False) + features = q["x"] + num_vars = q["num_vars"] + code_ppl = q["code_perplexity"] + prob_ppl = q["prob_perplexity"] + curr_temp = q["temp"] + features = self.project_inp(features) + + if mask: + x, mask_indices = self.apply_mask( + features, + padding_mask, + mask_indices=mask_indices, + mask_channel_indices=mask_channel_indices, + ) + if not is_xla_tensor(x) and mask_indices is not None: + # tpu-comment: reducing the size in a dynamic way causes + # too many recompilations on xla. + y = unmasked_features[mask_indices].view( + unmasked_features.size(0), -1, unmasked_features.size(-1) + ) + else: + y = unmasked_features + else: + x = features + y = unmasked_features + mask_indices = None + + x, layer_results = self.encoder( + x, padding_mask=padding_mask, layer=layer, corpus_key=corpus_key + ) + + if features_only: + return { + "x": x, + "padding_mask": padding_mask, + "features": unmasked_features, + "layer_results": layer_results, + } + + if self.quantizer: + if self.negatives_from_everywhere: + q = self.quantizer(unmasked_features, produce_targets=False) + y = q["x"] + num_vars = q["num_vars"] + code_ppl = q["code_perplexity"] + prob_ppl = q["prob_perplexity"] + curr_temp = q["temp"] + y = self.project_q(y) + + negs, _ = self.sample_negatives( + y, + mask_indices[0].sum(), + padding_count=padding_count, + ) + y = y[mask_indices].view(y.size(0), -1, y.size(-1)) + + else: + q = self.quantizer(y, produce_targets=False) + y = q["x"] + num_vars = q["num_vars"] + code_ppl = q["code_perplexity"] + prob_ppl = q["prob_perplexity"] + curr_temp = q["temp"] + + y = self.project_q(y) + + negs, _ = self.sample_negatives( + y, + y.size(1), + padding_count=padding_count, + ) + + if self.codebook_negatives > 0: + cb_negs = self.quantizer.sample_from_codebook( + y.size(0) * y.size(1), self.codebook_negatives + ) + cb_negs = cb_negs.view( + self.codebook_negatives, y.size(0), y.size(1), -1 + ) # order doesnt matter + cb_negs = self.project_q(cb_negs) + negs = torch.cat([negs, cb_negs], dim=0) + else: + y = self.project_q(y) + + if self.negatives_from_everywhere: + negs, _ = self.sample_negatives( + unmasked_features, + y.size(1), + padding_count=padding_count, + ) + negs = self.project_q(negs) + else: + negs, _ = self.sample_negatives( + y, + y.size(1), + padding_count=padding_count, + ) + + if not is_xla_tensor(x): + # tpu-comment: reducing the size in a dynamic way causes + # too many recompilations on xla. + x = x[mask_indices].view(x.size(0), -1, x.size(-1)) + + if self.target_glu: + y = self.target_glu(y) + negs = self.target_glu(negs) + + x = self.final_proj(x) + x = self.compute_preds(x, y, negs) + + result = { + "x": x, + "padding_mask": padding_mask, + "features_pen": features_pen, + } + + if prob_ppl is not None: + result["prob_perplexity"] = prob_ppl + result["code_perplexity"] = code_ppl + result["num_vars"] = num_vars + result["temp"] = curr_temp + + return result + + def quantize(self, x): + assert self.quantizer is not None + x = self.feature_extractor(x) + x = x.transpose(1, 2) + x = self.layer_norm(x) + return self.quantizer.forward_idx(x) + + def extract_features( + self, source, padding_mask, mask=False, layer=None, corpus_key=None + ): + res = self.forward( + source, + padding_mask, + mask=mask, + features_only=True, + layer=layer, + corpus_key=corpus_key, + ) + return res + + def get_logits(self, net_output): + logits = net_output["x"] + logits = logits.transpose(0, 2) + logits = logits.reshape(-1, logits.size(-1)) + return logits + + def get_targets(self, sample, net_output, expand_steps=True): + x = net_output["x"] + return x.new_zeros(x.size(1) * x.size(2), dtype=torch.long) + + def get_extra_losses(self, net_output): + pen = [] + + if "prob_perplexity" in net_output: + pen.append( + (net_output["num_vars"] - net_output["prob_perplexity"]) + / net_output["num_vars"] + ) + + if "features_pen" in net_output: + pen.append(net_output["features_pen"]) + + return pen + + def remove_pretraining_modules(self, last_layer=None): + self.quantizer = None + self.project_q = None + self.target_glu = None + self.final_proj = None + + if last_layer is not None: + self.encoder.layers = nn.ModuleList( + l for i, l in enumerate(self.encoder.layers) if i <= last_layer + ) + + +class ConvFeatureExtractionModel(nn.Module): + def __init__( + self, + conv_layers: List[Tuple[int, int, int]], + dropout: float = 0.0, + mode: str = "default", + conv_bias: bool = False, + ): + super().__init__() + + assert mode in {"default", "layer_norm"} + + def block( + n_in, + n_out, + k, + stride, + is_layer_norm=False, + is_group_norm=False, + conv_bias=False, + ): + def make_conv(): + conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) + nn.init.kaiming_normal_(conv.weight) + return conv + + assert ( + is_layer_norm and is_group_norm + ) == False, "layer norm and group norm are exclusive" + + if is_layer_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + nn.Sequential( + TransposeLast(), + Fp32LayerNorm(dim, elementwise_affine=True), + TransposeLast(), + ), + nn.GELU(), + ) + elif is_group_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + Fp32GroupNorm(dim, dim, affine=True), + nn.GELU(), + ) + else: + return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) + + in_d = 1 + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3, "invalid conv definition: " + str(cl) + (dim, k, stride) = cl + + self.conv_layers.append( + block( + in_d, + dim, + k, + stride, + is_layer_norm=mode == "layer_norm", + is_group_norm=mode == "default" and i == 0, + conv_bias=conv_bias, + ) + ) + in_d = dim + + def forward(self, x): + + # BxT -> BxCxT + x = x.unsqueeze(1) + + for conv in self.conv_layers: + x = conv(x) + + return x + + +def make_conv_pos(e, k, g, is_batch_norm=False): + pos_conv = nn.Conv1d( + e, + e, + kernel_size=k, + padding=k // 2, + groups=g, + ) + dropout = 0 + std = math.sqrt((4 * (1.0 - dropout)) / (k * e)) + nn.init.normal_(pos_conv.weight, mean=0, std=std) + nn.init.constant_(pos_conv.bias, 0) + + if not is_batch_norm: + pos_conv = nn.utils.weight_norm(pos_conv, name="weight", dim=2) + pos_conv = nn.Sequential(pos_conv, SamePad(k), nn.GELU()) + else: + batch_norm = nn.BatchNorm1d(e) + pos_conv = nn.Sequential(batch_norm, pos_conv, SamePad(k), nn.GELU()) + + return pos_conv + + +class TransformerEncoder(nn.Module): + def build_encoder_layer(self, args: Wav2Vec2Config, **kwargs): + if args.layer_type == "transformer": + layer = TransformerSentenceEncoderLayer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=args.encoder_ffn_embed_dim, + num_attention_heads=args.encoder_attention_heads, + dropout=self.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + activation_fn=args.activation_fn, + layer_norm_first=args.layer_norm_first, + ) + elif args.layer_type == "conformer": + layer = ConformerWav2Vec2EncoderLayer( + embed_dim=self.embedding_dim, + ffn_embed_dim=args.encoder_ffn_embed_dim, + attention_heads=args.encoder_attention_heads, + dropout=args.dropout, + depthwise_conv_kernel_size=args.depthwise_conv_kernel_size, + activation_fn="swish", + attn_type=args.attn_type, + use_fp16=args.fp16, + pos_enc_type="abs", + ) + elif args.layer_type == "trf_adp": + use_adp = False + if args.adp_trf_idx == "all": + use_adp = True + else: + adp_trf_idx = list(range(*[int(g) for g in args.adp_trf_idx.split(":")])) + if kwargs.get("layer_idx", None) in adp_trf_idx: + use_adp = True + if use_adp: + layer = TransformerSentenceEncoderWithAdapterLayer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=args.encoder_ffn_embed_dim, + num_attention_heads=args.encoder_attention_heads, + dropout=self.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + activation_fn=args.activation_fn, + layer_norm_first=args.layer_norm_first, + adapter_num=args.adp_num, + adapter_dim=args.adp_dim, + adapter_act_fn=args.adp_act_fn, + ) + else: + layer = TransformerSentenceEncoderLayer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=args.encoder_ffn_embed_dim, + num_attention_heads=args.encoder_attention_heads, + dropout=self.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + activation_fn=args.activation_fn, + layer_norm_first=args.layer_norm_first, + ) + + layer = fsdp_wrap(layer) + if args.checkpoint_activations: + layer = checkpoint_wrapper(layer) + return layer + + def __init__(self, args: Wav2Vec2Config, skip_pos_conv: bool = False, override_encoder_layer: int = None): + super().__init__() + + self.dropout = args.dropout + self.embedding_dim = args.encoder_embed_dim + self.required_seq_len_multiple = args.required_seq_len_multiple + + pos_conv_depth = getattr(args, "pos_conv_depth", 1) + if pos_conv_depth > 1: + num_layers = args.pos_conv_depth + k = max(3, args.conv_pos // num_layers) + + def make_conv_block(e, k, g, l): + return nn.Sequential( + *[ + nn.Sequential( + nn.Conv1d( + e, + e, + kernel_size=k, + padding=k // 2, + groups=g, + ), + SamePad(k), + TransposeLast(), + LayerNorm(e, elementwise_affine=False), + TransposeLast(), + nn.GELU(), + ) + for _ in range(l) + ] + ) + + self.pos_conv = make_conv_block( + self.embedding_dim, k, args.conv_pos_groups, num_layers + ) + elif skip_pos_conv: + self.pos_conv = None + else: + self.pos_conv = make_conv_pos( + self.embedding_dim, + args.conv_pos, + args.conv_pos_groups, + is_batch_norm=args.conv_pos_batch_norm + if hasattr(args, "conv_pos_batch_norm") + else False, + ) + + if override_encoder_layer is None: + encoder_layers = args.encoder_layers + else: + encoder_layers = override_encoder_layer + + self.layers = nn.ModuleList( + [self.build_encoder_layer(args, layer_idx=ii) for ii in range(encoder_layers)] + ) + self.layer_norm_first = args.layer_norm_first + self.layer_norm = LayerNorm(self.embedding_dim) + self.layerdrop = args.encoder_layerdrop + + self.apply(init_bert_params) + + def forward(self, x, padding_mask=None, layer=None, corpus_key=None): + x, layer_results = self.extract_features( + x, padding_mask, layer, corpus_key=corpus_key + ) + + if self.layer_norm_first and layer is None: + x = self.layer_norm(x) + + return x, layer_results + + def extract_features( + self, + x, + padding_mask=None, + tgt_layer=None, + min_layer=0, + corpus_key=None, + ): + + if padding_mask is not None: + x = index_put(x, padding_mask, 0) + + if self.pos_conv is not None: + x_conv = self.pos_conv(x.transpose(1, 2)) + x_conv = x_conv.transpose(1, 2) + x = x + x_conv + + if not self.layer_norm_first: + x = self.layer_norm(x) + + # pad to the sequence length dimension + x, pad_length = pad_to_multiple( + x, self.required_seq_len_multiple, dim=-2, value=0 + ) + if pad_length > 0 and padding_mask is None: + padding_mask = x.new_zeros((x.size(0), x.size(1)), dtype=torch.bool) + padding_mask[:, -pad_length:] = True + else: + padding_mask, _ = pad_to_multiple( + padding_mask, self.required_seq_len_multiple, dim=-1, value=True + ) + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + layer_results = [] + r = None + + for i, layer in enumerate(self.layers): + dropout_probability = np.random.random() if self.layerdrop > 0 else 1 + if not self.training or (dropout_probability > self.layerdrop): + layer_check = layer + if isinstance(layer, FullyShardedDataParallel): + layer_check = layer.unwrapped_module + if (corpus_key is None) or ( + not isinstance(layer_check, ( + TransformerSentenceEncoderWithAdapterLayer, + ) + ) + ): + x, (z, lr) = layer( + x, self_attn_padding_mask=padding_mask, need_weights=False + ) + else: + x, (z, lr) = layer( + x, + self_attn_padding_mask=padding_mask, + need_weights=False, + corpus_key=corpus_key, + ) + if i >= min_layer: + layer_results.append((x, z, lr)) + if i == tgt_layer: + r = x + break + + if r is not None: + x = r + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + # undo paddding + if pad_length > 0: + x = x[:, :-pad_length] + + def undo_pad(a, b, c): + return ( + a[:-pad_length], + b[:-pad_length] if b is not None else b, + c[:-pad_length], + ) + + layer_results = [undo_pad(*u) for u in layer_results] + + return x, layer_results + + def max_positions(self): + """Maximum output length supported by the encoder.""" + return self.args.max_positions + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + return state_dict + + +class ConformerEncoder(TransformerEncoder): + def build_encoder_layer(self, args): + layer = ConformerWav2Vec2EncoderLayer( + embed_dim=self.embedding_dim, + ffn_embed_dim=args.encoder_ffn_embed_dim, + attention_heads=args.encoder_attention_heads, + dropout=args.dropout, + depthwise_conv_kernel_size=args.depthwise_conv_kernel_size, + activation_fn="swish", + attn_type=args.attn_type, + pos_enc_type=args.pos_enc_type, + use_fp16=args.fp16, # only used for rope + ) + layer = fsdp_wrap(layer) + if args.checkpoint_activations: + layer = checkpoint_wrapper(layer) + return layer + + def __init__(self, args): + super().__init__(args) + self.args = args + self.dropout = args.dropout + self.embedding_dim = args.encoder_embed_dim + self.pos_enc_type = args.pos_enc_type + max_source_positions = self.max_positions() + + if self.pos_enc_type == "rel_pos": + self.embed_positions = RelPositionalEncoding( + max_source_positions, self.embedding_dim + ) + elif self.pos_enc_type == "rope": + self.embed_positions = None + else: + raise Exception("Unsupported positional encoding type") + + self.layers = nn.ModuleList( + [self.build_encoder_layer(args) for _ in range(args.encoder_layers)] + ) + self.layer_norm_first = args.layer_norm_first + self.layer_norm = LayerNorm(self.embedding_dim) + self.layerdrop = args.encoder_layerdrop + + self.apply(init_bert_params) + + def extract_features(self, x, padding_mask=None, tgt_layer=None): + if padding_mask is not None: + x = index_put(x, padding_mask, 0) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # B X T X C here + position_emb = None + if self.pos_enc_type == "rel_pos": + position_emb = self.embed_positions(x) + + if not self.layer_norm_first: + x = self.layer_norm(x) + + x = F.dropout(x, p=self.dropout, training=self.training) + + layer_results = [] + r = None + for i, layer in enumerate(self.layers): + dropout_probability = np.random.random() + if not self.training or (dropout_probability > self.layerdrop): + x, z = layer( + x, + self_attn_padding_mask=padding_mask, + need_weights=False, + position_emb=position_emb, + ) + if tgt_layer is not None: + layer_results.append((x, z)) + if i == tgt_layer: + r = x + break + + if r is not None: + x = r + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, layer_results + + +class TransformerSentenceEncoderLayer(nn.Module): + """ + Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained + models. + """ + + def __init__( + self, + embedding_dim: float = 768, + ffn_embedding_dim: float = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + activation_fn: str = "relu", + layer_norm_first: bool = False, + ) -> None: + + super().__init__() + # Initialize parameters + self.embedding_dim = embedding_dim + self.dropout = dropout + self.activation_dropout = activation_dropout + + # Initialize blocks + self.activation_fn = utils.get_activation_fn(activation_fn) + self.self_attn = MultiheadAttention( + self.embedding_dim, + num_attention_heads, + dropout=attention_dropout, + self_attention=True, + ) + + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(self.activation_dropout) + self.dropout3 = nn.Dropout(dropout) + + self.layer_norm_first = layer_norm_first + + # layer norm associated with the self attention layer + self.self_attn_layer_norm = LayerNorm(self.embedding_dim) + self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) + self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) + + # layer norm associated with the position wise feed-forward NN + self.final_layer_norm = LayerNorm(self.embedding_dim) + + def forward( + self, + x: torch.Tensor, + self_attn_mask: torch.Tensor = None, + self_attn_padding_mask: torch.Tensor = None, + need_weights: bool = False, + att_args=None, + ): + """ + LayerNorm is applied either before or after the self-attention/ffn + modules similar to the original Transformer imlementation. + """ + residual = x + + if self.layer_norm_first: + x = self.self_attn_layer_norm(x) + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + attn_mask=self_attn_mask, + need_weights=False, + ) + x = self.dropout1(x) + x = residual + x + + residual = x + x = self.final_layer_norm(x) + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + + layer_result = x + + x = self.dropout3(x) + x = residual + x + else: + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + need_weights=False, + ) + + x = self.dropout1(x) + x = residual + x + + x = self.self_attn_layer_norm(x) + + residual = x + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + + layer_result = x + + x = self.dropout3(x) + x = residual + x + x = self.final_layer_norm(x) + + return x, (attn, layer_result) + + +class AdapterFast(nn.Module): + def __init__(self, adapter_num, input_dim, hidden_dim, act_fn): + """ + Implements adapter modules directly with 3D tensor weight as parameters + and without using ModuleList orto speed up training throughput. + """ + super().__init__() + + self.adapter_num = adapter_num + self.input_dim = input_dim + self.hidden_dim = hidden_dim + self.W_a = nn.Parameter(torch.empty(adapter_num, hidden_dim, input_dim)) + self.W_b = nn.Parameter(torch.empty(adapter_num, input_dim, hidden_dim)) + self.b_a = nn.Parameter(torch.empty(adapter_num, hidden_dim)) + self.b_b = nn.Parameter(torch.empty(adapter_num, input_dim)) + + self.ln_W = nn.Parameter(torch.empty(adapter_num, input_dim)) + self.ln_b = nn.Parameter(torch.empty(adapter_num, input_dim)) + self.act_fn = nn.Identity() + if act_fn == "relu": + self.act_fn = nn.ReLU() + elif act_fn == "gelu": + self.act_fn = nn.GELU() + elif act_fn == "selu": + self.act_fn = nn.SELU() + else: + raise ValueError(f"unsupported {act_fn}") + + + self.input_dim = input_dim + self.reset_parameters() + + def reset_parameters(self): + for ii in range(self.adapter_num): + nn.init.kaiming_uniform_(self.W_a[ii], a=math.sqrt(5)) + nn.init.kaiming_uniform_(self.W_b[ii], a=math.sqrt(5)) + fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.W_a[ii]) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + nn.init.uniform_(self.b_a[ii], -bound, bound) + fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.W_b[ii]) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + nn.init.uniform_(self.b_b[ii], -bound, bound) + + nn.init.ones_(self.ln_W) + nn.init.zeros_(self.ln_b) + + def forward(self, x, adapter_id): + ii = adapter_id + h = x + h = F.layer_norm(h, (self.input_dim, ), self.ln_W[ii], self.ln_b[ii]) + h = F.linear(h, self.W_a[ii], self.b_a[ii]) + h = self.act_fn(h) + h = F.linear(h, self.W_b[ii], self.b_b[ii]) + outputs = h + return outputs + + def extra_repr(self): + return ('adapter={}, input_dim={}, hidden_dim={}'.format(self.adapter_num, self.input_dim, self.hidden_dim)) + + + +class TransformerSentenceEncoderWithAdapterLayer(TransformerSentenceEncoderLayer): + """ + Implements a Transformer Encoder Layer with adapters used in BERT/XLM style pre-trained + models. An adapter module is added along with vanilla Transformer module. + """ + + def __init__( + self, + embedding_dim: float = 768, + ffn_embedding_dim: float = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + activation_fn: str = "relu", + layer_norm_first: bool = False, + adapter_num=201, + adapter_dim=64, + adapter_act_fn="relu", + ) -> None: + + super().__init__( + embedding_dim=embedding_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + layer_norm_first=layer_norm_first, + + ) + + self.adapter_num = adapter_num + self.adapter_dim = adapter_dim + self.adapter_layer = AdapterFast(adapter_num, self.embedding_dim, self.adapter_dim, adapter_act_fn) + + def forward( + self, + x: torch.Tensor, + self_attn_mask: torch.Tensor = None, + self_attn_padding_mask: torch.Tensor = None, + need_weights: bool = False, + att_args=None, + corpus_key=None, + ): + + x, (attn, layer_result) = super().forward( + x=x, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_weights=need_weights, + att_args=att_args, + ) + assert corpus_key is not None + assert len(set(corpus_key)) == 1, f"corpus_key items are not same {corpus_key}" + y = self.adapter_layer(x, corpus_key[0]) + x = x + y + return x, (attn, layer_result) diff --git a/fairseq/fairseq/models/wav2vec/wav2vec2_asr.py b/fairseq/fairseq/models/wav2vec/wav2vec2_asr.py new file mode 100644 index 0000000..0403efe --- /dev/null +++ b/fairseq/fairseq/models/wav2vec/wav2vec2_asr.py @@ -0,0 +1,878 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import copy +import logging +import math +import re +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Any, Optional + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from omegaconf import II, MISSING, open_dict + +from fairseq import checkpoint_utils, tasks, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models import ( + BaseFairseqModel, + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, +) +from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES, LAYER_TYPE_CHOICES, AdapterFast +from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerDecoderLayer +from fairseq.tasks import FairseqTask + +logger = logging.getLogger(__name__) + + +@dataclass +class Wav2Vec2AsrConfig(FairseqDataclass): + w2v_path: str = field( + default=MISSING, metadata={"help": "path to wav2vec 2.0 model"} + ) + no_pretrained_weights: bool = field( + default=False, metadata={"help": "if true, does not load pretrained weights"} + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + + final_dropout: float = field( + default=0.0, + metadata={"help": "dropout after transformer and before final projection"}, + ) + dropout: float = field( + default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"} + ) + attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights inside wav2vec 2.0 model" + }, + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN inside wav2vec 2.0 model" + }, + ) + + # masking + apply_mask: bool = field( + default=False, metadata={"help": "apply masking during fine-tuning"} + ) + mask_length: int = field( + default=10, metadata={"help": "repeat the mask indices multiple times"} + ) + mask_prob: float = field( + default=0.5, + metadata={ + "help": "probability of replacing a token with mask (normalized by length)" + }, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose masks"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: Optional[int] = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + require_same_masks: bool = field( + default=True, + metadata={ + "help": "whether to number of masked timesteps must be the same across all " + "examples in a batch" + }, + ) + mask_dropout: float = field( + default=0.0, + metadata={"help": "percent of masks to unmask for each sample"}, + ) + + # channel masking + mask_channel_length: int = field( + default=10, metadata={"help": "length of the mask for features (channels)"} + ) + mask_channel_prob: float = field( + default=0.0, metadata={"help": "probability of replacing a feature with 0"} + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, metadata={"help": "whether to allow channel masks to overlap"} + ) + freeze_finetune_updates: int = field( + default=0, metadata={"help": "dont finetune wav2vec for this many updates"} + ) + feature_grad_mult: float = field( + default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"} + ) + layerdrop: float = field( + default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"} + ) + drop_path: float = 0 + mask_channel_min_space: Optional[int] = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + mask_channel_before: bool = False + normalize: bool = II("task.normalize") + update_alibi: bool = True + data: str = II("task.data") + # this holds the loaded wav2vec args + w2v_args: Any = None + offload_activations: bool = field( + default=False, metadata={"help": "offload_activations"} + ) + min_params_to_wrap: int = field( + default=int(1e8), + metadata={ + "help": "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + }, + ) + + checkpoint_activations: bool = field( + default=False, + metadata={"help": "recompute activations and save memory for extra compute"}, + ) + ddp_backend: str = II("distributed_training.ddp_backend") + + zero_mask: bool = False + load_ema: bool = False + + layer_decay: float = 1 + + + layer_type: LAYER_TYPE_CHOICES = field( + default="transformer", metadata={"help": "layer type in encoder"} + ) + # Adapter num + adp_num: int = field( + default=-1 + ) + adp_dim: int = field( + default=64 + ) + adp_act_fn: str = field( + default="relu" + ) + adp_trf_idx: str = field( + default="all", + ) + + freeze_regex: Optional[str] = field( + default=None, + ) + +@dataclass +class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig): + blank_weight: float = 0 + blank_mode: str = "add" + + +@register_model("wav2vec_ctc", dataclass=Wav2Vec2CtcConfig) +class Wav2VecCtc(BaseFairseqModel): + def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel): + super().__init__() + self.cfg = cfg + self.w2v_encoder = w2v_encoder + self.blank_weight = cfg.blank_weight + self.blank_mode = cfg.blank_mode + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask): + """Build a new model instance.""" + w2v_encoder = Wav2VecEncoder(cfg, len(task.target_dictionary)) + return cls(cfg, w2v_encoder) + + def get_logits(self, net_output, normalize=False): + logits = net_output["encoder_out"] + if self.blank_weight != 0: + if self.blank_mode == "add": + logits[..., 0] += self.blank_weight + elif self.blank_mode == "set": + logits[..., 0] = self.blank_weight + else: + raise Exception(f"invalid blank mode {self.blank_mode}") + + if net_output["padding_mask"] is not None and net_output["padding_mask"].any(): + number_of_classes = logits.size(-1) + masking_tensor = torch.ones( + number_of_classes, device=logits.device + ) * float("-inf") + masking_tensor[0] = 0 + + if logits.size(0) > net_output["padding_mask"].size(1): + net_output["padding_mask"] = F.pad( + net_output["padding_mask"], (1, 0), value=False + ) + + logits[net_output["padding_mask"].T] = masking_tensor.type_as(logits) + + if normalize: + logits = utils.log_softmax(logits.float(), dim=-1) + + return logits + + def get_normalized_probs(self, net_output, log_probs): + """Get normalized probabilities (or log probs) from a net's output.""" + + logits = self.get_logits(net_output) + + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def forward(self, **kwargs): + x = self.w2v_encoder(**kwargs) + return x + + +@dataclass +class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig): + decoder_embed_dim: int = field( + default=768, metadata={"help": "decoder embedding dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "decoder layerdrop chance"} + ) + decoder_attention_heads: int = field( + default=4, metadata={"help": "num decoder attention heads"} + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + decoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each decoder block"} + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings (outside self attention)" + }, + ) + decoder_dropout: float = field( + default=0.0, metadata={"help": "dropout probability in the decoder"} + ) + decoder_attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights inside the decoder" + }, + ) + decoder_activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN inside the decoder" + }, + ) + max_target_positions: int = field( + default=2048, metadata={"help": "max target positions"} + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + autoregressive: bool = II("task.autoregressive") + + +@register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig) +class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def build_model(cls, cfg: Wav2Vec2Seq2SeqConfig, task: FairseqTask): + """Build a new model instance.""" + + assert ( + cfg.autoregressive + ), "Please set task.autoregressive=true for seq2seq asr models" + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + emb = Embedding(num_embeddings, embed_dim, padding_idx) + return emb + + decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim) + + encoder = cls.build_encoder(cfg) + decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) + + return Wav2Vec2Seq2SeqModel(encoder, decoder) + + @classmethod + def build_encoder(cls, cfg: Wav2Vec2AsrConfig): + return Wav2VecEncoder(cfg) + + @classmethod + def build_decoder(cls, cfg: Wav2Vec2Seq2SeqConfig, tgt_dict, embed_tokens): + return TransformerDecoder(cfg, tgt_dict, embed_tokens) + + def forward(self, **kwargs): + encoder_out = self.encoder(**kwargs) + decoder_out = self.decoder(encoder_out=encoder_out, **kwargs) + return decoder_out + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + +class Wav2VecEncoder(FairseqEncoder): + def __init__(self, cfg: Wav2Vec2AsrConfig, output_size=None): + self.apply_mask = cfg.apply_mask + + arg_overrides = { + "dropout": cfg.dropout, + "activation_dropout": cfg.activation_dropout, + "dropout_input": cfg.dropout_input, + "attention_dropout": cfg.attention_dropout, + "mask_length": cfg.mask_length, + "mask_prob": cfg.mask_prob, + "require_same_masks": getattr(cfg, "require_same_masks", True), + "pct_holes": getattr(cfg, "mask_dropout", 0), + "mask_selection": cfg.mask_selection, + "mask_other": cfg.mask_other, + "no_mask_overlap": cfg.no_mask_overlap, + "mask_channel_length": cfg.mask_channel_length, + "mask_channel_prob": cfg.mask_channel_prob, + "mask_channel_before": cfg.mask_channel_before, + "mask_channel_selection": cfg.mask_channel_selection, + "mask_channel_other": cfg.mask_channel_other, + "no_mask_channel_overlap": cfg.no_mask_channel_overlap, + "encoder_layerdrop": cfg.layerdrop, + "feature_grad_mult": cfg.feature_grad_mult, + "checkpoint_activations": cfg.checkpoint_activations, + "offload_activations": cfg.offload_activations, + "min_params_to_wrap": cfg.min_params_to_wrap, + # d2v multi args + "encoder_dropout": cfg.dropout, + "drop_path": getattr(cfg, "drop_path", 0), + "mask_dropout": getattr(cfg, "mask_dropout", 0), + "zero_mask": getattr(cfg, "zero_mask", False), + "local_grad_mult": cfg.feature_grad_mult, + "layerdrop": cfg.layerdrop, + "prenet_layerdrop": cfg.layerdrop, + "prenet_dropout": cfg.dropout, + "post_mlp_drop": cfg.dropout, + "encoder_zero_mask": getattr(cfg, "zero_mask", False), + "inverse_mask": False, + "learned_alibi_scale": getattr(cfg, "update_alibi", True), + } + + if cfg.w2v_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) + w2v_args = state.get("cfg", None) + if w2v_args is None: + w2v_args = convert_namespace_to_omegaconf(state["args"]) + w2v_args.criterion = None + w2v_args.lr_scheduler = None + + cfg.w2v_args = w2v_args + + logger.info(w2v_args) + + else: + state = None + w2v_args = cfg.w2v_args + if isinstance(w2v_args, Namespace): + cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args) + + self.is_d2v_multi = "data2vec_multi" in w2v_args.model.get("_name", None) + + if not self.is_d2v_multi: + model_normalized = w2v_args.task.get( + "normalize", w2v_args.model.get("normalize", False) + ) + assert cfg.normalize == model_normalized, ( + "Fine-tuning works best when data normalization is the same. " + "Please check that --normalize is set or unset for both pre-training and here" + ) + + with open_dict(w2v_args): + args_replacement = ["checkpoint_activations", "layer_type", + "adp_num", "adp_dim", + "adp_act_fn", "adp_trf_idx"] + for _args in args_replacement: + if hasattr(cfg, _args) and getattr(cfg, _args, None) is not None: + w2v_args.model[_args] = getattr(cfg, _args, None) + + if hasattr(cfg, "checkpoint_activations") and cfg.checkpoint_activations: + with open_dict(w2v_args): + w2v_args.model.checkpoint_activations = cfg.checkpoint_activations + + w2v_args.task.data = cfg.data + task = tasks.setup_task(w2v_args.task, from_checkpoint=True) + model = task.build_model(w2v_args.model, from_checkpoint=True) + model.remove_pretraining_modules() + d = w2v_args.model.encoder_embed_dim + else: + assert cfg.normalize + + if hasattr(w2v_args.task, "audio"): + w2v_args.task.audio.data = cfg.data + else: + w2v_args.task.data = cfg.data + task = tasks.setup_task(w2v_args.task, from_checkpoint=True) + + model = task.build_model(w2v_args.model, from_checkpoint=True) + + model.remove_pretraining_modules(modality="audio") + d = w2v_args.model.embed_dim + + if state is not None and not cfg.no_pretrained_weights: + if cfg.load_ema: + assert "_ema" in state["model"] + for k in state["model"]["_ema"]: + mk = "encoder." + k + assert mk in state["model"], mk + state["model"][mk] = state["model"]["_ema"][k] + self.load_model_weights(state, model, cfg) + + super().__init__(task.source_dictionary) + + self.w2v_model = model + + self.final_dropout = nn.Dropout(cfg.final_dropout) + self.freeze_finetune_updates = cfg.freeze_finetune_updates + self.num_updates = 0 + + targ_d = None + self.proj = None + + if output_size is not None: + targ_d = output_size + elif getattr(cfg, "decoder_embed_dim", d) != d: + targ_d = cfg.decoder_embed_dim + + if targ_d is not None: + self.proj = Linear(d, targ_d) + + if cfg.freeze_regex is not None: + self.freeze_regex(cfg.freeze_regex) + + layer_decay = getattr(cfg, "layer_decay", 1) + if layer_decay < 1: + mod_encs = list(model.modality_encoders.values()) + assert len(mod_encs) == 1, len(mod_encs) + blocks = list(mod_encs[0].context_encoder.blocks) + list(model.blocks) + num_layers = len(blocks) + 1 + layer_scales = list( + layer_decay ** (num_layers - i) for i in range(num_layers + 1) + ) + + for i, b in enumerate(blocks): + lid = i + 1 + if layer_scales[lid] == 1.0: + continue + + for n, p in b.named_parameters(): + optim_override = getattr(p, "optim_overrides", {}) + if "optimizer" not in optim_override: + optim_override["optimizer"] = {} + + optim_override["optimizer"]["lr_scale"] = layer_scales[lid] + p.optim_overrides = optim_override + + def freeze_regex(self, pattern): + unfrozen_names = [] + for name, param in self.named_parameters(): + if re.fullmatch(pattern, name) is not None: + param.requires_grad_(False) + else: + unfrozen_names.append(name) + + def load_model_weights(self, state, model, cfg): + if cfg.ddp_backend == "fully_sharded": + from fairseq.distributed import FullyShardedDataParallel + + for name, module in model.named_modules(): + if "encoder.layers" in name and len(name.split(".")) == 3: + # Only for layers, we do a special handling and load the weights one by one + # We dont load all weights together as that wont be memory efficient and may + # cause oom + new_dict = { + k.replace(name + ".", ""): v + for (k, v) in state["model"].items() + if name + "." in k + } + assert isinstance(module, FullyShardedDataParallel) + with module.summon_full_params(): + module.load_state_dict(new_dict, strict=True) + module._reset_lazy_init() + + # Once layers are loaded, filter them out and load everything else. + r = re.compile("encoder.layers.\d.") + filtered_list = list(filter(r.match, state["model"].keys())) + + new_big_dict = { + k: v for (k, v) in state["model"].items() if k not in filtered_list + } + + model.load_state_dict(new_big_dict, strict=False) + else: + to_delete = {"_ema", "target_proj", "decoder"} + for k in to_delete: + if k in state["model"]: + del state["model"][k] + + if hasattr(model, "modality_encoders"): + if "modality_encoders.AUDIO.encoder_mask" not in state["model"]: + model.modality_encoders["AUDIO"].encoder_mask = None + elif not cfg.zero_mask: + model.modality_encoders["AUDIO"].encoder_mask = None + del state["model"]["modality_encoders.AUDIO.encoder_mask"] + + for k in list(state["model"].keys()): + if k.startswith("modality_encoders.") and not k.startswith( + "modality_encoders.AUDIO" + ): + del state["model"][k] + + print(model) + model.load_state_dict(state["model"], strict=True) + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward(self, source, padding_mask, **kwargs): + + w2v_args = { + "source": source, + "padding_mask": padding_mask, + "mask": self.apply_mask and self.training, + } + if "corpus_key" in kwargs: + w2v_args["corpus_key"] = kwargs["corpus_key"] + + if self.is_d2v_multi: + w2v_args["mode"] = "AUDIO" + + ft = self.freeze_finetune_updates <= self.num_updates + + with torch.no_grad() if not ft else contextlib.ExitStack(): + res = self.w2v_model.extract_features(**w2v_args) + + x = res["x"] + padding_mask = res["padding_mask"] + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + x = self.final_dropout(x) + + if self.proj: + x = self.proj(x) + + return { + "encoder_out": x, # T x B x C + "padding_mask": padding_mask, # B x T, + "layer_results": res["layer_results"], + } + + def forward_torchscript(self, net_input): + if torch.jit.is_scripting(): + return self.forward(net_input["source"], net_input["padding_mask"]) + else: + return self.forward_non_torchscript(net_input) + + def reorder_encoder_out(self, encoder_out, new_order): + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + if encoder_out["padding_mask"] is not None: + encoder_out["padding_mask"] = encoder_out["padding_mask"].index_select( + 0, new_order + ) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return None + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +class TransformerDecoder(FairseqIncrementalDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + cfg: Wav2Vec2Seq2SeqConfig, + dictionary, + embed_tokens, + no_encoder_attn=False, + ): + super().__init__(dictionary) + + self.dropout = cfg.decoder_dropout + self.share_input_output_embed = cfg.share_decoder_input_output_embed + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = cfg.decoder_embed_dim + self.output_embed_dim = cfg.decoder_embed_dim + + self.layerdrop = cfg.decoder_layerdrop + + self.padding_idx = embed_tokens.padding_idx + self.max_target_positions = cfg.max_target_positions + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + cfg.max_target_positions, + embed_dim, + self.padding_idx, + learned=cfg.decoder_learned_pos, + ) + if not cfg.no_token_positional_embeddings + else None + ) + + # TODO: update this when transformer gets converted to dataclass configs + transformer_cfg = copy.deepcopy(cfg) + with open_dict(transformer_cfg): + transformer_cfg.dropout = transformer_cfg.decoder_dropout + transformer_cfg.attention_dropout = ( + transformer_cfg.decoder_attention_dropout + ) + transformer_cfg.activation_dropout = ( + transformer_cfg.decoder_activation_dropout + ) + + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + TransformerDecoderLayer(transformer_cfg, no_encoder_attn) + for _ in range(transformer_cfg.decoder_layers) + ] + ) + + if not self.share_input_output_embed: + self.embed_out = nn.Parameter( + torch.Tensor(len(dictionary), self.output_embed_dim) + ) + nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim**-0.5) + + if transformer_cfg.decoder_normalize_before: + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (Tensor, optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + if type(prev_output_tokens) == list: + max_len = max((len(x) for x in prev_output_tokens)) + tmp = torch.zeros( + [len(prev_output_tokens), max_len], device=prev_output_tokens[0].device + ) + for (i, p) in enumerate(prev_output_tokens): + tmp[i, : len(p)] = p + prev_output_tokens = tmp + + prev_output_tokens = prev_output_tokens.long() + x, extra = self.extract_features( + prev_output_tokens, encoder_out, incremental_state + ) + x = self.output_layer(x) + return x, extra + + def extract_features( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): + """ + Similar to *forward* but only return features. + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + + # embed positions + positions = ( + self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + attn = None + + inner_states = [x] + + # decoder layers + self_attn_padding_mask = None + if prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + for layer in self.layers: + dropout_probability = np.random.random() + if not self.training or (dropout_probability > self.layerdrop): + x, attn, _ = layer( + x, + encoder_out["encoder_out"] if encoder_out is not None else None, + encoder_out["padding_mask"] if encoder_out is not None else None, + incremental_state, + self_attn_mask=self.buffered_future_mask(x) + if incremental_state is None + else None, + self_attn_padding_mask=self_attn_padding_mask, + ) + inner_states.append(x) + + if self.layer_norm: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, {"attn": attn, "inner_states": inner_states} + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + # project back to size of vocabulary + if self.share_input_output_embed: + return F.linear(features, self.embed_tokens.weight) + else: + return F.linear(features, self.embed_out) + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embed_positions is None: + return self.max_target_positions + return min(self.max_target_positions, self.embed_positions.max_positions) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m diff --git a/fairseq/fairseq/models/wav2vec/wav2vec2_classification.py b/fairseq/fairseq/models/wav2vec/wav2vec2_classification.py new file mode 100644 index 0000000..c9bbaab --- /dev/null +++ b/fairseq/fairseq/models/wav2vec/wav2vec2_classification.py @@ -0,0 +1,348 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import logging +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from omegaconf import II, MISSING, open_dict + +from fairseq import checkpoint_utils, tasks, utils +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model +from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES, Wav2Vec2Config +from fairseq.models.wav2vec.wav2vec2_asr import Embedding, Linear, Wav2VecEncoder, Wav2Vec2AsrConfig +from fairseq.tasks import FairseqTask + +logging.basicConfig(level=logging.DEBUG) + + +@dataclass +class Wav2Vec2ClassificationConfig(Wav2Vec2AsrConfig): + latent_embed_dim: Optional[int] = field( + default=None, metadata={"help": "latent dim (encoder w2v -> latent -> class"} + ) + pooling: str = field( + default="first_token", + metadata={"help": "pooling layer choices"}, + ) + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="gelu", metadata={"help": "activation function to use"} + ) + + +@register_model("wav2vec_classification", dataclass=Wav2Vec2ClassificationConfig) +class Wav2VecClassification(BaseFairseqModel): + # TODO: Can be shared/merged with ASR model class as w2v_encoder params are common. + def __init__( + self, + cfg: Wav2Vec2ClassificationConfig, + w2v_encoder: BaseFairseqModel, + pooling_layer, + ): + super().__init__() + self.cfg = cfg + self.w2v_encoder = w2v_encoder + self.pooling_layer = pooling_layer + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: Wav2Vec2ClassificationConfig, task: FairseqTask): + """Build a new model instance.""" + w2v_encoder = Wav2VecEncoder(cfg, None) + pooling_layer = get_pooling_layer( + cfg, + w2v_encoder.w2v_model.encoder.layers[-1].embedding_dim, + len(task.target_dictionary), + len(w2v_encoder.w2v_model.encoder.layers), + ) + return cls(cfg, w2v_encoder, pooling_layer) + + def get_normalized_probs(self, net_output, log_probs): + """Get normalized probabilities (or log probs) from a net's output.""" + logits = net_output + + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def get_logits(self, net_output): + return net_output + + def forward(self, **kwargs): + encoder_out_dict = self.w2v_encoder(**kwargs) + w2v_encoder_out = encoder_out_dict["encoder_out"] # TxBxC + w2v_encoder_padding_mask = encoder_out_dict["padding_mask"] # BxT + # w2v_encoder_layer_results = encoder_out_dict["layer_results"] + return self.pooling_layer( + last_layer_feats=w2v_encoder_out, + padding_mask=w2v_encoder_padding_mask, + # all_layer_feats=w2v_encoder_layer_results, + ) + + # def forward_latent(self, **kwargs): + # encoder_out_dict = self.w2v_encoder(**kwargs) + # w2v_encoder_out = encoder_out_dict["encoder_out"] + # w2v_encoder_padding_mask = encoder_out_dict["encoder_padding_mask"] + # w2v_encoder_layer_results = encoder_out_dict["layer_results"] + # return self.pooling_layer.forward_latent( + # last_layer_feats=w2v_encoder_out, + # padding_mask=w2v_encoder_padding_mask, + # all_layer_feats=w2v_encoder_layer_results, + # ) + + +def get_pooling_layer( + cfg: Wav2Vec2ClassificationConfig, + encoder_embed_dim: int, + num_targets: int, + encoder_layers: int, +): + assert cfg.pooling == 'mean' + if cfg.pooling == "first_token": + return FirstToken(cfg, encoder_embed_dim, num_targets) + # elif cfg.pooling == "mean": + # return MeanPooling(cfg, encoder_embed_dim, num_targets) + elif cfg.pooling == "mean": + return MeanPoolingFast(cfg, encoder_embed_dim, num_targets) + elif cfg.pooling == "mean_amsoftmax": + return MeanPoolingFastAMSoftmax(cfg, encoder_embed_dim, num_targets) + elif cfg.pooling == "max": + return MaxPoolingFast(cfg, encoder_embed_dim, num_targets) + elif cfg.pooling == "elmo": + return LayerWeightedMeanPooling( + cfg, encoder_embed_dim, num_targets, encoder_layers + ) + else: + raise NotImplementedError(f"{cfg.pooling} has not been implemented yet.") + + +class Pooling(nn.Module): + def __init__( + self, + cfg: Wav2Vec2ClassificationConfig, + encoder_embed_dim: int, + num_targets: int, + ): + super().__init__() + self.projection = Linear(encoder_embed_dim, num_targets) + + def forward(self, last_layer_feats, **kwargs): + raise NotImplementedError() + + +class FirstToken(Pooling): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, last_layer_feats, **kwargs): + return self.projection(last_layer_feats[:, 0]) + + +# class MeanPooling(Pooling): +# def __init__( +# self, +# cfg: Wav2VecClassificationConfig, +# encoder_embed_dim: int, +# num_targets: int, +# **kwargs, +# ): +# super().__init__(cfg, encoder_embed_dim, num_targets) +# self.activation_fn = utils.get_activation_fn(cfg.activation_fn) +# self.linear = Linear(encoder_embed_dim, encoder_embed_dim) + +# def forward(self, last_layer_feats, padding_mask, **kwargs): +# # last_layer_feats: [BxTxD] +# # padding_mask: [BxT] +# last_layer_feats = self.linear(self.activation_fn(last_layer_feats)) +# input_lengths = (1 - padding_mask.long()).sum(-1) +# pooled_feature_list = [] +# for i in range(len(last_layer_feats)): +# length = input_lengths[i] +# pooled_feature = torch.mean(last_layer_feats[i][:length], dim=0) +# pooled_feature_list.append(pooled_feature) +# return self.projection(torch.stack(pooled_feature_list)) + + +def fn_mean(x, mask): + """ + Args: + x: TxBxD + mask: BxT + Return: + y: BxD + """ + if mask is not None: + mask = mask.t()[:, :, None] + return (x * mask).sum(0) / mask.sum(0) + else: + return x.sum(0) / x.shape[0] + + +class MeanPoolingFast(nn.Module): + def __init__( + self, + cfg: Wav2Vec2ClassificationConfig, + encoder_embed_dim: int, + num_targets: int, + **kwargs, + ): + super().__init__() + self.activation_fn = utils.get_activation_fn(cfg.activation_fn) + self.latent_embed_dim = ( + cfg.latent_embed_dim + if cfg.latent_embed_dim is not None + else encoder_embed_dim + ) + logging.debug(f"| {self.latent_embed_dim=}") + self.linear = Linear(encoder_embed_dim, self.latent_embed_dim) + self.projection = Linear(self.latent_embed_dim, num_targets) + + def forward(self, last_layer_feats, padding_mask, **kwargs): + """ + Arguments + features - [TxBxD] Acoustic feature with shape + padding_mask - [BxT] Padding Mask + """ + if padding_mask is not None: + feat_mask = (~padding_mask).to(last_layer_feats.dtype) + else: + feat_mask = None + feat = self.linear(last_layer_feats) + feat = fn_mean(feat, feat_mask) + feat = self.activation_fn(feat) + return self.projection(feat) + + def forward_latent(self, last_layer_feats, padding_mask, **kwargs): + """ + Arguments + features - [TxBxD] Acoustic feature with shape + padding_mask - [BxT] Padding Mask + """ + if padding_mask is not None: + feat_mask = (~padding_mask).to(last_layer_feats.dtype) + else: + feat_mask = None + feat = self.linear(last_layer_feats) + feat = fn_mean(feat, feat_mask) + return feat + + +class MeanPoolingFastAMSoftmax(MeanPoolingFast): + def __init__( + self, + cfg: Wav2Vec2ClassificationConfig, + encoder_embed_dim: int, + num_targets: int, + **kwargs, + ): + super().__init__(cfg, encoder_embed_dim, num_targets, **kwargs) + self.projection = Linear(self.latent_embed_dim, num_targets, bias=False) + nn.init.xavier_normal_(self.projection.weight, gain=1) + + def forward(self, last_layer_feats, padding_mask, **kwargs): + + """ + Arguments + features - [BxTxD] Acoustic feature with shape + padding_mask - [BxT] Padding Mask + """ + feat_mask = (~padding_mask).to(last_layer_feats.dtype) # T,B -> B,T + feat = self.linear(last_layer_feats) # B,T,D + feat = fn_mean(feat, feat_mask) # B,D + feat = self.activation_fn(feat) + # normalize feat + feat_norm = F.normalize(feat, p=2, dim=-1) # B,D + weight_norm = F.normalize(self.projection.weight.t(), p=2, dim=-1) # D,K + cos_fw = feat_norm @ weight_norm + return cos_fw + + +def fn_max(x, mask): + """ + Args: + x: TxBxD + mask: BxT + Return: + y: BxD + """ + mask = mask.t()[:, :, None].to(torch.bool) + return x.masked_fill(~mask, -1e-8).max(0)[0] + + +class MaxPoolingFast(Pooling): + def __init__( + self, + cfg: Wav2Vec2ClassificationConfig, + encoder_embed_dim: int, + num_targets: int, + **kwargs, + ): + super().__init__(cfg, encoder_embed_dim, num_targets) + self.activation_fn = utils.get_activation_fn(cfg.activation_fn) + self.linear = Linear(encoder_embed_dim, encoder_embed_dim) + + def forward(self, last_layer_feats, padding_mask, **kwargs): + + """ + Arguments + features - [TxBxD] Acoustic feature with shape + padding_mask - [BxT] Padding Mask + """ + feat_mask = (~padding_mask).to(last_layer_feats.dtype) + feat = self.linear(last_layer_feats) + feat = fn_max(feat, feat_mask) + feat = self.activation_fn(feat) + return self.projection(feat) + + +class LayerWeightedMeanPooling(MeanPoolingFast): + """Elmo-style weighted average representation.""" + + def __init__( + self, + cfg: Wav2Vec2ClassificationConfig, + encoder_embed_dim: int, + num_targets: int, + encoder_layers: int, + ): + super().__init__(cfg, encoder_embed_dim, num_targets) + self.num_layers = encoder_layers + self.weights = nn.Parameter(torch.ones(encoder_layers)) + + def forward(self, last_layer_feats, padding_mask, all_layer_feats): + # last_layer_feats: [BxTxD] + # padding_mask: [BxT] + if not self.training: + msg = ( + f"Number of layers in input features = {len(all_layer_feats)}." + f" Expected {self.num_layers} layers." + ) + assert len(all_layer_feats) == self.num_layers, msg + + # Stack up all layers and reshape to (num_layers, features) + all_layer_feats_stacked = torch.stack(all_layer_feats, dim=0) + num_layers, *original_feat_shape = all_layer_feats_stacked.shape + all_layer_feats_stacked_flat = all_layer_feats_stacked.view(num_layers, -1) + + # Weighted average + normalized_weights = F.softmax(self.weights, dim=-1) + weighted_avg_features = ( + normalized_weights.unsqueeze(-1) * all_layer_feats_stacked_flat + ).sum(dim=0) + weighted_avg_features = weighted_avg_features.view(*original_feat_shape) + + # Mean Pooling on weighted average features. + return super().forward(weighted_avg_features, padding_mask) \ No newline at end of file diff --git a/fairseq/fairseq/models/wav2vec/wav2vec2_laser.py b/fairseq/fairseq/models/wav2vec/wav2vec2_laser.py new file mode 100644 index 0000000..ff89759 --- /dev/null +++ b/fairseq/fairseq/models/wav2vec/wav2vec2_laser.py @@ -0,0 +1,39 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.wav2vec.wav2vec2_asr import ( + Wav2Vec2CtcConfig, + Wav2VecCtc, + Wav2VecEncoder, +) +from fairseq.tasks import FairseqTask + + +@register_model("wav2vec2_laser", dataclass=Wav2Vec2CtcConfig) +class Wav2VecLaser(Wav2VecCtc): + def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel): + super().__init__(cfg, w2v_encoder) + self.num_updates = 0 + self.freeze_finetune_updates = cfg.freeze_finetune_updates + + @classmethod + def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask): + """Build a new model instance.""" + w2v_encoder = Wav2VecEncoder(cfg, 1024) + return cls(cfg, w2v_encoder) + + def forward(self, **kwargs): + output = super().forward(**kwargs) + x_out = output["encoder_out"] * 0.01 + out_pad_mask = output["padding_mask"] + # Set padded outputs to -inf so they are not selected by max-pooling + if out_pad_mask is not None and out_pad_mask.any(): + x_out = ( + x_out.float() + .masked_fill_(out_pad_mask.T.unsqueeze(-1), float("-inf")) + .type_as(x_out) + ) + return x_out.max(dim=0)[0] diff --git a/fairseq/fairseq/models/xmod/__init__.py b/fairseq/fairseq/models/xmod/__init__.py new file mode 100644 index 0000000..bbf7694 --- /dev/null +++ b/fairseq/fairseq/models/xmod/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .model import * # noqa +from .transformer_layer_xmod import * # noqa diff --git a/fairseq/fairseq/models/xmod/hub_interface.py b/fairseq/fairseq/models/xmod/hub_interface.py new file mode 100644 index 0000000..909bb42 --- /dev/null +++ b/fairseq/fairseq/models/xmod/hub_interface.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from fairseq.models.roberta.hub_interface import RobertaHubInterface +import torch +import torch.nn.functional as F + + +class XMODHubInterface(RobertaHubInterface): + def extract_features( + self, + tokens: torch.LongTensor, + return_all_hiddens: bool = False, + lang_id=None, + ) -> torch.Tensor: + if tokens.dim() == 1: + tokens = tokens.unsqueeze(0) + if tokens.size(-1) > self.model.max_positions(): + raise ValueError( + "tokens exceeds maximum length: {} > {}".format( + tokens.size(-1), self.model.max_positions() + ) + ) + features, extra = self.model( + tokens.to(device=self.device), + features_only=True, + return_all_hiddens=return_all_hiddens, + lang_id=lang_id, + ) + if return_all_hiddens: + # convert from T x B x C -> B x T x C + inner_states = extra["inner_states"] + return [inner_state.transpose(0, 1) for inner_state in inner_states] + else: + return features # just the last layer's features + + def predict( + self, + head: str, + tokens: torch.LongTensor, + return_logits: bool = False, + lang_id=None, + ): + features = self.extract_features(tokens.to(device=self.device), lang_id=lang_id) + logits = self.model.classification_heads[head](features) + if return_logits: + return logits + return F.log_softmax(logits, dim=-1) diff --git a/fairseq/fairseq/models/xmod/model.py b/fairseq/fairseq/models/xmod/model.py new file mode 100644 index 0000000..fb6c7a8 --- /dev/null +++ b/fairseq/fairseq/models/xmod/model.py @@ -0,0 +1,742 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from ..roberta.model_xlmr import XLMRModel +from fairseq.models.xmod.transformer_layer_xmod import XMODTransformerEncoderLayerBase +from ..roberta.model import base_architecture, RobertaEncoder +from fairseq.models.transformer import TransformerEncoder +from fairseq.modules.transformer_sentence_encoder import init_bert_params +from typing import Optional +from fairseq.models.xmod.hub_interface import XMODHubInterface +import torch +from fairseq.distributed import fsdp_wrap +from fairseq.models import ( + register_model, + register_model_architecture, +) + +from fairseq.modules.checkpoint_activations import checkpoint_wrapper + +DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8) + + +@register_model("xmod") +class XMODModel(XLMRModel): + @classmethod + def hub_models(cls): + return { + "xmod.base": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.81.1M.tar.gz", + "xmod.large.prenorm": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.large.prenorm.81.500k.tar.gz", + "xmod.base.13.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.13.125k.tar.gz", + "xmod.base.30.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.30.125k.tar.gz", + "xmod.base.30.195k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.30.195k.tar.gz", + "xmod.base.60.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.60.125k.tar.gz", + "xmod.base.60.265k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.60.265k.tar.gz", + "xmod.base.75.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.75.125k.tar.gz", + "xmod.base.75.269k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.75.269k.tar.gz", + } + + @classmethod + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="sentencepiece", + **kwargs, + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + bpe=bpe, + load_checkpoint_heads=True, + **kwargs, + ) + return XMODHubInterface(x["args"], x["task"], x["models"][0]) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + from omegaconf import OmegaConf + + if OmegaConf.is_config(args): + OmegaConf.set_struct(args, False) + + # make sure all arguments are present + base_architecture(args) + + if not hasattr(args, "max_positions"): + if not hasattr(args, "tokens_per_sample"): + args.tokens_per_sample = task.max_positions() + args.max_positions = args.tokens_per_sample + + encoder = XMODEncoder(args, task.source_dictionary) + + if OmegaConf.is_config(args): + OmegaConf.set_struct(args, True) + + return cls(args, encoder) + + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + classification_head_name=None, + lang_id=None, + **kwargs, + ): + if classification_head_name is not None: + features_only = True + x, extra = self.encoder( + src_tokens, features_only, return_all_hiddens, lang_id=lang_id, **kwargs + ) + + if classification_head_name is not None: + x = self.classification_heads[classification_head_name](x) + return x, extra + + +class XMODEncoder(RobertaEncoder): + """XMOD encoder.""" + + def build_encoder(self, args, dictionary, embed_tokens): + encoder = XMODTransformerEncoder(args, dictionary, embed_tokens) + encoder.apply(init_bert_params) + return encoder + + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + masked_tokens=None, + lang_id=None, + **unused, + ): + """ + Args: + src_tokens (LongTensor): input tokens of shape `(batch, src_len)` + features_only (bool, optional): skip LM head and just return + features. If True, the output will be of shape + `(batch, src_len, embed_dim)`. + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + + Returns: + tuple: + - the LM output of shape `(batch, src_len, vocab)` + - a dictionary of additional data, where 'inner_states' + is a list of hidden states. Note that the hidden + states have shape `(src_len, batch, vocab)`. + """ + x, extra = self.extract_features( + src_tokens, return_all_hiddens=return_all_hiddens, lang_id=lang_id + ) + if not features_only: + x = self.output_layer(x, masked_tokens=masked_tokens) + return x, extra + + def extract_features( + self, src_tokens, return_all_hiddens=False, lang_id=None, **kwargs + ): + encoder_out = self.sentence_encoder( + src_tokens, + return_all_hiddens=return_all_hiddens, + lang_id=lang_id, + token_embeddings=kwargs.get("token_embeddings", None), + ) + # T x B x C -> B x T x C + features = encoder_out["encoder_out"][0].transpose(0, 1) + inner_states = encoder_out["encoder_states"] if return_all_hiddens else None + return features, {"inner_states": inner_states} + + +class XMODTransformerEncoder(TransformerEncoder): + def build_encoder_layer(self, cfg): + layer = XMODTransformerEncoderLayerBase(cfg) + checkpoint = cfg.checkpoint_activations + if checkpoint: + offload_to_cpu = cfg.offload_activations + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + # if we are checkpointing, enforce that FSDP always wraps the + # checkpointed layer, regardless of layer size + min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 + layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) + return layer + + def forward( + self, + src_tokens, + src_lengths: Optional[torch.Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[torch.Tensor] = None, + lang_id=None, + ): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + """ + return self.forward_scriptable( + src_tokens, + src_lengths, + return_all_hiddens, + token_embeddings, + lang_id=lang_id, + ) + # TorchScript doesn't support super() method so that the scriptable Subclass + # can't access the base class model in Torchscript. + # Current workaround is to add a helper function with different name and + # call the helper function from scriptable Subclass. + + def forward_scriptable( + self, + src_tokens, + src_lengths: Optional[torch.Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[torch.Tensor] = None, + lang_id=None, + ): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + """ + # compute padding mask + encoder_padding_mask = src_tokens.eq(self.padding_idx) + has_pads = src_tokens.device.type == "xla" or encoder_padding_mask.any() + + x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings) + + # account for padding while computing the representation + if has_pads: + x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x)) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + encoder_states = [] + + if return_all_hiddens: + encoder_states.append(x) + + # encoder layers + for layer in self.layers: + x = layer( + x, + encoder_padding_mask=encoder_padding_mask if has_pads else None, + lang_id=lang_id, + ) + if return_all_hiddens: + assert encoder_states is not None + encoder_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in + # `forward` so we use a dictionary instead. + # TorchScript does not support mixed values so the values are all lists. + # The empty list is equivalent to None. + src_lengths = ( + src_tokens.ne(self.padding_idx) + .sum(dim=1, dtype=torch.int32) + .reshape(-1, 1) + .contiguous() + ) + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask], # B x T + "encoder_embedding": [encoder_embedding], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [src_lengths], + } + + +@register_model_architecture("xmod", "xmod_base_13") +def roberta_base_architecture(args): + args.ffn_modules = getattr(args, "ffn_modules", False) + args.adapter_modules = getattr(args, "adapter_modules", True) + args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) + args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) + args.ln_before_adapter = getattr(args, "ln_before_adapter", True) + args.languages = getattr( + args, + "languages", + [ + "ar_AR", + "en_XX", + "fi_FI", + "fr_XX", + "hi_IN", + "id_ID", + "ka_GE", + "ko_KR", + "ru_RU", + "sw_KE", + "ta_IN", + "th_TH", + "vi_VN", + ], + ) + base_architecture(args) + + +@register_model_architecture("xmod", "xmod_base_30") +def roberta_base_architecture(args): + args.ffn_modules = getattr(args, "ffn_modules", False) + args.adapter_modules = getattr(args, "adapter_modules", True) + args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) + args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) + args.ln_before_adapter = getattr(args, "ln_before_adapter", True) + args.languages = getattr( + args, + "languages", + [ + "ar_AR", + "cs_CZ", + "en_XX", + "eu_ES", + "fi_FI", + "fr_XX", + "hi_IN", + "hr_HR", + "hu_HU", + "hy_AM", + "id_ID", + "it_IT", + "ka_GE", + "ko_KR", + "lt_LT", + "ml_IN", + "mn_MN", + "ms_MY", + "pl_PL", + "ro_RO", + "ru_RU", + "si_LK", + "sk_SK", + "sq_AL", + "sv_SE", + "sw_KE", + "ta_IN", + "th_TH", + "tl_XX", + "vi_VN", + ], + ) + base_architecture(args) + + +@register_model_architecture("xmod", "xmod_base_60") +def roberta_base_architecture(args): + args.ffn_modules = getattr(args, "ffn_modules", False) + args.adapter_modules = getattr(args, "adapter_modules", True) + args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) + args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) + args.ln_before_adapter = getattr(args, "ln_before_adapter", True) + args.languages = getattr( + args, + "languages", + [ + "af_ZA", + "am_ET", + "ar_AR", + "be_BY", + "bn_IN", + "ca_ES", + "cs_CZ", + "cy_GB", + "da_DK", + "en_XX", + "eo_EO", + "et_EE", + "eu_ES", + "fa_IR", + "fi_FI", + "fr_XX", + "ga_IE", + "gl_ES", + "gu_IN", + "ha_NG", + "hi_IN", + "hr_HR", + "hu_HU", + "hy_AM", + "id_ID", + "is_IS", + "it_IT", + "ka_GE", + "ko_KR", + "ku_TR", + "la_VA", + "lt_LT", + "lv_LV", + "mk_MK", + "ml_IN", + "mn_MN", + "ms_MY", + "ne_NP", + "nl_XX", + "no_XX", + "pl_PL", + "ps_AF", + "pt_XX", + "ro_RO", + "ru_RU", + "sa_IN", + "sd_PK", + "si_LK", + "sk_SK", + "sl_SI", + "so_SO", + "sq_AL", + "sr_RS", + "sv_SE", + "sw_KE", + "ta_IN", + "te_IN", + "th_TH", + "tl_XX", + "vi_VN", + ], + ) + base_architecture(args) + + +@register_model_architecture("xmod", "xmod_base_75") +def roberta_base_architecture(args): + args.ffn_modules = getattr(args, "ffn_modules", False) + args.adapter_modules = getattr(args, "adapter_modules", True) + args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) + args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) + args.ln_before_adapter = getattr(args, "ln_before_adapter", True) + args.languages = getattr( + args, + "languages", + [ + "af_ZA", + "am_ET", + "ar_AR", + "as_IN", + "be_BY", + "bn_IN", + "br_FR", + "bs_BA", + "ca_ES", + "cs_CZ", + "cy_GB", + "da_DK", + "en_XX", + "eo_EO", + "et_EE", + "eu_ES", + "fa_IR", + "fi_FI", + "fr_XX", + "fy_NL", + "ga_IE", + "gd_GB", + "gl_ES", + "gu_IN", + "ha_NG", + "hi_IN", + "hr_HR", + "hu_HU", + "hy_AM", + "id_ID", + "is_IS", + "it_IT", + "jv_ID", + "ka_GE", + "kn_IN", + "ko_KR", + "ku_TR", + "la_VA", + "lt_LT", + "lv_LV", + "mg_MG", + "mk_MK", + "ml_IN", + "mn_MN", + "mr_IN", + "ms_MY", + "ne_NP", + "nl_XX", + "no_XX", + "om_KE", + "or_IN", + "pa_IN", + "pl_PL", + "ps_AF", + "pt_XX", + "ro_RO", + "ru_RU", + "sa_IN", + "sd_PK", + "si_LK", + "sk_SK", + "sl_SI", + "so_SO", + "sq_AL", + "sr_RS", + "su_ID", + "sv_SE", + "sw_KE", + "ta_IN", + "te_IN", + "th_TH", + "tl_XX", + "vi_VN", + "xh_ZA", + "yi_DE", + ], + ) + base_architecture(args) + + +@register_model_architecture("xmod", "xmod_base") +def roberta_base_architecture(args): + args.ffn_modules = getattr(args, "ffn_modules", False) + args.adapter_modules = getattr(args, "adapter_modules", True) + args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) + args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) + args.ln_before_adapter = getattr(args, "ln_before_adapter", True) + args.languages = getattr( + args, + "languages", + [ + "en_XX", + "id_ID", + "vi_VN", + "ru_RU", + "fa_IR", + "sv_SE", + "ja_XX", + "fr_XX", + "de_DE", + "ro_RO", + "ko_KR", + "hu_HU", + "es_XX", + "fi_FI", + "uk_UA", + "da_DK", + "pt_XX", + "no_XX", + "th_TH", + "pl_PL", + "bg_BG", + "nl_XX", + "zh_CN", + "he_IL", + "el_GR", + "it_IT", + "sk_SK", + "hr_HR", + "tr_TR", + "ar_AR", + "cs_CZ", + "lt_LT", + "hi_IN", + "zh_TW", + "ca_ES", + "ms_MY", + "sl_SI", + "lv_LV", + "ta_IN", + "bn_IN", + "et_EE", + "az_AZ", + "sq_AL", + "sr_RS", + "kk_KZ", + "ka_GE", + "tl_XX", + "ur_PK", + "is_IS", + "hy_AM", + "ml_IN", + "mk_MK", + "be_BY", + "la_VA", + "te_IN", + "eu_ES", + "gl_ES", + "mn_MN", + "kn_IN", + "ne_NP", + "sw_KE", + "si_LK", + "mr_IN", + "af_ZA", + "gu_IN", + "cy_GB", + "eo_EO", + "km_KH", + "ky_KG", + "uz_UZ", + "ps_AF", + "pa_IN", + "ga_IE", + "ha_NG", + "am_ET", + "lo_LA", + "ku_TR", + "so_SO", + "my_MM", + "or_IN", + "sa_IN", + ], + ) + base_architecture(args) + + +@register_model_architecture("xmod", "xmod_large_prenorm") +def roberta_base_architecture(args): + args.ffn_modules = getattr(args, "ffn_modules", False) + args.adapter_modules = getattr(args, "adapter_modules", True) + args.adapter_layer_norm = getattr(args, "adapter_layer_norm", True) + args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", False) + args.ln_before_adapter = getattr(args, "ln_before_adapter", False) + # args.bottleneck = getattr(args, "bottleneck", 8) + args.bottleneck = getattr(args, "bottleneck", 4) + args.languages = getattr( + args, + "languages", + [ + "en_XX", + "id_ID", + "vi_VN", + "ru_RU", + "fa_IR", + "sv_SE", + "ja_XX", + "fr_XX", + "de_DE", + "ro_RO", + "ko_KR", + "hu_HU", + "es_XX", + "fi_FI", + "uk_UA", + "da_DK", + "pt_XX", + "no_XX", + "th_TH", + "pl_PL", + "bg_BG", + "nl_XX", + "zh_CN", + "he_IL", + "el_GR", + "it_IT", + "sk_SK", + "hr_HR", + "tr_TR", + "ar_AR", + "cs_CZ", + "lt_LT", + "hi_IN", + "zh_TW", + "ca_ES", + "ms_MY", + "sl_SI", + "lv_LV", + "ta_IN", + "bn_IN", + "et_EE", + "az_AZ", + "sq_AL", + "sr_RS", + "kk_KZ", + "ka_GE", + "tl_XX", + "ur_PK", + "is_IS", + "hy_AM", + "ml_IN", + "mk_MK", + "be_BY", + "la_VA", + "te_IN", + "eu_ES", + "gl_ES", + "mn_MN", + "kn_IN", + "ne_NP", + "sw_KE", + "si_LK", + "mr_IN", + "af_ZA", + "gu_IN", + "cy_GB", + "eo_EO", + "km_KH", + "ky_KG", + "uz_UZ", + "ps_AF", + "pa_IN", + "ga_IE", + "ha_NG", + "am_ET", + "lo_LA", + "ku_TR", + "so_SO", + "my_MM", + "or_IN", + "sa_IN", + ], + ) + + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.encoder_layers = getattr(args, "encoder_layers", 24) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + base_architecture(args) diff --git a/fairseq/fairseq/models/xmod/transformer_layer_xmod.py b/fairseq/fairseq/models/xmod/transformer_layer_xmod.py new file mode 100644 index 0000000..47a91cd --- /dev/null +++ b/fairseq/fairseq/models/xmod/transformer_layer_xmod.py @@ -0,0 +1,179 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.modules.transformer_layer import TransformerEncoderLayer +from typing import Optional +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.modules import LayerNorm +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.quant_noise import quant_noise +from torch import Tensor + + +class Adapter(nn.Module): + def __init__(self, cfg, red_fac=2): + super(Adapter, self).__init__() + self.cfg = cfg + self.embed_dim = cfg.encoder_embed_dim + self.quant_noise = getattr(cfg, "quant_noise_pq", 0) + self.quant_noise_block_size = getattr(cfg, "quant_noise_pq_block_size", 8) or 8 + self.activation_fn = utils.get_activation_fn( + activation=getattr(cfg, "activation_fn", "relu") or "relu" + ) + self.fc1 = quant_noise( + nn.Linear(self.embed_dim, self.embed_dim // red_fac), + p=self.quant_noise, + block_size=self.quant_noise_block_size, + ) + self.fc2 = quant_noise( + nn.Linear(self.embed_dim // red_fac, self.embed_dim), + p=self.quant_noise, + block_size=self.quant_noise_block_size, + ) + activation_dropout_p = getattr(cfg, "activation_dropout", 0) or 0 + if activation_dropout_p == 0: + # for backwards compatibility with models that use cfg.relu_dropout + activation_dropout_p = getattr(cfg, "relu_dropout", 0) or 0 + self.activation_dropout_module = FairseqDropout( + float(activation_dropout_p), module_name=self.__class__.__name__ + ) + + def forward(self, x): + x = self.activation_fn(self.fc1(x)) + if not hasattr(self.cfg, "adapter_dropout") or self.cfg.adapter_dropout: + x = self.activation_dropout_module(x) + x = self.fc2(x) + return x + + +class XMODTransformerEncoderLayerBase(TransformerEncoderLayer): + """Encoder layer block. + + In the original paper each operation (multi-head attention or FFN) is + postprocessed with: `dropout -> add residual -> layernorm`. In the + tensor2tensor code they suggest that learning is more robust when + preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *cfg.encoder.normalize_before* to ``True``. + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + + def __init__(self, cfg): + super().__init__(cfg) + if hasattr(cfg, "adapter_modules") and cfg.adapter_modules: + export = getattr(cfg, "export", False) + if cfg.adapter_layer_norm: + self.adapter_layer_norm = LayerNorm(self.embed_dim, export=export) + self.adapter_modules = nn.ModuleDict(dict()) + if hasattr(self.cfg, "bottleneck"): + bottleneck = self.cfg.bottleneck + else: + bottleneck = 2 + for language in cfg.languages: + self.adapter_modules[str(language)] = Adapter(cfg, red_fac=bottleneck) + + def lang_adapter(self, lang_id, x): + # If language adapters exist pass throught them + if hasattr(self.cfg, "adapter_modules") and self.cfg.adapter_modules: + if lang_id is None: + lang_id = ["en_XX"] * x.shape[1] + d_langs = [lang_id[0]] + lang_lengths = [1] + for lang in lang_id[1:]: + if lang == d_langs[-1]: + lang_lengths[-1] += 1 + else: + d_langs.append(lang) + lang_lengths.append(1) + + if ( + not hasattr(self.cfg, "ln_before_adapter") + or not self.cfg.ln_before_adapter + ): + residual = x + if self.cfg.adapter_layer_norm: + x = self.adapter_layer_norm(x) + elif self.cfg.adapter_reuse_layer_norm: + x = self.final_layer_norm(x) + if hasattr(self.cfg, "ln_before_adapter") and self.cfg.ln_before_adapter: + residual = x + + split_x = torch.split(x, lang_lengths, 1) + x_ = [] + for i, (lang, s_x) in enumerate(zip(d_langs, split_x)): + lang = lang.replace("_rom", "").replace("_zaw", "") + x_.append(self.adapter_modules[str(lang)](s_x)) + x = torch.cat(x_, 1) + + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + + return x + + def forward( + self, + x, + encoder_padding_mask: Optional[Tensor], + attn_mask: Optional[Tensor] = None, + lang_id: Optional[list] = None, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor): binary ByteTensor of shape + `(batch, seq_len)` where padding elements are indicated by ``1``. + attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`, + where `tgt_len` is the length of output and `src_len` is the + length of input, though here both are equal to `seq_len`. + `attn_mask[tgt_i, src_j] = 1` means that when calculating the + embedding for `tgt_i`, we exclude (mask out) `src_j`. This is + useful for strided self-attention. + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + # anything in original attn_mask = 1, becomes -1e8 + # anything in original attn_mask = 0, becomes 0 + # Note that we cannot use -inf here, because at some edge cases, + # the attention weight (before softmax) for some padded element in query + # will become -inf, which results in NaN in model parameters + if attn_mask is not None: + attn_mask = attn_mask.masked_fill(attn_mask.to(torch.bool), -1e8) + + residual = x + if self.normalize_before: + x = self.self_attn_layer_norm(x) + x, _ = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=encoder_padding_mask, + need_weights=False, + attn_mask=attn_mask, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + + x = self.lang_adapter(lang_id, x) + + if not self.normalize_before: + x = self.final_layer_norm(x) + return x diff --git a/fairseq/fairseq/modules/__init__.py b/fairseq/fairseq/modules/__init__.py new file mode 100644 index 0000000..dcfda9b --- /dev/null +++ b/fairseq/fairseq/modules/__init__.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +from .adaptive_input import AdaptiveInput +from .adaptive_softmax import AdaptiveSoftmax +from .base_layer import BaseLayer +from .beamable_mm import BeamableMM +from .character_token_embedder import CharacterTokenEmbedder +from .conv_tbc import ConvTBC +from .cross_entropy import cross_entropy +from .downsampled_multihead_attention import DownsampledMultiHeadAttention +from .dynamic_convolution import DynamicConv, DynamicConv1dTBC, DynamicConv_scripatable +from .dynamic_crf_layer import DynamicCRF +from .ema_module import EMAModuleConfig, EMAModule +from .fairseq_dropout import FairseqDropout +from .fp32_batch_norm import Fp32BatchNorm +from .fp32_group_norm import Fp32GroupNorm +from .fp32_instance_norm import Fp32InstanceNorm +from .gelu import gelu, gelu_accurate +from .grad_multiply import GradMultiply +from .gumbel_vector_quantizer import GumbelVectorQuantizer +from .kmeans_vector_quantizer import KmeansVectorQuantizer +from .layer_drop import LayerDropModuleList +from .layer_norm import Fp32LayerNorm, LayerNorm +from .learned_positional_embedding import LearnedPositionalEmbedding +from .lightweight_convolution import LightweightConv, LightweightConv1dTBC +from .linearized_convolution import LinearizedConvolution +from .location_attention import LocationAttention +from .lstm_cell_with_zoneout import LSTMCellWithZoneOut +from .multihead_attention import MultiheadAttention +from .positional_embedding import PositionalEmbedding +from .same_pad import SamePad, SamePad2d +from .scalar_bias import ScalarBias +from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding +from .transformer_sentence_encoder_layer import TransformerSentenceEncoderLayer +from .transformer_sentence_encoder import TransformerSentenceEncoder +from .transpose_last import TransposeLast +from .unfold import unfold1d +from .transformer_layer import TransformerDecoderLayer, TransformerEncoderLayer +from .vggblock import VGGBlock +from .espnet_multihead_attention import ( + ESPNETMultiHeadedAttention, + RelPositionMultiHeadedAttention, + RotaryPositionMultiHeadedAttention, +) +from .rotary_positional_embedding import RotaryPositionalEmbedding +from .positional_encoding import ( + RelPositionalEncoding, +) + +__all__ = [ + "AdaptiveInput", + "AdaptiveSoftmax", + "BaseLayer", + "BeamableMM", + "CharacterTokenEmbedder", + "ConvTBC", + "cross_entropy", + "DownsampledMultiHeadAttention", + "DynamicConv1dTBC", + "DynamicConv", + "DynamicConv_scripatable", + "DynamicCRF", + "EMAModule", + "EMAModuleConfig", + "FairseqDropout", + "Fp32BatchNorm", + "Fp32GroupNorm", + "Fp32LayerNorm", + "Fp32InstanceNorm", + "gelu", + "gelu_accurate", + "GradMultiply", + "GumbelVectorQuantizer", + "KmeansVectorQuantizer", + "LayerDropModuleList", + "LayerNorm", + "LearnedPositionalEmbedding", + "LightweightConv1dTBC", + "LightweightConv", + "LinearizedConvolution", + "LocationAttention", + "LSTMCellWithZoneOut", + "MultiheadAttention", + "PositionalEmbedding", + "SamePad", + "SamePad2d", + "ScalarBias", + "SinusoidalPositionalEmbedding", + "TransformerSentenceEncoderLayer", + "TransformerSentenceEncoder", + "TransformerDecoderLayer", + "TransformerEncoderLayer", + "TransposeLast", + "VGGBlock", + "unfold1d", + "ESPNETMultiheadedAttention", + "PositionalEmbedding", + "RelPositionMultiHeadedAttention", + "RelPositionalEncoding", + "RotaryPositionalEmbedding", + "RotaryPositionMultiHeadedAttention", +] diff --git a/fairseq/fairseq/modules/adaptive_input.py b/fairseq/fairseq/modules/adaptive_input.py new file mode 100644 index 0000000..01ac4ac --- /dev/null +++ b/fairseq/fairseq/modules/adaptive_input.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from typing import List + +import torch +from torch import nn + +from fairseq.modules.quant_noise import quant_noise + + +class AdaptiveInput(nn.Module): + def __init__( + self, + vocab_size: int, + padding_idx: int, + initial_dim: int, + factor: float, + output_dim: int, + cutoff: List[int], + q_noise: float = 0, + qn_block_size: int = 8, + ): + super().__init__() + + if vocab_size > cutoff[-1]: + cutoff = cutoff + [vocab_size] + else: + assert ( + vocab_size == cutoff[-1] + ), "cannot specify cutoff larger than vocab size" + + self.cutoff = cutoff + self.embedding_dim = output_dim + self.padding_idx = padding_idx + + self.embeddings = nn.ModuleList() + for i in range(len(self.cutoff)): + prev = self.cutoff[i - 1] if i > 0 else 0 + size = self.cutoff[i] - prev + dim = int(initial_dim // (factor**i)) + seq = nn.Sequential( + nn.Embedding(size, dim, self.padding_idx), + quant_noise( + nn.Linear(dim, output_dim, bias=False), q_noise, qn_block_size + ), + ) + + self.embeddings.append(seq) + self.padding_idx = None + self.padding_idx = padding_idx + + def init_weights(m): + if isinstance(m, nn.Embedding): + nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5) + nn.init.constant_(m.weight[padding_idx], 0) + elif hasattr(m, "weight"): + nn.init.xavier_uniform_(m.weight) + + self.apply(init_weights) + + self.register_buffer("_float_tensor", torch.FloatTensor(1)) + + def weights_for_band(self, band: int): + return self.embeddings[band][0].weight, self.embeddings[band][1].weight + + def forward(self, input: torch.Tensor): + result = self._float_tensor.new(input.shape + (self.embedding_dim,)) + for i in range(len(self.cutoff)): + mask = input.lt(self.cutoff[i]) + if i > 0: + mask.mul_(input.ge(self.cutoff[i - 1])) + chunk_input = input[mask] - self.cutoff[i - 1] + else: + chunk_input = input[mask] + if mask.any(): + result[mask] = self.embeddings[i](chunk_input) + return result diff --git a/fairseq/fairseq/modules/adaptive_softmax.py b/fairseq/fairseq/modules/adaptive_softmax.py new file mode 100644 index 0000000..ae0c77b --- /dev/null +++ b/fairseq/fairseq/modules/adaptive_softmax.py @@ -0,0 +1,268 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +import operator + +import torch +import torch.nn.functional as F +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.quant_noise import quant_noise +from torch import nn + + +class TiedLinear(nn.Module): + def __init__(self, weight, transpose): + super().__init__() + self.weight = weight + self.transpose = transpose + + def forward(self, input): + return F.linear(input, self.weight.t() if self.transpose else self.weight) + + +class TiedHeadModule(nn.Module): + def __init__(self, weights, input_dim, num_classes, q_noise, qn_block_size): + super().__init__() + tied_emb, _ = weights + self.num_words, emb_dim = tied_emb.size() + + self.word_proj = quant_noise( + TiedLinear(tied_emb, transpose=False), q_noise, qn_block_size + ) + if input_dim != emb_dim: + self.word_proj = nn.Sequential( + quant_noise( + nn.Linear(input_dim, emb_dim, bias=False), q_noise, qn_block_size + ), + self.word_proj, + ) + + self.class_proj = quant_noise( + nn.Linear(input_dim, num_classes, bias=False), q_noise, qn_block_size + ) + self.out_dim = self.num_words + num_classes + + self.register_buffer("_float_tensor", torch.FloatTensor(1)) + + def forward(self, input): + inp_sz = functools.reduce(operator.mul, input.shape[:-1], 1) + out = self._float_tensor.new(inp_sz, self.out_dim) + out[:, : self.num_words] = self.word_proj(input.view(inp_sz, -1)) + out[:, self.num_words :] = self.class_proj(input.view(inp_sz, -1)) + return out + + +class AdaptiveSoftmax(nn.Module): + """ + This is an implementation of the efficient softmax approximation for + graphical processing units (GPU), described in the paper "Efficient softmax + approximation for GPUs" (http://arxiv.org/abs/1609.04309). + """ + + def __init__( + self, + vocab_size, + input_dim, + cutoff, + dropout, + factor=4.0, + adaptive_inputs=None, + tie_proj=False, + q_noise=0, + qn_block_size=8, + ): + super().__init__() + + if vocab_size > cutoff[-1]: + cutoff = cutoff + [vocab_size] + else: + assert ( + vocab_size == cutoff[-1] + ), "cannot specify cutoff larger than vocab size" + + output_dim = cutoff[0] + len(cutoff) - 1 + + self.vocab_size = vocab_size + self.cutoff = cutoff + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.input_dim = input_dim + self.factor = factor + self.q_noise = q_noise + self.qn_block_size = qn_block_size + + self.lsm = nn.LogSoftmax(dim=1) + + if adaptive_inputs is not None: + self.head = TiedHeadModule( + adaptive_inputs.weights_for_band(0), + input_dim, + len(cutoff) - 1, + self.q_noise, + self.qn_block_size, + ) + else: + self.head = quant_noise( + nn.Linear(input_dim, output_dim, bias=False), + self.q_noise, + self.qn_block_size, + ) + + self._make_tail(adaptive_inputs, tie_proj) + + def init_weights(m): + if ( + hasattr(m, "weight") + and not isinstance(m, TiedLinear) + and not isinstance(m, TiedHeadModule) + ): + nn.init.xavier_uniform_(m.weight) + + self.apply(init_weights) + + self.register_buffer("version", torch.LongTensor([1])) + + def _make_tail(self, adaptive_inputs=None, tie_proj=False): + self.tail = nn.ModuleList() + for i in range(len(self.cutoff) - 1): + dim = int(self.input_dim // self.factor ** (i + 1)) + + tied_emb, tied_proj = ( + adaptive_inputs.weights_for_band(i + 1) + if adaptive_inputs is not None + else (None, None) + ) + + if tied_proj is not None: + if tie_proj: + proj = quant_noise( + TiedLinear(tied_proj, transpose=True), + self.q_noise, + self.qn_block_size, + ) + else: + proj = quant_noise( + nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False), + self.q_noise, + self.qn_block_size, + ) + else: + proj = quant_noise( + nn.Linear(self.input_dim, dim, bias=False), + self.q_noise, + self.qn_block_size, + ) + + if tied_emb is None: + out_proj = nn.Linear( + dim, self.cutoff[i + 1] - self.cutoff[i], bias=False + ) + else: + out_proj = TiedLinear(tied_emb, transpose=False) + + m = nn.Sequential( + proj, + nn.Dropout(self.dropout_module.p), + quant_noise(out_proj, self.q_noise, self.qn_block_size), + ) + + self.tail.append(m) + + def upgrade_state_dict_named(self, state_dict, name): + version_name = name + ".version" + if version_name not in state_dict: + raise Exception("This version of the model is no longer supported") + + def adapt_target(self, target): + """ + In order to be efficient, the AdaptiveSoftMax does not compute the + scores for all the word of the vocabulary for all the examples. It is + thus necessary to call the method adapt_target of the AdaptiveSoftMax + layer inside each forward pass. + """ + + target = target.view(-1) + new_target = [target.clone()] + target_idxs = [] + + for i in range(len(self.cutoff) - 1): + mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1])) + new_target[0][mask] = self.cutoff[0] + i + + if mask.any(): + target_idxs.append(mask.nonzero(as_tuple=False).squeeze(1)) + new_target.append(target[mask].add(-self.cutoff[i])) + else: + target_idxs.append(None) + new_target.append(None) + + return new_target, target_idxs + + def forward(self, input, target): + """ + Args: + input: (b x t x d) + target: (b x t) + Returns: + 2 lists: output for each cutoff section and new targets by cut off + """ + + input = input.contiguous().view(-1, input.size(-1)) + input = self.dropout_module(input) + + new_target, target_idxs = self.adapt_target(target) + output = [self.head(input)] + + for i in range(len(target_idxs)): + if target_idxs[i] is not None: + output.append(self.tail[i](input.index_select(0, target_idxs[i]))) + else: + output.append(None) + + return output, new_target + + def get_log_prob(self, input, target): + """ + Computes the log probabilities for all the words of the vocabulary, + given a 2D tensor of hidden vectors. + """ + + bsz, length, dim = input.size() + input = input.contiguous().view(-1, dim) + + if target is not None: + _, target_idxs = self.adapt_target(target) + else: + target_idxs = None + + head_y = self.head(input) + log_probs = head_y.new_zeros(input.size(0), self.vocab_size) + + head_sz = self.cutoff[0] + len(self.tail) + log_probs[:, :head_sz] = self.lsm(head_y) + tail_priors = log_probs[:, self.cutoff[0] : head_sz].clone() + + for i in range(len(self.tail)): + start = self.cutoff[i] + end = self.cutoff[i + 1] + + if target_idxs is None: + tail_out = log_probs[:, start:end] + tail_out.copy_(self.tail[i](input)) + log_probs[:, start:end] = self.lsm(tail_out).add_( + tail_priors[:, i, None] + ) + elif target_idxs[i] is not None: + idxs = target_idxs[i] + tail_out = log_probs[idxs, start:end] + tail_out.copy_(self.tail[i](input[idxs])) + log_probs[idxs, start:end] = self.lsm(tail_out).add_( + tail_priors[idxs, i, None] + ) + + log_probs = log_probs.view(bsz, length, -1) + return log_probs diff --git a/fairseq/fairseq/modules/base_layer.py b/fairseq/fairseq/modules/base_layer.py new file mode 100644 index 0000000..e823f7b --- /dev/null +++ b/fairseq/fairseq/modules/base_layer.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch +import sys +from fairseq import utils +from fairseq.distributed import utils as distributed_utils +from fairseq.modules.layer_norm import LayerNorm + + +class BaseLayer(nn.Module): + def __init__(self, args): + super().__init__() + self.num_workers = distributed_utils.get_data_parallel_world_size() + expert_centroids = torch.empty(self.num_workers, args.decoder_embed_dim) + torch.nn.init.orthogonal_(expert_centroids, gain=0.1) + self.register_parameter( + "expert_centroids", torch.nn.Parameter(expert_centroids) + ) + self.expert_network = nn.Sequential( + *([BaseSublayer(args) for _ in range(args.base_sublayers)]) + ) + self.expert_id = distributed_utils.get_data_parallel_rank() + self.shuffle = args.base_shuffle + self.cpp = self.load_assignment() + + # Add a special attribute to the expert parameters, so we know not to sync their gradients + for param in self.expert_network.parameters(): + param.expert = True + + def forward(self, input_features, *args, **kwargs): + features = input_features.reshape(-1, input_features.size(-1)) + is_training = input_features.requires_grad + + if self.shuffle and is_training: + # Send each token to a random worker, to break correlations within the batch + shuffle_sort = torch.randperm(features.size(0), device=features.device) + features = All2All.apply(features[shuffle_sort]) + + with torch.no_grad(): + # Compute similarity of each token to each expert, for routing + token_expert_affinities = features.matmul( + self.expert_centroids.transpose(0, 1) + ) + + # Compute which token goes to which expert + sort_by_expert, input_splits, output_splits = ( + self.balanced_assignment(token_expert_affinities) + if is_training + else self.greedy_assignment(token_expert_affinities) + ) + # Swap these tokens for the right ones for our expert + routed_features = All2All.apply( + features[sort_by_expert], output_splits, input_splits + ) + + if routed_features.size(0) > 0: + # Mix in the expert network based on how appropriate it is for these tokens + alpha = torch.sigmoid( + routed_features.mv(self.expert_centroids[self.expert_id]) + ).unsqueeze(1) + routed_features = ( + alpha * self.expert_network(routed_features) + + (1 - alpha) * routed_features + ) + # Return to original worker and ordering + result = All2All.apply(routed_features, input_splits, output_splits)[ + self.inverse_sort(sort_by_expert) + ] + + if self.shuffle and is_training: + # Undo shuffling + result = All2All.apply(result)[self.inverse_sort(shuffle_sort)] + + # Return additional Nones for compatibility with TransformerDecoderLayer + return result.view(input_features.size()), None, None + + def inverse_sort(self, order): + # Creates an index that undoes a sort: xs==xs[order][inverse_sort(order)] + return torch.empty_like(order).scatter_( + 0, order, torch.arange(0, order.size(0), device=order.device) + ) + + def balanced_assignment(self, scores): + ok = scores.isfinite() + if not ok.all(): + # NaNs here can break the assignment algorithm + scores[~ok] = scores[ok].min() + return self.cpp.balanced_assignment(scores), None, None + + # Assigns each token to the top k experts + def greedy_assignment(self, scores, k=1): + token_to_workers = torch.topk(scores, dim=1, k=k, largest=True).indices.view(-1) + token_to_workers, sort_ordering = torch.sort(token_to_workers) + worker2token = sort_ordering // k + + # Find how many tokens we're sending to each other worker (being careful for sending 0 tokens to some workers) + output_splits = torch.zeros( + (self.num_workers,), dtype=torch.long, device=scores.device + ) + workers, counts = torch.unique_consecutive(token_to_workers, return_counts=True) + output_splits[workers] = counts + # Tell other workers how many tokens to expect from us + input_splits = All2All.apply(output_splits) + return worker2token, input_splits.tolist(), output_splits.tolist() + + def load_assignment(self): + try: + from fairseq import libbase + + return libbase + + except ImportError as e: + sys.stderr.write( + "ERROR: missing libbase. run `python setup.py build_ext --inplace`\n" + ) + raise e + + +class BaseSublayer(nn.Module): + def __init__(self, args): + super().__init__() + self.activation_fn = utils.get_activation_fn( + activation=getattr(args, "activation_fn", "relu") or "relu" + ) + self.norm = LayerNorm(args.decoder_embed_dim, export=False) + self.ff1 = torch.nn.Linear(args.decoder_embed_dim, args.decoder_ffn_embed_dim) + self.ff2 = torch.nn.Linear(args.decoder_ffn_embed_dim, args.decoder_embed_dim) + self.ff2.weight.data.zero_() + + def forward(self, xs): + return xs + self.ff2(self.activation_fn(self.ff1(self.norm(xs)))) + + +# Wraps torch.distributed.all_to_all_single as a function that supports autograd +class All2All(torch.autograd.Function): + @staticmethod + def forward(ctx, xs, input_splits=None, output_splits=None): + ctx.input_splits = input_splits + ctx.output_splits = output_splits + + ys = ( + torch.empty_like(xs) + if output_splits is None + else xs.new_empty(size=[sum(output_splits)] + list(xs.size()[1:])) + ) + torch.distributed.all_to_all_single( + ys, xs, output_split_sizes=output_splits, input_split_sizes=input_splits + ) + return ys + + @staticmethod + def backward(ctx, grad_output): + result = ( + torch.empty_like(grad_output) + if ctx.input_splits is None + else grad_output.new_empty( + size=[sum(ctx.input_splits)] + list(grad_output.size()[1:]) + ) + ) + torch.distributed.all_to_all_single( + result, + grad_output, + output_split_sizes=ctx.input_splits, + input_split_sizes=ctx.output_splits, + ) + return result, None, None diff --git a/fairseq/fairseq/modules/beamable_mm.py b/fairseq/fairseq/modules/beamable_mm.py new file mode 100644 index 0000000..eff1a46 --- /dev/null +++ b/fairseq/fairseq/modules/beamable_mm.py @@ -0,0 +1,49 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + + +class BeamableMM(nn.Module): + """This module provides an optimized MM for beam decoding with attention. + + It leverage the fact that the source-side of the input is replicated beam + times and the target-side of the input is of width one. This layer speeds up + inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)} + with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}. + """ + + def __init__(self, beam_size=None): + super(BeamableMM, self).__init__() + self.beam_size = beam_size + + def forward(self, input1, input2): + if ( + not self.training + and self.beam_size is not None # test mode + and input1.dim() == 3 # beam size is set + and input1.size(1) # only support batched input + == 1 # single time step update + ): + bsz, beam = input1.size(0), self.beam_size + + # bsz x 1 x nhu --> bsz/beam x beam x nhu + input1 = input1[:, 0, :].unfold(0, beam, beam).transpose(2, 1) + + # bsz x sz2 x nhu --> bsz/beam x sz2 x nhu + input2 = input2.unfold(0, beam, beam)[:, :, :, 0] + + # use non batched operation if bsz = beam + if input1.size(0) == 1: + output = torch.mm(input1[0, :, :], input2[0, :, :]) + else: + output = input1.bmm(input2) + return output.view(bsz, 1, -1) + else: + return input1.bmm(input2) + + def set_beam_size(self, beam_size): + self.beam_size = beam_size diff --git a/fairseq/fairseq/modules/character_token_embedder.py b/fairseq/fairseq/modules/character_token_embedder.py new file mode 100644 index 0000000..181221b --- /dev/null +++ b/fairseq/fairseq/modules/character_token_embedder.py @@ -0,0 +1,214 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import List, Tuple + +import torch +import torch.nn.functional as F +from fairseq.data import Dictionary +from torch import nn + + +CHAR_PAD_IDX = 0 +CHAR_EOS_IDX = 257 + + +logger = logging.getLogger(__name__) + + +class CharacterTokenEmbedder(torch.nn.Module): + def __init__( + self, + vocab: Dictionary, + filters: List[Tuple[int, int]], + char_embed_dim: int, + word_embed_dim: int, + highway_layers: int, + max_char_len: int = 50, + char_inputs: bool = False, + ): + super(CharacterTokenEmbedder, self).__init__() + + self.onnx_trace = False + self.embedding_dim = word_embed_dim + self.max_char_len = max_char_len + self.char_embeddings = nn.Embedding(257, char_embed_dim, padding_idx=0) + self.symbol_embeddings = nn.Parameter(torch.FloatTensor(2, word_embed_dim)) + self.eos_idx, self.unk_idx = 0, 1 + self.char_inputs = char_inputs + + self.convolutions = nn.ModuleList() + for width, out_c in filters: + self.convolutions.append( + nn.Conv1d(char_embed_dim, out_c, kernel_size=width) + ) + + last_dim = sum(f[1] for f in filters) + + self.highway = Highway(last_dim, highway_layers) if highway_layers > 0 else None + + self.projection = nn.Linear(last_dim, word_embed_dim) + + assert ( + vocab is not None or char_inputs + ), "vocab must be set if not using char inputs" + self.vocab = None + if vocab is not None: + self.set_vocab(vocab, max_char_len) + + self.reset_parameters() + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def set_vocab(self, vocab, max_char_len): + word_to_char = torch.LongTensor(len(vocab), max_char_len) + + truncated = 0 + for i in range(len(vocab)): + if i < vocab.nspecial: + char_idxs = [0] * max_char_len + else: + chars = vocab[i].encode() + # +1 for padding + char_idxs = [c + 1 for c in chars] + [0] * (max_char_len - len(chars)) + if len(char_idxs) > max_char_len: + truncated += 1 + char_idxs = char_idxs[:max_char_len] + word_to_char[i] = torch.LongTensor(char_idxs) + + if truncated > 0: + logger.info( + "truncated {} words longer than {} characters".format( + truncated, max_char_len + ) + ) + + self.vocab = vocab + self.word_to_char = word_to_char + + @property + def padding_idx(self): + return Dictionary().pad() if self.vocab is None else self.vocab.pad() + + def reset_parameters(self): + nn.init.xavier_normal_(self.char_embeddings.weight) + nn.init.xavier_normal_(self.symbol_embeddings) + nn.init.xavier_uniform_(self.projection.weight) + + nn.init.constant_( + self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.0 + ) + nn.init.constant_(self.projection.bias, 0.0) + + def forward( + self, + input: torch.Tensor, + ): + if self.char_inputs: + chars = input.view(-1, self.max_char_len) + pads = chars[:, 0].eq(CHAR_PAD_IDX) + eos = chars[:, 0].eq(CHAR_EOS_IDX) + if eos.any(): + if self.onnx_trace: + chars = torch.where(eos.unsqueeze(1), chars.new_zeros(1), chars) + else: + chars[eos] = 0 + + unk = None + else: + flat_words = input.view(-1) + chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as( + input + ) + pads = flat_words.eq(self.vocab.pad()) + eos = flat_words.eq(self.vocab.eos()) + unk = flat_words.eq(self.vocab.unk()) + + word_embs = self._convolve(chars) + if self.onnx_trace: + if pads.any(): + word_embs = torch.where( + pads.unsqueeze(1), word_embs.new_zeros(1), word_embs + ) + if eos.any(): + word_embs = torch.where( + eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs + ) + if unk is not None and unk.any(): + word_embs = torch.where( + unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs + ) + else: + if pads.any(): + word_embs[pads] = 0 + if eos.any(): + word_embs[eos] = self.symbol_embeddings[self.eos_idx] + if unk is not None and unk.any(): + word_embs[unk] = self.symbol_embeddings[self.unk_idx] + + return word_embs.view(input.size()[:2] + (-1,)) + + def _convolve( + self, + char_idxs: torch.Tensor, + ): + char_embs = self.char_embeddings(char_idxs) + char_embs = char_embs.transpose(1, 2) # BTC -> BCT + + conv_result = [] + + for conv in self.convolutions: + x = conv(char_embs) + x, _ = torch.max(x, -1) + x = F.relu(x) + conv_result.append(x) + + x = torch.cat(conv_result, dim=-1) + + if self.highway is not None: + x = self.highway(x) + x = self.projection(x) + + return x + + +class Highway(torch.nn.Module): + """ + A `Highway layer <https://arxiv.org/abs/1505.00387>`_. + Adopted from the AllenNLP implementation. + """ + + def __init__(self, input_dim: int, num_layers: int = 1): + super(Highway, self).__init__() + self.input_dim = input_dim + self.layers = nn.ModuleList( + [nn.Linear(input_dim, input_dim * 2) for _ in range(num_layers)] + ) + self.activation = nn.ReLU() + + self.reset_parameters() + + def reset_parameters(self): + for layer in self.layers: + # As per comment in AllenNLP: + # We should bias the highway layer to just carry its input forward. We do that by + # setting the bias on `B(x)` to be positive, because that means `g` will be biased to + # be high, so we will carry the input forward. The bias on `B(x)` is the second half + # of the bias vector in each Linear layer. + nn.init.constant_(layer.bias[self.input_dim :], 1) + + nn.init.constant_(layer.bias[: self.input_dim], 0) + nn.init.xavier_normal_(layer.weight) + + def forward(self, x: torch.Tensor): + for layer in self.layers: + projection = layer(x) + proj_x, gate = projection.chunk(2, dim=-1) + proj_x = self.activation(proj_x) + gate = torch.sigmoid(gate) + x = gate * x + (gate.new_tensor([1]) - gate) * proj_x + return x diff --git a/fairseq/fairseq/modules/checkpoint_activations.py b/fairseq/fairseq/modules/checkpoint_activations.py new file mode 100644 index 0000000..aa0b592 --- /dev/null +++ b/fairseq/fairseq/modules/checkpoint_activations.py @@ -0,0 +1,242 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +from typing import Any, Dict, List, Tuple, Union + +import torch +import torch.utils.checkpoint as checkpoint +from fairseq import utils + + +def checkpoint_wrapper(m, offload_to_cpu=False): + """ + A friendlier wrapper for performing activation checkpointing. + + Compared to the PyTorch version, this version: + - wraps an nn.Module, so that all subsequent calls will use checkpointing + - handles keyword arguments in the forward + - handles non-Tensor outputs from the forward + + Usage:: + + checkpointed_module = checkpoint_wrapper(my_module, offload_to_cpu=True) + a, b = checkpointed_module(x, y=3, z=torch.Tensor([1])) + """ + # should I check whether original_forward has already been set? + assert not hasattr( + m, "precheckpoint_forward" + ), "checkpoint function has already been applied?" + m.precheckpoint_forward = m.forward + m.forward = functools.partial( + _checkpointed_forward, + m.precheckpoint_forward, # original_forward + offload_to_cpu, + ) + return m + + +def unwrap_checkpoint(m: torch.nn.Module): + """ + unwrap a module and its children from checkpoint_wrapper + """ + for module in m.modules(): + if hasattr(module, "precheckpoint_forward"): + module.forward = module.precheckpoint_forward + del module.precheckpoint_forward + if hasattr(module, "old_deepcopy_method"): + module.__deepcopy__ = module.old_deepcopy_method + del module.old_deepcopy_method + return m + + +def _checkpointed_forward(original_forward, offload_to_cpu, *args, **kwargs): + # Autograd Functions in PyTorch work best with positional args, since + # the backward must return gradients (or None) for every input argument. + # We can flatten keyword arguments to make this easier. + kwarg_keys, flat_args = pack_kwargs(*args, **kwargs) + parent_ctx_dict = {"offload": offload_to_cpu} + output = CheckpointFunction.apply( + original_forward, parent_ctx_dict, kwarg_keys, *flat_args + ) + if isinstance(output, torch.Tensor): + return output + else: + packed_non_tensor_outputs = parent_ctx_dict["packed_non_tensor_outputs"] + if packed_non_tensor_outputs: + output = unpack_non_tensors(output, packed_non_tensor_outputs) + return output + + +def pack_kwargs(*args, **kwargs) -> Tuple[List[str], List[Any]]: + """ + Usage:: + + kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4) + args, kwargs = unpack_kwargs(kwarg_keys, flat_args) + assert args == [1, 2] + assert kwargs == {"a": 3, "b": 4} + """ + kwarg_keys = [] + flat_args = list(args) + for k, v in kwargs.items(): + kwarg_keys.append(k) + flat_args.append(v) + return kwarg_keys, flat_args + + +def unpack_kwargs( + kwarg_keys: List[str], flat_args: List[Any] +) -> Tuple[List[Any], Dict[str, Any]]: + if len(kwarg_keys) == 0: + return flat_args, {} + args = flat_args[: -len(kwarg_keys)] + kwargs = {k: v for k, v in zip(kwarg_keys, flat_args[-len(kwarg_keys) :])} + return args, kwargs + + +def split_non_tensors( + mixed: Union[torch.Tensor, Tuple[Any]] +) -> Tuple[Tuple[torch.Tensor], Dict[str, List[Any]]]: + """ + Usage:: + + x = torch.Tensor([1]) + y = torch.Tensor([2]) + tensors, packed_non_tensors = split_non_tensors((x, y, None, 3)) + recon = unpack_non_tensors(tensors, packed_non_tensors) + assert recon == (x, y, None, 3) + """ + if isinstance(mixed, torch.Tensor): + return (mixed,), None + tensors = [] + packed_non_tensors = {"is_tensor": [], "objects": []} + for o in mixed: + if isinstance(o, torch.Tensor): + packed_non_tensors["is_tensor"].append(True) + tensors.append(o) + else: + packed_non_tensors["is_tensor"].append(False) + packed_non_tensors["objects"].append(o) + return tuple(tensors), packed_non_tensors + + +def unpack_non_tensors( + tensors: Tuple[torch.Tensor], + packed_non_tensors: Dict[str, List[Any]], +) -> Tuple[Any]: + if packed_non_tensors is None: + return tensors + assert isinstance(packed_non_tensors, dict) + mixed = [] + is_tensor_list = packed_non_tensors["is_tensor"] + objects = packed_non_tensors["objects"] + assert len(tensors) + len(objects) == len(is_tensor_list) + obj_i = tnsr_i = 0 + for is_tensor in is_tensor_list: + if is_tensor: + mixed.append(tensors[tnsr_i]) + tnsr_i += 1 + else: + mixed.append(objects[obj_i]) + obj_i += 1 + return tuple(mixed) + + +class CheckpointFunction(torch.autograd.Function): + """Similar to the torch version, but support non-Tensor outputs. + + The caller is expected to provide a dict (*parent_ctx_dict*) that will hold + the non-Tensor outputs. These should be combined with the Tensor *outputs* + by calling ``unpack_non_tensors``. + """ + + @staticmethod + def forward(ctx, run_function, parent_ctx_dict, kwarg_keys, *args): + if torch.is_grad_enabled(): # grad may be disabled, e.g., during validation + checkpoint.check_backward_validity(args) + + ctx.run_function = run_function + ctx.kwarg_keys = kwarg_keys + ctx.fwd_rng_state = utils.get_rng_state() + + tensor_inputs, packed_non_tensor_inputs = split_non_tensors(args) + if parent_ctx_dict["offload"]: + ctx.fwd_device = tuple(x.device for x in tensor_inputs) + ctx.grad_requirements = tuple(x.requires_grad for x in tensor_inputs) + tensor_inputs = tuple( + x.to(torch.device("cpu"), non_blocking=True) for x in tensor_inputs + ) + + else: + ctx.fwd_device, ctx.grad_requirements = None, None + + ctx.save_for_backward(*tensor_inputs) + ctx.packed_non_tensor_inputs = packed_non_tensor_inputs + + with torch.no_grad(): + unpacked_args, unpacked_kwargs = unpack_kwargs(kwarg_keys, args) + outputs = run_function(*unpacked_args, **unpacked_kwargs) + + if isinstance(outputs, torch.Tensor): + return outputs + else: + # Autograd Functions don't like non-Tensor outputs. We can split the + # non-Tensor and Tensor outputs, returning the former by reference + # through *parent_ctx_dict* and returning the latter directly. + outputs, packed_non_tensor_outputs = split_non_tensors(outputs) + parent_ctx_dict["packed_non_tensor_outputs"] = packed_non_tensor_outputs + return outputs + + @staticmethod + def backward(ctx, *args): + if not torch.autograd._is_checkpoint_valid(): + raise RuntimeError( + "Checkpointing is not compatible with .grad(), please use .backward() if possible" + ) + + tensor_inputs: Tuple = ctx.saved_tensors + tensor_inputs = checkpoint.detach_variable(tensor_inputs) + if ctx.fwd_device is not None: + tensor_inputs = [ + t.to(ctx.fwd_device[i], non_blocking=True) + for i, t in enumerate(tensor_inputs) + ] + for i, need_grad in enumerate(ctx.grad_requirements): + tensor_inputs[i].requires_grad = need_grad + inputs = unpack_non_tensors(tensor_inputs, ctx.packed_non_tensor_inputs) + + # Store the current states. + bwd_rng_state = utils.get_rng_state() + + # Set the states to what it used to be before the forward pass. + utils.set_rng_state(ctx.fwd_rng_state) + + with torch.enable_grad(): + unpacked_args, unpacked_kwargs = unpack_kwargs(ctx.kwarg_keys, inputs) + outputs = ctx.run_function(*unpacked_args, **unpacked_kwargs) + tensor_outputs, _ = split_non_tensors(outputs) + # Set the states back to what it was at the start of this function. + utils.set_rng_state(bwd_rng_state) + + # Run backward() with only Tensors that require grad + outputs_with_grad = [] + args_with_grad = [] + for i in range(len(tensor_outputs)): + if tensor_outputs[i].requires_grad: + outputs_with_grad.append(tensor_outputs[i]) + args_with_grad.append(args[i]) + if len(outputs_with_grad) == 0: + raise RuntimeError( + "None of the outputs have requires_grad=True, " + "this checkpoint() is not necessary" + ) + + torch.autograd.backward(outputs_with_grad, args_with_grad) + + grads = tuple( + inp.grad if isinstance(inp, torch.Tensor) else None for inp in inputs + ) + return (None, None, None) + grads diff --git a/fairseq/fairseq/modules/conformer_layer.py b/fairseq/fairseq/modules/conformer_layer.py new file mode 100644 index 0000000..964af24 --- /dev/null +++ b/fairseq/fairseq/modules/conformer_layer.py @@ -0,0 +1,301 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from typing import Optional + +import torch + +from fairseq.modules import ( + ESPNETMultiHeadedAttention, + LayerNorm, + MultiheadAttention, + RelPositionMultiHeadedAttention, + RotaryPositionMultiHeadedAttention, +) +from fairseq.utils import get_activation_fn + + +class ConvolutionModule(torch.nn.Module): + """Convolution block used in the conformer block""" + + def __init__( + self, + embed_dim, + channels, + depthwise_kernel_size, + dropout, + activation_fn="swish", + bias=False, + export=False, + ): + """ + Args: + embed_dim: Embedding dimension + channels: Number of channels in depthwise conv layers + depthwise_kernel_size: Depthwise conv layer kernel size + dropout: dropout value + activation_fn: Activation function to use after depthwise convolution kernel + bias: If bias should be added to conv layers + export: If layernorm should be exported to jit + """ + super(ConvolutionModule, self).__init__() + assert ( + depthwise_kernel_size - 1 + ) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding" + self.layer_norm = LayerNorm(embed_dim, export=export) + self.pointwise_conv1 = torch.nn.Conv1d( + embed_dim, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.glu = torch.nn.GLU(dim=1) + self.depthwise_conv = torch.nn.Conv1d( + channels, + channels, + depthwise_kernel_size, + stride=1, + padding=(depthwise_kernel_size - 1) // 2, + groups=channels, + bias=bias, + ) + self.batch_norm = torch.nn.BatchNorm1d(channels) + self.activation = get_activation_fn(activation_fn)(channels) + self.pointwise_conv2 = torch.nn.Conv1d( + channels, + embed_dim, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.dropout = torch.nn.Dropout(dropout) + + def forward(self, x): + """ + Args: + x: Input of shape B X T X C + Returns: + Tensor of shape B X T X C + """ + x = self.layer_norm(x) + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = self.glu(x) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + x = self.batch_norm(x) + x = self.activation(x) + + x = self.pointwise_conv2(x) + x = self.dropout(x) + return x.transpose(1, 2) + + +class FeedForwardModule(torch.nn.Module): + """Positionwise feed forward layer used in conformer""" + + def __init__( + self, + input_feat, + hidden_units, + dropout1, + dropout2, + activation_fn="swish", + bias=True, + ): + """ + Args: + input_feat: Input feature dimension + hidden_units: Hidden unit dimension + dropout1: dropout value for layer1 + dropout2: dropout value for layer2 + activation_fn: Name of activation function + bias: If linear layers should have bias + """ + + super(FeedForwardModule, self).__init__() + self.layer_norm = LayerNorm(input_feat) + self.w_1 = torch.nn.Linear(input_feat, hidden_units, bias=bias) + self.w_2 = torch.nn.Linear(hidden_units, input_feat, bias=bias) + self.dropout1 = torch.nn.Dropout(dropout1) + self.dropout2 = torch.nn.Dropout(dropout2) + self.activation = get_activation_fn(activation_fn)(hidden_units) + + def forward(self, x): + """ + Args: + x: Input Tensor of shape T X B X C + Returns: + Tensor of shape T X B X C + """ + x = self.layer_norm(x) + x = self.w_1(x) + x = self.activation(x) + x = self.dropout1(x) + x = self.w_2(x) + return self.dropout2(x) + + +class ConformerEncoderLayer(torch.nn.Module): + """Conformer block based on https://arxiv.org/abs/2005.08100. We currently don't support relative positional encoding in MHA""" + + def __init__( + self, + embed_dim, + ffn_embed_dim, + attention_heads, + dropout, + use_fp16, + depthwise_conv_kernel_size=31, + activation_fn="swish", + attn_type=None, + pos_enc_type="abs", + ): + """ + Args: + embed_dim: Input embedding dimension + ffn_embed_dim: FFN layer dimension + attention_heads: Number of attention heads in MHA + dropout: dropout value + depthwise_conv_kernel_size: Size of kernel in depthwise conv layer in convolution module + activation_fn: Activation function name to use in convulation block and feed forward block + attn_type: MHA implementation from ESPNET vs fairseq + pos_enc_type: Positional encoding type - abs, rope, rel_pos + """ + self.pos_enc_type = pos_enc_type + super(ConformerEncoderLayer, self).__init__() + + self.ffn1 = FeedForwardModule( + embed_dim, + ffn_embed_dim, + dropout, + dropout, + ) + + self.self_attn_layer_norm = LayerNorm(embed_dim, export=False) + self.self_attn_dropout = torch.nn.Dropout(dropout) + if attn_type == "espnet": + if self.pos_enc_type == "rel_pos": + self.self_attn = RelPositionMultiHeadedAttention( + embed_dim, + attention_heads, + dropout=dropout, + ) + elif self.pos_enc_type == "rope": + self.self_attn = RotaryPositionMultiHeadedAttention( + embed_dim, attention_heads, dropout=dropout, precision=use_fp16 + ) + elif self.pos_enc_type == "abs": + self.self_attn = ESPNETMultiHeadedAttention( + embed_dim, + attention_heads, + dropout=dropout, + ) + else: + raise Exception(f"Unsupported attention type {self.pos_enc_type}") + else: + # Default to fairseq MHA + self.self_attn = MultiheadAttention( + embed_dim, + attention_heads, + dropout=dropout, + ) + + self.conv_module = ConvolutionModule( + embed_dim=embed_dim, + channels=embed_dim, + depthwise_kernel_size=depthwise_conv_kernel_size, + dropout=dropout, + activation_fn=activation_fn, + ) + + self.ffn2 = FeedForwardModule( + embed_dim, + ffn_embed_dim, + dropout, + dropout, + activation_fn=activation_fn, + ) + self.final_layer_norm = LayerNorm(embed_dim, export=False) + + def forward( + self, + x, + encoder_padding_mask: Optional[torch.Tensor], + position_emb: Optional[torch.Tensor] = None, + ): + """ + Args: + x: Tensor of shape T X B X C + encoder_padding_mask: Optional mask tensor + positions: + Returns: + Tensor of shape T X B X C + """ + residual = x + x = self.ffn1(x) + x = x * 0.5 + residual + residual = x + x = self.self_attn_layer_norm(x) + if self.pos_enc_type == "rel_pos": + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=encoder_padding_mask, + pos_emb=position_emb, + need_weights=False, + ) + else: + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=encoder_padding_mask, + need_weights=False, + ) + x = self.self_attn_dropout(x) + x = x + residual + + residual = x + # TBC to BTC + x = x.transpose(0, 1) + x = self.conv_module(x) + # BTC to TBC + x = x.transpose(0, 1) + x = residual + x + + residual = x + x = self.ffn2(x) + + layer_result = x + + x = x * 0.5 + residual + + x = self.final_layer_norm(x) + return x, (attn, layer_result) + + +class ConformerWav2Vec2EncoderLayer(ConformerEncoderLayer): + """Encoder layer for Wav2vec2 encoder""" + + def forward( + self, + x: torch.Tensor, + self_attn_mask: torch.Tensor = None, + self_attn_padding_mask: torch.Tensor = None, + need_weights: bool = False, + att_args=None, + position_emb=None, + ): + return super().forward(x, self_attn_padding_mask, position_emb) diff --git a/fairseq/fairseq/modules/conv_tbc.py b/fairseq/fairseq/modules/conv_tbc.py new file mode 100644 index 0000000..65e17ec --- /dev/null +++ b/fairseq/fairseq/modules/conv_tbc.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn +from torch.nn.modules.utils import _single +from torch import Tensor + + +class ConvTBC(torch.nn.Module): + """1D convolution over an input of shape (time x batch x channel) + + The implementation uses gemm to perform the convolution. This implementation + is faster than cuDNN for small kernel sizes. + """ + + def __init__(self, in_channels, out_channels, kernel_size, padding=0): + super(ConvTBC, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _single(kernel_size) + self.padding = _single(padding) + + self.weight = torch.nn.Parameter( + torch.Tensor(self.kernel_size[0], in_channels, out_channels) + ) + self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) + + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_normal_(self.weight) + nn.init.zeros_(self.bias) + + def conv_tbc(self, input: Tensor): + return torch.conv_tbc( + input.contiguous(), self.weight, self.bias, self.padding[0] + ) + + def forward(self, input: Tensor): + return self.conv_tbc(input) + + def __repr__(self): + s = ( + "{name}({in_channels}, {out_channels}, kernel_size={kernel_size}" + ", padding={padding}" + ) + if self.bias is None: + s += ", bias=False" + s += ")" + return s.format(name=self.__class__.__name__, **self.__dict__) diff --git a/fairseq/fairseq/modules/cross_entropy.py b/fairseq/fairseq/modules/cross_entropy.py new file mode 100644 index 0000000..286c00e --- /dev/null +++ b/fairseq/fairseq/modules/cross_entropy.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +import torch.nn.functional as F + +logger = logging.getLogger(__name__) + + +def _cross_entropy_pytorch(logits, target, ignore_index=None, reduction="mean"): + lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) + return F.nll_loss( + lprobs, + target, + ignore_index=ignore_index, + reduction=reduction, + ) + + +try: + import xentropy_cuda + from apex.contrib import xentropy + + def cross_entropy(logits, target, ignore_index=-100, reduction="mean"): + if logits.device == torch.device("cpu"): + return _cross_entropy_pytorch(logits, target, ignore_index, reduction) + else: + if not getattr(cross_entropy, "_has_logged_once", False): + logger.info("using fused cross entropy") + cross_entropy._has_logged_once = True + + half_to_float = logits.dtype == torch.half + losses = xentropy.SoftmaxCrossEntropyLoss.apply( + logits, + target, + 0.0, + ignore_index, + half_to_float, + ) + if reduction == "sum": + return losses.sum() + elif reduction == "mean": + if ignore_index >= 0: + return losses.sum() / target.ne(ignore_index).sum() + else: + return losses.mean() + elif reduction == "none": + return losses + else: + raise NotImplementedError + +except ImportError: + + def cross_entropy(logits, target, ignore_index=-100, reduction="mean"): + return _cross_entropy_pytorch(logits, target, ignore_index, reduction) diff --git a/fairseq/fairseq/modules/cuda_utils.cu b/fairseq/fairseq/modules/cuda_utils.cu new file mode 100644 index 0000000..924f852 --- /dev/null +++ b/fairseq/fairseq/modules/cuda_utils.cu @@ -0,0 +1,202 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +template <typename U, typename V> +constexpr __host__ __device__ auto divUp(U a, V b) -> decltype(a + b) { + return (a + b - 1) / b; +} + +template <int FS, int SB, int padding_l, typename scalar_t> +__inline__ __device__ void zeroSharedMem(scalar_t* data) { + /* + Given an array of length FS + SB, zero out the first padding_l and last + (FS - padding_l) values in the array + */ + + int tid = threadIdx.x; + + if (FS < SB) { + // zero all if we have enough threads in a block to do all of them + if (tid < padding_l || tid > SB - FS + padding_l - 1) { + data[tid] = scalar_t(0.0); + } + } else { + // otherwise zero out one block at a time + const int numIterations = divUp<int, int>(FS, SB); + for (int i = 0; i < numIterations; i++) { + int offset = i * SB; + if (tid + offset < padding_l) { + data[tid + offset] = scalar_t(0.0); + } else if (tid + offset < FS) { + data[SB + tid + offset] = scalar_t(0.0); + } + } + } +} + +template <typename scalar_t> +__inline__ __device__ scalar_t warpReduce(scalar_t data) { + /* + Reduce an array within each warp. After processing all values in warp will + caontain the sum of all original values in that warp. + + data - pointer to data to reduce + */ + data += __shfl_xor_sync(SHFL_MASK, data, 16); + data += __shfl_xor_sync(SHFL_MASK, data, 8); + data += __shfl_xor_sync(SHFL_MASK, data, 4); + data += __shfl_xor_sync(SHFL_MASK, data, 2); + data += __shfl_xor_sync(SHFL_MASK, data, 1); + return data; +} + +template <typename scalar_t> +__inline__ __device__ scalar_t blockReduce(scalar_t data) { + /* + Reduce an entire array on the block level. After processing, the + first value in the array will contain the reduced sum. + + data - pointer to data to reduce + */ + + static __shared__ scalar_t warpSum[32]; + const int tid = threadIdx.x; + int wid = tid / 32; + int lane = tid % 32; + + __syncthreads(); + + // reduce each warp then write to shared memory + scalar_t sum = warpReduce(data); + if (lane == 0) { + warpSum[wid] = sum; + } + + __syncthreads(); + + scalar_t v; + // perform final sum of partial warp sums + if (tid < blockDim.x / 32) { + v = warpSum[lane]; + } else { + v = scalar_t(0.0); + } + + if (wid == 0) { + v = warpReduce(v); + } + __syncthreads(); + + return v; +} + +void checkCudaStatus(cudaError_t status, int lineNumber = -1) { + if (status != cudaSuccess) { + std::cout << cudaGetErrorString(status) << " at line " << lineNumber + << std::endl; + std::cout << "Exiting" << std::endl; + exit(1); + } +} + +template <int FS, int SB, int padding_l, typename scalar_t> +__device__ void load_input_to_shared( + const scalar_t* input, // global memory + int inputOffset, + int sequenceLength, + int iteration, + int numIterations, + bool no_prev, + scalar_t* output /* shared memory */) { + /* + Load a block size of input into shared memory with + right and left overhang of total size FS. If previously + loaded memory, overlap will be shifted over to reduce + global memory access + + input - pointer to start of channel sequence + inputOffset - how far in the sequence to start loading + sequenceLength - total length of sequence + iteration - which block of sequence we are loading + numIterations - total number of blocks to load + no_prev - whether to load the whole block if the previous block + wasn't loaded + output - shared memory to write input to + */ + + const int tid = threadIdx.x; + + // Load the left "overhang" of input + if (iteration > 0) { + if (padding_l < SB) { + // load all at once + if (tid < padding_l) { + output[tid] = + (no_prev) ? input[inputOffset - padding_l + tid] : output[tid + SB]; + } + } else { + // load in chunks of size SB + int numIterations = divUp<int, int>(padding_l, SB); + for (int i = 0; i < numIterations; i++) { + int offset = i * SB; + if ((tid + offset) < padding_l) { + output[tid + offset] = (no_prev) + ? input[inputOffset - padding_l + tid + offset] + : output[tid + offset + SB]; + } + } + } + } + + // Load the right "overhang" of input + if (iteration < (numIterations - 1)) { + const int elementsLeft = sequenceLength - (iteration + 1) * SB; + + if ((FS - padding_l) < SB) { + // load all at once + if (tid < (FS - padding_l)) { + output[padding_l + SB + tid] = (tid < elementsLeft) + ? input[inputOffset + SB + tid] + : scalar_t(0.0); + } + } else { + // load in chunks of size SB + int numIterations = divUp<int, int>(FS - padding_l, SB); + for (int i = 0; i < numIterations; i++) { + int offset = i * SB; + if ((tid + offset) < (FS - padding_l)) { + output[padding_l + SB + tid + offset] = + ((tid + offset) < elementsLeft) + ? input[inputOffset + SB + tid + offset] + : scalar_t(0.0); + } + } + } + } + + // We should also clear out the right "overhang" + if (iteration == (numIterations - 1)) { + if ((FS - padding_l) < SB) { + // clear out all at once + if (tid < (FS - padding_l)) { + output[padding_l + SB + tid] = scalar_t(0.0); + } + } else { + // clear in chunks of size SB + int numIterations = divUp<int, int>(FS - padding_l, SB); + for (int i = 0; i < numIterations; i++) { + int offset = i * SB; + if ((tid + offset) < (FS - padding_l)) { + output[padding_l + SB + tid + offset] = scalar_t(0.0); + } + } + } + } + output[tid + padding_l] = ((inputOffset + tid) < sequenceLength) + ? input[inputOffset + tid] + : scalar_t(0.0); +} diff --git a/fairseq/fairseq/modules/downsampled_multihead_attention.py b/fairseq/fairseq/modules/downsampled_multihead_attention.py new file mode 100644 index 0000000..5e42942 --- /dev/null +++ b/fairseq/fairseq/modules/downsampled_multihead_attention.py @@ -0,0 +1,317 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.scalar_bias import scalar_bias + + +class SingleHeadAttention(nn.Module): + """ + Single-head attention that supports Gating and Downsampling + """ + + def __init__( + self, + out_channels, + embed_dim, + head_dim, + head_index, + dropout=0.0, + bias=True, + project_input=True, + gated=False, + downsample=False, + num_heads=1, + ): + super().__init__() + self.embed_dim = embed_dim + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.head_index = head_index + self.head_dim = head_dim + self.project_input = project_input + self.gated = gated + self.downsample = downsample + self.num_heads = num_heads + self.projection = None + + k_layers = [] + v_layers = [] + if self.downsample: + k_layers.append(Downsample(self.head_index)) + v_layers.append(Downsample(self.head_index)) + out_proj_size = self.head_dim + else: + out_proj_size = self.head_dim * self.num_heads + if self.gated: + k_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias)) + self.in_proj_q = GatedLinear(self.embed_dim, out_proj_size, bias=bias) + v_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias)) + else: + k_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias)) + self.in_proj_q = Linear(self.embed_dim, out_proj_size, bias=bias) + v_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias)) + + self.in_proj_k = nn.Sequential(*k_layers) + self.in_proj_v = nn.Sequential(*v_layers) + + if self.downsample: + self.out_proj = Linear(out_proj_size, self.head_dim, bias=bias) + else: + self.out_proj = Linear(out_proj_size, out_channels, bias=bias) + + self.scaling = self.head_dim**-0.5 + + def forward( + self, + query, + key, + value, + mask_future_timesteps=False, + key_padding_mask=None, + use_scalar_bias=False, + ): + """Input shape: Time x Batch x Channel + Self-attention can be implemented by passing in the same arguments for + query, key and value. Future timesteps can be masked with the + `mask_future_timesteps` argument. Padding elements can be excluded from + the key by passing a binary ByteTensor (`key_padding_mask`) with shape: + batch x src_len, where padding elements are indicated by 1s. + """ + src_len, bsz, out_channels = key.size() + tgt_len = query.size(0) + assert list(query.size()) == [tgt_len, bsz, out_channels] + assert key.size() == value.size() + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if self.downsample: + size = bsz + else: + size = bsz * self.num_heads + + k = key + v = value + q = query + if self.project_input: + q = self.in_proj_q(q) + k = self.in_proj_k(k) + v = self.in_proj_v(v) + src_len = k.size()[0] + q *= self.scaling + + if not self.downsample: + q = q.view(tgt_len, size, self.head_dim) + k = k.view(src_len, size, self.head_dim) + v = v.view(src_len, size, self.head_dim) + + q = q.transpose(0, 1) + k = k.transpose(0, 1) + v = v.transpose(0, 1) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + if mask_future_timesteps: + assert ( + query.size() == key.size() + ), "mask_future_timesteps only applies to self-attention" + attn_weights *= torch.tril( + attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(), + diagonal=-1, + )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0) + attn_weights += torch.triu( + attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(), + diagonal=0, + )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0) + tgt_size = tgt_len + if use_scalar_bias: + attn_weights = scalar_bias(attn_weights, 2) + v = scalar_bias(v, 1) + tgt_size += 1 + + if key_padding_mask is not None: + # don't attend to padding symbols + if key_padding_mask.max() > 0: + if self.downsample: + attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len) + else: + attn_weights = attn_weights.view( + size, self.num_heads, tgt_len, src_len + ) + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + -math.inf, + ) + attn_weights = attn_weights.view(size, tgt_len, src_len) + attn_weights = F.softmax(attn_weights, dim=-1) + attn_weights = self.dropout_module(attn_weights) + + attn = torch.bmm(attn_weights, v) + if self.downsample: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.head_dim) + else: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim) + + attn = self.out_proj(attn) + + return attn, attn_weights + + +class DownsampledMultiHeadAttention(nn.ModuleList): + """ + Multi-headed attention with Gating and Downsampling + """ + + def __init__( + self, + out_channels, + embed_dim, + num_heads, + dropout=0.0, + bias=True, + project_input=True, + gated=False, + downsample=False, + ): + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.downsample = downsample + self.gated = gated + self.project_input = project_input + assert self.head_dim * num_heads == embed_dim + + if self.downsample: + attention_heads = [] + for index in range(self.num_heads): + attention_heads.append( + SingleHeadAttention( + out_channels, + self.embed_dim, + self.head_dim, + index, + dropout, + bias, + self.project_input, + self.gated, + self.downsample, + self.num_heads, + ) + ) + super().__init__(modules=attention_heads) + self.out_proj = Linear(embed_dim, out_channels, bias=bias) + else: + # either we have a list of attention heads, or just one attention head + # if not being downsampled, we can do the heads with one linear layer instead of separate ones + super().__init__() + self.attention_module = SingleHeadAttention( + out_channels, + self.embed_dim, + self.head_dim, + 1, + dropout, + bias, + self.project_input, + self.gated, + self.downsample, + self.num_heads, + ) + + def forward( + self, + query, + key, + value, + mask_future_timesteps=False, + key_padding_mask=None, + use_scalar_bias=False, + ): + src_len, bsz, embed_dim = key.size() + tgt_len = query.size(0) + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + assert key.size() == value.size() + + tgt_size = tgt_len + if use_scalar_bias: + tgt_size += 1 + + attn = [] + attn_weights = [] + if self.downsample: + for attention_head_number in range(self.num_heads): + # call the forward of each attention head + _attn, _attn_weight = self[attention_head_number]( + query, + key, + value, + mask_future_timesteps, + key_padding_mask, + use_scalar_bias, + ) + attn.append(_attn) + attn_weights.append(_attn_weight) + full_attn = torch.cat(attn, dim=2) + full_attn = self.out_proj(full_attn) + return full_attn, attn_weights[0].clone() + else: + _attn, _attn_weight = self.attention_module( + query, + key, + value, + mask_future_timesteps, + key_padding_mask, + use_scalar_bias, + ) + attn.append(_attn) + attn_weights.append(_attn_weight) + full_attn = torch.cat(attn, dim=2) + full_attn_weights = torch.cat(attn_weights) + full_attn_weights = full_attn_weights.view( + bsz, self.num_heads, tgt_size, src_len + ) + full_attn_weights = full_attn_weights.sum(dim=1) / self.num_heads + return full_attn, full_attn_weights + + +class Downsample(nn.Module): + """ + Selects every nth element, where n is the index + """ + + def __init__(self, index): + super().__init__() + self.index = index + + def forward(self, x): + return x[:: self.index + 1] + + +def Linear(in_features, out_features, dropout=0.0, bias=True): + """Weight-normalized Linear layer (input: B x T x C)""" + m = nn.Linear(in_features, out_features, bias=bias) + m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features)) + m.bias.data.zero_() + return nn.utils.weight_norm(m) + + +def GatedLinear(in_features, out_features, dropout=0.0, bias=True): + """Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units""" + return nn.Sequential( + Linear(in_features, out_features * 4, dropout, bias), + nn.GLU(), + Linear(out_features * 2, out_features * 2, dropout, bias), + nn.GLU(), + Linear(out_features, out_features, dropout, bias), + ) diff --git a/fairseq/fairseq/modules/dynamic_convolution.py b/fairseq/fairseq/modules/dynamic_convolution.py new file mode 100644 index 0000000..0ff02cd --- /dev/null +++ b/fairseq/fairseq/modules/dynamic_convolution.py @@ -0,0 +1,526 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.incremental_decoding_utils import ( + FairseqIncrementalState, + with_incremental_state, +) +from fairseq.modules.fairseq_dropout import FairseqDropout +from torch import Tensor + +from .unfold import unfold1d + + +def DynamicConv( + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + renorm_padding=False, + bias=False, + conv_bias=False, + query_size=None, + in_proj=False, +): + if torch.cuda.is_available(): + try: + from fairseq.modules.dynamicconv_layer import DynamicconvLayer + + return DynamicconvLayer( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + renorm_padding=renorm_padding, + bias=bias, + conv_bias=conv_bias, + query_size=query_size, + ) + except ImportError as e: + print(e) + return DynamicConv1dTBC( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + renorm_padding=renorm_padding, + bias=bias, + conv_bias=conv_bias, + query_size=query_size, + ) + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m + + +@with_incremental_state +class DynamicConv1dTBC(nn.Module): + """Dynamic lightweight convolution taking T x B x C inputs + Args: + input_size: # of channels of the input + kernel_size: convolution channels + padding_l: padding to the left when using "same" padding + num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size) + weight_dropout: the drop rate of the DropConnect to drop the weight + weight_softmax: normalize the weight with softmax before the convolution + renorm_padding: re-normalize the filters to ignore the padded part (only the non-padding parts sum up to 1) + bias: use bias + conv_bias: bias of the convolution + query_size: specified when feeding a different input as the query + in_proj: project the input and generate the filter together + + Shape: + Input: TxBxC, i.e. (timesteps, batch_size, input_size) + Output: TxBxC, i.e. (timesteps, batch_size, input_size) + + Attributes: + weight: the learnable weights of the module of shape + `(num_heads, 1, kernel_size)` + bias: the learnable bias of the module of shape `(input_size)` + """ + + def __init__( + self, + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + renorm_padding=False, + bias=False, + conv_bias=False, + query_size=None, + in_proj=False, + ): + super().__init__() + self.input_size = input_size + self.query_size = input_size if query_size is None else query_size + self.kernel_size = kernel_size + self.padding_l = padding_l + self.num_heads = num_heads + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) + self.weight_softmax = weight_softmax + self.renorm_padding = renorm_padding + + if in_proj: + self.weight_linear = Linear( + self.input_size, self.input_size + num_heads * kernel_size * 1 + ) + else: + self.weight_linear = Linear( + self.query_size, num_heads * kernel_size * 1, bias=bias + ) + if conv_bias: + self.conv_bias = nn.Parameter(torch.Tensor(input_size)) + else: + self.conv_bias = None + self.reset_parameters() + + @property + def in_proj(self): + return ( + self.weight_linear.out_features + == self.input_size + self.num_heads * self.kernel_size + ) + + def reset_parameters(self): + self.weight_linear.reset_parameters() + if self.conv_bias is not None: + nn.init.constant_(self.conv_bias, 0.0) + + def forward(self, x, incremental_state=None, query=None, unfold=None): + """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C + args: + x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) + incremental_state: A dict to keep the state + unfold: unfold the input or not. If not, we use the matrix trick instead + query: use the specified query to predict the conv filters + """ + unfold = ( + x.size(0) > 512 if unfold is None else unfold + ) # use unfold mode as default for long sequence to save memory + unfold = unfold or (incremental_state is not None) + assert query is None or not self.in_proj + + if query is None: + query = x + if unfold: + output = self._forward_unfolded(x, incremental_state, query) + else: + output = self._forward_expanded(x, incremental_state, query) + + if self.conv_bias is not None: + output = output + self.conv_bias.view(1, 1, -1) + return output + + def _forward_unfolded(self, x, incremental_state, query): + """The conventional implementation of convolutions. + Unfolding the input by having a window shifting to the right.""" + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + assert R * H == C == self.input_size + + if self.in_proj: + proj = self.weight_linear(x) + x = proj.narrow(2, 0, self.input_size).contiguous() + weight = ( + proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1) + ) + else: + weight = self.weight_linear(query).view(T * B * H, -1) + + # renorm_padding is only implemented in _forward_expanded + assert not self.renorm_padding or incremental_state is not None + + if incremental_state is not None: + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is None: + input_buffer = x.new() + x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) + if self.kernel_size > 1: + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) + else: + padding_l = self.padding_l + if K > T and padding_l == K - 1: + weight = weight.narrow(1, K - T, T) + K, padding_l = T, T - 1 + # unfold the input: T x B x C --> T' x B x C x K + x_unfold = unfold1d(x, K, padding_l, 0) + x_unfold = x_unfold.view(T * B * H, R, K) + + if self.weight_softmax and not self.renorm_padding: + weight = F.softmax(weight, dim=1) + weight = weight.narrow(1, 0, K) + + if incremental_state is not None: + weight = weight[:, -x_unfold.size(2) :] + K = weight.size(1) + + if self.weight_softmax and self.renorm_padding: + weight = F.softmax(weight, dim=1) + + weight = self.weight_dropout_module(weight, inplace=False) + + output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1 + output = output.view(T, B, C) + return output + + def _forward_expanded(self, x, incremental_stat, query): + """Turn the convolution filters into band matrices and do matrix multiplication. + This is faster when the sequence is short, but less memory efficient. + This is not used in the decoder during inference. + """ + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + assert R * H == C == self.input_size + if self.in_proj: + proj = self.weight_linear(x) + x = proj.narrow(2, 0, self.input_size).contiguous() + weight = ( + proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1) + ) + else: + weight = self.weight_linear(query).view(T * B * H, -1) + + if not self.renorm_padding: + if self.weight_softmax: + weight = F.softmax(weight, dim=1) + weight = self.weight_dropout_module(weight, inplace=False) + weight = weight.narrow(1, 0, K).contiguous() + weight = weight.view(T, B * H, K).transpose(0, 1) + + x = x.view(T, B * H, R).transpose(0, 1) + if self.weight_softmax and self.renorm_padding: + # turn the convolution filters into band matrices + weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf")) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) + weight_expanded = weight_expanded.narrow(2, self.padding_l, T) + # normalize the weight over valid positions like self-attention + weight_expanded = F.softmax(weight_expanded, dim=2) + weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False) + else: + P = self.padding_l + # For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length + if K > T and P == K - 1: + weight = weight.narrow(2, K - T, T) + K, P = T, T - 1 + # turn the convolution filters into band matrices + weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) + weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T + output = torch.bmm(weight_expanded, x) + output = output.transpose(0, 1).contiguous().view(T, B, C) + return output + + def reorder_incremental_state(self, incremental_state, new_order): + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + input_buffer = input_buffer.index_select(1, new_order) + self._set_input_buffer(incremental_state, input_buffer) + + def _get_input_buffer(self, incremental_state): + return utils.get_incremental_state(self, incremental_state, "input_buffer") + + def _set_input_buffer(self, incremental_state, new_buffer): + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) + + def extra_repr(self): + s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}".format( + self.input_size, + self.kernel_size, + self.padding_l, + self.num_heads, + self.weight_softmax, + self.conv_bias is not None, + self.renorm_padding, + self.in_proj, + ) + + if self.query_size != self.input_size: + s += ", query_size={}".format(self.query_size) + if self.weight_dropout_module.p > 0.0: + s += ", weight_dropout={}".format(self.weight_dropout_module.p) + return s + + +class DynamicConv_scripatable(nn.Module, FairseqIncrementalState): + """Dynamic lightweight convolution taking T x B x C inputs + Args: + input_size: # of channels of the input + kernel_size: convolution channels + padding_l: padding to the left when using "same" padding + num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size) + weight_dropout: the drop rate of the DropConnect to drop the weight + weight_softmax: normalize the weight with softmax before the convolution + renorm_padding: re-normalize the filters to ignore the padded part (only the non-padding parts sum up to 1) + bias: use bias + conv_bias: bias of the convolution + query_size: specified when feeding a different input as the query + in_proj: project the input and generate the filter together + + Shape: + Input: TxBxC, i.e. (timesteps, batch_size, input_size) + Output: TxBxC, i.e. (timesteps, batch_size, input_size) + + Attributes: + weight: the learnable weights of the module of shape + `(num_heads, 1, kernel_size)` + bias: the learnable bias of the module of shape `(input_size)` + """ + + def __init__( + self, + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + renorm_padding=False, + bias=False, + conv_bias=False, + query_size=None, + in_proj=False, + ): + super().__init__() + self.input_size = input_size + self.query_size = input_size if query_size is None else query_size + self.kernel_size = kernel_size + self.padding_l = padding_l + self.num_heads = num_heads + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) + self.weight_softmax = weight_softmax + self.renorm_padding = renorm_padding + + if in_proj: + self.weight_linear = Linear( + self.input_size, self.input_size + num_heads * kernel_size * 1 + ) + else: + self.weight_linear = Linear( + self.query_size, num_heads * kernel_size * 1, bias=bias + ) + self.in_proj = ( + self.weight_linear.out_features + == self.input_size + self.num_heads * self.kernel_size + ) + self.has_conv_bias = conv_bias + self.conv_bias = nn.Parameter(torch.Tensor(input_size).view(1, 1, -1)) + self.init_incremental_state() + + self.reset_parameters() + + def reset_parameters(self): + self.weight_linear.reset_parameters() + if self.has_conv_bias: + nn.init.constant_(self.conv_bias, 0.0) + + def forward( + self, + x, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + query: Optional[Tensor] = None, + ): + """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C + args: + x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) + incremental_state: A dict to keep the state + unfold: unfold the input or not. If not, we use the matrix trick instead + query: use the specified query to predict the conv filters + """ + assert query is None or not self.in_proj + + if query is None: + query = x + + output = self._forward_unfolded(x, incremental_state, query) + + if self.has_conv_bias: + output = output + self.conv_bias + return output + + def _forward_unfolded( + self, + x, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + query, + ): + """The conventional implementation of convolutions. + Unfolding the input by having a window shifting to the right.""" + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + assert R * H == C == self.input_size + + TxBxH = T * B * H + + if self.in_proj: + proj = self.weight_linear(x) + x = proj.narrow(2, 0, self.input_size).contiguous() + weight = proj.narrow(2, self.input_size, H * K).contiguous().view(TxBxH, -1) + else: + weight = self.weight_linear(query).view(TxBxH, -1) + + # renorm_padding is only implemented in _forward_expanded + assert not self.renorm_padding or incremental_state is not None + + if incremental_state is not None: + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) + else: + x_unfold = x.unsqueeze(3).clone() + if self.kernel_size > 1: + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(TxBxH, R, -1) + else: + padding_l = self.padding_l + if K > T and padding_l == K - 1: + weight = weight.narrow(1, K - T, T) + K, padding_l = T, T - 1 + # unfold the input: T x B x C --> T' x B x C x K + x_unfold = unfold1d(x, K, padding_l, 0.0) + x_unfold = x_unfold.view(TxBxH, R, K) + + if self.weight_softmax and not self.renorm_padding: + weight = F.softmax(weight, dim=1) + weight = weight.narrow(1, 0, K) + + if incremental_state is not None: + weight = weight[:, -(x_unfold.size(2)) :] + K = weight.size(1) + + if self.weight_softmax and self.renorm_padding: + weight = F.softmax(weight, dim=1) + + weight = self.weight_dropout_module(weight, inplace=False) + + output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T x B x H x R x 1 + output = output.view(T, B, C) + return output + + def reorder_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + new_order: Tensor, + ): + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + input_buffer = input_buffer.index_select(1, new_order) + self._set_input_buffer(incremental_state, input_buffer) + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ): + result = self.get_incremental_state(incremental_state, "input_buffer") + if result is not None and "input_buffer" in result: + return result["input_buffer"] + else: + return None + + def _set_input_buffer( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + new_buffer: Optional[Tensor], + ): + result = self.set_incremental_state( + incremental_state, "input_buffer", {"input_buffer": new_buffer} + ) + if result is not None: + incremental_state = result + return incremental_state + + def extra_repr(self): + s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}".format( # noqa + self.input_size, + self.kernel_size, + self.padding_l, + self.num_heads, + self.weight_softmax, + self.conv_bias is not None, + self.renorm_padding, + self.in_proj, + ) + + if self.query_size != self.input_size: + s += ", query_size={}".format(self.query_size) + if self.weight_dropout_module.p > 0.0: + s += ", weight_dropout={}".format(self.weight_dropout_module.p) + return s diff --git a/fairseq/fairseq/modules/dynamic_crf_layer.py b/fairseq/fairseq/modules/dynamic_crf_layer.py new file mode 100644 index 0000000..8fcc6b8 --- /dev/null +++ b/fairseq/fairseq/modules/dynamic_crf_layer.py @@ -0,0 +1,189 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +This file is to re-implemented the low-rank and beam approximation of CRF layer +Proposed by: + +Sun, Zhiqing, et al. +Fast Structured Decoding for Sequence Models +https://arxiv.org/abs/1910.11555 + +The CRF implementation is mainly borrowed from +https://github.com/kmkurn/pytorch-crf/blob/master/torchcrf/__init__.py + +""" + +import numpy as np +import torch +import torch.nn as nn + + +def logsumexp(x, dim=1): + return torch.logsumexp(x.float(), dim=dim).type_as(x) + + +class DynamicCRF(nn.Module): + """Dynamic CRF layer is used to approximate the traditional + Conditional Random Fields (CRF) + $P(y | x) = 1/Z(x) exp(sum_i s(y_i, x) + sum_i t(y_{i-1}, y_i, x))$ + + where in this function, we assume the emition scores (s) are given, + and the transition score is a |V| x |V| matrix $M$ + + in the following two aspects: + (1) it used a low-rank approximation for the transition matrix: + $M = E_1 E_2^T$ + (2) it used a beam to estimate the normalizing factor Z(x) + """ + + def __init__(self, num_embedding, low_rank=32, beam_size=64): + super().__init__() + + self.E1 = nn.Embedding(num_embedding, low_rank) + self.E2 = nn.Embedding(num_embedding, low_rank) + + self.vocb = num_embedding + self.rank = low_rank + self.beam = beam_size + + def extra_repr(self): + return "vocab_size={}, low_rank={}, beam_size={}".format( + self.vocb, self.rank, self.beam + ) + + def forward(self, emissions, targets, masks, beam=None): + """ + Compute the conditional log-likelihood of a sequence of target tokens given emission scores + + Args: + emissions (`~torch.Tensor`): Emission score are usually the unnormalized decoder output + ``(batch_size, seq_len, vocab_size)``. We assume batch-first + targets (`~torch.LongTensor`): Sequence of target token indices + ``(batch_size, seq_len) + masks (`~torch.ByteTensor`): Mask tensor with the same size as targets + + Returns: + `~torch.Tensor`: approximated log-likelihood + """ + numerator = self._compute_score(emissions, targets, masks) + denominator = self._compute_normalizer(emissions, targets, masks, beam) + return numerator - denominator + + def forward_decoder(self, emissions, masks=None, beam=None): + """ + Find the most likely output sequence using Viterbi algorithm. + + Args: + emissions (`~torch.Tensor`): Emission score are usually the unnormalized decoder output + ``(batch_size, seq_len, vocab_size)``. We assume batch-first + masks (`~torch.ByteTensor`): Mask tensor with the same size as targets + + Returns: + `~torch.LongTensor`: decoded sequence from the CRF model + """ + return self._viterbi_decode(emissions, masks, beam) + + def _compute_score(self, emissions, targets, masks=None): + batch_size, seq_len = targets.size() + emission_scores = emissions.gather(2, targets[:, :, None])[:, :, 0] # B x T + transition_scores = (self.E1(targets[:, :-1]) * self.E2(targets[:, 1:])).sum(2) + + scores = emission_scores + scores[:, 1:] += transition_scores + + if masks is not None: + scores = scores * masks.type_as(scores) + return scores.sum(-1) + + def _compute_normalizer(self, emissions, targets=None, masks=None, beam=None): + # HACK: we include "target" which is a hueristic for training + # HACK: we use a beam of tokens to approximate the normalizing factor (which is bad?) + + beam = beam if beam is not None else self.beam + batch_size, seq_len = emissions.size()[:2] + if targets is not None: + _emissions = emissions.scatter(2, targets[:, :, None], np.float("inf")) + beam_targets = _emissions.topk(beam, 2)[1] + beam_emission_scores = emissions.gather(2, beam_targets) + else: + beam_emission_scores, beam_targets = emissions.topk(beam, 2) + beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D + beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D + beam_transition_matrix = torch.bmm( + beam_transition_score1.view(-1, beam, self.rank), + beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2), + ) + beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam) + + # compute the normalizer in the log-space + score = beam_emission_scores[:, 0] # B x K + for i in range(1, seq_len): + next_score = score[:, :, None] + beam_transition_matrix[:, i - 1] + next_score = logsumexp(next_score, dim=1) + beam_emission_scores[:, i] + + if masks is not None: + score = torch.where(masks[:, i : i + 1], next_score, score) + else: + score = next_score + + # Sum (log-sum-exp) over all possible tags + return logsumexp(score, dim=1) + + def _viterbi_decode(self, emissions, masks=None, beam=None): + # HACK: we use a beam of tokens to approximate the normalizing factor (which is bad?) + + beam = beam if beam is not None else self.beam + batch_size, seq_len = emissions.size()[:2] + beam_emission_scores, beam_targets = emissions.topk(beam, 2) + beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D + beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D + beam_transition_matrix = torch.bmm( + beam_transition_score1.view(-1, beam, self.rank), + beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2), + ) + beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam) + + traj_tokens, traj_scores = [], [] + finalized_tokens, finalized_scores = [], [] + + # compute the normalizer in the log-space + score = beam_emission_scores[:, 0] # B x K + dummy = ( + torch.arange(beam, device=score.device).expand(*score.size()).contiguous() + ) + + for i in range(1, seq_len): + traj_scores.append(score) + _score = score[:, :, None] + beam_transition_matrix[:, i - 1] + _score, _index = _score.max(dim=1) + _score = _score + beam_emission_scores[:, i] + + if masks is not None: + score = torch.where(masks[:, i : i + 1], _score, score) + index = torch.where(masks[:, i : i + 1], _index, dummy) + else: + score, index = _score, _index + traj_tokens.append(index) + + # now running the back-tracing and find the best + best_score, best_index = score.max(dim=1) + finalized_tokens.append(best_index[:, None]) + finalized_scores.append(best_score[:, None]) + + for idx, scs in zip(reversed(traj_tokens), reversed(traj_scores)): + previous_index = finalized_tokens[-1] + finalized_tokens.append(idx.gather(1, previous_index)) + finalized_scores.append(scs.gather(1, previous_index)) + + finalized_tokens.reverse() + finalized_tokens = torch.cat(finalized_tokens, 1) + finalized_tokens = beam_targets.gather(2, finalized_tokens[:, :, None])[:, :, 0] + + finalized_scores.reverse() + finalized_scores = torch.cat(finalized_scores, 1) + finalized_scores[:, 1:] = finalized_scores[:, 1:] - finalized_scores[:, :-1] + + return finalized_scores, finalized_tokens diff --git a/fairseq/fairseq/modules/dynamicconv_layer/__init__.py b/fairseq/fairseq/modules/dynamicconv_layer/__init__.py new file mode 100644 index 0000000..22dc6f4 --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .dynamicconv_layer import DynamicconvLayer # noqa diff --git a/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py b/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py new file mode 100644 index 0000000..9304f99 --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py @@ -0,0 +1,223 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +def gen_forward(): + + kernels = [3, 5, 7, 15, 31, 63, 127, 255] + blocks = [32, 64, 128, 256] + + head = """ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "dynamicconv_cuda.cuh" + +std::vector<at::Tensor> dynamicconv_cuda_forward(at::Tensor input, at::Tensor weight, int padding_l) { + + at::DeviceGuard g(input.device()); + const auto minibatch = input.size(0); + const auto numFeatures = input.size(1); + const auto sequenceLength = input.size(2); + + const auto numHeads = weight.size(1); + const auto filterSize = weight.size(2); + + const auto numFiltersInBlock = numFeatures / numHeads; + const dim3 blocks(minibatch, numFeatures); + + auto output = at::zeros_like(input); + auto stream = at::cuda::getCurrentCUDAStream(); +""" + + switch = """ + switch(filterSize) { +""" + + case_k = """ + case {k}: +""" + + main_block = """ + if (padding_l == {pad}) {{ + AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "dynamicconv_forward", ([&] {{ + dynamicconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t> + <<<blocks, {b_size}, 0, stream>>>( + input.data<scalar_t>(), + weight.data<scalar_t>(), + minibatch, + sequenceLength, + numFeatures, + numFiltersInBlock, + numHeads, + output.data<scalar_t>()); + }})); + }} else +""" + + bad_padding = """ + { + std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl; + } + break;\n +""" + + end = """ + default: + std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl; + } + + return {output}; +} +""" + + with open("dynamicconv_cuda_forward.cu", "w") as forward: + forward.write(head) + forward.write(switch) + for k in kernels: + b_size = 32 + for b in blocks: + if b > k: + b_size = b + break + forward.write(case_k.format(k=k)) + for pad in [k // 2, k - 1]: + forward.write(main_block.format(k=k, b_size=b_size, pad=pad)) + forward.write(bad_padding) + forward.write(end) + + +def gen_backward(): + + kernels = [3, 5, 7, 15, 31, 63, 127, 255] + thresh = [512, 512, 512, 512, 512, 380, 256, 256] + min_block = [64, 64, 64, 64, 64, 64, 128, 256] + seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]] + + head = """ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "dynamicconv_cuda.cuh" + +std::vector<at::Tensor> dynamicconv_cuda_backward(at::Tensor gradOutput, int padding_l, at::Tensor input, at::Tensor weight) { + + at::DeviceGuard g(input.device()); + const auto minibatch = input.size(0); + const auto numFeatures = input.size(1); + const auto sequenceLength = input.size(2); + + const auto numHeads = weight.size(1); + const auto filterSize = weight.size(2); + + const auto numFiltersInBlock = numFeatures / numHeads; + auto numChunks = 1; + + auto gradInput = at::zeros_like(input); + auto gradWeight = at::zeros_like(weight); + auto stream = at::cuda::getCurrentCUDAStream(); + + dim3 blocks(minibatch, numHeads, numChunks); +""" + + sequence_if = """ + if (sequenceLength < {seq}) {{ + switch(filterSize) {{ +""" + + case_k = """ + case {k}: +""" + + chunks_reset = """ + numChunks = int(ceilf(sequenceLength/float({b_size}))); + blocks = dim3(minibatch, numHeads, numChunks); +""" + + main_block = """ + if (padding_l == {p}) {{ + AT_DISPATCH_FLOATING_TYPES_AND_HALF(gradOutput.scalar_type(), "dynamicconv_backward", ([&] {{ + dynamicconv_backward_kernel<{k}, {b_size}, {p}, scalar_t> + <<<blocks, {b_size}, 0, stream>>>( + gradOutput.data<scalar_t>(), + input.data<scalar_t>(), + weight.data<scalar_t>(), + minibatch, + sequenceLength, + numFeatures, + numFiltersInBlock, + numHeads, + gradWeight.data<scalar_t>(), + gradInput.data<scalar_t>()); + }})); + }} else +""" + + bad_padding = """ + { + std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl; + } + break;\n +""" + + bad_filter = """ + default: + std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl; + } +""" + + con_else = """ + } else +""" + + final_else = """ + { + switch(filterSize) { +""" + + last_return = """ + } + return {gradInput, gradWeight}; +} +""" + + with open("dynamicconv_cuda_backward.cu", "w") as backward: + backward.write(head) + for seq in seqs: + backward.write(sequence_if.format(seq=seq)) + for k, t, m in zip(kernels, thresh, min_block): + backward.write(case_k.format(k=k)) + if seq <= t: + b_size = seq + else: + b_size = m + backward.write(chunks_reset.format(b_size=b_size)) + for p in [k // 2, k - 1]: + backward.write(main_block.format(k=k, b_size=b_size, p=p)) + backward.write(bad_padding) + backward.write(bad_filter) + backward.write(con_else) + backward.write(final_else) + for k, m in zip(kernels, min_block): + backward.write(case_k.format(k=k)) + backward.write(chunks_reset.format(b_size=m)) + for p in [k // 2, k - 1]: + backward.write(main_block.format(k=k, b_size=m, p=p)) + backward.write(bad_padding) + backward.write(bad_filter) + backward.write(last_return) + + +if __name__ == "__main__": + gen_forward() + gen_backward() diff --git a/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp new file mode 100644 index 0000000..744c363 --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp @@ -0,0 +1,51 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <torch/extension.h> +#include <vector> + +std::vector<at::Tensor> +dynamicconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l); + +std::vector<at::Tensor> dynamicconv_cuda_backward( + at::Tensor gradOutput, + int padding_l, + at::Tensor input, + at::Tensor filters); + +#define CHECK_CUDA(x) \ + AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +std::vector<at::Tensor> +dynamicconv_forward(at::Tensor input, at::Tensor filters, int padding_l) { + CHECK_INPUT(input); + CHECK_INPUT(filters); + + return dynamicconv_cuda_forward(input, filters, padding_l); +} + +std::vector<at::Tensor> dynamicconv_backward( + at::Tensor gradOutput, + int padding_l, + at::Tensor input, + at::Tensor filters) { + CHECK_INPUT(gradOutput); + CHECK_INPUT(input); + CHECK_INPUT(filters); + + return dynamicconv_cuda_backward(gradOutput, padding_l, input, filters); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &dynamicconv_forward, "dynamicconv forward (CUDA)"); + m.def("backward", &dynamicconv_backward, "dynamicconv backward (CUDA)"); +} diff --git a/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh new file mode 100644 index 0000000..44baf21 --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh @@ -0,0 +1,50 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <ATen/ATen.h> +#include <c10/cuda/CUDAStream.h> + +#include <cuda.h> +#include <cuda_fp16.h> +#include <cuda_runtime.h> + +#include <algorithm> +#include <functional> +#include <iostream> +#include <stdexcept> +#include <utility> +#include <vector> + +#include <assert.h> +#include <math.h> +#include <stdlib.h> + +#define SHFL_MASK 0xffffffff + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_forward_kernel( + const scalar_t* input, + const scalar_t* weight, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + scalar_t* output); + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_backward_kernel( + const scalar_t* gradOutput, // B * C * T + const scalar_t* input, // B * C * T + const scalar_t* weight, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + scalar_t* gradWeight, + scalar_t* gradInput); // B * H * k * T diff --git a/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu new file mode 100644 index 0000000..4630f1e --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu @@ -0,0 +1,176 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "../cuda_utils.cu" +#include "dynamicconv_cuda.cuh" +#include "dynamicconv_cuda_backward.cu" +#include "dynamicconv_cuda_forward.cu" + +// FS is filter size and kernels are specialized for filter sizes +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_forward_kernel( + const scalar_t* input, + const scalar_t* weight, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + scalar_t* output) { + assert(blockDim.x == SB); + + const int tid = threadIdx.x; + const int batchIdx = blockIdx.x; + const int featureIdx = blockIdx.y; + const int head = featureIdx / numFiltersInBlock; + + const int IOOffset = + batchIdx * numFeatures * sequenceLength + featureIdx * sequenceLength; + const scalar_t* inputFeature = &input[IOOffset]; + scalar_t* outputFeature = &output[IOOffset]; + + scalar_t filter[FS]; + + __shared__ scalar_t tempInput[SB + FS]; + zeroSharedMem<FS, SB, padding_l>(tempInput); + + const int numIterations = divUp<int, int>(sequenceLength, SB); + + for (int i = 0; i < numIterations; ++i) { + __syncthreads(); + const int inputOffset = i * SB; + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempInput); + __syncthreads(); + if (inputOffset + tid < sequenceLength) { +#pragma unroll + for (int k = 0; k < FS; ++k) { + const int filterOffset = batchIdx * numHeads * FS * sequenceLength + + head * FS * sequenceLength + k * sequenceLength + i * SB + tid; + filter[k] = weight[filterOffset]; + } + + scalar_t out = scalar_t(0.0); +#pragma unroll + for (int k = 0; k < FS; ++k) { + out += filter[k] * tempInput[tid + k]; + } + + outputFeature[inputOffset + tid] = out; + } + } +} + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_backward_kernel( + const scalar_t* gradOutput, // B * C * T + const scalar_t* input, // B * C * T + const scalar_t* weight, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + scalar_t* gradWeight, + scalar_t* gradInput) { // B * H * k * T + + assert(blockDim.x == SB); + + // each block operates on a single batch and filter head + const int tid = threadIdx.x; + const int batchIdx = blockIdx.x; + const int headIdx = blockIdx.y; + const int chunkIdx = blockIdx.z; + + const int numChunks = divUp<int, int>(sequenceLength, SB); + const int inputOffset = chunkIdx * SB; + + // initialize shared memory for output gradient and input + __shared__ scalar_t tempGradOutput[SB + FS]; + __shared__ scalar_t tempInput[SB + FS]; + const int padding = FS - padding_l - 1; + + zeroSharedMem<FS, SB, padding>(tempGradOutput); + zeroSharedMem<FS, SB, padding_l>(tempInput); + + // initialize local filter and weight gradient sum arrays + scalar_t tempGradSum[FS]; + scalar_t bfilter[FS]; + for (int k = 0; k < FS; ++k) { + tempGradSum[k] = scalar_t(0.0); + + int idxOffset = inputOffset + tid + k - padding; + if (idxOffset >= 0 && idxOffset < sequenceLength) { + int bfilterOffset = batchIdx * numHeads * FS * sequenceLength + + headIdx * FS * sequenceLength + (FS - k - 1) * sequenceLength + + idxOffset; + bfilter[k] = weight[bfilterOffset]; + } else { + bfilter[k] = scalar_t(0.0); + } + } + + // iterate over filter block + for (int featureIdx = 0; featureIdx < numFiltersInBlock; ++featureIdx) { + __syncthreads(); + + // load input and output gradient for this channel and chunk + const int IOOffset = batchIdx * numFeatures * sequenceLength + + (headIdx * numFiltersInBlock + featureIdx) * sequenceLength; + const scalar_t* inputFeature = &input[IOOffset]; + const scalar_t* gradOutputFeature = &gradOutput[IOOffset]; + scalar_t* gradInputFeature = &gradInput[IOOffset]; + + load_input_to_shared<FS, SB, padding>( + gradOutputFeature, + inputOffset, + sequenceLength, + chunkIdx, + numChunks, + true, + tempGradOutput); + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + chunkIdx, + numChunks, + true, + tempInput); + __syncthreads(); + + // sum input and weight gradients + scalar_t out = scalar_t(0.0); +#pragma unroll + for (int k = 0; k < FS; ++k) { + tempGradSum[k] += tempInput[tid + k] * tempGradOutput[tid + padding]; + out += bfilter[k] * tempGradOutput[tid + k]; + } + + if (inputOffset + tid < sequenceLength) { + gradInputFeature[inputOffset + tid] = out; + } + } + + const int gradOffset = + batchIdx * numHeads * FS * sequenceLength + headIdx * FS * sequenceLength; + scalar_t* gradWeightFeature = &gradWeight[gradOffset]; + + // write weight gradient + if (inputOffset + tid < sequenceLength) { + for (int k = 0; k < FS; ++k) { + const int outputOffset = k * sequenceLength + inputOffset + tid; + gradWeightFeature[outputOffset] = tempGradSum[k]; + } + } +} diff --git a/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py new file mode 100644 index 0000000..711ed03 --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py @@ -0,0 +1,227 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import dynamicconv_cuda +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.unfold import unfold1d +from torch import nn +from torch.autograd import Function + + +class dynamicconvFunction(Function): + @staticmethod + def forward(ctx, x, weights, padding_l): + ctx.padding_l = padding_l + outputs = dynamicconv_cuda.forward(x, weights, padding_l) + variables = [x, weights] + ctx.save_for_backward(*variables) + return outputs[0] + + @staticmethod + def backward(ctx, grad_output): + outputs = dynamicconv_cuda.backward( + grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors + ) + grad_input, grad_weights = outputs + return grad_input, grad_weights, None + + +@with_incremental_state +class DynamicconvLayer(nn.Module): + def __init__( + self, + input_size, + kernel_size=1, + padding_l=None, + weight_softmax=False, + num_heads=1, + weight_dropout=0.0, + bias=False, + renorm_padding=False, + conv_bias=False, + query_size=None, + ): + + super(DynamicconvLayer, self).__init__() + self.input_size = input_size + self.query_size = input_size if query_size is None else query_size + self.kernel_size = kernel_size + self.padding_l = padding_l + self.num_heads = num_heads + self.weight_softmax = weight_softmax + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) + self.renorm_padding = renorm_padding + self.bias = bias + + self.weight_linear = nn.Linear(input_size, num_heads * kernel_size, bias) + if conv_bias: + self.conv_bias = nn.Parameter(torch.Tensor(input_size)) + else: + self.conv_bias = None + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_uniform_(self.weight_linear.weight) + if self.conv_bias is not None: + nn.init.constant_(self.conv_bias, 0.0) + nn.init.constant_(self.weight_linaer.bias, 0.0) + + def forward(self, x, incremental_state=None, query=None, unfold=None): + + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + # R = C // H + + # during inference time, incremental BMM is faster + if incremental_state is not None: + unfold = ( + x.size(0) > 512 if unfold is None else unfold + ) # use unfold mode as default for long sequence to save memory + unfold = unfold or (incremental_state is not None) + assert query is None + + if query is None: + query = x + if unfold: + output = self._forward_unfolded(x, incremental_state, query) + else: + output = self._forward_expanded(x, incremental_state, query) + + if self.conv_bias is not None: + output = output + self.conv_bias.view(1, 1, -1) + + return output + + # during training time, use CUDA kernel + else: + weight = self.weight_linear(x).view(T, B, H, K) + if self.weight_softmax: + weight = F.softmax(weight, dim=-1) + if self.weight_dropout_module.p: + weight = self.weight_dropout_module(weight) + + weight = weight.permute(1, 2, 3, 0).contiguous() + self.filters = weight + x = x.permute(1, 2, 0).contiguous() + output = dynamicconvFunction.apply(x, weight, self.padding_l).permute( + 2, 0, 1 + ) + if self.conv_bias is not None: + output = output + self.conv_bias.view(1, 1, -1) + return output + + def reorder_incremental_state(self, incremental_state, new_order): + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + input_buffer = input_buffer.index_select(1, new_order) + self._set_input_buffer(incremental_state, input_buffer) + + def _get_input_buffer(self, incremental_state): + return utils.get_incremental_state(self, incremental_state, "input_buffer") + + def _set_input_buffer(self, incremental_state, new_buffer): + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) + + def _forward_unfolded(self, x, incremental_state, query): + """The conventional implementation of convolutions. + Unfolding the input by having a window shifting to the right.""" + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + assert R * H == C == self.input_size + + weight = self.weight_linear(query).view(T * B * H, -1) + + # renorm_padding is only implemented in _forward_expanded + assert not self.renorm_padding or incremental_state is not None + + if incremental_state is not None: + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is None: + input_buffer = x.new() + x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) + if self.kernel_size > 1: + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) + else: + padding_l = self.padding_l + if K > T and padding_l == K - 1: + weight = weight.narrow(1, K - T, T) + K, padding_l = T, T - 1 + # unfold the input: T x B x C --> T' x B x C x K + x_unfold = unfold1d(x, K, padding_l, 0) + x_unfold = x_unfold.view(T * B * H, R, K) + + if self.weight_softmax and not self.renorm_padding: + weight = F.softmax(weight, dim=1) + weight = weight.narrow(1, 0, K) + + if incremental_state is not None: + weight = weight[:, -x_unfold.size(2) :] + K = weight.size(1) + + if self.weight_softmax and self.renorm_padding: + weight = F.softmax(weight, dim=1) + + weight = self.weight_dropout_module(weight, inplace=False) + + output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1 + output = output.view(T, B, C) + return output + + def _forward_expanded(self, x, incremental_stat, query): + """Turn the convolution filters into band matrices and do matrix multiplication. + This is faster when the sequence is short, but less memory efficient. + This is not used in the decoder during inference. + """ + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + assert R * H == C == self.input_size + weight = self.weight_linear(query).view(T * B * H, -1) + + if not self.renorm_padding: + if self.weight_softmax: + weight = F.softmax(weight, dim=1) + weight = self.weight_dropout_module(weight, inplace=False) + weight = weight.narrow(1, 0, K).contiguous() + weight = weight.view(T, B * H, K).transpose(0, 1) + + x = x.view(T, B * H, R).transpose(0, 1) + if self.weight_softmax and self.renorm_padding: + # turn the convolution filters into band matrices + weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf")) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) + weight_expanded = weight_expanded.narrow(2, self.padding_l, T) + # normalize the weight over valid positions like self-attention + weight_expanded = F.softmax(weight_expanded, dim=2) + weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False) + else: + P = self.padding_l + # For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length + if K > T and P == K - 1: + weight = weight.narrow(2, K - T, T) + K, P = T, T - 1 + # turn the convolution filters into band matrices + weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) + weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T + output = torch.bmm(weight_expanded, x) + output = output.transpose(0, 1).contiguous().view(T, B, C) + return output diff --git a/fairseq/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp b/fairseq/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp new file mode 100644 index 0000000..d7e57c8 --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp @@ -0,0 +1,29 @@ +#include <torch/torch.h> +#include <vector> + +std::vector<float*> +dynamicconv_cpu_forward(float* input, float* filters, int padding_l); + +std::vector<float*> dynamicconv_cpu_backward( + float* gradOutput, + int padding_l, + float* input, + float* filters); + +std::vector<float*> +dynamicconv_forward(float* input, float* filters, int padding_l) { + return dynamicconv_cpu_forward(input, filters, padding_l); +} + +std::vector<float*> dynamicconv_backward( + float* gradOutput, + int padding_l, + float* input, + float* filters) { + return dynamicconv_cpu_backward(gradOutput, padding_l, input, filters); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &dynamicconv_forward, "dynamicconv forward (CPU)"); + m.def("backward", &dynamicconv_backward, "dynamicconv backward (CPU)"); +} diff --git a/fairseq/fairseq/modules/dynamicconv_layer/setup.py b/fairseq/fairseq/modules/dynamicconv_layer/setup.py new file mode 100644 index 0000000..6a21f7e --- /dev/null +++ b/fairseq/fairseq/modules/dynamicconv_layer/setup.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + + +setup( + name="dynamicconv_layer", + ext_modules=[ + CUDAExtension( + name="dynamicconv_cuda", + sources=[ + "dynamicconv_cuda.cpp", + "dynamicconv_cuda_kernel.cu", + ], + ), + ], + cmdclass={"build_ext": BuildExtension}, +) diff --git a/fairseq/fairseq/modules/ema_module.py b/fairseq/fairseq/modules/ema_module.py new file mode 100644 index 0000000..f0ece84 --- /dev/null +++ b/fairseq/fairseq/modules/ema_module.py @@ -0,0 +1,215 @@ +#!/usr/bin/env python3 + +""" +Used for EMA tracking a given pytorch module. The user is responsible for calling step() +and setting the appropriate decay +""" + +import copy +from dataclasses import dataclass, field +import logging + +import torch + +from omegaconf import II +from fairseq.dataclass import FairseqDataclass + +try: + from amp_C import multi_tensor_l2norm + + multi_tensor_l2norm_available = True +except ImportError: + multi_tensor_l2norm_available = False + +logger = logging.getLogger(__name__) + + +@dataclass +class EMAModuleConfig(FairseqDataclass): + ema_decay: float = field( + default=0.9999, metadata={"help": "decay for exponential moving average model"} + ) + ema_fp32: bool = field( + default=False, + metadata={"help": "If true, store EMA model in fp32 even if model is in fp16"}, + ) + add_missing_params: bool = True + log_norms: bool = False + + +class EMAModule: + """Exponential Moving Average of Fairseq Models""" + + def __init__( + self, + model, + config: EMAModuleConfig, + copy_model=True, + device=None, + skip_keys=None, + ): + """ + @param model model to initialize the EMA with + @param config EMAConfig object with configuration like + ema_decay, ema_update_freq, ema_fp32 + @param device If provided, copy EMA to this device (e.g. gpu). + Otherwise EMA is in the same device as the model. + """ + + self.config = config + + if copy_model: + self.model = copy.deepcopy(model) + self.model.requires_grad_(False) + else: + self.model = model + + self.config = config + self.decay = config.ema_decay + self.skip_keys = skip_keys or set() + self.add_missing_params = config.add_missing_params + self.fp32_params = {} + + if device is not None: + logging.info(f"Copying EMA model to device {device}") + self.model = self.model.to(device=device) + + if self.config.ema_fp32: + self.build_fp32_params() + + self.log_norms = config.log_norms and multi_tensor_l2norm_available + self.logs = {} + + def build_fp32_params(self, state_dict=None): + """ + Store a copy of the EMA params in fp32. + If state dict is passed, the EMA params is copied from + the provided state dict. Otherwise, it is copied from the + current EMA model parameters. + """ + if not self.config.ema_fp32: + raise RuntimeError( + "build_fp32_params should not be called if ema_fp32=False. " + "Use ema_fp32=True if this is really intended." + ) + + if state_dict is None: + state_dict = self.model.state_dict() + + def _to_float(t): + return t.float() if torch.is_floating_point(t) else t + + for param_key in state_dict: + if param_key in self.fp32_params: + if param_key == "__sq_mom": + self.fp32_params[param_key] = state_dict[param_key] + else: + self.fp32_params[param_key].copy_(state_dict[param_key]) + else: + self.fp32_params[param_key] = _to_float(state_dict[param_key]) + if "__sq_mom" in self.fp32_params: + self.fp32_params["__sq_mom"][param_key] = torch.zeros_like( + self.fp32_params[param_key] + ) + + def restore(self, state_dict, build_fp32_params=False): + """Load data from a model spec into EMA model""" + self.model.load_state_dict(state_dict, strict=False) + if build_fp32_params: + self.build_fp32_params(state_dict) + + def set_decay(self, decay, weight_decay=None): + self.decay = decay + if weight_decay is not None: + self.weight_decay = weight_decay + + def get_decay(self): + return self.decay + + def _step_internal(self, new_model): + """One update of the EMA model based on new model weights""" + decay = self.decay + + ema_state_dict = {} + ema_params = ( + self.fp32_params if self.config.ema_fp32 else self.model.state_dict() + ) + + new_p = [] + ema_p = [] + + for key, param in new_model.named_parameters(): + if isinstance(param, dict): + continue + + if not self.add_missing_params and key not in ema_params: + continue + + try: + ema_param = ema_params[key] + except KeyError: + ema_param = ( + param.float().clone() if param.ndim == 1 else copy.deepcopy(param) + ) + ema_params[key] = ema_param + + if param.shape != ema_param.shape: + raise ValueError( + "incompatible tensor shapes between model param and ema param" + + "{} vs. {}".format(param.shape, ema_param.shape) + ) + + if "version" in key: + # Do not decay a model.version pytorch param + continue + + lr = 1 - decay + + if key in self.skip_keys or not param.requires_grad: + ema_params[key].copy_(param.to(dtype=ema_param.dtype).data) + ema_param = ema_params[key] + else: + if self.log_norms: + new_p.append(param) + ema_p.append(ema_param) + + ema_param.mul_(1 - lr) + ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=lr) + + ema_state_dict[key] = ema_param + + for key, param in new_model.named_buffers(): + ema_state_dict[key] = param + + if self.log_norms: + if "model_norm" in self.logs: + self.prev_model_norm = self.logs["model_norm"] + + chunk_size = 2048 * 32 + has_inf = torch.zeros( + (1, 1), dtype=torch.int, device=next(new_model.parameters()).device + ) + + new_norm = multi_tensor_l2norm(chunk_size, has_inf, [new_p], False) + old_norm = multi_tensor_l2norm(chunk_size, has_inf, [ema_p], False) + + self.logs["model_norm"] = new_norm[0] + self.logs["ema_norm"] = old_norm[0] + + self.restore(ema_state_dict, build_fp32_params=False) + + @torch.no_grad() + def step(self, new_model): + self._step_internal(new_model) + + def reverse(self, model): + """ + Load the model parameters from EMA model. + Useful for inference or fine-tuning from the EMA model. + """ + d = self.model.state_dict() + if "_ema" in d: + del d["_ema"] + + model.load_state_dict(d, strict=False) + return model diff --git a/fairseq/fairseq/modules/espnet_multihead_attention.py b/fairseq/fairseq/modules/espnet_multihead_attention.py new file mode 100644 index 0000000..82bc0d7 --- /dev/null +++ b/fairseq/fairseq/modules/espnet_multihead_attention.py @@ -0,0 +1,256 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Multi-Head Attention layer definition.""" + +import math + +import torch +from torch import nn + +from fairseq.modules.rotary_positional_embedding import ( + RotaryPositionalEmbedding, + apply_rotary_pos_emb, +) + + +class ESPNETMultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + Args: + n_head: The number of heads. + n_feat: The number of features. + dropout: Dropout rate. + """ + + def __init__(self, n_feat, n_head, dropout): + """Construct an MultiHeadedAttention object.""" + super(ESPNETMultiHeadedAttention, self).__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.attn = None + self.dropout = nn.Dropout(p=dropout) + + def forward_qkv(self, query, key, value, **kwargs): + """Transform query, key and value. + Args: + query: Query tensor B X T1 X C + key: Key tensor B X T2 X C + value: Value tensor B X T2 X C + Returns: + torch.Tensor: Transformed query tensor B X n_head X T1 X d_k + torch.Tensor: Transformed key tensor B X n_head X T2 X d_k + torch.Tensor: Transformed value tensor B X n_head X T2 X d_k + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + return q, k, v + + def forward_attention(self, value, scores, mask): + """Compute attention context vector. + Args: + value: Transformed value B X n_head X T2 X d_k. + scores: Attention score B X n_head X T1 X T2 + mask: Mask T2 X B + Returns: + torch.Tensor: Transformed value B X T1 X d_model + weighted by the attention score B X T1 X T2 + """ + n_batch = value.size(0) + if mask is not None: + scores = scores.masked_fill( + mask.unsqueeze(1).unsqueeze(2).to(bool), + float("-inf"), # (batch, head, time1, time2) + ) + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, query, key, value, key_padding_mask=None, **kwargs): + """Compute scaled dot product attention. + Args: + query (torch.Tensor): Query tensor T X B X C + key (torch.Tensor): Key tensor T X B X C + value (torch.Tensor): Value tensor T X B X C + mask (torch.Tensor): Mask tensor T X B + Returns: + torch.Tensor: Output tensor T X B X D. + """ + query = query.transpose(0, 1) + key = key.transpose(0, 1) + value = value.transpose(0, 1) + + q, k, v = self.forward_qkv(query, key, value) + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + scores = self.forward_attention(v, scores, key_padding_mask) + scores = scores.transpose(0, 1) + return scores, None + + +class RelPositionMultiHeadedAttention(ESPNETMultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head: The number of heads. + n_feat: The number of features. + dropout: Dropout rate. + zero_triu: Whether to zero the upper triangular part of attention matrix. + """ + + def __init__(self, n_feat, n_head, dropout, zero_triu=False): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_feat, n_head, dropout) + self.zero_triu = zero_triu + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.zeros(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.zeros(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x): + """Compute relative positional encoding. + Args: + x: Input tensor B X n_head X T X 2T-1 + Returns: + torch.Tensor: Output tensor. + """ + zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + + if self.zero_triu: + ones = torch.ones((x.size(2), x.size(3)), device=x.device) + x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] + + return x + + def forward(self, query, key, value, pos_emb, key_padding_mask=None, **kwargs): + """Compute scaled dot product attention. + Args: + query: Query tensor T X B X C + key: Key tensor T X B X C + value: Value tensor T X B X C + pos_emb: Positional embedding tensor B X 2T-1 X C + key_padding_mask: Mask tensor T X B + Returns: + torch.Tensor: Output tensor T X B X C. + """ + query = query.transpose(0, 1) + key = key.transpose(0, 1) + value = value.transpose(0, 1) + pos_emb = pos_emb.transpose(0, 1) + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, 2*time1-1) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k + ) # (batch, head, time1, time2) + + scores = self.forward_attention(v, scores, key_padding_mask) + scores = scores.transpose(0, 1) + return scores, None + + +class RotaryPositionMultiHeadedAttention(ESPNETMultiHeadedAttention): + def __init__( + self, + n_feat, + n_head, + dropout, + precision, + rotary_emd_base=10000, + ): + """Construct an RotaryPositionMultiHeadedAttention object.""" + super().__init__(n_feat, n_head, dropout) + precision = torch.float + self.rotary_ndims = self.d_k # also try self.d_k//2 + if precision == "fp16": + precision = torch.half + + self.rotary_emb = RotaryPositionalEmbedding( + self.rotary_ndims, base=rotary_emd_base, precision=precision + ) + + def forward(self, query, key, value, key_padding_mask=None, **kwargs): + """Compute rotary position attention. + Args: + query: Query tensor T X B X C + key: Key tensor T X B X C + value: Value tensor T X B X C + key_padding_mask: Mask tensor T X B + Returns: + torch.Tensor: Output tensor T X B X D. + Notes: + Assumes self attn + """ + + T, B, C = value.size() + query = query.view(T, B, self.h, self.d_k) + key = key.view(T, B, self.h, self.d_k) + value = value.view(T, B, self.h, self.d_k) + cos, sin = self.rotary_emb(value, seq_len=T) + query, key = apply_rotary_pos_emb( + query, key, cos, sin, offset=0 + ) # offset is based on layer_past + + query = query.view(T, B, self.h * self.d_k) + key = key.view(T, B, self.h * self.d_k) + value = value.view(T, B, self.h * self.d_k) + + # TBD to BTD + query = query.transpose(0, 1) + key = key.transpose(0, 1) + value = value.transpose(0, 1) + + q, k, v = self.forward_qkv(query, key, value) + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + scores = self.forward_attention(v, scores, key_padding_mask) + scores = scores.transpose(0, 1) + return scores, None diff --git a/fairseq/fairseq/modules/fairseq_dropout.py b/fairseq/fairseq/modules/fairseq_dropout.py new file mode 100644 index 0000000..3cddca7 --- /dev/null +++ b/fairseq/fairseq/modules/fairseq_dropout.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import List, Optional + +import torch.nn as nn +import torch.nn.functional as F + + +logger = logging.getLogger(__name__) + + +class FairseqDropout(nn.Module): + def __init__(self, p, module_name=None): + super().__init__() + self.p = p + self.module_name = module_name + self.apply_during_inference = False + + def forward(self, x, inplace: bool = False): + if self.p > 0 and (self.training or self.apply_during_inference): + return F.dropout(x, p=self.p, training=True, inplace=inplace) + else: + return x + + def make_generation_fast_( + self, + name: str, + retain_dropout: bool = False, + retain_dropout_modules: Optional[List[str]] = None, + **kwargs + ): + if retain_dropout: + if retain_dropout_modules is not None and self.module_name is None: + logger.warning( + "Cannot enable dropout during inference for module {} " + "because module_name was not set".format(name) + ) + elif ( + retain_dropout_modules is None # if None, apply to all modules + or self.module_name in retain_dropout_modules + ): + logger.info( + "Enabling dropout during inference for module: {}".format(name) + ) + self.apply_during_inference = True + else: + logger.info("Disabling dropout for module: {}".format(name)) diff --git a/fairseq/fairseq/modules/fp32_batch_norm.py b/fairseq/fairseq/modules/fp32_batch_norm.py new file mode 100644 index 0000000..c560f33 --- /dev/null +++ b/fairseq/fairseq/modules/fp32_batch_norm.py @@ -0,0 +1,44 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +batch norm done in fp32 (for fp16 training) +""" +import torch +import torch.nn as nn + + +class Fp32BatchNorm(nn.Module): + def __init__(self, sync=False, *args, **kwargs): + super().__init__() + + if sync: + from fairseq.distributed import utils + + if utils.get_global_world_size() == 1: + sync = False + + if sync: + self.bn = nn.SyncBatchNorm(*args, **kwargs) + else: + self.bn = nn.BatchNorm1d(*args, **kwargs) + + self.sync = sync + + def forward(self, input): + if self.bn.running_mean.dtype != torch.float: + if self.sync: + self.bn.running_mean = self.bn.running_mean.float() + self.bn.running_var = self.bn.running_var.float() + if self.bn.affine: + try: + self.bn.weight = self.bn.weight.float() + self.bn.bias = self.bn.bias.float() + except: + self.bn.float() + else: + self.bn.float() + + output = self.bn(input.float()) + return output.type_as(input) diff --git a/fairseq/fairseq/modules/fp32_group_norm.py b/fairseq/fairseq/modules/fp32_group_norm.py new file mode 100644 index 0000000..d03aac0 --- /dev/null +++ b/fairseq/fairseq/modules/fp32_group_norm.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Layer norm done in fp32 (for fp16 training) +""" + +import torch.nn as nn +import torch.nn.functional as F + + +class Fp32GroupNorm(nn.GroupNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.group_norm( + input.float(), + self.num_groups, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) diff --git a/fairseq/fairseq/modules/fp32_instance_norm.py b/fairseq/fairseq/modules/fp32_instance_norm.py new file mode 100644 index 0000000..30a5449 --- /dev/null +++ b/fairseq/fairseq/modules/fp32_instance_norm.py @@ -0,0 +1,35 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Layer norm done in fp32 (for fp16 training) +""" + +import torch.nn as nn +import torch.nn.functional as F + + +class Fp32InstanceNorm(nn.InstanceNorm1d): + def __init__(self, *args, **kwargs): + self.transpose_last = "transpose_last" in kwargs and kwargs["transpose_last"] + if "transpose_last" in kwargs: + del kwargs["transpose_last"] + super().__init__(*args, **kwargs) + + def forward(self, input): + if self.transpose_last: + input = input.transpose(1, 2) + output = F.instance_norm( + input.float(), + running_mean=self.running_mean, + running_var=self.running_var, + weight=self.weight.float() if self.weight is not None else None, + bias=self.bias.float() if self.bias is not None else None, + use_input_stats=self.training or not self.track_running_stats, + momentum=self.momentum, + eps=self.eps, + ) + if self.transpose_last: + output = output.transpose(1, 2) + return output.type_as(input) diff --git a/fairseq/fairseq/modules/gelu.py b/fairseq/fairseq/modules/gelu.py new file mode 100644 index 0000000..a2f1ecf --- /dev/null +++ b/fairseq/fairseq/modules/gelu.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with +the corresponding GitHub repo: https://github.com/hendrycks/GELUs +""" + +import math + +import torch +import torch.nn as nn + + +def gelu_accurate(x): + if not hasattr(gelu_accurate, "_a"): + gelu_accurate._a = math.sqrt(2 / math.pi) + return ( + 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3)))) + ) + + +def gelu(x: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.gelu(x.float()).type_as(x) diff --git a/fairseq/fairseq/modules/grad_multiply.py b/fairseq/fairseq/modules/grad_multiply.py new file mode 100644 index 0000000..08d15f5 --- /dev/null +++ b/fairseq/fairseq/modules/grad_multiply.py @@ -0,0 +1,18 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + + +class GradMultiply(torch.autograd.Function): + @staticmethod + def forward(ctx, x, scale): + ctx.scale = scale + res = x.new(x) + return res + + @staticmethod + def backward(ctx, grad): + return grad * ctx.scale, None diff --git a/fairseq/fairseq/modules/gumbel_vector_quantizer.py b/fairseq/fairseq/modules/gumbel_vector_quantizer.py new file mode 100644 index 0000000..867b019 --- /dev/null +++ b/fairseq/fairseq/modules/gumbel_vector_quantizer.py @@ -0,0 +1,212 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class GumbelVectorQuantizer(nn.Module): + def __init__( + self, + dim, + num_vars, + temp, + groups, + combine_groups, + vq_dim, + time_first, + activation=nn.GELU(), + weight_proj_depth=1, + weight_proj_factor=1, + hard=True, + std=0, + ): + """Vector quantization using gumbel softmax + + Args: + dim: input dimension (channels) + num_vars: number of quantized vectors per group + temp: temperature for training. this should be a tuple of 3 elements: (start, stop, decay factor) + groups: number of groups for vector quantization + combine_groups: whether to use the vectors for all groups + vq_dim: dimensionality of the resulting quantized vector + time_first: if true, expect input in BxTxC format, otherwise in BxCxT + activation: what activation to use (should be a module). this is only used if weight_proj_depth is > 1 + weight_proj_depth: number of layers (with activation in between) to project input before computing logits + weight_proj_factor: this is used only if weight_proj_depth is > 1. scales the inner dimensionality of + projections by this factor + """ + super().__init__() + + self.groups = groups + self.combine_groups = combine_groups + self.input_dim = dim + self.num_vars = num_vars + self.time_first = time_first + self.hard = hard + + assert ( + vq_dim % groups == 0 + ), f"dim {vq_dim} must be divisible by groups {groups} for concatenation" + + var_dim = vq_dim // groups + num_groups = groups if not combine_groups else 1 + + self.vars = nn.Parameter(torch.FloatTensor(1, num_groups * num_vars, var_dim)) + if std == 0: + nn.init.uniform_(self.vars) + else: + nn.init.normal_(self.vars, mean=0, std=std) + + if weight_proj_depth > 1: + + def block(input_dim, output_dim): + return nn.Sequential(nn.Linear(input_dim, output_dim), activation) + + inner_dim = self.input_dim * weight_proj_factor + self.weight_proj = nn.Sequential( + *[ + block(self.input_dim if i == 0 else inner_dim, inner_dim) + for i in range(weight_proj_depth - 1) + ], + nn.Linear(inner_dim, groups * num_vars), + ) + else: + self.weight_proj = nn.Linear(self.input_dim, groups * num_vars) + nn.init.normal_(self.weight_proj.weight, mean=0, std=1) + nn.init.zeros_(self.weight_proj.bias) + + if isinstance(temp, str): + import ast + + temp = ast.literal_eval(temp) + assert len(temp) == 3, f"{temp}, {len(temp)}" + + self.max_temp, self.min_temp, self.temp_decay = temp + self.curr_temp = self.max_temp + self.codebook_indices = None + + def set_num_updates(self, num_updates): + self.curr_temp = max( + self.max_temp * self.temp_decay**num_updates, self.min_temp + ) + + def get_codebook_indices(self): + if self.codebook_indices is None: + from itertools import product + + p = [range(self.num_vars)] * self.groups + inds = list(product(*p)) + self.codebook_indices = torch.tensor( + inds, dtype=torch.long, device=self.vars.device + ).flatten() + + if not self.combine_groups: + self.codebook_indices = self.codebook_indices.view( + self.num_vars**self.groups, -1 + ) + for b in range(1, self.groups): + self.codebook_indices[:, b] += self.num_vars * b + self.codebook_indices = self.codebook_indices.flatten() + return self.codebook_indices + + def codebook(self): + indices = self.get_codebook_indices() + return ( + self.vars.squeeze(0) + .index_select(0, indices) + .view(self.num_vars**self.groups, -1) + ) + + def sample_from_codebook(self, b, n): + indices = self.get_codebook_indices() + indices = indices.view(-1, self.groups) + cb_size = indices.size(0) + assert ( + n < cb_size + ), f"sample size {n} is greater than size of codebook {cb_size}" + sample_idx = torch.randint(low=0, high=cb_size, size=(b * n,)) + indices = indices[sample_idx] + + z = self.vars.squeeze(0).index_select(0, indices.flatten()).view(b, n, -1) + return z + + def to_codebook_index(self, indices): + res = indices.new_full(indices.shape[:-1], 0) + for i in range(self.groups): + exponent = self.groups - i - 1 + res += indices[..., i] * (self.num_vars**exponent) + return res + + def forward_idx(self, x): + res = self.forward(x, produce_targets=True) + return res["x"], res["targets"] + + def forward(self, x, produce_targets=False): + + result = {"num_vars": self.num_vars * self.groups} + + if not self.time_first: + x = x.transpose(1, 2) + + bsz, tsz, fsz = x.shape + x = x.reshape(-1, fsz) + x = self.weight_proj(x) + x = x.view(bsz * tsz * self.groups, -1) + + with torch.no_grad(): + _, k = x.max(-1) + hard_x = ( + x.new_zeros(*x.shape) + .scatter_(-1, k.view(-1, 1), 1.0) + .view(bsz * tsz, self.groups, -1) + ) + hard_probs = torch.mean(hard_x.float(), dim=0) + result["code_perplexity"] = torch.exp( + -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) + ).sum() + + avg_probs = torch.softmax( + x.view(bsz * tsz, self.groups, -1).float(), dim=-1 + ).mean(dim=0) + result["prob_perplexity"] = torch.exp( + -torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1) + ).sum() + + result["temp"] = self.curr_temp + + if self.training: + x = F.gumbel_softmax(x.float(), tau=self.curr_temp, hard=self.hard).type_as( + x + ) + else: + x = hard_x + + x = x.view(bsz * tsz, -1) + + vars = self.vars + if self.combine_groups: + vars = vars.repeat(1, self.groups, 1) + + if produce_targets: + result["targets"] = ( + x.view(bsz * tsz * self.groups, -1) + .argmax(dim=-1) + .view(bsz, tsz, self.groups) + .detach() + ) + + x = x.unsqueeze(-1) * vars + x = x.view(bsz * tsz, self.groups, self.num_vars, -1) + x = x.sum(-2) + x = x.view(bsz, tsz, -1) + + if not self.time_first: + x = x.transpose(1, 2) # BTC -> BCT + + result["x"] = x + + return result diff --git a/fairseq/fairseq/modules/kmeans_attention.py b/fairseq/fairseq/modules/kmeans_attention.py new file mode 100644 index 0000000..0088d1e --- /dev/null +++ b/fairseq/fairseq/modules/kmeans_attention.py @@ -0,0 +1,744 @@ +import math +from functools import reduce, wraps +from inspect import isfunction +from operator import mul + +import torch +import torch.nn as nn +import torch.nn.functional as F +from aml.multimodal_video.utils.einops.lib import rearrange, repeat +from aml.multimodal_video.utils.einops.lib.layers.torch import Rearrange + +from fairseq.modules.local_attention import LocalAttention + +# constants + +TOKEN_SELF_ATTN_VALUE = -5e4 +KMEAN_INIT_ITERS = 10 + +# helper functions + + +def exists(val): + return val is not None + + +def identity(x, *args, **kwargs): + return x + + +def default(x, d): + if not exists(x): + return d if not isfunction(d) else d() + return x + + +def cast_tuple(x): + return x if isinstance(x, tuple) else (x,) + + +def cache_fn(f): + cache = None + + @wraps(f) + def cached_fn(*args, **kwargs): + nonlocal cache + if exists(cache): + return cache + cache = f(*args, **kwargs) + return cache + + return cached_fn + + +def to(t): + return {"device": t.device, "dtype": t.dtype} + + +def find_modules(nn_module, type): + return [module for module in nn_module.modules() if isinstance(module, type)] + + +def is_empty(t): + return t.nelement() == 0 + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +def batched_index_select(values, indices): + last_dim = values.shape[-1] + return values.gather(2, expand_dim(indices, -1, last_dim)) + + +def merge_dims(ind_from, ind_to, tensor): + shape = list(tensor.shape) + arr_slice = slice(ind_from, ind_to + 1) + shape[arr_slice] = [reduce(mul, shape[arr_slice])] + return tensor.reshape(*shape) + + +def expand_dim(t, dim, k): + t = t.unsqueeze(dim) + expand_shape = [-1] * len(t.shape) + expand_shape[dim] = k + return t.expand(*expand_shape) + + +def scatter_mean(src, t, index, dim, eps=1e-5): + numer = src.scatter_add(dim, index, t) + denom = src.scatter_add(dim, index, torch.ones_like(t)) + return numer / (denom + eps) + + +def split_at_index(dim, index, t): + pre_slices = (slice(None),) * dim + l = (*pre_slices, slice(None, index)) + r = (*pre_slices, slice(index, None)) + return t[l], t[r] + + +def reshape_dim(t, dim, split_dims): + shape = list(t.shape) + num_dims = len(shape) + dim = (dim + num_dims) % num_dims + shape[dim : dim + 1] = split_dims + return t.reshape(shape) + + +def ema(old, new, decay): + if not exists(old): + return new + return old * decay + new * (1 - decay) + + +def ema_inplace(moving_avg, new, decay): + if is_empty(moving_avg): + moving_avg.data.copy_(new) + return + moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) + + +# helper classes + + +def map_first_tuple_or_el(x, fn): + if isinstance(x, tuple): + return (fn(x[0]),) + x[1:] + return fn(x) + + +class Chunk(nn.Module): + def __init__(self, chunks, fn, along_dim=-1): + super().__init__() + self.dim = along_dim + self.chunks = chunks + self.fn = fn + + def forward(self, x, **kwargs): + if self.chunks <= 1: + return self.fn(x, **kwargs) + chunks = x.chunk(self.chunks, dim=self.dim) + return torch.cat([self.fn(c, **kwargs) for c in chunks], dim=self.dim) + + +class PreNorm(nn.ModuleList): + def __init__(self, norm_class, dim, fn): + super().__init__() + self.norm = norm_class(dim) + self.fn = fn + + def forward(self, x, **kwargs): + x = self.norm(x) + return self.fn(x, **kwargs) + + +class ReZero(nn.Module): + def __init__(self, fn): + super().__init__() + self.residual_weight = nn.Parameter(torch.zeros(1)) + self.fn = fn + + def forward(self, x, **kwargs): + x = self.fn(x, **kwargs) + return map_first_tuple_or_el(x, lambda t: t * self.residual_weight) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.g = nn.Parameter(torch.ones(1)) + self.eps = eps + + def forward(self, x): + def norm(t): + n = torch.norm(t, dim=-1, keepdim=True).clamp(min=self.eps) + return t / n * self.g + + return map_first_tuple_or_el(x, norm) + + +class ProjectInOut(nn.Module): + def __init__(self, fn, dim_in, dim_out, project_out=True): + super().__init__() + self.fn = fn + self.project_in = nn.Linear(dim_in, dim_out) + self.project_out = nn.Linear(dim_out, dim_in) if project_out else identity + + def forward(self, x, **kwargs): + x = self.project_in(x) + x, loss = self.fn(x, **kwargs) + x = self.project_out(x) + return x, loss + + +class MatrixMultiply(nn.Module): + def __init__(self, tensor, transpose=False): + super().__init__() + self.tensor = tensor + self.transpose = transpose + + def forward(self, x): + tensor = self.tensor + if self.transpose: + tensor = tensor.t() + return x @ tensor + + +# positional embeddings + + +class DepthWiseConv1d(nn.Module): + def __init__(self, dim_in, dim_out, kernel_size, stride=1, bias=True, causal=False): + super().__init__() + self.padding = ( + ((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2) + ) + + self.net = nn.Sequential( + nn.Conv1d( + dim_in, + dim_in, + kernel_size=kernel_size, + groups=dim_in, + stride=stride, + bias=bias, + ), + nn.Conv1d(dim_in, dim_out, 1, bias=bias), + ) + + def forward(self, x): + x = F.pad(x, self.padding, value=0.0) + return self.net(x) + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + position = torch.arange(0, max_seq_len, dtype=torch.float) + sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + self.register_buffer("emb", emb) + + def forward(self, x): + return self.emb[None, : x.shape[1], :].to(x) + + +def rotate_every_two(x): + x = rearrange(x, "... (d j) -> ... d j", j=2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return rearrange(x, "... d j -> ... (d j)") + + +def apply_rotary_pos_emb(q, k, sinu_pos): + sinu_pos = rearrange(sinu_pos, "() n (j d) -> n j d", j=2) + sin, cos = sinu_pos.unbind(dim=-2) + sin, cos = map(lambda t: repeat(t, "b n -> b (n j)", j=2), (sin, cos)) + q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) + return q, k + + +# kmeans related function and class + + +def update_kmeans_on_backwards(module): + module.kmean_modules = find_modules(module, Kmeans) + + def hook(_, grad_in, grad_out): + for m in module.kmean_modules: + m.update() + + return module.register_backward_hook(hook) + + +def similarity(x, means): + return torch.einsum("bhld,hcd->bhlc", x, means) + + +def dists_and_buckets(x, means): + dists = similarity(x, means) + _, buckets = torch.max(dists, dim=-1) + return dists, buckets + + +def batched_bincount(index, num_classes, dim=-1): + shape = list(index.shape) + shape[dim] = num_classes + out = index.new_zeros(shape) + out.scatter_add_(dim, index, torch.ones_like(index, dtype=index.dtype)) + return out + + +def kmeans_iter(x, means, buckets=None): + b, h, _, d, dtype, num_clusters = *x.shape, x.dtype, means.shape[1] + + if not exists(buckets): + _, buckets = dists_and_buckets(x, means) + + bins = batched_bincount(buckets, num_clusters).sum(0, keepdim=True) + zero_mask = bins.long() == 0 + + means_ = buckets.new_zeros(b, h, num_clusters, d, dtype=dtype) + means_.scatter_add_(-2, expand_dim(buckets, -1, d), x) + means_ = F.normalize(means_.sum(0, keepdim=True), dim=-1).type(dtype) + + means = torch.where(zero_mask.unsqueeze(-1), means, means_) + means = means.squeeze(0) + return means + + +def distribution(dists, window_size): + _, topk_indices = dists.topk(k=window_size, dim=-2) + indices = topk_indices.transpose(-2, -1) + return indices.reshape(*indices.size()[:2], -1) + + +class Kmeans(nn.Module): + def __init__( + self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4 + ): + super().__init__() + self.commitment = commitment + self.ema_decay = ema_decay + + self.register_buffer("means", torch.randn(num_heads, num_clusters, head_dim)) + self.register_buffer("initted", torch.tensor(False)) + self.num_new_means = 0 + self.new_means = None + + @torch.no_grad() + def init(self, x): + if self.initted: + return + _, h, _, d, device, _ = *x.shape, x.device, x.dtype + + num_clusters = self.means.shape[1] + + means = x.transpose(0, 1).contiguous().view(h, -1, d) + num_samples = means.shape[1] + + if num_samples >= num_clusters: + indices = torch.randperm(num_samples, device=device)[:num_clusters] + else: + indices = torch.randint(0, num_samples, (num_clusters,), device=device) + + means = means[:, indices] + + for _ in range(KMEAN_INIT_ITERS): + means = kmeans_iter(x, means) + + self.num_new_means = 0 + self.means.data.copy_(means) + self.initted.data.copy_(torch.tensor(True)) + + @torch.no_grad() + def update(self, new_means=None): + new_means = default(new_means, self.new_means) + assert exists(new_means), "new kmeans has not been supplied" + ema_inplace(self.means, new_means, self.ema_decay) + + del self.new_means + self.new_means = None + self.num_new_means = 0 + + def forward(self, x, update_means=False): + self.init(x) + + b, dtype = x.shape[0], x.dtype + means = self.means.type(dtype) + x = F.normalize(x, 2, dim=-1).type(dtype) + + with torch.no_grad(): + dists, buckets = dists_and_buckets(x, means) + + routed_means = batched_index_select(expand_dim(means, 0, b), buckets) + loss = F.mse_loss(x, routed_means) * self.commitment + + if update_means: + with torch.no_grad(): + means = kmeans_iter(x, means, buckets) + self.new_means = ema( + self.new_means, means, self.num_new_means / (self.num_new_means + 1) + ) + self.num_new_means += 1 + + return dists, loss + + +# kmeans attention class + + +class KmeansAttention(nn.Module): + def __init__( + self, + num_clusters, + window_size, + num_heads, + head_dim, + causal=False, + dropout=0.0, + ema_decay=0.999, + commitment=1e-4, + context_window_size=None, + receives_context=False, + num_mem_kv=0, + shared_qk=False, + ): + super().__init__() + self.num_heads = num_heads + self.num_clusters = num_clusters + self.head_dim = head_dim + + self.window_size = window_size + self.context_window_size = default(context_window_size, window_size) + self.causal = causal + + self.shared_qk = shared_qk + self.receives_context = receives_context + self.kmeans = Kmeans(num_heads, head_dim, num_clusters, ema_decay, commitment) + self.dropout = nn.Dropout(dropout) + + self.num_mem_kv = max(num_mem_kv, 1 if causal and not shared_qk else 0) + self.mem_key = nn.Parameter( + torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim) + ) + self.mem_value = nn.Parameter( + torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim) + ) + + def forward(self, q, k, v, query_mask=None, key_mask=None, **kwargs): + b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = ( + *q.shape, + k.shape[2], + self.window_size, + self.context_window_size, + self.num_clusters, + q.device, + q.dtype, + ) + is_reverse = kwargs.pop("_reverse", False) + + out = torch.zeros_like(q, dtype=dtype) + + update_kmeans = self.training and not is_reverse + + key_mask = ( + default(key_mask, query_mask) if not self.receives_context else key_mask + ) + kv_wsz = wsz if not self.receives_context else c_wsz + + wsz = min(wsz, t) + kv_wsz = min(kv_wsz, kv_t) + + if not self.shared_qk or self.receives_context: + dists, aux_loss = self.kmeans(torch.cat((q, k), dim=2), update_kmeans) + q_dists, k_dists = split_at_index(2, t, dists) + indices = distribution(q_dists, wsz) + kv_indices = distribution(k_dists, kv_wsz) + else: + dists, aux_loss = self.kmeans(q, update_kmeans) + k = F.normalize(k, dim=-1).to(q) + indices = distribution(dists, wsz) + kv_indices = indices + + q = batched_index_select(q, indices) + k = batched_index_select(k, kv_indices) + v = batched_index_select(v, kv_indices) + + reshape_with_window = lambda x: x.reshape(b, h, nc, -1, d) + q, k, v = map(reshape_with_window, (q, k, v)) + + m_k, m_v = map( + lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value) + ) + k, v = map(lambda x: torch.cat(x, dim=3), ((m_k, k), (m_v, v))) + + dots = torch.einsum("bhnid,bhnjd->bhnij", q, k) * (d**-0.5) + + mask_value = max_neg_value(dots) + + if exists(query_mask) or exists(key_mask): + query_mask = default( + query_mask, lambda: torch.ones((b, t), device=device).bool() + ) + key_mask = default( + key_mask, lambda: torch.ones((b, kv_t), device=device).bool() + ) + + q_mask = expand_dim(query_mask, 1, h).gather(2, indices) + kv_mask = expand_dim(key_mask, 1, h).gather(2, kv_indices) + q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (q_mask, kv_mask)) + mask = q_mask[:, :, :, :, None] * kv_mask[:, :, :, None, :] + mask = F.pad(mask, (self.num_mem_kv, 0), value=1) + dots.masked_fill_(~mask, mask_value) + del mask + + if self.causal: + q_mask, kv_mask = map( + lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices) + ) + mask = q_mask[:, :, :, :, None] >= kv_mask[:, :, :, None, :] + mask = F.pad(mask, (self.num_mem_kv, 0), value=1) + dots.masked_fill_(~mask, mask_value) + del mask + + if self.shared_qk: + q_mask, kv_mask = map( + lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices) + ) + mask = q_mask[:, :, :, :, None] == kv_mask[:, :, :, None, :] + mask = F.pad(mask, (self.num_mem_kv, 0), value=0) + dots.masked_fill_(mask, TOKEN_SELF_ATTN_VALUE) + del mask + + dots = dots.softmax(dim=-1) + dots = self.dropout(dots) + + bo = torch.einsum("bhcij,bhcjd->bhcid", dots, v) + so = torch.reshape(bo, (b, h, -1, bo.shape[-1])).type(dtype) + out = scatter_mean(out, so, indices.unsqueeze(-1).expand_as(so), -2) + return out, aux_loss + + +# feedforward + + +class GELU_(nn.Module): + def forward(self, x): + return ( + 0.5 + * x + * ( + 1 + + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))) + ) + ) + + +GELU = nn.GELU if hasattr(nn, "GELU") else GELU_ + + +class FeedForward(nn.Module): + def __init__(self, dim, mult=4, dropout=0.0, activation=None, glu=False): + super().__init__() + activation = default(activation, GELU) + + self.glu = glu + self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1)) + self.act = activation() + self.dropout = nn.Dropout(dropout) + self.w2 = nn.Linear(dim * mult, dim) + + def forward(self, x, **kwargs): + if not self.glu: + x = self.w1(x) + x = self.act(x) + else: + x, v = self.w1(x).chunk(2, dim=-1) + x = self.act(x) * v + + x = self.dropout(x) + x = self.w2(x) + return x + + +# self attention + + +class SelfAttention(nn.Module): + def __init__( + self, + dim, + max_seq_len, + heads, + local_attn_heads, + window_size, + dim_head=None, + local_attn_window_size=None, + local_attn_radius_blocks=1, + causal=False, + attn_dropout=0.0, + dropout=0.0, + kmeans_ema_decay=0.999, + commitment_factor=1e-4, + receives_context=False, + context_window_size=None, + rel_pos_emb=True, + num_mem_kv=0, + shared_qk=False, + conv_query_kernel=9, + ): + super().__init__() + assert ( + dim_head or (dim % heads) == 0 + ), "hidden dimension must be divisible by number of heads" + assert ( + max_seq_len % window_size + ) == 0, "maximum sequence length must be divisible by the target window size" + assert ( + local_attn_heads <= heads + ), "number of local attention heads must be less than total heads" + assert not ( + receives_context and local_attn_heads > 0 + ), "local attention cannot be used for self attention with context" + assert not ( + receives_context and causal + ), "contextual attention layer cannot be causal" + + local_attn_window_size = default(local_attn_window_size, window_size) + context_window_size = default(context_window_size, window_size) + + self.shared_qk = shared_qk + self.receives_context = receives_context + self.heads = heads + self.local_attn_heads = local_attn_heads + self.global_attn_heads = heads - local_attn_heads + + self.causal = causal + self.window_size = window_size + + dim_head = default(dim_head, dim // heads) + dim_heads = dim_head * heads + self.dim_head = dim_head + + num_clusters = max_seq_len // window_size + + # local + + local_dim_heads = dim_head * self.local_attn_heads + + if self.local_attn_heads > 0: + rel_pos_emb_config = (dim_head, local_attn_heads) if rel_pos_emb else None + self.local_attn = LocalAttention( + dim=dim_head, + window_size=local_attn_window_size, + causal=causal, + dropout=attn_dropout, + rel_pos_emb_config=rel_pos_emb_config, + look_backward=local_attn_radius_blocks, + look_forward=0 if causal else local_attn_radius_blocks, + ) + self.local_to_qkv = nn.Linear(dim, 3 * local_dim_heads) + + # global + + global_dim_heads = dim_head * self.global_attn_heads + + if self.global_attn_heads > 0: + self.global_attn = KmeansAttention( + num_clusters, + window_size, + self.global_attn_heads, + dim_head, + causal=causal, + dropout=attn_dropout, + ema_decay=kmeans_ema_decay, + commitment=commitment_factor, + receives_context=receives_context, + num_mem_kv=num_mem_kv, + shared_qk=shared_qk, + ) + + self.to_q = nn.Sequential( + Rearrange("b n c -> b c n"), + DepthWiseConv1d(dim, global_dim_heads, conv_query_kernel, causal=causal), + Rearrange("b c n -> b n c"), + ) + + self.to_v = nn.Linear(dim, global_dim_heads, bias=False) + + if not self.shared_qk: + self.to_k = nn.Linear(dim, global_dim_heads, bias=False) + + # out + + self.to_out = nn.Linear(dim_heads, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward( + self, + query, + key, + value, + context=None, + key_padding_mask=None, + context_mask=None, + pos_emb=None, + **kwargs + ): + assert not ( + self.receives_context and not exists(context) + ), "context must be passed if self attention is set to receive context" + input_mask = key_padding_mask + x = query.transpose(0, 1) + b, t, _, h, dh = *x.shape, self.heads, self.dim_head + has_local, has_global = map( + lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads) + ) + + split_heads = ( + lambda v: reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous() + ) + + if has_local: + local_qkv = self.local_to_qkv(x).chunk(3, dim=-1) + lq, lk, lv = map(split_heads, local_qkv) + + if has_global: + kv_input = x if not self.receives_context else context + + q, v = self.to_q(x), self.to_v(kv_input) + + if not self.shared_qk: + k = self.to_k(kv_input) + else: + k = self.to_q(kv_input) if self.receives_context else q + + q, k, v = map(split_heads, (q, k, v)) + + out = [] + total_loss = torch.tensor(0.0, requires_grad=True, **to(x)) + + if has_local: + local_out = self.local_attn(lq, lk, lv, input_mask=input_mask) + out.append(local_out) + + if has_global: + if not self.receives_context and exists(pos_emb): + q, k = apply_rotary_pos_emb(q, k, pos_emb) + + global_out, loss = self.global_attn( + q, k, v, query_mask=input_mask, key_mask=context_mask + ) + total_loss = total_loss + loss + + out.append(global_out) + + out = torch.cat(out, dim=1) + out = out.reshape(b, h, t, -1).transpose(1, 2).reshape(b, t, -1) + out = self.dropout(out.transpose(0, 1)) + # out = self.to_out(out) + return out, total_loss diff --git a/fairseq/fairseq/modules/kmeans_vector_quantizer.py b/fairseq/fairseq/modules/kmeans_vector_quantizer.py new file mode 100644 index 0000000..1015c38 --- /dev/null +++ b/fairseq/fairseq/modules/kmeans_vector_quantizer.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from fairseq.modules import Fp32GroupNorm + + +class KmeansVectorQuantizer(nn.Module): + def __init__( + self, dim, num_vars, groups, combine_groups, vq_dim, time_first, gamma=0.25 + ): + """Vector quantization using straight pass-through estimator (i.e. kmeans) + + Args: + dim: input dimension (channels) + num_vars: number of quantized vectors per group + groups: number of groups for vector quantization + combine_groups: whether to use the vectors for all groups + vq_dim: dimensionality of the resulting quantized vector + time_first: if true, expect input in BxTxC format, otherwise in BxCxT + gamma: commitment loss coefficient + """ + super().__init__() + + self.groups = groups + self.combine_groups = combine_groups + self.input_dim = dim + self.num_vars = num_vars + self.vq_dim = vq_dim + self.time_first = time_first + + assert ( + vq_dim % groups == 0 + ), f"dim {vq_dim} must be divisible by groups {groups} for concatenation" + + self.var_dim = vq_dim // groups + num_groups = groups if not combine_groups else 1 + + self.embedding = nn.Parameter( + 0.01 * torch.randn(num_vars, num_groups, self.var_dim) + ) + self.projection = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size=1, groups=groups, bias=False), + Fp32GroupNorm(groups, dim), + ) + self.gamma = gamma + self.mse_mean = nn.MSELoss(reduction="mean") + + def _pass_grad(self, x, y): + """Manually set gradient for backward pass. + for y = f(x), ensure that during the backward pass, + dL/dy = dL/dx regardless of f(x). + Returns: + y, with the gradient forced to be dL/dy = dL/dx. + """ + + return y.detach() + (x - x.detach()) + + @property + def expand_embedding(self): + if self.combine_groups: + return self.embedding.expand(self.num_vars, self.groups, self.var_dim) + return self.embedding + + def forward_idx(self, x): + res = self.forward(x, produce_targets=True) + return res["x"], res["targets"] + + def forward(self, x, produce_targets=False): + + result = {"num_vars": self.num_vars} + + if self.time_first: + x = x.transpose(1, 2) + + bsz, fsz, tsz = x.shape + + ze = self.projection(x) + ze_ = ze.view(bsz, self.groups, self.var_dim, tsz).permute(0, 3, 1, 2) + d = ( + (ze_.unsqueeze(0) - self.expand_embedding.unsqueeze(1).unsqueeze(1)) + .view(self.num_vars, bsz, tsz, self.groups, -1) + .norm(dim=-1, p=2) + ) + idx = d.argmin(dim=0) + zq = ( + torch.stack( + [ + self.expand_embedding[idx[..., group], group] + for group in range(self.groups) + ], + dim=-2, + ) + .view(bsz, tsz, self.groups * self.var_dim) + .permute(0, 2, 1) + ) + assert ze.shape == zq.shape, (ze.shape, zq.shape) + x = self._pass_grad(ze, zq) + + with torch.no_grad(): + hard_x = ( + idx.new_zeros(bsz * tsz * self.groups, self.num_vars) + .scatter_(-1, idx.view(-1, 1), 1.0) + .view(bsz * tsz, self.groups, -1) + ) + hard_probs = torch.mean(hard_x.float(), dim=0) + result["code_perplexity"] = torch.exp( + -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) + ).sum() + + if produce_targets: + result["targets"] = idx + + if self.time_first: + x = x.transpose(1, 2) # BCT -> BTC + result["x"] = x + + ze = ze.float() + zq = zq.float() + latent_loss = self.mse_mean(zq, ze.detach()) + commitment_loss = self.mse_mean(ze, zq.detach()) + + result["kmeans_loss"] = latent_loss + self.gamma * commitment_loss + + return result diff --git a/fairseq/fairseq/modules/layer_drop.py b/fairseq/fairseq/modules/layer_drop.py new file mode 100644 index 0000000..8961d8b --- /dev/null +++ b/fairseq/fairseq/modules/layer_drop.py @@ -0,0 +1,44 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +LayerDrop as described in https://arxiv.org/abs/1909.11556. +""" + +import torch +import torch.nn as nn + + +class LayerDropModuleList(nn.ModuleList): + """ + A LayerDrop implementation based on :class:`torch.nn.ModuleList`. + + We refresh the choice of which layers to drop every time we iterate + over the LayerDropModuleList instance. During evaluation we always + iterate over all layers. + + Usage:: + + layers = LayerDropList(p=0.5, modules=[layer1, layer2, layer3]) + for layer in layers: # this might iterate over layers 1 and 3 + x = layer(x) + for layer in layers: # this might iterate over all layers + x = layer(x) + for layer in layers: # this might not iterate over any layers + x = layer(x) + + Args: + p (float): probability of dropping out each layer + modules (iterable, optional): an iterable of modules to add + """ + + def __init__(self, p, modules=None): + super().__init__(modules) + self.p = p + + def __iter__(self): + dropout_probs = torch.empty(len(self)).uniform_() + for i, m in enumerate(super().__iter__()): + if not self.training or (dropout_probs[i] > self.p): + yield m diff --git a/fairseq/fairseq/modules/layer_norm.py b/fairseq/fairseq/modules/layer_norm.py new file mode 100644 index 0000000..0b276ce --- /dev/null +++ b/fairseq/fairseq/modules/layer_norm.py @@ -0,0 +1,48 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +try: + from apex.normalization import FusedLayerNorm as _FusedLayerNorm + + has_fused_layernorm = True + + class FusedLayerNorm(_FusedLayerNorm): + @torch.jit.unused + def forward(self, x): + if not x.is_cuda: + return super().forward(x) + else: + with torch.cuda.device(x.device): + return super().forward(x) + +except ImportError: + has_fused_layernorm = False + + +def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): + if torch.jit.is_scripting() or torch.jit.is_tracing(): + export = True + if not export and torch.cuda.is_available() and has_fused_layernorm: + return FusedLayerNorm(normalized_shape, eps, elementwise_affine) + return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) + + +class Fp32LayerNorm(nn.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.layer_norm( + input.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) diff --git a/fairseq/fairseq/modules/learned_positional_embedding.py b/fairseq/fairseq/modules/learned_positional_embedding.py new file mode 100644 index 0000000..378d0f7 --- /dev/null +++ b/fairseq/fairseq/modules/learned_positional_embedding.py @@ -0,0 +1,61 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from torch import Tensor + + +class LearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + Padding ids are ignored by either offsetting based on padding_idx + or by setting padding_idx to None and ensuring that the appropriate + position ids are passed to the forward function. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.onnx_trace = False + if self.padding_idx is not None: + self.max_positions = self.num_embeddings - self.padding_idx - 1 + else: + self.max_positions = self.num_embeddings + + def forward( + self, + input: Tensor, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + positions: Optional[Tensor] = None, + ): + """Input is expected to be of size [bsz x seqlen].""" + assert (positions is None) or ( + self.padding_idx is None + ), "If positions is pre-computed then padding_idx should not be set." + + if positions is None: + if incremental_state is not None: + # positions is the same for every token when decoding a single step + # Without the int() cast, it doesn't work in some cases when exporting to ONNX + positions = torch.zeros( + (1, 1), device=input.device, dtype=input.dtype + ).fill_(int(self.padding_idx + input.size(1))) + else: + positions = utils.make_positions( + input, self.padding_idx, onnx_trace=self.onnx_trace + ) + return F.embedding( + positions, + self.weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) diff --git a/fairseq/fairseq/modules/lightconv_layer/__init__.py b/fairseq/fairseq/modules/lightconv_layer/__init__.py new file mode 100644 index 0000000..3b2a99c --- /dev/null +++ b/fairseq/fairseq/modules/lightconv_layer/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .lightconv_layer import LightconvLayer # noqa diff --git a/fairseq/fairseq/modules/lightconv_layer/cuda_function_gen.py b/fairseq/fairseq/modules/lightconv_layer/cuda_function_gen.py new file mode 100644 index 0000000..a25433d --- /dev/null +++ b/fairseq/fairseq/modules/lightconv_layer/cuda_function_gen.py @@ -0,0 +1,289 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +def gen_forward(): + + kernels = [3, 5, 7, 15, 31, 63, 127, 255] + seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]] + + head = """ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "lightconv_cuda.cuh" + +std::vector<at::Tensor> lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l) { + + at::DeviceGuard g(input.device()); + const auto minibatch = input.size(0); + const auto numFeatures = input.size(1); + const auto sequenceLength = input.size(2); + + const auto numHeads = filters.size(0); + const auto filterSize = filters.size(1); + + const auto numFiltersInBlock = numFeatures / numHeads; + + const dim3 blocks(minibatch, numFeatures); + + auto output = at::zeros_like(input); + auto stream = at::cuda::getCurrentCUDAStream(); +""" + + sequence_if = """ + if (sequenceLength <= {seq}) {{ + switch(filterSize) {{ +""" + + case_k = """ + case {k}: +""" + + main_block = """ + if (padding_l == {pad}) {{ + AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_forward", ([&] {{ + lightconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t> + <<<blocks, {b_size}, 0, stream>>>( + input.data<scalar_t>(), + filters.data<scalar_t>(), + minibatch, + sequenceLength, + numFeatures, + numFiltersInBlock, + output.data<scalar_t>()); + }})); + }} else +""" + + bad_padding = """ + { + std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl; + } + break; +""" + + bad_filter = """ + default: + std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl; + } +""" + + con_else = """ + } else +""" + + final_else = """ + { + switch(filterSize) { +""" + + final_return = """ + } + + return {output}; +} +""" + + with open("lightconv_cuda_forward.cu", "w") as forward: + forward.write(head) + for seq in seqs: + forward.write(sequence_if.format(seq=seq)) + for k in kernels: + forward.write(case_k.format(k=k)) + for pad in [k // 2, k - 1]: + forward.write(main_block.format(k=k, b_size=seq, pad=pad)) + forward.write(bad_padding) + forward.write(bad_filter) + forward.write(con_else) + + forward.write(final_else) + for k in kernels: + forward.write(case_k.format(k=k)) + for pad in [k // 2, k - 1]: + forward.write(main_block.format(k=k, b_size=seq, pad=pad)) + forward.write(bad_padding) + forward.write(bad_filter) + forward.write(final_return) + + +def gen_backward(): + + head = """ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "lightconv_cuda.cuh" + +std::vector<at::Tensor> lightconv_cuda_backward( + at::Tensor gradOutput, + int padding_l, + at::Tensor input, + at::Tensor filters) { + + // gradWrtInput + const int minibatch = input.size(0); + const int numFeatures = input.size(1); + const int sequenceLength = input.size(2); + + const int numHeads = filters.size(0); + const int filterSize = filters.size(1); + + const dim3 gradBlocks(minibatch, numFeatures); + const dim3 weightGradFirstpassShortBlocks(minibatch, numHeads); + const dim3 weightGradSecondpassBlocks(numHeads, filterSize); + + const int numFiltersInBlock = numFeatures / numHeads; + + auto gradInput = at::zeros_like(input); + auto gradFilters = at::zeros_like(filters); + + at::DeviceGuard g(input.device()); + auto stream = at::cuda::getCurrentCUDAStream(); + + switch(filterSize) { +""" + + sequence_if = """ + if (sequenceLength <= {seq}) {{ +""" + + case_k = """ + case {k}: +""" + + main_block = """ + if (padding_l == {p}) {{ + AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_backward", ([&] {{ + lightconv_grad_wrt_input_kernel<{k}, {b_size}, {p}, scalar_t> + <<<gradBlocks, {b_size}, 0, stream>>>( + gradOutput.data<scalar_t>(), + filters.data<scalar_t>(), + minibatch, + sequenceLength, + numFeatures, + numFiltersInBlock, + gradInput.data<scalar_t>()); + +""" + + weight_grad_short = """ + at::Tensor tempSumGradFilters = at::zeros({{minibatch, numHeads, filterSize}}, input.options().dtype(at::kFloat)); + lightconv_grad_wrt_weights_firstpass_short_kernel<{k}, {b_size}, {p}, scalar_t> + <<<weightGradFirstpassShortBlocks, {b_size}, 0, stream>>>( + input.data<scalar_t>(), + gradOutput.data<scalar_t>(), + minibatch, + sequenceLength, + numFeatures, + numFiltersInBlock, + numHeads, + tempSumGradFilters.data<float>() + ); + + lightconv_grad_wrt_weights_secondpass_short_kernel<{k}, {b_size}, scalar_t> + <<<weightGradSecondpassBlocks, {b_size}, 0, stream>>>( + tempSumGradFilters.data<float>(), + minibatch, + numFiltersInBlock, + gradFilters.data<scalar_t>() + ); + }})); + }} else +""" + + weight_grad = """ + at::Tensor tempSumGradFilters = at::zeros({{minibatch, numFeatures, filterSize}}, input.options().dtype(at::kFloat)); + lightconv_grad_wrt_weights_firstpass_kernel<{k}, {b_size}, {p}, scalar_t> + <<<gradBlocks, {b_size}, 0, stream>>>( + input.data<scalar_t>(), + gradOutput.data<scalar_t>(), + minibatch, + sequenceLength, + numFeatures, + numFiltersInBlock, + tempSumGradFilters.data<float>() + ); + + lightconv_grad_wrt_weights_secondpass_kernel<{k}, {b_size}, scalar_t> + <<<weightGradSecondpassBlocks, {b_size}, 0, stream>>>( + tempSumGradFilters.data<float>(), + minibatch, + numFiltersInBlock, + gradFilters.data<scalar_t>() + ); + }})); + }} else +""" + + bad_padding = """ + { + std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl; + } +""" + + breakout = """ + break; +""" + + bad_filter = """ + default: + std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl; +""" + + con_else = """ + } else +""" + + final_else = """ + { + switch(filterSize) { +""" + + last_return = """ + } + return {gradInput, gradFilters}; +} +""" + + kernels = [3, 5, 7, 15, 31, 63, 127, 255] + seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]] + thresh = [32, 32, 64, 128, 256, -1, -1, -1] + max_mem = [-1, -1, -1, -1, -1, 192, 96, 64] + + with open("lightconv_cuda_backward.cu", "w") as backward: + backward.write(head) + for (k, t, mem) in zip(kernels, thresh, max_mem): + backward.write(case_k.format(k=k)) + for seq in seqs: + if (t == -1 or seq <= t) and (mem == -1 or seq < mem): + backward.write(sequence_if.format(seq=seq)) + for p in [k // 2, k - 1]: + backward.write(main_block.format(k=k, b_size=seq, p=p)) + backward.write(weight_grad_short.format(k=k, b_size=seq, p=p)) + backward.write(bad_padding) + else: + for p in [k // 2, k - 1]: + backward.write(main_block.format(k=k, b_size=32, p=p)) + backward.write(weight_grad.format(k=k, b_size=32, p=p)) + backward.write(bad_padding) + backward.write(breakout) + break + backward.write(con_else) + backward.write(bad_filter) + backward.write(last_return) + + +if __name__ == "__main__": + gen_forward() + gen_backward() diff --git a/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp b/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp new file mode 100644 index 0000000..ece47a8 --- /dev/null +++ b/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cpp @@ -0,0 +1,51 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <torch/extension.h> +#include <vector> + +std::vector<at::Tensor> +lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l); + +std::vector<at::Tensor> lightconv_cuda_backward( + at::Tensor gradOutput, + int padding_l, + at::Tensor input, + at::Tensor filters); + +#define CHECK_CUDA(x) \ + AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +std::vector<at::Tensor> +lightconv_forward(at::Tensor input, at::Tensor filters, int padding_l) { + CHECK_INPUT(input); + CHECK_INPUT(filters); + + return lightconv_cuda_forward(input, filters, padding_l); +} + +std::vector<at::Tensor> lightconv_backward( + at::Tensor gradOutput, + int padding_l, + at::Tensor input, + at::Tensor filters) { + CHECK_INPUT(gradOutput); + CHECK_INPUT(input); + CHECK_INPUT(filters); + + return lightconv_cuda_backward(gradOutput, padding_l, input, filters); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &lightconv_forward, "lighconv forward (CUDA)"); + m.def("backward", &lightconv_backward, "lighconv backward (CUDA)"); +} diff --git a/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cuh b/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cuh new file mode 100644 index 0000000..610ab39 --- /dev/null +++ b/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda.cuh @@ -0,0 +1,79 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <ATen/ATen.h> +#include <c10/cuda/CUDAStream.h> + +#include <cuda.h> +#include <cuda_runtime.h> + +#include <algorithm> +#include <functional> +#include <iostream> +#include <stdexcept> +#include <utility> +#include <vector> + +#include <assert.h> +#include <stdlib.h> + +#define SHFL_MASK 0xffffffff + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_forward_kernel( + const scalar_t* input, + const scalar_t* filters, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + scalar_t* output); + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_input_kernel( + const scalar_t* input, + const scalar_t* filters, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + scalar_t* output); + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_short_kernel( + const scalar_t* input, + const scalar_t* gradInput, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + float* output); + +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_short_kernel( + const float* input, + const int minibatch, + const int numFiltersInBlock, + scalar_t* output); + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_kernel( + const scalar_t* input, + const scalar_t* gradInput, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + float* output); + +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_kernel( + const float* input, + const int minibatch, + const int numFiltersInBlock, + scalar_t* output); diff --git a/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu b/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu new file mode 100644 index 0000000..cdf31d5 --- /dev/null +++ b/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu @@ -0,0 +1,400 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include "../cuda_utils.cu" +#include "lightconv_cuda.cuh" +#include "lightconv_cuda_backward.cu" +#include "lightconv_cuda_forward.cu" + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_forward_kernel( + const scalar_t* input, + const scalar_t* filters, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + scalar_t* output) { + const int tid = threadIdx.x; + const int batchIdx = blockIdx.x; + const int featureIdx = blockIdx.y; + const int filterIdx = featureIdx / numFiltersInBlock; + + const int IOOffset = + numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; + const scalar_t* inputFeature = &input[IOOffset]; + scalar_t* outputFeature = &output[IOOffset]; + const scalar_t* inputFilter = &filters[filterIdx * FS]; + + assert(blockDim.x == SB); + + scalar_t filter[FS]; +#pragma unroll + for (int i = 0; i < FS; ++i) { + filter[i] = inputFilter[i]; + } + + __shared__ scalar_t temp[SB + FS]; + zeroSharedMem<FS, SB, padding_l>(temp); + + const int numIterations = divUp<int, int>(sequenceLength, SB); + + for (int i = 0; i < numIterations; ++i) { + // Read input into shared memory + const int inputOffset = i * SB; + + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + (numIterations == 1), + temp); + + __syncthreads(); + + scalar_t out = 0; +#pragma unroll + for (int j = 0; j < FS; ++j) { + out += filter[j] * temp[tid + j]; + } + + // Write output + const int outputOffset = inputOffset; + if ((outputOffset + tid) < sequenceLength) { + outputFeature[outputOffset + tid] = out; + } + + __syncthreads(); + } +} + +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_input_kernel( + const scalar_t* input, + const scalar_t* filters, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + scalar_t* output) { + // input grad kernel is similar to forward kernel + const int tid = threadIdx.x; + const int batchIdx = blockIdx.x; + const int featureIdx = blockIdx.y; + const int filterIdx = featureIdx / numFiltersInBlock; + + const int IOOffset = + numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; + const scalar_t* inputFeature = &input[IOOffset]; + scalar_t* outputFeature = &output[IOOffset]; + const scalar_t* inputFilter = &filters[filterIdx * FS]; + + assert(blockDim.x == SB); + + scalar_t filter[FS]; + +// The only change is loading the filter in reverse +#pragma unroll + for (int i = 0; i < FS; ++i) { + filter[i] = inputFilter[FS - i - 1]; + } + + __shared__ scalar_t temp[SB + FS]; + const int padding = FS - padding_l - 1; + zeroSharedMem<FS, SB, padding>(temp); + + __syncthreads(); + + const int numIterations = divUp<int, int>(sequenceLength, SB); + + for (int i = 0; i < numIterations; ++i) { + // Read input into shared memory + const int inputOffset = i * SB; + + load_input_to_shared<FS, SB, padding>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + temp); + + __syncthreads(); + + scalar_t out = 0; +#pragma unroll + for (int j = 0; j < FS; ++j) { + out += filter[j] * temp[tid + j]; + } + + // Write output + const int outputOffset = inputOffset; + if ((outputOffset + tid) < sequenceLength) { + outputFeature[outputOffset + tid] = out; + } + + __syncthreads(); + } +} + +// This is by far the most expensive kernel in terms of time taken. +// Can be 16x slower than the forward or grad_wrt_input when filter size is 31 +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_short_kernel( + const scalar_t* input, + const scalar_t* gradInput, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + float* output) { + const int tid = threadIdx.x; + const int batchIdx = blockIdx.x; + const int filterIdx = blockIdx.y; + + const int numIterations = divUp<int, int>(sequenceLength, SB); + + float* tempOutputGradWeight = &output[filterIdx * FS * minibatch]; + + assert(blockDim.x == SB); + + __shared__ scalar_t tempInput[SB + FS]; + __shared__ scalar_t tempGradInput[SB + FS]; + + // local weight accumulation + float accumWeights[FS]; + + // Initialize memory + for (int i = 0; i < FS; ++i) { + accumWeights[i] = float(0.0); + } + + // loop over each sequence within filterblock + for (int idxInFilterBlock = 0; idxInFilterBlock < numFiltersInBlock; + ++idxInFilterBlock) { + const int featureOffset = batchIdx * numFeatures * sequenceLength + + (filterIdx * numFiltersInBlock + idxInFilterBlock) * sequenceLength; + const scalar_t* inputFeature = &input[featureOffset]; + const scalar_t* gradInputFeature = &gradInput[featureOffset]; + + zeroSharedMem<FS, SB, padding_l>(tempInput); + zeroSharedMem<FS, SB, (FS / 2)>(tempGradInput); + __syncthreads(); + + for (int i = 0; i < numIterations; ++i) { + const int inputOffset = i * SB; + + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempInput); + load_input_to_shared<FS, SB, (FS / 2)>( + gradInputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempGradInput); + + __syncthreads(); + + const int gradIndex = (FS / 2) + tid; + scalar_t tempGrad = tempGradInput[gradIndex]; + +#pragma unroll + for (int j = 0; j < FS; j++) { + const int inputIndex = tid + j; + accumWeights[j] += tempInput[inputIndex] * tempGrad; + } + + __syncthreads(); + } + } + + // Row-major sum + for (int filterWeightIdx = 0; filterWeightIdx < FS; ++filterWeightIdx) { + float temp; + if (tid < sequenceLength) { + temp = accumWeights[filterWeightIdx]; + } else { + temp = float(0.0); + } + + const int outputOffset = filterWeightIdx * minibatch + batchIdx; + + temp = blockReduce(temp); + + if (tid == 0) { + tempOutputGradWeight[outputOffset] = temp; + } + } +} + +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_short_kernel( + const float* input, + const int minibatch, + const int numFiltersInBlock, + scalar_t* output) { + assert(blockDim.x == SB); + + const int tid = threadIdx.x; + + const int filterIdx = blockIdx.x; + const int filterWeightIdx = blockIdx.y; + + const int inputOffset = + filterIdx * FS * minibatch + filterWeightIdx * minibatch; + const float* tempInput = &input[inputOffset]; + + // read into shared memory for reduction + int readIndex = tid; + + float sum = 0.0; + while (readIndex < minibatch) { + sum += tempInput[readIndex]; + readIndex += SB; + } + + float temp = blockReduce(sum); + + if (tid == 0) { + output[blockIdx.x * FS + blockIdx.y] = temp; + } +} + +// This is by far the most expensive kernel in terms of time taken. +// Can be 16x slower than the forward or grad_wrt_input when filter size is 31 +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_kernel( + const scalar_t* input, + const scalar_t* gradInput, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + float* output) { + assert(blockDim.x == SB); + + const int tid = threadIdx.x; + const int batchIdx = blockIdx.x; + const int featureIdx = blockIdx.y; + const int filterIdx = featureIdx / numFiltersInBlock; + const int idxInFilterBlock = featureIdx % numFiltersInBlock; + + const int numIterations = divUp<int, int>(sequenceLength, SB); + + float temp; + + __shared__ scalar_t tempInput[SB + FS]; + __shared__ scalar_t tempGradInput[SB + FS]; + zeroSharedMem<FS, SB, padding_l>(tempInput); + zeroSharedMem<FS, SB, (FS / 2)>(tempGradInput); + __syncthreads(); + + float accumWeights[FS]; + + for (int i = 0; i < FS; ++i) { + accumWeights[i] = float(0.0); + } + + const int IOOffset = + batchIdx * numFeatures * sequenceLength + featureIdx * sequenceLength; + const scalar_t* inputFeature = &input[IOOffset]; + const scalar_t* gradInputFeature = &gradInput[IOOffset]; + float* tempOutputGradWeight = + &output[filterIdx * FS * minibatch * numFiltersInBlock]; + + for (int i = 0; i < numIterations; ++i) { + const int inputOffset = i * SB; + + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempInput); + load_input_to_shared<FS, SB, (FS / 2)>( + gradInputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempGradInput); + __syncthreads(); + +#pragma unroll + for (int j = 0; j < FS; ++j) { + accumWeights[j] += tempInput[tid + j] * tempGradInput[tid + (FS / 2)]; + } + + __syncthreads(); + } + + // Row-major sum + for (int filterWeightIdx = 0; filterWeightIdx < FS; ++filterWeightIdx) { + // Write to shared memory before reduction + if (tid < sequenceLength) { + temp = accumWeights[filterWeightIdx]; + } else { + temp = float(0.0); + } + + temp = blockReduce(temp); + + const int outputOffset = filterWeightIdx * minibatch * numFiltersInBlock + + batchIdx * numFiltersInBlock + idxInFilterBlock; + + if (tid == 0) { + tempOutputGradWeight[outputOffset] = temp; + } + } +} + +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_kernel( + const float* input, + const int minibatch, + const int numFiltersInBlock, + scalar_t* output) { + assert(blockDim.x == SB); + const int tid = threadIdx.x; + + // What is the id within a minibatch + const int filterIdx = blockIdx.x; + const int filterWeightIdx = blockIdx.y; + + const int inputOffset = filterIdx * FS * minibatch * numFiltersInBlock + + filterWeightIdx * minibatch * numFiltersInBlock; + const float* tempInput = &input[inputOffset]; + + int readIndex = tid; + + float sum = float(0.0); + while (readIndex < (minibatch * numFiltersInBlock)) { + sum += tempInput[readIndex]; + readIndex += SB; + } + + float temp = blockReduce(sum); + + if (tid == 0) { + output[blockIdx.x * FS + blockIdx.y] = temp; + } +} diff --git a/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py b/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py new file mode 100644 index 0000000..e7e597f --- /dev/null +++ b/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py @@ -0,0 +1,137 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import lightconv_cuda +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout +from torch import nn +from torch.autograd import Function + + +class lightconvFunction(Function): + @staticmethod + def forward(ctx, x, weights, padding_l): + ctx.padding_l = padding_l + outputs = lightconv_cuda.forward(x, weights, padding_l) + variables = [x, weights] + ctx.save_for_backward(*variables) + return outputs[0] + + @staticmethod + def backward(ctx, grad_output): + outputs = lightconv_cuda.backward( + grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors + ) + grad_input, grad_weights = outputs + return grad_input, grad_weights, None + + +@with_incremental_state +class LightconvLayer(nn.Module): + def __init__( + self, + input_size, + kernel_size=1, + padding_l=None, + weight_softmax=False, + num_heads=1, + weight_dropout=0.0, + bias=False, + ): + super(LightconvLayer, self).__init__() + self.input_size = input_size + self.kernel_size = kernel_size + self.padding_l = padding_l + self.num_heads = num_heads + self.weight_softmax = weight_softmax + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) + + self.weight = nn.Parameter(torch.Tensor(num_heads, kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(input_size)) + else: + self.bias = None + self.reset_parameters() + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + for k, v in state_dict.items(): + if k.endswith(prefix + "weight"): + if v.dim() == 3 and v.size(1) == 1: + state_dict[k] = v.squeeze(1) + + def reset_parameters(self): + nn.init.xavier_uniform_(self.weight) + if self.bias is not None: + nn.init.constant_(self.bias, 0.0) + + def forward(self, x, incremental_state=None): + + # during inference time, incremental BMM is faster + if incremental_state is not None: + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is None: + input_buffer = x.new() + x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) + if self.kernel_size > 1: + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) + + weight = self.weight + if self.weight_softmax: + weight = F.softmax(weight.float(), dim=1).type_as(weight) + + weight = weight[:, -x_unfold.size(2) :] + + K = weight.size(1) + + weight = ( + weight.view(1, H, K) + .expand(T * B, H, K) + .contiguous() + .view(T * B * H, K, 1) + ) + + weight = self.weight_dropout_module(weight) + output = torch.bmm(x_unfold, weight) # T*B*H x R x 1 + output = output.view(T, B, C) + return output + + # during training time, use CUDA kernel + else: + x = x.permute(1, 2, 0).contiguous() + weight = self.weight + if self.weight_softmax: + weight = F.softmax(self.weight, -1) + if self.weight_dropout_module.p: + weight = self.weight_dropout_module(weight) + return lightconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1) + + def reorder_incremental_state(self, incremental_state, new_order): + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + input_buffer = input_buffer.index_select(1, new_order) + self._set_input_buffer(incremental_state, input_buffer) + + def _get_input_buffer(self, incremental_state): + return utils.get_incremental_state(self, incremental_state, "input_buffer") + + def _set_input_buffer(self, incremental_state, new_buffer): + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) + + def half(self): + return self._apply(lambda t: t.half() if t.is_floating_point() else t) diff --git a/fairseq/fairseq/modules/lightconv_layer/setup.py b/fairseq/fairseq/modules/lightconv_layer/setup.py new file mode 100644 index 0000000..052635b --- /dev/null +++ b/fairseq/fairseq/modules/lightconv_layer/setup.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + + +setup( + name="lightconv_layer", + ext_modules=[ + CUDAExtension( + "lightconv_cuda", + [ + "lightconv_cuda.cpp", + "lightconv_cuda_kernel.cu", + ], + ), + ], + cmdclass={"build_ext": BuildExtension}, +) diff --git a/fairseq/fairseq/modules/lightweight_convolution.py b/fairseq/fairseq/modules/lightweight_convolution.py new file mode 100644 index 0000000..ec11a95 --- /dev/null +++ b/fairseq/fairseq/modules/lightweight_convolution.py @@ -0,0 +1,310 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.unfold import unfold1d + + +def LightweightConv( + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + bias=False, +): + if torch.cuda.is_available(): + try: + from fairseq.modules.lightconv_layer import LightconvLayer + + return LightconvLayer( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + bias=bias, + ) + except ImportError as e: + print(e) + return LightweightConv1dTBC( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + bias=bias, + ) + + +class LightweightConv1d(nn.Module): + """Lightweight Convolution assuming the input is BxCxT + This is just an example that explains LightConv clearer than the TBC version. + We don't use this module in the model. + + Args: + input_size: # of channels of the input and output + kernel_size: convolution channels + padding: padding + num_heads: number of heads used. The weight is of shape + `(num_heads, 1, kernel_size)` + weight_softmax: normalize the weight with softmax before the convolution + + Shape: + Input: BxCxT, i.e. (batch_size, input_size, timesteps) + Output: BxCxT, i.e. (batch_size, input_size, timesteps) + + Attributes: + weight: the learnable weights of the module of shape + `(num_heads, 1, kernel_size)` + bias: the learnable bias of the module of shape `(input_size)` + """ + + def __init__( + self, + input_size, + kernel_size=1, + padding=0, + num_heads=1, + weight_softmax=False, + bias=False, + weight_dropout=0.0, + ): + super().__init__() + self.input_size = input_size + self.kernel_size = kernel_size + self.num_heads = num_heads + self.padding = padding + self.weight_softmax = weight_softmax + self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size)) + + if bias: + self.bias = nn.Parameter(torch.Tensor(input_size)) + else: + self.bias = None + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_uniform_(self.weight) + if self.bias is not None: + nn.init.constant_(self.bias, 0.0) + + def forward(self, input): + """ + input size: B x C x T + output size: B x C x T + """ + B, C, T = input.size() + H = self.num_heads + + weight = self.weight + if self.weight_softmax: + weight = F.softmax(weight, dim=-1) + + weight = self.weight_dropout_module(weight) + # Merge every C/H entries into the batch dimension (C = self.input_size) + # B x C x T -> (B * C/H) x H x T + # One can also expand the weight to C x 1 x K by a factor of C/H + # and do not reshape the input instead, which is slow though + input = input.view(-1, H, T) + output = F.conv1d(input, weight, padding=self.padding, groups=self.num_heads) + output = output.view(B, C, T) + if self.bias is not None: + output = output + self.bias.view(1, -1, 1) + + return output + + +@with_incremental_state +class LightweightConv1dTBC(nn.Module): + """Lightweight Convolution assuming the input is TxBxC + Args: + input_size: # of channels of the input + kernel_size: convolution channels + padding_l: padding to the left when using "same" padding + num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size) + weight_dropout: the drop rate of the DropConnect to drop the weight + weight_softmax: normalize the weight with softmax before the convolution + bias: use bias + + Shape: + Input: TxBxC, i.e. (timesteps, batch_size, input_size) + Output: TxBxC, i.e. (timesteps, batch_size, input_size) + + Attributes: + weight: the learnable weights of the module of shape + `(num_heads, 1, kernel_size)` + bias: the learnable bias of the module of shape `(input_size)` + """ + + def __init__( + self, + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + bias=False, + ): + super().__init__() + self.input_size = input_size + self.kernel_size = kernel_size + self.padding_l = padding_l + self.num_heads = num_heads + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) + self.weight_softmax = weight_softmax + + self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(input_size)) + else: + self.bias = None + + self.reset_parameters() + self.onnx_trace = False + + def reset_parameters(self): + nn.init.xavier_uniform_(self.weight) + if self.bias is not None: + nn.init.constant_(self.bias, 0.0) + + def forward(self, x, incremental_state=None, unfold=False): + """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C + args: + x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) + incremental_state: A dict to keep the state + unfold: unfold the input or not. If not, we use the matrix trick instead + """ + unfold = unfold or (incremental_state is not None) + + if unfold: + output = self._forward_unfolded(x, incremental_state) + else: + output = self._forward_expanded(x, incremental_state) + + if self.bias is not None: + output = output + self.bias.view(1, 1, -1) + return output + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def _forward_unfolded(self, x, incremental_state): + """The conventional implementation of convolutions. + Unfolding the input by having a window shifting to the right.""" + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + assert R * H == C == self.input_size + + weight = self.weight.view(H, K) + if incremental_state is not None: + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is None: + input_buffer = x.new() + x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) + if self.kernel_size > 1: + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) + else: + # unfold the input: T x B x C --> T' x B x C x K + x_unfold = unfold1d(x, self.kernel_size, self.padding_l, 0) + x_unfold = x_unfold.view(T * B * H, R, K) + + if self.weight_softmax: + weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( + weight + ) + + if incremental_state is not None: + weight = weight[:, -x_unfold.size(2) :] + K = weight.size(1) + + weight = ( + weight.view(1, H, K).expand(T * B, H, K).contiguous().view(T * B * H, K, 1) + ) + + weight = self.weight_dropout_module(weight) + output = torch.bmm(x_unfold, weight) # T*B*H x R x 1 + output = output.view(T, B, C) + return output + + def _forward_expanded(self, x, incremental_state): + """Turn the convolution filters into band matrices and do matrix multiplication. + This is faster when the sequence is short, but less memory efficient. + This is not used in the decoder during inference. + """ + T, B, C = x.size() + K, H = self.kernel_size, self.num_heads + R = C // H + assert R * H == C == self.input_size + + weight = self.weight.view(H, K) + if self.weight_softmax: + weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( + weight + ) + weight = weight.view(1, H, K).expand(T * B, H, K).contiguous() + weight = weight.view(T, B * H, K).transpose(0, 1) + + x = x.view(T, B * H, R).transpose(0, 1) + P = self.padding_l + if K > T and P == K - 1: + weight = weight.narrow(2, K - T, T) + K, P = T, T - 1 + # turn the convolution filters into band matrices + weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) + weight_expanded.as_strided((B * H, T, K), (T * (T + K - 1), T + K, 1)).copy_( + weight + ) + weight_expanded = weight_expanded.narrow(2, P, T) + weight_expanded = self.weight_dropout_module(weight_expanded) + + output = torch.bmm(weight_expanded, x) + output = output.transpose(0, 1).contiguous().view(T, B, C) + return output + + def reorder_incremental_state(self, incremental_state, new_order): + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + input_buffer = input_buffer.index_select(1, new_order) + self._set_input_buffer(incremental_state, input_buffer) + + def _get_input_buffer(self, incremental_state): + return utils.get_incremental_state(self, incremental_state, "input_buffer") + + def _set_input_buffer(self, incremental_state, new_buffer): + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) + + def extra_repr(self): + s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, bias={}".format( + self.input_size, + self.kernel_size, + self.padding_l, + self.num_heads, + self.weight_softmax, + self.bias is not None, + ) + if self.weight_dropout_module.p > 0.0: + s += ", weight_dropout={}".format(self.weight_dropout_module.p) + return s diff --git a/fairseq/fairseq/modules/linearized_convolution.py b/fairseq/fairseq/modules/linearized_convolution.py new file mode 100644 index 0000000..1c7a9f0 --- /dev/null +++ b/fairseq/fairseq/modules/linearized_convolution.py @@ -0,0 +1,125 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.incremental_decoding_utils import with_incremental_state + +from .conv_tbc import ConvTBC + +from typing import Dict, Optional +from torch import Tensor + + +@with_incremental_state +class LinearizedConvolution(ConvTBC): + """An optimized version of nn.Conv1d. + + At training time, this module uses ConvTBC, which is an optimized version + of Conv1d. At inference time, it optimizes incremental generation (i.e., + one time step at a time) by replacing the convolutions with linear layers. + Note that the input order changes from training to inference. + """ + + def __init__(self, in_channels, out_channels, kernel_size, **kwargs): + super().__init__(in_channels, out_channels, kernel_size, **kwargs) + self._linearized_weight = None + self.register_backward_hook(self._clear_linearized_weight) + + def state_dict(self, destination=None, prefix="", keep_vars=False): + state = ConvTBC.state_dict(self, destination, prefix, keep_vars=keep_vars) + # don't store redundant _linearized_weight in checkpoints + if prefix + "_linearized_weight" in state: + del state[prefix + "_linearized_weight"] + return state + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + if prefix + "_linearized_weight" in state_dict: + del state_dict[prefix + "_linearized_weight"] + + @torch.jit.export + def forward( + self, + input, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + """ + Args: + incremental_state: Used to buffer signal; if not None, then input is + expected to contain a single frame. If the input order changes + between time steps, call reorder_incremental_state. + Input: + Time x Batch x Channel during training + Batch x Time x Channel during inference + """ + if incremental_state is None: + output = self.conv_tbc(input) + if self.kernel_size[0] > 1 and self.padding[0] > 0: + # remove future timesteps added by padding + output = output[: -self.padding[0], :, :] + return output + + # reshape weight + weight = self._get_linearized_weight() + kw = self.kernel_size[0] + + bsz = input.size(0) # input: bsz x len x dim + if kw > 1: + input = input.data + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is None: + input_buffer = input.new(bsz, kw, input.size(2)).zero_() + self._set_input_buffer(incremental_state, input_buffer) + else: + # shift buffer + input_buffer[:, :-1, :] = input_buffer[:, 1:, :].clone() + # append next input + input_buffer[:, -1, :] = input[:, -1, :] + input = input_buffer + with torch.no_grad(): + output = F.linear(input.view(bsz, -1), weight, self.bias) + return output.view(bsz, 1, -1) + + @torch.jit.unused + def reorder_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + new_order, + ): + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + input_buffer = input_buffer.index_select(0, new_order) + self._set_input_buffer(incremental_state, input_buffer) + + @torch.jit.unused + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ): + return utils.get_incremental_state(self, incremental_state, "input_buffer") + + @torch.jit.unused + def _set_input_buffer( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + new_buffer, + ): + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) + + @torch.jit.unused + def _get_linearized_weight(self): + if self._linearized_weight is None: + kw = self.kernel_size[0] + weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous() + assert weight.size() == (self.out_channels, kw, self.in_channels) + return weight.view(self.out_channels, -1) + return self._linearized_weight + + @torch.jit.unused + def _clear_linearized_weight(self, *args): + self._linearized_weight = None diff --git a/fairseq/fairseq/modules/location_attention.py b/fairseq/fairseq/modules/location_attention.py new file mode 100644 index 0000000..dbbbfb9 --- /dev/null +++ b/fairseq/fairseq/modules/location_attention.py @@ -0,0 +1,83 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch +import torch.nn.functional as F + + +class LocationAttention(nn.Module): + """ + Attention-Based Models for Speech Recognition + https://arxiv.org/pdf/1506.07503.pdf + + :param int encoder_dim: # projection-units of encoder + :param int decoder_dim: # units of decoder + :param int attn_dim: attention dimension + :param int conv_dim: # channels of attention convolution + :param int conv_kernel_size: filter size of attention convolution + """ + + def __init__( + self, + attn_dim, + encoder_dim, + decoder_dim, + attn_state_kernel_size, + conv_dim, + conv_kernel_size, + scaling=2.0, + ): + super(LocationAttention, self).__init__() + self.attn_dim = attn_dim + self.decoder_dim = decoder_dim + self.scaling = scaling + self.proj_enc = nn.Linear(encoder_dim, attn_dim) + self.proj_dec = nn.Linear(decoder_dim, attn_dim, bias=False) + self.proj_attn = nn.Linear(conv_dim, attn_dim, bias=False) + self.conv = nn.Conv1d( + attn_state_kernel_size, + conv_dim, + 2 * conv_kernel_size + 1, + padding=conv_kernel_size, + bias=False, + ) + self.proj_out = nn.Sequential(nn.Tanh(), nn.Linear(attn_dim, 1)) + + self.proj_enc_out = None # cache + + def clear_cache(self): + self.proj_enc_out = None + + def forward(self, encoder_out, encoder_padding_mask, decoder_h, attn_state): + """ + :param torch.Tensor encoder_out: padded encoder hidden state B x T x D + :param torch.Tensor encoder_padding_mask: encoder padding mask + :param torch.Tensor decoder_h: decoder hidden state B x D + :param torch.Tensor attn_prev: previous attention weight B x K x T + :return: attention weighted encoder state (B, D) + :rtype: torch.Tensor + :return: previous attention weights (B x T) + :rtype: torch.Tensor + """ + bsz, seq_len, _ = encoder_out.size() + if self.proj_enc_out is None: + self.proj_enc_out = self.proj_enc(encoder_out) + + # B x K x T -> B x C x T + attn = self.conv(attn_state) + # B x C x T -> B x T x C -> B x T x D + attn = self.proj_attn(attn.transpose(1, 2)) + + if decoder_h is None: + decoder_h = encoder_out.new_zeros(bsz, self.decoder_dim) + dec_h = self.proj_dec(decoder_h).view(bsz, 1, self.attn_dim) + + out = self.proj_out(attn + self.proj_enc_out + dec_h).squeeze(2) + out.masked_fill_(encoder_padding_mask, -float("inf")) + + w = F.softmax(self.scaling * out, dim=1) + c = torch.sum(encoder_out * w.view(bsz, seq_len, 1), dim=1) + return c, w diff --git a/fairseq/fairseq/modules/lstm_cell_with_zoneout.py b/fairseq/fairseq/modules/lstm_cell_with_zoneout.py new file mode 100644 index 0000000..2733089 --- /dev/null +++ b/fairseq/fairseq/modules/lstm_cell_with_zoneout.py @@ -0,0 +1,37 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn + + +class LSTMCellWithZoneOut(nn.Module): + """ + Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations + https://arxiv.org/abs/1606.01305 + """ + + def __init__( + self, prob: float, input_size: int, hidden_size: int, bias: bool = True + ): + super(LSTMCellWithZoneOut, self).__init__() + self.lstm_cell = nn.LSTMCell(input_size, hidden_size, bias=bias) + self.prob = prob + if prob > 1.0 or prob < 0.0: + raise ValueError( + "zoneout probability must be in the range from " "0.0 to 1.0." + ) + + def zoneout(self, h, next_h, prob): + if isinstance(h, tuple): + return tuple([self.zoneout(h[i], next_h[i], prob) for i in range(len(h))]) + + if self.training: + mask = h.new_zeros(*h.size()).bernoulli_(prob) + return mask * h + (1 - mask) * next_h + + return prob * h + (1 - prob) * next_h + + def forward(self, x, h): + return self.zoneout(h, self.lstm_cell(x, h), self.prob) diff --git a/fairseq/fairseq/modules/multihead_attention.py b/fairseq/fairseq/modules/multihead_attention.py new file mode 100644 index 0000000..262132d --- /dev/null +++ b/fairseq/fairseq/modules/multihead_attention.py @@ -0,0 +1,910 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn import Parameter + +try: + from xformers.components.attention import build_attention + from xformers.components.attention.utils import maybe_merge_masks + + _xformers_available = True +except ImportError: + _xformers_available = False + +from fairseq import utils +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.quant_noise import quant_noise +from fairseq.models.fairseq_incremental_decoder import FairseqIncrementalDecoder + + +# TODO: move this into xformers? +# TODO: uint8 input type should just output a bool +def _mask_for_xformers(mask: Tensor, to_dtype: Optional[torch.dtype] = None): + """ + call to pytorch multihead accepts three mask types: + - ByteTensor where non-zero means to mask + - FloatTensor which is an additive mask + - BoolTensor where True means to mask + xFormers currently accepts boolean and additive maks. For boolean masks + the values have opposite meaning. For a BoolTensor True mean to keep the value. + """ + float_types = [torch.float, torch.float16] + # If an input mask is a float it is an additive mask. Otherwise it is either uint8 or bool. + additive = mask.dtype in float_types + # If to_dype is not specified, keep same dtype as mask. + to_dtype = mask.dtype if to_dtype is None else to_dtype + to_additive = to_dtype in float_types + + if additive: + if to_additive: + return mask.to(to_dtype) + mask = mask < 0 + + if to_additive: + # return additive mask + new_mask = torch.zeros_like(mask, dtype=to_dtype) + new_mask = new_mask.masked_fill_(mask, -float("inf")) + return new_mask + + # In xFormers True is value to keep rather than value to mask + mask = ~mask.to(torch.bool) + mask = mask.to(to_dtype) + return mask + + +class MultiheadAttention(FairseqIncrementalDecoder): + """Multi-headed attention. + + See "Attention Is All You Need" for more details. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + dictionary=None, + q_noise=0.0, + qn_block_size=8, + # TODO: pass in config rather than string. + # config defined in xformers.components.attention.AttentionConfig + xformers_att_config: Optional[str] = None, + xformers_blocksparse_layout: Optional[ + torch.Tensor + ] = None, # This should be part of the config + xformers_blocksparse_blocksize: Optional[ + int + ] = 16, # This should be part of the config + ): + super().__init__(dictionary) + + xformers_att_config = utils.eval_str_dict(xformers_att_config) + self.use_xformers = xformers_att_config is not None + if self.use_xformers and not _xformers_available: + raise ImportError("\n\n Please install xFormers.") + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim**-0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, ( + "Self-attention requires query, key and " "value to be of the same size" + ) + + self.k_proj = quant_noise( + nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.v_proj = quant_noise( + nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.q_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + self.out_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + self.beam_size = 1 + self.reset_parameters() + + if self.use_xformers: + xformers_att_config["dropout"] = xformers_att_config.get("dropout", dropout) + xformers_att_config["num_heads"] = xformers_att_config.get( + "num_heads", num_heads + ) + + if xformers_blocksparse_layout is not None: + # Could be part of a single config passed only once + xformers_att_config["block_size"] = xformers_blocksparse_blocksize + xformers_att_config["layout"] = xformers_blocksparse_layout + xformers_att_config["name"] = "blocksparse" + + self.attention = build_attention(xformers_att_config) + + self.onnx_trace = False + self.skip_embed_dim_check = False + self.init_incremental_state() + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def reset_parameters(self): + if self.qkv_same_dim: + # Empirically observed the convergence to be much better with + # the scaled initialization + nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) + else: + nn.init.xavier_uniform_(self.k_proj.weight) + nn.init.xavier_uniform_(self.v_proj.weight) + nn.init.xavier_uniform_(self.q_proj.weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.out_proj.bias is not None: + nn.init.constant_(self.out_proj.bias, 0.0) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + + def _get_reserve_head_index(self, num_heads_to_keep: int): + k_proj_heads_norm = [] + q_proj_heads_norm = [] + v_proj_heads_norm = [] + + for i in range(self.num_heads): + start_idx = i * self.head_dim + end_idx = (i + 1) * self.head_dim + k_proj_heads_norm.append( + torch.sum( + torch.abs( + self.k_proj.weight[ + start_idx:end_idx, + ] + ) + ).tolist() + + torch.sum(torch.abs(self.k_proj.bias[start_idx:end_idx])).tolist() + ) + q_proj_heads_norm.append( + torch.sum( + torch.abs( + self.q_proj.weight[ + start_idx:end_idx, + ] + ) + ).tolist() + + torch.sum(torch.abs(self.q_proj.bias[start_idx:end_idx])).tolist() + ) + v_proj_heads_norm.append( + torch.sum( + torch.abs( + self.v_proj.weight[ + start_idx:end_idx, + ] + ) + ).tolist() + + torch.sum(torch.abs(self.v_proj.bias[start_idx:end_idx])).tolist() + ) + + heads_norm = [] + for i in range(self.num_heads): + heads_norm.append( + k_proj_heads_norm[i] + q_proj_heads_norm[i] + v_proj_heads_norm[i] + ) + + sorted_head_index = sorted( + range(self.num_heads), key=lambda k: heads_norm[k], reverse=True + ) + reserve_head_index = [] + for i in range(num_heads_to_keep): + start = sorted_head_index[i] * self.head_dim + end = (sorted_head_index[i] + 1) * self.head_dim + reserve_head_index.append((start, end)) + return reserve_head_index + + def _adaptive_prune_heads(self, reserve_head_index: List[Tuple[int, int]]): + new_q_weight = [] + new_q_bias = [] + new_k_weight = [] + new_k_bias = [] + new_v_weight = [] + new_v_bias = [] + new_out_proj_weight = [] + + for ele in reserve_head_index: + start_idx, end_idx = ele + new_q_weight.append( + self.q_proj.weight[ + start_idx:end_idx, + ] + ) + new_q_bias.append(self.q_proj.bias[start_idx:end_idx]) + + new_k_weight.append( + self.k_proj.weight[ + start_idx:end_idx, + ] + ) + + new_k_bias.append(self.k_proj.bias[start_idx:end_idx]) + + new_v_weight.append( + self.v_proj.weight[ + start_idx:end_idx, + ] + ) + new_v_bias.append(self.v_proj.bias[start_idx:end_idx]) + + new_out_proj_weight.append(self.out_proj.weight[:, start_idx:end_idx]) + + new_q_weight = torch.cat(new_q_weight).detach() + new_k_weight = torch.cat(new_k_weight).detach() + new_v_weight = torch.cat(new_v_weight).detach() + new_out_proj_weight = torch.cat(new_out_proj_weight, dim=-1).detach() + new_q_weight.requires_grad = True + new_k_weight.requires_grad = True + new_v_weight.requires_grad = True + new_out_proj_weight.requires_grad = True + + new_q_bias = torch.cat(new_q_bias).detach() + new_q_bias.requires_grad = True + + new_k_bias = torch.cat(new_k_bias).detach() + new_k_bias.requires_grad = True + + new_v_bias = torch.cat(new_v_bias).detach() + new_v_bias.requires_grad = True + + self.q_proj.weight = torch.nn.Parameter(new_q_weight) + self.q_proj.bias = torch.nn.Parameter(new_q_bias) + + self.k_proj.weight = torch.nn.Parameter(new_k_weight) + self.k_proj.bias = torch.nn.Parameter(new_k_bias) + + self.v_proj.weight = torch.nn.Parameter(new_v_weight) + self.v_proj.bias = torch.nn.Parameter(new_v_bias) + + self.out_proj.weight = torch.nn.Parameter(new_out_proj_weight) + + self.num_heads = len(reserve_head_index) + self.embed_dim = self.head_dim * self.num_heads + self.q_proj.out_features = self.embed_dim + self.k_proj.out_features = self.embed_dim + self.v_proj.out_features = self.embed_dim + + def _set_skip_embed_dim_check(self): + self.skip_embed_dim_check = True + + def _pad_masks( + self, + key_padding_mask: Optional[Tensor], + attn_mask: Optional[Tensor], + ) -> Tuple[Optional[Tensor], Optional[Tensor]]: + if attn_mask is not None: + shape = attn_mask.size()[:-1] + torch.Size([1]) + attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(shape)], dim=-1) + if key_padding_mask is not None: + shape = key_padding_mask.size()[:-1] + torch.Size([1]) + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(shape), + ], + dim=-1, + ) + return key_padding_mask, attn_mask + + def _add_bias( + self, + k: Tensor, + v: Tensor, + key_padding_mask: Optional[Tensor], + attn_mask: Optional[Tensor], + bsz: int, + ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]: + assert self.bias_k is not None + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + key_padding_mask, attn_mask = self._pad_masks( + key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + return k, v, key_padding_mask, attn_mask + + def _append_zero_attn( + self, + k: Tensor, + v: Tensor, + key_padding_mask: Optional[Tensor], + attn_mask: Optional[Tensor], + ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]: + zero_attn_shape = k.size()[:-2] + torch.Size([1]) + k.size()[-1:] + k = torch.cat( + [k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=-2 + ) + v = torch.cat( + [v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=-2 + ) + key_padding_mask, attn_mask = self._pad_masks( + key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + return k, v, key_padding_mask, attn_mask + + def _xformers_attn_forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + + tgt_len, bsz, embed_dim = query.size() + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == tgt_len + + if self.self_attention: + key = query + value = query + elif self.encoder_decoder_attention: + value = key + + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + + if self.bias_k is not None: + assert self.bias_v is not None + k, v, attn_mask, key_padding_mask = self._add_bias( + k, v, attn_mask, key_padding_mask, bsz + ) + + def fold_heads(x): + return ( + x.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + def split_heads(x): + return ( + x.contiguous() + .view(-1, bsz, self.num_heads, self.head_dim) + .transpose(0, 1) + .transpose(1, 2) + ) + + massage = split_heads if self.attention.requires_head_dimension else fold_heads + q = massage(q) + if k is not None: + k = massage(k) + if v is not None: + v = massage(v) + + if self.add_zero_attn: + k, v, key_padding_mask, attn_mask = self._append_zero_attn( + k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + + kwargs = {} + + if attn_mask is not None and self.attention.supports_attention_mask: + attn_mask = _mask_for_xformers(attn_mask, to_dtype=q.dtype) + kwargs["att_mask"] = attn_mask + + if key_padding_mask is not None: + key_padding_mask = _mask_for_xformers(key_padding_mask, to_dtype=torch.bool) + if not self.attention.requires_separate_masks: + attn_mask = maybe_merge_masks( + attn_mask, + key_padding_mask, + batch_size=bsz, + src_len=k.size(-2), + tgt_len=q.size(-2), + num_heads=self.num_heads, + ) + key_padding_mask = None + kwargs["att_mask"] = attn_mask + if self.attention.supports_key_padding_mask: + kwargs["key_padding_mask"] = key_padding_mask + + y = self.attention(q, k, v, **kwargs) + + y = ( + y.view(bsz, self.num_heads, tgt_len, self.head_dim) + .transpose(1, 2) + .flatten(start_dim=2, end_dim=3) + .transpose(0, 1) + ) + assert list(y.size()) == [tgt_len, bsz, embed_dim] + + # Dropout not needed because already applied in attention. + # It is applied to the attention weights before matmul with v. + y = self.out_proj(y) + + # TODO: support returning attention weights if needed. + return y, None + + def forward( + self, + query: Tensor, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + before_softmax: bool = False, + need_head_weights: bool = False, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + is_tpu = query.device.type == "xla" + + tgt_len, bsz, embed_dim = query.size() + src_len = tgt_len + if not self.skip_embed_dim_check: + assert ( + embed_dim == self.embed_dim + ), f"query dim {embed_dim} != {self.embed_dim}" + assert list(query.size()) == [tgt_len, bsz, embed_dim] + if key is not None: + src_len, key_bsz, _ = key.size() + if not torch.jit.is_scripting(): + assert value is not None + assert src_len, key_bsz == value.shape[:2] + + if ( + not self.onnx_trace + and not is_tpu # don't use PyTorch version on TPUs + and incremental_state is None + and not static_kv + # A workaround for quantization to work. Otherwise JIT compilation + # treats bias in linear module as method. + and not torch.jit.is_scripting() + # The Multihead attention implemented in pytorch forces strong dimension check + # for input embedding dimention and K,Q,V projection dimension. + # Since pruning will break the dimension check and it is not easy to modify the pytorch API, + # it is preferred to bypass the pytorch MHA when we need to skip embed_dim_check + and not self.skip_embed_dim_check + ): + assert key is not None and value is not None + + if self.use_xformers: + return self._xformers_attn_forward( + query, key, value, key_padding_mask, need_weights, attn_mask + ) + + else: + return F.multi_head_attention_forward( + query, + key, + value, + self.embed_dim, + self.num_heads, + torch.empty([0]), + torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), + self.bias_k, + self.bias_v, + self.add_zero_attn, + self.dropout_module.p, + self.out_proj.weight, + self.out_proj.bias, + self.training or self.dropout_module.apply_during_inference, + key_padding_mask.bool() if key_padding_mask is not None else None, + need_weights, + attn_mask, + use_separate_proj_weight=True, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + ) + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + if self.beam_size > 1 and bsz == key.size(1): + # key is [T, bsz*beam_size, C], reduce to [T, bsz, C] + key = key.view(key.size(0), -1, self.beam_size, key.size(2))[ + :, :, 0, : + ] + if key_padding_mask is not None: + key_padding_mask = key_padding_mask.view( + -1, self.beam_size, key_padding_mask.size(1) + )[:, 0, :] + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k, v, attn_mask, key_padding_mask = self._add_bias( + k, v, attn_mask, key_padding_mask, bsz + ) + + q = ( + q.contiguous() + .view(tgt_len, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + kv_bsz = bsz # need default value for scripting + if k is not None: + kv_bsz = k.size(1) + k = ( + k.contiguous() + .view(-1, kv_bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + if v is not None: + v = ( + v.contiguous() + .view(-1, kv_bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + kv_bsz = _prev_key.size(0) + prev_key = _prev_key.view(kv_bsz * self.num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + src_len = k.size(1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + assert kv_bsz == _prev_value.size(0) + prev_value = _prev_value.view( + kv_bsz * self.num_heads, -1, self.head_dim + ) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=kv_bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + + saved_state["prev_key"] = k.view(kv_bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_value"] = v.view( + kv_bsz, self.num_heads, -1, self.head_dim + ) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + assert k.size(1) == src_len + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.dim() == 0: + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == kv_bsz + assert key_padding_mask.size(1) == src_len + + if self.add_zero_attn: + assert v is not None + src_len += 1 + k, v, key_padding_mask, attn_mask = self._append_zero_attn( + k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + + if self.encoder_decoder_attention and bsz != kv_bsz: + attn_weights = torch.einsum( + "bxhtd,bhsd->bxhts", + q.view((kv_bsz, -1, self.num_heads) + q.size()[1:]), + k.view((kv_bsz, self.num_heads) + k.size()[1:]), + ) + attn_weights = attn_weights.reshape((-1,) + attn_weights.size()[-2:]) + else: + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + if self.onnx_trace: + attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) + attn_weights += attn_mask + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + if not is_tpu: + attn_weights = attn_weights.view( + kv_bsz, -1, self.num_heads, tgt_len, src_len + ) + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .to(torch.bool), + float("-inf"), + ) + else: + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if before_softmax: + return attn_weights, v + + attn_weights_float = utils.softmax( + attn_weights, dim=-1, onnx_trace=self.onnx_trace + ) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = self.dropout_module(attn_weights) + + assert v is not None + attn: Optional[Tensor] = None + if self.encoder_decoder_attention and bsz != kv_bsz: + attn = torch.einsum( + "bxhts,bhsd->bxhtd", + attn_probs.view( + ( + kv_bsz, + -1, + self.num_heads, + ) + + attn_probs.size()[1:] + ), + v.view( + ( + kv_bsz, + self.num_heads, + ) + + v.size()[1:] + ), + ) + attn = attn.reshape((-1,) + attn.size()[-2:]) + else: + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + if self.onnx_trace and attn.size(1) == 1: + # when ONNX tracing a single decoder step (sequence length == 1) + # the transpose is a no-op copy before view, thus unnecessary + attn = attn.contiguous().view(tgt_len, bsz, self.embed_dim) + else: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim) + attn = self.out_proj(attn) + attn_weights: Optional[Tensor] = None + if need_weights: + attn_weights = attn_weights_float.view( + bsz, self.num_heads, tgt_len, src_len + ).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + + return attn, attn_weights + + @staticmethod + def _append_prev_key_padding_mask( + key_padding_mask: Optional[Tensor], + prev_key_padding_mask: Optional[Tensor], + batch_size: int, + src_len: int, + static_kv: bool, + ) -> Optional[Tensor]: + # saved key padding masks have shape (bsz, seq_len) + if prev_key_padding_mask is not None and static_kv: + new_key_padding_mask = prev_key_padding_mask + elif prev_key_padding_mask is not None and key_padding_mask is not None: + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 + ) + # During incremental decoding, as the padding token enters and + # leaves the frame, there will be a time when prev or current + # is None + elif prev_key_padding_mask is not None: + if src_len > prev_key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - prev_key_padding_mask.size(1)), + device=prev_key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + else: + new_key_padding_mask = prev_key_padding_mask.float() + elif key_padding_mask is not None: + if src_len > key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - key_padding_mask.size(1)), + device=key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [filler.float(), key_padding_mask.float()], dim=1 + ) + else: + new_key_padding_mask = key_padding_mask.float() + else: + new_key_padding_mask = prev_key_padding_mask + return new_key_padding_mask + + @torch.jit.export + def reorder_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + new_order: Tensor, + ): + """Reorder buffered internal state (for incremental generation).""" + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + for k in input_buffer.keys(): + input_buffer_k = input_buffer[k] + if input_buffer_k is not None: + if self.encoder_decoder_attention: + if input_buffer_k.size(0) * self.beam_size == new_order.size(0): + return incremental_state + elif self.beam_size > 1: + input_buffer[k] = input_buffer_k.index_select( + 0, + new_order.reshape(-1, self.beam_size)[:, 0] + // self.beam_size, + ) + else: + input_buffer[k] = input_buffer_k.index_select(0, new_order) + else: + input_buffer[k] = input_buffer_k.index_select(0, new_order) + incremental_state = self._set_input_buffer(incremental_state, input_buffer) + return incremental_state + + def set_beam_size(self, beam_size): + """Used for effiecient beamable enc-dec attention""" + self.beam_size = beam_size + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ) -> Dict[str, Optional[Tensor]]: + result = self.get_incremental_state(incremental_state, "attn_state") + if result is not None: + return result + else: + empty_result: Dict[str, Optional[Tensor]] = {} + return empty_result + + def _set_input_buffer( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + buffer: Dict[str, Optional[Tensor]], + ): + return self.set_incremental_state(incremental_state, "attn_state", buffer) + + def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): + return attn_weights + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + items_to_add = {} + keys_to_remove = [] + for k in state_dict.keys(): + if k.endswith(prefix + "in_proj_weight"): + # in_proj_weight used to be q + k + v with same dimensions + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] + items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] + items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] + + keys_to_remove.append(k) + + k_bias = prefix + "in_proj_bias" + if k_bias in state_dict.keys(): + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] + items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][ + dim : 2 * dim + ] + items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] + + keys_to_remove.append(prefix + "in_proj_bias") + + for k in keys_to_remove: + del state_dict[k] + + for key, value in items_to_add.items(): + state_dict[key] = value diff --git a/fairseq/fairseq/modules/positional_embedding.py b/fairseq/fairseq/modules/positional_embedding.py new file mode 100644 index 0000000..fbc13d8 --- /dev/null +++ b/fairseq/fairseq/modules/positional_embedding.py @@ -0,0 +1,37 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn + +from .learned_positional_embedding import LearnedPositionalEmbedding +from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding + + +def PositionalEmbedding( + num_embeddings: int, + embedding_dim: int, + padding_idx: int, + learned: bool = False, + auto_expand: bool = True, +): + if learned: + # if padding_idx is specified then offset the embedding ids by + # this index and adjust num_embeddings appropriately + # TODO: The right place for this offset would be inside + # LearnedPositionalEmbedding. Move this there for a cleaner implementation. + if padding_idx is not None: + num_embeddings = num_embeddings + padding_idx + 1 + m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) + if padding_idx is not None: + nn.init.constant_(m.weight[padding_idx], 0) + else: + m = SinusoidalPositionalEmbedding( + embedding_dim, + padding_idx, + init_size=num_embeddings + padding_idx + 1, + auto_expand=auto_expand, + ) + return m diff --git a/fairseq/fairseq/modules/positional_encoding.py b/fairseq/fairseq/modules/positional_encoding.py new file mode 100644 index 0000000..67f6353 --- /dev/null +++ b/fairseq/fairseq/modules/positional_encoding.py @@ -0,0 +1,129 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import math +import torch + + +class PositionalEncoding(nn.Module): + """Positional encoding. + + Args: + d_model: Embedding dimension. + dropout_rate: Dropout rate. + max_len: Maximum input length. + reverse: Whether to reverse the input position. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): + """Construct an PositionalEncoding object.""" + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.reverse = reverse + self.xscale = math.sqrt(self.d_model) + self.dropout = nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.d_model) + if self.reverse: + position = torch.arange( + x.size(1) - 1, -1, -1.0, dtype=torch.float32 + ).unsqueeze(1) + else: + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor B X T X C + Returns: + torch.Tensor: Encoded tensor B X T X C + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class RelPositionalEncoding(nn.Module): + """Relative positional encoding module (new implementation). + + Args: + d_model: Embedding dimension. + dropout_rate: Dropout rate. + max_len: Maximum input length. + """ + + def __init__(self, max_len, d_model): + """Construct an PositionalEncoding object.""" + super(RelPositionalEncoding, self).__init__() + self.d_model = d_model + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i<j). + pe_positive = torch.zeros(x.size(1), self.d_model) + pe_negative = torch.zeros(x.size(1), self.d_model) + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe_positive[:, 0::2] = torch.sin(position * div_term) + pe_positive[:, 1::2] = torch.cos(position * div_term) + pe_negative[:, 0::2] = torch.sin(-1 * position * div_term) + pe_negative[:, 1::2] = torch.cos(-1 * position * div_term) + + # Reserve the order of positive indices and concat both positive and + # negative indices. This is used to support the shifting trick + # as in https://arxiv.org/abs/1901.02860 + pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0) + pe_negative = pe_negative[1:].unsqueeze(0) + pe = torch.cat([pe_positive, pe_negative], dim=1) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + Args: + x : Input tensor T X B X C. + Returns: + torch.Tensor: Encoded tensor T X B X C. + + """ + x = x.transpose(0, 1) # Change TBC to BTC + self.extend_pe(x) + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - x.size(1) + 1 : self.pe.size(1) // 2 + x.size(1), + ] + pos_emb = pos_emb.transpose(0, 1) # change to TBC + return pos_emb diff --git a/fairseq/fairseq/modules/quant_noise.py b/fairseq/fairseq/modules/quant_noise.py new file mode 100644 index 0000000..d777dfb --- /dev/null +++ b/fairseq/fairseq/modules/quant_noise.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + + +def quant_noise(module, p, block_size): + """ + Wraps modules and applies quantization noise to the weights for + subsequent quantization with Iterative Product Quantization as + described in "Training with Quantization Noise for Extreme Model Compression" + + Args: + - module: nn.Module + - p: amount of Quantization Noise + - block_size: size of the blocks for subsequent quantization with iPQ + + Remarks: + - Module weights must have the right sizes wrt the block size + - Only Linear, Embedding and Conv2d modules are supported for the moment + - For more detail on how to quantize by blocks with convolutional weights, + see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks" + - We implement the simplest form of noise here as stated in the paper + which consists in randomly dropping blocks + """ + + # if no quantization noise, don't register hook + if p <= 0: + return module + + # supported modules + assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d)) + + # test whether module.weight has the right sizes wrt block_size + is_conv = module.weight.ndim == 4 + + # 2D matrix + if not is_conv: + assert ( + module.weight.size(1) % block_size == 0 + ), "Input features must be a multiple of block sizes" + + # 4D matrix + else: + # 1x1 convolutions + if module.kernel_size == (1, 1): + assert ( + module.in_channels % block_size == 0 + ), "Input channels must be a multiple of block sizes" + # regular convolutions + else: + k = module.kernel_size[0] * module.kernel_size[1] + assert k % block_size == 0, "Kernel size must be a multiple of block size" + + def _forward_pre_hook(mod, input): + # no noise for evaluation + if mod.training: + if not is_conv: + # gather weight and sizes + weight = mod.weight + in_features = weight.size(1) + out_features = weight.size(0) + + # split weight matrix into blocks and randomly drop selected blocks + mask = torch.zeros( + in_features // block_size * out_features, device=weight.device + ) + mask.bernoulli_(p) + mask = mask.repeat_interleave(block_size, -1).view(-1, in_features) + + else: + # gather weight and sizes + weight = mod.weight + in_channels = mod.in_channels + out_channels = mod.out_channels + + # split weight matrix into blocks and randomly drop selected blocks + if mod.kernel_size == (1, 1): + mask = torch.zeros( + int(in_channels // block_size * out_channels), + device=weight.device, + ) + mask.bernoulli_(p) + mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels) + else: + mask = torch.zeros( + weight.size(0), weight.size(1), device=weight.device + ) + mask.bernoulli_(p) + mask = ( + mask.unsqueeze(2) + .unsqueeze(3) + .repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1]) + ) + + # scale weights and apply mask + mask = mask.to( + torch.bool + ) # x.bool() is not currently supported in TorchScript + s = 1 / (1 - p) + mod.weight.data = s * weight.masked_fill(mask, 0) + + module.register_forward_pre_hook(_forward_pre_hook) + return module diff --git a/fairseq/fairseq/modules/quantization/__init__.py b/fairseq/fairseq/modules/quantization/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/fairseq/modules/quantization/pq/__init__.py b/fairseq/fairseq/modules/quantization/pq/__init__.py new file mode 100644 index 0000000..c142a80 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .utils import SizeTracker, get_param, attrsetter, quantize_model_ # NOQA diff --git a/fairseq/fairseq/modules/quantization/pq/em.py b/fairseq/fairseq/modules/quantization/pq/em.py new file mode 100644 index 0000000..6f15c3e --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/em.py @@ -0,0 +1,211 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import random +from collections import Counter + +import torch + + +class EM: + """ + EM algorithm used to quantize the columns of W to minimize + + ||W - W_hat||^2 + + Args: + - W: weight matrix of size (in_features x out_features) + - n_iter: number of k-means iterations + - n_centroids: number of centroids (size of codebook) + - eps: for cluster reassignment when an empty cluster is found + - max_tentatives for cluster reassignment when an empty cluster is found + - verbose: print error after each iteration + + Remarks: + - If one cluster is empty, the most populated cluster is split into + two clusters + - All the relevant dimensions are specified in the code + """ + + def __init__( + self, W, n_centroids=256, n_iter=20, eps=1e-6, max_tentatives=30, verbose=True + ): + self.W = W + self.n_centroids = n_centroids + self.n_iter = n_iter + self.eps = eps + self.max_tentatives = max_tentatives + self.verbose = verbose + self.centroids = torch.Tensor() + self.assignments = torch.Tensor() + self.objective = [] + + def initialize_centroids(self): + """ + Initializes the centroids by sampling random columns from W. + """ + + in_features, out_features = self.W.size() + indices = torch.randint( + low=0, high=out_features, size=(self.n_centroids,) + ).long() + self.centroids = self.W[:, indices].t() # (n_centroids x in_features) + + def step(self, i): + """ + There are two standard steps for each iteration: expectation (E) and + minimization (M). The E-step (assignment) is performed with an exhaustive + search and the M-step (centroid computation) is performed with + the exact solution. + + Args: + - i: step number + + Remarks: + - The E-step heavily uses PyTorch broadcasting to speed up computations + and reduce the memory overhead + """ + + # assignments (E-step) + distances = self.compute_distances() # (n_centroids x out_features) + self.assignments = torch.argmin(distances, dim=0) # (out_features) + n_empty_clusters = self.resolve_empty_clusters() + + # centroids (M-step) + for k in range(self.n_centroids): + W_k = self.W[:, self.assignments == k] # (in_features x size_of_cluster_k) + self.centroids[k] = W_k.mean(dim=1) # (in_features) + + # book-keeping + obj = (self.centroids[self.assignments].t() - self.W).norm(p=2).item() + self.objective.append(obj) + if self.verbose: + logging.info( + f"Iteration: {i},\t" + f"objective: {obj:.6f},\t" + f"resolved empty clusters: {n_empty_clusters}" + ) + + def resolve_empty_clusters(self): + """ + If one cluster is empty, the most populated cluster is split into + two clusters by shifting the respective centroids. This is done + iteratively for a fixed number of tentatives. + """ + + # empty clusters + counts = Counter(map(lambda x: x.item(), self.assignments)) + empty_clusters = set(range(self.n_centroids)) - set(counts.keys()) + n_empty_clusters = len(empty_clusters) + + tentatives = 0 + while len(empty_clusters) > 0: + # given an empty cluster, find most populated cluster and split it into two + k = random.choice(list(empty_clusters)) + m = counts.most_common(1)[0][0] + e = torch.randn_like(self.centroids[m]) * self.eps + self.centroids[k] = self.centroids[m].clone() + self.centroids[k] += e + self.centroids[m] -= e + + # recompute assignments + distances = self.compute_distances() # (n_centroids x out_features) + self.assignments = torch.argmin(distances, dim=0) # (out_features) + + # check for empty clusters + counts = Counter(map(lambda x: x.item(), self.assignments)) + empty_clusters = set(range(self.n_centroids)) - set(counts.keys()) + + # increment tentatives + if tentatives == self.max_tentatives: + logging.info( + f"Could not resolve all empty clusters, {len(empty_clusters)} remaining" + ) + raise EmptyClusterResolveError + tentatives += 1 + + return n_empty_clusters + + def compute_distances(self): + """ + For every centroid m, computes + + ||M - m[None, :]||_2 + + Remarks: + - We rely on PyTorch's broadcasting to speed up computations + and reduce the memory overhead + - Without chunking, the sizes in the broadcasting are modified as: + (n_centroids x n_samples x out_features) -> (n_centroids x out_features) + - The broadcasting computation is automatically chunked so that + the tensors fit into the memory of the GPU + """ + + nb_centroids_chunks = 1 + + while True: + try: + return torch.cat( + [ + (self.W[None, :, :] - centroids_c[:, :, None]).norm(p=2, dim=1) + for centroids_c in self.centroids.chunk( + nb_centroids_chunks, dim=0 + ) + ], + dim=0, + ) + except RuntimeError: + nb_centroids_chunks *= 2 + + def assign(self): + """ + Assigns each column of W to its closest centroid, thus essentially + performing the E-step in train(). + + Remarks: + - The function must be called after train() or after loading + centroids using self.load(), otherwise it will return empty tensors + """ + + distances = self.compute_distances() # (n_centroids x out_features) + self.assignments = torch.argmin(distances, dim=0) # (out_features) + + def save(self, path, layer): + """ + Saves centroids and assignments. + + Args: + - path: folder used to save centroids and assignments + """ + + torch.save(self.centroids, os.path.join(path, "{}_centroids.pth".format(layer))) + torch.save( + self.assignments, os.path.join(path, "{}_assignments.pth".format(layer)) + ) + torch.save(self.objective, os.path.join(path, "{}_objective.pth".format(layer))) + + def load(self, path, layer): + """ + Loads centroids and assignments from a given path + + Args: + - path: folder use to load centroids and assignments + """ + + self.centroids = torch.load( + os.path.join(path, "{}_centroids.pth".format(layer)) + ) + self.assignments = torch.load( + os.path.join(path, "{}_assignments.pth".format(layer)) + ) + self.objective = torch.load( + os.path.join(path, "{}_objective.pth".format(layer)) + ) + + +class EmptyClusterResolveError(Exception): + pass diff --git a/fairseq/fairseq/modules/quantization/pq/modules/__init__.py b/fairseq/fairseq/modules/quantization/pq/modules/__init__.py new file mode 100644 index 0000000..b67c8e8 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/modules/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .qconv import PQConv2d # NOQA +from .qemb import PQEmbedding # NOQA +from .qlinear import PQLinear # NOQA diff --git a/fairseq/fairseq/modules/quantization/pq/modules/qconv.py b/fairseq/fairseq/modules/quantization/pq/modules/qconv.py new file mode 100644 index 0000000..d15ec19 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/modules/qconv.py @@ -0,0 +1,115 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair + + +class PQConv2d(nn.Module): + """ + Quantized counterpart of nn.Conv2d module. Stores the centroid, the assignments + and the non-quantized biases. The full weight is re-instantiated at each forward + pass and autograd automatically computes the gradients with respect to the + centroids. + + Args: + - centroids: centroids of size n_centroids x block_size + - assignments: assignments of the centroids to the subvectors + of size self.out_channels x n_blocks + - bias: the non-quantized bias, must be either torch.Tensor or None + + Remarks: + - We refer the reader to the official documentation of the nn.Conv2d module + for the other arguments and the behavior of the module. + - Performance tests on GPU show that this implementation is 10% slower than + the non-quantized nn.Conv2d module for a standard training loop. + - During the backward, the gradients are averaged by cluster and not summed. + This explains the hook registered to the centroids. + """ + + def __init__( + self, + centroids, + assignments, + bias, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + padding_mode="zeros", + ): + super(PQConv2d, self).__init__() + self.block_size = centroids.size(1) + self.n_centroids = centroids.size(0) + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.padding_mode = padding_mode + # check compatibility + if in_channels // groups * np.prod(self.kernel_size) % self.block_size != 0: + raise ValueError("Wrong PQ sizes") + if len(assignments) % out_channels != 0: + raise ValueError("Wrong PQ sizes") + if in_channels % groups != 0: + raise ValueError("in_channels must be divisible by groups") + if out_channels % groups != 0: + raise ValueError("out_channels must be divisible by groups") + # define parameters + self.centroids = nn.Parameter(centroids, requires_grad=True) + self.register_buffer("assignments", assignments) + self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) + if bias is not None: + self.bias = nn.Parameter(bias) + else: + self.register_parameter("bias", None) + # register hook for averaging gradients per centroids instead of summing + self.centroids.register_hook(lambda x: x / self.counts[:, None]) + + @property + def weight(self): + return ( + self.centroids[self.assignments] + .reshape(-1, self.out_channels, self.block_size) + .permute(1, 0, 2) + .reshape( + self.out_channels, self.in_channels // self.groups, *self.kernel_size + ) + ) + + def forward(self, x): + return F.conv2d( + x, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + ) + + def extra_repr(self): + s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}" + if self.padding != (0,) * len(self.padding): + s += ", padding={padding}" + if self.dilation != (1,) * len(self.dilation): + s += ", dilation={dilation}" + if self.groups != 1: + s += ", groups={groups}" + if self.bias is None: + s += ", bias=False" + if self.padding_mode != "zeros": + s += ", padding_mode={padding_mode}" + s += ", n_centroids={n_centroids}, block_size={block_size}" + return s.format(**self.__dict__) diff --git a/fairseq/fairseq/modules/quantization/pq/modules/qemb.py b/fairseq/fairseq/modules/quantization/pq/modules/qemb.py new file mode 100644 index 0000000..3a74ad3 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/modules/qemb.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PQEmbedding(nn.Module): + """ + Quantized counterpart of nn.Embedding module. Stores the centroids and + the assignments. The full weight is re-instantiated at each forward + pass. + + Args: + - centroids: centroids of size n_centroids x block_size + - assignments: assignments of the centroids to the subvectors + of size self.out_features x n_blocks + - bias: the non-quantized bias + + Remarks: + - We refer the reader to the official documentation of the nn.Embedding module + for the other arguments and the behavior of the module + - Performance tests on GPU show that this implementation is 10% slower than + the non-quantized nn.Embedding module for a standard training loop. + """ + + def __init__( + self, + centroids, + assignments, + num_embeddings, + embedding_dim, + padding_idx=None, + max_norm=None, + norm_type=2.0, + scale_grad_by_freq=False, + sparse=False, + _weight=None, + ): + super(PQEmbedding, self).__init__() + self.block_size = centroids.size(1) + self.n_centroids = centroids.size(0) + self.num_embeddings = num_embeddings + self.embedding_dim = embedding_dim + if padding_idx is not None: + if padding_idx > 0: + assert ( + padding_idx < self.num_embeddings + ), "Padding_idx must be within num_embeddings" + elif padding_idx < 0: + assert ( + padding_idx >= -self.num_embeddings + ), "Padding_idx must be within num_embeddings" + padding_idx = self.num_embeddings + padding_idx + self.padding_idx = padding_idx + self.max_norm = max_norm + self.norm_type = norm_type + self.scale_grad_by_freq = scale_grad_by_freq + self.sparse = sparse + # check compatibility + if self.embedding_dim % self.block_size != 0: + raise ValueError("Wrong PQ sizes") + if len(assignments) % self.num_embeddings != 0: + raise ValueError("Wrong PQ sizes") + # define parameters + self.centroids = nn.Parameter(centroids, requires_grad=True) + self.register_buffer("assignments", assignments) + self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) + + @property + def weight(self): + return ( + self.centroids[self.assignments] + .reshape(-1, self.num_embeddings, self.block_size) + .permute(1, 0, 2) + .flatten(1, 2) + ) + + def forward(self, input): + return F.embedding( + input, + self.weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) + + def extra_repr(self): + s = "{num_embeddings}, {embedding_dim}" + if self.padding_idx is not None: + s += ", padding_idx={padding_idx}" + if self.max_norm is not None: + s += ", max_norm={max_norm}" + if self.norm_type != 2: + s += ", norm_type={norm_type}" + if self.scale_grad_by_freq is not False: + s += ", scale_grad_by_freq={scale_grad_by_freq}" + if self.sparse is not False: + s += ", sparse=True" + s += ", n_centroids={n_centroids}, block_size={block_size}" + + return s.format(**self.__dict__) diff --git a/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py b/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py new file mode 100644 index 0000000..9bdd25a --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PQLinear(nn.Module): + """ + Quantized counterpart of nn.Linear module. Stores the centroid, the assignments + and the non-quantized biases. The full weight is re-instantiated at each forward + pass. + + Args: + - centroids: centroids of size n_centroids x block_size + - assignments: assignments of the centroids to the subvectors + of size self.out_features x n_blocks + - bias: the non-quantized bias + + Remarks: + - We refer the reader to the official documentation of the nn.Linear module + for the other arguments and the behavior of the module + - Performance tests on GPU show that this implementation is 15% slower than + the non-quantized nn.Linear module for a standard training loop. + """ + + def __init__(self, centroids, assignments, bias, in_features, out_features): + super(PQLinear, self).__init__() + self.block_size = centroids.size(1) + self.n_centroids = centroids.size(0) + self.in_features = in_features + self.out_features = out_features + # check compatibility + if self.in_features % self.block_size != 0: + raise ValueError("Wrong PQ sizes") + if len(assignments) % self.out_features != 0: + raise ValueError("Wrong PQ sizes") + # define parameters + self.centroids = nn.Parameter(centroids, requires_grad=True) + self.register_buffer("assignments", assignments) + self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) + if bias is not None: + self.bias = nn.Parameter(bias) + else: + self.register_parameter("bias", None) + + @property + def weight(self): + return ( + self.centroids[self.assignments] + .reshape(-1, self.out_features, self.block_size) + .permute(1, 0, 2) + .flatten(1, 2) + ) + + def forward(self, x): + return F.linear( + x, + self.weight, + self.bias, + ) + + def extra_repr(self): + return f"in_features={self.in_features},\ + out_features={self.out_features},\ + n_centroids={self.n_centroids},\ + block_size={self.block_size},\ + bias={self.bias is not None}" diff --git a/fairseq/fairseq/modules/quantization/pq/pq.py b/fairseq/fairseq/modules/quantization/pq/pq.py new file mode 100644 index 0000000..eddc2eb --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/pq.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .em import EM, EmptyClusterResolveError + + +class PQ(EM): + """ + Quantizes the layer weights W with the standard Product Quantization + technique. This learns a codebook of codewords or centroids of size + block_size from W. For further reference on using PQ to quantize + neural networks, see "And the Bit Goes Down: Revisiting the Quantization + of Neural Networks", Stock et al., ICLR 2020. + + PQ is performed in two steps: + (1) The matrix W (weights or fully-connected or convolutional layer) + is reshaped to (block_size, -1). + - If W is fully-connected (2D), its columns are split into + blocks of size block_size. + - If W is convolutional (4D), its filters are split along the + spatial dimension. + (2) We apply the standard EM/k-means algorithm to the resulting reshaped matrix. + + Args: + - W: weight matrix to quantize of size (in_features x out_features) + - block_size: size of the blocks (subvectors) + - n_centroids: number of centroids + - n_iter: number of k-means iterations + - eps: for cluster reassignment when an empty cluster is found + - max_tentatives for cluster reassignment when an empty cluster is found + - verbose: print information after each iteration + + Remarks: + - block_size be compatible with the shape of W + """ + + def __init__( + self, + W, + block_size, + n_centroids=256, + n_iter=20, + eps=1e-6, + max_tentatives=30, + verbose=True, + ): + self.block_size = block_size + W_reshaped = self._reshape(W) + super(PQ, self).__init__( + W_reshaped, + n_centroids=n_centroids, + n_iter=n_iter, + eps=eps, + max_tentatives=max_tentatives, + verbose=verbose, + ) + + def _reshape(self, W): + """ + Reshapes the matrix W as expained in step (1). + """ + + # fully connected: by convention the weight has size out_features x in_features + if len(W.size()) == 2: + self.out_features, self.in_features = W.size() + assert ( + self.in_features % self.block_size == 0 + ), "Linear: n_blocks must be a multiple of in_features" + return ( + W.reshape(self.out_features, -1, self.block_size) + .permute(2, 1, 0) + .flatten(1, 2) + ) + + # convolutional: we reshape along the spatial dimension + elif len(W.size()) == 4: + self.out_channels, self.in_channels, self.k_h, self.k_w = W.size() + assert ( + self.in_channels * self.k_h * self.k_w + ) % self.block_size == 0, ( + "Conv2d: n_blocks must be a multiple of in_channels * k_h * k_w" + ) + return ( + W.reshape(self.out_channels, -1, self.block_size) + .permute(2, 1, 0) + .flatten(1, 2) + ) + # not implemented + else: + raise NotImplementedError(W.size()) + + def encode(self): + """ + Performs self.n_iter EM steps. + """ + + self.initialize_centroids() + for i in range(self.n_iter): + try: + self.step(i) + except EmptyClusterResolveError: + break + + def decode(self): + """ + Returns the encoded full weight matrix. Must be called after + the encode function. + """ + + # fully connected case + if "k_h" not in self.__dict__: + return ( + self.centroids[self.assignments] + .reshape(-1, self.out_features, self.block_size) + .permute(1, 0, 2) + .flatten(1, 2) + ) + + # convolutional case + else: + return ( + self.centroids[self.assignments] + .reshape(-1, self.out_channels, self.block_size) + .permute(1, 0, 2) + .reshape(self.out_channels, self.in_channels, self.k_h, self.k_w) + ) diff --git a/fairseq/fairseq/modules/quantization/pq/utils.py b/fairseq/fairseq/modules/quantization/pq/utils.py new file mode 100644 index 0000000..eceeef8 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/pq/utils.py @@ -0,0 +1,376 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import re +from operator import attrgetter, itemgetter +import torch +import numpy as np +import torch.distributed as dist +import torch.nn as nn + +from .modules import PQConv2d, PQEmbedding, PQLinear +from .pq import PQ + + +def quantize_model_( + model, + size_tracker, + layers_to_quantize, + block_sizes_config, + n_centroids_config, + step=0, + n_iter=15, + eps=1e-6, + max_tentatives=100, + remove_weights=False, + verbose=True, + state_dict=None, +): + """ + Quantize a model in-place by stages. All the targeted + layers are replaced by their quantized counterpart, + and the model is ready for the finetuning of the + centroids in a standard training loop (no modifications + required). Note that we do not quantize biases. + + Args: + - model: a nn.Module + - size_tracker: useful for tracking quatization statistics + - layers_to_quantize: a list containing regexps for + filtering the layers to quantize at each stage according + to their name (as in model.named_parameters()) + - block_sizes_config: dict like + { + 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), + 'Linear': ('in_features', {'*': 8}) + } + For instance, all conv2d layers with kernel size 3x3 have + a block size of 9 and all Linear layers are quantized with + a block size of 8, irrespective of their size. + - n_centroids_config: dict like + { + 'Conv2d': ('kernel_size', {'*': 256}), + 'Linear': ('in_features', {'*': 256}) + } + For instance, all conv2d layers are quantized with 256 centroids + - step: the layers to quantize inplace corresponding + to layers_to_quantize[step] + """ + + quantized_layers = get_layers( + model, layers_to_quantize[step], remove_weights=remove_weights + ) + + for layer in quantized_layers: + + # book-keeping + is_master_process = (not dist.is_initialized()) or ( + dist.is_initialized() and dist.get_rank() == 0 + ) + verbose = verbose and is_master_process + + # get block size and centroids + module = attrgetter(layer)(model) + block_size = get_param(module, layer, block_sizes_config) + n_centroids = get_param(module, layer, n_centroids_config) + if verbose: + logging.info( + f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids" + ) + + # quantize layer + weight = module.weight.data.clone() + is_bias = "bias" in [x[0] for x in module.named_parameters()] + bias = module.bias.data.clone() if is_bias else None + quantizer = PQ( + weight, + block_size, + n_centroids=n_centroids, + n_iter=n_iter, + eps=eps, + max_tentatives=max_tentatives, + verbose=verbose, + ) + + # quantization performed on all GPUs with same seed + quantizer.encode() + centroids = quantizer.centroids.contiguous() + assignments = quantizer.assignments.contiguous() + + # If n_iter = 0 and state_dict is provided, then + # we initialize random assignments and centroids to + # random values of the appropriate dimensions + # because the quantized model parameters will + # overwritten by the state_dict later on. + if n_iter == 0 and state_dict: + # Initialize random centroids of the correct size + centroids = torch.rand(centroids.size()) + centroids.cuda() + # Get counts and assignment keys from layer in loaded checkpoint. + counts_key = layer + "." + "counts" + assignment_key = layer + "." + "assignments" + # Get number of different bins to include. + counts = list(state_dict[counts_key].shape)[0] + print(layer) + print(state_dict[counts_key]) + print(counts) + # Initialize random assignments of the correct size + # with an appropriate number of bins. + num_assignments = list(state_dict[assignment_key].shape)[0] + num_extra = num_assignments - counts + print(num_assignments) + print(num_extra) + assignments_bins = torch.arange(counts) + assignments_rand = torch.randint(0, counts - 1, (num_extra,)) + assignments = torch.cat((assignments_bins, assignments_rand), 0) + # assignments = assignments.type(torch.IntTensor) + assignments.cuda() + print("assignments") + print(assignments) + + # broadcast results to make sure weights are up-to-date + if dist.is_initialized(): + dist.broadcast(centroids, 0) + dist.broadcast(assignments, 0) + + # instantiate the quantized counterpart + if isinstance(module, nn.Linear): + out_features, in_features = map( + lambda k: module.__dict__[k], ["out_features", "in_features"] + ) + quantized_module = PQLinear( + centroids, assignments, bias, in_features, out_features + ) + elif isinstance(module, nn.Embedding): + num_embeddings, embedding_dim = map( + lambda k: module.__dict__[k], ["num_embeddings", "embedding_dim"] + ) + quantized_module = PQEmbedding( + centroids, assignments, num_embeddings, embedding_dim + ) + elif isinstance(module, nn.Conv2d): + out_channels, in_channels, kernel_size = map( + lambda k: module.__dict__[k], + ["out_channels", "in_channels", "kernel_size"], + ) + stride, padding, dilation, groups, padding_mode = map( + lambda k: module.__dict__[k], + ["stride", "padding", "dilation", "groups", "padding_mode"], + ) + + quantized_module = PQConv2d( + centroids, + assignments, + bias, + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + padding_mode=padding_mode, + ) + else: + raise ValueError(f"Module {module} not yet supported for quantization") + + # replace layer by its quantized counterpart + attrsetter(layer)(model, quantized_module) + + # update statistics + size_tracker.update(weight, block_size, n_centroids) + + # return name of quantized layers + return quantized_layers + + +def get_layers(model, filter_regexp, remove_weights=False): + """ + Filters out the layers according to a regexp. Note that + we omit biases. + + Args: + - model: a nn.Module + - filter_regexp: a regexp to filter the layers to keep + according to their name in model.named_parameters(). + For instance, the regexp: + + down_layers\\.[123456]\\.(conv[12]|identity\\.conv)) + + is keeping blocks down_layers from 1 to 6, and inside + each block is keeping conv1, conv2 and identity.conv. + + Remarks: + - We add (module\\.)? at the beginning of the regexp to + account for the possible use of nn.parallel.DataParallel + """ + + # get all parameter names + all_layers = map(itemgetter(0), model.named_parameters()) + + # remove biases + all_layers = filter(lambda x: "bias" not in x, all_layers) + + # remove .weight in all other names (or .weight_orig is spectral norm) + all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers) + # remove weights indicates whether the weights extension should be removed, in addition to + # weight_orig and weight extension on names + if remove_weights: + all_layers = map(lambda x: x.replace(".weights", ""), all_layers) + all_layers = map(lambda x: x.replace(".weight", ""), all_layers) + + # return filtered layers + filter_regexp = "(module\\.)?" + "(" + filter_regexp + ")" + r = re.compile(filter_regexp) + + return list(filter(r.match, all_layers)) + + +def get_param(module, layer_name, param_config): + """ + Given a quantization configuration, get the right parameter + for the module to be quantized. + + Args: + - module: a nn.Module + - layer_name: the name of the layer + - param_config: a dict like + { + 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), + 'Linear': ('in_features', {'*': 8}) + } + For instance, all conv2d layers with kernel size 3x3 have + a block size of 9 and all Linear layers are quantized with + a block size of 8, irrespective of their size. + + Remarks: + - if 'fuzzy_name' is passed as a parameter, layers whose layer_name + include 'fuzzy_name' will be assigned the given parameter. + In the following example, conv.expand layers will have a block + size of 9 while conv.reduce will have a block size of 4 and all + other layers will have a block size of 2. + { + 'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}), + 'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4}) + } + + """ + + layer_type = module.__class__.__name__ + + if layer_type not in param_config: + raise KeyError(f"Layer type {layer_type} not in config for layer {module}") + + feature, params = param_config[module.__class__.__name__] + + if feature != "fuzzy_name": + feature_value = str(getattr(module, feature)) + if feature_value not in params: + if "*" in params: + feature_value = "*" + else: + raise KeyError( + f"{feature}={feature_value} not in config for layer {module}" + ) + else: + feature_values = [name for name in params if name in layer_name] + if len(feature_values) == 0: + if "*" in params: + feature_value = "*" + else: + raise KeyError(f"name={layer_name} not in config for {module}") + else: + feature_value = feature_values[0] + + return params[feature_value] + + +class SizeTracker(object): + """ + Class to keep track of the compressed network size with iPQ. + + Args: + - model: a nn.Module + + Remarks: + - The compressed size is the sum of three components + for each layer in the network: + (1) Storing the centroids given by iPQ in fp16 + (2) Storing the assignments of the blocks in int8 + (3) Storing all non-compressed elements such as biases + - This cost in only valid if we use 256 centroids (then + indexing can indeed by done with int8). + """ + + def __init__(self, model): + self.model = model + self.size_non_compressed_model = self.compute_size() + self.size_non_quantized = self.size_non_compressed_model + self.size_index = 0 + self.size_centroids = 0 + self.n_quantized_layers = 0 + + def compute_size(self): + """ + Computes the size of the model (in MB). + """ + + res = 0 + for _, p in self.model.named_parameters(): + res += p.numel() + return res * 4 / 1024 / 1024 + + def update(self, W, block_size, n_centroids): + """ + Updates the running statistics when quantizing a new layer. + """ + + # bits per weights + bits_per_weight = np.log2(n_centroids) / block_size + self.n_quantized_layers += 1 + + # size of indexing the subvectors of size block_size (in MB) + size_index_layer = bits_per_weight * W.numel() / 8 / 1024 / 1024 + self.size_index += size_index_layer + + # size of the centroids stored in float16 (in MB) + size_centroids_layer = n_centroids * block_size * 2 / 1024 / 1024 + self.size_centroids += size_centroids_layer + + # size of non-compressed layers, e.g. LayerNorms or biases (in MB) + size_uncompressed_layer = W.numel() * 4 / 1024 / 1024 + self.size_non_quantized -= size_uncompressed_layer + + def __repr__(self): + size_compressed = ( + self.size_index + self.size_centroids + self.size_non_quantized + ) + compression_ratio = self.size_non_compressed_model / size_compressed # NOQA + return ( + f"Non-compressed model size: {self.size_non_compressed_model:.2f} MB. " + f"After quantizing {self.n_quantized_layers} layers, size " + f"(indexing + centroids + other): {self.size_index:.2f} MB + " + f"{self.size_centroids:.2f} MB + {self.size_non_quantized:.2f} MB = " + f"{size_compressed:.2f} MB, compression ratio: {compression_ratio:.2f}x" + ) + + +def attrsetter(*items): + def resolve_attr(obj, attr): + attrs = attr.split(".") + head = attrs[:-1] + tail = attrs[-1] + + for name in head: + obj = getattr(obj, name) + return obj, tail + + def g(obj, val): + for attr in items: + resolved_obj, resolved_attr = resolve_attr(obj, attr) + setattr(resolved_obj, resolved_attr, val) + + return g diff --git a/fairseq/fairseq/modules/quantization/quantization_options.py b/fairseq/fairseq/modules/quantization/quantization_options.py new file mode 100644 index 0000000..b46d682 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/quantization_options.py @@ -0,0 +1,44 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +def parse_config_yaml(yaml_data): + # Initialize to default options. + quantization_options = { + "n_centroids": { + "Linear": ["in_features", {"*": 256}], + "Embedding": ["embedding_dim", {"*": 256}], + }, + "block_sizes": { + "Linear": ["fuzzy_name", {"fc": 8, "attn": 4, "emb": 4}], + "Embedding": ["fuzzy_name", {"emb": 8}], + }, + "layers_to_quantize": [ + "decoder\\.layers\\.\\d+\\.fc[12]", + "decoder\\.embed_tokens\\.embeddings\\.[012]\\.[01]", + "decoder\\.layers\\.\\d+\\.self_attn\\.(k_proj|v_proj|q_proj|out_proj)", + ], + } + + if "n_centroids" in yaml_data: + quantization_options["n_centroids"] = { + layer: convert_yaml_to_tuple(layer_data) + for layer, layer_data in yaml_data["n_centroids"].items() + } + if "block_sizes" in yaml_data: + quantization_options["block_sizes"] = { + layer: convert_yaml_to_tuple(layer_data) + for layer, layer_data in yaml_data["block_sizes"].items() + } + if "layers_to_quantize" in yaml_data: + quantization_options["layers_to_quantize"] = yaml_data["layers_to_quantize"] + + return quantization_options + + +def convert_yaml_to_tuple(yaml_dictionary): + """Converts a yaml dictionary with two keys: `key` and `value` into a two + argument tuple of those values.""" + return (yaml_dictionary["key"], yaml_dictionary["value"]) diff --git a/fairseq/fairseq/modules/quantization/scalar/__init__.py b/fairseq/fairseq/modules/quantization/scalar/__init__.py new file mode 100644 index 0000000..143834f --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .utils import quantize_model_ # NOQA diff --git a/fairseq/fairseq/modules/quantization/scalar/modules/__init__.py b/fairseq/fairseq/modules/quantization/scalar/modules/__init__.py new file mode 100644 index 0000000..8031d9c --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/modules/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .qact import ActivationQuantizer # NOQA +from .qconv import IntConv2d # NOQA +from .qemb import IntEmbedding # NOQA +from .qlinear import IntLinear # NOQA diff --git a/fairseq/fairseq/modules/quantization/scalar/modules/qact.py b/fairseq/fairseq/modules/quantization/scalar/modules/qact.py new file mode 100644 index 0000000..b362c30 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/modules/qact.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from ..ops import emulate_int + + +class ActivationQuantizer: + """ + Fake scalar quantization of the activations using a forward hook. + + Args: + - module. a nn.Module for which we quantize the *post-activations* + - p: proportion of activations to quantize, set by default to 1 + - update_step: to recompute quantization parameters + - bits: number of bits for quantization + - method: choose among {"tensor", "histogram", "channel"} + - clamp_threshold: to prevent gradients overflow + + Remarks: + - Parameters scale and zero_point are recomputed every update_step + forward pass to reduce the overhead + - For the list of quantization methods and number of bits, see ops.py + - To remove the hook from the module, simply call self.handle.remove() + - At test time, the activations are fully quantized + - We use the straight-through estimator so that the gradients + back-propagate nicely in the network, this is implemented with + the detach() trick + - The activations are hard-clamped in [-clamp_threshold, clamp_threshold] + to prevent overflow during the backward pass + """ + + def __init__( + self, + module, + p=1, + update_step=1000, + bits=8, + method="histogram", + clamp_threshold=5, + ): + self.module = module + self.p = p + self.update_step = update_step + self.counter = 0 + self.bits = bits + self.method = method + self.clamp_threshold = clamp_threshold + self.handle = None + self.register_hook() + + def register_hook(self): + # forward hook + def quantize_hook(module, x, y): + + # update parameters every 1000 iterations + if self.counter % self.update_step == 0: + self.scale = None + self.zero_point = None + self.counter += 1 + + # train with QuantNoise and evaluate the fully quantized network + p = self.p if self.module.training else 1 + + # quantize activations + y_q, self.scale, self.zero_point = emulate_int( + y.detach(), + bits=self.bits, + method=self.method, + scale=self.scale, + zero_point=self.zero_point, + ) + + # mask to apply noise + mask = torch.zeros_like(y) + mask.bernoulli_(1 - p) + noise = (y_q - y).masked_fill(mask.bool(), 0) + + # using straight-through estimator (STE) + clamp_low = -self.scale * self.zero_point + clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) + return torch.clamp(y, clamp_low.item(), clamp_high.item()) + noise.detach() + + # register hook + self.handle = self.module.register_forward_hook(quantize_hook) diff --git a/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py b/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py new file mode 100644 index 0000000..2974474 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py @@ -0,0 +1,149 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from torch.nn.modules.conv import _ConvNd +from torch.nn.modules.utils import _pair + +from ..ops import emulate_int + + +class IntConv2d(_ConvNd): + """ + Quantized counterpart of the nn.Conv2d module that applies QuantNoise during training. + + Args: + - standard nn.Conv2d parameters + - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights) + - bits: number of bits + - method: choose among {"tensor", "histogram", "channel"} + - update_step: recompute scale and zero_point every update_steps iterations + + Remarks: + - We use the straight-thgourh estimator so that the gradients + back-propagate nicely in the network, this is implemented with + the detach() trick + - Parameters scale and zero_point are recomputed every update_step + forward pass to reduce the overhead + - At test time, the weights are fully quantized + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode="zeros", + p=0, + bits=8, + method="histogram", + update_step=1000, + ): + kernel_size = _pair(kernel_size) + stride = _pair(stride) + padding = _pair(padding) + dilation = _pair(dilation) + super(IntConv2d, self).__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + False, + _pair(0), + groups, + bias, + padding_mode, + ) + + # quantization parameters + self.p = p + self.bits = bits + self.method = method + self.update_step = update_step + self.counter = 0 + + def _conv_forward(self, input, weight): + if self.padding_mode != "zeros": + return F.conv2d( + F.pad(input, self._padding_repeated_twice, mode=self.padding_mode), + weight, + self.bias, + self.stride, + _pair(0), + self.dilation, + self.groups, + ) + return F.conv2d( + input, + weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + ) + + def forward(self, input): + # train with QuantNoise and evaluate the fully quantized network + p = self.p if self.training else 1 + + # update parameters every 100 iterations + if self.counter % self.update_step == 0: + self.scale = None + self.zero_point = None + self.counter += 1 + + # quantize weight + weight_quantized, self.scale, self.zero_point = emulate_int( + self.weight.detach(), + bits=self.bits, + method=self.method, + scale=self.scale, + zero_point=self.zero_point, + ) + + # mask to apply noise + mask = torch.zeros_like(self.weight) + mask.bernoulli_(1 - p) + noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) + + # using straight-through estimator (STE) + clamp_low = -self.scale * self.zero_point + clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) + weight = ( + torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + + noise.detach() + ) + + # return output + output = self._conv_forward(input, weight) + return output + + def extra_repr(self): + return ( + "in_channels={}, out_channels={}, kernel_size={}, stride={}, " + "padding={}, dilation={}, groups={}, bias={}, quant_noise={}, " + "bits={}, method={}".format( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + self.padding, + self.dilation, + self.groups, + self.bias is not None, + self.p, + self.bits, + self.method, + ) + ) diff --git a/fairseq/fairseq/modules/quantization/scalar/modules/qemb.py b/fairseq/fairseq/modules/quantization/scalar/modules/qemb.py new file mode 100644 index 0000000..3b293ac --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/modules/qemb.py @@ -0,0 +1,147 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..ops import emulate_int + + +class IntEmbedding(nn.Module): + """ + Quantized counterpart of the nn.Embedding module that applies QuantNoise during training. + + Args: + - num_embeddings: number of tokens + - embedding_dim: embedding dimension + - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights) + - bits: number of bits + - method: choose among {"tensor", "histogram", "channel"} + - update_step: recompute scale and zero_point every update_steps iterations + + Remarks: + - We use the straight-through estimator so that the gradients + back-propagate nicely in the network, this is implemented with + the detach() trick + - Parameters scale and zero_point are recomputed every update_step + forward pass to reduce the overhead + - At test time, the weights are fully quantized + """ + + def __init__( + self, + num_embeddings, + embedding_dim, + padding_idx=None, + max_norm=None, + norm_type=2.0, + scale_grad_by_freq=False, + sparse=False, + _weight=None, + p=0, + update_step=1000, + bits=8, + method="histogram", + ): + super(IntEmbedding, self).__init__() + self.num_embeddings = num_embeddings + self.embedding_dim = embedding_dim + if padding_idx is not None: + if padding_idx > 0: + assert ( + padding_idx < self.num_embeddings + ), "Padding_idx must be within num_embeddings" + elif padding_idx < 0: + assert ( + padding_idx >= -self.num_embeddings + ), "Padding_idx must be within num_embeddings" + padding_idx = self.num_embeddings + padding_idx + self.padding_idx = padding_idx + self.max_norm = max_norm + self.norm_type = norm_type + self.scale_grad_by_freq = scale_grad_by_freq + if _weight is None: + self.weight = nn.Parameter(torch.Tensor(num_embeddings, embedding_dim)) + self.reset_parameters() + else: + assert list(_weight.shape) == [ + num_embeddings, + embedding_dim, + ], "Shape of weight does not match num_embeddings and embedding_dim" + self.weight = nn.Parameter(_weight) + self.sparse = sparse + + # quantization parameters + self.p = p + self.bits = bits + self.method = method + self.update_step = update_step + self.counter = 0 + + def reset_parameters(self): + nn.init.normal_(self.weight) + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def forward(self, input): + # train with QuantNoise and evaluate the fully quantized network + p = self.p if self.training else 1 + + # update parameters every 1000 iterations + if self.counter % self.update_step == 0: + self.scale = None + self.zero_point = None + self.counter += 1 + + # quantize weight + weight_quantized, self.scale, self.zero_point = emulate_int( + self.weight.detach(), + bits=self.bits, + method=self.method, + scale=self.scale, + zero_point=self.zero_point, + ) + + # mask to apply noise + mask = torch.zeros_like(self.weight) + mask.bernoulli_(1 - p) + noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) + + # using straight-through estimator (STE) + clamp_low = -self.scale * self.zero_point + clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) + weight = ( + torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + + noise.detach() + ) + + # return output + output = F.embedding( + input, + weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) + return output + + def extra_repr(self): + s = "{num_embeddings}, {embedding_dim}" + if self.padding_idx is not None: + s += ", padding_idx={padding_idx}" + if self.max_norm is not None: + s += ", max_norm={max_norm}" + if self.norm_type != 2: + s += ", norm_type={norm_type}" + if self.scale_grad_by_freq is not False: + s += ", scale_grad_by_freq={scale_grad_by_freq}" + if self.sparse is not False: + s += ", sparse=True" + s += "quant_noise={p}, bits={bits}, method={method}" + return s.format(**self.__dict__) diff --git a/fairseq/fairseq/modules/quantization/scalar/modules/qlinear.py b/fairseq/fairseq/modules/quantization/scalar/modules/qlinear.py new file mode 100644 index 0000000..78606a2 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/modules/qlinear.py @@ -0,0 +1,113 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..ops import emulate_int + + +class IntLinear(nn.Module): + """ + Quantized counterpart of the nn.Linear module that applies QuantNoise during training. + + Args: + - in_features: input features + - out_features: output features + - bias: bias or not + - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights) + - bits: number of bits + - method: choose among {"tensor", "histogram", "channel"} + - update_step: recompute scale and zero_point every update_steps iterations + + Remarks: + - We use the straight-through estimator so that the gradients + back-propagate nicely in the network, this is implemented with + the detach() trick. + - Parameters scale and zero_point are recomputed every update_step + forward pass to reduce the overhead + - At test time, the weights are fully quantized + """ + + def __init__( + self, + in_features, + out_features, + bias=True, + p=0, + update_step=3000, + bits=8, + method="histogram", + ): + super(IntLinear, self).__init__() + self.in_features = int(in_features) + self.out_features = int(out_features) + self.weight = torch.nn.Parameter(torch.Tensor(out_features, in_features)) + self.chosen_bias = bias + if self.chosen_bias: + self.bias = torch.nn.Parameter(torch.Tensor(out_features)) + else: + self.register_parameter("bias", None) + self.reset_parameters() + + # quantization parameters + self.p = p + self.bits = bits + self.method = method + self.update_step = update_step + self.counter = 0 + + def reset_parameters(self): + nn.init.xavier_uniform_(self.weight) + if self.chosen_bias: + nn.init.constant_(self.bias, 0.0) + return + + def forward(self, input): + # train with QuantNoise and evaluate the fully quantized network + p = self.p if self.training else 1 + + # update parameters every 100 iterations + if self.counter % self.update_step == 0: + self.scale = None + self.zero_point = None + self.counter += 1 + + # quantize weight + weight_quantized, self.scale, self.zero_point = emulate_int( + self.weight.detach(), + bits=self.bits, + method=self.method, + scale=self.scale, + zero_point=self.zero_point, + ) + + # mask to apply noise + mask = torch.zeros_like(self.weight) + mask.bernoulli_(1 - p) + noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) + + # using straight-through estimator (STE) + clamp_low = -self.scale * self.zero_point + clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) + weight = ( + torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + + noise.detach() + ) + + # return output + output = F.linear(input, weight, self.bias) + return output + + def extra_repr(self): + return "in_features={}, out_features={}, bias={}, quant_noise={}, bits={}, method={}".format( + self.in_features, + self.out_features, + self.bias is not None, + self.p, + self.bits, + self.method, + ) diff --git a/fairseq/fairseq/modules/quantization/scalar/ops.py b/fairseq/fairseq/modules/quantization/scalar/ops.py new file mode 100644 index 0000000..e0f9a0c --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/ops.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +try: + import torch.ao.quantization as quantization +except ImportError: + import torch.quantization as quantization + + +def emulate_int(w, bits, method, scale=None, zero_point=None): + q = globals()[f"emulate_int8_{method}"] + return q(w, scale=scale, zero_point=zero_point, bits=bits) + + +def quantize(w, scale, zero_point, bits=8): + # In the default behavior, max_val = 255. + max_val = 2**bits - 1 + return ( + torch.clamp(torch.round(w / scale + zero_point), 0, max_val) - zero_point + ) * scale + + +def emulate_int8_histogram(w, scale=None, zero_point=None, bits=8): + if scale is None: + obs = quantization.observer.HistogramObserver() + obs.to(device=w.device) + _ = obs(w.float()) + scale, zero_point = obs.calculate_qparams() + scale = scale.cuda().type_as(w) + zero_point = zero_point.cuda().type_as(w) + return quantize(w, scale, zero_point, bits=bits), scale, zero_point + + +def emulate_int8_channel(w, scale=None, zero_point=None, bits=8): + if scale is None: + obs = quantization.observer.PerChannelMinMaxObserver( + ch_axis=-1, qscheme=torch.per_channel_symmetric + ) + obs.to(device=w.device) + _ = obs(w) + scale, zero_point, ch_axis = obs.get_qparams() + scale = scale.cuda().type_as(w) + zero_point = zero_point.cuda().type_as(w) + return quantize(w, scale, zero_point, bits=bits), scale, zero_point + + +def emulate_int8_tensor(w, scale=None, zero_point=None, bits=8): + if scale is None: + obs = quantization.observer.MinMaxObserver() + obs.to(device=w.device) + _ = obs(w) + scale, zero_point = obs.calculate_qparams() + scale = scale.cuda().type_as(w) + zero_point = zero_point.cuda().type_as(w) + return quantize(w, scale, zero_point, bits=bits), scale, zero_point diff --git a/fairseq/fairseq/modules/quantization/scalar/utils.py b/fairseq/fairseq/modules/quantization/scalar/utils.py new file mode 100644 index 0000000..d4b1cc2 --- /dev/null +++ b/fairseq/fairseq/modules/quantization/scalar/utils.py @@ -0,0 +1,80 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from operator import attrgetter + +import torch.distributed as dist +import torch.nn as nn + +from ..pq.utils import attrsetter, get_layers +from .modules import ActivationQuantizer, IntConv2d, IntEmbedding, IntLinear + + +MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d} + + +def quantize_model_( + model, p=0.2, bits=8, update_step=3000, method="histogram", remove_weights=False +): + """ + Replaces all modules with their scalar quantized counterpart and + registers hooks to quantize the post-ativations of those modules. + + Args: + - model: a nn.Module + - p: amount of noise (0 for no noise, 1 to quantize all the weights/activations) + - bits: number of bits + - update_step: update quantization parameters every update_step steps + """ + # quantize all layers + # remove weights indicates whether the weights extension should be removed, in addition to + # weight_orig and weight extension on names + quantized_layers = get_layers(model, "(.*?)", remove_weights=remove_weights) + + for layer in quantized_layers: + + # book-keeping + is_master_process = (not dist.is_initialized()) or ( + dist.is_initialized() and dist.get_rank() == 0 + ) + + # recover module + module = attrgetter(layer)(model) + if is_master_process: + logging.info( + f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}" + ) + + # quantization params + q_params = { + "p": p, + "update_step": update_step, + "bits": bits, + "method": method, + "counter": 0, + } + + # instantiate the quantized counterpart + if isinstance(module, tuple(MAPPING.keys())): + QuantizedModule = MAPPING[module.__class__] + quantized_module = QuantizedModule.__new__(QuantizedModule) + params = module.__dict__ + params.update(q_params) + quantized_module.__dict__.update(params) + + else: + if is_master_process: + logging.info(f"Module {module} not yet supported for quantization") + continue + + # activation quantization + a_q = ActivationQuantizer(quantized_module, p=0, bits=bits, method=method) + + # replace layer by its quantized counterpart + attrsetter(layer)(model, quantized_module) + + # return name of quantized layers + return quantized_layers diff --git a/fairseq/fairseq/modules/rotary_positional_embedding.py b/fairseq/fairseq/modules/rotary_positional_embedding.py new file mode 100644 index 0000000..b74028b --- /dev/null +++ b/fairseq/fairseq/modules/rotary_positional_embedding.py @@ -0,0 +1,50 @@ +import torch + + +class RotaryPositionalEmbedding(torch.nn.Module): + def __init__(self, dim, base=10000, precision=torch.half): + """Rotary positional embedding + Reference : https://blog.eleuther.ai/rotary-embeddings/ + Paper: https://arxiv.org/pdf/2104.09864.pdf + Args: + dim: Dimension of embedding + base: Base value for exponential + precision: precision to use for numerical values + """ + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + self.seq_len_cached = 0 + self.cos_cached = torch.empty(self.seq_len_cached, 1, 1, dim) + self.sin_cached = torch.empty(self.seq_len_cached, 1, 1, dim) + self.precision = precision + + def forward(self, x, seq_len: int = 0): + """ + Args: + x: Input x with T X B X C + seq_len: Sequence length of input x + """ + if seq_len > self.seq_len_cached: + self.seq_len_cached = seq_len + t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + self.cos_cached = emb.cos().view(emb.size(0), 1, 1, emb.size(1)) + self.sin_cached = emb.sin().view(emb.size(0), 1, 1, emb.size(1)) + return self.cos_cached, self.sin_cached + +# rotary pos emb helpers: +def rotate_half(x): + x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] + return torch.cat( + (-x2, x1), dim=x1.ndim - 1 + ) # dim=-1 triggers a bug in earlier torch versions + + +def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0): + cos, sin = ( + cos[offset : q.shape[0] + offset, ...], + sin[offset : q.shape[0] + offset, ...], + ) + return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin) diff --git a/fairseq/fairseq/modules/same_pad.py b/fairseq/fairseq/modules/same_pad.py new file mode 100644 index 0000000..a3ce413 --- /dev/null +++ b/fairseq/fairseq/modules/same_pad.py @@ -0,0 +1,33 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from torch import nn + + +class SamePad(nn.Module): + def __init__(self, kernel_size, causal=False): + super().__init__() + if causal: + self.remove = kernel_size - 1 + else: + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + if self.remove > 0: + x = x[:, :, : -self.remove] + return x + + +class SamePad2d(nn.Module): + def __init__(self, kernel_size): + super().__init__() + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + assert len(x.size()) == 4 + if self.remove > 0: + x = x[:, :, : -self.remove, : -self.remove] + return x diff --git a/fairseq/fairseq/modules/scalar_bias.py b/fairseq/fairseq/modules/scalar_bias.py new file mode 100644 index 0000000..c96247c --- /dev/null +++ b/fairseq/fairseq/modules/scalar_bias.py @@ -0,0 +1,31 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# + +import torch + + +class ScalarBias(torch.autograd.Function): + """ + Adds a vector of scalars, used in self-attention mechanism to allow + the model to optionally attend to this vector instead of the past + """ + + @staticmethod + def forward(ctx, input, dim, bias_init): + size = list(input.size()) + size[dim] += 1 + output = input.new(*size).fill_(bias_init) + output.narrow(dim, 1, size[dim] - 1).copy_(input) + ctx.dim = dim + return output + + @staticmethod + def backward(ctx, grad): + return grad.narrow(ctx.dim, 1, grad.size(ctx.dim) - 1), None, None + + +def scalar_bias(input, dim, bias_init=0): + return ScalarBias.apply(input, dim, bias_init) diff --git a/fairseq/fairseq/modules/sinusoidal_positional_embedding.py b/fairseq/fairseq/modules/sinusoidal_positional_embedding.py new file mode 100644 index 0000000..dd93ddc --- /dev/null +++ b/fairseq/fairseq/modules/sinusoidal_positional_embedding.py @@ -0,0 +1,122 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Optional + +import torch +import torch.onnx.operators +from fairseq import utils +from torch import nn, Tensor + + +class SinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length. + + Padding symbols are ignored. + """ + + def __init__(self, embedding_dim, padding_idx, init_size=1024, auto_expand=True): + super().__init__() + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx if padding_idx is not None else 0 + self.register_buffer( + "weights", + SinusoidalPositionalEmbedding.get_embedding( + init_size, embedding_dim, padding_idx + ), + persistent=False, + ) + self.max_positions = int(1e5) + self.auto_expand = auto_expand + self.onnx_trace = False + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + # Ignore some deprecated keys that were used in older versions + deprecated_keys = ["weights", "_float_tensor"] + for key in deprecated_keys: + if prefix + key in state_dict: + del state_dict[prefix + key] + super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + @staticmethod + def get_embedding( + num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None + ): + """Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze( + 1 + ) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view( + num_embeddings, -1 + ) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + return emb + + def forward( + self, + input, + incremental_state: Optional[Any] = None, + timestep: Optional[Tensor] = None, + positions: Optional[Any] = None, + ): + """Input is expected to be of size [bsz x seqlen].""" + bspair = torch.onnx.operators.shape_as_tensor(input) + bsz, seq_len = bspair[0], bspair[1] + max_pos = self.padding_idx + 1 + seq_len + weights = self.weights + + if max_pos > self.weights.size(0): + # If the input is longer than the number of pre-computed embeddings, + # compute the extra embeddings on the fly. + # Only store the expanded embeddings if auto_expand=True. + # In multithreading environments, mutating the weights of a module + # may cause trouble. Set auto_expand=False if this happens. + weights = SinusoidalPositionalEmbedding.get_embedding( + max_pos, self.embedding_dim, self.padding_idx + ).to(self.weights) + if self.auto_expand: + self.weights = weights + + if incremental_state is not None: + # positions is the same for every token when decoding a single step + pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len + if self.onnx_trace: + return ( + weights.index_select(index=self.padding_idx + pos, dim=0) + .unsqueeze(1) + .repeat(bsz, 1, 1) + ) + return weights[self.padding_idx + pos, :].expand(bsz, 1, -1) + + positions = utils.make_positions( + input, self.padding_idx, onnx_trace=self.onnx_trace + ) + if self.onnx_trace: + flat_embeddings = weights.detach().index_select(0, positions.view(-1)) + embedding_shape = torch.cat( + (bsz.view(1), seq_len.view(1), torch.tensor([-1], dtype=torch.long)) + ) + embeddings = torch.onnx.operators.reshape_from_tensor_shape( + flat_embeddings, embedding_shape + ) + return embeddings + return ( + weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() + ) diff --git a/fairseq/fairseq/modules/sparse_multihead_attention.py b/fairseq/fairseq/modules/sparse_multihead_attention.py new file mode 100644 index 0000000..3cbd9d6 --- /dev/null +++ b/fairseq/fairseq/modules/sparse_multihead_attention.py @@ -0,0 +1,140 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch + +from .multihead_attention import MultiheadAttention + + +class SparseMultiheadAttention(MultiheadAttention): + """Sparse Multi-Headed Attention. + + "Generating Long Sequences with Sparse Transformers". Implements + fixed factorized self attention, where l=stride and c=expressivity. + A(1) includes all words in the stride window and A(2) takes a summary of c + words from the end of each stride window. + If is_bidirectional=False, we do not include any words past the current word, + as in the paper. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + stride=32, + expressivity=8, + is_bidirectional=True, + ): + + super().__init__( + embed_dim, + num_heads, + kdim, + vdim, + dropout, + bias, + add_bias_kv, + add_zero_attn, + self_attention, + encoder_decoder_attention, + ) + + self.is_bidirectional = is_bidirectional + self.stride = stride + self.expressivity = expressivity + assert self.stride > 0 and self.stride >= self.expressivity + + # Used for Ai(2) calculations - beginning of [l-c, l] range + def compute_checkpoint(self, word_index): + if word_index % self.stride == 0 and word_index != 0: + checkpoint_index = word_index - self.expressivity + else: + checkpoint_index = ( + math.floor(word_index / self.stride) * self.stride + + self.stride + - self.expressivity + ) + return checkpoint_index + + # Computes Ai(2) + def compute_subset_summaries(self, absolute_max): + checkpoint_index = self.compute_checkpoint(0) + subset_two = set() + while checkpoint_index <= absolute_max - 1: + summary = set( + range( + checkpoint_index, + min(checkpoint_index + self.expressivity + 1, absolute_max), + ) + ) + subset_two = subset_two.union(summary) + checkpoint_index = self.compute_checkpoint(checkpoint_index + self.stride) + return subset_two + + # Sparse Transformer Fixed Attention Pattern: https://arxiv.org/pdf/1904.10509.pdf + def compute_fixed_attention_subset(self, word_index, tgt_len): + # +1s account for range function; [min, max) -> [min, max] + if not self.is_bidirectional: + absolute_max = word_index + 1 + else: + absolute_max = tgt_len + + # Subset 1 - whole window + rounded_index = ( + math.floor((word_index + self.stride) / self.stride) * self.stride + ) + if word_index % self.stride == 0 and word_index != 0: + subset_one = set( + range(word_index - self.stride, min(absolute_max, word_index + 1)) + ) + else: + subset_one = set( + range( + max(0, rounded_index - self.stride), + min(absolute_max, rounded_index + 1), + ) + ) + + # Subset 2 - summary per window + # If bidirectional, subset 2 is the same for every index + subset_two = set() + if not self.is_bidirectional: + subset_two = self.compute_subset_summaries(absolute_max) + + return subset_one.union(subset_two) + + # Compute sparse mask - if bidirectional, can pre-compute and store + def buffered_sparse_mask(self, tensor, tgt_len, src_len): + assert tgt_len > self.stride + sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float("-inf")) + + # If bidirectional, subset 2 is the same for every index + subset_summaries = set() + if self.is_bidirectional: + subset_summaries = self.compute_subset_summaries(tgt_len) + + for i in range(tgt_len): + fixed_attention_subset = self.compute_fixed_attention_subset(i, tgt_len) + fixed_attention_subset = fixed_attention_subset.union(subset_summaries) + included_word_indices = torch.LongTensor(list(fixed_attention_subset)) + sparse_mask[i].index_fill_(0, included_word_indices, 0) + return sparse_mask.type_as(tensor) + + def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): + sparse_mask = self.buffered_sparse_mask(attn_weights, tgt_len, src_len) + sparse_mask = sparse_mask.unsqueeze(0).expand( + bsz * self.num_heads, tgt_len, src_len + ) + attn_weights += sparse_mask diff --git a/fairseq/fairseq/modules/sparse_transformer_sentence_encoder.py b/fairseq/fairseq/modules/sparse_transformer_sentence_encoder.py new file mode 100644 index 0000000..f41ec09 --- /dev/null +++ b/fairseq/fairseq/modules/sparse_transformer_sentence_encoder.py @@ -0,0 +1,96 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +from fairseq.modules import TransformerSentenceEncoder +from fairseq.modules.sparse_transformer_sentence_encoder_layer import ( + SparseTransformerSentenceEncoderLayer, +) + + +class SparseTransformerSentenceEncoder(TransformerSentenceEncoder): + """ + Sparse implementation of the TransformerSentenceEncoder + - see SparseMultiheadAttention + """ + + def __init__( + self, + padding_idx: int, + vocab_size: int, + num_encoder_layers: int = 6, + embedding_dim: int = 768, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + max_seq_len: int = 256, + num_segments: int = 2, + use_position_embeddings: bool = True, + offset_positions_by_padding: bool = True, + encoder_normalize_before: bool = False, + apply_bert_init: bool = False, + activation_fn: str = "relu", + learned_pos_embedding: bool = True, + embed_scale: float = None, + freeze_embeddings: bool = False, + n_trans_layers_to_freeze: int = 0, + export: bool = False, + is_bidirectional: bool = True, + stride: int = 32, + expressivity: int = 8, + ) -> None: + + super().__init__( + padding_idx, + vocab_size, + num_encoder_layers, + embedding_dim, + ffn_embedding_dim, + num_attention_heads, + dropout, + attention_dropout, + activation_dropout, + max_seq_len, + num_segments, + use_position_embeddings, + offset_positions_by_padding, + encoder_normalize_before, + apply_bert_init, + activation_fn, + learned_pos_embedding, + embed_scale, + freeze_embeddings, + n_trans_layers_to_freeze, + export, + ) + + self.layers = nn.ModuleList( + [ + SparseTransformerSentenceEncoderLayer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + export=export, + is_bidirectional=is_bidirectional, + stride=stride, + expressivity=expressivity, + ) + for _ in range(num_encoder_layers) + ] + ) + + def freeze_module_params(m): + if m is not None: + for p in m.parameters(): + p.requires_grad = False + + for layer in range(n_trans_layers_to_freeze): + freeze_module_params(self.layers[layer]) diff --git a/fairseq/fairseq/modules/sparse_transformer_sentence_encoder_layer.py b/fairseq/fairseq/modules/sparse_transformer_sentence_encoder_layer.py new file mode 100644 index 0000000..d95da59 --- /dev/null +++ b/fairseq/fairseq/modules/sparse_transformer_sentence_encoder_layer.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.modules import TransformerSentenceEncoderLayer +from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention + + +class SparseTransformerSentenceEncoderLayer(TransformerSentenceEncoderLayer): + """ + Implements a Sprase Transformer Encoder Layer (see SparseMultiheadAttention) + """ + + def __init__( + self, + embedding_dim: int = 768, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + activation_fn: str = "relu", + export: bool = False, + is_bidirectional: bool = True, + stride: int = 32, + expressivity: int = 8, + ) -> None: + + super().__init__( + embedding_dim, + ffn_embedding_dim, + num_attention_heads, + dropout, + attention_dropout, + activation_dropout, + activation_fn, + export, + ) + + self.self_attn = SparseMultiheadAttention( + self.embedding_dim, + num_attention_heads, + dropout=attention_dropout, + add_bias_kv=False, + add_zero_attn=False, + self_attention=True, + is_bidirectional=is_bidirectional, + stride=stride, + expressivity=expressivity, + ) diff --git a/fairseq/fairseq/modules/transformer_layer.py b/fairseq/fairseq/modules/transformer_layer.py new file mode 100644 index 0000000..19e035d --- /dev/null +++ b/fairseq/fairseq/modules/transformer_layer.py @@ -0,0 +1,562 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import utils +from fairseq.models.transformer import TransformerConfig +from fairseq.modules import LayerNorm, MultiheadAttention +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.quant_noise import quant_noise + + +class TransformerEncoderLayerBase(nn.Module): + """Encoder layer block. + + In the original paper each operation (multi-head attention or FFN) is + postprocessed with: `dropout -> add residual -> layernorm`. In the + tensor2tensor code they suggest that learning is more robust when + preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *cfg.encoder.normalize_before* to ``True``. + + Args: + cfg (argparse.Namespace): parsed command-line arguments + """ + + def __init__(self, cfg, return_fc=False): + super().__init__() + self.cfg = cfg + self.return_fc = return_fc + self.embed_dim = cfg.encoder.embed_dim + self.quant_noise = cfg.quant_noise.pq + self.quant_noise_block_size = cfg.quant_noise.pq_block_size + self.self_attn = self.build_self_attention(self.embed_dim, cfg) + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=self.__class__.__name__ + ) + self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn) + activation_dropout_p = cfg.activation_dropout + if activation_dropout_p == 0: + # for backwards compatibility with models that use cfg.relu_dropout + activation_dropout_p = cfg.relu_dropout or 0 + self.activation_dropout_module = FairseqDropout( + float(activation_dropout_p), module_name=self.__class__.__name__ + ) + self.normalize_before = cfg.encoder.normalize_before + self.fc1 = self.build_fc1( + self.embed_dim, + cfg.encoder.ffn_embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + self.fc2 = self.build_fc2( + cfg.encoder.ffn_embed_dim, + self.embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + + self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) + + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise( + nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size + ) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise( + nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size + ) + + def _get_fc_rank(self, remove_num: int) -> List[int]: + f1_filter_param = [] + for i in range(self.fc1.out_features): + f1_filter_param.append( + torch.sum(torch.abs(self.fc1.weight[i])) + + torch.sum(torch.abs(self.fc2.weight[:, i])) + + torch.abs(self.fc1.bias[i]) + ) + return sorted( + range(len(f1_filter_param)), key=lambda k: f1_filter_param[k], reverse=False + )[0:remove_num] + + def _prune_fc_layer(self, remove_index: List[int]): + new_fc1_weight = [] + new_fc1_bias = [] + for i in range(self.fc1.out_features): + if i not in remove_index: + new_fc1_weight.append(self.fc1.weight[i]) + new_fc1_bias.append(self.fc1.bias[i]) + + new_fc1_weight = torch.stack(new_fc1_weight).detach() + new_fc1_weight.requires_grad = True + + new_fc1_bias = torch.stack(new_fc1_bias).detach() + new_fc1_bias.requires_grad = True + + self.fc1 = quant_noise( + nn.Linear(self.fc1.in_features, self.fc1.out_features - len(remove_index)), + p=self.quant_noise, + block_size=self.quant_noise_block_size, + ) + self.fc1.weight = torch.nn.Parameter(new_fc1_weight) + self.fc1.bias = torch.nn.Parameter(new_fc1_bias) + + new_fc2_weight = [] + new_fc2_bias = [] + for i in range(self.fc2.in_features): + if i not in remove_index: + new_fc2_weight.append(self.fc2.weight[:, i]) + new_fc2_bias = self.fc2.bias.detach() + + new_fc2_weight = torch.stack(new_fc2_weight, dim=-1).detach() + new_fc2_weight.requires_grad = True + + new_fc2_bias = self.fc2.bias.detach() + new_fc2_bias.requires_grad = True + + self.fc2 = quant_noise( + nn.Linear(self.fc2.in_features - len(remove_index), self.fc2.out_features), + p=self.quant_noise, + block_size=self.quant_noise_block_size, + ) + self.fc2.weight = torch.nn.Parameter(new_fc2_weight) + self.fc2.bias = torch.nn.Parameter(new_fc2_bias) + + def build_self_attention(self, embed_dim, cfg): + return MultiheadAttention( + embed_dim, + cfg.encoder.attention_heads, + dropout=cfg.attention_dropout, + self_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + xformers_att_config=cfg.encoder.xformers_att_config, + ) + + def residual_connection(self, x, residual): + return residual + x + + def upgrade_state_dict_named(self, state_dict, name): + """ + Rename layer norm states from `...layer_norms.0.weight` to + `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to + `...final_layer_norm.weight` + """ + layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"} + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layer_norms.{}.{}".format(name, old, m) + if k in state_dict: + state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k] + del state_dict[k] + + def forward( + self, + x, + encoder_padding_mask: Optional[Tensor], + attn_mask: Optional[Tensor] = None, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor): binary ByteTensor of shape + `(batch, seq_len)` where padding elements are indicated by ``1``. + attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`, + where `tgt_len` is the length of output and `src_len` is the + length of input, though here both are equal to `seq_len`. + `attn_mask[tgt_i, src_j] = 1` means that when calculating the + embedding for `tgt_i`, we exclude (mask out) `src_j`. This is + useful for strided self-attention. + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + # anything in original attn_mask = 1, becomes -1e8 + # anything in original attn_mask = 0, becomes 0 + # Note that we cannot use -inf here, because at some edge cases, + # the attention weight (before softmax) for some padded element in query + # will become -inf, which results in NaN in model parameters + if attn_mask is not None: + attn_mask = attn_mask.masked_fill( + attn_mask.to(torch.bool), -1e8 if x.dtype == torch.float32 else -1e4 + ) + + residual = x + if self.normalize_before: + x = self.self_attn_layer_norm(x) + x, _ = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=encoder_padding_mask, + need_weights=False, + attn_mask=attn_mask, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + + fc_result = x + + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.final_layer_norm(x) + + if self.return_fc and not torch.jit.is_scripting(): + return x, fc_result + return x + + +# backward compatible with the legacy argparse format +class TransformerEncoderLayer(TransformerEncoderLayerBase): + def __init__(self, args): + super().__init__(TransformerConfig.from_namespace(args)) + self.args = args + + def build_self_attention(self, embed_dim, args): + return super().build_self_attention( + embed_dim, TransformerConfig.from_namespace(args) + ) + + +class TransformerDecoderLayerBase(nn.Module): + """Decoder layer block. + + In the original paper each operation (multi-head attention, encoder + attention or FFN) is postprocessed with: `dropout -> add residual -> + layernorm`. In the tensor2tensor code they suggest that learning is more + robust when preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *cfg.decoder.normalize_before* to ``True``. + + Args: + args (argparse.Namespace): parsed command-line arguments + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, cfg, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__() + self.embed_dim = cfg.decoder.embed_dim + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=self.__class__.__name__ + ) + self.quant_noise = cfg.quant_noise.pq + self.quant_noise_block_size = cfg.quant_noise.pq_block_size + + self.cross_self_attention = cfg.cross_self_attention + + self.self_attn = self.build_self_attention( + self.embed_dim, + cfg, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + self.attn_ln = ( + LayerNorm(self.embed_dim) + if utils.safe_getattr(cfg, "scale_attn", False) + else None + ) + self.nh = self.self_attn.num_heads + self.head_dim = self.self_attn.head_dim + scale_heads = utils.safe_getattr(cfg, "scale_heads", False) + self.c_attn = ( + nn.Parameter(torch.ones((self.nh,)), requires_grad=True) + if scale_heads + else None + ) + + self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn) + activation_dropout_p = cfg.activation_dropout + if activation_dropout_p == 0: + # for backwards compatibility with models that use cfg.relu_dropout + activation_dropout_p = cfg.relu_dropout or 0 + self.activation_dropout_module = FairseqDropout( + float(activation_dropout_p), module_name=self.__class__.__name__ + ) + self.normalize_before = cfg.decoder.normalize_before + + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) + + if no_encoder_attn: + self.encoder_attn = None + self.encoder_attn_layer_norm = None + else: + self.encoder_attn = self.build_encoder_attention(self.embed_dim, cfg) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) + + self.ffn_layernorm = ( + LayerNorm(cfg.decoder.ffn_embed_dim) + if utils.safe_getattr(cfg, "scale_fc", False) + else None + ) + self.w_resid = ( + nn.Parameter( + torch.ones( + self.embed_dim, + ), + requires_grad=True, + ) + if utils.safe_getattr(cfg, "scale_resids", False) + else None + ) + + self.fc1 = self.build_fc1( + self.embed_dim, + cfg.decoder.ffn_embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + self.fc2 = self.build_fc2( + cfg.decoder.ffn_embed_dim, + self.embed_dim, + self.quant_noise, + self.quant_noise_block_size, + ) + + self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) + self.need_attn = True + + self.onnx_trace = False + + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_self_attention( + self, embed_dim, cfg, add_bias_kv=False, add_zero_attn=False + ): + return MultiheadAttention( + embed_dim, + cfg.decoder.attention_heads, + dropout=cfg.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=not cfg.cross_self_attention, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + xformers_att_config=cfg.decoder.xformers_att_config, + ) + + def build_encoder_attention(self, embed_dim, cfg): + return MultiheadAttention( + embed_dim, + cfg.decoder.attention_heads, + kdim=cfg.encoder.embed_dim, + vdim=cfg.encoder.embed_dim, + dropout=cfg.attention_dropout, + encoder_decoder_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + xformers_att_config=cfg.encoder.xformers_att_config, + ) + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def residual_connection(self, x, residual): + return residual + x + + def forward( + self, + x, + encoder_out: Optional[torch.Tensor] = None, + encoder_padding_mask: Optional[torch.Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + prev_self_attn_state: Optional[List[torch.Tensor]] = None, + prev_attn_state: Optional[List[torch.Tensor]] = None, + self_attn_mask: Optional[torch.Tensor] = None, + self_attn_padding_mask: Optional[torch.Tensor] = None, + need_attn: bool = False, + need_head_weights: bool = False, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor, optional): binary + ByteTensor of shape `(batch, src_len)` where padding + elements are indicated by ``1``. + need_attn (bool, optional): return attention weights + need_head_weights (bool, optional): return attention weights + for each head (default: return average over heads). + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + if need_head_weights: + need_attn = True + + residual = x + if self.normalize_before: + x = self.self_attn_layer_norm(x) + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[2] + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state, saved_state) + _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state) + if self.cross_self_attention and not ( + incremental_state is not None + and _self_attn_input_buffer is not None + and "prev_key" in _self_attn_input_buffer + ): + if self_attn_mask is not None: + assert encoder_out is not None + self_attn_mask = torch.cat( + (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1 + ) + if self_attn_padding_mask is not None: + if encoder_padding_mask is None: + assert encoder_out is not None + encoder_padding_mask = self_attn_padding_mask.new_zeros( + encoder_out.size(1), encoder_out.size(0) + ) + self_attn_padding_mask = torch.cat( + (encoder_padding_mask, self_attn_padding_mask), dim=1 + ) + assert encoder_out is not None + y = torch.cat((encoder_out, x), dim=0) + else: + y = x + + x, attn = self.self_attn( + query=x, + key=y, + value=y, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + need_weights=False, + attn_mask=self_attn_mask, + ) + if self.c_attn is not None: + tgt_len, bsz = x.size(0), x.size(1) + x = x.view(tgt_len, bsz, self.nh, self.head_dim) + x = torch.einsum("tbhd,h->tbhd", x, self.c_attn) + x = x.reshape(tgt_len, bsz, self.embed_dim) + if self.attn_ln is not None: + x = self.attn_ln(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + if self.encoder_attn is not None and encoder_out is not None: + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + if self.ffn_layernorm is not None: + x = self.ffn_layernorm(x) + x = self.fc2(x) + x = self.dropout_module(x) + if self.w_resid is not None: + residual = torch.mul(self.w_resid, residual) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.final_layer_norm(x) + if self.onnx_trace and incremental_state is not None: + saved_state = self.self_attn._get_input_buffer(incremental_state) + assert saved_state is not None + if self_attn_padding_mask is not None: + self_attn_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + saved_state["prev_key_padding_mask"], + ] + else: + self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]] + return x, attn, self_attn_state + return x, attn, None + + def make_generation_fast_(self, need_attn: bool = False, **kwargs): + self.need_attn = need_attn + + +# backward compatible with the legacy argparse format +class TransformerDecoderLayer(TransformerDecoderLayerBase): + def __init__( + self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__( + TransformerConfig.from_namespace(args), + no_encoder_attn=no_encoder_attn, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + self.args = args + + def build_self_attention( + self, embed_dim, args, add_bias_kv=False, add_zero_attn=False + ): + return super().build_self_attention( + embed_dim, + TransformerConfig.from_namespace(args), + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + + def build_encoder_attention(self, embed_dim, args): + return super().build_encoder_attention( + embed_dim, + TransformerConfig.from_namespace(args), + ) diff --git a/fairseq/fairseq/modules/transformer_layer_aug.py b/fairseq/fairseq/modules/transformer_layer_aug.py new file mode 100644 index 0000000..7eb8169 --- /dev/null +++ b/fairseq/fairseq/modules/transformer_layer_aug.py @@ -0,0 +1,315 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional + +import torch +from numpy.random import uniform +from torch import Tensor + +from fairseq.modules import LayerNorm +from fairseq.modules.transformer_layer import TransformerDecoderLayerBase + + +class AugTransformerDecoderLayerBase(TransformerDecoderLayerBase): + """Decoder layer block augmented with an additional cross-attention. + + This decoder block is processed with the sequence of the following sub-modules. + self-attention -> cross-attention (first) -> cross-attention (second) -> FFN + + Args: + cfg (argparse.Namespace): parsed command-line arguments + encoder_attn_merge_type (str, optional): the way to combine outputs from + two cross-attention modules. If "sequential" is set, two cross-attention + modules are stacked sequentially. If "parallel" is set, they are processed + in parallel and combined before feeding it to FFN (default: sequential). + dropnet_ratio (float, optional): a probability to drop each cross-attention + module during training (default: 0.0). + """ + + def __init__( + self, + cfg, + add_bias_kv=False, + add_zero_attn=False, + encoder_attn_merge_type="sequential", + dropnet_ratio=0.0, + ): + super().__init__( + cfg, + no_encoder_attn=False, + add_bias_kv=add_bias_kv, + add_zero_attn=False, + ) + self.encoder_attn = self.build_encoder_attention(self.embed_dim, cfg) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) + self.encoder_attn2 = self.build_encoder_attention(self.embed_dim, cfg) + if encoder_attn_merge_type == "sequential": + self.encoder_attn_layer_norm2 = LayerNorm(self.embed_dim, export=cfg.export) + else: + self.encoder_attn_layer_norm2 = None + + self.encoder_attn_merge_type = encoder_attn_merge_type + self.dropnet_ratio = dropnet_ratio + + def forward( + self, + x, + encoder_out: Optional[torch.Tensor] = None, + encoder_padding_mask: Optional[torch.Tensor] = None, + encoder_out_aug: Optional[torch.Tensor] = None, + encoder_padding_mask2: Optional[torch.Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + prev_self_attn_state: Optional[List[torch.Tensor]] = None, + prev_attn_state: Optional[List[torch.Tensor]] = None, + self_attn_mask: Optional[torch.Tensor] = None, + self_attn_padding_mask: Optional[torch.Tensor] = None, + need_attn: bool = False, + need_head_weights: bool = False, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor, optional): binary + ByteTensor of shape `(batch, src_len)` where padding + elements are indicated by ``1``. + need_attn (bool, optional): return attention weights + need_head_weights (bool, optional): return attention weights + for each head (default: return average over heads). + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + if need_head_weights: + need_attn = True + + residual = x + if self.normalize_before: + x = self.self_attn_layer_norm(x) + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[2] + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state, saved_state) + _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state) + if self.cross_self_attention and not ( + incremental_state is not None + and _self_attn_input_buffer is not None + and "prev_key" in _self_attn_input_buffer + ): + if self_attn_mask is not None: + assert encoder_out is not None + self_attn_mask = torch.cat( + (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1 + ) + if self_attn_padding_mask is not None: + if encoder_padding_mask is None: + assert encoder_out is not None + encoder_padding_mask = self_attn_padding_mask.new_zeros( + encoder_out.size(1), encoder_out.size(0) + ) + self_attn_padding_mask = torch.cat( + (encoder_padding_mask, self_attn_padding_mask), dim=1 + ) + assert encoder_out is not None + y = torch.cat((encoder_out, x), dim=0) + else: + y = x + + x, attn = self.self_attn( + query=x, + key=y, + value=y, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + need_weights=False, + attn_mask=self_attn_mask, + ) + if self.c_attn is not None: + tgt_len, bsz = x.size(0), x.size(1) + x = x.view(tgt_len, bsz, self.nh, self.head_dim) + x = torch.einsum("tbhd,h->tbhd", x, self.c_attn) + x = x.reshape(tgt_len, bsz, self.embed_dim) + if self.attn_ln is not None: + x = self.attn_ln(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + assert encoder_out is not None + assert encoder_out_aug is not None + + if self.encoder_attn_merge_type == "sequential": + ratios = self.get_dropnet_ratio() + + # first encoder attention + if ratios[0] > 0: + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm(x) + x = ratios[0] * x + + # second encoder attention + if ratios[1] > 0: + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm2(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn2._set_input_buffer(incremental_state, saved_state) + + x, attn2 = self.encoder_attn2( + query=x, + key=encoder_out_aug, + value=encoder_out_aug, + key_padding_mask=encoder_padding_mask2, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm2(x) + x = ratios[1] * x + + elif self.encoder_attn_merge_type == "parallel": + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + + x1, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x2, attn2 = self.encoder_attn2( + query=x, + key=encoder_out_aug, + value=encoder_out_aug, + key_padding_mask=encoder_padding_mask2, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x1 = self.dropout_module(x1) + x2 = self.dropout_module(x2) + ratios = self.get_dropnet_ratio() + x = ratios[0] * x1 + ratios[1] * x2 + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm(x) + + else: + raise NotImplementedError(self.encoder_attn_merge_type) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + if self.ffn_layernorm is not None: + x = self.ffn_layernorm(x) + x = self.fc2(x) + x = self.dropout_module(x) + if self.w_resid is not None: + residual = torch.mul(self.w_resid, residual) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.final_layer_norm(x) + if self.onnx_trace and incremental_state is not None: + saved_state = self.self_attn._get_input_buffer(incremental_state) + assert saved_state is not None + if self_attn_padding_mask is not None: + self_attn_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + saved_state["prev_key_padding_mask"], + ] + else: + self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]] + return x, attn, attn2, self_attn_state + return x, attn, attn2, None + + def get_dropnet_ratio(self): + if self.encoder_attn_merge_type == "sequential": + if self.dropnet_ratio > 0: + frand = float(uniform(0, 1)) + if frand < self.dropnet_ratio and self.training: + return [2, 0] + elif frand > 1 - self.dropnet_ratio and self.training: + return [0, 2] + else: + return [1, 1] + else: + return [1, 1] + + elif self.encoder_attn_merge_type == "parallel": + if self.dropnet_ratio > 0: + frand = float(uniform(0, 1)) + if frand < self.dropnet_ratio and self.training: + return [1, 0] + elif frand > 1 - self.dropnet_ratio and self.training: + return [0, 1] + else: + return [0.5, 0.5] + else: + return [0.5, 0.5] diff --git a/fairseq/fairseq/modules/transformer_sentence_encoder.py b/fairseq/fairseq/modules/transformer_sentence_encoder.py new file mode 100644 index 0000000..5d2db91 --- /dev/null +++ b/fairseq/fairseq/modules/transformer_sentence_encoder.py @@ -0,0 +1,291 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional, Tuple + +import torch +import torch.nn as nn +from fairseq.modules import ( + FairseqDropout, + LayerDropModuleList, + LayerNorm, + MultiheadAttention, + PositionalEmbedding, + TransformerSentenceEncoderLayer, +) +from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ + + +def init_bert_params(module): + """ + Initialize the weights specific to the BERT Model. + This overrides the default initializations depending on the specified arguments. + 1. If normal_init_linear_weights is set then weights of linear + layer will be initialized using the normal distribution and + bais will be set to the specified value. + 2. If normal_init_embed_weights is set then weights of embedding + layer will be initialized using the normal distribution. + 3. If normal_init_proj_weights is set then weights of + in_project_weight for MultiHeadAttention initialized using + the normal distribution (to be validated). + """ + + def normal_(data): + # with FSDP, module params will be on CUDA, so we cast them back to CPU + # so that the RNG is consistent with and without FSDP + data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device)) + + if isinstance(module, nn.Linear): + normal_(module.weight.data) + if module.bias is not None: + module.bias.data.zero_() + if isinstance(module, nn.Embedding): + normal_(module.weight.data) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + if isinstance(module, MultiheadAttention): + normal_(module.q_proj.weight.data) + normal_(module.k_proj.weight.data) + normal_(module.v_proj.weight.data) + + +class TransformerSentenceEncoder(nn.Module): + """ + Implementation for a Bi-directional Transformer based Sentence Encoder used + in BERT/XLM style pre-trained models. + + This first computes the token embedding using the token embedding matrix, + position embeddings (if specified) and segment embeddings + (if specified). After applying the specified number of + TransformerEncoderLayers, it outputs all the internal states of the + encoder as well as the final representation associated with the first + token (usually CLS token). + + Input: + - tokens: B x T matrix representing sentences + - segment_labels: B x T matrix representing segment label for tokens + + Output: + - a tuple of the following: + - a list of internal model states used to compute the + predictions where each tensor has shape T x B x C + - sentence representation associated with first input token + in format B x C. + """ + + def __init__( + self, + padding_idx: int, + vocab_size: int, + num_encoder_layers: int = 6, + embedding_dim: int = 768, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + layerdrop: float = 0.0, + max_seq_len: int = 256, + num_segments: int = 2, + use_position_embeddings: bool = True, + offset_positions_by_padding: bool = True, + encoder_normalize_before: bool = False, + apply_bert_init: bool = False, + activation_fn: str = "relu", + learned_pos_embedding: bool = True, + embed_scale: float = None, + freeze_embeddings: bool = False, + n_trans_layers_to_freeze: int = 0, + export: bool = False, + traceable: bool = False, + q_noise: float = 0.0, + qn_block_size: int = 8, + ) -> None: + + super().__init__() + self.padding_idx = padding_idx + self.vocab_size = vocab_size + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.layerdrop = layerdrop + self.max_seq_len = max_seq_len + self.embedding_dim = embedding_dim + self.num_segments = num_segments + self.use_position_embeddings = use_position_embeddings + self.apply_bert_init = apply_bert_init + self.learned_pos_embedding = learned_pos_embedding + self.traceable = traceable + + self.embed_tokens = self.build_embedding( + self.vocab_size, self.embedding_dim, self.padding_idx + ) + self.embed_scale = embed_scale + + if q_noise > 0: + self.quant_noise = apply_quant_noise_( + nn.Linear(self.embedding_dim, self.embedding_dim, bias=False), + q_noise, + qn_block_size, + ) + else: + self.quant_noise = None + + self.segment_embeddings = ( + nn.Embedding(self.num_segments, self.embedding_dim, padding_idx=None) + if self.num_segments > 0 + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + self.max_seq_len, + self.embedding_dim, + padding_idx=(self.padding_idx if offset_positions_by_padding else None), + learned=self.learned_pos_embedding, + ) + if self.use_position_embeddings + else None + ) + + if encoder_normalize_before: + self.emb_layer_norm = LayerNorm(self.embedding_dim, export=export) + else: + self.emb_layer_norm = None + + if self.layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + self.build_transformer_sentence_encoder_layer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=self.dropout_module.p, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + export=export, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + for _ in range(num_encoder_layers) + ] + ) + + # Apply initialization of model params after building the model + if self.apply_bert_init: + self.apply(init_bert_params) + + def freeze_module_params(m): + if m is not None: + for p in m.parameters(): + p.requires_grad = False + + if freeze_embeddings: + freeze_module_params(self.embed_tokens) + freeze_module_params(self.segment_embeddings) + freeze_module_params(self.embed_positions) + freeze_module_params(self.emb_layer_norm) + + for layer in range(n_trans_layers_to_freeze): + freeze_module_params(self.layers[layer]) + + def build_embedding(self, vocab_size, embedding_dim, padding_idx): + return nn.Embedding(vocab_size, embedding_dim, padding_idx) + + def build_transformer_sentence_encoder_layer( + self, + embedding_dim, + ffn_embedding_dim, + num_attention_heads, + dropout, + attention_dropout, + activation_dropout, + activation_fn, + export, + q_noise, + qn_block_size, + ): + return TransformerSentenceEncoderLayer( + embedding_dim=embedding_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + export=export, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + + def forward( + self, + tokens: torch.Tensor, + segment_labels: torch.Tensor = None, + last_state_only: bool = False, + positions: Optional[torch.Tensor] = None, + token_embeddings: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + is_tpu = tokens.device.type == "xla" + + # compute padding mask. This is needed for multi-head attention + padding_mask = tokens.eq(self.padding_idx) + if not self.traceable and not is_tpu and not padding_mask.any(): + padding_mask = None + + if token_embeddings is not None: + x = token_embeddings + else: + x = self.embed_tokens(tokens) + + if self.embed_scale is not None: + x = x * self.embed_scale + + if self.embed_positions is not None: + x = x + self.embed_positions(tokens, positions=positions) + + if self.segment_embeddings is not None and segment_labels is not None: + x = x + self.segment_embeddings(segment_labels) + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if self.emb_layer_norm is not None: + x = self.emb_layer_norm(x) + + x = self.dropout_module(x) + + # account for padding while computing the representation + if padding_mask is not None: + x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + inner_states = [] + if not last_state_only: + inner_states.append(x) + + for layer in self.layers: + x, _ = layer( + x, self_attn_padding_mask=padding_mask, self_attn_mask=attn_mask + ) + if not last_state_only: + inner_states.append(x) + + sentence_rep = x[0, :, :] + + if last_state_only: + inner_states = [x] + + if self.traceable: + return torch.stack(inner_states), sentence_rep + else: + return inner_states, sentence_rep diff --git a/fairseq/fairseq/modules/transformer_sentence_encoder_layer.py b/fairseq/fairseq/modules/transformer_sentence_encoder_layer.py new file mode 100644 index 0000000..f869c4b --- /dev/null +++ b/fairseq/fairseq/modules/transformer_sentence_encoder_layer.py @@ -0,0 +1,139 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable, Optional + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.modules import LayerNorm, MultiheadAttention +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.quant_noise import quant_noise + + +class TransformerSentenceEncoderLayer(nn.Module): + """ + Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained + models. + """ + + def __init__( + self, + embedding_dim: int = 768, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + activation_fn: str = "relu", + export: bool = False, + q_noise: float = 0.0, + qn_block_size: int = 8, + init_fn: Callable = None, + ) -> None: + super().__init__() + + if init_fn is not None: + init_fn() + + # Initialize parameters + self.embedding_dim = embedding_dim + self.num_attention_heads = num_attention_heads + self.attention_dropout = attention_dropout + self.q_noise = q_noise + self.qn_block_size = qn_block_size + + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.activation_dropout_module = FairseqDropout( + activation_dropout, module_name=self.__class__.__name__ + ) + + # Initialize blocks + self.activation_fn = utils.get_activation_fn(activation_fn) + self.self_attn = self.build_self_attention( + self.embedding_dim, + num_attention_heads, + dropout=attention_dropout, + self_attention=True, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + + # layer norm associated with the self attention layer + self.self_attn_layer_norm = LayerNorm(self.embedding_dim, export=export) + + self.fc1 = self.build_fc1( + self.embedding_dim, + ffn_embedding_dim, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + self.fc2 = self.build_fc2( + ffn_embedding_dim, + self.embedding_dim, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + + # layer norm associated with the position wise feed-forward NN + self.final_layer_norm = LayerNorm(self.embedding_dim, export=export) + + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_self_attention( + self, + embed_dim, + num_attention_heads, + dropout, + self_attention, + q_noise, + qn_block_size, + ): + return MultiheadAttention( + embed_dim, + num_attention_heads, + dropout=dropout, + self_attention=True, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + + def forward( + self, + x: torch.Tensor, + self_attn_mask: Optional[torch.Tensor] = None, + self_attn_padding_mask: Optional[torch.Tensor] = None, + ): + """ + LayerNorm is applied either before or after the self-attention/ffn + modules similar to the original Transformer implementation. + """ + residual = x + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + need_weights=False, + attn_mask=self_attn_mask, + ) + x = self.dropout_module(x) + x = residual + x + x = self.self_attn_layer_norm(x) + + residual = x + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = residual + x + x = self.final_layer_norm(x) + return x, attn diff --git a/fairseq/fairseq/modules/transpose_last.py b/fairseq/fairseq/modules/transpose_last.py new file mode 100644 index 0000000..d7cca9a --- /dev/null +++ b/fairseq/fairseq/modules/transpose_last.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +transpose last 2 dimensions of the input +""" + +import torch.nn as nn + + +class TransposeLast(nn.Module): + def __init__(self, deconstruct_idx=None, tranpose_dim=-2): + super().__init__() + self.deconstruct_idx = deconstruct_idx + self.tranpose_dim = tranpose_dim + + def forward(self, x): + if self.deconstruct_idx is not None: + x = x[self.deconstruct_idx] + return x.transpose(self.tranpose_dim, -1) diff --git a/fairseq/fairseq/modules/unfold.py b/fairseq/fairseq/modules/unfold.py new file mode 100644 index 0000000..bbaafbd --- /dev/null +++ b/fairseq/fairseq/modules/unfold.py @@ -0,0 +1,19 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn.functional as F + + +def unfold1d(x, kernel_size: int, padding_l: int, pad_value: float = 0): + """unfold T x B x C to T x B x C x K""" + if kernel_size > 1: + T, B, C = x.size() + x = F.pad( + x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value + ) + x = x.as_strided((T, B, C, kernel_size), (B * C, C, 1, B * C)) + else: + x = x.unsqueeze(3) + return x diff --git a/fairseq/fairseq/modules/vggblock.py b/fairseq/fairseq/modules/vggblock.py new file mode 100644 index 0000000..ee5ee19 --- /dev/null +++ b/fairseq/fairseq/modules/vggblock.py @@ -0,0 +1,116 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import absolute_import, division, print_function, unicode_literals + +from collections.abc import Iterable +from itertools import repeat + +import torch +import torch.nn as nn + + +def _pair(v): + if isinstance(v, Iterable): + assert len(v) == 2, "len(v) != 2" + return v + return tuple(repeat(v, 2)) + + +def infer_conv_output_dim(conv_op, input_dim, sample_inchannel): + sample_seq_len = 200 + sample_bsz = 10 + x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim) + # N x C x H x W + # N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim + x = conv_op(x) + # N x C x H x W + x = x.transpose(1, 2) + # N x H x C x W + bsz, seq = x.size()[:2] + per_channel_dim = x.size()[3] + # bsz: N, seq: H, CxW the rest + return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim + + +class VGGBlock(torch.nn.Module): + """ + VGG motibated cnn module https://arxiv.org/pdf/1409.1556.pdf + + Args: + in_channels: (int) number of input channels (typically 1) + out_channels: (int) number of output channels + conv_kernel_size: convolution channels + pooling_kernel_size: the size of the pooling window to take a max over + num_conv_layers: (int) number of convolution layers + input_dim: (int) input dimension + conv_stride: the stride of the convolving kernel. + Can be a single number or a tuple (sH, sW) Default: 1 + padding: implicit paddings on both sides of the input. + Can be a single number or a tuple (padH, padW). Default: None + layer_norm: (bool) if layer norm is going to be applied. Default: False + + Shape: + Input: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features) + Output: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features) + """ + + def __init__( + self, + in_channels, + out_channels, + conv_kernel_size, + pooling_kernel_size, + num_conv_layers, + input_dim, + conv_stride=1, + padding=None, + layer_norm=False, + ): + assert ( + input_dim is not None + ), "Need input_dim for LayerNorm and infer_conv_output_dim" + super(VGGBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.conv_kernel_size = _pair(conv_kernel_size) + self.pooling_kernel_size = _pair(pooling_kernel_size) + self.num_conv_layers = num_conv_layers + self.padding = ( + tuple(e // 2 for e in self.conv_kernel_size) + if padding is None + else _pair(padding) + ) + self.conv_stride = _pair(conv_stride) + + self.layers = nn.ModuleList() + for layer in range(num_conv_layers): + conv_op = nn.Conv2d( + in_channels if layer == 0 else out_channels, + out_channels, + self.conv_kernel_size, + stride=self.conv_stride, + padding=self.padding, + ) + self.layers.append(conv_op) + if layer_norm: + conv_output_dim, per_channel_dim = infer_conv_output_dim( + conv_op, input_dim, in_channels if layer == 0 else out_channels + ) + self.layers.append(nn.LayerNorm(per_channel_dim)) + input_dim = per_channel_dim + self.layers.append(nn.ReLU()) + + if self.pooling_kernel_size is not None: + pool_op = nn.MaxPool2d(kernel_size=self.pooling_kernel_size, ceil_mode=True) + self.layers.append(pool_op) + self.total_output_dim, self.output_dim = infer_conv_output_dim( + pool_op, input_dim, out_channels + ) + + def forward(self, x): + for i, _ in enumerate(self.layers): + x = self.layers[i](x) + return x diff --git a/fairseq/fairseq/nan_detector.py b/fairseq/fairseq/nan_detector.py new file mode 100644 index 0000000..bd0f911 --- /dev/null +++ b/fairseq/fairseq/nan_detector.py @@ -0,0 +1,108 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch + + +logger = logging.getLogger(__name__) + + +class NanDetector: + """ + Detects the first NaN or Inf in forward and/or backward pass and logs, together with the module name + """ + + def __init__(self, model, forward=True, backward=True): + self.bhooks = [] + self.fhooks = [] + self.forward = forward + self.backward = backward + self.named_parameters = list(model.named_parameters()) + self.reset() + + for name, mod in model.named_modules(): + mod.__module_name = name + self.add_hooks(mod) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, exc_traceback): + # Dump out all model gnorms to enable better debugging + norm = {} + gradients = {} + for name, param in self.named_parameters: + if param.grad is not None: + grad_norm = torch.norm(param.grad.data.float(), p=2) + norm[name] = param.norm().item() + if torch.isnan(grad_norm).any() or torch.isinf(grad_norm).any(): + gradients[name] = param.grad.data + if len(gradients) > 0: + logger.info("Detected nan/inf grad norm, dumping norms...") + logger.info(f"norms: {norm}") + logger.info(f"gradients: {gradients}") + + self.close() + + def add_hooks(self, module): + if self.forward: + self.fhooks.append(module.register_forward_hook(self.fhook_fn)) + if self.backward: + self.bhooks.append(module.register_backward_hook(self.bhook_fn)) + + def reset(self): + self.has_printed_f = False + self.has_printed_b = False + + def _detect(self, tensor, name, backward): + err = None + if ( + torch.is_floating_point(tensor) + # single value tensors (like the loss) will not provide much info + and tensor.numel() >= 2 + ): + with torch.no_grad(): + if torch.isnan(tensor).any(): + err = "NaN" + elif torch.isinf(tensor).any(): + err = "Inf" + if err is not None: + err = f"{err} detected in output of {name}, shape: {tensor.shape}, {'backward' if backward else 'forward'}" + return err + + def _apply(self, module, inp, x, backward): + if torch.is_tensor(x): + if isinstance(inp, tuple) and len(inp) > 0: + inp = inp[0] + err = self._detect(x, module.__module_name, backward) + if err is not None: + if torch.is_tensor(inp) and not backward: + err += ( + f" input max: {inp.max().item()}, input min: {inp.min().item()}" + ) + + has_printed_attr = "has_printed_b" if backward else "has_printed_f" + logger.warning(err) + setattr(self, has_printed_attr, True) + elif isinstance(x, dict): + for v in x.values(): + self._apply(module, inp, v, backward) + elif isinstance(x, list) or isinstance(x, tuple): + for v in x: + self._apply(module, inp, v, backward) + + def fhook_fn(self, module, inp, output): + if not self.has_printed_f: + self._apply(module, inp, output, backward=False) + + def bhook_fn(self, module, inp, output): + if not self.has_printed_b: + self._apply(module, inp, output, backward=True) + + def close(self): + for hook in self.fhooks + self.bhooks: + hook.remove() diff --git a/fairseq/fairseq/ngram_repeat_block.py b/fairseq/fairseq/ngram_repeat_block.py new file mode 100644 index 0000000..4eb5030 --- /dev/null +++ b/fairseq/fairseq/ngram_repeat_block.py @@ -0,0 +1,120 @@ +# Originally from Microsoft Corporation. +# Licensed under the MIT License. + +""" Wrapper for ngram_repeat_block cuda extension """ +import math +import warnings +from typing import List + +import torch +from torch import nn + +try: + from fairseq import ngram_repeat_block_cuda + + EXTENSION_BUILT = True +except ImportError: + EXTENSION_BUILT = False + + +def is_cuda_extension_usable() -> bool: + """Check whether ngram_repeat_block_cuda is built properly""" + if not EXTENSION_BUILT or not torch.cuda.is_available(): + return False + bsz = 2 + tokens = torch.tensor([[4, 4, 3, 2], [1, 2, 3, 4]], dtype=torch.long, device="cuda") + lprobs = torch.rand((8, 12), device="cuda") + try: + outputs = ngram_repeat_block_cuda.forward(tokens, lprobs, bsz, 3, 4, 3) + outputs = outputs + 4 # This line breaks if the extension is built incorrectly. + return True + except RuntimeError: + warnings.warn( + "NGramRepeatBlock extension must be rebuilt." + 'Run TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0" python setup.py build_ext --inplace' + ) + return False + + +class NGramRepeatBlock(nn.Module): + """Wrapper class for calling ngram_repeat_block cuda extension""" + + def __init__(self, no_repeat_ngram_size: int, use_extension: bool = True): + super().__init__() + self.use_extension = is_cuda_extension_usable() if use_extension else False + self.no_repeat_ngram_size = no_repeat_ngram_size + + def reset_parameters(self): + pass + + @torch.jit.unused + def call_cuda_extension( + self, + tokens, + lprobs, + bsz: int, + beam_size: int, + step: int, + ): + return ngram_repeat_block_cuda.forward( + tokens, lprobs, bsz, step, beam_size, self.no_repeat_ngram_size + ) + + def forward( + self, + tokens, + lprobs, + bsz: int, + beam_size: int, + step: int, + ): + """ + Args: + tokens(Tensor): Input tokens(Bsz*beam, seq_len) + lprobs(Tensor): likelihood probability, + Expected to be updated in place.(Bsz*beam, vocab_size) + bsz(int): batch size + step(int): current step + beam_size(int): beam size + no_repeat_ngram_size(int): Ngram size + """ + msg = f"expected {bsz *beam_size} got" + assert tokens.size(0) == bsz * beam_size, f"{msg} {tokens.size(0)}" + assert lprobs.size(0) == bsz * beam_size, f"{msg} {lprobs.size(0)}" + if self.use_extension: + return self.call_cuda_extension(tokens, lprobs, bsz, beam_size, step) + + else: + return self._no_repeat_ngram( + tokens, + lprobs, + bsz, + beam_size, + step, + ) + + def _no_repeat_ngram(self, tokens, lprobs, bsz: int, beam_size: int, step: int): + """For each hypothesis generate a list of previous ngrams and set associated lprobs to -inf""" + banned_tokens = [ + torch.jit.annotate(List[int], []) for bbsz_idx in range(bsz * beam_size) + ] + if step + 2 - self.no_repeat_ngram_size >= 0: + cpu_tokens: List[List[int]] = tokens.cpu().tolist() + check_start_pos = step + 2 - self.no_repeat_ngram_size + for bbsz_idx in range(bsz * beam_size): + ngram_to_check = cpu_tokens[bbsz_idx][ + -(self.no_repeat_ngram_size - 1) : + ] + for i in range(check_start_pos): + if ( + ngram_to_check + == cpu_tokens[bbsz_idx][i : i + self.no_repeat_ngram_size - 1] + ): + banned_tokens[bbsz_idx].append( + cpu_tokens[bbsz_idx][i + self.no_repeat_ngram_size - 1] + ) + for bbsz_idx in range(bsz * beam_size): + lprobs[bbsz_idx][ + torch.tensor(banned_tokens[bbsz_idx], dtype=torch.int64) + ] = torch.tensor(-math.inf).to(lprobs) + return lprobs diff --git a/fairseq/fairseq/optim/__init__.py b/fairseq/fairseq/optim/__init__.py new file mode 100644 index 0000000..be783be --- /dev/null +++ b/fairseq/fairseq/optim/__init__.py @@ -0,0 +1,48 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import importlib +import os + +from fairseq import registry +from fairseq.optim.bmuf import FairseqBMUF # noqa +from fairseq.optim.fairseq_optimizer import ( # noqa + FairseqOptimizer, + LegacyFairseqOptimizer, +) +from fairseq.optim.amp_optimizer import AMPOptimizer +from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer +from fairseq.optim.shard import shard_ +from omegaconf import DictConfig + +__all__ = [ + "AMPOptimizer", + "FairseqOptimizer", + "FP16Optimizer", + "MemoryEfficientFP16Optimizer", + "shard_", +] + +( + _build_optimizer, + register_optimizer, + OPTIMIZER_REGISTRY, + OPTIMIZER_DATACLASS_REGISTRY, +) = registry.setup_registry("--optimizer", base_class=FairseqOptimizer, required=True) + + +def build_optimizer(cfg: DictConfig, params, *extra_args, **extra_kwargs): + if all(isinstance(p, dict) for p in params): + params = [t for p in params for t in p.values()] + params = list(filter(lambda p: p.requires_grad, params)) + return _build_optimizer(cfg, params, *extra_args, **extra_kwargs) + + +# automatically import any Python files in the optim/ directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.optim." + file_name) diff --git a/fairseq/fairseq/optim/adadelta.py b/fairseq/fairseq/optim/adadelta.py new file mode 100644 index 0000000..f1a2154 --- /dev/null +++ b/fairseq/fairseq/optim/adadelta.py @@ -0,0 +1,47 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.optim + +from . import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("adadelta") +class Adadelta(LegacyFairseqOptimizer): + def __init__(self, args, params): + super().__init__(args) + self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config) + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO', + help='coefficient used for computing a running average of squared gradients') + parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS', + help='term added to the denominator to improve numerical stability') + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.args.lr[0], + "rho": self.args.adadelta_rho, + "eps": self.args.adadelta_eps, + "weight_decay": self.args.weight_decay, + } + + @property + def supports_flat_params(self): + return True diff --git a/fairseq/fairseq/optim/adafactor.py b/fairseq/fairseq/optim/adafactor.py new file mode 100644 index 0000000..042ae92 --- /dev/null +++ b/fairseq/fairseq/optim/adafactor.py @@ -0,0 +1,268 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.optim + +from . import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("adafactor") +class FairseqAdafactor(LegacyFairseqOptimizer): + def __init__(self, args, params): + super().__init__(args) + self._optimizer = Adafactor(params, **self.optimizer_config) + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--adafactor-eps', default='(1e-30, 1e-3)', metavar="E", + help='epsilons for Adafactor optimizer') + parser.add_argument('--clip-threshold', type=float, default=1.0, metavar="C", + help='threshold for clipping update root mean square') + parser.add_argument('--decay-rate', type=float, default=-0.8, metavar="D", + help='decay rate of the second moment estimator') + parser.add_argument('--beta1', type=float, default=None, metavar="B", + help='beta for first moment estimator. Optional') + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + parser.add_argument('--scale-parameter', action='store_true', + help='scale learning rate by root mean square of parameter') + parser.add_argument('--relative-step', action='store_true', + help='set learning rate to inverse square root of timestep,' + 'otherwise use external learning rate') + parser.add_argument('--warmup-init', action='store_true', + help='use relative step for warm-up learning rate schedule') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + Note : Convergence issues empirically observed with fp16 on. + Might require search for appropriate configuration. + """ + return { + "lr": self.args.lr[0], + "eps": eval(self.args.adafactor_eps), + "clip_threshold": self.args.clip_threshold, + "decay_rate": self.args.decay_rate, + "beta1": self.args.beta1, + "weight_decay": self.args.weight_decay, + "scale_parameter": self.args.scale_parameter, # defaults to False + "relative_step": self.args.relative_step, # defaults to False + "warmup_init": self.args.warmup_init, + } + + +class Adafactor(torch.optim.Optimizer): + """Implements Adafactor algorithm. + + This implementation is based on: + `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` + (see https://arxiv.org/abs/1804.04235) + + Note that this optimizer internally adjusts the learning rate + depending on the *scale_parameter*, *relative_step* and + *warmup_init* options. To use a manual (external) learning rate + schedule you should set `scale_parameter=False` and + `relative_step=False`. + + Args: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): external learning rate (default: None) + eps (tuple[float, float]): regularization constans for square gradient + and parameter scale respectively (default: (1e-30, 1e-3)) + clip_threshold (float): threshold of root mean square of + final gradient update (default: 1.0) + decay_rate (float): coefficient used to compute running averages of square + gradient (default: -0.8) + beta1 (float): coefficient used for computing running averages of gradient + (default: None) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + scale_parameter (bool): if True, learning rate is scaled by root mean square of + parameter (default: True) + relative_step (bool): if True, time-dependent learning rate is computed + instead of external learning rate (default: True) + warmup_init (bool): time-dependent learning rate computation depends on + whether warm-up initialization is being used (default: False) + """ + + def __init__( + self, + params, + lr=None, + eps=(1e-30, 1e-3), + clip_threshold=1.0, + decay_rate=-0.8, + beta1=None, + weight_decay=0.0, + scale_parameter=True, + relative_step=True, + warmup_init=False, + ): + if lr is not None and relative_step: + raise ValueError("Cannot combine manual lr and relative_step options") + if warmup_init and not relative_step: + raise ValueError("warmup_init requires relative_step=True") + + defaults = dict( + lr=lr, + eps=eps, + clip_threshold=clip_threshold, + decay_rate=decay_rate, + beta1=beta1, + weight_decay=weight_decay, + scale_parameter=scale_parameter, + relative_step=relative_step, + warmup_init=warmup_init, + ) + super(Adafactor, self).__init__(params, defaults) + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return False + + def _get_lr(self, param_group, param_state): + rel_step_sz = param_group["lr"] + if param_group["relative_step"]: + min_step = ( + 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2 + ) + rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"])) + param_scale = 1.0 + if param_group["scale_parameter"]: + param_scale = max(param_group["eps"][1], param_state["RMS"]) + return param_scale * rel_step_sz + + def _get_options(self, param_group, param_shape): + factored = len(param_shape) >= 2 + use_first_moment = param_group["beta1"] is not None + return factored, use_first_moment + + def _rms(self, tensor): + return tensor.norm(2) / (tensor.numel() ** 0.5) + + def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col): + r_factor = ( + (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)) + .rsqrt_() + .unsqueeze(-1) + ) + c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() + return torch.mul(r_factor, c_factor) + + def step(self, closure=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad.data + if grad.dtype in {torch.float16, torch.bfloat16}: + grad = grad.float() + if grad.is_sparse: + raise RuntimeError("Adafactor does not support sparse gradients.") + + state = self.state[p] + grad_shape = grad.shape + + factored, use_first_moment = self._get_options(group, grad_shape) + # State Initialization + if len(state) == 0: + state["step"] = 0 + + if use_first_moment: + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(grad) + if factored: + state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad) + state["exp_avg_sq_col"] = torch.zeros( + grad_shape[:-2] + grad_shape[-1:] + ).to(grad) + else: + state["exp_avg_sq"] = torch.zeros_like(grad) + + state["RMS"] = 0 + else: + if use_first_moment: + state["exp_avg"] = state["exp_avg"].to(grad) + if factored: + state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) + state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) + else: + state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) + + p_data_fp32 = p.data + if p.data.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + state["step"] += 1 + state["RMS"] = self._rms(p_data_fp32) + group["lr"] = self._get_lr(group, state) + + beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) + update = (grad**2) + group["eps"][0] + if factored: + exp_avg_sq_row = state["exp_avg_sq_row"] + exp_avg_sq_col = state["exp_avg_sq_col"] + + exp_avg_sq_row.mul_(beta2t).add_( + update.mean(dim=-1), alpha=1.0 - beta2t + ) + exp_avg_sq_col.mul_(beta2t).add_( + update.mean(dim=-2), alpha=1.0 - beta2t + ) + + # Approximation of exponential moving average of square of gradient + update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) + update.mul_(grad) + else: + exp_avg_sq = state["exp_avg_sq"] + + exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t) + update = exp_avg_sq.rsqrt().mul_(grad) + + update.div_( + (self._rms(update) / group["clip_threshold"]).clamp_(min=1.0) + ) + update.mul_(group["lr"]) + + if use_first_moment: + exp_avg = state["exp_avg"] + exp_avg.mul_(group["beta1"]).add_(update, alpha=1 - group["beta1"]) + update = exp_avg + + if group["weight_decay"] != 0: + p_data_fp32.add_( + p_data_fp32, alpha=-group["weight_decay"] * group["lr"] + ) + + p_data_fp32.add_(-update) + + if p.data.dtype in {torch.float16, torch.bfloat16}: + p.data.copy_(p_data_fp32) + + return loss diff --git a/fairseq/fairseq/optim/adagrad.py b/fairseq/fairseq/optim/adagrad.py new file mode 100644 index 0000000..4f53954 --- /dev/null +++ b/fairseq/fairseq/optim/adagrad.py @@ -0,0 +1,40 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.optim + +from . import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("adagrad") +class Adagrad(LegacyFairseqOptimizer): + def __init__(self, args, params): + super().__init__(args) + self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config) + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.args.lr[0], + "weight_decay": self.args.weight_decay, + } + + @property + def supports_flat_params(self): + return False diff --git a/fairseq/fairseq/optim/adam.py b/fairseq/fairseq/optim/adam.py new file mode 100644 index 0000000..678ec7c --- /dev/null +++ b/fairseq/fairseq/optim/adam.py @@ -0,0 +1,239 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import Any, List + +import torch +import torch.distributed as dist +import torch.optim +from fairseq.dataclass import FairseqDataclass +from fairseq.optim import FairseqOptimizer, register_optimizer +from fairseq.optim.fused_adam import get_fused_adam_class +from omegaconf import II, OmegaConf + + +logger = logging.getLogger(__name__) + + +@dataclass +class FairseqAdamConfig(FairseqDataclass): + adam_betas: Any = field( + default=(0.9, 0.999), metadata={"help": "betas for Adam optimizer"} + ) + adam_eps: float = field( + default=1e-8, metadata={"help": "epsilon for Adam optimizer"} + ) + weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) + use_old_adam: bool = field( + default=False, metadata={"help": "Use fairseq.optim.adam.Adam"} + ) + fp16_adam_stats: bool = field( + default=False, metadata={"help": "use FP16 stats (with automatic scaling)"} + ) + # TODO common vars below in parent + tpu: bool = II("common.tpu") + lr: List[float] = II("optimization.lr") + + +@register_optimizer("adam", dataclass=FairseqAdamConfig) +class FairseqAdam(FairseqOptimizer): + """Adam optimizer for fairseq. + + Important note: this optimizer corresponds to the "AdamW" variant of + Adam in its weight decay behavior. As such, it is most closely + analogous to torch.optim.AdamW from PyTorch. + """ + + def __init__(self, cfg: FairseqAdamConfig, params): + super().__init__(cfg) + fused_adam_cls = get_fused_adam_class() + use_fused_adam = ( + not getattr(cfg, "use_old_adam", False) + and fused_adam_cls is not None + and torch.cuda.is_available() + ) + if getattr(cfg, "tpu", False): + if self.cfg.fp16_adam_stats: + raise NotImplementedError("--fp16-adam-stats is only supported on GPU") + # on TPUs we use the Adam defined here, since it + # automatically casts gradients to FP32 + self._optimizer = Adam(params, **self.optimizer_config) + elif use_fused_adam: + logger.info("using FusedAdam") + self._optimizer = fused_adam_cls( + params, use_fp16_stats=self.cfg.fp16_adam_stats, **self.optimizer_config + ) + else: + if self.cfg.fp16_adam_stats: + raise NotImplementedError( + "--fp16-adam-stats is only supported with FusedAdamV1" + ) + self._optimizer = Adam(params, **self.optimizer_config) + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.cfg.lr[0] + if isinstance(self.cfg.lr, Collection) + else self.cfg.lr, + "betas": eval(self.cfg.adam_betas) + if isinstance(self.cfg.adam_betas, str) + else OmegaConf.to_container(self.cfg.adam_betas), + "eps": self.cfg.adam_eps, + "weight_decay": self.cfg.weight_decay, + } + + def average_params(self): + """Reduce Params is only used during BMUF distributed training.""" + state_dict = self.optimizer.state_dict() + total_gpus = float(dist.get_world_size()) + + for _, value in state_dict["state"].items(): + value["exp_avg"] /= total_gpus + value["exp_avg_sq"] /= total_gpus + dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM) + dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM) + + +class Adam(torch.optim.Optimizer): + r"""Implements Adam algorithm. + + This implementation is modified from torch.optim.Adam based on: + `Fixed Weight Decay Regularization in Adam` + (see https://arxiv.org/abs/1711.05101) + + It has been proposed in `Adam: A Method for Stochastic Optimization`_. + + Args: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + amsgrad (boolean, optional): whether to use the AMSGrad variant of this + algorithm from the paper `On the Convergence of Adam and Beyond`_ + + .. _Adam\: A Method for Stochastic Optimization: + https://arxiv.org/abs/1412.6980 + .. _On the Convergence of Adam and Beyond: + https://openreview.net/forum?id=ryQu7f-RZ + """ + + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + ): + defaults = dict( + lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad + ) + super(Adam, self).__init__(params, defaults) + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + def step(self, closure=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad.data + if grad.dtype in {torch.float16, torch.bfloat16}: + grad = grad.float() + if grad.is_sparse: + raise RuntimeError( + "Adam does not support sparse gradients, please consider SparseAdam instead" + ) + amsgrad = group.get("amsgrad", False) + + p_data_fp32 = p.data + if p.data.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + state = self.state[p] + + # State initialization + if len(state) == 0: + state["step"] = 0 + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(p_data_fp32) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32) + else: + state["exp_avg"] = state["exp_avg"].to(p_data_fp32) + state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32) + if amsgrad: + state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to( + p_data_fp32 + ) + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + if amsgrad: + max_exp_avg_sq = state["max_exp_avg_sq"] + beta1, beta2 = group["betas"] + + state["step"] += 1 + + # Decay the first and second moment running average coefficient + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) + if amsgrad: + # Maintains the maximum of all 2nd moment running avg. till now + torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) + # Use the max. for normalizing running avg. of gradient + denom = max_exp_avg_sq.sqrt().add_(group["eps"]) + else: + denom = exp_avg_sq.sqrt().add_(group["eps"]) + + bias_correction1 = 1 - beta1 ** state["step"] + bias_correction2 = 1 - beta2 ** state["step"] + step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 + + if group["weight_decay"] != 0: + p_data_fp32.add_( + p_data_fp32, alpha=-group["weight_decay"] * group["lr"] + ) + + p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size) + + if p.data.dtype in {torch.float16, torch.bfloat16}: + p.data.copy_(p_data_fp32) + + return loss diff --git a/fairseq/fairseq/optim/adamax.py b/fairseq/fairseq/optim/adamax.py new file mode 100644 index 0000000..98ff8ad --- /dev/null +++ b/fairseq/fairseq/optim/adamax.py @@ -0,0 +1,172 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.optim + +from . import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("adamax") +class FairseqAdamax(LegacyFairseqOptimizer): + def __init__(self, args, params): + super().__init__(args) + self._optimizer = Adamax(params, **self.optimizer_config) + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--adamax-betas', default='(0.9, 0.999)', metavar='B', + help='betas for Adam optimizer') + parser.add_argument('--adamax-eps', type=float, default=1e-8, metavar='D', + help='epsilon for Adam optimizer') + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + parser.add_argument('--no-bias-correction', default=False, action='store_true', + help='disable bias correction') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.args.lr[0], + "betas": eval(self.args.adamax_betas), + "eps": self.args.adamax_eps, + "weight_decay": self.args.weight_decay, + "bias_correction": not self.args.no_bias_correction, + } + + +class Adamax(torch.optim.Optimizer): + """Implements Adamax algorithm (a variant of Adam based on infinity norm). + + It has been proposed in `Adam: A Method for Stochastic Optimization`__. + + Compared to the version in PyTorch, this version implements a fix for weight decay. + + Args: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 2e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + bias_correction (bool, optional): enable bias correction (default: True) + + __ https://arxiv.org/abs/1412.6980 + """ + + def __init__( + self, + params, + lr=2e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + bias_correction=True, + ): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= weight_decay: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + + defaults = dict( + lr=lr, + betas=betas, + eps=eps, + weight_decay=weight_decay, + bias_correction=bias_correction, + ) + super(Adamax, self).__init__(params, defaults) + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + def step(self, closure=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad.data.float() + if grad.is_sparse: + raise RuntimeError("Adamax does not support sparse gradients") + + p_data_fp32 = p.data + if p.data.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + state = self.state[p] + + # State initialization + if len(state) == 0: + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p_data_fp32) + state["exp_inf"] = torch.zeros_like(p_data_fp32) + else: + state["exp_avg"] = state["exp_avg"].to(p_data_fp32) + state["exp_inf"] = state["exp_inf"].to(p_data_fp32) + + exp_avg, exp_inf = state["exp_avg"], state["exp_inf"] + beta1, beta2 = group["betas"] + eps = group["eps"] + + state["step"] += 1 + + # Update biased first moment estimate. + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + + # Update the exponentially weighted infinity norm. + torch.max( + exp_inf.mul_(beta2), + grad.abs_(), + out=exp_inf, + ) + + step_size = group["lr"] + if group["bias_correction"]: + bias_correction = 1 - beta1 ** state["step"] + step_size /= bias_correction + + if group["weight_decay"] != 0: + p_data_fp32.add_( + p_data_fp32, alpha=-group["weight_decay"] * group["lr"] + ) + + p_data_fp32.addcdiv_(exp_avg, exp_inf.add(eps), value=-step_size) + + if p.data.dtype in {torch.float16, torch.bfloat16}: + p.data.copy_(p_data_fp32) + + return loss diff --git a/fairseq/fairseq/optim/amp_optimizer.py b/fairseq/fairseq/optim/amp_optimizer.py new file mode 100644 index 0000000..cfe57d0 --- /dev/null +++ b/fairseq/fairseq/optim/amp_optimizer.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +from fairseq import optim +from omegaconf import DictConfig + +logger = logging.getLogger(__name__) + + +class AMPOptimizer(optim.FairseqOptimizer): + """ + Wrap an *optimizer* to support AMP (automatic mixed precision) training. + """ + + def __init__(self, cfg: DictConfig, params, fp32_optimizer, **kwargs): + super().__init__(cfg.optimizer) + self.fp32_optimizer = fp32_optimizer + amp_kwargs = {"init_scale": cfg.common.fp16_init_scale} + if getattr(cfg.common, "amp_scale_window", None) is not None: + amp_kwargs["growth_interval"] = cfg.common.amp_init_scale + self._grad_scaler = torch.cuda.amp.GradScaler(**amp_kwargs) + self.min_loss_scale = cfg.common.min_loss_scale + + @classmethod + def build_optimizer(cls, cfg: DictConfig, params, **kwargs): + """ + Args: + cfg (omegaconf.DictConfig): fairseq args + params (iterable): iterable of parameters to optimize + """ + fp32_optimizer = optim.build_optimizer(cfg.optimizer, params) + return cls(cfg, params, fp32_optimizer, **kwargs) + + def backward(self, loss): + """Computes the sum of gradients of the given tensor w.r.t. graph leaves. + + Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this + function additionally dynamically scales the loss to avoid gradient + underflow. + """ + self._grad_scaler.scale(loss).backward() + + def step(self): + self.scaler.step(self.fp32_optimizer) + self.scaler.update() + + def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): + """Clips gradient norm.""" + self.scaler.unscale_(self.optimizer) + grad_norm = self.fp32_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) + if not torch.isfinite(grad_norm).all(): + new_loss_scale = self.next_loss_scale + if new_loss_scale <= self.min_loss_scale: + raise FloatingPointError( + ( + "AMP: Minimum loss scale reached ({}). Your loss is probably exploding. " + "Try restarting training or use fp32. {}" + ).format(self.min_loss_scale, new_loss_scale) + ) + else: + logger.info( + "AMP: overflow detected, setting scale to " f"to {new_loss_scale}" + ) + return grad_norm + + @property + def scaler(self): + return self._grad_scaler + + @property + def next_loss_scale(self): + return self.scaler.get_scale() * self.scaler.get_backoff_factor() + + @property + def optimizer(self): + return self.fp32_optimizer.optimizer + + @optimizer.setter + def optimizer(self, optimizer): + self.fp32_optimizer.optimizer = optimizer + + @property + def lr_scheduler(self): + return getattr(self.fp32_optimizer, "lr_scheduler", None) + + @property + def optimizer_config(self): + return self.fp32_optimizer.optimizer_config + + def get_lr(self): + return self.fp32_optimizer.get_lr() + + def set_lr(self, lr): + self.fp32_optimizer.set_lr(lr) + + def all_reduce_grads(self, module): + self.fp32_optimizer.all_reduce_grads(module) + + @property + def supports_flat_params(self): + return self.fp32_optimizer.supports_flat_params diff --git a/fairseq/fairseq/optim/bmuf.py b/fairseq/fairseq/optim/bmuf.py new file mode 100644 index 0000000..d6d0e04 --- /dev/null +++ b/fairseq/fairseq/optim/bmuf.py @@ -0,0 +1,200 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +import torch +import torch.distributed as dist +from fairseq.dataclass.configs import FairseqBMUFConfig +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.optim.fairseq_optimizer import FairseqOptimizer + + +class FairseqBMUF(FairseqOptimizer): + """ + Implements incremental block distributed data parallelism similar to + https://ieeexplore.ieee.org/document/7472805 + + Paper title: Scalable training of deep learning machines by incremental + block training with intra-block parallel optimization and blockwise + model-update filtering + """ + + def __init__(self, cfg: FairseqBMUFConfig, optimizer): + super().__init__(cfg) + self._optimizer = optimizer + self._num_updates = 0 + self.sync_iter = cfg.global_sync_iter + self.block_momentum = cfg.block_momentum + self.block_lr = cfg.block_lr + self._reset_local_data() + self.warmup_iteration = cfg.warmup_iterations + self.use_nbm = cfg.use_nbm + self.initial_state = self._optimizer.state_dict() + self.average_sync = self.cfg.average_sync + self.world_size = self.cfg.distributed_world_size + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + gen_parser_from_dataclass(parser, FairseqBMUFConfig()) + + @property + def optimizer(self): + return self._optimizer.optimizer + + @property + def optimizer_config(self): + return self._optimizer.optimizer_config + + def get_lr(self): + return self._optimizer.get_lr() + + def set_lr(self, lr): + self._optimizer.set_lr(lr) + + def state_dict(self): + return self._optimizer.state_dict() + + def load_state_dict(self, state_dict, optimizer_overrides=None): + self._optimizer.load_state_dict(state_dict, optimizer_overrides) + self.initial_state = self._optimizer.state_dict() + + def multiply_grads(self, c): + """Multiplies grads by a constant *c*.""" + self._optimizer.multiply_grads(c) + + def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): + """Clips gradient norm.""" + return self._optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) + + def average_params(self): + self._optimizer.average_params() + + def _block_sync(self): + if self.world_size <= 1: + return + # Update the global model using local models from all GPUs + # (Step-1) Calculate grad between previously synced model and + # currrent local model + if self.block_momentum != 0: + self._calc_grad() + + # (Step-2) Average gradient from all GPUs + self._avg_grad_from_all_gpus() + + # (Step-3) Calculate global momentum and update the global model + if self.block_momentum != 0: + self._update_global_model() + + # (Step-4) Average local optimizer params + if self.average_sync: + self.average_params() + + def _is_warmup_end(self): + # Check whether train iterations is equal to warmup iter + if self.get_num_updates() == self.warmup_iteration: + return True + return False + + def _is_bmuf_iter(self): + # Check whether train iterations is equal to bmuf sync iter + if (self.get_num_updates() > self.warmup_iteration) and ( + self.get_num_updates() % self.sync_iter == 0 + ): + return True + return False + + def _warmup_sync(self, root_rank=0): + if self.world_size <= 1: + return + # Broadcast the local model to all gpus + for param in self.params: + dist.broadcast(param.data, src=root_rank) + + # Update local optimizer state + if self.average_sync: + self._optimizer.average_params() + else: + self._optimizer.load_state_dict(self.initial_state) + + self._reset_local_data() + + def step(self, closure=None): + """Performs a single optimization step.""" + self._optimizer.step(closure) + self.set_num_updates(self.get_num_updates() + 1) + if self._is_warmup_end(): + self._warmup_sync() + elif self._is_bmuf_iter(): + self._block_sync() + + def zero_grad(self): + """Clears the gradients of all optimized parameters.""" + self._optimizer.zero_grad() + + def get_num_updates(self): + """Get the number of parameters updates.""" + return self._num_updates + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + self._num_updates = num_updates + + @torch.no_grad() + def _reset_local_data(self): + # (Step-0) Initialize global momentum parameters and store global copy on each gpu + self.global_params = [torch.zeros_like(p.data) for p in self.params] + self.smoothed_grads = [p.data.new_zeros(p.data.size()) for p in self.params] + self.grads = [p.data.new_zeros(p.data.size()) for p in self.params] + + # saving the global model locally for calculating gradient during bmuf sync + for param, global_param in zip(self.params, self.global_params): + global_param.copy_(param.data) + + @torch.no_grad() + def _calc_grad(self): + # global_params is basically the global copy from the previously finished + # synchronisation. param.data is local parameter after block_sync_freq + # for the local gpu. so grad is difference between previously synced + # model and currrent local model. + for index, (param, global_param) in enumerate( + zip(self.params, self.global_params) + ): + self.grads[index] = global_param - param.data + + def _avg_grad_from_all_gpus(self): + for index, param in enumerate(self.params): + sync_para = param.data if self.block_momentum == 0 else self.grads[index] + sync_para /= float(dist.get_world_size()) + dist.all_reduce(sync_para, op=dist.ReduceOp.SUM) + + @torch.no_grad() + def _update_global_model(self): + for index, (param, global_param, smoothed_grad, grad) in enumerate( + zip( + self.params, + self.global_params, + self.smoothed_grads, + # all gpus would share the same value of smoothed_grad, since it is + # always computed on synchronized gradients. + self.grads, + ) + ): + # global_param is basically last syncrhornized parameter. though + # smoothed_grad is local, all processes will have same value of + # smoothed_grad and hence param is globally synchronized copy. + # smoothed_grad(t) = BM * smoothed_grad(t-1) + BM_lr * grad(t) + smoothed_grad = self.block_momentum * smoothed_grad + self.block_lr * grad + param.data.copy_(global_param - smoothed_grad) + + # A Nesterov momentum here is to do a partial weight update before + # calculating the gradient + if self.use_nbm: + param.data.copy_(param.data - self.block_momentum * smoothed_grad) + + # backup for the next synchronization. + self.smoothed_grads[index] = smoothed_grad + global_param.copy_(param.data) diff --git a/fairseq/fairseq/optim/composite.py b/fairseq/fairseq/optim/composite.py new file mode 100644 index 0000000..1ef0114 --- /dev/null +++ b/fairseq/fairseq/optim/composite.py @@ -0,0 +1,273 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from collections import defaultdict +from dataclasses import dataclass, field +from typing import Dict, Any, List, Optional + +import torch.optim +from fairseq.dataclass import FairseqDataclass +from fairseq.optim import FairseqOptimizer, register_optimizer, _build_optimizer +from fairseq.optim.lr_scheduler import FairseqLRScheduler, build_lr_scheduler +from omegaconf import II, open_dict +import copy + + +logger = logging.getLogger(__name__) + + +@dataclass +class OptimizerAndSchedulerConfig(FairseqDataclass): + optimizer: Any = None + lr_scheduler: Optional[Any] = None + lr: List = II("optimization.lr") + lr_float: Optional[ + float + ] = None # this makes it easier to sweep on learning rate with auto sweepers + + +@dataclass +class CompositeOptimizerConfig(FairseqDataclass): + groups: Dict[str, Any] = field( + default_factory=lambda: {}, + metadata={ + "help": "optimizer name -> optimizer OptimizerAndSchedulerConfig. " + "Configures a different optimizer and (optionally) lr scheduler for each parameter group" + }, + ) + dynamic_groups: bool = field( + default=False, + metadata={ + "help": "create groups dynamically based on parameters, if set to False, all parameters needs to have group_names" + }, + ) + + +@register_optimizer("composite", dataclass=CompositeOptimizerConfig) +class FairseqCompositeOptimizer(FairseqOptimizer): + + optimizers: Dict[str, FairseqOptimizer] = {} + lr_schedulers: Dict[str, FairseqLRScheduler] = {} + lr_scheduler: FairseqLRScheduler = None + _optimizer: torch.optim.Optimizer + + def __init__(self, cfg: CompositeOptimizerConfig, params): + super().__init__(cfg) + + assert ( + len(params) > 1 + ), "Composite optimizer only works when there are multiple parameter groups (try fp16_no_flatten_grads: true)" + + def dict_hash(dictionary: Dict[str, Any]) -> str: + import hashlib + import json + + dhash = hashlib.md5() + encoded = json.dumps(dictionary, sort_keys=True).encode() + dhash.update(encoded) + return dhash.hexdigest() + + groupped_params = defaultdict(list) + overrides = defaultdict(dict) + if not cfg.dynamic_groups: + for p in params: + group = getattr(p, "param_group", "default") + override_config = getattr(p, "optim_overrides", None) + if override_config is not None and bool(override_config): + overrides[group] = override_config + else: + assert ( + override_config == None or override_config == overrides[group] + ), f"For group {group}, different overrides found {override_config} v/s {overrides[group]}" + groupped_params[group].append(p) + + for p, params in groupped_params.items(): + override_config = getattr(params[0], "optim_overrides", None) + if override_config is not None: + for pp in params[1:]: + assert override_config == getattr( + pp, "optim_overrides", None + ), f" {str(override_config)} != {str(getattr(pp, 'optim_overrides', None))}" + else: + for p in params: + group = getattr(p, "param_group", "default") + override_config = getattr(p, "optim_overrides", None) + if override_config is not None: + override_config["group_name"] = group + group_name = dict_hash(override_config) + overrides[group_name] = override_config + else: + group_name = group + groupped_params[group_name].append(p) + + self.optimizers_config = {} + for group, group_params in groupped_params.items(): + p_group = group + if group in overrides and "group_name" in overrides[group]: + p_group = overrides[group]["group_name"] + if group in cfg.groups: + group_cfg = cfg.groups[group] + optimizer_config = copy.deepcopy(group_cfg.optimizer) + scheduler_config = copy.deepcopy(group_cfg.lr_scheduler) + explicit_group_present = True + else: + group_cfg = cfg.groups[p_group] + optimizer_config = copy.deepcopy(group_cfg.optimizer) + scheduler_config = copy.deepcopy(group_cfg.lr_scheduler) + explicit_group_present = False + + if getattr(group_cfg, "lr_float", None) is not None: + with open_dict(optimizer_config): + optimizer_config.lr = [group_cfg.lr_float] + + if group in overrides and "optimizer" in overrides[group]: + with open_dict(optimizer_config): + if "lr_scale" in overrides[group]["optimizer"]: + lr_scale = overrides[group]["optimizer"]["lr_scale"] + optimizer_config.lr = [ + lr * lr_scale for lr in optimizer_config.lr + ] + + if explicit_group_present: + logger.info( + f"For group:{group}, config as well as override present for lr" + ) + + if ( + "weight_decay_scale" in overrides[group]["optimizer"] + and "optimizer_config" in optimizer_config + ): + weight_decay_scale = overrides[group]["optimizer"][ + "weight_decay_scale" + ] + optimizer_config.weight_decay = ( + optimizer_config.weight_decay * weight_decay_scale + ) + if explicit_group_present: + logger.info( + f"For group:{group}, config as well as override present for weight_decay" + ) + + with open_dict(scheduler_config): + scheduler_config.lr = optimizer_config.lr + self.optimizers[group] = _build_optimizer(optimizer_config, group_params) + self.optimizers_config[group] = optimizer_config + if scheduler_config is not None: + self.lr_schedulers[group] = build_lr_scheduler( + scheduler_config, self.optimizers[group] + ) + logger.info("Optimizers for different groups are as below") + for group in self.optimizers_config.keys(): + logger.info(f"Group : {group}:{self.optimizers_config[group]}") + if len(self.lr_schedulers) > 0: + assert len(self.lr_schedulers) == len(self.optimizers), ( + f"Please provide an lr scheduler for each optimizer to use pass_through scheduler. " + f"Optimizers: {self.optimizers}; Lr scheds: {self.lr_schedulers}" + ) + self.lr_scheduler = CompositeLRScheduler(self.lr_schedulers) + + self._optimizer = CompositeOptimizer(self.optimizers) + + @property + def supports_groups(self): + return True + + @property + def param_groups(self): + for opt in self.optimizers.values(): + for group in opt.param_groups: + yield group + + def get_lr(self): + """Return the current learning rate.""" + k = ( + "default" + if "default" in self.optimizers + else next(iter(self.optimizers.keys())) + ) + return self.optimizers[k].param_groups[0]["lr"] + + def state_dict(self): + """Return the LR scheduler state dict.""" + return {k: s.state_dict() for k, s in self.optimizers.items()} + + def load_state_dict(self, state_dict, optimizer_overrides=None): + """Load an LR scheduler state dict.""" + for k, state in state_dict.items(): + if k not in self.optimizers: + # skip extra keys like "loss_scale" added by fp16 optimizer + continue + + overrides = ( + optimizer_overrides[k] + if isinstance(optimizer_overrides, dict) and k in optimizer_overrides + else None + ) + self.optimizers[k].load_state_dict(state, optimizer_overrides=overrides) + + +class CompositeOptimizer(torch.optim.Optimizer): + def __init__(self, optimizers: Dict[str, FairseqOptimizer]): + self.optimizers = optimizers + + @property + def supports_memory_efficient_fp16(self): + return all(o.supports_memory_efficient_fp16 for o in self.optimizers.values()) + + @property + def supports_flat_params(self): + return all(o.supports_flat_params for o in self.optimizers.values()) + + def step(self, closure=None, groups=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for k, opt in self.optimizers.items(): + if groups is None or k in groups: + opt.step() + + return loss + + def zero_grad(self): + for opt in self.optimizers.values(): + opt.zero_grad() + + +class CompositeLRScheduler(FairseqLRScheduler): + def __init__(self, lr_schedulers): + super().__init__(None, None) + + self.lr_schedulers = lr_schedulers + + def state_dict(self): + """Return the LR scheduler state dict.""" + return {k: s.state_dict() for k, s in self.lr_schedulers.items()} + + def load_state_dict(self, state_dict): + """Load an LR scheduler state dict.""" + for k, state in state_dict.items(): + self.lr_schedulers[k].load_state_dict(state) + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + for s in self.lr_schedulers.values(): + s.step_begin_epoch(epoch) + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + for s in self.lr_schedulers.values(): + s.step(epoch) + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + return {k: s.step_update(num_updates) for k, s in self.lr_schedulers.items()} diff --git a/fairseq/fairseq/optim/cpu_adam.py b/fairseq/fairseq/optim/cpu_adam.py new file mode 100644 index 0000000..b218934 --- /dev/null +++ b/fairseq/fairseq/optim/cpu_adam.py @@ -0,0 +1,210 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.optim import FairseqOptimizer, register_optimizer +from omegaconf import II, DictConfig + + +try: + import deepspeed + + has_deepspeed = True +except ImportError as e: + has_deepspeed = False + + +def _get_cpu_adam(): + try: + from deepspeed.ops.op_builder import CPUAdamBuilder + + return CPUAdamBuilder().load() + except ImportError: + # fbcode + from deepspeed.ops.adam import DeepSpeedCPUAdam as ds_opt_adam + + return ds_opt_adam + + +@dataclass +class FairseqCPUAdamConfig(FairseqDataclass): + adam_betas: str = field( + default="(0.9, 0.999)", metadata={"help": "betas for Adam optimizer"} + ) + adam_eps: float = field( + default=1e-8, metadata={"help": "epsilon for Adam optimizer"} + ) + weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) + fp16_adam_stats: bool = field( + default=False, metadata={"help": "use FP16 stats (with automatic scaling)"} + ) + # TODO common vars below in parent + lr: List[float] = II("optimization.lr") + + +@register_optimizer("cpu_adam", dataclass=FairseqCPUAdamConfig) +class FairseqCPUAdam(FairseqOptimizer): + """Adam optimizer for fairseq, optimized for CPU tensors. + + Important note: this optimizer corresponds to the "AdamW" variant of + Adam in its weight decay behavior. As such, it is most closely + analogous to torch.optim.AdamW from PyTorch. + """ + + def __init__(self, cfg: DictConfig, params): + super().__init__(cfg) + self._optimizer = CPUAdam(params, **self.optimizer_config) + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.cfg.lr[0] + if isinstance(self.cfg.lr, Collection) + else self.cfg.lr, + "betas": eval(self.cfg.adam_betas), + "eps": self.cfg.adam_eps, + "weight_decay": self.cfg.weight_decay, + "use_fp16_stats": self.cfg.fp16_adam_stats, + } + + +class CPUAdam(torch.optim.Optimizer): + + optimizer_id = 0 + + def __init__( + self, + params, + lr=1e-3, + bias_correction=True, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + use_fp16_stats=False, + ): + defaults = { + "lr": lr, + "bias_correction": bias_correction, + "betas": betas, + "eps": eps, + "weight_decay": weight_decay, + } + super().__init__(params, defaults) + + self.use_fp16_stats = use_fp16_stats + self.FLOAT16_MAX = 65504.0 + + if not has_deepspeed: + raise ImportError("Please install DeepSpeed: pip install deepspeed") + + self.opt_id = CPUAdam.optimizer_id + CPUAdam.optimizer_id = CPUAdam.optimizer_id + 1 + + self.ds_opt_adam = _get_cpu_adam() + adamw_mode = True + self.ds_opt_adam.create_adam( + self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode + ) + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + torch.cuda.synchronize() + + for group_id, group in enumerate(self.param_groups): + for param_id, p in enumerate(group["params"]): + if p.grad is None: + continue + + state = self.state[p] + if len(state) == 0: + state["step"] = 0 + dtype = torch.float16 if self.use_fp16_stats else p.data.dtype + # gradient momentums + state["exp_avg"] = torch.zeros_like( + p.data, dtype=dtype, device="cpu" + ) + # gradient variances + state["exp_avg_sq"] = torch.zeros_like( + p.data, dtype=dtype, device="cpu" + ) + if self.use_fp16_stats: + assert torch.is_floating_point(p.data) + state["exp_avg_scale"] = 1.0 + state["exp_avg_sq_scale"] = 1.0 + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + + p_data_bak = p.data # backup of the original data pointer + + p.data = p.data.to(dtype=torch.float32, device="cpu") + p.grad.data = p.grad.data.to(dtype=torch.float32, device="cpu") + + if self.use_fp16_stats: + exp_avg = exp_avg.float() * state["exp_avg_scale"] + exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"] + + state["step"] += 1 + beta1, beta2 = group["betas"] + + self.ds_opt_adam.adam_update( + self.opt_id, + state["step"], + group["lr"], + beta1, + beta2, + group["eps"], + group["weight_decay"], + group["bias_correction"], + p.data, + p.grad.data, + exp_avg, + exp_avg_sq, + ) + + if p_data_bak.data_ptr() != p.data.data_ptr(): + p_data_bak.copy_(p.data) + p.data = p_data_bak + + if self.use_fp16_stats: + + def inf_norm(t): + return torch.norm(t, float("inf")) + + # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py + state["exp_avg_scale"], state["exp_avg_sq_scale"] = ( + 1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX, + 1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX, + ) + state["exp_avg"], state["exp_avg_sq"] = ( + (exp_avg / state["exp_avg_scale"]).half(), + (exp_avg_sq / state["exp_avg_sq_scale"]).half(), + ) + + return loss diff --git a/fairseq/fairseq/optim/dynamic_loss_scaler.py b/fairseq/fairseq/optim/dynamic_loss_scaler.py new file mode 100644 index 0000000..60c47b8 --- /dev/null +++ b/fairseq/fairseq/optim/dynamic_loss_scaler.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +class DynamicLossScaler(object): + def __init__( + self, + init_scale=2.0**15, + scale_factor=2.0, + scale_window=2000, + tolerance=0.0, + threshold=None, + min_loss_scale=1e-4, + ): + self.loss_scale = init_scale + self.scale_factor = scale_factor + self.scale_window = scale_window + self.tolerance = tolerance + self.threshold = threshold + self._iter = 0 + self._last_overflow_iter = -1 + self._last_rescale_iter = -1 + self._overflows_since_rescale = 0 + self.min_loss_scale = min_loss_scale + + def scale(self, outputs): + return self.loss_scale * outputs + + def update(self): + if (self._iter - self._last_overflow_iter) % self.scale_window == 0: + self.loss_scale *= self.scale_factor + self._last_rescale_iter = self._iter + self._iter += 1 + + def _decrease_loss_scale(self): + self.loss_scale /= self.scale_factor + if self.threshold is not None: + self.loss_scale = max(self.loss_scale, self.threshold) + + def check_overflow(self, grad_norm): + # detect inf and nan + if grad_norm == float("inf") or grad_norm != grad_norm: + # overflow has occured + prev_scale = self.loss_scale + iter_since_rescale = self._iter - self._last_rescale_iter + + self._last_overflow_iter = self._iter + self._overflows_since_rescale += 1 + pct_overflow = self._overflows_since_rescale / float(iter_since_rescale) + if pct_overflow >= self.tolerance: + self._decrease_loss_scale() + self._last_rescale_iter = self._iter + self._overflows_since_rescale = 0 + + if self.loss_scale <= self.min_loss_scale: + # Use FloatingPointError as an uncommon error that parent + # functions can safely catch to stop training. + self.loss_scale = prev_scale + raise FloatingPointError( + ( + "Minimum loss scale reached ({}). Your loss is probably exploding. " + "Try lowering the learning rate, using gradient clipping or " + "increasing the batch size." + ).format(self.min_loss_scale) + ) + + self._iter += 1 + raise OverflowError("setting loss scale to: " + str(self.loss_scale)) diff --git a/fairseq/fairseq/optim/fairseq_optimizer.py b/fairseq/fairseq/optim/fairseq_optimizer.py new file mode 100644 index 0000000..73c7c69 --- /dev/null +++ b/fairseq/fairseq/optim/fairseq_optimizer.py @@ -0,0 +1,187 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq import utils +from fairseq.dataclass.utils import gen_parser_from_dataclass +from collections import defaultdict + + +class FairseqOptimizer(object): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + + @classmethod + def add_args(cls, parser): + """Add optimizer-specific arguments to the parser.""" + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) + + @property + def optimizer(self): + """Return a torch.optim.optimizer.Optimizer instance.""" + if not hasattr(self, "_optimizer"): + raise NotImplementedError + if not isinstance(self._optimizer, torch.optim.Optimizer): + raise ValueError("_optimizer must be an instance of torch.optim.Optimizer") + return self._optimizer + + @optimizer.setter + def optimizer(self, optimizer): + """Reset optimizer instance.""" + if not hasattr(self, "_optimizer"): + raise NotImplementedError + if not isinstance(self._optimizer, torch.optim.Optimizer): + raise ValueError("_optimizer must be an instance of torch.optim.Optimizer") + self._optimizer = optimizer + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + raise NotImplementedError + + @property + def params(self): + """Return an iterable of the parameters held by the optimizer.""" + for param_group in self.param_groups: + for p in param_group["params"]: + yield p + + @property + def param_groups(self): + return self.optimizer.param_groups + + def __getstate__(self): + return self._optimizer.__getstate__() + + def get_lr(self): + """Return the current learning rate.""" + return self.param_groups[0]["lr"] + + def set_lr(self, lr): + """Set the learning rate.""" + for param_group in self.param_groups: + param_group["lr"] = lr + + def state_dict(self): + """Return the optimizer's state dict.""" + return self.optimizer.state_dict() + + def load_state_dict(self, state_dict, optimizer_overrides=None): + """Load an optimizer state dict. + + In general we should prefer the configuration of the existing optimizer + instance (e.g., learning rate) over that found in the state_dict. This + allows us to resume training from a checkpoint using a new set of + optimizer args. + """ + self.optimizer.load_state_dict(state_dict) + + if optimizer_overrides is not None and len(optimizer_overrides) > 0: + # override learning rate, momentum, etc. with latest values + for group in self.param_groups: + group.update(optimizer_overrides) + + def backward(self, loss): + """Computes the sum of gradients of the given tensor w.r.t. graph leaves.""" + loss.backward() + + def all_reduce_grads(self, module): + """Manually all-reduce gradients (if required).""" + if hasattr(module, "all_reduce_grads"): + module.all_reduce_grads() + + def multiply_grads(self, c): + """Multiplies grads by a constant *c*.""" + per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list)) + for p in self.params: + if p.grad is not None: + if p.grad.is_sparse: + p.grad.data.mul_(c.to(p.grad.device) if torch.is_tensor(c) else c) + else: + per_device_and_dtype_grads[p.grad.device][p.grad.dtype].append( + p.grad.data + ) + for device, per_dtype_grads in per_device_and_dtype_grads.items(): + for grads in per_dtype_grads.values(): + torch._foreach_mul_(grads, c.to(device) if torch.is_tensor(c) else c) + + def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): + """Clips gradient norm.""" + return utils.clip_grad_norm_(self.params, max_norm, aggregate_norm_fn) + + def step(self, closure=None, scale=1.0, groups=None): + """Performs a single optimization step.""" + if self.supports_step_with_scale: + if self.supports_groups: + self.optimizer.step(closure, scale=scale, groups=groups) + else: + self.optimizer.step(closure, scale=scale) + else: + if scale != 1.0: + self.multiply_grads(1.0 / scale) + if self.supports_groups: + self.optimizer.step(closure, groups=groups) + else: + self.optimizer.step(closure) + + def zero_grad(self): + """Clears the gradients of all optimized parameters.""" + for p in self.params: + p.grad = None + self.optimizer.zero_grad() + + @property + def supports_memory_efficient_fp16(self): + if hasattr(self.optimizer, "supports_memory_efficient_fp16"): + return self.optimizer.supports_memory_efficient_fp16 + return False + + @property + def supports_step_with_scale(self): + if hasattr(self.optimizer, "supports_step_with_scale"): + return self.optimizer.supports_step_with_scale + return False + + @property + def supports_groups(self): + if hasattr(self.optimizer, "supports_groups"): + return self.optimizer.supports_groups + return False + + @property + def supports_flat_params(self): + """ + Whether the optimizer supports collapsing of the model + parameters/gradients into a single contiguous Tensor. + """ + if hasattr(self.optimizer, "supports_flat_params"): + return self.optimizer.supports_flat_params + return False + + def average_params(self): + pass + + def broadcast_global_state_dict(self, state_dict): + """ + Broadcasts a global state dict to all ranks. + Useful for optimizers that shard state between ranks. + """ + if hasattr(self.optimizer, "broadcast_global_state_dict"): + return self.optimizer.broadcast_global_state_dict(state_dict) + else: + return state_dict + + +class LegacyFairseqOptimizer(FairseqOptimizer): + def __init__(self, args): + self.args = args diff --git a/fairseq/fairseq/optim/fp16_optimizer.py b/fairseq/fairseq/optim/fp16_optimizer.py new file mode 100644 index 0000000..6a4da34 --- /dev/null +++ b/fairseq/fairseq/optim/fp16_optimizer.py @@ -0,0 +1,558 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections import defaultdict +from itertools import chain + +import torch +from omegaconf import DictConfig + +from fairseq import optim + +from .dynamic_loss_scaler import DynamicLossScaler + + +class _FP16OptimizerMixin(object): + def __init__(self, *args, **kwargs): + # forward __init__ call to the next class in mro(method resolution order) + super().__init__(*args, **kwargs) + self._multiply_factor = 1.0 + + @property + def has_flat_params(self): + return torch.is_tensor(self.fp32_params) or ( + isinstance(self.fp32_params, dict) + and all(torch.is_tensor(t) for t in self.fp32_params.values()) + ) + + @classmethod + def build_fp32_params(cls, args, params, flatten=True): + # create FP32 copy of parameters and grads + if flatten: + is_pipeline_parallel = getattr( + args, "pipeline_model_parallel", False + ) and getattr(args, "distributed_no_spawn", False) + total_param_size = sum(p.data.numel() for p in params) + devices = [torch.cuda.current_device()] + if is_pipeline_parallel: + devices = list(set(args.pipeline_devices)) + fp32_params = {} + for device in devices: + if is_pipeline_parallel: + device_param_size = sum( + p.data.numel() for p in params if p.device.index == device + ) + device_params = [p for p in params if p.device.index == device] + else: + device_param_size = total_param_size + device_params = params + fp32_params[device] = ( + device_params[0].new(0).float().new(device_param_size) + ) + offset = 0 + for p in device_params: + numel = p.data.numel() + fp32_params[device][offset : offset + numel].copy_(p.data.view(-1)) + offset += numel + fp32_params[device] = torch.nn.Parameter(fp32_params[device]) + fp32_params[device].grad = fp32_params[device].data.new( + device_param_size + ) + return fp32_params + else: + fp32_params = [] + for p in params: + p32 = torch.nn.Parameter(p.data.float()) + if hasattr(p, "expert"): + p32.expert = True + elif hasattr(p, "base_expert"): + p32.base_expert = True + p32.grad = torch.zeros_like(p32.data) + if hasattr(p, "param_group"): + p32.param_group = p.param_group + if hasattr(p, "optim_overrides"): + p32.optim_overrides = p.optim_overrides + fp32_params.append(p32) + return fp32_params + + def state_dict(self): + """Return the optimizer's state dict.""" + state_dict = self.fp32_optimizer.state_dict() + if self.scaler is not None: + state_dict["loss_scale"] = self.scaler.loss_scale + return state_dict + + def load_state_dict(self, state_dict, optimizer_overrides=None): + """Load an optimizer state dict. + + In general we should prefer the configuration of the existing optimizer + instance (e.g., learning rate) over that found in the state_dict. This + allows us to resume training from a checkpoint using a new set of + optimizer args. + """ + if "loss_scale" in state_dict and self.scaler is not None: + self.scaler.loss_scale = state_dict["loss_scale"] + self.fp32_optimizer.load_state_dict(state_dict, optimizer_overrides) + + def backward(self, loss): + """Computes the sum of gradients of the given tensor w.r.t. graph leaves. + + Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this + function additionally dynamically scales the loss to avoid gradient + underflow. + """ + if self.scaler is not None: + loss = self.scaler.scale(loss) + loss.backward() + self._needs_sync = True + + def _sync_fp16_grads_to_fp32(self): + if self._needs_sync: + # copy FP16 grads to FP32 + if self.has_flat_params: + devices = list(self.fp32_params.keys()) + device_params_dict = defaultdict(list) + for p in self.fp16_params: + if p.requires_grad: + device_params_dict[p.device.index].append(p) + for device in devices: + device_params = device_params_dict[device] + offset = 0 + for p in device_params: + grad_data = ( + p.grad.data + if p.grad is not None + else p.data.new_zeros(p.data.shape) + ) + numel = grad_data.numel() + self.fp32_params[device].grad.data[ + offset : offset + numel + ].copy_(grad_data.view(-1)) + offset += numel + else: + for p, p32 in zip(self.fp16_params, self.fp32_params): + if not p.requires_grad: + continue + if p.grad is not None: + if p32.grad is None: + p32.grad = p.grad.data.float() + else: + p32.grad.data.copy_(p.grad.data) + else: + p32.grad = torch.zeros_like(p.data, dtype=torch.float) + + self._needs_sync = False + + def _sync_fp32_params_to_fp16(self): + # copy FP32 params back into FP16 model + if self.has_flat_params: + devices = list(self.fp32_params.keys()) + device_params_dict = defaultdict(list) + for p in self.fp16_params: + device_params_dict[p.device.index].append(p) + for device in devices: + device_params = device_params_dict[device] + offset = 0 + for p in device_params: + numel = p.data.numel() + p.data.copy_( + self.fp32_params[device] + .data[offset : offset + numel] + .view_as(p.data) + ) + offset += numel + else: + for p, p32 in zip(self.fp16_params, self.fp32_params): + if not p.requires_grad: + continue + p.data.copy_(p32.data) + + def _unscale_grads(self): + self._sync_fp16_grads_to_fp32() + if ( + # Skip the multiplication if it's a no-op (i.e., if _multiply_factor + # is 1.0). At the same time, we want to avoid the device-to-host + # transfer by comparing it to 1.0. Since _multiply_factor starts as + # a Python float, we roughly assume that if it's a tensor then it's + # probably not =1.0 anymore and we do the multiplication. Otherwise + # we can safely check the value without a D2H transfer. + torch.is_tensor(self._multiply_factor) + or self._multiply_factor != 1.0 + ): + self.fp32_optimizer.multiply_grads(self._multiply_factor) + self._multiply_factor = 1.0 + + def multiply_grads(self, c): + """Multiplies grads by a constant ``c``.""" + self._multiply_factor *= c + + def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): + """Clips gradient norm and updates dynamic loss scaler.""" + self._sync_fp16_grads_to_fp32() + + grad_norm = self._multiply_factor * self.fp32_optimizer.clip_grad_norm( + 0, aggregate_norm_fn + ) + + if torch.is_tensor(self._multiply_factor): + self._multiply_factor = self._multiply_factor.to(grad_norm.device) + + if self.scaler is not None: + if grad_norm > max_norm > 0.0: + self._multiply_factor *= max_norm / grad_norm + + self.scaler.check_overflow(grad_norm) + elif max_norm > 0.0: + clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1) + self._multiply_factor *= clip_coef + + return grad_norm + + def step(self, closure=None, groups=None): + """Performs a single optimization step.""" + self._sync_fp16_grads_to_fp32() + + if getattr(self, "supports_step_with_scale", False): + self.fp32_optimizer.step( + closure, scale=(1.0 / self._multiply_factor), groups=groups + ) + else: + self._unscale_grads() + self.fp32_optimizer.step(closure, groups=groups) + + if self.scaler is not None: + self.scaler.update() + + self._sync_fp32_params_to_fp16() + + def zero_grad(self): + """Clears the gradients of all optimized parameters.""" + for p in self.fp16_params: + p.grad = None + if self.has_flat_params: + if torch.is_tensor(self.fp32_params): + self.fp32_params.grad.zero_() + elif isinstance(self.fp32_params, dict): + for fp32_params in self.fp32_params.values(): + fp32_params.grad.zero_() + else: + raise RuntimeError("self.fp32_params must be a tensor or dict") + else: + for p32 in self.fp32_params: + if p32.grad is not None: + p32.grad.zero_() + self._needs_sync = False + + if self.scaler is not None: + self._multiply_factor = 1.0 / float(self.scaler.loss_scale) + + +class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer): + """ + Wrap an *optimizer* to support FP16 (mixed precision) training. + """ + + def __init__(self, cfg: DictConfig, params, fp32_optimizer, fp32_params, **kwargs): + super().__init__(cfg.optimizer) + self.fp16_params = params + self.fp32_optimizer = fp32_optimizer + self.fp32_params = fp32_params + + if getattr(cfg.common, "fp16_scale_window", None) is None: + if len(cfg.optimization.update_freq) > 1: + raise ValueError( + "--fp16-scale-window must be given explicitly when using a " + "custom --update-freq schedule" + ) + data_parallel_size = int( + cfg.distributed_training.distributed_world_size + / cfg.common.model_parallel_size + ) + scale_window = int( + 2**14 / data_parallel_size / cfg.optimization.update_freq[0] + ) + else: + scale_window = cfg.common.fp16_scale_window + + if not getattr(cfg.common, "bf16", False): + self.scaler = DynamicLossScaler( + init_scale=cfg.common.fp16_init_scale, + scale_window=scale_window, + tolerance=cfg.common.fp16_scale_tolerance, + threshold=cfg.common.threshold_loss_scale, + min_loss_scale=cfg.common.min_loss_scale, + ) + else: + # disable loss scaling for bfloat16 + self.scaler = None + + @classmethod + def build_optimizer(cls, cfg: DictConfig, params, **kwargs): + """ + Args: + cfg (omegaconf.DictConfig): fairseq args + params (iterable): iterable of parameters to optimize + """ + flatten = not getattr(cfg.common, "fp16_no_flatten_grads", False) + if getattr(cfg.common, "bf16", False): + flatten = False # mixed precision is faster on TPUs without flat grads + fp32_params = cls.build_fp32_params(cfg.optimizer, params, flatten=flatten) + if flatten: + fp32_optimizer = optim.build_optimizer(cfg.optimizer, [fp32_params]) + else: + fp32_optimizer = optim.build_optimizer(cfg.optimizer, fp32_params) + if flatten and not fp32_optimizer.supports_flat_params: + raise RuntimeError( + f"chosen optimizer {fp32_optimizer.__class__.__name__} does not support flat params, please set --fp16-no-flatten-grads" + ) + return cls(cfg, params, fp32_optimizer, fp32_params, **kwargs) + + @property + def optimizer(self): + return self.fp32_optimizer.optimizer + + @optimizer.setter + def optimizer(self, optimizer): + self.fp32_optimizer.optimizer = optimizer + + @property + def lr_scheduler(self): + return getattr(self.fp32_optimizer, "lr_scheduler", None) + + @property + def optimizer_config(self): + return self.fp32_optimizer.optimizer_config + + def get_lr(self): + return self.fp32_optimizer.get_lr() + + def set_lr(self, lr): + self.fp32_optimizer.set_lr(lr) + + def all_reduce_grads(self, module): + self.fp32_optimizer.all_reduce_grads(module) + + @property + def supports_flat_params(self): + return self.fp32_optimizer.supports_flat_params + + +class _MemoryEfficientFP16OptimizerMixin(object): + def __init__(self, *args, **kwargs): + # forward __init__ call to the next class in MRO (method resolution order) + super().__init__(*args, **kwargs) + self._multiply_factor = 1.0 + + @property + def has_flat_params(self): + return False + + def state_dict(self): + """Return the optimizer's state dict.""" + state_dict = self.wrapped_optimizer.state_dict() + if self.scaler is not None: + state_dict["loss_scale"] = self.scaler.loss_scale + return state_dict + + def load_state_dict(self, state_dict, optimizer_overrides=None): + """Load an optimizer state dict. + + In general we should prefer the configuration of the existing optimizer + instance (e.g., learning rate) over that found in the state_dict. This + allows us to resume training from a checkpoint using a new set of + optimizer args. + """ + if "loss_scale" in state_dict and self.scaler is not None: + self.scaler.loss_scale = state_dict["loss_scale"] + + self.wrapped_optimizer.load_state_dict(state_dict, optimizer_overrides) + + # Hack: PyTorch automatically casts the optimizer state to match the + # type of the current parameters. But with --memory-efficient-fp16 the + # params are FP16 while the optimizer state is FP32 and we don't want + # to cast. A workaround is to manually copy back the original state + # after the optimizer has been loaded. + if not getattr(self.optimizer, "disable_mem_eff_fp16_loading_hack", False): + groups = self.optimizer.param_groups + saved_groups = state_dict["param_groups"] + id_map = { + old_id: p + for old_id, p in zip( + chain(*(g["params"] for g in saved_groups)), + chain(*(g["params"] for g in groups)), + ) + } + for k, v in state_dict["state"].items(): + if k in id_map: + param = id_map[k] + self.optimizer.state[param] = v + + def backward(self, loss): + """Computes the sum of gradients of the given tensor w.r.t. graph leaves. + + Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this + function additionally dynamically scales the loss to avoid gradient + underflow. + """ + if self.scaler is not None: + loss = self.scaler.scale(loss) + loss.backward() + + def _unscale_grads(self): + if ( + # Skip the multiplication if it's a no-op (i.e., if _multiply_factor + # is 1.0). At the same time, we want to avoid the device-to-host + # transfer by comparing it to 1.0. Since _multiply_factor starts as + # a Python float, we roughly assume that if it's a tensor then it's + # probably not =1.0 anymore and we do the multiplication. Otherwise + # we can safely check the value without a D2H transfer. + torch.is_tensor(self._multiply_factor) + or self._multiply_factor != 1.0 + ): + self.wrapped_optimizer.multiply_grads(self._multiply_factor) + self._multiply_factor = 1.0 + + def multiply_grads(self, c): + """Multiplies grads by a constant *c*.""" + self._multiply_factor *= c + + def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): + """Clips gradient norm and updates dynamic loss scaler.""" + max_norm = float(max_norm) + grad_norm = self._multiply_factor * self.wrapped_optimizer.clip_grad_norm( + 0, aggregate_norm_fn + ) + + if self.scaler is not None: + grad_norm_cpu = float(grad_norm) + if grad_norm_cpu > max_norm > 0.0: + self._multiply_factor *= max_norm / grad_norm_cpu + + # detect overflow and adjust loss scale + self.scaler.check_overflow(grad_norm_cpu) + elif max_norm > 0.0: + clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1) + self._multiply_factor *= clip_coef + + return grad_norm + + def step(self, closure=None, groups=None): + """Performs a single optimization step.""" + if getattr(self, "supports_step_with_scale", False): + # NOTE(msb) optimizer divides by scale factor + self.wrapped_optimizer.step( + closure, scale=(1.0 / self._multiply_factor), groups=groups + ) + else: + self._unscale_grads() + self.wrapped_optimizer.step(closure, groups=groups) + + if self.scaler is not None: + self.scaler.update() + + def zero_grad(self): + """Clears the gradients of all optimized parameters.""" + self.wrapped_optimizer.zero_grad() + if self.scaler is not None: + self._multiply_factor = 1.0 / float(self.scaler.loss_scale) + else: + self._multiply_factor = 1.0 + + @property + def supports_flat_params(self): + return self.wrapped_optimizer.supports_flat_params + + +class MemoryEfficientFP16Optimizer( + _MemoryEfficientFP16OptimizerMixin, optim.FairseqOptimizer +): + """ + Wrap an *optimizer* to support FP16 (mixed precision) training. + + Compared to :class:`fairseq.optim.FP16Optimizer`, this version does not + maintain an FP32 copy of the model. We instead expect the optimizer to + convert the gradients to FP32 internally and sync the results back to the + FP16 model params. This significantly reduces memory usage but slightly + increases the time spent in the optimizer. + + Since this wrapper depends on specific functionality in the wrapped + optimizer (i.e., on-the-fly conversion of grads to FP32), only certain + optimizers can be wrapped. This is determined by the + *supports_memory_efficient_fp16* property. + """ + + def __init__( + self, cfg: DictConfig, params, optimizer, allow_unsupported=False, **kwargs + ): + if not allow_unsupported and not optimizer.supports_memory_efficient_fp16: + raise ValueError( + "Unsupported optimizer: {}".format(optimizer.__class__.__name__) + ) + + super().__init__(getattr(cfg, "optimizer", None)) + self.wrapped_optimizer = optimizer + + if getattr(cfg.common, "fp16_scale_window", None) is None: + if len(cfg.optimization.update_freq) > 1: + raise ValueError( + "--fp16-scale-window must be given explicitly when using a " + "custom --update-freq schedule" + ) + data_parallel_size = int( + cfg.distributed_training.distributed_world_size + / cfg.common.model_parallel_size + ) + scale_window = int( + 2**14 / data_parallel_size / cfg.optimization.update_freq[0] + ) + else: + scale_window = cfg.common.fp16_scale_window + + if not getattr(cfg.common, "bf16", False): + self.scaler = DynamicLossScaler( + init_scale=cfg.common.fp16_init_scale, + scale_window=scale_window, + tolerance=cfg.common.fp16_scale_tolerance, + threshold=cfg.common.threshold_loss_scale, + min_loss_scale=cfg.common.min_loss_scale, + ) + else: + # disable loss scaling for bfloat16 + self.scaler = None + + @classmethod + def build_optimizer(cls, cfg: DictConfig, params, **kwargs): + """ + Args: + args (argparse.Namespace): fairseq args + params (iterable): iterable of parameters to optimize + """ + fp16_optimizer = optim.build_optimizer(cfg.optimizer, params) + return cls(cfg, params, fp16_optimizer, **kwargs) + + @property + def optimizer(self): + return self.wrapped_optimizer.optimizer + + @optimizer.setter + def optimizer(self, optimizer): + self.wrapped_optimizer.optimizer = optimizer + + @property + def optimizer_config(self): + return self.wrapped_optimizer.optimizer_config + + @property + def lr_scheduler(self): + return getattr(self.wrapped_optimizer, "lr_scheduler", None) + + def get_lr(self): + return self.wrapped_optimizer.get_lr() + + def set_lr(self, lr): + self.wrapped_optimizer.set_lr(lr) + + def all_reduce_grads(self, module): + self.wrapped_optimizer.all_reduce_grads(module) diff --git a/fairseq/fairseq/optim/fused_adam.py b/fairseq/fairseq/optim/fused_adam.py new file mode 100644 index 0000000..39a2a83 --- /dev/null +++ b/fairseq/fairseq/optim/fused_adam.py @@ -0,0 +1,389 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import types + +import torch + + +def get_fused_adam_class(): + """ + Look for the FusedAdam optimizer from apex. We first try to load the + "contrib" interface, which is a bit faster than the main interface, + but is technically deprecated. + """ + try: + # The "deprecated" interface in recent versions of apex is a bit + # faster than the main interface, since we don't use the apex + # optimizer. This can be installed by passing the + # `--deprecated_fused_adam` option when building apex. + global fused_adam_cuda + import importlib + + fused_adam_cuda = importlib.import_module("fused_adam_cuda") + return FusedAdamV1 + except ImportError: + try: + # fallback to the newer interface + from apex.multi_tensor_apply import multi_tensor_applier + from apex.optimizers import FusedAdam as _FusedAdam # noqa + + if multi_tensor_applier.available: + return FusedAdamV2 + except ImportError: + pass + return None + + +class FusedAdamV1(torch.optim.Optimizer): + """ + Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via + ``python setup.py install --cuda_ext --cpp_ext``. + + It has been proposed in `Adam: A Method for Stochastic Optimization`_. + + Compared to the original version in Apex, the fairseq version casts grads + and params to FP32 internally to support ``--memory-efficient-fp16``. + + Args: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups. + lr (float, optional): learning rate. (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square. (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability. (default: 1e-8) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + amsgrad (boolean, optional): whether to use the AMSGrad variant of this + algorithm from the paper `On the Convergence of Adam and Beyond`_ + (default: False) NOT SUPPORTED in FusedAdam! + eps_inside_sqrt (boolean, optional): in the 'update parameters' step, + adds eps to the bias-corrected second moment estimate before + evaluating square root instead of adding it to the square root of + second moment estimate as in the original paper. (default: False) + .. _Adam: A Method for Stochastic Optimization: + https://arxiv.org/abs/1412.6980 + .. _On the Convergence of Adam and Beyond: + https://openreview.net/forum?id=ryQu7f-RZ + """ + + def __init__( + self, + params, + lr=1e-3, + bias_correction=True, + betas=(0.9, 0.999), + eps=1e-8, + eps_inside_sqrt=False, + weight_decay=0.0, + max_grad_norm=0.0, + amsgrad=False, + use_fp16_stats=False, + ): + global fused_adam_cuda + import importlib + + fused_adam_cuda = importlib.import_module("fused_adam_cuda") + + if amsgrad: + raise RuntimeError("FusedAdam does not support the AMSGrad variant.") + defaults = { + "lr": lr, + "bias_correction": bias_correction, + "betas": betas, + "eps": eps, + "weight_decay": weight_decay, + "max_grad_norm": max_grad_norm, + } + super().__init__(params, defaults) + self.eps_mode = 0 if eps_inside_sqrt else 1 + + self.use_fp16_stats = use_fp16_stats + self.FLOAT16_MAX = 65504.0 + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + @property + def supports_step_with_scale(self): + return True + + def step(self, closure=None, grads=None, scale=1.0, grad_norms=None): + """Performs a single optimization step. + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + grads (list of tensors, optional): weight gradient to use for the + optimizer update. If gradients have type torch.half, parameters + are expected to be in type torch.float. (default: None) + output params (list of tensors, optional): A reduced precision copy + of the updated weights written out in addition to the regular + updated weights. Have to be of same type as gradients. (default: None) + scale (float, optional): factor to divide gradient tensor values + by before applying to weights. (default: 1) + """ + loss = None + if closure is not None: + loss = closure() + + if grads is None: + grads_group = [None] * len(self.param_groups) + # backward compatibility + # assuming a list/generator of parameter means single group + elif isinstance(grads, types.GeneratorType): + grads_group = [grads] + elif type(grads[0]) != list: + grads_group = [grads] + else: + grads_group = grads + + if grad_norms is None: + grad_norms = [None] * len(self.param_groups) + + for group, grads_this_group, grad_norm in zip( + self.param_groups, grads_group, grad_norms + ): + if grads_this_group is None: + grads_this_group = [None] * len(group["params"]) + + # compute combined scale factor for this group + combined_scale = scale + if group.get("max_grad_norm", 0) > 0: + # norm is in fact norm*scale + clip = ((grad_norm / scale) + 1e-6) / group["max_grad_norm"] + if clip > 1: + combined_scale = clip * scale + + bias_correction = 1 if group.get("bias_correction", 1) else 0 + + for p, grad in zip(group["params"], grads_this_group): + # note: p.grad should not ever be set for correct + # operation of mixed precision optimizer that sometimes + # sends None gradients + if p.grad is None and grad is None: + continue + if grad is None: + grad = p.grad.data + if grad.is_sparse: + raise RuntimeError( + "FusedAdam does not support sparse gradients, " + "please consider SparseAdam instead" + ) + + if p.device.type == "cpu": + p_data_fp32 = p.data.cuda(non_blocking=True).float() + out_p = torch.tensor([], dtype=torch.float) + else: + p_data_fp32 = p.data.float() + out_p = p.data + + state = self.state[p] + + # State initialization + dtype = torch.float16 if self.use_fp16_stats else p_data_fp32.dtype + if len(state) == 0: + state["step"] = 0 + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(p_data_fp32, dtype=dtype) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like(p_data_fp32, dtype=dtype) + if self.use_fp16_stats: + state["exp_avg_scale"] = 1.0 + state["exp_avg_sq_scale"] = 1.0 + else: + device = p_data_fp32.device + state["exp_avg"] = state["exp_avg"].to(device, dtype) + state["exp_avg_sq"] = state["exp_avg_sq"].to(device, dtype) + + exp_avg = state["exp_avg"] + exp_avg_sq = state["exp_avg_sq"] + if self.use_fp16_stats: + assert exp_avg.dtype == torch.float16 + exp_avg = exp_avg.float() * state["exp_avg_scale"] + exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"] + beta1, beta2 = group["betas"] + + if "step" not in state: + state["step"] = group["step"] + + state["step"] += 1 + + with torch.cuda.device(p_data_fp32.device): + fused_adam_cuda.adam( + p_data_fp32, + out_p, + exp_avg, + exp_avg_sq, + grad, + group["lr"], + beta1, + beta2, + group["eps"], + combined_scale, + state["step"], + self.eps_mode, + bias_correction, + group["weight_decay"], + ) + + if p.device.type == "cpu": + p.data.copy_(p_data_fp32, non_blocking=True) + + if self.use_fp16_stats: + + def inf_norm(t): + return torch.norm(t, float("inf")) + + # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py + state["exp_avg_scale"], state["exp_avg_sq_scale"] = ( + 1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX, + 1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX, + ) + state["exp_avg"], state["exp_avg_sq"] = ( + (exp_avg / state["exp_avg_scale"]).half(), + (exp_avg_sq / state["exp_avg_sq_scale"]).half(), + ) + + return loss + + +try: + from apex.multi_tensor_apply import multi_tensor_applier + from apex.optimizers import FusedAdam + + class FusedAdamV2(FusedAdam): + """ + Compared to the original version in Apex, the fairseq version casts grads + and params to FP32 internally to support ``--memory-efficient-fp16``. + """ + + def __init__(self, *args, use_fp16_stats=False, **kwargs): + if use_fp16_stats: + raise NotImplementedError( + "--fp16-adam-stats is only supported with FusedAdamV1" + ) + super().__init__(*args, **kwargs) + if not hasattr(self, "multi_tensor_adam"): + raise Exception( + "Apex installation is outdated. Please install an updated version of apex." + ) + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + def step( + self, + closure=None, + grads=None, + output_params=None, + scale=None, + grad_norms=None, + ): + """Performs a single optimization step.""" + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + bias_correction = 1 if group["bias_correction"] else 0 + beta1, beta2 = group["betas"] + + # assume same step across group now to simplify things + # per parameter step can be easily support by making it tensor, or pass list into kernel + if "step" in group: + group["step"] += 1 + else: + group["step"] = 1 + + # create lists for multi-tensor apply + g_16, p_16, orig_p_16, m_16, v_16 = [], [], [], [], [] + g_32, p_32, m_32, v_32 = [], [], [], [] + + for p in group["params"]: + if p.grad is None: + continue + if p.grad.data.is_sparse: + raise RuntimeError( + "FusedAdam does not support sparse gradients, " + "please consider SparseAdam instead" + ) + + state = self.state[p] + # State initialization + if len(state) == 0: + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(p.data, dtype=torch.float) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like( + p.data, dtype=torch.float + ) + else: + state["exp_avg"] = state["exp_avg"].to( + device=p.data.device, dtype=torch.float + ) + state["exp_avg_sq"] = state["exp_avg_sq"].to( + device=p.data.device, dtype=torch.float + ) + + if p.dtype == torch.float16: + g_16.append(p.grad.data.float()) + p_16.append(p.data.float()) + orig_p_16.append(p.data) + m_16.append(state["exp_avg"]) + v_16.append(state["exp_avg_sq"]) + elif p.dtype == torch.float32: + g_32.append(p.grad.data) + p_32.append(p.data) + m_32.append(state["exp_avg"]) + v_32.append(state["exp_avg_sq"]) + else: + raise RuntimeError("FusedAdam only support fp16 and fp32.") + + with torch.cuda.device(p.device): + if len(g_16) > 0: + multi_tensor_applier( + self.multi_tensor_adam, + self._dummy_overflow_buf, + [g_16, p_16, m_16, v_16], + group["lr"], + beta1, + beta2, + group["eps"], + group["step"], + self.adam_w_mode, + bias_correction, + group["weight_decay"], + ) + for orig_p, p in zip(orig_p_16, p_16): + orig_p.copy_(p.data) + if len(g_32) > 0: + multi_tensor_applier( + self.multi_tensor_adam, + self._dummy_overflow_buf, + [g_32, p_32, m_32, v_32], + group["lr"], + beta1, + beta2, + group["eps"], + group["step"], + self.adam_w_mode, + bias_correction, + group["weight_decay"], + ) + + return loss + +except ImportError: + pass diff --git a/fairseq/fairseq/optim/fused_lamb.py b/fairseq/fairseq/optim/fused_lamb.py new file mode 100644 index 0000000..f4f2bdb --- /dev/null +++ b/fairseq/fairseq/optim/fused_lamb.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.optim import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("lamb") +class FairseqLAMB(LegacyFairseqOptimizer): + """LAMB optimizer.""" + + def __init__(self, args, params): + super().__init__(args) + try: + from apex.optimizers import FusedLAMB + + self._optimizer = FusedLAMB(params, **self.optimizer_config) + except ImportError: + raise ImportError("Please install apex to use LAMB optimizer") + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--lamb-betas', default='(0.9, 0.999)', metavar='B', + help='betas for LAMB optimizer') + parser.add_argument('--lamb-eps', type=float, default=1e-8, metavar='D', + help='epsilon for LAMB optimizer') + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.args.lr[0], + "betas": eval(self.args.lamb_betas), + "eps": self.args.lamb_eps, + "weight_decay": self.args.weight_decay, + } + + @property + def supports_flat_params(self): + return False diff --git a/fairseq/fairseq/optim/lr_scheduler/__init__.py b/fairseq/fairseq/optim/lr_scheduler/__init__.py new file mode 100644 index 0000000..5b3dbc0 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/__init__.py @@ -0,0 +1,36 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import importlib +import os + +from fairseq import registry +from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa + FairseqLRScheduler, + LegacyFairseqLRScheduler, +) +from omegaconf import DictConfig + + +( + build_lr_scheduler_, + register_lr_scheduler, + LR_SCHEDULER_REGISTRY, + LR_SCHEDULER_DATACLASS_REGISTRY, +) = registry.setup_registry( + "--lr-scheduler", base_class=FairseqLRScheduler, default="fixed" +) + + +def build_lr_scheduler(cfg: DictConfig, optimizer): + return build_lr_scheduler_(cfg, optimizer) + + +# automatically import any Python files in the optim/lr_scheduler/ directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.optim.lr_scheduler." + file_name) diff --git a/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py b/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py new file mode 100644 index 0000000..5fcaea2 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py @@ -0,0 +1,146 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class CosineLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = field( + default=II("optimization.lr"), + metadata={"help": "max learning rate, must be more than cfg.min_lr"}, + ) + min_lr: float = field(default=0.0, metadata={"help": "min learning rate"}) + t_mult: float = field( + default=1.0, metadata={"help": "factor to grow the length of each period"} + ) + lr_period_updates: float = field( + default=-1, metadata={"help": "initial number of updates per period"} + ) + lr_shrink: float = field( + default=0.1, metadata={"help": "shrink factor for annealing"} + ) + # This is not required, but is for convenience in inferring lr_period_updates + max_update: int = II("optimization.max_update") + + +@register_lr_scheduler("cosine", dataclass=CosineLRScheduleConfig) +class CosineLRSchedule(FairseqLRScheduler): + """Assign LR based on a cyclical schedule that follows the cosine function. + + See https://arxiv.org/pdf/1608.03983.pdf for details. + + We also support a warmup phase where we linearly increase the learning rate + from some initial learning rate (``--warmup-init-lr``) until the configured + max learning rate (``--lr``). + + During warmup:: + + lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates) + lr = lrs[update_num] + + After warmup:: + + lr = cfg.min_lr + 0.5*(cfg.lr - cfg.min_lr)*(1 + cos(t_curr / t_i)) + + where ``t_curr`` is current percentage of updates within the current period + range and ``t_i`` is the current period range, which is scaled by ``t_mul`` + after every iteration. + """ + + def __init__(self, cfg: CosineLRScheduleConfig, fairseq_optimizer): + super().__init__(cfg, fairseq_optimizer) + if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1: + raise ValueError( + "Cannot use a fixed learning rate schedule with cosine." + f" Consider --lr-scheduler=fixed instead. ({cfg.lr})" + ) + + self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr + if self.max_lr < cfg.min_lr: + cfg.min_lr = self.max_lr + + warmup_end_lr = self.max_lr + if cfg.warmup_init_lr < 0: + cfg.warmup_init_lr = cfg.min_lr + + self.t_mult = cfg.t_mult + self.period = cfg.lr_period_updates + + if self.period <= 0: + assert ( + cfg.max_update > 0 + ), "Either --max_update or --lr-period-updates must be set" + self.period = cfg.max_update - cfg.warmup_updates + + if cfg.warmup_updates > 0: + # linearly warmup for the first cfg.warmup_updates + self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates + else: + self.lr_step = 1 + + self.warmup_updates = cfg.warmup_updates + self.lr_shrink = cfg.lr_shrink + + # initial learning rate + self.lr = cfg.warmup_init_lr + self.optimizer.set_lr(self.lr) + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + super().step(epoch, val_loss) + # we don't change the learning rate at epoch boundaries + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + if num_updates < self.cfg.warmup_updates: + self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step + else: + curr_updates = num_updates - self.cfg.warmup_updates + if self.t_mult != 1: + i = math.floor( + math.log( + 1 - curr_updates / self.period * (1 - self.t_mult), self.t_mult + ) + ) + t_i = self.t_mult**i * self.period + t_curr = ( + curr_updates + - (1 - self.t_mult**i) / (1 - self.t_mult) * self.period + ) + else: + i = math.floor(curr_updates / self.period) + t_i = self.period + t_curr = curr_updates - (self.period * i) + + lr_shrink = self.lr_shrink**i + min_lr = self.cfg.min_lr * lr_shrink + max_lr = self.max_lr * lr_shrink + + self.lr = min_lr + 0.5 * (max_lr - min_lr) * ( + 1 + math.cos(math.pi * t_curr / t_i) + ) + + self.optimizer.set_lr(self.lr) + return self.lr diff --git a/fairseq/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py b/fairseq/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py new file mode 100644 index 0000000..6c12fa5 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from argparse import Namespace + +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.optim import FairseqOptimizer + + +class FairseqLRScheduler(object): + def __init__(self, cfg, optimizer): + super().__init__() + if optimizer is not None and not isinstance(optimizer, FairseqOptimizer): + raise ValueError("optimizer must be an instance of FairseqOptimizer") + self.cfg = cfg + self.optimizer = optimizer + self.best = None + + @classmethod + def add_args(cls, parser): + """Add arguments to the parser for this LR scheduler.""" + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) + + def state_dict(self): + """Return the LR scheduler state dict.""" + return {"best": self.best} + + def load_state_dict(self, state_dict): + """Load an LR scheduler state dict.""" + self.best = state_dict["best"] + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + pass + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + if val_loss is not None: + if self.best is None: + self.best = val_loss + else: + self.best = min(self.best, val_loss) + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + return self.optimizer.get_lr() + + +class LegacyFairseqLRScheduler(FairseqLRScheduler): + def __init__(self, args: Namespace, optimizer): + if not isinstance(optimizer, FairseqOptimizer): + raise ValueError("optimizer must be an instance of FairseqOptimizer") + self.args = args + self.optimizer = optimizer + self.best = None diff --git a/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py b/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py new file mode 100644 index 0000000..d0e7e14 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py @@ -0,0 +1,76 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +from typing import Optional, List +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class FixedLRScheduleConfig(FairseqDataclass): + force_anneal: Optional[int] = field( + default=None, + metadata={"help": "force annealing at specified epoch"}, + ) + lr_shrink: float = field( + default=0.1, + metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"}, + ) + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig) +class FixedLRSchedule(FairseqLRScheduler): + """Decay the LR on a fixed schedule.""" + + def __init__(self, cfg: FixedLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + + self.lr = cfg.lr[0] + if cfg.warmup_updates > 0: + self.warmup_factor = 1.0 / cfg.warmup_updates + else: + self.warmup_factor = 1 + + def state_dict(self): + return {"lr": self.lr} + + def load_state_dict(self, state_dict): + if "lr" in state_dict: + self.lr = state_dict["lr"] + + def get_next_lr(self, epoch): + lrs = self.cfg.lr + if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal: + # use fixed LR schedule + next_lr = lrs[min(epoch - 1, len(lrs) - 1)] + else: + # annneal based on lr_shrink + next_lr = lrs[-1] * self.cfg.lr_shrink ** ( + epoch + 1 - self.cfg.force_anneal + ) + return next_lr + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + self.lr = self.get_next_lr(epoch) + self.optimizer.set_lr(self.warmup_factor * self.lr) + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates: + self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates) + self.optimizer.set_lr(self.warmup_factor * self.lr) + else: + self.optimizer.set_lr(self.lr) + return self.optimizer.get_lr() diff --git a/fairseq/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py b/fairseq/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py new file mode 100644 index 0000000..987c905 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class InverseSquareRootLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=4000, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("inverse_sqrt", dataclass=InverseSquareRootLRScheduleConfig) +class InverseSquareRootSchedule(FairseqLRScheduler): + """Decay the LR based on the inverse square root of the update number. + + We also support a warmup phase where we linearly increase the learning rate + from some initial learning rate (``--warmup-init-lr``) until the configured + learning rate (``--lr``). Thereafter we decay proportional to the number of + updates, with a decay factor set to align with the configured learning rate. + + During warmup:: + + lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates) + lr = lrs[update_num] + + After warmup:: + + decay_factor = cfg.lr * sqrt(cfg.warmup_updates) + lr = decay_factor / sqrt(update_num) + """ + + def __init__(self, cfg: InverseSquareRootLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1: + raise ValueError( + "Cannot use a fixed learning rate schedule with inverse_sqrt." + " Consider --lr-scheduler=fixed instead." + ) + warmup_end_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr + if cfg.warmup_init_lr < 0: + cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr + + # linearly warmup for the first cfg.warmup_updates + self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates + + # then, decay prop. to the inverse square root of the update number + self.decay_factor = warmup_end_lr * cfg.warmup_updates**0.5 + + # initial learning rate + self.lr = cfg.warmup_init_lr + self.optimizer.set_lr(self.lr) + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + super().step(epoch, val_loss) + # we don't change the learning rate at epoch boundaries + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + if num_updates < self.cfg.warmup_updates: + self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step + else: + self.lr = self.decay_factor * num_updates**-0.5 + self.optimizer.set_lr(self.lr) + return self.lr diff --git a/fairseq/fairseq/optim/lr_scheduler/manual_lr_scheduler.py b/fairseq/fairseq/optim/lr_scheduler/manual_lr_scheduler.py new file mode 100644 index 0000000..57edc25 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/manual_lr_scheduler.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import LegacyFairseqLRScheduler, register_lr_scheduler +import logging +import ast + +logger = logging.getLogger(__name__) +logger.setLevel(logging.WARNING) + + +@register_lr_scheduler("manual") +class ManualSchedule(LegacyFairseqLRScheduler): + """Decay the LR on a manual schedule.""" + + def __init__(self, args, optimizer): + super().__init__(args, optimizer) + + self.epoch2lr = self.parse_manuallr_args(args.epoch2lr) + self.update2lr = self.parse_manuallr_args(args.update2lr) + logger.info("@@@ ManualSchedule epoch2lr={}".format(self.epoch2lr)) + logger.info("@@@ ManualSchedule update2lr={}".format(self.update2lr)) + + if 1 in self.epoch2lr: + self.lr = self.epoch2lr[1] + elif 1 in self.update2lr: + self.lr = self.update2lr[1] + else: + self.lr = args.lr[0] + self.optimizer.set_lr(self.lr) # Set the beginning of the epoch. + + def parse_manuallr_args(self, lr_args_str): + lr_dict = ast.literal_eval(lr_args_str.replace(" ", "")) + if not isinstance(lr_dict, dict): + raise ValueError("epoch2lr/update2lr must be abel to evaluated to a dict") + + lr_args = {} + logger.info("@@@ after parsing input dictionary lr_dict = {}".format(lr_dict)) + for key, val in lr_dict.items(): + if "," in key: + for k in key.split(","): + lr_args[int(k)] = float(val) + elif "-" in key: + s = int(key.split("-")[0]) + e = int(key.split("-")[1]) + for k in range(s, e + 1, 1): + lr_args[k] = float(val) + else: + lr_args[int(key)] = float(val) + + return lr_args + + @staticmethod + def add_args(parser): + """Add arguments to the parser for this LR scheduler.""" + # fmt: off + parser.add_argument( + "--epoch2lr", + type=str, + metavar="DICT", + default="{}", + help="a dictionary used to set lr for each epoch manually", + ) + parser.add_argument( + "--update2lr", + type=str, + metavar="DICT", + default="{}", + help="a dictionary used to set lr for each update manually", + ) + # fmt: on + + def state_dict(self): + return {"lr": self.lr} + + def load_state_dict(self, state_dict): + if "lr" in state_dict: + self.lr = state_dict["lr"] + + def get_next_lr(self, epoch): + manual_keys = [k for k in self.epoch2lr if k <= epoch] + if manual_keys: + manual_lr = self.epoch2lr[max(manual_keys)] + else: + logger.warning( + "@@@ epoch={} does not exist in manual lr input. epoch2lr={}...".format( + epoch, + list(self.epoch2lr.items())[ + : min(10, len(self.epoch2lr.keys()) - 1) + ], + ) + ) + manual_lr = self.optimizer.get_lr() + return manual_lr + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + self.lr = self.get_next_lr(epoch) + self.optimizer.set_lr(self.lr) + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + manual_keys = [k for k in self.update2lr if k <= num_updates] + if manual_keys: + manual_lr = self.update2lr[max(manual_keys)] + else: + logger.warning( + "epoch={} does not exist in manual lr input update2lr={}...".format( + num_updates, + list(self.update2lr.items())[ + : min(10, len(self.update2lr.keys()) - 1) + ], + ) + ) + manual_lr = self.optimizer.get_lr() + + self.optimizer.set_lr(manual_lr) + return self.optimizer.get_lr() diff --git a/fairseq/fairseq/optim/lr_scheduler/pass_through.py b/fairseq/fairseq/optim/lr_scheduler/pass_through.py new file mode 100644 index 0000000..2f93db3 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/pass_through.py @@ -0,0 +1,39 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class PassThroughScheduleConfig(FairseqDataclass): + pass + + +@register_lr_scheduler("pass_through", dataclass=PassThroughScheduleConfig) +class PassThroughScheduleSchedule(FairseqLRScheduler): + """Delegate lr scheduling to the optimizer.""" + + def __init__(self, cfg: PassThroughScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + assert ( + hasattr(optimizer, "lr_scheduler") and optimizer.lr_scheduler is not None + ), "Pass-through schedule can only be used with optimizers with their own schedulers" + + def state_dict(self): + return self.optimizer.lr_scheduler.state_dict() + + def load_state_dict(self, state_dict): + self.optimizer.lr_scheduler.load_state_dict(state_dict) + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + return self.optimizer.lr_scheduler.step_begin_epoch(epoch) + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + return self.optimizer.lr_scheduler.step_update(num_updates) diff --git a/fairseq/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py b/fairseq/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py new file mode 100644 index 0000000..b8109a7 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py @@ -0,0 +1,89 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +from typing import Optional, List +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class PolynomialDecayLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + force_anneal: Optional[int] = field( + default=None, + metadata={"help": "force annealing at specified epoch"}, + ) + end_learning_rate: float = field( + default=0.0, + metadata={"help": "learning rate to decay to"}, + ) + power: float = field( + default=1.0, + metadata={"help": "decay exponent"}, + ) + total_num_update: float = field( + default=II("optimization.max_update"), + metadata={"help": "total number of updates over which to decay learning rate"}, + ) + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("polynomial_decay", dataclass=PolynomialDecayLRScheduleConfig) +class PolynomialDecayLRSchedule(FairseqLRScheduler): + """Decay the LR on a fixed schedule.""" + + def __init__(self, cfg: PolynomialDecayLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + + assert cfg.total_num_update > 0 + + self.lr = cfg.lr[0] + if cfg.warmup_updates > 0: + self.warmup_factor = 1.0 / cfg.warmup_updates + else: + self.warmup_factor = 1 + self.end_learning_rate = cfg.end_learning_rate + self.total_num_update = cfg.total_num_update + self.power = cfg.power + self.optimizer.set_lr(self.warmup_factor * self.lr) + + def get_next_lr(self, epoch): + lrs = self.cfg.lr + if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal: + # use fixed LR schedule + next_lr = lrs[min(epoch, len(lrs) - 1)] + else: + # annneal based on lr_shrink + next_lr = self.optimizer.get_lr() + return next_lr + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + self.lr = self.get_next_lr(epoch) + self.optimizer.set_lr(self.warmup_factor * self.lr) + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + if self.cfg.warmup_updates > 0 and num_updates <= self.cfg.warmup_updates: + self.warmup_factor = num_updates / float(self.cfg.warmup_updates) + lr = self.warmup_factor * self.lr + elif num_updates >= self.total_num_update: + lr = self.end_learning_rate + else: + warmup = self.cfg.warmup_updates + lr_range = self.lr - self.end_learning_rate + pct_remaining = 1 - (num_updates - warmup) / ( + self.total_num_update - warmup + ) + lr = lr_range * pct_remaining ** (self.power) + self.end_learning_rate + self.optimizer.set_lr(lr) + return self.optimizer.get_lr() diff --git a/fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py b/fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py new file mode 100644 index 0000000..5ee9c1b --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +from typing import List + +import torch.optim.lr_scheduler +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class ReduceLROnPlateauLRScheduleConfig(FairseqDataclass): + lr_shrink: float = field( + default=0.1, metadata={"help": "shrink factor for annealing"} + ) + lr_threshold: float = field( + default=1e-4, + metadata={ + "help": ( + "threshold for measuring the new optimum, to only focus on " + "significant changes" + ) + }, + ) + lr_patience: int = field( + default=0, + metadata={ + "help": ( + "number of epochs with no improvement after which learning rate will " + "be reduced" + ) + }, + ) + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = II("optimization.lr") + maximize_best_checkpoint_metric: bool = II( + "checkpoint.maximize_best_checkpoint_metric" + ) + + +@register_lr_scheduler( + "reduce_lr_on_plateau", dataclass=ReduceLROnPlateauLRScheduleConfig +) +class ReduceLROnPlateauLRSchedule(FairseqLRScheduler): + """ + Decay the LR by a factor every time the validation loss plateaus. + Also comes with optional warmup phase, where we linearly increase + the learning rate from some initial learning rate + (``--warmup-init-lr``) until the configured learning rate + (``--lr``). Thereafter the lr is adjusted according to original + reduce_on_plateau scheme. + + During warmup:: + + lrs = torch.linspace( + cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates + ) + lr = lrs[update_num] + """ + + def __init__(self, cfg: ReduceLROnPlateauLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if len(cfg.lr) > 1: + raise ValueError( + "Cannot use a fixed learning rate schedule with reduce_lr_on_plateau." + " Consider --lr-scheduler=fixed instead." + ) + self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + self.optimizer.optimizer, + patience=cfg.lr_patience, + factor=cfg.lr_shrink, + mode="max" if cfg.maximize_best_checkpoint_metric else "min", + threshold=cfg.lr_threshold, + ) + warmup_end_lr = cfg.lr[0] + # if no warm up, sets initial lr to be cfg.lr[0] + if cfg.warmup_init_lr < 0: + cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr + + # linearly warmup for the first cfg.warmup_updates + if cfg.warmup_updates > 0: + self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates + + # this flag is either set from arg when no warm up, or set by + # step_update() when warmup finishes + self.warmup_end = True if cfg.warmup_updates <= 0 else False + + # initial learning rate + # this self.lr is used only during init and/or warm up period + self.lr = warmup_end_lr if self.warmup_end else cfg.warmup_init_lr + self.optimizer.set_lr(self.lr) + + def state_dict(self): + """Return the LR scheduler state dict.""" + return { + "best": self.lr_scheduler.best, + "last_epoch": self.lr_scheduler.last_epoch, + } + + def load_state_dict(self, state_dict): + """Load an LR scheduler state dict.""" + self.lr_scheduler.best = state_dict["best"] + if "last_epoch" in state_dict: + self.lr_scheduler.last_epoch = state_dict["last_epoch"] + + def step(self, epoch, val_loss=None): + """ + Update the learning rate at the end of the given epoch if warmup + finishes otherwise no update of lr on epoch boundaries + """ + if val_loss is not None and self.warmup_end is True: + self.lr_scheduler.step(val_loss) + else: + self.lr_scheduler.last_epoch = epoch + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """ + Update the learning rate after each update.""" + # if there is warmup + if self.cfg.warmup_updates > 0: + if num_updates <= self.cfg.warmup_updates: + self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step + self.optimizer.set_lr(self.lr) + else: + if self.warmup_end is False: + self.warmup_end = True + # else do nothing + return self.optimizer.get_lr() diff --git a/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py b/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py new file mode 100644 index 0000000..db99d4e --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class StepLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = field( + default=II("optimization.lr"), + metadata={"help": "max learning rate, must be more than cfg.min_lr"}, + ) + min_lr: float = field(default=0.0, metadata={"help": "min learning rate"}) + lr_deacy_period: int = field(default=25000, metadata={"help": "decay period"}) + lr_decay: float = field(default=0.5, metadata={"help": "decay factor"}) + + +@register_lr_scheduler("step", dataclass=StepLRScheduleConfig) +class StepLRSchedule(FairseqLRScheduler): + """Decay learning rate every k updates by a fixed factor""" + + def __init__(self, cfg: StepLRScheduleConfig, fairseq_optimizer): + super().__init__(cfg, fairseq_optimizer) + self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr + self.min_lr = cfg.min_lr + self.lr_deacy_period = cfg.lr_deacy_period + self.lr_decay = cfg.lr_decay + self.warmup_updates = cfg.warmup_updates + self.warmup_init_lr = ( + cfg.warmup_init_lr if cfg.warmup_init_lr >= 0 else self.min_lr + ) + + assert self.lr_deacy_period > 0 + assert self.lr_decay <= 1 + assert self.min_lr >= 0 + assert self.max_lr > self.min_lr + + if cfg.warmup_updates > 0: + # linearly warmup for the first cfg.warmup_updates + self.warmup_lr_step = ( + self.max_lr - self.warmup_init_lr + ) / self.warmup_updates + else: + self.warmup_lr_step = 1 + + # initial learning rate + self.lr = self.warmup_init_lr + self.optimizer.set_lr(self.lr) + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + super().step(epoch, val_loss) + # we don't change the learning rate at epoch boundaries + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + if num_updates < self.cfg.warmup_updates: + self.lr = self.warmup_init_lr + num_updates * self.warmup_lr_step + else: + curr_updates = num_updates - self.cfg.warmup_updates + lr_mult = self.lr_decay ** (curr_updates // self.lr_deacy_period) + self.lr = max(self.max_lr * lr_mult, self.min_lr) + + self.optimizer.set_lr(self.lr) + return self.lr diff --git a/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py b/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py new file mode 100644 index 0000000..4d5547c --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py @@ -0,0 +1,175 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import Optional, List, Tuple +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class TriStageLRScheduleConfig(FairseqDataclass): + warmup_steps: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + hold_steps: int = field( + default=0, + metadata={"help": "steps in hold stage"}, + ) + decay_steps: int = field( + default=0, + metadata={"help": "steps in decay stages"}, + ) + phase_ratio: Optional[Tuple[float, float, float]] = field( + default=None, + metadata={ + "help": ( + "if set, automatically sets warmup/hold/decay steps to the ratio " + "specified here from max_updates. the ratios must add up to 1.0" + ) + }, + ) + init_lr_scale: float = field( + default=0.01, + metadata={"help": "initial learning rate scale during warmup phase"}, + ) + final_lr_scale: float = field( + default=0.01, + metadata={"help": "final learning rate scale"}, + ) + max_update: float = II("optimization.max_update") + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("tri_stage", dataclass=TriStageLRScheduleConfig) +class TriStageLRSchedule(FairseqLRScheduler): + """Tristage learning rate schedulr + + Implement the learning rate scheduler in https://arxiv.org/pdf/1904.08779.pdf + + Similar to inverse_squre_root scheduler, but tri_stage learning rate employs + three stages LR scheduling: + + - warmup stage, starting from `lr` * `init_lr_scale`, linearly + increased to `lr` in `warmup_steps` iterations + + - hold stage, after `warmup_steps`, keep the LR as `lr` for `hold_steps` + iterations + + - decay stage, after hold stage, decay LR exponetially to + `lr` * `final_lr_scale` in `decay_steps`; + after that LR is keep as `final_lr_scale` * `lr` + + During warmup:: + + init_lr = cfg.init_lr_scale * cfg.lr + lrs = torch.linspace(init_lr, cfg.lr, cfg.warmup_steps) + lr = lrs[update_num] + + During hold:: + + lr = cfg.lr + + During decay:: + + decay_factor = - math.log(cfg.final_lr_scale) / cfg.decay_steps + lr = cfg.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor) + + After that:: + + lr = cfg.lr * cfg.final_lr_scale + """ + + def __init__(self, cfg: TriStageLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if len(cfg.lr) > 1: + raise ValueError( + "Cannot use a fixed learning rate schedule with tri-stage lr." + " Consider --lr-scheduler=fixed instead." + ) + + # calculate LR at each point + self.peak_lr = cfg.lr[0] + self.init_lr = cfg.init_lr_scale * cfg.lr[0] + self.final_lr = cfg.final_lr_scale * cfg.lr[0] + + if cfg.phase_ratio is not None: + assert cfg.max_update > 0 + assert sum(cfg.phase_ratio) == 1, "phase ratios must add up to 1" + self.warmup_steps = int(cfg.max_update * cfg.phase_ratio[0]) + self.hold_steps = int(cfg.max_update * cfg.phase_ratio[1]) + self.decay_steps = int(cfg.max_update * cfg.phase_ratio[2]) + else: + self.warmup_steps = cfg.warmup_steps + self.hold_steps = cfg.hold_steps + self.decay_steps = cfg.decay_steps + + assert ( + self.warmup_steps + self.hold_steps + self.decay_steps > 0 + ), "please specify steps or phase_ratio" + + self.warmup_rate = ( + (self.peak_lr - self.init_lr) / self.warmup_steps + if self.warmup_steps != 0 + else 0 + ) + self.decay_factor = -math.log(cfg.final_lr_scale) / self.decay_steps + + # initial learning rate + self.lr = self.init_lr + self.optimizer.set_lr(self.lr) + + def _decide_stage(self, update_step): + """ + return stage, and the corresponding steps within the current stage + """ + if update_step < self.warmup_steps: + # warmup state + return 0, update_step + + offset = self.warmup_steps + + if update_step < offset + self.hold_steps: + # hold stage + return 1, update_step - offset + + offset += self.hold_steps + + if update_step <= offset + self.decay_steps: + # decay stage + return 2, update_step - offset + + offset += self.decay_steps + + # still here ? constant lr stage + return 3, update_step - offset + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + super().step(epoch, val_loss) + # we don't change the learning rate at epoch boundaries + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + stage, steps_in_stage = self._decide_stage(num_updates) + if stage == 0: + self.lr = self.init_lr + self.warmup_rate * steps_in_stage + elif stage == 1: + self.lr = self.peak_lr + elif stage == 2: + self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage) + elif stage == 3: + self.lr = self.final_lr + else: + raise ValueError("Undefined stage") + + self.optimizer.set_lr(self.lr) + + return self.lr diff --git a/fairseq/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py b/fairseq/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py new file mode 100644 index 0000000..2a32bd1 --- /dev/null +++ b/fairseq/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py @@ -0,0 +1,83 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import List + +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class TriangularLRScheduleConfig(FairseqDataclass): + max_lr: float = field( + default="???", metadata={"help": "max learning rate, must be more than cfg.lr"} + ) + lr_period_updates: float = field( + default=5000, + metadata={"help": "initial number of updates per period (cycle length)"}, + ) + lr_shrink: float = field( + default=0.1, metadata={"help": "shrink factor for annealing"} + ) + shrink_min: bool = field( + default=False, metadata={"help": "if set, also shrinks min lr"} + ) + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("triangular", dataclass=TriangularLRScheduleConfig) +class TriangularLRSchedule(FairseqLRScheduler): + """Assign LR based on a triangular cyclical schedule. + + See https://arxiv.org/pdf/1506.01186.pdf for details. + """ + + def __init__(self, cfg: TriangularLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if len(cfg.lr) > 1: + raise ValueError( + "Cannot use a fixed learning rate schedule with triangular." + " Consider --lr-scheduler=fixed instead." + ) + + lr = cfg.lr[0] + + assert cfg.max_lr > lr, "max_lr must be more than lr" + self.min_lr = lr + self.max_lr = cfg.max_lr + self.stepsize = cfg.lr_period_updates // 2 + self.lr_shrink = cfg.lr_shrink + self.shrink_min = cfg.shrink_min + + # initial learning rate + self.lr = self.min_lr + self.optimizer.set_lr(self.lr) + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + super().step(epoch, val_loss) + # we don't change the learning rate at epoch boundaries + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + cycle = math.floor(num_updates / (2 * self.stepsize)) + + lr_shrink = self.lr_shrink**cycle + max_lr = self.max_lr * lr_shrink + if self.shrink_min: + min_lr = self.min_lr * lr_shrink + else: + min_lr = self.min_lr + + x = abs(num_updates / self.stepsize - 2 * (cycle + 1) + 1) + self.lr = min_lr + (max_lr - min_lr) * max(0, (1 - x)) + + self.optimizer.set_lr(self.lr) + return self.lr diff --git a/fairseq/fairseq/optim/nag.py b/fairseq/fairseq/optim/nag.py new file mode 100644 index 0000000..c30a6c0 --- /dev/null +++ b/fairseq/fairseq/optim/nag.py @@ -0,0 +1,111 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +import torch +from fairseq.dataclass import FairseqDataclass +from omegaconf import II, DictConfig +from torch.optim.optimizer import Optimizer, required + +from . import FairseqOptimizer, register_optimizer + + +@dataclass +class FairseqNAGConfig(FairseqDataclass): + momentum: float = field(default=0.99, metadata={"help": "momentum factor"}) + weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) + # TODO common vars in parent class + lr: List[float] = II("optimization.lr") + + +@register_optimizer("nag", dataclass=FairseqNAGConfig) +class FairseqNAG(FairseqOptimizer): + def __init__(self, cfg: DictConfig, params): + super().__init__(cfg) + self._optimizer = NAG(params, **self.optimizer_config) + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.cfg.lr[0] + if isinstance(self.cfg.lr, Collection) + else self.cfg.lr, + "momentum": self.cfg.momentum, + "weight_decay": self.cfg.weight_decay, + } + + +class NAG(Optimizer): + def __init__(self, params, lr=required, momentum=0, weight_decay=0): + defaults = dict(lr=lr, lr_old=lr, momentum=momentum, weight_decay=weight_decay) + super(NAG, self).__init__(params, defaults) + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + def step(self, closure=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + weight_decay = group["weight_decay"] + momentum = group["momentum"] + lr = group["lr"] + lr_old = group.get("lr_old", lr) + lr_correct = lr / lr_old if lr_old > 0 else lr + + for p in group["params"]: + if p.grad is None: + continue + + p_data_fp32 = p.data + if p_data_fp32.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + d_p = p.grad.data.float() + param_state = self.state[p] + if "momentum_buffer" not in param_state: + param_state["momentum_buffer"] = torch.zeros_like(d_p) + else: + param_state["momentum_buffer"] = param_state["momentum_buffer"].to( + d_p + ) + + buf = param_state["momentum_buffer"] + + if weight_decay != 0: + p_data_fp32.mul_(1 - lr * weight_decay) + p_data_fp32.add_(buf, alpha=momentum * momentum * lr_correct) + p_data_fp32.add_(d_p, alpha=-(1 + momentum) * lr) + + buf.mul_(momentum * lr_correct).add_(d_p, alpha=-lr) + + if p.data.dtype in {torch.float16, torch.bfloat16}: + p.data.copy_(p_data_fp32) + + group["lr_old"] = lr + + return loss diff --git a/fairseq/fairseq/optim/sgd.py b/fairseq/fairseq/optim/sgd.py new file mode 100644 index 0000000..8e34fb9 --- /dev/null +++ b/fairseq/fairseq/optim/sgd.py @@ -0,0 +1,43 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.optim + +from . import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("sgd") +class SGD(LegacyFairseqOptimizer): + def __init__(self, args, params): + super().__init__(args) + self._optimizer = torch.optim.SGD(params, **self.optimizer_config) + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--momentum', default=0.0, type=float, metavar='M', + help='momentum factor') + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.args.lr[0], + "momentum": self.args.momentum, + "weight_decay": self.args.weight_decay, + } + + @property + def supports_flat_params(self): + return True diff --git a/fairseq/fairseq/optim/shard.py b/fairseq/fairseq/optim/shard.py new file mode 100644 index 0000000..9d7f2eb --- /dev/null +++ b/fairseq/fairseq/optim/shard.py @@ -0,0 +1,58 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Any, Dict + +from fairseq.distributed import utils + + +try: + from fairscale.optim import OSS + + _has_fairscale = True +except ImportError: + _has_fairscale = False + + +def shard_(optimizer, group): + if not _has_fairscale: + raise ImportError( + "\n\nPlease install the fairscale package:" "\n\n pip install fairscale" + ) + + class FairseqOSS(OSS): + @property + def disable_mem_eff_fp16_loading_hack(self): + return True + + def __getattr__(self, name): + if name.startswith("supports") and hasattr(self.optim, name): + return getattr(self.optim, name) + raise AttributeError( + "'FairseqOSS' object has no attribute {0!r}".format(name) + ) + + def broadcast_global_state_dict( + self, state_dict: Dict[str, Any] + ) -> Dict[str, Any]: + """ + Broadcasts the entire state_dict to all other ranks + each rank is responsible to load their own partition of data + """ + return utils.broadcast_object( + state_dict, + src_rank=0, + group=self.group, + ) + + torch_optimizer = optimizer.optimizer + optim_cls = type(torch_optimizer) + + optimizer.optimizer = FairseqOSS( + torch_optimizer.param_groups, + optim_cls, + group=group, + **optimizer.optimizer_config + ) diff --git a/fairseq/fairseq/options.py b/fairseq/fairseq/options.py new file mode 100644 index 0000000..9205916 --- /dev/null +++ b/fairseq/fairseq/options.py @@ -0,0 +1,413 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from pathlib import Path +from typing import Callable, List, Optional, Union + +import torch +from fairseq import utils +from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.dataclass.configs import ( + CheckpointConfig, + CommonConfig, + CommonEvalConfig, + DatasetConfig, + DistributedTrainingConfig, + EvalLMConfig, + GenerationConfig, + InteractiveConfig, + OptimizationConfig, + EMAConfig, +) +from fairseq.dataclass.utils import gen_parser_from_dataclass + +# this import is for backward compatibility +from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list # noqa + + +def get_preprocessing_parser(default_task="translation"): + parser = get_parser("Preprocessing", default_task) + add_preprocess_args(parser) + return parser + + +def get_training_parser(default_task="translation"): + parser = get_parser("Trainer", default_task) + add_dataset_args(parser, train=True) + add_distributed_training_args(parser) + add_model_args(parser) + add_optimization_args(parser) + add_checkpoint_args(parser) + add_ema_args(parser) + return parser + + +def get_generation_parser(interactive=False, default_task="translation"): + parser = get_parser("Generation", default_task) + add_dataset_args(parser, gen=True) + add_distributed_training_args(parser, default_world_size=1) + add_generation_args(parser) + add_checkpoint_args(parser) + if interactive: + add_interactive_args(parser) + return parser + + +def get_speech_generation_parser(default_task="text_to_speech"): + parser = get_parser("Speech Generation", default_task) + add_dataset_args(parser, gen=True) + add_distributed_training_args(parser, default_world_size=1) + add_speech_generation_args(parser) + return parser + + +def get_interactive_generation_parser(default_task="translation"): + return get_generation_parser(interactive=True, default_task=default_task) + + +def get_eval_lm_parser(default_task="language_modeling"): + parser = get_parser("Evaluate Language Model", default_task) + add_dataset_args(parser, gen=True) + add_distributed_training_args(parser, default_world_size=1) + add_eval_lm_args(parser) + return parser + + +def get_validation_parser(default_task=None): + parser = get_parser("Validation", default_task) + add_dataset_args(parser, train=True) + add_distributed_training_args(parser, default_world_size=1) + group = parser.add_argument_group("Evaluation") + gen_parser_from_dataclass(group, CommonEvalConfig()) + return parser + + +def parse_args_and_arch( + parser: argparse.ArgumentParser, + input_args: List[str] = None, + parse_known: bool = False, + suppress_defaults: bool = False, + modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None, +): + """ + Args: + parser (ArgumentParser): the parser + input_args (List[str]): strings to parse, defaults to sys.argv + parse_known (bool): only parse known arguments, similar to + `ArgumentParser.parse_known_args` + suppress_defaults (bool): parse while ignoring all default values + modify_parser (Optional[Callable[[ArgumentParser], None]]): + function to modify the parser, e.g., to set default values + """ + if suppress_defaults: + # Parse args without any default values. This requires us to parse + # twice, once to identify all the necessary task/model args, and a second + # time with all defaults set to None. + args = parse_args_and_arch( + parser, + input_args=input_args, + parse_known=parse_known, + suppress_defaults=False, + ) + suppressed_parser = argparse.ArgumentParser(add_help=False, parents=[parser]) + suppressed_parser.set_defaults(**{k: None for k, v in vars(args).items()}) + args = suppressed_parser.parse_args(input_args) + return argparse.Namespace( + **{k: v for k, v in vars(args).items() if v is not None} + ) + + from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY, MODEL_REGISTRY + + # Before creating the true parser, we need to import optional user module + # in order to eagerly import custom tasks, optimizers, architectures, etc. + usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) + usr_parser.add_argument("--user-dir", default=None) + usr_args, _ = usr_parser.parse_known_args(input_args) + utils.import_user_module(usr_args) + + if modify_parser is not None: + modify_parser(parser) + + # The parser doesn't know about model/criterion/optimizer-specific args, so + # we parse twice. First we parse the model/criterion/optimizer, then we + # parse a second time after adding the *-specific arguments. + # If input_args is given, we will parse those args instead of sys.argv. + args, _ = parser.parse_known_args(input_args) + + # Add model-specific args to parser. + if hasattr(args, "arch"): + model_specific_group = parser.add_argument_group( + "Model-specific configuration", + # Only include attributes which are explicitly given as command-line + # arguments or which have default values. + argument_default=argparse.SUPPRESS, + ) + if args.arch in ARCH_MODEL_REGISTRY: + ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group) + elif args.arch in MODEL_REGISTRY: + MODEL_REGISTRY[args.arch].add_args(model_specific_group) + else: + raise RuntimeError() + + if hasattr(args, "task"): + from fairseq.tasks import TASK_REGISTRY + + TASK_REGISTRY[args.task].add_args(parser) + if getattr(args, "use_bmuf", False): + # hack to support extra args for block distributed data parallelism + from fairseq.optim.bmuf import FairseqBMUF + + FairseqBMUF.add_args(parser) + + # Add *-specific args to parser. + from fairseq.registry import REGISTRIES + + for registry_name, REGISTRY in REGISTRIES.items(): + choice = getattr(args, registry_name, None) + if choice is not None: + cls = REGISTRY["registry"][choice] + if hasattr(cls, "add_args"): + cls.add_args(parser) + elif hasattr(cls, "__dataclass"): + gen_parser_from_dataclass(parser, cls.__dataclass()) + + # Modify the parser a second time, since defaults may have been reset + if modify_parser is not None: + modify_parser(parser) + + # Parse a second time. + if parse_known: + args, extra = parser.parse_known_args(input_args) + else: + args = parser.parse_args(input_args) + extra = None + # Post-process args. + if ( + hasattr(args, "batch_size_valid") and args.batch_size_valid is None + ) or not hasattr(args, "batch_size_valid"): + args.batch_size_valid = args.batch_size + if hasattr(args, "max_tokens_valid") and args.max_tokens_valid is None: + args.max_tokens_valid = args.max_tokens + if getattr(args, "memory_efficient_fp16", False): + args.fp16 = True + if getattr(args, "memory_efficient_bf16", False): + args.bf16 = True + args.tpu = getattr(args, "tpu", False) + args.bf16 = getattr(args, "bf16", False) + if args.bf16: + args.tpu = True + if args.tpu and args.fp16: + raise ValueError("Cannot combine --fp16 and --tpu, use --bf16 on TPUs") + + if getattr(args, "seed", None) is None: + args.seed = 1 # default seed for training + args.no_seed_provided = True + else: + args.no_seed_provided = False + + if getattr(args, "update_epoch_batch_itr", None) is None: + if hasattr(args, "grouped_shuffling"): + args.update_epoch_batch_itr = args.grouped_shuffling + else: + args.grouped_shuffling = False + args.update_epoch_batch_itr = False + + # Apply architecture configuration. + if hasattr(args, "arch") and args.arch in ARCH_CONFIG_REGISTRY: + ARCH_CONFIG_REGISTRY[args.arch](args) + + if parse_known: + return args, extra + else: + return args + + +def get_parser(desc, default_task="translation"): + # Before creating the true parser, we need to import optional user module + # in order to eagerly import custom tasks, optimizers, architectures, etc. + usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) + usr_parser.add_argument("--user-dir", default=None) + usr_args, _ = usr_parser.parse_known_args() + utils.import_user_module(usr_args) + + parser = argparse.ArgumentParser(allow_abbrev=False) + gen_parser_from_dataclass(parser, CommonConfig()) + + from fairseq.registry import REGISTRIES + + for registry_name, REGISTRY in REGISTRIES.items(): + parser.add_argument( + "--" + registry_name.replace("_", "-"), + default=REGISTRY["default"], + choices=REGISTRY["registry"].keys(), + ) + + # Task definitions can be found under fairseq/tasks/ + from fairseq.tasks import TASK_REGISTRY + + parser.add_argument( + "--task", + metavar="TASK", + default=default_task, + choices=TASK_REGISTRY.keys(), + help="task", + ) + # fmt: on + return parser + + +def add_preprocess_args(parser): + group = parser.add_argument_group("Preprocessing") + # fmt: off + group.add_argument("-s", "--source-lang", default=None, metavar="SRC", + help="source language") + group.add_argument("-t", "--target-lang", default=None, metavar="TARGET", + help="target language") + group.add_argument("--trainpref", metavar="FP", default=None, + help="train file prefix (also used to build dictionaries)") + group.add_argument("--validpref", metavar="FP", default=None, + help="comma separated, valid file prefixes " + "(words missing from train set are replaced with <unk>)") + group.add_argument("--testpref", metavar="FP", default=None, + help="comma separated, test file prefixes " + "(words missing from train set are replaced with <unk>)") + group.add_argument("--align-suffix", metavar="FP", default=None, + help="alignment file suffix") + group.add_argument("--destdir", metavar="DIR", default="data-bin", + help="destination dir") + group.add_argument("--thresholdtgt", metavar="N", default=0, type=int, + help="map words appearing less than threshold times to unknown") + group.add_argument("--thresholdsrc", metavar="N", default=0, type=int, + help="map words appearing less than threshold times to unknown") + group.add_argument("--tgtdict", metavar="FP", + help="reuse given target dictionary") + group.add_argument("--srcdict", metavar="FP", + help="reuse given source dictionary") + group.add_argument("--nwordstgt", metavar="N", default=-1, type=int, + help="number of target words to retain") + group.add_argument("--nwordssrc", metavar="N", default=-1, type=int, + help="number of source words to retain") + group.add_argument("--alignfile", metavar="ALIGN", default=None, + help="an alignment file (optional)") + parser.add_argument('--dataset-impl', metavar='FORMAT', default='mmap', + choices=get_available_dataset_impl(), + help='output dataset implementation') + group.add_argument("--joined-dictionary", action="store_true", + help="Generate joined dictionary") + group.add_argument("--only-source", action="store_true", + help="Only process the source language") + group.add_argument("--padding-factor", metavar="N", default=8, type=int, + help="Pad dictionary size to be multiple of N") + group.add_argument("--workers", metavar="N", default=1, type=int, + help="number of parallel workers") + group.add_argument("--dict-only", action='store_true', + help="if true, only builds a dictionary and then exits") + # fmt: on + return parser + + +def add_dataset_args(parser, train=False, gen=False): + group = parser.add_argument_group("dataset_data_loading") + gen_parser_from_dataclass(group, DatasetConfig()) + # fmt: on + return group + + +def add_distributed_training_args(parser, default_world_size=None): + group = parser.add_argument_group("distributed_training") + if default_world_size is None: + default_world_size = max(1, torch.cuda.device_count()) + gen_parser_from_dataclass( + group, DistributedTrainingConfig(distributed_world_size=default_world_size) + ) + return group + + +def add_optimization_args(parser): + group = parser.add_argument_group("optimization") + # fmt: off + gen_parser_from_dataclass(group, OptimizationConfig()) + # fmt: on + return group + + +def add_checkpoint_args(parser): + group = parser.add_argument_group("checkpoint") + # fmt: off + gen_parser_from_dataclass(group, CheckpointConfig()) + # fmt: on + return group + + +def add_common_eval_args(group): + gen_parser_from_dataclass(group, CommonEvalConfig()) + + +def add_eval_lm_args(parser): + group = parser.add_argument_group("LM Evaluation") + add_common_eval_args(group) + gen_parser_from_dataclass(group, EvalLMConfig()) + + +def add_generation_args(parser): + group = parser.add_argument_group("Generation") + add_common_eval_args(group) + gen_parser_from_dataclass(group, GenerationConfig()) + return group + + +def add_speech_generation_args(parser): + group = parser.add_argument_group("Speech Generation") + add_common_eval_args(group) # NOTE: remove_bpe is not needed + # fmt: off + group.add_argument('--eos_prob_threshold', default=0.5, type=float, + help='terminate when eos probability exceeds this') + # fmt: on + return group + + +def add_interactive_args(parser): + group = parser.add_argument_group("Interactive") + gen_parser_from_dataclass(group, InteractiveConfig()) + + +def add_model_args(parser): + group = parser.add_argument_group("Model configuration") + # fmt: off + + # Model definitions can be found under fairseq/models/ + # + # The model architecture can be specified in several ways. + # In increasing order of priority: + # 1) model defaults (lowest priority) + # 2) --arch argument + # 3) --encoder/decoder-* arguments (highest priority) + from fairseq.models import ARCH_MODEL_REGISTRY + group.add_argument('--arch', '-a', metavar='ARCH', + choices=ARCH_MODEL_REGISTRY.keys(), + help='model architecture') + # fmt: on + return group + + +def get_args( + data: Union[str, Path], + task: str = "translation", + arch: str = "transformer", + **overrides +): + parser = get_training_parser(task) + args = parse_args_and_arch(parser, [str(data), "--task", task, "--arch", arch]) + + for k, v in overrides.items(): + setattr(args, k, v) + + return args + + +def add_ema_args(parser): + group = parser.add_argument_group("EMA configuration") + gen_parser_from_dataclass(group, EMAConfig()) diff --git a/fairseq/fairseq/pdb.py b/fairseq/fairseq/pdb.py new file mode 100644 index 0000000..1ba6ef0 --- /dev/null +++ b/fairseq/fairseq/pdb.py @@ -0,0 +1,47 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import multiprocessing +import os +import pdb +import sys + + +__all__ = ["set_trace"] + + +_stdin = [None] +_stdin_lock = multiprocessing.Lock() +try: + _stdin_fd = sys.stdin.fileno() +except Exception: + _stdin_fd = None + + +class MultiprocessingPdb(pdb.Pdb): + """A Pdb wrapper that works in a multiprocessing environment. + + Usage: `from fairseq import pdb; pdb.set_trace()` + """ + + def __init__(self): + pdb.Pdb.__init__(self, nosigint=True) + + def _cmdloop(self): + stdin_bak = sys.stdin + with _stdin_lock: + try: + if _stdin_fd is not None: + if not _stdin[0]: + _stdin[0] = os.fdopen(_stdin_fd) + sys.stdin = _stdin[0] + self.cmdloop() + finally: + sys.stdin = stdin_bak + + +def set_trace(): + pdb = MultiprocessingPdb() + pdb.set_trace(sys._getframe().f_back) diff --git a/fairseq/fairseq/quantization_utils.py b/fairseq/fairseq/quantization_utils.py new file mode 100644 index 0000000..11fc414 --- /dev/null +++ b/fairseq/fairseq/quantization_utils.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +from fairseq.modules.quantization import pq, quantization_options, scalar +from omegaconf import DictConfig + + +logger = logging.getLogger(__name__) + + +def quantize_model_scalar(model, model_cfg: DictConfig): + quant_noise_scalar = getattr(model_cfg, "quant_noise_scalar", 0) or 0 + if quant_noise_scalar > 0: + # quantize_model edits the model in place + scalar.quantize_model_(model, p=quant_noise_scalar, bits=8, update_step=1000) + return model + + +class Quantizer(object): + def __init__(self, config_path, max_epoch, max_update): + try: + import yaml + except ImportError: + raise ImportError("Please install yaml with: pip install yaml") + + # parse config + if config_path: + with open(config_path) as config_file: + config = quantization_options.parse_config_yaml( + yaml.safe_load(config_file) + ) + else: + config = quantization_options.parse_config_yaml({}) + + self.n_centroids_config = config["n_centroids"] + self.block_sizes_config = config["block_sizes"] + self.layers_to_quantize = config["layers_to_quantize"] + + # We assume that training will run for a fixed number of epochs + # (or updates) and that we should train for equal durations + # between iterations of PQ. + num_iterations = len(self.layers_to_quantize) + if max_epoch > 0: + assert max_epoch % num_iterations == 0, ( + "for iterative PQ, --max-epoch (={}) must be evenly divisible by " + "len(layers_to_quantize) (={})".format(max_epoch, num_iterations) + ) + self.epoch_schedule = max_epoch // num_iterations + else: + self.epoch_schedule = None + if max_update > 0: + assert max_update % num_iterations == 0, ( + "for iterative PQ, --max-update (={}) must be evenly divisible by " + "len(layers_to_quantize) (={})".format(max_update, num_iterations) + ) + self.update_schedule = max_update // num_iterations + else: + self.update_schedule = None + assert (self.epoch_schedule is not None) ^ ( + self.update_schedule is not None + ), "for iterative PQ, cannot specify both --max-update and --max-epoch" + + # 0 is a special value for quantization step, which will force + # the first call to begin_epoch() to call step() + self.quantization_step = 0 + + def set_trainer(self, trainer): + self.trainer = trainer + self.size_tracker = pq.SizeTracker(self.trainer.get_model()) + + def step(self): + """Move to the next stage of quantization.""" + if self.quantization_step >= len(self.layers_to_quantize): + # Maybe we just finished the last training step or we loaded + # a checkpoint for an iterative PQ model which previously + # finished training. Either way, don't quantize again. + return + + logger.info( + "quantizing model (step={}; layers_to_quantize[step]={})".format( + self.quantization_step, self.layers_to_quantize[self.quantization_step] + ) + ) + quantized_layers = pq.quantize_model_( + self.trainer.get_model(), + self.size_tracker, + self.layers_to_quantize, + self.block_sizes_config, + self.n_centroids_config, + step=self.quantization_step, + ) + logger.info("quantized layers: {}".format(quantized_layers)) + logger.info(self.size_tracker) + + self.quantization_step += 1 + + # reintialize the Trainer since model parameters have changed + self.trainer.reinitialize() + + def begin_epoch(self, epoch): + """Called at the beginning of each epoch (epochs start at 1).""" + if ( + ( + self.epoch_schedule is not None + and epoch > 0 + and (epoch - 1) % self.epoch_schedule == 0 + ) + # we always step once in the beginning, even if using + # update-based quantization + or self.quantization_step == 0 + ): + self.step() + + def step_update(self, num_updates): + """Called at the end of each step.""" + if ( + self.update_schedule is not None + and num_updates > 0 + and num_updates % self.update_schedule == 0 + ): + self.step() + + def state_dict(self): + return { + "n_centroids_config": self.n_centroids_config, + "block_sizes_config": self.block_sizes_config, + "layers_to_quantize": self.layers_to_quantize, + "epoch_schedule": self.epoch_schedule, + "update_schedule": self.update_schedule, + "quantization_step": self.quantization_step, + } + + def load_state_dict(self, state_dict): + self.n_centroids_config = state_dict["n_centroids_config"] + self.block_sizes_config = state_dict["block_sizes_config"] + self.layers_to_quantize = state_dict["layers_to_quantize"] + self.epoch_schedule = state_dict["epoch_schedule"] + self.update_schedule = state_dict["update_schedule"] + self.quantization_step = state_dict["quantization_step"] diff --git a/fairseq/fairseq/registry.py b/fairseq/fairseq/registry.py new file mode 100644 index 0000000..904ffcd --- /dev/null +++ b/fairseq/fairseq/registry.py @@ -0,0 +1,104 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from argparse import Namespace + +from typing import Union +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import merge_with_parent +from hydra.core.config_store import ConfigStore +from omegaconf import DictConfig + +REGISTRIES = {} + + +def setup_registry(registry_name: str, base_class=None, default=None, required=False): + assert registry_name.startswith("--") + registry_name = registry_name[2:].replace("-", "_") + + REGISTRY = {} + REGISTRY_CLASS_NAMES = set() + DATACLASS_REGISTRY = {} + + # maintain a registry of all registries + if registry_name in REGISTRIES: + return # registry already exists + REGISTRIES[registry_name] = { + "registry": REGISTRY, + "default": default, + "dataclass_registry": DATACLASS_REGISTRY, + } + + def build_x(cfg: Union[DictConfig, str, Namespace], *extra_args, **extra_kwargs): + if isinstance(cfg, DictConfig): + choice = cfg._name + + if choice and choice in DATACLASS_REGISTRY: + from_checkpoint = extra_kwargs.get("from_checkpoint", False) + dc = DATACLASS_REGISTRY[choice] + cfg = merge_with_parent(dc(), cfg, remove_missing=from_checkpoint) + elif isinstance(cfg, str): + choice = cfg + if choice in DATACLASS_REGISTRY: + cfg = DATACLASS_REGISTRY[choice]() + else: + choice = getattr(cfg, registry_name, None) + if choice in DATACLASS_REGISTRY: + cfg = DATACLASS_REGISTRY[choice].from_namespace(cfg) + + if choice is None: + if required: + raise ValueError("{} is required!".format(registry_name)) + return None + + cls = REGISTRY[choice] + if hasattr(cls, "build_" + registry_name): + builder = getattr(cls, "build_" + registry_name) + else: + builder = cls + + if "from_checkpoint" in extra_kwargs: + del extra_kwargs["from_checkpoint"] + + return builder(cfg, *extra_args, **extra_kwargs) + + def register_x(name, dataclass=None): + def register_x_cls(cls): + if name in REGISTRY: + raise ValueError( + "Cannot register duplicate {} ({})".format(registry_name, name) + ) + if cls.__name__ in REGISTRY_CLASS_NAMES: + raise ValueError( + "Cannot register {} with duplicate class name ({})".format( + registry_name, cls.__name__ + ) + ) + if base_class is not None and not issubclass(cls, base_class): + raise ValueError( + "{} must extend {}".format(cls.__name__, base_class.__name__) + ) + + if dataclass is not None and not issubclass(dataclass, FairseqDataclass): + raise ValueError( + "Dataclass {} must extend FairseqDataclass".format(dataclass) + ) + + cls.__dataclass = dataclass + if cls.__dataclass is not None: + DATACLASS_REGISTRY[name] = cls.__dataclass + + cs = ConfigStore.instance() + node = dataclass() + node._name = name + cs.store(name=name, group=registry_name, node=node, provider="fairseq") + + REGISTRY[name] = cls + + return cls + + return register_x_cls + + return build_x, register_x, REGISTRY, DATACLASS_REGISTRY diff --git a/fairseq/fairseq/scoring/__init__.py b/fairseq/fairseq/scoring/__init__.py new file mode 100644 index 0000000..58f2f56 --- /dev/null +++ b/fairseq/fairseq/scoring/__init__.py @@ -0,0 +1,55 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import importlib +import os +from abc import ABC, abstractmethod + +from fairseq import registry +from omegaconf import DictConfig + + +class BaseScorer(ABC): + def __init__(self, cfg): + self.cfg = cfg + self.ref = [] + self.pred = [] + + def add_string(self, ref, pred): + self.ref.append(ref) + self.pred.append(pred) + + @abstractmethod + def score(self) -> float: + pass + + @abstractmethod + def result_string(self) -> str: + pass + + +_build_scorer, register_scorer, SCORER_REGISTRY, _ = registry.setup_registry( + "--scoring", default="bleu" +) + + +def build_scorer(choice, tgt_dict): + _choice = choice._name if isinstance(choice, DictConfig) else choice + + if _choice == "bleu": + from fairseq.scoring import bleu + + return bleu.Scorer( + bleu.BleuConfig(pad=tgt_dict.pad(), eos=tgt_dict.eos(), unk=tgt_dict.unk()) + ) + return _build_scorer(choice) + + +# automatically import any Python files in the current directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + module = file[: file.find(".py")] + importlib.import_module("fairseq.scoring." + module) diff --git a/fairseq/fairseq/scoring/bertscore.py b/fairseq/fairseq/scoring/bertscore.py new file mode 100644 index 0000000..6d5a845 --- /dev/null +++ b/fairseq/fairseq/scoring/bertscore.py @@ -0,0 +1,44 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +import numpy as np + +from fairseq.dataclass import FairseqDataclass +from fairseq.scoring import BaseScorer, register_scorer + + +@dataclass +class BertScoreScorerConfig(FairseqDataclass): + bert_score_lang: str = field(default="en", metadata={"help": "BERTScore language"}) + + +@register_scorer("bert_score", dataclass=BertScoreScorerConfig) +class BertScoreScorer(BaseScorer): + def __init__(self, cfg): + super(BertScoreScorer, self).__init__(cfg) + try: + import bert_score as _bert_score + except ImportError: + raise ImportError("Please install BERTScore: pip install bert-score") + + self.cfg = cfg + self._bert_score = _bert_score + self.scores = None + + def add_string(self, ref, pred): + self.ref.append(ref) + self.pred.append(pred) + + def score(self, order=4): + _, _, self.scores = self._bert_score.score( + self.pred, self.ref, lang=self.cfg.bert_score_lang + ) + self.scores = self.scores.numpy() + return np.mean(self.scores) + + def result_string(self, order=4): + return f"BERTScore: {self.score():.4f}" diff --git a/fairseq/fairseq/scoring/bleu.py b/fairseq/fairseq/scoring/bleu.py new file mode 100644 index 0000000..e55bd2f --- /dev/null +++ b/fairseq/fairseq/scoring/bleu.py @@ -0,0 +1,168 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ctypes +import math +import sys +from dataclasses import dataclass, field + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.scoring import BaseScorer, register_scorer +from fairseq.scoring.tokenizer import EvaluationTokenizer + + +class BleuStat(ctypes.Structure): + _fields_ = [ + ("reflen", ctypes.c_size_t), + ("predlen", ctypes.c_size_t), + ("match1", ctypes.c_size_t), + ("count1", ctypes.c_size_t), + ("match2", ctypes.c_size_t), + ("count2", ctypes.c_size_t), + ("match3", ctypes.c_size_t), + ("count3", ctypes.c_size_t), + ("match4", ctypes.c_size_t), + ("count4", ctypes.c_size_t), + ] + + +@dataclass +class SacrebleuConfig(FairseqDataclass): + sacrebleu_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field( + default="13a", metadata={"help": "tokenizer"} + ) + sacrebleu_lowercase: bool = field( + default=False, metadata={"help": "apply lowercasing"} + ) + sacrebleu_char_level: bool = field( + default=False, metadata={"help": "evaluate at character level"} + ) + + +@register_scorer("sacrebleu", dataclass=SacrebleuConfig) +class SacrebleuScorer(BaseScorer): + def __init__(self, cfg): + super(SacrebleuScorer, self).__init__(cfg) + import sacrebleu + + self.sacrebleu = sacrebleu + self.tokenizer = EvaluationTokenizer( + tokenizer_type=cfg.sacrebleu_tokenizer, + lowercase=cfg.sacrebleu_lowercase, + character_tokenization=cfg.sacrebleu_char_level, + ) + + def add_string(self, ref, pred): + self.ref.append(self.tokenizer.tokenize(ref)) + self.pred.append(self.tokenizer.tokenize(pred)) + + def _score(self, order=4): + if order != 4: + raise NotImplementedError + # tokenization and lowercasing are performed by self.tokenizer instead. + return self.sacrebleu.corpus_bleu(self.pred, [self.ref], tokenize="none") + + def score(self, order=4): + return self._score(order).score + + def result_string(self, order=4): + return self._score(order).format() + + +@dataclass +class BleuConfig(FairseqDataclass): + pad: int = field(default=1, metadata={"help": "padding index"}) + eos: int = field(default=2, metadata={"help": "eos index"}) + unk: int = field(default=3, metadata={"help": "unk index"}) + + +@register_scorer("bleu", dataclass=BleuConfig) +class Scorer(object): + def __init__(self, cfg): + self.stat = BleuStat() + self.pad = cfg.pad + self.eos = cfg.eos + self.unk = cfg.unk + + try: + from fairseq import libbleu + except ImportError as e: + sys.stderr.write( + "ERROR: missing libbleu.so. run `pip install --editable .`\n" + ) + raise e + + self.C = ctypes.cdll.LoadLibrary(libbleu.__file__) + + self.reset() + + def reset(self, one_init=False): + if one_init: + self.C.bleu_one_init(ctypes.byref(self.stat)) + else: + self.C.bleu_zero_init(ctypes.byref(self.stat)) + + def add(self, ref, pred): + if not isinstance(ref, torch.IntTensor): + raise TypeError("ref must be a torch.IntTensor (got {})".format(type(ref))) + if not isinstance(pred, torch.IntTensor): + raise TypeError("pred must be a torch.IntTensor(got {})".format(type(pred))) + + # don't match unknown words + rref = ref.clone() + assert not rref.lt(0).any() + rref[rref.eq(self.unk)] = -999 + + rref = rref.contiguous().view(-1) + pred = pred.contiguous().view(-1) + + self.C.bleu_add( + ctypes.byref(self.stat), + ctypes.c_size_t(rref.size(0)), + ctypes.c_void_p(rref.data_ptr()), + ctypes.c_size_t(pred.size(0)), + ctypes.c_void_p(pred.data_ptr()), + ctypes.c_int(self.pad), + ctypes.c_int(self.eos), + ) + + def score(self, order=4): + psum = sum( + math.log(p) if p > 0 else float("-Inf") for p in self.precision()[:order] + ) + return self.brevity() * math.exp(psum / order) * 100 + + def precision(self): + def ratio(a, b): + return a / b if b > 0 else 0 + + return [ + ratio(self.stat.match1, self.stat.count1), + ratio(self.stat.match2, self.stat.count2), + ratio(self.stat.match3, self.stat.count3), + ratio(self.stat.match4, self.stat.count4), + ] + + def brevity(self): + r = self.stat.reflen / self.stat.predlen + return min(1, math.exp(1 - r)) + + def result_string(self, order=4): + assert order <= 4, "BLEU scores for order > 4 aren't supported" + fmt = "BLEU{} = {:2.2f}, {:2.1f}" + for _ in range(1, order): + fmt += "/{:2.1f}" + fmt += " (BP={:.3f}, ratio={:.3f}, syslen={}, reflen={})" + bleup = [p * 100 for p in self.precision()[:order]] + return fmt.format( + order, + self.score(order=order), + *bleup, + self.brevity(), + self.stat.predlen / self.stat.reflen, + self.stat.predlen, + self.stat.reflen + ) diff --git a/fairseq/fairseq/scoring/chrf.py b/fairseq/fairseq/scoring/chrf.py new file mode 100644 index 0000000..5df5a1c --- /dev/null +++ b/fairseq/fairseq/scoring/chrf.py @@ -0,0 +1,36 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from dataclasses import dataclass + +from fairseq.dataclass import FairseqDataclass +from fairseq.scoring import BaseScorer, register_scorer + + +@dataclass +class ChrFScorerConfig(FairseqDataclass): + pass + + +@register_scorer("chrf", dataclass=ChrFScorerConfig) +class ChrFScorer(BaseScorer): + def __init__(self, args): + super(ChrFScorer, self).__init__(args) + import sacrebleu + + self.sacrebleu = sacrebleu + + def add_string(self, ref, pred): + self.ref.append(ref) + self.pred.append(pred) + + def score(self, order=4): + return self.result_string(order).score + + def result_string(self, order=4): + if order != 4: + raise NotImplementedError + return self.sacrebleu.corpus_chrf(self.pred, [self.ref]).format() diff --git a/fairseq/fairseq/scoring/meteor.py b/fairseq/fairseq/scoring/meteor.py new file mode 100644 index 0000000..3271995 --- /dev/null +++ b/fairseq/fairseq/scoring/meteor.py @@ -0,0 +1,42 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +from dataclasses import dataclass + +from fairseq.dataclass import FairseqDataclass +from fairseq.scoring import BaseScorer, register_scorer + + +@dataclass +class MeteorScorerConfig(FairseqDataclass): + pass + + +@register_scorer("meteor", dataclass=MeteorScorerConfig) +class MeteorScorer(BaseScorer): + def __init__(self, args): + super(MeteorScorer, self).__init__(args) + try: + import nltk + except ImportError: + raise ImportError("Please install nltk to use METEOR scorer") + + self.nltk = nltk + self.scores = [] + + def add_string(self, ref, pred): + self.ref.append(ref) + self.pred.append(pred) + + def score(self, order=4): + self.scores = [ + self.nltk.translate.meteor_score.single_meteor_score(r, p) + for r, p in zip(self.ref, self.pred) + ] + return np.mean(self.scores) + + def result_string(self, order=4): + return f"METEOR: {self.score():.4f}" diff --git a/fairseq/fairseq/scoring/tokenizer.py b/fairseq/fairseq/scoring/tokenizer.py new file mode 100644 index 0000000..b0cedd5 --- /dev/null +++ b/fairseq/fairseq/scoring/tokenizer.py @@ -0,0 +1,80 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unicodedata + +import sacrebleu as sb + +from fairseq.dataclass import ChoiceEnum + +SACREBLEU_V2_ABOVE = int(sb.__version__[0]) >= 2 + + +class EvaluationTokenizer(object): + """A generic evaluation-time tokenizer, which leverages built-in tokenizers + in sacreBLEU (https://github.com/mjpost/sacrebleu). It additionally provides + lowercasing, punctuation removal and character tokenization, which are + applied after sacreBLEU tokenization. + + Args: + tokenizer_type (str): the type of sacreBLEU tokenizer to apply. + lowercase (bool): lowercase the text. + punctuation_removal (bool): remove punctuation (based on unicode + category) from text. + character_tokenization (bool): tokenize the text to characters. + """ + + SPACE = chr(32) + SPACE_ESCAPE = chr(9601) + _ALL_TOKENIZER_TYPES = ( + sb.BLEU.TOKENIZERS + if SACREBLEU_V2_ABOVE + else ["none", "13a", "intl", "zh", "ja-mecab"] + ) + ALL_TOKENIZER_TYPES = ChoiceEnum(_ALL_TOKENIZER_TYPES) + + def __init__( + self, + tokenizer_type: str = "13a", + lowercase: bool = False, + punctuation_removal: bool = False, + character_tokenization: bool = False, + ): + + assert ( + tokenizer_type in self._ALL_TOKENIZER_TYPES + ), f"{tokenizer_type}, {self._ALL_TOKENIZER_TYPES}" + self.lowercase = lowercase + self.punctuation_removal = punctuation_removal + self.character_tokenization = character_tokenization + if SACREBLEU_V2_ABOVE: + self.tokenizer = sb.BLEU(tokenize=str(tokenizer_type)).tokenizer + else: + self.tokenizer = sb.tokenizers.TOKENIZERS[tokenizer_type]() + + @classmethod + def remove_punctuation(cls, sent: str): + """Remove punctuation based on Unicode category.""" + return cls.SPACE.join( + t + for t in sent.split(cls.SPACE) + if not all(unicodedata.category(c)[0] == "P" for c in t) + ) + + def tokenize(self, sent: str): + tokenized = self.tokenizer(sent) + + if self.punctuation_removal: + tokenized = self.remove_punctuation(tokenized) + + if self.character_tokenization: + tokenized = self.SPACE.join( + list(tokenized.replace(self.SPACE, self.SPACE_ESCAPE)) + ) + + if self.lowercase: + tokenized = tokenized.lower() + + return tokenized diff --git a/fairseq/fairseq/scoring/wer.py b/fairseq/fairseq/scoring/wer.py new file mode 100644 index 0000000..633dc47 --- /dev/null +++ b/fairseq/fairseq/scoring/wer.py @@ -0,0 +1,58 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +from fairseq.dataclass import FairseqDataclass +from fairseq.scoring import BaseScorer, register_scorer +from fairseq.scoring.tokenizer import EvaluationTokenizer + + +@dataclass +class WerScorerConfig(FairseqDataclass): + wer_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field( + default="none", metadata={"help": "sacreBLEU tokenizer to use for evaluation"} + ) + wer_remove_punct: bool = field( + default=False, metadata={"help": "remove punctuation"} + ) + wer_char_level: bool = field( + default=False, metadata={"help": "evaluate at character level"} + ) + wer_lowercase: bool = field(default=False, metadata={"help": "lowercasing"}) + + +@register_scorer("wer", dataclass=WerScorerConfig) +class WerScorer(BaseScorer): + def __init__(self, cfg): + super().__init__(cfg) + self.reset() + try: + import editdistance as ed + except ImportError: + raise ImportError("Please install editdistance to use WER scorer") + self.ed = ed + self.tokenizer = EvaluationTokenizer( + tokenizer_type=self.cfg.wer_tokenizer, + lowercase=self.cfg.wer_lowercase, + punctuation_removal=self.cfg.wer_remove_punct, + character_tokenization=self.cfg.wer_char_level, + ) + + def reset(self): + self.distance = 0 + self.ref_length = 0 + + def add_string(self, ref, pred): + ref_items = self.tokenizer.tokenize(ref).split() + pred_items = self.tokenizer.tokenize(pred).split() + self.distance += self.ed.eval(ref_items, pred_items) + self.ref_length += len(ref_items) + + def result_string(self): + return f"WER: {self.score():.2f}" + + def score(self): + return 100.0 * self.distance / self.ref_length if self.ref_length > 0 else 0 diff --git a/fairseq/fairseq/search.py b/fairseq/fairseq/search.py new file mode 100644 index 0000000..c7378bb --- /dev/null +++ b/fairseq/fairseq/search.py @@ -0,0 +1,892 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +from typing import List, Optional + +import torch +import torch.nn as nn +from fairseq.token_generation_constraints import ( + ConstraintState, + OrderedConstraintState, + UnorderedConstraintState, +) +from torch import Tensor + + +class Search(nn.Module): + def __init__(self, tgt_dict): + super().__init__() + self.pad = tgt_dict.pad() + self.unk = tgt_dict.unk() + self.eos = tgt_dict.eos() + self.vocab_size = len(tgt_dict) + self.src_lengths = torch.tensor(-1) + self.supports_constraints = False + self.stop_on_max_len = False + + def step( + self, step, lprobs, scores, prev_output_tokens=None, original_batch_idxs=None + ): + """Take a single search step. + + Args: + step: the current search step, starting at 0 + lprobs: (bsz x input_beam_size x vocab_size) + the model's log-probabilities over the vocabulary at the current step + scores: (bsz x input_beam_size x step) + the historical model scores of each hypothesis up to this point + prev_output_tokens: (bsz x step) + the previously generated oputput tokens + original_batch_idxs: (bsz) + the tensor with the batch indices, in the range [0, bsz) + this is useful in case there has been applied a re-ordering + and we need to know the orignal indices + + Return: A tuple of (scores, indices, beams) where: + scores: (bsz x output_beam_size) + the scores of the chosen elements; output_beam_size can be + larger than input_beam_size, e.g., we may return + 2*input_beam_size to account for EOS + indices: (bsz x output_beam_size) + the indices of the chosen elements + beams: (bsz x output_beam_size) + the hypothesis ids of the chosen elements, in the range [0, input_beam_size) + """ + raise NotImplementedError + + @torch.jit.export + def set_src_lengths(self, src_lengths): + self.src_lengths = src_lengths + + @torch.jit.export + def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int): + """Initialize constraint states for constrained decoding (if supported). + + Args: + batch_constraints: (torch.Tensor, optional) + the list of constraints, in packed form + beam_size: (int) + the beam size + Returns: + *encoder_out* rearranged according to *new_order* + """ + pass + + def prune_sentences(self, batch_idxs: Tensor): + """ + Removes constraint states for completed sentences (if supported). + This is called from sequence_generator._generate() when sentences are + deleted from the batch. + + Args: + batch_idxs: Indices of *sentences* whose constraint state should be *kept*. + """ + pass + + def update_constraints(self, active_hypos: Tensor): + """ + Updates the constraint states by selecting the beam items that are retained. + This is called at each time step of sequence_generator._generate() when + the set of 2 * {beam_size} candidate hypotheses are reduced to the beam size. + + Args: + active_hypos: (batch size, beam size) + list of integers denoting, for each sentence, which beam candidate items + should be kept. + """ + pass + + +class BeamSearch(Search): + def __init__(self, tgt_dict): + super().__init__(tgt_dict) + self.constraint_states = None + + @torch.jit.export + def step( + self, + step: int, + lprobs, + scores: Optional[Tensor], + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + candidate_multiple: int = 2, + ): + bsz, beam_size, vocab_size = lprobs.size() + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + lprobs = lprobs[:, ::beam_size, :].contiguous() + else: + # make probs contain cumulative scores for each hypothesis + assert scores is not None + lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) + + top_prediction = torch.topk( + lprobs.view(bsz, -1), + k=min( + # Take the best `candidate_muliple`(default 2) x beam_size predictions. We'll choose the first + # beam_size of these which don't predict eos to continue with. + candidate_multiple * beam_size, + lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad + ), + ) + scores_buf = top_prediction[0] + indices_buf = top_prediction[1] + # Project back into relative indices and beams + beams_buf = torch.div(indices_buf, vocab_size, rounding_mode="trunc") + indices_buf = indices_buf.fmod(vocab_size) + + # At this point, beams_buf and indices_buf are single-dim and contain relative indices + return scores_buf, indices_buf, beams_buf + + +class PrefixConstrainedBeamSearch(Search): + def __init__(self, tgt_dict, prefix_allowed_tokens_fn): + super().__init__(tgt_dict) + self.prefix_allowed_tokens_fn = prefix_allowed_tokens_fn + self.stop_on_max_len = True + + @torch.jit.export + def apply_mask(self, x, prev_output_tokens, original_batch_idxs): + beam_size = x.shape[0] // original_batch_idxs.shape[0] + original_batch_idxs = ( + original_batch_idxs.unsqueeze(-1).repeat((1, beam_size)).flatten().tolist() + ) + + mask = torch.full_like(x, -math.inf) + for sent_i, (sent, batch_i) in enumerate( + zip(prev_output_tokens, original_batch_idxs) + ): + mask[sent_i, :, self.prefix_allowed_tokens_fn(batch_i, sent)] = 0 + + return mask + + @torch.jit.export + def step( + self, + step: int, + lprobs: Tensor, + scores: Tensor, + prev_output_tokens: Tensor, + original_batch_idxs: Tensor, + ): + bsz, beam_size, vocab_size = lprobs.size() + + lprobs += self.apply_mask( + lprobs.view(bsz * beam_size, 1, vocab_size), + prev_output_tokens, + original_batch_idxs, + ).view(bsz, beam_size, vocab_size) + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + lprobs = lprobs[:, ::beam_size, :].contiguous() + else: + # make probs contain cumulative scores for each hypothesis + assert scores is not None + lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) + + top_prediction = torch.topk( + lprobs.view(bsz, -1), + k=min( + # Take the best beam_size predictions. We'll choose the first + # beam_size of these which don't predict eos to continue with. + beam_size, + lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad + ), + ) + scores_buf = top_prediction[0] + indices_buf = top_prediction[1] + beams_buf = indices_buf // vocab_size + indices_buf = indices_buf.fmod(vocab_size) + return scores_buf, indices_buf, beams_buf + + +class LexicallyConstrainedBeamSearch(Search): + """Implements lexically constrained beam search as described in + + Fast Lexically Constrained Decoding with Dynamic Beam + Allocation for Neural Machine Translation. Post & Vilar, + NAACL 2018. https://www.aclweb.org/anthology/N18-1119/ + + and + + Improved Lexically Constrained Decoding for Translation and + Monolingual Rewriting. Hu et al, NAACL + 2019. https://www.aclweb.org/anthology/N19-1090/ + + This is accomplished by maintaining, for each beam hypothesis, a + ConstraintState object (see constraints.py) that tracks which + constraints have been generated and using this information to + shape the beam for each input sentence. + """ + + def __init__(self, tgt_dict, representation): + super().__init__(tgt_dict) + self.representation = representation + self.vocab_size = len(tgt_dict) + self.num_cands = 0 + self.supports_constraints = True + + @torch.jit.export + def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int): + self.constraint_states = [] + for constraint_tensor in batch_constraints: + if self.representation == "ordered": + constraint_state = OrderedConstraintState.create(constraint_tensor) + elif self.representation == "unordered": + constraint_state = UnorderedConstraintState.create(constraint_tensor) + + self.constraint_states.append([constraint_state for i in range(beam_size)]) + + @torch.jit.export + def prune_sentences(self, batch_idxs: Tensor): + self.constraint_states = [ + self.constraint_states[i] for i in batch_idxs.tolist() + ] + + @torch.jit.export + def update_constraints(self, active_hypos: Tensor): + if self.constraint_states: + batch_size = active_hypos.size(0) + for sentid in range(batch_size): + self.constraint_states[sentid] = [ + self.constraint_states[sentid][i] for i in active_hypos[sentid] + ] + + @torch.jit.export + def step( + self, + step: int, + lprobs: Tensor, + scores: Optional[Tensor], + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + """ + A constrained step builds a large candidates list from the following: + - the top 2 * {beam_size} items over the whole beam + - for each item in the beam + - the top {each_k} (default 1) + - all next constraints + We then compute the constrained state of each beam item, and assign + stripe codes: 0 to the best in each bank, 1 to the 2nd-best, and so + on. We then sort by (stripe, score), and truncate the list at + 2 * beam size. + + Args: + step: the decoder step + lprobs: (batch size, beam size, target vocab) + the target-vocab distributions for each item in the beam. + Retrun: A tuple of (scores, indices, beams, constraints) where: + scores: (batch, output beam size) + the scores of the chosen elements + indices: (batch, output beam size) + the target vocab indices of the chosen elements + beams: (batch, output beam size) + the 0-indexed hypothesis ids of the chosen elements + constraints: (batch, output beam size) + the new constraint states + """ + each_k = 1 + device = lprobs.device + + batch_size, beam_size, vocab_size = lprobs.size() + + self.num_cands = min( + # Just take the k-best. We'll get another k from the 1-best from each + # row, plus more from the constraints + beam_size * 2, + lprobs.view(batch_size, -1).size(1) - 1, # -1 so we never select pad + ) + + # STEP 0: Preliminary. Prevent EOS for unfinished hyps across all batch items + constraint_states = self.constraint_states + if constraint_states and step > 0: + not_finished_indices = [] + for sentno, sent_constraints in enumerate(constraint_states): + for beamno, state in enumerate(sent_constraints): + index = sentno * beam_size + beamno + if not state.finished: + not_finished_indices.append(index) + not_finished_indices = torch.tensor(not_finished_indices) + if not_finished_indices.numel() > 0: + lprobs.view(batch_size * beam_size, -1)[ + not_finished_indices, self.eos + ] = -math.inf + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam entry for each batch item + lprobs = lprobs[:, ::beam_size, :].contiguous() + else: + # make probs contain cumulative scores for each hypothesis + assert scores is not None + lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) + + top_prediction = torch.topk( + lprobs.view(batch_size, -1), + self.num_cands, + ) + scores_buf, indices_buf = top_prediction + # Project back into relative indices and beams + beams_buf = indices_buf // vocab_size + indices_buf = indices_buf.fmod(vocab_size) + + # Short circuit if there are no constraints in this batch + if not constraint_states: + return scores_buf, indices_buf, beams_buf + + # STEP 1: get top-1 from each hypothesis across all sentences in the batch + if step > 0: + top_scores, top_indices = torch.topk( + lprobs.view(batch_size * beam_size, -1), + k=each_k, + dim=1, + ) + top_scores = top_scores.view(batch_size, -1) + top_indices = top_indices.view(batch_size, -1) + scores_buf = torch.cat((scores_buf, top_scores), dim=1) + indices_buf = torch.cat((indices_buf, top_indices), dim=1) + new_beams = torch.arange(0, beam_size, device=device).repeat(batch_size, 1) + beams_buf = torch.cat((beams_buf, new_beams), dim=1) + + # Now, process sentences in the batch one by one. + new_scores_buf = torch.zeros((batch_size, 2 * beam_size), device=device) + new_indices_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long() + new_beams_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long() + for sentno, states in enumerate(constraint_states): + scores, indices, beams, new_states = self.step_sentence( + step, + sentno, + lprobs[sentno], + constraint_states[sentno], + beams_buf[sentno].clone(), + indices_buf[sentno].clone(), + scores_buf[sentno].clone(), + ) + new_scores_buf[sentno] = scores + new_indices_buf[sentno] = indices + new_beams_buf[sentno] = beams + self.constraint_states[sentno] = new_states + + return new_scores_buf, new_indices_buf, new_beams_buf + + @torch.jit.export + def step_sentence( + self, + step: int, + sentno: int, + lprobs: Tensor, + constraint_states: List[List[ConstraintState]], + beams_buf: Tensor, + indices_buf: Tensor, + scores_buf: Tensor, + ): + """Does per-sentence processing. Adds all constraints for each + hypothesis to the list of candidates; then removes duplicates, + sorts, and dynamically stripes across the banks. All tensor inputs + are collapsed to those pertaining to a single input sentence. + """ + device = lprobs.device + + # STEP 2: Add all constraints for each beam item + for beamno, state in enumerate(constraint_states): + next_tokens = torch.tensor(list(state.next_tokens()), device=device).long() + if next_tokens.numel() != 0: + indices_buf = torch.cat((indices_buf, next_tokens)) + next_beams = ( + torch.tensor(beamno, device=device) + .repeat(next_tokens.size(0)) + .long() + ) + beams_buf = torch.cat((beams_buf, next_beams)) + next_values = lprobs[beamno].take(next_tokens.view(-1)) + scores_buf = torch.cat((scores_buf, next_values)) + + # At the 0th time step, there is just one beam item + if step == 0: + break + + # STEP 3: Compute the "bank" for each candidate. This is the + # number of constraints it's generated. We need this so that + # we can do round-robin allocation of the beam across these + # banks. If C is the number of constraints, we select the best + # item in bank C, then the best in bank C-1, etc, followed by + # the 2nd-best in bank C, the 2nd-best in bank C-1, etc, and so + # on, until the maximum beam size. We accomplish this by + # creating a sort key and striping across the banks. + + # Compute the new states for all candidates + cands_size = indices_buf.size(0) + constraint_states = [ + constraint_states[beams_buf[i]].advance(indices_buf[i]) + for i in range(cands_size) + ] + + banks = torch.tensor([state.bank for state in constraint_states], device=device) + + # STEP 4: Sort + num_constraint_tokens = len(state.tokens) + + # Sort by keys (bank, score) (i.e., sort banks together, and scores + # within banks). AFAIK pytorch doesn't support either stable sort or + # multi-key sorting, so we have to hack this. + MAX_SCORE = -100 + sort_key = (num_constraint_tokens - banks) * MAX_SCORE + scores_buf + sort_values, sort_indices = sort_key.sort(dim=0, descending=True) + scores_buf = scores_buf[sort_indices] + indices_buf = indices_buf[sort_indices] + beams_buf = beams_buf[sort_indices] + banks = banks[sort_indices] + + # Sort the constraints to follow suit + constraint_states = [constraint_states[i] for i in sort_indices] + + # STEP 5: Remove duplicates. The topk calls (overall and + # per-row) plus the per-row generation of constraints will + # produce duplicates. Here we remove them. + + def roll(t): + """Rolls a 1d tensor left by 1. + + [0, 1, 2, 3, 4] becomes [4, 0, 1, 2, 3] + """ + return torch.cat((t[-1].unsqueeze(0), t[0:-1]), dim=0) + + # We map candidates (beam, token_id) to a single dimension. + # This is then shifted by 1. We can then easily identify + # duplicates and create a mask that identifies unique + # extensions. + uniques_mask = beams_buf * (self.vocab_size + 1) + indices_buf + uniques_mask = roll(uniques_mask) != uniques_mask + + # Use the mask to pare down the data structures + scores_buf = torch.masked_select(scores_buf, uniques_mask) + indices_buf = torch.masked_select(indices_buf, uniques_mask) + beams_buf = torch.masked_select(beams_buf, uniques_mask) + banks = torch.masked_select(banks, uniques_mask) + i = 1 + for mask in uniques_mask[1:]: + if not mask: + constraint_states.pop(i) + i += mask + + # STEP 6: Assign IDs round-robin across banks, sort, and + # truncate. Now that the candidates are sorted by (bank, + # score) and uniqed, we dynamically allocate the {beam_size} + # beam by striping across the candidates. These stripes will + # be used as sort keys to do round-robin selection. This is + # accomplished in a single pass with offsets. Sorting by + # highest-banks (furthest-along hypotheses) first ensures + # progress through the constraints. + # + # e.g., BANKS: 3 3 3 2 2 2 2 1 1 1 0 0 + # OLD STRIPES: 0 1 2 0 1 2 3 0 1 2 0 1 + # NEW STRIPES: 0 1+4 2+8 0+1 1+5 2+9 3+11 0+2 1+6 2+10 0+3 1+7 + # = 0 5 10 1 6 11 13 2 7 12 3 8 + # + # Sorting by this then gives the following banks: + # + # 3 2 1 0 3 2 1 0 3 2 1 2 + # + # We'll take the top {beam_size} of these. + stripe_offsets = [offset * (len(banks) + 1) for offset in range(len(banks) + 1)] + stripes = torch.zeros_like(banks) + cur_bank_count = -1 + cur_bank = banks[0] + for i, bank in enumerate(banks): + if bank != cur_bank: + cur_bank_count = 0 + cur_bank = bank + else: + cur_bank_count += 1 + stripes[i] = num_constraint_tokens - bank + stripe_offsets[cur_bank_count] + + # STEP 7: Sort by the stripes values + sort_values, sort_indices = stripes.sort(dim=0) + scores_buf = scores_buf[sort_indices] + indices_buf = indices_buf[sort_indices] + beams_buf = beams_buf[sort_indices] + constraint_states = [constraint_states[i] for i in sort_indices] + + # STEP 8: Truncate to the candidates size! + scores_buf = scores_buf[: self.num_cands] + indices_buf = indices_buf[: self.num_cands] + beams_buf = beams_buf[: self.num_cands] + + return scores_buf, indices_buf, beams_buf, constraint_states + + +class LengthConstrainedBeamSearch(Search): + def __init__(self, tgt_dict, min_len_a, min_len_b, max_len_a, max_len_b): + super().__init__(tgt_dict) + self.min_len_a = min_len_a + self.min_len_b = min_len_b + self.max_len_a = max_len_a + self.max_len_b = max_len_b + self.beam = BeamSearch(tgt_dict) + self.needs_src_lengths = True + + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + min_lens = self.min_len_a * self.src_lengths + self.min_len_b + max_lens = self.max_len_a * self.src_lengths + self.max_len_b + lprobs[step < min_lens, :, self.eos] = -math.inf + lprobs[step >= max_lens, :, self.eos] = 0 + return self.beam.step(step, lprobs, scores) + + +class DiverseBeamSearch(Search): + """Diverse Beam Search. + + See "Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence + Models" for details. + + We implement cumulative diversity penalty here as default, optionally provide Hamming diversity described + in the original paper, and a way to interpolate between the two through diversity_discount. + + Take the example below for illustration of cumulative diversity implemented. + A) I like dogs. + B) I like ____. + C) There are ___. + And we are at step=2, trying to fill in the blank: + + Hamming diversity: + Penalty for B from A is 1 for "dogs" and 0 for any other words like "cats". + Penalty for C from A is 1 for "dogs" and 0 for any other words like "cats". + + Cumulative diversity (default): + Penalty for B from A is 3 for "dogs" and 0 for any other words like "cats". + Penalty for C from A is 1 for "dogs" and 0 for any other words like "cats". + B and C differ because B matches with A for "I" and "like" at respective steps incurring 2 cumulative penalty. + + Using divesrity_discount to interpolate between the two: + if diverstiy_discount = 0.5, then + Penalty for B from A is 1.75 (1 + 0.5 + 0.25) for "dogs" and 0 for any other words like "cats". + Penalty for C from A is 1 for "dogs" and 0 for any other words like "cats". + "I" and "like" matched for B and A at step 0 and 1 respectively. Since "I" is two steps away and "like" is one step away, they are discounted by (0.5)^2 and 0.5 respectively. + When diversity_discount = 0, we recover Hammning diversity and when diversity_discount = 1, we recover cumulative diversity. + + NB: During beam search for each diversity group, `candidate_mutiple` is set to 1 rather than BeamSearch default(2). + This is to ensure we have final `beam_size` candidates so that no diversity groups would be dropped during final token selection in sequence generation. + For full backwards compatibility, use diversity_discount=0 and candidate_multiple=2. + + """ + + def __init__( + self, + tgt_dict, + num_groups, + diversity_strength, + diversity_discount=1.0, + candidate_multiple=1, + ): + super().__init__(tgt_dict) + self.num_groups = num_groups + self.diversity_strength = -diversity_strength + self.beam = BeamSearch(tgt_dict) + self.diversity_discount = diversity_discount + self.candidate_multiple = candidate_multiple + + # Float tensor to keep track of overlap between groups. + # Each token shared at the same step between two groups is counted as one. + # Then token counts are discounted by `diversity_discount` for every next timestep. + # Once initialized, dimension is batch_size * num_groups * num_groups. + self.group_overlap = torch.empty(0) + + @torch.jit.export + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + bsz, beam_size, vocab_size = lprobs.size() + if beam_size % self.num_groups != 0: + raise ValueError( + "DiverseBeamSearch requires --beam to be divisible by the number of groups" + ) + + # initialize diversity penalty + diversity_buf = torch.zeros(lprobs[:, 0, :].size()).to(lprobs) + + scores_G, beams_G = [], [] + + # pre-allocating tensor for indices for all groups + indices_G_stacked = torch.empty( + bsz, + int(beam_size / self.num_groups) * self.candidate_multiple, + self.num_groups, + dtype=torch.long, + device=lprobs.device, + ) + + for g in range(self.num_groups): + lprobs_g = lprobs[:, g :: self.num_groups, :] + scores_g = scores[:, g :: self.num_groups, :] if step > 0 else None + + diversity_buf.zero_() + # apply diversity penalty + if g > 0: + indices_ = indices_G_stacked[:, :, :g] + if step > 0: + penalty_val = 1 + self.group_overlap[original_batch_idxs, g, :g] + penalty_val = penalty_val.unsqueeze(1) + else: + penalty_val = torch.ones(bsz, 1, 1) + diversity_buf.scatter_add_( + 1, + indices_.reshape(bsz, -1), + penalty_val.expand(indices_.size()) + .reshape(bsz, -1) + .to(diversity_buf), + ) + + lprobs_g = torch.add( + lprobs_g, + other=diversity_buf.unsqueeze(1), + alpha=self.diversity_strength, + ) + else: + lprobs_g = lprobs_g.contiguous() + + scores_buf, indices_buf, beams_buf = self.beam.step( + step, lprobs_g, scores_g, candidate_multiple=self.candidate_multiple + ) + beams_buf.mul_(self.num_groups).add_(g) + + scores_G.append(scores_buf.clone()) + beams_G.append(beams_buf.clone()) + + indices_G_stacked[:, :, g] = indices_buf + + # interleave results from different groups + scores_buf = torch.stack(scores_G, dim=2).view(bsz, -1) + indices_buf = indices_G_stacked.view(bsz, -1) + beams_buf = torch.stack(beams_G, dim=2).view(bsz, -1) + # find num of overlapped tokens for each group pair + # then discount it for next timestamp + overlap = self.diversity_discount * torch.sum( + indices_G_stacked.unsqueeze(2).eq(indices_G_stacked.unsqueeze(3)), dim=1 + ) + if step == 0: + self.group_overlap = overlap + else: + self.group_overlap[original_batch_idxs] = ( + self.group_overlap[original_batch_idxs] * self.diversity_discount + + overlap + ) + + return scores_buf, indices_buf, beams_buf + + +class Sampling(Search): + sampling_topk: int + sampling_topp: float + + def __init__(self, tgt_dict, sampling_topk=-1, sampling_topp=-1.0): + super().__init__(tgt_dict) + self.sampling_topk = sampling_topk + self.sampling_topp = sampling_topp + + def _sample_topp(self, lprobs): + """Sample among the smallest set of elements whose cumulative probability mass exceeds p. + + See `"The Curious Case of Neural Text Degeneration" + (Holtzman et al., 2019) <https://arxiv.org/abs/1904.09751>`_. + + Args: + lprobs: (bsz x input_beam_size x vocab_size) + the model's log-probabilities over the vocabulary at the current step + + Return: A tuple of (trimed_probs, truncated_indices) where: + trimed_probs: (bsz x input_beam_size x ?) + the model's probabilities over the elements selected to sample from. The + width of the third dimension is determined by top-P. + truncated_indices: (bsz x input_beam_size x ?) + the indices of the chosen elements. + """ + probs = lprobs.exp_() + + # sort the last dimension (vocab dimension) in descending order + sorted_probs, sorted_indices = probs.sort(descending=True) + + # compute a mask to indicate the words to be included in the top-P set. + cumsum_probs = sorted_probs.cumsum(dim=2) + mask = cumsum_probs.lt(self.sampling_topp) + + # note that mask was computed by 'lt'. One more word needs to be included + # so that the cumulative probability mass can exceed p. + cumsum_mask = mask.cumsum(dim=2) + last_included = cumsum_mask[:, :, -1:] + last_included.clamp_(0, mask.size()[2] - 1) + mask = mask.scatter_(2, last_included, 1) + + # truncate unnecessary dims. + max_dim = last_included.max() + truncated_mask = mask[:, :, : max_dim + 1] + truncated_probs = sorted_probs[:, :, : max_dim + 1] + truncated_indices = sorted_indices[:, :, : max_dim + 1] + + # trim the words that are not in top-P by setting their probabilities + # to 0, so that they would not be sampled later. + trim_mask = ~truncated_mask + trimed_probs = truncated_probs.masked_fill_(trim_mask, 0) + return trimed_probs, truncated_indices + + @torch.jit.export + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + bsz, beam_size, vocab_size = lprobs.size() + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + lprobs = lprobs[:, ::beam_size, :].contiguous() + + if self.sampling_topp > 0: + # only sample from the smallest set of words whose cumulative probability mass exceeds p + probs, top_indices = self._sample_topp(lprobs) + elif self.sampling_topk > 0: + # only sample from top-k candidates + lprobs, top_indices = lprobs.topk(self.sampling_topk) + probs = lprobs.exp_() + else: + probs = lprobs.exp_() + + # dummy data to be consistent with true branch for type check + top_indices = torch.empty(0).to(probs) + # sample + if step == 0: + indices_buf = torch.multinomial( + probs.view(bsz, -1), + beam_size, + replacement=True, + ).view(bsz, beam_size) + else: + indices_buf = torch.multinomial( + probs.view(bsz * beam_size, -1), + 1, + replacement=True, + ).view(bsz, beam_size) + + if step == 0: + # expand to beam size + probs = probs.expand(bsz, beam_size, -1) + + # gather scores + scores_buf = torch.gather(probs, dim=2, index=indices_buf.unsqueeze(-1)) + scores_buf = scores_buf.log_().view(bsz, -1) + + # remap indices if using top-k or top-P sampling + if self.sampling_topk > 0 or self.sampling_topp > 0: + indices_buf = torch.gather( + top_indices.expand(bsz, beam_size, -1), + dim=2, + index=indices_buf.unsqueeze(-1), + ).squeeze(2) + + if step == 0: + beams_buf = indices_buf.new_zeros(bsz, beam_size) + else: + beams_buf = torch.arange(0, beam_size).to(indices_buf).repeat(bsz, 1) + # make scores cumulative + scores_buf.add_( + torch.gather(scores[:, :, step - 1], dim=1, index=beams_buf) + ) + + return scores_buf, indices_buf, beams_buf + + +class DiverseSiblingsSearch(Search): + """ + Beam search with diverse siblings. + + See "A Simple, Fast Diverse Decoding Algorithm for Neural Generation" for details. + https://arxiv.org/abs/1611.08562 + + 1/ Calculate hypotheses for each beam + 2/ Intra-sibling ordering + 3/ Rewrite scores + 4/ Choose top K hypotheses + + if diversity_rate == 0 is equivalent to BeamSearch + """ + + def __init__(self, tgt_dict, diversity_rate): + super().__init__(tgt_dict) + self.diversity_rate = diversity_rate + self.beam = BeamSearch(tgt_dict) + + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + bsz, beam_size, vocab_size = lprobs.size() + k = min( + # Take the best 2 x beam_size predictions. We'll choose the first + # beam_size of these which don't predict eos to continue with. + beam_size * 2, + lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad + ) + s_list: List[Tensor] + i_list: List[Tensor] + s_list = [torch.empty(0).to(lprobs) for i in range(beam_size)] + i_list = [torch.LongTensor().to(device=lprobs.device) for i in range(beam_size)] + sibling_score = torch.arange(1, k + 1).to(lprobs) * self.diversity_rate + + if step == 0: + return self.beam.step(step, lprobs, scores) + lprobs.add_(scores[:, :, step - 1].unsqueeze(-1)) + + # 1/ Calculate hypotheses for each beam + for i in range(beam_size): + torch.topk(lprobs[:, i, :].view(bsz, -1), k, out=(s_list[i], i_list[i])) + i_list[i].fmod_(vocab_size) + + # 2/ Intra-sibling ordering by default from topk + 3/ Rewrite scores + s_list[i].sub_(sibling_score) + + # 4/ Choose top K hypotheses + indices = torch.stack(i_list, dim=1).view(bsz, -1) + + final_scores = torch.empty(0).to(lprobs) + final_indices = torch.LongTensor().to(device=lprobs.device) + final_beams = torch.LongTensor().to(device=lprobs.device) + (final_scores, final_indices) = torch.topk( + torch.stack(s_list, dim=1).view(bsz, -1), + k, + ) + + final_beams = final_indices // k + + for i in range(bsz): + final_indices[i] = indices[i][final_indices[i]] + + return final_scores, final_indices, final_beams diff --git a/fairseq/fairseq/sequence_generator.py b/fairseq/fairseq/sequence_generator.py new file mode 100644 index 0000000..78db504 --- /dev/null +++ b/fairseq/fairseq/sequence_generator.py @@ -0,0 +1,1020 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import sys +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from fairseq import search, utils +from fairseq.data import data_utils +from fairseq.models import FairseqIncrementalDecoder +from fairseq.ngram_repeat_block import NGramRepeatBlock + + +class SequenceGenerator(nn.Module): + def __init__( + self, + models, + tgt_dict, + beam_size=1, + max_len_a=0, + max_len_b=200, + max_len=0, + min_len=1, + normalize_scores=True, + len_penalty=1.0, + unk_penalty=0.0, + temperature=1.0, + match_source_len=False, + no_repeat_ngram_size=0, + search_strategy=None, + eos=None, + symbols_to_strip_from_output=None, + lm_model=None, + lm_weight=1.0, + tokens_to_suppress=(), + ): + """Generates translations of a given source sentence. + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models, + currently support fairseq.models.TransformerModel for scripting + beam_size (int, optional): beam width (default: 1) + max_len_a/b (int, optional): generate sequences of maximum length + ax + b, where x is the source length + max_len (int, optional): the maximum length of the generated output + (not including end-of-sentence) + min_len (int, optional): the minimum length of the generated output + (not including end-of-sentence) + normalize_scores (bool, optional): normalize scores by the length + of the output (default: True) + len_penalty (float, optional): length penalty, where <1.0 favors + shorter, >1.0 favors longer sentences (default: 1.0) + unk_penalty (float, optional): unknown word penalty, where <0 + produces more unks, >0 produces fewer (default: 0.0) + temperature (float, optional): temperature, where values + >1.0 produce more uniform samples and values <1.0 produce + sharper samples (default: 1.0) + match_source_len (bool, optional): outputs should match the source + length (default: False) + """ + super().__init__() + if isinstance(models, EnsembleModel): + self.model = models + else: + self.model = EnsembleModel(models) + self.tgt_dict = tgt_dict + self.pad = tgt_dict.pad() + self.unk = tgt_dict.unk() + self.eos = tgt_dict.eos() if eos is None else eos + self.symbols_to_strip_from_output = ( + symbols_to_strip_from_output.union({self.eos}) + if symbols_to_strip_from_output is not None + else {self.eos} + ) + + self.token_indices_to_suppress: Optional[Tensor] = None + token_indices_to_suppress = [] + for token_string in tokens_to_suppress: + token_index = tgt_dict.index(token_string) + assert token_index != self.unk + token_indices_to_suppress.append(token_index) + if len(token_indices_to_suppress) > 0: + self.token_indices_to_suppress = torch.Tensor( + token_indices_to_suppress + ).long() + + self.vocab_size = len(tgt_dict) + self.beam_size = beam_size + # the max beam size is the dictionary size - 1, since we never select pad + self.beam_size = min(beam_size, self.vocab_size - 1) + self.model.set_decoder_beam_size(self.beam_size) + self.max_len_a = max_len_a + self.max_len_b = max_len_b + self.min_len = min_len + self.max_len = max_len or self.model.max_decoder_positions() + + self.normalize_scores = normalize_scores + self.len_penalty = len_penalty + self.unk_penalty = unk_penalty + self.temperature = temperature + self.match_source_len = match_source_len + + if no_repeat_ngram_size > 0: + self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size) + else: + self.repeat_ngram_blocker = None + + assert temperature > 0, "--temperature must be greater than 0" + + self.search = ( + search.BeamSearch(tgt_dict) if search_strategy is None else search_strategy + ) + # We only need to set src_lengths in LengthConstrainedBeamSearch. + # As a module attribute, setting it would break in multithread + # settings when the model is shared. + self.should_set_src_lengths = ( + hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths + ) + + self.model.eval() + + self.lm_model = lm_model + self.lm_weight = lm_weight + if self.lm_model is not None: + self.lm_model.eval() + + def cuda(self): + self.model.cuda() + return self + + @torch.no_grad() + def forward( + self, + sample: Dict[str, Dict[str, Tensor]], + prefix_tokens: Optional[Tensor] = None, + bos_token: Optional[int] = None, + ): + """Generate a batch of translations. + + Args: + sample (dict): batch + prefix_tokens (torch.LongTensor, optional): force decoder to begin + with these tokens + bos_token (int, optional): beginning of sentence token + (default: self.eos) + """ + return self._generate(sample, prefix_tokens, bos_token=bos_token) + + # TODO(myleott): unused, deprecate after pytorch-translate migration + def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None): + """Iterate over a batched dataset and yield individual translations. + Args: + cuda (bool, optional): use GPU for generation + timer (StopwatchMeter, optional): time generations + """ + for sample in data_itr: + s = utils.move_to_cuda(sample) if cuda else sample + if "net_input" not in s: + continue + input = s["net_input"] + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in input.items() if k != "prev_output_tokens" + } + if timer is not None: + timer.start() + with torch.no_grad(): + hypos = self.generate(encoder_input) + if timer is not None: + timer.stop(sum(len(h[0]["tokens"]) for h in hypos)) + for i, id in enumerate(s["id"].data): + # remove padding + src = utils.strip_pad(input["src_tokens"].data[i, :], self.pad) + ref = ( + utils.strip_pad(s["target"].data[i, :], self.pad) + if s["target"] is not None + else None + ) + yield id, src, ref, hypos[i] + + @torch.no_grad() + def generate( + self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs + ) -> List[List[Dict[str, Tensor]]]: + """Generate translations. Match the api of other fairseq generators. + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + sample (dict): batch + prefix_tokens (torch.LongTensor, optional): force decoder to begin + with these tokens + constraints (torch.LongTensor, optional): force decoder to include + the list of constraints + bos_token (int, optional): beginning of sentence token + (default: self.eos) + """ + return self._generate(sample, **kwargs) + + def _generate( + self, + sample: Dict[str, Dict[str, Tensor]], + prefix_tokens: Optional[Tensor] = None, + constraints: Optional[Tensor] = None, + bos_token: Optional[int] = None, + ): + incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(self.model.models_size) + ], + ) + net_input = sample["net_input"] + + if "src_tokens" in net_input: + src_tokens = net_input["src_tokens"] + # length of the source text being the character length except EndOfSentence and pad + # if src_lengths exists in net_input (speech_to_text dataset case), then use it + if "src_lengths" in net_input: + src_lengths = net_input["src_lengths"] + else: + src_lengths = ( + (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)) + .long() + .sum(dim=1) + ) + elif "source" in net_input: + src_tokens = net_input["source"] + src_lengths = ( + net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) + if net_input["padding_mask"] is not None + else torch.tensor(src_tokens.size(-1)).to(src_tokens) + ) + elif "features" in net_input: + src_tokens = net_input["features"] + src_lengths = ( + net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) + if net_input["padding_mask"] is not None + else torch.tensor(src_tokens.size(-1)).to(src_tokens) + ) + else: + raise Exception( + "expected src_tokens or source in net input. input keys: " + + str(net_input.keys()) + ) + + # bsz: total number of sentences in beam + # Note that src_tokens may have more than 2 dimensions (i.e. audio features) + bsz, src_len = src_tokens.size()[:2] + beam_size = self.beam_size + + if constraints is not None and not self.search.supports_constraints: + raise NotImplementedError( + "Target-side constraints were provided, but search method doesn't support them" + ) + + # Initialize constraints, when active + self.search.init_constraints(constraints, beam_size) + + max_len: int = -1 + if self.match_source_len: + max_len = src_lengths.max().item() + else: + max_len = min( + int(self.max_len_a * src_len + self.max_len_b), + self.max_len - 1, + ) + assert ( + self.min_len <= max_len + ), "min_len cannot be larger than max_len, please adjust these!" + # compute the encoder output for each beam + with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"): + encoder_outs = self.model.forward_encoder(net_input) + + # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores + new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) + new_order = new_order.to(src_tokens.device).long() + encoder_outs = self.model.reorder_encoder_out(encoder_outs, new_order) + # ensure encoder_outs is a List. + assert encoder_outs is not None + + # initialize buffers + scores = ( + torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() + ) # +1 for eos; pad is never chosen for scoring + tokens = ( + torch.zeros(bsz * beam_size, max_len + 2) + .to(src_tokens) + .long() + .fill_(self.pad) + ) # +2 for eos and pad + tokens[:, 0] = self.eos if bos_token is None else bos_token + attn: Optional[Tensor] = None + + # A list that indicates candidates that should be ignored. + # For example, suppose we're sampling and have already finalized 2/5 + # samples. Then cands_to_ignore would mark 2 positions as being ignored, + # so that we only finalize the remaining 3 samples. + cands_to_ignore = ( + torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) + ) # forward and backward-compatible False mask + + # list of completed sentences + finalized = torch.jit.annotate( + List[List[Dict[str, Tensor]]], + [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], + ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step + + # a boolean array indicating if the sentence at the index is finished or not + finished = [False for i in range(bsz)] + num_remaining_sent = bsz # number of sentences remaining + + # number of candidate hypos per step + cand_size = 2 * beam_size # 2 x beam size in case half are EOS + + # offset arrays for converting between different indexing schemes + bbsz_offsets = ( + (torch.arange(0, bsz) * beam_size) + .unsqueeze(1) + .type_as(tokens) + .to(src_tokens.device) + ) + cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) + + reorder_state: Optional[Tensor] = None + batch_idxs: Optional[Tensor] = None + + original_batch_idxs: Optional[Tensor] = None + if "id" in sample and isinstance(sample["id"], Tensor): + original_batch_idxs = sample["id"] + else: + original_batch_idxs = torch.arange(0, bsz).type_as(tokens) + + for step in range(max_len + 1): # one extra step for EOS marker + # reorder decoder internal states based on the prev choice of beams + if reorder_state is not None: + if batch_idxs is not None: + # update beam indices to take into account removed sentences + corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( + batch_idxs + ) + reorder_state.view(-1, beam_size).add_( + corr.unsqueeze(-1) * beam_size + ) + original_batch_idxs = original_batch_idxs[batch_idxs] + self.model.reorder_incremental_state(incremental_states, reorder_state) + encoder_outs = self.model.reorder_encoder_out( + encoder_outs, reorder_state + ) + with torch.autograd.profiler.record_function( + "EnsembleModel: forward_decoder" + ): + lprobs, avg_attn_scores = self.model.forward_decoder( + tokens[:, : step + 1], + encoder_outs, + incremental_states, + self.temperature, + ) + + if self.lm_model is not None: + lm_out = self.lm_model(tokens[:, : step + 1]) + probs = self.lm_model.get_normalized_probs( + lm_out, log_probs=True, sample=None + ) + probs = probs[:, -1, :] * self.lm_weight + lprobs += probs + + lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) + + lprobs[:, self.pad] = -math.inf # never select pad + lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty + + # handle max length constraint + if step >= max_len: + lprobs[:, : self.eos] = -math.inf + lprobs[:, self.eos + 1 :] = -math.inf + + # handle prefix tokens (possibly with different lengths) + if ( + prefix_tokens is not None + and step < prefix_tokens.size(1) + and step < max_len + ): + lprobs, tokens, scores = self._prefix_tokens( + step, lprobs, scores, tokens, prefix_tokens, beam_size + ) + else: + if step < self.min_len: + # minimum length constraint (does not apply if using prefix_tokens) + lprobs[:, self.eos] = -math.inf + + if self.token_indices_to_suppress is not None: + lprobs[:, self.token_indices_to_suppress] = -math.inf + + # Record attention scores, only support avg_attn_scores is a Tensor + if avg_attn_scores is not None: + if attn is None: + attn = torch.empty( + bsz * beam_size, avg_attn_scores.size(1), max_len + 2 + ).to(scores) + attn[:, :, step + 1].copy_(avg_attn_scores) + + scores = scores.type_as(lprobs) + eos_bbsz_idx = torch.empty(0).to( + tokens + ) # indices of hypothesis ending with eos (finished sentences) + eos_scores = torch.empty(0).to( + scores + ) # scores of hypothesis ending with eos (finished sentences) + + if self.should_set_src_lengths: + self.search.set_src_lengths(src_lengths) + + if self.repeat_ngram_blocker is not None: + lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step) + + # Shape: (batch, cand_size) + cand_scores, cand_indices, cand_beams = self.search.step( + step, + lprobs.view(bsz, -1, self.vocab_size), + scores.view(bsz, beam_size, -1)[:, :, :step], + tokens[:, : step + 1], + original_batch_idxs, + ) + + # cand_bbsz_idx contains beam indices for the top candidate + # hypotheses, with a range of values: [0, bsz*beam_size), + # and dimensions: [bsz, cand_size] + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + + # finalize hypotheses that end in eos + # Shape of eos_mask: (batch size, beam size) + eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) + eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) + + # only consider eos when it's among the top beam_size indices + # Now we know what beam item(s) to finish + # Shape: 1d list of absolute-numbered + eos_bbsz_idx = torch.masked_select( + cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + + finalized_sents: List[int] = [] + if eos_bbsz_idx.numel() > 0: + eos_scores = torch.masked_select( + cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + + finalized_sents = self.finalize_hypos( + step, + eos_bbsz_idx, + eos_scores, + tokens, + scores, + finalized, + finished, + beam_size, + attn, + src_lengths, + max_len, + ) + num_remaining_sent -= len(finalized_sents) + + assert num_remaining_sent >= 0 + if num_remaining_sent == 0: + break + if self.search.stop_on_max_len and step >= max_len: + break + assert step < max_len, f"{step} < {max_len}" + + # Remove finalized sentences (ones for which {beam_size} + # finished hypotheses have been generated) from the batch. + if len(finalized_sents) > 0: + new_bsz = bsz - len(finalized_sents) + + # construct batch_idxs which holds indices of batches to keep for the next pass + batch_mask = torch.ones( + bsz, dtype=torch.bool, device=cand_indices.device + ) + batch_mask[finalized_sents] = False + # TODO replace `nonzero(as_tuple=False)` after TorchScript supports it + batch_idxs = torch.arange( + bsz, device=cand_indices.device + ).masked_select(batch_mask) + + # Choose the subset of the hypothesized constraints that will continue + self.search.prune_sentences(batch_idxs) + + eos_mask = eos_mask[batch_idxs] + cand_beams = cand_beams[batch_idxs] + bbsz_offsets.resize_(new_bsz, 1) + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + cand_scores = cand_scores[batch_idxs] + cand_indices = cand_indices[batch_idxs] + + if prefix_tokens is not None: + prefix_tokens = prefix_tokens[batch_idxs] + src_lengths = src_lengths[batch_idxs] + cands_to_ignore = cands_to_ignore[batch_idxs] + + scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + if attn is not None: + attn = attn.view(bsz, -1)[batch_idxs].view( + new_bsz * beam_size, attn.size(1), -1 + ) + bsz = new_bsz + else: + batch_idxs = None + + # Set active_mask so that values > cand_size indicate eos hypos + # and values < cand_size indicate candidate active hypos. + # After, the min values per row are the top candidate active hypos + + # Rewrite the operator since the element wise or is not supported in torchscript. + + eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) + active_mask = torch.add( + eos_mask.type_as(cand_offsets) * cand_size, + cand_offsets[: eos_mask.size(1)], + ) + + # get the top beam_size active hypotheses, which are just + # the hypos with the smallest values in active_mask. + # {active_hypos} indicates which {beam_size} hypotheses + # from the list of {2 * beam_size} candidates were + # selected. Shapes: (batch size, beam size) + new_cands_to_ignore, active_hypos = torch.topk( + active_mask, k=beam_size, dim=1, largest=False + ) + + # update cands_to_ignore to ignore any finalized hypos. + cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] + # Make sure there is at least one active item for each sentence in the batch. + assert (~cands_to_ignore).any(dim=1).all() + + # update cands_to_ignore to ignore any finalized hypos + + # {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam + # can be selected more than once). + active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) + active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) + + active_bbsz_idx = active_bbsz_idx.view(-1) + active_scores = active_scores.view(-1) + + # copy tokens and scores for active hypotheses + + # Set the tokens for each beam (can select the same row more than once) + tokens[:, : step + 1] = torch.index_select( + tokens[:, : step + 1], dim=0, index=active_bbsz_idx + ) + # Select the next token for each of them + tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( + cand_indices, dim=1, index=active_hypos + ) + if step > 0: + scores[:, :step] = torch.index_select( + scores[:, :step], dim=0, index=active_bbsz_idx + ) + scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( + cand_scores, dim=1, index=active_hypos + ) + + # Update constraints based on which candidates were selected for the next beam + self.search.update_constraints(active_hypos) + + # copy attention for active hypotheses + if attn is not None: + attn[:, :, : step + 2] = torch.index_select( + attn[:, :, : step + 2], dim=0, index=active_bbsz_idx + ) + + # reorder incremental state in decoder + reorder_state = active_bbsz_idx + + # sort by score descending + for sent in range(len(finalized)): + scores = torch.tensor( + [float(elem["score"].item()) for elem in finalized[sent]] + ) + _, sorted_scores_indices = torch.sort(scores, descending=True) + finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] + finalized[sent] = torch.jit.annotate( + List[Dict[str, Tensor]], finalized[sent] + ) + return finalized + + def _prefix_tokens( + self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int + ): + """Handle prefix tokens""" + prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) + prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + prefix_mask = prefix_toks.ne(self.pad) + lprobs[prefix_mask] = torch.tensor(-math.inf).to(lprobs) + lprobs[prefix_mask] = lprobs[prefix_mask].scatter( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] + ) + # if prefix includes eos, then we should make sure tokens and + # scores are the same across all beams + eos_mask = prefix_toks.eq(self.eos) + if eos_mask.any(): + # validate that the first beam matches the prefix + first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[ + :, 0, 1 : step + 1 + ] + eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] + target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] + assert (first_beam == target_prefix).all() + + # copy tokens, scores and lprobs from the first beam to all beams + tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size) + scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size) + lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size) + return lprobs, tokens, scores + + def replicate_first_beam(self, tensor, mask, beam_size: int): + tensor = tensor.view(-1, beam_size, tensor.size(-1)) + tensor[mask] = tensor[mask][:, :1, :] + return tensor.view(-1, tensor.size(-1)) + + def finalize_hypos( + self, + step: int, + bbsz_idx, + eos_scores, + tokens, + scores, + finalized: List[List[Dict[str, Tensor]]], + finished: List[bool], + beam_size: int, + attn: Optional[Tensor], + src_lengths, + max_len: int, + ): + """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. + A sentence is finalized when {beam_size} finished items have been collected for it. + + Returns number of sentences (not beam items) being finalized. + These will be removed from the batch and not processed further. + Args: + bbsz_idx (Tensor): + """ + assert bbsz_idx.numel() == eos_scores.numel() + + # clone relevant token and attention tensors. + # tokens is (batch * beam, max_len). So the index_select + # gets the newly EOS rows, then selects cols 1..{step + 2} + tokens_clone = tokens.index_select(0, bbsz_idx)[ + :, 1 : step + 2 + ] # skip the first index, which is EOS + + tokens_clone[:, step] = self.eos + attn_clone = ( + attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] + if attn is not None + else None + ) + + # compute scores per token position + pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] + pos_scores[:, step] = eos_scores + # convert from cumulative to per-position scores + pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] + + # normalize sentence-level scores + if self.normalize_scores: + eos_scores /= (step + 1) ** self.len_penalty + + # cum_unfin records which sentences in the batch are finished. + # It helps match indexing between (a) the original sentences + # in the batch and (b) the current, possibly-reduced set of + # sentences. + cum_unfin: List[int] = [] + prev = 0 + for f in finished: + if f: + prev += 1 + else: + cum_unfin.append(prev) + cum_fin_tensor = torch.tensor(cum_unfin, dtype=torch.int).to(bbsz_idx) + + unfin_idx = torch.div(bbsz_idx, beam_size, rounding_mode="trunc") + sent = unfin_idx + torch.index_select(cum_fin_tensor, 0, unfin_idx) + + # Create a set of "{sent}{unfin_idx}", where + # "unfin_idx" is the index in the current (possibly reduced) + # list of sentences, and "sent" is the index in the original, + # unreduced batch + # For every finished beam item + # sentence index in the current (possibly reduced) batch + seen = (sent << 32) + unfin_idx + unique_seen: List[int] = torch.unique(seen).tolist() + + if self.match_source_len: + condition = step > torch.index_select(src_lengths, 0, unfin_idx) + eos_scores = torch.where(condition, torch.tensor(-math.inf), eos_scores) + sent_list: List[int] = sent.tolist() + for i in range(bbsz_idx.size()[0]): + # An input sentence (among those in a batch) is finished when + # beam_size hypotheses have been collected for it + if len(finalized[sent_list[i]]) < beam_size: + if attn_clone is not None: + # remove padding tokens from attn scores + hypo_attn = attn_clone[i] + else: + hypo_attn = torch.empty(0) + + finalized[sent_list[i]].append( + { + "tokens": tokens_clone[i], + "score": eos_scores[i], + "attention": hypo_attn, # src_len x tgt_len + "alignment": torch.empty(0), + "positional_scores": pos_scores[i], + } + ) + + newly_finished: List[int] = [] + for unique_s in unique_seen: + # check termination conditions for this sentence + unique_sent: int = unique_s >> 32 + unique_unfin_idx: int = unique_s - (unique_sent << 32) + + if not finished[unique_sent] and self.is_finished( + step, unique_unfin_idx, max_len, len(finalized[unique_sent]), beam_size + ): + finished[unique_sent] = True + newly_finished.append(unique_unfin_idx) + + return newly_finished + + def is_finished( + self, + step: int, + unfin_idx: int, + max_len: int, + finalized_sent_len: int, + beam_size: int, + ): + """ + Check whether decoding for a sentence is finished, which + occurs when the list of finalized sentences has reached the + beam size, or when we reach the maximum length. + """ + assert finalized_sent_len <= beam_size + if finalized_sent_len == beam_size or step == max_len: + return True + return False + + +class EnsembleModel(nn.Module): + """A wrapper around an ensemble of models.""" + + def __init__(self, models): + super().__init__() + self.models_size = len(models) + # method '__len__' is not supported in ModuleList for torch script + self.single_model = models[0] + self.models = nn.ModuleList(models) + + self.has_incremental: bool = False + if all( + hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) + for m in models + ): + self.has_incremental = True + + def forward(self): + pass + + def has_encoder(self): + return hasattr(self.single_model, "encoder") + + def has_incremental_states(self): + return self.has_incremental + + def max_decoder_positions(self): + return min( + [ + m.max_decoder_positions() + for m in self.models + if hasattr(m, "max_decoder_positions") + ] + + [sys.maxsize] + ) + + def set_decoder_beam_size(self, beam_size): + """Set beam size for efficient beamable enc-dec attention.""" + if beam_size > 1: + for model in self.models: + if hasattr(model, "set_beam_size"): + model.set_beam_size(beam_size) + + @torch.jit.export + def forward_encoder(self, net_input: Dict[str, Tensor]): + if not self.has_encoder(): + return None + return [model.encoder.forward_torchscript(net_input) for model in self.models] + + @torch.jit.export + def forward_decoder( + self, + tokens, + encoder_outs: List[Dict[str, List[Tensor]]], + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + temperature: float = 1.0, + ): + log_probs = [] + avg_attn: Optional[Tensor] = None + encoder_out: Optional[Dict[str, List[Tensor]]] = None + for i, model in enumerate(self.models): + if self.has_encoder(): + encoder_out = encoder_outs[i] + # decode each model + if self.has_incremental_states(): + decoder_out = model.decoder.forward( + tokens, + encoder_out=encoder_out, + incremental_state=incremental_states[i], + ) + else: + if hasattr(model, "decoder"): + decoder_out = model.decoder.forward(tokens, encoder_out=encoder_out) + else: + decoder_out = model.forward(tokens) + + attn: Optional[Tensor] = None + decoder_len = len(decoder_out) + if decoder_len > 1 and decoder_out[1] is not None: + if isinstance(decoder_out[1], Tensor): + attn = decoder_out[1] + else: + attn_holder = decoder_out[1]["attn"] + if isinstance(attn_holder, Tensor): + attn = attn_holder + elif attn_holder is not None: + attn = attn_holder[0] + if attn is not None: + attn = attn[:, -1, :] + + decoder_out_tuple = ( + decoder_out[0][:, -1:, :].div_(temperature), + None if decoder_len <= 1 else decoder_out[1], + ) + probs = model.get_normalized_probs( + decoder_out_tuple, log_probs=True, sample=None + ) + probs = probs[:, -1, :] + if self.models_size == 1: + return probs, attn + + log_probs.append(probs) + if attn is not None: + if avg_attn is None: + avg_attn = attn + else: + avg_attn.add_(attn) + + avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( + self.models_size + ) + + if avg_attn is not None: + avg_attn.div_(self.models_size) + return avg_probs, avg_attn + + @torch.jit.export + def reorder_encoder_out( + self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order + ): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + new_outs: List[Dict[str, List[Tensor]]] = [] + if not self.has_encoder(): + return new_outs + for i, model in enumerate(self.models): + assert encoder_outs is not None + new_outs.append( + model.encoder.reorder_encoder_out(encoder_outs[i], new_order) + ) + return new_outs + + @torch.jit.export + def reorder_incremental_state( + self, + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + new_order, + ): + if not self.has_incremental_states(): + return + for i, model in enumerate(self.models): + model.decoder.reorder_incremental_state_scripting( + incremental_states[i], new_order + ) + + +class SequenceGeneratorWithAlignment(SequenceGenerator): + def __init__( + self, models, tgt_dict, left_pad_target=False, print_alignment="hard", **kwargs + ): + """Generates translations of a given source sentence. + + Produces alignments following "Jointly Learning to Align and + Translate with Transformer Models" (Garg et al., EMNLP 2019). + + Args: + left_pad_target (bool, optional): Whether or not the + hypothesis should be left padded or not when they are + teacher forced for generating alignments. + """ + super().__init__(EnsembleModelWithAlignment(models), tgt_dict, **kwargs) + self.left_pad_target = left_pad_target + + if print_alignment == "hard": + self.extract_alignment = utils.extract_hard_alignment + elif print_alignment == "soft": + self.extract_alignment = utils.extract_soft_alignment + + @torch.no_grad() + def generate(self, models, sample, **kwargs): + finalized = super()._generate(sample, **kwargs) + + src_tokens = sample["net_input"]["src_tokens"] + bsz = src_tokens.shape[0] + beam_size = self.beam_size + ( + src_tokens, + src_lengths, + prev_output_tokens, + tgt_tokens, + ) = self._prepare_batch_for_alignment(sample, finalized) + if any(getattr(m, "full_context_alignment", False) for m in self.model.models): + attn = self.model.forward_align(src_tokens, src_lengths, prev_output_tokens) + else: + attn = [ + finalized[i // beam_size][i % beam_size]["attention"].transpose(1, 0) + for i in range(bsz * beam_size) + ] + + if src_tokens.device != "cpu": + src_tokens = src_tokens.to("cpu") + tgt_tokens = tgt_tokens.to("cpu") + attn = [i.to("cpu") for i in attn] + + # Process the attn matrix to extract hard alignments. + for i in range(bsz * beam_size): + alignment = self.extract_alignment( + attn[i], src_tokens[i], tgt_tokens[i], self.pad, self.eos + ) + finalized[i // beam_size][i % beam_size]["alignment"] = alignment + return finalized + + def _prepare_batch_for_alignment(self, sample, hypothesis): + src_tokens = sample["net_input"]["src_tokens"] + bsz = src_tokens.shape[0] + src_tokens = ( + src_tokens[:, None, :] + .expand(-1, self.beam_size, -1) + .contiguous() + .view(bsz * self.beam_size, -1) + ) + src_lengths = sample["net_input"]["src_lengths"] + src_lengths = ( + src_lengths[:, None] + .expand(-1, self.beam_size) + .contiguous() + .view(bsz * self.beam_size) + ) + prev_output_tokens = data_utils.collate_tokens( + [beam["tokens"] for example in hypothesis for beam in example], + self.pad, + self.eos, + self.left_pad_target, + move_eos_to_beginning=True, + ) + tgt_tokens = data_utils.collate_tokens( + [beam["tokens"] for example in hypothesis for beam in example], + self.pad, + self.eos, + self.left_pad_target, + move_eos_to_beginning=False, + ) + return src_tokens, src_lengths, prev_output_tokens, tgt_tokens + + +class EnsembleModelWithAlignment(EnsembleModel): + """A wrapper around an ensemble of models.""" + + def __init__(self, models): + super().__init__(models) + + def forward_align(self, src_tokens, src_lengths, prev_output_tokens): + avg_attn = None + for model in self.models: + decoder_out = model(src_tokens, src_lengths, prev_output_tokens) + attn = decoder_out[1]["attn"][0] + if avg_attn is None: + avg_attn = attn + else: + avg_attn.add_(attn) + if len(self.models) > 1: + avg_attn.div_(len(self.models)) + return avg_attn diff --git a/fairseq/fairseq/sequence_scorer.py b/fairseq/fairseq/sequence_scorer.py new file mode 100644 index 0000000..411d4df --- /dev/null +++ b/fairseq/fairseq/sequence_scorer.py @@ -0,0 +1,153 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +import torch +from fairseq import utils + + +class SequenceScorer(object): + """Scores the target for a given source sentence.""" + + def __init__( + self, + tgt_dict, + softmax_batch=None, + compute_alignment=False, + eos=None, + symbols_to_strip_from_output=None, + ): + self.pad = tgt_dict.pad() + self.eos = tgt_dict.eos() if eos is None else eos + self.softmax_batch = softmax_batch or sys.maxsize + assert self.softmax_batch > 0 + self.compute_alignment = compute_alignment + self.symbols_to_strip_from_output = ( + symbols_to_strip_from_output.union({self.eos}) + if symbols_to_strip_from_output is not None + else {self.eos} + ) + + @torch.no_grad() + def generate(self, models, sample, **kwargs): + """Score a batch of translations.""" + net_input = sample["net_input"] + + def batch_for_softmax(dec_out, target): + # assumes decoder_out[0] is the only thing needed (may not be correct for future models!) + first, rest = dec_out[0], dec_out[1:] + bsz, tsz, dim = first.shape + if bsz * tsz < self.softmax_batch: + yield dec_out, target, True + else: + flat = first.contiguous().view(1, -1, dim) + flat_tgt = target.contiguous().view(flat.shape[:-1]) + s = 0 + while s < flat.size(1): + e = s + self.softmax_batch + yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False + s = e + + def gather_target_probs(probs, target): + probs = probs.gather( + dim=2, + index=target.unsqueeze(-1), + ) + return probs + + orig_target = sample["target"] + + # compute scores for each model in the ensemble + avg_probs = None + avg_attn = None + for model in models: + model.eval() + decoder_out = model(**net_input) + attn = decoder_out[1] if len(decoder_out) > 1 else None + if type(attn) is dict: + attn = attn.get("attn", None) + + batched = batch_for_softmax(decoder_out, orig_target) + probs, idx = None, 0 + for bd, tgt, is_single in batched: + sample["target"] = tgt + curr_prob = model.get_normalized_probs( + bd, log_probs=len(models) == 1, sample=sample + ).data + if is_single: + probs = gather_target_probs(curr_prob, orig_target) + else: + if probs is None: + probs = curr_prob.new(orig_target.numel()) + step = curr_prob.size(0) * curr_prob.size(1) + end = step + idx + tgt_probs = gather_target_probs( + curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt + ) + probs[idx:end] = tgt_probs.view(-1) + idx = end + sample["target"] = orig_target + + probs = probs.view(sample["target"].shape) + + if avg_probs is None: + avg_probs = probs + else: + avg_probs.add_(probs) + if attn is not None: + if torch.is_tensor(attn): + attn = attn.data + else: + attn = attn[0] + if avg_attn is None: + avg_attn = attn + else: + avg_attn.add_(attn) + if len(models) > 1: + avg_probs.div_(len(models)) + avg_probs.log_() + if avg_attn is not None: + avg_attn.div_(len(models)) + + bsz = avg_probs.size(0) + hypos = [] + start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz + for i in range(bsz): + # remove padding from ref + ref = ( + utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad) + if sample["target"] is not None + else None + ) + tgt_len = ref.numel() + avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len] + score_i = avg_probs_i.sum() / tgt_len + if avg_attn is not None: + avg_attn_i = avg_attn[i] + if self.compute_alignment: + alignment = utils.extract_hard_alignment( + avg_attn_i, + sample["net_input"]["src_tokens"][i], + sample["target"][i], + self.pad, + self.eos, + ) + else: + alignment = None + else: + avg_attn_i = alignment = None + hypos.append( + [ + { + "tokens": ref, + "score": score_i, + "attention": avg_attn_i, + "alignment": alignment, + "positional_scores": avg_probs_i, + } + ] + ) + return hypos diff --git a/fairseq/fairseq/speech_generator.py b/fairseq/fairseq/speech_generator.py new file mode 100644 index 0000000..f2cc8b5 --- /dev/null +++ b/fairseq/fairseq/speech_generator.py @@ -0,0 +1,427 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch + +from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig + + +class SpeechGenerator(object): + def __init__(self, model, vocoder, data_cfg: S2TDataConfig): + self.model = model + self.vocoder = vocoder + stats_npz_path = data_cfg.global_cmvn_stats_npz + self.gcmvn_stats = None + if stats_npz_path is not None: + self.gcmvn_stats = np.load(stats_npz_path) + + def gcmvn_denormalize(self, x): + # x: B x T x C + if self.gcmvn_stats is None: + return x + mean = torch.from_numpy(self.gcmvn_stats["mean"]).to(x) + std = torch.from_numpy(self.gcmvn_stats["std"]).to(x) + assert len(x.shape) == 3 and mean.shape[0] == std.shape[0] == x.shape[2] + x = x * std.view(1, 1, -1).expand_as(x) + return x + mean.view(1, 1, -1).expand_as(x) + + def get_waveform(self, feat): + # T x C -> T + return None if self.vocoder is None else self.vocoder(feat).squeeze(0) + + +class AutoRegressiveSpeechGenerator(SpeechGenerator): + def __init__( + self, + model, + vocoder, + data_cfg, + max_iter: int = 6000, + eos_prob_threshold: float = 0.5, + ): + super().__init__(model, vocoder, data_cfg) + self.max_iter = max_iter + self.eos_prob_threshold = eos_prob_threshold + + @torch.no_grad() + def generate(self, model, sample, has_targ=False, **kwargs): + model.eval() + + src_tokens = sample["net_input"]["src_tokens"] + src_lengths = sample["net_input"]["src_lengths"] + bsz, src_len = src_tokens.size()[:2] + n_frames_per_step = model.decoder.n_frames_per_step + out_dim = model.decoder.out_dim + raw_dim = out_dim // n_frames_per_step + + # initialize + encoder_out = model.forward_encoder( + src_tokens, src_lengths, speaker=sample["speaker"] + ) + incremental_state = {} + feat, attn, eos_prob = [], [], [] + finished = src_tokens.new_zeros((bsz,)).bool() + out_lens = src_lengths.new_zeros((bsz,)).long().fill_(self.max_iter) + + prev_feat_out = encoder_out["encoder_out"][0].new_zeros(bsz, 1, out_dim) + for step in range(self.max_iter): + cur_out_lens = out_lens.clone() + cur_out_lens.masked_fill_(cur_out_lens.eq(self.max_iter), step + 1) + _, cur_eos_out, cur_extra = model.forward_decoder( + prev_feat_out, + encoder_out=encoder_out, + incremental_state=incremental_state, + target_lengths=cur_out_lens, + speaker=sample["speaker"], + **kwargs, + ) + cur_eos_prob = torch.sigmoid(cur_eos_out).squeeze(2) + feat.append(cur_extra["feature_out"]) + attn.append(cur_extra["attn"]) + eos_prob.append(cur_eos_prob) + + cur_finished = cur_eos_prob.squeeze(1) > self.eos_prob_threshold + out_lens.masked_fill_((~finished) & cur_finished, step + 1) + finished = finished | cur_finished + if finished.sum().item() == bsz: + break + prev_feat_out = cur_extra["feature_out"] + + feat = torch.cat(feat, dim=1) + feat = model.decoder.postnet(feat) + feat + eos_prob = torch.cat(eos_prob, dim=1) + attn = torch.cat(attn, dim=2) + alignment = attn.max(dim=1)[1] + + feat = feat.reshape(bsz, -1, raw_dim) + feat = self.gcmvn_denormalize(feat) + + eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1) + attn = attn.repeat_interleave(n_frames_per_step, dim=2) + alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) + out_lens = out_lens * n_frames_per_step + + finalized = [ + { + "feature": feat[b, :out_len], + "eos_prob": eos_prob[b, :out_len], + "attn": attn[b, :, :out_len], + "alignment": alignment[b, :out_len], + "waveform": self.get_waveform(feat[b, :out_len]), + } + for b, out_len in zip(range(bsz), out_lens) + ] + + if has_targ: + assert sample["target"].size(-1) == out_dim + tgt_feats = sample["target"].view(bsz, -1, raw_dim) + tgt_feats = self.gcmvn_denormalize(tgt_feats) + tgt_lens = sample["target_lengths"] * n_frames_per_step + for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): + finalized[b]["targ_feature"] = f[:l] + finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) + return finalized + + +class MultiDecoderSpeechGenerator(SpeechGenerator): + def __init__( + self, + models, + args, + vocoder, + data_cfg, + tgt_dict_mt, + max_iter: int = 6000, + eos_prob_threshold: float = 0.5, + eos_mt=None, + symbols_to_strip_from_output=None, + ): + super().__init__(models[0], vocoder, data_cfg) + self.max_iter = max_iter + self.eos_prob_threshold = eos_prob_threshold + + self.tgt_dict_mt = tgt_dict_mt + self.eos_mt = eos_mt + + from examples.speech_to_speech.unity.sequence_generator import SequenceGenerator + from fairseq import search + + self.text_generator = SequenceGenerator( + models, + tgt_dict_mt, + beam_size=max(1, getattr(args, "beam", 5)), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 200), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + search_strategy=search.BeamSearch(tgt_dict_mt), + eos=eos_mt, + symbols_to_strip_from_output=symbols_to_strip_from_output, + ) + + @torch.no_grad() + def generate(self, model, sample, has_targ=False, **kwargs): + model.eval() + + src_tokens = sample["net_input"]["src_tokens"] + src_lengths = sample["net_input"]["src_lengths"] + bsz, src_len = src_tokens.size()[:2] + n_frames_per_step = model.decoder.n_frames_per_step + out_dim = model.decoder.out_dim + raw_dim = out_dim // n_frames_per_step + + # initialize + encoder_out = model.forward_encoder( + src_tokens, src_lengths, speaker=sample["speaker"] + ) + + prefix_tokens = None + constraints = None + bos_token = None + + mt_decoder = getattr(model, f"{model.mt_task_name}_decoder") + + # 1. MT decoder + finalized_mt = self.text_generator.generate_decoder( + [encoder_out], + src_tokens, + src_lengths, + sample, + prefix_tokens, + constraints, + bos_token, + aux_task_name=model.mt_task_name, + ) + + # extract decoder output corresponding to the best hypothesis + max_tgt_len = max([len(hypo[0]["tokens"]) for hypo in finalized_mt]) + prev_output_tokens_mt = ( + src_tokens.new_zeros(src_tokens.shape[0], max_tgt_len) + .fill_(mt_decoder.padding_idx) + .int() + ) # B x T + for i, hypo in enumerate(finalized_mt): + i_beam = 0 + tmp = hypo[i_beam]["tokens"].int() # hyp + eos + prev_output_tokens_mt[i, 0] = self.text_generator.eos + if tmp[-1] == self.text_generator.eos: + tmp = tmp[:-1] + prev_output_tokens_mt[i, 1 : len(tmp) + 1] = tmp + + text = "".join([self.tgt_dict_mt[c] for c in tmp]) + text = text.replace("_", " ") + text = text.replace("▁", " ") + text = text.replace("<unk>", " ") + text = text.replace("<s>", "") + text = text.replace("</s>", "") + if len(text) > 0 and text[0] == " ": + text = text[1:] + sample_id = sample["id"].tolist()[i] + print("{} (None-{})".format(text, sample_id)) + + mt_decoder_out = mt_decoder( + prev_output_tokens_mt, + encoder_out=encoder_out, + features_only=True, + ) + x = mt_decoder_out[0].transpose(0, 1) + + mt_decoder_padding_mask = None + if prev_output_tokens_mt.eq(mt_decoder.padding_idx).any(): + mt_decoder_padding_mask = prev_output_tokens_mt.eq(mt_decoder.padding_idx) + + # 2. TTS encoder + if getattr(model, "synthesizer_encoder", None) is not None: + synthesizer_encoder_out = model.synthesizer_encoder( + x, + mt_decoder_padding_mask, + ) + else: + synthesizer_encoder_out = { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [mt_decoder_padding_mask] + if mt_decoder_padding_mask is not None + else [], # B x T + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + # 3. TTS decoder + incremental_state = {} + feat, attn, eos_prob = [], [], [] + finished = src_tokens.new_zeros((bsz,)).bool() + out_lens = src_lengths.new_zeros((bsz,)).long().fill_(self.max_iter) + + prev_feat_out = encoder_out["encoder_out"][0].new_zeros(bsz, 1, out_dim) + for step in range(self.max_iter): + cur_out_lens = out_lens.clone() + cur_out_lens.masked_fill_(cur_out_lens.eq(self.max_iter), step + 1) + _, cur_eos_out, cur_extra = model.forward_decoder( + prev_feat_out, + encoder_out=synthesizer_encoder_out, + incremental_state=incremental_state, + target_lengths=cur_out_lens, + speaker=sample["speaker"], + **kwargs, + ) + cur_eos_prob = torch.sigmoid(cur_eos_out).squeeze(2) + feat.append(cur_extra["feature_out"]) + attn.append(cur_extra["attn"]) + eos_prob.append(cur_eos_prob) + + cur_finished = cur_eos_prob.squeeze(1) > self.eos_prob_threshold + out_lens.masked_fill_((~finished) & cur_finished, step + 1) + finished = finished | cur_finished + if finished.sum().item() == bsz: + break + prev_feat_out = cur_extra["feature_out"] + + feat = torch.cat(feat, dim=1) + feat = model.decoder.postnet(feat) + feat + eos_prob = torch.cat(eos_prob, dim=1) + attn = torch.cat(attn, dim=2) + alignment = attn.max(dim=1)[1] + + feat = feat.reshape(bsz, -1, raw_dim) + feat = self.gcmvn_denormalize(feat) + + eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1) + attn = attn.repeat_interleave(n_frames_per_step, dim=2) + alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) + out_lens = out_lens * n_frames_per_step + + finalized = [ + { + "feature": feat[b, :out_len], + "eos_prob": eos_prob[b, :out_len], + "attn": attn[b, :, :out_len], + "alignment": alignment[b, :out_len], + "waveform": self.get_waveform(feat[b, :out_len]), + } + for b, out_len in zip(range(bsz), out_lens) + ] + + if has_targ: + assert sample["target"].size(-1) == out_dim + tgt_feats = sample["target"].view(bsz, -1, raw_dim) + tgt_feats = self.gcmvn_denormalize(tgt_feats) + tgt_lens = sample["target_lengths"] * n_frames_per_step + for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): + finalized[b]["targ_feature"] = f[:l] + finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) + return finalized + + +class NonAutoregressiveSpeechGenerator(SpeechGenerator): + @torch.no_grad() + def generate(self, model, sample, has_targ=False, **kwargs): + model.eval() + + bsz, max_src_len = sample["net_input"]["src_tokens"].size() + n_frames_per_step = model.encoder.n_frames_per_step + out_dim = model.encoder.out_dim + raw_dim = out_dim // n_frames_per_step + + feat, feat_post, out_lens, log_dur_out, _, _ = model( + src_tokens=sample["net_input"]["src_tokens"], + src_lengths=sample["net_input"]["src_lengths"], + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + incremental_state=None, + target_lengths=sample["target_lengths"], + speaker=sample["speaker"], + ) + if feat_post is not None: + feat = feat_post + + feat = feat.view(bsz, -1, raw_dim) + feat = self.gcmvn_denormalize(feat) + + dur_out = torch.clamp(torch.round(torch.exp(log_dur_out) - 1).long(), min=0) + + def get_dur_plot_data(d): + r = [] + for i, dd in enumerate(d): + r += [i + 1] * dd.item() + return r + + out_lens = out_lens * n_frames_per_step + finalized = [ + { + "feature": feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]), + "waveform": self.get_waveform( + feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]) + ), + "attn": feat.new_tensor(get_dur_plot_data(dur_out[b])), + } + for b, l in zip(range(bsz), out_lens) + ] + + if has_targ: + tgt_feats = sample["target"].view(bsz, -1, raw_dim) + tgt_feats = self.gcmvn_denormalize(tgt_feats) + tgt_lens = sample["target_lengths"] * n_frames_per_step + for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): + finalized[b]["targ_feature"] = f[:l] + finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) + return finalized + + +class TeacherForcingAutoRegressiveSpeechGenerator(AutoRegressiveSpeechGenerator): + @torch.no_grad() + def generate(self, model, sample, has_targ=False, **kwargs): + model.eval() + + src_tokens = sample["net_input"]["src_tokens"] + src_lens = sample["net_input"]["src_lengths"] + prev_out_tokens = sample["net_input"]["prev_output_tokens"] + tgt_lens = sample["target_lengths"] + n_frames_per_step = model.decoder.n_frames_per_step + raw_dim = model.decoder.out_dim // n_frames_per_step + bsz = src_tokens.shape[0] + + feat, eos_prob, extra = model( + src_tokens, + src_lens, + prev_out_tokens, + incremental_state=None, + target_lengths=tgt_lens, + speaker=sample["speaker"], + ) + + attn = extra["attn"] # B x T_s x T_t + alignment = attn.max(dim=1)[1] + feat = feat.reshape(bsz, -1, raw_dim) + feat = self.gcmvn_denormalize(feat) + eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1) + attn = attn.repeat_interleave(n_frames_per_step, dim=2) + alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) + tgt_lens = sample["target_lengths"] * n_frames_per_step + + finalized = [ + { + "feature": feat[b, :tgt_len], + "eos_prob": eos_prob[b, :tgt_len], + "attn": attn[b, :, :tgt_len], + "alignment": alignment[b, :tgt_len], + "waveform": self.get_waveform(feat[b, :tgt_len]), + } + for b, tgt_len in zip(range(bsz), tgt_lens) + ] + + if has_targ: + tgt_feats = sample["target"].view(bsz, -1, raw_dim) + tgt_feats = self.gcmvn_denormalize(tgt_feats) + for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): + finalized[b]["targ_feature"] = f[:l] + finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) + return finalized diff --git a/fairseq/fairseq/tasks/__init__.py b/fairseq/fairseq/tasks/__init__.py new file mode 100644 index 0000000..6da1f00 --- /dev/null +++ b/fairseq/fairseq/tasks/__init__.py @@ -0,0 +1,138 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import argparse +import importlib +import os + +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import merge_with_parent +from hydra.core.config_store import ConfigStore + +from .fairseq_task import FairseqTask, LegacyFairseqTask # noqa + + +# register dataclass +TASK_DATACLASS_REGISTRY = {} +TASK_REGISTRY = {} +TASK_CLASS_NAMES = set() + + +def setup_task(cfg: FairseqDataclass, **kwargs): + task = None + task_name = getattr(cfg, "task", None) + + if isinstance(task_name, str): + # legacy tasks + task = TASK_REGISTRY[task_name] + if task_name in TASK_DATACLASS_REGISTRY: + dc = TASK_DATACLASS_REGISTRY[task_name] + cfg = dc.from_namespace(cfg) + else: + task_name = getattr(cfg, "_name", None) + + if task_name and task_name in TASK_DATACLASS_REGISTRY: + remove_missing = "from_checkpoint" in kwargs and kwargs["from_checkpoint"] + dc = TASK_DATACLASS_REGISTRY[task_name] + cfg = merge_with_parent(dc(), cfg, remove_missing=remove_missing) + task = TASK_REGISTRY[task_name] + + assert ( + task is not None + ), f"Could not infer task type from {cfg}. Available argparse tasks: {TASK_REGISTRY.keys()}. Available hydra tasks: {TASK_DATACLASS_REGISTRY.keys()}" + + return task.setup_task(cfg, **kwargs) + + +def register_task(name, dataclass=None): + """ + New tasks can be added to fairseq with the + :func:`~fairseq.tasks.register_task` function decorator. + + For example:: + + @register_task('classification') + class ClassificationTask(FairseqTask): + (...) + + .. note:: + + All Tasks must implement the :class:`~fairseq.tasks.FairseqTask` + interface. + + Args: + name (str): the name of the task + """ + + def register_task_cls(cls): + if name in TASK_REGISTRY: + return TASK_REGISTRY[name] + + if not issubclass(cls, FairseqTask): + raise ValueError( + "Task ({}: {}) must extend FairseqTask".format(name, cls.__name__) + ) + if cls.__name__ in TASK_CLASS_NAMES: + raise ValueError( + "Cannot register task with duplicate class name ({})".format( + cls.__name__ + ) + ) + TASK_REGISTRY[name] = cls + TASK_CLASS_NAMES.add(cls.__name__) + + if dataclass is not None and not issubclass(dataclass, FairseqDataclass): + raise ValueError( + "Dataclass {} must extend FairseqDataclass".format(dataclass) + ) + + cls.__dataclass = dataclass + if dataclass is not None: + TASK_DATACLASS_REGISTRY[name] = dataclass + + cs = ConfigStore.instance() + node = dataclass() + node._name = name + cs.store(name=name, group="task", node=node, provider="fairseq") + + return cls + + return register_task_cls + + +def get_task(name): + return TASK_REGISTRY[name] + + +def import_tasks(tasks_dir, namespace): + for file in os.listdir(tasks_dir): + path = os.path.join(tasks_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + task_name = file[: file.find(".py")] if file.endswith(".py") else file + importlib.import_module(namespace + "." + task_name) + + # expose `task_parser` for sphinx + if task_name in TASK_REGISTRY: + parser = argparse.ArgumentParser(add_help=False) + group_task = parser.add_argument_group("Task name") + # fmt: off + group_task.add_argument('--task', metavar=task_name, + help='Enable this task with: ``--task=' + task_name + '``') + # fmt: on + group_args = parser.add_argument_group( + "Additional command-line arguments" + ) + TASK_REGISTRY[task_name].add_args(group_args) + globals()[task_name + "_parser"] = parser + + +# automatically import any Python files in the tasks/ directory +tasks_dir = os.path.dirname(__file__) +import_tasks(tasks_dir, "fairseq.tasks") diff --git a/fairseq/fairseq/tasks/audio_classification.py b/fairseq/fairseq/tasks/audio_classification.py new file mode 100644 index 0000000..4c21d23 --- /dev/null +++ b/fairseq/fairseq/tasks/audio_classification.py @@ -0,0 +1,269 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +from collections import OrderedDict +import itertools +import logging +import os +import sys +from dataclasses import dataclass, field +from typing import Optional + +import numpy as np +import torch +from omegaconf import II, MISSING +from sklearn import metrics as sklearn_metrics + +from fairseq.data import AddTargetDataset, Dictionary, FileAudioDataset +from fairseq.data.multi_corpus_dataset import MultiCorpusDataset +from fairseq.data.text_compressor import TextCompressionLevel, TextCompressor +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks.audio_pretraining import AudioPretrainingConfig, AudioPretrainingTask +from fairseq.tasks.audio_finetuning import label_len_fn, LabelEncoder + +from .. import utils +from ..logging import metrics +from . import FairseqTask, register_task + +logger = logging.getLogger(__name__) + +@dataclass +class AudioClassificationConfig(AudioPretrainingConfig): + target_dictionary: Optional[str] = field( + default=None, metadata={"help": "override default dictionary location"} + ) + + +@register_task("audio_classification", dataclass=AudioClassificationConfig) +class AudioClassificationTask(AudioPretrainingTask): + """Task for audio classification tasks.""" + + cfg: AudioClassificationConfig + + def __init__( + self, + cfg: AudioClassificationConfig, + ): + super().__init__(cfg) + self.state.add_factory("target_dictionary", self.load_target_dictionary) + logging.info(f"=== Number of labels = {len(self.target_dictionary)}") + + def load_target_dictionary(self): + if self.cfg.labels: + target_dictionary = self.cfg.data + if self.cfg.target_dictionary: # override dict + target_dictionary = self.cfg.target_dictionary + dict_path = os.path.join(target_dictionary, f"dict.{self.cfg.labels}.txt") + logger.info("Using dict_path : {}".format(dict_path)) + return Dictionary.load(dict_path, add_special_symbols=False) + return None + + def load_dataset( + self, split: str, task_cfg: AudioClassificationConfig = None, **kwargs + ): + super().load_dataset(split, task_cfg, **kwargs) + task_cfg = task_cfg or self.cfg + assert task_cfg.labels is not None + text_compression_level = getattr( + TextCompressionLevel, str(self.cfg.text_compression_level) + ) + data_path = self.cfg.data + if task_cfg.multi_corpus_keys is None: + label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}") + skipped_indices = getattr(self.datasets[split], "skipped_indices", set()) + text_compressor = TextCompressor(level=text_compression_level) + with open(label_path, "r") as f: + labels = [ + text_compressor.compress(l) + for i, l in enumerate(f) + if i not in skipped_indices + ] + + assert len(labels) == len(self.datasets[split]), ( + f"labels length ({len(labels)}) and dataset length " + f"({len(self.datasets[split])}) do not match" + ) + + process_label = LabelEncoder(self.target_dictionary) + + self.datasets[split] = AddTargetDataset( + self.datasets[split], + labels, + pad=self.target_dictionary.pad(), + eos=self.target_dictionary.eos(), + batch_targets=True, + process_label=process_label, + label_len_fn=label_len_fn, + add_to_input=False, + # text_compression_level=text_compression_level, + ) + else: + target_dataset_map = OrderedDict() + + multi_corpus_keys = [ + k.strip() for k in task_cfg.multi_corpus_keys.split(",") + ] + corpus_idx_map = {k: idx for idx, k in enumerate(multi_corpus_keys)} + + data_keys = [k.split(":") for k in split.split(",")] + + multi_corpus_sampling_weights = [ + float(val.strip()) + for val in task_cfg.multi_corpus_sampling_weights.split(",") + ] + data_weights = [] + for key, file_name in data_keys: + k = key.strip() + label_path = os.path.join( + data_path, f"{file_name.strip()}.{task_cfg.labels}" + ) + skipped_indices = getattr( + self.dataset_map[split][k], "skipped_indices", set() + ) + text_compressor = TextCompressor(level=text_compression_level) + with open(label_path, "r") as f: + labels = [ + text_compressor.compress(l) + for i, l in enumerate(f) + if i not in skipped_indices + ] + + assert len(labels) == len(self.dataset_map[split][k]), ( + f"labels length ({len(labels)}) and dataset length " + f"({len(self.dataset_map[split][k])}) do not match" + ) + + process_label = LabelEncoder(self.target_dictionary) + + # TODO: Remove duplication of code from the if block above + target_dataset_map[k] = AddTargetDataset( + self.dataset_map[split][k], + labels, + pad=self.target_dictionary.pad(), + eos=self.target_dictionary.eos(), + batch_targets=True, + process_label=process_label, + label_len_fn=label_len_fn, + add_to_input=False, + # text_compression_level=text_compression_level, + ) + + data_weights.append(multi_corpus_sampling_weights[corpus_idx_map[k]]) + + if len(target_dataset_map) == 1: + self.datasets[split] = list(target_dataset_map.values())[0] + else: + self.datasets[split] = MultiCorpusDataset( + target_dataset_map, + distribution=data_weights, + seed=0, + sort_indices=True, + ) + + @property + def source_dictionary(self): + return None + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.state.target_dictionary + + def train_step(self, sample, model, *args, **kwargs): + sample["target"] = sample["target"].to(dtype=torch.long) + loss, sample_size, logging_output = super().train_step( + sample, model, *args, **kwargs + ) + self._log_metrics(sample, model, logging_output) + return loss, sample_size, logging_output + + def valid_step(self, sample, model, criterion): + sample["target"] = sample["target"].to(dtype=torch.long) + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + self._log_metrics(sample, model, logging_output) + return loss, sample_size, logging_output + + def _log_metrics(self, sample, model, logging_output): + metrics = self._inference_with_metrics( + sample, + model, + ) + """ + logging_output["_precision"] = metrics["precision"] + logging_output["_recall"] = metrics["recall"] + logging_output["_f1"] = metrics["f1"] + logging_output["_eer"] = metrics["eer"] + logging_output["_accuracy"] = metrics["accuracy"] + """ + logging_output["_correct"] = metrics["correct"] + logging_output["_total"] = metrics["total"] + + def _inference_with_metrics(self, sample, model): + def _compute_eer(target_list, lprobs): + # from scipy.optimize import brentq + # from scipy.interpolate import interp1d + + y_one_hot = np.eye(len(self.state.target_dictionary))[target_list] + fpr, tpr, thresholds = sklearn_metrics.roc_curve( + y_one_hot.ravel(), lprobs.ravel() + ) + # Revisit the interpolation approach. + # eer = brentq(lambda x: 1.0 - x - interp1d(fpr, tpr)(x), 0.0, 1.0) + + fnr = 1 - tpr + eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))] + + return eer + + with torch.no_grad(): + net_output = model(**sample["net_input"]) + lprobs = ( + model.get_normalized_probs(net_output, log_probs=True).cpu().detach() + ) + target_list = sample["target"][:, 0].detach().cpu() + predicted_list = torch.argmax(lprobs, 1).detach().cpu() # B,C->B + + metrics = { + "correct": torch.sum(target_list == predicted_list).item(), + "total": len(target_list), + } + return metrics + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + zero = torch.scalar_tensor(0.0) + correct, total = 0, 0 + for log in logging_outputs: + correct += log.get("_correct", zero) + total += log.get("_total", zero) + metrics.log_scalar("_correct", correct) + metrics.log_scalar("_total", total) + + if total > 0: + def _fn_accuracy(meters): + if meters["_total"].sum > 0: + return utils.item(meters["_correct"].sum / meters["_total"].sum) + return float("nan") + + metrics.log_derived("accuracy", _fn_accuracy) + """ + prec_sum, recall_sum, f1_sum, acc_sum, eer_sum = 0.0, 0.0, 0.0, 0.0, 0.0 + for log in logging_outputs: + prec_sum += log.get("_precision", zero).item() + recall_sum += log.get("_recall", zero).item() + f1_sum += log.get("_f1", zero).item() + acc_sum += log.get("_accuracy", zero).item() + eer_sum += log.get("_eer", zero).item() + + metrics.log_scalar("avg_precision", prec_sum / len(logging_outputs)) + metrics.log_scalar("avg_recall", recall_sum / len(logging_outputs)) + metrics.log_scalar("avg_f1", f1_sum / len(logging_outputs)) + metrics.log_scalar("avg_accuracy", acc_sum / len(logging_outputs)) + metrics.log_scalar("avg_eer", eer_sum / len(logging_outputs)) + """ \ No newline at end of file diff --git a/fairseq/fairseq/tasks/audio_finetuning.py b/fairseq/fairseq/tasks/audio_finetuning.py new file mode 100644 index 0000000..d79553c --- /dev/null +++ b/fairseq/fairseq/tasks/audio_finetuning.py @@ -0,0 +1,404 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +from fairseq.data.multi_corpus_dataset import MultiCorpusDataset +import torch +import json + +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Optional, Any, OrderedDict + +from fairseq.data import AddTargetDataset, Dictionary, encoders +from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.configs import GenerationConfig +from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel + +from . import register_task +from .. import utils +from ..logging import metrics + + +logger = logging.getLogger(__name__) + + +class LabelEncoder(object): + def __init__(self, dictionary): + self.dictionary = dictionary + + def __call__(self, label): + return self.dictionary.encode_line( + label, append_eos=False, add_if_not_exist=False + ) + + +def label_len_fn(label): + return len(label.split(" ")) + + +@dataclass +class AudioFinetuningConfig(AudioPretrainingConfig): + # Options for reporting WER metrics during validation. Only applicable to + # Seq2Seq models during fine-tuning + eval_wer: bool = field( + default=False, metadata={"help": "compute WER for Seq2Seq models"} + ) + eval_wer_config: GenerationConfig = field( + default_factory=lambda: GenerationConfig(), + metadata={"help": "beam search config for evaluating wer during training"}, + ) + eval_wer_tokenizer: Any = field( + default=None, + metadata={"help": "tokenizer config for evaluating wer during training"}, + ) + eval_wer_post_process: str = field( + default="letter", + metadata={ + "help": "remove BPE tokens before scoring (can be sentencepiece, letter, and more)" + }, + ) + eval_bleu: bool = field( + default=False, metadata={"help": "evaluation with BLEU scores"} + ) + eval_bleu_detok: Optional[str] = field( + default=None, + metadata={ + "help": "detokenize before computing BLEU (e.g., 'moses'); " + "required if using --eval-bleu; use 'space' to disable " + "detokenization; see fairseq.data.encoders for other options" + }, + ) + eval_bleu_detok_args: str = field( + default="{}", metadata={"help": "args for building the tokenizer, if needed"} + ) + eval_tokenized_bleu: bool = field( + default=False, metadata={"help": "compute tokenized BLEU instead of sacrebleu"} + ) + eval_bleu_remove_bpe: Optional[str] = field( + default=None, metadata={"help": "remove BPE before computing BLEU"} + ) + eval_bleu_args: str = field( + default="{}", + metadata={ + "help": "generation args for BLUE scoring, e.g., " + '\'{"beam": 4, "lenpen": 0.6}\'' + }, + ) + eval_bleu_print_samples: bool = field( + default=False, metadata={"help": "print sample generations during validation"} + ) + autoregressive: bool = field( + default=False, + metadata={ + "help": "required for autoregressive decoders (like seq2seq models); " + "adds 'prev_output_tokens' to input and appends eos to target" + }, + ) + rebuild_batches: bool = True + target_dictionary: Optional[str] = field( + default=None, + metadata={ + "help": "override default dictionary location" + } + ) + +@register_task("audio_finetuning", dataclass=AudioFinetuningConfig) +class AudioFinetuningTask(AudioPretrainingTask): + """ """ + + cfg: AudioFinetuningConfig + + def __init__( + self, + cfg: AudioFinetuningConfig, + ): + super().__init__(cfg) + self.blank_symbol = "<s>" + + self.state.add_factory("target_dictionary", self.load_target_dictionary) + + def load_target_dictionary(self): + if self.cfg.labels: + target_dictionary = self.cfg.data + if self.cfg.target_dictionary: # override dict + target_dictionary = self.cfg.target_dictionary + dict_path = os.path.join(target_dictionary, f"dict.{self.cfg.labels}.txt") + logger.info('Using dict_path : {}'.format(dict_path)) + return Dictionary.load(dict_path) + return None + + def load_dataset( + self, split: str, task_cfg: AudioFinetuningConfig = None, **kwargs + ): + super().load_dataset(split, task_cfg, **kwargs) + + task_cfg = task_cfg or self.cfg + assert task_cfg.labels is not None + text_compression_level = getattr( + TextCompressionLevel, str(self.cfg.text_compression_level) + ) + data_path = self.cfg.data + if task_cfg.multi_corpus_keys is None: + label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}") + skipped_indices = getattr(self.datasets[split], "skipped_indices", set()) + text_compressor = TextCompressor(level=text_compression_level) + with open(label_path, "r") as f: + labels = [ + text_compressor.compress(l) + for i, l in enumerate(f) + if i not in skipped_indices + ] + + assert len(labels) == len(self.datasets[split]), ( + f"labels length ({len(labels)}) and dataset length " + f"({len(self.datasets[split])}) do not match" + ) + + process_label = LabelEncoder(self.target_dictionary) + + self.datasets[split] = AddTargetDataset( + self.datasets[split], + labels, + pad=self.target_dictionary.pad(), + eos=self.target_dictionary.eos(), + batch_targets=True, + process_label=process_label, + label_len_fn=label_len_fn, + add_to_input=task_cfg.get("autoregressive", False), + text_compression_level=text_compression_level, + ) + else: + + target_dataset_map = OrderedDict() + + multi_corpus_keys = [k.strip() for k in task_cfg.multi_corpus_keys.split(",")] + corpus_idx_map = {k: idx for idx, k in enumerate(multi_corpus_keys)} + + data_keys = [k.split(":") for k in split.split(",")] + + multi_corpus_sampling_weights = [float(val.strip()) for val in task_cfg.multi_corpus_sampling_weights.split(",")] + data_weights = [] + for key, file_name in data_keys: + k = key.strip() + label_path = os.path.join(data_path, f"{file_name.strip()}.{task_cfg.labels}") + skipped_indices = getattr(self.dataset_map[split][k], "skipped_indices", set()) + text_compressor = TextCompressor(level=text_compression_level) + with open(label_path, "r") as f: + labels = [ + text_compressor.compress(l) + for i, l in enumerate(f) + if i not in skipped_indices + ] + + assert len(labels) == len(self.dataset_map[split][k]), ( + f"labels length ({len(labels)}) and dataset length " + f"({len(self.dataset_map[split][k])}) do not match" + ) + + process_label = LabelEncoder(self.target_dictionary) + + # TODO: Remove duplication of code from the if block above + target_dataset_map[k] = AddTargetDataset( + self.dataset_map[split][k], + labels, + pad=self.target_dictionary.pad(), + eos=self.target_dictionary.eos(), + batch_targets=True, + process_label=process_label, + label_len_fn=label_len_fn, + add_to_input=task_cfg.get("autoregressive", False), + text_compression_level=text_compression_level, + ) + + data_weights.append(multi_corpus_sampling_weights[corpus_idx_map[k]]) + + if len(target_dataset_map) == 1: + self.datasets[split] = list(target_dataset_map.values())[0] + else: + self.datasets[split] = MultiCorpusDataset(target_dataset_map, distribution=data_weights, seed=0, sort_indices=True) + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.state.target_dictionary + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + if self.cfg.eval_wer and self.cfg.autoregressive: + metrics = self._inference_with_wer(self.sequence_generator, sample, model) + logging_output["_num_char_errors"] = metrics["num_char_errors"] + logging_output["_num_chars"] = metrics["num_chars"] + logging_output["_num_word_errors"] = metrics["num_word_errors"] + logging_output["_num_words"] = metrics["num_words"] + if self.cfg.eval_bleu and self.cfg.autoregressive: + metrics = self._inference_with_bleu(self.sequence_generator, sample, model) + logging_output["_bleu_sys_len"] = metrics.sys_len + logging_output["_bleu_ref_len"] = metrics.ref_len + # we split counts into separate entries so that they can be + # summed efficiently across workers using fast-stat-sync + assert len(metrics.counts) == 4 + for i in range(4): + logging_output[f"_bleu_counts_{i}"] = metrics.counts[i] + logging_output[f"_bleu_totals_{i}"] = metrics.totals[i] + return loss, sample_size, logging_output + + def build_model(self, model_cfg: FairseqDataclass, from_checkpoint=False): + model = super().build_model(model_cfg, from_checkpoint) + + if self.cfg.eval_wer and self.cfg.autoregressive: + self.sequence_generator = self.build_generator( + [model], + self.cfg.eval_wer_config, + ) + if self.cfg.eval_wer_tokenizer: + self.tokenizer = encoders.build_tokenizer(self.cfg.eval_wer_tokenizer) + else: + self.tokenizer = None + if self.cfg.eval_bleu and self.cfg.autoregressive: + assert self.cfg.eval_bleu_detok is not None, ( + "--eval-bleu-detok is required if using --eval-bleu; " + "try --eval-bleu-detok=moses (or --eval-bleu-detok=space " + "to disable detokenization, e.g., when using sentencepiece)" + ) + detok_args = json.loads(self.cfg.eval_bleu_detok_args) + self.tokenizer = encoders.build_tokenizer( + Namespace(tokenizer=self.cfg.eval_bleu_detok, **detok_args) + ) + gen_args = json.loads(self.cfg.eval_bleu_args) + gen_args = Namespace(**gen_args) + self.sequence_generator = self.build_generator([model], gen_args) + + return model + + def _inference_with_wer(self, generator, sample, model): + import editdistance + + def decode(toks): + s = self.target_dictionary.string( + toks.int().cpu(), + self.cfg.eval_wer_post_process, + escape_unk=True, + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + num_word_errors, num_char_errors = 0, 0 + num_chars, num_words = 0, 0 + gen_out = self.inference_step(generator, [model], sample, None) + for i in range(len(gen_out)): + hyp = decode(gen_out[i][0]["tokens"]) + ref = decode( + utils.strip_pad(sample["target"][i], self.target_dictionary.pad()), + ) + num_char_errors += editdistance.eval(hyp, ref) + num_chars += len(ref) + hyp_words = hyp.split() + ref_words = ref.split() + num_word_errors += editdistance.eval(hyp_words, ref_words) + num_words += len(ref_words) + + return { + "num_char_errors": num_char_errors, + "num_chars": num_chars, + "num_word_errors": num_word_errors, + "num_words": num_words, + } + + def _inference_with_bleu(self, generator, sample, model): + import sacrebleu + + def decode(toks, is_ref): + s = self.target_dictionary.string( + toks.int().cpu(), + self.cfg.eval_bleu_remove_bpe, + # The default unknown string in fairseq is `<unk>`, but + # this is tokenized by sacrebleu as `< unk >`, inflating + # BLEU scores. Instead, we use a somewhat more verbose + # alternative that is unlikely to appear in the real + # reference, but doesn't get split into multiple tokens. + unk_string=("UNKNOWNTOKENINREF" if is_ref else "UNKNOWNTOKENINHYP"), + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + gen_out = self.inference_step(generator, [model], sample) + hyps, refs = [], [] + for i in range(len(gen_out)): + hyps.append(decode(gen_out[i][0]["tokens"], is_ref=False)) + refs.append( + decode( + utils.strip_pad(sample["target"][i], self.target_dictionary.pad()), + is_ref=True, # don't count <unk> as matches to the hypo + ) + ) + if self.cfg.eval_bleu_print_samples: + logger.info("H-{} {}".format(sample["id"][0], hyps[0])) + logger.info("T-{} {}".format(sample["id"][0], refs[0])) + + eval_tokenization = "none" if self.cfg.eval_tokenized_bleu else "13a" + return sacrebleu.corpus_bleu(hyps, [refs], tokenize=eval_tokenization) + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + if self.cfg.eval_wer: + zero = torch.scalar_tensor(0.0) + num_char_errors = sum( + log.get("_num_char_errors", zero) for log in logging_outputs + ) + num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) + num_word_errors = sum( + log.get("_num_word_errors", zero) for log in logging_outputs + ) + num_words = sum(log.get("_num_words", zero) for log in logging_outputs) + metrics.log_scalar("_num_char_errors", num_char_errors) + metrics.log_scalar("_num_chars", num_chars) + metrics.log_scalar("_num_word_errors", num_word_errors) + metrics.log_scalar("_num_words", num_words) + if num_chars > 0: + metrics.log_derived( + "uer", + lambda meters: meters["_num_char_errors"].sum + * 100.0 + / meters["_num_chars"].sum + if meters["_num_chars"].sum > 0 + else float("nan"), + ) + if num_words > 0: + metrics.log_derived( + "wer", + lambda meters: meters["_num_word_errors"].sum + * 100.0 + / meters["_num_words"].sum + if meters["_num_words"].sum > 0 + else float("nan"), + ) + if self.cfg.eval_bleu: + len_keys = ["_bleu_sys_len", "_bleu_ref_len"] + count_keys = [f"_bleu_counts_{i}" for i in range(4)] + total_keys = [f"_bleu_totals_{i}" for i in range(4)] + for k in len_keys + count_keys + total_keys: + metrics.log_scalar(k, sum(log.get(k, 0) for log in logging_outputs)) + + import sacrebleu + + metrics.log_derived( + "bleu", + lambda meters: sacrebleu.compute_bleu( + correct=[meters[k].sum for k in count_keys], + total=[meters[k].sum for k in total_keys], + sys_len=meters["_bleu_sys_len"].sum, + ref_len=meters["_bleu_ref_len"].sum, + smooth_method="exp", + ).score, + ) diff --git a/fairseq/fairseq/tasks/audio_pretraining.py b/fairseq/fairseq/tasks/audio_pretraining.py new file mode 100644 index 0000000..3e91303 --- /dev/null +++ b/fairseq/fairseq/tasks/audio_pretraining.py @@ -0,0 +1,253 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +import sys + +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Optional, OrderedDict +from fairseq.data.multi_corpus_dataset import MultiCorpusDataset +from omegaconf import MISSING, II, OmegaConf + +from fairseq.data import BinarizedAudioDataset, FileAudioDataset, SubsampleDataset +from fairseq.dataclass import FairseqDataclass, ChoiceEnum +from fairseq.data.text_compressor import TextCompressionLevel + +from . import FairseqTask, register_task + + +logger = logging.getLogger(__name__) + + +@dataclass +class AudioMaskingConfig: + feature_encoder_spec: str = II("model.modalities.audio.feature_encoder_spec") + mask_prob: float = II("model.modalities.audio.mask_prob") + mask_prob_adjust: float = II("model.modalities.audio.mask_prob_adjust") + mask_length: int = II("model.modalities.audio.mask_length") + inverse_mask: bool = II("model.modalities.audio.inverse_mask") + mask_dropout: float = II("model.modalities.audio.mask_dropout") + clone_batch: int = II("model.clone_batch") + expand_adjacent: bool = False + non_overlapping: bool = False + + +@dataclass +class AudioPretrainingConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + labels: Optional[str] = field( + default=None, + metadata={"help": "extension of the label file to load, used for fine-tuning"}, + ) + multi_corpus_keys: Optional[str] = field( + default=None, + metadata={"help": "Comma separated names for loading multi corpus datasets"}) + multi_corpus_sampling_weights: Optional[str] = field( + default=None, + metadata={"help": "Comma separated string of sampling weights corresponding to the multi_corpus_keys"}) + binarized_dataset: bool = field( + default=False, + metadata={ + "help": "if true, loads binarized dataset (useful for very large datasets). " + "See examples/wav2vec/scripts/binarize_manifest.sh" + }, + ) + sample_rate: int = field( + default=16_000, + metadata={ + "help": "target sample rate. audio files will be up/down sampled to this rate" + }, + ) + normalize: bool = field( + default=False, + metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, + ) + enable_padding: bool = field( + default=False, metadata={"help": "pad shorter samples instead of cropping"} + ) + max_sample_size: Optional[int] = field( + default=None, metadata={"help": "max sample size to crop to for batching"} + ) + min_sample_size: Optional[int] = field( + default=None, metadata={"help": "min sample size to skip small examples"} + ) + num_batch_buckets: int = field( + default=0, + metadata={"help": "number of buckets"}, + ) + tpu: bool = II("common.tpu") + text_compression_level: ChoiceEnum([x.name for x in TextCompressionLevel]) = field( + default="none", + metadata={ + "help": "compression level for texts (e.g. audio filenames, " + "target texts): none/low/high (default: none). " + }, + ) + + rebuild_batches: bool = True + precompute_mask_config: Optional[AudioMaskingConfig] = None + + post_save_script: Optional[str] = None + + subsample: float = 1 + seed: int = II("common.seed") + + +@register_task("audio_pretraining", dataclass=AudioPretrainingConfig) +class AudioPretrainingTask(FairseqTask): + """ """ + + cfg: AudioPretrainingConfig + + @classmethod + def setup_task(cls, cfg: AudioPretrainingConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (AudioPretrainingConfig): configuration of this task + """ + + return cls(cfg) + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + task_cfg = task_cfg or self.cfg + + # upgrade old task + if isinstance(task_cfg, Namespace): + if not hasattr(task_cfg, "autoregressive"): + task_cfg.autoregressive = not task_cfg.criterion == "ctc" + + text_compression_level = getattr( + TextCompressionLevel, str(self.cfg.text_compression_level) + ) + + compute_mask = getattr(task_cfg, "precompute_mask_config", None) is not None + mask_args = {} + if compute_mask: + mask_args = task_cfg.precompute_mask_config + + if getattr(task_cfg, "binarized_dataset", False): + self.datasets[split] = BinarizedAudioDataset( + data_path, + split=split, + sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), + max_sample_size=self.cfg.max_sample_size, + min_sample_size=self.cfg.min_sample_size, + pad=task_cfg.labels is not None or task_cfg.enable_padding, + normalize=task_cfg.normalize, + num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), + compute_mask=compute_mask, + **mask_args, + ) + else: + if task_cfg.multi_corpus_keys is None: + manifest_path = os.path.join(data_path, "{}.tsv".format(split)) + + self.datasets[split] = FileAudioDataset( + manifest_path=manifest_path, + sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), + max_sample_size=self.cfg.max_sample_size, + min_sample_size=self.cfg.min_sample_size, + pad=task_cfg.labels is not None or task_cfg.enable_padding, + normalize=task_cfg.normalize, + num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), + text_compression_level=text_compression_level, + compute_mask=compute_mask, + **mask_args, + ) + else: + dataset_map = OrderedDict() + self.dataset_map = {} + multi_corpus_keys = [k.strip() for k in task_cfg.multi_corpus_keys.split(",")] + corpus_idx_map = {k: idx for idx, k in enumerate(multi_corpus_keys)} + data_keys = [k.split(":") for k in split.split(",")] + + multi_corpus_sampling_weights = [float(val.strip()) for val in task_cfg.multi_corpus_sampling_weights.split(",")] + data_weights = [] + + for key, file_name in data_keys: + + k = key.strip() + manifest_path = os.path.join(data_path, "{}.tsv".format(file_name.strip())) + + # TODO: Remove duplication of code from the if block above + dataset_map[k] = FileAudioDataset( + manifest_path=manifest_path, + sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), + max_sample_size=self.cfg.max_sample_size, + min_sample_size=self.cfg.min_sample_size, + pad=task_cfg.labels is not None or task_cfg.enable_padding, + normalize=task_cfg.normalize, + num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), + text_compression_level=text_compression_level, + compute_mask=compute_mask, + corpus_key=corpus_idx_map[k], + **mask_args, + ) + + data_weights.append(multi_corpus_sampling_weights[corpus_idx_map[k]]) + + self.dataset_map[split] = dataset_map + + if len(dataset_map) == 1: + self.datasets[split] = list(dataset_map.values())[0] + else: + self.datasets[split] = MultiCorpusDataset(dataset_map, distribution=data_weights, seed=0, sort_indices=True) + + if getattr(task_cfg, "subsample", 1) < 1: + self.datasets[split] = SubsampleDataset( + self.datasets[split], + task_cfg.subsample, + shuffle=True, + seed=task_cfg.seed, + ) + + if self.cfg.tpu and task_cfg.inferred_w2v_config.mask_channel_prob == 0.0: + logger.info( + "Pretraining on TPUs may suffer convergence " + "issues when training with `mask_channel_prob` value of " + "0. You may want to set this to a low value close to 0." + ) + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return sys.maxsize, sys.maxsize + + def build_model(self, model_cfg: FairseqDataclass, from_checkpoint=False): + model = super().build_model(model_cfg, from_checkpoint) + + actualized_cfg = getattr(model, "cfg", None) + if actualized_cfg is not None: + # if "w2v_args" in actualized_cfg: + if hasattr(actualized_cfg, "w2v_args"): + model_cfg.w2v_args = actualized_cfg.w2v_args + + return model + + def post_save(self, cp_path, num_updates): + if self.cfg.post_save_script is not None: + logger.info(f"launching {self.cfg.post_save_script}") + import os.path as osp + from fairseq.file_io import PathManager + + eval_cp_path = osp.join( + osp.dirname(cp_path), f"checkpoint_eval_{num_updates}.pt" + ) + + print(cp_path, eval_cp_path, osp.dirname(cp_path)) + + assert PathManager.copy( + cp_path, eval_cp_path, overwrite=True + ), f"Failed to copy {cp_path} to {eval_cp_path}" + + import subprocess + import shlex + + subprocess.call(shlex.split(f"{self.cfg.post_save_script} {eval_cp_path}")) diff --git a/fairseq/fairseq/tasks/cross_lingual_lm.py b/fairseq/fairseq/tasks/cross_lingual_lm.py new file mode 100644 index 0000000..8f8fe7e --- /dev/null +++ b/fairseq/fairseq/tasks/cross_lingual_lm.py @@ -0,0 +1,191 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import logging +import os +from collections import OrderedDict + +import numpy as np +from fairseq import tokenizer, utils +from fairseq.data import ConcatDataset, Dictionary, TokenBlockDataset, data_utils +from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset +from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary +from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset +from fairseq.tasks import LegacyFairseqTask, register_task + + +logger = logging.getLogger(__name__) + + +@register_task("cross_lingual_lm") +class CrossLingualLMTask(LegacyFairseqTask): + """ + Task for training cross-lingual language models. + + For more details look at: https://arxiv.org/pdf/1901.07291.pdf + + Args: + dictionary (Dictionary): the dictionary for the input of the task + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument( + "data", + help="colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner", + ) + parser.add_argument( + "--tokens-per-sample", + default=512, + type=int, + help="max number of total tokens over all segments" " per sample", + ) + parser.add_argument( + "--monolingual-langs", + default="en", + type=str, + help="comma separated list of languages for which we" + " want to train XLM on", + ) + parser.add_argument( + "--shuffle", + action="store_true", + help="shuffle each monolingual dataset while" " training", + ) + + def __init__(self, args, dictionary): + super().__init__(args) + self.dictionary = dictionary + self.seed = args.seed + self.distributed_world_size = args.distributed_world_size + self.langs2id = self._lang_to_id(args.monolingual_langs) + + def _lang_to_id(self, languages: str): + """ + Build a map from languages to ids. These ids are used as segment labels + for cross-lingual LM training. + """ + lang2id = {} + langs = [l.strip() for l in languages.split(",")] + for id, lang in enumerate(langs): + lang2id[lang] = id + return lang2id + + @classmethod + def load_dictionary(cls, filename): + return MaskedLMDictionary.load(filename) + + @classmethod + def build_dictionary( + cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 + ): + d = MaskedLMDictionary() + for filename in filenames: + Dictionary.add_file_to_dictionary( + filename, d, tokenizer.tokenize_line, workers + ) + d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) + return d + + @property + def target_dictionary(self): + return self.dictionary + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task.""" + dictionary = MaskedLMDictionary.load(os.path.join(args.data, "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + return cls(args, dictionary) + + def _load_single_lang_dataset(self, split, epoch): + loaded_datasets = [] + + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + for k in itertools.count(): + split_k = split + (str(k) if k > 0 else "") + path = os.path.join(data_path, split_k) + + ds = data_utils.load_indexed_dataset( + path, self.dictionary, self.args.dataset_impl + ) + if ds is None: + if k > 0: + break + else: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) + + # Since we append each block with the classification_token, + # we need to effectively create blocks of length + # tokens_per_sample-1 + loaded_datasets.append( + TokenBlockDataset( + ds, + ds.sizes, + self.args.tokens_per_sample - 1, + pad=self.dictionary.pad(), + eos=self.dictionary.eos(), + ) + ) + + logger.info( + "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1])) + ) + + if len(loaded_datasets) == 1: + dataset = loaded_datasets[0] + sizes = dataset.sizes + else: + dataset = ConcatDataset(loaded_datasets) + sizes = np.concatenate([ds.sizes for ds in loaded_datasets]) + + return dataset, sizes + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + dataset_map = OrderedDict() + + for lang in self.langs2id.keys(): + # Datasets are expected to be in "split.lang" format (Eg: train.en) + language_split = "{}.{}".format(split, lang) + + block_dataset, sizes = self._load_single_lang_dataset( + split=language_split, epoch=epoch + ) + + dataset_map[lang] = MaskedLMDataset( + dataset=block_dataset, + sizes=sizes, + vocab=self.dictionary, + pad_idx=self.dictionary.pad(), + mask_idx=self.dictionary.mask(), + classif_token_idx=self.dictionary.eos(), + sep_token_idx=self.dictionary.eos(), + shuffle=getattr(self.args, "shuffle", False), + has_pairs=False, + segment_id=self.langs2id[lang], + seed=self.seed, + ) + + self.datasets[split] = MultiCorpusSampledDataset(dataset_map) + logger.info( + "{} {} {} examples".format( + utils.split_paths(self.args.data)[epoch - 1], + split, + len(self.datasets[split]), + ) + ) diff --git a/fairseq/fairseq/tasks/denoising.py b/fairseq/fairseq/tasks/denoising.py new file mode 100644 index 0000000..57b824d --- /dev/null +++ b/fairseq/fairseq/tasks/denoising.py @@ -0,0 +1,296 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field +from typing import Any, Optional + +import numpy as np +from omegaconf import II, MISSING + +from fairseq import utils +from fairseq.data import ( + AppendTokenDataset, + DenoisingDataset, + Dictionary, + IdDataset, + NestedDictionaryDataset, + NumelDataset, + PadDataset, + PrependTokenDataset, + StripTokenDataset, + TokenBlockDataset, + data_utils, +) +from fairseq.data.encoders.utils import get_whole_word_mask +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + +from ..data.indexed_dataset import get_available_dataset_impl + +logger = logging.getLogger(__name__) + +SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) +MASK_LENGTH_CHOICES = ChoiceEnum(["subword", "word", "span-poisson"]) + + +@dataclass +class DenoisingConfig(FairseqDataclass): + data: str = field( + default=MISSING, + metadata={"help": "path to data directory"}, + ) + bpe: Optional[str] = field( + default=None, + metadata={"help": "TODO"}, + ) + tokens_per_sample: int = field( + default=512, + metadata={ + "help": "max number of total tokens over all segments " + "per sample for dataset" + }, + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="complete_doc", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + replace_length: int = field( + default=0, + metadata={"help": "TODO, should only allow -1, 0 and 1"}, + ) + mask: float = field( + default=0.0, + metadata={"help": "fraction of words/subwords that will be masked"}, + ) + mask_random: float = field( + default=0.0, + metadata={"help": "instead of using [MASK], use random token this often"}, + ) + insert: float = field( + default=0.0, + metadata={"help": "insert this percentage of additional random tokens"}, + ) + permute: float = field( + default=0.0, + metadata={"help": "take this proportion of subwords and permute them"}, + ) + rotate: float = field( + default=0.5, + metadata={"help": "rotate this proportion of inputs"}, + ) + poisson_lambda: float = field( + default=3.0, + metadata={"help": "randomly shuffle sentences for this proportion of inputs"}, + ) + shuffle_instance: float = field( + default=0.0, + metadata={"help": "shuffle this proportion of sentences in all inputs"}, + ) + mask_length: MASK_LENGTH_CHOICES = field( + default="subword", + metadata={"help": "mask length to choose"}, + ) + permute_sentences: int = field( + default=-1, + metadata={ + "help": "when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)" + }, + ) + seed: int = II("common.seed") + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + max_source_positions: int = field( + default=1024, + metadata={"help": "max number of tokens in the source sequence"}, + ) + max_target_positions: int = field( + default=1024, + metadata={"help": "max number of tokens in the target sequence"}, + ) + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + + +@register_task("denoising", dataclass=DenoisingConfig) +class DenoisingTask(FairseqTask): + """ + Denoising task for applying sequence to sequence denoising. (ie. BART) + """ + + cfg: DenoisingConfig + + def __init__(self, cfg, dictionary): + super().__init__(cfg) + self.dictionary = dictionary + + # add mask token + self.mask_idx = self.dictionary.add_symbol("<mask>") + + @classmethod + def setup_task(cls, cfg: DenoisingConfig, **kwargs): + """Setup the task.""" + paths = utils.split_paths(cfg.data) + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + if not hasattr(cfg, "shuffle_instance"): + cfg.shuffle_instance = False + return cls(cfg, dictionary) + + def _load_dataset_split(self, split, epoch, combine): + paths = utils.split_paths(self.cfg.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + split_path = os.path.join(data_path, split) + + dataset = data_utils.load_indexed_dataset( + split_path, + self.dictionary, + self.cfg.dataset_impl, + combine=combine, + ) + if dataset is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + dataset = StripTokenDataset(dataset, self.dictionary.eos()) + + dataset = maybe_shorten_dataset( + dataset, + split, + self.cfg.shorten_data_split_list, + self.cfg.shorten_method, + self.cfg.tokens_per_sample, + self.cfg.seed, + ) + + # create continuous blocks of tokens + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + self.cfg.tokens_per_sample - 2, + # one less for <s> and one for </s> + pad=self.dictionary.pad(), + eos=self.dictionary.eos(), + break_mode=self.cfg.sample_break_mode, + document_sep_len=0, + ) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) + + # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) + dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) + dataset = AppendTokenDataset(dataset, self.source_dictionary.eos()) + return dataset + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + dataset = self._load_dataset_split(split, epoch, combine) + + mask_whole_words = ( + get_whole_word_mask(self.cfg.bpe, self.source_dictionary) + if self.cfg.mask_length != "subword" + else None + ) + + self.datasets[split] = DenoisingDataset( + dataset, + dataset.sizes, + self.dictionary, + self.mask_idx, + mask_whole_words, + shuffle=self.cfg.shuffle_instance, + seed=self.cfg.seed, + mask=self.cfg.mask, + mask_random=self.cfg.mask_random, + insert=self.cfg.insert, + rotate=self.cfg.rotate, + permute_sentences=self.cfg.permute_sentences, + bpe=self.cfg.bpe, + replace_length=self.cfg.replace_length, + mask_length=self.cfg.mask_length, + poisson_lambda=self.cfg.poisson_lambda, + ) + logger.info( + "Split: {0}, Loaded {1} samples of denoising_dataset".format( + split, + len(self.datasets[split]), + ) + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + """ + Generate batches for inference. We assume that the input begins with a + bos symbol (`<s>`) and ends with an eos symbol (`</s>`). + """ + pad = self.source_dictionary.pad() + eos = self.source_dictionary.eos() + src_dataset = TokenBlockDataset( + src_tokens, + src_lengths, + block_size=self.cfg.tokens_per_sample - 2, # for <s> and </s> + pad=pad, + eos=eos, + break_mode=self.cfg.sample_break_mode, + document_sep_len=0, + ) + prev_output_tokens = PrependTokenDataset( + StripTokenDataset(src_dataset, eos), eos + ) + src_dataset = PadDataset(src_dataset, pad_idx=pad, left_pad=False) + return NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": src_dataset, + "src_lengths": NumelDataset(src_dataset, reduce=False), + "prev_output_tokens": PadDataset( + prev_output_tokens, pad_idx=pad, left_pad=False + ), + }, + "target": src_dataset, + }, + sizes=[np.array(src_lengths)], + ) + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + return (self.cfg.max_source_positions, self.cfg.max_target_positions) + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary`.""" + return self.dictionary + + @property + def target_dictionary(self): + """Return the target :class:`~fairseq.data.Dictionary`.""" + return self.dictionary diff --git a/fairseq/fairseq/tasks/fairseq_task.py b/fairseq/fairseq/tasks/fairseq_task.py new file mode 100644 index 0000000..e39d1d6 --- /dev/null +++ b/fairseq/fairseq/tasks/fairseq_task.py @@ -0,0 +1,708 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import warnings +from argparse import Namespace +from typing import Any, Callable, Dict, List + +import torch +from fairseq import search, tokenizer, utils +from fairseq.logging import metrics +from fairseq.data import Dictionary, FairseqDataset, data_utils, encoders, iterators +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.optim.amp_optimizer import AMPOptimizer +from omegaconf import DictConfig + + +logger = logging.getLogger(__name__) + + +class StatefulContainer(object): + def __init__(self): + self._state = dict() + self._factories = dict() + + def add_factory(self, name, factory: Callable[[], Any]): + self._factories[name] = factory + + def merge_state_dict(self, state_dict: Dict[str, Any]): + self._state.update(state_dict) + + @property + def state_dict(self) -> Dict[str, Any]: + return self._state + + def __getattr__(self, name): + if name not in self._state and name in self._factories: + self._state[name] = self._factories[name]() + + if name in self._state: + return self._state[name] + + raise AttributeError(f"Task state has no factory for attribute {name}") + + +class FairseqTask(object): + """ + Tasks store dictionaries and provide helpers for loading/iterating over + Datasets, initializing the Model/Criterion and calculating the loss. + + Tasks have limited statefulness. In particular, state that needs to be + saved to/loaded from checkpoints needs to be stored in the `self.state` + :class:`StatefulContainer` object. For example:: + + self.state.add_factory("dictionary", self.load_dictionary) + print(self.state.dictionary) # calls self.load_dictionary() + + This is necessary so that when loading checkpoints, we can properly + recreate the task state after initializing the task instance. + """ + + @classmethod + def add_args(cls, parser): + """Add task-specific arguments to the parser.""" + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) + + @staticmethod + def logging_outputs_can_be_summed(criterion) -> bool: + """ + Whether the logging outputs returned by `train_step` and `valid_step` can + be summed across workers prior to calling `aggregate_logging_outputs`. + Setting this to True will improves distributed training speed. + """ + return criterion.logging_outputs_can_be_summed() + + def __init__(self, cfg: FairseqDataclass, **kwargs): + self.cfg = cfg + self.datasets = dict() + self.dataset_to_epoch_iter = dict() + self.state = StatefulContainer() + + @classmethod + def load_dictionary(cls, filename): + """Load the dictionary from the filename + + Args: + filename (str): the filename + """ + return Dictionary.load(filename) + + @classmethod + def build_dictionary( + cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 + ): + """Build the dictionary + + Args: + filenames (list): list of filenames + workers (int): number of concurrent workers + threshold (int): defines the minimum word count + nwords (int): defines the total number of words in the final dictionary, + including special symbols + padding_factor (int): can be used to pad the dictionary size to be a + multiple of 8, which is important on some hardware (e.g., Nvidia + Tensor Cores). + """ + d = Dictionary() + for filename in filenames: + Dictionary.add_file_to_dictionary( + filename, d, tokenizer.tokenize_line, workers + ) + d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) + return d + + @classmethod + def setup_task(cls, cfg: DictConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (omegaconf.DictConfig): parsed command-line arguments + """ + return cls(cfg, **kwargs) + + def has_sharded_data(self, split): + return os.pathsep in getattr(self.cfg, "data", "") + + def load_dataset( + self, + split: str, + combine: bool = False, + task_cfg: FairseqDataclass = None, + **kwargs, + ): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + combine (bool): combines a split segmented into pieces into one dataset + task_cfg (FairseqDataclass): optional task configuration stored in the checkpoint that can be used + to load datasets + """ + raise NotImplementedError + + def dataset(self, split): + """ + Return a loaded dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + + Returns: + a :class:`~fairseq.data.FairseqDataset` corresponding to *split* + """ + from fairseq.data import FairseqDataset + + if split not in self.datasets: + raise KeyError("Dataset not loaded: " + split) + if not isinstance(self.datasets[split], FairseqDataset): + raise TypeError("Datasets are expected to be of type FairseqDataset") + return self.datasets[split] + + def filter_indices_by_size( + self, indices, dataset, max_positions=None, ignore_invalid_inputs=False + ): + """ + Filter examples that are too large + + Args: + indices (np.array): original array of sample indices + dataset (~fairseq.data.FairseqDataset): dataset to batch + max_positions (optional): max sentence length supported by the + model (default: None). + ignore_invalid_inputs (bool, optional): don't raise Exception for + sentences that are too long (default: False). + Returns: + np.array: array of filtered sample indices + """ + indices, ignored = dataset.filter_indices_by_size(indices, max_positions) + if len(ignored) > 0: + if not ignore_invalid_inputs: + raise Exception( + ( + "Size of sample #{} is invalid (={}) since max_positions={}, " + "skip this example with --skip-invalid-size-inputs-valid-test" + ).format(ignored[0], dataset.size(ignored[0]), max_positions) + ) + logger.warning( + ( + "{:,} samples have invalid sizes and will be skipped, " + "max_positions={}, first few sample ids={}" + ).format(len(ignored), max_positions, ignored[:10]) + ) + return indices + + def can_reuse_epoch_itr(self, dataset): + # We can reuse the epoch iterator across epochs as long as the dataset + # hasn't disabled it. We default to ``False`` here, although in practice + # this will be ``True`` for most datasets that inherit from + # ``FairseqDataset`` due to the base implementation there. + return getattr(dataset, "can_reuse_epoch_itr_across_epochs", False) + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + data_buffer_size=0, + disable_iterator_cache=False, + skip_remainder_batch=False, + grouped_shuffling=False, + update_epoch_batch_itr=False, + ): + """ + Get an iterator that yields batches of data from the given dataset. + + Args: + dataset (~fairseq.data.FairseqDataset): dataset to batch + max_tokens (int, optional): max number of tokens in each batch + (default: None). + max_sentences (int, optional): max number of sentences in each + batch (default: None). + max_positions (optional): max sentence length supported by the + model (default: None). + ignore_invalid_inputs (bool, optional): don't raise Exception for + sentences that are too long (default: False). + required_batch_size_multiple (int, optional): require batch size to + be a multiple of N (default: 1). + seed (int, optional): seed for random number generator for + reproducibility (default: 1). + num_shards (int, optional): shard the data iterator into N + shards (default: 1). + shard_id (int, optional): which shard of the data iterator to + return (default: 0). + num_workers (int, optional): how many subprocesses to use for data + loading. 0 means the data will be loaded in the main process + (default: 0). + epoch (int, optional): the epoch to start the iterator from + (default: 1). + data_buffer_size (int, optional): number of batches to + preload (default: 0). + disable_iterator_cache (bool, optional): don't cache the + EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`) + (default: False). + skip_remainder_batch (bool, optional): if set, discard the last + batch in each training epoch, as the last batch is often smaller than + local_batch_size * distributed_word_size (default: ``True``). + grouped_shuffling (bool, optional): group batches with each groups + containing num_shards batches and shuffle groups. Reduces difference + between sequence lengths among workers for batches sorted by length. + update_epoch_batch_itr (bool optional): if true then donot use the cached + batch iterator for the epoch + + Returns: + ~fairseq.iterators.EpochBatchIterator: a batched iterator over the + given dataset split + """ + can_reuse_epoch_itr = ( + not disable_iterator_cache + and not update_epoch_batch_itr + and self.can_reuse_epoch_itr(dataset) + ) + logger.info(f"can_reuse_epoch_itr = {can_reuse_epoch_itr}") + if can_reuse_epoch_itr and dataset in self.dataset_to_epoch_iter: + logger.debug("reusing EpochBatchIterator for epoch {}".format(epoch)) + return self.dataset_to_epoch_iter[dataset] + + assert isinstance(dataset, FairseqDataset) + + # initialize the dataset with the correct starting epoch + dataset.set_epoch(epoch) + + def make_batches(dataset, epoch): + logger.info(f"creating new batches for epoch {epoch}") + + # get indices ordered by example size + with data_utils.numpy_seed(seed + epoch): + indices = dataset.ordered_indices() + + # filter examples that are too large + if max_positions is not None: + indices = self.filter_indices_by_size( + indices, dataset, max_positions, ignore_invalid_inputs + ) + + # create mini-batches with given size constraints + batches = dataset.batch_by_size( + indices, + max_tokens=max_tokens, + max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + return batches + + reuse_dataloader = getattr(self.cfg, "reuse_dataloader", True) + persistent_workers = getattr(self.cfg, "persistent_workers", True) + rebuild_batches = getattr(self.cfg, "rebuild_batches", False) + logger.info(f"reuse_dataloader = {reuse_dataloader}") + logger.info(f"rebuild_batches = {rebuild_batches}") + + if rebuild_batches: + logger.info("batches will be rebuilt for each epoch") + batch_sampler = make_batches + else: + batch_sampler = make_batches(dataset, epoch) + + # return a reusable, sharded iterator + epoch_iter = iterators.EpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_sampler=batch_sampler, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + buffer_size=data_buffer_size, + skip_remainder_batch=skip_remainder_batch, + grouped_shuffling=grouped_shuffling, + reuse_dataloader=reuse_dataloader, + persistent_workers=persistent_workers, + ) + + if can_reuse_epoch_itr: + self.dataset_to_epoch_iter[dataset] = epoch_iter + + return epoch_iter + + def build_model(self, cfg: FairseqDataclass, from_checkpoint=False): + """ + Build the :class:`~fairseq.models.BaseFairseqModel` instance for this + task. + + Args: + cfg (FairseqDataclass): configuration object + + Returns: + a :class:`~fairseq.models.BaseFairseqModel` instance + """ + from fairseq import models, quantization_utils + + model = models.build_model(cfg, self, from_checkpoint) + model = quantization_utils.quantize_model_scalar(model, cfg) + return model + + def build_criterion(self, cfg: DictConfig, from_checkpoint=False): + """ + Build the :class:`~fairseq.criterions.FairseqCriterion` instance for + this task. + + Args: + cfg (omegaconf.DictConfig): configration object + + Returns: + a :class:`~fairseq.criterions.FairseqCriterion` instance + """ + from fairseq import criterions + + return criterions.build_criterion(cfg, self, from_checkpoint=from_checkpoint) + + def build_generator( + self, + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=None, + prefix_allowed_tokens_fn=None, + ): + """ + Build a :class:`~fairseq.SequenceGenerator` instance for this + task. + + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + args (fairseq.dataclass.configs.GenerationConfig): + configuration object (dataclass) for generation + extra_gen_cls_kwargs (Dict[str, Any]): extra options to pass + through to SequenceGenerator + prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]]): + If provided, this function constrains the beam search to + allowed tokens only at each step. The provided function + should take 2 arguments: the batch ID (`batch_id: int`) + and a unidimensional tensor of token ids (`inputs_ids: + torch.Tensor`). It has to return a `List[int]` with the + allowed tokens for the next generation step conditioned + on the previously generated tokens (`inputs_ids`) and + the batch ID (`batch_id`). This argument is useful for + constrained generation conditioned on the prefix, as + described in "Autoregressive Entity Retrieval" + (https://arxiv.org/abs/2010.00904) and + https://github.com/facebookresearch/GENRE. + """ + if getattr(args, "score_reference", False): + from fairseq.sequence_scorer import SequenceScorer + + return SequenceScorer( + self.target_dictionary, + compute_alignment=getattr(args, "print_alignment", False), + ) + + from fairseq.sequence_generator import ( + SequenceGenerator, + SequenceGeneratorWithAlignment, + ) + + # Choose search strategy. Defaults to Beam Search. + sampling = getattr(args, "sampling", False) + sampling_topk = getattr(args, "sampling_topk", -1) + sampling_topp = getattr(args, "sampling_topp", -1.0) + diverse_beam_groups = getattr(args, "diverse_beam_groups", -1) + diverse_beam_strength = getattr(args, "diverse_beam_strength", 0.5) + match_source_len = getattr(args, "match_source_len", False) + diversity_rate = getattr(args, "diversity_rate", -1) + constrained = getattr(args, "constraints", False) + if prefix_allowed_tokens_fn is None: + prefix_allowed_tokens_fn = getattr(args, "prefix_allowed_tokens_fn", None) + if ( + sum( + int(cond) + for cond in [ + sampling, + diverse_beam_groups > 0, + match_source_len, + diversity_rate > 0, + ] + ) + > 1 + ): + raise ValueError("Provided Search parameters are mutually exclusive.") + assert sampling_topk < 0 or sampling, "--sampling-topk requires --sampling" + assert sampling_topp < 0 or sampling, "--sampling-topp requires --sampling" + + if sampling: + search_strategy = search.Sampling( + self.target_dictionary, sampling_topk, sampling_topp + ) + elif diverse_beam_groups > 0: + search_strategy = search.DiverseBeamSearch( + self.target_dictionary, diverse_beam_groups, diverse_beam_strength + ) + elif match_source_len: + # this is useful for tagging applications where the output + # length should match the input length, so we hardcode the + # length constraints for simplicity + search_strategy = search.LengthConstrainedBeamSearch( + self.target_dictionary, + min_len_a=1, + min_len_b=0, + max_len_a=1, + max_len_b=0, + ) + elif diversity_rate > -1: + search_strategy = search.DiverseSiblingsSearch( + self.target_dictionary, diversity_rate + ) + elif constrained: + search_strategy = search.LexicallyConstrainedBeamSearch( + self.target_dictionary, args.constraints + ) + elif prefix_allowed_tokens_fn: + search_strategy = search.PrefixConstrainedBeamSearch( + self.target_dictionary, prefix_allowed_tokens_fn + ) + else: + search_strategy = search.BeamSearch(self.target_dictionary) + + extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} + if seq_gen_cls is None: + if getattr(args, "print_alignment", False): + seq_gen_cls = SequenceGeneratorWithAlignment + extra_gen_cls_kwargs["print_alignment"] = args.print_alignment + else: + seq_gen_cls = SequenceGenerator + + return seq_gen_cls( + models, + self.target_dictionary, + beam_size=getattr(args, "beam", 5), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 200), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + search_strategy=search_strategy, + **extra_gen_cls_kwargs, + ) + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + """ + Do forward and backward, and return the loss as computed by *criterion* + for the given *model* and *sample*. + + Args: + sample (dict): the mini-batch. The format is defined by the + :class:`~fairseq.data.FairseqDataset`. + model (~fairseq.models.BaseFairseqModel): the model + criterion (~fairseq.criterions.FairseqCriterion): the criterion + optimizer (~fairseq.optim.FairseqOptimizer): the optimizer + update_num (int): the current update + ignore_grad (bool): multiply loss by 0 if this is set to True + + Returns: + tuple: + - the loss + - the sample size, which is used as the denominator for the + gradient + - logging outputs to display while training + """ + model.train() + model.set_num_updates(update_num) + with torch.autograd.profiler.record_function("forward"): + with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))): + loss, sample_size, logging_output = criterion(model, sample) + if ignore_grad: + loss *= 0 + with torch.autograd.profiler.record_function("backward"): + optimizer.backward(loss) + return loss, sample_size, logging_output + + def valid_step(self, sample, model, criterion): + model.eval() + with torch.no_grad(): + loss, sample_size, logging_output = criterion(model, sample) + return loss, sample_size, logging_output + + def optimizer_step(self, optimizer, model, update_num): + optimizer.step() + + def build_dataset_for_inference( + self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs + ) -> torch.utils.data.Dataset: + raise NotImplementedError + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, constraints=constraints + ) + + def begin_epoch(self, epoch, model): + """Hook function called before the start of each epoch.""" + pass + + def begin_valid_epoch(self, epoch, model): + """Hook function called before the start of each validation epoch.""" + pass + + def aggregate_logging_outputs(self, logging_outputs, criterion): + """[deprecated] Aggregate logging outputs from data parallel training.""" + utils.deprecation_warning( + "The aggregate_logging_outputs API is deprecated. " + "Please use the reduce_metrics API instead." + ) + with metrics.aggregate() as agg: + self.reduce_metrics(logging_outputs, criterion) + return agg.get_smoothed_values() + + def reduce_metrics(self, logging_outputs, criterion): + """Aggregate logging outputs from data parallel training.""" + # backward compatibility for tasks that override aggregate_logging_outputs + base_func = FairseqTask.aggregate_logging_outputs + self_func = getattr(self, "aggregate_logging_outputs").__func__ + if self_func is not base_func: + utils.deprecation_warning( + "Tasks should implement the reduce_metrics API. " + "Falling back to deprecated aggregate_logging_outputs API." + ) + agg_logging_outputs = self.aggregate_logging_outputs( + logging_outputs, criterion + ) + for k, v in agg_logging_outputs.items(): + metrics.log_scalar(k, v) + return + + if not any("ntokens" in log for log in logging_outputs): + warnings.warn( + "ntokens not found in Criterion logging outputs, cannot log wpb or wps" + ) + else: + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + metrics.log_scalar("wpb", ntokens, priority=180, round=1) + metrics.log_speed("wps", ntokens, priority=90, round=1) + + if not any("nsentences" in log for log in logging_outputs): + warnings.warn( + "nsentences not found in Criterion logging outputs, cannot log bsz" + ) + else: + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + metrics.log_scalar("bsz", nsentences, priority=190, round=1) + + criterion.__class__.reduce_metrics(logging_outputs) + + def state_dict(self): + if self.state is not None: + return self.state.state_dict + return {} + + def load_state_dict(self, state_dict: Dict[str, Any]): + if self.state is not None: + self.state.merge_state_dict(state_dict) + + def max_positions(self): + """Return the max input length allowed by the task.""" + return None + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary` (if applicable + for this task).""" + return None + + @property + def target_dictionary(self): + """Return the target :class:`~fairseq.data.Dictionary` (if applicable + for this task).""" + return None + + def build_tokenizer(self, args): + """Build the pre-tokenizer for this task.""" + return encoders.build_tokenizer(args) + + def build_bpe(self, args): + """Build the tokenizer for this task.""" + return encoders.build_bpe(args) + + def get_interactive_tokens_and_lengths(self, lines, encode_fn): + tokens = [ + self.source_dictionary.encode_line( + encode_fn(src_str), add_if_not_exist=False + ).long() + for src_str in lines + ] + lengths = [t.numel() for t in tokens] + return tokens, lengths + + +class LegacyFairseqTask(FairseqTask): + def __init__(self, args: Namespace): + super().__init__(None) + self.args = args + self.datasets = {} + self.dataset_to_epoch_iter = {} + + @classmethod + def setup_task(cls, args: Namespace, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + return cls(args, **kwargs) + + def has_sharded_data(self, split): + return os.pathsep in getattr(self.args, "data", "") + + def build_model(self, args: Namespace, from_checkpoint=False): + """ + Build the :class:`~fairseq.models.BaseFairseqModel` instance for this + task. + + Args: + args (argparse.Namespace): parsed command-line arguments + + Returns: + a :class:`~fairseq.models.BaseFairseqModel` instance + """ + from fairseq import models, quantization_utils + + model = models.build_model(args, self, from_checkpoint) + model = quantization_utils.quantize_model_scalar(model, args) + return model + + def build_criterion(self, args: Namespace): + """ + Build the :class:`~fairseq.criterions.FairseqCriterion` instance for + this task. + + Args: + args (argparse.Namespace): parsed command-line arguments + + Returns: + a :class:`~fairseq.criterions.FairseqCriterion` instance + """ + from fairseq import criterions + + return criterions.build_criterion(args, self) diff --git a/fairseq/fairseq/tasks/frm_text_to_speech.py b/fairseq/fairseq/tasks/frm_text_to_speech.py new file mode 100644 index 0000000..667f5f8 --- /dev/null +++ b/fairseq/fairseq/tasks/frm_text_to_speech.py @@ -0,0 +1,55 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +from fairseq.data.audio.frm_text_to_speech_dataset import FrmTextToSpeechDatasetCreator +from fairseq.tasks import register_task +from fairseq.tasks.text_to_speech import TextToSpeechTask + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=logging.INFO, +) +logger = logging.getLogger(__name__) + + +@register_task("frm_text_to_speech") +class FrmTextToSpeechTask(TextToSpeechTask): + @staticmethod + def add_args(parser): + TextToSpeechTask.add_args(parser) + parser.add_argument("--do_chunk", action="store_true", help="train on chunks") + parser.add_argument("--chunk_bound", default=-1, type=int) + parser.add_argument("--chunk_init", default=50, type=int) + parser.add_argument("--chunk_incr", default=5, type=int) + parser.add_argument("--add_eos", action="store_true") + parser.add_argument("--dedup", action="store_true") + parser.add_argument("--ref_fpu", default=-1, type=float) + + def load_dataset(self, split, **unused_kwargs): + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + self.datasets[split] = FrmTextToSpeechDatasetCreator.from_tsv( + self.args.data, + self.data_cfg, + split, + self.src_dict, + pre_tokenizer, + bpe_tokenizer, + is_train_split=is_train_split, + n_frames_per_step=self.args.n_frames_per_step, + speaker_to_id=self.speaker_to_id, + do_chunk=self.args.do_chunk, + chunk_bound=self.args.chunk_bound, + chunk_init=self.args.chunk_init, + chunk_incr=self.args.chunk_incr, + add_eos=self.args.add_eos, + dedup=self.args.dedup, + ref_fpu=self.args.ref_fpu, + ) diff --git a/fairseq/fairseq/tasks/hubert_pretraining.py b/fairseq/fairseq/tasks/hubert_pretraining.py new file mode 100644 index 0000000..1a3605f --- /dev/null +++ b/fairseq/fairseq/tasks/hubert_pretraining.py @@ -0,0 +1,191 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +import sys +from typing import Dict, List, Optional, Tuple + +import numpy as np + +from dataclasses import dataclass, field +from fairseq.data import Dictionary, HubertDataset +from fairseq.dataclass.configs import FairseqDataclass +from fairseq.tasks import register_task +from fairseq.tasks.fairseq_task import FairseqTask +from omegaconf import MISSING + +logger = logging.getLogger(__name__) + + +class LabelEncoder(object): + def __init__(self, dictionary: Dictionary) -> None: + self.dictionary = dictionary + + def __call__(self, label: str) -> List[str]: + return self.dictionary.encode_line( + label, + append_eos=False, + add_if_not_exist=False, + ) + + +@dataclass +class HubertPretrainingConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + fine_tuning: bool = field( + default=False, metadata={"help": "set to true if fine-tuning Hubert"} + ) + labels: List[str] = field( + default_factory=lambda: ["ltr"], + metadata={ + "help": ( + "extension of the label files to load, frame-level labels for" + " pre-training, and sequence-level label for fine-tuning" + ) + }, + ) + label_dir: Optional[str] = field( + default=None, + metadata={ + "help": "if set, looks for labels in this directory instead", + }, + ) + label_rate: float = field( + default=-1.0, + metadata={"help": "label frame rate. -1.0 for sequence label"}, + ) + sample_rate: int = field( + default=16_000, + metadata={ + "help": "target sample rate. audio files will be up/down " + "sampled to this rate" + }, + ) + normalize: bool = field( + default=False, + metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, + ) + enable_padding: bool = field( + default=False, + metadata={"help": "pad shorter samples instead of cropping"}, + ) + max_keep_size: Optional[int] = field( + default=None, + metadata={"help": "exclude sample longer than this"}, + ) + max_sample_size: Optional[int] = field( + default=None, + metadata={"help": "max sample size to crop to for batching"}, + ) + min_sample_size: Optional[int] = field( + default=None, + metadata={"help": "min sample size to crop to for batching"}, + ) + single_target: Optional[bool] = field( + default=False, + metadata={ + "help": "if set, AddTargetDatasets outputs same keys " "as AddTargetDataset" + }, + ) + random_crop: Optional[bool] = field( + default=True, + metadata={"help": "always crop from the beginning if false"}, + ) + pad_audio: Optional[bool] = field( + default=False, + metadata={"help": "pad audio to the longest one in the batch if true"}, + ) + + +@register_task("hubert_pretraining", dataclass=HubertPretrainingConfig) +class HubertPretrainingTask(FairseqTask): + + cfg: HubertPretrainingConfig + + def __init__( + self, + cfg: HubertPretrainingConfig, + ) -> None: + super().__init__(cfg) + + logger.info(f"current directory is {os.getcwd()}") + logger.info(f"HubertPretrainingTask Config {cfg}") + + self.cfg = cfg + self.fine_tuning = cfg.fine_tuning + + if cfg.fine_tuning: + self.state.add_factory("target_dictionary", self.load_dictionaries) + else: + self.state.add_factory("dictionaries", self.load_dictionaries) + + self.blank_symbol = "<s>" + + @property + def source_dictionary(self) -> Optional[Dictionary]: + return None + + @property + def target_dictionary(self) -> Optional[Dictionary]: + return self.state.target_dictionary + + @property + def dictionaries(self) -> List[Dictionary]: + return self.state.dictionaries + + @classmethod + def setup_task( + cls, cfg: HubertPretrainingConfig, **kwargs + ) -> "HubertPretrainingTask": + return cls(cfg) + + def load_dictionaries(self): + label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir + dictionaries = [ + Dictionary.load(f"{label_dir}/dict.{label}.txt") + for label in self.cfg.labels + ] + return dictionaries[0] if self.cfg.fine_tuning else dictionaries + + def get_label_dir(self) -> str: + if self.cfg.label_dir is None: + return self.cfg.data + return self.cfg.label_dir + + def load_dataset(self, split: str, **kwargs) -> None: + manifest = f"{self.cfg.data}/{split}.tsv" + dicts = [self.target_dictionary] if self.cfg.fine_tuning else self.dictionaries + pad_list = [dict.pad() for dict in dicts] + eos_list = [dict.eos() for dict in dicts] + procs = [LabelEncoder(dict) for dict in dicts] + paths = [f"{self.get_label_dir()}/{split}.{l}" for l in self.cfg.labels] + + # hubert v1: pad_audio=True, random_crop=False; + self.datasets[split] = HubertDataset( + manifest, + sample_rate=self.cfg.sample_rate, + label_paths=paths, + label_rates=self.cfg.label_rate, + pad_list=pad_list, + eos_list=eos_list, + label_processors=procs, + max_keep_sample_size=self.cfg.max_keep_size, + min_keep_sample_size=self.cfg.min_sample_size, + max_sample_size=self.cfg.max_sample_size, + pad_audio=self.cfg.pad_audio, + normalize=self.cfg.normalize, + store_labels=False, + random_crop=self.cfg.random_crop, + single_target=self.cfg.single_target, + ) + + def max_positions(self) -> Tuple[int, int]: + return (sys.maxsize, sys.maxsize) + + def filter_indices_by_size(self, indices: np.array, *args, **kwargs) -> np.array: + return indices diff --git a/fairseq/fairseq/tasks/language_modeling.py b/fairseq/fairseq/tasks/language_modeling.py new file mode 100644 index 0000000..44d5324 --- /dev/null +++ b/fairseq/fairseq/tasks/language_modeling.py @@ -0,0 +1,383 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field +from typing import Optional + +import numpy as np +import torch +from fairseq import utils +from fairseq.data import ( + AppendTokenDataset, + Dictionary, + IdDataset, + LMContextWindowDataset, + MonolingualDataset, + NestedDictionaryDataset, + NumelDataset, + PadDataset, + PrependTokenDataset, + StripTokenDataset, + TokenBlockDataset, + TruncatedDictionary, + data_utils, +) +from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import LegacyFairseqTask, register_task +from omegaconf import II + + +SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) +logger = logging.getLogger(__name__) + + +@dataclass +class LanguageModelingConfig(FairseqDataclass): + data: Optional[str] = field( + default=None, metadata={"help": "path to data directory"} + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="none", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sample for LM dataset"}, + ) + output_dictionary_size: int = field( + default=-1, metadata={"help": "limit the size of output dictionary"} + ) + self_target: bool = field(default=False, metadata={"help": "include self target"}) + future_target: bool = field( + default=False, metadata={"help": "include future target"} + ) + past_target: bool = field(default=False, metadata={"help": "include past target"}) + add_bos_token: bool = field( + default=False, metadata={"help": "prepend beginning of sentence token (<s>)"} + ) + max_target_positions: Optional[int] = field( + default=None, metadata={"help": "max number of tokens in the target sequence"} + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + pad_to_fixed_length: Optional[bool] = field( + default=False, + metadata={"help": "pad to fixed length"}, + ) + pad_to_fixed_bsz: Optional[bool] = field( + default=False, + metadata={"help": "boolean to pad to fixed batch size"}, + ) + + # TODO common vars below add to parent + seed: int = II("common.seed") + batch_size: Optional[int] = II("dataset.batch_size") + batch_size_valid: Optional[int] = II("dataset.batch_size_valid") + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + data_buffer_size: int = II("dataset.data_buffer_size") + tpu: bool = II("common.tpu") + use_plasma_view: bool = II("common.use_plasma_view") + plasma_path: str = II("common.plasma_path") + + +@register_task("language_modeling", dataclass=LanguageModelingConfig) +class LanguageModelingTask(LegacyFairseqTask): + """ + Train a language model. + + Args: + dictionary (~fairseq.data.Dictionary): the dictionary for the input of + the language model + output_dictionary (~fairseq.data.Dictionary): the dictionary for the + output of the language model. In most cases it will be the same as + *dictionary*, but could possibly be a more limited version of the + dictionary (if ``--output-dictionary-size`` is used). + targets (List[str]): list of the target types that the language model + should predict. Can be one of "self", "future", and "past". + Defaults to "future". + + .. note:: + + The language modeling task is compatible with :mod:`fairseq-train`, + :mod:`fairseq-generate`, :mod:`fairseq-interactive` and + :mod:`fairseq-eval-lm`. + + The language modeling task provides the following additional command-line + arguments: + + .. argparse:: + :ref: fairseq.tasks.language_modeling_parser + :prog: + """ + + def __init__(self, args, dictionary, output_dictionary=None, targets=None): + super().__init__(args) + self.dictionary = dictionary + self.output_dictionary = output_dictionary or dictionary + + if targets is None: + targets = ["future"] + self.targets = targets + + @classmethod + def setup_dictionary(cls, args, **kwargs): + dictionary = None + output_dictionary = None + if args.data: + paths = utils.split_paths(args.data) + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + output_dictionary = dictionary + if args.output_dictionary_size >= 0: + output_dictionary = TruncatedDictionary( + dictionary, args.output_dictionary_size + ) + return (dictionary, output_dictionary) + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + dictionary, output_dictionary = cls.setup_dictionary(args, **kwargs) + + # upgrade old checkpoints + if getattr(args, "exclude_self_target", False): + args.self_target = False + + targets = [] + if getattr(args, "self_target", False): + targets.append("self") + if getattr(args, "future_target", False): + targets.append("future") + if getattr(args, "past_target", False): + targets.append("past") + if len(targets) == 0: + # standard language modeling + targets = ["future"] + + return cls(args, dictionary, output_dictionary, targets=targets) + + def build_model(self, args, from_checkpoint=False): + model = super().build_model(args, from_checkpoint) + for target in self.targets: + if target not in model.supported_targets: + raise ValueError( + "Unsupported language modeling target: {}".format(target) + ) + + return model + + def load_dataset( + self, split: str, epoch=1, combine=False, **kwargs + ) -> MonolingualDataset: + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, valid1, test) + """ + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + + data_path = paths[(epoch - 1) % len(paths)] + split_path = os.path.join(data_path, split) + + # each process has its own copy of the raw data (likely to be an np.memmap) + dataset = data_utils.load_indexed_dataset( + split_path, self.dictionary, self.args.dataset_impl, combine=combine + ) + if dataset is None: + raise FileNotFoundError(f"Dataset not found: {split} ({split_path})") + + dataset = maybe_shorten_dataset( + dataset, + split, + self.args.shorten_data_split_list, + self.args.shorten_method, + self.args.tokens_per_sample, + self.args.seed, + ) + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + self.args.tokens_per_sample, + pad=self.dictionary.pad(), + eos=self.dictionary.eos(), + break_mode=self.args.sample_break_mode, + include_targets=True, + use_plasma_view=self.args.use_plasma_view, + split_path=split_path, + plasma_path=self.args.plasma_path, + ) + + add_eos_for_other_targets = ( + self.args.sample_break_mode is not None + and self.args.sample_break_mode != "none" + ) + fixed_pad_length = None + if self.args.pad_to_fixed_length: + fixed_pad_length = self.args.tokens_per_sample + + pad_to_bsz = None + if self.args.pad_to_fixed_bsz: + pad_to_bsz = ( + self.args.batch_size_valid if "valid" in split else self.args.batch_size + ) + + self.datasets[split] = MonolingualDataset( + dataset=dataset, + sizes=dataset.sizes, + src_vocab=self.dictionary, + tgt_vocab=self.output_dictionary, + add_eos_for_other_targets=add_eos_for_other_targets, + shuffle=True, + targets=self.targets, + add_bos_token=self.args.add_bos_token, + fixed_pad_length=fixed_pad_length, + pad_to_bsz=pad_to_bsz, + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + """ + Generate batches for inference. We prepend an eos token to src_tokens + (or bos if `--add-bos-token` is set) and we append a <pad> to target. + This is convenient both for generation with a prefix and LM scoring. + """ + dataset = StripTokenDataset( + TokenBlockDataset( + src_tokens, + src_lengths, + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ), + # remove eos from (end of) target sequence + self.source_dictionary.eos(), + ) + src_dataset = PrependTokenDataset( + dataset, + token=( + self.source_dictionary.bos() + if getattr(self.args, "add_bos_token", False) + else self.source_dictionary.eos() + ), + ) + tgt_dataset = AppendTokenDataset(dataset, token=self.source_dictionary.pad()) + return NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": PadDataset( + src_dataset, + pad_idx=self.source_dictionary.pad(), + left_pad=False, + ), + "src_lengths": NumelDataset(src_dataset, reduce=False), + }, + "target": PadDataset( + tgt_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False + ), + }, + sizes=[np.array(src_lengths)], + ) + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + # Generation will always be conditioned on bos_token + if getattr(self.args, "add_bos_token", False): + bos_token = self.source_dictionary.bos() + else: + bos_token = self.source_dictionary.eos() + + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the language_modeling task is not supported" + ) + + # SequenceGenerator doesn't use src_tokens directly, we need to + # pass the `prefix_tokens` argument instead + if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement(): + prefix_tokens = sample["net_input"]["src_tokens"] + if prefix_tokens[:, 0].eq(bos_token).all(): + prefix_tokens = prefix_tokens[:, 1:] + + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token + ) + + def eval_lm_dataloader( + self, + dataset, + max_tokens: Optional[int] = 36000, + batch_size: Optional[int] = None, + max_positions: Optional[int] = None, + num_shards: int = 1, + shard_id: int = 0, + num_workers: int = 1, + data_buffer_size: int = 10, + # ensures that every evaluated token has access to a context of at least + # this size, if possible + context_window: int = 0, + ): + if context_window > 0: + dataset = LMContextWindowDataset( + dataset=dataset, + tokens_per_sample=self.args.tokens_per_sample, + context_window=context_window, + pad_idx=self.source_dictionary.pad(), + ) + return self.get_batch_iterator( + dataset=dataset, + max_tokens=max_tokens, + max_sentences=batch_size, + max_positions=max_positions, + ignore_invalid_inputs=True, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + data_buffer_size=data_buffer_size, + ).next_epoch_itr(shuffle=False) + + @property + def source_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.dictionary + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.output_dictionary diff --git a/fairseq/fairseq/tasks/legacy_masked_lm.py b/fairseq/fairseq/tasks/legacy_masked_lm.py new file mode 100644 index 0000000..9754976 --- /dev/null +++ b/fairseq/fairseq/tasks/legacy_masked_lm.py @@ -0,0 +1,152 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import logging +import os + +import numpy as np +from fairseq import tokenizer, utils +from fairseq.data import ConcatDataset, Dictionary, data_utils, indexed_dataset +from fairseq.data.legacy.block_pair_dataset import BlockPairDataset +from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset +from fairseq.data.legacy.masked_lm_dictionary import BertDictionary +from fairseq.tasks import LegacyFairseqTask, register_task + + +logger = logging.getLogger(__name__) + + +@register_task("legacy_masked_lm") +class LegacyMaskedLMTask(LegacyFairseqTask): + """ + Task for training Masked LM (BERT) model. + Args: + dictionary (Dictionary): the dictionary for the input of the task + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument( + "data", + help="colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner", + ) + parser.add_argument( + "--tokens-per-sample", + default=512, + type=int, + help="max number of total tokens over all segments" + " per sample for BERT dataset", + ) + parser.add_argument( + "--break-mode", default="doc", type=str, help="mode for breaking sentence" + ) + parser.add_argument("--shuffle-dataset", action="store_true", default=False) + + def __init__(self, args, dictionary): + super().__init__(args) + self.dictionary = dictionary + self.seed = args.seed + + @classmethod + def load_dictionary(cls, filename): + return BertDictionary.load(filename) + + @classmethod + def build_dictionary( + cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 + ): + d = BertDictionary() + for filename in filenames: + Dictionary.add_file_to_dictionary( + filename, d, tokenizer.tokenize_line, workers + ) + d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) + return d + + @property + def target_dictionary(self): + return self.dictionary + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task.""" + paths = utils.split_paths(args.data) + assert len(paths) > 0 + dictionary = BertDictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + + return cls(args, dictionary) + + def load_dataset(self, split, epoch=1, combine=False): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + loaded_datasets = [] + + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + logger.info("data_path", data_path) + + for k in itertools.count(): + split_k = split + (str(k) if k > 0 else "") + path = os.path.join(data_path, split_k) + ds = indexed_dataset.make_dataset( + path, + impl=self.args.dataset_impl, + fix_lua_indexing=True, + dictionary=self.dictionary, + ) + + if ds is None: + if k > 0: + break + else: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) + + with data_utils.numpy_seed(self.seed + k): + loaded_datasets.append( + BlockPairDataset( + ds, + self.dictionary, + ds.sizes, + self.args.tokens_per_sample, + break_mode=self.args.break_mode, + doc_break_size=1, + ) + ) + + logger.info( + "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1])) + ) + + if not combine: + break + + if len(loaded_datasets) == 1: + dataset = loaded_datasets[0] + sizes = dataset.sizes + else: + dataset = ConcatDataset(loaded_datasets) + sizes = np.concatenate([ds.sizes for ds in loaded_datasets]) + + self.datasets[split] = MaskedLMDataset( + dataset=dataset, + sizes=sizes, + vocab=self.dictionary, + pad_idx=self.dictionary.pad(), + mask_idx=self.dictionary.mask(), + classif_token_idx=self.dictionary.cls(), + sep_token_idx=self.dictionary.sep(), + shuffle=self.args.shuffle_dataset, + seed=self.seed, + ) diff --git a/fairseq/fairseq/tasks/masked_lm.py b/fairseq/fairseq/tasks/masked_lm.py new file mode 100644 index 0000000..b064907 --- /dev/null +++ b/fairseq/fairseq/tasks/masked_lm.py @@ -0,0 +1,327 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field + +import numpy as np +from omegaconf import II, MISSING, OmegaConf + +from fairseq import utils +from fairseq.data import ( + Dictionary, + IdDataset, + MaskTokensDataset, + NestedDictionaryDataset, + NumelDataset, + NumSamplesDataset, + PrependTokenDataset, + RightPadDataset, + RightPaddingMaskDataset, + SortDataset, + TokenBlockDataset, + data_utils, +) +from fairseq.data.encoders.utils import get_whole_word_mask +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + +from .language_modeling import SAMPLE_BREAK_MODE_CHOICES, SHORTEN_METHOD_CHOICES + +logger = logging.getLogger(__name__) + + +@dataclass +class MaskedLMConfig(FairseqDataclass): + data: str = field( + default=MISSING, + metadata={ + "help": "colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner" + }, + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="none", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sample for LM dataset"}, + ) + mask_prob: float = field( + default=0.15, + metadata={"help": "probability of replacing a token with mask"}, + ) + leave_unmasked_prob: float = field( + default=0.1, + metadata={"help": "probability that a masked token is unmasked"}, + ) + random_token_prob: float = field( + default=0.1, + metadata={"help": "probability of replacing a token with a random token"}, + ) + freq_weighted_replacement: bool = field( + default=False, + metadata={"help": "sample random replacement words based on word frequencies"}, + ) + mask_whole_words: bool = field( + default=False, + metadata={"help": "mask whole words; you may also want to set --bpe"}, + ) + mask_multiple_length: int = field( + default=1, + metadata={"help": "repeat the mask indices multiple times"}, + ) + mask_stdev: float = field( + default=0.0, + metadata={"help": "stdev of the mask length"}, + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + seed: int = II("common.seed") + + include_target_tokens: bool = field( + default=False, + metadata={ + "help": "include target tokens in model input. this is used for data2vec" + }, + ) + include_index: bool = field( + default=True, + metadata={"help": "include index in model input. this is used for data2vec"}, + ) + skip_masking: bool = field( + default=False, + metadata={"help": "skip masking at dataset"}, + ) + # subsample_train: float = field( + # default=1, + # metadata={"help": "shorten training set for debugging"}, + # ) + d2v2_multi: bool = field( + default=False, + metadata={"help": "prepare dataset for data2vec_multi"}, + ) + + +@register_task("masked_lm", dataclass=MaskedLMConfig) +class MaskedLMTask(FairseqTask): + + cfg: MaskedLMConfig + + """Task for training masked language models (e.g., BERT, RoBERTa).""" + + def __init__(self, cfg: MaskedLMConfig, dictionary=None): + super().__init__(cfg) + self.dictionary = dictionary or self.load_dict(cfg) + + # add mask token + self.mask_idx = self.dictionary.add_symbol("<mask>") + + @classmethod + def setup_task(cls, cfg: MaskedLMConfig, **kwargs): + dictionary = cls.load_dict(cfg) + return cls(cfg, dictionary) + + @classmethod + def load_dict(cls, cfg): + paths = utils.split_paths(cfg.data) + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + return dictionary + + def _load_dataset_split(self, split, epoch, combine): + paths = utils.split_paths(self.cfg.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + split_path = os.path.join(data_path, split) + + dataset = data_utils.load_indexed_dataset( + split_path, + self.source_dictionary, + combine=combine, + ) + if dataset is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + dataset = maybe_shorten_dataset( + dataset, + split, + self.cfg.shorten_data_split_list, + self.cfg.shorten_method, + self.cfg.tokens_per_sample, + self.cfg.seed, + ) + + # create continuous blocks of tokens + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + self.cfg.tokens_per_sample - 1, # one less for <s> + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode=self.cfg.sample_break_mode, + ) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) + + # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) + return PrependTokenDataset(dataset, self.source_dictionary.bos()) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + dataset = self._load_dataset_split(split, epoch, combine) + + # create masked input and targets + mask_whole_words = ( + get_whole_word_mask(self.args, self.source_dictionary) + if self.cfg.mask_whole_words + else None + ) + + src_dataset, tgt_dataset = MaskTokensDataset.apply_mask( + dataset, + self.source_dictionary, + pad_idx=self.source_dictionary.pad(), + mask_idx=self.mask_idx, + seed=self.cfg.seed, + mask_prob=self.cfg.mask_prob, + leave_unmasked_prob=self.cfg.leave_unmasked_prob, + random_token_prob=self.cfg.random_token_prob, + freq_weighted_replacement=self.cfg.freq_weighted_replacement, + mask_whole_words=mask_whole_words, + mask_multiple_length=self.cfg.mask_multiple_length, + mask_stdev=self.cfg.mask_stdev, + skip_masking=self.cfg.skip_masking, + ) + + with data_utils.numpy_seed(self.cfg.seed): + shuffle = np.random.permutation(len(src_dataset)) + + target_dataset = RightPadDataset( + tgt_dataset, + pad_idx=self.source_dictionary.pad(), + ) + + if self.cfg.d2v2_multi: + dataset = self._d2v2_multi_dataset(src_dataset) + else: + dataset = self._regular_dataset(src_dataset, target_dataset) + + self.datasets[split] = SortDataset( + dataset, sort_order=[shuffle, src_dataset.sizes] + ) + + def _regular_dataset(self, src_dataset, target_dataset): + input_dict = { + "src_tokens": RightPadDataset( + src_dataset, + pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": NumelDataset(src_dataset, reduce=False), + } + if self.cfg.include_target_tokens: + input_dict["target_tokens"] = target_dataset + if self.cfg.include_index: + input_dict["src_id"] = IdDataset() + + dataset = NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": input_dict, + "target": target_dataset, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_dataset, reduce=True), + }, + sizes=[src_dataset.sizes], + ) + return dataset + + def _d2v2_multi_dataset(self, src_dataset): + input_dict = { + "source": RightPadDataset( + src_dataset, + pad_idx=self.source_dictionary.pad(), + ), + "id": IdDataset(), + "padding_mask": RightPaddingMaskDataset(src_dataset), + } + + dataset = NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": input_dict, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_dataset, reduce=True), + }, + sizes=[src_dataset.sizes], + ) + return dataset + + def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): + src_dataset = RightPadDataset( + TokenBlockDataset( + src_tokens, + src_lengths, + self.cfg.tokens_per_sample - 1, # one less for <s> + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ), + pad_idx=self.source_dictionary.pad(), + ) + src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos()) + src_dataset = NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": src_dataset, + "src_lengths": NumelDataset(src_dataset, reduce=False), + }, + }, + sizes=src_lengths, + ) + if sort: + src_dataset = SortDataset(src_dataset, sort_order=[src_lengths]) + return src_dataset + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + def begin_epoch(self, epoch, model): + model.set_epoch(epoch) + + def max_positions(self): + return self.cfg.tokens_per_sample diff --git a/fairseq/fairseq/tasks/multilingual_denoising.py b/fairseq/fairseq/tasks/multilingual_denoising.py new file mode 100644 index 0000000..cb5ee34 --- /dev/null +++ b/fairseq/fairseq/tasks/multilingual_denoising.py @@ -0,0 +1,268 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import logging +import os +from dataclasses import dataclass, field +from typing import Optional + +import numpy as np +from omegaconf import II + +from fairseq.data import ( + AppendTokenDataset, + ConcatDataset, + DenoisingDataset, + Dictionary, + PrependTokenDataset, + ResamplingDataset, + SortDataset, + TokenBlockDataset, + data_utils, +) +from fairseq.data.encoders.utils import get_whole_word_mask +from fairseq.tasks import register_task + +from .denoising import DenoisingConfig, DenoisingTask + +logger = logging.getLogger(__name__) + + +@dataclass +class MultilingualDenoisingConfig(DenoisingConfig): + multilang_sampling_alpha: float = field( + default=1.0, + metadata={"help": "smoothing alpha for sample ratios across multiple datasets"}, + ) + add_lang_token: bool = field( + default=False, + metadata={"help": ""}, + ) + langs: Optional[str] = field( + default=None, + metadata={"help": "language ids we are considering"}, + ) + no_whole_word_mask_langs: str = field( + default="", + metadata={ + "help": "languages without spacing between words don't support whole word masking" + }, + ) + train_subset: str = II("common.train_subset") + valid_subset: str = II("common.valid_subset") + + +@register_task("multilingual_denoising", dataclass=MultilingualDenoisingConfig) +class MultilingualDenoisingTask(DenoisingTask): + + cfg: MultilingualDenoisingConfig + + @classmethod + def setup_task(cls, cfg: MultilingualDenoisingConfig, **kwargs): + """Setup the task.""" + paths = cfg.data.split(":") + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + + data_path = paths[0] + if cfg.langs is None: + languages = sorted( + [ + name + for name in os.listdir(data_path) + if os.path.isdir(os.path.join(data_path, name)) + ] + ) + else: + languages = cfg.langs.split(",") + + if cfg.add_lang_token: + for lang in languages: + dictionary.add_symbol("[{}]".format(lang)) + + logger.info("dictionary: {} types".format(len(dictionary))) + if not hasattr(cfg, "shuffle_instance"): + cfg.shuffle_instance = False + return cls(cfg, dictionary) + + def __init__(self, cfg: MultilingualDenoisingConfig, dictionary): + super().__init__(cfg, dictionary) + self.dictionary = dictionary + + # add mask token + self.mask_idx = self.dictionary.add_symbol("<mask>") + self.cfg = cfg + + def _get_sample_prob(self, dataset_lens): + """ + Get smoothed sampling probability by languages. This helps low resource + languages by upsampling them. + """ + prob = dataset_lens / dataset_lens.sum() + smoothed_prob = prob**self.cfg.multilang_sampling_alpha + smoothed_prob = smoothed_prob / smoothed_prob.sum() + return smoothed_prob + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + paths = self.cfg.data.split(":") + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + split_path = os.path.join(data_path, split) + + if self.cfg.langs is None: + languages = sorted( + [ + name + for name in os.listdir(data_path) + if os.path.isdir(os.path.join(data_path, name)) + ] + ) + else: + languages = self.cfg.langs.split(",") + for name in languages: + p = os.path.join(data_path, name) + assert os.path.exists(p), "data not found: {}".format(p) + + logger.info("Training on {0} languages: {1}".format(len(languages), languages)) + logger.info( + "Language to id mapping: ", {lang: id for id, lang in enumerate(languages)} + ) + + mask_whole_words = get_whole_word_mask(self.cfg.bpe, self.dictionary) + language_without_segmentations = self.cfg.no_whole_word_mask_langs.split(",") + lang_datasets = [] + for language in languages: + split_path = os.path.join(data_path, language, split) + + dataset = data_utils.load_indexed_dataset( + split_path, + self.source_dictionary, + self.cfg.dataset_impl, + combine=combine, + ) + if dataset is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + end_token = ( + self.source_dictionary.index("[{}]".format(language)) + if self.cfg.add_lang_token + else self.source_dictionary.eos() + ) + + # create continuous blocks of tokens + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + self.cfg.tokens_per_sample - 2, # one less for <s> + pad=self.source_dictionary.pad(), + eos=end_token, + break_mode=self.cfg.sample_break_mode, + ) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) + + # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) + dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) + dataset = AppendTokenDataset(dataset, end_token) + + lang_mask_whole_words = ( + mask_whole_words + if language not in language_without_segmentations + else None + ) + lang_dataset = DenoisingDataset( + dataset, + dataset.sizes, + self.dictionary, + self.mask_idx, + lang_mask_whole_words, + shuffle=self.cfg.shuffle_instance, + seed=self.cfg.seed, + mask=self.cfg.mask, + mask_random=self.cfg.mask_random, + insert=self.cfg.insert, + rotate=self.cfg.rotate, + permute_sentences=self.cfg.permute_sentences, + bpe=self.cfg.bpe, + replace_length=self.cfg.replace_length, + mask_length=self.cfg.mask_length, + poisson_lambda=self.cfg.poisson_lambda, + eos=None + if not self.cfg.add_lang_token + else self.source_dictionary.index("[{}]".format(language)), + ) + lang_datasets.append(lang_dataset) + + dataset_lengths = np.array( + [len(d) for d in lang_datasets], + dtype=float, + ) + logger.info( + "loaded total {} blocks for all languages".format( + int(dataset_lengths.sum()), + ) + ) + if split == self.cfg.train_subset: + # For train subset, additionally up or down sample languages. + sample_probs = self._get_sample_prob(dataset_lengths) + logger.info( + "Sample probability by language: {}".format( + { + lang: "{0:.4f}".format(sample_probs[id]) + for id, lang in enumerate(languages) + } + ) + ) + size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths + logger.info( + "Up/Down Sampling ratio by language: {}".format( + { + lang: "{0:.2f}".format(size_ratio[id]) + for id, lang in enumerate(languages) + } + ) + ) + + resampled_lang_datasets = [ + ResamplingDataset( + lang_datasets[i], + size_ratio=size_ratio[i], + seed=self.cfg.seed, + epoch=epoch, + replace=size_ratio[i] >= 1.0, + ) + for i, d in enumerate(lang_datasets) + ] + dataset = ConcatDataset( + resampled_lang_datasets, + ) + else: + dataset = ConcatDataset(lang_datasets) + lang_splits = [split] + for lang_id, lang_dataset in enumerate(lang_datasets): + split_name = split + "_" + languages[lang_id] + lang_splits.append(split_name) + self.datasets[split_name] = lang_dataset + + if split in self.cfg.valid_subset: + self.cfg.valid_subset = self.cfg.valid_subset.replace( + split, ",".join(lang_splits) + ) + + with data_utils.numpy_seed(self.cfg.seed + epoch): + shuffle = np.random.permutation(len(dataset)) + + self.datasets[split] = SortDataset( + dataset, + sort_order=[ + shuffle, + dataset.sizes, + ], + ) diff --git a/fairseq/fairseq/tasks/multilingual_language_modeling.py b/fairseq/fairseq/tasks/multilingual_language_modeling.py new file mode 100644 index 0000000..8fd5e59 --- /dev/null +++ b/fairseq/fairseq/tasks/multilingual_language_modeling.py @@ -0,0 +1,627 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field +from typing import Optional + +import numpy as np +import torch +from omegaconf import II + +from fairseq import utils +from fairseq.data import ( + AppendTokenDataset, + ConcatDataset, + Dictionary, + IdDataset, + LMContextWindowDataset, + MonolingualDataset, + NestedDictionaryDataset, + NumelDataset, + PadDataset, + PrependTokenDataset, + ResamplingDataset, + SortDataset, + StripTokenDataset, + TokenBlockDataset, + TruncatedDictionary, + data_utils, +) +from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import LegacyFairseqTask, register_task + +SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) +logger = logging.getLogger(__name__) + + +def lang_token(lang): + return f"<{lang}>" + + +@dataclass +class MultilingualLanguageModelingConfig(FairseqDataclass): + # TODO common var add to parent + data: Optional[str] = field( + default=None, metadata={"help": "path to data directory"} + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="none", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sample for LM dataset"}, + ) + output_dictionary_size: int = field( + default=-1, metadata={"help": "limit the size of output dictionary"} + ) + self_target: bool = field(default=False, metadata={"help": "include self target"}) + future_target: bool = field( + default=False, metadata={"help": "include future target"} + ) + past_target: bool = field(default=False, metadata={"help": "include past target"}) + add_bos_token: bool = field( + default=False, metadata={"help": "prepend lang id token <dialect>"} + ) + max_source_positions: Optional[int] = field( + default=None, metadata={"help": "max number of tokens in the source sequence"} + ) + max_target_positions: Optional[int] = field( + default=None, metadata={"help": "max number of tokens in the target sequence"} + ) + pad_to_fixed_length: Optional[bool] = field( + default=False, metadata={"help": "pad to fixed length"} + ) + pad_to_fixed_bsz: Optional[bool] = field( + default=False, metadata={"help": "boolean to pad to fixed batch size"} + ) + + multilang_sampling_alpha: Optional[float] = field( + default=1.0, + metadata={ + "help": "smoothing alpha for sample rations across multiple datasets" + }, + ) + + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + + langs: str = field( + default="", + metadata={ + "help": "comma-separated list of languages (default: all directories in data path)" + }, + ) + baseline_model_langs: str = field( + default="", + metadata={ + "help": "comma-separated list of languages in the baseline model (default: none)" + }, + ) + # TODO: legacy parameter kept for compatibility + baseline_model: str = field( + default="", + metadata={"help": "path to the baseline model (default: none)"}, + ) + + lang_to_offline_shard_ratio: str = field( + default="", + metadata={ + "help": "absolute path of tsv file location to indicate lang to offline shard ratio.", + }, + ) + # TODO common vars below add to parent + seed: int = II("common.seed") + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + data_buffer_size: int = II("dataset.data_buffer_size") + tpu: bool = II("common.tpu") + batch_size: Optional[int] = II("dataset.batch_size") + batch_size_valid: Optional[int] = II("dataset.batch_size_valid") + train_subset: str = II("common.train_subset") + valid_subset: str = II("common.valid_subset") + + +@register_task( + "multilingual_language_modeling", dataclass=MultilingualLanguageModelingConfig +) +class MultilingualLanguageModelingTask(LegacyFairseqTask): + """ + Train a language model. + + Args: + dictionary (~fairseq.data.Dictionary): the dictionary for the input of + the language model + output_dictionary (~fairseq.data.Dictionary): the dictionary for the + output of the language model. In most cases it will be the same as + *dictionary*, but could possibly be a more limited version of the + dictionary (if ``--output-dictionary-size`` is used). + targets (List[str]): list of the target types that the language model + should predict. Can be one of "self", "future", and "past". + Defaults to "future". + + .. note:: + + The language modeling task is compatible with :mod:`fairseq-train`, + :mod:`fairseq-generate`, :mod:`fairseq-interactive` and + :mod:`fairseq-eval-lm`. + + The language modeling task provides the following additional command-line + arguments: + + .. argparse:: + :ref: fairseq.tasks.language_modeling_parser + :prog: + """ + + def __init__(self, args, dictionary, output_dictionary=None, targets=None): + super().__init__(args) + self.dictionary = dictionary + self.output_dictionary = output_dictionary or dictionary + + if targets is None: + targets = ["future"] + self.targets = targets + + @staticmethod + def _get_langs(args, epoch=1): + paths = utils.split_paths(args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + languages = sorted( + name + for name in os.listdir(data_path) + if os.path.isdir(os.path.join(data_path, name)) + ) + if args.langs: + keep_langs = set(args.langs.split(",")) + languages = [lang for lang in languages if lang in keep_langs] + assert len(languages) == len(keep_langs) + + return languages, data_path + + @classmethod + def setup_dictionary(cls, args, **kwargs): + dictionary = None + output_dictionary = None + if args.data: + paths = utils.split_paths(args.data) + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + if args.add_bos_token: + languages, _ = cls._get_langs(args) + logger.info("----------------") + for lang in languages: + dictionary.add_symbol(lang_token(lang)) + logger.info(f"add language token: {lang_token(lang)}") + logger.info("----------------") + + logger.info("dictionary: {} types".format(len(dictionary))) + output_dictionary = dictionary + if args.output_dictionary_size >= 0: + output_dictionary = TruncatedDictionary( + dictionary, args.output_dictionary_size + ) + return (dictionary, output_dictionary) + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + dictionary, output_dictionary = cls.setup_dictionary(args, **kwargs) + + # upgrade old checkpoints + if hasattr(args, "exclude_self_target"): + args.self_target = not args.exclude_self_target + + targets = [] + if getattr(args, "self_target", False): + targets.append("self") + if getattr(args, "future_target", False): + targets.append("future") + if getattr(args, "past_target", False): + targets.append("past") + if len(targets) == 0: + # standard language modeling + targets = ["future"] + + return cls(args, dictionary, output_dictionary, targets=targets) + + def build_model(self, args, from_checkpoint=False): + model = super().build_model(args, from_checkpoint) + for target in self.targets: + if target not in model.supported_targets: + raise ValueError( + f"Unsupported language modeling target: {target} not in {model.supported_targets}" + ) + + return model + + def _get_sample_prob(self, dataset_lens): + """ + Get smoothed sampling porbability by languages. This helps low resource + languages by upsampling them. + """ + prob = dataset_lens / dataset_lens.sum() + smoothed_prob = prob**self.args.multilang_sampling_alpha + smoothed_prob = smoothed_prob / smoothed_prob.sum() + return smoothed_prob + + def load_dataset(self, split: str, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + languages, data_path = MultilingualLanguageModelingTask._get_langs( + self.args, epoch + ) + lang_to_offline_shard_ratio = None + if self.args.lang_to_offline_shard_ratio != "": + lang_to_offline_shard_ratio = {} + assert os.path.exists( + self.args.lang_to_offline_shard_ratio + ), "provided offline shard ratio file doesn't exist: {0}".format( + self.args.lang_to_offline_shard_ratio + ) + with open(self.args.lang_to_offline_shard_ratio) as fin: + for line in fin: + lang, ratio = line.strip().split("\t") + ratio = float(ratio) + lang_to_offline_shard_ratio[lang] = ratio + + logger.info( + "Found offline sharded ratio: %s", + lang_to_offline_shard_ratio, + ) + + if split == self.args.train_subset: + logger.info( + "Training on {0} languages: {1}".format(len(languages), languages) + ) + else: + logger.info( + "Evaluating on {0} languages: {1}".format(len(languages), languages) + ) + + tokens_per_sample = self.args.tokens_per_sample - int(self.args.add_bos_token) + + fixed_pad_length = None + if self.args.pad_to_fixed_length: + fixed_pad_length = self.args.tokens_per_sample + + pad_to_bsz = None + if self.args.pad_to_fixed_bsz: + pad_to_bsz = ( + self.args.batch_size_valid if "valid" in split else self.args.batch_size + ) + + lang_datasets = [] + for lang_id, language in enumerate(languages): + split_path = os.path.join(data_path, language, split) + dataset = data_utils.load_indexed_dataset( + split_path, self.dictionary, self.args.dataset_impl, combine=combine + ) + # print('len(dataset) =', len(dataset)) + if dataset is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + dataset = maybe_shorten_dataset( + dataset, + split, + self.args.shorten_data_split_list, + self.args.shorten_method, + tokens_per_sample, + self.args.seed, + ) + + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + tokens_per_sample, + pad=self.dictionary.pad(), + eos=self.dictionary.eos(), + break_mode=self.args.sample_break_mode, + include_targets=True, + ) + + add_eos_for_other_targets = ( + self.args.sample_break_mode is not None + and self.args.sample_break_mode != "none" + ) + src_lang_idx, tgt_lang_idx = None, None + if self.args.add_bos_token: + src_lang_idx = self.dictionary.index(lang_token(language)) + tgt_lang_idx = self.output_dictionary.index(lang_token(language)) + + lang_datasets.append( + MonolingualDataset( + dataset=dataset, + sizes=dataset.sizes, + src_vocab=self.dictionary, + tgt_vocab=self.output_dictionary, + add_eos_for_other_targets=add_eos_for_other_targets, + shuffle=True, + targets=self.targets, + fixed_pad_length=fixed_pad_length, + pad_to_bsz=pad_to_bsz, + add_bos_token=self.args.add_bos_token, + src_lang_idx=src_lang_idx, + tgt_lang_idx=tgt_lang_idx, + ) + ) + + dataset_lengths = np.array( + [len(d) for d in lang_datasets], + dtype=float, + ) + logger.info( + "loaded total {} blocks for all languages".format( + dataset_lengths.sum(), + ) + ) + if split == self.args.train_subset: + dataset_lengths_ratio_multiplier = np.ones(len(dataset_lengths)) + if lang_to_offline_shard_ratio is not None: + dataset_lengths_ratio_multiplier = [] + for lang in languages: + assert ( + lang in lang_to_offline_shard_ratio + ), "Lang: {0} missing in offline shard ratio file: {1}".format( + lang, + self.args.lang_to_offline_shard_ratio, + ) + dataset_lengths_ratio_multiplier.append( + lang_to_offline_shard_ratio[lang] + ) + dataset_lengths_ratio_multiplier = np.array( + dataset_lengths_ratio_multiplier + ) + true_dataset_lengths = ( + dataset_lengths * dataset_lengths_ratio_multiplier + ) + else: + true_dataset_lengths = dataset_lengths + # For train subset, additionally up or down sample languages. + sample_probs = self._get_sample_prob(true_dataset_lengths) + + logger.info( + "Sample probability by language: %s", + { + lang: "{0:.4f}".format(sample_probs[id]) + for id, lang in enumerate(languages) + }, + ) + size_ratio = (sample_probs * true_dataset_lengths.sum()) / dataset_lengths + # TODO: add an option for shrinking all size ratios to below 1 + # if self.args.multilang_sampling_alpha != 1: + # size_ratio /= size_ratio.max() + + # Fix numeric errors in size ratio computation + # 0.999999999999999999 -> 1 + # 1.000000000000000002 -> 1 + for i in range(len(size_ratio)): + size_ratio[i] = round(size_ratio[i], 8) + + logger.info( + "Up/Down Sampling ratio by language: %s", + { + lang: "{0:.2f}".format(size_ratio[id]) + for id, lang in enumerate(languages) + }, + ) + logger.info( + "Actual dataset size by language: %s", + { + lang: "{0:.2f}".format(len(lang_datasets[id])) + for id, lang in enumerate(languages) + }, + ) + resampled_lang_datasets = [ + ResamplingDataset( + lang_datasets[i], + size_ratio=size_ratio[i], + seed=self.args.seed, + epoch=epoch, + replace=size_ratio[i] > 1.0, + ) + for i, d in enumerate(lang_datasets) + ] + logger.info( + "Resampled dataset size by language: %s", + { + lang: "{0:.2f}".format(len(resampled_lang_datasets[id])) + for id, lang in enumerate(languages) + }, + ) + dataset = ConcatDataset(resampled_lang_datasets) + else: + dataset = ConcatDataset(lang_datasets) + lang_splits = [split] + for lang_id, lang_dataset in enumerate(lang_datasets): + split_name = split + "_" + languages[lang_id] + lang_splits.append(split_name) + self.datasets[split_name] = lang_dataset + + # [TODO]: This is hacky for now to print validation ppl for each + # language individually. Maybe need task API changes to allow it + # in more generic ways. + if split in self.args.valid_subset: + self.args.valid_subset = self.args.valid_subset.replace( + split, ",".join(lang_splits) + ) + + with data_utils.numpy_seed(self.args.seed + epoch): + shuffle = np.random.permutation(len(dataset)) + + self.datasets[split] = SortDataset( + dataset, + sort_order=[ + shuffle, + dataset.sizes, + ], + ) + + def build_dataset_for_inference( + self, src_tokens, src_lengths, language="en_XX", **kwargs + ): + """ + Generate batches for inference. We prepend an eos token to src_tokens + (or bos if `--add-bos-token` is set) and we append a <pad> to target. + This is convenient both for generation with a prefix and LM scoring. + """ + dataset = StripTokenDataset( + TokenBlockDataset( + src_tokens, + src_lengths, + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ), + # remove eos from (end of) target sequence + self.source_dictionary.eos(), + ) + + src_lang_idx = self.dictionary.index(lang_token(language)) + src_dataset = PrependTokenDataset( + dataset, + token=( + (src_lang_idx or self.source_dictionary.bos()) + if getattr(self.args, "add_bos_token", False) + else self.source_dictionary.eos() + ), + ) + + max_seq_len = max(src_lengths) + 1 + tgt_dataset = AppendTokenDataset(dataset, token=self.source_dictionary.pad()) + return NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": PadDataset( + src_dataset, + pad_idx=self.source_dictionary.pad(), + left_pad=False, + pad_length=max_seq_len, + ), + "src_lengths": NumelDataset(src_dataset, reduce=False), + }, + "target": PadDataset( + tgt_dataset, + pad_idx=self.source_dictionary.pad(), + left_pad=False, + pad_length=max_seq_len, + ), + }, + sizes=[np.array(src_lengths)], + ) + + @torch.no_grad() + def inference_step( + self, + generator, + models, + sample, + language="en_XX", + prefix_tokens=None, + constraints=None, + ): + # Generation will always be conditioned on bos_token + if getattr(self.args, "add_bos_token", False): + src_lang_idx = self.dictionary.index(lang_token(language)) + bos_token = src_lang_idx or self.source_dictionary.bos() + else: + bos_token = self.source_dictionary.eos() + + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the language_modeling task is not supported" + ) + + # SequenceGenerator doesn't use src_tokens directly, we need to + # pass the `prefix_tokens` argument instead + if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement(): + prefix_tokens = sample["net_input"]["src_tokens"] + if prefix_tokens[:, 0].eq(bos_token).all(): + prefix_tokens = prefix_tokens[:, 1:] + + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token + ) + + def eval_lm_dataloader( + self, + dataset, + max_tokens: Optional[int] = 36000, + batch_size: Optional[int] = None, + max_positions: Optional[int] = None, + num_shards: int = 1, + shard_id: int = 0, + num_workers: int = 1, + data_buffer_size: int = 10, + # ensures that every evaluated token has access to a context of at least + # this size, if possible + context_window: int = 0, + ): + if context_window > 0: + dataset = LMContextWindowDataset( + dataset=dataset, + tokens_per_sample=self.args.tokens_per_sample, + context_window=context_window, + pad_idx=self.source_dictionary.pad(), + ) + return self.get_batch_iterator( + dataset=dataset, + max_tokens=max_tokens, + max_sentences=batch_size, + max_positions=max_positions, + ignore_invalid_inputs=True, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + data_buffer_size=data_buffer_size, + ) + + @property + def source_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.dictionary + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.output_dictionary diff --git a/fairseq/fairseq/tasks/multilingual_masked_lm.py b/fairseq/fairseq/tasks/multilingual_masked_lm.py new file mode 100644 index 0000000..156d085 --- /dev/null +++ b/fairseq/fairseq/tasks/multilingual_masked_lm.py @@ -0,0 +1,338 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os + +import numpy as np +import torch + +from fairseq import utils +from fairseq.data import ( + ConcatDataset, + Dictionary, + IdDataset, + MaskTokensDataset, + NestedDictionaryDataset, + NumelDataset, + NumSamplesDataset, + PadDataset, + PrependTokenDataset, + RawLabelDataset, + ResamplingDataset, + SortDataset, + TokenBlockDataset, + data_utils, + encoders, +) +from fairseq.tasks import LegacyFairseqTask, register_task + +logger = logging.getLogger(__name__) + + +@register_task("multilingual_masked_lm") +class MultiLingualMaskedLMTask(LegacyFairseqTask): + """Task for training masked language models (e.g., BERT, RoBERTa).""" + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument( + "data", + help="colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner", + ) + parser.add_argument( + "--sample-break-mode", + default="complete", + choices=["none", "complete", "complete_doc", "eos"], + help='If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.', + ) + parser.add_argument( + "--tokens-per-sample", + default=512, + type=int, + help="max number of total tokens over all segments " + "per sample for BERT dataset", + ) + parser.add_argument( + "--mask-prob", + default=0.15, + type=float, + help="probability of replacing a token with mask", + ) + parser.add_argument( + "--leave-unmasked-prob", + default=0.1, + type=float, + help="probability that a masked token is unmasked", + ) + parser.add_argument( + "--random-token-prob", + default=0.1, + type=float, + help="probability of replacing a token with a random token", + ) + parser.add_argument( + "--freq-weighted-replacement", + action="store_true", + help="sample random replacement words based on word frequencies", + ) + parser.add_argument( + "--mask-whole-words", + default=False, + action="store_true", + help="mask whole words; you may also want to set --bpe", + ) + parser.add_argument( + "--multilang-sampling-alpha", + type=float, + default=1.0, + help="smoothing alpha for sample rations across multiple datasets", + ) + + def __init__(self, args, dictionary): + super().__init__(args) + self.dictionary = dictionary + self.seed = args.seed + + # add mask token + self.mask_idx = dictionary.add_symbol("<mask>") + + @classmethod + def setup_task(cls, args, **kwargs): + paths = utils.split_paths(args.data) + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + return cls(args, dictionary) + + def _get_whole_word_mask(self): + # create masked input and targets + if self.args.mask_whole_words: + bpe = encoders.build_bpe(self.args) + if bpe is not None: + + def is_beginning_of_word(i): + if i < self.source_dictionary.nspecial: + # special elements are always considered beginnings + return True + tok = self.source_dictionary[i] + if tok.startswith("madeupword"): + return True + try: + return bpe.is_beginning_of_word(tok) + except ValueError: + return True + + mask_whole_words = torch.ByteTensor( + list(map(is_beginning_of_word, range(len(self.source_dictionary)))) + ) + else: + mask_whole_words = None + return mask_whole_words + + def _get_sample_prob(self, dataset_lens): + """ + Get smoothed sampling porbability by languages. This helps low resource + languages by upsampling them. + """ + prob = dataset_lens / dataset_lens.sum() + smoothed_prob = prob**self.args.multilang_sampling_alpha + smoothed_prob = smoothed_prob / smoothed_prob.sum() + return smoothed_prob + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + languages = sorted( + name + for name in os.listdir(data_path) + if os.path.isdir(os.path.join(data_path, name)) + ) + + logger.info("Training on {0} languages: {1}".format(len(languages), languages)) + logger.info( + "Language to id mapping: ", {lang: id for id, lang in enumerate(languages)} + ) + + mask_whole_words = self._get_whole_word_mask() + lang_datasets = [] + for lang_id, language in enumerate(languages): + split_path = os.path.join(data_path, language, split) + + dataset = data_utils.load_indexed_dataset( + split_path, + self.source_dictionary, + self.args.dataset_impl, + combine=combine, + ) + if dataset is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + # create continuous blocks of tokens + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + self.args.tokens_per_sample - 1, # one less for <s> + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode=self.args.sample_break_mode, + ) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) + + # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) + dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) + + src_dataset, tgt_dataset = MaskTokensDataset.apply_mask( + dataset, + self.source_dictionary, + pad_idx=self.source_dictionary.pad(), + mask_idx=self.mask_idx, + seed=self.args.seed, + mask_prob=self.args.mask_prob, + leave_unmasked_prob=self.args.leave_unmasked_prob, + random_token_prob=self.args.random_token_prob, + freq_weighted_replacement=self.args.freq_weighted_replacement, + mask_whole_words=mask_whole_words, + ) + + lang_dataset = NestedDictionaryDataset( + { + "net_input": { + "src_tokens": PadDataset( + src_dataset, + pad_idx=self.source_dictionary.pad(), + left_pad=False, + ), + "src_lengths": NumelDataset(src_dataset, reduce=False), + }, + "target": PadDataset( + tgt_dataset, + pad_idx=self.source_dictionary.pad(), + left_pad=False, + ), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_dataset, reduce=True), + "lang_id": RawLabelDataset([lang_id] * src_dataset.sizes.shape[0]), + }, + sizes=[src_dataset.sizes], + ) + lang_datasets.append(lang_dataset) + + dataset_lengths = np.array( + [len(d) for d in lang_datasets], + dtype=float, + ) + logger.info( + "loaded total {} blocks for all languages".format( + dataset_lengths.sum(), + ) + ) + if split == self.args.train_subset: + # For train subset, additionally up or down sample languages. + sample_probs = self._get_sample_prob(dataset_lengths) + logger.info( + "Sample probability by language: ", + { + lang: "{0:.4f}".format(sample_probs[id]) + for id, lang in enumerate(languages) + }, + ) + size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths + logger.info( + "Up/Down Sampling ratio by language: ", + { + lang: "{0:.2f}".format(size_ratio[id]) + for id, lang in enumerate(languages) + }, + ) + + resampled_lang_datasets = [ + ResamplingDataset( + lang_datasets[i], + size_ratio=size_ratio[i], + seed=self.args.seed, + epoch=epoch, + replace=size_ratio[i] >= 1.0, + ) + for i, d in enumerate(lang_datasets) + ] + dataset = ConcatDataset(resampled_lang_datasets) + else: + dataset = ConcatDataset(lang_datasets) + lang_splits = [split] + for lang_id, lang_dataset in enumerate(lang_datasets): + split_name = split + "_" + languages[lang_id] + lang_splits.append(split_name) + self.datasets[split_name] = lang_dataset + + # [TODO]: This is hacky for now to print validation ppl for each + # language individually. Maybe need task API changes to allow it + # in more generic ways. + if split in self.args.valid_subset: + self.args.valid_subset = self.args.valid_subset.replace( + split, ",".join(lang_splits) + ) + + with data_utils.numpy_seed(self.args.seed + epoch): + shuffle = np.random.permutation(len(dataset)) + + self.datasets[split] = SortDataset( + dataset, + sort_order=[ + shuffle, + dataset.sizes, + ], + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): + src_dataset = PadDataset( + TokenBlockDataset( + src_tokens, + src_lengths, + self.args.tokens_per_sample - 1, # one less for <s> + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ), + pad_idx=self.source_dictionary.pad(), + left_pad=False, + ) + src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos()) + src_dataset = NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": src_dataset, + "src_lengths": NumelDataset(src_dataset, reduce=False), + }, + }, + sizes=src_lengths, + ) + if sort: + src_dataset = SortDataset(src_dataset, sort_order=[src_lengths]) + return src_dataset + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary diff --git a/fairseq/fairseq/tasks/multilingual_translation.py b/fairseq/fairseq/tasks/multilingual_translation.py new file mode 100644 index 0000000..cef7656 --- /dev/null +++ b/fairseq/fairseq/tasks/multilingual_translation.py @@ -0,0 +1,463 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import logging +import os +from collections import OrderedDict +from argparse import ArgumentError + +import torch +from fairseq import options, utils +from fairseq.logging import metrics +from fairseq.data import ( + Dictionary, + LanguagePairDataset, + RoundRobinZipDatasets, + TransformEosLangPairDataset, +) +from fairseq.models import FairseqMultiModel +from fairseq.tasks.translation import load_langpair_dataset + +from . import LegacyFairseqTask, register_task + + +logger = logging.getLogger(__name__) + + +def _lang_token(lang: str): + return "__{}__".format(lang) + + +def _lang_token_index(dic: Dictionary, lang: str): + """Return language token index.""" + idx = dic.index(_lang_token(lang)) + assert idx != dic.unk_index, "cannot find language token for lang {}".format(lang) + return idx + + +@register_task("multilingual_translation") +class MultilingualTranslationTask(LegacyFairseqTask): + """A task for training multiple translation models simultaneously. + + We iterate round-robin over batches from multiple language pairs, ordered + according to the `--lang-pairs` argument. + + The training loop is roughly: + + for i in range(len(epoch)): + for lang_pair in args.lang_pairs: + batch = next_batch_for_lang_pair(lang_pair) + loss = criterion(model_for_lang_pair(lang_pair), batch) + loss.backward() + optimizer.step() + + In practice, `next_batch_for_lang_pair` is abstracted in a FairseqDataset + (e.g., `RoundRobinZipDatasets`) and `model_for_lang_pair` is a model that + implements the `FairseqMultiModel` interface. + + During inference it is required to specify a single `--source-lang` and + `--target-lang`, which indicates the inference langauge direction. + `--lang-pairs`, `--encoder-langtok`, `--decoder-langtok` have to be set to + the same value as training. + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + parser.add_argument('data', metavar='DIR', help='path to data directory') + parser.add_argument('--lang-pairs', default=None, metavar='PAIRS', + help='comma-separated list of language pairs (in training order): en-de,en-fr,de-fr') + parser.add_argument('-s', '--source-lang', default=None, metavar='SRC', + help='source language (only needed for inference)') + parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET', + help='target language (only needed for inference)') + parser.add_argument('--left-pad-source', default='True', type=str, metavar='BOOL', + help='pad the source on the left (default: True)') + parser.add_argument('--left-pad-target', default='False', type=str, metavar='BOOL', + help='pad the target on the left (default: False)') + try: + parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the source sequence') + parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the target sequence') + except ArgumentError: + # this might have already been defined. Once we transition this to hydra it should be fine to add it here. + pass + parser.add_argument('--upsample-primary', default=1, type=int, + help='amount to upsample primary dataset') + parser.add_argument('--encoder-langtok', default=None, type=str, choices=['src', 'tgt'], + metavar='SRCTGT', + help='replace beginning-of-sentence in source sentence with source or target ' + 'language token. (src/tgt)') + parser.add_argument('--decoder-langtok', action='store_true', + help='replace beginning-of-sentence in target sentence with target language token') + # fmt: on + + def __init__(self, args, dicts, training): + super().__init__(args) + self.dicts = dicts + self.training = training + if training: + self.lang_pairs = args.lang_pairs + else: + self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] + # eval_lang_pairs for multilingual translation is usually all of the + # lang_pairs. However for other multitask settings or when we want to + # optimize for certain languages we want to use a different subset. Thus + # the eval_lang_pairs class variable is provided for classes that extend + # this class. + self.eval_lang_pairs = self.lang_pairs + # model_lang_pairs will be used to build encoder-decoder model pairs in + # models.build_model(). This allows multitask type of sub-class can + # build models other than the input lang_pairs + self.model_lang_pairs = self.lang_pairs + self.langs = list(dicts.keys()) + + @classmethod + def setup_task(cls, args, **kwargs): + dicts, training = cls.prepare(args, **kwargs) + return cls(args, dicts, training) + + @classmethod + def update_args(cls, args): + args.left_pad_source = utils.eval_bool(args.left_pad_source) + args.left_pad_target = utils.eval_bool(args.left_pad_target) + + if args.lang_pairs is None: + raise ValueError( + "--lang-pairs is required. List all the language pairs in the training objective." + ) + if isinstance(args.lang_pairs, str): + args.lang_pairs = args.lang_pairs.split(",") + + @classmethod + def prepare(cls, args, **kargs): + cls.update_args(args) + sorted_langs = sorted( + list({x for lang_pair in args.lang_pairs for x in lang_pair.split("-")}) + ) + if args.source_lang is not None or args.target_lang is not None: + training = False + else: + training = True + + # load dictionaries + dicts = OrderedDict() + for lang in sorted_langs: + paths = utils.split_paths(args.data) + assert len(paths) > 0 + dicts[lang] = cls.load_dictionary( + os.path.join(paths[0], "dict.{}.txt".format(lang)) + ) + if len(dicts) > 0: + assert dicts[lang].pad() == dicts[sorted_langs[0]].pad() + assert dicts[lang].eos() == dicts[sorted_langs[0]].eos() + assert dicts[lang].unk() == dicts[sorted_langs[0]].unk() + if args.encoder_langtok is not None or args.decoder_langtok: + for lang_to_add in sorted_langs: + dicts[lang].add_symbol(_lang_token(lang_to_add)) + logger.info("[{}] dictionary: {} types".format(lang, len(dicts[lang]))) + return dicts, training + + def get_encoder_langtok(self, src_lang, tgt_lang): + if self.args.encoder_langtok is None: + return self.dicts[src_lang].eos() + if self.args.encoder_langtok == "src": + return _lang_token_index(self.dicts[src_lang], src_lang) + else: + return _lang_token_index(self.dicts[src_lang], tgt_lang) + + def get_decoder_langtok(self, tgt_lang): + if not self.args.decoder_langtok: + return self.dicts[tgt_lang].eos() + return _lang_token_index(self.dicts[tgt_lang], tgt_lang) + + def alter_dataset_langtok( + self, + lang_pair_dataset, + src_eos=None, + src_lang=None, + tgt_eos=None, + tgt_lang=None, + ): + if self.args.encoder_langtok is None and not self.args.decoder_langtok: + return lang_pair_dataset + + new_src_eos = None + if ( + self.args.encoder_langtok is not None + and src_eos is not None + and src_lang is not None + and tgt_lang is not None + ): + new_src_eos = self.get_encoder_langtok(src_lang, tgt_lang) + else: + src_eos = None + + new_tgt_bos = None + if self.args.decoder_langtok and tgt_eos is not None and tgt_lang is not None: + new_tgt_bos = self.get_decoder_langtok(tgt_lang) + else: + tgt_eos = None + + return TransformEosLangPairDataset( + lang_pair_dataset, + src_eos=src_eos, + new_src_eos=new_src_eos, + tgt_bos=tgt_eos, + new_tgt_bos=new_tgt_bos, + ) + + def load_dataset(self, split, epoch=1, **kwargs): + """Load a dataset split.""" + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + def language_pair_dataset(lang_pair): + src, tgt = lang_pair.split("-") + langpair_dataset = load_langpair_dataset( + data_path, + split, + src, + self.dicts[src], + tgt, + self.dicts[tgt], + combine=True, + dataset_impl=self.args.dataset_impl, + upsample_primary=self.args.upsample_primary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + max_source_positions=self.args.max_source_positions, + max_target_positions=self.args.max_target_positions, + ) + return self.alter_dataset_langtok( + langpair_dataset, + src_eos=self.dicts[src].eos(), + src_lang=src, + tgt_eos=self.dicts[tgt].eos(), + tgt_lang=tgt, + ) + + self.datasets[split] = RoundRobinZipDatasets( + OrderedDict( + [ + (lang_pair, language_pair_dataset(lang_pair)) + for lang_pair in self.lang_pairs + ] + ), + eval_key=None + if self.training + else "%s-%s" % (self.args.source_lang, self.args.target_lang), + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the multilingual_translation task is not supported" + ) + + lang_pair = "%s-%s" % (self.args.source_lang, self.args.target_lang) + return RoundRobinZipDatasets( + OrderedDict( + [ + ( + lang_pair, + self.alter_dataset_langtok( + LanguagePairDataset( + src_tokens, src_lengths, self.source_dictionary + ), + src_eos=self.source_dictionary.eos(), + src_lang=self.args.source_lang, + tgt_eos=self.target_dictionary.eos(), + tgt_lang=self.args.target_lang, + ), + ) + ] + ), + eval_key=lang_pair, + ) + + def build_model(self, args, from_checkpoint=False): + def check_args(): + messages = [] + if ( + len(set(self.args.lang_pairs).symmetric_difference(args.lang_pairs)) + != 0 + ): + messages.append( + "--lang-pairs should include all the language pairs {}.".format( + args.lang_pairs + ) + ) + if self.args.encoder_langtok != args.encoder_langtok: + messages.append( + "--encoder-langtok should be {}.".format(args.encoder_langtok) + ) + if self.args.decoder_langtok != args.decoder_langtok: + messages.append( + "--decoder-langtok should {} be set.".format( + "" if args.decoder_langtok else "not" + ) + ) + + if len(messages) > 0: + raise ValueError(" ".join(messages)) + + # Update args -> the fact that the constructor here + # changes the args object doesn't mean you get the same one here + self.update_args(args) + + # Check if task args are consistant with model args + check_args() + + from fairseq import models + + model = models.build_model(args, self, from_checkpoint) + if not isinstance(model, FairseqMultiModel): + raise ValueError( + "MultilingualTranslationTask requires a FairseqMultiModel architecture" + ) + return model + + def _per_lang_pair_train_loss( + self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad + ): + loss, sample_size, logging_output = criterion( + model.models[lang_pair], sample[lang_pair] + ) + if ignore_grad: + loss *= 0 + optimizer.backward(loss) + return loss, sample_size, logging_output + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + model.train() + from collections import defaultdict + + agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, defaultdict(float) + curr_lang_pairs = [ + lang_pair + for lang_pair in self.model_lang_pairs + if sample[lang_pair] is not None and len(sample[lang_pair]) != 0 + ] + + for idx, lang_pair in enumerate(curr_lang_pairs): + + def maybe_no_sync(): + if ( + self.args.distributed_world_size > 1 + and hasattr(model, "no_sync") + and idx < len(curr_lang_pairs) - 1 + ): + return model.no_sync() + else: + return contextlib.ExitStack() # dummy contextmanager + + with maybe_no_sync(): + loss, sample_size, logging_output = self._per_lang_pair_train_loss( + lang_pair, + model, + update_num, + criterion, + sample, + optimizer, + ignore_grad, + ) + agg_loss += loss.detach().item() + # TODO make summing of the sample sizes configurable + agg_sample_size += sample_size + for k in logging_output: + agg_logging_output[k] += logging_output[k] + agg_logging_output[f"{lang_pair}:{k}"] += logging_output[k] + return agg_loss, agg_sample_size, agg_logging_output + + def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample): + return criterion(model.models[lang_pair], sample[lang_pair]) + + def valid_step(self, sample, model, criterion): + model.eval() + with torch.no_grad(): + from collections import defaultdict + + agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, defaultdict(float) + for lang_pair in self.eval_lang_pairs: + if ( + lang_pair not in sample + or sample[lang_pair] is None + or len(sample[lang_pair]) == 0 + ): + continue + loss, sample_size, logging_output = self._per_lang_pair_valid_loss( + lang_pair, model, criterion, sample + ) + agg_loss += loss.data.item() + # TODO make summing of the sample sizes configurable + agg_sample_size += sample_size + for k in logging_output: + agg_logging_output[k] += logging_output[k] + agg_logging_output[f"{lang_pair}:{k}"] += logging_output[k] + return agg_loss, agg_sample_size, agg_logging_output + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + if self.args.decoder_langtok: + bos_token = _lang_token_index( + self.target_dictionary, self.args.target_lang + ) + else: + bos_token = self.target_dictionary.eos() + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + bos_token=bos_token, + ) + + def reduce_metrics(self, logging_outputs, criterion): + with metrics.aggregate(): + # pass 'sample_size', 'nsentences', 'ntokens' stats to fairseq_task + super().reduce_metrics(logging_outputs, criterion) + for k in ["sample_size", "nsentences", "ntokens"]: + metrics.log_scalar(k, sum(l[k] for l in logging_outputs)) + + @property + def source_dictionary(self): + if self.training: + return next(iter(self.dicts.values())) + else: + return self.dicts[self.args.source_lang] + + @property + def target_dictionary(self): + if self.training: + return next(iter(self.dicts.values())) + else: + return self.dicts[self.args.target_lang] + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + if len(self.datasets.values()) == 0: + return { + "%s-%s" + % (self.args.source_lang, self.args.target_lang): ( + self.args.max_source_positions, + self.args.max_target_positions, + ) + } + return OrderedDict( + [ + (key, (self.args.max_source_positions, self.args.max_target_positions)) + for split in self.datasets.keys() + for key in self.datasets[split].datasets.keys() + ] + ) diff --git a/fairseq/fairseq/tasks/multires_hubert_pretraining.py b/fairseq/fairseq/tasks/multires_hubert_pretraining.py new file mode 100644 index 0000000..cfed147 --- /dev/null +++ b/fairseq/fairseq/tasks/multires_hubert_pretraining.py @@ -0,0 +1,204 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +import sys +from typing import Dict, List, Optional, Tuple + +import numpy as np + +from dataclasses import dataclass, field +from fairseq.data import Dictionary, HubertDataset +from fairseq.dataclass.configs import FairseqDataclass +from fairseq.tasks import register_task +from fairseq.tasks.fairseq_task import FairseqTask +from omegaconf import MISSING + +logger = logging.getLogger(__name__) + + +class LabelEncoder(object): + def __init__(self, dictionary: Dictionary) -> None: + self.dictionary = dictionary + + def __call__(self, label: str) -> List[str]: + return self.dictionary.encode_line( + label, + append_eos=False, + add_if_not_exist=False, + ) + + +@dataclass +class MultiresHubertPretrainingConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + fine_tuning: bool = field( + default=False, metadata={"help": "set to true if fine-tuning Hubert"} + ) + labels: List[str] = field( + default_factory=lambda: ["ltr50", "ltr25"], + metadata={ + "help": ( + "extension of the label files to load, frame-level labels for" + " pre-training, and sequence-level label for fine-tuning" + ) + }, + ) + label_dir: Optional[str] = field( + default=None, + metadata={ + "help": "if set, looks for labels in this directory instead", + }, + ) + label_rate: float = field( + default=-1.0, + metadata={"help": "label frame rate. -1.0 for sequence label"}, + ) + # label_rate: 1,2,2,5 + # (imply (1,2), (2,5)) + # if base label_rate = 50 + # (1,2), (2,5) --> label rates 50, 25, 10 + label_rate_ratios: List[int] = field(default=MISSING, metadata={"help": "tuple for label rates e.g., [(1,2), (2,5)]"}) + sample_rate: int = field( + default=16_000, + metadata={ + "help": "target sample rate. audio files will be up/down " + "sampled to this rate" + }, + ) + normalize: bool = field( + default=False, + metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, + ) + enable_padding: bool = field( + default=False, + metadata={"help": "pad shorter samples instead of cropping"}, + ) + max_keep_size: Optional[int] = field( + default=None, + metadata={"help": "exclude sample longer than this"}, + ) + max_sample_size: Optional[int] = field( + default=None, + metadata={"help": "max sample size to crop to for batching"}, + ) + min_sample_size: Optional[int] = field( + default=None, + metadata={"help": "min sample size to crop to for batching"}, + ) + random_crop: Optional[bool] = field( + default=True, + metadata={"help": "always crop from the beginning if false"}, + ) + pad_audio: Optional[bool] = field( + default=False, + metadata={"help": "pad audio to the longest one in the batch if true"}, + ) + + +@register_task("multires_hubert_pretraining", dataclass=MultiresHubertPretrainingConfig) +class MultiresHubertPretrainingTask(FairseqTask): + """ + Multiresolution HuBERT Pretraining Task. + The task is based on `HubertPretrainingTask` but extended to multiresolution. + """ + + cfg: MultiresHubertPretrainingConfig + + def __init__( + self, + cfg: MultiresHubertPretrainingConfig, + ) -> None: + super().__init__(cfg) + + logger.info(f"current directory is {os.getcwd()}") + logger.info(f"MultiresHubertPretrainingTask Config {cfg}") + + self.cfg = cfg + self.fine_tuning = cfg.fine_tuning + + if cfg.fine_tuning: + self.state.add_factory("target_dictionary", self.load_dictionaries) + self.res_number = 1 + else: + self.state.add_factory("dictionaries", self.load_dictionaries) + + self.blank_symbol = "<s>" + + @property + def source_dictionary(self) -> Optional[Dictionary]: + return None + + @property + def target_dictionary(self) -> Optional[Dictionary]: + return self.state.target_dictionary + + @property + def dictionaries(self) -> List[Dictionary]: + return self.state.dictionaries + + @classmethod + def setup_task( + cls, cfg: MultiresHubertPretrainingConfig, **kwargs + ) -> "MultiresHubertPretrainingTask": + return cls(cfg) + + def load_dictionaries(self): + label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir + self.res_number = len(label_dir) + dictionaries = [ (Dictionary.load(f"{label_dir}/dict.{label}.txt") if label is not "" else None ) for label in self.cfg.labels] + return dictionaries[0] if self.cfg.fine_tuning else dictionaries + + def get_label_dir(self) -> str: + if self.cfg.label_dir is None: + return self.cfg.data + return self.cfg.label_dir + + def load_dataset(self, split: str, **kwargs) -> None: + manifest = f"{self.cfg.data}/{split}.tsv" + dicts = [self.target_dictionary] if self.cfg.fine_tuning else self.dictionaries + pad_list = [(dict.pad() if dict is not None else None) for dict in dicts] + eos_list = [(dict.eos() if dict is not None else None) for dict in dicts] + procs = [LabelEncoder(dict) for dict in dicts] + paths = [(f"{self.get_label_dir()}/{split}.{l}" if l != "" else None) for l in self.cfg.labels] + + base_rate = self.cfg.label_rate + self.label_rates = [base_rate] + label_rate_ratios = self.cfg.label_rate_ratios + self.label_rate_ratios = [] + for i in range(len(label_rate_ratios) // 2): + + upsample_rate, downsample_rate = label_rate_ratios[i * 2], label_rate_ratios[i * 2 + 1] + # parse label rate ratios + self.label_rate_ratios.append((upsample_rate, downsample_rate)) + base_rate = base_rate * upsample_rate // downsample_rate + self.label_rates.append(base_rate) + + # hubert v1: pad_audio=True, random_crop=False; + self.datasets[split] = HubertDataset( + manifest, + sample_rate=self.cfg.sample_rate, + label_paths=paths, + label_rates=self.label_rates, + pad_list=pad_list, + eos_list=eos_list, + label_processors=procs, + max_keep_sample_size=self.cfg.max_keep_size, + min_keep_sample_size=self.cfg.min_sample_size, + max_sample_size=self.cfg.max_sample_size, + pad_audio=self.cfg.pad_audio, + normalize=self.cfg.normalize, + store_labels=False, + random_crop=self.cfg.random_crop, + ) + + def max_positions(self) -> Tuple[int, int]: + return (sys.maxsize, sys.maxsize) + + def filter_indices_by_size(self, indices: np.array, *args, **kwargs) -> np.array: + return indices diff --git a/fairseq/fairseq/tasks/nlu_finetuning.py b/fairseq/fairseq/tasks/nlu_finetuning.py new file mode 100644 index 0000000..a335021 --- /dev/null +++ b/fairseq/fairseq/tasks/nlu_finetuning.py @@ -0,0 +1,477 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +import torch +import json + +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Optional, Any + +from fairseq.data import AddTargetDataset, Dictionary, encoders +from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.configs import GenerationConfig +from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel + +from . import register_task +from .. import utils +from ..logging import metrics + + +logger = logging.getLogger(__name__) + + +class LabelEncoder(object): + def __init__(self, dictionary): + self.dictionary = dictionary + + def __call__(self, label): + return self.dictionary.encode_line( + label, append_eos=False, add_if_not_exist=False + ) + + +def label_len_fn(label): + return len(label.split(" ")) + + +@dataclass +class NLUFinetuningConfig(AudioPretrainingConfig): + # Options for reporting WER metrics during validation. Only applicable to + # Seq2Seq models during fine-tuning + eval_wer: bool = field( + default=False, metadata={"help": "compute WER for Seq2Seq models"} + ) + eval_wer_parse: bool = field( + default=False, metadata={"help": "compute WER for Seq2Seq models"} + ) + eval_wer_config: GenerationConfig = field( + default_factory=lambda: GenerationConfig(), + metadata={"help": "beam search config for evaluating wer during training"}, + ) + eval_wer_tokenizer: Any = field( + default=None, + metadata={"help": "tokenizer config for evaluating wer during training"}, + ) + eval_wer_post_process: str = field( + default="letter", + metadata={ + "help": "remove BPE tokens before scoring (can be sentencepiece, letter, and more)" + }, + ) + eval_bleu: bool = field( + default=False, metadata={"help": "evaluation with BLEU scores"} + ) + eval_bleu_detok: Optional[str] = field( + default=None, + metadata={ + "help": "detokenize before computing BLEU (e.g., 'moses'); " + "required if using --eval-bleu; use 'space' to disable " + "detokenization; see fairseq.data.encoders for other options" + }, + ) + eval_bleu_detok_args: str = field( + default="{}", metadata={"help": "args for building the tokenizer, if needed"} + ) + eval_tokenized_bleu: bool = field( + default=False, metadata={"help": "compute tokenized BLEU instead of sacrebleu"} + ) + eval_bleu_remove_bpe: Optional[str] = field( + default=None, metadata={"help": "remove BPE before computing BLEU"} + ) + eval_bleu_args: str = field( + default="{}", + metadata={ + "help": "generation args for BLUE scoring, e.g., " + '\'{"beam": 4, "lenpen": 0.6}\'' + }, + ) + eval_bleu_print_samples: bool = field( + default=False, metadata={"help": "print sample generations during validation"} + ) + autoregressive: bool = field( + default=False, + metadata={ + "help": "required for autoregressive decoders (like seq2seq models); " + "adds 'prev_output_tokens' to input and appends eos to target" + }, + ) + + +@register_task("nlu_finetuning", dataclass=NLUFinetuningConfig) +class NLUFinetuningTask(AudioPretrainingTask): + """ """ + + cfg: NLUFinetuningConfig + + def __init__( + self, + cfg: NLUFinetuningConfig, + ): + super().__init__(cfg) + self.blank_symbol = "<s>" + + self.state.add_factory("target_dictionary", self.load_target_dictionary) + + def load_target_dictionary(self): + if self.cfg.labels: + dict_path = os.path.join(self.cfg.data, f"dict.{self.cfg.labels}.txt") + return Dictionary.load(dict_path) + return None + + def load_dataset(self, split: str, task_cfg: NLUFinetuningConfig = None, **kwargs): + super().load_dataset(split, task_cfg, **kwargs) + + task_cfg = task_cfg or self.cfg + assert task_cfg.labels is not None + text_compression_level = getattr( + TextCompressionLevel, str(self.cfg.text_compression_level) + ) + data_path = self.cfg.data + label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}") + skipped_indices = getattr(self.datasets[split], "skipped_indices", set()) + text_compressor = TextCompressor(level=text_compression_level) + with open(label_path, "r") as f: + labels = [ + text_compressor.compress(l) + for i, l in enumerate(f) + if i not in skipped_indices + ] + + assert len(labels) == len(self.datasets[split]), ( + f"labels length ({len(labels)}) and dataset length " + f"({len(self.datasets[split])}) do not match" + ) + + process_label = LabelEncoder(self.target_dictionary) + + self.datasets[split] = AddTargetDataset( + self.datasets[split], + labels, + pad=self.target_dictionary.pad(), + eos=self.target_dictionary.eos(), + batch_targets=True, + process_label=process_label, + label_len_fn=label_len_fn, + add_to_input=task_cfg.get("autoregressive", False), + text_compression_level=text_compression_level, + ) + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.state.target_dictionary + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + if self.cfg.eval_wer_parse and self.cfg.autoregressive: + metrics = self._inference_with_wer_parse( + self.sequence_generator, sample, model + ) + logging_output["_num_char_errors"] = metrics["num_char_errors"] + logging_output["_num_chars"] = metrics["num_chars"] + logging_output["_num_word_errors"] = metrics["num_word_errors"] + logging_output["_num_words"] = metrics["num_words"] + logging_output["_num_em_errors"] = metrics["num_em_errors"] + logging_output["_num_ems"] = metrics["num_ems"] + logging_output["_num_tree_errors"] = metrics["num_tree_errors"] + logging_output["_num_trees"] = metrics["num_trees"] + if self.cfg.eval_wer and self.cfg.autoregressive: + metrics = self._inference_with_wer(self.sequence_generator, sample, model) + logging_output["_num_char_errors"] = metrics["num_char_errors"] + logging_output["_num_chars"] = metrics["num_chars"] + logging_output["_num_word_errors"] = metrics["num_word_errors"] + logging_output["_num_words"] = metrics["num_words"] + if self.cfg.eval_bleu and self.cfg.autoregressive: + metrics = self._inference_with_bleu(self.sequence_generator, sample, model) + logging_output["_bleu_sys_len"] = metrics.sys_len + logging_output["_bleu_ref_len"] = metrics.ref_len + # we split counts into separate entries so that they can be + # summed efficiently across workers using fast-stat-sync + assert len(metrics.counts) == 4 + for i in range(4): + logging_output[f"_bleu_counts_{i}"] = metrics.counts[i] + logging_output[f"_bleu_totals_{i}"] = metrics.totals[i] + return loss, sample_size, logging_output + + def build_model(self, model_cfg: FairseqDataclass): + model = super().build_model(model_cfg) + + if (self.cfg.eval_wer or self.cfg.eval_wer_parse) and self.cfg.autoregressive: + self.sequence_generator = self.build_generator( + [model], + self.cfg.eval_wer_config, + ) + if self.cfg.eval_wer_tokenizer: + self.tokenizer = encoders.build_tokenizer(self.cfg.eval_wer_tokenizer) + else: + self.tokenizer = None + if self.cfg.eval_bleu and self.cfg.autoregressive: + assert self.cfg.eval_bleu_detok is not None, ( + "--eval-bleu-detok is required if using --eval-bleu; " + "try --eval-bleu-detok=moses (or --eval-bleu-detok=space " + "to disable detokenization, e.g., when using sentencepiece)" + ) + detok_args = json.loads(self.cfg.eval_bleu_detok_args) + self.tokenizer = encoders.build_tokenizer( + Namespace(tokenizer=self.cfg.eval_bleu_detok, **detok_args) + ) + gen_args = json.loads(self.cfg.eval_bleu_args) + gen_args = Namespace(**gen_args) + self.sequence_generator = self.build_generator([model], gen_args) + + return model + + def _inference_with_wer_parse(self, generator, sample, model): + import editdistance + + def decode(toks): + s = self.target_dictionary.string( + toks.int().cpu(), + self.cfg.eval_wer_post_process, + escape_unk=True, + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + def decode_to_list(toks): + def token_string(i): + if i == self.target_dictionary.unk(): + return self.target_dictionary.unk_string(False) + else: + return self.target_dictionary[i] + + return [token_string(i) for i in toks] + + def is_ont_token(token): + return "[" in token or "]" in token + + def post_process(l): + o = [] + for w in l: + if w == self.target_dictionary.eos_word or w == "|": + continue + if w == "_": + o.append(" ") + else: + o.append(w) + if is_ont_token(w): + o.append(" ") + return o + + num_word_errors, num_char_errors = 0, 0 + num_chars, num_words = 0, 0 + num_em_errors, num_ems = 0, 0 + num_tree_errors, num_trees = 0, 0 + gen_out = self.inference_step(generator, [model], sample, None) + for i in range(len(gen_out)): + hyp_tokens = gen_out[i][0]["tokens"] + # hyp = decode(hyp_tokens) + ref_tokens = utils.strip_pad( + sample["target"][i], self.target_dictionary.pad() + ) + # ref = decode(ref_tokens) + hyp_list = decode_to_list(hyp_tokens) + ref_list = decode_to_list(ref_tokens) + + hyp_list = post_process(hyp_list) + ref_list = post_process(ref_list) + + hyp = "".join(hyp_list).strip() + ref = "".join(ref_list).strip() + num_chars += len(ref) + num_char_errors += editdistance.eval(hyp, ref) + hyp_words = hyp.split() + ref_words = ref.split() + hyp_tree = [word for word in hyp_list if ("[" in word or "]" in word)] + ref_tree = [word for word in ref_list if ("[" in word or "]" in word)] + # num_word_errors += editdistance.eval(hyp_words, ref_words) + hyp_before = decode(hyp_tokens).split() + ref_before = decode(ref_tokens).split() + + num_word_errors += editdistance.eval(hyp_before, ref_before) + num_words += len(ref_before) + if hyp != ref: + num_em_errors += 1 + if hyp_tree != ref_tree: + num_tree_errors += 1 + num_ems += 1 + num_trees += 1 + + return { + "num_char_errors": num_char_errors, + "num_chars": num_chars, + "num_word_errors": num_word_errors, + "num_words": num_words, + "num_ems": num_ems, + "num_em_errors": num_em_errors, + "num_trees": num_trees, + "num_tree_errors": num_tree_errors, + } + + def _inference_with_wer(self, generator, sample, model): + import editdistance + + def decode(toks): + s = self.target_dictionary.string( + toks.int().cpu(), + self.cfg.eval_wer_post_process, + escape_unk=True, + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + num_word_errors, num_char_errors = 0, 0 + num_chars, num_words = 0, 0 + gen_out = self.inference_step(generator, [model], sample, None) + for i in range(len(gen_out)): + hyp = decode(gen_out[i][0]["tokens"]) + ref = decode( + utils.strip_pad(sample["target"][i], self.target_dictionary.pad()), + ) + num_char_errors += editdistance.eval(hyp, ref) + num_chars += len(ref) + hyp_words = hyp.split() + ref_words = ref.split() + num_word_errors += editdistance.eval(hyp_words, ref_words) + num_words += len(ref_words) + + return { + "num_char_errors": num_char_errors, + "num_chars": num_chars, + "num_word_errors": num_word_errors, + "num_words": num_words, + } + + def _inference_with_bleu(self, generator, sample, model): + import sacrebleu + + def decode(toks, is_ref): + s = self.target_dictionary.string( + toks.int().cpu(), + self.cfg.eval_bleu_remove_bpe, + # The default unknown string in fairseq is `<unk>`, but + # this is tokenized by sacrebleu as `< unk >`, inflating + # BLEU scores. Instead, we use a somewhat more verbose + # alternative that is unlikely to appear in the real + # reference, but doesn't get split into multiple tokens. + unk_string=("UNKNOWNTOKENINREF" if is_ref else "UNKNOWNTOKENINHYP"), + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + gen_out = self.inference_step(generator, [model], sample) + hyps, refs = [], [] + for i in range(len(gen_out)): + hyps.append(decode(gen_out[i][0]["tokens"], is_ref=False)) + refs.append( + decode( + utils.strip_pad(sample["target"][i], self.target_dictionary.pad()), + is_ref=True, # don't count <unk> as matches to the hypo + ) + ) + if self.cfg.eval_bleu_print_samples: + logger.info("H-{} {}".format(sample["id"][0], hyps[0])) + logger.info("T-{} {}".format(sample["id"][0], refs[0])) + + eval_tokenization = "none" if self.cfg.eval_tokenized_bleu else "13a" + return sacrebleu.corpus_bleu(hyps, [refs], tokenize=eval_tokenization) + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + if self.cfg.eval_wer or self.cfg.eval_wer_parse: + zero = torch.scalar_tensor(0.0) + num_char_errors = sum( + log.get("_num_char_errors", zero) for log in logging_outputs + ) + num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) + num_word_errors = sum( + log.get("_num_word_errors", zero) for log in logging_outputs + ) + num_words = sum(log.get("_num_words", zero) for log in logging_outputs) + metrics.log_scalar("_num_char_errors", num_char_errors) + metrics.log_scalar("_num_chars", num_chars) + metrics.log_scalar("_num_word_errors", num_word_errors) + metrics.log_scalar("_num_words", num_words) + if num_chars > 0: + metrics.log_derived( + "uer", + lambda meters: meters["_num_char_errors"].sum + * 100.0 + / meters["_num_chars"].sum + if meters["_num_chars"].sum > 0 + else float("nan"), + ) + if num_words > 0: + metrics.log_derived( + "wer", + lambda meters: meters["_num_word_errors"].sum + * 100.0 + / meters["_num_words"].sum + if meters["_num_words"].sum > 0 + else float("nan"), + ) + if self.cfg.eval_wer_parse: + num_em_errors = sum( + log.get("_num_em_errors", zero) for log in logging_outputs + ) + num_ems = sum(log.get("_num_ems", zero) for log in logging_outputs) + metrics.log_scalar("_num_em_errors", num_em_errors) + metrics.log_scalar("_num_ems", num_ems) + num_tree_errors = sum( + log.get("_num_tree_errors", zero) for log in logging_outputs + ) + num_trees = sum(log.get("_num_trees", zero) for log in logging_outputs) + metrics.log_scalar("_num_tree_errors", num_tree_errors) + metrics.log_scalar("_num_trees", num_trees) + + if num_ems > 0: + metrics.log_derived( + "em_error", + lambda meters: meters["_num_em_errors"].sum + * 100.0 + / meters["_num_ems"].sum + if meters["_num_ems"].sum > 0 + else float("nan"), + ) + if num_trees > 0: + metrics.log_derived( + "tree_error", + lambda meters: meters["_num_tree_errors"].sum + * 100.0 + / meters["_num_trees"].sum + if meters["_num_trees"].sum > 0 + else float("nan"), + ) + + if self.cfg.eval_bleu: + len_keys = ["_bleu_sys_len", "_bleu_ref_len"] + count_keys = [f"_bleu_counts_{i}" for i in range(4)] + total_keys = [f"_bleu_totals_{i}" for i in range(4)] + for k in len_keys + count_keys + total_keys: + metrics.log_scalar(k, sum(log.get(k, 0) for log in logging_outputs)) + + import sacrebleu + + metrics.log_derived( + "bleu", + lambda meters: sacrebleu.compute_bleu( + correct=[meters[k].sum for k in count_keys], + total=[meters[k].sum for k in total_keys], + sys_len=meters["_bleu_sys_len"].sum, + ref_len=meters["_bleu_ref_len"].sum, + smooth_method="exp", + ).score, + ) diff --git a/fairseq/fairseq/tasks/online_backtranslation.py b/fairseq/fairseq/tasks/online_backtranslation.py new file mode 100644 index 0000000..da24fe8 --- /dev/null +++ b/fairseq/fairseq/tasks/online_backtranslation.py @@ -0,0 +1,683 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import json +import logging +import math +import os +from argparse import Namespace +from collections import OrderedDict, defaultdict +from pathlib import Path +from typing import Dict, Sequence, Tuple +from argparse import ArgumentError + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import fairseq +from fairseq import options, utils +from fairseq.logging import metrics +from fairseq.data import ( + FairseqDataset, + LanguagePairDataset, + NoisingDataset, + PrependTokenDataset, + RoundRobinZipDatasets, + TransformEosLangPairDataset, + data_utils, + encoders, +) +from fairseq.sequence_generator import SequenceGenerator +from fairseq.tasks import register_task +from fairseq.tasks.translation import TranslationTask, load_langpair_dataset + +logger = logging.getLogger(__name__) + + +class PiecewiseLinearFn: + """Piecewise linear function. Can be configured with a string.""" + + def __init__(self, pieces: Sequence[Tuple[int, float]]): + assert pieces == sorted( + pieces + ), f"PiecewiseLinearFn configuration should be sorted, received: {pieces}" + + self.pieces = pieces + + def __call__(self, x: int) -> float: + for i, (x_a, y_a) in enumerate(self.pieces[:-1]): + x_b, y_b = self.pieces[i + 1] + if x_a <= x <= x_b: + return y_a + (x - x_a) * (y_b - y_a) / (x_b - x_a) + + return self.pieces[-1][1] + + @staticmethod + def from_string(configuration: str) -> "PiecewiseLinearFn": + """ + Parse the configuration of lambda coefficient (for scheduling). + x = "3" # lambda will be a constant equal to x + x = "0:1,1000:0" # lambda will start from 1 and linearly decrease + # to 0 during the first 1000 iterations + x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 + # iterations, then will linearly increase to 1 until iteration 2000 + """ + if isinstance(configuration, float): + return PiecewiseLinearFn([(0, configuration)]) + + try: + parts = configuration.split(",") + if len(parts) == 1: + v = float(configuration) + return PiecewiseLinearFn([(0, v)]) + + split = [s.split(":") for s in parts] + pieces = [(int(t), float(v)) for t, v in split] + return PiecewiseLinearFn(pieces) + except Exception: + raise ValueError( + f"Invalid PiecewiseLinearFn configuration: {configuration!r}" + ) + + @staticmethod + def one() -> "PiecewiseLinearFn": + return PiecewiseLinearFn([(0, 1.0)]) + + +@register_task("online_backtranslation") +class OnlineBackTranslationTask(TranslationTask): + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + # Generic translation args + parser.add_argument('data', help='colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner; \ + however, valid and test data are always in the first directory to \ + avoid the need for repeating them in all directories') + parser.add_argument('--mono-langs', metavar='MONO_LANGS', + help='monolingual languages for training') + parser.add_argument('--valid-lang-pairs', default=None, metavar='VALID_LANG_PAIRS', + help='language pairs for validation') + parser.add_argument('--load-alignments', action='store_true', + help='load the binarized alignments') + parser.add_argument('--left-pad-source', default='False', type=str, metavar='BOOL', + help='pad the source on the left') + parser.add_argument('--left-pad-target', default='False', type=str, metavar='BOOL', + help='pad the target on the left') + parser.add_argument('--upsample-primary', default=1, type=int, + help='amount to upsample primary dataset') + try: + parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the source sequence') + parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the target sequence') + except ArgumentError: + # this might have already been defined. Once we transition this to hydra it should be fine to add it here. + pass + parser.add_argument('--truncate-source', action='store_true', default=False, + help='truncate source to max-source-positions') + parser.add_argument('--num-batch-buckets', default=0, type=int, metavar='N', + help='if >0, then bucket source and target lengths into N ' + 'buckets and pad accordingly; this is useful on TPUs ' + 'to minimize the number of compilations') + + # Denoising args + parser.add_argument('--max-word-shuffle-distance', default=3.0, type=float, metavar='N', + help='maximum word shuffle distance for denoising autoencoding data generation') + parser.add_argument('--word-dropout-prob', default=0.1, type=float, metavar='N', + help='word dropout probability for denoising autoencoding data generation') + parser.add_argument('--word-blanking-prob', default=0.2, type=float, metavar='N', + help='word blanking probability for denoising autoencoding data generation') + + # Backtranslation args + parser.add_argument('--lambda-bt', default="1.0", type=str, metavar='N', + help='back-translation weight') + parser.add_argument('--lambda-dae', default="1.0", type=str, metavar='N', + help='denoising auto-encoder weight') + + # Evaluation args + parser.add_argument('--generate-one-by-one', action='store_true', + help='generate one sentence at a time for backtranslation') + + parser.add_argument('--eval-bleu', action='store_true', + help='evaluation with BLEU scores') + parser.add_argument('--eval-bleu-detok', type=str, default="space", + help='detokenize before computing BLEU (e.g., "moses"); ' + 'required if using --eval-bleu; use "space" to ' + 'disable detokenization; see fairseq.data.encoders ' + 'for other options') + parser.add_argument('--eval-bleu-detok-args', type=str, metavar='JSON', + help='args for building the tokenizer, if needed') + parser.add_argument('--eval-tokenized-bleu', action='store_true', default=False, + help='compute tokenized BLEU instead of sacrebleu') + parser.add_argument('--eval-bleu-remove-bpe', nargs='?', const='@@ ', default=None, + help='remove BPE before computing BLEU') + parser.add_argument('--eval-bleu-args', type=str, metavar='JSON', + help='generation args for BLUE scoring, ' + 'e.g., \'{"beam": 4, "lenpen": 0.6}\'') + parser.add_argument('--eval-bleu-print-samples', action='store_true', + help='print sample generations during validation') + # fmt: on + + def __init__(self, args, common_dict, mono_langs, valid_lang_pairs): + super().__init__(args, common_dict, common_dict) + self.common_dict = common_dict + self.mono_langs = mono_langs + self.valid_lang_pairs = valid_lang_pairs + + self.SHOW_SAMPLES_INTERVAL = 1000 + # Start by showing samples + self._show_samples_ctr = self.SHOW_SAMPLES_INTERVAL + self.SHOW_SAMPLES_NUMBER = 5 + self.lambda_bt = PiecewiseLinearFn.from_string(args.lambda_bt) + self.lambda_dae = PiecewiseLinearFn.from_string(args.lambda_dae) + + self.args = args + self.data = utils.split_paths(self.args.data) + if len(self.data) == 1: + shards = list(Path(self.data[0]).glob("shard*")) + if len(shards) > 0: + # keep this as strings, since it can also be a manifold path + old_data = self.data + self.data = [str(shard) for shard in shards] + logging.warning(f"Expanded data directory {old_data} to {self.data}") + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + args.left_pad_source = options.eval_bool(args.left_pad_source) + args.left_pad_target = options.eval_bool(args.left_pad_target) + + paths = utils.split_paths(args.data) + assert len(paths) > 0 + assert args.mono_langs is not None + + mono_langs = args.mono_langs.split(",") + valid_lang_pairs = args.valid_lang_pairs.split(",") + + # load dictionary + dict_path = os.path.join(paths[0], "dict.txt") + common_dict = cls.load_dictionary(dict_path) + + return cls(args, common_dict, mono_langs, valid_lang_pairs) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs) -> FairseqDataset: + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if split == "train": + data_path = self.data[(epoch - 1) % len(self.data)] + dataset = self.load_train_dataset(data_path) + else: + # valid/test should always be the same. + dataset = self.load_translation_dataset(split, self.data[0]) + + self.datasets[split] = dataset + return dataset + + def load_train_dataset(self, data_path: str) -> FairseqDataset: + """The training dataset is made of backtranslation dataset and denoising dataset.""" + data = [] + for lang in self.mono_langs: + train_path = os.path.join(data_path, lang, "train") + # TODO: could we do the BT using denoise sample ? + # this would half the data loading work + data.append((f"{lang}-BT", self.load_bt_dataset(train_path, lang))) + data.append( + (f"{lang}-DENOISE", self.load_denoise_dataset(train_path, lang)) + ) + + return RoundRobinZipDatasets(OrderedDict(data)) + + def _langpair_dataset( + self, src: FairseqDataset, tgt: FairseqDataset + ) -> LanguagePairDataset: + return LanguagePairDataset( + src, + src.sizes, + self.dictionary, + tgt=tgt, + tgt_sizes=tgt.sizes, + tgt_dict=self.dictionary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + # TODO: should we shuffle ? we are already sorting batch by sizes so ? + # shuffle=True, + ) + + def _prepend_lang_bos_to_target( + self, dataset: LanguagePairDataset, lang: str + ) -> LanguagePairDataset: + bos = _lang_token_index(self.dictionary, lang) + return TransformEosLangPairDataset( + dataset, + src_eos=self.dictionary.eos(), + new_src_eos=self.dictionary.eos(), + tgt_bos=self.dictionary.eos(), + new_tgt_bos=bos, + ) + + def load_bt_dataset(self, data_path: str, lang: str) -> FairseqDataset: + """The BT dataset is generated with (tgt, tgt) pairs. + The actual translation to a (generated_src, tgt) pair + is done on the fly during training. + """ + mono_dataset = data_utils.load_indexed_dataset( + data_path, self.common_dict, self.args.dataset_impl + ) + assert mono_dataset is not None, f"No dataset found for {lang}" + + mono_dataset_src = PrependTokenDataset( + mono_dataset, _lang_token_index(self.dictionary, lang) + ) + + mono_dataset_bt = self._langpair_dataset(mono_dataset_src, mono_dataset) + logger.info( + f"mono_lang = {lang} " + f"lang token index = {_lang_token_index(self.dictionary, lang)} " + f"lang token = {_lang_token(lang)}" + ) + + mono_dataset_bt = self._prepend_lang_bos_to_target(mono_dataset_bt, lang) + return mono_dataset_bt + + def load_denoise_dataset(self, data_path: str, lang: str) -> FairseqDataset: + """Classic denoising dataset""" + dataset = data_utils.load_indexed_dataset( + data_path, self.common_dict, self.args.dataset_impl + ) + noisy_dataset = NoisingDataset( + dataset, + self.dictionary, + seed=1, + max_word_shuffle_distance=self.args.max_word_shuffle_distance, + word_dropout_prob=self.args.word_dropout_prob, + word_blanking_prob=self.args.word_blanking_prob, + ) + noisy_dataset = PrependTokenDataset( + noisy_dataset, _lang_token_index(self.dictionary, lang) + ) + + clean_dataset = data_utils.load_indexed_dataset( + data_path, self.common_dict, self.args.dataset_impl + ) + denoising_dataset = self._langpair_dataset(noisy_dataset, clean_dataset) + denoising_dataset = self._prepend_lang_bos_to_target(denoising_dataset, lang) + return denoising_dataset + + def load_translation_dataset( + self, split: str, data_path: str, combine: bool = False + ): + # only judging with one language pair for the moment, + # since ConcatDataset doesn't work as expected + assert len(self.valid_lang_pairs) == 1, "For now..." + valid_lang_pair = self.valid_lang_pairs[0] + src, tgt = valid_lang_pair.split("-") + + # use the same function than TranslationTask + src_tgt_dt = load_langpair_dataset( + data_path, + split, + src, + self.common_dict, + tgt, + self.common_dict, + combine=combine, + dataset_impl=self.args.dataset_impl, + upsample_primary=self.args.upsample_primary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + max_source_positions=self.args.max_source_positions, + max_target_positions=self.args.max_target_positions, + load_alignments=self.args.load_alignments, + truncate_source=self.args.truncate_source, + num_buckets=self.args.num_batch_buckets, + shuffle=(split != "test"), + prepend_bos_src=_lang_token_index(self.dictionary, src), + ) + + src_tgt_eos_dt = self._prepend_lang_bos_to_target(src_tgt_dt, tgt) + src_tgt_eos_dt.args = self.args + return src_tgt_eos_dt + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + raise NotImplementedError + + def build_model(self, args, from_checkpoint=False): + # torch.autograd.set_detect_anomaly(True) + model = super().build_model(args, from_checkpoint) + + add_secial_tokens_to_dict_and_model(self.common_dict, model, self.mono_langs) + + self.sequence_generators = {} + for mono_lang in self.mono_langs: + self.sequence_generators[mono_lang] = SequenceGenerator( + [model], + tgt_dict=self.dictionary, + beam_size=1, + max_len_a=1.3, + max_len_b=5, + min_len=5, + # keep 1 to be able to prepend bos + max_len=model.max_decoder_positions() - 1, + ) + + if getattr(args, "eval_bleu", False): + assert getattr(args, "eval_bleu_detok", None) is not None, ( + "--eval-bleu-detok is required if using --eval-bleu; " + "try --eval-bleu-detok=moses (or --eval-bleu-detok=space " + "to disable detokenization, e.g., when using sentencepiece)" + ) + detok_args = json.loads(getattr(args, "eval_bleu_detok_args", "{}") or "{}") + self.tokenizer = encoders.build_tokenizer( + Namespace( + tokenizer=getattr(args, "eval_bleu_detok", None), **detok_args + ) + ) + + gen_args = json.loads(getattr(args, "eval_bleu_args", "{}") or "{}") + self.bleu_sequence_generator = self.build_generator( + [model], Namespace(**gen_args) + ) + + return model + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + return (self.args.max_source_positions, self.args.max_target_positions) + + @property + def dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary`.""" + return self.common_dict + + def display_samples_once_in_a_while(self, smp, mono_lang, other_lang): + self._show_samples_ctr += 1 + if self._show_samples_ctr < self.SHOW_SAMPLES_INTERVAL: + return + self._show_samples_ctr = 0 + + ln = smp["net_input"]["src_tokens"].shape[0] + + logger.info( + f"(r:{self.args.distributed_rank}) : " + f"{other_lang} ---> {mono_lang} " + f"({other_lang} was generated by back-translation.) {ln} samples" + ) + + for i in range(min(ln, self.SHOW_SAMPLES_NUMBER)): + src_tokens = smp["net_input"]["src_tokens"][i] + tgt_tokens = smp["target"][i] + + src_str = self.dictionary.string(src_tokens, "sentencepiece") + tgt_str = self.dictionary.string(tgt_tokens, "sentencepiece") + logger.info( + f"\n{i}\t\t[{other_lang} generated] {src_str}\n" + f"\t\t[{mono_lang} original ] {tgt_str}\n" + f"\t\t[ src tokens] {src_tokens}\n" + ) + + def backtranslate_sample(self, smp, orig_lang, other_lang) -> None: + """ + * WARNING: smp is modified in place. + * At the start of this function, `smp` has the same input and target: + |--------------------------------------------------------| + | smp['net_input']['src_tokens'] | smp['target'] | + | (from data) __en__ hello world | __en__ hello world | + |--------------------------------------------------------| + + * We call generator.generate(smp, bos_token = token("ro")), + and copy the result as input + * At the end, `smp` has the translation to other language. + |--------------------------------------------------------| + | smp['net_input']['src_tokens'] | smp['target'] | + | (generated) __ro__ salut lume | __en__ hello world | + |--------------------------------------------------------| + + """ + bos_token = _lang_token_index(self.dictionary, other_lang) + generated = self.sequence_generators[orig_lang].generate( + models=[], sample=smp, bos_token=bos_token + ) + + max_lngth = max([gn[0]["tokens"].size(0) for gn in generated]) + net_input = smp["net_input"] + n_src_tokens = torch.empty( + size=(len(generated), max_lngth + 1), dtype=net_input["src_tokens"].dtype + ) + n_src_lengths = torch.empty( + len(generated), dtype=net_input["src_lengths"].dtype + ) + + for i, gn in enumerate(generated): + tokens = gn[0]["tokens"] + tokens_size = tokens.size(0) + padding_needed = max_lngth - tokens_size + tokens = torch.cat([tokens.new([bos_token]), tokens]) + tokens = F.pad(tokens, (0, padding_needed), value=self.dictionary.pad()) + n_src_tokens[i] = tokens + n_src_lengths[i] = tokens_size + 1 + + device = net_input["src_tokens"].device + # This seems to be important + del net_input["src_tokens"] + del net_input["src_lengths"] + net_input["src_tokens"] = n_src_tokens.to(device) + net_input["src_lengths"] = n_src_lengths.to(device) + + def generate(self, smp, model): + model.eval() + orig_lang = ( + self.dictionary[smp["net_input"]["src_tokens"][0][0]] + .replace(" ", "") + .replace("_", "") + ) + bos_token = smp["net_input"]["prev_output_tokens"][0][0] + with torch.no_grad(): + generated = self.sequence_generators[orig_lang].generate( + models=[model], sample=smp, bos_token=bos_token + ) + return generated + + def get_other_lang(self, lang): + # TODO: allow more complex mapping + if lang != self.mono_langs[0]: + return self.mono_langs[0] + if len(self.mono_langs) == 2: + return self.mono_langs[1] + return self.mono_langs[np.random.randint(1, len(self.mono_langs))] + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + + model.train() + model.set_num_updates(update_num) + + agg_loss, agg_sample_size = 0.0, 0.0 + agg_logging_output: Dict[str, float] = defaultdict(float) + + dataset_keys = self.datasets["train"].datasets.keys() + + weights = { + "BT": self.lambda_bt(update_num), + "DENOISE": self.lambda_dae(update_num), + } + log_keys = {"BT": "bt_", "DENOISE": "dae_"} + + for dataset_key in dataset_keys: + smp = sample[dataset_key] + mono_lang, task_subtype = dataset_key.split("-") + if weights[task_subtype] == 0: + continue + + if task_subtype == "BT": + with torch.autograd.profiler.record_function("backtranslation"): + model.eval() + # TODO: Could we translate to several language at once ? + # this would allow to share encoder_out and maximize GPU usage. + other_lang = self.get_other_lang(mono_lang) + self.backtranslate_sample(smp, mono_lang, other_lang) + self.display_samples_once_in_a_while(smp, mono_lang, other_lang) + model.train() + + # Like in FairseqTask.train_step + with torch.autograd.profiler.record_function("forward"): + loss, sample_size, logging_output = criterion(model, smp) + loss *= weights[task_subtype] + if ignore_grad: + loss *= 0 + with torch.autograd.profiler.record_function("backward"): + optimizer.backward(loss) + + agg_loss += loss.item() + agg_sample_size += sample_size + for k in logging_output: + agg_logging_output[log_keys[task_subtype] + k] += logging_output[k] + agg_logging_output[k] += logging_output[k] + + return agg_loss, agg_sample_size, agg_logging_output + + def get_bos_token_from_sample(self, sample): + net_input = sample["net_input"] + source_lang_token_id = torch.unique(net_input["src_tokens"][:, 0]).item() + source_lang_token = self.dictionary[source_lang_token_id].replace("_", "") + target_lang_token_id = _lang_token_index( + self.dictionary, self.get_other_lang(source_lang_token) + ) + + return target_lang_token_id + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + bt_sample_size = sum(x.get("bt_sample_size", 0) for x in logging_outputs) + if bt_sample_size: + bt_loss_sum = sum(x.get("bt_loss", 0) for x in logging_outputs) + bt_loss_sum *= 1 / bt_sample_size / math.log(2) + metrics.log_scalar("bt_loss", bt_loss_sum, bt_sample_size, round=3) + + bt_nll_loss_sum = sum(x.get("bt_nll_loss", 0) for x in logging_outputs) + bt_ntokens = sum(x.get("bt_ntokens", 0) for x in logging_outputs) + bt_nll_loss_sum *= 1 / bt_ntokens / math.log(2) + metrics.log_scalar("bt_nll_loss", bt_nll_loss_sum, bt_ntokens, round=3) + metrics.log_derived( + "bt_ppl", lambda meters: utils.get_perplexity(meters["bt_nll_loss"].avg) + ) + + dae_sample_size = sum(x.get("dae_sample_size", 0) for x in logging_outputs) + if dae_sample_size: + dae_loss_sum = sum(x.get("dae_loss", 0) for x in logging_outputs) + dae_loss_sum *= 1 / dae_sample_size / math.log(2) + metrics.log_scalar("dae_loss", dae_loss_sum, dae_sample_size, round=3) + + dae_nll_loss_sum = sum(x.get("dae_nll_loss", 0) for x in logging_outputs) + dae_ntokens = sum(x.get("dae_ntokens", 0) for x in logging_outputs) + dae_nll_loss_sum *= 1 / dae_ntokens / math.log(2) + metrics.log_scalar("dae_nll_loss", dae_nll_loss_sum, dae_ntokens, round=3) + metrics.log_derived( + "dae_ppl", + lambda meters: utils.get_perplexity(meters["dae_nll_loss"].avg), + ) + + +@torch.no_grad() +def extend_embedding( + emb: nn.Module, new_vocab_size: int, copy_from_token_id: int +) -> None: + old_emb_data = emb.weight.data + (old_vocab_size, dim) = old_emb_data.shape + assert new_vocab_size >= old_vocab_size + + if new_vocab_size > old_vocab_size: + emb.weight.data = torch.zeros((new_vocab_size, dim)) + emb.weight.data[:old_vocab_size, :] = old_emb_data + # initialize new embeddings + emb.weight.data[old_vocab_size:, :] = old_emb_data[copy_from_token_id] + if hasattr(emb, "num_embeddings"): + emb.num_embeddings = new_vocab_size + if hasattr(emb, "out_features"): + emb.out_features = new_vocab_size + + if getattr(emb, "bias", None) is None: + return + + # Fix the bias. + # Bias shape can be different from the previous vocab size + # if the weight matrix was shared and alread extended but not the bias. + (old_vocab_size,) = emb.bias.shape + assert new_vocab_size >= old_vocab_size + if new_vocab_size > old_vocab_size: + old_bias = emb.bias.data + new_bias = torch.zeros( + (new_vocab_size,), dtype=old_bias.dtype, device=old_bias.device + ) + new_bias[:old_vocab_size] = old_bias + emb.bias.data = new_bias + + +def add_secial_tokens_to_dict_and_model( + dictionary: "fairseq.data.Dictionary", + model: nn.Module, + mono_langs: Sequence[str], +) -> None: + embs = model.encoder.embed_tokens + vocab_size, embedding_dim = embs.weight.shape + + # The model may or may not have a '<mask>' embedding yet + assert ( + len(dictionary) <= vocab_size <= len(dictionary) + 1 + ), f"Dictionary len ({len(dictionary)}) doesn't match embs shape ({embs.weight.shape})" + # TODO: we should reuse the pretrained model dict which already has <mask> + dictionary.add_symbol("<mask>") + + for lang in mono_langs: + lang_token = _lang_token(lang) + dictionary.add_symbol(lang_token) + logger.info( + f"dictionary: {len(dictionary)} -> {vocab_size} tokens " + f"after adding {len(mono_langs)} lang tokens." + ) + + if len(dictionary) <= vocab_size: + return + + extend_embedding(embs, len(dictionary), dictionary.bos()) + dec_embs = model.decoder.embed_tokens + extend_embedding(dec_embs, len(dictionary), dictionary.bos()) + lm_head = model.decoder.output_projection + extend_embedding(lm_head, len(dictionary), dictionary.bos()) + assert lm_head.weight.shape == (len(dictionary), embedding_dim) + + +def _lang_token(lang: str) -> str: + return f"__{lang}__" + + +def _lang_token_index(dictionary, lang: str) -> int: + return dictionary.index(_lang_token(lang)) + + +@contextlib.contextmanager +def assert_weights_have_changed(model: nn.Module): + def checksum(model: nn.Module) -> float: + return sum(p.sum().item() for p in model.parameters()) + + initial_checksum = checksum(model) + yield model + final_checksum = checksum(model) + logger.info( + f"initial_checksum={initial_checksum} -> final_checksum={final_checksum}" + ) + assert initial_checksum != final_checksum, "Model hasn't changed !" diff --git a/fairseq/fairseq/tasks/semisupervised_translation.py b/fairseq/fairseq/tasks/semisupervised_translation.py new file mode 100644 index 0000000..432b8a5 --- /dev/null +++ b/fairseq/fairseq/tasks/semisupervised_translation.py @@ -0,0 +1,485 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from collections import OrderedDict + +from fairseq import utils +from fairseq.data import ( + BacktranslationDataset, + IndexedCachedDataset, + IndexedDataset, + IndexedRawTextDataset, + LanguagePairDataset, + NoisingDataset, + RoundRobinZipDatasets, + data_utils, + indexed_dataset, +) +from fairseq.models import FairseqMultiModel +from fairseq.sequence_generator import SequenceGenerator + +from . import register_task +from .multilingual_translation import MultilingualTranslationTask + + +logger = logging.getLogger(__name__) + + +def _get_bt_dataset_key(lang_pair): + return "bt:" + lang_pair + + +def _get_denoising_dataset_key(lang_pair): + return "denoising:" + lang_pair + + +# ported from UnsupervisedMT +def parse_lambda_config(x): + """ + Parse the configuration of lambda coefficient (for scheduling). + x = "3" # lambda will be a constant equal to x + x = "0:1,1000:0" # lambda will start from 1 and linearly decrease + # to 0 during the first 1000 iterations + x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 + # iterations, then will linearly increase to 1 until iteration 2000 + """ + split = x.split(",") + if len(split) == 1: + return float(x), None + else: + split = [s.split(os.pathsep) for s in split] + assert all(len(s) == 2 for s in split) + assert all(k.isdigit() for k, _ in split) + assert all( + int(split[i][0]) < int(split[i + 1][0]) for i in range(len(split) - 1) + ) + return float(split[0][1]), [(int(k), float(v)) for k, v in split] + + +@register_task("semisupervised_translation") +class SemisupervisedTranslationTask(MultilingualTranslationTask): + """A task for training multiple translation models simultaneously. + + We iterate round-robin over batches from multiple language pairs, ordered + according to the `--lang-pairs` argument. + + The training loop is roughly: + + for i in range(len(epoch)): + for lang_pair in args.lang_pairs: + batch = next_batch_for_lang_pair(lang_pair) + loss = criterion(model_for_lang_pair(lang_pair), batch) + loss.backward() + optimizer.step() + + In practice, `next_batch_for_lang_pair` is abstracted in a FairseqDataset + (e.g., `RoundRobinZipDatasets`) and `model_for_lang_pair` is a model that + implements the `FairseqMultiModel` interface. + + During inference it is required to specify a single `--source-lang` and + `--target-lang`, instead of `--lang-pairs`. + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + MultilingualTranslationTask.add_args(parser) + parser.add_argument('--lambda-parallel-config', default="1.0", type=str, metavar='CONFIG', + help='cross-entropy reconstruction coefficient (parallel data). ' + 'use fixed weight during training if set to floating point number. ' + 'use piecewise linear function over number of updates to schedule the ' + 'weight with the format: w0:step0,w1:step1,...') + parser.add_argument('--lambda-denoising-config', default="0.0", type=str, metavar='CONFIG', + help='Cross-entropy reconstruction coefficient (denoising autoencoding)' + 'use fixed weight during training if set to floating point number. ' + 'use piecewise linear function over number of updates to schedule the ' + 'weight with the format: w0:step0,w1:step1,...') + parser.add_argument('--lambda-otf-bt-config', default="0.0", type=str, metavar='CONFIG', + help='cross-entropy reconstruction coefficient (on-the-fly back-translation parallel data)' + 'use fixed weight during training if set to floating point number. ' + 'use piecewise linear function over number of updates to schedule the ' + 'weight with the format: w0:step0,w1:step1,...') + parser.add_argument('--bt-max-len-a', default=1.1, type=float, metavar='N', + help='generate back-translated sequences of maximum length ax + b, where x is the ' + 'source length') + parser.add_argument('--bt-max-len-b', default=10.0, type=float, metavar='N', + help='generate back-translated sequences of maximum length ax + b, where x is the ' + 'source length') + parser.add_argument('--bt-beam-size', default=1, type=int, metavar='N', + help='beam size used in beam search of online back-translation') + parser.add_argument('--max-word-shuffle-distance', default=3.0, type=float, metavar='N', + help='maximum word shuffle distance for denoising autoencoding data generation') + parser.add_argument('--word-dropout-prob', default=0.1, type=float, metavar='N', + help='word dropout probability for denoising autoencoding data generation') + parser.add_argument('--word-blanking-prob', default=0.2, type=float, metavar='N', + help='word blanking probability for denoising autoencoding data generation') + # fmt: on + + def __init__(self, args, dicts, training): + super().__init__(args, dicts, training) + self.lambda_parallel, self.lambda_parallel_steps = parse_lambda_config( + args.lambda_parallel_config + ) + self.lambda_otf_bt, self.lambda_otf_bt_steps = parse_lambda_config( + args.lambda_otf_bt_config + ) + self.lambda_denoising, self.lambda_denoising_steps = parse_lambda_config( + args.lambda_denoising_config + ) + if self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None: + denoising_lang_pairs = [ + "%s-%s" % (tgt, tgt) + for tgt in {lang_pair.split("-")[1] for lang_pair in args.lang_pairs} + ] + self.model_lang_pairs = self.model_lang_pairs + denoising_lang_pairs + self.backtranslate_datasets = {} + self.backtranslators = {} + + @classmethod + def setup_task(cls, args, **kwargs): + dicts, training = MultilingualTranslationTask.prepare(args, **kwargs) + return cls(args, dicts, training) + + def load_dataset(self, split, epoch=1, **kwargs): + """Load a dataset split.""" + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + def split_exists(split, src, tgt, lang): + if src is not None: + filename = os.path.join( + data_path, "{}.{}-{}.{}".format(split, src, tgt, lang) + ) + else: + filename = os.path.join( + data_path, "{}.{}-None.{}".format(split, src, tgt) + ) + return indexed_dataset.dataset_exists(filename, impl=self.args.dataset_impl) + + def load_indexed_dataset(path, dictionary): + return data_utils.load_indexed_dataset( + path, dictionary, self.args.dataset_impl + ) + + # load parallel datasets + src_datasets, tgt_datasets = {}, {} + if ( + self.lambda_parallel > 0.0 + or self.lambda_parallel_steps is not None + or not split.startswith("train") + ): + for lang_pair in self.lang_pairs: + src, tgt = lang_pair.split("-") + if split_exists(split, src, tgt, src): + prefix = os.path.join( + data_path, "{}.{}-{}.".format(split, src, tgt) + ) + elif split_exists(split, tgt, src, src): + prefix = os.path.join( + data_path, "{}.{}-{}.".format(split, tgt, src) + ) + else: + continue + src_datasets[lang_pair] = load_indexed_dataset( + prefix + src, self.dicts[src] + ) + tgt_datasets[lang_pair] = load_indexed_dataset( + prefix + tgt, self.dicts[tgt] + ) + logger.info( + "parallel-{} {} {} examples".format( + data_path, split, len(src_datasets[lang_pair]) + ) + ) + if len(src_datasets) == 0: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) + + # back translation datasets + backtranslate_datasets = {} + if ( + self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None + ) and split.startswith("train"): + for lang_pair in self.lang_pairs: + src, tgt = lang_pair.split("-") + if not split_exists(split, tgt, None, tgt): + raise FileNotFoundError( + "Dataset not found: backtranslation {} ({})".format( + split, data_path + ) + ) + filename = os.path.join( + data_path, "{}.{}-None.{}".format(split, tgt, tgt) + ) + dataset = load_indexed_dataset(filename, self.dicts[tgt]) + lang_pair_dataset_tgt = LanguagePairDataset( + dataset, + dataset.sizes, + self.dicts[tgt], + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + ) + lang_pair_dataset = LanguagePairDataset( + dataset, + dataset.sizes, + src_dict=self.dicts[src], + tgt=dataset, + tgt_sizes=dataset.sizes, + tgt_dict=self.dicts[tgt], + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + ) + backtranslate_datasets[lang_pair] = BacktranslationDataset( + tgt_dataset=self.alter_dataset_langtok( + lang_pair_dataset_tgt, + src_eos=self.dicts[tgt].eos(), + src_lang=tgt, + tgt_lang=src, + ), + backtranslation_fn=self.backtranslators[lang_pair], + src_dict=self.dicts[src], + tgt_dict=self.dicts[tgt], + output_collater=self.alter_dataset_langtok( + lang_pair_dataset=lang_pair_dataset, + src_eos=self.dicts[src].eos(), + src_lang=src, + tgt_eos=self.dicts[tgt].eos(), + tgt_lang=tgt, + ).collater, + ) + logger.info( + "backtranslate-{}: {} {} {} examples".format( + tgt, + data_path, + split, + len(backtranslate_datasets[lang_pair]), + ) + ) + self.backtranslate_datasets[lang_pair] = backtranslate_datasets[ + lang_pair + ] + + # denoising autoencoder + noising_datasets = {} + if ( + self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None + ) and split.startswith("train"): + for lang_pair in self.lang_pairs: + _, tgt = lang_pair.split("-") + if not split_exists(split, tgt, None, tgt): + continue + filename = os.path.join( + data_path, "{}.{}-None.{}".format(split, tgt, tgt) + ) + tgt_dataset1 = load_indexed_dataset(filename, self.dicts[tgt]) + tgt_dataset2 = load_indexed_dataset(filename, self.dicts[tgt]) + noising_dataset = NoisingDataset( + tgt_dataset1, + self.dicts[tgt], + seed=1, + max_word_shuffle_distance=self.args.max_word_shuffle_distance, + word_dropout_prob=self.args.word_dropout_prob, + word_blanking_prob=self.args.word_blanking_prob, + ) + noising_datasets[lang_pair] = self.alter_dataset_langtok( + LanguagePairDataset( + noising_dataset, + tgt_dataset1.sizes, + self.dicts[tgt], + tgt_dataset2, + tgt_dataset2.sizes, + self.dicts[tgt], + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + ), + src_eos=self.dicts[tgt].eos(), + src_lang=tgt, + tgt_eos=self.dicts[tgt].eos(), + tgt_lang=tgt, + ) + logger.info( + "denoising-{}: {} {} {} examples".format( + tgt, + data_path, + split, + len(noising_datasets[lang_pair]), + ) + ) + + def language_pair_dataset(lang_pair): + src, tgt = lang_pair.split("-") + src_dataset, tgt_dataset = src_datasets[lang_pair], tgt_datasets[lang_pair] + return self.alter_dataset_langtok( + LanguagePairDataset( + src_dataset, + src_dataset.sizes, + self.dicts[src], + tgt_dataset, + tgt_dataset.sizes, + self.dicts[tgt], + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + ), + self.dicts[src].eos(), + src, + self.dicts[tgt].eos(), + tgt, + ) + + self.datasets[split] = RoundRobinZipDatasets( + OrderedDict( + [ + (lang_pair, language_pair_dataset(lang_pair)) + for lang_pair in src_datasets.keys() + ] + + [ + (_get_bt_dataset_key(lang_pair), dataset) + for lang_pair, dataset in backtranslate_datasets.items() + ] + + [ + (_get_denoising_dataset_key(lang_pair), dataset) + for lang_pair, dataset in noising_datasets.items() + ] + ), + eval_key=None + if self.training + else "%s-%s" % (self.args.source_lang, self.args.target_lang), + ) + + def build_model(self, args, from_checkpoint=False): + from fairseq import models + + model = models.build_model(args, self, from_checkpoint) + if not isinstance(model, FairseqMultiModel): + raise ValueError( + "SemisupervisedTranslationTask requires a FairseqMultiModel architecture" + ) + + # create SequenceGenerator for each model that has backtranslation dependency on it + self.sequence_generators = {} + if ( + self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None + ) and self.training: + for lang_pair in self.lang_pairs: + src, tgt = lang_pair.split("-") + key = "{}-{}".format(tgt, src) + self.sequence_generators[key] = SequenceGenerator( + [model.models[key]], + tgt_dict=self.dicts[src], + beam_size=args.bt_beam_size, + max_len_a=args.bt_max_len_a, + max_len_b=args.bt_max_len_b, + ) + decoder_lang_tok_idx = self.get_decoder_langtok(src) + + def backtranslate_fn( + sample, + model=model.models[key], + bos_token=decoder_lang_tok_idx, + sequence_generator=self.sequence_generators[key], + ): + return sequence_generator.generate( + [model], + sample, + bos_token=bos_token, + ) + + self.backtranslators[lang_pair] = backtranslate_fn + + return model + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + model.train() + + if update_num > 0: + self.update_step(update_num) + + agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, {} + + def forward_backward(model, samples, logging_output_key, weight): + nonlocal agg_loss, agg_sample_size, agg_logging_output + if samples is None or len(samples) == 0: + return + loss, sample_size, logging_output = criterion(model, samples) + if ignore_grad: + loss *= 0 + else: + loss *= weight + optimizer.backward(loss) + agg_loss += loss.detach().item() + # TODO make summing of the sample sizes configurable + agg_sample_size += sample_size + for k in logging_output: + agg_logging_output[k] += logging_output[k] + agg_logging_output[logging_output_key] += logging_output[k] + + if self.lambda_parallel > 0.0: + for lang_pair in self.lang_pairs: + forward_backward( + model.models[lang_pair], + sample[lang_pair], + lang_pair, + self.lambda_parallel, + ) + + if self.lambda_otf_bt > 0.0: + for lang_pair in self.lang_pairs: + sample_key = _get_bt_dataset_key(lang_pair) + forward_backward( + model.models[lang_pair], + sample[sample_key], + sample_key, + self.lambda_otf_bt, + ) + + if self.lambda_denoising > 0.0: + for lang_pair in self.lang_pairs: + _, tgt = lang_pair.split("-") + sample_key = _get_denoising_dataset_key(lang_pair) + forward_backward( + model.models["{0}-{0}".format(tgt)], + sample[sample_key], + sample_key, + self.lambda_denoising, + ) + + return agg_loss, agg_sample_size, agg_logging_output + + def update_step(self, num_updates): + def lambda_step_func(config, n_iter): + """ + Update a lambda value according to its schedule configuration. + """ + ranges = [ + i + for i in range(len(config) - 1) + if config[i][0] <= n_iter < config[i + 1][0] + ] + if len(ranges) == 0: + assert n_iter >= config[-1][0] + return config[-1][1] + assert len(ranges) == 1 + i = ranges[0] + x_a, y_a = config[i] + x_b, y_b = config[i + 1] + return y_a + (n_iter - x_a) * float(y_b - y_a) / float(x_b - x_a) + + if self.lambda_parallel_steps is not None: + self.lambda_parallel = lambda_step_func( + self.lambda_parallel_steps, num_updates + ) + if self.lambda_denoising_steps is not None: + self.lambda_denoising = lambda_step_func( + self.lambda_denoising_steps, num_updates + ) + if self.lambda_otf_bt_steps is not None: + self.lambda_otf_bt = lambda_step_func(self.lambda_otf_bt_steps, num_updates) diff --git a/fairseq/fairseq/tasks/sentence_prediction.py b/fairseq/fairseq/tasks/sentence_prediction.py new file mode 100644 index 0000000..de80add --- /dev/null +++ b/fairseq/fairseq/tasks/sentence_prediction.py @@ -0,0 +1,303 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os + +import contextlib +from dataclasses import dataclass, field +from typing import Optional +from omegaconf import MISSING, II, open_dict, OmegaConf + +import numpy as np +from fairseq.data import ( + ConcatSentencesDataset, + Dictionary, + IdDataset, + NestedDictionaryDataset, + NumelDataset, + NumSamplesDataset, + OffsetTokensDataset, + PrependTokenDataset, + RawLabelDataset, + RightPadDataset, + RightPaddingMaskDataset, + RollDataset, + SortDataset, + StripTokenDataset, + data_utils, +) +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.tasks import FairseqDataclass, FairseqTask, register_task +from fairseq.dataclass import ChoiceEnum + + +logger = logging.getLogger(__name__) +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) + + +@dataclass +class SentencePredictionConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + num_classes: int = field( + default=-1, + metadata={"help": "number of classes or regression targets"}, + ) + init_token: Optional[int] = field( + default=None, + metadata={"help": "add token at the beginning of each batch item"}, + ) + separator_token: Optional[int] = field( + default=None, + metadata={"help": "add separator token between inputs"}, + ) + no_shuffle: bool = field( + default=False, + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed tokens_per_sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + add_prev_output_tokens: bool = field( + default=False, + metadata={ + "help": "add prev_output_tokens to sample, used for encoder-decoder arch" + }, + ) + max_positions: int = field( + default=512, + metadata={"help": "max tokens per example"}, + ) + + regression_target: bool = II("criterion.regression_target") + classification_head_name: str = II("criterion.classification_head_name") + seed: int = II("common.seed") + + d2v2_multi: bool = field( + default=False, + metadata={"help": "prepare dataset for data2vec_multi"}, + ) + + +@register_task("sentence_prediction", dataclass=SentencePredictionConfig) +class SentencePredictionTask(FairseqTask): + """ + Sentence (or sentence pair) prediction (classification or regression) task. + + Args: + dictionary (Dictionary): the dictionary for the input of the task + """ + + def __init__(self, cfg, data_dictionary, label_dictionary): + super().__init__(cfg) + self.dictionary = data_dictionary + self._label_dictionary = label_dictionary + + @classmethod + def load_dictionary(cls, filename): + """Load the dictionary from the filename + + Args: + filename (str): the filename + """ + dictionary = Dictionary.load(filename) + dictionary.add_symbol("<mask>") + return dictionary + + @classmethod + def setup_task(cls, cfg, **kwargs): + assert cfg.num_classes > 0, "Must set task.num_classes" + + # load data dictionary + data_dict = cls.load_dictionary( + os.path.join(cfg.data, "input0", "dict.txt"), + ) + logger.info("[input] dictionary: {} types".format(len(data_dict))) + + # load label dictionary + if not cfg.regression_target: + label_dict = cls.load_dictionary( + os.path.join(cfg.data, "label", "dict.txt"), + ) + logger.info("[label] dictionary: {} types".format(len(label_dict))) + else: + label_dict = data_dict + return cls(cfg, data_dict, label_dict) + + def load_dataset(self, split, combine=False, **kwargs): + """Load a given dataset split (e.g., train, valid, test).""" + + def get_path(key, split): + return os.path.join(self.cfg.data, key, split) + + def make_dataset(key, dictionary): + split_path = get_path(key, split) + + try: + dataset = data_utils.load_indexed_dataset( + split_path, + dictionary, + combine=combine, + ) + except Exception as e: + if "StorageException: [404] Path not found" in str(e): + logger.warning(f"dataset {e} not found") + dataset = None + else: + raise e + return dataset + + input0 = make_dataset("input0", self.source_dictionary) + assert input0 is not None, "could not find dataset: {}".format( + get_path("input0", split) + ) + input1 = make_dataset("input1", self.source_dictionary) + + if self.cfg.init_token is not None: + input0 = PrependTokenDataset(input0, self.cfg.init_token) + + if input1 is None: + src_tokens = input0 + else: + if self.cfg.separator_token is not None: + input1 = PrependTokenDataset(input1, self.cfg.separator_token) + + src_tokens = ConcatSentencesDataset(input0, input1) + + with data_utils.numpy_seed(self.cfg.seed): + shuffle = np.random.permutation(len(src_tokens)) + + src_tokens = maybe_shorten_dataset( + src_tokens, + split, + self.cfg.shorten_data_split_list, + self.cfg.shorten_method, + self.max_positions(), + self.cfg.seed, + ) + + if self.cfg.d2v2_multi: + net_input = { + "source": RightPadDataset( + src_tokens, + pad_idx=self.source_dictionary.pad(), + ), + "id": IdDataset(), + "padding_mask": RightPaddingMaskDataset(src_tokens), + } + else: + net_input = { + "src_tokens": RightPadDataset( + src_tokens, + pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": NumelDataset(src_tokens, reduce=False), + } + if self.cfg.add_prev_output_tokens: + prev_tokens_dataset = RightPadDataset( + RollDataset(src_tokens, 1), + pad_idx=self.dictionary.pad(), + ) + net_input.update( + prev_output_tokens=prev_tokens_dataset, + ) + + dataset = { + "id": IdDataset(), + "net_input": net_input, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens, reduce=True), + } + + if not self.cfg.regression_target: + label_dataset = make_dataset("label", self.label_dictionary) + if label_dataset is not None: + dataset.update( + target=OffsetTokensDataset( + StripTokenDataset( + label_dataset, + id_to_strip=self.label_dictionary.eos(), + ), + offset=-self.label_dictionary.nspecial, + ) + ) + else: + label_path = "{0}.label".format(get_path("label", split)) + if os.path.exists(label_path): + + def parse_regression_target(i, line): + values = line.split() + assert ( + len(values) == self.cfg.num_classes + ), f'expected num_classes={self.cfg.num_classes} regression target values on line {i}, found: "{line}"' + return [float(x) for x in values] + + with open(label_path) as h: + dataset.update( + target=RawLabelDataset( + [ + parse_regression_target(i, line.strip()) + for i, line in enumerate(h.readlines()) + ] + ) + ) + + nested_dataset = NestedDictionaryDataset( + dataset, + sizes=[src_tokens.sizes], + ) + + if self.cfg.no_shuffle: + dataset = nested_dataset + else: + dataset = SortDataset( + nested_dataset, + # shuffle + sort_order=[shuffle], + ) + + logger.info("Loaded {0} with #samples: {1}".format(split, len(dataset))) + + self.datasets[split] = dataset + return self.datasets[split] + + def build_model(self, cfg, from_checkpoint=False): + from fairseq import models + + with open_dict(cfg) if OmegaConf.is_config(cfg) else contextlib.ExitStack(): + cfg.max_positions = self.cfg.max_positions + + model = models.build_model(cfg, self, from_checkpoint) + + model.register_classification_head( + self.cfg.classification_head_name, + num_classes=self.cfg.num_classes, + ) + + return model + + def max_positions(self): + return self.cfg.max_positions + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + @property + def label_dictionary(self): + return self._label_dictionary diff --git a/fairseq/fairseq/tasks/sentence_prediction_adapters.py b/fairseq/fairseq/tasks/sentence_prediction_adapters.py new file mode 100644 index 0000000..afe5569 --- /dev/null +++ b/fairseq/fairseq/tasks/sentence_prediction_adapters.py @@ -0,0 +1,56 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import contextlib +from omegaconf import open_dict, OmegaConf + +from fairseq.tasks import register_task +from fairseq.tasks.sentence_prediction import ( + SentencePredictionTask, + SentencePredictionConfig, +) + + +logger = logging.getLogger(__name__) + + +@register_task("sentence_prediction_adapters", dataclass=SentencePredictionConfig) +class SentencePredictionAdapterTask(SentencePredictionTask): + def build_model(self, cfg): + from fairseq import models + + with open_dict(cfg) if OmegaConf.is_config(cfg) else contextlib.ExitStack(): + cfg.max_positions = self.cfg.max_positions + + model = models.build_model(cfg, self) + + model.register_classification_head( + self.cfg.classification_head_name, + num_classes=self.cfg.num_classes, + ) + + logger.info("Freezing Embedding Parameters") + for parameter in model.encoder.sentence_encoder.embed_positions.parameters(): + parameter.requires_grad = False + for ( + parameter + ) in model.encoder.sentence_encoder.layernorm_embedding.parameters(): + parameter.requires_grad = False + for parameter in model.encoder.sentence_encoder.embed_tokens.parameters(): + parameter.requires_grad = False + + logger.info("Freezing Adapters") + for k, v in model.encoder.sentence_encoder.layers._modules.items(): + logger.info("Freezing Adapters in Layer " + str(k)) + if hasattr(v, "adapter_layer_norm"): + logger.info("Freezing Adapter LN") + for parameter in v.adapter_layer_norm.parameters(): + parameter.requires_grad = False + for parameter in v.adapter_modules.parameters(): + parameter.requires_grad = False + + return model diff --git a/fairseq/fairseq/tasks/sentence_ranking.py b/fairseq/fairseq/tasks/sentence_ranking.py new file mode 100644 index 0000000..57f63aa --- /dev/null +++ b/fairseq/fairseq/tasks/sentence_ranking.py @@ -0,0 +1,219 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os + +import numpy as np +from fairseq import utils +from fairseq.data import ( + ConcatSentencesDataset, + Dictionary, + IdDataset, + NestedDictionaryDataset, + NumelDataset, + NumSamplesDataset, + PrependTokenDataset, + RawLabelDataset, + RightPadDataset, + SortDataset, + TruncateDataset, + data_utils, +) +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.tasks import LegacyFairseqTask, register_task + + +logger = logging.getLogger(__name__) + + +@register_task("sentence_ranking") +class SentenceRankingTask(LegacyFairseqTask): + """ + Ranking task on multiple sentences. + + Args: + dictionary (Dictionary): the dictionary for the input of the task + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument("data", metavar="FILE", help="file prefix for data") + parser.add_argument( + "--num-classes", type=int, help="number of sentences to be ranked" + ) + parser.add_argument( + "--init-token", + type=int, + help="add token at the beginning of each batch item", + ) + parser.add_argument( + "--separator-token", type=int, help="add separator token between inputs" + ) + parser.add_argument("--no-shuffle", action="store_true") + parser.add_argument( + "--shorten-method", + default="none", + choices=["none", "truncate", "random_crop"], + help="if not none, shorten sequences that exceed --tokens-per-sample", + ) + parser.add_argument( + "--shorten-data-split-list", + default="", + help="comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)', + ) + parser.add_argument( + "--max-option-length", type=int, help="max length for each option" + ) + + def __init__(self, args, dictionary): + super().__init__(args) + self.dictionary = dictionary + + @classmethod + def load_dictionary(cls, args, filename, source=True): + """Load the dictionary from the filename + + Args: + filename (str): the filename + """ + dictionary = Dictionary.load(filename) + dictionary.add_symbol("<mask>") + return dictionary + + @classmethod + def setup_task(cls, args, **kwargs): + assert ( + args.criterion == "sentence_ranking" + ), "Must set --criterion=sentence_ranking" + + # load data dictionary + data_dict = cls.load_dictionary( + args, + os.path.join(args.data, "input0", "dict.txt"), + source=True, + ) + logger.info("[input] dictionary: {} types".format(len(data_dict))) + return SentenceRankingTask(args, data_dict) + + def load_dataset(self, split, combine=False, **kwargs): + """Load a given dataset split (e.g., train, valid, test).""" + + def get_path(type, split): + return os.path.join(self.args.data, type, split) + + def make_dataset(type, dictionary): + split_path = get_path(type, split) + + dataset = data_utils.load_indexed_dataset( + split_path, + self.source_dictionary, + self.args.dataset_impl, + combine=combine, + ) + return dataset + + input0 = make_dataset("input0", self.source_dictionary) + input_options = [ + make_dataset("input{idx}".format(idx=idx + 1), self.source_dictionary) + for idx in range(self.args.num_classes) + ] + + if self.args.separator_token is not None: + input0 = PrependTokenDataset(input0, self.args.separator_token) + + src_tokens = [] + for input_option in input_options: + if self.args.init_token is not None: + input_option = PrependTokenDataset(input_option, self.args.init_token) + if self.args.max_option_length is not None: + input_option = TruncateDataset( + input_option, self.args.max_option_length + ) + src_token = ConcatSentencesDataset(input_option, input0) + src_token = maybe_shorten_dataset( + src_token, + split, + self.args.shorten_data_split_list, + self.args.shorten_method, + self.args.max_positions, + self.args.seed, + ) + src_tokens.append(src_token) + + with data_utils.numpy_seed(self.args.seed): + shuffle = np.random.permutation(len(src_tokens[0])) + + dataset = { + "id": IdDataset(), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens[0], reduce=True), + } + + for src_token_idx in range(len(src_tokens)): + dataset.update( + { + "net_input{idx}".format(idx=src_token_idx + 1): { + "src_tokens": RightPadDataset( + src_tokens[src_token_idx], + pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": NumelDataset( + src_tokens[src_token_idx], reduce=False + ), + } + } + ) + + label_path = "{}.label".format(get_path("label", split)) + if os.path.exists(label_path): + with open(label_path) as h: + dataset.update( + target=RawLabelDataset([int(x.strip()) for x in h.readlines()]) + ) + + nested_dataset = NestedDictionaryDataset( + dataset, + sizes=[np.maximum.reduce([src_token.sizes for src_token in src_tokens])], + ) + + if self.args.no_shuffle: + dataset = nested_dataset + else: + dataset = SortDataset( + nested_dataset, + # shuffle + sort_order=[shuffle], + ) + + logger.info("Loaded {0} with #samples: {1}".format(split, len(dataset))) + + self.datasets[split] = dataset + return self.datasets[split] + + def build_model(self, args, from_checkpoint=False): + from fairseq import models + + model = models.build_model(args, self, from_checkpoint) + + model.register_classification_head( + getattr(args, "ranking_head_name", "sentence_classification_head"), + num_classes=1, + ) + + return model + + def max_positions(self): + return self.args.max_positions + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary diff --git a/fairseq/fairseq/tasks/simultaneous_translation.py b/fairseq/fairseq/tasks/simultaneous_translation.py new file mode 100644 index 0000000..9576b26 --- /dev/null +++ b/fairseq/fairseq/tasks/simultaneous_translation.py @@ -0,0 +1,41 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from fairseq.tasks import register_task +from fairseq.tasks.speech_to_text import SpeechToTextTask +from fairseq.tasks.translation import TranslationTask, TranslationConfig + +try: + import examples.simultaneous_translation # noqa + + import_successful = True +except BaseException: + import_successful = False + + +logger = logging.getLogger(__name__) + + +def check_import(flag): + if not flag: + raise ImportError( + "'examples.simultaneous_translation' is not correctly imported. " + "Please considering `pip install -e $FAIRSEQ_DIR`." + ) + + +@register_task("simul_speech_to_text") +class SimulSpeechToTextTask(SpeechToTextTask): + def __init__(self, args, tgt_dict): + check_import(import_successful) + super().__init__(args, tgt_dict) + + +@register_task("simul_text_to_text", dataclass=TranslationConfig) +class SimulTextToTextTask(TranslationTask): + def __init__(self, cfg, src_dict, tgt_dict): + check_import(import_successful) + super().__init__(cfg, src_dict, tgt_dict) diff --git a/fairseq/fairseq/tasks/span_masked_lm.py b/fairseq/fairseq/tasks/span_masked_lm.py new file mode 100644 index 0000000..d746aa1 --- /dev/null +++ b/fairseq/fairseq/tasks/span_masked_lm.py @@ -0,0 +1,243 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field +from typing import Optional + +import numpy as np +from omegaconf import II, MISSING + +from fairseq import utils +from fairseq.data import ( + AppendTokenDataset, + Dictionary, + IdDataset, + NestedDictionaryDataset, + NumelDataset, + PadDataset, + PrependTokenDataset, + StripTokenDataset, + TokenBlockDataset, + data_utils, +) +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.data.span_mask_tokens_dataset import SpanMaskedTokensDataset +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + +from ..data.indexed_dataset import get_available_dataset_impl + +logger = logging.getLogger(__name__) + +SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) + + +@dataclass +class SpanMaskedLMConfig(FairseqDataclass): + shuffle: bool = field( + default=False, + ) + noise_density: float = field( + default=0.15, + metadata={"help": "What fraction of the tokens to select as noise"}, + ) + mean_noise_span_length: float = field( + default=3, + metadata={"help": "Mean noise span length, must be >= 1"}, + ) + data: str = field( + default=MISSING, + metadata={ + "help": "colon separated path to data directories list, " + "will be iterated upon during epochs in round-robin manner" + }, + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="none", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sample for LM dataset"}, + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + seed: int = II("common.seed") + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + max_source_positions: int = field( + default=1024, metadata={"help": "max number of tokens in the source sequence"} + ) + max_target_positions: int = field( + default=1024, metadata={"help": "max number of tokens in the target sequence"} + ) + include_target_tokens: bool = field( + default=False, + metadata={ + "help": "include target tokens in model input. this is used for data2vec" + }, + ) + + +@register_task("span_masked_lm", dataclass=SpanMaskedLMConfig) +class SpanMaskedLMTask(FairseqTask): + """ + Span masked language modeling task. (ie. T5) + """ + + cfg: SpanMaskedLMConfig + + def __init__(self, cfg, dictionary): + super().__init__(cfg) + self.dictionary = dictionary + + @classmethod + def setup_task(cls, cfg: SpanMaskedLMConfig, **kwargs): + """Setup the task.""" + paths = utils.split_paths(cfg.data) + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + if not hasattr(cfg, "shuffle"): + cfg.shuffle = False + return cls(cfg, dictionary) + + def _load_dataset_split(self, split, epoch, combine): + paths = utils.split_paths(self.cfg.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + split_path = os.path.join(data_path, split) + + dataset = data_utils.load_indexed_dataset( + split_path, + self.dictionary, + self.cfg.dataset_impl, + combine=combine, + ) + if dataset is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + dataset = StripTokenDataset(dataset, self.dictionary.eos()) + + dataset = maybe_shorten_dataset( + dataset, + split, + self.cfg.shorten_data_split_list, + self.cfg.shorten_method, + self.cfg.tokens_per_sample, + self.cfg.seed, + ) + + # create continuous blocks of tokens + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + self.cfg.tokens_per_sample - 2, # one less for <s> and one for </s> + pad=self.dictionary.pad(), + eos=self.dictionary.eos(), + break_mode=self.cfg.sample_break_mode, + document_sep_len=0, + ) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) + + # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) + dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) + dataset = AppendTokenDataset(dataset, self.source_dictionary.eos()) + return dataset + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + dataset = self._load_dataset_split(split, epoch, combine) + + self.datasets[split] = SpanMaskedTokensDataset( + dataset, + self.dictionary, + noise_density=self.cfg.noise_density, + mean_noise_span_length=self.cfg.mean_noise_span_length, + shuffle=self.cfg.shuffle, + seed=self.cfg.seed, + ) + logger.info( + "Split: {0}, Loaded {1} samples of span_masked_tokens_dataset".format( + split, + len(self.datasets[split]), + ) + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + """ + Generate batches for inference. We assume that the input begins with a + bos symbol (`<s>`) and ends with an eos symbol (`</s>`). + """ + pad = self.source_dictionary.pad() + eos = self.source_dictionary.eos() + src_dataset = TokenBlockDataset( + src_tokens, + src_lengths, + block_size=self.cfg.tokens_per_sample - 2, # for <s> and </s> + pad=pad, + eos=eos, + break_mode=self.cfg.sample_break_mode, + document_sep_len=0, + ) + prev_output_tokens = PrependTokenDataset( + StripTokenDataset(src_dataset, eos), eos + ) + src_dataset = PadDataset(src_dataset, pad_idx=pad, left_pad=False) + return NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": src_dataset, + "src_lengths": NumelDataset(src_dataset, reduce=False), + "prev_output_tokens": PadDataset( + prev_output_tokens, pad_idx=pad, left_pad=False + ), + }, + "target": src_dataset, + }, + sizes=[np.array(src_lengths)], + ) + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + return (self.cfg.max_source_positions, self.cfg.max_target_positions) + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary`.""" + return self.dictionary + + @property + def target_dictionary(self): + """Return the target :class:`~fairseq.data.Dictionary`.""" + return self.dictionary diff --git a/fairseq/fairseq/tasks/speech_dlm_task.py b/fairseq/fairseq/tasks/speech_dlm_task.py new file mode 100644 index 0000000..340732b --- /dev/null +++ b/fairseq/fairseq/tasks/speech_dlm_task.py @@ -0,0 +1,561 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field +from typing import Optional +from collections import OrderedDict + +import numpy as np +import torch +from fairseq import utils +from fairseq.data import ( + AppendTokenDataset, + Dictionary, + IdDataset, + LMContextWindowDataset, + MonolingualDataset, + NestedDictionaryDataset, + NumelDataset, + PadDataset, + PrependTokenDataset, + SpeechDLMDataset, + StripTokenDataset, + TokenBlockDataset, + TruncatedDictionary, + data_utils, +) +from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import LegacyFairseqTask, register_task +from omegaconf import II + + +SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) +logger = logging.getLogger(__name__) + + +@dataclass +class SpeechDLMConfig(FairseqDataclass): + data: Optional[str] = field( + default=None, metadata={"help": "path to data directory"} + ) + channels: Optional[str] = field( + default=None, + metadata={ + "help": 'comma-separated list of channels to load e.g., "unitA,unitB"' + "(default: load all possible channels in the data path)" + }, + ) + channel_weights: Optional[str] = field( + default=None, + metadata={ + "help": "comma-separated list of weights for different losses" + "(default: None, which means all losses are treated equally)" + }, + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="none", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sample for LM dataset"}, + ) + output_dictionary_size: int = field( + default=-1, metadata={"help": "limit the size of output dictionary"} + ) + # str type is a workaround to put **default=True** here + next_unit_prediction: str = field( + default="False", + metadata={ + "help": "Perform Next Unit Prediction, expected str input ('True' or 'False')" + }, + ) + edge_unit_prediction: str = field( + default="True", + metadata={ + "help": "Perform Edge Unit Prediction, expected str input ('True' or 'False')" + }, + ) + duration_prediction: str = field( + default="True", + metadata={ + "help": "Perform Duration Prediction, expected str input ('True' or 'False')" + }, + ) + delayed_duration_target: str = field( + default="True", + metadata={ + "help": "Perform Delayed Duration Prediction, expected str input ('True' or 'False')" + "(default: 'True')" + }, + ) + max_target_durations: Optional[int] = field( + default=256, + metadata={"help": "max duration considered (cut off to this value)"}, + ) + add_bos_token: bool = field( + default=False, metadata={"help": "prepend beginning of sentence token (<s>)"} + ) + max_target_positions: Optional[int] = field( + default=None, metadata={"help": "max number of tokens in the target sequence"} + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + # TODO common vars below add to parent + seed: int = II("common.seed") + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + data_buffer_size: int = II("dataset.data_buffer_size") + tpu: bool = II("common.tpu") + + +@register_task("speech_dlm_task", dataclass=SpeechDLMConfig) +class SpeechDLMTask(LegacyFairseqTask): + """Task for the SpeechDLM model as described in the paper: + https://arxiv.org/pdf/2203.16502.pdf + + It create a multi-channel dataset (SpeechDLMDataset) from multiple + dictionaries. + + Args: + dictionaries (Dict[str, ~fairseq.data.Dictionary]): the dictionaries for + each input channel of the SpeechDLM model + output_dictionaries (Dict[str, ~fairseq.data.Dictionary]): the dictionaries + for the output of each channel of the SpeechDLM model. In most cases it + will be the same as *dictionaries*. + targets (List[str]): list of the target types that the SpeechDLM model + should predict. Can be one of "next", "edge", "duration". + Defaults to "next". + + .. note:: + + The SpeechDLM task is only compatible with + :mod:`fairseq-train` and :mod:`fairseq-validate`. + To generate new samples, please refer to example codes + at examples/textless_nlp/dgslm . + """ + + def __init__(self, args, dicts, output_dicts=None, targets=None): + super().__init__(args) + self.dicts = dicts + self.output_dicts = output_dicts or dicts + + if targets is None: + targets = ["next"] + self.targets = targets + + self.channels = list(dicts.keys()) + + if args.channel_weights is not None: + self.channel_weights = [float(w) for w in args.channel_weights.split(",")] + else: + self.channel_weights = [1.0 for _ in self.channels] + assert len(self.channel_weights) == len( + self.channels + ), "number of channel_weights must be the same as number of channels" + + assert str(args.next_unit_prediction).lower() in [ + "true", + "false", + ], f"Expected to be a string of boolean, found {args.next_unit_prediction}" + assert str(args.edge_unit_prediction).lower() in [ + "true", + "false", + ], f"Expected to be a string of boolean, found {args.edge_unit_prediction}" + assert str(args.duration_prediction).lower() in [ + "true", + "false", + ], f"Expected to be a string of boolean, found {args.duration_prediction}" + assert str(args.delayed_duration_target).lower() in [ + "true", + "false", + ], f"Expected to be a string of boolean, found {args.delayed_duration_target}" + self.next_unit_prediction = bool( + str(args.next_unit_prediction).lower() == "true" + ) + self.edge_unit_prediction = bool( + str(args.edge_unit_prediction).lower() == "true" + ) + self.duration_prediction = bool(str(args.duration_prediction).lower() == "true") + self.delayed_duration_target = bool( + str(args.delayed_duration_target).lower() == "true" + ) + + self.max_target_durations = args.max_target_durations + + @classmethod + def setup_dictionary(cls, args, **kwargs): + """The dictionaries will be a dict over channel keys and values of type + ~fairseq.data.Dictionary. + """ + paths = utils.split_paths(args.data) + assert len(paths) > 0 + data_path = paths[0] + + dicts = None + output_dicts = None + if args.channels is None: + sorted_channels = sorted( + name[5:-4] + for name in os.listdir(data_path) + if name[:5] == "dict." and name[-4:] == ".txt" + ) + else: + sorted_channels = sorted(args.channels.split(",")) + logger.info("channels: {}".format(sorted_channels)) + # load dictionaries + dicts = OrderedDict() + output_dicts = OrderedDict() + for channel in sorted_channels: + dictionary = Dictionary.load( + os.path.join(data_path, "dict.{}.txt".format(channel)) + ) + logger.info("[{}] dictionary: {} types".format(channel, len(dictionary))) + output_dictionary = dictionary + if args.output_dictionary_size >= 0: + output_dictionary = TruncatedDictionary( + dictionary, args.output_dictionary_size + ) + dicts[channel] = dictionary + output_dicts[channel] = output_dictionary + if len(dicts) > 0: + assert dicts[channel].pad() == dicts[sorted_channels[0]].pad() + assert dicts[channel].bos() == dicts[sorted_channels[0]].bos() + assert dicts[channel].eos() == dicts[sorted_channels[0]].eos() + assert dicts[channel].unk() == dicts[sorted_channels[0]].unk() + return (dicts, output_dicts) + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + dicts, output_dicts = cls.setup_dictionary(args, **kwargs) + + targets = [] + if str(getattr(args, "next_unit_prediction", "false")).lower() == "true": + targets.append("next") + if str(getattr(args, "edge_unit_prediction", "false")).lower() == "true": + targets.append("edge") + if str(getattr(args, "duration_prediction", "false")).lower() == "true": + targets.append("duration") + if len(targets) == 0: + # standard language modeling + targets = ["next"] + + return cls(args, dicts, output_dicts, targets=targets) + + def build_model(self, args): + model = super().build_model(args) + for target in self.targets: + if target not in model.supported_targets: + raise ValueError("Unsupported SpeechDLM target: {}".format(target)) + return model + + def load_dataset( + self, split: str, epoch=1, combine=False, **kwargs + ) -> SpeechDLMDataset: + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + + data_path = paths[(epoch - 1) % len(paths)] + + channel_datasets = {} + for channel in self.channels: + split_path = os.path.join(data_path, split + "." + channel) + dictionary = self.dicts[channel] + output_dictionary = self.output_dicts[channel] + + dataset = data_utils.load_indexed_dataset( + split_path, dictionary, self.args.dataset_impl, combine=combine + ) + + if dataset is None: + raise FileNotFoundError( + "[{}] Dataset not found: {} ({})".format(channel, split, split_path) + ) + + dataset = maybe_shorten_dataset( + dataset, + split, + self.args.shorten_data_split_list, + self.args.shorten_method, + self.args.tokens_per_sample, + self.args.seed, + ) + + dataset = TokenBlockDataset( + dataset, + dataset.sizes, + self.args.tokens_per_sample, + pad=dictionary.pad(), + eos=dictionary.eos(), + break_mode=self.args.sample_break_mode, + include_targets=True, + ) + + add_eos_for_other_targets = ( + self.args.sample_break_mode is not None + and self.args.sample_break_mode != "none" + ) + + channel_datasets[channel] = MonolingualDataset( + dataset=dataset, + sizes=dataset.sizes, + src_vocab=dictionary, + tgt_vocab=output_dictionary, + add_eos_for_other_targets=add_eos_for_other_targets, + shuffle=False, + targets=["future"], + add_bos_token=self.args.add_bos_token, + ) + + self.datasets[split] = SpeechDLMDataset( + datasets=channel_datasets, + targets=self.targets, + max_target_durations=self.max_target_durations, + shuffle=True, + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + """ + Generate batches for inference. We prepend an eos token to src_tokens + (or bos if `--add-bos-token` is set) and we append a <pad> to target. + This is convenient both for generation with a prefix and LM scoring. + """ + src_datasets = {} + tgt_datasets = {} + for channel in src_tokens[0]: + dataset = StripTokenDataset( + TokenBlockDataset( + [src_tokens[i][channel] for i in range(len(src_tokens))], + src_lengths, + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionaries[channel].pad(), + eos=self.source_dictionaries[channel].eos(), + break_mode="eos", + ), + # remove eos from (end of) target sequence + self.source_dictionaries[channel].eos(), + ) + src_dataset = PrependTokenDataset( + dataset, + token=( + self.source_dictionaries[channel].bos() + if getattr(self.args, "add_bos_token", False) + else self.source_dictionaries[channel].eos() + ), + ) + tgt_dataset = AppendTokenDataset( + dataset, token=self.source_dictionaries[channel].pad() + ) + + src_datasets[channel] = src_dataset + tgt_datasets[channel] = tgt_dataset + + return NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": OrderedDict( + [ + ( + channel, + PadDataset( + src_datasets[channel], + pad_idx=self.source_dictionaries[channel].pad(), + left_pad=False, + ), + ) + for channel in src_datasets + ] + ), + "src_lengths": NumelDataset( + next(iter(src_datasets.values())), reduce=False + ), + }, + "target": OrderedDict( + [ + ( + channel, + PadDataset( + tgt_datasets[channel], + pad_idx=self.source_dictionaries[channel].pad(), + left_pad=False, + ), + ) + for channel in tgt_datasets + ] + ), + }, + sizes=[np.array(src_lengths)], + ) + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + # Generation will always be conditioned on bos_token + if getattr(self.args, "add_bos_token", False): + bos_token = self.source_dictionary.bos() + else: + bos_token = self.source_dictionary.eos() + + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the SpeechDLM task is not supported" + ) + # SequenceGenerator doesn't use src_tokens directly, we need to + # pass the `prefix_tokens` argument instead + if prefix_tokens is None: + prefix_tokens = {} + for channel in sample["net_input"]["src_tokens"]: + if sample["net_input"]["src_tokens"][channel].nelement(): + prefix_tokens_channel = sample["net_input"]["src_tokens"][ + channel + ] + if prefix_tokens_channel[:, 0].eq(bos_token).all(): + prefix_tokens_channel = prefix_tokens_channel[:, 1:] + prefix_tokens[channel] = prefix_tokens_channel + else: + prefix_tokens = None + break + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token + ) + + def eval_lm_dataloader( + self, + dataset, + max_tokens: Optional[int] = 36000, + batch_size: Optional[int] = None, + max_positions: Optional[int] = None, + num_shards: int = 1, + shard_id: int = 0, + num_workers: int = 1, + data_buffer_size: int = 10, + # ensures that every evaluated token has access to a context of at least + # this size, if possible + context_window: int = 0, + ): + if context_window > 0: + dataset = LMContextWindowDataset( + dataset=dataset, + tokens_per_sample=self.args.tokens_per_sample, + context_window=context_window, + pad_idx=self.source_dictionary.pad(), + ) + return self.get_batch_iterator( + dataset=dataset, + max_tokens=max_tokens, + max_sentences=batch_size, + max_positions=max_positions, + ignore_invalid_inputs=True, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + data_buffer_size=data_buffer_size, + ).next_epoch_itr(shuffle=False) + + @property + def source_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.dicts[self.channels[0]] + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.output_dicts[self.channels[0]] + + @property + def source_dictionaries(self): + """Return the dict of :class:`~fairseq.data.Dictionary` for the + multichannel language model.""" + return self.dicts + + @property + def target_dictionaries(self): + """Return the dict of :class:`~fairseq.data.Dictionary` for the + multichannel language model.""" + return self.output_dicts + + def build_generator(self, models, args, extra_gen_cls_kwargs=None): + + from fairseq.models.speech_dlm.sequence_generator import ( + multichannel_search, + MultichannelSequenceGenerator, + ) + + # Choose search strategy. Defaults to Beam Search. + sampling = getattr(args, "sampling", False) + sampling_topk = getattr(args, "sampling_topk", -1) + sampling_topp = getattr(args, "sampling_topp", -1.0) + assert ( + sampling_topk < 0 or sampling + ), "--sampling-topk requires sampling (not beam search)" + assert ( + sampling_topp < 0 or sampling + ), "--sampling-topp requires sampling (not beam search)" + + if sampling: + search_strategy = multichannel_search.ContiguousMultichannelSampling( + self.target_dictionaries, sampling_topk, sampling_topp + ) + else: + search_strategy = multichannel_search.ContiguousMultichannelBeamSearch( + self.target_dictionaries + ) + + extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} + + return MultichannelSequenceGenerator( + models, + self.target_dictionaries, + beam_size=getattr(args, "beam", 5), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 500), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + search_strategy=search_strategy, + duration_temperature=getattr(args, "duration_temperature", 1.0), + **extra_gen_cls_kwargs, + ) diff --git a/fairseq/fairseq/tasks/speech_to_speech.py b/fairseq/fairseq/tasks/speech_to_speech.py new file mode 100644 index 0000000..5aaaa95 --- /dev/null +++ b/fairseq/fairseq/tasks/speech_to_speech.py @@ -0,0 +1,597 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import logging +import math +from argparse import Namespace +from pathlib import Path +from typing import List + +import torch +import torch.nn as nn + +from fairseq import utils +from fairseq.data import Dictionary +from fairseq.data.audio.data_cfg import MultitaskConfig, S2SDataConfig +from fairseq.data.audio.speech_to_speech_dataset import SpeechToSpeechDatasetCreator +from fairseq.data.audio.speech_to_text_dataset import ( + SpeechToTextDataset, + TextTargetMultitaskData, +) +from fairseq.tasks import LegacyFairseqTask, register_task +from fairseq.tasks.speech_to_text import DummyMultiTask +from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion + +logger = logging.getLogger(__name__) + + +class StackUnitSequenceGenerator(nn.Module): + def __init__(self, tgt_dict, vocab_size): + super().__init__() + self.pad = tgt_dict.pad() + self.eos = tgt_dict.eos() + self.unk = tgt_dict.unk() + self.offset = len(tgt_dict) - vocab_size + self.vocab_size = vocab_size + + def pack_units(self, input: torch.Tensor, n_frames_per_step) -> torch.Tensor: + if n_frames_per_step <= 1: + return input + + bsz, _, n = input.shape + assert n == n_frames_per_step + + scale = [ + pow(self.vocab_size, n_frames_per_step - 1 - i) + for i in range(n_frames_per_step) + ] + scale = torch.LongTensor(scale).squeeze(0).to(input.device) + mask = input >= self.offset + res = ((input - self.offset) * scale * mask).sum(dim=2) + self.offset + return res + + @torch.no_grad() + def generate(self, models, sample, **kwargs): + # currently only support viterbi search for stacked units + model = models[0] + model.eval() + + max_len = model.max_decoder_positions() + # TODO: incorporate max_len_a and max_len_b + + src_tokens = sample["net_input"]["src_tokens"] + src_lengths = sample["net_input"]["src_lengths"] + bsz, src_len, _ = src_tokens.size() + n_frames_per_step = model.decoder.n_frames_per_step + + # initialize + encoder_out = model.forward_encoder( + src_tokens, src_lengths, speaker=sample["speaker"] + ) + incremental_state = {} + pred_out, attn, scores = [], [], [] + finished = src_tokens.new_zeros((bsz,)).bool() + + prev_output_tokens = src_lengths.new_zeros((bsz, 1)).long().fill_(self.eos) + for _ in range(max_len): + cur_out, cur_extra = model.forward_decoder( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + ) + + lprobs = model.get_normalized_probs([cur_out], log_probs=True) + # never select pad, unk + lprobs[:, :, self.pad] = -math.inf + lprobs[:, :, self.unk] = -math.inf + + cur_pred_lprob, cur_pred_out = torch.max(lprobs, dim=2) + scores.append(cur_pred_lprob) + pred_out.append(cur_pred_out) + + prev_output_tokens = torch.cat( + ( + prev_output_tokens, + self.pack_units( + cur_pred_out.view(bsz, 1, n_frames_per_step), n_frames_per_step + ), + ), + dim=1, + ) + + attn.append(cur_extra["attn"][0]) + + cur_finished = torch.any(cur_pred_out.squeeze(1) == self.eos, dim=1) + finished = finished | cur_finished + if finished.sum().item() == bsz: + break + + pred_out = torch.cat(pred_out, dim=1).view(bsz, -1) + attn = torch.cat(attn, dim=2) + alignment = attn.max(dim=1)[1] + attn = attn.repeat_interleave(n_frames_per_step, dim=2) + alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) + scores = torch.cat(scores, dim=1) + eos_idx = (pred_out == self.eos).nonzero(as_tuple=True) + out_lens = src_lengths.new_zeros((bsz,)).long().fill_(max_len) + for b, l in zip(eos_idx[0], eos_idx[1]): + out_lens[b] = min(l, out_lens[b]) + + hypos = [ + [ + { + "tokens": pred_out[b, :out_len], + "attn": attn[b, :, :out_len], + "alignment": alignment[b, :out_len], + "positional_scores": scores[b, :out_len], + "score": utils.item(scores[b, :out_len].sum().data), + } + ] + for b, out_len in zip(range(bsz), out_lens) + ] + + return hypos + + +@register_task("speech_to_speech") +class SpeechToSpeechTask(LegacyFairseqTask): + @classmethod + def add_args(cls, parser): + parser.add_argument("data", help="manifest root path") + parser.add_argument( + "--config-yaml", + type=str, + default="config.yaml", + help="Configuration YAML filename (under manifest root)", + ) + parser.add_argument( + "--multitask-config-yaml", + type=str, + default=None, + help="Configuration YAML filename for the multitasks (under manifest root)", + ) + parser.add_argument( + "--max-source-positions", + default=6000, + type=int, + metavar="N", + help="max number of tokens in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + parser.add_argument( + "--target-is-code", + action="store_true", + help="set if target is discrete unit instead of spectrogram", + ) + parser.add_argument( + "--target-code-size", type=int, default=None, help="# discrete units" + ) + parser.add_argument( + "--n-frames-per-step", + type=int, + default=1, + help="# stacked frames, use 0 for reduced discrete unit sequence", + ) + parser.add_argument("--eval-inference", action="store_true") + parser.add_argument( + "--eval-args", + type=str, + default="{}", + help='generation args for speech-to-unit model , e.g., \'{"beam": 5, "max_len_a": 1}\', as JSON string', + ) + parser.add_argument("--eos-prob-threshold", type=float, default=0.5) + parser.add_argument( + "--mcd-normalize-type", + type=str, + default="targ", + choices=["targ", "pred", "path"], + ) + parser.add_argument( + "--vocoder", + type=str, + default="griffin_lim", + choices=["griffin_lim", "hifigan", "code_hifigan"], + ) + parser.add_argument("--spec-bwd-max-iter", type=int, default=8) + parser.add_argument( + "--infer-target-lang", + type=str, + default="", + help="target language for inference", + ) + + def __init__(self, args, tgt_dict, infer_tgt_lang_id=None): + super().__init__(args) + self.tgt_dict = tgt_dict + self.data_cfg = S2SDataConfig(Path(args.data) / args.config_yaml) + + self.multitask_tasks = {} + self.tgt_dict_mt = None + self.eos_token_mt = None + if getattr(args, "multitask_config_yaml", None) is not None: + multitask_cfg = MultitaskConfig( + Path(args.data) / args.multitask_config_yaml + ) + first_pass_task_idx = multitask_cfg.first_pass_decoder_task_index + for i, (task_name, task_config) in enumerate( + multitask_cfg.get_all_tasks().items() + ): + task_obj = DummyMultiTask( + task_config, + task_config.tgt_dict, + first_pass=i == first_pass_task_idx, + ) + self.multitask_tasks[task_name] = task_obj + if task_obj.is_first_pass_decoder: + self.tgt_dict_mt = task_obj.target_dictionary + if task_config.prepend_bos_and_append_tgt_lang_tag: + self.eos_token_mt = task_config.eos_token + assert not isinstance(self.eos_token_mt, List) + + if not self.eos_token_mt: + raise Warning( + "Please provide eos_token in --multitask-config-yaml to replace eos in sequence generator" + ) + + self._infer_tgt_lang_id = infer_tgt_lang_id + + @classmethod + def setup_task(cls, args, **kwargs): + data_cfg = data_cfg = S2SDataConfig(Path(args.data) / args.config_yaml) + tgt_dict = None + infer_tgt_lang_id = None + if args.target_is_code: + if data_cfg.prepend_tgt_lang_tag_as_bos: + # dictionary with language tags + dict_path = Path(args.data) / data_cfg.vocab_filename + if not dict_path.is_file(): + raise FileNotFoundError( + f"Dict has to be provided when setting prepend_tgt_lang_tag_as_bos: true, but dict not found: {dict_path}" + ) + tgt_dict = Dictionary.load(dict_path.as_posix()) + + # target langauge for inference + if args.infer_target_lang != "": + tgt_lang_tag = SpeechToTextDataset.LANG_TAG_TEMPLATE.format( + args.infer_target_lang + ) + infer_tgt_lang_id = tgt_dict.index(tgt_lang_tag) + assert infer_tgt_lang_id != tgt_dict.unk() + else: + assert args.target_code_size is not None + + tgt_dict = Dictionary() + for i in range(args.target_code_size): + tgt_dict.add_symbol(str(i)) + logger.info(f"dictionary size: " f"{len(tgt_dict):,}") + + if getattr(args, "train_subset", None) is not None: + if not all(s.startswith("train") for s in args.train_subset.split(",")): + raise ValueError('Train splits should be named like "train*".') + + assert args.n_frames_per_step >= 1 + assert ( + not args.eval_inference + or (args.target_is_code and args.vocoder == "code_hifigan") + or (not args.target_is_code and args.vocoder != "code_hifigan") + ) + + return cls(args, tgt_dict, infer_tgt_lang_id=infer_tgt_lang_id) + + def build_criterion(self, args): + from fairseq import criterions + + if len(self.multitask_tasks) > 0: + if self.args.target_is_code and not args._name.startswith("speech_to_unit"): + raise ValueError( + "set --criterion speech_to_unit for speech-to-unit loss with multitask" + ) + elif not self.args.target_is_code and not args._name.startswith( + "speech_to_spectrogram" + ): + raise ValueError( + "set --criterion speech_to_spectrogram for speech-to-spectrogram loss with multitask" + ) + + return criterions.build_criterion(args, self) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + self.datasets[split] = SpeechToSpeechDatasetCreator.from_tsv( + root=self.args.data, + data_cfg=self.data_cfg, + splits=split, + is_train_split=split.startswith("train"), + epoch=epoch, + seed=self.args.seed, + target_is_code=self.args.target_is_code, + tgt_dict=self.target_dictionary, + n_frames_per_step=self.args.n_frames_per_step, + multitask=self.multitask_tasks, + ) + + @property + def target_dictionary(self): + return self.tgt_dict + + @property + def target_dictionary_mt(self): + return self.tgt_dict_mt + + @property + def source_dictionary(self): + return None + + def max_positions(self): + return self.args.max_source_positions, self.args.max_target_positions + + def build_model(self, args, from_checkpoint=False): + args.input_feat_per_channel = self.data_cfg.input_feat_per_channel + args.input_channels = self.data_cfg.input_transformed_channels + args.target_speaker_embed = self.data_cfg.target_speaker_embed is not None + args.n_frames_per_step = self.args.n_frames_per_step + + model = super().build_model(args, from_checkpoint) + + if len(self.multitask_tasks) > 0: + from fairseq.models.speech_to_speech.s2s_transformer import ( + S2STransformerMultitaskModelBase, + ) + + assert isinstance(model, S2STransformerMultitaskModelBase) + + if self.args.eval_inference: + self.eval_gen_args = json.loads(self.args.eval_args) + self.generator = self.build_generator( + [model], Namespace(**self.eval_gen_args) + ) + + return model + + def build_generator_dual_decoder( + self, + models, + args, + extra_gen_cls_kwargs=None, + ): + from examples.speech_to_speech.unity.sequence_generator_multi_decoder import ( + MultiDecoderSequenceGenerator, + ) + + return MultiDecoderSequenceGenerator( + models, + self.target_dictionary, + self.target_dictionary_mt, + beam_size=max(1, getattr(args, "beam", 1)), + beam_size_mt=max(1, getattr(args, "beam_mt", 1)), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 200), + max_len_a_mt=getattr(args, "max_len_a_mt", 0), + max_len_b_mt=getattr(args, "max_len_b_mt", 200), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + **extra_gen_cls_kwargs, + ) + + def build_generator( + self, + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=None, + ): + + if not self.args.target_is_code or self.args.eval_inference: + from fairseq.models.text_to_speech.vocoder import get_vocoder + + self.vocoder = get_vocoder(self.args, self.data_cfg) + self.vocoder = ( + self.vocoder.cuda() + if torch.cuda.is_available() and not self.args.cpu + else self.vocoder.cpu() + ) + + has_dual_decoder = getattr(models[0], "mt_task_name", None) is not None + + if self.args.target_is_code: + if self.args.n_frames_per_step == 1: + if has_dual_decoder: + seq_generator = self.build_generator_dual_decoder( + models, + args, + extra_gen_cls_kwargs=extra_gen_cls_kwargs, + ) + else: + seq_generator = super().build_generator( + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=extra_gen_cls_kwargs, + ) + else: + assert ( + getattr(args, "beam", 1) == 1 and getattr(args, "nbest", 1) == 1 + ), "only support viterbi search for stacked units" + seq_generator = StackUnitSequenceGenerator( + self.tgt_dict, + self.args.target_code_size, + ) + else: + if has_dual_decoder: + if getattr(args, "teacher_forcing", False): + raise NotImplementedError + else: + from fairseq.speech_generator import MultiDecoderSpeechGenerator + + generator = MultiDecoderSpeechGenerator + + lang_token_ids_aux = { + i + for s, i in self.tgt_dict_mt.indices.items() + if TextTargetMultitaskData.is_lang_tag(s) + } + + if extra_gen_cls_kwargs is None: + extra_gen_cls_kwargs = {} + extra_gen_cls_kwargs[ + "symbols_to_strip_from_output" + ] = lang_token_ids_aux + + eos_id_mt = ( + self.tgt_dict_mt.index(self.eos_token_mt) + if self.eos_token_mt + else None + ) + assert eos_id_mt != self.tgt_dict_mt.unk() + extra_gen_cls_kwargs["eos_mt"] = eos_id_mt + + seq_generator = generator( + models, + args, + self.vocoder, + self.data_cfg, + self.target_dictionary_mt, + max_iter=self.args.max_target_positions, + eos_prob_threshold=self.args.eos_prob_threshold, + **extra_gen_cls_kwargs, + ) + else: + if getattr(args, "teacher_forcing", False): + from fairseq.speech_generator import ( + TeacherForcingAutoRegressiveSpeechGenerator, + ) + + generator = TeacherForcingAutoRegressiveSpeechGenerator + logger.info("Teacher forcing mode for generation") + else: + from fairseq.speech_generator import AutoRegressiveSpeechGenerator + + generator = AutoRegressiveSpeechGenerator + + seq_generator = generator( + models[0], + self.vocoder, + self.data_cfg, + max_iter=self.args.max_target_positions, + eos_prob_threshold=self.args.eos_prob_threshold, + ) + + return seq_generator + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + for task_name, task_obj in self.multitask_tasks.items(): + criterion.set_multitask_loss_weight( + task_name, task_obj.args.get_loss_weight(update_num) + ) + if task_name in model.multitask_decoders: + model.multitask_decoders[task_name].train() + + loss, sample_size, logging_output = super().train_step( + sample, model, criterion, optimizer, update_num, ignore_grad + ) + return loss, sample_size, logging_output + + def valid_step(self, sample, model, criterion): + for task_name in self.multitask_tasks.keys(): + if task_name in model.multitask_decoders: + model.multitask_decoders[task_name].eval() + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + + if self.args.eval_inference: + hypos, inference_losses = self.valid_step_with_inference( + sample, model, self.generator + ) + for k, v in inference_losses.items(): + assert k not in logging_output + logging_output[k] = v + + return loss, sample_size, logging_output + + def valid_step_with_inference(self, sample, model, generator): + if self.args.target_is_code: + hypos = generator.generate([model], sample) + tgt_lens = ( + sample["target_lengths"] - 1 + ) * self.args.n_frames_per_step # strip <eos> + for b, (f, l) in enumerate(zip(sample["target"], tgt_lens)): + hypos[b][0]["targ_waveform"] = self.vocoder( + {"code": f[:l] - 4}, # remove <bos>, <pad>, <eos>, <unk> + dur_prediction=self.eval_gen_args.get("dur_prediction", False), + ) + if len(hypos[b][0]["tokens"]) > 0: + hypos[b][0]["waveform"] = self.vocoder( + {"code": hypos[b][0]["tokens"] - 4}, + dur_prediction=self.eval_gen_args.get("dur_prediction", False), + ) + else: + hypos[b][0]["waveform"] = torch.flip( + hypos[b][0]["targ_waveform"], dims=[0] + ) + else: + hypos = [ + [hypo] for hypo in generator.generate(model, sample, has_targ=True) + ] + + losses = { + "mcd_loss": 0.0, + "targ_frames": 0.0, + "pred_frames": 0.0, + "path_frames": 0.0, + "nins": 0.0, + "ndel": 0.0, + } + rets = batch_mel_cepstral_distortion( + [hypo[0]["targ_waveform"] for hypo in hypos], + [hypo[0]["waveform"] for hypo in hypos], + self.data_cfg.output_sample_rate, + normalize_type=None, + ) + for d, extra in rets: + pathmap = extra[-1] + losses["mcd_loss"] += d.item() + losses["targ_frames"] += pathmap.size(0) + losses["pred_frames"] += pathmap.size(1) + losses["path_frames"] += pathmap.sum().item() + losses["nins"] += (pathmap.sum(dim=1) - 1).sum().item() + losses["ndel"] += (pathmap.sum(dim=0) - 1).sum().item() + losses["norm_frames"] = losses[ + f"{getattr(self.args, 'mcd_normalize_type', 'targ')}_frames" + ] + + return hypos, losses + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + if self._infer_tgt_lang_id is not None: + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + bos_token=self._infer_tgt_lang_id, + ) + else: + return super().inference_step( + generator, + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + ) diff --git a/fairseq/fairseq/tasks/speech_to_text.py b/fairseq/fairseq/tasks/speech_to_text.py new file mode 100644 index 0000000..8840821 --- /dev/null +++ b/fairseq/fairseq/tasks/speech_to_text.py @@ -0,0 +1,350 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from argparse import Namespace +from pathlib import Path +from typing import List + +from fairseq.data import Dictionary, encoders +from fairseq.data.audio.audio_utils import get_features_or_waveform +from fairseq.data.audio.data_cfg import MultitaskConfig +from fairseq.data.audio.speech_to_text_dataset import ( + S2TDataConfig, + SpeechToTextDataset, + SpeechToTextDatasetCreator, + TextTargetMultitaskData, +) +from fairseq.tasks import LegacyFairseqTask, register_task + +logger = logging.getLogger(__name__) + + +@register_task("speech_to_text") +class SpeechToTextTask(LegacyFairseqTask): + @classmethod + def add_args(cls, parser): + parser.add_argument("data", help="manifest root path") + parser.add_argument( + "--config-yaml", + type=str, + default="config.yaml", + help="Configuration YAML filename (under manifest root)", + ) + parser.add_argument( + "--multitask-config-yaml", + type=str, + default=None, + help="Configuration YAML filename for the multitasks (under manifest root)", + ) + parser.add_argument( + "--max-source-positions", + default=6000, + type=int, + metavar="N", + help="max number of tokens in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + + def __init__(self, args, tgt_dict): + super().__init__(args) + self.tgt_dict = tgt_dict + self.data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml) + self.speaker_to_id = self._get_speaker_to_id() + if ( + self.data_cfg.prepend_tgt_lang_tag + and self.data_cfg.prepend_bos_and_append_tgt_lang_tag + ): + raise ValueError( + "Please set only one of the two options to avoid adding target token multiple times" + ) + + self.multitask_tasks = {} + self.tgt_dict_mt = None + self.eos_token_mt = None + if getattr(args, "multitask_config_yaml", None) is not None: + multitask_cfg = MultitaskConfig( + Path(args.data) / args.multitask_config_yaml + ) + first_pass_task_idx = multitask_cfg.first_pass_decoder_task_index + for i, (task_name, task_config) in enumerate( + multitask_cfg.get_all_tasks().items() + ): + task_obj = DummyMultiTask( + task_config, + task_config.tgt_dict, + first_pass=i == first_pass_task_idx, + ) + self.multitask_tasks[task_name] = task_obj + if task_obj.is_first_pass_decoder: + self.tgt_dict_mt = task_obj.target_dictionary + if task_config.prepend_bos_and_append_tgt_lang_tag: + self.eos_token_mt = task_config.eos_token + assert not isinstance(self.eos_token_mt, List) + + if not self.eos_token_mt: + raise Warning( + "Please provide eos_token in --multitask-config-yaml to replace eos in sequence generator" + ) + + def _get_speaker_to_id(self): + speaker_to_id = None + speaker_set_filename = self.data_cfg.config.get("speaker_set_filename") + if speaker_set_filename is not None: + speaker_set_path = Path(self.args.data) / speaker_set_filename + with open(speaker_set_path) as f: + speaker_to_id = {r.strip(): i for i, r in enumerate(f)} + return speaker_to_id + + @classmethod + def setup_task(cls, args, **kwargs): + data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml) + dict_path = Path(args.data) / data_cfg.vocab_filename + if not dict_path.is_file(): + raise FileNotFoundError(f"Dict not found: {dict_path.as_posix()}") + tgt_dict = Dictionary.load(dict_path.as_posix()) + logger.info( + f"dictionary size ({data_cfg.vocab_filename}): " f"{len(tgt_dict):,}" + ) + + if getattr(args, "train_subset", None) is not None: + if not all(s.startswith("train") for s in args.train_subset.split(",")): + raise ValueError('Train splits should be named like "train*".') + return cls(args, tgt_dict) + + def build_criterion(self, args): + from fairseq import criterions + + if self.data_cfg.prepend_tgt_lang_tag and args.ignore_prefix_size != 1: + raise ValueError( + 'Please set "--ignore-prefix-size 1" since ' + "target language ID token is prepended as BOS." + ) + return criterions.build_criterion(args, self) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + self.datasets[split] = SpeechToTextDatasetCreator.from_tsv( + root=self.args.data, + cfg=self.data_cfg, + splits=split, + tgt_dict=self.tgt_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + is_train_split=is_train_split, + epoch=epoch, + seed=self.args.seed, + speaker_to_id=self.speaker_to_id, + multitask=self.multitask_tasks, + ) + + @property + def target_dictionary(self): + return self.tgt_dict + + @property + def target_dictionary_mt(self): + return self.tgt_dict_mt + + @property + def source_dictionary(self): + return None + + def max_positions(self): + return self.args.max_source_positions, self.args.max_target_positions + + def build_model(self, args, from_checkpoint=False): + args.input_feat_per_channel = self.data_cfg.input_feat_per_channel + args.input_channels = self.data_cfg.input_channels + args.speaker_to_id = self.speaker_to_id + return super(SpeechToTextTask, self).build_model(args, from_checkpoint) + + def build_generator_dual_decoder( + self, + models, + args, + extra_gen_cls_kwargs, + ): + from examples.speech_to_speech.unity.sequence_generator_multi_decoder import ( + MultiDecoderSequenceGenerator, + ) + + lang_token_ids_aux = { + i + for s, i in self.tgt_dict_mt.indices.items() + if TextTargetMultitaskData.is_lang_tag(s) + } + + extra_gen_cls_kwargs["symbols_to_strip_from_output"].update(lang_token_ids_aux) + + eos_id_mt = ( + self.tgt_dict_mt.index(self.eos_token_mt) if self.eos_token_mt else None + ) + assert eos_id_mt != self.tgt_dict_mt.unk() + extra_gen_cls_kwargs["eos_mt"] = eos_id_mt + + return MultiDecoderSequenceGenerator( + models, + self.target_dictionary, + self.target_dictionary_mt, + beam_size=max(1, getattr(args, "beam", 1)), + beam_size_mt=max(1, getattr(args, "beam_mt", 1)), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 200), + max_len_a_mt=getattr(args, "max_len_a_mt", 0), + max_len_b_mt=getattr(args, "max_len_b_mt", 0), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + len_penalty_mt=getattr(args, "lenpen_mt", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + **extra_gen_cls_kwargs, + ) + + def build_generator( + self, + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=None, + ): + if self.data_cfg.prepend_tgt_lang_tag and args.prefix_size != 1: + raise ValueError( + 'Please set "--prefix-size 1" since ' + "target language ID token is prepended as BOS." + ) + lang_token_ids = { + i + for s, i in self.tgt_dict.indices.items() + if SpeechToTextDataset.is_lang_tag(s) + } + + if extra_gen_cls_kwargs is None: + extra_gen_cls_kwargs = {} + extra_gen_cls_kwargs["symbols_to_strip_from_output"] = lang_token_ids + + eos_token = ( + args.eos_token + if "eos_token" in args and args.eos_token is not None + else self.data_cfg.config.get("eos_token", None) + ) + + if self.data_cfg.prepend_bos_and_append_tgt_lang_tag and not eos_token: + raise Warning( + "Please provide --eos_token to replace eos in sequence generator" + ) + + eos_id = self.tgt_dict.index(eos_token) if eos_token else None + extra_gen_cls_kwargs["eos"] = eos_id + + has_dual_decoder = getattr(models[0], "mt_task_name", None) is not None + + if has_dual_decoder: + return self.build_generator_dual_decoder( + models, + args, + extra_gen_cls_kwargs=extra_gen_cls_kwargs, + ) + else: + return super().build_generator( + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=extra_gen_cls_kwargs, + ) + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + for task_name, task_obj in self.multitask_tasks.items(): + criterion.set_multitask_loss_weight( + task_name, task_obj.args.get_loss_weight(update_num) + ) + if task_name in model.multitask_decoders: + model.multitask_decoders[task_name].train() + + loss, sample_size, logging_output = super().train_step( + sample, model, criterion, optimizer, update_num, ignore_grad + ) + return loss, sample_size, logging_output + + def valid_step(self, sample, model, criterion): + for task_name, task_obj in self.multitask_tasks.items(): + if task_name in model.multitask_decoders: + model.multitask_decoders[task_name].eval() + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + + return loss, sample_size, logging_output + + def build_tokenizer(self, args): + logger.info(f"pre-tokenizer: {self.data_cfg.pre_tokenizer}") + return encoders.build_tokenizer(Namespace(**self.data_cfg.pre_tokenizer)) + + def build_bpe(self, args): + logger.info(f"tokenizer: {self.data_cfg.bpe_tokenizer}") + return encoders.build_bpe(Namespace(**self.data_cfg.bpe_tokenizer)) + + def get_interactive_tokens_and_lengths(self, lines, encode_fn): + n_frames = [get_features_or_waveform(p).shape[0] for p in lines] + return lines, n_frames + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + return SpeechToTextDataset( + "interactive", False, self.data_cfg, src_tokens, src_lengths + ) + + +class DummyMultiTask(LegacyFairseqTask): + def __init__(self, args, tgt_dict, first_pass=False): + super().__init__(args) + self.tgt_dict = tgt_dict + self.first_pass = first_pass + + @property + def target_dictionary(self): + return self.tgt_dict + + @property + def is_first_pass_decoder(self): + return self.first_pass + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + if self.args.decoder_type == "ctc": + model = models[0] # only support single model + encoder_out = model(**sample) + if hasattr(model, "get_logits"): + emissions = model.get_logits( + encoder_out + ) # no need to normalize emissions + else: + emissions = model.get_normalized_probs(encoder_out, log_probs=True) + return generator.decode( + emissions.transpose(0, 1).float().cpu().contiguous() + ) + else: + raise NotImplementedError("only ctc decoder is supported at the moment") + + def build_generator( + self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None + ): + if self.args.decoder_type == "ctc": + from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder + + return W2lViterbiDecoder(args, self.tgt_dict) + else: + raise NotImplementedError("only ctc decoder is supported at the moment") diff --git a/fairseq/fairseq/tasks/speech_ulm_task.py b/fairseq/fairseq/tasks/speech_ulm_task.py new file mode 100644 index 0000000..b9d3019 --- /dev/null +++ b/fairseq/fairseq/tasks/speech_ulm_task.py @@ -0,0 +1,224 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import sys +import torch +from dataclasses import dataclass, field +from typing import List, Optional, Tuple + +from fairseq.data import Dictionary +from fairseq.data.codedataset import ExpressiveCodeDataConfig, CodeDataset +from fairseq.dataclass.configs import FairseqDataclass +from fairseq.tasks import register_task +from fairseq.tasks.fairseq_task import FairseqTask +from omegaconf import MISSING, DictConfig + + +logger = logging.getLogger(__name__) + + +class UnitDictionary(Dictionary): + """ + A fixed-sized Dictionary that operates on integer-valued tokens + wth a trivial (identity) token <-> id mapping. + Special symbols (bos, eos, ...) have ids above n_units. + """ + + def __init__( + self, + *, # begin keyword-only arguments + n_units, + bos="<s>", + pad="<pad>", + eos="</s>", + unk="<unk>", + extra_special_symbols=None, + clip=False, + ): + self.n_units = n_units + self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos + self.clip = clip + + self.symbols = [] + self.count = [] + self.indices = {} + for i in range(n_units): + self.add_symbol(str(i)) + + self.bos_index = self.add_symbol(bos) + self.pad_index = self.add_symbol(pad) + self.eos_index = self.add_symbol(eos) + self.unk_index = self.add_symbol(unk) + + if extra_special_symbols: + for s in extra_special_symbols: + self.add_symbol(s) + self.nspecial = len(self.symbols) + + def encode_line(self, line, append_eos=True, prepend_bos=False) -> torch.IntTensor: + words = [int(x) for x in line.split()] + if self.clip: + words = [min(self.n_units - 1, word) for word in words] + if prepend_bos: + words = [self.bos_index] + words + if append_eos: + words.append(self.eos_index) + ids = torch.IntTensor(words) + return ids + + +@dataclass +class SpeechUnitModelingConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "Path to data config.json"}) + max_token_duration: int = field( + default=20, metadata={"help": "all token durations are capped to this value"} + ) + tokens_per_sample: int = field( + default=1024, metadata={"help": "tokens in a sample"} + ) + max_target_positions: int = field( + default=1024, metadata={"help": "max target positions"} + ) + + # duration modeling + ignore_duration_input: bool = field( + default=False, metadata={"help": "whether token durations should be zeroed out"} + ) + discrete_duration: bool = field( + default=False, metadata={"help": "treat duration as discrete variable"} + ) + # F0 modeling + ignore_f0_input: bool = field( + default=False, metadata={"help": "whether F0 should be zeroed out"} + ) + discrete_f0: bool = field( + default=False, metadata={"help": "load quantized f0. get bin from config"} + ) + log_f0: bool = field( + default=False, metadata={"help": "whether f0 should be modeled in log space"} + ) + normalize_f0_mean: bool = field( + default=False, metadata={"help": "whether normalize f0 by speaker mean"} + ) + normalize_f0_std: bool = field( + default=False, metadata={"help": "whether normalize f0 by speaker stddev"} + ) + interpolate_f0: bool = field( + default=False, + metadata={"help": "whether interpolate f0 for non-voiced segments"}, + ) + + # input/output streams + stream_shifts: str = field( + default="0,0", + metadata={ + "help": ( + "comma-separated integer list denoting right-shift for " + "duration and pitch streams" + ) + }, + ) + + +@register_task("speech_unit_modeling", dataclass=SpeechUnitModelingConfig) +class SpeechUnitLanguageModelingTask(FairseqTask): + def __init__(self, cfg: SpeechUnitModelingConfig) -> None: + super().__init__(cfg) + assert not self.cfg.normalize_f0_std or self.cfg.normalize_f0_mean + + self.data_config = ExpressiveCodeDataConfig(cfg.data) + self._source_dictionary = self._target_dictionary = UnitDictionary( + n_units=self.data_config.n_units + ) + self._source_duration_dictionary = self._target_duration_dictionary = ( + UnitDictionary(n_units=self.cfg.max_token_duration + 1, clip=True) + if self.cfg.discrete_duration + else None + ) + self._source_f0_dictionary = self._target_f0_dictionary = ( + UnitDictionary(n_units=self.data_config.f0_vq_n_units) + if self.cfg.discrete_f0 + else None + ) + + self._channel_names = ["token", "duration", "f0"] + self._channel_sizes = [ + len(self.target_dictionary), + len(self.target_duration_dictionary) if self.cfg.discrete_duration else 1, + len(self.target_f0_dictionary) if self.cfg.discrete_f0 else 1, + ] + + @property + def source_dictionary(self) -> Optional[Dictionary]: + return self._source_dictionary + + @property + def source_duration_dictionary(self) -> Optional[Dictionary]: + return self._source_duration_dictionary + + @property + def source_f0_dictionary(self) -> Optional[Dictionary]: + return self._source_f0_dictionary + + @property + def channel_names(self) -> List[str]: + return self._channel_names + + @property + def channel_sizes(self) -> List[int]: + return self._channel_sizes + + @property + def dictionary(self) -> Optional[Dictionary]: + return self._source_dictionary + + @property + def target_dictionary(self) -> Optional[Dictionary]: + return self._target_dictionary + + @property + def target_duration_dictionary(self) -> Optional[Dictionary]: + return self._target_duration_dictionary + + @property + def target_f0_dictionary(self) -> Optional[Dictionary]: + return self._target_f0_dictionary + + @property + def dictionaries(self) -> List[Dictionary]: + return [self._dictionaries[l] for l in self.cfg.labels] + + @classmethod + def setup_task( + cls, cfg: SpeechUnitModelingConfig, **kwargs + ) -> "SpeechUnitLanguageModelingTask": + return cls(cfg) + + def load_dataset(self, split: str, **kwargs) -> None: + self.datasets[split] = CodeDataset( + manifest=self.data_config.manifests[split], + dictionary=self.source_dictionary, + dur_dictionary=self.source_duration_dictionary, + f0_dictionary=self.source_f0_dictionary, + config=self.data_config, + discrete_dur=self.cfg.discrete_duration, + discrete_f0=self.cfg.discrete_f0, + log_f0=self.cfg.log_f0, + normalize_f0_mean=self.cfg.normalize_f0_mean, + normalize_f0_std=self.cfg.normalize_f0_std, + interpolate_f0=self.cfg.interpolate_f0, + shifts=self.cfg.stream_shifts, + ) + + def max_positions(self) -> Tuple[int, int]: + return (sys.maxsize, sys.maxsize) + + def build_criterion(self, cfg: DictConfig): + import fairseq.criterions + + return fairseq.criterions.build_criterion(cfg, self) diff --git a/fairseq/fairseq/tasks/text_to_speech.py b/fairseq/fairseq/tasks/text_to_speech.py new file mode 100644 index 0000000..82e7e66 --- /dev/null +++ b/fairseq/fairseq/tasks/text_to_speech.py @@ -0,0 +1,501 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import os.path as op + +import torch +import torch.nn.functional as F +import numpy as np + +from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDatasetCreator +from fairseq.tasks import register_task +from fairseq.tasks.speech_to_text import SpeechToTextTask +from fairseq.speech_generator import ( + AutoRegressiveSpeechGenerator, + NonAutoregressiveSpeechGenerator, + TeacherForcingAutoRegressiveSpeechGenerator, +) + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=logging.INFO, +) +logger = logging.getLogger(__name__) + + +try: + from tensorboardX import SummaryWriter +except ImportError: + logger.info("Please install tensorboardX: pip install tensorboardX") + SummaryWriter = None + + +@register_task("text_to_speech") +class TextToSpeechTask(SpeechToTextTask): + @staticmethod + def add_args(parser): + parser.add_argument("data", help="manifest root path") + parser.add_argument( + "--config-yaml", + type=str, + default="config.yaml", + help="Configuration YAML filename (under manifest root)", + ) + parser.add_argument( + "--max-source-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1200, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + parser.add_argument("--n-frames-per-step", type=int, default=1) + parser.add_argument("--eos-prob-threshold", type=float, default=0.5) + parser.add_argument("--eval-inference", action="store_true") + parser.add_argument("--eval-tb-nsample", type=int, default=8) + parser.add_argument("--vocoder", type=str, default="griffin_lim") + parser.add_argument("--spec-bwd-max-iter", type=int, default=8) + + def __init__(self, args, src_dict): + super().__init__(args, src_dict) + self.src_dict = src_dict + self.sr = self.data_cfg.config.get("features").get("sample_rate") + + self.tensorboard_writer = None + self.tensorboard_dir = "" + if args.tensorboard_logdir and SummaryWriter is not None: + self.tensorboard_dir = os.path.join(args.tensorboard_logdir, "valid_extra") + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + self.datasets[split] = TextToSpeechDatasetCreator.from_tsv( + self.args.data, + self.data_cfg, + split, + self.src_dict, + pre_tokenizer, + bpe_tokenizer, + is_train_split=is_train_split, + epoch=epoch, + seed=self.args.seed, + n_frames_per_step=self.args.n_frames_per_step, + speaker_to_id=self.speaker_to_id, + ) + + @property + def target_dictionary(self): + return None + + @property + def source_dictionary(self): + return self.src_dict + + def get_speaker_embeddings_path(self): + speaker_emb_path = None + if self.data_cfg.config.get("speaker_emb_filename") is not None: + speaker_emb_path = op.join( + self.args.data, self.data_cfg.config.get("speaker_emb_filename") + ) + return speaker_emb_path + + @classmethod + def get_speaker_embeddings(cls, args): + embed_speaker = None + if args.speaker_to_id is not None: + if args.speaker_emb_path is None: + embed_speaker = torch.nn.Embedding( + len(args.speaker_to_id), args.speaker_embed_dim + ) + else: + speaker_emb_mat = np.load(args.speaker_emb_path) + assert speaker_emb_mat.shape[1] == args.speaker_embed_dim + embed_speaker = torch.nn.Embedding.from_pretrained( + torch.from_numpy(speaker_emb_mat), + freeze=True, + ) + logger.info( + f"load speaker embeddings from {args.speaker_emb_path}. " + f"train embedding? {embed_speaker.weight.requires_grad}\n" + f"embeddings:\n{speaker_emb_mat}" + ) + return embed_speaker + + def build_model(self, cfg, from_checkpoint=False): + cfg.pitch_min = self.data_cfg.config["features"].get("pitch_min", None) + cfg.pitch_max = self.data_cfg.config["features"].get("pitch_max", None) + cfg.energy_min = self.data_cfg.config["features"].get("energy_min", None) + cfg.energy_max = self.data_cfg.config["features"].get("energy_max", None) + cfg.speaker_emb_path = self.get_speaker_embeddings_path() + model = super().build_model(cfg, from_checkpoint) + self.generator = None + if getattr(cfg, "eval_inference", False): + self.generator = self.build_generator([model], cfg) + return model + + def build_generator(self, models, cfg, vocoder=None, **unused): + if vocoder is None: + vocoder = self.build_default_vocoder() + model = models[0] + if getattr(model, "NON_AUTOREGRESSIVE", False): + return NonAutoregressiveSpeechGenerator(model, vocoder, self.data_cfg) + else: + generator = AutoRegressiveSpeechGenerator + if getattr(cfg, "teacher_forcing", False): + generator = TeacherForcingAutoRegressiveSpeechGenerator + logger.info("Teacher forcing mode for generation") + return generator( + model, + vocoder, + self.data_cfg, + max_iter=self.args.max_target_positions, + eos_prob_threshold=self.args.eos_prob_threshold, + ) + + def build_default_vocoder(self): + from fairseq.models.text_to_speech.vocoder import get_vocoder + + vocoder = get_vocoder(self.args, self.data_cfg) + if torch.cuda.is_available() and not self.args.cpu: + vocoder = vocoder.cuda() + else: + vocoder = vocoder.cpu() + return vocoder + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + + if getattr(self.args, "eval_inference", False): + hypos, inference_losses = self.valid_step_with_inference( + sample, model, self.generator + ) + for k, v in inference_losses.items(): + assert k not in logging_output + logging_output[k] = v + + picked_id = 0 + if self.tensorboard_dir and (sample["id"] == picked_id).any(): + self.log_tensorboard( + sample, + hypos[: self.args.eval_tb_nsample], + model._num_updates, + is_na_model=getattr(model, "NON_AUTOREGRESSIVE", False), + ) + return loss, sample_size, logging_output + + def valid_step_with_inference(self, sample, model, generator): + hypos = generator.generate(model, sample, has_targ=True) + + losses = { + "mcd_loss": 0.0, + "targ_frames": 0.0, + "pred_frames": 0.0, + "nins": 0.0, + "ndel": 0.0, + } + rets = batch_mel_cepstral_distortion( + [hypo["targ_waveform"] for hypo in hypos], + [hypo["waveform"] for hypo in hypos], + self.sr, + normalize_type=None, + ) + for d, extra in rets: + pathmap = extra[-1] + losses["mcd_loss"] += d.item() + losses["targ_frames"] += pathmap.size(0) + losses["pred_frames"] += pathmap.size(1) + losses["nins"] += (pathmap.sum(dim=1) - 1).sum().item() + losses["ndel"] += (pathmap.sum(dim=0) - 1).sum().item() + + return hypos, losses + + def log_tensorboard(self, sample, hypos, num_updates, is_na_model=False): + if self.tensorboard_writer is None: + self.tensorboard_writer = SummaryWriter(self.tensorboard_dir) + tb_writer = self.tensorboard_writer + for b in range(len(hypos)): + idx = sample["id"][b] + text = sample["src_texts"][b] + targ = hypos[b]["targ_feature"] + pred = hypos[b]["feature"] + attn = hypos[b]["attn"] + + if is_na_model: + data = plot_tts_output( + [targ.transpose(0, 1), pred.transpose(0, 1)], + [f"target (idx={idx})", "output"], + attn, + "alignment", + ret_np=True, + suptitle=text, + ) + else: + eos_prob = hypos[b]["eos_prob"] + data = plot_tts_output( + [targ.transpose(0, 1), pred.transpose(0, 1), attn], + [f"target (idx={idx})", "output", "alignment"], + eos_prob, + "eos prob", + ret_np=True, + suptitle=text, + ) + + tb_writer.add_image( + f"inference_sample_{b}", data, num_updates, dataformats="HWC" + ) + + if hypos[b]["waveform"] is not None: + targ_wave = hypos[b]["targ_waveform"].detach().cpu().float() + pred_wave = hypos[b]["waveform"].detach().cpu().float() + tb_writer.add_audio( + f"inference_targ_{b}", targ_wave, num_updates, sample_rate=self.sr + ) + tb_writer.add_audio( + f"inference_pred_{b}", pred_wave, num_updates, sample_rate=self.sr + ) + + +def save_figure_to_numpy(fig): + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + return data + + +DEFAULT_V_MIN = np.log(1e-5) + + +def plot_tts_output( + data_2d, + title_2d, + data_1d, + title_1d, + figsize=(24, 4), + v_min=DEFAULT_V_MIN, + v_max=3, + ret_np=False, + suptitle="", +): + try: + import matplotlib.pyplot as plt + from mpl_toolkits.axes_grid1 import make_axes_locatable + except ImportError: + raise ImportError("Please install Matplotlib: pip install matplotlib") + + data_2d = [ + x.detach().cpu().float().numpy() if isinstance(x, torch.Tensor) else x + for x in data_2d + ] + fig, axes = plt.subplots(1, len(data_2d) + 1, figsize=figsize) + if suptitle: + fig.suptitle(suptitle[:400]) # capped at 400 chars + axes = [axes] if len(data_2d) == 0 else axes + for ax, x, name in zip(axes, data_2d, title_2d): + ax.set_title(name) + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size="5%", pad=0.05) + im = ax.imshow( + x, + origin="lower", + aspect="auto", + vmin=max(x.min(), v_min), + vmax=min(x.max(), v_max), + ) + fig.colorbar(im, cax=cax, orientation="vertical") + + if isinstance(data_1d, torch.Tensor): + data_1d = data_1d.detach().cpu().numpy() + axes[-1].plot(data_1d) + axes[-1].set_title(title_1d) + plt.tight_layout() + + if ret_np: + fig.canvas.draw() + data = save_figure_to_numpy(fig) + plt.close(fig) + return data + + +def antidiag_indices(offset, min_i=0, max_i=None, min_j=0, max_j=None): + """ + for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs + + offset=2 (1, 1), + offset=3 (2, 1), (1, 2) + offset=4 (2, 2), (1, 3) + offset=5 (2, 3) + + constraints: + i + j = offset + min_j <= j < max_j + min_i <= offset - j < max_i + """ + if max_i is None: + max_i = offset + 1 + if max_j is None: + max_j = offset + 1 + min_j = max(min_j, offset - max_i + 1, 0) + max_j = min(max_j, offset - min_i + 1, offset + 1) + j = torch.arange(min_j, max_j) + i = offset - j + return torch.stack([i, j]) + + +def batch_dynamic_time_warping(distance, shapes=None): + """full batched DTW without any constraints + + distance: (batchsize, max_M, max_N) matrix + shapes: (batchsize,) vector specifying (M, N) for each entry + """ + # ptr: 0=left, 1=up-left, 2=up + ptr2dij = {0: (0, -1), 1: (-1, -1), 2: (-1, 0)} + + bsz, m, n = distance.size() + cumdist = torch.zeros_like(distance) + backptr = torch.zeros_like(distance).type(torch.int32) - 1 + + # initialize + cumdist[:, 0, :] = distance[:, 0, :].cumsum(dim=-1) + cumdist[:, :, 0] = distance[:, :, 0].cumsum(dim=-1) + backptr[:, 0, :] = 0 + backptr[:, :, 0] = 2 + + # DP with optimized anti-diagonal parallelization, O(M+N) steps + for offset in range(2, m + n - 1): + ind = antidiag_indices(offset, 1, m, 1, n) + c = torch.stack( + [ + cumdist[:, ind[0], ind[1] - 1], + cumdist[:, ind[0] - 1, ind[1] - 1], + cumdist[:, ind[0] - 1, ind[1]], + ], + dim=2, + ) + v, b = c.min(axis=-1) + backptr[:, ind[0], ind[1]] = b.int() + cumdist[:, ind[0], ind[1]] = v + distance[:, ind[0], ind[1]] + + # backtrace + pathmap = torch.zeros_like(backptr) + for b in range(bsz): + i = m - 1 if shapes is None else (shapes[b][0] - 1).item() + j = n - 1 if shapes is None else (shapes[b][1] - 1).item() + dtwpath = [(i, j)] + while (i != 0 or j != 0) and len(dtwpath) < 10000: + assert i >= 0 and j >= 0 + di, dj = ptr2dij[backptr[b, i, j].item()] + i, j = i + di, j + dj + dtwpath.append((i, j)) + dtwpath = dtwpath[::-1] + indices = torch.from_numpy(np.array(dtwpath)) + pathmap[b, indices[:, 0], indices[:, 1]] = 1 + + return cumdist, backptr, pathmap + + +def compute_l2_dist(x1, x2): + """compute an (m, n) L2 distance matrix from (m, d) and (n, d) matrices""" + return torch.cdist(x1.unsqueeze(0), x2.unsqueeze(0), p=2).squeeze(0).pow(2) + + +def compute_rms_dist(x1, x2): + l2_dist = compute_l2_dist(x1, x2) + return (l2_dist / x1.size(1)).pow(0.5) + + +def get_divisor(pathmap, normalize_type): + if normalize_type is None: + return 1 + elif normalize_type == "len1": + return pathmap.size(0) + elif normalize_type == "len2": + return pathmap.size(1) + elif normalize_type == "path": + return pathmap.sum().item() + else: + raise ValueError(f"normalize_type {normalize_type} not supported") + + +def batch_compute_distortion(y1, y2, sr, feat_fn, dist_fn, normalize_type): + d, s, x1, x2 = [], [], [], [] + for cur_y1, cur_y2 in zip(y1, y2): + assert cur_y1.ndim == 1 and cur_y2.ndim == 1 + cur_x1 = feat_fn(cur_y1) + cur_x2 = feat_fn(cur_y2) + x1.append(cur_x1) + x2.append(cur_x2) + + cur_d = dist_fn(cur_x1, cur_x2) + d.append(cur_d) + s.append(d[-1].size()) + max_m = max(ss[0] for ss in s) + max_n = max(ss[1] for ss in s) + d = torch.stack( + [F.pad(dd, (0, max_n - dd.size(1), 0, max_m - dd.size(0))) for dd in d] + ) + s = torch.LongTensor(s).to(d.device) + cumdists, backptrs, pathmaps = batch_dynamic_time_warping(d, s) + + rets = [] + itr = zip(s, x1, x2, d, cumdists, backptrs, pathmaps) + for (m, n), cur_x1, cur_x2, dist, cumdist, backptr, pathmap in itr: + cumdist = cumdist[:m, :n] + backptr = backptr[:m, :n] + pathmap = pathmap[:m, :n] + divisor = get_divisor(pathmap, normalize_type) + + distortion = cumdist[-1, -1] / divisor + ret = distortion, (cur_x1, cur_x2, dist, cumdist, backptr, pathmap) + rets.append(ret) + return rets + + +def batch_mel_cepstral_distortion(y1, y2, sr, normalize_type="path", mfcc_fn=None): + """ + https://arxiv.org/pdf/2011.03568.pdf + + The root mean squared error computed on 13-dimensional MFCC using DTW for + alignment. MFCC features are computed from an 80-channel log-mel + spectrogram using a 50ms Hann window and hop of 12.5ms. + + y1: list of waveforms + y2: list of waveforms + sr: sampling rate + """ + + try: + import torchaudio + except ImportError: + raise ImportError("Please install torchaudio: pip install torchaudio") + + if mfcc_fn is None or mfcc_fn.sample_rate != sr: + melkwargs = { + "n_fft": int(0.05 * sr), + "win_length": int(0.05 * sr), + "hop_length": int(0.0125 * sr), + "f_min": 20, + "n_mels": 80, + "window_fn": torch.hann_window, + } + mfcc_fn = torchaudio.transforms.MFCC( + sr, n_mfcc=13, log_mels=True, melkwargs=melkwargs + ).to(y1[0].device) + return batch_compute_distortion( + y1, + y2, + sr, + lambda y: mfcc_fn(y).transpose(-1, -2), + compute_rms_dist, + normalize_type, + ) diff --git a/fairseq/fairseq/tasks/translation.py b/fairseq/fairseq/tasks/translation.py new file mode 100644 index 0000000..6897ebe --- /dev/null +++ b/fairseq/fairseq/tasks/translation.py @@ -0,0 +1,498 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +import itertools +import json +import logging +import os +from typing import Optional +from argparse import Namespace +from omegaconf import II + +import numpy as np +from fairseq import utils +from fairseq.logging import metrics +from fairseq.data import ( + AppendTokenDataset, + ConcatDataset, + LanguagePairDataset, + PrependTokenDataset, + StripTokenDataset, + TruncateDataset, + data_utils, + encoders, + indexed_dataset, +) +from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + + +EVAL_BLEU_ORDER = 4 + + +logger = logging.getLogger(__name__) + + +def load_langpair_dataset( + data_path, + split, + src, + src_dict, + tgt, + tgt_dict, + combine, + dataset_impl, + upsample_primary, + left_pad_source, + left_pad_target, + max_source_positions, + max_target_positions, + prepend_bos=False, + load_alignments=False, + truncate_source=False, + append_source_id=False, + num_buckets=0, + shuffle=True, + pad_to_multiple=1, + prepend_bos_src=None, +): + def split_exists(split, src, tgt, lang, data_path): + filename = os.path.join(data_path, "{}.{}-{}.{}".format(split, src, tgt, lang)) + return indexed_dataset.dataset_exists(filename, impl=dataset_impl) + + src_datasets = [] + tgt_datasets = [] + + for k in itertools.count(): + split_k = split + (str(k) if k > 0 else "") + + # infer langcode + if split_exists(split_k, src, tgt, src, data_path): + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, src, tgt)) + elif split_exists(split_k, tgt, src, src, data_path): + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, tgt, src)) + else: + if k > 0: + break + else: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) + + src_dataset = data_utils.load_indexed_dataset( + prefix + src, src_dict, dataset_impl + ) + if truncate_source: + src_dataset = AppendTokenDataset( + TruncateDataset( + StripTokenDataset(src_dataset, src_dict.eos()), + max_source_positions - 1, + ), + src_dict.eos(), + ) + src_datasets.append(src_dataset) + + tgt_dataset = data_utils.load_indexed_dataset( + prefix + tgt, tgt_dict, dataset_impl + ) + if tgt_dataset is not None: + tgt_datasets.append(tgt_dataset) + + logger.info( + "{} {} {}-{} {} examples".format( + data_path, split_k, src, tgt, len(src_datasets[-1]) + ) + ) + + if not combine: + break + + assert len(src_datasets) == len(tgt_datasets) or len(tgt_datasets) == 0 + + if len(src_datasets) == 1: + src_dataset = src_datasets[0] + tgt_dataset = tgt_datasets[0] if len(tgt_datasets) > 0 else None + else: + sample_ratios = [1] * len(src_datasets) + sample_ratios[0] = upsample_primary + src_dataset = ConcatDataset(src_datasets, sample_ratios) + if len(tgt_datasets) > 0: + tgt_dataset = ConcatDataset(tgt_datasets, sample_ratios) + else: + tgt_dataset = None + + if prepend_bos: + assert hasattr(src_dict, "bos_index") and hasattr(tgt_dict, "bos_index") + src_dataset = PrependTokenDataset(src_dataset, src_dict.bos()) + if tgt_dataset is not None: + tgt_dataset = PrependTokenDataset(tgt_dataset, tgt_dict.bos()) + elif prepend_bos_src is not None: + logger.info(f"prepending src bos: {prepend_bos_src}") + src_dataset = PrependTokenDataset(src_dataset, prepend_bos_src) + + eos = None + if append_source_id: + src_dataset = AppendTokenDataset( + src_dataset, src_dict.index("[{}]".format(src)) + ) + if tgt_dataset is not None: + tgt_dataset = AppendTokenDataset( + tgt_dataset, tgt_dict.index("[{}]".format(tgt)) + ) + eos = tgt_dict.index("[{}]".format(tgt)) + + align_dataset = None + if load_alignments: + align_path = os.path.join(data_path, "{}.align.{}-{}".format(split, src, tgt)) + if indexed_dataset.dataset_exists(align_path, impl=dataset_impl): + align_dataset = data_utils.load_indexed_dataset( + align_path, None, dataset_impl + ) + + tgt_dataset_sizes = tgt_dataset.sizes if tgt_dataset is not None else None + return LanguagePairDataset( + src_dataset, + src_dataset.sizes, + src_dict, + tgt_dataset, + tgt_dataset_sizes, + tgt_dict, + left_pad_source=left_pad_source, + left_pad_target=left_pad_target, + align_dataset=align_dataset, + eos=eos, + num_buckets=num_buckets, + shuffle=shuffle, + pad_to_multiple=pad_to_multiple, + ) + + +@dataclass +class TranslationConfig(FairseqDataclass): + data: Optional[str] = field( + default=None, + metadata={ + "help": "colon separated path to data directories list, will be iterated upon during epochs " + "in round-robin manner; however, valid and test data are always in the first directory " + "to avoid the need for repeating them in all directories" + }, + ) + source_lang: Optional[str] = field( + default=None, + metadata={ + "help": "source language", + "argparse_alias": "-s", + }, + ) + target_lang: Optional[str] = field( + default=None, + metadata={ + "help": "target language", + "argparse_alias": "-t", + }, + ) + load_alignments: bool = field( + default=False, metadata={"help": "load the binarized alignments"} + ) + left_pad_source: bool = field( + default=True, metadata={"help": "pad the source on the left"} + ) + left_pad_target: bool = field( + default=False, metadata={"help": "pad the target on the left"} + ) + max_source_positions: int = field( + default=1024, metadata={"help": "max number of tokens in the source sequence"} + ) + max_target_positions: int = field( + default=1024, metadata={"help": "max number of tokens in the target sequence"} + ) + upsample_primary: int = field( + default=-1, metadata={"help": "the amount of upsample primary dataset"} + ) + truncate_source: bool = field( + default=False, metadata={"help": "truncate source to max-source-positions"} + ) + num_batch_buckets: int = field( + default=0, + metadata={ + "help": "if >0, then bucket source and target lengths into " + "N buckets and pad accordingly; this is useful on TPUs to minimize the number of compilations" + }, + ) + train_subset: str = II("dataset.train_subset") + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + required_seq_len_multiple: int = II("dataset.required_seq_len_multiple") + + # options for reporting BLEU during validation + eval_bleu: bool = field( + default=False, metadata={"help": "evaluation with BLEU scores"} + ) + eval_bleu_args: Optional[str] = field( + default="{}", + metadata={ + "help": 'generation args for BLUE scoring, e.g., \'{"beam": 4, "lenpen": 0.6}\', as JSON string' + }, + ) + eval_bleu_detok: str = field( + default="space", + metadata={ + "help": "detokenize before computing BLEU (e.g., 'moses'); required if using --eval-bleu; " + "use 'space' to disable detokenization; see fairseq.data.encoders for other options" + }, + ) + eval_bleu_detok_args: Optional[str] = field( + default="{}", + metadata={"help": "args for building the tokenizer, if needed, as JSON string"}, + ) + eval_tokenized_bleu: bool = field( + default=False, metadata={"help": "compute tokenized BLEU instead of sacrebleu"} + ) + eval_bleu_remove_bpe: Optional[str] = field( + default=None, + metadata={ + "help": "remove BPE before computing BLEU", + "argparse_const": "@@ ", + }, + ) + eval_bleu_print_samples: bool = field( + default=False, metadata={"help": "print sample generations during validation"} + ) + + +@register_task("translation", dataclass=TranslationConfig) +class TranslationTask(FairseqTask): + """ + Translate from one (source) language to another (target) language. + + Args: + src_dict (~fairseq.data.Dictionary): dictionary for the source language + tgt_dict (~fairseq.data.Dictionary): dictionary for the target language + + .. note:: + + The translation task is compatible with :mod:`fairseq-train`, + :mod:`fairseq-generate` and :mod:`fairseq-interactive`. + """ + + cfg: TranslationConfig + + def __init__(self, cfg: TranslationConfig, src_dict, tgt_dict): + super().__init__(cfg) + self.src_dict = src_dict + self.tgt_dict = tgt_dict + + @classmethod + def setup_task(cls, cfg: TranslationConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + + paths = utils.split_paths(cfg.data) + assert len(paths) > 0 + # find language pair automatically + if cfg.source_lang is None or cfg.target_lang is None: + cfg.source_lang, cfg.target_lang = data_utils.infer_language_pair(paths[0]) + if cfg.source_lang is None or cfg.target_lang is None: + raise Exception( + "Could not infer language pair, please provide it explicitly" + ) + + # load dictionaries + src_dict = cls.load_dictionary( + os.path.join(paths[0], "dict.{}.txt".format(cfg.source_lang)) + ) + tgt_dict = cls.load_dictionary( + os.path.join(paths[0], "dict.{}.txt".format(cfg.target_lang)) + ) + assert src_dict.pad() == tgt_dict.pad() + assert src_dict.eos() == tgt_dict.eos() + assert src_dict.unk() == tgt_dict.unk() + logger.info("[{}] dictionary: {} types".format(cfg.source_lang, len(src_dict))) + logger.info("[{}] dictionary: {} types".format(cfg.target_lang, len(tgt_dict))) + + return cls(cfg, src_dict, tgt_dict) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + paths = utils.split_paths(self.cfg.data) + assert len(paths) > 0 + if split != self.cfg.train_subset: + # if not training data set, use the first shard for valid and test + paths = paths[:1] + data_path = paths[(epoch - 1) % len(paths)] + + # infer langcode + src, tgt = self.cfg.source_lang, self.cfg.target_lang + + self.datasets[split] = load_langpair_dataset( + data_path, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=combine, + dataset_impl=self.cfg.dataset_impl, + upsample_primary=self.cfg.upsample_primary, + left_pad_source=self.cfg.left_pad_source, + left_pad_target=self.cfg.left_pad_target, + max_source_positions=self.cfg.max_source_positions, + max_target_positions=self.cfg.max_target_positions, + load_alignments=self.cfg.load_alignments, + truncate_source=self.cfg.truncate_source, + num_buckets=self.cfg.num_batch_buckets, + shuffle=(split != "test"), + pad_to_multiple=self.cfg.required_seq_len_multiple, + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + return LanguagePairDataset( + src_tokens, + src_lengths, + self.source_dictionary, + tgt_dict=self.target_dictionary, + constraints=constraints, + ) + + def build_model(self, cfg, from_checkpoint=False): + model = super().build_model(cfg, from_checkpoint) + if self.cfg.eval_bleu: + detok_args = json.loads(self.cfg.eval_bleu_detok_args) + self.tokenizer = encoders.build_tokenizer( + Namespace(tokenizer=self.cfg.eval_bleu_detok, **detok_args) + ) + + gen_args = json.loads(self.cfg.eval_bleu_args) + self.sequence_generator = self.build_generator( + [model], Namespace(**gen_args) + ) + return model + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + if self.cfg.eval_bleu: + bleu = self._inference_with_bleu(self.sequence_generator, sample, model) + logging_output["_bleu_sys_len"] = bleu.sys_len + logging_output["_bleu_ref_len"] = bleu.ref_len + # we split counts into separate entries so that they can be + # summed efficiently across workers using fast-stat-sync + assert len(bleu.counts) == EVAL_BLEU_ORDER + for i in range(EVAL_BLEU_ORDER): + logging_output["_bleu_counts_" + str(i)] = bleu.counts[i] + logging_output["_bleu_totals_" + str(i)] = bleu.totals[i] + return loss, sample_size, logging_output + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + if self.cfg.eval_bleu: + + def sum_logs(key): + import torch + + result = sum(log.get(key, 0) for log in logging_outputs) + if torch.is_tensor(result): + result = result.cpu() + return result + + counts, totals = [], [] + for i in range(EVAL_BLEU_ORDER): + counts.append(sum_logs("_bleu_counts_" + str(i))) + totals.append(sum_logs("_bleu_totals_" + str(i))) + + if max(totals) > 0: + # log counts as numpy arrays -- log_scalar will sum them correctly + metrics.log_scalar("_bleu_counts", np.array(counts)) + metrics.log_scalar("_bleu_totals", np.array(totals)) + metrics.log_scalar("_bleu_sys_len", sum_logs("_bleu_sys_len")) + metrics.log_scalar("_bleu_ref_len", sum_logs("_bleu_ref_len")) + + def compute_bleu(meters): + import inspect + + try: + from sacrebleu.metrics import BLEU + + comp_bleu = BLEU.compute_bleu + except ImportError: + # compatibility API for sacrebleu 1.x + import sacrebleu + + comp_bleu = sacrebleu.compute_bleu + + fn_sig = inspect.getfullargspec(comp_bleu)[0] + if "smooth_method" in fn_sig: + smooth = {"smooth_method": "exp"} + else: + smooth = {"smooth": "exp"} + bleu = comp_bleu( + correct=meters["_bleu_counts"].sum, + total=meters["_bleu_totals"].sum, + sys_len=int(meters["_bleu_sys_len"].sum), + ref_len=int(meters["_bleu_ref_len"].sum), + **smooth, + ) + return round(bleu.score, 2) + + metrics.log_derived("bleu", compute_bleu) + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + return (self.cfg.max_source_positions, self.cfg.max_target_positions) + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary`.""" + return self.src_dict + + @property + def target_dictionary(self): + """Return the target :class:`~fairseq.data.Dictionary`.""" + return self.tgt_dict + + def _inference_with_bleu(self, generator, sample, model): + import sacrebleu + + def decode(toks, escape_unk=False): + s = self.tgt_dict.string( + toks.int().cpu(), + self.cfg.eval_bleu_remove_bpe, + # The default unknown string in fairseq is `<unk>`, but + # this is tokenized by sacrebleu as `< unk >`, inflating + # BLEU scores. Instead, we use a somewhat more verbose + # alternative that is unlikely to appear in the real + # reference, but doesn't get split into multiple tokens. + unk_string=("UNKNOWNTOKENINREF" if escape_unk else "UNKNOWNTOKENINHYP"), + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + gen_out = self.inference_step(generator, [model], sample, prefix_tokens=None) + hyps, refs = [], [] + for i in range(len(gen_out)): + hyps.append(decode(gen_out[i][0]["tokens"])) + refs.append( + decode( + utils.strip_pad(sample["target"][i], self.tgt_dict.pad()), + escape_unk=True, # don't count <unk> as matches to the hypo + ) + ) + if self.cfg.eval_bleu_print_samples: + logger.info("example hypothesis: " + hyps[0]) + logger.info("example reference: " + refs[0]) + if self.cfg.eval_tokenized_bleu: + return sacrebleu.corpus_bleu(hyps, [refs], tokenize="none") + else: + return sacrebleu.corpus_bleu(hyps, [refs]) diff --git a/fairseq/fairseq/tasks/translation_from_pretrained_bart.py b/fairseq/fairseq/tasks/translation_from_pretrained_bart.py new file mode 100644 index 0000000..0fd7a5b --- /dev/null +++ b/fairseq/fairseq/tasks/translation_from_pretrained_bart.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq import utils +from fairseq.data import LanguagePairDataset + +from . import register_task +from .translation import TranslationTask, load_langpair_dataset + + +@register_task("translation_from_pretrained_bart") +class TranslationFromPretrainedBARTTask(TranslationTask): + """ + Translate from source language to target language with a model initialized with a multilingual pretrain. + + Args: + src_dict (~fairseq.data.Dictionary): dictionary for the source language + tgt_dict (~fairseq.data.Dictionary): dictionary for the target language + + .. note:: + + The translation task is compatible with :mod:`fairseq-train`, + :mod:`fairseq-generate` and :mod:`fairseq-interactive`. + + The translation task provides the following additional command-line + arguments: + + .. argparse:: + :ref: fairseq.tasks.translation_parser + :prog: + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + TranslationTask.add_args(parser) + parser.add_argument('--langs', type=str, metavar='LANG', + help='comma-separated list of monolingual language, ' + 'for example, "en,de,fr". These should match the ' + 'langs from pretraining (and be in the same order). ' + 'You should always add all pretraining language idx ' + 'during finetuning.') + parser.add_argument('--prepend-bos', action='store_true', + help='prepend bos token to each sentence, which matches ' + 'mBART pretraining') + # fmt: on + + def __init__(self, args, src_dict, tgt_dict): + super().__init__(args, src_dict, tgt_dict) + self.langs = args.langs.split(",") + for d in [src_dict, tgt_dict]: + for l in self.langs: + d.add_symbol("[{}]".format(l)) + d.add_symbol("<mask>") + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + paths = utils.split_paths(self.args.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + # infer langcode + src, tgt = self.args.source_lang, self.args.target_lang + + self.datasets[split] = load_langpair_dataset( + data_path, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=combine, + dataset_impl=self.args.dataset_impl, + upsample_primary=self.args.upsample_primary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + max_source_positions=getattr(self.args, "max_source_positions", 1024), + max_target_positions=getattr(self.args, "max_target_positions", 1024), + load_alignments=self.args.load_alignments, + prepend_bos=getattr(self.args, "prepend_bos", False), + append_source_id=True, + ) + + def build_generator(self, models, args, **unused): + if getattr(args, "score_reference", False): + from fairseq.sequence_scorer import SequenceScorer + + return SequenceScorer( + self.target_dictionary, + eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)), + ) + else: + from fairseq.sequence_generator import SequenceGenerator + + return SequenceGenerator( + models, + self.target_dictionary, + beam_size=getattr(args, "beam", 5), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 200), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)), + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + src_lang_id = self.source_dictionary.index("[{}]".format(self.args.source_lang)) + source_tokens = [] + for s_t in src_tokens: + s_t = torch.cat([s_t, s_t.new(1).fill_(src_lang_id)]) + source_tokens.append(s_t) + dataset = LanguagePairDataset( + source_tokens, + src_lengths, + self.source_dictionary, + tgt_dict=self.target_dictionary, + constraints=constraints, + ) + return dataset diff --git a/fairseq/fairseq/tasks/translation_from_pretrained_xlm.py b/fairseq/fairseq/tasks/translation_from_pretrained_xlm.py new file mode 100644 index 0000000..a05f289 --- /dev/null +++ b/fairseq/fairseq/tasks/translation_from_pretrained_xlm.py @@ -0,0 +1,39 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass +from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary +from fairseq.tasks.translation import TranslationConfig, TranslationTask + +from . import register_task + + +@dataclass +class TranslationFromPretrainedXLMConfig(TranslationConfig): + pass + + +@register_task( + "translation_from_pretrained_xlm", dataclass=TranslationFromPretrainedXLMConfig +) +class TranslationFromPretrainedXLMTask(TranslationTask): + """ + Same as TranslationTask except use the MaskedLMDictionary class so that + we can load data that was binarized with the MaskedLMDictionary class. + + This task should be used for the entire training pipeline when we want to + train an NMT model from a pretrained XLM checkpoint: binarizing NMT data, + training NMT with the pretrained XLM checkpoint, and subsequent evaluation + of that trained model. + """ + + @classmethod + def load_dictionary(cls, filename): + """Load the masked LM dictionary from the filename + + Args: + filename (str): the filename + """ + return MaskedLMDictionary.load(filename) diff --git a/fairseq/fairseq/tasks/translation_lev.py b/fairseq/fairseq/tasks/translation_lev.py new file mode 100644 index 0000000..b45fecd --- /dev/null +++ b/fairseq/fairseq/tasks/translation_lev.py @@ -0,0 +1,195 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +import torch +from fairseq import utils +from fairseq.data import LanguagePairDataset +from fairseq.dataclass import ChoiceEnum +from fairseq.tasks import register_task +from fairseq.tasks.translation import ( + TranslationConfig, + TranslationTask, + load_langpair_dataset, +) +from fairseq.utils import new_arange + + +NOISE_CHOICES = ChoiceEnum(["random_delete", "random_mask", "no_noise", "full_mask"]) + + +@dataclass +class TranslationLevenshteinConfig(TranslationConfig): + noise: NOISE_CHOICES = field( + default="random_delete", + metadata={"help": "type of noise"}, + ) + + +@register_task("translation_lev", dataclass=TranslationLevenshteinConfig) +class TranslationLevenshteinTask(TranslationTask): + """ + Translation (Sequence Generation) task for Levenshtein Transformer + See `"Levenshtein Transformer" <https://arxiv.org/abs/1905.11006>`_. + """ + + cfg: TranslationLevenshteinConfig + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + paths = utils.split_paths(self.cfg.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + + # infer langcode + src, tgt = self.cfg.source_lang, self.cfg.target_lang + + self.datasets[split] = load_langpair_dataset( + data_path, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=combine, + dataset_impl=self.cfg.dataset_impl, + upsample_primary=self.cfg.upsample_primary, + left_pad_source=self.cfg.left_pad_source, + left_pad_target=self.cfg.left_pad_target, + max_source_positions=self.cfg.max_source_positions, + max_target_positions=self.cfg.max_target_positions, + prepend_bos=True, + ) + + def inject_noise(self, target_tokens): + def _random_delete(target_tokens): + pad = self.tgt_dict.pad() + bos = self.tgt_dict.bos() + eos = self.tgt_dict.eos() + + max_len = target_tokens.size(1) + target_mask = target_tokens.eq(pad) + target_score = target_tokens.clone().float().uniform_() + target_score.masked_fill_( + target_tokens.eq(bos) | target_tokens.eq(eos), 0.0 + ) + target_score.masked_fill_(target_mask, 1) + target_score, target_rank = target_score.sort(1) + target_length = target_mask.size(1) - target_mask.float().sum( + 1, keepdim=True + ) + + # do not delete <bos> and <eos> (we assign 0 score for them) + target_cutoff = ( + 2 + + ( + (target_length - 2) + * target_score.new_zeros(target_score.size(0), 1).uniform_() + ).long() + ) + target_cutoff = target_score.sort(1)[1] >= target_cutoff + + prev_target_tokens = ( + target_tokens.gather(1, target_rank) + .masked_fill_(target_cutoff, pad) + .gather(1, target_rank.masked_fill_(target_cutoff, max_len).sort(1)[1]) + ) + prev_target_tokens = prev_target_tokens[ + :, : prev_target_tokens.ne(pad).sum(1).max() + ] + + return prev_target_tokens + + def _random_mask(target_tokens): + pad = self.tgt_dict.pad() + bos = self.tgt_dict.bos() + eos = self.tgt_dict.eos() + unk = self.tgt_dict.unk() + + target_masks = ( + target_tokens.ne(pad) & target_tokens.ne(bos) & target_tokens.ne(eos) + ) + target_score = target_tokens.clone().float().uniform_() + target_score.masked_fill_(~target_masks, 2.0) + target_length = target_masks.sum(1).float() + target_length = target_length * target_length.clone().uniform_() + target_length = target_length + 1 # make sure to mask at least one token. + + _, target_rank = target_score.sort(1) + target_cutoff = new_arange(target_rank) < target_length[:, None].long() + prev_target_tokens = target_tokens.masked_fill( + target_cutoff.scatter(1, target_rank, target_cutoff), unk + ) + return prev_target_tokens + + def _full_mask(target_tokens): + pad = self.tgt_dict.pad() + bos = self.tgt_dict.bos() + eos = self.tgt_dict.eos() + unk = self.tgt_dict.unk() + + target_mask = ( + target_tokens.eq(bos) | target_tokens.eq(eos) | target_tokens.eq(pad) + ) + return target_tokens.masked_fill(~target_mask, unk) + + if self.cfg.noise == "random_delete": + return _random_delete(target_tokens) + elif self.cfg.noise == "random_mask": + return _random_mask(target_tokens) + elif self.cfg.noise == "full_mask": + return _full_mask(target_tokens) + elif self.cfg.noise == "no_noise": + return target_tokens + else: + raise NotImplementedError + + def build_generator(self, models, args, **unused): + # add models input to match the API for SequenceGenerator + from fairseq.iterative_refinement_generator import IterativeRefinementGenerator + + return IterativeRefinementGenerator( + self.target_dictionary, + eos_penalty=getattr(args, "iter_decode_eos_penalty", 0.0), + max_iter=getattr(args, "iter_decode_max_iter", 10), + beam_size=getattr(args, "iter_decode_with_beam", 1), + reranking=getattr(args, "iter_decode_with_external_reranker", False), + decoding_format=getattr(args, "decoding_format", None), + adaptive=not getattr(args, "iter_decode_force_max_iter", False), + retain_history=getattr(args, "retain_iter_history", False), + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + if constraints is not None: + # Though see Susanto et al. (ACL 2020): https://www.aclweb.org/anthology/2020.acl-main.325/ + raise NotImplementedError( + "Constrained decoding with the translation_lev task is not supported" + ) + + return LanguagePairDataset( + src_tokens, src_lengths, self.source_dictionary, append_bos=True + ) + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + model.train() + sample["prev_target"] = self.inject_noise(sample["target"]) + loss, sample_size, logging_output = criterion(model, sample) + if ignore_grad: + loss *= 0 + optimizer.backward(loss) + return loss, sample_size, logging_output + + def valid_step(self, sample, model, criterion): + model.eval() + with torch.no_grad(): + sample["prev_target"] = self.inject_noise(sample["target"]) + loss, sample_size, logging_output = criterion(model, sample) + return loss, sample_size, logging_output diff --git a/fairseq/fairseq/tasks/translation_multi_simple_epoch.py b/fairseq/fairseq/tasks/translation_multi_simple_epoch.py new file mode 100644 index 0000000..5db36a7 --- /dev/null +++ b/fairseq/fairseq/tasks/translation_multi_simple_epoch.py @@ -0,0 +1,441 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import datetime +import logging +import time + +import torch +from fairseq.data import ( + FairseqDataset, + LanguagePairDataset, + ListDataset, + data_utils, + iterators, +) +from fairseq.data.multilingual.multilingual_data_manager import ( + MultilingualDatasetManager, +) +from fairseq.data.multilingual.sampling_method import SamplingMethod +from fairseq.tasks import LegacyFairseqTask, register_task +from fairseq.utils import FileContentsAction + + +### +def get_time_gap(s, e): + return ( + datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s) + ).__str__() + + +### + + +logger = logging.getLogger(__name__) + + +@register_task("translation_multi_simple_epoch") +class TranslationMultiSimpleEpochTask(LegacyFairseqTask): + """ + Translate from one (source) language to another (target) language. + + Args: + langs (List[str]): a list of languages that are being supported + dicts (Dict[str, fairseq.data.Dictionary]): mapping from supported languages to their dictionaries + training (bool): whether the task should be configured for training or not + + .. note:: + + The translation task is compatible with :mod:`fairseq-train`, + :mod:`fairseq-generate` and :mod:`fairseq-interactive`. + + The translation task provides the following additional command-line + arguments: + + .. argparse:: + :ref: fairseq.tasks.translation_parser + :prog: + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + parser.add_argument('-s', '--source-lang', default=None, metavar='SRC', + help='inference source language') + parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET', + help='inference target language') + parser.add_argument('--lang-pairs', default=None, metavar='PAIRS', + help='comma-separated list of language pairs (in training order): en-de,en-fr,de-fr', + action=FileContentsAction) + parser.add_argument('--keep-inference-langtok', action='store_true', + help='keep language tokens in inference output (e.g. for analysis or debugging)') + + SamplingMethod.add_arguments(parser) + MultilingualDatasetManager.add_args(parser) + # fmt: on + + def __init__(self, args, langs, dicts, training): + super().__init__(args) + self.langs = langs + self.dicts = dicts + self.training = training + if training: + self.lang_pairs = args.lang_pairs + else: + self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] + # eval_lang_pairs for multilingual translation is usually all of the + # lang_pairs. However for other multitask settings or when we want to + # optimize for certain languages we want to use a different subset. Thus + # the eval_lang_pairs class variable is provided for classes that extend + # this class. + self.eval_lang_pairs = self.lang_pairs + # model_lang_pairs will be used to build encoder-decoder model pairs in + # models.build_model(). This allows multitask type of sub-class can + # build models other than the input lang_pairs + self.model_lang_pairs = self.lang_pairs + self.source_langs = [d.split("-")[0] for d in self.lang_pairs] + self.target_langs = [d.split("-")[1] for d in self.lang_pairs] + self.check_dicts(self.dicts, self.source_langs, self.target_langs) + + self.sampling_method = SamplingMethod.build_sampler(args, self) + self.data_manager = MultilingualDatasetManager.setup_data_manager( + args, self.lang_pairs, langs, dicts, self.sampling_method + ) + + def check_dicts(self, dicts, source_langs, target_langs): + if self.args.source_dict is not None or self.args.target_dict is not None: + # no need to check whether the source side and target side are sharing dictionaries + return + src_dict = dicts[source_langs[0]] + tgt_dict = dicts[target_langs[0]] + for src_lang in source_langs: + assert ( + src_dict == dicts[src_lang] + ), "Diffrent dictionary are specified for different source languages; " + "TranslationMultiSimpleEpochTask only supports one shared dictionary across all source languages" + for tgt_lang in target_langs: + assert ( + tgt_dict == dicts[tgt_lang] + ), "Diffrent dictionary are specified for different target languages; " + "TranslationMultiSimpleEpochTask only supports one shared dictionary across all target languages" + + @classmethod + def setup_task(cls, args, **kwargs): + langs, dicts, training = MultilingualDatasetManager.prepare( + cls.load_dictionary, args, **kwargs + ) + return cls(args, langs, dicts, training) + + def has_sharded_data(self, split): + return self.data_manager.has_sharded_data(split) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if split in self.datasets: + dataset = self.datasets[split] + if self.has_sharded_data(split): + if self.args.virtual_epoch_size is not None: + if dataset.load_next_shard: + shard_epoch = dataset.shard_epoch + else: + # no need to load next shard so skip loading + # also this avoid always loading from beginning of the data + return + else: + shard_epoch = epoch + else: + # estimate the shard epoch from virtual data size and virtual epoch size + shard_epoch = self.data_manager.estimate_global_pass_epoch(epoch) + logger.info(f"loading data for {split} epoch={epoch}/{shard_epoch}") + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + if split in self.datasets: + del self.datasets[split] + logger.info("old dataset deleted manually") + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + self.datasets[split] = self.data_manager.load_dataset( + split, + self.training, + epoch=epoch, + combine=combine, + shard_epoch=shard_epoch, + **kwargs, + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the multilingual_translation task is not supported" + ) + + src_data = ListDataset(src_tokens, src_lengths) + dataset = LanguagePairDataset(src_data, src_lengths, self.source_dictionary) + src_langtok_spec, tgt_langtok_spec = self.args.langtoks["main"] + if self.args.lang_tok_replacing_bos_eos: + dataset = self.data_manager.alter_dataset_langtok( + dataset, + src_eos=self.source_dictionary.eos(), + src_lang=self.args.source_lang, + tgt_eos=self.target_dictionary.eos(), + tgt_lang=self.args.target_lang, + src_langtok_spec=src_langtok_spec, + tgt_langtok_spec=tgt_langtok_spec, + ) + else: + dataset.src = self.data_manager.src_dataset_tranform_func( + self.args.source_lang, + self.args.target_lang, + dataset=dataset.src, + spec=src_langtok_spec, + ) + return dataset + + def build_generator( + self, + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=None, + ): + if not getattr(args, "keep_inference_langtok", False): + _, tgt_langtok_spec = self.args.langtoks["main"] + if tgt_langtok_spec: + tgt_lang_tok = self.data_manager.get_decoder_langtok( + self.args.target_lang, tgt_langtok_spec + ) + extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} + extra_gen_cls_kwargs["symbols_to_strip_from_output"] = {tgt_lang_tok} + + return super().build_generator( + models, args, seq_gen_cls=None, extra_gen_cls_kwargs=extra_gen_cls_kwargs + ) + + def build_model(self, args, from_checkpoint=False): + return super().build_model(args, from_checkpoint) + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + return loss, sample_size, logging_output + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + _, tgt_langtok_spec = self.args.langtoks["main"] + if not self.args.lang_tok_replacing_bos_eos: + if prefix_tokens is None and tgt_langtok_spec: + tgt_lang_tok = self.data_manager.get_decoder_langtok( + self.args.target_lang, tgt_langtok_spec + ) + src_tokens = sample["net_input"]["src_tokens"] + bsz = src_tokens.size(0) + prefix_tokens = ( + torch.LongTensor([[tgt_lang_tok]]).expand(bsz, 1).to(src_tokens) + ) + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + ) + else: + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + bos_token=self.data_manager.get_decoder_langtok( + self.args.target_lang, tgt_langtok_spec + ) + if tgt_langtok_spec + else self.target_dictionary.eos(), + ) + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + return (self.args.max_source_positions, self.args.max_target_positions) + + @property + def source_dictionary(self): + return self.data_manager.get_source_dictionary(self.source_langs[0]) + + @property + def target_dictionary(self): + return self.data_manager.get_target_dictionary(self.target_langs[0]) + + def create_batch_sampler_func( + self, + max_positions, + ignore_invalid_inputs, + max_tokens, + max_sentences, + required_batch_size_multiple=1, + seed=1, + ): + def construct_batch_sampler(dataset, epoch): + splits = [ + s for s, _ in self.datasets.items() if self.datasets[s] == dataset + ] + split = splits[0] if len(splits) > 0 else None + # NEW implementation + if epoch is not None: + # initialize the dataset with the correct starting epoch + dataset.set_epoch(epoch) + + # get indices ordered by example size + start_time = time.time() + logger.info(f"start batch sampler: mem usage: {data_utils.get_mem_usage()}") + + with data_utils.numpy_seed(seed): + indices = dataset.ordered_indices() + logger.info( + f"[{split}] @batch_sampler order indices time: {get_time_gap(start_time, time.time())}" + ) + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + + # filter examples that are too large + if max_positions is not None: + my_time = time.time() + indices = self.filter_indices_by_size( + indices, dataset, max_positions, ignore_invalid_inputs + ) + logger.info( + f"[{split}] @batch_sampler filter_by_size time: {get_time_gap(my_time, time.time())}" + ) + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + + # create mini-batches with given size constraints + my_time = time.time() + batch_sampler = dataset.batch_by_size( + indices, + max_tokens=max_tokens, + max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + + logger.info( + f"[{split}] @batch_sampler batch_by_size time: {get_time_gap(my_time, time.time())}" + ) + logger.info( + f"[{split}] per epoch batch_sampler set-up time: {get_time_gap(start_time, time.time())}" + ) + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + + return batch_sampler + + return construct_batch_sampler + + # we need to override get_batch_iterator because we want to reset the epoch iterator each time + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + data_buffer_size=0, + disable_iterator_cache=False, + skip_remainder_batch=False, + grouped_shuffling=False, + update_epoch_batch_itr=False, + ): + """ + Get an iterator that yields batches of data from the given dataset. + + Args: + dataset (~fairseq.data.FairseqDataset): dataset to batch + max_tokens (int, optional): max number of tokens in each batch + (default: None). + max_sentences (int, optional): max number of sentences in each + batch (default: None). + max_positions (optional): max sentence length supported by the + model (default: None). + ignore_invalid_inputs (bool, optional): don't raise Exception for + sentences that are too long (default: False). + required_batch_size_multiple (int, optional): require batch size to + be a multiple of N (default: 1). + seed (int, optional): seed for random number generator for + reproducibility (default: 1). + num_shards (int, optional): shard the data iterator into N + shards (default: 1). + shard_id (int, optional): which shard of the data iterator to + return (default: 0). + num_workers (int, optional): how many subprocesses to use for data + loading. 0 means the data will be loaded in the main process + (default: 0). + epoch (int, optional): the epoch to start the iterator from + (default: 0). + data_buffer_size (int, optional): number of batches to + preload (default: 0). + disable_iterator_cache (bool, optional): don't cache the + EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`) + (default: False). + grouped_shuffling (bool, optional): group batches with each groups + containing num_shards batches and shuffle groups. Reduces difference + between sequence lengths among workers for batches sorted by length. + update_epoch_batch_itr (bool optional): if true then donot use the cached + batch iterator for the epoch + + Returns: + ~fairseq.iterators.EpochBatchIterator: a batched iterator over the + given dataset split + """ + # initialize the dataset with the correct starting epoch + assert isinstance(dataset, FairseqDataset) + if dataset in self.dataset_to_epoch_iter: + return self.dataset_to_epoch_iter[dataset] + if self.args.sampling_method == "RoundRobin": + batch_iter = super().get_batch_iterator( + dataset, + max_tokens=max_tokens, + max_sentences=max_sentences, + max_positions=max_positions, + ignore_invalid_inputs=ignore_invalid_inputs, + required_batch_size_multiple=required_batch_size_multiple, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + data_buffer_size=data_buffer_size, + disable_iterator_cache=disable_iterator_cache, + skip_remainder_batch=skip_remainder_batch, + update_epoch_batch_itr=update_epoch_batch_itr, + ) + self.dataset_to_epoch_iter[dataset] = batch_iter + return batch_iter + + construct_batch_sampler = self.create_batch_sampler_func( + max_positions, + ignore_invalid_inputs, + max_tokens, + max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + seed=seed, + ) + + epoch_iter = iterators.EpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_sampler=construct_batch_sampler, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + ) + return epoch_iter diff --git a/fairseq/fairseq/token_generation_constraints.py b/fairseq/fairseq/token_generation_constraints.py new file mode 100644 index 0000000..e708dc5 --- /dev/null +++ b/fairseq/fairseq/token_generation_constraints.py @@ -0,0 +1,506 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Implements tracking of constraints for a beam item. + +A list of constraints is given as a list of one or more token +sequences, each of length at least one token. For example, for an input sentence + +> Die maschinelle Übersetzung ist schwer zu kontrollieren. + +We could have the constraints: +* to influence +* hard + +There are two implementations: +* OrderedConstraintState: Tracks progress through an ordered list of multitoken constraints. +* UnorderedConstraintState: Tracks progress through an unordered list of multitoken constraints. + +The difference is that in the first, the constraints are assumed to be +in order; the algorithm will permit zero or more tokens between them. +In the second, the constraints are not ordered, so many orderings will +be explored. + +The same sequence can be present any number of times, and will appear +that many times in the output. +""" + +from collections import Counter +from typing import List, Optional, Set, Tuple + +import torch + + +class ConstraintState: + def __init__(self): + pass + + +def pack_constraints(batch_constraints: List[List[torch.Tensor]]) -> torch.Tensor: + """Takes a list of list of constraints in tensor form (a list of + tensor constraints for each sentence) and transforms it into a + packed Tensor. For example, here is a batch of size 3 with 3, 0, + and 1 constraints: + + [ [ [3 1 2], [3], [4 5 6 7], ] + [], + [ [1 8 9 10 1 4 11 12], ] + ] + + Its corresponding packed structure is: + + [ [ 3 3 1 2 0 3 0 4 5 6 7 0], + [ 0 0 0 0 0 0 0 0 0 0 0 0], + [ 1 1 8 9 10 1 4 11 12 0 0 0] ] + + The packed tensor has shape (batch size, maxlen), where + maxlen is defined below. Each row contains concatenated + constraint tokens for that sentence, with 0 appended after + each constraint. The first item in each row is the number + of constraints for that sentence. So maxlen is the maximum + of + + (number of constraints) + (sum length of constraints) + 1. + + across all sentences in the batch. + """ + # The maximum word length of concatenated constraints for any sentence + max_constraints_len = 1 + for sentence_constraints in batch_constraints: + if len(sentence_constraints): + # number of constraints, plus sum of constrain lens, plus a zero after each + constraints_len = ( + 1 + + sum([c.size(0) for c in sentence_constraints]) + + len(sentence_constraints) + ) + max_constraints_len = max(max_constraints_len, constraints_len) + + batch_size = len(batch_constraints) + constraints_tensor = torch.zeros((batch_size, max_constraints_len)).long() + for i, sentence_constraints in enumerate(batch_constraints): + constraints_tensor[i, 0] = len(sentence_constraints) + offset = 1 + for j, constraint in enumerate(sentence_constraints): + this_len = constraint.size(0) + constraints_tensor[i, offset : offset + this_len] = constraint + offset += this_len + 1 + + return constraints_tensor.long() + + +def unpack_constraints(constraint_tensor: torch.Tensor) -> List[torch.Tensor]: + """ + Transforms *one row* of a packed constraint tensor (e.g., for one + sentence in the batch) into a list of constraint tensors. + """ + constraint_list = [] + num_constraints = constraint_tensor[0] + constraints = constraint_tensor.tolist() + offset = 1 + for i in range(num_constraints): + where = constraints.index(0, offset) + constraint_list.append(constraint_tensor[offset:where]) + offset = where + 1 + + return constraint_list + + +class ConstraintNode: + """ + Represents a node in a trie managing unordered constraints. + """ + + def __init__(self, token: int = None, parent=None): + # The token associate with this node (None for the root) + self.token = int(token) if token is not None else None + # The parent (None at the root) + self.parent = parent + # Whether this node is a completed constraint + self.terminal = 0 + # List of child nodes + self.children = {} + + # The cumulative number of constraints from this point in the + # trie forward + self.num_constraints = 0 + + @property + def id(self): + return self.token + + def __str__(self): + term = self.terminal != 0 + return f"[{self.token}].{term}#{self.num_constraints}" + + def __getitem__(self, key: int): + return self.children.get(key, None) + + def next_tokens(self) -> Set[int]: + """The set of child labels.""" + return set(self.children.keys()) + + @staticmethod + def create(constraints: List[List[int]]): + root = ConstraintNode() + for sequence in constraints: + root.add_sequence(sequence) + + return root + + @staticmethod + def print_graph(node: "ConstraintNode"): + if len(node.children) == 0: + return str(node) + else: + s = f"({node}" + for child in node.children.values(): + s += " " + ConstraintNode.print_graph(child) + s += ")" + return s + + def token_counts(self) -> Counter: + """Returns a counter of the number of times each token is used + in a constraint. + """ + token_counts = Counter() + kids = list(self.children.values()) + while len(kids) > 0: + kid = kids.pop() + token_counts[kid.id] += kid.num_constraints + kids += list(kid.children.values()) + + return token_counts + + def tokens(self) -> Set[int]: + """Returns the set of tokens in constraints.""" + return set(self.token_counts().keys()) + + def add_sequence(self, sequence: List[int]): + """Adds a constraint, represented as a list of integers, to + the trie.""" + assert len(sequence) > 0 + + token = int(sequence[0]) + if token not in self.children: + self.children[token] = ConstraintNode(token, parent=self) + + node = self.children[token] + if len(sequence) == 1: + node.terminal += 1 + node.num_constraints += 1 + parent = node.parent + while parent is not None: + parent.num_constraints += 1 + parent = parent.parent + else: + node.add_sequence(sequence[1:]) + + +class UnorderedConstraintState(ConstraintState): + """ + Records progress through the set of constraints for each item in the beam + using a trie. + """ + + def __init__(self, node: ConstraintNode, copy_from: "ConstraintState" = None): + self.node = node + + if copy_from is None: + # The root node + self.root = node + # The set of states in the graph that have been completed + self.completed = Counter() + # The... + self.generated = Counter() + # The list of tokens we need to generate + self.needed_tokens = self.root.tokens() + else: + self.completed = Counter(copy_from.completed) + self.generated = Counter(copy_from.generated) + self.root = copy_from.root + + # Mark the node as generated + if self.node != self.root: + self.generated[node] += 1 + + @staticmethod + def create(constraint_tensor: torch.Tensor): + constraint_list = unpack_constraints(constraint_tensor) + constraint_trie_root = ConstraintNode.create(constraint_list) + return UnorderedConstraintState(constraint_trie_root) + + def __str__(self): + gen_str = ",".join([str(node) for node in self.generated]) + return f"{self.name}/{self.bank}({gen_str})x{self.num_completed}" + + def __copy__(self): + copied_state = UnorderedConstraintState(self.node, copy_from=self) + return copied_state + + def copy(self): + return self.__copy__() + + @property + def name(self): + if self.node.id is None: + return "ROOT" + else: + return str(self.node.id) + + @property + def is_root(self): + return self.node == self.root + + @property + def bank(self): + return sum(self.generated.values()) + + @property + def num_completed(self): + """The number of constraints (not constraint tokens) that are completed. + In addition to the already-completed states, we need to account for the + current state, which might get marked as completed when another token + is generated. + """ + in_final = self.node.terminal and self.completed[self.node] < self.node.terminal + return sum(self.completed.values()) + in_final + + @property + def finished(self): + return self.root.num_constraints - self.num_completed == 0 + + @property + def token_counts(self): + return self.root.token_counts() + + @property + def tokens(self): + return self.root.tokens() + + @property + def num_constraint_tokens(self): + return sum(self.token_counts.values()) + + def next_tokens(self) -> Set[int]: + """Returns the list of tokens that could come next. + These are (a) all tokens extending the root state and, for + non-root states, additionally all tokens extending the current + state.""" + + if self.node != self.root: + return self.root.next_tokens().union(self.node.next_tokens()) + else: + return self.root.next_tokens() + + def advance(self, token: int): + """Reads in a token and advances the state. Here's how it works. + + We can advance to the next state if: + - there is a matching child + - its path isn't blocked + + A path is blocked when all constraints that are descendants of + that node have already been generated, in the current state. + + If we are not able to advance from the current state, we "fall + off the graph" and return to the root state. There, we again + try to advance, checking the same criteria. + + In any case, when falling off the graph, we need to do some + bookkeeping. We: + - check whether any constraints were met (all prefixes of + current state) + - if one is found, mark it as completed + - adjust visited nodes accordingly + """ + token = int(token) + + next_state = None + child = self.node[token] + if child is not None and self.generated[child] < child.num_constraints: + next_state = UnorderedConstraintState(child, copy_from=self) + + def rewind(): + """If we're mid-trie and an "illegal" token is chosen next, we need + to reset our state to the root state. However, along the way, we need + to check whether a prefix of the current trie state represents a state + we could mark as completed. + """ + node = self.node + while node != self.root: + if node.terminal and self.completed[node] < node.terminal: + next_state.completed[node] += 1 + return + + next_state.generated[node] -= 1 + node = node.parent + + # Fall off the graph, check the root + if next_state is None and token in self.root.next_tokens(): + child = self.root[token] + # We can only traverse this edge if it's not saturated + if self.generated[child] < child.num_constraints: + next_state = UnorderedConstraintState(child, copy_from=self) + else: + next_state = UnorderedConstraintState(self.root, copy_from=self) + + # Rewind + rewind() + + elif next_state is None: + next_state = UnorderedConstraintState(self.root, copy_from=self) + # Rewind + rewind() + + return next_state + + +class ConstraintSequence: + def __init__(self, sequences: List[List[int]]): + """Represents a set of possibly multitoken constraints by + concatenating them and internally recording the end points. + """ + self.sequences = [] + self.endpoints = [] + self.num_tokens = 0 + self.tokens = set() + for sequence in sequences: + for token in sequence: + self.tokens.add(token) + self.num_tokens += len(sequence) + self.endpoints += [False for x in range(len(sequence) - 1)] + [True] + self.sequences += sequence + + def __getitem__(self, key: int): + return self.sequences[key] + + def __len__(self): + return len(self.sequences) + + def __str__(self): + return str(self.sequences) + + +class OrderedConstraintState(ConstraintState): + """ + Records progress through the set of linear nonbranching constraints with gaps. + """ + + def __init__(self, sequence: ConstraintSequence, state: int = -1): + self.sequence = sequence + self.state = state + + @staticmethod + def create(constraint_tensor: torch.Tensor): + constraint_list = unpack_constraints(constraint_tensor) + return OrderedConstraintState(ConstraintSequence(constraint_list), -1) + + def __str__(self): + return f"{self.state}/{self.bank}x{self.num_completed}" + + def __copy__(self): + return OrderedConstraintState(self.sequence, self.state) + + def copy(self): + return self.__copy__() + + @property + def num_completed(self): + if self.state == -1: + return 0 + count = len( + list(filter(lambda x: x, self.sequence.endpoints[0 : self.state + 1])) + ) + return count + + @property + def is_root(self): + return self.state == -1 + + @property + def name(self): + if self.state == -1: + return "ROOT" + else: + return str(self.sequence[self.state]) + + @property + def bank(self) -> int: + return self.state + 1 + + @property + def finished(self): + return self.state + 1 == len(self.sequence) + + @property + def token_counts(self): + return self.sequence.token_counts() + + @property + def tokens(self): + return self.sequence.tokens + + @property + def num_constraint_tokens(self): + return sum(self.token_counts.values()) + + def next_tokens(self) -> Set[int]: + """Returns the list of tokens that could come next. + These are (a) all tokens extending the root state and, for + non-root states, additionally all tokens extending the current + state.""" + + tokens = set() + if self.state > 0: + tokens.add(self.sequence[0]) + if not self.finished: + tokens.add(self.sequence[self.state + 1]) + return tokens + + def advance(self, token: int): + """Reads in a token and advances the state. Here's how it works. + + We can advance to the next state if: + - there is a matching child + - its path isn't blocked + + A path is blocked when all constraints that are descendants of + that node have already been generated, in the current state. + + If we are not able to advance from the current state, we "fall + off the graph" and return to the root state. There, we again + try to advance, checking the same criteria. + + In any case, when falling off the graph, we need to do some + bookkeeping. We: + - check whether any constraints were met (all prefixes of + current state) + - if one is found, mark it as completed + - adjust visited nodes accordingly + """ + token = int(token) + # print(f"{self} ADVANCE({token}) {self.sequence} -> ", end="") + + if self.finished: + # Accept anything + next_state = self.copy() + + elif self.sequence[self.state + 1] == token: + # Advance to the next token + next_state = OrderedConstraintState(self.sequence, self.state + 1) + + elif self.sequence.endpoints[self.state]: + # Accept anything between constraints (*) + next_state = self.copy() + + elif token == self.sequence[0]: + # Start over having generated the first token + next_state = OrderedConstraintState(self.sequence, 0) + else: + # Start over from the root + next_state = OrderedConstraintState(self.sequence, -1) + + return next_state diff --git a/fairseq/fairseq/tokenizer.py b/fairseq/fairseq/tokenizer.py new file mode 100644 index 0000000..42131f7 --- /dev/null +++ b/fairseq/fairseq/tokenizer.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re + + +SPACE_NORMALIZER = re.compile(r"\s+") + + +def tokenize_line(line): + line = SPACE_NORMALIZER.sub(" ", line) + line = line.strip() + return line.split() diff --git a/fairseq/fairseq/trainer.py b/fairseq/fairseq/trainer.py new file mode 100644 index 0000000..16b1b91 --- /dev/null +++ b/fairseq/fairseq/trainer.py @@ -0,0 +1,1622 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Train a network across multiple GPUs. +""" + +import contextlib +import logging +import os +import sys +import time +from argparse import Namespace +from itertools import chain +from typing import Any, Dict, List + +import torch +from omegaconf import OmegaConf + +from fairseq import checkpoint_utils, models, optim, utils +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.distributed import utils as distributed_utils +from fairseq.file_io import PathManager +from fairseq.logging import meters, metrics +from fairseq.models.ema import build_ema +from fairseq.nan_detector import NanDetector +from fairseq.optim import lr_scheduler +from fairseq.utils import safe_hasattr + +logger = logging.getLogger(__name__) + + +class Trainer(object): + """Main class for data parallel training. + + This class supports synchronous distributed data parallel training, + where multiple workers each have a full model replica and gradients + are accumulated across workers before each update. We use + :class:`~torch.nn.parallel.DistributedDataParallel` to handle + communication of the gradients across workers. + """ + + def __init__(self, cfg: FairseqConfig, task, model, criterion, quantizer=None): + + if isinstance(cfg, Namespace): + logger.warning( + "argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf" + ) + cfg = convert_namespace_to_omegaconf(cfg) + + self.cfg = cfg + self.task = task + + # catalog shared parameters + shared_params = _catalog_shared_params(model) + self.tpu = cfg.common.tpu + self.cuda = torch.cuda.is_available() and not cfg.common.cpu and not self.tpu + if self.cuda: + self.device = torch.device("cuda") + elif self.tpu: + self.device = utils.get_tpu_device() + else: + self.device = torch.device("cpu") + + if self.is_fsdp: + import fairscale + + if self.cfg.common.bf16: + raise ValueError( + "FullyShardedDataParallel is not compatible with --bf16 or " + "--memory-efficient-bf16" + ) + if self.cfg.distributed_training.zero_sharding != "none": + raise ValueError( + "FullyShardedDataParallel is not compatible with --zero-sharding " + "option (it's already built in)" + ) + if ( + max(self.cfg.optimization.update_freq) > 1 + and fairscale.__version__ < "0.4.0" + ): + raise RuntimeError( + "Please update to fairscale 0.4.0 or newer when combining " + "--update-freq with FullyShardedDataParallel" + ) + else: + if ( + hasattr(self.cfg.distributed_training, "cpu_offload") + and self.cfg.distributed_training.cpu_offload + ): + raise ValueError("--cpu-offload requires --ddp-backend=fully_sharded") + + # copy model and criterion to current device/dtype + self._criterion = criterion + self._model = model + if not self.is_fsdp: + if cfg.common.fp16: + assert not cfg.common.amp, "Cannot use fp16 and AMP together" + self._criterion = self._criterion.half() + self._model = self._model.half() + elif cfg.common.bf16: + self._criterion = self._criterion.to(dtype=torch.bfloat16) + self._model = self._model.to(dtype=torch.bfloat16) + elif cfg.common.amp: + self._amp_retries = 0 + if ( + not cfg.distributed_training.pipeline_model_parallel + # the DistributedFairseqModel wrapper will handle moving to device, + # so only handle cases which don't use the wrapper + and not self.use_distributed_wrapper + ): + self._criterion = self._criterion.to(device=self.device) + self._model = self._model.to(device=self.device) + self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel + self.last_device = None + if self.cuda and self.pipeline_model_parallel: + self.last_device = torch.device( + cfg.distributed_training.pipeline_devices[-1] + ) + + # check that shared parameters are preserved after device transfer + for shared_param in shared_params: + ref = _get_module_by_path(self._model, shared_param[0]) + for path in shared_param[1:]: + logger.info( + "detected shared parameter: {} <- {}".format(shared_param[0], path) + ) + _set_module_by_path(self._model, path, ref) + + self._dummy_batch = None # indicates we don't have a dummy batch at first + self._lr_scheduler = None + self._num_updates = 0 + self._num_xla_compiles = 0 # for TPUs + self._optim_history = None + self._optimizer = None + self._warn_once = set() + self._wrapped_criterion = None + self._wrapped_model = None + self._ema = None + + # TODO(myleott): support tpu + if self.cuda and self.data_parallel_world_size > 1: + self._grad_norm_buf = torch.cuda.DoubleTensor(self.data_parallel_world_size) + else: + self._grad_norm_buf = None + + self.quantizer = quantizer + if self.quantizer is not None: + self.quantizer.set_trainer(self) + + # get detailed cuda environment + if self.cuda: + self.cuda_env = utils.CudaEnvironment() + if self.data_parallel_world_size > 1: + self.cuda_env_arr = distributed_utils.all_gather_list( + self.cuda_env, group=distributed_utils.get_global_group() + ) + else: + self.cuda_env_arr = [self.cuda_env] + if self.data_parallel_rank == 0: + utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr) + else: + self.cuda_env = None + self.cuda_env_arr = None + + metrics.log_start_time("wall", priority=790, round=0) + + self._start_time = time.time() + self._previous_training_time = 0 + self._cumulative_training_time = None + + def reinitialize(self): + """Reinitialize the Trainer, typically after model params change.""" + self._lr_scheduler = None + self._optimizer = None + self._wrapped_criterion = None + self._wrapped_model = None + + @property + def data_parallel_world_size(self): + if self.cfg.distributed_training.distributed_world_size == 1: + return 1 + return distributed_utils.get_data_parallel_world_size() + + @property + def data_parallel_process_group(self): + return distributed_utils.get_data_parallel_group() + + @property + def data_parallel_rank(self): + if self.cfg.distributed_training.distributed_world_size == 1: + return 0 + return distributed_utils.get_data_parallel_rank() + + @property + def is_data_parallel_master(self): + # NOTE: this returns true for all model parallel replicas with data + # parallel rank 0 + return self.data_parallel_rank == 0 + + @property + def use_distributed_wrapper(self) -> bool: + return ( + self.data_parallel_world_size > 1 and not self.cfg.optimization.use_bmuf + ) or (self.is_fsdp and self.cfg.distributed_training.cpu_offload) + + @property + def should_save_checkpoint_on_current_rank(self) -> bool: + """Indicates whether to save checkpoints on the current DDP rank.""" + if ( + self.is_fsdp and self.cfg.distributed_training.use_sharded_state + ) or getattr(self.cfg.model, "base_layers", 0) > 0: + return True + else: + return self.is_data_parallel_master + + @property + def always_call_state_dict_during_save_checkpoint(self) -> bool: + if self.is_fsdp and not self.cfg.distributed_training.use_sharded_state: + # FSDP calls communication collective when consolidating checkpoints + return True + else: + return False + + @property + def checkpoint_suffix(self) -> str: + """Suffix to add to the checkpoint file name.""" + if self.is_fsdp and self.cfg.distributed_training.use_sharded_state: + return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format( + self.data_parallel_rank + ) + else: + return self.cfg.checkpoint.checkpoint_suffix or "" + + @property + def criterion(self): + if self._wrapped_criterion is None: + if utils.has_parameters(self._criterion) and self.use_distributed_wrapper: + self._wrapped_criterion = models.DistributedFairseqModel( + self.cfg.distributed_training, + self._criterion, + process_group=self.data_parallel_process_group, + device=self.device, + ) + else: + self._wrapped_criterion = self._criterion + return self._wrapped_criterion + + @property + def model(self): + if self._wrapped_model is None: + if self.use_distributed_wrapper: + self._wrapped_model = models.DistributedFairseqModel( + self.cfg.distributed_training, + self._model, + process_group=self.data_parallel_process_group, + device=self.device, + ) + else: + self._wrapped_model = self._model + return self._wrapped_model + + @property + def ema(self): + if self._ema is None: + self._build_ema() + return self._ema + + def _build_ema(self): + if self.cfg.ema.store_ema: + self._ema = build_ema(self._model, self.cfg.ema, self.device) + logger.info("Exponential Moving Average Shadow Model is initialized.") + + @property + def optimizer(self): + if self._optimizer is None: + self._build_optimizer() + return self._optimizer + + @property + def lr_scheduler(self): + if self._lr_scheduler is None: + self._build_optimizer() # this will initialize self._lr_scheduler + return self._lr_scheduler + + def _build_optimizer(self): + + if ( + self.cfg.optimization.debug_param_names + and self.cfg.common.fp16_no_flatten_grads + ): + params = [] + self.param_names = [] + + for n, p in chain( + self.model.named_parameters(), self.criterion.named_parameters() + ): + if p.requires_grad: + params.append(p) + self.param_names.append(n) + else: + params = list( + filter( + lambda p: p.requires_grad, + chain(self.model.parameters(), self.criterion.parameters()), + ) + ) + + if self.is_fsdp and self.cfg.common.fp16: + # FullyShardedDataParallel always uses MemoryEfficientFP16 wrapper, + # mostly for the grad scaling. But if we don't have the + # --memory-efficient-fp16 flag set, then we're effectively doing + # regular --fp16 and can allow the use of optimizers that would + # otherwise be unsupported by MemoryEfficientFP16Optimizer. + allow_unsupported = not self.cfg.common.memory_efficient_fp16 + self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( + self.cfg, params, allow_unsupported=allow_unsupported + ) + elif self.cfg.common.fp16 or self.cfg.common.bf16 or self.cfg.common.amp: + if self.cuda and torch.cuda.get_device_capability(0)[0] < 7: + logger.info( + "NOTE: your device does NOT support faster training with --fp16 or --amp, " + "please switch to FP32 which is likely to be faster" + ) + if ( + self.cfg.common.memory_efficient_fp16 + or self.cfg.common.memory_efficient_bf16 + ): + self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( + self.cfg, params + ) + elif self.cfg.common.amp: + self._optimizer = optim.AMPOptimizer.build_optimizer(self.cfg, params) + else: + self._optimizer = optim.FP16Optimizer.build_optimizer(self.cfg, params) + else: + if self.cuda and torch.cuda.get_device_capability(0)[0] >= 7: + logger.info( + "NOTE: your device may support faster training with --fp16 or --amp" + ) + self._optimizer = optim.build_optimizer(self.cfg.optimizer, params) + + if self.is_fsdp: + assert ( + not self.cfg.optimization.use_bmuf + ), "--ddp-backend=fully_sharded is not compatible with BMUF" + assert self._optimizer.supports_flat_params, ( + "--ddp-backend=fully_sharded is only compatible with pointwise " + "optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.). " + "However, the sharding will result in slightly different results when " + "using non-pointwise optimizers (e.g., Adagrad, Adafactor, LAMB)" + ) + + if self.cfg.optimization.use_bmuf: + self._optimizer = optim.FairseqBMUF( + self.cfg.bmuf, + self._optimizer, + ) + + if self.cfg.distributed_training.zero_sharding == "os": + if ( + self.cfg.common.fp16 + and not self.cfg.common.memory_efficient_fp16 + and not self.cfg.common.memory_efficient_bf16 + ) and not self.cfg.common.fp16_no_flatten_grads: + raise ValueError( + "ZeRO is incomptabile with fp16 and flattened grads. " + "Please use --fp16-no-flatten-grads" + ) + else: + optim.shard_(self._optimizer, self.data_parallel_process_group) + + # We should initialize the learning rate scheduler immediately after + # building the optimizer, so that the initial learning rate is set. + self._lr_scheduler = lr_scheduler.build_lr_scheduler( + self.cfg.lr_scheduler, + self.optimizer, + ) + self._lr_scheduler.step_update(0) + + @property + def is_fsdp(self): + return self.cfg.distributed_training.ddp_backend == "fully_sharded" + + def consolidate_optimizer(self): + """For OSS, we need to consolidate the state dict.""" + if self.cfg.checkpoint.no_save_optimizer_state: + return + self._gathered_optim_state = None + if hasattr(self.optimizer.optimizer, "consolidate_state_dict"): + self.optimizer.optimizer.consolidate_state_dict() + elif self.is_fsdp and not self.model.use_sharded_state: + st = self.model.gather_full_optim_state_dict( + self.optimizer + ) # only returns on rank 0 + self._gathered_optim_state = st + + def state_dict(self): + state_dict = { + "args": None, # legacy + "cfg": ( + OmegaConf.to_container(self.cfg, resolve=True, enum_to_str=True) + if OmegaConf.is_config(self.cfg) + else self.cfg + ), + "model": self.model.state_dict(), + "criterion": ( + self.criterion.state_dict() + if utils.has_parameters(self.criterion) + else None + ), + "optimizer_history": (self._optim_history or []) + + [ + { + "criterion_name": self.get_criterion().__class__.__name__, + "optimizer_name": self.optimizer.__class__.__name__, + "lr_scheduler_state": self.lr_scheduler.state_dict(), + "num_updates": self.get_num_updates(), + } + ], + "task_state": self.task.state_dict() if self.task is not None else {}, + "extra_state": { + "metrics": metrics.state_dict(), + "previous_training_time": self.cumulative_training_time(), + }, + } + if self.cfg.ema.store_ema: + # Save EMA model state as extra state + state_dict["extra_state"]["ema"] = self.ema.get_model().state_dict() + if self.cfg.ema.ema_fp32: + # Save EMA params in fp32 + state_dict["extra_state"]["ema_fp32_params"] = self.ema.fp32_params + if not self.cfg.checkpoint.no_save_optimizer_state: + if self._gathered_optim_state is not None: + state_dict["last_optimizer_state"] = self._gathered_optim_state + self._gathered_optim_state = None + else: + state_dict["last_optimizer_state"] = self.optimizer.state_dict() + if self.is_fsdp: + # save meta data for recombining checkpoint upon loading + state_dict["fsdp_metadata"] = self.model.local_metadata_dict() + return state_dict + + def save_checkpoint(self, filename, extra_state): + """Save all training state in a checkpoint file.""" + if self.should_save_checkpoint_on_current_rank: + + logger.info(f"Saving checkpoint to {os.path.abspath(filename)}") + # call state_dict on all ranks in case it needs internal communication + state_dict = utils.move_to_cpu(self.state_dict()) + state_dict["extra_state"].update(extra_state) + + checkpoint_utils.torch_persistent_save( + state_dict, + filename, + async_write=self.cfg.checkpoint.write_checkpoints_asynchronously, + ) + logger.info(f"Finished saving checkpoint to {os.path.abspath(filename)}") + return os.path.abspath(filename) + return None + + def load_checkpoint( + self, + filename, + reset_optimizer=False, + reset_lr_scheduler=False, + optimizer_overrides=None, + reset_meters=False, + ): + """ + Load all training state from a checkpoint file. + rank = 0 will load the checkpoint, and then broadcast it to all + other ranks. + """ + extra_state, self._optim_history, last_optim_state = None, [], None + + logger.info(f"Preparing to load checkpoint {filename}") + is_distributed = self.data_parallel_world_size > 1 + bexists = PathManager.isfile(filename) + if bexists: + load_on_all_ranks = ( + self.cfg.checkpoint.load_checkpoint_on_all_dp_ranks + # TPUs don't support broadcast yet, so load checkpoints + # on every worker for now + or self.tpu + # FSDP requires loading checkpoint shards on all ranks + or (self.is_fsdp and self.cfg.distributed_training.use_sharded_state) + or getattr(self.cfg.model, "base_layers", 0) > 0 + ) + + if load_on_all_ranks or self.data_parallel_rank == 0: + state = checkpoint_utils.load_checkpoint_to_cpu( + filename, load_on_all_ranks=load_on_all_ranks + ) + last_optim_state = state.get("last_optimizer_state", None) + + # If doing zero_sharding, do not broadcast global optimizer + # state. Later we will broadcast sharded states to each rank + # to avoid memory from exploding. + if ( + not load_on_all_ranks + and self.cfg.distributed_training.zero_sharding == "os" + and "last_optimizer_state" in state + and is_distributed + ): + state["last_optimizer_state"] = "SHARDED" + else: + last_optim_state = None + state = None + + if is_distributed and not load_on_all_ranks: + state = distributed_utils.broadcast_object( + state, + src_rank=0, + group=self.data_parallel_process_group, + dist_device=self.device, + ) + if self.data_parallel_rank > 0: + last_optim_state = state.get("last_optimizer_state", None) + + # load model parameters + try: + if ( + "optimizer_history" in state + and len(state["optimizer_history"]) > 0 + and "num_updates" in state["optimizer_history"][-1] + ): + self.model.set_num_updates( + state["optimizer_history"][-1]["num_updates"] + ) + + # this is the code related to AdaPrune + # In short, it removes redundant heads in multi-head attention module based on heads importance provided + # For more info, please refer to the paper: https://openreview.net/forum?id=_CMSV7FTzGI + # The idea of prune in mha can be summarized as + # Fine tune model (e.g. roberta encoder) on a certain datasets with regularization + # After the model is trained. User could use get_reserve_head_index and _adaptive_prune_heads functions to get the top X heads with most importance. + # Then user uses the rank to prune a new roberta encoder and save the pruned ckpt manually. + # User will fine tune the the new roberta encoder via the ckpt saved above + # To get rid of registering different pruned version of Roberta, I use the argument --mha-heads-to-keep to prune the Roberta model into a pruned version which matches the pruned ckpt. + if ( + safe_hasattr(self.model, "args") + and safe_hasattr(self.model.args, "mha_heads_to_keep") + and self.model.args.mha_heads_to_keep != -1 + ): + logger.info( + f"Prune model: keep {self.model.args.mha_heads_to_keep} heads for each multihead attention module" + ) + for layer in self.model.encoder.sentence_encoder.layers: + reserve_head_index = layer.self_attn._get_reserve_head_index( + num_heads_to_keep=self.model.args.mha_heads_to_keep + ) + layer.self_attn._adaptive_prune_heads( + reserve_head_index=reserve_head_index + ) + layer.self_attn._set_skip_embed_dim_check() + logger.info(self.model) + # this is the code related to AdaPrune + # In short, it removes redundant units in feedforward layer in each transformer layer based on importance + # For more info, please refer to the paper: https://openreview.net/forum?id=_CMSV7FTzGI + # The idea of prune in ffn can be summarized as + # Fine tune model (e.g. roberta encoder) on a certain datasets with regularization + # After the model is trained. User could use _get_fc_rank and _prune_fc_layer functions to get the top X units with most importance. + # Then user uses the rank to prune a new roberta encoder and save the pruned ckpt manually. + # User will fine tune the the new roberta encoder via the ckpt saved above + # To get rid of registering different pruned version of Roberta, I use the argument --ffn-blocks-to-remove to prune the Roberta model into a pruned version which matches the pruned ckpt. + if ( + safe_hasattr(self.model, "args") + and safe_hasattr(self.model.args, "ffn_blocks_to_remove") + and self.model.args.ffn_blocks_to_remove != -1 + ): + logger.info( + f"Prune model: remove {self.model.args.ffn_blocks_to_remove} ffn blocks for each transformer layer" + ) + for layer in self.model.encoder.sentence_encoder.layers: + remove_index = layer._get_fc_rank( + remove_num=self.model.args.ffn_blocks_to_remove + ) + layer._prune_fc_layer(remove_index=remove_index) + logger.info(self.model) + + self.model.load_state_dict( + state["model"], strict=True, model_cfg=self.cfg.model + ) + # save memory for later steps + del state["model"] + if utils.has_parameters(self.get_criterion()): + self.get_criterion().load_state_dict( + state["criterion"], strict=True + ) + del state["criterion"] + + except Exception: + raise Exception( + "Cannot load model parameters from checkpoint {}; " + "please ensure that the architectures match.".format(filename) + ) + extra_state = state["extra_state"] + self._optim_history = state["optimizer_history"] + + if last_optim_state is not None and not reset_optimizer: + # rebuild optimizer after loading model, since params may have changed + self._build_optimizer() + + # only reload optimizer and lr_scheduler if they match + last_optim = self._optim_history[-1] + assert ( + last_optim["criterion_name"] == self.get_criterion().__class__.__name__ + ), f"Criterion does not match; please reset the optimizer (--reset-optimizer). {last_optim['criterion_name']} vs {self.get_criterion().__class__.__name__}" + assert ( + last_optim["optimizer_name"] == self.optimizer.__class__.__name__ + ), f"Optimizer does not match; please reset the optimizer (--reset-optimizer). {last_optim['optimizer_name']} vs {self.optimizer.__class__.__name__}" + + if not reset_lr_scheduler: + self.lr_scheduler.load_state_dict(last_optim["lr_scheduler_state"]) + + if self.is_fsdp and not self.model.use_sharded_state: + # if use_sharded_state, the last_optim_state is already sharded, skip this + last_optim_state = self.model.get_shard_from_optim_state_dict( + last_optim_state + ) + elif not load_on_all_ranks and is_distributed: + last_optim_state = self.optimizer.broadcast_global_state_dict( + last_optim_state + ) + + self.optimizer.load_state_dict(last_optim_state, optimizer_overrides) + + self.set_num_updates(last_optim["num_updates"]) + + if extra_state is not None: + itr_state = extra_state["train_iterator"] + epoch = itr_state["epoch"] + + if "previous_training_time" in extra_state: + self._previous_training_time = extra_state["previous_training_time"] + self._start_time = time.time() + + self.lr_step(epoch) + + if ( + itr_state.get("version", 1) >= 2 + and itr_state["iterations_in_epoch"] == 0 + ): + # reset meters at start of epoch + reset_meters = True + + if "metrics" in extra_state and not reset_meters: + metrics.load_state_dict(extra_state["metrics"]) + + # reset TimeMeters, since their start times don't make sense anymore + for meter in metrics.get_meters("default"): + if isinstance(meter, meters.TimeMeter): + meter.reset() + + if self.cfg.ema.store_ema: + if "ema" not in extra_state: + logger.warn( + "EMA not found in checkpoint. But store_ema is True. " + "EMA is re-initialized from checkpoint." + ) + self.ema.restore( + state["model"], build_fp32_params=self.cfg.ema.ema_fp32 + ) + else: + logger.info("Loading EMA from checkpoint") + self.ema.restore(extra_state["ema"], build_fp32_params=False) + + if self.cfg.ema.ema_fp32: + if "ema_fp32_params" in extra_state: + logger.info("Loading EMA fp32 params from checkpoint") + self.ema.build_fp32_params(extra_state["ema_fp32_params"]) + else: + logger.info( + "Building EMA fp32 params from EMA model in checkpoint" + ) + self.ema.build_fp32_params() + + logger.info( + "Loaded checkpoint {} (epoch {} @ {} updates)".format( + filename, epoch, self.get_num_updates() + ) + ) + + else: + logger.info("No existing checkpoint found {}".format(filename)) + + return extra_state + + def get_train_iterator( + self, + epoch, + combine=True, + load_dataset=True, + data_selector=None, + shard_batch_itr=True, + disable_iterator_cache=False, + ): + """Return an EpochBatchIterator over the training set for a given epoch.""" + if load_dataset: + logger.info("loading train data for epoch {}".format(epoch)) + self.task.load_dataset( + self.cfg.dataset.train_subset, + epoch=epoch, + combine=combine, + data_selector=data_selector, + tpu=self.tpu, + ) + batch_iterator = self.task.get_batch_iterator( + dataset=self.task.dataset(self.cfg.dataset.train_subset), + max_tokens=self.cfg.dataset.max_tokens, + max_sentences=self.cfg.dataset.batch_size, + max_positions=utils.resolve_max_positions( + self.task.max_positions(), + self.model.max_positions(), + self.cfg.dataset.max_tokens, + ), + ignore_invalid_inputs=True, + required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, + seed=(self.cfg.common.seed + epoch) + if self.cfg.dataset.update_ordered_indices_seed + else self.cfg.common.seed, + num_shards=self.data_parallel_world_size if shard_batch_itr else 1, + shard_id=self.data_parallel_rank if shard_batch_itr else 0, + num_workers=self.cfg.dataset.num_workers, + epoch=epoch, + data_buffer_size=self.cfg.dataset.data_buffer_size, + disable_iterator_cache=disable_iterator_cache, + skip_remainder_batch=self.cfg.optimization.skip_remainder_batch, + grouped_shuffling=self.cfg.dataset.grouped_shuffling, + update_epoch_batch_itr=self.cfg.dataset.update_epoch_batch_itr, + ) + self.reset_dummy_batch(batch_iterator.first_batch) + return batch_iterator + + def get_valid_iterator( + self, + subset, + disable_iterator_cache=False, + ): + """Return an EpochBatchIterator over given validation subset for a given epoch.""" + batch_iterator = self.task.get_batch_iterator( + dataset=self.task.dataset(subset), + max_tokens=self.cfg.dataset.max_tokens_valid, + max_sentences=self.cfg.dataset.batch_size_valid, + max_positions=utils.resolve_max_positions( + self.task.max_positions(), + self.model.max_positions(), + ), + ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, + seed=self.cfg.common.seed, + num_shards=self.data_parallel_world_size, + shard_id=self.data_parallel_rank, + num_workers=self.cfg.dataset.num_workers, + # always pass a fixed "epoch" to keep validation data consistent + # across training epochs + epoch=1, + data_buffer_size=self.cfg.dataset.data_buffer_size, + disable_iterator_cache=disable_iterator_cache, + skip_remainder_batch=False, + ) + self.reset_dummy_batch(batch_iterator.first_batch) + return batch_iterator + + def begin_epoch(self, epoch): + """Called at the beginning of each epoch.""" + logger.info("begin training epoch {}".format(epoch)) + + self.lr_step_begin_epoch(epoch) + + if self.quantizer is not None: + self.quantizer.begin_epoch(epoch) + + # task specific setup per epoch + self.task.begin_epoch(epoch, self.get_model()) + + if self.tpu: + import torch_xla.core.xla_model as xm + + xm.rendezvous("begin_epoch") # wait for all workers + xm.mark_step() + + def begin_valid_epoch(self, epoch): + """Called at the beginning of each validation epoch.""" + + # task specific setup per validation epoch + self.task.begin_valid_epoch(epoch, self.get_model()) + + def reset_dummy_batch(self, batch): + self._dummy_batch = batch + + @metrics.aggregate("train") + def train_step(self, samples, raise_oom=False): + """Do forward, backward and parameter update.""" + self._set_seed() + self.model.train() + self.criterion.train() + self.zero_grad() + + metrics.log_start_time("train_wall", priority=800, round=0) + + # If EMA is enabled through store_ema=True + # and task.uses_ema is True, pass the EMA model as a keyword + # argument to the task. + extra_kwargs = {} + if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False): + extra_kwargs["ema_model"] = self.ema.get_model() + + has_oom = False + + # forward and backward pass + logging_outputs, sample_size, ooms = [], 0, 0 + for i, sample in enumerate(samples): # delayed update loop + sample, is_dummy_batch = self._prepare_sample(sample) + + def maybe_no_sync(): + """ + Whenever *samples* contains more than one mini-batch, we + want to accumulate gradients locally and only call + all-reduce in the last backwards pass. + """ + if ( + self.data_parallel_world_size > 1 + and hasattr(self.model, "no_sync") + and i < len(samples) - 1 + # The no_sync context manager results in increased memory + # usage with FSDP, since full-size gradients will be + # accumulated on each GPU. It's typically a better tradeoff + # to do the extra communication with FSDP. + and not self.is_fsdp + ): + return self.model.no_sync() + else: + return contextlib.ExitStack() # dummy contextmanager + + try: + with maybe_no_sync(): + # forward and backward + loss, sample_size_i, logging_output = self.task.train_step( + sample=sample, + model=self.model, + criterion=self.criterion, + optimizer=self.optimizer, + update_num=self.get_num_updates(), + ignore_grad=is_dummy_batch, + **extra_kwargs, + ) + del loss + + logging_outputs.append(logging_output) + sample_size += sample_size_i + + # emptying the CUDA cache after the first step can + # reduce the chance of OOM + if self.cuda and self.get_num_updates() == 0: + torch.cuda.empty_cache() + except RuntimeError as e: + if "out of memory" in str(e): + self._log_oom(e) + has_oom = True + if raise_oom: + raise e + else: + raise e + except Exception: + self.consolidate_optimizer() + self.save_checkpoint( + os.path.join(self.cfg.checkpoint.save_dir, "crash.pt"), {} + ) + raise + + if has_oom: + logger.warning( + "attempting to recover from OOM in forward/backward pass" + ) + ooms += 1 + self.zero_grad() + if self.cuda: + torch.cuda.empty_cache() + + if self.cfg.distributed_training.distributed_world_size == 1: + return None + + if self.tpu and i < len(samples) - 1: + # tpu-comment: every XLA operation before marking step is + # appended to the IR graph, and processing too many batches + # before marking step can lead to OOM errors. + # To handle gradient accumulation use case, we explicitly + # mark step here for every forward pass without a backward pass + self._xla_markstep_and_send_to_cpu() + + if is_dummy_batch: + if torch.is_tensor(sample_size): + sample_size.zero_() + else: + sample_size *= 0.0 + + if torch.is_tensor(sample_size): + sample_size = sample_size.float() + else: + sample_size = float(sample_size) + + # gather logging outputs from all replicas + if self._sync_stats(): + train_time = self._local_cumulative_training_time() + ( + logging_outputs, + ( + sample_size, + ooms, + total_train_time, + ), + ) = self._aggregate_logging_outputs( + logging_outputs, sample_size, ooms, train_time, ignore=is_dummy_batch + ) + self._cumulative_training_time = ( + total_train_time / self.data_parallel_world_size + ) + + overflow = False + try: + with torch.autograd.profiler.record_function("reduce-grads"): + # reduce gradients across workers + self.optimizer.all_reduce_grads(self.model) + if utils.has_parameters(self.criterion): + self.optimizer.all_reduce_grads(self.criterion) + + with torch.autograd.profiler.record_function("multiply-grads"): + # multiply gradients by (data_parallel_size / sample_size) since + # DDP normalizes by the number of data parallel workers for + # improved fp16 precision. + # Thus we get (sum_of_gradients / sample_size) at the end. + # In case of fp16, this step also undoes loss scaling. + # (Debugging note: Some optimizers perform this scaling on the + # fly, so inspecting model.parameters() or optimizer.params may + # still show the original, unscaled gradients.) + numer = ( + self.data_parallel_world_size + if not self.cfg.optimization.use_bmuf or self._sync_stats() + else 1 + ) + self.optimizer.multiply_grads(numer / (sample_size or 1.0)) + # Note: (sample_size or 1.0) handles the case of a zero gradient, in a + # way that avoids CPU/device transfers in case sample_size is a GPU or + # TPU object. The assumption is that the gradient itself is also 0. + + with torch.autograd.profiler.record_function("clip-grads"): + # clip grads + grad_norm = self.clip_grad_norm(self.cfg.optimization.clip_norm) + + # check that grad norms are consistent across workers + # on tpu check tensor is slow + if not self.tpu: + if ( + not self.cfg.optimization.use_bmuf + and self.cfg.distributed_training.ddp_backend != "slowmo" + ): + self._check_grad_norms(grad_norm) + if not torch.isfinite(grad_norm).all(): + # in case of AMP, if gradients are Nan/Inf then + # optimizer step is still required + if self.cfg.common.amp: + overflow = True + else: + # check local gradnorm single GPU case, trigger NanDetector + raise FloatingPointError("gradients are Nan/Inf") + + with torch.autograd.profiler.record_function("optimizer"): + # take an optimization step + self.task.optimizer_step( + self.optimizer, model=self.model, update_num=self.get_num_updates() + ) + if self.cfg.common.amp and overflow: + if self._amp_retries == self.cfg.common.amp_batch_retries: + logger.info("AMP: skipping this batch.") + self._amp_retries = 0 + else: + self._amp_retries += 1 + return self.train_step( + samples, raise_oom + ) # recursion to feed in same batch + + except FloatingPointError: + + self.consolidate_optimizer() + self.save_checkpoint( + os.path.join(self.cfg.checkpoint.save_dir, "crash.pt"), {} + ) + + # re-run the forward and backward pass with hooks attached to print + # out where it fails + self.zero_grad() + with NanDetector(self.get_model()): + for _, sample in enumerate(samples): + sample, _ = self._prepare_sample(sample) + self.task.train_step( + sample, + self.model, + self.criterion, + self.optimizer, + self.get_num_updates(), + ignore_grad=False, + **extra_kwargs, + ) + raise + except OverflowError as e: + overflow = True + logger.info( + f"NOTE: gradient overflow detected, ignoring gradient, {str(e)}" + ) + + if hasattr(self, "param_names") and hasattr( + self.optimizer, "fp32_optimizer" + ): + for p, n in zip(self.optimizer.fp32_optimizer.params, self.param_names): + if torch.isinf(p.grad).any() or torch.isnan(p.grad).any(): + logger.info(f"overflow in param {n}") + + grad_norm = torch.tensor(0.0).cuda() + self.zero_grad() + except RuntimeError as e: + if "out of memory" in str(e): + self._log_oom(e) + logger.error("OOM during optimization, irrecoverable") + raise e + + # Some distributed wrappers (e.g., SlowMo) need access to the optimizer + # after the step + if hasattr(self.model, "perform_slowmo"): + self.model.perform_slowmo( + self.optimizer.optimizer, getattr(self.optimizer, "fp32_params", None) + ) + + logging_output = None + if not overflow or self.cfg.distributed_training.ddp_backend == "slowmo": + self.set_num_updates(self.get_num_updates() + 1) + + if self.cfg.ema.store_ema: + # Step EMA forward with new model. + self.ema.step( + self.get_model(), + self.get_num_updates(), + ) + metrics.log_scalar( + "ema_decay", + self.ema.get_decay(), + priority=10000, + round=5, + weight=0, + ) + + if self.tpu: + import torch_xla.core.xla_model as xm + + # mark step on TPUs + self._xla_markstep_and_send_to_cpu() + + # only log stats every log_interval steps + # this causes wps to be misreported when log_interval > 1 + logging_output = {} + if self.get_num_updates() % self.cfg.common.log_interval == 0: + # log memory usage + mem_info = xm.get_memory_info(self.device) + gb_free = mem_info["kb_free"] / 1024 / 1024 + gb_total = mem_info["kb_total"] / 1024 / 1024 + metrics.log_scalar( + "gb_free", gb_free, priority=1500, round=1, weight=0 + ) + metrics.log_scalar( + "gb_total", gb_total, priority=1600, round=1, weight=0 + ) + logging_outputs = self._xla_markstep_and_send_to_cpu( + logging_outputs + ) + logging_output = self._reduce_and_log_stats( + logging_outputs, sample_size, grad_norm + ) + + # log whenever there's an XLA compilation, since these + # slow down training and may indicate opportunities for + # optimization + self._check_xla_compilation() + else: + if self.cuda and self.cuda_env is not None: + # log minimum free memory over the iteration + gb_used = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024 + torch.cuda.reset_peak_memory_stats() + gb_free = self.cuda_env.total_memory_in_GB - gb_used + metrics.log_scalar( + "gb_free", gb_free, priority=1500, round=1, weight=0 + ) + + # log stats + logging_output = self._reduce_and_log_stats( + logging_outputs, sample_size, grad_norm + ) + + # clear CUDA cache to reduce memory fragmentation + if ( + self.cuda + and self.cfg.common.empty_cache_freq > 0 + and ( + (self.get_num_updates() + self.cfg.common.empty_cache_freq - 1) + % self.cfg.common.empty_cache_freq + ) + == 0 + ): + torch.cuda.empty_cache() + + if self.cfg.common.fp16 or self.cfg.common.amp: + metrics.log_scalar( + "loss_scale", + ( + self.optimizer.scaler.loss_scale + if self.cfg.common.fp16 + else self.optimizer.scaler.get_scale() + ), + priority=700, + round=4, + weight=0, + ) + + metrics.log_stop_time("train_wall") + return logging_output + + @metrics.aggregate("valid") + def valid_step(self, sample, raise_oom=False): + """Do forward pass in evaluation mode.""" + if self.tpu: + import torch_xla.core.xla_model as xm + + xm.rendezvous("valid_step") # wait for all workers + + # If EMA is enabled through store_ema=True + # and task.uses_ema is True, pass the EMA model as a keyword + # argument to the task. + extra_kwargs = {} + if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False): + extra_kwargs["ema_model"] = self.ema.get_model() + + with torch.no_grad(): + self.model.eval() + self.criterion.eval() + + sample, is_dummy_batch = self._prepare_sample(sample) + + try: + _loss, sample_size, logging_output = self.task.valid_step( + sample, self.model, self.criterion, **extra_kwargs + ) + except RuntimeError as e: + if "out of memory" in str(e): + self._log_oom(e) + if not raise_oom: + logger.warning( + "ran out of memory in validation step, retrying batch" + ) + for p in self.model.parameters(): + if p.grad is not None: + p.grad = None # free some memory + if self.cuda: + torch.cuda.empty_cache() + return self.valid_step(sample, raise_oom=True) + raise e + + logging_outputs = [logging_output] + if is_dummy_batch: + if torch.is_tensor(sample_size): + sample_size.zero_() + else: + sample_size *= 0.0 + + # gather logging outputs from all replicas + if self.data_parallel_world_size > 1: + logging_outputs, (sample_size,) = self._aggregate_logging_outputs( + logging_outputs, + sample_size, + ignore=is_dummy_batch, + ) + + # log validation stats + if self.tpu: + logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs) + logging_output = self._reduce_and_log_stats(logging_outputs, sample_size) + + return logging_output + + def zero_grad(self): + self.optimizer.zero_grad() + + def lr_step_begin_epoch(self, epoch): + """Adjust the learning rate at the beginning of the epoch.""" + self.lr_scheduler.step_begin_epoch(epoch) + # prefer updating the LR based on the number of steps + return self.lr_step_update() + + def lr_step(self, epoch, val_loss=None): + """Adjust the learning rate at the end of the epoch.""" + self.lr_scheduler.step(epoch, val_loss) + # prefer updating the LR based on the number of steps + return self.lr_step_update() + + def lr_step_update(self): + """Update the learning rate after each update.""" + new_lr = self.lr_scheduler.step_update(self.get_num_updates()) + if isinstance(new_lr, dict): + for k, v in new_lr.items(): + metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300) + new_lr = new_lr.get("default", next(iter(new_lr.values()))) + else: + metrics.log_scalar("lr", new_lr, weight=0, priority=300) + return new_lr + + def get_lr(self): + """Get the current learning rate.""" + return self.optimizer.get_lr() + + def get_model(self): + """Get the (non-wrapped) model instance.""" + return self._model + + def get_criterion(self): + """Get the (non-wrapped) criterion instance.""" + return self._criterion + + def get_meter(self, name): + """[deprecated] Get a specific meter by name.""" + from fairseq import meters + + if "get_meter" not in self._warn_once: + self._warn_once.add("get_meter") + utils.deprecation_warning( + "Trainer.get_meter is deprecated. Please use fairseq.metrics instead." + ) + + train_meters = metrics.get_meters("train") + if train_meters is None: + train_meters = {} + + if name == "train_loss" and "loss" in train_meters: + return train_meters["loss"] + elif name == "train_nll_loss": + # support for legacy train.py, which assumed this meter is + # always initialized + m = train_meters.get("nll_loss", None) + return m or meters.AverageMeter() + elif name == "wall": + # support for legacy train.py, which assumed this meter is + # always initialized + m = metrics.get_meter("default", "wall") + return m or meters.TimeMeter() + elif name == "wps": + m = metrics.get_meter("train", "wps") + return m or meters.TimeMeter() + elif name in {"valid_loss", "valid_nll_loss"}: + # support for legacy train.py, which assumed these meters + # are always initialized + k = name[len("valid_") :] + m = metrics.get_meter("valid", k) + return m or meters.AverageMeter() + elif name == "oom": + return meters.AverageMeter() + elif name in train_meters: + return train_meters[name] + return None + + def get_num_updates(self): + """Get the number of parameters updates.""" + return self._num_updates + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + self._num_updates = num_updates + self.lr_step_update() + if self.quantizer: + self.quantizer.step_update(self._num_updates) + metrics.log_scalar("num_updates", self._num_updates, weight=0, priority=200) + + def clip_grad_norm(self, clip_norm): + def agg_norm_fn(total_norm): + total_norm = total_norm.cuda().float() ** 2 + total_norm = distributed_utils.all_reduce( + total_norm, group=self.data_parallel_process_group + ) + return total_norm**0.5 + + should_agg_norm = self.is_fsdp and ( + self.data_parallel_process_group is not None + or torch.distributed.is_initialized() + ) + return self.optimizer.clip_grad_norm( + clip_norm, aggregate_norm_fn=agg_norm_fn if should_agg_norm else None + ) + + def cumulative_training_time(self): + if self._cumulative_training_time is None: + # single GPU + return self._local_cumulative_training_time() + else: + return self._cumulative_training_time + + def _local_cumulative_training_time(self): + """Aggregate training time in seconds.""" + return time.time() - self._start_time + self._previous_training_time + + def _fp_convert_sample(self, sample): + def apply_half(t): + if t.dtype is torch.float32: + return t.to(dtype=torch.half) + return t + + def apply_bfloat16(t): + if t.dtype is torch.float32: + return t.to(dtype=torch.bfloat16) + return t + + if self.cfg.common.fp16: + sample = utils.apply_to_sample(apply_half, sample) + + if self.cfg.common.bf16: + sample = utils.apply_to_sample(apply_bfloat16, sample) + + return sample + + def _prepare_sample(self, sample, is_dummy=False): + if sample == "DUMMY": + raise Exception( + "Trying to use an uninitialized 'dummy' batch. This usually indicates " + "that the total number of batches is smaller than the number of " + "participating GPUs. Try reducing the batch size or using fewer GPUs." + ) + + if sample is None or len(sample) == 0: + assert ( + self._dummy_batch is not None and len(self._dummy_batch) > 0 + ), "Invalid dummy batch: {}".format(self._dummy_batch) + sample, _ = self._prepare_sample(self._dummy_batch, is_dummy=True) + return sample, True + + # Given that PCIe/NVLink bandwidth is significantly smaller than DRAM bandwidth + # it makes sense to do the format conversion on the CPU and then transfer + # a smaller buffer to the device. This also saves GPU memory capacity. + + if self.cfg.common.on_cpu_convert_precision: + sample = self._fp_convert_sample(sample) + + if self.cuda: + if self.pipeline_model_parallel: + if "target" in sample: + sample["target"] = utils.move_to_cuda( + sample["target"], device=self.last_device + ) + else: + sample = utils.move_to_cuda(sample) + elif self.tpu and is_dummy: + # the dummy batch may not be on the appropriate device + sample = utils.move_to_cuda(sample, device=self.device) + + if not self.cfg.common.on_cpu_convert_precision: + sample = self._fp_convert_sample(sample) + + if self._dummy_batch == "DUMMY": + self._dummy_batch = sample + + return sample, False + + def _set_seed(self): + # Set seed based on args.seed and the update number so that we get + # reproducible results when resuming from checkpoints + seed = self.cfg.common.seed + self.get_num_updates() + utils.set_torch_seed(seed) + + def _sync_stats(self): + # Return True if it's using multiple GPUs and DDP or multiple GPUs with + # BMUF and it's a bmuf sync with warmup iterations completed before. + if self.data_parallel_world_size == 1: + return False + elif self.cfg.optimization.use_bmuf: + return ( + self.get_num_updates() + 1 + ) % self.cfg.bmuf.global_sync_iter == 0 and ( + self.get_num_updates() + 1 + ) > self.cfg.bmuf.warmup_iterations + else: + return True + + def _log_oom(self, exc): + msg = "OOM: Ran out of memory with exception: {}".format(exc) + logger.warning(msg) + if torch.cuda.is_available() and hasattr(torch.cuda, "memory_summary"): + for device_idx in range(torch.cuda.device_count()): + logger.warning(torch.cuda.memory_summary(device=device_idx)) + sys.stderr.flush() + + def _aggregate_logging_outputs( + self, + logging_outputs: List[Dict[str, Any]], + *extra_stats_to_sum, + ignore=False, + ): + if self.task.__class__.logging_outputs_can_be_summed(self.get_criterion()): + return self._fast_stat_sync_sum( + logging_outputs, *extra_stats_to_sum, ignore=ignore + ) + else: + return self._all_gather_list_sync( + logging_outputs, *extra_stats_to_sum, ignore=ignore + ) + + def _all_gather_list_sync( + self, + logging_outputs: List[Dict[str, Any]], + *extra_stats_to_sum, + ignore=False, + ): + """ + Sync logging outputs across workers. all_gather_list_sync is + suitable when logging outputs are complex types. + """ + if self.tpu: + raise NotImplementedError + if ignore: + logging_outputs = [] + results = list( + zip( + *distributed_utils.all_gather_list( + [logging_outputs] + list(extra_stats_to_sum), + max_size=getattr(self.cfg.common, "all_gather_list_size", 16384), + group=self.data_parallel_process_group, + ) + ) + ) + logging_outputs, extra_stats_to_sum = results[0], results[1:] + logging_outputs = list(chain.from_iterable(logging_outputs)) + extra_stats_to_sum = [sum(s) for s in extra_stats_to_sum] + return logging_outputs, extra_stats_to_sum + + def _fast_stat_sync_sum( + self, + logging_outputs: List[Dict[str, Any]], + *extra_stats_to_sum, + ignore=False, + ): + """ + Sync logging outputs across workers. fast_stat_sync_sum is + faster than all_gather_list_sync, but is only suitable when + logging outputs are scalars and can be summed. Note that + *logging_outputs* cannot contain any nested dicts/lists. + """ + data = {} + for i, stat in enumerate(extra_stats_to_sum): + data["extra_stats_" + str(i)] = stat + if len(logging_outputs) > 0: + log_keys = list(logging_outputs[0].keys()) + for k in log_keys: + if not ignore: + v = sum(log[k] for log in logging_outputs if k in log) + else: + v = logging_outputs[0][k] + v = torch.zeros_like(v) if torch.is_tensor(v) else 0 + data["logging_outputs_" + k] = v + else: + log_keys = None + + data = distributed_utils.all_reduce_dict( + data, device=self.device, group=self.data_parallel_process_group + ) + + extra_stats_to_sum = [ + data["extra_stats_" + str(i)] for i in range(len(extra_stats_to_sum)) + ] + if log_keys is not None: + logging_outputs = [{k: data["logging_outputs_" + k] for k in log_keys}] + else: + logging_outputs = [] + return logging_outputs, extra_stats_to_sum + + def _check_grad_norms(self, grad_norm): + """Check that grad norms are consistent across workers.""" + if self._grad_norm_buf is not None: + self._grad_norm_buf.zero_() + self._grad_norm_buf[self.data_parallel_rank] = grad_norm + distributed_utils.all_reduce( + self._grad_norm_buf, group=self.data_parallel_process_group + ) + + def is_consistent(tensor): + max_abs_diff = torch.max(torch.abs(tensor - tensor[0])) + return ( + ( + torch.isfinite(tensor).all() + and (max_abs_diff / (tensor[0] + 1e-6) < 1e-6).all() + ) + or (self.cfg.common.amp and not torch.isfinite(tensor).all()) + # in case of amp non-finite grads are fine + ) + + if not is_consistent(self._grad_norm_buf): + pretty_detail = "\n".join( + "rank {:3d} = {:.8f}".format(r, n) + for r, n in enumerate(self._grad_norm_buf.tolist()) + ) + error_detail = "grad_norm across the workers:\n{}\n".format( + pretty_detail + ) + # use FloatingPointError to trigger NanDetector + raise FloatingPointError( + "Fatal error: gradients are inconsistent between workers. " + "Try --ddp-backend=legacy_ddp. " + "Or are you mixing up different generation of GPUs in training?" + + "\n" + + "-" * 80 + + "\n{}\n".format(error_detail) + + "-" * 80 + ) + + def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None): + if grad_norm is not None and ( + not torch.is_tensor(grad_norm) or torch.isfinite(grad_norm) + ): + metrics.log_speed("ups", 1.0, priority=100, round=2) + metrics.log_scalar("gnorm", grad_norm, priority=400, round=3) + if self.cfg.optimization.clip_norm > 0: + metrics.log_scalar( + "clip", + torch.where( + grad_norm > self.cfg.optimization.clip_norm, + grad_norm.new_tensor(100), + grad_norm.new_tensor(0), + ), + priority=500, + round=1, + ) + + with metrics.aggregate() as agg: + if logging_outputs is not None: + self.task.reduce_metrics(logging_outputs, self.get_criterion()) + del logging_outputs + + # extra warning for criterions that don't properly log a loss value + if "loss" not in agg: + if "loss" not in self._warn_once: + self._warn_once.add("loss") + logger.warning( + "Criterion.reduce_metrics did not log a 'loss' value, " + "which may break some functionality" + ) + metrics.log_scalar("loss", -1) + + # support legacy interface + if self.tpu: + logging_output = {} + else: + logging_output = agg.get_smoothed_values() + logging_output["sample_size"] = sample_size + for key_to_delete in ["ppl", "wps", "wpb", "bsz"]: + if key_to_delete in logging_output: + del logging_output[key_to_delete] + return logging_output + + def _check_xla_compilation(self): + import torch_xla.debug.metrics as met + + compile_stats = met.metric_data("CompileTime") + if compile_stats is None: + return + num_xla_compiles = compile_stats[0] + if num_xla_compiles > self._num_xla_compiles: + logger.warning( + "XLA compilation detected on device #{}; too many of these can lead " + "to slow training, but we expect a few in the beginning".format( + self.cfg.distributed_training.distributed_rank + ) + ) + self._num_xla_compiles = num_xla_compiles + + def _xla_markstep_and_send_to_cpu(self, data=None): + import torch_xla.core.xla_model as xm + + xm.mark_step() + if data is not None: + from fairseq.utils import xla_device_to_cpu + + return xla_device_to_cpu(data) + + +def _catalog_shared_params(module, memo=None, prefix=""): + if memo is None: + first_call = True + memo = {} + else: + first_call = False + for name, param in module._parameters.items(): + param_prefix = prefix + ("." if prefix else "") + name + if param not in memo: + memo[param] = [] + memo[param].append(param_prefix) + for name, m in module._modules.items(): + if m is None: + continue + submodule_prefix = prefix + ("." if prefix else "") + name + _catalog_shared_params(m, memo, submodule_prefix) + if first_call: + return [x for x in memo.values() if len(x) > 1] + + +def _get_module_by_path(module, path): + path = path.split(".") + for name in path: + module = getattr(module, name) + return module + + +def _set_module_by_path(module, path, value): + path = path.split(".") + for name in path[:-1]: + module = getattr(module, name) + setattr(module, path[-1], value) diff --git a/fairseq/fairseq/utils.py b/fairseq/fairseq/utils.py new file mode 100644 index 0000000..4d4b350 --- /dev/null +++ b/fairseq/fairseq/utils.py @@ -0,0 +1,951 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import collections +import contextlib +import copy +import importlib +import logging +import os +import sys +import warnings +from itertools import accumulate +from typing import TYPE_CHECKING, Callable, Dict, List, Optional + +import torch +import torch.nn.functional as F +from torch import Tensor + +if TYPE_CHECKING: + from fairseq.modules.multihead_attention import MultiheadAttention + +try: + from amp_C import multi_tensor_l2norm + + multi_tensor_l2norm_available = True +except ImportError: + multi_tensor_l2norm_available = False + +try: + import torch_xla.core.xla_model as xm +except ImportError: + xm = None + + +logger = logging.getLogger(__name__) + + +MANIFOLD_PATH_SEP = "|" + + +class FileContentsAction(argparse.Action): + def __init__(self, option_strings, dest, nargs=None, **kwargs): + if nargs is not None: + raise ValueError("nargs not allowed") + super(FileContentsAction, self).__init__(option_strings, dest, **kwargs) + + def __call__(self, parser, namespace, values, option_string=None): + from fairseq.file_io import PathManager + + if PathManager.isfile(values): + with PathManager.open(values) as f: + argument = f.read().strip() + else: + argument = values + setattr(namespace, self.dest, argument) + + +def split_paths(paths: str, separator=os.pathsep) -> List[str]: + return ( + paths.split(separator) if "://" not in paths else paths.split(MANIFOLD_PATH_SEP) + ) + + +def load_ensemble_for_inference(filenames, task, model_arg_overrides=None): + from fairseq import checkpoint_utils + + deprecation_warning( + "utils.load_ensemble_for_inference is deprecated. " + "Please use checkpoint_utils.load_model_ensemble instead." + ) + return checkpoint_utils.load_model_ensemble( + filenames, arg_overrides=model_arg_overrides, task=task + ) + + +def apply_to_sample(f, sample): + if hasattr(sample, "__len__") and len(sample) == 0: + return {} + + def _apply(x): + if torch.is_tensor(x): + return f(x) + elif isinstance(x, collections.OrderedDict): + # OrderedDict has attributes that needs to be preserved + od = collections.OrderedDict( + (key, _apply(value)) for key, value in x.items() + ) + od.__dict__ = x.__dict__ + return od + elif isinstance(x, dict): + return {key: _apply(value) for key, value in x.items()} + elif isinstance(x, list): + return [_apply(x) for x in x] + elif isinstance(x, tuple): + return tuple(_apply(x) for x in x) + elif isinstance(x, set): + return {_apply(x) for x in x} + else: + return x + + return _apply(sample) + + +def move_to_cuda(sample, device=None): + device = device or torch.cuda.current_device() + + def _move_to_cuda(tensor): + # non_blocking is ignored if tensor is not pinned, so we can always set + # to True (see github.com/PyTorchLightning/pytorch-lightning/issues/620) + return tensor.to(device=device, non_blocking=True) + + return apply_to_sample(_move_to_cuda, sample) + + +def move_to_cpu(sample): + def _move_to_cpu(tensor): + # PyTorch has poor support for half tensors (float16) on CPU. + # Move any such tensors to float32. + if tensor.dtype in {torch.bfloat16, torch.float16}: + tensor = tensor.to(dtype=torch.float32) + return tensor.cpu() + + return apply_to_sample(_move_to_cpu, sample) + + +def move_to_tpu(sample): + + import torch_xla.core.xla_model as xm + + device = xm.xla_device() + + def _move_to_tpu(tensor): + return tensor.to(device) + + return apply_to_sample(_move_to_tpu, sample) + + +def get_incremental_state( + module: "MultiheadAttention", + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + key: str, +) -> Optional[Dict[str, Optional[Tensor]]]: + """Helper for getting incremental state for an nn.Module.""" + return module.get_incremental_state(incremental_state, key) + + +def set_incremental_state( + module: "MultiheadAttention", + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + key: str, + value: Dict[str, Optional[Tensor]], +) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]: + """Helper for setting incremental state for an nn.Module.""" + if incremental_state is not None: + result = module.set_incremental_state(incremental_state, key, value) + if result is not None: + incremental_state = result + return incremental_state + + +def load_align_dict(replace_unk): + if replace_unk is None: + align_dict = None + elif isinstance(replace_unk, str) and len(replace_unk) > 0: + # Load alignment dictionary for unknown word replacement if it was passed as an argument. + align_dict = {} + with open(replace_unk, "r") as f: + for line in f: + cols = line.split() + align_dict[cols[0]] = cols[1] + else: + # No alignment dictionary provided but we still want to perform unknown word replacement by copying the + # original source word. + align_dict = {} + return align_dict + + +def print_embed_overlap(embed_dict, vocab_dict): + embed_keys = set(embed_dict.keys()) + vocab_keys = set(vocab_dict.symbols) + overlap = len(embed_keys & vocab_keys) + logger.info("found {}/{} types in embedding file".format(overlap, len(vocab_dict))) + + +def parse_embedding(embed_path): + """Parse embedding text file into a dictionary of word and embedding tensors. + + The first line can have vocabulary size and dimension. The following lines + should contain word and embedding separated by spaces. + + Example: + 2 5 + the -0.0230 -0.0264 0.0287 0.0171 0.1403 + at -0.0395 -0.1286 0.0275 0.0254 -0.0932 + """ + embed_dict = {} + with open(embed_path) as f_embed: + next(f_embed) # skip header + for line in f_embed: + pieces = line.rstrip().split(" ") + embed_dict[pieces[0]] = torch.Tensor( + [float(weight) for weight in pieces[1:]] + ) + return embed_dict + + +def load_embedding(embed_dict, vocab, embedding): + for idx in range(len(vocab)): + token = vocab[idx] + if token in embed_dict: + embedding.weight.data[idx] = embed_dict[token] + return embedding + + +def replace_unk(hypo_str, src_str, alignment, align_dict, unk): + from fairseq import tokenizer + + # Tokens are strings here + hypo_tokens = tokenizer.tokenize_line(hypo_str) + # TODO: Very rare cases where the replacement is '<eos>' should be handled gracefully + src_tokens = tokenizer.tokenize_line(src_str) + ["<eos>"] + for i, ht in enumerate(hypo_tokens): + if ht == unk: + src_token = src_tokens[alignment[i]] + # Either take the corresponding value in the aligned dictionary or just copy the original value. + hypo_tokens[i] = align_dict.get(src_token, src_token) + return " ".join(hypo_tokens) + + +def post_process_prediction( + hypo_tokens, + src_str, + alignment, + align_dict, + tgt_dict, + remove_bpe=None, + extra_symbols_to_ignore=None, +): + hypo_str = tgt_dict.string( + hypo_tokens, remove_bpe, extra_symbols_to_ignore=extra_symbols_to_ignore + ) + if align_dict is not None: + hypo_str = replace_unk( + hypo_str, src_str, alignment, align_dict, tgt_dict.unk_string() + ) + if align_dict is not None or remove_bpe is not None: + # Convert back to tokens for evaluating with unk replacement or without BPE + # Note that the dictionary can be modified inside the method. + hypo_tokens = tgt_dict.encode_line(hypo_str, add_if_not_exist=True) + return hypo_tokens, hypo_str, alignment + + +def make_positions(tensor, padding_idx: int, onnx_trace: bool = False): + """Replace non-padding symbols with their position numbers. + + Position numbers begin at padding_idx+1. Padding symbols are ignored. + """ + # The series of casts and type-conversions here are carefully + # balanced to both work with ONNX export and XLA. In particular XLA + # prefers ints, cumsum defaults to output longs, and ONNX doesn't know + # how to handle the dtype kwarg in cumsum. + mask = tensor.ne(padding_idx).int() + return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx + + +def strip_pad(tensor, pad): + return tensor[tensor.ne(pad)] + + +def buffered_arange(max, device="cpu"): + if not hasattr(buffered_arange, "buf"): + buffered_arange.buf = torch.LongTensor().to(device) + if max > buffered_arange.buf.numel(): + buffered_arange.buf.resize_(max) + torch.arange(max, out=buffered_arange.buf) + return buffered_arange.buf[:max] + + +def convert_padding_direction( + src_tokens, padding_idx, right_to_left: bool = False, left_to_right: bool = False +): + assert right_to_left ^ left_to_right + pad_mask = src_tokens.eq(padding_idx) + if not pad_mask.any(): + # no padding, return early + return src_tokens + if left_to_right and not pad_mask[:, 0].any(): + # already right padded + return src_tokens + if right_to_left and not pad_mask[:, -1].any(): + # already left padded + return src_tokens + max_len = src_tokens.size(1) + buffered = torch.empty(0).long() + if max_len > 0: + torch.arange(max_len, out=buffered) + range = buffered.type_as(src_tokens).expand_as(src_tokens) + num_pads = pad_mask.long().sum(dim=1, keepdim=True) + if right_to_left: + index = torch.remainder(range - num_pads, max_len) + else: + index = torch.remainder(range + num_pads, max_len) + return src_tokens.gather(1, index) + + +def item(tensor): + # tpu-comment: making this a no-op for xla devices. + if torch.is_tensor(tensor) and tensor.device.type == "xla": + return tensor.detach() + if hasattr(tensor, "item"): + return tensor.item() + if hasattr(tensor, "__getitem__"): + return tensor[0] + return tensor + + +def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor: + per_device_grads = {} + norms = [] + for grad in grads: + device = grad.device + cur_device_grads = per_device_grads.get(device) + if cur_device_grads is None: + cur_device_grads = [] + per_device_grads[device] = cur_device_grads + cur_device_grads.append(grad) + for device in per_device_grads.keys(): + cur_device_grads = per_device_grads[device] + if device.type == "cuda": + # TODO(msb) return has_inf + has_inf = torch.zeros((1, 1), dtype=torch.int, device=device) + with torch.cuda.device(device): + norm = multi_tensor_l2norm( + chunk_size, has_inf, [cur_device_grads], False + ) + norms.append(norm[0].to(torch.cuda.current_device())) + else: + norms += [torch.norm(g, p=2, dtype=torch.float32) for g in cur_device_grads] + total_norm = torch.norm(torch.stack(norms)) + return total_norm + + +@torch.no_grad() +def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor: + def grad_exists(p): + return p is not None and getattr(p, "grad", None) is not None + + if isinstance(params, torch.Tensor): + params = [params] + params = list(params) + grads = [ + p.grad.detach() for p in params if grad_exists(p) and not hasattr(p, "expert") + ] + expert_grads = [ + p.grad.detach() for p in params if grad_exists(p) and hasattr(p, "expert") + ] + + if len(grads) == 0: + if len(params) > 0: + return params[0].new_tensor(0.0) + else: + return torch.tensor(0.0) + + if len(grads) == 1: + total_norm = torch.norm(grads[0], p=2, dtype=torch.float32) + else: + if multi_tensor_l2norm_available: + total_norm = multi_tensor_total_norm(grads) + else: + if torch.cuda.is_available(): + warnings.warn( + "amp_C fused kernels unavailable, disabling multi_tensor_l2norm; " + "you may get better performance by installing NVIDIA's apex library" + ) + device = torch.cuda.current_device() + elif grads[0].device.type == "xla": + device = grads[0].device + else: + device = torch.device("cpu") + total_norm = torch.norm( + torch.stack( + [torch.norm(g, p=2, dtype=torch.float32).to(device) for g in grads] + ) + ) + + if aggregate_norm_fn is not None: + total_norm = aggregate_norm_fn(total_norm) + + if max_norm > 0: + max_norm = float(max_norm) + clip_coef = (max_norm / (total_norm + 1e-6)).clamp_(max=1) + torch._foreach_mul_(grads + expert_grads, clip_coef) + + return total_norm + + +def fill_with_neg_inf(t): + """FP16-compatible function that fills a tensor with -inf.""" + return t.float().fill_(float("-inf")).type_as(t) + + +def _match_types(arg1, arg2): + """Convert the numerical argument to the same type as the other argument""" + + def upgrade(arg_number, arg_structure): + if isinstance(arg_structure, tuple): + return tuple([arg_number] * len(arg_structure)) + elif isinstance(arg_structure, dict): + arg = copy.deepcopy(arg_structure) + for k in arg: + arg[k] = upgrade(arg_number, arg_structure[k]) + return arg + else: + return arg_number + + if isinstance(arg1, float) or isinstance(arg1, int): + return upgrade(arg1, arg2), arg2 + elif isinstance(arg2, float) or isinstance(arg2, int): + return arg1, upgrade(arg2, arg1) + + return arg1, arg2 + + +def resolve_max_positions(*args): + """Resolve max position constraints from multiple sources.""" + + def map_value_update(d1, d2): + updated_value = copy.deepcopy(d1) + for key in d2: + if key not in updated_value: + updated_value[key] = d2[key] + else: + updated_value[key] = min(d1[key], d2[key]) + return updated_value + + def nullsafe_min(l): + minim = None + for item in l: + if minim is None: + minim = item + elif item is not None and item < minim: + minim = item + return minim + + max_positions = None + for arg in args: + if max_positions is None: + max_positions = arg + elif arg is not None: + max_positions, arg = _match_types(max_positions, arg) + if isinstance(arg, float) or isinstance(arg, int): + max_positions = min(max_positions, arg) + elif isinstance(arg, dict): + max_positions = map_value_update(max_positions, arg) + else: + max_positions = tuple(map(nullsafe_min, zip(max_positions, arg))) + + return max_positions + + +def import_user_module(args): + module_path = getattr(args, "user_dir", None) + if module_path is not None: + module_path = os.path.abspath(args.user_dir) + if not os.path.exists(module_path) and not os.path.isfile( + os.path.dirname(module_path) + ): + fairseq_rel_path = os.path.join(os.path.dirname(__file__), args.user_dir) + if os.path.exists(fairseq_rel_path): + module_path = fairseq_rel_path + else: + fairseq_rel_path = os.path.join( + os.path.dirname(__file__), "..", args.user_dir + ) + if os.path.exists(fairseq_rel_path): + module_path = fairseq_rel_path + else: + raise FileNotFoundError(module_path) + + # ensure that user modules are only imported once + import_user_module.memo = getattr(import_user_module, "memo", set()) + if module_path not in import_user_module.memo: + import_user_module.memo.add(module_path) + + module_parent, module_name = os.path.split(module_path) + if module_name not in sys.modules: + sys.path.insert(0, module_parent) + importlib.import_module(module_name) + + tasks_path = os.path.join(module_path, "tasks") + if os.path.exists(tasks_path): + from fairseq.tasks import import_tasks + + import_tasks(tasks_path, f"{module_name}.tasks") + + models_path = os.path.join(module_path, "models") + if os.path.exists(models_path): + from fairseq.models import import_models + + import_models(models_path, f"{module_name}.models") + elif module_path in sys.modules[module_name].__path__: + logger.info(f"--user-dir={module_path} has already been imported.") + else: + raise ImportError( + "Failed to import --user-dir={} because the corresponding module name " + "({}) is not globally unique. Please rename the directory to " + "something unique and try again.".format(module_path, module_name) + ) + + +def softmax(x, dim: int, onnx_trace: bool = False): + if onnx_trace: + return F.softmax(x.float(), dim=dim) + else: + return F.softmax(x, dim=dim, dtype=torch.float32) + + +def log_softmax(x, dim: int, onnx_trace: bool = False): + if onnx_trace: + return F.log_softmax(x.float(), dim=dim) + else: + return F.log_softmax(x, dim=dim, dtype=torch.float32) + + +def get_perplexity(loss, round=2, base=2): + from fairseq.logging.meters import safe_round + + if loss is None: + return 0.0 + try: + return safe_round(base**loss, round) + except OverflowError: + return float("inf") + + +def deprecation_warning(message, stacklevel=3): + # don't use DeprecationWarning, since it's ignored by default + warnings.warn(message, stacklevel=stacklevel) + + +def relu_squared(x: torch.Tensor): + return F.relu(x).pow(2) + + +def get_activation_fn(activation: str) -> Callable: + """Returns the activation function corresponding to `activation`""" + from fairseq.modules import gelu, gelu_accurate + + if activation == "relu": + return F.relu + elif activation == "relu_squared": + return relu_squared + elif activation == "gelu": + return gelu + elif activation == "gelu_fast": + deprecation_warning( + "--activation-fn=gelu_fast has been renamed to gelu_accurate" + ) + return gelu_accurate + elif activation == "gelu_accurate": + return gelu_accurate + elif activation == "tanh": + return torch.tanh + elif activation == "linear": + return lambda x: x + elif activation == "swish": + return torch.nn.SiLU + else: + raise RuntimeError("--activation-fn {} not supported".format(activation)) + + +def get_available_activation_fns() -> List: + return [ + "relu", + "gelu", + "gelu_fast", # deprecated + "gelu_accurate", + "tanh", + "linear", + ] + + +@contextlib.contextmanager +def model_eval(model): + is_training = model.training + model.eval() + yield + model.train(is_training) + + +def has_parameters(module): + try: + next(module.parameters()) + return True + except StopIteration: + return False + + +def get_rng_state(): + state = {"torch_rng_state": torch.get_rng_state()} + if xm is not None: + state["xla_rng_state"] = xm.get_rng_state() + if torch.cuda.is_available(): + state["cuda_rng_state"] = torch.cuda.get_rng_state() + return state + + +def set_rng_state(state): + torch.set_rng_state(state["torch_rng_state"]) + if xm is not None: + xm.set_rng_state(state["xla_rng_state"]) + if torch.cuda.is_available(): + torch.cuda.set_rng_state(state["cuda_rng_state"]) + + +class set_torch_seed(object): + def __init__(self, seed): + assert isinstance(seed, int) + self.rng_state = get_rng_state() + + torch.manual_seed(seed) + if xm is not None: + xm.set_rng_state(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + + def __enter__(self): + return self + + def __exit__(self, *exc): + set_rng_state(self.rng_state) + + +def parse_alignment(line): + """ + Parses a single line from the alingment file. + + Args: + line (str): String containing the alignment of the format: + <src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> .. + <src_idx_m>-<tgt_idx_m>. All indices are 0 indexed. + + Returns: + torch.IntTensor: packed alignments of shape (2 * m). + """ + alignments = line.strip().split() + parsed_alignment = torch.IntTensor(2 * len(alignments)) + for idx, alignment in enumerate(alignments): + src_idx, tgt_idx = alignment.split("-") + parsed_alignment[2 * idx] = int(src_idx) + parsed_alignment[2 * idx + 1] = int(tgt_idx) + return parsed_alignment + + +def get_token_to_word_mapping(tokens, exclude_list): + n = len(tokens) + word_start = [int(token not in exclude_list) for token in tokens] + word_idx = list(accumulate(word_start)) + token_to_word = {i: word_idx[i] for i in range(n)} + return token_to_word + + +def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos): + tgt_valid = ( + ((tgt_sent != pad) & (tgt_sent != eos)).nonzero(as_tuple=False).squeeze(dim=-1) + ) + src_invalid = ( + ((src_sent == pad) | (src_sent == eos)).nonzero(as_tuple=False).squeeze(dim=-1) + ) + src_token_to_word = get_token_to_word_mapping(src_sent, [eos, pad]) + tgt_token_to_word = get_token_to_word_mapping(tgt_sent, [eos, pad]) + alignment = [] + if len(tgt_valid) != 0 and len(src_invalid) < len(src_sent): + attn_valid = attn[tgt_valid] + attn_valid[:, src_invalid] = float("-inf") + _, src_indices = attn_valid.max(dim=1) + for tgt_idx, src_idx in zip(tgt_valid, src_indices): + alignment.append( + ( + src_token_to_word[src_idx.item()] - 1, + tgt_token_to_word[tgt_idx.item()] - 1, + ) + ) + return alignment + + +def extract_soft_alignment(attn, src_sent, tgt_sent, pad, eos): + tgt_valid = ((tgt_sent != pad)).nonzero(as_tuple=False) + src_valid = ((src_sent != pad)).nonzero(as_tuple=False).squeeze(dim=-1) + alignment = [] + if len(tgt_valid) != 0 and len(src_valid) != 0: + attn_valid = attn[tgt_valid, src_valid] + alignment = [ + ["{:.6f}".format(p) for p in src_probs.tolist()] for src_probs in attn_valid + ] + return alignment + + +def new_arange(x, *size): + """ + Return a Tensor of `size` filled with a range function on the device of x. + If size is empty, using the size of the variable x. + """ + if len(size) == 0: + size = x.size() + return torch.arange(size[-1], device=x.device).expand(*size).contiguous() + + +def get_tpu_device(): + return xm.xla_device() + + +def tpu_data_loader(itr): + import torch_xla.core.xla_model as xm + import torch_xla.distributed.parallel_loader as pl + + from fairseq.data import iterators + + xm.rendezvous("tpu_data_loader") # wait for all workers + xm.mark_step() + device = xm.xla_device() + return iterators.CountingIterator( + pl.ParallelLoader(itr, [device]).per_device_loader(device), + start=getattr(itr, "n", 0), + total=len(itr), + ) + + +def is_xla_tensor(tensor): + return torch.is_tensor(tensor) and tensor.device.type == "xla" + + +def index_put(tensor, indices, value): + if is_xla_tensor(tensor): + for _ in range(indices.dim(), tensor.dim()): + indices = indices.unsqueeze(-1) + if indices.size(-1) < tensor.size(-1): + indices = indices.expand_as(tensor) + tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices) + else: + tensor[indices] = value + return tensor + + +def xla_device_to_cpu(dat): + import torch_xla.core.xla_model as xm + + return xm._maybe_convert_to_cpu(dat) + + +class CudaEnvironment(object): + def __init__(self): + cur_device = torch.cuda.current_device() + prop = torch.cuda.get_device_properties("cuda:{}".format(cur_device)) + self.name = prop.name + self.major = prop.major + self.minor = prop.minor + self.total_memory_in_GB = prop.total_memory / 1024 / 1024 / 1024 + + @staticmethod + def pretty_print_cuda_env_list(cuda_env_list): + """ + Given a list of CudaEnviorments, pretty print them + """ + num_workers = len(cuda_env_list) + center = "CUDA enviroments for all {} workers".format(num_workers) + banner_len = 40 - len(center) // 2 + first_line = "*" * banner_len + center + "*" * banner_len + logger.info(first_line) + for r, env in enumerate(cuda_env_list): + logger.info( + "rank {:3d}: ".format(r) + + "capabilities = {:2d}.{:<2d} ; ".format(env.major, env.minor) + + "total memory = {:.3f} GB ; ".format(env.total_memory_in_GB) + + "name = {:40s}".format(env.name) + ) + logger.info(first_line) + + +def csv_str_list(x): + return x.split(",") + + +def eval_str_list(x, type=float): + if x is None: + return None + if isinstance(x, str): + x = eval(x) + try: + return list(map(type, x)) + except TypeError: + return [type(x)] + + +def eval_str_dict(x, type=dict): + if x is None: + return None + if isinstance(x, str): + x = eval(x) + return x + + +def eval_bool(x, default=False): + if x is None: + return default + try: + return bool(eval(x)) + except TypeError: + return default + + +def reset_logging(): + root = logging.getLogger() + for handler in root.handlers: + root.removeHandler(handler) + root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper()) + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter( + logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + ) + root.addHandler(handler) + + +def safe_getattr(obj, k, default=None): + """Returns obj[k] if it exists and is not None, otherwise returns default.""" + from omegaconf import OmegaConf + + if OmegaConf.is_config(obj): + return obj[k] if k in obj and obj[k] is not None else default + + return getattr(obj, k, default) + + +def safe_hasattr(obj, k): + """Returns True if the given key exists and is not None.""" + return getattr(obj, k, None) is not None + + +def hotreload_function(name=None): + """ + Decorator to function to enable hot-reload for debugging. + It allows you to debug a function without having reloading all heavy models, dataset loading and + preprocessing, allow faster debugging. + If you want to change model or dataset loading, consider relaunching your code + ----------------------------------- + This will run the decorated function func: + if func run successful: + It will pause, allow user to edit code, and prompt user to: + Press enter to re-run the function with updated code + Type "done" to finish the function, return output + Type "disable" to stop pausing this function and let code continue without pause + Ctril + C to terminal + if func raise error: + it will prompt user to + 1. Edit code, and press enter to retry + 2. Ctrl + C to terminate + 3. Type "raise" to raise that exception + * Requirements: + 0. Fairseq was installed with `pip install --editable .` + 1. pip install jurigged[develoop] + 2. set environment HOTRELOAD_PAUSE=1 CUDA_LAUNCH_BLOCKING=1 + 3. Run on only 1 GPU (no distributed) + * How to use: + 1. in python, import and decorate the top-level function to be re-run after code edits: + ```python + from fairseq.utils import hotreload_function + .... + @hotreload_function("train_step") + def train_step(self, sample ....): + .... + .... + ``` + 2. in bash run scripts: + ```bash + watch_dir=<home>/fairseq-py/fairseq/tasks # directory to watch for file changes + export CUDA_VISIBLE_DEVICES=0 # single-gpu + HOTRELOAD_PAUSE=1 CUDA_LAUNCH_BLOCKING=1 python -m jurigged -w ${watch_dir} --poll 2 -v train.py ...... + ``` + * NOTE: + 1. -w ${watch_dir} specify all the files to be watched for changes + once functions, class, ... code are changed, all instances in the process will get updated (hot-reload) + * Limitation: + * Currently distributed debugging not working + * Need to launch train.py locally (cannot submit jobs) + """ + try: + import jurigged + except ImportError as e: + logger.warning("Please install jurigged: pip install jurigged[develoop]") + raise e + from fairseq.distributed import utils as distributed_utils + import traceback + + def hotreload_decorator(func): + assert callable(func), f"not callable: {func}" + jname = name or func.__name__ + logger.info(f"jurigged-hotreload:Apply jurigged on {jname}:{func.__name__}") + HOTRELOAD_PAUSE = bool(os.environ.get("HOTRELOAD_PAUSE", 0)) + cublk = bool(os.environ.get("CUDA_LAUNCH_BLOCKING", 0)) + prefix = f"HOTRELOAD:{jname}:[cublk={cublk}]" + hot_reload_state = {"disable": False} + + def func_wrapper(*args, **kwargs): + if not HOTRELOAD_PAUSE or hot_reload_state["disable"]: + return func(*args, **kwargs) + world_size = distributed_utils.get_global_world_size() + assert ( + world_size <= 1 + ), f"HOTRELOAD_PAUSE:{jname} currently cannot do distributed training" + success = False + while not success: + try: + output = func(*args, **kwargs) + # success = True + end_action = input( + f"{prefix}: PAUSE, you may edit code now. Enter to re-run, ctrl+C to terminate, " + f'type "done" to continue (function still being watched), or type "disable" to stop pausing this function :' + ) + if end_action.strip().lower() in ["disable", "done"]: + success = True + else: + logger.warning( + f"{prefix}: action={end_action} function will re-run now." + ) + except Exception as e: + action = input( + f"{prefix}:ERROR: \n{traceback.format_exc()}\n" + f'Edit code to try again: enter to continue, ctrl+C to terminate, or type "raise" to raise the exception: ' + ) + if action.strip().lower() == "raise": + raise e + + if end_action.strip().lower() == "disable": + logger.warning( + f"{prefix}: Stop pausing {jname}. The function is still being watched and newly editted code will take effect " + f"if the {jname} is called again later." + f' "unset HOTRELOAD_PAUSE" before relaunch to disable hotreload and' + f" remove @hotreload_function decorator in the code." + ) + hot_reload_state["disable"] = True + return output + + return func_wrapper + + return hotreload_decorator diff --git a/fairseq/fairseq/version.txt b/fairseq/fairseq/version.txt new file mode 100644 index 0000000..26acbf0 --- /dev/null +++ b/fairseq/fairseq/version.txt @@ -0,0 +1 @@ +0.12.2 diff --git a/fairseq/fairseq_cli/__init__.py b/fairseq/fairseq_cli/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/fairseq_cli/eval_lm.py b/fairseq/fairseq_cli/eval_lm.py new file mode 100644 index 0000000..dbd1450 --- /dev/null +++ b/fairseq/fairseq_cli/eval_lm.py @@ -0,0 +1,347 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Evaluate the perplexity of a trained language model. +""" + +import logging +import math +import os +import sys +from argparse import Namespace +from typing import Iterable, List, Optional + +import torch +from omegaconf import DictConfig + +import fairseq +from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.logging import progress_bar +from fairseq.logging.meters import StopwatchMeter +from fairseq.sequence_scorer import SequenceScorer + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("fairseq_cli.eval_lm") + + +def eval_lm( + models: List[fairseq.models.FairseqModel], + source_dictionary: fairseq.data.Dictionary, + batch_iterator: Iterable, + post_process: Optional[str] = None, + output_word_probs: bool = False, + output_word_stats: bool = False, + target_dictionary: Optional[fairseq.data.Dictionary] = None, + softmax_batch: int = 0, + remove_bos_token: bool = False, + device: Optional[torch.device] = None, +): + """ + Args: + models (List[~fairseq.models.FairseqModel]): list of models to + evaluate. Models are essentially `nn.Module` instances, but + must be compatible with fairseq's `SequenceScorer`. + source_dictionary (~fairseq.data.Dictionary): dictionary for + applying any relevant post processing or outputing word + probs/stats. + batch_iterator (Iterable): yield batches of data + post_process (Optional[str]): post-process text by removing BPE, + letter segmentation, etc. Valid options can be found in + fairseq.data.utils.post_process, although not all options + are implemented here. + output_word_probs (Optional[bool]): output words and their + predicted log probabilities + output_word_stats (Optional[bool]): output word statistics such + as word count and average probability + target_dictionary (Optional[~fairseq.data.Dictionary]): output + dictionary (defaults to *source_dictionary*) + softmax_batch (Optional[bool]): if BxT is more than this, will + batch the softmax over vocab to this amount of tokens, in + order to fit into GPU memory + remove_bos_token (Optional[bool]): if True, confirm that the + first token is the beginning-of-sentence symbol (according + to the relevant dictionary) and remove it from the output + device (Optional[torch.device]): device to use for evaluation + (defaults to device of first model parameter) + """ + if target_dictionary is None: + target_dictionary = source_dictionary + if device is None: + device = next(models[0].parameters()).device + + gen_timer = StopwatchMeter() + scorer = SequenceScorer(target_dictionary, softmax_batch) + + score_sum = 0.0 + count = 0 + + if post_process is not None: + if post_process in {"subword_nmt", "@@ "}: + bpe_cont = post_process.rstrip() + bpe_toks = { + i + for i in range(len(source_dictionary)) + if source_dictionary[i].endswith(bpe_cont) + } + else: + raise NotImplementedError( + f"--post-process={post_process} is not implemented" + ) + bpe_len = len(bpe_cont) + else: + bpe_toks = None + bpe_len = 0 + + word_stats = dict() + + for sample in batch_iterator: + if "net_input" not in sample: + continue + + sample = utils.move_to_cuda(sample, device=device) + + gen_timer.start() + hypos = scorer.generate(models, sample) + gen_timer.stop(sample["ntokens"]) + + for i, hypos_i in enumerate(hypos): + hypo = hypos_i[0] + sample_id = sample["id"][i] + + tokens = hypo["tokens"] + tgt_len = tokens.numel() + pos_scores = hypo["positional_scores"].float() + + if remove_bos_token: + assert hypo["tokens"][0].item() == target_dictionary.bos() + tokens = tokens[1:] + pos_scores = pos_scores[1:] + + skipped_toks = 0 + if bpe_toks is not None: + for i in range(tgt_len - 1): + if tokens[i].item() in bpe_toks: + skipped_toks += 1 + pos_scores[i + 1] += pos_scores[i] + pos_scores[i] = 0 + + inf_scores = pos_scores.eq(float("inf")) | pos_scores.eq(float("-inf")) + if inf_scores.any(): + logger.info( + "skipping tokens with inf scores:", + target_dictionary.string(tokens[inf_scores.nonzero()]), + ) + pos_scores = pos_scores[(~inf_scores).nonzero()] + score_sum += pos_scores.sum().cpu() + count += pos_scores.numel() - skipped_toks + + if output_word_probs or output_word_stats: + w = "" + word_prob = [] + is_bpe = False + for i in range(len(tokens)): + w_ind = tokens[i].item() + w += source_dictionary[w_ind] + if bpe_toks is not None and w_ind in bpe_toks: + w = w[:-bpe_len] + is_bpe = True + else: + word_prob.append((w, pos_scores[i].item())) + + next_prob = None + ind = i + 1 + while ind < len(tokens): + if pos_scores[ind].item() != 0: + next_prob = pos_scores[ind] + break + ind += 1 + + word_stats.setdefault(w, WordStat(w, is_bpe)).add( + pos_scores[i].item(), next_prob + ) + is_bpe = False + w = "" + if output_word_probs: + logger.info( + str(int(sample_id)) + + " " + + ( + "\t".join( + "{} [{:2f}]".format(x[0], x[1]) for x in word_prob + ) + ) + ) + + avg_nll_loss = ( + -score_sum / count / math.log(2) if count > 0 else 0 + ) # convert to base 2 + logger.info( + "Evaluated {:,} tokens in {:.1f}s ({:.2f} tokens/s)".format( + gen_timer.n, gen_timer.sum, 1.0 / gen_timer.avg if gen_timer.avg > 0 else 0 + ) + ) + + if output_word_stats: + for ws in sorted(word_stats.values(), key=lambda x: x.count, reverse=True): + logger.info(ws) + + return { + "loss": avg_nll_loss, + "perplexity": 2**avg_nll_loss, + } + + +class WordStat(object): + def __init__(self, word, is_bpe): + self.word = word + self.is_bpe = is_bpe + self.log_prob = 0 + self.next_word_prob = 0 + self.count = 0 + self.missing_next_words = 0 + + def add(self, log_prob, next_word_prob): + """increments counters for the sum of log probs of current word and next + word (given context ending at current word). Since the next word might be at the end of the example, + or it might be not counted because it is not an ending subword unit, + also keeps track of how many of those we have seen""" + if next_word_prob is not None: + self.next_word_prob += next_word_prob + else: + self.missing_next_words += 1 + self.log_prob += log_prob + self.count += 1 + + def __str__(self): + return "{}\t{}\t{}\t{}\t{}\t{}".format( + self.word, + self.count, + self.log_prob, + self.is_bpe, + self.next_word_prob, + self.count - self.missing_next_words, + ) + + +def main(cfg: DictConfig, **unused_kwargs): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + utils.import_user_module(cfg.common) + + logger.info(cfg) + + if cfg.eval_lm.context_window > 0: + # reduce tokens per sample by the required context window size + cfg.task.tokens_per_sample -= cfg.eval_lm.context_window + + # Initialize the task using the current *cfg* + task = tasks.setup_task(cfg.task) + + # Load ensemble + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, model_args, task = checkpoint_utils.load_model_ensemble_and_task( + [cfg.common_eval.path], + arg_overrides=eval(cfg.common_eval.model_overrides), + suffix=cfg.checkpoint.checkpoint_suffix, + strict=(cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.checkpoint.checkpoint_shard_count, + task=task, + ) + + use_fp16 = cfg.common.fp16 + use_cuda = torch.cuda.is_available() and not cfg.common.cpu + if use_cuda: + torch.cuda.set_device(cfg.distributed_training.device_id) + + # Optimize ensemble for generation and set the source and dest dicts on the model + # (required by scorer) + for model in models: + if use_fp16: + model.half() + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: + model.cuda() + model.prepare_for_inference_(cfg) + + assert len(models) > 0 + + logger.info( + "num. model params: {:,}".format(sum(p.numel() for p in models[0].parameters())) + ) + + # Load dataset splits + task.load_dataset(cfg.dataset.gen_subset) + dataset = task.dataset(cfg.dataset.gen_subset) + logger.info( + "{} {} {:,} examples".format( + cfg.task.data, cfg.dataset.gen_subset, len(dataset) + ) + ) + + itr = task.eval_lm_dataloader( + dataset=dataset, + max_tokens=cfg.dataset.max_tokens or 36000, + batch_size=cfg.dataset.batch_size, + max_positions=utils.resolve_max_positions( + *[model.max_positions() for model in models] + ), + num_shards=max( + cfg.dataset.num_shards, + cfg.distributed_training.distributed_world_size, + ), + shard_id=max( + cfg.dataset.shard_id, + cfg.distributed_training.distributed_rank, + ), + num_workers=cfg.dataset.num_workers, + data_buffer_size=cfg.dataset.data_buffer_size, + context_window=cfg.eval_lm.context_window, + ) + + itr = progress_bar.progress_bar( + itr, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + ) + + results = eval_lm( + models=models, + source_dictionary=task.source_dictionary, + batch_iterator=itr, + post_process=cfg.common_eval.post_process, + output_word_probs=cfg.eval_lm.output_word_probs, + output_word_stats=cfg.eval_lm.output_word_stats, + target_dictionary=task.target_dictionary, + softmax_batch=cfg.eval_lm.softmax_batch, + remove_bos_token=getattr(cfg.task, "add_bos_token", False), + ) + + logger.info( + "Loss (base 2): {:.4f}, Perplexity: {:.2f}".format( + results["loss"], results["perplexity"] + ) + ) + + return results + + +def cli_main(): + parser = options.get_eval_lm_parser() + args = options.parse_args_and_arch(parser) + + distributed_utils.call_main(convert_namespace_to_omegaconf(args), main) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/generate.py b/fairseq/fairseq_cli/generate.py new file mode 100644 index 0000000..b875783 --- /dev/null +++ b/fairseq/fairseq_cli/generate.py @@ -0,0 +1,417 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Translate pre-processed data with a trained model. +""" + +import ast +import logging +import math +import os +import sys +from argparse import Namespace +from itertools import chain + +import numpy as np +import torch +from omegaconf import DictConfig + +from fairseq import checkpoint_utils, options, scoring, tasks, utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.logging import progress_bar +from fairseq.logging.meters import StopwatchMeter, TimeMeter + + +def main(cfg: DictConfig): + + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + assert cfg.common_eval.path is not None, "--path required for generation!" + assert ( + not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam + ), "--sampling requires --nbest to be equal to --beam" + assert ( + cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw" + ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)" + + if cfg.common_eval.results_path is not None: + os.makedirs(cfg.common_eval.results_path, exist_ok=True) + output_path = os.path.join( + cfg.common_eval.results_path, + "generate-{}.txt".format(cfg.dataset.gen_subset), + ) + with open(output_path, "w", buffering=1, encoding="utf-8") as h: + return _main(cfg, h) + else: + return _main(cfg, sys.stdout) + + +def get_symbols_to_strip_from_output(generator): + if hasattr(generator, "symbols_to_strip_from_output"): + return generator.symbols_to_strip_from_output + else: + return {generator.eos} + + +def _main(cfg: DictConfig, output_file): + logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=output_file, + ) + logger = logging.getLogger("fairseq_cli.generate") + + utils.import_user_module(cfg.common) + + if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: + cfg.dataset.max_tokens = 12000 + logger.info(cfg) + + # Fix seed for stochastic decoding + if cfg.common.seed is not None and not cfg.generation.no_seed_provided: + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) + + use_cuda = torch.cuda.is_available() and not cfg.common.cpu + + # Load dataset splits + task = tasks.setup_task(cfg.task) + + # Set dictionaries + try: + src_dict = getattr(task, "source_dictionary", None) + except NotImplementedError: + src_dict = None + tgt_dict = task.target_dictionary + + overrides = ast.literal_eval(cfg.common_eval.model_overrides) + + # Load ensemble + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, saved_cfg = checkpoint_utils.load_model_ensemble( + utils.split_paths(cfg.common_eval.path), + arg_overrides=overrides, + task=task, + suffix=cfg.checkpoint.checkpoint_suffix, + strict=(cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.checkpoint.checkpoint_shard_count, + ) + + # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config + task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task) + + if cfg.generation.lm_path is not None: + overrides["data"] = cfg.task.data + + try: + lms, _ = checkpoint_utils.load_model_ensemble( + [cfg.generation.lm_path], arg_overrides=overrides, task=None + ) + except: + logger.warning( + f"Failed to load language model! Please make sure that the language model dict is the same " + f"as target dict and is located in the data dir ({cfg.task.data})" + ) + raise + + assert len(lms) == 1 + else: + lms = [None] + + # Optimize ensemble for generation + for model in chain(models, lms): + if model is None: + continue + if cfg.common.fp16: + model.half() + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: + model.cuda() + model.prepare_for_inference_(cfg) + + # Load alignment dictionary for unknown word replacement + # (None if no unknown word replacement, empty if no path to align dictionary) + align_dict = utils.load_align_dict(cfg.generation.replace_unk) + + # Load dataset (possibly sharded) + itr = task.get_batch_iterator( + dataset=task.dataset(cfg.dataset.gen_subset), + max_tokens=cfg.dataset.max_tokens, + max_sentences=cfg.dataset.batch_size, + max_positions=utils.resolve_max_positions( + task.max_positions(), *[m.max_positions() for m in models] + ), + ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=cfg.dataset.required_batch_size_multiple, + seed=cfg.common.seed, + num_shards=cfg.distributed_training.distributed_world_size, + shard_id=cfg.distributed_training.distributed_rank, + num_workers=cfg.dataset.num_workers, + data_buffer_size=cfg.dataset.data_buffer_size, + ).next_epoch_itr(shuffle=False) + progress = progress_bar.progress_bar( + itr, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + ) + + # Initialize generator + gen_timer = StopwatchMeter() + + extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight} + generator = task.build_generator( + models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs + ) + + # Handle tokenization and BPE + tokenizer = task.build_tokenizer(cfg.tokenizer) + bpe = task.build_bpe(cfg.bpe) + + def decode_fn(x): + if bpe is not None: + x = bpe.decode(x) + if tokenizer is not None: + x = tokenizer.decode(x) + return x + + scorer = scoring.build_scorer(cfg.scoring, tgt_dict) + + num_sentences = 0 + has_target = True + wps_meter = TimeMeter() + for sample in progress: + sample = utils.move_to_cuda(sample) if use_cuda else sample + if "net_input" not in sample: + continue + + prefix_tokens = None + if cfg.generation.prefix_size > 0: + prefix_tokens = sample["target"][:, : cfg.generation.prefix_size] + + constraints = None + if "constraints" in sample: + constraints = sample["constraints"] + + gen_timer.start() + hypos = task.inference_step( + generator, + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + ) + num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) + gen_timer.stop(num_generated_tokens) + + for i, sample_id in enumerate(sample["id"].tolist()): + has_target = sample["target"] is not None + + # Remove padding + if "src_tokens" in sample["net_input"]: + src_tokens = utils.strip_pad( + sample["net_input"]["src_tokens"][i, :], tgt_dict.pad() + ) + else: + src_tokens = None + + target_tokens = None + if has_target: + target_tokens = ( + utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu() + ) + + # Either retrieve the original sentences or regenerate them from tokens. + if align_dict is not None: + src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text( + sample_id + ) + target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text( + sample_id + ) + else: + if src_dict is not None: + src_str = src_dict.string(src_tokens, cfg.common_eval.post_process) + else: + src_str = "" + if has_target: + target_str = tgt_dict.string( + target_tokens, + cfg.common_eval.post_process, + escape_unk=True, + extra_symbols_to_ignore=get_symbols_to_strip_from_output( + generator + ), + ) + + src_str = decode_fn(src_str) + if has_target: + target_str = decode_fn(target_str) + + if not cfg.common_eval.quiet: + if src_dict is not None: + print("S-{}\t{}".format(sample_id, src_str), file=output_file) + if has_target: + print("T-{}\t{}".format(sample_id, target_str), file=output_file) + + # Process top predictions + for j, hypo in enumerate(hypos[i][: cfg.generation.nbest]): + hypo_tokens, hypo_str, alignment = utils.post_process_prediction( + hypo_tokens=hypo["tokens"].int().cpu(), + src_str=src_str, + alignment=hypo["alignment"], + align_dict=align_dict, + tgt_dict=tgt_dict, + remove_bpe=cfg.common_eval.post_process, + extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator), + ) + detok_hypo_str = decode_fn(hypo_str) + if not cfg.common_eval.quiet: + score = hypo["score"] / math.log(2) # convert to base 2 + # original hypothesis (after tokenization and BPE) + print( + "H-{}\t{}\t{}".format(sample_id, score, hypo_str), + file=output_file, + ) + # detokenized hypothesis + print( + "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str), + file=output_file, + ) + print( + "P-{}\t{}".format( + sample_id, + " ".join( + map( + lambda x: "{:.4f}".format(x), + # convert from base e to base 2 + hypo["positional_scores"] + .div_(math.log(2)) + .tolist(), + ) + ), + ), + file=output_file, + ) + + if cfg.generation.print_alignment == "hard": + print( + "A-{}\t{}".format( + sample_id, + " ".join( + [ + "{}-{}".format(src_idx, tgt_idx) + for src_idx, tgt_idx in alignment + ] + ), + ), + file=output_file, + ) + if cfg.generation.print_alignment == "soft": + print( + "A-{}\t{}".format( + sample_id, + " ".join( + [",".join(src_probs) for src_probs in alignment] + ), + ), + file=output_file, + ) + + if cfg.generation.print_step: + print( + "I-{}\t{}".format(sample_id, hypo["steps"]), + file=output_file, + ) + + if cfg.generation.retain_iter_history: + for step, h in enumerate(hypo["history"]): + _, h_str, _ = utils.post_process_prediction( + hypo_tokens=h["tokens"].int().cpu(), + src_str=src_str, + alignment=None, + align_dict=None, + tgt_dict=tgt_dict, + remove_bpe=None, + ) + print( + "E-{}_{}\t{}".format(sample_id, step, h_str), + file=output_file, + ) + + # Score only the top hypothesis + if has_target and j == 0: + if ( + align_dict is not None + or cfg.common_eval.post_process is not None + ): + # Convert back to tokens for evaluation with unk replacement and/or without BPE + target_tokens = tgt_dict.encode_line( + target_str, add_if_not_exist=True + ) + hypo_tokens = tgt_dict.encode_line( + detok_hypo_str, add_if_not_exist=True + ) + if hasattr(scorer, "add_string"): + scorer.add_string(target_str, detok_hypo_str) + else: + scorer.add(target_tokens, hypo_tokens) + + wps_meter.update(num_generated_tokens) + progress.log({"wps": round(wps_meter.avg)}) + num_sentences += ( + sample["nsentences"] if "nsentences" in sample else sample["id"].numel() + ) + + logger.info("NOTE: hypothesis and token scores are output in base 2") + logger.info( + "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format( + num_sentences, + gen_timer.n, + gen_timer.sum, + num_sentences / gen_timer.sum, + 1.0 / gen_timer.avg, + ) + ) + if has_target: + if cfg.bpe and not cfg.generation.sacrebleu: + if cfg.common_eval.post_process: + logger.warning( + "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization" + ) + else: + logger.warning( + "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words. Use --sacrebleu for standard 13a BLEU tokenization" + ) + # use print to be consistent with other main outputs: S-, H-, T-, D- and so on + print( + "Generate {} with beam={}: {}".format( + cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string() + ), + file=output_file, + ) + + return scorer + + +def cli_main(): + parser = options.get_generation_parser() + # TODO: replace this workaround with refactoring of `AudioPretraining` + parser.add_argument( + "--arch", + "-a", + metavar="ARCH", + default="wav2vec2", + help="Model architecture. For constructing tasks that rely on " + "model args (e.g. `AudioPretraining`)", + ) + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/hydra_train.py b/fairseq/fairseq_cli/hydra_train.py new file mode 100644 index 0000000..607340a --- /dev/null +++ b/fairseq/fairseq_cli/hydra_train.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os + +import hydra +import torch +from hydra.core.hydra_config import HydraConfig +from omegaconf import OmegaConf, open_dict + +from fairseq import distributed_utils, metrics +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.initialize import add_defaults, hydra_init +from fairseq.dataclass.utils import omegaconf_no_object_check +from fairseq.utils import reset_logging +from fairseq_cli.train import main as pre_main + +logger = logging.getLogger("fairseq_cli.hydra_train") + + +@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config") +def hydra_main(cfg: FairseqConfig) -> float: + _hydra_main(cfg) + + +def _hydra_main(cfg: FairseqConfig, **kwargs) -> float: + add_defaults(cfg) + + if cfg.common.reset_logging: + reset_logging() # Hydra hijacks logging, fix that + else: + # check if directly called or called through hydra_main + if HydraConfig.initialized(): + with open_dict(cfg): + # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) + cfg.job_logging_cfg = OmegaConf.to_container( + HydraConfig.get().job_logging, resolve=True + ) + + with omegaconf_no_object_check(): + cfg = OmegaConf.create( + OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) + ) + OmegaConf.set_struct(cfg, True) + + try: + if cfg.common.profile: + with torch.cuda.profiler.profile(): + with torch.autograd.profiler.emit_nvtx(): + distributed_utils.call_main(cfg, pre_main, **kwargs) + else: + distributed_utils.call_main(cfg, pre_main, **kwargs) + except BaseException as e: + if not cfg.common.suppress_crashes: + raise + else: + logger.error("Crashed! " + str(e)) + + # get best val and return - useful for sweepers + try: + best_val = metrics.get_smoothed_value( + "valid", cfg.checkpoint.best_checkpoint_metric + ) + except: + best_val = None + + if best_val is None: + best_val = float("inf") + + return best_val + + +def cli_main(): + try: + from hydra._internal.utils import get_args + + cfg_name = get_args().config_name or "config" + except: + logger.warning("Failed to get config name from hydra args") + cfg_name = "config" + + hydra_init(cfg_name) + hydra_main() + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/hydra_validate.py b/fairseq/fairseq_cli/hydra_validate.py new file mode 100644 index 0000000..cb6f761 --- /dev/null +++ b/fairseq/fairseq_cli/hydra_validate.py @@ -0,0 +1,188 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys +from itertools import chain + +import torch +from hydra.core.hydra_config import HydraConfig +from omegaconf import OmegaConf, open_dict +import hydra + +from fairseq import checkpoint_utils, distributed_utils, utils +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.initialize import add_defaults, hydra_init +from fairseq.dataclass.utils import omegaconf_no_object_check +from fairseq.distributed import utils as distributed_utils +from fairseq.logging import metrics, progress_bar +from fairseq.utils import reset_logging + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("fairseq_cli.validate") + + +@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config") +def hydra_main(cfg: FairseqConfig) -> float: + return _hydra_main(cfg) + + +def _hydra_main(cfg: FairseqConfig, **kwargs) -> float: + add_defaults(cfg) + + if cfg.common.reset_logging: + reset_logging() # Hydra hijacks logging, fix that + else: + # check if directly called or called through hydra_main + if HydraConfig.initialized(): + with open_dict(cfg): + # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) + cfg.job_logging_cfg = OmegaConf.to_container( + HydraConfig.get().job_logging, resolve=True + ) + + with omegaconf_no_object_check(): + cfg = OmegaConf.create( + OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) + ) + OmegaConf.set_struct(cfg, True) + + assert ( + cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None + ), "Must specify batch size either with --max-tokens or --batch-size" + + distributed_utils.call_main(cfg, validate, **kwargs) + + +def validate(cfg): + utils.import_user_module(cfg.common) + + use_fp16 = cfg.common.fp16 + use_cuda = torch.cuda.is_available() and not cfg.common.cpu + + if use_cuda: + torch.cuda.set_device(cfg.distributed_training.device_id) + + if cfg.distributed_training.distributed_world_size > 1: + data_parallel_world_size = distributed_utils.get_data_parallel_world_size() + data_parallel_rank = distributed_utils.get_data_parallel_rank() + else: + data_parallel_world_size = 1 + data_parallel_rank = 0 + + overrides = {"task": {"data": cfg.task.data}} + + # Load ensemble + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [cfg.common_eval.path], + arg_overrides=overrides, + suffix=cfg.checkpoint.checkpoint_suffix, + ) + model = models[0] + + # Move models to GPU + for model in models: + model.eval() + if use_fp16: + model.half() + if use_cuda: + model.cuda() + + # Print args + logger.info(saved_cfg) + + # Build criterion + criterion = task.build_criterion(saved_cfg.criterion, from_checkpoint=True) + criterion.eval() + + for subset in cfg.dataset.valid_subset.split(","): + try: + task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task) + dataset = task.dataset(subset) + except KeyError: + raise Exception("Cannot find dataset: " + subset) + + # Initialize data iterator + itr = task.get_batch_iterator( + dataset=dataset, + max_tokens=cfg.dataset.max_tokens, + max_sentences=cfg.dataset.batch_size, + max_positions=utils.resolve_max_positions( + task.max_positions(), + *[m.max_positions() for m in models], + ), + ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=cfg.dataset.required_batch_size_multiple, + seed=cfg.common.seed, + num_shards=data_parallel_world_size, + shard_id=data_parallel_rank, + num_workers=cfg.dataset.num_workers, + data_buffer_size=cfg.dataset.data_buffer_size, + ).next_epoch_itr(shuffle=False) + progress = progress_bar.progress_bar( + itr, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, + prefix=f"valid on '{subset}' subset", + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + ) + + def apply_half(t): + if t.dtype is torch.float32: + return t.to(dtype=torch.half) + return t + + log_outputs = [] + for i, sample in enumerate(progress): + sample = utils.move_to_cuda(sample) if use_cuda else sample + + if use_fp16: + sample = utils.apply_to_sample(apply_half, sample) + + _loss, _sample_size, log_output = task.valid_step(sample, model, criterion) + with metrics.aggregate() as agg: + task.reduce_metrics([log_output], criterion) + progress.log(agg.get_smoothed_values(), step=i) + # progress.log(log_output, step=i) from vision + log_outputs.append(log_output) + + if data_parallel_world_size > 1: + log_outputs = distributed_utils.all_gather_list( + log_outputs, + max_size=cfg.common.all_gather_list_size, + group=distributed_utils.get_data_parallel_group(), + ) + log_outputs = list(chain.from_iterable(log_outputs)) + + with metrics.aggregate() as agg: + task.reduce_metrics(log_outputs, criterion) + log_output = agg.get_smoothed_values() + + progress.print(log_output, tag=subset, step=i) + + +def cli_main(): + try: + from hydra._internal.utils import get_args + + cfg_name = get_args().config_name or "config" + except: + logger.warning("Failed to get config name from hydra args") + cfg_name = "config" + + hydra_init(cfg_name) + hydra_main() + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/interactive.py b/fairseq/fairseq_cli/interactive.py new file mode 100644 index 0000000..03265d0 --- /dev/null +++ b/fairseq/fairseq_cli/interactive.py @@ -0,0 +1,317 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Translate raw text with a trained model. Batches data on-the-fly. +""" + +import ast +import fileinput +import logging +import math +import os +import sys +import time +from argparse import Namespace +from collections import namedtuple + +import numpy as np +import torch + +from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.token_generation_constraints import pack_constraints, unpack_constraints +from fairseq_cli.generate import get_symbols_to_strip_from_output + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("fairseq_cli.interactive") + + +Batch = namedtuple("Batch", "ids src_tokens src_lengths constraints") +Translation = namedtuple("Translation", "src_str hypos pos_scores alignments") + + +def buffered_read(input, buffer_size): + buffer = [] + with fileinput.input(files=[input], openhook=fileinput.hook_encoded("utf-8")) as h: + for src_str in h: + buffer.append(src_str.strip()) + if len(buffer) >= buffer_size: + yield buffer + buffer = [] + + if len(buffer) > 0: + yield buffer + + +def make_batches(lines, cfg, task, max_positions, encode_fn): + def encode_fn_target(x): + return encode_fn(x) + + if cfg.generation.constraints: + # Strip (tab-delimited) contraints, if present, from input lines, + # store them in batch_constraints + batch_constraints = [list() for _ in lines] + for i, line in enumerate(lines): + if "\t" in line: + lines[i], *batch_constraints[i] = line.split("\t") + + # Convert each List[str] to List[Tensor] + for i, constraint_list in enumerate(batch_constraints): + batch_constraints[i] = [ + task.target_dictionary.encode_line( + encode_fn_target(constraint), + append_eos=False, + add_if_not_exist=False, + ) + for constraint in constraint_list + ] + + if cfg.generation.constraints: + constraints_tensor = pack_constraints(batch_constraints) + else: + constraints_tensor = None + + tokens, lengths = task.get_interactive_tokens_and_lengths(lines, encode_fn) + + itr = task.get_batch_iterator( + dataset=task.build_dataset_for_inference( + tokens, lengths, constraints=constraints_tensor + ), + max_tokens=cfg.dataset.max_tokens, + max_sentences=cfg.dataset.batch_size, + max_positions=max_positions, + ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, + ).next_epoch_itr(shuffle=False) + for batch in itr: + ids = batch["id"] + src_tokens = batch["net_input"]["src_tokens"] + src_lengths = batch["net_input"]["src_lengths"] + constraints = batch.get("constraints", None) + + yield Batch( + ids=ids, + src_tokens=src_tokens, + src_lengths=src_lengths, + constraints=constraints, + ) + + +def main(cfg: FairseqConfig): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + start_time = time.time() + total_translate_time = 0 + + utils.import_user_module(cfg.common) + + if cfg.interactive.buffer_size < 1: + cfg.interactive.buffer_size = 1 + if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: + cfg.dataset.batch_size = 1 + + assert ( + not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam + ), "--sampling requires --nbest to be equal to --beam" + assert ( + not cfg.dataset.batch_size + or cfg.dataset.batch_size <= cfg.interactive.buffer_size + ), "--batch-size cannot be larger than --buffer-size" + + logger.info(cfg) + + # Fix seed for stochastic decoding + if cfg.common.seed is not None and not cfg.generation.no_seed_provided: + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) + + use_cuda = torch.cuda.is_available() and not cfg.common.cpu + + # Setup task, e.g., translation + task = tasks.setup_task(cfg.task) + + # Load ensemble + overrides = ast.literal_eval(cfg.common_eval.model_overrides) + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, _model_args = checkpoint_utils.load_model_ensemble( + utils.split_paths(cfg.common_eval.path), + arg_overrides=overrides, + task=task, + suffix=cfg.checkpoint.checkpoint_suffix, + strict=(cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.checkpoint.checkpoint_shard_count, + ) + + # Set dictionaries + src_dict = task.source_dictionary + tgt_dict = task.target_dictionary + + # Optimize ensemble for generation + for model in models: + if model is None: + continue + if cfg.common.fp16: + model.half() + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: + model.cuda() + model.prepare_for_inference_(cfg) + + # Initialize generator + generator = task.build_generator(models, cfg.generation) + + # Handle tokenization and BPE + tokenizer = task.build_tokenizer(cfg.tokenizer) + bpe = task.build_bpe(cfg.bpe) + + def encode_fn(x): + if tokenizer is not None: + x = tokenizer.encode(x) + if bpe is not None: + x = bpe.encode(x) + return x + + def decode_fn(x): + if bpe is not None: + x = bpe.decode(x) + if tokenizer is not None: + x = tokenizer.decode(x) + return x + + # Load alignment dictionary for unknown word replacement + # (None if no unknown word replacement, empty if no path to align dictionary) + align_dict = utils.load_align_dict(cfg.generation.replace_unk) + + max_positions = utils.resolve_max_positions( + task.max_positions(), *[model.max_positions() for model in models] + ) + + if cfg.generation.constraints: + logger.warning( + "NOTE: Constrained decoding currently assumes a shared subword vocabulary." + ) + + if cfg.interactive.buffer_size > 1: + logger.info("Sentence buffer size: %s", cfg.interactive.buffer_size) + logger.info("NOTE: hypothesis and token scores are output in base 2") + logger.info("Type the input sentence and press return:") + start_id = 0 + for inputs in buffered_read(cfg.interactive.input, cfg.interactive.buffer_size): + results = [] + for batch in make_batches(inputs, cfg, task, max_positions, encode_fn): + bsz = batch.src_tokens.size(0) + src_tokens = batch.src_tokens + src_lengths = batch.src_lengths + constraints = batch.constraints + if use_cuda: + src_tokens = src_tokens.cuda() + src_lengths = src_lengths.cuda() + if constraints is not None: + constraints = constraints.cuda() + + sample = { + "net_input": { + "src_tokens": src_tokens, + "src_lengths": src_lengths, + }, + } + translate_start_time = time.time() + translations = task.inference_step( + generator, models, sample, constraints=constraints + ) + translate_time = time.time() - translate_start_time + total_translate_time += translate_time + list_constraints = [[] for _ in range(bsz)] + if cfg.generation.constraints: + list_constraints = [unpack_constraints(c) for c in constraints] + for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)): + src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad()) + constraints = list_constraints[i] + results.append( + ( + start_id + id, + src_tokens_i, + hypos, + { + "constraints": constraints, + "time": translate_time / len(translations), + }, + ) + ) + + # sort output to match input order + for id_, src_tokens, hypos, info in sorted(results, key=lambda x: x[0]): + src_str = "" + if src_dict is not None: + src_str = src_dict.string(src_tokens, cfg.common_eval.post_process) + print("S-{}\t{}".format(id_, src_str)) + print("W-{}\t{:.3f}\tseconds".format(id_, info["time"])) + for constraint in info["constraints"]: + print( + "C-{}\t{}".format( + id_, + tgt_dict.string(constraint, cfg.common_eval.post_process), + ) + ) + + # Process top predictions + for hypo in hypos[: min(len(hypos), cfg.generation.nbest)]: + hypo_tokens, hypo_str, alignment = utils.post_process_prediction( + hypo_tokens=hypo["tokens"].int().cpu(), + src_str=src_str, + alignment=hypo["alignment"], + align_dict=align_dict, + tgt_dict=tgt_dict, + remove_bpe=cfg.common_eval.post_process, + extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator), + ) + detok_hypo_str = decode_fn(hypo_str) + score = hypo["score"] / math.log(2) # convert to base 2 + # original hypothesis (after tokenization and BPE) + print("H-{}\t{}\t{}".format(id_, score, hypo_str)) + # detokenized hypothesis + print("D-{}\t{}\t{}".format(id_, score, detok_hypo_str)) + print( + "P-{}\t{}".format( + id_, + " ".join( + map( + lambda x: "{:.4f}".format(x), + # convert from base e to base 2 + hypo["positional_scores"].div_(math.log(2)).tolist(), + ) + ), + ) + ) + if cfg.generation.print_alignment: + alignment_str = " ".join( + ["{}-{}".format(src, tgt) for src, tgt in alignment] + ) + print("A-{}\t{}".format(id_, alignment_str)) + + # update running id_ counter + start_id += len(inputs) + + logger.info( + "Total time: {:.3f} seconds; translation time: {:.3f}".format( + time.time() - start_time, total_translate_time + ) + ) + + +def cli_main(): + parser = options.get_interactive_generation_parser() + args = options.parse_args_and_arch(parser) + distributed_utils.call_main(convert_namespace_to_omegaconf(args), main) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/preprocess.py b/fairseq/fairseq_cli/preprocess.py new file mode 100644 index 0000000..2ba9e09 --- /dev/null +++ b/fairseq/fairseq_cli/preprocess.py @@ -0,0 +1,393 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Data pre-processing: build vocabularies and binarize training data. +""" + +import logging +import os +import shutil +import sys +import typing as tp +from argparse import Namespace +from itertools import zip_longest + +from fairseq import options, tasks, utils +from fairseq.binarizer import ( + AlignmentDatasetBinarizer, + FileBinarizer, + VocabularyDatasetBinarizer, +) +from fairseq.data import Dictionary + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("fairseq_cli.preprocess") + +##################################################################### +# file name tools +##################################################################### + + +def _train_path(lang, trainpref): + return "{}{}".format(trainpref, ("." + lang) if lang else "") + + +def _file_name(prefix, lang): + fname = prefix + if lang is not None: + fname += ".{lang}".format(lang=lang) + return fname + + +def _dest_path(prefix, lang, destdir): + return os.path.join(destdir, _file_name(prefix, lang)) + + +def _dict_path(lang, destdir): + return _dest_path("dict", lang, destdir) + ".txt" + + +def dataset_dest_prefix(args, output_prefix, lang): + base = os.path.join(args.destdir, output_prefix) + if lang is not None: + lang_part = f".{args.source_lang}-{args.target_lang}.{lang}" + elif args.only_source: + lang_part = "" + else: + lang_part = f".{args.source_lang}-{args.target_lang}" + + return "{}{}".format(base, lang_part) + + +def dataset_dest_file(args, output_prefix, lang, extension): + return "{}.{}".format(dataset_dest_prefix(args, output_prefix, lang), extension) + + +##################################################################### +# dictionary tools +##################################################################### + + +def _build_dictionary( + filenames, + task, + args, + src=False, + tgt=False, +): + assert src ^ tgt + return task.build_dictionary( + filenames, + workers=args.workers, + threshold=args.thresholdsrc if src else args.thresholdtgt, + nwords=args.nwordssrc if src else args.nwordstgt, + padding_factor=args.padding_factor, + ) + + +##################################################################### +# bin file creation logic +##################################################################### + + +def _make_binary_dataset( + vocab: Dictionary, + input_prefix: str, + output_prefix: str, + lang: tp.Optional[str], + num_workers: int, + args: Namespace, +): + logger.info("[{}] Dictionary: {} types".format(lang, len(vocab))) + + binarizer = VocabularyDatasetBinarizer( + vocab, + append_eos=True, + ) + + input_file = "{}{}".format(input_prefix, ("." + lang) if lang is not None else "") + full_output_prefix = dataset_dest_prefix(args, output_prefix, lang) + + final_summary = FileBinarizer.multiprocess_dataset( + input_file, + args.dataset_impl, + binarizer, + full_output_prefix, + vocab_size=len(vocab), + num_workers=num_workers, + ) + + logger.info(f"[{lang}] {input_file}: {final_summary} (by {vocab.unk_word})") + + +def _make_binary_alignment_dataset( + input_prefix: str, output_prefix: str, num_workers: int, args: Namespace +): + + binarizer = AlignmentDatasetBinarizer(utils.parse_alignment) + + input_file = input_prefix + full_output_prefix = dataset_dest_prefix(args, output_prefix, lang=None) + + final_summary = FileBinarizer.multiprocess_dataset( + input_file, + args.dataset_impl, + binarizer, + full_output_prefix, + vocab_size=None, + num_workers=num_workers, + ) + + logger.info( + "[alignments] {}: parsed {} alignments".format( + input_file, final_summary.num_seq + ) + ) + + +##################################################################### +# routing logic +##################################################################### + + +def _make_dataset( + vocab: Dictionary, + input_prefix: str, + output_prefix: str, + lang: tp.Optional[str], + args: Namespace, + num_workers: int, +): + if args.dataset_impl == "raw": + # Copy original text file to destination folder + output_text_file = _dest_path( + output_prefix + ".{}-{}".format(args.source_lang, args.target_lang), + lang, + args.destdir, + ) + shutil.copyfile(_file_name(input_prefix, lang), output_text_file) + else: + _make_binary_dataset( + vocab, input_prefix, output_prefix, lang, num_workers, args + ) + + +def _make_all(lang, vocab, args): + if args.trainpref: + _make_dataset( + vocab, args.trainpref, "train", lang, args=args, num_workers=args.workers + ) + if args.validpref: + for k, validpref in enumerate(args.validpref.split(",")): + outprefix = "valid{}".format(k) if k > 0 else "valid" + _make_dataset( + vocab, validpref, outprefix, lang, args=args, num_workers=args.workers + ) + if args.testpref: + for k, testpref in enumerate(args.testpref.split(",")): + outprefix = "test{}".format(k) if k > 0 else "test" + _make_dataset( + vocab, testpref, outprefix, lang, args=args, num_workers=args.workers + ) + + +def _make_all_alignments(args): + if args.trainpref and os.path.exists(args.trainpref + "." + args.align_suffix): + _make_binary_alignment_dataset( + args.trainpref + "." + args.align_suffix, + "train.align", + num_workers=args.workers, + args=args, + ) + if args.validpref and os.path.exists(args.validpref + "." + args.align_suffix): + _make_binary_alignment_dataset( + args.validpref + "." + args.align_suffix, + "valid.align", + num_workers=args.workers, + args=args, + ) + if args.testpref and os.path.exists(args.testpref + "." + args.align_suffix): + _make_binary_alignment_dataset( + args.testpref + "." + args.align_suffix, + "test.align", + num_workers=args.workers, + args=args, + ) + + +##################################################################### +# align +##################################################################### + + +def _align_files(args, src_dict, tgt_dict): + assert args.trainpref, "--trainpref must be set if --alignfile is specified" + src_file_name = _train_path(args.source_lang, args.trainpref) + tgt_file_name = _train_path(args.target_lang, args.trainpref) + freq_map = {} + with open(args.alignfile, "r", encoding="utf-8") as align_file: + with open(src_file_name, "r", encoding="utf-8") as src_file: + with open(tgt_file_name, "r", encoding="utf-8") as tgt_file: + for a, s, t in zip_longest(align_file, src_file, tgt_file): + si = src_dict.encode_line(s, add_if_not_exist=False) + ti = tgt_dict.encode_line(t, add_if_not_exist=False) + ai = list(map(lambda x: tuple(x.split("-")), a.split())) + for sai, tai in ai: + srcidx = si[int(sai)] + tgtidx = ti[int(tai)] + if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk(): + assert srcidx != src_dict.pad() + assert srcidx != src_dict.eos() + assert tgtidx != tgt_dict.pad() + assert tgtidx != tgt_dict.eos() + if srcidx not in freq_map: + freq_map[srcidx] = {} + if tgtidx not in freq_map[srcidx]: + freq_map[srcidx][tgtidx] = 1 + else: + freq_map[srcidx][tgtidx] += 1 + align_dict = {} + for srcidx in freq_map.keys(): + align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get) + with open( + os.path.join( + args.destdir, + "alignment.{}-{}.txt".format(args.source_lang, args.target_lang), + ), + "w", + encoding="utf-8", + ) as f: + for k, v in align_dict.items(): + print("{} {}".format(src_dict[k], tgt_dict[v]), file=f) + + +##################################################################### +# MAIN +##################################################################### + + +def main(args): + # setup some basic things + utils.import_user_module(args) + + os.makedirs(args.destdir, exist_ok=True) + + logger.addHandler( + logging.FileHandler( + filename=os.path.join(args.destdir, "preprocess.log"), + ) + ) + logger.info(args) + + assert ( + args.dataset_impl != "huffman" + ), "preprocessing.py doesn't support Huffman yet, use HuffmanCodeBuilder directly." + + # build dictionaries + + target = not args.only_source + + if not args.srcdict and os.path.exists(_dict_path(args.source_lang, args.destdir)): + raise FileExistsError(_dict_path(args.source_lang, args.destdir)) + + if ( + target + and not args.tgtdict + and os.path.exists(_dict_path(args.target_lang, args.destdir)) + ): + raise FileExistsError(_dict_path(args.target_lang, args.destdir)) + + task = tasks.get_task(args.task) + + if args.joined_dictionary: + assert ( + not args.srcdict or not args.tgtdict + ), "cannot use both --srcdict and --tgtdict with --joined-dictionary" + + if args.srcdict: + src_dict = task.load_dictionary(args.srcdict) + elif args.tgtdict: + src_dict = task.load_dictionary(args.tgtdict) + else: + assert ( + args.trainpref + ), "--trainpref must be set if --srcdict is not specified" + src_dict = _build_dictionary( + { + _train_path(lang, args.trainpref) + for lang in [args.source_lang, args.target_lang] + }, + task=task, + args=args, + src=True, + ) + tgt_dict = src_dict + else: + if args.srcdict: + src_dict = task.load_dictionary(args.srcdict) + else: + assert ( + args.trainpref + ), "--trainpref must be set if --srcdict is not specified" + src_dict = _build_dictionary( + [_train_path(args.source_lang, args.trainpref)], + task=task, + args=args, + src=True, + ) + + if target: + if args.tgtdict: + tgt_dict = task.load_dictionary(args.tgtdict) + else: + assert ( + args.trainpref + ), "--trainpref must be set if --tgtdict is not specified" + tgt_dict = _build_dictionary( + [_train_path(args.target_lang, args.trainpref)], + task=task, + args=args, + tgt=True, + ) + else: + tgt_dict = None + + # save dictionaries + + src_dict.save(_dict_path(args.source_lang, args.destdir)) + if target and tgt_dict is not None: + tgt_dict.save(_dict_path(args.target_lang, args.destdir)) + + if args.dict_only: + return + + _make_all(args.source_lang, src_dict, args) + if target: + _make_all(args.target_lang, tgt_dict, args) + + # align the datasets if needed + if args.align_suffix: + _make_all_alignments(args) + + logger.info("Wrote preprocessed data to {}".format(args.destdir)) + + if args.alignfile: + _align_files(args, src_dict=src_dict, tgt_dict=tgt_dict) + + +def cli_main(): + parser = options.get_preprocessing_parser() + args = parser.parse_args() + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/score.py b/fairseq/fairseq_cli/score.py new file mode 100644 index 0000000..0b207be --- /dev/null +++ b/fairseq/fairseq_cli/score.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +BLEU scoring of generated translations against reference translations. +""" + +import argparse +import os +import sys + +from fairseq.data import dictionary +from fairseq.scoring import bleu + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Command-line script for BLEU scoring." + ) + # fmt: off + parser.add_argument('-s', '--sys', default='-', help='system output') + parser.add_argument('-r', '--ref', required=True, help='references') + parser.add_argument('-o', '--order', default=4, metavar='N', + type=int, help='consider ngrams up to this order') + parser.add_argument('--ignore-case', action='store_true', + help='case-insensitive scoring') + parser.add_argument('--sacrebleu', action='store_true', + help='score with sacrebleu') + parser.add_argument('--sentence-bleu', action='store_true', + help='report sentence-level BLEUs (i.e., with +1 smoothing)') + # fmt: on + return parser + + +def cli_main(): + parser = get_parser() + args = parser.parse_args() + print(args) + + assert args.sys == "-" or os.path.exists( + args.sys + ), "System output file {} does not exist".format(args.sys) + assert os.path.exists(args.ref), "Reference file {} does not exist".format(args.ref) + + dict = dictionary.Dictionary() + + def readlines(fd): + for line in fd.readlines(): + if args.ignore_case: + yield line.lower() + else: + yield line + + if args.sacrebleu: + import sacrebleu + + def score(fdsys): + with open(args.ref) as fdref: + print(sacrebleu.corpus_bleu(fdsys, [fdref]).format()) + + elif args.sentence_bleu: + + def score(fdsys): + with open(args.ref) as fdref: + scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk()) + for i, (sys_tok, ref_tok) in enumerate( + zip(readlines(fdsys), readlines(fdref)) + ): + scorer.reset(one_init=True) + sys_tok = dict.encode_line(sys_tok) + ref_tok = dict.encode_line(ref_tok) + scorer.add(ref_tok, sys_tok) + print(i, scorer.result_string(args.order)) + + else: + + def score(fdsys): + with open(args.ref) as fdref: + scorer = bleu.Scorer( + bleu.BleuConfig( + pad=dict.pad(), + eos=dict.eos(), + unk=dict.unk(), + ) + ) + for sys_tok, ref_tok in zip(readlines(fdsys), readlines(fdref)): + sys_tok = dict.encode_line(sys_tok) + ref_tok = dict.encode_line(ref_tok) + scorer.add(ref_tok, sys_tok) + print(scorer.result_string(args.order)) + + if args.sys == "-": + score(sys.stdin) + else: + with open(args.sys, "r") as f: + score(f) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/train.py b/fairseq/fairseq_cli/train.py new file mode 100644 index 0000000..f771bff --- /dev/null +++ b/fairseq/fairseq_cli/train.py @@ -0,0 +1,581 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Train a new model on one or across multiple GPUs. +""" + +import argparse +import logging +import math +import os +import sys +from typing import Any, Callable, Dict, List, Optional, Tuple + +# We need to setup root logger before importing any fairseq libraries. +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("fairseq_cli.train") + +import numpy as np +import torch +from omegaconf import DictConfig, OmegaConf + +from fairseq import checkpoint_utils, options, quantization_utils, tasks, utils +from fairseq.data import data_utils, iterators +from fairseq.data.plasma_utils import PlasmaStore +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.initialize import add_defaults +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap +from fairseq.distributed import utils as distributed_utils +from fairseq.file_io import PathManager +from fairseq.logging import meters, metrics, progress_bar +from fairseq.model_parallel.megatron_trainer import MegatronTrainer +from fairseq.trainer import Trainer + + +def main(cfg: FairseqConfig) -> None: + if isinstance(cfg, argparse.Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + utils.import_user_module(cfg.common) + add_defaults(cfg) + + if ( + distributed_utils.is_master(cfg.distributed_training) + and "job_logging_cfg" in cfg + ): + # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) + logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg)) + + assert ( + cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None + ), "Must specify batch size either with --max-tokens or --batch-size" + metrics.reset() + + if cfg.common.log_file is not None: + handler = logging.FileHandler(filename=cfg.common.log_file) + logger.addHandler(handler) + + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) + + if distributed_utils.is_master(cfg.distributed_training): + checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir) + + # Print args + logger.info(cfg) + + if cfg.checkpoint.write_checkpoints_asynchronously: + try: + import iopath # noqa: F401 + except ImportError: + logging.exception( + "Asynchronous checkpoint writing is specified but iopath is " + "not installed: `pip install iopath`" + ) + return + + # Setup task, e.g., translation, language modeling, etc. + task = tasks.setup_task(cfg.task) + + assert cfg.criterion, "Please specify criterion to train a model" + + # Build model and criterion + if cfg.distributed_training.ddp_backend == "fully_sharded": + with fsdp_enable_wrap(cfg.distributed_training): + model = fsdp_wrap(task.build_model(cfg.model)) + else: + model = task.build_model(cfg.model) + criterion = task.build_criterion(cfg.criterion) + logger.info(model) + logger.info("task: {}".format(task.__class__.__name__)) + logger.info("model: {}".format(model.__class__.__name__)) + logger.info("criterion: {}".format(criterion.__class__.__name__)) + logger.info( + "num. shared model params: {:,} (num. trained: {:,})".format( + sum( + p.numel() for p in model.parameters() if not getattr(p, "expert", False) + ), + sum( + p.numel() + for p in model.parameters() + if not getattr(p, "expert", False) and p.requires_grad + ), + ) + ) + + logger.info( + "num. expert model params: {} (num. trained: {})".format( + sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)), + sum( + p.numel() + for p in model.parameters() + if getattr(p, "expert", False) and p.requires_grad + ), + ) + ) + + # Load valid dataset (we load training data below, based on the latest checkpoint) + # We load the valid dataset AFTER building the model + if not cfg.dataset.disable_validation: + data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg) + if cfg.dataset.combine_valid_subsets: + task.load_dataset("valid", combine=True, epoch=1) + else: + for valid_sub_split in cfg.dataset.valid_subset.split(","): + task.load_dataset(valid_sub_split, combine=False, epoch=1) + + # (optionally) Configure quantization + if cfg.common.quantization_config_path is not None: + quantizer = quantization_utils.Quantizer( + config_path=cfg.common.quantization_config_path, + max_epoch=cfg.optimization.max_epoch, + max_update=cfg.optimization.max_update, + ) + else: + quantizer = None + + # Build trainer + if cfg.common.model_parallel_size == 1: + trainer = Trainer(cfg, task, model, criterion, quantizer) + else: + trainer = MegatronTrainer(cfg, task, model, criterion) + logger.info( + "training on {} devices (GPUs/TPUs)".format( + cfg.distributed_training.distributed_world_size + ) + ) + logger.info( + "max tokens per device = {} and max sentences per device = {}".format( + cfg.dataset.max_tokens, + cfg.dataset.batch_size, + ) + ) + + # Load the latest checkpoint if one is available and restore the + # corresponding train iterator + extra_state, epoch_itr = checkpoint_utils.load_checkpoint( + cfg.checkpoint, + trainer, + # don't cache epoch iterators for sharded datasets + disable_iterator_cache=task.has_sharded_data("train"), + ) + if cfg.common.tpu: + import torch_xla.core.xla_model as xm + + xm.rendezvous("load_checkpoint") # wait for all workers + + max_epoch = cfg.optimization.max_epoch or math.inf + lr = trainer.get_lr() + + # TODO: a dry run on validation set to pin the memory + valid_subsets = cfg.dataset.valid_subset.split(",") + if not cfg.dataset.disable_validation: + for subset in valid_subsets: + logger.info('begin dry-run validation on "{}" subset'.format(subset)) + itr = trainer.get_valid_iterator(subset).next_epoch_itr( + shuffle=False, set_dataset_epoch=False # use a fixed valid set + ) + if cfg.common.tpu: + itr = utils.tpu_data_loader(itr) + for _ in itr: + pass + # TODO: end of dry run section + + train_meter = meters.StopwatchMeter() + train_meter.start() + while epoch_itr.next_epoch_idx <= max_epoch: + if lr <= cfg.optimization.stop_min_lr: + logger.info( + f"stopping training because current learning rate ({lr}) is smaller " + "than or equal to minimum learning rate " + f"(--stop-min-lr={cfg.optimization.stop_min_lr})" + ) + break + + # train for one epoch + valid_losses, should_stop = train(cfg, trainer, task, epoch_itr) + if should_stop: + break + + # only use first validation loss to update the learning rate + lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0]) + + epoch_itr = trainer.get_train_iterator( + epoch_itr.next_epoch_idx, + # sharded data: get train iterator for next epoch + load_dataset=task.has_sharded_data("train"), + # don't cache epoch iterators for sharded datasets + disable_iterator_cache=task.has_sharded_data("train"), + ) + train_meter.stop() + logger.info("done training in {:.1f} seconds".format(train_meter.sum)) + + # ioPath implementation to wait for all asynchronous file writes to complete. + if cfg.checkpoint.write_checkpoints_asynchronously: + logger.info( + "ioPath PathManager waiting for all asynchronous checkpoint " + "writes to finish." + ) + PathManager.async_close() + logger.info("ioPath PathManager finished waiting.") + + +def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool: + # skip check if no validation was done in the current epoch + if valid_loss is None: + return False + if cfg.checkpoint.patience <= 0: + return False + + def is_better(a, b): + return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b + + prev_best = getattr(should_stop_early, "best", None) + if prev_best is None or is_better(valid_loss, prev_best): + should_stop_early.best = valid_loss + should_stop_early.num_runs = 0 + return False + else: + should_stop_early.num_runs += 1 + if should_stop_early.num_runs >= cfg.checkpoint.patience: + logger.info( + "early stop since valid performance hasn't improved for last {} runs".format( + cfg.checkpoint.patience + ) + ) + return True + else: + return False + + +@metrics.aggregate("train") +def train( + cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr +) -> Tuple[List[Optional[float]], bool]: + """Train the model for one epoch and return validation losses.""" + # Initialize data iterator + itr = epoch_itr.next_epoch_itr( + fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus, + shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum), + ) + update_freq = ( + cfg.optimization.update_freq[epoch_itr.epoch - 1] + if epoch_itr.epoch <= len(cfg.optimization.update_freq) + else cfg.optimization.update_freq[-1] + ) + itr = iterators.GroupedIterator( + itr, + update_freq, + skip_remainder_batch=cfg.optimization.skip_remainder_batch, + ) + if cfg.common.tpu: + itr = utils.tpu_data_loader(itr) + progress = progress_bar.progress_bar( + itr, + log_format=cfg.common.log_format, + log_file=cfg.common.log_file, + log_interval=cfg.common.log_interval, + epoch=epoch_itr.epoch, + aim_repo=( + cfg.common.aim_repo + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + aim_run_hash=( + cfg.common.aim_run_hash + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + aim_param_checkpoint_dir=cfg.checkpoint.save_dir, + tensorboard_logdir=( + cfg.common.tensorboard_logdir + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + wandb_project=( + cfg.common.wandb_project + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + wandb_run_name=os.environ.get( + "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) + ), + azureml_logging=( + cfg.common.azureml_logging + if distributed_utils.is_master(cfg.distributed_training) + else False + ), + ) + progress.update_config(_flatten_config(cfg)) + + trainer.begin_epoch(epoch_itr.epoch) + + valid_subsets = cfg.dataset.valid_subset.split(",") + should_stop = False + num_updates = trainer.get_num_updates() + logger.info("Start iterating over samples") + for i, samples in enumerate(progress): + with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function( + "train_step-%d" % i + ): + log_output = trainer.train_step(samples) + + if log_output is not None: # not OOM, overflow, ... + # log mid-epoch stats + num_updates = trainer.get_num_updates() + if num_updates % cfg.common.log_interval == 0: + stats = get_training_stats(metrics.get_smoothed_values("train_inner")) + progress.log(stats, tag="train_inner", step=num_updates) + + # reset mid-epoch stats after each log interval + # the end-of-epoch stats will still be preserved + metrics.reset_meters("train_inner") + + end_of_epoch = not itr.has_next() + valid_losses, should_stop = validate_and_save( + cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch + ) + + if should_stop: + break + + # log end-of-epoch stats + logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch)) + stats = get_training_stats(metrics.get_smoothed_values("train")) + progress.print(stats, tag="train", step=num_updates) + + # reset epoch-level meters + metrics.reset_meters("train") + return valid_losses, should_stop + + +def _flatten_config(cfg: DictConfig): + config = OmegaConf.to_container(cfg) + # remove any legacy Namespaces and replace with a single "args" + namespace = None + for k, v in list(config.items()): + if isinstance(v, argparse.Namespace): + namespace = v + del config[k] + if namespace is not None: + config["args"] = vars(namespace) + return config + + +def validate_and_save( + cfg: DictConfig, + trainer: Trainer, + task: tasks.FairseqTask, + epoch_itr, + valid_subsets: List[str], + end_of_epoch: bool, +) -> Tuple[List[Optional[float]], bool]: + num_updates = trainer.get_num_updates() + max_update = cfg.optimization.max_update or math.inf + + # Stopping conditions (and an additional one based on validation loss later + # on) + should_stop = False + if num_updates >= max_update: + should_stop = True + logger.info( + f"Stopping training due to " + f"num_updates: {num_updates} >= max_update: {max_update}" + ) + + training_time_hours = trainer.cumulative_training_time() / (60 * 60) + if ( + cfg.optimization.stop_time_hours > 0 + and training_time_hours > cfg.optimization.stop_time_hours + ): + should_stop = True + logger.info( + f"Stopping training due to " + f"cumulative_training_time: {training_time_hours} > " + f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)" + ) + + do_save = ( + (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0) + or should_stop + or ( + cfg.checkpoint.save_interval_updates > 0 + and num_updates > 0 + and num_updates % cfg.checkpoint.save_interval_updates == 0 + and num_updates >= cfg.dataset.validate_after_updates + ) + ) + do_validate = ( + ( + (not end_of_epoch and do_save) # validate during mid-epoch saves + or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0) + or should_stop + or ( + cfg.dataset.validate_interval_updates > 0 + and num_updates > 0 + and num_updates % cfg.dataset.validate_interval_updates == 0 + ) + ) + and not cfg.dataset.disable_validation + and num_updates >= cfg.dataset.validate_after_updates + ) + + # Validate + valid_losses = [None] + if do_validate: + valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets) + + should_stop |= should_stop_early(cfg, valid_losses[0]) + + # Save checkpoint + if do_save or should_stop: + cp_path = checkpoint_utils.save_checkpoint( + cfg.checkpoint, trainer, epoch_itr, valid_losses[0] + ) + if cp_path is not None and hasattr(task, "post_save"): + task.post_save(cp_path, num_updates) + + return valid_losses, should_stop + + +def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]: + stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0) + return stats + + +def validate( + cfg: DictConfig, + trainer: Trainer, + task: tasks.FairseqTask, + epoch_itr, + subsets: List[str], +) -> List[Optional[float]]: + """Evaluate the model on the validation set(s) and return the losses.""" + + if cfg.dataset.fixed_validation_seed is not None: + # set fixed seed for every validation + utils.set_torch_seed(cfg.dataset.fixed_validation_seed) + + trainer.begin_valid_epoch(epoch_itr.epoch) + valid_losses = [] + for subset_idx, subset in enumerate(subsets): + logger.info('begin validation on "{}" subset'.format(subset)) + + # Initialize data iterator + itr = trainer.get_valid_iterator(subset).next_epoch_itr( + shuffle=False, set_dataset_epoch=False # use a fixed valid set + ) + if cfg.common.tpu: + itr = utils.tpu_data_loader(itr) + progress = progress_bar.progress_bar( + itr, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, + epoch=epoch_itr.epoch, + prefix=f"valid on '{subset}' subset", + aim_repo=( + cfg.common.aim_repo + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + aim_run_hash=( + cfg.common.aim_run_hash + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + aim_param_checkpoint_dir=cfg.checkpoint.save_dir, + tensorboard_logdir=( + cfg.common.tensorboard_logdir + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + wandb_project=( + cfg.common.wandb_project + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + wandb_run_name=os.environ.get( + "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) + ), + ) + + # create a new root metrics aggregator so validation metrics + # don't pollute other aggregators (e.g., train meters) + with metrics.aggregate(new_root=True) as agg: + for i, sample in enumerate(progress): + if ( + cfg.dataset.max_valid_steps is not None + and i > cfg.dataset.max_valid_steps + ): + break + trainer.valid_step(sample) + + # log validation stats + # only tracking the best metric on the 1st validation subset + tracking_best = subset_idx == 0 + stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values(), tracking_best) + + if hasattr(task, "post_validate"): + task.post_validate(trainer.get_model(), stats, agg) + + progress.print(stats, tag=subset, step=trainer.get_num_updates()) + + valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric]) + return valid_losses + + +def get_valid_stats( + cfg: DictConfig, + trainer: Trainer, + stats: Dict[str, Any], + tracking_best: bool, +) -> Dict[str, Any]: + stats["num_updates"] = trainer.get_num_updates() + if tracking_best and hasattr(checkpoint_utils.save_checkpoint, "best"): + key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric) + best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min + stats[key] = best_function( + checkpoint_utils.save_checkpoint.best, + stats[cfg.checkpoint.best_checkpoint_metric], + ) + return stats + + +def cli_main( + modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None +) -> None: + parser = options.get_training_parser() + args = options.parse_args_and_arch(parser, modify_parser=modify_parser) + + cfg = convert_namespace_to_omegaconf(args) + + if cfg.common.use_plasma_view: + server = PlasmaStore(path=cfg.common.plasma_path) + logger.info( + f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}" + ) + + if args.profile: + with torch.cuda.profiler.profile(): + with torch.autograd.profiler.emit_nvtx(): + distributed_utils.call_main(cfg, main) + else: + distributed_utils.call_main(cfg, main) + + # if cfg.common.use_plasma_view: + # server.server.kill() + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/fairseq_cli/validate.py b/fairseq/fairseq_cli/validate.py new file mode 100644 index 0000000..4617b6d --- /dev/null +++ b/fairseq/fairseq_cli/validate.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys +from argparse import Namespace +from itertools import chain + +import torch +from omegaconf import DictConfig + +from fairseq import checkpoint_utils, distributed_utils, options, utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.logging import metrics, progress_bar +from fairseq.utils import reset_logging + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("fairseq_cli.validate") + + +def main(cfg: DictConfig, override_args=None): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + utils.import_user_module(cfg.common) + + reset_logging() + + assert ( + cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None + ), "Must specify batch size either with --max-tokens or --batch-size" + + use_fp16 = cfg.common.fp16 + use_cuda = torch.cuda.is_available() and not cfg.common.cpu + + if use_cuda: + torch.cuda.set_device(cfg.distributed_training.device_id) + + if cfg.distributed_training.distributed_world_size > 1: + data_parallel_world_size = distributed_utils.get_data_parallel_world_size() + data_parallel_rank = distributed_utils.get_data_parallel_rank() + else: + data_parallel_world_size = 1 + data_parallel_rank = 0 + + if override_args is not None: + overrides = vars(override_args) + overrides.update(eval(getattr(override_args, "model_overrides", "{}"))) + else: + overrides = None + + # Load ensemble + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [cfg.common_eval.path], + arg_overrides=overrides, + suffix=cfg.checkpoint.checkpoint_suffix, + ) + model = models[0] + + # Move models to GPU + for model in models: + model.eval() + if use_fp16: + model.half() + if use_cuda: + model.cuda() + + # Print args + logger.info(saved_cfg) + + # Build criterion + criterion = task.build_criterion(saved_cfg.criterion) + criterion.eval() + + for subset in cfg.dataset.valid_subset.split(","): + try: + task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task) + dataset = task.dataset(subset) + except KeyError: + raise Exception("Cannot find dataset: " + subset) + + # Initialize data iterator + itr = task.get_batch_iterator( + dataset=dataset, + max_tokens=cfg.dataset.max_tokens, + max_sentences=cfg.dataset.batch_size, + max_positions=utils.resolve_max_positions( + task.max_positions(), + *[m.max_positions() for m in models], + ), + ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=cfg.dataset.required_batch_size_multiple, + seed=cfg.common.seed, + num_shards=data_parallel_world_size, + shard_id=data_parallel_rank, + num_workers=cfg.dataset.num_workers, + data_buffer_size=cfg.dataset.data_buffer_size, + ).next_epoch_itr(shuffle=False) + progress = progress_bar.progress_bar( + itr, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, + prefix=f"valid on '{subset}' subset", + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + ) + + log_outputs = [] + for i, sample in enumerate(progress): + sample = utils.move_to_cuda(sample) if use_cuda else sample + _loss, _sample_size, log_output = task.valid_step(sample, model, criterion) + progress.log(log_output, step=i) + log_outputs.append(log_output) + + if data_parallel_world_size > 1: + log_outputs = distributed_utils.all_gather_list( + log_outputs, + max_size=cfg.common.all_gather_list_size, + group=distributed_utils.get_data_parallel_group(), + ) + log_outputs = list(chain.from_iterable(log_outputs)) + + with metrics.aggregate() as agg: + task.reduce_metrics(log_outputs, criterion) + log_output = agg.get_smoothed_values() + + progress.print(log_output, tag=subset, step=i) + + +def cli_main(): + parser = options.get_validation_parser() + args = options.parse_args_and_arch(parser) + + # only override args that are explicitly given on the command line + override_parser = options.get_validation_parser() + override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True) + + distributed_utils.call_main( + convert_namespace_to_omegaconf(args), main, override_args=override_args + ) + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq/hubconf.py b/fairseq/hubconf.py new file mode 100644 index 0000000..5949e27 --- /dev/null +++ b/fairseq/hubconf.py @@ -0,0 +1,73 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import functools +import importlib + + +dependencies = [ + "dataclasses", + "hydra", + "numpy", + "omegaconf", + "regex", + "requests", + "torch", +] + + +# Check for required dependencies and raise a RuntimeError if any are missing. +missing_deps = [] +for dep in dependencies: + try: + importlib.import_module(dep) + except ImportError: + # Hack: the hydra package is provided under the "hydra-core" name in + # pypi. We don't want the user mistakenly calling `pip install hydra` + # since that will install an unrelated package. + if dep == "hydra": + dep = "hydra-core" + missing_deps.append(dep) +if len(missing_deps) > 0: + raise RuntimeError("Missing dependencies: {}".format(", ".join(missing_deps))) + + +# only do fairseq imports after checking for dependencies +from fairseq.hub_utils import ( # noqa; noqa + BPEHubInterface as bpe, + TokenizerHubInterface as tokenizer, +) +from fairseq.models import MODEL_REGISTRY # noqa + + +# torch.hub doesn't build Cython components, so if they are not found then try +# to build them here +try: + import fairseq.data.token_block_utils_fast # noqa +except ImportError: + try: + import cython # noqa + import os + from setuptools import sandbox + + sandbox.run_setup( + os.path.join(os.path.dirname(__file__), "setup.py"), + ["build_ext", "--inplace"], + ) + except ImportError: + print( + "Unable to build Cython components. Please make sure Cython is " + "installed if the torch.hub model you are loading depends on it." + ) + + +# automatically expose models defined in FairseqModel::hub_models +for _model_type, _cls in MODEL_REGISTRY.items(): + for model_name in _cls.hub_models().keys(): + globals()[model_name] = functools.partial( + _cls.from_pretrained, + model_name, + ) diff --git a/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/__init__.py b/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/__init__.py new file mode 100644 index 0000000..4884f5b --- /dev/null +++ b/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +__version__ = "0.1" diff --git a/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/config.py b/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/config.py new file mode 100644 index 0000000..91926c4 --- /dev/null +++ b/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/config.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from dataclasses import dataclass, field + +from hydra.core.config_store import ConfigStore + +from hydra_plugins.hydra_submitit_launcher.config import SlurmQueueConf + + +@dataclass +class DependencySubmititConf(SlurmQueueConf): + """Slurm configuration overrides and specific parameters""" + + _target_: str = ( + "hydra_plugins.dependency_submitit_launcher.launcher.DependencySubmititLauncher" + ) + + +ConfigStore.instance().store( + group="hydra/launcher", + name="dependency_submitit_slurm", + node=DependencySubmititConf(), + provider="dependency_submitit_slurm", +) diff --git a/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/launcher.py b/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/launcher.py new file mode 100644 index 0000000..b3fcf79 --- /dev/null +++ b/fairseq/hydra_plugins/dependency_submitit_launcher/hydra_plugins/dependency_submitit_launcher/launcher.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import os +import subprocess +from pathlib import Path +from typing import Any, List, Sequence + +from hydra.core.singleton import Singleton +from hydra.core.utils import JobReturn, filter_overrides +from omegaconf import OmegaConf + +log = logging.getLogger(__name__) + +from .config import DependencySubmititConf +from hydra_plugins.hydra_submitit_launcher.submitit_launcher import BaseSubmititLauncher + + +class DependencySubmititLauncher(BaseSubmititLauncher): + _EXECUTOR = "slurm" + + def launch( + self, job_overrides: Sequence[Sequence[str]], initial_job_idx: int + ) -> Sequence[JobReturn]: + + # lazy import to ensure plugin discovery remains fast + import submitit + + assert self.config is not None + + num_jobs = len(job_overrides) + assert num_jobs > 0 + + next_script = None + + for jo in job_overrides: + if next_script is None: + for item in jo: + if "next_script=" in item: + next_script = item + break + assert ( + next_script is not None + ), "job overrides must contain +next_script=path/to/next/script" + jo.remove(next_script) + + idx = next_script.find("=") + next_script = next_script[idx + 1 :] + + params = self.params + # build executor + init_params = {"folder": self.params["submitit_folder"]} + specific_init_keys = {"max_num_timeout"} + + init_params.update( + **{ + f"{self._EXECUTOR}_{x}": y + for x, y in params.items() + if x in specific_init_keys + } + ) + init_keys = specific_init_keys | {"submitit_folder"} + executor = submitit.AutoExecutor(cluster=self._EXECUTOR, **init_params) + + # specify resources/parameters + baseparams = set(OmegaConf.structured(DependencySubmititConf).keys()) + params = { + x if x in baseparams else f"{self._EXECUTOR}_{x}": y + for x, y in params.items() + if x not in init_keys + } + executor.update_parameters(**params) + + log.info( + f"Submitit '{self._EXECUTOR}' sweep output dir : " + f"{self.config.hydra.sweep.dir}" + ) + sweep_dir = Path(str(self.config.hydra.sweep.dir)) + sweep_dir.mkdir(parents=True, exist_ok=True) + if "mode" in self.config.hydra.sweep: + mode = int(str(self.config.hydra.sweep.mode), 8) + os.chmod(sweep_dir, mode=mode) + + job_params: List[Any] = [] + for idx, overrides in enumerate(job_overrides): + idx = initial_job_idx + idx + lst = " ".join(filter_overrides(overrides)) + log.info(f"\t#{idx} : {lst}") + job_params.append( + ( + list(overrides), + "hydra.sweep.dir", + idx, + f"job_id_for_{idx}", + Singleton.get_state(), + ) + ) + + jobs = executor.map_array(self, *zip(*job_params)) + + for j, jp in zip(jobs, job_params): + job_id = str(j.job_id) + task_id = "0" if "_" not in job_id else job_id.split("_")[1] + sweep_config = self.config_loader.load_sweep_config(self.config, jp[0]) + dir = sweep_config.hydra.sweep.dir + + dir = ( + dir.replace("[", "") + .replace("]", "") + .replace("{", "") + .replace("}", "") + .replace(",", "_") + .replace("'", "") + .replace('"', "") + ) + + subprocess.call( + [next_script, job_id, task_id, dir], + shell=False, + ) + + return [j.results()[0] for j in jobs] diff --git a/fairseq/hydra_plugins/dependency_submitit_launcher/setup.py b/fairseq/hydra_plugins/dependency_submitit_launcher/setup.py new file mode 100644 index 0000000..bf79546 --- /dev/null +++ b/fairseq/hydra_plugins/dependency_submitit_launcher/setup.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# type: ignore +from pathlib import Path + +from read_version import read_version +from setuptools import find_namespace_packages, setup + +setup( + name="dependency-submitit-launcher", + version=read_version("hydra_plugins/dependency_submitit_launcher", "__init__.py"), + author="Alexei Baevski", + author_email="abaevski@fb.com", + description="Dependency-supporting Submitit Launcher for Hydra apps", + packages=find_namespace_packages(include=["hydra_plugins.*"]), + classifiers=[ + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Operating System :: MacOS", + "Operating System :: POSIX :: Linux", + "Development Status :: 4 - Beta", + ], + install_requires=[ + "hydra-core>=1.0.4", + "submitit>=1.0.0", + ], + include_package_data=True, +) diff --git a/fairseq/pyproject.toml b/fairseq/pyproject.toml new file mode 100644 index 0000000..4d84c9b --- /dev/null +++ b/fairseq/pyproject.toml @@ -0,0 +1,23 @@ +[build-system] +requires = [ + "setuptools>=18.0", + "wheel", + "cython", + "numpy>=1.21.3", + "torch>=1.10", +] +build-backend = "setuptools.build_meta" + +[tool.black] +extend-exclude = ''' +( +^/examples/| +^/fairseq/model_parallel/megatron| +^/build/ +) +''' + +[tool.isort] +profile = "black" +known_third_party = "_cffi_backend,agg_results,aml,bitarray,boto3,botocore,dump_hubert_feature,dynamicconv_cuda,editdistance,faiss,fasttext,feature_utils,ffmpeg,g2p_en,h5py,hydra,hypothesis,indicnlp,inflect,iopath,joblib,kaldi_io,kenlm,libfb,librosa,lightconv_cuda,matplotlib,misc,mmpt,mmpt_cli,model,nltk,npy_append_array,numpy,omegaconf,pandas,pathbuilder,preprocessing,progressbar,pythainlp,random_sequence_shuffler,regex,sacrebleu,sacremoses,scipy,sentencepiece,setuptools,six,sklearn,soundfile,sweep,sweep_wmt_en2de_transformer_big_common,tabulate,torch,torchaudio,tqdm,unidecode,utils,videoreader,wav2vec_cluster_faiss,wget,yaml" +skip_gitignore = true diff --git a/fairseq/release_utils.py b/fairseq/release_utils.py new file mode 100644 index 0000000..69a5e8d --- /dev/null +++ b/fairseq/release_utils.py @@ -0,0 +1,72 @@ +import argparse +from typing import Tuple + + +def get_next_version(release_type) -> Tuple[Tuple[int, int, int], str, str]: + current_ver = find_version("fairseq/version.txt") + version_list = [int(x) for x in current_ver.strip("'").split(".")] + major, minor, patch = version_list[0], version_list[1], version_list[2] + if release_type == "patch": + patch += 1 + elif release_type == "minor": + minor += 1 + patch = 0 + elif release_type == "major": + major += 1 + minor = patch = 0 + else: + raise ValueError( + "Incorrect release type specified. Acceptable types are major, minor and patch." + ) + + new_version_tuple = (major, minor, patch) + new_version_str = ".".join([str(x) for x in new_version_tuple]) + new_tag_str = "v" + new_version_str + return new_version_tuple, new_version_str, new_tag_str + + +def find_version(version_file_path) -> str: + with open(version_file_path) as f: + version = f.read().strip() + return version + + +def update_version(new_version_str) -> None: + """ + given the current version, update the version to the + next version depending on the type of release. + """ + + with open("fairseq/version.txt", "w") as writer: + writer.write(new_version_str) + + +def main(args): + if args.release_type in ["major", "minor", "patch"]: + new_version_tuple, new_version, new_tag = get_next_version(args.release_type) + else: + raise ValueError("Incorrect release type specified") + + if args.update_version: + update_version(new_version) + + print(new_version, new_tag) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Versioning utils") + parser.add_argument( + "--release-type", + type=str, + required=True, + help="type of release = major/minor/patch", + ) + parser.add_argument( + "--update-version", + action="store_true", + required=False, + help="updates the version in fairseq/version.txt", + ) + + args = parser.parse_args() + main(args) diff --git a/fairseq/scripts/__init__.py b/fairseq/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/scripts/average_checkpoints.py b/fairseq/scripts/average_checkpoints.py new file mode 100644 index 0000000..49f4f9d --- /dev/null +++ b/fairseq/scripts/average_checkpoints.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import collections +import os +import re + +import torch + +from fairseq.file_io import PathManager + + +def average_checkpoints(inputs): + """Loads checkpoints from inputs and returns a model with averaged weights. + + Args: + inputs: An iterable of string paths of checkpoints to load from. + + Returns: + A dict of string keys mapping to various values. The 'model' key + from the returned dict should correspond to an OrderedDict mapping + string parameter names to torch Tensors. + """ + params_dict = collections.OrderedDict() + params_keys = None + new_state = None + num_models = len(inputs) + + for fpath in inputs: + with PathManager.open(fpath, "rb") as f: + state = torch.load( + f, + map_location=( + lambda s, _: torch.serialization.default_restore_location(s, "cpu") + ), + ) + # Copies over the settings from the first checkpoint + if new_state is None: + new_state = state + + model_params = state["model"] + + model_params_keys = list(model_params.keys()) + if params_keys is None: + params_keys = model_params_keys + elif params_keys != model_params_keys: + raise KeyError( + "For checkpoint {}, expected list of params: {}, " + "but found: {}".format(f, params_keys, model_params_keys) + ) + + for k in params_keys: + p = model_params[k] + if isinstance(p, torch.HalfTensor): + p = p.float() + if k not in params_dict: + params_dict[k] = p.clone() + # NOTE: clone() is needed in case of p is a shared parameter + else: + params_dict[k] += p + + averaged_params = collections.OrderedDict() + for k, v in params_dict.items(): + averaged_params[k] = v + if averaged_params[k].is_floating_point(): + averaged_params[k].div_(num_models) + else: + averaged_params[k] //= num_models + new_state["model"] = averaged_params + return new_state + + +def last_n_checkpoints(paths, n, update_based, upper_bound=None): + assert len(paths) == 1 + path = paths[0] + if update_based: + pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt") + else: + pt_regexp = re.compile(r"checkpoint(\d+)\.pt") + files = PathManager.ls(path) + + entries = [] + for f in files: + m = pt_regexp.fullmatch(f) + if m is not None: + sort_key = int(m.group(1)) + if upper_bound is None or sort_key <= upper_bound: + entries.append((sort_key, m.group(0))) + if len(entries) < n: + raise Exception( + "Found {} checkpoint files but need at least {}", len(entries), n + ) + return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]] + + +def main(): + parser = argparse.ArgumentParser( + description="Tool to average the params of input checkpoints to " + "produce a new checkpoint", + ) + # fmt: off + parser.add_argument('--inputs', required=True, nargs='+', + help='Input checkpoint file paths.') + parser.add_argument('--output', required=True, metavar='FILE', + help='Write the new checkpoint containing the averaged weights to this path.') + num_group = parser.add_mutually_exclusive_group() + num_group.add_argument('--num-epoch-checkpoints', type=int, + help='if set, will try to find checkpoints with names checkpoint_xx.pt in the ' + 'path specified by input, and average last this many of them.') + num_group.add_argument('--num-update-checkpoints', type=int, + help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by' + ' input, and average last this many of them.') + num_group.add_argument('--num-best-checkpoints', type=int, default=0, + help='if set, will try to find checkpoints with names checkpoint_best_ee_xx.pt in the path specified by' + ' input, and average last this many of them.') + parser.add_argument('--checkpoint-upper-bound', type=int, + help='when using --num-epoch-checkpoints, this will set an upper bound on which epoch to use, ' + 'when using --num-update-checkpoints, this will set an upper bound on which update to use' + 'e.g., with --num-epoch-checkpoints=10 --checkpoint-upper-bound=50, checkpoints 41-50 would be' + ' averaged.' + 'e.g., with --num-update-checkpoints=10 --checkpoint-upper-bound=50000, checkpoints 40500-50000 would' + ' be averaged assuming --save-interval-updates 500' + ) + # fmt: on + args = parser.parse_args() + print(args) + + num = None + is_update_based = False + if args.num_update_checkpoints is not None: + num = args.num_update_checkpoints + is_update_based = True + elif args.num_epoch_checkpoints is not None: + num = args.num_epoch_checkpoints + + assert args.checkpoint_upper_bound is None or ( + args.num_epoch_checkpoints is not None + or args.num_update_checkpoints is not None + ), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints" + assert ( + args.num_epoch_checkpoints is None or args.num_update_checkpoints is None + ), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints" + + if num is not None: + args.inputs = last_n_checkpoints( + args.inputs, + num, + is_update_based, + upper_bound=args.checkpoint_upper_bound, + ) + print("averaging checkpoints: ", args.inputs) + + if args.num_best_checkpoints > 0: + args.inputs = list( + sorted( + args.inputs, + key=lambda x: float( + os.path.basename(x).split("_")[-1].replace(".pt", "") + ), + ) + ) + args.inputs = args.inputs[: args.num_best_checkpoints] + for path in args.inputs: + print(os.path.basename(path)) + new_state = average_checkpoints(args.inputs) + with PathManager.open(args.output, "wb") as f: + torch.save(new_state, f) + print("Finished writing averaged checkpoint to {}".format(args.output)) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/build_sym_alignment.py b/fairseq/scripts/build_sym_alignment.py new file mode 100644 index 0000000..0ca5c18 --- /dev/null +++ b/fairseq/scripts/build_sym_alignment.py @@ -0,0 +1,97 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Use this script in order to build symmetric alignments for your translation +dataset. +This script depends on fast_align and mosesdecoder tools. You will need to +build those before running the script. +fast_align: + github: http://github.com/clab/fast_align + instructions: follow the instructions in README.md +mosesdecoder: + github: http://github.com/moses-smt/mosesdecoder + instructions: http://www.statmt.org/moses/?n=Development.GetStarted +The script produces the following files under --output_dir: + text.joined - concatenation of lines from the source_file and the + target_file. + align.forward - forward pass of fast_align. + align.backward - backward pass of fast_align. + aligned.sym_heuristic - symmetrized alignment. +""" + +import argparse +import os +from itertools import zip_longest + + +def main(): + parser = argparse.ArgumentParser(description="symmetric alignment builer") + # fmt: off + parser.add_argument('--fast_align_dir', + help='path to fast_align build directory') + parser.add_argument('--mosesdecoder_dir', + help='path to mosesdecoder root directory') + parser.add_argument('--sym_heuristic', + help='heuristic to use for symmetrization', + default='grow-diag-final-and') + parser.add_argument('--source_file', + help='path to a file with sentences ' + 'in the source language') + parser.add_argument('--target_file', + help='path to a file with sentences ' + 'in the target language') + parser.add_argument('--output_dir', + help='output directory') + # fmt: on + args = parser.parse_args() + + fast_align_bin = os.path.join(args.fast_align_dir, "fast_align") + symal_bin = os.path.join(args.mosesdecoder_dir, "bin", "symal") + sym_fast_align_bin = os.path.join( + args.mosesdecoder_dir, "scripts", "ems", "support", "symmetrize-fast-align.perl" + ) + + # create joined file + joined_file = os.path.join(args.output_dir, "text.joined") + with open(args.source_file, "r", encoding="utf-8") as src, open( + args.target_file, "r", encoding="utf-8" + ) as tgt: + with open(joined_file, "w", encoding="utf-8") as joined: + for s, t in zip_longest(src, tgt): + print("{} ||| {}".format(s.strip(), t.strip()), file=joined) + + bwd_align_file = os.path.join(args.output_dir, "align.backward") + + # run forward alignment + fwd_align_file = os.path.join(args.output_dir, "align.forward") + fwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v > {FWD}".format( + FASTALIGN=fast_align_bin, JOINED=joined_file, FWD=fwd_align_file + ) + assert os.system(fwd_fast_align_cmd) == 0 + + # run backward alignment + bwd_align_file = os.path.join(args.output_dir, "align.backward") + bwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v -r > {BWD}".format( + FASTALIGN=fast_align_bin, JOINED=joined_file, BWD=bwd_align_file + ) + assert os.system(bwd_fast_align_cmd) == 0 + + # run symmetrization + sym_out_file = os.path.join(args.output_dir, "aligned") + sym_cmd = "{SYMFASTALIGN} {FWD} {BWD} {SRC} {TGT} {OUT} {HEURISTIC} {SYMAL}".format( + SYMFASTALIGN=sym_fast_align_bin, + FWD=fwd_align_file, + BWD=bwd_align_file, + SRC=args.source_file, + TGT=args.target_file, + OUT=sym_out_file, + HEURISTIC=args.sym_heuristic, + SYMAL=symal_bin, + ) + assert os.system(sym_cmd) == 0 + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/check_installation.py b/fairseq/scripts/check_installation.py new file mode 100644 index 0000000..e5a9d9d --- /dev/null +++ b/fairseq/scripts/check_installation.py @@ -0,0 +1,36 @@ +from pathlib import Path +import os + +cwd = Path(".").resolve() +print("running 'check_installation.py' from:", cwd) + +# Old versions of numpy/torch can prevent loading the .so files +import torch + +print("torch:", torch.__version__) +import numpy + +print("numpy:", numpy.__version__) + +import fairseq + +print("Fairseq installed at:", fairseq.__file__) +import fairseq.criterions +import fairseq.dataclass.configs + +import _imp + +print("Should load following .so suffixes:", _imp.extension_suffixes()) + +so_files = list(Path(fairseq.__file__).parent.glob("*.so")) +so_files.extend(Path(fairseq.__file__).parent.glob("data/*.so")) +print("Found following .so files:") +for so_file in so_files: + print(f"- {so_file}") + +from fairseq import libbleu + +print("Found libbleu at", libbleu.__file__) +from fairseq.data import data_utils_fast + +print("Found data_utils_fast at", data_utils_fast.__file__) diff --git a/fairseq/scripts/compare_namespaces.py b/fairseq/scripts/compare_namespaces.py new file mode 100644 index 0000000..bc24db6 --- /dev/null +++ b/fairseq/scripts/compare_namespaces.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python +"""Helper script to compare two argparse.Namespace objects.""" + +from argparse import Namespace # noqa + + +def main(): + + ns1 = eval(input("Namespace 1: ")) + ns2 = eval(input("Namespace 2: ")) + + def keys(ns): + ks = set() + for k in dir(ns): + if not k.startswith("_"): + ks.add(k) + return ks + + k1 = keys(ns1) + k2 = keys(ns2) + + def print_keys(ks, ns1, ns2=None): + for k in ks: + if ns2 is None: + print("{}\t{}".format(k, getattr(ns1, k, None))) + else: + print( + "{}\t{}\t{}".format(k, getattr(ns1, k, None), getattr(ns2, k, None)) + ) + + print("Keys unique to namespace 1:") + print_keys(k1 - k2, ns1) + print() + + print("Keys unique to namespace 2:") + print_keys(k2 - k1, ns2) + print() + + print("Overlapping keys with different values:") + ks = [k for k in k1 & k2 if getattr(ns1, k, "None") != getattr(ns2, k, "None")] + print_keys(ks, ns1, ns2) + print() + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/compound_split_bleu.sh b/fairseq/scripts/compound_split_bleu.sh new file mode 100644 index 0000000..1972fdd --- /dev/null +++ b/fairseq/scripts/compound_split_bleu.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +if [ $# -ne 1 ]; then + echo "usage: $0 GENERATE_PY_OUTPUT" + exit 1 +fi + +GEN=$1 + +SYS=$GEN.sys +REF=$GEN.ref + +if [ $(tail -n 1 $GEN | grep BLEU | wc -l) -ne 1 ]; then + echo "not done generating" + exit +fi + +grep ^H $GEN | awk -F '\t' '{print $NF}' | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $SYS +grep ^T $GEN | cut -f2- | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $REF +fairseq-score --sys $SYS --ref $REF diff --git a/fairseq/scripts/constraints/extract.py b/fairseq/scripts/constraints/extract.py new file mode 100644 index 0000000..437b373 --- /dev/null +++ b/fairseq/scripts/constraints/extract.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Extracts random constraints from reference files.""" + +import argparse +import random +import sys + + +def get_phrase(words, index, length): + assert index < len(words) - length + 1 + phr = " ".join(words[index : index + length]) + for i in range(index, index + length): + words.pop(index) + return phr + + +def main(args): + + if args.seed: + random.seed(args.seed) + + for line in sys.stdin: + constraints = [] + + def add_constraint(constraint): + constraints.append(constraint) + + source = line.rstrip() + if "\t" in line: + source, target = line.split("\t") + if args.add_sos: + target = f"<s> {target}" + if args.add_eos: + target = f"{target} </s>" + + if len(target.split()) >= args.len: + words = [target] + + num = args.number + + choices = {} + for i in range(num): + if len(words) == 0: + break + segmentno = random.choice(range(len(words))) + segment = words.pop(segmentno) + tokens = segment.split() + phrase_index = random.choice(range(len(tokens))) + choice = " ".join( + tokens[phrase_index : min(len(tokens), phrase_index + args.len)] + ) + for j in range( + phrase_index, min(len(tokens), phrase_index + args.len) + ): + tokens.pop(phrase_index) + if phrase_index > 0: + words.append(" ".join(tokens[0:phrase_index])) + if phrase_index + 1 < len(tokens): + words.append(" ".join(tokens[phrase_index:])) + choices[target.find(choice)] = choice + + # mask out with spaces + target = target.replace(choice, " " * len(choice), 1) + + for key in sorted(choices.keys()): + add_constraint(choices[key]) + + print(source, *constraints, sep="\t") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--number", "-n", type=int, default=1, help="number of phrases") + parser.add_argument("--len", "-l", type=int, default=1, help="phrase length") + parser.add_argument( + "--add-sos", default=False, action="store_true", help="add <s> token" + ) + parser.add_argument( + "--add-eos", default=False, action="store_true", help="add </s> token" + ) + parser.add_argument("--seed", "-s", default=0, type=int) + args = parser.parse_args() + + main(args) diff --git a/fairseq/scripts/constraints/validate.py b/fairseq/scripts/constraints/validate.py new file mode 100644 index 0000000..d531ad9 --- /dev/null +++ b/fairseq/scripts/constraints/validate.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + + +"""Reads in a fairseq output file, and verifies that the constraints +(C- lines) are present in the output (the first H- line). Assumes that +constraints are listed prior to the first hypothesis. +""" + +constraints = [] +found = 0 +total = 0 +for line in sys.stdin: + if line.startswith("C-"): + constraints.append(line.rstrip().split("\t")[1]) + elif line.startswith("H-"): + text = line.split("\t")[2] + + for constraint in constraints: + total += 1 + if constraint in text: + found += 1 + else: + print(f"No {constraint} in {text}", file=sys.stderr) + + constraints = [] + +print(f"Found {found} / {total} = {100 * found / total:.1f}%") diff --git a/fairseq/scripts/convert_dictionary.lua b/fairseq/scripts/convert_dictionary.lua new file mode 100644 index 0000000..14ee8c9 --- /dev/null +++ b/fairseq/scripts/convert_dictionary.lua @@ -0,0 +1,34 @@ +-- Copyright (c) Facebook, Inc. and its affiliates. +-- +-- This source code is licensed under the MIT license found in the +-- LICENSE file in the root directory of this source tree. +-- +-- Usage: convert_dictionary.lua <dict.th7> +require 'fairseq' +require 'torch' +require 'paths' + +if #arg < 1 then + print('usage: convert_dictionary.lua <dict.th7>') + os.exit(1) +end +if not paths.filep(arg[1]) then + print('error: file does not exit: ' .. arg[1]) + os.exit(1) +end + +dict = torch.load(arg[1]) +dst = paths.basename(arg[1]):gsub('.th7', '.txt') +assert(dst:match('.txt$')) + +f = io.open(dst, 'w') +for idx, symbol in ipairs(dict.index_to_symbol) do + if idx > dict.cutoff then + break + end + f:write(symbol) + f:write(' ') + f:write(dict.index_to_freq[idx]) + f:write('\n') +end +f:close() diff --git a/fairseq/scripts/convert_model.lua b/fairseq/scripts/convert_model.lua new file mode 100644 index 0000000..61b9213 --- /dev/null +++ b/fairseq/scripts/convert_model.lua @@ -0,0 +1,108 @@ +-- Copyright (c) Facebook, Inc. and its affiliates. +-- +-- This source code is licensed under the MIT license found in the +-- LICENSE file in the root directory of this source tree. +-- +-- Usage: convert_model.lua <model_epoch1.th7> +require 'torch' +local fairseq = require 'fairseq' + +model = torch.load(arg[1]) + +function find_weight_norm(container, module) + for _, wn in ipairs(container:listModules()) do + if torch.type(wn) == 'nn.WeightNorm' and wn.modules[1] == module then + return wn + end + end +end + +function push_state(dict, key, module) + if torch.type(module) == 'nn.Linear' then + local wn = find_weight_norm(model.module, module) + assert(wn) + dict[key .. '.weight_v'] = wn.v:float() + dict[key .. '.weight_g'] = wn.g:float() + elseif torch.type(module) == 'nn.TemporalConvolutionTBC' then + local wn = find_weight_norm(model.module, module) + assert(wn) + local v = wn.v:float():view(wn.viewOut):transpose(2, 3) + dict[key .. '.weight_v'] = v + dict[key .. '.weight_g'] = wn.g:float():view(module.weight:size(3), 1, 1) + else + dict[key .. '.weight'] = module.weight:float() + end + if module.bias then + dict[key .. '.bias'] = module.bias:float() + end +end + +encoder_dict = {} +decoder_dict = {} +combined_dict = {} + +function encoder_state(encoder) + luts = encoder:findModules('nn.LookupTable') + push_state(encoder_dict, 'embed_tokens', luts[1]) + push_state(encoder_dict, 'embed_positions', luts[2]) + + fcs = encoder:findModules('nn.Linear') + assert(#fcs >= 2) + local nInputPlane = fcs[1].weight:size(1) + push_state(encoder_dict, 'fc1', table.remove(fcs, 1)) + push_state(encoder_dict, 'fc2', table.remove(fcs, #fcs)) + + for i, module in ipairs(encoder:findModules('nn.TemporalConvolutionTBC')) do + push_state(encoder_dict, 'convolutions.' .. tostring(i - 1), module) + if nInputPlane ~= module.weight:size(3) / 2 then + push_state(encoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1)) + end + nInputPlane = module.weight:size(3) / 2 + end + assert(#fcs == 0) +end + +function decoder_state(decoder) + luts = decoder:findModules('nn.LookupTable') + push_state(decoder_dict, 'embed_tokens', luts[1]) + push_state(decoder_dict, 'embed_positions', luts[2]) + + fcs = decoder:findModules('nn.Linear') + local nInputPlane = fcs[1].weight:size(1) + push_state(decoder_dict, 'fc1', table.remove(fcs, 1)) + push_state(decoder_dict, 'fc2', fcs[#fcs - 1]) + push_state(decoder_dict, 'fc3', fcs[#fcs]) + + table.remove(fcs, #fcs) + table.remove(fcs, #fcs) + + for i, module in ipairs(decoder:findModules('nn.TemporalConvolutionTBC')) do + if nInputPlane ~= module.weight:size(3) / 2 then + push_state(decoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1)) + end + nInputPlane = module.weight:size(3) / 2 + + local prefix = 'attention.' .. tostring(i - 1) + push_state(decoder_dict, prefix .. '.in_projection', table.remove(fcs, 1)) + push_state(decoder_dict, prefix .. '.out_projection', table.remove(fcs, 1)) + push_state(decoder_dict, 'convolutions.' .. tostring(i - 1), module) + end + assert(#fcs == 0) +end + + +_encoder = model.module.modules[2] +_decoder = model.module.modules[3] + +encoder_state(_encoder) +decoder_state(_decoder) + +for k, v in pairs(encoder_dict) do + combined_dict['encoder.' .. k] = v +end +for k, v in pairs(decoder_dict) do + combined_dict['decoder.' .. k] = v +end + + +torch.save('state_dict.t7', combined_dict) diff --git a/fairseq/scripts/count_docs.py b/fairseq/scripts/count_docs.py new file mode 100644 index 0000000..58d85af --- /dev/null +++ b/fairseq/scripts/count_docs.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Count the number of documents and average number of lines and tokens per +document in a large file. Documents should be separated by a single empty line. +""" + +import argparse +import gzip +import sys + +import numpy as np + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("input") + parser.add_argument("--gzip", action="store_true") + args = parser.parse_args() + + def gopen(): + if args.gzip: + return gzip.open(args.input, "r") + else: + return open(args.input, "r", encoding="utf-8") + + num_lines = [] + num_toks = [] + with gopen() as h: + num_docs = 1 + num_lines_in_doc = 0 + num_toks_in_doc = 0 + for i, line in enumerate(h): + if len(line.strip()) == 0: # empty line indicates new document + num_docs += 1 + num_lines.append(num_lines_in_doc) + num_toks.append(num_toks_in_doc) + num_lines_in_doc = 0 + num_toks_in_doc = 0 + else: + num_lines_in_doc += 1 + num_toks_in_doc += len(line.rstrip().split()) + if i % 1000000 == 0: + print(i, file=sys.stderr, end="", flush=True) + elif i % 100000 == 0: + print(".", file=sys.stderr, end="", flush=True) + print(file=sys.stderr, flush=True) + + print("found {} docs".format(num_docs)) + print("average num lines per doc: {}".format(np.mean(num_lines))) + print("average num toks per doc: {}".format(np.mean(num_toks))) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/read_binarized.py b/fairseq/scripts/read_binarized.py new file mode 100644 index 0000000..a414095 --- /dev/null +++ b/fairseq/scripts/read_binarized.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse + +from fairseq.data import Dictionary, data_utils, indexed_dataset + + +def get_parser(): + parser = argparse.ArgumentParser( + description="writes text from binarized file to stdout" + ) + # fmt: off + parser.add_argument('--dataset-impl', help='dataset implementation', + choices=indexed_dataset.get_available_dataset_impl()) + parser.add_argument('--dict', metavar='FP', help='dictionary containing known words', default=None) + parser.add_argument('--input', metavar='FP', required=True, help='binarized file to read') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + dictionary = Dictionary.load(args.dict) if args.dict is not None else None + dataset = data_utils.load_indexed_dataset( + args.input, + dictionary, + dataset_impl=args.dataset_impl, + default="lazy", + ) + + for tensor_line in dataset: + if dictionary is None: + line = " ".join([str(int(x)) for x in tensor_line]) + else: + line = dictionary.string(tensor_line) + + print(line) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/rm_pt.py b/fairseq/scripts/rm_pt.py new file mode 100644 index 0000000..6cd063d --- /dev/null +++ b/fairseq/scripts/rm_pt.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import re +import shutil +import sys + + +pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt") +pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt") +pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt") + + +def parse_checkpoints(files): + entries = [] + for f in files: + m = pt_regexp_epoch_based.fullmatch(f) + if m is not None: + entries.append((int(m.group(1)), m.group(0))) + else: + m = pt_regexp_update_based.fullmatch(f) + if m is not None: + entries.append((int(m.group(1)), m.group(0))) + return entries + + +def last_n_checkpoints(files, n): + entries = parse_checkpoints(files) + return [x[1] for x in sorted(entries, reverse=True)[:n]] + + +def every_n_checkpoints(files, n): + entries = parse_checkpoints(files) + return [x[1] for x in sorted(sorted(entries)[::-n])] + + +def main(): + parser = argparse.ArgumentParser( + description=( + "Recursively delete checkpoint files from `root_dir`, " + "but preserve checkpoint_best.pt and checkpoint_last.pt" + ) + ) + parser.add_argument("root_dirs", nargs="*") + parser.add_argument( + "--save-last", type=int, default=0, help="number of last checkpoints to save" + ) + parser.add_argument( + "--save-every", type=int, default=0, help="interval of checkpoints to save" + ) + parser.add_argument( + "--preserve-test", + action="store_true", + help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)", + ) + parser.add_argument( + "--delete-best", action="store_true", help="delete checkpoint_best.pt" + ) + parser.add_argument( + "--delete-last", action="store_true", help="delete checkpoint_last.pt" + ) + parser.add_argument( + "--no-dereference", action="store_true", help="don't dereference symlinks" + ) + args = parser.parse_args() + + files_to_desymlink = [] + files_to_preserve = [] + files_to_delete = [] + for root_dir in args.root_dirs: + for root, _subdirs, files in os.walk(root_dir): + if args.save_last > 0: + to_save = last_n_checkpoints(files, args.save_last) + else: + to_save = [] + if args.save_every > 0: + to_save += every_n_checkpoints(files, args.save_every) + for file in files: + if not pt_regexp.fullmatch(file): + continue + full_path = os.path.join(root, file) + if ( + not os.path.basename(root).startswith("test_") or args.preserve_test + ) and ( + (file == "checkpoint_last.pt" and not args.delete_last) + or (file == "checkpoint_best.pt" and not args.delete_best) + or file in to_save + ): + if os.path.islink(full_path) and not args.no_dereference: + files_to_desymlink.append(full_path) + else: + files_to_preserve.append(full_path) + else: + files_to_delete.append(full_path) + + if len(files_to_desymlink) == 0 and len(files_to_delete) == 0: + print("Nothing to do.") + sys.exit(0) + + files_to_desymlink = sorted(files_to_desymlink) + files_to_preserve = sorted(files_to_preserve) + files_to_delete = sorted(files_to_delete) + + print("Operations to perform (in order):") + if len(files_to_desymlink) > 0: + for file in files_to_desymlink: + print(" - preserve (and dereference symlink): " + file) + if len(files_to_preserve) > 0: + for file in files_to_preserve: + print(" - preserve: " + file) + if len(files_to_delete) > 0: + for file in files_to_delete: + print(" - delete: " + file) + while True: + resp = input("Continue? (Y/N): ") + if resp.strip().lower() == "y": + break + elif resp.strip().lower() == "n": + sys.exit(0) + + print("Executing...") + if len(files_to_desymlink) > 0: + for file in files_to_desymlink: + realpath = os.path.realpath(file) + print("rm " + file) + os.remove(file) + print("cp {} {}".format(realpath, file)) + shutil.copyfile(realpath, file) + if len(files_to_delete) > 0: + for file in files_to_delete: + print("rm " + file) + os.remove(file) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/sacrebleu.sh b/fairseq/scripts/sacrebleu.sh new file mode 100644 index 0000000..c10bf2b --- /dev/null +++ b/fairseq/scripts/sacrebleu.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +if [ $# -ne 4 ]; then + echo "usage: $0 TESTSET SRCLANG TGTLANG GEN" + exit 1 +fi + +TESTSET=$1 +SRCLANG=$2 +TGTLANG=$3 + +GEN=$4 + +if ! command -v sacremoses &> /dev/null +then + echo "sacremoses could not be found, please install with: pip install sacremoses" + exit +fi + +grep ^H $GEN \ +| sed 's/^H\-//' \ +| sort -n -k 1 \ +| cut -f 3 \ +| sacremoses detokenize \ +> $GEN.sorted.detok + +sacrebleu --test-set $TESTSET --language-pair "${SRCLANG}-${TGTLANG}" < $GEN.sorted.detok diff --git a/fairseq/scripts/shard_docs.py b/fairseq/scripts/shard_docs.py new file mode 100644 index 0000000..97232c3 --- /dev/null +++ b/fairseq/scripts/shard_docs.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Split a large file into shards while respecting document boundaries. Documents +should be separated by a single empty line. +""" + +import argparse +import contextlib + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("input") + parser.add_argument("--num-shards", type=int) + args = parser.parse_args() + + assert args.num_shards is not None and args.num_shards > 1 + + with open(args.input, "r", encoding="utf-8") as h: + with contextlib.ExitStack() as stack: + outputs = [ + stack.enter_context( + open(args.input + ".shard" + str(i), "w", encoding="utf-8") + ) + for i in range(args.num_shards) + ] + + doc = [] + first_doc = [True] * args.num_shards + + def output_doc(i): + if not first_doc[i]: + outputs[i].write("\n") + first_doc[i] = False + for line in doc: + outputs[i].write(line) + doc.clear() + + num_docs = 0 + for line in h: + if line.strip() == "": # empty line indicates new document + output_doc(num_docs % args.num_shards) + num_docs += 1 + else: + doc.append(line) + output_doc(num_docs % args.num_shards) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/split_train_valid_docs.py b/fairseq/scripts/split_train_valid_docs.py new file mode 100644 index 0000000..ff15978 --- /dev/null +++ b/fairseq/scripts/split_train_valid_docs.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Split a large file into a train and valid set while respecting document +boundaries. Documents should be separated by a single empty line. +""" + +import argparse +import random +import sys + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("input") + parser.add_argument("sample_output", help="train output file") + parser.add_argument("remainder_output", help="valid output file") + parser.add_argument("-k", type=int, help="remainder size") + parser.add_argument( + "--lines", action="store_true", help="split lines instead of docs" + ) + args = parser.parse_args() + + assert args.k is not None + + sample = [] + remainder = [] + num_docs = [0] + + def update_sample(doc): + if len(sample) < args.k: + sample.append(doc.copy()) + else: + i = num_docs[0] + j = random.randrange(i + 1) + if j < args.k: + remainder.append(sample[j]) + sample[j] = doc.copy() + else: + remainder.append(doc.copy()) + num_docs[0] += 1 + doc.clear() + + with open(args.input, "r", encoding="utf-8") as h: + doc = [] + for i, line in enumerate(h): + if line.strip() == "": # empty line indicates new document + update_sample(doc) + else: + doc.append(line) + if args.lines: + update_sample(doc) + if i % 1000000 == 0: + print(i, file=sys.stderr, end="", flush=True) + elif i % 100000 == 0: + print(".", file=sys.stderr, end="", flush=True) + if len(doc) > 0: + update_sample(doc) + print(file=sys.stderr, flush=True) + + assert len(sample) == args.k + + with open(args.sample_output, "w", encoding="utf-8") as out: + first = True + for doc in sample: + if not first and not args.lines: + out.write("\n") + first = False + for line in doc: + out.write(line) + + with open(args.remainder_output, "w", encoding="utf-8") as out: + first = True + for doc in remainder: + if not first and not args.lines: + out.write("\n") + first = False + for line in doc: + out.write(line) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/spm_decode.py b/fairseq/scripts/spm_decode.py new file mode 100644 index 0000000..7d7b68b --- /dev/null +++ b/fairseq/scripts/spm_decode.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import argparse + +import sentencepiece as spm + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--model", required=True, help="sentencepiece model to use for decoding" + ) + parser.add_argument("--input", required=True, help="input file to decode") + parser.add_argument("--input_format", choices=["piece", "id"], default="piece") + args = parser.parse_args() + + sp = spm.SentencePieceProcessor() + sp.Load(args.model) + + if args.input_format == "piece": + + def decode(input): + return "".join(sp.DecodePieces(input)) + + elif args.input_format == "id": + + def decode(input): + return "".join(sp.DecodeIds(input)) + + else: + raise NotImplementedError + + def tok2int(tok): + # remap reference-side <unk> (represented as <<unk>>) to 0 + return int(tok) if tok != "<<unk>>" else 0 + + with open(args.input, "r", encoding="utf-8") as h: + for line in h: + if args.input_format == "id": + print(decode(list(map(tok2int, line.rstrip().split())))) + elif args.input_format == "piece": + print(decode(line.rstrip().split())) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/spm_encode.py b/fairseq/scripts/spm_encode.py new file mode 100644 index 0000000..f91e0bb --- /dev/null +++ b/fairseq/scripts/spm_encode.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import argparse +import contextlib +import sys + +import sentencepiece as spm + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--model", required=True, help="sentencepiece model to use for encoding" + ) + parser.add_argument( + "--inputs", nargs="+", default=["-"], help="input files to filter/encode" + ) + parser.add_argument( + "--outputs", nargs="+", default=["-"], help="path to save encoded outputs" + ) + parser.add_argument("--output_format", choices=["piece", "id"], default="piece") + parser.add_argument( + "--min-len", + type=int, + metavar="N", + help="filter sentence pairs with fewer than N tokens", + ) + parser.add_argument( + "--max-len", + type=int, + metavar="N", + help="filter sentence pairs with more than N tokens", + ) + args = parser.parse_args() + + assert len(args.inputs) == len( + args.outputs + ), "number of input and output paths should match" + + sp = spm.SentencePieceProcessor() + sp.Load(args.model) + + if args.output_format == "piece": + + def encode(input): + return sp.EncodeAsPieces(input) + + elif args.output_format == "id": + + def encode(input): + return list(map(str, sp.EncodeAsIds(input))) + + else: + raise NotImplementedError + + if args.min_len is not None or args.max_len is not None: + + def valid(line): + return (args.min_len is None or len(line) >= args.min_len) and ( + args.max_len is None or len(line) <= args.max_len + ) + + else: + + def valid(lines): + return True + + with contextlib.ExitStack() as stack: + inputs = [ + stack.enter_context(open(input, "r", encoding="utf-8")) + if input != "-" + else sys.stdin + for input in args.inputs + ] + outputs = [ + stack.enter_context(open(output, "w", encoding="utf-8")) + if output != "-" + else sys.stdout + for output in args.outputs + ] + + stats = { + "num_empty": 0, + "num_filtered": 0, + } + + def encode_line(line): + line = line.strip() + if len(line) > 0: + line = encode(line) + if valid(line): + return line + else: + stats["num_filtered"] += 1 + else: + stats["num_empty"] += 1 + return None + + for i, lines in enumerate(zip(*inputs), start=1): + enc_lines = list(map(encode_line, lines)) + if not any(enc_line is None for enc_line in enc_lines): + for enc_line, output_h in zip(enc_lines, outputs): + print(" ".join(enc_line), file=output_h) + if i % 10000 == 0: + print("processed {} lines".format(i), file=sys.stderr) + + print("skipped {} empty lines".format(stats["num_empty"]), file=sys.stderr) + print("filtered {} lines".format(stats["num_filtered"]), file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/fairseq/scripts/spm_train.py b/fairseq/scripts/spm_train.py new file mode 100644 index 0000000..9db668f --- /dev/null +++ b/fairseq/scripts/spm_train.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import sys + +import sentencepiece as spm + + +if __name__ == "__main__": + spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:])) diff --git a/fairseq/scripts/test_fsdp.sh b/fairseq/scripts/test_fsdp.sh new file mode 100644 index 0000000..1f428a0 --- /dev/null +++ b/fairseq/scripts/test_fsdp.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash +rm -rf fsdp_dummy +mkdir -p fsdp_dummy +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 256 --batch-size 8 \ + --arch transformer_lm_gpt2_tiny \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 5 --log-format json --log-interval 1 \ + --save-interval-updates 5 --save-dir fsdp_dummy --disable-validation \ + --restore-file x.pt "$@" + +# Now we try to load the checkpoint +CUDA_VISIBLE_DEVICES=0,1 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 256 --batch-size 8 \ + --arch transformer_lm_gpt2_tiny \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 2 --log-format json --log-interval 1 \ + --save-interval-updates 2 --save-dir fsdp_dummy diff --git a/fairseq/setup.cfg b/fairseq/setup.cfg new file mode 100644 index 0000000..3fa679d --- /dev/null +++ b/fairseq/setup.cfg @@ -0,0 +1,4 @@ +[flake8] +max-line-length = 127 +extend-ignore = E203, W503 +extend-exclude = fairseq/model_parallel/megatron diff --git a/fairseq/setup.py b/fairseq/setup.py new file mode 100644 index 0000000..dae0608 --- /dev/null +++ b/fairseq/setup.py @@ -0,0 +1,257 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import subprocess +import sys + +from setuptools import Extension, find_packages, setup +from torch.utils import cpp_extension + +if sys.version_info < (3, 6): + sys.exit("Sorry, Python >= 3.6 is required for fairseq.") + + +def write_version_py(): + with open(os.path.join("fairseq", "version.txt")) as f: + version = f.read().strip() + + # write version info to fairseq/version.py + with open(os.path.join("fairseq", "version.py"), "w") as f: + f.write('__version__ = "{}"\n'.format(version)) + return version + + +version = write_version_py() + + +with open("README.md") as f: + readme = f.read() + + +if sys.platform == "darwin": + extra_compile_args = ["-stdlib=libc++", "-O3"] +else: + extra_compile_args = ["-std=c++11", "-O3"] + + +class NumpyExtension(Extension): + """Source: https://stackoverflow.com/a/54128391""" + + def __init__(self, *args, **kwargs): + self.__include_dirs = [] + super().__init__(*args, **kwargs) + + @property + def include_dirs(self): + import numpy + + return self.__include_dirs + [numpy.get_include()] + + @include_dirs.setter + def include_dirs(self, dirs): + self.__include_dirs = dirs + + +extensions = [ + Extension( + "fairseq.libbleu", + sources=[ + "fairseq/clib/libbleu/libbleu.cpp", + "fairseq/clib/libbleu/module.cpp", + ], + extra_compile_args=extra_compile_args, + ), + NumpyExtension( + "fairseq.data.data_utils_fast", + sources=["fairseq/data/data_utils_fast.pyx"], + language="c++", + extra_compile_args=extra_compile_args, + ), + NumpyExtension( + "fairseq.data.token_block_utils_fast", + sources=["fairseq/data/token_block_utils_fast.pyx"], + language="c++", + extra_compile_args=extra_compile_args, + ), +] + + +extensions.extend( + [ + cpp_extension.CppExtension( + "fairseq.libbase", + sources=[ + "fairseq/clib/libbase/balanced_assignment.cpp", + ], + ), + cpp_extension.CppExtension( + "fairseq.libnat", + sources=[ + "fairseq/clib/libnat/edit_dist.cpp", + ], + ), + cpp_extension.CppExtension( + "alignment_train_cpu_binding", + sources=[ + "examples/operators/alignment_train_cpu.cpp", + ], + ), + ] +) +if "CUDA_HOME" in os.environ: + extensions.extend( + [ + cpp_extension.CppExtension( + "fairseq.libnat_cuda", + sources=[ + "fairseq/clib/libnat_cuda/edit_dist.cu", + "fairseq/clib/libnat_cuda/binding.cpp", + ], + ), + cpp_extension.CppExtension( + "fairseq.ngram_repeat_block_cuda", + sources=[ + "fairseq/clib/cuda/ngram_repeat_block_cuda.cpp", + "fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu", + ], + ), + cpp_extension.CppExtension( + "alignment_train_cuda_binding", + sources=[ + "examples/operators/alignment_train_kernel.cu", + "examples/operators/alignment_train_cuda.cpp", + ], + ), + ] + ) + +cmdclass = {"build_ext": cpp_extension.BuildExtension} + +if "READTHEDOCS" in os.environ: + # don't build extensions when generating docs + extensions = [] + if "build_ext" in cmdclass: + del cmdclass["build_ext"] + + # use CPU build of PyTorch + dependency_links = [ + "https://download.pytorch.org/whl/cpu/torch-1.7.0%2Bcpu-cp36-cp36m-linux_x86_64.whl" + ] +else: + dependency_links = [] + + +if "clean" in sys.argv[1:]: + # Source: https://bit.ly/2NLVsgE + print("deleting Cython files...") + + subprocess.run( + ["rm -f fairseq/*.so fairseq/**/*.so fairseq/*.pyd fairseq/**/*.pyd"], + shell=True, + ) + + +extra_packages = [] +if os.path.exists(os.path.join("fairseq", "model_parallel", "megatron", "mpu")): + extra_packages.append("fairseq.model_parallel.megatron.mpu") + + +def do_setup(package_data): + setup( + name="fairseq", + version=version, + description="Facebook AI Research Sequence-to-Sequence Toolkit", + url="https://github.com/pytorch/fairseq", + classifiers=[ + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + ], + long_description=readme, + long_description_content_type="text/markdown", + install_requires=[ + "cffi", + "cython", + "hydra-core>=1.0.7,<1.1", + "omegaconf<2.1", + "numpy>=1.21.3", + "regex", + "sacrebleu>=1.4.12", + "torch>=1.13", + "tqdm", + "bitarray", + "torchaudio>=0.8.0", + "scikit-learn", + "packaging", + ], + extras_require={ + "dev": ["flake8", "pytest", "black==22.3.0"], + "docs": ["sphinx", "sphinx-argparse"], + }, + dependency_links=dependency_links, + packages=find_packages( + exclude=[ + "examples", + "examples.*", + "scripts", + "scripts.*", + "tests", + "tests.*", + ] + ) + + extra_packages, + package_data=package_data, + ext_modules=extensions, + test_suite="tests", + entry_points={ + "console_scripts": [ + "fairseq-eval-lm = fairseq_cli.eval_lm:cli_main", + "fairseq-generate = fairseq_cli.generate:cli_main", + "fairseq-hydra-train = fairseq_cli.hydra_train:cli_main", + "fairseq-interactive = fairseq_cli.interactive:cli_main", + "fairseq-preprocess = fairseq_cli.preprocess:cli_main", + "fairseq-score = fairseq_cli.score:cli_main", + "fairseq-train = fairseq_cli.train:cli_main", + "fairseq-validate = fairseq_cli.validate:cli_main", + ], + }, + cmdclass=cmdclass, + zip_safe=False, + ) + + +def get_files(path, relative_to="fairseq"): + all_files = [] + for root, _dirs, files in os.walk(path, followlinks=True): + root = os.path.relpath(root, relative_to) + for file in files: + if file.endswith(".pyc"): + continue + all_files.append(os.path.join(root, file)) + return all_files + + +if __name__ == "__main__": + try: + # symlink examples into fairseq package so package_data accepts them + fairseq_examples = os.path.join("fairseq", "examples") + if "build_ext" not in sys.argv[1:] and not os.path.exists(fairseq_examples): + os.symlink(os.path.join("..", "examples"), fairseq_examples) + + package_data = { + "fairseq": ( + get_files(fairseq_examples) + + get_files(os.path.join("fairseq", "config")) + ) + } + do_setup(package_data) + finally: + if "build_ext" not in sys.argv[1:] and os.path.islink(fairseq_examples): + os.unlink(fairseq_examples) diff --git a/fairseq/tests/__init__.py b/fairseq/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/tests/distributed/__init__.py b/fairseq/tests/distributed/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/tests/distributed/test_bmuf.py b/fairseq/tests/distributed/test_bmuf.py new file mode 100644 index 0000000..995d0db --- /dev/null +++ b/fairseq/tests/distributed/test_bmuf.py @@ -0,0 +1,204 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import functools +import random +import unittest +from multiprocessing import Manager + +import torch +import torch.nn as nn +from omegaconf import OmegaConf + +from fairseq import optim +from fairseq.distributed import utils as distributed_utils + + +class Model(nn.Module): + def __init__(self, input_size, output_size): + super(Model, self).__init__() + self.fc = nn.Linear(input_size, output_size) + + def forward(self, input): + output = self.fc(input) + return output + + +def setup_model_loss_criterion(cfg, args, rank, is_cuda): + """ + setup model, criterion and optimizer based on input args + """ + args.distributed_rank = rank + cfg.distributed_training.distributed_rank = args.distributed_rank + if cfg.distributed_training.distributed_world_size > 1: + distributed_utils.distributed_init(cfg) + torch.manual_seed(1) + model = Model(args.input_size, args.nb_classes) + loss_fn = nn.CrossEntropyLoss() + if is_cuda: + model = model.cuda() + loss_fn = loss_fn.cuda() + + optimizer = optim.sgd.SGD(args, model.parameters()) + optimizer = optim.FairseqBMUF(cfg=cfg.bmuf, optimizer=optimizer) + + return model, loss_fn, optimizer + + +def train_step(input, target, model, loss_fn, optimizer, **unused): + """Do forward, backward and parameter update.""" + model.train() + output = model(input) + loss = loss_fn(output, target) + optimizer.backward(loss) + optimizer.step() + + +def single_gpu_training(cfg, args, rank, iterations, shared_results): + + is_cuda = torch.cuda.is_available() + if is_cuda: + torch.cuda.set_device(rank) + + model, loss_fn, optimizer = setup_model_loss_criterion(cfg, args, rank, is_cuda) + + for _ in range(iterations): + input = torch.randn(1, args.input_size) + target = torch.empty(args.batch_size, dtype=torch.long).random_(args.nb_classes) + + if is_cuda: + input = input.cuda() + target = target.cuda() + train_step(input, target, model, loss_fn, optimizer) + + results = [] + for param in model.parameters(): + if len(results) == 0: + results = param.flatten().cpu().data + else: + results = torch.cat((results, param.flatten().cpu().data), 0) + + shared_results[rank] = results + + +def setup_args(): + args = argparse.Namespace() + args.global_sync_iter = 20 + args.block_momentum = 0.875 + args.block_lr = 0.5 + args.input_size = 5 + args.nb_classes = 2 + args.batch_size = 1 + args.lr = [1e-3] + args.momentum = 0 + args.weight_decay = 0 + args.warmup_iterations = 0 + args.use_nbm = True + args.average_sync = True + args.global_sync_iter = 1 + args.model_parallel_size = 1 + args.distributed_backend = "gloo" + + args.distributed_world_size = 2 + port = random.randint(10000, 20000) + args.distributed_init_method = "tcp://localhost:{port}".format(port=port) + args.distributed_init_host = "localhost" + args.distributed_port = port + 1 + args.local_world_size = args.distributed_world_size + + cfg = OmegaConf.create() + cfg.optimization = OmegaConf.create() + cfg.common = OmegaConf.create() + cfg.distributed_training = OmegaConf.create() + cfg.dataset = OmegaConf.create() + cfg.bmuf = OmegaConf.create() + cfg.optimizer = OmegaConf.create() + + cfg.bmuf.global_sync_iter = args.global_sync_iter + cfg.bmuf.block_momentum = args.block_momentum + cfg.bmuf.block_lr = args.block_lr + cfg.dataset.batch_size = args.batch_size + cfg.optimization.lr = args.lr + cfg.optimizer.momentum = args.momentum + cfg.optimizer.weight_decay = args.weight_decay + cfg.bmuf.warmup_iterations = args.warmup_iterations + cfg.bmuf.use_nbm = args.use_nbm + cfg.bmuf.average_sync = args.average_sync + cfg.common.model_parallel_size = args.model_parallel_size + cfg.distributed_training.distributed_backend = args.distributed_backend + cfg.distributed_training.distributed_world_size = args.distributed_world_size + cfg.bmuf.distributed_world_size = args.distributed_world_size + cfg.distributed_training.distributed_init_method = args.distributed_init_method + cfg.distributed_training.distributed_port = args.distributed_port + + return cfg, args + + +@unittest.skipIf(torch.cuda.device_count() < 2, "test requires 2 GPUs") +class TestBMUF(unittest.TestCase): + def bmuf_process(self, cfg, args, iterations): + results = Manager().dict() + torch.multiprocessing.spawn( + fn=functools.partial(single_gpu_training, cfg, args), + args=(iterations, results), + nprocs=args.distributed_world_size, + join=True, + ) + return results + + def test_bmuf_sync(self): + # Train model for 1 iteration and do bmuf sync without doing warmup + cfg, args = setup_args() + iterations = 1 + results = self.bmuf_process(cfg, args, iterations) + # Make sure params in both machines are same + assert len(results) == 2 + self.assertAlmostEqual(results[0], results[1]) + + def test_warmup_sync(self): + # Train model for 20 iteration and do warmup sync without doing bmuf sync + cfg, args = setup_args() + args.warmup_iterations = 20 + cfg.bmuf.warmup_iterations = args.warmup_iterations + iterations = 20 + results = self.bmuf_process(cfg, args, iterations) + # Make sure params in both machines are same + assert len(results) == 2 + self.assertAlmostEqual(results[0], results[1]) + + def test_warmup_sync_bmuf_sync(self): + # Train model for 25 iteration and do warmup sync after 20 iteration + # and bmuf sync after 25 iteration + cfg, args = setup_args() + args.warmup_iterations = 20 + args.global_sync_iter = 5 + cfg.bmuf.warmup_iterations = args.warmup_iterations + cfg.bmuf.global_sync_iter = args.global_sync_iter + iterations = 25 + results = self.bmuf_process(cfg, args, iterations) + # Make sure params in both machines are same + assert len(results) == 2 + self.assertAlmostEqual(results[0], results[1]) + + def test_single_gpu_bmuf(self): + # Train model for 5 iterations and use GPU 1 + cfg, args = setup_args() + args.distributed_world_size = 1 + args.warmup_iterations = 5 + cfg.distributed_training.distributed_world_size = args.distributed_world_size + cfg.bmuf.distributed_world_size = args.distributed_world_size + cfg.bmuf.warmup_iterations = args.warmup_iterations + iterations = 20 + results = self.bmuf_process(cfg, args, iterations) + assert len(results) == 1 + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess((t1 - t2).abs().max(), 1e-4) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/distributed/test_distributed_timeout_wrapper.py b/fairseq/tests/distributed/test_distributed_timeout_wrapper.py new file mode 100644 index 0000000..996093c --- /dev/null +++ b/fairseq/tests/distributed/test_distributed_timeout_wrapper.py @@ -0,0 +1,52 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import signal +import time +import unittest + +import torch +from torch import nn + +from fairseq.distributed import DistributedTimeoutWrapper + + +class ModuleWithDelay(nn.Module): + def __init__(self, delay): + super().__init__() + self.delay = delay + + def forward(self, x): + time.sleep(self.delay) + return x + + +class TestDistributedTimeoutWrapper(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_no_timeout(self): + module = DistributedTimeoutWrapper(ModuleWithDelay(1), 0, signal.SIGINT) + module(torch.rand(5)) + module.stop_timeout() + + def test_timeout_safe(self): + module = DistributedTimeoutWrapper(ModuleWithDelay(1), 10, signal.SIGINT) + module(torch.rand(5)) + module.stop_timeout() + + def test_timeout_killed(self): + with self.assertRaises(KeyboardInterrupt): + module = DistributedTimeoutWrapper(ModuleWithDelay(5), 1, signal.SIGINT) + module(torch.rand(5)) + module.stop_timeout() + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/distributed/test_module_proxy_wrapper.py b/fairseq/tests/distributed/test_module_proxy_wrapper.py new file mode 100644 index 0000000..2ac1a87 --- /dev/null +++ b/fairseq/tests/distributed/test_module_proxy_wrapper.py @@ -0,0 +1,74 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from torch import nn + +from fairseq.distributed import ModuleProxyWrapper + +from .utils import objects_are_equal + + +class MockDDPWrapper(nn.Module): + """A simple wrapper with an interface similar to DistributedDataParallel.""" + + def __init__(self, module): + super().__init__() + self.module = module + + def forward(self, x): + return self.module(x) + + +class Model(nn.Module): + def __init__(self): + super().__init__() + self.linear = nn.Linear(5, 10) + self.xyz = "hello" + + def forward(self, x): + return self.linear(x) + + def get_xyz(self): + return self.xyz + + +class TestModuleProxyWrapper(unittest.TestCase): + def _get_module(self): + module = Model() + wrapped_module = MockDDPWrapper(module) + wrapped_module = ModuleProxyWrapper(wrapped_module) + return wrapped_module, module + + def test_getattr_forwarding(self): + wrapped_module, module = self._get_module() + assert module.xyz == "hello" + assert module.get_xyz() == "hello" + assert wrapped_module.xyz == "hello" + + wrapped_module.xyz = "world" + assert wrapped_module.xyz == "world" + assert module.get_xyz() == "hello" + + def test_state_dict(self): + wrapped_module, module = self._get_module() + assert objects_are_equal(wrapped_module.state_dict(), module.state_dict()) + + def test_load_state_dict(self): + wrapped_module, module = self._get_module() + wrapped_module.load_state_dict(module.state_dict()) + input = torch.rand(4, 5) + torch.testing.assert_allclose(wrapped_module(input), module(input)) + + def test_forward(self): + wrapped_module, module = self._get_module() + input = torch.rand(4, 5) + torch.testing.assert_allclose(wrapped_module(input), module(input)) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/distributed/test_utils.py b/fairseq/tests/distributed/test_utils.py new file mode 100644 index 0000000..30f995b --- /dev/null +++ b/fairseq/tests/distributed/test_utils.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +import sys +import unittest + +import torch + +from fairseq.distributed import utils as dist_utils + +from .utils import objects_are_equal, spawn_and_init + + +class DistributedTest(unittest.TestCase): + def setUp(self): + if not torch.cuda.is_available(): + raise unittest.SkipTest("CUDA not available, skipping test") + if sys.platform == "win32": + raise unittest.SkipTest("NCCL doesn't support Windows, skipping test") + if torch.cuda.device_count() < 2: + raise unittest.SkipTest("distributed tests require 2+ GPUs, skipping") + + +class TestBroadcastObject(DistributedTest): + def test_str(self): + spawn_and_init( + functools.partial( + TestBroadcastObject._test_broadcast_object, "hello world" + ), + world_size=2, + ) + + def test_tensor(self): + spawn_and_init( + functools.partial( + TestBroadcastObject._test_broadcast_object, + torch.rand(5), + ), + world_size=2, + ) + + def test_complex(self): + spawn_and_init( + functools.partial( + TestBroadcastObject._test_broadcast_object, + { + "a": "1", + "b": [2, torch.rand(2, 3), 3], + "c": (torch.rand(2, 3), 4), + "d": {5, torch.rand(5)}, + "e": torch.rand(5), + "f": torch.rand(5).int().cuda(), + }, + ), + world_size=2, + ) + + @staticmethod + def _test_broadcast_object(ref_obj, rank, group): + obj = dist_utils.broadcast_object( + ref_obj if rank == 0 else None, src_rank=0, group=group + ) + assert objects_are_equal(ref_obj, obj) + + +class TestAllGatherList(DistributedTest): + def test_str_equality(self): + spawn_and_init( + functools.partial( + TestAllGatherList._test_all_gather_list_equality, + "hello world", + ), + world_size=2, + ) + + def test_tensor_equality(self): + spawn_and_init( + functools.partial( + TestAllGatherList._test_all_gather_list_equality, + torch.rand(5), + ), + world_size=2, + ) + + def test_complex_equality(self): + spawn_and_init( + functools.partial( + TestAllGatherList._test_all_gather_list_equality, + { + "a": "1", + "b": [2, torch.rand(2, 3), 3], + "c": (torch.rand(2, 3), 4), + "d": {5, torch.rand(5)}, + "e": torch.rand(5), + "f": torch.rand(5).int(), + }, + ), + world_size=2, + ) + + @staticmethod + def _test_all_gather_list_equality(ref_obj, rank, group): + objs = dist_utils.all_gather_list(ref_obj, group) + for obj in objs: + assert objects_are_equal(ref_obj, obj) + + def test_rank_tensor(self): + spawn_and_init( + TestAllGatherList._test_all_gather_list_rank_tensor, world_size=2 + ) + + @staticmethod + def _test_all_gather_list_rank_tensor(rank, group): + obj = torch.tensor([rank]) + objs = dist_utils.all_gather_list(obj, group) + for i, obj in enumerate(objs): + assert obj.item() == i + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/distributed/utils.py b/fairseq/tests/distributed/utils.py new file mode 100644 index 0000000..be4e19c --- /dev/null +++ b/fairseq/tests/distributed/utils.py @@ -0,0 +1,65 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +import tempfile + +import torch + + +def spawn_and_init(fn, world_size, args=None): + if args is None: + args = () + with tempfile.NamedTemporaryFile(delete=False) as tmp_file: + torch.multiprocessing.spawn( + fn=functools.partial(init_and_run, fn, args), + args=( + world_size, + tmp_file.name, + ), + nprocs=world_size, + join=True, + ) + + +def distributed_init(rank, world_size, tmp_file): + torch.distributed.init_process_group( + backend="nccl", + init_method="file://{}".format(tmp_file), + world_size=world_size, + rank=rank, + ) + torch.cuda.set_device(rank) + + +def init_and_run(fn, args, rank, world_size, tmp_file): + distributed_init(rank, world_size, tmp_file) + group = torch.distributed.new_group() + fn(rank, group, *args) + + +def objects_are_equal(a, b) -> bool: + if type(a) is not type(b): + return False + if isinstance(a, dict): + if set(a.keys()) != set(b.keys()): + return False + for k in a.keys(): + if not objects_are_equal(a[k], b[k]): + return False + return True + elif isinstance(a, (list, tuple, set)): + if len(a) != len(b): + return False + return all(objects_are_equal(x, y) for x, y in zip(a, b)) + elif torch.is_tensor(a): + return ( + a.size() == b.size() + and a.dtype == b.dtype + and a.device == b.device + and torch.all(a == b) + ) + else: + return a == b diff --git a/fairseq/tests/gpu/__init__.py b/fairseq/tests/gpu/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/tests/gpu/test_binaries_gpu.py b/fairseq/tests/gpu/test_binaries_gpu.py new file mode 100644 index 0000000..5caf94c --- /dev/null +++ b/fairseq/tests/gpu/test_binaries_gpu.py @@ -0,0 +1,590 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import json +import logging +import os +import tempfile +import unittest +from io import StringIO + +import torch + +from fairseq import options +from fairseq_cli import train +from tests.utils import ( + create_dummy_data, + generate_main, + preprocess_lm_data, + preprocess_translation_data, + train_language_model, + train_translation_model, +) + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestMultiGPU(unittest.TestCase): + @staticmethod + def parse_logs(logfile): + logs = [] + for ln in open(logfile, "r").readlines(): + try: + logs.append(json.loads(ln)) + except json.JSONDecodeError: + continue + return logs + + @property + def world_size(self): + return torch.cuda.device_count() + + def train_flags(self, mu): + return [ + "--memory-efficient-fp16", + "--update-freq", + "1", + "--seed", + "1", + "--log-format", + "json", + "--max-update", + str(mu), + "--tokens-per-sample", + "20", + "--batch-size", + "2", + "--share-decoder-input-output-embed", + "--optimizer", + "adam", + "--max-valid-steps", + "1", + "--pad-to-fixed-length", + "--sample-break-mode", + "none", + ] + + def _test_resume_multilingual_training( + self, extra_clargs, arch="transformer_lm_gpt2_tiny" + ): + languages = ["en_XX", "fr_XX", "zh_CN"] + save_interval = 5 + mu = 10 + flags = ( + self.train_flags(mu) + + ["--save-interval-updates", str(save_interval), "--log-interval", "1"] + + extra_clargs + ) + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fp16") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data( + data_dir, + num_examples=int( + mu * 20 * self.world_size * 1.5 + ), # make sure enough data for max updates + languages=languages, + ) + preprocess_lm_data(data_dir, languages) + train_language_model( + data_dir, + arch, + flags + ["--log-file", log], + task="multilingual_language_modeling", + world_size=self.world_size, + ) + log2 = os.path.join(data_dir, "resume.log") + ckpt_name = f"checkpoint_1_{save_interval}.pt" + restore_file = os.path.join(data_dir, ckpt_name) + train_language_model( + data_dir, + arch, + flags + + ["--log-file", log2, "--restore-file", restore_file, "--no-save"], + task="multilingual_language_modeling", + world_size=self.world_size, + ) + + l1 = self.parse_logs(log) + assert ( + int(l1[-1]["train_num_updates"]) == mu + ), f"The first run did not complete {mu} updates. Add more data" + l2 = self.parse_logs(log2) + + if int(l2[0]["num_updates"]) != save_interval + 1: + all_ckpt_files = [ + x for x in os.listdir(data_dir) if x.endswith(".pt") + ] + import shutil + + shutil.move(data_dir, "last_failed_resume") + raise AssertionError( + f"Likely failed to load {ckpt_name}. {all_ckpt_files} \n LOGS: {l1} \n\n {l2}. " + ) + for k in [ + "train_loss", + "train_num_updates", + "train_ppl", + "train_gnorm", + ]: + from_scratch, resumed = float(l1[-1][k]), float(l2[-1][k]) + # This fails without rounding! + assert ( + from_scratch == resumed + ), f"difference at {k} {from_scratch} != {resumed}" + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestTranslationGPU(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_fp16_multigpu(self): + self._test_multigpu("test_fp16", ["--fp16"]) + + def test_slowmo_multigpu(self): + self._test_multigpu( + "test_slowmo", ["--ddp-backend", "slowmo", "--nprocs-per-node", "1"] + ) + + def test_slowmo_single_node_multigpu(self): + self._test_multigpu( + "test_slowmo_single_node", + ["--ddp-backend", "slowmo", "--nprocs-per-node", "2"], + ) + + def _test_multigpu(self, test_name, test_args): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory(test_name) as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + test_args + ["--log-file", log], + world_size=min(torch.cuda.device_count(), 2), + ) + generate_main(data_dir) + assert os.path.exists(log) + + @staticmethod + def parse_logs(logfile): + logs = [] + for ln in open(logfile, "r").readlines(): + try: + logs.append(json.loads(ln)) + except json.JSONDecodeError: + continue + return logs + + def test_resume_training_fsdp(self): + self._test_resume_training(["--ddp-backend", "fully_sharded"]) + + def test_resume_training_fsdp_sharded_state(self): + self._test_resume_training( + ["--ddp-backend", "fully_sharded", "--use-sharded-state"] + ) + + def test_resume_training_noc10d(self): + self._test_resume_training([]) + + def _test_resume_training(self, extra_clargs, arch="fconv_iwslt_de_en"): + flags = [ + "--fp16", + "--log-format", + "json", + "--max-update", + "10", + "--save-interval-updates", + "2", + "--log-interval", + "1", + ] + extra_clargs + world_size = min(torch.cuda.device_count(), 2) + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fp16") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + arch, + flags + ["--log-file", log], + world_size=world_size, + ) + log2 = os.path.join(data_dir, "resume.log") + restore_file = os.path.join(data_dir, "checkpoint_1_2.pt") + train_translation_model( + data_dir, + arch, + flags + ["--log-file", log2, "--restore-file", restore_file], + world_size=world_size, + ) + + l1 = self.parse_logs(log) + l2 = self.parse_logs(log2) + assert int(l2[0]["num_updates"]) == 3, f"{l1}\n\n {l2}" + for k in [ + "train_loss", + "train_num_updates", + "train_ppl", + "train_gnorm", + ]: + from_scratch, resumed = l1[-1][k], l2[-1][k] + assert ( + from_scratch == resumed + ), f"difference at {k} {from_scratch} != {resumed}" + + def test_memory_efficient_fp16(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_memory_efficient_fp16") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, "fconv_iwslt_de_en", ["--memory-efficient-fp16"] + ) + generate_main(data_dir) + + def test_transformer_fp16(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "64", + "--decoder-embed-dim", + "64", + "--fp16", + ], + run_validation=True, + ) + generate_main(data_dir) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_amp(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_amp") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model(data_dir, "fconv_iwslt_de_en", ["--amp"]) + generate_main(data_dir) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_transformer_amp(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "64", + "--decoder-embed-dim", + "64", + "--amp", + ], + run_validation=True, + ) + generate_main(data_dir) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_levenshtein_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_levenshtein_transformer" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "levenshtein_transformer", + [ + "--apply-bert-init", + "--early-exit", + "6,6,6", + "--criterion", + "nat_loss", + ], + task="translation_lev", + ) + gen_config = [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ] + # non-ensemble generation + generate_main(data_dir, gen_config) + # ensemble generation + generate_main( + data_dir, + gen_config, + path=os.pathsep.join( + [ + os.path.join(data_dir, "checkpoint_last.pt"), + os.path.join(data_dir, "checkpoint_last.pt"), + ] + ), + ) + + def test_fsdp_checkpoint_generate(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + world_size = min(torch.cuda.device_count(), 2) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + ["--log-file", log, "--ddp-backend", "fully_sharded"], + world_size=world_size, + ) + generate_main(data_dir) + assert os.path.exists(log) + + def test_fsdp_sharded_checkpoint_generate(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + world_size = min(torch.cuda.device_count(), 2) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + [ + "--log-file", + log, + "--ddp-backend", + "fully_sharded", + "--use-sharded-state", + ], + world_size=world_size, + ) + generate_main(data_dir, ["--checkpoint-shard-count", str(world_size)]) + assert os.path.exists(log) + + +def _quantize_language_model(data_dir, arch, extra_flags=None, run_validation=False): + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + "language_modeling", + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + train.main(train_args) + + # try scalar quantization + scalar_quant_train_parser = options.get_training_parser() + scalar_quant_train_args = options.parse_args_and_arch( + scalar_quant_train_parser, + [ + "--task", + "language_modeling", + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-update", + "3", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + "--quant-noise-scalar", + "0.5", + ] + + (extra_flags or []), + ) + train.main(scalar_quant_train_args) + + # try iterative PQ quantization + quantize_parser = options.get_training_parser() + quantize_args = options.parse_args_and_arch( + quantize_parser, + [ + "--task", + "language_modeling", + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + "--max-tokens", + "50", + "--tokens-per-sample", + "50", + "--max-update", + "6", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + "--restore-file", + os.path.join(data_dir, "checkpoint_last.pt"), + "--reset-optimizer", + "--quantization-config-path", + os.path.join( + os.path.dirname(__file__), "transformer_quantization_config.yaml" + ), + ] + + (extra_flags or []), + ) + train.main(quantize_args) + + +@unittest.skipIf( + int(torch.__version__[2]) < 10, reason="quantized kernels are only supported on CPU" +) +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestQuantization(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_quantization(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_quantization") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + # tests both scalar and iterative PQ quantization + _quantize_language_model(data_dir, "transformer_lm") + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestOptimizersGPU(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_flat_grads(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_flat_grads") as data_dir: + # Use just a bit of data and tiny model to keep this test runtime reasonable + create_dummy_data(data_dir, num_examples=10, maxlen=5) + preprocess_translation_data(data_dir) + with self.assertRaises(RuntimeError): + # adafactor isn't compatible with flat grads, which + # are used by default with --fp16 + train_translation_model( + data_dir, + "lstm", + [ + "--required-batch-size-multiple", + "1", + "--encoder-layers", + "1", + "--encoder-hidden-size", + "32", + "--decoder-layers", + "1", + "--optimizer", + "adafactor", + "--fp16", + ], + ) + # but it should pass once we set --fp16-no-flatten-grads + train_translation_model( + data_dir, + "lstm", + [ + "--required-batch-size-multiple", + "1", + "--encoder-layers", + "1", + "--encoder-hidden-size", + "32", + "--decoder-layers", + "1", + "--optimizer", + "adafactor", + "--fp16", + "--fp16-no-flatten-grads", + ], + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/gpu/test_ema_gpu.py b/fairseq/tests/gpu/test_ema_gpu.py new file mode 100644 index 0000000..33fb560 --- /dev/null +++ b/fairseq/tests/gpu/test_ema_gpu.py @@ -0,0 +1,215 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from copy import deepcopy +from dataclasses import dataclass +from typing import Optional + +import torch + +from fairseq.models.ema import EMA + + +class DummyModule(torch.nn.Module): + def __init__(self) -> None: + """LightningModule for testing purposes + + Args: + epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum + validation loss for testing purposes (zero based). If None this is ignored. Defaults to None. + """ + super().__init__() + self.layer = torch.nn.Linear(in_features=32, out_features=2) + self.another_layer = torch.nn.Linear(in_features=2, out_features=2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.layer(x) + return self.another_layer(x) + + +@dataclass +class EMAConfig(object): + ema_decay: float = 0.99 + ema_start_update: int = 0 + ema_fp32: bool = False + ema_seed_model: Optional[str] = None + ema_update_freq: int = 1 + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestEMAGPU(unittest.TestCase): + def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None): + diff = x.float() - y.float() + diff_norm = torch.norm(diff) + other_norm = torch.norm(y.float()) + + if msg is None: + msg = "|input - other| > {} + {} * |other|".format(atol, rtol) + + self.assertLessEqual( + diff_norm, + atol + rtol * other_norm, + msg=msg, + ) + + def test_ema(self): + model = DummyModule().cuda() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig() + ema = EMA(model, config) + + # set decay + ema._set_decay(config.ema_decay) + self.assertEqual(ema.get_decay(), config.ema_decay) + + # get model + self.assertEqual(ema.get_model(), ema.model) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # EMA step + x = torch.randn(32).cuda() + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + ema_state_dict = ema.get_model().state_dict() + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema_state_dict[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # Load EMA into model + model2 = DummyModule().cuda() + ema.reverse(model2) + + for key, param in model2.state_dict().items(): + ema_param = ema_state_dict[key] + self.assertTrue(torch.allclose(ema_param, param)) + + def test_ema_fp32(self): + model = DummyModule().cuda().half() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=True) + ema = EMA(model, config) + + x = torch.randn(32).cuda() + y = model(x.half()) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertIn(key, ema.fp32_params) + + # EMA update is done in fp32, and hence the EMA param must be + # closer to the EMA update done in fp32 than in fp16. + self.assertLessEqual( + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param.float() + + (1 - config.ema_decay) * param.float() + ) + .half() + .float() + ), + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param + (1 - config.ema_decay) * param + ).float() + ), + ) + self.assertTorchAllClose( + ema_param, + ( + config.ema_decay * prev_param.float() + + (1 - config.ema_decay) * param.float() + ).half(), + ) + + def test_ema_fp16(self): + model = DummyModule().cuda().half() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=False) + ema = EMA(model, config) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + x = torch.randn(32).cuda() + y = model(x.half()) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + + # EMA update is done in fp16, and hence the EMA param must be + # closer to the EMA update done in fp16 than in fp32. + self.assertLessEqual( + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param + (1 - config.ema_decay) * param + ).float() + ), + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param.float() + + (1 - config.ema_decay) * param.float() + ) + .half() + .float() + ), + ) + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/gpu/transformer_quantization_config.yaml b/fairseq/tests/gpu/transformer_quantization_config.yaml new file mode 100644 index 0000000..de31d81 --- /dev/null +++ b/fairseq/tests/gpu/transformer_quantization_config.yaml @@ -0,0 +1,28 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This file defines example configuration arguments for quantizing +# a transformer model with product quantization + +n_centroids: + Linear: + key: in_features + value: {"*": 8} + Embedding: + key: embedding_dim + value: {"*": 8} + +block_sizes: + Linear: + key: fuzzy_name + value: {fc: 8, attn: 4, emb: 4} + Embedding: + key: fuzzy_name + value: {emb: 8} + +layers_to_quantize: + - decoder\\.layers\\.\d+\\.fc[12] + - decoder\\.embed_tokens\\.embeddings\\.[012]\\.[01] + - decoder\\.layers\\.\d+\\.self_attn\\.(k_proj|v_proj|q_proj|out_proj) diff --git a/fairseq/tests/speech/__init__.py b/fairseq/tests/speech/__init__.py new file mode 100644 index 0000000..dba99e4 --- /dev/null +++ b/fairseq/tests/speech/__init__.py @@ -0,0 +1,210 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from argparse import Namespace +import os +import re +import unittest +from pathlib import Path +from tqdm import tqdm +from typing import List, Dict, Optional +import torch +from fairseq.checkpoint_utils import load_model_ensemble_and_task +from fairseq.scoring.wer import WerScorer +from fairseq.scoring.bleu import SacrebleuScorer +from fairseq import utils +import zipfile + +S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq" + + +class TestFairseqSpeech(unittest.TestCase): + @classmethod + def download(cls, base_url: str, out_root: Path, filename: str): + url = f"{base_url}/{filename}" + path = out_root / filename + if not path.exists(): + torch.hub.download_url_to_file(url, path.as_posix(), progress=True) + return path + + def _set_up(self, dataset_id: str, s3_dir: str, data_filenames: List[str]): + self.use_cuda = torch.cuda.is_available() + self.root = Path.home() / ".cache" / "fairseq" / dataset_id + self.root.mkdir(exist_ok=True, parents=True) + os.chdir(self.root) + self.base_url = ( + s3_dir if re.search("^https:", s3_dir) else f"{S3_BASE_URL}/{s3_dir}" + ) + for filename in data_filenames: + self.download(self.base_url, self.root, filename) + + def set_up_librispeech(self): + self._set_up( + "librispeech", + "s2t/librispeech", + [ + "cfg_librispeech.yaml", + "spm_librispeech_unigram10000.model", + "spm_librispeech_unigram10000.txt", + "librispeech_test-other.tsv", + "librispeech_test-other.zip", + ], + ) + + def set_up_ljspeech(self): + self._set_up( + "ljspeech", + "s2/ljspeech", + [ + "cfg_ljspeech_g2p.yaml", + "ljspeech_g2p_gcmvn_stats.npz", + "ljspeech_g2p.txt", + "ljspeech_test.tsv", + "ljspeech_test.zip", + ], + ) + + def set_up_sotasty_es_en(self): + self._set_up( + "sotasty_es_en", + "s2t/big/es-en", + [ + "cfg_es_en.yaml", + "spm_bpe32768_es_en.model", + "spm_bpe32768_es_en.txt", + "sotasty_es_en_test_ted.tsv", + "sotasty_es_en_test_ted.zip", + ], + ) + + def set_up_mustc_de_fbank(self): + self._set_up( + "mustc_de_fbank", + "https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de", + [ + "config.yaml", + "spm.model", + "dict.txt", + "src_dict.txt", + "tgt_dict.txt", + "tst-COMMON.tsv", + "tst-COMMON.zip", + ], + ) + + def download_and_load_checkpoint( + self, + checkpoint_filename: str, + arg_overrides: Optional[Dict[str, str]] = None, + strict: bool = True, + ): + path = self.download(self.base_url, self.root, checkpoint_filename) + _arg_overrides = arg_overrides or {} + _arg_overrides["data"] = self.root.as_posix() + models, cfg, task = load_model_ensemble_and_task( + [path.as_posix()], arg_overrides=_arg_overrides, strict=strict + ) + if self.use_cuda: + for model in models: + model.cuda() + + return models, cfg, task, self.build_generator(task, models, cfg) + + def build_generator( + self, + task, + models, + cfg, + ): + return task.build_generator(models, cfg) + + @classmethod + def get_batch_iterator(cls, task, test_split, max_tokens, max_positions): + task.load_dataset(test_split) + return task.get_batch_iterator( + dataset=task.dataset(test_split), + max_tokens=max_tokens, + max_positions=max_positions, + num_workers=1, + ).next_epoch_itr(shuffle=False) + + @classmethod + def get_wer_scorer( + cls, tokenizer="none", lowercase=False, remove_punct=False, char_level=False + ): + scorer_args = { + "wer_tokenizer": tokenizer, + "wer_lowercase": lowercase, + "wer_remove_punct": remove_punct, + "wer_char_level": char_level, + } + return WerScorer(Namespace(**scorer_args)) + + @classmethod + def get_bleu_scorer(cls, tokenizer="13a", lowercase=False, char_level=False): + scorer_args = { + "sacrebleu_tokenizer": tokenizer, + "sacrebleu_lowercase": lowercase, + "sacrebleu_char_level": char_level, + } + return SacrebleuScorer(Namespace(**scorer_args)) + + @torch.no_grad() + def base_test( + self, + ckpt_name, + reference_score, + score_delta=0.3, + dataset="librispeech_test-other", + max_tokens=65_536, + max_positions=(4_096, 1_024), + arg_overrides=None, + strict=True, + score_type="wer", + ): + models, _, task, generator = self.download_and_load_checkpoint( + ckpt_name, arg_overrides=arg_overrides, strict=strict + ) + if not self.use_cuda: + return + + batch_iterator = self.get_batch_iterator( + task, dataset, max_tokens, max_positions + ) + if score_type == "bleu": + scorer = self.get_bleu_scorer() + elif score_type == "wer": + scorer = self.get_wer_scorer() + else: + raise Exception(f"Unsupported score type {score_type}") + + progress = tqdm(enumerate(batch_iterator), total=len(batch_iterator)) + for batch_idx, sample in progress: + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + hypo = task.inference_step(generator, models, sample) + for i, sample_id in enumerate(sample["id"].tolist()): + tgt_str, hypo_str = self.postprocess_tokens( + task, + sample["target"][i, :], + hypo[i][0]["tokens"].int().cpu(), + ) + if batch_idx == 0 and i < 3: + print(f"T-{sample_id} {tgt_str}") + print(f"H-{sample_id} {hypo_str}") + scorer.add_string(tgt_str, hypo_str) + + print(scorer.result_string() + f" (reference: {reference_score})") + self.assertAlmostEqual(scorer.score(), reference_score, delta=score_delta) + + def postprocess_tokens(self, task, target, hypo_tokens): + tgt_tokens = utils.strip_pad(target, task.tgt_dict.pad()).int().cpu() + tgt_str = task.tgt_dict.string(tgt_tokens, "sentencepiece") + hypo_str = task.tgt_dict.string(hypo_tokens, "sentencepiece") + return tgt_str, hypo_str + + def unzip_files(self, zip_file_name): + zip_file_path = self.root / zip_file_name + with zipfile.ZipFile(zip_file_path, "r") as zip_ref: + zip_ref.extractall(self.root / zip_file_name.strip(".zip")) diff --git a/fairseq/tests/speech/test_convtransformer_simul_trans.py b/fairseq/tests/speech/test_convtransformer_simul_trans.py new file mode 100644 index 0000000..0562404 --- /dev/null +++ b/fairseq/tests/speech/test_convtransformer_simul_trans.py @@ -0,0 +1,33 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from tests.speech import TestFairseqSpeech + +S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq/" + + +class TestConvtransformerSimulTrans(TestFairseqSpeech): + def setUp(self): + self._set_up( + "simul", + "speech_tests/simul", + ["config_gcmvn_specaug.yaml", "dict.txt", "dev.tsv"], + ) + + def test_waitk_checkpoint(self): + """Only test model loading since fairseq currently doesn't support inference of simultaneous models""" + _, _, _, _ = self.download_and_load_checkpoint( + "checkpoint_best.pt", + arg_overrides={ + "config_yaml": "config_gcmvn_specaug.yaml", + "load_pretrained_encoder_from": None, + }, + ) + return + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_dual_input_wav_transformer.py b/fairseq/tests/speech/test_dual_input_wav_transformer.py new file mode 100644 index 0000000..3581bc1 --- /dev/null +++ b/fairseq/tests/speech/test_dual_input_wav_transformer.py @@ -0,0 +1,76 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from collections import namedtuple +from pathlib import Path + +import torch +from tqdm import tqdm + +import fairseq +from fairseq import utils +from fairseq.checkpoint_utils import load_model_ensemble_and_task +from fairseq.scoring.bleu import SacrebleuScorer +from fairseq.tasks import import_tasks +from tests.speech import S3_BASE_URL, TestFairseqSpeech + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestLibrispeechDualInputWavTransformer(TestFairseqSpeech): + def setUp(self): + dataset_id = "librispeech_wvtrasnformer" + base_url = "https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned" + data_filenames = [ + "checkpoint_ave_10.pt", + "spm.model", + "src_dict.txt", + "tgt_dict.txt", + "config.yaml", + ] + self._set_up( + dataset_id, + "s2t", + [ + "librispeech_flac_test-other.tsv", + "librispeech_flac_test-other.zip", + ], + ) + for filename in data_filenames: + self.download(base_url, self.root, filename) + + def import_user_module(self): + user_dir = ( + Path(fairseq.__file__).parent.parent / "examples/speech_text_joint_to_text" + ) + Arg = namedtuple("Arg", ["user_dir"]) + arg = Arg(user_dir.__str__()) + utils.import_user_module(arg) + + @torch.no_grad() + def test_librispeech_dualinput_wav_transformer_checkpoint(self): + self.import_user_module() + checkpoint_filename = "checkpoint_ave_10.pt" + arg_overrides = { + "config_yaml": "config.yaml", + "load_pretrained_speech_text_encoder": "", + "load_pretrained_speech_text_decoder": "", + "beam": 10, + "nbest": 1, + "lenpen": 1.0, + "load_speech_only": True, + } + self.base_test( + checkpoint_filename, + 4.6, + dataset="librispeech_flac_test-other", + max_tokens=800000, + max_positions=(800000, 1024), + arg_overrides=arg_overrides, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_dualinput_s2t_transformer.py b/fairseq/tests/speech/test_dualinput_s2t_transformer.py new file mode 100644 index 0000000..76675b9 --- /dev/null +++ b/fairseq/tests/speech/test_dualinput_s2t_transformer.py @@ -0,0 +1,110 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from argparse import Namespace +from collections import namedtuple +from pathlib import Path + +import torch +from tqdm import tqdm + +import fairseq +from fairseq import utils +from fairseq.checkpoint_utils import load_model_ensemble_and_task +from fairseq.scoring.bleu import SacrebleuScorer +from fairseq.tasks import import_tasks +from tests.speech import TestFairseqSpeech + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestDualInputS2TTransformer(TestFairseqSpeech): + def setUp(self): + self.set_up_mustc_de_fbank() + + def import_user_module(self): + user_dir = ( + Path(fairseq.__file__).parent.parent / "examples/speech_text_joint_to_text" + ) + Arg = namedtuple("Arg", ["user_dir"]) + arg = Arg(user_dir.__str__()) + utils.import_user_module(arg) + + @torch.no_grad() + def test_mustc_de_fbank_dualinput_s2t_transformer_checkpoint(self): + self.import_user_module() + checkpoint_filename = "checkpoint_ave_10.pt" + path = self.download(self.base_url, self.root, checkpoint_filename) + models, cfg, task = load_model_ensemble_and_task( + [path.as_posix()], + arg_overrides={ + "data": self.root.as_posix(), + "config_yaml": "config.yaml", + "load_pretrain_speech_encoder": "", + "load_pretrain_text_encoder_last": "", + "load_pretrain_decoder": "", + "beam": 10, + "nbest": 1, + "lenpen": 1.0, + "load_speech_only": True, + }, + ) + if self.use_cuda: + for model in models: + model.cuda() + generator = task.build_generator(models, cfg) + test_split = "tst-COMMON" + task.load_dataset(test_split) + batch_iterator = task.get_batch_iterator( + dataset=task.dataset(test_split), + max_tokens=250_000, + max_positions=(10_000, 1_024), + num_workers=1, + ).next_epoch_itr(shuffle=False) + + tokenizer = task.build_tokenizer(cfg.tokenizer) + bpe = task.build_bpe(cfg.bpe) + + def decode_fn(x): + if bpe is not None: + x = bpe.decode(x) + if tokenizer is not None: + x = tokenizer.decode(x) + return x + + scorer_args = { + "sacrebleu_tokenizer": "13a", + "sacrebleu_lowercase": False, + "sacrebleu_char_level": False, + } + scorer = SacrebleuScorer(Namespace(**scorer_args)) + progress = tqdm(enumerate(batch_iterator), total=len(batch_iterator)) + for batch_idx, sample in progress: + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + hypo = task.inference_step(generator, models, sample) + for i, sample_id in enumerate(sample["id"].tolist()): + tgt_tokens = ( + utils.strip_pad(sample["target"][i, :], task.tgt_dict.pad()) + .int() + .cpu() + ) + + tgt_str = task.tgt_dict.string(tgt_tokens, "sentencepiece") + hypo_str = task.tgt_dict.string( + hypo[i][0]["tokens"].int().cpu(), "sentencepiece" + ) + if batch_idx == 0 and i < 3: + print(f"T-{sample_id} {tgt_str}") + print(f"D-{sample_id} {hypo_str}") + scorer.add_string(tgt_str, hypo_str) + reference_bleu = 27.3 + result = scorer.result_string() + print(result + f" (reference: {reference_bleu})") + res_bleu = float(result.split()[2]) + self.assertAlmostEqual(res_bleu, reference_bleu, delta=0.3) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_fastspeech2.py b/fairseq/tests/speech/test_fastspeech2.py new file mode 100644 index 0000000..7150a3b --- /dev/null +++ b/fairseq/tests/speech/test_fastspeech2.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from tqdm import tqdm + +from fairseq import utils +from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion +from tests.speech import TestFairseqSpeech + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestFastSpeech2(TestFairseqSpeech): + def setUp(self): + self.set_up_ljspeech() + + @torch.no_grad() + def test_ljspeech_fastspeech2_checkpoint(self): + models, cfg, task, generator = self.download_and_load_checkpoint( + "ljspeech_fastspeech2_g2p.pt", + arg_overrides={ + "config_yaml": "cfg_ljspeech_g2p.yaml", + "vocoder": "griffin_lim", + "fp16": False, + }, + ) + + batch_iterator = self.get_batch_iterator(task, "ljspeech_test", 65_536, 4_096) + progress = tqdm(batch_iterator, total=len(batch_iterator)) + mcd, n_samples = 0.0, 0 + for sample in progress: + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + hypos = generator.generate(models[0], sample, has_targ=True) + rets = batch_mel_cepstral_distortion( + [hypo["targ_waveform"] for hypo in hypos], + [hypo["waveform"] for hypo in hypos], + sr=task.sr, + ) + mcd += sum(d.item() for d, _ in rets) + n_samples += len(sample["id"].tolist()) + + mcd = round(mcd / n_samples, 1) + reference_mcd = 3.2 + print(f"MCD: {mcd} (reference: {reference_mcd})") + self.assertAlmostEqual(mcd, reference_mcd, delta=0.1) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_s2s_transformer.py b/fairseq/tests/speech/test_s2s_transformer.py new file mode 100644 index 0000000..180f463 --- /dev/null +++ b/fairseq/tests/speech/test_s2s_transformer.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from tests.speech import TestFairseqSpeech +from fairseq import utils + +S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq/" + + +class TestS2STransformer(TestFairseqSpeech): + def setUp(self): + self._set_up( + "s2s", + "speech_tests/s2s", + [ + "dev_shuf200.tsv", + "src_feat.zip", + "config_specaug_lb.yaml", + "vocoder", + "vocoder_config.json", + ], + ) + + def test_s2s_transformer_checkpoint(self): + self.base_test( + ckpt_name="s2u_transformer_reduced_fisher.pt", + reference_score=38.3, + dataset="dev_shuf200", + arg_overrides={ + "config_yaml": "config_specaug_lb.yaml", + "multitask_config_yaml": None, + "target_is_code": True, + "target_code_size": 100, + "eval_inference": False, + }, + score_type="bleu", + strict=False, + ) + + def postprocess_tokens(self, task, target, hypo_tokens): + tgt_tokens = utils.strip_pad(target, task.tgt_dict.pad()).int().cpu() + tgt_str = task.tgt_dict.string(tgt_tokens) + hypo_str = task.tgt_dict.string(hypo_tokens) + return tgt_str, hypo_str + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_s2t_conformer.py b/fairseq/tests/speech/test_s2t_conformer.py new file mode 100644 index 0000000..5aaa4a0 --- /dev/null +++ b/fairseq/tests/speech/test_s2t_conformer.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from tests.speech import TestFairseqSpeech + + +class TestS2TConformer(TestFairseqSpeech): + def setUp(self): + self.set_up_librispeech() + + def test_librispeech_s2t_conformer_s_checkpoint(self): + self.base_test( + ckpt_name="librispeech_conformer_rel_pos_s.pt", + reference_score=12, + arg_overrides={"config_yaml": "cfg_librispeech.yaml"}, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_s2t_transformer.py b/fairseq/tests/speech/test_s2t_transformer.py new file mode 100644 index 0000000..172f548 --- /dev/null +++ b/fairseq/tests/speech/test_s2t_transformer.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from tests.speech import TestFairseqSpeech + + +class TestS2TTransformer(TestFairseqSpeech): + def setUp(self): + self.set_up_librispeech() + + def test_librispeech_s2t_transformer_s_checkpoint(self): + self.base_test( + ckpt_name="librispeech_transformer_s.pt", + reference_score=9, + arg_overrides={"config_yaml": "cfg_librispeech.yaml"}, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_tts_transformer.py b/fairseq/tests/speech/test_tts_transformer.py new file mode 100644 index 0000000..b6330c6 --- /dev/null +++ b/fairseq/tests/speech/test_tts_transformer.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from tqdm import tqdm + +from fairseq import utils +from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion +from tests.speech import TestFairseqSpeech + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestTTSTransformer(TestFairseqSpeech): + def setUp(self): + self.set_up_ljspeech() + + @torch.no_grad() + def test_ljspeech_tts_transformer_checkpoint(self): + models, cfg, task, generator = self.download_and_load_checkpoint( + "ljspeech_transformer_g2p.pt", + arg_overrides={ + "config_yaml": "cfg_ljspeech_g2p.yaml", + "vocoder": "griffin_lim", + "fp16": False, + }, + ) + + batch_iterator = self.get_batch_iterator(task, "ljspeech_test", 65_536, 1024) + progress = tqdm(batch_iterator, total=len(batch_iterator)) + mcd, n_samples = 0.0, 0 + for sample in progress: + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + hypos = generator.generate(models[0], sample, has_targ=True) + rets = batch_mel_cepstral_distortion( + [hypo["targ_waveform"] for hypo in hypos], + [hypo["waveform"] for hypo in hypos], + sr=task.sr, + ) + mcd += sum(d.item() for d, _ in rets) + n_samples += len(sample["id"].tolist()) + + mcd = round(mcd / n_samples, 1) + reference_mcd = 3.3 + print(f"MCD: {mcd} (reference: {reference_mcd})") + self.assertAlmostEqual(mcd, reference_mcd, delta=0.1) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_wav2vec2.py b/fairseq/tests/speech/test_wav2vec2.py new file mode 100644 index 0000000..eff6114 --- /dev/null +++ b/fairseq/tests/speech/test_wav2vec2.py @@ -0,0 +1,90 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +import torch +from tests.speech import TestFairseqSpeech +from fairseq.data.data_utils import post_process +from fairseq import utils +from omegaconf import open_dict + +S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq" + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestWav2Vec2(TestFairseqSpeech): + def setUp(self): + self._set_up( + "librispeech_w2v2", + "conformer/wav2vec2/librispeech", + [ + "test_librispeech-other.ltr", + "test_librispeech-other.tsv", + "test_librispeech-other_small.ltr_100", + "test_librispeech-other_small.tsv", + "test-other.zip", + "dict.ltr.txt", + "dict.ltr_100.txt", + ], + ) + self.unzip_files( + "test-other.zip", + ) + + def test_transformer_w2v2(self): + self.base_test( + ckpt_name="transformer_oss_small_100h.pt", + reference_score=38, + score_delta=1, + dataset="test_librispeech-other", + max_tokens=1000000, + max_positions=(700000, 1000), + arg_overrides={ + "task": "audio_finetuning", + "labels": "ltr", + "nbest": 1, + "tpu": False, + }, + strict=False, + ) + + def test_conformer_w2v2(self): + self.base_test( + ckpt_name="conformer_LS_PT_LS_FT_rope.pt", + reference_score=4.5, + score_delta=1, + dataset="test_librispeech-other_small", + max_tokens=1000000, + max_positions=(700000, 1000), + arg_overrides={ + "task": "audio_finetuning", + "labels": "ltr_100", + "nbest": 1, + "tpu": False, + }, + strict=True, + ) + + def build_generator(self, task, models, cfg): + try: + from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder + except Exception: + raise Exception("Cannot run this test without flashlight dependency") + with open_dict(cfg): + cfg.nbest = 1 + return W2lViterbiDecoder(cfg, task.target_dictionary) + + def postprocess_tokens(self, task, target, hypo_tokens): + tgt_tokens = utils.strip_pad(target, task.target_dictionary.pad()).int().cpu() + tgt_str = task.target_dictionary.string(tgt_tokens) + tgt_str = post_process(tgt_str, "letter") + + hypo_pieces = task.target_dictionary.string(hypo_tokens) + hypo_str = post_process(hypo_pieces, "letter") + return tgt_str, hypo_str + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech/test_xm_transformer.py b/fairseq/tests/speech/test_xm_transformer.py new file mode 100644 index 0000000..0a55094 --- /dev/null +++ b/fairseq/tests/speech/test_xm_transformer.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from tests.speech import TestFairseqSpeech + + +class TestXMTransformer(TestFairseqSpeech): + def setUp(self): + self.set_up_sotasty_es_en() + + # TODO: investigate increases BLEU score (30.42 -> 31.74) + def test_sotasty_es_en_600m_checkpoint(self): + self.base_test( + ckpt_name="xm_transformer_600m_es_en_md.pt", + reference_score=31.74, + score_delta=0.2, + max_tokens=3_000_000, + max_positions=(1_000_000, 1_024), + dataset="sotasty_es_en_test_ted", + arg_overrides={"config_yaml": "cfg_es_en.yaml"}, + score_type="bleu", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech_recognition/__init__.py b/fairseq/tests/speech_recognition/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fairseq/tests/speech_recognition/asr_test_base.py b/fairseq/tests/speech_recognition/asr_test_base.py new file mode 100644 index 0000000..8c5d414 --- /dev/null +++ b/fairseq/tests/speech_recognition/asr_test_base.py @@ -0,0 +1,557 @@ +#!/usr/bin/env python3 + +import argparse +import os +import unittest +from inspect import currentframe, getframeinfo + +import numpy as np +import torch +from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask +from fairseq.data import data_utils as fairseq_data_utils +from fairseq.data.dictionary import Dictionary +from fairseq.models import ( + BaseFairseqModel, + FairseqDecoder, + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqEncoderModel, + FairseqModel, +) +from fairseq.tasks.fairseq_task import LegacyFairseqTask + + +DEFAULT_TEST_VOCAB_SIZE = 100 + + +# /////////////////////////////////////////////////////////////////////////// +# utility function to setup dummy dict/task/input +# /////////////////////////////////////////////////////////////////////////// + + +def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): + dummy_dict = Dictionary() + # add dummy symbol to satisfy vocab size + for id, _ in enumerate(range(vocab_size)): + dummy_dict.add_symbol("{}".format(id), 1000) + return dummy_dict + + +class DummyTask(LegacyFairseqTask): + def __init__(self, args): + super().__init__(args) + self.dictionary = get_dummy_dictionary() + if getattr(self.args, "ctc", False): + self.dictionary.add_symbol("<ctc_blank>") + self.tgt_dict = self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + +def get_dummy_task_and_parser(): + """ + to build a fariseq model, we need some dummy parse and task. This function + is used to create dummy task and parser to faciliate model/criterion test + + Note: we use FbSpeechRecognitionTask as the dummy task. You may want + to use other task by providing another function + """ + parser = argparse.ArgumentParser( + description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS + ) + DummyTask.add_args(parser) + args = parser.parse_args([]) + task = DummyTask.setup_task(args) + return task, parser + + +def get_dummy_input(T=100, D=80, B=5, K=100): + forward_input = {} + # T max sequence length + # D feature vector dimension + # B batch size + # K target dimension size + feature = torch.randn(B, T, D) + # this (B, T, D) layout is just a convention, you can override it by + # write your own _prepare_forward_input function + src_lengths = torch.from_numpy( + np.random.randint(low=1, high=T, size=B, dtype=np.int64) + ) + src_lengths[0] = T # make sure the maximum length matches + prev_output_tokens = [] + for b in range(B): + token_length = np.random.randint(low=1, high=src_lengths[b].item() + 1) + tokens = np.random.randint(low=0, high=K, size=token_length, dtype=np.int64) + prev_output_tokens.append(torch.from_numpy(tokens)) + + prev_output_tokens = fairseq_data_utils.collate_tokens( + prev_output_tokens, + pad_idx=1, + eos_idx=2, + left_pad=False, + move_eos_to_beginning=False, + ) + src_lengths, sorted_order = src_lengths.sort(descending=True) + forward_input["src_tokens"] = feature.index_select(0, sorted_order) + forward_input["src_lengths"] = src_lengths + forward_input["prev_output_tokens"] = prev_output_tokens + + return forward_input + + +def get_dummy_encoder_output(encoder_out_shape=(100, 80, 5)): + """ + This only provides an example to generate dummy encoder output + """ + (T, B, D) = encoder_out_shape + encoder_out = {} + + encoder_out["encoder_out"] = torch.from_numpy( + np.random.randn(*encoder_out_shape).astype(np.float32) + ) + seq_lengths = torch.from_numpy(np.random.randint(low=1, high=T, size=B)) + # some dummy mask + encoder_out["encoder_padding_mask"] = torch.arange(T).view(1, T).expand( + B, -1 + ) >= seq_lengths.view(B, 1).expand(-1, T) + encoder_out["encoder_padding_mask"].t_() + + # encoer_padding_mask is (T, B) tensor, with (t, b)-th element indicate + # whether encoder_out[t, b] is valid (=0) or not (=1) + return encoder_out + + +def _current_postion_info(): + cf = currentframe() + frameinfo = " (at {}:{})".format( + os.path.basename(getframeinfo(cf).filename), cf.f_back.f_lineno + ) + return frameinfo + + +def check_encoder_output(encoder_output, batch_size=None): + """we expect encoder_output to be a dict with the following + key/value pairs: + - encoder_out: a Torch.Tensor + - encoder_padding_mask: a binary Torch.Tensor + """ + if not isinstance(encoder_output, dict): + msg = ( + "FairseqEncoderModel.forward(...) must be a dict" + _current_postion_info() + ) + return False, msg + + if "encoder_out" not in encoder_output: + msg = ( + "FairseqEncoderModel.forward(...) must contain encoder_out" + + _current_postion_info() + ) + return False, msg + + if "encoder_padding_mask" not in encoder_output: + msg = ( + "FairseqEncoderModel.forward(...) must contain encoder_padding_mask" + + _current_postion_info() + ) + return False, msg + + if not isinstance(encoder_output["encoder_out"], torch.Tensor): + msg = "encoder_out must be a torch.Tensor" + _current_postion_info() + return False, msg + + if encoder_output["encoder_out"].dtype != torch.float32: + msg = "encoder_out must have float32 dtype" + _current_postion_info() + return False, msg + + mask = encoder_output["encoder_padding_mask"] + if mask is not None: + if not isinstance(mask, torch.Tensor): + msg = ( + "encoder_padding_mask must be a torch.Tensor" + _current_postion_info() + ) + return False, msg + if mask.dtype != torch.uint8 and ( + not hasattr(torch, "bool") or mask.dtype != torch.bool + ): + msg = ( + "encoder_padding_mask must have dtype of uint8" + + _current_postion_info() + ) + return False, msg + + if mask.dim() != 2: + msg = ( + "we expect encoder_padding_mask to be a 2-d tensor, in shape (T, B)" + + _current_postion_info() + ) + return False, msg + + if batch_size is not None and mask.size(1) != batch_size: + msg = ( + "we expect encoder_padding_mask to be a 2-d tensor, with size(1)" + + " being the batch size" + + _current_postion_info() + ) + return False, msg + return True, None + + +def check_decoder_output(decoder_output): + """we expect output from a decoder is a tuple with the following constraint: + - the first element is a torch.Tensor + - the second element can be anything (reserved for future use) + """ + if not isinstance(decoder_output, tuple): + msg = "FariseqDecoder output must be a tuple" + _current_postion_info() + return False, msg + + if len(decoder_output) != 2: + msg = "FairseqDecoder output must be 2-elem tuple" + _current_postion_info() + return False, msg + + if not isinstance(decoder_output[0], torch.Tensor): + msg = ( + "FariseqDecoder output[0] must be a torch.Tensor" + _current_postion_info() + ) + return False, msg + + return True, None + + +# /////////////////////////////////////////////////////////////////////////// +# Base Test class +# /////////////////////////////////////////////////////////////////////////// + + +class TestBaseFairseqModelBase(unittest.TestCase): + """ + This class is used to facilitate writing unittest for any class derived from + `BaseFairseqModel`. + """ + + @classmethod + def setUpClass(cls): + if cls is TestBaseFairseqModelBase: + raise unittest.SkipTest("Skipping test case in base") + super().setUpClass() + + def setUpModel(self, model): + self.assertTrue(isinstance(model, BaseFairseqModel)) + self.model = model + + def setupInput(self): + pass + + def setUp(self): + self.model = None + self.forward_input = None + pass + + +class TestFairseqEncoderDecoderModelBase(TestBaseFairseqModelBase): + """ + base code to test FairseqEncoderDecoderModel (formally known as + `FairseqModel`) must be derived from this base class + """ + + @classmethod + def setUpClass(cls): + if cls is TestFairseqEncoderDecoderModelBase: + raise unittest.SkipTest("Skipping test case in base") + super().setUpClass() + + def setUpModel(self, model_cls, extra_args_setters=None): + self.assertTrue( + issubclass(model_cls, (FairseqEncoderDecoderModel, FairseqModel)), + msg="This class only tests for FairseqModel subclasses", + ) + + task, parser = get_dummy_task_and_parser() + model_cls.add_args(parser) + + args = parser.parse_args([]) + + if extra_args_setters is not None: + for args_setter in extra_args_setters: + args_setter(args) + model = model_cls.build_model(args, task) + self.model = model + + def setUpInput(self, input=None): + self.forward_input = get_dummy_input() if input is None else input + + def setUp(self): + super().setUp() + + def test_forward(self): + if self.model and self.forward_input: + forward_output = self.model.forward(**self.forward_input) + # for FairseqEncoderDecoderModel, forward returns a tuple of two + # elements, the first one is a Torch.Tensor + succ, msg = check_decoder_output(forward_output) + if not succ: + self.assertTrue(succ, msg=msg) + self.forward_output = forward_output + + def test_get_normalized_probs(self): + if self.model and self.forward_input: + forward_output = self.model.forward(**self.forward_input) + logprob = self.model.get_normalized_probs(forward_output, log_probs=True) + prob = self.model.get_normalized_probs(forward_output, log_probs=False) + + # in order for different models/criterion to play with each other + # we need to know whether the logprob or prob output is batch_first + # or not. We assume an additional attribute will be attached to logprob + # or prob. If you find your code failed here, simply override + # FairseqModel.get_normalized_probs, see example at + # https://fburl.com/batch_first_example + self.assertTrue(hasattr(logprob, "batch_first")) + self.assertTrue(hasattr(prob, "batch_first")) + + self.assertTrue(torch.is_tensor(logprob)) + self.assertTrue(torch.is_tensor(prob)) + + +class TestFairseqEncoderModelBase(TestBaseFairseqModelBase): + """ + base class to test FairseqEncoderModel + """ + + @classmethod + def setUpClass(cls): + if cls is TestFairseqEncoderModelBase: + raise unittest.SkipTest("Skipping test case in base") + super().setUpClass() + + def setUpModel(self, model_cls, extra_args_setters=None): + self.assertTrue( + issubclass(model_cls, FairseqEncoderModel), + msg="This class is only used for testing FairseqEncoderModel", + ) + task, parser = get_dummy_task_and_parser() + model_cls.add_args(parser) + args = parser.parse_args([]) + if extra_args_setters is not None: + for args_setter in extra_args_setters: + args_setter(args) + + model = model_cls.build_model(args, task) + self.model = model + + def setUpInput(self, input=None): + self.forward_input = get_dummy_input() if input is None else input + # get_dummy_input() is originally for s2s, here we delete extra dict + # items, so it can be used for EncoderModel / Encoder as well + self.forward_input.pop("prev_output_tokens", None) + + def setUp(self): + super().setUp() + + def test_forward(self): + if self.forward_input and self.model: + bsz = self.forward_input["src_tokens"].size(0) + forward_output = self.model.forward(**self.forward_input) + + # we expect forward_output to be a dict with the following + # key/value pairs: + # - encoder_out: a Torch.Tensor + # - encoder_padding_mask: a binary Torch.Tensor + succ, msg = check_encoder_output(forward_output, batch_size=bsz) + if not succ: + self.assertTrue(succ, msg=msg) + self.forward_output = forward_output + + def test_get_normalized_probs(self): + if self.model and self.forward_input: + forward_output = self.model.forward(**self.forward_input) + logprob = self.model.get_normalized_probs(forward_output, log_probs=True) + prob = self.model.get_normalized_probs(forward_output, log_probs=False) + + # in order for different models/criterion to play with each other + # we need to know whether the logprob or prob output is batch_first + # or not. We assume an additional attribute will be attached to logprob + # or prob. If you find your code failed here, simply override + # FairseqModel.get_normalized_probs, see example at + # https://fburl.com/batch_first_example + self.assertTrue(hasattr(logprob, "batch_first")) + self.assertTrue(hasattr(prob, "batch_first")) + + self.assertTrue(torch.is_tensor(logprob)) + self.assertTrue(torch.is_tensor(prob)) + + +class TestFairseqEncoderBase(unittest.TestCase): + """ + base class to test FairseqEncoder + """ + + @classmethod + def setUpClass(cls): + if cls is TestFairseqEncoderBase: + raise unittest.SkipTest("Skipping test case in base") + super().setUpClass() + + def setUpEncoder(self, encoder): + self.assertTrue( + isinstance(encoder, FairseqEncoder), + msg="This class is only used for test FairseqEncoder", + ) + self.encoder = encoder + + def setUpInput(self, input=None): + self.forward_input = get_dummy_input() if input is None else input + # get_dummy_input() is originally for s2s, here we delete extra dict + # items, so it can be used for EncoderModel / Encoder as well + self.forward_input.pop("prev_output_tokens", None) + + def setUp(self): + self.encoder = None + self.forward_input = None + + def test_forward(self): + if self.encoder and self.forward_input: + bsz = self.forward_input["src_tokens"].size(0) + + forward_output = self.encoder.forward(**self.forward_input) + succ, msg = check_encoder_output(forward_output, batch_size=bsz) + if not succ: + self.assertTrue(succ, msg=msg) + self.forward_output = forward_output + + +class TestFairseqDecoderBase(unittest.TestCase): + """ + base class to test FairseqDecoder + """ + + @classmethod + def setUpClass(cls): + if cls is TestFairseqDecoderBase: + raise unittest.SkipTest("Skipping test case in base") + super().setUpClass() + + def setUpDecoder(self, decoder): + self.assertTrue( + isinstance(decoder, FairseqDecoder), + msg="This class is only used for test FairseqDecoder", + ) + self.decoder = decoder + + def setUpInput(self, input=None): + self.forward_input = get_dummy_encoder_output() if input is None else input + + def setUpPrevOutputTokens(self, tokens=None): + if tokens is None: + self.encoder_input = get_dummy_input() + self.prev_output_tokens = self.encoder_input["prev_output_tokens"] + else: + self.prev_output_tokens = tokens + + def setUp(self): + self.decoder = None + self.forward_input = None + self.prev_output_tokens = None + + def test_forward(self): + if ( + self.decoder is not None + and self.forward_input is not None + and self.prev_output_tokens is not None + ): + forward_output = self.decoder.forward( + prev_output_tokens=self.prev_output_tokens, + encoder_out=self.forward_input, + ) + succ, msg = check_decoder_output(forward_output) + if not succ: + self.assertTrue(succ, msg=msg) + self.forward_input = forward_output + + +class DummyEncoderModel(FairseqEncoderModel): + def __init__(self, encoder): + super().__init__(encoder) + + @classmethod + def build_model(cls, args, task): + return cls(DummyEncoder()) + + def get_logits(self, net_output): + # Inverse of sigmoid to use with BinaryCrossEntropyWithLogitsCriterion as + # F.binary_cross_entropy_with_logits combines sigmoid and CE + return torch.log( + torch.div(net_output["encoder_out"], 1 - net_output["encoder_out"]) + ) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + lprobs = super().get_normalized_probs(net_output, log_probs, sample=sample) + lprobs.batch_first = True + return lprobs + + +class DummyEncoder(FairseqEncoder): + def __init__(self): + super().__init__(None) + + def forward(self, src_tokens, src_lengths): + mask, max_len = lengths_to_encoder_padding_mask(src_lengths) + return {"encoder_out": src_tokens, "encoder_padding_mask": mask} + + +class CrossEntropyCriterionTestBase(unittest.TestCase): + @classmethod + def setUpClass(cls): + if cls is CrossEntropyCriterionTestBase: + raise unittest.SkipTest("Skipping base class test case") + super().setUpClass() + + def setUpArgs(self): + args = argparse.Namespace() + args.sentence_avg = False + args.threshold = 0.1 # to use with BinaryCrossEntropyWithLogitsCriterion + return args + + def setUp(self): + args = self.setUpArgs() + self.model = DummyEncoderModel(encoder=DummyEncoder()) + self.criterion = self.criterion_cls.build_criterion(args, task=DummyTask(args)) + + def get_src_tokens(self, correct_prediction, aggregate): + """ + correct_prediction: True if the net_output (src_tokens) should + predict the correct target + aggregate: True if the criterion expects net_output (src_tokens) + aggregated across time axis + """ + predicted_idx = 0 if correct_prediction else 1 + if aggregate: + src_tokens = torch.zeros((2, 2), dtype=torch.float) + for b in range(2): + src_tokens[b][predicted_idx] = 1.0 + else: + src_tokens = torch.zeros((2, 10, 2), dtype=torch.float) + for b in range(2): + for t in range(10): + src_tokens[b][t][predicted_idx] = 1.0 + return src_tokens + + def get_target(self, soft_target): + if soft_target: + target = torch.zeros((2, 2), dtype=torch.float) + for b in range(2): + target[b][0] = 1.0 + else: + target = torch.zeros((2, 10), dtype=torch.long) + return target + + def get_test_sample(self, correct, soft_target, aggregate): + src_tokens = self.get_src_tokens(correct, aggregate) + target = self.get_target(soft_target) + L = src_tokens.size(1) + return { + "net_input": {"src_tokens": src_tokens, "src_lengths": torch.tensor([L])}, + "target": target, + "ntokens": src_tokens.size(0) * src_tokens.size(1), + } diff --git a/fairseq/tests/speech_recognition/test_collaters.py b/fairseq/tests/speech_recognition/test_collaters.py new file mode 100644 index 0000000..6a5029a --- /dev/null +++ b/fairseq/tests/speech_recognition/test_collaters.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import numpy as np +import torch +from examples.speech_recognition.data.collaters import Seq2SeqCollater + + +class TestSeq2SeqCollator(unittest.TestCase): + def test_collate(self): + + eos_idx = 1 + pad_idx = 0 + collater = Seq2SeqCollater( + feature_index=0, label_index=1, pad_index=pad_idx, eos_index=eos_idx + ) + + # 2 frames in the first sample and 3 frames in the second one + frames1 = np.array([[7, 8], [9, 10]]) + frames2 = np.array([[1, 2], [3, 4], [5, 6]]) + target1 = np.array([4, 2, 3, eos_idx]) + target2 = np.array([3, 2, eos_idx]) + sample1 = {"id": 0, "data": [frames1, target1]} + sample2 = {"id": 1, "data": [frames2, target2]} + batch = collater.collate([sample1, sample2]) + + # collate sort inputs by frame's length before creating the batch + self.assertTensorEqual(batch["id"], torch.tensor([1, 0])) + self.assertEqual(batch["ntokens"], 7) + self.assertTensorEqual( + batch["net_input"]["src_tokens"], + torch.tensor( + [[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [pad_idx, pad_idx]]] + ), + ) + self.assertTensorEqual( + batch["net_input"]["prev_output_tokens"], + torch.tensor([[eos_idx, 3, 2, pad_idx], [eos_idx, 4, 2, 3]]), + ) + self.assertTensorEqual(batch["net_input"]["src_lengths"], torch.tensor([3, 2])) + self.assertTensorEqual( + batch["target"], + torch.tensor([[3, 2, eos_idx, pad_idx], [4, 2, 3, eos_idx]]), + ) + self.assertEqual(batch["nsentences"], 2) + + def assertTensorEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertEqual(t1.ne(t2).long().sum(), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/speech_recognition/test_cross_entropy.py b/fairseq/tests/speech_recognition/test_cross_entropy.py new file mode 100644 index 0000000..b05400e --- /dev/null +++ b/fairseq/tests/speech_recognition/test_cross_entropy.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from examples.speech_recognition.criterions.cross_entropy_acc import ( + CrossEntropyWithAccCriterion, +) + +from .asr_test_base import CrossEntropyCriterionTestBase + + +class CrossEntropyWithAccCriterionTest(CrossEntropyCriterionTestBase): + def setUp(self): + self.criterion_cls = CrossEntropyWithAccCriterion + super().setUp() + + def test_cross_entropy_all_correct(self): + sample = self.get_test_sample(correct=True, soft_target=False, aggregate=False) + loss, sample_size, logging_output = self.criterion( + self.model, sample, "sum", log_probs=True + ) + assert logging_output["correct"] == 20 + assert logging_output["total"] == 20 + assert logging_output["sample_size"] == 20 + assert logging_output["ntokens"] == 20 + + def test_cross_entropy_all_wrong(self): + sample = self.get_test_sample(correct=False, soft_target=False, aggregate=False) + loss, sample_size, logging_output = self.criterion( + self.model, sample, "sum", log_probs=True + ) + assert logging_output["correct"] == 0 + assert logging_output["total"] == 20 + assert logging_output["sample_size"] == 20 + assert logging_output["ntokens"] == 20 diff --git a/fairseq/tests/speech_recognition/test_data_utils.py b/fairseq/tests/speech_recognition/test_data_utils.py new file mode 100644 index 0000000..a72e0b6 --- /dev/null +++ b/fairseq/tests/speech_recognition/test_data_utils.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import unittest + +import torch +from examples.speech_recognition.data import data_utils + + +class DataUtilsTest(unittest.TestCase): + def test_normalization(self): + sample_len1 = torch.tensor( + [ + [ + -0.7661, + -1.3889, + -2.0972, + -0.9134, + -0.7071, + -0.9765, + -0.8700, + -0.8283, + 0.7512, + 1.3211, + 2.1532, + 2.1174, + 1.2800, + 1.2633, + 1.6147, + 1.6322, + 2.0723, + 3.1522, + 3.2852, + 2.2309, + 2.5569, + 2.2183, + 2.2862, + 1.5886, + 0.8773, + 0.8725, + 1.2662, + 0.9899, + 1.1069, + 1.3926, + 1.2795, + 1.1199, + 1.1477, + 1.2687, + 1.3843, + 1.1903, + 0.8355, + 1.1367, + 1.2639, + 1.4707, + ] + ] + ) + out = data_utils.apply_mv_norm(sample_len1) + assert not torch.isnan(out).any() + assert (out == sample_len1).all() diff --git a/fairseq/tests/speech_recognition/test_vggtransformer.py b/fairseq/tests/speech_recognition/test_vggtransformer.py new file mode 100644 index 0000000..4dc73b8 --- /dev/null +++ b/fairseq/tests/speech_recognition/test_vggtransformer.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python3 + +# import models/encoder/decoder to be tested +from examples.speech_recognition.models.vggtransformer import ( + TransformerDecoder, + VGGTransformerEncoder, + VGGTransformerModel, + vggtransformer_1, + vggtransformer_2, + vggtransformer_base, +) + +# import base test class +from .asr_test_base import ( + DEFAULT_TEST_VOCAB_SIZE, + TestFairseqDecoderBase, + TestFairseqEncoderBase, + TestFairseqEncoderDecoderModelBase, + get_dummy_dictionary, + get_dummy_encoder_output, + get_dummy_input, +) + + +class VGGTransformerModelTest_mid(TestFairseqEncoderDecoderModelBase): + def setUp(self): + def override_config(args): + """ + vggtrasformer_1 use 14 layers of transformer, + for testing purpose, it is too expensive. For fast turn-around + test, reduce the number of layers to 3. + """ + args.transformer_enc_config = ( + "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" + ) + + super().setUp() + extra_args_setter = [vggtransformer_1, override_config] + + self.setUpModel(VGGTransformerModel, extra_args_setter) + self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) + + +class VGGTransformerModelTest_big(TestFairseqEncoderDecoderModelBase): + def setUp(self): + def override_config(args): + """ + vggtrasformer_2 use 16 layers of transformer, + for testing purpose, it is too expensive. For fast turn-around + test, reduce the number of layers to 3. + """ + args.transformer_enc_config = ( + "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" + ) + + super().setUp() + extra_args_setter = [vggtransformer_2, override_config] + + self.setUpModel(VGGTransformerModel, extra_args_setter) + self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) + + +class VGGTransformerModelTest_base(TestFairseqEncoderDecoderModelBase): + def setUp(self): + def override_config(args): + """ + vggtrasformer_base use 12 layers of transformer, + for testing purpose, it is too expensive. For fast turn-around + test, reduce the number of layers to 3. + """ + args.transformer_enc_config = ( + "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 3" + ) + + super().setUp() + extra_args_setter = [vggtransformer_base, override_config] + + self.setUpModel(VGGTransformerModel, extra_args_setter) + self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) + + +class VGGTransformerEncoderTest(TestFairseqEncoderBase): + def setUp(self): + super().setUp() + + self.setUpInput(get_dummy_input(T=50, D=80, B=5)) + + def test_forward(self): + print("1. test standard vggtransformer") + self.setUpEncoder(VGGTransformerEncoder(input_feat_per_channel=80)) + super().test_forward() + print("2. test vggtransformer with limited right context") + self.setUpEncoder( + VGGTransformerEncoder( + input_feat_per_channel=80, transformer_context=(-1, 5) + ) + ) + super().test_forward() + print("3. test vggtransformer with limited left context") + self.setUpEncoder( + VGGTransformerEncoder( + input_feat_per_channel=80, transformer_context=(5, -1) + ) + ) + super().test_forward() + print("4. test vggtransformer with limited right context and sampling") + self.setUpEncoder( + VGGTransformerEncoder( + input_feat_per_channel=80, + transformer_context=(-1, 12), + transformer_sampling=(2, 2), + ) + ) + super().test_forward() + print("5. test vggtransformer with windowed context and sampling") + self.setUpEncoder( + VGGTransformerEncoder( + input_feat_per_channel=80, + transformer_context=(12, 12), + transformer_sampling=(2, 2), + ) + ) + + +class TransformerDecoderTest(TestFairseqDecoderBase): + def setUp(self): + super().setUp() + + dict = get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE) + decoder = TransformerDecoder(dict) + dummy_encoder_output = get_dummy_encoder_output(encoder_out_shape=(50, 5, 256)) + + self.setUpDecoder(decoder) + self.setUpInput(dummy_encoder_output) + self.setUpPrevOutputTokens() diff --git a/fairseq/tests/tasks/test_denoising.py b/fairseq/tests/tasks/test_denoising.py new file mode 100644 index 0000000..5c22168 --- /dev/null +++ b/fairseq/tests/tasks/test_denoising.py @@ -0,0 +1,96 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import unittest +from tempfile import TemporaryDirectory + +from fairseq import options +from fairseq.binarizer import FileBinarizer, VocabularyDatasetBinarizer +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.tasks.denoising import DenoisingTask +from tests.utils import build_vocab, make_data + + +class TestDenoising(unittest.TestCase): + def test_denoising(self): + with TemporaryDirectory() as dirname: + + # prep input file + raw_file = os.path.join(dirname, "raw") + data = make_data(out_file=raw_file) + vocab = build_vocab(data) + + # binarize + binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False) + split = "train" + bin_file = os.path.join(dirname, split) + dataset_impl = "mmap" + FileBinarizer.multiprocess_dataset( + input_file=raw_file, + binarizer=binarizer, + dataset_impl=dataset_impl, + vocab_size=len(vocab), + output_prefix=bin_file, + ) + + # setup task + train_args = options.parse_args_and_arch( + options.get_training_parser(), + [ + "--task", + "denoising", + "--arch", + "bart_base", + "--seed", + "42", + "--mask-length", + "word", + "--permute-sentences", + "1", + "--rotate", + "0", + "--replace-length", + "-1", + "--mask", + "0.2", + dirname, + ], + ) + cfg = convert_namespace_to_omegaconf(train_args) + task = DenoisingTask(cfg.task, binarizer.dict) + + # load datasets + original_dataset = task._load_dataset_split(bin_file, 1, False) + task.load_dataset(split) + masked_dataset = task.dataset(split) + + iterator = task.get_batch_iterator( + dataset=masked_dataset, + max_tokens=65_536, + max_positions=4_096, + ).next_epoch_itr(shuffle=False) + mask_index = task.source_dictionary.index("<mask>") + for batch in iterator: + for sample in range(len(batch)): + net_input = batch["net_input"] + masked_src_tokens = net_input["src_tokens"][sample] + masked_src_length = net_input["src_lengths"][sample] + masked_tgt_tokens = batch["target"][sample] + + sample_id = batch["id"][sample] + original_tokens = original_dataset[sample_id] + original_tokens = original_tokens.masked_select( + masked_src_tokens[:masked_src_length] == mask_index + ) + masked_tokens = masked_tgt_tokens.masked_select( + masked_src_tokens == mask_index + ) + + assert masked_tokens.equal(original_tokens) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/tasks/test_masked_lm.py b/fairseq/tests/tasks/test_masked_lm.py new file mode 100644 index 0000000..215cd35 --- /dev/null +++ b/fairseq/tests/tasks/test_masked_lm.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import unittest +from tempfile import TemporaryDirectory + +from fairseq.binarizer import FileBinarizer, VocabularyDatasetBinarizer +from fairseq.tasks.masked_lm import MaskedLMConfig, MaskedLMTask +from tests.utils import build_vocab, make_data + + +class TestMaskedLM(unittest.TestCase): + def test_masks_tokens(self): + with TemporaryDirectory() as dirname: + + # prep input file + raw_file = os.path.join(dirname, "raw") + data = make_data(out_file=raw_file) + vocab = build_vocab(data) + + # binarize + binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False) + split = "train" + bin_file = os.path.join(dirname, split) + FileBinarizer.multiprocess_dataset( + input_file=raw_file, + binarizer=binarizer, + dataset_impl="mmap", + vocab_size=len(vocab), + output_prefix=bin_file, + ) + + # setup task + cfg = MaskedLMConfig( + data=dirname, + seed=42, + mask_prob=0.5, # increasing the odds of masking + random_token_prob=0, # avoiding random tokens for exact match + leave_unmasked_prob=0, # always masking for exact match + ) + task = MaskedLMTask(cfg, binarizer.dict) + + original_dataset = task._load_dataset_split(bin_file, 1, False) + + # load datasets + task.load_dataset(split) + masked_dataset = task.dataset(split) + + mask_index = task.source_dictionary.index("<mask>") + iterator = task.get_batch_iterator( + dataset=masked_dataset, + max_tokens=65_536, + max_positions=4_096, + ).next_epoch_itr(shuffle=False) + for batch in iterator: + for sample in range(len(batch)): + net_input = batch["net_input"] + masked_src_tokens = net_input["src_tokens"][sample] + masked_src_length = net_input["src_lengths"][sample] + masked_tgt_tokens = batch["target"][sample] + + sample_id = batch["id"][sample] + original_tokens = original_dataset[sample_id] + original_tokens = original_tokens.masked_select( + masked_src_tokens[:masked_src_length] == mask_index + ) + masked_tokens = masked_tgt_tokens.masked_select( + masked_tgt_tokens != task.source_dictionary.pad() + ) + + assert masked_tokens.equal(original_tokens) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/tasks/test_multilingual_denoising.py b/fairseq/tests/tasks/test_multilingual_denoising.py new file mode 100644 index 0000000..a0227f6 --- /dev/null +++ b/fairseq/tests/tasks/test_multilingual_denoising.py @@ -0,0 +1,98 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import unittest +from tempfile import TemporaryDirectory + +from fairseq import options +from fairseq.binarizer import FileBinarizer, VocabularyDatasetBinarizer +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.tasks.multilingual_denoising import MultilingualDenoisingTask +from tests.utils import build_vocab, make_data + + +class TestMultilingualDenoising(unittest.TestCase): + def test_multilingual_denoising(self): + with TemporaryDirectory() as dirname: + + # prep input file + lang_dir = os.path.join(dirname, "en") + os.mkdir(lang_dir) + raw_file = os.path.join(lang_dir, "raw") + data = make_data(out_file=raw_file) + vocab = build_vocab(data) + + # binarize + binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False) + split = "train" + bin_file = os.path.join(lang_dir, split) + dataset_impl = "mmap" + FileBinarizer.multiprocess_dataset( + input_file=raw_file, + binarizer=binarizer, + dataset_impl=dataset_impl, + vocab_size=len(vocab), + output_prefix=bin_file, + ) + + # setup task + train_args = options.parse_args_and_arch( + options.get_training_parser(), + [ + "--task", + "multilingual_denoising", + "--arch", + "bart_base", + "--seed", + "42", + "--mask-length", + "word", + "--permute-sentences", + "1", + "--rotate", + "0", + "--replace-length", + "-1", + "--mask", + "0.2", + dirname, + ], + ) + cfg = convert_namespace_to_omegaconf(train_args) + task = MultilingualDenoisingTask(cfg.task, binarizer.dict) + + # load datasets + original_dataset = task._load_dataset_split(bin_file, 1, False) + task.load_dataset(split) + masked_dataset = task.dataset(split) + + iterator = task.get_batch_iterator( + dataset=masked_dataset, + max_tokens=65_536, + max_positions=4_096, + ).next_epoch_itr(shuffle=False) + mask_index = task.source_dictionary.index("<mask>") + for batch in iterator: + for sample in range(len(batch)): + net_input = batch["net_input"] + masked_src_tokens = net_input["src_tokens"][sample] + masked_src_length = net_input["src_lengths"][sample] + masked_tgt_tokens = batch["target"][sample] + + sample_id = batch["id"][sample] + original_tokens = original_dataset[sample_id] + original_tokens = original_tokens.masked_select( + masked_src_tokens[:masked_src_length] == mask_index + ) + masked_tokens = masked_tgt_tokens.masked_select( + masked_src_tokens == mask_index + ) + + assert masked_tokens.equal(original_tokens) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/tasks/test_span_masked_lm.py b/fairseq/tests/tasks/test_span_masked_lm.py new file mode 100644 index 0000000..d289cf8 --- /dev/null +++ b/fairseq/tests/tasks/test_span_masked_lm.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import unittest +from tempfile import TemporaryDirectory + +from fairseq import options +from fairseq.binarizer import FileBinarizer, VocabularyDatasetBinarizer +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.tasks.span_masked_lm import SpanMaskedLMTask +from tests.utils import build_vocab, make_data + + +class TestSpanMaskedLM(unittest.TestCase): + def test_masks_token_spans(self): + with TemporaryDirectory() as dirname: + + # prep input file + raw_file = os.path.join(dirname, "raw") + data = make_data(out_file=raw_file) + vocab = build_vocab(data) + + # binarize + binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False) + split = "train" + bin_file = os.path.join(dirname, split) + dataset_impl = "mmap" + + FileBinarizer.multiprocess_dataset( + input_file=raw_file, + binarizer=binarizer, + dataset_impl=dataset_impl, + vocab_size=len(vocab), + output_prefix=bin_file, + ) + + # adding sentinel tokens + for i in range(100): + vocab.add_symbol(f"<extra_id_{i}>") + + # setup task + train_args = options.parse_args_and_arch( + options.get_training_parser(), + [ + "--task", + "span_masked_lm", + "--arch", + "bart_base", + "--seed", + "42", + dirname, + ], + ) + cfg = convert_namespace_to_omegaconf(train_args) + task = SpanMaskedLMTask(cfg.task, binarizer.dict) + + # load datasets + original_dataset = task._load_dataset_split(bin_file, 1, False) + task.load_dataset(split) + masked_dataset = task.dataset(split) + + iterator = task.get_batch_iterator( + dataset=masked_dataset, + max_tokens=65_536, + max_positions=4_096, + ).next_epoch_itr(shuffle=False) + num_tokens = len(vocab) + for batch in iterator: + for sample in range(len(batch)): + sample_id = batch["id"][sample] + original_tokens = original_dataset[sample_id] + masked_src_tokens = batch["net_input"]["src_tokens"][sample] + masked_src_length = batch["net_input"]["src_lengths"][sample] + masked_tgt_tokens = batch["target"][sample] + + original_offset = 0 + masked_tgt_offset = 0 + extra_id_token = len(vocab) - 1 + for masked_src_token in masked_src_tokens[:masked_src_length]: + if masked_src_token == extra_id_token: + assert ( + masked_src_token == masked_tgt_tokens[masked_tgt_offset] + ) + extra_id_token -= 1 + masked_tgt_offset += 1 + while ( + original_offset < len(original_tokens) + and masked_tgt_tokens[masked_tgt_offset] + != extra_id_token + ): + assert ( + original_tokens[original_offset] + == masked_tgt_tokens[masked_tgt_offset] + ) + original_offset += 1 + masked_tgt_offset += 1 + else: + assert original_tokens[original_offset] == masked_src_token + original_offset += 1 + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_activation_checkpointing.py b/fairseq/tests/test_activation_checkpointing.py new file mode 100644 index 0000000..647a957 --- /dev/null +++ b/fairseq/tests/test_activation_checkpointing.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +import torch.nn as nn +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from torch.utils.checkpoint import checkpoint + + +class Model(nn.Module): + def __init__( + self, use_pytorch_checkpoint=False, use_fairseq_checkpoint=False, **kwargs + ): + super().__init__() + torch.manual_seed(0) + self.use_pytorch_checkpoint = use_pytorch_checkpoint + self.ffn = nn.Sequential( + nn.Linear(32, 128), + # add a Dropout layer to test RNG save/restore + nn.Dropout(p=0.5), + nn.Linear(128, 32), + ) + if use_fairseq_checkpoint: + self.ffn = checkpoint_wrapper(self.ffn, **kwargs) + self.out = nn.Linear(32, 1) + + def forward(self, x): + if self.use_pytorch_checkpoint: + x = checkpoint(self.ffn, x) + else: + x = self.ffn(x) + return self.out(x) + + +class TestActivationCheckpointing(unittest.TestCase): + def _test_checkpoint_wrapper(self, device, log_memory_usage=False): + def get_loss_and_gnorm(model): + torch.manual_seed(1) + input = torch.rand(2, 16, 32).requires_grad_(True).to(device) + model.zero_grad() + loss = model(input).sum() + loss.backward() + gnorm = torch.norm( + torch.stack([torch.norm(p.grad.detach()) for p in model.parameters()]) + ) + return {"loss": loss, "gnorm": gnorm} + + model = Model().to(device) + no_cpt = get_loss_and_gnorm(model) + + model = Model(use_pytorch_checkpoint=True).to(device) + pyt_cpt = get_loss_and_gnorm(model) + torch.testing.assert_allclose(no_cpt["loss"], pyt_cpt["loss"]) + torch.testing.assert_allclose(no_cpt["gnorm"], pyt_cpt["gnorm"]) + + model = Model(use_fairseq_checkpoint=True).to(device) + fairseq_cpt = get_loss_and_gnorm(model) + torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt["loss"]) + torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt["gnorm"]) + + model = Model(use_fairseq_checkpoint=True, offload_to_cpu=True).to(device) + fairseq_cpt_offload = get_loss_and_gnorm(model) + torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt_offload["loss"]) + torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt_offload["gnorm"]) + + def test_checkpoint_wrapper_cpu(self): + self._test_checkpoint_wrapper(device=torch.device("cpu")) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_checkpoint_wrapper_cuda(self): + self._test_checkpoint_wrapper(device=torch.device("cuda")) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_amp_optimizer.py b/fairseq/tests/test_amp_optimizer.py new file mode 100644 index 0000000..4d6073a --- /dev/null +++ b/fairseq/tests/test_amp_optimizer.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import copy +import unittest + +import torch +from torch.cuda.amp import GradScaler, autocast + +from fairseq.optim import build_optimizer + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestGradientScalingAMP(unittest.TestCase): + def setUp(self): + self.x = torch.tensor([2.0]).cuda().half() + weight = 3.0 + bias = 5.0 + self.error = 1.0 + self.target = torch.tensor([self.x * weight + bias + self.error]).cuda() + self.loss_fn = torch.nn.L1Loss() + + self.model = torch.nn.Linear(1, 1) + self.model.weight.data = torch.tensor([[weight]]) + self.model.bias.data = torch.tensor([bias]) + self.model.cuda() + self.params = list(self.model.parameters()) + + self.namespace_dls = argparse.Namespace( + optimizer="adam", + lr=[0.1], + adam_betas="(0.9, 0.999)", + adam_eps=1e-8, + weight_decay=0.0, + threshold_loss_scale=1, + min_loss_scale=1e-4, + ) + self.scaler = GradScaler( + init_scale=1, + growth_interval=1, + ) + + def run_iter(self, model, params, optimizer): + optimizer.zero_grad() + with autocast(): + y = model(self.x) + loss = self.loss_fn(y, self.target) + self.scaler.scale(loss).backward() + self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) + + self.scaler.unscale_(optimizer) + grad_norm = optimizer.clip_grad_norm(0) + self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) + + self.scaler.step(optimizer) + self.scaler.update() + self.assertEqual( + model.weight, + torch.tensor([[3.1]], device="cuda:0", requires_grad=True), + ) + self.assertEqual( + model.bias, + torch.tensor([5.1], device="cuda:0", requires_grad=True), + ) + self.assertEqual(self.scaler.get_scale(), 2.0) + + def test_automatic_mixed_precision(self): + model = copy.deepcopy(self.model) + params = list(model.parameters()) + optimizer = build_optimizer(self.namespace_dls, params) + + self.run_iter(model, params, optimizer) diff --git a/fairseq/tests/test_average_checkpoints.py b/fairseq/tests/test_average_checkpoints.py new file mode 100644 index 0000000..f348b56 --- /dev/null +++ b/fairseq/tests/test_average_checkpoints.py @@ -0,0 +1,134 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import collections +import os +import shutil +import tempfile +import unittest + +import numpy as np +import torch +from scripts.average_checkpoints import average_checkpoints +from torch import nn + + +class ModelWithSharedParameter(nn.Module): + def __init__(self): + super(ModelWithSharedParameter, self).__init__() + self.embedding = nn.Embedding(1000, 200) + self.FC1 = nn.Linear(200, 200) + self.FC2 = nn.Linear(200, 200) + # tie weight in FC2 to FC1 + self.FC2.weight = nn.Parameter(self.FC1.weight) + self.FC2.bias = nn.Parameter(self.FC1.bias) + + self.relu = nn.ReLU() + + def forward(self, input): + return self.FC2(self.ReLU(self.FC1(input))) + self.FC1(input) + + +class TestAverageCheckpoints(unittest.TestCase): + def test_average_checkpoints(self): + params_0 = collections.OrderedDict( + [ + ("a", torch.DoubleTensor([100.0])), + ("b", torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])), + ("c", torch.IntTensor([7, 8, 9])), + ] + ) + params_1 = collections.OrderedDict( + [ + ("a", torch.DoubleTensor([1.0])), + ("b", torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])), + ("c", torch.IntTensor([2, 2, 2])), + ] + ) + params_avg = collections.OrderedDict( + [ + ("a", torch.DoubleTensor([50.5])), + ("b", torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])), + # We expect truncation for integer division + ("c", torch.IntTensor([4, 5, 5])), + ] + ) + + fd_0, path_0 = tempfile.mkstemp() + fd_1, path_1 = tempfile.mkstemp() + torch.save(collections.OrderedDict([("model", params_0)]), path_0) + torch.save(collections.OrderedDict([("model", params_1)]), path_1) + + output = average_checkpoints([path_0, path_1])["model"] + + os.close(fd_0) + os.remove(path_0) + os.close(fd_1) + os.remove(path_1) + + for (k_expected, v_expected), (k_out, v_out) in zip( + params_avg.items(), output.items() + ): + self.assertEqual( + k_expected, + k_out, + "Key mismatch - expected {} but found {}. " + "(Expected list of keys: {} vs actual list of keys: {})".format( + k_expected, k_out, params_avg.keys(), output.keys() + ), + ) + np.testing.assert_allclose( + v_expected.numpy(), + v_out.numpy(), + err_msg="Tensor value mismatch for key {}".format(k_expected), + ) + + def test_average_checkpoints_with_shared_parameters(self): + def _construct_model_with_shared_parameters(path, value): + m = ModelWithSharedParameter() + nn.init.constant_(m.FC1.weight, value) + torch.save({"model": m.state_dict()}, path) + return m + + tmpdir = tempfile.mkdtemp() + paths = [] + path = os.path.join(tmpdir, "m1.pt") + m1 = _construct_model_with_shared_parameters(path, 1.0) + paths.append(path) + + path = os.path.join(tmpdir, "m2.pt") + m2 = _construct_model_with_shared_parameters(path, 2.0) + paths.append(path) + + path = os.path.join(tmpdir, "m3.pt") + m3 = _construct_model_with_shared_parameters(path, 3.0) + paths.append(path) + + new_model = average_checkpoints(paths) + self.assertTrue( + torch.equal( + new_model["model"]["embedding.weight"], + (m1.embedding.weight + m2.embedding.weight + m3.embedding.weight) / 3.0, + ) + ) + + self.assertTrue( + torch.equal( + new_model["model"]["FC1.weight"], + (m1.FC1.weight + m2.FC1.weight + m3.FC1.weight) / 3.0, + ) + ) + + self.assertTrue( + torch.equal( + new_model["model"]["FC2.weight"], + (m1.FC2.weight + m2.FC2.weight + m3.FC2.weight) / 3.0, + ) + ) + shutil.rmtree(tmpdir) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_backtranslation_dataset.py b/fairseq/tests/test_backtranslation_dataset.py new file mode 100644 index 0000000..dffc3b4 --- /dev/null +++ b/fairseq/tests/test_backtranslation_dataset.py @@ -0,0 +1,123 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import tests.utils as test_utils +import torch +from fairseq.data import ( + BacktranslationDataset, + LanguagePairDataset, + TransformEosDataset, +) +from fairseq.sequence_generator import SequenceGenerator + + +class TestBacktranslationDataset(unittest.TestCase): + def setUp(self): + ( + self.tgt_dict, + self.w1, + self.w2, + self.src_tokens, + self.src_lengths, + self.model, + ) = test_utils.sequence_generator_setup() + + dummy_src_samples = self.src_tokens + + self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples) + self.cuda = torch.cuda.is_available() + + def _backtranslation_dataset_helper( + self, + remove_eos_from_input_src, + remove_eos_from_output_src, + ): + tgt_dataset = LanguagePairDataset( + src=self.tgt_dataset, + src_sizes=self.tgt_dataset.sizes, + src_dict=self.tgt_dict, + tgt=None, + tgt_sizes=None, + tgt_dict=None, + ) + + generator = SequenceGenerator( + [self.model], + tgt_dict=self.tgt_dict, + max_len_a=0, + max_len_b=200, + beam_size=2, + unk_penalty=0, + ) + + backtranslation_dataset = BacktranslationDataset( + tgt_dataset=TransformEosDataset( + dataset=tgt_dataset, + eos=self.tgt_dict.eos(), + # remove eos from the input src + remove_eos_from_src=remove_eos_from_input_src, + ), + src_dict=self.tgt_dict, + backtranslation_fn=( + lambda sample: generator.generate([self.model], sample) + ), + output_collater=TransformEosDataset( + dataset=tgt_dataset, + eos=self.tgt_dict.eos(), + # if we remove eos from the input src, then we need to add it + # back to the output tgt + append_eos_to_tgt=remove_eos_from_input_src, + remove_eos_from_src=remove_eos_from_output_src, + ).collater, + cuda=self.cuda, + ) + dataloader = torch.utils.data.DataLoader( + backtranslation_dataset, + batch_size=2, + collate_fn=backtranslation_dataset.collater, + ) + backtranslation_batch_result = next(iter(dataloader)) + + eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2 + + # Note that we sort by src_lengths and add left padding, so actually + # ids will look like: [1, 0] + expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]]) + if remove_eos_from_output_src: + expected_src = expected_src[:, :-1] + expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]]) + generated_src = backtranslation_batch_result["net_input"]["src_tokens"] + tgt_tokens = backtranslation_batch_result["target"] + + self.assertTensorEqual(expected_src, generated_src) + self.assertTensorEqual(expected_tgt, tgt_tokens) + + def test_backtranslation_dataset_no_eos_in_output_src(self): + self._backtranslation_dataset_helper( + remove_eos_from_input_src=False, + remove_eos_from_output_src=True, + ) + + def test_backtranslation_dataset_with_eos_in_output_src(self): + self._backtranslation_dataset_helper( + remove_eos_from_input_src=False, + remove_eos_from_output_src=False, + ) + + def test_backtranslation_dataset_no_eos_in_input_src(self): + self._backtranslation_dataset_helper( + remove_eos_from_input_src=True, + remove_eos_from_output_src=False, + ) + + def assertTensorEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertEqual(t1.ne(t2).long().sum(), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_binaries.py b/fairseq/tests/test_binaries.py new file mode 100644 index 0000000..41d9210 --- /dev/null +++ b/fairseq/tests/test_binaries.py @@ -0,0 +1,1915 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import json +import logging +import os +import random +import sys +import tempfile +import unittest +from packaging import version +from io import StringIO +from typing import Dict, List + +import torch + +from fairseq import options +from fairseq_cli import eval_lm, train +from tests.utils import ( + create_dummy_data, + create_laser_data_and_config_json, + generate_main, + preprocess_lm_data, + preprocess_summarization_data, + preprocess_translation_data, + train_language_model, + train_translation_model, +) + +try: + import transformers # noqa + + has_hf_transformers = True +except ImportError: + has_hf_transformers = False + + +class TestTranslation(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_fconv(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fconv") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model(data_dir, "fconv_iwslt_de_en") + generate_main(data_dir) + + def test_raw(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fconv_raw") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, ["--dataset-impl", "raw"]) + train_translation_model( + data_dir, "fconv_iwslt_de_en", ["--dataset-impl", "raw"] + ) + generate_main(data_dir, ["--dataset-impl", "raw"]) + + def test_update_freq(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_update_freq") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, "fconv_iwslt_de_en", ["--update-freq", "3"] + ) + generate_main(data_dir) + + def test_max_positions(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_max_positions") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + with self.assertRaises(Exception) as context: + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + ["--max-target-positions", "5"], + ) + self.assertTrue( + "skip this example with --skip-invalid-size-inputs-valid-test" + in str(context.exception) + ) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + [ + "--max-target-positions", + "5", + "--skip-invalid-size-inputs-valid-test", + ], + ) + with self.assertRaises(Exception) as context: + generate_main(data_dir) + generate_main(data_dir, ["--skip-invalid-size-inputs-valid-test"]) + + def test_generation(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_sampling") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model(data_dir, "fconv_iwslt_de_en") + generate_main( + data_dir, + [ + "--sampling", + "--temperature", + "2", + "--beam", + "2", + "--nbest", + "2", + ], + ) + generate_main( + data_dir, + [ + "--sampling", + "--sampling-topk", + "3", + "--beam", + "2", + "--nbest", + "2", + ], + ) + generate_main( + data_dir, + [ + "--sampling", + "--sampling-topp", + "0.2", + "--beam", + "2", + "--nbest", + "2", + ], + ) + generate_main( + data_dir, + [ + "--diversity-rate", + "0.5", + "--beam", + "6", + ], + ) + with self.assertRaises(ValueError): + generate_main( + data_dir, + [ + "--diverse-beam-groups", + "4", + "--match-source-len", + ], + ) + generate_main(data_dir, ["--prefix-size", "2"]) + generate_main(data_dir, ["--retain-dropout"]) + + def test_eval_bleu(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_eval_bleu") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + [ + "--eval-bleu", + "--eval-bleu-print-samples", + "--eval-bleu-remove-bpe", + "--eval-bleu-detok", + "space", + "--eval-bleu-args", + '{"beam": 4, "min_len": 10}', + ], + ) + + def test_lstm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_lstm") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "lstm_wiseman_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-out-embed-dim", + "8", + ], + ) + generate_main(data_dir) + + def test_lstm_bidirectional(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_lstm_bidirectional") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "lstm", + [ + "--encoder-layers", + "2", + "--encoder-bidirectional", + "--encoder-hidden-size", + "16", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-out-embed-dim", + "8", + "--decoder-layers", + "2", + ], + ) + generate_main(data_dir) + + def test_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + run_validation=True, + ) + generate_main(data_dir) + + def test_multilingual_transformer(self): + # test with all combinations of encoder/decoder lang tokens + encoder_langtok_flags = [ + [], + ["--encoder-langtok", "src"], + ["--encoder-langtok", "tgt"], + ] + decoder_langtok_flags = [[], ["--decoder-langtok"]] + with contextlib.redirect_stdout(StringIO()): + for i in range(len(encoder_langtok_flags)): + for j in range(len(decoder_langtok_flags)): + enc_ltok_flag = encoder_langtok_flags[i] + dec_ltok_flag = decoder_langtok_flags[j] + with tempfile.TemporaryDirectory( + f"test_multilingual_transformer_{i}_{j}" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + arch="multilingual_transformer", + task="multilingual_translation", + extra_flags=[ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out,out-in"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "multilingual_translation", + "--lang-pairs", + "in-out,out-in", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + @unittest.skipIf( + sys.platform.lower() == "darwin", "skip latent depth test on MacOS" + ) + def test_multilingual_translation_latent_depth(self): + # test with latent depth in encoder, decoder, or both + encoder_latent_layer = [[], ["--encoder-latent-layer"]] + decoder_latent_layer = [[], ["--decoder-latent-layer"]] + with contextlib.redirect_stdout(StringIO()): + for i in range(len(encoder_latent_layer)): + for j in range(len(decoder_latent_layer)): + if i == 0 and j == 0: + continue + enc_ll_flag = encoder_latent_layer[i] + dec_ll_flag = decoder_latent_layer[j] + with tempfile.TemporaryDirectory( + f"test_multilingual_translation_latent_depth_{i}_{j}" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data( + data_dir, extra_flags=["--joined-dictionary"] + ) + train_translation_model( + data_dir, + arch="latent_multilingual_transformer", + task="multilingual_translation_latent_depth", + extra_flags=[ + "--user-dir", + "examples/latent_depth/latent_depth_src", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--share-encoders", + "--share-decoders", + "--sparsity-weight", + "0.1", + ] + + enc_ll_flag + + dec_ll_flag, + lang_flags=["--lang-pairs", "in-out,out-in"], + run_validation=True, + extra_valid_flags=[ + "--user-dir", + "examples/latent_depth/latent_depth_src", + ] + + enc_ll_flag + + dec_ll_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--user-dir", + "examples/latent_depth/latent_depth_src", + "--task", + "multilingual_translation_latent_depth", + "--lang-pairs", + "in-out,out-in", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ll_flag + + dec_ll_flag, + ) + + def test_translation_multi_simple_epoch(self): + # test with all combinations of encoder/decoder lang tokens + encoder_langtok_flags = [ + [], + ["--encoder-langtok", "src"], + ["--encoder-langtok", "tgt"], + ] + decoder_langtok_flags = [[], ["--decoder-langtok"]] + with contextlib.redirect_stdout(StringIO()): + for i in range(len(encoder_langtok_flags)): + for j in range(len(decoder_langtok_flags)): + enc_ltok_flag = encoder_langtok_flags[i] + dec_ltok_flag = decoder_langtok_flags[j] + with tempfile.TemporaryDirectory( + f"test_translation_multi_simple_epoch_{i}_{j}" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data( + data_dir, extra_flags=["--joined-dictionary"] + ) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", + extra_flags=[ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + "--virtual-epoch-size", + "1000", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out,out-in"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out,out-in", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + def test_translation_multi_simple_epoch_no_vepoch(self): + # test with all combinations of encoder/decoder lang tokens + with contextlib.redirect_stdout(StringIO()): + enc_ltok_flag = ["--encoder-langtok", "src"] + dec_ltok_flag = ["--decoder-langtok"] + with tempfile.TemporaryDirectory( + "test_translation_multi_simple_epoch_dict" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, extra_flags=[]) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", + extra_flags=[ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + def test_translation_multi_simple_epoch_dicts(self): + # test with all combinations of encoder/decoder lang tokens + with contextlib.redirect_stdout(StringIO()): + enc_ltok_flag = ["--encoder-langtok", "src"] + dec_ltok_flag = ["--decoder-langtok"] + with tempfile.TemporaryDirectory( + "test_translation_multi_simple_epoch_dict" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, extra_flags=[]) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", + extra_flags=[ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + "--virtual-epoch-size", + "1000", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + def test_translation_multi_simple_epoch_src_tgt_dict_spec(self): + # test the specification of explicit --src-dict and --tgt-dict + with contextlib.redirect_stdout(StringIO()): + enc_ltok_flag = ["--encoder-langtok", "src"] + dec_ltok_flag = ["--decoder-langtok"] + with tempfile.TemporaryDirectory( + "test_translation_multi_simple_epoch_dict" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, extra_flags=[]) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", + extra_flags=[ + "--source-dict", + f"{data_dir}/dict.in.txt", + "--target-dict", + f"{data_dir}/dict.out.txt", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + "--virtual-epoch-size", + "1000", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + def test_transformer_cross_self_attention(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_transformer_cross_self_attention" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--no-cross-attention", + "--cross-self-attention", + ], + run_validation=True, + ) + generate_main(data_dir, extra_flags=[]) + + @unittest.skipIf( + version.parse(torch.__version__) > version.parse("1.8"), + "skip for latest torch versions", + ) + def test_transformer_pointer_generator(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_transformer_pointer_generator" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_summarization_data(data_dir) + train_translation_model( + data_dir, + "transformer_pointer_generator", + extra_flags=[ + "--user-dir", + "examples/pointer_generator/pointer_generator_src", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--alignment-layer", + "-1", + "--alignment-heads", + "1", + "--source-position-markers", + "0", + ], + run_validation=True, + extra_valid_flags=[ + "--user-dir", + "examples/pointer_generator/pointer_generator_src", + ], + ) + generate_main( + data_dir, + extra_flags=[ + "--user-dir", + "examples/pointer_generator/pointer_generator_src", + ], + ) + + def test_lightconv(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_lightconv") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "lightconv_iwslt_de_en", + [ + "--encoder-conv-type", + "lightweight", + "--decoder-conv-type", + "lightweight", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + ) + generate_main(data_dir) + + def test_dynamicconv(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_dynamicconv") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "lightconv_iwslt_de_en", + [ + "--encoder-conv-type", + "dynamic", + "--decoder-conv-type", + "dynamic", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + ) + generate_main(data_dir) + + def test_cmlm_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_cmlm_transformer") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "cmlm_transformer", + [ + "--apply-bert-init", + "--criterion", + "nat_loss", + "--noise", + "full_mask", + "--pred-length-offset", + "--length-loss-factor", + "0.1", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) + + def test_nonautoregressive_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_nonautoregressive_transformer" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "nonautoregressive_transformer", + [ + "--apply-bert-init", + "--src-embedding-copy", + "--criterion", + "nat_loss", + "--noise", + "full_mask", + "--pred-length-offset", + "--length-loss-factor", + "0.1", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "0", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) + + # def test_nat_crf_transformer(self): + # with contextlib.redirect_stdout(StringIO()): + # with tempfile.TemporaryDirectory('test_nat_crf_transformer') as data_dir: + # create_dummy_data(data_dir) + # preprocess_translation_data(data_dir, ['--joined-dictionary']) + # train_translation_model(data_dir, 'nacrf_transformer', [ + # '--apply-bert-init', '--criterion', + # 'nat_loss', '--noise', 'full_mask', '--pred-length-offset', + # '--length-loss-factor', '0.1', + # '--word-ins-loss-factor', '0.5', + # '--crf-lowrank-approx', '1', + # '--crf-beam-approx', '1' + # ], task='translation_lev') + # generate_main(data_dir, [ + # '--task', 'translation_lev', + # '--iter-decode-max-iter', '0', + # '--iter-decode-eos-penalty', '0', + # '--print-step', + # ]) + + def test_iterative_nonautoregressive_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_iterative_nonautoregressive_transformer" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "iterative_nonautoregressive_transformer", + [ + "--apply-bert-init", + "--src-embedding-copy", + "--criterion", + "nat_loss", + "--noise", + "full_mask", + "--stochastic-approx", + "--dae-ratio", + "0.5", + "--train-step", + "3", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) + + def test_insertion_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_insertion_transformer") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "insertion_transformer", + [ + "--apply-bert-init", + "--criterion", + "nat_loss", + "--noise", + "random_mask", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) + + def test_mixture_of_experts(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_moe") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--task", + "translation_moe", + "--user-dir", + "examples/translation_moe/translation_moe_src", + "--method", + "hMoElp", + "--mean-pool-gating-network", + "--num-experts", + "3", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + ) + generate_main( + data_dir, + [ + "--task", + "translation_moe", + "--user-dir", + "examples/translation_moe/translation_moe_src", + "--method", + "hMoElp", + "--mean-pool-gating-network", + "--num-experts", + "3", + "--gen-expert", + "0", + ], + ) + + def test_alignment(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_alignment") as data_dir: + create_dummy_data(data_dir, alignment=True) + preprocess_translation_data(data_dir, ["--align-suffix", "align"]) + train_translation_model( + data_dir, + "transformer_align", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--load-alignments", + "--alignment-layer", + "1", + "--criterion", + "label_smoothed_cross_entropy_with_alignment", + ], + run_validation=True, + ) + generate_main(data_dir) + + def test_laser_lstm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_laser_lstm") as data_dir: + laser_config_file = create_laser_data_and_config_json(data_dir) + train_translation_model( + laser_config_file.name, + "laser_lstm", + [ + "--user-dir", + "examples/laser/laser_src", + "--weighting-alpha", + "0.3", + "--encoder-bidirectional", + "--encoder-hidden-size", + "512", + "--encoder-layers", + "5", + "--decoder-layers", + "1", + "--encoder-embed-dim", + "320", + "--decoder-embed-dim", + "320", + "--decoder-lang-embed-dim", + "32", + "--save-dir", + data_dir, + "--disable-validation", + ], + task="laser", + lang_flags=[], + ) + + def test_laser_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_laser_transformer") as data_dir: + laser_config_file = create_laser_data_and_config_json(data_dir) + train_translation_model( + laser_config_file.name, + "laser_transformer", + [ + "--user-dir", + "examples/laser/laser_src", + "--weighting-alpha", + "0.3", + "--encoder-embed-dim", + "320", + "--decoder-embed-dim", + "320", + "--decoder-lang-embed-dim", + "32", + "--save-dir", + data_dir, + "--disable-validation", + ], + task="laser", + lang_flags=[], + ) + + def test_alignment_full_context(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_alignment") as data_dir: + create_dummy_data(data_dir, alignment=True) + preprocess_translation_data(data_dir, ["--align-suffix", "align"]) + train_translation_model( + data_dir, + "transformer_align", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--load-alignments", + "--alignment-layer", + "1", + "--criterion", + "label_smoothed_cross_entropy_with_alignment", + "--full-context-alignment", + ], + run_validation=True, + ) + generate_main(data_dir) + + def test_transformer_layerdrop(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_layerdrop") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "3", + "--decoder-layers", + "3", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--encoder-layerdrop", + "0.01", + "--decoder-layerdrop", + "0.01", + ], + ) + generate_main(data_dir) + generate_main( + data_dir, + [ + "--model-overrides", + "{'encoder_layers_to_keep':'0,2','decoder_layers_to_keep':'1'}", + ], + ) + + +class TestStories(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_fconv_self_att_wp(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fconv_self_att_wp") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + config = [ + "--encoder-layers", + "[(128, 3)] * 2", + "--decoder-layers", + "[(128, 3)] * 2", + "--decoder-attention", + "True", + "--encoder-attention", + "False", + "--gated-attention", + "True", + "--self-attention", + "True", + "--project-input", + "True", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-out-embed-dim", + "8", + "--multihead-self-attention-nheads", + "2", + ] + train_translation_model(data_dir, "fconv_self_att_wp", config) + generate_main(data_dir) + + # fusion model + os.rename( + os.path.join(data_dir, "checkpoint_last.pt"), + os.path.join(data_dir, "pretrained.pt"), + ) + config.extend( + [ + "--pretrained", + "True", + "--pretrained-checkpoint", + os.path.join(data_dir, "pretrained.pt"), + "--save-dir", + os.path.join(data_dir, "fusion_model"), + ] + ) + train_translation_model(data_dir, "fconv_self_att_wp", config) + + +class TestLanguageModeling(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_fconv_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fconv_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "fconv_lm", + [ + "--decoder-layers", + "[(850, 3)] * 2 + [(1024,4)]", + "--decoder-embed-dim", + "280", + "--optimizer", + "nag", + "--lr", + "0.1", + ], + ) + eval_lm_main(data_dir) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_transformer_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "transformer_lm", + ["--add-bos-token", "--nval", "1"], + run_validation=True, + ) + eval_lm_main(data_dir) + eval_lm_main(data_dir, extra_flags=["--context-window", "25"]) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_normformer_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "transformer_lm", + [ + "--add-bos-token", + "--nval", + "1", + "--scale-fc", + "--scale-heads", + "--scale-attn", + "--scale-fc", + ], + run_validation=True, + ) + eval_lm_main(data_dir) + eval_lm_main(data_dir, extra_flags=["--context-window", "25"]) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_transformer_lm_with_adaptive_softmax(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_transformer_lm_with_adaptive_softmax" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "transformer_lm", + [ + "--add-bos-token", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + ], + run_validation=True, + ) + eval_lm_main(data_dir) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_lightconv_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_lightconv_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "lightconv_lm", + ["--add-bos-token"], + run_validation=True, + ) + eval_lm_main(data_dir) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_lstm_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_lstm_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "lstm_lm", + ["--add-bos-token"], + run_validation=True, + ) + eval_lm_main(data_dir) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_lstm_lm_residuals(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_lstm_lm_residuals") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "lstm_lm", + ["--add-bos-token", "--residuals"], + run_validation=True, + ) + eval_lm_main(data_dir) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + @unittest.skipIf(not has_hf_transformers, "skip test if transformers is missing") + def test_transformer_xl_bptt_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_xl_bptt_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + task_flags = [ + "--user-dir", + "examples/truncated_bptt", + "--task", + "truncated_bptt_lm", + "--batch-size", + "2", + "--tokens-per-sample", + "50", + ] + train_language_model( + data_dir=data_dir, + arch="transformer_xl", + extra_flags=task_flags + + [ + "--n-layer", + "2", + ], + task="truncated_bptt_lm", + run_validation=True, + extra_valid_flags=task_flags, + ) + eval_lm_main(data_dir, extra_flags=task_flags) + # Train with activation offloading + train_language_model( + data_dir=data_dir, + arch="transformer_xl", + extra_flags=task_flags + + [ + "--n-layer", + "2", + "--offload-activations", + ], + task="truncated_bptt_lm", + run_validation=True, + extra_valid_flags=task_flags, + ) + + +class TestMaskedLanguageModel(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_legacy_masked_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_legacy_mlm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_legacy_masked_language_model(data_dir, "masked_lm") + + def test_roberta_masked_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_roberta_mlm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_masked_lm( + data_dir, "roberta_base", extra_flags=["--encoder-layers", "2"] + ) + + def test_roberta_sentence_prediction(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_roberta_head") as data_dir: + create_dummy_roberta_head_data(data_dir, num_classes=num_classes) + preprocess_lm_data(os.path.join(data_dir, "input0")) + preprocess_lm_data(os.path.join(data_dir, "label")) + train_roberta_head(data_dir, "roberta_base", num_classes=num_classes) + + def test_roberta_regression_single(self): + num_classes = 1 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_roberta_regression_single" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "roberta_base", + num_classes=num_classes, + extra_flags=["--regression-target"], + ) + + def test_roberta_regression_multiple(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_roberta_regression_multiple" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "roberta_base", + num_classes=num_classes, + extra_flags=["--regression-target"], + ) + + def test_linformer_roberta_masked_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_linformer_roberta_mlm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_masked_lm( + data_dir, + "linformer_roberta_base", + extra_flags=[ + "--user-dir", + "examples/linformer/linformer_src", + "--encoder-layers", + "2", + ], + ) + + def test_linformer_roberta_sentence_prediction(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_linformer_roberta_head") as data_dir: + create_dummy_roberta_head_data(data_dir, num_classes=num_classes) + preprocess_lm_data(os.path.join(data_dir, "input0")) + preprocess_lm_data(os.path.join(data_dir, "label")) + train_roberta_head( + data_dir, + "linformer_roberta_base", + num_classes=num_classes, + extra_flags=["--user-dir", "examples/linformer/linformer_src"], + ) + + def test_linformer_roberta_regression_single(self): + num_classes = 1 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_linformer_roberta_regression_single" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "linformer_roberta_base", + num_classes=num_classes, + extra_flags=[ + "--regression-target", + "--user-dir", + "examples/linformer/linformer_src", + ], + ) + + def test_linformer_roberta_regression_multiple(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_linformer_roberta_regression_multiple" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "linformer_roberta_base", + num_classes=num_classes, + extra_flags=[ + "--regression-target", + "--user-dir", + "examples/linformer/linformer_src", + ], + ) + + def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_only): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_mlm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_legacy_masked_language_model( + data_dir, + arch="masked_lm", + extra_args=("--encoder-learned-pos",) if learned_pos_emb else (), + ) + with tempfile.TemporaryDirectory( + "test_mlm_translation" + ) as translation_dir: + create_dummy_data(translation_dir) + preprocess_translation_data( + translation_dir, extra_flags=["--joined-dictionary"] + ) + # Train transformer with data_dir/checkpoint_last.pt + train_translation_model( + translation_dir, + arch="transformer_from_pretrained_xlm", + extra_flags=[ + "--decoder-layers", + "1", + "--decoder-embed-dim", + "32", + "--decoder-attention-heads", + "1", + "--decoder-ffn-embed-dim", + "32", + "--encoder-layers", + "1", + "--encoder-embed-dim", + "32", + "--encoder-attention-heads", + "1", + "--encoder-ffn-embed-dim", + "32", + "--pretrained-xlm-checkpoint", + "{}/checkpoint_last.pt".format(data_dir), + "--activation-fn", + "gelu", + "--max-source-positions", + "500", + "--max-target-positions", + "500", + ] + + ( + ["--encoder-learned-pos", "--decoder-learned-pos"] + if learned_pos_emb + else [] + ) + + (["--init-encoder-only"] if encoder_only else []), + task="translation_from_pretrained_xlm", + ) + + def test_pretrained_masked_lm_for_translation_learned_pos_emb(self): + self._test_pretrained_masked_lm_for_translation(True, False) + + def test_pretrained_masked_lm_for_translation_sinusoidal_pos_emb(self): + self._test_pretrained_masked_lm_for_translation(False, False) + + def test_pretrained_masked_lm_for_translation_encoder_only(self): + self._test_pretrained_masked_lm_for_translation(True, True) + + def test_r4f_roberta(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_r4f_roberta_head") as data_dir: + create_dummy_roberta_head_data(data_dir, num_classes=num_classes) + preprocess_lm_data(os.path.join(data_dir, "input0")) + preprocess_lm_data(os.path.join(data_dir, "label")) + train_roberta_head( + data_dir, + "roberta_base", + num_classes=num_classes, + extra_flags=[ + "--user-dir", + "examples/rxf/rxf_src", + "--criterion", + "sentence_prediction_r3f", + "--spectral-norm-classification-head", + ], + ) + + +def train_legacy_masked_language_model(data_dir, arch, extra_args=()): + train_parser = options.get_training_parser() + # TODO: langs should be in and out right? + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + "cross_lingual_lm", + data_dir, + "--arch", + arch, + # Optimizer args + "--optimizer", + "adam", + "--lr-scheduler", + "reduce_lr_on_plateau", + "--lr-shrink", + "0.5", + "--lr", + "0.0001", + "--stop-min-lr", + "1e-09", + # dropout, attention args + "--dropout", + "0.1", + "--attention-dropout", + "0.1", + # MLM args + "--criterion", + "legacy_masked_lm_loss", + "--masked-lm-only", + "--monolingual-langs", + "in,out", + "--num-segment", + "5", + # Transformer args: use a small transformer model for fast training + "--encoder-layers", + "1", + "--encoder-embed-dim", + "32", + "--encoder-attention-heads", + "1", + "--encoder-ffn-embed-dim", + "32", + # Other training args + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--dataset-impl", + "raw", + "--num-workers", + "0", + ] + + list(extra_args), + ) + train.main(train_args) + + +class TestOptimizers(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_optimizers(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_optimizers") as data_dir: + # Use just a bit of data and tiny model to keep this test runtime reasonable + create_dummy_data(data_dir, num_examples=10, maxlen=5) + preprocess_translation_data(data_dir) + optimizers = ["adafactor", "adam", "nag", "adagrad", "sgd", "adadelta"] + last_checkpoint = os.path.join(data_dir, "checkpoint_last.pt") + for optimizer in optimizers: + if os.path.exists(last_checkpoint): + os.remove(last_checkpoint) + train_translation_model( + data_dir, + "lstm", + [ + "--required-batch-size-multiple", + "1", + "--encoder-layers", + "1", + "--encoder-hidden-size", + "32", + "--decoder-layers", + "1", + "--optimizer", + optimizer, + ], + ) + generate_main(data_dir) + + +def read_last_log_entry( + logs: List[logging.LogRecord], logger_name: str +) -> Dict[str, float]: + for x in reversed(logs): + if x.name == logger_name: + return json.loads(x.message) + raise ValueError(f"No entries from {logger_name} found in captured logs") + + +class TestActivationCheckpointing(unittest.TestCase): + base_flags = [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--restore-file", + "x.pt", + "--log-format", + "json", + "--log-interval", + "1", + "--max-update", + "2", + ] + + def _train(self, data_dir, extra_flags): + with self.assertLogs() as logs: + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + self.base_flags + extra_flags, + run_validation=True, + extra_valid_flags=["--log-format", "json"], + ) + return logs.records + + def test_activation_offloading_does_not_change_metrics(self): + """Neither ----checkpoint-activations nor --offload-activations should change loss""" + with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: + + with self.assertLogs(): + create_dummy_data(data_dir, num_examples=20) + preprocess_translation_data(data_dir) + offload_logs = self._train(data_dir, ["--offload-activations"]) + baseline_logs = self._train(data_dir, []) + + assert len(baseline_logs) == len(offload_logs) + + baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") + offload_valid_stats = read_last_log_entry(offload_logs, "valid") + baseline_train_stats = read_last_log_entry(baseline_logs, "train") + offload_train_stats = read_last_log_entry(offload_logs, "train") + + assert ( + baseline_train_stats["train_loss"] == offload_train_stats["train_loss"] + ) + assert ( + baseline_valid_stats["valid_loss"] == offload_valid_stats["valid_loss"] + ) + + def test_activation_checkpointing_does_not_change_metrics(self): + """--checkpoint-activations should not change loss""" + + with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: + with self.assertLogs(): + create_dummy_data(data_dir, num_examples=20) + preprocess_translation_data(data_dir) + ckpt_logs = self._train(data_dir, ["--checkpoint-activations"]) + baseline_logs = self._train(data_dir, []) + assert len(baseline_logs) == len(ckpt_logs) + + baseline_train_stats = read_last_log_entry(baseline_logs, "train") + ckpt_train_stats = read_last_log_entry(ckpt_logs, "train") + assert baseline_train_stats["train_loss"] == ckpt_train_stats["train_loss"] + + baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") + ckpt_valid_stats = read_last_log_entry(ckpt_logs, "valid") + assert baseline_valid_stats["valid_loss"] == ckpt_valid_stats["valid_loss"] + + +def create_dummy_roberta_head_data( + data_dir, num_examples=100, maxlen=10, num_classes=2, regression=False +): + input_dir = "input0" + + def _create_dummy_data(filename): + random_data = torch.rand(num_examples * maxlen) + input_data = 97 + torch.floor(26 * random_data).int() + if regression: + output_data = torch.rand((num_examples, num_classes)) + else: + output_data = 1 + torch.floor(num_classes * torch.rand(num_examples)).int() + with open(os.path.join(data_dir, input_dir, filename + ".out"), "w") as f_in: + label_filename = filename + ".label" if regression else filename + ".out" + with open(os.path.join(data_dir, "label", label_filename), "w") as f_out: + offset = 0 + for i in range(num_examples): + # write example input + ex_len = random.randint(1, maxlen) + ex_str = " ".join(map(chr, input_data[offset : offset + ex_len])) + print(ex_str, file=f_in) + # write example label + if regression: + class_str = " ".join(map(str, output_data[i].numpy())) + print(class_str, file=f_out) + else: + class_str = "class{}".format(output_data[i]) + print(class_str, file=f_out) + offset += ex_len + + os.mkdir(os.path.join(data_dir, input_dir)) + os.mkdir(os.path.join(data_dir, "label")) + _create_dummy_data("train") + _create_dummy_data("valid") + _create_dummy_data("test") + + +def train_masked_lm(data_dir, arch, extra_flags=None): + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + "masked_lm", + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "masked_lm", + "--batch-size", + "500", + "--required-batch-size-multiple", + "1", + "--save-dir", + data_dir, + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + train.main(train_args) + + +def train_roberta_head(data_dir, arch, num_classes=2, extra_flags=None): + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + "sentence_prediction", + data_dir, + "--arch", + arch, + "--encoder-layers", + "2", + "--num-classes", + str(num_classes), + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "sentence_prediction", + "--max-tokens", + "500", + "--max-positions", + "500", + "--batch-size", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + train.main(train_args) + + +def eval_lm_main(data_dir, extra_flags=None): + eval_lm_parser = options.get_eval_lm_parser() + eval_lm_args = options.parse_args_and_arch( + eval_lm_parser, + [ + data_dir, + "--path", + os.path.join(data_dir, "checkpoint_last.pt"), + "--no-progress-bar", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + eval_lm.main(eval_lm_args) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_binarizer.py b/fairseq/tests/test_binarizer.py new file mode 100644 index 0000000..50075ea --- /dev/null +++ b/fairseq/tests/test_binarizer.py @@ -0,0 +1,122 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import typing as tp +import unittest +from tempfile import TemporaryDirectory + +from fairseq.binarizer import BinarizeSummary, FileBinarizer, VocabularyDatasetBinarizer +from fairseq.data import Dictionary, indexed_dataset +from tests.utils import make_data, sizes + + +def build_vocab(data: tp.List[tp.List[str]]) -> Dictionary: + d = Dictionary() + for s in data: + for token in s: + d.add_symbol(token) + d.finalize() + return d + + +class TestBinarizer(unittest.TestCase): + def compare_ds_data(self, summary, data, prefix, impl, vocab): + self.assertEqual(summary.num_seq, len(data)) + self.assertEqual(summary.num_tok, sum([len(s) for s in data])) + + dataset = indexed_dataset.make_dataset(prefix, impl) + + self.assertEqual(len(dataset), len(data)) + decoded = [vocab.string(dataset[i]).split() for i in range(0, len(dataset))] + + self.assertEqual(decoded, data) + data_sizes = [i.item() for i in dataset.sizes] + self.assertEqual(data_sizes, sizes(data)) + + def test_can_binarize_line(self): + data = make_data(length=1) + vocab = build_vocab(data) + + binarizer = VocabularyDatasetBinarizer( + vocab, + ) + + sentence = data[0] + summary = BinarizeSummary() + + tensor = binarizer.binarize_line( + " ".join(sentence), + summary, + ) + + self.assertEqual(len(tensor), len(sentence) + 1) + + self.assertEqual(summary.num_tok, len(sentence) + 1) + self.assertEqual(summary.num_seq, 1) + + def test_can_binarize_file_chunk(self): + # test without multiprocess logic + with TemporaryDirectory() as dirname: + raw_file = os.path.join(dirname, "raw1") + prefix = os.path.join(dirname, "test1") + impl = "mmap" + + data = make_data(out_file=raw_file) + vocab = build_vocab(data) + + binarizer = VocabularyDatasetBinarizer( + vocab, + append_eos=False, + ) + + summary = FileBinarizer._binarize_chunk_and_finalize( + binarizer, + raw_file, + offset_start=0, + offset_end=-1, + output_prefix=prefix, + dataset_impl=impl, + vocab_size=len(vocab), + ) + + self.compare_ds_data(summary, data, prefix, impl, vocab) + + def test_can_multiprocess(self): + with TemporaryDirectory() as dirname: + raw_file = os.path.join(dirname, "raw1") + prefix = os.path.join(dirname, "test1") + impl = "mmap" + data = make_data(out_file=raw_file) + vocab = build_vocab(data) + binarizer = VocabularyDatasetBinarizer( + vocab, + append_eos=False, + ) + # with one worker + summary = FileBinarizer.multiprocess_dataset( + raw_file, + impl, + binarizer, + output_prefix=prefix, + vocab_size=len(vocab), + num_workers=1, + ) + + self.compare_ds_data(summary, data, prefix, impl, vocab) + + # with multiple worker + prefix_multi = os.path.join(dirname, "test2") + summary = FileBinarizer.multiprocess_dataset( + raw_file, + impl, + binarizer, + output_prefix=prefix_multi, + vocab_size=len(vocab), + num_workers=3, + ) + + self.compare_ds_data(summary, data, prefix_multi, impl, vocab) diff --git a/fairseq/tests/test_character_token_embedder.py b/fairseq/tests/test_character_token_embedder.py new file mode 100644 index 0000000..24940eb --- /dev/null +++ b/fairseq/tests/test_character_token_embedder.py @@ -0,0 +1,48 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from fairseq.data import Dictionary +from fairseq.modules import CharacterTokenEmbedder + + +class TestCharacterTokenEmbedder(unittest.TestCase): + def test_character_token_embedder(self): + vocab = Dictionary() + vocab.add_symbol("hello") + vocab.add_symbol("there") + + embedder = CharacterTokenEmbedder( + vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2 + ) + + test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]] + max_len = max(len(s) for s in test_sents) + input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad()) + for i in range(len(test_sents)): + input[i][0] = vocab.eos() + for j in range(len(test_sents[i])): + input[i][j + 1] = vocab.index(test_sents[i][j]) + input[i][j + 2] = vocab.eos() + embs = embedder(input) + + assert embs.size() == (len(test_sents), max_len + 2, 5) + self.assertAlmostEqual(embs[0][0], embs[1][0]) + self.assertAlmostEqual(embs[0][0], embs[0][-1]) + self.assertAlmostEqual(embs[0][1], embs[2][1]) + self.assertAlmostEqual(embs[0][3], embs[1][1]) + + embs.sum().backward() + assert embedder.char_embeddings.weight.grad is not None + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess((t1 - t2).abs().max(), 1e-6) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_checkpoint_utils.py b/fairseq/tests/test_checkpoint_utils.py new file mode 100644 index 0000000..f8cd943 --- /dev/null +++ b/fairseq/tests/test_checkpoint_utils.py @@ -0,0 +1,125 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import logging +import os +import tempfile +import unittest +from io import StringIO +from unittest.mock import patch + +from fairseq import checkpoint_utils +from tests.utils import ( + create_dummy_data, + preprocess_translation_data, + train_translation_model, +) +import torch + + +class TestCheckpointUtils(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + @contextlib.contextmanager + def _train_transformer(self, seed, extra_args=None): + if extra_args is None: + extra_args = [] + with tempfile.TemporaryDirectory(f"_train_transformer_seed{seed}") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "3", + "--decoder-layers", + "3", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--seed", + str(seed), + ] + + extra_args, + ) + yield os.path.join(data_dir, "checkpoint_last.pt") + + def test_load_model_ensemble_and_task(self): + # with contextlib.redirect_stdout(StringIO()): + with self._train_transformer(seed=123) as model1: + with self._train_transformer(seed=456) as model2: + ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task( + filenames=[model1, model2] + ) + self.assertEqual(len(ensemble), 2) + + # after Transformer has been migrated to Hydra, this will probably + # become cfg.common.seed + self.assertEqual(ensemble[0].args.seed, 123) + self.assertEqual(ensemble[1].args.seed, 456) + + # the task from the first model should be returned + self.assertTrue("seed123" in task.cfg.data) + + # last cfg is saved + self.assertEqual(cfg.common.seed, 456) + + def test_prune_state_dict(self): + with contextlib.redirect_stdout(StringIO()): + extra_args = ["--encoder-layerdrop", "0.01", "--decoder-layerdrop", "0.01"] + with self._train_transformer(seed=1, extra_args=extra_args) as model: + ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task( + filenames=[model], + arg_overrides={ + "encoder_layers_to_keep": "0,2", + "decoder_layers_to_keep": "1", + }, + ) + self.assertEqual(len(ensemble), 1) + self.assertEqual(len(ensemble[0].encoder.layers), 2) + self.assertEqual(len(ensemble[0].decoder.layers), 1) + + def test_torch_persistent_save_async(self): + state_dict = {} + filename = "async_checkpoint.pt" + + with patch(f"{checkpoint_utils.__name__}.PathManager.opena") as mock_opena: + with patch( + f"{checkpoint_utils.__name__}._torch_persistent_save" + ) as mock_save: + checkpoint_utils.torch_persistent_save( + state_dict, filename, async_write=True + ) + mock_opena.assert_called_with(filename, "wb") + mock_save.assert_called() + + def test_load_ema_from_checkpoint(self): + dummy_state = {"a": torch.tensor([1]), "b": torch.tensor([0.1])} + with patch(f"{checkpoint_utils.__name__}.PathManager.open") as mock_open, patch( + f"{checkpoint_utils.__name__}.torch.load" + ) as mock_load: + + mock_load.return_value = {"extra_state": {"ema": dummy_state}} + filename = "ema_checkpoint.pt" + state = checkpoint_utils.load_ema_from_checkpoint(filename) + + mock_open.assert_called_with(filename, "rb") + mock_load.assert_called() + + self.assertIn("a", state["model"]) + self.assertIn("b", state["model"]) + self.assertTrue(torch.allclose(dummy_state["a"], state["model"]["a"])) + self.assertTrue(torch.allclose(dummy_state["b"], state["model"]["b"])) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_checkpoint_utils_for_task_level_attributes.py b/fairseq/tests/test_checkpoint_utils_for_task_level_attributes.py new file mode 100644 index 0000000..53ab401 --- /dev/null +++ b/fairseq/tests/test_checkpoint_utils_for_task_level_attributes.py @@ -0,0 +1,172 @@ +#!/usr/bin/env fbpython +# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary. + +import contextlib +import logging +import unittest +from io import StringIO +from unittest.mock import MagicMock, patch + +import torch +from fairseq import checkpoint_utils, data +from omegaconf import OmegaConf + + +def mock_trainer(epoch, num_updates, iterations_in_epoch): + trainer = MagicMock() + trainer.load_checkpoint.return_value = { + "train_iterator": { + "epoch": epoch, + "iterations_in_epoch": iterations_in_epoch, + "shuffle": False, + }, + "FakeTask": checkpoint_dict()["FakeTask"], + } + trainer.get_num_updates.return_value = num_updates + trainer.task.__class__.__name__ = "FakeTask" + trainer.task.get_checkpoint_dict.return_value = checkpoint_dict() + trainer.task.set_checkpoint_dict = MagicMock() + + return trainer + + +def checkpoint_dict(): + return { + "FakeTask": { + "observer_stats": { + ( + 4, + 16, + "MovingAveragePerChannelMinMax", + "MovingAveragePerChannelMinMax", + ): {"mod1": 1, "mod2": 2, "mod3": 3} + } + } + } + + +def mock_dict(): + d = MagicMock() + d.pad.return_value = 1 + d.eos.return_value = 2 + d.unk.return_value = 3 + return d + + +def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoch): + tokens = torch.LongTensor(list(range(epoch_size))).view(1, -1) + tokens_ds = data.TokenBlockDataset( + tokens, + sizes=[tokens.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + trainer = mock_trainer(epoch, num_updates, iterations_in_epoch) + dataset = data.LanguagePairDataset( + tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False + ) + epoch_itr = data.EpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_sampler=[[i] for i in range(epoch_size)], + ) + return trainer, epoch_itr + + +def get_mock_cfg(finetune_from_model): + cfg_mock = OmegaConf.create( + { + "checkpoint": { + "save_dir": None, + "optimizer_overrides": "{}", + "reset_dataloader": False, + "reset_meters": False, + "reset_optimizer": False, + "reset_lr_scheduler": False, + "finetune_from_model": finetune_from_model, + "model_parallel_size": 1, + "restore_file": "checkpoint_last.pt", + "no_save": False, + "save_interval_updates": 0, + "no_last_checkpoints": False, + "keep_interval_updates": 0, + "keep_last_epochs": 0, + "keep_best_checkpoints": 0, + }, + "common": { + "model_parallel_size": 1, + }, + } + ) + return cfg_mock + + +class TestCheckpointsForTaskLevelAttributes(unittest.TestCase): + def setUp(self) -> None: + self.cfg_mock = get_mock_cfg(None) + self.patches = { + "os.makedirs": MagicMock(), + "os.path.join": MagicMock(), + "os.path.isfile": MagicMock(return_value=True), + "os.path.isabs": MagicMock(return_value=False), + "fairseq.file_io.PathManager.exists": MagicMock(return_value=False), + } + self.applied_patches = [patch(p, d) for p, d in self.patches.items()] + [p.start() for p in self.applied_patches] + logging.disable(logging.CRITICAL) + + self.trainer, self.epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50) + self.trainer.get_train_iterator = MagicMock(return_value=self.epoch_itr) + self.epoch_itr.next_epoch_itr(shuffle=False) + + checkpoint_utils.save_checkpoint( + self.cfg_mock.checkpoint, self.trainer, self.epoch_itr, None + ) + + def tearDown(self): + patch.stopall() + logging.disable(logging.NOTSET) + + def test_verify_checkpoint(self) -> None: + cp_dict = self.trainer.task.get_checkpoint_dict() + self.assertTrue(len(cp_dict) == 1) + self.assertTrue("FakeTask" in cp_dict) + self.assertTrue("observer_stats" in cp_dict["FakeTask"]) + self.assertTrue(len(cp_dict["FakeTask"]["observer_stats"]) == 1) + self.assertTrue( + ( + 4, + 16, + "MovingAveragePerChannelMinMax", + "MovingAveragePerChannelMinMax", + ) + in cp_dict["FakeTask"]["observer_stats"] + ) + self.assertTrue( + cp_dict["FakeTask"]["observer_stats"][ + ( + 4, + 16, + "MovingAveragePerChannelMinMax", + "MovingAveragePerChannelMinMax", + ) + ] + == {"mod1": 1, "mod2": 2, "mod3": 3} + ) + + def test_load_checkpoint(self) -> None: + with contextlib.redirect_stdout(StringIO()): + # Now, load checkpoint to ensure the respective logic works as expected + _, epoch_itr = checkpoint_utils.load_checkpoint( + self.cfg_mock.checkpoint, self.trainer + ) + + self.trainer.task.set_checkpoint_dict.assert_called_once_with( + checkpoint_dict()["FakeTask"] + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_concat_dataset.py b/fairseq/tests/test_concat_dataset.py new file mode 100644 index 0000000..d94aeff --- /dev/null +++ b/fairseq/tests/test_concat_dataset.py @@ -0,0 +1,58 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from fairseq.data import LanguagePairDataset, TokenBlockDataset +from fairseq.data.concat_dataset import ConcatDataset +from tests.test_train import mock_dict + + +class TestConcatDataset(unittest.TestCase): + def setUp(self): + d = mock_dict() + tokens_1 = torch.LongTensor([1]).view(1, -1) + tokens_ds1 = TokenBlockDataset( + tokens_1, + sizes=[tokens_1.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_1 = LanguagePairDataset( + tokens_ds1, tokens_ds1.sizes, d, shuffle=False + ) + tokens_2 = torch.LongTensor([2]).view(1, -1) + tokens_ds2 = TokenBlockDataset( + tokens_2, + sizes=[tokens_2.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_2 = LanguagePairDataset( + tokens_ds2, tokens_ds2.sizes, d, shuffle=False + ) + + def test_concat_dataset_basics(self): + d = ConcatDataset([self.dataset_1, self.dataset_2]) + assert len(d) == 2 + assert d[0]["source"][0] == 1 + assert d[1]["source"][0] == 2 + + d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[1, 2]) + assert len(d) == 3 + assert d[0]["source"][0] == 1 + assert d[1]["source"][0] == 2 + assert d[2]["source"][0] == 2 + + d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[2, 1]) + assert len(d) == 3 + assert d[0]["source"][0] == 1 + assert d[1]["source"][0] == 1 + assert d[2]["source"][0] == 2 diff --git a/fairseq/tests/test_constraints.py b/fairseq/tests/test_constraints.py new file mode 100644 index 0000000..d766d51 --- /dev/null +++ b/fairseq/tests/test_constraints.py @@ -0,0 +1,275 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from typing import List + +import torch + +from fairseq.token_generation_constraints import ( + ConstraintNode, + OrderedConstraintState, + UnorderedConstraintState, + pack_constraints, +) + + +def tensorize(constraints: List[List[int]]) -> torch.Tensor: + return [torch.tensor(x) for x in constraints] + + +class TestHelperRoutines(unittest.TestCase): + def setUp(self): + self.examples = [ + ([[]], torch.tensor([[0]])), + ([[], []], torch.tensor([[0], [0]])), + ([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])), + ( + [ + [ + torch.tensor([3, 1, 2]), + torch.tensor([3]), + torch.tensor([4, 5, 6, 7]), + ], + [], + [torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])], + ], + torch.tensor( + [ + [3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0], + ] + ), + ), + ] + + def test_packing(self): + """Ensures the list of lists of tensors gets packed correctly.""" + for batch_constraints, expected_tensor in self.examples: + packed = pack_constraints(batch_constraints) + assert torch.equal(packed, expected_tensor) + + +class TestUnorderedConstraintState(unittest.TestCase): + def setUp(self): + # Tuples of (contraint set, expected printed graph, token counts per node) + self.examples = [ + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + "([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))", # noqa + {1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1}, + ), + ([], "[None].False#0", {}), + (tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}), + ( + tensorize([[100000, 1, 2, 3, 4, 5]]), + "([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))", + {100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, + ), + ( + tensorize([[1, 2], [1, 2]]), + "([None].False#2 ([1].False#2 [2].True#2))", + {1: 2, 2: 2}, + ), + ( + tensorize([[1, 2], [3, 4]]), + "([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))", + {1: 1, 2: 1, 3: 1, 4: 1}, + ), + ] + + self.sequences = [ + ( + self.examples[0][0], + [], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + [1, 2], + {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, + ), + ( + self.examples[0][0], + [1, 2, 94], + {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + [1, 3, 999, 1, 4], + {"bank": 4, "num_completed": 2, "finished": False, "is_root": False}, + ), + ( + self.examples[0][0], + [1, 3, 999, 1, 4, 999], + {"bank": 4, "num_completed": 2, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + [4, 5, 6, 8], + {"bank": 2, "num_completed": 1, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + # Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5] + # [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]], + [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, + ), + ( + self.examples[0][0], + [1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": True}, + ), + ( + tensorize([[1], [2, 3]]), + # Should not be able to get credit for entering 1 a second time + [1, 1], + {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, + ), + ( + self.examples[4][0], + [1, 2, 1, 2], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ( + self.examples[4][0], + [1, 2, 1, 2, 1], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, + ), + ( + self.examples[5][0], + [1, 2, 3, 4, 5], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, + ), + ] + + def test_graphs(self): + """ + Test whether unordered graph systems are created correctly. + """ + for example in self.examples: + constraints, expected, gold_counts = example + c = ConstraintNode.create(constraints) + assert ( + ConstraintNode.print_graph(c) == expected + ), f"got {ConstraintNode.print_graph(c)}, expected {expected}" + assert ( + c.token_counts() == gold_counts + ), f"{c} got {c.token_counts()} wanted {gold_counts}" + + def test_next_tokens(self): + """ + Tests that the set of next tokens is correct. + """ + for example in self.examples: + constraints, expected, gold_counts = example + root = ConstraintNode.create(constraints) + + root_tokens = set(root.children.keys()) + for sequence in constraints: + state = UnorderedConstraintState(root) + for token in sequence: + all_tokens = root_tokens.union(state.node.children.keys()) + assert ( + all_tokens == state.next_tokens() + ), f"ALL {all_tokens} NEXT {state.next_tokens()}" + state = state.advance(token) + + def test_sequences(self): + for constraints, tokens, expected in self.sequences: + state = UnorderedConstraintState.create(pack_constraints([constraints])[0]) + for token in tokens: + state = state.advance(token) + result = {} + for attr in expected.keys(): + result[attr] = getattr(state, attr) + + assert ( + result == expected + ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" + + +class TestOrderedConstraintState(unittest.TestCase): + def setUp(self): + self.sequences = [ + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2], + {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 94], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 3, 999, 1, 4], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 3, 999, 999], + {"bank": 3, "num_completed": 1, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 3, 77, 1, 3, 1], + {"bank": 6, "num_completed": 2, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, + ), + ( + tensorize([[1], [2, 3]]), + [1, 1], + {"bank": 1, "num_completed": 1, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2], [1, 2]]), + [1, 2, 1, 2], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ( + tensorize([[1, 2], [1, 2]]), + [1, 2, 1, 2, 1], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ( + tensorize([[1, 2], [3, 4]]), + [1, 2, 3, 4, 5], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ] + + def test_sequences(self): + for i, (constraints, tokens, expected) in enumerate(self.sequences): + state = OrderedConstraintState.create(pack_constraints([constraints])[0]) + for token in tokens: + state = state.advance(token) + result = {} + for attr in expected.keys(): + result[attr] = getattr(state, attr) + assert ( + result == expected + ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_convtbc.py b/fairseq/tests/test_convtbc.py new file mode 100644 index 0000000..3a3c9b9 --- /dev/null +++ b/fairseq/tests/test_convtbc.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +import torch.nn as nn +from fairseq.modules import ConvTBC + + +class TestConvTBC(unittest.TestCase): + def test_convtbc(self): + # ksz, in_channels, out_channels + conv_tbc = ConvTBC(4, 5, kernel_size=3, padding=1) + # out_channels, in_channels, ksz + conv1d = nn.Conv1d(4, 5, kernel_size=3, padding=1) + + conv_tbc.weight.data.copy_(conv1d.weight.data.transpose(0, 2)) + conv_tbc.bias.data.copy_(conv1d.bias.data) + + input_tbc = torch.randn(7, 2, 4, requires_grad=True) + input1d = input_tbc.data.transpose(0, 1).transpose(1, 2) + input1d.requires_grad = True + + output_tbc = conv_tbc(input_tbc) + output1d = conv1d(input1d) + + self.assertAlmostEqual( + output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data + ) + + grad_tbc = torch.randn(output_tbc.size()) + grad1d = grad_tbc.transpose(0, 1).transpose(1, 2).contiguous() + + output_tbc.backward(grad_tbc) + output1d.backward(grad1d) + + self.assertAlmostEqual( + conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data + ) + self.assertAlmostEqual(conv_tbc.bias.grad.data, conv1d.bias.grad.data) + self.assertAlmostEqual( + input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data + ) + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess((t1 - t2).abs().max(), 1e-4) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_data_utils.py b/fairseq/tests/test_data_utils.py new file mode 100644 index 0000000..c48d02c --- /dev/null +++ b/fairseq/tests/test_data_utils.py @@ -0,0 +1,136 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import numpy as np + +from fairseq.data.data_utils_fast import batch_by_size_fn, batch_by_size_vec + + +class TestBatchBySize(unittest.TestCase): + @classmethod + def batch_by_size_baseline( + cls, + indices, + num_tokens_vec, + max_tokens, + max_sentences, + bsz_mult, + ): + """Simple, reliable and slow implementation of batch by size""" + batches = [] + start = 0 + while start < len(indices): + for end in range(start + 1, len(indices) + 1): + max_val = max(num_tokens_vec[pos] for pos in range(start, end)) + sent_count = end - start + num_tokens = max_val * sent_count + overflow = num_tokens > max_tokens > 0 or sent_count > max_sentences > 0 + terminate = overflow or end == len(indices) + if overflow: + sent_count -= 1 + if terminate: + if sent_count > bsz_mult: + sent_count = sent_count - sent_count % bsz_mult + batches.append(indices[start : start + sent_count]) + start = start + sent_count + break + return batches + + @classmethod + def _get_error_message( + cls, max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results + ): + return f"""Reference batch_by_size implementation should produce + same output as the baseline method. + Params: + max_sentences={max_sentences}, + max_tokens={max_tokens}, + bsz_mult={bsz_mult}, + num_tokens_vec={num_tokens_vec}, + expected_batches={validation}, + returned_batches={results}""" + + def _compare_results( + self, + indices_len, + batch_by_size_impl, + max_sentences, + max_tokens, + bsz_mult, + num_tokens_vec, + ): + indices = np.array(list(range(indices_len))) + validation = self.batch_by_size_baseline( + indices, + num_tokens_vec, + max_tokens=max_tokens, + max_sentences=max_sentences, + bsz_mult=bsz_mult, + ) + results = batch_by_size_impl( + indices, + num_tokens_vec, + max_tokens=max_tokens, + max_sentences=max_sentences, + bsz_mult=bsz_mult, + ) + error_msg = self._get_error_message( + max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results + ) + self.assertEqual(len(validation), len(results), error_msg) + for first, second in zip(validation, results): + self.assertTrue(np.array_equal(first, second), error_msg) + + def _run_compare_with_baseline_sweep(self, batch_by_size_impl): + """Compare reference batch_by_size implementation with batch_by_size_baseline + across a dense grid of hyperparam values""" + MAX_MAX_TOKENS = 10 + NUM_TOKENS_VECS_COUNT = 5 + for indices_len in [10, 11]: # try odd and even len of indices + for max_sentences in range(0, indices_len + 2): + for max_tokens in range(0, MAX_MAX_TOKENS): + for bsz_mult in range(1, max(MAX_MAX_TOKENS, indices_len) + 2): + for _ in range(NUM_TOKENS_VECS_COUNT): + num_tokens_vec = np.random.randint( + 0, max_tokens + 1, size=indices_len + ) + self._compare_results( + indices_len, + batch_by_size_impl, + max_sentences, + max_tokens, + bsz_mult, + num_tokens_vec, + ) + + +class TestBatchBySizeVec(TestBatchBySize): + def test_compare_with_baseline(self): + self._run_compare_with_baseline_sweep(batch_by_size_vec) + + +class TestBatchBySizeFn(TestBatchBySize): + def test_compare_with_baseline(self): + def batch_by_size_fn_wrapper( + indices, + num_tokens_vec, + max_tokens, + max_sentences, + bsz_mult, + ): + def num_tokens_fn(idx): + return num_tokens_vec[idx] + + return batch_by_size_fn( + indices, num_tokens_fn, max_tokens, max_sentences, bsz_mult + ) + + self._run_compare_with_baseline_sweep(batch_by_size_fn_wrapper) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_dataclass_utils.py b/fairseq/tests/test_dataclass_utils.py new file mode 100644 index 0000000..231f86b --- /dev/null +++ b/fairseq/tests/test_dataclass_utils.py @@ -0,0 +1,87 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from argparse import ArgumentParser +from dataclasses import dataclass, field + +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import gen_parser_from_dataclass + + +@dataclass +class A(FairseqDataclass): + data: str = field(default="test", metadata={"help": "the data input"}) + num_layers: int = field(default=200, metadata={"help": "more layers is better?"}) + + +@dataclass +class B(FairseqDataclass): + bar: A = field(default=A()) + foo: int = field(default=0, metadata={"help": "not a bar"}) + + +@dataclass +class D(FairseqDataclass): + arch: A = field(default=A()) + foo: int = field(default=0, metadata={"help": "not a bar"}) + + +@dataclass +class C(FairseqDataclass): + data: str = field(default="test", metadata={"help": "root level data input"}) + encoder: D = field(default=D()) + decoder: A = field(default=A()) + lr: int = field(default=0, metadata={"help": "learning rate"}) + + +class TestDataclassUtils(unittest.TestCase): + def test_argparse_convert_basic(self): + parser = ArgumentParser() + gen_parser_from_dataclass(parser, A(), True) + args = parser.parse_args(["--num-layers", "10", "the/data/path"]) + self.assertEqual(args.num_layers, 10) + self.assertEqual(args.data, "the/data/path") + + def test_argparse_recursive(self): + parser = ArgumentParser() + gen_parser_from_dataclass(parser, B(), True) + args = parser.parse_args(["--num-layers", "10", "--foo", "10", "the/data/path"]) + self.assertEqual(args.num_layers, 10) + self.assertEqual(args.foo, 10) + self.assertEqual(args.data, "the/data/path") + + def test_argparse_recursive_prefixing(self): + self.maxDiff = None + parser = ArgumentParser() + gen_parser_from_dataclass(parser, C(), True, "") + args = parser.parse_args( + [ + "--encoder-arch-data", + "ENCODER_ARCH_DATA", + "--encoder-arch-num-layers", + "10", + "--encoder-foo", + "10", + "--decoder-data", + "DECODER_DATA", + "--decoder-num-layers", + "10", + "--lr", + "10", + "the/data/path", + ] + ) + self.assertEqual(args.encoder_arch_data, "ENCODER_ARCH_DATA") + self.assertEqual(args.encoder_arch_num_layers, 10) + self.assertEqual(args.encoder_foo, 10) + self.assertEqual(args.decoder_data, "DECODER_DATA") + self.assertEqual(args.decoder_num_layers, 10) + self.assertEqual(args.lr, 10) + self.assertEqual(args.data, "the/data/path") + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_dataset.py b/fairseq/tests/test_dataset.py new file mode 100644 index 0000000..a3e3970 --- /dev/null +++ b/fairseq/tests/test_dataset.py @@ -0,0 +1,66 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import unittest +from typing import Sequence + +from fairseq.data import LanguagePairDataset, ListDataset, RoundRobinZipDatasets +from tests.test_train import mock_dict + + +def lang_pair_dataset(lengths: Sequence[int]) -> LanguagePairDataset: + tokens = [[i] * l for i, l in enumerate(lengths)] + return LanguagePairDataset(ListDataset(tokens), lengths, mock_dict()) + + +def sample(id: int, length: int): + return {"id": id, "source": [id] * length, "target": None} + + +class TestDataset(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_round_robin_zip_datasets(self): + long_dataset = lang_pair_dataset([10, 9, 8, 11]) + short_dataset = lang_pair_dataset([11, 9]) + + dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) + # Dataset is now sorted by sentence length + dataset.ordered_indices() + assert dataset.longest_dataset is long_dataset + self.assertEqual(dict(dataset[0]), {"a": sample(2, 8), "b": sample(1, 9)}) + # The item 2 of dataset 'a' is with item (2 % 2 = 0) of dataset 'b' + self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 9)}) + + def test_round_robin_zip_datasets_filtered(self): + long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12]) + short_dataset = lang_pair_dataset([11, 20, 9, 1000]) + + dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) + # Dataset is now sorted by sentence length + idx = dataset.ordered_indices() + idx, _ = dataset.filter_indices_by_size(idx, {"a": 19, "b": 900}) + self.assertEqual(list(idx), [0, 1, 2, 3, 4]) + self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)}) + self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 20)}) + self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(0, 11)}) + + def test_round_robin_zip_datasets_filtered_with_tuple(self): + long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12]) + short_dataset = lang_pair_dataset([11, 20, 9, 1000]) + + dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) + # Dataset is now sorted by sentence length + idx = dataset.ordered_indices() + idx, _ = dataset.filter_indices_by_size(idx, 19) + self.assertEqual(list(idx), [0, 1, 2, 3, 4]) + self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)}) + self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(2, 9)}) + self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(2, 9)}) diff --git a/fairseq/tests/test_dictionary.py b/fairseq/tests/test_dictionary.py new file mode 100644 index 0000000..dc9d71b --- /dev/null +++ b/fairseq/tests/test_dictionary.py @@ -0,0 +1,145 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import io +import os +import string +import tempfile +import unittest + +import torch +from fairseq import tokenizer +from fairseq.data import Dictionary + + +class TestDictionary(unittest.TestCase): + def test_finalize(self): + txt = [ + "A B C D", + "B C D", + "C D", + "D", + ] + ref_ids1 = list( + map( + torch.IntTensor, + [ + [4, 5, 6, 7, 2], + [5, 6, 7, 2], + [6, 7, 2], + [7, 2], + ], + ) + ) + ref_ids2 = list( + map( + torch.IntTensor, + [ + [7, 6, 5, 4, 2], + [6, 5, 4, 2], + [5, 4, 2], + [4, 2], + ], + ) + ) + + # build dictionary + d = Dictionary() + for line in txt: + d.encode_line(line, add_if_not_exist=True) + + def get_ids(dictionary): + ids = [] + for line in txt: + ids.append(dictionary.encode_line(line, add_if_not_exist=False)) + return ids + + def assertMatch(ids, ref_ids): + for toks, ref_toks in zip(ids, ref_ids): + self.assertEqual(toks.size(), ref_toks.size()) + self.assertEqual(0, (toks != ref_toks).sum().item()) + + ids = get_ids(d) + assertMatch(ids, ref_ids1) + + # check finalized dictionary + d.finalize() + finalized_ids = get_ids(d) + assertMatch(finalized_ids, ref_ids2) + + # write to disk and reload + with tempfile.NamedTemporaryFile(mode="w") as tmp_dict: + d.save(tmp_dict.name) + d = Dictionary.load(tmp_dict.name) + reload_ids = get_ids(d) + assertMatch(reload_ids, ref_ids2) + assertMatch(finalized_ids, reload_ids) + + def test_overwrite(self): + # for example, Camembert overwrites <unk>, <s> and </s> + dict_file = io.StringIO( + "<unk> 999 #fairseq:overwrite\n" + "<s> 999 #fairseq:overwrite\n" + "</s> 999 #fairseq:overwrite\n" + ", 999\n" + "▁de 999\n" + ) + d = Dictionary() + d.add_from_file(dict_file) + self.assertEqual(d.index("<pad>"), 1) + self.assertEqual(d.index("foo"), 3) + self.assertEqual(d.index("<unk>"), 4) + self.assertEqual(d.index("<s>"), 5) + self.assertEqual(d.index("</s>"), 6) + self.assertEqual(d.index(","), 7) + self.assertEqual(d.index("▁de"), 8) + + def test_no_overwrite(self): + # for example, Camembert overwrites <unk>, <s> and </s> + dict_file = io.StringIO( + "<unk> 999\n" "<s> 999\n" "</s> 999\n" ", 999\n" "▁de 999\n" + ) + d = Dictionary() + with self.assertRaisesRegex(RuntimeError, "Duplicate"): + d.add_from_file(dict_file) + + def test_space(self): + # for example, character models treat space as a symbol + dict_file = io.StringIO(" 999\n" "a 999\n" "b 999\n") + d = Dictionary() + d.add_from_file(dict_file) + self.assertEqual(d.index(" "), 4) + self.assertEqual(d.index("a"), 5) + self.assertEqual(d.index("b"), 6) + + def test_add_file_to_dict(self): + counts = {} + num_lines = 100 + per_line = 10 + with tempfile.TemporaryDirectory("test_sampling") as data_dir: + filename = os.path.join(data_dir, "dummy.txt") + with open(filename, "w", encoding="utf-8") as data: + for c in string.ascii_letters: + line = f"{c} " * per_line + for _ in range(num_lines): + data.write(f"{line}\n") + counts[c] = per_line * num_lines + per_line += 5 + + dict = Dictionary() + Dictionary.add_file_to_dictionary( + filename, dict, tokenizer.tokenize_line, 10 + ) + dict.finalize(threshold=0, nwords=-1, padding_factor=8) + + for c in string.ascii_letters: + count = dict.get_count(dict.index(c)) + self.assertEqual( + counts[c], count, f"{c} count is {count} but should be {counts[c]}" + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_ema.py b/fairseq/tests/test_ema.py new file mode 100644 index 0000000..bd2cf2c --- /dev/null +++ b/fairseq/tests/test_ema.py @@ -0,0 +1,275 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from copy import deepcopy +from dataclasses import dataclass +import pytest +from typing import Optional +from unittest.mock import patch + +import torch + +from fairseq.models.ema import EMA + + +class DummyModule(torch.nn.Module): + def __init__(self) -> None: + """LightningModule for testing purposes + + Args: + epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum + validation loss for testing purposes (zero based). If None this is ignored. Defaults to None. + """ + super().__init__() + self.layer = torch.nn.Linear(in_features=32, out_features=2) + self.another_layer = torch.nn.Linear(in_features=2, out_features=2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.layer(x) + return self.another_layer(x) + + +@dataclass +class EMAConfig(object): + ema_decay: float = 0.99 + ema_start_update: int = 0 + ema_fp32: bool = False + ema_seed_model: Optional[str] = None + ema_update_freq: int = 1 + + +class TestEMA(unittest.TestCase): + def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None): + diff = x.float() - y.float() + diff_norm = torch.norm(diff) + other_norm = torch.norm(y.float()) + + if msg is None: + msg = "|input - other| > {} + {} * |other|".format(atol, rtol) + + self.assertLessEqual( + diff_norm, + atol + rtol * other_norm, + msg=msg, + ) + + def test_ema(self): + model = DummyModule() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig() + ema = EMA(model, config) + + # set decay + ema._set_decay(config.ema_decay) + self.assertEqual(ema.get_decay(), config.ema_decay) + + # get model + self.assertEqual(ema.get_model(), ema.model) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # EMA step + x = torch.randn(32) + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + ema_state_dict = ema.get_model().state_dict() + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema_state_dict[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # Load EMA into model + model2 = DummyModule() + ema.reverse(model2) + + for key, param in model2.state_dict().items(): + ema_param = ema_state_dict[key] + self.assertTrue(torch.allclose(ema_param, param)) + + # Check that step_internal is called once + with patch.object(ema, "_step_internal", return_value=None) as mock_method: + ema.step(model) + mock_method.assert_called_once_with(model, None) + + def _test_ema_start_update(self, updates): + model = DummyModule() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_start_update=1) + ema = EMA(model, config) + + # EMA step + x = torch.randn(32) + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model, updates=updates) + ema_state_dict = ema.get_model().state_dict() + + self.assertEqual(ema.get_decay(), 0 if updates == 0 else config.ema_decay) + + for key, param in model.state_dict().items(): + ema_param = ema_state_dict[key] + prev_param = state[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + if updates == 0: + self.assertTorchAllClose( + ema_param, + param, + ) + else: + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Check that step_internal is called once + with patch.object(ema, "_step_internal", return_value=None) as mock_method: + ema.step(model, updates=updates) + mock_method.assert_called_once_with(model, updates) + + def test_ema_before_start_update(self): + self._test_ema_start_update(updates=0) + + def test_ema_after_start_update(self): + self._test_ema_start_update(updates=1) + + def test_ema_fp32(self): + dtype = torch.float + + model = DummyModule().to(dtype) + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=True) + ema = EMA(model, config) + + x = torch.randn(32) + y = model(x.to(dtype)) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertIn(key, ema.fp32_params) + + # EMA update is done in fp32, and hence the EMA param must be + # closer to the EMA update done in fp32 than in fp16. + self.assertLessEqual( + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param.float() + + (1 - config.ema_decay) * param.float() + ) + .to(dtype) + .float() + ), + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param + (1 - config.ema_decay) * param + ).float() + ), + ) + self.assertTorchAllClose( + ema_param, + ( + config.ema_decay * prev_param.float() + + (1 - config.ema_decay) * param.float() + ).to(dtype), + ) + + @pytest.mark.skipif( + not torch.cuda.is_available(), + reason="CPU no longer supports Linear in half precision", + ) + def test_ema_fp16(self): + model = DummyModule().cuda().half() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=False) + ema = EMA(model, config) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + x = torch.randn(32).cuda() + y = model(x.half()) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + + # EMA update is done in fp16, and hence the EMA param must be + # closer to the EMA update done in fp16 than in fp32. + self.assertLessEqual( + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param + (1 - config.ema_decay) * param + ).float() + ), + torch.norm( + ema_param.float() + - ( + config.ema_decay * prev_param.float() + + (1 - config.ema_decay) * param.float() + ) + .half() + .float() + ), + ) + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_espnet_multihead_attention.py b/fairseq/tests/test_espnet_multihead_attention.py new file mode 100644 index 0000000..ee71dd0 --- /dev/null +++ b/fairseq/tests/test_espnet_multihead_attention.py @@ -0,0 +1,176 @@ +import torch +import numpy as np +import unittest +from fairseq.modules import ( + ESPNETMultiHeadedAttention, + RelPositionMultiHeadedAttention, + RotaryPositionMultiHeadedAttention, +) + +torch.use_deterministic_algorithms(True) + + +class TestESPNETMultiHeadedAttention(unittest.TestCase): + def setUp(self) -> None: + self.T = 3 + self.B = 1 + self.C = 2 + torch.manual_seed(0) + self.sample = torch.randn(self.T, self.B, self.C) # TBC + self.sample_scores = torch.randn(self.B, 1, self.T, self.T) + self.MHA = ESPNETMultiHeadedAttention(self.C, 1, dropout=0) + + def test_forward(self): + expected_scores = torch.tensor( + [[[0.1713, -0.3776]], [[0.2263, -0.4486]], [[0.2243, -0.4538]]] + ) + scores, _ = self.MHA(self.sample, self.sample, self.sample) + self.assertTrue( + np.allclose( + expected_scores.cpu().detach().numpy(), + scores.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + def test_forward_qkv(self): + expected_query = torch.tensor( + [[[[-1.0235, 0.0409], [0.4008, 1.3077], [0.5396, 2.0698]]]] + ) + expected_key = torch.tensor( + [[[[0.5053, -0.4965], [-0.3730, -0.9473], [-0.7019, -0.1935]]]] + ) + expected_val = torch.tensor( + [[[[-0.9940, 0.5403], [0.5924, -0.7619], [0.7504, -1.0892]]]] + ) + sample_t = self.sample.transpose(0, 1) + query, key, val = self.MHA.forward_qkv(sample_t, sample_t, sample_t) + self.assertTrue( + np.allclose( + expected_query.cpu().detach().numpy(), + query.cpu().detach().numpy(), + atol=1e-4, + ) + ) + self.assertTrue( + np.allclose( + expected_key.cpu().detach().numpy(), + key.cpu().detach().numpy(), + atol=1e-4, + ) + ) + self.assertTrue( + np.allclose( + expected_val.cpu().detach().numpy(), + val.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + def test_forward_attention(self): + expected_scores = torch.tensor( + [[[0.1627, -0.6249], [-0.2547, -0.6487], [-0.0711, -0.8545]]] + ) + scores = self.MHA.forward_attention( + self.sample.transpose(0, 1).view(self.B, 1, self.T, self.C), + self.sample_scores, + mask=None, + ) + self.assertTrue( + np.allclose( + expected_scores.cpu().detach().numpy(), + scores.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + +class TestRelPositionMultiHeadedAttention(unittest.TestCase): + def setUp(self) -> None: + self.T = 3 + self.B = 1 + self.C = 2 + torch.manual_seed(0) + self.sample = torch.randn(self.T, self.B, self.C) # TBC + self.sample_x = torch.randn(self.B, 1, self.T, self.T * 2 - 1) + self.sample_pos = torch.randn(self.B, self.T * 2 - 1, self.C) + self.MHA = RelPositionMultiHeadedAttention(self.C, 1, dropout=0) + + def test_rel_shift(self): + expected_x = torch.tensor( + [ + [ + [ + [-0.7193, -0.4033, -0.5966], + [-0.8567, 1.1006, -1.0712], + [-0.5663, 0.3731, -0.8920], + ] + ] + ] + ) + x = self.MHA.rel_shift(self.sample_x) + self.assertTrue( + np.allclose( + expected_x.cpu().detach().numpy(), + x.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + def test_forward(self): + expected_scores = torch.tensor( + [ + [[-0.9609, -0.5020]], + [[-0.9308, -0.4890]], + [[-0.9473, -0.4948]], + [[-0.9609, -0.5020]], + [[-0.9308, -0.4890]], + [[-0.9473, -0.4948]], + [[-0.9609, -0.5020]], + [[-0.9308, -0.4890]], + [[-0.9473, -0.4948]], + [[-0.9609, -0.5020]], + [[-0.9308, -0.4890]], + [[-0.9473, -0.4948]], + [[-0.9609, -0.5020]], + [[-0.9308, -0.4890]], + [[-0.9473, -0.4948]], + ] + ) + scores, _ = self.MHA(self.sample, self.sample, self.sample, self.sample_pos) + self.assertTrue( + np.allclose( + expected_scores.cpu().detach().numpy(), + scores.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + +class TestRotaryPositionMultiHeadedAttention(unittest.TestCase): + def setUp(self) -> None: + self.T = 3 + self.B = 1 + self.C = 2 + torch.manual_seed(0) + self.sample = torch.randn(self.T, self.B, self.C) # TBC + self.MHA = RotaryPositionMultiHeadedAttention( + self.C, 1, dropout=0, precision=None + ) + + def test_forward(self): + expected_scores = torch.tensor( + [[[-0.3220, -0.4726]], [[-1.2813, -0.0979]], [[-0.3138, -0.4758]]] + ) + scores, _ = self.MHA(self.sample, self.sample, self.sample) + self.assertTrue( + np.allclose( + expected_scores.cpu().detach().numpy(), + scores.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_export.py b/fairseq/tests/test_export.py new file mode 100644 index 0000000..3e9a48d --- /dev/null +++ b/fairseq/tests/test_export.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import tempfile +import unittest + +import torch + +from fairseq.data.dictionary import Dictionary +from fairseq.models.transformer import TransformerModel +from fairseq.modules import multihead_attention, sinusoidal_positional_embedding +from fairseq.tasks.fairseq_task import LegacyFairseqTask + +DEFAULT_TEST_VOCAB_SIZE = 100 + + +class DummyTask(LegacyFairseqTask): + def __init__(self, args): + super().__init__(args) + self.dictionary = get_dummy_dictionary() + if getattr(self.args, "ctc", False): + self.dictionary.add_symbol("<ctc_blank>") + self.src_dict = self.dictionary + self.tgt_dict = self.dictionary + + @property + def source_dictionary(self): + return self.src_dict + + @property + def target_dictionary(self): + return self.dictionary + + +def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): + dummy_dict = Dictionary() + # add dummy symbol to satisfy vocab size + for id, _ in enumerate(range(vocab_size)): + dummy_dict.add_symbol("{}".format(id), 1000) + return dummy_dict + + +def get_dummy_task_and_parser(): + """ + Return a dummy task and argument parser, which can be used to + create a model/criterion. + """ + parser = argparse.ArgumentParser( + description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS + ) + DummyTask.add_args(parser) + args = parser.parse_args([]) + task = DummyTask.setup_task(args) + return task, parser + + +def _test_save_and_load(scripted_module): + with tempfile.NamedTemporaryFile() as f: + scripted_module.save(f.name) + torch.jit.load(f.name) + + +class TestExportModels(unittest.TestCase): + def test_export_multihead_attention(self): + module = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) + scripted = torch.jit.script(module) + _test_save_and_load(scripted) + + def test_incremental_state_multihead_attention(self): + module1 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) + module1 = torch.jit.script(module1) + module2 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) + module2 = torch.jit.script(module2) + + state = {} + state = module1.set_incremental_state(state, "key", {"a": torch.tensor([1])}) + state = module2.set_incremental_state(state, "key", {"a": torch.tensor([2])}) + v1 = module1.get_incremental_state(state, "key")["a"] + v2 = module2.get_incremental_state(state, "key")["a"] + + self.assertEqual(v1, 1) + self.assertEqual(v2, 2) + + def test_positional_embedding(self): + module = sinusoidal_positional_embedding.SinusoidalPositionalEmbedding( + embedding_dim=8, padding_idx=1 + ) + scripted = torch.jit.script(module) + _test_save_and_load(scripted) + + @unittest.skipIf( + torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" + ) + def test_export_transformer(self): + task, parser = get_dummy_task_and_parser() + TransformerModel.add_args(parser) + args = parser.parse_args([]) + model = TransformerModel.build_model(args, task) + scripted = torch.jit.script(model) + _test_save_and_load(scripted) + + @unittest.skipIf( + torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" + ) + def test_export_transformer_no_token_pos_emb(self): + task, parser = get_dummy_task_and_parser() + TransformerModel.add_args(parser) + args = parser.parse_args([]) + args.no_token_positional_embeddings = True + model = TransformerModel.build_model(args, task) + scripted = torch.jit.script(model) + _test_save_and_load(scripted) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_file_chunker_utils.py b/fairseq/tests/test_file_chunker_utils.py new file mode 100644 index 0000000..5cded04 --- /dev/null +++ b/fairseq/tests/test_file_chunker_utils.py @@ -0,0 +1,63 @@ +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import shutil +import tempfile +import unittest +from typing import Optional + + +class TestFileChunker(unittest.TestCase): + _tmpdir: Optional[str] = None + _tmpfile: Optional[str] = None + _line_content = "Hello, World\n" + _num_bytes = None + _num_lines = 200 + _num_splits = 20 + + @classmethod + def setUpClass(cls) -> None: + cls._num_bytes = len(cls._line_content.encode("utf-8")) + cls._tmpdir = tempfile.mkdtemp() + with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f: + cls._tmpfile = f.name + for _i in range(cls._num_lines): + f.write(cls._line_content) + f.flush() + + @classmethod + def tearDownClass(cls) -> None: + # Cleanup temp working dir. + if cls._tmpdir is not None: + shutil.rmtree(cls._tmpdir) # type: ignore + + def test_find_offsets(self): + from fairseq.file_chunker_utils import find_offsets + + offsets = find_offsets(self._tmpfile, self._num_splits) + self.assertEqual(len(offsets), self._num_splits + 1) + (zero, *real_offsets, last) = offsets + self.assertEqual(zero, 0) + for i, o in enumerate(real_offsets): + self.assertEqual( + o, + self._num_bytes + + ((i + 1) * self._num_bytes * self._num_lines / self._num_splits), + ) + self.assertEqual(last, self._num_bytes * self._num_lines) + + def test_readchunks(self): + from fairseq.file_chunker_utils import Chunker, find_offsets + + offsets = find_offsets(self._tmpfile, self._num_splits) + for start, end in zip(offsets, offsets[1:]): + with Chunker(self._tmpfile, start, end) as lines: + all_lines = list(lines) + num_lines = self._num_lines / self._num_splits + self.assertAlmostEqual( + len(all_lines), num_lines, delta=1 + ) # because we split on the bites, we might end up with one more/less line in a chunk + self.assertListEqual( + all_lines, [self._line_content for _ in range(len(all_lines))] + ) diff --git a/fairseq/tests/test_file_io.py b/fairseq/tests/test_file_io.py new file mode 100644 index 0000000..af7c4ce --- /dev/null +++ b/fairseq/tests/test_file_io.py @@ -0,0 +1,59 @@ +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import shutil +import sys +import tempfile +import unittest +from typing import Optional +from unittest.mock import MagicMock + + +class TestFileIO(unittest.TestCase): + + _tmpdir: Optional[str] = None + _tmpfile: Optional[str] = None + _tmpfile_contents = "Hello, World" + + @classmethod + def setUpClass(cls) -> None: + cls._tmpdir = tempfile.mkdtemp() + with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f: + cls._tmpfile = f.name + f.write(cls._tmpfile_contents) + f.flush() + + @classmethod + def tearDownClass(cls) -> None: + # Cleanup temp working dir. + if cls._tmpdir is not None: + shutil.rmtree(cls._tmpdir) # type: ignore + + def test_file_io(self): + from fairseq.file_io import PathManager + + with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: + s = f.read() + self.assertEqual(s, self._tmpfile_contents) + + def test_file_io_oss(self): + # Mock iopath to simulate oss environment. + sys.modules["iopath"] = MagicMock() + from fairseq.file_io import PathManager + + with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: + s = f.read() + self.assertEqual(s, self._tmpfile_contents) + + def test_file_io_async(self): + # ioPath `PathManager` is initialized after the first `opena` call. + try: + from fairseq.file_io import PathManager + + _asyncfile = os.path.join(self._tmpdir, "async.txt") + f = PathManager.opena(_asyncfile, "wb") + f.close() + + finally: + self.assertTrue(PathManager.async_close()) diff --git a/fairseq/tests/test_fp16_optimizer.py b/fairseq/tests/test_fp16_optimizer.py new file mode 100644 index 0000000..27085a1 --- /dev/null +++ b/fairseq/tests/test_fp16_optimizer.py @@ -0,0 +1,111 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +import logging +import unittest + +import torch +from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer +from omegaconf import OmegaConf + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestGradientScaling(unittest.TestCase): + def setUp(self): + self.x = torch.tensor([2.0]).cuda().half() + weight = 3.0 + bias = 5.0 + self.error = 1.0 + self.target = torch.tensor([self.x * weight + bias + self.error]).cuda().half() + self.loss_fn = torch.nn.L1Loss() + + self.model = torch.nn.Linear(1, 1) + self.model.weight.data = torch.tensor([[weight]]) + self.model.bias.data = torch.tensor([bias]) + self.model.cuda().half() + self.params = list(self.model.parameters()) + + self.cfg_dls = OmegaConf.create( + { + "optimization": { + "lr": [0.1], + }, + "optimizer": { + "_name": "adam", + "lr": [0.1], + "adam_betas": "(0.9, 0.999)", + "adam_eps": 1e-8, + "weight_decay": 0.0, + }, + "common": { + "fp16_init_scale": 1, + "fp16_scale_window": 1, + "fp16_scale_tolerance": 1, + "threshold_loss_scale": 1, + "min_loss_scale": 1e-4, + "tpu": False, + }, + } + ) + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def run_iter(self, model, params, optimizer): + optimizer.zero_grad() + y = model(self.x) + loss = self.loss_fn(y, self.target) + optimizer.backward(loss) + self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) + + grad_norm = optimizer.clip_grad_norm(0) + self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) + + optimizer.step() + self.assertEqual( + model.weight, + torch.tensor( + [[3.0996]], device="cuda:0", dtype=torch.float16, requires_grad=True + ), + ) + self.assertEqual( + model.bias, + torch.tensor( + [5.1016], device="cuda:0", dtype=torch.float16, requires_grad=True + ), + ) + self.assertEqual(optimizer.scaler.loss_scale, 2.0) + + def test_mixed_precision(self): + model = copy.deepcopy(self.model) + params = list(model.parameters()) + optimizer = FP16Optimizer.build_optimizer(self.cfg_dls, params) + + self.run_iter(model, params, optimizer) + self.assertTrue( + all( + torch.all( + fp32_params.eq( + torch.tensor( + [3.1000, 5.1000], device="cuda:0", requires_grad=True + ) + ) + ) + for fp32_params in optimizer.fp32_params.values() + ) + ) + + def test_memory_efficient(self): + model = copy.deepcopy(self.model) + params = list(model.parameters()) + optimizer = MemoryEfficientFP16Optimizer.build_optimizer(self.cfg_dls, params) + + self.run_iter(model, params, optimizer) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_hf_hub.py b/fairseq/tests/test_hf_hub.py new file mode 100644 index 0000000..5cfef70 --- /dev/null +++ b/fairseq/tests/test_hf_hub.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch + +try: + import huggingface_hub +except ImportError: + huggingface_hub = None + +from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub + + +@unittest.skipIf(not huggingface_hub, "Requires huggingface_hub install") +class TestHuggingFaceHub(unittest.TestCase): + @torch.no_grad() + def test_hf_fastspeech2(self): + hf_model_id = "facebook/fastspeech2-en-ljspeech" + models, cfg, task = load_model_ensemble_and_task_from_hf_hub(hf_model_id) + self.assertTrue(len(models) > 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_huffman.py b/fairseq/tests/test_huffman.py new file mode 100644 index 0000000..85d0c72 --- /dev/null +++ b/fairseq/tests/test_huffman.py @@ -0,0 +1,179 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import typing as tp +import unittest +from collections import Counter +from tempfile import NamedTemporaryFile, TemporaryDirectory + +from fairseq.data import Dictionary, indexed_dataset +from fairseq.data.huffman import ( + HuffmanCodeBuilder, + HuffmanCoder, + HuffmanMMapIndexedDataset, + HuffmanMMapIndexedDatasetBuilder, +) +from tests.utils import POPULATION, make_data, sizes + + +def make_counts(data: tp.List[tp.List[str]]) -> Counter: + return Counter([symbol for sentence in data for symbol in sentence]) + + +def make_code_builder(data: tp.List[tp.List[str]]) -> HuffmanCodeBuilder: + builder = HuffmanCodeBuilder() + for sentence in data: + builder.add_symbols(*sentence) + return builder + + +class TestCodeBuilder(unittest.TestCase): + def test_code_builder_can_count(self): + data = make_data() + counts = make_counts(data) + builder = make_code_builder(data) + + self.assertEqual(builder.symbols, counts) + + def test_code_builder_can_add(self): + data = make_data() + counts = make_counts(data) + builder = make_code_builder(data) + + new_builder = builder + builder + + self.assertEqual(new_builder.symbols, counts + counts) + + def test_code_builder_can_io(self): + data = make_data() + builder = make_code_builder(data) + + with NamedTemporaryFile() as tmp_fp: + builder.to_file(tmp_fp.name) + other_builder = HuffmanCodeBuilder.from_file(tmp_fp.name) + + self.assertEqual(builder.symbols, other_builder.symbols) + + +class TestCoder(unittest.TestCase): + def test_coder_can_io(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + with NamedTemporaryFile() as tmp_fp: + coder.to_file(tmp_fp.name) + other_coder = HuffmanCoder.from_file(tmp_fp.name) + + self.assertEqual(coder, other_coder) + + def test_coder_can_encode_decode(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + encoded = [coder.encode(sentence) for sentence in data] + decoded = [[n.symbol for n in coder.decode(enc)] for enc in encoded] + + self.assertEqual(decoded, data) + + unseen_data = make_data() + unseen_encoded = [coder.encode(sentence) for sentence in unseen_data] + unseen_decoded = [ + [n.symbol for n in coder.decode(enc)] for enc in unseen_encoded + ] + self.assertEqual(unseen_decoded, unseen_data) + + +def build_dataset(prefix, data, coder): + with HuffmanMMapIndexedDatasetBuilder(prefix, coder) as builder: + for sentence in data: + builder.add_item(sentence) + + +class TestHuffmanDataset(unittest.TestCase): + def test_huffman_can_encode_decode(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + with TemporaryDirectory() as dirname: + prefix = os.path.join(dirname, "test1") + build_dataset(prefix, data, coder) + dataset = HuffmanMMapIndexedDataset(prefix) + + self.assertEqual(len(dataset), len(data)) + decoded = [list(dataset.get_symbols(i)) for i in range(0, len(dataset))] + + self.assertEqual(decoded, data) + data_sizes = [i.item() for i in dataset.sizes] + self.assertEqual(data_sizes, sizes(data)) + + def test_huffman_compresses(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + with TemporaryDirectory() as dirname: + prefix = os.path.join(dirname, "huffman") + build_dataset(prefix, data, coder) + + prefix_mmap = os.path.join(dirname, "mmap") + mmap_builder = indexed_dataset.make_builder( + indexed_dataset.data_file_path(prefix_mmap), + "mmap", + vocab_size=len(POPULATION), + ) + dictionary = Dictionary() + for c in POPULATION: + dictionary.add_symbol(c) + dictionary.finalize() + for sentence in data: + mmap_builder.add_item(dictionary.encode_line(" ".join(sentence))) + mmap_builder.finalize(indexed_dataset.index_file_path(prefix_mmap)) + + huff_size = os.stat(indexed_dataset.data_file_path(prefix)).st_size + mmap_size = os.stat(indexed_dataset.data_file_path(prefix_mmap)).st_size + self.assertLess(huff_size, mmap_size) + + def test_huffman_can_append(self): + data1 = make_data() + builder = make_code_builder(data1) + coder = builder.build_code() + + with TemporaryDirectory() as dirname: + prefix1 = os.path.join(dirname, "test1") + build_dataset(prefix1, data1, coder) + + data2 = make_data() + prefix2 = os.path.join(dirname, "test2") + build_dataset(prefix2, data2, coder) + + prefix3 = os.path.join(dirname, "test3") + + with HuffmanMMapIndexedDatasetBuilder(prefix3, coder) as builder: + builder.append(prefix1) + builder.append(prefix2) + + dataset = HuffmanMMapIndexedDataset(prefix3) + + self.assertEqual(len(dataset), len(data1) + len(data2)) + + decoded1 = [list(dataset.get_symbols(i)) for i in range(0, len(data1))] + self.assertEqual(decoded1, data1) + + decoded2 = [ + list(dataset.get_symbols(i)) for i in range(len(data1), len(dataset)) + ] + self.assertEqual(decoded2, data2) + + data_sizes = [i.item() for i in dataset.sizes] + self.assertEqual(data_sizes[: len(data1)], sizes(data1)) + self.assertEqual(data_sizes[len(data1) : len(dataset)], sizes(data2)) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_inference_dropout.py b/fairseq/tests/test_inference_dropout.py new file mode 100644 index 0000000..353ac67 --- /dev/null +++ b/fairseq/tests/test_inference_dropout.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import unittest + +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models.transformer import TransformerModel +from tests.test_sequence_generator import get_dummy_task_and_parser + + +class TestInferenceDropout(unittest.TestCase): + def setUp(self): + self.task, self.parser = get_dummy_task_and_parser() + TransformerModel.add_args(self.parser) + self.args = self.parser.parse_args([]) + self.args.encoder_layers = 2 + self.args.decoder_layers = 1 + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_sets_inference_dropout_to_true(self): + self.args.retain_dropout = True + self.transformer_model = TransformerModel.build_model(self.args, self.task) + cfg = convert_namespace_to_omegaconf(self.args) + self.transformer_model.prepare_for_inference_(cfg) + assert self.transformer_model.encoder.dropout_module.apply_during_inference + assert self.transformer_model.decoder.dropout_module.apply_during_inference + for layer in self.transformer_model.encoder.layers: + assert layer.dropout_module.apply_during_inference + + def test_inference_dropout_false_by_default(self): + self.transformer_model = TransformerModel.build_model(self.args, self.task) + cfg = convert_namespace_to_omegaconf(self.args) + self.transformer_model.prepare_for_inference_(cfg) + assert not self.transformer_model.encoder.dropout_module.apply_during_inference + assert not self.transformer_model.decoder.dropout_module.apply_during_inference + for layer in self.transformer_model.encoder.layers: + assert not layer.dropout_module.apply_during_inference + for layer in self.transformer_model.decoder.layers: + assert not layer.dropout_module.apply_during_inference + + def test_applies_training_mode(self): + self.transformer_model = TransformerModel.build_model(self.args, self.task) + assert self.transformer_model.encoder.dropout_module.training + for layer in self.transformer_model.encoder.layers: + assert layer.dropout_module.training + + self.transformer_model.eval() + assert not self.transformer_model.decoder.dropout_module.training + for layer in self.transformer_model.encoder.layers: + assert not layer.dropout_module.training + + def test_retain_modules(self): + self.args.retain_dropout = True + self.args.retain_dropout_modules = [ + "TransformerEncoder", + "TransformerEncoderLayer", + ] + self.transformer_model = TransformerModel.build_model(self.args, self.task) + cfg = convert_namespace_to_omegaconf(self.args) + self.transformer_model.prepare_for_inference_(cfg) + assert self.transformer_model.encoder.dropout_module.apply_during_inference + assert not self.transformer_model.decoder.dropout_module.apply_during_inference + for layer in self.transformer_model.decoder.layers: + assert not layer.dropout_module.apply_during_inference diff --git a/fairseq/tests/test_iopath.py b/fairseq/tests/test_iopath.py new file mode 100644 index 0000000..48230a6 --- /dev/null +++ b/fairseq/tests/test_iopath.py @@ -0,0 +1,28 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from unittest import mock + + +class TestIOPath(unittest.TestCase): + def test_no_iopath(self): + from .test_reproducibility import TestReproducibility + + with mock.patch.dict("sys.modules", {"iopath": None}): + # reuse reproducibility tests, which are e2e tests that should cover + # most checkpoint related functionality + TestReproducibility._test_reproducibility(self, "test_reproducibility") + + def test_no_supports_rename(self): + from .test_reproducibility import TestReproducibility + + with mock.patch("fairseq.file_io.PathManager.supports_rename") as mock_fn: + mock_fn.return_value = False + TestReproducibility._test_reproducibility(self, "test_reproducibility") + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_iterators.py b/fairseq/tests/test_iterators.py new file mode 100644 index 0000000..2e2eb2f --- /dev/null +++ b/fairseq/tests/test_iterators.py @@ -0,0 +1,194 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +from fairseq.data import iterators, ListDataset + + +class TestIterators(unittest.TestCase): + def test_counting_iterator_index(self, ref=None, itr=None): + # Test the indexing functionality of CountingIterator + if ref is None: + assert itr is None + ref = list(range(10)) + itr = iterators.CountingIterator(ref) + else: + assert len(ref) == 10 + assert itr is not None + + self.assertTrue(itr.has_next()) + self.assertEqual(itr.n, 0) + self.assertEqual(next(itr), ref[0]) + self.assertEqual(itr.n, 1) + self.assertEqual(next(itr), ref[1]) + self.assertEqual(itr.n, 2) + itr.skip(3) + self.assertEqual(itr.n, 5) + self.assertEqual(next(itr), ref[5]) + itr.skip(2) + self.assertEqual(itr.n, 8) + self.assertEqual(list(itr), [ref[8], ref[9]]) + self.assertFalse(itr.has_next()) + + def test_counting_iterator_length_mismatch(self): + ref = list(range(10)) + # When the underlying iterable is longer than the CountingIterator, + # the remaining items in the iterable should be ignored + itr = iterators.CountingIterator(ref, total=8) + self.assertEqual(list(itr), ref[:8]) + # When the underlying iterable is shorter than the CountingIterator, + # raise an IndexError when the underlying iterable is exhausted + itr = iterators.CountingIterator(ref, total=12) + self.assertRaises(IndexError, list, itr) + + def test_counting_iterator_take(self): + # Test the "take" method of CountingIterator + ref = list(range(10)) + itr = iterators.CountingIterator(ref) + itr.take(5) + self.assertEqual(len(itr), len(list(iter(itr)))) + self.assertEqual(len(itr), 5) + + itr = iterators.CountingIterator(ref) + itr.take(5) + self.assertEqual(next(itr), ref[0]) + self.assertEqual(next(itr), ref[1]) + itr.skip(2) + self.assertEqual(next(itr), ref[4]) + self.assertFalse(itr.has_next()) + + def test_grouped_iterator(self): + # test correctness + x = list(range(10)) + itr = iterators.GroupedIterator(x, 1) + self.assertEqual(list(itr), [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]) + itr = iterators.GroupedIterator(x, 4) + self.assertEqual(list(itr), [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9]]) + itr = iterators.GroupedIterator(x, 5) + self.assertEqual(list(itr), [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) + + # test the GroupIterator also works correctly as a CountingIterator + x = list(range(30)) + ref = list(iterators.GroupedIterator(x, 3)) + itr = iterators.GroupedIterator(x, 3) + self.test_counting_iterator_index(ref, itr) + + def test_sharded_iterator(self): + # test correctness + x = list(range(10)) + itr = iterators.ShardedIterator(x, num_shards=1, shard_id=0) + self.assertEqual(list(itr), x) + itr = iterators.ShardedIterator(x, num_shards=2, shard_id=0) + self.assertEqual(list(itr), [0, 2, 4, 6, 8]) + itr = iterators.ShardedIterator(x, num_shards=2, shard_id=1) + self.assertEqual(list(itr), [1, 3, 5, 7, 9]) + itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0) + self.assertEqual(list(itr), [0, 3, 6, 9]) + itr = iterators.ShardedIterator(x, num_shards=3, shard_id=1) + self.assertEqual(list(itr), [1, 4, 7, None]) + itr = iterators.ShardedIterator(x, num_shards=3, shard_id=2) + self.assertEqual(list(itr), [2, 5, 8, None]) + + # test CountingIterator functionality + x = list(range(30)) + ref = list(iterators.ShardedIterator(x, num_shards=3, shard_id=0)) + itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0) + self.test_counting_iterator_index(ref, itr) + + def test_counting_iterator_buffered_iterator_take(self): + ref = list(range(10)) + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr) + itr.take(5) + self.assertEqual(len(itr), len(list(iter(itr)))) + self.assertEqual(len(itr), 5) + + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr) + itr.take(5) + self.assertEqual(len(buffered_itr), 5) + self.assertEqual(len(list(iter(buffered_itr))), 5) + + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr) + itr.take(5) + self.assertEqual(next(itr), ref[0]) + self.assertEqual(next(itr), ref[1]) + itr.skip(2) + self.assertEqual(next(itr), ref[4]) + self.assertFalse(itr.has_next()) + self.assertRaises(StopIteration, next, buffered_itr) + + ref = list(range(4, 10)) + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr, start=4) + itr.take(5) + self.assertEqual(len(itr), 5) + self.assertEqual(len(buffered_itr), 1) + self.assertEqual(next(itr), ref[0]) + self.assertFalse(itr.has_next()) + self.assertRaises(StopIteration, next, buffered_itr) + + def test_epoch_batch_iterator_skip_remainder_batch(self): + reference = [1, 2, 3] + itr1 = _get_epoch_batch_itr(reference, 2, True) + self.assertEqual(len(itr1), 1) + itr2 = _get_epoch_batch_itr(reference, 2, False) + self.assertEqual(len(itr2), 2) + itr3 = _get_epoch_batch_itr(reference, 1, True) + self.assertEqual(len(itr3), 2) + itr4 = _get_epoch_batch_itr(reference, 1, False) + self.assertEqual(len(itr4), 3) + itr5 = _get_epoch_batch_itr(reference, 4, True) + self.assertEqual(len(itr5), 0) + self.assertFalse(itr5.has_next()) + itr6 = _get_epoch_batch_itr(reference, 4, False) + self.assertEqual(len(itr6), 1) + + def test_grouped_iterator_skip_remainder_batch(self): + reference = [1, 2, 3, 4, 5, 6, 7, 8, 9] + itr1 = _get_epoch_batch_itr(reference, 3, False) + grouped_itr1 = iterators.GroupedIterator(itr1, 2, True) + self.assertEqual(len(grouped_itr1), 1) + + itr2 = _get_epoch_batch_itr(reference, 3, False) + grouped_itr2 = iterators.GroupedIterator(itr2, 2, False) + self.assertEqual(len(grouped_itr2), 2) + + itr3 = _get_epoch_batch_itr(reference, 3, True) + grouped_itr3 = iterators.GroupedIterator(itr3, 2, True) + self.assertEqual(len(grouped_itr3), 1) + + itr4 = _get_epoch_batch_itr(reference, 3, True) + grouped_itr4 = iterators.GroupedIterator(itr4, 2, False) + self.assertEqual(len(grouped_itr4), 1) + + itr5 = _get_epoch_batch_itr(reference, 5, True) + grouped_itr5 = iterators.GroupedIterator(itr5, 2, True) + self.assertEqual(len(grouped_itr5), 0) + + itr6 = _get_epoch_batch_itr(reference, 5, True) + grouped_itr6 = iterators.GroupedIterator(itr6, 2, False) + self.assertEqual(len(grouped_itr6), 1) + + +def _get_epoch_batch_itr(ref, bsz, skip_remainder_batch): + dsz = len(ref) + indices = range(dsz) + starts = indices[::bsz] + batch_sampler = [indices[s : s + bsz] for s in starts] + dataset = ListDataset(ref) + itr = iterators.EpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_sampler=batch_sampler, + skip_remainder_batch=skip_remainder_batch, + ) + return itr.next_epoch_itr() + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_label_smoothing.py b/fairseq/tests/test_label_smoothing.py new file mode 100644 index 0000000..04c0f97 --- /dev/null +++ b/fairseq/tests/test_label_smoothing.py @@ -0,0 +1,123 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import copy +import unittest + +import tests.utils as test_utils +import torch +from fairseq.criterions.cross_entropy import CrossEntropyCriterion +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, +) + + +class TestLabelSmoothing(unittest.TestCase): + def setUp(self): + # build dictionary + self.d = test_utils.dummy_dictionary(3) + vocab = len(self.d) + self.assertEqual(vocab, 4 + 3) # 4 special + 3 tokens + self.assertEqual(self.d.pad(), 1) + self.assertEqual(self.d.eos(), 2) + self.assertEqual(self.d.unk(), 3) + pad, eos, unk, w1, w2, w3 = 1, 2, 3, 4, 5, 6 # noqa: F841 + + # build dataset + self.data = [ + # the first batch item has padding + { + "source": torch.LongTensor([w1, eos]), + "target": torch.LongTensor([w1, eos]), + }, + { + "source": torch.LongTensor([w1, eos]), + "target": torch.LongTensor([w1, w1, eos]), + }, + ] + self.sample = next(test_utils.dummy_dataloader(self.data)) + + # build model + self.args = argparse.Namespace() + self.args.sentence_avg = False + self.args.report_accuracy = False + self.args.probs = ( + torch.FloatTensor( + [ + # pad eos unk w1 w2 w3 + [0.05, 0.05, 0.1, 0.05, 0.3, 0.4, 0.05], + [0.05, 0.10, 0.2, 0.05, 0.2, 0.3, 0.10], + [0.05, 0.15, 0.3, 0.05, 0.1, 0.2, 0.15], + ] + ) + .unsqueeze(0) + .expand(2, 3, 7) + ) # add batch dimension + self.task = test_utils.TestTranslationTask.setup_task(self.args, self.d, self.d) + self.model = self.task.build_model(self.args) + + def test_nll_loss(self): + self.args.label_smoothing = 0.1 + nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) + smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion( + self.args, self.task + ) + nll_loss, nll_sample_size, nll_logging_output = nll_crit( + self.model, self.sample + ) + smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit( + self.model, self.sample + ) + self.assertLess(abs(nll_loss - nll_logging_output["loss"]), 1e-6) + self.assertLess(abs(nll_loss - smooth_logging_output["nll_loss"]), 1e-6) + + def test_padding(self): + self.args.label_smoothing = 0.1 + crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) + loss, _, logging_output = crit(self.model, self.sample) + + def get_one_no_padding(idx): + # create a new sample with just a single batch item so that there's + # no padding + sample1 = next(test_utils.dummy_dataloader([self.data[idx]])) + args1 = copy.copy(self.args) + args1.probs = args1.probs[idx, :, :].unsqueeze(0) + model1 = self.task.build_model(args1) + loss1, _, _ = crit(model1, sample1) + return loss1 + + loss1 = get_one_no_padding(0) + loss2 = get_one_no_padding(1) + self.assertAlmostEqual(loss, loss1 + loss2) + + def test_reduction(self): + self.args.label_smoothing = 0.1 + crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) + loss, _, logging_output = crit(self.model, self.sample, reduce=True) + unreduced_loss, _, _ = crit(self.model, self.sample, reduce=False) + self.assertAlmostEqual(loss, unreduced_loss.sum()) + + def test_zero_eps(self): + self.args.label_smoothing = 0.0 + nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) + smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion( + self.args, self.task + ) + nll_loss, nll_sample_size, nll_logging_output = nll_crit( + self.model, self.sample + ) + smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit( + self.model, self.sample + ) + self.assertAlmostEqual(nll_loss, smooth_loss) + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess((t1 - t2).abs().max(), 1e-6) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_lm_context_window.py b/fairseq/tests/test_lm_context_window.py new file mode 100644 index 0000000..165e04a --- /dev/null +++ b/fairseq/tests/test_lm_context_window.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch + +from fairseq.data import MonolingualDataset +from fairseq.tasks.language_modeling import LanguageModelingConfig, LanguageModelingTask +from tests import utils as test_utils + + +class TestLMContextWindow(unittest.TestCase): + def test_eval_dataloader(self): + dictionary = test_utils.dummy_dictionary(10) + assert len(dictionary) == 14 # 4 extra special symbols + assert dictionary.pad() == 1 + + dataset = test_utils.TestDataset( + [ + torch.tensor([4, 5, 6, 7], dtype=torch.long), + torch.tensor([8, 9, 10, 11], dtype=torch.long), + torch.tensor([12, 13], dtype=torch.long), + ] + ) + dataset = MonolingualDataset(dataset, sizes=[4, 4, 2], src_vocab=dictionary) + + config = LanguageModelingConfig(tokens_per_sample=4) + task = LanguageModelingTask(config, dictionary) + + eval_dataloader = task.eval_lm_dataloader( + dataset=dataset, + batch_size=1, + context_window=2, + num_workers=0, + ) + + batch = next(eval_dataloader) + assert batch["net_input"]["src_tokens"][0].tolist() == [4, 5, 6, 7, 1, 1] + assert batch["target"][0].tolist() == [4, 5, 6, 7, 1, 1] + + batch = next(eval_dataloader) + assert batch["net_input"]["src_tokens"][0].tolist() == [6, 7, 8, 9, 10, 11] + assert batch["target"][0].tolist() == [1, 1, 8, 9, 10, 11] + + batch = next(eval_dataloader) + assert batch["net_input"]["src_tokens"][0].tolist() == [10, 11, 12, 13] + assert batch["target"][0].tolist() == [1, 1, 12, 13] + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_lstm_jitable.py b/fairseq/tests/test_lstm_jitable.py new file mode 100644 index 0000000..38f79d1 --- /dev/null +++ b/fairseq/tests/test_lstm_jitable.py @@ -0,0 +1,115 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import tempfile +import unittest + +import torch +from fairseq.data.dictionary import Dictionary +from fairseq.models.lstm import LSTMModel +from fairseq.tasks.fairseq_task import LegacyFairseqTask + + +DEFAULT_TEST_VOCAB_SIZE = 100 + + +class DummyTask(LegacyFairseqTask): + def __init__(self, args): + super().__init__(args) + self.dictionary = get_dummy_dictionary() + if getattr(self.args, "ctc", False): + self.dictionary.add_symbol("<ctc_blank>") + self.src_dict = self.dictionary + self.tgt_dict = self.dictionary + + @property + def source_dictionary(self): + return self.src_dict + + @property + def target_dictionary(self): + return self.dictionary + + +def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): + dummy_dict = Dictionary() + # add dummy symbol to satisfy vocab size + for id, _ in enumerate(range(vocab_size)): + dummy_dict.add_symbol("{}".format(id), 1000) + return dummy_dict + + +def get_dummy_task_and_parser(): + """ + to build a fariseq model, we need some dummy parse and task. This function + is used to create dummy task and parser to faciliate model/criterion test + + Note: we use FbSpeechRecognitionTask as the dummy task. You may want + to use other task by providing another function + """ + parser = argparse.ArgumentParser( + description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS + ) + DummyTask.add_args(parser) + args = parser.parse_args([]) + task = DummyTask.setup_task(args) + return task, parser + + +class TestJitLSTMModel(unittest.TestCase): + def _test_save_and_load(self, scripted_module): + with tempfile.NamedTemporaryFile() as f: + scripted_module.save(f.name) + torch.jit.load(f.name) + + def assertTensorEqual(self, t1, t2): + t1 = t1[~torch.isnan(t1)] # can cause size mismatch errors if there are NaNs + t2 = t2[~torch.isnan(t2)] + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertEqual(t1.ne(t2).long().sum(), 0) + + def test_jit_and_export_lstm(self): + task, parser = get_dummy_task_and_parser() + LSTMModel.add_args(parser) + args = parser.parse_args([]) + args.criterion = "" + model = LSTMModel.build_model(args, task) + scripted_model = torch.jit.script(model) + self._test_save_and_load(scripted_model) + + def test_assert_jit_vs_nonjit_(self): + task, parser = get_dummy_task_and_parser() + LSTMModel.add_args(parser) + args = parser.parse_args([]) + args.criterion = "" + model = LSTMModel.build_model(args, task) + model.eval() + scripted_model = torch.jit.script(model) + scripted_model.eval() + idx = len(task.source_dictionary) + iter = 100 + # Inject random input and check output + seq_len_tensor = torch.randint(1, 10, (iter,)) + num_samples_tensor = torch.randint(1, 10, (iter,)) + for i in range(iter): + seq_len = seq_len_tensor[i] + num_samples = num_samples_tensor[i] + src_token = (torch.randint(0, idx, (num_samples, seq_len)),) + src_lengths = torch.randint(1, seq_len + 1, (num_samples,)) + src_lengths, _ = torch.sort(src_lengths, descending=True) + # Force the first sample to have seq_len + src_lengths[0] = seq_len + prev_output_token = (torch.randint(0, idx, (num_samples, 1)),) + result = model(src_token[0], src_lengths, prev_output_token[0], None) + scripted_result = scripted_model( + src_token[0], src_lengths, prev_output_token[0], None + ) + self.assertTensorEqual(result[0], scripted_result[0]) + self.assertTensorEqual(result[1], scripted_result[1]) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_memory_efficient_fp16.py b/fairseq/tests/test_memory_efficient_fp16.py new file mode 100644 index 0000000..2bf2f29 --- /dev/null +++ b/fairseq/tests/test_memory_efficient_fp16.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import unittest + +import torch +from fairseq.optim.adam import FairseqAdam +from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer +from omegaconf import OmegaConf + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestMemoryEfficientFP16(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_load_state_dict(self): + # define simple FP16 model + model = torch.nn.Linear(5, 5).cuda().half() + params = list(model.parameters()) + + # initialize memory efficient FP16 optimizer + # with pseudo DictConfigs + optimizer = FairseqAdam( + cfg=OmegaConf.create( + vars( + argparse.Namespace( + adam_betas="(0.9, 0.999)", + adam_eps=1e-8, + weight_decay=0.0, + lr=[0.00001], + ) + ) + ), + params=params, + ) + me_optimizer = MemoryEfficientFP16Optimizer( + cfg=OmegaConf.create( + { + "common": vars( + argparse.Namespace( + fp16_init_scale=1, + fp16_scale_window=1, + fp16_scale_tolerance=1, + threshold_loss_scale=1, + min_loss_scale=1e-4, + ) + ) + } + ), + params=params, + optimizer=optimizer, + ) + + # optimizer state is created in the first step + loss = model(torch.rand(5).cuda().half()).sum() + me_optimizer.backward(loss) + me_optimizer.step() + + # reload state + state = me_optimizer.state_dict() + me_optimizer.load_state_dict(state) + for k, v in me_optimizer.optimizer.state.items(): + self.assertTrue(k.dtype == torch.float16) + for v_i in v.values(): + if torch.is_tensor(v_i): + self.assertTrue(v_i.dtype == torch.float32) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_metrics.py b/fairseq/tests/test_metrics.py new file mode 100644 index 0000000..fc93b48 --- /dev/null +++ b/fairseq/tests/test_metrics.py @@ -0,0 +1,77 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +import uuid + +from fairseq.logging import metrics + + +class TestMetrics(unittest.TestCase): + def test_nesting(self): + with metrics.aggregate() as a: + metrics.log_scalar("loss", 1) + with metrics.aggregate() as b: + metrics.log_scalar("loss", 2) + + self.assertEqual(a.get_smoothed_values()["loss"], 1.5) + self.assertEqual(b.get_smoothed_values()["loss"], 2) + + def test_new_root(self): + with metrics.aggregate() as a: + metrics.log_scalar("loss", 1) + with metrics.aggregate(new_root=True) as b: + metrics.log_scalar("loss", 2) + + self.assertEqual(a.get_smoothed_values()["loss"], 1) + self.assertEqual(b.get_smoothed_values()["loss"], 2) + + def test_nested_new_root(self): + with metrics.aggregate() as layer1: + metrics.log_scalar("loss", 1) + with metrics.aggregate(new_root=True) as layer2: + metrics.log_scalar("loss", 2) + with metrics.aggregate() as layer3: + metrics.log_scalar("loss", 3) + with metrics.aggregate(new_root=True) as layer4: + metrics.log_scalar("loss", 4) + metrics.log_scalar("loss", 1.5) + + self.assertEqual(layer4.get_smoothed_values()["loss"], 4) + self.assertEqual(layer3.get_smoothed_values()["loss"], 3) + self.assertEqual(layer2.get_smoothed_values()["loss"], 2.5) + self.assertEqual(layer1.get_smoothed_values()["loss"], 1.25) + + def test_named(self): + name = str(uuid.uuid4()) + metrics.reset_meters(name) + + with metrics.aggregate(name): + metrics.log_scalar("loss", 1) + + metrics.log_scalar("loss", 3) + + with metrics.aggregate(name): + metrics.log_scalar("loss", 2) + + self.assertEqual(metrics.get_smoothed_values(name)["loss"], 1.5) + + def test_nested_duplicate_names(self): + name = str(uuid.uuid4()) + metrics.reset_meters(name) + + with metrics.aggregate(name): + metrics.log_scalar("loss", 1) + with metrics.aggregate() as other: + with metrics.aggregate(name): + metrics.log_scalar("loss", 2) + metrics.log_scalar("loss", 6) + + self.assertEqual(metrics.get_smoothed_values(name)["loss"], 3) + self.assertEqual(other.get_smoothed_values()["loss"], 2) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_multi_corpus_dataset.py b/fairseq/tests/test_multi_corpus_dataset.py new file mode 100644 index 0000000..79900ab --- /dev/null +++ b/fairseq/tests/test_multi_corpus_dataset.py @@ -0,0 +1,82 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from collections import OrderedDict + +import torch + +from fairseq.data import LanguagePairDataset, TokenBlockDataset +from fairseq.data.multi_corpus_dataset import MultiCorpusDataset +from tests.test_train import mock_dict + + +class TestMultiCorpusDataset(unittest.TestCase): + def setUp(self): + d = mock_dict() + tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, -1) + tokens_ds1 = TokenBlockDataset( + tokens_1, + sizes=[tokens_1.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_1 = LanguagePairDataset( + tokens_ds1, tokens_ds1.sizes, d, shuffle=False + ) + tokens_2 = torch.LongTensor([i for i in range(0, 5000, 2)]).view(1, -1) + tokens_ds2 = TokenBlockDataset( + tokens_2, + sizes=[tokens_2.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_2 = LanguagePairDataset( + tokens_ds2, tokens_ds2.sizes, d, shuffle=False + ) + + def _test_sample_helper( + self, + distribution, + ): + m = MultiCorpusDataset( + OrderedDict({0: self.dataset_1, 1: self.dataset_2}), + distribution=distribution, + seed=0, + sort_indices=True, + ) + m.set_epoch(1) + indices = m.ordered_indices() + count_sample_from_first_dataset = 0 + items = set() + for i in indices: + item = m[i]["source"].item() + if item % 2 == 1: + count_sample_from_first_dataset += 1 + + items.add(item) + sample_from_first_ds_percentage = ( + 1.0 * count_sample_from_first_dataset / len(indices) + ) + self.assertLess( + abs(sample_from_first_ds_percentage - distribution[0]), + 0.01, + ) + self.assertEqual( + len(items), + int( + min(len(self.dataset_1), len(indices) * distribution[0]) + + min(len(self.dataset_1), len(indices) * distribution[1]) + ), + ) + print(distribution) + + def test_multi_corpus_dataset(self): + for distribution in [[0.5, 0.5], [0.1, 0.9], [0.9, 0.1], [0.0, 1.0]]: + self._test_sample_helper(distribution=distribution) diff --git a/fairseq/tests/test_multi_corpus_sampled_dataset.py b/fairseq/tests/test_multi_corpus_sampled_dataset.py new file mode 100644 index 0000000..88f0817 --- /dev/null +++ b/fairseq/tests/test_multi_corpus_sampled_dataset.py @@ -0,0 +1,95 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from fairseq.data import LanguagePairDataset, TokenBlockDataset +from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset +from tests.test_train import mock_dict + + +class TestMultiCorpusSampledDataset(unittest.TestCase): + def setUp(self): + d = mock_dict() + tokens_1 = torch.LongTensor([1]).view(1, -1) + tokens_ds1 = TokenBlockDataset( + tokens_1, + sizes=[tokens_1.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_1 = LanguagePairDataset( + tokens_ds1, tokens_ds1.sizes, d, shuffle=False + ) + tokens_2 = torch.LongTensor([2]).view(1, -1) + tokens_ds2 = TokenBlockDataset( + tokens_2, + sizes=[tokens_2.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_2 = LanguagePairDataset( + tokens_ds2, tokens_ds2.sizes, d, shuffle=False + ) + + def _test_sample_helper( + self, + expected_sample_from_first_ds_percentage, + num_samples=1000, + sampling_func=None, + ): + # To make sure test is not flaky + np.random.seed(0) + if sampling_func is None: + m = MultiCorpusSampledDataset( + OrderedDict({0: self.dataset_1, 1: self.dataset_2}), + ) + else: + m = MultiCorpusSampledDataset( + OrderedDict({0: self.dataset_1, 1: self.dataset_2}), + sampling_func=sampling_func, + ) + m.ordered_indices() + count_sample_from_first_dataset = 0 + for _ in range(num_samples): + if m.collater([m[0], m[1]])["net_input"]["src_tokens"][0] == 1: + count_sample_from_first_dataset += 1 + sample_from_first_ds_percentage = ( + 1.0 * count_sample_from_first_dataset / num_samples + ) + self.assertLess( + abs( + sample_from_first_ds_percentage + - expected_sample_from_first_ds_percentage + ), + 0.01, + ) + + def test_multi_corpus_sampled_dataset_uniform_sample(self): + self._test_sample_helper(expected_sample_from_first_ds_percentage=0.5) + + def test_multi_corpus_sampled_dataset_weighted_sample(self): + def naive_weighted_sample(weights): + def f(input): + v = np.random.random() + agg = 0 + for i, weight in enumerate(weights): + agg += weight + if agg > v: + return i + + return f + + self._test_sample_helper( + expected_sample_from_first_ds_percentage=0.9, + sampling_func=naive_weighted_sample(weights=[0.9, 0.1]), + ) diff --git a/fairseq/tests/test_multihead_attention.py b/fairseq/tests/test_multihead_attention.py new file mode 100644 index 0000000..4a0b430 --- /dev/null +++ b/fairseq/tests/test_multihead_attention.py @@ -0,0 +1,488 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import unittest + +import pytest +import torch + +from fairseq.modules.multihead_attention import MultiheadAttention, _mask_for_xformers + +BATCH = [20, 41, 97] +SEQ = [64] +EMB = [48] +HEADS = [4] +DROP = 0.1 +DEVICE = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"] +ATTN_MASK_DTYPE = [None, torch.uint8, torch.bool, torch.float] +KEY_PADDING_MASK_DTYPE = [None, torch.uint8, torch.bool] + + +# FIXME: some tests fail when decimal=2, fix this and set decimal to 2 +def assert_almost_equal(x, y, decimal=1, err_msg=""): + import numpy.testing as npt + + if isinstance(x, torch.Tensor): + x = x.cpu().detach().numpy() + if isinstance(y, torch.Tensor): + y = y.cpu().detach().numpy() + npt.assert_array_almost_equal(x, y, err_msg=err_msg, decimal=decimal) + + +def _reset_seeds(): + torch.manual_seed(0) + torch.random.manual_seed(0) + random.seed(0) + torch.cuda.manual_seed_all(0) + + +def _get_mask(to_dtype: torch.dtype, dim0: int, dim1: int): + if to_dtype == torch.float: + mask = torch.randint(0, 2, (dim0, dim1)).to(dtype=torch.bool) + return mask.to(dtype=to_dtype).masked_fill(mask, -float("inf")) + return torch.randint(0, 2, (dim0, dim1)).to(dtype=to_dtype) + + +def test_mask_for_xformers(): + # Additive Mask + m_float_add = torch.tensor([float("-inf"), 0]).to(torch.float) + m_float_add_flipped = torch.tensor([0, float("-inf")]).to(torch.float) + m_float16_add = torch.tensor([float("-inf"), 0]).to(torch.float16) + m_float16_add_flipped = torch.tensor([0, float("-inf")]).to(torch.float16) + m_uint = torch.tensor([1, 0]).to(torch.uint8) + m_uint_flipped = torch.tensor([0, 1]).to(torch.uint8) + m_bool = torch.tensor([False, True]) + + assert torch.equal(_mask_for_xformers(m_float_add), m_float_add) + assert torch.equal(_mask_for_xformers(m_float16_add), m_float16_add) + assert torch.equal(_mask_for_xformers(m_uint), m_uint_flipped) + assert torch.equal(_mask_for_xformers(m_bool), ~m_bool) + + assert torch.equal( + _mask_for_xformers(m_float_add, to_dtype=torch.float16), m_float16_add + ) + assert torch.equal( + _mask_for_xformers(m_float_add, to_dtype=torch.float), m_float_add + ) + assert torch.equal(_mask_for_xformers(m_float_add, to_dtype=torch.bool), m_bool) + assert torch.equal( + _mask_for_xformers(m_float_add, to_dtype=torch.uint8), m_uint_flipped + ) + + assert torch.equal( + _mask_for_xformers(m_float16_add, to_dtype=torch.float16), m_float16_add + ) + assert torch.equal( + _mask_for_xformers(m_float16_add, to_dtype=torch.float), m_float_add + ) + assert torch.equal(_mask_for_xformers(m_float16_add, to_dtype=torch.bool), m_bool) + assert torch.equal( + _mask_for_xformers(m_float16_add, to_dtype=torch.uint8), m_uint_flipped + ) + + assert torch.equal( + _mask_for_xformers(m_bool, to_dtype=torch.float16), m_float16_add_flipped + ) + assert torch.equal( + _mask_for_xformers(m_bool, to_dtype=torch.float), m_float_add_flipped + ) + assert torch.equal(_mask_for_xformers(m_bool, to_dtype=torch.bool), ~m_bool) + assert torch.equal(_mask_for_xformers(m_bool, to_dtype=torch.uint8), m_uint) + + assert torch.equal( + _mask_for_xformers(m_uint, to_dtype=torch.float16), m_float16_add + ) + assert torch.equal(_mask_for_xformers(m_uint, to_dtype=torch.float), m_float_add) + assert torch.equal(_mask_for_xformers(m_uint, to_dtype=torch.bool), m_bool) + assert torch.equal(_mask_for_xformers(m_uint, to_dtype=torch.uint8), m_uint_flipped) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="blocksparse requires gpu") +@pytest.mark.skip(reason="not part of latest xformers") +@pytest.mark.parametrize("device", ["cuda"]) +@pytest.mark.parametrize("add_zero_attn", [False]) +@pytest.mark.parametrize("batch_size", [20]) +@pytest.mark.parametrize("embedding", [64]) +@pytest.mark.parametrize("seq_len", [64]) +@pytest.mark.parametrize("num_heads", [4]) +def test_xformers_blocksparse_parity( + device, + add_zero_attn, + batch_size, + embedding, + seq_len, + num_heads, +): + + xformers_att_config = '{"name": "scaled_dot_product"}' + xformers_blocksparse_blocksize = 16 + xformers_blocksparse_layout = torch.ones( + seq_len // xformers_blocksparse_blocksize, + seq_len // xformers_blocksparse_blocksize, + dtype=torch.int32, + ) + + q = torch.rand(seq_len, batch_size, embedding).to(device).half() + q.requires_grad = True + k = torch.rand(seq_len, batch_size, embedding).to(device).half() + k.requires_grad = True + v = torch.rand(seq_len, batch_size, embedding).to(device).half() + v.requires_grad = True + + q_ = q.detach().clone().half() + q_.requires_grad = True + k_ = k.detach().clone().half() + k_.requires_grad = True + v_ = v.detach().clone().half() + v_.requires_grad = True + + _reset_seeds() + xf_blocksparse_mha = ( + MultiheadAttention( + embedding, + num_heads, + dropout=0.0, + add_zero_attn=add_zero_attn, + xformers_att_config=xformers_att_config, + xformers_blocksparse_layout=xformers_blocksparse_layout, + xformers_blocksparse_blocksize=xformers_blocksparse_blocksize, + ) + .to(device) + .half() + ) + + xf_blocksparse_output, _ = xf_blocksparse_mha( + q, + k, + v, + ) + + _reset_seeds() + xformers_mha = ( + MultiheadAttention( + embedding, + num_heads, + dropout=0.0, + add_zero_attn=add_zero_attn, + xformers_att_config=xformers_att_config, + xformers_blocksparse_layout=None, + ) + .to(device) + .half() + ) + + xformers_output, _ = xformers_mha( + q_, + k_, + v_, + ) + + # # account for when nan != nan + rand = random.uniform(0, 1) + xformers_output = xformers_output.masked_fill(xformers_output.isnan(), rand) + xf_blocksparse_output = xf_blocksparse_output.masked_fill( + xf_blocksparse_output.isnan(), rand + ) + + assert_almost_equal(xformers_output, xf_blocksparse_output) + + loss_blocksparse = torch.norm(xformers_output) + loss_original = torch.norm(xf_blocksparse_output) + loss_blocksparse.backward() + loss_original.backward() + + q.masked_fill(q.isnan(), rand) + q_.masked_fill(q_.isnan(), rand) + k.masked_fill(k.isnan(), rand) + k_.masked_fill(k_.isnan(), rand) + v.masked_fill(v.isnan(), rand) + v_.masked_fill(v_.isnan(), rand) + + assert_almost_equal(q.grad, q_.grad) + assert_almost_equal(k.grad, k_.grad) + assert_almost_equal(v.grad, v_.grad) + + +@pytest.mark.parametrize("device", DEVICE) +@pytest.mark.parametrize("attn_dtype", ATTN_MASK_DTYPE) +@pytest.mark.parametrize("key_padding_dtype", KEY_PADDING_MASK_DTYPE) +@pytest.mark.parametrize("add_bias_kv", [True, False]) +@pytest.mark.parametrize("add_zero_attn", [True, False]) +# TODO: test with static_kv True +@pytest.mark.parametrize("static_kv", [False]) +@pytest.mark.parametrize("batch_size", BATCH) +@pytest.mark.parametrize("embedding", EMB) +@pytest.mark.parametrize("seq_len", SEQ) +@pytest.mark.parametrize("num_heads", HEADS) +def test_xformers_single_forward_parity( + device, + attn_dtype, + key_padding_dtype, + add_bias_kv, + add_zero_attn, + static_kv, + batch_size, + embedding, + seq_len, + num_heads, +): + + xformers_att_config = '{"name": "scaled_dot_product"}' + + attn_mask = ( + None + if attn_dtype is None + else _get_mask(to_dtype=attn_dtype, dim0=seq_len, dim1=seq_len).to(device) + ) + key_padding_mask = ( + None + if key_padding_dtype is None + else _get_mask(to_dtype=key_padding_dtype, dim0=batch_size, dim1=seq_len).to( + device + ) + ) + + q = torch.rand(seq_len, batch_size, embedding).to(device) + q.requires_grad = True + k = torch.rand(seq_len, batch_size, embedding).to(device) + k.requires_grad = True + v = torch.rand(seq_len, batch_size, embedding).to(device) + v.requires_grad = True + + q_ = q.detach().clone() + q_.requires_grad = True + k_ = k.detach().clone() + k_.requires_grad = True + v_ = v.detach().clone() + v_.requires_grad = True + + # TODO: dropouts in the two implementations lead to different entries dropped. + _reset_seeds() + xformers_mha = MultiheadAttention( + embedding, + num_heads, + dropout=0.0, + xformers_att_config=xformers_att_config, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ).to(device) + xformers_output, _ = xformers_mha( + q, + k, + v, + key_padding_mask=key_padding_mask, + attn_mask=attn_mask, + static_kv=static_kv, + ) + + _reset_seeds() + original_mha = MultiheadAttention( + embedding, + num_heads, + dropout=0.0, + xformers_att_config=None, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ).to(device) + original_output, _ = original_mha( + q_, + k_, + v_, + key_padding_mask=key_padding_mask, + attn_mask=attn_mask, + static_kv=static_kv, + ) + + # account for when nan != nan + if xformers_output.isnan().any() or original_output.isnan().any(): + rand = random.uniform(0, 1) + xformers_output = xformers_output.masked_fill(xformers_output.isnan(), rand) + original_output = original_output.masked_fill(original_output.isnan(), rand) + + # torch.equal works for cpu, on cuda allclose is needed. + assert torch.allclose( + xformers_output, original_output, atol=1e-06 + ), f"max diff is {torch.max(torch.abs(xformers_output - original_output))}" + + loss_xformers = torch.norm(xformers_output) + loss_original = torch.norm(original_output) + loss_xformers.backward() + loss_original.backward() + + # torch.equal works for cpu, on cuda allclose is needed. + assert torch.allclose( + q.grad, q_.grad + ), f"max diff is {torch.max(torch.abs(q.grad - q_.grad))}" + assert torch.allclose( + k.grad, k_.grad + ), f"max diff is {torch.max(torch.abs(k.grad - k_.grad))}" + assert torch.allclose( + v.grad, v_.grad + ), f"max diff is {torch.max(torch.abs(v.grad - v_.grad))}" + + +def test_mask_padding_parity(): + def old_padding_code(key_padding_mask, attn_mask): + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask), + ], + dim=1, + ) + return key_padding_mask, attn_mask + + # values don't matter for this test. + mha = MultiheadAttention( + embed_dim=8, + num_heads=2, + dropout=0.0, + add_bias_kv=True, + add_zero_attn=True, + ) + + key_padding_mask = torch.rand((8, 64)) + attn_mask = torch.rand((64, 64)) + + kp_mask_orig, a_mask_orig = old_padding_code(key_padding_mask, attn_mask) + kp_mask_new, a_mask_new = mha._pad_masks(key_padding_mask, attn_mask) + + assert kp_mask_orig.size() == kp_mask_new.size() + assert a_mask_orig.size() == a_mask_new.size() + assert torch.equal(kp_mask_orig, kp_mask_new) + assert torch.equal(a_mask_orig, a_mask_new) + + +def test_add_bias_parity(): + # values don't matter for this test. + mha = MultiheadAttention( + embed_dim=8, + num_heads=2, + dropout=0.0, + add_bias_kv=True, + add_zero_attn=True, + ) + + def old_bias_code(k, v, key_padding_mask, attn_mask, bsz): + k = torch.cat([k, mha.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, mha.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(key_padding_mask.size(0), 1), + ], + dim=1, + ) + return k, v, key_padding_mask, attn_mask + + seq_len = 64 + bsz = 8 + embedding = 8 + key_padding_mask = torch.rand((bsz, seq_len)) + attn_mask = torch.rand((seq_len, seq_len)) + k = torch.rand((seq_len, bsz, embedding)) + v = torch.rand((seq_len, bsz, embedding)) + + k_orig, v_orig, kp_mask_orig, a_mask_orig = old_bias_code( + k, v, key_padding_mask, attn_mask, bsz + ) + k_new, v_new, kp_mask_new, a_mask_new = mha._add_bias( + k, v, key_padding_mask, attn_mask, bsz + ) + + assert torch.equal(k_orig, k_new) + assert torch.equal(v_orig, v_new) + assert torch.equal(kp_mask_orig, kp_mask_new) + assert torch.equal(a_mask_orig, a_mask_new) + + +class TestMultiheadAttention(unittest.TestCase): + def test_append_prev_key_padding_mask(self): + bsz = 1 + src_len = 4 + + cases = [ + # no padding mask + (None, None, None), + # current padding mask only + ( + torch.tensor([[1]]).bool(), + None, + torch.tensor([[0, 0, 0, 1]]).bool(), + ), + # previous padding mask only + ( + None, + torch.tensor([[0, 1, 0]]).bool(), + torch.tensor([[0, 1, 0, 0]]).bool(), + ), + # both padding masks + ( + torch.tensor([[1]]).bool(), + torch.tensor([[0, 1, 0]]).bool(), + torch.tensor([[0, 1, 0, 1]]).bool(), + ), + # prev_key_padding_mask already full + ( + torch.tensor([[0, 1, 0, 1]]).bool(), + None, + torch.tensor([[0, 1, 0, 1]]).bool(), + ), + # key_padding_mask already full + ( + None, + torch.tensor([[0, 1, 0, 1]]).bool(), + torch.tensor([[0, 1, 0, 1]]).bool(), + ), + ] + for c in cases: + key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( + c[0], + c[1], + batch_size=bsz, + src_len=src_len, + static_kv=False, + ) + + if key_padding_mask is not None: + self.assertTrue( + torch.all(torch.eq(key_padding_mask, c[2])), + f"Unexpected resultant key padding mask: {key_padding_mask}" + f" given current: {c[0]} and previous: {c[1]}", + ) + self.assertEqual(key_padding_mask.size(0), bsz) + self.assertEqual(key_padding_mask.size(1), src_len) + else: + self.assertIsNone(c[2]) + + def test_pruning_heads(self): + embed_dim = 768 + num_heads = 12 + num_heads_to_keep = 8 + dummy_input = torch.randn(32, 2, embed_dim) + mha = MultiheadAttention(embed_dim=embed_dim, num_heads=num_heads) + reserve_head_index = mha._get_reserve_head_index( + num_heads_to_keep=num_heads_to_keep + ) + mha._adaptive_prune_heads(reserve_head_index=reserve_head_index) + mha._set_skip_embed_dim_check() + mha(query=dummy_input, key=dummy_input, value=dummy_input) + self.assertEqual(mha.head_dim, embed_dim / num_heads) + self.assertEqual(mha.num_heads, num_heads_to_keep) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_noising.py b/fairseq/tests/test_noising.py new file mode 100644 index 0000000..1956f6a --- /dev/null +++ b/fairseq/tests/test_noising.py @@ -0,0 +1,531 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from typing import Dict, List + +import torch + +import tests.utils as test_utils +from fairseq import utils +from fairseq.data import ( + Dictionary, + LanguagePairDataset, + TransformEosDataset, + data_utils, + noising, +) + + +class TestDataNoising(unittest.TestCase): + def _get_test_data_with_bpe_cont_marker(self, append_eos=True): + """ + Args: + append_eos: if True, each input sentence in the source tokens tensor + will have an EOS appended to the end. + + Returns: + vocabs: BPE vocab with continuation markers as suffixes to denote + non-end of word tokens. This is the standard BPE format used in + fairseq's preprocessing. + x: input tensor containing numberized source tokens, with EOS at the + end if append_eos is true + src_lengths: and source lengths. + """ + vocab = Dictionary() + vocab.add_symbol("he@@") + vocab.add_symbol("llo") + vocab.add_symbol("how") + vocab.add_symbol("are") + vocab.add_symbol("y@@") + vocab.add_symbol("ou") + vocab.add_symbol("n@@") + vocab.add_symbol("ew") + vocab.add_symbol("or@@") + vocab.add_symbol("k") + + src_tokens = [ + ["he@@", "llo", "n@@", "ew", "y@@", "or@@", "k"], + ["how", "are", "y@@", "ou"], + ] + x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor( + vocab=vocab, src_tokens=src_tokens, append_eos=append_eos + ) + return vocab, x, src_lengths + + def _get_test_data_with_bpe_end_marker(self, append_eos=True): + """ + Args: + append_eos: if True, each input sentence in the source tokens tensor + will have an EOS appended to the end. + + Returns: + vocabs: BPE vocab with end-of-word markers as suffixes to denote + tokens at the end of a word. This is an alternative to fairseq's + standard preprocessing framework and is not generally supported + within fairseq. + x: input tensor containing numberized source tokens, with EOS at the + end if append_eos is true + src_lengths: and source lengths. + """ + vocab = Dictionary() + vocab.add_symbol("he") + vocab.add_symbol("llo_EOW") + vocab.add_symbol("how_EOW") + vocab.add_symbol("are_EOW") + vocab.add_symbol("y") + vocab.add_symbol("ou_EOW") + vocab.add_symbol("n") + vocab.add_symbol("ew_EOW") + vocab.add_symbol("or") + vocab.add_symbol("k_EOW") + + src_tokens = [ + ["he", "llo_EOW", "n", "ew_EOW", "y", "or", "k_EOW"], + ["how_EOW", "are_EOW", "y", "ou_EOW"], + ] + x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor( + vocab=vocab, src_tokens=src_tokens, append_eos=append_eos + ) + return vocab, x, src_lengths + + def _get_test_data_with_word_vocab(self, append_eos=True): + """ + Args: + append_eos: if True, each input sentence in the source tokens tensor + will have an EOS appended to the end. + + Returns: + vocabs: word vocab + x: input tensor containing numberized source tokens, with EOS at the + end if append_eos is true + src_lengths: and source lengths. + """ + vocab = Dictionary() + + vocab.add_symbol("hello") + vocab.add_symbol("how") + vocab.add_symbol("are") + vocab.add_symbol("you") + vocab.add_symbol("new") + vocab.add_symbol("york") + src_tokens = [ + ["hello", "new", "york", "you"], + ["how", "are", "you", "new", "york"], + ] + x, src_lengths = self._convert_src_tokens_to_tensor( + vocab=vocab, src_tokens=src_tokens, append_eos=append_eos + ) + return vocab, x, src_lengths + + def _convert_src_tokens_to_tensor( + self, vocab: Dictionary, src_tokens: List[List[str]], append_eos: bool + ): + src_len = [len(x) for x in src_tokens] + # If we have to append EOS, we include EOS in counting src length + if append_eos: + src_len = [length + 1 for length in src_len] + + x = torch.LongTensor(len(src_tokens), max(src_len)).fill_(vocab.pad()) + for i in range(len(src_tokens)): + for j in range(len(src_tokens[i])): + x[i][j] = vocab.index(src_tokens[i][j]) + if append_eos: + x[i][j + 1] = vocab.eos() + + x = x.transpose(1, 0) + return x, torch.LongTensor(src_len) + + def assert_eos_at_end(self, x, x_len, eos): + """Asserts last token of every sentence in x is EOS""" + for i in range(len(x_len)): + self.assertEqual( + x[x_len[i] - 1][i], + eos, + ( + "Expected eos (token id {eos}) at the end of sentence {i} " + "but got {other} instead" + ).format(i=i, eos=eos, other=x[i][-1]), + ) + + def assert_word_dropout_correct(self, x, x_noised, x_len, l_noised): + # Expect only the first word (2 bpe tokens) of the first example + # was dropped out + self.assertEqual(x_len[0] - 2, l_noised[0]) + for i in range(l_noised[0]): + self.assertEqual(x_noised[i][0], x[i + 2][0]) + + def test_word_dropout_with_eos(self): + vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True) + + with data_utils.numpy_seed(1234): + noising_gen = noising.WordDropout(vocab) + x_noised, l_noised = noising_gen.noising(x, x_len, 0.2) + self.assert_word_dropout_correct( + x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised + ) + self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) + + def assert_word_blanking_correct(self, x, x_noised, x_len, l_noised, unk): + # Expect only the first word (2 bpe tokens) of the first example + # was blanked out + self.assertEqual(x_len[0], l_noised[0]) + for i in range(l_noised[0]): + if i < 2: + self.assertEqual(x_noised[i][0], unk) + else: + self.assertEqual(x_noised[i][0], x[i][0]) + + def test_word_blank_with_eos(self): + vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True) + + with data_utils.numpy_seed(1234): + noising_gen = noising.WordDropout(vocab) + x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk()) + self.assert_word_blanking_correct( + x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk() + ) + self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) + + def generate_unchanged_shuffle_map(self, length): + return {i: i for i in range(length)} + + def assert_word_shuffle_matches_expected( + self, + x, + x_len, + max_shuffle_distance: int, + vocab: Dictionary, + expected_shufle_maps: List[Dict[int, int]], + expect_eos_at_end: bool, + bpe_end_marker=None, + ): + """ + This verifies that with a given x, x_len, max_shuffle_distance, and + vocab, we get the expected shuffle result. + + Args: + x: Tensor of shape (T x B) = (sequence_length, batch_size) + x_len: Tensor of length B = batch_size + max_shuffle_distance: arg to pass to noising + expected_shuffle_maps: List[mapping] where mapping is a + Dict[old_index, new_index], mapping x's elements from their + old positions in x to their new positions in x. + expect_eos_at_end: if True, check the output to make sure there is + an EOS at the end. + bpe_end_marker: str denoting the BPE end token. If this is not None, we + set the BPE cont token to None in the noising classes. + """ + bpe_cont_marker = None + if bpe_end_marker is None: + bpe_cont_marker = "@@" + + with data_utils.numpy_seed(1234): + word_shuffle = noising.WordShuffle( + vocab, bpe_cont_marker=bpe_cont_marker, bpe_end_marker=bpe_end_marker + ) + x_noised, l_noised = word_shuffle.noising( + x, x_len, max_shuffle_distance=max_shuffle_distance + ) + + # For every example, we have a different expected shuffle map. We check + # that each example is shuffled as expected according to each + # corresponding shuffle map. + for i in range(len(expected_shufle_maps)): + shuffle_map = expected_shufle_maps[i] + for k, v in shuffle_map.items(): + self.assertEqual(x[k][i], x_noised[v][i]) + + # Shuffling should not affect the length of each example + for pre_shuffle_length, post_shuffle_length in zip(x_len, l_noised): + self.assertEqual(pre_shuffle_length, post_shuffle_length) + if expect_eos_at_end: + self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) + + def test_word_shuffle_with_eos(self): + vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True) + + # Assert word shuffle with max shuffle distance 0 causes input to be + # unchanged + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + max_shuffle_distance=0, + vocab=vocab, + expected_shufle_maps=[ + self.generate_unchanged_shuffle_map(example_len) + for example_len in x_len + ], + expect_eos_at_end=True, + ) + + # Assert word shuffle with max shuffle distance 3 matches our expected + # shuffle order + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + vocab=vocab, + max_shuffle_distance=3, + expected_shufle_maps=[ + self.generate_unchanged_shuffle_map(x_len[0]), + {0: 0, 1: 3, 2: 1, 3: 2}, + ], + expect_eos_at_end=True, + ) + + def test_word_shuffle_with_eos_nonbpe(self): + """The purpose of this is to test shuffling logic with word vocabs""" + vocab, x, x_len = self._get_test_data_with_word_vocab(append_eos=True) + + # Assert word shuffle with max shuffle distance 0 causes input to be + # unchanged + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + max_shuffle_distance=0, + vocab=vocab, + expected_shufle_maps=[ + self.generate_unchanged_shuffle_map(example_len) + for example_len in x_len + ], + expect_eos_at_end=True, + ) + + # Assert word shuffle with max shuffle distance 3 matches our expected + # shuffle order + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + vocab=vocab, + max_shuffle_distance=3, + expected_shufle_maps=[ + {0: 0, 1: 1, 2: 3, 3: 2}, + {0: 0, 1: 2, 2: 1, 3: 3, 4: 4}, + ], + expect_eos_at_end=True, + ) + + def test_word_shuffle_without_eos(self): + """Same result as word shuffle with eos except no EOS at end""" + vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False) + + # Assert word shuffle with max shuffle distance 0 causes input to be + # unchanged + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + max_shuffle_distance=0, + vocab=vocab, + expected_shufle_maps=[ + self.generate_unchanged_shuffle_map(example_len) + for example_len in x_len + ], + expect_eos_at_end=False, + ) + + # Assert word shuffle with max shuffle distance 3 matches our expected + # shuffle order + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + vocab=vocab, + max_shuffle_distance=3, + expected_shufle_maps=[ + self.generate_unchanged_shuffle_map(x_len[0]), + {0: 0, 1: 3, 2: 1, 3: 2}, + ], + expect_eos_at_end=False, + ) + + def test_word_shuffle_without_eos_with_bpe_end_marker(self): + """Same result as word shuffle without eos except using BPE end token""" + vocab, x, x_len = self._get_test_data_with_bpe_end_marker(append_eos=False) + + # Assert word shuffle with max shuffle distance 0 causes input to be + # unchanged + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + max_shuffle_distance=0, + vocab=vocab, + expected_shufle_maps=[ + self.generate_unchanged_shuffle_map(example_len) + for example_len in x_len + ], + expect_eos_at_end=False, + bpe_end_marker="_EOW", + ) + + # Assert word shuffle with max shuffle distance 3 matches our expected + # shuffle order + self.assert_word_shuffle_matches_expected( + x=x, + x_len=x_len, + vocab=vocab, + max_shuffle_distance=3, + expected_shufle_maps=[ + self.generate_unchanged_shuffle_map(x_len[0]), + {0: 0, 1: 3, 2: 1, 3: 2}, + ], + expect_eos_at_end=False, + bpe_end_marker="_EOW", + ) + + def assert_no_eos_at_end(self, x, x_len, eos): + """Asserts that the last token of each sentence in x is not EOS""" + for i in range(len(x_len)): + self.assertNotEqual( + x[x_len[i] - 1][i], + eos, + "Expected no eos (token id {eos}) at the end of sentence {i}.".format( + eos=eos, i=i + ), + ) + + def test_word_dropout_without_eos(self): + """Same result as word dropout with eos except no EOS at end""" + vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False) + + with data_utils.numpy_seed(1234): + noising_gen = noising.WordDropout(vocab) + x_noised, l_noised = noising_gen.noising(x, x_len, 0.2) + self.assert_word_dropout_correct( + x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised + ) + self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) + + def test_word_blank_without_eos(self): + """Same result as word blank with eos except no EOS at end""" + vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False) + + with data_utils.numpy_seed(1234): + noising_gen = noising.WordDropout(vocab) + x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk()) + self.assert_word_blanking_correct( + x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk() + ) + self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) + + def _get_noising_dataset_batch( + self, + src_tokens_no_pad, + src_dict, + append_eos_to_tgt=False, + ): + """ + Constructs a NoisingDataset and the corresponding + ``LanguagePairDataset(NoisingDataset(src), src)``. If + *append_eos_to_tgt* is True, wrap the source dataset in + :class:`TransformEosDataset` to append EOS to the clean source when + using it as the target. + """ + src_dataset = test_utils.TestDataset(data=src_tokens_no_pad) + + noising_dataset = noising.NoisingDataset( + src_dataset=src_dataset, + src_dict=src_dict, + seed=1234, + max_word_shuffle_distance=3, + word_dropout_prob=0.2, + word_blanking_prob=0.2, + noising_class=noising.UnsupervisedMTNoising, + ) + tgt = src_dataset + language_pair_dataset = LanguagePairDataset( + src=noising_dataset, tgt=tgt, src_sizes=None, src_dict=src_dict + ) + language_pair_dataset = TransformEosDataset( + language_pair_dataset, + src_dict.eos(), + append_eos_to_tgt=append_eos_to_tgt, + ) + + dataloader = torch.utils.data.DataLoader( + dataset=language_pair_dataset, + batch_size=2, + collate_fn=language_pair_dataset.collater, + ) + denoising_batch_result = next(iter(dataloader)) + return denoising_batch_result + + def test_noising_dataset_with_eos(self): + src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker( + append_eos=True + ) + + # Format data for src_dataset + src_tokens = torch.t(src_tokens) + src_tokens_no_pad = [] + for src_sentence in src_tokens: + src_tokens_no_pad.append( + utils.strip_pad(tensor=src_sentence, pad=src_dict.pad()) + ) + denoising_batch_result = self._get_noising_dataset_batch( + src_tokens_no_pad=src_tokens_no_pad, src_dict=src_dict + ) + + eos, pad = src_dict.eos(), src_dict.pad() + + # Generated noisy source as source + expected_src = torch.LongTensor( + [[4, 5, 10, 11, 8, 12, 13, eos], [pad, pad, pad, 6, 8, 9, 7, eos]] + ) + # Original clean source as target (right-padded) + expected_tgt = torch.LongTensor( + [[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]] + ) + generated_src = denoising_batch_result["net_input"]["src_tokens"] + tgt_tokens = denoising_batch_result["target"] + + self.assertTensorEqual(expected_src, generated_src) + self.assertTensorEqual(expected_tgt, tgt_tokens) + + def test_noising_dataset_without_eos(self): + """ + Similar to test noising dataset with eos except that we have to set + *append_eos_to_tgt* to ``True``. + """ + + src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker( + append_eos=False + ) + + # Format data for src_dataset + src_tokens = torch.t(src_tokens) + src_tokens_no_pad = [] + for src_sentence in src_tokens: + src_tokens_no_pad.append( + utils.strip_pad(tensor=src_sentence, pad=src_dict.pad()) + ) + denoising_batch_result = self._get_noising_dataset_batch( + src_tokens_no_pad=src_tokens_no_pad, + src_dict=src_dict, + append_eos_to_tgt=True, + ) + + eos, pad = src_dict.eos(), src_dict.pad() + + # Generated noisy source as source + expected_src = torch.LongTensor( + [[4, 5, 10, 11, 8, 12, 13], [pad, pad, pad, 6, 8, 9, 7]] + ) + # Original clean source as target (right-padded) + expected_tgt = torch.LongTensor( + [[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]] + ) + + generated_src = denoising_batch_result["net_input"]["src_tokens"] + tgt_tokens = denoising_batch_result["target"] + + self.assertTensorEqual(expected_src, generated_src) + self.assertTensorEqual(expected_tgt, tgt_tokens) + + def assertTensorEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertEqual(t1.ne(t2).long().sum(), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_online_backtranslation.py b/fairseq/tests/test_online_backtranslation.py new file mode 100644 index 0000000..0ae7e77 --- /dev/null +++ b/fairseq/tests/test_online_backtranslation.py @@ -0,0 +1,206 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import tempfile +import unittest +from pathlib import Path +from typing import Any, Dict, Sequence + +import fairseq.data.indexed_dataset as indexed_dataset +import fairseq.options +import fairseq.tasks.online_backtranslation as obt +import torch +from tests import utils + + +def mk_sample(tokens: Sequence[int], batch_size: int = 2) -> Dict[str, Any]: + batch = torch.stack([torch.tensor(tokens, dtype=torch.long)] * batch_size) + sample = { + "net_input": { + "src_tokens": batch, + "prev_output_tokens": batch, + "src_lengths": torch.tensor([len(tokens)] * batch_size, dtype=torch.long), + }, + "target": batch[:, 1:], + } + return sample + + +def mk_dataset(num_samples: int, max_len: int, output: Path): + output.parent.mkdir(exist_ok=True) + idx = indexed_dataset.IndexedDatasetBuilder(str(output)) + data = torch.randint(5, 100, (num_samples, max_len)) + lengths = torch.randint(3, max_len, (num_samples,)) + for d, l in zip(data, lengths): + d[0] = 0 + idx.add_item(d[:l]) + idx.finalize(output.with_suffix(".idx")) + assert output.exists() + assert output.with_suffix(".idx").exists() + + +class OnlineBacktranslationTest(unittest.TestCase): + + tmp_dir = Path(tempfile.mkdtemp(suffix="OnlineBacktranslationTest")) + + @classmethod + def obt_task( + cls, languages: Sequence[str], data: Path = None, language_mapping: str = None + ): + dict_path = cls.tmp_dir / "dict.txt" + if not dict_path.exists(): + dictionary = utils.dummy_dictionary(100) + dictionary.save(str(dict_path)) + + if data is not None: + (data / "dict.txt").write_text(dict_path.read_text()) + else: + data = cls.tmp_dir + assert len(languages) >= 2 + + kwargs = { + "arch": "transformer", + # --max-sentences=1 for better predictability of batches + "max_sentences": 1, + # Use characteristics dimensions + "encoder_layers": 3, + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "encoder_attention_heads": 4, + "decoder_layers": 3, + "decoder_embed_dim": 12, + "decoder_output_dim": 12, + "decoder_ffn_embed_dim": 14, + "decoder_attention_heads": 4, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + } + + args = fairseq.options.get_args( + data, + task="online_backtranslation", + mono_langs=",".join(languages), + valid_lang_pairs=f"{languages[0]}-{languages[1]}", + tokens_per_sample=256, + language_mapping=language_mapping, + **kwargs, + ) + task = obt.OnlineBackTranslationTask.setup_task(args) + # we need to build the model to have the correct dictionary + model = task.build_model(task.args) + return task, model + + def tmp_path(self, test_case: str) -> Path: + return Path(tempfile.mkdtemp(test_case, dir=self.tmp_dir)) + + def test_lang_tokens(self): + task, model = self.obt_task(["en", "ro", "zh"]) + assert obt._lang_token("en") in task.dictionary + assert obt._lang_token("ro") in task.dictionary + assert obt._lang_token("zh") in task.dictionary + + en_bos = obt._lang_token_index(task.common_dict, "en") + assert "en" == task.common_dict[en_bos].strip("_") + zh_bos = obt._lang_token_index(task.common_dict, "zh") + assert "zh" == task.common_dict[zh_bos].strip("_") + zh_sample = mk_sample([zh_bos, 16, 14, 12, 10]) + + # we expect to receive the bos token for translation + assert task.get_bos_token_from_sample(zh_sample) == en_bos + + def test_backtranslate_sample(self): + task, model = self.obt_task(["en", "ro", "zh"]) + + en_bos = obt._lang_token_index(task.common_dict, "en") + zh_bos = obt._lang_token_index(task.common_dict, "zh") + sample = mk_sample([zh_bos, 16, 14, 12, 10]) + + task.backtranslate_sample(sample, "zh", "en") + target_zh = list(sample["target"][0]) + assert target_zh == [16, 14, 12, 10] # original zh sentence + generated_en = sample["net_input"]["src_tokens"][0] + assert generated_en[0] == en_bos + + def test_train_dataset(self): + data = self.tmp_path("test_train_dataset") + mk_dataset(20, 10, data / "en" / "train.bin") + mk_dataset(10, 10, data / "zh" / "train.bin") + task, model = self.obt_task(["en", "zh"], data) + task.load_dataset("train") + + en_bos = obt._lang_token_index(task.common_dict, "en") + zh_bos = obt._lang_token_index(task.common_dict, "zh") + + train = task.datasets["train"] + train.ordered_indices() + train.prefetch([0, 19]) + sample_0 = train[0] + sample_19 = train[19] + self.assertEqual( + set(sample_0.keys()), {"en-BT", "en-DENOISE", "zh-BT", "zh-DENOISE"} + ) + for sample in (sample_0, sample_19): + self.assertEqual(sample["en-BT"]["source"][0], en_bos) + # bt target isn't ready to look at. + self.assertEqual(sample["en-DENOISE"]["source"][0], en_bos) + # TODO What could we check on the target side ? + + for i in range(10): + # Zh dataset is shorter, and is wrapped around En dataset. + train.prefetch([i, i + 10]) + self.assertEqual( + list(train[i]["zh-DENOISE"]["source"]), + list(train[i + 10]["zh-DENOISE"]["source"]), + ) + self.assertEqual(train[i]["zh-DENOISE"]["source"][0].item(), zh_bos) + + # Sorted by increasing len + self.assertLess( + len(sample_0["en-BT"]["source"]), len(sample_19["en-BT"]["source"]) + ) + + def test_valid_dataset(self): + data = self.tmp_path("test_valid_dataset") + mk_dataset(10, 21, data / "valid.en-zh.en.bin") + mk_dataset(10, 21, data / "valid.en-zh.zh.bin") + + task, model = self.obt_task(["en", "zh"], data) + valid = task.load_dataset("valid") + en_bos = obt._lang_token_index(task.common_dict, "en") + + assert valid is not None + valid.prefetch(range(10)) + sample_0 = valid[0] + sample_9 = valid[9] + self.assertEqual(sample_0["id"], 0) + self.assertEqual(sample_9["id"], 9) + self.assertEqual(sample_0["source"][0], en_bos) + self.assertEqual(sample_9["source"][0], en_bos) + # TODO: could we test the target side ? + + def assertFnMatch(self, fn, values): + for x, y in values.items(): + fn_x = fn(x) + self.assertEqual(fn_x, y, f"Fn has wrong value: fn({x}) = {fn_x} != {y}") + + def test_piecewise_linear_fn(self): + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("1.0"), {0: 1, 100: 1, 500: 1, 1000: 1} + ) + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("0:1,1000:0"), + {0: 1, 500: 0.5, 1000: 0, 2000: 0}, + ) + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("0:0,1000:1"), + {0: 0, 500: 0.5, 1000: 1, 2000: 1}, + ) + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("0:0,1000:1,2000:0"), + {0: 0, 500: 0.5, 1000: 1, 1500: 0.5, 2000: 0, 3000: 0}, + ) diff --git a/fairseq/tests/test_plasma_utils.py b/fairseq/tests/test_plasma_utils.py new file mode 100644 index 0000000..7286c6c --- /dev/null +++ b/fairseq/tests/test_plasma_utils.py @@ -0,0 +1,127 @@ +import contextlib +import tempfile +import unittest +from io import StringIO + +import numpy as np + +from tests.utils import create_dummy_data, preprocess_lm_data, train_language_model + +try: + from pyarrow import plasma + + from fairseq.data.plasma_utils import PlasmaStore, PlasmaView + + PYARROW_AVAILABLE = True +except ImportError: + PYARROW_AVAILABLE = False + +dummy_path = "dummy" + + +@unittest.skipUnless(PYARROW_AVAILABLE, "") +class TestPlasmaView(unittest.TestCase): + def setUp(self) -> None: + self.tmp_file = tempfile.NamedTemporaryFile() # noqa: P201 + self.path = self.tmp_file.name + self.server = PlasmaStore.start(path=self.path, nbytes=10000) + self.client = plasma.connect(self.path, num_retries=10) + + def tearDown(self) -> None: + self.client.disconnect() + self.tmp_file.close() + self.server.kill() + + def test_two_servers_do_not_share_object_id_space(self): + data_server_1 = np.array([0, 1]) + data_server_2 = np.array([2, 3]) + server_2_path = self.path + with tempfile.NamedTemporaryFile() as server_1_path: + server = PlasmaStore.start(path=server_1_path.name, nbytes=10000) + arr1 = PlasmaView( + data_server_1, dummy_path, 1, plasma_path=server_1_path.name + ) + assert len(arr1.client.list()) == 1 + assert (arr1.array == data_server_1).all() + arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=server_2_path) + assert (arr2.array == data_server_2).all() + assert (arr1.array == data_server_1).all() + server.kill() + + def test_hash_collision(self): + data_server_1 = np.array([0, 1]) + data_server_2 = np.array([2, 3]) + arr1 = PlasmaView(data_server_1, dummy_path, 1, plasma_path=self.path) + assert len(arr1.client.list()) == 1 + arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=self.path) + assert len(arr1.client.list()) == 1 + assert len(arr2.client.list()) == 1 + assert (arr2.array == data_server_1).all() + # New hash key based on tuples + arr3 = PlasmaView( + data_server_2, dummy_path, (1, 12312312312, None), plasma_path=self.path + ) + assert ( + len(arr2.client.list()) == 2 + ), "No new object was created by using a novel hash key" + assert ( + arr3.object_id in arr2.client.list() + ), "No new object was created by using a novel hash key" + assert ( + arr3.object_id in arr3.client.list() + ), "No new object was created by using a novel hash key" + del arr3, arr2, arr1 + + @staticmethod + def _assert_view_equal(pv1, pv2): + np.testing.assert_array_equal(pv1.array, pv2.array) + + def test_putting_same_array_twice(self): + data = np.array([4, 4, 4]) + arr1 = PlasmaView(data, dummy_path, 1, plasma_path=self.path) + assert len(self.client.list()) == 1 + arr1b = PlasmaView( + data, dummy_path, 1, plasma_path=self.path + ) # should not change contents of store + arr1c = PlasmaView( + None, dummy_path, 1, plasma_path=self.path + ) # should not change contents of store + + assert len(self.client.list()) == 1 + self._assert_view_equal(arr1, arr1b) + self._assert_view_equal(arr1, arr1c) + PlasmaView( + data, dummy_path, 2, plasma_path=self.path + ) # new object id, adds new entry + assert len(self.client.list()) == 2 + + new_client = plasma.connect(self.path) + assert len(new_client.list()) == 2 # new client can access same objects + assert isinstance(arr1.object_id, plasma.ObjectID) + del arr1b + del arr1c + + def test_plasma_store_full_raises(self): + with tempfile.NamedTemporaryFile() as new_path: + server = PlasmaStore.start(path=new_path.name, nbytes=10000) + with self.assertRaises(plasma.PlasmaStoreFull): + # 2000 floats is more than 2000 bytes + PlasmaView( + np.random.rand(10000, 1), dummy_path, 1, plasma_path=new_path.name + ) + server.kill() + + def test_object_id_overflow(self): + PlasmaView.get_object_id("", 2**21) + + def test_training_lm_plasma(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "transformer_lm", + ["--use-plasma-view", "--plasma-path", self.path], + run_validation=True, + ) diff --git a/fairseq/tests/test_positional_encoding.py b/fairseq/tests/test_positional_encoding.py new file mode 100644 index 0000000..4e38c43 --- /dev/null +++ b/fairseq/tests/test_positional_encoding.py @@ -0,0 +1,63 @@ +import unittest + +import torch +from fairseq.modules import RelPositionalEncoding +import numpy as np + + +class TestRelPositionalEncoding(unittest.TestCase): + def setUp(self) -> None: + self.T = 3 + self.B = 1 + self.C = 2 + torch.manual_seed(0) + self.sample = torch.randn(self.T, self.B, self.C) # TBC + self.rel_pos_enc = RelPositionalEncoding(max_len=4, d_model=self.C) + + def test_extend_pe(self): + inp = self.sample.transpose(0, 1) + self.rel_pos_enc.extend_pe(inp) + expected_pe = torch.tensor( + [ + [ + [0.1411, -0.9900], + [0.9093, -0.4161], + [0.8415, 0.5403], + [0.0000, 1.0000], + [-0.8415, 0.5403], + [-0.9093, -0.4161], + [-0.1411, -0.9900], + ] + ] + ) + + self.assertTrue( + np.allclose( + expected_pe.cpu().detach().numpy(), + self.rel_pos_enc.pe.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + def test_forward(self): + pos_enc = self.rel_pos_enc(self.sample) + expected_pos_enc = torch.tensor( + [ + [[0.9093, -0.4161]], + [[0.8415, 0.5403]], + [[0.0000, 1.0000]], + [[-0.8415, 0.5403]], + [[-0.9093, -0.4161]], + ] + ) + self.assertTrue( + np.allclose( + pos_enc.cpu().detach().numpy(), + expected_pos_enc.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_reproducibility.py b/fairseq/tests/test_reproducibility.py new file mode 100644 index 0000000..b285593 --- /dev/null +++ b/fairseq/tests/test_reproducibility.py @@ -0,0 +1,148 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import tempfile +import unittest + +import torch + +from . import test_binaries + + +class TestReproducibility(unittest.TestCase): + def _test_reproducibility( + self, + name, + extra_flags=None, + delta=0.0001, + resume_checkpoint="checkpoint1.pt", + max_epoch=3, + ): + def get_last_log_stats_containing_string(log_records, search_string): + for log_record in logs.records[::-1]: + if isinstance(log_record.msg, str) and search_string in log_record.msg: + return json.loads(log_record.msg) + + if extra_flags is None: + extra_flags = [] + + with tempfile.TemporaryDirectory(name) as data_dir: + with self.assertLogs() as logs: + test_binaries.create_dummy_data(data_dir) + test_binaries.preprocess_translation_data(data_dir) + + # train epochs 1 and 2 together + with self.assertLogs() as logs: + test_binaries.train_translation_model( + data_dir, + "fconv_iwslt_de_en", + [ + "--dropout", + "0.0", + "--log-format", + "json", + "--log-interval", + "1", + "--max-epoch", + str(max_epoch), + ] + + extra_flags, + ) + train_log = get_last_log_stats_containing_string(logs.records, "train_loss") + valid_log = get_last_log_stats_containing_string(logs.records, "valid_loss") + + # train epoch 2, resuming from previous checkpoint 1 + os.rename( + os.path.join(data_dir, resume_checkpoint), + os.path.join(data_dir, "checkpoint_last.pt"), + ) + with self.assertLogs() as logs: + test_binaries.train_translation_model( + data_dir, + "fconv_iwslt_de_en", + [ + "--dropout", + "0.0", + "--log-format", + "json", + "--log-interval", + "1", + "--max-epoch", + str(max_epoch), + ] + + extra_flags, + ) + train_res_log = get_last_log_stats_containing_string( + logs.records, "train_loss" + ) + valid_res_log = get_last_log_stats_containing_string( + logs.records, "valid_loss" + ) + + for k in ["train_loss", "train_ppl", "train_num_updates", "train_gnorm"]: + self.assertAlmostEqual( + float(train_log[k]), float(train_res_log[k]), delta=delta + ) + for k in [ + "valid_loss", + "valid_ppl", + "valid_num_updates", + "valid_best_loss", + ]: + self.assertAlmostEqual( + float(valid_log[k]), float(valid_res_log[k]), delta=delta + ) + + def test_reproducibility(self): + self._test_reproducibility("test_reproducibility") + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_reproducibility_fp16(self): + self._test_reproducibility( + "test_reproducibility_fp16", + [ + "--fp16", + "--fp16-init-scale", + "4096", + ], + delta=0.011, + ) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_reproducibility_memory_efficient_fp16(self): + self._test_reproducibility( + "test_reproducibility_memory_efficient_fp16", + [ + "--memory-efficient-fp16", + "--fp16-init-scale", + "4096", + ], + ) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_reproducibility_amp(self): + self._test_reproducibility( + "test_reproducibility_amp", + [ + "--amp", + "--fp16-init-scale", + "4096", + ], + delta=0.011, + ) + + def test_mid_epoch_reproducibility(self): + self._test_reproducibility( + "test_mid_epoch_reproducibility", + ["--save-interval-updates", "3"], + resume_checkpoint="checkpoint_1_3.pt", + max_epoch=1, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_resampling_dataset.py b/fairseq/tests/test_resampling_dataset.py new file mode 100644 index 0000000..ccb53a2 --- /dev/null +++ b/fairseq/tests/test_resampling_dataset.py @@ -0,0 +1,103 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import collections +import unittest + +import numpy as np +from fairseq.data import ListDataset, ResamplingDataset + + +class TestResamplingDataset(unittest.TestCase): + def setUp(self): + self.strings = ["ab", "c", "def", "ghij"] + self.weights = [4.0, 2.0, 7.0, 1.5] + self.size_ratio = 2 + self.dataset = ListDataset( + self.strings, np.array([len(s) for s in self.strings]) + ) + + def _test_common(self, resampling_dataset, iters): + assert len(self.dataset) == len(self.strings) == len(self.weights) + assert len(resampling_dataset) == self.size_ratio * len(self.strings) + + results = {"ordered_by_size": True, "max_distribution_diff": 0.0} + + totalfreqs = 0 + freqs = collections.defaultdict(int) + + for epoch_num in range(iters): + resampling_dataset.set_epoch(epoch_num) + + indices = resampling_dataset.ordered_indices() + assert len(indices) == len(resampling_dataset) + + prev_size = -1 + + for i in indices: + cur_size = resampling_dataset.size(i) + # Make sure indices map to same sequences within an epoch + assert resampling_dataset[i] == resampling_dataset[i] + + # Make sure length of sequence is correct + assert cur_size == len(resampling_dataset[i]) + + freqs[resampling_dataset[i]] += 1 + totalfreqs += 1 + + if prev_size > cur_size: + results["ordered_by_size"] = False + + prev_size = cur_size + + assert set(freqs.keys()) == set(self.strings) + for s, weight in zip(self.strings, self.weights): + freq = freqs[s] / totalfreqs + expected_freq = weight / sum(self.weights) + results["max_distribution_diff"] = max( + results["max_distribution_diff"], abs(expected_freq - freq) + ) + + return results + + def test_resampling_dataset_batch_by_size_false(self): + resampling_dataset = ResamplingDataset( + self.dataset, + self.weights, + size_ratio=self.size_ratio, + batch_by_size=False, + seed=0, + ) + + results = self._test_common(resampling_dataset, iters=1000) + + # For batch_by_size = False, the batches should be returned in + # arbitrary order of size. + assert not results["ordered_by_size"] + + # Allow tolerance in distribution error of 2%. + assert results["max_distribution_diff"] < 0.02 + + def test_resampling_dataset_batch_by_size_true(self): + resampling_dataset = ResamplingDataset( + self.dataset, + self.weights, + size_ratio=self.size_ratio, + batch_by_size=True, + seed=0, + ) + + results = self._test_common(resampling_dataset, iters=1000) + + # For batch_by_size = True, the batches should be returned in + # increasing order of size. + assert results["ordered_by_size"] + + # Allow tolerance in distribution error of 2%. + assert results["max_distribution_diff"] < 0.02 + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_roberta.py b/fairseq/tests/test_roberta.py new file mode 100644 index 0000000..14f01f9 --- /dev/null +++ b/fairseq/tests/test_roberta.py @@ -0,0 +1,344 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +import unittest +from typing import Any, Dict, Sequence + +import fairseq +import fairseq.options +import fairseq.tasks +import torch +from tests.utils import dummy_dictionary + +VOCAB_SIZE = 100 + + +@fairseq.tasks.register_task("fake_task") +class FakeTask(fairseq.tasks.LegacyFairseqTask): + def __init__(self, args): + super().__init__(args) + self.dictionary = dummy_dictionary(VOCAB_SIZE - 4) + assert len(self.dictionary) == VOCAB_SIZE + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + +@functools.lru_cache() +def get_toy_model( + device: str, + architecture: str = "roberta_enc_dec", + **extra_args: Any, +): + assert device in ("gpu", "cpu") + kwargs = { + "arch": architecture, + # Use characteristics dimensions + "encoder_layers": 3, + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "encoder_attention_heads": 4, + "decoder_layers": 3, + "decoder_embed_dim": 12, + "decoder_ffn_embed_dim": 14, + "decoder_attention_heads": 4, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + # required args + "tokens_per_sample": 256, + "data": "/tmp/test_roberta", + } + kwargs.update(extra_args) + fake_task = FakeTask(kwargs) + args = fairseq.options.get_args( + task="online_backtranslation", + mono_langs="en,ro", + valid_lang_pairs="en-ro", + **kwargs, + ) + torch.manual_seed(0) + model = fake_task.build_model(args) + if device == "gpu": + model.cuda() + return fake_task, model + + +def mk_sample( + lang: str, device: str, tok: Sequence[int] = None, batch_size: int = 2 +) -> Dict[str, Any]: + assert device in ("gpu", "cpu") + if not tok: + if lang == "en": + tok = [10, 11, 12, 13, 14, 15, 2] + else: + tok = [20, 21, 22, 23, 24, 25, 26, 27, 2] + + batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size) + if device == "gpu": + batch = batch.cuda() + sample = { + "net_input": { + "src_tokens": batch, + "prev_output_tokens": batch, + "src_lengths": torch.tensor( + [len(tok)] * batch_size, dtype=torch.long, device=batch.device + ), + }, + "target": batch[:, 1:], + } + return sample + + +def cpu_gpu(fn): + def helper(self): + fn(self, "cpu") + if torch.cuda.is_available(): + fn(self, "gpu") + + return helper + + +def architectures(fn): + def helper(self): + for arch in ["roberta_enc_dec", "transformer"]: + fn(self, arch) + + return helper + + +class RobertaTest(unittest.TestCase): + def assertTensorEqual(self, t1, t2, delta: float = 1e-6): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + if delta == 0.0: + self.assertEqual(t1.ne(t2).long().sum(), 0) + else: + self.assertEqual(((t2 - t1).abs() > delta).long().sum(), 0) + + def assertSharing(self, model, link_groups: Sequence[Sequence[str]]): + ids = {} + for group in link_groups: + group_ids = {name: id(params(model, name)) for name in group} + shared_id = group_ids[group[0]] + self.assertEqual(group_ids, {name: shared_id for name in group}) + self.assertNotIn(shared_id, ids) + ids[shared_id] = group + + def test_roberta_shared_params(self): + _, roberta = get_toy_model("cpu", architecture="roberta") + self.assertSharing( + roberta, + [ + [ + "encoder.sentence_encoder.embed_tokens.weight", + "encoder.lm_head.weight", + ] + ], + ) + + _, roberta = get_toy_model( + "cpu", architecture="roberta", untie_weights_roberta=True + ) + self.assertSharing( + roberta, + [ + ["encoder.sentence_encoder.embed_tokens.weight"], + ["encoder.lm_head.weight"], + ], + ) + + def test_roberta_enc_dec_shared_params(self): + # 3 distinct embeddings + _, enc_dec = get_toy_model("cpu", architecture="roberta_enc_dec") + self.assertSharing( + enc_dec, + [ + ["encoder.embed_tokens.weight"], + ["decoder.embed_tokens.weight"], + ["decoder.output_projection.weight"], + ], + ) + + # 2 distinct embeddings, one for encoder, one for decoder + _, enc_dec = get_toy_model( + "cpu", architecture="roberta_enc_dec", share_decoder_input_output_embed=True + ) + self.assertSharing( + enc_dec, + [ + ["encoder.embed_tokens.weight"], + [ + "decoder.embed_tokens.weight", + "decoder.output_projection.weight", + ], + ], + ) + + # shared embeddings + _, enc_dec = get_toy_model( + "cpu", architecture="roberta_enc_dec", share_all_embeddings=True + ) + self.assertSharing( + enc_dec, + [ + [ + "encoder.embed_tokens.weight", + "decoder.embed_tokens.weight", + "decoder.output_projection.weight", + ] + ], + ) + + def test_roberta_max_positions_is_correctly_set(self): + device = "cpu" + task, model = get_toy_model(device) + max_pos = model.max_decoder_positions() + self.assertEqual(max_pos, 256) + self.assertEqual(max_pos, model.decoder.max_positions()) + self.assertEqual(max_pos, model.encoder.max_positions()) + self.assertEqual(max_pos, model.encoder.embed_positions.max_positions) + + sentence = [31 for _ in range(max_pos)] + sample = mk_sample("en", device, sentence, batch_size=1) + self.assertEqual(list(sample["net_input"]["src_lengths"]), [max_pos]) + self.assertEqual(len(sample["net_input"]["src_tokens"][0]), max_pos) + x, _ = model.forward(**sample["net_input"]) + self.assertEqual(x.shape, (1, max_pos, VOCAB_SIZE)) + + @cpu_gpu + def test_roberta_forward_backward(self, device: str): + _, model = get_toy_model(device) + sample = mk_sample("en", device) + en_tokens = sample["net_input"]["src_tokens"] + (bs, l) = en_tokens.shape + # Forward + logits, _ = model(**sample["net_input"]) + self.assertEqual(logits.shape, (bs, l, VOCAB_SIZE)) + + # Backward + loss = logits.sum() + loss.backward() + + @cpu_gpu + def test_roberta_forward_backward_bs1(self, device: str): + _, model = get_toy_model(device) + sample = mk_sample("en", device, batch_size=1) + o, _ = model.forward(**sample["net_input"]) + loss = o.sum() + sample2 = mk_sample("ro", device, batch_size=1) + o, _ = model.forward(**sample2["net_input"]) + loss += o.sum() + loss.backward() + + @cpu_gpu + def test_roberta_batching(self, device: str): + """ + Checks that the batch of size 2 give twice the same results than the batch of size 1. + """ + _, model = get_toy_model(device) + sample = mk_sample("en", device, batch_size=1) + slen = sample["net_input"]["src_lengths"][0] + sample2 = mk_sample("en", device, batch_size=2) + with torch.no_grad(): + z = model.encoder.forward( + sample["net_input"]["src_tokens"], sample["net_input"]["src_lengths"] + ) + z = z["encoder_out"][-1] + logits, _ = model.forward(**sample["net_input"]) + + z2 = model.encoder.forward( + sample2["net_input"]["src_tokens"], sample["net_input"]["src_lengths"] + ) + z2 = z2["encoder_out"][-1] + logits2, _ = model.forward(**sample2["net_input"]) + + self.assertEqual(z.shape, (slen, 1, 12)) + self.assertEqual(z2.shape, (slen, 2, 12)) + self.assertTensorEqual(logits2[0], logits2[1]) + self.assertTensorEqual(logits[0], logits2[0]) + + @cpu_gpu + def test_roberta_incremental_decoder(self, device: str): + """ + Checks that incremental decoding yields the same result than non incremental one. + """ + task, model = get_toy_model(device) + + en_sample = mk_sample("en", device) + en_tokens = en_sample["net_input"]["src_tokens"] + ro_sample = mk_sample("ro", device) + ro_tokens = ro_sample["net_input"]["src_tokens"] + + en_enc = model.encoder.forward( + en_tokens, src_lengths=en_sample["net_input"]["src_lengths"] + ) + (bs, tgt_len) = ro_tokens.shape + + # Decode without incremental state + ro_dec, _ = model.decoder.forward(ro_tokens, encoder_out=en_enc) + self.assertEqual(ro_dec.shape, (bs, tgt_len, VOCAB_SIZE)) + self.assertTensorEqual(ro_dec[0], ro_dec[1]) + + # Decode with incremental state + inc_state = {} + ro_dec_inc = [] + for i in range(tgt_len): + ro, _ = model.decoder.forward( + ro_tokens[:, : i + 1], encoder_out=en_enc, incremental_state=inc_state + ) + self.assertEqual(ro.shape, (bs, 1, VOCAB_SIZE)) + ro_dec_inc.append(ro) + + for i in range(tgt_len): + # Intra-batch + self.assertTensorEqual(ro_dec_inc[i][0], ro_dec_inc[i][1]) + # Incremental vs non-incremental + self.assertTensorEqual(ro_dec_inc[i][:, 0], ro_dec[:, i]) + + @cpu_gpu + def test_regularize_for_adaprune_in_roberta(self, device: str): + _, model = get_toy_model( + device=device, + architecture="roberta_base", + mha_reg_scale_factor=0.000375, + ffn_reg_scale_factor=0.000375, + ) + sample = mk_sample("en", device, batch_size=1) + task_loss, _ = model.forward(**sample["net_input"]) + head_loss = model._get_adaptive_head_loss() + ffn_loss = model._get_adaptive_ffn_loss() + loss = task_loss.sum() + head_loss + ffn_loss + loss.backward() + + @cpu_gpu + def test_ffn_prune_for_adaprune_in_roberta(self, device: str): + _, model = get_toy_model( + device=device, + architecture="roberta_base", + ) + sample = mk_sample("en", device, batch_size=1) + for layer in model.encoder.sentence_encoder.layers: + fc1_original_size = layer.fc1.out_features + remove_index = layer._get_fc_rank(remove_num=2) + layer._prune_fc_layer(remove_index=remove_index) + self.assertEqual(layer.fc1.out_features, fc1_original_size - 2) + + task_loss, _ = model.forward(**sample["net_input"]) + + +def params(model, name): + if "." not in name: + return getattr(model, name) + + prefix, name = name.split(".", 1) + return params(getattr(model, prefix), name) diff --git a/fairseq/tests/test_rotary_positional_embedding.py b/fairseq/tests/test_rotary_positional_embedding.py new file mode 100644 index 0000000..7c44e86 --- /dev/null +++ b/fairseq/tests/test_rotary_positional_embedding.py @@ -0,0 +1,85 @@ +import torch +import numpy as np +import unittest +from fairseq.modules.rotary_positional_embedding import apply_rotary_pos_emb +from fairseq.modules import RotaryPositionalEmbedding + + +class TestRotaryPositionalEmbedding(unittest.TestCase): + def setUp(self) -> None: + self.T = 3 + self.B = 1 + self.C = 2 + torch.manual_seed(0) + self.sample = torch.randn(self.T, self.B, self.C) # TBC + self.rope_pos_emd = RotaryPositionalEmbedding(dim=self.C) + + def test_forward(self): + expected_cos = torch.tensor( + [[[[1.0000, 1.0000]]], [[[0.5403, 0.5403]]], [[[-0.4161, -0.4161]]]] + ) + expected_sin = torch.tensor( + [[[[0.0000, 0.0000]]], [[[0.8415, 0.8415]]], [[[0.9093, 0.9093]]]] + ) + cos, sin = self.rope_pos_emd(self.sample, self.T) + self.assertTrue( + np.allclose( + expected_cos.cpu().detach().numpy(), + cos.cpu().detach().numpy(), + atol=1e-4, + ) + ) + self.assertTrue( + np.allclose( + expected_sin.cpu().detach().numpy(), + sin.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + def test_apply_rotary_pos_emb(self): + cos, sin = self.rope_pos_emd(self.sample, self.T) + query = self.sample.view(self.T, self.B, 1, self.C) + expected_query = torch.tensor( + [[[[1.5410, -0.2934]]], [[[-1.6555, -1.5263]]], [[[1.7231, -0.4041]]]] + ) + new_query, new_key = apply_rotary_pos_emb(query, query, cos, sin) + self.assertTrue( + np.allclose( + expected_query.cpu().detach().numpy(), + new_query.cpu().detach().numpy(), + atol=1e-4, + ) + ) + self.assertTrue( + np.allclose( + expected_query.cpu().detach().numpy(), + new_key.cpu().detach().numpy(), + atol=1e-4, + ) + ) + + def test_jit_compile_rope_module(self): + module_scripted = torch.jit.script(self.rope_pos_emd) + apply_rotary_scripted = torch.jit.script(apply_rotary_pos_emb) + # Test several different lengths + for T in [3, 5, 10]: + sample = torch.randn(T, self.B, self.C) + # Run forward pass with the original module + cos_original, sin_original = self.rope_pos_emd(sample, T) + query = sample.view(T, self.B, 1, self.C) + new_query, new_key = apply_rotary_pos_emb(query, query, cos_original, sin_original) + + # Run forward pass with the scripted module + cos_scripted, sin_scripted = module_scripted(sample, T) + new_query_scripted, new_key_scripted = apply_rotary_scripted(query, query, cos_scripted, sin_scripted) + + # Ensure the outputs are the same + self.assertTrue(torch.allclose(cos_original, cos_scripted)) + self.assertTrue(torch.allclose(sin_original, sin_scripted)) + self.assertTrue(torch.allclose(new_query, new_query_scripted)) + self.assertTrue(torch.allclose(new_key, new_key_scripted)) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_sequence_generator.py b/fairseq/tests/test_sequence_generator.py new file mode 100644 index 0000000..2e42df0 --- /dev/null +++ b/fairseq/tests/test_sequence_generator.py @@ -0,0 +1,744 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import math +import tempfile +import unittest + +import numpy as np +import torch + +import tests.utils as test_utils +from fairseq import search +from fairseq.data.dictionary import Dictionary +from fairseq.models.transformer import TransformerModel +from fairseq.ngram_repeat_block import NGramRepeatBlock +from fairseq.sequence_generator import EnsembleModel, SequenceGenerator +from fairseq.tasks.fairseq_task import LegacyFairseqTask + +DEFAULT_TEST_VOCAB_SIZE = 100 + + +class DummyTask(LegacyFairseqTask): + def __init__(self, args): + super().__init__(args) + self.dictionary = get_dummy_dictionary() + if getattr(self.args, "ctc", False): + self.dictionary.add_symbol("<ctc_blank>") + self.src_dict = self.dictionary + self.tgt_dict = self.dictionary + + @property + def source_dictionary(self): + return self.src_dict + + @property + def target_dictionary(self): + return self.dictionary + + +def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): + dummy_dict = Dictionary() + # add dummy symbol to satisfy vocab size + for id, _ in enumerate(range(vocab_size)): + dummy_dict.add_symbol("{}".format(id), n=1000) + return dummy_dict + + +def get_dummy_task_and_parser(): + """ + to build a fariseq model, we need some dummy parse and task. This function + is used to create dummy task and parser to faciliate model/criterion test + + Note: we use FbSpeechRecognitionTask as the dummy task. You may want + to use other task by providing another function + """ + parser = argparse.ArgumentParser( + description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS + ) + DummyTask.add_args(parser) + args = parser.parse_args([]) + task = DummyTask.setup_task(args) + return task, parser + + +class TestJitSequenceGeneratorBase(unittest.TestCase): + def setUp(self): + self.task, self.parser = get_dummy_task_and_parser() + eos = self.task.tgt_dict.eos() + src_tokens = torch.randint(3, 50, (2, 10)).long() + src_tokens = torch.cat((src_tokens, torch.LongTensor([[eos], [eos]])), -1) + src_lengths = torch.LongTensor([2, 10]) + self.sample = { + "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths} + } + TransformerModel.add_args(self.parser) + args = self.parser.parse_args([]) + args.encoder_layers = 2 + args.decoder_layers = 1 + self.transformer_model = TransformerModel.build_model(args, self.task) + + def assertOutputEqual(self, hypo, pos_probs): + pos_scores = torch.FloatTensor(pos_probs).log() + self.assertTensorSizeEqual(hypo["positional_scores"], pos_scores) + self.assertTensorSizeEqual(pos_scores.numel(), hypo["tokens"].numel()) + + def assertTensorSizeEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess((t1 - t2).abs().max(), 1e-4) + + def assertTensorEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertEqual(t1.ne(t2).long().sum(), 0) + + def assertHypoEqual(self, h1, h2): + "Check two hypos are equal" + self.assertTensorEqual(h1["tokens"], h2["tokens"]) + self.assertAlmostEqual(h1["positional_scores"], h2["positional_scores"]) + self.assertLess(abs(h1["score"] - h2["score"]), 1e-6) + self.assertAlmostEqual(h1["attention"], h2["attention"]) + + def _test_save_and_load(self, scripted_module): + with tempfile.NamedTemporaryFile() as f: + scripted_module.save(f.name) + torch.jit.load(f.name) + + +JIT_MSG = "Targeting OSS scriptability for the 1.6 release" + + +@unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG) +class TestJitSequenceGenerator(TestJitSequenceGeneratorBase): + def test_export_transformer(self): + model = self.transformer_model + torch.jit.script(model) + + def test_ensemble_sequence_generator(self): + model = self.transformer_model + generator = SequenceGenerator( + [model], + self.task.tgt_dict, + beam_size=2, + no_repeat_ngram_size=2, + max_len_b=10, + ) + scripted_model = torch.jit.script(generator) + self._test_save_and_load(scripted_model) + + def test_export_ensemble_model(self): + model = self.transformer_model + ensemble_models = EnsembleModel([model]) + torch.jit.script(ensemble_models) + + +class TestExportSearch(unittest.TestCase): + def setUp(self): + task, _ = get_dummy_task_and_parser() + self.tgt_dict = task.tgt_dict + self.min_top1_prob = 0.4 + + def test_export_diverse_bs(self): + search_strategy = search.DiverseBeamSearch( + self.tgt_dict, num_groups=2, diversity_strength=0.0 + ) + torch.jit.script(search_strategy) + + def test_export_sampling(self): + low_sampling_topp = self.min_top1_prob / 2.0 + search_strategy = search.Sampling( + self.tgt_dict, sampling_topp=low_sampling_topp + ) + torch.jit.script(search_strategy) + + def test_export_diverse_siblings_search(self): + search_strategy = search.DiverseSiblingsSearch( + self.tgt_dict, diversity_rate=0.5 + ) + torch.jit.script(search_strategy) + + +class TestSequenceGeneratorBase(unittest.TestCase): + def assertHypoTokens(self, hypo, tokens): + self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens)) + + def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): + pos_scores = torch.FloatTensor(pos_probs).log() + self.assertAlmostEqual(hypo["positional_scores"], pos_scores) + self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) + score = pos_scores.sum() + if normalized: + score /= pos_scores.numel() ** lenpen + self.assertLess(abs(score - hypo["score"]), 1e-6) + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess((t1 - t2).abs().max(), 1e-4) + + def assertTensorEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertEqual(t1.ne(t2).long().sum(), 0) + + +class TestSequenceGenerator(TestSequenceGeneratorBase): + def setUp(self): + ( + self.tgt_dict, + self.w1, + self.w2, + src_tokens, + src_lengths, + self.model, + ) = test_utils.sequence_generator_setup() + self.sample = { + "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths} + } + + def test_with_normalization(self): + generator = SequenceGenerator([self.model], self.tgt_dict, beam_size=2) + hypos = generator.forward(self.sample) + eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, eos]) + self.assertHypoScore(hypos[0][0], [0.9, 1.0]) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos]) + self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0]) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos]) + self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0]) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w1, w2, eos]) + self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6]) + + def test_without_normalization(self): + # Sentence 1: unchanged from the normalized case + # Sentence 2: beams swap order + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, normalize_scores=False + ) + hypos = generator.forward(self.sample) + eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, eos]) + self.assertHypoScore(hypos[0][0], [0.9, 1.0], normalized=False) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos]) + self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], normalized=False) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) + self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], normalized=False) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos]) + self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], normalized=False) + + def test_with_lenpen_favoring_short_hypos(self): + lenpen = 0.6 + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen + ) + hypos = generator.forward(self.sample) + eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, eos]) + self.assertHypoScore(hypos[0][0], [0.9, 1.0], lenpen=lenpen) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos]) + self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) + self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], lenpen=lenpen) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos]) + self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen) + + def test_with_lenpen_favoring_long_hypos(self): + lenpen = 5.0 + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen + ) + hypos = generator.forward(self.sample) + eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w2, w1, w2, eos]) + self.assertHypoScore(hypos[0][0], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w1, eos]) + self.assertHypoScore(hypos[0][1], [0.9, 1.0], lenpen=lenpen) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos]) + self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w1, w2, eos]) + self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6], lenpen=lenpen) + + def test_maxlen(self): + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, max_len_b=2 + ) + hypos = generator.forward(self.sample) + eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, eos]) + self.assertHypoScore(hypos[0][0], [0.9, 1.0]) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w2, w2, eos]) + self.assertHypoScore(hypos[0][1], [0.1, 0.1, 0.6]) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) + self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6]) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w2, w2, eos]) + self.assertHypoScore(hypos[1][1], [0.3, 0.9, 0.01]) + + def test_encoder_with_different_output_len(self): + args = self.model.encoder.args + task = test_utils.TestTranslationTask.setup_task( + args, self.tgt_dict, self.tgt_dict + ) + reshaping_model = test_utils.TestReshapingModel.build_model(args, task) + generator = SequenceGenerator( + [reshaping_model], self.tgt_dict, beam_size=2, max_len_b=2 + ) + hypos = generator.forward(self.sample) + for sent in [0, 1]: + for beam in [0, 1]: + assert hypos[sent][beam]["attention"] is not None + + def test_generation_with_additional_input(self): + args = self.model.encoder.args + task = test_utils.TestTranslationTask.setup_task( + args, self.tgt_dict, self.tgt_dict + ) + add_input_model = test_utils.TestAdditionalInputModel.build_model(args, task) + generator = SequenceGenerator([add_input_model], self.tgt_dict, beam_size=2) + sample = self.sample.copy() + sample["net_input"]["fancy_other_input"] = sample["net_input"]["src_tokens"] + hypos = generator.forward(self.sample) + eos, w1 = self.tgt_dict.eos(), self.w1 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, eos]) + self.assertHypoScore(hypos[0][0], [0.9, 1.0]) + + +@unittest.skipUnless(torch.cuda.is_available(), "") +class TestRepeatNgramBlocking(TestSequenceGeneratorBase): + @classmethod + def setUpClass(cls): + ( + cls.tgt_dict, + cls.w1, + cls.w2, + src_tokens, + src_lengths, + cls.model, + ) = test_utils.sequence_generator_setup() + return cls + + def test_finds_repetitive_tokens(self): + bsz, vocab_size, beam_size, step = 2, 4, 1, 3 + generated_tok = torch.tensor( + [[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda" + ) + lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda") + desired_result = lprobs.new_tensor( + [[0.0, 0.0, -math.inf, 0.0], [0.0, 0.0, 0.0, -math.inf]] + ) + + cuda_ext_result, baseline_result = self._compare_cuda_ext_to_default_implem( + bsz, beam_size, generated_tok, lprobs, step, 2 + ) + self.assertTensorEqual(cuda_ext_result, desired_result) + self.assertTensorEqual(baseline_result, desired_result) + + @unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG) + def test_jit_no_extension(self): + bsz, vocab_size, beam_size, step = 2, 4, 1, 3 + generated_tok = torch.tensor( + [[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda" + ) + lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda") + blocker = NGramRepeatBlock(2, use_extension=False) + base_result = blocker(generated_tok, lprobs.clone(), bsz, beam_size, step) + scripted_blocker = torch.jit.script(blocker) + jit_result = scripted_blocker( + generated_tok, lprobs.clone(), bsz, beam_size, step + ) + self.assertTensorEqual(base_result, jit_result) + + def test_ngram_blocking_same_as_default_implem(self): + """Test that cuda extension returns same things as default impl in many settings.""" + vocab_size = 4 + step = 6 + for _ in range(2): + block_param = np.random.choice([1, 2, 3, 4]) + batch_size = np.random.randint(1, 8) + beam_size = np.random.choice([1, 2, 4, 8]) + lprobs = torch.zeros((beam_size * batch_size, vocab_size), device="cuda") + + generated_tok = torch.tensor( + np.random.randint( + 0, vocab_size, size=(batch_size * beam_size, step + 1) + ), + device="cuda", + dtype=torch.long, + ) + self._compare_cuda_ext_to_default_implem( + batch_size, + beam_size, + generated_tok, + lprobs, + step, + block_param, + ) + + def _compare_cuda_ext_to_default_implem( + self, bsz, beam_size, generated_tok, lprobs, step, block_param + ): + """Assert that cuda extension and default implem return the same thing.""" + blocker = NGramRepeatBlock(block_param) + assert blocker.use_extension, "Extension not compiled" + cuda_ext_result = blocker( + generated_tok, + lprobs.clone(), + bsz, + beam_size, + step, + ) + blocker.use_extension = False + baseline_result = blocker( + generated_tok, + lprobs.clone(), + bsz, + beam_size, + step, + ) + self.assertTensorEqual(cuda_ext_result, baseline_result) + blocker.use_extension = True + return cuda_ext_result, baseline_result + + +class TestDiverseBeamSearch(TestSequenceGeneratorBase): + def setUp(self): + # construct dummy dictionary + d = test_utils.dummy_dictionary(vocab_size=2) + self.assertEqual(d.pad(), 1) + self.assertEqual(d.eos(), 2) + self.assertEqual(d.unk(), 3) + self.eos = d.eos() + self.w1 = 4 + self.w2 = 5 + + # construct source data + self.src_tokens = torch.LongTensor( + [ + [self.w1, self.w2, self.eos], + [self.w1, self.w2, self.eos], + ] + ) + self.src_lengths = torch.LongTensor([2, 2]) + + args = argparse.Namespace() + unk = 0.0 + args.beam_probs = [ + # step 0: + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [0.0, unk, 0.9, 0.1], # beam 1 + [0.0, unk, 0.9, 0.1], # beam 2 + # sentence 2: + [0.0, unk, 0.7, 0.3], + [0.0, unk, 0.7, 0.3], + ] + ), + # step 1: + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [0.0, unk, 0.6, 0.4], + [0.0, unk, 0.6, 0.4], + # sentence 2: + [0.25, unk, 0.35, 0.4], + [0.25, unk, 0.35, 0.4], + ] + ), + # step 2: + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + # sentence 2: + [0.9, unk, 0.1, 0.0], + [0.9, unk, 0.1, 0.0], + ] + ), + ] + + task = test_utils.TestTranslationTask.setup_task(args, d, d) + self.model = task.build_model(args) + self.tgt_dict = task.target_dictionary + + def test_diverse_beam_search(self): + search_strategy = search.DiverseBeamSearch( + self.tgt_dict, num_groups=2, diversity_strength=0.0 + ) + generator = SequenceGenerator( + [self.model], + self.tgt_dict, + beam_size=2, + search_strategy=search_strategy, + ) + sample = { + "net_input": { + "src_tokens": self.src_tokens, + "src_lengths": self.src_lengths, + } + } + hypos = generator.forward(sample) + eos, w1, w2 = self.eos, self.w1, self.w2 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, w1, eos]) + self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0]) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w1, w1, eos]) + self.assertHypoScore(hypos[0][1], [0.9, 0.6, 1.0]) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) + self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9]) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w1, w2, eos]) + self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.9]) + + +class TestDiverseSiblingsSearch(TestDiverseBeamSearch): + def assertHypoScore( + self, hypo, pos_probs, sibling_rank, diversity_rate, normalized=True, lenpen=1.0 + ): + pos_scores = torch.FloatTensor(pos_probs).log() + pos_scores.sub_(torch.Tensor(sibling_rank) * diversity_rate) + self.assertAlmostEqual(hypo["positional_scores"], pos_scores) + self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) + score = pos_scores.sum() + if normalized: + score /= pos_scores.numel() ** lenpen + self.assertLess(abs(score - hypo["score"]), 1e-6) + + def test_diverse_beam_search(self): + search_strategy = search.DiverseSiblingsSearch( + self.tgt_dict, diversity_rate=0.5 + ) + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy + ) + sample = { + "net_input": { + "src_tokens": self.src_tokens, + "src_lengths": self.src_lengths, + } + } + hypos = generator.forward(sample) + eos, w1, w2 = self.eos, self.w1, self.w2 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, w1, eos]) + self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0], [0, 1, 1], 0.5) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w1, w2, eos]) + self.assertHypoScore(hypos[0][1], [0.9, 0.4, 1.0], [0, 2, 1], 0.5) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) + self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9], [0, 1, 1], 0.5) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w1, w1, eos]) + self.assertHypoScore(hypos[1][1], [0.7, 0.35, 0.9], [0, 2, 1], 0.5) + + +class TestTopPSamplingSearch(TestSequenceGeneratorBase): + def setUp(self): + # construct dummy dictionary + d = test_utils.dummy_dictionary(vocab_size=2) + self.assertEqual(d.pad(), 1) + self.assertEqual(d.eos(), 2) + self.assertEqual(d.unk(), 3) + self.eos = d.eos() + self.w1 = 4 + self.w2 = 5 + + # construct source data + self.src_tokens = torch.LongTensor( + [ + [self.w1, self.w2, self.eos], + [self.w1, self.w2, self.eos], + ] + ) + self.src_lengths = torch.LongTensor([2, 2]) + + args = argparse.Namespace() + unk = 0.0 + # The minimal probability of top 2 tokens. + self.min_top2_prob = 0.75 + # The minimal probability of the top 1 token. + self.min_top1_prob = 0.4 + + w1_prob = self.min_top1_prob + w2_prob = self.min_top2_prob - self.min_top1_prob + eos_prob = 1 - self.min_top2_prob + + args.beam_probs = [ + # step 0: + torch.FloatTensor( + [ + # eos w1 w2 + [0.0, unk, 1.0, 0.0], + [0.0, unk, 1.0, 0.0], + [0.0, unk, 1.0, 0.0], + [0.0, unk, 1.0, 0.0], + ] + ), + # step 1: + torch.FloatTensor( + [ + # eos w1 w2 + [eos_prob, unk, w1_prob, w2_prob], + [eos_prob, unk, w1_prob, w2_prob], + [eos_prob, unk, w1_prob, w2_prob], + [eos_prob, unk, w1_prob, w2_prob], + ] + ), + # step 2: + torch.FloatTensor( + [ + # eos w1 w2 + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + ] + ), + ] + + task = test_utils.TestTranslationTask.setup_task(args, d, d) + self.model = task.build_model(args) + self.tgt_dict = task.target_dictionary + + def test_topp_sampling_search_low_prob(self): + # Given a prob low enough to top-P sampling, we expect only the top + # 1 token to be sampled, which always results in the same output. + low_sampling_topp = self.min_top1_prob / 2.0 + search_strategy = search.Sampling( + self.tgt_dict, sampling_topp=low_sampling_topp + ) + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy + ) + sample = { + "net_input": { + "src_tokens": self.src_tokens, + "src_lengths": self.src_lengths, + } + } + hypos = generator.forward(sample) + eos, w1 = self.eos, self.w1 + # sentence 1, beam 1 + self.assertHypoTokens(hypos[0][0], [w1, w1, eos]) + self.assertHypoScore(hypos[0][0], [1.0, 0.4, 1.0]) + # sentence 1, beam 2 + self.assertHypoTokens(hypos[0][1], [w1, w1, eos]) + self.assertHypoScore(hypos[0][1], [1.0, 0.4, 1.0]) + # sentence 2, beam 1 + self.assertHypoTokens(hypos[1][0], [w1, w1, eos]) + self.assertHypoScore(hypos[1][0], [1.0, 0.4, 1.0]) + # sentence 2, beam 2 + self.assertHypoTokens(hypos[1][1], [w1, w1, eos]) + self.assertHypoScore(hypos[1][1], [1.0, 0.4, 1.0]) + + def test_topp_sampling_search_high_prob(self): + # Given a prob high enough to top-P sampling, any of the top 2 + # tokens could be sampled. This can cause different outputs. + high_sampling_topp = (self.min_top1_prob + self.min_top2_prob) / 2.0 + search_strategy = search.Sampling( + self.tgt_dict, sampling_topp=high_sampling_topp + ) + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy + ) + sample = { + "net_input": { + "src_tokens": self.src_tokens, + "src_lengths": self.src_lengths, + } + } + hypos = generator.forward(sample) + eos, w1, w2 = self.eos, self.w1, self.w2 + # sentence 1, beam 1 + self.assertTrue( + self.hypoTokens(hypos[0][0], [w1, w1, eos]) + or self.hypoTokens(hypos[0][0], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[0][0], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[0][0], [1.0, 0.35, 1.0]) + ) + + # sentence 1, beam 2 + self.assertTrue( + self.hypoTokens(hypos[0][1], [w1, w1, eos]) + or self.hypoTokens(hypos[0][1], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[0][1], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[0][1], [1.0, 0.35, 1.0]) + ) + + # sentence 2, beam 1 + self.assertTrue( + self.hypoTokens(hypos[1][0], [w1, w1, eos]) + or self.hypoTokens(hypos[1][0], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[1][0], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[1][0], [1.0, 0.35, 1.0]) + ) + + # sentence 2, beam 2 + self.assertTrue( + self.hypoTokens(hypos[1][1], [w1, w1, eos]) + or self.hypoTokens(hypos[1][1], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[1][1], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[1][1], [1.0, 0.35, 1.0]) + ) + + def hypoTokens(self, hypo, tokens): + return self.tensorEqual(hypo["tokens"], torch.LongTensor(tokens)) + + def hypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): + pos_scores = torch.FloatTensor(pos_probs).log() + if not self.almostEqual(hypo["positional_scores"], pos_scores): + return False + if pos_scores.numel() != hypo["tokens"].numel(): + return False + score = pos_scores.sum() + if normalized: + score /= pos_scores.numel() ** lenpen + return abs(score - hypo["score"]) < 1e-6 + + def almostEqual(self, t1, t2): + return t1.size() == t2.size() and (t1 - t2).abs().max() < 1e-4 + + def tensorEqual(self, t1, t2): + return t1.size() == t2.size() and t1.ne(t2).long().sum() == 0 + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_sequence_scorer.py b/fairseq/tests/test_sequence_scorer.py new file mode 100644 index 0000000..42f9447 --- /dev/null +++ b/fairseq/tests/test_sequence_scorer.py @@ -0,0 +1,120 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import unittest + +import tests.utils as test_utils +import torch +from fairseq.sequence_scorer import SequenceScorer + + +class TestSequenceScorer(unittest.TestCase): + def test_sequence_scorer(self): + # construct dummy dictionary + d = test_utils.dummy_dictionary(vocab_size=2) + self.assertEqual(d.pad(), 1) + self.assertEqual(d.eos(), 2) + self.assertEqual(d.unk(), 3) + eos = d.eos() + w1 = 4 + w2 = 5 + + # construct dataloader + data = [ + { + "source": torch.LongTensor([w1, w2, eos]), + "target": torch.LongTensor([w1, w2, w1, eos]), + }, + { + "source": torch.LongTensor([w2, eos]), + "target": torch.LongTensor([w2, w1, eos]), + }, + { + "source": torch.LongTensor([w2, eos]), + "target": torch.LongTensor([w2, eos]), + }, + ] + data_itr = test_utils.dummy_dataloader(data) + + # specify expected output probabilities + args = argparse.Namespace() + unk = 0.0 + args.beam_probs = [ + # step 0: + torch.FloatTensor( + [ + # eos w1 w2 + [0.0, unk, 0.6, 0.4], # sentence 1 + [0.0, unk, 0.4, 0.6], # sentence 2 + [0.0, unk, 0.7, 0.3], # sentence 3 + ] + ), + # step 1: + torch.FloatTensor( + [ + # eos w1 w2 + [0.0, unk, 0.2, 0.7], # sentence 1 + [0.0, unk, 0.8, 0.2], # sentence 2 + [0.7, unk, 0.1, 0.2], # sentence 3 + ] + ), + # step 2: + torch.FloatTensor( + [ + # eos w1 w2 + [0.10, unk, 0.50, 0.4], # sentence 1 + [0.15, unk, 0.15, 0.7], # sentence 2 + [0.00, unk, 0.00, 0.0], # sentence 3 + ] + ), + # step 3: + torch.FloatTensor( + [ + # eos w1 w2 + [0.9, unk, 0.05, 0.05], # sentence 1 + [0.0, unk, 0.00, 0.0], # sentence 2 + [0.0, unk, 0.00, 0.0], # sentence 3 + ] + ), + ] + expected_scores = [ + [0.6, 0.7, 0.5, 0.9], # sentence 1 + [0.6, 0.8, 0.15], # sentence 2 + [0.3, 0.7], # sentence 3 + ] + + task = test_utils.TestTranslationTask.setup_task(args, d, d) + model = task.build_model(args) + scorer = SequenceScorer(task.target_dictionary) + for sample in data_itr: + hypos = task.inference_step(scorer, [model], sample) + for id, hypos_id in zip(sample["id"].tolist(), hypos): + self.assertHypoTokens(hypos_id[0], data[id]["target"]) + self.assertHypoScore(hypos_id[0], expected_scores[id]) + + def assertHypoTokens(self, hypo, tokens): + self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens)) + + def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): + pos_scores = torch.FloatTensor(pos_probs).log() + self.assertAlmostEqual(hypo["positional_scores"], pos_scores) + self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) + score = pos_scores.sum() + if normalized: + score /= pos_scores.numel() ** lenpen + self.assertLess(abs(score - hypo["score"]), 1e-6) + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess((t1 - t2).abs().max(), 1e-4) + + def assertTensorEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertEqual(t1.ne(t2).long().sum(), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_sparse_multihead_attention.py b/fairseq/tests/test_sparse_multihead_attention.py new file mode 100644 index 0000000..3e32b25 --- /dev/null +++ b/fairseq/tests/test_sparse_multihead_attention.py @@ -0,0 +1,114 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention + + +class TestSparseMultiheadAttention(unittest.TestCase): + def test_sparse_multihead_attention(self): + attn_weights = torch.randn(1, 8, 8) + bidirectional_sparse_mask = torch.tensor( + [ + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + ] + ) + + bidirectional_attention = SparseMultiheadAttention( + 16, 1, stride=4, expressivity=1, is_bidirectional=True + ) + bidirectional_attention_sparse_mask = ( + bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8) + ) + torch.all( + torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask) + ) + + sparse_mask = torch.tensor( + [ + [ + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [ + 0, + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [ + 0, + 0, + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [ + 0, + 0, + 0, + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), float("-inf")], + [ + float("-inf"), + float("-inf"), + float("-inf"), + 0, + 0, + 0, + float("-inf"), + float("-inf"), + ], + [ + float("-inf"), + float("-inf"), + float("-inf"), + 0, + 0, + 0, + 0, + float("-inf"), + ], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + ] + ) + + attention = SparseMultiheadAttention( + 16, 1, stride=4, expressivity=1, is_bidirectional=False + ) + attention_sparse_mask = attention.buffered_sparse_mask(attn_weights, 8, 8) + + torch.all(torch.eq(attention_sparse_mask, sparse_mask)) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_token_block_dataset.py b/fairseq/tests/test_token_block_dataset.py new file mode 100644 index 0000000..c4d7b76 --- /dev/null +++ b/fairseq/tests/test_token_block_dataset.py @@ -0,0 +1,92 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import tests.utils as test_utils +import torch +from fairseq.data import TokenBlockDataset + + +class TestTokenBlockDataset(unittest.TestCase): + def _build_dataset(self, data, **kwargs): + sizes = [len(x) for x in data] + underlying_ds = test_utils.TestDataset(data) + return TokenBlockDataset(underlying_ds, sizes, **kwargs) + + def test_eos_break_mode(self): + data = [ + torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), + torch.tensor([1], dtype=torch.long), + torch.tensor([8, 7, 6, 1], dtype=torch.long), + ] + ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos") + self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) + self.assertEqual(ds[1].tolist(), [1]) + self.assertEqual(ds[2].tolist(), [8, 7, 6, 1]) + + data = [ + torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), + torch.tensor([8, 7, 6, 1], dtype=torch.long), + torch.tensor([1], dtype=torch.long), + ] + ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos") + self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) + self.assertEqual(ds[1].tolist(), [8, 7, 6, 1]) + self.assertEqual(ds[2].tolist(), [1]) + + def test_block_break_mode(self): + data = [ + torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), + torch.tensor([8, 7, 6, 1], dtype=torch.long), + torch.tensor([9, 1], dtype=torch.long), + ] + ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode="none") + self.assertEqual(ds[0].tolist(), [5, 4, 3]) + self.assertEqual(ds[1].tolist(), [2, 1, 8]) + self.assertEqual(ds[2].tolist(), [7, 6, 1]) + self.assertEqual(ds[3].tolist(), [9, 1]) + + def test_complete_break_mode(self): + data = [ + torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), + torch.tensor([8, 7, 6, 1], dtype=torch.long), + torch.tensor([9, 1], dtype=torch.long), + ] + ds = self._build_dataset( + data, block_size=6, pad=0, eos=1, break_mode="complete" + ) + self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) + self.assertEqual(ds[1].tolist(), [8, 7, 6, 1, 9, 1]) + + data = [ + torch.tensor([4, 3, 2, 1], dtype=torch.long), + torch.tensor([5, 1], dtype=torch.long), + torch.tensor([1], dtype=torch.long), + torch.tensor([6, 1], dtype=torch.long), + ] + ds = self._build_dataset( + data, block_size=3, pad=0, eos=1, break_mode="complete" + ) + self.assertEqual(ds[0].tolist(), [4, 3, 2, 1]) + self.assertEqual(ds[1].tolist(), [5, 1, 1]) + self.assertEqual(ds[2].tolist(), [6, 1]) + + def test_4billion_tokens(self): + """Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745""" + data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000 + ds = self._build_dataset( + data, block_size=6, pad=0, eos=1, break_mode="complete" + ) + ds[-1] # __getitem__ works + start, end = ds.slice_indices[-1] + assert end > 4294967295 # data must be sufficiently large to overflow uint32 + assert not isinstance( + end + 1, float + ) # this would also raise, since np.uint64(1) + 1 => 2.0 + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_train.py b/fairseq/tests/test_train.py new file mode 100644 index 0000000..02ef94c --- /dev/null +++ b/fairseq/tests/test_train.py @@ -0,0 +1,247 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import logging +import unittest +from io import StringIO +from unittest.mock import MagicMock, patch + +import torch +from fairseq import checkpoint_utils, data +from omegaconf import OmegaConf + + +def mock_trainer(epoch, num_updates, iterations_in_epoch): + trainer = MagicMock() + trainer.load_checkpoint.return_value = { + "train_iterator": { + "epoch": epoch, + "iterations_in_epoch": iterations_in_epoch, + "shuffle": False, + }, + } + trainer.get_num_updates.return_value = num_updates + return trainer + + +def mock_dict(): + d = MagicMock() + d.pad.return_value = 1 + d.eos.return_value = 2 + d.unk.return_value = 3 + return d + + +def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoch): + tokens = torch.LongTensor(list(range(epoch_size))).view(1, -1) + tokens_ds = data.TokenBlockDataset( + tokens, + sizes=[tokens.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + trainer = mock_trainer(epoch, num_updates, iterations_in_epoch) + dataset = data.LanguagePairDataset( + tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False + ) + epoch_itr = data.EpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_sampler=[[i] for i in range(epoch_size)], + ) + return trainer, epoch_itr + + +def get_mock_cfg(finetune_from_model): + cfg_mock = OmegaConf.create( + { + "checkpoint": { + "save_dir": None, + "optimizer_overrides": "{}", + "reset_dataloader": False, + "reset_meters": False, + "reset_optimizer": False, + "reset_lr_scheduler": False, + "finetune_from_model": finetune_from_model, + "model_parallel_size": 1, + "restore_file": "checkpoint_last.pt", + }, + "common": { + "model_parallel_size": 1, + }, + } + ) + return cfg_mock + + +class TestLoadCheckpoint(unittest.TestCase): + def setUp(self): + self.cfg_mock = get_mock_cfg(None) + self.patches = { + "os.makedirs": MagicMock(), + "os.path.join": MagicMock(), + "os.path.isfile": MagicMock(return_value=True), + "os.path.isabs": MagicMock(return_value=False), + "fairseq.file_io.PathManager.exists": MagicMock(return_value=False), + } + self.applied_patches = [patch(p, d) for p, d in self.patches.items()] + [p.start() for p in self.applied_patches] + logging.disable(logging.CRITICAL) + + def tearDown(self): + patch.stopall() + logging.disable(logging.NOTSET) + + def test_load_partial_checkpoint(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + + _, epoch_itr = checkpoint_utils.load_checkpoint( + self.cfg_mock.checkpoint, trainer + ) + + self.assertEqual(epoch_itr.epoch, 2) + self.assertEqual(epoch_itr.iterations_in_epoch, 50) + + itr = epoch_itr.next_epoch_itr(shuffle=False) + self.assertEqual(epoch_itr.epoch, 2) + self.assertEqual(epoch_itr.iterations_in_epoch, 50) + + self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 50) + self.assertEqual(epoch_itr.iterations_in_epoch, 51) + + for _ in range(150 - 52): + next(itr) + self.assertEqual(epoch_itr.iterations_in_epoch, 149) + self.assertTrue(itr.has_next()) + next(itr) + self.assertFalse(itr.has_next()) + + itr = epoch_itr.next_epoch_itr(shuffle=False) + self.assertTrue(itr.has_next()) + self.assertEqual(epoch_itr.epoch, 3) + self.assertEqual(epoch_itr.iterations_in_epoch, 0) + + def test_load_full_checkpoint(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 300, 150) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + + _, epoch_itr = checkpoint_utils.load_checkpoint( + self.cfg_mock.checkpoint, trainer + ) + itr = epoch_itr.next_epoch_itr(shuffle=False) + + self.assertEqual(epoch_itr.epoch, 3) + self.assertEqual(epoch_itr.iterations_in_epoch, 0) + self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0) + + def test_load_no_checkpoint(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + self.patches["os.path.isfile"].return_value = False + + _, epoch_itr = checkpoint_utils.load_checkpoint( + self.cfg_mock.checkpoint, trainer + ) + itr = epoch_itr.next_epoch_itr(shuffle=False) + + self.assertEqual(epoch_itr.epoch, 1) + self.assertEqual(epoch_itr.iterations_in_epoch, 0) + self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0) + + def test_finetune_from_model_args_conflict(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + + for arg in [ + "reset_optimizer", + "reset_lr_scheduler", + "reset_meters", + "reset_dataloader", + ]: + with self.subTest(arg=arg): + cfg_mock = get_mock_cfg("/temp/checkpoint_pretrained.pt") + cfg_mock["checkpoint"][arg] = True + with self.assertRaises(Exception) as context: + _, _ = checkpoint_utils.load_checkpoint( + cfg_mock.checkpoint, trainer + ) + + self.assertTrue( + "--finetune-from-model can not be set together with either --reset-optimizer" + " or reset_lr_scheduler or reset_meters or reset_dataloader" + in str(context.exception) + ) + + def test_finetune_from_model(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + from_model_path = "/temp/checkpoint_pretrained.pt" + + def mock_finetune_exist(path): + if path == from_model_path: + return True + else: + return False + + self.patches[ + "fairseq.file_io.PathManager.exists" + ].side_effect = mock_finetune_exist + cfg_mock = get_mock_cfg(from_model_path) + cfg_mock.checkpoint.restore_file = "checkpoint_last.pt" + _, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer) + ( + checkpoint_path, + reset_optimizer, + reset_lr_scheduler, + optimizer_overrides, + ) = trainer.load_checkpoint.call_args[0] + reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"] + self.assertTrue(reset_optimizer) + self.assertTrue(reset_lr_scheduler) + self.assertTrue(reset_meters) + + def test_finetune_from_model_resume(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + from_model_path = "/temp/checkpoint_pretrained.pt" + + # launch second time + # both restore_file=checkpoint_last.pt and finetune_from_model are set + def mock_finetune_exist(path): + if path == from_model_path or path.endsWith("checkpoint_last.pt"): + return True + else: + return False + + self.patches[ + "fairseq.file_io.PathManager.exists" + ].side_effect = mock_finetune_exist + cfg_mock = get_mock_cfg(from_model_path) + cfg_mock.checkpoint.restore_file = "checkpoint_last.pt" + _, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer) + ( + checkpoint_path, + reset_optimizer, + reset_lr_scheduler, + optimizer_overrides, + ) = trainer.load_checkpoint.call_args[0] + reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"] + self.assertFalse(reset_optimizer) + self.assertFalse(reset_lr_scheduler) + self.assertFalse(reset_meters) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_transformer.py b/fairseq/tests/test_transformer.py new file mode 100644 index 0000000..de5c5bd --- /dev/null +++ b/fairseq/tests/test_transformer.py @@ -0,0 +1,65 @@ +import argparse +import unittest +from typing import Any, Dict, Sequence + +import torch +from fairseq.models import transformer + +from tests.test_roberta import FakeTask + + +def mk_sample(tok: Sequence[int] = None, batch_size: int = 2) -> Dict[str, Any]: + if not tok: + tok = [10, 11, 12, 13, 14, 15, 2] + + batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size) + sample = { + "net_input": { + "src_tokens": batch, + "prev_output_tokens": batch, + "src_lengths": torch.tensor( + [len(tok)] * batch_size, dtype=torch.long, device=batch.device + ), + }, + "target": batch[:, 1:], + } + return sample + + +def mk_transformer(**extra_args: Any): + overrides = { + # Use characteristics dimensions + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "decoder_embed_dim": 12, + "decoder_ffn_embed_dim": 14, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + } + overrides.update(extra_args) + # Overrides the defaults from the parser + args = argparse.Namespace(**overrides) + transformer.tiny_architecture(args) + + torch.manual_seed(0) + task = FakeTask(args) + return transformer.TransformerModel.build_model(args, task) + + +class TransformerTestCase(unittest.TestCase): + def test_forward_backward(self): + model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=12) + sample = mk_sample() + o, _ = model.forward(**sample["net_input"]) + loss = o.sum() + loss.backward() + + def test_different_encoder_decoder_embed_dim(self): + model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=16) + sample = mk_sample() + o, _ = model.forward(**sample["net_input"]) + loss = o.sum() + loss.backward() diff --git a/fairseq/tests/test_utils.py b/fairseq/tests/test_utils.py new file mode 100644 index 0000000..7919590 --- /dev/null +++ b/fairseq/tests/test_utils.py @@ -0,0 +1,114 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from fairseq import utils + + +class TestUtils(unittest.TestCase): + def test_convert_padding_direction(self): + pad = 1 + left_pad = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [1, 7, 8, 9, 10], + [1, 1, 1, 11, 12], + ] + ) + right_pad = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [7, 8, 9, 10, 1], + [11, 12, 1, 1, 1], + ] + ) + + self.assertAlmostEqual( + right_pad, + utils.convert_padding_direction( + left_pad, + pad, + left_to_right=True, + ), + ) + self.assertAlmostEqual( + left_pad, + utils.convert_padding_direction( + right_pad, + pad, + right_to_left=True, + ), + ) + + def test_make_positions(self): + pad = 1 + left_pad_input = torch.LongTensor( + [ + [9, 9, 9, 9, 9], + [1, 9, 9, 9, 9], + [1, 1, 1, 9, 9], + ] + ) + left_pad_output = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [1, 2, 3, 4, 5], + [1, 1, 1, 2, 3], + ] + ) + right_pad_input = torch.LongTensor( + [ + [9, 9, 9, 9, 9], + [9, 9, 9, 9, 1], + [9, 9, 1, 1, 1], + ] + ) + right_pad_output = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [2, 3, 4, 5, 1], + [2, 3, 1, 1, 1], + ] + ) + + self.assertAlmostEqual( + left_pad_output, + utils.make_positions(left_pad_input, pad), + ) + self.assertAlmostEqual( + right_pad_output, + utils.make_positions(right_pad_input, pad), + ) + + def test_clip_grad_norm_(self): + params = torch.nn.Parameter(torch.zeros(5)).requires_grad_(False) + grad_norm = utils.clip_grad_norm_(params, 1.0) + self.assertTrue(torch.is_tensor(grad_norm)) + self.assertEqual(grad_norm, 0.0) + + params = [torch.nn.Parameter(torch.zeros(5)) for i in range(3)] + for p in params: + p.grad = torch.full((5,), fill_value=2.0) + grad_norm = utils.clip_grad_norm_(params, 1.0) + exp_grad_norm = torch.full((15,), fill_value=2.0).norm() + self.assertTrue(torch.is_tensor(grad_norm)) + self.assertEqual(grad_norm, exp_grad_norm) + + grad_norm = utils.clip_grad_norm_(params, 1.0) + self.assertAlmostEqual(grad_norm, torch.tensor(1.0)) + + def test_resolve_max_positions_with_tuple(self): + resolved = utils.resolve_max_positions(None, (2000, 100, 2000), 12000) + self.assertEqual(resolved, (2000, 100, 2000)) + + def assertAlmostEqual(self, t1, t2): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + self.assertLess(utils.item((t1 - t2).abs().max()), 1e-4) + + +if __name__ == "__main__": + unittest.main() diff --git a/fairseq/tests/test_valid_subset_checks.py b/fairseq/tests/test_valid_subset_checks.py new file mode 100644 index 0000000..c39fb89 --- /dev/null +++ b/fairseq/tests/test_valid_subset_checks.py @@ -0,0 +1,143 @@ +import os +import shutil +import tempfile +import unittest + +from fairseq import options +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.data.data_utils import raise_if_valid_subsets_unintentionally_ignored +from .utils import create_dummy_data, preprocess_lm_data, train_language_model + + +def make_lm_config( + data_dir=None, + extra_flags=None, + task="language_modeling", + arch="transformer_lm_gpt2_tiny", +): + task_args = [task] + if data_dir is not None: + task_args += [data_dir] + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + *task_args, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + ] + + (extra_flags or []), + ) + cfg = convert_namespace_to_omegaconf(train_args) + return cfg + + +def write_empty_file(path): + with open(path, "w"): + pass + assert os.path.exists(path) + + +class TestValidSubsetsErrors(unittest.TestCase): + """Test various filesystem, clarg combinations and ensure that error raising happens as expected""" + + def _test_case(self, paths, extra_flags): + with tempfile.TemporaryDirectory() as data_dir: + [ + write_empty_file(os.path.join(data_dir, f"{p}.bin")) + for p in paths + ["train"] + ] + cfg = make_lm_config(data_dir, extra_flags=extra_flags) + raise_if_valid_subsets_unintentionally_ignored(cfg) + + def test_default_raises(self): + with self.assertRaises(ValueError): + self._test_case(["valid", "valid1"], []) + with self.assertRaises(ValueError): + self._test_case( + ["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"] + ) + + def partially_specified_valid_subsets(self): + with self.assertRaises(ValueError): + self._test_case( + ["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"] + ) + # Fix with ignore unused + self._test_case( + ["valid", "valid1", "valid2"], + ["--valid-subset", "valid,valid1", "--ignore-unused-valid-subsets"], + ) + + def test_legal_configs(self): + self._test_case(["valid"], []) + self._test_case(["valid", "valid1"], ["--ignore-unused-valid-subsets"]) + self._test_case(["valid", "valid1"], ["--combine-val"]) + self._test_case(["valid", "valid1"], ["--valid-subset", "valid,valid1"]) + self._test_case(["valid", "valid1"], ["--valid-subset", "valid1"]) + self._test_case( + ["valid", "valid1"], ["--combine-val", "--ignore-unused-valid-subsets"] + ) + self._test_case( + ["valid1"], ["--valid-subset", "valid1"] + ) # valid.bin doesn't need to be ignored. + + def test_disable_validation(self): + self._test_case([], ["--disable-validation"]) + self._test_case(["valid", "valid1"], ["--disable-validation"]) + + def test_dummy_task(self): + cfg = make_lm_config(task="dummy_lm") + raise_if_valid_subsets_unintentionally_ignored(cfg) + + def test_masked_dummy_task(self): + cfg = make_lm_config(task="dummy_masked_lm") + raise_if_valid_subsets_unintentionally_ignored(cfg) + + +class TestCombineValidSubsets(unittest.TestCase): + def _train(self, extra_flags): + with self.assertLogs() as logs: + with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: + create_dummy_data(data_dir, num_examples=20) + preprocess_lm_data(data_dir) + + shutil.copyfile(f"{data_dir}/valid.bin", f"{data_dir}/valid1.bin") + shutil.copyfile(f"{data_dir}/valid.idx", f"{data_dir}/valid1.idx") + train_language_model( + data_dir, + "transformer_lm", + ["--max-update", "0", "--log-format", "json"] + extra_flags, + run_validation=False, + ) + return [x.message for x in logs.records] + + def test_combined(self): + flags = ["--combine-valid-subsets", "--required-batch-size-multiple", "1"] + logs = self._train(flags) + assert any(["valid1" in x for x in logs]) # loaded 100 examples from valid1 + assert not any(["valid1_ppl" in x for x in logs]) # metrics are combined + + def test_subsets(self): + flags = [ + "--valid-subset", + "valid,valid1", + "--required-batch-size-multiple", + "1", + ] + logs = self._train(flags) + assert any(["valid_ppl" in x for x in logs]) # loaded 100 examples from valid1 + assert any(["valid1_ppl" in x for x in logs]) # metrics are combined diff --git a/fairseq/tests/utils.py b/fairseq/tests/utils.py new file mode 100644 index 0000000..af3f714 --- /dev/null +++ b/fairseq/tests/utils.py @@ -0,0 +1,797 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import json +import os +import random +import shutil +import string +import sys +import typing as tp +from io import StringIO + +import torch +import torch.nn.functional as F + +import fairseq.distributed.utils as distributed_utils +from fairseq import options, utils +from fairseq.data import Dictionary +from fairseq.data.language_pair_dataset import collate +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, +) +from fairseq.models.fairseq_encoder import EncoderOut +from fairseq.tasks import LegacyFairseqTask +from fairseq_cli import generate, interactive, preprocess, train, validate + + +def dummy_dictionary(vocab_size, prefix="token_"): + d = Dictionary() + for i in range(vocab_size): + token = prefix + str(i) + d.add_symbol(token) + d.finalize(padding_factor=1) # don't add extra padding symbols + return d + + +def dummy_dataloader( + samples, + padding_idx=1, + eos_idx=2, + batch_size=None, +): + if batch_size is None: + batch_size = len(samples) + + # add any missing data to samples + for i, sample in enumerate(samples): + if "id" not in sample: + sample["id"] = i + + # create dataloader + dataset = TestDataset(samples) + dataloader = torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)), + ) + return iter(dataloader) + + +def sequence_generator_setup(): + # construct dummy dictionary + d = dummy_dictionary(vocab_size=2) + + eos = d.eos() + w1 = 4 + w2 = 5 + + # construct source data + src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]]) + src_lengths = torch.LongTensor([2, 2]) + + args = argparse.Namespace() + unk = 0.0 + args.beam_probs = [ + # step 0: + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [0.0, unk, 0.9, 0.1], # beam 1 + [0.0, unk, 0.9, 0.1], # beam 2 + # sentence 2: + [0.0, unk, 0.7, 0.3], + [0.0, unk, 0.7, 0.3], + ] + ), + # step 1: + torch.FloatTensor( + [ + # eos w1 w2 prefix + # sentence 1: + [1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 <eos>: 0.9*1.0) + [0.0, unk, 0.9, 0.1], # w2: 0.1 + # sentence 2: + [0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 <eos>: 0.7*0.25) + [0.00, unk, 0.10, 0.9], # w2: 0.3 + ] + ), + # step 2: + torch.FloatTensor( + [ + # eos w1 w2 prefix + # sentence 1: + [0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9 + [ + 0.6, + unk, + 0.2, + 0.2, + ], # w2 w2: 0.1*0.1 (emit: w2 w2 <eos>: 0.1*0.1*0.6) + # sentence 2: + [ + 0.60, + unk, + 0.4, + 0.00, + ], # w1 w2: 0.7*0.4 (emit: w1 w2 <eos>: 0.7*0.4*0.6) + [0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9 + ] + ), + # step 3: + torch.FloatTensor( + [ + # eos w1 w2 prefix + # sentence 1: + [ + 1.0, + unk, + 0.0, + 0.0, + ], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0) + [ + 1.0, + unk, + 0.0, + 0.0, + ], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0) + # sentence 2: + [ + 0.1, + unk, + 0.5, + 0.4, + ], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1) + [ + 1.0, + unk, + 0.0, + 0.0, + ], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0) + ] + ), + ] + + task = TestTranslationTask.setup_task(args, d, d) + model = task.build_model(args) + tgt_dict = task.target_dictionary + + return tgt_dict, w1, w2, src_tokens, src_lengths, model + + +def create_dummy_data( + data_dir, num_examples=100, maxlen=20, alignment=False, languages=None +): + def _create_dummy_data(dir, filename): + data = torch.rand(num_examples * maxlen) + data = 97 + torch.floor(26 * data).int() + with open(os.path.join(dir, filename), "w") as h: + offset = 0 + for _ in range(num_examples): + ex_len = random.randint(1, maxlen) + ex_str = " ".join(map(chr, data[offset : offset + ex_len])) + print(ex_str, file=h) + offset += ex_len + + def _create_dummy_alignment_data(filename_src, filename_tgt, filename): + with open(os.path.join(data_dir, filename_src), "r") as src_f, open( + os.path.join(data_dir, filename_tgt), "r" + ) as tgt_f, open(os.path.join(data_dir, filename), "w") as h: + for src, tgt in zip(src_f, tgt_f): + src_len = len(src.split()) + tgt_len = len(tgt.split()) + avg_len = (src_len + tgt_len) // 2 + num_alignments = random.randint(avg_len // 2, 2 * avg_len) + src_indices = torch.floor(torch.rand(num_alignments) * src_len).int() + tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int() + ex_str = " ".join( + [ + "{}-{}".format(src, tgt) + for src, tgt in zip(src_indices, tgt_indices) + ] + ) + print(ex_str, file=h) + + files_to_write = [ + "train.in", + "train.out", + "valid.in", + "valid.out", + "test.in", + "test.out", + ] + if languages is None: # En only dummy dataset + for f in files_to_write: + _create_dummy_data(data_dir, f) + else: + for lang in languages: + lang_dir = os.path.join(data_dir, lang) + os.makedirs(lang_dir, exist_ok=True) + for f in files_to_write: + _create_dummy_data(lang_dir, f) + + if alignment: + _create_dummy_alignment_data("train.in", "train.out", "train.align") + _create_dummy_alignment_data("valid.in", "valid.out", "valid.align") + _create_dummy_alignment_data("test.in", "test.out", "test.align") + + +def preprocess_lm_data(data_dir, languages=None): + preprocess_parser = options.get_preprocessing_parser() + if languages is None: + preprocess_args = preprocess_parser.parse_args( + [ + "--only-source", + "--trainpref", + os.path.join(data_dir, "train.out"), + "--validpref", + os.path.join(data_dir, "valid.out"), + "--testpref", + os.path.join(data_dir, "test.out"), + "--destdir", + data_dir, + ] + ) + preprocess.main(preprocess_args) + else: + for lang in languages: + lang_dir = os.path.join(data_dir, lang) + assert os.path.exists(lang_dir) + preprocess_args = preprocess_parser.parse_args( + [ + "--only-source", + "--trainpref", + os.path.join(lang_dir, "train.out"), + "--validpref", + os.path.join(lang_dir, "valid.out"), + "--testpref", + os.path.join(lang_dir, "test.out"), + "--destdir", + lang_dir, + ] + ) + preprocess.main(preprocess_args) + shutil.copyfile( + os.path.join(data_dir, languages[0], "dict.txt"), + os.path.join(data_dir, "dict.txt"), + ) + + +def preprocess_translation_data(data_dir, extra_flags=None): + preprocess_parser = options.get_preprocessing_parser() + preprocess_args = preprocess_parser.parse_args( + [ + "--source-lang", + "in", + "--target-lang", + "out", + "--trainpref", + os.path.join(data_dir, "train"), + "--validpref", + os.path.join(data_dir, "valid"), + "--testpref", + os.path.join(data_dir, "test"), + "--thresholdtgt", + "0", + "--thresholdsrc", + "0", + "--destdir", + data_dir, + ] + + (extra_flags or []), + ) + preprocess.main(preprocess_args) + + +def preprocess_summarization_data(data_dir, extra_flags=None): + preprocess_parser = options.get_preprocessing_parser() + preprocess_args = preprocess_parser.parse_args( + [ + "--source-lang", + "in", + "--target-lang", + "out", + "--trainpref", + os.path.join(data_dir, "train"), + "--validpref", + os.path.join(data_dir, "valid"), + "--testpref", + os.path.join(data_dir, "test"), + "--thresholdtgt", + "0", + "--thresholdsrc", + "0", + "--joined-dictionary", + "--destdir", + data_dir, + ] + + (extra_flags or []), + ) + preprocess.main(preprocess_args) + + +def create_laser_data_and_config_json(data_dir): + src_langs = ["de", "fr", "ru", "tr", "zh"] + tgt_langs = ["en", "es"] + config_json = {} + config_train_json = [] + src_vocab = None + tgt_vocab = None + + for src_lang in src_langs: + for tgt_lang in tgt_langs: + langpair_folder = f"{src_lang}-{tgt_lang}" + + langpair_path = os.path.join(data_dir, langpair_folder) + os.mkdir(langpair_path) + create_dummy_data(langpair_path) + preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"]) + + src_vocab = os.path.join(langpair_path, "dict.in.txt") + tgt_vocab = os.path.join(langpair_path, "dict.out.txt") + config_train_json.append( + { + "id": 0 if tgt_lang == "en" else 1, + "src": os.path.join(langpair_path, "train.in-out.in"), + "tgt": os.path.join(langpair_path, "train.in-out.out"), + } + ) + + config_json["src_vocab"] = src_vocab + config_json["tgt_vocab"] = tgt_vocab + config_json["train"] = config_train_json + + with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file: + json.dump(config_json, config_file) + + return config_file + + +def train_translation_model( + data_dir, + arch, + extra_flags=None, + task="translation", + run_validation=False, + lang_flags=None, + extra_valid_flags=None, + world_size=1, +): + if lang_flags is None: + lang_flags = [ + "--source-lang", + "in", + "--target-lang", + "out", + ] + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + task, + data_dir, + "--save-dir", + data_dir, + "--arch", + arch, + "--optimizer", + "nag", + "--lr", + "0.05", + "--max-tokens", + "500", + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + str(world_size), + "--num-workers", + "0", + ] + + lang_flags + + (extra_flags or []), + ) + + cfg = convert_namespace_to_omegaconf(train_args) + distributed_utils.call_main(cfg, train.main) + + if run_validation: + # test validation + validate_parser = options.get_validation_parser() + validate_args = options.parse_args_and_arch( + validate_parser, + [ + "--task", + task, + data_dir, + "--path", + os.path.join(data_dir, "checkpoint_last.pt"), + "--valid-subset", + "valid", + "--max-tokens", + "500", + "--no-progress-bar", + "--num-workers", + "0", + ] + + lang_flags + + (extra_valid_flags or []), + ) + validate.main(validate_args) + + +def generate_main(data_dir, extra_flags=None, path=None): + if extra_flags is None: + extra_flags = [ + "--print-alignment", + ] + if path is None: + path = os.path.join(data_dir, "checkpoint_last.pt") + generate_parser = options.get_generation_parser() + generate_args = options.parse_args_and_arch( + generate_parser, + [ + data_dir, + "--path", + path, + "--beam", + "3", + "--batch-size", + "64", + "--max-len-b", + "5", + "--gen-subset", + "valid", + "--no-progress-bar", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + + # evaluate model in batch mode + generate.main(generate_args) + + # evaluate model interactively + generate_args.buffer_size = 0 + generate_args.input = "-" + generate_args.batch_size = None + orig_stdin = sys.stdin + sys.stdin = StringIO("h e l l o\n") + interactive.main(generate_args) + sys.stdin = orig_stdin + + +class TestDataset(torch.utils.data.Dataset): + def __init__(self, data): + super().__init__() + self.data = data + self.sizes = None + + def __getitem__(self, index): + return self.data[index] + + def __len__(self): + return len(self.data) + + +class TestTranslationTask(LegacyFairseqTask): + def __init__(self, args, src_dict, tgt_dict, model): + super().__init__(args) + self.src_dict = src_dict + self.tgt_dict = tgt_dict + self.model = model + + @classmethod + def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None): + return cls(args, src_dict, tgt_dict, model) + + def build_model(self, args, from_checkpoint=False): + return TestModel.build_model(args, self) + + @property + def source_dictionary(self): + return self.src_dict + + @property + def target_dictionary(self): + return self.tgt_dict + + +class TestModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def build_model(cls, args, task): + encoder = TestEncoder(args, task.source_dictionary) + decoder = TestIncrementalDecoder(args, task.target_dictionary) + return cls(encoder, decoder) + + +class TestEncoder(FairseqEncoder): + def __init__(self, args, dictionary): + super().__init__(dictionary) + self.args = args + + def forward(self, src_tokens, src_lengths=None, **kwargs): + return EncoderOut( + encoder_out=src_tokens, + encoder_padding_mask=None, + encoder_embedding=None, + encoder_states=None, + src_tokens=None, + src_lengths=None, + ) + + def reorder_encoder_out(self, encoder_out, new_order): + return EncoderOut( + encoder_out=encoder_out.encoder_out.index_select(0, new_order), + encoder_padding_mask=None, + encoder_embedding=None, + encoder_states=None, + src_tokens=None, + src_lengths=None, + ) + + +class TestIncrementalDecoder(FairseqIncrementalDecoder): + def __init__(self, args, dictionary): + super().__init__(dictionary) + assert hasattr(args, "beam_probs") or hasattr(args, "probs") + args.max_decoder_positions = getattr(args, "max_decoder_positions", 100) + self.args = args + + def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + bbsz = prev_output_tokens.size(0) + vocab = len(self.dictionary) + src_len = encoder_out.encoder_out.size(1) + tgt_len = prev_output_tokens.size(1) + + # determine number of steps + if incremental_state is not None: + # cache step number + step = utils.get_incremental_state(self, incremental_state, "step") + if step is None: + step = 0 + utils.set_incremental_state(self, incremental_state, "step", step + 1) + steps = [step] + else: + steps = list(range(tgt_len)) + + # define output in terms of raw probs + if hasattr(self.args, "probs"): + assert ( + self.args.probs.dim() == 3 + ), "expected probs to have size bsz*steps*vocab" + probs = self.args.probs.index_select(1, torch.LongTensor(steps)) + else: + probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_() + for i, step in enumerate(steps): + # args.beam_probs gives the probability for every vocab element, + # starting with eos, then unknown, and then the rest of the vocab + if step < len(self.args.beam_probs): + probs[:, i, self.dictionary.eos() :] = self.args.beam_probs[step] + else: + probs[:, i, self.dictionary.eos()] = 1.0 + + # random attention + attn = torch.rand(bbsz, tgt_len, src_len) + + dev = prev_output_tokens.device + return probs.to(dev), {"attn": [attn.to(dev)]} + + def get_normalized_probs(self, net_output, log_probs, _): + # the decoder returns probabilities directly + probs = net_output[0] + if log_probs: + return probs.log() + else: + return probs + + def max_positions(self): + return self.args.max_decoder_positions + + +class TestReshapingEncoder(FairseqEncoder): + def __init__(self, args, dictionary): + super().__init__(dictionary) + self.args = args + + def forward(self, src_tokens, src_lengths=None, **kwargs): + b_sz, t_sz = src_tokens.shape + padding_needed = t_sz % 2 + x = src_tokens + if padding_needed > 0: + padding_needed = 2 - padding_needed + x = F.pad(x, (0, padding_needed)) + + return EncoderOut( + encoder_out=x.view(b_sz, -1, 2), + encoder_padding_mask=None, + encoder_embedding=None, + encoder_states=None, + src_tokens=None, + src_lengths=None, + ) + + def reorder_encoder_out(self, encoder_out, new_order): + return EncoderOut( + encoder_out=encoder_out.encoder_out.index_select(0, new_order), + encoder_padding_mask=None, + encoder_embedding=None, + encoder_states=None, + src_tokens=None, + src_lengths=None, + ) + + +class TestReshapingModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def build_model(cls, args, task): + encoder = TestReshapingEncoder(args, task.source_dictionary) + decoder = TestIncrementalDecoder(args, task.target_dictionary) + return cls(encoder, decoder) + + +class TestAdditionalInputEncoder(FairseqEncoder): + def __init__(self, args, dictionary): + super().__init__(dictionary) + self.args = args + + def forward(self, src_tokens, src_lengths=None, **kwargs): + assert "fancy_other_input" in kwargs + assert kwargs["fancy_other_input"] is not None + return EncoderOut( + encoder_out=src_tokens, + encoder_padding_mask=None, + encoder_embedding=None, + encoder_states=None, + src_tokens=None, + src_lengths=None, + ) + + def reorder_encoder_out(self, encoder_out, new_order): + return EncoderOut( + encoder_out=encoder_out.encoder_out.index_select(0, new_order), + encoder_padding_mask=None, + encoder_embedding=None, + encoder_states=None, + src_tokens=None, + src_lengths=None, + ) + + +class TestAdditionalInputModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def build_model(cls, args, task): + encoder = TestAdditionalInputEncoder(args, task.source_dictionary) + decoder = TestIncrementalDecoder(args, task.target_dictionary) + return cls(encoder, decoder) + + def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + decoder_out = self.decoder( + prev_output_tokens, encoder_out=encoder_out, **kwargs + ) + return decoder_out + + +def train_language_model( + data_dir, + arch, + extra_flags=None, + run_validation=False, + extra_valid_flags=None, + task="language_modeling", + world_size=1, +): + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + task, + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + str(world_size), + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + cfg = convert_namespace_to_omegaconf(train_args) + distributed_utils.call_main(cfg, train.main) + + if run_validation: + # test validation + validate_parser = options.get_validation_parser() + validate_args = options.parse_args_and_arch( + validate_parser, + [ + "--task", + task, + data_dir, + "--path", + os.path.join(data_dir, "checkpoint_last.pt"), + "--valid-subset", + "valid", + "--max-tokens", + "500", + "--no-progress-bar", + "--num-workers", + "0", + ] + + (extra_valid_flags or []), + ) + validate.main(validate_args) + + +def sizes(data): + return [len(sentence) for sentence in data] + + +POPULATION = string.ascii_letters + string.digits + + +def make_sentence() -> tp.List[str]: + length = random.randint(10, 50) + return random.choices( + population=POPULATION, k=length, weights=range(1, len(POPULATION) + 1) + ) + + +def make_data(length=1000, out_file=None) -> tp.List[tp.List[str]]: + data = ( + [make_sentence() for _ in range(0, length)] + # add all the symbols at least once + + [list(string.ascii_letters), list(string.digits)] + ) + if out_file is not None: + with open(out_file, "w", encoding="utf-8") as out: + for s in data: + print(" ".join(s), file=out) + + return data + + +def build_vocab(data: tp.List[tp.List[str]]) -> Dictionary: + d = Dictionary() + for s in data: + for token in s: + d.add_symbol(token) + d.finalize() + return d diff --git a/fairseq/train.py b/fairseq/train.py new file mode 100644 index 0000000..321de3d --- /dev/null +++ b/fairseq/train.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Legacy entry point. Use fairseq_cli/train.py or fairseq-train instead. +""" + +from fairseq_cli.train import cli_main + + +if __name__ == "__main__": + cli_main() diff --git a/inference_av2av.py b/inference_av2av.py new file mode 100644 index 0000000..6f6ddae --- /dev/null +++ b/inference_av2av.py @@ -0,0 +1,165 @@ +import os +import argparse +import numpy as np +import torch +import torch.nn.functional as F + +from fairseq import utils +from fairseq_cli.generate import get_symbols_to_strip_from_output + +from av2unit.inference import load_model as load_av2unit_model +from unit2unit.inference import load_model as load_unit2unit_model +from unit2av.inference import load_model as load_unit2av_model, load_speaker_encoder_model + +from util import process_units, extract_audio_from_video, save_video + +class AVSpeechToAVSpeechPipeline: + def __init__(self, + av2unit_model, av2unit_task, + unit2unit_task, unit2unit_generator, + unit2av_model, speaker_encoder, + use_cuda=False + ): + self.av2unit_model = av2unit_model + self.av2unit_task = av2unit_task + self.unit2unit_task = unit2unit_task + self.unit2unit_generator = unit2unit_generator + self.unit2av_model = unit2av_model + self.speaker_encoder = speaker_encoder + self.use_cuda = use_cuda + + def process_av2unit(self, lip_video_path, audio_path): + task = self.av2unit_task + video_feats, audio_feats = task.dataset.load_feature((lip_video_path, audio_path)) + audio_feats, video_feats = torch.from_numpy(audio_feats.astype(np.float32)) if audio_feats is not None else None, torch.from_numpy(video_feats.astype(np.float32)) if video_feats is not None else None + if task.dataset.normalize and 'audio' in task.dataset.modalities: + with torch.no_grad(): + audio_feats = F.layer_norm(audio_feats, audio_feats.shape[1:]) + + collated_audios, _, _ = task.dataset.collater_audio([audio_feats], len(audio_feats)) + collated_videos, _, _ = task.dataset.collater_audio([video_feats], len(video_feats)) + + sample = {"source": { + "audio": collated_audios, "video": collated_videos, + }} + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + + pred = task.inference( + self.av2unit_model, + sample, + ) + pred_str = task.dictionaries[0].string(pred.int().cpu()) + + return pred_str + + def process_unit2unit(self, unit): + task = self.unit2unit_task + unit = list(map(int, unit.strip().split())) + unit = task.source_dictionary.encode_line( + " ".join(map(lambda x: str(x), process_units(unit, reduce=True))), + add_if_not_exist=False, + append_eos=True, + ).long() + unit = torch.cat([ + unit.new([task.source_dictionary.bos()]), + unit, + unit.new([task.source_dictionary.index("[{}]".format(task.source_language))]) + ]) + + sample = {"net_input": { + "src_tokens": torch.LongTensor(unit).view(1,-1), + }} + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + + pred = task.inference_step( + self.unit2unit_generator, + None, + sample, + )[0][0] + + pred_str = task.target_dictionary.string( + pred["tokens"].int().cpu(), + extra_symbols_to_ignore=get_symbols_to_strip_from_output(self.unit2unit_generator) + ) + + return pred_str + + def process_unit2av(self, unit, audio_path, video_path, bbox_path): + unit = list(map(int, unit.strip().split())) + + sample = { + "code": torch.LongTensor(unit).view(1,-1), + "spkr": torch.from_numpy(self.speaker_encoder.get_embed(audio_path)).view(1,1,-1), + } + sample = utils.move_to_cuda(sample) if self.use_cuda else sample + + wav, video, full_video, bbox = self.unit2av_model(sample, video_path, bbox_path, dur_prediction=True) + + return wav, video, full_video, bbox + +def main(args): + use_cuda = torch.cuda.is_available() and not args.cpu + + av2unit_model, av2unit_task = load_av2unit_model(args.av2unit_path, args.modalities, use_cuda=use_cuda) + unit2unit_task, unit2unit_generator = load_unit2unit_model(args.utut_path, args.src_lang, args.tgt_lang, use_cuda=use_cuda) + cfg_path = os.path.join("unit2av", "config.json") + unit2av_model = load_unit2av_model(args.unit2av_path, cfg_path, args.tgt_lang, use_cuda=use_cuda) + speaker_encoder_model = load_speaker_encoder_model(os.path.join("unit2av", "encoder.pt"), use_cuda=use_cuda) + + pipeline = AVSpeechToAVSpeechPipeline( + av2unit_model, av2unit_task, + unit2unit_task, unit2unit_generator, + unit2av_model, speaker_encoder_model, + use_cuda=use_cuda + ) + + temp_audio_path = os.path.splitext(args.in_vid_path)[0]+".temp.wav" + lip_video_path = os.path.splitext(args.in_vid_path)[0]+".lip.mp4" + bbox_path = os.path.splitext(args.in_vid_path)[0]+".bbox.pkl" + extract_audio_from_video(args.in_vid_path, temp_audio_path) + + src_unit = pipeline.process_av2unit(lip_video_path, temp_audio_path) + tgt_unit = pipeline.process_unit2unit(src_unit) + tgt_audio, tgt_video, full_video, bbox = pipeline.process_unit2av(tgt_unit, temp_audio_path, args.in_vid_path, bbox_path) + + save_video(tgt_audio, tgt_video, full_video, bbox, args.out_vid_path) + + os.remove(temp_audio_path) + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-vid-path", type=str, required=True, help="File path of source video input" + ) + parser.add_argument( + "--out-vid-path", type=str, required=True, help="File path of translated video output" + ) + parser.add_argument( + "--src-lang", type=str, required=True, + choices=["en","es","fr","it","pt"], + help="source language" + ) + parser.add_argument( + "--tgt-lang", type=str, required=True, + choices=["en","es","fr","it","pt"], + help="target language" + ) + parser.add_argument( + "--modalities", type=str, default="audio,video", help="input modalities", + choices=["audio,video","audio","video"], + ) + parser.add_argument( + "--av2unit-path", type=str, required=True, help="path to the mAV-HuBERT pre-trained model" + ) + parser.add_argument( + "--utut-path", type=str, required=True, help="path to the UTUT pre-trained model" + ) + parser.add_argument( + "--unit2av-path", type=str, required=True, help="path to the Unit AV Renderer" + ) + parser.add_argument("--cpu", action="store_true", help="run on CPU") + args = parser.parse_args() + main(args) + +if __name__ == "__main__": + cli_main() diff --git a/notebooks/check_checkpoint.ipynb b/notebooks/check_checkpoint.ipynb new file mode 100644 index 0000000..b599267 --- /dev/null +++ b/notebooks/check_checkpoint.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "2e8cb32a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Top Level Keys ===\n", + "dict_keys(['audio', 'video'])\n", + "\n", + "\n", + "=== Audio model Keys ===\n", + "dict_keys(['en', 'es', 'fr', 'it', 'pt'])\n", + "\n", + "=== Keys (Layer Names) - First 10 ===\n", + "conv_pre.bias\n", + "conv_pre.weight_g\n", + "conv_pre.weight_v\n", + "ups.0.bias\n", + "ups.0.weight_g\n", + "ups.0.weight_v\n", + "ups.1.bias\n", + "ups.1.weight_g\n", + "ups.1.weight_v\n", + "ups.2.bias\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# 체크포인트 파일 경로 (확인하고 싶은 파일 경로로 변경하세요)\n", + "checkpoint_path = \"/home/2022113135/av2av/checkpoints/unit_av_renderer.pt\" \n", + "\n", + "try:\n", + " # CPU로 로드 (GPU가 없어도 확인 가능하게)\n", + " checkpoint = torch.load(checkpoint_path, map_location='cpu')\n", + " \n", + " # 1. 최상위 키(Key) 확인\n", + " print(\"=== Top Level Keys ===\")\n", + " print(checkpoint.keys())\n", + " print(\"\\n\")\n", + "\n", + " checkpoint = checkpoint['audio']\n", + " print(\"=== Audio model Keys ===\")\n", + " print(checkpoint.keys())\n", + " print()\n", + " checkpoint = checkpoint['en']\n", + "\n", + " # 2. 'generator' 혹은 모델 State Dict 키 내부 확인\n", + " # (보통 'generator', 'model', 'state_dict' 등의 키를 사용함)\n", + " if 'generator' in checkpoint:\n", + " print(\"=== Generator State Dict Keys (Layer Names) - First 10 ===\")\n", + " # 너무 많으니까 처음 10개만 출력\n", + " for key in list(checkpoint['generator'].keys())[:10]:\n", + " print(key)\n", + " \n", + " # 3. 모델의 Weight Shape 확인 (하나만 예시로)\n", + " first_key = list(checkpoint['generator'].keys())[0]\n", + " print(f\"\\nShape of '{first_key}': {checkpoint['generator'][first_key].shape}\")\n", + " \n", + " elif 'state_dict' in checkpoint:\n", + " print(\"=== State Dict Keys (Layer Names) - First 10 ===\")\n", + " for key in list(checkpoint['state_dict'].keys())[:10]:\n", + " print(key)\n", + "\n", + " else:\n", + " # 키가 없고 바로 State Dict인 경우\n", + " print(\"=== Keys (Layer Names) - First 10 ===\")\n", + " for key in list(checkpoint.keys())[:10]:\n", + " print(key)\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"Error: 파일을 찾을 수 없습니다. 경로를 확인해주세요: {checkpoint_path}\")\n", + "except Exception as e:\n", + " print(f\"Error 발생: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "de3a590a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Top Level Keys ===\n", + "dict_keys(['generator'])\n", + "\n", + "\n", + "=== Generator Keys ===\n", + "conv_pre.bias\n", + "conv_pre.weight_g\n", + "conv_pre.weight_v\n", + "ups.0.bias\n", + "ups.0.weight_g\n", + "ups.0.weight_v\n", + "ups.1.bias\n", + "ups.1.weight_g\n", + "ups.1.weight_v\n", + "ups.2.bias\n", + "ups.2.weight_g\n", + "ups.2.weight_v\n", + "ups.3.bias\n", + "ups.3.weight_g\n", + "ups.3.weight_v\n", + "ups.4.bias\n", + "ups.4.weight_g\n", + "ups.4.weight_v\n", + "resblocks.0.convs1.0.bias\n", + "resblocks.0.convs1.0.weight_g\n", + "resblocks.0.convs1.0.weight_v\n", + "resblocks.0.convs1.1.bias\n", + "resblocks.0.convs1.1.weight_g\n", + "resblocks.0.convs1.1.weight_v\n", + "resblocks.0.convs1.2.bias\n", + 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"resblocks.12.convs2.1.weight_g\n", + "resblocks.12.convs2.1.weight_v\n", + "resblocks.12.convs2.2.bias\n", + "resblocks.12.convs2.2.weight_g\n", + "resblocks.12.convs2.2.weight_v\n", + "resblocks.13.convs1.0.bias\n", + "resblocks.13.convs1.0.weight_g\n", + "resblocks.13.convs1.0.weight_v\n", + "resblocks.13.convs1.1.bias\n", + "resblocks.13.convs1.1.weight_g\n", + "resblocks.13.convs1.1.weight_v\n", + "resblocks.13.convs1.2.bias\n", + "resblocks.13.convs1.2.weight_g\n", + "resblocks.13.convs1.2.weight_v\n", + "resblocks.13.convs2.0.bias\n", + "resblocks.13.convs2.0.weight_g\n", + "resblocks.13.convs2.0.weight_v\n", + "resblocks.13.convs2.1.bias\n", + "resblocks.13.convs2.1.weight_g\n", + "resblocks.13.convs2.1.weight_v\n", + "resblocks.13.convs2.2.bias\n", + "resblocks.13.convs2.2.weight_g\n", + "resblocks.13.convs2.2.weight_v\n", + "resblocks.14.convs1.0.bias\n", + "resblocks.14.convs1.0.weight_g\n", + "resblocks.14.convs1.0.weight_v\n", + "resblocks.14.convs1.1.bias\n", + "resblocks.14.convs1.1.weight_g\n", + "resblocks.14.convs1.1.weight_v\n", + "resblocks.14.convs1.2.bias\n", + "resblocks.14.convs1.2.weight_g\n", + "resblocks.14.convs1.2.weight_v\n", + "resblocks.14.convs2.0.bias\n", + "resblocks.14.convs2.0.weight_g\n", + "resblocks.14.convs2.0.weight_v\n", + "resblocks.14.convs2.1.bias\n", + "resblocks.14.convs2.1.weight_g\n", + "resblocks.14.convs2.1.weight_v\n", + "resblocks.14.convs2.2.bias\n", + "resblocks.14.convs2.2.weight_g\n", + "resblocks.14.convs2.2.weight_v\n", + "conv_post.bias\n", + "conv_post.weight_g\n", + "conv_post.weight_v\n", + "dict.weight\n", + "spkr.weight\n", + "spkr.bias\n", + "dur_predictor.conv1.0.weight\n", + "dur_predictor.conv1.0.bias\n", + "dur_predictor.ln1.weight\n", + "dur_predictor.ln1.bias\n", + "dur_predictor.conv2.0.weight\n", + "dur_predictor.conv2.0.bias\n", + "dur_predictor.ln2.weight\n", + "dur_predictor.ln2.bias\n", + "dur_predictor.proj.weight\n", + "dur_predictor.proj.bias\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# 체크포인트 파일 경로 (확인하고 싶은 파일 경로로 변경하세요)\n", + "checkpoint_path = \"/home/2022113135/gyucheol/NetfLips/av2av-main/unit2av/checkpoint/zeroth-hubert/g_00500000\"\n", + "# CPU로 로드 (GPU가 없어도 확인 가능하게)\n", + "checkpoint_ko = torch.load(checkpoint_path, map_location='cpu')\n", + "\n", + "# 1. 최상위 키(Key) 확인\n", + "print(\"=== Top Level Keys ===\")\n", + "print(checkpoint_ko.keys())\n", + "print(\"\\n\")\n", + "\n", + "checkpoint_ko = checkpoint_ko['generator']\n", + "print(\"=== Generator Keys ===\")\n", + "for key in checkpoint_ko.keys():\n", + " print(key)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a7ff3197", + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint_path = \"/home/2022113135/av2av/checkpoints/unit_av_renderer.pt\" \n", + "checkpoint = torch.load(checkpoint_path)\n", + "\n", + "checkpoint['audio']['ko'] = checkpoint_ko" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e521077", + "metadata": 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'resblocks.14.convs2.1.bias', 'resblocks.14.convs2.1.weight_g', 'resblocks.14.convs2.1.weight_v', 'resblocks.14.convs2.2.bias', 'resblocks.14.convs2.2.weight_g', 'resblocks.14.convs2.2.weight_v', 'conv_post.bias', 'conv_post.weight_g', 'conv_post.weight_v', 'dict.weight', 'spkr.weight', 'spkr.bias', 'dur_predictor.conv1.0.weight', 'dur_predictor.conv1.0.bias', 'dur_predictor.ln1.weight', 'dur_predictor.ln1.bias', 'dur_predictor.conv2.0.weight', 'dur_predictor.conv2.0.bias', 'dur_predictor.ln2.weight', 'dur_predictor.ln2.bias', 'dur_predictor.proj.weight', 'dur_predictor.proj.bias'])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint['audio']['ko'].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ceea9d6f", + "metadata": {}, + "outputs": [], + "source": [ + "output_path = '/home/2022113135/av2av/checkpoints/unit_av_renderer_withKO.pt'\n", + "torch.save(checkpoint, output_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d852a6ac", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lip-bbox", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/sample_inference_g_500000.ipynb b/notebooks/sample_inference_g_500000.ipynb new file mode 100644 index 0000000..4537b77 --- /dev/null +++ b/notebooks/sample_inference_g_500000.ipynb @@ -0,0 +1,1426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "aee17ce7", + "metadata": {}, + "source": [ + "# Sample Inference Results\n", + "\n", + "This notebook runs the `inference_unit2a.py` script to generate audio and displays the results.\n", + "\n", + "**Model Checkpoint**: `g_best` (Zeroth-Hubert)\n", + "**Dataset**: All subjects in `test_data_01/003`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "98dc1317", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scanning for files in /home/2022113135/datasets/zeroth/test_data_01/003...\n", + "Subject 104: found 3 pairs\n", + "Subject 105: found 1 pairs\n", + "Subject 112: found 1 pairs\n", + "Subject 118: found 3 pairs\n", + "Subject 121: found 3 pairs\n", + "Subject 126: found 3 pairs\n", + "Subject 132: found 2 pairs\n", + "Subject 137: found 2 pairs\n", + "Subject 147: found 1 pairs\n", + "Subject 149: found 3 pairs\n", + "Total pairs found: 22\n", + "Output directory: /home/2022113135/gyucheol/NetfLips/av2av-main/output/g_500000\n" + ] + } + ], + "source": [ + "import os\n", + "import subprocess\n", + "import IPython.display as ipd\n", + "\n", + "# Paths\n", + "ROOT_DIR = \"/home/2022113135\"\n", + "PROJECT_DIR = os.path.join(ROOT_DIR, \"gyucheol/NetfLips/av2av-main\")\n", + "INFERENCE_SCRIPT = os.path.join(PROJECT_DIR, \"inference_unit2a.py\")\n", + "CHECKPOINT = os.path.join(PROJECT_DIR, \"unit2av/checkpoint/zeroth-hubert/g_00500000\")\n", + "CONFIG = os.path.join(PROJECT_DIR, \"unit2av/checkpoint/zeroth-hubert/config.json\")\n", + "OUTPUT_DIR = os.path.join(PROJECT_DIR, \"output/g_500000\")\n", + "\n", + "# Directories\n", + "WAV_ROOT = \"/home/2022113135/datasets/zeroth/test_data_01/003\"\n", + "PT_ROOT = \"/home/2022113135/datasets/final_unit2a_split/test\"\n", + "\n", + "# Find all subjects\n", + "subjects = sorted([d for d in os.listdir(WAV_ROOT) if os.path.isdir(os.path.join(WAV_ROOT, d))])\n", + "\n", + "file_pairs = []\n", + "\n", + "print(f\"Scanning for files in {WAV_ROOT}...\")\n", + "\n", + "for subject in subjects:\n", + " subject_dir = os.path.join(WAV_ROOT, subject)\n", + " wav_files = sorted([f for f in os.listdir(subject_dir) if f.endswith('.wav')])\n", + " \n", + " found_count = 0\n", + " for wav_file in wav_files:\n", + " if found_count >= 3:\n", + " break\n", + " \n", + " # Construct expected pt filename\n", + " # items: 104_003_0253.wav -> 104_003_0253_preprocessed.pt\n", + " base_name = os.path.splitext(wav_file)[0]\n", + " pt_filename = f\"{base_name}_preprocessed.pt\"\n", + " pt_path = os.path.join(PT_ROOT, pt_filename)\n", + " \n", + " if os.path.exists(pt_path):\n", + " file_pairs.append({\n", + " \"subject\": subject,\n", + " \"wav\": os.path.join(subject_dir, wav_file),\n", + " \"pt\": pt_path\n", + " })\n", + " found_count += 1\n", + " \n", + " print(f\"Subject {subject}: found {found_count} pairs\")\n", + "\n", + "print(f\"Total pairs found: {len(file_pairs)}\")\n", + "print(f\"Output directory: {OUTPUT_DIR}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "55f538b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 104_003_0253_preprocessed.pt...\n", + "Processing 104_003_0577_preprocessed.pt...\n", + "Processing 104_003_0993_preprocessed.pt...\n", + "Processing 105_003_2905_preprocessed.pt...\n", + "Processing 112_003_0107_preprocessed.pt...\n", + "Processing 118_003_0522_preprocessed.pt...\n", + "Processing 118_003_0836_preprocessed.pt...\n", + "Processing 118_003_1091_preprocessed.pt...\n", + "Processing 121_003_0527_preprocessed.pt...\n", + "Processing 121_003_0791_preprocessed.pt...\n", + "Processing 121_003_2994_preprocessed.pt...\n", + "Processing 126_003_1107_preprocessed.pt...\n", + "Processing 126_003_2205_preprocessed.pt...\n", + "Processing 126_003_2432_preprocessed.pt...\n", + "Processing 132_003_1366_preprocessed.pt...\n", + "Processing 132_003_2657_preprocessed.pt...\n", + "Processing 137_003_1351_preprocessed.pt...\n", + "Processing 137_003_1614_preprocessed.pt...\n", + "Processing 147_003_1675_preprocessed.pt...\n", + "Processing 149_003_0927_preprocessed.pt...\n", + "Processing 149_003_2332_preprocessed.pt...\n", + "Processing 149_003_2621_preprocessed.pt...\n" + ] + } + ], + "source": [ + "# Run Inference for each file\n", + "for pair in file_pairs:\n", + " input_pt = pair['pt']\n", + " print(f\"Processing {os.path.basename(input_pt)}...\")\n", + " \n", + " command = [\n", + " \"python\", INFERENCE_SCRIPT,\n", + " \"--checkpoint\", CHECKPOINT,\n", + " \"--config\", CONFIG,\n", + " \"--input_file\", input_pt,\n", + " \"--output_folder\", OUTPUT_DIR,\n", + " \"--device\", \"cuda\"\n", + " ]\n", + " \n", + " # Run the command\n", + " result = subprocess.run(command, capture_output=True, text=True, cwd=PROJECT_DIR)\n", + " \n", + " if result.returncode != 0:\n", + " print(f\"Error processing {input_pt}:\")\n", + " print(result.stderr)\n", + " else:\n", + " # Optional: Print only if needed, to avoid clutter\n", + " # print(result.stdout)\n", + " pass\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "578d74d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + "g_500000 Inference Results\n", + "------------------------------------------------------------\n", + "\n", + "################################################################################\n", + " SUBJECT: 104\n", + "################################################################################\n", + "\n", + "============================================================\n", + "File ID: 104_003_0253\n", + "Original Audio: 104_003_0253.wav\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " <audio controls=\"controls\" >\n", + " <source 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ACAPr/BQD8/wQA/////wIAAAADAP3/AwD//wEA//8BAP//AgD+/wMA/f8EAPz/BAD//wIA//8DAPz/BAD8/wUA/f8BAAAA/v8BAP//AAD+/wIA/P8DAP7///8AAAEA/f8EAPv/AQD8/wIA/f8BAP3/AQAAAAMA/v8EAPv/BgD7/wUA/P8EAAAAAAADAP//AwD+/wIAAAADAP//AwD//wIA/f8EAP7/AgD//wAABAD//wMA//8EAP//BAAAAAMABAAAAAIA//8DAAAABAABAAEA//8CAP7/BQD+/wIAAQABAAEAAQACAP//AQABAAEAAQD+//3/BAD8/wQA/f8AAP7/AwD6/wUA+v8FAP3/AwD9/wQA/P///wIA+v8FAP3/AAD//wIA+v8IAPr/BQD9/wIAAQAAAAYA/P8IAP3/BgAAAAIAAwACAAMAAAAEAP7/BQABAAUAAAAEAP//BgD+/wUA/v8EAAIA//8GAP7/AwABAAUA/f8HAPn/CQD6/wYA/v8CAAEA+/8IAPf/CAD4/wcA/P8EAPn/BQD5/wkA+v8GAPr/BwD6/wcA+P8GAPf/BwD4/wUA+/8AAAAAAwD7/wcA/f8FAAEAAQAEAAAAAAACAP//AwABAP7/BQAAAAQAAAADAAEAAAAFAPz/CAD6/wkA+f8IAP//AQAEAP7/BwD//wkA+v8JAP3/AwD+/wAAAQD9/wIA/f8BAP3/AgD//wAAAAD+/wEA/P8JAPf/CAD4/wQAAAD8/wQA9/8GAPv/AgAAAP3/AAADAP3/AwD9/wQA/P8FAP//AgAFAAEAAwADAAMABAAAAAcA/f8HAAIAAQAIAP//CAD7/wkA/v8CAAcA/P8HAP//AQAGAPz/BwD8/wYA//8BAP//AwD//wMA/v8DAP//AAAAAPz/BwD4/wUA+/8CAAMA+/8GAPj/BwD8/wQA//8DAP7/BAD8/wMA/f8CAAAAAAD//wEA+/8FAPr/BAD5/wUA+/8AAAEA+/8EAP3/AwD9/wIA/v8CAP3/AQD9/wMA/P8FAPv/BQD+/wAABAD9/wMA/f8FAPz/BAAAAAAAAgAAAAIAAgACAAIAAgACAP//AQADAPr/CAD8//7/AQD7/wIAAQD7//7//v8AAP/////8/wMA+/8BAP7//P8GAPz/AgD+/wEABAD8/wcA+/8EAAAA/v8HAPv/BwD+//3/+/8AAPn/BQD7/wEAAgD8/wMA/f8CAPv/AQD6/wIA+f8CAP3/AwD//wEAAgD+/wQA//8CAAEAAgACAP3/BgD5/woA9/8JAPb/CQD7/wMA/v8DAP//BQD8/wYAAAABAAMA//8EAP//BQD9/wIAAwD8/wgA//8BAAIAAwD9/wkA9/8JAPj/BwD7/wQA/v8BAAAA/v8CAPz/AQD///v/AgD4/wEA+/8AAP///v/8/wQA+/8AAAAA+/8FAPv///8CAAAAAQAAAP//AwD//wUA///+/wUA+v8FAPr/BgD6/wMA/f8AAAEA/P/////////+/wAA/f8FAP3/BAD+/wAAAQABAP7/AwD8/wMA/v8AAP3/BAD6/wcA+f8AAP///v////z/+//9//7//v/8/wIA9/8HAPn/BQD9/wIAAAABAAAAAAACAAAAAgD9/wQA/f8FAPz/AgABAP7/BwD6/wgA/f8FAAEAAwABAAQA/P8IAPz/AgD/////AgAFAPz/CAD8/wgAAAAHAAIAAwAEAPz/BgD7/wMA/f8BAP///v8BAP7///8EAPz/BgD6/wUA/f///wEA/f8AAP//AAD+/wAA/v8AAPz/AAAAAP3/AwD9/wMA//8AAAEA+/8HAPb/BQD9//3/AQD///3/AwD8/wAABQD4/wgA+v8EAP//AAABAAEA/f8GAPv/BQD5/woA9/8KAPr/BAD/////AAAAAAAAAgD9/wIA//8BAAIA//8BAP3/AgABAP7/CAD5/wYA/f8AAAMAAQACAP3/AwAAAAIAAwD9/wMA//8BAAEAAQD//wAAAwAAAAIAAgAAAAIAAgACAAMAAQAAAAEAAQD+/wEA/v8BAP//AwD8/wQA/f8DAP//AgD+/wEA//8BAP3/BAD8/wQA/v8BAAEAAQD+/wIA/v8BAAEA//8DAP3/AAAAAAEA/v8DAP7///8FAP7/BAAAAAEA/v8FAPz/AgD//wAA/v///////v/+/////v8DAP//AgD//wMAAAACAAMA/v8EAAAA//8GAPv/AgABAP3/AgACAP7/AQACAPz/BQD//wEAAAD9/wEA+/8IAPX/CQD2/wYA+v8IAPf/BQD6/wEA/v8AAPn/BAD6/wQA/P////z//////wEA///+/wYA+f8HAPz/AwD//wIA/v8CAAAAAwD9/wIAAAACAAAAAgD9/wUA/P8FAP3/BAD8/wMA/P8CAAAA//8AAAIA+/8GAPv/AwAAAP7/AgD+/wIA/P8FAP7/AwD9/wMAAQADAAEAAwD+/wgA+/8HAP//AwABAAEAAQACAAEAAQABAP7/AgD9/wQA/P8BAPz/AAD+/wMA+f8FAPn/BQD6/wQA+/8CAPv/AAD+/wEA/f8BAP7/AQD8/wMA+/8FAP3/AQACAAIA/P8HAPj/BQD8//7/AgD9/wAA/f8AAPz////9//r//v/9//3/AgD6/wAA///+/wAA/f8EAPj/BQD8/wAA///9/wMA/v8BAP7/AwD9/wQA/v8BAAIA/////wMA/v8CAPz/BQD7/wUA/v8BAAIA//8CAP//AgAAAAAA/v8DAPv/AgD9/wEAAgD8/wYA+f8IAPr/AgACAP3/BAD8/wUA/P8FAAQA/P8JAPz/BAACAAEAAQAAAAMA/P8GAP7/AgADAP3/AwD+/wEAAAABAP7/BQD+/wMAAAD///7/AgD5/wIAAAD6/wUA+v8CAP7/AQD//wQA/P8EAP7/AwAAAP7/AgD+/wAA///8/wAA//8BAPr/AwD8/wAA///8/wAAAQD5/wIA9/8BAPv/AAD8/wMA+v8CAPz/AQD///7/BAD3/wkA9/8FAPn/BAD//wEA/P8FAPn/BQD9/wIA/v8BAP//AgD9/wYA+/8FAAIA//8CAAEA/v8AAP//AAAAAAEA/v///wAAAAABAAEA//8FAPv/CgD4/wwA+P8IAP//AgADAP7/BAAAAAQA/f8FAPz/AgD9/wYA9/8GAPr/BAAAAP//AQACAPv/AwD9////AgD6/wIA/P8AAAIA+/8FAPn/AgD//wAA//8DAPv/BAD+/wEABAD7/wUA+/8HAPv/AwD9/wQAAAADAP7/AQD//wMA/P8AAP///v8BAP7/AgD8/wQA+P8IAPn/BgD4/wUA+v8BAPr/AQD7/////P////3/AgD8/wEAAAAAAAEA/P8CAP7/AgD//wEAAAADAAMA/f8HAPv/CAD+/wAABAD5/wAAAAD8/wEA/v/8/wUA/P8BAP3/AQD+/wMA+v8EAPr/AgD//wAA//8CAAEA/f8FAP3/AwACAP7/CAD7/wUAAQD//wUAAQD8/wkA+f8IAP3/AAAFAP3/BAD7/wMA/f8CAPz/AQD7/wAA///8/wIA/P8FAPn/BQD6/wYA+/8IAPn/BgD7/wUA/P8AAP///f8BAP7/AQAAAAAAAQD+/wYA+v8FAP3///8DAPz/BwD4/wgA+P8FAPr/AwD7/wAA/v/+/wIA//////7/AAD//wEA//8CAP3/AQADAP7/BgD7/wMAAAABAAEA//8BAAIA/////wIA/f8EAPv/BQD8/wIA/v/+/wEAAAAAAP3/BAD6/wIA/v8AAAEAAwD5/wYA+/8GAP3/BgD+/wQAAAD//wIA/P8GAPz/BwD7/wYA//8GAPz/AgACAAEA//8FAPv/BgD7/wQA/P8EAPb/CwD2/wcA+P8DAP7/AgD+/wIAAAACAP//AAAEAPv/BAD+/wAAAwD+/wEAAAABAPz/AwD7/wEAAgD8/wYA9/8FAPz/AgACAPz/BgD8/wMA/v8CAP//AAD///3/AwD6/wQA+f8GAPj/CQD2/wcA/P8BAAIA/v8FAP3/BQD8/wYA+/8IAP3/AAAEAPr/BgD4/wUA/v8BAPz/BgD8/wIAAQAAAAEAAwD+/wIAAAAAAAMA+f8IAPr/BAABAP3/BAD//wAAAQACAPv/AgD7/wEA///9/wQA/P8EAPz/BAD+/wEAAgD//wAAAwD6/wcA/P8EAAIA/v8DAAAAAAACAAAA///+/wEA/P8FAP3/AAACAPz/BAABAP7/BAD6/wUA+f8IAPj/BgD7/wIA/f8BAP7/AgD+//3/AgD//wIA//8CAAAAAgABAAEAAgABAAAAAQABAP7/AgAAAP3/AgD+//3/AgAAAP3/BAD4/wgA+P8IAPz///8EAP7/AgACAP7/AwABAAAAAQABAAAAAAAAAAAAAAABAP//AAABAP//AgD9/wIAAAD+/wIA/v///wAA/v8AAP/////+//////////3/AQD8/wAA/P8BAP7//P8AAP7/AQD//wAA/f8CAP3/BQD8/wMAAAABAAIA/v8EAP3/BgD8/wMAAAD//wMAAAD9/wQA/P8DAP//AgD+/wIA/v8AAP3/AwD9/wYA+P8CAPz/AwD///7/AQD8/wYA+P8HAPj/CAD6/wUA/v8CAAEAAAAAAAIA/v8GAPr/BwD3/wQA/f8BAAAAAAD9/wIA/f8CAAAA//8AAP7/AgD+/wIAAAD+/wUA/P8EAPz/AAAEAPn/BwD4/wUA+v8GAPr/AwABAP//AgD//wAABAD+/wMAAAAAAAQA/////wIA+/8CAAAA/P8DAAAA+/8IAPX/CQD8/wMA//8CAP//AQABAP3/AAD+/wEA//8AAP7/AAD+/wEA///9/wAA/f/+/wEA/f8BAP///f8DAP3/AQD9/wQA+f8GAPn/BgD6/wEA/f8BAP3/AwD4/wcA9/8JAPf/CAD5/wYAAAD//wYA+v8IAPz/BQD9/wQA/f8EAPv/BQD//wEAAAD//wEAAgABAAAAAwD8/wMAAAABAAIAAQD+/wIA/f8DAP//AAABAP7/AwD//wAA//8BAPz/BAD7/wMA//8AAAEAAQACAP7/AwD9/wYA/P8GAPz/AwD+/wIAAAD//wEA/v8AAAAA//8CAP7/AgD7/wYA+/8EAP///////wEA/v8AAAMA+/8DAAAAAQABAP//BgD5/wkA+f8BAAEAAQD9/wUA+/8FAP//BAD8/wUA+/8DAPz/AwD9////AAD8/wAA/v/9/wQA+P8FAPz/AgD///z/AwD9/wIA////////BAD8/wMA/P8DAP7/AQD9/wEA/f8EAPv/BAD+//7/BAD+/wIA//8BAAIA/P8HAP3/BQD+/wEAAAABAAAAAAACAP3/AwD6/wYA9/8IAPv/AQACAAAA//8DAAAAAAAGAPn/CgD9/wQAAQD//wIAAwD+/wMAAAABAP7/BgD8/wcA+f8GAP7/AwABAP7/AQD9/wQA+f8JAPT/BwD5/wMA///+/wAA/f///wAA/P8EAPj/BwD3/woA9/8GAP3/AgD//wEA//8BAAMA/f8DAP///f8EAP3/BQD9/wEAAAD9/wQA+/8CAAAA/f8DAP//AAACAPr/BAD7/wIAAQD5/wYA9v8HAPf/BAD8/wIA//8AAP7/AgD//wAAAQD8/wQA//8BAAAAAgABAAIA/v8DAP7/AwD+/wAAAAACAPz/AwD9/wAAAQD+/wEA/v8DAP3/BAD9/wQA/v8EAPr/BAD9/wYA/P8DAPv/BQD9/wEA//8BAAAAAwAAAAQAAAD//wYA/f8FAP7/BQAAAAMABAD9/wcA/P8JAPz/BQD//wAAAQD+/wEA/v8BAAEA/P8DAPz/AQD+/wEA/v8AAAAA/P8DAPv/AwD7/wIA/P8BAP//AQD9/wAAAgD+/wEA/v8AAAAA/f8DAPv///////z/AAADAPj/BAD5/wQA+/8BAAAA+/8EAPn/AQD9//7/AAD///3/BAD5/wYA+/8FAP3/AQABAP//BAD9/wMA//8DAAAA//8HAPn/BQD7/wIA//8BAAAA//8GAPr/CwD7/wcA//8DAAIAAgACAAIAAAACAPn/CQD5/wYA/P8DAP7/BAD8/wUA/f8BAAIAAAAEAP3/AgD9/wMAAAD+/wIA/v8AAAEA////////AAD+/wIA/P8DAPz/BQD6/wYA/P8FAP//AQABAAEA//8EAP3/BAD8/wYA/P8CAP//AAACAP///v8CAP7/AAD+/wAAAQAAAP///v///wEA/f8CAPz/AwD7/wAA/v/+/wIA+/8CAPv/AQD+/wAA/v8AAP7///8AAPv/AwD8/wAA/v8AAP3/AgD8/wAAAAD///7//f8DAPv/BAD8/wIAAQD9/wYA+P8KAPn/BwD//wEAAgABAAEABAD//wAAAQAAAAIAAgD9/wcA/P8HAPv/BQAAAAEABAD8/wcA/v8DAAQA//8GAAAABQAAAAYA/v8HAAIAAQAGAAAAAgAFAP7/BQD//wQA/v8BAAIAAAACAP//AgD9/wYA9/8GAPz/AwD+////AgD/////AQD9/wEAAAD//wEAAAD9/wIA/f///wAA/////wMA+/8BAP/////+/wIA/f8DAPz/AAABAP3/AQD6/wMA+f8EAPn/AgD7/wMA/f///wIA//8AAAMA/f8CAAIA//8BAAQA/v8GAP7/AwABAAMAAAAHAP3/BAD//wMAAgADAP//AwABAAAABAD+/wcA+/8IAPr/BAD+////BAD9/wAAAAD7/wMA/P///wEA/v/8/wIA+f8HAPr/BAD///7/AQACAPz/BwD5/wYA/f8DAAEAAwAAAP//BAD+/wIAAgD//wYA//8CAAMA/P8KAPj/CQD8/wcA/f8FAAIAAAAEAP//AAAHAPr/CQD8/wYAAAACAAIA/f8IAPf/CgD5/wcA/v8DAP7/BQD9/wcA/f8DAAQA/P8HAPz/AwD//wEAAAABAP///v8DAP7/AgD+/wIA+/8GAPz/BAD9/wIA//8DAP3/AwD+/wAAAgD7/wQA/P8CAAIA/P8FAPr/BwD3/wgA+f8EAPr/AwD6/wUA+/8FAPv/BwD8/wQAAQD9/wcA+/8EAAEA+/8JAPf/BwD5/wMA/v8BAP///v8BAAMA/v8AAAIA+/8GAPr/BQD9/wEA//8AAP7/AgD+/wMA//8CAAEAAQADAPr/CAD7/wQAAgD5/wUA+v8FAP3/AgAAAAIA//8FAPr/BQD9/wEABAD8/wUA+v8GAPv/AgD+////AAD+/wIA/v8CAP//AAAAAAEAAgD//wUA/v8EAP7/AwACAP//AwD//wAAAgD//wUA+/8CAP//AAACAP//AgD//wEAAAD+/wUA+f8IAPv/AgD9/wMAAAD//wEA/P8FAPr/BwD6/wMA/v8AAAAA/v8EAPr/BgD8/wEAAwD6/wYA/P8HAPv/AQABAP7/AgD+//7/AgD7/wQA+v8EAPn/BAD+//7/AAD7/wMA/f///wAAAQD9/wUA/P8EAPz/AQABAPz/BAD5/wMA/f///////f8CAPz/AwD7/wUA/f8DAP////8CAP3/AAAAAP//AgD+/wAA/v///wIAAQAEAP3/BAABAAMAAQD9/wMA/f8FAP//AgD+/wIA+f8HAPr/AgABAP7/AgAAAAAAAQABAP3/AwD//wEA/////wMA/f8BAAAA/v8BAAIAAAAAAAAA//8BAAEA/f8DAPz/AgAAAAAA//////////8BAP3/AgD+/wMA/P8CAAEAAQAEAPz/BQD6/wkA9/8JAPf/CAD7/wQA/v8DAP7/BQD6/wcA/P8CAAAAAgD7/wcA/P8FAPv/AgD6/wUA+v8BAPz///////7/AAD///7/AAD9///////+/wAA/P/+/wAA/v8AAPz/AgD///7/AAAAAPv/BAD3/wcA9/8EAPv/AQD/////AAD9/wUAAAAEAAAAAQACAAAABAD//wMAAgD9/wUA+/8FAP7/AgAAAAIA/v8EAPz/BgD7/wYA+/8FAPz/BQD/////AgD8/wMA+/8BAP///f8CAP3///8CAPz/BAD+/wMA+v8GAPj/BgD7/wQA+/8DAP3///8BAP7///8AAP7////9/wEA/f/9/wQA+v8EAP7/AgD//wUA//8EAAIA//8GAPz/BQAAAAQA//8CAP//AwD+/wEA//8DAP//AwD+/wQA/v8AAAMA/P8GAPz/AQABAPv/CQD0/wwA9/8GAPz/AAD/////AAD+/wMA/v/9/wUA9v8LAPX/BgD7/wIA/f8BAAEA+P8GAPn/AgD///7////+/////f/9/////v8CAP3/AgD+/wEA/v8GAPz/CAD6/wgA//8BAAMA/f8HAPz/BgD+/wQAAQADAAAAAgADAP//BAABAP7/AwABAAQAAAACAP7/AAADAP7/AgD9////AgD8/wUA+/8BAAEA/f8BAP////8DAPv/AwD6/wYA+/8DAP3///8AAP///v/+/wAA+/8CAP///f8CAPv/BQD8/wIA/P8CAPz/AAACAPz/BgD4/wUA/f8DAP3/AgD//wIA/f8DAP3/BQD//wAAAgD//wYA/f8JAPz/BgD+/wUA//8FAP7/BgABAAIABAD//wEAAgD+/wQA/v8DAP3/BAD//wEAAwAAAAMAAAADAP//AgACAPz/BgD5/wgA+P8HAPr/AgABAP3/AgD+/wAA/v8BAP3////+/wAAAQAAAP3/AAD+//////8BAP3/AQAAAPz/AgD7///////8/wIA/f///wAA//8DAPv/AwD//wAABQD7/wIAAgD9/wIAAAD+//7/AgD9/wMA/P///wIAAAD/////AQABAP//AQD//wAABAD8/wEA///7/wUA+v8EAP///P8DAP3/AgAAAP//AgD9/wYA+/8FAPv/BgD8/wYA/P8FAAMAAAAFAP7/AwD//wQA//8EAP//AwD+/wcA+P8KAPn/BwD5/wYA+P8HAPj/BgD6/wEA///+/wMA/f///wMA+/8BAP7///8CAP///P8DAP3/AgAAAP3/AQAAAP7////9/wQA+P8HAPX/BQD8////AQD6/wMA+f8FAPf/AgD9//7/AQD///3/AwD5/wEA/f///////v/+//7/AQD5/wUA+/8BAP////8AAP///////wEA/f8EAAAA//8FAPr/CAD8/wQA/v8BAAIAAQD9/wYA/P8DAAIA/P8EAPr/CAD4/wkA+f8EAPz/BAD9/wEAAgD//wAAAQD/////AgD8//7/BAD6/wcA/v8BAP//BQD9/wcA+v8JAPr/BwD8/wMAAgD//wMA/////wMA/f8BAAEA/P8CAP3/AQD//wMA+/8DAPv/AAACAPv/AwD9/wAAAAABAP7///////3/AgD7/wMA/v//////AAD+/wIA///+/wAA/////wMA+/8DAP7//v8BAAAA/f8EAPn//v8BAP3//f8BAPj/AwD7/wIA+v8DAPj/AwD9/wIA//8DAP3/AQABAP7/AgAAAPz/BAD7/wQA/v/+/wQA/f8DAP7///8AAAAA/v///wMA+v8GAPr/AgACAP//AAACAP3/AgD+/wAAAQD//wAAAAABAP7/AAACAP//BQD8/wEABQD8/wUAAAD+/wcA+f8JAP3/AwABAAIAAQD9/wMA/P8DAP7///8DAPv/AwD9/wMA//8AAAEAAQAAAAEA//8AAAIAAAAAAAEA//8CAAAAAwD6/wYA+/8CAAAA/f8DAPz/AwD9/wQA/P///wMA/P8EAPz///8AAAAA/v/8/wEA+P8FAPv//f8FAPb/CQD3/wgA+P8GAP7/AQACAPz/AgD+/wIA/v8BAP/////+/////v8AAPz/BQD3/wcA+P8EAP3/AAD+/wIA//8BAP7//f8DAP7/BQD6/wMA/P8BAP7/AgD9////AgD6/wYA/P8EAP////8FAP7/BgD7/wQAAwD7/wkA+f8GAP//AAABAAAA//8AAAAAAAD+/wQA/f///wYA+f8JAPz/AgACAAEAAQD/////AAAAAAAA//////7/AgD9/wIA/v/+/wIA/f8EAP7/AwAEAP//AgD//wMAAAADAAMA/v8GAPz/BgD9/wUA/v/+/wYA+/8GAP3/AwD9/wQA/////wYA+v8GAP3/AAAAAP7/AQD8/wAA9/8EAPv///////3/AAD///v/BAD9/wAAAwD8/wUA/f8EAP3/BAD+/wAAAwD+/wEAAAD//wAAAwD6/wQA+P8BAP7//f8AAP7////+/wIA/f8CAAIA+/8IAPr/CAD8/wEAAwD+/wEAAgD8/wMA/v8GAPr/CQD4/wYA//8DAAAABAABAAMAAgAFAAAAAgABAP7/BwD7/wMA/v/9/wEA/////wMA+v8FAPr/BQD+/wEAAwD//wUA//8BAAMAAAADAAAAAwAAAAIAAgD8/wQA//8AAAIA/f8DAP7/AgAFAPz/BgD+/wEA//8CAP//AQACAPz/BAD9/wUA/P8AAAEA/P8DAPz///8FAPz/BAD+/wMAAAACAP//AwACAAMA//8EAP7/BAD+/wMA/f8FAP7/BgD//wAAAwABAAEAAgD//wMAAQD//wAAAwD8/wIA/f8CAP3/AQAAAP3/BAD7/wUA/P8EAP7/AAAFAPv/BQD8/wIA//8BAAIA/f8GAPr/BQD9/wIAAQD8/wMA/f8AAP///v8CAP7//v8BAP7/BAD8/wMA+/8CAP//AAACAAEAAAD+/wQA/f8FAP7/AgAAAAIA//8DAP3/BAD+/wQAAQD//wMA//8CAAMAAgACAAEABAD+/wUA///+/wYA+/8FAPz/AwD//wIA/f8CAAAAAAABAP//AwD//wAAAgD//wUA/P8EAAIA/v8DAP7/AQD//wMA/v/+/wAA/v8AAP7/AAAAAP7/AgD/////BAD7/wYA/P8FAPr/BwD8/wMAAAD//wMA//8AAAQA/P8EAPz/BwD7/wcA/v8BAAMAAQADAAIAAgADAAMAAAAFAP7/BwD9/wMAAQAEAP//BQD9/wMA/f8BAP//AQD+/wEAAAD///////8CAPz/AQD///7/AwD9/wEA/f8CAP3/AgD5/wUA+v8FAPz/AQD+////AgD8/wUA+v8FAP//AgD//wUA+/8GAPr/AwD8/wEA/v////7///////7/AQD9/wQA+/8EAPz/BQD8/wUA/P8HAPv/CAD6/wgA/P8BAAMA//8FAP7/BQD7/wcA+/8FAP//BQD7/wsA9v8NAPn/BgAAAP7/BQD8/wUA+v8GAPr/BwD6/wMA+f8GAPf/BQD5/wIA/f/+/wQA+v8FAPz/BAD8/wQA+v8GAPj/BgD6/wMAAQD+/wQA/f8BAAAA/f8GAPn/BgD8/wIA//8CAP7/AQAAAAEA//8CAP7/AAAAAP///f8DAPn/BQD7/wIA/v///wEA/v8DAPv/BAD9/wQA/P8CAP7/AAACAPr/AgD9/wEA//////z/AQABAPz/AgD9/wAAAAAAAP3/AQD8/wEA/v8AAP7//f8AAPz/AQAAAPz/AQD8//7////+//7//v/6/wMA+v8FAPz/AwD+/wEA/v8AAAAA//8DAPv/BQD8/wIA/v/+/wQA/f8CAP//AAACAP7/AQD5/wIA/P8GAPz/BQD//wEAAAAAAPz/AAD+//f/BQD1/wMA+/8DAAAAAAADAPn/CgD7/wcA/f8BAAMA//8BAP///v8AAP7//v/+//3////7/wMA+P8HAPj/BgD7/wAAAAD7/wQA/P8FAPv/BAD7/wQA/v8DAP7/AwAAAAAAAAD///7/AQAAAP7/AwD7/wYA+v8EAPv/AwD9//z/AQD3/wMA+f/9//3/+/8AAPn//v/+//v/AgD6/wMA9/8HAPv/BQD9/wIA//8FAPz/CgD6/wYA/f8EAP7/AgD//wIA+P8HAPn/AwD3//3//f/9//v/AAD7/wIAAAD/////AQD//wQA//8AAP///f8DAP3////+//7/AAAAAP7///////z/AAD4///////5/wYA9/8AAAIA+v8GAPv/BgD+/wIA//8CAAAAAAABAP7/AQACAP///v8EAP7/AQAEAP//BQD//wMAAQACAAUA/v8BAAAA/v8AAP7/AQD+/wMA/v8CAAAABgD8/wsA/f8IAP7///8FAPv/AQAAAPv/BQD5/wIAAgD7/wUA/P8AAAMA+f8HAPT/CQD1/wYA+f8EAPr/BQD7/wQA+f8KAPb/CAD5/wYA+/8GAP3//v8FAPz/AQD+/wIA/P8GAPn/CAD5/wcA+v8EAP///f8IAPn/CQD7/wMAAQD9/wYA+/8DAPr/CQD2/wsA+f8CAAMA+f8JAPf/BwD9/wMA/P8FAPz/CAD7/wEA///9/wUA/v8GAPr/CAD7/wMA/v8CAAQA/P8FAP//BAABAAAA//8EAP//BgAAAAEAAAADAAQAAQACAAEAAwAEAAIABQACAAEAAwD//wMAAgD//wIAAgD+/wYA/f8FAPz/BAD/////BQD5/wUA/f8CAAEA/f8GAPn/CQD7/wMA//8BAAAAAwD9/wUA/P8FAP7//////wEA/v8DAPz/BgD7/wUA/P8AAAAA/v///wAA/v/8/wIA+v8FAPr///8CAP3/BQD9/wEABgD8/wcAAAABAAIAAwD9/wgA+/8DAAEA/P8AAAQA/f8DAAAA/P8HAP//AgAFAPj/BwD8/wMA//8AAAEAAQAAAAQAAAD+/wEA/v8BAP7//v8CAP7/BQD7/wQA+v8DAPr/BQD9/wIAAAACAPz/CQD7/wAABgD5/wkA/f8GAP//AAAHAPr/CQD9/wEABQD5/wkA+v8HAPr/AwD8/wAA/v8AAPz/AgD5/wYA+v8HAPr/BQD+/wMAAgD//wUA//8DAAIA//8JAPv/BgACAP7/CQD7/wQABAD9/wUAAwD+/wYA/f8EAAEAAAD+/wMAAAABAAAA+/8DAPv/AwD8/////v////7//v////3//P////7///8BAPz/AwD8/wEA+/8EAPv/BQD5/wgA+v8HAPz/BQD3/wkA9f8GAP3//v///wAA/P8BAPr/AAD7/wEAAQD+//3/AgD8/wMA/f/7/wYA+P8CAP///f/+/wAA//8BAAEAAwD7/wgA9/8NAPr/BgACAAAAAQABAAAA//8FAP3/AgAEAP3/BQD5/wkA9/8JAPv/AgD+/wMA///+/wIA//8CAAEA/f8CAAAAAAABAAAA//8AAP7/AwAAAAEABAD//wQABQD8/wkA/P8FAP7/AQD+/wcA+/8IAPz/AgACAPz/AgABAPz/BAABAAAABAD9/wEAAAD///3/BQD5/wQA/P8CAAEA/P8IAPz/BAD//wAAAgABAPz/BAD6/wQA/P8AAP////8BAP///v8AAAEA/P8FAPr/BwD8//7//f/6/wEA+f8CAPn/AwD5/wEAAgD9/wMA+/8EAPn/CQD3/wcA+f8DAAIA//////7//////wIA+/8DAPr/BAD///7/CAD4/wgAAAACAAMA/v8DAPr/AAAAAP7/BAD9//7/BQD7/wcA/P8EAAIAAwADAP//BgD9/wYA//8EAAIAAAAEAAIABQD9/wQA/v8BAAIAAAD7/wQA+v8EAP7/AgAAAAQA+P8LAPb/BwD7////AAD8/wMA/f8EAPv/AwD5/wIA/P8DAPz///8AAPz/AwD///7/AAD//wEA/v///wAAAQD+/wMA/v/+/wIAAgD+/wEA+/8EAP//AgD//wIA/////wUA/v8CAP//AQD+/wMA+P8FAPb/BwD3/wMA/f8BAPz/AgD/////BAD2/wIAAwD6/wsA9P8KAP//BQABAAAABQAAAAQA+/8BAAAA9/8EAPf/AgD5/wAA/P8DAPv//////wEA/v8CAP3//P8EAPn/BwD6/wMAAwD5/woA9/8LAPb/CgD7/wUAAAABAAAAAAAEAPr/CwD1/woA+/8CAAcA+/8IAPj/CwD7/wQA/f8BAPz/BAD5/wMA/f//////AgD8/wUA/P8CAAIAAwD9/wUA/P8DAPv/AgD8/wMA+/8DAP7/AAADAPv/CAD5/wUA/f8AAAMA/f8FAPb/DAD0/wkA+f8AAAEA+/8BAAEA/f8EAPv/AAD///7///8BAP3///8AAP//BAACAP7/AwD//wAAAgD9/wIAAQD5/wIA///+/wIA+v8EAP7/+/8GAPX/CgD4/wIAAgD+/wEA//8AAAQA/P8HAPf/CQD9/wMABQD+/woA+v8HAP3/AAAFAP3/BQACAP3/DAD6/wkA+v8DAAQAAAD//wQA+v8JAPX/CAD3/wQA+P8AAAEA/P8BAPv/AgD9/wQA/P8FAP////8BAAAAAgD6/wYA+P8DAAIA+f8HAPv/AAACAPn/CAD4/wcA/P/+/wQA+v8DAAEA/P8GAP7//f8FAP3/BQD4/wYA9/8HAPr/AQD5/wQA+/8DAP//+/8EAP3/BQD+/wMA/v8EAP//AwD9/wYAAgD+/wYA+v8FAPv/AQABAP7/AQAEAPz/AwD9/wQA/P8GAPj/BAD8/wEAAgD3/wcA+/8DAAAA/v8DAP3/BAD+/wAA/v/9//7/BAD7/wEAAwD9/wcA/P8EAAAAAgABAAIA/v8EAP3/AwD+/wQAAQACAAAAAAD//wEAAAABAPv/BAD3/wwA9v8FAAAA/P8FAP//AAAEAPn/AQD+/wEA/P8CAP3/AQD8/wAA+v8CAPv////6/wMA+/8CAAAA/////wIA//8DAAEAAAAAAAMA/P8EAAIA+/8JAPf/AgAAAPz/AQD8//7/AQAAAAEAAQD7/wcA/P8EAP7//f8EAPz/AwD9/wMA+/8GAPr/BQD9/wIAAAABAAIAAgAAAAQA/v8DAP//AgD8/wUA+/8FAAAAAgD//wAAAAABAAAA/f/+/wIA/P8DAPr/AAD8//z/AQD6/wIA/P8AAP3/AAAAAP7//v8CAP/////+/wAA//8CAPr/BgD7/wUA/P8CAP3/AQD9/wMAAAAAAP7/AQAAAP///v/+/wIA///9//7////8/wQA+f8CAPv/AAD9////AQD8/wQA+/8DAPz/BQD6/wYA///+/wQA/P8DAP7/+/8GAPv/BwD6////BAD7/wgA/f8BAAIA/f8DAAEAAAAFAP7/CQD9/wYA/v8CAAQA/f8AAAIA+/8GAPz/AgD//wIAAwD//wEA//8EAP//AQABAPv/BQD5/wQA+/////7//v/+/wAAAAD+/wIA/f8DAP//AAD9/wgA9P8KAPb/BAD8//3//f////z//v/+//n/AQD4////+v/9//v/+v8BAPr/AAD9//7////9/wAAAQD9/wQA+f8FAPz/AQADAP7/BQD9/wEAAQD//wYA/f8BAAIA/v8HAPz/CgD+/wMABAD//wYA/P8EAP3/AQD//wIAAAD//wIA//8HAP3/CQD4/wUAAQD+/wYA+P8GAP3/AAD9/wQA+/8GAPv/BAD7/wQA+f8GAPv//f8DAPr/BgD8/wUA/f8DAAEA/P8EAAAAAAAFAAAAAQAAAAQA/P8LAPf/CQD6/wEABAD+////AwD6/wMAAAD6/wkA+v8FAPv/AgAAAAAA//8AAP//AwACAPn/CQD8//7/BAD4/wMA///7/wMA/f///wAAAAACAP3///8AAAAAAAD////////8/////f/9/wAA+/////7/AQD///z//P8CAPr/BgD4/wcA+/8EAAEA//8DAAEA//8EAP3/AwADAAEA//8FAP3/AAAGAPz/DADz/wwA+/8DAAcA/v8IAPv/BgD+/wQAAAD+/wEA/v8DAP/////7/wQA+v8EAP3/AAAAAP//AwD7/wYA+f8GAP3/BgD9/wQA//8FAP//AgABAAIA/v8EAP3/BQD9/wIA//8DAAMAAQAAAAQA/f8FAAEA+/8DAP3/BAD+/wAABAD6/wkA+v8EAAAA/v8EAPf/DAD2/wkA+/8BAAIA//8CAAIA//8FAP7/AwD4/wgA+/8FAP///v8CAP3/BAD8/wAAAQD8/wIA/////wEA/f/9/wEA/v8AAP///v8AAP3///8AAP7/AgD8/wQA/f8CAAEA/f8HAPj/CAD7/wMAAQD//wQA//8AAAMA/P8FAP3/AAACAP3/AgD//wEA/P8FAPj/BwD7/wQA/f8BAAIAAAACAP///v8BAAEAAwD+/wEA///+/wUA+v8GAPv/BAABAAAABQD9/wQAAgD//wIAAwAAAAQAAQAEAAAAAwAAAAQAAQABAAAAAwD8/wIA/v///wMA/P8CAP7/AAD9/wIA/P8DAP3///8BAPz/AQD///z/AgD9////AQD+/wAA/f8GAPn/BgD6/wIA/////wAA///6/wMA+f8FAPv/AgD6/wEA/v/+/wEA///9/wEA+/8AAPz/AQD+/wAA/f8BAP7/AgD+/wQA+/8FAP3/AQAGAPj/CAD8/wIABAD7/woA+P8DAP7///8EAPv/BAD//wMAAAADAAEAAwACAAAABQD//wUA/f8FAP7//v8CAAEA//8DAP3/AQACAAAAAwD8/wMAAAABAAQA/f8BAAAA/v8DAP////8AAAEA/v8CAP3/AAABAP3/AQD///7/BAD9/wAAAAACAP//BQD9/wMA/v8EAPz/CAD4/wgA+v8GAPz/AwD+/wIA/v8BAP//AQD9/wEA/f8EAPz/AgD///v/BgD4/wcA+P8FAPv///8BAPv/AwD8/wEA/f////////8AAP/////+/////f8AAAIA+P8GAPj/BAD9/////v8AAP/////+/wAA/f8DAP3/AwD+/wAAAwD+/wIAAgD+/wYA/f8FAP//AwACAAAAAgD+/wIAAgAAAAEAAwD//wQA/v8DAAIA//8FAPz/BgAAAAIAAwACAAIABAADAAEABQD//wcAAQAEAAMAAgABAAYA/f8GAP3/BQD//wEAAgD+/wUA+/8GAPz/BQD4/wUA/P8GAPv/AAACAP//AAD///7/AwD8/wMA/f8CAP///f8DAPn/BQD9////AwD6/wMA/v///wAA/v8BAAAA/P8DAPz/AwD8//z/AwD3/wcA9/8FAPj/BAD9/wAAAQD//wIA//8CAP3/BgD7/wcA+/8KAPr/BwD9/wUA//8FAAAABAD//wEABAD+/wgA+v8IAP3/AwAAAAIABAD+/wMA//8CAPv/BgD7/wYA/P/9/wEA/P8EAPr/AwD6/wEA///8/wQA/P8BAAIA+/8FAAAA/P8FAPv/BAABAAAAAAAFAP3/BgD6/wgA+f8IAP7/AgAEAP//BAD9/wUAAAABAAMAAgD+/wgA/v8CAAMAAQD//wUAAAD//wgA+/8IAP7/BAD8/wgA9v8NAPb/CQD8/wQAAAACAP//BwD7/wgA/f8EAP//BQD5/wcA+/8EAP//AAD8/wUA/P8CAAAA//8BAP3/BAD9/wQA/f8BAAIA//8CAP///f8EAPz/BQD6/wQA/f8DAP////8DAPv/BAD8/wEA/v////7/AgD+/wAAAgAAAAMA//8DAP3/BgD8/wUA/v/+/wYA+v8EAPz/AAABAP7/AwD5/wgA+v8FAP7/AgD+////AQD//wMA/P8FAPj/BwD5/wUA//8CAP//BAD+/wMA/v8DAP//AgAAAP3/AgD8/wQA/v8BAAAAAQACAAAAAgD8/wMA/v8FAPz/BgD3/wsA9v8HAPn/AgD+/wEAAAD//wEA/v8DAPz/BwD6/wkA+/8FAAAAAQACAAIA/v8EAP//AQAAAAIAAAD//wEA/f8FAPz/AQADAP3/BQD6/wUA/f8DAP7/AQD//wEA//8DAPz/AwD8/wMA/v8CAP////8BAP7/AgD7/wcA+P8HAPv/AwD/////AgD//wQA/P8EAPv/BQD7/wIA/v8AAP3/AAAAAP7//v8AAAAA/v8BAPv///8DAPj/CgD3/wUA/f8DAP//AQD//wAA/f8EAPf/CAD5/wEA///8/wAAAQD9/wIAAAD//wEAAgD8/wQA/f///wIA/v8BAP//AAD///7/AgABAAIABAD7/wgA/f8FAPv/BQD8/wcA+/8GAPr/BgD4/wUA/P8BAAEA//8AAAEA/v8EAP7/AAABAP7/AgAAAP3/BQD7/wMA/v8AAAAAAgD//wAAAAABAP7/AwD7/wUA+/8DAP////8DAPv/AQD+/wAAAQD+/wEA/v8BAAAAAwD+/wQA//8CAP3/BQD7/wYA+/8EAPz/BQD9/wQA//8BAP//BAD8/wMAAAABAP//AQABAAEA///+/wAA/f8CAPr/AgD7/wIA/P8BAP7/AAD9///////9/wIA/P/9///////9/wQA9v8JAPn/AQD9/wIA/f////7//v/9/wIA+/8DAP3/AAD+/wEAAgABAAQAAAABAAMA/v8GAP3/BgD9/wQA/v8BAAIA/P8HAPr/CAD6/wQA//8BAAAAAgD//wIA/v8CAAIA/v8CAPz/AQD9/wAA/v8AAP7/AAD///7/AgD+/wQA/f8CAP7//v8CAPz/BgD4/wgA9f8JAPf/BQD7/wMA+v8EAPn/AgD///r/BQD7/wQA+v8KAPj/BwAAAAIAAwACAP//AwACAAAABAD//wIAAAACAP3/BAD8/wQA//8CAP//BAD8/wQA/f8EAPz/BgD5/wYA+/8DAAAA//8BAAAA/v8CAP3///8AAP//AQD///7/AQD9/wIA/P8BAAAA/v///wEA/f8AAP/////9/wEA///9/wIA+f8CAPv/AAD+/wEA/v8AAAIA/v8BAAAAAgADAAAAAwAAAAAABAD+/wUA/v8FAPz/CQD8/wYA/v8EAAMA/v8FAP3/AwABAAEABAD//wMA/f8DAP7/AQACAPr/BAD8/wIA/v8DAPr/BwD4/wQA/f8CAAAA/f8BAPz/BAD9/wEA/P8DAPz/AgD9//z/AwD4/wYA+/8AAAAA/v8AAAIA/v/9/wIA/P8BAAAAAAD//wAA//8AAAQA+v8FAP7///8DAP3/AwABAP//AQACAAEAAQAEAAIAAQAFAPz/CAD9/wQAAgABAAUAAQADAAAAAAACAAAAAAAGAPn/BgD9/wMAAAAFAP3/BgD9/wUA//8CAAEA/P8IAPf/CQD6/wIAAAD9/wQA+/8FAPn/BwD3/wUA+//+/wMA/f8AAAEA/P8CAPz/AQD8/wUA+v8DAP//+/8FAPf/AwD+//r/BQD6/wIA/f8DAP////8AAP//AwAAAP//AQAAAAEA///+/wQA9/8JAPn/AwD9/wEA/v8EAPv/AgABAP7/AwD+/wEAAAACAP7/AQD9/wAA/v8CAP3/AgD8/wMA/v8BAP//AAACAP7/AwD//wAAAQD+/wQAAAAAAAQA//8HAP7/BAD+/wMAAQACAAEAAQACAP//BgD5/wkA+/8EAPz/AwD9////AAAAAP3/AgD6/wUA+/8DAP7///8BAPv/AgD+/wIA/f///wMA+f8IAPr/AwD9/wEA/v////3/BAD6/wEA/f/9/wEA///8/wIA+////////f/9/wAA/f8AAAIA+v8DAPv//v8CAPr/AwD6/wAA///+//7/////////AAD///////8BAP7/AAD//wIAAAACAAAAAQABAAAAAQABAP//BAD9/wIAAgD9/wYA/P8EAPz///8GAPr/CAD5/wMA/v8CAAEA+/8IAPj/CQD6/wMA/f8BAP7///8BAP3/BAD//wEAAQAAAAMAAAADAP//AgABAP//BQD9/wIAAgD+/wIAAQD7/wYA+v8FAPr/AAABAP//AwD8//7/AwD6/wUA+f8CAP//AAD//wEA/f8BAPr/BQD5/wQA/P8BAP///f8CAP7/AAACAPv/AgD+/wAAAwD5/wYA+/8CAP///v8CAPz/AwD2/wcA9/8CAP3/+/8DAPr/AgD6/wIA+v8EAPv/AgAAAAEAAAAAAP7/AgD+/wUA+f8DAPz/AwD+/wEAAgD7/wcA+P8IAPj/AwD///3/BQD5/wYA+/8CAAAAAQD//wQA+/8DAP3/AAACAP//AQD8/wQA/P8EAP7/AgAAAAEAAQ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AAAP7/AAD+//3//v8BAPr/AwD6/wMA+/8DAPr/AwD9/wMA/P8CAP//AgAAAP7/BQD7/woA+f8DAP///v///wAA/f8AAAUA+f8IAPj/BQAAAP3/AQD9/wEA/f8BAP7///8AAP//AwD8/wUA+f8IAPz/BQD//wIABAD+/wQA/P8HAPj/CQD7/wYAAgABAP//BwD7/wgA/P8BAAIA/v8EAP////8CAPz/AAD+//7/AQD+//////8AAP3/AgD6/wIA/v/+//3/AQD7/wEA+v8HAPn/BgD4/wQA/P8DAPz/BQD7/wUA+P8IAPr/BQD6/wQA/P8DAPr/AwD///z/BAD7/wIA/P8AAPz/AwD9/wAAAQD6/wQA+v8DAP7/AAAAAAAA///+/wQA+/8DAP//AQACAP7//v8FAPz/CAD8/wEABAD+/wEABQD8/wYA//8FAAAABQABAP7/BwD8/wEABAD6/wcA/P8CAAEA/f8HAPn/BgD8/wQA/v/8/wcA9f8GAPr///8DAPf/CgDz/woA+f8AAAMA+/8GAP3/AQD8/wEAAAD9/wIA+v8DAP3/AAD9//7////8/wEA+v8AAP3//f/+//z////6/wMA+f8AAAAA/P///wIA/P8DAP7/AgD9/wQA/P8GAP3/CAD9/wUA/v8EAP//BgD8/wMA//8DAP3/BQD//wQA//8CAAIAAQD//wEA///+/wMA+v8IAPn/BgD9/wQA/v8GAPv/BQD8/wIAAAD8/wIA///9/wMA/P8EAP7/AwD6/wQA+v8EAP7////+////AgD//wEAAQD//wIA/v8AAAAAAAABAAEAAAADAP3/BQD9/wYA/f8HAPj/CAD9/wMA/P8DAPz/AgD///z/CAD4/wgA+/8BAAUA+P8HAP3/AAAAAAIA+v8GAPr/AAACAPr/AwD7/wEAAAD9/////v///wAA///+/wEAAAD9/wMA//8BAAAA+v8EAPz/AgD+//z/BQD5/wYA+v///wEA/f8AAAEA//8DAP7/BQD9/wQAAAACAAEAAgAAAAYA//8AAAUA/P8FAP7/AwACAP7/AgABAAAABwD7/wYA/f8FAP//AQACAP3/AQD+/wIAAgD+/wEA/v/+/wEA/f8AAAAAAAD7/wQA+f8EAPv///8DAP3/AgAAAAAAAgD/////AgD+/wUA+v8DAP3/AgD+/wMAAQABAAMAAQAAAAQAAAABAAEAAAAAAAQA/P8KAPn/CwD6/wcA//8CAAEA/f8EAP7/AQABAAAA//8CAP7/BAD//wIA//8DAPr/BgD7/wQA///+/wIA/v8DAP3/AwD9/wEA//8AAP7/AwD9/wEA/////wEA//8EAPv/BAD9/wMAAAD9/wIAAAACAP////8CAAAAAwD8/wQA+f8HAPn/BQD+/wEAAgD+/wAAAwD+/wUA+v8JAPj/CQD8/wIA//8BAP7/BAAAAAEAAwD7/wgA/P8HAPz/BwD7/woA+/8JAPv/BQD+/wEAAQABAAEA//8DAP3/AAACAP///f8FAPr/BAAAAAAAAAADAP3/AQAAAAAAAQD+/wEAAAABAAMAAAAAAAQA+v8EAP7/AAACAP3/AgD+/wIAAwD+/wMA+/8FAPv/BQD8/wQA+/8EAAAAAwD+/wAA/f8CAP7/AwAAAAIA/v8AAAIA//8AAAMA/P8GAPv/BgD7/wYA/////wIA+/8IAPn/AwD///3/BAD7/wYA+v8DAP3/AAACAAAAAQAAAAEAAAADAP7/BAD7/wcA9v8MAPT/CAD3/wMA+/8AAP3/AgD7/wMA+v8FAPz/AQD//wIA//8BAP7/AAADAPr/BwD7/wMA/////wEA//8BAP//AgD+/wMA/v8CAAEABAD//wEAAgABAAQAAAACAAEABAD+/wYA+v8DAAEA/f8EAP3/AgACAP//AwABAAAAAQADAAAA//8AAAIA//8AAP7//v8AAAAA/v///wEA+v8GAPr/BAD9/wAAAgAAAAMAAgD//wIAAAAAAAQA/v///wAA/f///wAA/P8CAP3/AAADAPz/BAD+////AgD7/wMAAAD//wEA/f8FAP3/BAD+/wMA/f8EAPv/AAABAPv/BAD5/wUA+f8GAPb/BgD8/wAA//8BAAAAAAABAP//AgD//wIA/f8EAP7/AAAAAAAAAgD7/wQA+v8EAP7/AgD9/wUA/f8DAP////8CAAEAAgAAAAAAAAD//wEAAgD9/wIA/v8CAP//AgABAAMAAAABAAMAAwAAAAMAAAACAAMAAAAAAAUA/v8CAP//BQD8/wUA/v///wQA/v8DAAAAAgD//wEAAgD//wUA/v8EAP3/AgABAAEA//8CAP3/BQD6/wMA+v8CAPz/AgD8//7/AQD+/wAA/////wIA/f8FAPr/AgD+//z/AwD5/wQA/f///wEA/P8CAPz/AgD8/wIA/f/7/wQA+f8CAP3//////wAA/v8DAAEAAAADAP7/AgABAP////8EAPz/BAD9/wEA//8DAP//BQD9/wQA//8GAP////8EAP//AgD//wMA/P8FAPv/BgD7/wQA/f8EAP//AgD8/wMA/P8EAPz/BAD9/wEABAD+/wMAAAABAAEAAAABAP7/AwD9/wQA/P8CAAAAAAD//wMA/v8CAP//AAD9/wYA+f8FAPv///8DAPv/AgD8/wAA//8BAPz/AwD8/wMA/f8DAP3/BwD8/wMAAgAAAAQAAgD//wIABAD7/wUA/P8AAP7/AgD9/wEA////////AQD7/wUA/P8BAP////8EAP3/BAD/////AgD//wMA+/8DAPr///8AAPv/AgD7/////v/+/wAA/v8BAPz/AwD8/wAA/v/9/wIA+/8DAP3/AAAAAP3/AQD//wQA+v8EAP3/AAACAP3/AgD9//7/AgD+/wAAAAD+/wEABAD7/wYA/P8BAAMA///9/wYA+P8JAPj/BQD///7/CAD6/wYAAAAAAAMA///+/wMAAAD//wQA+P8HAPz/AwD9/wAAAgD8/wQA+v8DAP3///////v/BAD5/wMA/P8BAAAA///9/wMA+v8EAP3//v8BAPz/AgD7/wMA+/8BAP7/AwD8/wUA+v8GAP3/AwABAP//AgACAP7/AgAAAP3/AgD8/wEA/////wAA/v///wAAAAABAAAAAQABAPz/AgABAAAAAAACAPv/BAD///3/BgD//wEAAQD+/wMA/P8BAAAA/f8EAP3//v8CAP7///8DAPz/AAD///7/AAAAAP3//f8BAPj/BAD4/wQA+P8CAP3///8AAAAA//8AAP7/AQD+/wAA///9/wUA+f8FAP3/AwAAAAEAAAADAP//AwD//wAAAQD+/wUA/P8DAP////8GAPv/AwD9/wEA/v8CAP7/AgD+////AQD//wIA/P8FAPj/CgD1/w0A9v8JAPz/AgABAAEAAQD//wIA/f8FAPn/BAD6/wEA///+//7//v8BAP3/AAD9/wAA/v8FAPn/BAD8/wIAAAABAP//AgD7/wIA/v8AAAMA+P8HAPv/AgAAAP3/AwD9/wEA//8AAP//AgABAAAAAgD//wIA//////7/AwD8/wIA/v///wEA+/8EAPj/BAD+//////////7/AAD9/wAA+v8CAP3//v8AAPz/AQD8///////7/wIA+/8CAPn/AgD5/wEA+/8EAPr/BAD9////BQD3/wYA+/8HAPr/BAD8/wEABAD6/wcA+/8AAAUA+f8HAPz/AgAAAAAAAgD9/wMA/P8CAP7/AAACAPv/BQD9////AgD8/wIA///8/wMA/P///wEA+v8FAPv/AQD///7/AwD+/wAAAgD8/wQA/f8BAAIA/v8BAP//AAABAPv/AwD8/wQAAAD6/wEA/P8CAP7///////7/AQD+//z/BAD5/wYA+v8FAPr/AwD//wAAAQD//wAAAQD8/wUA+v8HAP3/AAACAPv/BAD9/wEA+v8EAPn/BQD9/wAAAgD8/wAA//8CAP3/AwD8/wAA/v8AAPv/BAD6/wMA/P8BAP3////9/wEA/v8AAP3/AAD9/wAA+/8EAPz/AwD8/wQA/v8EAP//AQAEAP7/BQD7/wUA//8BAAEAAAABAP//BAD8/wAAAQD9/wEABQD3/wkA+v8EAAAABQD6/wcA+/8FAP3/BAD8/wIA/v8BAAIA+v8FAPz/BAD/////AgAAAAMA/f8DAP//AQD//wEA//8CAP7/AwD9/wQA/P8EAP//AgD//wMA/P8EAPz/BQD9/wEAAAD+/wEA//8AAP7/AgD8/wMA/v///wAAAQD9/wQA+/8BAPz/AgD9/wEA/f8BAAAAAwD+/wQA+/8GAPv/BQD8/wQAAAAAAAMA//8DAP7/AgAAAAMA//8DAP//AgD9/wQA/v8CAP//AAAEAP//AwD//wQA//8EAAAAAwAEAAAAAgD//wMAAAAEAAEAAQD//wIA/v8FAP7/AgABAAEAAQABAAIA//8BAAEAAQABAP7//f8EAPz/BAD9/wAA/v8DAPr/BQD6/wUA/f8DAP3/BAD8////AgD6/wUA/f8AAP//AgD6/wgA+v8FAP3/AgABAAAABgD8/wgA/f8GAAAAAgADAAIAAwAAAAQA/v8FAAEABQAAAAQA//8GAP7/BQD+/wQAAgD//wYA/v8DAAEABQD9/wcA+f8JAPr/BgD+/wIAAQD7/wgA9/8IAPj/BwD8/wQA+f8FAPn/CQD6/wYA+v8HAPr/BwD4/wYA9/8HAPj/BQD7/wAAAAADAPv/BwD9/wUAAQABAAQAAAAAAAIA//8DAAEA/v8FAAAABAAAAAMAAQAAAAUA/P8IAPr/CQD5/wgA//8BAAQA/v8HAP//CQD6/wkA/f8DAP7/AAABAP3/AgD9/wEA/f8CAP//AAAAAP7/AQD8/wkA9/8IAPj/BAAAAPz/BAD3/wYA+/8CAAAA/f8AAAMA/f8DAP3/BAD8/wUA//8CAAUAAQADAAMAAwAEAAAABwD9/wcAAgABAAgA//8IAPv/CQD+/wIABwD8/wcA//8BAAYA/P8HAPz/BgD//wEA//8DAP//AwD+/wMA//8AAAAA/P8HAPj/BQD7/wIAAwD7/wYA+P8HAPz/BAD//wMA/v8EAPz/AwD9/wIAAAAAAP//AQD7/wUA+v8EAPn/BQD7/wAAAQD7/wQA/f8DAP3/AgD+/wIA/f8BAP3/AwD8/wUA+/8FAP7/AAAEAP3/AwD9/wUA/P8EAAAAAAACAAAAAgACAAIAAgACAAIA//8BAAMA+v8IAPz//v8BAPv/AgABAPv//v/+/wAA//////z/AwD7/wEA/v/8/wYA/P8CAP7/AQAEAPz/BwD7/wQAAAD+/wcA+/8HAP7//f/7/wAA+f8FAPv/AQACAPz/AwD9/wIA+/8BAPr/AgD5/wIA/f8DAP//AQACAP7/BAD//wIAAQACAAIA/f8GAPn/CgD3/wkA9v8JAPv/AwD+/wMA//8FAPz/BgAAAAEAAwD//wQA//8FAP3/AgADAPz/CAD//wEAAgADAP3/CQD3/wkA+P8HAPv/BAD+/wEAAAD+/wIA/P8BAP//+/8CAPj/AQD7/wAA///+//z/BAD7/wAAAAD7/wUA+////wIAAAABAAAA//8DAP//BQD///7/BQD6/wUA+v8GAPr/AwD9/wAAAQD8//////////7/AAD9/wUA/f8EAP7/AAABAAEA/v8DAPz/AwD+/wAA/f8EAPr/BwD5/wAA///+/////P/7//3//v/+//z/AgD3/wcA+f8FAP3/AgAAAAEAAAAAAAIAAAACAP3/BAD9/wUA/P8CAAEA/v8HAPr/CAD9/wUAAQADAAEABAD8/wgA/P8CAP////8CAAUA/P8IAPz/CAAAAAcAAgADAAQA/P8GAPv/AwD9/wEA///+/wEA/v///wQA/P8GAPr/BQD9////AQD9/wAA//8AAP7/AAD+/wAA/P8AAAAA/f8DAP3/AwD//wAAAQD7/wcA9v8FAP3//f8BAP///f8DAPz/AAAFAPj/CAD6/wQA//8AAAEAAQD9/wYA+/8FAPn/CgD3/woA+v8EAP////8AAAAAAAACAP3/AgD//wEAAgD//wEA/f8CAAEA/v8IAPn/BgD9/wAAAwABAAIA/f8DAAAAAgADAP3/AwD//wEAAQABAP//AAADAAAAAgACAAAAAgACAAIAAwABAAAAAQABAP7/AQD+/wEA//8DAPz/BAD9/wMA//8CAP7/AQD//wEA/f8EAPz/BAD+/wEAAQABAP7/AgD+/wEAAQD//wMA/f8AAAAAAQD+/wMA/v///wUA/v8EAAAAAQD+/wUA/P8CAP//AAD+///////+//7////+/wMA//8CAP//AwAAAAIAAwD+/wQAAAD//wYA+/8CAAEA/f8CAAIA/v8BAAIA/P8FAP//AQAAAP3/AQD7/wgA9f8JAPb/BgD6/wgA9/8FAPr/AQD+/wAA+f8EAPr/BAD8/////P//////AQD///7/BgD5/wcA/P8DAP//AgD+/wIAAAADAP3/AgAAAAIAAAACAP3/BQD8/wUA/f8EAPz/AwD8/wIAAAD//wAAAgD7/wYA+/8DAAAA/v8CAP7/AgD8/wUA/v8DAP3/AwABAAMAAQADAP7/CAD7/wcA//8DAAEAAQABAAIAAQABAAEA/v8CAP3/BAD8/wEA/P8AAP7/AwD5/wUA+f8FAPr/BAD7/wIA+/8AAP7/AQD9/wEA/v8BAPz/AwD7/wUA/f8BAAIAAgD8/wcA+P8FAPz//v8CAP3/AAD9/wAA/P////3/+v/+//3//f8CAPr/AAD///7/AAD9/wQA+P8FAPz/AAD///3/AwD+/wEA/v8DAP3/BAD+/wEAAgD/////AwD+/wIA/P8FAPv/BQD+/wEAAgD//wIA//8CAAAAAAD+/wMA+/8CAP3/AQACAPz/BgD5/wgA+v8CAAIA/f8EAPz/BQD8/wUABAD8/wkA/P8EAAIAAQABAAAAAwD8/wYA/v8CAAMA/f8DAP7/AQAAAAEA/v8FAP7/AwAAAP///v8CAPn/AgAAAPr/BQD6/wIA/v8BAP//BAD8/wQA/v8DAAAA/v8CAP7/AAD///z/AAD//wEA+v8DAPz/AAD///z/AAABAPn/AgD3/wEA+/8AAPz/AwD6/wIA/P8BAP///v8EAPf/CQD3/wUA+f8EAP//AQD8/wUA+f8FAP3/AgD+/wEA//8CAP3/BgD7/wUAAgD//wIAAQD+/wAA//8AAAAAAQD+////AAAAAAEAAQD//wUA+/8KAPj/DAD4/wgA//8CAAMA/v8EAAAABAD9/wUA/P8CAP3/BgD3/wYA+v8EAAAA//8BAAIA+/8DAP3///8CAPr/AgD8/wAAAgD7/wUA+f8CAP//AAD//wMA+/8EAP7/AQAEAPv/BQD7/wcA+/8DAP3/BAAAAAMA/v8BAP//AwD8/wAA///+/wEA/v8CAPz/BAD4/wgA+f8GAPj/BQD6/wEA+v8BAPv////8/////f8CAPz/AQAAAAAAAQD8/wIA/v8CAP//AQAAAAMAAwD9/wcA+/8IAP7/AAAEAPn/AAAAAPz/AQD+//z/BQD8/wEA/f8BAP7/AwD6/wQA+v8CAP//AAD//wIAAQD9/wUA/f8DAAIA/v8IAPv/BQABAP//BQABAPz/CQD5/wgA/f8AAAUA/f8EAPv/AwD9/wIA/P8BAPv/AAD///z/AgD8/wUA+f8FAPr/BgD7/wgA+f8GAPv/BQD8/wAA///9/wEA/v8BAAAAAAABAP7/BgD6/wUA/f///wMA/P8HAPj/CAD4/wUA+v8DAPv/AAD+//7/AgD//////v8AAP//AQD//wIA/f8BAAMA/v8GAPv/AwAAAAEAAQD//wEAAgD/////AgD9/wQA+/8FAPz/AgD+//7/AQAAAAAA/f8EAPr/AgD+/wAAAQADAPn/BgD7/wYA/f8GAP7/BAAAAP//AgD8/wYA/P8HAPv/BgD//wYA/P8CAAIAAQD//wUA+/8GAPv/BAD8/wQA9v8LAPb/BwD4/wMA/v8CAP7/AgAAAAIA//8AAAQA+/8EAP7/AAADAP7/AQAAAAEA/P8DAPv/AQACAPz/BgD3/wUA/P8CAAIA/P8GAPz/AwD+/wIA//8AAP///f8DAPr/BAD5/wYA+P8JAPb/BwD8/wEAAgD+/wUA/f8FAPz/BgD7/wgA/f8AAAQA+v8GAPj/BQD+/wEA/P8GAPz/AgABAAAAAQADAP7/AgAAAAAAAwD5/wgA+v8EAAEA/f8EAP//AAABAAIA+/8CAPv/AQD///3/BAD8/wQA/P8EAP7/AQACAP//AAADAPr/BwD8/wQAAgD+/wMAAAAAAAIAAAD///7/AQD8/wUA/f8AAAIA/P8EAAEA/v8EAPr/BQD5/wgA+P8GAPv/AgD9/wEA/v8CAP7//f8CAP//AgD//wIAAAACAAEAAQACAAEAAAABAAEA/v8CAAAA/f8CAP7//f8CAAAA/f8EAPj/CAD4/wgA/P///wQA/v8CAAIA/v8DAAEAAAABAAEAAAAAAAAAAAAAAAEA//8AAAEA//8CAP3/AgAAAP7/AgD+////AAD+/wAA//////7//////////f8BAPz/AAD8/wEA/v/8/wAA/v8BAP//AAD9/wIA/f8FAPz/AwAAAAEAAgD+/wQA/f8GAPz/AwAAAP//AwAAAP3/BAD8/wMA//8CAP7/AgD+/wAA/f8DAP3/BgD4/wIA/P8DAP///v8BAPz/BgD4/wcA+P8IAPr/BQD+/wIAAQAAAAAAAgD+/wYA+v8HAPf/BAD9/wEAAAAAAP3/AgD9/wIAAAD//wAA/v8CAP7/AgAAAP7/BQD8/wQA/P8AAAQA+f8HAPj/BQD6/wYA+v8DAAEA//8CAP//AAAEAP7/AwAAAAAABAD/////AgD7/wIAAAD8/wMAAAD7/wgA9f8JAPz/AwD//wIA//8BAAEA/f8AAP7/AQD//wAA/v8AAP7/AQD///3/AAD9//7/AQD9/wEA///9/wMA/f8BAP3/BAD5/wYA+f8GAPr/AQD9/wEA/f8DAPj/BwD3/wkA9/8IAPn/BgAAAP//BgD6/wgA/P8FAP3/BAD9/wQA+/8FAP//AQAAAP//AQACAAEAAAADAPz/AwAAAAEAAgABAP7/AgD9/wMA//8AAAEA/v8DAP//AAD//wEA/P8EAPv/AwD//wAAAQABAAIA/v8DAP3/BgD8/wYA/P8DAP7/AgAAAP//AQD+/wAAAAD//wIA/v8CAPv/BgD7/wQA////////AQD+/wAAAwD7/wMAAAABAAEA//8GAPn/CQD5/wEAAQABAP3/BQD7/wUA//8EAPz/BQD7/wMA/P8DAP3///8AAPz/AAD+//3/BAD4/wUA/P8CAP///P8DAP3/AgD///////8EAPz/AwD8/wMA/v8BAP3/AQD9/wQA+/8EAP7//v8EAP7/AgD//wEAAgD8/wcA/f8FAP7/AQAAAAEAAAAAAAIA/f8DAPr/BgD3/wgA+/8BAAIAAAD//wMAAAAAAAYA+f8KAP3/BAABAP//AgADAP7/AwAAAAEA/v8GAPz/BwD5/wYA/v8DAAEA/v8BAP3/BAD5/wkA9P8HAPn/AwD///7/AAD9////AAD8/wQA+P8HAPf/CgD3/wYA/f8CAP//AQD//wEAAwD9/wMA///9/wQA/f8FAP3/AQAAAP3/BAD7/wIAAAD9/wMA//8AAAIA+v8EAPv/AgABAPn/BgD2/wcA9/8EAPz/AgD//wAA/v8CAP//AAABAPz/BAD//wEAAAACAAEAAgD+/wMA/v8DAP7/AAAAAAIA/P8DAP3/AAABAP7/AQD+/wMA/f8EAP3/BAD+/wQA+v8EAP3/BgD8/wMA+/8FAP3/AQD//wEAAAADAAAABAAAAP//BgD9/wUA/v8FAAAAAwAEAP3/BwD8/wkA/P8FAP//AAABAP7/AQD+/wEAAQD8/wMA/P8BAP7/AQD+/wAAAAD8/wMA+/8DAPv/AgD8/wEA//8BAP3/AAACAP7/AQD+/wAAAAD9/wMA+////////P8AAAMA+P8EAPn/BAD7/wEAAAD7/wQA+f8BAP3//v8AAP///f8EAPn/BgD7/wUA/f8BAAEA//8EAP3/AwD//wMAAAD//wcA+f8FAPv/AgD//wEAAAD//wYA+v8LAPv/BwD//wMAAgACAAIAAgAAAAIA+f8JAPn/BgD8/wMA/v8EAPz/BQD9/wEAAgAAAAQA/f8CAP3/AwAAAP7/AgD+/wAAAQD///////8AAP7/AgD8/wMA/P8FAPr/BgD8/wUA//8BAAEAAQD//wQA/f8EAPz/BgD8/wIA//8AAAIA///+/wIA/v8AAP7/AAABAAAA///+////AQD9/wIA/P8DAPv/AAD+//7/AgD7/wIA+/8BAP7/AAD+/wAA/v///wAA+/8DAPz/AAD+/wAA/f8CAPz/AAAAAP///v/9/wMA+/8EAPz/AgABAP3/BgD4/woA+f8HAP//AQACAAEAAQAEAP//AAABAAAAAgACAP3/BwD8/wcA+/8FAAAAAQAEAPz/BwD+/wMABAD//wYAAAAFAAAABgD+/wcAAgABAAYAAAACAAUA/v8FAP//BAD+/wEAAgAAAAIA//8CAP3/BgD3/wYA/P8DAP7///8CAP////8BAP3/AQAAAP//AQAAAP3/AgD9////AAD/////AwD7/wEA//////7/AgD9/wMA/P8AAAEA/f8BAPr/AwD5/wQA+f8CAPv/AwD9////AgD//wAAAwD9/wIAAgD//wEABAD+/wYA/v8DAAEAAwAAAAcA/f8EAP//AwACAAMA//8DAAEAAAAEAP7/BwD7/wgA+v8EAP7///8EAP3/AAAAAPv/AwD8////AQD+//z/AgD5/wcA+v8EAP///v8BAAIA/P8HAPn/BgD9/wMAAQADAAAA//8EAP7/AgACAP//BgD//wIAAwD8/woA+P8JAPz/BwD9/wUAAgAAAAQA//8AAAcA+v8JAPz/BgAAAAIAAgD9/wgA9/8KAPn/BwD+/wMA/v8FAP3/BwD9/wMABAD8/wcA/P8DAP//AQAAAAEA///+/wMA/v8CAP7/AgD7/wYA/P8EAP3/AgD//wMA/f8DAP7/AAACAPv/BAD8/wIAAgD8/wUA+v8HAPf/CAD5/wQA+v8DAPr/BQD7/wUA+/8HAPz/BAABAP3/BwD7/wQAAQD7/wkA9/8HAPn/AwD+/wEA///+/wEAAwD+/wAAAgD7/wYA+v8FAP3/AQD//wAA/v8CAP7/AwD//wIAAQABAAMA+v8IAPv/BAACAPn/BQD6/wUA/f8CAAAAAgD//wUA+v8FAP3/AQAEAPz/BQD6/wYA+/8CAP7///8AAP7/AgD+/wIA//8AAAAAAQACAP//BQD+/wQA/v8DAAIA//8DAP//AAACAP//BQD7/wIA//8AAAIA//8CAP//AQAAAP7/BQD5/wgA+/8CAP3/AwAAAP//AQD8/wUA+v8HAPr/AwD+/wAAAAD+/wQA+v8GAPz/AQADAPr/BgD8/wcA+/8BAAEA/v8CAP7//v8CAPv/BAD6/wQA+f8EAP7//v8AAPv/AwD9////AAABAP3/BQD8/wQA/P8BAAEA/P8EAPn/AwD9///////9/wIA/P8DAPv/BQD9/wMA/////wIA/f8AAAAA//8CAP7/AAD+////AgABAAQA/f8EAAEAAwABAP3/AwD9/wUA//8CAP7/AgD5/wcA+v8CAAEA/v8CAAAAAAABAAEA/f8DAP//AQD/////AwD9/wEAAAD+/wEAAgAAAAAAAAD//wEAAQD9/wMA/P8CAAAAAAD//////////wEA/f8CAP7/AwD8/wIAAQABAAQA/P8FAPr/CQD3/wkA9/8IAPv/BAD+/wMA/v8FAPr/BwD8/wIAAAACAPv/BwD8/wUA+/8CAPr/BQD6/wEA/P///////v8AAP///v8AAP3///////7/AAD8//7/AAD+/wAA/P8CAP///v8AAAAA+/8EAPf/BwD3/wQA+/8BAP////8AAP3/BQAAAAQAAAABAAIAAAAEAP//AwACAP3/BQD7/wUA/v8CAAAAAgD+/wQA/P8GAPv/BgD7/wUA/P8FAP////8CAPz/AwD7/wEA///9/wIA/f///wIA/P8EAP7/AwD6/wYA+P8GAPv/BAD7/wMA/f///wEA/v///wAA/v////3/AQD9//3/BAD6/wQA/v8CAP//BQD//wQAAgD//wYA/P8FAAAABAD//wIA//8DAP7/AQD//wMA//8DAP7/BAD+/wAAAwD8/wYA/P8BAAEA+/8JAPT/DAD3/wYA/P8AAP////8AAP7/AwD+//3/BQD2/wsA9f8GAPv/AgD9/wEAAQD4/wYA+f8CAP///v////7////9//3////+/wIA/f8CAP7/AQD+/wYA/P8IAPr/CAD//wEAAwD9/wcA/P8GAP7/BAABAAMAAAACAAMA//8EAAEA/v8DAAEABAAAAAIA/v8AAAMA/v8CAP3///8CAPz/BQD7/wEAAQD9/wEA/////wMA+/8DAPr/BgD7/wMA/f///wAA///+//7/AAD7/wIA///9/wIA+/8FAPz/AgD8/wIA/P8AAAIA/P8GAPj/BQD9/wMA/f8CAP//AgD9/wMA/f8FAP//AAACAP//BgD9/wkA/P8GAP7/BQD//wUA/v8GAAEAAgAEAP//AQACAP7/BAD+/wMA/f8EAP//AQADAAAAAwAAAAMA//8CAAIA/P8GAPn/CAD4/wcA+v8CAAEA/f8CAP7/AAD+/wEA/f////7/AAABAAAA/f8AAP7//////wEA/f8BAAAA/P8CAPv///////z/AgD9////AAD//wMA+/8DAP//AAAFAPv/AgACAP3/AgAAAP7//v8CAP3/AwD8////AgAAAP////8BAAEA//8BAP//AAAEAPz/AQD///v/BQD6/wQA///8/wMA/f8CAAAA//8CAP3/BgD7/wUA+/8GAPz/BgD8/wUAAwAAAAUA/v8DAP//BAD//wQA//8DAP7/BwD4/woA+f8HAPn/BgD4/wcA+P8GAPr/AQD///7/AwD9////AwD7/wEA/v///wIA///8/wMA/f8CAAAA/f8BAAAA/v////3/BAD4/wcA9f8FAPz///8BAPr/AwD5/wUA9/8CAP3//v8BAP///f8DAPn/AQD9///////+//7//v8BAPn/BQD7/wEA/////wAA////////AQD9/wQAAAD//wUA+v8IAPz/BAD+/wEAAgABAP3/BgD8/wMAAgD8/wQA+v8IAPj/CQD5/wQA/P8EAP3/AQACAP//AAABAP////8CAPz//v8EAPr/BwD+/wEA//8FAP3/BwD6/wkA+v8HAPz/AwACAP//AwD/////AwD9/wEAAQD8/wIA/f8BAP//AwD7/wMA+/8AAAIA+/8DAP3/AAAAAAEA/v///////f8CAPv/AwD+//////8AAP7/AgD///7/AAD/////AwD7/wMA/v/+/wEAAAD9/wQA+f/+/wEA/f/9/wEA+P8DAPv/AgD6/wMA+P8DAP3/AgD//wMA/f8BAAEA/v8CAAAA/P8EAPv/BAD+//7/BAD9/wMA/v///wAAAAD+////AwD6/wYA+v8CAAIA//8AAAIA/f8CAP7/AAABAP//AAAAAAEA/v8AAAIA//8FAPz/AQAFAPz/BQAAAP7/BwD5/wkA/f8DAAEAAgABAP3/AwD8/wMA/v///wMA+/8DAP3/AwD//wAAAQABAAAAAQD//wAAAgAAAAAAAQD//wIAAAADAPr/BgD7/wIAAAD9/wMA/P8DAP3/BAD8////AwD8/wQA/P///wAAAAD+//z/AQD4/wUA+//9/wUA9v8JAPf/CAD4/wYA/v8BAAIA/P8CAP7/AgD+/wEA//////7////+/wAA/P8FAPf/BwD4/wQA/f8AAP7/AgD//wEA/v/9/wMA/v8FAPr/AwD8/wEA/v8CAP3///8CAPr/BgD8/wQA/////wUA/v8GAPv/BAADAPv/CQD5/wYA//8AAAEAAAD//wAAAAAAAP7/BAD9////BgD5/wkA/P8CAAIAAQABAP////8AAAAAAAD//////v8CAP3/AgD+//7/AgD9/wQA/v8DAAQA//8CAP//AwAAAAMAAwD+/wYA/P8GAP3/BQD+//7/BgD7/wYA/f8DAP3/BAD/////BgD6/wYA/f8AAAAA/v8BAPz/AAD3/wQA+////////f8AAP//+/8EAP3/AAADAPz/BQD9/wQA/f8EAP7/AAADAP7/AQAAAP//AAADAPr/BAD4/wEA/v/9/wAA/v////7/AgD9/wIAAgD7/wgA+v8IAPz/AQADAP7/AQACAPz/AwD+/wYA+v8JAPj/BgD//wMAAAAEAAEAAwACAAUAAAACAAEA/v8HAPv/AwD+//3/AQD/////AwD6/wUA+v8FAP7/AQADAP//BQD//wEAAwAAAAMAAAADAAAAAgACAPz/BAD//wAAAgD9/wMA/v8CAAUA/P8GAP7/AQD//wIA//8BAAIA/P8EAP3/BQD8/wAAAQD8/wMA/P///wUA/P8EAP7/AwAAAAIA//8DAAIAAwD//wQA/v8EAP7/AwD9/wUA/v8GAP//AAADAAEAAQACAP//AwABAP//AAADAPz/AgD9/wIA/f8BAAAA/f8EAPv/BQD8/wQA/v8AAAUA+/8FAPz/AgD//wEAAgD9/wYA+v8FAP3/AgABAPz/AwD9/wAA///+/wIA/v/+/wEA/v8EAPz/AwD7/wIA//8AAAIAAQAAAP7/BAD9/wUA/v8CAAAAAgD//wMA/f8EAP7/BAABAP//AwD//wIAAwACAAIAAQAEAP7/BQD///7/BgD7/wUA/P8DAP//AgD9/wIAAAAAAAEA//8DAP//AAACAP//BQD8/wQAAgD+/wMA/v8BAP//AwD+//7/AAD+/wAA/v8AAAAA/v8CAP////8EAPv/BgD8/wUA+v8HAPz/AwAAAP//AwD//wAABAD8/wQA/P8HAPv/BwD+/wEAAwABAAMAAgACAAMAAwAAAAUA/v8HAP3/AwABAAQA//8FAP3/AwD9/wEA//8BAP7/AQAAAP///////wIA/P8BAP///v8DAP3/AQD9/wIA/f8CAPn/BQD6/wUA/P8BAP7///8CAPz/BQD6/wUA//8CAP//BQD7/wYA+v8DAPz/AQD+/////v///////v8BAP3/BAD7/wQA/P8FAPz/BQD8/wcA+/8IAPr/CAD8/wEAAwD//wUA/v8FAPv/BwD7/wUA//8FAPv/CwD2/w0A+f8GAAAA/v8FAPz/BQD6/wYA+v8HAPr/AwD5/wYA9/8FAPn/AgD9//7/BAD6/wUA/P8EAPz/BAD6/wYA+P8GAPr/AwABAP7/BAD9/wEAAAD9/wYA+f8GAPz/AgD//wIA/v8BAAAAAQD//wIA/v8AAAAA///9/wMA+f8FAPv/AgD+////AQD+/wMA+/8EAP3/BAD8/wIA/v8AAAIA+v8CAP3/AQD//////P8BAAEA/P8CAP3/AAAAAAAA/f8BAPz/AQD+/wAA/v/9/wAA/P8BAAAA/P8BAPz//v////7//v/+//r/AwD6/wUA/P8DAP7/AQD+/wAAAAD//wMA+/8FAPz/AgD+//7/BAD9/wIA//8AAAIA/v8BAPn/AgD8/wYA/P8FAP//AQAAAAAA/P8AAP7/9/8FAPX/AwD7/wMAAAAAAAMA+f8KAPv/BwD9/wEAAwD//wEA///+/wAA/v/+//7//f////v/AwD4/wcA+P8GAPv/AAAAAPv/BAD8/wUA+/8EAPv/BAD+/wMA/v8DAAAAAAAAAP///v8BAAAA/v8DAPv/BgD6/wQA+/8DAP3//P8BAPf/AwD5//3//f/7/wAA+f/+//7/+/8CAPr/AwD3/wcA+/8FAP3/AgD//wUA/P8KAPr/BgD9/wQA/v8CAP//AgD4/wcA+f8DAPf//f/9//3/+/8AAPv/AgAAAP////8BAP//BAD//wAA///9/wMA/f////7//v8AAAAA/v///////P8AAPj///////n/BgD3/wAAAgD6/wYA+/8GAP7/AgD//wIAAAAAAAEA/v8BAAIA///+/wQA/v8BAAQA//8FAP//AwABAAIABQD+/wEAAAD+/wAA/v8BAP7/AwD+/wIAAAAGAPz/CwD9/wgA/v///wUA+/8BAAAA+/8FAPn/AgACAPv/BQD8/wAAAwD5/wcA9P8JAPX/BgD5/wQA+v8FAPv/BAD5/woA9v8IAPn/BgD7/wYA/f/+/wUA/P8BAP7/AgD8/wYA+f8IAPn/BwD6/wQA///9/wgA+f8JAPv/AwABAP3/BgD7/wMA+v8JAPb/CwD5/wIAAwD5/wkA9/8HAP3/AwD8/wUA/P8IAPv/AQD///3/BQD+/wYA+v8IAPv/AwD+/wIABAD8/wUA//8EAAEAAAD//wQA//8GAAAAAQAAAAMABAABAAIAAQADAAQAAgAFAAIAAQADAP//AwACAP//AgACAP7/BgD9/wUA/P8EAP////8FAPn/BQD9/wIAAQD9/wYA+f8JAPv/AwD//wEAAAADAP3/BQD8/wUA/v//////AQD+/wMA/P8GAPv/BQD8/wAAAAD+////AAD+//z/AgD6/wUA+v///wIA/f8FAP3/AQAGAPz/BwAAAAEAAgADAP3/CAD7/wMAAQD8/wAABAD9/wMAAAD8/wcA//8CAAUA+P8HAPz/AwD//wAAAQABAAAABAAAAP7/AQD+/wEA/v/+/wIA/v8FAPv/BAD6/wMA+v8FAP3/AgAAAAIA/P8JAPv/AAAGAPn/CQD9/wYA//8AAAcA+v8JAP3/AQAFAPn/CQD6/wcA+v8DAPz/AAD+/wAA/P8CAPn/BgD6/wcA+v8FAP7/AwACAP//BQD//wMAAgD//wkA+/8GAAIA/v8JAPv/BAAEAP3/BQADAP7/BgD9/wQAAQAAAP7/AwAAAAEAAAD7/wMA+/8DAPz////+/////v/+/////f/8/////v///wEA/P8DAPz/AQD7/wQA+/8FAPn/CAD6/wcA/P8FAPf/CQD1/wYA/f/+////AAD8/wEA+v8AAPv/AQABAP7//f8CAPz/AwD9//v/BgD4/wIA///9//7/AAD//wEAAQADAPv/CAD3/w0A+v8GAAIAAAABAAEAAAD//wUA/f8CAAQA/f8FAPn/CQD3/wkA+/8CAP7/AwD///7/AgD//wIAAQD9/wIAAAAAAAEAAAD//wAA/v8DAAAAAQAEAP//BAAFAPz/CQD8/wUA/v8BAP7/BwD7/wgA/P8CAAIA/P8CAAEA/P8EAAEAAAAEAP3/AQAAAP///f8FAPn/BAD8/wIAAQD8/wgA/P8EAP//AAACAAEA/P8EAPr/BAD8/wAA/////wEA///+/wAAAQD8/wUA+v8HAPz//v/9//r/AQD5/wIA+f8DAPn/AQACAP3/AwD7/wQA+f8JAPf/BwD5/wMAAgD//////v//////AgD7/wMA+v8EAP///v8IAPj/CAAAAAIAAwD+/wMA+v8AAAAA/v8EAP3//v8FAPv/BwD8/wQAAgADAAMA//8GAP3/BgD//wQAAgAAAAQAAgAFAP3/BAD+/wEAAgAAAPv/BAD6/wQA/v8CAAAABAD4/wsA9v8HAPv///8AAPz/AwD9/wQA+/8DAPn/AgD8/wMA/P///wAA/P8DAP///v8AAP//AQD+////AAABAP7/AwD+//7/AgACAP7/AQD7/wQA//8CAP//AgD/////BQD+/wIA//8BAP7/AwD4/wUA9v8HAPf/AwD9/wEA/P8CAP////8EAPb/AgADAPr/CwD0/woA//8FAAEAAAAFAAAABAD7/wEAAAD3/wQA9/8CAPn/AAD8/wMA+///////AQD+/wIA/f/8/wQA+f8HAPr/AwADAPn/CgD3/wsA9v8KAPv/BQAAAAEAAAAAAAQA+v8LAPX/CgD7/wIABwD7/wgA+P8LAPv/BAD9/wEA/P8EAPn/AwD9//////8CAPz/BQD8/wIAAgADAP3/BQD8/wMA+/8CAPz/AwD7/wMA/v8AAAMA+/8IAPn/BQD9/wAAAwD9/wUA9v8MAPT/CQD5/wAAAQD7/wEAAQD9/wQA+/8AAP///v///wEA/f///wAA//8EAAIA/v8DAP//AAACAP3/AgABAPn/AgD///7/AgD6/wQA/v/7/wYA9f8KAPj/AgACAP7/AQD//wAABAD8/wcA9/8JAP3/AwAFAP7/CgD6/wcA/f8AAAUA/f8FAAIA/f8MAPr/CQD6/wMABAAAAP//BAD6/wkA9f8IAPf/BAD4/wAAAQD8/wEA+/8CAP3/BAD8/wUA/////wEAAAACAPr/BgD4/wMAAgD5/wcA+/8AAAIA+f8IAPj/BwD8//7/BAD6/wMAAQD8/wYA/v/9/wUA/f8FAPj/BgD3/wcA+v8BAPn/BAD7/wMA///7/wQA/f8FAP7/AwD+/wQA//8DAP3/BgACAP7/BgD6/wUA+/8BAAEA/v8BAAQA/P8DAP3/BAD8/wYA+P8EAPz/AQACAPf/BwD7/wMAAAD+/wMA/f8EAP7/AAD+//3//v8EAPv/AQADAP3/BwD8/wQAAAACAAEAAgD+/wQA/f8DAP7/BAABAAIAAAAAAP//AQAAAAEA+/8EAPf/DAD2/wUAAAD8/wUA//8AAAQA+f8BAP7/AQD8/wIA/f8BAPz/AAD6/wIA+/////r/AwD7/wIAAAD/////AgD//wMAAQAAAAAAAwD8/wQAAgD7/wkA9/8CAAAA/P8BAPz//v8BAAAAAQABAPv/BwD8/wQA/v/9/wQA/P8DAP3/AwD7/wYA+v8FAP3/AgAAAAEAAgACAAAABAD+/wMA//8CAPz/BQD7/wUAAAACAP//AAAAAAEAAAD9//7/AgD8/wMA+v8AAPz//P8BAPr/AgD8/wAA/f8AAAAA/v/+/wIA//////7/AAD//wIA+v8GAPv/BQD8/wIA/f8BAP3/AwAAAAAA/v8BAAAA///+//7/AgD///3//v////z/BAD5/wIA+/8AAP3///8BAPz/BAD7/wMA/P8FAPr/BgD///7/BAD8/wMA/v/7/wYA+/8HAPr///8EAPv/CAD9/wEAAgD9/wMAAQAAAAUA/v8JAP3/BgD+/wIABAD9/wAAAgD7/wYA/P8CAP//AgADAP//AQD//wQA//8BAAEA+/8FAPn/BAD7/////v/+//7/AAAAAP7/AgD9/wMA//8AAP3/CAD0/woA9v8EAPz//f/9/////P/+//7/+f8BAPj////6//3/+//6/wEA+v8AAP3//v////3/AAABAP3/BAD5/wUA/P8BAAMA/v8FAP3/AQABAP//BgD9/wEAAgD+/wcA/P8KAP7/AwAEAP//BgD8/wQA/f8BAP//AgAAAP//AgD//wcA/f8JAPj/BQABAP7/BgD4/wYA/f8AAP3/BAD7/wYA+/8EAPv/BAD5/wYA+//9/wMA+v8GAPz/BQD9/wMAAQD8/wQAAAAAAAUAAAABAAAABAD8/wsA9/8JAPr/AQAEAP7///8DAPr/AwAAAPr/CQD6/wUA+/8CAAAAAAD//wAA//8DAAIA+f8JAPz//v8EAPj/AwD///v/AwD9////AAAAAAIA/f///wAAAAAAAP////////z////9//3/AAD7/////v8BAP///P/8/wIA+v8GAPj/BwD7/wQAAQD//wMAAQD//wQA/f8DAAMAAQD//wUA/f8AAAYA/P8MAPP/DAD7/wMABwD+/wgA+/8GAP7/BAAAAP7/AQD+/wMA//////v/BAD6/wQA/f8AAAAA//8DAPv/BgD5/wYA/f8GAP3/BAD//wUA//8CAAEAAgD+/wQA/f8FAP3/AgD//wMAAwABAAAABAD9/wUAAQD7/wMA/f8EAP7/AAAEAPr/CQD6/wQAAAD+/wQA9/8MAPb/CQD7/wEAAgD//wIAAgD//wUA/v8DAPj/CAD7/wUA///+/wIA/f8EAPz/AAABAPz/AgD/////AQD9//3/AQD+/wAA///+/wAA/f///wAA/v8CAPz/BAD9/wIAAQD9/wcA+P8IAPv/AwABAP//BAD//wAAAwD8/wUA/f8AAAIA/f8CAP//AQD8/wUA+P8HAPv/BAD9/wEAAgAAAAIA///+/wEAAQADAP7/AQD///7/BQD6/wYA+/8EAAEAAAAFAP3/BAACAP//AgADAAAABAABAAQAAAADAAAABAABAAEAAAADAPz/AgD+////AwD8/wIA/v8AAP3/AgD8/wMA/f///wEA/P8BAP///P8CAP3///8BAP7/AAD9/wYA+f8GAPr/AgD/////AAD///r/AwD5/wUA+/8CAPr/AQD+//7/AQD///3/AQD7/wAA/P8BAP7/AAD9/wEA/v8CAP7/BAD7/wUA/f8BAAYA+P8IAPz/AgAEAPv/CgD4/wMA/v///wQA+/8EAP//AwAAAAMAAQADAAIAAAAFAP//BQD9/wUA/v/+/wIAAQD//wMA/f8BAAIAAAADAPz/AwAAAAEABAD9/wEAAAD+/wMA/////wAAAQD+/wIA/f8AAAEA/f8BAP///v8EAP3/AAAAAAIA//8FAP3/AwD+/wQA/P8IAPj/CAD6/wYA/P8DAP7/AgD+/wEA//8BAP3/AQD9/wQA/P8CAP//+/8GAPj/BwD4/wUA+////wEA+/8DAPz/AQD9/////////wAA//////7////9/wAAAgD4/wYA+P8EAP3////+/wAA//////7/AAD9/wMA/f8DAP7/AAADAP7/AgACAP7/BgD9/wUA//8DAAIAAAACAP7/AgACAAAAAQADAP//BAD+/wMAAgD//wUA/P8GAAAAAgADAAIAAgAEAAMAAQAFAP//BwABAAQAAwACAAEABgD9/wYA/f8FAP//AQACAP7/BQD7/wYA/P8FAPj/BQD8/wYA+/8AAAIA//8AAP///v8DAPz/AwD9/wIA///9/wMA+f8FAP3///8DAPr/AwD+////AAD+/wEAAAD8/wMA/P8DAPz//P8DAPf/BwD3/wUA+P8EAP3/AAABAP//AgD//wIA/f8GAPv/BwD7/woA+v8HAP3/BQD//wUAAAAEAP//AQAEAP7/CAD6/wgA/f8DAAAAAgAEAP7/AwD//wIA+/8GAPv/BgD8//3/AQD8/wQA+v8DAPr/AQD///z/BAD8/wEAAgD7/wUAAAD8/wUA+/8EAAEAAAAAAAUA/f8GAPr/CAD5/wgA/v8CAAQA//8EAP3/BQAAAAEAAwACAP7/CAD+/wIAAwABAP//BQAAAP//CAD7/wgA/v8EAPz/CAD2/w0A9v8JAPz/BAAAAAIA//8HAPv/CAD9/wQA//8FAPn/BwD7/wQA//8AAPz/BQD8/wIAAAD//wEA/f8EAP3/BAD9/wEAAgD//wIA///9/wQA/P8FAPr/BAD9/wMA/////wMA+/8EAPz/AQD+/////v8CAP7/AAACAAAAAwD//wMA/f8GAPz/BQD+//7/BgD6/wQA/P8AAAEA/v8DAPn/CAD6/wUA/v8CAP7///8BAP//AwD8/wUA+P8HAPn/BQD//wIA//8EAP7/AwD+/wMA//8CAAAA/f8CAPz/BAD+/wEAAAABAAIAAAACAPz/AwD+/wUA/P8GAPf/CwD2/wcA+f8CAP7/AQAAAP//AQD+/wMA/P8HAPr/CQD7/wUAAAABAAIAAgD+/wQA//8BAAAAAgAAAP//AQD9/wUA/P8BAAMA/f8FAPr/BQD9/wMA/v8BAP//AQD//wMA/P8DAPz/AwD+/wIA/////wEA/v8CAPv/BwD4/wcA+/8DAP////8CAP//BAD8/wQA+/8FAPv/AgD+/wAA/f8AAAAA/v/+/wAAAAD+/wEA+////wMA+P8KAPf/BQD9/wMA//8BAP//AAD9/wQA9/8IAPn/AQD///z/AAABAP3/AgAAAP//AQACAPz/BAD9////AgD+/wEA//8AAP///v8CAAEAAgAEAPv/CAD9/wUA+/8FAPz/BwD7/wYA+v8GAPj/BQD8/wEAAQD//wAAAQD+/wQA/v8AAAEA/v8CAAAA/f8FAPv/AwD+/wAAAAACAP//AAAAAAEA/v8DAPv/BQD7/wMA/////wMA+/8BAP7/AAABAP7/AQD+/wEAAAADAP7/BAD//wIA/f8FAPv/BgD7/wQA/P8FAP3/BAD//wEA//8EAPz/AwAAAAEA//8BAAEAAQD///7/AAD9/wIA+v8CAPv/AgD8/wEA/v8AAP3///////3/AgD8//3///////3/BAD2/wkA+f8BAP3/AgD9/////v/+//3/AgD7/wMA/f8AAP7/AQACAAEABAAAAAEAAwD+/wYA/f8GAP3/BAD+/wEAAgD8/wcA+v8IAPr/BAD//wEAAAACAP//AgD+/wIAAgD+/wIA/P8BAP3/AAD+/wAA/v8AAP///v8CAP7/BAD9/wIA/v/+/wIA/P8GAPj/CAD1/wkA9/8FAPv/AwD6/wQA+f8CAP//+v8FAPv/BAD6/woA+P8HAAAAAgADAAIA//8DAAIAAAAEAP//AgAAAAIA/f8EAPz/BAD//wIA//8EAPz/BAD9/wQA/P8GAPn/BgD7/wMAAAD//wEAAAD+/wIA/f///wAA//8BAP///v8BAP3/AgD8/wEAAAD+////AQD9/wAA//////3/AQD///3/AgD5/wIA+/8AAP7/AQD+/wAAAgD+/wEAAAACAAMAAAADAAAAAAAEAP7/BQD+/wUA/P8JAPz/BgD+/wQAAwD+/wUA/f8DAAEAAQAEAP//AwD9/wMA/v8BAAIA+v8EAPz/AgD+/wMA+v8HAPj/BAD9/wIAAAD9/wEA/P8EAP3/AQD8/wMA/P8CAP3//P8DAPj/BgD7/wAAAAD+/wAAAgD+//3/AgD8/wEAAAAAAP//AAD//wAABAD6/wUA/v///wMA/f8DAAEA//8BAAIAAQABAAQAAgABAAUA/P8IAP3/BAACAAEABQABAAMAAAAAAAIAAAAAAAYA+f8GAP3/AwAAAAUA/f8GAP3/BQD//wIAAQD8/wgA9/8JAPr/AgAAAP3/BAD7/wUA+f8HAPf/BQD7//7/AwD9/wAAAQD8/wIA/P8BAPz/BQD6/wMA///7/wUA9/8DAP7/+v8FAPr/AgD9/wMA/////wAA//8DAAAA//8BAAAAAQD///7/BAD3/wkA+f8DAP3/AQD+/wQA+/8CAAEA/v8DAP7/AQAAAAIA/v8BAP3/AAD+/wIA/f8CAPz/AwD+/wEA//8AAAIA/v8DAP//AAABAP7/BAAAAAAABAD//wcA/v8EAP7/AwABAAIAAQABAAIA//8GAPn/CQD7/wQA/P8DAP3///8AAAAA/f8CAPr/BQD7/wMA/v///wEA+/8CAP7/AgD9////AwD5/wgA+v8DAP3/AQD+/////f8EAPr/AQD9//3/AQD///z/AgD7///////9//3/AAD9/wAAAgD6/wMA+//+/wIA+v8DAPr/AAD///7//v////////8AAP///////wEA/v8AAP//AgAAAAIAAAABAAEAAAABAAEA//8EAP3/AgACAP3/BgD8/wQA/P///wYA+v8IAPn/AwD+/wIAAQD7/wgA+P8JAPr/AwD9/wEA/v///wEA/f8EAP//AQABAAAAAwAAAAMA//8CAAEA//8FAP3/AgACAP7/AgABAPv/BgD6/wUA+v8AAAEA//8DAPz//v8DAPr/BQD5/wIA//8AAP//AQD9/wEA+v8FAPn/BAD8/wEA///9/wIA/v8AAAIA+/8CAP7/AAADAPn/BgD7/wIA///+/wIA/P8DAPb/BwD3/wIA/f/7/wMA+v8CAPr/AgD6/wQA+/8CAAAAAQAAAAAA/v8CAP7/BQD5/wMA/P8DAP7/AQACAPv/BwD4/wgA+P8DAP///f8FAPn/BgD7/wIAAAABAP//BAD7/wMA/f8AAAIA//8BAPz/BAD8/wQA/v8CAAAAAQABAAEAAgD+/wcA+v8IAPn/CQD9/wQAAQABAAEA/////wEA//8AAP//AQD///7/AAABAAEAAQD//wEA//8DAP7/AQABAP//AwD+/wEAAgD9/wcA+f8FAPz/AwD7/wQA/f8AAAEA/////wMA+f8IAPn/BAD///3/AQD///3/AAD9//v/AgD8////AwD4/wUA+/8EAP7/AAACAP////8DAPv/AwD//wEA/f8CAPz/AQD+//7/AAD6/wgA9v8HAPn/AgD+/wAA//8BAAAA///+////AgD9/wYA+f8EAP3//////wAAAgD6/wUA+f8HAPv/BQD8/wMAAgABAAAAAwD+/wUA/////wYA+f8LAPb/CQD6/wMA/f8BAAAA//8DAPz/AgABAP//BAD//wIAAAACAAEA//8AAP7/AQABAP3/AQD8/wMA/v//////AQD9/wIAAAD+/wcA/v8FAPz/BgD9/wQAAAAEAAAABAD//wIAAQABAAIA/f8EAP7/AwABAP3/AwAAAP//BAD+/wAABQD5/wcA+f8FAPr/AQD8//3//v////3///////z/AwD6/wIA///+/wYA+P8JAPj/CQD6/wUA/v8BAAAAAAAAAAEAAAD+/wIA/f8CAPr//v8BAPv/AgD7/wIA/f8BAAAA//8DAP3/BAD//wQA/v8BAAIA/////wYA9/8JAPn/BwD+/wIAAAAAAAIAAwD+/wYA//8GAP//BwD//wIAAwD9/wMAAwD8/wMA+v8CAP7/AgD///3/AwD8/wIAAgD9/wcA/P8EAAIA//8EAAEA//8FAP7/BQD//wIA///+/wUA/f8CAP//AAAAAAMAAgD//wQA//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ACAP3/BAD//wEAAQAAAP//AwD8/wUA/////wQA/f8GAP//BQD//wQAAgABAAIAAQAAAAIA/P8EAP//AwACAAEAAwADAAMABQADAAQAAQACAP7/BAD6/wQA/v///wAA////////AgD//wIA/v8BAAAA/v8AAP7/AAD+/wMA+f8GAPr/AQD9//7/AgD8/wQA/P8DAP7/AwD7/wMA///8/wQA+P8FAPr/BAD8/wAA//8AAAMA+/8EAPv/BQD8/wQA/P8FAPv/BwD6/wUA+f8LAPf/CgD4/wYA/f8BAP7/AgD+/wQA/P8BAAMA/P8HAPz/AAABAP7/AwD//wQA/f8DAAAA//8CAAIAAAD//wUA/P8HAP3/AQACAP7/BQD8/wQA/f8CAAIAAAACAAIAAAACAAMAAQADAAIA/v8EAP3/AQD//wAAAQD9/wQA/P8FAPz/BQD7/wYA+/8FAPr/BAD8/wMA/////wMA/v8BAAMA+/8FAPz/AgABAP//AwD8/wUA+P8IAPj/BgD/////AgAAAAMAAAACAP//AQAAAAAA//8BAPz/AQD+/////f//////AQACAP//AQABAAIA//8FAP7/BAAAAP7/BwD6/wUA/v///wIA//8DAP3/AwD+/wAABgD5/wgA9/8DAP7/AAD//wEA+v8EAPz/BQD8///////9/wEA/f/9/wIA+v8EAPr/AQD8/wAA/f8CAPz/AwAAAAAAAAACAP7/AgABAP3/BgD7/wcA+/8CAAEAAAADAP////8FAPn/CgD4/wgA+P8HAPn/AwABAPv/BwD6/wIAAAD//wIA//8BAP7/AgD+/wEAAQD//wMA/f8FAP//AwACAAAAAwACAAAABQD+/wYA/P8GAP7/AwACAP7/BAD8/wMA/f8DAP3/AQD7/wEA/f8DAPz///8BAPv/BQD6/wMA/f/8/wEA/v///wEA/f8AAAAA/f///wIA/v8DAP7/AgAAAAMA/P8GAPf/BgD5/wUA+v8CAPz/AAD+//3//f/9//r////9/wEA+v8CAPz/AgD8/wAAAAD+////AQD9////AAD//wAAAAABAP7/AwD9/wMAAAAAAAEA//8AAAIA/v8AAAEA/v8DAP//AQAAAAIA//8EAPv/CAD4/wUA/v///wAA/v8BAP7/BAD8/wUA/f8BAP//AgD//wEAAAD//wMAAAAFAP7/BgD+/wMABAD+/wUA/P8EAP7/AwACAP7/BQD8/wMAAAD+/wUA+f8HAPz/BgD+/wIA/v///wEA+/8AAP///v///wIA+v8FAPz/AgACAP3/BAD+/wQA/f8DAP3/AQD8/wMA+v8DAP3//f8DAPj/BgD7/////v8AAP3/AQD5//3////8/wAA/P8BAPv/AwD7/wEA/v8AAAEA/P8DAPr/BAD8/wEA//8CAPz/BAD8/wAAAwD9/wIA/f8DAAAA//8DAP3/BAADAP7/BAD9/wIA/v///wEA/v8DAPv/AwD9/wMA//8AAAIAAAAEAP7/BAACAAAAAwABAAIAAQABAAIAAwABAP//AgAAAP//AQAAAP3/AQD+/wEAAgD+/wIAAAD+/wAAAAD//////v/+//////8AAAAA///+//3/AwD9/wMA/f8CAP//AgD9/wYA+/8FAPv/BwD6/wQA/v8BAAIAAgAAAAAA//8BAAAA/P8CAPz/AwD8/wEA//8AAP////8AAAAA//////3////9//7//f/9//7////8/wEA///9/wYA+P8IAPj/AwAAAP7/BAD8/wQAAQADAP//AwD//wUAAAD//wMA+v8CAPv/AQD+//7/AQD9/wIA/v8AAP///f8EAPz/AQD9//7/AgAAAP//AQABAP7/BAD+/wQA/f8HAP7/AgADAP//BQD9/wcA+v8HAP7/AgABAAAAAQABAAIA/P8DAP3/AQD8/wMA+P8EAPr/AgD8/wIA/v8AAAAA//8AAAIA//8DAP///v8GAPj/AgAAAPv/BAD6/wQA/f8EAPz/AgADAP7/AwD6/wMA//8AAAYA9v8KAPX/BwD7/wAA/f////z/BAD7/wMA/f//////AAAAAAEA/v8BAP//BAD8/wkA9/8LAPb/CgD4/wcA//8AAAIA/P8CAAAAAAABAP3/AwD8/wIA/v///wIA/P8DAP7/////////AQD+/wUA+P8IAPr/BQD+/wUA//8EAP7/AQAAAP//AgD//wQA/v8FAP//BAAAAP7/BQD//wIAAAACAP7/AQABAPz/BgD2/wgA+/8AAP////8BAAAAAQD//wEABAD7/wUA//8AAAIA/v8AAAEAAgD8/wcA+P8GAPn/AgD+/wIA/v8BAPz/AwD8/wMA/v8CAAEAAQD+/wAAAwD+/wEA/v/+/wEA/f8AAP7/AAD//wIA/P8CAAAA/v8EAP3/BQD//wAAAwD//wIAAgD//wIAAQD9/wMA/P8BAAIA/P8BAAMA/P8FAPz/BwD5/wsA9v8KAPn/BwD8/wAAAQAAAAAAAgD//wIA//8BAAEAAAD///7//f8BAP7//v8DAP3/AgAAAP//AwD+/wEAAgD9/wYA+P8IAPz/AwADAP7/AwABAP3/BQD8/wUA+v8FAPj/BwD8/wAABQD5/wcA/f8AAAUA+f8DAP7/AQD+/wQA+P8HAPr/AAACAP3/AgD8/wAAAQABAAAAAgABAP//AwABAAEAAgD//wQA/P8EAPv/BgD6/wUA+v8AAAEA/v8CAP3/AAD//wEAAAAAAAAAAAABAAEAAgD9/wUA/f8FAP7/AgD9/wMAAAD//wEA/v8BAAAAAgD9/wIA/v8BAAIA+/8GAPn/BAD8/wEAAAD9/wIA+v8DAP3/AQD6/wMA+/8BAPz/AgD5/wIA+/8CAAAA/P8DAP3/AAABAP//AQAAAAMA/f8HAPj/CgD6/wcA/P8DAP//AQABAAIA/P8DAP7/AQD//wQA+f8KAPb/BgD5/wMAAAACAPz/////////AgD9/wAA//8BAP3/AgAAAP7/AwD9/wQA//8DAP3/AwAAAP//BQD7/wMA///8/wQA+/8FAPv/AwD8/wIA//8BAP//AAD/////AAADAP3/AgAAAAAAAgD8/wIAAAD+/wEA/v8AAAAAAAD+/wEAAQABAAAAAQD//wMAAAAAAAUA/f8FAPz/AgACAPv/AwD9//7/AwD+////BAD3/woA+P8IAPr/BwD9////BgD1/wkA+P8EAP3/AgD6/wYA+f8EAP7//P8CAPr/BAD5/wUA+/8BAAAA/f8EAPr/BQD8/wIA/P8DAP3/AwD6/wIA/f8AAAAA/f8AAP7/AwD8/wQA/P8EAAEA/v8GAP3/AwABAAAAAQABAAEA//8BAP7/BAAAAP//AAABAAEAAgABAP7/AwD+/wMAAAABAAIA//8AAP//AgD+/wAAAgD+/wMA/v/+/wMA/f8AAAEA/v8BAP7/AgD//wQA//8BAP//AQACAP7/CAD4/wYA/f///wgA9f8KAPf/BQD///z/BwD6/wUA+/8EAP7/AAACAP7/AQD9/wEA//8CAP3/AwD9/wQA//8DAAAAAQD+/wIA/v8BAAAA//8BAAAAAQABAAQA/P8DAP7/AAAAAP//AAD///3////+//7/AQD+//7/AAD+/wMA+/8DAPz/AQABAP3/AwD9/wIA/////wEAAAD//wEA/P8CAPz/BwD4/wUA/f/+/wYA/f8AAAEA//8CAP//AgACAAEA//8CAP7/BQD9/wEAAAABAP//AAD9/wEA/v8CAP//AQACAP7/AgABAAIAAQAAAAUA/v8FAP7/BAD+/wUA/f8EAP//AQABAAAAAgACAP7/AgAAAAEAAQAAAAAA/f8DAPv/BgD5/wMA+f8HAPr/AgD+//z/AwD8//7/AgD7/wMA/f8CAP//AQD+/wMA/P8GAPv/AwADAPr/CQD3/wYA/P8FAP3/BAD7/wQA/P8DAP3/AAAAAP//AAABAP//AQD7/wMA/f8AAAEA+f8GAPj/BAD5/wQA+/8DAP7/AQD8/wcA+P8HAPv/AAACAP//AwD//wEAAgAAAAEAAQD//wEAAQD7/wYA/v/9/wUA+f8EAP7/AQD8/wUA+/8EAP3/AwAAAAIA/v8AAP//AQADAP7/AAACAPv/BwD4/wYA/f8BAAUA/P8JAPj/CAD//wIAAAAEAAAAAwAEAP//BgD+/wMAAgADAP7/BQD9/wAAAgD6/wUA/P8DAP7/AQD7/wMA/P8CAP///v8BAP7//////////v8BAPz/AQAAAP3/AgD9/wIAAQD8/wQA+/8CAP7/AQD9//7////9/wAAAAD8/wEA/P8CAPv/AwD7/wMA+v8FAPX/BgD5/wMA/P8BAAAA/P8FAPv/BAD///7/AwD//wQA+/8GAPr/CwD3/wgA/f8DAP7/AAD8/wUA/f8DAP//AQACAAIAAwABAAMAAQADAAEAAgABAAQA+/8EAPv/BQD//wAAAQD+/wQA/P8IAPj/BQAAAP//CAD3/wcA+/8CAAIA/f///wQA+/8FAPz/AQD+/wAAAQD+/wEA/f8BAAIA/v8BAP//AwAAAAIA//8CAAAAAQABAAEA/v8FAPr/BwD6/wUA+/8FAPz/AwD8/wAAAAD//wMA+/8FAPj/BQD8/wEA/v8AAP7/AQD6/wMA+/8DAPv/AwD5/wMA/f///wAAAAD8/wMA+//+/wEA///9/wMA+P8FAPv/AQD+////AQD8/wAA/v8BAP7/AwD8/wQA/f8DAP//AgAAAAEABQD7/wgA/P8FAAEAAQD//wIA/f8HAPv/BQABAP7/CAD5/wYAAwD7/wkA+v8GAAEAAAAFAP//BQACAAMAAgAFAP3/CQD//wcA//8GAP3/CAD//wEABAD+/wMAAAABAAIA/v8DAP//AQABAPz/AQAAAAEAAAD8/wUA/v/+/wQA+P8HAPv/AQACAPz/BAD5/wUA+P8HAPr/AwD+////AAD//wAA/f8BAP//AQD9/wIA/P8BAP//+/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AAAAAAAQD+/wEAAAABAAMAAAAAAAQA+v8EAP7/AAACAP3/AgD+/wIAAwD+/wMA+/8FAPv/BQD8/wQA+/8EAAAAAwD+/wAA/f8CAP7/AwAAAAIA/v8AAAIA//8AAAMA/P8GAPv/BgD7/wYA/////wIA+/8IAPn/AwD///3/BAD7/wYA+v8DAP3/AAACAAAAAQAAAAEAAAADAP7/BAD7/wcA9v8MAPT/CAD3/wMA+/8AAP3/AgD7/wMA+v8FAPz/AQD//wIA//8BAP7/AAADAPr/BwD7/wMA/////wEA//8BAP//AgD+/wMA/v8CAAEABAD//wEAAgABAAQAAAACAAEABAD+/wYA+v8DAAEA/f8EAP3/AgACAP//AwABAAAAAQADAAAA//8AAAIA//8AAP7//v8AAAAA/v///wEA+v8GAPr/BAD9/wAAAgAAAAMAAgD//wIAAAAAAAQA/v///wAA/f///wAA/P8CAP3/AAADAPz/BAD+////AgD7/wMAAAD//wEA/f8FAP3/BAD+/wMA/f8EAPv/AAABAPv/BAD5/wUA+f8GAPb/BgD8/wAA//8BAAAAAAABAP//AgD//wIA/f8EAP7/AAAAAAAAAgD7/wQA+v8EAP7/AgD9/wUA/f8DAP////8CAAEAAgAAAAAAAAD//wEAAgD9/wIA/v8CAP//AgABAAMAAAABAAMAAwAAAAMAAAACAAMAAAAAAAUA/v8CAP//BQD8/wUA/v///wQA/v8DAAAAAgD//wEAAgD//wUA/v8EAP3/AgABAAEA//8CAP3/BQD6/wMA+v8CAPz/AgD8//7/AQD+/wAA/////wIA/f8FAPr/AgD+//z/AwD5/wQA/f///wEA/P8CAPz/AgD8/wIA/f/7/wQA+f8CAP3//////wAA/v8DAAEAAAADAP7/AgABAP////8EAPz/BAD9/wEA//8DAP//BQD9/wQA//8GAP////8EAP//AgD//wMA/P8FAPv/BgD7/wQA/f8EAP//AgD8/wMA/P8EAPz/BAD9/wEABAD+/wMAAAABAAEAAAABAP7/AwD9/wQA/P8CAAAAAAD//wMA/v8CAP//AAD9/wYA+f8FAPv///8DAPv/AgD8/wAA//8BAPz/AwD8/wMA/f8DAP3/BwD8/wMAAgAAAAQAAgD//wIABAD7/wUA/P8AAP7/AgD9/wEA////////AQD7/wUA/P8BAP////8EAP3/BAD/////AgD//wMA+/8DAPr///8AAPv/AgD7/////v/+/wAA/v8BAPz/AwD8/wAA/v/9/wIA+/8DAP3/AAAAAP3/AQD//wQA+v8EAP3/AAACAP3/AgD9//7/AgD+/wAAAAD+/wEABAD7/wYA/P8BAAMA///9/wYA+P8JAPj/BQD///7/CAD6/wYAAAAAAAMA///+/wMAAAD//wQA+P8HAPz/AwD9/wAAAgD8/wQA+v8DAP3///////v/BAD5/wMA/P8BAAAA///9/wMA+v8EAP3//v8BAPz/AgD7/wMA+/8BAP7/AwD8/wUA+v8GAP3/AwABAP//AgACAP7/AgAAAP3/AgD8/wEA/////wAA/v///wAAAAABAAAAAQABAPz/AgABAAAAAAACAPv/BAD///3/BgD//wEAAQD+/wMA/P8BAAAA/f8EAP3//v8CAP7///8DAPz/AAD///7/AAAAAP3//f8BAPj/BAD4/wQA+P8CAP3///8AAAAA//8AAP7/AQD+/wAA///9/wUA+f8FAP3/AwAAAAEAAAADAP//AwD//wAAAQD+/wUA/P8DAP////8GAPv/AwD9/wEA/v8CAP7/AgD+////AQD//wIA/P8FAPj/CgD1/w0A9v8JAPz/AgABAAEAAQD//wIA/f8FAPn/BAD6/wEA///+//7//v8BAP3/AAD9/wAA/v8FAPn/BAD8/wIAAAABAP//AgD7/wIA/v8AAAMA+P8HAPv/AgAAAP3/AwD9/wEA//8AAP//AgABAAAAAgD//wIA//////7/AwD8/wIA/v///wEA+/8EAPj/BAD+//////////7/AAD9/wAA+v8CAP3//v8AAPz/AQD8///////7/wIA+/8CAPn/AgD5/wEA+/8EAPr/BAD9////BQD3/wYA+/8HAPr/BAD8/wEABAD6/wcA+/8AAAUA+f8HAPz/AgAAAAAAAgD9/wMA/P8CAP7/AAACAPv/BQD9////AgD8/wIA///8/wMA/P///wEA+v8FAPv/AQD///7/AwD+/wAAAgD8/wQA/f8BAAIA/v8BAP//AAABAPv/AwD8/wQAAAD6/wEA/P8CAP7///////7/AQD+//z/BAD5/wYA+v8FAPr/AwD//wAAAQD//wAAAQD8/wUA+v8HAP3/AAACAPv/BAD9/wEA+v8EAPn/BQD9/wAAAgD8/wAA//8CAP3/AwD8/wAA/v8AAPv/BAD6/wMA/P8BAP3////9/wEA/v8AAP3/AAD9/wAA+/8EAPz/AwD8/wQA/v8EAP//AQAEAP7/BQD7/wUA//8BAAEAAAABAP//BAD8/wAAAQD9/wEABQD3/wkA+v8EAAAABQD6/wcA+/8FAP3/BAD8/wIA/v8BAAIA+v8FAPz/BAD/////AgAAAAMA/f8DAP//AQD//wEA//8CAP7/AwD9/wQA/P8EAP//AgD//wMA/P8EAPz/BQD9/wEAAAD+/wEA//8AAP7/AgD8/wMA/v///wAAAQD9/wQA+/8BAPz/AgD9/wEA/f8BAAAAAwD+/wQA+/8GAPv/BQD8/wQAAAAAAAMA//8DAP7/AgAAAAMA//8DAP//AgD9/wQA/v8CAP//AAAEAP//AwD//wQA//8EAAAAAwAEAAAAAgD//wMAAAAEAAEAAQD//wIA/v8FAP7/AgABAAEAAQABAAIA//8BAAEAAQABAP7//f8EAPz/BAD9/wAA/v8DAPr/BQD6/wUA/f8DAP3/BAD8////AgD6/wUA/f8AAP//AgD6/wgA+v8FAP3/AgABAAAABgD8/wgA/f8GAAAAAgADAAIAAwAAAAQA/v8FAAEABQAAAAQA//8GAP7/BQD+/wQAAgD//wYA/v8DAAEABQD9/wcA+f8JAPr/BgD+/wIAAQD7/wgA9/8IAPj/BwD8/wQA+f8FAPn/CQD6/wYA+v8HAPr/BwD4/wYA9/8HAPj/BQD7/wAAAAADAPv/BwD9/wUAAQABAAQAAAAAAAIA//8DAAEA/v8FAAAABAAAAAMAAQAAAAUA/P8IAPr/CQD5/wgA//8BAAQA/v8HAP//CQD6/wkA/f8DAP7/AAABAP3/AgD9/wEA/f8CAP//AAAAAP7/AQD8/wkA9/8IAPj/BAAAAPz/BAD3/wYA+/8CAAAA/f8AAAMA/f8DAP3/BAD8/wUA//8CAAUAAQADAAMAAwAEAAAABwD9/wcAAgABAAgA//8IAPv/CQD+/wIABwD8/wcA//8BAAYA/P8HAPz/BgD//wEA//8DAP//AwD+/wMA//8AAAAA/P8HAPj/BQD7/wIAAwD7/wYA+P8HAPz/BAD//wMA/v8EAPz/AwD9/wIAAAAAAP//AQD7/wUA+v8EAPn/BQD7/wAAAQD7/wQA/f8DAP3/AgD+/wIA/f8BAP3/AwD8/wUA+/8FAP7/AAAEAP3/AwD9/wUA/P8EAAAAAAACAAAAAgACAAIAAgACAAIA//8BAAMA+v8IAPz//v8BAPv/AgABAPv//v/+/wAA//////z/AwD7/wEA/v/8/wYA/P8CAP7/AQAEAPz/BwD7/wQAAAD+/wcA+/8HAP7//f/7/wAA+f8FAPv/AQACAPz/AwD9/wIA+/8BAPr/AgD5/wIA/f8DAP//AQACAP7/BAD//wIAAQACAAIA/f8GAPn/CgD3/wkA9v8JAPv/AwD+/wMA//8FAPz/BgAAAAEAAwD//wQA//8FAP3/AgADAPz/CAD//wEAAgADAP3/CQD3/wkA+P8HAPv/BAD+/wEAAAD+/wIA/P8BAP//+/8CAPj/AQD7/wAA///+//z/BAD7/wAAAAD7/wUA+////wIAAAABAAAA//8DAP//BQD///7/BQD6/wUA+v8GAPr/AwD9/wAAAQD8//////////7/AAD9/wUA/f8EAP7/AAABAAEA/v8DAPz/AwD+/wAA/f8EAPr/BwD5/wAA///+/////P/7//3//v/+//z/AgD3/wcA+f8FAP3/AgAAAAEAAAAAAAIAAAACAP3/BAD9/wUA/P8CAAEA/v8HAPr/CAD9/wUAAQADAAEABAD8/wgA/P8CAP////8CAAUA/P8IAPz/CAAAAAcAAgADAAQA/P8GAPv/AwD9/wEA///+/wEA/v///wQA/P8GAPr/BQD9////AQD9/wAA//8AAP7/AAD+/wAA/P8AAAAA/f8DAP3/AwD//wAAAQD7/wcA9v8FAP3//f8BAP///f8DAPz/AAAFAPj/CAD6/wQA//8AAAEAAQD9/wYA+/8FAPn/CgD3/woA+v8EAP////8AAAAAAAACAP3/AgD//wEAAgD//wEA/f8CAAEA/v8IAPn/BgD9/wAAAwABAAIA/f8DAAAAAgADAP3/AwD//wEAAQABAP//AAADAAAAAgACAAAAAgACAAIAAwABAAAAAQABAP7/AQD+/wEA//8DAPz/BAD9/wMA//8CAP7/AQD//wEA/f8EAPz/BAD+/wEAAQABAP7/AgD+/wEAAQD//wMA/f8AAAAAAQD+/wMA/v///wUA/v8EAAAAAQD+/wUA/P8CAP//AAD+///////+//7////+/wMA//8CAP//AwAAAAIAAwD+/wQAAAD//wYA+/8CAAEA/f8CAAIA/v8BAAIA/P8FAP//AQAAAP3/AQD7/wgA9f8JAPb/BgD6/wgA9/8FAPr/AQD+/wAA+f8EAPr/BAD8/////P//////AQD///7/BgD5/wcA/P8DAP//AgD+/wIAAAADAP3/AgAAAAIAAAACAP3/BQD8/wUA/f8EAPz/AwD8/wIAAAD//wAAAgD7/wYA+/8DAAAA/v8CAP7/AgD8/wUA/v8DAP3/AwABAAMAAQADAP7/CAD7/wcA//8DAAEAAQABAAIAAQABAAEA/v8CAP3/BAD8/wEA/P8AAP7/AwD5/wUA+f8FAPr/BAD7/wIA+/8AAP7/AQD9/wEA/v8BAPz/AwD7/wUA/f8BAAIAAgD8/wcA+P8FAPz//v8CAP3/AAD9/wAA/P////3/+v/+//3//f8CAPr/AAD///7/AAD9/wQA+P8FAPz/AAD///3/AwD+/wEA/v8DAP3/BAD+/wEAAgD/////AwD+/wIA/P8FAPv/BQD+/wEAAgD//wIA//8CAAAAAAD+/wMA+/8CAP3/AQACAPz/BgD5/wgA+v8CAAIA/f8EAPz/BQD8/wUABAD8/wkA/P8EAAIAAQABAAAAAwD8/wYA/v8CAAMA/f8DAP7/AQAAAAEA/v8FAP7/AwAAAP///v8CAPn/AgAAAPr/BQD6/wIA/v8BAP//BAD8/wQA/v8DAAAA/v8CAP7/AAD///z/AAD//wEA+v8DAPz/AAD///z/AAABAPn/AgD3/wEA+/8AAPz/AwD6/wIA/P8BAP///v8EAPf/CQD3/wUA+f8EAP//AQD8/wUA+f8FAP3/AgD+/wEA//8CAP3/BgD7/wUAAgD//wIAAQD+/wAA//8AAAAAAQD+////AAAAAAEAAQD//wUA+/8KAPj/DAD4/wgA//8CAAMA/v8EAAAABAD9/wUA/P8CAP3/BgD3/wYA+v8EAAAA//8BAAIA+/8DAP3///8CAPr/AgD8/wAAAgD7/wUA+f8CAP//AAD//wMA+/8EAP7/AQAEAPv/BQD7/wcA+/8DAP3/BAAAAAMA/v8BAP//AwD8/wAA///+/wEA/v8CAPz/BAD4/wgA+f8GAPj/BQD6/wEA+v8BAPv////8/////f8CAPz/AQAAAAAAAQD8/wIA/v8CAP//AQAAAAMAAwD9/wcA+/8IAP7/AAAEAPn/AAAAAPz/AQD+//z/BQD8/wEA/f8BAP7/AwD6/wQA+v8CAP//AAD//wIAAQD9/wUA/f8DAAIA/v8IAPv/BQABAP//BQABAPz/CQD5/wgA/f8AAAUA/f8EAPv/AwD9/wIA/P8BAPv/AAD///z/AgD8/wUA+f8FAPr/BgD7/wgA+f8GAPv/BQD8/wAA///9/wEA/v8BAAAAAAABAP7/BgD6/wUA/f///wMA/P8HAPj/CAD4/wUA+v8DAPv/AAD+//7/AgD//////v8AAP//AQD//wIA/f8BAAMA/v8GAPv/AwAAAAEAAQD//wEAAgD/////AgD9/wQA+/8FAPz/AgD+//7/AQAAAAAA/f8EAPr/AgD+/wAAAQADAPn/BgD7/wYA/f8GAP7/BAAAAP//AgD8/wYA/P8HAPv/BgD//wYA/P8CAAIAAQD//wUA+/8GAPv/BAD8/wQA9v8LAPb/BwD4/wMA/v8CAP7/AgAAAAIA//8AAAQA+/8EAP7/AAADAP7/AQAAAAEA/P8DAPv/AQACAPz/BgD3/wUA/P8CAAIA/P8GAPz/AwD+/wIA//8AAP///f8DAPr/BAD5/wYA+P8JAPb/BwD8/wEAAgD+/wUA/f8FAPz/BgD7/wgA/f8AAAQA+v8GAPj/BQD+/wEA/P8GAPz/AgABAAAAAQADAP7/AgAAAAAAAwD5/wgA+v8EAAEA/f8EAP//AAABAAIA+/8CAPv/AQD///3/BAD8/wQA/P8EAP7/AQACAP//AAADAPr/BwD8/wQAAgD+/wMAAAAAAAIAAAD///7/AQD8/wUA/f8AAAIA/P8EAAEA/v8EAPr/BQD5/wgA+P8GAPv/AgD9/wEA/v8CAP7//f8CAP//AgD//wIAAAACAAEAAQACAAEAAAABAAEA/v8CAAAA/f8CAP7//f8CAAAA/f8EAPj/CAD4/wgA/P///wQA/v8CAAIA/v8DAAEAAAABAAEAAAAAAAAAAAAAAAEA//8AAAEA//8CAP3/AgAAAP7/AgD+////AAD+/wAA//////7//////////f8BAPz/AAD8/wEA/v/8/wAA/v8BAP//AAD9/wIA/f8FAPz/AwAAAAEAAgD+/wQA/f8GAPz/AwAAAP//AwAAAP3/BAD8/wMA//8CAP7/AgD+/wAA/f8DAP3/BgD4/wIA/P8DAP///v8BAPz/BgD4/wcA+P8IAPr/BQD+/wIAAQAAAAAAAgD+/wYA+v8HAPf/BAD9/wEAAAAAAP3/AgD9/wIAAAD//wAA/v8CAP7/AgAAAP7/BQD8/wQA/P8AAAQA+f8HAPj/BQD6/wYA+v8DAAEA//8CAP//AAAEAP7/AwAAAAAABAD/////AgD7/wIAAAD8/wMAAAD7/wgA9f8JAPz/AwD//wIA//8BAAEA/f8AAP7/AQD//wAA/v8AAP7/AQD///3/AAD9//7/AQD9/wEA///9/wMA/f8BAP3/BAD5/wYA+f8GAPr/AQD9/wEA/f8DAPj/BwD3/wkA9/8IAPn/BgAAAP//BgD6/wgA/P8FAP3/BAD9/wQA+/8FAP//AQAAAP//AQACAAEAAAADAPz/AwAAAAEAAgABAP7/AgD9/wMA//8AAAEA/v8DAP//AAD//wEA/P8EAPv/AwD//wAAAQABAAIA/v8DAP3/BgD8/wYA/P8DAP7/AgAAAP//AQD+/wAAAAD//wIA/v8CAPv/BgD7/wQA////////AQD+/wAAAwD7/wMAAAABAAEA//8GAPn/CQD5/wEAAQABAP3/BQD7/wUA//8EAPz/BQD7/wMA/P8DAP3///8AAPz/AAD+//3/BAD4/wUA/P8CAP///P8DAP3/AgD///////8EAPz/AwD8/wMA/v8BAP3/AQD9/wQA+/8EAP7//v8EAP7/AgD//wEAAgD8/wcA/f8FAP7/AQAAAAEAAAAAAAIA/f8DAPr/BgD3/wgA+/8BAAIAAAD//wMAAAAAAAYA+f8KAP3/BAABAP//AgADAP7/AwAAAAEA/v8GAPz/BwD5/wYA/v8DAAEA/v8BAP3/BAD5/wkA9P8HAPn/AwD///7/AAD9////AAD8/wQA+P8HAPf/CgD3/wYA/f8CAP//AQD//wEAAwD9/wMA///9/wQA/f8FAP3/AQAAAP3/BAD7/wIAAAD9/wMA//8AAAIA+v8EAPv/AgABAPn/BgD2/wcA9/8EAPz/AgD//wAA/v8CAP//AAABAPz/BAD//wEAAAACAAEAAgD+/wMA/v8DAP7/AAAAAAIA/P8DAP3/AAABAP7/AQD+/wMA/f8EAP3/BAD+/wQA+v8EAP3/BgD8/wMA+/8FAP3/AQD//wEAAAADAAAABAAAAP//BgD9/wUA/v8FAAAAAwAEAP3/BwD8/wkA/P8FAP//AAABAP7/AQD+/wEAAQD8/wMA/P8BAP7/AQD+/wAAAAD8/wMA+/8DAPv/AgD8/wEA//8BAP3/AAACAP7/AQD+/wAAAAD9/wMA+////////P8AAAMA+P8EAPn/BAD7/wEAAAD7/wQA+f8BAP3//v8AAP///f8EAPn/BgD7/wUA/f8BAAEA//8EAP3/AwD//wMAAAD//wcA+f8FAPv/AgD//wEAAAD//wYA+v8LAPv/BwD//wMAAgACAAIAAgAAAAIA+f8JAPn/BgD8/wMA/v8EAPz/BQD9/wEAAgAAAAQA/f8CAP3/AwAAAP7/AgD+/wAAAQD///////8AAP7/AgD8/wMA/P8FAPr/BgD8/wUA//8BAAEAAQD//wQA/f8EAPz/BgD8/wIA//8AAAIA///+/wIA/v8AAP7/AAABAAAA///+////AQD9/wIA/P8DAPv/AAD+//7/AgD7/wIA+/8BAP7/AAD+/wAA/v///wAA+/8DAPz/AAD+/wAA/f8CAPz/AAAAAP///v/9/wMA+/8EAPz/AgABAP3/BgD4/woA+f8HAP//AQACAAEAAQAEAP//AAABAAAAAgACAP3/BwD8/wcA+/8FAAAAAQAEAPz/BwD+/wMABAD//wYAAAAFAAAABgD+/wcAAgABAAYAAAACAAUA/v8FAP//BAD+/wEAAgAAAAIA//8CAP3/BgD3/wYA/P8DAP7///8CAP////8BAP3/AQAAAP//AQAAAP3/AgD9////AAD/////AwD7/wEA//////7/AgD9/wMA/P8AAAEA/f8BAPr/AwD5/wQA+f8CAPv/AwD9////AgD//wAAAwD9/wIAAgD//wEABAD+/wYA/v8DAAEAAwAAAAcA/f8EAP//AwACAAMA//8DAAEAAAAEAP7/BwD7/wgA+v8EAP7///8EAP3/AAAAAPv/AwD8////AQD+//z/AgD5/wcA+v8EAP///v8BAAIA/P8HAPn/BgD9/wMAAQADAAAA//8EAP7/AgACAP//BgD//wIAAwD8/woA+P8JAPz/BwD9/wUAAgAAAAQA//8AAAcA+v8JAPz/BgAAAAIAAgD9/wgA9/8KAPn/BwD+/wMA/v8FAP3/BwD9/wMABAD8/wcA/P8DAP//AQAAAAEA///+/wMA/v8CAP7/AgD7/wYA/P8EAP3/AgD//wMA/f8DAP7/AAACAPv/BAD8/wIAAgD8/wUA+v8HAPf/CAD5/wQA+v8DAPr/BQD7/wUA+/8HAPz/BAABAP3/BwD7/wQAAQD7/wkA9/8HAPn/AwD+/wEA///+/wEAAwD+/wAAAgD7/wYA+v8FAP3/AQD//wAA/v8CAP7/AwD//wIAAQABAAMA+v8IAPv/BAACAPn/BQD6/wUA/f8CAAAAAgD//wUA+v8FAP3/AQAEAPz/BQD6/wYA+/8CAP7///8AAP7/AgD+/wIA//8AAAAAAQACAP//BQD+/wQA/v8DAAIA//8DAP//AAACAP//BQD7/wIA//8AAAIA//8CAP//AQAAAP7/BQD5/wgA+/8CAP3/AwAAAP//AQD8/wUA+v8HAPr/AwD+/wAAAAD+/wQA+v8GAPz/AQADAPr/BgD8/wcA+/8BAAEA/v8CAP7//v8CAPv/BAD6/wQA+f8EAP7//v8AAPv/AwD9////AAABAP3/BQD8/wQA/P8BAAEA/P8EAPn/AwD9///////9/wIA/P8DAPv/BQD9/wMA/////wIA/f8AAAAA//8CAP7/AAD+////AgABAAQA/f8EAAEAAwABAP3/AwD9/wUA//8CAP7/AgD5/wcA+v8CAAEA/v8CAAAAAAABAAEA/f8DAP//AQD/////AwD9/wEAAAD+/wEAAgAAAAAAAAD//wEAAQD9/wMA/P8CAAAAAAD//////////wEA/f8CAP7/AwD8/wIAAQABAAQA/P8FAPr/CQD3/wkA9/8IAPv/BAD+/wMA/v8FAPr/BwD8/wIAAAACAPv/BwD8/wUA+/8CAPr/BQD6/wEA/P///////v8AAP///v8AAP3///////7/AAD8//7/AAD+/wAA/P8CAP///v8AAAAA+/8EAPf/BwD3/wQA+/8BAP////8AAP3/BQAAAAQAAAABAAIAAAAEAP//AwACAP3/BQD7/wUA/v8CAAAAAgD+/wQA/P8GAPv/BgD7/wUA/P8FAP////8CAPz/AwD7/wEA///9/wIA/f///wIA/P8EAP7/AwD6/wYA+P8GAPv/BAD7/wMA/f///wEA/v///wAA/v////3/AQD9//3/BAD6/wQA/v8CAP//BQD//wQAAgD//wYA/P8FAAAABAD//wIA//8DAP7/AQD//wMA//8DAP7/BAD+/wAAAwD8/wYA/P8BAAEA+/8JAPT/DAD3/wYA/P8AAP////8AAP7/AwD+//3/BQD2/wsA9f8GAPv/AgD9/wEAAQD4/wYA+f8CAP///v////7////9//3////+/wIA/f8CAP7/AQD+/wYA/P8IAPr/CAD//wEAAwD9/wcA/P8GAP7/BAABAAMAAAACAAMA//8EAAEA/v8DAAEABAAAAAIA/v8AAAMA/v8CAP3///8CAPz/BQD7/wEAAQD9/wEA/////wMA+/8DAPr/BgD7/wMA/f///wAA///+//7/AAD7/wIA///9/wIA+/8FAPz/AgD8/wIA/P8AAAIA/P8GAPj/BQD9/wMA/f8CAP//AgD9/wMA/f8FAP//AAACAP//BgD9/wkA/P8GAP7/BQD//wUA/v8GAAEAAgAEAP//AQACAP7/BAD+/wMA/f8EAP//AQADAAAAAwAAAAMA//8CAAIA/P8GAPn/CAD4/wcA+v8CAAEA/f8CAP7/AAD+/wEA/f////7/AAABAAAA/f8AAP7//////wEA/f8BAAAA/P8CAPv///////z/AgD9////AAD//wMA+/8DAP//AAAFAPv/AgACAP3/AgAAAP7//v8CAP3/AwD8////AgAAAP////8BAAEA//8BAP//AAAEAPz/AQD///v/BQD6/wQA///8/wMA/f8CAAAA//8CAP3/BgD7/wUA+/8GAPz/BgD8/wUAAwAAAAUA/v8DAP//BAD//wQA//8DAP7/BwD4/woA+f8HAPn/BgD4/wcA+P8GAPr/AQD///7/AwD9////AwD7/wEA/v///wIA///8/wMA/f8CAAAA/f8BAAAA/v////3/BAD4/wcA9f8FAPz///8BAPr/AwD5/wUA9/8CAP3//v8BAP///f8DAPn/AQD9///////+//7//v8BAPn/BQD7/wEA/////wAA////////AQD9/wQAAAD//wUA+v8IAPz/BAD+/wEAAgABAP3/BgD8/wMAAgD8/wQA+v8IAPj/CQD5/wQA/P8EAP3/AQACAP//AAABAP////8CAPz//v8EAPr/BwD+/wEA//8FAP3/BwD6/wkA+v8HAPz/AwACAP//AwD/////AwD9/wEAAQD8/wIA/f8BAP//AwD7/wMA+/8AAAIA+/8DAP3/AAAAAAEA/v///////f8CAPv/AwD+//////8AAP7/AgD///7/AAD/////AwD7/wMA/v/+/wEAAAD9/wQA+f/+/wEA/f/9/wEA+P8DAPv/AgD6/wMA+P8DAP3/AgD//wMA/f8BAAEA/v8CAAAA/P8EAPv/BAD+//7/BAD9/wMA/v///wAAAAD+////AwD6/wYA+v8CAAIA//8AAAIA/f8CAP7/AAABAP//AAAAAAEA/v8AAAIA//8FAPz/AQAFAPz/BQAAAP7/BwD5/wkA/f8DAAEAAgABAP3/AwD8/wMA/v///wMA+/8DAP3/AwD//wAAAQABAAAAAQD//wAAAgAAAAAAAQD//wIAAAADAPr/BgD7/wIAAAD9/wMA/P8DAP3/BAD8////AwD8/wQA/P///wAAAAD+//z/AQD4/wUA+//9/wUA9v8JAPf/CAD4/wYA/v8BAAIA/P8CAP7/AgD+/wEA//////7////+/wAA/P8FAPf/BwD4/wQA/f8AAP7/AgD//wEA/v/9/wMA/v8FAPr/AwD8/wEA/v8CAP3///8CAPr/BgD8/wQA/////wUA/v8GAPv/BAADAPv/CQD5/wYA//8AAAEAAAD//wAAAAAAAP7/BAD9////BgD5/wkA/P8CAAIAAQABAP////8AAAAAAAD//////v8CAP3/AgD+//7/AgD9/wQA/v8DAAQA//8CAP//AwAAAAMAAwD+/wYA/P8GAP3/BQD+//7/BgD7/wYA/f8DAP3/BAD/////BgD6/wYA/f8AAAAA/v8BAPz/AAD3/wQA+////////f8AAP//+/8EAP3/AAADAPz/BQD9/wQA/f8EAP7/AAADAP7/AQAAAP//AAADAPr/BAD4/wEA/v/9/wAA/v////7/AgD9/wIAAgD7/wgA+v8IAPz/AQADAP7/AQACAPz/AwD+/wYA+v8JAPj/BgD//wMAAAAEAAEAAwACAAUAAAACAAEA/v8HAPv/AwD+//3/AQD/////AwD6/wUA+v8FAP7/AQADAP//BQD//wEAAwAAAAMAAAADAAAAAgACAPz/BAD//wAAAgD9/wMA/v8CAAUA/P8GAP7/AQD//wIA//8BAAIA/P8EAP3/BQD8/wAAAQD8/wMA/P///wUA/P8EAP7/AwAAAAIA//8DAAIAAwD//wQA/v8EAP7/AwD9/wUA/v8GAP//AAADAAEAAQACAP//AwABAP//AAADAPz/AgD9/wIA/f8BAAAA/f8EAPv/BQD8/wQA/v8AAAUA+/8FAPz/AgD//wEAAgD9/wYA+v8FAP3/AgABAPz/AwD9/wAA///+/wIA/v/+/wEA/v8EAPz/AwD7/wIA//8AAAIAAQAAAP7/BAD9/wUA/v8CAAAAAgD//wMA/f8EAP7/BAABAP//AwD//wIAAwACAAIAAQAEAP7/BQD///7/BgD7/wUA/P8DAP//AgD9/wIAAAAAAAEA//8DAP//AAACAP//BQD8/wQAAgD+/wMA/v8BAP//AwD+//7/AAD+/wAA/v8AAAAA/v8CAP////8EAPv/BgD8/wUA+v8HAPz/AwAAAP//AwD//wAABAD8/wQA/P8HAPv/BwD+/wEAAwABAAMAAgACAAMAAwAAAAUA/v8HAP3/AwABAAQA//8FAP3/AwD9/wEA//8BAP7/AQAAAP///////wIA/P8BAP///v8DAP3/AQD9/wIA/f8CAPn/BQD6/wUA/P8BAP7///8CAPz/BQD6/wUA//8CAP//BQD7/wYA+v8DAPz/AQD+/////v///////v8BAP3/BAD7/wQA/P8FAPz/BQD8/wcA+/8IAPr/CAD8/wEAAwD//wUA/v8FAPv/BwD7/wUA//8FAPv/CwD2/w0A+f8GAAAA/v8FAPz/BQD6/wYA+v8HAPr/AwD5/wYA9/8FAPn/AgD9//7/BAD6/wUA/P8EAPz/BAD6/wYA+P8GAPr/AwABAP7/BAD9/wEAAAD9/wYA+f8GAPz/AgD//wIA/v8BAAAAAQD//wIA/v8AAAAA///9/wMA+f8FAPv/AgD+////AQD+/wMA+/8EAP3/BAD8/wIA/v8AAAIA+v8CAP3/AQD//////P8BAAEA/P8CAP3/AAAAAAAA/f8BAPz/AQD+/wAA/v/9/wAA/P8BAAAA/P8BAPz//v////7//v/+//r/AwD6/wUA/P8DAP7/AQD+/wAAAAD//wMA+/8FAPz/AgD+//7/BAD9/wIA//8AAAIA/v8BAPn/AgD8/wYA/P8FAP//AQAAAAAA/P8AAP7/9/8FAPX/AwD7/wMAAAAAAAMA+f8KAPv/BwD9/wEAAwD//wEA///+/wAA/v/+//7//f////v/AwD4/wcA+P8GAPv/AAAAAPv/BAD8/wUA+/8EAPv/BAD+/wMA/v8DAAAAAAAAAP///v8BAAAA/v8DAPv/BgD6/wQA+/8DAP3//P8BAPf/AwD5//3//f/7/wAA+f/+//7/+/8CAPr/AwD3/wcA+/8FAP3/AgD//wUA/P8KAPr/BgD9/wQA/v8CAP//AgD4/wcA+f8DAPf//f/9//3/+/8AAPv/AgAAAP////8BAP//BAD//wAA///9/wMA/f////7//v8AAAAA/v///////P8AAPj///////n/BgD3/wAAAgD6/wYA+/8GAP7/AgD//wIAAAAAAAEA/v8BAAIA///+/wQA/v8BAAQA//8FAP//AwABAAIABQD+/wEAAAD+/wAA/v8BAP7/AwD+/wIAAAAGAPz/CwD9/wgA/v///wUA+/8BAAAA+/8FAPn/AgACAPv/BQD8/wAAAwD5/wcA9P8JAPX/BgD5/wQA+v8FAPv/BAD5/woA9v8IAPn/BgD7/wYA/f/+/wUA/P8BAP7/AgD8/wYA+f8IAPn/BwD6/wQA///9/wgA+f8JAPv/AwABAP3/BgD7/wMA+v8JAPb/CwD5/wIAAwD5/wkA9/8HAP3/AwD8/wUA/P8IAPv/AQD///3/BQD+/wYA+v8IAPv/AwD+/wIABAD8/wUA//8EAAEAAAD//wQA//8GAAAAAQAAAAMABAABAAIAAQADAAQAAgAFAAIAAQADAP//AwACAP//AgACAP7/BgD9/wUA/P8EAP////8FAPn/BQD9/wIAAQD9/wYA+f8JAPv/AwD//wEAAAADAP3/BQD8/wUA/v//////AQD+/wMA/P8GAPv/BQD8/wAAAAD+////AAD+//z/AgD6/wUA+v///wIA/f8FAP3/AQAGAPz/BwAAAAEAAgADAP3/CAD7/wMAAQD8/wAABAD9/wMAAAD8/wcA//8CAAUA+P8HAPz/AwD//wAAAQABAAAABAAAAP7/AQD+/wEA/v/+/wIA/v8FAPv/BAD6/wMA+v8FAP3/AgAAAAIA/P8JAPv/AAAGAPn/CQD9/wYA//8AAAcA+v8JAP3/AQAFAPn/CQD6/wcA+v8DAPz/AAD+/wAA/P8CAPn/BgD6/wcA+v8FAP7/AwACAP//BQD//wMAAgD//wkA+/8GAAIA/v8JAPv/BAAEAP3/BQADAP7/BgD9/wQAAQAAAP7/AwAAAAEAAAD7/wMA+/8DAPz////+/////v/+/////f/8/////v///wEA/P8DAPz/AQD7/wQA+/8FAPn/CAD6/wcA/P8FAPf/CQD1/wYA/f/+////AAD8/wEA+v8AAPv/AQABAP7//f8CAPz/AwD9//v/BgD4/wIA///9//7/AAD//wEAAQADAPv/CAD3/w0A+v8GAAIAAAABAAEAAAD//wUA/f8CAAQA/f8FAPn/CQD3/wkA+/8CAP7/AwD///7/AgD//wIAAQD9/wIAAAAAAAEAAAD//wAA/v8DAAAAAQAEAP//BAAFAPz/CQD8/wUA/v8BAP7/BwD7/wgA/P8CAAIA/P8CAAEA/P8EAAEAAAAEAP3/AQAAAP///f8FAPn/BAD8/wIAAQD8/wgA/P8EAP//AAACAAEA/P8EAPr/BAD8/wAA/////wEA///+/wAAAQD8/wUA+v8HAPz//v/9//r/AQD5/wIA+f8DAPn/AQACAP3/AwD7/wQA+f8JAPf/BwD5/wMAAgD//////v//////AgD7/wMA+v8EAP///v8IAPj/CAAAAAIAAwD+/wMA+v8AAAAA/v8EAP3//v8FAPv/BwD8/wQAAgADAAMA//8GAP3/BgD//wQAAgAAAAQAAgAFAP3/BAD+/wEAAgAAAPv/BAD6/wQA/v8CAAAABAD4/wsA9v8HAPv///8AAPz/AwD9/wQA+/8DAPn/AgD8/wMA/P///wAA/P8DAP///v8AAP//AQD+////AAABAP7/AwD+//7/AgACAP7/AQD7/wQA//8CAP//AgD/////BQD+/wIA//8BAP7/AwD4/wUA9v8HAPf/AwD9/wEA/P8CAP////8EAPb/AgADAPr/CwD0/woA//8FAAEAAAAFAAAABAD7/wEAAAD3/wQA9/8CAPn/AAD8/wMA+///////AQD+/wIA/f/8/wQA+f8HAPr/AwADAPn/CgD3/wsA9v8KAPv/BQAAAAEAAAAAAAQA+v8LAPX/CgD7/wIABwD7/wgA+P8LAPv/BAD9/wEA/P8EAPn/AwD9//////8CAPz/BQD8/wIAAgADAP3/BQD8/wMA+/8CAPz/AwD7/wMA/v8AAAMA+/8IAPn/BQD9/wAAAwD9/wUA9v8MAPT/CQD5/wAAAQD7/wEAAQD9/wQA+/8AAP///v///wEA/f///wAA//8EAAIA/v8DAP//AAACAP3/AgABAPn/AgD///7/AgD6/wQA/v/7/wYA9f8KAPj/AgACAP7/AQD//wAABAD8/wcA9/8JAP3/AwAFAP7/CgD6/wcA/f8AAAUA/f8FAAIA/f8MAPr/CQD6/wMABAAAAP//BAD6/wkA9f8IAPf/BAD4/wAAAQD8/wEA+/8CAP3/BAD8/wUA/////wEAAAACAPr/BgD4/wMAAgD5/wcA+/8AAAIA+f8IAPj/BwD8//7/BAD6/wMAAQD8/wYA/v/9/wUA/f8FAPj/BgD3/wcA+v8BAPn/BAD7/wMA///7/wQA/f8FAP7/AwD+/wQA//8DAP3/BgACAP7/BgD6/wUA+/8BAAEA/v8BAAQA/P8DAP3/BAD8/wYA+P8EAPz/AQACAPf/BwD7/wMAAAD+/wMA/f8EAP7/AAD+//3//v8EAPv/AQADAP3/BwD8/wQAAAACAAEAAgD+/wQA/f8DAP7/BAABAAIAAAAAAP//AQAAAAEA+/8EAPf/DAD2/wUAAAD8/wUA//8AAAQA+f8BAP7/AQD8/wIA/f8BAPz/AAD6/wIA+/////r/AwD7/wIAAAD/////AgD//wMAAQAAAAAAAwD8/wQAAgD7/wkA9/8CAAAA/P8BAPz//v8BAAAAAQABAPv/BwD8/wQA/v/9/wQA/P8DAP3/AwD7/wYA+v8FAP3/AgAAAAEAAgACAAAABAD+/wMA//8CAPz/BQD7/wUAAAACAP//AAAAAAEAAAD9//7/AgD8/wMA+v8AAPz//P8BAPr/AgD8/wAA/f8AAAAA/v/+/wIA//////7/AAD//wIA+v8GAPv/BQD8/wIA/f8BAP3/AwAAAAAA/v8BAAAA///+//7/AgD///3//v////z/BAD5/wIA+/8AAP3///8BAPz/BAD7/wMA/P8FAPr/BgD///7/BAD8/wMA/v/7/wYA+/8HAPr///8EAPv/CAD9/wEAAgD9/wMAAQAAAAUA/v8JAP3/BgD+/wIABAD9/wAAAgD7/wYA/P8CAP//AgADAP//AQD//wQA//8BAAEA+/8FAPn/BAD7/////v/+//7/AAAAAP7/AgD9/wMA//8AAP3/CAD0/woA9v8EAPz//f/9/////P/+//7/+f8BAPj////6//3/+//6/wEA+v8AAP3//v////3/AAABAP3/BAD5/wUA/P8BAAMA/v8FAP3/AQABAP//BgD9/wEAAgD+/wcA/P8KAP7/AwAEAP//BgD8/wQA/f8BAP//AgAAAP//AgD//wcA/f8JAPj/BQABAP7/BgD4/wYA/f8AAP3/BAD7/wYA+/8EAPv/BAD5/wYA+//9/wMA+v8GAPz/BQD9/wMAAQD8/wQAAAAAAAUAAAABAAAABAD8/wsA9/8JAPr/AQAEAP7///8DAPr/AwAAAPr/CQD6/wUA+/8CAAAAAAD//wAA//8DAAIA+f8JAPz//v8EAPj/AwD///v/AwD9////AAAAAAIA/f///wAAAAAAAP////////z////9//3/AAD7/////v8BAP///P/8/wIA+v8GAPj/BwD7/wQAAQD//wMAAQD//wQA/f8DAAMAAQD//wUA/f8AAAYA/P8MAPP/DAD7/wMABwD+/wgA+/8GAP7/BAAAAP7/AQD+/wMA//////v/BAD6/wQA/f8AAAAA//8DAPv/BgD5/wYA/f8GAP3/BAD//wUA//8CAAEAAgD+/wQA/f8FAP3/AgD//wMAAwABAAAABAD9/wUAAQD7/wMA/f8EAP7/AAAEAPr/CQD6/wQAAAD+/wQA9/8MAPb/CQD7/wEAAgD//wIAAgD//wUA/v8DAPj/CAD7/wUA///+/wIA/f8EAPz/AAABAPz/AgD/////AQD9//3/AQD+/wAA///+/wAA/f///wAA/v8CAPz/BAD9/wIAAQD9/wcA+P8IAPv/AwABAP//BAD//wAAAwD8/wUA/f8AAAIA/f8CAP//AQD8/wUA+P8HAPv/BAD9/wEAAgAAAAIA///+/wEAAQADAP7/AQD///7/BQD6/wYA+/8EAAEAAAAFAP3/BAACAP//AgADAAAABAABAAQAAAADAAAABAABAAEAAAADAPz/AgD+////AwD8/wIA/v8AAP3/AgD8/wMA/f///wEA/P8BAP///P8CAP3///8BAP7/AAD9/wYA+f8GAPr/AgD/////AAD///r/AwD5/wUA+/8CAPr/AQD+//7/AQD///3/AQD7/wAA/P8BAP7/AAD9/wEA/v8CAP7/BAD7/wUA/f8BAAYA+P8IAPz/AgAEAPv/CgD4/wMA/v///wQA+/8EAP//AwAAAAMAAQADAAIAAAAFAP//BQD9/wUA/v/+/wIAAQD//wMA/f8BAAIAAAADAPz/AwAAAAEABAD9/wEAAAD+/wMA/////wAAAQD+/wIA/f8AAAEA/f8BAP///v8EAP3/AAAAAAIA//8FAP3/AwD+/wQA/P8IAPj/CAD6/wYA/P8DAP7/AgD+/wEA//8BAP3/AQD9/wQA/P8CAP//+/8GAPj/BwD4/wUA+////wEA+/8DAPz/AQD9/////////wAA//////7////9/wAAAgD4/wYA+P8EAP3////+/wAA//////7/AAD9/wMA/f8DAP7/AAADAP7/AgACAP7/BgD9/wUA//8DAAIAAAACAP7/AgACAAAAAQADAP//BAD+/wMAAgD//wUA/P8GAAAAAgADAAIAAgAEAAMAAQAFAP//BwABAAQAAwACAAEABgD9/wYA/f8FAP//AQACAP7/BQD7/wYA/P8FAPj/BQD8/wYA+/8AAAIA//8AAP///v8DAPz/AwD9/wIA///9/wMA+f8FAP3///8DAPr/AwD+////AAD+/wEAAAD8/wMA/P8DAPz//P8DAPf/BwD3/wUA+P8EAP3/AAABAP//AgD//wIA/f8GAPv/BwD7/woA+v8HAP3/BQD//wUAAAAEAP//AQAEAP7/CAD6/wgA/f8DAAAAAgAEAP7/AwD//wIA+/8GAPv/BgD8//3/AQD8/wQA+v8DAPr/AQD///z/BAD8/wEAAgD7/wUAAAD8/wUA+/8EAAEAAAAAAAUA/f8GAPr/CAD5/wgA/v8CAAQA//8EAP3/BQAAAAEAAwACAP7/CAD+/wIAAwABAP//BQAAAP//CAD7/wgA/v8EAPz/CAD2/w0A9v8JAPz/BAAAAAIA//8HAPv/CAD9/wQA//8FAPn/BwD7/wQA//8AAPz/BQD8/wIAAAD//wEA/f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4F/Kf5OfcU9S3zwfH88HrwpPAO8kP0V/Z1+A/8HAECBYAGBQhqCvsKxQhgBlgFCQQvAaT+Pf6Y/vn9D/3e/MT82fua+gn6tPmA+TX6G/ye/mMBGwWaCTENLA96ENgRnRJgEbkOOgxnCRoFBQD6+735ovhG+Pn4ufrf/Kj+4/8EAWsCpwNRBFkEPwRdBBQEHQPNAZIAa//P/dj79/kK+BH2TPSj8pnwre7e7T/uOO/38D70t/h0/fUB1QXGCNIKqQsOC20JvAcZBiMEbQKJARwBCwAE/ib8yPrl+In2MfX/9d/34fl4/Pj/3ANOB28KKQ0BD0UQTBHFERoReA9gDYgKuQZqAq/+G/yV+hj6fvp5++H8S/6a/wcBYQKjA44ExgSABOoD6gJ4ARsAaP/X/tH9pfzH+9r6Kfn29tb0q/IS8Mbt8Oxu7bzuB/E49PH3NvyzAKwE9QehCg4MwwugClYJcAcCBdYCKAGm//T9Rfzu+sb5y/hF+G34HPkL+nz7o/0TALgCIwYDCtoMgA7/D0MRNRF6D0ANTwtICfwGjQRvAuQA5/82/yH+SP1n/ez9jv5q/7kAOQIhA10D2wKxAYcAf/+2/hn+jP1Z/XX9Vf12/Bj7tvnM97f0EfFQ7tjs+OsR7GHtvu+Y88j4JP4RA80HqgtkDTgNXQzUCkIIPwXOAiMBzv+c/pL9zPxO/Iz7avrB+cf51fkM+hz7IP3P//wCTgZdCS8MEA9eEfURRxGREJ8PmQ2vCu4HpwVLAyYBlf9J/o79kP3d/UX+4P7R/7oAIgH6AIUAPgAUAJ7/JP9J/8v/6v+F/77+YP1z++v4qvUP8sHujOzD6+vr3+zr7tvxYfVn+Y79eAENBbAH8gicCScK3AmSCCoHAQZ/BIYChACy/iv9t/tP+ov5ivnj+VD6/Ppj/DD+GgBUAoQEggbRCG8Lbw1zDqEPQRHPEZQQjA6fDDgK6Aa0A08Bbf8K/kX9N/2+/YH+Zf9GALgAjgAlANr/YP+R/gH+Dv5J/hP+p/1o/cD8NfsS+ab26vMf8T/vi+4v7kDubu+L8Qv0+/aL+qL+iQIZBcwG5QhTCpkJFwhyB5YGWwQNApoAP/+y/YL88fuh+2L7Vftx+7r7f/y//Vj/BwGmArAEMweFCXoLhA3DD4gRGxK7Eb4Q8A4vDAAJBwZMA/cAZf+G/jb+bv4G/5r/1f/j/8T/Lv9d/rz9Q/3t/MP82PzY/IL8A/xe+3T6Ovmn9+n1TfQA8/Hx4fAh8CLw7/CF8vX0Nfj7+1X/2AE2BHsGewcmB/MGCwc6BsIElAMhAjAApP5z/QD8Dfte+wH8KvzW/HX+5/+pAGwBpAL7AzUFvgbsCH0LBw4wELURMhJ8EeMPmQ2iCl8HmwTEAmgBUQDl/y8AaAD//3P/Rv/p/hb+c/0//Qv9kfw4/D78BPw1+2L66PmC+cr40/f49jb2VvVk9Hbzs/Iv8g7yrPI/9JH2FvlX++X9EAGZA80E4QU/B3oHZgZrBTME6QH9/wz/v/1K/Fb8ef3W/Z79jv4JAH0AhgA5AVgCXAOKBGgGqAjPCvkM6g4JEFcQOBCXD/4NoAtdCW4HdwVmA9cBIQGeAO3/f/+k/8L/R/+f/kD+uf2t/IT73fqR+uv5Lfka+ZX52/mR+RX5V/gm99f1nvRf84PyUvKQ8mXzIfVf9wz5c/q6/Ar/9P9wAA0CwgMeBCgEewTfA1QCTwF+APz+CP5p/vr+6P5b/7oAwQELAmAC9gJPA4cDIgQsBWgG7gfbCc0LhQ3dDsEPGBCLDy4ObAyUCoMITAZGBJsCUwFxABEA6v+m/yj/nP7z/cX8Z/uY+lz6/Plb+Tr5xPkc+r35DPlW+Ef36vXt9D/0h/NH87fze/Sh9S33JPhK+Aj5mfpP+7L7hf3d/0MBMgKBA0EE5gNjA7ACjQHPAMcA3gDAAAsBuwHwAdMBHAJcAi4CCgJhAhYD1QMiBTMHawmQC30NIw9ZEMcQcxBfD5sNjgtJCbUGXgSiAp4BAwF7AB8Ay/9p/6X+c/1O/G/7lvrw+Yn5P/lT+ZP5iPn0+Cb4Qvco9jD1i/TS80Hzb/Mp9EH1HPZI9o/2fPeU+B/5hPp+/aj/mwD3AbsDawQpBO8DWgOWAi0CswHCAFsAoABqAOL/6v+uAD4BWAGzAXUChwNuBEEFnQZ8CFIKtQsJDYkOqA/0D5EPzw65De8LgAlkB+kFrwSQA48CwgHvAAAA7v7A/QH9Xvxb+2P62vlz+ff4iPgz+LH36PYT9kX1u/Rz9Af02/OG9P71PvdA9zH3n/gp+ur5d/kK+wv9VP2G/VP/+gAwAScBdgGHAXQBgQERAToA/f8NAIv/Lv/k//EAXAG4AcgCDgTpBH4FUwZxB34IYwlMCnEL5QwuDuAOFw/dDvgNZAyXCusINweJBUEEUQNoAqcBFAFTAFr/dP6E/TH85/oc+o75G/nr+Mn4e/ge+Jv3x/br9Uz1sfQs9Fb0fvXC9uX2mPa691z5Evkw+G75cPu+++f7wf14/8P/GQDxADwBKgFsAUwBdAD0/woAsf8T/2j/hABAAbMBzwJXBGMF9AWiBnwHEAhbCLUIWQltCtILBg3RDUYOGQ4qDf8L5AqfCT0I9wanBTgE8QLcAZcAV/9r/pH9rvwQ/MD7iPtO++D6JPpQ+ZP4pfeQ9uT1pPU/9dL0/vQs9pf3f/cx9mH2+vfn95D2bPf5+c36tfpH/EH+6f50/14AjACUAHAB1gHoAEcAhgAPAOP+0P7c/5QA5ADYAWoDvQTDBdoGzwc8CEAIXgiqCC4JJgpPCxsMjQzZDMQMTwy6C+cKtwlVCLsG0AQaA8gBhAB2/+r+rP5X/uP9Wv3j/ID8vPua+qf57vgJ+CT3mPYy9tD1qfUy9l/3xPeo9hv2ePcv+Oz2ufaj+J35Q/lZ+n78af3T/f/+5v8vANYAeQE4Ab4AigD1//P+X/5+/uf+Uf/f/+wAfwL1AwgFMwaJBzwIMAhnCDkJAwp+CjULIAxUDKcLIwsrC9UKwQnICCkIHgdmBbIDXQIuAf3/9v6A/nX+F/50/V79lP39/On7U/um+h/5mffo9pP2VPbK9sT3wfe29pP2pve+98b2L/fO+BX5iPib+aL7gvyh/FT9gv6S/0YAfgChAAwB7QDC/6T+gf6B/g/++/2//rH/qwAFAmwDogQBBjQHjAe3B64IxwkECgoKtAozC3kKZwlfCccJQgldCCoI9AfLBhwFjAMiAuYAzP/O/lX+Vv4h/sr93P3k/Un9hvzr++b6aflP+FT4Z/lc+rf5K/gj+Ef5rfjY9h732Pis+IT3QPjv+an6IfvU+0r8Uv05/yMAo/+1/5oAbQAR/17+wP7p/mr+Sf4b/0MA8ABUARECUAOPBFgFwQVZBn4HoQgGCQoJcAnCCTUJWAhFCIwIMAhsBxUH+QZXBusESAMXAk8BTQDx/jr+eP6W/uj9f/3x/RL+B/33+6T7cfsT+277fvxz/A77WPri+n/60fgi+AL5J/lN+Dr4GPnT+S/6Wvqg+rj7U/0w/kn+4f6q/4//Gf8o/zf/7P7o/i3/Of+Y/6cASAFEAdkBIQPhAxYEvwTDBUcGaQbaBm0HdgcOB+sGIwcHB6AGdQZoBvcFMwVqBIEDcAJ2Aa8A+P9O/+X+4f7o/pD+Kv5G/kb+fP2//D79nv6U/z7/CP6Z/R7+bP1Q+3X6Cftr+gv5OvkR+v35BPpd+vP5/PmP+6/8ZvzZ/EX+rv5K/pv+KP9D/27/hf9y/yoAWwGAASQB2AHcAtAClgI4AxkElATYBOcEzgTsBBcFzwRNBD4EewRLBMMDpgPLA18DiQIOAt4BZQGrAPv/jf+B/3D/5v6I/tf+5/4X/nj9Dv5k/4cA2ACCAGoAlwDw/67+Mf4V/tT8EPuI+t76yvqU+tT6+Prd+i77x/sL/Fz88PwN/c78M/0M/nH+qP5L/wsAngBLASICxAIDAw8DPQN3A48DyAM9BFwEBwTTA6ADEAOWAo4CegIsAgACDgIRAtcBbwEdAe0AlgAkAPX/+P/H/3//aP+E/4L/Uf8j/wX/0P7q/vv/HAEMAaoAZAESAmcBpwDnAPAACQAR/xr+8fx0/If8wPuo+hb7T/xa/Mv7L/wY/VX9B/3W/Ar9o/1V/rT+EP8gAKMBgAKjAjUDVgStBOYDlgN8BC0FmgSwA1sDBAMKAvcAbQA7AAAAyP+6/7//pP9b/xr/Gf8O/5/+Mf5J/on+aP5S/rP+/f7k/vn+V/+8/6cA4wHsARMBeAGvAlcCFQEyAfYBngHmALYAbAD3/7n//P6C/bX87Pza/Cj8+/uO/Pz8/fws/bj9IP5Y/qH+Gv+y/6AAuQGHAgwDtQNeBIYEfATLBDsFIwW+BHYEFAQ5AzwCVAFIAC3/Xf7w/Zf9N/3l/NT89Pwj/T39Kf0T/WL92v3L/Yz9y/0N/tv9O/6e/84ALQGhAYECEgMXAwED7wKuAlUC5wFBAaMAfgCQACcAdf88/2v/K/92/vX94v3S/YT9MP0k/YP9/v1N/o3+HP/g/4kAQAFoArkDaQRfBGQE6wReBTMFuQRyBEsE6gNRA7YCOQKlAZsAKv/9/Vf9rfys+9z6jPp++qf69fr/+vL6dfs6/Ib8r/xZ/RH+nv6I/4kA1gDuALYBewJyAlsC5AJDAwQDwwKkAhwCRQGgACYAkf8V/+7+0P5//lb+sf4l/0H/Jv8e/zj/Zv+U/9z/QwCIALYAUgFTAhkDgQPoA3sECgVfBWoFVQUjBaME5wM0A5kCAwJPAUsAL/9r/sP91PwA/I77APs4+un5LPpF+gX6Cfpz+sD64/qK+wz91f71/2MAIAF+AkcD+AK9AgcD4wJHAv0BwAFMAUMBVwGdAL3/uf+4/+3+H/7S/Zf9bf2X/d79Yf5p/2EArwDpAK0BiAL7AjIDggP4A1kEXQQzBEQEfQSGBGMEJATJA4wDfQMeA2YCzwE1ASIA6P7z/Rf9N/yE+/j6jPpV+jn6Mvpa+or6j/qg+tz6MPvS+9D8pP05/jL/kgCoAV4CCQN2A4cDjwN/A/sCNAKFAdEA+P9A/9v+lf4w/tH9qP1s/Rb9HP19/bf98f20/rv/kABNASsCFgP1A6kEKAWtBUAGfwZIBvUFqQUsBYUE/QNwA5UCvwFiAQoBQABm/9z+U/6J/a/87ftZ+wD7zvq4+tL6EPs2+yT7IPuQ+1b8Ff2w/T3+wf53/2sAEQErATsBhAG7AegBLwJIAikCKAIFAkYBRgCH/9H+5P0y/RP9Gf3W/K38FP2Q/aT93/22/qX/PgDrAPMBCwMHBM0ENQV8BfQFUAY4BvYFzAVnBbgEKQS0A+sC2wHxACwAQf80/kz9rPw4/Mr7V/sT+yn7bvum++P7PPyN/KX8tfwX/bD9HP5m/u3+rP9mABIBngHLAa8BnwGVAU8B3wCbAIoAbwAjAMr/hv8p/4f+4P2K/V39JP0T/U39tf1G/ub+Qf9w//j/tgAsAaQBjgJ6A/gDbAQBBVMFSAUkBeIEbwT3A54DSgPiAm8C7wE/AWYApP8D/0n+dv3H/E385/up+7T70fvX+/H7NPyB/Nj8U/3S/TX+pf4+/93/aQDtAGYBsgHTAfgBMAJGAv4BfAEQAcoAaQC8//X+af4N/pr9Kf0H/R39N/1r/dD9Vv76/qD/DwBcANsAdQHmATgCkwL2AlsDvAP9AxAE+wO3A0EDwwJtAkUCHQLOAXUBPwEHAZsAHQC5/zr/iP7u/Zj9Wf0U/dv8nPxF/Bf8O/x1/Jr80fw8/cX9X/4f//X/pgAMAUABagGTAcYB/wEVAvkB1wHBAX4BCwGKANL/2v4I/o39J/3k/AP9Sf2I/QL+vP5W/8H/OwC+ADwBwgE1AnUCvwInA1gDOQMkAzQDIQPKAmICEALJAZEBbwFHAfwApgBWAPP/gf8t/+3+if4N/r39r/20/ZX9Tf0Q/QP9Ef0V/Rb9QP2p/TL+sv5E//j/lwAHAWYBuwHlAfcBBgL/AdwBuAGUAV0BDgGkABwAjP8Y/7D+Qv7z/ev9Df4l/k/+rP4p/6r/KwCtACIBigHsAU4CrQL1AgYD7gLMArQCjAIxArYBWAEpAfEAmwBYAC0A3/9f/+v+k/49/vT9xf2Y/W79d/2p/bf9pv3A/QT+Jf4c/kb+yP5T/5X/rv/u/1MAlwCcAJoAwwD9ABABDgEpAUUBIAHEAGwAIQDK/2v/Dv/D/q/+2f4R/zj/aP+0/wcATACDALgA/AA3AUwBXQGjAQkCRAJBAigCJQI0Ai8C8wGXAVIBMAECAbIAdQBmAEsA7/+C/0H/Gf/c/qP+j/6B/l/+Q/5F/kb+Mv4l/ib+Kv5K/pn+/f5R/6D/4v8DABYAPABPACAA5P/g/+n/yv+w/87/7f/a/73/s/+Z/3L/Wv9L/z7/Yf+6//3/GwBiANwAKgEyAUYBgwGjAY0BigG7AegB2wGkAX8BhQGLAVwBDgHwAAQBAAHSALUAvQC3AIQANgDn/6T/Zf8O/53+Rf4d/vv9wv2h/b798P0C/g/+Tv6n/u7+J/9l/53/3/9AAIgAjwCSALIAtwCOAGsATQATANz/tv95/z//Kf8J/8/+wf7i/uX+8f5P/73/+P86ALEAHQFSAXcBnwHDAeUB9QHwAegB4AG8AYkBfAFsATMB+wDaAMkApQB9AG4AWwBJADIAIQAUAPD/1P+3/3X/Ov8K/9H+gv5C/jz+N/5D/oP+zf79/h3/ZP+p/73/3v8BAP3/6f/3/yMAGwD7//P/6P/O/6P/gf9h/0L/JP/x/tj+9f4U/xP/Fv9W/6X/0/8LAGAAvAD7ACUBUAF2AZoBpgF0AT0BLgE3ASgBAQH+AAgB9wDRALAArACVAGAAQQA3AC8AGQALAA0A7//Q/7L/l/+R/5H/m/+M/2P/U/9J/0b/Ov8n/yb/Kf9G/3H/if+g/6v/qf+c/5n/rv+l/47/j/+Q/4b/hP+e/7D/m/+K/5j/sf/L/+D/8f8GABwAMABFAF8AdwB2AHUAiQCiAK0AqgCwAL0AzQDlAPcA8QDrAPkA/QDZAKwAogCVAHsAdQCEAIIAaQBWAEEACwDM/5n/a/9C/y7/Nf9X/4f/vv/c/9f/zf/D/7r/qf+H/27/Zf9m/2n/Yv9S/z3/Mf87/zv/Lv8v/0f/Yf9r/3z/p//K/9//6f/6/w8AHAAnACMAHQAtAEAAMQAJAP//IAAlAAQA/f8vAGgAewCMALUA2gDwAP4ABgH8APMA9QDlAL8ArACwAKAAbgBUAFgARQATAOv/1/+5/5X/hv+L/43/of/A/9H/5P8RAD0APgAsACwAKwATAOr/xv+m/4f/a/9J/yL/F/8u/zv/Lf8k/0T/c/+F/4j/n//R/wMAHwAyAEwAZgByAGAAQAAxADIAKQAOAPT/6P/p/+z/5f/U/9b/7P/7//z/DQA2AGcAjAClALYAzwDuAO8A0gC1AKYAigBQACIAEQADAOr/1P/P/8r/tv+h/5X/j/+J/4L/hf+e/8v/5//p/+z//v8IAPH/0f/F/8f/vf+v/6f/of+b/57/p/+v/7v/0P/t/wcAGAArAEAATwBNAEIAOQA7AD0ANgAeAAsACQAOAAQA5v/H/7z/wf/B/7r/vv/V//X/EgAvAFAAbAB+AIQAigCRAI4AfQBlAFQASgA0ABIA9//p/97/y/+7/7//zf/R/8H/tv++/8j/w/+4/8L/3P/v//H/8P/5////+P/n/9z/4P/t//D/7f/y/wIADgAOAAcACAARABQADgAHAA0AFgAWABEADQAWAB8AJAAiACEAIQAeAA8A+P/p/+n/7f/r/+T/6P/1//v/8f/j/+D/7v/7////CQAeADYARgBKAEUAPQAyACoAJgAjABsAFAAWABoAEQD6/97/w/+u/57/kf+L/5X/rv/J/93/7////w0AFwAiADAANgA3ADwARABEADcAJQASAPj/2v+9/6r/nv+O/3j/cP9//5H/mv+i/7z/3P/5/xQALgA8AEkAVQBTAE4ATQBDACYACQD7/+z/2//O/8v/0v/g//T/BwAdACkANgBFAFAASwBQAFcAUQBeAG8AWgA1ABoAEwAAAOb/yf/N/+j/2P+3/6b/iwFWAqn/Sf5G/8kAgwGyAF3/aP/AAPMAOQDG/1n/vP9BAMH/OP+H/5//YP9f/1L/H/+R/9j/af+sAEoBvP/p/kH/SQBOAYEANv+c/0MADgBi/0v/YwACAQMA0v6X/qv/fQAkAHP/Jf/gAOgBPgCI/+z/ZgARAVUAPv/M/7sAagAXABQAq//O/+P/lv8KANMAhACO/1X/5P9wAI8AZwBFAI4AQQE3AcQA6QAPATMBtQAfAA4AtP+d/1X/tP7U/t3+Wf79/aj97v1P/jX+MP51/gf/Qv9S/53/AwB3AIgAVQBHAH4A8wAhAfMA7AA5AXYBHQG+AMkABAEuARwB8wD9ACMBNwE3AfwADgFuATQByADIAL0AqwCKAFEAIgDx/8//lv9H/zL/LP/1/gP/3f55/lX+fv6x/rb+0P69/ln+dv7k/vn+Ef+3/nj++f4q/4/+OP4y/zcAZP+m/tT/5AC4AIgA2ABfAS0CpwIpApEBDQKzAtgCrALwAY0BygGpAYEBRQFSABAASgCN/3j+p/5D/+r+c/4+/hv+Wf7A/pz+bv7T/k7/i/8UADoAKgDcADkBBQEBAYQACQBHAFkAVQC2/8r+fP5+/rH+7/2s/KL9yv6K/eT8ef64/+f+tv4ZAMsASQFBAhsCrQGrArYDtwNNAyoDFwMuA0sDlwKGAeUAtgCrACMA6f73/U7+vf4p/sH9p/3W/Xb+zv6B/kP+uP63/34AXwAGADIADwHKATsB3/9N/yEA4wCS/5n9q/2X/ir+Pv4lAH3/tPtX/J0BqgLZ/qj9/f/LAe4CDgMCAS0A+wJ/BTsEoAGCATMDowODAhsBFADU/ygASADw/g/9U/03/mH9bvyZ/Or8Af2g/RL+Wv0r/ZL+UAA1ARIBxAAwAVQCSQPMAg4BKQBlAcgCXgFG/nf9mv4c/qX9gP+1/qz5vfmsAIQCWv3H+y7/oAFBAyUEJAKqAKoDZQe5BkIDTwJ4BJoFSASQAiABvv/0/4cAaf6g++37OP1M/B77Yvt0+377Gv2D/sr9Gv3F/lQBjwJMAr0B/AElA3sEgwRSAi0AFgHaAmcBof3j+7/8afwV/IP+q/2s94/3nv/MAnb91/rQ/pAC+wOPBBUDpgFZBDcIjwcCBDoDJQWhBRkE4QLJAef/GP9r/0v+QfzN+xH8fftd+x38sPve+kT8cf5t/l79Iv7CAMcC8QJCAuYB/QLtBAAFnwJHAKUABgNBAqH9IftK/GP8pfs2/s7++vd+9a3+KAQV/vf5Mf5UAtkDLwW9A8gAFQPNCCcJUQRXAtYEQga4BLUCIwGq/1D/qv8c/rz7m/sL/BX7rvpk+0n70foC/P/9JP40/Rb+GgGZA6wDnAKmAi8EHgZUBn4DbQDqAOcDZQP2/V362Pu8/Cj7MvzE/SL5L/XT+9QC4P7X+T39MgKWA6kELQUbAzsDzAfLCTMGfwP+BEsG8gSLA4gCcgAc/x//KP4h/Dz7QPvh+oj6xfrG+pP6aPs//fP9+fyJ/aUANwOBA7YCfALrA6wFCgYFBNYAhgBHA0UDWf5X+v76n/vI+U77TP6z+bjzpPkhAyoBxPqM/DoCiATMBYEGNQRRA/8HiQv+B7kD8gQOB5sFXQMTAmcA5/4w/gn9tfsd+6z6APph+Zf5jfrh+ub6avwg/tb9mP1JALADTwQ3AwIDUgQgBqwGqwSMAbAADwOXA7b+Ivru+tL7MPmO+bz9tvum9Ab3+ABvApj8l/w6AWIDQwWcB7kFygLvBd8KNQlQBOwD/gVcBToD/wGVAMH+uf0l/Sj8EvuL+p36ePoQ+q76nvvA++L8v/50/tf9XgD2A+sEoQPaAgkEAgazBvoEogHY/+MBtQOo/735ePky+yb5J/lL/XP7/vP79cwAKwOK/I37JwGSBPMFUQfKBYMDhAZeC9AJmgTyA3wG9gUJA3YBmAD5/mD9U/xj+7P6VPoQ+qj5Vfn3+Sf7uPt+/B/+cv7k/QIA5QMrBaADwgJoBMEGNAdgBUMCUQAuAoIEjwDY+UP5wfuv+fn38fsG/X32tPTQ/XwDu/7x+4wAQwSVBdsH1Qe4BJIFrwolCwgGlAOVBfMFIAPwABsAX/58/L779vrI+WT5sPlb+bn4cvn9+oP7TPyD/lD/TP7H/zMEjQbxBEgDewS1BoQHWgYfAxkA8ACqA1gBqfow+FP6T/m69s/54vx99xfzn/r+ApEAj/yz//0D8AVnCEAJYwagBQUKNww9CKUEcQUVBs8DngHTAPz+TPwy+wf7EPoj+R75+fhe+AP58fqx+/H7Ef68//7+wv/IA2UGQwX6A+gEYQbnBkgGqANVADwA2wJgAdX6h/d5+fL4J/YZ+QH9H/jJ8mr5wAKfAfD8OP8YBFMGjQilCSEHBAbHCTcM3QjHBM0E3AUlBHQBEACm/mL84Ppt+tf5E/kF+U/5zPje+Lz6AvxV/Ff+FQAT/3f/9gNUB9cFogNrBGUGQweSBpwD8v+k/3sCsgE7+yj34/jc+K/14fcG/Z35n/Js94kCcwO//YH+3wP/BkIJgwr3Bw0GoAkADRYKJQVXBIkFGgQ0AX//+f2q+/75cfn8+Hr4QPg8+Dn49vjL+gH8YPx5/v0AewAUANID3QecB4MFEQUvBrwH/Qf+BJAALv+aAeABGfzK9oD3N/g79Rj2gftL+hHzHvVDAAgEIv9S/isD/AZ9CUwLjQmuBtAIGQ2yCz4G+gMfBaEEuAFz/+z9hvtv+ej4uvgd+Jr3pvf797v4ePrU+z/8W/4+AecA/v9EA6cH/QfgBSsFJQaKB/gHcQXjANn+AwHXAYP8wvYP9yT45fSs9NX6KPzM9IbzAf4OBaYBDv+/ArMGXQkfDF0LbQfPB3AMnwxKB9EDXAQjBHIBHf+y/Vb79fg3+CP42fe098f3vfc2+C/6Wvzz/DP+CwGsAdIAaAPHB34IYwZtBS0GXAfJB5kFGAFy/kMAmgHZ/M72YPaY9wn1dfQe+ir8SPVB82r9GwUHAij/0QIhB8wJQQytCyoISAhLDJcM1gdIBDYE0QM4Acv+fv2F+974W/dj99H3qvdb93v3Kfgp+nn8MP1i/kEBLgJZAYID/wdeCSIHmgVJBocH3ge8BSIB+P1v/2cBMf009iL1gvdr9Rfzv/fq+//2BvPW+h8EUAPy/5gCAge7CWUM3AyjCXQIBQxjDe4IogQ8BPUDLwGM/kz9aPuu+O/2n/Y195j3HPfr9tb34/k//Gb9cf4aAbMCDgJmA6oH4Qk8CEoGMwZXBzYIqQY4AkT+t/4wAUD++Pav9DP31/Vn8rD1lfs0+ZLzAvgIAk4EMgFaAmMG8Ai8C9oNVAtRCLUKVQ0iCmcFOATWAz8Bp/6J/bj7wfiz9lv2//Zl9xX36fak94H5+fuR/X7+iACNApICMQO6BmkJXgiSBl4G9waTB8IGxQJR/lb+8ACZ/qv3qfSi9vf1A/MP9uP7M/kl80r4MQPJBMkAYALpBmIJDgy/DesKUgjiChoN0glkBR4EbwP6AK3+if2l+8f4m/YZ9v32nfci9wP30vd2+RH80/2V/rkA1gJPAo0CuAY4Cq0IwgWWBecGwgeyBk8Cpv3s/QcB1f499/TztvZ09qLzM/cQ/Jz3AvPR+m4ETwPg/88CPgcsCusMlQwBCacIQQzBDHgI5gQQBBkD8gDe/vX81/oF+VX3EPaa9rz3RvfV9j74a/oi/E39yv43AeQCfgLVAnQGBwosCQAGAAVLBnwHWQbZAXP99f2CAMT98faE9LX2APaf9Cz5svt39Qz0nv6WBc8Bfv+KAxYI0gvzDSQLgAeoCbUNMgwKB1YE7gN+AmUArP5J/PD5+Pii99j1mfaJ+Nr3o/bO+Eb8PP0e/TD/VQJ/A8wCJwNdBt0JbgnrBQ8ESwUvBx0GAwGf/H/9zv8F/e32kPS39Xv1gPbU+vb5d/Px9aIBdAUQAB3/LQTmCIAMMg0lCakGWwq1DaoKmAUIBJ8DawHK/+X+6vv1+Kz4FvhT9v721PjB9832QPqy/QT9qfwBADkDaAOSAkEDRQa6CYkJigUgA+AEIAdzBTUAnPys/TP/YfxN9+z0WvX99oH6t/sd9jXzJPxcBSEDL/5sACkG6gpeDd4KYgZ/B5QM6AzIBzAEEQQbA5wAVP/R/X/6zfhU+Q74HvaD9z35t/fQ9zT8TP5u/In94AF4A7wCMAN8BJsGWgnGCKME4gI1BYMGfgPk/iP93f2C/RH7m/fQ9KP0gPcV+4j6T/Xg9N/94gRdAtH+TQHiBs8LMA3HCbAG9QjZDPoLIQcABPgDEAN0AK3+w/zt+R/5d/lm98b1x/fq+Db3fPjn/Kz95Ps3/o8CPwNeAmAD0AS0Bm4JkAgGBNICuAVNBsMCyv4v/Zf9iv0f+0r36vQv9an3Afuy+pP1LfUo/v0EYAL5/rYBOgeFC40MlwnoBvoIbQxjC9oGHwQvBEgDpACW/rH8nfoC+mn5Kfep9tX4D/ll9xP5z/xG/Yb8+/77AWsCkAKcA6MEqwYVCccHwAMuA+8FAAZFApn+hP1r/vj9dfrv9uH1+PU+92r69vpZ9kT1Uf0tBDQCSv8EAtEGMgqGCxwK2AewCIoLMgtMB1ME9gOAA00Brv5g/MP6xPoR+kX3k/ah+PP4Rfjg+dz7Ifzw/Jz/mQGtAfUBQgPDBNoGjAj6Bo0DlgN5BjUGLwK2/tr9yv6Q/vz6A/cW9lL2J/aZ+On78/gI9BT5MAOIBAoAXADLBLQIoQvIC1UI3wZACkYMzwh/BMkDLAQbAgP/Q/1F/Hf7Dvqn9zf3HvlQ+ST4Afnl+tP7vvx3/jIAWAHoAUsCxgOdBnwIEwfoA50DgwYxB9MD8/8z/j3/NADj/Or3u/ZH9//1WvdU/CD71vOa9QMBlAW4AM7+ugLXBlUKXgxjCaQFXQjfDLYKGAW7Ay4FugM7AJH+7f1h/H365/ga+JH4NfmT+Pn3Yfl9+zr8ovxR/qkAsgETAbABQgVcCEQHzwM4A28GHghYBTkBJ//E/y0BLf/4+T331/ce9/j1NPlh/BP4OfNt+XoDrQOn/kT/iANtB3gLhAtJBjoFIgsEDaMHOQR+BUQFNAI7ACEAgf4i++/5WPpQ+br4gvm1+Ob39/ke/Mv7CPwR/1wBIACA/zIDKge2BhYEzAPYBWAH2AZ4BIwBUQBTAU0Bqf2f+YT4Qvjb9r32d/nw+kb31PQt+3sCVgFX/noA+gMjB1IKvAmLBl0H0Qp7CnMHQwYbBjwEJAJFAsUBsf2Z+nf7ZvtH+Tf5i/mo95n3l/pm+wX65fuP/5L/Hv57AIAEXwVZBOkE8wX/BbcGUAdnBdwC9QFtAV4Ahf6a++r4tPfc9qP2Wvme+7n3zvNe+a4BKwLG/tX+vgEHBj0KHAoWBisFPwmTC/oI/AUhBZEEJgSsAyMBef0A/G78g/uy+W/5Dfln98333/k6+nP6LvwW/Yr97v8QAtMBSQJJBSsH7wUWBdsGNQjTBkMEBQL/AFkBUQBW/LT4wvfJ93/3dfhx+iT5O/XB9nH+2gJnAE390/6+BHMKaAq3BTQElAgjDFkKrQYiBbUFIQZzBIABK/+v/d38Lvy1+k/5vvjW9wH3NPhK+nb6SvlC+jz+CAG+/3n+nQEjBmQHJAZjBVIGVgjiCAgGWAKYAaoCdQFW/dv5GPkc+db3Lfew+fr6PPac84L7hwOMACX7qf3pAwgIeglkBysEQAbqC3sM7AdfBX4GEgcsBdoCEwG0/t38jfyR+5P5MvhA97r26veu+f/4Kvd8+aH+1/+T/aX9KgF+BQ0IHAe2BFwFHgl+CkUHwwPmAmQCrgAw/5H9NPpI9xz3UvcI+Ir65vi38tH09/+tAxP9G/pQ//wFFAonCSUEpQOBCqkO7Qp7BkYGewdEB5YF6gI3ADj+w/wG/G/7FPlO9pn2LvjV9zT38fc5+Vj7Cf7I/rv9E/8sBEcIrwc8BZ8F2wjTCl8JQgaYAxAC7QESAgQAlPvb92L3Xfje92P3cfgC977zcvcWAM4Atvr2+i0CLweZB2EGfAUrB4ELiQ2YCgEHDweLCKIH9gTKAksATf0h/EH8jvqb9y/2L/ZU9hn3IPji9wj4F/uU/vH+Ov5EAOIEGQjFB34GIAcMCYoKtwkkBvACsAK7A4YCJf6i+Tr4Jvkh+Xb2rfQN96X4+/Wk9tT8GP+O/JL99gGZBFUG5Ae4B+EHvgriDOMKxweJB7cIuAfuBHkCyv+n/NP7gPxr+lL2m/T69Xf3Y/fV9vz2T/jp+8P/f/+X/WAAFwaiCAkIQAcoB2YIbAr6CXIG3AMUBIcDIgBP/Rn88/kB+Pr3o/Zg9E32avlg9yz18PlL/5H+Zf10AN4DOwarCBUJOwjsCWEMggsUCSwITwjtBx8GWgJi/kr9SP6Z/Az42fVZ9jf2jfaZ95j27/Uu+XT94P4n/jH+/ABhBUMIWQgFB64GYQguCq4JTQcCBbwDjAJWAD/+Lv0L+//3aPf593/1W/MQ9wv7Dvg99Vv6AgDe/7z/NwKkA70FLwqOC4oI6wdECzkMSQm7B0UIDgarAUYAfQC5/Wj6fvkL+PX1jPa+94P2yPXw90n6TPvE/L7+c/8mAEQDvAZxBx4H4wfwB6sHQQmcCS8GMANxAysDuv9k/C/85vtM+W33mfb88xXz3/i2/QL4kPGM+OQDYARr/o790wJzCeAMWwrgBfYGfQzVDbUJXgb4BSwF2AIuARUAXP1D+ij5oPi398b34veO9jr20vhk+377nvvQ/XkA3wE9AtQDEwdrCPcGUwZhB2AIdAjeBikErALMAoMBFf4S/JP8m/s1+P71Z/Yn9xD3GfiJ+YD4QfjC/FwAL/94/9IDMAbBBXsHAQp5CW8IZgmvCUgIIAejBZgC5gCzAU0Amvtu+ff6sPoQ+AD4gfkv+Cr3RPrT/N/7L/wk/4gAuwD0AkYFWwUXBdcFugZEBz0HUAbfBBoE8QPgAnEABf46/Wz98fsL+ab39ffP9wn3X/hQ+xz6WPbQ+RQCewNE/8z+3QKiB7kKNgmRBIMEYQr9DCUIqgJUAqUElgRhATv+nvzF++z7BPwZ+hX4XPhw+Vz6iPts+0z6zvvm/xMCuwBI/00BswV2B0YFigPRBP4Gsgf0BQYDUgLOAz0DEAAN/pX9lfxE+2j6WvnN92D2bPYp+UP8K/vI9935iADXAn8AtABzA2oF9AeoCfAGnATrB5cKMwdSA60DewS5AqQAbf/q/bz8QfwB+yX6D/sY+zD5yfhA+1f9u/zQ+0T96v+YAdgBRQHqAQAFGwdvBYwD/QQpB7kGUgTZApED2wNNAVb+uv14/eD7mvpr+UD3v/Yq+Gj4Rvm5+8T6Jfil+zACLgNrAGcAQANzBroIOgiwBQIF+wZRCAMHPQRnAkoCggLDAc3/QP3p+8b8fv37+yn6Ffqu+h371ftf/ID8Q/3F/rX/jv/M/7ABzwN5BC4EPAQhBSgGAgYEBZgEWQRkAw0CcQCT/rr9CP7+/AL6m/je+SH6S/iC99n59Pxe/JX5kvs6AYkCHQA1AMACLQXXBikGsAPxAz8H0geGBKcCmwMKBOYCWgEhAHD/DP91/l/9cvxh/FT8tPtP+8r7d/xc/FT8cP2R/vX+Zv93ACQCkQPUA4cD4QMPBaQF0gTeA54DhQMKA5EB8f+a/yb/of3K/Ef8yfok+vr6fPqZ+K/5cv1I/XL5X/ry/58B7P4o/nUAMQMSBdgEAAP+Am0FwAbDBaoElARuBLwDkwPcA3MCc/8G/hn/8v9h/jz7OPl0+k/9Sf0Q+gz5UPw0/5r+7/yU/b4AYAMbA7EB3gEJBF4GJQbBAxADoQQIBbED9wI6AtX/vv6RAGMAWPyk+Vb6Cvvp+Zr4R/ms+qj5K/hU+oH9rv1X/dH+FABxAYYEKwbKBLkETgf2CLcI1we1BhoGWQYVBjcEsAFNAOD/mv6L/GT7+/r2+fH4OfnP+V35Lvlx+tn7a/zo/DP+ewCdAigDAgPZA50FXAfXB5kGbAULBpcHUwdaBLgBmwGxAR0Ak/3k+n/5s/m2+Af2qvSI9WP3cPjd9pX1avmg/iv/GP4TAEUDrQXIB98IwQheCekKFwvKCSIJCwlyB8sEewNYA8kBlP4X/P/6Yfro+a74ZPaz9aj35/gO+Df4nvqQ/OX8x/1gADkDvQTrBF4FLAcaCWgJEwhaB88IggmeBncDVgOCAxkBsf38+0b7S/nC9rP1dPXu9Obz1PJ09OL4J/on9wT4u/4kAygCjgEYBJIHnAoIDKcK/AhsCswM0QtiCMYGAwfOBRIDNwHG/0H9Lft1+iT5W/cV9w73HvZZ9hP4N/nM+dn6UvxJ/mcAlAGvAjwFfQedB3oHhQicCfcJdwk7CC4HZAYOBVYDngGk/439v/sA+nz4j/dW9p/04PNk9Mb0nPSQ9az40PqQ+dX5Z/9XBKIDDwLEBIIJ9wuDCxQKvgk2C5MMaAtfCBAGZQXSBPsCgwDp/Sr75flq+r758fbq9E71vvbd91P49fcz+BL7yP7W/0n/pwACBMkGxwe9B84HmwgeChgL4wlzB34GxgbkBeUDwgER/9r8Tvx0++j4k/bW9ZX16PRn9Gv0hPRV9NL0a/hi/SP9nPnb/E0FBQhhBTsFBQggCxYOQg6QCmwICQswDSsKcgX0AzUE0wJ9AAj++/oZ+Xf5KPmv9rT0+vRF9mb3HPhc+A/5HPvI/YP/PABIAXMDHgYdCJ0ISQj/CMYKggtiCq0IZAfWBmYGtQTwAb7/Hv5x/Db7yvlY91T1/PQS9WP0bPMk87TzDvVO9jz3YPpr/tr9MPyDAZsIewi6BdYH1wtMDYsNHQ0BC/cJkQuSC88HLgQjA5ECkADV/YT7lPnp98z29PUE9Zj06fQ+9bb1KPcJ+UT6sftb/vkACALgAk0FeAi+CowL+AqoCkUM8w2+DHAJWQeGBzQHJAR2AKr+M/2y+pz48/az9EzzMvMq8unwKPIV9Jrz4fIn9X/4DfpF/PsA2gK6AIsDlAuhDeUJNwq9DRMPhg/dDgYLWgjmCSMKnQX5ALr/LP/l/CP68vfv9br0EvU39aLzZfIB9OT2Q/i4+Dj6wvzT/wQDzgQXBboGlQp8DZINlAxtDFMNMQ46DVAKqwd0BmgFaANyAGL9DPse+TD3mPUq9JjygvFz8TDy//Jz88bzxfQD95f52fp7+wT9WQAUBqIJiwXuAUMJtxGfDgUITQhjC+oMpwwnCEQCZgJ5BREDpf3Y+8T7ofnQ91P4NfjW9VP0qfWs91/4UPiH+Ar6hv3tAHYBvwC9AkMHFAoNCYYHrAmbDVMOzwvUCe0JrArzCfIGiQMMApUBZf8I/Dz6dfmK9571/fRX9I3zyfN99Mr0ePXP9in4ffkc+3X8hf0l/+8AowGeApUGEwr1BkkCZQaYDUEL+gNoA8kGLAfeBfcDOQDj/fT/zQGL/sv5rPkp/CD8a/rp+cD5e/ms+hP8zPvp+xH+Z/85/8sAtAOvBMsEPQaCB5oHUQj3CZcKvgmECEwHZQaUBlsGVANp/3P+Xf9v/p37O/n69+D33/jI+Ij2SPU194v5Nfpm+uD6ifsn/QD/If+u/qn/0wDuAKsAvP+5ALoFDAaG/Tf7AgXrCJYAkPsV/yUCCgMKA3j/S/yw//MD6AFR/t/+ggAbAOf/3gB8AFb+yf3s/z4Bbf81/a/9WwDVAoICiv/K/gQDwAa9BB4BPAKSBlYIIQZgA0EDAgW/BYQEkQIuAesA2wB8/zD+U/5w/Qz74/rj/BD9+/qe+Xv6hvy7/c/8z/q3+l39Df+B/YP7afsT/ND89PxR+7D7+v/A/2b5P/oCA0gE1f1R/d4C3QX1BREFVgP0A6UHvAhNBewCtwQPBigD4P9xAIwB6/50+1/71/z5/Kv88vtj+oT7wf/HAOP9Kv5SAi0EMwOeA8wFdQevB6kG3wUKB94IPQjnBGQCaQMKBcIC5f73/SP+Jf3Q/Dz8d/kS+Ib6Ivzo+ar3Q/jo+YX6L/rh+b/5Pfk/+bz6o/sv+tL4tfkQ/Y4BFAFx+kn6IQUKC68EtP/eA1UJJgvUCgEIbwSxBQ4KjwkABKYAegELApoA+v49/Yf6Svm3+4/9nfrD9w76d/0r/mT+/f4Y/ycBBAWxBd0DbQVeCUsK5wijCCYJJAmRCEgH/QUuBQsEdQKbANf+RP6r/Qf7vvgY+YT5QPj49qv2J/dQ+MH4jvcY9wH5lPrt+Vz5bvqC+637tvvK+5H8Ef5I/lf/swNSBML+eP9OCEYKaARFA3MGtQfrCB8J+QT6AeQEbwdLBNn/Mf+aAPL/K/0D/JD9qP0S+7n6V/0O/jv96/1x/if/qgKiBHYCDwNQB5YHVwUWB88JAAktB2AGCAaYBrUG+wO1AKcA/gF1AMX8XfuA/DT80fl9+MD4zPhk+Ov3j/cP+Hb5F/oL+eD3GvkQ/Cf9hftt+qb7df1C/hb+Nv20/An+8f+vAf8DVANq/qL+4AZwCh0EX//VAq4HNAitBcECaQE3A8UFfwRrACv/VgGIAo4BZgCQ//f+xv9TAewAE/8h/+EApQHkARADLAO7AYUCdwVNBZACwAIJBZAFTwWiBBoC9gA6A+gDEwH4/oj+8/3p/Uz+yfy++aD4ofoB/Mv5NPct+Hf6gPqb+dj5JPor+kT7y/xJ/S79kf0z/q3+V/+2/6j+mP1m/kb/Mv64/UMAlAG6/Rr7BgC8BLIBIf58AEcDnQMIBcsFQwPyAjkHyAjGBXEFMwiTBwMF/wWrB3wFtQK3AhwDNwI1AWsAjP/p/sP+4v6M/gD+X/56/gH90Pyl/zUBAP9I/eP+9wCGAVcBPwDd/hcA1wIHAnH+yv3k/y0Anf5E/R78zPsm/dj9/PsO+v/6D/3Q/BP7APth/O78n/zB/AT9XvyR+5j7jvvT+hz6m/lZ+S769fy//zj+R/rN/PYEMwfCAhECqAYYCocLPAwAC54JzgsHD+0N5Qm/CEsKcQk8BsgEngRmAjn/zf0z/er74vqr+U73ofYv+WH6u/cR9nj4efvY+1f77vwMAIQBDQF+AYwDXgURBtQFMwV4BTQGdwUeBPwDLwOTANP+eP5z/Rj8QvvE+S/4lvh5+T/4Evf297L4nPgo+S35kviy+fD66vlu+W/7oPyO+yn77/zi/kMA7QJOBX8DcAGLBmcNDAyNB3QJlA2mDToNAQ5+DAQKAgpFCmUIaQZcBUcDUABb/7b/ff1M+YX3M/gF+MT2A/Za9SL11vaf+Pf3lPdv+q79Zf61/uQAuAPQBekGsQZABuAHNgqLCfIGPAaiBlIFLwMJAs4AGP5O+3v6sPqF+VD3Dfbx9UD21/ZK9yf37Pa195L5sfp++jH7PP05/gL+7v7BAD8BhQCYAKgBlgLlAtgCaATTB64HrgLHAqIKoA2vB3gErwfsCVwKNwutCFsDSAPMB9EHBQN4AFsA1P5R/bT91vxE+ab23vbK9zn4kfj391n2xfap+tX92PxK+0794gDeAvkDDAUBBccEYwYUCAEHSAWLBaEF0gN0AigCsQBh/k392/xT+935xvlz+SL4Kvh7+WL51vgR+nb7ePuu+7/8wv2k/pX/LAAzAF0AFgG/AYQBpwAeAJ0AjAGkAccAnwAjA/QF6QOu/0YC8AjICOID7wNbBz8IbQjgCL4GUQS2BUYH4gQPAu8BWAGS/sP86vxn/Df6/Pf99kD3NPj3+Br4Cfaw9ln7M/4f/ND66v1SAUoCyQLHA7sEvQVFBoEF0ATSBcsGVgWjArYBwQJIA44BZP6Q/Ev9M/7Y/ND6oPp1+1b7ofoW+4D8qfxG+zH7s/3o/4r/Jv41/sz/eQHPAWkA8v55/1EBpgHt/7b+Z/8nAJIAyQJlBGgB3/4+A4sISAfTA3AEGwdNCDAIIge5BW0FywXeBPoCtgHNAPP+jPxu+5f7svoQ+CX2xPZJ+Az4e/Zd9kv4I/r4+q773Pyc/pYAvQEgAn0DxgXWBiIGZQXtBW0HXQg4B/0EQQQqBRkFHgM6AXUAsf+Z/tz9K/09/H770Po++qb6iPs/+z76aPod/Kz9lv3q/LT9K/+N/9b/wwCqAJH/o//NAEEB0AAxAKT/OADvAlMF4gOlAE8BwAXuB8UFhwM3BOYFdAYUBh8FsQO1AmkCvAFjAC//ZP5Q/Zz7Fvq4+RT62/mr+FH3CvfO+GL7tPvS+er5Mv30/6QARAHfAcIBDgNpBmoIWweuBZ0FBQerCLwIYgbrA+UD2QQQBBkCgwAh/+n9Xv0O/Vf8Jvuq+QD5Avoi+5v6lPn8+W37pvxz/bv9s/2q/p0AbgHiABcBAgLuAXMBpQGmARgByQCUAAgABACnAWwD0wEO/mb+EAOoBNEBjwDLAUYCBQODBIcDAgH9AOcB0AAEAAcBmADD/Sv8SP1c/mf9cvtN+rP6EfxG/V79W/wx/G3+1AAeATwBbQKmAn8CqwQYB04GkwT/BHwFogQfBVEGJwSaAPAA5AKJASH/y/4k/nn8rPyp/T38IvqD+iL8p/xk/GP8tPxL/Tr+Iv+W/9D/KgB1ANkAigG8AfAABgCk/7b/pv/Y/tD9N/2t/FX8//xw/mT/if5z/Jr8ZACjA9gCjQCXAOoCRAXcBYwEKwN0A7IEwgROA7ECkgPsAu//Nv4+/0UAMP+t/Pv62/sU/nz+xvzB+7/8P/4l/8n/OQBTALkAaAHqAe8CTQTNA3MB8gCPA7cFvgQSApUAXAGvAqoCjwEZAMP+jf5O/4n/3v4E/i39vPyB/bv+af62/O77yvwg/sz+IP7o/C39wf4Y/839O/0E/nn+3P3R/Fj8Ef3e/Un9Yfxk/TwAewEM/zr9egD5BP8EkQKdArkEEQZKBtwFJQUPBVEFfwQsA48DwQQRAyb/5v3T/7MA0/4O/Jv6Uvvq/EP9NvyM+178ov32/T7+3v8DAbP/j/7ZAGAEzwRVAuEAWQKmBFoFNAQeAh4BfgJ2A84BdwDlADkAov6N/pz+lf1p/a/9WPw4+2b8i/2u/ML7GPyx/Gv9cf6q/hn+OP7T/uD+Pf/s/yH/lf1r/UH+y/5y/iz9ZPyR/fX/HwF9/0X9lP6YAi0EvAJBAmYDNgQCBRcGGgYyBbgEsgTQBCcF/gSlAwcCNAGyACoAFgCK/5X9EPxd/CL9UP3L/Kj7Ifsa/Fv9zP3I/cP96f2i/vP/RwHbAScBMwDiALsCrgNTA0cCOgFOAZkCUQMdAisArP+PAMAAlP+c/rb+rf71/Xn9lv3r/ez9Jv25/J79g/5u/ir+B/7s/Ub+of48/sP9z/2t/UD9GP0P/fD8GP01/Wr9/P66AL//B/4oACYE1gRFA18D5wQIBrUG1QZSBjwGngZABl0FHgUZBS4EcALMADIAjQANAKb9ZvtF+yj8EvzA+nj5qPnT+hP7nPog+y/8j/zk/PD9M/9GAAEBQgGVAaoC3wPjAysDXQNTBH0EpgPoAt4CCwOtAsoB0gADANT/IQB1/+39ev32/Z/9vvx5/I78qPzw/Mb8APwG/Dz9yv34/Gn89fyv/RT+Gv5V/bD8sf09/3P/f/+yADQBUgDFAE8D8gSQBCEEVASoBLIFBQfKBl8F+wSVBY8FxAQLBJcD4QJ4ARoAzf+w/zD+OPys+9z7YPvK+nT6zfm1+dP6dfsA+zT7avxh/Rn+TP9+AAABRwHhAbwCtAM+BL0DQgP8A90ErwTvAwwDbQK/AvECpgE8ADMAYAC5/9z+Tv4K/uz9ZP1X/AL8+/y0/fD8Afxa/En92v33/ZT9Iv2O/V/+af4P/jL+Zf5G/mb+8v6t/2YAcQDC/wIAuwEEAwED9QI/A3EDQgSSBZQFkgSXBE8FOAW3BG0E7AM2A7UCUgKcAVcABv9l/iP+hf24/A/8M/uF+gH77/u8+xD7ZPv7+xP85vyO/iL/uf4X////egBKAWoCiAIcAogCBAOtAroCpwPvAyYDkAJ3AmoCnwLaAi0CGwHvAAUBNQBY/23/pv///un9Zv2m/fL9qf0L/bb8xvza/Pf8S/1k/Qj9w/zY/DT9zP0l/t79of0G/uT+JQBaAVEBYQB6AMkBywJLA7QDiANDAx4EQQUtBaUExATZBEUEwwPQA8QDBAPJAZgA//8YANn/WP6m/F/85Pyo/NL7dft8+2n7pPty/P/85fwP/dj9gv7o/nD/zf/Z/ywABAGuAZsBVAG1AWsCgQIeAkEC9wI5A7wCfwLzAiUDqwJBAh4C4QGrAWQBpQARAE0AfQDB/8n+dv5u/jL+8v26/Sz9qfzK/PH8h/xP/Kz84Pyz/Kj86fx3/Uz+0f6p/nv+7P7J/50ALQFfAUQBYgEZAuQCFwPzAvICHQNtA78DswM5A6wCfgKsAo8CygH4AMoA0wBeANX/xP99/6D+Vf7t/vP+Gf69/fr97f3I/eL9rf1h/bz9Jv4C/hH+kv6//sT+MP+j/xEA7gCsAaYBjgEhAswC3gKgAqAC2AL2ArICDgKlAcwBxQEZAYcAcwBCAKz/IP/q/s7+e/4b/uf9iP34/Nj8Ov1e/Qb9sfy//Bj9hf3e/Qn+HP50/iH/of/O/x4AxQCJARsCPwJCApMCFwN2A6ADgAMtA+wCvgKNAncCTALBAQ4BlQBMAA8A8//X/3b//f7u/i3/If/Z/tz++v7V/q7+sP6a/oj+wv78/sT+af5j/pz+4/4K/wf/JP96/6r/2f9WALoA0gDtAPsA5wAZAYYBcwHlAMwANQFQAQcBwwB0AEQAdgBrAMz/aP+N/5r/Zv9G/yz/7v7I/uP+9v7d/tz+/P7h/pv+q/4W/2f/dP9+/5P/g/+E/wQAtQDGAEsAMwC6AEcBgQF3ATwBGgFRAaoBwQGFASsB9gD8AA4B+ADHAI8AQQD9//3/FgDl/4r/a/9r/zf/Gf88/0j/Jv8q/1L/Yv9T/1X/hf/G/+H/zf+o/6f/5/80ADoA8P+q/67/6f8MAOT/n/+E/57/v/+//7T/zP/l/9L/tP/I/w8ARAAlAOj/8P8TAAkA8v/2/////f/c/7D/yf8iAE8AIwDZ/8z/KgCkAMAAfAA5AEoArAAMARABrwBsAKIA2ACmAHkAhwBgABAADwA3ADAA+//T/+v/KgBAAAsA6P8rAIYAdgAnABsAVABrACsAxf+Z/7T/qf9Q/xb/If8P/9f+0f4D/xj/B//9/gP/Qf+6/+n/j/9Z/5r/zP/D/8j/r/9X/zv/e/+n/7D/yv/Z/8n/0f/4/yEAVQB3AHcAfgCFAGoAewDmACcB6QCFAF8AlQABASIBuQBJAFoApAC+AK4AgwA/ACYAZACaAHIALgAXAB4AKgA3ADcAMgAzAB4A/P8BACYAKQAMAAMABgD0/+z/BgAJAN7/xP/M/7z/mv+D/2L/SP9b/2j/Lv/0/hn/Y/9t/0D/JP9D/4T/n/9v/1D/jv/m//P/wf+T/5X/sv+l/2v/Vf9v/27/T/9U/47/3v8YAAcA3/8aAIkApgCbANAADwEcARoBIgE7AXUBigFMARcBMQFMAUQBOgEcAeEA0gDhAKgAUwBOAFkAIADv//H/3P+u/7v/yv9//zn/Pv9M/y3/A/8O/zT/J/8A/xb/af94/0f/Vf+O/6n/vv/l//b/2f/o/wMA1v/N//r/3P9s/0X/mv+4/27/OP8y/z7/Rv9E/yX/Ef9R/33/VP9M/3z/mP+a/73/3v+7/7L/+v9DAEgAOgBlAJsArADAAOMAAQEIARABLAFNAXIBcAFQAT8BXgGGAXgBOgEYATABOAEPAfMA9gDEAIsAhgB1ABoA8f8UAPL/i/9b/3z/k/+F/1T/Iv8q/2n/eP9K/yP/Fv8n/1D/Sf8C//D+Of9X/yv/Nf9Q/0X/Q/9g/2X/Rf9Z/4z/i/+F/7L/3P/V/5f/jv+f/33/f/+Q/6X/nP+I/9z/KQBLAEEACQA3AJYArgCOAFYAgwC4ALcAuwClANUA7gDLALkA2QAPAQEBzwC6ANIA+AD7ALAAeQB4AJ4AsQB5ABcA6/8rAEgAGwDu/+v/6f/2/wsA3f+u/8P/v/+C/27/l/+e/2//W/9V/2L/Yv9I/y3/Kv83/y//Nf83/xr/Af8t/1H/Sf83/0T/X/9x/5z/q/+g/5r/xv/e/8X/0P8CAC8AIAATAB4ASgCAAIwAZwBYAJQA3ADfAKYAqwDdAAMB5gCrALEA4wACAfsABAEpAUcBYAGAAToB/QAnATAB5QCEAH8AdAAxAAsA0v+K/4z/mP9//0X/Qv9v/2z/cv9m/0z/N/8i/y//KP8i/yH/HP8p/yP/Sv9q/2n/W/9m/57/1f8NAP7/3v/2/x0AAQDA/9j//P/0/9D/zP/k/wEAEwDh/7n/u//c/8P/qP+p/6b/vv/R/97/1P/f/w0ADAAYACcAMgA3ACQAVwCJALEApQCQALYA1wD4APcA7QD/AAQBHQEhARABGQEJAfYA0wC7AKEAdABRACcAAwDv/8T/lf9o/1b/Xf9E/xP//v71/vX+8f72/gD/4f7r/v7+G/81/zr/Sf9D/1P/jP+r/7//wP/j/yQAOwBIAFYAhgCpAJwAmQCgAJwAyQDBAIkASgBcAJIAfQBxADwAIAArAFMAXQAWAPv/6v8YACYA//8AABsAOAAAAAIACAD2/+n/3v8EAPn/8f/x/9z/7v8pACgA5f/i/ygAQQBGAEUAPgBcAF4AXgBkAEkAPQAwACIADQDc/8L/t/+c/3z/WP9M/0v/PP9D/yP/K/80/x//KP9N/2b/Rf88/3X/rv+a/6L/s//K/+T/AwAaAPn/CwBDAF4AWwBTAGYAkwCVAI0AiQCYAJ4AlQCwAL4AtgCpAKcAugDJANAAvACEAGAAWwBlAD4AGgD2/9H/2f/I/7//u//C/73/uf/C/6T/sv/T/+j/wP+J/53/nf+h/6D/qv+4/6v/pv/G/+z/AQAEAPX/+f/s/+//+f/8/wMA/f/t/8n/8f8OAAIA/P/n//b/6f/j/+j/8P8cABgA9f/u//b//f8HAOb/1f/c/9L/wf/A/9v/8v/2/w4ACQD6/yUAOAAXAAwALwA1AA8ADQAmACMALABDACgAAAAhAGAAVQA1ADYATgBuAFUAPgAyAB8AFQD///P/yf/L/9T/xv+6/6v/m/9+/47/f/9u/5n/mv96/2z/d/+Y/7X/wv+V/5z/wP/N/9X/z//g/9L/8f///+v/+f8NABMAGwAxAFEAVgBlAHYASAB1AJ0AlwB5AGAAcgBpAIAAZwBUAFoAUQAwABUAKQAWAAEACAD3/8b/0f/j/8b/u/+g/6r/0f/S/67/ov/D/8r/2v/q/+j/2f/u/wwA8v8KABsAFAAOABYAFgAJAB0ACwAHABsAEgDy//f/CAD9////+v/m/93/6P/c/7z/xP++/5v/o/+4/5X/fv+Q/6T/t/+3/5j/k//I/+P/3//S/+n/AQD7/xYAHgAlAC4ANgBUAFQAVwBqAHcAgACDAKYAqwClAJwAiACeAJgAcABsAGoASwAnAB0ALAANAAsAAgDi/93/2v/i/7b/oP+t/4//i/93/3f/lf+C/4P/jf+p/8L/0v/g/8r/yP/Y/9X/zf/R/9n/yv++/9z/7f8HAB4AKgBIAE4APgBXAGcAZwBYADMADQD3/w0A9P/d/9H/3v/R/6z/qv/H/+j/3v/R/+7/DQAYAAoADwAdABoALQALAPD/DgBDAFsAWwBUAGQAjwCSAIkAigCEAIgAjwCNAIMAYwB2AHQARwA8ABgAAwDw/8z/0P++/7r/sv+M/5f/lv+S/5P/gv+E/3f/hf+O/3T/gP9+/5X/k/97/5n/sv+5/63/wP/c/+7/+f/x/xsAMAA5AEMAQQBWAFIAUwBOAFcAYwBRAEUAPAA7AEAAKQAcADgAJgAKACcALQAIAPj/EgAWAP3//v/7////AQD//yoAIAAIAAsAEgAiAOj//P8QAPP/CgAFAA4ACgAfADAAFAAUACAACgAHACcAJwAGAPf/FAAfAA8AAgAFAPH/2//g/8H/xP+x/4n/ef9V/3//lf+U/6v/k/+2/+z/7f/v/+z/AwD+//b/9P8eAC4AGwAPAAMAEQApAFAAOgA8AGgAkACEALkAugC0AMcAkAB1AFUAbAB1AEgAJQAQAOj/7f/p/9n/5f/x/8D/xf/q/7//1v/5/7//w/++/5L/rP++/+H/3v/l/9j/1f/J//D/FQDq/yMAIwASAPP/0v9IACsACwAGAL//4f/S//r/DgAPAB4AAADP/7z/w//k/+7/rf/U/+T/BAA6ADEACwC6/8f/kP9x/83/l/+Q/7T/z/8WAPz/MwBiAEIAYABvANYA0wCfAIsAaAB6AB0AGwALAMj/5f/O/63/lv/U/xEA6//4/wgA5v/Y//T/7/8GACoAEgDL/+X/CgC+//X//P/h//X/8v/u/+7/JAAKAP//GADw/87/AQATAP7/SAAzAAYAIgAqAEEAQABBACwAIQBMADkAWgBkAA4AGQAVAL7/y//m//r/AwAEAAMAv//o/9f/tP/x/+v/3f+//7v/4v8NACUA7//K/wIA9v/1/wcADgA+ADIAGgDb/9j/DQDv/9T/6/8IAPj/7P/l/9X/3f8HAPf/CwBDABoA+//l//r/FQAeAA4A+/8JAL//rv/x/+v/5v8DAOr/zf+9/8P/7f/9/+v/8v/6/6D/iP/I//n/0P/A/8j/hv+0/97/7v8IADcAQwAKABQAHgBcAHgARABpAJcAbgA9AE4AKwA1AHIAVQAmAOb/9P8OAAYAGQAqAB0A1v/K/wgA6f/0/00A6P+7/+H/5v/0/+7/7//r/3oA1AC8AMsAtQCPAJQApQBzAF4ASwDO/3f/o/96/yf/MP/p/tH+A//9/h3/LP9U/2z/rP8dACUAKABmAJsAvwDiANkAywCZALMArQA5ABwA/v/e/7r/k/+O/5r/r/9n/3L/vP+Q/6X/u/+x/83/0P/N/8P/2P8ZACEAHwA2AE0AUQBHAFoAYAB5AH4AXABHAEEAIQALAAMA0f/B/63/gv9r/2b/ef9h/3f/bf+P/6z/Nv9U/4L/jf/A/8b/uv+0/6b/vf/h/y8AZgA0AFoAVAAtAF8AUAAhADMAawCRAKsAwACtAMsAJwFMAYABxgHjAeIBzgHoAQcC7wHLAVkBHgH1AGkAGwC+/5L/fv9p/yL/qf6E/hn+of13/ZL9uf2m/Wr9Tf1K/UP9Of1K/V39Kf0P/e/88/xl/cL9FP5S/y4BOgKkAnMDZwT0BG0F7AVABjkGXgUIBOoCBAIHAdT/7P5V/rX9QP00/ZX9L/6w/iL/kP89AA4BdgHXAVgCMgLRAbMBPwGcAN//Gf8x/i/9MPx7+4H7jPtf+2f7XvvX+oD64fr0+on7vP7xAkUFEgZEB2gILAjEB0oItwgrCLUGMwRlAUv/Sf0H+6r5hvln+fr4Rvmo+lX8xf0f/54AKwIKAy0DeAPyA7sDFQN3AsQB2gDC/6/+lf3O/If8o/xP/TD+F/4B/T/8FPxE/Nn8x/2I/mP+rf2B/D/8Uf+UBAMI1ghLCRcJOQfpBckGFghJCI8G8wLk/vz7KPry+Ev5a/qO+uH5sfm1+r/8+/4aAfQCDATAA4kCIQKlAiMDGwOoAsUBQwCu/sn9cv1A/S/9MP1J/YL9Sv0q/JP7MPy4/Nz8Uf0n/jD+Av0G/Jj9XQIIB7QINwm7CUUIvgUkBd4F4QX5BH8CVv4e+4352Pcl95T4RvoU+9j7RP0Y/x0BCwPGBCsGrQYBBrQEegOWAtwByACj//n+Rf5B/T78bvv4+kn7qfyn/i4ATwAS/239x/xU/UX+fP+XAEEALv6g+z37c/9wBcoHgQfpB5gGnwLOAGECuQOnA0ICz/74+hz5rfgj+Yf7y/4VAHL/UP9VAJAB6QKVBHgF1wQnA/QAL/+W/t3+R/+M/57/XP8b/7r+V/61/vb/iQHFAlsDyAKyACH+s/yK/KX8CP1q/bH8NPuB+Wn4X/upAmgHGAcCB7kHXgV+Al4DXAVuBe4D7gAK/bP6aPpV+sv67fyE/tT98/zc/bD/FwEpAhgDoAMNA4cBLwDP/y4AogAdAZ0BxwFoAbsAvv/q/gX/z/+4AH0BFwF+/uD6q/hz+B/5cfpB/Cv9HPzw+T75R/3JBMIJ0Qp6C1QL4AcuBOYDOwVPBbQDfQC8/CX60fhJ+GX5T/x+/lP+oP2Y/mYAtAHaAiAEyQQDBBwCZgDj/1oATwFAAnwCFQJZAbn/z/03/dP9dv4//9//1v4E/MX46vZw92L5hvux/Rr/lf6f/N77nv/RBqALLQwhDJcLuAchA/UBrwJVAqsA8/3I+rP41veM96n4uvuD/hj/9P4eAPgB+wKLA2cECAV8BNgCPgGRAKwACAGoAUoCoAJoAvQAkP40/XD9F/4h/00A/P9v/fL5nffI96/5Gfyd/jkAq/8G/Q370/3mBAcK1QofCwwLEgd2AWH/HABIAFX/3P3y+x363/gv+Kj4NPt8/hEAuwCIAkYEEwQbAwcDIAONAuwBrwFzARIBtABhAF4A+QC1AW8BEQCl/rb9TP3B/ez+dP9E/sr7uPlv+W/6t/tu/Uv/pP++/dX7BP6NBHwJxwm9CcUKbQgwA5wAGgHZAFL/Av6s/Fj7q/oB+oL54fqS/Tn/KQAsAjkEEARiAnEBhgGYAZEB9wFhAvIB9QBlAGkA1gCiAbcBTwCa/nX9mPyf/Of94P7v/ZT7lvkW+br54/rU/DX/BgAq/pD8mf/ABQgJRwmhCngL3wfmAuoAZQBG/1n+s/2Z/Mf7aPtl+qf5LPvn/bf/PAGHA/QEJQR6An8BRQGNAScCkgKLAvsB9QADAMf/cABoAUABl//i/f38kvzG/N79xv7l/Wz7n/mf+Tz64fpz/IT+6P4+/RX9gQEtByEJQAn8Cj4LMge4AvgA+f+R/sj9Nv02/Jj7LfsA+m/5J/va/dH/yAEwBEEFIgR7AmwB3QBQAbgCjwMwA0gCPQENADj/1P9jAW8Biv/d/Rr9cvxE/Bn91f0a/R77hvlY+Rz6SPsk/S7/q/8P/yMBTQaZCdIJ0QodDHcJKQSNAI7+n/zm+5v8pfx5+z76b/k2+VT6+vxGADADLwXABcsEPAPuAcEAIwAeAdkC3wI3Afn/T/88/vD95v8GAngBG/+U/S/9CP1P/UT+1f7t/eL7DPp0+R/6hvs2/cL+4f/PAWYFdghDCcMJ+wooCl0GrwJMAK39fPt1+2L89vuB+q353vnX+rj8KP+yASUEbAWFBOoCNQIfART/rP7cALUCRgIQAWIAhf9u/tH+/gCIAoMBHP+j/ZH9rf1z/Xj9sv3q/Jn6ePiC+DX61/v2/Ob+QwNuCHkK2gmHCrMLVAm3BAECVQA3/Xz6Nvrk+sz61fmz+Nv4PPtW/pAA4gL2BZEHFgavA7AC0wGD/wD+kv8AAkACDwEfAC7/Uf61/iEATwGjAaYA5f4z/qj+T/7W/BT8V/xe++/49Pdn+fD6XPuR/TIEVwtADdkLJgwHDGIHwQHp/1v/CP3++rv6oPq6+YH4Zfff93X7LQAhAzMFpAdMCMEFkgIaARsAX/6X/Rf/TgHsAd4AcP8p/rn93f5bAAwBrQHkAYYA+/7n/lv+/fuW+jr7EPt6+QH5BfpQ+lH6M/6CBikNuQ7gDjgP2wu/BJb/Gv7O/Aj7x/pI+3T6ufiD9+r20ffv+4kBgAVNCJgK4wntBWwCnwCG/rz8cv2B/6MA7gCcAOL+rfy4/B7/9QBFAcoBtgI4AjYAfP75/O36rPnA+cL5h/n5+R76MfnP+gwCEwqTDWoPQRLQEKsI9gDW/XL7yfgW+W77D/yX+tT4MPdo9rn4Ff6KA+MH0QtoDW4KfQXTAZ/+nPso+2L9XP8TAHIAov/+/Er7nPwZ/+EAUwKYA+IDIQNDAdr9j/pU+Q/5fPiC+L75efpx+WH62ACICD4MhQ5MEugSiAxHBDP/hfvc9+X2Pvlu+2/7Q/qH+PD2ivdQ+x4BGgflCzMOHg1pCVkEzf6P+mr59Prr/ED+o//w/6r9M/s8+/P8A/+TAWYEKQY9BpwE0wD/+634Wfe89sf2gviR+ln6tfpFAHIHnQqNDPsQ6RIHDrAHywPE/rL4SPZX9z/47Ph/+qb62/jz+Nj7ZP/2A8AJYQ2pDCsKjAeUAiT80Phc+eH6NPwP/mv/ev6k/Nj7xPvi/PX/pQM/BtcHAgh0BDX+kvkF9+n0lfQg9+j5UPog+0cAoAavCfMLRxClEhQPMAkxBSsBavu89pD1gfYS95n3SvnU+kT7cvy9/wUE6QfUCsULQQp3B8wDFf/0+ij5Wvl7+k388v3W/df8zvy+/Ej86P3fAXUFwgfYCPMG+wGz/Dr4mvRU8x/1uveV+X79KwSQCIkJzgu2D/cPywtbCEsGAAKK/CL5E/f09B30WPUd91T51vw8AM4C/AVaCX8KeAkyCBMG7gEH/lr8XfsA+t/5d/uO/EX8bvxE/Vb9W/35/toBuAQwB08IXAZOAm3+g/oB9jnzDPTX9X73Ev1uBXwJyQm0DHAQYQ5fCeUH+AYZAtP8e/px+Cj1NPPI85L1hvjJ/OkA4wQoCUELBgpzCKsHywS+/8D84/xq/Hf6b/qE/AX9vvsb/Nr9oP2O/Gj+EAJjBL8FpgaiBYACzv44+3n39fSF9N/0K/hZAJYHigkGC7sOyg58CSIGpQYJBbMA+/1M/Jv4XfR28pHyaPTU+Dz+0QKuB5EMug2HCp8HZAYHA9r9xPvT/HD8MPoF+qv7avtk+rn7yv1F/sj++AArA/IDIQThA3ACOgAG/p/7F/kY9yj2XPiv/gsFagg/C1MOcQ08CHUEyQPGAggBjwAMANT89vcL9KzxbfG09Mj6KAELB5IMgA/2DSsKpQbEAlD+9ftT/Cz8hfrn+WX6f/ke+CP5f/vX/HX+HgKvBQoGBwRyAjkB6f5z/LL7IfyV+4P6WPzvANwD4QTLB2ILvwrwBs4ENASgAjgBZAE4AbX+lPpa9gDzD/Jr9Pz4pf5LBXwLZw6xDWQLGghpAyr/jf1C/Q78p/oy+pL5F/hP92P4KvrK+0b+/wFbBSwGSgQRAncAYP4H/Gf7nfz+/Cn9uwBiBf4F1wTjBiQJawaBAnQCDQMsAQYAlQF3Ag4ABPww+OX0gfMz9Sb5Xv43BBUJwQupDBQMKQlVBJIAYf9M/sP7Cfpu+in64fer9sf3s/hZ+VT8XwEvBQwGRQWEA4UAff3J+3n73/td/XUBwQZgCbII0gd/B5UExP+f/Vr+KP/R/2gB8gLMAs0A9vww+DX1QPWS97D7hAE2B+AJ6gmTCZgI1gW6AnwB/AD4/m38wvpH+Y33evbI9rL3ufio+tv9bAHTA1cEtwN8AnsAEf6o/M38vP2SAA0GdwrxCi0K7wnDBuH/zfow+tv6jPtR/qEC4QTcAxgBGP24+Fv2Dvcz+gn/rwTsCNgJqgjpBlcElAGUAEQBCgEy/1z9cvu/+H72Gfbf9tT32vlE/WsA8QEzAsMBRgBj/mL9g/1g/vT/wQN+CRoNBQ0oDFMLYAc6AKD6HPid9tv2BvoQ/hwBXgP2A0gBD/1h+pT5ovqJ/gQExwcJCT0JjgcEAzf/aP/fAEsAPf92/0L+Ovrw9lz2Z/Zd9iX4J/zp/9IBqAJsAnUAmf2x+3L7CfyU/jQF+AyWEHsQLRAuDnwHff+g+k73FvTr83n3HvtP/Yf/YAEnAbf/G/8b/zj/BAGhBAkHEQf0BqsGugOA/w7+4P6u/vr9Wf4Z/tz7f/k++AP3SfbO9wP7Ff5/ACcCNQKLADD+D/yo+q/6GP7IBbUNohEmE8kTURAcCB0A3fpo9tbyi/JH9Yz4EPvz/CH+sf5c/2gAzQEQBDoHlAl/Cc4H6gVyA1sAVv4q/k7+vv0l/Yb8U/tR+iP6pfmN+If45PlY+8j8+v4fAVkBw/8R/qj8MfvL+z8BHAk1Dr8QWBNpE4UNNwWs/8D7JvcI9C706PVt96v46fk5+8X8Xv5IALUDLwgFCzILmwqdCTkGmAE+/6n+Y/1J/Jb8VvxZ+t74Evkh+YL4BPnv+lD8wfzm/YH/yP8I/3r+cf3C+8j8ngIbCakMyg9zEwoT0AywBSgBZf12+XH3lPfh97T3mPdf92r3Tvm4/IH/LAK0BkYLegwlC/EJ6AfZA70AMABT/0H9N/zo+/X5+fa+9XL2HvfE98r5kPxF/vD+uP9EAND//v4C/vP8rf5cBGcJlQu8DoAS4hBJChcFowGV/JP4C/mU+uf5cflF+kn5qPa49hb6qv1RAY0GQguSDJcL/gmSBuIBxv9kACIA9f4H/37+8/od98P16PSl86P0//fW+tX8N//fALQAIgD1/6j+of2OAfoI1wxQDfgPTxKzDXsFXQF8/+36RPfR+NT7A/z/+sv6A/pL+N332fmJ/V4CXwe3ChIMWwyZCuUFQQGt/yf/Zv2m/GP+Ef+A/Lb5cPhb9l/z7vKe9aj4Qvsc/k0AEgEhAWMA7P4wAP4Fhgt5DaYPLRMEEqUKxwMGAGr7Y/aK9Sz4HPoK+0f8kPwr+xj6gPrE+4L+dANACHEKPwvCC+IJLgUmAY//c/7w/FL8hPwT/P/68PmA+OX2A/Zx9Ur1TPcE+6r93f5IAFgBlQB2ADkEoQkADWgPZRLqEoIOPwhVA7b+lvkJ9nj1yfZI+HL5SPqr+uv6V/sM/Pv96gFtBjUJNwrICiMKGAenAwYC5wDP/iH9wPzT+675P/iv91T2AvVW9SX2rfau+Pn7I/5n//AAWQF0ASEFxQo3DTkOuxFKEywOoge/BLYBHfx9+Kf4zfiJ9xv3u/cG+Hb45vkU/BH/2gIyBigIcQlYCowJ9Qa6BMIDkALEAJT/tv7r/Kj6x/jC9qX0jvNl85rz6vRl9+f5YPxK/xEB0ABCAukHNw3gDsgQQBRDE3IMhwZSA7v+6/kM+X/6hPrc+cz5n/it9n/21veO+Sj9DgOqByYJMgofCwkJ6QS+AokCaAFKADEBhwFl/zf9rvtb+PTzAfIu8iryIfMt9jD5GvtR/Wr/HQCLAvkIsQ6HEHYSqxUcFHEMwwVrAuz9kfgm92v5//oh+1b7qvqt+FD3d/eb+ID76gA6Bu0IWQo4C6QJywXfAn4Bhf8r/oL/vgB0/yX+Ev76+1n3c/Qd9GPzcfKW83n2DvkZ+zz9JABLBWELJg9RETwUoBXPER4LGAYiAnT98Plc+WL6IPuA+4T7b/qu+Lb3E/in+c78LQHqBB4HjQjlCFAHHgW+AzYCHwBi/y8A7v9N/nn90fwg+qv2OPV/9bf12fWt9rb3TfjC+FD6nv5VBbwLdRA7FDgWAhRUDncITANt/kP71/rs+x39Ef4e/o/8JPpD+EL3Vfe6+VL+pwKTBdQHxAjjBqsDNwL4AZcAgP/lAIICgwGo/8z+0PyH+Jr0PfOk8170n/Xi9076V/sz+x39hwLgB10L6g8HFRsV1Q8eC7oHzgEg+8r4Nvpm+yf8Bf53/9H+B/0K+0v5Rvl6+zj+0QBOBKMHDAgRBlcErAK8/2j9+/3M/2wA2QAGAm4BiP3V+LX1ufM58kfykPSp9wv66Pv5/vQDYgiaCjENYRHfEg0PMQqvB7cEg/+E+wv7xvuw+737nfxZ/WX9Cf14/Gv8tf18/60AFQIhBP0E+QMwAzkDvAHM/qf9rP4O/57+f//xAA8Ay/yz+cr31vWA83Pyk/OE9YT3nfvUAnsJ5AzEDyoTCRM2Dj0JIgaWAt3+Wv10/Wr9lf0A/kH9tPsJ+xr7Mfsj/Hr+3QByAvQDEwVzBMwCCQLKAYIA+f7I/hj/vP6n/nX/wf+E/nr8kvoP+dn3l/Z29Tn1ffW89VX4DP9WBswK1w7XE9YUrQ8XCigHOwPT/Zf79vzX/bv9t/6N/1X+Tvzm+gb6SPpf/Pn+AgGYA1AGdgZNBNYCQwKEAEL+3/28/tD+6f4OAH0Asf4E/Dz63PgX9/X1Nfba9sT27PYm+jQAOAWbCFoNVRKOEiQOSwqyB0QDZP61/IX99v0O/gL/pP/r/uD9zPx0+/P6E/yk/cP+yACTA7UEPgQWBKkDngFV/7P+bv6E/ev9r/8fAH/+hfxQ+9j5evfi9Rf2oPYT9sf20vtPAvgF3Aj1DdcRFBB5C8AIggYvAvT9ufym/WP+3P7N/58ApwDv/5z+UP3R/Ov86/xx/Sj/wwBeARECMgM8A8MBZQDb/xj/Lf5i/pz/CQCu/tb82Pvh+s74v/Y49hv2b/VI9yn9yQLgBbYJfA6HD3sM0Qk0CDwFswGz/9v+IP7V/Tj+qP4e/+n/UgAcADYAiAC+/zD+sv3z/YP9ev1F/zQBbAELAT0BlgDY/of+8/+dAPP/S/+3/m39l/vF+Qb4dfZF9aH1NPmf/uUCiwbkCmcN/QtDCa8HDQbEA04C3QFYAY4Axf/a/gr+8P04/mb+Wf9oAYYCgQE+ALn/O/7k+5H7iP3S/tX+rf/lACcAh/6b/o//kv9B/2P/Zv/7/ub9tvsk+Qn3rPXQ9kD7GgCtA5sHQws9CxUItgVqBEECdADkAGMC9gKhAjMChgFmAC//ZP6O/sr/QwHgAaEBNwEqAM79fPv9+tr7lvxx/RX/NwDy/8b/YgA9ABv/0/66//b/6f63/Xj8I/pr93T3HPsK/+0B/wUxCmEKIQepBPcCHQA4/kD/IgE1AjgDzwPaAoIB5gDb/8z+9v9eAugC+wEBAsUB5/5w+4z6Lfsu+3n7Jv2D/lP+RP5a//3/h/9w/2YAOgEQAUkA0P7u+3H4N/eq+Uz9UACRBNYJgAtPCNoEAAMJAIX8IfyK/sQAiAJZBNgEugOJAjkBOf9n/iwALgJTAkwCEgMfApP+ifvR+sr6nvqY+3n9Nv65/dT9iv47/kL91/0AAH4BqwGBAToAvPyi+Uv6Fv33/rIBCgfECj8J1AXiA0oBBf2q+tD7Af4SAI4CXgR7BP0DWgO1ARAAdwDMAdABWwHMAXkBGv+i/KX7JPvN+r37b/0r/gX+Q/6z/kj+Xf1j/Zz+9//XADcBawAR/uv7GfzR/Vz/wwHlBcwI9QeRBfMDyQFZ/hH8RvyU/R7/GwHeAqUD0QN+A1QCPwFxAQACwQGeARsCgQE3/zL9CfyJ+mj5SfoD/ND8jf0f/x8AlP+0/r3+W//X/z4AswBAACr+O/yq/Ef+Lf/nALgElQcNB00FOgRpAmP/UP0D/VH9Kf4qAD4CFwNeA7ADMQPvAZkBFQKqAbkA4wAYAZv/vP36/P/7WPoa+k773fsj/Mn9mf+c/97+TP96APgABAGHAXkBYv/p/O78e/4l/1AAhgPhBS0F1ANdA/QBcv8R/hv+a/4b/2oApwGpAosDmAPKAk8CcQImAhMBNADz/3r/Wv5b/Rf93/wU/JT7+vtJ/Ob7C/ye/QP/1/7f/qIASAIpAq0BbAG1/4792f1Q//f/4wFvBYMGYAT6AnEC2/8F/Q79Kf5I/iz/aAF7Ak0CGgP/AxwDQQJEA9EDKAKPACkAvv5O/Hf7A/wA/Bb8Nv3Y/Tz9svyR/Fz8qPx+/Vz+DQCuAigEqgONAtgARv7H/GT9tv6/ABIESQaRBRcEHwPUAKL9gfxk/QX+xv7SAJgC6QJHA+4DHQOgAQACDAMJAjoAyP8v/wr9UvtN+8L7R/xW/TT+Xv5y/l/+oP39/DD9vf2s/pEAogJZA3cC3wCZ/wz/xP7o/u8AYgT4BcsE0gOzA7QBNv7X/L/9F/4l/uv/EgJ8ApECnwO7A1IC6AGiAvwBNAAx/zX+SPzf+qb6sPp6+3399/4V/0v/vv8m/9/9PP1X/QL+bv8yAU8C+QFlAA7/6/79/vf+1wCGBG8GbwWGBGgEcQLq/k793/0I/vr9kv+lARgCJgJLA7MDZgLmAeUCigJ/AEL/dP4//BX6wfki+rf6ofwN/xwAXQDNAIwAEf+r/X79Wf6l/yEBeQKTApAAKf6w/RX+3/0//3UDowYkBhUFRwWzA8X/c/3T/f79w/1k/8IBTgIiAgADSgPfAQ8B2wHSAV8Aof8e/+H8bfrz+TX6Pvql+zX+wP9rAFMBjgFlABP/g/6e/n//BQE/Ak4C5AC4/mb9RP1P/TL+VwHxBA0GkAWwBRQFLQJF/3P+Q/7I/X3+TQBfAeAB4gJYA2ICjAGmASABkf+K/h/+v/zK+gz6YPrE+tH75v2T/0QA8gCMARwBBgBk/33/BgC2AEsBcgF6ACj+LPw0/DX9IP57AHMEvgZJBtMFxgXPA8YAav8b/3H+nf73/5sAuADlAd4C7gHsAH8BoQH4/6/+vv7h/Y77TPrV+kX7j/tC/Zz/oQDTAJcBCAIfASAAMACOAKEAzgDaANf/pv0y++L5iPp9/AT/dAJFBlMIBgjwBpoFYgPnAFP/wP70/q7/HgBHAAgBpgG5AF7/bP/b/wP/Cf5U/pv+iv1b/JX8cP3W/ZT+KwAmAdAAqgA+AfwA0P+l/4wAvQBBADgA3v8U/oD7KPn+92L53fxDAJwDCghAC48KHwjLBg8FWAFM/hz+wf5B/hb+f/+tABMA4v5l/hL+av37/AL9Sv3R/Wn+vP4+/2oAfgHUAe4B9gFSAXsASABAAOf////ZAHsBGwEHAIn+XPyM+SH3Hfb89i366/5AAwcHGAt7DRgMTAmTBzgFXwHg/rD+f/7v/Xz+Kf9C/sf86vve+or5Rfkm+jT7tPwx/6wBOwN5BK8FtQUuBJACmwFjAA7/Pf+1AFEBAQFIAXABpf+R/AT6/PcW9nT11vYH+bD7EQA1BXgIdQr7DPkNdQtmCA4HuASJABz+S/7Y/Tz8ufva+6T63Pjf9wj3Zvah96f6zf0xAZ4FPQkFCiUJEQjRBVQC7/9u/1v/mf/gAAkCwAGbADH/7vwo+m/4CfgK+Jf4KvrU+9n8Ff4mAIQC0gQsB34JgAtwDHkLOQntBj0EbgC6/Ij6Q/kE+Bf3svZh9jL2q/b+90L6k/2LAUwFPggUCmYKJwkcBx8FcQMeAnwBjwGkATMBZQAY/8z8DPo2+Ir3ePcx+BT6Ufzw/SD/WgAUAfwAhgHDAx0GXwcwCdULPwyGCXIGyAOZ/4v6MveN9U/0AfRF9TH3UvkA/Jf+XwANAtoDwwToBHwFQwZPBiAGagZVBj4F8APHAtsAGv7++xz7SvoQ+bT41flP+3L8Cf6EAOMCGwRVBA4EMAOdAdD/fv74/SD+qP6W/y0B1gIwAzgCOwEVANb9xPvF++z8n/2//ucAEgIRAZD/pv54/Ub8qPy3/jUBwwNnBlYIygikByIFKALG/979/vvn+kv7EPwP/Pj7pPwk/Z78Evxs/PX8S/1k/mUAZwJlBIcGBQiuCOoI6QcRBQYCy/8e/Tf6QPn3+Uj6nvow/I/9ef1U/fL9MP40/o//8wH0A8gFCAiXCXUJUAjVBr0EIQKO/yD98vpv+Wb4ZfcN9yL43vlb+zn9DgDRAosElwU1BvoF+gS1A1EC/gAIAFf/wv5q/j7+B/7I/Xr9Hv0A/Tz9kf0X/hn/JQC/AFABGQJjAvkBxwEgAiMChwEDAcYAaQDY/xv/Gv45/YL8bPvG+hb8Wv6n/yIBAgTOBeME6QMuBOIClf/P/TX+0/2R/N78LP5b/iL+9/4UANQAAAJMA5UDXwNaA3oCbwDk/l/+fv1//BT9w/7R/7UAawKVA/UC5gF7AdkAuf9K/9P/YgCwABUBfQGqAYoB/QATABT/Hv49/cX88fxk/bL9Df6//l//SP/F/qT+zf7C/gr/YQAZAiEDvQNXBCAEfgJtAO3+nP0T/BT7YPto/E39Gv5W/9AAvgHxARACkAIVAy8DHQMsAxMDawJpAYwA5f86/4z+Cv7M/cr94/3z/Q3+W/7S/iz/Tv9e/4b/tf/d/yAAqgB5AVUCCwNcAxUDSQI4AQAAy/7z/a/9xv0K/pf+Qv+E/27/b/9b/+7+0P6g/8QArAG2At8DYAT2AyMDIwLnAJ7/df6C/Qv9G/1L/Xn98v2H/pv+UP5X/rb+4P7p/mn/bQB4ATsCzAJMA4QD7gJ6Ad7/pv6A/Uj84fuw/Mj9o/7R/0kBOQKyAjoDfANOA0UDPgOGAo8BAQE6ANH+0f3S/Rb+UP7//tT/DgDF/0j/Uv4a/Yv8tvz9/Ln9jf+TAYwC5gJxA4MDewJdAR4BCAFVAI//OP/J/tf92/xx/Jb89fx//XP+xv/6AMMBaQINA1MDPAMjAwMDewKNAZkAvf/1/mj+Ov5b/rP+IP9b/0L/B//K/of+Zv67/nP/RgAkAQICbwIdAmIBqgDx/03/OP/Y/5QA+wAyAR4BRwDM/lj9UPzb+y38S/38/uwAjgJ6A8IDggOUAlUBcwD//7j/x/81AIYAnACxAIgAx//W/ib+e/3i/Pb8xP2y/qP/2gD0AV8CWgI4ApIBVABj/xz/6v7Y/n7/gADiALEAgAAKAPL+zv1K/Tf9Tv32/Vz/3gD7Ac4CWgNcA+wCYwLCAf8AXQDk/1z/3v6y/r/+0/4P/4f/AwBcAKAAtwCLAEoAHwDs/67/pf/e/xMAGAAPAB4AIADY/2v/OP8w/+z+ov7G/gH/2/62/gn/bv+g/xUA4ABpAYgBnQGDAfAAUwAgAP7/vv+y/9v/2f+7/7f/qf+b/9f/JQBAAHYA8gAxAQwB/gAJAaoACQCy/4v/JP+c/lj+Tf5I/mX+xP5T/+f/UgCLAL8A+QD1AKMAawBwAGMAPwBwAPIAMAEPAd0AigDp/0f//P7h/u3+Qf+0/woAagDaAAIB2ACqAHsAJgDZ/7T/nv+m/+j/GQACAOn/7P+q/xf/qf6l/s3+2v73/oP/TwCzAKsA0QAXAdAAHADA/73/nP+E/9j/awDbABcBJAHhAEcAk/8C/6P+eP6u/kH/6P+HADUBxgHmAakBTQHLACEAmP9r/3r/qP/u/zAAYQCGAHAA//94/yP/1/55/mP+wP4x/33/4P9mAMcA9gAUAQgBtwBVAPP/hv87/z7/W/96/7//GgBLAFYAXAA8AOX/i/9T/zH/SP+u/x4AZgClAOkA4QB+ADQAMwAxABcALQB0AJYAdAArAML/Rf/k/rz+z/4m/67/NQCfAOAA3wCYADAAy/97/1r/dP+6/xIAawCtALQAiwBgADgADgAEAC0ASwA8AB4A3/9u/wT/zv63/tL+Wf8ZAK8ALAGrAeMBoQEnAa4ANQDf/8b/z//q/x4ANAACALn/cP/6/nL+Nf5M/ob+7f6G/xkAiADeAAwB/wDnANcAqQBbACQAFgADAOj/5//0/97/o/9g/y3/EP8H/w3/K/95/+//XQDDADUBpQHVAbsBiwFeASYB3ACLAEwAKgD8/5b/Kf/o/p/+Iv7O/ev9Nf6E/hf/5P+FAOsAPgFfATUB+ADLAI4AUQBEAE8AOAAMAO7/w/9f/+j+qP6l/rj+1f4a/5X/EABUAH4AxQASASYBEwEaAT8BRQEOAcEAhQAvAJ3/Cv/J/sn+zf7p/kT/sv/t//v/AQD6/9L/tv/J//L/IQBsALgAwgCfAHsAPADh/6L/nP+4/+//OwCBAL8A6QDGAGMADQDK/2f/F/8z/4n/uv/1/2IAtwDFAMIAxQChAGkARwAhAO7/6f8MABEAAQAgAD0A/v+h/3D/M//I/pL+r/7I/uj+Wv/m/zYAeADcAA8B4gCkAIgATwDk/5n/l/+p/63/yP8DACEAEADs/8D/hf9X/1b/a/+b/wYAggDPAAABNAE1AekAmABzAFMAJwAXADcATAAdANL/nP9V/+z+qv67/ur+G/91/9//HwBAAF8AYwBGAD4AUwBPADQAMAA4ABIAzv+k/5b/if+M/7//DgBZAJ0A0wDbAK4AegBQABAAw/+t/9P/7P/t/w4ATQBnAFcAUwBXAEAAGQD3/9j/t/+i/5n/jv+E/4L/ff9r/2D/af9y/4L/t/8GAEoAdgCkAMsAwQCHAD4AAgDK/5//mP+9//7/SACSAMIAyAC0AJkAYAARAN3/1P/Q/8r/4v8VADgAQABGAEMAJAD8/9v/u/+c/5T/nv+d/4n/d/9h/zT/Bf/3/hT/Q/+H/+//WQCVAKoAtQCfAFUAAADO/6z/hP92/5P/v//h/wsAQQBrAIkArQDBALMAngCaAH4AQgAXABIACQDy//P/CgAOAP//+f/p/77/lv+M/4P/cf95/5//wv/R/+n/BgAKAAgAGwAzADMANgBZAHsAawBMAEQANgAJANj/wP+5/7n/z//7/yUATwCIALMAsACNAG0AUQAsAAwABQAfAEgAawBlAEAAFQDc/4T/L/8P/xj/I/8+/3X/oP+o/6j/tv+1/6b/tP/q/x0APABfAIEAfwBVAB0A8v/N/67/of+u/8n/5P/5/wMABgAKABkAMQBTAHsAmwC2AMYAuQCQAG8AZgBWADAAGgATAPP/sv92/1j/Qf8u/zj/ZP+V/7r/1f/u//3//f/1//r/DgAcACAAMQBDADIACADy/+3/1f/E/+H/GQA6AEYAWwBmAEsAIAAIAP7/+/8KACoASgBpAHkAZwBIADYAIQDz/8//3P/t/9j/yf/j//P/2P/C/9L/0/+3/6f/tP+x/5n/m/+y/7f/uf/T//H/+v8EABQAGQAYACsARgBZAHQAnQCwAKUAjQBrACgA1/+p/5b/g/+H/8j/FAA2AE8AgACZAHcATABIAEIAHgAFAA0AEQD5/97/x/+g/3X/Wf9B/zD/P/9u/5v/xP/6/ycAMwAtACAADAD+/wgAHgA1AFkAiQCcAIMAZQBKABgA2P+2/7n/wv/I/+T/EAAuADQANgBEAE4ARgA8AEUAUQBCABkA+P/e/6r/af9I/0v/Vv9m/43/v//m//n//f/4//D/6f/f/9z/6//+/wQAAgABAPz/8f/p//D/AgAWACcAOABFAEcAPQA7AEAAQgBFAFUAYABWAEcAOQAdAPv/5v/g/9j/1P/e/+X/3P/X/9f/zf/J/93/+v8RACwATQBcAFEAOgAjAAEA2f+8/7D/q/+t/7z/zf/W/+D/7//4/wAAEgAtAEgAYwCAAJcAoACfAI8AbwBEABcA7P/E/6b/lf+W/53/pP+q/6//sP+n/5z/lP+Y/6n/w//u/yQAVgB8AJMAlQB6AEYADADQ/5f/af9Y/2D/df+Q/7H/0P/p//v/CAAbADEATQBsAIkApQC4ALYApQCNAG0AQAAVAP3/8v/o/+f/9P////L/2P+8/5T/YP87/zb/Sv94/8H/IAB3ALgA3ADbALcAdQAmANv/nv92/2X/Z/90/4P/jv+Y/57/qv+8/9P/7/8NACkAOAA/AEYASgBKAFEAYwB0AHkAeQB3AGYAQgAcAPz/2P+u/4//fv9u/2H/Zv98/5n/vv/z/ysAYACKAKoAuACwAJoAfABWADEAFQAAAO3/2//M/7n/of+M/4D/ff+G/6H/x//z/x0ARgBlAHYAfQB+AHYAaABeAFkAUgBMAEkAPgAmAAQA2v+n/23/QP8m/yD/M/9i/5//3/8cAFAAcQB/AIIAggB4AGkAWwBJADAAEADv/87/r/+Z/4v/hf+I/5H/oP+y/8n/4P/4/w8AJQA4AEUATQBQAFAATABIAEIAOQAtABwABgDu/9X/v/+w/6v/rv+8/87/5P/6/xAAIgA1AEkAWwBsAHcAdABeAC8A6f+T/zX/3/6i/of+kP67/vz+Rf+J/8L/6/8IACIAPwBmAJIAwwDwABYBMAE9AUIBQwFHAUoBSQFAASoB/gDBAHMAIgDU/5T/af9W/1L/W/9n/3H/cv9v/23/cP97/4z/ov+0/7z/t/+m/5H/ff91/3v/kf+z/9f/9P8FAAkAAQD4/+3/7P/z/wMAFgAqADcAPwBGAEwAVgBlAHgAjACbAJ8AlwB/AF4AOQAXAP7/7f/o/+f/6P/k/9n/yv+8/7L/rf+y/8b/4/8DACgATQBxAI4AnQCiAJ0AiwBrAD8ADgDd/6r/cP84/wn/3v6w/oX+bv5f/lv+Z/6J/r/+Av9P/6H/9P9IAJcA3gAfAWUBpgHVAfkBEgITAvUBwgF8ASMBwQBXAOr/fv8g/8v+f/5O/jr+OP5K/nz+vf4C/1P/pv/0/0UAmgDsADoBjgHZAQsCLgJGAj4CHQL2AcYBgQE7Ae4AjgAiAK7/I/+T/hH+mf0u/en8wfyq/Lb83vwP/Vn9xv1C/sX+Yv8CAJAAFAGAAc4BFQJPAmoChAKqAqwChgJZAgwCgAHiAEQAi//b/l7+8P2J/Wb9cv1z/ZL99P1c/rL+MP/Q/1UAywBcAd8BQQKhAv0CMwNOA2EDSwMGA7cCWQLVAUwB1wBcANj/af8A/4r+Hv69/Vr9Cv3X/K38jPyW/LH8w/zx/ET9lP3r/Wj+7/5l/+P/XgC9ABMBZgGkAesBPAJxApcCxALBAnYCKALOASwBhgAVAJT/DP/I/qX+Z/5J/l3+X/5Y/oP+u/7l/i7/kf/w/1kA3ABbAc8BSAK4AgQDMwNNA0UDHQPtAq4CXAILArcBSAHNAFMAxP8k/5j+Fv6S/Sb92/yX/Ff8M/wm/Bj8Jvxg/LP8Gf2g/T3+2P5t//r/fwD/AGoBzAFCAqoC3AIFAysD+gKFAi8CwAH4AFYA9v9S/6D+Vf4H/oD9Sv1p/Vz9Yf3J/S/+bP7e/nj/6f9iAB4BzwFaAv8CqQMUBFcElwSwBJcEcwQ1BNADXgPbAjACeAHDAPn/KP9l/q/9Af1s/PD7gfst+/r64vrm+gj7WPvQ+1D86Pyl/Vj+Av/D/2oA6QCAAQUCVgLHAjcDQwNNA4ADMgOIAikCrgG0AOH/WP+R/tb9l/1b/fX89Pw//Ur9Vv3H/Uv+qf4w/+T/hAAtAfUBmQIZA7IDPwSSBNIECQUpBTEFEwXCBGAE9ANfA6kC5wEXAUUAb/+B/p397vxg/N/7kPuE+4/7pfvR+/T7APwb/Ef8b/y5/ED96P2d/lb/8P9pAMoA9gD2AA0BUwGWAb0B9AE4AioCuAFBAcMA+P86/83+Wf7m/eT9Av7X/c79Fv4z/iH+Wv69/gP/Zv8LAJwADAGpAVoCwwIQA44DAwQ7BGgEmQSmBJYEdwQ6BOADhAMmA60CBAJVAb4AFwBH/5r+MP7T/Xb9Pf0k/RX9Ev0Q/Rf9O/10/an96/0m/lP+j/65/rb+vf7P/q3+d/5d/jP+9/3m/fb9MP6q/hD/af8OAIAAWABaAJsAXAASAGAAlAByAMoASQEbAcwA8gDXADEAzv/h/8n/lv/L/yoAUgCTABQBeAGtARgCxQJTA6gDGwTBBCIFHwUiBSAFuQQtBK8D1gLIARABTQAk/zn+xP0y/Zf8UPwj/AP8Ffwa/AH8HfxP/Gf8lfzi/DL9o/0a/kj+W/6M/qv+m/5//pz+Jf+v/9D/DACoAM8AZQBEAEsA5/+u/+b/zf+C/8L/DACp/0b/Z/9d//b+2P4K/xv/PP+s/xsAZQDlAJUBFwJpAuACqANoBNUEWAVPBu0Gzga/BrYG4QXMBB0ECAOQAb8AHQDh/vP9tv0l/Vr8Lfwe/MX7xvsQ/Pr72fsj/Gb8cPyw/C39l/3c/RP+L/4s/iD+B/7f/er9ev45/5P/3/+IAOEAfQAlABQAr/87/1j/f/9o/8f/bABkAPv/5/+8/x3/lf5u/mf+d/7S/mL/4/9XAPIArQE0Ap0CWANJBCwFLwY+B+MHOwheCOAH6QYhBlgFHATmAu0B1AC5/9j+1/3K/C/8x/tE+wL7Hfsq+yL7IPsi+1L7svsR/H38If3R/Wf+zf72/vn+8P7E/qn+Dv+b/83/CQCeAJQA0/+C/2D/mP4M/m7+oP6P/if/yv+I/y//PP/y/kr+F/5e/oz+z/5h/woAZwCbAAwBzAFPArQC5wNuBU8GLQd/CM8IPAgZCKwHSwaPBWkFTAT/Am4CZQGi/3f+lf1k/KT7lPt++2D7gfuH+y772vrg+gH7PfvQ+578bP1D/t3+DP8e//P+c/4p/n7+Lf/F/yQAeAC5ADMAS//V/mv+qv2P/RL+JP49/uv+Qf/M/q/+9f7l/rD+Bv+G/7D/4/9dAKgAmwDZAIMBMQLbAuIDCwXLBVUGAwdPB/cGvwa4BjcGvQWhBSwFMwRIAy4CrQBT/2L+sv0t/e78z/y7/HP85Ps2+8z6k/qK+vH6rPtd/Bz9A/5v/m7+cP5n/vP9nP0R/lb/XQCKAKoAGwGHAAX/XP4j/mX9M/0T/kH+9v2j/lT/sv4N/oT+7v7P/hf/2f8zAD4AfADDAKwAuQBtAWYC/gKhA7kEcAV9Bb4FRQY+BvUFHwYgBqMFbAVZBWME9wIKAgkBpv+7/nT+Ef6w/YT9JP15/Nb7S/v2+uj6BvuZ+3r8DP1m/QH+Yf5L/lT+mP6W/nH+6f4fAPgAsAA8AEEAnf8m/ob9pP1M/S398f0o/pj91/2M/jP+pf1y/qH/7P8uAPcAUwEEAfEAIAEOAS0BEAIFA00DhgM+BKUEPwQ6BPYEWQVPBa4F7wWSBUEFAQUCBL0CBwJfAXsAxf9t/yT/wP4X/k39qvwl/LH7e/t0+3/73vtU/GP8Z/zy/In91/0e/n3+qP61/uH+bf88AGYA/f/L/8H/9f5B/kf+H/6x/eP9Ov7W/db9av59/iv+m/56//H/RAC5APYA5ADmAPkAIAF9AQsCmQIOA1UDnwMTBDMEFgSMBGMFkAVoBZoFpgU9BcQEIQQRAzACngHoABMAl/9P/+X+M/5J/XT8/Pu5+277Uft1+8H7BfxK/JD8Af3J/W/+lv66/gP/E//2/rD/lQCWAFsAgQASAIb+If6V/jP+o/3l/Qn+U/0d/aL9g/2N/Xv+dP8QAHkAAAEtAfgA3AA3AbwBJwKVAjQDdQNSA6YDFwQRBCUE4ASABWQFPAVOBQkFsQRmBIIDYwKgAe8AEQBH/+f+sP5K/oX9yPxh/CH8zfut+7b7vvsT/H383Pw+/cf9ev4Q/x//C/8g/y//kf9aAMsAqQClAHIAVf9O/kX+Pf6L/Tn9Uf0U/ef8Mv12/XD98P3a/of/9P+YAFcBiwFsAaEBCQI1AoICEgNLA0QDpgMYBB8EFARbBJIEcgRHBCME8AP5AwoEkwPLAhUCQwFMAKD/Q//x/rP+j/4+/tr9jP1N/Q/94Pzb/A/9Uf16/dH9Rf55/nz+tv7E/n/+Sv5e/rH+7f7z/vv+zf70/VH9Qv0x/fj89vwC/eD87fwd/Uz9s/2E/ln/MADsAIsBPAKnApwCjgKrAr0C0ALpAgoDHQM6A0oDQQNAAzUDDQMAAw4DCgMhA2kDnQN4AyIDsgIRAm4B9ACUAIMAlwBOABEAFwDP/1P/JP/u/pD+VP46/v79wf3K/cf9ff05/TX9J/3v/KL8ePx0/JH8t/x7/PX7xPsB/AX8yPvI+yn8Y/yH/BD9ov0q/hj/MQDZAFkBQQInA3UDpQMCBDAELgQyBB8EygOeA8kDyAN0A0oDWgMnA7sCnAKfAooCuAL+AvoCxAKcAkUCwQFXASUBFAEQARYB+QDgAMkAfQAMAHX/+/64/kb+xf1w/T398PyQ/GL8BPyE+3D7mvuf+2z7PPso+0T7gPt2+0/7m/s1/Ij8zvxX/cX9Af51/gr/h/8bAPUAwwEpAmICxQIvA0oDQwNYA1wDTgNxA4cDagN0A6wDsgOKA3QDYANeA44DvAPBA8wD2wOxA0sD6AKhAl0CBwKuAWMBKAEBAc4AVgDG/3b/SP/k/l3+8f2X/TH9zPxK/Kz7T/s/+x77Fft9+7/7Y/sG+zP7g/uT+6r78fs//Kj8J/11/aP9//16/rj+xv7x/nb/LQC+ABABVwHEASkCRgIlAhICNAJnAoMCnQLJAigDqgP+AwIEAAQqBFIEYgSHBLAErwScBIYEPQTUA40DJQONAicC5gF/ASMB2gBcAM7/Yv/2/mP+6/2s/WX94vxi/Av8ofsh+9D6vPrW+kj71fvR+2P7Ufui+7n7ofvV+1j80vxA/bn9Bf42/oP+zP65/qH+Ev+6/xcAWgDLAC4BYgF1AXEBWAFcAboBKwI4AkACzwJ9A8YD7QM6BHsEnwTZBAsFGgUzBUwFHAXPBKEEagTrAzQDkwIvAs4BKAGBABwA3P94/+7+aP79/bv9k/1S/dT8a/w//Av8kfsh+yf7qvsy/Dv81Pub+/D7S/wh/NL7AfyK/PX8H/0t/VD9rv0O/gn+y/0G/sT+Qv9N/27/3P9YAJsAkgB4AJwABQF5AbQB1AFKAiQDxQP6A1cECQWMBbwF3AXpBfEFFgYJBogFEgXwBJMEtwPbAj0CuAEvAZEA9v+p/3b/2f4W/qf9X/0c/e78rvxj/Gn8j/xl/AP89Ptu/Bn9WP0J/dT8Lf2W/XL9Bv3n/CX9T/1B/SH9Lf1w/cD9vv1U/TT9yv1p/l3+Mf6R/jL/if+g/7T/6f9hAPwAYQGcARkC4gKCA9ADJgTFBIEF+gUhBjcGawaUBmgG8AV3BSsF1gQnBE4DrQIzAosBtgACAJD/NP+Q/rL9E/3x/On8ofw1/AH8JPxa/En89/vo+4D8Y/3T/af9iv0G/pz+lf4i/vr9Mf5W/jb+7v3K/fj9Nv7+/XH9S/2z/fT9vv2H/bf9L/6f/sz+yf7u/nX/GQBwALEATAEkArsCGwOoA3MEOQXMBRkGQwaIBsgGlgb/BYUFXAUaBXIEpwMXA7gCHwIrAT0AzP+b/w//I/54/VT9Sv37/H38MPw3/Hr8l/xN/DD80vzf/Un++v3P/WH+Gv8c/7H+h/7P/uT+pv5C/gP+Iv5i/j3+jP08/az9Ef7E/Vr9gv3q/T/+cf6P/qj+E/+w/wEASQAKAQUCkwLnAnwDOgTUBEgFrQX8BUoGewZQBscFSQUaBdwENARzA/4CtAISAiEBWgDs/6f/Lv9m/rP9c/13/UP9wvxt/H38t/y5/HL8bvwK/fb9WP4W/hP+wP5p/3D/I/8b/1n/bf8t/7z+fP6f/sb+f/7Z/YL9vv3r/Y/9Fv0c/X79zf37/R/+Pv6f/kf/vP/9/5sArAFzAs0CSQMCBJQE/wR/BfAFKgZDBi4GwgU6BfYEtgQXBEwD0QJ7As8B5AAkAM//mv8q/2n+vP2H/YX9SP3S/J/8v/zj/Nf8xfz+/Kr9Y/6D/kj+jf5m/+n/v/+d/9z/EgD+/8b/cP8a/xv/N//A/gD+3v03/hX+gP1H/XD9iP2W/bL9vv3k/Wf+7f4s/5P/fAB5AQ4CdQINA80DXQSxBBUFjwXXBcwFkQVDBfoEvARWBKED5AJtAvYBMQFGAKD/bv9G/6r+yv1T/U39L/3Z/Jz8mPy9/Ob89vwU/aL9Zf6z/pz+zP5n//r/MgArADQAcwCmAHoAFADM/7X/ov9L/67+TP5c/l7++v2V/Yj9pv2s/Zr9kf3X/Wn+1/4P/1//DgDtAJ0B+gFZAg4D2gNGBHEExAQtBVsFKwXYBJwEhgRNBKUDvAIKApEBAwE2AGL/3f6z/nj+1v0n/eT87vzN/H/8Uvx9/Mf8AP1b/f/9oP7s/h7/ef/X/ycAgADBAM0A0gD4AAUB0ACEAFEAEwCU//j+mP5m/g/+uv2h/Zb9dP1o/XP9b/2f/S/+tf71/kf/9f/TAIYBAAJuAvQCpgNWBLsE0QTmBCEFKgXGBFcEHgTkA18DlgLCAQkBaADB/wL/Vv7j/Zj9Uv3s/Hv8Tfxb/E/8Evz6+0H8xvxt/Qr+a/7C/lP/BwB3AIsAuAAqAYQBhwFoAWoBagE7AewAdgDg/1//A/+P/vT9kf2M/Xz9O/0c/Sz9Sf10/cv9Uf7X/lT/7f+6AI4BQQLRAkkDwQNlBAEFNwUUBfcEFQULBZcEBgSlA0ADigKbAbUA3/8d/4X+5f0i/YH8Rfwy/PH7ofuW+7r7zvvW+yX85vy9/UP+of5F/yMAzQAfAVEBlAHgARICEwL4Ad8BzQGlATsBngAjAMr/Nf9p/tr9mP1h/SD97/zb/AP9YP2s/eL9Uf4G/6n/GgClAGoBLwLMAkgDuQMwBLQEEgUFBboEpAS0BHIE0AMkA5IC8AEYARsAKv9v/tz9Nf1r/M37mfua+2/7KfsU+0P7lPv9+4T8E/2i/VH+Mv/+/4sADQGiARMCRAJkAooCmQKLAmwCMQLLAVoB6QBaALf/JP+b/hH+rP2C/Vv9K/0v/Xn9zP0T/m7+6P53/xsAxQBZAekBlwJGA8EDDQRSBJ8E3wTuBL0EaAQeBNsDXwORAq8B+ABLAHv/kv6y/fL8Z/z++5L7OvsW+wT74Prg+kH78vuS/OH8L/3o/ez+vf9QANgAawHuAVECjgKrArsCtgKXAkgC2QF3ASYBngDd/0z/8v55/ub9iv1s/XP9hv2a/cr9Lv6e/gL/eP8MALIAZgH4AWIC2QJuA9YD/QMZBDsEXARlBCsEtgNbAx0DqQLnAQcBQgCi//z+IP5B/bX8Y/wN/K77Y/sy+yb7P/uL+xH8lfzv/Gv9L/7t/pD/PgDeAFIBtQEZAm0CpAKxAqQCmgJsAvcBegEWAZoAAQB8/wb/k/41/ub9qP2l/c/9+f0m/mj+yv5Q/+f/bQDxAIgBIAKoAiADegO0A+4DGAQSBPoD5wO4A2QDCQOaAgwCfAHfACYAav+x/uz9N/2s/D388/vG+5D7Z/uE+9j7NfyD/ML8IP3N/ZH+JP+s/00A1QAuAXkBxQEYAlQCPQL/AfkBBwLNAVQBxwA9AMv/Yv/l/nn+O/4T/gT+HP46/mP+tP4B/z3/pP86AMoARgG7ATACrQIUA0sDaQOQA6wDowN5A0kDJgMAA6cCHAKWARoBiADt/07/oP7//Zb9Rv3m/Jz8gfx3/Gr8cvy4/Cr9dv2L/cn9WP70/nT/7P9XAKsA+gA3AV4BiAGoAaIBfwFWASgBAwHLAF0A1/93/y3/2f6P/l/+Uv5u/qP+2P4Q/1D/kP/h/0gAqwARAYYB9QFUArICBAM1A1QDXgNGAxwD7AKvAnkCUwIUAqsBMgGxACgAqv8s/6f+Nv7m/af9gv13/WL9QP00/UD9Z/2u/ev9EP5W/sn+Pf+i////RgB9AKgAwwDbAO0A7gDnANsArwBzAEkAHwDV/37/KP/e/rH+lf58/oX+vv78/jf/ef+5////WACnAOoAOQGIAdMBLQKAAq0CygLkAusC3wK9AoECSAIjAvwBxwGLAUMB5QB1APr/gv8k/9X+j/5V/iv+Fv4Y/hj+Bf7w/fH9Cf4s/k/+ef7C/iT/ff+4/+L/CAAvAEwATgA+ADoAQgA7AB0A8P/C/5v/cf81//b+xP6g/pH+o/7O/gL/PP94/7D/8v88AIYAzAAKATgBYgGVAdgBGwJGAk8CSgJKAkoCOwIUAtwBoQFzAUwBHQHhAJ8AWAAJALX/Yv8d/+j+vP6Y/n3+b/5r/mv+Z/5e/l/+bP6F/rH+7v43/33/t//n/xgAPwBKADkAHgAUABkAEgDx/8j/qf+M/2L/Mf8F/+T+yv6z/rD+0P4G/zr/Zv+X/97/NQCDAL0A8wApAV0BhQGmAckB6QH7AfUB5gHYAc0BuQGTAWMBMwEJAdkAnwBkADQABgDN/4r/T/8r/xn/B//s/tH+xf7J/s3+0f7W/ub+/P4T/zL/YP+U/8X/5P/8/xcAJwAfAAcA7//g/8v/qv+D/2L/Sv82/xr/+f7d/tH+1P7c/u3+E/9Q/5H/xf/4/zkAggC+AOAA+QAdAUABUgFZAWcBcQFuAWABUAFBASwBDgHwANQAuACcAIQAcABaAEYANgAlAA8A8//U/7X/nf+O/3f/Zv9q/3L/cf9x/4D/nP+u/6T/k/+T/6L/qf+q/6T/mP+R/4//ef9R/y//Ff/4/tL+t/68/tX+1v7I/tn+B/8w/0n/ZP+V/9T/CwAyAFgAgACcAK8AvwDOAOYABwEUAQgB9wDqANMAogBqAE0ARgA4ACQAMgBsAKEApACBAE0AKAAoAEEASgArAPL/yf+6/7j/uP+8/8n/2//0/xoAPgBQAE4APwA2AEMAWABjAE0ALQAdAA4A9v/O/5b/Yf86/xz/AP/y/vr+BP8O/yL/Mv88/0f/VP9v/53/1P8GACsARwBXAGYAfACTAJcAjQB8AGsAZABhAGEAWgBPAEMALgAdAB0AGQASABMAGAAYABsAIwAiACMAKwAeAAAA8//o/9f/2f/p//X/BAAbABoADQAcACsAJgAjAB0ADAAAAP7/8//X/7//rv+X/33/ZP9N/0H/PP81/yz/LP83/0r/Xf90/47/q//F/9j/9P8TADMAUgBhAGcAdgB4AFUAKAAZABgAEgAQAA0ABgAPACoAMwAtADYANgAvADMAMAAyAFYAfwB1AFEATgBGADIAOwBLAEoASwBSAEwAPQBEAE8ATwBaAF8AXQBXAEYANAAxADQAFQDt/9z/yf+a/3X/ev+K/4f/fv9x/23/if+2/9D/x/+7/8v/3//g/9f/3P/m/9n/yv/T/+7////9/+//6P/2/wwAFwASAAkABwALAA0A+f/n//L/BQAEAPL/7/8DABcAIQAjACcAMQA5AD4AOAA2AEcAWwBfAFAARQBNAFsAZgBiAFsATgA9ADEALAAbAAQA8P/d/8D/pv+V/4r/e/9q/1j/Rf83/zP/Nf84/zn/Rf9f/3r/iv+X/6z/vf/D/7z/sP+s/7H/wf/i/wYAJQBBAF8AdQB6AHUAaQBXAEcARgBQAFUAVABZAG4AgQCGAIUAjQCeAKsAtAC6ALsAuwDDAM4A2QDYANAAxwC4AKcAlgB3AE4AKQAaAA4A9//c/83/x/+7/6f/mP+T/5//sf+6/6//m/+K/3r/Y/9M/0L/Q/9A/0H/S/9V/1P/Sf9C/zv/NP8x/zT/Q/9X/2r/bf9p/3b/kv+u/7r/vP/J/+X/BgAjADsAWQB3AJAAmgCeAKkAwADRANIAyADBAMgA1QDeAOAA2QDUANEA0ADHALkArACeAIwAeABkAFAAOwAmAA4A/f/y/+7/5//V/7r/o/+W/5D/iP9+/3X/cP9y/4D/kP+d/6H/nv+W/4//jf+O/4n/fv9z/3H/c/94/3//g/+G/4n/j/+V/6L/s//G/9L/3v/r//n/BQANABMAGwAlAC0AOQBIAFgAZgB1AH4AggCKAJAAkwCaAKIAsADCANMA4gDsAPAA7QDbAMUArgCQAHsAZwBSAEUANwAsACAAEQAGAPj/7P/o/+P/4P/d/9T/y/+8/6n/lf97/2T/Vv9K/0T/QP9D/0j/U/9g/23/df96/3z/fP9+/4P/if+P/5b/oP+q/7P/u//C/8j/z//Y/9//7P/9/w4AHQAnAC4AMgA4AD4AQgBGAEwAVgBlAHAAewCFAI4AlgCXAJQAjgCLAIYAggB6AHYAcgBuAGkAZgBhAFsAUwBLAEAAMwAoACAAGgARAAcA+//y/+r/3v/W/8//yv/C/73/vP+4/7H/p/+Z/43/g/94/3P/dP98/4L/h/+R/6D/q/+w/7z/zP/Z/+D/6f/3/wMABQAEAAUABAD4//D/7v/v//T/+f8HABkAKwA1AEIATQBUAFIATQBLAEoAQwA8AEAARwBIAEgASgBNAE4ASgBEAEcASABHAEcASABLAEgAQwA5AC4AIwATAAQA+//z/+v/5v/h/97/2P/T/8//yP/A/7n/tP+u/6v/qf+n/6b/o/+g/57/nv+h/6j/rv+2/77/xv/P/9T/1//b/9r/2v/Z/9z/4f/o//P/+P///wUACQAQABIAEgAUABMAEgAUABYAGwAbAB8AIwAmACsALQAxADUANgA3ADgAPQA/AEAARABGAEUAQwBCAD8AOAAyACoAJQAiAB8AHAAXABMADAAGAAEA+//2//D/6f/l/+P/4P/e/9v/1v/T/9L/1P/W/9b/1//U/9T/0//T/9L/0f/P/87/0P/S/9f/2//e/+H/5P/k/+j/6v/r/+7/8P/0//f/+v/9//7//v/+/wAABAAGAAwAEAAVABoAHwAjACQAIwAiAB8AHgAbABkAFgATABAAEQAPABEAFQAXABoAGAAcABwAGAAWABIADgAIAAEA/f/2//P/8v/t/+z/6f/p/+v/5//l/+j/5//n/+X/4//j/+P/4v/g/97/3P/Z/9j/1f/U/9X/0//Z/+D/3//j/+n/6//s/+z/7//u//L/9v/2//v///8FAAoADQAVABsAGwAYABgAGgAaABwAIAAdABgAGQAiACcAKAApACcAIwAnACgAIwAhABwAGQATABAAEAAKAAUADAAPAA8AAwD+/wYABAAGAAgACAADAPj/+//2//X/CQAJACkAHQBEAOwAcwDm/wUAqP9f/2n/c/9I/0v/AwB8AGMADwDB/3MAhQCU/3b/VP8T/zT/Tf8Q/+L+F/8D/zP/w//8/xAAFQARAIQA3QA/ATUCqwGYAI0A1wDhANAA1wBWAB4Axf+v/xUA8f9m/zz/1//P//f/GQDq/7cABwGQAHgAxwChAOgAKQHFAHIAl/8T/+b+Sv/o/mH+ZP6d/bz9Nv75/l3/Yf9V/w7/if8GAJgAqQCfAJ8AcACiANIADAGuAB8A7P+7/7H/qP/l/yYA1v9m/4D/mv+h/7P/mP+O/1f/nf+9/7L/DQD6/0AAUwAqAH8A0wAdAQUBEAElAe0ABQEhAeIABgErAa8ATwBNAG0AVgCDAF4AEQAuADIAQwCAAKUAUwAIAPL/QwCKAFwAcwA8APX/CQARACsA8v+d/0L/MP9O/0P/Xf9G//v+7P4g/yH/Rv9S/9j+6v40/0v/c/+k/5f/ev/U/8b/3v8cAEgAjwBfAEkALwCEALIALgD1/9r/wP/U/7f/wv8VAOT/0v8BAAsAOgAyABEADADb/8r/nv+k//P/AgDj/6j/s//J/+v/OgB1AEwAEADf//X/lgCxALYAiAAUAPn/KQBsAFQAXwAnAKv/uf///1UAgABKAN//3P89AFUAfgByADAAOQBOAE8AogCnAIgAmwBmAKEAmABhACUAxP/I/9j/x/+S/33/t//a/9v/FgCy/wX/Hv8y/zf/Xv8j/4P+Rv62/u/+Nv81/6r+c/6R/gf/g//7/w0A/f8oAIsAPAH5ATwCqAFdAakBNAKRAmsCJwI1AjYCBgJRApkCJwKQAfsAtgDTAI4A8v+D/6f/lf83/yL/8f5+/g7+Zf3p/Fz9nP1A/b/8sfzn/Az9bf0b/cD8+/xX/eT9Wf6k/rn+zP71/mb/QADFAAwBvAFhAssC+wMDBcoEYgQvBB8EbgTUBC8EMAOnAvkBrAGwAUEBBQDN/i/+Gv6h/pH+ov34/OH8Dv01/Vz9G/1u/AD8j/yp/ff9sf1a/U39yP1//o7+sP7p/mz+b/4v/2QAygFJAjoBWgG5AwwFZAUoBskFNwU5BrEGEgbnBScFbgN3AiACngErAQ0AaP7O/cL9Ov2O/Cb8sftq+6T7l/ut++P7zfu8++L7g/yd/FX87PwV/kz+YP5I/3H/6f7d/vH+8P5r/xb/WP+8AY0CZQHFAQADxAOKBZoG0wXqBbEGyAZVB50HBQZ5BF8DOgJQAgkCXwDm/tv98vzK/Kf8g/tn+t75ofkt+u/6BPsL+wf7Cvv/+9P8jPyX/G39MP4a/wAADACW/zf/u/6Q/sv+7/5NAGUCawJhASQCrAOvBM8FMAa0BTkGCQfoBgEHSQdrBo8EJwNBAv8BhAGe/+H9G/2j/Db8rvvU+un5iPli+cv5zvoy+yD7Svvg+y79O/6S/XD9y/7Q/9oAvgEtAR0AGADZ/zv/Bv8m/w8BUgMeAr4ANQOqBd4FLgZwBn0GtwfGCF8IAwjHBxEGGgQ/A7YCqwG5/0L9vPta+9n6zPnC+Mr3s/aH9pb3qfgX+ZT5RvoE+2T87P1F/nb9sf1D/4EATgGEAX0AhP+n/1j/df5h/tn/YAI5A9gBUQIhBewGZQcTCLQI2whZCeMJDAqRCRQImgVTA20CHQKsAOn9rvuU+sD52Pgj+HP3YfZA9Qf1o/aq+HL51/mK+uv7M/6y/w7/fP67/x0BtAExAjkCDQF8/63+JP7f/Z//tAI0Ax0BKQEoBM4GDgigCJkI6gjuCYEK4grsCiAJCQatA5cCJQIZATL+0vpH+Zr4v/c792n2BfUw9JP0+PUJ+L/5G/p7+k/88/7qANoAdP+4/+YB+AINA+8CSAH7/lT+F/5+/eT/PQPeAT3/LgHlBOoGBggTCO4HlQn7Cv4Kswu5C/sI7QU4BCkDiAKMAHr8t/lb+Vz40/Yu9lX1WfQR9GL0dvVQ9+L4svmz+sn8Zf/KAE4AhP+eABMDAAQOAygCIgFX/zv+m/2s/IH+cALBAQH+j//OBPAG6AaJBykIHgm9CqIL5gvVCwcKyga3BIkEMgSMAUT9X/qy+RP5c/cM9iz1WPTF8y70pPVL95P4oPm++tP81/8yARwAYf8sAb4DWwRdAyoCGgA6/kb+tP2o/G3/qgJBAH/93AC8BVgHcgdzByoIgApLDEUMYwzgC2AJ1wZZBYoE1QPZAOj7jvm3+Zb4ZfYB9RD0c/N88+fz3vRV9vj3qvkr+zz97P+3AGb/nP9NAsAE1AS6AqkAyv8//y3+7vyC/bgAOwLJ/zX/QQOmBlUHzAc/CFYJ4AsBDVQMVQyJC6IINAYYBfADHgLw/j77a/nu+JT3lvU29GfzBPNa8xb0yfQY9kH4Kvq3++X9+/90AMv/UgAVA4sF0QRNAmkAOv+J/pH9tvy6/pkBKADu/agACwX5BocHvwcsCAsKHAzPDAQNoAxvCnAH0QVZBTIEEwEa/Wz6Tvmj+DL3G/W780Tz9PKO89f0afV99uv4NPtb/br/ngDt/wYAaQKvBXQGHwRyAZ3/wv6q/rb9rP11AOgAyv2//sIDAwZfBpIHFAjRCCILiAyKDKQMgAuRCFAGzgUEBUACbv53+8f5vPib98L1B/Rh8x3zIPPv89v0wfWk91v64/zd/kIAowBDAGwBsgSVBk0F4QJcAHb+Rv7A/cz86P4eAYz+9fxAAVEF5wWcBsQHrQiWChwMTAy8DI8MhwoGCDoGNwXyA64Arfxw+hj5nfeT9kH1SvN88hzz0fNM9N70R/YR+fb7G/7d/6sAWgDFAEkDbQb7BkgEBAHz/kD+Gv5t/Rf+SgAU/+f7UP4+BGIG3wVXBqIH8wmaDEMNqgxjDD4LGgmKB3AG5gQfAhP+Afvw+cL44vZv9erzevKL8s/zkPSG9B71j/ck+y7+4/9yAGcAnAB2AsgFhAeqBRcCHv/8/S3+qP3u/U4Ajv93+6T8FQNtBuMFtAWaBugICQw3Dc8MnAxSCz0JNwhpB9QFEgP6/uD74vp9+Wb3JPa49LPyJPJm86H0vvSS9Ov1SPkX/bn/jQAKAO7/sAHgBDAHdAZEA+3/8P26/bz9Rf2q/vz/Hv06+/X/TQUDBqAFdgb+B68KHQ1mDeEMTAyjCroI7QfuBiwESQAV/V379vm697r14/Tf82fyFfKK8wT1NfW69XD4RfxR/+UAEgG7AHYBOARCB5IHBQWtAcn+Yf0L/VL85/wD/8r9P/pF/NYC3AVdBbQF7QYuCZEMDg5sDfYM3wvTCaYI6QfhBSMCy/1C+4/6APmC9vv02POM8lLyPfNX9Dz19fW892T7OP+AARUCdwFtAbAD8QZHCJYGFgPN/w7+Wv1R/L77Ff1P/YP6fvrB/8MDSARRBcEG7QffCp8Nqg1MDd8M0go9CfsIYQfsA9r/e/wG++r5Y/cy9ST0EPN78uPyk/NL9B31zPY6+v79fQDVAewBdQGvAroF1AcMBxQEvQBo/oP9nPwE+477kP3++3P5nPxFAmYE2wQmBrwHNQoYDRMO0w29DZIMewo5CV4ISAZcAt39Xvt5+oH48PV99F/zQ/Id8uLy9PPI9Mb1c/if/BMA6QFXAvwBkQIXBdUHLwi0BT8Cc//P/br84fpG+sv8UP25+ef5sP+hA3YEpQXYBp8IBAwgDgUO8A1rDX8Ldwk/CEwHswTD/777h/pN+fX2GPWt81/y+/GK8pHznfSC9Vr34vr4/vcB6QJ5AqsCWQTZBqIIlAfmA10AMf4F/c37wfmB+bf7MvvY+LL7ZAGGA0gEaAZMCK4KrQ20Do8O+w45DsMLkQlJCJwG3wIM/kP7FvrN91L1DPTH8o7xhvE98k7zuPRb9gD55fyBAJ0CCwOoAisDXwXZB/sHOgXKASj/Wf3V+7H51vhN+238Vfn0+Hn+OQOxBBcGggcdCXcMHg82DxMP2w7dDEEKngh/Bx0FWQC0+7j5lfi/9hf1V/Oi8WrxZPKO87P0zfXA90z7aP+IAroDSAPMAssDlQbsCKMHYwNd/y/9hvxd+7D5sfpO/Nb5LPgB/acCFgTZBGcGyAf6CqwO/g78DQ4OJg0FC1EJ6AfPBdkBGP2i+rz53fet9fPzUvK/8Y7ymfNY9Pf0RPZ8+QL+qAEhA6kC6QHLAoAFNQhBCMgEQgB1/bP8Cvxz+nP6Afyd+kH4W/tPAfcDigSDBdUG0wnEDRoPQw6oDeUMzwukCrcIWAYqA7X+qPvD+hX5tPYr9Xnz9vFR8tjz5/QX9WX1jvfr+3YA2gKkApIB0QERBDIHmAhQBr0B8P1e/Br8aPvp+sH77/rl91j5NAAxBH0D8ANlBuQIMwzFDk4OFA2/DBYMGwvkCZ4HIATa/6/8wftx+ob3WvUt9NLyd/LI8xr1WPVR9ar2j/q5//sC9QJ5ARgBIwOwBr8IDgfQAtH+tvxJ/Jv7kfpV+7D7s/j098P9egMPBIID7wTQB8wLjg4BDgQNSg2SDAMLEgp1CDEF6wAg/af7E/t8+HT1NfRs89TydPNR9Lr0a/WX9iP5zf0NAhoD7wEPASECbAU8CF0HugP3/zX9Jvy++7X6LPts/N/5O/fP+/ACjgSAA9cEYQcbClcNvg6UDaAMOgzrCtMJQAmoBuEBAP5q/Gv7Y/nt9lL1PfQv8yLza/Sc9eD1N/Yd+CD8xwAuA18CvAAgARkEOAdqB6AE1gB7/ef7pfvM+tb6ifxJ+8X3GPqBAREFSgTBBP8GggmDDJQOMQ7GDK4Llgq2CRsJPgcWA27+Avx6+yD6mPfH9d307POe86v08PVZ9of2x/f8+oP/wwLpAkMBeQBgAhIGyQeZBZsB1f2P+1z7Jfu4+iH8J/yW+IX4Uf8MBS0FqQR+BsUIYwteDhoPQg1xC2oKfgnkCOcHawQi/9n7U/uQ+nH4ePZO9WH0rfM29Ob12vap9iT3uvk3/lICRQOKATgAcQHOBGMHiQbnAr3+2fsQ+xj7KPth/JT8Ofn39/P9qgRpBTsEzwWFCO8KdA3PDvMN6gsCCgUJ/QhICCgFOgBV/Dr7Lvud+SX33vUq9Q70L/QT9kj3GvdJ9yD57vxuAXoDAgLq/1QAWgNpBssG2AMw/6n70fr8+h37rPya/Vr6zPej/FMEwAZCBVYFtweKChANog4BDmML8ggiCEsIGwjjBd8AAPy1+lX7Zfpc+Aj3ufU99L/0F/c++I/3Ffdd+B38GAGwAzQCfv9U/0cCxwX8Bo8Eff9N+xn6avrD+1v+WP7A+ST3cvzNBLUHVQVvBCYHKgpXDFIOOw4SC5IHZgabB6sIIgZTAM77Cvvb+0D7Jvmc99T2qPVn9WD3BflH+AL32fd3+z0A7wLIAa7+6v1LAWgFnQaGBL//Ffvf+dD6uPwWAAwA5Pm69lT9KgZPCJAFUASfBgIKZAzRDZUNigp8BssEaQZ1CFgGFwBQ+xr7gvw//DT6GPgL98f2JvdJ+Cb51fj990r4DPtQ/wQCAQHj/VP9EAH5BLEFmgNi/0T7Dvrj+nr9tQFeAUf6TffM/mQHqgitBS4EFAarCewLgQwHDCoJvgTvAgEFRgdoBZv/Gvs1+wD90Pza+v/4Ofg3+In4IPmD+Tj5mvjk+EH7vv6EAGD/QP1U/ZUAPQTzBDgCVf7E+3D61vpd//sDxADE+Tj6wgG1B5MIGwYoBA4G3QmTC0EL8An2BlQD9wHYA+IF9gPj/pr75fs8/Vv9J/yF+kL5Ffn3+Wn60Pk/+dT49vg/+zf+vv5o/Wf89vxhAKoEygTJAGj91Pvh+sf8PwLRBO3/UvqY/GkEPQlgCLEFtgRMBpAJvgvMCtYHiwTQAUcBWwPDBEIC0f2w+1L8ev3r/f/8w/pJ+SX6oftC+5b5rPiq+Gj5yPsR/l39afuR+7T9MwGqBNMDz/7h+1f8pvyp/qwDcgSH/mf7GwB7BukIhQcfBe4EYQfmCb8KcAkJBqwCfQHrAbgCugKkAMX9p/wI/Xv9iP3A/CH7IPr0+i38Tvsp+Yn4U/lB+g/8ff0V/HL6vPuV/tUBIATrAQ79l/us/If9NgFiBaACSf1H/n4DpgdGCVcHiASbBcUI/wm/Cc4HdAOWAF8BQwKPAWYAyv51/Vv9l/2h/a39Cf2w+zr7R/z3/I/7cPnI+Jr5TvsK/a/8i/or+pj85f/WAqoDigA6/DP76vzA/90DOgXhAFj9MQCUBYwIPggDBowEmQXtB3YJ4ghpBd4ARP/pAC8CIQHZ/qL9/P02/vr9C/7y/R796fuu+wn9nv1u+834jfgx+jn8XP1w/Iz6m/qo/dQBPwQ6A8//N/0q/bb+uwG/BMsDHACU/kIAeARMCMwGjQJqAlwFCQedB/sGrwNkADwA3gH5AhQCbv8F/sz+g/9G/3X+Uf1F/KH7vvtq/DT8hPrI+Iz4Z/pD/Ur++PwW/AT94P8sBDYGsAOC/+z8u/zR/Sv/0ADsABH+UPyP/5QERgboBOkDCgVJB9AIUAltCHgFdQKdAQgCGwLDADP+6/zw/Yj+Mv3++x/8Kfya+9z7zfxO/I764/nA+qb8Ef+9/zr+pv0K/0MBAAQoBckCG//E/AH89vsj/Jb9Yf8q/jb8C/8rBOAFhAU8BmgHkAjgCfwJUAgeBjYEXwLrAEEASv9g/U382fzY/JL7/vqb+wT8IPyt/Pv8RfyL+9D7kvzG/bf/hABd///+SQCKASsDhASqAtP+t/xJ/F77ifll+bn89f4X/Xf9rQKlBRwFkAYoCcIJKwp9CqsI7gbFBt4E+QDS/oX+kv1V/ED8H/ys+qT5e/oG/Cr9lv3I/I/7tvsL/VT+nP/QAA8BeAA4ACABnAJWA5QCgQB+/nT9Ofw1+gv44fYy+WP92v3A/FkA+wSBBWsGTwpiDJQLnAp5CYYIUwhcBg8CjP4s/bn8Hvxo+7L6gPmP+C35EftX/YH+VP31+/v8yP6I/4MA4gHtASwBNgEPAvcCvAIcAZH/vf7k/Tj8Zvny9lD2T/ft+c/80v3U/j0C7QX6B9MJ4wu7DDkMxQouCeQI2weSA+v+rvwF/OP7Bfs3+Yb4Hflw+T764fwn/y7+Mvwn/dD/yQAKAXgCSwOQAjQCxwJNA98CLAF2/9H+Fv7N+3H4CfYS9gn3ufaw90H86P94AHICyQYKCvoLZg0KDc4LpgvuCikI8gT2AXr+r/vK+i760vjv9+P3TPip+Qb8Bf6h/sH+mP+9AIgBXQKwA4sE6wP9AssCnAIKAr8A3v7n/YX9h/sT+Lz10vW99uj2jPZc94T7GgG2A2cEPwddC9oMFgxyDGcNLQzoCEYFvgJYARj/2/tT+SL4M/ia+An53/oz/af9kP1P/38BGwIhApICGgPZA1kEEwR+AxsCTACd/4j//P6r/SX7gvhh9273u/d+95n2OPa992/7if+DAmcFHggCCdAJMgybDd4MGQvnB5UEwAOPA90A0vza+Wj4dPiY+d/6Dvzy/EP9E/67/0sBcALnArQC+gL8A6YELgSSAp0AT/9L/wwAaf8//M/4N/c79w74XviL9/T22fcW+ur8kP/KAZEDQQWTBwcKcwvaCzELxwjkBfYEEQVmAz4Akf3p+zX7PPt0+x78Kf3U/Y/+6P8RAcMBYgLCArsCEgNFBIEEngKTAHX/ZP7V/U/+F/4W/Ev6gPmE+Nj34vf690L4UfkN++n8C/8dAeIBPAL9A1oGnAfHB3MHnAYhBtoGBgduBZcDwgGc/2f+Rf48/lf+zv4e/8b+fP4z/40AIgG5AFIBDgO2A2IDCAP2AXsAU/8j/rX8BvyR/Dn8V/q/+Az49feM+KD5VvqM+qH7B/4bADgB7gHdAqMEngUOBesEsQVWBk8GaQU8BBgE9wMRAvz/LQBkAfwA0v+O/6H/kv+z/zYA6QBOAfUAqABRAQwC0AFsAYwBBgFY//D9+fyD+4z6Tfql+Sj5U/lT+W/5+vlv+jv79Pzw/u//ngDcAZcCxgLrAqYD/ASKBVUFMAXIBJIDjgIFA/4D+QNQA2sCKAFqAEwAJQA8AHkAQwAEAHcAtwAaAAUA0QA5AUABYgEiAWUAHv/t/KT6RvkU+bf55vk++Qb5v/m++sL74/wj/kr/FwDLAGQBswFMAoEDxASRBU4FVQS1A0YDxgJrAp4CuANVBFwDIwK0AYsBKQG8AKkA9gBmAY8BIQGXAB8Agv+E/wAAlv/X/iL/SP+9/dD7Xvtc+/n5ePhN+Uj7u/uI+zX8Af1C/fH9OP/a/9b/CwC8ABUC+wN+BYoFiQTSA3YDeQMXBCoENQMPAocB/QGAAuEB5QAbAb8BggGRAU4CJQJCAbcAQwCb/+z+//2G/Qb++/0V/bz8xPxe/Fn7YvrC+tL7Gvww/P/8yP3A/ZP9nP2t/ZX+CAC4AD8BTALdArgCGAMKBB8EpQPYA6kDlgJBArwCoAIWAgYCTAIsAhsCtALqAmYCTALtAVIALP9l/3X/1P4U/jL9ZfxC/L78Gv3p/L38Ff1F/ez8lfw//Of7ePyR/d79zv1G/hP/8P+YAKIAXACaAHoBZwLvAggDEAOSA0IEUgTbA4sDUwOhAuYB/gGjAkEDfQP2AhUCZgHAAA0AhP83/8D+Jv61/Xb9Q/3h/GX8EPwV/LT8Z/2d/Yv9Vf1F/Vb9/fz6/PP9hv5o/oT+Ev/X/2QAmQB/ADoAfQBUAR0CtwJiA0EEggThA5UDpwMyA/YCUwMrA20CHwJSAvkBRgEIAdcAuACtAK3/4P33/FP9xP2S/U39H/3O/LX84PwW/U/9kP2D/ej8Xvwb/G78CP6T/xsAgQCdAC4A1P/0/0MAEwDv/6IAqgH/AXwBxQHSAlcDDwQIBTkFBwVJBT4FBwSqArIBpAALAFoAwwBdAFf/X/6//bz9KP5C/tD9B/1b/D38pPwy/dL9E/5K/U38dPwc/Rz9EP0e/un/IQEtAW8A/P+//9r+mf7u/z8BdwE+AfkAcwDPAHECTgP9AycGjAe7BnQFugQhBMUC8wA6AGwA/v/I/uj95f1O/pL+aP5R/n3+7/3J/B78ZPxn/dv9p/34/VP+7/1c/Sf92vyY/Mf9uf+GACMANP9//hb++P0O/5EAZAHFAb4BogHdAUkC1wITBEMGtAd7BxIHqgY5BS4DxAEhAWcAJf8r/gb+Cv7S/cv9e/6Y/5//iP68/SH9Vfy6+9372/y2/ZL9B/3D/I38EPz/+6H92P9GAJb/cP8X/yf+yP1g/mz/mACMAfABVAKAAg8CzwJVBaEHyAj3CBwIXAY3BJECxAEpAVQAbv+K/sP9cf2a/Qv+9v77/yIAnv/r/qj97/uq+o76jPvW/LD93/1Q/Z/8pPwU/QX+3v/hANj/av6c/d38bvzF/Jv9//6cAHEBKgL0AyYG8gdKCQsKmAn3BxkGDwT/AfUAcADO/2H/5/5H/q79lf1w/qL/SgBfAAcAOv/a/Vv8ffsq+zf71fuJ/LP8hfxg/IH8svw1/Yn+jP8X/+T9L/07/bj9b/4o/5r/CwC4AP0BbwQJB50IkAlECu0JZwh4BlwEagI6AWgAZ/+w/nP+Zf5M/rX+0v+dAN8A4gAjAIX+A/0p/HH7vvrW+pH7EPwj/B/8VfyK/I38zvxs/f39Lv6k/e38av1j/j3+3f2V/o//WwC7At0GiAnBCfMJDAqMCGUG6wQaBE0DYQJgAeD/l/4f/sj96f1F//gAgwEOAdMAYwC3/vD8M/yq+7v6PPqn+k/7tfvU+8v7ufug++771Pwb/aD8wfym/Yn+zP49/qD9z/1X/xIDrwdmCnELUAzOCxgJYQbpBJ8DYAK8AcsAQv8I/kX9E/1I/rsA2gK7A6MDvAL7APL+fP2k/M37q/qn+Ur5ZPnL+Z36cvvz+wv8xvvD+zz8P/ze+6r8pv7l/3H/Rv43/Zz9ZAH7BsYKZwyEDVINNApnBrwE4gOYAvQBbwGu/7H9hvwd/Df9SgCnA18FYQWRBNwCwP/C/LT7UPso+hP5bvj59134r/mf+uz6RfsO+x/66/l5+vL6D/xC/j8AvACg/6n9QP1sAUYIfAyhDTUOeQ0XCtsFWwPSAigDngMLAxgBIf+n/Xf8z/y8/3oDYwVFBVYEhgKo/wD9uPtk+wD7QfoL+ZX3+fax9974J/o++//6vPns+K34CvlT+xH/mQHXAWwAa/4T/xsEhAmLDP4O1g99DGEHtwM6AT8AoAFVA0AD9wFTAEP+1vzO/RUB5QR1B8oH3gVkAo/+rPtT+iX6Yvo7+hb5Y/eG9hz3dPgk+pP7JvtC+Un4iPhu+Tb8cAANA8MCkQAO/h3+fQJoCJMMEg+eD54MOAdoApj/Dv+DAJoC3wOOA60BWv8n/v3+rgERBY0HJwinBjYDBf/Z+yv6bvmE+dP5A/n49o31A/bM9076hfxq/IL6Tfn3+Ir5cPy8AHcDRQPLACD+pP5JA3AIlwvUDaIOpwv1BUEBMP87/8IA5gIrBHYDBAGJ/pL9uf7xAQIG0wgnCRYHVwPO/vX6DfnQ+JD5O/ou+dL2e/UO9t/3evrn/AD9FPuy+Xz5gvqZ/bgBaQTmA1oA//zf/R0DjgjgC0QOCQ+WCw0Fw/+b/fD9WABUA34ElQOwAVj/hP1Z/lMCrwb/CAsJFAdpAyn/1vsG+jD5NvlA+dz3CPar9ZD2GPja+kH+Nv/J/Db6avkc+uf8UQGBBDwEcwDg+z37FQB2BuwKyQ3/DpkMxAYPATz+Lv76/54CNgRnA+EAbP7p/Ff9HAFZBkgJLQmGBy4ENv86+7j5WfmB+c75z/im9nn1YPaO+If7Dv/0ABj/gPtv+bz5WPzGAF4EagS9AED77/fI+rMCIAqIDukQURA/C2YEGQBl/+8AZQNRBcAEiAHV/Sr7mvrA/XUD5AddCawI+gVCAYz86PkM+ev49Phd+Af3BPY29uf3Ifsj/xsCKQL7/sr6i/jV+dL9zwFqA8cBrvy09j32sP1QB88NqhHtEqwPXAnuA+kAXQByAhAF3gSqAdH9pPoS+fH6QQC/BegIiAmJBxsDLP6f+t74jPj0+A75L/jh9k72WvcI+t79QQJkBUQE2/6W+Tv4q/qD/jEBKAH5/VD40PPr9Tz/oAkVEBkTmBNqEF4K+QRyAq0CHwR+BEsCQv4h+oD35veZ+98AoQWxCPcIJAadAUX9G/qD+D74q/gu+Qn5RPgr+Mj5uvyJAKoE+QajBIb+hfmo+JX6HP3E/hL+r/rj9c3y9PUtAGgLUBJQFVYVjRFVC+wFNQMRA/IDvQPnAO37S/c+9UH2FPra/5oFEgkjCTcGoQHJ/Cb5vvc4+Jb5vPqn+qT5a/nS+rz9+QGCBqgIOgZJAJT6+fd5+Hr6uPvl+m34H/Vx8oT0E/6gCtoSwhV+FpsUxQ52CHgFigSKA/sBv/5j+df0s/Nc9fr4vP7fBDgI/QdzBXkB1/yS+Qb5Dfrr+p/7Pfy5+3z61Pr0/b8CRQe3CYsIOAMG/Cj3BPal9qv3l/gP+L71l/PS9Bn8kgcJEaIVqxdrFyITugzoBzsFWwMqAan92Pi59CXzDfQJ9yL8/gHvBbcG7wRnAXH99Pp8+kf7xfxq/g3/+P0o/NT7+/20AZ0FbAh6CH0Ebv0I94/0MvWc9vz3ovjW9x/2svTU9tD/0QtyE5kWbRjPFoUQNwr3BtcEjAIhAGb8aff980/zEvSP9sT7PgF0A6UCEAE3/xr9PPy1/U4AGwJnAhUBQP6o+6L7Sv4yAkgGsgj9BhgB8Pnm9CbzB/SE9hX5DPoz+SP3UfWU910A8guNFOEYuRmUFqgQUgvGB+gENAJ0/7b78PYu8+3x4fLH9Qf6uP2U/w0At//9/v/+pAAmAwMFVAXUA8kAL/3D+kf7jv6ZApcFMgaCAzj+MviL83vyHvXM+PX6cfr694v1lPSE9wABnw1OFn8ZghlMFnEQgAvCCDQGSQMuAIb7qfV+8S7wzfBh88P3+/tw/oD/JADHAAwCngRUB/4HdQbNA/v/3Ps2+jf81f/7AvEE5wT9AcX8BPdQ89fzLPeZ+Xr5sffU9QP1VvXC+IACSA//FiEYAheBFDoQnQw5CiUHzgOHAEP7qfRR8CLv5e+H8tH2J/v8/TT/8P+tAYkEBwcYCOgHbgZ5A7H/yfx//Nr+CQL/A0oETAM6AC37xPYA9bv1Jvc+90H2W/Xi9Fj12/Zm+gED2Q4ZFmcWJxV1FJkRKg0DCs4H7gTnAF/7RfVG8ZzwwPGS86f2Uvpq/ET9N/81Am8EwAW8BrAGTgVvA8ABoQDrANICmgRWBJwCwQA0/sn65fff9U/0rfOo85Pz2PMm9Yv3Jvrr/M0CUAx7E+oUHBQBE1EQHA0KCwcJwgV7AZL8vvey9Bv0ufSm9fP2e/iZ+S/6Ufv+/UQBsQNFBVcGjgYQBmAF9gSYBeQG9gbMBKcBB/97/Fv5m/ZB9NzxC/Ee8h7zK/R/9p/5Zvw2/j0BnwipENMS7BA1EPoPGg7WC6sJigYdAy0AGv1H+qX4cff59Sv1n/Vf9uL2N/gQ+zz+lQDLAn0FyQfxCFwJqwkGCuMJXwgtBUMBBP59+5X4avUJ82rxn/Ap8ZjyrPR991/6zvxy/sj/oANnChEPIQ+2DhQQGRB3DaIKngjgBmkFVwMCAND8Mfq89vXyG/Gc8TXzTfUU+Ev7Tv5MAbIE4AdBCg0MLw19DbIMBQqrBRcBT/12+vn3ZvVb8/Dxp/B68AvycfRL9wn6rPuQ/GD9N/9fBGgLMg9yDw0QnxClDhUMgAtwC3oKywheBfD/qvow9rfxze5H76vxs/OK9Vf44/s9/2gCMAZBCjgNgQ6vDrwNzgpABrEBA/4n+yX50fdX9i30vPEi8I7wuPIz9XD3avms+kj7avxOAKYHSw48EPAPuhDeEE8Plw5PD0YPLg2qCEsCLPxR9//yC/AV8PvxXPMc9Iz1Bfhe+7T/hgTjCDYMYQ1EDO4KsgnfBjYDiwCm/vn8Y/sc+ff1E/Pr8NHvwvAW8xz1p/bq9zL5RfuJ/soDkwryDmgP2A+VEfkRLRE0ERMR4w6TCuIEHv+c+gL3DfQE86/zC/TP8/zzHfXd9/T7CwDXA0oHCgm5CPIHqQc6B0QGJQXKAwwCEQAg/eL4IfXX8g7xYPC+8bXzBPUW9mX3Wfnz+6v/gQUoCxkNOw0qDzoRWRFjEcsRPRBzDAgInAPm/0X9iPqW9wX2nfXX9LjzfvM09ZT4yvsE/p8AUQPZAzMDdAQPB3UIRQgMBw0FvALc/yD8rfhk9oL0G/My8zT02PQu9Rb2A/hi+ob91QL6B1MJgwk4DCoPSRDuEBQRmA8lDUwKDwfXBJ4DrwBh/L75dPjN9i31LfQ79NH1y/cF+aP6Xv1s/w4AggGgBDQHtwekBgcFjgPaAUL/N/yv+fL34/Zb9iL2KPZh9uP24/cy+R/8aQHRBC8E+ASjCWYNQg6JDqMOxw2HDB0LlAmoCJIHWQSs/378ZvsA+nX3k/Un9YX1HPbL9k34sPph/Gz9Q/+bAZIDewTOAxkDowNkAy8BkP6T/Hv71/py+fH3pvfs9xv4jfgN+mz9vwBEAVEBuASrCVIMdQzkC+oLbQxHDI0LMwtMCr8HYwQsAbP+TP2s+zT5SfdE9ob1aPUm9mj31fgt+pr7//wA/hD/mwAqAmUD/gNzA8QBzf+5/k/+NP1z+/j5Efn1+Eb5K/p//Ev+H/4o/6MCAwaQCCEKzwl1CdoKEwzsC74LTwubCXcHbAUbA5wAM/4r/ID6vfjo9ob1IvWz9VP2FPec+P75jfpD++78wv+fAuADzgM6AyACVAEQASIAoP42/eX7E/vX+vb6Kvxg/Z/8qvxpAC4E5wT2BNYFBgewCBMKkwoHC1ALqApaCQ4IywbgBPQBM/+g/Tn8z/k196z1TvWf9Qj2Xfb19p73Ivhz+UL8g/9+AQICKgI9AisChQKYAm8BTADi/1j/n/63/fr8fv3N/eb80P32AJUCIALxAREDOAUmB94HNQjtCKAJkQmPCHYHsQZbBSQD5QAm/5P9ivsz+aj3cvey94v3CPeN9qn2uPeS+fj7VP6g/+f/XwBkAUQCxQLhAqQClAKnAiQCPQEyALr+6/2X/j7/JP9Q/7H/9f+YAGgBRAJcAzEEDAVTBvUGswY0BjcFVAQqBGwDywFZAO7+av01/CT7fPpx+lf61Pnz+D/4+fjh+lT8Nv0z/iH/JAAJARMBHwEnAgED9wLPAnQCggGhAAcAg/+L/+//yP9n/7b/PwAnAA4A0ADsAcgChQPyAw8EJwSvA6UCMgJsAlMCcAEOAAr/qP75/QT90PwZ/Sz97Pwr/IL74vuy/Ez9E/7s/qT/LwAeAPb/qACMAdEBwQGDAQQBgQAGAK7/y/8QAOT/j/+r/wYAMwAsACQAqADqAeUC1AKKAr4CvAIZAl0B8wAUAU0BlQBk/wP/5/44/qD9fv2r/TD+U/6n/Tr9lv0u/s/+eP8kAKYAkAA3AGEAyADuAOEAoQBtAF4A7v9M/1L/zv/2/73/kP+u/+n/9f/9/08A9wC9AQACrQGYAe0BDgLIAUkB9gAKAQMBcwDj/6H/U//S/kr+6v3z/VD+hv4i/qP9A/7W/gn/Kv/V/0wAbwCuAKUAYAB0AIcAYQBrAGQA/v++//j/XwCtAJMAIwD5/xcA//8TAJMA0wCyAIcAUgBmANcADAElAVoBIAG8ANUACQHxALUAUwDl/6H/aP8U/97+8/78/sT+lv5+/mb+jv7V/vr+Zv/Z/5L/A//n/h7/cf+e/47/tP8SAEkAdgC4AOoABQHpAKwA5gBbASsBgwAJAL//qP/g/xwASAB/AJAAawBTAJcAEAEoAfQA9QAFAeAAlQASAJr/uf8kABUAfv8P///+4f7j/nv/DADK/wn/a/40/kr+ZP6D/sL+Bv9W/5n/s/8JAKAA7QARAYsB/wHZAR4BQwDX/+f/+f8AADAAXABrAHQAcACEANUAJAE/AUgBZwFxASABcQDN/3//l/8UAFkAx/8W/xv/Vf9s/57/xP+W/zn/wf4v/rb9l/3C/QX+Xv7a/lT/k/+3/ykA5QB9AeIBLALoAeoAGAAJADoAQgBIAGsArwDWAMUAtwChAJwADwGxAeUBuAFJAZAAz/9D/yH/hP8NACUAvf8p/+v+Pf96/0D/Pv+l/7H/+P7Y/Qz99/xP/b/9Nf6o/hL/S/9d//f/HwHpASgCKwLXAU8B3QCYAJ8AwQDBAL4AygDPAMEAfwBMAKIATQG6AcMBeQHOAPP/Yv9M/4//AABEADEA8/9q/+T+Af97/8r/FwBUAAUAGv/z/TT9PP2z/ST+h/7g/v3+5f4K/8X/3QDCAQMC0wG+AbkBZAHwANYAGAFVAUgBAQGvAGcAMAAwAIoA7QAFAdkAcQDI/yb/xf7M/kX/tv+9/43/Ov+z/m3+2v6w/1oAkwBmAO3/QP+K/g3+Bf5r/uv+G//a/nL+ZP7q/qD/UgA3Ad8B6gH1ARECwAFyAZwB3QHqAagBHwGpAHAAXwBcAHMAswDwAMIAJwCQ/zb/Af/3/jL/Yf88/+f+f/41/mb+/f6x/2AAoABBAN//pv80/8f+xf79/in/B/+E/hr+Mv6c/if/8v/rAJIBqQGUAacBwAHSAeQB6QHzAcUBLgGwAK8AtQCBAIQA3wAQAb8AHQCB/xr/9P76/vP+1f6o/kD+6P0r/rr+L//A/1sAqwCrAGwAKAAEANT/sv+v/4T/Lf+7/j/+GP5k/gL/+v/XAB8BLQFgAZEBrQG7Ab4BzAHZAbMBSwHhAL0AsgCaAK4A3QDSAJkAKQBl/8X+vP7N/oH+Kf7w/ar9if3S/VL+0P5V/+b/VQBwAE0AOwBSAFYAHADd/7r/dP/5/o/+dP7M/oX/OACQAMIAFAFbAXoBkQGXAaIB2QHaAZABXgE2AQ0BEgEbAQ8BDQEBAeQAdQCs/yH///7G/mD+8/2j/ZD9pP3X/UP+xP5H/9X/QQBgAFMAUwCMALEAZgD+/7f/bf8x/wT/4/4n/8L/NgB8AMkAGgFRAVcBLwEpAXABkwFaAS8BNQEqAf0A1QDYAOgAzgCmAHgABwCE/yj/0P6K/j7+xv13/Y395f0+/nr+0v5W/8j/GgAvABcANwB/AJgAWADu/6b/qf+q/3j/h/8CAFgAfgCWAMYAAwFAAeUAUwFcA8QCOgBt/wwAawEDAtcAuf8vAOMAoQAyALL/Wv9v/yj/mP5+/ob+Bf7S/Sr+fv68/hH/d//K/+n/+P8fAEMAbgCHAFEAGAAgAOH/dP9M/5P/bQBVATkBfgAIABkAuwBCAS0B3QDYAPAAzgDTAP0A4QAAASUBsABbAFwAPwA4ABQAzP+s/1r/2/5q/j7+WP5Y/oj+3f4A/zf/df+C/4D/nP/z/+D/rQBtAsYBzP8N/2H/BwBFANH/zf9wAIgA2P8eAO0AuAC9AGcAhf9dAFoBbgD//0MAagDOAPYA9P/v/1YB1gDW//j/DgBYADUAxv40/p7+Zv7K/Rv+FP/s/wAAEv9l/tH+qP/T/9v/CABKADAAif/E/4sAsADkABYB5ADlANwAgwDPAGgBUwH7AJMA/f/Q/1oAqABSAEQATgA9AIwAYQDB/6j/2P/L/8D/yf+t/3n/ef9a/zD/Sv8w/4H/CgDc/4j/gv+a/7T/1f/N/5z/HwCUAOX/Qf8H/1X/KQBNAEAAgwBjADcAMQBmAOcALgExAQMBygD+ACcBzQB6AKYAAQH0AGAAtP96/5D/ff8p/+7+/v4g/wb/wv54/p7+J/9n/6z/0f+Y/47/vf/5/1AAZwBGAB4ADgAuACsA///Y////HQASABMACQAvAG0AWwBUAI0AywDaAJsAmQC9ALsApQBrAHYA0AClAG8AagAXALz/c/93/4v/sv/U/2X/0/6w/sP+Bf8x/z//bv9H/83+lf7Z/gn/Ev8U//T+E/+K/3f/FP84/6r/CAA6AFQAggAZAXEBPwFnAesBNQJ9AoICQgJfAr0C3wLFAsUClAIwAsQBGgGEAFUA7/9m/wH/lP4r/u39bf2g/Ff8sPzq/Dr8aPug+zv8DvzO+9b7APyt/ET9If7q/7cA3/9mAEYDWAVKBWMFtAU6BrsHHAjUBgoG4AVPBXEESQPOAW0Amf/d/g/+SP1C/CP7ZPo1+lz62fqR+877zPsD/R/+9/06/s3+u/6Y/oH+Xf7v/j3/9v2q/A/9lf/7AlcCxv2w/R8D6gVVBPACxwLQAzcG+AYiBRQEawQnBHADQQPrArMB6v/r/o//sf8G/ob8H/x7/Ar9p/wa/JT8Q/1m/RH9Bv36/b7+7/3g/M/9lP+q/3v+ef04/df9WP6u/W78YvxU/+UC5QFR/iH/hQO6BYMFDQWXBCwFNQc9CFcHXgazBX8EqQPqA7MDAAJ//z3+t/5t/qL8IPuW+of6l/qh+uP6Bfvw+ir7zPvi/IP+HP/i/Uf9qv4uAFYAsf/p/p7+P/9x//T9rP1cAWUEiwEN/soAfAVcBlUF1QS2BKAFCgfuBtsFZQWHBJkCjAG0AYUBzf8P/dr7yfwy/ej7ffrS+eX5pfpO+0r7p/tk/LL8Uv0W/7wAsQC1/7D/PAHzAtAC0QBs/57/WAAsAFn+t/sC+zb+xwFTAJb8S/10ASMExgSdBC4E6ATlBkQIYAi9B24G0ATdA8IDaAOCAZH+xPym/Hf8cPtE+iT5Dfig9234Ffpc+1D7xfqw+53+cQEgAlIBLAF3AsMD/ANVAwICewDw/7P/RP42/Cr7f/xn/9r/Rf0e/bsAvgO1BCgFLwW2BeEHtQmACZcIYAdTBboDFwNrAuEAK/6C+5H6afql+bT4z/fF9rT2NfgD+kj7Rfyy/Db9iv/LAkUEnQPEAvYCBQTSBFoEdAITAGP+jv0G/dP7vPly+dz8X/95/RP8Hv8XAycFTgb8BmEHhgjNCdQJVQnGCPQGKAR3AlUCmwFd/pv6RPkQ+Qf40vbK9fb0qvWT98r4Zfko+4D9m/7p/+8CPwVqBfIEnwRKBdkGsgbtA+AAK//R/pj+u/x0+eX3Vvo9/u3+m/zK/CEBYAUIB5YH5AdeCIoJBAqJCWwJewjfBBEB8f/8/zz+3/oX+Or2sPZe9rP1O/XP9Xv39fj6+WL82f+lAe8BWwNABjAInQe5BfIE3wVvBpoEIQFf/lT91vxL+6f4p/Zp99/6nf0D/eL7K/5yAlMFfAYbBw8IYQnhCcIJWApWCuIHKwRpAWoACAAG/qH6c/in98P2NvaK9jX3C/jI+Ib5Zvut/n8BLgIzAu4DYAb+BsoFbAQOBJ0EHwRkAY3+K/34+zX6t/jJ9yP3j/dp+vD9n/7S/fP/KgT9Bs0ITwqBCmMKEgtFC6IKqglgB4YDOgCb/lz9dftn+Zb3/vUK9e70zfWR9wT5MfmT+eL7eP9iAokDkQM1BKUFPQaxBSQFvATfA44C6gA9/8v9T/yk+pX5/fjv93r4nfwJAOP+3P3wABYFzQdeCWIJJQk2Co0KaAn+CKUIAAbsAXv+t/xJ/Ez7AfkF9yn2z/XS9Z72VPgf+tj6Bvu8/DYAFgPUA/AD6ASoBU4FDgU/Bb0EXAOLAYH/KP6c/UL85fld+P73Ifgb+tD9ef9g/i//+QL3BXoHFAn0Cd8J4wl7Cc8I4wgbCJoERwDi/T39QPxj+tD43/cF9zH28fUk9675ZfsJ++D6Q/3AAK4CKwPGA48EqQRRBFwEkgQUBIkCaADh/p/+HP7h+6b5Vvm2+f35/fsR/xkAAgDAARIEawVXB4oJEApxCW8IGwfVBksHzQVHAj3/jf1J/Nj6tvlX+d/4gPcd9iz2Sfgr+3H8+Ps6/FP++gDMAqoDagQWBacEowOyA1cE2QMVAjQAOv/B/iz94fpC+hz7/fp4+iX8L/+wAA0BLgKTA7QEkwaNCAMJSAgoByoG+wW9BRcE1wEGADv+VPzc+lf6vvrv+r353/eI97L5XPwk/a/8IP3J/oYApAFjAjUDoQPhAtkB3gFAAsoBtQC7/wD/Cv5p/Af7LfsI/E78E/0p/8AABwGyAUgDwgT2BQ0H2QcsCJAHBAYFBSYF2gQtA/oAC/9o/Qv8PPtT+5P7vfrd+IH3OfjO+sz8uvxD/A/9m/4FACcBDQKFAkcCvQHTAYwC4AJMAloBpADn/73+mv1P/c39JP7p/Sz+y/9cAbYB/AHQAnQDJwRfBXUGiQY4BWUDyQI/AwwDzwEKAA7+qvw4/B78Pfxv/Jv73Pkw+bT6Dv0J/nb9W/1o/n7/TQAcAYIBfwGiAd8B5wHTAa0BWgH9AK4AOABO/0D+N/47/3b/if6E/i0A6wGHAlUCFgJnAlIDJgQ9BI0DZAKSAX8BggHvAMn/cf54/eT8h/y2/FX9gP3C/Kz7dPvc/ML+Z/8Y/x3/d//l/8AAugEUAvUBvwFxATIBOQE+AQcB/wA4AdgAhP9s/s/+8v8dAIb/0v/rAIEBegFMATABvAGnAsYCPAKPAWcAYv+g/y4Aqv98/nb9u/xq/Ij86vx8/ev9yv1V/Xr9y/5TALQAcQDLAIAB6QE8An0CTALpAb0BrgGOAVkB3QAWAKj/5P/V/+f+av4k/8b/iP9i/xUAGwGeAWMB7QD9AJQB+gEZAgIC+gBR/57+CP8r/3L+V/1//DH8HPwf/G389PyA/bP9hv0C/pX/5QBDAbABfgL1AgoDKwNJAzsDOgMfA5sCIQL/AXgBfwAVACMAsv/1/rH+0f7j/s3+9P6s/30AqABNAAoAEwBjAA4BtQFzASkAHv8r/4T/Sv+d/gX+uP2D/Qv9nfzc/Jv9F/76/ef9g/50/wYAfQBBAf8BSAJMAn8CuwKwAqkCvAJ2AhIC1gE9AXcATQBNALD/Ev8z/4H/QP/T/uv+if84AFoA4/+U/+H/WwCpAMQAcgDb/5X/mf9y/yn/8v7k/vf+zf5d/jj+gP7J/sf+lv6l/hz/j/+x/9X/QwDFAAABIgFgAX8BgAG/AfUB1AG2AZ4BKgHFAM0AuQAuAKD/gf+g/4X/O/9C/6D/4f+1/0z/Sf+0/+P/6f8uAB8Ae/8a/0b/gf+L/2v/TP9r/7b/0f+u/47/of/g/yoAaQCAAG8AdQCqANYA1ADTAAIBMQEEAasAqADQALUAgQBMAOv/sP/I/6b/Nf8N/1b/ev86/xn/Zf/J/+n/uP+C/6//AwAiAFQAdADo/zL/Pf+//wAAzP9P/xr/af/B/9n/5P8IADEANwAyAGkApgCVAIsAsgDHALsAvwDhAN4AgwA+AGAAgwBuAE4A//+F/0v/Sv8v/yL/Wf95/y//9P48/7X/9f/2/7v/i//M/z8AhgC9AK0AJQDD//z/XwBoAAAAfv90/8L/0/+3/7r/wf/Y/w8ASACGALcAwADGANgA3QDxABQBCAGzAFYAOgBXAE4AFQDx/67/F/+s/sT+EP9C/0L/Cf/e/gn/af/J/w0AEwDv//X/KABVAI0AtQB4APv/vv/k/xcA8P96/0T/gP/A/8P/sf/G//7/EgANAFUA4gA2ATkBGQHoAN8AEwEsARsB8wCXAD4AGwD8//T/+P94/5r+R/6S/uL+9P7j/tv+7/4g/3T/1v8oAGgAiQCHAIYAkwC7AOIArAAmANP/3//8/9//dv8S/xr/Vv9V/07/j/+//5//p/8hAM0AKwEHAcMAywAFAS4BNgEdAdgAdAAbANb/u//k/9f/JP92/nv+2P4P/xf/B/8i/3n/vv/n/yUAZwCgAMYAtgCPAK8AAQECAYkABQDY//b/CgDX/3X/R/9Z/0z/Jv9B/47/pP+C/5v/JQC2AO8A5ADMAMUA5QAxAWEBMwHEAGcAPQAvAC0AGAC//zr/6P7l/gz/Jv8a/wz/Mv9t/3r/hP/E/w0ALwBBAFQAWwB1AKgAowBLAAEA/P8UAA4A4P+v/57/qv+j/4v/oP/N/8P/rf/w/14AkACTAJgAmwCaALwA+AALAdYAhABPAEMAPgAnAPv/vv9y/yL/+P4P/0H/TP8x/x3/Jv9P/5P/yv/Z/+X/EgA4ADkATQB7AHcARQAjAA0A8//m/9H/pP+M/5T/h/99/7L/6//c/7v/0/8NAE0AcwBrAF8AgAC6AOAA3gDIALsAvwCtAHwAXgBcAC0Au/9T/zb/Sv9g/1//Rf8y/1P/kP+r/7T/8v9YAIoAagBGAFkAhQCbAH4AOgAJAAYACgDt/87/1f/g/8H/pf+8/9r/xP+h/73/DgBJAEwANwA+AGsAkwCYAJUApwC7AK8AeQBDADkAKgDT/2X/OP9B/zj/Gf8G/xP/QP9t/3L/Yv+E/+L/KQAuACcARABqAG4AWABFAD8ASQBSAEUAKgAqAD8AKgDz/+f/AwD2/7L/jf+4//7/EgDw/9j/9/8wAFIAXgBvAJIAogB9ADkAIgA7ADcA7/+d/4H/iv+G/2L/SP9Q/2X/av9u/43/u//V/8r/vf/d/ycAWgBYAEYARwBaAHYAiACLAI0AjAB3AE4ALAAlAC0AHQDk/77/y//Z/77/q//F//L/EwAnACgAIQAvAEQAOwAlADQAVwBEAOz/pf+q/8r/yP+x/6b/nv+T/4f/fP+K/7r/3P/E/7H/4v8kADwARQBSAFgAbACaAKwAkACNAK8ApgBlAEAATgBHABAA2P/F/9H/3P/F/5r/mv/R//v/7//X/9r/9f8JAAkA+/8IAC0AJwDn/7z/xf/E/6v/ov+k/4z/bf9d/1T/Zf+Z/7z/s/+z/+j/HAAvAD4AWgB7AKIAyADGAK0ArQC/ALAAkACCAHQATwAaAOX/x//S/9j/rP96/37/ov+o/5f/jv+l/9X/8P/d/9H///8yACAA6//b/+z/9v/0/+7/0/+s/5n/m/+s/87/7//q/9r/9/8qAD0ASQBwAJYApgCxAL4AxwDUAOEA1wC4AJsAgQBZACMA5v+7/6X/kP9n/z7/Of9J/1b/X/9w/5b/xf/f/97/4////yMAMgAjABMAFQAUAAMA/f8FAP7/5v/X/9b/3P/n/+z/4P/c//P/FAApADwAVwB0AJIAqQCvALQAwgDCAKoAhgBfADgAFQDn/6n/ev9p/13/QP8n/y3/Qv9T/2b/g/+u/9f/6f/r//r/FAArADkAQgBEADoAKgAbAB4AJgAZAPv/4v/c/9z/2P/N/8b/2P8BABwAIwAxAFEAdQCSAKgAsQCwAK0AngB7AFYAOAAZAPT/zP+f/3f/Y/9e/1L/RP9G/1j/a/93/4j/sP/k/w8AHwArAEYAbACJAJEAkgCMAHQAWgBVAFgAOwAEAN3/2v/f/8n/qf+j/8r/8//8////GQBAAF8AegCKAIkAgAB7AHMAWgApAPX/1P/D/6j/ev9T/0D/O/84/zr/Tf9m/3b/gP+Z/8v/+/8RAB8ARwB7AJkAlgCLAJAAmACJAGkATgA0AAwA3v/G/8D/tf+d/4//pP/O/+j/7P/1/xcARgBrAHkAcwBvAHYAgAB3AFIAIwABAPL/4f+7/4r/af9k/2r/Zf9V/1L/aP+I/6P/vv/i/woAKgBJAG8AjgCTAIQAfgCFAHwAWQAvABIA+v/W/7L/pP+n/6P/lv+Z/7n/4f/3/wAAEQAvAFIAaQByAHIAbgBsAGkAVgAwAAYA7f/d/77/kf9t/2f/bf9p/1//Yv9+/6P/wP/Z//3/KQBOAGQAegCYAKoApACXAJMAkgB4AEsAKwAfAA0A5//C/7X/uP+1/6v/rP+8/87/3f/u/wYAGgAnADIAPQBDAEEAOgAtABcA/P/k/87/t/+c/3r/X/9W/2D/aP9m/2f/ev+i/8r/5v8BACYATQBoAHQAgACQAJYAkQCFAHMAWQA7ACgAGgD+/9L/sf+u/7H/pv+Z/57/r//B/8//6v8OACkAMAA2AEYAXABnAF4ASAArAA0A+f/v/+X/yv+g/4H/hP+c/6v/qf+p/73/3////x0APgBfAHMAewCEAJUApwCsAJwAggBjAEoAOgArABAA5/+9/6T/nv+c/5D/h/+P/53/oP+n/8b/+P8SAAsAAgAYAD0ASwA7ACIAEQAGAPn/6//c/8X/q/+b/53/pP+k/6T/tP/R/+T/7/8FAC0AVQBmAGMAZgB6AIwAigB0AFwARgAvABsADwD7/9L/qf+c/6L/o/+T/4v/mf+r/7H/u//a/wIAFQAWABsANQBRAFYASwBAADoALQAXAAQA+f/r/9T/u/+y/7j/vf/A/8f/2f/s//j/BwAmAEYAUQBMAE8AXwBnAFoAQwA5AC4AGQD//+//5P/Q/7b/rf+x/6//qv+0/8n/1f/N/9P/+P8jAC8AIAAfADcATQBLAD0ANwAxACEACQD9//v/9f/h/8n/wf/K/9f/3P/g/+f/8//9/wgAGgAzAEAAOwAyADUAQgBGAEAAMgAhABAABQAAAPr/7P/X/8f/wf/B/8X/zf/S/8v/yv/d////FAATABMAHAAqAC4ALwAxADAAHgAAAO7/8P/x/9//wv+2/8D/y//O/9D/2//t//v/BAARACEAKgAoACMAJAAmACMAHAAZABkAFQAMAAgACQAIAAMAAAD7//D/5P/h/+b/6P/g/9r/3//v//r//v8FAA4AFQAUABIAEwAVABAACAABAPn/8v/u//H/9f/z/+n/5v/w/wEACQAIAAkADQAPABMAFQAWABcAFAASABQAFgAUABIAEwAYABYACQD/////AAD5/+7/5//o/+j/5v/n/+7/9v/8//r/+v8AAAYABwAEAAEA///+//v//P///wIABAAEAAUABgAIAAgADAAPAAoAAwAAAAQABwAFAP7//P8AAAYACgALAAwADAAQABQAEQAMAAQAAwAEAAAA9v/s/+z/7v/v/+z/5v/n/+r/7P/s/+7/8v/z//P/8//2//z//f///wIABwAJAAkADAAPABEADgAJAAsADgAKAAUAAQABAAMAAAD/////AgAEAAQABgAIAAkACQAMAA0ACQACAAAAAQAAAPr/9P/x//D/7//t/+n/6v/t/+z/6//u//T/9//4//r/AgAHAAoADgAUABwAHQAcAB4AIQAgABsAFQAUABAACAACAAEA/f/5//P/8v/1//b/+P/9////AAACAAcADQANAAcAAgAEAAUAAgD8//f/9v/0//H/7P/s/+z/6//t/+7/8f/2//z/AAAEAAgADQASABUAFwAaABsAGgAZABkAFgAQAAkABAACAPz/9//z/+//7P/o/+b/6f/w//T/9P/2//z/AwAKAAwACwAGAAQABAAEAPv/8v/q/+j/5f/d/9j/2f/e/9//3v/g/+n/8f/3//v/AQAIAA0AFAAaABwAHAAdAB0AHgAeABgAEAAMAAgABAD9//f/9f/1//D/6//s//X//v/+//7///8IABEAFwAZABMADAAIAAwADAAHAPz/9P/v/+z/6//r/+3/7//w//L/9v/9/wcADQAQABMAGAAgACcALQArACYAIwAjACIAGwAQAAQA/f/4/+7/5P/f/+D/4f/c/9j/2f/j/+//9f/z//P/+v8GABAAEgAMAAYABwALAAoAAwD4//H/7P/o/+T/4f/f/+P/5v/p/+//+f8JABUAHQAgACQALAA1AD0AOwAyACkAJAAiAB0AEQAAAPH/6f/j/93/1P/O/8n/x//I/8z/1f/d/+L/5f/q//X/AgAKAAoABQACAAMABwAFAPz/8//r/+b/5P/j/+T/5f/m/+b/6f/y/wIAEAAcACQAKgAyADsARABKAEkAQQA1ACsAJAAdABIAAgDx/+P/3f/c/9j/1P/S/9P/2f/i/+v/9P/7/wEACAAPABYAGgAdABwAGAAVABMAEQANAAYAAAD4//L/7//w//H/8f/x//T/+v8DAA0AFgAaAB4AIwAnACgAKQAmACAAGAAOAAUA+v/v/+X/2//U/83/yv/J/8r/zP/R/9r/4v/q//L/+f8CAAsAEwAbAB4AHwAeAB0AHAAaABYAEAAIAAAA/P/6//n/+//8//7/AAAEAA4AFQAZABwAHQAcABoAGwAZABIACgD+//X/6//k/9z/0//I/8D/uv+4/7j/uf+7/77/w//K/9b/4f/t//r/AwAOABkAIQApACsAKwApACQAHwAYAA8ABwD///n/9//4//r///8GAA4AFwAjACsAMQA1ADcANAA0ADEALAAlABwAFQAOAAkABAD+//j/8v/t/+v/6f/q/+v/7f/v//T/+P///wYADQATABcAGwAfACIAIgAiAB4AGgAXABMADQAHAPz/9v/w/+z/6v/r/+n/6//s/+7/9f/4//n/+v/4//X/9//0//L/7//p/+b/5f/h/+P/4//e/93/2v/c/+H/5f/p/+r/7v/6/wkAFwAiACQAMAAzADQARAA4AC4AKwARAAsA/P/y//H/4f/W/8z/yP/L/9X/0f/Y/+r//P8HAA0AEgAxADsALwBaAFgAbgB0AHQAfgCsAH4BhgH+AJEAIADP/3H/M//e/pf+L/7S/dD9C/5b/qb+Hf+D//H/PwBwAMIAJAFjAWQBiQF9AUEB6wC4AKwAfwB6AEEACQDW/7f/kf9p/3b/ZP+W/7T/1v/N/4T/b/9n//f/PQBGAFMAMwAfAAoAYAB/ALIAsgB1AEMA6//Y/77/AgAXAPr/7v+4/6v/lf+//+n/GgBFACsACQDw//z/CQAmADMALQAOAPr/6f/Q/+P/7f/2/9j/3P/Q/67/t/+P/2z/QP8o/xv//f78/t7+0/7z/hn/T/9g/2b/cP9y/5H/zv/w/y8AmADrADoBkwEAAk4CpQIOA0ADXQODA20DGAO9AjECmgEPAYUA4f8x//D+u/5z/l3+WP5S/mj+qP6o/or+Xv7Z/Uf9Tv2F/T/96fyi/BD8YvsN+8P6svon+3T7gPtL/AT/XgNmBt4GkgcYCSoJbQg/CWoK4gk+CJ4FEAJN/7T9xvv9+WD5//gc+Fb3gPfa+NP6avzV/Xj/9gAqAmID4gQjBvgGPQdyBkoFiQSBA8ABNABv/wn/p/5k/jP+2P2Q/Wb9iP1A/lH/TgCuAIkABwAO/9D9Bf0E/RX9lPy7+zb77Pqj+uX6cPva+xf+8wJPBiIGLQYNCIEILAdCByQIawfxBI8BL/7b+7b6OfmJ9zD35fc3+Gv4L/pC/fL/dQHCAn0E1wU+Bj8Goga4BvUFlwQKA7oB4gAOAKv+gv1R/aL9Nv5d/5gADgH+ANkA4AA7AaoBvQEWAUr/lvyi+iX6Gvr1+e/5dPku+FT3bPf19wz6r//BBsgJjwhECAYKiQoCCiILAwzTCV0FfACH/J76HPqj+IX2xPX+9c/1c/bO+VT+OwGSAhAEfQUTBoIGWgcaCOgHRQZtA5UA/P6Y/hz+4fyK+9D61/rZ+yb+uAA+Ap0CZgI+AtsCLAQyBUYFCQSXAX/+V/uR+YT5h/l9+AP31vUq9TP1RfYr+Ar7swDKB3YK2gimCfYMXQ3jC0wMIwzWCDkEuf8U/Jz64vlH91L0wvMm9ab2evjx+z4A0gIhA2wDxQQZBtMGMwfzBpQFPAOWAJv+Kv7c/vX+vP1P/A/8Jf3v/kYBYQMmBKQDZAJLAcwBmwOCBJQDfQEN/4v8Nvo1+QL63vr6+RD4Dfcz99T3HPnV+jX8kf88BqcKnglfCb8MZA3NCbAHjgeFBc0BRP4T+734S/dm9X3zEvRu98j65/yB/xEDYAWvBf0FaQeQCCMIeQY+BLYBRv99/S38YPvh+7/85fuS+rn7Ff+kAqsFcgdhB4cG9gWGBRkFHQXYBIsCOv5s+rL4v/ds9n71a/U39a/05fST9kb5FfxW/sD/LACaAZkHmQ8UEqcPDQ/jDmUKJgW8AyMDewDB/Ev4kfMG8Qbxq/GA8xP4dv1fAIkBwAOnBlIIZQnfClwLQwlfBWABL/5J/Kb7LvsZ+kv5c/mC+Wz5V/vd/9kEMwi/CUAKZwo3CkYJ5wd0Bo4EmQGe/R36Xfhe96D11PMu8zLzPfOS9HD3MvoA/ET9F/4i/6MBSgSfB8QNRxJAD8cJygiiCHIFKQPRAtz/a/px9tX0hfSb9X73/fjF+pX9XwDSAvAFDQlHCmgJ9wfNBpEFwAOYAXz/FP0X+qD30/aE9175Bvwh/hj/NgATAqEEfQidDOcNkwsSCCgFcgKJ/4X9/fxH/Cb60/es9m/2b/aa9in3nfje+mb8OvyD/DP/5QFDAiIC3gL/AwcHCgvwCrIHXgdDCKcEV//S/cj9zPv4+dX5QfnV90730fdi+Ub9ZQKMBWsG8wYdB0IG0QXMBkkHLQUvATT9Lvqd+N/4G/pw+yf9FP/1//f/MwH7BOcJrgz/C8YJqgfqBGQB7f5O/vf9Jfw3+Wf3mvcQ+Kj3mvdp+E/5Z/r0+5z9T//vAJ0BVwEzAUwBOAEIAbEBIQWDCX0JIgZABZgFRQIB/ob9Mf7I/Pr6Ifo4+S34APjA+Lf6q/43A/UFOwcMCFMHKgUCBF8E6QO+AVT/iv2a+1v5qPin+gP+CAETAwwEZQT0BOkF+gZaCKYJAQn6BacCCAA1/an6zPnP+Ur5mPj590/3pPeH+cn7mv0e/zoA0AAOARYB5wDiABQBpwCM/5n+y/3l/hcEswjrBkgDtAO9A6T/Bv2V/lT/f/3g+9/6rPlr+VL6gvsT/lYCHQXEBCYE8QQXBc0DFwNyAwsDLAHw/kj9O/ze+2H8/P3UAKsDtgRSBBgEBgQgBMEFTwgfCYIHjwSLAFD8cvkX+AX4mfld+0j7Sfoy+gX75vy//7UBEgJWAkkCuQAJ/6n+u/6b/o7+xv2v+4/5HPrB/k4E5QW1BPAEDgXgAXr+1f6XAJwAlv9S/lP8Y/pv+UL5p/qj/rcCxwNNAxQE3ATzA3kDwgQ7BUUDsAAj/3j+RP6t/rD/JAGpApMDiAMIA98CQQPpA3gEjQSKAzQBWf4K/G76zPl5+oD79/uL/L39Bv8xAFcBYQIWA9ECRQG9/yr/jP57/ej8tPzg+1D6pPju93T4Tvpk/+0GVwqnB7sFDAf9BYoBdP/TAAABh/7S+8j5Bfjq9ij3T/lE/ZEBGASPBJgE+ATRBD4EjwSUBWcFhgN2Aez/q/5F/uD+9///AY0EIQX1AuIAFgF4AlUDrQO7A54C1f/a/G37cvvr+4j8cf3H/qEA2wHnAI//pQAaAvkAP/9S/x3/3fwZ+xL7pPqR+TH5RvlZ+Tz67/ug/RYBkgbACDwGuwRjBZsDw/+1/sz/Q/+r/dP8/fvi+rH6pvsm/VL/BgLAA/IDxAOyA8wCfQGVAY4CGQK7AHIAsgBGACMAxAGcBK4G1wbWBXcEewJtAAgAJQFpARUAcf7+/Jz7lPot+kj7jv5JAhsEUwRqBCkExQIhAZgA1ACGADb/H/3Y+jP5Ovhg9wr33ffx+Gn59Pk++wj8y/wHAb8HdQqCCJUHCwjdBScC2wAQAZgA7f/Q/lP8IvqT+XT5Yfmr+kT9Kf/D/44A7QFjAv8BjALCBA4HxwdHB5EGHQZFBhgGngRGA+gCWAGJ/VH6z/mL+j/7Yfyq/cD+rP9dAFYB0AJgBCQGcAf4BoYFFQTxAUn/m/3E/Jj7LPr6+J73Qfbx9Zj2Vfc3+M35s/vy/Fz96v26/rz/PAOyCIoKSQg/By0H6AOd/xL/8f/Y/lb+Kf/2/QP7//hM+Fb4hPnq+03+vADaA7MFTgUqBV0GiQcgCLwIOQnsCMkHiQUqAhP/V/1H/CL7L/oZ+sn66vuq/an/+wDUAfACZATnBTYH5QdxB7AFVwN4Ad3/7/2Q/EL8sfss+sv4LPjA9/73Gfnf+S36sPop+237DPxY/Uz+Pv71/Xb+dwEKBrcHkQYdB14IrAXgAOj/kAEYAW//Vv4n/DD5S/fv9V716ff7/N0A8wItBdgGswayBpQIswoQCzQKUQnuB/EExwBx/fj70PsI/PH7h/tf+8D7sfxY/rAANANYBf0GtgdoB58GkQUuBKQCPwE1AGf/Z/7e/B37wvmw+O/3NPg6+dz56flr+X34XPib+eL6ifty/MD9Wv5l/vb+gwDsAyEIZgksCEsIdAgDBTsAi/74/kr+tvxr+1n6uvll+bz4CfnC+1j/qAGdA5wGMAmjCeQIeggqCFwHkAbaBWUEdgIAAXb/+/wO+3r7nfxS/Dj8Hv6EAP4BPwN/BO4EGQXzBQ8GdQSWAzMEXwPjAG3/l/7F/Ir7i/u8+j/5zfjK+AD4EPcT99X3n/iI+af6tfvi/CX+EP+g/zAAxwAwAYACcAU7BxkGQAUnBk4FqAEl/1P/Pf/b/er8dPyO+6r6G/q6+VX64fxSAPMC3QQDB+AIUQlpCJEHAQjkCJYIdQd/BtcEkQHb/Zr7+vrm+r366/o5/Ff+EgBNAXkCoQM2BfcGfwfQBj8GwgUqBHcBLv+t/a/7aPkU+Bb30/Ua9Tn1u/WM9vn3mvmy+pT7qfzB/Rf/UgDCAOMAQQFXAWMA3/9uArEFdwVQBHkFUAVuATP+Tv6Y/oz9a/3W/RH9Xfwy/Gb7XPul/YIAPgIQBOsGvwhmCPoHZQh9CLcHxAaUBnAG6AR4AkAAj/50/ZP8Cvyt/F/+8//oAPgBNAOhA3oDwgM/BEEE5AOHA80CMQEY/9X8avrA+Fv4MPi995f38veF+Nf4wfgF+Uv6vvuI/Eb9LP5e/jP+j/7T/pD+zv6T/47/CgDSAg4FTAT8A5wF1AQjAWP/v/+a/jX9Gf7k/tz9eP0j/nT9ZvzT/YsAUQJABPQGkQjACFAJ8wkLCaIHCQcJBkwEUAPhAoQBqf+//kL+Jf1J/Lj8EP7l/9oBewN4BGUEeQOBAoMBIADy/pr+YP6f/QH9V/y5+h/5pvh1+Cj42viH+rb7G/xv/JX8evyY/Jr8TPyM/Hj9MP5z/pH+pP57/tj98Pwx/XD/agHYAYsDgAaFBvADrAJKAq4AYf++/3UARQGaAp0CmgAz/0H/0P5Y/kYA8gOpBvsHAQlECRAIdgZvBbMEGgQJBHUEeQSIA0wCJAGk/+j9jfwx/Az9S/4d/7b/JAAGAGP/af4X/Rj8YPxY/cL9HP4E/0f/GP53/If7IfuH+j/6R/sL/UH+M/7//L/7Bvsq+u74efi2+Zj7pvwt/dD9Wf5m/hn+pf7SAAwDIARMBcgG3QZzBUUEygM9A6kCYQIRAvQBdAKjAukBWQGwATcCaAKiAgwDVwOTAxMElQShBJUE5gQABVwErQObA8MDwgPAA2cDQQLiAKP/6P0k/Gn7Ofvr+hL7s/sG/PH79PtB/Ln8Vf0M/sD+av/j/x8ANQDm/x//R/5m/Tv8C/uA+n/6OPrK+fr5jfr0+iz7H/vy+kj7CPyz/Hr9hP5q/2AAYwGAAWcB7gLrBIMFXQaPCLIJ6ghBCOwHxgZ2BcEEuwMYAvoAbgCe/+P+1P4f/2n/fv/N/+YAQgJcA7QEOwYAB9EGUwajBXAEMgMwAgcB9v8j/+b9j/zH+xH7N/oB+nP6tPrW+nn7JPxk/An9Dv5v/k3+fv7U/rL+Rf4k/jf+Hv7F/R39evxi/Hv8HvyT+4T7u/tq+7T6fPqZ+o36ovri+jP7MPwQ/t3/JgEUA+oF8gclCe4KUwwQDE0LpgpaCb0Htwa7BSYExQLQAXUAF/9f/rj9av0i/t3+8v5v/6EAnwEZApICVgMDBDcE6QNUAwoD+wKGAqgBlACS//z+Qf7v/Pv76vv9+8b7ofuH+0v7Sful+/j7GPxN/J38t/yn/Kn8ivx+/Pb8Z/1a/XH9Bv5v/kL+Af4A/ur9dv3E/DL85/um+2v7Sfs8+577uvz+/R7/4ABzA8YFngekCTkL4QtVDFkMWwstCm8Jqwh+BwIGbgTNAlEB3/8V/qb8UfxI/B38dfxF/SP+1v5E/9r/zQCPAfEBTwKbAm0C7QGbASwBgwBFAC4Ak/8D/9z+bf6N/dP8V/zM+zb7vPpr+mb6u/oM+y37gfsU/En8Evwh/JL8+fx//Vn+4/7m/hj/gv9p/+T+iP5i/kb+E/64/WT9YP2V/dz9gf69/yABbALSA0UFlwapB0AIcwi2CAgJ9AiICCoIwQcIBzcGdAWKBGEDMQI0ATUA7/7O/T39+fzm/EP94f0y/hr+A/4L/tf9ef2P/Sb+m/7G/kP/IgBjAOz/5/8kAIz/2v7y/hv/Af8r/yf/W/5k/bL8nfs4+pP5v/n++Ur68/qg++/7IPx//Ab9yP3j/jIAiAGUAu8C0AKCArQBggC7/5P/df8z/zf/d/+T/6j/2/8vABcBkwLoA+8ECwYKB2sHWQc4BwEHxwbHBroGVQa5Bf8EEQTSAokBlQDO/yb/8P4N/+L+YP75/ZH9r/ya+wD79voZ+1/7A/zO/Dz9Vv15/aX9zP0t/tL+av/f/0wAcAASAJb/L/91/on9F/0k/Rj9xvx5/G38ify3/Ar9jP0u/gH/EQATAbgBJQKZAswCVAJ/AegAeQDj/2j/VP9m/1//Pv8c/x3/iv9IAA4B7wHzAtEDXQSwBN8EDwVzBdoF4wWwBY0FLQVQBH4DHwO8AgoCewFDAQQBfADZ/2n/Fv+J/qr92fxV/N37Sfvm+u/6M/t1+7H7DPyi/EL9pf32/Wj+u/7B/rn+vf60/sD+4/7K/n7+Z/6H/o/+hv6n/vj+cv/x/zgAbADlAHoBygHWAeUB/wHWAU8BwABmAAQAgf8n/xL/FP8q/1T/dP+g/x0AywBSAc0BfwIoA2UDaANiAzkDFQM0A2QDZgNJA/4CjgIxAuYBlAF9AbUByQGDASUBwwBLANL/U//P/lv+4P02/Yr8FvzP+7X71fse/HT8xPzy/B79e/3e/f39Gf59/tT+z/64/t3+Hf9D/0v/Sv9l/5b/v//Y/xQAigABASoBOQF/AaUBbgFSAXoBYQH5AMsAwwBZALf/Xf9J/1D/j//3/zwAgADqAB0BFwFYAd4BNAJYAn8CmQKAAk8CHgLwAdQB2wHOAYQBJgHjAKMAUAALAAYARACFAJIAdABBAOj/cf8I/6n+Pf7k/bX9gv1C/SX9LP1C/WD9Zf1g/YT9s/2//fL9eP74/jv/aP98/2z/iv/g/xEAPADKAF0BbgFDATMBAgGeAHAAigCPAIsAwQDTAHkAKgAOAND/i/+Z/8r/+/9aAMQA6AD1ABYBAAG6AKsAvQDIAA8BhAGlAXIBUAEuAeMAlwB1AH8AtQD0AAEB7QDjALcAWAARAPP/zP/A/+L/3f+u/6b/pf9Z//D+rv54/kj+Sv5x/o3+p/64/pL+N/7a/Yz9a/2w/TT+of4D/5D/FQBMAFAAZACZANcA7wDUANIACwEsARAB+wAZATQBKgEHAd4ApQB0AGEATgAZAPX/FQAoAPX/0P/5/ygAKAAqAFwAmACzAMAA4QAHAQUB5ADDAKgAeAA0APv/1/+4/5j/h/+U/67/xf/Y/+X/3P/F/8D/yv/S/+L/AAAdADQAQwA1AAEAy/+l/3b/Nf8E///+Df/z/sn+xf7I/p/+ev6K/q3+0/4d/3n/xP8NAE0AWwBQAFgAYgBoAI8A1QAOATgBWgFdAT8BHAH7ANQAugCjAHcAUQBCACMAAAASADkAQwBaAIoAigBcADcAIQD5/93/1P/F/7z/v/+u/4z/iv+w/8n/zf/j/wQAAQDg/9T/4P/h/9v/6f8AAAsAEAAVAB8AOwBZAF4AVQBXAEkAGwDg/63/gv9Z/zz/N/9K/1r/UP9H/0b/LP/6/ur+/f4M/yH/T/+O/93/NQBqAIIAsQDgANsAyQDxADwBdgGgAb4BuwGTAUwB5QCQAHcAawBTAFwAcQBFAOn/qP9z/yf/8/4C/y7/Sf9Y/2r/fP+F/3b/Tf8y/zn/Pf8i/yD/Y/+r/73/zP8DACMABwD5/xsAOQBMAG8AjwCdALgAzwC2AIwAdABVAB4A7//X/7v/m/+K/37/cv9j/1X/Uf9o/4n/lf+e/8T/9f/8/+v/9v8gAEsAawCMALAAzgDZANQAyQDCALsAtAC1ALcAsQCqAKQAmgCCAG4AbQBnAFkAVABRADEAAADa/7f/hv9R/yf/+/7P/q/+n/6Y/pn+pv7G/uH+5/71/iv/dv+0/+//PwCGAJ4AkwCFAH4AawBLAEEASABHAEQAUABZAFQAWwB4AIAAbABqAHoAfwB8AH0AdwBgAEoANgAPAO7/8P8AAPv/+P8DAAAA5//I/7H/rP+6/8j/xf/Z/xoARgBBAD0AUwBsAHQAeQB7AHUAdwBtAEIABwDX/9D/w/+x/5z/df9f/z7/HP8C/+L+2/65/rT+1f7C/sD+1v4C/yz/Hv9J/5H/x/8SAEIAhgC4ALwA4ADpAOYA7gDdANYAxACxAKMAhACjANIA0QDaAPQABgH8AM8AtwC9ALMApQCMAIAAiABQAA8A6f+0/5L/d/9f/17/bv+c/6b/lP+8/9z/4/8AABoAOwBgAG0AXgA2ABQA3P+C/0//Of8X/xD/Hv8q/yn/Hv8i/xb//v4S/zP/R/9d/3b/gP9m/1n/bv9w/3j/rP/m/wkAMABeAHgAigCwAMwAwwDOAPQA/QABARIBDgHrAMkAsgCSAH8AkQCoALoA1QDlANMAvgCzAJ4AfABeAEwAMwAZABAADQAAAPT/+//6/+T/2f/d/97/4f/y//n/8P/m/9T/rf98/1D/Kv/7/sX+of55/kr+JP4X/hn+E/4j/l/+nv7N/vb+LP9i/4T/rf/j/xIATwCgANYA7AAPAT0BRAE3AU4BYwFcAWMBcQFIAQYB2wCoAFMAHQAbABoAGAA0AE0ASwBQAF4AWABOAGcAiwCmAMsA+gAdATIBQQE5AR8BBwHwAM8AowB+AGUASgAWAN3/uv+c/23/Sv8//zX/Mv87/zD/GP8S/wb/y/6P/nT+RP74/cj9rv2L/XX9g/2c/cH9BP5T/pn+5f5H/5X/w/8IAG4AtADsAFUByQEJAjsCegKWAoUCeQJmAjgCCQLcAZMBMAHOAG0AAACS/0X/JP8P/wj/Iv9S/4j/v//3/zIAfQDJAAkBUgGgAdUB/wEiAh8CAALdAagBVQH4AJcALQDE/2L/Af+w/nD+Qv4i/gP+3/3D/a39k/1r/Uf9PP1F/UL9Mv0z/UH9P/07/U79a/2b/QP+iv7//o7/bwBKAeMBlAJpA/QDNgSDBKcEaAQUBNIDTwOUAgEChQHkAEQA2P9z/wj/vf6O/lX+IP4X/hn+Dv4l/nX+3f5h/w8A1wCpAXYCKQO+AzUEgwSgBI0ETATkA1gDpgLfARYBUACG/8n+Gv5z/dr8Xvz5+577Vfss+x77Dfvr+u36H/tM+3374/tk/On8dP35/XH+2f4+/8//ZwC+AEYBTwIPA1gDBATTBPUE7QQdBdAEGQShAwUD6wHwAFEAo//b/mf+RP4O/tX96/0L/ub99f1N/mz+lv46/+r/gABcAUUCDwPuA6kEEwVvBb8FxAVxBeAEOARuA1ECIgEhABD///0q/Wb8wPtn+xv72frc+uH60Prs+g77JPtt+877HfyI/Aj9f/3q/TP+ef7X/vf+HP/r/7IA5wChAQIDtgP2A78EXwU1BfsExQTwA+ECNAJ2AUEAN//V/oH+yv1z/bf9wv2k/fH9PP4+/o/+F/9W/63/cQBXATsCNgNjBJ0FhAYYB4cHjgcvB6wGvAVhBCcD6wFYANz+r/2Y/Lr7HvuU+j36JPoE+uv56Pnx+SL6bfqn+vr6evv++5T8Kv2n/Sn+mv7H/sH+4/6K/1UAoAApAbEC6QMiBLUEqQW2BTkF6QQSBLACqwG4ADz/3f1d/TL9r/xm/OX8Wv1c/bb9Vf6E/r3+Zf/m/zMA8AAUAjADNwR7BQIHKAiqCAoJLgmfCLoHowb3BCwDtQEeAG/+M/1Q/Ir78vp/+jP6F/oA+vD5Cvol+jz6evq7+uL6Qvvv+5L8If3L/XL+6f4c/wj/T/9NANQArACpAX0D8gPWA+EEkwXMBBUEpgM5AoIApv+t/gT9IfyB/Jz8KPyY/KP91/3Y/Yz+4P6g/gL/wv/t/zoAaQHAAsADCQXtBpoIbwnuCU0K4gnWCL8HJwb6AxsCiwDJ/iX9GPyC+wH7gfpo+qr6qPqN+tP68vqe+o360vrQ+sj6Wvsj/LL8bP1X/uD+8P7x/mD/WADeAKQAZAFKA/4DnQNVBDAFZwQ5A5QCPQFO/y7+b/3z+9j6bvss/ND7LvzR/Yn+Z/4v/+//lv+Q/zIAPgAiAAMBaAJdA1YESwZ4CGMJpwlRClkKJwncB4MGawRVAuIAVP+Q/X38Gfyn+yf7G/tm+4b7nvvl+/r71PvU++b73fvg+yX8sfw6/Z79Ev5//nb+Iv5X/lr/9f+j/0sAVgIkA6wCgQOcBMUDgwIIArcAlv50/fL8mvt++j77Wvwx/Jr8if6Y/2f/7P+DAOf/aP+//7//a/8aALUB7gLPA54F7AcECSAJfwmSCY8ILQfJBesD5gFnADD/zP2z/Hf8gvwj/OT7Sfyo/Kf8wfzv/Of83fzx/Az9Lf1k/eT9e/60/tn+IP/Q/v79Av79/mT/BP/E/7kBfAIQAq4CjQOoAlUBzgB6/0T9JfzI+5n6q/mw+lL8yfx0/Xb/2QDFAAkBnAHtAOz/8f/S/yb/lv8hATwCTANgBbUHFQmFCaYJlgm4CPEGFAVFAzEBaP85/hr9Ovwq/Hv8k/y1/CT9k/3F/dT9yf2i/X39bP1u/XP9pP1E/g//lv/p/zEADQBT/w3/5v9DAGv/xf+2AToCaQEEAvYC3AE4AKf/dv4c/M/6tfq6+aD4r/mj+1X8Nv2V/2wBsQEhAtACHwLUAKIAcwBO/xn/qwD1AYACPgT1BoYI4AhUCZoJuggNB00FOQPQAAX/1v1y/Gr7jPsR/Eb8pPxe/f/9Zv63/tH+lP5O/j/+GP7H/cf9W/4f/6v/GAChAMUAIwAIABQBQgE9AK4AVwJGAiIBiAEuAtAADv+N/nz9Lfv7+RT6RPlX+JD5ovtz/Gj9z/+tAewBYgJNA8sCYgEYAQcBxv8y/4YAqwH/AYsDQAbsB0EIughWCbMI9QZjBZ8DCgHq/r/9XfwF++L6bPuq+wb8+vzh/VX+1v5V/yn/vP6y/pf+J/4N/pj+Rf+4/1MAGQElAcoAkgHNAmECjwGgArMDjAJUAdYBsQGS/9H9Nf23+4T50/ju+P/3n/ds+UD73vtW/fn/hAHjAcECnAMPAw0C5AF5ATIACgBsATYCdAIWBHIGeAeGBw4IhwilB/8FigSaAigAW/7z/DT7JPps+tb66Pqf+wD95/1e/iH/qf9T//D++P6i/gT+KP7q/l7/yP/eAMsBxQFnAmIEFAUUBJEEEAYjBQADpQJqAi0Av/2S/BL7zPih94731PZW9tL3z/nF+jH8wP7BAK0BnQKXA7EDLAPwAowChQH9AIwBFwJnAlwD5wQ9BvMGNgdoB2EHkgYcBVEDTgGE/+n99/ti+gX6IvoE+kj6V/uG/EH95P28/ib/6f7i/vf+W/7Y/V7+Af8l/8z/KQERAtoCxARiBkcGtwZwCCAIxwXBBD4EoQF6/qr8tPoC+If2afas9fn0k/Ya+RL6KPsd/qIAawFnAsAD2wNQA1kDGgPFAfIAvgF0Aj0CDwP+BO0FKwb5BmsH6gZcBqkF8QOjAQIAy/6m/GH6x/ny+Wb5Ofk8+lX7IPwL/eb9Vf6A/rf+xf5E/tn9Kf6H/rb+b/91AIkBdQPRBQ4HoQc8CcsK/gneB8cGgAU6AqX+UvzL+bL2+/Sx9PrzcPMx9Rv4p/nd+rT9mQDQAasC4wM7BNYD3wPAA6gC2wFyAgcD1gJqA+oEiwWEBTIGtwYPBlUF8QSlA4gB/f/6/jr9GPsv+hn6Y/nf+I35OfqW+q37yfzm/Cb9Qv7D/hb+AP78/k//Iv8eAFoB3gGNA28GrwfRB5MJfAugCncIfwc8BtYCIP+n/Pn5yvbs9E30fvMr86z0K/cp+Qj7qP1KAAgCXAN+BM4EnwSkBFsETwOFAosCigJcAuAC5ANZBIwETAXmBXwF4ASkBMQD+QFuAE//r/3L+676Mvpz+df4MPnC+e/5wPoj/JH8l/y4/eL+mv5Q/mH/PgANAI0A2wFeAiwDgQUhBx8HCQjOCXAJSQf8BfMEKAKk/j38D/pT94P1CPVu9Dr00/Ug+Ov5HPwE/3wBHAOJBMMFGwa1BWoF4QR3A0MC9wF0AagA4wDFAScCVQICA9gDHATRA5kDOQMQAsMA0f96/sz81ft8+976O/p0+jr7pfsS/BP9xP3Q/Vv+N/8p/9H+T//w//v/QQA2AQgCsALoAzIFoAXtBakGhQYFBY0DaQJwAMj9pfsG+kr47PaW9rD25/Yr+HP6ePw5/q0APgO9BKAFtQZSB8sG2AUVBdwDFgLcABwA8f4U/mT+6P73/mb/iwCMAd4BDAKBAocC0wFCAbEAcP9M/vP9ev2I/Bj8fvwA/T39uv2f/kj/q/8tAGcAOABCAHIAYQBsAMwAPgHFAVgCzQIuA4sDtQNYA1cCOAElAJP+qPwO+875v/gg+Bv4mfhu+bX6jfyO/ksAEwINBJUFSwa4BjEHHAclBgwFQAQRA3UBSAB1/3D+uP3C/fH9/P2G/qD/XQCoAEkB7gHQAXABLwFsAFH/nP4k/nb92fzi/H39F/6U/lD/UQAwAaIBsgG+AdMBfgHoAKQAeQArADwAnAC5AMgAKQF3ATsBlgDl/yj/Bf6O/FT7fvrN+V35f/kk+gz7Mvyw/VH/vgAZAokDrwROBbEFAAYABowFyAQOBFoDVQI3AWYAq//t/oX+c/5q/pz+NP/J/ygAoAAXASYB8wDGAFYAlv/0/oj+Df6g/Zj9AP6u/l//CwDxAAQCngKRAnwCjQIpAlABsQBDALX/UP9U/1f/Mf9Z/8//4v99/zD/8/5Z/mX9hPzy+3j79frV+kz76/uP/JD91v71//kAEwIRA7IDJgSdBNEEkwQ9BOYDTgOOAswB+ABAANP/cv8E/+L+Mv+U/8P/DwCcAPIA9gD0AMsATwDG/0n/vP5D/v/9/P1E/tD+ev8yAAMB6QGNArYCvQLHAmsCvAEqAagAGgCj/2P/Wf9S/0D/cf+2/5D/R/8m/9P+I/5t/fD8j/wV/Mj7C/x//M78Xv1U/jX/4f+fAG8BBQJlAtUCKQMXA+8C7QKaAvcBiQEdAXkAFQAEAMb/g//E/0MAWwBPALYAGgHpAK0ApABHAKv/Pv/o/nb+HP4j/oz+//5o/yUAFAHJAUwCuALzAvYCwgJlAgACggH5AJgASAAFAOX/zP+9/8T/rP9j/xr/xf5G/rH9Lv3X/Iz8QvxH/KD86vw4/db9iP4Q/57/SADUACkBgQHjAQkC9QHuAd0BkAFCAQoBqQBcAFQASAAlADkAlQDbAN0A+gBIAT0B3QCYAEUAqv8G/5P+Pv7c/aT9/f2E/sj+T/9NAAYBYQHeAVsCfgJpAmYCWQIMArsBogF1AScBAwHrALEAfgBPAP7/nv9E/+b+eP4K/rz9hv0//R39OP1D/Tr9d/3W/QD+OP6r/hL/XP+7/ycAcAChANEA7gDkANMAywCwAJAAngC8AMcA7QBCAYYBmgGyAdcBvwFfAfkAlgD7/0L/uv5i/gL+t/3O/TX+kf7n/oT/SwDRACcBlwHxAfgB+gEbAgECuQGuAcQBnAFiAV4BWwEjAekAvQB0ABoA1/+T/zf/6f64/of+Tv4s/hf++f3w/QL+B/4O/j/+ef6V/sP+F/9P/2T/kP/C/9T/3P/5/xYAJwBQAIwAvwDvADYBdQGNAZwBqQGOATwB4ACJAA4Acv/x/p7+T/4D/vf9Mv55/r7+Kf+//0MAnAD5AFcBhwGaAbsBwwGeAYMBjAGDAU8BKAEpAR8B8gDKALQAlgBqAEEAHQDx/8H/mf92/0P/Ef/w/sn+ov6M/nT+Yf5u/oD+iP6j/tX+AP8h/0f/d/+a/63/zf/3/xUAMgBeAIsAtgDoAA4BGgEfASgBCAG7AGsAJgDA/0D/5v6t/nT+Tv5m/qP+3f4t/6H/GABsALAA/wA8AU0BTwFcAVUBNwEqASsBFQHvAOwA+gDiAMIAzQDVALQAmgCfAJIAZABDADQADwDX/7L/kP9a/y7/Fv/2/tH+yf7O/sb+0f77/hL/G/9G/4L/lP+T/77/9P/7//v/LABcAGQAdACZAKcAkQB9AGgALADg/5z/TP/2/sf+rv6M/or+xv4N/zv/ff/l/zUAXACNAMUA2wDdAOQA5QDTAMcAwwC0AJ4AmgCYAIwAewB/AJAAlQCXAK8AxwDFALsAswCgAHoAUgAuAPn/x/+v/5j/bv9b/2v/cv9n/37/s//O/9r/AwAnAA4A3//L/6z/Z/8+/1L/cP98/5z/0P/n/+D/2f/V/8v/wf+9/7P/qf+a/3//Yf9W/2j/hv+t/+r/KwBXAG0AggCTAI0AdQBkAFUANQAVAAUA/P/y/+3/8//3//n/CgApAEcAbgCmANIA6AD5AAQB9gDNAKQAiABbACoAIgAuACIAHAAvAC4AFAAHAAoAAADv//H/6v/R/8T/x/+5/6v/q/+o/5T/kP+p/8D/yP/I/7//mP9c/yr/Bv/s/t3+3P7q/gj/Mv9i/53/2v8KACUAOQBLAFoAYwBkAFsASgA5ADAAIgAVAAwAAADv/+3//f8KABUANgBxAJ4AqACkAK0AxQDOAL4ApQCWAIMAXwBBADkALwAcACMARQBZAFQAXwB6AIEAaQBEACAA/v/l/9L/v/+k/4T/cf9s/3T/f/+R/6v/wv/J/7r/m/98/2n/V/9O/2D/iv+8/+v/FAAjAA0A7f/l/+L/yP+3/8r/4P/m//b/FgAlABwAEwAGAO3/4v/x//H/yv+r/6P/m/+W/7X/7P8RACgASwB2AJkAtgDPANMAxwDBAMQAvgCvAK8AuACxAJEAbQBbAFsAZABoAFsAOQAKAOP/zf/F/8j/0v/b/9X/xf+8/8H/yf/E/7X/qv+o/6X/nv+C/0X/+P7E/sT+7P4h/2X/qv/X/+n/+f8RACYAPQBlAKUA4AD8AOIAlAA7APL/rf9T///+5f4I/z7/af9+/4v/pP/L/wMATwCtAAEBLAEkAf0AwQBwACQA/v8LAD4AggC2ALUAfQBHAEYAaQB7AHEAXgBXAFUATwA+ACEA///o/+f/9f/7/9//pf97/3X/ev9u/1r/WP9l/2//b/9j/1j/Vf9m/5L/2/81AHEAbwBSAFkAfACGAHYAcwBhABcAwP+M/0X/xv5h/m/+3f5h/+f/YACWAGYAEADl/+b/+P8tAJgAAgEaAc0AWAD//+7/NQC0AB0BQgE/ATIBBAGjAD4ABwD6/wEAMwCGAL0AvACwAMcA5QDXAJoARADs/6r/lf+F/zv/wP5a/i3+G/4D/v39M/6t/lX/+v9bAFcAIgAJACoAcwCrAJoAMQCr/zT/yf5o/i/+Pf6J/gj/jP/Q/9j/7/8pAGoAwQAdATAB6gCXAFwAIADo/7z/m/9+/4P/wP8HACgAQQB9ANMATQHeAS8CKALnAakB0AFQAqYCogJZAuMBZQHdACoAe/8V/wb/Ov9Q//r+cv4R/g7+gP4S/2P/i/+//wcAWQBuAAwAkP9Z/0f/J//h/nv+L/4M/tL9lf2p/Rr+rv4h/37/DgDPAFsBfAFIAeEAWQDI/1j/R/9b/xz/qP53/rX+VP8TALgAVgHRAe4B8wEUAh8CHQIuAiUC1wE6AXIA4/+8/+n/cwAYAVsBTQFDASkB7gC+ALYAwQCtAFwA5v9J/3v+uf1U/Wr9Cv4k/zoAswB5AAMAzP/w/ycAFgCf/+H+HP6j/YX9hv1//Yj94f2//sj/WABPABMAAwBXAOEACAF0AGL/eP5N/sn+UP+J/53/7/+5AMIBfAKRAjMC0wHAAdkBqwH4AAkAZv9q/xEA7gB4AWoBDwENAaIBXwK3AogCBgJbAY0Aov+z/uv9k/3i/Zv+Kv9G/1L/z/+9AKcBIwIYAp0B9gBQAIL/fP6C/eH8i/xM/B/8PvzF/HH9Mv5R/6IAiAHCAakBZwHdACIAav/K/mb+aP6W/pP+hP7B/lv/JgD5AKQBEgJbApcCtwKWAhsCYAGrABkAn/9U/2L/0/96AAoBOgEUAeQA8QA8AXIBSQHfAJEAbAAvAMf/gf+d/9j/3v/H/8//CgBpAMkA7QCRAOD/Uv8V//3+3/6r/lX+7v2d/XH9cf2s/R7+ov4G/zD/E/++/oT+wv5m/wEARQAlAAQAYAD5AEkBbgGCAWgBfgG2AWcB0ACJAGEAGwCs/xT/2v5B/7H/EACMAMkA6ABJAYkBkQHbAVsCvwK4Au8B0AAqAP7/8P/a/5n/XP9o/5b/x/8TAG0A2QBFAU8B2AA2AM3/+f99AHsAy/8O/6H+cP5h/jL+tv0F/Vn8Efxl/PX8Xf23/eT9rP1+/QH+aP9jAfoCTgPLAl0CQwJbAnMCRgK/AfoABgAr/7D+cP5T/l/+f/6v/gf/l/9zAGkBCQJAAkcCMwIDAsQBfwE4Ae4AqAB3AFYAPwBgAKoAuwB9AEgAYQDHADsBXQEUAZ8AOwAVAB8A+f94/+b+W/7u/RX+zv5h/2r/Cf9o/sX9Yf1c/Zj9uP2F/Tf92/xZ/DX8rvwz/e391v9mAtoD9AMDBIMEyQSRBAgEGgPnAd4A4v+3/qb95fyL/Mn8b/0Z/sT+jP9tAHABgQJZA8wDvQMYAzAChgFPAWwBfQEZAVMAuP+t//7/NwBiAN4AggHRAbcBWgHZAJEAowB3AMn/JP/i/sH+mv55/kH+2/1//YD9sf2z/Xv9Jv3G/I38rfwU/Y/96v3x/ZX9Iv0v/e/98P41ACYCzQPMA+ACoQInA7UD1QNBAyUC/gAMAGj/+P6H/j/+QP4d/uv9V/5T/0oABwFfAV0BlAEzArkC1gK2AoMCWwI+AkMCdgKBAgsCPAGLAEIAawDhAFoBWgGZAI7/E/9o/wAAFABx/7T+bP52/o7+ov6X/kX+z/13/Ur9VP2y/SH+Jf7T/ZP9av0u/ej8tvyc/KL8svyn/Lz8+P3WAJQDMQRoAycDuQOZBHMFnAWfBO8CPQH4/zH/nf4T/pb9//x9/LH8qf3d/tT/iAA1AfkB6wLtA4sEfwRABD8EbASFBCcEQgNSAoUBawAn/3L+iv4F/2T/QP+V/gj+If6+/ov/YADQAHwAvP9T/5j/HgA1AI//R/7l/Er8pfwm/S79vvzq++/6RvpD+u367PuW/ID8JPxX/C39uP6kAQMFEAabBJEDNwRJBfIFIQaOBSMEMAJAAAX/kf45/m39MPwh+zj7oPxs/sz/kAASAeUBOgPWBEgGJAdmBz4HiAZfBT4EPgM1AvYAQ/9p/TD85ftp/Df9c/0m/Wf9lv45APMBIAMXAzcCcgEhAToBVAHAAHL/BP6b/C77Xvqf+kL7Vvuc+nn5wfgU+RT6Gfvm+zb87/vW+8j8gf4iAN4BPwQiBuQFjASSBCkGggeBBzcGIgQHAosAuf8y/3/+Zv05/IT7jPtR/Mf9j//xAKoBUQJ+AxYFsAbNB/QHIAfNBZYEvAPuAssBegAN/2/9EvyV+xz8Xf2U/uf+jP6f/pz//QASAocCWwLMAT8B8ADHAIgA7//M/jj9rvu5+nb6YPrP+eL4WPie+FH59Pl8+u76SPvN+7X83/0Y//b/DAD7/7cAQQJuBHMGYQZlBGADGAS3BJ8EMARXA1ECcQGOAO3/y/+m/z//FP9w/zQAHAG9AdMBwgFLAnIDVQSABFYEBwRtA8gCbwJZAhoCWwFtANr/ev/7/rj+9f50/+z/2v8i/8/+ff9CAF8AIgDY/3H/xP7X/UP9av2S/QD98vsB+6X65voq+w774voD+4j7Hvwg/Lj76PvO/KL9Hf5s/rv+Dv8z/yv/Zv8eALoBDATdBPgCYgGnAusE5QXEBSoFdAQTBO4DwQOMAyQDLALgAAEAAgCuAIAB7wF9AWkA7P+nANABbQJsAhYCfgEPAU4BCgKUApECMgKaAeQAgQC5ABwB/gBUAGb/Z/6z/X79T/3F/D/8+Pu7+4D7bvua+wL8b/ys/Nr8Bv1Q/e39kv6//oL+Ov78/cb9pP19/ST9vfyo/OX8J/1V/XH9U/0D/RT99f0e//3/LwHxAsMDRgO/AwIG6QdJCA8IswcqB/UGHgfeBu4FmgQLA6wBNgFwAS0BFQD7/lP+C/5U/gH/Y/9j/2z/lf8KACQBaQLeAoQCEQLgAeUBxAElAUMAiP/X/uH9ufzV+5X7p/t/+xj78/qB+2v85vz6/EL9yf1h/t7+4f6f/rj+Hv9X/0n/C/+o/jX+5P3t/ST+Av5Q/aL8mfzs/Pz8v/xo/Cv8Tvyu/Bv9//2k/94BTQTaBekFGQbcB9wJmgrECtUKQwo5CT0IRgdyBroFZAQ2Aj0Aav8z/4r+jP0M/RT9J/0X/Qf9Ov29/Sz+MP4b/o7+ev8DALv/S/9U/3f/QP8Q/1r/xf+5//n+9v2i/UP+0P5z/rv9e/2Y/Z79n/3W/fv9l/3L/Ev8mvyQ/XX+mv4u/v/9Zv4F/33/qP+R/0X/rv7Z/UT9P/1y/Wn9K/0h/X/9F/7R/u3/vAEtBDYGawa6Bd0GmQnsCkMKbgn6CGAIrQcLB3IGtgVnBGMCkQD1/xgArP9a/un8EPzl++/7p/sT+676tPoP+5X7OPzq/Hv9zf0v/uj+xf98AP8ATQFyAbAB7QGZAdUAZQA5AI3/ZP6Z/Xr9W/3P/F38bPya/I/8j/zy/MD9rv4o///+yv4P/4P/1//8/6n/Ev/m/gf//f7y/vv+/P4o/4b/5f8+AGAAMwBBAPMA4AGFAjEDpgQ5BjIGJAWkBZ4HcgiGB2EGxwWlBboFVgUIBFcCCwFrABMAof8N/yP+nfwX+4v67fpA+8n6y/lR+eb5Evsw/N/8Ef00/bT9iP6M/34A5gDZAPUAUQGqAegBtQHyAFAAPgBQAAUAdP/5/pn+Rv4y/lD+Q/4t/lT+UP4W/kf+u/7Y/s/++v45/2T/X/8e/xT/m/87AHEAYwBQAFwAjgCvAK8A+gCrARkC3AGcAfcBogJHA+UD+AMtA7oCnQOYBF0EyAPWA/0DtANAA7IC7AEtAZkAEgCB/+T+Jv47/Wj88Pu2+4D7T/sk+wP7F/th+7j7Pfz+/Jn95P1K/gL/t/8OABoAYwAkAcIBjgG6ADQAkQBYAY8BBgFhADUAkAD7AAIBuwBUAML/dP+1/wwA/P99/8/+df6a/r7+iP5H/m/+/P55/5T/lf/U/ygASwBkALUADwEeAfMAzwDbAAkBQAGcATwCmwI2ArsBPAJYA8gDSQO+ArsC3gK3AkQCjAHFAFYAOQDZ/wf/Iv5v/Qr9Ef1O/TL9i/zZ+9H7Zvzd/NH8cfw7/Kf8lv1j/tP+C/8q/4H/QAADAXsBvAGrAV4BgwFrAkwDLQNHAs4BEwI9AukBbgHlAEcA0f+G/zP/xP5K/vL91v21/ZP90v0y/j3+Uv7Z/mb/kv+G/5b/3f9IAKMAuACdAJEAhwCLAPMAgAGcAawBCgL5AX0BvQGqAgkDlQLbAT8BJgGTAdYBdgG+ADcAJgBOAD4AyP8c/5z+dv5n/hv+pf1g/VL9Of0j/UL9av1t/Z/9Lv7C/hj/Y//V/1IAuwAtAaQB4wEIAlAChQJvAkwCOwL+AX8BDQHaAKAAEABm/wL/3/7a/sD+av41/l3+cv5b/oD+uP6K/jr+UP7K/jv/Q//3/s/+K//e/0cAPAAlADkAgQDyADkBWAG/Af8BnAGBAT4CrQJbAhYC+QHMAeABGwLtAWgB2wBwAFAAYgBBAM//MP+b/lz+ZP5T/jr+RP5F/kH+cf7A/g//ZP+h/8D/CABxAKQApQC1AOIAEQEzAS0B9gC2AKEArwCeAE0A4/+c/4//jv9t/0D/Jv8f/yf/KP8c/zP/Xv9H/xL/KP9K/xn/4v7+/jn/U/9N/z3/T/+d/+z/AQD9/xgATQCKANMAEQEkASMBLgE7AVcBkwG3AYUBHQHVAM0A8wAMAdYAXQAKAAQACwD1/9z/2P/R/6v/ev+G/83/AwAUABUA9f/b/yUAmQC6AKIAfAA8ACIAewDmAOcAjAArAAgAHwArAAYAzP+T/13/Wf+R/7P/gv8i/+H+3v76/hr/Nf84/yb/OP9p/2X/M/82/3T/l/98/2D/b/+O/5r/p//M/9z/yP+//8n/1//y/wsAFwAnAEIAYQCOALYAvgDVABwBTwFCATkBRgEiAeMA0gDMAJ8AfgBtADEA6f/l/wQABADj/8P/t//K/9z/zv+6/8D/7f82AG4AcgBrAIYAmACGAIIAqwDTAMYAiABTAFEAZQBgAEkALgAUAA8ADQDT/3n/Uv9D/xP/+P4W/xv/2P6W/p/+zP7X/sH+tv6+/r7+0P4Q/0j/Of8d/0b/lv/H/+P/EgBBAFwAdgCgAMwA8gAAAfsACgEvAVYBcwF3AT4B8wDqAAkB6gCZAFgANQAlACAAAwDQ/7T/qv+S/3P/ZP9Y/03/VP9f/1f/RP9C/2//tv/q//D/5f/2/zEAcQCBAHAAewCpAMgAugCeAKsA7AAeAQMBuQB+AFsASAA8ABIArv9B/wL/9f77/vH+zP6l/pL+kP6r/uD+/P7s/u7+Jf9z/7f/3P/h/+H///87AIMAugDGALoAvgDQAOMA+AANAR0BIwEeARoBKgFDAUkBMAEGAesA5wDaAKIATQASAAAA6/+3/3P/Pv8q/yr/GP/q/s7+2f70/gn/HP80/z3/OP84/1P/hP+3/97/9/8MAC8AaACYALgAywDGALEAsADDAMYAqAByADQADAAAAOH/of9w/1z/Mf/s/tP+6v7w/tL+yv7p/g7/Mf9V/4D/uP/v/w0AFgAgAEIAfQC5AMcAsgC1AOAABwEaASkBJQELAQgBIAEmAQ0BAwEBAdwAqgCbAKIAjQBTABYA+P/4/+//wv+H/2f/aP9l/z3///7o/gj/Kf8p/yj/Ov9Y/4P/tf/M/8r/1//1/wAAAwAgAEsAWQBMAFMAewCZAIIAUwA+AEEAKwD7/9n/wf+a/2v/Tv9M/1D/Tf9D/z7/SP9i/4X/pf++/93/AQAbACAALQBdAI8AjwBvAG0AkQCqAKMAkgCFAIgAmQCcAIcAeACEAI4AgQBzAHUAdgBiAEAALAAvACoAAQDO/7r/xv/J/7b/m/+Q/5r/qf+v/6z/rP+n/5b/j/+m/77/tv+f/5P/mf+q/73/zP/V/9f/1f/g/wAAIQAoABgACwAQABgADgABAAkAGgAUAPv/7//+/xYAGgD+/9j/zv/h//H/6v/X/9D/2P/r//j/+v8AAAwAFgAgADAAPQA9AD0ATgBrAH0AfAB6AIEAiQCEAG8ATgAuAB4AFQANAPn/2f+6/7f/z//b/9z/4f/o/+j/7/8NACsAMgAeAAQAAgAWABYA9f/Z/9b/1f/E/7X/qf+a/4//lv+o/7f/vf/G/+H//v8PABoALQBAAD8ANwA6AEMASQBIAD0ALwArAC4AJgAIAOz/6//x/9z/uf+o/7D/sf+e/5H/lv+n/7L/tP+3/8X/5f8HABMADgATADAAUABXAEYANAAvADQAOAAxAB8ABgDt/+j/7v/x/+z/4f/W/8//0v/m/wAADwAMAAkAGQAuADQAKQAaAAoA9f/a/8L/u/++/7n/q/+k/7H/zP/p/wMAFAAcAB4ALABGAFgAWwBbAGYAewCDAH4AegCFAJgAmwCCAFsAPwA2AC4AFgDt/8P/rP+p/6v/qP+d/5j/mv+f/6b/sv/D/8//0//W/+L/9/8HABEAGQAiACQAFQAFAAkAEwAQAAIA8f/q//P//v/4/+7/9P8AAPL/2v/X/+L/5P/W/8X/v//H/9P/0f/I/8f/yv/H/73/t/+6/8X/zP/I/8L/0P/v/wYAFAAqAEQAVABaAGMAcQB/AIgAgABwAGMAXgBZAFIASQA8ACkAGQAQABAAGAAVAAgA/f8AAAoACAD8//b//f8EAP//8P/p//D/9f/s/97/2v/f/93/0//I/83/2//g/9j/0v/Z/+j/8v/2//n/+v/0/+v/7v/5//v/7P/X/9L/2f/b/9D/yP/J/9P/3f/i/+v/9f/6/wIADgAdACkAKQApADAAOQA9ADcANAA6AD4ANQAmACIAKwAsAB4ACgAFAA0AFAAMAP//+/8DABAAFAASABAAFAAYABsAGgAXABgAFgAPAAoABwAHAAMA/P/0/+//7P/s/+v/6P/j/+H/5f/q/+3/7f/t/+z/7P/u//L/8//w/+f/4P/f/+H/5f/m/9//3P/c/9v/3//k/+v/7P/o/+j/8P/6/wQABgAMABEAFQAZABwAJwAtAC0AJgAkACsALwAxACsAIwAhAB0AGgAUAA0ACgAEAP7/+//4//v//f8AAAIAAwAKABEAFQAWABIAFAATABYAEgALAAgAAwD+//b/8v/1//X/8v/u/+3/8P/0//j/+f/7//3//f/+//v/+//7//n/9v/u/+v/6v/q/+7/8f/1//T/8v/z//n////+//z//f/7//r/+v/8/wIAAgD///7/AQAHAAoACgALAAsACwALAAsADgAQABIAEAAPAA8AEAAQAA0ACQAHAAYAAQABAAIAAAD///3//v8CAAMAAQAAAAAAAQADAAMAAQD+////AAD///7///8BAAEA/P/6//j/9//0/+//7f/t/+3/7P/q/+z/7//x//H/8//2//z//P/7//v//v8BAAIAAAABAAMAAQABAAEAAAACAP///P/8////AAD8//n/AAAGAAkACQAHAAsADQASABcAFgAWABcAHAAmAC0ALwApABwAGAAWAA4ADgAVABkAEgACAPv/BgARAA4AAAD6/wAABAD//wAAAwAGAPX/8f8DAAcACgAOAAkA/P/r/+n/5//P/8X/xP/I/87/xf/H/8n/1f/m/9T/9v/V/zgAFQHs/+T+nf8iAFgAAQBr/1P/9P9CAe8APP/f/qn/ewB9AKT/Lf/O/4oAVAC9/3n/vf86ACYAx//Q/zEAbgBAAA4ADAAtAG0AeABzAEIA8v9AAOcAywAIALf/v/8SAHkAFgB4/4D/8/9UABAAgv9s/43/wf/Y/7T/o//T/x8AJwDJ/+H/GQAqAP4AAAEVACIAggCWAIwAy/8Y/53/BQDNAM4A5/6j/n7/8/8YAJf/QP+1/1gAFQBr/3H/4P/+/+j/9/9zAMUAcACr/47/OwCjAKEABQCe/9v/AwADANH/9v9AAEkAVgA5AFoAOgDY/9D/CABbADcACQCt/4T/1v+i/53/4P8BABAA5P/f/xAALgAdAA0AGwBAAEcAHwADAA4A+P88AF8A3v/1/+3/0v/2/7r/zP8dAP3/x//+//n/3v/x/+T/+//Q/5P/8v8EANn/0f+b/9v/PwApANb/uf/5/1wAhgAXAAEATQBfAH0ATgAmAJsAoQANAOb/9v/0/yIAov9B/9r/1f/E/97/pf+4/7D/mv+h/9P/PgBGACwABwD2/2UAYQAkADIAHgAyAHMALwDU/zEASQAcABYA3v/c/+r/xP/g//f/qf+X/4b/Tf+S//D/JgD9/7v/BQBNAGgAYwARAPf/MwA5AEMANwD7//v/GABbAHwAUwAyAD8ALADx/9f/m//U/ycAuP+k/5T/RP/I/+L/Xf+4/63/ov8iALH/EwCHAAIAMQBlAHwAiABiAD4AMQBQAI8AYwDf//P/DgD1/+X/sf/F/6z/eP/D/9L/9f8cAKn/mP/i//z/MQAdALz/rP/5/1QAKQDK/9//DAASAE0AXwArAD0AMwBAAHkAeABsAB4Axf8ZAHUAQAASAOf/mv/e/zkA+v/V/7r/k/+l/6D/sv/L/5n/iv+9/+b/CgAcAAwABAAOADkAFgDZ/xEAGwANADEAPQA9AE4ALQAGAAQA/P8TADsAGQDn/9X/yv8KAAQAtP+a/9D/9v/c//T/1v+8/+3/5v/g/yIAcgCGADgA9v8EADEAaQCQAHoAIADj//b/NQAxAAwA9//H/8v/AgAnACIAAADX/6//oP/H/8n/1//s/7f/t/+0/57/4f/s/57/kf+E/7P/LwD6/3//bv9r/6v/+v/M/4X/bf9w/7j/2f+3/8D/1f/S/9n/AQA1AEQARgBGABIAVgDmAAUB4gCxAMsAEQFcAZMBZQEtAX8BugGNAYcBgAFcAV8BRQESAdoAewCaAM8AXwAWAOz/yP/5//L/b/8M//3+Ef8+/+r+nf6k/oH+jP5U/iD+p/7M/n/+dv5O/oP+vP5q/lv+T/4v/nX+UP7g/ez9w/2i/eT96/3v/Tn+Sv5z/rX+EP+j/1sAoAFTAkwCIAMsBFkEvAR0BZIFVAVaBdgE2gOVA0oDVAJsAcIAAAAb/43+S/4N/tX9sv2n/Z39Gf7//pX/q//U/1sA+QCpARoCRgJpAm8CQgL3AcwB0QGFAbQArf+7/g3+wf1u/eD8TvwJ/M37H/vg+gT7FPtL+6L7gft8+xL8kvzV/An9mf2q/pD/HgBeASEDVgS5BA8FewXLBU8GhgbsBSQFegRlA64BawAUAG3/fP6d/fL8i/za/Kv9Fv5L/tP+fP/X/1sAYwGCAkUDxwMQBCgENASHBK8EUQQFBKAD/gKKAiIChgGXAIb/zv4f/j79dvz2+2r72vqG+lj6Qvp1+u/6P/t8+6/7wPs0/Pf8Nv0x/WP9df1U/Y39/f1t/kv/aQD1Aa0DEQSyAyoE8wRdBbEFsAUFBVMEiQMrAhwBpAAiAHD/kv74/eP94P3p/TH+Zv63/m7/4/8BAHAAOwEWArwCWAPkAwMEGgQ3BDkEYQRgBC8E3gNoAx4DmwJ1AWoAvP/f/gn+Rf1x/NP7avvc+jX66/k0+qL67/pJ+5376vuL/CT9V/2x/QH+PP6P/nX+T/5i/lr+n/4M/1v/4f8AAXYC+AK3AukCMQM2A08DgAOBAz8D2wLcAW0A1v8PAAIAj/9m/3X/O//9/vn+OP/s/9QAewG7AeEBWQINA7IDawRFBe0F7gU+BZgEIQSQA14DGAM2AlYBRwDb/oH9q/yI/GL8APyo+2T7Q/sf+zj7k/vd+4n8V/2M/Y/93v1Y/rv+r/5Z/vr9fv0o/dv8afw0/BT8//v6++P7YPy2/Z//ogGMApICQgM1BKQETAUlBoAGiAbbBTIExgIUAl4BjQDi/0r/uf4b/nj9OP2w/ZL+j/+GAEMBLgJgAzIErwRBBdcFNwYpBqYF/QQkBOMCrwG+ABUA6/9k/zP+N/1v/An8TfyU/N/8Yf2u/Zn9df1l/Wb9sf0Y/k7+Yv5G/tP9Nv2a/Aj8nvtE++f6jfr++YT5V/lZ+eb5yPqs++/84f5zAasDsQSRBQgHAghJCLcI8Qh/CMMHcwZxBI8CFgGW/9/9p/xf/E78DvwH/Fb86/wQ/s//cgECA9UE/gVEBpQG6Qb3BgMH1QY9BiIFiQPnAX8Ahf8N/9X+Xf53/Zv8/PuP+6v7Xvw1/e79Vv5N/jX+O/5L/mz+d/5E/uH9fv3w/PP7IvvT+mn60/lH+a74SPg4+HT4DvkM+k37evx5/eL+hAG+BL0GdwdXCEAJZwkQCcoISwh3B6UGUAUQA/QAlf9D/uL8ZPzM/DP9jP3Y/cr9Ev5V/xsBngLXA0AFXAZiBgQG4AWrBZMFrwVuBckE7wN8ApYAIP9+/lH+J/7A/f78J/x4+xH7Dvta+yb8FP01/QX9Df39/A79LP06/V/9Vf0W/XX8kvs5+1j7mvvU+9r74fsA/Ej8qPz5/Jf9aP4U/5D/s//Q/zsA8gBLApMD8wMUBFEEXAQyBEAErAT9BFwFjwXeBDEE8AN7A/8CzAIIA3kDqwOiAwsDCwKPAYoBWwESAfsAygBCAK//Pf8V/0n/fP+U/6f/m/+I/43/lP+7/ygAWwDh/yn/m/78/Wv9QP0p/fT8wvxn/Lz7Evvu+l77yfvU+8z7D/yL/B39sf0G/jf+sv46/4b/6P+BABYBWwE5Ad0ATQDd/7z/k/9O/wH/qP4r/qr9kv3t/aj+nf+CAFkBJALoAroDqATfBfYGqAcpCGUIcAhSCOYHRAd6BpYFmgRsAzcCIgE3AH3/t/4I/pT9Cv2d/GH8OPx6/Oz8OP1m/XH9jf20/bv90v3h/QH+Ov4r/g3+5P2K/WH9Xf1p/bL94/3w/Q/+H/4g/hj+Ff5e/tf+RP++/zMAdQCNAJEAjgCUAK8ArABfAOH/J/9q/vH9lP1Z/WD9pv0V/nn+6P6Q/0QA/ADKAbcCsQN3BCYFpAW/Bb0FrAVzBScFxAR0BPIDDQNaAswBHwGsAI4AeAA4AN7/f//8/p7+gf5L/iD+Mf5G/jL+3/2X/YP9XP1R/aL9/v0//nX+ff5l/mT+l/7p/jD/f/+9/63/iP9u/0X/I/8E/8v+mP6W/qX+jP6A/rH+4v7+/jD/af+M/6j/uv+b/3//sf/Z/8X/7P+AABIBcwHNAScChwIAA2MDgQOYA88D9APLA28DOgMlA+0CggLkAToB0ACPACUAiv8k/wH/0v6Y/nj+aP58/p/+iv5I/hj+Ff4y/jz+NP5T/pj+t/6q/qT+wf4T/3n/wf/2/yAAQgBZAFAARABBAEEALADr/6T/TP/i/qj+kv5o/jD+Bv4O/jP+Vf6R/t3+IP9J/03/XP+V/93/FwAzAGwAxwABAS8BcQHAARsCcgK9AucCBwM9Az4D9gK0ArsC9wINA+ICpQJQAuwBjAEcAa4AdwCAAGMA5v9b/+z+gv4//jn+Q/4+/iv+7/2F/UT9aP2m/dH9B/46/mL+pP7x/i3/df/O//b/8P/5////9P8EAB0AAQDF/6X/iv9a/z//UP9i/1r/Wf9j/1v/WP91/5n/yP8OAFQAegCLAK4A2ADxABABPAFWAVYBTQFMAU4BOAEbARIBCgH9AOgAyQDRABIBTwFVAT4BTwGKAZIBYAE7ARcB7gDgALgAYQA2AEIAMgDw/8H/r/+F/0z/Kf8J/+r+6P7a/pP+TP43/jj+Of5O/l/+YP5g/mH+W/5Y/mX+p/7//i//N/8x/yb/MP9J/1v/f/+3//P/FwAEAOn/8v8IAC4AbgCgANAADwFCAV4BawF1AX8BjgGrAcUByQHCAa4BhwFRARcB5QC2AJ4ArwC5AKgAlgBsAEAAQwBRAEYAOwA+ADgAGQDz/8v/ov+l/9H/7P8AACAAKgATAAEAEAAgACkAJAD0/6z/h/9w/zz/CP/z/tz+tP6Q/nD+TP5C/lv+a/5v/n7+kP6Q/o7+lv6l/sX+Af9H/37/qP/U//r/EAAqAFQAhgC8APIAIQFQAXwBmwGoAa0BuwHGAbcBmgGDAXIBVQEgAd0AqgCJAGgARAAqACQAIQAQAPf/5//p//f/AwADAP//AgAIAAUAAQACAP7/7f/f/9v/2//i//L/9v/j/8P/nP94/2D/Vf9I/yn/+/7L/qn+mP6d/rv+5v4C/w7/EP8E//3+Dv8q/z7/Vv90/4j/lv+i/6P/rP/M//H/CwAjADgAUgB6AKkA2gATAUsBcQGLAZ8BsQG2AcEBygG8AZkBVwH8AK0AfwBaACUA9v/S/6//jf96/33/oP/V//b/7f/b/97/9f8UADUAXQCCAJMAigBtAFYAUQBZAFYAMgD6/7n/ef9B/xL/6v7A/qH+jf5x/ln+U/5g/n/+qf7J/uD+/v4j/0f/a/+G/5v/qv+w/6j/nP+m/8D/6v8WADQAPwBBAFAAcQCsAPUALgFRAWIBagF1AYUBnAGuAbYBoQFkARoB2gCsAIQAVQAbAOD/t/+U/2f/RP9C/1n/dP+N/6P/vf/e/wEAHgA7AF8AhQCUAJMAkQCIAHIAUwAzAA0A5/+9/4z/X/8//x//7v7C/q3+qP6u/rn+wv7S/u/+BP8R/y3/Wv+C/5z/pv+p/7D/tf+t/6b/tv/U/+v/7//w//v/GwBCAGIAgQCyAOcABAETASQBMwFIAVoBWAFLAUkBRgEjAfgA3gC9AI8AZQBBABgAAAD7/+b/0//c/+v/5//l//v/FgA0AF0AcQBpAF4AUQA3ABsACgD2/93/wv+f/3X/VP88/yX/Ef/5/uP+0f7L/sn+y/7O/tf+5P74/hH/MP9U/3//qv/I/97/+P8NABkAHwAcABoAJQA2ADcANgBBAE4AWwBqAHgAhwCfALAAqwCcAJ0ApQCqAK8AsACpAKEAkQBzAFoAUABOAFIAVQBPAEQAPwA7ADQAOgBSAGgAdgB7AHQAZQBbAFMAQwA6ADUAJQAGAN//t/+U/3//a/9S/z//Mf8b//3+5v7h/ub+8v7//gn/E/8f/yz/Pv9Y/37/of+9/9P/5f/v//r/CQAZACkANwBAAEIARABFAEAAQgBPAGIAcQB9AIYAjgCUAJgAlQCWAJwAowCjAKIAoQCiAKQAoQCaAJMAjAB+AGoAUwA/AC8AIgAYABQAFwAiAC0AMgA6AEAAPQAzACUAFQABAO7/1v+2/5b/d/9a/z7/J/8U/wj//f70/u3+6/7z/gT/F/8u/0n/Y/99/5b/r//M/+v/BQAbACYAKQAoACkALAAxADgAPQA7AC8AIgAYABUAGQAkACwALwAoACEAGgAXAB0AKwA7AEsAVQBcAGAAaQB0AIAAjACVAJoAmQCVAI4AiACBAHsAdwB0AHEAbABlAFwATwBCADQAJAAUAAQA8//h/9H/wP+v/6L/mP+N/4D/c/9k/1L/RP85/zP/Lv8s/yv/Mf86/0j/Xv96/5j/t//U/+7/BAAXACcAOQBDAEwAUgBZAF0AXgBgAGMAZABiAF4AWABPAEUAOQApABsADAD+//P/7//u/+7/8v/3//7/CwAYACQANQBGAFIAVgBYAFgAWABWAFAATQBKAEcAOQAlABMACAD6/+r/3//e/+H/3//Y/9L/zv/M/8v/wv+7/7f/sf+j/5H/iP+H/4j/hf+E/4z/lv+f/6f/sf++/9L/4//v//j/BAASABoAHQAfACIAJQAlACEAGwAXABQAEQALAAoADQASABUAGAAcACYALgAzADcAPgBDAEMAQQA/AD4AOgA3ADMALwAsACkAJgAkACIAIQAjACAAHwAdABcAEAAIAP7/9//v/+j/4f/b/9X/0P/M/8n/yf/J/8r/yP/H/8X/w//D/8P/xf/H/8j/zP/R/9f/4P/q//P//v8EAAoADQAQABMAFAAWABYAFAAQAA0ABwAEAAIAAgADAAEA///9//r/+f/6//3/AwAIAA0AEAASABQAGgAhACYAKgAuADAALgAnACMAIQAiACAAHwAfABkAEQAIAAQABAAGAAkACQAFAP//+P/x/+z/6P/m/+D/1//O/8H/s/+r/6j/pv+j/6H/oP+c/5j/mP+e/6X/rv+7/8b/0f/f//D/BgAbADEAQQBMAFIAVQBTAE4ASQBBADgAKwAdAA0AAwD8//r//v8GAA8AFwAeACcAMAA9AEgAVABdAGEAYABbAFYATQBGAD0ANAApABsADwADAPX/7P/j/93/1//U/9H/0f/T/9X/1//c/+L/6f/s/+7/8P/v/+3/6f/j/97/2P/S/8z/xv/A/73/vf+9/73/v//D/8j/zf/R/9n/4P/p//L/+v8DAA0AFQAdACIAJwApACgAJQAgABoAFQANAAQA/v/5//f/9v/4//7/CAASAB0ALAA5AEUATwBYAFwAXwBdAFcATgBEADYAKAAZAAsA/v/y/+b/3f/V/87/yv/I/8n/yv/M/9D/1v/a/9z/4f/k/+f/6f/q/+j/5f/k/+D/2//X/9P/0v/Q/8//0f/V/9z/5v/x//3/CwAZACcAMgA8AEMASgBLAEgAQgA5AC0AIAASAAUA+P/t/+T/3v/b/9v/3f/j/+v/9v8BAA0AHAApADYAQABJAE0AUABPAEkAQgA2ACsAHgAPAAAA9P/n/97/1//S/8//z//P/9L/0//W/9r/3v/i/+b/6P/q/+v/6//p/+X/4v/f/9n/1P/P/8r/xv/F/8T/xv/L/9L/2v/j/+7/+f8EABAAGwAmADAANwA+AEEAQgBBADwAMgAoABwAEAADAPr/8//v/+7/8P/1//z/BAAMABUAHgAmAC4ANgA7AEEAQwBFAEIAPQA2ACwAIQAUAAUA+P/s/+T/3f/Y/9b/1v/Z/9v/3//j/+b/6v/t/+//8f/z//f/+f/8//z//f/+/////v/9//z/+v/4//X/8//w/+7/7f/v/+//8P/z//X/9//5//3/AAAEAAgADAAOAA8AEQARABAADwANAAkABQACAAAA/f/6//r/+//9/wIABwALABIAGAAeACQAKAAqACsAKwApACQAIgAeABcAEQAMAAcAAgD///r/9//2//P/8//z//P/9P/0//T/9f/2//T/9P/y//L/8f/x//H/8f/y//X/9//5//r//P//////AAABAAMAAgACAAIAAgAEAAIAAgADAAIAAgABAAIAAgACAAEAAAD+//3//v/+//7//v/+//////8AAAEAAwAHAAgACwANAA0ADgAOAA4ADQAOAA0ADQALAAsACgAIAAcABQAGAAUABgAFAAUABQAFAAQABAAEAAQAAwACAAEAAAAAAP3//P/7//r/+f/4//f/9//3//b/9//3//f/9//2//f/+P/4//r/+//8//3//v////7//v/+///////+//7//////////v8BAAEAAgADAAIAAwADAAQAAwADAAQABQAFAAYABQAGAAUABQAFAAQAAwADAAMAAgABAAAAAQAAAAAAAAD/////AAD/////////////AAD//wAAAQAAAAAAAQAAAP7//v/+//7//v/+//z//f/9//z/+//8//z//P/7//z//P/9//3//P/9//z//P/7//3//f/8//z//P/8//3//f/9//7//f///wAAAAAAAAIAAwAEAAQABQAGAAYABgAHAAcABgAGAAUABAAEAAMABAAEAAMAAwACAAIAAQAAAAEAAAAAAP////////////////7//f/9//z/+//7//v/+v/7//v/+//8//3//f/+//7//v/+/wAAAQABAAAAAAD//wAA///9//7//P/9//v//P/7//r/+//7//z//f/+//7//v/+/wAAAAAAAAAA/////////////wAA/////////v/+//////////////8AAAAAAAAAAAAAAAAAAAAAAAD/////AAAAAP7/AAD+//7//f/8//3//f/9//z//f/9//z//P/9//7////////////////////////////+//////////3//v////7//v///////v/+//7//f/+/wAA////////AQAAAAAAAAAAAP/////+/////v/+/////////////v////////8AAP7//v/+///////+//7////+//7//v/+//7//v/9//3//v/9//7//f/+//7////9//7//f/9//7//v/+//7//v/+//////////7///////7//v/9//z//f/7//v/+//7//z/+//8//z//f/9//7//f////7///8AAP/////+//7//v/+//3//f/8//v/+//7//r/+v/7//z//f/9//7//////wEAAAABAAEAAgADAAIAAQABAAAAAAD///3//f/7//v/+v/6//v/+//8//z//f/+/////////wAAAAAAAAAAAQD//////v/+//3//f/8//z//f/8//z/+//8//z/+//9//z//f/+/wAA//8BAAEAAQACAAMAAgACAAMAAQAAAAAA///+//z//f/7//v//P/9//z//P//////AgD//wMAAgACAAQAAwAFAAMABQACAAMABgABAAMABwADAP////8CAAIABAAEAAcAEQASAAsACAAJAA4ACwAQABQABgAEAAMAAADy/+z/9f/s/w8ADgDO/7v/p//K/7f/+v94AN//nf/P//f/KQAsAEgA7gCdASsBkgBkABkAo/+a/5D/Vf9I/wT/of65/g3/AP8e/13/nv+y/5z/vv/T/97/4/8PADUANAAWAYYBEgElAUABVgGHATABmwBgAA4Aj/8h/zT/PP8r/0f/0/6R/r/+0P7d/lr/9P8lAFMAlADgAAgBLAGFAZMBngGwAV4BCQHwAOYAkQAlANP/gv97/5j/cf8l/w3/E/8K/yb/WP9t/5P/h/9Y/5b/3v8EAC0AOQBPADUA8f/c/9f/1f/e/7j/Xv8J/9H+jP5Z/lz+av5b/lv+Y/47/jD+Xf6c/rz+D/9v/6z/HwCfAHMByQIEBJgEAQVQBToF6wSwBIQEPQTxAxsDrQFoAFX/R/6D/TL9E/0F/RH9IP1K/ab9P/7t/qz/gAA8AbAB7gH1AfYBJAIqAtUBcgEWAXYAtP/v/lj+Bv7W/aP9Wv3f/Gz8BPzT+yD8i/wO/Vr9O/3+/Nz8xPwI/Yr9L/63/tL+zP7d/lX/ggArA9cGjwgcBzIGigZBBXMDiQMsBPMD0wJlAAX9gvrv+W35Nvmi+lz8Mv1t/U79tv2Q/zECcQRkBuAHIAidB6kGhAXaBCIFLwUABHUC+gDW/p/85fv2/Kb+v//Y/wD/RP5H/rf+Tf/WAN8CcgNdArEAYf+t/mT+av5b/uf9Hf2P+5T5fviJ+Gf5q/qZ+xP8Nfzr+5H7e/sk/Ln9Av+Y/xIAFwDu/0MAGQGIAywIaguQCaIGmAYNBpEDvwLRA8QDQAKa/6j7wPid+GL5JPrr+2H+/P9UAC4AvADIAq8F9QcICSYJJggSBtUD6AG9ANYA8gDb/yf+tvxr+3z6Dvt6/ZEAuQL/AukB7QC7AAoBpwGdAlwD9wIIAVH+efxZ/P78qf0+/jX+ef2w/Aj8xft5/Kv94f0V/Vz8ifu5+rn6/fpi+x38/PsC+436w/qD+zX9Bv9CAFwBgQNyB44LOwyqCQMJmApeCdwFIQSkA/cB+f7j+6b5L/kn+hr7o/tp/acAFQMCBBYF5QZ9CFIJAgnIBygGOwSDAYf+LPzU+lH6rPmf+Hr4vfnO+gr8BP87AxkHEgmhCEEHPAZiBUoEswIdAYH/4PxL+WL2tvW79t334fgW+lL7v/xK/oX/sADqAX8CrQG+/1D+8f29/Qf9GvxH+1r6Oflm+Jn40/mA+7v82f1e/zABvwLFBJgJ+w9/EacMMwnRCbEI0ATxAu0CXAFG/vz6yPcc9rT3tPqe/LX+BQKaBFsFcwUDBlcHuAiyCKMGogNGAV3/SP0c+8b54Pn++TP5S/kv+/z8fP7RAL8DQgZYB4oG0wQ+A8cBhQAj/2T91/ux+n75e/ip+AL6/Ps1/hsANgEVAuICwALPAQoBjADd/rr7A/ms9wf35/aL9/T4nvoK/Nz8Nf04/sX/CQEXAjsDpwRGBkoHngeZCbANmg4OCvUF3AUOBTUBQf/W/z//rPza+cP4Jvnf+pT9k/9KAV8DCAWpBd0FLAbrBSAF0gPWAVr//vwQ+5X5wfgr+Or3nvjA+VX7wv3E/+YAgwJBBWQHvQeyBjMF7wOFAtQAAP9r/fD8j/wz+w36c/rB+7n80f3w/qn/AQDC/9T+Ef4C/oj9yvv6+an5ffpr+2T8Pf44APcAbwCj/2//pf8WAAEBCAL+AWYBewE8AiEEqAi5De0NhgnABn0GYgQxAfIAEwJBAFH8UPlG9yv2ifeg+ij9F/8+ASYDNQTNBFQFggV7BVEF6wP9AFX+Mf1G/LL6kvn5+R774Pvg/Df+Bf+Z/zMBagOpBHgEywMOAwQCGQHWAPIA1gBkAEH/sf2P/Dv8UPzH/Kv9Qv75/Ur99fzL/M38iP2d/g//Jv9i/5//GQDVAGQBswHRATQBxP8F/mP8W/sN+zP7nftx/Or95f/dAesEYQsqElYSAw28CTcJjAYgApL/Rf6++x74vPRf8tvxAfQN+AX8zv9CBCUI8gkuCsUJywiCBywGFQSGAIH86vkV+LD1uPSo9mn53vtH/pr/vf/zADgEUQePCIIIgQcKBewB6v8m/wn/q/8+AG3/g/36+6f7RvyR/ff+c//r/lf+ZP69/iD/9/8sAd4BWQFIANj/OwDHAMsAIQBA/1f+1PyK+pn4Xfid+Qz7ovv/+3j9lv+VASgEdQgHDzMViBTbDEsHJgcSBWj+i/nH+Mb3lfS68RvxmPJU9r37BgHsBRcLcA74DXYLHQn7BrIEGQLX/mT7L/hV9TPzn/Ko9O34Vv29ANsDVwb3BkAGYgZeCE4KUQl+BZcBBf84/dH7Wfty/IX+Yf94/gL+Ff8sAEQANgBRAAAAUv8o/xr/aP7W/RP+Vf5s/hP/4/8rAA4A8//W/yb/Jf3V+kr6T/uU+5n68/lA+ir7Iv39/yoD4wjBEX8WRRFWCbUHiAgWBNr8UfkO+XX3xPP08OjwkPMp+HH9nQJaCIoNRw+fDUULGQm5BlcEnQE+/rz6qfc69cXzW/S+90/84v/nAsEFhgbaBNgDfQXSB/sHmQUqAqv+uPvY+c/4nPlr/nwE7QV5A+IBPAEDAHb/9/9VADIACP9Q/Ib5w/gZ+iH8Ev5zAOoCEgS3A8gCCQLKASYB6f7D+zv5mfcf9sr0//RT97H57/rI/DEA4ANJCGAPdxabFlQPNAkFCDoFkP2V9zD38PcT9mDzY/LQ88D3vv05BNQJVQ7FEJAPwws8CL8FpQNSAQr+I/oL9930QvMW86n1/vq4AEIEwgVNBqkFQQTyA6sFuwdMB4kDlv6V+vv3BPe5+JP9fAPpBq4GRQR9AbD/Ef8M/xn/uf7U/aT8pvrA+Dn5FPwC/xUBuQLQA9YD+QLCAWMA2f6z/PD5tPeQ9o/14vTR9d73FPka+jv9zQG6BYMKAxIRGJEWVQ9TCnkIpgMd/Er4/Pdx9q/zf/IS89f01Phs/3AG7wvmD74RexBVDeIJsQX/AIz9NPsl+JD0/vEm8WXyDvZD+7MAxQWqCZcK8AfcBPkEFQdZB7sEhAAQ/Ff4rfWG9GT2pftnAkkHCAhfBkIF8AM+ARn/YP56/en7jPqR+TX57PnV+0/+RwB5AdoCLwQ8BDoDqwEA/7D7MPmi95n2VvYY99X3yPcU+C76Zf1xAdAGbgzrEQ8XXhe9EI0KEQkRBkL+mfdF9fbz9/F88XDzTfbw+VD/OgUiCo8OvRHKETwPAwvWBFz+SPp4+Mv2vfSM83Tz8PNs9g/8mwJsB0MKDwvjCMYEXQJ/A/wETgMa/5H6cfaX82rzlfZH/LICngc0CT0ITweWBpcEcgJTAXz/6/uW+PX2yPaz91f5WfvP/WsAXAKvA9EEgQVbBdgDhACW/Lz5F/g59xb3MPdP9/z3LPmC+jn9gwLwCGAPsRUmGIITDw2FCuAHjgBc+Sb3UfY38+7wZ/KC9UP4FfwbArcI9w0BETsR5Q4/C0UGu/8Y+qz3pfaj9M3yCPMJ9QX4GPxIAZcGPQoLCw0JcAVvAvABvQLyAc3+5fqF90b1FvW69578kQJZCEALJQorCD8HIAX0AU0A6f5f+9r3FPd+9xX3Pve5+ZL9FgGaAzQFOQb9BugGqwRIAEn8aPo3+Tv3WvWl9Oz09PVq92f5ZP3yA2sKeA/qFCIZIhegEDAM6wieAQH5BvXk83Xxde8F8Z70qPf6+ocAOgelDAYQYRGBEIMNGgiHACX6wPcS9yr1bvPH80j1Jfee+isA0gWiCWoL6gqcB0YDEAF8Ad0B0/8J/Ib4LfaA9Yb3DfzxARUIwQvhCv4HLQZuBCcCvQBe/yX8rPg99+T2K/Zh9jb5cv28AM0CkATeBY8GkwYJBZwBEv6T+1f5C/cC9nT2wfan9lb35vht+5wA8wcVD1QVSRk0F1UQ2AptB4QB9/nr9eP00vJW8KPwLPPv9eb5jgDaBx8NRRDIEUURdw7iCC4B6vqt+Ov3fvUH8xfz0fTj9mL61P+SBe0JEwwJCzMHOAOyAVYCLgJ8/6/7vfix9oT1pvb6+sEA2QWSCUALWgoGCN0F6gPGAXf/2/wL+g74Hvda9hf2pPfH+j7+egFuBKsG3gcICJQGEQPY/gj8mvoI+RP3yvVd9Rv1E/VZ9rz56f7ZBGUK0g+gFcsYhxWTDpYJywVx/1n4W/SM8v3w+/BV8/L1Q/ht/OYCQAmvDX0QwhGKEBIM1ARY/e340/ck91r1MvT49Hj2Vvgv/LgBswYWCnUL6Am8BbEBngCpATIBLf7/+gT5r/e39y76Vf6PAwsJtgs/CqgH8AXiA1kBcf9m/YD6bfjw90D3U/Zk97H6Kf7tAKMDYgZLCMQINQc+A3n+kfuR+lz5fPdA9t/1PfVe9Lb0XvdL/KsClQgxDYMSkxhaGgwVKA0NB1MBVfqq9N7xZPA98Jzyb/Vq9hr40P30BcQMUREkFJEUExF/CVsAk/nq9nr2vvXU9AT1+vX+9iz5vf1qAy4IPAveCw8JJgTeANQAUgGl/7X8gfoa+VT4F/mc+17/LgTQCHEK2wj6BuwFHQREASD+4/pL+IH3vPdo90X3Ovno/A8ANgKXBIcHmAkrCdEF5gCo/Fv6MvnT92L2mvXn9LnznvPB9ZL5pf7KBE4KpA7qE8MZ0BpbFEcLnwQb/zb4H/KP7+XvtPFe9H32pvdn+rIAcgiQDqIS+BRMFDAPqway/U73yvTi9Cf17/RU9b72qfhc+27/PwTFCC0MxAwRCYgDmACeAAwAh/0H+9H5TflA+SX6IPxp/xUERwiKCaMI7wcRB5IEKAHG/Ur6n/dD9//32ff192P6Bf7uAGQDEQZhCGYJCgjLA2r+nvox+Yz4O/ez9cz0QPTz88L0Gffp+qkAtwcwDVkQqRQcGv8ZihGYB6IBuPzn9Vrw8+488IvyYPWE9/j4h/xcA9QKWhCzE8YUhhJ+DPADdPu39ZbzsvNZ9AX14vUy93P5B/18AdMFrgl9DCwM4QeeArz/6f4Z/qz8Avun+Wj5TfpW++n8VQCQBLoH4wkACwoKaAdiBN8AU/wW+Hf2fffW+Cj5avkk+1j+SgEoA1QFSgixCZsHBwMk/mP6U/ig9z33YfYk9Wf0xPTK9Wz39PoMAdsHqAwbDwcSzhboGBkTeQi6APH8L/jR8XvuBvCr8+P2HPnG+oH95wLMCRoPqxEhElMQpwt9BGH82fX/8oDzB/X99d32uvhr+yf+9wBVBAcI3Ao9C1oImQO//0r+EP4o/Tr7yvnm+Zr65fro+9j+hgJ4BSsI+goMDEAK3wa7AnX9mfiq9lH3MPiR+MD5W/wt/+QAHAJSBEoHoQgJB5gD7/90/Dn5G/e+9v72WvZb9VD1NfaB9x/69f65BBwJngtNDa0PQBPQFNsPLAbN/or7offs8a/vAfOg91v6SPxj/pwA4gOaCMoMgg4oDjsM+welAVP7uvac9HX19feK+Rj6pfvl/Qb/7P+7AmUGMwjSBxwG+wJm/4H9Q/3Z/Af8zPvy++T7Xvwh/pIALQMzBgoJbwpZCkEJPQYOATn8zfm6+Lj3d/fk+MP78f4IAd0B1AJXBBAFlwTnAwED6gDH/Zr63feu9Y/07/Q59jX34Pet+ST96gCgA18FDQdhCfsLpQ08DuUNtQpOA2j7pvcE91D2jPZ4+e38s/7K/xYBCgJdAzIGRAk0CpcImgVsAn//i/yh+SL4Hvkw+0j8Tfyl/Ab+GAAQAjYDmAMJBM0EggTFAcD9p/uk/D3+9v2//N38ef5BAH0BrAK2BL0HHwrlCUMHBAQIAe39xfpJ+Db3Ifj0+kv+rwAjAlUDDwTeA1oDYgOpAxwDSgFR/nP6tvaC9Ab0lPQr9p/46vqA/PH9wP+6AaMDfwUSB9QHqQd6BikEWgIkA3IEZwKZ/sX9U//3/gP9Xv0tABcCMgI1AnAC3QHoAJcApgC7AFMBPQJ6AoUBkf9//Xn8vvx0/Vn+v//0AKcASf+y/ir/Xf8j/3v/EgCC/xf+5/2r/5kBMwJsAo0D/QQuBesDxwIhAygE5APNAS3/3fyp+i75mvnF+6P+ygFTBN0E5wNEAxgDWwJEAckATAAP/lz6kveQ9v/1yvUm97b5F/zl/TT/7v9+AGUBfwJfA9IDywNZA4YC0gBz/lD+1QJFCIAIowTGAnQDSwID/1r9Y/4VAB8BOgE6AOT+VP5h/qD+uf/PAY0DBQSLA0QCKABq/jL+i/4i/rT9Nv6Y/sz9sPxQ/Kr8l/3s/uj/SADtAI0CmQTmBccFhwQ9A5oCFgImAW0AswAmASsAxf3a+7P7vPwH/q7/ywGyA8YE/gRWBAQD3wEkAdX/h/14+2T6Svm990/38PgY+038Nf2U/qb/wv+D/4j/s//w/1EAewDr/8D+y/3v/RP/4//3/78BTgdaDQMOFQk5BEgChgBs/a/7Ev2L/7sAEwDP/Qf76PmD+4z+hgFvBFEHJAmoCGoFjgDb/B/85Pzd/Bv8tvtv+7v6//nm+Tf7r/6OA1kHRwgJB5QF1QTyAxICMAAgAI8B3wHJ/yz9TPwK/fH9mf6W/zIB8gJKBLcE3wNlApkBuwFjAZD/If1Y+1j6vvmh+Sn6HPt8/HL+UQDMAKv/Nf5+/Uf9zfwL/OD7wPzW/ST+1/3S/Zv+HwC0AX4CYALLAqEFTArNDBQKtgSrATEB1f88/Vj8rf28/kv+Ov02/I/7Pvzf/oYCswW9B60IVQgwBkoCFP5U+z36p/kU+R75BfpW+6f83v1N/6oB7QSNB9cHzgUXA2gB+QB4AD//iv5c/24AFgD2/s/+0f/WAGEBAgIiAzEEVgRrA9oB+f8//nD9rf3h/TL9TvxE/Ov8fv0I/gr/SgDtAJMAlv9y/iv9mfsL+kX5pfnG+hv8bv2V/l//7f+TAE8B2AEeAjMC+QFpAUMBAgPjBhYKPAkoBe8BAQERAPj9hPzf/Bf+I/+Q/7v+Of1l/RQA3ALzA7YEOQaqBjYExP90+8n4cfjN+VT7r/y9/ukASwH1/yj/IQD/AUQDBgOcAaQA9wBdAeUArAC8AQoDHAMBAtUAVABUAEgA/f/U/xYAawBiAPL/X/8Q/2D/GgBlANn/Tf+k/1YARwCC//f+6f68/vv94/z3+3L7Ivvi+tT6HPu8+8/8W/7Q/54A4QARAYcBBAKBAYP/eP0D/Y/9zP1I/qUATwVzCtkM2gq8BgkE0ALKAFH+0v0O/27/Tv75/Kv7ffoL+/T9OQE+A3wESAXmBAgDTQC//aH8cP20/qz+q/0M/eL8ZfzG+/X7eP0hABgDDQV+BY4FdwagB3AHZgWXAkcAdv5B/Nf5BvnH+oX9Wv+MACkCtQP9AxMDGQKdAVQB4gA+AHH/Yv4d/SH80vvw+1T8a/0S/+D/D//B/Tj9Hf20/HP8H/14/qj/7P8P/w7+Vf5j/xr/Y/2v/AH+jf/X/4P/qP9IAAYBbwKbBe4JOAwpCt0FJAMgAj8AqP0G/Xr+iP8+/3P+Kv1x+w37+/yS/14BEAP2BJgF6gOeAFb9q/sg/EX9f/04/ZP9J/4G/tv9A/+tASQFXgjQCY4IzgWPA/gBlv80/M75MfqM/GH+rf7G/gcA5AEkA7sDYAT5BKsE8wIIALr8Z/od+lz7tfyh/WL+0P6S/vj9m/31/Tr/7gDfAS0BPP9W/U/8yfte+5j7yvwh/tT+Sf8LAOEAawG0AbgBLwEPALv+Uv3F+8L6P/sg/SgA/gS1CpMNBgx/CY8I2Qb2Avf/g//h/oH8JPqG+Kf2bvX+9hX7sf/YAyAHmQj0B+8FKgNbAMP+hv4c/uf8BPzy+wv8bPz3/YkAMAOTBUgH/gZ9BBYCoQGTAbj/J/1q/HL9Mf73/QD+Lv/iAB8CswLmAs0CYAKtAZsA5/7s/NP7UfyO/T7+Qf48/jf+6v2z/Qn+mv7+/l3/pv8r/wD+Yf3b/aL+Cv8x/1X/t/+pALoB/wFMAUMAK/+2/er7g/r/+VD6kPvr/ccAowN6B4MMnQ8bDlIKygeNBVkB4/zh+lv6W/lv+H/45viw+Rn83/9FA64FlAeOCLYHNQXiAYT+8fuy+kL6G/q6+nz8j/41AJQB1gLxAyEFLwbwBQgEEAJjAewACP9u/Cj7iPtD/OP8Bv7S/8YBqQM5Bc4FEAV3A8EBJgBl/pD8YPtf+wP8cfy2/FD94v3A/Xb9K/6X/3AAfgCNAKwARQBY/0/+df08/c39av6h/lz/GgGNAmAC+ABv/y/+Iv0n/HT7i/uM/Pv9Zf+zAGcCtAWnCjUOYw2eCXIGCQSIAKr8vPq3+i373/uV/D/8Xfsi/NP+RAGNAsUDKAWkBckEuwK5/wj9Rfz5/D395fxq/R//zgCcAbEB6AEXA7wE9QQkA0IB+wAbAa7/K/2M+7b7L/04/+QAlAH0Af0CFgTlA28C9wBJAP3/Dv8g/U37DPtJ/G/9df0D/Un9df6P/9T/qf/y/7oAHAF/AGX/q/6B/on+fP5R/lD+E/+UAJ8BOgE9AMf/bP9Y/gv9qPxj/WX+0/6f/pj+Nf8XAIYB9wQTClsNWAzgCMkF7AI2/777VPrl+i38Ov1R/SL8wfrq+uf8k/8oAqQEwwadB1EG4wK4/uP7Jftw+/f7W/3u/0wCGQO5AmcCtAJgA8wDSAPOAWkAx/8d/4H9n/sT+3z8/v4pAUkC6gLnA/UE2QRQA5cBrQD6/1L+vfui+UH5cfof/Jv99P47AB8BRAGuANP/fP8cACABYQGGAF3/rf5E/pL90vz+/KL+BAG7AuoC8QHWAAQA6P5P/WD88vzU/YP9m/yR/Ez9Ef5T/+YBygVACqUN1g2DCuEF4AGd/hH84/r9+of7J/yS/BX8BPsd+x792/9kAtUEvwb6BmgFBwOMAFX+Cf0y/XX+3//FABkBCwHMAIAAXwCiADkBrwGEAbQArv/I/iD+2f0u/jD/mgDnAYwClAKKAoMC3AFvACr/sf5i/oL9d/z4+/b7NPz+/Fn+lv9fABUBpwF+AcYAaQCiAL4ASQBz/6b+M/4b/hL+9v0+/lb/6QARAh8CNwEPABz/Ov5d/f38Tv2+/eD96/0K/kP+Dv/wALYD0ga5CUELLQrkBiED7f9H/Xz7IPsD/GT9pv4//9L+Bf4J/hT/fgD7AZEDqQRyBNsCewAV/ov8hfyt/Sb/pQBGApADlwNEApcAff8Y/xz/Hf/E/jL++v04/lL+Kv6R/hMAHAKsA04EHwRgAy0CgAB8/rb82/se/Pn8tv0j/qH+g/+RAD8BTwEPAdgAmQAKAAT/9v2Y/fr9aP5x/ov+J/8BAIoAdwAVAAoAkAAiAS8BoQCb/0X+BP09/OL7y/sr/Bj9K/4O/+z/7wDjAd8CYgRnBvAHDwjABrIEkwKjAOT+o/1//YT+vf8oAJz/qv7z/bj95P2A/rT/TQF+ApwCtAF2AKf/rv9MAA8B2gG2AiwDcgJYAM/9Nvw2/EH9S/7s/mX/vv+W/+j+ZP6p/tf/kQEoA8cDMAP5AcUAfv/9/fD8Lv1n/k3/Nf+s/lH+Cv61/Wn9Jf0l/ej9Hv+l/zb/7v5b/8L/qf+T//D/bwCKACMAWP+d/nv+Gf8EAKwA6QD8ABYBBgGcAB8AFwCaAD4BuAEDAhUC5gGlAW0BIgHkAPUAOgFBAfkAvQCxALwA5ABSAesBQgIPAlYBRwAJ/+H9Hv37/Hr9Sv4J/5j/CABSAG4AhwC9AAEBUAGwAcQBEgGt/zP+Iv2N/Gf8mvz//HL91f3w/bb9lf0P/iz/dwB6AQACCwK0Af8A5f+3/hb+Vf76/l3/Vf8H/4H+1P1C/ev89/zU/Y3/PQHyAcsBggFtAV4BQQFSAbUBSQKuAooCzQHfAF8AiQAIAXUBvQHaAWsBNwDC/tD9qv0v/iD/TQCJAaQCSAMYAy4CMwGeAFAAEgDo/8n/pf+T/5P/W//z/vn+vf+vABsB7wCKABUAaf9r/nD9BP1U/fj9dv64/uz+QP++/0cAqQDKAKQAIAA1/yn+Y/33/NL8HP3P/Vr+Uf4P/in+hP7L/iX/6v//APgBmALGAn4C7gFWAcsATAAGADEAfQBUALH/J//8/uf+4f5s/6cA5AGHAqQCiAJEAu0BtgGoAZwBjQGBASUBJADb/gr+4f0U/pP+ev9rAOAA6ADUAKEANQDk/wQAaQC3ANgAwAA1AC7/E/57/Zz9Pf4V//P/tgAhAQkBhwDu/57/0v9jAM4AqgAIAEL/kf70/X/9bf3p/b7+cP+n/3n/T/9z/9D/KQB9ANoAJAERAY4Aw//2/n7+jf7n/ir/Uv+m/xoASwAvAEIAugBOAb8BDgI3Ah8CxQFHAdEAoADdAEwBcAEbAZoALACj/+b+av61/qb/lQAUARsB0gB1ADMAEQD5//v/KQBeAE0A1/8s/57+b/63/l3/HgC2APwA5wBwAKb/0v5k/pr+WP9FAAsBcQFaAc0AAgBe/yz/Z//P/yMAVgBiAB8AkP8N/wz/nf9eAPIAUgF5AUcBsgD//3T/J/8y/4n/zP+g/0T/N/+H/+L/KAB/APMAUwFjAQgBZgDW/4f/Tv8C/8r+4/4k/0H/L/8f/zb/ef/g/2MA9QCCAc8BoAEEAVQA7P/Y//T/EQAdACEAMQBAAB4Ayv+P/6X/8/8+AGsAfAB6AHMAaQBbAE4AXACKAKEAaAD1/5X/ff+d/83/AwAxAEkAQAASAML/a/9c/9f/rQBTAYcBcAE0Ab4AJQC+/7n/9f8/AJEA2gDkAKYAVwAxAEIAgADKAOkAtABGANX/aP/l/nr+ff7y/m3/mP+Q/5z/uf+q/23/QP9K/2//if+Y/5n/ev9D/xv/G/84/3b/4/9pAMsA5QDPAKgAcgA6ACsAVQCCAHsAWQBZAHsAlwCrANsAGAExARcB5gCtAGIAGADv/+P/5f/4/xIAFgADAPb/DABAAIIA0AALAQEBqAAyAMn/gP9w/7j/OwCTAIQAMgDQ/2X/+P7K/hD/qf9SAOEAOQE7AegAZADU/1b/FP8o/3P/uf/Z/9D/pf9e/xz/C/8y/3T/vv8NADYACgCV/yX/8P7p/gP/Qv+d/+7/GwApACsAIwAcACMAKgAUAPH/2v/V/9j/5v8QAEUAYQBfAFgAWwBjAF8AWABzAMEAIgFeAV0BMwH/AM4AowCEAH4AigCZAJwAgABGAAkA6f/l/+P/4v/w/xIAKAAfAAMA7P/z/wsAFQD4/8z/sv+f/3P/N/8g/0T/g/+n/5X/a/9h/4X/oP+S/4f/tf/y//n/0v+x/57/hf9w/3T/gv+X/8f/EQA8ACEA2/+e/4D/ev+V/8v/BwAqADAAGwDq/7X/sP///3YA0wD4APgA4wCmAEIA5P+6/8P/3f/v//z/BwAMABAAEgAdADwAcAClAL0AswChAIYATwAOAPj/IwBaAGsAZwBtAF8AHwDM/5//qP/Q/wUALwBAAD4AOgAnAPD/pf91/2v/af9Z/0n/XP+S/8j/1v/A/6z/sf+q/3z/R/80/0T/W/9t/4b/qv/X/wUAJQApABkADQAYAC8AOQAtAB8AIQAxADwAPgBUAI8A2wALAQYB0wCSAF0ANgAXAPj/5v/4/ykATQBLAC4AIAAiABgA8f/J/8H/0P/d/+L/8f8JABwAGgAFAP3/+//+/////////wAA/v8DAPz/BAD9//z/AwD3/wQA+f8AAAAAAAABAAAAAgD+/wUA/P8FAP//AgAFAPn/BgD/////BQD7/wIAAAAAAAEA//8CAAIA//8DAAEAAgAAAAQA/v8FAP//AwD+/wUA+/8IAPv/CQD8/wQAAwD8/wgA+v8FAP7/AQD+/wQA+/8FAP7//v8EAPf/CAD3/wQA+f8AAPv//v8AAPz/AgD7/wEAAAD6/wYA+P8FAPv/AgAAAAEA//8CAP7/BQD7/wsA9/8JAPf/CAD3/woA9/8HAPf/CQD0/w0A8f8HAPz//f8BAP7//f8GAPv/CAD3/wgA+v8HAPv/AgD//wAAAAD/////AQD9/wMA/P8BAP3//v8AAPn/AAD5/wEA+//+//7//v8BAP////8AAAEAAgD+/wMA//8DAP7/AgD//wIAAAACAP3/AwD/////CAD4/woA/f8DAAMAAAAEAAMA/v8DAAAA/v8CAAAAAQADAP//BAACAAQAAwAEAAQAAwADAP//AgD/////AAD//wEA/f8AAAAA/v8EAP3/AwD/////AgD8/wIA/v/+/wEA/v///wAA/v8CAPj/BAD8/wEAAQD8/wcA+P8IAPn/AwD///7//////////f8DAPr/BgD2/wkA+/8AAAIA/f8DAP//AQD+/wUA+f8IAPz/AwD8/wYA+/8HAPv/BQD9/wEA/f8FAPv/BgD6/wMA//8EAP3/BQD6/wMA/v8CAAEAAgD//wAAAwD7/wgA/f8DAP3/BQD9/wgA+/8EAP7/AQAEAPv/BgD7/wQA/v8FAPz/BwD9/wMABAD9/wgA/v8AAAQA/P8CAP////8AAAEA/v8DAP7/AQABAP7/BQD7/wQA+/8EAPv/BQD8/wMAAAD+/wQA/////wIA/v8AAAMA/f8FAPv/AwD8/wMA/v8BAAEA/v8DAAEAAAAEAP7/AgAAAP//AwD9/wEA/P8CAP3//v8AAPv/AwD//wEAAQAAAAIAAQABAAMAAAACAAEA//8FAPz/BAD9/wIA/f8FAP3/AwD+/wMA+/8JAPj/BgD6/wEAAAD+////AAD8/wMA/P8GAPn/AwD6/wQA+f8EAPn/AgD9/wEA+/8DAPn/AAABAPv/BAD9/wIAAAAAAAAAAgD9/wcA+P8KAPf/CQD8/wAABQD8/wUA/f8DAAAA//8DAP3/BwD2/wsA8/8JAP3//f8GAPv/AAAEAPr/BQAAAPz/BgD6/wIAAQD9/wgA+P8IAPv/BAAEAP7/BgD+/wMAAgADAP7/BwD7/wcA/f8EAAAAAQADAPz/AgABAPz/BAD8///////+/wEA/f8AAP3/AgD9/wEA///8/wEA/P8BAP7/AQD8/wMA/f//////AAD//wQA+/8HAPv/BgD7/wQA+/8DAPz/AAAAAPz/AgD8/////v/8//v////5/wUA+P8CAP3//v8BAPz/AgD+/////f8EAPv/AAABAPz/AwD9/wMA/v8DAPz/BQD8/wQA/////wIA/v8CAP7/AQD//wAAAgAAAAAAAgAAAP//BAD+/wEA/v8CAP3/AgD6/wYA+f8JAPf/CAD7/wMA/v8DAP3/AwD//wEAAAADAAIAAgACAAEAAwABAAIAAAABAAQA+/8GAP7/AQAGAPv/AwD///3/BwD4/wgA/f8BAAUA+/8FAPj/CAD0/wcA+v///wEA/f8AAAAA/f8FAPz/BAD+/wMAAAAAAAIA+/8GAPj/BQD5/wMA/P8AAP7//v8BAPz/AAD9////AAD8//3/+/////3//v/+/wAA+/8DAP3//f8EAPr/BQD5/wYA+P8GAPn/BAD+/wEA/v8BAAAA/f8EAP3/AQD//wIA/v8CAAEA//8CAAMAAQD+/wUA+v8DAP7/AAD+/wQA+f8GAPv/AwD//wAAAQADAAEAAAAEAP7/BgD9/wYA//8CAAEAAQACAAMA//8BAAEA/v8AAAIA/f///wEA/v8CAAIA/P8GAPj/BwD5/wMA/v/+/wAA+/8CAP//AAD///7//v8BAP//AQD//wEAAAD//wIA//8DAP////8EAPv/BAD+/wIAAAAEAPz/BQD7/wMAAAD8/wIA+/8DAP///f8EAPr/BAD9/wAAAAD//////v/+//v/AwD3/wQA9v8FAPn/AwD///v/BwD6/wQA/v/7/wcA+f8HAPv/BAAAAAUA/P8IAPr/CgD6/wYA+/8EAPj/AwD9//z/BAD6/wQA/f8AAP7/AAD+/wMA+/8CAPz///8AAAIA/P8EAAAA/f8EAP//AwAAAAEAAwD//wQAAgD+/wUAAAD//wUA/P8IAPr/BgD+/wIAAgD8/wIA/v8BAP7//v/+//3/AgD7/wEA/f8DAPz/AwD9/wEAAQAAAAIA//8AAAIA/P8AAP///v8AAP7/AAACAP3/BAD8/wYA/f8CAPz/AwD//wEAAgD7/wYA+f8FAPv/AgD6/wEA/v/9/wQA+v8FAPr/AgD9/wIAAAD+/wMA/P8GAP3/BQD9/wMA//8AAAQA+/8HAP3/AQAAAP7/AwD+/wIA/v8BAP///////wAAAAD+/wQA+f8EAPz/AAACAP3/BAD7/wQA/f8DAAAAAwADAP//AwD8/wQA/v8AAAQA/P8FAAEAAQADAAAA/P8KAPr/BQD//wEAAQD+/wIA/f8EAPf/CQD3/wYA+f8DAP//AQD/////BQD+/wIA/v8DAP7/AwD8/wUA+v8IAPj/CQD4/wMA/v/+/wMA+/8DAP7///8CAPz/AgABAP3/BQD9/wEAAgD9/wMA/P8CAPz/AwD5/wUA+f8FAPz/AgD+////AwD9/wMAAQD//wUA+/8HAP3/AgAEAP3/BAD9/wIA///9/wQA/P8CAPz/BQD9/wMA/v8EAPz/CQD4/wgA/P8DAAAA/v8CAP7/BQD6/wgA+f8HAP3/AQACAPz/AQD7/wIA/f8BAAAA/P8GAPz/AwABAPz/BAAAAP//AwD9/wIAAQAAAAQA/v8EAP//AQAAAAEAAAD+/wIA+/8EAP////8CAP////8FAPv/BwD4/wUA+/8EAPz/AwD7/wYA+f8DAP3/AAACAPz/AAACAP7/AgADAP3/BQD+/wMAAgD//wQA/f8DAP7/AQACAPr/BgD5/wMA/f8CAP3/AgD8/wIA//8AAAIA+/8HAPv/BgD8/wQA/v8DAAAAAQABAP7/BAD6/wcA+f8GAP7//f8GAPr/BAD+/wAAAgD8/wQA+v8GAPn/AwD+//7/AwD5/wMA/f8CAPn/AwD7/wEA/v////v/AQD8/wEAAQD8/wMA/f8AAP//AwD9/wQA/v8DAP//AQADAP3/BwD5/wcA+/8GAPz/BAD8/wMA/f8EAPz/BQD7/wYA+v8EAPr/BQD9/wMA/f/9/wEA/v8EAPr/BAD6/wYA+v8DAP//AAABAP//AgD//wQA/f8BAAUA+P8KAPj/BwD7////AQD9/wUA+/8CAP3/AgD//wIA+/8FAPr/BAD+/wAAAgD9/wUA/P8EAPz/AQABAP3/AgD///7/AAACAPv/BQD+/wIAAAAAAAAAAQAEAPz/BwD8/wIABAD6/wcA+f8AAAEA/f8CAAAA/v8BAP3/AwD//wQA/f8DAAAA/v8FAPn/BQD6/wMA/f8BAP3/AQD/////AAD8/wAA/v/+/wAA//8AAP///v8BAAAA/v8BAP//AAD+/wEA/f8DAPr/AwD8/wEA/f8BAPz/AgAAAP3/BQD6/wUAAAD+/wkA+P8IAPz/BAAAAAAAAQAAAP//AQABAAMA/P8CAAEA/v8IAPr/BgD6/wYA+/8JAPj/CgD4/wUA/f8AAAMA/P8FAPz/AgAAAAAA//8AAP7/AQAAAP7/AgD//wEAAgABAPz/BwD7/wYA/f8EAP////8DAPv/CQD3/wgA+P8DAP//AAAAAAMA/P8BAAEA/f8FAP3/AQD9/wMA/P8DAP///f8HAPn/BwD7/wcA/v8CAP//AAD+/wQA/P8CAAAA//8EAP7/BAD9/wEAAgD7/wQA/P8CAP3/AAD8/wEA/P8CAP3//////wEA/v8AAP////8AAP7/AwD8/wQA/f8BAAEA/P8EAP3/AgD7/wMA/P8EAP3/AAAAAP7/BAD+/wIA/f8FAPz/AgACAAAABQD6/wYA+/8HAPr/BgD7/wQA/v/+/wIA+/8FAPr/BQD9/wUA/f8AAAQA/v8EAAIA/f8IAPz/BQAAAP//BAD+/wQA//8BAP//AgAAAAUA/P8DAP7/BAD+/wMA/f///wQA+P8IAPb/BgD5/wYA+f8DAP3///8AAP3//v8AAP//AAD9/wUA+f8IAPr/AwAAAP//AgAAAAAAAgAAAP////8BAAEAAgD9/wIA/////wIA+v8FAPz/AgD+/wIA/f8FAPj/BgD4/wUA/f////7/AAD9/wAA/f8BAP////8DAPr/BQD9/wAAAgD9/wAABAD8/wYA+/8IAPr/CA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ABAPr/AwD9/////f8AAP7/AAD+/wAA+/8GAPv/AQAAAP3/AwAAAP7/BAD7/wMA+v8HAPb/CQD5/wIAAAD9/wMA/////wAAAAD+/wMA/P8EAP//AgAAAAEAAQD///7/AQD+/wIA/P8FAPn/BQD5/wQA+v8DAPz/AwD7/wMA/P/9/wEA/P///////f/+////AAD8/wAA/f////3/AAD6/wQA9/8EAPj/AAD+//3/AwD7/wQA/P8FAPf/CAD3/wkA/P///wIA/f8FAP3/AQAAAP//AgD//wIA/P8GAPv/AwABAP3/AQAAAP///v8AAAEA/v8EAPz//v8EAPr/BgD6/wIA/f///wAA/f8CAPv/AwD9/wAA//8BAAAA//8CAP3/AQAAAAEA//8BAP//AAAAAP///f8DAPv/BQD+//3////9/wIA/v/9/wMA+/8DAPv/AAD+/wIA/P8CAP7/AAAAAP7/AwD9/wMA////////AwD7/wcA+/8DAP7/AAAAAP//AAD7/wMA+v8EAP7/AAACAP3//f8EAPz/AgD//////v8BAPr/AwD6/wQA+/8DAPv///////7/AQD7/wQA+P8HAPf/AQD+////AwD7/wQA/P8FAP7/BAD9/wYAAAD+/wQA/f8EAAAA/v8EAAAA/v8GAPf/BgD///z/BQD8/wIAAgD+/wAABAAAAAAAAwD9/wQA/v8BAAIA/f8BAP//AQD+/wIA+/8IAPr/BAAAAP7/BQD+/wEAAQABAPz/BQD7/wYA+/8FAPz/BAD9/wMAAQD+/wUA/P8CAAAA/v8EAP3/AgD+/wAA/v8DAPv/BQD4/wcA+/8CAP3/AgD9/wYA9/8GAPr///8DAPn/AwD+//3/BwD6/wcA+/8FAPz/BAD8/wQAAQAAAAEAAAADAP//AwD9/wQA//8EAP3/AwABAP3/BAD+/wAAAgD//wQA/v8FAPz/CAD6/wkA/P8IAPz/CgD3/woA/P8DAAMA//8EAAAAAAD//wMAAAAAAAQA//8CAAAAAgAAAAEAAgD//wEA///+/wQA+v8HAPn/BAD8/wIA/P8DAP3/AQAAAAAAAAABAP7///8AAP////8AAP////8DAPv/BAD+/wEAAQAAAAEAAgACAAAABgD9/wgA/f8FAP//BgD//wQA/v8FAP//BgABAAEABAACAAMAAQAAAAQAAAAEAP//AwABAAEABQD+/wQAAAD+/wYA/v8BAAMA/f8DAP3/AwD+/wIA/P8FAPv/CAD2/wgA9v8KAPz/AgAAAAEA/v8EAPr/BAD6/wMA/P8CAPz/AQD+/wMA/v8FAP7/AwADAP//CQD6/wQA//8BAAMAAQD+/wQAAAAGAP7/BQD+/wQAAgD9/wYA/f8HAPz/BAABAAAABQD+/wYAAQAFAP//BAAAAAEAAAD+/wMA+v8EAPv/BQD6/wIA//8AAAAA//8BAPv/CAD5/wUA/f///wMA+/8DAPn/AwD9/wEAAgD5/wUA/f8DAP7/AQAAAAAAAQACAAEABQAAAAUA//8HAAIAAgAFAP3/CAABAAQAAwACAAUAAQACAAEABAD//wgA/f8CAAUA/v8FAAAA//8GAP7/AgD//wIAAAABAAIA/f8GAPv/AQD//wIA/f8BAP3/AQAEAPv/BAD7/wMA//8CAAAAAwD+/wMA/f8BAAEA//8CAP3/AgD///7/AgD8/wEA/f8BAP///P8DAPv/AgAAAP7/AwD+/wEA/v///wEA/v8BAAAA/v8CAAAAAAABAAIA/v8DAP3/BgD6/wcA/f8BAAIAAQACAAMAAAACAAMAAQADAP3/BAD6/wgA+/8BAP3//v8AAAEA+v8CAPz///8AAP3///8BAPz/AQD9////AgD9/wMA/v8BAAMA/f8DAAAAAQABAP//AwD+/wgA/P/+//v//f8AAP7/AAAAAP7/AwD7/wUA+/8BAPv////+//7//P8CAP//AAAGAPr/BQD//wAABQD9/wUA//8AAAIA//8CAAIA/P8DAP7/AwD9/wMA//8DAAEAAAABAAUA/P8JAPr/BwD9/wUA//8CAAAAAQABAAMAAgABAAMA/v8FAP3/AgAAAP//AwD+/wEAAAD+/wMA/f///wEA/P8BAPr/AAD7/wAA+/8CAP3//v8BAPz/AQD///7/AAD/////AgD+/wMA/v8CAAAAAQAEAP///v8EAP3/AgD+/wAAAAD+/wIA/v///////P8DAPn/BQD5/wQA/v8BAAQA+/8FAP3/AwD+/wEA//8BAAEA+v8HAPf/BgD+//7/AQD6/wMA+//8//7/+v8CAPj/AgD6/wIA/f8AAAEA/f8GAPz/AgACAP3/BAD//wEAAQAAAP//AwD8/wUA/////wQA/f8GAP//BQD//wQAAgABAAIAAQAAAAIA/P8EAP//AwACAAEAAwADAAMABQADAAQAAQACAP7/BAD6/wQA/v///wAA////////AgD//wIA/v8BAAAA/v8AAP7/AAD+/wMA+f8GAPr/AQD9//7/AgD8/wQA/P8DAP7/AwD7/wMA///8/wQA+P8FAPr/BAD8/wAA//8AAAMA+/8EAPv/BQD8/wQA/P8FAPv/BwD6/wUA+f8LAPf/CgD4/wYA/f8BAP7/AgD+/wQA/P8BAAMA/P8HAPz/AAABAP7/AwD//wQA/f8DAAAA//8CAAIAAAD//wUA/P8HAP3/AQACAP7/BQD8/wQA/f8CAAIAAAACAAIAAAACAAMAAQADAAIA/v8EAP3/AQD//wAAAQD9/wQA/P8FAPz/BQD7/wYA+/8FAPr/BAD8/wMA/////wMA/v8BAAMA+/8FAPz/AgABAP//AwD8/wUA+P8IAPj/BgD/////AgAAAAMAAAACAP//AQAAAAAA//8BAPz/AQD+/////f//////AQACAP//AQABAAIA//8FAP7/BAAAAP7/BwD6/wUA/v///wIA//8DAP3/AwD+/wAABgD5/wgA9/8DAP7/AAD//wEA+v8EAPz/BQD8///////9/wEA/f/9/wIA+v8EAPr/AQD8/wAA/f8CAPz/AwAAAAAAAAACAP7/AgABAP3/BgD7/wcA+/8CAAEAAAADAP////8FAPn/CgD4/wgA+P8HAPn/AwABAPv/BwD6/wIAAAD//wIA//8BAP7/AgD+/wEAAQD//wMA/f8FAP//AwACAAAAAwACAAAABQD+/wYA/P8GAP7/AwACAP7/BAD8/wMA/f8DAP3/AQD7/wEA/f8DAPz///8BAPv/BQD6/wMA/f/8/wEA/v///wEA/f8AAAAA/f///wIA/v8DAP7/AgAAAAMA/P8GAPf/BgD5/wUA+v8CAPz/AAD+//3//f/9//r////9/wEA+v8CAPz/AgD8/wAAAAD+////AQD9////AAD//wAAAAABAP7/AwD9/wMAAAAAAAEA//8AAAIA/v8AAAEA/v8DAP//AQAAAAIA//8EAPv/CAD4/wUA/v///wAA/v8BAP7/BAD8/wUA/f8BAP//AgD//wEAAAD//wMAAAAFAP7/BgD+/wMABAD+/wUA/P8EAP7/AwACAP7/BQD8/wMAAAD+/wUA+f8HAPz/BgD+/wIA/v///wEA+/8AAP///v///wIA+v8FAPz/AgACAP3/BAD+/wQA/f8DAP3/AQD8/wMA+v8DAP3//f8DAPj/BgD7/////v8AAP3/AQD5//3////8/wAA/P8BAPv/AwD7/wEA/v8AAAEA/P8DAPr/BAD8/wEA//8CAPz/BAD8/wAAAwD9/wIA/f8DAAAA//8DAP3/BAADAP7/BAD9/wIA/v///wEA/v8DAPv/AwD9/wMA//8AAAIAAAAEAP7/BAACAAAAAwABAAIAAQABAAIAAwABAP//AgAAAP//AQAAAP3/AQD+/wEAAgD+/wIAAAD+/wAAAAD//////v/+//////8AAAAA///+//3/AwD9/wMA/f8CAP//AgD9/wYA+/8FAPv/BwD6/wQA/v8BAAIAAgAAAAAA//8BAAAA/P8CAPz/AwD8/wEA//8AAP////8AAAAA//////3////9//7//f/9//7////8/wEA///9/wYA+P8IAPj/AwAAAP7/BAD8/wQAAQADAP//AwD//wUAAAD//wMA+v8CAPv/AQD+//7/AQD9/wIA/v8AAP///f8EAPz/AQD9//7/AgAAAP//AQABAP7/BAD+/wQA/f8HAP7/AgADAP//BQD9/wcA+v8HAP7/AgABAAAAAQABAAIA/P8DAP3/AQD8/wMA+P8EAPr/AgD8/wIA/v8AAAAA//8AAAIA//8DAP///v8GAPj/AgAAAPv/BAD6/wQA/f8EAPz/AgADAP7/AwD6/wMA//8AAAYA9v8KAPX/BwD7/wAA/f////z/BAD7/wMA/f//////AAAAAAEA/v8BAP//BAD8/wkA9/8LAPb/CgD4/wcA//8AAAIA/P8CAAAAAAABAP3/AwD8/wIA/v///wIA/P8DAP7/////////AQD+/wUA+P8IAPr/BQD+/wUA//8EAP7/AQAAAP//AgD//wQA/v8FAP//BAAAAP7/BQD//wIAAAACAP7/AQABAPz/BgD2/wgA+/8AAP////8BAAAAAQD//wEABAD7/wUA//8AAAIA/v8AAAEAAgD8/wcA+P8GAPn/AgD+/wIA/v8BAPz/AwD8/wMA/v8CAAEAAQD+/wAAAwD+/wEA/v/+/wEA/f8AAP7/AAD//wIA/P8CAAAA/v8EAP3/BQD//wAAAwD//wIAAgD//wIAAQD9/wMA/P8BAAIA/P8BAAMA/P8FAPz/BwD5/wsA9v8KAPn/BwD8/wAAAQAAAAAAAgD//wIA//8BAAEAAAD///7//f8BAP7//v8DAP3/AgAAAP//AwD+/wEAAgD9/wYA+P8IAPz/AwADAP7/AwABAP3/BQD8/wUA+v8FAPj/BwD8/wAABQD5/wcA/f8AAAUA+f8DAP7/AQD+/wQA+P8HAPr/AAACAP3/Ag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ADAP3/BAD+/wEAAgD9/wEA//8BAP7/AgD7/wgA+v8EAAAA/v8FAP7/AQABAAEA/P8FAPv/BgD7/wUA/P8EAP3/AwABAP7/BQD8/wIAAAD+/wQA/f8CAP7/AAD+/wMA+/8FAPj/BwD7/wIA/f8CAP3/BgD3/wYA+v///wMA+f8DAP7//f8HAPr/BwD7/wUA/P8EAPz/BAABAAAAAQAAAAMA//8DAP3/BAD//wQA/f8DAAEA/f8EAP7/AAACAP//BAD+/wUA/P8IAPr/CQD8/wgA/P8KAPf/CgD8/wMAAwD//wQAAAAAAP//AwAAAAAABAD//wIAAAACAAAAAQACAP//AQD///7/BAD6/wcA+f8EAPz/AgD8/wMA/f8BAAAAAAAAAAEA/v///wAA/////wAA/////wMA+/8EAP7/AQABAAAAAQACAAIAAAAGAP3/CAD9/wUA//8GAP//BAD+/wUA//8GAAEAAQAEAAIAAwABAAAABAAAAAQA//8DAAEAAQAFAP7/BAAAAP7/BgD+/wEAAwD9/wMA/f8DAP7/AgD8/wUA+/8IAPb/CAD2/woA/P8CAAAAAQD+/wQA+v8EAPr/AwD8/wIA/P8BAP7/AwD+/wUA/v8DAAMA//8JAPr/BAD//wEAAwABAP7/BAAAAAYA/v8FAP7/BAACAP3/BgD9/wcA/P8EAAEAAAAFAP7/BgABAAUA//8EAAAAAQAAAP7/AwD6/wQA+/8FAPr/AgD//wAAAAD//wEA+/8IAPn/BQD9////AwD7/wMA+f8DAP3/AQACAPn/BQD9/wMA/v8BAAAAAAABAAIAAQAFAAAABQD//wcAAgACAAUA/f8IAAEABAADAAIABQABAAIAAQAEAP//CAD9/wIABQD+/wUAAAD//wYA/v8CAP//AgAAAAEAAgD9/wYA+/8BAP//AgD9/wEA/f8BAAQA+/8EAPv/AwD//wIAAAADAP7/AwD9/wEAAQD//wIA/f8CAP///v8CAPz/AQD9/wEA///8/wMA+/8CAAAA/v8DAP7/AQD+////AQD+/wEAAAD+/wIAAAAAAAEAAgD+/wMA/f8GAPr/BwD9/wEAAgABAAIAAwAAAAIAAwABAAMA/f8EAPr/CAD7/wEA/f/+/wAAAQD6/wIA/P///wAA/f///wEA/P8BAP3///8CAP3/AwD+/wEAAwD9/wMAAAABAAEA//8DAP7/CAD8//7/+//9/wAA/v8AAAAA/v8DAPv/BQD7/wEA+/////7//v/8/wIA//8AAAYA+v8FAP//AAAFAP3/BQD//wAAAgD//wIAAgD8/wMA/v8DAP3/AwD//wMAAQAAAAEABQD8/wkA+v8HAP3/BQD//wIAAAABAAEAAwACAAEAAwD+/wUA/f8CAAAA//8DAP7/AQAAAP7/AwD9////AQD8/wEA+v8AAPv/AAD7/wIA/f/+/wEA/P8BAP///v8AAP////8CAP7/AwD+/wIAAAABAAQA///+/wQA/f8CAP7/AAAAAP7/AgD+///////8/wMA+f8FAPn/BAD+/wEABAD7/wUA/f8DAP7/AQD//wEAAQD6/wcA9/8GAP7//v8BAPr/AwD7//z//v/6/wIA+P8CAPr/AgD9/wAAAQD9/wYA/P8CAAIA/f8EAP//AQABAAAA//8DAPz/BQD/////BAD9/wYA//8FAP//BAACAAEAAgABAAAAAgD8/wQA//8DAAIAAQADAAMAAwAFAAMABAABAAIA/v8EAPr/BAD+////AAD///////8CAP//AgD+/wEAAAD+/wAA/v8AAP7/AwD5/wYA+v8BAP3//v8CAPz/BAD8/wMA/v8DAPv/AwD///z/BAD4/wUA+v8EAPz/AAD//wAAAwD7/wQA+/8FAPz/BAD8/wUA+/8HAPr/BQD5/wsA9/8KAPj/BgD9/wEA/v8CAP7/BAD8/wEAAwD8/wcA/P8AAAEA/v8DAP//BAD9/wMAAAD//wIAAgAAAP//BQD8/wcA/f8BAAIA/v8FAPz/BAD9/wIAAgAAAAIAAgAAAAIAAwABAAMAAgD+/wQA/f8BAP//AAABAP3/BAD8/wUA/P8FAPv/BgD7/wUA+v8EAPz/AwD/////AwD+/wEAAwD7/wUA/P8CAAEA//8DAPz/BQD4/wgA+P8GAP////8CAAAAAwAAAAIA//8BAAAAAAD//wEA/P8BAP7////9//////8BAAIA//8BAAEAAgD//wUA/v8EAAAA/v8HAPr/BQD+////AgD//wMA/f8DAP7/AAAGAPn/CAD3/wMA/v8AAP//AQD6/wQA/P8FAPz///////3/AQD9//3/AgD6/wQA+v8BAPz/AAD9/wIA/P8DAAAAAAAAAAIA/v8CAAEA/f8GAPv/BwD7/wIAAQAAAAMA/////wUA+f8KAPj/CAD4/wcA+f8DAAEA+/8HAPr/AgAAAP//AgD//wEA/v8CAP7/AQABAP//AwD9/wUA//8DAAIAAAADAAIAAAAFAP7/BgD8/wYA/v8DAAIA/v8EAPz/AwD9/wMA/f8BAPv/AQD9/wMA/P///wEA+/8FAPr/AwD9//z/AQD+////AQD9/wAAAAD9////AgD+/wMA/v8CAAAAAwD8/wYA9/8GAPn/BQD6/wIA/P8AAP7//f/9//3/+v////3/AQD6/wIA/P8CAPz/AAAAAP7///8BAP3///8AAP//AAAAAAEA/v8DAP3/AwAAAAAAAQD//wAAAgD+/wAAAQD+/wMA//8BAAAAAgD//wQA+/8IAPj/BQD+////AAD+/wEA/v8EAPz/BQD9/wEA//8CAP//AQAAAP//AwAAAAUA/v8GAP7/AwAEAP7/BQD8/wQA/v8DAAIA/v8FAPz/AwAAAP7/BQD5/wcA/P8GAP7/AgD+////AQD7/wAA///+////AgD6/wUA/P8CAAIA/f8EAP7/BAD9/wMA/f8BAPz/AwD6/wMA/f/9/wMA+P8GAPv////+/wAA/f8BAPn//f////z/AAD8/wEA+/8DAPv/AQD+/wAAAQD8/wMA+v8EAPz/AQD//wIA/P8EAPz/AAADAP3/AgD9/wMAAAD//wMA/f8EAAMA/v8EAP3/AgD+////AQD+/wMA+/8DAP3/AwD//wAAAgAAAAQA/v8EAAIAAAADAAEAAgABAAEAAgADAAEA//8CAAAA//8BAAAA/f8BAP7/AQACAP7/AgAAAP7/AAAAAP/////+//7//////wAAAAD///7//f8DAP3/AwD9/wIA//8CAP3/BgD7/wUA+/8HAPr/BAD+/wEAAgACAAAAAAD//wEAAAD8/wIA/P8DAPz/AQD//wAA/////wAAAAD//////f////3//v/9//3//v////z/AQD///3/BgD4/wgA+P8DAAAA/v8EAPz/BAABAAMA//8DAP//BQAAAP//AwD6/wIA+/8BAP7//v8BAP3/AgD+/wAA///9/wQA/P8BAP3//v8CAAAA//8BAAEA/v8EAP7/BAD9/wcA/v8CAAMA//8FAP3/BwD6/wcA/v8CAAEAAAABAAEAAgD8/wMA/f8BAPz/AwD4/wQA+v8CAPz/AgD+/wAAAAD//wAAAgD//wMA///+/wYA+P8CAAAA+/8EAPr/BAD9/wQA/P8CAAMA/v8DAPr/AwD//wAABgD2/woA9f8HAPv/AAD9/////P8EAPv/AwD9//////8AAAAAAQD+/wEA//8EAPz/CQD3/wsA9v8KAPj/BwD//wAAAgD8/wIAAAAAAAEA/f8DAPz/AgD+////AgD8/wMA/v////////8BAP7/BQD4/wgA+v8FAP7/BQD//wQA/v8BAAAA//8CAP//BAD+/wUA//8EAAAA/v8FAP//AgAAAAIA/v8BAAEA/P8GAPb/CAD7/wAA/////wEAAAABAP//AQAEAPv/BQD//wAAAgD+/wAAAQACAPz/BwD4/wYA+f8CAP7/AgD+/wEA/P8DAPz/AwD+/wIAAQABAP7/AAADAP7/AQD+//7/AQD9/wAA/v8AAP//AgD8/wIAAAD+/wQA/f8FAP//AAADAP//AgACAP//AgABAP3/AwD8/wEAAgD8/wEAAwD8/wUA/P8HAPn/CwD2/woA+f8HAPz/AAABAAAAAAACAP//AgD//wEAAQAAAP///v/9/wEA/v/+/wMA/f8CAAAA//8DAP7/AQACAP3/BgD4/wgA/P8DAAMA/v8DAAEA/f8FAPz/BQD6/wUA+P8HAPz/AAAFAPn/BwD9/wAABQD5/wMA/v8BAP7/BAD4/wcA+v8AAAIA/f8CAPz/AAABAAEAAAACAAEA//8DAAEAAQACAP//BAD8/wQA+/8GAPr/BQD6/wAAAQD+/wIA/f8AAP//AQAAAAAAAAAAAAEAAQACAP3/BQD9/wUA/v8CAP3/AwAAAP//AQD+/wEAAAACAP3/AgD+/wEAAgD7/wYA+f8EAPz/AQAAAP3/AgD6/wMA/f8BAPr/AwD7/wEA/P8CAPn/AgD7/wIAAAD8/wMA/f8AAAEA//8BAAAAAwD9/wcA+P8KAPr/BwD8/wMA//8BAAEAAgD8/wMA/v8BAP//BAD5/woA9v8GAPn/AwAAAAIA/P////////8CAP3/AAD//wEA/f8CAAAA/v8DAP3/BAD//wMA/f8DAAAA//8FAPv/AwD///z/BAD7/wUA+/8DAPz/AgD//wEA//8AAP////8AAAMA/f8CAAAAAAACAPz/AgAAAP7/AQD+/wAAAAAAAP7/AQABAAEAAAABAP//AwAAAAAABQD9/wUA/P8CAAIA+/8DAP3//v8DAP7///8EAPf/CgD4/wgA+v8HAP3///8GAPX/CQD4/wQA/f8CAPr/BgD5/wQA/v/8/wIA+v8EAPn/BQD7/wEAAAD9/wQA+v8FAPz/AgD8/wMA/f8DAPr/AgD9/wAAAAD9/wAA/v8DAPz/BAD8/wQAAQD+/wYA/f8DAAEAAAABAAEAAQD//wEA/v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/m//z/FQAuAEgAXgBsAHAAbgBiAE0AMQAUAPf/3v/J/7r/s/+z/7j/vv/G/9T/4v/p/+//8f/w/+v/5//h/9z/2//c/+L/6//3/wcAGAAqADsARgBSAFoAXgBdAF4AWQBMAD4AKwAWAP//5P/O/7z/rP+g/5v/mP+Z/6L/rv+4/8z/4v/1/wwAJAA1AEUAUABSAE4ASAA5ACgAGQAJAPr/8v/r/+b/6v/w//P///8LAA0AFQAfABgAEwATAP//7P/m/9T/xP/I/87/1v/u/woAIAA8AFQAYABqAGoAYABSAD4AIwAHAOn/y/+v/5f/hP91/23/b/90/3//k/+r/77/1f/u/wEAEwAlAC4AMwA1ADUAMQAsACYAIQAbABUAFAAWABgAHAAmADEAOwBHAFQAWQBbAF8AXwBUAEkAPAArABkADQAHAP///v8IABIAGgApADkAQABEAEkAQgAvABwABwDp/8j/rP+R/3b/Y/9Y/1L/UP9b/2z/fP+N/6X/vf/R/+P/9P8CAA0AFgAdACIAJAAmACkAKQAsADIAOAA7AEAASABJAEkASQBJAEQAPAA3ADEAKgAkAB8AGwAbABkAEwANAAcABAD+//n/+f/+/wYAEAAbACkAMwA2ADAAJQASAPX/0P+o/4T/Yf9C/y7/KP8s/zv/UP9t/5P/u//h/wQAIAA3AEQARgBBADYAJgAZAAwABgAGAA4AGwAuAEIAVQBmAHEAeAB7AHUAbABgAE4APAAoABcABwD6//H/6f/l/+X/5v/v//r/CAAYACgANgBCAEoATABKAEIANgAjAAsA8P/S/7P/lf96/2j/YP9f/2v/gf+d/7v/2v/3/w8AIAAmACUAHQASAAYA+//w/+r/6f/q//D/+v8DAA0AFgAdACUAKgAqACkAJQAhABoAEAAIAP//+P/z//H/8f/2//r///8GAAoADwATABkAIAAoADMAPABAAEMAQgA5ACkAGAAAAOn/0v+6/6j/nf+b/5z/ov+u/77/0P/d/+n/8P/v/+3/5v/d/9f/0v/T/9n/3//o//T/AAALABMAGgAeACEAHwAbABgAFAAUABIAEgAVABcAGgAcAB0AHQAeAB4AHgAcABwAHAAdAB4AHwAiACQAKAAqACkAJgAhABoAEQAGAPv/8P/l/9r/z//L/83/0P/W/+L/7v/6/wQABQAHAAQA/v/z/+f/3//X/9T/0v/a/+X/8P///w0AFgAZABsAHQASAAcAAADw/+b/3f/V/8v/yv/N/8z/1f/j//j/CAAZAC8ANgA5AD0ANwAtACoAJQAXABEAFgAJAAAA/v/3//D/5v/i/9n/2P/X/9H/1P/h//P/8v/6/xIAEwASABQADAAHAAMA+v/w//T/+P/y//r/BAAMABIAEgAYABwAGAAVABMADwANAAgAAgD///v/9f/1//j/+P/8//7/AAAGAAkABQAFAAgABAADAAQAAAAAAAMABQAJAAwADAAMAA4ACwAFAP//+//2//T/9P/2//n//v8CAAYACgAIAAgABgAGAAUAAgAAAP7/+//1//D/7v/w//b/+P/9/wMABQAGAAYABAABAP7//f/8//z/9//x/+7/7//1//r/AQAGAAsADwAQAAsACQAHAAMAAgD//wAA/v///////f/8//3////9////AgAEAAIAAQD+//v//f////7/AAAGAAYABAACAAAA/v///wEAAgACAAMAAwABAAAAAQABAAIABgAIAAoADQAPAAoABgAEAAcABgACAP//+v/3//f/+P/3//r//v8CAAQABAAIAAgABgAFAAUAAgABAP7/+f/1//X/9P/2//b/9P/z//L/7//w//L/9v/4//v//////wAAAwAFAAQABwAHAAgACQAKAAgACAAJAAUAAwAEAAAAAgAFAAYACAAKAA0ADgANAA0ACgAFAAEA///5//P/8f/t/+7/8f/0//v/AQADAAgADAAIAAcABAD+//r/+P/3//b/9P/4//r/+f/8/////v/+/////v/9//z//v/+//7/AQAEAAYABgAKAAsACwAMAA0ACgAJAAkABgAEAAIA/v/8//z/+//7//r/+P/6//n/+f/7//3//P/8//z/+//7//7///8AAAEABAAHAAcABgAIAAgABwAFAAUABAABAAAAAAABAAAAAAABAAAAAQD+//r/9//z//L/8P/u//D/8P/x//T/+f/+/wEABQAHAAgABwAIAAgABgAEAAIAAgADAAQABQAGAAYACAAJAAoACgAKAAoACAAHAAIA///8//r/+f/5//j/+P/7//z//f8AAAIAAwADAAQAAwAAAP///f////7//v/+//////8AAP//AQABAP3//v/7//j/+P/1//b/+P/5//v//f/+/wIABAAEAAcABgADAAEAAAD//wAAAAAAAAEAAAACAAUABQAGAAQAAgABAP7//f/7//j/9//3//n/+//8/wAAAgAGAAgADAANAA0ADQAKAAoACQAGAAUABAACAAIAAgD//////v/8//z/+//6//j/+P/5//j/+//8//7////+/wAAAQACAAMAAQAAAP///v/+//7//f8AAAAAAwAEAAQABgAHAAcABgAGAAQAAwABAAAA///9//z/+//5//v//P/8//z//v8AAAEAAgAEAAQABAAFAAQAAgADAAIAAQADAAMAAQD//wAA///9//3//P/7//z//f/9////AQABAAIAAQADAAIAAgAAAP/////+//7//v8AAAEAAwADAAQAAwAEAAMAAQABAAAA///9//r/+v/5//n/+f/7////AQADAAUABgAGAAQABAAEAAIAAgAAAAAAAAABAAIAAgACAAIAAwADAAIABAADAAIAAgAAAP///v/+/wAAAQACAAQABAAEAAQAAwACAAAA/////////////wAAAQACAAEAAgABAAAA///+//v/+v/6//n/+v/8//7//f/8//v/+v/7//3//P/8//3//v//////AQABAAIAAQAEAAUABQAGAAYABQAEAAIAAQD+//3//f/9//3//v///wAABAAFAAUABgAGAAQAAgABAAAA/v////3//f/9/////v8AAAMAAwAEAAUABQAEAAIAAAD+//v/+v/5//j/+v/6//z//f////7///8AAP////8BAAIAAQAAAP////////////8AAAEAAgABAAEA//8AAP///v/9//7//v///wEAAwAGAAkACAAHAAYAAwABAAAA/v/+//7//////wAAAgADAAQABQAFAAQAAwABAP///v/9//z/+v/6//r/+v/6//n/+//7//3/AAABAAEA///+//3//P/7//v//v//////////////AAD///////////7//f/8//z//P/+//7/AAACAAMABQAFAAYABAACAAIAAAD+//////////////8BAAMABQAGAAkACQAIAAcABQADAP/////8//r/+P/4//f/9//4//f/+P/5//r/+//9//7//v///wAAAQACAAIAAwACAAIAAgADAAMAAgABAAEAAAABAAIAAwAEAAMAAwACAAMABAAEAAMAAwACAAAA/v/9//z//f///wAAAQADAAUABwAGAAcACAAHAAgABwAHAAYAAwABAP7/+v/5//f/9//4//j/+v/6//r//f/9//3//f/9//3//P/6//r/+f/7//z//v8BAAIABAADAAMAAwACAAIAAQACAAIAAQACAAEAAAAAAP7/AAABAAAA//8AAP///v/9//3//v/+/wAAAAACAAIABAAEAAMABAADAAEAAQABAAEAAQAAAAEAAAABAAEAAAD/////AAAAAAAAAAACAAMAAgACAAEAAAAAAP//AAAAAAAAAQABAAEAAAAAAAEAAAAAAAAAAAABAAIAAwADAAIAAgABAAEAAgABAAAAAAABAP///v/+//7////+/wEAAwAFAAYABwAHAAcABwAFAAQAAwACAAIAAQABAAEAAQABAAEAAAAAAAAAAQD///z/+//6//f/9f/x//H/8//5/wMABQAGAPz/+v/7//z/AAAAAAMA/f/6//n/+v8HAA8AFwAWABEAEwAKAAIA/f/8//n/8f/0//f/+f8AAAwAFgANAP7/+//z/+H/6P/2/wAA/P/3//T/7f/n/+r/8f8JABcAHAAnAC4AIAAOAAgA+P/j/8j/xv/H/73/1//8/wkAKAD6//7/vP9tAMwBhwGeAA4ALQFEAef/5v4a/sP9t/2X/mH/BQGfArYBZgAsAF0BAwKZAQIB9P6s/i3/df4h/47/af9U/2X+uP1r/f/9CgBtAewBuAGzAeMB+QHVApcCygI+A4MBWwA6/4b96vx2/A/8rfwz/fL84vy//Nf8Uv0P/+UACQJGA6wDJwSKBLcE4QTiBL4EQANIAan/rv1v/Br8JPyW/Cv8Evzf/C78n/wk/hz+sf81AT4BxAH2ARACHAI9ArQC0QLcAqYCbAHs//n+vv7d/tn+2P6v/oP+U/4c/mn+Mv8PALUAmgAEAAAAEQDu/54AggGPATkB3wDUALUA8ACEAUcB1wBFAHn/wv6m/iX/U/9L/z7/jv+j/6v/RwCpALUAZgBUAOb/I/9h/7j/rf91/6T/NgA/AHwAmgB1AC4BpgEcAUwA/f/h/zT/wf7f/lb/vf/Z//z/oP9Y/6P/2P8DAAQA2P+c/4z/Vf8H/wT/OP8VACEBFQHlAIwBbQEoASgBFwFSAEr/T/9U/nv94v7AAHwAy/9CAMH/Kv8UAB8BAQGyANYAfQCu/mT9xv6sAPYALwEkASD/JP4TAJ0BPwE4AQgChQC5/A77ffzC/roA7QFqAaMAkgAsALb/q/9pAGABswAv/gX9Pv6Q/y4AvQD+AIIBuQJxAm4A1f+oAOEAdgAPAOb/Yf5s/Vz+dv/LAJsBWgFqAF//Jv6k/pwAcgGJAXEAN//I/qf+m/9cAVgCsQFXAEL/ev8kAAgAdwAWAPv+Ov8OACIAJQDnAF0AAP+S/pr/2wBxAGn/wP49/8L/OgAPAc8B7AEtADL/g/+E/87+2/2F/Wr+bv94//QAzQKPAo8Alf9PAUgCTwFdANz/cf8r/xb/dP8JAMD/nP7h/Vf+Zf/QANwBygGsAPX+bv53/8sAhwGDASgBXwD8/pv+IwAIAVIANv/D/ZL8YfzJ/RsAmwGGATgB8ACn/8b+iv/FAMYAgQC6/37+yv5EAI0BCAI0AlICzADf/uz+2P+KAHYAvv8E/93+hf99AAYBLgEYAcsAQgBi/2L/1/+2/3n/L//a/lf/PQAjAfgBqQFdAM7+9v32/rkARwHQAHMA/f8d/8z+m/92AIAAtwDuAGYAyf+R/z4ABgHnAN7/wf6w/mH/IAClADcA8f4n/i3+U/+AAJAAqgDcAMYAfwA+AJYADQF9AMv/qv8T/5T+Af/q/5sAzwC3ALcAawDv/zAApwB4AND/Cf8m/hf+FP8NAC4BswFDASoBSwC4/hb+y/5RACQBGADf/kv/AwAXAKMAYQHqAEoAxf9B/xf/BP85/3b/Av+o/i3/aP+S/0AA/wDvANb/W//9/1kAhQBcAUgCCAK7AMb/of/g/+X/av8U/3//OgCcAKQABAHYAcQBdABS/yH/gf9fAJoB5wGTANX+Kv6Q/kL/7/+NAOEASwAJ/0n+b/4+/10A7gBCADj/Q/8SALoAygBiABAAgf/d/hH/4v+MAL4AWwCa/+n+4P7t/+gA6gBqAMz/p/8yAB8BwgFrAT8ASv9Q/8j/bAA+AXgBAQGJAD0AgP9+/j7+6v7F/xMAAwDu/6z/AP9z/ob+6P6A//b/EwACAOn/RADFALYASwDe/5H/Uf/u/s7+f/+7AGgBBwENAEz/bf9zAJQBQQIkAuwAHv+2/dv9Zv/rAEsBlQCb/y//av97/z//Sf+f/9//CQDp/+n/NwBAAFkAdwBQAAsABgBdALsASgHYAa8BAwEQAGv/lf+z/87/LQBnAKcAvwBcAO//rf+Q/5H/bP9L/2b/5P9fAEUAvP8Y/53+kP4C/8L/ggDGAJUAfgCUAGcA6/+P/9r/SwBSABgAy//x/4oAqgDj/9r+Tf51/hj/VQCIASMCuAHv//D9Lv1F/mwACwI5AngBcgBg/1f+CP68/ikApAE4AtEBtgDT/6H/1v////z/JwCbANQArQBrANf/CP+y/vz+Xv+q/zAA+QAHARYADf9Y/kH+sf5h/1cA+wAZAYoAKf9X/qz+jf+DAD4BpwE7AUQAhf8z/7X/mAD3ALkAMgD9/zAAggC1AIEATAAXALT/YP8b/0j/DgDlACEBewCN/+r+pP7B/hL/qf+cAGMBdgHPANv/Qv89/4L/xf/s/wQANQBJAAoAnv8e/9z+Jf+s/xEAagC5APoA1gBmADgALwD6/8L/5P8wADIAUwCSACsAmP8k/w7/T/9k/2f/Vf90/xEAwQAQAdcAVQDc/17/Jf9l//f/pQAoAUkBAgGmAEgAOwBmAGUAhwADAYEBjQEWAYkANgD4/8f/jv+E/7n/FgBkAF0AFwDC/17/9f7g/gr/OP9O/2D/Uv8G/8H+z/7X/oz+ev7m/nL/mP+I/6r/yv9l/5H+3f2m/en9j/5H/6n/0/8DABcA+P/l/+7/LAC1AEUBhQFlAeMANgD7/04AwQBJAfMBggKfAjgCywF/ATQBQAGiAdkB/wFrAp4CMAKAAcwAJwCj/2z/kP/N/xQAYgB7ADEAh//h/n/+af6Q/q/+ov55/ln+S/4J/pD9Lf3M/JD85fyC/Rb+dv5G/o79m/wF/A/8wfwK/iT/r/+L/8j+Qv5n/lL/uQABAu8CIwPfAk4CtgEbAqICsgJkA/YDqgNGAw4D8gLBAukCdgOuA+4DTAQiBI0D1AI4AqsBHAHwAPQA2QCzAHUAMQDz/8f/FwBHANP/TP+W/o394vy6/Lz86/xF/WX9Kf3I/GX8Kfwv/Iz8Bv0x/Rv94/yL/DH8FfxW/L78G/1e/Wb9Kv3S/JH8fvy8/In9z/5FAK8BtwIHA7sCbQJRAqQCgQMiBG8EfQTlAzQDygKHApsC6wKAAyQEZASJBGQE1ANoAwoDwgKtAmwCPQIkArABJwGTAPj/hf///sb+yv6v/tT+1P6S/nv+Yv6N/s7+ov5N/sH9Nf0h/VT9oP3R/dj91P2H/S39Gf0Y/TT9hv3O/df9s/2U/X79Yv1l/XX9l/31/Wb+0P44/4n//v+mAGEBSgIXA50DJQSPBIsEJwSsAzADxgK6AgADNQNNA1cDQAMUAw8DQQN3A4kDjgOIA0wD7AJrAsQBMAHgALAAUQC9/yH/gf4V/vX96/3b/a39h/2O/Zf9u/3a/bL9pv3L/er9Bv7V/Uz9uvxn/Ir80fwh/Wf9TP0G/bb8aPx5/N38Zf3h/RT+GP4A/uP9x/2P/Xz9wv1Z/j3/SQBiAWQCGQN4A7cDEQRxBLAErQRYBPADrQN9A2EDWwNsA58D6QMnBFgEfAR9BGUEPgTfA1EDwwI9ApYB6wCJAE4ACgCq/wn/S/6//YX9kP2t/dL9AP4N/uP9r/2a/ZH9hv18/ZL95P0z/lH+A/5s/Sj9Sv2a/e39Jv5V/kr+IP4N/v/9Lv6A/q7+t/65/tH+wP5d/vD9mf16/bD9Mf7b/o7/bgB0AVcCKAPVAygEMQQHBMIDYwP/ArsCegJJAlkCowImA7EDDQQ4BCwEAQTPA2sD6AJiAtMBTAG9AC8Ao/8U/7f+ZP70/aX9bf1C/UP9aP23/Qr+Sf59/nr+Zf52/nb+Tv4T/tf90/0L/jf+K/7n/Zj9ev2O/c79Ev43/l7+Zv5Z/nP+qP7v/ib/O/8x/wX/zf51/uP9O/2n/GH8svyU/cv+IQByAZ8CgwMHBCEEBgQTBCwEFAS0AzsD2wKdApEC2AJzAy0EwQTpBMMEdQT4A1EDlgIAAoUBFQGnAEYA9v+V/yj/0v60/pr+SP7X/ZD9jv20/df94v35/Rv+Mf5D/mv+l/6D/jL+4/30/Uz+jf6B/iP+zP2l/aT9m/2s/fv9Sv5w/oj+zv4V/1P/ev99/3//c/86/9D+cf4D/nv92fxe/JT8ef3F/lgA/gEkA58DuQOcA6UD8AMXBNgDdQMEA3UCEAIUAooCJQO+A0MEiAR4BAIEPgNkAq8BGQF/AAUA1/+9/27//v6P/jL+1P2y/ff9Qv5P/jT+Hf4d/kb+iP7Y/if/T/8w/8/+gf5o/kz+L/56/hD/VP8y/9/+cf4T/vb9Fv5k/u3+Xv9h/1z/n//K/7//y//7/xsAFQDG/zH/jv70/Ub9mvwU/Mf7TPy9/cb/EAJrA5sDbQM6AxkDagMDBDME7AMhAzwCwwHPAVMC3gKBAzMEmwRyBNUDEwMVAjABewARAPv/7f/c/7n/d//u/kb+tP2P/ej9Lv5Q/l/+WP5K/k/+mf4o/7j/+v8FAOn/nP8s/7v+lf7z/m3/hv+A/z7/pP4j/vX9Df5R/qT+0/4E/1n/lv+Q/33/m//C/8//xP+F/wH/aP7K/Sf9p/xQ/M/7cPsi/Lr98f/rAe0CSgNSA3ADnAMsBLMEgwTSA80CHQLPAfwBjgI5A/0DWQRTBPEDTgNxAmsBzwBvADoAGwDu/9r/xv+X/xv/jP4y/h3+Zv7M/gb/F//+/tf+3v4m/5r/8/8MAOz/mv8m/6b+UP5r/sb+A/8Z/wv/xv5b/gb+y/2w/dD9F/6F/gb/Yf+D/57/0f/s/+v/6f/B/1n/w/4u/qb9Lf3M/G38GPyw+6374Pwt/5gBEgO4A+4D5wPMA9ADIQRdBBsEOgNaAiICMAJhAtMCcwMVBGEEYwQPBE0DRAI9AZ0AWwA/AB4A6v/1//j/nf8G/2b+Hv4//qn+I/93/4r/Of/y/g3/Yv/Q/x0AJgDb/0T/nf4e/gz+V/6i/sb+wP6U/jr+z/2S/YT9qf3o/TT+of4O/0P/Mv8r/zb/Mf83/y3/Bf+2/kT+sv0R/ab8SPz1+677sPu5/On+jgFBA9wDBgTpA9kD5gNGBK4EigTNA8kCWAJeAokCFQPRA5gE/QTqBHIEeANFAvgADQDG/8P/1P/M/9P/1/97/+7+Tf7y/fj9M/6f/tv+6P67/mn+V/5+/u/+Wf+c/5n/Hv+M/gz+yf3C/Rn+t/7s/sL+cP4O/rP9e/2Y/dv9TP67/gT/Tf93/43/lf+h/6f/l/+h/3n/AP9w/vD9hv0Y/cr8bfwl/OX8+f6IASoDuAPoA/cD/wMbBJkEJQXnBOgDygJOAlsCkAIkA+wDqwT4BL0ENAQ9AxUC6AAPAM//u/+X/0//Of9d/yL/mP4I/sD9wv3s/Wb+xf69/mn+Ev4a/mX+3f5z/+r/8v9w/8H+Mf7T/df9Sf6t/r/+s/6N/kb+AP4H/j7+aP6c/tv+Rf+c/6z/wf/V/+j/4P/i//7/tP8t/5b+9f14/er8jfwi/Jn76Pt4/Q4AIQIFA2UDZANSAz8DgwMiBEYEwAPLAhgC8wH5AUsC8gK+A00EZwQvBIcDagInARIAlv+f/6n/qP/E//v/2v9A/5n+Kf4L/jD+df71/lz/P//z/uf+KP9t/8T/PgBeABQAi//z/rL+mf6K/pT+pv7P/r/+m/53/jz+P/5G/m3+6P5k/87/6/8GADQAEADs/8X/oP9v/+L+Zv7k/Vz95Pxc/C786vu++7z8BP+OAdwCQANxA0oDLANPA9gDYQQnBFMDZgIFAgICEgKWAkMD3QM2BBIEnwPDAqcBjgDZ/97//v8FABsAPQBSAO//Nf+L/i/+HP4v/or+AP8j/9z+nP7B/ir/nv8RAIMAlQAmAI//+v6S/lD+Rv5m/oj+t/7B/qL+gv5g/k7+VP6W/gz/Yf+D/3T/Y/9X/yf/Dv8a/xT/1/5g/gD+sf1E/c/8Xfz1+2P7Lfum/I//8gH2AkgDgANvAzsDfwM1BNQEgwRrA8ICjAJ3ApwCCQPUA4EEuwSIBOwD/wK/AbUAVABdAFwAKgAjAFsAYgDi/xj/f/5C/iz+Qv7H/kr/PP/2/tv+AP9C/47/BgBcADwAsP/j/kH+5f2+/eT9Lf58/sf+xf6Q/nT+Zv5G/iv+df7z/kX/gf+h/7P/pP9t/0j/Lv8Q/8v+gP5O/vn9ff0A/ZL8K/y6+4L7tPx//90B4wIyA20DrAOqA+IDdgQFBeUE0AMIA9cCrgKUAqECSgMXBF4EOwSgA7kCigFDAKf/oP+2/6T/mf/c/wsAvP/+/lz+Lf4y/m7+4f5S/17/8/64/r3+5f4z/5T/+/8IAKP/Af9w/iD+8/3j/ez9J/6N/rj+r/6h/of+VP44/n3+7P5a/6r/w//L/6z/Vf8K//f+7P7F/o3+Xf4i/pv9G/20/Ez85vuq+w79zP/bAdwCQwOqA/0D8QNRBA8FgQUtBQgEHgO9Am4CYQKuAkUDBAQ+BN0DSwOCAmQBPwCh/6j/t/+V/7P/EwBaAA0AKv+P/m7+bP6Z/vb+SP8s/6D+SP5H/o3+Hf+2/zcAVgAKAHr/6v6D/ib+3/3M/QH+eP6m/pv+nv6T/mL+Q/6G/g7/dP93/8r/s/+n/07/3f7q/pv+rv6E/nj+UP6w/TX9svwq/Lv7rvs+/ej/zAGcAtgCRQPJA+ADLwS8BCMFzwSQA8ECVQJWAi8CQAIGA9wDcAQMBIEDvAKFAXAAo/93/33/Vf9w/8//HwDy/yX/kf6O/sP+CP9D/4r/hv8h/8D+qP71/n//8/8YABkA9v+I/+r+f/5C/gD+0v3g/Vn+qf66/tD+wf6l/pr+0P4v/3r/mv+E/2n/Uf8H/7L+jP6L/of+cP4//g/+rP0Y/ZH8A/yB+yf8Kv5NAOcBfgLIAmMDpgP1A34E6gQ0Bc4E7gNNAx4D8QKsAskCJgO2AwsEogPyAisC8gDQ/1H/z/6i/vr+Lv+u/1sALQCa/3T/L/8c/5f/hv9S/13/4v6C/r/++v5m/xYANgACAPb/Qv9f/vX9aP0S/Rb9Sv2+/Qz+Yf6h/o/+g/6J/rr+8f4P/x7/F/8c/xL/2/69/qT+ev52/lT++v2a/Rf9W/yT+/D68/s2/lgAWANTBeIFgQfYBzoHxAcCB3cFpwQ9A10BywCCALn/MQAXAVUBJgKwAuQBJgF8ADz/iP5//k/+kf6F/1YACwF+AUYB5wBbAOv/f//K/p3+Tf6Z/Zr9/v0Y/sX+yf8TAEkAbADU/yn/sf4U/qD9ff2L/cf9KP6a/vj+L/9A/xj/Av8L/+7+6P4P/zj/Mv/2/qr+cP5B/hT++f27/Vj94vxL/Kj7+/px+1X9Rv8NAt4E/wUqBwgI5AfRB3cHfwYiBQkEngJOAQ8BswCDAMYACQFbAWABHQF4AHT/iP6o/QX95/xP/Vb+lP8DAQ8CRwJ/AiUCkAFUAcEARADP/y3/of5k/oj+ov73/lf/T/85//L+e/4G/mf9JP0A/e78gf3z/Xj+Lf+c/+j/4v/p/7r/ff99/0//RP81//r+lP4R/sX9bP0k/QL9qPxO/Oj7Zvs2+yn8Bv7W/z0CTwRPBVkG3QbnBtMGfgbzBRoFYwSgAxgD6gLIAs8CtgLHAr0C/QEeAREAt/6T/a78Jfz0+1r8Pv09/mn/SwDnADsBNAFpAWcBRQFWAR8B4QCrAIYAaQA9ADkA4v9S/73+9f00/Xj8//u/+537+/uD/Aj9tf1L/rr+Hf98/7z/5v8oAGUApgDwABEB4gBcANr/Rf+N/iv+x/0r/Z38+ftZ+1j7RPyf/WP/RgGZApMDSgS2BPkEIQUcBeUEvwR+BFUEYARIBFAESQQLBN8DZQOVAoEBOwD4/rv9z/wr/N/7F/yb/GX9Fv6y/kD/ef/W/0kArgA5AZkBzgHqAeUBzgGcAWkBFgGPANj//f5A/n/94Px2/BT8+vsM/D38lvz2/F39sf0j/pf+D/+6/0sA7wCKAeYBHgIJArkBRAG2ACYAm/8I/5X+KP6o/Vb9/fzS/CX9vf2b/sD/vgCBASQChQLYAhgDQwOIA68DzQPtA+YD5wP4A/MD3QPJA4wDBgM7AjoBIAD0/v/9SP3A/I38uvwQ/V/91v1a/qr+EP+P/9f/IAB4ALMA1gACASUBJAEeAf8AwABiAOr/e//p/mT+Cv6c/U79K/0L/fz8Sv2U/dT9Z/6y/vT+nP/F/ywAtQDIADEBOwEwAQwBmQBvABcA1v/n/6v/rv+Z/2j/bP9b/0//Zv9n/0j/nv+q/wQAZgCfAB8BNgGTAeUB3AH7ARMC8gEIAi4CMAJFAjYCKgIcAugB1QGUAS4BwwBCALb/QP8I/+H+5/4N/0P/TP9f/2z/Rf84/xv/Gf8Z/zH/hP+v/+L/DwD7/9f/tv+H/0v/MP/5/qz+tf6G/ov+5f7r/jf/af92/6L/tP/K/9r/5v+7/8D/rv91/6H/rv+v//r/BAAKABkABgA0AEMAkwCqAL8ArQBoAGMABwAPAAIA9//p/+n/7//C//X/1v/w/ykAGQBVAHAAawBRAF0AQQATAEkADwAdADUAAQAKABYAMABDAGUAlQC5AMUA6gDvAOQA1wB8AFUAEQDV/9v/xf/L/8T/sP+M/4f/Z/9t/2T/XP9c/2b/jf95/+v/9/8tAGUAjACSANEA7wBLAAYBQQDX/08Akf/a/97/7//v/+H/6f+//7b/tf/8/8//IQAeAPb/HQDU/9//yv/h/8z/yP+1/73/rP9c/6H/gv9v/4v/qf+y/6b/wv/Y/6z/7v/y/+b/KgDo//r/9v/n//H/AABKAFgAggDRALsA8wCpALYAdQAmAH4ACACDAD0AWQAxAMj/NACI/8X/vP+M/43/X/+K/7T/0v///ykA/f8NAOv/MgAYACoAiwDl/3AA5P+O/xEAVP8jACQA8/9AAHX/tv+W/5P/RADR/wwABgCe/wsA+P8PADcAhQD9/yUAdACf/0wALQDN/3gAEADb/3MA4P8vAC0A8/98AP//OwDc/woAWwCs/zkANwDZ/zkA3P+Q//7/KwAWAMEAUwANACIAYP/5/+//3f/b/63/pf+i/5H/jf/V/3D/YP9i/zr/NP+u//3/BgCqAIMA+v98AFIAQQDBALEAYwD+/xcA6v8bACsA6P+DALQARQBnAKgAaACUAMUAPwBtAE8A3//i/+f/dwDe/5v/m/86/3D/ZP+K/1v/V/8c/3H/qv8Y/8T/zf/A/zQAAwAnAPz/3P/P/3r/tf+g/zb/jv/N/wMAUAANAHYAPwAHAFoAy/8IACIA6/8vADkAMgAQAOj/xf/U/00AFAAcAD4A1P84ACoALABlAKcA6wCyAAcBVAEkAT4B+QCvAO0AowCrAIkARgBvAAkAsP/E/8j/v//X//n/+//d/7v/T/9g/2//aP+f/7P/rf+a/8H/U/8D/+f+kv6m/tP+8/43/2D/if9U/5//nv9T//r/6v8yAF0AMAANALH/of/P/wsAHwBAADoAGwAGADgAJwAsAFAAFAD8/8j/z/8CAAAASwCRAM8AvACkAOEAlwC9APcA9ADvAKcAuwCxAKoA1gDLAMYAvADKAM8AogCKAFoALQApADYALwBAAGsAXwBPAEEA///H/4L/Uv9e/0z/gf92/2r/W//7/tn+hP50/pT+eP6F/qn+sP6Y/m3+VP5e/q/+Bv9V/8r/9f///xsADgAOAAcAy/++/7H/u/+5/5X/pv+Q/3//kP+b/8P/IwA5AF8A7wAXAT0BrwEGAqkCOgNbA68DtQNMA/ICeAJEAjwCFAL5AZ0BUAERAXsABgCy/0T/9P58/hL+Bf7i/S/+ev6E/u3+I/9s/8f/u//e/+f/uv+z/43/hf+I/3b/V//o/qH+Tv7U/Yf9Ov0p/Rv95PzU/Oz8//wy/ZX95P1b/rv+5P4g/z//UP9c/2D/VP+H/wQAWwDtANQBzAJ9Aw4EoQTbBA0F9gSzBIoENQTwA6cDQwPmApYCIAKRAfAAiQA5AJn/Df9z/vn9u/2J/ab96v1K/ob+qf7v/iD/gf+j/6//DgAfAE4AnADBAOEA3wDOAK4AZAAYAL7/KP/k/mn+9v3h/Xb9Yf1w/VT9V/15/Y/9d/2X/Zz9i/2t/ar9uf3b/QP+Ev4S/vb9ov0y/Tz91/02/hn/wgBwAu8DRQVXBisH1wfEB4kHKwd3BuwFIgViBJUD1gI/AicBKwBe/1f+ev2f/Nb7hftj+4D76vuc/Lf9a/4b/w0AkgANATABNAF+AaUBxwHuAfQB0AGJAfgABgBU//T+P/7C/Yb9Jf0k/fj8rfzR/AX9Nv2N/QH+T/69/gj//f4T/w//9f7S/pn+Yf77/Yj99Pwv/KL7nPtU/Cz9Yv7TADEDGwUBB0QIbwkdCr0JaAnYCOgHPQdMBiEFJQQ0AyEClAAX//r93vyy+5j63Pl/+WP5h/k++lP7hfxW/uj/jwCIAYYCzAKwAqIC2QIEA9UCZAIRAowBjQCC/8L+8P1d/R79l/wn/BX8PPxb/HL8vvxX/en9Rf7q/oX/3f82AE8AXQBoAEYA6P9K/4/+4P0w/Sr8Bfsk+kb6JvsC/Cv+fgE/BJgGtggXCt8KLQu5ChIKXQkcCAwHGAaeBFoDJQKEAOf+Sf3M+4T6Jvk6+Of30vcw+Db5qPop/L39RP+oADgCTgPLA3cE6wT3BMgETQTAAyMDDgKTAE//TP4//XD8AvzB+8n7EfxE/G/8vfwK/V39vP0n/s/+g/8PAHsA1QAUAeMAdwAXAG3/r/73/f/8KfxM+1T6VfnW+N35nPtE/q4BhQR8B/AJ9wpzC3IL1gpQCnUJMAhbB2EGNgXZA90BMwCn/sj8JfuD+f33P/e49qn2cPd9+ED6H/zo/c7/NAFgAlMDWwQJBWIFDQZjBkMGowWOBPcCIAFx/7b9T/yY+yb7/vpT+4r70PuL/N78If3S/U3+8P6s/zoAxAAiAXwBrAF+AUABywD3/zz/WP4U/ej77/oh+oH50PgO+Mv4Mfv0/e0A5wNBB+oJ+go5C0gLJAtoCoIJKQgUB3gGUAXwAyACRQC//tH8sfoO+en3Cvdb9jP20fbc92z5cvtr/V//4QAdAlMDEATKBKcFNwafBrMGJgY2BekDHwJmANP+V/1N/JX7Y/ut+9n75PtC/LT8Mf20/QD+vf6p/z8AmQD7AHUBnAFtAf0AkwAfAGL/c/5U/Wz8i/uV+sL5FflV+DT4YvqO/XsA0wM9B98JAgvfCj8K4glVCWcIkAeXBv4FhQVTBKgCCQGE/9X9z/v5+fj4WfjG94r39/fj+AH6nftV/ez+cwBVAQAC2gJmAwoEwgRGBdgF2AXnBK4DPgJ5AND+df2Y/D/8IfxA/HL8hfzE/Af9Mv2l/Q7+kP5W/+H/TwCXANMADwHeAJIAaQD//3X/0/7f/QP9Pvxy+8z6I/qi+Qz5Evla+3b+8QAdBJwH6gnRClIKXgnqCDoIHwdXBuEFgwUMBdwDKALEAF3/mP3H+2j66fnU+bX5y/mG+nP7SPyB/b3+yv/AAFkBowEqAsECBAN2A+UDOAREBHsDWAJFARAAu/6e/fP81vzJ/Mf88/wW/Xr9pP2h/fb9Xv7J/lD/sf/f/yUAWgBZACAA5f/l/6P/Jv+w/g7+Yv2u/Pr7bfsJ+6z6PPqk+W36g/0FAAICewVFCCsJKAmKCA0I9gdIB3oGMQbbBbEF8gQ3A+ABygA1/5/9W/yW+1n7BfvF+vj6kPsz/Mf84/0l/yIA7gBCAXAB6QEhAjoC2wJWA4wDjwO8ArsB6ADI/6j+2/1l/UH96fyK/J38r/zN/Pj8D/2Q/Sn+lP4N/27/tP/4/xYADgAbAGgAjgBbACYA1P8c/0z+nf0H/Yj8JfzT+3v70fpq+jH85v6DAAgDAwYKBwYHzwY8BiIGWwb1BcMF4QXABXUFTgQYA2kCGwGO/3f+o/0B/Y38A/yD+5z7+vsk/LX80P3F/m7/yP/1/1IAtADyALUB4gKyAzgEMAR9A9IC9QG4AKv/8v57/gv+YP0B/dX8XPw3/DX8Kvyt/DX9fv3v/Wb+vP4T/3f/+/+RABABggGvAZQBQgGjAPv/bP/p/m7+Bf6c/fv8OvxV+yH7zvyG/s3/PQISBDoEIQQrBA0EbQQPBUQFqwXsBfQFqAXoBFQEsAPBAt4BUgGjAMf/9P7k/QX9kfxN/Bj8g/ws/XL9mP2H/Zf9xf07/jL/igARAkcD+QP6A7ADEQMGAhUBXADZ/2v/6v5d/uf9Z/3Q/HP8T/xX/ID8wvwN/Un9kf31/Wn+9f6Y/0YA3gBZAZwBnwF1AScB7gCtAEMAwP9B/4v+vf0M/Sn8Xvta+1T8mv31/hgASwABAPT/KQCBAE4BbgKEAzwEjgTjBB8FMAUQBQAFJgVJBSQFcwR+A2ACHgG2/3/+yv1X/Sj9C/3z/N/8vPyc/Mb8lf3S/kAAewFeAuYC6AKUAhsCxQGOAYcBiQFGAfIAUgCC/6b+5v1w/Rr97fzo/BL9Mv1T/WH9Zv2s/RD+mf4k/7D/KwBbAGQAOgD5/7v/ff8//wj/zf5n/vf9df3j/GP8iPx+/b3+zf/p/1f/6P7G/g//5P9FAbwC5ANpBKwE9gQZBUEFNwVeBdgFNgYLBkEFPQQDA8kBmAC2/z3/4f6m/kH+w/0p/XX87vvx+8D8+f0z/yEA1gAJAcYAfwA2AEwAngDoAA4B9ACHAMT/4v4D/n79R/1Y/ZX9xf33/fb9wf1u/Uf9a/2t/R3+of4l/3r/ov+m/5H/mP+n/8v/+P8VAP7/o/8a/2b+vf0B/cP8pf3s/sn/6v95//b+6P47/+n/KQGQAp4D5wPMAwgEcgTLBEQF+gWyBkgHMQdIBg4FrwNBAukAEQC1/23/Af98/tL97/wZ/Hf7eftb/Lj9FP8NAJkApAA2AJ7/XP+L/+v/YQCuALMAXwCg/7/+/P2p/b/9+f1I/pX+nf48/qr9Mv31/Av9f/1D/gj/m//w/+r/sv+D/3P/if+8//f/CgDT/2H/zv4p/n799Pyc/Pf8PP6Y/0QADABQ/7z+xf5S/2YAIwLUA9gEEQUBBSIFUAWMBe4FhwYbB1YHyQaFBSYErQJGAUQAxP9//yP/pP7y/SH9Mfxy+y/7n/vJ/Bf+If/C//P/pv8Y/9D+H//Z/5QANQFiARQBdgBh/0D+rv2s/e79Xv7O/v3+0f5E/rH9X/1o/dP9U/7t/o7/3P/d/5P/M/8C//j+EP9q/8r/1v+k/zf/n/4l/sv9nf1p/Yf9f/6f/zkAFwBt/+z+Ef+x/6sAKAKsA3kEWATgA8wDAQRfBPIEswV8BuwGhAZmBT4EMAMlAk8B7gDQAGcAm/+Z/mj9Pvxa++D6Kvtj/Mv93/6w/wwAzv9J/xT/Yf/8/8MAUQFoAR4BbQBj/37+Nv5e/rT+Df9U/1T/1v4m/nD9Dv0w/Y/9Gf6//jj/Rv8K/7j+kP6u/vL+df8YAJcAvQB6AP//bv/u/nz+NP4G/tX9K/77/qf/wP88/5r+dP70/tj/MQG0Ar8D7AN6A0kDjwMVBNcEyQXWBpkHggeABjQF+wO2AogBwgBdAOT/F/8Y/h79WfzG+3j7ufu9/An++v6I/7r/hf8X/97+H//a/8kAcgGqAXYBzwDQ/9/+bv58/sb+C/8i/+j+Of5U/ZX8OvxV/L78VP38/Yb+x/7P/uP+IP9w/9z/ZgDvAEMBLgHEADgAlv8G/5T+Kf7s/b39y/2G/mj/vP91/+f+rP4Z/+7/FgGBApAD6AOcA1MDnwM2BPgExwWLBhUH4gbsBa0EmQOrAu0BdQE4AekAFgD5/tr93/wp/KT7o/tu/JX9gv4l/3z/gv9o/1H/mP9QABEBnQHEAWkBuQDO//X+hv53/p3+zP7Y/qX+EP40/Yz8Rvxj/O38mP1L/un+Jv8b/w//MP9u/7P/CQBkAJsAhwA1ALj/Lf+1/lr+Ev7r/cL9oP0Y/uP+O////nf+Lv6W/or/vAArAmED0QOSAzgDbgMgBPsE5wW+BkkHPAd9BlQFRgRYA2oCvAFDAbcAzP+W/mn9h/z1+7b7+vvJ/O/90v48/3D/ev9Y/0n/g/8EAKQAAgEAAbcAKABX/3z+BP4F/jT+VP5T/i/+1v1J/cT8p/z2/HH98f13/gD/U/9l/2H/f//J/wsASgCPAM0A0gCIAAkAhf8N/5L+J/7U/X39KP1R/fn9fv6f/mb+QP7C/r7/2wAjAlYD+gP5A6UDvwNuBEkFLgbyBmkHcAeuBloFLgQ+A2kC0AFkAQUBYAA9/wj+Kv2u/Hz8gPzt/MX9e/68/sz+2/7r/gb/Jv9//w0AdwB+ACUAtP82/6P+Rv5I/nn+p/6r/nP+Fv6r/Uz9Ov19/fD9av7Y/k//ov+V/1P/Lf9K/5b/6v84AHoAhABBALP/Gf+n/j7+5P2u/ZT9g/3E/YH+Mf+P/27/Jv9c/w4A5wDNAbICMQM8A/UC0QJVAyEE+QSlBQcGOgbnBQsFIQSLAyYDzgJtAsIB5ACv/1D+T/3M/Ln8xfzs/EP9rf3f/c/95f0k/on+Bf91//7/dwCXAGMADwDG/3v/IP/V/sD+1P7g/t7+3v7M/qP+b/5M/mT+lP7B/vT+FP8M/9T+of6p/vP+U/+r/+j/AQDw/7D/cP9Z/0f/R/9M/zz/Dv/M/vP+cP/W/+X/lf9k/6L/LwDLAH8BKAJdAjQCCAJzAl8DbARhBQEGXQZJBqkFzwQmBMADUwPLAhUCLwEbANz+yP0d/dn8rvyQ/LX8A/1M/Xj9rP0G/ob+HP+Z/wkAZwCOAGYAJQAJAPb/1f+n/4z/hP9y/zr/7P6j/l7+F/7d/dD96f0N/jj+W/5k/lj+S/5Z/qL+Cv9t/83/HQBcAGgAQQAkACIAJwAZAOr/qP9I/8z+kf6+/vT+/P67/nL+kf4F/7H/kgBmAewBIQI9AqYCZQMtBOAEWwWZBZoFPAWqBDoE5QOFAxIDbgKnAcAAsv+8/iP+2P2r/YD9aP1//aH9sv3P/Qj+bv72/mT/r//l//D/3v/N/7n/sP+t/47/V/8X/9X+pf58/mL+X/5X/lL+UP5A/kH+V/5r/oj+qf7R/hP/Zv++/yEAaACEAIkAZAA1ABgA+v/c/7b/cf8R/6n+Tf5Q/rz+MP90/1//Iv8c/1L/tP9aAA0BjwHWAeQBEgKYAjkD1gNSBKAExwSwBFoECwTTA5IDOgOuAvIBGwEkADn/kP4q/ub9sP2I/YT9n/29/ej9L/6M/u7+Nf9W/3H/kP+p/8b/3//u/+3/vf9y/y3/9/7U/rL+kP6C/nr+bP5y/pb+yP75/hz/Qv9+/7L/1//1/wYADQAMAAIA/v8GAAsACQAHAPX/yf+L/0j/Dv/o/sn+yP73/if/Of8k//T+7/4x/6f/SADgAEEBdAGGAaEBAgKSAi0DvQMbBE4EUAQPBLIDWAP5ApYCIAKVAQoBbADA/zL/y/5//kL+/v3G/b390f38/UD+hP7F/vb+FP8//33/q//F/8X/rv+Y/3T/RP8g/wT/7f7f/tb+4P78/gn/B/8T/zP/a/+r/9b/9v8JAAUA+v/+/xcAPwBdAG8AcwB1AHAAZQBRADwALQAkAB8AGgAVAA0A+f/S/6L/hP+K/6f/x//b/+X/7f/y//z/EwA5AF0AeQCTALsA/wBMAZcB2gEMAioCOAIyAh0C/gHPAY0BOQHYAHcAGwDN/5X/cP9U/z7/Jf8N/wP/Bf8b/0H/bf+Y/7H/tv+p/5P/e/9q/2H/Xv9g/2L/Yv9f/1j/Tf9E/zv/N/89/0//ZP99/4r/jf+I/4P/h/+X/7f/3/8JACwAQgBGADwAMAAlACkAOgBRAF8AWgA+ABcA+P/u//3/HQBDAGAAbQBrAGIAWgBbAGUAcQB6AHUAWwAvAPf/wP+U/3n/bv9x/3j/ef94/3j/gf+V/7T/2v8FAC8AVwB9AJ4AuQDQAN8A5wDpAOEAzwC2AJkAewBfAEUAKQAJAOb/yf+2/7H/uf/E/8v/zP/L/8v/1f/q/wkAIwAzADEAHQAGAO3/3f/a/9//5v/q/+b/1v+8/57/hf90/3H/fv+V/6j/sv+z/7H/s//F/+f/FgBKAHIAhwCGAHYAXQBBACsAHQAUAA8ABwD8//D/4v/X/8//yf/J/8v/0f/X/97/6f/z//7/CwAYACgANABAAEkATABOAEsARwBGAEgATABLAEMAMgAUAPD/zP+w/6H/ov+s/7n/w//F/77/tf+w/7T/xf/f//z/EwAeAB4AEwAAAOz/3v/Y/9n/4f/n/+b/2//I/7L/pP+i/6v/vv/U/+f/9P/6/wAACQAYAC8ARABRAFIARQAwABsAEAARABwAJwAtACcAGwALAP7/+f/5/wAACAAUACMAMgBCAFAATwBAACoAEwADAAMAEAAlADQAOgA0ACYAFQAIAAIAAQAAAP3/9f/q/97/1P/M/8X/xf/H/8//3P/x/wgAHgAtADMAMgAvACwAMAA2ADQAKQAUAPv/5f/Y/9P/1//Z/9P/vP+c/4r/gP91/3D/cv97/4L/hP+S/6H/pf+q/7L/v//e////HwA8AEsAUABGADcANQA2ADYAOwBHAEsAYQByAJgAuwCtAKQAgwBZADQAMQAVABwAQQBMAHoAjgB5AGoAVQA0ACMAMAAcADsAGQAcANn/PQBqASMAAf/c/g7/Dv8J/1n/Qf+P/zr/6f4u/1r/YP90/6D/AgBeAHoAVQBCABIAo/+Y/37/3/8NAcMAIgBuAAkAgf8s/1j/ov8NAFUADwASACoAOwA/AEYAZgCTAJQAZgCUAE4ABQAqALH/9f8QALz/EwB2ACMA2f/y/9n/7f+z/2IAiABIAIgAiwCdAA0ANABOAH4ALgAVALMA5//Z/9j/5/+c/4n/ZwBk/5P/AQCP/2j/gP82ADH/5f/z/3L/MgCK//L/EgDp/27/5/9OAKL/KACBAHsAJQAaAHUALQCL/+T/UQBEALH/SACRAD3/agDp/2T/rgAKAHf/JAD7//D/LQCN/6AA8v+2//n/8P8vAPP/SgBS/3sAPABw/00ATv8AAfr/H//+ADz/HQABAPP/LACQ/68AHQAu/wcAiABc/8AAOACQ/4QAIgCf/9X/PAB+/0IAxv+//y4AYf9hAHn/pv9mAJn/iQB3/x4AqwCQ/93/UQCXAJ3/RwBBAB4AbwASANn/tP9JAMb/Wf/K//b/FQBKAF8ACgAsAEkABABKAL3/1//D/1X/l/+Q//X/4/8sAA8A9/+kAKUAwgDhANkAzQBsACsAEgD1/xAAEgDa/4f/R//7/oT+uP4X/+P+Df9n/0H/Nf/s/zgAVQD7AC8B4AD8AEYB5gArAS8B2ACwAC8A+v+o/5v/mP+V/y//0P6p/jv+lf7L/sj+MP9c/2n/Y//v/2QAYADXAPAAAAExAQ0B/QAmAfcA4gCwAGkAkAA7AOr/v/+k/3H/dP+Q/03/pP8JAAkA9f/6/2IAdgCbAOYA8wA4AUoBIgHwAPAAzQB2AEgACwDO/4z/Zv8y/wT//P7v/uj+yP7K/tH+mP6E/nf+OP42/mb+Z/6O/gj/Cf/X/vn+0P6E/tD+Kf8l/1L/HQAAAg8DWAPOA8ADIgQvBOEDOQSiBK0EHQSzApEBIwFlAMj/jf8//xD/N/7B/O374Ps//Gr85Pxn/Z39HP5t/uL+MwDvAO4AhgG8AcgBCAKxAYMBqwElAQwA8f7n/QT9N/xF+3H69fk1+eX3l/ba9Wj1rfW69zL7YAGiBx0Jhwn4Cq0LhwzMDU4PaBGQET8OIwneA78AOf/J/fT8Mvx9+k/3Y/MK8VXxr/P29o75ffvE/Y//eAFLBCkHFAqYC6ELBwuvCZQI2QesBioFHAO+/4L8xvms9zv3PPdY91f3pfbz9Rn2Pvck+cD78f3i/uf+Zf6+/UD9Jv0C/S79p/v3+fYA0wldCcEHdgmpCYgIqAYnBrIJwwxxC8AG5wAK/fH7gfsE+3v8E/9W/gL6Ifa09On2tPui/o4AHwMbBFQE9QQCBgIIdAlACdkHOwXWAmgB/f8o//r+gv5K/Af57/dV+Pz4mPq3+8b7xvsL+7X57/kH/A3+3v7+/d37XfrO+F72hPQ29aj+ywtpDlILyQtcDAYLwggZCH0M5RAID5wH4v4s+U/3dPf196X5Xv0i/lf5zvTD8+X2u/3tAvQEqwahB7YHEwjnCD8KwwpGCfcFjAE9/uj8hfzu/Dr9XPyd+mv4Fvc6+OX6of2N/+H/9f7E/Zf87PsX/QT/Pf96/Zr6uvcC9VLyfu9Y8Eb9Lw3JEL4OMw8iD34MRAimB1INwBF9DiIF1vrW8xjy0fOA9Xv4SP0L/m354/V/9jH7AQIkBtoHGwlNCUEJqQkDCkYJtQaQAyAANvza+Sj6//u7/QH+IP08/MT63/nj+s/8Uf93AZUBq/9T/Vj7V/o8+6H8Zfyr+uH2j/Li7sPrRvOfBrwTVhV5FGgTdRCmCqsFmQeEDUsORQcQ/azz6+4j8GTyW/S++Yv/bQAc/oL8W/7QAqwFnAYCCMIJaAunDMELNwhHA3f+j/oj+Db40PpJ/jUAIP8F/N/4ePdQ+QH9UQChAhED2wDz/AL64fmj+7P9KP7y+6/3QfKH7L/pz/FEBAkUqxnHGNQVHhHXCeMDlATLCZULqQZO/qL1r++17tPwcPQz+scA7wQ2BfoCXgHeAbECXgPLBP8G5wnxCw4LAAe/AJj62vbP9Sv3eftmAdkDdQEC/Y74UPey+Sz9NQFVBDwEOAF2/YX6f/ls+i/7NPqO983yC+zL6QH0ZAaRFIIa2BozFywQ9gZzAWkDtwe6B90CZvsA9D7wj+9a8N70Yf2RBb8J6wgIBQYCpwCz//T/BwJgBccIlgp1CQoFuP4X+af2mfcz+/L/0QIrAsH+BPtO+cz5Wvut/af/XwCaADcAsP6w/P/6NPl39uzyne0b67/zNAVaFZ0cXxsXF4URLgnmAbEACwQABlMDe/2q9tvxs++Y7/jym/rFBHEMLg2lCOwDNgGx/l38bf35AVgHGQvWCh4GAv+I+KL1SPcj+2D+pABIAfr/2/3u+nr4p/jm+nX9AwBAAoUCgwCC/DL3gvIu78LrVusM9XMHvRg8ILIdthf3EB0IjgDv/Sn/lwH7AQr/uvmF84Pu2+3+8pb8UAeDDkIPcwsGBtz/4vqo+FT6qgAiCC8M4Av2B4IBHPvw98v3UvlP/Gz+6/6k/vH8fvq7+P/31vj2+0gAZQPLA5sBFPzi9B/t8eZG6If2DA1FHhYktiAtGsIPbQIV+kb5WPwG/3wArv/b+pvzs+5a7z327QCHCuoPyxD6DfUHnP7c9Xvz6ffy/ikF4QkwDL0JuAJs+z33h/ao+C38Jv+OAHMAbP7J+iz3m/Vh93P7xP+ZAnMCZP1p9VDsCObz6ZP5wBAZIh0lyiIcHccP2QAa9/j0Q/cf+/L/wQBD+9/zte/28R/5ZQGaCQ4RjBSmELMG7fpA82bzOvgD/icFXg1WEXAMbwFr9xj0ZPWH9oj49PxcAbsC+/+f+r32zfZ8+aX81/+rABD+n/fB7ermxOyTADoViiKFJfsiWR2EDvf9UvON7zbyYfnL/97+/Pnl9o/29/ig/FMCQAtxEmQScgtCAWX4OvSh89L1dvyuBrMPihFbCtv/rPhz9LLx+/Gl9YD7EQFeAu7+APoO9qP10/gj/Dv9Gvzv9xrwS+3p9z8JbRguIUQiph+mFy0JGfxb9BzyHPWI+ov8Ofly9jf3Mfro/YoC3QksEagSkA0KBYH8mfb283/0e/njAjANuxKwD0sGPvxU9YnxofC88vH2S/wIAO/+6fkK9eXz7vVS+Pv4Zvjy9EHxIPj5BZgSIhwlILogBRxiD+cBOvjx8pHyQ/bB+rr6a/lL+gT8d/7XAAQGfA1pEV0QjgqkAuX6FfQ48cTydvnCBP8O5RF0DNcDrfsO9ZvxbvHi9DX7WwDmAQ7/y/n59SX1WfWL9GLzNvBZ8gj/ZAw7FlwbpRwEHLAUfQiA/sP4A/dh9+f4p/j59Un2BflZ/DgApwRCC0wQ8A92C+wE2P6H+dv1cvUt+VMBewpID8IMfAUb/a31jPCJ7lfwIvWY+gv+uv1d+yT5Bvje98r2i/NT88n8Qwk6EqkXMhnSGG0USwp2AKL65/gk+v37hvxY+hr5pfkm+g38KgCFB28PEBG4C5oETP6h+FX02fO6+JABrgpfEAgQ5wk+AUH5jfKs7Xzsc++y9M745PpJ+9n6P/r4+LT10vOg+7YIshL5F/8XlxazEooJzQCd+jj4gfpw/SX+D/s9+Yn6ZvtV/UQAngVeDLsOlwwqB4UA9/qP9wj35vgD//4H3w5TEO4L0wRe/Tz2g/Ae7ePs+u6G8ur1IvfE97X4E/nn+Hn+yQjnDm0SOhSUEtAODQgtAQv+lvwh/dv/FQF2/vT6d/ob+4v7Qv1AAaoGZgktB/UC0/9x/af61Pk2/LAAhQaHCgUK+QZkAxj/9fpf98b0tvRg9jf3rPcJ+Mf37PcK+Nj2hfgwAMQHAg3aDz4P0A78DNoH+AOGAfL/mwAmAST/lvw5/KL8if07/4MAJgN1BS8EBQLk/179J/w5/GD8lf3RAA8EyAW7BRkERwLN/377Sfer9GTz1vPb9Vz3Y/hB+iP8u/wS/uQBnAVhCJoJ4AlGC1sKFgdhBeUDWQK7AIr/Tv8d/xgAsQE7A7kDvwLiArYCfgCR/tb9SP6s/mf+Pf4p/nX+8v6v/zsAdAASAcIApv63+9341vbp9W72u/c3+Q/73fvD+6r9rABeA7gFCwYfBjoHaQdQB9YHZQcCBhAFvwPMAAn+F/1f/kwBaQIdAtUCygL+AJb/ZP9O/9f/NADM/4EAGQGiACwAn/6Y/ND7Yfs3+hb5mviS+G/50vkv+R/54/lI+4r+sQJmBNME9QRiA60ClQJMAosEOwalBqkHWAd4BfICogHGAegBlQHWAN4AGgD+/f/9mP/aAE0BtwByAL0AfgCw/2X/N/9N/iv9G/tO+E32gvVZ9hP41vnG+x7+KgAyANX/OwF3Am0CjAHnAJ0BiwKaA0cFrwZRB6oHogdLBp0EngP+AsgCxQGxAMEAjAAnAEL/gv4U//D/6wBsATABAgFeAA3/Xf0C/Ev7lvoF+mX5Dfk2+VX5YfkG+cv4ivk5+gD8d/8ZAaUBcAN2BKcDXwJeAr8E2QbeBo8GLQaPBewEggTIBC4FvwW2Bq0G/gQ1ASj9cfvs+oD7IP0r/mb/6wACAdj/m/5W/qb+PP4c/TD8D/sj+Tz3CPYN9lX3Mfnr+pv9UgB0AbkBLwE6AVIC9AI6BCsGzgbgBo4FoQO0AzYETQQ6BbwGRAffBZMCjf/q/ZT8YfwN/oAA5gIiBNED0gJ8AXr/IP1r+1X66fnZ+R35r/hL+dn5Nfpq+n77Vv3A/i7/a/7S/oT/df/O/84AcAPaBS0GAwbHBeIEqAOwAnECsgNxBZYFtgSpA1YCawGCAJv/hv9s/4X/jP/U/l3+fP70/oX/AQCu/0L+cPy++rX5EvlH+Ab4TPhh+UT7V/0NAAICNALWAd8BOQI+AuIB4QEeAjgCpwLPArsCgAP+BPYFdAYYBi8FZgS2AsQATf+f/nn+iv7Q/jD/vf8gAOX/H/9O/sX9b/07/Tj9Kf0r/Rj9vPwH/Fb7FPuf+sr6XP24/z0AFwCgAL0CzwNaA/oCXwMWBIsE7wMGBEsGjQfVBfwCPgJuAjIBxP5U/cX9o/5+/pz9Cv4//6P/Gf98/vz9p/0T/fX7Lvs5+7r7xfsm+2z7I/35/Xz9e/2n/vL/LwAKAOkApgG/AeIBkwI7BJUFLwbQBjgIXgkRCQEH/wOHAS4A5f41/Zn8Yf0G/nv9mfy//P/96f6f/1oAigBvAAEAjP7R/DL8Sfzf+9n6bvrt+gT79vl/+aH6aPwP/uL/XAI0BVcHwQcPB3gG4Qb8B2IIUQetBakEPwNOAKj97/wq/Zv9L/5J/9oArgHNAYkB4wCmAMwAPwBu/7j+Jf6a/R/8Hvre+Kn47vh++Sz6ZvrX+VP6mPzZ/q0AMwIYBLQFwQVfBaAF1AWVBXkF2ARtAwoChQAP/3X+uP6g/9MAcgFrAVcBOgE/ASIBZgAXAMsAdQEvAcn/FP69/DX7cfnQ+An6Gvyn/ef9Of1Q/ET8ff3F/pwAIwOWBdUGpwWgA5QCVgL8AfQBOQK7ATkAmv7U/W/9pf3D/u3/vAAfATQBEwETATcB+AB6ADMASABjAOr/5v5Q/vf9l/2b/bf9fP3L/ZP+f/6K/rn/hAHmAqYD2QMLBMcD0AKdAT0A8//N/2H/vP+TAFsB8AGgAuUCyQH//33+7Pzi+7z7rPt5+4f7CfzR/Nr94v8CAo0CUwISAg0BM/9k/T78Gvyw/K/9eP/rAYYDUATjBOgEggSnA9cCSwLWAWABXQC+/zQApAB+AH8ATQFKAdL/l/5a/sP9WfyX+3H7mPtI/O/8Ef7U/xMBYQGiAJ3/Yf75/MD8Wf2x/e/93f59AKIBnAKMA88DnAONAw0EYwTYBEoFPQVBBMYCxAGUAG//sf55/qn+PP4t/T78tPu2+0z8uf0+/1wAAQEPAdgANgAs/+n9B/3m/Cr9n/2z/f/8Q/xZ/L/97/8MAlsEqQYrCEUI5AYCBUYDOgIpAbT/GP/4/rz+e/7w/uj/jgDCALQAawDN//H+qP1j/GT7/fo4+xH8oP00/4cAwwA2AM7/xP4T/Wr74PoM/BH+JQD3AbUDVwVsBm4GmAUDBW4EmQPsApwBOwAw/2n+lf56/9QA7AFhAtIBkQCI/2n+pf2c/bL9Vv0H/U39Pv0T/R/9WP06/hX/Zf84/8r+cf7j/Zr9Ff4l/38AzAH2Ak8DIAMlAwoDvgKQAj0DBwQmBMQD6gIUAt4A9P+4/2D/Pv+z/yMAif///sD+K/6n/UT96vy2/Db9+v1o/lD+SP6//tT+a/7K/RH9qPzQ/In9QP4a/10AsgFJA4cEAQVNBYYFaQVdBKwCmgEJAUcAif+4/38AEgF0ASEBZADZ/5v/FP83/vL9Jv5H/jn+VP6O/u/+Jf/d/q7+lv5Q/j7+Iv7R/b/9xf2s/bT9bP6A/60A/gExA6sDIAOlAhwCCgGJAOwAiAHJAaUBKwEcAZ8B4gFEAooCqgJAAjIB9v+F/uz8kvtm+wT85/wy/o3/gQAIAU8BawE/AZsAlv+G/pL9Iv0O/V/9Af7Y/gIAEwFNAucC5wLbAtgCnwK4AdIAUwALALT/R/8u/2D/cf93/0D/hf85AMcAQQFZAaQByQEGAfT/8f7p/fz8x/zH/OH8zP2T/gP/Tf+Z/7//xv8tAJIAxgC8AF0AJACNAMoA5gASAZcBVAI8AqgBPwFJAdEA9P9f/+3+L/+M/7D/uf/y/4AAhQByACgArP9t/zj/X/9I/yr/Zv+P/8j/4f8BABoAvf84/67+VP5G/pj+s/+yAOUAkAD5/8X/0f8mAAUBDQLYAuYCRAJwAeUAoACeAMUAtwAzAKv/a//w/tb+Sf/J/8H/VP8U/6j+ff60/t7+SP/N/yMANABfAJwALQCt/0b/+P7n/uT+O/+W/woAXAA0AOf/rf/z/y0ASgCTANcA+AARAXIBjQFuAUoBGwG0AKQA2gBsADIAEwCe/8n+W/6B/m3++/7V/1MAUgBCAOn/0v4t/vv9N/6h/mP/OwDPAHoBhQHsABIAh/8v/+f+Lv+0/0MAzQD3AP0A0wCrAMoA4wDSAGkAEgAgACIAPwCPABABZAEAAb4AiwA/AAYAzv+4/4H/Kf8J/9P+h/7M/gn/KP8t/0//pP+D/4H/qv+K/7b/aAAdAbEBQQI7AowBewBs/83+If4P/p7+6P5Y//j/QABJADUAvQBHAQcBVQHlAK7/Cf9C/iP+tv6d/5UAIAF9AVsBHgB6/6X/P/9Y/3L/jP+Z/0v/Fv/C/g7/CwDzAGwBAQJVAogBpwAYALP/bv9e/93/LQDBAFsBHAE0AH7/eP8K/7L+3f75/mH/+f/2/7v/o/+z//z/EACCAAIB0ADJAGcAcwDlAAEBBQFNADEAQAD2/5b/kv/L/9D/IAAhAMr/iv9A/yL/dv/8/24AswAzAVYBZgE0AUEA0f82/wv+QP47/jf+W/+P/+j/WwCaAI8AkwCaABgA9v+N/4L/Cv9T/v7+gf6r/m4AxwC0AZgBFAFeAeH/DwBUAKL/qAC1AKwAjQDJ/+3/GP9K/0T/+f4lAM7/pv/o/8T/3P9XAGkAxwBFAd0AiQB3AN3/c/5X/kP+FP4I/5T/7/9SADYARACeANQA8wBKAegAswDfAEYAkQCJAFEAygCrARYCQAGgADkAoP/D/nr+Xf68/nv/2f/OAJ8AtwDGAHz/0/+u/9L/egB9/4//PP+Y/h//yP5e/0kAbADTACsA0P9W/1f+PP6N/hD/9P+GAG8AyAAdAqMBxgBHAU4ACgBB/xj/kv9k/6EA6AACAVAB0ADCAEoA5P/7/zv/Rf+T/gb+Av8o/43/EgC/ACABwADNAZwAxP8lAKb+Hf+M/lj+qf5y/20APv+2ANIALABuAWoAyAB0Aa4ASQGNAO//UwBg/z3/zP4M/5L/Bv8PANb/Yv9zAF4AkABMAWEBLQHkAKwAAwDM/1T/zP65/5X/L/+4/3j/Tf+p/3YAlwAPAf4BFQHDALAA2P9e/+j+j/4A/z3/ov/q/1b/OQAjAJ//7QB8APj/XwDv/4f/cv/t/8n/pf/w/+v/6v8nALn/SP8Z/1b/3f8rAN4AlAD1AHwAqgAeAcz/dQA5AK//SQDw/wcAuf+Y/xgABgBeADwA2P/H/+H/8/8DAHL/hv/G/1f/Sf/e/mT/AQC2/1f/iv+6/5z/kv+t/5cA9QA4AXYB/gDLANIA2ACl/9P/VwBw/xIAPADE/+3/v/8E/+3+Zv/d/r/+0v93AN4A9QB6AVgB9gC1AdQAPADKAJQAIAD6/hr/kv9j/l3+vP4v/z7/b/84AP7/p/+/AAMBYwAhAVYBfAFcAe0Asf/d/pr+af7P/lf/SQB0ABwAtf/F//v/u/9MAPYAFQAjAasAaP5c/xIAt/9q/73/eADG/+D/kwAsAHsAQwBvAPr/y/8qALj/zf+q/x0A2/+K//D/TP9t/+j/qf/r/7z/HQDDAIoA4ABmALr/WQAhAI//rv+4AM8Ar//r/6L/3P7c/xIAjv91ANQARACd/6j/xf+s/6MACgFsAKkALAB9/7T/xf9D/7b+d/+6/4n/5//M//P/cwCBADIBYwHoAIsAUQDh/0//Tf87/iT+Vv+w/3P/bwBBAtUBPgGVAd8A6P+q/5n/lf8wAEIApv/+/nf/zP8J/1sAKAGFAEQAHABHAD7/A/+j/xD/Tf9cAGsAZP96/5wAGgCS/tT/ggECAB0AnQEGAfb/aADJAPD/QQDs/3//HwCY/53/g/94/1EATAAtAOQA1gBk/wz/V/8C/gP9HP7E/j/+C/9kAHoA/P/BAOkBWQFqAX0CJwKOAML/UgDm/3r/o/9y/xQA5/8V/xL/Iv9f/8T/PABtAJQAyAAVAcMAwP+HAE4BQgD6/87/N//7/nX+If9x/zb/pQDJAekBKgHsANIA/P7A/q3/wf7a/jMABgBz/+z/NADH/7v/NgAlAKX/+v9EANT/of8tAAUAs//T/zkA0P8Z/w0BZwHm/1sAfAH1ALz/iABFAUMAeQA6ASkAlv5l/nT+p/3B/Z7+2f8dAJ0AxgF/AW8ABABPALX/x/5W/1YAIABmAAgBHgGSAT0BEgBpAK0A4P+3/63/bv9I//b+l/60/lP/4f+2/9//TAA2AMH/p/89ACsA7v9ZAK8AvAC1AFMB0AGuAMEAhQF8ANf//P9OAAD/Hf6p/4b/Vv77/av+0P5F/gD/j/+K/0j/3f+aAEgAFwHiAbQBmwGKAXAB2wD5/z3/VP+H/yv/O/8DAAkAy/8VADoAiQD9/3T/iv8F/4P+k/7h/uf++f6E/1UAzQD/AOkBRwKwAXUBgwEUAVgAVwBIAMz/gP8Z/+T+u/6K/vP+A//K/p3/iQCDAF8A8QCHAV0BrwAyAHIAZQDU/97/IACh/yr/oP++/7D/fADhADwApf8y/6X+V/6J/jD/7f+KAAEBHAGXAD8AlgCEAAgAHwA4ABMACgDq/6X/o/+l/zL/PP+f/87/JQBbAGwAhgCGAKAAygAJAUcBRQH2ADcAgv8S/7D+vP5C/87/BgDw/97/0f+t/3r/l/8aAFQANAAEAOf/z/9d/wz/Kf9h/23/YP/S/z8AdQDTAPMA3gDcALwAXQD6/8P/o/+l/9v/1P/d/xwAIgARABQAVACKAJAAUADI/3n/Gf/F/gH/Nv96/yMAZABRAIsAowBtAFYAVwDt/5j/rf/o/0IAYwCtAAEB0QCCAFwA6P9J/xz/Jv8T/x7/Xf+w/wUARgBqAKgAvACrAKMAYABFAD0A+f/c//z/CwAqADoALQAaAP7/DQAjABQA6P/f/9X/ef8C/w7/a/96/27/pv/r/7r/af+u/zIAMwAWAGYAmwAgALL/EgBnAFsAUgB3AI8AGAC+/+b/5v/U//v/FQDv/+X/PABrAGMAgACmAG4Av/9w/4P/mf/D/93/GwAhAM//p//Z/x8AGAAJADgAlACJAPz/ov+v/6L/Ov8V/1v/tP/s/zAAjQC9AJ4AVgAoANr/u//g/+H/vP+1/9b/2f/w/zgAngD3AAsB/gDwAJ8ACgDd/wIAuP9e/yT/5f4L/3H/1P8uADoAKwAvAA0A8f/+/xYA8P+9/9H/8v/h/73/t/+2/7H/wv8CAFcAagB2AK4A4wDkAHEACwDh/6j/kf+3/8//uP+y/6n/s//C/6v/wf8WAGYAhgCTAIEAMgDb/5f/XP8n/yf/WP+G/8H/CQBVADkA1f/V/+H/xv/F/xQAZQBBACsAbQBzAEgARwCJAOgABAHwAOMAvQCIADoA4f/Y//D/+P8fAE0ASQArABIA/P+x/2n/if/O/+j/CQAxABYAvf95/2j/Zf+G/8X/7P8OACAAFwDr/8D/zf/W/7X/wf/+/zMAVgAoAN7/8v8BAN7//v9hAJ8AcAAUAOf/0f+e/27/nP8dAH0AkgB/AGYAQAD7/67/w/87AGgAQQAlACIABQDO/7v/tf+1/7L/dv8//0X/Vf9y/7T/AABNAH8AiABjABgAzv+W/53/zP8dAGMAfgB7ADsA8f+3/67/+/9GAHIAqQC7AIkAIADi/9P/nv+n/+H/IQA9AFAAYgAiAOz/z//r/xQAIwBTAGUAPwDf/2z/BP+//rj+5P40/4L/pv/C/+7/5//Q//n/QQB/ALYA7gABAdoAkwBcADQAFAAZAGEAwgABATgBTgE6ASEB6gCSAF4AWAAsAOD/sv+d/2v/U/91/5z/w//W/5P/Cf+P/jf+2/2y/fT9Zv7B/gv/T/99/53/3v8xAFoAdACaAJgAWQAsAEgAhQCyAPIAdQEQAnACdQJGAiUC1QFoARwB6QCzAFAA/v/i/73/cf8x//f+pv56/nL+Vv4+/kb+K/7w/b79lf1f/Qv93PwI/W796f2v/rz/sACUAX0CQAOxA+YDBwT8A5wDIgO3AhwCaAH1AM8AwQDJANYA1QDdAKAAMgDo/5//QP/y/rj+d/77/WD9zPw7/ND7j/tu+1L7ovtr/Az90/3b/hMAUgEvAhMD4QM1BCoE+wPnA5kDOgMXAwYD2wJWAvUByQFWAcUASgAmABIA1//y/0IAewB5AGIAZAD5/yn/K/7k/Jz7a/qF+en4mPjU+J357fpp/Pn9mv8MAV0CYgMgBKwE5ATdBJkEGQSbAxYDiQINAqUBdAFJAQQB1QDPAPMAJQFOAX8BvgHhAb8BaQHwAD0APf8L/sz8hftO+jj5g/hL+Gj4EPlA+rn7Wv3S/mQA+wEtAzYE8QRbBW0FIwXqBH0EzgMPA0oCuAE0AcQAjwBZADoASABdAHQAvgA4AacB9QEgAhMCowGxAGv/7v1e/Nv6dPl9+CL4Rvi5+HH5j/oA/Ij9Kv/zAIcC0wPYBHcFuQWiBXsFKQWIBMoD+gI4AlEBZADX/23/HP/5/jz/zv9XAAYBzAFqAsECxwKbAvwB4gCB/+/9Sfyg+kz5gPgO+Bj4n/iH+b76HPzU/cX/nAFGA4EEZgXeBd8FtgUpBVUEgQOxAggCVwHLAF0A1P9c///+Bf8+/3v/GQDCAGkB5QEpAnUCRQK4AfYA6/+l/uv8Jvuy+aP4HPgu+Mf4vfkQ+7f8iv5mACAClQODBOsEEgUcBRIFxgRZBOADXAPoAm8CAgJxAaEA0P8W/6L+b/6P/ib/6v+3AG0BEAJ+ApACVwKoAaMAJ/9O/Vf7YvlF+Ov3Fviy+IT5Bvuz/FP+HwCxAR4D9QNRBLQEwATUBN8EmAQuBJUDHgOQAtQBSwG0AAgAY//j/r7+zv47/wwA8wCQAd4BLgI5AusBWgFzAC//c/2L+5r5Lfjp91n4Lvk3+ov7LP16/pj/xgAgAj0DjAO4A+8D3gORA1oDbwNlAz0DOANGAzEDqwLzAUMBgACx/zL/Qf+e/zQAwwAPAVMBWgEWAcQASgCj/6f+Xv2w+5H5NfgQ+In4ePmi+hz8jv15/o//+QA6AvsCOgNvA2MDAgOqAmoCngLvAvEC+QIdAyQDxgJLAu8BewHvAGIARACLAOIAVAGGAX4BQwHOAHsADABz/6P+U/2n+3b5Y/e19mL34vjB+rj8t/4DAKAAiQGUAkUDegNTAw8DcwK8ARwBtwD4AJEBawJqAwsEPwT1A0kDYwJzAcAARwApAHsANgHIAbgBeQEgAbAARQCv/wj//v2D/LP6Svg49vD1Q/dU+ZL7zv3X/wEBlQFaAjYD4QP4A6MDPgN7AqYB6wBLAEwAuQCBAV4C8AI/AxQDtAICAl8BKAEKAUoBtgFbAr8CUgK6AQABWACo//P+Z/5l/QX8Yfpf+E32efXW9kz5z/sF/gkAVQGFAcIBgAJOA6UDdAM9A7ECvAHGAO//uP8bAMAA1wHfAlcDUQPfAiQCOgG7AM4APAECAucCrgO2AxEDMgJHAXYAof8K/zz+3/wf+w75uPaP9F/0jfaa+Xb89P4zAWECmAIVA+EDsATHBFgEtwOQAjgB0P/Y/rP+Hv8nAJoB1wJ8AzcDjwK+AR0BFAGYAaQCnwNhBMkERwQsAwQCFAElACn/g/6K/e/73fnS99H18fNb9DP3qfq//SMAPgIYA8oCHQPxA8gE3gRaBM0DjQLxAEr/+P2L/av9k/4WAIcBkAKzAmcC+QGhAd4BlgKxA88EngXLBeQEQwNxAQgA+/79/YL90fyd+wb6Jfgw9h70APSh9iT6cP0rAK0CCATBA8oDZAQSBQsFLQRmA/cBCgAx/qP8BvwQ/Bz9Jf8iAbMChAPFA30D2QLSAnsDawRTBQEGKQYfBV0DpQFUAFH/Pf60/Sz93PsS+iX4XPZg9Ozze/ZG+uT9xABDA78EcwQ0BJcEDwX9BCoETAPYAbv/mv3n+zj7Ufta/F7+lABxAn8D/gMTBLEDrwMzBBQF6QVpBoUGggWoA50B0f94/iT9Yvz5+wb7xvlA+NL2HfVe9Hr2BPqd/acAQQMkBUQF+AQeBUAFDwXXA3wC7gDj/u/8Qfui+sr6svux/QYAXAL/A+AEWQUDBcYE0QQrBcQFDwY5BmAFfgNwAYL/AP6r/Bj87/s3+0b68vhs94D1w/Tc9kD6yf3NAF4D8QTMBKIEyASzBEcEJQMMAn4Alf7j/GH7yvrL+sb7A/6EAOgCkwSQBegFiQVzBXMFoAUKBk8GLwboBPcC+gAR/439V/zx+8P7+vom+gf5qPfu9Wj1bvdV+o39jgAMA3AEKgT1A/MDtQNKAzoCUQEDAFv+6/yt+2f7qPu//Ob+NgFyA/MEyQXxBV0FJgX6BAgFYQWxBdIFygQqA2UBmP8b/tv8aPwE/DP7bfpG+dr3PPYi9h34sfqe/VUAugLqA8kD5QPtA8gDMQMgAiwBr/8n/uX8+vvQ+x78i/3B/9QBigOZBEQFMQXoBA8FMQVfBX4FowU2BeADdwL3AH7/H/4x/df8Hfwm+//5o/gW9+/12fYM+Xz7BP54AFkCwgLwApMDzwOwAyMDZwIqAYT/Nv75/Dz8Jvzi/ID+SAAXArYD7QRiBSYFRgVuBVQFSAVLBRkF9AOZAmsBAgCt/o79Bf1w/HH7hfpZ+dT3jvbf9p74o/ra/EL/SAELAncCQgOqA5ADHAOlArsBRwAi/x3+V/0P/Zn9//5UAIEBvQLiA4wEmATPBA8F8QTwBB4FGQVUBDkDGgKjADD/AP47/YH8ePuD+mX5APjl9hT3p/ht+lj8sf6/AKAB+QHPAnYDhQMjA7UCCALGAJz/qf7w/Zn9zP3u/mAAqwELA0IE8gTvBM4EzQSGBFUEZQREBJ0DoAKkAWwAH/8q/oP93PzU+6X6e/kZ+Dn3mff2+Jj6MPxM/i0AOQEdAvECiQNTA8ACXgKOAZ0Auv8a/8z+rP48/xMA5AC3AYsCbQPaA+kD/gPkA6oDaANuA3ID7gI3AmIBdwBr/2b+uP3v/O77yPqh+ZP49vdy+MH5Pfuy/ET+sv+LAEUBPwIWA2ADSwMVA4UCqwHhAEoA///8/18ABAGeATYCtgIwA3UDewOLA4IDYQNCAykDCAODAsEB7gAIACP/Pf5x/Zz8lPt9+m35g/gx+LH40vkN+238If6J/3wAWgE2AskCyQKYAksCwAEqAZUANgAOACUAmwAqAcQBTQLFAjQDYwN1A2IDOQMHA7QChQJeAgQCagGiAAEATP+J/uP9I/1J/D/7RfqO+VP54/ni+gb8PP2D/qr/cwAaAacBCQIVAvIB4QHNAZ0BNAHhAMQAxAD6AGEB7wFUAp4C3wLzAusC2wLdArgCbwIyAuABaAG0APP/QP97/s39Ov28/DL8ifv7+o76g/oF+877pfxt/Wn+W//6/30A2QAiAS0BJwE9ATwBVAFUAUoBTgFcAbsBMgKrAvkCHwMxAwUDygKfAo8CcAI5AgICpgEvAZkA6v8//5f+Gf7F/YH9Rv3z/KX8Y/xB/G/8z/xK/a/9DP5v/tP+O/+N/+b/LABgAJ4A2QAcATsBWgFrAXMBsQEKAmsCmwKoArUCnwKRAnkCQwIMAsMBeAEJAZ0AWAD9/4//Fv+6/nb+IP7S/Xj9H/2z/En8M/xj/Nr8V/3g/WX+qf7s/jf/hP+v/7P/0//5/zIAggDiAEIBegGvAe4BMQJrAowCmgKGAnUCcQJoAmwCbgJfAi0C4wGKAQsBewDh/0X/rv4n/r79Z/0p/QD96Pz7/DP9hf3o/Uj+qv7k/vb++f7v/t7+w/7F/un+Af8u/4r///9tAOgAfgEEAnwC7wI/A2MDVAMzAwADxgKlAoMCWwIpAvYBvwFjAfEAbADP/y//qv5E/vP9wf23/cX95v0u/oT+yv77/hn/Gv/2/sL+e/4h/sj9jv17/Y/90/0+/sj+TP/O/1YA3wBnAdkBOwJ0AnsCawJLAj0CMgIuAi0CGQL1AboBeAEqAc8AeAAjANL/if9J/xX/8f7k/vb+Fv9F/2z/gf+E/2H/Lf/o/pj+Sv4G/tz9vv27/dv9EP5c/rb+I/+U//n/UwCeAM4A3ADMAL4AtAC1AM8A7QAWASwBQgFXAW0BfAF6AW0BTQEbAd8ApABkAB0A4//C/67/qf+w/8D/wP+0/5v/d/9E/wv/5f7O/sH+xP7f/gT/KP9O/4D/pP+x/6//qf+f/4r/hf+P/53/sP/Q/wsATwCXAPIASwGVAb8B2QHYAbIBgQFJARkB4wC2AJwAhgCDAIsAoQC3ALwAuwChAHkARwAFAMj/iP9Y/zj/J/8p/yb/KP8z/zL/Mv80/0f/Uf9N/0//Sf88/yr/LP9D/1r/gv+v/+D/CQAjAEIARwA3AAsAyv+B/x7/w/6A/lf+W/54/rn+Df9Z/6D/zv/0/wcABAD5/+X/1f/G/7j/vP/T//P/GQBFAHgAngC2AMcA2wDyAAcBKgFYAZcB0wENAlMCkAK9AssCwgKpAmoCGAK1AU8B5ABvAAEAl/9E//7+z/65/q/+tf69/sT+wv61/qD+hP5s/lf+Uf5b/nD+k/62/tr+8v70/un+0P60/pn+iv6R/qP+z/4P/2H/yv8+AL8AMAGMAcsB3AHFAYcBOgHgAIwAVAA6ADgARABTAFsAVABIADwANgA5AEUAUABSAEkANQAZAP7/8P/2/xAANgBcAHgAhACAAHYAcgB9AJYAvADjAAQBGgErATIBNgE6ATcBJQH/AMUAdQASAKP/Nf/P/nf+Mv4H/vT99/0M/jH+YP6V/sv+AP8v/1T/cf+D/4T/d/9n/1f/S/9K/1P/ZP91/37/hf+N/5T/pv/K//z/PACEANEAGgFVAYIBoQGyAbIBpwGSAXABSQEfAfUAzQCsAJkAlACfALUA0wDwAAkBHQElAR4BBQHeAK8AdAAzAO3/nf9H/+7+lv5F/vn9vP2O/Xn9ff2X/cf9B/5T/qf+Bv9q/8v/IwBvAKkA0wDmAOIAxACPAFIAFADg/7n/oP+U/5z/t//j/xkAUwCKALwA4wD5AP0A8gDdAMgAuACvAK4AsgC0ALAAoQB/AE4AFgDi/7r/of+b/6L/tf/O/+v/DAAvAFQAeACVAKUAnwB9AEAA8f+b/1D/Gv/+/v7+E/8z/1D/af96/4b/lP+r/8///P8qAE0AWgBQADEACADi/8b/tv+u/6j/nf+M/3v/bv90/4v/t//y/zAAbQClANQA/AAfAUABWQFmAWABPwEHAbsAZgATAM//of+K/4n/lv+o/77/1//w/woAIwA5AEUAQwAyABMA7f/I/6n/lf+I/33/bP9R/y3/Bv/n/tv+6P4P/0j/iv/L/wYANwBgAIQApQC/AM0AygC0AIgATQAKAMz/mv98/3H/dP+A/47/nf+t/8H/2f/3/xsAQABhAHQAdwBrAFIAMgATAP3/8v/v/+//7f/i/8//vv+w/7H/xf/o/xgASwB7AKIAugDFAMYAwgC3AKoAlQB1AEcACwDF/4P/Tv8v/yz/RP9v/6H/z//3/xEAIwAwADwASABSAFgAWABOADwAJAANAPX/4P/K/7L/l/93/1r/Rf8+/07/cv+p/+3/NQB4AK0AzwDbANQAvACXAGgALgDw/7L/fv9g/1r/b/+f/9v/FwBMAHMAhgCIAH0AawBUAEAAMgArACsAMQA4ADwAOwAvABUA7//A/4v/Xf87/yf/J/83/1f/hP+6//b/MgBnAIoAmgCSAHoAVQApAPz/0v+t/4//e/91/3z/jv+s/8z/7f8MACUAOABDAEwAVABcAGUAcQB5AH0AeQBvAF0ASAAtAA8A7//O/6//lv+J/4f/mP+4/+P/EwBDAGoAfQB/AG0ASgAYAN7/of9o/zn/F/8E/wb/G/87/2X/lf/F//D/FAAwAEYAUwBdAGIAZwBuAHQAfgCGAIsAigCCAHAAVwA1ABAA6//K/7D/n/+e/6r/xf/s/xcAQQBmAHsAfwBzAFYALwABANH/qf+K/3j/cf97/4//q//L/+n/BgAeAC8AQQBNAFYAXgBlAGsAbQBuAG0AZQBYAEcAMgAcAAUA8v/g/9H/yP/F/8n/0//j//b/DQAiADIAPQA9ADUAIgALAPD/0/+2/5z/iP95/3L/c/96/4r/nf+2/9D/6P8BABUAJAAtAC4AMAAtACkAJwAlACAAGgATAAwAAgD2/+3/6P/m/+f/8f///w4AIAAxAD4ARwBJAEQAOQAkAA0A8f/X/73/qf+f/5r/mv+i/7X/xv/Y/+n/9/8DAAgACwAQABIAFQAYABoAGwAcABwAGwAXABAACQACAPz/9//1//b/9////wgAFQAhACoANQA5ADsAOgAyACYAGwAMAP//9P/r/+X/4f/e/93/4P/n/+3/9P/9/wUACwAQABUAFwAWABMAEAALAAcABAABAP///P/6//n/+P/3//f/9f/2//f/+P/6//3/AAAFAAkACgAMAAwACwAKAAUAAAD7//j/9P/0//H/7v/u/+z/7P/v//H/8//3//z/AgAGAAsADwAPAA8ADgAMAAoACAAGAAQAAwADAAQABQAFAAQAAQD///r/+P/3//f/9//5//3/AQAGAAgACQAIAAUAAgAAAPv/+P/1//P/8f/y//T/9//6//3/AQAFAAgACQAKAAoADAALAAsACwAJAAgABgADAAAAAAD//wAAAQACAAMABQAGAAcABwAFAAIA/v/6//f/9v/0//X/8//0//b/9v/2//b/9f/z//D/7//x//T/9//8/wEABQAKAAwADgANAA4ADAAKAAgABAACAP///v/+/wEAAwAHAAsADgAPAA4ADgAOAA0ACwAJAAkACQALAAwACwAMAAoABgACAP//+v/2//P/8f/v/+//8f/z//X/9//5//v//f///wAAAQD//////v/6//r/+P/3//f/+f/8////AwAHAAsADwATABQAFQAVABMADwAMAAoABwAGAAUAAwABAP3/+P/2//P/8P/w/+3/6//s/+//9P/3//v/AQAFAAkACwALAAgABAD+//j/8v/v/+7/8v/2//r/AgAHAA0AEgAUABMAEgARAA8ACwAHAAEAAAACAAAAAgAFAAQA/v/4/+//6f/k/9//3//f/+H/6P/1/wEACQARABsAIQAbABQAEQAIAPv/8//t/+r/6f/w//j/+v/6//z//f/+/wMABgANAA0ACwAOAAoABAABAPj/9P/6//7/CQANAAMAFgAEAPP/+P/x/wcA/P9WADEAuf///wEAAgAQABUAMgAzABgA9P/l/+T/3v/i//D/7/8CABIAIgAkABkA5P+s/8P/3f/2/xIAIAAvAMn/x/8VAPj/NgCRAJIA+wBWAQYBlQC0AF4BwgDy/xH/v/5K/1T/5P9HAK7/oP7k/vv+XP9cAAcAvv+e/9L/VQAkAVsBUwEjAXsAsACpAIEAWAAOAE7/cf4g/qL9KP7L/uH+/f7A/lb+if5N/5n/RwCmAGAASQBvANEABQF+AYQBEwGHACIANwBIAFwA3f88/8H+av5z/q/+OP9c/0v/Nf9L/8j/XQDrABEBOAFaASsBTAGfAZcBmwGeATcB4gCfAGMAOwDg/3L/8f54/mf+ov4M/yv/0P7E/ur+I/+S/ycAmADTAOIA6gA8AV8BgQGEAVYBBAGhAGgA/P/Y/9T/gv9u/23/QP86/3H/g/+K/7z/0P8KAD0ALQBkALwAxgDdAMkAjwBXACAAGwAfAC4Arv8r//H+yv75/h7/Q/9V/0X/Lf9q/9v/OQCdALQAmwCCAGsAfQCMAIQAegBXAAsA3//w/9z//f8nANX/h/9h/zb/RP99/3v/Pv9J/3z/nP8SAEwATgCIAGgANwAyADQAOQDw/3z/c//U/+L/xP+f/4n//v85ABQAOwBuAC0AFABTAE0AawBmAFUAeQCKAF8AFgAwAAUAwv/w/x8AUQCJAFIA9v/u//L/3v/X//z/OABiAFIAGQAgAAsAl/+J//f/QAA7AA0AHAAVAJf/YP+a/7b/q//r/wUAPQCjAFsAXQB6AA8Atv/s/x8AAAAbAB4ABwDL/7T/QAChAE8AGgDv/8L/3v+m/2H/Rf/y/tv+Yf+7/9//BQCr/7r/VABRAEQAiQAyAM3/8/9MAMoA5wBAAKz/2v82AGUAXgBxAG0A9f+u/3b/bf/W/ysAHwDE/7b/EwBJAD0AKwAsAEIA+/+K/4r/uf8mABYAef9Z/8j/DgCs/2//pP/Q/8//0/8UABoABAAxAFUAvAC7AGQAvQD4AHMAMwBfAD0A7/+o/5D/if+7/5T/f//p/wAA1P/G//H/6f8eAGIAZQB3AIwAfwA+AEAANwDS/wkANwCg/5r/GgATAL//uv+u/8v/nP8F/zv/1f/0/5X/ZP+u/wgAYwCnAJ4AxAD5AKcAHQALABwA4f/O/9L/ov+3//r/FgAtAB8AAQAaABQA1f8CAEkA8/95/33/eP99/7P/9v8PAAEAFgDa/7v/5P/q/9n/zv/5/zsAHgDt/wYAOwAmAN3/DwA2ABMAFAAVADYAXgAsAPz/VgCJAHoAlwBCAOb/GABfAIIAPgDb/xcAmgCfAC4A7P/x//v/rf+T//r/7//G/77/vv+s/2n/WP+G/57/r/+b/67/6f+Z/3z/sf/y/yAA1P+S/wYApgA9AI7/i//K/87/tP8dAGkAUAAcAOn/YQAvAQcBYwA6AHUABwE5AVQBIgEzAK3/FQDbABcBcwCh/4z///9gAGYAIAB//7f+fP4g//f/5v8B/4H+1v5q/5r/iP9S//H+q/7e/pb/3f93/73+af7G/j7/ff8y/wz/pf9UAIcAXQDJAKUBzAGuAQECYgIdAksCKQMGA0gC5wGKAacBGgKDATgBZgHFANX/Of+G/8L/D/9C/j3+sf6N/vD9lv22/fD9AP4y/hX+x/2Z/Rb92PzR/LH8ivw7/L78Xf0M/dP8HP0c/0MBbAGPAZ8BegLKBEQGfwa+BXUFDgYPBiMGjgbxBWAEnQMqAz0C2wH1AEf/Rf7S/bP9Xf2c/CT8FfyU/CH9Nv38/Ov8Of2Q/bb9kv02/bv8RPxj/M38Sfw6+2b6V/ua/eD94fwN/d3/dQJIAowDFQW6BMcF0QYfBzYIHAgdBykGDAYmB6QGXQR5Ag8CNAJBAa7/RP5u/cH8Tfys/M78c/xB/DT8vvz7/VT+Qf3x/K/9y/2c/f38vvtb+wP7evr4+d/4k/kk+w/7V/vN/Hn+OQC0AQ0DAwUJB6sH4wfOCJwJCAofCbMIDgmAB0kGdwVCBAcDjgDD/rz+iv7H/H37xfuq+5D7tPuu+1n85vzV/C/9Gf7S/m3+rv2g/en9w/3E/GH7L/r3+aj5Zfh9+Hf5Xfqn+2b86f00AAEC8wP1BdcHGAn5CbQKdAqkCjkLWgp/CBUHMAaVBL8CbgEEACv+U/xW+7j7Mvxb+8X6L/se/P38If24/Z7+/f4f/zn/s/8dAET///3Q/W398/t7+mP5Tfim98X3e/h/+gj89/vW/ekA6QJSBbUHXAlbCgcL1AvnC3sLWAs9CkoIzgYhBUcDRQHv/lD9evwt+9f5Z/my+d765fpt+o37m/yl/Yj+1P7g/74AvQARAUABqgDD/9v+0f1t/Jr6Uvhd9qX1+/a++PT4Vfmy+lP9hgAsA5IG+AgpCkcLLwu5DFwO7gweC+8IdAd5BtMDnQFI/8r8APsW+R/5Yfn892v3Pvgg+lX7bvtV/PP9jf+pAHUBjgJ9A5wDDwPBAooCbAHB/5X9vfsa+r33yPV09PT12vgg+RD6yvvG/fwBPwXoB5cKlgu9DIUNsQ1eDnMNIwt2CAgG/gQLA8z/3vxp+uj4ufcE91j3BfeM9qP3x/kB/Ff94v3f/usA/gIcBOwEfgVlBQMFYwTEA/cC8gAn/gH8Qfru96v1kfOB8rr1PPmC+An5Gvwo/4oDdAfOCQ8MKA3XDZQObg6iDmANgAlaBrcEZAPzACv90fmh97X2Pvao9eP1+/VS9ir4IPqW/JX+4P7A/zEC8ARhBosGSQbsBfUFpwV1BCIDMwGI/gj8M/p7+MX1D/P48OLw3fU5+pH5P/mP++8AtwZcCToLVwwMDagOsg4fDskNeAuqBzkEhwLgAbb/xvsH+Dr2RfZ79gH2jPWC9nP4avox/E3++AAwApQCRARVBgAIdQhgB4MGSAbWBacEsgKEAC7+zfvR+QT4svU+8x3xTO8v8RD3ePor+hb6bP0GBAEJMguWDIkNqw4/D3cOgA1qDBUKGAZ6AvEASgC5/rb6APet9g33F/ct95X2BfjK+gr8aP0+/2oBiwPVA0gEOwb3B5gInwf0BQ0FvATeA9sBTv///Br7aPmJ97316PPd8V7wc+828lL5mPxZ+6b7M/8JBvAKtwuWDDgNIA7oDl4NVwysCyAJFQV5AeEALwEt/zX7mfdO9134RPgN+Fb3Tfiu+yr9dP0M/yUBfANABN0DcAV7B+UH1gYTBVYEfARKA4YAGv6p/GX7s/l197n16PTJ8zzyQvHf8BT08Ppw/WH8kP3+ANQGPwvhC5AMhg1ZDp0OIA11DGwM4QmOBXkCxwG4ATcAcfxY+Hv3k/is+CX4Mvek9976Av0H/Sr+EgAoAtIDnQM8BGEGBgcrBr0EpwPmA0IDzwBf/uD85PuN+ob4k/ao9QH1t/NB8s/xV/Jw9vr8bP5K/Vf/qgPMCB4MNA1tDtMO5A41D0EOlg26DCoJtARKAqsB3gBE/nD6RPc09gT3hfeu9tj19fbO+Vj8c/1o/hEAwAGVA/QEgQWHBgkHUwbLBfIE/gPKAlIAJP7Y/Kb7TfpU+EX2D/Vm9N3z//KO8lXyD/RO+2QAFf95/xADIggODSMOwg7wD6oPNRAVD88MfAwvCqwFNAIeAJb/h/4G+wv3FPWo9Z/2VPZu9cf1nfid+y79E/5B/2EBXgPiBCsG2wZhB0IHLAZrBY0EKwNOAbf+0vze++76dflG93715vTy9N70SvT58/Dzs/Yn/hICPgDqABEFGwrdDY8OGw+ED+AOTQ85DtUL5Ap/COcDaQAw/7H+CP3R+V/2IPWK9nr39PZB9gD3Nfql/Fr9if7O/18BBQPhA/4EzQXmBcYFwATeA4MDbwKsAJD+6fwF/DH7C/ps+Pv2TfYn9mP2Ufbe9fv1KfYj+QgAdANLArMCxwU1CpMNWA6vDgEOHQ3BDSwMvAlPCSsH/gIjAA7/jP5j/Rj7NfjO9rj3fPhE+MH3HPiN+tP8mP1V/i7/iwA3AuYCtQPYBDQF5QT3A1sDZgOvAgYBIP99/Zr8L/xD+4f5KvjK98v3//f092z3+/Y/9wX7ZAFSA0oBwgFgBaEJIwxbDHMMGQzBC2MMLQtbCSoJJQcRA1QAcP8Y/4z9lPq792X2+PbV91T3GvaC9hT53Pqs+zX9Xv57/wIB1wFHAwcFiQVIBUkE6gOpBHMEFQNTAYr/g/4x/o79Hvyh+oX5ofhb+Ez4tfde9+n2sPfZ/M0BwAGrAGQCdAYkCl4L8ws9DJYL5guZC2AJfggZCBgFWQF2/wP/PP4S/C/57/Yt9vb2yvcI9/r1h/c9+l/7Uvzy/Wj/CAFiArIDwwUvB4QHAQcZBkcGwAbcBegDoQH8/3H/s/4J/SD7oPmo+Az42feg99r2JPbZ9bj3Q/1YAXYAbP/JARgGcAmYCugKyArUCqUL7wogCZkIngefBD0BUP+e/pv9k/uN+DL2bfav96j3bPZZ9gT5qfuY/HT9pP5SAE8ClwO6BAkGDgeBB+oGGwYZBhgGEgURA/AAof/w/s79vvuy+Wj4nvcT9472sPXo9J70+vQt+Hb9e/9v/k//sQKaBpQJ/AqPC3MLfgtCDI8LCAqVCdkHfwQgAq4Aff8r/uX7F/k19zb3L/iz+G/4gvjP+Uj7r/yd/gIAEQGUApADmARaBrcHOwiOB1UGzwWIBfAEhQP/ALf+cf0x/KL6IvnA96L23fWW9UT14fQ49bD1yfft/EUA4f8sAP8C6gbFCTMLOQz4C38LKQxzC+MJcwkkCB0F7QHy/yf/5f39+6L5FPdj9tL3Ifl3+Gn3Cfme+wb9Y/50/4cAQQJ3A1kEOQUABh0H0QYDBUsEgAQ1BMsCWQBB/kb9vvy++/D5JPhG9yL32Pbd9Tb1jfUs9rr4g/3m/1f/LwAXA1gGGwkACwkM4Qt/C/gLpguGCiIKvAh2BdcCcgH1/4z+5PxD+ub3Ifer91n4Kfj/9/T4Lfp4+yD9YP6c/1wBlAJeA68EKwYlBxEHEwZJBQUFtwTKA8ABYP/y/fn8evuy+Qv46PaE9jf2gvX29OH0mfXc+Gz9Fv/a/pEA0AMvBzYKNAwIDdcMrQynDKEL8ArSCrMIIQWCApkA1P6D/dH7KPno9kj26vaP9273xPdv+ez6/vuH/QT/sQCtAsIDWARbBXcGQgf4BrkF1gRjBKEDMQIGAPT9vPzN+zb6UvgT93f2afYd9hf1pvQq9Yb3k/wdAAcAWwDuAj0GNgmjC10NZQ1rDDAMgQszChcKpAnGBjgD4gBY/wH+6fwk+174ZvY19tL2PPfh91f5Xvqt+uP7o/1V/1ABtAI+A7YDewSqBVIG7wVTBYkEqgPfApcBGADm/rr9Uvyj+vr48feq96/3Tfdy9pv15fVW+Wj+ggAwAAkBYgNBBv8IfQsCDZIM7wuhCxQKOgntCQwJ5QWLAv7/Jf7+/Bn8afof+Jr2Ufan9uP20Pev+dD6Lvvy+w79/P6QAUkDLATTBIsFkgb4BpgGaAYPBi0FxwPDAf3/6v7V/UP8Vfp5+Df35va79hj2pfWi9WT3vPvy/lz/RwB9AtUEMwdVCSgLCQzkC9ULpwqeCIAIvQikBq0DGgGi/sz8tvtQ+mj4C/e49qv2KvZ19pz4lvpY+2H8Xv1Y/o8A4QIdBPME1QWtBt8GdwZ2Bn0G5AXZBCMD4QBJ/2P+Hv1f+7L5QPg892n2rfUc9Rn1pPcJ/DD+VP6M/7oBNQTGBi4JAguFC7EL7Qt6CtwIKAkSCQYHRASiASD/T/0t/NH6DPmb9+T2i/YB9pb2ovgw+jz7Xvz1/A7+eQDoApIEnQWsBs0HHAjGB5UHHwdcBmAFgAMTATz/tv3w+zT6l/g09y72F/UM9JHzkPRE+Db8eP0U/tr/7QFvBEIHuAlNC+8LawwIDCMKFglCCV8IdgZABEEBNP5g/Ab7Yfnx9yv3qvbz9ZD1qfa2+Kb6cPyn/UP+o//qAbID8wQ1BlcH+AfYBykHYgaVBdMEwAO/Aa7/R/6o/Kb6GPkW+Fr3hfaA9Tz0O/Sb9877k/2Z/kgA0AGNA8wFEQi7CeUKMAwvDBEKoQiACIsHEQazBGUCif9i/bn7Hfrs+JD4nvgT+Dz3svc0+Zv6PPyi/Vf+kv8sARkCFwOeBDEGVQdxB9MGJgZDBVoEbAPrAWMAV//0/fP71PkG+Oz2ePa69Q/1k/ac+aP7Dv3W/kYAsAFxA3UFawfJCDQKRAtlCjIJIQmHCDwH4AXrA9sBYQC0/oz8sPqo+S75afhX91P3Qfj9+Ab6KvvD+wv9/f46AE8B2gKgBEAGBgf/BsUGQQaSBawEKgOHAUUAzf7X/P36Oflu9+b1g/RL9I/2NvmH+gP89/18/wkB1ALKBM8GuAiVCjcLhApRClgKaAlLCPkG6gTkAjkBT/8b/UX76PmX+D/3b/ap9nz3Xfik+ez62/tJ/f/+bgAcAvYDkwX5Bu4HLAjQBwUH4wVPBIcCDgGb/7H9jPtT+fn2/PQn9C31MveI+NL5Gfwe/nn//ADPAqUEpwb5CJcK3woeC3cLqApfCW8IEQf9BB0DVwEc/xj9sPsb+kH40vZr9uL2bvcp+DP5G/pi+xv9iP4GAMwBpQNzBdQGoAfQBzQHMwbqBEADpgEtAEv+DPza+aP3ZPX083b0DPY89834S/tC/Zf+RwBBAgYE/gWDCGoK9wqjC3MM2guBCm4JzAeHBb8DRQI8ADb+s/zl+q747fZm9pL2uvZX92H4V/m5+jL8Mv2a/sQAEwMFBV0GKAeFB4UHIAfmBQ0EUAKwAKL+MfwJ+gH4+PWS9YD21PbQ93b6x/wG/lv/PAERA7YE/gYdCekJ2wplDI4MYQtnCi8JEgc2Bb0DigF3/yf+e/x3+r/4v/dW99L22PaS9yL4Bfk2+gv7T/xl/qQAaQLhAxkF3gVBBlwGDAY5BTAE6QIoAab+zvu7+N/1LPXX9fz1UfcB+iP82v3G/7cBEANSBMoFvQY4B5YIRAqlCnsKgQrWCXQIUwfoBaADeAFP/138fvmd9yX3T/d290z4TPm3+UL6svq/+nf78/xv/t7/wgGyAzAFXgb8BqIGqAU5BEACk//k/FH64fcq96v3rfdn+N36Q/2T/sv/XwFMAgIDSAT9BDIFfgZHCFMJFAoTCy0L4wmPCO4GQwSdAUT/mPwq+u74C/lf+Vz5s/ks+iP6IfoO+qj57vk6+8v8aP52AJUCOQRtBRsG4gXaBIEDlgHY/hf8rfkc+CX4ovgb+fr6x/2t/38AYAEzAl0CxAJpA3UD8AOpBUwHGwgXCTcKCwoYCTgIcAb3A4wB6v5U/G36qfne+Q36L/rA+h77B/vj+pv6WPqt+q/75PxF/jYAOgLHA+IEQgXABLkDRwIcAEf9RPpT+AL4XfgI+Zr6+vxJ/+AADAIWA0ADHwNCA9gCsQLiAyoF4AX1BlsIAwkPCccIaAdaBXsDSgGL/h784fqu+pb6qPoW+2b7lvvE+3f73/rf+k37ovuK/FT+KQDDASQD/AMYBLgDvwLIAKj9jfoe+aD4CvjA+On6Vv3L/+sBrAPLBDYFNgVSBAMD0AI2A0EDZgOiBDwGEQfCB+gHxwZrBbQDHwGy/tr82ftL++v6Wfs2/Mb8Rf1o/TL9IP0h/dn8m/wm/eb9eP6T/+sA4wFiAvsB+AAp//P8wPuy+k/5c/nr+oX8bf6wAOwCYgRzBUQG0wXbBGwEpAN7AvEBhgJTA6oDfgRABRcF1QQWBDMCFwCL/nD9Ovxs+3z7tPv6+6b8Sf2m/Q/+lP6v/nP+oP7r/tX++P5w/6P/iP9Z/5D+2PzG+7j7Fvu8+sf7Lf1j/sL/zgGSA3MEogVHBrQFagUoBTIEGwO4AtwClAJ+AuUCwwJ/AikCJwH7//n+Sf5//Yz8bvyx/KL86fxs/dr9af4S/3r/ev+d/9v/s/+R/6v/f//g/lL+e/0S/I37tfsu+yz7d/wW/oH/3QCtAgQEtATtBXkGyAV3BTgFSQSGA5QDrQMnA+QC+AKgAhUCXQE+AAL/8v1H/Wv8sfvX+9v7z/ty/Bf9pP1h/jL/7f9LANwAWwEwAewAtQD3/8/+pP1S/C77yfqF+oT6o/tL/dn+TgAWAt0D3ASqBT4G5QVyBR8FVgRwAyYDKwPLAosCmgJhAhQCnwHEAM3/lf6H/Yv8f/s2+yb7pvrF+m77IPz9/NX9tP6F/0cA3AANAR8BGAF7AGb/Hv4w/bP8Ivzs+3X8Zv2u/hEAdQERA2AESgXVBaAFIQW6BA8EKAOBAlACJAISAmICkAKQAoUC/AEzAV8AY/9L/gP9Bvxv+9j6h/qv+vT6e/th/GD9Sf5N/zsAuwAWAVsBBwEdAOD+1f1R/aL8Gvx8/Gb9u/5XAOkBqgNPBacGhgd8BwAHYwYfBWkDGgJkAbwAJQAPAC4AYwDOAAsB6ACLABcAd/9R/hz9O/xa+536ZvqS+hj79fvq/Mv90f71/7EAGwFOAQYBKgDY/tH9N/1G/K/7Ivz//Cb+pP90AWcDAQWBBoAHhgdRB9YGqgU5BPkC9wHIAOX/wf+S/5X/7f8eADsALADo/4L/nf62/cX8cft1+vn5pvmw+T76CvsP/HP9+P4uADsB8gENAnABnAA+AIL/Nf7b/Vz+7P7P/xABngIiBKsFJAezB6QHlQfbBloF3APAAm0B2f/6/oj+Kv5R/pn+1P4f/0f/ev9b/9L+SP5y/Wz8s/sG+276MfpQ+sf6ifuU/Lb9tf56/+7/2P+Y/7v/kP/v/u7+fP8eAO4AHgKkA/oEUga+B1sIngjuCHYIQQfdBV4EiQKlADD/BP4V/ZD8VfxS/Gn8rvwQ/S/9UP1i/RD9nPwt/Ij72vps+lL6gPr8+s37xPyo/Xz+GP9Q/73/TQBJADoAuwBAAZEBCgIMAxoE1QQHBjMHrAckCGAIwgfNBvwFDQWVAxMCCQH9/xj/q/5Y/gr+y/2g/Vn99vzX/KX88vtK+836R/rS+Yv5fvmz+Sn65fqu+2j8Jv2w/TL+2f5S/8L/fgBEAfEBlAKGA74EoAWEBnUH6gdCCHQIIQiJB9wGBwa/BEEDDALSAKP/yv4S/o39Lv3y/PD8+vw1/Wz9SP0X/cb8JfyP+wv7hvoL+sD5wvnd+R/6jvrR+nL7mPxw/TX+WP+uANwBygL/AzEF5wW8BoAHrAfDB+sHvQcYB2IGuQWgBGMDiwK0AbUA9v+K/xf/cv4J/uj9p/1t/Rb9h/wc/MH7afsQ+6/6oPq0+r365voA+y37Yvui+zT8t/w2/Sv+Z/+6AOwBIAOIBJoFdAZCB3EHWQdgB/4GMQZbBaAEzQPEAvUBUwGSAPf/lv8g/63+b/5g/i7++v3s/a39VP0d/dH8Xfzp+5z7e/tG+yD7IPs0+0/7hfsB/H/8BP0F/j3/XAB1Aa8CPQSOBX0GaQfyBywITgjoBxEHEgYWBf4DgAIoATIASv+e/ir+yv2G/XD9pf2u/Xj9bP1B/eb8lfwz/OD7g/s4+zD7D/sS+2f7qvvO+zb88/x4/e/9CP87ACwBTAK0AyQFFwbwBusHQQgqCBkIgAdhBkQFTQQPA58BpQDn/xz/nv6F/n3+RP5K/oP+Mv7f/bj9GP1Z/Mv7PfuV+uP5jvla+RD5LPmB+bf5Qfpc+138PP3b/rQAMwKvAxwFrgbXB6cIlgncCccJ2QlXCTUICwfuBYQE0gJoATcA2v6v/QT9oPxK/Dr8l/y9/Nb8Pf14/Wb9Jf2e/PD7L/t/+vD5PvnW+Lr4pfgc+Rz6Fvs1/Nv9rP9RAesCtQRmBqQHwgi1CRMKLAolCowJbAg5Bw4GnQQNA8YBsACf/87+WP77/an9rP3L/aD9aP03/e38e/zi+0b7lfrO+T75u/g5+Pn36/c++O/4mfmm+lr8Tf48ABICDwQWBrcHGwkWClgKPArZCQUJxQd1BiEFnANFAlkBogAGAJD/Yf9C/xr/I/8P/8D+e/4o/r/9P/2r/BH8UfuB+tv5Qfm3+Dr46vcW+KT4aPmb+k/8O/4VAPQBIgQqBr4H8gihCd0J6wmsCcUITQfjBYUE0gI9AQ8AAf/+/Vn9K/0t/Uf9n/38/Tr+hf7I/rv+Z/77/Vn9jvzH+w37Vfqo+TL5VPnt+Yj6XPvN/Kj+dQAdAvUDuwUHByUI5QgPCfMIoQjQB2YGBQXrA3gC1wCZ/7r+Av5u/UL9Xv1s/aD97f0L/hv+Gf7e/Vn9vvwo/Gb7lvoH+q35TfkH+Xb5Z/os+0P8Ff42ADsCGgQyBi4IkwnBCm8LLguECooJCQgQBgAEMAIrADj+HP1y/Of7ofu2+zL8uPw4/b/99/0N/hT+yf0//a/8Jfxx+5v6CvrA+WL5OvnV+bb6k/sP/TL/aQFgA3oF2gePCZ4KhAu0CzELXgolCWcHTQWQA/wB5/8f/hf9UPyg+yX7N/uV+9H7Mfyv/Ar9Pv00/f38iPzd+zP7dPq/+Ur54/it+Cn5UPp8+9v8E/+tAfgDBgY/CEYKhQtHDJQMDwz9CoAJhwdJBQ8DHgEg/yv92vsq+8v6lvrS+o37H/yR/Cz9sP3k/b79iP0X/S/8RvuI+rz5JfnG+Kb4OPlV+p/7Uf2j/0oC2gQlB4YJrgsnDe0N5A0cDe8LKAq6BxkFugKHAD3+WvxD+836a/on+oH6CPs9+537Ffxp/GD87fuA++z6EPpW+cX4Tvga+NX3KPiX+Tn7Ev2s/64CqwU7CJwK9QyJDlwPcg+FDhYNWwv6CC0GkQNPAQ//6fxk+536Ifqs+df5efrK+hb7mvsK/Cz84vun+1z7jfqj+d34Ivif9zb3Ivf290D51Poq/SkAUwNMBi8J7wvoDSsPuA8zD/oNXgwnClkHlgRCAvj/tP0q/G377vp6+o36Ift++637Hvx//Ef8qPss+5/6oPme+Of3Rfe39oT2JfeT+CX6EfzS/hACKQXdB4MK1gxJDiIPHQ8VDrEM/QqqCOUFegOaAY//p/2t/Cv8kvsi+1j7xvug+3P7zfv3+5H7F/vY+nz6u/kM+Z/4OPjm9xH4G/ll+r37+P3nAMgDXQbsCJALRQ0yDtkOVQ7wDHELeQnPBvcD2QH6/7T9HPyK+wL7QPoW+qT6yvpo+n763/q1+i/66vmt+RX5dPgx+CX48/c9+LD5UvvX/C7/XAJ+BdcHGgqqDAMORw5mDpINwAuzCZsHEgUwAjQA6f43/d/7nvu5+237NPuf+9P7dPs0+zn79foc+lf58Pg++Gz3/fb49gD3jvd3+Z37hP1gAB4EoQcYClkM5Q4EENsPcQ8zDiUMqwkeB4EEpAGJ/xD+TvwC+7L6wfqJ+kf6t/oM+6b6b/qi+n/6tvkI+ej4i/jS94D3rPfQ94b4W/pG/En+LQG+BOIHFgqNDPkOvg+UDxoP9w0ODIgJAwdXBJoBkv/T/Qf81/p5+lz65/m0+Sf6W/rz+bP58vnu+T35wPid+DP4s/er9+b3TPiz+cX71f1TALADVwcLChkMoA5GEF8Quw95DroMIgohB2oEqgFK/4/9EvwR+5X6lPqJ+jP6Rvp5+hv6f/k2+Sv5qvjt99b39Pe/97f3Mfje+Pv5uvul/eP/pQLZBcsIzQrzDBAP0g+jD78Ogg3jCz4JogYqBKsBvP/q/Wj8a/uq+j36ovkS+f34vfgp+Mf32vfs94/3SfeB99T32fcc+MH4yfmX+3H9bP8eAjIFSAiFCm4Mog6uD6YP+w6tDTYMzAnvBn4ECQLh/w7+jfyZ+/b6ofpX+gT62fmY+fz4Zfgy+PH3PPes9rD26fbz9if33fcX+Qr7FP1H/0UCogXcCEALLA1dD2UQRhCUDyAOnQxjCp8HPwXWArsA2f4h/Sv8c/vA+gz6fvk3+a/42/c89+72ofYa9tD1AfZt9tX2TPcv+LH57Psn/kkAGgN+BpcJuAuODZ0PnBBbELIPgg6+DFYKuQc1BYYCGQA4/nz8Afsh+pP52Pg0+P/35vdf98P23PYb97z2VvaP9gT3IfdW9yX4nPnC+/j9ZABxA9QG9wkgDPUNyA+TEGsQcA8MDoAMGwplB90EdwI5ACX+a/wi+zv6ePnY+Ij4Xfgo+MP3cfdj9wf3Vfbo9er1/fU09r32//dx+hX9fv+dAkoGzglIDOgNsA+cEDoQDw9dDaELdQnBBkYEJQJYALH+Nv0r/Jb7HPtd+tf5oPky+V74nvdP9+r2CfZf9YP17/VX9hj3a/jg+rj9/v+9Av0FLgl6C6wMEw4DD7AOog33C1wKgggKBrMDuwEkAIn+M/1i/NX7dvvU+l36S/oB+nL51fii+If46fdQ9zv3ive49wX4wvhV+uP8Cv8qASwEVwcgCs8L7QxmDvkORQ65DAMLlglBB1IE+wFFAMT+Ef3l+5n7efsQ+4H6Y/pf+tH5CPl5+E344vcF96b2M/cD+JD4WvkH+6f9MgDdAfEDtQYMCTUKmwqnC3IMzQuwCrIJtQg0BzAFhAMaAq4AOf8K/mf98vx5/Nb7XPs1+8369/kX+ab4Svhb96H2tfYF9zz3yff5+Ij6kPzj/hQBdAPeBScIvwmJCoYLEAymC7UKjwmcCAwH/wRkA/EBdgAJ//j9Zv0E/a/8Wfw6/B/8v/sk+176yPkj+SX4OPe99uH2K/eZ95r4KPpc/Lf+1gA3A80FMAijCX0KrgtZDBQMRwtdCm0JzwfpBSgEjQICAVj/FP4j/YD8Fvy4+6b7ivtj+wX7mPp4+g/6T/md+Ir4vvim+N/4rPnN+vP7nf2l/z8BCQMGBdAG9ge3COIJfgpSCt0JawnVCFEHqAVIBNsCWAHd/8/+1/0D/X788fti++H6ffr0+WH5IfnQ+Eb4APhX+N74Q/nm+Rn7Wfxi/Sb//wBDApoDOwXfBl4HxwfOCA0JtQgDCLMHSAfgBa8EpAOjAoIBVQCn/8f+AP56/bv8BPx1++D6CfpZ+Sr5+Ph0+D/44/iL+fL50fom/Hf9IP4I//EAHgKJArkDZgVLBlYGBQcTCCsIrwdzB3gH2QaGBX0ExgPWAqMBlwC5/9j+HP5L/XT86Ptv+6z6zPmM+ZH5J/m8+PD4rfkT+kH6E/tN/Cr9t/3t/pkAmwFmAtIDVwUMBocGYAfuBwYIwQdzBysHSwZBBX0EhANYAnoBngCd/+f+S/6E/bT8DPx3+9P6KfrI+c35lPlf+dz5Svp7+vn6tftm/Oj8mv2W/tP/2wCrAQwDKATYBIsFJAbBBgIHOAciB8wGlgYpBm8FIQROA7ACQQHd//r+jv6k/Yb8MvwV/Ln77vrb+iz7xfqY+qj6/Po3+zX7ofsp/Mr8Nf3H/ab+af9sACcB6QHzAtYDcwTOBHEF/gUjBgMG0AXPBXsFcwR1A9UC1wGXAJf//P6V/vD9cv1H/Vn9FP1//ID8Wvz8+7L7jPu0+837CfxZ/P78uP0q/uP+if8RAIsA/QBvAbgBOgK6AkADtQMdBMIE9wT2BN0EeQQPBFcDZgJ/AcoA9P/k/kb+zv1N/dj8mvyr/LX8uvzL/DX9gv2V/Qv+I/5i/u7+Bf82/4H/CQBEACYAwgA2AWQBpwGYARkCTQLvAeEB0QHCAWAB3wB/ADgAHwCU/zH/Kf8n/zD/vv7Z/mf/hf94/3z/+/+GAF8AUAD7AE4BNwErATsBlgGBARsBvgCAAJQALgCP/3H/o/+h/yL/7f5g/43/aP9W/3n/y/+2/4z/qf+j/2b/Qf8Q/8v+pf6C/oH+lv63/vT+OP+m/yUAkQDqAFMBuwH0AfYB6AHXAaEBOgHLAHIA/P+k/4n/bf9F/0X/jv/F/7z/vP/o/xAA6/+h/4D/if9m/w//5/72/gj/9v7j/vj+S/+P/37/lv8FAHIAjACIAPUAcwGKAWEBYwGtAaEBKwHiAOwA2gBmAAEAAAAEAMP/ff9r/43/kv9m/1f/hf+w/53/fP+W/7r/l/9U/0b/Uf8j/9j+yf70/gP/BP9B/63/HgBxAMQALgGOAcYByQHGAcwBqQFgARkB3ACdAEQAAADf/6//h/93/4b/lP+Z/7P/1f/0/wkADwAaABoAAwDp/9b/tP+F/2v/Wv9I/z7/Rf9d/4H/qv/Z/xMASAB2AKMAsAC/AMsAtgCfAH4AWgA1AAMA3P+w/4L/Z/9S/z//N/9B/1n/ev+j/9j/BgAxAFkAZwBkAE8ALwAEAMn/lf9n/0P/Mv8v/0H/av+d/9n/HgBgAJ8AygDtAAgB/QDiAMYAnABXAAwA0/+d/2H/Of8r/zH/P/9h/5X/0f8YAGAAmAC9AOQA/QDsALwAlgB3ADcA6P+7/6r/kv92/4L/r//T//X/JQBgAJAApQC1ALwAsgCVAGMALQDz/7j/eP87/xD/8v7f/t/+9v4a/0v/k//c/xsAVwCZAMQAyAC/ALUAlABYABwA7f/D/5v/g/+F/6D/yP/+/0QAkQDbABYBQgFcAVkBOAH3AKQAQgDT/1n/6f6Q/lD+Iv4Q/in+Y/6s/gH/ZP/N/yUAaQCbALcAuACiAHgAQwAIANP/pP+A/3X/g/+o/9v/HgB4ANYAKAFqAaMByQHFAZwBXwERAaYAIwCn/zv/3P6K/ln+VP5u/p/+5f5B/6L/+/9IAHwAlwCeAIkAWgAXANL/j/9L/w3/6P7n/v7+Jf9o/8n/PACpAA8BdAHHAfgBAQLsAb0BaAHzAG0A5v9j/+j+iP5P/j/+Uv6J/uT+Uf/E/zMAmQDmAA0BEwH9AMgAdwAXALn/Y/8W/93+xf7O/u7+JP9z/9P/MwCQAOwANwFjAXEBZwE+AfAAiAAXAKX/M//N/oX+Zv5s/pL+3f5Q/9X/WgDcAFMBrAHYAdgBtAFrAfwAdwDy/3T/A/+m/nH+Z/5//rH+A/9t/9//SAChAOcAEQEWAfIAswBeAPn/jP8k/9P+nf6D/or+uv4N/3n/8v9yAO4AVwGhAcYByQGtAWwBDgGjADkA0v9x/yb/+v7u/vX+FP9I/47/1/8aAFcAjACoAKcAkABlACQA0P93/yT/3f6l/oj+kP64/v7+Xf/T/1UA1QBIAaoB8AETAhIC6gGhAT0ByABNANb/b/8f/+7+3v7r/hX/Uv+e/+v/LwBlAIkAlgCKAGQALADn/5j/Rv/5/rz+kP59/on+sv73/lL/xf89ALUAIQF6AbgB0wHJAZ0BVgH6AJIAKgDO/4b/Vv9A/0f/Z/+V/87/CQBAAGkAfAB5AGAAMgDx/6T/VP8H/8b+lv6B/oP+n/7Q/hn/cf/Q/zIAjwDfAB4BRgFXAU0BLAH4ALcAcQAqAO7/wv+l/53/ov+3/9T/9v8VAC4AQQBFADgAHgD2/8j/lP9h/zP/Ev///gH/Fv88/3L/tv///0kAiwDHAPYAFQEmASIBEQHwAMcAlwBkADMACQDq/9j/0v/X/+j/AAAdADgATwBbAF0ATgAyAAYA0P+T/1T/F//j/r/+rv62/tX+CP9S/63/DABrAMQADAFAAVsBXAFFAR0B5QCkAGIAIwDv/8j/sv+t/7n/0f/v/xMAMQBGAE0AQwAmAPT/tv9t/yH/1/6X/mr+U/5U/nP+rf7+/mD/zv89AKQA/AA/AWcBcAFgAToBAQG+AHwAQAARAPf/7f/0/wsAKwBOAGsAfgCBAHMAUAAeAOD/mP9Q/w//2/63/qj+tP7V/gj/SP+T/+H/KgBoAJkAugDLAMoAugCgAIAAXgA9ACQAEwAOABMAIwA8AFYAdQCRAKcAswC1AKoAkgBvAEIADADR/5P/WP8j//b+1v7H/sX+1P7z/h//WP+Y/9v/HQBYAIkAqwC+AL8AsQCYAHMATAAlAAYA8//v//v/FgA+AHIAqADaAP4AEgENAe4AtQBkAAMAnP82/9z+mP5t/mL+cv6f/uD+Mf+J/+P/MgB1AKcAxADIALoAmgBrADkAAgDU/7H/nv+c/6//3P8WAFcAmADPAPwADgEGAegAugB8ACgA0v+A/zX//P7U/r/+x/7k/gX/Mf9w/6b/1f8NADMAVgCAAJcAnwCWAIEAZwBGACsAGAAPABQAIwA3AE4AawCYAMQA2ADaAMsAqAB9AEMA8v+i/1L/Af+6/oT+bP5e/o3+w/6S/50BFQKl/yv9j/0jAAoB1gAZAtsC1ABd/V39LgHGAroA1P5C/1cAXv+0/Sj++//6AYMD5AKTATwCYQNaAsD/Yv62/sj+/v3d/e3+T/9G/oL9qP3j/YT+Wv/W//z/k/+g/wEBfgJ9AhIC9AIMBJADzAH4AE8BUgGBAOL+U/4f/1H/6/5k/k3+Kv5Y/bH8bfwF/Xb9fv1H/iP/tP8uADUBWgLKAqYCawL3AvsCJQKcAVkB0gCg/w3//P79/vv+0/5I/wz/XP4y/ir+1/44/1r/QwDuAB8BPgHQAWsCcQJUAuABoAGDAf4AowBCANn/S/+z/p3+iP5c/mD+uf7g/nn+mf4b/0b/Zf9o/4X/4/8sAFgAfgDhADMBDAHLAKMAxQAOASIBLwE0Ae8AkwBfAFAAOQDl/3v//P4q/nf9VP14/dL9Zf7t/oD/4f8uALgAAgE/AYsBsgF+AQUB/QDgAGQAEAAfAE4AEgDl/x0ADgCs/3r/dP93/3D/l/8xAF4AJQAxABoA4//M/xkAZQBBADYACgDK/3b/Dv8z/0j/U/+D/6D/+f8sADsALwAfAEAALwA9AFoAgwDfALIAQgAwADYAKQA2AEsAXwCGAGUALQAnAPn/sv+M/2b/Mv8h/zL/UP9a/03/Sf9J/zn/Jv99//j/MQB/ALQAzgDaALcAxgD+APkAzgDFAKQAVABKAF8AOAALAAQAFQAuADsAWQCSAGcA4/+y/67/if9d/yv/Bf/2/tP+yv78/jf/i//5/0cAYABoAFUALQAhABEAEQBAAGUAfQCRAHIAUwB0AI0AmgCiAIgAjgCAACsA2P+w/7z/4v/1//r/BgDe/2n/GP8p/0f/W/90/5b/4f/s/7L/jP+L/8X/5f/T/+H/KwBRADcAXQCoAOcA8ADqAAEB0QBhAL7/eP9n/xb/G/8x/17/j/94/5v/v//H/8//4//9/+D/z//M/+n/JgA5AGoAoAClAJsAiwBWAAwA6P/G/6X/ff9d/5H/x/+9/9v/TwCWAKIAzgD1ACwBQwEVAQ4B+gDHAJcAWAAtACAAAwDN/6H/h/9Z/yf/Gf8n/zj/L/8t/1f/Xv9L/1b/a/9//5X/sf/V/wMANQBdAKAA1wDuAAIB7wDPAMgAvgCCAEMAAgDP/73/d/9j/3j/aP9q/3f/iP+e/7P/q/+6/+T/CwBHAFIAVAB+AI4AdwBvAHgAVgAaANT/uf+1/4H/dv+7//r/FwAxAFwAgwCHAGwAZQBHAPv/vf9+/1L/RP9X/5D/qf+7/9r/4//f/97/5v/7/wAA6P/p/wIA//8JABsAGQATAOz/t/+0/8v/yf/V/wMAMABjAHcAigDCAOIA4QDFAKoAigBlAEgANgAlAAIA4v/E/7L/of+e/73/xP/Z//j/FQBSAHgAhQCKAGMAGgDb/4n/KP/2/tv+w/6//sL+3P4S/0H/Y/+k//z/JgBTAHoAewCNAHEALgD7/8j/rf+u/6j/lf/K//r/6//6/x8ATgBiAE0ARABkAFQAGQAlADMAIgAcABEAKgBfAH0AowD5AEQBcAHCAf8BIAJLAjsCGALnAYwBTAERAZAACgC5/1r/8P6j/mr+W/5Y/kj+Xv6D/q/+6P4R/xv/Kv9G/y//B//4/uj+1P6s/qb+wv7I/sD+3P4I/wP/8v75/hf/Lf8b/zj/g/+c/6P/4v8tAEsATwBpAKcAugC4APcAVAF+AZ0B8gFjArkCwQLeAjUDNgP8As8CtQKaAj4CxQFvAS0B4ACYAFEAFQAJANv/ov+K/1v/Dv+1/mv+IP63/T79+PzR/Iv8UPxN/JH8AP1F/Yr9CP6I/uH+Df84/3r/lf9r/1L/S/8V/+v+0/7L/tT+3v4j/2z/kP/G/wQAOQB9AOMAZQHXARYCbgLPAvcCQwOmA/IDPARvBLUE+gToBNYE0QSEBB4EqAMXA4MCwQEAAW4Anv/K/kH+u/1N/e78qfyr/JL8Xfx9/K78uPzb/Bv9fP3G/dr98/0Y/gv+4f3V/b39jP1f/S/9Hv02/UD9Wf1y/Yj90v0a/lX+vf4y/5j/8v9EALUANQGXARICsQItA5ED1QPwAxYEIgQoBF0EgwS0BNwEowRdBBQEkAMgA6gCRwIgAsEBaQFKASUB/AC/AJEAhwBfAA0Awf+O/yv/jf4O/rz9iv1Z/Rn97Pyg/BL8hPsM+8b6uvrB+uD6C/sx+0j7RvtZ+4T7w/sA/EL8rvwG/WP97P16/jn/DgDjAMcBcQINA6gD/gNFBIIEuwTuBPIEBgUlBSoFKwUhBSQFLwUyBSQF6QS0BHMEBQSMAysDBQO+AkYCCgLLAXYBEAGYAGEAAgBc//T+fP7r/Xz98fyG/Cv8tfuR+4P7XPtT+zn7Fvv7+tD6uPqs+rX6wPrG+u36E/tS+5z7xvsM/FT8j/z4/Fz94v2n/lD/+f/DAJEBWgIAA6UDdQQHBVIFoAX2BTAGHgYkBnEGkgaHBoAGcwZFBr8FOAXWBEsEywNtAxQDuwJtAi8C3AGLAUcB+ACXABAAmP8S/0z+sP0o/Y/8IPy5+4T7Wfv9+tj6uvpq+jT6Cvry+dn5oPmp+cn5wPnT+Qn6W/qp+uz6Z/vi+z/8tfw3/dn9mP5j/2AATgE3AksDKgTkBKcFMAaiBtIG2AYCB9YGjAZrBjMGAgbFBZ4FnwVaBRsF/QSeBEAE0gN0AyoDkgIaAtQBWwHzAK4AZwAiALX/QP/s/nP+3f1f/eT8Vfzb+3f7J/sF+8r6j/p4+kn6GPru+a/5rPm7+aT5wfn/+Sn6UPp6+rz6G/tZ+6n7TPzm/Hb9Ov4T//v/3QDFAfICCwTwBNYFmQYpB4wHzgcMCCEIDQjmB6kHPgeqBjkGyQUwBcoEaQQFBLUDJAPEAooC+AGyAYUBHgEMAcMAWwAvAJT/Jv/J/uz9g/0d/Wn8/Ptn+xf79/py+mT6mfqf+rT6m/qi+pn6M/r9+ev56fn3+fH5HvpM+lz6i/qs+gb7ovtL/Cr9Ef4H/xAA5gDeAQUDEQQTBQAG2QaIB90H/AcQCBII+wfyB/oH3AeFB+0GQAatBf0ESAS+AzwDzAJLAr4BUAHNAFoAGADz////BwD0/73/XP8Q/7b+Q/6+/Rn9pvwq/I37GPuS+jX68/mS+a75/fkq+lv6SPpa+oH6X/py+o/6s/r2+gj7OftU+zH7Uvui+1f8U/07/lr/dACgAe8CBwREBXQGeQdbCLcIFwlTCQYJwAhSCAMI4AdXB80GHgY5BYoEvgMMA5cCNAIJAsQBggFcAQgB0QCXAG4AbwAuAP3/vf81/9H+Qf7I/Yf9B/2m/B/8hvsz+6H6OPr/+bb5wPm6+dr5QPpk+p36sfqa+r/6z/oJ+137h/vL+9H7r/uq+8X7Yfw9/ST+L/8gAC4BUAJjA5oEuQW/BqEHKQiYCNkI1Qi0CFcI/geuBz4H0gY5BmgFjQSbA68CzQENAasAgQBjAD4AFwARAB4AMQBbAJEAxADFAIgAGQCE/+n+Uf7F/Tv9o/z++1X7vfpN+gT61vnB+eb5OPqp+hH7dPvu+0D8Yvxu/Gr8efxr/Dz8JPz++8b7l/ui+yz8/vze/cv+wf/RANYB2gL9AyMFNAb3BncH8AdGCHoIiAhyCG0IPwjgB3cH8AZWBoIFbwRsA24CkQHUADoA8f/G/6L/h/92/6L/0f/m/wIAFgAfAOf/af/2/ob+Bv6M/Q/9qfwa/FT7ufpQ+hH63/m5+eL5OPqO+vz6gPsU/I38yfz+/D79bv2B/V39L/3j/ID8FPzp+038FP3y/b7+hP9gAD8BGQIoA1kEfwVaBtsGUAezB+8HDwgdCEkIVwgqCM4HSQeZBp4FaARBA04CegHEACAAuP9v/xX/wv6d/sv+GP9W/5H/0v/5/9H/dv8r//z+yv5y/hL+vP02/Wj8kPvi+nD6CfrA+dn5Kvp++rj6Dfuc+yX8gfzL/C79lP3H/bX9l/1t/Rr9nvwt/EH83Pyn/Wn+Jf/7/9gAoQF8AooDrwSZBRUGZAbDBi4HdAeWB9MHHAg/CCYIxwdGB4EGawU4BBcDJwJQAYgA8f91/xT/1P6t/rT+3f4b/1f/g/+i/67/s/+o/37/Pv/w/qn+SP7J/ST9afy5+wn7bfoB+tv57PkO+j/6j/r3+nX75ftF/KL88/wu/Vr9av12/VT9Bf2Q/Ef8nvxs/VL+E//U/54AWgH6AcQCrAOPBCUFcgW/BTEGqgYEB0wHsgcrCHcIcggRCHcHjwZkBSwEFAMWAjwBiwADAIz/Lv8H/+D+rf6N/rP++P4c/yT/Nf8x/+3+lv5f/kn+IP7i/Yv9B/1V/JX76fpX+u/5x/ne+RD6Ufqe+v/6Xfu0+xj8f/zm/Dz9hP2S/XX9Nv3r/Gz8D/xn/HD9lP5m/0UALgHkAVICBAMUBBwFwgURBlAGpAb8BjEHawfRB1YIugjGCHkI7Af+BrUFSAQMAwoCPAGhAC0AsP84//j+wv5w/h3+Dv4m/jz+Of4v/hz+8v21/Yj9jv2h/Zb9Vf3g/EX8qPsZ+536S/pB+mz6pvrh+i37g/vb+y78hfzr/FT9mf2y/bH9kP1d/SP9yfxJ/Ef8Kv13/oz/dwBhARsClAISA+gD/gT5BYIGrgbfBjwHgQefB98HagjvCPsIhQjaB/kGuAVKBBUDLQJoAbkANwC5/xL/gv5e/mv+P/7z/ez9Iv4V/rX9Y/1A/Qf9p/xU/Ev8c/xp/BH8pPto+037LfsM+yv7h/vp+yj8Z/zF/B/9Vf1u/Yr9iv1o/Tv9Kf0N/dX8jvxL/N37m/tA/MT9av/GAA0CLgPzA3AEKQUPBtwGTwdYBxgH7gbtBt8GywbjBioHTgcjB7QGFgYlBfIDzALxAVQB4gCPAFEA5/9U//D+2v7I/nz+Hf7f/Zz9F/2M/EP8HPzq+8v74vs8/KL8yvy2/J/8nfyj/Jv8uvwI/UD9Xf2C/Z39mP2O/Yr9j/1z/T799Pyy/Hr8Pfz/+/T75vvD+2L8MP5sACMCZQN1BDwFlQX8BakGSgeEBykHZAa5BWcFIwXkBPAEXQXKBfoF7QWzBScFRgRSA6cCQQLmAYYBIAF9AIv/nP7v/XT9+fyA/CT88vuy+1j7H/s3+3777PuH/Eb9Bf6D/pD+XP48/iz++v2+/aT9if1I/fr8w/yd/IX8Zfxk/IL8n/yR/Iv8rfzm/Bj9U/13/V79t/0b/xgBqAKrA1cEsgSwBK4EAQWVBfYFywU4BcEEugTvBDkFqgVHBssG/QbQBlgGkwWDBEgDNwJ9Af4AhAAHAG3/nf7Z/Wv9PP0F/dP8xPzT/M38rfyY/KD8u/zo/D39vf0+/oP+ff5L/ib+HP4H/uv97f3p/b/9hf1S/R/9+fzm/Nv83fz0/AD9+Pz0/OL8yfzN/Nj8ovzJ/AX+/v+lAbsChwMTBE4EZgS7BEAFngVrBcwEQgQsBFMEegS2BDAFzAUzBiUGtAUKBRkE9QIDAnMBGAHLAIMADgBk/7D+Jf7A/Wv9M/0Z/R79Ev3Z/Jn8k/yv/PD8cv0g/rv+Gv8Z/8L+YP4U/sn9g/1m/VP9Lf38/Nv8vPy2/M386/wP/U39dv2H/ZD9ef0v/QD90fyQ/PL8gv6HAOsBzAKDA/oDBAQgBJwEPAVzBQ8FXQTbA6YDlwOhA+4DgQQPBTwFCgWwBBoEMwNKAsoBjQFpAUwBHwGWAN3/Kf+L/g3+yv2s/Yb9Zf0w/e/8wfzR/Av9ev0c/s/+UP95/z7/0v56/ir+yf15/U/9Jv3g/Jz8bfxi/G/8hPy3/An9V/1r/Wn9Q/3+/K/8dPwj/BH8AP37/v4AMQLtApQDBgQXBFEE5gRiBTAFWgRqA+ACvQLHAg0DtQOMBCUFWgVNBQYFYAR9A8QCWgIRAsABcAH5AC8ARf+J/hH+y/3A/cz94/3n/dH9n/2Q/bb9Bv5z/un+V/+H/1T/wP4//gX+1P2K/W79gP1w/Tr9Ev3z/Mz8tPyk/JP8k/yV/Hn8Tvwa/OP7x/u7+6T7JPzk/UEA8wHYAoQD/wMQBAYETgTHBOYEWARuA9ACugLoAjkDxwOKBEAFoAWdBVkFywThA/ECWQIHAsMBpQGWAT8BmQDZ/yj/pf5o/kD+If4a/hr+6f2n/Zr9uv0C/oD+E/9x/4L/Qf/P/mj+Gf7G/YH9Z/1L/Qz9zvyd/Gn8OPwr/C/8VPx8/Jz8ofyE/Dv84fuo+2z7wvtQ/cL/oAGuAoYDCAQVBCQErwQsBUIF1QTmAwQDhwKQArYCBgO0A5AERwV3BXMFLQVoBHYD1wKRAksCGALzAXABtQD0/zL/lf5T/mb+bP5u/mb+Kf7b/ZP9gv3A/UX+7v5h/4r/Qf+8/lT+9P2g/WH9Tf00/fj8tvxj/CL8B/z2++/7Bvws/D78Mvwm/PX7s/tl+0T7E/we/sMA6gKFBIUF0AWpBaQFzQWzBUQFXwQKA80BNgE5AZcBZgKOA7QEkwUGBgEGZAVcBFMDpQI8AvAByQGyAYoBMwGsAP3/bv8Z/7n+T/7s/YH97fxo/Cj8NfzC/LX9tP5y/9z/1v9e/7r+Gv59/fb8lfxU/Bn83fu7+8D75/sN/EL8k/zT/N38ofxY/Ob7VPum+qb6KPzs/vQBhwSNBnMHSAfjBrIGKQY3BTsE9gJbASEA6P97AGEBogIgBIMFSQZqBuoFwARTAwsCbgE/AVEBlQEfAooCWQKgAbQA5f/2/vz9Qf2w/CH8v/uv+/37x/wL/l7/VwDPANYASAAg/9D9tvzX+z37KvuT+xL8d/zG/P386/yk/Gr8J/zO+0D70fqE+j/61vlF+mv8uv9eA68GQwkoClsJGQg1B/UFKATKAsUBhABq/2j/mAAFAjcDfQTCBVIGEwYrBbsDRgIQAYYA0QCfAYsCngMyBLYDZgKZALL+4Pxq+6n6ifqm+hT70vvA/Pn9af+cABUBxwDq/4r+0Pw9+2T6GPo9+hL7UPxy/R7+N/7i/ST9D/wQ+1H60Pmd+aj5Ffql+vT6hPy8/1ADrQaoCX0LIgsPCUwHYwa+BNUC/QGoAccA1P87ALEB6gJxAx4EGAVEBaQEfQNMAp4BMQEyARwCjQPrBGwFvQRUA+EA8/2C+/j5c/lH+fn5nPv8/Ar+Iv9+AIwBbQG8APz/ff50/JP6ufn1+Vf6R/vW/C7+vf5X/pH9i/wG+5n55fje+Fb55/np+vP7V/z1/SABYARtBxgKqwtOC+II8AbvBc8DvgEPAdUAKwBt/+//qQGiAuQCywPfBBoFfASQA8ACLQKWAX0BhQIEBDMFYgVdBG0Cvf+3/Dj67Piu+ET5gPos/O/9b/+CAF0BqQEtASUArP4t/YH7/vl5+cX5mfrg+yf9Uf6f/qH9W/z2+mv5Uvja91T4gfma+t77wPys/WkA7QMZB0sK7AyXDagLvQhAB7oF3gLdAHAAGgAL/7D+WgCLAiwDUANtBJgFeQU+BDgD9gKIAsYB7gGGA28FywWwBPkCUwDH/D/5Ovdi90j4g/mP++n97v8JAa4BJQKyAXgA1f4T/YL7D/pp+dz50vo3/Kn9xP4u/0H+oPz++kj58vdY96P3I/mu+sz7rPy8/akAQQQgB7sK1A03DvsLgQh9BpAEAgHl/gv/AP83/kb+cAAHA7oDzAPrBOoFOgXYAywDKQMFA5ICFgMjBeUG3AahBXkDDgDu+xD44/UV9lz3g/lz/Ab/IQE/AkACygGtACf/af18+0b6w/mk+Tr6e/sn/W7+2f7J/uP9CPz++Sn4H/cQ97n3V/k3+538GP0W/poBogXQCEQMHA+nD70MTAgmBu0Dyf9w/Z399P2m/SL+HAFbBOQEpQSMBdwFaQSiAvMBgwKzAokCFwSpBjIIzAe5BRMDOv8n+mD2DvXk9dn3Ufq3/QsB4QJdA/EC3wFPABD+rfso+mP5Cfli+XD6Ufw7/tv+1v6u/mD9MPsl+bn3gvfF9234RvoQ/Ln8ivz4/ZsCPweDChwOzRBWEP4LtgYuBNQB4v0K/M38ov0m/lr/RwJCBboFQgWkBRMFLAOYAf4AhgFEAt4CxATdB3AJ0Af7BKsBz/yo9yD0UvRN95n5TPxVABoDsgPPArAB1wDR/vb7jvom+oT5h/lx+uf7qP1y/kz+S/50/V37WfnJ9z33rvfz9zD5dfu5/GD8sfvb/k8FnAn1DCkR+BJaEK4J9wMcAh3/Mfv2+rz88P3+/toADgQdBnEFGwVbBc0DAgJ/AbYBhwIkA3AEfAdXCT4IwgWqAnn+Nvna9Cz0p/Zx+Tr8/f9XAzIE7AJ1AWAAxf6T/A773vra+i/6H/o3+3X8UP2b/aL9hP1T/DD6ovjz97r3/Pe9+CX6s/v6++f6pfxhA+4JzA1XEbMTaxJBDEMEEQAn/g77xPm8+yn+/P+lAbADaAUMBREEDwT3AggB8AAXAgoDtgOmBCYHVQlPCKgF4QJE/6H6+/VX9OD2Ovqi/Hb/XgKjA7UC4ADE/7v+G/29+yj7MfvW+jD6n/qa+zT83PxR/UT9tfxC+6j5qPjf94P35ffG+ND5h/oy+sf6jQAUCZoOHhLEFCYU1Q7kBe3+0vx6+1H61/ul/vUAiQKNA20ERQRBAyYD7QJoARcB3QIzBGAEwwR4BpQIiAhQBogDSgBo/B34TvU19nj5qvw+/34BCgPKAkkB7f+x/pP90Pwy/Mr7Jfsm+i/6Bvto+wX8Of31/Z79H/xP+iz5+Pew9rb22fdD+Xr6p/oY+qD8zARUDZwR8hP9FDESlwoXAaT7PPs7+yD7Kf3w//8BdQPfA4YDBwPFAg4DeQIhAeIBJQS0BNMDGARDBiUIPgdcBJ8Brv6b+sr2cvV+9zT7Lv5mAB4CtAIuAhYBsP8r/k/97vw2/Er7SvoD+uz6jPux+9D8Mf4x/tr82Ppo+Zj4ZPe09nP30fj/+Y/63Pln+nwB9AvlES0UgBUMFBsOJQTt+1P6QPto+738Gf9AARQD2wOXAwYD1gK6AywEoQLEAX0D/gQyBA4D+gN7BigI3gZdA3MAv/3B+XL2APbj+Ev96//8AP8BIwKmAaUAtv6r/Wn9ofwx/D77iPn5+a/70/u9+5X8h/3L/dr79/gQ+Or3+/Zd9hr3D/mA+r35Efno/m8KIBKtFEsWHRVpDygG7vxK+f757PrL/DH/ZwDFAYEDygOsAskB1gLwBHAEXgIdAw0FngTlAnwCwgRHB1QGewP6AAn+EPtq+AX3Gvnh/H//TwEgAr0BlgFSAd3/af6l/VH9Tf1D/Fz6DvqD+4z8dfxv/Bz9ef0B/Df5ZfdQ92f31Pbt9g345vgJ+HH3If7dCkwTTBabF5YV7Q7tBDL8Xfk4+oT7FP59AM4ADAElArcCBQJNAR0DJAYtBjIECATWBEUEkQKuAXQDrAU7BpEFLgIH/vP7TfkD9yP4QPvT/m8B0gFgASwBhgBV/yr+Zv0Q/RT9Ff37+3n6+Pqu/CH9afzM+yP8b/yx+ir4ffcC+AL4r/eh9z74aPhp9w38iAgKEkMV5BaKFXMP3AXX/Jf5Vvo8+539kQDdAAYAUQAtAVEB4QBlAu0FWQfoBeMEwwQrBL8ChgHXAqAFogaiBZgCqP4y/A367/fG+DT8Pf8UAdMBiQEDAYwAmP90/tv9Uf0d/U79V/wY+0H7H/zq/BP9gvxS/G38fvvR+a74Z/iT+Gb4wfeP9xX4T/cS9zH/mgwPFCkWnBZhE8cLwQGm+uj5Y/u6/NL/7gElACH+bP6Z/10A9gC9A8cHtggrB4oFsgNqApsBKwHmAogFsgbyBScCRf3E+g/5ufd8+S/9VwBcAoYCKQEJADD/wf31/Fn9tv30/df97/z4+zj7/PqZ+zL8FfzL+7z7Wft2+ir5EPjq9wn45vfg9xH4cPfN+EsCTA7HE9sVLxbOEbEJawAg+8z7/Pwa/k4BGwI0//z8x/xF/ngAOgI1BWUI6wjGB34FiwJxAXABrAGiA/sFXAeMBr0Bv/yH+o74Cvi1+kL+hQEkA+oBBADs/gP+Pv07/Vr+kv/D/9T+Qv38+xn7fvrR+rD7GPw0/Cb8uvsi+/X5Y/it99L31Pe89/X3Y/eO+O0A0Qt6Ea0T6hOEEIIJ2gAu/Br9R/4Z/3IB8QE9/zb88vqj/Nn/kwKpBQEIbQj5B2AFoQF6AFQB0wIcBSUHHwiMBtQB2fwa+b/2hfcu+y7/vQGsAt4BYv93/RT9S/1w/rv/ggDPAEv/zPz9+tz50Pmk+n77S/wB/fL8zvty+mn5jPj69xX4svgu+fD46vdb+jMD0gvoD3cS4BI1D4sImAG6/mz/yf/lAFwCPwG8/kP83/pX/Mf/rQPNBrcHgQfPBjEEWQHTAEoChwWeCJsIDgevBJn/V/oz91b2Bfm2/AP/PwHoASIAD/6t/Af9xf48ADoBPAElAEj+Svvs+M/4wvnf+sn7BPzo+0z7BvrU+ET4L/ie+GD5tvm5+c/4pvnp/+MGiAtKD2sQAA/mCrUEzgGtAdEAdAFyAl4BMAA5/vn7gvyU/pwB+wQWBowGmwatBHIDhgN7A6sEegbtBgYGXwPi/y/9t/oL+bD5fvs7/Y7/6AAcAEX/gv7+/ZH+RP9ZACcBVgDw/hj9+PrB+WX5rfkZ+oz6JvsB+4X6SPra+W35afnB+Qj6u/lV+YT7RQDZA18Hlgv/DAgMjgmMBlYFIwTcAk0D8AKxAT0Bzv9I/of+tP/RAd0D2wRIBgQHkAUyBFkDqQKoA8sExQQRBXQEAgJi/9/8e/sC/Or89P0P/yb/ff5t/U38N/xQ/b/+z/8lANf/7/5S/b37wfoM+vL5R/o6+jH6V/pK+hH6yvmk+Zj5kvnZ+Wv6yfux/hABZwJaBVQI8QhRCFcHkgZ+BXsDpQLFAkcCUAITAhIB1QARAQMC8AJ2AxwF/gVTBeIEEgReA3wDPgPxAhQDCwPJAuYBOABV/yv/m/4v/gD+z/3e/a39Ov0W/Sf9oP1o/qX+2f5D/8n++f1H/SL8K/uA+sH5n/n6+U769Ppr+y77BvvK+nT6ovpb+4n9FgArAWQCggTeBcEFSwWKBasF/gRqBMgDCAMQAxYDhgIkAoQCPAN7A2QDfQPSA/sD8QPRA10DbwPwAzoDZAKFAnwCPAKZAdMAoABCALj/LP9D/sT9hP3h/Jn8xfwM/Vn9pP0D/jX+Gv7S/W79Cf1v/Lf7H/uC+jv6I/on+rX6OvuG++P7/PvD+877S/w7/Wr+fv8VASYDfQSXBD4EkgTpBLwDTQKLAggD4AKxArYCcQMkBCoE8AMGBHMEiwRcBLADGgNRAxoDigJzAkYCHwL0AZYBrAHXAagBCgEUAF3/rv7P/Rr9vPyp/KL8p/y7/AD9sf0C/tf95v3X/Xf97Pw1/Jf7RPsI++L6KPvG+078efxl/DP8A/y9+6D7cfyr/ej+OgBqAQAD/APNA6gDMAOnAsECEgM6AwwDHwNGAx0DfwMqBK4ELwU4BRgFwwT3A24D9QKHAp0CygJuAvIBMQJhAq0BNwGEAaoBeQGxAKn/vf6s/ar8yPun+278Av0T/Ub9g/21/ZP9A/2u/OP8Cv10/OX7vPu/+937+fs3/Kv82/zs/OH8X/wB/Az8SfzV/N79Q//FACYC1QLEApwCtQG3AHcBrQKzAocC6gJOA3EDdQOyA80D7wNiBBUEYwNJA3wDaAO2Aj0COgIEAjICUgLsAeYBTwJjApYB1wDaAFEAGv8J/lz9S/2i/dz95f0H/mv+aP7j/cL92f2y/Xz9Df1t/Bf8CPwh/FP8uPwJ/ez88fw1/SH9yfyp/LP84vyN/b/+6v/kAPcBsAJOAoQBaQFbATsBVgFtAc4BKwJDAhQCIgKqAjADYwMyA1gDbgMjA+kCwgK4ApsCOwLZAbkB8wE6Ah4C/gEgAuQBAAEgAEb/cf7I/X39b/1x/QX+Yv5f/qD+qP5l/hr+IP5J/h/+AP6V/Qr9A/3x/Ob8A/0l/Yz9if3Z/L38/vzh/Lb8NP1r/i7/9f8GAY4BSgGdAGQADgAKAMsAOQFxAeUBNQJCAq4CGgPhApcClgJMAuwBGwJVAigCnAG4AfABvgGGAgwDhAJXAs4CdgKFAcMBLALyAKX/YP9s/gH+5P5S//X+Ef+F/7z+CP4m/+z/af81/1z/Kf/I/cr9Cv65/Ar9hv3S/P78wP2j/Yb97/zQ/J795/3A/n7/k//QAIQBNwCs/2kAKADo/tb/OwHtAE0AwgCyAYwA1gDNAnsBLwFQAy4D4AHWAUoC0gHlADYB/gEuAd0BzQIKApQChwJtAk8CbwD2AD0A8v4lAL3+Df9qACEAfP/m/lEAYQA6/8X/8f8bAA0ADP+e/mX+N/7p/Rr9gf1l/kX9Pv4T/sj8Wv6e/bv98/6t/lMAMABb/ysADf+s/jf+e/5b/2j/OgAIAJYA6wA/AEwA+gAsAt4BugHrAvACtAFzARUBuwAtAcEAcAHJAW4BywEyApoBQwH4ATYCZAE3ATYBOgB7AJH/wP/sAHEAVwBXAbH/Nv/AAVoAY/8/AFYB4/+G/iYAf/8T/iz+pP4n/d38Kf94/kH9UP4o/lL99v2I/+oAU/+XAPYBS/2M/Ur/6f4L/or/AQKg/4j/VwEZAef/HgCHAWUAjf84AtcBcwAIAYoADQC0/0kAOwHNAU4AuQB6AdD/ewCFAYkBfwBPASwCIwCt/7sANgFU/5X/GALWACsAQAEDAbf/TgDWAXkAEQCfAMz/B/8t/v3+Jf/C/Tr90v11/x7+Tv2V/5T/Qf7r/vP/nv7l/5gAO/0a/mj/Q/7p/R//RgDCANj/Nf9xAdP/TP+hAlQA9v5GAnABkP9eAJAB6wAHAJkAGgJTADn+TwHt//n99/8fAQsAUgD8AX4AggHDApwCxAGEAXYCKAHfAG0BEALdAGsBkAJiAEMA2gCu/xH+nv51/r/+Jf4b/Yr+iv46/rz9p/8a/qb+LwCO/p4Ap/9I/xL+gv5S//L9oP/O/9P+ZgA6AAn/lP8V/0oAEv+o/nUBjwCy/xoBj/+z/nwBawCo/n4AIwBpAOYAQAEFAdwA0gEOAYcBlgF3AQ8BQAFEATwBO/+YAtwBKP5TAsMA4ABPAMv/IgIR/y3+hQGx/ln9c/+m/rD+bv6W/4r+ZP2RAI//6vzA/1wB4f7e//YAkP+V/8j/Rf81/vn/4v+B/Tb/NQBm/eX+TwDa/rr+DgECAQb9WwFwAZL+LQH5AAIB7QBnAbsB+wAuAO4AZQH1/vsAuQG0AHMBCwCnARIBZv+hAUEBuQD2AbMA2f/XAZEBtf+FAY4Cuv99/qsAbgCR/fz9WQCM/9T7b/0YAfr8XP1mAdr+qf4t/2//SgAT/7z/mgCy/fL8/v87/lv+of9D/rj+x/4P/3L/6QBMAEIAMgHO/1gBUAKhALYAwwHPAP0AdwJZ/3kBBwMr/68AbAHT/+z/6wFaAZD/ZwDJAvf+O/0bA0gAOv0yArYA4f10AbABCf9MAcMCBf9F/y4Bt/+O/tL/xP9H/jT+BwAI/6b8iv+JAHT9yf6e/6z+rAHv/hX+dQH//RP9d/+J/tv+TAACAHX/2f8K/6//9wD0/3AAygDdALoBfwFbANkCRQLO/6oB4gDB/1UCbABW/8gCowAT/6b/WAFVARcAXQBQAJoALv78/iwBdQBPAIT/AgEUAFcA7gB+/+QBzP98ADEAd/4VAYr/d/6J/iD///2a/Dn/If5o/4n/P/y5AVMAfv2+AC8A2v8//Xz/qP9C/lcApP4LARwAQf/t/xMBPgD5/usBaP/c/xACqQFW/w4A5ANYADT9vAKuA1H+jv9rAnUAN/8MAjsChAHDAWYAiACFAQQAeP4mAl//tf2mAKH+IwHb/17/qQHd/pj+PAA5ACT/Wf9SAFf/oP2Z/zYAbP48/zsA4P44/nH/AP9r/60A6gDr/fT+YQFV/V7+v/9M/1/+Pf9TAR//DgG2Ab3/YgAmAn0BF/82AbMCtf+B/gADGQFt/HYCIAKc+4EA4QJR//3+KwI8A3z/4wHTAQABHgE4AKYBlP/M/xsB/P6S/6YB7/9K/94AzP7T/p0Aof03AIj+N/4zAQj9bP5/AAX/CP5p/wb/ev7j/0X/eP6rAIABrf56/wgBNf6e/tb/P/+U/tD+NwHA/sf+8wHk/3IAJwLl/3YANgKLATX/bwD8AlMAuv85AU0AQwC0AGkA5f4vACQCsAAdAdgATQH6ANX/fABHAUgBUP9u/4EAn/8e/0IAqwEi/8r+qQD8/qv/gv/K/4EAq/6T/6r/p/4kAND/t/5R/+kAT/5c/isB3/5j/2P/Y/9n/ur92P+J/nv+QABgADL+c//FAWsACgC+ACYBfACY/yMBJgGWAP4AjQCPAaYA1P/nAFsAGf+0ANEBxv6hAL4CmP9AAMIBhAAcAPIA+f8pAOL/PP9nAU7+rv4PAqv/nv7iAc//O/7cAS//tv/A/7/+YQC6/of/Yv8A/6//0v+n/yL/LQB6/xD/cv/9/97+MP9lAMj97P7M/wr/k//z/sQA5QBh/lcAFAGw/x0ASwFgAb//kQGJASEAIALZAKL/QQBkAJr/TwCPAPz+wP+l/wgBF/+4/9EBI/9fAL4AfgB+/2gAZgDc/+sBa//xABoCh/4MAEsAMv47AJAAvv0BAEUBrv0kAJYAz/yVABgBRP5l/3cAyf9tAP0AX/6jASkAsv6iAPr9zACl/179DQC4ACT/pwDeAeX+YwA5AYX/2QA7AWUAPwDvAIgAoP/JAB4AYf97ALf/Qf+x//D/y/8oANL/lv+ZAfX/f/9ZAfX/RwCwAOf+4/+6AfH/XP9fAPn/DQApAIT/PQCMADIAof8c/9n/GADA/nf90/9A/w3/JgAU/w0AEgBN/9D/aQDs/1sAQP/6/xAAnf5nALT/Zf99AAgBLQCy/w4BiwBM/57/IwFGADYAPwDN/6EA+v/wAEAAJP/HALEA4P5kAE8BB/8RANUAhf/aAFwBLABxAIkBAwD1/fQAnADP/oj/AwAnAR4Amf98AG4Al/8aABcAZv4zAKP/hf0HAPD+Yf6x/xsA4f9H/7cA2/9hAEj/HgCKAbr9mACTAFD+5wCL/8H/0AFZAFL/DQH8AIf/pgAOAOb/igDg/1wAkf9lAHwAXv+r//D/DwAE/jMAMgDF/eYAGAD5/oUBxQC7/8kCDAEe/94At//5/xoAAf9XAK0Adf9cASoAJf/SAJUAIv/U/goB3v9D/4H/rP56/9r/uf9iAJYAQQAgAUsAdP+qAKIA3P84/67+EwCw///9HwBKAFoAwP+2/2sB1v8qAB4A6f86AN4A+P9G/+cAOQBi/zcAYQBa/lAARwFG/lH/cgAZAG3/h//MAN4ADgGTAMUAywDyAJv/Gv6zAGn/6v78AGX/6/4OAT4BP/7v/isBswD//hz/nQA/AAgAjv6BAEQBi/6lAOoAAwBsAF8AUwCJ/wgAOADI/6z+qP6LAB//oP7wACwAhP4jABoBlf84AEsAFACy/97/DQFUAG0AfQCYABIAfgBoAJ//MwHu/5/+IwG1AEn/PABzAI4AvwAXADIATgAkAAcAB/8///T/IQBS/4//dQBN/y7/AgGi/9L+GgGxAO7+bAAEADb/lQBa/5T/gQAtARUAPgAcAZH/jgCOAOL+zf9L/+P+Rf+2/vD/pf8sAGIAqP/jAEsBaf+L/wEBxP7C/+sADgA7AFP/1wETAGL+OgKUALv+JQAzAJ7/R/9tAIoARP8wAWMAyP4pABABLQA0/kL/LwAO/z4AMwBP/6IB1P8//0sBFwAmAAUBl/+K//cAx/9zABwA5P+BAAIArv9HAB0BcP5VAD0BGP4KAHAAOv7E/nQAev/H/cv/AQGVAGcACwB0AY7/Sf+OAU7/Hv+qAD0BZP9e/ygBWwHJ/4f+LQGxAJz+Rf89AJH+WP8ZAZ3/DwBNAAIA+ABj/7wA8wFh/pb/CgGGAH//4P9KAYP/E/8lAGQA6P4XAE4BJv7f/zIAdP+6/3b+OgAIAZQAh/7lAIcByP7NAFIBx/+g/+z/pv87/9/+LwCOAHf+rv+WAjH/jP5sAqX/5/1EAGUA1/9IAN4AFgBeAX8Aof8+Anb/rf6XAVYA3f29/hgA4P8Z/5v/ZgHh/4f/4gAn/6cAdQGm/rb/uv/XAHwAI/+mAXL+of88Ah//YP+WAOwAB//Z/pMA7v8RAOD/3v8wASMAGAAOAEr/aABRAP7+2f/+/yX/tgDi/Y3+uwHc/0//3/9eAaYAeADV/1T/wACg/67/Df/w/wACTf8e/4MBfv/Z/zMBsv6k/7MAov97/rX+9wBbAAwAigCBACUApf9VAUsASv7BAFQBLf6+/4YBagDg//f+UwDIAH0Asf9q/twARABD/rz/lP8MAOb/gv8QAIEAtQHzAMj+B//YASgB3P4HANwA4/7M/4wABv9T/8AAAQEH/ysAvwDL/7n/tP59/6kA5ADa/63/AAClAL4AFQDA/63/BwFy/rP+4wBT/xgAEgCZ/5wA5wCIAND/fv/IAI0AHf/T/5X/EAA7ANT/RAAHALMANP9J/68AKwBYACT/Bv/P/zMAjv+p/xgBkP//ABEBCQAIAYX/XwDl/2D/4/+T/yIAuP4S/2UA5f6E/zYBxP9b/20AFgGYAM//RQAfAWMAv/96Adz/K/9VAEAAVf/3/uMASv+W//3/C//u/4D/LQBLAL7/rgAWAVj/Pf+oABUAm/9HADX/h//nABoAfwDOACEAeP96AHcAwP+dAJv/0P4s//v/4f+P/yoATACn/54A8gCn//P/w//f/4n/PgD1/1r/+v/x/1wA6v9sACgAEgDO/3gA1ADK/6gAtP94/0UAOABzAMb/qP9sAFH/ff/0/xD/4v8NAOX/r/92AKgAmwCyAEcA9wAyAFAAy/8w/zkAGwD5/hv/dABRANL/MAALAHL/hQAmAGf/3P8QAHUA6f7f/7UARf+KADwA0v+8AFYBxQALAH0A5f8DAHj/R/8a/zb/0f/0/oH/Q/8bANgAPv/3/zgBbQCd//IA/f+J/7kB6P9z//EAWgAF/xf/OAD+/zUA/f88/2T/YADWAKT/0f/hAB4AZv+s/0oAUgD//5r/hv/O/yYAwACi/zL/0gCYAKP/FAATAOD//P98/xv/vP/c/ycAnv+z/3YBwAB0AFYAYwDUAOn/m/9u/63/t/9b/2r/Mf9ZADcARP/jABMB1//a/3oAwv/L/7AAIABw//H/zADy/77/lwA4AHv/r/97AHD/cv8CAW3/Wv8LAWAApv+KAKoAD//D/34AQv8p/6P/LgC//3n/9//x/+X/RgCCABQAPQCcADQAxf9eAOn/of8EAM//q/8UAJoAJQBrACUArv/U/8r/sv/L/6D/f//o/7b/3//4/0kA2ABVABUA6gCWANz/4gCmAKn/BQCiAAEATP/h//P/mP9L/yr/Gf8v/6//t/9N/3n/vP+y/8j/7/8RACYAVwBUACoAVADPAIoAMACrAMYApwDGAIQADwBYAKAA6f+n/wMArf+R/6L/WP9H/2T/i/92/6n/PABHAP3/+P8NAPz/5P8WAND/vv9PAEYARACKAJAAYwB2AFsAOwBWAPb/wf+T/6b/sv9f/5f/Wv9K/7H/x/+h/5H/xP/R/+H/IQAEAIn/yP/+/9T/AAArABYA2v/W//f/7v/P/53/X/+Z/8b/r//m//7/LAB/AKoAygAGAVMBewGoAd0B8wEKAvAB+wEqAsIBtwHTAT0B0QB3AOb/jP8l/7f+qP5+/hv++v3U/af9k/1m/Vb9QP09/Tn9GP0u/WD9jf2e/cf9/v3n/db93v3P/Uf9GP4VAGEAgAAaAcQB2wLtA5oEugQpBckFsgV2BcAFvgU0BWYEtwOlA38D8gLrAVYAkv8X//79AP2M/B/9Zvz9+vv6Xvub/OL8XPwd/Vv+xP+mAJwAJQFcAs0C6AIHA2ADeAOtAtABNwHsAB0Ajf4M/Qz8gfuy+of5nvjZ92D3Jvdt9pn2PPmc+wD8Q/2A/1UBeQP4BSYIKwkQChULIQtmC7gLIwsCCqIIZQdNBq0E2wLwALf+Dv1++y/6R/mM+D74Rvcw9y34vPg5+ln70vs5/f3+IgEYAxEE4wT2BSQH5AfdB00H+gWoBHgD5gGcADb/Yv2R+9v5yfhm+Iz3kvYS9sX1Mfbq9vP2Nfej9zv4Afy1AVYDdAI8BIwHOwr2C9gM3QyUDJ4McAvyCe8JZwmmBsQCiAA2AIn/0/0s+7/4YPjo+Pb4Rviw95j4R/qu+yL8u/zn/tYANwF5Am0EnQW0BscGhQbrBj0H3gYZBRwDEwLHALz+lvyt+if5G/hT92r2xvWx9c31Kfag9iv3wvco+Pz3J/rG/+UDnwRZBP8FUwk8DLgNyw0JDSwMRgvqCRAJrQgLB7sDPQBb/v/9Tv15+7/4/vbi9h33SPcQ9wn4Ffom/PP9aP7S/+ICCAV2BYQFYAbDB1UIfAe1BpQGcwY9BbECgABH/3n+1Pxq+tj4APh69332a/WB9Wn2Wvfn9574gvml+sX7Sfxk/B/8Mf7CBKQJGwi9BXgHsgocDMULxApyCRgIsga5BHoD1ANCA+3/DfwP+8L8g/3q+/T53/hY+Zn6Ivtq+8b8c/97AZ8C/gI1A+MEdAZZBqgFiAUNBiQGyARFA2kC7QH3AHL+6ftm+7D7Nvvw+YH4Tfju+Bv5kvhw+GL5dPpJ+1r7gPtH/K38RvyK+yD7hPzIAoEIDgcABGMFzgh6ChwKFQm6CPYHLgZyBF4D9AMWBNcAh/wK+9L8Sv4//YT7Fvq5+an6P/tX+4z8FwCdAogCpgJ1A0gF6wYuBpAEfQSDBdcFNAQxAkYBcQBa/xD94foC+3D70fri+cz42/jj+Sj6zfn1+UP7rvxp/aP9gf3d/c/9x/w7/C78WPs1+2UBnQgvB38DpAS7BwQKkArGCQEJngfrBWwE+QJSA8UD/gDU/Fr6N/uL/SD9XPsP+hb5J/oL/Hj8tv2lAVQEhQTnAzYDTQX2ByEHjgRIA/ADSgSiAn8AYP+H/on9o/uc+Tf6h/tN+0L67PgV+b76tvuE+5v7xfxI/vD+Nf6e/bD9VP11/Nv6/PmW+Uz6pAGaCPoGHAR3BdIIhQsdDE0LdAp3CM0F0QOtAgoDQgNjALn7xfh2+dT7w/tZ+tP5Wvkw+oX8k/17/7IDjgY8B78FMARTBsYIUAcpBEMCDQLRAen/4f3g/JP8IfxP+t34u/mP+2r8mPtQ+o76Pvxl/ZX9A/6k/p7/CwDM/oD98/xo/JH7+fm3+L33BfiW/8EH7gaCBHoG7QmQDEQNXAwlCwMJSAbfA/UBgwGXAQH/Pvr69pX3Mvp6+mP5Zvnq+XT7JP64/0cBoQUrCYQJuwd7BZkGgQiSBhIDngCN/1T/lf0++5D6J/vo++L6ZflR+iv8Jv0K/fD7L/xb/uX/+P/C//n/fQBVAL7+0vwp/O77lPoA+X73ufax9Zr3owHhCDgIqgilC1IOtg87DgoLPgmiByAGxwTtAaD/dP16+en1sfTx9kv6jvoV+nb7LP1X/7EBewI0BD0IcQvyC4UJVQeHB7oF0wDz/Gb7n/ty/B/8vPre+dX6v/s++nT5VPtr/Wb/ggC3/97/KgEqABv+hP0W/qD/QgCA/hb8cfrT+K328/Rd9Mf0nvVg/PwIIg6HDLoNsA8uEMgPjQwYCZgInQcVBcwBSv2r+b72r/Nt8mX0OPkf/WP9Af1n/qH/IgE8A2gEQAdNDDQPMA26B6QD7gFj/jT6bvmz+nX8kP3W++X5+fmL+kz74/t9/QgBugMjBJQC+/8B/2r/v/4W/sD+wP/q/0v+3/rW94L2+/Wb9c310PaX+Cv5nPodBBsOcw/hD2wSYxLnEGgNcwdOBOUDHgOfAPb7yPdJ9bLzvvND9Uv48PwfAJgA+QD4AREDbwQLBUMGvgn4DBANvggpAmH+5/wT+mT4fvl7+zn9bv0o/Nz69Pqs/E/+LP+8AagE6gQfAwMAb/0y/fH98f2x/dv9Nv52/dD6LfjP9oz2TPct+Ab5KPr3+kr6wf2tCDoQKREdE5gU8hHlDWQIsAL5ALYBEAEg/gH67PYY9dPzgfRF97P74wD2AxwE6AM3BBUExQOFAw4FFwnoC2cKHgWZ/2L8AvqH99X25vhB/O7+NP9X/aP7kPve/H7+igD0AnAEWwSDAiH/sfyf/Mb8kvzd/I39Vf7L/VH7xPjB9zz4G/mh+UP6LPuE+z76EvziBe4OExEdE4YU0hDSC5wGrABX/or/x/9p/Yn5Fvcx9gD1g/XV+Hj9agJMBmYHYwbeBMsDbQOgAiIDcwafCWgJ4QRm/9n7yvh99qL2u/it/P4A3QFw//783vx2/kr/ZwD5AnoEkwScAyQAjvzO+zr8UfzB/Mz9G//p/oD8D/rH+LT4lvk1+nj6//qX+6r69PozA9wN+hEHFKkVthHDC30GYAAh/Y79Rv5b/XD67Pd895z2EPYd+T/+DAMLB6YINwfpBC8DbQJAATkA4wIIB1AH+gPX/9j7j/if9vL2xfnQ/XkB1gI6AZj+pv1w/pH+wf5rATAEXgTfAv3/ivww++P7ifyp/D/9tf4X/zv9AfvV+Tb5EPmZ+TH6pfrh+vn5E/hE+zEGPA+kEp4VoBVYD7sIqgNv/nH8z/3E/lT9w/q4+X35y/e19wL8igGsBckHywdCBtoDBQL7APP+nP65ApMGeQZPA1v/SvyF+a73ZPlM/XkAtQJIAx8BZP7c/SL+9P1W/5oC6ARvBBACSv8Q/Sv8LPwx/Nz8Iv4y/73+H/yz+S/5S/lZ+ZT5APpp+gH6Yvi49kP7awcCEfQU5Rc3FtwOKAj3AsD+Hv1f/ff9gv3b+936/vmz9633yPy8AsoFoQajBr8FzQKU/+z9tPyI/dEBVQY2B/QDdAAR/jT6y/dY+in+ZwDkAZwCoAE2/1z95vwZ/ST/DgMKBbIDZwGL/7D9vftF+qv6dPxt/br9Mf1i+5r5wPgy+L73OPi1+aP6Tvop+XH45P3UCX0SuRZdGikYtA9RCEUDif5k+6L6S/sP/H77kPp6+ZD3Ifhd/eoCygQjBU8GkAanA/T/av7W/Rb+qAD6BJAHigW7AeD+S/ut+Ib66P1I/ygAEAJMAjn/hPzK/HL+ZgDQAlkE6QMCAg8ARf7w+wf6OfrB+yP8CPxw/A78c/r7+NP4Z/nN+Xv6Lfuw+lX5qPgx/msKiRMEGMAbrxjDDlMHZgLj/E75x/j9+ZH7XPty+in6wPjT+FT9swIoBcwFzwYHBz4EaACS/sn98/0WAc0FpQcWBVMBaf5c+2b5nfot/dT+XgAvAg4CsP/n/bX9Kv5f/2sB9gIDA8QBwP+w/cf7IPrl+ef6qfsc/Hz8H/w1+536dfqO+qL6u/q2+kf6Svnk94P7EAdnEZYWOxqOGGoPpwaPAaL8yPip91H56fsF/A37KPsX+jr5ofwsAooFnAZ5Bw0IwQVkAe/+Cf7O/UEAzQShB5cGCANW/zr85vkt+rb8qP5LAJwCJgP7AI3+hv3N/ef+XACPAd0B2wAf/4z9Bfxu+rv5kvrs+8f8gP2P/Vv8CPtL+hn6EPoY+jb6E/pM+bX3o/kvBP4PbhacGjYaLRMcCuMDVv+h+s/3Nvje+vr7Pvtv+yb7y/lt+4cA0QRYBukGkQdkBvYC1/8Q/kb9m/6tAosGuAaXA+D/u/w3+sv5uPu4/Uj/cQGMAgwBxv5j/d/8bP0Y/98AuwFgAQIAdP7a/PH62vlD+if7CfwR/U/9Uvwn+376Ofow+lf6cvoc+pX5Vvhn+EcA9gxWFV0aAB30F+QNKQbsAOf7LPho91r5j/un+wL7pfqS+eL5DP6LA4sGlwecCA4IzQRDAbz+Ff1t/R0BeAZvCMMF/wH7/cL5e/iN+rL8Xf4bAVADVAJO//v8BPzk+0T9BAD+AeYBgQD9/iX9mPrA+Cz5ffp3+wr9GP7j/NP6jPnM+Hn4Fflt+gn7k/qE+TX64gG8DbkVqBoHHaMXcg23BUsA6Pr89j/2HvgK+m36t/re+sn5dvon/4EEUAe1CMsJAwm/BesBI/+T/eP9GwFcBZsGTgTdAA79ovnd+LH6zPyz/gUBfwJwAev+Ov2r/NX8R/6dAPYBqAFCAG/+pfyo+mn57/kg+0v8f/3+/Q39YfuV+m76Wvr2+tj75ftf+076Fvmw/YIJgxMUGYYcXBl5D74GBQHA+4v3+PXG98n6nfsp+yz7a/pY+kj+KgThBzcJ2wlsCSgGbgFK/nX8ovtI/msDGAaSBC4Bbf2r+QL4s/l+/GX+NABoAocCrf8C/R38d/zp/SEADQJDApMAhf6v/H/65fhJ+QT7e/zI/av+7/0U/Kj6Gvoc+tr6zvtk/Gn8pvtq+a348/8xDJUUdBmoG5UWvgz8BLn/Mfsy+Fr4Zfut/Sv9pPxO/Lj6Q/sZAG0F7QdRCFYIWAdjA+f+5PwG/JT8bQACBegFFgO2/9v8KvpT+XT7dv5DALUB0AJdARH+NPxR/AD9e/55AD8BWgDG/kj9A/zY+sr6evwk/tH+E/96/sf8M/tw+ir6aPoq+9H7W/tQ+jn5Z/fF+bIEiRDlFo8a4BkCEvkIHQNw/vL64fkg++/9y/61/Pb6aPkG+I/6lAAPBZwGaAerB2wF+gC0/Zf8rvxU/3UE4weUBocC8v7M+xP5Kfnk+1D+3f95AakBF//o+6P6JfuV/O7+HgGgAckAqP+K/jH9B/xR/BL+Z/+O/zT//v3j+zn6S/nm+Hr5c/og+3f7Pvtc+uz4lfteBnARiRZ4GZwY/BDiCEgEdwBQ/ar8Hf6R/1v+X/s6+Rr3E/bQ+VoAbASYBZ8GGQekBMAA4/6a/iD/QgLgBpIIrAVYAf79evrZ9+D4BPxZ/qf/eQDd/1n9GPu8+rP7sf2YAM8C4gJtAdD/Tf7P/Bn87/ym/oz/Vv8z/tz7e/lQ+Oj3HfhE+dT6Bfx//Ej8m/sS+lr75wSOEAYWqxjeGG8S+AlKBEYAEP4d/kP/HQB2/sn6APiW9QT0SPdL/poD+AUiBxoH/QTjAUIABQBwADYD6QfzCccGjAGk/TD6JPc19/357vxN/3cAlv9Q/Vf75PrQ+6P9YgAgA80DHAKZ/3/9hfyN/CP9IP7d/uz+Bv7G+0H5BvgB+L34MfrF++f8c/0t/a78vfuw+qD/PQsYE9kUthXzEuILQAZqAtf/oQCdAtQCdwCd+8D3P/a39Jb1GfsUAe0EwAbuBW4DLwGUAGIB6gGSA6oHUgpnCBcDUv2Y+dz35vcZ+s38u/7p/3v/Kv3S+q36R/z+/Q0AVwKFA+0CsADb/WL8Av13/rj/JgBA/xD+2/y4+rX4L/gJ+dr6ifwF/d384Pyb/ED8xPtQ+0QAJgu8EQYS9RGRD2YK4AasA5cBvwO+BYgEQgGj+xj3DPa+9TH3hvuo/w4D3QT2AhsAIf/O/6YBXgNyBZIIowk9B6EC6fwQ+XP4WvkY+xr9lf5O/xn+OvtM+ZH5avvr/T4A2gGKAlcCNAEc/5T9ZP5mAHkBNwHq/0L+ifxt+ub4o/hF+er6jfyy/AD8ufuz+7f7OftX+tD+Cwo/EeQQOhAbD9ELbAnlBskEOAbvB9YGSwPi/BL3lfXG9Yn2jfl9/dsAOAJtAE7+Gf4d/ykBXQNwBWgIWwqHCIsDPv6I+5z7a/xz/eb+rv/t/sL83vl89yH3XPmX/CD/1QAYAuwCYgJ0AKL/oQCZAToCLQKMAK7+Tf2a+zr6pvnq+V37gvwf/B77OPrH+bL55flD+iL71wDkCrcPbg41DkQOfgyWCmIIUgctCJgHIQU6AUz7T/c696H3TviG+l39Y/+e/jL8hvsv/HP9GACzAtwEYAfSCL0HPQTXAHoAJgFpAPP/MgB//3D9o/r+92T25/Z9+TH8Nf4cAKgBbALdAVEAKACxAcQCQQMVA7YBhQCJ/+P9ovwS/M77G/zP+3n6ZPmZ+C74ePgS+Rv6Kvs4/hwG0wwkDaYMsg07DTUM+wqRCcwJcQkKB8EEhwF2/fz7nPup+XT4ffm6+hX6LviO9034I/nm+s391QAiBJAHGwlBB38EDQRJBBcDFAL9AbMBaADM/Qn7Pflm+B75nfpf+2v8Mf4c/7D+df3o/EH+7P/SANsBuwInA0gDKAIcALv+Tf5Y/gT+Ef0w/Dv78Pnm+CP4+vdm+G759P0mBY8ITwhGCZwKpgoXCn8JWAr4CwcMhQuYCmMHxwOgATj/A/zS+VL56vhR9931mfWD9eH1NvcH+dj7Pv8QAu0D7wOAA8YEoQUuBYAFVgbQBsYGYwUaAyIBb/8D/un8pfun+pH6qfr0+eD4y/ji+QP7+vtG/cn+TABaAYUBRgFAAXwBowGiAXMB5ACBAGMA1P/J/rb9I/2t/PX7hPym/gAAAABXAHMBqAKJAw8E5wQnBg4HtAcxCOYHwgaJBYoEPAOGATYAxf9j/3L+Wv1W/H77pPqG+ST53/l6+q/66vol+877/fwJ/iT/0QCbAgIExgStBO4DMAPPAlwCugGVAdIBxgF0AdAA5/8x/5j+3f0+/cv8ovzj/CH9R/2k/TX+9/7x/+AAjQH/AVsCmQJ0AvABWQHhAGIA5P/B/wMAUAA3AAAAKwA0AM3/gf9H/9b+Yf4C/r/9cf0v/Y39Yf7q/i7/rP9VAMIA+QBAAYcB0AExAmgCUgI/AkwCQwIWAuoBvQFeAc0AOACI/97+kv6G/sf+X//l/0gAxAASAfkAogBJAC4AHwDo/8H/qv+D/4X/s//d//z/RgCtANQAtACHAFAA1P8n/5T+Jv7X/cn98f0+/oT+o/7U/jH/U/9o/7j/y/+Q/4j/n/94/yr/Dv8s/zj/B//R/pb+Kf7c/fj9Pv50/rb+GP+D//z/qQB2AS8CxQJQA+kDPgT+A5EDMQO9AogCpAK5ArICegIcArUBEwFeAPv/vv98/zj/1/55/lr+Sf4g/jb+uP5+/2MAGAFeAV0BLAG3ACkAp/8X/5r+Vv4u/if+J/7h/a39/f1i/pf+8v5B/y//EP8L//T+4f4C/2j/z//+/0gAfQAWAJz/m/+9/9T/5v/n/9j/v/+m/6b/rP+g/5r/rv+9/63/l/+C/2X/bv/H/3AAUwEiAp4C0gLcAuQCFANGA1kDWwMnA54C1wHqAPr/Rf/n/uT+PP/M/0IAXwBNACcA1P+J/3L/Zf9X/3j/t//S/8v/xv+2/5b/n//O/+H/s/9Y//j+of4+/vD94P3h/QD+Yv6o/pr+if6b/tz+Rv+C/6//DwBXAGwAgACCAIcAjABcAAsAvP90/0b/Jv8D/wP/VP/E/wsAEgDN/1z/M/9z/8X/GgCDAMkA4ADkAM4AvADQAPwAUAHXAXACAgNtA4ADLgOyAlwCDwKMAf8AnwBeABgAwP9o/z7/Rf9X/4P/w//b/+b/+P/P/43/ef9g/zX/Cv/T/sP+3v7Q/qP+jf6L/qL+vP7D/sb+0f4S/4L/y//Z/9//0v+5/7P/pP+T/9n/bQDdABkBQwFLASMBxABBAOT/6P8SACgALQAIAK7/Sf/m/mX+5/3N/ST+jv7n/kj/h/+P/5P/nf+l/9H/IQB5AMAA2wDpAA8BOwFTAWMBlgH3AT8CSAI2AgwCyAGDAUkBDwHOAIsAUgAJALn/jf98/27/XP9T/2z/nv/T/xUAUgByAHwAaQAkAKH/Jv/s/s/+wf7U/gX/R/9r/1n/WP9e/1D/ff/E/9P/5v8XAC4ALQAbAP7/8f/O/5T/iP+F/2H/eP/D/+X/BABCAGEARQAMANb/sv9r/xj/7/7T/rb+wP7e/uz+/P4K/yb/W/+P/9X/RACdAMwA1gDAAMEA5AAUAWkBsAG4Ab0B0wHQAdAB7AENAiMCGQLVAVoBswARAJf/Rf8w/1z/p//Z/+X/4f/S/7X/uP/t/yUARQBKAB8A4/+t/2r/MP8Z//f+z/66/pX+Wv4o/jP+n/5A/+b/jgANATUBKAEAAaIAIADD/7n/8f8nAC4A//+6/37/Zv93/5v/uP/R/9//vf9z/wf/ff4T/vf9EP45/kj+L/4N/g/+Sv63/jz/6P+xAFABogG0AZoBfAFtAYABvQEPAmMCnAKLAlACJwIOAhACFwIDAuoBxAFkAd8AWQDj/67/pP+S/3v/df+E/4//fP9v/5X/2v8pAHQAhABHAPb/n/8Y/4X+Q/5V/oj+z/4T/zH/KP8s/2n/yf8dAHEA0wAAAdkAlQBQAP//uP+o/6f/df8b/8j+k/5+/qH+D/+a//3/RACCAI8AZwAoAM7/b/86/yH/+f6i/jf+Bf4c/lb+sf4y/7n/NQCQALcAwwDoADABhQHeAS0CcQKyAsQChAI6AiUCKwI0AiQC5AGIAR4BmAAFAHb/+f7I/uj+Kf92/8T/9f/1/9z/5P8xAJIA0gDgALEAVADY/0f/vf5X/in+L/41/vX9lf1e/VH9af3U/Xz+Jf/d/40A+AAsAUwBWQFiAVQBGAHeAKgASgDg/4b/PP8+/6f/FQBGAFYAVQA6AAwAwP9+/4P/p//C/97/0P+c/4r/hv91/3z/mP+6/9n/1v/A/8r/2P/S/+X/FQBSALgANQGSAdYBBgIrAmwCrQLQAuoC5gKRAg4CYwGMANr/ef89/xn/+f69/of+X/4x/jH+bf6r/vf+Wv+s//v/UABuAFIAKgD8/9H/nv83/7j+cP5d/m7+ov7U/gT/Rf94/4//qv/Q//P/EgAVAAYADAAiAB8ABwACACoAcACvAMEApgB2AEYAIwD0/5v/Mf/g/qf+hf57/n7+if6d/r/+8/4y/3j/2P84AHYApgDLANMA0wDlABQBbAHFAewB4wG0AXUBUAE/ASIBHgFJAYABowGeAW4BMwEFAdYAogB2AE4AJwAIAOv/2v/n/wgAFwAUABMADQADAOr/sv9q/yz/5/6g/mL+F/7T/cf97f0r/n/+2v4q/2f/j/+3/9j/xv+h/5z/lP+D/6T/3//1/wAAHQA1ADgAJgAhADUALwAPAAIA3f+I/1v/Xv9d/13/ZP9a/yb/vf5X/jP+GP70/Rn+ef7Q/i7/nv/y/x4AWwC+ACEBWQGZAQECVAJyApgCvwK4Ap0CkwJ4AkMCHwIIAtABcAEGAa4AXgAAAKf/dv9y/4b/sf/1/0IAbgBzAIkAogBiANz/cv8j/8T+jP6w/uX+6/7v/ib/dv+S/3j/Uv82/zz/av+c/6T/oP++//X/NwCSAPMAAwGeAAsAcP/N/lb+R/5u/m7+Tv5F/kv+KP7p/cz9uf1r/QH94Pxr/af+w/+v/8n+pf7t/44BKAKfARABVgE9AjAD0wP3A8YD2AN3BBsFJQXUBLQEsgRqBOYDdAMJAyMCqQBS/5j+7v3d/NT7UftC+2P7lPup+5X7o/sT/Lj8TP25/e798f0m/t7+8f/vAJIBBgKhAnADDgTuA/oCDgIaAswC/wJgAsYB1gEBApEB2wBJALP/D/+d/lz+E/6f/Rz9rvxv/JX89fwD/YT84fvQ+1X8lfw0/ML70/wmAPQBIP+4/KX/zANPBLMCHgJ+A6cFtwaJBuYFMAVnBaoG4gakBU4FGQaDBZ8DAQNwA5QCcABm/j/9Yv1k/Tj7F/jW9un3nvmO+bz3iPf7+fb7D/zZ+3f8pv3F/on/RgBtAe4C0ANxA80CDwPTA/oDRAP0AtkDiAQZBJMDPgOxAlcCLgK8ASEBfgDj/3//CP9m/ub9Qv1k/P/7+fuE++X6t/qn+qn6RfsC/CD8EPxu/Lz8t/xj/uUBOAJ8/mj+vgOiBuUEWwPcA2YF+AYgB0IGpAXKBXsGUwYeBQwF0gW1BL4CrgKnA70CLwBh/nn9Cv2B/eD84Pkt+Hj5wfoi+lb4ePfI+Hz6YPti/On8PP3c/nIAugAoAQ8CrgIDA0wD+wOdBH0EUwReBBQEOwSTBLUDKAJoAX0BPQE0AEn/Bv/E/lz+3f3I/JT7BPvj+sP6sPqy+vD6UPue++v7HPxM/IH8yvzz/Dj+ewGUAgj/vv0vAjgFYQRtAyID3QMtBuYGpgUtBa8FigbYBlkGkgbxBmMFqwN9A8cDLwNQAT7/SP5f/oj+Wv1a+mb43PiU+Wb5lPi693D4XvpB+5T7cfx8/ZX+0P/BAIABOQKUAqACGwNIBNEEUgT9A+sDUQPoAtUCBALrAHkAhQB3AAQAIf+D/lr+If7c/X799vyP/Fj8G/zm+8P7o/u0++/7Tvyy/P78//z6/PP8bP1gAEIDMgFL/uIAdgSKBIoDogKOAuAEwwYXBoEF2QWUBt0GGAbyBZEGfAWaA+gC4wL8AuABe//e/aX96P2n/XD7xPiE+Hn59/mm+Zz4vvhy+oj7I/zt/E39Gv5Q/z4AWQFuAt8CMAOcAxEEuwQQBQAF1gRaBNEDoAMkAw0CuwDh/+X/t//n/hX+dP0N/d38TvyK+0X7M/s++3X7h/t7+677vfum++j7fPz0/DT9QP26/dUA1AO9Acz+MwG+BEUFugSlAzADPwUkB6MG5QXgBXQGogbrBfAFWQZZBekDJwPSAgMD3wEa/zf9xvzj/IX8OvqQ90v3WPgn+fj4+/de+D76nvvA/On9f/6h/wQBswGlAuADcgStBOsEZwUWBhkGYQWHBP0D1QN2A4UCpgHuAB4Am//l/tz9I/1b/Jr7U/tM+1v7avsx+zj7uPv4+/L79fsC/EL8uPxA/cD9v/3A/c39rP91A2ADoP/W/9ED7AU0BkcF1gOcBOMGegeLBm8FbAUNBmAF/gSdBfsEVAMnAkYBeQGRAZP/G/24+6/7Uvwf+/D3FvZl9uX3Nfmj+A34e/k7+3f8sv1v/n7/LQFbAmADqgSzBUkG8gVUBegFVgZqBYEE+AOiA7cDPQPnAbsA6P8r/4D+zf1R/e38afwV/OP7xfuj+w37ifoD+7z7Nfy8/Ab9Lf2+/Tn+Vv5j/pT+df6h/p0BdQQLAsr+sQAmBLEF+QW0BJIDGgVMB2oHMAY+BZEFrQXuBCoFZAUxBOUCqgHDAEMB7gCn/kT8xvrq+rX7bfqt9zb2vfau+J35bPgJ+JD5MvuN/NH95/6wAG8CSwMABPMEDAZvBvgF3QUSBhgGMwaMBQEE0gIBAhoBQgBW/3X+G/6//Tj95Pxl/Mj7V/sU+zL7avuC+6D7i/uy+1L8qvwG/Zz9AP6H/nL/fP9L//X+CP8MAgUE4QBI/soA+QRVB7cFOgMmA8wE7QZFB80GrQZXBq4FQQW+BcsF0ARvAzgB0v8hANP/+/1s+6v5Evo3+5f6SPjD9m33Evmq+Sj5o/ln+/j8DP6s/qP/ZgGFAtkCVAOABDEGMAdlBhwFBgVXBQEFwwMuAlgBcQEyAdn/Tf6m/az9Jv24/N78//wf/fT8aPxO/In8TPz8+yH8ofw5/aD9wv3B/cz9Wv7f/rT+Yf7c/uT+LP/kAW8DQgF8/4gAPwItBEkFJwSgAiYDxgTQBRYGFAY2BsQFYQWEBY8FagW8BKsC2ACAABEANf+h/Ur7FPpj+qT6Cfru+J74nPlJ+kr6sPp5+7P8O/7Q/sn++f/JAcAC5gLzAokDiwQ5BekELgQ1BHcElAMIAisBIgFGAaoAa/95/mT+pP5h/sv9ef2y/eD9u/2O/VL9Pv1q/U797fwI/cH9Uf5a/lv+tf4r/4H/v//x/xwAUwBsADIApAD7AWcClQHbAN4A1AHNAnUCTwHeACYBMwEeAYkBaQIFA8wCIALUAQQCXgJBAtEBiwFQAfoAaQDk/0r/Gv9N/0P/Av9u/lj+dv42/vL93f0Y/p7+4v70/kL/nf+c/z3/BP8q/7P/RQBwAB4Ay/+o/63/cv8J/wX/K/+h//D/xv+q/5//tP/b/wIADgAZADYATABCAA0A2f/N/+T/DQBdAHYAZwBaAE0AMQALACEAQgBTAHYAkgCAAGEARgAnACQARABhAHgArQDAAIcATwA/AD0AIwD+/9L/xf/c/97/qf9n/0X/G/8K/zL/Uv90/4z/eP93/6P/w//A/6//u//X/+v/4f/C/7X/vP/T/+b/6f/z/wUAKwBOAGMAeQB8AGoAaQBzAGgAVgB1AMMA5wDgANQA4gAOARkB4gCeAJAAmACRAHcAWwBHACgA+v/P/6b/rP/a/9b/xv/Y//v/KgA6ACcAFgARAB4APAA+ABwACwD1/8X/n/93/0L/Ev8C/xL/Hf8U/w3/A/8L/yP/Jv8m/z3/Xv9s/2H/Vf9O/1X/gf+i/5j/rf/3/ysAPABYAGYAPwAhABYAFQA4AEkAOABDAGcAkQC+AMoAvgC9AL8AzADOAK0AnACTAH0AcABXADYALAAYAPf/5//V/8f/zv/A/5//nf+e/4b/d/+E/6r/zv/X/9b/2P/a//X/DwAHABAAMQBOAHsAoACNAHYAfACKAJEAgwBzAH8AfABeAEIAJgAcABAA1f+k/8H/9f8IAPb/wP+b/5v/kP9o/0H/QP9z/6L/pv+r/8P/0f/g//L/9//8/wcAEAARABEAFgAiACEAHgAtAE8AagBhADoANwBkAIsAkQBuADYAFwAHAOL/t/+P/3T/a/9Y/zr/HP/5/ub+3/7U/uL+Df8y/1H/Zv9m/37/tP/O/9r/BQAuAEsAdACIAIgAnAC3AM0A8AAVAS0BOwE6AS0BIQEIAeMAygC7ALMAtQCpAHgATwBUAFkANwAQAPf/2//V/97/xP+U/4f/mv+p/6D/gf9y/4D/mv+8/9j/4//u/+7/5P/Z/8v/yP/V/9b/2P/8/yEAJgAZABYAGwAXAAsAAQDx//L/GwAuAAkA5//n/+T/1P+6/6H/lP+E/3P/cf9q/17/Y/9p/3P/m//S/+3/6v/h/+n//f8PAA0AAQANADkAYwCCAJoAmgCLAH0AdgB/AIkAegBdAFMAZgCDAIgAZwA+AC0ANgA4ACUAGgAkACoAHwAIAOj/wv+s/6j/mv+B/23/Yf9i/3n/lv+h/67/1v8LADIARQBKAFwAhwDIABEBUAFvAVABHQFVAc4BbwEIAAf/J/9g///+u/7+/hf/nv5D/nz+3v7w/sj+w/7q/gb/Iv+R/z4AzgADASEBUwEoAZYATwCXAN0A3QC7AOYAWwFLAZoATQDJAFABdgHYAQID6wMxAyUBwP+I//j+rv3W/LL8L/xg+wL7J/sp+676cfri+uD7E/2M/v//uwDSABMB6QHmAgYEQwUaBi0GXgX6A9QCgQKBAjsCsAH2ABcAP//S/p3+P/6z/T39Lf2O/RL+oP5T/9P/8f8AAD0AqQAuAcwBRgJIAtMBPgHgANAA5ADPAIEADACa/zr/4P6a/nD+Rv4Q/gX+Lv5V/nr+rv7N/sL+1P5B/73/BAAyAGwAiAB0AGIAiQDbAAAB4wCxAIYAVQAwADoAZABtADkA6P+//+T/LQBXAGEAbQCPAL8A8AAhATwBPAE9AVcBYwFEASMBEAHhAI8AUwA8ABUAzP+P/2//PP/Z/m3+Jf4V/iv+O/4l/hP+Lf5X/m7+hf6q/t/+H/9R/2L/bv+G/4D/R//9/tb+3/7x/u3+4f7a/s7+rP5x/iT+Av4w/oP+u/7c/l3/qgBPAiQD6wLHApgD4gTJBRwGIAYBBr8FTAXFBGwEOgTHA84CdQEoAED/nP7J/cL86vtV+9n6Zvof+j361PqA+8/7B/y0/O39af/CAL8BhgJRAw8EqAQxBakFwgVBBV0EgAPJAhECRgFZADX/9P3V/Pf7S/u8+m76Yfp5+p764Ppa+xn8zvxG/bT9LP6q/jv/zv8TAAcA0/+7/6D/VP/l/ov+3/1j/az/gQRbBrkCkf9yARIFmgbTBvgG7AaQBvIFRAVKBTMGxQbPBUYDbQAn/6z/sf/x/bb7PPpL+ZL4Hfhw+O75bPtZ+1f6gfqV/Hf/qgGqAvQCgwOmBL8FLgZCBl0GGwYWBYwDMQJwAeAAq//y/Zv87/tj+7D6J/oY+oz6TvsB/G386fzu/VH/mACWAU4C8AK3A1cEQwSjAx4D0QJGAjUBzf93/nn9qvys+6v6/flo+dP4q/gd+UP6nPx4/+0A8gCoAdoDTgYkCHEJNwpvCicKZAl/CPQHfgdmBn8ECgKO/839uvxz+775NPg097T2qfbz9qj33/gs+jb7Svzl/f7/OAIuBKYFvAaxB0wIRAjxB8AHawd/BhcFdwPMATYAtP4w/c37s/q4+eD4ePiL+O74lflm+j/7Kvw4/Wz+z/85AVsC+wInAwYDzQKjAk4CnwGpAHn/K/7j/JL7ePpf+R/5jfwLAp8CWv5F/QYBoASnBiQIeggDCOoHDQjUB6oHyQdjB3QFAgLN/tL9Ef6//DH6RfjT9tz1F/am9nf3i/mC+7H7kPvf/Hn/oAJfBacGxwYeB9AHGQgACL0H5QaDBdQDyQHB/03+Df2I+9/5m/gz+Fr4gPjt+A36evun/Kb9xP4LAJ0BgwM5BRkGMgbkBaEFkQUjBQQEzQKbAf//OP7D/Iv7ZfpP+UD4Z/cV9233L/j8+Ln5oPql/H8AbQQ0BrgG5QfBCW0LbgyjDDsMVAvWCd8HrAWlA/8BLQCf/av6VfhL9/H2TfaX9XX1CPYn94/4Mfp3/DH/eAH3AhUEMgWEBvQHFQmSCWEJoAhNB5kF4wNIAqIA4P4M/W77GPrj+Db4Yfiw+K747/jw+Yv7Tf3L/iAAegGdAmoDBQSVBDYFsgWDBWEEqAIuASIA2/4q/ZD7Pfom+R34IPez9tn2C/dg9yf4gfofABsGXwfWBcUGvwl6DLcOeA9fDuIMVQstCdYGywQpA2IBW/5p+q331PZH9j31ivRS9G30rfWv95D5RPz0/50CoAM4BEEFHgddCbQKugr4CbQI/wYaBR0DQAHb/1r+8ftv+ev3X/eJ9+r31/cB+FT5IPum/Dr+QQBiAgsE6gQiBWoFTAYdBwIH8wU4BGkCAAGC/6/9FvzF+ln5+ffy9lr2QvaX9h333ff++E76uPte/ar/jAMzCKkKfwpDCg0LSgxZDUwNvQt9CT0H3ARoAk8AmP4M/Vr7Hfkc94f28PZp9wL4uPiW+R/7Kv0v/2QBywOjBWkGbwZIBlEGtgYoB+8GugXHA54B2v+B/iz9Evws+xv6YPlS+Vb5q/m7+sf7f/x+/en+gwAQAk0DCwRVBHAEZgQwBP0DrAMGAwECkADT/kf9JPwT++T5+/h3+Br4+/c3+Kv4Zvks+hj7Gvx6/eUB2wi3C3gJXgjRCZ4LbA3XDSgMMAoeCFsFfwLq/2P+8f1K/Ov4R/aS9fP1hfYI93f3//dk+af7jf23/yQD8wW+BnYG8AU9BsEHtAhDCB4HJAXFAsAAvv4g/ZL8D/y3+iv5MPhV+JX54vqA+yj8fP0L/6YAWALBA9wEnwWLBdQEXQR9BMAEYgQHA/4A9v5p/Wn8nPu3+vX5aPmy+A/4Efi1+I75P/r0+t374fwu/nr/RgGXBXMKRgtqCb8IPwlRCn0L8ArICJAGXgQuAjIAV/45/Zn89vqU+C73JvcE+Dv5Cvpy+h/7Z/wY/vT/QgK/BAUGvAXtBG0EuwSXBecFMwW1A8UBCAC8/ob9kvwx/Kf7lPoJ+lz62frM+/X8Xf3b/Rr/UACYARQDBAR1BKkESQSQAxED2QKSAgQCCAFx/7X9jvzu+0v7o/oZ+rD5f/mW+bb5APrg+tn7Vvz9/AD+a/9sA/EIlwqICL4HhwiyCTULbwsmCrQIhAaXAxwBJ/9L/i3+lfyn+cP3Uvee90347/hk+R76bvsA/Wv+fgBZA1QFygU0BUIEXgSbBVwGHQbsBO0CHAG1/y7+If30/LD80fvy+nv6d/ob+xH8zPyp/dL+rv+GAJ0BdQJGAyUEHQRRA84CqgKRAjICDQFW/8H9ffyE+9/6cPrp+Wf5Hvm5+J74k/m5+lH7FPwN/c/9Mf8bA6EIIwvWCeQIXQkuCogLGwwCC2UJRwdqBK4Bo//S/p7+7PwG+hn4a/dn9+L3Z/gM+Rb6Pvtj/Iv9pv8lA8kFRga8BfEE/AQiBtUGwwbtBdwD1wFNAFz+Df3M/CP8//oU+oD5ivkx+uj6e/s2/FH9gf6R/8AAFwJpA2YEkwQABHMDQwNCA0gDugI2AWD/3v2m/LP7/fpp+uX5Qfl8+DP4nvhX+Sf6vvor+yH8Zf31/3YF2gmnCccIKwmICcsKAAySC7MKGAk0BlcD5AB+/4n/uP4s/OH5bPi89x34o/j4+KT5gPqX+7H8BP7PAMQD9QQeBcYEYwQJBf4FJgaWBRkFmQRuAon/UP7m/Vf90fxp+xn6YPrC+qH6BPvx+2j97/6C/8L/3QCtAikEkAQuBMcDdQM9A/QCPQKeARIBrP/V/VX8Kful+nz62Pkj+eb42PgP+Zj5G/re+i78Lf12/qYCrQcZCb0INwljCRcKggtwC6QK7QnvByAFfAJIAGL/zf7R/Er6dvhq9zH3TPdw9xL4EPkb+gv7Bfwu/lIBjgOMBL0EfwQfBX0GIgdABwEH5QV4BNgC7QDH/0X/Tv76/Jr7jfpb+nX6RfpQ+vf6IPxb/SH+8v5oAAgCRgPiA/YDHwR1BG0E5gNFA5UClwFUAO7+ef1j/MD7A/sj+nb5D/kM+VD5jvnw+aD6lPud/P/9vgBxBNAGZwe5B08ILwlFCs4KfgrACX0IfgYnBFYCXAFYAL7+2PzR+kT5rvhV+BX4bvgH+Yb5G/r6+pn82f7dACsCxAIMA9AD7QSPBfYFIAaRBc0EAQS6AqkBHwFEACn/J/4V/Vb89ft++xz7MPvM+4b86fxT/S7+VP+QAHcB4gFaAuUCAQPbAqUCQQL0AYgBigB2/5j+m/2x/A38gvsf+9P6S/rE+a35Hvra+pP7M/1TAAMDIAS1BFYFbwYGCCQJhgmPCU0JdQjzBmgFXgSgA6EC9ADU/u78k/uP+qj57/iQ+Jb4tvjL+G755/qX/CH+Hv+T/3gA6wFdA4wEVAXuBTgG1wVZBfUEZwTvA08D6QGFAJb/Yf4x/WL8oftQ+2T7K/vu+jL7vftz/ED97v3G/sn/oQAlAXYB8QE8AhQC5QGSASEBuwAcAEr/nv4l/pD93/wh/E37z/oD+w37Afs1/XkAbwF1ASICzQJwBMAGrAcaCAwJEQlPCIUHtgaYBq4GrQW0A40B3/+z/ln96fvc+vz5SPmP+Lj3//dD+SX6zfou+1j7rPyr/igAfQHDAssDvAQgBf8ERAW6Bc4FVQVKBE8DhQKeAbcAmf9r/pj94vwI/GP7EfsN+077aPuH+/f7hPxJ/Rv+of4x/9j/RgDGAF4BjwGlAY4BDwG1AH8AJACj/8v+AP6C/SX9sPxM/FH9P//a/6X/yv8BAAEBrQKBAxsEKQWuBbQFrAW2BSUGxwbuBiUG2wTqAycDDgL4APf/xP7R/dj8bPvI+vX6yvqe+l/6w/kv+ob7Yvws/TH+9v6//5kAIwHPAdECkgPGA40DWANWA1wDNwOaAtIBTwGiAKz/BP+Y/jP+3P1Z/cz8ofzK/P78Hv0v/VH9ov37/Sz+X/7J/jb/hf+9/9//IgCNAL0AjQBbADUAAADG/63/pv/q/6kA7gDHALgAoACtAOEACwFGAbUBzAGaAYQBnAH6AVkCgAKVAocCZwJWAhcCuAFiAQABeADr/3H/Gf8o//v+af7n/Vv9DP37/Pz8H/2M/ev99P0t/pb+Af+b/ysAfwCmAN0APQF7Aa8BsgGaAbMBpgF6AUwBSgESAbYAgAD//5j/a/9G/wX/4f7O/o3+hf5x/nX+qv4h/1v/df/R/+T/EwBCAGUAVwBvAKoAgwBuADsAEgDm/+H/9v/r/wEA9P/n/8H/yv+u/4j/mv+U/83/6P/u/+n/+/8SAEsAjgCSAMMA3QDGANsAEgEhASkBAgGuAGoASAAuAAgA3v+W/4L/Vf9A/2b/T/90/5P/d/93/3b/qf/x//f/+/8LAAUAEwAvAEYAVQBfAEQAGQD//wIAHQAPAAwA7/+//6f/h/9y/3b/Z/9g/3//nv+z/6v/1P/h//7/UgBNAHQAiACfAMMAxwDiAM0AyQDNAMUAhwBqAG0ARAA0AO7/nf+L/4j/fv96/3z/eP9w/3v/h/+S/7z/2f8KACoASwB4AH4AugCiAF4AXwBtAFYAQQAMANn/4f/B/4f/UP9P/1z/dv9z/2X/V/9o/47/k/+//9n/8/8IAAYAIwBZAG8AWgA6AB8ADwAYAB4A6f+//5X/bf9i/1f/Wf9E/33/x//i//H/BgAvAEIAgACMAKwA2gDbAOcA3QDYAOcAIgEEAfYA6gCkAJwAlQB2AC4AGQD7/+z/EwDn/7v/2//s/9z/7P/j/+7/AwAAAAcAEAA5ADcAEAAIAPH/uP+//8T/l/+O/2D/Mf8f/y3/LP89/2n/Nv83/2X/Yv+0/+3/5P/2/zIAQgA4AGgAYQBtAHkAhgBdABgAFAD4//r/0P+R/3b/Qf9E/1v/NP8n/zb/QP9r/6n/n/+w//X/LABAAGAAhABqAMUA0ACxAKYAqADpAMMAxACBAHYAjQBxAIgAMwAsAHAALgA+ACAA8/8/ABgAMAAvACoAFgANADIA7f8rAFEAHgAeAAIA1/8DAPP/8f/q/7j/xf90/5P/ZP9o/8n/Zf+o/4H/l//Q/8L/EADp/1IAXQBUAI4ASgC2ALcAaADRAEkARQBRAAYADQCu/6j/Tf9b/zn/6/4T/xP/0f4m/zj/H/9+/1D/0//N/87/AgA6ACQAIACBAP//LwAzADUAQwD6/xIA9v8hAAUA+v8yAAoAKQBSADwAKABJAKAAeAAxAKsAfQCCAI0ASwCgAHUADwAmAJEAEADS/xQA1P/B/83/z/+y/4r/wv+U/7X/8f+m/+T/7/8pAPf/+f96AGEATgC3AI8AYgDJAJUAswDFAL0AngB4AGQAPwBEAOH/6v/u/3v/ev9W/3P/P/8f/7L/Jf9m/4v/oP+q/2L/HQCd/6L/FwDj/9D/2P/w/5r/qv/Z/9P/0/98/+7/uf9l//X/sv/D/+j/6P/G/9n/5//r/yYAyv8qAGoAn/8PAJsAOwAOAGEAaQAPAFwAAAAQAFoAIAAMAOH/pf8CAPb/mv/1/67/FwC9/6///f///ywAyv9nACgAPQB/AB4ATQBtAHkAfwADAFIAcwC2/3AAbwDv/5j/LAAYAHL/QQC5/wEABgDx/+r/+/9PAKb/sAAKACwAVwD1/5EAv/+XAJMAyv8ZAKEAFQDB/y8AXAAqAIf/v/9XAOP/Zf9oALP/Yf+x/6z/FwCa/xkAWP+u/xIAYv+qAEb/0f9HAUz/0/+VACcA/P/Y/7UAqP8mAOH/nP9VAB3/cwAg/wQAo//g/q8AOf+6/3T/NADR/xn/TQAVAFQAz//a/2EAFgC9/14ARwC+/yEAp/+D/87/mwBo/6j/3gD0/zkAnAD2AGUADQFnAYsABAGnAcIBkgEQAfwA2wBLAOH/U/+3/2r/2f4J/9T+mP7g/h//s/4w/17/Mf8XANX/LAAZAb0AmgAAAYQBAQFLAbsB+QAKAb0A1QCZAG0AgwBfAGAAbv8cAIH/Ff+///r+e/+N/zf/q/+b/3H/kv9l/9//MABI/4z/IwCi/2T/wf/v/3P/lf+P/4T/zf8hAPf/W//K/7f/dP+E/+z/OQAn/+j/8v8v/87/v/8kAI3/0v/k/+j/5/8uAKsA/v+uAE4AHQBuAKEAvwByAIwAIgDR//wAQwAMAMYADQAdAGcAfgDr/+D/QADQ/27/SgCa/zIA5v+c/1gANP+OAEMAp/9oAG0AcwAzAK0AKAABAKQArv94AKD/ev+ZAOL+Tf+s/3r/Df+W/yIAI/+SAEcAjv8mAWkAxv+xAMMArAC8AD4AlwB4AMr/3v+9/2v/Pf9MAP3+GP8NALz+pP92/77/1P97/y4AqP+P/3MAoQARAOn/NwCI/4P/gwH9/9f+egCz/3r/GgACAEUAr/88AEIA0v8jACEADwEUAC0AzgB8/3YANwC6/8MADgBEAJj/OwDg/4f/5ABz/yYAO/9//6gA8/5EABUA3f83AET/MACQ/z8AAABF/xgAcf9QAJP/Lv+BAEMASAAxACQASAAnANEACgFtAB0AvQDi//3/xAALAHUAMgD1/2f/gf/P/+//CwCH/83/af9x/5X/pv+c/9v/TwBX/7T/6f+Z/2EA5f9dAKgA6v83AHAAcAB/AFcA7f+o/8v/i/8K/+H/wv/g/nf/zv8b/7//PgDk/zoAwP80ADwAzP+LAFQAeACNAIQAQgD2/64AWwBqAEwADQD7/7L/zv+D//L/6/8fAM3/Ev/w//D/DgBcAOv/+v/M/zMAjAAtAFcAHAA4AP7/vP9iACcASQBiAIP/jv8gAOf/cP///0MAmP+0/8X/Yf+X/y4ADADt/ycA8v8SABoAEACgAJEAdAC3AEAAOQB6AHwAQwA+AEwAyP/7//z/lf+7/4H/Yv+B/yv/R/+t/3X/Xv+V/4X/0P8RAAUAKwBGAEUAcABoAEEAcgBGADQAWwDV/7r/RgDX/3//vv/E/7H/jf+j/9X/wP+7/wQA5P/N/wgALgA+AEUARwAwACoASABrAF4ASQBbAB4Ayf8lAEcA6P8qAFQAyP++/xkABgAlAF8AKQAYACUATQBgAGwAjABYAFgARgAiADcARQBJAMf/nP/B/6b/tP+O/4X/of+B/4P/m/+W/6P/3v/5/9j/3/////n/CQA8AC4AOwA0APb/MwAhABwASQD1/+r/+v/S/wEAKgAAAMD/6/8eABMATwBNAB4APQCHAHEAKQBzAIIATgB5AHEAGQAHAD8AEADr////3f/G/6n/dP9m/3f/dv9v/03/Mf8r/zP/Tf81/yT/Q/9b/1r/MP81/0//Wf99/1P/WP9v/0X/Yf97/3T/nP+V/3j/h/96/5//xP+9/8D/rf/X/93/3v80AFoAggCeAPQAiAG/AQ4CawJ1AvQCdgNOA0kDjwOPAycDAgPmAlgC9QGwARsBtACRACAAiP8w//X+X/76/Qf+3P3B/Rz+dv6C/pP+9f44/2D/5v8lAP3/LgBcAA0A3f/c/5D/Mf/t/mv+4/24/V39qPw6/Br88/vB+6P7wvsj/Jr8vvzd/C/9lv1T/gD/Mf87/7v/PQHJAlIDywNcBK0ElQU8BusFAwZDBrQFuwQbBLgDMAPbAlkCJQFiAEAAev+k/kr+0v1r/Wv9Kv2+/C392/0E/kz+jP58/hr/DQB5ANkASgFeAaoBkgKXAgUCTQIWAigBBAHCAMD/kv+4/8f+Wv73/qT+pP3y/UX+CP6w/sf+AP4P/rr+z/65/h//FP/M/j3/Uf/Z/u/+8f50/hj+4v21/aj9lv1T/fD8wPyn/I38C/1v/yUCqQLeAnoD5QNSBQ8HUQc8B5YHBgcpBkwFiwQvBDIDAwKjAAj/Qf7P/cf8l/vm+k36EPpD+l/6HvtN/Cv94P0R/pD+RQDjAcECkAMvBD8EQAQyBPsDzQOFA8gCTAHK/1r/av/k/vv9yPzP+wv8uvzl/Ov8TP3T/RP+Ov6z/n3/XQAEAekAVgBzAOIA1gBRAKb/M/+0/hr+aP3V/MT8gvzk+/P6vfqM/UMBvwIwA9kDbASUBWoHKwhNCHYJzAncB+kFzwTbA2oDgAI/APb9yPwI/ML6ZfmK+Cn4DPhQ+MD4u/nK+7H9iv7D/mj/+QDXAjwExgQGBSYF5ARhBGADqgKzAjMC3QDs/63+w/wk/D38nvtQ+5H7jPv4+w792P3Z/rIAqAFxAegBvAJnA0IEeQScA8ECSAKaAcoA+/8k/07+nP2//LH7PfsM+//6MPv5+g/7zPuq/CP9uv14/iD/swLWBhQHxgbDB7MH9AdSCZ8IcgfNB3YGagNaAT8AY/+v/o/9rvsa+uX5GPqy+XD55Pm3+pf7o/yv/Tn/OwFzAusCnAJYApsD1QS2BF0E3QOGAocByQCV//j/iAAj/8L9afxE++j7nfyl/Nv9if4Z/pz+Hv9l/6gA9QHoAWMBRwEgAeUA/wDGAOT/Jv+M/qz9B/30/JL8sPsn+9/6r/oM+3j7q/sJ/Eb8Bfwn/jAEPgjjB8QHGAjNB7gJaAs/Cr8JoQlOB9oDBgHo/5v/jP4I/df6mfhk+CL5Yfiu97/43/n3+o/8Gv59AOoCbgT9BCwECwQCBksH1QZyBh8F4QKlAY0A3/7x/en9FP0r+5T58/g0+RL69vqP+1D8Ov6YAL4BWwK3AxsFngWqBTsFmwThBJsF0QQrAhIAFv8f/m79qfxs+7n6vvos+hj5SPm9+qf7GPya/Af9Cf57/2sAXQBGAOIAKAHTAGMA7v/y////z/6E/vAAQQPOA5kDyAJCAlEDrATvBLcE3wTABKgDYALPAcEBygH8ARMBCv8A/vH9fv3M/ID8lPy9/Pz8nP1V/hH/OgAAAYAAUwBoAYICQQObAzoDVgKYAR4BbgD//3QAkQCO/6L+o/09/eT9uP64AJgBAAB6/7b/e/91AJsBVgHWALIA4v+g/sr+t/+C/9T+LP4J/TT8Nfw7/If7/PpH+/D6kfpD+8H7NPzP/Oz81/x5/M7/0AbnCBQHRgfnBg0GMAhOCcsHvAe9B9UEwQCj/qv+v/5i/rL9qvuv+fv52PpF+jz61fsj/df9kP7r/+sBYgOqBKgEvQJkAjcEsAQMBO8DfwJ2AFz/Ef7i/Nj8e/2U/Wf8Bfvd+o/7hPzj/R3///8KAeMB9QLvA2wEwwWtBsQFuwQXBI4DZAPSAlABgP9W/ur9Lf0i/PL7QfwV/Mn7v/vl+2H8Z/0j/vz9CP6a/iL/dv98/2r/Nf/C/mT+2P1V/bz9Qv6R/ab+1QKIBLYDhQTSBFIEFAZcB0cG5QUCBpUEMwLLAGcAeP+d/n3+J/0m+8n65voJ+uf5Jfv3+4f8hf3S/jwAbwEGA+kDDwMpA5AEjAQUBFoEawPOAfcAn//g/TD9Wf1C/Wz8t/uJ+4v7OPzS/Wv/ZQB/AYgCwgL4AgQEYAW7BSQFTgQOA9wBhgEeAQMAJ/99/lv9KfyI+3f7afty+2r75/rF+kn7JPwR/cb9Vf6f/p/+u/7//nf/8f9gAOECHQY8BocFBwZGBdgERgYNBpEEiQT1A7QBrv/b/of+Df76/ff9+PwL/H/8kvzB+0n8ev0a/un+yv/IAK4BXQIlA+oC5gEhAuYCbQIpAjwCCQHc/0r/CP7k/N38Nv07/RH98Pzm/P78Vf18/q3/pADbAU4CHgJSAtYCdgP4A/wDbANvAqoBOgGPANf/Zv/p/jL+hv3V/FD8S/x8/IT8VfwR/AT8Rvy8/Gr9/v0+/k7+K/7j/fL9h/4F/43/EAJcBfsFwwU1BoUFfgXzBv4GLQYPBmQFmAOKATIAh//o/qT+a/4z/cD7cPtc+7v69vr9+6b8XP0h/jL/XgADARsCmgLIAf4B3ALJAtICFANnAkIBNwD+/tb9N/1c/Y/9+vxF/Bb87fvh+9D8SP5k/3QAYwGiAbQBRAJEA+cDCwRcBD0EeAMHA5ICnwHfAIEA0v+3/tX9UP3H/GX8WPwa/Lv7rvvT+/b7N/zS/GX9q/31/Uv+gf6Z/tn+Ev+B/6kBcgRVBUoFTwW+BIQEJwVcBRoFKQUfBRMESQINAW8A5v/l//3/H//v/Xj9G/1k/Df8lfzR/P38V/3t/Wb+5/7J/ysAEgCnAIcB2gEjAqECpQJGAv0BaQGeADkADACw/y7/p/4w/sj9ff2I/b/9Av56/vP+Tf+Z//3/bwDMAEwBwgEjApECpgKFAl4CGQLWAawBVQHDAC4Am/8A/1L+q/0C/Vb85Puo+3v7Vftq+7D78/tV/MH8H/2U/Rv+pv7I/5wBKgMHBG8EhgRvBIYE8QQzBV4FlgVUBXYEcAOaAgUCqAFoAScBkQDJ/yH/P/5h/Qb9Av0F/eb87/wL/Qn9Nv11/ab9AP6k/lf/4P90AAIBUgGXAdwB7AHpAQMCAgL2AeIBlQE8AekAkgBMAAoAzv+W/2j/Of8O/xH/Iv86/2z/h/+f/8v/8/8yAG4AlQCyAKkAjABnADcAAADF/5P/UP8O/+L+pP5s/jv+Gf4i/ib+M/5c/nf+of7V/uD+6v4S/0f/kP8DAIgA4wAXATQBMwEyAUkBYwFxAXcBgwGLAYMBeQF0AXoBewGAAZ8BkwFcATEB6wCIAEkAKwAAANX/v/+M/0b/Ev/m/tr+1f7O/vX+Gf8z/1z/Z/9u/5f/xP/u/w8ALABQAGsAfQBqADcAGQAaACAAJgAhAB8AFQAWAD0AVQBaAHUAfwB1AIAAjQCQAJsAoQCBAEcAGgDq/8H/r/+m/5r/hf97/3z/b/93/4j/d/9b/0T/Nv8t/yf/Nf9C/0n/a/+E/47/sf/n/xYAPABQAEoAVAB3AIwAjwCIAHkAZwBbAFgARQA7AEAAJwD+/97/vv+y/7b/rv+T/4b/kv+b/6n/sP+U/4X/i/+J/4X/ff95/4P/lv+j/6//tv/D/+T/AwAaAEIAcQCWALYAygDOANAA2ADrAAUBIgE5ATMBDQH/AOYAtACbAHgASwArAAQAy/+d/5j/lf+N/4r/h/+O/6H/w//O/87/1f/Q/8n/w//K/8//zP+6/57/hv91/4P/iv+A/4f/nP+w/8H/3P/y/wYALQBJAFYAXQByAIMAgwCCAH0AawBSAE0ASQA8ADoAJwD5/9T/x/+4/53/hP9f/0L/P/9D/0X/NP8o/zT/Qf9R/2T/cv+C/53/v//Q/+r/GABGAHgAoQCyAMAAyQDaAO8A+QD5AOoA0ACjAHcAVgApAAQA6v/C/5v/hP99/3v/g/+T/5f/oP+2/9L/8/8KACEAOAA/AEIAOgAqACEAFgD+/+L/1P/L/8L/xf/E/8D/yf/a/+n/+v8ZADEAPgBIAFAAWABjAHEAdgBzAG4AaQBbAE0APwA0ACgAFgD+/+f/2//a/9v/2v/W/9r/3//k/+r/6P/k/+P/4//k/+T/5P/g/9n/0f/J/8T/vf+3/67/of+X/5b/lv+Y/6T/s//E/93/9f8PAC0ASQBXAGAAbABxAHQAdgBrAFwAUwBGADUAJAAUAAIA9//z/+z/5f/k/+b/5f/k/+r/8P/1//n/9//w//D/+/8DAAYABQACAP7/AgAKAAoABgAEAP//+f/6//3//P/5//j/9v/4////AgAEAAgADgAOAA8AEwAWABMADgAJAAQAAQABAPz/9P/x//D/6//t/+//7P/p/+r/6//p/+v/7f/o/+j/7//z//T/+f8CAAMACgAVABoAHgAkACcAJAAhAB8AFwANAAUA/f/v/+f/4f/b/9n/2//d/+H/6v/x//n/AgAJABEAFwAcAB8AIgAhACEAIgAdABgAFgASAAoABAD+//X/7//x/+3/7P/z//X/9v/+/wQACAAKAA8AEgARABMAEgANAAgABgAEAP7//v/9//v//f/+//v/+v/9//7//f////z//P/8//v/9//2//X/8//z//P/8f/z//b/+P/3//j/+f/5//r/+//8//3/AAACAP//AAABAAAA///+/////P/6//b/8//y//P/8P/r/+z/7//w//H/9P/3//3/AwAGAAgACwAPABIADgAKAAkACAAHAAMA+//1//H/8P/t/+f/6P/o/+n/7f/w//j/BQANAA8AEQAYACAAJAAnACQAHgAdABsAFgAQAAwABQD///7//f///wQACAAHAAcADAATAB0AIwAlACEAHQAaABYADQAAAPf/6v/X/8j/uf+z/7X/uv+4/7n/wv/S/+P/9v8EAA8AGAAdAB0AGQAaABsAFgAKAPn/6//k/+b/6//v/+3/6//p/+X/5v/u//X/+//+//3//v8DAA8AHAAlACgAIQAXABAADwANAAgABwAFAPv/8//s/+j/6v/z//n/+v8AAAYACAAHAAUABQAIAA4AEwAOAAkACwAIAAgADwAXAB8AIQAiACgALgA4AEAAPgA3ADEAKgAcACQAOQAzABsACADj/7j/mv+W/5n/pP+0/67/of+N/4H/gP+C/5b/u//b//3/GQAnACkALAAyADgASgBmAIAAjQB9AGoANgAHAPv/5/8AABMADQANAOj/zP+2/6P/wf/a/+7/EgAJAAQA+v/T/8T/oP++/9v/2f8HAOb/0f/O/8D/0P/q/w4AHwAtABsAHAAVAAUACQDq/9D/uf+t/7D/vf+//8r/0v/X/+//7f8rAEkAcACrAHsAqwCOAH8AswB4AJ4AigBxAGYAIgAfAPP/DQA5AEgAaQBlAHAAZgBWAGYAgACXADMB8wHBAaMByAFCAW4Aqv/x/jf+xP2Z/TP93PwM/Sv9Qv2R/Qv+kP7U/kv/uP8FAHAA2ACCAdgB8AFoAqwCoQJKAu8BkwH0AGsA9P+C/xr/3P7V/oz+Rf5s/on+jf6o/gP/Yf90/87/LwA7AGoAuwD7APsAAAErARABygCcAHsAXwA5ACkAKAD0/7v/ov90/0X/O/9W/2j/Rv80/0r/Qf82/2T/u//w/xQAWABuAFcAZwCcAK4AmACqALYAhQBbAFQANQABAO//CwAmACsAQgBYAD4AHgAnAEAAYACSAMUA0QC9ALcAvgDBANAA4gDtAN4AuwCWAF4AJwACAOX/1P/F/7v/vf+w/4v/ZP8//x3/JP9L/1T/Rv9G/0P/J/8n/0b/Sf8x/y3/HP/h/tX+9f4T/yz/Iv8P//L+uf6i/pb+bP5R/kH+QP4o/uv90v3P/ej9LP5e/mP+S/79/j8B8AMLBpUHMgjnB0MHiwaFBVoEtQNWA4oCAQE2/4j99vsi+yz7h/sG/ND8t/0f/i7+if5M/3MAGQL6A1gF7gWTBWQEzQIOAfb/n/9a/2L/b//R/vb9Mv14/CX8evxi/Tz+vP4d//3+Zf79/f/9Sv6w/k7/tf9X/5L+s/25/OL7VfsQ++v6jvo7+p/5tfgP+Bv4b/1JCG0PexEdE3cRKAxMCVIISwUMAz4EQAWqAYr7b/fZ9K7y7PP7+Gz8Pf1i/4gBogBW/uv+JgIsBO4GmAugDGwIWgN2/4v7hvjG+HX7af36/UP/of+//Iz6CPyf/oYAHgPDBbIFlgMJAooAKP5J/d/+XgChAGoAzv9l/q782vsn/OH8z/1B/1UAHgB2/+3+qP6e/rn+Nv+O/zL/iv7l/fT8vPvH+pf62fod+xP85P83BiwLuA3SDlUNzQl5BywGJgRVAtUBlwGt/4r8Dvqk+OD3v/iv+0b+Mv/j/8IAogDO/w8AiQGgApoDIwWEBXcDRwC6/bj7o/p8+1f9xP6T/zQAaQDf/03/pP+vAA0COAMoA5kC2wEvAMv+V/70/bf9D/5V/qT9ifxN/G38KPxU/Bb9uv3b/b79xv09/Xv8jvz4/Nz8Jvxw+3n6WPhx90P+JAswE4IVMRcEFK4LDAdXBS4BJv7R//gBDP+6+Cf1BfUD9WP3Pv4rA2AD3QMvBfgD1wAzALEC6gPZA1MFzQUWAkT94fqi+V34kPj4+oP9iv67/+gBNgKVANAAYgJgAh8C1AKmAggBc//k/rv+O/4b/rb+kf5o/Sr98P1O/jj+eP7o/p7+Uf7c/lj/dP/e/9kARgFqAFr/X/73/ID7qvop+kX52vhe+Rj6kfpr/CMDigufD0YRzREMDo0IqwVvA0kAb/4B/4X/Nv25+Wv4s/gX+YP7n/+mAYYB6QH6AgwDOAKTApQDHQOAAukCVwIrADD+bv1v/QX9z/wj/lj/qf/qADcCxgEEAW0BkgFwAKn/xv+l/27/4f9MAN//Vv82/6f+bv3V/Ef94v0t/mX+gf7p/WD9Cf7X/gX/UP+r/4n/o/5b/Rf8g/o/+RD5bPmG+c746fuSBsEPKxKhE7ISAwxlBssECQLp/Yv8R/6O/sz6EvcG9yn4Qfnx/BoByQH5AA8C+wOvA7IBGAEeASMAawB2AjYDbgFF/x/+tPzL+rr6Gv3a/0ACVwSoBHcCHgBC/7v+Gv6c/lYAeQGoAU0B0f/2/Vv9+v2n/gb/lP8PAGP/5v3u/Gv8bPz9/U4AjwGcASUBXQAB/zD9yfsT+0f6mvm9+Tn6pvn0+FT+SglVEPoSVRUFE6oLqgZXBIUAtvx9/K/+MP78+en2wfbI9gv4j/y1AK4BygGMA0MFCATQAT8BkgC5//sAFQPxAmYAuf0I/O36VPpW+xT+BAFvA+wEMQSfAWv/pP7m/q3/ngAqAWIBXgFHAHL+Tf0T/UL93/3m/nf/5P7Q/SH9s/xo/D/9Qv+iAL0AXQCy/1/+rPyE+w/7evrA+bv5J/r2+S75vPy0Bg0PchLLFNYTOw1qBx0FIQKo/h7++P/U/z38pPjB97z3VvgV/IEA9wEXAmEDugTFA3EBcADp/1L/8QDXA4gEbAK5/2b9IPvn+fr65/0GAU4DfgSIA1MAxv0b/Sv9Pv4sAMQBeQIMAqoACv/Q/Y79Bf7V/rD/9P+L/3z+Ev0s/Az8lvy+/cr+cP+V/xH/N/4H/cb7EPvd+vf6Mvuw+2r7GPpX/skIHQ/WELYS5Q8mCD0DYwHJ/hL9Af/NAYsAGvwA+g37cPt7/GYAUgNKA3cDmgTlA5MAIP5Y/kD++/2iACQEjQQ7AlH/hPw2+gn6Qv1lAXcDbAT/BDoDx//n/e79wP4hAGoBygGjAIH+5vwa/Nb7Zfy3/WD/RQDw/0T/mf7//eT9Sv6f/oT+RP5H/iX+hf36/Kr8HPxa+wX7OPtm+5P7qfrG+6AE+w10EKQRABGtCVUCaP8H/d378P6FAwUEkP9O+0X7VPzP/CMANgQVBc4E/gRkA2H/UvwZ/bH+jf6AAKEEswXcAsz+Gvtk+fn5lfzKADUDDQNcA7UCM/+b/ID9wf+8ANsAWAHRANX+cf1K/V/9zv1t/xABnQAH/3n+if7P/UT9If4G/+z+iv4H/in9tPxF/RX+l/0c/Iv7x/t0+0P7yfpY+0gDAA5iEXYRtBDcCeoBZf7I++T6xf62AyIEjP8O+8T6H/wi/acACgVXBikGgwWNAj/+uvuQ/AX++v3o//MD3gTvARX+ivqG+CL5GPzQ/woC7wLCA4sCnP6V/Hb+XQC6AE8BVgHf/3D+nf2d/NH71vwi//v/7P4U/nv+6f54/kT+iv6D/sn+Tv/U/sb9o/1P/kv+DP3u+w38dPw7/BX8Q/uD/FIFvw7FEKwQCA/lBzEBN/4e/Cz9MgKsBbEDQP7P+lj7Mvxr/asBbQVLBngGNAVkAf/9rv0R/wL/kv5iAdQEAARrACv9dvpG+eH6y/3G/wsBuAL2Aqn/+PtF/Pj+JABnABABxwDA/0//kf4I/c/8eP5U/zT+8vwi/Rf+Kv6Z/Z391v0U/qr+1P5c/lD+0v7A/qL9L/yI+8D7WftH+qf5n/mx/pAJww8PEHoQiwxRBIL/dP0V/SUB1gU3Bd3/yvqg+c76yPsy/7AE5gbDBlYGVQOz/x//XQC1ANf/1ADyAyUE7QDI/UL7t/l/+uz8TP8yAe4CRAN/AKn8GPyi/k4AxgBvAT4BSwCi/8X+q/2q/Sf/4v+C/jr9R/2k/df9qP2J/TP+0f70/tn+WP5E/o/+6v3P/Ff8S/xh/En8mfvI+ur5Qfx8BboNLA/7D0wOgQaMAK7+kf0cAEMFtwWrAK/7wfkK+lj7p/6OAxcGEgZ8Bd4Cx//a/ywBKAHSAI4BEANZA44Bl/5E+z/5o/kd+/H8iv/gATwCFQBM/YX8+f0RAGUBzAGsAecAmf9D/k/9Uf1U/u3+S/6v/cT93P3L/Z/9mf0w/vn+lf/y/ygAhACNAIf/Jv7a/TD+3/12/R39//tJ+kP6CACUCEgMQA23DUEJhwO/AdAAWwF6BQQHXQIG/e36xvqe+zb+6QHrA+wDfANtAdf+lf/JAfMBoAE0AvACCQMwAvsAov6E+7r6XvtX+0P9agCjAPz+BP4Y/ZL8B/4FAMIALwFCAer/RP6g/aj97/1y/p/+Mf7o/dP9m/2L/ar9/v1x/rz+/f4p/yP//v53/rX9WP1r/Yn9sv2C/fX8Pvxl+3f+TQbPCgYL5AsGCm8FZwOXAgoDUgabB/sD3v7c+2/7Nfz8/UEBaANWAw0DigG8/y8BNgOnAi0CVQIrAiMC5AHrAb0Alf37+0L75/nR+yT/gP9E/zr/c/3q+xb9Tf+yADMCCAN1AVH/Vv51/eX8B/4F/yb+RP0Y/Y38bvz6/E39u/16/uH+sf6P/gL/Rv+7/lv+W/7X/WD9hf1V/Vf8UPuV/boDgQfxBwUJHQjyBMoDdwMaBC4HBwheBEkAFf47/ZT9KP9JAWwCPwJoAaL/nv5bAMwBIQHvANkA5v9LAPsBrQJMAlgBev8W/fD7vPwb/iT/7/9t/279A/zt+5X8Ff4kAF4BKwFPAC3/w/12/az+ZP9z/7b/Wv+M/kv+HP7r/VT+tv5b/uj9+v1C/jL+BP5P/oP+Wf5F/jn+CP4R/rn9c/2XACIFJAZSBjgHhgW0A5MDVgN1BCkGDwXuAd//hP/K/1gAhgFPArkB5AA/AG7/IgCtATIBPQDv/9X+dv6d/7IA6AFrAuYAEf/x/V79Cv4T/73/UgC4/+X9p/yF/PX8uv3W/qb/wv+h/wH/E/4Q/m3+CP7E/QP+Av4c/pf+tf7o/lf/8P4+/j7+YP5M/lT+aP5L/iP+IP4W/iX+jf66/oT+qf+jAnEEkQQhBasEAgPBAhcDiQPbBCYFdQOKAbgADQECAvUCnQNyA5ECwgEGAc8AegGcAc0ADQAb/1r+d/7o/ib/ef/b/0b/Uv4s/kP+d/5T/7f/d/97/xf/gP6m/vr+5/4S/x//vv6c/qT+d/5A/jv+HP7J/av9Dv6f/h7/qv8OACMA9/9z/8j+Vv4m/hT+Bf7X/Y39Uf1i/cT9Ff5O/qz+Kf9FAKYBNAJEAtsB6QC+ADMBsAGlAjoDvwIZAsYB9AEMA00E2QTwBMMETAQFBP0D4gN4A7gC2AHdAAAArP+X/3D/Uv8U/7f+Ov7l/Sb+ff6x/jb/tv/5/0EAfgCDAFEAFgDe/5r/af8+/wD/1v6i/iz+n/0Z/ZL8Vvx7/Mb8Hv2E/ev9Qv51/nf+dP6C/nj+d/59/hr+ov2o/cf9rP2t/b39rP0//o//gwDeANYAnQDtAKUBPgLYAhMDtwJUAjMCqALkA+QEBwXfBMQEzgT+BBYF5wROBFsDSAI6AYAATwBDAAQAkv/r/ib+iv1X/YL90/0Q/jL+Tv5u/sr+Z//Y//r/6f++/7D/6P8ZAAUArv8a/2L+s/0d/cP8x/wO/UP9SP1Y/Y79AP6t/jn/hP+k/43/Tv8E/7v+of6g/n/+V/4t/vr9yP3H/Zn+y/9xAOAA+QDpAJcBXAK2AioDfwN1A04DigNDBMsEvAQ4BJMDdwMJBJUEsQR2BMEDkwKDAf0A4wDnALEADQAf/xv+Mf2V/GH8tPxF/Xn9ZP15/dv9Yf7R/in/iP/J/9v/9f80AF8AGwBW/2X+tP1m/Vf9Tv1G/SL9yvyI/Ib88/zk/bf+B/8q/0r/SP8q/03/q/+3/4P/Tv///rj+iv5H/tT9gf3+/RT/+P+WAPYAOgGwATsC5ALBA2kEowRxBPsDqAORA4UDggOpAwcEbgSRBEMErQMqA8kCaQILAsUBZwG7APD/Lv9d/pT9IP0L/TL9bf2p/f79af67/gL/M/9C/4r/GACGAM0A2ABvAML/Ev94/h/+8/2g/VH9b/2y/a39pf3H/d79Bf6K/iX/Zv91/4b/kf+Q/4j/eP9J/wD/z/6Z/in+sv2N/af9uP0P/uj+sf8wAKgAHgG9AXkC4QL+AvwCywKRAogCxAL+AvACywK+AvMCkwM1BH4EkARUBL0DFwNsArAB6QDi/9D+DP6D/Sn9Bf0M/Tf9g/3g/Vb+7P50/8///v8uAHwAwQC8ALAAhAAPAOT/mv87/8z+df47/gX+Mv5J/lb+Nf44/oT+t/4i/6P/BgBsALYAygCdAEUA4f9N/+T+nP52/oj+b/5i/kX+Cv7e/Qr+x/6b/3UA9gDXAJQAZgBXAHAAsQAVAXEBugEAAiUCUwKkAhQDnwM+BNEECgXoBGAEiQOdArEBxwD5/3P/D/+m/lD+//2s/XX9j/0F/p7+Q//I/+//zf/F/+D///9FAJwAzADQAIwADQCW/yn/1/6n/of+bv53/o/+l/7S/jf/if/I/wkAIgANAN3/j/8//wP/8/4O/yH/KP8k/xT//P7l/uH+/P4g//z+e/7c/YT9o/0J/nP+2v4r/4n/FwB3ALoARwHWARgCfAIuA7EDCgRmBJsEyQT5BNQEPwR7A6wC1gH+ADQAlf8K/4H+Pf5n/pj+hP5q/k7+Tv6U/tj+C/9f/8P/+v8NAAgA8v/a/8f/u/+0/5//cP8c/7/+f/5p/pP+4f4p/1X/XP82//b+uv6N/n3+q/4P/3L/x/8WAFcAjwDfAEkBoQHWAbwBUAHHACAAWv+h/g3+pf15/Xf9Zf1L/WD9fP1//br9Uv7t/nP/FACXANoAHgF4AcIBCQJbAqQC4gITAyIDDAPJAoACcwKLAocCdgJTAgACoAE4AaEAAwCO/0//PP8s/wL/4f7X/uL+Df80/0r/gf/R/x0AeADdABcBHQEgAS8BFwHFAEYAof/w/mf+A/6p/WX9SP1i/aX9/f1r/vr+e//H/9r/u/9//zX/3f6S/lP+Af7I/cj9xf20/cP9zv28/b797P1T/sf+M/+n//3/BgABACIAcQDnAGsB4QFWArsC9wI6A5QD3gMNBDUETQQfBNMDlQMcA1IClgETAZEAIgADAOn/mf9A//z+wv6Q/pb+0P47/+T/egDaAPgAxABlACYAJAD6/4v/MP/A/jX+qf1V/Rz9zfzd/Dr9mP3//Xb+xv7e/u3+9/7//jP/cf+0/yMAiQCmAL0A6wD1AAMBPgFvAZIBzAEGAh8CBgKvAUMB2gByAEMAPAAdAOn/uv+d/5j/m/+i/93/DgD0/9P/qv9i/zX/NP8z/zD/P/9i/2T/Kv/5/vD+2f67/qv+o/6v/rr+v/7i/hP/Uf+7/xsAagC4ANcA1wDcAM8A3gATAT8BcAG3Ae8BDwIpAjYCZgKmAtIC8QLkApkCJwKwATsB1ACYAGsAVwBoAD4A6//k/+v/x//D//7/PwB2AKIAjwBIAOr/dP8b/9n+d/74/Zf9X/0g/fj87/zr/PT89Pzj/Lb8dPxN/E/8Tvw9/Nv8Bf7//d/9mv8AAYEAlgDKATECHgKbAigDVwMyAzgDzQMWBNwDngNwA4gDYQOGAgUC2wHvABAA9f+q/0f/Xv9f/4P/KgCTAIIAiADSAOsAuQC6AMAAVwAFABMA3/9w/z//9P5y/kP+Jv7N/bD91v3w/er93P3T/bb9u/0H/kr+bv6T/rr+6f4l/3n/FQDMAEUBiwG0AZgBYwE3AQIB1ACnAHEAYQBaAE0AXwBvAGUAXgBqAHEAZQBrAJAAvgDxAEoBjgF2AUcBPgEtAQcB8QAAARcBHQEGAcoAegAuAP3/6f/0/w8AOABRADoA+v+R/zf/Lf8B/43+q/4b/9j+e/7L/gP/2v7a/gz/If8V/y//o//2/9P/o/+Q/3f/Yf86/wT/Bf8Y//3+6/7j/rv+y/4d/0j/WP+F/5L/Y/80/yb///7W/vj+Qf+O/wcAfACdAKsA6gAxAUIBSAGTAf0BOwJ3ArUCqgJ+AnsCigKDAlwCIALyAb4BZgEHAa8AXgAZAOH/xv/C/6j/k/+j/6f/lP+b/6r/m/95/0T/Cv/m/uT+BP8f/xf/Cf8E/8/+h/6I/rf+5P4P/xj/Af/j/pT+Q/5H/mr+fP6v/gX/Tv9v/4D/tv/2/ywAfAC/AMEAnQBqACsA8f+u/3H/aP+L/6v/rP9s/w3/uf5W/ir+iP71/jT/sP9VAMUABgFJAYgBvQHqATACcQJsAkwCQQIqAg0C7wG+AbcB6gEPAg8C1wFTAdIAfgA2APj/vP9Y/+r+pf6J/m3+Uf5x/tv+UP+t//H/GQBBAIYAwwDPAL8ApABuABsAw/9y/yb/3v6Z/ln+NP4n/iT+H/4l/kv+ev6K/pT+vP73/jv/bv+G/6//+f9hANQAIAE2ATUBJgEXAQ8B8wDrABABFAHSAJwAkgCEAHQAeAB0AFUAOgA8ADwAFgDy/w4ANQAUAOr/BAApABgA8//u//z/BAAbAEcAQQDx/63/f/8m/87+v/6y/ob+ZP41/gH+B/4b/hT+Jv4//kn+iv7y/iz/Ov9S/8z/jgDwABIBmgE7AoMCwwIbA08DTwMqAyIDLQPrApwCmQKEAk4CQQIiAtIBeAEHAXsA+v+H/yH/1f60/sT+4P7q/gb/Pf+L/+D/CQAIABgAGgD4/9T/rP9m/y7/Cf+7/lX+Gf7v/aP9VP0m/RL9Mf19/bj9zv3Z/fX9G/5A/nP+yP4o/4j/4P8bAFUAogDwACwBYQGMAbABvAG8Ab0BqQGHAYIBfwFgAXUBuAG6AZoBtQG0AWQBHgH1AKwAXgA2AEgAdwCGAJ8A7AAPAe0A0QCdADYA1/+K/zr///7T/qf+ff5V/jz+LP4S/v79Av4G/hb+Jv4d/hT+Hf4c/jP+Xf5j/mb+sP4g/2//bv9g/+n/xQD7AN0AYwERAi4CLAKJAscCpQKqAgwDPgMcAyUDSQMsA+0CpAIqArEBWgHvAHUAGgC9/0D/0f6o/rv+2P7z/iD/X/+N/43/cP9U/0z/Qf8z/zr/QP80/z7/Vf81//L+yP6q/n3+R/4B/q/9X/0p/RP9Ev0z/ZP9Ff57/sb+F/9m/8r/ZADiAAsBUwHpASIC2QG7AeoB+wHjAdcB1wHHAawBmgF8AUkBKAEhARYB/gDQAIIAOQD//7D/YP9N/2r/gv+J/4//jf+C/3n/a/9f/3b/k/+E/1X/L/8R/wf/Hv8t/xz//f7h/rf+cf4p/gH+7P0N/nT+p/6Y/gD/1v8yAC4ApAB4AdsB6QExAmUCKQIBAkUCggJ/ApUCywK+AmoCHQLVAYoBawFYAQ0BsQBiAAIAkf8v//X+7P7r/t3+7P4k/1n/gv+y//z/MQAuACsAGwD1//b/xv89/zL/iv9I/8/+7f4r/wn/5/4K/y3/Gv8I/y7/Of8C/wn/WP98/5L/y//k/+D/2v+k/0n/F/8H/9v+j/5N/jf+M/4c/vz9BP48/nr+nf5F/zcADACY/7gAxgHAAPb/LQFYAggCygFkAqoCSgJyAlIDiwNAA3sDvgNaAwsDrgLdAVYBFAFSAFb/y/6N/mL+/P2w/e79Sf4J/vD9sv5i/3f/g/8CAEsAOQAxAF4AcQBLADoANgBFACwA//+6/5H/Vv/f/sb+1v6r/p3+Bv/p/qX+6f42/yv/Wf/i/zYAiwDcAD0BTQE5AVkBhAFbATMBSgELAbgAlgBKAJn/Gv/9/gP/9v7X/tn+2v65/pb+l/63/vv+Pv9r/47/gv9q/4X/pv+N/5f/6v8VAA8AQwCdALwA2QAZATsBPwFnAakB0gHwARMCEQLbAb0ByQGyAXgBWgEvAbwARAD9/8H/g/9W/yj/7v67/oz+dv6v/hP/Zv/B/yIARwBYAKMA8QACAQkBPQFXAScB2gCNAEQABADS/5X/VP8U/9n+g/4a/rj9Vv0A/c/8r/x7/Er8Mfw9/FX8fvy7/CH9kf3n/R/+o/5v/8j/pP/l/x0BCgLFAU4BHQIuA2gDWAOsAx4ETQSFBLoE4QS1BIsEhAR+BD4EwQMoA44C+wERAQ0AYv8W/7b+L/7B/aj9sv1x/S/9Yv3n/VP+nf70/j//W/9c/3f/lP+h/9n/CADF/3D/hf9w/9v+jf7q/hP/ov5L/nn+qP59/mb+l/7n/jj/mP/a//7/NQB0AJEAkwCeAKQAkQBxAFAA//+J/z3/Hf/T/ln+DP4P/gP+t/2J/az9y/3q/Vn++f4R/+j+lP/DAO8ARgCSAMMBgAJ4AoMC2gJKA54D2QP3AxwEZARrBBgEwQNkA5sC1gGBASwBcgCa/wD/o/41/pH9Qf2Z/er94P0H/pP+Cf+F/w4AUgDSAMcBEwJ4AaIBcQJGAjwB5gAmAfsAWwDM/4L/M//h/nz+HP7U/dT9r/1C/RL9Ff23/D/8Tfxh/ET8X/y7/PX8Ff0s/R79V/27/d/9Bv6c/ib/tv9mACoA4/8xAZMC8gE5ARwCOgNEA/ACMgPHA00ErgT6BOAE2wQVBegEYAQvBM8DywL2AWEBgQBh/23+0v2X/SX9jfxq/KP80Pzf/Cr97f2w/vb+YP8AAIIA3QD0AM4A/QA4ARIB+QCUAE8AvwDNALf/E/9//83/bP/U/rn+Ff9Q/wT/0/7g/hn/bf+C/1//mv/d/5r/dP+Y/37/Iv8a/x//z/52/mT+fv6W/nj+Fv78/Q7+Y/5j/4f/Lv5//mIBZgKfABIAzAE2A14DNwNIA8YDigQWBZ8EHASpBBAFZQT7AzAEeAMfAlYB7QAVAB3/Ov6A/QT9hfwA/Mv7/Psh/CD8cPxQ/f39ZP7n/kn/sP9rAMAAbADAAJ8BbwG/AAgBUwGXAPb/7f+0/z3/9P6z/kf+H/5H/in+y/3n/VH+VP4m/kb+fP6T/rL+xv7q/kn/lP+D/5D/yv/f/9L/+/8QANX/pP+a/9b/lgAIAfL/PP/JAJQC9AGQAOIAWwJyA54DQQMcAw8EPQV1BdMEygRRBUwFwQRbBOID0ALKAesAFQA3/6/+lP19/DP87/u6+2b7OPss+677iPwu/bL9KP7U/sb/dgDDAMgA+wCvAcABOgETAUAB4wBZADQA8P97/yf/z/5s/iv+Dv7g/X79VP12/ZP9gf2j/fT9LP5a/ov+4f4i/zz/R/9q/4H/dP9s/2b/XP8h//T+uf6t/jn/JQD5/9f+Jf8AAQkCWAHWAFABfwKwAxsEgQNSA2IEfgWrBUIFTgVnBUYFFQXTBP0D9gILAvcAHABg/3D+Mf0g/Iv7d/uV+3j7Gvsj+2D8hf21/d/9tf52/xMAtAABAUEBewGmAZ8BmwFbAfYAlgBwAEoAuv8C/2j+Iv7r/br9ef1D/U79hv2S/ZH9wf34/f39G/55/qr+0P4J/03/eP+i/7v/ov+D/5j/gf85/+n+tv70/o3/QgDe/xD/m/8uAeEBkgFNAa8BrQKtAxIE2QMEBM0EtgX0BfcF/AWvBSUFzARaBJIDjAI3AfD/Mf/G/vX93Py1+/z6DfuL+7X7gPtj+wP8TP1q/tb+9v48/+P/HwHCAYQBSAFVATwB+QDaAJkAEwB9/xT/5v7Q/pf+EP5+/UX9aP14/S797fwA/Uz9vP0o/m7+jf7Z/mz/8f9JAG0ASABNAGcAjQBzACYA3f92/3b/d//i/5EA7wBDAE3/PwDKAVoCJwKXAckBmAKwAxoEsQPVAyEEoAS4BMQEzAQ4BKYDOAPjAo4C3AGTACb/Xf4u/rz9Av3s+zD7KPuj+xz8E/w+/Kb8OP0P/vr+ef+c/yAA5QAgAU4BuQHeAZ4BSgEpAewA1ACZAO7/XP9V/3H/Vf8a/+7+5v65/sv+Bv/1/g7/Hv/a/hr/PP+H/6j/kv/I/6P/6f8aACQAnv9g/4f/bf93/2T/Rf/U/gv/zf+bAMYAQABQANwAZQG0AaUBmgGrAQACMQJoAvMCLwNXAwMD+gJBAzwDAQNhAsgBTwHqAGYAiv/h/mH+7P2p/RD9n/x1/JP8wvzK/CX9df0H/p/+EP/F/wYAbgDzANAA7AA7AVYBFwHoAMwAoADHALAAXgAnAAwAWwAqALD/tv90/5H/lv+l/4j/hP+z/3b/2f/v/wMAy//2/wkA4v9dAAUA9v/a/9T/DwDb/+//tP+k/8P/bf+4/xj/Iv///zsANgDM/wwA3P8PAIkAdACxANwAhwDKAL4APQGoAUoBeAEuAW0BNwE9AWYBDwEmAasAagBrAPP/EwCH/zr/I/9s/qz+nP6F/mn+rf7T/rf+NP+B/5f/CQBsAGQASgB+AJIAlAC6AK8AmwBvAHsANwA8AB8AVQAhAAAA0f+N/+r/Mf/d/oL+pf7a/qT+5P46/yP/Y/8+/+3+G/8T/5j/lf8RABsAoP+9/53/WQCRAIgAxABaALYAxQDWAPwAxQCyAKP/x/9jAEsAygA2ABIAh/9t/zIA5f/8ALMAfQCnADUAGQEVAbYBFQKJAbkBQAFaAVABYwFiAT4ATwB5/xv/WP8p/27/kP6b/lL+B/7O/u/+M//5/tT+FP/D/kj/7v/h/xwA2//L/7n/rf8zALD/zP8vAL7/EgDW/6D/zf9j/63/9P4L/4v/Iv8OAIb/oP9c/0b/sf9r/68AdAAEAakA1v9bANf/kwBTAH0AEgFiABABoACLAO0ANQB9ACkAYABMACEAPgBa/9j/qP+Y/5r/jf8FAPH/SQAFAA4AaABgAEsAWwCCANgA7QCdAM8AwADxAOcAfwAoATYBOQHyAH4AnQBAADEA4v9w/7//k/8f/87+w/7n/s7+6v69/jT/Jf9O/+3++f57/yX/hf/0/qv/r/+4/+n/xv8YAID/RACe//3/uQDZ/28A4v9QAMz/3f+DAE//cgCK/yYAHADR/wQBqP+IAIz/1P8rABIARwEhAJMAxv99/1EA8P/RAGoA4P8RABv/zP/C/xIA9P+0/9T/7v72/5z/LgBSANv/KgCF/wUAcgB2ADABsQCvAGMAQQB0AFwATQG1AC0BUgBMAFYA0/8VAWAAlQA3AJn/u//p/moAQwDT/04A0v6X/zv/4f+XAN//cgAP/3f/Ev+U/1wAVv9wAGH/r/+//4sA5gCL/88A/f+u/x4AqP/7//z/awB0ANb/BwDo/xIA9v9FADAAhv85/7v+SP+I//7/PgBt/7n/8P/A/zQAjACuAMz/h//t/1//cf/W/0L/D/9s/zb/+v5b/+P/kQBSACkAyv9Y/5kAYADLAOcAmwBzAIX/twCfAGkBUAGBAKUALv8IAAoA7/8uAKD/y/9N/+P/+P91/4gAHgD//x4AZ//l/ycAbAD6AKMAIQA/AP3/SAAqAKQA2wBvAEwA7v9fAGoAzwAcAcUAgQAh/9H/agAe/1kAeP84/2z/Lv+V/1b/AQCG/ygAFABa/7P/mf+j//X/a//f/8T/o//x/9L/FwDG/zEA4P9PANH/0P8y/xD/IgBf//P/iv+j/zMApf9WAIUAUAC5AAUANwBvAGMAQQAjAKAAvgDFADQAlv+j/7L/BgA6AJYAxf/L/oT/8f4UAI8A8f/m/7v/N/9X/6b/nP+8AHAAwwAgAOX/ZQBPACUB5ABsAIUAkv+U//n/SgDdAFQAMwB3/5L/w//3/4cAUQA2AAUAGQDr/24A2ACJAEsBcQDQAJsAMQB2AHv//v/x/q3/g//Z/rP+B/66/ir/NP9I/wQAq//L/3r/r/++/2z/IgDo/1cBYgBh/1MAHv9OAO8AiAAHALX/cABY/xQARgAUAD0AtP4fABT/zf9FAGr/uwAs/w0Bnf91/+8A7f+mAAMASgFyAKT/wP/j/kQAiAC+ACsA9P9+AHP/OgA0AKH/EwBR/9D/NQAgAO4Agv8NAKwAbQDIABIAZAH2/8f/rQE//8kASAAY/wMBbf6VAI7/I//fANf+rgA1/7f/YP84/0wBif6CAB0Am/7h/xr/2wAb/9f/HgEr/5wAHv9pAOcA+v4bApz/lP9+Acj+9wCB/6j/aABp/usAbv8nAFcAXf/OANj+tQCbAOL/iQCK/9MAIP9TAHkBQwAxAS7/wP9g/2b/ZgAh/93/zf47//H+yv4NANv/VgDZ/4EAFgCQ//AAhAAnAb4AEgBpADD/w/+WAPb/bAD2AMH/GP/I/1IA9P/7/7D/0f9eAOP/vf/H/6v/RwAvAPT/oQBkAGH/hP9ZALH/hABBAFL/e//u/5IArAADAfb/sABDACwAVQFTAL4AMgDG/wQA+P+x/8/+Q/9b/2T/2/+G/+3+f//J/+7/gwATAPH/ov+7/ykAeQC+AAEAZQArAK3/1wBvAGYApQBgAL3/B/+m/2b/rP8DAOL/z/9J/2D/Tf+j/1EA5QCHAAYA9v+C/ykAJwBLAKkAMwBKAIH/cP+x/9z/GAAmAOoAIACJ/23/Gv/Q/6AAwACBAEgADACi/4f/+f96ACoB6gB7ANP/Ef9K/07/5/+ZAJAA5//v/vj+O/8SAOQA7ADQABMAmf+Y/xcAnACCAHsA7P9R/2v/sP+jAOoAxQDaAAEAuv/t/x8AOwD2/5r/Hf8c/yj/C/+V/+L/DQDk/6X/JgAAABkAgABjAEwAKQBWAFUAgADcAFUA9v/h/+D/v/+U/9f/qP9n/zT/Q/+C/7T/PgAhAPf/HQDJ/+H/EQBfAK0ApgDPAEYA1/8XADwAhACKAHsATQDL/7//ff9k/93/DwDU/3z/fv+i/9D/UgCTAHwABQC1/7X/hv/3/0oASAAWAN3/jv8y/3L/yv/8/8v/l/+C/wr/8f5K/47/+/85AFcASgAOABAA5f/n/0sARQATAPX/DABNAD4AZABgAD0AbgC/AMoAxQAEAewAqwCZACgBewFyAfYBUgI3At8BagFfAYwBjQFwAUMBQgGwAA8Ao/9O/4D/Xf9o/0X/8/71/pL+Jf75/R/+Mf4x/lX+Kv4u/kH+D/7y/en9DP4T/vj9DP4k/iH+B/4F/gD+Of69/r7+pP4i/l398vxs/G/8ePz2+1D8Xf5dAOcBNwR0BlgH7we2CIoIPgiMCEgI9wYHBg8FdgPQAVYAuP8o/4P+U/7t/XD9u/1c/jL+5P3G/sj/KQC0ADkBlgGDAQQBkwAjAAUACAD+/7b/Kf+2/uT9C/1V/Bn8PvwX/Ez8Svwk/ED8W/yS/Kb88/yW/dP9uv2X/WP99/x3/Fb84vtg+xP7Vfug/Cf+3f8vAhMF0Qe0CfMKvgvLC3ILswo+CXcHywUSBPIB6v9//pT96vyj/Lb8mvzU/E790P2H/nP/owCKAVgCBgP4AmgCAgKCAZEAof80/+3+0P5//qH9JP0V/TX9k/2J/jz/w/7z/QP9Uvxb/LP8dP18/gP/tf76/Rj9cPxI/BH8ovtu+wv7EPpV+fH4jvgg+RL7JP5LAa4EpAmmDaUP4A+NDsANuwyjCmcIMwZ8A9kAjP7x+xf60vmL+oL7LvwS/TT+E/+//1YADQENAtYDFAVcBIkDLQNeAh8BE/9o/f/8xPwh/ND7zPsJ/AX9Lv5e/if+Xf90AF0A6f8///P+FP/n/kv+0P2H/Wr9Cv3n+6X6k/pF+xn7X/oW+uz54fl4+a/4+fjT+mX+vgIIBwYMeBCHErES5hChDaYK5QdBBcwCKQAp/qv83PpC+af4dfk6+1X9U//eANUBgQLCAigCnQHhAb4CMARhBYMFTQS6Ad3+QvyF+uH5Vvqm+/P8xP3w/Z/9W/3c/QL/tAD7ASICvwHNAGn/7v2C/L/7Dvx3/MX83/xV/HL7WfqC+ST5Cfl5+fH59Pnz+bb5K/p9/EAAEAYADmgUwxbcFaISuw2CCPgD4AB8/8/+Fv6v/FP68/fr9s33evrp/WABgQTKBTIFyAO3AQ8A9v/yAHkCcwN5A3MCawCp/qz8Q/u6++r8A/7c/gX/Wv6s/fj8W/yh/BT+AACWAW0DzgMPAn0A8/6S/eP8r/yN/BH8zfta+2j6Evp3+sX6aPs3/HT8XfxJ+yf57/Zf9477bAJGDGwVIhmJGIwVYA9YCN4CRf80/sf+G//I/fH6tffu9Wj27vgw/XwBqwSHBj0GIwSPASn/Ef4Z/zkBnAMUBVQE8gGk/mD7qPmk+SL7K/51AY4DlwO9AVH/3vxP+3z7Cv3r/8ICqAOkAr8A6/6w/Xf9qP0s/v/+If8D/hD8G/rJ+Bf5WvqM+7T8i/2M/Yf8tfq7+CX2VPTT+A4FhRL3Gqkd9xoBFPQLOgTH/b/6zPud/iIASP5/+Vn15/PQ9Yz6t/8HBDYHrgigBysEFgBN/Yb8//2KAU8F4AaQBdEBv/wm+Pr1WvYW+iYBiAZ5CIEHDgN7/bz5PPgA+Rv8MABVAzwEZAPiAd//Vf5K/ur+Nv97/zr//PwB+jv4lPcH+Ob5cvzD/jkAdADU/s37uvgR9pLzyfIK+zILJxjpHYEeGRmWD4wHZwCg+mz5bPxHADkB6f21+AD17vPA9lj8IAGpBMEH9wj7BtkCrv5q/ED8Nf6vArUG/AZGBA4A0Pqi9mv1PPcf+wIALAXRBwIGJQL6/mv8Nvt4/BH/SAHwAnYEvQMHAaP+Kf2S/O38E/4C/xP/Yf4m/ar7evo0+ib76/zF/tz/7f/z/tP8G/oF+Mb2cPXX8yL5nQiIFe0azxwHGWAQ9gglAwT9L/o9/A4AQwHW/aL4u/R88iT0Ivpr/4MCOAbcCP0HfgQuAOf9ZP0c/gACQAZeBlYDnf/T+u31E/Tq9cD5dv7EA2QHiwbSAgkAR/6B/Jr8U//lAU4DmQRGBHEBTP78/NT8Dv3W/RP/8/9A/5j9/vuH+u75//r5/Kv+t//T/+T+9fxb+nf4Yffe9nH18vc8BZUT2RnlG3AaghIiCtMEtv4x+rr6gv5qAKT9x/hU9TTz8fPk+TMAXANZBg0JiQgfBQABvf4L/sn9VABlBP8ELgKs/mL67/U99Er2Tfqm/nkDsAfkBxgEuQDi/lL9D/3l/hwBFwKNAvMC4wH8/j79of3f/fT90v6A/8T+BP3t+0T7KPp1+nD8Hf75/jj/sP44/TH7xfk7+Yb49PcZ9/v5rQW1EfsWYBk4GE8RlgrbBZ3/BPvJ+8n+mP84/Qb5tvX+8/30uPlJ/swA/QMhBwgHbgSoAR8Ai/9x/20BWwSZBHUC/P9r/Dz4jvZq+K37Ov+0AyYHzAbBA1UBiP+O/WX9eP+qAcoCjwNjAw4BbP6C/aP9of2+/ZH+c/8M/7v9kfxK+0/6D/sS/Wr+GP95/9b+Qf2f+2n6qPny+NL4kPg5+VkBNg2IEwkWkhYSEtQLVQfxASH9C/yz/dL+3/wl+UH2ovRi9HH34vuA/qgBdQWMBkoFYwPrARkBNQD6AJcDwASQA7UBhf4J+pz3QPgc+3T+YwFVBAQF0wK9AFz/pP2J/SsA+QIpBJsErAQJA1UA2P6D/vj92v3Q/oj/sf71/KT7kfoq+kr7Z/07/0QApgDQ/wf+ZfxL+6b6T/om+nb6n/m3+koDjQyQENUS7xLEDsQK2gchAyP/E/7Z/pP+LPye+dr3nvbW9nf56fts/XAAiAORBB0EPwOdArwBJQFqAlwEpwSxA90BRP6S+gv5M/nS+VD7EP6VAM8B+wHAAVMBugAcATUC7QKmAz8EKwTFAnoAn/4l/f373Pu4/Hf9aP0b/d78Ofx6+4z7R/wY/dz9bP4+/jv9Vvyp+/n6jfoz+qL5p/g//D4GzQ26EL8S1hHjDe4K5wd9A9QAOwE7As0Atv3R+nn45/ZT93/5CfvT/CcAmQLcApMCRgLqAdEBLwLQA3cFaQVvBHICD/9h/Jf7lvu8+738if7i/wkAq/+t/4//iv9RACUB1gEJAz0ETQQRA4gBHAB//uf8+PvN+8z77vtY/Gz8yPsT+wL7Nftb+wf8y/wK/SP9Jf1//Jb74fpk+hH62vn+/r8IYg6FEN0RyQ/GC5UJ6AZyA8gCCAQUBIMB3v2B+tT3W/YT9//4//n8++z+tf8r/zH/WP8hALQBoANHBg0IJgjyBtwDIgAV/pv9UP0+/Xj9tP1+/Zb8Fvxh/PP8YP6/ALECjwPqA8UDlAL2ABgAz/8m/5/+SP4j/cz7M/so+0D7g/sT/Iv8M/zC+5j7HPsL+xH8G/3F/WP+N/45/bn7JP7XBRELIQ2QD5MPtwzACpUIwwQyA3EE4AQUA6QARv5++yP5Svgx+Mz3NvkC/Pb89Pyd/UH+7f46AAgCOQRlBgAIBQi0BbkCEAEuAFL/7v4A//H+gf6V/RT8o/oz+hf76vyy/gUADgFTAdIADQCh/9n/fQCXAVwC9QHaAF7/v/11/KX7f/vI+/j7BvzV+/36Q/qC+gv7f/t+/HX9kv2j/Jj98wLdBzIKgAyrDaMMAgxtC74IwgYDB+8G1AQXAr3/7vyT+qv5F/kT+GL4QvoG+/H6pvtY/Lf8iP3o/osAmwIcBaQG8QV5BOUDbAN7AhICBwLSAbYBKAE9/5r8uvoI+v75VvpZ+778Dv4I/2r/Xf8l/1X/GADcAHMB5gHNAQkB5v+t/rn9Mv01/cr9dP6P/i3+2v0n/QL8X/sX+9z6k/oa+4X+kQKcBGgG6QcVCGcIDQnNCKcIbwkZCloJRAdYBYIDfQE5AGP/Ev5P/Zv96vw/+xP6FflI+AL4GvjA+AX63fvC/ZH+4f4bAJ4BwgL1AxcF2QVjBnEGagWfAywCYgHBAEYAIgAWANX/Yv9x/hL9x/vc+mn6VPpu+r36OfuW+wb8wPyq/bT+zv8UAT4CwALeAuwCoQIbAqwBKgFyAL7/W//0/oL+E//z/xQAPAC1AOkA0gC0AG8AGAD5/wUAMQCtAFYBOAJCA9oDZAT4BPEEjgTuA/kC9AHOAK//rP5+/d787/zm/Oj8Kf17/ZP9Z/0w/fT86vxp/UL+8f6Q/5kAmAEqAnwCqAKsApQCcAIKAlkB0ACbAFcA2v9e/9L+T/4o/kr+hP7H/jH/s/8iAGoAjACHAHIAYABOAFQARgAlABIA/v8QADwAUgBNADoAJQDu/4n/5v4Z/nn9IP0O/Tn9uv12/k7/QQDWAPIA9QDyAMUAhQBOAPL/kP9P/zn/SP9v/+z/owBGAdYBRgJEAuUBnQFlASsBKgFWAVUBMAE7AUIB8QCcAH0AWgBEAF8AfgCCAKUA0wCoAEQAx/88/+f+xf7M/v3+M/9n/5b/o/+i/53/cf9S/0X/GP/+/hf/Rf96/7f/DQBgAJMAoQCOAEcA0f9m//b+hP5X/m/+ov7M/gP/Tv9r/37/l/+B/27/e/92/0P/6f6D/jv+Mf5v/tr+O/+8/4cANQGfAfcBNQJAAjUCQwJLAisCHwIUAtsBwwHVAaUBPgEDAe8A3gDmAOcAsQB9AGwARAALAMv/k/+M/6j/1/8UABwA+//k/7P/ZP8o/9n+af4c/t/9kv17/aX94P0t/pX+DP90/8n/IwBsAIYAlQCrAKAAbwA9AAAAqP97/6L/1//6/zQAZQBeAFwAbgBVADgAMQAsADEALwAQAN//p/+g/9n//f8SAEEAWgBJADAABQDO/7H/wP/Z/+7/EAAOAMv/g/9n/2j/iP/I/ycAmgAQAXABpQGqAZsBhwF9AY8BqQHHAdMBogFDAeQAggAJAHn/8P51/v79y/3t/R/+bv70/nz/AACeAD0BnQGPAUAB8AB1AMj/H/96/vr99/1J/pH+1f45/7H/HgCAANgABQH7ANoAogA6ANj/pP9+/2f/ev+f/6z/o/+2/9r/3v/r/xsAOgBSAHIAYwAfANP/iv8r/7b+Zv5S/lr+i/7g/h3/Uf+g/+n/DwAVABQAIAAvAFkAmQDGAOgACAEJAQcBFgESAeYAlgAfAMj/4/9eAPgAmgEzAqEC1AK8AjQCRQFYAMn/k/90/0T/CP/T/sz+//5F/4n//f+bAB8BdwGeAV8BygAmAH3/0f5g/i/+Ef4Q/jH+Sf5I/lP+gf7F/hv/hf/m/zAAYwByAGMARQArACsAJgAKAOr/1//P/9j/6/8BAEIAtwAgAU4BQAEIAbUAgwB3AFoAMQAlACYA//+7/3H/B/+c/nD+bP51/sT+av8NAHcAtADKAMwA0QDFAIMAHgDc/7v/iP8u/9b+ov6h/uj+Vf/L/2EAIAHMAS8CSwInArcBNAHFAFQA9f/P/77/mv9//2r/Rf8q/z//e//R/0AAtAAHASQBAAGeACsAz/+Q/2T/O/8a/xT/KP88/0r/X/+M/9f/HgA9AEYARwA1ACkAKAAaAA0AEwAHAN//qv9//27/Z/9V/z//KP8l/0//lv/N/xcAfwDoADIBLAHGADgAwP9x/zX//P7Q/sz+8P4w/5D/CwCnAGMBFwJ/AqECmwJYAuQBYAHSAFMABADd/7P/j/+J/5v/v/8CAF0AtgAmAYMBegEYAY8A7f90/1X/XP9t/5T/of+B/0j/Af/h/u3+B/87/3T/e/+E/6L/jP9X/0v/Tv9R/1P/NP/m/on+Uf5o/pv+t/7j/h7/Rf99/9P/EwBHAJkA7wAXAQABtABaAAgAyf+l/5X/hf+H/6T/zP/8/0wAxgBPAcgBEQImAvQBdgHJACUAq/91/4f/wv8AADkAdQCdAJcAhQCSALkA8QAsAUgBLgH7ANEArgCRAH4AdABaACoA5P+N/zT/8f7d/vT+H/9f/6z/5P8CAAgA3v+T/1D/Ff/U/qL+kP6X/rf++P4+/2D/Zf9r/3z/mv/Z/zwAlQDeABgBHgHiAIwAPQABANz/xv+0/6H/n/+3/+r/NwCbABYBmQENAkwCRQIEApQBCgGJABoAuP9i/yj/AP/R/pv+fP54/oj+y/4q/3b/yP85AJgAzQDvAPgA1ACVADwAy/9J/+f+yP7B/rn+1P4K/zr/dv+8/+P//f8pAEMAJADw/87/s/+Y/5T/oP+d/5f/vf/z/wYAEQA7AGAAdQCsAPYAFQEVASMBLAEMAdEAjgBAAAAA+v8ZABQA6f/Y//T/KAB4ANQAHQFhAagBtwFwAe4AXADf/3n/Gf/S/sD+0P7w/hn/KP8M//7+Hv9H/3b/4/9nAMAAAQEdAekAkAA/AOL/jv9O/zD/N/8s/xb/MP9U/2j/qv/v////GQBGADQA9f/D/4z/Vv89/0P/Wv90/5L/sf+y/5n/kf+Y/6X/4f9NALkAEAFfAYQBdgFVAS4B7ACsAIUAWgAMAKv/Uf8S//z+IP+A//v/gAD+AEABLwHtAJ0ASwAJANH/of+K/37/Vv8c/93+lP5x/pf+1v4g/5r/NgDGADcBeQGEAWsBUAEzAfUAmQBHAAYAt/9l/yr/Df8h/3T/2v8dAEgAcAB5AFQAHADl/7//u//S/+H/1v/H/77/sP+W/37/cv96/6D/5P86AJMA5wAwAV0BWwEzAfMAoQBCANT/Xv/y/qn+h/6G/p/+3v5G/8T/NgB/AJ4AsQDCAL0AlQBXAAgAxf+Z/3T/Vf8t//T+zf7C/sT+8P5W/+D/YADaAEQBbgFOARwB6AC3AAQBdAFoAfkAjwBGAP7/4//J/7j/lf89/wn/+v7p/rb+rP6d/oP+hv6X/uv+Hv+b/ysAWABCAEsAhACXAAUBpAErAlECPgIZAq8BPAHnAKwAaAA1ABMAv/9k/yX/y/6a/n3+f/7T/l3/5P9HAJcAwADKAKwAfQBrAGQAqgBGAW0BIgG+AFkA4P+a/7L/rf/e/wQA8//O/4v/OP/d/tj+zP7C/r7+uv7E/rz+4f4r/47/8/9YAKkABQFyAZQBSgHLAFEABADY/7//v/+7/7//p/+A/zj/6v7C/pX+sf78/mz/yf/3/yYAOgBhAHoAlACgAJ4AsACpAHYAHADV/7P/qP/n/08AtwARATwBQwEdAd0AnwBqAE8AQwBVAGsAcQCKAI4AfQBZADoAKAAlAFUAngDuAP4A5QDIAJEAewCFAJAAhABkACgAvv9i/xT/3P7c/vb+Kf91/6j/t/+a/03/2P5F/tb9r/28/ef9HP4+/j7+S/5M/jT+Kf4e/jX+bv6X/p/+nf7L/j7/AwD6AM4BOAJaApsC6wI3A58DHgShBBIFPgUHBZkEIASxA0MDtAL+AQ0B5f+z/oz9f/zF+3L7W/th+2f7Zft6+6X7/PuW/GT9fP6z/7cAgQEmAqYCCwNwA60DwgO0A2oD+AJbAtcBmwFYAQ0BDwEcAY8AhP95/pT95/xu/Df8VPzE/En9lP3k/YD+Vf8eAKsAFwF/AaIBUgGzACEAxf9i/+L+O/6j/R39f/z6+8L70Pu5+6X70vv/+3T8ef3B/2ECUwOtA5UEKgU/BakFqwbdBxIJGwn5B60GagXBBPID6AJGAngB1P9y/Sv7YfmW+J/4pvjS+Cb5fPnJ+er5YvqV+1P9c/+FASQDggSaBVoGygYTB0oHOge2BmgFmgPUAcQA+/+7/iD+H/6G/X78qPsX++X6Vfva+0P88fyj/UP+1f6G/6AAuwFKAnkChAJFAv8BowEiAbQAAwAW/wD+tPyt++76Pfqw+Sz5y/jH+EP5evrz/F8AtwKwA+YELwYXB/YHDQk+ChALLwsnCk4IlgZ1BZIEJwOBAeP/z/1A+9v4JPdP9nr2/fZs9+L3lvjN+Sv7i/wT/uH/1gFgA1QE7QSKBUQG0QYQB/YGewajBWoErQI1Aa8A7P9n/jb9XPxZ+576Y/qD+in77Ptp/AT9vf2Y/sP/AgEzAnEDTAQ4BKsDJAOGAtwBDQEPAOf+k/1n/FH7Nvp2+R351/iy+M/42/g9+X36G/1OAVsEJgVXBg8I8AihCcIK0QtuDBMMKgqiB1wF3wM1AwMCVwAA/0z9nvob+Mj2ePYE9473n/eu98b3ufiO+mb8lP4bAXUDGQUHBqAGIwcNCLUIYwiJBzQGhAS3AroAaP8A/5H9UPsJ+oz5WPkD+Uf5jvqP+1b8CP3P/SX/gQD4ARMDpANGBHYE+AOPA4kDdwPwArEBOwDN/jz9+/vy+hb6p/lB+eH4cfgT+EL45Pgi+v77Mf9mA34FvwXeBh0IAAlWCoULKQwuDEELUAnoBuQEvgPxAj8B4f6n/Dv6APjZ9ob27Pbi94340fj++I/5cfsZ/jwA9QFuA4gEXQXqBWwGNQcOCDgIdAfzBSgE5QLqAZMAJf/h/cz8fvsJ+iT5/Pi9+Wz6Ovp3+m37g/zv/Wf/xwA3AkQDjANhA2cDjAOeA5gD/gL2AfwAf//V/bj8sfvc+kT6kPn1+Hz4RPiB+Ob4yPlC+6z9kAGaBFwFNQYECDIJ2AmUCu4Kygo7CtwIyAYUBRcEbQM5Ah8AE/58/Jf68fg++O33A/hX+Dv48fdm+PL5K/wF/oH/7wBGArAD9gTdBcgG0AdQCN8HpwZRBS4EfQNWAygCkP/2/V/9K/zw+sT6P/tA+9z6jPpF+v36yfwq/i//LQAoAe0BzwGDAf8BqQLtAm0CawFqAEf/mP4D/gP9h/xS/J777/pd+jL6mPre+iz7DvyB/qcBvgLJAisEngV5Bp0HhAjfCAsJ5AjIB0IGWwW+BJsDqwFS/5/9Yvwd+5/6d/os+jX6JvrB+fr5LvsR/cz+nP/2/2wAFwHtAcsCnwNtBMkEhwSTA2QCswFRAdgA/f/x/lf++v2a/VL9Hv2N/SH+0f1z/av9MP4Z/+r/PgB7AOQAKgEFASEBtAHrAecBqAG9AOX/Pv9K/pf9M/21/Cz8sPtK+/v6Bvtk+6H7C/yi/Lf9tQDtA6QE0AQkBqkGKgY1BmMGGQbsBXEFwQMaAqkBQwE5ACr/Tv7d/aH9RP1I/b79K/6B/pb+ef7h/vn/JAHlAQoCpAErARUBBwHWACMBowGEAf0AWwCq/zv/IP/7/oD+D/41/kv+CP4P/j3+gv7D/o7+gf7n/nb/UQDiAOEA4QDSAIcASABUAHEAMgDS/z//W/7Y/YX99/zF/Nv81fzk/PP8+vz8/Cr9of2z/aP9+P1u/kQAAQPPA+IDDQVoBa8EpAQSBfMEuQSYBGsDDwICAs4B5QBlANT/MP8P/wz/Cv8u/0z/Nv/w/uH+Mf/X/9sAnQHJAZcBMwHtAL0AfACdAOEAtQBCAJv/5P5e/i3+M/4R/gf+nP4t/z7/dv8AAAgAkf+K/6T/VP+B/wYAvv9J/0f/Gv/J/hH/lP+v/9H/BACZ/xn//P6p/kf+QP43/gb+9f3w/a79Uv06/Tn9J/0t/YL98/47AXQCBAM3BLEEKwQ7BIcETgRXBH8EnAOEAmoCDQIBAaMAkwA5AG4A7gDkAKoAnwBMALb/nf8BAFUAxgAyAQwBrABnABQAsv9h/zf/Of9Z/3X/bP9c/zb//P7T/qP+nP4P/3r/k/+w/6f/Ff+O/pv+nv56/sn+C//X/tH+8v7L/sX+P/+N/4D/rf/F/2H/E//X/lD+/P31/fX9AP40/mT+Sv4a/hn+C/4O/h3+Rf5u/yQBHgLfAq4DsQNeA4EDvQO+A/QDKQSUA+sCsAIpAqgBtQG0AacBCgJYAgkCbgHcAC8Alf93/27/Ov9v/7f/hf9X/2P/R/8l/13/r//e/xEAFQC8/33/e/9l/0v/Z/+m/+L//v/Z/53/fv9L/wn//v4E/w3/Bf/C/mP+Gv4F/hX+V/7d/kj/h/+n/3D/Q/9G/wn/xv69/pb+Wf4n/gT+2/2W/Yr9g/1D/Tj9Qf2X/ev+XABeATQCewJnAoUC1AJeA+ADRARNBMgDSgPgAngCcgKHApMC7AIbA94CSQJpAZ0AAgCE/1P/Sf9d/53/o/9b//z+rP5+/o/+4P43/2v/oP+R/zf/D/8Z/zX/g//Y/wkADwD0/9X/mf82/+v+z/7H/v3+PP8w/wH/y/6V/pn+y/4p/4H/oP+w/5L/R/8I/9b+pv6Y/qn+sf6W/m3+W/40/vb94v23/XH9Vv1K/f39d//XANgBLQL4Af0BSgLDAlADvAMdBAQEfgMWA9YCzwLxAuQC9QI+A08DBwNzAroBCAFyAP7/qP9q/2v/Uv/w/pv+bf42/ib+b/7j/kL/bv9h/xv/6f4X/1L/a/+Y/8T/yP/A/8r/u/99/yb/3P61/sH+9v4I/9f+r/6Z/oH+g/61/iH/jf/A/7//n/9c/xr/5v6u/pL+mP6a/m7+Mv4C/uH94v32/cX9b/0s/ZH97/5vAIABGAJGAm8C2AJOA90DUgSYBFsErAM6Az8DgQPFA8wDrgPJA9UDcAOaAr4BHwGZACMA+P/q/9z/xv91/xn/yP5u/j7+Q/6Y/vD+Av/W/nr+QP5i/p3+2/4G/yr/Rv8U/9b+uv6q/pb+hP6X/sD+zv7Z/tP+qf6R/rb+8v45/5X/2P/g/7P/ef9S/yL/7v7k/vf+9P68/ob+SP4I/vH98f3s/ef91v2p/Yj9Wf4MAGkBGgJwAq4C5QJDA/ADiAS8BMAEXgSnAzoDXwOvA7EDbANQA3cDVQO8Ag4CiwEVAZcAPQAfAA0AAwDz/6//UP/6/sD+sP7L/h3/cP9u/wb/hv5b/n7+tP4S/33/nv93/yj/vf5w/kz+Kv4M/hP+Nv5b/mH+Kv72/QT+Qv6f/ib/ov/i/+b/xv+J/0X/Hv8Y/w7/Bf8D/9/+l/5C/hj+CP7t/eL90f2d/X/9Vf34/d3/hQFSAswCAwPxAu4CZAMeBFAEKgTaA0UDygK2At0CCQP5Au8CLwMpA50C7QFZAe0AqQB1AIkAtwC4AIgAMwDT/2z/Af/W/ur+FP9R/2X/IP+e/kv+XP6R/tb+MP9g/1//Pv8S//D+1f7S/sD+sP7R/u/+4f63/pH+eP53/rH+Gv+E/8v/5v/X/5z/YP8z/wT/5P62/o7+ZP4X/sf9gv1d/V79Xv1V/UL9Lf0j/WT9x/7HAOoBbgIGA1MDGAMEA50DTQRGBMUDMwPOAqgCigKBAqAClQJ0AmcCMgK4AR4BrwB1AEoAOABUAGkAZQBEAAQAxf+A/yn//f4b/1j/aP85//L+q/6F/pX+y/4X/0n/Sv8z/xX/+v7v/vr+F/8s/zn/V/9k/1z/Tv84/y7/QP94/9b/FQAOAPj/4//D/5z/cv9k/1z/Mf8C/7H+Pv7h/Yj9Q/0r/ST9E/3z/MX8lvzi/Gz+pgANApUCIgOoA38D9wIqAx4ElATmA9cCeAKKAjgCrwGkAf4BFQK9AV8BRAEgAZwACwDv/zAAVwA3ABcAGgAAAKX/Rv8R/+/+4P4L/1T/X/8N/6j+cf54/q3+AP9g/57/i/9A/w7/Cf8F//f+/f4A/+z+z/61/qD+nv6y/uz+XP/a/yMANgBOAGoAYAAiANz/xf/L/67/av8A/4j+Lf7b/Xj9N/0p/Sf9HP0D/f38nP2c/w8CQgOJA0IEAAWYBLED2gMCBXgFXQTWAm8C1QKhAqcBOQHNATwCogHTAM8AHQHQABoAz/9AAMYAjgD3/+v/IgDB/wr/sf62/rn+tv7J/tX+p/5L/vf9AP5i/qj+qf7C/uv+uf51/pr+2/7e/s7+xf7O/tb+rf50/n3+rv7L/gD/gf8EAB0A8//W/8n/o/9k/zz/Pf8z//X+df4F/tf9q/10/Vn9VP1y/X79WP1f/VL+2QCOA3cERQTGBHMF4ASZA4kD1ASZBXgEfQLhAZkCowJsAZMAMwEvAsoBkwA6ANMAGwFrAI7/5v/7ABwBZgBFAK4AlgDc/yb/+f4s/0n/M/8f//f+pf5E/jf+mv7r/tj+4P4u/0v/J/8q/2//q/+J/xL/v/7I/tP+kv5R/nv+8P4z/0H/Xf+O/7D/s/+a/5n/1/8CAML/Wf8V/7D+D/5//T79Lv0y/T39Sf1J/T79HP1Z/SD/SAJ2BJoEcgQGBSgFOgRjA8YD5wT/BD0DYAEzAc4BOAGt/zf/ZABMAcQA3//i/7AADAFDAI//TABrAWQBzgC+AOwAlQCs/9X+of7n/iH/D//W/sr+1/6f/nv+xv79/vj+PP+c/7L/v//P/8P/wv+r/0L/9/4p/17/F//M/gb/b/+R/4D/e/+Y/9n/AQDe/7r/4f/Z/0b/r/5n/g3+jP0r/fj86fz7/A39BP0P/Tj9Xf0f/qoACgSSBQ0FvQQcBcIEkgP+AtsDBAWMBIYCHQFFAYgBigA6/3H/0AA0AVUAwP8bAKcAeQCy/5//mwA0AeYAxwAfAQsBPwBY//P+A/8e/z3/af9z/0T/2/5x/pf+Af/j/qn+Lv/c/+T/ov+d/87/0/9T/6L+kf4W/0f/9P7R/iz/iv9//1P/bf/B/w4ACwDP/8n/6/+Z/9H+S/4b/tT9e/1V/V79Xv05/f38r/yF/LL8D/0b/vQAeQTPBfoEgQTTBIMEkQN0A7YExwXbBHYCuwBHAL7/jP4M/kv/AAE5ATMAef94/4b/Of8t/zkA1AGMAi8CvgFNATcAB/+3/if/mf/E/8L/gf/q/hn+af1w/VP+Sf+o/9H/NABTANP/af+s/zAAaABHAB0AGgANAJj/Bf8B/3D/rP+x/9r/FgArAB4A///b/8L/nP9A/83+lP5g/vn9rv2F/U39Hf0c/S/9Ov1f/aL92v2C/p8A8wMMBqgF2QQKBRIFRASxA2cEkwUmBeQCvQD1/5z/x/5l/lb/qADCAI//hv5m/pz+wP4C/93/QAH2AYoBAgGrAO//MP8d/3b/lv9j/xT/qf7i/eH8UfyV/Af+DwBrACL/6P6o/4j/DP9u/6IAYQGWACT/vf5S/1H/mP7l/i4A0wBPAI//s/80AC0A9f/p/0kApgDx/9r+ev5g/gz+sv3E/Rz+Hf7C/YX9c/1T/Un9mP13/rYAKARFBsEFuAS5BMUELgTwA/cEIAaDBUMDHQEEAHr/6v78/jAAXQEVAbr/1P62/rr+4/5s/3UAtAEFAjoBlgBpAPP/T/9A/6//6P+8/1v/xP4T/pH9bv2p/S7+wf4C/+3+yv7A/gD/fv/s/y8AOAALALb/Tv8T/yj/XP9m/1D/Q/8y/xr/Cf8O/1H/pv+2/3X/IP/g/pn+SP4w/kT+Vf5G/gT+uf2k/b79z/3D/db9G/5n/mX/JwJeBRYGnATuA5oE4ARpBGMEKQWIBUUEzQHF//H+2P5P/yQAdwDw/zD/tv5c/hD+Uf6K/xMB8AH8AasBlAHJAZIB4QCkAPYACgGRAMf/Bf+n/nv+MP4K/jn+kf7t/gf/y/6o/h//EACIACcAyv/j//L/yv+O/3T/uP/x/4v/6/6j/rH+8/49/0//Qv9U/0//6/55/k7+Wv59/oL+L/69/ZP9m/2I/WP9Rf1I/Yz92P3t/cv+2AGWBXAGlwS2A5AECwXJBNQEWwWXBXwEBgKf/2P+af5o/1cAGQAp/5T+N/6h/UD90P1G/7MAXAFUAf4A7QBQAY4BZAFEAWQBhAE0AUYAO//B/tL+4f6g/kz+Yf7K/s7+U/4X/qf+1f+hAF4A2f/h/xkAIQARAP3/KgB7ADcAYv/O/tz+RP+s/7v/X/8m/yv/3P5Y/iL+Ov5f/lP+9v19/V/9qv3e/fH9Av7h/dj9Cv4r/iH/SgLwBYMGmQTOA3ME5gQRBVkFyQXNBYUECwKW/1H+cv59/1cA+P/b/gj+df3t/NX8k/3f/goAzQDfAFgAWwA6AcwBsgGJAYkBjAEkARQADf/U/vf+oP4Z/sL91/0m/xQAg/4b/Xb+UAC0AEoADgCpADwBqACJ/zD/5f9tAEkA1/8D/8T+hf/O/zH/3P5K/5P/MP+8/kz+AP5v/sD+Bv45/Sr9wP1o/jH+gf2A/UP+j/5C/vn/RAQQBx8GLgTiA54EMgWXBfYFLgaCBVgDwgD4/hn+f/7j/30Adv/n/c78cfzJ/Gj9AP4V/3wA6wA2AMn/lwDUASMCogFSAVQBHAFDAE//J/9i/xD/eP4X/r/9dv2O/df97/3+/Xf+bf8jAOv/i//z/8gARwEjAcgAwAC2AEYAyf+q//b/OwAzAM//DP+A/nP+gP5t/iP+vf10/VT9N/0T/T790v05/if+3/2o/er9YP4h//kBKga5B9YFJwR2BCUFjgUdBpkGewYRBYQCLADU/m/+Dv82AHUANf9l/Sr8Cfy+/I39Z/6H/4YAuwAcAPP/MgFbAjwC3gHeAZEByAASAMn/1f++/x7/gP4d/nD98vxD/cX98/0D/kz+F//F/5L/U//k/6wAGAEqAesAqQB+ADsAAwD+/wIA4f/B/33/xf4h/hj+RP4w/sj9N/3Z/MH8yPzO/AP9oP0N/sr9fP2F/bX9Sv7y/10D+gZ0B0kFFQSwBIIFCgaWBs8GHgY7BJQBgv+6/q3+XP9uAEwAv/73/PH7VPyM/YD+Qf8nANIAzQBtAMIAAALhAqYCNgIkAr8BBwGwAKcAjQAcACv/T/7E/UH9HP15/cr92v3k/T/+3v4d//j+Pf8JALUA/wD4ALwAeAAzAAIAFQAtAPT/qf9w//j+Sv7e/dL95/3C/S/9kPxK/Ff8hvzJ/E791P3O/YP9g/3H/T/+jP/OAuoGQggiBhcELwQvBfsFlAbnBmgGjgTVAbP/s/5Q/sb+DQB+AA//wvww+0D7l/z4/cj+Zf8FAEIAGgCMAOwBAQP/Ap4CfAIlAnkBDQEMATAB/AATAO/+Kv6n/Wz9qP34/QT++/0i/pz+I/9W/6D/WgAUAXgBiQFPARMB1AB/AGsAmQCEABkAtf9O/7P+Lv7w/cL9lf0u/Yv8IfwH/BH8NPxx/PP8Yf1O/Sz9cP36/dH+uwB3BPoH6AdSBQkExQTqBbIGBgfGBp4FagPXABz/Y/4x/s/+zP9+/5X9aPtg+h37zPwR/qn+Mv+x/7z/yv/MAGQCLAPyAr8CvAJQAqUBPwE5AVQB/QDt/7z+5f1t/V79pf3m/e396v0k/rv+Nf89/4H/UwAVAW8BbgFCAT4BNgENAS4BfQFaAdAAYQDx/1z/6/6n/lH+z/0d/V/84/vF+9D75fst/K78D/0j/Tr9vP2E/sT/rwK8BpEI1waXBG8EnwWfBgIH3wYzBpAE9gGt/57+Q/5d/g7/X/86/hX8Xfo++rP7YP0o/m3+4f4u/x3/pP87AawC7QKbAokCiQI/As0BkQHHAdAB0ABR/17+7v2y/b/96v3j/an9fP3C/VT+of7N/mD/NQDCAL8AhwC8ACUBMAEqAX8BtAFkAecAewD6/3n/Af91/tj9Mf15/Nf7ifuV+7X7zfsj/Kn8z/y5/Cj9E/4f/0kBOAVxCBkIjgV1BLYFgAdPCCIImwd7Bi8EiQHo/07/KP9k/4j/sv7C/I36hfl9+mj8jv2i/ZT94/00/pT+5P/cAdwCkgJOAnsCjwJ6AmwCjgLFAjECnQAU/zL+KP4O/3z/I/51/CH8pvwv/XX9lv0y/i//ff8l/0X/KQAOAYcBtQGvAYQBRQEPAf0A6QCMANr/Hv95/of9c/wR/Br8qPv2+r36C/uH++/7H/x2/E79M/6e/yMDUwdlCFQG3QS+BZgH3AgOCYEIngcEBosDbgF/AB8A6//l/1L/jv06+8z5Hvqc+978+Px+/I78Ff2p/c3+qwAGAiEC7QE3ArYCHgNhA44DxQOSA20C4gD//8f/jv8h/5j+z/38/H/8N/zy+/z7hvwr/YH9mf3D/VD+L//n/1sA+AC9AREC2QGQAWkBTgEsAdQADAAA/wD+Iv2B/BL8n/s0+/v63vq7+rX6Hfvv+/X84/5+AuoFSgZ/BOwDkQXcB0YJdgn9CBwIigapBG4D6AJ3AvkBXQEnAAX+mftM+rr60vsl/IT76fr4+kX7v/s6/WH/mQCJAIgAXwGUAoADEAShBCcF7ATLA5wCAgLMAYoBCwEgANj+qP3y/H38E/z3+0v8l/x3/DD8Xvw0/UH+EP/I/7AAbAGwAd0BMQJ1ApECdwL7AREB5//F/vT9b/3b/AH8FvtX+sb5bfmA+Qz6xfqJ+0/91wArBKgEagOXA4gFjAf1CM0JvQlqCHEGNQXuBEQECANpAgsCYwCb/Vj7h/rG+hL79PqP+gz6svkV+pP7zv2q/0oARwCxAKEBxAIGBBgFkQVqBbwEzwMUA5gCQgILAoABOgDT/vv9av3H/Gb8ffy+/Lv8ZPwx/KH8kv2F/lL/7/9bAMcAWwHvAV8CrQLOAqICEgI3ATsAR/+U/iL+iP2f/Kz7z/or+vf5JvqG+t36dfur/XQB0QNWA7oC1gOaBVMHFwkxCtEJLwiiBigGzQWeBLUDoAPIAmEAuf33+yD71Prd+tX6LvoN+Wb48viH+lD8iv0l/nT+4/79/64BKwMZBLcEBgXpBHoE4gNqA1MDMgONAqIBpgBU/+z9F/3Q/Kf8evwj/ID7A/sl+9b7uvx8/R3+5f68/0UApgA3AeYBbwKhAnQC8AEoAWcAAgDJ/1j/sv7x/Qv9OPzX++v7IPws/Ir8Qv66APMBvQGrAUYCLQNgBOAFNwetBwEHKgbxBaUF3ARpBIEESwRCA6QB9/+T/nH9n/xS/DD8uvsX+7D6rfoG+3r70/tR/D39Wv5a/zUA2AAuAXQB5AFoAqsCnQJwAjkC4QF4AQkBiQAEAHz/8v50/gn+kP0P/df8Cv12/d79H/5J/of+4f43/5j/FgCFANgAIgFAARgB4QDZAAkBUAF5AWgBMAH2AMwAuACtAI8AaABPADoADwDb/8X/t/+L/2z/h/+m/6L/mf+d/5r/hP9m/1X/Uf9K/1X/hv/I//P/CwAlAEQAaQCLAK0A4gAZASoBFAEQAS4BOgEcAe8AzADEALEAaQASAOH/zP/A/73/tf+m/63/x//Z/+b/+/8JAAwAFQAOAN3/j/9R/zv/Rv9j/3v/gP9w/1//Wv9d/2T/gf+y/97/4//J/7L/s//a/xgAQwBWAF4AWgBMAFUAZgBcAEwAQwAtAP7/zv+5/7n/v//E/8L/qv97/1r/Zv+B/5L/nv+3/9D/yP+l/5T/sf/Y/+z/8v/z//L/+f/9//v///8DAAUAGQBBAFgAVABVAGgAgACGAHQAWwBMAD8AOAA/AEYASABJAFQAXwB1AIwAlwChALsA3QDfAL8AmACBAH0AiwCYAIsAXgAqAAMA6P/S/8H/xf/Z/9v/uP9//2P/cf+P/6D/o/+e/4z/f/9+/33/bv9Z/0//Uv9U/0f/Mv8o/yv/Qv9q/5L/pv+u/8P/5v8AAAgAGAA2AEUANAAfACQAMQAuABwADQD//+n/2//p/wMAEgAOABAAKQBDAEsAUABkAIIAmQCbAIQAYQBKAEkATwBPAFAAVwBZAEsAQQBFAEkARgBFAE4AWABRADYAEgDz/+n/8P/1/+f/y/+4/67/o/+W/5P/ov+1/7//wP/I/9f/7P8FABkAHQAUABYAJgAzAC8AJAAcABMAAwDq/9T/y//M/9z/8P8DABAAEwARABoANABRAGQAbQBrAF0AQgArACEAHQAOAOf/s/+M/3//iP+U/5b/jf+C/3z/f/+T/7L/z//j//P///8AAPj/9/8IACEAMwA8AEIASQBOAEoAQgBCAE8AVwBSAEcAPgApAAMA3f/J/8X/wf+1/6j/mf+A/2r/X/9m/3r/lf+v/8P/zf/Y/+z/DgArADsARQBPAFAASQBLAE4ATgBHAEAANwAqABwAGQAhACsAMwA6AEMARQBBAEQATgBdAGEAYQBeAFEAPAArACQAHgANAPP/1f+8/7D/rP+q/6T/nv+a/5//qP+z/7r/xv/V/+L/5//k/+L/5v/v//r//P/8//3/AgAEAAcACQASAB4AJQAlAB0AFAANAAcAAwD///z/9//u/+X/4f/g/+L/5P/n/+3/8//4//r//v8FABAAGgAgACMAIgAgAB4AIAAiACQAJgAkAB0AEwAIAP3/9v/7/wAAAAD+//r/9//4////CwAVABsAGwAYABUAEgARAA4ACgABAPb/7f/m/+H/3P/c/9v/2//d/+H/5v/t//L/+f8CAAgACgAHAAMABAAIAAkABwAEAAMABQAGAAQAAwADAAUABwAEAAMAAwAFAAYAAwADAAUABQABAPr/9P/y//T/9f/4//r//P/+//z//v8CAAkADAAOABAADgAMAAcABQAIAAsADQAKAAMA+v/1//D/7//x//H/8f/x//D/8P/1//7/CAAPAA4ADQALAAsACwAMAAsACgAFAP//+f/0//L/9P/0//P/8f/w//P/9v/8/wIACQAMAAwACgAJAAkADQAPAA0ACQAGAAUABAACAAEAAwAEAAYABQACAAEAAgACAAEAAAD////////8//j/9v/2//n/+v/6//v//P/8//v///8EAAUABgAFAAMAAwADAAMABgAKAAYAAAD+///////8//j/+P/7//z/+v/3//b/+P/9/wUACwANAAkABQAHAAsADQAMAAsACgAJAAQA/v/5//n/+//7//j/9v/3//j/9//4//v/AQAEAAEA/P/9/wEAAwAAAP7//f8CAAYABAACAAEAAQADAAUABwALAAwACQAFAAMAAAD7//n/+f/4//X/8//x//H/8f/x//L/8//2//z/AQAGAAgACQAKAAsAEAAXABoAGwAZABgAFgAQAAgABQAEAAQAAwD///z/+P/z//L/9P/4////AQD+//v/+P/7//3/AQADAAIAAQD9//f/9f/0//X/9//3//T/8f/w//P/+P///wYACQAHAAMABQAKAAoABwALABAADQAFAP/////+//r/+v/6//X/6//p/+z/7//4//7/+//x/+L/4//q//D/9P/1/wMAAwD5//n/DQAnAC4AMQA2AEAAQgA0AC0AGgAGABoATQBNAOr/if+G/9n/IgAfAN3/pf+z/8P/1//9/yAANwAfABYADAANACAALQBmAFMAJAA7AHwAkQAuAMv/mP/i/14AYwAOAJj/cP9m/13/cP+L/8n/3f/R/7f/dv9g/4//+P9KACUA8f/+/wIA9f8pAJcAZwDG/+r/qAATAcsAWgAYANP/8/+EAP4AswDZ/47/wv8CAAUAuv95/1n/tP9IAFgAyP83/1P/u/8HAEQARQADAN//JABdAEoAEQDZ/9H/AwBjAHgAAQCb/8n/MwAfALP/gP+e//j/PgAkAOr/0v/V/9f/4/8VADgAFQDh/+v/OgCNAIUADQB5/3r/FwCaAKMAFwCf/+7/fwCPAP//Qv84/w0A2ACwAPf/s//G/9n/1v/V//X/GwA3ABMAxv+j//T/EgCW/1r/2P+EAH8AAwCT/3b/z/9iAJwADgB3/6H/XgDaAIwAyv+B//z/cgA0AN7/GAA0AM3/of/3/yIA+f/k/+v/of+w/zkAZAAIAHb/gv8fAKgAQACL/5T/MgCIACUAuP+o//r/SwA6AA4ABQAoADAA5P+l/63/PACxAFMAiv9n/woATQDo/57//f8wAAoABgD5//D/4v8qAD4Az/+a/+T/kgCgAP3/of/X/04AJgC7/9r/MABeABcA9P8cAPT/q/+F/+r/SgA4AP3/xv/Y/9z/6P8IANr/sP+q//T/QwD9/5j/hv/n/0gAawBAAKn/i/81ALoAeAD9/1AAzgBsAKL/qP9WAIkARgANAPn/6f/o//P/0v+D/17/xv8NAK3/aP/f/zsA1f9s/5z/FQCXAMAAPwC//xsA+wA1AY4AwP+b/z8AwABRAJT/QP+A/4n/Wf+J/6n/m/9p/63/FADs/8L/zv8aACUA7f9/ABoBigBW/2T/mAC8ADcA4f/+/zsA8f/n/zoAJwCh/8z/NQDb/57/LABRALr/sf8KADQAFQA8AE8A4P/M/xAAfQCCAPL/qP8CAEEAlP+C/x0AAgBs/2P/zv9u/17/4f8WANX/mP/y/04AVwBKAFoABADG/2sAIAG8AOb/0P8QAGAATwD5/+j/t/+i/87/FQAMAN3/1f+P/0f/yP+KAEkAwP/e/z8ANQARAO//mP/k/6IAtAD+/4X/8v+IAFMAdv9w/zwAeQAfAMT/4v8rAD4A5f96/8b/WACHAGgAEwD4/z4AKwDI//T/YAB4ABsA2/8lADwAIwDP/6P/pP92//L/WgD+/4P/Z/+m/6j/d/+U/9f/+/8NAAMA8/8FAE0AbgAnAPj/HQCMAMsAVwDQ/+L/aACQAEMAy/91/8D/NgAsAIr/Xf/F/83/z//P/5X/uv8NAOL/jf+7/04AUwDJ/7L/HACOADkA+f86AD8APAAcABEA6f8SAHwAbgDz/4r/0f9TAE0Auf+p/wMAMQAtAP7/yf+x/xIARAD8/83/NwCyADkAsP/R/yoAbABYAML/l/9QAKYAKACS/3j/HgCdAMr/JP8DAMIAYwDJ/2f/gv8zAHYA8P+x/yMAiABdALf/XP/t/3oAGgCy/9X/EgBVADIAnv9//+n/AQDY/+b/3v8OAGEADwCr/+D/JAAPAAAA6v/7/2AANgDQ/+L/+f/e////HgAZACIACQDv/9z/8v/L/83/EQD4/+r/8f/z/8v/4/8CAMb/w//2/ycAFQAEACYAMgAFAAgAVAAxAA8AWgCPAD0A6P/+/yUAOgAsAP7/wf/K/wAA6/+q/6z///8iAJv/Sv/w/3AAJgDW/+f/CQA9AG4AOQADACcAaAB8ABwA0/8qAEUA6f+8/wYAPwDg/57/xP/b/9n/zf/b/7r/mv/k/xwAAwDP/9//+v8OADkAGgALABQAIAA5ADgAKgAWACMAAQDw/z8AQADm/8L///8/ACAA3f/g/+L/2f8GABkA5/+9/9v/EADt/8r/3f/Z/9f/EQAgAMf/nv/4/0gANQARAOj/yv/7/z8APgAtAOj/xf8TAEEABwDz/yIA///s/w8AHQAWAOr/vP/y/1oAOgDv/8v/wv/0/ygAGgDz//D/8////wYA+v/5//f/5//1/x8AKgASANP/qP/y/1wARQDy/8v/yv8FAD4AGgDl//P/9//2/xUADwDe/+D/DAAMAA0AFQDy/+P/+f///wMAGwAYAO//4P/s/w8AKwAIAOv/3//s/w4AFgDs/87/8P8EABcADgDS/7z/4v8UABgA9//W/+f/AwD4//b/EAASAPr//f8WADMALAAKAAkAMwBTAEkAKgASABUARwBqADoABQAHACEAJQA3ADQA+//q/w4AKAAjABsAEADz/+z/CwAyADAABwDa/8P/6P8fAPv/ov+K/63/xv+5/4n/Vv9R/3f/hP9o/1L/Tf8//0P/cP97/1X/Tv9o/23/cf+H/5f/pP+p/5n/of/R//r/9P/M/8z/BwAuABcA4P/D/+n/JgBCACcA8P/x/y4ARwBLAGAAZgCMANkA0wC6AB8BgQF+AYYBsgHgASoCSwIcAhICOQI4AiQCDALHAY8BfgFEAdsAgwBDABMA1v9o/+3+rP6J/lb+FP7W/ar9kP13/V79Uv1X/Wb9Yv1d/Xz9sv3j/Qb+KP5S/nv+rv7i/gn/Lf9R/2L/bv9y/2f/d/+n/9r/2/+s/6T/wP/L/2wAfgFtAaYA9AAJAsoCQgNVAx0DUAP6A4wExgSPBE4EdgSZBFsEDgTAAzwDnQIAAlQBmgD+/1b/Tf47/c78wfx1/On7Rfv4+nT7S/ye/JP8xfxY/UX+O/+9//3/ewAEAWUBvwH5AfMBzwGJARMBtACGADAAb/+D/uz9o/1U/db8Kvyg+5X7y/vb+8T7o/u2+xn8uPxW/dP9bf4i/8X/TwC5AIUBOgO3BKsE+wMZBC0FbAYIB4wGwwWEBa8F/AX9BWAF0QR2BMoDQwPTAkACvAH0AMP/x/4s/qT9Cv03/En72frZ+pf6TvpV+pT6Pfva+0n8BP23/af+CgD+AHYB7gFxAjQD7AMOBLkDbQM2A+gCcgLaAToBnwC//83+Pf7Q/Tv9jvwP/Nb7i/tL+2z7nfvC+9H7/Puh/Ff9yf0s/pH+Gv+0/ykAjQDWABMBTwE1AvcDBAV0BC4DngKzA44FLQZBBf8DqgNNBL0EfgQ7BC4E4wM0A1ACmAFSASQBbAAM/5b9rPxP/Bn8u/tD+8n6RPrM+Z/57PnS+gP82/w5/ZP9X/6o/xgBLQLRAiQDcAP2A24ElgRuBAEEfQP4AloCnwHxAH0ABwA5/0/+nf1d/Wb9N/2//HH8fvy1/Nz86/wV/Yv9J/6K/rj+Hf/k/6QA9wDdAMsABAFuAZ4BRwHTALQAwwAEAZIB/QHBAeUAMABwAG4BFQLQASkB7AAiAUcBFQH0AGEBFwJVAtIB8gBuAJ4A7wC7APP/Hf+6/pj+Tf4O/gr+Jv4g/uP9u/3y/X/+Gf90/3z/kv/t/0gASQAoAEEAnwDhAJ8AIADk//b/FAD1/6r/j/+r/8z/vf+d/7T/8P8QAAEA+f8mAGoAnACnAJoAqADRAN8AzwC1AKAAmACKAGYAOgAIANr/uP+T/2//U/89/yr/Ff8G/wb/Cv8J///++/4E/x3/RP9f/2T/ev+Z/7L/yv/f/wIAKwA4ACoAGwAmAEYAVgBDAB8ABQAHABQAFQAKAAgAFAAlADMAKQAcADUAdQCwANEAzQCtAJkAkQCWAIwAdQBlAHIAjQCLAGoARAArACMAIAAfACAAHwAmADgAWgBlAFwAUwBfAGQADwCi/8H/zgCqAV4BWgBW//T+F/9q/3T/df/G/yMAMAC7/wr/e/6J/gL/ef/G/9j/0v/I/77/cf8F/9L+3P4o/6z/PABrACQAkv8a/xn/bv/J/xcAdgDAAM4AmgAyANb/uv/m/0MAqQDgANAApwBuACAA7P/1/xYAOwBtAJwArQCRAEcA+//e/+v/9/8OADsAWwBAAAQA2f/J/8n/zv/Z/+7/CQAXAAsA9P/q/+v/7f/t/+X/7P/3/+//3//r//X/yv+s//r/hQCGAKD/e/4t/uH+nf+d/yL/5v44/8f/GgATAOn/3v8IAE0AeABwAGAAfgC0ANUAywCpAKIAsgDEAMEAnQBlAB0A7/8LAGcAvACvAEgA6f/Z/wMAIAAWAAgAFgA2AE4ASQA2ACUAKgBCAE4APwAhAAgA4f+m/3j/b/92/1//T/9m/53/vf+6/7z/yf/l/xAASQBzAJ8A7gBaAYIBIAFnAOX/z//F/33/DP/A/rP+0/7f/tH+xv7X/gf/S/+q/wAANgBOAGYAkAC7AMIApQCfANEAGQE5AQ8BrAAyAMH/cP9I/zf/Mv9K/4j/s/+b/1X/Lf84/2L/mP/K/+P/0//d/wwAQQAtANn/mv+U/7v/z//q/wsAJwAhAPX/zP+0/7T/yP8KAG0AyQDwAN0AygDVAP0AFwEtAUMBWgFlAWEBYgFAAQABwgC9AMwAqgBRABIALABjAEoA7P+e/5r/pP+E/0f/Gv8g/y3/Q/9D/0b/Kf8I/wb/JP9j/3n/df9v/5f/wP+1/4j/av97/5z/sP+0/7H/jf9u/2X/cf9q/1H/Tf9P/1r/X/9+/6P/k/90/2r/lP+s/6P/lf+g/8r/7f8MACAANwBWAKYAIQGdAeAB4QHEAbkBzAHkAf0BAQILAh0CNgIsAugBiwEzAfEAqgBLAOL/jf9d/0z/PP8d/+T+qP6P/p3+wv7Z/u7+Fv9g/8X/FgBEAEAAIwARABEAIgAkABIA9f/W/8T/m/9Q//L+nf6B/nX+av5I/hv+C/4B/gX+/P3//SH+Uv6K/rT+2f4M/0f/kv/u/0kAkwCXAGYALwAgAEcAfAC/ADwBBQLcAlgDWgMrAwgD9AK/AmsCMAIlAjsCOwIQAtIBmgFuAS0BvQA0ALf/Wv8J/7P+Z/43/ib+Jf4h/hb+Bv71/d39wf2+/e/9W/7o/nL/5/9GAJAAuQC2AJgAiwC4AAsBSwFbAVcBYAFhAR0BiADj/3b/Uv9K/0H/Qv9g/4//sv+7/7X/p/+b/4H/VP8e/+z+xP6g/oH+c/5+/pf+nv6W/pD+kv6T/oD+aP5n/ob+t/7b/uT+6P4r/+7/IgFgAkQDwgMWBGUEkARiBOgDaAMhAxED+AKoAisCtwFdAfMARABS/1n+lf0H/ZL8Mfz/+w38S/yp/Cv9zv1j/rb+zP7m/kf/6v+oAGYBJgLpAoQDxAOTAxQDkAJAAhMC1QFxAQgBuwB0AAkAcf/P/k3+9/3H/bP9vf3m/S7+i/7v/kX/eP99/1L/CP+1/mn+Iv7h/bv9y/0R/mT+k/6U/nX+RP7//bb9gf1r/XX91P3Y/pEAiQIdBAUFiQX4BToG+AUvBWMEAgQABPMDoANAAxcDAAOOAosBIwCw/mT9Sfxi+7z6c/qQ+gL7vvux/Lj9f/7T/tX+6f4//8T/TQDfAKoBuAK5A00EUATxA1wDmgKcAXQAY/+3/pv+4P4u/1D/av+o/+7/5f9s/9r+nf7E/gj/Pv+J/yEA6wCMAccBqAFTAdEADwAY/xL+Iv1e/Oj71vsL/EX8Xfxk/Gr8ZvxG/CP8HPw6/IH8Rf3s/mUB6APBBfsGFggaCXgJ0QiaB5gGBAZvBYUEkgMRAwYD3gInAu8Al/9J/u38dvsi+jf5yPjF+DH5H/p6+/b8M/4V/7D/MACcAPMAQgG5AXACTAP/A2AEcwRLBNwD+AKuAToA4P65/dz8bPx5/MP8DP1i/fL9vv58/wQAaQDHAAUBGAErAXcB6AE8Al4CdQKMAmYC1wEJAT4Aef+P/ob9ofwM/Kn7SPv1+tr69foa+zn7avvV+2T83vw9/dv9K/8uAU0D9gQ0BmkHlQgzCeEI7gcFB2AGqwWWBGIDlAI7Au0BTwFuAIv/s/7E/ar8gPtw+pb5G/k0+eL55voM/E39lf6z/4IADgGBAe8BTAKYAu8CWAOvA8oDsAN8Ax8DcgJtATsAHv9V/vb92f28/aj93f1o/vv+Pv8x/y7/X/+I/3j/bv/C/2MA/wBuAbYB3gHeAaIBIwFkAGD/J/4I/U/87Pub+2H7f/vg+yP8G/z8+wr8KfwQ/Az8EP1T/7sBWwOtBHoGcAh3CTIJegglCOcH2wYjBdwDcQMiA1YCZwHAACYAG/+f/SH83Pqd+Vr4i/eq92X4SPl7+kD8Hf5s/zsAFQEZAt0CFAMOA2YDJASWBHAERgRtBF0EmANdAhoB7v+y/kD97Pte+5z77/sY/J78tv3N/nb/xf8sAO8AsgHwAesBTwL6AkwDMQMZAwoDsgLiAc0AuP+1/p79cvyC++T6V/rT+ab50/n3+fr5MPrK+oX7Cfxk/P78IP6s/3YBWwMoBbIGDggxCdAJrwn+CCYIRAcYBo4EGAMfAnIBsADh/z3/tv4H/h79G/wp+1X6mfkg+TH5s/l2+qP7Rf3i/gwA7gDWAaoCHwMsAxwDTgO1A9oDrwOZA60DfgPdAv4B9wDH/67+3/1C/br8W/xu/D79eP4z/03/sP+JAOEAdwAMADIAyQA8AS4BKgG8AUYCFQJ7AeIAEQDo/pz9TPwP+yv6yvnL+e35DPpE+ub6z/tO/Cb8//uY/Cn+KwDGARkDJwXUB8EJSgoWCvoJ4wnzCPkGEQXyAxED8QHjACgAkP/m/h/+IP3O+2j6P/lW+Mj3nffX9/D4CfsS/Wr+yP+FAfsCuAPhA9wDGgSABIcETARUBGkEIASYA98CvgFcAPT+j/0x/PD6RPqO+j77t/tm/Lf9Xv+mABABOQH+Ac0C0wKsAu8CXgPEA/oD4gO6A20DoAKEAU4AwP4I/aH7hfqD+cP4ivjf+Gf5w/kg+sL6h/sy/Kv8Ef2o/Zn+IQBSAnEEEgbXB+AJIgsLCzAKUAl7CAoH1wT4Av0BKQEaAEH/yP5O/pL9ofyi+6D6kfml+Cr4KviC+FL55voN/QT/XAB2AawCpAPrA7wDpQPVAwkEBATnA+8D7wOSA90C8gHHAIL/Xv5S/VD8ivtJ+577Nfzd/Lv94P4uAFgB+AE5ArECMwNDAw4D3QLIAtkCtwIkAnwB5QASAAH/1/2R/GX7f/qw+QH5pfii+Az5wPli+gj74/vB/I79Jv6T/vn/lwKoBM4FjQfeCWkLqQvcCv8JiQk6CNQFAwT3AqMBVABy/5L+qf3h/Br8Q/s0+vz4TPhS+G/4hvhA+fj6K/3x/iwAjAEjAzAEbARNBEEEYwR6BE0E/QO8A3YDFANtAjgBxv/R/g7+1/yo+zz7g/sR/Ir8EP0z/rv/rAATAaABPQKiAtwC7wLzAhkDNgMdA9ECQwJ5AZ0AmP9V/gL92vv2+j/6m/ks+S35h/n0+WT67PqB+yT8yPxF/b79Yv5W/ywBoANaBaAGiwhWCuQKZApoCaEI4wcrBg0E7AI+AhoBDQBx/+f+Mf5b/XP8hPt9+p/5P/ks+TL5qvni+pf8Rf6b/70A2AGnAusC1gKyAqQCtQLFAsACvwLQAsMCVwKUAacAsv/X/v/9Iv2s/M78PP3d/Zz+UP8eAO0ASQFGAVMBgAG2Ad8B1wHjAT4CbgIZApUBCgFEADf/Df77/Bz8Zfvi+pv6f/qV+uz6UPuC+5H71/tK/Kj8C/1q/Sz+ZgAbA3sEuQUBCMMJIAqsCdIIWQjJB/gFMwStAwcD5AE+AZgAfv+i/u79xfxQ+xL6dvla+SH5+Pi5+Uf7yPwW/l7/ZgA1AfABYgJ4AoECxwJCA5IDeANsA6wDhgOqAqQBuwDY/+T+yv3i/Jn8nfye/NP8Mv2b/S7+v/77/jL/xf9TAJsA/ACJAQMCXgJyAikC1QFhAYUAmf/r/if+Xf3q/JT8NfwC/Of7y/vJ+7L7jvu4+wf8S/yk/Gn9Xf/RAR4DJwRGBhAIYggcCNYHvQeJB3EGJgWsBCEEFwNnAr0BmACw/wz/3v1k/Hn7Hvux+gL6yPly+l/7FfwG/VX+av8pAAwB5wE6AnoCMgPTA8oDjAOaA4kD3gL1AT4BgACV/8/+Ov6Z/QX9wPzO/OD8w/zc/Hj99/0T/nv+O//A/yAArAAvAX4BxAEEAggC0QGOAUUB2AA9AJj/Dv+g/iv+pf1D/fz8rfxz/EP8GPwz/If8yvwX/bT9yf4IAOkAwQENAzkEvwT9BEEFagVJBeYEgQQ0BNcDgQMyA6UCCwKmASwBYACK//b+df7g/V39Fv3+/AH9E/1R/Z/90P0b/qb+/f4Y/3f/+v82AFUAkgDLANkAywC3AJYAXQASANH/nP9g/zX/J/8a/xf/NP9O/2j/m//C/+H/FQBBAGAAmADOAN0A8wAgAR8B6gDSANUAtQB/AGAAVwA/ABIA4f+u/2n/KP/+/tz+x/7J/uP+Ff9F/2P/gf+a/57/uf/a/8X/uP/3/zQALQAvAFoAdwB4AIkArgC6AKsAsgDZAOIAxgC9AMcAzADcAOcAxgCXAH4AbwBLABsA8P/Y/9X/0//H/7T/kv9t/2T/Z/9X/z7/Qf9a/3D/iv+m/63/rf/I/+D/3f/f//3/EQACAPf/HQBFAEAALQA8AFYAaABzAHYAcgB0AHkAfABvAEoAOQA9ACkACwAFAAIA8f/k/9b/z//G/6T/j/+k/6b/jP+Z/7r/wv+//7v/tv+4/7n/rf+Z/5H/sP/P/8r/z//6/w4ACAAWACQAFQAUAC8ARQBVAGAAbgCHAJ8AmQB7AGwAYwBFABYABgALAAUA+/////v/8v/z/+j/uP+E/3L/av9V/0L/P/9N/2f/gv+R/5H/kf+c/6z/uf/H/+X/FgBFAG8AmgDCANgA4QDrAO0A4QDaANsA1wDMAMUAwQC5AKEAfwBlAE0AKgACAOH/xv+q/5L/gP9v/2T/Zv9u/3D/cP99/5T/nf+R/4n/k/+g/5//mP+f/7L/xf/V/93/3P/g/+v/8P/t/+v/9f8IACAANABEAFEAZAB5AIAAdABpAGYAYABTAEEALgAbAA8ABgD6/+P/z//N/87/xv+9/7j/s/+t/6r/nP+I/4D/iP+P/5X/oP+w/8r/5f/v//H//P8KABQAHgApADwAYQCEAJMApAC2ALoAswCnAIoAawBgAFYAPAAoAB8AFgAOAAEA6f/W/9D/z//J/8n/zf/Q/9z/5//k/9//4f/e/9z/4P/b/9T/3v/r/+b/4v/o/+f/4v/e/9f/2f/s//r/BAAYADIARwBYAGAAVABHAEIANQAbAAkA///5//7/BQAEAAoAEwARAAsABgD2/+X/3P/Q/8b/w//C/8H/x//I/8b/y//R/87/zf/T/9X/2P/h/+n/7v/5/wMACQAOABEAEQATABgAGAAZABoAGwAcACAAHQAVABAACQAAAPv/+v/5//v///8FAAwAEAATABIAEQANAAsACwALAAoADgASABUAFwAYABgAFAAQAAgAAwD6//T/7//s/+z/7v/w//P/9v/8/wEAAwAEAAYACQAKAAoACgAJAAkACgAKAAkACQAHAAQAAgAAAP3/+//4//T/8//z//P/8v/v/+v/6v/o/+j/6v/t//H/+f///wMACAAJAAkABQADAAIAAAAAAAAAAgAFAAoACwANAA4ACwAHAAQA///7//n/9//0//X/+P/6//v//v8AAP///v/+//z//f/+//z//f/9//3//f/+///////////////+//7//////wAAAgACAAUABgAHAAgABwAJAAoADAAOAA8ADgAMAAkABwAFAAMAAwADAAQABAAHAAcACgAKAAoACgAIAAQA/v/7//n/9f/y//H/8P/w//H/8//1//f/+f/9//7/AQABAAIAAgACAAMABQAHAAgACwAMAAwACwAIAAUAAwD///z/+f/3//T/8//z//P/9P/1//X/9v/4//v//P/9//3//P/+//7////+/wIAAwADAAIAAgD//////P/6//n/9//4//n/+//6//z///8CAAQABAAFAAUABwAHAAgACAAIAAkACwAMAAwADAANABAAEQANAAsACAAGAAQAAAD8//r/+f/6//n/+P/3//j/+//8//3/AAD//wAAAQD///7//v8AAAEAAQABAAMABgAJAAoACgALAAgACAAGAAIA/f/7//v/+f/4//j/+f/8//z//f/+//z//P/7//r//P/9/wAAAQADAAYACAAJAAcABgADAAAA/f/7//v/+P/3//b/9v/0//X/8//0//X/9//6//7/AQAEAAYACQAIAAgACQAHAAYABAAFAAUABgAJAAoADQAMAA0ADQAKAAcABQACAAAA/v/8//v//f/8//z//P/9//v/+//6//n/+v/8//3//v/9//7///8AAAIAAgACAAMAAQAAAP7//f/+//7//f/+////AAAAAAAA///+//3//f/9//3//v8BAAIABAAEAAUABAADAAEAAAD///7//v/9//3//f/8//z/+v/5//r/+v/5//r/+v/9//7/AAD//wAAAgADAAQAAgACAAIAAQACAAMABAADAAQABAAEAAQABAAEAAQAAwAEAAMAAQAAAP////////7//v////////////////8AAAAA//8AAAEA//////7//////wAA//////3//f/9//z//f/9//3//P/8//z/+v/6//v/+v/7//3/////////AAAAAAAAAQAAAAEAAAABAAIAAgACAAEAAAAAAP///v/9//7//f/9//3//P/8//3//v/+////AQABAAEAAAD//wAAAAAAAP/////+//7//f///wAAAAABAAEAAgAEAAMABAAEAAIAAQAAAP////8AAP//AAABAAAAAQACAAMABAAFAAYABgAHAAgACQAIAAcABQADAAIAAQABAAAA//////7//f/9//v//P/9//v/+//7//v/+//7//v//P/+/wAAAwAEAAYABgAGAAYABgAEAAEA///+//7//f/+//z//P/8//z/+//5//r/+v/6//r/+//9//3/AAAAAAAAAQAAAP//AAD///3//v///wEAAgACAAIAAQAAAP///f/8//r/9//3//f/+P/6//v//P/7//3//v/9//z/+//8//7//v///wMABQAGAAgACgAKAAoACwAKAAoACgAIAAoACgAIAAYABQABAP7//P/7//r/+P/5//z//////wEAAgAFAAYABwAHAAcACAAIAAcABwAHAAcACAAIAAkACgAKAAkABgAEAAIAAAD//////////////f/6//r/+v/5//n/+P/6//v//f8BAAQABgAJAAkACQAJAAgABQAEAAMAAQD///7//f/7//r/9//1//L/8P/v/+//8P/x//X/+v/+/wAAAwAFAAcACQAKAAwADAANAAsACgAJAAgABgAFAAQABAABAP7//P/4//f/9v/1//b/9//3//f/+f/7//3//v//////AAAAAP//AAACAAMABQAGAAYABwAGAAUABAAEAAQAAwACAAEAAQD//////v/+//7//v////////8BAAIAAwAFAAQABAAFAAUABAAEAAMAAgACAAEAAQACAAIAAgAAAAEAAAAAAP///////wAAAQACAAMAAQACAAMAAgABAAEAAgACAAMABAAEAAUABAAEAAUABQADAAMAAgABAAAAAAAAAP7//f/8//v/+v/7//r/+//8//3//f/9//3//f/+//7//////wEAAgACAAMABQAEAAMAAQACAP///v/+//3//f/+//////////////8AAAAAAgABAAAA/v/+////AAD//wAA///////////+/////v/+//7//v/9//7//v///wAAAAD///7//P/7//j/9//4//j/+v/8//7///8BAAEAAQABAP/////9//7//////wAAAAACAAEAAgADAAAAAAD+//7///8BAAIAAwAFAAUACAAJAAgABwAHAAcABAACAAMAAwACAAIAAAABAP//AAD//wAA/v/+//7//f/9//3//f/+//7////+//7//v////7//////wAAAQABAAIABAAEAAMABAAEAAMAAgABAAAAAAABAAIAAQAAAP///f/8//v/+f/4//r/+//8//z//v8AAAAAAQABAAEAAAD///7//v/+//3//////wAA/////////v/+//3//P/7//r/+f/5//n/+f/6//z/+//8//v//P/7//z//P/8//3//v//////AQACAAQABAABAAIAAQABAAAAAAD//wAAAAAAAP///////wAAAAD///7///8BAAIAAQACAAEAAQD/////AAD///////8AAAEAAgADAAIAAgADAAQAAgABAAEAAQABAAEAAQACAAMAAwAEAAUABQADAAIAAgACAAMABAAEAAQABQAFAAUABQAGAAMAAQAAAP///f/7//v/+//7//z//f/+//7////+//7//f/8//z//P/7//z//f/8//7///////7//v/+//7/AAAAAP//AAABAAEAAQD//wAA//////3//f/+/wAA/////wAAAAAAAP//AAABAAEA//8AAAAA///9//3//P/9//////8AAAEAAgACAAIAAwADAAIAAgABAAAA/v///wEAAwACAAMAAwACAAMAAgABAAMAAwAEAAUABgAEAAQAAgD///7//P/8//7//v8AAAEABAAHAAgACgAJAAYABQAEAAAA/v/8//z//f/+/wAAAwAEAAYABAACAAAA/P/7//r/+P/5//v//f8AAP//AQACAAEAAQAAAP7//v/9//3//f/+/wAAAgACAAQAAwACAAMAAAD+//3/+//8//z//f8BAAQABgAIAAcABAADAAEA+//5//b/+P/6////AwAGAAgACAAJAAcAAwD///v/+f/6//3/AgAFAAkADQANAA0ACwAIAAQAAAD8//r/+v/7//3/AAADAAUABgAFAAMAAAD+//z//P/9//7//////wIABAAEAAQABAADAAEA///9//v//P/8//3//v8AAAEAAQABAAAAAQABAAIAAgACAAMABAAEAAIAAQAAAP///f/8//v//P/8//3//v/9//7//v/+//3//f/9/////f/9//v/+//7//3/+//7//z//f/9//7//f/+/wAAAAD9//z//f///wEAAwAFAAcABwAGAAUAAwAAAP///f/8//3//v8AAAEAAgADAAMABwAHAAUAAwAEAAEA/v/9//3/+//9/wMAAAAAAPn/AAAKAAIAAwAEAAEA+v/3//3/+P8BAAMABQARAB8ALgAxACkAKgAgABYAFQANABAAGQAEAF8AwwB6ACMAzv/M/5j/fP+k/8P/EwDt/8v/lf+O/77/t//R/w0ATQBFAAMA6f/q/w8AJAAUAIMAcwBMALEAKAAaACoA5v9NANQAugHrAKb/I/8y/jb+Uv5p/ub+CP8W/6n+gf4A/0X/rP/R/8z/xf/h/1gAgADiAOQAxQABAagAuQDzAP4A9QDIALsB5AEgAbcArf+Z/4f/Mf8G/7v+5/5e/vL9Nf7Z/gP/Ef9Q/2b/uf+0/0AAmwBqAKEAxQBmAf8BfwIpA3YDlAO3AjcC7gHBACIAjP8h//j+Hf/v/qX+5v6O/hD+s/0F/pn9DP2T/bn9Av4h/sj+6P+cAIUBDgI1AnYCtgKvAjcCygGzASUBcgA1AOT/AAD2/6P/W//I/oP+Iv6u/az9/v1i/sb+TP+f/woAfQDQAEEBpAEOAmUCawI1AtoBXAEFAccAgAArAOr/w/9S//D+j/5N/kb+S/6D/rv+Fv9k/6z//v8yAG8AawCLAMIAowClAJsAZgAuANz/sf91/y//Jf8S/xr/Iv8S/xL/5v7g/h//cP/D/wYAbADEAL8AiACTANIAEQELAbkArwCcAH8AVAAkAEoAnADLAKQASQAfACwALwAsACwAQQA7ACMAKAA1AHAAxQDcAOkA4AChAFEA3v+J/4P/jv+T/5f/df8v/9r+uP67/tX+Ef9U/7H/rf9d/w3/xP6//uD++/7b/pz+pv7X/qL+Y/5s/rn+EP8l/07/d/9R/xD/F/8t/7X/sACnAf0CMwRSBR4GhAa0BmsGFgawBYAFEgXsA5ECGAGg/4j+5P2U/df9X/7B/sP+b/4f/iX+aP6b/sn+6P7g/oL+4f0o/cv81fxw/Jv7MPuG+7z7XPvf+sD6w/o5+l35s/gm+Cj4s/fw98D8lgI+B7oLQQ5LEA0Rfw8KDicMEwrRCDcGWAKB/l37pvm/+DX68/x6/isAVwFEAaYAd//C/y8BgAE6Ag4DCAOkAucAMf8B/lH96f0H/nj9EP2X/PT7vPqf+bj5Pfq++jj7SftX+3X72/sE/Of7TPwN/FH6YPd49wf88/+kA3AIhwxlD/sPnA5QDS8LRAmnCCwGqAJ7/8r8u/r0+Ef52/uJ/+8CuwPoAh4CLgGuAH3/0P4fAZoDdAQrA48AVf++/jX+A/7s/ab+Xf+m/uz8Lvue+mb7yPua+737RfzU/Hv8oPsn+y/7iPs6+9X5Lvg/9kb0KfcO/ZQBBQfuC2QPAhG9D34NYAv6CCkHZgXCAuf/KP2Y+0j66vnZ+/T9QwBcA1YEPAMCAjEB9ABi/27+vf9lAQQDtwJ4AVEBoADV/8/+P/08/eH98/1z/Zf83vw7/WT8q/vF++/8e/7u/i/+1/yO+036RPid9rn12fUV9QT0X/kLAb0GwQz+EMISTBKWDpsKdAgyBkYElQLc/4/8t/qC+sb6UPz1/pUB4QMSBgEHGQZHBMgCLAGd/vL9aP9WAf4CQQL7AJQAv/4U/GL6V/qw+1P9Ff6D/RX9Y/2S/Ev7ovts/ZP/lQDL/8L9nPt4+U/31fXa9av2QvdS9sjzNPdkAIAHvwypEdEUTBToD0sKAwYMA4ABBgC//fn7bvtw/ET9f/3m/rkBWwRKBrwH9weNBhQEygGr/qr8ev6tAdEDvwMzAlsAiP6v+7v4svgA+7z9jP+K/8P+yP59/g79efz6/dL/kADZ/5/9Mfs6+Yn3BfY/9ZH1FPab9eLyIvQc/QcG2QzDEvQVthVrEUYKAgS3AFb/0/4+/vf8NPzP/FH9XP0U/mwA7gOOBgsIfggQB0IEiAHQ/hr9GP8uAz8GoQb9BFoCn/3f+Jv2K/dR+gj+fgBWAc8Aa//c/d/8xfzE/WX/lP8l/mv8X/oV+GH2fPUa9UX1bPU39QHz1PNh/bgHtQ6TFPUWURU4ELcHsAAJ/sv9TP45/l39vvwl/Y79Zv3f/agACQXNBwAJowlkCN8EPwHr/s79HgDkBM8HdgckBEYAjvwj+F32Xvhb/JgAlQJBAtcALv+o/XP8NPzZ/MP9A/65/Of6nvl8+Eb3O/aS9TL1q/S383rxV/O2/YkIYhBhFqMXyBTCDrcF8f4M/WH9rv5B/4T+lf0a/Rb9Ff0v/rgBcQZyCVsKRQoKCAgEewCR/u3+CwIXBiAI5Aa/Arb96vh+9Z/1HfnP/dwBjwPMAsUAh/4O/XX8wvyV/eL9X/1R/CL7IPo9+WT4efdt9o715vQE9GvxB/J2+yQHRhCTFgsYehU1D3kFvP1c+1r8Bf+mACEA8v60/cb8kvyW/VQBEQefCv0KxQnyBmQDZgCf/i7/UwIhBvwHCgZTAQL9Evmw9cf1VfkS/gUCfgOcAhABif/S/W/8+PtB/Ln8tPwQ/F773vot+vX4zvf49t31tPS/8/3wS/Ey+9kGIg8hFjUYBhX2DnsF0/0Z/Ez92P9bAX8ASP+//Rf8DPy0/a0BiQezCjUK+wiTBuUCaQCi////uwJbBkYHJAX+AGn8svg+9oj2sfns/ZkBIwPIAncBsP8l/uP8CPwH/Lf8Bv2a/AL8hfvL+ob5E/i79pL1svTt83HxhPFI+7IHhhBxFygZjxXVDqUE6fyH+0H9VwDOAWkAlf62/ED7d/uo/TcC2weiCm8JcwdlBT8CFwAPAA8BVwMWBtoGPARE/6f6ovci94j45fq2/v4BzwLEATAA9P4j/hH9nfv6+pr7Pfxy/JP8vPyn/JX77fnJ95H1I/Rr8xfxsO7F9XkDrw1MFU4ZfRd6EY0Ha/6l+2z9nAAyAvsA9f6p/NX6vPqn/PoA0AZhCq8JJAdfBUgDsgCrADgC6wN3BqkHdwUIARD9H/rA96v3Yfoo/oQB5gJLAkEBLwDp/mL9VPw5/Fb8cvyE/Hv8nfyx/Mz7Gfp++NP2BfXU8x7zU/Bv8Bf7qQjfEesXHhkfFUYNAgMv/NX71/7/AWgCWADf/aT7bvri+tf9VgPcCLIKjAgyBr0EaQLtALABJwMqBb0GHAbiAjL+S/o1+Df4oPkv/KT/8gEsAk0BMAA8/0X+7fyn+wT7S/uv+8n7KfyW/I38T/sN+QL3ZvXD8+zyN/LW7wzzhf9wCyYT4BedF38SvQmUAND8WP5TAUkDEgJF/9P8qvrQ+YH71P+yBQUKJgq+B/IF0wQAAzkCsQODBWIGKgaLBOYA2fwp+tP4S/lJ+wX+fwClAaEBEAEQAN/+kf0A/Mb6Zfq0+lf7VPwi/Vn9zfwC+7f4fvZb9B3zCPPG8vjw+/MZANYL3hKdF34XRxJXCqgBbP3W/qYBEQOpAfH+vPwi+976xfxZARwHxwqkCgEI8wW3BL8C8wFJA8cEWgX2BKwD/wBZ/Yv6bfmt+fn6Hv34/v//jAAuAO/+EP5q/R/8FvsN+6r7h/yS/eb9QP0t/Df6Z/f99Hvzv/Ih827zkfE/83P+yAohEvUWmxceE4sLwQLB/df+xAE0AwMCgv9h/Xv7/PrE/M8AfAaKCjkKUQdKBVkEwQL7AUwDvgQmBYUEPAM5ATn+gftz+bf43/ol/Qn+V/9lAG4Asf+h/rL9Cf2T/PL7r/uL/Hz9ef3F/JL7DPri9471DvSd82b0bvXU9MbzOPkQBX8ONhQoFxkV/g7zBvr/Wf66ABUDqgOVAcr+3/yb+w386P6jA2MICQoNCHEFVgTqA1QDtQPgBFwFqgQ5A7wBjf9//PH59ffc97P68vz6/az/2wDMAOH/sP7y/aH9J/2C/Gf8FP1x/Zb8XvtT+gf5Yffh9TT12fUD93f3RvZp9Av5awRADVkSWRXEEy4OdQa6/8D+vwGIBOcELwKe/7f9N/uu+n/9VwINB4cIygb6BIIEOQR1A9wDZQXOBbsERwO+AXD/G/wG+fX2Tfen+h79kf7pAEwC3gEmAIf+7v1w/Zr86fvf+6L8tfxQ+x/60PmJ+cz4bPia+M746/jW91b1svJs9fj/9QkREKcU2BTwD/YI/QHd/+QCVAVlBRsD2P/u/Cn6KvnC+woBUga2CKoHEwZzBaEEcgPCA0QFqAXNBDUDcQF1/3X8efnW95j4n/sE/qH/4QExA2cCMgBt/t/9XP2M/O778Pto/C38Kfua+hb72fsz+6j5xvg7+Cr3P/X38+fy4/L2+VMFnQ1rEwIWGRPkDKwFywBYAeQDFAXWA28AG/0A+oT4fPpF//8ExAjuCEEH4wXPBOcD/ANFBfIFXQWhA1ABz/8X/o/7rvk7+YX6ZP1m/0oAVQFXAZv/Tf3F/Hf9kP0r/Wb84fsD/Kb71PpO+1H88/sD+i34Pvda9rf1H/X59NP0vfRh+igFaQ1vEg8V+hJJDTkGCAHzADYDbgSRA9wASf5E/E779Pz5AJ4FqwidCLIGFAUJBDID6AKnAxoE0wN1AxwC7QA6AGL+P/z8+tP6+fvL/Ur/KgA0AF//H/50/ZH9cf1l/YX9OP3F/Cv8gvtU+5H7IPvT+cX4GPhG93L22vWa9UD2CfZX9hX9FgceDkMSOhPcEDcMvgV6AVUC7gRzBjoFAwJH/5j8/Prt+w3/cwNNBuEFEQTaAmoCgAJ+A3sFIQenB+0GiwTMAUH/dfz3+Wj4Ufhj+R37Ev16/j3/a/8u/1f/5/8GAPL/jv9w/hv92/v2+oX6fPow+jD5S/iR9wj3kvY39kP2Ifev9yL30fqXAxsLrw9kEvIRVA4aCYAE5gPuBUsHRwaBArj+6vsE+mf6ZP2qAQcF3gXzBKMEDgVxBW8GAwixCI4HiwXaAt//0P0w/GD6+Pin+CL5P/qG/Cr/7wDcAbgBtwDy/wr/LP7z/Xv9lvyX+0D6A/m3+Mj4w/ge+Vj5KfkO+Y349ff494T4evhP90L6MgLlCEkNrBCAEdAOuglMBa0E6gV9BlUF+gHQ/gL8G/qq+qD9LQKOBWEG5gVkBekELAQOBMoE4wQABMkCggHCANb/Uf4w/d37nPqA+k/7tv2AAHsBWAGoAKj/m/6b/Uz9v/3p/d78NPsd+gL6lfqS+5z8Tf01/eL76/kp+AD3uvb79l/3APcN+Pz9GgVaCvMOmhGLEMgMOwhVBYYFZwb6BcMD2wBq/hT8afsp/W4ASwMZBHIDkwI6AisCpAImBMsFbgYJBgQF+APkAhUBEf+g/ST8wPpk+gb7pvwy/kb+iP0N/cD8HfwT/Fz95P69/6f/5f75/T39EfzF+nj6OvpT+SD4GPfQ9vH2Tvcr+Ir5zvrA+jH8xgGpB5sLug7jD8MNBwqaBk0FUgb3BgsGPQPq/xn92/rW+lP9JgG5A1QETgQhBBMEyQNKBBYGSAfVBjcFlQOZAmIBa//X/d38ofsT+uX4C/mm+kb8Jf0r/q7/1gDjAOMAPwH8AKz/dv1i+/n58Pgk+GL46/k++3v7H/t8+pz5y/ht+Nn4P/pp+xL7SPxdAbgGHwr2DGUOugxECeYFgQSfBZYGFQYeBJUBkP/s/db9DgAMA7MElgRBA64BtwB5AJkBywOUBSAGTQVyA80BYADJ/tv9Zv0T/aD8N/yM/GL9Hv4X/pf9Pf3H/B78z/tX/E39EP4e/sz9oP0t/XX8F/xg/JT8C/xT+4/6ifni+MT4APmT+TT6PPqJ+Sb7IQC2BPEHMgvFDAMLdAg4B5gHCwlvCU0IygVdAif/yvyc/OD+JgH8AeoBLAFhALj/oP8cAYIDSgURBlUGOAYRBi8FkAMOAv//Z/26+u34ZviG+F75YPpu+/X8Vv4h/wEA/wBfAS8BfwCL/5f+ev0x/Ev7JPvi+jn6tPkg+VT4Cvha+CH5RvpE+8f7aPsn/PX/iATsB58KNgziChAIWwY6BrEHtQgiCPMFXAIU/2f8QPvT/Gv/JgFcAiQDUANwA80D2gQ/Bt4GSQa+BJQChADJ/mz9AP0R/TT9CP3u/Eb9Lv0f/Y39Hf68/lH/if9B//f+of7k/UX9Bf2s/Pb7DPu1+hf7cvvf+2T8xPyv/Cf8zvvS+9/75PvD+3H7MvtV+y39CAHTBAcHDgiKB0cFkANIA34EaQZgB0MHywXkA9cCTwKSAtsDlwT9A6gC2ABe/6X+yP4cAOcBNQPPA6gD5QIWAkUBngApAK7/gf9D/9j+h/7l/UL9Ef31/Mn8o/x2/I/8F/2W/TD+6f4W/+L+WP5u/eP8yvy5/Gv8HPzk+4H7e/u4+9r7O/yM/D78b/vL+vr6yvxHAFYDQwXFBpMGYwULBWYFfQZFB+cGWwWvApoA4//3/2YBvgMuBZoFSgUdBPsCcwJoAtcCPgNNA/UC5gGaAHb/gP4V/gL+Ff5z/tb+Af/b/oz+g/7C/kr/xP/P/7X/SP8z/vH8FfzF+9H7Dvw1/C78bPy+/Mz8+/wu/QX91vzr/Cn9df3X/fX9i/0a/cb8WPwu/Fv9yP9KAcABywG2AB4A3wBMAn0EYgYRBxMGJwRYA1YDmwO1BMkFFwbiBSIFCwRFA+UCywLhAgcDQAMbA0UCZQHCABcAkf9A/3D/HwCfAIcAjP8y/kv9xfy6/D799P11/jr+rv15/YH9p/3k/TL+WP4g/u39pv0f/fL8zPw1/MX7oftr+0D7Svt2+5n7wvvm++f7APyV/GH+vAAtAuACmQJuAQUBZQEyApQD0QQrBRYEigL+AVoCRQOEBK0FjAYCB6cGrwUdBfkE5wS2BGcEOAR1A/YBrgCe/5r+3f00/d38Nv2t/ff9Df4R/lf+cv7F/tb/nQDVAJIAzv9K/9/+MP6p/Tr90Pxf/Nb7hPuC+677o/t0+8P7TPyU/N/8N/1f/WT9Tv3S/F/8U/w+/Kb8bP5gAFwBtgEjAUgAYwA7AbsCYQSVBeUF1wSxA4EDAgQOBUYGLAdnB9kGvQWgBNADcQOWA6cDlANdA1cCAAFFAOf/cP8F/8f+pP6l/rP+of58/nD+Vv5H/pL+9v5S/03/vv5Z/iT+0f28/a/9YP3+/JL8Mvzp+6P7bPs4+y37b/uY+6b78/sl/Bn8MPxc/Kr8K/3E/Vf+kf5t/98AVAF3AXsB5wDbAEcB7wH3Ar8DDwRsA3sCnwI4Aw0EbgWeBkoHigc8B3cGnAX7BJsECARZAxYDgQJpAagACABl//r+zv71/kb/q//6/7X/Jv/Z/pP+Wv5y/ov+W/4U/sb9eP00/fL8svxL/N37lvth+0r7SfuN+9r7GPzD/F/9oP3l/cf9K/24/I78d/ye/CL9rP3h/cP9Nf6f/+wA2gF6AisCnAFqAacBpgLtAx4FZQWTBEwEjQS/BFAFDgaXBqwGOwZrBXAEAQRQBIIEdwSaBFcEPwPfAa8Auf/z/nn+U/4+/jX+Nf68/QP9kfwn/OT7HfyO/Az9T/1s/av9xf3l/e39gP0K/ZX8HPzY+6P7nPuQ+1D7YPul+/f7mfxV/fD9Lv4V/uv9qP2X/eT9IP5B/r/+6f81ARUCpgKTAh8CJwJoAvECDgQkBcsFqQUkBR4FUAWZBScGiAbDBtgGZAaeBQMFzgTVBIkE4AMJA6kBGQAU/3D+A/7L/ab9jP17/Y39k/1R/Sr9Gf3g/N38Dv0k/ST96fyV/GH8Ifz2+w38Pvxy/JP8kfx5/HL8lfzQ/ED97f19/sP+4v7E/lj+1/1t/Tr9jf1d/j7/4v9XAMcAiAGuAp8DQARyBP8DmAOOA+cDhgTiBO8EXgRuA/kCzQLxAowDLgSMBKMEnARLBO0D7wPcA3MD0QIWAiAB0//i/kP+dP3j/H38Cvz9+z/8Z/xo/HH8kvx+/F78nvz1/Dr9jP3K/ff9NP5W/jP+3P17/R79xfyb/Mj8Gv1k/bD9+P0u/k/+ev7A/iv/u/9UAOAAXgG1Ac0BrQF8AXABkwHeAS4CPgIVAtQBsQEbAvUC/APQBAIFtwQEBCkDxwLPAh4DmwPoA/UDkgPHAh4CpQFWATEB2QBMAMP/I/9w/gn+9P3f/aT9dv2G/a79z/3m/ej95/0E/in+Nf4v/iT+Bv7H/W79HP3Y/Ib8Qvwz/FP8t/xl/SP+z/5k/8r/AgA2AGAAZABTADQA7/+j/6r/CQCkAE8BpwGSAV4BLQEPAScBkwEaAoICvwKwAmUCDALJAcYB6AENAiQCAQKZASMBzgCJAFQAQQA+ADgAKgAIANj/yP8BAHAAAgGYAeYBwgE4AYIAxf8Y/5P+RP41/iz+Af6//YP9eP2i/Qz+vf53/wkAPQDp/2T/Bf/X/tz+FP9k/5f/k/94/0L/Av/s/uX+7/5F/7L/CABjALUA8AAIARkBSAFtAX4BkAF3AU8BLQEAAdsAxQCdAF0ADQDJ/5v/Z/9E/2H/nf/q/0oArQAEASgBEgHwAL8ApwC8AMMAwQCpAD8Arf8y/+n+4P7r/v7+LP9Z/5L/0P/l//j/EwD8/8r/k/9X/zf/L/9N/4P/mv+X/3r/OP8W/yH/Of9R/1//Y/9n/4H/xv8pAIYA1gAIARQBDwH0ALUAgwBeAC0ABADr/+H/BgBAAHAAjgCLAGcAIQDO/5//mf+y/+n/JwBbAIcAjQBgACgACwAbAEoAZgBhAEEABwDS/7L/ov+o/8f/2//L/5b/Yv9N/3P/3P9RAK4A5QDTAI8ASgAVAAIAEgAiABoA8P+u/3z/gf/Q/0cAuADuAM8AawDt/4z/av+O/9//NwB8AKYAugDGAMkAyADAAJ8AWADy/3v/BP+u/o7+qf7t/kD/iv/A//r/QgCEAKwApwBtABIAr/9f/yX//v7g/r7+l/6D/pf+1P43/6j/AAA0ADwAIAD9/+r/9/8cAEIAVABKAC8AFQATADoAjADoABcB9QCMAAYAk/9Y/2v/zP9TAMkAEAEmASQBHQEVAQ4BDAEDAd0AjQAeALH/Vf8S//T+B/9T/73/JQB+AMkA+gADAeEApwBlABkAvf9f/xP/5/7M/sf+5v4n/3P/qP+7/7j/qv+Y/5H/qv/f/xoAMwAeAPL/yv++/9f/GgB2AMYA1gCcADIAy/+P/4P/qP/3/1AAhQB+AE8AJQAjAEcAfQCsAMQAuQB8ACEA0v+y/7f/uf+o/5//tf/j/xMAQABwAJUAjABLAPn/v/+i/4j/bv9n/37/ov+6/8X/zv/Q/7j/iP9b/03/Vv9c/03/Nv8n/yj/N/9Z/5b/5P8oAEkASAA3ACEACAD1//f/HABaAJgAxgDiAO0A2wCzAIcAcgB3AIEAhAB9AGgAQAALAOP/4v8JAEIAewCoAMUAzAC3AI4AYgA5ABAA6//Q/8X/yv/Z/+T/6f/v//7/FQAsAEEAVABiAF8ASwAoAAMA2/+y/4//d/9w/3X/f/+D/3z/b/9f/1X/Wf9p/4X/pf/B/9j/6//9/xIAHwAaABIACgAEAAMACgATABMA/P/f/8r/wf+9/77/zv/x/xgAMwBMAGEAXwBFAB0AAAACAAsADgASABEABwDy/9z/3v/6/xUAJwA0ADcANQAvACcAKgAuAC0AJgAbAAoABAAEAAYABwD+//L/4P/G/7L/rv+5/9b/8f/+//3/8P/e/8r/wf/M/+f/BgAaABwAEQAAAOv/3v/e/+z/BQAgADQAQgBOAFcAWgBSADgAFwD3/9//3f/w/xYAQQBcAGMAXQBLADcAJgAfACIAKAAnACQAHwAZABgAGgAhACMAHgARAPT/x/+e/4D/av9s/4f/sP/X//r/GwAqABcA8//S/7L/nP+m/8v/9v8OABUABADU/6T/gf9y/3n/i/+M/2//U/9L/13/l/8AAE0ATgAhANn/jv9j/37/x/8JACwAPAA3AC4APABhAIUAiQBwAE4AJwAIAAwAMABMAF4AeACTAJoAmgChAIMATAAlABAAAwAPADEARgA3ACAAGgAPAP7//f8KABIAHAAqAD0ASAA5ABYA7f/I/6n/kv+U/6r/tf+2/8T/y//H/9P/+f8ZACUAKAAbANf/iP9b/0b/TP99/87/EAApAC0AIwAUABIAKQBUAIIAlwCFAGQATQAlAAkADAANAAcAEQAhACUAJQAkACAAFgAXABoAHgAzAE0ASAAuACQAGwADAP//EgAqADIAHwAHAOX/nP9U/1v/jv99/2T/rP/y/83/oP+b/4n/aP9n/6L//v9FAD8A6P9y/yv/Gv8s/17/tP/f/6L/Xf9K/0//iP8BAIUA2gAJAR4B9gCjAFMAAQDD/8T/8P8WADMAXwCEAIkAbQBkAJwA2ADPALMAzgDhAKAATwBIAFEAGQAHAG0ApgB0AC4AFgA2AD4AJAApADYAJQC5/zj/Rf+j/83/0P/m/yMAXQBVACAAFAAbANL/TP/7/tX+tv6m/qb+7/5t/8b/zf98/3j/4P8SAPf/AwA/AEMAAwDR/+H/GAA0AAcA0P/G/6f/Wv+A/1UA9wD2AN4A/ADbAGYAHwA2AGMATQAEAAEAHAD//zMAigBNAPD/LQCPAJIAOADp/6j/+P59/vr+xf8lAEoASwAjAO3/qf+0/yYAbwAvAMX/Fv9p/r/+pv90ABUBOAGuAMf/4f6s/vz+X/8pAAABYAEhAZ4ARADp/4H/iv/I/+f/IQB1AJQAVADb/4H/jf/S/xcAgAAdAW8BGgFgANX/gf9W/8T/kQDeALEAhwA8ALX/e/+f/63/uP+x/6n//P9fAJwApABKAPj/tv9V/0P/ov8CAAsA8P/r/53/QP9E/4D/uf+R/5H/MwA0AH//dv/w/yEAAwAYAFcAOQAUABQA2v/M/8D/+P8BABH/7f6x/0UAVADA/8z/BgCk/6L/OADhAPsA8AD5AHQA4v/Y/5//3f9/AAwA1v8kAPX/zf+u/yYA3ADVAJ8AhQCDAAkAkv+0/8L/kf++/ygAHADw/xUAQADl/3T/XP8+/5D/CwA2AI8AdgDH/5v/DgCEALoAkQCQAHsAHwDE/7L/TgBoAM//sP8gAA8Ap/+b/2P/V//U/9j/h//G/2gAgwDo/5b/FAC9AFoAqf/M/ysAcQBuAKoA2gA4AOH/HAABANb/QwBJAJ//KP/v/gX/VP/o/3sAjQCTAIYA2/94/6b/nv+S/7//WAChAP3/iP+V/5D/i/+3/+r/LQAfAPP/NwB9AE4A3f/v/xIAIAA7ADYAHADT/6b/Sv/+/n3/OQB4AJEAuAC6AJYAHwAZAHsANwDu/+n/z//V/xQAMgAoAN7/P/9f/+b/wP+h/2IAdgCr/xMAcQAaAB8AQABrAEsA7v84ACIBGAFiACkA8v9T/0n/+f81AFMAOACi///+zP7e/nn/IgBAAMUA2wBDAO//NgBxAO3/wf8fABUA3P8JAIcAfwBo/7P+Rv/A/8H/5P89ABwA2//Z/93/EgDI/57/JgCyAKwASADd/53/KgBJAIz/e/8cAMMA1AA2AJ7/ff+C/2L/zv+sALkAUwAMAIb/ov8YABMA8v8EAAcA0f9v/6T/lgDZAGAAOQCQAEsAeP+U/xQAwP8f/8j+SP9cAJcAXQA5APr/FgDs/43/7v9/AIAA/P/N/+r/5f/D/3D/BADUAHYAFwAdAO//BgALALT/v/8vAMYA+AA5AH3/m/+B/xH/UP8KAJQAmgCDAFMAMQDs/5P/w/83AIcATQC3/3D/uv/+/9v//P+AAGYANwC/AIUAiv9R/3f/Qv/1/m//YgBhAB0AxAAyAQ0AMP++/9z/8f+VAA8BpgBh/+j+kv/m/3j/qf9vAFIAdf87/+3/PADS/+H/cwDoAEsAtv9GAGwATgDR/0L/V/9m/7T/SwC5AMIAHQB3/8P/VQDJAKgAtP92/zj/Fv+k/+7/XgCSADwA8/8yAC8ABgAjAE4AdgDt/2v/Tv/2/8wAcACA/xD/L/+j/3kALwEaAUMAuf+i/9D/yf/N/34AigC9/yr/Xf8PAGcAHgBIABgB6wAWADYANAC2/2n/0v7a/lv/af+l/7kARgHGADsAzv/o/zoAZgB8AFIAHgAMAH3/xf7W/ov/MwAfAMb/NACzAA8Axv8wAPj/z//w/10AAgG2ABcA4v91/13/gP9Q//X/kwDk//f/iABpAC8Anf8W/zf/BACvAL4AfwA+AAMAyP++/xsAgQAiAAUAVAD9/0n/3f6C/yIAGADW/6H/HAAdAJj/EgBKAKn/rf+s/7f/ugBEAZMAzP+E//7/KQCK/8f/kACdAPL/IACvAOD/qP9wANAAHgGcAMr/ov8o/wP/mP/U/+D/LgA0AIv/G/9u/9L/AwAxAI4AQwEdASYAzf/k/xIAXgBXAC8AcgA5AJz/Xf97/+X/BgCt/3P/0/9KAAwAp/+R/3j/Vf+M/xUAfQDNAMkAOgBY/yH/n/9cAPgAyAB1AIMA//8m/1b/xP88AHUAo/+5/1sAGwDq/4H/GP9F/6L/VADPAIYA+v+q/7P/dP8Q/13/XgAXASIBhQB7/3z/VQBfAMf/LwDYALAAw//A/kD/VQALANH/iADFANEAhAAEAI7/pf91AFkA6P9CAHgA5f+i/9D/Yv80/w8AmAA4AK3/MP8U/6r/OwB7AFkAkv/9//UAqwDU/3L/7v+I/2X/gADhAOH/d/9iAGUAlf83////OwF0AbMA5/8i/xr/UQDvAH0AVAAOAGn/Ff8h/8P/9f9s/4v/5P+s/wsA2wCsABMAy/8XAGQA1f+a/4YANwEGAO7+DP/Y/20A/v8rAOkAhgDZ/oz+LwDlAD8ADwBVAAsARv/+/uz/yQB8ACIAZwCGAEwAAgDT/4b/O/+n/wYAFgCJ/xn/tP+g/2P/TQAzAQABWQBJAG4AAACL/7v/8//V/zsAcgDb/93/VgAiADj/Gf/a/14ADACU/3gAhQBB/5D/sACZAML/6f9UAFYAaADt/wMAmQAmAF3/BADyAG0Arv9b/4D/tP9L/1j/WgCVAI7/D/8IAPMAUgCX/2QAHwFNADP/Pv8BAFsAaQBUAGgATQA0/yL/RgAsAJn/rf8OAJEAUQDf//L/TwBKAIP/ev9UAOYAYQAj/x//KwDr/6f/cgALALf/HQBnAD8AIgCHAMr/Gf+I/ykAIgDK/+j/gACQALH/8v/NAGIAuP+R/4j/LgB9AAQAMwC6AEkAqv/E/4v/9P96ACEAWP+R/uP+fP/v/+z/qv+p/4//HQC1AHYAawBdAAwA2f9W/5j/vAB7ARMBu/87/8L/6f/w/3gADQHaAPj/m/+x/9r/RwAhAK//7P+r//z+O/8EAPX/FP+y/68A/f/n/8cATwG2AHn/i/8eADEAWACkABQBXAA6/93/LwCO/0D/0P94AMD/+v5p/+v/8f99ACsAVv9l/1wAqgFVARoBHgGS/9/+1//8AAYB//9I//P+Cv/y/vn+SgAbAboAPwAv/7v+DwCoAFAAtQA0AGL/1v5A/j//KgBGAC0AUv8k/0MAugANAIX/KACmAPD/6/8FAVcBuf9S/rn/LAH2/0T/jwBJAVAA+P4G/8//PP9W/xsBXAGoAGgAKACl/6n/SwBWAHYAYQAiAHkA2P/c/pj+ZP7W/kAArgDe/8z/ggCgADEAfwAaAagA2P9KAPgA+QAdAEn/7v+AAPP/5//N/wH/4v5y/+T/BQCQAGsAhv/r/rv/JwGHAAoAVQFBAaT/pf54/p3//QAIAd4AewBs/5n/lf8x/wkA+v82/yf/wv/+/53/rf9JAAkBYwAG/8r/6wB8ALb/cP9uAKcAYv9b/2IAHgGQAHP/z/+yAD0AoP++/67/pf9A/zD/TgCwAOT/e/+8/zQAyAAzAHn/TgCFAO//1f/z/1EAWwAEACcAOwDz/zYANgDT/4L/Ev8a/8P/WgAGAE0AyQBRAFAAlACMANL/Bv8IALMAXv8k//P/dQDPAFQAy/94ANEAGADJ/+3/FADS/8///P/I/5P/wf9AACsAyP/L//X/qP9I/wn/Z/+mAOAAHADz/yAABwDD/wUA7QAZAaYAEwBV/xv/9v6H/5cAyQASAHj/oP/K/z//Jf9wAMYAJwDL/ysA5QBtACIACADD/+T/mf+3/+7/7/9kAMcAYgDL/nL+SwAeAUAA2//K/6H/g//j/jP/fgAzAfMBpAGc/wf/LACsAIb/Bf9bAOkApv++/Tb+EAB2ABEBxQHJAFD/Sv8bAJT/zv6y/80AlwAEACYAJgAGADQAcgBnAEQAXADXAJwAt/9N/xb/HP+q/+P/AwAOAawAiv+w/+n/u//S/xwAIABhAJ8A6/8n/w3/kf94AIQApgCkACsAEABq/zz/BgDBAAQBZQDn/xoA3P/m/pD+w/+rAO8AnQDK/xcAs/+b/i//QgBlAI8A/AD9/yn/QwCNAED/9f4DAN4AFwAd/ykAiAAM/9T+JgDCAOn/Pf9MADQB7/8s/0UAjgAvABMATACzAHwAzP8r/zH/pv/P/2P/Uv8HAE8AOgDyAJMBHQHr/4r/HAAAAFP/sf+tAL4AagDY/3D/df+y//z/kQAkAcwAqv/E/rL+Kv/T//v/VQCuAIYAiwBaAET/jv6D/8AA6AByAJwA5QAqAOH+0P59/47/yf9cANAAGAHbAIP/tf4Y/4b/mgCYAScB7wAkAB/+B/44/8H/dQC6ATgCqwCd/ir+6v79/6wArACnAJ4ARgBk/+X+NP9b/4b/2v93AKgAWABaAL7/Sv/I/5v/YP8EADMBHQGH/8T+/f7p//r/h/84APYApwDY/+D/qQD3/0b+v/6FALQAMADUAA8BNwAm/3L+p/7J/2IB0AEhAXMA0f8p/9/+1//8AFYBTAH+AC4AAf+4/lr/TAD6AOIAMAAu/8b+wv/tAKkA6v8oAGsAcv/U/jwAegHTABkABADB/yT/+f75/6kAmgDWAEYAO//L/sn+nf9tAPIAJwEaAO7+1P6s/w8AEQD9ACUB8f9T/0P/0v9jAIAAEwGbALf/cv8d/1P/MACPAPj/Tf/6/ub+Mf+U/6v/TAB3ANX/eP+9/tj/BgL7AccArP+1/yQAWP/Q/isARAFFAFL/UP9r/xn/ff5G/2YAbgB3AKUAmAAEAMj/3//W/40AyAAQAL3/VQCfAJH/K//Z/y8A+v+b/0kAngDi//P/9P+Q/1sAJwEyAJT/RAAYAdAABgDw/4P/Tv/2/2YAFwDn/4cAOgGpAOX/WQDxACEAD/8hAEMCygFN/wD/9f8X/8n9P/9xAQMBBQDE/1b/Wv88/2//5QCmARMBEQAa/wz/qv/h/wYAgQBGABz/Nf83AOL/aP+q/7//uv/p/0IAnwBSAFj/Rf/3/+H/kf/V/3UA0gDF/4L/+P/9/kv/NgFaAfv/7P9AACUAvv9h/5z/cgBLAXoANf8e/y0AZwAv/yj/bAAxAPL+Jf+eAJMBrgDF/3r/b/9k/3H/vgCYAdQAm/92/4v/KP/y/xABOQHuABIAEv9u/+r/f//n/1kAQwAPABEAuAChAFkA2gD0AFMAbv9V/x8Ay//e/8kATQBz/0b/F/9x/zsAdQB7AKz/2v+8APn/PP8J/1j/IQAFAcIAN/+M/7YAoAB8AAIAp/+PAAIB/f8AAI4AFwC2/8r/KQA8AOP/qv9A/xb/3/+BALgA/f9L/0j/Rv8PAHwAjQD4AHYAYf/h/iL/tP8CABUArACcARwB2P4i/uD/XADz/yEAjP/R/zEAT/8oAKIADgATAOX/e/91/4MAqQD5/xoAx//9/sz/cAArAHMAwwC+AKz/ev4P/1EAcQCi/1D/dQDdAAAA+P/eAPgAAgDf/x0Aw//A/8D/x/9YAGEA4/9SAI8A3/8VAOj/e/9PALsAvQCoAKn/sP7V/r3/GwANAD4A0f+A//3/qP9C/xcAagCHAEIBRAHPAOb/W/8bABIA3v8mAAYAYP8O/8j/LwBw/13+Wv8cAdcAMAAsAOz/BwD7//z/OABgAE0AAADF/3T/xf/s/8L/GAAPAED/JP9JAB0BRgDV/2gAHAAJ/xH/TgDlAKgAUwBsAKT/JP93/7H/TABYAGIAwgCa/2f+IP+3/+v/iQAwAfcA7f/H/+v/fv/O/2MA5f/m//sACQGx/xL/PABJAJf+p/4RALEAHgDz/5sALQBO/0T/JADFAJUAzAAWAQUALf96/7n/rf/m/6gA6QCbAEIAmP/t/ln/RwB0AKn/qP8UAcoAMf9z/20A/P9m/5j/sf+w//v/XACzAGwAYP9F/83/QACIABkAaADDAPL/Z/9z/4X/9//MAGwBSAF6ANP/V/+L/vr9wf58AA4BEgBy/47/3f91/6D/1gDNAPv/l/8yALUAi//q/tz/AgDD/zQAkQAoAWcBGAAF/+T+Fv+p/wUA2f9fAC8BCwAM/5T/FQCnAK4AogCJAQQB8v+b/9j+SP4g/3gAwQCCAM3/dP98//L+8f7B/wEBygHIAKv/IQBmAGb/Bf9hAHgBRwBH/wkAbAD2/9n/6v/0/34AagA9///+sAAdAeb/Iv8B/wUAjQCi/wEAUwE+AVYATP/7/i4AJgGkAN7/Sf9W/4P/pf52/vT/TwHeAMP/kP+d//H/8f8qAO4AcgBjAMAAyv9X/9L/3f+0/ycAowD7/4v/GgBsAKv/7f5y/0AAfAAQAOD/gQBGAO/+uv5n/7T/WQC1AFwAXgANAJH/vP9n/1D/iwDIAR4BnP+g/6X/bv9b/4H/zgBBAY0AqQBLAFj/ZP/W/ij/XABCAL0AyQCV/37/mf8g/67/FgBSALsAtgDBAGUA9//+/9P/Vv+e/5wAQQEfAY0AZgBLANj+Dv5K/8D/GgDMAEcA3//I/y7/Cv9s/0AANAFQAQcBhAAvAAQAHwBEAH7/p//JABMBagCQ/4f/f/+u/n/+N/9BAPIARgC+/9D/5P84ADUAEwBmAK8A/v++/tv+dAAMAW4Ar//x/44Ap/8f/+H/ZACSAD0AWP8C//H+Tv/e/1oAEwHdAEUAFACM/yP/dP/T/3MAxwBaAAwAif9C//j/hgCqAMAAfADG/yn/fP/Q//P/dgBpAPP/DwApALP/fP8IAHAAYgAyAPf/DQAmAHb/Nv/b/3v/dP8dACsALwAJALD/5f9ZAEAAiQALAcIAEQBI/yz/jf9J/2z/IgB6AH0AKgADAEUAnQDRAJUAZAArAJ//U/9y/9//eQCcAEYAOQD0/2v/E/8v/8X/WwBeANX/q/8rAIMAOwDx/zQARgCV/7f/hgCsADkAj/+6/wcAmP91/+r/jgB5AA0A0P82/y//lP94/7f/fQDQAKEAXQBsAGAAb//F/mj/RQCfAOMA/gBdABD/4f6j/3D/UP/k/8IA+AAbAJb/wv+//9f/AQA8ABQB5gCs/2n/if9D/2f/4v+dAJIAkf+A/zkA8v9g/+3/sADVADYA4f9hAEQAd/+h/yAApv+R/0MATgCr/2n/DACKADsAEgB2AK8AMwC+/+T/wP+A/83/7/8EAFMAZQAfANr/x/8WAGwABADh/0MAJwC0/2z/hv/P//X/CwAzACEA6f/z/1UARgDq/zsAGwDr/xoA9//p/zAATQC4/0b/lf8SABQAUQCbAEIA8f/u/5j/sf8mABwABgDK/+T/GAAkAAsA//8DAAsA+P+6/8D/CwBiAGgAQwD8/6b/wf81ACkAFABEAFwA8/8q/x//jv/e/w4A6v8FAFMA2v9U/8P/aQBbACkAUABRAOH/wf8WAMn/ff/s/0UALwDi//z/SgABAIL/r/80AFgATQA8AOb/wP8KAA4AIQBaAH4AkgA3AAoAbgAvAHL/a/8GAEAA8P/s/xsACQCm/6z/+/+1/8D/WACEAB4Az//k/zsALAD+/3MAVADh/zQAWQD+/8P/uf/X/8T/7P95ALMAbQC//5H/vP+K/3b/3f9lAJEARQD1/4v/6v4e/ywAsQCdAI8AaQD//4f/bP+j/+H/5P8QAJUAYgC5/7L/xP+i/6b/+/9+AMIAeQDw/9H/lP/1/jn/HwBNAG4AywC2ABEAff91/6n/8P82AMEA/wBDAFP///4O/2v/LACZANoAAgFTAM7/vf9I/zD/o/8OAGsAeQApADUAGACb/7H/9/9MALoAsgCcAHUAzv9S/43/zf/A/yYAugChABwA4P/p/5b/Pv+1/5AAugBTAEYAPwABAPT/BQDr/xIAYQAxAPL/vf9U/2v/2v8AAEUAiwB/ADgA1P+9/9H/1f/3/wIAFADR/5P/5P/9//j/DgAcAHAAWwDQ/5T/uv8EALH/bv/N/yQAPAAIAOD/BgAIAPn/AADQ/9D/AwARAB8ANQAiAP7/6P+6/77/EABnAFcA7v+z/8D/uv+W/9r/OwBBAC0A9f/A/7v/s//3/xgA6f8hADoAJwApAAQAy//i/zwAOAD9/wQARgBQAOT/jf+s/w4ANwDw/xwAlQBEAN7/AwApAA8A9/8VAE0AKwDu/y4ATgA6AC8Ayf+F/6H/2f8WABoAGwAgAAYA0f/N/wMA+f/I/9f/EwAaAOz/CABPAC4A5f/y/zgAMAAOAFEAZQDF/5D/8v/3/+b/BQBdAGkAx/9j/53/0f/G/6j/s//I/8L/rP99/5v/6f/0/+L/9P8lAFUAHwCz/87/AQCt/6r/GQA4AAoA1/+4/4f/Vf+N/9v/xv8HAHkAPgC9/6n/FwBDALz/lP8+AKgAPgC1/+X/TADw/2X/tP9HAF8AMAAjADIA+/+d/5n/4P8lAGMAQADo/yYAXQAJANf/3f8NADUA/f/h/z8AiAAyANz/6P/e/9r/+P8oAGYAYAAeAP7/y/+M/8z/GwA5AGEAWwAzAPX/2v/5/+T/tP+9/+b//P8uAGYAZwAvANb/s/+d/43/+v9nAEoA6v+//8v/vv/I/wwAWQCLAHoAKQDX/7//0/+x/4P/wf/x/+H/8f/z/xUAKwC4/4v/3f8KABgAAgDm/+n/1v+8/8z/FwBfAFUAIAD6//n/4/+//8H/0P/U/+j/7v/L/9X/GAA3ABAA9/8fADYAFQD3/wUADwDs/9X/1v/g/xAAMAAmADYARAAWAN3/9P8SAOT/4P8MAEEAVgAlAC0AcABuADMA/P8CACMAFgAHAP3/6f/Q/7v/1f8RAE4AewBiADsAQQAQANH/+P8tACkA/v/5/xAA3f/L//X/+/8MAC8AQwApAAAA+v/c/9L/DwAvACsAEAD0//f/1f+j/6T/wv/q/xAAHwArADQAJQAHAOH/zf/Q/9X/1v/b/9b/xf+t/5r/tP/a//b/HQAyACgAAgDW/8H/rv+t/8z/5f/4/xEAGQAFAPT/CAAXABEABwDw/+3/7P/f/+///v/+//X/BAAmACIAHQAyADEAFgAEAOz/5/8QAA8A/v8WAA4A+/8EACQASQBKADkAMAAjAAwA9v/3/wwADQALAA4ACAAEAPj/9/8kADYAIgAZABEAAAD1//z/BwAEAAcAFQADAOv/9/8SAB0ADgANABcADQD9//n/8v/k/+P/+v8NABIACgAKAA0A6f/Y/wYAHAALAPv//f/6/+T/6v8EAPj/4v/j/+j/6f/5/w0ABwD0/+b/2//Z/+b/CgAkAB0AFQAGAPD/7v/y//n/DQAeABYA9P/m/+v/6//v/wAACgADAPD/6f/u//X///8AAP7//v/7//n///8IAA8AFQAVAAwABwAIAAYA+//9/wkACgAHAAMAAQABAP//+//9//7//v8BAP///f/+///////+////AQAGAA0ACwAKAA0AEwAWAA4AAwADAAgABgD8//X/9//+/wAAAAAHAAkABwD///j/+f/6//z/AAAAAP3/+P/1//j//f8EAAwAEQASAA0ABQAAAP7/+//6//b/+v/8//r/+P/5//3/AQADAAQABAADAAIAAgADAAEA+v/3//j/9//2//z/AQAAAAAAAgD+//z/AQACAP7/+//3//P/8f/0//r/AAAGAAoACgAIAAoACwALAAoACgAIAAQA///8//r/+P/4//3/AAADAAUAAwD///v/9P/z//P/8//3//z//P/8//3//P/8//7/AwAKAA8AEAAOAAoACwAMAA0ACgAMAA0ACgAGAAMAAgACAAAA/f/8/wAAAgAEAAMAAgACAP///f///wEAAQAAAP///f/8//7/AAABAAMABAADAAMAAgAAAAAAAwABAAAABAAEAAIAAgAAAAIAAgACAAEAAAD+/////v/8//z/+v/6//v//v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AAAAEA//8CAP7/BQD7/wsA9/8JAPf/CAD3/woA9/8HAPf/CQD0/w0A8f8HAPz//f8BAP7//f8GAPv/CAD3/wgA+v8HAPv/AgD//wAAAAD/////AQD9/wMA/P8BAP3//v8AAPn/AAD5/wEA+//+//7//v8BAP////8AAAEAAgD+/wMA//8DAP7/AgD//wIAAAACAP3/AwD/////CAD4/woA/f8DAAMAAAAEAAMA/v8DAAAA/v8CAAAAAQADAP//BAACAAQAAwAEAAQAAwADAP//AgD/////AAD//wEA/f8AAAAA/v8EAP3/AwD/////AgD8/wIA/v/+/wEA/v///wAA/v8CAPj/BAD8/wEAAQD8/wcA+P8IAPn/AwD///7//////////f8DAPr/BgD2/wkA+/8AAAIA/f8DAP//AQD+/wUA+f8IAPz/AwD8/wYA+/8HAPv/BQD9/wEA/f8FAPv/BgD6/wMA//8EAP3/BQD6/wMA/v8CAAEAAgD//wAAAwD7/wgA/f8DAP3/BQD9/wgA+/8EAP7/AQAEAPv/BgD7/wQA/v8FAPz/BwD9/wMABAD9/wgA/v8AAAQA/P8CAP////8AAAEA/v8DAP7/AQABAP7/BQD7/wQA+/8EAPv/BQD8/wMAAAD+/wQA/////wIA/v8AAAMA/f8FAPv/AwD8/wMA/v8BAAEA/v8DAAEAAAAEAP7/AgAAAP//AwD9/wEA/P8CAP3//v8AAPv/AwD//wEAAQAAAAIAAQABAAMAAAACAAEA//8FAPz/BAD9/wIA/f8FAP3/AwD+/wMA+/8JAPj/BgD6/wEAAAD+////AAD8/wMA/P8GAPn/AwD6/wQA+f8EAPn/AgD9/wEA+/8DAPn/AAABAPv/BAD9/wIAAAAAAAAAAgD9/wcA+P8KAPf/CQD8/wAABQD8/wUA/f8DAAAA//8DAP3/BwD2/wsA8/8JAP3//f8GAPv/AAAEAPr/BQAAAPz/BgD6/wIAAQD9/wgA+P8IAPv/BAAEAP7/BgD+/wMAAgADAP7/BwD7/wcA/f8EAAAAAQADAPz/AgABAPz/BAD8///////+/wEA/f8AAP3/AgD9/wEA///8/wEA/P8BAP7/AQD8/wMA/f//////AAD//wQA+/8HAPv/BgD7/wQA+/8DAPz/AAAAAPz/AgD8/////v/8//v////5/wUA+P8CAP3//v8BAPz/AgD+/////f8EAPv/AAABAPz/AwD9/wMA/v8DAPz/BQD8/wQA/////wIA/v8CAP7/AQD//wAAAgAAAAAAAgAAAP//BAD+/wEA/v8CAP3/AgD6/wYA+f8JAPf/CAD7/wMA/v8DAP3/AwD//wEAAAADAAIAAgACAAEAAwABAAIAAAABAAQA+/8GAP7/AQAGAPv/AwD///3/BwD4/wgA/f8BAAUA+/8FAPj/CAD0/wcA+v///wEA/f8AAAAA/f8FAPz/BAD+/wMAAAAAAAIA+/8GAPj/BQD5/wMA/P8AAP7//v8BAPz/AAD9////AAD8//3/+/////3//v/+/wAA+/8DAP3//f8EAPr/BQD5/wYA+P8GAPn/BAD+/wEA/v8BAAAA/f8EAP3/AQD//wIA/v8CAAEA//8CAAMAAQD+/wUA+v8DAP7/AAD+/wQA+f8GAPv/AwD//wAAAQADAAEAAAAEAP7/BgD9/wYA//8CAAEAAQACAAMA//8BAAEA/v8AAAIA/f///wEA/v8CAAIA/P8GAPj/BwD5/wMA/v/+/wAA+/8CAP//AAD///7//v8BAP//AQD//wEAAAD//wIA//8DAP////8EAPv/BAD+/wIAAAAEAPz/BQD7/wMAAAD8/wIA+/8DAP///f8EAPr/BAD9/wAAAAD//////v/+//v/AwD3/wQA9v8FAPn/AwD///v/BwD6/wQA/v/7/wcA+f8HAPv/BAAAAAUA/P8IAPr/CgD6/wYA+/8EAPj/AwD9//z/BAD6/wQA/f8AAP7/AAD+/wMA+/8CAPz///8AAAIA/P8EAAAA/f8EAP//AwAAAAEAAwD//wQAAgD+/wUAAAD//wUA/P8IAPr/BgD+/wIAAgD8/wIA/v8BAP7//v/+//3/AgD7/wEA/f8DAPz/AwD9/wEAAQAAAAIA//8AAAIA/P8AAP///v8AAP7/AAACAP3/BAD8/wYA/f8CAPz/AwD//wEAAgD7/wYA+f8FAPv/AgD6/wEA/v/9/wQA+v8FAPr/AgD9/wIAAAD+/wMA/P8GAP3/BQD9/wMA//8AAAQA+/8HAP3/AQAAAP7/AwD+/wIA/v8BAP///////wAAAAD+/wQA+f8EAPz/AAACAP3/BAD7/wQA/f8DAAAAAwADAP//AwD8/wQA/v8AAAQA/P8FAAEAAQADAAAA/P8KAPr/BQD//wEAAQD+/wIA/f8EAPf/CQD3/wYA+f8DAP//AQD/////BQD+/wIA/v8DAP7/AwD8/wUA+v8IAPj/CQD4/wMA/v/+/wMA+/8DAP7///8CAPz/AgABAP3/BQD9/wEAAgD9/wMA/P8CAPz/AwD5/wUA+f8FAPz/AgD+////AwD9/wMAAQD//wUA+/8HAP3/AgAEAP3/BAD9/wIA///9/wQA/P8CAPz/BQD9/wMA/v8EAPz/CQD4/wgA/P8DAAAA/v8CAP7/BQD6/wgA+f8HAP3/AQACAPz/AQD7/wIA/f8BAAAA/P8GAPz/AwABAPz/BAAAAP//AwD9/wIAAQAAAAQA/v8EAP//AQAAAAEAAAD+/wIA+/8EAP////8CAP////8FAPv/BwD4/wUA+/8EAPz/AwD7/wYA+f8DAP3/AAACAPz/AAACAP7/AgADAP3/BQD+/wMAAgD//wQA/f8DAP7/AQACAPr/BgD5/wMA/f8CAP3/AgD8/wIA//8AAAIA+/8HAPv/BgD8/wQA/v8DAAAAAQABAP7/BAD6/wcA+f8GAP7//f8GAPr/BAD+/wAAAgD8/wQA+v8GAPn/AwD+//7/AwD5/wMA/f8CAPn/AwD7/wEA/v////v/AQD8/wEAAQD8/wMA/f8AAP//AwD9/wQA/v8DAP//AQADAP3/BwD5/wcA+/8GAPz/BAD8/wMA/f8EAPz/BQD7/wYA+v8EAPr/BQD9/wMA/f/9/wEA/v8EAPr/BAD6/wYA+v8DAP//AAABAP//AgD//wQA/f8BAAUA+P8KAPj/BwD7////AQD9/wUA+/8CAP3/AgD//wIA+/8FAPr/BAD+/wAAAgD9/wUA/P8EAPz/AQABAP3/AgD///7/AAACAPv/BQD+/wIAAAAAAAAAAQAEAPz/BwD8/wIABAD6/wcA+f8AAAEA/f8CAAAA/v8BAP3/AwD//wQA/f8DAAAA/v8FAPn/BQD6/wMA/f8BAP3/AQD/////AAD8/wAA/v/+/wAA//8AAP///v8BAAAA/v8BAP//AAD+/wEA/f8DAPr/AwD8/wEA/f8BAPz/AgAAAP3/BQD6/wUAAAD+/wkA+P8IAPz/BAAAAAAAAQAAAP//AQABAAMA/P8CAAEA/v8IAPr/BgD6/wYA+/8JAPj/CgD4/wUA/f8AAAMA/P8FAPz/AgAAAAAA//8AAP7/AQAAAP7/AgD//wEAAgABAPz/BwD7/wYA/f8EAP////8DAPv/CQD3/wgA+P8DAP//AAAAAAMA/P8BAAEA/f8FAP3/AQD9/wMA/P8DAP///f8HAPn/BwD7/wcA/v8CAP//AAD+/wQA/P8CAAAA//8EAP7/BAD9/wEAAgD7/wQA/P8CAP3/AAD8/wEA/P8CAP3//////wEA/v8AAP////8AAP7/AwD8/wQA/f8BAAEA/P8EAP3/AgD7/wMA/P8EAP3/AAAAAP7/BAD+/wIA/f8FAPz/AgACAAAABQD6/wYA+/8HAPr/BgD7/wQA/v/+/wIA+/8FAPr/BQD9/wUA/f8AAAQA/v8EAAIA/f8IAPz/BQAAAP//BAD+/wQA//8BAP//AgAAAAUA/P8DAP7/BAD+/wMA/f///wQA+P8IAPb/BgD5/wYA+f8DAP3///8AAP3//v8AAP//AAD9/wUA+f8IAPr/AwAAAP//AgAAAAAAAgAAAP////8BAAEAAgD9/wIA/////wIA+v8FAPz/AgD+/wIA/f8FAPj/BgD4/wUA/f////7/AAD9/wAA/f8BAP////8DAPr/BQD9/wAAAgD9/wAABAD8/wYA+/8IAPr/CAD4/wgA+v8EAP///v8DAP7//v8DAPz/BAD6/wUA+/8EAP7/AAACAP//AwD+/wIA+v8IAPr/CAD5/wUA+v8GAPv/BAD8/wUA//8CAAQA/f8EAAQA/P8EAAEAAwACAAIAAgABAAQAAAACAAQA/f8GAP7//v8DAPv/BAD9/wIA/f8DAPr/AwD9/wIA/f8BAP7/AAD+/wAA/v8CAPn/BAD7/wQA/P8BAPz/BgD7/wMA/P8CAP7///8AAP///P8CAPj/BgD6/wIA+/8BAP7//v8AAP///f8CAPr/AAD9////AAD9/wEA/v///wIA+/8JAPf/BgD+/wAABgD3/woA+f8IAP7///8GAPz/AAACAPv/BAD+/wIAAAABAAEAAwACAAMAAAAEAAEAAwABAAEAAwD9/wIA/f8DAP//AgD//wAAAgD+/wUA/P8CAAMA/P8KAPf/BgD9/wAAAQACAPr/CAD4/wQA///+/wEA/f8DAPz/AgD8/wQA/f8CAP////8FAP3/BAD//wIA//8BAAIA//8BAAAAAQD//wIA/f8DAP7/AQD///////8AAP7/BQD4/wcA+P8FAPz///8CAPz/AQD+//z/BAD4/wYA+P8EAPv/AgD7/wQA+v8EAPz/AgD4/wcA9f8JAPj/AwD6/wQA+v8EAPz///8BAPv/AwD7/wIA/v8BAAEA/f8DAP7/AwAAAP//BAAAAAEABAD//wIAAgACAP7/BAD7/wcA/P8FAP7/BAD//wIAAgAAAAIAAQABAAEABAD+/wcA/f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AAAAAAAgD7/wQA+v8EAP7/AgD9/wUA/f8DAP////8CAAEAAgAAAAAAAAD//wEAAgD9/wIA/v8CAP//AgABAAMAAAABAAMAAwAAAAMAAAACAAMAAAAAAAUA/v8CAP//BQD8/wUA/v///wQA/v8DAAAAAgD//wEAAgD//wUA/v8EAP3/AgABAAEA//8CAP3/BQD6/wMA+v8CAPz/AgD8//7/AQD+/wAA/////wIA/f8FAPr/AgD+//z/AwD5/wQA/f///wEA/P8CAPz/AgD8/wIA/f/7/wQA+f8CAP3//////wAA/v8DAAEAAAADAP7/AgABAP////8EAPz/BAD9/wEA//8DAP//BQD9/wQA//8GAP////8EAP//AgD//wMA/P8FAPv/BgD7/wQA/f8EAP//AgD8/wMA/P8EAPz/BAD9/wEABAD+/wMAAAABAAEAAAABAP7/AwD9/wQA/P8CAAAAAAD//wMA/v8CAP//AAD9/wYA+f8FAPv///8DAPv/AgD8/wAA//8BAPz/AwD8/wMA/f8DAP3/BwD8/wMAAgAAAAQAAgD//wIABAD7/wUA/P8AAP7/AgD9/wEA////////AQD7/wUA/P8BAP////8EAP3/BAD/////AgD//wMA+/8DAPr///8AAPv/AgD7/////v/+/wAA/v8BAPz/AwD8/wAA/v/9/wIA+/8DAP3/AAAAAP3/AQD//wQA+v8EAP3/AAACAP3/AgD9//7/AgD+/wAAAAD+/wEABAD7/wYA/P8BAAMA///9/wYA+P8JAPj/BQD///7/CAD6/wYAAAAAAAMA///+/wMAAAD//wQA+P8HAPz/AwD9/wAAAgD8/wQA+v8DAP3///////v/BAD5/wMA/P8BAAAA///9/wMA+v8EAP3//v8BAPz/AgD7/wMA+/8BAP7/AwD8/wUA+v8GAP3/AwABAP//AgACAP7/AgAAAP3/AgD8/wEA/////wAA/v///wAAAAABAAAAAQABAPz/AgABAAAAAAACAPv/BAD///3/BgD//wEAAQD+/wMA/P8BAAAA/f8EAP3//v8CAP7///8DAPz/AAD///7/AAAAAP3//f8BAPj/BAD4/wQA+P8CAP3///8AAAAA//8AAP7/AQD+/wAA///9/wUA+f8FAP3/AwAAAAEAAAADAP//AwD//wAAAQD+/wUA/P8DAP////8GAPv/AwD9/wEA/v8CAP7/AgD+////AQD//wIA/P8FAPj/CgD1/w0A9v8JAPz/AgABAAEAAQD//wIA/f8FAPn/BAD6/wEA///+//7//v8BAP3/AAD9/wAA/v8FAPn/BAD8/wIAAAABAP//AgD7/wIA/v8AAAMA+P8HAPv/AgAAAP3/AwD9/wEA//8AAP//AgABAAAAAgD//wIA//////7/AwD8/wIA/v///wEA+/8EAPj/BAD+//////////7/AAD9/wAA+v8CAP3//v8AAPz/AQD8///////7/wIA+/8CAPn/AgD5/wEA+/8EAPr/BAD9////BQD3/wYA+/8HAPr/BAD8/wEABAD6/wcA+/8AAAUA+f8HAPz/AgAAAAAAAgD9/wMA/P8CAP7/AAACAPv/BQD9////AgD8/wIA///8/wMA/P///wEA+v8FAPv/AQD///7/AwD+/wAAAgD8/wQA/f8BAAIA/v8BAP//AAABAPv/AwD8/wQAAAD6/wEA/P8CAP7///////7/AQD+//z/BAD5/wYA+v8FAPr/AwD//wAAAQD//wAAAQD8/wUA+v8HAP3/AAACAPv/BAD9/wEA+v8EAPn/BQD9/wAAAgD8/wAA//8CAP3/AwD8/wAA/v8AAPv/BAD6/wMA/P8BAP3////9/wEA/v8AAP3/AAD9/wAA+/8EAPz/AwD8/wQA/v8EAP//AQAEAP7/BQD7/wUA//8BAAEAAAABAP//BAD8/wAAAQD9/wEABQD3/wkA+v8EAAAABQD6/wcA+/8FAP3/BAD8/wIA/v8BAAIA+v8FAPz/BAD/////AgAAAAMA/f8DAP//AQD//wEA//8CAP7/AwD9/wQA/P8EAP//AgD//wMA/P8EAPz/BQD9/wEAAAD+/wEA//8AAP7/AgD8/wMA/v///wAAAQD9/wQA+/8BAPz/AgD9/wEA/f8BAAAAAwD+/wQA+/8GAPv/BQD8/wQAAAAAAAMA//8DAP7/AgAAAAMA//8DAP//AgD9/wQA/v8CAP//AAAEAP//AwD//wQA//8EAAAAAwAEAAAAAgD//wMAAAAEAAEAAQD//wIA/v8FAP7/AgABAAEAAQABAAIA//8BAAEAAQABAP7//f8EAPz/BAD9/wAA/v8DAPr/BQD6/wUA/f8DAP3/BAD8////AgD6/wUA/f8AAP//AgD6/wgA+v8FAP3/AgABAAAABgD8/wgA/f8GAAAAAgADAAIAAwAAAAQA/v8FAAEABQAAAAQA//8GAP7/BQD+/wQAAgD//wYA/v8DAAEABQD9/wcA+f8JAPr/BgD+/wIAAQD7/wgA9/8IAPj/BwD8/wQA+f8FAPn/CQD6/wYA+v8HAPr/BwD4/wYA9/8HAPj/BQD7/wAAAAADAPv/BwD9/wUAAQABAAQAAAAAAAIA//8DAAEA/v8FAAAABAAAAAMAAQAAAAUA/P8IAPr/CQD5/wgA//8BAAQA/v8HAP//CQD6/wkA/f8DAP7/AAABAP3/AgD9/wEA/f8CAP//AAAAAP7/AQD8/wkA9/8IAPj/BAAAAPz/BAD3/wYA+/8CAAAA/f8AAAMA/f8DAP3/BAD8/wUA//8CAAUAAQADAAMAAwAEAAAABwD9/wcAAgABAAgA//8IAPv/CQD+/wIABwD8/wcA//8BAAYA/P8HAPz/BgD//wEA//8DAP//AwD+/wMA//8AAAAA/P8HAPj/BQD7/wIAAwD7/wYA+P8HAPz/BAD//wMA/v8EAPz/AwD9/wIAAAAAAP//AQD7/wUA+v8EAPn/BQD7/wAAAQD7/wQA/f8DAP3/AgD+/wIA/f8BAP3/AwD8/wUA+/8FAP7/AAAEAP3/AwD9/wUA/P8EAAAAAAACAAAAAgACAAIAAgACAAIA//8BAAMA+v8IAPz//v8BAPv/AgABAPv//v/+/wAA//////z/AwD7/wEA/v/8/wYA/P8CAP7/AQAEAPz/BwD7/wQAAAD+/wcA+/8HAP7//f/7/wAA+f8FAPv/AQACAPz/AwD9/wIA+/8BAPr/AgD5/wIA/f8DAP//AQACAP7/BAD//wIAAQACAAIA/f8GAPn/CgD3/wkA9v8JAPv/AwD+/wMA//8FAPz/BgAAAAEAAwD//wQA//8FAP3/AgADAPz/CAD//wEAAgADAP3/CQD3/wkA+P8HAPv/BAD+/wEAAAD+/wIA/P8BAP//+/8CAPj/AQD7/wAA///+//z/BAD7/wAAAAD7/wUA+////wIAAAABAAAA//8DAP//BQD///7/BQD6/wUA+v8GAPr/AwD9/wAAAQD8//////////7/AAD9/wUA/f8EAP7/AAABAAEA/v8DAPz/AwD+/wAA/f8EAPr/BwD5/wAA///+/////P/7//3//v/+//z/AgD3/wcA+f8FAP3/AgAAAAEAAAAAAAIAAAACAP3/BAD9/wUA/P8CAAEA/v8HAPr/CAD9/wUAAQADAAEABAD8/wgA/P8CAP////8CAAUA/P8IAPz/CAAAAAcAAgADAAQA/P8GAPv/AwD9/wEA///+/wEA/v///wQA/P8GAPr/BQD9////AQD9/wAA//8AAP7/AAD+/wAA/P8AAAAA/f8DAP3/AwD//wAAAQD7/wcA9v8FAP3//f8BAP///f8DAPz/AAAFAPj/CAD6/wQA//8AAAEAAQD9/wYA+/8FAPn/CgD3/woA+v8EAP////8AAAAAAAACAP3/AgD//wEAAgD//wEA/f8CAAEA/v8IAPn/BgD9/wAAAwABAAIA/f8DAAAAAgADAP3/AwD//wEAAQABAP//AAADAAAAAgACAAAAAgACAAIAAwABAAAAAQABAP7/AQD+/wEA//8DAPz/BAD9/wMA//8CAP7/AQD//wEA/f8EAPz/BAD+/wEAAQABAP7/AgD+/wEAAQD//wMA/f8AAAAAAQD+/wMA/v///wUA/v8EAAAAAQD+/wUA/P8CAP//AAD+///////+//7////+/wMA//8CAP//AwAAAAIAAwD+/wQAAAD//wYA+/8CAAEA/f8CAAIA/v8BAAIA/P8FAP//AQAAAP3/AQD7/wgA9f8JAPb/BgD6/wgA9/8FAPr/AQD+/wAA+f8EAPr/BAD8/////P//////AQD///7/BgD5/wcA/P8DAP//AgD+/wIAAAADAP3/AgAAAAIAAAACAP3/BQD8/wUA/f8EAPz/AwD8/wIAAAD//wAAAgD7/wYA+/8DAAAA/v8CAP7/AgD8/wUA/v8DAP3/AwABAAMAAQADAP7/CAD7/wcA//8DAAEAAQABAAIAAQABAAEA/v8CAP3/BAD8/wEA/P8AAP7/AwD5/wUA+f8FAPr/BAD7/wIA+/8AAP7/AQD9/wEA/v8BAPz/AwD7/wUA/f8BAAIAAgD8/wcA+P8FAPz//v8CAP3/AAD9/wAA/P////3/+v/+//3//f8CAPr/AAD///7/AAD9/wQA+P8FAPz/AAD///3/AwD+/wEA/v8DAP3/BAD+/wEAAgD/////AwD+/wIA/P8FAPv/BQD+/wEAAgD//wIA//8CAAAAAAD+/wMA+/8CAP3/AQACAPz/BgD5/wgA+v8CAAIA/f8EAPz/BQD8/wUABAD8/wkA/P8EAAIAAQABAAAAAwD8/wYA/v8CAAMA/f8DAP7/AQAAAAEA/v8FAP7/AwAAAP///v8CAPn/AgAAAPr/BQD6/wIA/v8BAP//BAD8/wQA/v8DAAAA/v8CAP7/AAD///z/AAD//wEA+v8DAPz/AAD///z/AAABAPn/AgD3/wEA+/8AAPz/AwD6/wIA/P8BAP///v8EAPf/CQD3/wUA+f8EAP//AQD8/wUA+f8FAP3/AgD+/wEA//8CAP3/BgD7/wUAAgD//wIAAQD+/wAA//8AAAAAAQD+////AAAAAAEAAQD//wUA+/8KAPj/DAD4/wgA//8CAAMA/v8EAAAABAD9/wUA/P8CAP3/BgD3/wYA+v8EAAAA//8BAAIA+/8DAP3///8CAPr/AgD8/wAAAgD7/wUA+f8CAP//AAD//wMA+/8EAP7/AQAEAPv/BQD7/wcA+/8DAP3/BAAAAAMA/v8BAP//AwD8/wAA///+/wEA/v8CAPz/BAD4/wgA+f8GAPj/BQD6/wEA+v8BAPv////8/////f8CAPz/AQAAAAAAAQD8/wIA/v8CAP//AQAAAAMAAwD9/wcA+/8IAP7/AAAEAPn/AAAAAPz/AQD+//z/BQD8/wEA/f8BAP7/AwD6/wQA+v8CAP//AAD//wIAAQD9/wUA/f8DAAIA/v8IAPv/BQABAP//BQABAPz/CQD5/wgA/f8AAAUA/f8EAPv/AwD9/wIA/P8BAPv/AAD///z/AgD8/wUA+f8FAPr/BgD7/wgA+f8GAPv/BQD8/wAA///9/wEA/v8BAAAAAAABAP7/BgD6/wUA/f///wMA/P8HAPj/CAD4/wUA+v8DAPv/AAD+//7/AgD//////v8AAP//AQD//wIA/f8BAAMA/v8GAPv/AwAAAAEAAQD//wEAAgD/////AgD9/wQA+/8FAPz/AgD+//7/AQAAAAAA/f8EAPr/AgD+/wAAAQADAPn/BgD7/wYA/f8GAP7/BAAAAP//AgD8/wYA/P8HAPv/BgD//wYA/P8CAAIAAQD//wUA+/8GAPv/BAD8/wQA9v8LAPb/BwD4/wMA/v8CAP7/AgAAAAIA//8AAAQA+/8EAP7/AAADAP7/AQAAAAEA/P8DAPv/AQACAPz/BgD3/wUA/P8CAAIA/P8GAPz/AwD+/wIA//8AAP///f8DAPr/BAD5/wYA+P8JAPb/BwD8/wEAAgD+/wUA/f8FAPz/BgD7/wgA/f8AAAQA+v8GAPj/BQD+/wEA/P8GAPz/AgABAAAAAQADAP7/AgAAAAAAAwD5/wgA+v8EAAEA/f8EAP//AAABAAIA+/8CAPv/AQD///3/BAD8/wQA/P8EAP7/AQACAP//AAADAPr/BwD8/wQAAgD+/wMAAAAAAAIAAAD///7/AQD8/wUA/f8AAAIA/P8EAAEA/v8EAPr/BQD5/wgA+P8GAPv/AgD9/wEA/v8CAP7//f8CAP//AgD//wIAAAACAAEAAQACAAEAAAABAAEA/v8CAAAA/f8CAP7//f8CAAAA/f8EAPj/CAD4/wgA/P///wQA/v8CAAIA/v8DAAEAAAABAAEAAAAAAAAAAAAAAAEA//8AAAEA//8CAP3/AgAAAP7/AgD+////AAD+/wAA//////7//////////f8BAPz/AAD8/wEA/v/8/wAA/v8BAP//AAD9/wIA/f8FAPz/AwAAAAEAAgD+/wQA/f8GAPz/AwAAAP//AwAAAP3/BAD8/wMA//8CAP7/AgD+/wAA/f8DAP3/BgD4/wIA/P8DAP///v8BAPz/BgD4/wcA+P8IAPr/BQD+/wIAAQAAAAAAAgD+/wYA+v8HAPf/BAD9/wEAAAAAAP3/AgD9/wIAAAD//wAA/v8CAP7/AgAAAP7/BQD8/wQA/P8AAAQA+f8HAPj/BQD6/wYA+v8DAAEA//8CAP//AAAEAP7/AwAAAAAABAD/////AgD7/wIAAAD8/wMAAAD7/wgA9f8JAPz/AwD//wIA//8BAAEA/f8AAP7/AQD//wAA/v8AAP7/AQD///3/AAD9//7/AQD9/wEA///9/wMA/f8BAP3/BAD5/wYA+f8GAPr/AQD9/wEA/f8DAPj/BwD3/wkA9/8IAPn/BgAAAP//BgD6/wgA/P8FAP3/BAD9/wQA+/8FAP//AQAAAP//AQACAAEAAAADAPz/AwAAAAEAAgABAP7/AgD9/wMA//8AAAEA/v8DAP//AAD//wEA/P8EAPv/AwD//wAAAQABAAIA/v8DAP3/BgD8/wYA/P8DAP7/AgAAAP//AQD+/wAAAAD//wIA/v8CAPv/BgD7/wQA////////AQD+/wAAAwD7/wMAAAABAAEA//8GAPn/CQD5/wEAAQABAP3/BQD7/wUA//8EAPz/BQD7/wMA/P8DAP3///8AAPz/AAD+//3/BAD4/wUA/P8CAP///P8DAP3/AgD///////8EAPz/AwD8/wMA/v8BAP3/AQD9/wQA+/8EAP7//v8EAP7/AgD//wEAAgD8/wcA/f8FAP7/AQAAAAEAAAAAAAIA/f8DAPr/BgD3/wgA+/8BAAIAAAD//wMAAAAAAAYA+f8KAP3/BAABAP//AgADAP7/AwAAAAEA/v8GAPz/BwD5/wYA/v8DAAEA/v8BAP3/BAD5/wkA9P8HAPn/AwD///7/AAD9////AAD8/wQA+P8HAPf/CgD3/wYA/f8CAP//AQD//wEAAwD9/wMA///9/wQA/f8FAP3/AQAAAP3/BAD7/wIAAAD9/wMA//8AAAIA+v8EAPv/AgABAPn/BgD2/wcA9/8EAPz/AgD//wAA/v8CAP//AAABAPz/BAD//wEAAAACAAEAAgD+/wMA/v8DAP7/AAAAAAIA/P8DAP3/AAABAP7/AQD+/wMA/f8EAP3/BAD+/wQA+v8EAP3/BgD8/wMA+/8FAP3/AQD//wEAAAADAAAABAAAAP//BgD9/wUA/v8FAAAAAwAEAP3/BwD8/wkA/P8FAP//AAABAP7/AQD+/wEAAQD8/wMA/P8BAP7/AQD+/wAAAAD8/wMA+/8DAPv/AgD8/wEA//8BAP3/AAACAP7/AQD+/wAAAAD9/wMA+////////P8AAAMA+P8EAPn/BAD7/wEAAAD7/wQA+f8BAP3//v8AAP///f8EAPn/BgD7/wUA/f8BAAEA//8EAP3/AwD//wMAAAD//wcA+f8FAPv/AgD//wEAAAD//wYA+v8LAPv/BwD//wMAAgACAAIAAgAAAAIA+f8JAPn/BgD8/wMA/v8EAPz/BQD9/wEAAgAAAAQA/f8CAP3/AwAAAP7/AgD+/wAAAQD///////8AAP7/AgD8/wMA/P8FAPr/BgD8/wUA//8BAAEAAQD//wQA/f8EAPz/BgD8/wIA//8AAAIA///+/wIA/v8AAP7/AAABAAAA///+////AQD9/wIA/P8DAPv/AAD+//7/AgD7/wIA+/8BAP7/AAD+/wAA/v///wAA+/8DAPz/AAD+/wAA/f8CAPz/AAAAAP///v/9/wMA+/8EAPz/AgABAP3/BgD4/woA+f8HAP//AQACAAEAAQAEAP//AAABAAAAAgACAP3/BwD8/wcA+/8FAAAAAQAEAPz/BwD+/wMABAD//wYAAAAFAAAABgD+/wcAAgABAAYAAAACAAUA/v8FAP//BAD+/wEAAgAAAAIA//8CAP3/BgD3/wYA/P8DAP7///8CAP////8BAP3/AQAAAP//AQAAAP3/AgD9////AAD/////AwD7/wEA//////7/AgD9/wMA/P8AAAEA/f8BAPr/AwD5/wQA+f8CAPv/AwD9////AgD//wAAAwD9/wIAAgD//wEABAD+/wYA/v8DAAEAAwAAAAcA/f8EAP//AwACAAMA//8DAAEAAAAEAP7/BwD7/wgA+v8EAP7///8EAP3/AAAAAPv/AwD8////AQD+//z/AgD5/wcA+v8EAP///v8BAAIA/P8HAPn/BgD9/wMAAQADAAAA//8EAP7/AgACAP//BgD//wIAAwD8/woA+P8JAPz/BwD9/wUAAgAAAAQA//8AAAcA+v8JAPz/BgAAAAIAAgD9/wgA9/8KAPn/BwD+/wMA/v8FAP3/BwD9/wMABAD8/wcA/P8DAP//AQAAAAEA///+/wMA/v8CAP7/AgD7/wYA/P8EAP3/AgD//wMA/f8DAP7/AAACAPv/BAD8/wIAAgD8/wUA+v8HAPf/CAD5/wQA+v8DAPr/BQD7/wUA+/8HAPz/BAABAP3/BwD7/wQAAQD7/wkA9/8HAPn/AwD+/wEA///+/wEAAwD+/wAAAgD7/wYA+v8FAP3/AQD//wAA/v8CAP7/AwD//wIAAQABAAMA+v8IAPv/BAACAPn/BQD6/wUA/f8CAAAAAgD//wUA+v8FAP3/AQAEAPz/BQD6/wYA+/8CAP7///8AAP7/AgD+/wIA//8AAAAAAQACAP//BQD+/wQA/v8DAAIA//8DAP//AAACAP//BQD7/wIA//8AAAIA//8CAP//AQAAAP7/BQD5/wgA+/8CAP3/AwAAAP//AQD8/wUA+v8HAPr/AwD+/wAAAAD+/wQA+v8GAPz/AQADAPr/BgD8/wcA+/8BAAEA/v8CAP7//v8CAPv/BAD6/wQA+f8EAP7//v8AAPv/AwD9////AAABAP3/BQD8/wQA/P8BAAEA/P8EAPn/AwD9///////9/wIA/P8DAPv/BQD9/wMA/////wIA/f8AAAAA//8CAP7/AAD+////AgABAAQA/f8EAAEAAwABAP3/AwD9/wUA//8CAP7/AgD5/wcA+v8CAAEA/v8CAAAAAAABAAEA/f8DAP//AQD/////AwD9/wEAAAD+/wEAAgAAAAAAAAD//wEAAQD9/wMA/P8CAAAAAAD//////////wEA/f8CAP7/AwD8/wIAAQABAAQA/P8FAPr/CQD3/wkA9/8IAPv/BAD+/wMA/v8FAPr/BwD8/wIAAAACAPv/BwD8/wUA+/8CAPr/BQD6/wEA/P///////v8AAP///v8AAP3///////7/AAD8//7/AAD+/wAA/P8CAP///v8AAAAA+/8EAPf/BwD3/wQA+/8BAP////8AAP3/BQAAAAQAAAABAAIAAAAEAP//AwACAP3/BQD7/wUA/v8CAAAAAgD+/wQA/P8GAPv/BgD7/wUA/P8FAP////8CAPz/AwD7/wEA///9/wIA/f///wIA/P8EAP7/AwD6/wYA+P8GAPv/BAD7/wMA/f///wEA/v///wAA/v////3/AQD9//3/BAD6/wQA/v8CAP//BQD//wQAAgD//wYA/P8FAAAABAD//wIA//8DAP7/AQD//wMA//8DAP7/BAD+/wAAAwD8/wYA/P8BAAEA+/8JAPT/DAD3/wYA/P8AAP////8AAP7/AwD+//3/BQD2/wsA9f8GAPv/AgD9/wEAAQD4/wYA+f8CAP///v////7////9//3////+/wIA/f8CAP7/AQD+/wYA/P8IAPr/CAD//wEAAwD9/wcA/P8GAP7/BAABAAMAAAACAAMA//8EAAEA/v8DAAEABAAAAAIA/v8AAAMA/v8CAP3///8CAPz/BQD7/wEAAQD9/wEA/////wMA+/8DAPr/BgD7/wMA/f///wAA///+//7/AAD7/wIA///9/wIA+/8FAPz/AgD8/wIA/P8AAAIA/P8GAPj/BQD9/wMA/f8CAP//AgD9/wMA/f8FAP//AAACAP//BgD9/wkA/P8GAP7/BQD//wUA/v8GAAEAAgAEAP//AQACAP7/BAD+/wMA/f8EAP//AQADAAAAAwAAAAMA//8CAAIA/P8GAPn/CAD4/wcA+v8CAAEA/f8CAP7/AAD+/wEA/f////7/AAABAAAA/f8AAP7//////wEA/f8BAAAA/P8CAPv///////z/AgD9////AAD//wMA+/8DAP//AAAFAPv/AgACAP3/AgAAAP7//v8CAP3/AwD8////AgAAAP////8BAAEA//8BAP//AAAEAPz/AQD///v/BQD6/wQA///8/wMA/f8CAAAA//8CAP3/BgD7/wUA+/8GAPz/BgD8/wUAAwAAAAUA/v8DAP//BAD//wQA//8DAP7/BwD4/woA+f8HAPn/BgD4/wcA+P8GAPr/AQD///7/AwD9////AwD7/wEA/v///wIA///8/wMA/f8CAAAA/f8BAAAA/v////3/BAD4/wcA9f8FAPz///8BAPr/AwD5/wUA9/8CAP3//v8BAP///f8DAPn/AQD9///////+//7//v8BAPn/BQD7/wEA/////wAA////////AQD9/wQAAAD//wUA+v8IAPz/BAD+/wEAAgABAP3/BgD8/wMAAgD8/wQA+v8IAPj/CQD5/wQA/P8EAP3/AQACAP//AAABAP////8CAPz//v8EAPr/BwD+/wEA//8FAP3/BwD6/wkA+v8HAPz/AwACAP//AwD/////AwD9/wEAAQD8/wIA/f8BAP//AwD7/wMA+/8AAAIA+/8DAP3/AAAAAAEA/v///////f8CAPv/AwD+//////8AAP7/AgD///7/AAD/////AwD7/wMA/v/+/wEAAAD9/wQA+f/+/wEA/f/9/wEA+P8DAPv/AgD6/wMA+P8DAP3/AgD//wMA/f8BAAEA/v8CAAAA/P8EAPv/BAD+//7/BAD9/wMA/v///wAAAAD+////AwD6/wYA+v8CAAIA//8AAAIA/f8CAP7/AAABAP//AAAAAAEA/v8AAAIA//8FAPz/AQAFAPz/BQAAAP7/BwD5/wkA/f8DAAEAAgABAP3/AwD8/wMA/v///wMA+/8DAP3/AwD//wAAAQABAAAAAQD//wAAAgAAAAAAAQD//wIAAAADAPr/BgD7/wIAAAD9/wMA/P8DAP3/BAD8////AwD8/wQA/P///wAAAAD+//z/AQD4/wUA+//9/wUA9v8JAPf/CAD4/wYA/v8BAAIA/P8CAP7/AgD+/wEA//////7////+/wAA/P8FAPf/BwD4/wQA/f8AAP7/AgD//wEA/v/9/wMA/v8FAPr/AwD8/wEA/v8CAP3///8CAPr/BgD8/wQA/////wUA/v8GAPv/BAADAPv/CQD5/wYA//8AAAEAAAD//wAAAAAAAP7/BAD9////BgD5/wkA/P8CAAIAAQABAP////8AAAAAAAD//////v8CAP3/AgD+//7/AgD9/wQA/v8DAAQA//8CAP//AwAAAAMAAwD+/wYA/P8GAP3/BQD+//7/BgD7/wYA/f8DAP3/BAD/////BgD6/wYA/f8AAAAA/v8BAPz/AAD3/wQA+////////f8AAP//+/8EAP3/AAADAPz/BQD9/wQA/f8EAP7/AAADAP7/AQAAAP//AAADAPr/BAD4/wEA/v/9/wAA/v////7/AgD9/wIAAgD7/wgA+v8IAPz/AQADAP7/AQACAPz/AwD+/wYA+v8JAPj/BgD//wMAAAAEAAEAAwACAAUAAAACAAEA/v8HAPv/AwD+//3/AQD/////AwD6/wUA+v8FAP7/AQADAP//BQD//wEAAwAAAAMAAAADAAAAAgACAPz/BAD//wAAAgD9/wMA/v8CAAUA/P8GAP7/AQD//wIA//8BAAIA/P8EAP3/BQD8/wAAAQD8/wMA/P///wUA/P8EAP7/AwAAAAIA//8DAAIAAwD//wQA/v8EAP7/AwD9/wUA/v8GAP//AAADAAEAAQACAP//AwABAP//AAADAPz/AgD9/wIA/f8BAAAA/f8EAPv/BQD8/wQA/v8AAAUA+/8FAPz/AgD//wEAAgD9/wYA+v8FAP3/AgABAPz/AwD9/wAA///+/wIA/v/+/wEA/v8EAPz/AwD7/wIA//8AAAIAAQAAAP7/BAD9/wUA/v8CAAAAAgD//wMA/f8EAP7/BAABAP//AwD//wIAAwACAAIAAQAEAP7/BQD///7/BgD7/wUA/P8DAP//AgD9/wIAAAAAAAEA//8DAP//AAACAP//BQD8/wQAAgD+/wMA/v8BAP//AwD+//7/AAD+/wAA/v8AAAAA/v8CAP////8EAPv/BgD8/wUA+v8HAPz/AwAAAP//AwD//wAABAD8/wQA/P8HAPv/BwD+/wEAAwABAAMAAgACAAMAAwAAAAUA/v8HAP3/AwABAAQA//8FAP3/AwD9/wEA//8BAP7/AQAAAP///////wIA/P8BAP///v8DAP3/AQD9/wIA/f8CAPn/BQD6/wUA/P8BAP7///8CAPz/BQD6/wUA//8CAP//BQD7/wYA+v8DAPz/AQD+/////v///////v8BAP3/BAD7/wQA/P8FAPz/BQD8/wcA+/8IAPr/CAD8/wEAAwD//wUA/v8FAPv/BwD7/wUA//8FAPv/CwD2/w0A+f8GAAAA/v8FAPz/BQD6/wYA+v8HAPr/AwD5/wYA9/8FAPn/AgD9//7/BAD6/wUA/P8EAPz/BAD6/wYA+P8GAPr/AwABAP7/BAD9/wEAAAD9/wYA+f8GAPz/AgD//wIA/v8BAAAAAQD//wIA/v8AAAAA///9/wMA+f8FAPv/AgD+////AQD+/wMA+/8EAP3/BAD8/wIA/v8AAAIA+v8CAP3/AQD//////P8BAAEA/P8CAP3/AAAAAAAA/f8BAPz/AQD+/wAA/v/9/wAA/P8BAAAA/P8BAPz//v////7//v/+//r/AwD6/wUA/P8DAP7/AQD+/wAAAAD//wMA+/8FAPz/AgD+//7/BAD9/wIA//8AAAIA/v8BAPn/AgD8/wYA/P8FAP//AQAAAAAA/P8AAP7/9/8FAPX/AwD7/wMAAAAAAAMA+f8KAPv/BwD9/wEAAwD//wEA///+/wAA/v/+//7//f////v/AwD4/wcA+P8GAPv/AAAAAPv/BAD8/wUA+/8EAPv/BAD+/wMA/v8DAAAAAAAAAP///v8BAAAA/v8DAPv/BgD6/wQA+/8DAP3//P8BAPf/AwD5//3//f/7/wAA+f/+//7/+/8CAPr/AwD3/wcA+/8FAP3/AgD//wUA/P8KAPr/BgD9/wQA/v8CAP//AgD4/wcA+f8DAPf//f/9//3/+/8AAPv/AgAAAP////8BAP//BAD//wAA///9/wMA/f////7//v8AAAAA/v///////P8AAPj///////n/BgD3/wAAAgD6/wYA+/8GAP7/AgD//wIAAAAAAAEA/v8BAAIA///+/wQA/v8BAAQA//8FAP//AwABAAIABQD+/wEAAAD+/wAA/v8BAP7/AwD+/wIAAAAGAPz/CwD9/wgA/v///wUA+/8BAAAA+/8FAPn/AgACAPv/BQD8/wAAAwD5/wcA9P8JAPX/BgD5/wQA+v8FAPv/BAD5/woA9v8IAPn/BgD7/wYA/f/+/wUA/P8BAP7/AgD8/wYA+f8IAPn/BwD6/wQA///9/wgA+f8JAPv/AwABAP3/BgD7/wMA+v8JAPb/CwD5/wIAAwD5/wkA9/8HAP3/AwD8/wUA/P8IAPv/AQD///3/BQD+/wYA+v8IAPv/AwD+/wIABAD8/wUA//8EAAEAAAD//wQA//8GAAAAAQAAAAMABAABAAIAAQADAAQAAgAFAAIAAQADAP//AwACAP//AgACAP7/BgD9/wUA/P8EAP////8FAPn/BQD9/wIAAQD9/wYA+f8JAPv/AwD//wEAAAADAP3/BQD8/wUA/v//////AQD+/wMA/P8GAPv/BQD8/wAAAAD+////AAD+//z/AgD6/wUA+v///wIA/f8FAP3/AQAGAPz/BwAAAAEAAgADAP3/CAD7/wMAAQD8/wAABAD9/wMAAAD8/wcA//8CAAUA+P8HAPz/AwD//wAAAQABAAAABAAAAP7/AQD+/wEA/v/+/wIA/v8FAPv/BAD6/wMA+v8FAP3/AgAAAAIA/P8JAPv/AAAGAPn/CQD9/wYA//8AAAcA+v8JAP3/AQAFAPn/CQD6/wcA+v8DAPz/AAD+/wAA/P8CAPn/BgD6/wcA+v8FAP7/AwACAP//BQD//wMAAgD//wkA+/8GAAIA/v8JAPv/BAAEAP3/BQADAP7/BgD9/wQAAQAAAP7/AwAAAAEAAAD7/wMA+/8DAPz////+/////v/+/////f/8/////v///wEA/P8DAPz/AQD7/wQA+/8FAPn/CAD6/wcA/P8FAPf/CQD1/wYA/f/+////AAD8/wEA+v8AAPv/AQABAP7//f8CAPz/AwD9//v/BgD4/wIA///9//7/AAD//wEAAQADAPv/CAD3/w0A+v8GAAIAAAABAAEAAAD//wUA/f8CAAQA/f8FAPn/CQD3/wkA+/8CAP7/AwD///7/AgD//wIAAQD9/wIAAAAAAAEAAAD//wAA/v8DAAAAAQAEAP//BAAFAPz/CQD8/wUA/v8BAP7/BwD7/wgA/P8CAAIA/P8CAAEA/P8EAAEAAAAEAP3/AQAAAP///f8FAPn/BAD8/wIAAQD8/wgA/P8EAP//AAACAAEA/P8EAPr/BAD8/wAA/////wEA///+/wAAAQD8/wUA+v8HAPz//v/9//r/AQD5/wIA+f8DAPn/AQACAP3/AwD7/wQA+f8JAPf/BwD5/wMAAgD//////v//////AgD7/wMA+v8EAP///v8IAPj/CAAAAAIAAwD+/wMA+v8AAAAA/v8EAP3//v8FAPv/BwD8/wQAAgADAAMA//8GAP3/BgD//wQAAgAAAAQAAgAFAP3/BAD+/wEAAgAAAPv/BAD6/wQA/v8CAAAABAD4/wsA9v8HAPv///8AAPz/AwD9/wQA+/8DAPn/AgD8/wMA/P///wAA/P8DAP///v8AAP//AQD+////AAABAP7/AwD+//7/AgACAP7/AQD7/wQA//8CAP//AgD/////BQD+/wIA//8BAP7/AwD4/wUA9v8HAPf/AwD9/wEA/P8CAP////8EAPb/AgADAPr/CwD0/woA//8FAAEAAAAFAAAABAD7/wEAAAD3/wQA9/8CAPn/AAD8/wMA+///////AQD+/wIA/f/8/wQA+f8HAPr/AwADAPn/CgD3/wsA9v8KAPv/BQAAAAEAAAAAAAQA+v8LAPX/CgD7/wIABwD7/wgA+P8LAPv/BAD9/wEA/P8EAPn/AwD9//////8CAPz/BQD8/wIAAgADAP3/BQD8/wMA+/8CAPz/AwD7/wMA/v8AAAMA+/8IAPn/BQD9/wAAAwD9/wUA9v8MAPT/CQD5/wAAAQD7/wEAAQD9/wQA+/8AAP///v///wEA/f///wAA//8EAAIA/v8DAP//AAACAP3/AgABAPn/AgD///7/AgD6/wQA/v/7/wYA9f8KAPj/AgACAP7/AQD//wAABAD8/wcA9/8JAP3/AwAFAP7/CgD6/wcA/f8AAAUA/f8FAAIA/f8MAPr/CQD6/wMABAAAAP//BAD6/wkA9f8IAPf/BAD4/wAAAQD8/wEA+/8CAP3/BAD8/wUA/////wEAAAACAPr/BgD4/wMAAgD5/wcA+/8AAAIA+f8IAPj/BwD8//7/BAD6/wMAAQD8/wYA/v/9/wUA/f8FAPj/BgD3/wcA+v8BAPn/BAD7/wMA///7/wQA/f8FAP7/AwD+/wQA//8DAP3/BgACAP7/BgD6/wUA+/8BAAEA/v8BAAQA/P8DAP3/BAD8/wYA+P8EAPz/AQACAPf/BwD7/wMAAAD+/wMA/f8EAP7/AAD+//3//v8EAPv/AQADAP3/BwD8/wQAAAACAAEAAgD+/wQA/f8DAP7/BAABAAIAAAAAAP//AQAAAAEA+/8EAPf/DAD2/wUAAAD8/wUA//8AAAQA+f8BAP7/AQD8/wIA/f8BAPz/AAD6/wIA+/////r/AwD7/wIAAAD/////AgD//wMAAQAAAAAAAwD8/wQAAgD7/wkA9/8CAAAA/P8BAPz//v8BAAAAAQABAPv/BwD8/wQA/v/9/wQA/P8DAP3/AwD7/wYA+v8FAP3/AgAAAAEAAgACAAAABAD+/wMA//8CAPz/BQD7/wUAAAACAP//AAAAAAEAAAD9//7/AgD8/wMA+v8AAPz//P8BAPr/AgD8/wAA/f8AAAAA/v/+/wIA//////7/AAD//wIA+v8GAPv/BQD8/wIA/f8BAP3/AwAAAAAA/v8BAAAA///+//7/AgD///3//v////z/BAD5/wIA+/8AAP3///8BAPz/BAD7/wMA/P8FAPr/BgD///7/BAD8/wMA/v/7/wYA+/8HAPr///8EAPv/CAD9/wEAAgD9/wMAAQAAAAUA/v8JAP3/BgD+/wIABAD9/wAAAgD7/wYA/P8CAP//AgADAP//AQD//wQA//8BAAEA+/8FAPn/BAD7/////v/+//7/AAAAAP7/AgD9/wMA//8AAP3/CAD0/woA9v8EAPz//f/9/////P/+//7/+f8BAPj////6//3/+//6/wEA+v8AAP3//v////3/AAABAP3/BAD5/wUA/P8BAAMA/v8FAP3/AQABAP//BgD9/wEAAgD+/wcA/P8KAP7/AwAEAP//BgD8/wQA/f8BAP//AgAAAP//AgD//wcA/f8JAPj/BQABAP7/BgD4/wYA/f8AAP3/BAD7/wYA+/8EAPv/BAD5/wYA+//9/wMA+v8GAPz/BQD9/wMAAQD8/wQAAAAAAAUAAAABAAAABAD8/wsA9/8JAPr/AQAEAP7///8DAPr/AwAAAPr/CQD6/wUA+/8CAAAAAAD//wAA//8DAAIA+f8JAPz//v8EAPj/AwD///v/AwD9////AAAAAAIA/f///wAAAAAAAP////////z////9//3/AAD7/////v8BAP///P/8/wIA+v8GAPj/BwD7/wQAAQD//wMAAQD//wQA/f8DAAMAAQD//wUA/f8AAAYA/P8MAPP/DAD7/wMABwD+/wgA+/8GAP7/BAAAAP7/AQD+/wMA//////v/BAD6/wQA/f8AAAAA//8DAPv/BgD5/wYA/f8GAP3/BAD//wUA//8CAAEAAgD+/wQA/f8FAP3/AgD//wMAAwABAAAABAD9/wUAAQD7/wMA/f8EAP7/AAAEAPr/CQD6/wQAAAD+/wQA9/8MAPb/CQD7/wEAAgD//wIAAgD//wUA/v8DAPj/CAD7/wUA///+/wIA/f8EAPz/AAABAPz/AgD/////AQD9//3/AQD+/wAA///+/wAA/f///wAA/v8CAPz/BAD9/wIAAQD9/wcA+P8IAPv/AwABAP//BAD//wAAAwD8/wUA/f8AAAIA/f8CAP//AQD8/wUA+P8HAPv/BAD9/wEAAgAAAAIA///+/wEAAQADAP7/AQD///7/BQD6/wYA+/8EAAEAAAAFAP3/BAACAP//AgADAAAABAABAAQAAAADAAAABAABAAEAAAADAPz/AgD+////AwD8/wIA/v8AAP3/AgD8/wMA/f///wEA/P8BAP///P8CAP3///8BAP7/AAD9/wYA+f8GAPr/AgD/////AAD///r/AwD5/wUA+/8CAPr/AQD+//7/AQD///3/AQD7/wAA/P8BAP7/AAD9/wEA/v8CAP7/BAD7/wUA/f8BAAYA+P8IAPz/AgAEAPv/CgD4/wMA/v///wQA+/8EAP//AwAAAAMAAQADAAIAAAAFAP//BQD9/wUA/v/+/wIAAQD//wMA/f8BAAIAAAADAPz/AwAAAAEABAD9/wEAAAD+/wMA/////wAAAQD+/wIA/f8AAAEA/f8BAP///v8EAP3/AAAAAAIA//8FAP3/AwD+/wQA/P8IAPj/CAD6/wYA/P8DAP7/AgD+/wEA//8BAP3/AQD9/wQA/P8CAP//+/8GAPj/BwD4/wUA+////wEA+/8DAPz/AQD9/////////wAA//////7////9/wAAAgD4/wYA+P8EAP3////+/wAA//////7/AAD9/wMA/f8DAP7/AAADAP7/AgACAP7/BgD9/wUA//8DAAIAAAACAP7/AgACAAAAAQADAP//BAD+/wMAAgD//wUA/P8GAAAAAgADAAIAAgAEAAMAAQAFAP//BwABAAQAAwACAAEABgD9/wYA/f8FAP//AQACAP7/BQD7/wYA/P8FAPj/BQD8/wYA+/8AAAIA//8AAP///v8DAPz/AwD9/wIA///9/wMA+f8FAP3///8DAPr/AwD+////AAD+/wEAAAD8/wMA/P8DAPz//P8DAPf/BwD3/wUA+P8EAP3/AAABAP//AgD//wIA/f8GAPv/BwD7/woA+v8HAP3/BQD//wUAAAAEAP//AQAEAP7/CAD6/wgA/f8DAAAAAgAEAP7/AwD//wIA+/8GAPv/BgD8//3/AQD8/wQA+v8DAPr/AQD///z/BAD8/wEAAgD7/wUAAAD8/wUA+/8EAAEAAAAAAAUA/f8GAPr/CAD5/wgA/v8CAAQA//8EAP3/BQAAAAEAAwACAP7/CAD+/wIAAwABAP//BQAAAP//CAD7/wgA/v8EAPz/CAD2/w0A9v8JAPz/BAAAAAIA//8HAPv/CAD9/wQA//8FAPn/BwD7/wQA//8AAPz/BQD8/wIAAAD//wEA/f8EAP3/BAD9/wEAAgD//wIA///9/wQA/P8FAPr/BAD9/wMA/////wMA+/8EAPz/AQD+/////v8CAP7/AAACAAAAAwD//wMA/f8GAPz/BQD+//7/BgD6/wQA/P8AAAEA/v8DAPn/CAD6/wUA/v8CAP7///8BAP//AwD8/wUA+P8HAPn/BQD//wIA//8EAP7/AwD+/wMA//8CAAAA/f8CAPz/BAD+/wEAAAABAAIAAAACAPz/AwD+/wUA/P8GAPf/CwD2/wcA+f8CAP7/AQAAAP//AQD+/wMA/P8HAPr/CQD7/wUAAAABAAIAAgD+/wQA//8BAAAAAgAAAP//AQD9/wUA/P8BAAMA/f8FAPr/BQD9/wMA/v8BAP//AQD//wMA/P8DAPz/AwD+/wIA/////wEA/v8CAPv/BwD4/wcA+/8DAP////8CAP//BAD8/wQA+/8FAPv/AgD+/wAA/f8AAAAA/v/+/wAAAAD+/wEA+////wMA+P8KAPf/BQD9/wMA//8BAP//AAD9/wQA9/8IAPn/AQD///z/AAABAP3/AgAAAP//AQACAPz/BAD9////AgD+/wEA//8AAP///v8CAAEAAgAEAPv/CA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D+/wAAAgD7/wYA+/8DAAAA/v8GAP3/BQD//wIAAAADAP3/AwD+/wMA//8AAP//AgD+/wEA//8AAAAA/v8AAP7/AgD6/wIA+f8FAPn/AwD7/wIA//////7/AwD5/wYA+f8DAP7//f8CAPv/AwD5/wIA//8CAP3/AgD9/wQA/f8FAP7/AwAAAAAAAQADAPv/BAD7/wIA/f8BAP3/AAACAPn/BwD4/wgA+/8FAP3//v8FAPv/BQD8/wMA/////wMA+v8KAPr/BwD7/wUA/P8CAPz/BAD7/wQA/f/9/wQA/f8AAAAAAAD9/wEA/v/9/wQA+//+/wAA+f8CAPz//v/+//7///////7/AgD9/wQA+/8AAAIA+f8IAPj/BAD8/wEAAQAAAAEAAQAAAAMA/v8EAP7/AgD+/wAABAD9/wMA/v8BAAEAAQAAAP7/AgD6/wcA+/8EAPz///8EAP3/AgD9/wEAAAABAP//AwD//wEAAQACAP7/BAD+/wMA/f8EAPz/AAABAPr/AwD9/////f8AAP7/AAD+/wAA+/8GAPv/AQAAAP3/AwAAAP7/BAD7/wMA+v8HAPb/CQD5/wIAAAD9/wMA/////wAAAAD+/wMA/P8EAP//AgAAAAEAAQD///7/AQD+/wIA/P8FAPn/BQD5/wQA+v8DAPz/AwD7/wMA/P/9/wEA/P///////f/+////AAD8/wAA/f////3/AAD6/wQA9/8EAPj/AAD+//3/AwD7/wQA/P8FAPf/CAD3/wkA/P///wIA/f8FAP3/AQAAAP//AgD//wIA/P8GAPv/AwABAP3/AQAAAP///v8AAAEA/v8EAPz//v8EAPr/BgD6/wIA/f///wAA/f8CAPv/AwD9/wAA//8BAAAA//8CAP3/AQAAAAEA//8BAP//AAAAAP///f8DAPv/BQD+//3////9/wIA/v/9/wMA+/8DAPv/AAD+/wIA/P8CAP7/AAAAAP7/AwD9/wMA////////AwD7/wcA+/8DAP7/AAAAAP//AAD7/wMA+v8EAP7/AAACAP3//f8EAPz/AgD//////v8BAPr/AwD6/wQA+/8DAPv///////7/AQD7/wQA+P8HAPf/AQD+////AwD7/wQA/P8FAP7/BAD9/wYAAAD+/wQA/f8EAAAA/v8EAAAA/v8GAPf/BgD///z/BQD8/wIAAgD+/wAABAAAAAAAAwD9/wQA/v8BAAIA/f8BAP//AQD+/wIA+/8IAPr/BAAAAP7/BQD+/wEAAQABAPz/BQD7/wYA+/8FAPz/BAD9/wMAAQD+/wUA/P8CAAAA/v8EAP3/AgD+/wAA/v8DAPv/BQD4/wcA+/8CAP3/AgD9/wYA9/8GAPr///8DAPn/AwD+//3/BwD6/wcA+/8FAPz/BAD8/wQAAQAAAAEAAAADAP//AwD9/wQA//8EAP3/AwABAP3/BAD+/wAAAgD//wQA/v8FAPz/CAD6/wkA/P8IAPz/CgD3/woA/P8DAAMA//8EAAAAAAD//wMAAAAAAAQA//8CAAAAAgAAAAEAAgD//wEA///+/wQA+v8HAPn/BAD8/wIA/P8DAP3/AQAAAAAAAAABAP7///8AAP////8AAP////8DAPv/BAD+/wEAAQAAAAEAAgACAAAABgD9/wgA/f8FAP//BgD//wQA/v8FAP//BgABAAEABAACAAMAAQAAAAQAAAAEAP//AwABAAEABQD+/wQAAAD+/wYA/v8BAAMA/f8DAP3/AwD+/wIA/P8FAPv/CAD2/wgA9v8KAPz/AgAAAAEA/v8EAPr/BAD6/wMA/P8CAPz/AQD+/wMA/v8FAP7/AwADAP//CQD6/wQA//8BAAMAAQD+/wQAAAAGAP7/BQD+/wQAAgD9/wYA/f8HAPz/BAABAAAABQD+/wYAAQAFAP//BAAAAAEAAAD+/wMA+v8EAPv/BQD6/wIA//8AAAAA//8BAPv/CAD5/wUA/f///wMA+/8DAPn/AwD9/wEAAgD5/wUA/f8DAP7/AQAAAAAAAQACAAEABQAAAAUA//8HAAIAAgAFAP3/CAABAAQAAwACAAUAAQACAAEABAD//wgA/f8CAAUA/v8FAAAA//8GAP7/AgD//wIAAAABAAIA/f8GAPv/AQD//wIA/f8BAP3/AQAEAPv/BAD7/wMA//8CAAAAAwD+/wMA/f8BAAEA//8CAP3/AgD///7/AgD8/wEA/f8BAP///P8DAPv/AgAAAP7/AwD+/wEA/v///wEA/v8BAAAA/v8CAAAAAAABAAIA/v8DAP3/BgD6/wcA/f8BAAIAAQACAAMAAAACAAMAAQADAP3/BAD6/wgA+/8BAP3//v8AAAEA+v8CAPz///8AAP3///8BAPz/AQD9////AgD9/wMA/v8BAAMA/f8DAAAAAQABAP//AwD+/wgA/P/+//v//f8AAP7/AAAAAP7/AwD7/wUA+/8BAPv////+//7//P8CAP//AAAGAPr/BQD//wAABQD9/wUA//8AAAIA//8CAAIA/P8DAP7/AwD9/wMA//8DAAEAAAABAAUA/P8JAPr/BwD9/wUA//8CAAAAAQABAAMAAgABAAMA/v8FAP3/AgAAAP//AwD+/wEAAAD+/wMA/f///wEA/P8BAPr/AAD7/wAA+/8CAP3//v8BAPz/AQD///7/AAD/////AgD+/wMA/v8CAAAAAQAEAP///v8EAP3/AgD+/wAAAAD+/wIA/v///////P8DAPn/BQD5/wQA/v8BAAQA+/8FAP3/AwD+/wEA//8BAAEA+v8HAPf/BgD+//7/AQD6/wMA+//8//7/+v8CAPj/AgD6/wIA/f8AAAEA/f8GAPz/AgACAP3/BAD//wEAAQAAAP//AwD8/wUA/////wQA/f8GAP//BQD//wQAAgABAAIAAQAAAAIA/P8EAP//AwACAAEAAwADAAMABQADAAQAAQACAP7/BAD6/wQA/v///wAA////////AgD//wIA/v8BAAAA/v8AAP7/AAD+/wMA+f8GAPr/AQD9//7/AgD8/wQA/P8DAP7/AwD7/wMA///8/wQA+P8FAPr/BAD8/wAA//8AAAMA+/8EAPv/BQD8/wQA/P8FAPv/BwD6/wUA+f8LAPf/CgD4/wYA/f8BAP7/AgD+/wQA/P8BAAMA/P8HAPz/AAABAP7/AwD//wQA/f8DAAAA//8CAAIAAAD//wUA/P8HAP3/AQACAP7/BQD8/wQA/f8CAAIAAAACAAIAAAACAAMAAQADAAIA/v8EAP3/AQD//wAAAQD9/wQA/P8FAPz/BQD7/wYA+/8FAPr/BAD8/wMA/////wMA/v8BAAMA+/8FAPz/AgABAP//AwD8/wUA+P8IAPj/BgD/////AgAAAAMAAAACAP//AQAAAAAA//8BAPz/AQD+/////f//////AQACAP//AQABAAIA//8FAP7/BAAAAP7/BwD6/wUA/v///wIA//8DAP3/AwD+/wAABgD5/wgA9/8DAP7/AAD//wEA+v8EAPz/BQD8///////9/wEA/f/9/wIA+v8EAPr/AQD8/wAA/f8CAPz/AwAAAAAAAAACAP7/AgABAP3/BgD7/wcA+/8CAAEAAAADAP////8FAPn/CgD4/wgA+P8HAPn/AwABAPv/BwD6/wIAAAD//wIA//8BAP7/AgD+/wEAAQD//wMA/f8FAP//AwACAAAAAwACAAAABQD+/wYA/P8GAP7/AwACAP7/BAD8/wMA/f8DAP3/AQD7/wEA/f8DAPz///8BAPv/BQD6/wMA/f/8/wEA/v///wEA/f8AAAAA/f///wIA/v8DAP7/AgAAAAMA/P8GAPf/BgD5/wUA+v8CAPz/AAD+//3//f/9//r////9/wEA+v8CAPz/AgD8/wAAAAD+////AQD9////AAD//wAAAAABAP7/AwD9/wMAAAAAAAEA//8AAAIA/v8AAAEA/v8DAP//AQAAAAIA//8EAPv/CAD4/wUA/v///wAA/v8BAP7/BAD8/wUA/f8BAP//AgD//wEAAAD//wMAAAAFAP7/BgD+/wMABAD+/wUA/P8EAP7/AwACAP7/BQD8/wMAAAD+/wUA+f8HAPz/BgD+/wIA/v///wEA+/8AAP///v///wIA+v8FAPz/AgACAP3/BAD+/wQA/f8DAP3/AQD8/wMA+v8DAP3//f8DAPj/BgD7/////v8AAP3/AQD5//3////8/wAA/P8BAPv/AwD7/wEA/v8AAAEA/P8DAPr/BAD8/wEA//8CAPz/BAD8/wAAAwD9/wIA/f8DAAAA//8DAP3/BAADAP7/BAD9/wIA/v///wEA/v8DAPv/AwD9/wMA//8AAAIAAAAEAP7/BAACAAAAAwABAAIAAQABAAIAAwABAP//AgAAAP//AQAAAP3/AQD+/wEAAgD+/wIAAAD+/wAAAAD//////v/+//////8AAAAA///+//3/AwD9/wMA/f8CAP//AgD9/wYA+/8FAPv/BwD6/wQA/v8BAAIAAgAAAAAA//8BAAAA/P8CAPz/AwD8/wEA//8AAP////8AAAAA//////3////9//7//f/9//7////8/wEA///9/wYA+P8IAPj/AwAAAP7/BAD8/wQAAQADAP//AwD//wUAAAD//wMA+v8CAPv/AQD+//7/AQD9/wIA/v8AAP///f8EAPz/AQD9//7/AgAAAP//AQABAP7/BAD+/wQA/f8HAP7/AgADAP//BQD9/wcA+v8HAP7/AgABAAAAAQABAAIA/P8DAP3/AQD8/wMA+P8EAPr/AgD8/wIA/v8AAAAA//8AAAIA//8DAP///v8GAPj/AgAAAPv/BAD6/wQA/f8EAPz/AgADAP7/AwD6/wMA//8AAAYA9v8KAPX/BwD7/wAA/f////z/BAD7/wMA/f//////AAAAAAEA/v8BAP//BAD8/wkA9/8LAPb/CgD4/wcA//8AAAIA/P8CAAAAAAABAP3/AwD8/wIA/v///wIA/P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8JAPj/BQD///7/CAD6/wYAAAAAAAMA///+/wMAAAD//wQA+P8HAPz/AwD9/wAAAgD8/wQA+v8DAP3///////v/BAD5/wMA/P8BAAAA///9/wMA+v8EAP3//v8BAPz/AgD7/wMA+/8BAP7/AwD8/wUA+v8GAP3/AwABAP//AgACAP7/AgAAAP3/AgD8/wEA/////wAA/v///wAAAAABAAAAAQABAPz/AgABAAAAAAACAPv/BAD///3/BgD//wEAAQD+/wMA/P8BAAAA/f8EAP3//v8CAP7///8DAPz/AAD///7/AAAAAP3//f8BAPj/BAD4/wQA+P8CAP3///8AAAAA//8AAP7/AQD+/wAA///9/wUA+f8FAP3/AwAAAAEAAAADAP//AwD//wAAAQD+/wUA/P8DAP////8GAPv/AwD9/wEA/v8CAP7/AgD+////AQD//wIA/P8FAPj/CgD1/w0A9v8JAPz/AgABAAEAAQD//wIA/f8FAPn/BAD6/wEA///+//7//v8BAP3/AAD9/wAA/v8FAPn/BAD8/wIAAAABAP//AgD7/wIA/v8AAAMA+P8HAPv/AgAAAP3/AwD9/wEA//8AAP//AgABAAAAAgD//wIA//////7/AwD8/wIA/v///wEA+/8EAPj/BAD+//////////7/AAD9/wAA+v8CAP3//v8AAPz/AQD8///////7/wIA+/8CAPn/AgD5/wEA+/8EAPr/BAD9////BQD3/wYA+/8HAPr/BAD8/wEABAD6/wcA+/8AAAUA+f8HAPz/AgAAAAAAAgD9/wMA/P8CAP7/AAACAPv/BQD9////AgD8/wIA///8/wMA/P///wEA+v8FAPv/AQD///7/AwD+/wAAAgD8/wQA/f8BAAIA/v8BAP//AAABAPv/AwD8/wQAAAD6/wEA/P8CAP7///////7/AQD+//z/BAD5/wYA+v8FAPr/AwD//wAAAQD//wAAAQD8/wUA+v8HAP3/AAACAPv/BAD9/wEA+v8EAPn/BQD9/wAAAgD8/wAA//8CAP3/AwD8/wAA/v8AAPv/BAD6/wMA/P8BAP3////9/wEA/v8AAP3/AAD9/wAA+/8EAPz/AwD8/wQA/v8EAP//AQAEAP7/BQD7/wUA//8BAAEAAAABAP//BAD8/wAAAQD9/wEABQD3/wkA+v8EAAAABQD6/wcA+/8FAP3/BAD8/wIA/v8BAAIA+v8FAPz/BAD/////AgAAAAMA/f8DAP//AQD//wEA//8CAP7/AwD9/wQA/P8EAP//AgD//wMA/P8EAPz/BQD9/wEAAAD+/wEA//8AAP7/AgD8/wMA/v///wAAAQD9/wQA+/8BAPz/AgD9/wEA/f8BAAAAAwD+/wQA+/8GAPv/BQD8/wQAAAAAAAMA//8DAP7/AgAAAAMA//8DAP//AgD9/wQA/v8CAP//AAAEAP//AwD//wQA//8EAAAAAwAEAAAAAgD//wMAAAAEAAEAAQD//wIA/v8FAP7/AgABAAEAAQABAAIA//8BAAEAAQABAP7//f8EAPz/BAD9/wAA/v8DAPr/BQD6/wUA/f8DAP3/BAD8////AgD6/wUA/f8AAP//AgD6/wgA+v8FAP3/AgABAAAABgD8/wgA/f8GAAAAAgADAAIAAwAAAAQA/v8FAAEABQAAAAQA//8GAP7/BQD+/wQAAgD//wYA/v8DAAEABQD9/wcA+f8JAPr/BgD+/wIAAQD7/wgA9/8IAPj/BwD8/wQA+f8FAPn/CQD6/wYA+v8HAPr/BwD4/wYA9/8HAPj/BQD7/wAAAAADAPv/BwD9/wUAAQABAAQAAAAAAAIA//8DAAEA/v8FAAAABAAAAAMAAQAAAAUA/P8IAPr/CQD5/wgA//8BAAQA/v8HAP//CQD6/wkA/f8DAP7/AAABAP3/AgD9/wEA/f8CAP//AAAAAP7/AQD8/wkA9/8IAPj/BAAAAPz/BAD3/wYA+/8CAAAA/f8AAAMA/f8DAP3/BAD8/wUA//8CAAUAAQADAAMAAwAEAAAABwD9/wcAAgABAAgA//8IAPv/CQD+/wIABwD8/wcA//8BAAYA/P8HAPz/BgD//wEA//8DAP//AwD+/wMA//8AAAAA/P8HAPj/BQD7/wIAAwD7/wYA+P8HAPz/BAD//wMA/v8EAPz/AwD9/wIAAAAAAP//AQD7/wUA+v8EAPn/BQD7/wAAAQD7/wQA/f8DAP3/AgD+/wIA/f8BAP3/AwD8/wUA+/8FAP7/AAAEAP3/AwD9/wUA/P8EAAAAAAACAAAAAgACAAIAAgACAAIA//8BAAMA+v8IAPz//v8BAPv/AgABAPv//v/+/wAA//////z/AwD7/wEA/v/8/wYA/P8CAP7/AQAEAPz/BwD7/wQAAAD+/wcA+/8HAP7//f/7/wAA+f8FAPv/AQACAPz/AwD9/wIA+/8BAPr/AgD5/wIA/f8DAP//AQACAP7/BAD//wIAAQACAAIA/f8GAPn/CgD3/wkA9v8JAPv/AwD+/wMA//8FAPz/BgAAAAEAAwD//wQA//8FAP3/AgADAPz/CAD//wEAAgADAP3/CQD3/wkA+P8HAPv/BAD+/wEAAAD+/wIA/P8BAP//+/8CAPj/AQD7/wAA///+//z/BAD7/wAAAAD7/wUA+////wIAAAABAAAA//8DAP//BQD///7/BQD6/wUA+v8GAPr/AwD9/wAAAQD8//////////7/AAD9/wUA/f8EAP7/AAABAAEA/v8DAPz/AwD+/wAA/f8EAPr/BwD5/wAA///+/////P/7//3//v/+//z/AgD3/wcA+f8FAP3/AgAAAAEAAAAAAAIAAAACAP3/BAD9/wUA/P8CAAEA/v8HAPr/CAD9/wUAAQADAAEABAD8/wgA/P8CAP////8CAAUA/P8IAPz/CAAAAAcAAgADAAQA/P8GAPv/AwD9/wEA///+/wEA/v///wQA/P8GAPr/BQD9////AQD9/wAA//8AAP7/AAD+/wAA/P8AAAAA/f8DAP3/AwD//wAAAQD7/wcA9v8FAP3//f8BAP///f8DAPz/AAAFAPj/CAD6/wQA//8AAAEAAQD9/wYA+/8FAPn/CgD3/woA+v8EAP////8AAAAAAAACAP3/AgD//wEAAgD//wEA/f8CAAEA/v8IAPn/BgD9/wAAAwABAAIA/f8DAAAAAgADAP3/AwD//wEAAQABAP//AAADAAAAAgACAAAAAgACAAIAAwABAAAAAQABAP7/AQD+/wEA//8DAPz/BAD9/wMA//8CAP7/AQD//wEA/f8EAPz/BAD+/wEAAQABAP7/AgD+/wEAAQD//wMA/f8AAAAAAQD+/wMA/v///wUA/v8EAAAAAQD+/wUA/P8CAP//AAD+///////+//7////+/wMA//8CAP//AwAAAAIAAwD+/wQAAAD//wYA+/8CAAEA/f8CAAIA/v8BAAIA/P8FAP//AQAAAP3/AQD7/wgA9f8JAPb/BgD6/wgA9/8FAPr/AQD+/wAA+f8EAPr/BAD8/////P//////AQD///7/BgD5/wcA/P8DAP//AgD+/wIAAAADAP3/AgAAAAIAAAACAP3/BQD8/wUA/f8EAPz/AwD8/wIAAAD//wAAAgD7/wYA+/8DAAAA/v8CAP7/AgD8/wUA/v8DAP3/AwABAAMAAQADAP7/CAD7/wcA//8DAAEAAQABAAIAAQABAAEA/v8CAP3/BAD8/wEA/P8AAP7/AwD5/wUA+f8FAPr/BAD7/wIA+/8AAP7/AQD9/wEA/v8BAPz/AwD7/wUA/f8BAAIAAgD8/wcA+P8FAPz//v8CAP3/AAD9/wAA/P////3/+v/+//3//f8CAPr/AAD///7/AAD9/wQA+P8FAPz/AAD///3/AwD+/wEA/v8DAP3/BAD+/wEAAgD/////AwD+/wIA/P8FAPv/BQD+/wEAAgD//wIA//8CAAAAAAD+/wMA+/8CAP3/AQACAPz/BgD5/wgA+v8CAAIA/f8EAPz/BQD8/wUABAD8/wkA/P8EAAIAAQABAAAAAwD8/wYA/v8CAAMA/f8DAP7/AQAAAAEA/v8FAP7/AwAAAP///v8CAPn/AgAAAPr/BQD6/wIA/v8BAP//BAD8/wQA/v8DAAAA/v8CAP7/AAD///z/AAD//wEA+v8DAPz/AAD///z/AAABAPn/AgD3/wEA+/8AAPz/AwD6/wIA/P8BAP///v8EAPf/CQD3/wUA+f8EAP//AQD8/wUA+f8FAP3/AgD+/wEA//8CAP3/BgD7/wUAAgD//wIAAQD+/wAA//8AAAAAAQD+////AAAAAAEAAQD//wUA+/8KAPj/DAD4/wgA//8CAAMA/v8EAAAABAD9/wUA/P8CAP3/BgD3/wYA+v8EAAAA//8BAAIA+/8DAP3///8CAPr/AgD8/wAAAgD7/wUA+f8CAP//AAD//wMA+/8EAP7/AQAEAPv/BQD7/wcA+/8DAP3/BAAAAAMA/v8BAP//AwD8/wAA///+/wEA/v8CAPz/BAD4/wgA+f8GAPj/BQD6/wEA+v8BAPv////8/////f8CAPz/AQAAAAAAAQD8/wIA/v8CAP//AQAAAAMAAwD9/wcA+/8IAP7/AAAEAPn/AAAAAPz/AQD+//z/BQD8/wEA/f8BAP7/AwD6/wQA+v8CAP//AAD//wIAAQD9/wUA/f8DAAIA/v8IAPv/BQABAP//BQABAPz/CQD5/wgA/f8AAAUA/f8EAPv/AwD9/wIA/P8BAPv/AAD///z/AgD8/wUA+f8FAPr/BgD7/wgA+f8GAPv/BQD8/wAA///9/wEA/v8BAAAAAAABAP7/BgD6/wUA/f///wMA/P8HAPj/CAD4/wUA+v8DAPv/AAD+//7/AgD//////v8AAP//AQD//wIA/f8BAAMA/v8GAPv/AwAAAAEAAQD//wEAAgD/////AgD9/wQA+/8FAPz/AgD+//7/AQAAAAAA/f8EAPr/AgD+/wAAAQADAPn/BgD7/wYA/f8GAP7/BAAAAP//AgD8/wYA/P8HAPv/BgD//wYA/P8CAAIAAQD//wUA+/8GAPv/BAD8/wQA9v8LAPb/BwD4/wMA/v8CAP7/AgAAAAIA//8AAAQA+/8EAP7/AAADAP7/AQAAAAEA/P8DAPv/AQACAPz/BgD3/wUA/P8CAAIA/P8GAPz/AwD+/wIA//8AAP///f8DAPr/BAD5/wYA+P8JAPb/BwD8/wEAAgD+/wUA/f8FAPz/BgD7/wgA/f8AAAQA+v8GAPj/BQD+/wEA/P8GAPz/AgABAAAAAQADAP7/AgAAAAAAAwD5/wgA+v8EAAEA/f8EAP//AAABAAIA+/8CAPv/AQD///3/BAD8/wQA/P8EAP7/AQACAP//AAADAPr/BwD8/wQAAgD+/wMAAAAAAAIAAAD///7/AQD8/wUA/f8AAAIA/P8EAAEA/v8EAPr/BQD5/wgA+P8GAPv/AgD9/wEA/v8CAP7//f8CAP//AgD//wIAAAACAAEAAQACAAEAAAABAAEA/v8CAAAA/f8CAP7//f8CAAAA/f8EAPj/CAD4/wgA/P///wQA/v8CAAIA/v8DAAEAAAABAAEAAAAAAAAAAAAAAAEA//8AAAEA//8CAP3/AgAAAP7/AgD+////AAD+/wAA//////7//////////f8BAPz/AAD8/wEA/v/8/wAA/v8BAP//AAD9/wIA/f8FAPz/AwAAAAEAAgD+/wQA/f8GAPz/AwAAAP//AwAAAP3/BAD8/wMA//8CAP7/AgD+/wAA/f8DAP3/BgD4/wIA/P8DAP///v8BAPz/BgD4/wcA+P8IAPr/BQD+/wIAAQAAAAAAAgD+/wYA+v8HAPf/BAD9/wEAAAAAAP3/AgD9/wIAAAD//wAA/v8CAP7/AgAAAP7/BQD8/wQA/P8AAAQA+f8HAPj/BQD6/wYA+v8DAAEA//8CAP//AAAEAP7/AwAAAAAABAD/////AgD7/wIAAAD8/wMAAAD7/wgA9f8JAPz/AwD//wIA//8BAAEA/f8AAP7/AQD//wAA/v8AAP7/AQD///3/AAD9//7/AQD9/wEA///9/wMA/f8BAP3/BAD5/wYA+f8GAPr/AQD9/wEA/f8DAPj/BwD3/wkA9/8IAPn/BgAAAP//BgD6/wgA/P8FAP3/BAD9/wQA+/8FAP//AQAAAP//AQACAAEAAAADAPz/AwAAAAEAAgABAP7/AgD9/wMA//8AAAEA/v8DAP//AAD//wEA/P8EAPv/AwD//wAAAQABAAIA/v8DAP3/BgD8/wYA/P8DAP7/AgAAAP//AQD+/wAAAAD//wIA/v8CAPv/BgD7/wQA////////AQD+/wAAAwD7/wMAAAABAAEA//8GAPn/CQD5/wEAAQABAP3/BQD7/wUA//8EAPz/BQD7/wMA/P8DAP3///8AAPz/AAD+//3/BAD4/wUA/P8CAP///P8DAP3/AgD///////8EAPz/AwD8/wMA/v8BAP3/AQD9/wQA+/8EAP7//v8EAP7/AgD//wEAAgD8/wcA/f8FAP7/AQAAAAEAAAAAAAIA/f8DAPr/BgD3/wgA+/8BAAIAAAD//wMAAAAAAAYA+f8KAP3/BAABAP//AgADAP7/AwAAAAEA/v8GAPz/BwD5/wYA/v8DAAEA/v8BAP3/BAD5/wkA9P8HAPn/AwD///7/AAD9////AAD8/wQA+P8HAPf/CgD3/wYA/f8CAP//AQD//wEAAwD9/wMA///9/wQA/f8FAP3/AQAAAP3/BAD7/wIAAAD9/wMA//8AAAIA+v8EAPv/AgABAPn/BgD2/wcA9/8EAPz/AgD//wAA/v8CAP//AAABAPz/BAD//wEAAAACAAEAAgD+/wMA/v8DAP7/AAAAAAIA/P8DAP3/AAABAP7/AQD+/wMA/f8EAP3/BAD+/wQA+v8EAP3/BgD8/wMA+/8FAP3/AQD//wEAAAADAAAABAAAAP//BgD9/wUA/v8FAAAAAwAEAP3/BwD8/wkA/P8FAP//AAABAP7/AQD+/wEAAQD8/wMA/P8BAP7/AQD+/wAAAAD8/wMA+/8DAPv/AgD8/wEA//8BAP3/AAACAP7/AQD+/wAAAAD9/wMA+////////P8AAAMA+P8EAPn/BAD7/wEAAAD7/wQA+f8BAP3//v8AAP///f8EAPn/BgD7/wUA/f8BAAEA//8EAP3/AwD//wMAAAD//wcA+f8FAPv/AgD//wEAAAD//wYA+v8LAPv/BwD//wMAAgACAAIAAgAAAAIA+f8JAPn/BgD8/wMA/v8EAPz/BQD9/wEAAgAAAAQA/f8CAP3/AwAAAP7/AgD+/wAAAQD///////8AAP7/AgD8/wMA/P8FAPr/BgD8/wUA//8BAAEAAQD//wQA/f8EAPz/BgD8/wIA//8AAAIA///+/wIA/v8AAP7/AAABAAAA///+////AQD9/wIA/P8DAPv/AAD+//7/AgD7/wIA+/8BAP7/AAD+/wAA/v///wAA+/8DAPz/AAD+/wAA/f8CAPz/AAAAAP///v/9/wMA+/8EAPz/AgABAP3/BgD4/woA+f8HAP//AQACAAEAAQAEAP//AAABAAAAAgACAP3/BwD8/wcA+/8FAAAAAQAEAPz/BwD+/wMABAD//wYAAAAFAAAABgD+/wcAAgABAAYAAAACAAUA/v8FAP//BAD+/wEAAgAAAAIA//8CAP3/BgD3/wYA/P8DAP7///8CAP////8BAP3/AQAAAP//AQAAAP3/AgD9////AAD/////AwD7/wEA//////7/AgD9/wMA/P8AAAEA/f8BAPr/AwD5/wQA+f8CAPv/AwD9////AgD//wAAAwD9/wIAAgD//wEABAD+/wYA/v8DAAEAAwAAAAcA/f8EAP//AwACAAMA//8DAAEAAAAEAP7/BwD7/wgA+v8EAP7///8EAP3/AAAAAPv/AwD8////AQD+//z/AgD5/wcA+v8EAP///v8BAAIA/P8HAPn/BgD9/wMAAQADAAAA//8EAP7/AgACAP//BgD//wIAAwD8/woA+P8JAPz/BwD9/wUAAgAAAAQA//8AAAcA+v8JAPz/BgAAAAIAAgD9/wgA9/8KAPn/BwD+/wMA/v8FAP3/BwD9/wMABAD8/wcA/P8DAP//AQAAAAEA///+/wMA/v8CAP7/AgD7/wYA/P8EAP3/AgD//wMA/f8DAP7/AAACAPv/BAD8/wIAAgD8/wUA+v8HAPf/CAD5/wQA+v8DAPr/BQD7/wUA+/8HAPz/BAABAP3/BwD7/wQAAQD7/wkA9/8HAPn/AwD+/wEA///+/wEAAwD+/wAAAgD7/wYA+v8FAP3/AQD//wAA/v8CAP7/AwD//wIAAQABAAMA+v8IAPv/BAACAPn/BQD6/wUA/f8CAAAAAgD//wUA+v8FAP3/AQAEAPz/BQD6/wYA+/8CAP7///8AAP7/AgD+/wIA//8AAAAAAQACAP//BQD+/wQA/v8DAAIA//8DAP//AAACAP//BQD7/wIA//8AAAIA//8CAP//AQAAAP7/BQD5/wgA+/8CAP3/AwAAAP//AQD8/wUA+v8HAPr/AwD+/wAAAAD+/wQA+v8GAPz/AQADAPr/BgD8/wcA+/8BAAEA/v8CAP7//v8CAPv/BAD6/wQA+f8EAP7//v8AAPv/AwD9////AAABAP3/BQD8/wQA/P8BAAEA/P8EAPn/AwD9///////9/wIA/P8DAPv/BQD9/wMA/////wIA/f8AAAAA//8CAP7/AAD+////AgABAAQA/f8EAAEAAwABAP3/AwD9/wUA//8CAP7/AgD5/wcA+v8CAAEA/v8CAAAAAAABAAEA/f8DAP//AQD/////AwD9/wEAAAD+/wEAAgAAAAAAAAD//wEAAQD9/wMA/P8CAAAAAAD//////////wEA/f8CAP7/AwD8/wIAAQABAAQA/P8FAPr/CQD3/wkA9/8IAPv/BAD+/wMA/v8FAPr/BwD8/wIAAAACAPv/BwD8/wUA+/8CAPr/BQD6/wEA/P///////v8AAP///v8AAP3///////7/AAD8//7/AAD+/wAA/P8CAP///v8AAAAA+/8EAPf/BwD3/wQA+/8BAP////8AAP3/BQAAAAQAAAABAAIAAAAEAP//AwACAP3/BQD7/wUA/v8CAAAAAgD+/wQA/P8GAPv/BgD7/wUA/P8FAP////8CAPz/AwD7/wEA///9/wIA/f///wIA/P8EAP7/AwD6/wYA+P8GAPv/BAD7/wMA/f///wEA/v///wAA/v////3/AQD9//3/BAD6/wQA/v8CAP//BQD//wQAAgD//wYA/P8FAAAABAD//wIA//8DAP7/AQD//wMA//8DAP7/BAD+/wAAAwD8/wYA/P8BAAEA+/8JAPT/DAD3/wYA/P8AAP////8AAP7/AwD+//3/BQD2/wsA9f8GAPv/AgD9/wEAAQD4/wYA+f8CAP///v////7////9//3////+/wIA/f8CAP7/AQD+/wYA/P8IAPr/CAD//wEAAwD9/wcA/P8GAP7/BAABAAMAAAACAAMA//8EAAEA/v8DAAEABAAAAAIA/v8AAAMA/v8CAP3///8CAPz/BQD7/wEAAQD9/wEA/////wMA+/8DAPr/BgD7/wMA/f///wAA///+//7/AAD7/wIA///9/wIA+/8FAPz/AgD8/wIA/P8AAAIA/P8GAPj/BQD9/wMA/f8CAP//AgD9/wMA/f8FAP//AAACAP//BgD9/wkA/P8GAP7/BQD//wUA/v8GAAEAAgAEAP//AQACAP7/BAD+/wMA/f8EAP//AQADAAAAAwAAAAMA//8CAAIA/P8GAPn/CAD4/wcA+v8CAAEA/f8CAP7/AAD+/wEA/f////7/AAABAAAA/f8AAP7//////wEA/f8BAAAA/P8CAPv///////z/AgD9////AAD//wMA+/8DAP//AAAFAPv/AgACAP3/AgAAAP7//v8CAP3/AwD8////AgAAAP////8BAAEA//8BAP//AAAEAPz/AQD///v/BQD6/wQA///8/wMA/f8CAAAA//8CAP3/BgD7/wUA+/8GAPz/BgD8/wUAAwAAAAUA/v8DAP//BAD//wQA//8DAP7/BwD4/woA+f8HAPn/BgD4/wcA+P8GAPr/AQD///7/AwD9////AwD7/wEA/v///wIA///8/wMA/f8CAAAA/f8BAAAA/v////3/BAD4/wcA9f8FAPz///8BAPr/AwD5/wUA9/8CAP3//v8BAP///f8DAPn/AQD9///////+//7//v8BAPn/BQD7/wEA/////wAA////////AQD9/wQAAAD//wUA+v8IAPz/BAD+/wEAAgABAP3/BgD8/wMAAgD8/wQA+v8IAPj/CQD5/wQA/P8EAP3/AQACAP//AAABAP////8CAPz//v8EAPr/BwD+/wEA//8FAP3/BwD6/wkA+v8HAPz/AwACAP//AwD/////AwD9/wEAAQD8/wIA/f8BAP//AwD7/wMA+/8AAAIA+/8DAP3/AAAAAAEA/v///////f8CAPv/AwD+//////8AAP7/AgD///7/AAD/////AwD7/wMA/v/+/wEAAAD9/wQA+f/+/wEA/f/9/wEA+P8DAPv/AgD6/wMA+P8DAP3/AgD//wMA/f8BAAEA/v8CAAAA/P8EAPv/BAD+//7/BAD9/wMA/v///wAAAAD+////AwD6/wYA+v8CAAIA//8AAAIA/f8CAP7/AAABAP//AAAAAAEA/v8AAAIA//8FAPz/AQAFAPz/BQAAAP7/BwD5/wkA/f8DAAEAAgABAP3/AwD8/wMA/v///wMA+/8DAP3/AwD//wAAAQABAAAAAQD//wAAAgAAAAAAAQD//wIAAAADAPr/BgD7/wIAAAD9/wMA/P8DAP3/BAD8////AwD8/wQA/P///wAAAAD+//z/AQD4/wUA+//9/wUA9v8JAPf/CAD4/wYA/v8BAAIA/P8CAP7/AgD+/wEA//////7////+/wAA/P8FAPf/BwD4/wQA/f8AAP7/AgD//wEA/v/9/wMA/v8FAPr/AwD8/wEA/v8CAP3///8CAPr/BgD8/wQA/////wUA/v8GAPv/BAADAPv/CQD5/wYA//8AAAEAAAD//wAAAAAAAP7/BAD9////BgD5/wkA/P8CAAIAAQABAP////8AAAAAAAD//////v8CAP3/AgD+//7/AgD9/wQA/v8DAAQA//8CAP//AwAAAAMAAwD+/wYA/P8GAP3/BQD+//7/BgD7/wYA/f8DAP3/BAD/////BgD6/wYA/f8AAAAA/v8BAPz/AAD3/wQA+////////f8AAP//+/8EAP3/AAADAPz/BQD9/wQA/f8EAP7/AAADAP7/AQAAAP//AAADAPr/BAD4/wEA/v/9/wAA/v////7/AgD9/wIAAgD7/wgA+v8IAPz/AQADAP7/AQACAPz/AwD+/wYA+v8JAPj/BgD//wMAAAAEAAEAAwACAAUAAAACAAEA/v8HAPv/AwD+//3/AQD/////AwD6/wUA+v8FAP7/AQADAP//BQD//wEAAwAAAAMAAAADAAAAAgACAPz/BAD//wAAAgD9/wMA/v8CAAUA/P8GAP7/AQD//wIA//8BAAIA/P8EAP3/BQD8/wAAAQD8/wMA/P///wUA/P8EAP7/AwAAAAIA//8DAAIAAwD//wQA/v8EAP7/AwD9/wUA/v8GAP//AAADAAEAAQACAP//AwABAP//AAADAPz/AgD9/wIA/f8BAAAA/f8EAPv/BQD8/wQA/v8AAAUA+/8FAPz/AgD//wEAAgD9/wYA+v8FAP3/AgABAPz/AwD9/wAA///+/wIA/v/+/wEA/v8EAPz/AwD7/wIA//8AAAIAAQAAAP7/BAD9/wUA/v8CAAAAAgD//wMA/f8EAP7/BAABAP//AwD//wIAAwACAAIAAQAEAP7/BQD///7/BgD7/wUA/P8DAP//AgD9/wIAAAAAAAEA//8DAP//AAACAP//BQD8/wQAAgD+/wMA/v8BAP//AwD+//7/AAD+/wAA/v8AAAAA/v8CAP////8EAPv/BgD8/wUA+v8HAPz/AwAAAP//AwD//wAABAD8/wQA/P8HAPv/BwD+/wEAAwABAAMAAgACAAMAAwAAAAUA/v8HAP3/AwABAAQA//8FAP3/AwD9/wEA//8BAP7/AQAAAP///////wIA/P8BAP///v8DAP3/AQD9/wIA/f8CAPn/BQD6/wUA/P8BAP7///8CAPz/BQD6/wUA//8CAP//BQD7/wYA+v8DAPz/AQD+/////v///////v8BAP3/BAD7/wQA/P8FAPz/BQD8/wcA+/8IAPr/CAD8/wEAAwD//wUA/v8FAPv/BwD7/wUA//8FAPv/CwD2/w0A+f8GAAAA/v8FAPz/BQD6/wYA+v8HAPr/AwD5/wYA9/8FAPn/AgD9//7/BAD6/wUA/P8EAPz/BAD6/wYA+P8GAPr/AwABAP7/BAD9/wEAAAD9/wYA+f8GAPz/AgD//wIA/v8BAAAAAQD//wIA/v8AAAAA///9/wMA+f8FAPv/AgD+////AQD+/wMA+/8EAP3/BAD8/wIA/v8AAAIA+v8CAP3/AQD//////P8BAAEA/P8CAP3/AAAAAAAA/f8BAPz/AQD+/wAA/v/9/wAA/P8BAAAA/P8BAPz//v////7//v/+//r/AwD6/wUA/P8DAP7/AQD+/wAAAAD//wMA+/8FAPz/AgD+//7/BAD9/wIA//8AAAIA/v8BAPn/AgD8/wYA/P8FAP//AQAAAAAA/P8AAP7/9/8FAPX/AwD7/wMAAAAAAAMA+f8KAPv/BwD9/wEAAwD//wEA///+/wAA/v/+//7//f////v/AwD4/wcA+P8GAPv/AAAAAPv/BAD8/wUA+/8EAPv/BAD+/wMA/v8DAAAAAAAAAP///v8BAAAA/v8DAPv/BgD6/wQA+/8DAP3//P8BAPf/AwD5//3//f/7/wAA+f/+//7/+/8CAPr/AwD3/wcA+/8FAP3/AgD//wUA/P8KAPr/BgD9/wQA/v8CAP//AgD4/wcA+f8DAPf//f/9//3/+/8AAPv/AgAAAP////8BAP//BAD//wAA///9/wMA/f////7//v8AAAAA/v///////P8AAPj///////n/BgD3/wAAAgD6/wYA+/8GAP7/AgD//wIAAAAAAAEA/v8BAAIA///+/wQA/v8BAAQA//8FAP//AwABAAIABQD+/wEAAAD+/wAA/v8BAP7/AwD+/wIAAAAGAPz/CwD9/wgA/v///wUA+/8BAAAA+/8FAPn/AgACAPv/BQD8/wAAAwD5/wcA9P8JAPX/BgD5/wQA+v8FAPv/BAD5/woA9v8IAPn/BgD7/wYA/f/+/wUA/P8BAP7/AgD8/wYA+f8IAPn/BwD6/wQA///9/wgA+f8JAPv/AwABAP3/BgD7/wMA+v8JAPb/CwD5/wIAAwD5/wkA9/8HAP3/AwD8/wUA/P8IAPv/AQD///3/BQD+/wYA+v8IAPv/AwD+/wIABAD8/wUA//8EAAEAAAD//wQA//8GAAAAAQAAAAMABAABAAIAAQADAAQAAgAFAAIAAQADAP//AwACAP//AgACAP7/BgD9/wUA/P8EAP////8FAPn/BQD9/wIAAQD9/wYA+f8JAPv/AwD//wEAAAADAP3/BQD8/wUA/v//////AQD+/wMA/P8GAPv/BQD8/wAAAAD+////AAD+//z/AgD6/wUA+v///wIA/f8FAP3/AQAGAPz/BwAAAAEAAgADAP3/CAD7/wMAAQD8/wAABAD9/wMAAAD8/wcA//8CAAUA+P8HAPz/AwD//wAAAQABAAAABAAAAP7/AQD+/wEA/v/+/wIA/v8FAPv/BAD6/wMA+v8FAP3/AgAAAAIA/P8JAPv/AAAGAPn/CQD9/wYA//8AAAcA+v8JAP3/AQAFAPn/CQD6/wcA+v8DAPz/AAD+/wAA/P8CAPn/BgD6/wcA+v8FAP7/AwACAP//BQD//wMAAgD//wkA+/8GAAIA/v8JAPv/BAAEAP3/BQADAP7/BgD9/wQAAQAAAP7/AwAAAAEAAAD7/wMA+/8DAPz////+/////v/+/////f/8/////v///wEA/P8DAPz/AQD7/wQA+/8FAPn/CAD6/wcA/P8FAPf/CQD1/wYA/f/+////AAD8/wEA+v8AAPv/AQABAP7//f8CAPz/AwD9//v/BgD4/wIA///9//7/AAD//wEAAQADAPv/CAD3/w0A+v8GAAIAAAABAAEAAAD//wUA/f8CAAQA/f8FAPn/CQD3/wkA+/8CAP7/AwD///7/AgD//wIAAQD9/wIAAAAAAAEAAAD//wAA/v8DAAAAAQAEAP//BAAFAPz/CQD8/wUA/v8BAP7/BwD7/wgA/P8CAAIA/P8CAAEA/P8EAAEAAAAEAP3/AQAAAP///f8FAPn/BAD8/wIAAQD8/wgA/P8EAP//AAACAAEA/P8EAPr/BAD8/wAA/////wEA///+/wAAAQD8/wUA+v8HAPz//v/9//r/AQD5/wIA+f8DAPn/AQACAP3/AwD7/wQA+f8JAPf/BwD5/wMAAgD//////v//////AgD7/wMA+v8EAP///v8IAPj/CAAAAAIAAwD+/wMA+v8AAAAA/v8EAP3//v8FAPv/BwD8/wQAAgADAAMA//8GAP3/BgD//wQAAgAAAAQAAgAFAP3/BAD+/wEAAgAAAPv/BAD6/wQA/v8CAAAABAD4/wsA9v8HAPv///8AAPz/AwD9/wQA+/8DAPn/AgD8/wMA/P///wAA/P8DAP///v8AAP//AQD+////AAABAP7/AwD+//7/AgACAP7/AQD7/wQA//8CAP//AgD/////BQD+/wIA//8BAP7/AwD4/wUA9v8HAPf/AwD9/wEA/P8CAP////8EAPb/AgADAPr/CwD0/woA//8FAAEAAAAFAAAABAD7/wEAAAD3/wQA9/8CAPn/AAD8/wMA+///////AQD+/wIA/f/8/wQA+f8HAPr/AwADAPn/CgD3/wsA9v8KAPv/BQAAAAEAAAAAAAQA+v8LAPX/CgD7/wIABwD7/wgA+P8LAPv/BAD9/wEA/P8EAPn/AwD9//////8CAPz/BQD8/wIAAgADAP3/BQD8/wMA+/8CAPz/AwD7/wMA/v8AAAMA+/8IAPn/BQD9/wAAAwD9/wUA9v8MAPT/CQD5/wAAAQD7/wEAAQD9/wQA+/8AAP///v///wEA/f///wAA//8EAAIA/v8DAP//AAACAP3/AgABAPn/AgD///7/AgD6/wQA/v/7/wYA9f8KAPj/AgACAP7/AQD//wAABAD8/wcA9/8JAP3/AwAFAP7/CgD6/wcA/f8AAAUA/f8FAAIA/f8MAPr/CQD6/wMABAAAAP//BAD6/wkA9f8IAPf/BAD4/wAAAQD8/wEA+/8CAP3/BAD8/wUA/////wEAAAACAPr/BgD4/wMAAgD5/wcA+/8AAAIA+f8IAPj/BwD8//7/BAD6/wMAAQD8/wYA/v/9/wUA/f8FAPj/BgD3/wcA+v8BAPn/BAD7/wMA///7/wQA/f8FAP7/AwD+/wQA//8DAP3/BgACAP7/BgD6/wUA+/8BAAEA/v8BAAQA/P8DAP3/BAD8/wYA+P8EAPz/AQACAPf/BwD7/wMAAAD+/wMA/f8EAP7/AAD+//3//v8EAPv/AQADAP3/BwD8/wQAAAACAAEAAgD+/wQA/f8DAP7/BAABAAIAAAAAAP//AQAAAAEA+/8EAPf/DAD2/wUAAAD8/wUA//8AAAQA+f8BAP7/AQD8/wIA/f8BAPz/AAD6/wIA+/////r/AwD7/wIAAAD/////AgD//wMAAQAAAAAAAwD8/wQAAgD7/wkA9/8CAAAA/P8BAPz//v8BAAAAAQABAPv/BwD8/wQA/v/9/wQA/P8DAP3/AwD7/wYA+v8FAP3/AgAAAAEAAgACAAAABAD+/wMA//8CAPz/BQD7/wUAAAACAP//AAAAAAEAAAD9//7/AgD8/wMA+v8AAPz//P8BAPr/AgD8/wAA/f8AAAAA/v/+/wIA//////7/AAD//wIA+v8GAPv/BQD8/wIA/f8BAP3/AwAAAAAA/v8BAAAA///+//7/AgD///3//v////z/BAD5/wIA+/8AAP3///8BAPz/BAD7/wMA/P8FAPr/BgD///7/BAD8/wMA/v/7/wYA+/8HAPr///8EAPv/CAD9/wEAAgD9/wMAAQAAAAUA/v8JAP3/BgD+/wIABAD9/wAAAgD7/wYA/P8CAP//AgADAP//AQD//wQA//8BAAEA+/8FAPn/BAD7/////v/+//7/AAAAAP7/AgD9/wMA//8AAP3/CAD0/woA9v8EAPz//f/9/////P/+//7/+f8BAPj////6//3/+//6/wEA+v8AAP3//v////3/AAABAP3/BAD5/wUA/P8BAAMA/v8FAP3/AQABAP//BgD9/wEAAgD+/wcA/P8KAP7/AwAEAP//BgD8/wQA/f8BAP//AgAAAP//AgD//wcA/f8JAPj/BQABAP7/BgD4/wYA/f8AAP3/BAD7/wYA+/8EAPv/BAD5/wYA+//9/wMA+v8GAPz/BQD9/wMAAQD8/wQAAAAAAAUAAAABAAAABAD8/wsA9/8JAPr/AQAEAP7///8DAPr/AwAAAPr/CQD6/wUA+/8CAAAAAAD//wAA//8DAAIA+f8JAPz//v8EAPj/AwD///v/AwD9////AAAAAAIA/f///wAAAAAAAP////////z////9//3/AAD7/////v8BAP///P/8/wIA+v8GAPj/BwD7/wQAAQD//wMAAQD//wQA/f8DAAMAAQD//wUA/f8AAAYA/P8MAPP/DAD7/wMABwD+/wgA+/8GAP7/BAAAAP7/AQD+/wMA//////v/BAD6/wQA/f8AAAAA//8DAPv/BgD5/wYA/f8GAP3/BAD//wUA//8CAAEAAgD+/wQA/f8FAP3/AgD//wMAAwABAAAABAD9/wUAAQD7/wMA/f8EAP7/AAAEAPr/CQD6/wQAAAD+/wQA9/8MAPb/CQD7/wEAAgD//wIAAgD//wUA/v8DAPj/CAD7/wUA///+/wIA/f8EAPz/AAABAPz/AgD/////AQD9//3/AQD+/wAA///+/wAA/f///wAA/v8CAPz/BAD9/wIAAQD9/wcA+P8IAPv/AwABAP//BAD//wAAAwD8/wUA/f8AAAIA/f8CAP//AQD8/wUA+P8HAPv/BAD9/wEAAgAAAAIA///+/wEAAQADAP7/AQD///7/BQD6/wYA+/8EAAEAAAAFAP3/BAACAP//AgADAAAABAABAAQAAAADAAAABAABAAEAAAADAPz/AgD+////AwD8/wIA/v8AAP3/AgD8/wMA/f///wEA/P8BAP///P8CAP3///8BAP7/AAD9/wYA+f8GAPr/AgD/////AAD///r/AwD5/wUA+/8CAPr/AQD+//7/AQD///3/AQD7/wAA/P8BAP7/AAD9/wEA/v8CAP7/BAD7/wUA/f8BAAYA+P8IAPz/AgAEAPv/CgD4/wMA/v///wQA+/8EAP//AwAAAAMAAQADAAIAAAAFAP//BQD9/wUA/v/+/wIAAQD//wMA/f8BAAIAAAADAPz/AwAAAAEABAD9/wEAAAD+/wMA/////wAAAQD+/wIA/f8AAAEA/f8BAP///v8EAP3/AAAAAAIA//8FAP3/AwD+/wQA/P8IAPj/CAD6/wYA/P8DAP7/AgD+/wEA//8BAP3/AQD9/wQA/P8CAP//+/8GAPj/BwD4/wUA+////wEA+/8DAPz/AQD9/////////wAA//////7////9/wAAAgD4/wYA+P8EAP3////+/wAA//////7/AAD9/wMA/f8DAP7/AAADAP7/AgACAP7/BgD9/wUA//8DAAIAAAACAP7/AgACAAAAAQADAP//BAD+/wMAAgD//wUA/P8GAAAAAgADAAIAAgAEAAMAAQAFAP//BwABAAQAAwACAAEABgD9/wYA/f8FAP//AQACAP7/BQD7/wYA/P8FAPj/BQD8/wYA+/8AAAIA//8AAP///v8DAPz/AwD9/wIA///9/wMA+f8FAP3///8DAPr/AwD+////AAD+/wEAAAD8/wMA/P8DAPz//P8DAPf/BwD3/wUA+P8EAP3/AAABAP//AgD//wIA/f8GAPv/BwD7/woA+v8HAP3/BQD//wUAAAAEAP//AQAEAP7/CAD6/wgA/f8DAAAAAgAEAP7/AwD//wIA+/8GAPv/BgD8//3/AQD8/wQA+v8DAPr/AQD///z/BAD8/wEAAgD7/wUAAAD8/wUA+/8EAAEAAAAAAAUA/f8GAPr/CAD5/wgA/v8CAAQA//8EAP3/BQAAAAEAAwACAP7/CAD+/wIAAwABAP//BQAAAP//CAD7/wgA/v8EAPz/CAD2/w0A9v8JAPz/BAAAAAIA//8HAPv/CAD9/wQA//8FAPn/BwD7/wQA//8AAPz/BQD8/wIAAAD//wEA/f8EAP3/BAD9/wEAAgD//wIA///9/wQA/P8FAPr/BAD9/wMA/////wMA+/8EAPz/AQD+/////v8CAP7/AAACAAAAAwD//wMA/f8GAPz/BQD+//7/BgD6/wQA/P8AAAEA/v8DAPn/CAD6/wUA/v8CAP7///8BAP//AwD8/wUA+P8HAPn/BQD//wIA//8EAP7/AwD+/wMA//8CAAAA/f8CAPz/BAD+/wEAAAABAAIAAAACAPz/AwD+/wUA/P8GAPf/CwD2/wcA+f8CAP7/AQAAAP//AQD+/wMA/P8HAPr/CQD7/wUAAAABAAIAAgD+/wQA//8BAAAAAgAAAP//AQD9/wUA/P8BAAMA/f8FAPr/BQD9/wMA/v8BAP//AQD//wMA/P8DAPz/AwD+/wIA/////wEA/v8CAPv/BwD4/wcA+/8DAP////8CAP//BAD8/wQA+/8FAPv/AgD+/wAA/f8AAAAA/v/+/wAAAAD+/wEA+////wMA+P8KAPf/BQD9/wMA//8BAP//AAD9/wQA9/8IAPn/AQD///z/AAABAP3/AgAAAP//AQACAPz/BAD9////AgD+/wEA//8AAP///v8CAAEAAgAEAPv/CAD9/wUA+/8FAPz/BwD7/wYA+v8GAPj/BQD8/wEAAQD//wAAAQD+/wQA/v8AAAEA/v8CAAAA/f8FAPv/AwD+/wAAAAACAP//AAAAAAEA/v8DAPv/BQD7/wMA/////wMA+/8BAP7/AAABAP7/AQD+/wEAAAADAP7/BAD//wIA/f8FAPv/BgD7/wQA/P8FAP3/BAD//wEA//8EAPz/AwAAAAEA//8BAAEAAQD///7/AAD9/wIA+v8CAPv/AgD8/wEA/v8AAP3///////3/AgD8//3///////3/BAD2/wkA+f8BAP3/AgD9/////v/+//3/AgD7/wMA/f8AAP7/AQACAAEABAAAAAEAAwD+/wYA/f8GAP3/BAD+/wEAAgD8/wcA+v8IAPr/BAD//wEAAAACAP//AgD+/wIAAgD+/wIA/P8BAP3/AAD+/wAA/v8AAP///v8CAP7/BAD9/wIA/v/+/wIA/P8GAPj/CAD1/wkA9/8FAPv/AwD6/wQA+f8CAP//+v8FAPv/BAD6/woA+P8HAAAAAgADAAIA//8DAAIAAAAEAP//AgAAAAIA/f8EAPz/BAD//wIA//8EAPz/BAD9/wQA/P8GAPn/BgD7/wMAAAD//wEAAAD+/wIA/f///wAA//8BAP///v8BAP3/AgD8/wEAAAD+////AQD9/wAA//////3/AQD///3/AgD5/wIA+/8AAP7/AQD+/wAAAgD+/wEAAAACAAMAAAADAAAAAAAEAP7/BQD+/wUA/P8JAPz/BgD+/wQAAwD+/wUA/f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VJBSoEdgLFAGX/jf73/Y/90P3D/i0AIwGXAQQCQAI0AqMB2gAUAFD/3/7//rn/cQDrAIsBRgLWArUCEwKNAQsBTwCs/1n/Wf+9/1cA8QA3AfsA3wDuANgAjgD6/2P/3f5W/oP9W/wW++b5NflQ+YP6nvyJ/msAWgIZBCoFpQQnA7EBiADi/5H/Z/9//9f/sABpAZ8BIQGMAD8Arf8u/7L+bP6o/gT/wv/AAIkBGQJWAoACZAIJAp8BRQETAbsAXgAgAAsAEQAgAEMAYgBiAF4AUgA0AO7/qv+M/23/Sf/h/iP+M/0M/OP63PmZ+d766vwX/9MAgAIgBLwERATZAloBVADC//7/iADzACQBVgGmAXUBtQBq/2n+D/4D/kP+fv7j/qL/kwC0AW0CewJNAjgCcwJQAr4BBwFnACwA+P/X/6//kf/J/z8AtADcAKcAOQDn/8D/dP/a/l3+Nv4s/r398/z7+xz7cvp5+pn79fxF/tD/3QG2A2MEBgRTA8UCYwL9AYcB8ACGALAABwH0APj/pP78/RP+Vf4x/gX+eP66/z8BegIJA/kC2QIMAz4DuQJ1AS8A4v8TAAkAnf8y/2j/DgC8ABgB2wBqAEUAXgAyAHn/v/6W/hr/nf+c//b+Bv7//OX7tPqM+TP5M/rN+8v9DQBBAiEEJgVeBdcEFwT0AvoBSwGdABAAov98/0n/nv7P/Y/91f0g/kv+CP86AIkBogJPA8MDqQMFA2gCogGbALf/lf8RAGEAYABdANAAFwHmALwATwCU/wP/4v4W/y7/Tf/2/xQBqQFoAZYADP8R/f76+/hg9+f2Evh5+jL9GwAaA7AFYwfTB/4GPgVRA3UB///j/g7+tv3Y/Vn+s/58/kn+Sf6K/hv/fv/7/9EA4QHUAnUDUAOyAlsCJQLtAWsBuwCMAJoApgCZAFYA7P+C/0X/I/8u/x3/Yf8QAMQASwGBAaQB3AF8AXAAEv9u/b774/ku+PD22PZh+M36Zv33/40CMQXOBucG3gU7BNcCpQHCAA8Ah/93/6P/y/+O/+T+Tv4K/jj+mf7M/h//3//uABMCyAIEA0kDtAMTBOkDCAMFAlcBywACABH/g/5Q/p7+Ov/d/0QARwCQAOkAAgHwALUAwwDTAGEAf/84/rP8HvuG+Sf4fvca+Av6hvyh/nUAewIqBAwFrgShA/gCsQJbAvEBewHsAI4ANACV/8H+yv0r/X39C/5k/rj+XP+WANABiwL+AnYD6QNJBF0EvwOwAs4BIQGTAMb/wP4u/lf+7P4m/xP/8/4k/w8ABAE0AREBVwG+AcoBQAHs/+z9S/xg+3P6Uvl6+JL4Gfol/LP9x/7W/0UBlgI2A4cDuwNWBPYE5gR6BFID0wGFADv/y/09/Gn7ufub/Lj9uv7c/1YBigJmA+ED7QPIA54DiwMcAzwCdQE4ARoBfACV/9/+gf5W/i3+Jv5c/uL+5P/kAJABwwHJAeABugHsAJX/H/79/ET8bvtp+n35IPnO+Qj79/up/MX9p//FAWMDOQTCBF8FpgV0Ba4EMgOMAT8ATf9W/kD9hvzE/Mb9t/5Q//D/yQC1AYACyAK2Ao8CtgIkA2sDFgOFAjECvQH+AMT/jf7M/Xv9if3F/Sj+uf6a/7cAdAG1AdAB3QHJATQBEQDU/qr9j/xY+zH6UfnE+P746/kr+3z8s/1Z/10B2wKzAzEE1AQ/BfUERgRtA4QCZwFKAHP/xf5B/lH+3v5W/5H/y/97AC4BVAFDAWABygFFAowCugKvAoUCYgIOAlQBKgAh/5L+aP4u/vr9FP6j/on/fQAdAVYBfgG+AewBaAFWADf/aP6q/ab8g/uX+gj63PkT+pv6OvsH/FT9E//NAO0B2ALmA9YENAXjBFEEmAOeAokBnQDr/0//4/7w/hv/T/+E/+H/WACXAMQAFwGSAeQBBQIPAh0CBgLFAVYBzQAZAGX/+f7H/qz+kv7A/mD/JwCpAOYADAE+AUkBEAG0AEUAz/9k/+n+Uv57/ZH83Psu+4H6DfpC+vv67vsA/Vv+8v97AccCvwM9BE8ESAQRBKYD3QIaAo8BNgHzALwAkgBvAF4ATgBLAC8AEADn/+j/CQAxAFcAlgDtABYB4wCFADYA6/+K/yf/Iv9h/7n/CgBXAJ8AxADEALAAkgBjAEMAPwBMACcAxP9N/8n+JP5G/UP8RPuL+lX6n/on+9j73/xV/ggAhwGOAg8DPgNdA2sDKwOSAgcC5wEhAkgCQwIpAhAC7AGvAUQBogDl/2j/W/+E/5X/kv/H/zEAfABkABIAsf9i/zP/Sv+R/+j/QQChAPwAKAEgAfIAtwBtADIAHAAyAFYAZABGAP//jP/u/hv+F/0O/Dz7wPqj+tX6VPs3/Gz9t/7V/7gAXAHbATkCeQKjArcCzAIOA2oDkgN3Az0DAwOnAgwCNAFcAKn/Nf8J/wb///7//jD/gf+9/6b/Zv9G/1f/e/+1/xsAkADvACYBRAE7AfIAkwBsAGgAYABYAH0AswDHAKwAYQDg/xz/Vf6d/cf87vtg+zj7RPto+9f7mvx+/UD+Bv/k/70AZQHrAXwC+gJgA5sD3APyA78DXwP3Ao4C8gFTAc0AXgDf/2//Sf9H/yf/7/7U/tX+zv7O/vj+Xf/I/zcAwQA/AX4BbQFAAQMBrQBFAAEA//8dAEIAaQCfANAA5gDTAJIAGwB8/8b+C/5I/YX82vtj+yT7Jfts+/X7q/x9/Xb+hv+IAF0BGwLOAnUD7QMsBDoEJwQBBMADTgObAt0BPwGtAPr/N/+s/mj+Qv4R/vf9GP52/v/+hv/3/1AAvABDAa4BvgGDAT0BBQHIAGkAAQDC/8//GABqAKIAzAAAASYBEAGlAAkAU/+b/vv9Wv24/CX80Pu/+7X7u/vz+3j8Jv3y/eb+8v8JAQQC+AK3AzAEaARyBGQEFwSyAyYDhALOAQMBQQCC/9n+Qv7e/bX9w/3s/Sz+l/4f/67/IQCMAO0AQgF8AYoBdAE1AeQAogBwADkADgALAC8AVQBmAH0AnQCZAFoA+v+X/yf/rf42/r79Mv2W/Bv8yPuJ+237o/s4/An9Av48/5sA2QHSAp0DTATCBOkE3QS5BGEEyAMGAz0CZAFpAH3/yf5P/u/9wf3p/UL+jf7S/kX/1f9EAIEAwAAOAUoBagF3AWkBLgHgAKwAiwBKAPb/zf/k////9f/3/ysAbABzAE0AGADP/0v/j/6+/eH8/fs8+836svrc+lX7M/xe/aL+5/8gAUYCPwMCBIYExwTQBKcEVwTMAxADNgJeAYIAqP/3/n/+Mf7w/er9L/6Q/tf+HP+O/w4AawCoAO4ANAFUAVYBUgFBAQMBuQCGAFoADQDK/83/9f8IAAgAPACOALkArACYAHgADQBV/4b+tv3D/MH7+/qU+n36vPpr+4L8vv0O/3oA5wELA8UDUgTRBB0F+gSKBAQEdAO2AroBrgDC/w7/hf4j/uX93P0M/mT+vv4G/1X/uf82AKcA+QA7AXoBtgHPAbkBeAEkAdQAggArAMz/fv9k/3n/oP/W/yQAiwD6AC4BBQGXAP3/K/8j/vP81PsG+4v6X/qZ+jP7GPxB/X3+yP8CAScCGgPjA2cEtQTiBL8EUQSXA9UCAAIhAScAU//E/mn+Q/4+/lz+jf7q/ln/uf/l/wEASgCnAOkA+gACASMBSwFRAREBswBcACQA7P+s/3D/X/+N/+L/SACRAL4A2QDkAKEAAwAl/0D+aP2L/K37A/ux+sv6Q/v8++H86/0l/3wAwwHBApUDUgTgBBQF4wR9BPMDRANZAl0BYgCS/wj/sf57/lj+av6p/v3+Lv9V/53/8/9NAJMA3QAfAV0BdwF9AV4B/wCaAEgAEQDI/3//Wf+A/8b/+P89AI4A5QARAQwBzgBPAJP/tf7Q/cT8svvb+ln6RfqE+ij7HvxX/br+PwCsAbkClgNRBN8E/QS9BE8E0wMrA0sCZwGiAAcAj/8x/+T+uP6l/qj+w/7p/hz/cv/s/2EAxwAcAWkBoAGiAXoBRAH8AIsAHwDT/53/av9J/2X/sP8CAEMAkgDcAP8A6gCsAEQAmf+v/rv97fwW/Dr7ffpU+rT6U/sd/C/9v/5hAN4B9gLYA38E0ATTBGIEvAPxAkEClwHYACoAqv+M/3f/T/8b/x3/Q/9Y/2X/eP/c/0EArAAJAWcBpAGpAY4BLwHBACoAtv9f/xj/6/7t/kD/q/8gAHIAwwAQAT8BOwHxAHkA3f81/2n+hP2R/LX7DvuR+nH6uvpw+1T8hf0H/7IAMAJYA1YEAwVVBTAFzQQSBCQDLwJTAZYAzf9C//v+8P7d/ub+Dv9D/4T/xf80AJUA5QAiAYYB0AHPAaQBYQErAbcAIACC/yP/2v60/tn+IP+X//v/ZgDIAAMBBwHwAMwAXQC9///+S/52/XH8ffvC+lL6PPqi+nf7kfzO/Wv/KAGDAnYDPgTbBCwF6gQ2BJoD0gLJAdcAEwBc/97+jv59/sT+6f4k/6T/FABoANIAMgGUAeUB/gELAvwBiwEVAaMACwB4/wP/1P7d/gH/JP+i/xQAOQBfAIkAswDCAKoAWgAcAJ3/4/4k/if9APwM+4D6Yvq8+lL7OPyn/Ur/2AAtAhsD0QN0BMoEiQTkAxwDcgLfAQUBBgBn/z7/Nv87/yn/Lf+T//r/OAB/ALYAFwGuARECMQIzAgEC0QGKAdcALACf/zX//P7S/rH+5f5B/4P/xv/c//z/RgBwAFsAKwDe/37/CP8m/iz9Svxr+7L6Zfq/+pb7ofzJ/Wn/IgF/AnwDLQSuBLkEUwSnA+ICAQIoAWkAp/8n/+n+3/7n/ub+Cv9v/+L/OgCYABIBqAEwAowCzwLvAs8CWwKtAf4AMABE/3j+/P3q/Qz+Sv6x/jH/uP8LACoAPABIAFAAMgDW/1z/3v4//mv9Xfw/+4L6Ufqz+lP7OPyT/Vr/agHxAs4DewT2BEwFIgU/BEgDfAKwAfEA9P/W/jf+Hf5O/lD+RP6p/pj/oAAsAbQBegI3A9QD9QOyA2EDvgLzATgBKgAd/2P+AP4I/gL+/f1c/hn/sf/j/wUALwBMAFUAFgCr/07/s/4h/m79TPw1+3j6FPo/+uz62Ps4/f7+8gCiAs8DXQSxBM4EbATGA9gCvAH7AJgA6v83/6P+gP66/p/+dP6d/hf/xP+LADgB/QHZAqoDVAR/BD4EtQMUAzICBQG//6v+Bv6k/Xr9gv3p/Yn+JP+M/9z/HwBLAIgAiABTAPn/d//l/g3+zfyK+4H60/ni+Yv6iPvg/Lz+7gDzAhYEowQoBTcFqwR2A0MCXgGhANz/Dv+F/hf+Gv5Q/nD+df6r/l//QwD9AJIBoALJA4YEzgTBBKUEGwT/Aq0BbAAk/wX+ZP0R/QP9Qf3O/Yf+Cf9A/6r/MAB5AKcAyQDmAOsAkgDR/+v+g/3i+0z6HfnE+Dn5dPr6+zn+tgAWAwEF5gU8Bi0GewUoBMgCQgElADj/Tv7S/Zb9r/38/WL+iP65/jv/1f+VAFYBNAJuA4wEJgWNBYMF3wT6A5oCFgGB/9b9yPxT/Bv8FvyM/GX9Vv4H/5X/KACmAOEADQEpAekATQBy/4b+Hv1k+575m/iI+DD5bfpW/OD+nQE4BNcFxQbjBoUGlwUQBGMC3ADv/zX/nP76/ar91P0p/jz+G/5L/sH+gP86ADcBgwLBA+QE0QV+BnoG4AXVBHIDsAG//+v9k/zK+1/7kfsW/Lf8if1x/kr/AQCJAP0AlAEMAvcBawFyAPz+Sv1K+y75jvcN9733e/mQ++T97QDAA6cFkAakBjgGggUlBMwCoAGDAM3/bv/P/jL+2f27/en9tv1g/b/9nP57/8QALgLmA6wFwQZmB34HogZHBZsDpwGW/8b9lvwW/N77sfsV/Pr8s/0//sz+c/9GAOQAagH1ARECowHXAIf/nf1I+yH5ovdH98T3Kflr+zX+NgHHA3sFNgZYBtUFvwRHA+0B+wCGAEcACQDZ/3X/Of/2/jX+iP3u/NX8ef04/nT/lAHMA+MFZAcECD4IigfaBbYDXAEE/0f9EfyD+6f7H/zr/PL9f/6p/gL/S/+h/97/JAC8AEIBEAFZAEz/hf2D+4H5SPhv+Oz4Gvpg/Cv//gHwAy0FIQZJBlsFRwQfA+oBFQFaAPb/hf8M/87+1f5o/oj9Gv0O/YH9A/73/scAHQMVBYYGsQctCMsHeQamBI0CUgAt/sP8D/zS+wP8kPxq/Rv+ev66/hP/cv+Y/7H/PgC+AOgArwDt/7T+2vyw+uL4VviL+Ef5NPur/X4AFAOGBIwFIAamBeoE4AOBAqQB8gB6ADkAp/81///+r/79/Un91vz5/Jb9RP6n/5cBsgPKBV8HGggsCHgHAwZIBO8BkP/L/Xz82vuu+8r7Ofz7/HD9zv0P/mr+PP/i/6oAYgH2ATQCnAEtAEv+5vti+XX35/Z095n4wPqj/QcBmwMBBe8FdQYIBuUEgQNeArgBIgGiAEYAIgCl/0v/4v7J/Qv9pPyN/C798P2T/yYCcwQ5BrkHnAjMCP4HHAb+A9UBo//i/en8Rfxl/MP8BP1//Zf9lP3W/Qb+Uv7i/rz/zwCzAc0BTQE/ADL+wftp+Q/44PdR+Jj5K/ww/wsC7wMdBQEG0QX/BIkDCwL7AE0A9v/Z/5n/rP/S/7f/Mv83/or9CP0E/WX9LP7V/yUCYQSBBrkHbQinCIQH3AXDA2IBQv+f/YD8Hvzt+wn8tPxH/XD9gf3r/aH+Rf+c/zMA4QAoAd8Ax/8w/lL8Jvr8+PL4OvmJ+rH8l/9gAuYDzwS5Bf0F7ARDA6ABuwBWAMr/kP/B/+X/DQAZAH3/w/7E/Q79EP0b/bH9Vv+LAcADtwUUBy0IcQhvBwsGeARTAjQAdv5I/cf8Sfw//Mf8Mv0p/TP9hf0K/nf+yf50/ycAfgBnAM3/U/6X/KX6ofm1+bP5y/rY/Ib/NQKuA3sEWgV5BaIEMQPaAR0BogBkAP//BQAyAHAAcwC4/4v+jf0N/aj8g/wp/dP+FQFyA04FVAemCMQIKgjRBjMFCwONAJ7+Uf2D/BL8DfyX/CL9kv3B/db9O/5z/qn+1f7w/iv/J//J/sb9Pvyg+un5VPrS+sH7r/0PALIC9AMdBJ0ETASZA60CNwGRAEgAJABiAD8AMQBvAHgAz/+p/o398fzA/ID85vx0/ocA6AJXBR4HiAjgCCwIWQeUBScD2AAI/9P9RP3Q/MT8cf2+/fH9+f3h/Sj+bf5h/p3+rP6u/sT+O/5N/Sj85Pqz+m37CPwg/UT+CADqAdQCFQM8Az4DIAOoArABUgE/ASMBKgHtAJEAxQBgAIf/of5x/dn8sfyN/Cf9vv6DALsCnQQCBkUHoAcrB0UGDwVtA8ABDAD7/sr+cv4Y/hL+J/5u/mL+6f3x/dD9if1W/SH9H/0A/Zz87vs++4X6u/ra+8f8Bf5+/0oBHAO8A30DfAMKA1ACfwG/ALcA1wDTAK8A5QCoAHUACQA2/43+o/3//A/9aP0j/p7/OgEOA5wEygWOBpcGyQX0BDEE/gKbAUsAxv+R/z3/7f7Z/vP+Cv+S/iD++P2J/Z/9b/3q/Pj81vyA/Ov7y/pN+g37Gfw8/cn+TQD9AY4DzgORA1UDtQJ0AvwBJQEYATsB+wDfAGwA6//n/0j/Y/68/ST9DP1d/c39Bv/nAHoCDgRkBRQGcgboBcYE5gPmAgQCEAEBAJr/4v89AB0Ar/9m/2b/Ff85/rr9mf19/fT8FPz4+xP8s/sO+0v6MfoO+yP8c/0N/3UA9AH1AgMDDAPzAsICaAIEAgoCPwI9ArEBDwF3AJX/rv67/Qj9y/ys/AX9jP2M/vX/iAENAyIEAgWcBbYFPwUpBB0DnwLuAXMB2gCDANoA7wDDAGsAzv9a/93+K/6o/Vz9Jf22/FT8Afzs+5D7xvoo+in6VPuP/NL9Uv/TAGcCPQNyA4EDngNWA9QCWwIhAhICsQHtACcAi////pD+JP7n/ZH9kv2r/Sb+2P7r/5ABBgNYBP0EjwWVBc0EnANxAsYBLAGyAGYAeADPAAUBHQHpAEkA0v9I/9P+Nv5w/Sn9gvzD+zL71/qF+vj5aPnU+VT7mPwh/rj/GwGTAlQDogMUBOkDjANZA+wCjwIdAlABZgCe/43+DP7P/Vv9O/0q/V796f2X/o//2gBnAr0D0QSCBYYFWQWLBE8DNQI9AYgAJwDh//D/kwAfAWgBTAHCAF4A4/8y/6L+Nf79/Yr99/x5/Oj7Mfta+p35R/nF+df6YvwQ/k3/pwDBAYgCPwOuAxUERwQuBAQEhQOsAooBWQBa/2r+w/1P/Sz9Jf0O/WD94f2f/rP/FQGmAucDqwQ1BXsFNgVUBEwDlALsAWkB7gDRABoBSAFVAfQAdgD+/07/t/4t/sD9mf1b/Q/93/x3/A78pvsB+5z62fqJ+2z8bv1X/pL/1AClAXQCJgOvA/cD3QOLA+0CCgICAQUASf/H/mT+Lv4f/iL+R/5B/jX+jf48/xoAIgEOAtgCeQPaA/0DrgMgA5ACRQLsAZcBZQGIAa0BggE4AbcASwCn//3+jP5K/gz+zf3J/cX9p/1X/ez8jfwW/LD7vfsT/Jr8Lv3U/bj+lf9zAEAB+gFyAqkCkgI0As4BOQHMAJoAhQB/AGwATwAxAPD/bv/8/s/+6P5v/yUA3gCJAQsCZwKMAlQC+AHlAe8B+gH8AQcCXAK0AroCdQLaAScBTQCF/+7+bv4Z/uj9EP51/r7+uv6I/iT+gP29/Bf8q/uT+7P7Ivz6/Of90v65/3oA7AD+ALIAXwAoABIAJABlAMQANQGlAe4BBAK4AUMBuABNACcAMwB7AN4AQAGxARoCOAIiAvwB2QHAAZ0BbQFdAXwBhwGEAX0BOgH3AHwA4/+N/0P/Df/o/vr+Lf9Y/2v/NP+8/vT9Cf11/Oj7ePt6++37w/yR/Uj+4/5Z/3//Zf81/8b+cP55/tf+e/85ABIB+QGsAuMC0AKbAkMC7AGaAYQBogHSARwCiQLeAsUCcQIdAs8BjQE3AeoAmQBGAD8ATgBGABcA8P/a/5f/Sv8f/y3/Wf96/7T/5//6/93/gP/d/gz+TP2z/Eb8E/xQ/Pb8sf1Y/tj+Mv9N/zn/1f48/tD9wv1G/gH/5P/3APEBqwISAzED8wKKAggCoQF+AZIB5AFQAq4CAQMmA/sClgI1ArQBIwGnADcAAAD0//3/7//d/8b/sf+K/y//DP8r/zL/R/9s/4T/j/90/y7/oP7c/Rr9ofxU/DD8a/z//L79Yf7Y/ir/J//y/n3+8/2x/an9Hf7k/sf/3gDfAbECJgMjA88CXgL/AbEBvAHcARkCbwKsAgIDIAP4ApsCDQKSASgBwABdABcADQArAEUAVAB9AIQAWgAkAP//AwAPAP7/4f/G/43/Of+6/u/9Hv10/Nz7pPvA+zX85vyY/TX+xv4P//H+uP5T/tr9rv3U/Wj+RP8QAP8A4QF3AskC4gKjAlMCIQIcAk0CcAJ2AqQC3QL1At0CfALtAUoBugBYABwA2P+p/7j/CABMAIsA1AAQARUB2wC3AMMAzgCWAEoA+/+Z/xb/dv7B/f38K/yS+277p/sm/L/8Zf3z/VD+ev58/lP+9/2n/an9Ef64/m//JgDZAHsBAgJgAoUCaAJRAksCYgKWArYC2ALvAgEDDQP3ApcCEQKPAQMBoAA/AOX/wP/F/wwAawC/AO4ABgERAfcA3wCuAIMATgD+/7n/YP/2/kX+Yf2g/A/8vPuf+8T7JPym/CP9lf0O/j/+JP75/eH9//1c/tP+f/83AMgAXgHxAVgCjQKMAmkCYwJcAlgCggKrAqICmwKUAqIClQIsArEBQQHWAHUANAARAAEAGQBWANMARgFsAXoBcwFQARwB6ACkAE0A1f9I/+b+ZP6Z/cj8E/yU+0j7Pvt4+/v7dPzL/ET9oP3O/dr94f0I/lf+w/5Q/xgA4wCNASICfQKuArkCrwKmApICawJDAlcCZwJZAkMCKwISAsQBRwHdAJgAYAAzACUALQBKAJQAAAF7AcUB4AHnAeAByAGlAXwBRAHjAFkAvv8e/2b+jv2t/OX7YvsJ++H69/pN+777Cvxa/Kv8Cv1I/X799f2I/hz/tv+HAGkBEgJ5ArwC+gIFA+ICzAKyAoICTAJKAmwCaAI4AgcC5QGVARwBqwBaABcA2f/M//v/QACJAOQATAGfAdQBAwItAkQCLQISAv8B0AFZAZMArv/N/ub98vwR/F/73fqG+n/6x/oy+3r7t/sQ/HL8z/w2/dX9l/5I//P/xQCoAVkCxAL+Ag8D+QLKAqcCjQJiAkECQQJLAjYCCALhAb8BbQHmAHAAJQDw/8b/vf/Y/wcAPACHAO8ATAGQAc4BCAIxAk0CawKJAoACKAKKAcAA1v/O/r39rvy8+/r6cfot+if6SPp/+rn6/Ppd++n7lPxX/Sv+Ef8CAOcAwAGEAhYDWANeA1UDVQNLAyID8ALGAqkCigJvAksCEAK0AUYB6ACIACgA1/+q/6D/qP/I//n/NwB3AMYAIAF5AcQBAAJCAoACrwK0AoUCEgJdAYEAkP+T/nr9Yvxv+776P/r2+d356PkA+i36ivoQ+7r7h/yA/YP+gf95AG8BSALqAl0DnwO0A6IDiQNnAzsDBgPZArcCjgJmAjoCEALKAWMB5gByABwA2P+v/5T/lP+f/8j/EgBpAMgAHAFzAb4BEwJkAqACvgK7AokCBAJJAW8AjP+L/oX9ivyd+9T6Ofrd+av5qPnC+fT5U/r1+s77sfyl/af+pf98AEkBEwK6AhsDTwN2A38DZgNAAzIDIgP+AtACuQKkAnsCQQL+AagBOAHJAGwAJQDh/6v/lv+d/7H/0f8UAG8AywAiAYgB+wFeAqACuwKwAmcC5gE7AXQAkP+Q/oz9l/y6++r6Mvqu+Wz5X/l9+cn5Rfrt+sL7u/zL/dH+uf+HAEEB6QF4AuECFwMiAx0DHAMmAzQDQgNKA0UDOgMvAx0D7AKUAhUCkAEOAZ4AQAD1/7X/h/92/43/xv8ZAHsA4ABBAaMBBwJdAowCiAJcAgsClgH5AD4AeP+v/uf9H/1Y/JP72vo8+tj5r/m0+dj5Jfqs+mr7RPwm/RD++P7H/3sAKwHRAU8CjwKwAs8C5gLqAvUCIgNVA2wDbgN3A3QDTAMFA7ECQAKsAR0BvAB6ADQA7v++/6j/rP/Y/ysAgwDNABsBewHWAQUCDwL6AccBbgEAAYQA9P9A/4T+y/0I/TP8Z/vB+kH69fnh+Qj6Rvqk+jL79vvD/Ib9Q/4H/7z/YAAFAZsBAAIrAk0CfgK4AugCJQNnA50DuQPUA/MD7wOzA1UD8AKBAgwCnwFGAeoAhwA/ACkALQAsADAATwCAAKsA0wD8ABUBDgH9APUA5QCyAGsAKQDe/2r/1f46/qH9BP1r/OL7XPvY+n36aPqO+sT6Cvt5+xL8wPyA/VL+FP+h/wYAdADiAC0BVAGMAeUBRgKgAgkDcAOtA8YD6wMUBAcEvgN7A1YDJQPLAmkCEAKrAUcBAQHYAJgAPwAAAPr/BgADAPX/8f/0//b/CgAtAEAALQAKAO//zP+C/xv/uf5j/gH+if0A/XH86/uJ+2f7ffuV+6L7zfs6/Mv8Uf3F/S/+iP7X/jv/uv8tAIMA5QB4ARwCpgIXA4MD4wMoBGEEmASwBJEEXwRABBoEwQNHA9gCdgIGAooBCwGCAOn/af8e//b+xf6Q/nv+k/7H/vz+L/9U/2f/df+T/7v/z//A/5v/b/8x/+D+fv4S/pz9IP2t/E78Dvzs++H76Pv9+x38Q/x//Nv8Sv24/S3+wf5t/xsAyAB7ASYCtwJBA9gDZgTPBBYFVAWEBZQFhAViBScFyARFBLIDDwNKAmkBiwDI/xv/ff7+/bH9i/11/Xr9q/36/U7+oP77/lz/uP8TAG0AtQDTAMMAoQCDAFgACQCZ/xj/jf74/Wj95vxp/Ov7ffsu+wD78PoC+z77mfsE/In8L/3x/cH+mP93AFcBLALxAq8DXwT1BG0FzgUUBjUGKQb4BaUFMwWbBOYDHwNGAmgBkgDR/x7/gP70/Yr9Q/0d/R39Pv2B/eT9YP7m/m7/7/9uAOcAVAGrAeQB9gHlAcIBjQE8AcwAQACg//H+Nv57/b38Cvxr+/H6nvpx+mv6ifrJ+jH7yfuF/E79GP7q/sX/qQCRAXwCRwPfA1EEtAQFBSMFEAXiBJ4EPgTVA2kD8wJgAr8BIwGIAOX/Tv/a/oj+Sv4e/g/+Iv5Z/rH+Hv+G/+X/QAChAAIBVAGHAZsBpAGrAasBmQFtASsB3QCNADoA2/9k/9v+Uv7a/Wv99vx7/BH8zfur+6L7sfvi+zf8ufxk/SH+0f5o//j/kAAYAXkBsQHLAdsB5QHwAfcB6wG/AYoBZAFZAUwBMgEOAfIA4QDfAOUA4gDWAMwA3AAGAS4BOQEuASIBIQEhARQB8QDKAKsAnQCZAJAAewBbAEgARwBOAEkAPwA1ADMANAAvABwA+v/U/7X/nP9//1L/Gv/j/rn+n/6H/mT+NP4E/uL91P3L/bf9k/1q/U79Q/1E/Ub9Sv1Z/Yf91f07/qT+Df94//T/gwAWAZoBDQJxAs4CIgNhA38DeANbAy8D+QKzAlkC6gFyAfwAjgAuAN7/m/9j/0L/Nv8//1j/ff+x//X/RwCpABIBegHaAS8CewK3AuEC7wLgArQCdQIjAr4BPwGnAPn/Ov92/rL98fw0/IP77Pp0+hv65PnQ+eH5E/pk+tT6X/sD/Lb8c/0x/vD+rf9lABQBrAEwApoC9QI9A2sDfANvA0gDDwPNAoACHAKoATQBywBwACIA4f+r/4j/jP++/xMAcwDXAEUBwAFIAswCOgOHA7sD5AMKBB0EBgS/A1cD5gJ1Av0BcAHTADQAqv83/8j+U/7c/W/9F/3c/K/8ffxF/B/8FfwW/BP8C/wK/A78Lfxr/Lj8+vw4/YT95v1Z/sX+Ff9V/5P/2v8lAFwAdABxAGUAXwBfAFkATABEAFAAewC5AAEBTAGWAeUBOQKPAtsCGQNMA28DggN+A2EDLgPtAqMCVQIMAsgBjAFaATQBHgEUARUBGgEhAScBKwEsASgBFwH2AMUAigBLAAYAv/91/yj/4/6j/mz+Pv4T/uX9tv2H/Vj9K/0A/dL8pPx5/Fb8Qfw6/ED8Vfx8/Lv8Dv1x/dz9Sf7A/jn/r/8eAH0AywAIATcBVAFiAVwBSwE3ASgBIQEhASgBNAFHAWUBkAHEAfsBLQJfAowCtwLfAv4CDAMLA/8C6wLVAroCmgJzAkkCIwIGAvEB3wHJAa0BjQFnATgB/QCzAF0AAgCo/1H/+/6s/mb+Kv77/db9tv2a/Yb9ef1y/W39Zf1a/U39P/0r/Qz94/y1/In8X/xC/C/8J/wv/E78hvzU/C79kf37/W3+5/5k/93/TgC0ABYBdwHSASECYgKVAsEC7AIRAy8DSwNiA30DlgOxA8UDzAPJA7oDowODA1kDJQPuArYCgwJRAiIC+QHYAbwBqQGZAYsBfAFvAV0BQwEbAeQAnwBIAOb/d////ob+Dv6e/Tv94vyV/Ff8KPwF/Ov72PvN+8r7y/vT+9376Pv0+wP8GPwx/Ev8avyS/Mb8Cf1W/bH9E/6A/vj+cv/r/10AxAAfAXABuQH3AS0CXQKKArUC3wIJAzADVQN3A5YDtQPTA+8DBwQaBCQEJAQTBPADugNwAxgDtQJRAuwBjwE7AfcAvwCUAHQAWABDAC0AGAABAOb/xv+f/3D/O//9/rb+ZP4O/rL9Vv3//LD8bfw0/Az88fvh+9v73vvn+/T7BPwa/DX8Vfx9/Kz85fwl/W/9wf0X/nH+0f4z/5f//v9kAMUAIQF0AbwB9gEgAjsCRwJDAjYCJQIRAv4B8gHrAe4B+wERAi0CTQJxApUCuQLdAv0CFwMsAzsDQgM/AzEDFwPxAr4CggI9Au0BmAE+AeMAhAAkAML/Yf8B/6H+SP7w/aH9W/0e/er8v/yh/JL8jPyN/Jf8rfzN/PT8IP1P/YH9tf3l/Q3+LP5C/lX+YP5k/mT+Zv5w/nr+fv6F/pv+zv4R/0z/fv+7/xIAeADYAB4BUgGIAcUB/wEuAlECbgKOArACzQLbAtsC1QLUAuAC8AL4AvQC5gLVArgCiAJQAiQCBwLmAbkBjwFsAUIBAAG0AH8AeACDAHYAQwAAAL//ev8q/97+pP56/jv+zv1M/eX8rPyE/Fj8Jvz1+9X7zvvl+w38Kvw+/F38mfzr/D39if3Y/Sr+df63/v/+Vf+x/wMASABzAIoArADrAC4BTgFJAUIBWAGGAa4BuAG2Ac4BCgJOAnsClQK6AvsCTQOaA8sD5gP+AxoEMgQ+BDkEGwThA5UDPgPgAokCQQLlAVwBzgBpABoA4v+g/wb/a/5M/mP+Hv6j/Wr9Rf36/NT82PzP/Kn8c/xL/Eb8hfzM/Lv8bfxW/JX88fwl/Rz98/zS/CX9jP1Y/Tj9j/3Z/ff9HP5o/p7+/v6M//H/VwC8AD4B6gFgApwCBAOMA+kDKwRDBD8ELARSBKsEkwQbBLEDrgPdA8EDYwP6AtICuQKIAmQCNgL8AdYBxQGIATcBMQFaAToB3ACFAGQAiACCABcAkv9g/1v/Nv/g/k/+0P22/af9KP2V/CL87/vT+477D/vA+tf6rfpK+kz6u/r3+vv6wfrE+q37rPzw/I/8vvz9/UP/lP9Z/+v/CwHGARYCagLPAnEDIQT5A5wDDAQGBUoFiAS/AwMECwU7BUcEYAN+AxkEXwTwAyYD0gL7AjYDDAOgAkQCPQIdArABlQHHAcoBQAG0AJQA4QA3AQcBbgDX/9z/SQBqAM3/B//X/u3+rP4l/sD9Kv16/O/7l/sr+7z6Svql+e/4j/gI+Xv5G/kY+AD4Nflo+oX6//k/+kr76PwX/n/+nP4//8gAWwJEA0wDpwOKBEIFmAXnBTsGIwbtBZkFfgWjBZgFHAUvBIkDmQMHBM8D3wIeAvIBaAK+AogC2wGLAd8BTQJzAiECDwImAigCAwIRAjIC/QGsAVwBOAEyAR8BpwD1/2H/Lv8q/8L+1P3r/Hf8Ivy9+zD7ePrH+Vz5KPkA+b34Rfjc9/T3i/jJ+Jn4nPj4+Nz5svoH+5f7MPwV/S/+Kf/i/4QAUAE8Ai4D1gOnBOkEQQWVBQ4GoQaDBnYGQAZfBmoGKQa7BWoFEAXiBMsETQT9A3QDUgMpA9wCtwJ0AjgCDAL1AckB1AGfAYkBUwEVAS0BNQE5AcYAhgCKAJYAaQAEAJj/Sf8Z/7P+Q/6W/fv8nfwg/Gj7pvpB+tv5N/mS+FD4P/jn93f3Gveq9yH4IvgH+P737fgi+gP7/PpN+zz8Bv5t/5r/KQA6AdMCyAOIBOQEagVXBgIHZAceBxIHOQeSBzAHlAZIBhoG+QVdBf0EnwRXBBwE0QNZA/cCCAMFA9QCMwLuATMCXgISAn4BLwFFAX4BUgH7AFgAJQCFAJUADQBf/yz/Jv8M/5b+Kf6V/SP98vx9/Pz7Yfv3+pn6FPpm+Tj56/iM+Of3yvYx9yf4bPg5+Uj5BPi09xn4+fiW+vv7X/wM/Ur+Cf+V/34A2QCMAa0DkgWCBu4G8AZ4BiEH4wcbCIQIugiGCGUILggbByYGZAUuBWAFpQU6BQwEXAO8AksCSwJsAkMCNQLZAWwBdAFAAQABqAC/AMAA9AAAAUUAmP84/2H/dv9h/7H+7f2S/VP99/xs/PX7T/sU+9D6d/oS+oD5/PiS+If4kfi1+Gr4FfgW+G/4zfgL+UT5OfmY+Vn6oftI/IX8yvxZ/bT+3/8VAbQBOQIDAwwERQX+BXEGwAY5B7oHXwiXCEkI6weZB7wH7QewB+QGNAbuBc8FvAVMBWYEpQNSAz8DHAOjAiMCjwEwAewAkwCQAIYADACM/0X/Nv9m/zL/m/4e/gv+F/4i/rP9+fyN/Ij8h/wb/Iv7/Prk+sn6g/o2+g/62vnW+RH65fm7+cP52vm7+fH5W/qo+vv65PpM+0P8zPzX/DH95P27/tX/YgC/ADEBIwI9A/EDLwR4BH4FZQbpBsAGlgajBjsH8gfFB0wH5gbbBvIG0AY6BsYFsAVqBfYEggTuA4sDRwO0AiUCxwFvAQMBbwCq/yL/Af/u/rL+Rf6//Tr9//wA/eX82Pxr/Ov7/fsJ/Nr7mPs6+x37a/us+7b7iPsj+y37tPsw/Dv8+Pv4+0f8rvz2/Or8tvzF/AH9SP1A/UT9hv2E/Z39D/5W/o7+I/95/8j/ngBOAcIBMgJ9AjQDYAQ/BaUFuQWzBS0G7AZzB4sHKwcYB0cHSwcPB1UGmQVWBR0F4wRRBGcDnQImAqcBHAGTAPr/jf8e/8/+F/6l/bX9hv1P/TD95fyI/JP8gPyD/Hn8Rfxk/G78R/w//Ev8Mvwz/Er8cfx//G38u/wN/QT9Ef1b/Xz9rv0G/iH+M/5p/sz+FP8b/wb/Bf9b/8X/+f/I/8//LABrAOoAFwEDAScBkQEEAlYCowLQAhEDdAO4A74D+QNuBL0E1wT1BPgEvgTzBDoF6wTSBLAEQATKA3MD+wJqAvgBVwGWACYA3/8d/2v+vv1W/UD9Mf0E/Vz8Dfwl/Av83/vy+wH8MvyM/KT8hPyJ/NT84/wT/Xn9fP2j/Q/+Iv4T/mv+o/7G/iv/Z/+U/5D/kv+N/5//6P/8/x4ASgBSAGcAqQCaAHYAnwDxAGEBgwFcAXMBqwHtARMCIwJZAnwCzALaAqIC3wIXA+gCQwODA+IC4AJhA+4CgQLFApICNAI0AiECdAEgAVMB0AA9ABoAjf8Z/zf/0P5D/iH+vf0p/Rf9L/3S/Lv8w/yV/HX8wvzz/Kz8yPwm/Xf9oP3N/fT9Lv58/rf+9/4J/zr/d/+m/8r/BwBeAG4AdQCEAGwAoADcAM0AugC8AOUADgETAesA0gDbAAgBRwFnAYwBTQE3AbwBDALnAbsBxAGeAZ4BCgI9At4BlAGWAWsBTgFhAUwB/gC+AKsAgQB1AG8AAQC+/7P/av9T/3n/Qf/0/t3+xv6Y/oD+bP4n/uT96v0Q/vH9C/4F/sz9sv3P/e/97P1A/nH+cv5z/rP+9v4I/1L/fP+Y/9T/FAAzAGMAngCmAOYANAFGAUcBZAFkAXQBzwHCAY0BbwFqAXgBhQFyARwB/gArAUwBPgEgAfEAxADOABIBQAH/AMsAzQDXAN8A0ADLAGMA//8yAFYAKgD+/7L/a/9X/1T/Yv9d/wz/sf7E/sj+k/6G/nr+V/49/lr+df6M/pj+jP52/oP+zP7w/ur+0v7b/uD+Cv9N/1n/Tf9A/0b/Z/+p/8X/4P8LABYAMwCAAKsAogCvAOYAFAFDAYEBjwFyAXwBnQGQAY4BmgGEAZUBxwGUATsBKwEiAekA4wDnALoAnwCeAHcAGgABAPT/6v/z/9n/of+K/7T/jf9g/2f/OP8R/0P/V/8O/+n++f71/uj+7P7a/qj+lf6m/pP+Zf5+/pb+eP6C/rb+xP6+/t3+5/4K/17/q//G/9j/9P/4/zMAdQB9AI0AswDMAPAAEQEGAQ4BJwE6AVkBcgGMAZgBjgGQAZ8BegFoAYcBkgFqAU0BWQEeAd8A5ADhALQAmgCSAHgAcwBdAEcANAAEANT/uP+9/4//V/8w//f+xv69/rL+d/5S/kr+O/4n/lT+ZP4p/jn+ev6G/pD+pP6Y/pD+sf7t/gT/Bv8V/w7/Gv9P/1n/dP+d/6T/2P8RAA8AMwByAG4AdQCNALIAwQC/APMACwHzABcBSwFFAU4BZAGIAbIByQHRAb0BqwGvAb8BzAHKAakBrgGWAVEBTQEnAdIAxADPAJEAXwBGAAQAuP+E/3P/Tf88/zz/Ef/p/tP+vP6m/pH+a/5n/m/+Uf5O/lj+UP47/kX+U/5S/mv+gP6U/pn+mv6k/t3+Av8J/yL/LP9C/3P/qf/E/9r/+f8ZADMAUgBdAF4AhACoAMMA4AD1ABQBJwEtAVsBeQGMAbcBvgG/Ac8B2gHwAfIB0AHJAdMB5AHfAbkBrAGLAW8BbQFMAREB6gDIAK4AiQBTACcA5v+8/5H/UP8e/wD/zP6O/lP+L/4g/gL+8f3V/bj9tv3U/cj9of2g/cb99P0T/jj+Mv4u/k7+b/6B/rb+7/7q/gT/Lv9W/4z/xP/H/87/AgA2AGgAmQC1AL8A8gAYATcBRgFUAWsBhwGiAa0BtQHFAdMBywHVAdMB1wHdAdgB4QHOAbgBzAHKAaYBlAF2AWkBcwFaAS4BCAH0AOwAzQCzAJIAZQBEADYAAgC7/4v/Xv80/wf/1P6T/mL+O/7//bn9lP2A/W39UP03/SD9F/0r/Ur9TP1I/Xb9of3E/eL9Av4j/m/+yv4O/1T/mf/V/xoAbACQAKYA2QD+ABkBUgF6AXABegGlAbgBxwHhAdsBwAHSAdsBxAGvAaQBiwF7AYwBhQGBAYsBmwGYAaEBpgGRAXwBagFSATwBQQE1ARUBFAEbAQ8B9wDYAJUAVwA8AAwAyf+J/0z/Bv/e/r3+f/44/gP+1v2h/X79W/0n/fL82vzZ/Nr86Pzz/O387fwW/T79W/2A/bP96/08/o7+1P4l/33/3f80AJYA1AAVAVABfQGWAcMBBgIyAmUCgQKPAoMChwJsAkkCKAIOAvUB4gHeAb8BrAGiAZQBgAGBAX0BXQFQAUQBJwEWARYBFQEKASkBQAFIAT4BNAEkAQcB+QDeALoAgQBOAAcAtf9j/w3/v/5q/iv+7P20/XP9PP0R/eb8yfyp/In8UPwl/Aj88vv8+yr8Z/yh/PD8PP2A/c/9Kf6E/uj+Y//X/0UAsgAdAXcBxgEPAkwCjQLKAv4CCQMLA/YC2wK5Ap0CgQJmAlICMgIJAscBjQFcAUUBMwEvATUBSgFYAVwBXAFYAVMBVQFvAYcBnAGmAaIBkwGEAXcBawFlAU8BFgHRAIcAKQC1/zv/yv5o/iX+5v2m/VH99Pyo/G78O/wN/Nz7q/uF+3r7e/t/+5372fsw/JX8Cv1l/cP9Jv6W/hT/lv8WAIUAAAFrAdABHwJwAq4C2AL2AgMDBQP8AvYC3QK6ApICagI7AhAC7AHGAagBlgGFAXoBdwF4AXMBdAF2AXkBhQGWAasBtwHSAekB9wH3Ae0B0AGmAYABWQEgAeAAqQBqACQA3v+N/xn/m/4h/p79H/3N/JH8WPws/A382Pud+3f7UPss+x/7M/tT+4z71vsj/G/84fxt/Rn+x/6C/y4AzwBXAbQB7gEOAkICdgK0As4C4ALfAuQC4ALZAsoCqwKQAloCGQK+AXsBPgEVAQQBBQEtAWABrQHfAQ0CPQJnAowCiAKAAlECMwIyAjoCRQJHAkoCNAIdAvgBuwFcAeQAZQDT/0X/tP4s/qr9OP3W/I38V/wu/A382fuh+2b7Q/si+/z61/rE+sz6/vpD+4z79Pts/Bz9x/10/gf/jv8mAKYAHwF1AcwB/wE1AlwCcQKIAoAClAKEAn0CZwJZAkMCHQL/AdQBywGhAZcBdwFkAVwBaQGlAcsBEQJbAq4C9AIgAz4DLgMWA/0C5ALJAqICgQJeAjMC/AG2AWsBCgGkADMAsP8X/3v++v2B/Qv9mfwt/NL7mPuC+3v7Y/tG+x/7Cfvn+sD6pvqn+uf6Kvua++37afz7/Kr9fP4R/8D/PwDUAEQBfQG+Ac8BDAIfAkACTQJXAnMCbgJ0AmECaAJpAmkCSAIQAtoBuQGiAZ0BpQG2AeYBDwJTAo0C1AINAzADRAM4AykD/wLTApoCcAJjAloCVwI6AgsCyAFtAQkBjwADAHn/3P5Y/tL9Uf3f/Gv8FvzL+4z7UPsO+9r6s/qZ+ov6c/pu+nj6i/qn+r/6/fpP+9f7evwz/RT+7P7P/4oAHgGYAe8BOgJxApMCkQKJAnwCdgJ9An4ClgKjAqYCnwJwAj0C+wHBAaABcgFpAW8BkAHIAfwBOgJ/As8CHwNpA4cDqwPGA9gD3AOlA2kDFwPUApoCWQIIAqYBVAH0AKkAMACu/xf/a/7X/Sf9iPzi+1z7//rN+rj6rvq6+q76r/qN+mf6Sfot+kn6Zvqu+hX7qftm/Cj9/f3A/pj/WwAFAXsBtAHbAeAB8QHyAfsBAQITAiACHgIYAv4B/AHuAegBywGrAZgBjwGbAaIBvgHMAfcBKQJaAqAC2QI0A3sDxgP9AxkEMAQfBAgEwQN9AzAD6gKrAlgC+gGFAR4BtQBTANv/U/+4/h/+lv0L/Yz8Efyu+2H7Ifv0+s76xfrN+tL6yvq0+qr6u/rj+hD7RvuL+/r7kvw8/fT9q/5m/xsAtgAiAXIBoQHaAfwBAwIBAuIB0gG0AZoBewFqAWoBgQGsAdABCAIhAkECQQI4AjICJwI5AlQCiwLCAg4DSAOCA7ED0APyA/MDAQT6A+oDwQOGAz8D4wJ+AgECiwEAAXwA9f9o/97+TP7I/Ur92vx0/CL83fug+2v7P/sn+xz7K/tI+1j7WPtR+1X7a/uJ+737BPxi/N/8ev0q/s7+Y//d/0gAlwDVAAEBDAELAfwABwEYAScBNQE0AUIBVQGGAbUB1wHxAQgCMgJUAncCjwKmAsMC5AIIAxkDLAM/A1UDZgNxA5IDtAPVA9kDwgOcA2cDMAPXAnAC/QGOASsBuABLAN3/if84/+D+fP4F/pH9Hv26/GD8Hfz3++f78vsG/CD8MPwz/DX8NPw9/Ez8Y/x8/KH81vwh/X393v1C/qX+C/9r/7j/8P8TACsAPQBPAGIAfQCbALwA4QAEATIBWQGGAbQB2wEBAiUCRgJlAocCrALUAvcCGAM1A1UDcAOJA5MDlAONA38DcQNVAzMDBQPQApwCYAIjAtsBkAE/AeEAfAALAJj/JP+z/kn+6/2c/WD9Of0f/RH9A/30/Of82fzQ/M780fze/PX8EP0q/UH9Vv1p/X79mP3A/fH9Lv5x/rD+7P4h/1D/ff+j/8X/5v8CAB8ANgBPAGsAjgC5AOwAKAFlAaMB2QEJAjACUwJ6AqECzQL1AhYDKQMqAx4DEwMGA/8C/gIFAwgDAgP2AtcCsQJ6AjkC7QGTATUBzABlAP3/n/9R/xD/4v64/pL+Zf42/gD+yP2R/V/9Pv0k/Rj9FP0Z/Sn9OP1N/Vr9bP17/Y/9rP3N/f39Mf5z/rf+/P46/2X/gf+M/4//if+G/4f/kf+n/8P/6v8UAEYAeACuAOcAIAFYAYsBuQHhAQQCJwJHAmUCgAKVAqQCqgKsAqgCogKiAqYCrwK3ArkCrQKOAmMCKgLnAZwBRgHuAJQAOwDg/47/QP/0/rf+hv5g/kL+Mf4h/g/+Av7z/eP91v3H/bL9ov2b/Zn9of23/dD97/0U/jr+XP5//p3+s/7I/tr+7f7//hL/Jv82/03/Y/97/5X/q//B/9T/7v8LADEAXQCLAL4A8QAiAVABfAGkAckB6gEMAisCSQJlAnkChAKFAnsCaAJRAjwCKQIWAgMC7QHTAbEBiAFWASEB5QCkAGEAFwDG/3D/Hf/Q/pL+Z/5O/kP+Q/5E/kj+S/5K/kT+OP4o/hv+Ev4O/hD+FP4c/ij+Nv5I/lr+bP5+/pH+pv6+/tb+7f4C/xH/G/8j/yj/Lf8z/zz/SP9b/3n/of/T/wsARwCFAMAA9wAoAVIBcwGOAaQBuQHLAd0B8AEAAg4CGAIgAiACGwIPAv8B6QHOAbEBkgFuAUUBGQHpALgAgwBNABkA5f+3/4v/YP87/xb/9f7V/rv+pP6Q/n/+df5u/mr+a/5t/nL+c/52/nf+df5y/m3+a/5r/nD+ef6G/pf+qv69/tD+4/70/gT/Ef8i/zL/QP9S/2r/gf+a/7n/3v8BACkAVQCCAKoA0gD6ABoBNAFJAV0BagF4AYUBkgGcAacBsQG2AbQBsAGmAZIBewFiAUMBIAH+ANoAsgCJAGMAOwASAOz/yP+i/3//X/9C/yn/F/8L/wb/Av8B/wD/+P7r/tr+xf6t/pf+hv56/nb+eP6D/pP+pv69/tD+4v7v/vf++f72/vH+7f7s/u/++v4N/yr/Tv97/6z/3v8PADoAXQB8AJQAqQC8ANAA5gD8ABIBKQE+AU0BWwFlAWsBbwFvAW0BZAFWAUIBJgEGAecAzACzAKEAlQCMAIMAeABpAFQAPAAgAAMA5//O/7b/of+P/4D/dv9u/2r/Z/9h/1f/R/8v/xb/+v7i/s/+xf7B/sP+yf7N/tH+0v7U/tb+3P7h/ur+9v4B/w//Hf8v/0X/Xv98/5v/vf/b//z/GgA1AFMAbgCKAKcAwgDcAPIABQEVASABJQElASEBGQERAQgB/wD4APQA7wDtAOoA5gDeANQAyQC6AKgAkgB7AGQATAA2ACMAEwAFAPn/7v/i/9b/y/+//7D/pf+Z/47/hv9//3n/cv9q/2H/U/9D/zD/Gf8D//D+4/7d/t7+6f76/g7/JP85/0n/Uv9Z/1v/Xf9h/2n/eP+N/6v/z//5/yUATwB2AJoAtwDOAOAA7gD7AAQBDAERARQBEgEQAQoBBwEAAfoA9QDvAOgA4ADXAMsAvgCxAKMAlgCIAH0AcgBrAGMAWgBQAEQANQAmABUAAgDy/+T/1v/L/8L/u/+0/67/p/+c/5D/g/9w/17/Sf80/yP/Ff8O/w3/Ef8b/yr/OP9E/0z/Uf9Q/0v/Rv9D/0f/T/9g/3f/lP+0/9L/8P8KACAANgBLAGEAeQCRAKcAvADNANoA4wDqAO8A9AD5APwAAAEDAQQBAwEBAfoA8ADgAMwAtwCiAI8AgAB1AG4AaABhAFkATQA/AC8AHgAPAAIA9v/s/+P/2v/S/8z/yP/F/8L/vf+1/6v/nf+P/4L/dv9t/2f/Y/9i/13/V/9Q/0b/Pf86/zv/QP9K/1L/Wf9e/2P/bf+A/5r/vP/g/wMAIgA2AEQATQBWAF4AZwB1AH8AiQCQAJYAnAClALMAwgDPANYA1ADHALQAnACFAHQAbABqAG8AdgB9AIEAfwB4AG0AXgBQAEAALwAfABEAAwD6//H/7P/j/9j/yv+5/6n/mP+K/4P/f/9//4P/gP95/3P/bP9i/1v/Wv9g/2b/bf92/3z/gv+F/4r/kv+c/6X/sv+//8r/1P/f/+j/8f/4/wUAEgAfAC4AOQBEAEwAVABaAFwAYgBoAGwAcQB2AHkAegB5AHgAeQB5AHYAdQB0AHEAawBnAGQAXwBaAFUAUABKAEMAPgA4ADIAKwAkAB0AFgAOAAcAAQD7//b/8f/s/+f/4f/X/8//yP/A/7n/tv+y/67/q/+m/5//nv+d/57/pP+s/7L/uP+6/7r/uv+5/7v/wv/L/9X/4f/r//P//P8FAAsAFQAdACUAKAArAC4ALAAmACYAKgAlACEAJwAqACcAKgAwAC8ALwA0AC0ALgAyACUAHgAlACYAHgAgAC4AMAAsAC0AKwAlABoADAAJAAgAAwD7//7/BQABAPr//f/9//f/7//o/+X/3v/T/8z/y//K/8j/yP/L/87/zf/K/8z/0P/S/9P/2v/h/+P/5v/p/+v/7f/u//L/+f///wUADAAUABsAHwAgACEAHQAYABQAEAASABQAGAAhACYAKwAuAC0AKQAmACMAHgAbABsAGgAaABoAGgAZABgAGAAXABYAGAAYABUAEwAQAAsABAABAP7/+//6//j/9//1//P/8f/v/+7/7P/r/+j/6P/k/+D/3f/a/9b/1f/W/9j/2f/c/+D/4f/e/+D/4P/h/+P/6f/x//f//f8CAAMAAwADAAEAAQACAAUACAAMAA8AEwAUABMAFAAWABcAGQAbABwAHAAaABgAFAARAA8ADwAOAA0ADwAPAA8ADwAPABAADwAQABAADgANAAsABgACAAAA/f/5//r//P/8//z///////7//v/+//r/+P/3//X/8f/w//H/7v/t/+3/7//t/+3/7//t/+7/7v/v//H/8//z//b/9v/4//j/+f/6//z//f8AAAQABgAIAAoACwALAAsADAAMAAsACgAMAAwACwAJAAsACgALAAwADQAMAA0ADAAJAAgABgAFAAQABAAFAAcABwAIAAcABgAGAAYABAACAAIAAgAAAAEAAAD//////v/+//7//P/9//z/+v/5//j/+P/3//f/9//4//n/+f/6//n/+f/6//v//P/7//r/+v/6//n/+f/5//v/+//8//3//f//////AQACAAEAAQABAAEAAAABAAAAAQACAAIAAwAEAAQABQAGAAUABAAEAAYABQAGAAUABQAFAAMAAwACAAEAAgACAAIAAQABAAIAAQABAAEAAAABAAEAAAABAAAA//////z//P/9//3//f/9//z//P////7//v//////AAD//wAAAAD///7//v/+//3//f/9/////v///wEAAQABAAEAAgABAAIAAgACAAEAAQACAAEAAQACAAIAAwAEAAMAAgACAAMAAgACAAIAAgADAAIAAwADAAQAAgADAAMAAgACAAIAAgACAAIAAgACAAIAAQAAAP///v/+//3//f///////f/+//7//f/9//v/+//6//v/+//9//3//f/9//7//v/+//3//P/7//3//f/9//7//v////7////+//7///8AAAAAAgADAAMAAwADAAMAAgADAAIAAgADAAMABAADAAQABAAEAAUABQAFAAUABQAFAAUAAwAEAAQABAAEAAMAAwAEAAIAAwACAAEAAgACAAMAAwACAAMAAQAAAAAAAAAAAAAAAAD///////////////////3//f/9//3//f/9//3//v////////8AAAAAAAABAAAAAAABAAEAAQAAAAEAAQABAAIAAgACAAMAAgADAAIAAwACAAEAAQABAAMAAgACAAMAAwACAAMAAwACAAIAAgACAAMAAgADAAQAAwADAAMAAwADAAIAAgABAAEAAQD/////AAD//wAAAAAAAAAA/////////////////v/+//////8AAAAA//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/8/zIAWwCIAMsAPwGHAZABggGSAYcBYQFJARQBzgB+ABIAt/94/0L/Qv8c/7n+k/53/pr+Dv9O/1L/Y/+6/x0AjgACAR4BWwGWAXQBkQGpAaABsQFhAeYArAB8AEEAOQDT/1v/J//T/sL+r/6L/pP+UP4+/mv+g/7O/gr/C/8j/3f/sv8VAHMAkACsANkAIgFhAYcBlgFxAUcBJQEJAe8AxQB9ANH/jP9w/0D/Sv8M/7T+kf6h/tf++v4r/0//Yv+V/+X/PACVAN0A9AD6AP8ATgFTAUkBUAHZAJ8AjgBwAFAACQDO/3D/Rf8v///+LP8n/+z+2f7V/v/+Qv9U/3r/k/+r/9P/CABlAIgAjgCnAMwA8gANAQEBCAH6AMYAsQC0AI8AYQAlAOP/vv+N/4D/ev9U/yz/MP8n/yn/S/+N/7z/s/+7/9f/GgB4AJkAmACNAHMApQC2AJgAkQBeACgACQDb/77/nv+R/2n/F/8V/yj/JP8c/zH/Sf9B/1n/iv/F/9z//f82AEwASQCYAMgAqwDxAMsA3wDrANwACQHHAMcApwCPAIkAZQBbADQA8P/g/+X/vf/T/8n/lP+H/4T/0f/r/87/6P/F/+T/GAAVAEsAIwAGADYAGwDY/9D/7f/E/6z/av9Y/03/C/9K/zz/8v4L/zv/Kv8n/1T/d/+p/5r/1P8dACMAQQBsAJ4AlADQAAUB9ADnAOEA4wDgAM8AuACjAI8AkQBoACkASQBbABYAFwDt/9v/+v8IACcA4P+1/+3/EQD0//7/JADu/xQAJQAQABcA+/8eAPT/9v/w/9b/y/+J/4D/iP9x/zv/Uv9w/xT/3P4Q//7+H/95/zv/Mf+H/6P/rP/A//v/MQBSAIgAhQCZAK8AuwDqAM0AxADDAMAAsAB+AHQAfwCKAFAAWQBXADUAMAAcADIAKABBADoA4P/n/xEADwACAPD/BAARAAkAEwAfAP3/CgAbANf/8/9QACEAsP+6/6n/nP/b/8P/Rf8i/2X/Rv8d/yD/HP8c/2r/kP9A/0b/r//v/wsA/P/f/ysAaACjALwAkwCdAMcA6gCkAIIAxgDgAMkAqAClAJIArwDDAJQAXgBmAIAAMgDd/7f/9v8AAM3/4/+c/1D/hv9i/0T/gP+o/97/mf9Z/2L/h/+8/6j/mv+h/5j/qf8qAPv/ZP9n/1T/Y/9q/1v/hv9u/2//pP99/63/BQAsADcAHQBLAGcAcwC6AAkBOQFpAYQBhgFWATUBewFrAScBAwHwAOQAxACqAGoAKwAuAEYAGADk/6r/d/9q/0//cv9r/0T/Tf82/+v+yP75/hz/6f7D/qr+fv6D/mr+Y/5T/kn+nv6//pD+qP7x/jH/a/+Z/+H/TwCsAA4BYQFnAcUBRwI+AkoCPgInAmUCRgIrAhMCrQGcAXgB2gB0AEYAJQAZAAIAwP+F/2P/Uf8//yr/Wf+r//L/4f/U//D/+v9BAHAAWgB3AIAATADx/3n/OP8G/5r+/f1X/f78s/yw/O78w/zh/BX9Sf2k/cv9R/42/9L/nABFAYgBEgKVAvkCTwOGA74DCwTPA08DyAJAAuIBkQEmAZ8AAACA/wb/qf6C/o7+xv6W/nP+n/7p/lf/w/8RAJ4A4QAlAbMBlAH8AXQCRQJDAtcBpwGVAecAdQCu/6z+I/5d/Xf8ofvi+nH6lfq2+uP6X/uO+x38+PyG/aH++//rAPUBjAJLAxQEngTxBA0FKwUoBe4EXgRzA8UCGwIKAVUAQf+4/m/+7v25/WL9HP1f/YD93P2C/tv+df/g/2YABgGaAUQC0QIeA0YDKgM4Az4DAQP5AmYCmAEBAQsASv9e/lv90/zc+9j6CPoz+WH4s/ib+Sr6Bft2+1H8hP1F/r//WgFhAvADBQV6BQUGEAY7Bj0GqQVRBccEqQPHAq8BggCU/7P+9P1o/fH8pvyP/G38mPwV/bP9IP67/l7/FgAGAawBVQLfAuoCQQOJA3sDsAOWA0QDsAINAqgBAwEnAIH/qP7c/R39Jfw++3X6v/lh+f74Rfgs+Wn6TfuM/Bj99P1j/zgArQH4AgYExwWSBuwGtQZzBmwG1gXlBDUEYwOHAo8BZgD3/rX9B/16/E38WfyW/Mb8QP1c/cv9a/4K/zAALQGvARACOAKBAuQC/QJWA0cDBwO0AhwClAEjAY4AWwD0/0X/tv4M/k/9rPwK/Hz7Q/vR+l76Pvq5+UP6zvtj/Jf9VP6//iUA3QDMAUUDEAQoBR0GJAYKBswFUQWjBNwD+gJ6AqsBggCD/3n+/P2c/fv8t/zj/Gz98P0W/vn9IP7q/qf/UgAPAVsB4AFUAnwC9AL8AvkCAgOCAuwBWAH+AKYA9v9t/x3/6/6l/kn+v/0v/e78rPxO/Mj7Svth+2f7MPv1+8/8j/1m/vH+sP+mAJcBpQKEAxoE/wSmBYcF1QRbBJUDngK/AdsALQBY/7X+Kv5t/QD9Nf2v/XP+Nv8BALoAKAF5AcEB1gERApgCtgKGAgYCbgH9AHcAKgAMALn/kf8s//v+4/7O/jf/w/8pAEsAHwDq/8//Vv+5/if+iP0l/bD87Ps/+6z6IvvF+zv8HP3S/b3+jP83AH8BtAK/A+4EZwXHBZUF/gRcBCEDHwKRAcQA3v8M/xv+m/0V/eb8Sf3i/cn+wP9VAKsA3gD6AF8BugE0AokCgwIpAr4BUgG9AF8APAAbABwA4P+V/5r/l//M/xAAUwBnAFUATwDl/1v/8f4o/qb9CP1U/Ob7Rfuu+of6K/vT+478b/2r/gsABQHSAbwC4gMfBeIFQQZGBooF1QS9Az4C1gC9/xT/cf6r/Rz93PzE/JT8lPw//RT+Pv9UAPMASwHGAV0C0wL6AvsCGAPxAmwC6AGMASgBkgAJAMP/W/9K/2f/hP+i/4H/iv+v/2b/M/8T/73+d/4H/n/9Gf15/MP7efsI+1P7QPzV/MP9hf43/3kALQH9AUcDXwRSBeEF5gVABWkEUwPzAc4Alv+v/kn+mf3//KH8f/yt/O/8kP14/nf/hwB/ATsC1wI6A10DiwNjAxMD5AKPAj0C0AEbAVQAff/s/rP+r/7j/hP/X/+n/7r/u/+x/5b/uv/H/6X/Yf/f/lP+3f02/aj8JPyv+xX8cvz1/KX9Mf4q/wUArQCxAZQCkwNVBIcEiwQIBJsD1QKeAXwAUf92/s/9Mv34/Pr8DP1N/bX9R/4O//T/7QDkAaQCQgOzA9cDqwNiA/cCawLRATMBxAA1AE//m/4s/vP9Fv5O/sj+XP+9/yMAWwB1ALMA1ADCAI0ARgAHAKr/Hv9T/ln9bfyu+4v7wfv7+4H8JP3//d3+qP9hACUBDAKoAi0DhQN8A4gDDgMuAi4BAwBg/+r+qv6U/rD+Mf+H/67/wP/b/00A8ACVARQCZwKJAnICOAKsAQ8BmAAiAND/cP8w/wb/+P4b/0D/oP8SAH4ABwGKAeYBKwItAikCBgLKAXYB6QBjAMH/4/4M/gj9Avw/+4v6M/om+lz66PrW+9T80f27/oL/YgBIAQ0CjwL8AnIDxgO8A1wDwAIxArgBWQEfAa4AXwAzAND/iP9P/0r/lv+r/77/zP+s/73/zv/F/5j/jP+n/5v/pv/K/xYAgADGAEYBxgELAnoC1QL+AhgDEwP6ArcCQALIAUYBjACs/9r+CP4u/Wf8m/ve+iH6kvlk+Zf5NvoY+yT8Sv01/j7/RQAOAdMBUgLiAnwDwQP1A+4DiQMmA78CgwI0ArUBPgGqAP//lv9X/yz/Kf8E/wn/Cv/O/uP+Gv8e/zb/T/+R/87/9P9fAMoAHwGcAQUCbAK5AtUCGgMsA+wCygKhAjcC8wGLAQABZwCM/9z+Sv6Y/RP9e/zd+1z7v/qH+oX6svpG+/n7rvxq/QL+pf5j//P/hgAoAaABEwJxAqYCyQKVAnACZAI1AhcC6AGUAUAB5QC6AKAASAAWAPn/wv+W/4r/qf/C/5v/mf+W/4L/nP/F//v/JQB1APUATwF9AcgBFAI5Ai8CIgInAikCMwImAtkBWAHwAI4AJgCk/zH/zf5H/qD9Ff2g/D78Dvzn+/H7FfxJ/JD8vvzt/DH9ev3l/UL+lP4C/1f/4P9+ABABmAEAAnQCyALrAgoDFwMTA+sCtQKiAnQCHAK4AVIB9ACRADsA7f+L/z7/G/8L//j+3f4A/0r/j/+6/+X/KABpAKkAFQF0AcIBFQJPAo8CiQJYAk8CQgIeAu4BigEWAYgA2/82/4L+yf0h/ZL8Cfyb+zH75vra+t/69/op+3v7+/t3/AD9rv1p/i//+f/NAKEBVwL0AnsD2gMSBDIEKAT5A7ADTQPkAngC/gGMARwBrwBaAO7/dv8U/7z+iP53/ob+uP7L/t/+A/80/4z/4v9UAOwAeQEFAmQCmwLnAhQDPgNwA28DTwMIA5YCLgKnAfkATAB5/37+gf2L/Ln7Dvt7+iv6Ivo2+lj6i/rf+j37pvs//AP91P2S/kH///+0AEEB0gFrAuUCOgN6A6wDzQO3A4ADSQP9ArICaAIbAtkBdgHxAH0AEQC4/2f/D//P/pj+Yv5e/of+sv7o/kX/wf9AALMALwHKAWEC1QI5A5YD1APxAwUE8gO6A2ID4QJJAoABjACY/6b+sf3O/Bb8ifsO+5/6WPo0+hf6KPpz+tH6Ofuv+zr8zfxZ/ez9lv5R/wMAuAB5ASECtgJCA6gD7QMeBDcEQgQtBPIDoAM2A70COgKfAe0APACc/wf/bv7k/Yj9Y/1l/Xz9nv3b/Un+9f7I/5sAXQEYAtgCgQMBBGYEvwQDBRwFCgXSBHQE6wMzA0sCQQE6AE3/cP6L/bb8D/yO+w/7lPo0+gX6Bfol+ln6lPrj+lb75vt5/AT9k/0y/uP+pf9tADIB7wGVAiYDrgMgBGIEaAQ5BAcE2AOCA/ICOwJ0AbcADQBs/9r+WP7u/bT9p/2g/an94/1S/vL+rv+DAGYBKwK/Ak0DzgMnBFoEeQSXBJoEZAQWBL0DNAONAvEBVgGvAP3/Xf/S/kf+vf1R/fv8j/wZ/Lf7VPvy+rb6r/rP+vn6Kvt0+9D7PvzJ/HH9C/6d/k3/CgC4AEUBvgE/ArUCCgNJA2QDUAMLA7UCUgLeAVoB1AB3ACkA2/+e/1v/Jv8F//v+Jf9k/6f/6/8/AKUACwFtAbsBCAJWAoYCvgL6AicDRwNYA2YDaQNXAzIDCQPYApICQwLtAXwB+QBhAL3/Cf8//nP9tvwG/Fr7uPo3+tr5sfm9+fX5Tvq5+jb7xPtQ/NT8XP3h/WH+7v6N/zMA2QBoAdcBJQJaAoQCqAKvApgCbgI4AvkBsgFtASAB1ACHAD4A9v+w/3b/Vf9G/0H/Uf93/7n/FQCEAP4AgwEIApkCNAO6AyEEbQSjBMgE4ATeBLwEagTrA1kDwwIaAlgBjQDI/w//Wv6t/f/8S/yo+zH76fq1+o36hPqb+sT68/or+2b7nPvY+yT8fPzP/CH9fv3n/VT+yv5V/+j/YwDAABABZwG7AfMBAgL0AdoBwgGtAZcBbQEtAfAAzQC5AJoAZgA2AC0AUACWAO4AQwGWAfMBZALiAksDnAPYAwwEOwRWBFEEJQTYA3sDGwO2Ak8C6AGDASEBwQBkABEAuf9h/xf/3v6x/nP+Gf6x/T/90fxn/AP8n/s1+9P6fvpC+hj6B/oe+kz6ivrZ+kL71/t7/CL9uP1B/tL+Y//y/2kAwwAQAVQBmgHRAfEB/gECAhUCJQImAh8CGwIsAkQCXAJuAnoCiAKaAqwCtgK1ArYCvALJAs8CzALEAsACyQLYAuQC7wL5Av4C/wL+AvkC9ALmAsYChQIjAqYBHQGKAO//Tv+y/ib+s/1O/ev8f/wO/Kb7UfsK+9H6mPpm+kf6QfpI+lL6YvqH+sT6F/tu+7b79vsw/Hz82vw7/Zv9Dv6g/k7/+/+YACgBpwEcAo8C+wJXA50D1wMTBEkEXwRPBCwEAwTcA7UDjANeAzoDJgMiAyUDGgMHAwIDFAMqAy0DGAP6AuACywK8AqoCiwJWAhoC4gGnAVsBBgG6AIIASAAJAMf/gf8//wT/y/6C/hn+mP0T/Y78B/x1++L6Xvrx+Zf5R/kA+cb4nviN+KD41Pgq+Z35KvrL+n/7SPwk/Qv+6P6o/1UA/gCwAVsC6QJVA6cD7wMqBFQEYQRTBEIEPAREBE8ERQQkBPcDzQOxA58DkAN6A1sDNQMUAwED+AL0AvIC+QIEAw4DGgMhAx4DEgP/AukC0AK0ApACVgIBApEBEwGXAB4Anf8K/2L+r/0E/WT8zPs6+7P6TfoM+uj5x/mk+Yv5ifmk+dL5/fkZ+jT6VPqA+qz61voS+2z77/uQ/Dr96f2f/l//JQDmAJgBOALFAkQDswMRBFgEgASMBH4EWwQmBNkDeAMXA8sCnAKCAnECbwKJAsoCLQOgAw0EZgSqBNsE/QQMBQIF6QTSBL8EpQRxBBAEhwPiAjUCjQHmAEAAqf8s/8b+Z/4B/on9Av11/Pf7kvtE+/j6qfpt+lf6W/pm+mz6c/qE+qj65fox+377vfvw+x78PvxE/Cz8//vY+8z71vv0+zX8rPxi/UX+PP9CAGUBjAKGAz4EvAQIBToFVwVPBSMF1ARqBP4DmgNAA/gCvwKnAroC9QJUA9YDaAT2BH8F7wU8BmEGYgYvBtkFaAXVBCoEXANyAn0BmgDH//r+QP6g/Tv9Ev0U/SD9G/0R/Q/9IP0w/S/9HP3y/M78pfxw/Cf8w/tU+/X6wvqx+rX6x/rn+iD7ZvvB+yL8i/z8/HX98/1P/on+k/5y/i3+0/2L/Uz9Fv3v/B/9wf2i/qD/oADSAQ4DLgQSBcoFbwbrBkAHYgdQB/EGVganBe4EMgSMAzwDMQM8A0UDVwOSA8UD8AMKBCwEPQQyBBYE0wNOA3YCnQHMAP3/+/7q/R/9lvw2/NH7dPtC+yz7WPu0+yv8nPwS/cj9c/7a/tf+2/7T/qj+TP7Z/ZX9JP2q/E388Puq+2T7Z/vK+zb8oPwF/Xb90/0H/h/+Yv6E/pD+fP5D/ij+2v2q/av9xf2U/u//oQFRA4MErQWGBvEGDAcRBxsHYAeNB4IHBAe6BV8E8QK3AdkAWgB8ANQAKwF1AXgBUAH+AAIBWwHRAR0CUAJmAi0CdwFgAIb/zv41/q/9Vv0Z/dT8hfxV/C38FfxK/Oz8lf0V/jP+bv68/tb+yP5V/vP9qP2b/Y39Tv3f/Nf8HP2J/c/9u/26/a79yf0b/oz+RP8GAJ0AOQGXAb0BowFLARMB/QDeANcAowB/AEcA7v+l/4P/z/9zAG0BbwJcA+8DCATuA48DaAOaA+MDTQRvBB8EmAPGAq0B4gDy/53/kf+Z/7X/ZP/T/g3+VP36/GL9EP4H/7T/LAA5AN7/f/9E/0j/cf+a/9f/sf8D/y7+Ov3N/Jj88vyo/Vn+2f4t/3f/hP90/03/u/9sAO8AOAHmAJMA7v8+/7L+Rf47/lT+vv4H/2X/uf8iAHYAhwC+ABcBxQEcAiEC+QGdATYBegDE/zz/Af/h/iX/v/9TAOoAGQErAQwBBQFUAeUBZAKlArYCXwLBAdMAKwCu/3D/V/9Y/07/Gf/G/nX+Mv70/SX+f/4t/5f/8v82AE4AdwBWAEMADgDr//L/wv9T/6/+R/4u/kj+Xf6E/tb+R//F//H/JABDAIUAxQDDANcA1ADIAJsAUAA2ABIA6/+1/5X/iv+6/wUAdACyANYAJAFdAZoBfAGtAegBCwLNAVgBEgHKAHwACACc/zv/8/6i/ln+Bf7g/ff9J/5g/mX+Y/5K/kP+gv7Y/mj/s/8PACwAXgCLAIQAqACEANcAwACmAAYAUP/X/kH+Nf4O/n/+u/4I/0T/c//E/9n/VQC9AFQBngG1AZoBMQH3AMIA5wD8ADABTQFIAUoBPAFRAT4BSAFiAXwBXgE/ARwBAAHJAKgAtgDBAOAAzQDNAKoAkwB2AGMAUgA+AC4ABQDk/6j/hP8//yT/BP/w/sr+a/4Z/rT9pf2a/Yz9mv28/cz9mv0w/dH8DP1t/TD+xf4y/4v/Vv+F/5v/9/8nAKcAKwFqASoBsgBPAMz/7f/J//P/nP/q/xcABQD4//v/rwDhAFoBcgGfAZ8BtQH8ARICZgKnAgsD3wKLAi0CtQGAARoBQwEwATQBEAGjAGIA3v/B/5P/1v80AH4AhgBxAHkAQAAfAN//7f/N/6T/q/9l/2D/J/8m/x3/+P4u/zP/cP9//5X/m/9g/xz/tv5y/hD+1P3C/b79qP15/Xn9XP1g/Xv9wP3w/fT9V/6A/qb+t/4T/6n/5/9uAK0AvQCnAK4ACwEWAUwBnwH4ARYCFALxAbgBpgG+AQACzgHhAd0BngFoARsBKQHpANsA7gClAHUAeAC/AOIA8gAqAVoBWgFzAXwBMQEgASYBWQEqAd8AwABRAPf/kv9P/xr/Bv9C/1P/O/8k/yT/E//4/vX+AP8e/yL/Mf/2/tP+xv6o/nn+F/4W/g/+8v3T/cv9Cf4//of+0f70/g7/Lf9H/0z/Yv+f/+v/+v/+/9z/l/+A/4L/zf8XAH8A3wDdAOEA5QD+AA0BCQE4AV8BowGhAXwBSwEYAQsB9ADyAMUArQCcAJgAlwCcALUAugC9AKYAiQB1AH0AnADDAPEA/wD0ALUAZAD//8P/tv+0/7n/lP+F/1//Nf8J/+L+2P7m/hr/Uv90/5H/uf+//7j/v//X/9n/t/+p/5X/av9O/1T/bv92/4L/mP+l/6L/0/8AADwAZwCPALMAlACcAH4AbgBPAEEAUQA8ADUANQBHAE0AXQBuAIcAkQCNAJIAcABSACoABgAHAPn/AgDo/9b/yv+n/47/bf9i/zb/Gv/t/tn+zf7b/hT/LP9o/53/4f/6/wIAFAAWABIA/f/1/9f/wv+l/4b/X/8//0T/O/8u/xj/JP9D/3n/uP/o/woAGgAqADAANwBMAG8AnQC/AL8AuQCoAKQAkQCPAKcA0AAPAUQBeQGGAYUBagFIAScBEQETAQcBBgHvANMAtACcAJYAhACFAIMAiAB6AG8AbQBjAFEANwAiAPL/vf+Q/3z/dv9//5X/ov+W/2v/M//1/s/+w/7C/sX+u/6x/o/+cv5k/nj+p/7a/hn/Q/9s/3b/ef91/3z/kP+i/7P/sf+h/4X/Zv9K/zr/Pf9P/2b/f/+a/7//4/8IACkAVQB/ALMA3wD+AAoB/QDxAOcA7gD5AAEB/wDyAOIA4ADhAPoAFgE1AUIBQQE5AS4BKQEkASkBHgESAewAvwCHAE8AKgAHAPb/4v/a/9X/zP/J/77/xv/Q/+v/AQAPAA0A8v/H/4L/Sf8F/+D+vP6p/pj+iP6I/n7+j/6X/sH+8f44/3v/tf/k//f/DQAFAA4A9v/s/9H/wf+7/6z/q/+j/7b/yf/v/woAMQBKAGcAfwCOAKsAuADRANQA0wDLAMAAwAC2ALMApgChAJsAngCoALIAzwDeAP0A/wABAfEA4ADVAMEAuACfAI4AcgBNACMA6//G/6D/jP96/2v/Zv9m/3X/e/+E/4X/iP+P/5L/mf+Y/57/qP+q/6r/jv97/1//Uv9N/0P/Uv9R/2j/Z/9x/3T/gP+f/7j/4P/x/woABgAFAPz/9f/9/wYAHAAiADEALgAyADIAMgA5ADcAQgA9AEAAPQBAAEUATgBcAGoAdwCFAJIAmgChAKcAqwCoAJ8AjQB5AGAAUgBIAEUARABBAD8APQA5ADQALgAuADIANAA2ACMABADX/6v/iP9x/2n/aP9q/2r/bP9v/3r/jf+u/9P/+P8NABYADgACAPT/5f/f/9n/2P/U/83/vf+q/5L/e/9r/2v/d/+O/6v/xP/a/+b/9P8BABQALABEAFwAbAB9AIIAhwCBAHsAbwBgAFIATQBPAFsAaABzAHsAdABuAF4AWwBVAFkAVQBIADQAEwD0/9T/xv/B/83/1v/g/+f/5v/r/+r/9/8GABwALQAyADMAIgAXAP7/7f/Z/8L/q/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D9/wEAAgABAAIAAwAAAAIAAwABAAMA/f8EAPr/CAD7/wEA/f/+/wAAAQD6/wIA/P///wAA/f///wEA/P8BAP3///8CAP3/AwD+/wEAAwD9/wMAAAABAAEA//8DAP7/CAD8//7/+//9/wAA/v8AAAAA/v8DAPv/BQD7/wEA+/////7//v/8/wIA//8AAAYA+v8FAP//AAAFAP3/BQD//wAAAgD//wIAAgD8/wMA/v8DAP3/AwD//wMAAQAAAAEABQD8/wkA+v8HAP3/BQD//wIAAAABAAEAAwACAAEAAwD+/wUA/f8CAAAA//8DAP7/AQAAAP7/AwD9////AQD8/wEA+v8AAPv/AAD7/wIA/f/+/wEA/P8BAP///v8AAP////8CAP7/AwD+/wIAAAABAAQA///+/wQA/f8CAP7/AAAAAP7/AgD+///////8/wMA+f8FAPn/BAD+/wEABAD7/wUA/f8DAP7/AQD//wEAAQD6/wcA9/8GAP7//v8BAPr/AwD7//z//v/6/wIA+P8CAPr/AgD9/wAAAQD9/wYA/P8CAAIA/f8EAP//AQABAAAA//8DAPz/BQD/////BAD9/wYA//8FAP//BAACAAEAAgABAAAAAgD8/wQA//8DAAIAAQADAAMAAwAFAAMABAABAAIA/v8EAPr/BAD+////AAD///////8CAP//AgD+/wEAAAD+/wAA/v8AAP7/AwD5/wYA+v8BAP3//v8CAPz/BAD8/wMA/v8DAPv/AwD///z/BAD4/wUA+v8EAPz/AAD//wAAAwD7/wQA+/8FAPz/BAD8/wUA+/8HAPr/BQD5/wsA9/8KAPj/BgD9/wEA/v8CAP7/BAD8/wEAAwD8/wcA/P8AAAEA/v8DAP//BAD9/wMAAAD//wIAAgAAAP//BQD8/wcA/f8BAAIA/v8FAPz/BAD9/wIAAgAAAAIAAgAAAAIAAwABAAMAAgD+/wQA/f8BAP//AAABAP3/BAD8/wUA/P8FAPv/BgD7/wUA+v8EAPz/AwD/////AwD+/wEAAwD7/wUA/P8CAAEA//8DAPz/BQD4/wgA+P8GAP////8CAAAAAwAAAAIA//8BAAAAAAD//wEA/P8BAP7////9//////8BAAIA//8BAAEAAgD//wUA/v8EAAAA/v8HAPr/BQD+////AgD//wMA/f8DAP7/AAAGAPn/CAD3/wMA/v8AAP//AQD6/wQA/P8FAPz///////3/AQD9//3/AgD6/wQA+v8BAPz/AAD9/wIA/P8DAAAAAAAAAAIA/v8CAAEA/f8GAPv/BwD7/wIAAQAAAAMA/////wUA+f8KAPj/CAD4/wcA+f8DAAEA+/8HAPr/AgAAAP//AgD//wEA/v8CAP7/AQABAP//AwD9/wUA//8DAAIAAAADAAIAAAAFAP7/BgD8/wYA/v8DAAIA/v8EAPz/AwD9/wMA/f8BAPv/AQD9/wMA/P///wEA+/8FAPr/AwD9//z/AQD+////AQD9/wAAAAD9////AgD+/wMA/v8CAAAAAwD8/wYA9/8GAPn/BQD6/wIA/P8AAP7//f/9//3/+v////3/AQD6/wIA/P8CAPz/AAAAAP7///8BAP3///8AAP//AAAAAAEA/v8DAP3/AwAAAAAAAQD//wAAAgD+/wAAAQD+/wMA//8BAAAAAgD//wQA+/8IAPj/BQD+////AAD+/wEA/v8EAPz/BQD9/wEA//8CAP//AQAAAP//AwAAAAUA/v8GAP7/AwAEAP7/BQD8/wQA/v8DAAIA/v8FAPz/AwAAAP7/BQD5/wcA/P8GAP7/AgD+////AQD7/wAA///+////AgD6/wUA/P8CAAIA/f8EAP7/BAD9/wMA/f8BAPz/AwD6/wMA/f/9/wMA+P8GAPv////+/wAA/f8BAPn//f////z/AAD8/wEA+/8DAPv/AQD+/wAAAQD8/wMA+v8EAPz/AQD//wIA/P8EAPz/AAADAP3/AgD9/wMAAAD//wMA/f8EAAMA/v8EAP3/AgD+////AQD+/wMA+/8DAP3/AwD//wAAAgAAAAQA/v8EAAIAAAADAAEAAgABAAEAAgADAAEA//8CAAAA//8BAAAA/f8BAP7/AQACAP7/AgAAAP7/AAAAAP/////+//7//////wAAAAD///7//f8DAP3/AwD9/wIA//8CAP3/BgD7/wUA+/8HAPr/BAD+/wEAAgACAAAAAAD//wEAAAD8/wIA/P8DAPz/AQD//wAA/////wAAAAD//////f////3//v/9//3//v////z/AQD///3/BgD4/wgA+P8DAAAA/v8EAPz/BAABAAMA//8DAP//BQAAAP//AwD6/wIA+/8BAP7//v8BAP3/AgD+/wAA///9/wQA/P8BAP3//v8CAAAA//8BAAEA/v8EAP7/BAD9/wcA/v8CAAMA//8FAP3/BwD6/wcA/v8CAAEAAAABAAEAAgD8/wMA/f8BAPz/AwD4/wQA+v8CAPz/AgD+/wAAAAD//wAAAgD//wMA///+/wYA+P8CAAAA+/8EAPr/BAD9/wQA/P8CAAMA/v8DAPr/AwD//wAABgD2/woA9f8HAPv/AAD9/////P8EAPv/AwD9//////8AAAAAAQD+/wEA//8EAPz/CQD3/wsA9v8KAPj/BwD//wAAAgD8/wIAAAAAAAEA/f8DAPz/AgD+////AgD8/wMA/v////////8BAP7/BQD4/wgA+v8FAP7/BQD//wQA/v8BAAAA//8CAP//BAD+/wUA//8EAAAA/v8FAP//AgAAAAIA/v8BAAEA/P8GAPb/CAD7/wAA/////wEAAAABAP//AQAEAPv/BQD//wAAAgD+/wAAAQACAPz/BwD4/wYA+f8CAP7/AgD+/wEA/P8DAPz/AwD+/wIAAQABAP7/AAADAP7/AQD+//7/AQD9/wAA/v8AAP//AgD8/wIAAAD+/wQA/f8FAP//AAADAP//AgACAP//AgABAP3/AwD8/wEAAgD8/wEAAwD8/wUA/P8HAPn/CwD2/woA+f8HAPz/AAABAAAAAAACAP//AgD//wEAAQAAAP///v/9/wEA/v/+/wMA/f8CAAAA//8DAP7/AQACAP3/BgD4/wgA/P8DAAMA/v8DAAEA/f8FAPz/BQD6/wUA+P8HAPz/AAAFAPn/BwD9/wAABQD5/wMA/v8BAP7/BAD4/wcA+v8AAAIA/f8CAPz/AAABAAEAAAACAAEA//8DAAEAAQACAP//BAD8/wQA+/8GAPr/BQD6/wAAAQD+/wIA/f8AAP//AQAAAAAAAAAAAAEAAQACAP3/BQD9/wUA/v8CAP3/AwAAAP//AQD+/wEAAAACAP3/AgD+/wEAAgD7/wYA+f8EAPz/AQAAAP3/AgD6/wMA/f8BAPr/AwD7/wEA/P8CAPn/AgD7/wIAAAD8/wMA/f8AAAEA//8BAAAAAwD9/wcA+P8KAPr/BwD8/wMA//8BAAEAAgD8/wMA/v8BAP//BAD5/woA9v8GAPn/AwAAAAIA/P////////8CAP3/AAD//wEA/f8CAAAA/v8DAP3/BAD//wMA/f8DAAAA//8FAPv/AwD///z/BAD7/wUA+/8DAPz/AgD//wEA//8AAP////8AAAMA/f8CAAAAAAACAPz/AgAAAP7/AQD+/wAAAAAAAP7/AQABAAEAAAABAP//AwAAAAAABQD9/wUA/P8CAAIA+/8DAP3//v8DAP7///8EAPf/CgD4/wgA+v8HAP3///8GAPX/CQD4/wQA/f8CAPr/BgD5/wQA/v/8/wIA+v8EAPn/BQD7/wEAAAD9/wQA+v8FAPz/AgD8/wMA/f8DAPr/AgD9/wAAAAD9/wAA/v8DAPz/BAD8/wQAAQD+/wYA/f8DAAEAAAABAAEAAQD//wEA/v8EAAAA//8AAAEAAQACAAEA/v8DAP7/AwAAAAEAAgD//wAA//8CAP7/AAACAP7/AwD+//7/AwD9/wAAAQD+/wEA/v8CAP//BAD//wEA//8BAAIA/v8IAPj/BgD9////CAD1/woA9/8FAP///P8HAPr/BQD7/wQA/v8AAAIA/v8BAP3/AQD//wIA/f8DAP3/BAD//wMAAAABAP7/AgD+/wEAAAD//wEAAAABAAEABAD8/wMA/v8AAAAA//8AAP///f////7//v8BAP7//v8AAP7/AwD7/wMA/P8BAAEA/f8DAP3/AgD/////AQAAAP//AQD8/wIA/P8HAPj/BQD9//7/BgD9/wAAAQD//wIA//8CAAIAAQD//wIA/v8FAP3/AQAAAAEA//8AAP3/AQD+/wIA//8BAAIA/v8CAAEAAgABAAAABQD+/wUA/v8EAP7/BQD9/wQA//8BAAEAAAACAAIA/v8CAAAAAQABAAAAAAD9/wMA+/8GAPn/AwD5/wcA+v8CAP7//P8DAPz//v8CAPv/AwD9/wIA//8BAP7/AwD8/wYA+/8DAAMA+v8JAPf/BgD8/wUA/f8EAPv/BAD8/wMA/f8AAAAA//8AAAEA//8BAPv/AwD9/wAAAQD5/wYA+P8EAPn/BAD7/wMA/v8BAPz/BwD4/wcA+/8AAAIA//8DAP//AQACAAAAAQABAP//AQABAPv/BgD+//3/BQD5/wQA/v8BAPz/BQD7/wQA/f8DAAAAAgD+/wAA//8BAAMA/v8AAAIA+/8HAPj/BgD9/wEABQD8/wkA+P8IAP//AgAAAAQAAAADAAQA//8GAP7/AwACAAMA/v8FAP3/AAACAPr/BQD8/wMA/v8BAPv/AwD8/wIA///+/wEA/v/////////+/wEA/P8BAAAA/f8CAP3/AgABAPz/BAD7/wIA/v8BAP3//v////3/AAAAAPz/AQD8/wIA+/8DAPv/AwD6/wUA9f8GAPn/AwD8/wEAAAD8/wUA+/8EAP///v8DAP//BAD7/wYA+v8LAPf/CAD9/wMA/v8AAPz/BQD9/wMA//8BAAIAAgADAAEAAwABAAMAAQ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ABAAUAAAAEAP//BgD+/wUA/v8EAAIA//8GAP7/AwABAAUA/f8HAPn/CQD6/wYA/v8CAAEA+/8IAPf/CAD4/wcA/P8EAPn/BQD5/wkA+v8GAPr/BwD6/wcA+P8GAPf/BwD4/wUA+/8AAAAAAwD7/wcA/f8FAAEAAQAEAAAAAAACAP//AwABAP7/BQAAAAQAAAADAAEAAAAFAPz/CAD6/wkA+f8IAP//AQAEAP7/BwD//wkA+v8JAP3/AwD+/wAAAQD9/wIA/f8BAP3/AgD//wAAAAD+/wEA/P8JAPf/CAD4/wQAAAD8/wQA9/8GAPv/AgAAAP3/AAADAP3/AwD9/wQA/P8FAP//AgAFAAEAAwADAAMABAAAAAcA/f8HAAIAAQAIAP//CAD7/wkA/v8CAAcA/P8HAP//AQAGAPz/BwD8/wYA//8BAP//AwD//wMA/v8DAP//AAAAAPz/BwD4/wUA+/8CAAMA+/8GAPj/BwD8/wQA//8DAP7/BAD8/wMA/f8CAAAAAAD//wEA+/8FAPr/BAD5/wUA+/8AAAEA+/8EAP3/AwD9/wIA/v8CAP3/AQD9/wMA/P8FAPv/BQD+/wAABAD9/wMA/f8FAPz/BAAAAAAAAgAAAAIAAgACAAIAAgACAP//AQADAPr/CAD8//7/AQD7/wIAAQD7//7//v8AAP/////8/wMA+/8BAP7//P8GAPz/AgD+/wEABAD8/wcA+/8EAAAA/v8HAPv/BwD+//3/+/8AAPn/BQD7/wEAAgD8/wMA/f8CAPv/AQD6/wIA+f8CAP3/AwD//wEAAgD+/wQA//8CAAEAAgACAP3/BgD5/woA9/8JAPb/CQD7/wMA/v8DAP//BQD8/wYAAAABAAMA//8EAP//BQD9/wIAAwD8/wgA//8BAAIAAwD9/wkA9/8JAPj/BwD7/wQA/v8BAAAA/v8CAPz/AQD///v/AgD4/wEA+/8AAP///v/8/wQA+/8AAAAA+/8FAPv///8CAAAAAQAAAP//AwD//wUA///+/wUA+v8FAPr/BgD6/wMA/f8AAAEA/P/////////+/wAA/f8FAP3/BAD+/wAAAQABAP7/AwD8/wMA/v8AAP3/BAD6/wcA+f8AAP///v////z/+//9//7//v/8/wIA9/8HAPn/BQD9/wIAAAABAAAAAAACAAAAAgD9/wQA/f8FAPz/AgABAP7/BwD6/wgA/f8FAAEAAwABAAQA/P8IAPz/AgD/////AgAFAPz/CAD8/wgAAAAHAAIAAwAEAPz/BgD7/wMA/f8BAP///v8BAP7///8EAPz/BgD6/wUA/f///wEA/f8AAP//AAD+/wAA/v8AAPz/AAAAAP3/AwD9/wMA//8AAAEA+/8HAPb/BQD9//3/AQD///3/AwD8/wAABQD4/wgA+v8EAP//AAABAAEA/f8GAPv/BQD5/woA9/8KAPr/BAD/////AAAAAAAAAgD9/wIA//8BAAIA//8BAP3/AgABAP7/CAD5/wYA/f8AAAMAAQACAP3/AwAAAAIAAwD9/wMA//8BAAEAAQD//wAAAwAAAAIAAgAAAAIAAgACAAMAAQAAAAEAAQD+/wEA/v8BAP//AwD8/wQA/f8DAP//AgD+/wEA//8BAP3/BAD8/wQA/v8BAAEAAQD+/wIA/v8BAAEA//8DAP3/AAAAAAEA/v8DAP7///8FAP7/BAAAAAEA/v8FAPz/AgD//wAA/v///////v/+/////v8DAP//AgD//wMAAAACAAMA/v8EAAAA//8GAPv/AgABAP3/AgACAP7/AQACAPz/BQD//wEAAAD9/wEA+/8IAPX/CQD2/wYA+v8IAPf/BQD6/wEA/v8AAPn/BAD6/wQA/P////z//////wEA///+/wYA+f8HAPz/AwD//wIA/v8CAAAAAwD9/wIAAAACAAAAAgD9/wUA/P8FAP3/BAD8/wMA/P8CAAAA//8AAAIA+/8GAPv/AwAAAP7/AgD+/wIA/P8FAP7/AwD9/wMAAQADAAEAAwD+/wgA+/8HAP//AwABAAEAAQACAAEAAQABAP7/AgD9/wQA/P8BAPz/AAD+/wMA+f8FAPn/BQD6/wQA+/8CAPv/AAD+/wEA/f8BAP7/AQD8/wMA+/8FAP3/AQACAAIA/P8HAPj/BQD8//7/AgD9/wAA/f8AAPz////9//r//v/9//3/AgD6/wAA///+/wAA/f8EAPj/BQD8/wAA///9/wMA/v8BAP7/AwD9/wQA/v8BAAIA/////wMA/v8CAPz/BQD7/wUA/v8BAAIA//8CAP//AgAAAAAA/v8DAPv/AgD9/wEAAgD8/wYA+f8IAPr/AgACAP3/BAD8/wUA/P8FAAQA/P8JAPz/BAACAAEAAQAAAAMA/P8GAP7/AgADAP3/AwD+/wEAAAABAP7/BQD+/wMAAAD///7/AgD5/wIAAAD6/wUA+v8CAP7/AQD//wQA/P8EAP7/AwAAAP7/AgD+/wAA///8/wAA//8BAPr/AwD8/wAA///8/wAAAQD5/wIA9/8BAPv/AAD8/wMA+v8CAPz/AQD///7/BAD3/wkA9/8FAPn/BAD//wEA/P8FAPn/BQD9/wIA/v8BAP//AgD9/wYA+/8FAAIA//8CAAEA/v8AAP//AAAAAAEA/v///wAAAAABAAEA//8FAPv/CgD4/wwA+P8IAP//AgADAP7/BAAAAAQA/f8FAPz/AgD9/wYA9/8GAPr/BAAAAP//AQACAPv/AwD9////AgD6/wIA/P8AAAIA+/8FAPn/AgD//wAA//8DAPv/BAD+/wEABAD7/wUA+/8HAPv/AwD9/wQAAAADAP7/AQD//wMA/P8AAP///v8BAP7/AgD8/wQA+P8IAPn/BgD4/wUA+v8BAPr/AQD7/////P////3/AgD8/wEAAAAAAAEA/P8CAP7/AgD//wEAAAADAAMA/f8HAPv/CAD+/wAABAD5/wAAAAD8/wEA/v/8/wUA/P8BAP3/AQD+/wMA+v8EAPr/AgD//wAA//8CAAEA/f8FAP3/AwACAP7/CAD7/wUAAQD//wUAAQD8/wkA+f8IAP3/AAAFAP3/BAD7/wMA/f8CAPz/AQD7/wAA///8/wIA/P8FAPn/BQD6/wYA+/8IAPn/BgD7/wUA/P8AAP///f8BAP7/AQAAAAAAAQD+/wYA+v8FAP3///8DAPz/BwD4/wgA+P8FAPr/AwD7/wAA/v/+/wIA//////7/AAD//wEA//8CAP3/AQADAP7/BgD7/wMAAAABAAEA//8BAAIA/////wIA/f8EAPv/BQD8/wIA/v/+/wEAAAAAAP3/BAD6/wIA/v8AAAEAAwD5/wYA+/8GAP3/BgD+/wQAAAD//wIA/P8GAPz/BwD7/wYA//8GAPz/AgACAAEA//8FAPv/BgD7/wQA/P8EAPb/CwD2/wcA+P8DAP7/AgD+/wIAAAACAP//AAAEAPv/BAD+/wAAAwD+/wEAAAABAPz/AwD7/wEAAgD8/wYA9/8FAPz/AgACAPz/BgD8/wMA/v8CAP//AAD///3/AwD6/wQA+f8GAPj/CQD2/wcA/P8BAAIA/v8FAP3/BQD8/wYA+/8IAP3/AAAEAPr/BgD4/wUA/v8BAPz/BgD8/wIAAQAAAAEAAwD+/wIAAAAAAAMA+f8IAPr/BAABAP3/BAD//wAAAQACAPv/AgD7/wEA///9/wQA/P8EAPz/BAD+/wEAAgD//wAAAwD6/wcA/P8EAAIA/v8DAAAAAAACAAAA///+/wEA/P8FAP3/AAACAPz/BAABAP7/BAD6/wUA+f8IAPj/BgD7/wIA/f8BAP7/AgD+//3/AgD//wIA//8CAAAAAgABAAEAAgABAAAAAQABAP7/AgAAAP3/AgD+//3/AgAAAP3/BAD4/wgA+P8IAPz///8EAP7/AgACAP7/AwABAAAAAQABAAAAAAAAAAAAAAABAP//AAABAP//AgD9/wIAAAD+/wIA/v///wAA/v8AAP/////+//////////3/AQD8/wAA/P8BAP7//P8AAP7/AQD//wAA/f8CAP3/BQD8/wMAAAABAAIA/v8EAP3/BgD8/wMAAAD//wMAAAD9/wQA/P8DAP//AgD+/wIA/v8AAP3/AwD9/wYA+P8CAPz/AwD///7/AQD8/wYA+P8HAPj/CAD6/wUA/v8CAAEAAAAAAAIA/v8GAPr/BwD3/wQA/f8BAAAAAAD9/wIA/f8CAAAA//8AAP7/AgD+/wIAAAD+/wUA/P8EAPz/AAAEAPn/BwD4/wUA+v8GAPr/AwABAP//AgD//wAABAD+/wMAAAAAAAQA/////wIA+/8CAAAA/P8DAAAA+/8IAPX/CQD8/wMA//8CAP//AQABAP3/AAD+/wEA//8AAP7/AAD+/wEA///9/wAA/f/+/wEA/f8BAP///f8DAP3/AQD9/wQA+f8GAPn/BgD6/wEA/f8BAP3/AwD4/wcA9/8JAPf/CAD5/wYAAAD//wYA+v8IAPz/BQD9/wQA/f8EAPv/BQD//wEAAAD//wEAAgABAAAAAwD8/wMAAAABAAIAAQD+/wIA/f8DAP//AAABAP7/AwD//wAA//8BAPz/BAD7/wMA//8AAAEAAQACAP7/AwD9/wYA/P8GAPz/AwD+/wIAAAD//wEA/v8AAAAA//8CAP7/AgD7/wYA+/8EAP///////wEA/v8AAAMA+/8DAAAAAQABAP//BgD5/wkA+f8BAAEAAQD9/wUA+/8FAP//BAD8/wUA+/8DAPz/AwD9////AAD8/wAA/v/9/wQA+P8FAPz/AgD///z/AwD9/wIA////////BAD8/wMA/P8DAP7/AQD9/wEA/f8EAPv/BAD+//7/BAD+/wIA//8BAAIA/P8HAP3/BQD+/wEAAAABAAAAAAACAP3/AwD6/wYA9/8IAPv/AQACAAAA//8DAAAAAAAGAPn/CgD9/wQAAQD//wIAAwD+/wMAAAABAP7/BgD8/wcA+f8GAP7/AwABAP7/AQD9/wQA+f8JAPT/BwD5/wMA///+/wAA/f///wAA/P8EAPj/BwD3/woA9/8GAP3/AgD//wEA//8BAAMA/f8DAP///f8EAP3/BQD9/wEAAAD9/wQA+/8CAAAA/f8DAP//AAACAPr/BAD7/wIAAQD5/wYA9v8HAPf/BAD8/wIA//8AAP7/AgD//wAAAQD8/wQA//8BAAAAAgABAAIA/v8DAP7/AwD+/wAAAAACAPz/AwD9/wAAAQD+/wEA/v8DAP3/BAD9/wQA/v8EAPr/BAD9/wYA/P8DAPv/BQD9/wEA//8BAAAAAwAAAAQAAAD//wYA/f8FAP7/BQAAAAMABAD9/wcA/P8JAPz/BQD//wAAAQD+/wEA/v8BAAEA/P8DAPz/AQD+/wEA/v8AAAAA/P8DAPv/AwD7/wIA/P8BAP//AQD9/wAAAgD+/wEA/v8AAAAA/f8DAPv///////z/AAADAPj/BAD5/wQA+/8BAAAA+/8EAPn/AQD9//7/AAD///3/BAD5/wYA+/8FAP3/AQABAP//BAD9/wMA//8DAAAA//8HAPn/BQD7/wIA//8BAAAA//8GAPr/CwD7/wcA//8DAAIAAgACAAIAAAACAPn/CQD5/wYA/P8DAP7/BAD8/wUA/f8BAAIAAAAEAP3/AgD9/wMAAAD+/wIA/v8AAAEA////////AAD+/wIA/P8DAPz/BQD6/wYA/P8FAP//AQABAAEA//8EAP3/BAD8/wYA/P8CAP//AAACAP///v8CAP7/AAD+/wAAAQAAAP///v///wEA/f8CAPz/AwD7/wAA/v/+/wIA+/8CAPv/AQD+/wAA/v8AAP7///8AAPv/AwD8/wAA/v8AAP3/AgD8/wAAAAD///7//f8DAPv/BAD8/wIAAQD9/wYA+P8KAPn/BwD//wEAAgABAAEABAD//wAAAQAAAAIAAgD9/wcA/P8HAPv/BQAAAAEABAD8/wcA/v8DAAQA//8GAAAABQAAAAYA/v8HAAIAAQAGAAAAAgAFAP7/BQD//wQA/v8BAAIAAAACAP//AgD9/wYA9/8GAPz/AwD+////AgD/////AQD9/wEAAAD//wEAAAD9/wIA/f///wAA/////wMA+/8BAP/////+/wIA/f8DAPz/AAABAP3/AQD6/wMA+f8EAPn/AgD7/wMA/f///wIA//8AAAMA/f8CAAIA//8BAAQA/v8GAP7/AwABAAMAAAAHAP3/BAD//wMAAgADAP//AwABAAAABAD+/wcA+/8IAPr/BAD+////BAD9/wAAAAD7/wMA/P///wEA/v/8/wIA+f8HAPr/BAD///7/AQACAPz/BwD5/wYA/f8DAAEAAwAAAP//BAD+/wIAAgD//wYA//8CAAMA/P8KAPj/CQD8/wcA/f8FAAIAAAAEAP//AAAHAPr/CQD8/wYAAAACAAIA/f8IAPf/CgD5/wcA/v8DAP7/BQD9/wcA/f8DAAQA/P8HAPz/AwD//wEAAAABAP///v8DAP7/AgD+/wIA+/8GAPz/BAD9/wIA//8DAP3/AwD+/wAAAgD7/wQA/P8CAAIA/P8FAPr/BwD3/wgA+f8EAPr/AwD6/wUA+/8FAPv/BwD8/wQAAQD9/wcA+/8EAAEA+/8JAPf/BwD5/wMA/v8BAP///v8BAAMA/v8AAAIA+/8GAPr/BQD9/wEA//8AAP7/AgD+/wMA//8CAAEAAQADAPr/CAD7/wQAAgD5/wUA+v8FAP3/AgAAAAIA//8FAPr/BQD9/wEABAD8/wUA+v8GAPv/AgD+////AAD+/wIA/v8CAP//AAAAAAEAAgD//wUA/v8EAP7/AwACAP//AwD//wAAAgD//wUA+/8CAP//AAACAP//AgD//wEAAAD+/wUA+f8IAPv/AgD9/wMAAAD//wEA/P8FAPr/BwD6/wMA/v8AAAAA/v8EAPr/BgD8/wEAAwD6/wYA/P8HAPv/AQABAP7/AgD+//7/AgD7/wQA+v8EAPn/BAD+//7/AAD7/wMA/f///wAAAQD9/wUA/P8EAPz/AQABAPz/BAD5/wMA/f///////f8CAPz/AwD7/wUA/f8DAP////8CAP3/AAAAAP//AgD+/wAA/v///wIAAQAEAP3/BAABAAMAAQD9/wMA/f8FAP//AgD+/wIA+f8HAPr/AgABAP7/AgAAAAAAAQABAP3/AwD//wEA/////wMA/f8BAAAA/v8BAAIAAAAAAAAA//8BAAEA/f8DAPz/AgAAAAAA//////////8BAP3/AgD+/wMA/P8CAAEAAQAEAPz/BQD6/wkA9/8JAPf/CAD7/wQA/v8DAP7/BQD6/wcA/P8CAAAAAgD7/wcA/P8FAPv/AgD6/wUA+v8BAPz///////7/AAD///7/AAD9///////+/wAA/P/+/wAA/v8AAPz/AgD///7/AAAAAPv/BAD3/wcA9/8EAPv/AQD/////AAD9/wUAAAAEAAAAAQACAAAABAD//wMAAgD9/wUA+/8FAP7/AgAAAAIA/v8EAPz/BgD7/wYA+/8FAPz/BQD/////AgD8/wMA+/8BAP///f8CAP3///8CAPz/BAD+/wMA+v8GAPj/BgD7/wQA+/8DAP3///8BAP7///8AAP7////9/wEA/f/9/wQA+v8EAP7/AgD//wUA//8EAAIA//8GAPz/BQAAAAQA//8CAP//AwD+/wEA//8DAP//AwD+/wQA/v8AAAMA/P8GAPz/AQABAPv/CQD0/wwA9/8GAPz/AAD/////AAD+/wMA/v/9/wUA9v8LAPX/BgD7/wIA/f8BAAEA+P8GAPn/AgD///7////+/////f/9/////v8CAP3/AgD+/wEA/v8GAPz/CAD6/wgA//8BAAMA/f8HAPz/BgD+/wQAAQADAAAAAgADAP//BAABAP7/AwABAAQAAAACAP7/AAADAP7/AgD9////AgD8/wUA+/8BAAEA/f8BAP////8DAPv/AwD6/wYA+/8DAP3///8AAP///v/+/wAA+/8CAP///f8CAPv/BQD8/wIA/P8CAPz/AAACAPz/BgD4/wUA/f8DAP3/AgD//wIA/f8DAP3/BQD//wAAAgD//wYA/f8JAPz/BgD+/wUA//8FAP7/BgABAAIABAD//wEAAgD+/wQA/v8DAP3/BAD//wEAAwAAAAMAAAADAP//AgACAPz/BgD5/wgA+P8HAPr/AgABAP3/AgD+/wAA/v8BAP3////+/wAAAQAAAP3/AAD+//////8BAP3/AQAAAPz/AgD7///////8/wIA/f///wAA//8DAPv/AwD//wAABQD7/wIAAgD9/wIAAAD+//7/AgD9/wMA/P///wIAAAD/////AQABAP//AQD//wAABAD8/wEA///7/wUA+v8EAP///P8DAP3/AgAAAP//AgD9/wYA+/8FAPv/BgD8/wYA/P8FAAMAAAAFAP7/AwD//wQA//8EAP//AwD+/wcA+P8KAPn/BwD5/wYA+P8HAPj/BgD6/wEA///+/wMA/f///wMA+/8BAP7///8CAP///P8DAP3/AgAAAP3/AQAAAP7////9/wQA+P8HAPX/BQD8////AQD6/wMA+f8FAPf/AgD9//7/AQD///3/AwD5/wEA/f///////v/+//7/AQD5/wUA+/8BAP////8AAP///////wEA/f8EAAAA//8FAPr/CAD8/wQA/v8BAAIAAQD9/wYA/P8DAAIA/P8EAPr/CAD4/wkA+f8EAPz/BAD9/wEAAgD//wAAAQD/////AgD8//7/BAD6/wcA/v8BAP//BQD9/wcA+v8JAPr/BwD8/wMAAgD//wMA/////wMA/f8BAAEA/P8CAP3/AQD//wMA+/8DAPv/AAACAPv/AwD9/wAAAAABAP7///////3/AgD7/wMA/v//////AAD+/wIA///+/wAA/////wMA+/8DAP7//v8BAAAA/f8EAPn//v8BAP3//f8BAPj/AwD7/wIA+v8DAPj/AwD9/wIA//8DAP3/AQABAP7/AgAAAPz/BAD7/wQA/v/+/wQA/f8DAP7///8AAAAA/v///wMA+v8GAPr/AgACAP//AAACAP3/AgD+/wAAAQD//wAAAAABAP7/AAACAP//BQD8/wEABQD8/wUAAAD+/wcA+f8JAP3/AwABAAIAAQD9/wMA/P8DAP7///8DAPv/AwD9/wMA//8AAAEAAQAAAAEA//8AAAIAAAAAAAEA//8CAAAAAwD6/wYA+/8CAAAA/f8DAPz/AwD9/wQA/P///wMA/P8EAPz///8AAAAA/v/8/wEA+P8FAPv//f8FAPb/CQD3/wgA+P8GAP7/AQACAPz/AgD+/wIA/v8BAP/////+/////v8AAPz/BQD3/wcA+P8EAP3/AAD+/wIA//8BAP7//f8DAP7/BQD6/wMA/P8BAP7/AgD9////AgD6/wYA/P8EAP////8FAP7/BgD7/wQAAwD7/wkA+f8GAP//AAABAAAA//8AAAAAAAD+/wQA/f///wYA+f8JAPz/AgACAAEAAQD/////AAAAAAAA//////7/AgD9/wIA/v/+/wIA/f8EAP7/AwAEAP//AgD//wMAAAADAAMA/v8GAPz/BgD9/wUA/v/+/wYA+/8GAP3/AwD9/wQA/////wYA+v8GAP3/AAAAAP7/AQD8/wAA9/8EAPv///////3/AAD///v/BAD9/wAAAwD8/wUA/f8EAP3/BAD+/wAAAwD+/wEAAAD//wAAAwD6/wQA+P8BAP7//f8AAP7////+/wIA/f8CAAIA+/8IAPr/CAD8/wEAAwD+/wEAAgD8/wMA/v8GAPr/CQD4/wYA//8DAAAABAABAAMAAgAFAAAAAgABAP7/BwD7/wMA/v/9/wEA/////wMA+v8FAPr/BQD+/wEAAwD//wUA//8BAAMAAAADAAAAAwAAAAIAAgD8/wQA//8AAAIA/f8DAP7/AgAFAPz/BgD+/wEA//8CAP//AQACAPz/BAD9/wUA/P8AAAEA/P8DAPz///8FAPz/BAD+/wMAAAACAP//AwACAAMA//8EAP7/BAD+/wMA/f8FAP7/BgD//wAAAwABAAEAAgD//wMAAQD//wAAAwD8/wIA/f8CAP3/AQAAAP3/BAD7/wUA/P8EAP7/AAAFAPv/BQD8/wIA//8BAAIA/f8GAPr/BQD9/wIAAQD8/wMA/f8AAP///v8CAP7//v8BAP7/BAD8/wMA+/8CAP//AAACAAEAAAD+/wQA/f8FAP7/AgAAAAIA//8DAP3/BAD+/wQAAQD//wMA//8CAAMAAgACAAEABAD+/wUA///+/wYA+/8FAPz/AwD//wIA/f8CAAAAAAABAP//AwD//wAAAgD//wUA/P8EAAIA/v8DAP7/AQD//wMA/v/+/wAA/v8AAP7/AAAAAP7/AgD/////BAD7/wYA/P8FAPr/BwD8/wMAAAD//wMA//8AAAQA/P8EAPz/BwD7/wcA/v8BAAMAAQADAAIAAgADAAMAAAAFAP7/BwD9/wMAAQAEAP//BQD9/wMA/f8BAP//AQD+/wEAAAD///////8CAPz/AQD///7/AwD9/wEA/f8CAP3/AgD5/wUA+v8FAPz/AQD+////AgD8/wUA+v8FAP//AgD//wUA+/8GAPr/AwD8/wEA/v////7///////7/AQD9/wQA+/8EAPz/BQD8/wUA/P8HAPv/CAD6/wgA/P8BAAMA//8FAP7/BQD7/wcA+/8FAP//BQD7/wsA9v8NAPn/BgAAAP7/BQD8/wUA+v8GAPr/BwD6/wMA+f8GAPf/BQD5/wIA/f/+/wQA+v8FAPz/BAD8/wQA+v8GAPj/BgD6/wMAAQD+/wQA/f8BAAAA/f8GAPn/BgD8/wIA//8CAP7/AQAAAAEA//8CAP7/AAAAAP///f8DAPn/BQD7/wIA/v///wEA/v8DAPv/BAD9/wQA/P8CAP7/AAACAPr/AgD9/wEA//////z/AQABAPz/AgD9/wAAAAAAAP3/AQD8/wEA/v8AAP7//f8AAPz/AQAAAPz/AQD8//7////+//7//v/6/wMA+v8FAPz/AwD+/wEA/v8AAAAA//8DAPv/BQD8/wIA/v/+/wQA/f8CAP//AAACAP7/AQD5/wIA/P8GAPz/BQD//wEAAAAAAPz/AAD+//f/BQD1/wMA+/8DAAAAAAADAPn/CgD7/wcA/f8BAAMA//8BAP///v8AAP7//v/+//3////7/wMA+P8HAPj/BgD7/wAAAAD7/wQA/P8FAPv/BAD7/wQA/v8DAP7/AwAAAAAAAAD///7/AQAAAP7/AwD7/wYA+v8EAPv/AwD9//z/AQD3/wMA+f/9//3/+/8AAPn//v/+//v/AgD6/wMA9/8HAPv/BQD9/wIA//8FAPz/CgD6/wYA/f8EAP7/AgD//wIA+P8HAPn/AwD3//3//f/9//v/AAD7/wIAAAD/////AQD//wQA//8AAP///f8DAP3////+//7/AAAAAP7///////z/AAD4///////5/wYA9/8AAAIA+v8GAPv/BgD+/wIA//8CAAAAAAABAP7/AQACAP///v8EAP7/AQAEAP//BQD//wMAAQACAAUA/v8BAAAA/v8AAP7/AQD+/wMA/v8CAAAABgD8/wsA/f8IAP7///8FAPv/AQAAAPv/BQD5/wIAAgD7/wUA/P8AAAMA+f8HAPT/CQD1/wYA+f8EAPr/BQD7/wQA+f8KAPb/CAD5/wYA+/8GAP3//v8FAPz/AQD+/wIA/P8GAPn/CAD5/wcA+v8EAP///f8IAPn/CQD7/wMAAQD9/wYA+/8DAPr/CQD2/wsA+f8CAAMA+f8JAPf/BwD9/wMA/P8FAPz/CAD7/wEA///9/wUA/v8GAPr/CAD7/wMA/v8CAAQA/P8FAP//BAABAAAA//8EAP//BgAAAAEAAAADAAQAAQACAAEAAwAEAAIABQACAAEAAwD//wMAAgD//wIAAgD+/wYA/f8FAPz/BAD/////BQD5/wUA/f8CAAEA/f8GAPn/CQD7/wMA//8BAAAAAwD9/wUA/P8FAP7//////wEA/v8DAPz/BgD7/wUA/P8AAAAA/v///wAA/v/8/wIA+v8FAPr///8CAP3/BQD9/wEABgD8/wcAAAABAAIAAwD9/wgA+/8DAAEA/P8AAAQA/f8DAAAA/P8HAP//AgAFAPj/BwD8/wMA//8AAAEAAQAAAAQAAAD+/wEA/v8BAP7//v8CAP7/BQD7/wQA+v8DAPr/BQD9/wIAAAACAPz/CQD7/wAABgD5/wkA/f8GAP//AAAHAPr/CQD9/wEABQD5/wkA+v8HAPr/AwD8/wAA/v8AAPz/AgD5/wYA+v8HAPr/BQD+/wMAAgD//wUA//8DAAIA//8JAPv/BgACAP7/CQD7/wQABAD9/wUAAwD+/wYA/f8EAAEAAAD+/wMAAAABAAAA+/8DAPv/AwD8/////v////7//v////3//P////7///8BAPz/AwD8/wEA+/8EAPv/BQD5/wgA+v8HAPz/BQD3/wkA9f8GAP3//v///wAA/P8BAPr/AAD7/wEAAQD+//3/AgD8/wMA/f/7/wYA+P8CAP///f/+/wAA//8BAAEAAwD7/wgA9/8NAPr/BgACAAAAAQABAAAA//8FAP3/AgAEAP3/BQD5/wkA9/8JAPv/AgD+/wMA///+/wIA//8CAAEA/f8CAAAAAAABAAAA//8AAP7/AwAAAAEABAD//wQABQD8/wkA/P8FAP7/AQD+/wcA+/8IAPz/AgACAPz/AgABAPz/BAABAAAABAD9/wEAAAD///3/BQD5/wQA/P8CAAEA/P8IAPz/BAD//wAAAgABAPz/BAD6/wQA/P8AAP////8BAP///v8AAAEA/P8FAPr/BwD8//7//f/6/wEA+f8CAPn/AwD5/wEAAgD9/wMA+/8EAPn/CQD3/wcA+f8DAAIA//////7//////wIA+/8DAPr/BAD///7/CAD4/wgAAAACAAMA/v8DAPr/AAAAAP7/BAD9//7/BQD7/wcA/P8EAAIAAwADAP//BgD9/wYA//8EAAIAAAAEAAIABQD9/wQA/v8BAAIAAAD7/wQA+v8EAP7/AgAAAAQA+P8LAPb/BwD7////AAD8/wMA/f8EAPv/AwD5/wIA/P8DAPz///8AAPz/AwD///7/AAD//wEA/v///wAAAQD+/wMA/v/+/wIAAgD+/wEA+/8EAP//AgD//wIA/////wUA/v8CAP//AQD+/wMA+P8FAPb/BwD3/wMA/f8BAPz/AgD/////BAD2/wIAAwD6/wsA9P8KAP//BQABAAAABQAAAAQA+/8BAAAA9/8EAPf/AgD5/wAA/P8DAPv//////wEA/v8CAP3//P8EAPn/BwD6/wMAAwD5/woA9/8LAPb/CgD7/wUAAAABAAAAAAAEAPr/CwD1/woA+/8CAAcA+/8IAPj/CwD7/wQA/f8BAPz/BAD5/wMA/f//////AgD8/wUA/P8CAAIAAwD9/wUA/P8DAPv/AgD8/wMA+/8DAP7/AAADAPv/CAD5/wUA/f8AAAMA/f8FAPb/DAD0/wkA+f8AAAEA+/8BAAEA/f8EAPv/AAD///7///8BAP3///8AAP//BAACAP7/AwD//wAAAgD9/wIAAQD5/wIA///+/wIA+v8EAP7/+/8GAPX/CgD4/wIAAgD+/wEA//8AAAQA/P8HAPf/CQD9/wMABQD+/woA+v8HAP3/AAAFAP3/BQACAP3/DAD6/wkA+v8DAAQAAAD//wQA+v8JAPX/CAD3/wQA+P8AAAEA/P8BAPv/AgD9/wQA/P8FAP////8BAAAAAgD6/wYA+P8DAAIA+f8HAPv/AAACAPn/CAD4/wcA/P/+/wQA+v8DAAEA/P8GAP7//f8FAP3/BQD4/wYA9/8HAPr/AQD5/wQA+/8DAP//+/8EAP3/BQD+/wMA/v8EAP//AwD9/wYAAgD+/wYA+v8FAPv/AQABAP7/AQAEAPz/AwD9/wQA/P8GAPj/BAD8/wEAAgD3/wcA+/8DAAAA/v8DAP3/BAD+/wAA/v/9//7/BAD7/wEAAwD9/wcA/P8EAAAAAgABAAIA/v8EAP3/AwD+/wQAAQACAAAAAAD//wEAAAABAPv/BAD3/wwA9v8FAAAA/P8FAP//AAAEAPn/AQD+/wEA/P8CAP3/AQD8/wAA+v8CAPv////6/wMA+/8CAAAA/////wIA//8DAAEAAAAAAAMA/P8EAAIA+/8JAPf/AgAAAPz/AQD8//7/AQAAAAEAAQD7/wcA/P8EAP7//f8EAPz/AwD9/wMA+/8GAPr/BQD9/wIAAAABAAIAAgAAAAQA/v8DAP//AgD8/wUA+/8FAAAAAgD//wAAAAABAAAA/f/+/wIA/P8DAPr/AAD8//z/AQD6/wIA/P8AAP3/AAAAAP7//v8CAP/////+/wAA//8CAPr/BgD7/wUA/P8CAP3/AQD9/wMAAAAAAP7/AQAAAP///v/+/wIA///9//7////8/wQA+f8CAPv/AAD9////AQD8/wQA+/8DAPz/BQD6/wYA///+/wQA/P8DAP7/+/8GAPv/BwD6////BAD7/wgA/f8BAAIA/f8DAAEAAAAFAP7/CQD9/wYA/v8CAAQA/f8AAAIA+/8GAPz/AgD//wIAAwD//wEA//8EAP//AQABAPv/BQD5/wQA+/////7//v/+/wAAAAD+/wIA/f8DAP//AAD9/wgA9P8KAPb/BAD8//3//f////z//v/+//n/AQD4////+v/9//v/+v8BAPr/AAD9//7////9/wAAAQD9/wQA+f8FAPz/AQADAP7/BQD9/wEAAQD//wYA/f8BAAIA/v8HAPz/CgD+/wMABAD//wYA/P8EAP3/AQD//wIAAAD//wIA//8HAP3/CQD4/wUAAQD+/wYA+P8GAP3/AAD9/wQA+/8GAPv/BAD7/wQA+f8GAPv//f8DAPr/BgD8/wUA/f8DAAEA/P8EAAAAAAAFAAAAAQAAAAQA/P8LAPf/CQD6/wEABAD+////AwD6/wMAAAD6/wkA+v8FAPv/AgAAAAAA//8AAP//AwACAPn/CQD8//7/BAD4/wMA///7/wMA/f///wAAAAACAP3///8AAAAAAAD////////8/////f/9/wAA+/////7/AQD///z//P8CAPr/BgD4/wcA+/8EAAEA//8DAAEA//8EAP3/AwADAAEA//8FAP3/AAAGAPz/DADz/wwA+/8DAAcA/v8IAPv/BgD+/wQAAAD+/wEA/v8DAP/////7/wQA+v8EAP3/AAAAAP//AwD7/wYA+f8GAP3/BgD9/wQA//8FAP//AgABAAIA/v8EAP3/BQD9/wIA//8DAAMAAQAAAAQA/f8FAAEA+/8DAP3/BAD+/wAABAD6/wkA+v8EAAAA/v8EAPf/DAD2/wkA+/8BAAIA//8CAAIA//8FAP7/AwD4/wgA+/8FAP///v8CAP3/BAD8/wAAAQD8/wIA/////wEA/f/9/wEA/v8AAP///v8AAP3///8AAP7/AgD8/wQA/f8CAAEA/f8HAPj/CAD7/wMAAQD//wQA//8AAAMA/P8FAP3/AAACAP3/AgD//wEA/P8FAPj/BwD7/wQA/f8BAAIAAAACAP///v8BAAEAAwD+/wEA///+/wUA+v8GAPv/BAABAAAABQD9/wQAAgD//wIAAwAAAAQAAQAEAAAAAwAAAAQAAQABAAAAAwD8/wIA/v///wMA/P8CAP7/AAD9/wIA/P8DAP3///8BAPz/AQD///z/AgD9////AQD+/wAA/f8GAPn/BgD6/wIA/////wAA///6/wMA+f8FAPv/AgD6/wEA/v/+/wEA///9/wEA+/8AAPz/AQD+/wAA/f8BAP7/AgD+/wQA+/8FAP3/AQAGAPj/CAD8/wIABAD7/woA+P8DAP7///8EAPv/BAD//wMAAAADAAEAAwACAAAABQD//wUA/f8FAP7//v8CAAEA//8DAP3/AQACAAAAAwD8/wMAAAABAAQA/f8BAAAA/v8DAP////8AAAEA/v8CAP3/AAABAP3/AQD///7/BAD9/wAAAAACAP//BQD9/wMA/v8EAPz/CAD4/wgA+v8GAPz/AwD+/wIA/v8BAP//AQD9/wEA/f8EAPz/AgD///v/BgD4/wcA+P8FAPv///8BAPv/AwD8/wEA/f////////8AAP/////+/////f8AAAIA+P8GAPj/BAD9/////v8AAP/////+/wAA/f8DAP3/AwD+/wAAAwD+/wIAAgD+/wYA/f8FAP//AwACAAAAAgD+/wIAAgAAAAEAAwD//wQA/v8DAAIA//8FAPz/BgAAAAIAAwACAAIABAADAAEABQD//wcAAQAEAAMAAgABAAYA/f8GAP3/BQD//wEAAgD+/wUA+/8GAPz/BQD4/wUA/P8GAPv/AAACAP//AAD///7/AwD8/wMA/f8CAP///f8DAPn/BQD9////AwD6/wMA/v///wAA/v8BAAAA/P8DAPz/AwD8//z/AwD3/wcA9/8FAPj/BAD9/wAAAQD//wIA//8CAP3/BgD7/wcA+/8KAPr/BwD9/wUA//8FAAAABAD//wEABAD+/wgA+v8IAP3/AwAAAAIABAD+/wMA//8CAPv/BgD7/wYA/P/9/wEA/P8EAPr/AwD6/wEA///8/wQA/P8BAAIA+/8FAAAA/P8FAPv/BAABAAAAAAAFAP3/BgD6/wgA+f8IAP7/AgAEAP//BAD9/wUAAAABAAMAAgD+/wgA/v8CAAMAAQD//wUAAAD//wgA+/8IAP7/BAD8/wgA9v8NAPb/CQD8/wQAAAACAP//BwD7/wgA/f8EAP//BQD5/wcA+/8EAP//AAD8/wUA/P8CAAAA//8BAP3/BAD9/wQA/f8BAAIA//8CAP///f8EAPz/BQD6/wQA/f8DAP////8DAPv/BAD8/wEA/v////7/AgD+/wAAAgAAAAMA//8DAP3/BgD8/wUA/v/+/wYA+v8EAPz/AAABAP7/AwD5/wgA+v8FAP7/AgD+////AQD//wMA/P8FAPj/BwD5/wUA//8CAP//BAD+/wMA/v8DAP//AgAAAP3/AgD8/wQA/v8BAAAAAQACAAAAAgD8/wMA/v8FAPz/BgD3/wsA9v8HAPn/AgD+/wEAAAD//wEA/v8DAPz/BwD6/wkA+/8FAAAAAQACAAIA/v8EAP//AQAAAAIAAAD//wEA/f8FAPz/AQADAP3/BQD6/wUA/f8DAP7/AQD//wEA//8DAPz/AwD8/wMA/v8CAP////8BAP7/AgD7/wcA+P8HAPv/AwD/////AgD//wQA/P8EAPv/BQD7/wIA/v8AAP3/AAAAAP7//v8AAAAA/v8BAPv///8DAPj/CgD3/wUA/f8DAP//AQD//wAA/f8EAPf/CAD5/wEA///8/wAAAQD9/wIAAAD//wEAAgD8/wQA/f///wIA/v8BAP//AAD///7/AgABAAIABAD7/wgA/f8FAPv/BQD8/wcA+/8GAPr/BgD4/wUA/P8BAAEA//8AAAEA/v8EAP7/AAABAP7/AgAAAP3/BQD7/wMA/v8AAAAAAgD//wAAAAABAP7/AwD7/wUA+/8DAP////8DAPv/AQD+/wAAAQD+/wEA/v8BAAAAAwD+/wQA//8CAP3/BQD7/wYA+/8EAPz/BQD9/wQA//8BAP//BAD8/wMAAAABAP//AQABAAEA///+/wAA/f8CAPr/AgD7/wIA/P8BAP7/AAD9///////9/wIA/P/9///////9/wQA9v8JAPn/AQD9/wIA/f////7//v/9/wIA+/8DAP3/AAD+/wEAAgABAAQAAAABAAMA/v8GAP3/BgD9/wQA/v8BAAIA/P8HAPr/CAD6/wQA//8BAAAAAgD//wIA/v8CAAIA/v8CAPz/AQD9/wAA/v8AAP7/AAD///7/AgD+/wQA/f8CAP7//v8CAPz/BgD4/wgA9f8JAPf/BQD7/wMA+v8EAPn/AgD///r/BQD7/wQA+v8KAPj/BwAAAAIAAwACAP//AwACAAAABAD//wIAAAACAP3/BAD8/wQA//8CAP//BAD8/wQA/f8EAPz/BgD5/wYA+/8DAAAA//8BAAAA/v8CAP3///8AAP//AQD///7/AQD9/wIA/P8BAAAA/v///wEA/f8AAP/////9/wEA///9/wIA+f8CAPv/AAD+/wEA/v8AAAIA/v8BAAAAAgADAAAAAwAAAAAABAD+/wUA/v8FAPz/CQD8/wYA/v8EAAMA/v8FAP3/AwABAAEABAD//wMA/f8DAP7/AQACAPr/BAD8/wIA/v8DAPr/BwD4/wQA/f8CAAAA/f8BAPz/BAD9/wEA/P8DAPz/AgD9//z/AwD4/wYA+/8AAAAA/v8AAAIA/v/9/wIA/P8BAAAAAAD//wAA//8AAAQA+v8FAP7///8DAP3/AwABAP//AQACAAEAAQAEAAIAAQAFAPz/CAD9/wQAAgABAAUAAQADAAAAAAACAAAAAAAGAPn/BgD9/wMAAAAFAP3/BgD9/wUA//8CAAEA/P8IAPf/CQD6/wIAAAD9/wQA+/8FAPn/BwD3/wUA+//+/wMA/f8AAAEA/P8CAPz/AQD8/wUA+v8DAP//+/8FAPf/AwD+//r/BQD6/wIA/f8DAP////8AAP//AwAAAP//AQAAAAEA///+/wQA9/8JAPn/AwD9/wEA/v8EAPv/AgABAP7/AwD+/wEAAAACAP7/AQD9/wAA/v8CAP3/AgD8/wMA/v8BAP//AAACAP7/AwD//wAAAQD+/wQAAAAAAAQA//8HAP7/BAD+/wMAAQACAAEAAQACAP//BgD5/wkA+/8EAPz/AwD9////AAAAAP3/AgD6/wUA+/8DAP7///8BAPv/AgD+/wIA/f///wMA+f8IAPr/AwD9/wEA/v////3/BAD6/wEA/f/9/wEA///8/wIA+////////f/9/wAA/f8AAAIA+v8DAPv//v8CAPr/AwD6/wAA///+//7/////////AAD///////8BAP7/AAD//wIAAAACAAAAAQABAAAAAQABAP//BAD9/wIAAgD9/wYA/P8EAPz///8GAPr/CAD5/wMA/v8CAAEA+/8IAPj/CQD6/wMA/f8BAP7///8BAP3/BAD//wEAAQAAAAMAAAADAP//AgABAP//BQD9/wIAAgD+/wIAAQD7/wYA+v8FAPr/AAABAP//AwD8//7/AwD6/wUA+f8CAP//AAD//wEA/f8BAPr/BQD5/wQA/P8BAP///f8CAP7/AAACAPv/AgD+/wAAAwD5/wYA+/8CAP///v8CAPz/AwD2/wcA9/8CAP3/+/8DAPr/AgD6/wIA+v8EAPv/AgAAAAEAAAAAAP7/AgD+/wUA+f8DAPz/AwD+/wEAAgD7/wcA+P8IAPj/AwD///3/BQD5/wYA+/8CAAAAAQD//wQA+/8DAP3/AAACAP//AQD8/wQA/P8EAP7/AgAAAAEAAQABAAIA/v8HAPr/CAD5/wkA/f8EAAEAAQABAP////8BAP//AAD//wEA///+/wAAAQABAAEA//8BAP//AwD+/wEAAQD//wMA/v8BAAIA/f8HAPn/BQD8/wMA+/8EAP3/AAABAP////8DAPn/CAD5/wQA///9/wEA///9/wAA/f/7/wIA/P///wMA+P8FAPv/BAD+/wAAAgD/////AwD7/wMA//8BAP3/AgD8/wEA/v/+/wAA+v8IAPb/BwD5/wIA/v8AAP//AQAAAP///v///wIA/f8GAPn/BAD9//////8AAAIA+v8FAPn/BwD7/wUA/P8DAAIAAQAAAAMA/v8FAP////8GAPn/CwD2/wkA+v8DAP3/AQAAAP//AwD8/wIAAQD//wQA//8CAAAAAgABAP//AAD+/wEAAQD9/wEA/P8DAP7//////wEA/f8CAAAA/v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8HAPz/BwD7/woA+/8JAPv/BQD+/wEAAQABAAEA//8DAP3/AAACAP///f8FAPr/BAAAAAAAAAADAP3/AQAAAAAAAQD+/wEAAAABAAMAAAAAAAQA+v8EAP7/AAACAP3/AgD+/wIAAwD+/wMA+/8FAPv/BQD8/wQA+/8EAAAAAwD+/wAA/f8CAP7/AwAAAAIA/v8AAAIA//8AAAMA/P8GAPv/BgD7/wYA/////wIA+/8IAPn/AwD///3/BAD7/wYA+v8DAP3/AAACAAAAAQAAAAEAAAADAP7/BAD7/wcA9v8MAPT/CAD3/wMA+/8AAP3/AgD7/wMA+v8FAPz/AQD//wIA//8BAP7/AAADAPr/BwD7/wMA/////wEA//8BAP//AgD+/wMA/v8CAAEABAD//wEAAgABAAQAAAACAAEABAD+/wYA+v8DAAEA/f8EAP3/AgACAP//AwABAAAAAQADAAAA//8AAAIA//8AAP7//v8AAAAA/v///wEA+v8GAPr/BAD9/wAAAgAAAAMAAgD//wIAAAAAAAQA/v///wAA/f///wAA/P8CAP3/AAADAPz/BAD+////AgD7/wMAAAD//wEA/f8FAP3/BAD+/wMA/f8EAPv/AAABAPv/BAD5/wUA+f8GAPb/BgD8/wAA//8BAAAAAAABAP//AgD//wIA/f8EAP7/AAAAAAAAAgD7/wQA+v8EAP7/AgD9/wUA/f8DAP////8CAAEAAgAAAAAAAAD//wEAAgD9/wIA/v8CAP//AgABAAMAAAABAAMAAwAAAAMAAAACAAMAAAAAAAUA/v8CAP//BQD8/wUA/v///wQA/v8DAAAAAgD//wEAAgD//wUA/v8EAP3/AgABAAEA//8CAP3/BQD6/wMA+v8CAPz/AgD8//7/AQD+/wAA/////wIA/f8FAPr/AgD+//z/AwD5/wQA/f///wEA/P8CAPz/AgD8/wIA/f/7/wQA+f8CAP3//////wAA/v8DAAEAAAADAP7/AgABAP////8EAPz/BAD9/wEA//8DAP//BQD9/wQA//8GAP////8EAP//AgD//wMA/P8FAPv/BgD7/wQA/f8EAP//AgD8/wMA/P8EAPz/BAD9/wEABAD+/wMAAAABAAEAAAABAP7/AwD9/wQA/P8CAAAAAAD//wMA/v8CAP//AAD9/wYA+f8FAPv///8DAPv/AgD8/wAA//8BAPz/AwD8/wMA/f8DAP3/BwD8/wMAAgAAAAQAAgD//wIABAD7/wUA/P8AAP7/AgD9/wEA////////AQD7/wUA/P8BAP////8EAP3/BAD/////AgD//wMA+/8DAPr///8AAPv/AgD7/////v/+/wAA/v8BAPz/AwD8/wAA/v/9/wIA+/8DAP3/AAAAAP3/AQD//wQA+v8EAP3/AAACAP3/AgD9//7/AgD+/wAAAAD+/wEABAD7/wYA/P8BAAMA///9/wYA+P8JAPj/BQD///7/CAD6/wYAAAAAAAMA///+/wMAAAD//wQA+P8HAPz/AwD9/wAAAgD8/wQA+v8DAP3///////v/BAD5/wMA/P8BAAAA///9/wMA+v8EAP3//v8BAPz/AgD7/wMA+/8BAP7/AwD8/wUA+v8GAP3/AwABAP//AgACAP7/AgAAAP3/AgD8/wEA/////wAA/v///wAAAAABAAAAAQABAPz/AgABAAAAAAACAPv/BAD///3/BgD//wEAAQD+/wMA/P8BAAAA/f8EAP3//v8CAP7///8DAPz/AAD///7/AAAAAP3//f8BAPj/BAD4/wQA+P8CAP3///8AAAAA//8AAP7/AQD+/wAA///9/wUA+f8FAP3/AwAAAAEAAAADAP//AwD//wAAAQD+/wUA/P8DAP////8GAPv/AwD9/wEA/v8CAP7/AgD+////AQD//wIA/P8FAPj/CgD1/w0A9v8JAPz/AgABAAEAAQD//wIA/f8FAPn/BAD6/wEA///+//7//v8BAP3/AAD9/wAA/v8FAPn/BAD8/wIAAAABAP//AgD7/wIA/v8AAAMA+P8HAPv/AgAAAP3/AwD9/wEA//8AAP//AgABAAAAAgD//wIA//////7/AwD8/wIA/v///wEA+/8EAPj/BAD+//////////7/AAD9/wAA+v8CAP3//v8AAPz/AQD8///////7/wIA+/8CAPn/AgD5/wEA+/8EAPr/BAD9////BQD3/wYA+/8HAPr/BAD8/wEABAD6/wcA+/8AAAUA+f8HAPz/AgAAAAAAAgD9/wMA/P8CAP7/AAACAPv/BQD9////AgD8/wIA///8/wMA/P///wEA+v8FAPv/AQD///7/AwD+/wAAAgD8/wQA/f8BAAIA/v8BAP//AAABAPv/AwD8/wQAAAD6/wEA/P8CAP7///////7/AQD+//z/BAD5/wYA+v8FAPr/AwD//wAAAQD//wAAAQD8/wUA+v8HAP3/AAACAPv/BAD9/wEA+v8EAPn/BQD9/wAAAgD8/wAA//8CAP3/AwD8/wAA/v8AAPv/BAD6/wMA/P8BAP3////9/wEA/v8AAP3/AAD9/wAA+/8EAPz/AwD8/wQA/v8EAP//AQAEAP7/BQD7/wUA//8BAAEAAAABAP//BAD8/wAAAQD9/wEABQD3/wkA+v8EAAAABQD6/wcA+/8FAP3/BAD8/wIA/v8BAAIA+v8FAPz/BAD/////AgAAAAMA/f8DAP//AQD//wEA//8CAP7/AwD9/wQA/P8EAP//AgD//wMA/P8EAPz/BQD9/wEAAAD+/wEA//8AAP7/AgD8/wMA/v///wAAAQD9/wQA+/8BAPz/AgD9/wEA/f8BAAAAAwD+/wQA+/8GAPv/BQD8/wQAAAAAAAMA//8DAP7/AgAAAAMA//8DAP//AgD9/wQA/v8CAP//AAAEAP//AwD//wQA//8EAAAAAwAEAAAAAgD//wMAAAAEAAEAAQD//wIA/v8FAP7/AgABAAEAAQABAAIA//8BAAEAAQABAP7//f8EAPz/BAD9/wAA/v8DAPr/BQD6/wUA/f8DAP3/BAD8////AgD6/wUA/f8AAP//AgD6/wgA+v8FAP3/AgABAAAABgD8/wgA/f8GAAAAAgADAAIAAwAAAAQA/v8FAAEABQAAAAQA//8GAP7/BQD+/wQAAgD//wYA/v8DAAEABQD9/wcA+f8JAPr/BgD+/wIAAQD7/wgA9/8IAPj/BwD8/wQA+f8FAPn/CQD6/wYA+v8HAPr/BwD4/wYA9/8HAPj/BQD7/wAAAAADAPv/BwD9/wUAAQABAAQAAAAAAAIA//8DAAEA/v8FAAAABAAAAAMAAQAAAAUA/P8IAPr/CQD5/wgA//8BAAQA/v8HAP//CQD6/wkA/f8DAP7/AAABAP3/AgD9/wEA/f8CAP//AAAAAP7/AQD8/wkA9/8IAPj/BAAAAPz/BAD3/wYA+/8CAAAA/f8AAAMA/f8DAP3/BAD8/wUA//8CAAUAAQADAAMAAwAEAAAABwD9/wcAAgABAAgA//8IAPv/CQD+/wIABwD8/wcA//8BAAYA/P8HAPz/BgD//wEA//8DAP//AwD+/wMA//8AAAAA/P8HAPj/BQD7/wIAAwD7/wYA+P8HAPz/BAD//wMA/v8EAPz/AwD9/wIAAAAAAP//AQD7/wUA+v8EAPn/BQD7/wAAAQD7/wQA/f8DAP3/AgD+/wIA/f8BAP3/AwD8/wUA+/8FAP7/AAAEAP3/AwD9/wUA/P8EAAAAAAACAAAAAgACAAIAAgACAAIA//8BAAMA+v8IAPz//v8BAPv/AgABAPv//v/+/wAA//////z/AwD7/wEA/v/8/wYA/P8CAP7/AQAEAPz/BwD7/wQAAAD+/wcA+/8HAP7//f/7/wAA+f8FAPv/AQACAPz/AwD9/wIA+/8BAPr/AgD5/wIA/f8DAP//AQACAP7/BAD//wIAAQACAAIA/f8GAPn/CgD3/wkA9v8JAPv/AwD+/wMA//8FAPz/BgAAAAEAAwD//wQA//8FAP3/AgADAPz/CAD//wEAAgADAP3/CQD3/wkA+P8HAPv/BAD+/wEAAAD+/wIA/P8BAP//+/8CAPj/AQD7/wAA///+//z/BAD7/wAAAAD7/wUA+////wIAAAABAAAA//8DAP//BQD///7/BQD6/wUA+v8GAPr/AwD9/wAAAQD8//////////7/AAD9/wUA/f8EAP7/AAABAAEA/v8DAPz/AwD+/wAA/f8EAPr/BwD5/wAA///+/////P/7//3//v/+//z/AgD3/wcA+f8FAP3/AgAAAAEAAAAAAAIAAAACAP3/BAD9/wUA/P8CAAEA/v8HAPr/CAD9/wUAAQADAAEABAD8/wgA/P8CAP////8CAAUA/P8IAPz/CAAAAAcAAgADAAQA/P8GAPv/AwD9/wEA///+/wEA/v///wQA/P8GAPr/BQD9////AQD9/wAA//8AAP7/AAD+/wAA/P8AAAAA/f8DAP3/AwD//wAAAQD7/wcA9v8FAP3//f8BAP///f8DAPz/AAAFAPj/CAD6/wQA//8AAAEAAQD9/wYA+/8FAPn/CgD3/woA+v8EAP////8AAAAAAAACAP3/AgD//wEAAgD//wEA/f8CAAEA/v8IAPn/BgD9/wAAAwABAAIA/f8DAAAAAgADAP3/AwD//wEAAQABAP//AAADAAAAAgACAAAAAgACAAIAAwABAAAAAQABAP7/AQD+/wEA//8DAPz/BAD9/wMA//8CAP7/AQD//wEA/f8EAPz/BAD+/wEAAQABAP7/AgD+/wEAAQD//wMA/f8AAAAAAQD+/wMA/v///wUA/v8EAAAAAQD+/wUA/P8CAP//AAD+///////+//7////+/wMA//8CAP//AwAAAAIAAwD+/wQAAAD//wYA+/8CAAEA/f8CAAIA/v8BAAIA/P8FAP//AQAAAP3/AQD7/wgA9f8JAPb/BgD6/wgA9/8FAPr/AQD+/wAA+f8EAPr/BAD8/////P//////AQD///7/BgD5/wcA/P8DAP//AgD+/wIAAAADAP3/AgAAAAIAAAACAP3/BQD8/wUA/f8EAPz/AwD8/wIAAAD//wAAAgD7/wYA+/8DAAAA/v8CAP7/AgD8/wUA/v8DAP3/AwABAAMAAQADAP7/CAD7/wcA//8DAAEAAQABAAIAAQABAAEA/v8CAP3/BAD8/wEA/P8AAP7/AwD5/wUA+f8FAPr/BAD7/wIA+/8AAP7/AQD9/wEA/v8BAPz/AwD7/wUA/f8BAAIAAgD8/wcA+P8FAPz//v8CAP3/AAD9/wAA/P////3/+v/+//3//f8CAPr/AAD///7/AAD9/wQA+P8FAPz/AAD///3/AwD+/wEA/v8DAP3/BAD+/wEAAgD/////AwD+/wIA/P8FAPv/BQD+/wEAAgD//wIA//8CAAAAAAD+/wMA+/8CAP3/AQACAPz/BgD5/wgA+v8CAAIA/f8EAPz/BQD8/wUABAD8/wkA/P8EAAIAAQABAAAAAwD8/wYA/v8CAAMA/f8DAP7/AQAAAAEA/v8FAP7/AwAAAP///v8CAPn/AgAAAPr/BQD6/wIA/v8BAP//BAD8/wQA/v8DAAAA/v8CAP7/AAD///z/AAD//wEA+v8DAPz/AAD///z/AAABAPn/AgD3/wEA+/8AAPz/AwD6/wIA/P8BAP///v8EAPf/CQD3/wUA+f8EAP//AQD8/wUA+f8FAP3/AgD+/wEA//8CAP3/BgD7/wUAAgD//wIAAQD+/wAA//8AAAAAAQD+////AAAAAAEAAQD//wUA+/8KAPj/DAD4/wgA//8CAAMA/v8EAAAABAD9/wUA/P8CAP3/BgD3/wYA+v8EAAAA//8BAAIA+/8DAP3///8CAPr/AgD8/wAAAgD7/wUA+f8CAP//AAD//wMA+/8EAP7/AQAEAPv/BQD7/wcA+/8DAP3/BAAAAAMA/v8BAP//AwD8/wAA///+/wEA/v8CAPz/BAD4/wgA+f8GAPj/BQD6/wEA+v8BAPv////8/////f8CAPz/AQAAAAAAAQD8/wIA/v8CAP//AQAAAAMAAwD9/wcA+/8IAP7/AAAEAPn/AAAAAPz/AQD+//z/BQD8/wEA/f8BAP7/AwD6/wQA+v8CAP//AAD//wIAAQD9/wUA/f8DAAIA/v8IAPv/BQABAP//BQABAPz/CQD5/wgA/f8AAAUA/f8EAPv/AwD9/wIA/P8BAPv/AAD///z/AgD8/wUA+f8FAPr/BgD7/wgA+f8GAPv/BQD8/wAA///9/wEA/v8BAAAAAAABAP7/BgD6/wUA/f///wMA/P8HAPj/CAD4/wUA+v8DAPv/AAD+//7/AgD//////v8AAP//AQD//wIA/f8BAAMA/v8GAPv/AwAAAAEAAQD//wEAAgD/////AgD9/wQA+/8FAPz/AgD+//7/AQAAAAAA/f8EAPr/AgD+/wAAAQADAPn/BgD7/wYA/f8GAP7/BAAAAP//AgD8/wYA/P8HAPv/BgD//wYA/P8CAAIAAQD//wUA+/8GAPv/BAD8/wQA9v8LAPb/BwD4/wMA/v8CAP7/AgAAAAIA//8AAAQA+/8EAP7/AAADAP7/AQAAAAEA/P8DAPv/AQACAPz/BgD3/wUA/P8CAAIA/P8GAPz/AwD+/wIA//8AAP///f8DAPr/BAD5/wYA+P8JAPb/BwD8/wEAAgD+/wUA/f8FAPz/BgD7/wgA/f8AAAQA+v8GAPj/BQD+/wEA/P8GAPz/AgABAAAAAQADAP7/AgAAAAAAAwD5/wgA+v8EAAEA/f8EAP//AAABAAIA+/8CAPv/AQD///3/BAD8/wQA/P8EAP7/AQACAP//AAADAPr/BwD8/wQAAgD+/wMAAAAAAAIAAAD///7/AQD8/wUA/f8AAAIA/P8EAAEA/v8EAPr/BQD5/wgA+P8GAPv/AgD9/wEA/v8CAP7//f8CAP//AgD//wIAAAACAAEAAQACAAEAAAABAAEA/v8CAAAA/f8CAP7//f8CAAAA/f8EAPj/CAD4/wgA/P///wQA/v8CAAIA/v8DAAEAAAABAAEAAAAAAAAAAAAAAAEA//8AAAEA//8CAP3/AgAAAP7/AgD+////AAD+/wAA//////7//////////f8BAPz/AAD8/wEA/v/8/wAA/v8BAP//AAD9/wIA/f8FAPz/AwAAAAEAAgD+/wQA/f8GAPz/AwAAAP//AwAAAP3/BAD8/wMA//8CAP7/AgD+/wAA/f8DAP3/BgD4/wIA/P8DAP///v8BAPz/BgD4/wcA+P8IAPr/BQD+/wIAAQAAAAAAAgD+/wYA+v8HAPf/BAD9/wEAAAAAAP3/AgD9/wIAAAD//wAA/v8CAP7/AgAAAP7/BQD8/wQA/P8AAAQA+f8HAPj/BQD6/wYA+v8DAAEA//8CAP//AAAEAP7/AwAAAAAABAD/////AgD7/wIAAAD8/wMAAAD7/wgA9f8JAPz/AwD//wIA//8BAAEA/f8AAP7/AQD//wAA/v8AAP7/AQD///3/AAD9//7/AQD9/wEA///9/wMA/f8BAP3/BAD5/wYA+f8GAPr/AQD9/wEA/f8DAPj/BwD3/wkA9/8IAPn/BgAAAP//BgD6/wgA/P8FAP3/BAD9/wQA+/8FAP//AQAAAP//AQACAAEAAAADAPz/AwAAAAEAAgABAP7/AgD9/wMA//8AAAEA/v8DAP//AAD//wEA/P8EAPv/AwD//wAAAQABAAIA/v8DAP3/BgD8/wYA/P8DAP7/AgAAAP//AQD+/wAAAAD//wIA/v8CAPv/BgD7/wQA////////AQD+/wAAAwD7/wMAAAABAAEA//8GAPn/CQD5/wEAAQABAP3/BQD7/wUA//8EAPz/BQD7/wMA/P8DAP3///8AAPz/AAD+//3/BAD4/wUA/P8CAP///P8DAP3/AgD///////8EAPz/AwD8/wMA/v8BAP3/AQD9/wQA+/8EAP7//v8EAP7/AgD//wEAAgD8/wcA/f8FAP7/AQAAAAEAAAAAAAIA/f8DAPr/BgD3/wgA+/8BAAIAAAD//wMAAAAAAAYA+f8KAP3/BAABAP//AgADAP7/AwAAAAEA/v8GAPz/BwD5/wYA/v8DAAEA/v8BAP3/BAD5/wkA9P8HAPn/AwD///7/AAD9////AAD8/wQA+P8HAPf/CgD3/wYA/f8CAP//AQD//wEAAwD9/wMA///9/wQA/f8FAP3/AQAAAP3/BAD7/wIAAAD9/wMA//8AAAIA+v8EAPv/AgABAPn/BgD2/wcA9/8EAPz/AgD//wAA/v8CAP//AAABAPz/BAD//wEAAAACAAEAAgD+/wMA/v8DAP7/AAAAAAIA/P8DAP3/AAABAP7/AQD+/wMA/f8EAP3/BAD+/wQA+v8EAP3/BgD8/wMA+/8FAP3/AQD//wEAAAADAAAABAAAAP//BgD9/wUA/v8FAAAAAwAEAP3/BwD8/wkA/P8FAP//AAABAP7/AQD+/wEAAQD8/wMA/P8BAP7/AQD+/wAAAAD8/wMA+/8DAPv/AgD8/wEA//8BAP3/AAACAP7/AQD+/wAAAAD9/wMA+////////P8AAAMA+P8EAPn/BAD7/wEAAAD7/wQA+f8BAP3//v8AAP///f8EAPn/BgD7/wUA/f8BAAEA//8EAP3/AwD//wMAAAD//wcA+f8FAPv/AgD//wEAAAD//wYA+v8LAPv/BwD//wMAAgACAAIAAgAAAAIA+f8JAPn/BgD8/wMA/v8EAPz/BQD9/wEAAgAAAAQA/f8CAP3/AwAAAP7/AgD+/wAAAQD///////8AAP7/AgD8/wMA/P8FAPr/BgD8/wUA//8BAAEAAQD//wQA/f8EAPz/BgD8/wIA//8AAAIA///+/wIA/v8AAP7/AAABAAAA///+////AQD9/wIA/P8DAPv/AAD+//7/AgD7/wIA+/8BAP7/AAD+/wAA/v///wAA+/8DAPz/AAD+/wAA/f8CAPz/AAAAAP///v/9/wMA+/8EAPz/AgABAP3/BgD4/woA+f8HAP//AQACAAEAAQAEAP//AAABAAAAAgACAP3/BwD8/wcA+/8FAAAAAQAEAPz/BwD+/wMABAD//wYAAAAFAAAABgD+/wcAAgABAAYAAAACAAUA/v8FAP//BAD+/wEAAgAAAAIA//8CAP3/BgD3/wYA/P8DAP7///8CAP////8BAP3/AQAAAP//AQAAAP3/AgD9////AAD/////AwD7/wEA//////7/AgD9/wMA/P8AAAEA/f8BAPr/AwD5/wQA+f8CAPv/AwD9////AgD//wAAAwD9/wIAAgD//wEABAD+/wYA/v8DAAEAAwAAAAcA/f8EAP//AwACAAMA//8DAAEAAAAEAP7/BwD7/wgA+v8EAP7///8EAP3/AAAAAPv/AwD8////AQD+//z/AgD5/wcA+v8EAP///v8BAAIA/P8HAPn/BgD9/wMAAQADAAAA//8EAP7/AgACAP//BgD//wIAAwD8/woA+P8JAPz/BwD9/wUAAgAAAAQA//8AAAcA+v8JAPz/BgAAAAIAAgD9/wgA9/8KAPn/BwD+/wMA/v8FAP3/BwD9/wMABAD8/wcA/P8DAP//AQAAAAEA///+/wMA/v8CAP7/AgD7/wYA/P8EAP3/AgD//wMA/f8DAP7/AAACAPv/BAD8/wIAAgD8/wUA+v8HAPf/CAD5/wQA+v8DAPr/BQD7/wUA+/8HAPz/BAABAP3/BwD7/wQAAQD7/wkA9/8HAPn/AwD+/wEA///+/wEAAwD+/wAAAgD7/wYA+v8FAP3/AQD//wAA/v8CAP7/AwD//wIAAQABAAMA+v8IAPv/BAACAPn/BQD6/wUA/f8CAAAAAgD//wUA+v8FAP3/AQAEAPz/BQD6/wYA+/8CAP7///8AAP7/AgD+/wIA//8AAAAAAQACAP//BQD+/wQA/v8DAAIA//8DAP//AAACAP//BQD7/wIA//8AAAIA//8CAP//AQAAAP7/BQD5/wgA+/8CAP3/AwAAAP//AQD8/wUA+v8HAPr/AwD+/wAAAAD+/wQA+v8GAPz/AQADAPr/BgD8/wcA+/8BAAEA/v8CAP7//v8CAPv/BAD6/wQA+f8EAP7//v8AAPv/AwD9////AAABAP3/BQD8/wQA/P8BAAEA/P8EAPn/AwD9///////9/wIA/P8DAPv/BQD9/wMA/////wIA/f8AAAAA//8CAP7/AAD+////AgABAAQA/f8EAAEAAwABAP3/AwD9/wUA//8CAP7/AgD5/wcA+v8CAAEA/v8CAAAAAAABAAEA/f8DAP//AQD/////AwD9/wEAAAD+/wEAAgAAAAAAAAD//wEAAQD9/wMA/P8CAAAAAAD//////////wEA/f8CAP7/AwD8/wIAAQABAAQA/P8FAPr/CQD3/wkA9/8IAPv/BAD+/wMA/v8FAPr/BwD8/wIAAAACAPv/BwD8/wUA+/8CAPr/BQD6/wEA/P///////v8AAP///v8AAP3///////7/AAD8//7/AAD+/wAA/P8CAP///v8AAAAA+/8EAPf/BwD3/wQA+/8BAP////8AAP3/BQAAAAQAAAABAAIAAAAEAP//AwACAP3/BQD7/wUA/v8CAAAAAgD+/wQA/P8GAPv/BgD7/wUA/P8FAP////8CAPz/AwD7/wEA///9/wIA/f///wIA/P8EAP7/AwD6/wYA+P8GAPv/BAD7/wMA/f///wEA/v///wAA/v////3/AQD9//3/BAD6/wQA/v8CAP//BQD//wQAAgD//wYA/P8FAAAABAD//wIA//8DAP7/AQD//wMA//8DAP7/BAD+/wAAAwD8/wYA/P8BAAEA+/8JAPT/DAD3/wYA/P8AAP////8AAP7/AwD+//3/BQD2/wsA9f8GAPv/AgD9/wEAAQD4/wYA+f8CAP///v////7////9//3////+/wIA/f8CAP7/AQD+/wYA/P8IAPr/CAD//wEAAwD9/wcA/P8GAP7/BAABAAMAAAACAAMA//8EAAEA/v8DAAEABAAAAAIA/v8AAAMA/v8CAP3///8CAPz/BQD7/wEAAQD9/wEA/////wMA+/8DAPr/BgD7/wMA/f///wAA///+//7/AAD7/wIA///9/wIA+/8FAPz/AgD8/wIA/P8AAAIA/P8GAPj/BQD9/wMA/f8CAP//AgD9/wMA/f8FAP//AAACAP//BgD9/wkA/P8GAP7/BQD//wUA/v8GAAEAAgAEAP//AQACAP7/BAD+/wMA/f8EAP//AQADAAAAAwAAAAMA//8CAAIA/P8GAPn/CAD4/wcA+v8CAAEA/f8CAP7/AAD+/wEA/f////7/AAABAAAA/f8AAP7//////wEA/f8BAAAA/P8CAPv///////z/AgD9////AAD//wMA+/8DAP//AAAFAPv/AgACAP3/AgAAAP7//v8CAP3/AwD8////AgAAAP////8BAAEA//8BAP//AAAEAPz/AQD///v/BQD6/wQA///8/wMA/f8CAAAA//8CAP3/BgD7/wUA+/8GAPz/BgD8/wUAAwAAAAUA/v8DAP//BAD//wQA//8DAP7/BwD4/woA+f8HAPn/BgD4/wcA+P8GAPr/AQD///7/AwD9////AwD7/wEA/v///wIA///8/wMA/f8CAAAA/f8BAAAA/v////3/BAD4/wcA9f8FAPz///8BAPr/AwD5/wUA9/8CAP3//v8BAP///f8DAPn/AQD9///////+//7//v8BAPn/BQD7/wEA/////wAA////////AQD9/wQAAAD//wUA+v8IAPz/BAD+/wEAAgABAP3/BgD8/wMAAgD8/wQA+v8IAPj/CQD5/wQA/P8EAP3/AQACAP//AAABAP////8CAPz//v8EAPr/BwD+/wEA//8FAP3/BwD6/wkA+v8HAPz/AwACAP//AwD/////AwD9/wEAAQD8/wIA/f8BAP//AwD7/wMA+/8AAAIA+/8DAP3/AAAAAAEA/v///////f8CAPv/AwD+//////8AAP7/AgD///7/AAD/////AwD7/wMA/v/+/wEAAAD9/wQA+f/+/wEA/f/9/wEA+P8DAPv/AgD6/wMA+P8DAP3/AgD//wMA/f8BAAEA/v8CAAAA/P8EAPv/BAD+//7/BAD9/wMA/v///wAAAAD+////AwD6/wYA+v8CAAIA//8AAAIA/f8CAP7/AAABAP//AAAAAAEA/v8AAAIA//8FAPz/AQAFAPz/BQAAAP7/BwD5/wkA/f8DAAEAAgABAP3/AwD8/wMA/v///wMA+/8DAP3/AwD//wAAAQABAAAAAQD//wAAAgAAAAAAAQD//wIAAAADAPr/BgD7/wIAAAD9/wMA/P8DAP3/BAD8////AwD8/wQA/P///wAAAAD+//z/AQD4/wUA+//9/wUA9v8JAPf/CAD4/wYA/v8BAAIA/P8CAP7/AgD+/wEA//////7////+/wAA/P8FAPf/BwD4/wQA/f8AAP7/AgD//wEA/v/9/wMA/v8FAPr/AwD8/wEA/v8CAP3///8CAPr/BgD8/wQA/////wUA/v8GAPv/BAADAPv/CQD5/wYA//8AAAEAAAD//wAAAAAAAP7/BAD9////BgD5/wkA/P8CAAIAAQABAP////8AAAAAAAD//////v8CAP3/AgD+//7/AgD9/wQA/v8DAAQA//8CAP//AwAAAAMAAwD+/wYA/P8GAP3/BQD+//7/BgD7/wYA/f8DAP3/BAD/////BgD6/wYA/f8AAAAA/v8BAPz/AAD3/wQA+////////f8AAP//+/8EAP3/AAADAPz/BQD9/wQA/f8EAP7/AAADAP7/AQAAAP//AAADAPr/BAD4/wEA/v/9/wAA/v////7/AgD9/wIAAgD7/wgA+v8IAPz/AQADAP7/AQACAPz/AwD+/wYA+v8JAPj/BgD//wMAAAAEAAEAAwACAAUAAAACAAEA/v8HAPv/AwD+//3/AQD/////AwD6/wUA+v8FAP7/AQADAP//BQD//wEAAwAAAAMAAAADAAAAAgACAPz/BAD//wAAAgD9/wMA/v8CAAUA/P8GAP7/AQD//wIA//8BAAIA/P8EAP3/BQD8/wAAAQD8/wMA/P///wUA/P8EAP7/AwAAAAIA//8DAAIAAwD//wQA/v8EAP7/AwD9/wUA/v8GAP//AAADAAEAAQACAP//AwABAP//AAADAPz/AgD9/wIA/f8BAAAA/f8EAPv/BQD8/wQA/v8AAAUA+/8FAPz/AgD//wEAAgD9/wYA+v8FAP3/AgABAPz/AwD9/wAA///+/wIA/v/+/wEA/v8EAPz/AwD7/wIA//8AAAIAAQAAAP7/BAD9/wUA/v8CAAAAAgD//wMA/f8EAP7/BAABAP//AwD//wIAAwACAAIAAQAEAP7/BQD///7/BgD7/wUA/P8DAP//AgD9/wIAAAAAAAEA//8DAP//AAACAP//BQD8/wQAAgD+/wMA/v8BAP//AwD+//7/AAD+/wAA/v8AAAAA/v8CAP////8EAPv/BgD8/wUA+v8HAPz/AwAAAP//AwD//wAABAD8/wQA/P8HAPv/BwD+/wEAAwABAAMAAgACAAMAAwAAAAUA/v8HAP3/AwABAAQA//8FAP3/AwD9/wEA//8BAP7/AQAAAP///////wIA/P8BAP///v8DAP3/AQD9/wIA/f8CAPn/BQD6/wUA/P8BAP7///8CAPz/BQD6/wUA//8CAP//BQD7/wYA+v8DAPz/AQD+/////v///////v8BAP3/BAD7/wQA/P8FAPz/BQD8/wcA+/8IAPr/CAD8/wEAAwD//wUA/v8FAPv/BwD7/wUA//8FAPv/CwD2/w0A+f8GAAAA/v8FAPz/BQD6/wYA+v8HAPr/AwD5/wYA9/8FAPn/AgD9//7/BAD6/wUA/P8EAPz/BAD6/wYA+P8GAPr/AwABAP7/BAD9/wEAAAD9/wYA+f8GAPz/AgD//wIA/v8BAAAAAQD//wIA/v8AAAAA///9/wMA+f8FAPv/AgD+////AQD+/wMA+/8EAP3/BAD8/wIA/v8AAAIA+v8CAP3/AQD//////P8BAAEA/P8CAP3/AAAAAAAA/f8BAPz/AQD+/wAA/v/9/wAA/P8BAAAA/P8BAPz//v////7//v/+//r/AwD6/wUA/P8DAP7/AQD+/wAAAAD//wMA+/8FAPz/AgD+//7/BAD9/wIA//8AAAIA/v8BAPn/AgD8/wYA/P8FAP//AQAAAAAA/P8AAP7/9/8FAPX/AwD7/wMAAAAAAAMA+f8KAPv/BwD9/wEAAwD//wEA///+/wAA/v/+//7//f////v/AwD4/wcA+P8GAPv/AAAAAPv/BAD8/wUA+/8EAPv/BAD+/wMA/v8DAAAAAAAAAP///v8BAAAA/v8DAPv/BgD6/wQA+/8DAP3//P8BAPf/AwD5//3//f/7/wAA+f/+//7/+/8CAPr/AwD3/wcA+/8FAP3/AgD//wUA/P8KAPr/BgD9/wQA/v8CAP//AgD4/wcA+f8DAPf//f/9//3/+/8AAPv/AgAAAP////8BAP//BAD//wAA///9/wMA/f////7//v8AAAAA/v///////P8AAPj///////n/BgD3/wAAAgD6/wYA+/8GAP7/AgD//wIAAAAAAAEA/v8BAAIA///+/wQA/v8BAAQA//8FAP//AwABAAIABQD+/wEAAAD+/wAA/v8BAP7/AwD+/wIAAAAGAPz/CwD9/wgA/v///wUA+/8BAAAA+/8FAPn/AgACAPv/BQD8/wAAAwD5/wcA9P8JAPX/BgD5/wQA+v8FAPv/BAD5/woA9v8IAPn/BgD7/wYA/f/+/wUA/P8BAP7/AgD8/wYA+f8IAPn/BwD6/wQA///9/wgA+f8JAPv/AwABAP3/BgD7/wMA+v8JAPb/CwD5/wIAAwD5/wkA9/8HAP3/AwD8/wUA/P8IAPv/AQD///3/BQD+/wYA+v8IAPv/AwD+/wIABAD8/wUA//8EAAEAAAD//wQA//8GAAAAAQAAAAMABAABAAIAAQADAAQAAgAFAAIAAQADAP//AwACAP//AgACAP7/BgD9/wUA/P8EAP////8FAPn/BQD9/wIAAQD9/wYA+f8JAPv/AwD//wEAAAADAP3/BQD8/wUA/v//////AQD+/wMA/P8GAPv/BQD8/wAAAAD+////AAD+//z/AgD6/wUA+v///wIA/f8FAP3/AQAGAPz/BwAAAAEAAgADAP3/CAD7/wMAAQD8/wAABAD9/wMAAAD8/wcA//8CAAUA+P8HAPz/AwD//wAAAQABAAAABAAAAP7/AQD+/wEA/v/+/wIA/v8FAPv/BAD6/wMA+v8FAP3/AgAAAAIA/P8JAPv/AAAGAPn/CQD9/wYA//8AAAcA+v8JAP3/AQAFAPn/CQD6/wcA+v8DAPz/AAD+/wAA/P8CAPn/BgD6/wcA+v8FAP7/AwACAP//BQD//wMAAgD//wkA+/8GAAIA/v8JAPv/BAAEAP3/BQADAP7/BgD9/wQAAQAAAP7/AwAAAAEAAAD7/wMA+/8DAPz////+/////v/+/////f/8/////v///wEA/P8DAPz/AQD7/wQA+/8FAPn/CAD6/wcA/P8FAPf/CQD1/wYA/f/+////AAD8/wEA+v8AAPv/AQABAP7//f8CAPz/AwD9//v/BgD4/wIA///9//7/AAD//wEAAQADAPv/CAD3/w0A+v8GAAIAAAABAAEAAAD//wUA/f8CAAQA/f8FAPn/CQD3/wkA+/8CAP7/AwD///7/AgD//wIAAQD9/wIAAAAAAAEAAAD//wAA/v8DAAAAAQAEAP//BAAFAPz/CQD8/wUA/v8BAP7/BwD7/wgA/P8CAAIA/P8CAAEA/P8EAAEAAAAEAP3/AQAAAP///f8FAPn/BAD8/wIAAQD8/wgA/P8EAP//AAACAAEA/P8EAPr/BAD8/wAA/////wEA///+/wAAAQD8/wUA+v8HAPz//v/9//r/AQD5/wIA+f8DAPn/AQACAP3/AwD7/wQA+f8JAPf/BwD5/wMAAgD//////v//////AgD7/wMA+v8EAP///v8IAPj/CAAAAAIAAwD+/wMA+v8AAAAA/v8EAP3//v8FAPv/BwD8/wQAAgADAAMA//8GAP3/BgD//wQAAgAAAAQAAgAFAP3/BAD+/wEAAgAAAPv/BAD6/wQA/v8CAAAABAD4/wsA9v8HAPv///8AAPz/AwD9/wQA+/8DAPn/AgD8/wMA/P///wAA/P8DAP///v8AAP//AQD+////AAABAP7/AwD+//7/AgACAP7/AQD7/wQA//8CAP//AgD/////BQD+/wIA//8BAP7/AwD4/wUA9v8HAPf/AwD9/wEA/P8CAP////8EAPb/AgADAPr/CwD0/woA//8FAAEAAAAFAAAABAD7/wEAAAD3/wQA9/8CAPn/AAD8/wMA+///////AQD+/wIA/f/8/wQA+f8HAPr/AwADAPn/CgD3/wsA9v8KAPv/BQAAAAEAAAAAAAQA+v8LAPX/CgD7/wIABwD7/wgA+P8LAPv/BAD9/wEA/P8EAPn/AwD9//////8CAPz/BQD8/wIAAgADAP3/BQD8/wMA+/8CAPz/AwD7/wMA/v8AAAMA+/8IAPn/BQD9/wAAAwD9/wUA9v8MAPT/CQD5/wAAAQD7/wEAAQD9/wQA+/8AAP///v///wEA/f///wAA//8EAAIA/v8DAP//AAACAP3/AgABAPn/AgD///7/AgD6/wQA/v/7/wYA9f8KAPj/AgACAP7/AQD//wAABAD8/wcA9/8JAP3/AwAFAP7/CgD6/wcA/f8AAAUA/f8FAAIA/f8MAPr/CQD6/wMABAAAAP//BAD6/wkA9f8IAPf/BAD4/wAAAQD8/wEA+/8CAP3/BAD8/wUA/////wEAAAACAPr/BgD4/wMAAgD5/wcA+/8AAAIA+f8IAPj/BwD8//7/BAD6/wMAAQD8/wYA/v/9/wUA/f8FAPj/BgD3/wcA+v8BAPn/BAD7/wMA///7/wQA/f8FAP7/AwD+/wQA//8DAP3/BgACAP7/BgD6/wUA+/8BAAEA/v8BAAQA/P8DAP3/BAD8/wYA+P8EAPz/AQACAPf/BwD7/wMAAAD+/wMA/f8EAP7/AAD+//3//v8EAPv/AQADAP3/BwD8/wQAAAACAAEAAgD+/wQA/f8DAP7/BAABAAIAAAAAAP//AQAAAAEA+/8EAPf/DAD2/wUAAAD8/wUA//8AAAQA+f8BAP7/AQD8/wIA/f8BAPz/AAD6/wIA+/////r/AwD7/wIAAAD/////AgD//wMAAQAAAAAAAwD8/wQAAgD7/wkA9/8CAAAA/P8BAPz//v8BAAAAAQABAPv/BwD8/wQA/v/9/wQA/P8DAP3/AwD7/wYA+v8FAP3/AgAAAAEAAgACAAAABAD+/wMA//8CAPz/BQD7/wUAAAACAP//AAAAAAEAAAD9//7/AgD8/wMA+v8AAPz//P8BAPr/AgD8/wAA/f8AAAAA/v/+/wIA//////7/AAD//wIA+v8GAPv/BQD8/wIA/f8BAP3/AwAAAAAA/v8BAAAA///+//7/AgD///3//v////z/BAD5/wIA+/8AAP3///8BAPz/BAD7/wMA/P8FAPr/BgD///7/BAD8/wMA/v/7/wYA+/8HAPr///8EAPv/CAD9/wEAAgD9/wMAAQAAAAUA/v8JAP3/BgD+/wIABAD9/wAAAgD7/wYA/P8CAP//AgADAP//AQD//wQA//8BAAEA+/8FAPn/BAD7/////v/+//7/AAAAAP7/AgD9/wMA//8AAP3/CAD0/woA9v8EAPz//f/9/////P/+//7/+f8BAPj////6//3/+//6/wEA+v8AAP3//v////3/AAABAP3/BAD5/wUA/P8BAAMA/v8FAP3/AQABAP//BgD9/wEAAgD+/wcA/P8KAP7/AwAEAP//BgD8/wQA/f8BAP//AgAAAP//AgD//wcA/f8JAPj/BQABAP7/BgD4/wYA/f8AAP3/BAD7/wYA+/8EAPv/BAD5/wYA+//9/wMA+v8GAPz/BQD9/wMAAQD8/wQAAAAAAAUAAAABAAAABAD8/wsA9/8JAPr/AQAEAP7///8DAPr/AwAAAPr/CQD6/wUA+/8CAAAAAAD//wAA//8DAAIA+f8JAPz//v8EAPj/AwD///v/AwD9////AAAAAAIA/f///wAAAAAAAP////////z////9//3/AAD7/////v8BAP///P/8/wIA+v8GAPj/BwD7/wQAAQD//wMAAQD//wQA/f8DAAMAAQD//wUA/f8AAAYA/P8MAPP/DAD7/wMABwD+/wgA+/8GAP7/BAAAAP7/AQD+/wMA//////v/BAD6/wQA/f8AAAAA//8DAPv/BgD5/wYA/f8GAP3/BAD//wUA//8CAAEAAgD+/wQA/f8FAP3/AgD//wMAAwABAAAABAD9/wUAAQD7/wMA/f8EAP7/AAAEAPr/CQD6/wQAAAD+/wQA9/8MAPb/CQD7/wEAAgD//wIAAgD//wUA/v8DAPj/CAD7/wUA///+/wIA/f8EAPz/AAABAPz/AgD/////AQD9//3/AQD+/wAA///+/wAA/f///wAA/v8CAPz/BAD9/wIAAQD9/wcA+P8IAPv/AwABAP//BAD//wAAAwD8/wUA/f8AAAIA/f8CAP//AQD8/wUA+P8HAPv/BAD9/wEAAgAAAAIA///+/wEAAQADAP7/AQD///7/BQD6/wYA+/8EAAEAAAAFAP3/BAACAP//AgADAAAABAABAAQAAAADAAAABAABAAEAAAADAPz/AgD+////AwD8/wIA/v8AAP3/AgD8/wMA/f///wEA/P8BAP///P8CAP3///8BAP7/AAD9/wYA+f8GAPr/AgD/////AAD///r/AwD5/wUA+/8CAPr/AQD+//7/AQD///3/AQD7/wAA/P8BAP7/AAD9/wEA/v8CAP7/BAD7/wUA/f8BAAYA+P8IAPz/AgAEAPv/CgD4/wMA/v///wQA+/8EAP//AwAAAAMAAQADAAIAAAAFAP//BQD9/wUA/v/+/wIAAQD//wMA/f8BAAIAAAADAPz/AwAAAAEABAD9/wEAAAD+/wMA/////wAAAQD+/wIA/f8AAAEA/f8BAP///v8EAP3/AAAAAAIA//8FAP3/AwD+/wQA/P8IAPj/CAD6/wYA/P8DAP7/AgD+/wEA//8BAP3/AQD9/wQA/P8CAP//+/8GAPj/BwD4/wUA+////wEA+/8DAPz/AQD9/////////wAA//////7////9/wAAAgD4/wYA+P8EAP3////+/wAA//////7/AAD9/wMA/f8DAP7/AAADAP7/AgACAP7/BgD9/wUA//8DAAIAAAACAP7/AgACAAAAAQADAP//BAD+/wMAAgD//wUA/P8GAAAAAgADAAIAAgAEAAMAAQAFAP//BwABAAQAAwACAAEABgD9/wYA/f8FAP//AQACAP7/BQD7/wYA/P8FAPj/BQD8/wYA+/8AAAIA//8AAP///v8DAPz/AwD9/wIA///9/wMA+f8FAP3///8DAPr/AwD+////AAD+/wEAAAD8/wMA/P8DAPz//P8DAPf/BwD3/wUA+P8EAP3/AAABAP//AgD//wIA/f8GAPv/BwD7/woA+v8HAP3/BQD//wUAAAAEAP//AQAEAP7/CAD6/wgA/f8DAAAAAgAEAP7/AwD//wIA+/8GAPv/BgD8//3/AQD8/wQA+v8DAPr/AQD///z/BAD8/wEAAgD7/wUAAAD8/wUA+/8EAAEAAAAAAAUA/f8GAPr/CAD5/wgA/v8CAAQA//8EAP3/BQAAAAEAAwACAP7/CAD+/wIAAwABAP//BQAAAP//CAD7/wgA/v8EAPz/CAD2/w0A9v8JAPz/BAAAAAIA//8HAPv/CAD9/wQA//8FAPn/BwD7/wQA//8AAPz/BQD8/wIAAAD//wEA/f8EAP3/BAD9/wEAAgD//wIA///9/wQA/P8FAPr/BAD9/wMA/////wMA+/8EAPz/AQD+/////v8CAP7/AAACAAAAAwD//wMA/f8GAPz/BQD+//7/BgD6/wQA/P8AAAEA/v8DAPn/CAD6/wUA/v8CAP7///8BAP//AwD8/wUA+P8HAPn/BQD//wIA//8EAP7/AwD+/wMA//8CAAAA/f8CAPz/BAD+/wEAAAABAAIAAAACAPz/AwD+/wUA/P8GAPf/CwD2/wcA+f8CAP7/AQAAAP//AQD+/wMA/P8HAPr/CQD7/wUAAAABAAIAAgD+/wQA//8BAAAAAgAAAP//AQD9/wUA/P8BAAMA/f8FAPr/BQD9/wMA/v8BAP//AQD//wMA/P8DAPz/AwD+/wIA/////wEA/v8CAPv/BwD4/wcA+/8DAP////8CAP//BAD8/wQA+/8FAPv/AgD+/wAA/f8AAAAA/v/+/wAAAAD+/wEA+////wMA+P8KAPf/BQD9/wMA//8BAP//AAD9/wQA9/8IAPn/AQD///z/AAABAP3/AgAAAP//AQACAPz/BAD9////AgD+/wEA//8AAP///v8CAAEAAgAEAPv/CAD9/wUA+/8FAPz/BwD7/wYA+v8GAPj/BQD8/wEAAQD//wAAAQD+/wQA/v8AAAEA/v8CAAAA/f8FAPv/AwD+/wAAAAACAP//AAAAAAEA/v8DAPv/BQD7/wMA/////wMA+/8BAP7/AAABAP7/AQD+/wEAAAADAP7/BAD//wIA/f8FAPv/BgD7/wQA/P8FAP3/BAD//wEA//8EAPz/AwAAAAEA//8BAAEAAQD///7/AAD9/wIA+v8CAPv/AgD8/wEA/v8AAP3///////3/AgD8//3///////3/BAD2/wkA+f8BAP3/AgD9/////v/+//3/AgD7/wMA/f8AAP7/AQACAAEABAAAAAEAAwD+/wYA/f8GAP3/BAD+/wEAAgD8/wcA+v8IAPr/BAD//wEAAAACAP//AgD+/wIAAgD+/wIA/P8BAP3/AAD+/wAA/v8AAP///v8CAP7/BAD9/wIA/v/+/wIA/P8GAPj/CAD1/wkA9/8FAPv/AwD6/wQA+f8CAP//+v8FAPv/BAD6/woA+P8HAAAAAgADAAIA//8DAAIAAAAEAP//AgAAAAIA/f8EAPz/BAD//wIA//8EAPz/BAD9/wQA/P8GAPn/BgD7/wMAAAD//wEAAAD+/wIA/f///wAA//8BAP///v8BAP3/AgD8/wEAAAD+////AQD9/wAA//////3/AQD///3/AgD5/wIA+/8AAP7/AQD+/wAAAgD+/wEAAAACAAMAAAADAAAAAAAEAP7/BQD+/wUA/P8JAPz/BgD+/wQAAwD+/wUA/f8DAAEAAQAEAP//AwD9/wMA/v8BAAIA+v8EAPz/AgD+/wMA+v8HAPj/BAD9/wIAAAD9/wEA/P8EAP3/AQD8/wMA/P8CAP3//P8DAPj/BgD7/wAAAAD+/wAAAgD+//3/AgD8/wEAAAAAAP//AAD//wAABAD6/wUA/v///wMA/f8DAAEA//8BAAIAAQABAAQAAgABAAUA/P8IAP3/BAACAAEABQABAAMAAAAAAAIAAAAAAAYA+f8GAP3/AwAAAAUA/f8GAP3/BQD//wIAAQD8/wgA9/8JAPr/AgAAAP3/BAD7/wUA+f8HAPf/BQD7//7/AwD9/wAAAQD8/wIA/P8BAPz/BQD6/wMA///7/wUA9/8DAP7/+v8FAPr/AgD9/wMA/////wAA//8DAAAA//8BAAAAAQD///7/BAD3/wkA+f8DAP3/AQD+/wQA+/8CAAEA/v8DAP7/AQAAAAIA/v8BAP3/AAD+/wIA/f8CAPz/AwD+/wEA//8AAAIA/v8DAP//AAABAP7/BAAAAAAABAD//wcA/v8EAP7/AwABAAIAAQABAAIA//8GAPn/CQD7/wQA/P8DAP3///8AAAAA/f8CAPr/BQD7/wMA/v///wEA+/8CAP7/AgD9////AwD5/wgA+v8DAP3/AQD+/////f8EAPr/AQD9//3/AQD///z/AgD7///////9//3/AAD9/wAAAgD6/wMA+//+/wIA+v8DAPr/AAD///7//v////////8AAP///////wEA/v8AAP//AgAAAAIAAAABAAEAAAABAAEA//8EAP3/AgACAP3/BgD8/wQA/P///wYA+v8IAPn/AwD+/wIAAQD7/wgA+P8JAPr/AwD9/wEA/v///wEA/f8EAP//AQABAAAAAwAAAAMA//8CAAEA//8FAP3/AgACAP7/AgABAPv/BgD6/wUA+v8AAAEA//8DAPz//v8DAPr/BQD5/wIA//8AAP//AQD9/wEA+v8FAPn/BAD8/wEA///9/wIA/v8AAAIA+/8CAP7/AAADAPn/BgD7/wIA///+/wIA/P8DAPb/BwD3/wIA/f/7/wMA+v8CAPr/AgD6/wQA+/8CAAAAAQAAAAAA/v8CAP7/BQD5/wMA/P8DAP7/AQACAPv/BwD4/wgA+P8DAP///f8FAPn/BgD7/wIAAAABAP//BAD7/wMA/f8AAAIA//8BAPz/BAD8/wQA/v8CAAAAAQABAAEAAgD+/wcA+v8IAPn/CQD9/wQAAQABAAEA/////wEA//8AAP//AQD///7/AAABAAEAAQD//wEA//8DAP7/AQABAP//AwD+/wEAAgD9/wcA+f8FAPz/AwD7/wQA/f8AAAEA/////wMA+f8IAPn/BAD///3/AQD///3/AAD9//v/AgD8////AwD4/wUA+/8EAP7/AAACAP////8DAPv/AwD//wEA/f8CAPz/AQD+//7/AAD6/wgA9v8HAPn/AgD+/wAA//8BAAAA///+////AgD9/wYA+f8EAP3//////wAAAgD6/wUA+f8HAPv/BQD8/wMAAgABAAAAAwD+/wUA/////wYA+f8LAPb/CQD6/wMA/f8BAAAA//8DAPz/AgABAP//BAD//wIAAAACAAEA//8AAP7/AQABAP3/AQD8/wMA/v//////AQD9/wIAAAD+/wcA/v8FAPz/BgD9/wQAAAAEAAAABAD//wIAAQABAAIA/f8EAP7/AwABAP3/AwAAAP//BAD+/wAABQD5/wcA+f8FAPr/AQD8//3//v////3///////z/AwD6/wIA///+/wYA+P8JAPj/CQD6/wUA/v8BAAAAAAAAAAEAAAD+/wIA/f8CAPr//v8BAPv/AgD7/wIA/f8BAAAA//8DAP3/BAD//wQA/v8BAAIA/////wYA9/8JAPn/BwD+/wIAAAAAAAIAAwD+/wYA//8GAP//BwD//wIAAwD9/wMAAwD8/wMA+v8CAP7/AgD///3/AwD8/wIAAgD9/wcA/P8EAAIA//8EAAEA//8FAP7/BQD//wIA///+/wUA/f8CAP//AAAAAAMAAgD//wQA//8BAAAAAQD//wMA/f8CAAAAAAADAPv/BAD7/wQA+v8DAP3/BAD7/wgA+v8HAPz/BAD//wUA//8EAP//AwABAAAAAgD+/wIAAgD+/wcA/f8EAAAAAgAAAAMA//8CAAEA//8CAAEA/f///wIA/f8BAAAA/f8BAAEA/f8DAP///v8GAPn/CQD5/wgA+P8GAPv/BQD//wAAAwD9/wIAAAD//wUA+v8CAAAA/P8DAPz/AgD///z/BAD7/wYA+/8DAPv/BAD8/wMA//8DAP7/AgAAAP//BQD8/wQA//8BAAEAAQD//wIA/v8GAP3/BQD8/wQAAQADAAEAAQADAAMAAAACAP//AgACAP7/AgD+/wMA//8BAP//AAABAAAAAQD+/wUA/P8EAP7/BAD+/wMA//8FAPz/BQD7/wUA/P8EAP3///////////8AAPz/BgD3/wgA+v8EAP//AQAAAAIA///+/wUA/P8FAP3/AwD9/wYA+P8MAPX/BwD//wIAAAABAAEAAwAAAAMAAQAEAAIAAQAGAP3/CAD9/wYA/v8DAAAABAABAAMA/f8EAPr/CAD4/wYA+v8FAPz/BQD4/wcA+P8FAPv/AwD+//7/AwD9/wIA/f8AAAAA/f8CAPv/BAD9/wAAAQD7/wUA+v8FAPv/BQD+/wMAAAABAAAA//8DAPv/AgD7/wEAAAD9/wEA+/8BAP//AAADAPj/BwD8/wMA//8BAP//BgD8/wUAAAAAAAQA+/8GAAAAAgABAAEA//8DAP//AwAAAAEAAQADAAAABAD9/wcA/f8BAAIA/f8GAPv/BAD8/wQA/v/9/wEA/v/+/wEA+f8DAP3///8CAP3/AgD//wIA///+/wQA+f8FAP3///8CAP7/AgABAAEA/P8EAPv/BQD9/wAAAgD9/wQA/f8CAP3/AwAAAP//AgD9/wIA/P8DAPz/AgD9//3/AwD8/wMA/f8AAAEA/v8BAP7/AwD+/wMA+v8HAPf/CgDx/wwA9P8IAPr/AAD+/wAA//////7/AAD+/wIA/f///wAA+/8DAP3/AAD9/wAA/P///wEA+/8EAPr/AAD8///////9//7//f/9/wAAAAABAP//AQD9/wQA+f8GAPz/AwD///7/AgAAAP7/AAAAAAAAAgD9/wQA+/8GAPz/BAAAAAIAAQACAAAABQD7/wcA+P8FAP3/AAD///////8BAPz/AwD+/wEAAAD9/wQA/P8EAP///P8GAPr/BAD9//7//v8BAPv/AgD4/wIA+v8BAPr/AAD+//3////7/wAA+/8AAP7//v8AAP3/AAABAPv/AgD7/wAAAwD7/wIA/P/9/wIAAQD+/wAA/v8AAAMA/f8BAAEA/P8CAP7/AAABAP3///////3/AAD9//v/AgD+//3/BAD5/wQA/f8CAAAA/f8EAP7/AwD9/wIA//8DAP//BAD9/wIAAQD8/wUA+/8CAP3//P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8EAPr/BAD9/wAAAgD9/wIA/f/+/wIA/v8AAAAA/v8BAAQA+/8GAPz/AQADAP///f8GAPj/CQD4/wUA///+/wgA+v8GAAAAAAADAP///v8DAAAA//8EAPj/BwD8/wMA/f8AAAIA/P8EAPr/AwD9///////7/wQA+f8DAPz/AQAAAP///f8DAPr/BAD9//7/AQD8/wIA+/8DAPv/AQD+/wMA/P8FAPr/BgD9/wMAAQD//wIAAgD+/wIAAAD9/wIA/P8BAP////8AAP7///8AAAAAAQAAAAEAAQD8/wIAAQAAAAAAAgD7/wQA///9/wYA//8BAAEA/v8DAPz/AQAAAP3/BAD9//7/AgD+////AwD8/wAA///+/wAAAAD9//3/AQD4/wQA+P8EAPj/AgD9////AAAAAP//AAD+/wEA/v8AAP///f8FAPn/BQD9/wMAAAABAAAAAwD//wMA//8AAAEA/v8FAPz/AwD/////BgD7/wMA/f8BAP7/AgD+/wIA/v///wEA//8CAPz/BQD4/woA9f8NAPb/CQD8/wIAAQABAAEA//8CAP3/BQD5/wQA+v8BAP///v/+//7/AQD9/wAA/f8AAP7/BQD5/wQA/P8CAAAAAQD//wIA+/8CAP7/AAADAPj/BwD7/wIAAAD9/wMA/f8BAP//AAD//wIAAQAAAAIA//8CAP/////+/wMA/P8CAP7///8BAPv/BAD4/wQA/v/////////+/wAA/f8AAPr/AgD9//7/AAD8/wEA/P//////+/8CAPv/AgD5/wIA+f8BAPv/BAD6/wQA/f///wUA9/8GAPv/BwD6/wQA/P8BAAQA+v8HAPv/AAAFAPn/BwD8/wIAAAAAAAIA/f8DAPz/AgD+/wAAAgD7/wUA/f///wIA/P8CAP///P8DAPz///8BAPr/BQD7/wEA///+/wMA/v8AAAIA/P8EAP3/AQACAP7/AQD//wAAAQD7/wMA/P8EAAAA+v8BAPz/AgD+///////+/wEA/v/8/wQA+f8GAPr/BQD6/wMA//8AAAEA//8AAAEA/P8FAPr/BwD9/wAAAgD7/wQA/f8BAPr/BAD5/wUA/f8AAAIA/P8AAP//AgD9/wMA/P8AAP7/AAD7/wQA+v8DAPz/AQD9/////f8BAP7/AAD9/wAA/f8AAPv/BAD8/wMA/P8EAP7/BAD//wEABAD+/wUA+/8FAP//AQABAAAAAQD//wQA/P8AAAEA/f8BAAUA9/8JAPr/BAAAAAUA+v8HAPv/BQD9/wQA/P8CAP7/AQACAPr/BQD8/wQA/////wIAAAADAP3/AwD//wEA//8BAP//AgD+/wMA/f8EAPz/BAD//wIA//8DAPz/BAD8/wUA/f8BAAAA/v8BAP//AAD+/wIA/P8DAP7///8AAAEA/f8EAPv/AQD8/wIA/f8BAP3/AQAAAAMA/v8EAPv/BgD7/wUA/P8EAAAAAAADAP//AwD+/wIAAAADAP//AwD//wIA/f8EAP7/AgD//wAABAD//wMA//8EAP//BAAAAAMABAAAAAIA//8DAAAABAABAAEA//8CAP7/BQD+/wIAAQABAAEAAQACAP//AQABAAEAAQD+//3/BAD8/wQA/f8AAP7/AwD6/wUA+v8FAP3/AwD9/wQA/P///wIA+v8FAP3/AAD//wIA+v8IAPr/BQD9/wIAAQAAAAYA/P8IAP3/BgAAAAIAAwACAAMAAAAEAP7/BQABAAUAAAAEAP//BgD+/wUA/v8EAAIA//8GAP7/AwABAAUA/f8HAPn/CQD6/wYA/v8CAAEA+/8IAPf/CAD4/wcA/P8EAPn/BQD5/wkA+v8GAPr/BwD6/wcA+P8GAPf/BwD4/wUA+/8AAAAAAwD7/wcA/f8FAAEAAQAEAAAAAAACAP//AwABAP7/BQAAAAQAAAADAAEAAAAFAPz/CAD6/wkA+f8IAP//AQAEAP7/BwD//wkA+v8JAP3/AwD+/wAAAQD9/wIA/f8BAP3/AgD//wAAAAD+/wEA/P8JAPf/CAD4/wQAAAD8/wQA9/8GAPv/AgAAAP3/AAADAP3/AwD9/wQA/P8FAP//AgAFAAEAAwADAAMABAAAAAcA/f8HAAIAAQAIAP//CAD7/wkA/v8CAAcA/P8HAP//AQAGAPz/BwD8/wYA//8BAP//AwD//wMA/v8DAP//AAAAAPz/BwD4/wUA+/8CAAMA+/8GAPj/BwD8/wQA//8DAP7/BAD8/wMA/f8CAAAAAAD//wEA+/8FAPr/BAD5/wUA+/8AAAEA+/8EAP3/AwD9/wIA/v8CAP3/AQD9/wMA/P8FAPv/BQD+/wAABAD9/wMA/f8FAPz/BAAAAAAAAgAAAAIAAgACAAIAAgACAP//AQADAPr/CAD8//7/AQD7/wIAAQD7//7//v8AAP/////8/wMA+/8BAP7//P8GAPz/AgD+/wEABAD8/wcA+/8EAAAA/v8HAPv/BwD+//3/+/8AAPn/BQD7/wEAAgD8/wMA/f8CAPv/AQD6/wIA+f8CAP3/AwD//wEAAgD+/wQA//8CAAEAAgACAP3/BgD5/woA9/8JAPb/CQD7/wMA/v8DAP//BQD8/wYAAAABAAMA//8EAP//BQD9/wIAAwD8/wgA//8BAAIAAwD9/wkA9/8JAPj/BwD7/wQA/v8BAAAA/v8CAPz/AQD///v/AgD4/wEA+/8AAP///v/8/wQA+/8AAAAA+/8FAPv///8CAAAAAQAAAP//AwD//wUA///+/wUA+v8FAPr/BgD6/wMA/f8AAAEA/P/////////+/wAA/f8FAP3/BAD+/wAAAQABAP7/AwD8/wMA/v8AAP3/BAD6/wcA+f8AAP///v////z/+//9//7//v/8/wIA9/8HAPn/BQD9/wIAAAABAAAAAAACAAAAAgD9/wQA/f8FAPz/AgABAP7/BwD6/wgA/f8FAAEAAwABAAQA/P8IAPz/AgD/////AgAFAPz/CAD8/wgAAAAHAAIAAwAEAPz/BgD7/wMA/f8BAP///v8BAP7///8EAPz/BgD6/wUA/f///wEA/f8AAP//AAD+/wAA/v8AAPz/AAAAAP3/AwD9/wMA//8AAAEA+/8HAPb/BQD9//3/AQD///3/AwD8/wAABQD4/wgA+v8EAP//AAABAAEA/f8GAPv/BQD5/woA9/8KAPr/BAD/////AAAAAAAAAgD9/wIA//8BAAIA//8BAP3/AgABAP7/CAD5/wYA/f8AAAMAAQACAP3/AwAAAAIAAwD9/wMA//8BAAEAAQD//wAAAwAAAAIAAgAAAAIAAgACAAMAAQAAAAEAAQD+/wEA/v8BAP//AwD8/wQA/f8DAP//AgD+/wEA//8BAP3/BAD8/wQA/v8BAAEAAQD+/wIA/v8BAAEA//8DAP3/AAAAAAEA/v8DAP7///8FAP7/BAAAAAEA/v8FAPz/AgD//wAA/v///////v/+/////v8DAP//AgD//wMAAAACAAMA/v8EAAAA//8GAPv/AgABAP3/AgACAP7/AQACAPz/BQD//wEAAAD9/wEA+/8IAPX/CQD2/wYA+v8IAPf/BQD6/wEA/v8AAPn/BAD6/wQA/P////z//////wEA///+/wYA+f8HAPz/AwD//wIA/v8CAAAAAwD9/wIAAAACAAAAAgD9/wUA/P8FAP3/BAD8/wMA/P8CAAAA//8AAAIA+/8GAPv/AwAAAP7/AgD+/wIA/P8FAP7/AwD9/wMAAQADAAEAAwD+/wgA+/8HAP//AwABAAEAAQACAAEAAQABAP7/AgD9/wQA/P8BAPz/AAD+/wMA+f8FAPn/BQD6/wQA+/8CAPv/AAD+/wEA/f8BAP7/AQD8/wMA+/8FAP3/AQACAAIA/P8HAPj/BQD8//7/AgD9/wAA/f8AAPz////9//r//v/9//3/AgD6/wAA///+/wAA/f8EAPj/BQD8/wAA///9/wMA/v8BAP7/AwD9/wQA/v8BAAIA/////wMA/v8CAPz/BQD7/wUA/v8BAAIA//8CAP//AgAAAAAA/v8DAPv/AgD9/wEAAgD8/wYA+f8IAPr/AgACAP3/BAD8/wUA/P8FAAQA/P8JAPz/BAACAAEAAQAAAAMA/P8GAP7/AgADAP3/AwD+/wEAAAABAP7/BQD+/wMAAAD///7/AgD5/wIAAAD6/wUA+v8CAP7/AQD//wQA/P8EAP7/AwAAAP7/AgD+/wAA///8/wAA//8BAPr/AwD8/wAA///8/wAAAQD5/wIA9/8BAPv/AAD8/wMA+v8CAPz/AQD///7/BAD3/wkA9/8FAPn/BAD//wEA/P8FAPn/BQD9/wIA/v8BAP//AgD9/wYA+/8FAAIA//8CAAEA/v8AAP//AAAAAAEA/v///wAAAAABAAEA//8FAPv/CgD4/wwA+P8IAP//AgADAP7/BAAAAAQA/f8FAPz/AgD9/wYA9/8GAPr/BAAAAP//AQACAPv/AwD9////AgD6/wIA/P8AAAIA+/8FAPn/AgD//wAA//8DAPv/BAD+/wEABAD7/wUA+/8HAPv/AwD9/wQAAAADAP7/AQD//wMA/P8AAP///v8BAP7/AgD8/wQA+P8IAPn/BgD4/wUA+v8BAPr/AQD7/////P////3/AgD8/wEAAAAAAAEA/P8CAP7/AgD//wEAAAADAAMA/f8HAPv/CAD+/wAABAD5/wAAAAD8/wEA/v/8/wUA/P8BAP3/AQD+/wMA+v8EAPr/AgD//wAA//8CAAEA/f8FAP3/AwACAP7/CAD7/wUAAQD//wUAAQD8/wkA+f8IAP3/AAAFAP3/BAD7/wMA/f8CAPz/AQD7/wAA///8/wIA/P8FAPn/BQD6/wYA+/8IAPn/BgD7/wUA/P8AAP///f8BAP7/AQAAAAAAAQD+/wYA+v8FAP3///8DAPz/BwD4/wgA+P8FAPr/AwD7/wAA/v/+/wIA//////7/AAD//wEA//8CAP3/AQADAP7/BgD7/wMAAAABAAEA//8BAAIA/////wIA/f8EAPv/BQD8/wIA/v/+/wEAAAAAAP3/BAD6/wIA/v8AAAEAAwD5/wYA+/8GAP3/BgD+/wQAAAD//wIA/P8GAPz/BwD7/wYA//8GAPz/AgACAAEA//8FAPv/BgD7/wQA/P8EAPb/CwD2/wcA+P8DAP7/AgD+/wIAAAACAP//AAAEAPv/BAD+/wAAAwD+/wEAAAABAPz/AwD7/wEAAgD8/wYA9/8FAPz/AgACAPz/BgD8/wMA/v8CAP//AAD///3/AwD6/wQA+f8GAPj/CQD2/wcA/P8BAAIA/v8FAP3/BQD8/wYA+/8IAP3/AAAEAPr/BgD4/wUA/v8BAPz/BgD8/wIAAQAAAAEAAwD+/wIAAAAAAAMA+f8IAPr/BAABAP3/BAD//wAAAQACAPv/AgD7/wEA///9/wQA/P8EAPz/BAD+/wEAAgD//wAAAwD6/wcA/P8EAAIA/v8DAAAAAAACAAAA///+/wEA/P8FAP3/AAACAPz/BAABAP7/BAD6/wUA+f8IAPj/BgD7/wIA/f8BAP7/AgD+//3/AgD//wIA//8CAAAAAgABAAEAAgABAAAAAQABAP7/AgAAAP3/AgD+//3/AgAAAP3/BAD4/wgA+P8IAPz///8EAP7/AgACAP7/AwABAAAAAQABAAAAAAAAAAAAAAABAP//AAABAP//AgD9/wIAAAD+/wIA/v///wAA/v8AAP/////+//////////3/AQD8/wAA/P8BAP7//P8AAP7/AQD//wAA/f8CAP3/BQD8/wMAAAABAAIA/v8EAP3/BgD8/wMAAAD//wMAAAD9/wQA/P8DAP//AgD+/wIA/v8AAP3/AwD9/wYA+P8CAPz/AwD///7/AQD8/wYA+P8HAPj/CAD6/wUA/v8CAAEAAAAAAAIA/v8GAPr/BwD3/wQA/f8BAAAAAAD9/wIA/f8CAAAA//8AAP7/AgD+/wIAAAD+/wUA/P8EAPz/AAAEAPn/BwD4/wUA+v8GAPr/AwABAP//AgD//wAABAD+/wMAAAAAAAQA/////wIA+/8CAAAA/P8DAAAA+/8IAPX/CQD8/wMA//8CAP//AQABAP3/AAD+/wEA//8AAP7/AAD+/wEA///9/wAA/f/+/wEA/f8BAP///f8DAP3/AQD9/wQA+f8GAPn/BgD6/wEA/f8BAP3/AwD4/wcA9/8JAPf/CAD5/wYAAAD//wYA+v8IAPz/BQD9/wQA/f8EAPv/BQD//wEAAAD//wEAAgABAAAAAwD8/wMAAAABAAIAAQD+/wIA/f8DAP//AAABAP7/AwD//wAA//8BAPz/BAD7/wMA//8AAAEAAQACAP7/AwD9/wYA/P8GAPz/AwD+/wIAAAD//wEA/v8AAAAA//8CAP7/AgD7/wYA+/8EAP///////wEA/v8AAAMA+/8DAAAAAQABAP//BgD5/wkA+f8BAAEAAQD9/wUA+/8FAP//BAD8/wUA+/8DAPz/AwD9////AAD8/wAA/v/9/wQA+P8FAPz/AgD///z/AwD9/wIA////////BAD8/wMA/P8DAP7/AQD9/wEA/f8EAPv/BAD+//7/BAD+/wIA//8BAAIA/P8HAP3/BQD+/wEAAAABAAAAAAACAP3/AwD6/wYA9/8IAPv/AQACAAAA//8DAAAAAAAGAPn/CgD9/wQAAQD//wIAAwD+/wMAAAABAP7/BgD8/wcA+f8GAP7/AwABAP7/AQD9/wQA+f8JAPT/BwD5/wMA///+/wAA/f///wAA/P8EAPj/BwD3/woA9/8GAP3/AgD//wEA//8BAAMA/f8DAP///f8EAP3/BQD9/wEAAAD9/wQA+/8CAAAA/f8DAP//AAACAPr/BAD7/wIAAQD5/wYA9v8HAPf/BAD8/wIA//8AAP7/AgD//wAAAQD8/wQA//8BAAAAAgABAAIA/v8DAP7/AwD+/wAAAAACAPz/AwD9/wAAAQD+/wEA/v8DAP3/BAD9/wQA/v8EAPr/BAD9/wYA/P8DAPv/BQD9/wEA//8BAAAAAwAAAAQAAAD//wYA/f8FAP7/BQAAAAMABAD9/wcA/P8JAPz/BQD//wAAAQD+/wEA/v8BAAEA/P8DAPz/AQD+/wEA/v8AAAAA/P8DAPv/AwD7/wIA/P8BAP//AQD9/wAAAgD+/wEA/v8AAAAA/f8DAPv///////z/AAADAPj/BAD5/wQA+/8BAAAA+/8EAPn/AQD9//7/AAD///3/BAD5/wYA+/8FAP3/AQABAP//BAD9/wMA//8DAAAA//8HAPn/BQD7/wIA//8BAAAA//8GAPr/CwD7/wcA//8DAAIAAgACAAIAAAACAPn/CQD5/wYA/P8DAP7/BAD8/wUA/f8BAAIAAAAEAP3/AgD9/wMAAAD+/wIA/v8AAAEA////////AAD+/wIA/P8DAPz/BQD6/wYA/P8FAP//AQABAAEA//8EAP3/BAD8/wYA/P8CAP//AAACAP///v8CAP7/AAD+/wAAAQAAAP///v///wEA/f8CAPz/AwD7/wAA/v/+/wIA+/8CAPv/AQD+/wAA/v8AAP7///8AAPv/AwD8/wAA/v8AAP3/AgD8/wAAAAD///7//f8DAPv/BAD8/wIAAQD9/wYA+P8KAPn/BwD//wEAAgABAAEABAD//wAAAQAAAAIAAgD9/wcA/P8HAPv/BQAAAAEABAD8/wcA/v8DAAQA//8GAAAABQAAAAYA/v8HAAIAAQAGAAAAAgAFAP7/BQD//wQA/v8BAAIAAAACAP//AgD9/wYA9/8GAPz/AwD+////AgD/////AQD9/wEAAAD//wEAAAD9/wIA/f///wAA/////wMA+/8BAP/////+/wIA/f8DAPz/AAABAP3/AQD6/wMA+f8EAPn/AgD7/wMA/f///wIA//8AAAMA/f8CAAIA//8BAAQA/v8GAP7/AwABAAMAAAAHAP3/BAD//wMAAgADAP//AwABAAAABAD+/wcA+/8IAPr/BAD+////BAD9/wAAAAD7/wMA/P///wEA/v/8/wIA+f8HAPr/BAD///7/AQACAPz/BwD5/wYA/f8DAAEAAwAAAP//BAD+/wIAAgD//wYA//8CAAMA/P8KAPj/CQD8/wcA/f8FAAIAAAAEAP//AAAHAPr/CQD8/wYAAAACAAIA/f8IAPf/CgD5/wcA/v8DAP7/BQD9/wcA/f8DAAQA/P8HAPz/AwD//wEAAAABAP///v8DAP7/AgD+/wIA+/8GAPz/BAD9/wIA//8DAP3/AwD+/wAAAgD7/wQA/P8CAAIA/P8FAPr/BwD3/wgA+f8EAPr/AwD6/wUA+/8FAPv/BwD8/wQAAQD9/wcA+/8EAAEA+/8JAPf/BwD5/wMA/v8BAP///v8BAAMA/v8AAAIA+/8GAPr/BQD9/wEA//8AAP7/AgD+/wMA//8CAAEAAQADAPr/CAD7/wQAAgD5/wUA+v8FAP3/AgAAAAIA//8FAPr/BQD9/wEABAD8/wUA+v8GAPv/AgD+////AAD+/wIA/v8CAP//AAAAAAEAAgD//wUA/v8EAP7/AwACAP//AwD//wAAAgD//wUA+/8CAP//AAACAP//AgD//wEAAAD+/wUA+f8IAPv/AgD9/wMAAAD//wEA/P8FAPr/BwD6/wMA/v8AAAAA/v8EAPr/BgD8/wEAAwD6/wYA/P8HAPv/AQABAP7/AgD+//7/AgD7/wQA+v8EAPn/BAD+//7/AAD7/wMA/f///wAAAQD9/wUA/P8EAPz/AQABAPz/BAD5/wMA/f///////f8CAPz/AwD7/wUA/f8DAP////8CAP3/AAAAAP//AgD+/wAA/v///wIAAQAEAP3/BAABAAMAAQD9/wMA/f8FAP//AgD+/wIA+f8HAPr/AgABAP7/AgAAAAAAAQABAP3/AwD//wEA/////wMA/f8BAAAA/v8BAAIAAAAAAAAA//8BAAEA/f8DAPz/AgAAAAAA//////////8BAP3/AgD+/wMA/P8CAAEAAQAEAPz/BQD6/wkA9/8JAPf/CAD7/wQA/v8DAP7/BQD6/wcA/P8CAAAAAgD7/wcA/P8FAPv/AgD6/wUA+v8BAPz///////7/AAD///7/AAD9///////+/wAA/P/+/wAA/v8AAPz/AgD///7/AAAAAPv/BAD3/wcA9/8EAPv/AQD/////AAD9/wUAAAAEAAAAAQACAAAABAD//wMAAgD9/wUA+/8FAP7/AgAAAAIA/v8EAPz/BgD7/wYA+/8FAPz/BQD/////AgD8/wMA+/8BAP///f8CAP3///8CAPz/BAD+/wMA+v8GAPj/BgD7/wQA+/8DAP3///8BAP7///8AAP7////9/wEA/f/9/wQA+v8EAP7/AgD//wUA//8EAAIA//8GAPz/BQAAAAQA//8CAP//AwD+/wEA//8DAP//AwD+/wQA/v8AAAMA/P8GAPz/AQABAPv/CQD0/wwA9/8GAPz/AAD/////AAD+/wMA/v/9/wUA9v8LAPX/BgD7/wIA/f8BAAEA+P8GAPn/AgD///7////+/////f/9/////v8CAP3/AgD+/wEA/v8GAPz/CAD6/wgA//8BAAMA/f8HAPz/BgD+/wQAAQADAAAAAgADAP//BAABAP7/AwABAAQAAAACAP7/AAADAP7/AgD9////AgD8/wUA+/8BAAEA/f8BAP////8DAPv/AwD6/wYA+/8DAP3///8AAP///v/+/wAA+/8CAP///f8CAPv/BQD8/wIA/P8CAPz/AAACAPz/BgD4/wUA/f8DAP3/AgD//wIA/f8DAP3/BQD//wAAAgD//wYA/f8JAPz/BgD+/wUA//8FAP7/BgABAAIABAD//wEAAgD+/wQA/v8DAP3/BAD//wEAAwAAAAMAAAADAP//AgACAPz/BgD5/wgA+P8HAPr/AgABAP3/AgD+/wAA/v8BAP3////+/wAAAQAAAP3/AAD+//////8BAP3/AQAAAPz/AgD7///////8/wIA/f///wAA//8DAPv/AwD//wAABQD7/wIAAgD9/wIAAAD+//7/AgD9/wMA/P///wIAAAD/////AQABAP//AQD//wAABAD8/wEA///7/wUA+v8EAP///P8DAP3/AgAAAP//AgD9/wYA+/8FAPv/BgD8/wYA/P8FAAMAAAAFAP7/AwD//wQA//8EAP//AwD+/wcA+P8KAPn/BwD5/wYA+P8HAPj/BgD6/wEA///+/wMA/f///wMA+/8BAP7///8CAP///P8DAP3/AgAAAP3/AQAAAP7////9/wQA+P8HAPX/BQD8////AQD6/wMA+f8FAPf/AgD9//7/AQD///3/AwD5/wEA/f///////v/+//7/AQD5/wUA+/8BAP////8AAP///////wEA/f8EAAAA//8FAPr/CAD8/wQA/v8BAAIAAQD9/wYA/P8DAAIA/P8EAPr/CAD4/wkA+f8EAPz/BAD9/wEAAgD//wAAAQD/////AgD8//7/BAD6/wcA/v8BAP//BQD9/wcA+v8JAPr/BwD8/wMAAgD//wMA/////wMA/f8BAAEA/P8CAP3/AQD//wMA+/8DAPv/AAACAPv/AwD9/wAAAAABAP7///////3/AgD7/wMA/v//////AAD+/wIA///+/wAA/////wMA+/8DAP7//v8BAAAA/f8EAPn//v8BAP3//f8BAPj/AwD7/wIA+v8DAPj/AwD9/wIA//8DAP3/AQABAP7/AgAAAPz/BAD7/wQA/v/+/wQA/f8DAP7///8AAAAA/v///wMA+v8GAPr/AgACAP//AAACAP3/AgD+/wAAAQD//wAAAAABAP7/AAACAP//BQD8/wEABQD8/wUAAAD+/wcA+f8JAP3/AwABAAIAAQD9/wMA/P8DAP7///8DAPv/AwD9/wMA//8AAAEAAQAAAAEA//8AAAIAAAAAAAEA//8CAAAAAwD6/wYA+/8CAAAA/f8DAPz/AwD9/wQA/P///wMA/P8EAPz///8AAAAA/v/8/wEA+P8FAPv//f8FAPb/CQD3/wgA+P8GAP7/AQACAPz/AgD+/wIA/v8BAP/////+/////v8AAPz/BQD3/wcA+P8EAP3/AAD+/wIA//8BAP7//f8DAP7/BQD6/wMA/P8BAP7/AgD9////AgD6/wYA/P8EAP////8FAP7/BgD7/wQAAwD7/wkA+f8GAP//AAABAAAA//8AAAAAAAD+/wQA/f///wYA+f8JAPz/AgACAAEAAQD/////AAAAAAAA//////7/AgD9/wIA/v/+/wIA/f8EAP7/AwAEAP//AgD//wMAAAADAAMA/v8GAPz/BgD9/wUA/v/+/wYA+/8GAP3/AwD9/wQA/////wYA+v8GAP3/AAAAAP7/AQD8/wAA9/8EAPv///////3/AAD///v/BAD9/wAAAwD8/wUA/f8EAP3/BAD+/wAAAwD+/wEAAAD//wAAAwD6/wQA+P8BAP7//f8AAP7////+/wIA/f8CAAIA+/8IAPr/CAD8/wEAAwD+/wEAAgD8/wMA/v8GAPr/CQD4/wYA//8DAAAABAABAAMAAgAFAAAAAgABAP7/BwD7/wMA/v/9/wEA/////wMA+v8FAPr/BQD+/wEAAwD//wUA//8BAAMAAAADAAAAAwAAAAIAAgD8/wQA//8AAAIA/f8DAP7/AgAFAPz/BgD+/wEA//8CAP//AQACAPz/BAD9/wUA/P8AAAEA/P8DAPz///8FAPz/BAD+/wMAAAACAP//AwACAAMA//8EAP7/BAD+/wMA/f8FAP7/BgD//wAAAwABAAEAAgD//wMAAQD//wAAAwD8/wIA/f8CAP3/AQAAAP3/BAD7/wUA/P8EAP7/AAAFAPv/BQD8/wIA//8BAAIA/f8GAPr/BQD9/wIAAQD8/wMA/f8AAP///v8CAP7//v8BAP7/BAD8/wMA+/8CAP//AAACAAEAAAD+/wQA/f8FAP7/AgAAAAIA//8DAP3/BAD+/wQAAQD//wMA//8CAAMAAgACAAEABAD+/wUA///+/wYA+/8FAPz/AwD//wIA/f8CAAAAAAABAP//AwD//wAAAgD//wUA/P8EAAIA/v8DAP7/AQD//wMA/v/+/wAA/v8AAP7/AAAAAP7/AgD/////BAD7/wYA/P8FAPr/BwD8/wMAAAD//wMA//8AAAQA/P8EAPz/BwD7/wcA/v8BAAMAAQADAAIAAgADAAMAAAAFAP7/BwD9/wMAAQAEAP//BQD9/wMA/f8BAP//AQD+/wEAAAD///////8CAPz/AQD///7/AwD9/wEA/f8CAP3/AgD5/wUA+v8FAPz/AQD+////AgD8/wUA+v8FAP//AgD//wUA+/8GAPr/AwD8/wEA/v////7///////7/AQD9/wQA+/8EAPz/BQD8/wUA/P8HAPv/CAD6/wgA/P8BAAMA//8FAP7/BQD7/wcA+/8FAP//BQD7/wsA9v8NAPn/BgAAAP7/BQD8/wUA+v8GAPr/BwD6/wMA+f8GAPf/BQD5/wIA/f/+/wQA+v8FAPz/BAD8/wQA+v8GAPj/BgD6/wMAAQD+/wQA/f8BAAAA/f8GAPn/BgD8/wIA//8CAP7/AQAAAAEA//8CAP7/AAAAAP///f8DAPn/BQD7/wIA/v///wEA/v8DAPv/BAD9/wQA/P8CAP7/AAACAPr/AgD9/wEA//////z/AQABAPz/AgD9/wAAAAAAAP3/AQD8/wEA/v8AAP7//f8AAPz/AQAAAPz/AQD8//7////+//7//v/6/wMA+v8FAPz/AwD+/wEA/v8AAAAA//8DAPv/BQD8/wIA/v/+/wQA/f8CAP//AAACAP7/AQD5/wIA/P8GAPz/BQD//wEAAAAAAPz/AAD+//f/BQD1/wMA+/8DAAAAAAADAPn/CgD7/wcA/f8BAAMA//8BAP///v8AAP7//v/+//3////7/wMA+P8HAPj/BgD7/wAAAAD7/wQA/P8FAPv/BAD7/wQA/v8DAP7/AwAAAAAAAAD///7/AQAAAP7/AwD7/wYA+v8EAPv/AwD9//z/AQD3/wMA+f/9//3/+/8AAPn//v/+//v/AgD6/wMA9/8HAPv/BQD9/wIA//8FAPz/CgD6/wYA/f8EAP7/AgD//wIA+P8HAPn/AwD3//3//f/9//v/AAD7/wIAAAD/////AQD//wQA//8AAP///f8DAP3////+//7/AAAAAP7///////z/AAD4///////5/wYA9/8AAAIA+v8GAPv/BgD+/wIA//8CAAAAAAABAP7/AQACAP///v8EAP7/AQAEAP//BQD//wMAAQACAAUA/v8BAAAA/v8AAP7/AQD+/wMA/v8CAAAABgD8/wsA/f8IAP7///8FAPv/AQAAAPv/BQD5/wIAAgD7/wUA/P8AAAMA+f8HAPT/CQD1/wYA+f8EAPr/BQD7/wQA+f8KAPb/CAD5/wYA+/8GAP3//v8FAPz/AQD+/wIA/P8GAPn/CAD5/wcA+v8EAP///f8IAPn/CQD7/wMAAQD9/wYA+/8DAPr/CQD2/wsA+f8CAAMA+f8JAPf/BwD9/wMA/P8FAPz/CAD7/wEA///9/wUA/v8GAPr/CAD7/wMA/v8CAAQA/P8FAP//BAABAAAA//8EAP//BgAAAAEAAAADAAQAAQACAAEAAwAEAAIABQACAAEAAwD//wMAAgD//wIAAgD+/wYA/f8FAPz/BAD/////BQD5/wUA/f8CAAEA/f8GAPn/CQD7/wMA//8BAAAAAwD9/wUA/P8FAP7//////wEA/v8DAPz/BgD7/wUA/P8AAAAA/v///wAA/v/8/wIA+v8FAPr///8CAP3/BQD9/wEABgD8/wcAAAABAAIAAwD9/wgA+/8DAAEA/P8AAAQA/f8DAAAA/P8HAP//AgAFAPj/BwD8/wMA//8AAAEAAQAAAAQAAAD+/wEA/v8BAP7//v8CAP7/BQD7/wQA+v8DAPr/BQD9/wIAAAACAPz/CQD7/wAABgD5/wkA/f8GAP//AAAHAPr/CQD9/wEABQD5/wkA+v8HAPr/AwD8/wAA/v8AAPz/AgD5/wYA+v8HAPr/BQD+/wMAAgD//wUA//8DAAIA//8JAPv/BgACAP7/CQD7/wQABAD9/wUAAwD+/wYA/f8EAAEAAAD+/wMAAAABAAAA+/8DAPv/AwD8/////v////7//v////3//P////7///8BAPz/AwD8/wEA+/8EAPv/BQD5/wgA+v8HAPz/BQD3/wkA9f8GAP3//v///wAA/P8BAPr/AAD7/wEAAQD+//3/AgD8/wMA/f/7/wYA+P8CAP///f/+/wAA//8BAAEAAwD7/wgA9/8NAPr/BgACAAAAAQABAAAA//8FAP3/AgAEAP3/BQD5/wkA9/8JAPv/AgD+/wMA///+/wIA//8CAAEA/f8CAAAAAAABAAAA//8AAP7/AwAAAAEABAD//wQABQD8/wkA/P8FAP7/AQD+/wcA+/8IAPz/AgACAPz/AgABAPz/BAABAAAABAD9/wEAAAD///3/BQD5/wQA/P8CAAEA/P8IAPz/BAD//wAAAgABAPz/BAD6/wQA/P8AAP////8BAP///v8AAAEA/P8FAPr/BwD8//7//f/6/wEA+f8CAPn/AwD5/wEAAgD9/wMA+/8EAPn/CQD3/wcA+f8DAAIA//////7//////wIA+/8DAPr/BAD///7/CAD4/wgAAAACAAMA/v8DAPr/AAAAAP7/BAD9//7/BQD7/wcA/P8EAAIAAwADAP//BgD9/wYA//8EAAIAAAAEAAIABQD9/wQA/v8BAAIAAAD7/wQA+v8EAP7/AgAAAAQA+P8LAPb/BwD7////AAD8/wMA/f8EAPv/AwD5/wIA/P8DAPz///8AAPz/AwD///7/AAD//wEA/v///wAAAQD+/wMA/v/+/wIAAgD+/wEA+/8EAP//AgD//wIA/////wUA/v8CAP//AQD+/wMA+P8FAPb/BwD3/wMA/f8BAPz/AgD/////BAD2/wIAAwD6/wsA9P8KAP//BQABAAAABQAAAAQA+/8BAAAA9/8EAPf/AgD5/wAA/P8DAPv//////wEA/v8CAP3//P8EAPn/BwD6/wMAAwD5/woA9/8LAPb/CgD7/wUAAAABAAAAAAAEAPr/CwD1/woA+/8CAAcA+/8IAPj/CwD7/wQA/f8BAPz/BAD5/wMA/f//////AgD8/wUA/P8CAAIAAwD9/wUA/P8DAPv/AgD8/wMA+/8DAP7/AAADAPv/CAD5/wUA/f8AAAMA/f8FAPb/DAD0/wkA+f8AAAEA+/8BAAEA/f8EAPv/AAD///7///8BAP3///8AAP//BAACAP7/AwD//wAAAgD9/wIAAQD5/wIA///+/wIA+v8EAP7/+/8GAPX/CgD4/wIAAgD+/wEA//8AAAQA/P8HAPf/CQD9/wMABQD+/woA+v8HAP3/AAAFAP3/BQACAP3/DAD6/wkA+v8DAAQAAAD//wQA+v8JAPX/CAD3/wQA+P8AAAEA/P8BAPv/AgD9/wQA/P8FAP////8BAAAAAgD6/wYA+P8DAAIA+f8HAPv/AAACAPn/CAD4/wcA/P/+/wQA+v8DAAEA/P8GAP7//f8FAP3/BQD4/wYA9/8HAPr/AQD5/wQA+/8DAP//+/8EAP3/BQD+/wMA/v8EAP//AwD9/wYAAgD+/wYA+v8FAPv/AQABAP7/AQAEAPz/AwD9/wQA/P8GAPj/BAD8/wEAAgD3/wcA+/8DAAAA/v8DAP3/BAD+/wAA/v/9//7/BAD7/wEAAwD9/wcA/P8EAAAAAgABAAIA/v8EAP3/AwD+/wQAAQACAAAAAAD//wEAAAABAPv/BAD3/wwA9v8FAAAA/P8FAP//AAAEAPn/AQD+/wEA/P8CAP3/AQD8/wAA+v8CAPv////6/wMA+/8CAAAA/////wIA//8DAAEAAAAAAAMA/P8EAAIA+/8JAPf/AgAAAPz/AQD8//7/AQAAAAEAAQD7/wcA/P8EAP7//f8EAPz/AwD9/wMA+/8GAPr/BQD9/wIAAAABAAIAAgAAAAQA/v8DAP//AgD8/wUA+/8FAAAAAgD//wAAAAABAAAA/f/+/wIA/P8DAPr/AAD8//z/AQD6/wIA/P8AAP3/AAAAAP7//v8CAP/////+/wAA//8CAPr/BgD7/wUA/P8CAP3/AQD9/wMAAAAAAP7/AQAAAP///v/+/wIA///9//7////8/wQA+f8CAPv/AAD9////AQD8/wQA+/8DAPz/BQD6/wYA///+/wQA/P8DAP7/+/8GAPv/BwD6////BAD7/wgA/f8BAAIA/f8DAAEAAAAFAP7/CQD9/wYA/v8CAAQA/f8AAAIA+/8GAPz/AgD//wIAAwD//wEA//8EAP//AQABAPv/BQD5/wQA+/////7//v/+/wAAAAD+/wIA/f8DAP//AAD9/wgA9P8KAPb/BAD8//3//f////z//v/+//n/AQD4////+v/9//v/+v8BAPr/AAD9//7////9/wAAAQD9/wQA+f8FAPz/AQADAP7/BQD9/wEAAQD//wYA/f8BAAIA/v8HAPz/CgD+/wMABAD//wYA/P8EAP3/AQD//wIAAAD//wIA//8HAP3/CQD4/wUAAQD+/wYA+P8GAP3/AAD9/wQA+/8GAPv/BAD7/wQA+f8GAPv//f8DAPr/BgD8/wUA/f8DAAEA/P8EAAAAAAAFAAAAAQAAAAQA/P8LAPf/CQD6/wEABAD+////AwD6/wMAAAD6/wkA+v8FAPv/AgAAAAAA//8AAP//AwACAPn/CQD8//7/BAD4/wMA///7/wMA/f///wAAAAACAP3///8AAAAAAAD////////8/////f/9/wAA+/////7/AQD///z//P8CAPr/BgD4/wcA+/8EAAEA//8DAAEA//8EAP3/AwADAAEA//8FAP3/AAAGAPz/DADz/wwA+/8DAAcA/v8IAPv/BgD+/wQAAAD+/wEA/v8DAP/////7/wQA+v8EAP3/AAAAAP//AwD7/wYA+f8GAP3/BgD9/wQA//8FAP//AgABAAIA/v8EAP3/BQD9/wIA//8DAAMAAQAAAAQA/f8FAAEA+/8DAP3/BAD+/wAABAD6/wkA+v8EAAAA/v8EAPf/DAD2/wkA+/8BAAIA//8CAAIA//8FAP7/AwD4/wgA+/8FAP///v8CAP3/BAD8/wAAAQD8/wIA/////wEA/f/9/wEA/v8AAP///v8AAP3///8AAP7/AgD8/wQA/f8CAAEA/f8HAPj/CAD7/wMAAQD//wQA//8AAAMA/P8FAP3/AAACAP3/AgD//wEA/P8FAPj/BwD7/wQA/f8BAAIAAAACAP///v8BAAEAAwD+/wEA///+/wUA+v8GAPv/BAABAAAABQD9/wQAAgD//wIAAwAAAAQAAQAEAAAAAwAAAAQAAQABAAAAAwD8/wIA/v///wMA/P8CAP7/AAD9/wIA/P8DAP3///8BAPz/AQD///z/AgD9////AQD+/wAA/f8GAPn/BgD6/wIA/////wAA///6/wMA+f8FAPv/AgD6/wEA/v/+/wEA///9/wEA+/8AAPz/AQD+/wAA/f8BAP7/AgD+/wQA+/8FAP3/AQAGAPj/CAD8/wIABAD7/woA+P8DAP7///8EAPv/BAD//wMAAAADAAEAAwACAAAABQD//wUA/f8FAP7//v8CAAEA//8DAP3/AQACAAAAAwD8/wMAAAABAAQA/f8BAAAA/v8DAP////8AAAEA/v8CAP3/AAABAP3/AQD///7/BAD9/wAAAAACAP//BQD9/wMA/v8EAPz/CAD4/wgA+v8GAPz/AwD+/wIA/v8BAP//AQD9/wEA/f8EAPz/AgD///v/BgD4/wcA+P8FAPv///8BAPv/AwD8/wEA/f////////8AAP/////+/////f8AAAIA+P8GAPj/BAD9/////v8AAP/////+/wAA/f8DAP3/AwD+/wAAAwD+/wIAAgD+/wYA/f8FAP//AwACAAAAAgD+/wIAAgAAAAEAAwD//wQA/v8DAAIA//8FAPz/BgAAAAIAAwACAAIABAADAAEABQD//wcAAQAEAAMAAgABAAYA/f8GAP3/BQD//wEAAgD+/wUA+/8GAPz/BQD4/wUA/P8GAPv/AAACAP//AAD///7/AwD8/wMA/f8CAP///f8DAPn/BQD9////AwD6/wMA/v///wAA/v8BAAAA/P8DAPz/AwD8//z/AwD3/wcA9/8FAPj/BAD9/wAAAQD//wIA//8CAP3/BgD7/wcA+/8KAPr/BwD9/wUA//8FAAAABAD//wEABAD+/wgA+v8IAP3/AwAAAAIABAD+/wMA//8CAPv/BgD7/wYA/P/9/wEA/P8EAPr/AwD6/wEA///8/wQA/P8BAAIA+/8FAAAA/P8FAPv/BAABAAAAAAAFAP3/BgD6/wgA+f8IAP7/AgAEAP//BAD9/wUAAAABAAMAAgD+/wgA/v8CAAMAAQD//wUAAAD//wgA+/8IAP7/BAD8/wgA9v8NAPb/CQD8/wQAAAACAP//BwD7/wgA/f8EAP//BQD5/wcA+/8EAP//AAD8/wUA/P8CAAAA//8BAP3/BAD9/wQA/f8BAAIA//8CAP///f8EAPz/BQD6/wQA/f8DAP////8DAPv/BAD8/wEA/v////7/AgD+/wAAAgAAAAMA//8DAP3/BgD8/wUA/v/+/wYA+v8EAPz/AAABAP7/AwD5/wgA+v8FAP7/AgD+////AQD//wMA/P8FAPj/BwD5/wUA//8CAP//BAD+/wMA/v8DAP//AgAAAP3/AgD8/wQA/v8BAAAAAQACAAAAAgD8/wMA/v8FAPz/BgD3/wsA9v8HAPn/AgD+/wEAAAD//wEA/v8DAPz/BwD6/wkA+/8FAAAAAQACAAIA/v8EAP//AQAAAAIAAAD//wEA/f8FAPz/AQADAP3/BQD6/wUA/f8DAP7/AQD//wEA//8DAPz/AwD8/wMA/v8CAP////8BAP7/AgD7/wcA+P8HAPv/AwD/////AgD//wQA/P8EAPv/BQD7/wIA/v8AAP3/AAAAAP7//v8AAAAA/v8BAPv///8DAPj/CgD3/wUA/f8DAP//AQD//wAA/f8EAPf/CAD5/wEA///8/wAAAQD9/wIAAAD//wEAAgD8/wQA/f///wIA/v8BAP//AAD///7/AgABAAIABAD7/wgA/f8FAPv/BQD8/wcA+/8GAPr/BgD4/wUA/P8BAAEA//8AAAEA/v8EAP7/AAABAP7/AgAAAP3/BQD7/wMA/v8AAAAAAgD//wAAAAABAP7/AwD7/wUA+/8DAP////8DAPv/AQD+/wAAAQD+/wEA/v8BAAAAAwD+/wQA//8CAP3/BQD7/wYA+/8EAPz/BQD9/wQA//8BAP//BAD8/wMAAAABAP//AQABAAEA///+/wAA/f8CAPr/AgD7/wIA/P8BAP7/AAD9///////9/wIA/P/9///////9/wQA9v8JAPn/AQD9/wIA/f////7//v/9/wIA+/8DAP3/AAD+/wEAAgABAAQAAAABAAMA/v8GAP3/BgD9/wQA/v8BAAIA/P8HAPr/CAD6/wQA//8BAAAAAgD//wIA/v8CAAIA/v8CAPz/AQD9/wAA/v8AAP7/AAD///7/AgD+/wQA/f8CAP7//v8CAPz/BgD4/wgA9f8JAPf/BQD7/wMA+v8EAPn/AgD///r/BQD7/wQA+v8KAPj/BwAAAAIAAwACAP//AwACAAAABAD//wIAAAACAP3/BAD8/wQA//8CAP//BAD8/wQA/f8EAPz/BgD5/wYA+/8DAAAA//8BAAAA/v8CAP3///8AAP//AQD///7/AQD9/wIA/P8BAAAA/v///wEA/f8AAP/////9/wEA///9/wIA+f8CAPv/AAD+/wEA/v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D9/wUA/f8DAP////8CAAEAAgAAAAAAAAD//wEAAgD9/wIA/v8CAP//AgABAAMAAAABAAMAAwAAAAMAAAACAAMAAAAAAAUA/v8CAP//BQD8/wUA/v///wQA/v8DAAAAAgD//wEAAgD//wUA/v8EAP3/AgABAAEA//8CAP3/BQD6/wMA+v8CAPz/AgD8//7/AQD+/wAA/////wIA/f8FAPr/AgD+//z/AwD5/wQA/f///wEA/P8CAPz/AgD8/wIA/f/7/wQA+f8CAP3//////wAA/v8DAAEAAAADAP7/AgABAP////8EAPz/BAD9/wEA//8DAP//BQD9/wQA//8GAP////8EAP//AgD//wMA/P8FAPv/BgD7/wQA/f8EAP//AgD8/wMA/P8EAPz/BAD9/wEABAD+/wMAAAABAAEAAAABAP7/AwD9/wQA/P8CAAAAAAD//wMA/v8CAP//AAD9/wYA+f8FAPv///8DAPv/AgD8/wAA//8BAPz/AwD8/wMA/f8DAP3/BwD8/wMAAgAAAAQAAgD//wIABAD7/wUA/P8AAP7/AgD9/wEA////////AQD7/wUA/P8BAP////8EAP3/BAD/////AgD//wMA+/8DAPr///8AAPv/AgD7/////v/+/wAA/v8BAPz/AwD8/wAA/v/9/wIA+/8DAP3/AAAAAP3/AQD//wQA+v8EAP3/AAACAP3/AgD9//7/AgD+/wAAAAD+/wEABAD7/wYA/P8BAAMA///9/wYA+P8JAPj/BQD///7/CAD6/wYAAAAAAAMA///+/wMAAAD//wQA+P8HAPz/AwD9/wAAAgD8/wQA+v8DAP3///////v/BAD5/wMA/P8BAAAA///9/wMA+v8EAP3//v8BAPz/AgD7/wMA+/8BAP7/AwD8/wUA+v8GAP3/AwABAP//AgACAP7/AgAAAP3/AgD8/wEA/////wAA/v///wAAAAABAAAAAQABAPz/AgABAAAAAAACAPv/BAD///3/BgD//wEAAQD+/wMA/P8BAAAA/f8EAP3//v8CAP7///8DAPz/AAD///7/AAAAAP3//f8BAPj/BAD4/wQA+P8CAP3///8AAAAA//8AAP7/AQD+/wAA///9/wUA+f8FAP3/AwAAAAEAAAADAP//AwD//wAAAQD+/wUA/P8DAP////8GAPv/AwD9/wEA/v8CAP7/AgD+////AQD//wIA/P8FAPj/CgD1/w0A9v8JAPz/AgABAAEAAQD//wIA/f8FAPn/BAD6/wEA///+//7//v8BAP3/AAD9/wAA/v8FAPn/BAD8/wIAAAABAP//AgD7/wIA/v8AAAMA+P8HAPv/AgAAAP3/AwD9/wEA//8AAP//AgABAAAAAgD//wIA//////7/AwD8/wIA/v///wEA+/8EAPj/BAD+//////////7/AAD9/wAA+v8CAP3//v8AAPz/AQD8///////7/wIA+/8CAPn/AgD5/wEA+/8EAPr/BAD9////BQD3/wYA+/8HAPr/BAD8/wEABAD6/wcA+/8AAAUA+f8HAPz/AgAAAAAAAgD9/wMA/P8CAP7/AAACAPv/BQD9////AgD8/wIA///8/wMA/P///wEA+v8FAPv/AQD///7/AwD+/wAAAgD8/wQA/f8BAAIA/v8BAP//AAABAPv/AwD8/wQAAAD6/wEA/P8CAP7///////7/AQD+//z/BAD5/wYA+v8FAPr/AwD//wAAAQD//wAAAQD8/wUA+v8HAP3/AAACAPv/BAD9/wEA+v8EAPn/BQD9/wAAAgD8/wAA//8CAP3/AwD8/wAA/v8AAPv/BAD6/wMA/P8BAP3////9/wEA/v8AAP3/AAD9/wAA+/8EAPz/AwD8/wQA/v8EAP//AQAEAP7/BQD7/wUA//8BAAEAAAABAP//BAD8/wAAAQD9/wEABQD3/wkA+v8EAAAABQD6/wcA+/8FAP3/BAD8/wIA/v8BAAIA+v8FAPz/BAD/////AgAAAAMA/f8DAP//AQD//wEA//8CAP7/AwD9/wQA/P8EAP//AgD//wMA/P8EAPz/BQD9/wEAAAD+/wEA//8AAP7/AgD8/wMA/v///wAAAQD9/wQA+/8BAPz/AgD9/wEA/f8BAAAAAwD+/wQA+/8GAPv/BQD8/wQAAAAAAAMA//8DAP7/AgAAAAMA//8DAP//AgD9/wQA/v8CAP//AAAEAP//AwD//wQA//8EAAAAAwAEAAAAAgD//wMAAAAEAAEAAQD//wIA/v8FAP7/AgABAAEAAQABAAIA//8BAAEAAQABAP7//f8EAPz/BAD9/wAA/v8DAPr/BQD6/wUA/f8DAP3/BAD8////AgD6/wUA/f8AAP//AgD6/wgA+v8FAP3/AgABAAAABgD8/wgA/f8GAAAAAgADAAIAAwAAAAQA/v8FAAEABQAAAAQA//8GAP7/BQD+/wQAAgD//wYA/v8DAAEABQD9/wcA+f8JAPr/BgD+/wIAAQD7/wgA9/8IAPj/BwD8/wQA+f8FAPn/CQD6/wYA+v8HAPr/BwD4/wYA9/8HAPj/BQD7/wAAAAADAPv/BwD9/wUAAQABAAQAAAAAAAIA//8DAAEA/v8FAAAABAAAAAMAAQAAAAUA/P8IAPr/CQD5/wgA//8BAAQA/v8HAP//CQD6/wkA/f8DAP7/AAABAP3/AgD9/wEA/f8CAP//AAAAAP7/AQD8/wkA9/8IAPj/BAAAAPz/BAD3/wYA+/8CAAAA/f8AAAMA/f8DAP3/BAD8/wUA//8CAAUAAQADAAMAAwAEAAAABwD9/wcAAgABAAgA//8IAPv/CQD+/wIABwD8/wcA//8BAAYA/P8HAPz/BgD//wEA//8DAP//AwD+/wMA//8AAAAA/P8HAPj/BQD7/wIAAwD7/wYA+P8HAPz/BAD//wMA/v8EAPz/AwD9/wIAAAAAAP//AQD7/wUA+v8EAPn/BQD7/wAAAQD7/wQA/f8DAP3/AgD+/wIA/f8BAP3/AwD8/wUA+/8FAP7/AAAEAP3/AwD9/wUA/P8EAAAAAAACAAAAAgACAAIAAgACAAIA//8BAAMA+v8IAPz//v8BAPv/AgABAPv//v/+/wAA//////z/AwD7/wEA/v/8/wYA/P8CAP7/AQAEAPz/BwD7/wQAAAD+/wcA+/8HAP7//f/7/wAA+f8FAPv/AQACAPz/AwD9/wIA+/8BAPr/AgD5/wIA/f8DAP//AQACAP7/BAD//wIAAQACAAIA/f8GAPn/CgD3/wkA9v8JAPv/AwD+/wMA//8FAPz/BgAAAAEAAwD//wQA//8FAP3/AgADAPz/CAD//wEAAgADAP3/CQD3/wkA+P8HAPv/BAD+/wEAAAD+/wIA/P8BAP//+/8CAPj/AQD7/wAA///+//z/BAD7/wAAAAD7/wUA+////wIAAAABAAAA//8DAP//BQD///7/BQD6/wUA+v8GAPr/AwD9/wAAAQD8//////////7/AAD9/wUA/f8EAP7/AAABAAEA/v8DAPz/AwD+/wAA/f8EAPr/BwD5/wAA///+/////P/7//3//v/+//z/AgD3/wcA+f8FAP3/AgAAAAEAAAAAAAIAAAACAP3/BAD9/wUA/P8CAAEA/v8HAPr/CAD9/wUAAQADAAEABAD8/wgA/P8CAP////8CAAUA/P8IAPz/CAAAAAcAAgADAAQA/P8GAPv/AwD9/wEA///+/wEA/v///wQA/P8GAPr/BQD9////AQD9/wAA//8AAP7/AAD+/wAA/P8AAAAA/f8DAP3/AwD//wAAAQD7/wcA9v8FAP3//f8BAP///f8DAPz/AAAFAPj/CAD6/wQA//8AAAEAAQD9/wYA+/8FAPn/CgD3/woA+v8EAP////8AAAAAAAACAP3/AgD//wEAAgD//wEA/f8CAAEA/v8IAPn/BgD9/wAAAwABAAIA/f8DAAAAAgADAP3/AwD//wEAAQABAP//AAADAAAAAgACAAAAAgACAAIAAwABAAAAAQABAP7/AQD+/wEA//8DAPz/BAD9/wMA//8CAP7/AQD//wEA/f8EAPz/BAD+/wEAAQABAP7/AgD+/wEAAQD//wMA/f8AAAAAAQD+/wMA/v///wUA/v8EAAAAAQD+/wUA/P8CAP//AAD+///////+//7////+/wMA//8CAP//AwAAAAIAAwD+/wQAAAD//wYA+/8CAAEA/f8CAAIA/v8BAAIA/P8FAP//AQAAAP3/AQD7/wgA9f8JAPb/BgD6/wgA9/8FAPr/AQD+/wAA+f8EAPr/BAD8/////P//////AQD///7/BgD5/wcA/P8DAP//AgD+/wIAAAADAP3/AgAAAAIAAAACAP3/BQD8/wUA/f8EAPz/AwD8/wIAAAD//wAAAgD7/wYA+/8DAAAA/v8CAP7/AgD8/wUA/v8DAP3/AwABAAMAAQADAP7/CAD7/wcA//8DAAEAAQABAAIAAQABAAEA/v8CAP3/BAD8/wEA/P8AAP7/AwD5/wUA+f8FAPr/BAD7/wIA+/8AAP7/AQD9/wEA/v8BAPz/AwD7/wUA/f8BAAIAAgD8/wcA+P8FAPz//v8CAP3/AAD9/wAA/P////3/+v/+//3//f8CAPr/AAD///7/AAD9/wQA+P8FAPz/AAD///3/AwD+/wEA/v8DAP3/BAD+/wEAAgD/////AwD+/wIA/P8FAPv/BQD+/wEAAgD//wIA//8CAAAAAAD+/wMA+/8CAP3/AQACAPz/BgD5/wgA+v8CAAIA/f8EAPz/BQD8/wUABAD8/wkA/P8EAAIAAQABAAAAAwD8/wYA/v8CAAMA/f8DAP7/AQAAAAEA/v8FAP7/AwAAAP///v8CAPn/AgAAAPr/BQD6/wIA/v8BAP//BAD8/wQA/v8DAAAA/v8CAP7/AAD///z/AAD//wEA+v8DAPz/AAD///z/AAABAPn/AgD3/wEA+/8AAPz/AwD6/wIA/P8BAP///v8EAPf/CQD3/wUA+f8EAP//AQD8/wUA+f8FAP3/AgD+/wEA//8CAP3/BgD7/wUAAgD//wIAAQD+/wAA//8AAAAAAQD+////AAAAAAEAAQD//wUA+/8KAPj/DAD4/wgA//8CAAMA/v8EAAAABAD9/wUA/P8CAP3/BgD3/wYA+v8EAAAA//8BAAIA+/8DAP3///8CAPr/AgD8/wAAAgD7/wUA+f8CAP//AAD//wMA+/8EAP7/AQAEAPv/BQD7/wcA+/8DAP3/BAAAAAMA/v8BAP//AwD8/wAA///+/wEA/v8CAPz/BAD4/wgA+f8GAPj/BQD6/wEA+v8BAPv////8/////f8CAPz/AQAAAAAAAQD8/wIA/v8CAP//AQAAAAMAAwD9/wcA+/8IAP7/AAAEAPn/AAAAAPz/AQD+//z/BQD8/wEA/f8BAP7/AwD6/wQA+v8CAP//AAD//wIAAQD9/wUA/f8DAAIA/v8IAPv/BQABAP//BQABAPz/CQD5/wgA/f8AAAUA/f8EAPv/AwD9/wIA/P8BAPv/AAD///z/AgD8/wUA+f8FAPr/BgD7/wgA+f8GAPv/BQD8/wAA///9/wEA/v8BAAAAAAABAP7/BgD6/wUA/f///wMA/P8HAPj/CAD4/wUA+v8DAPv/AAD+//7/AgD//////v8AAP//AQD//wIA/f8BAAMA/v8GAPv/AwAAAAEAAQD//wEAAgD/////AgD9/wQA+/8FAPz/AgD+//7/AQAAAAAA/f8EAPr/AgD+/wAAAQADAPn/BgD7/wYA/f8GAP7/BAAAAP//AgD8/wYA/P8HAPv/BgD//wYA/P8CAAIAAQD//wUA+/8GAPv/BAD8/wQA9v8LAPb/BwD4/wMA/v8CAP7/AgAAAAIA//8AAAQA+/8EAP7/AAADAP7/AQAAAAEA/P8DAPv/AQACAPz/BgD3/wUA/P8CAAIA/P8GAPz/AwD+/wIA//8AAP///f8DAPr/BAD5/wYA+P8JAPb/BwD8/wEAAgD+/wUA/f8FAPz/BgD7/wgA/f8AAAQA+v8GAPj/BQD+/wEA/P8GAPz/AgABAAAAAQADAP7/AgAAAAAAAwD5/wgA+v8EAAEA/f8EAP//AAABAAIA+/8CAPv/AQD///3/BAD8/wQA/P8EAP7/AQACAP//AAADAPr/BwD8/wQAAgD+/wMAAAAAAAIAAAD///7/AQD8/wUA/f8AAAIA/P8EAAEA/v8EAPr/BQD5/wgA+P8GAPv/AgD9/wEA/v8CAP7//f8CAP//AgD//wIAAAACAAEAAQACAAEAAAABAAEA/v8CAAAA/f8CAP7//f8CAAAA/f8EAPj/CAD4/wgA/P///wQA/v8CAAIA/v8DAAEAAAABAAEAAAAAAAAAAAAAAAEA//8AAAEA//8CAP3/AgAAAP7/AgD+////AAD+/wAA//////7//////////f8BAPz/AAD8/wEA/v/8/wAA/v8BAP//AAD9/wIA/f8FAPz/AwAAAAEAAgD+/wQA/f8GAPz/AwAAAP//AwAAAP3/BAD8/wMA//8CAP7/AgD+/wAA/f8DAP3/BgD4/wIA/P8DAP///v8BAPz/BgD4/wcA+P8IAPr/BQD+/wIAAQAAAAAAAgD+/wYA+v8HAPf/BAD9/wEAAAAAAP3/AgD9/wIAAAD//wAA/v8CAP7/AgAAAP7/BQD8/wQA/P8AAAQA+f8HAPj/BQD6/wYA+v8DAAEA//8CAP//AAAEAP7/AwAAAAAABAD/////AgD7/wIAAAD8/wMAAAD7/wgA9f8JAPz/AwD//wIA//8BAAEA/f8AAP7/AQD//wAA/v8AAP7/AQD///3/AAD9//7/AQD9/wEA///9/wMA/f8BAP3/BAD5/wYA+f8GAPr/AQD9/wEA/f8DAPj/BwD3/wkA9/8IAPn/BgAAAP//BgD6/wgA/P8FAP3/BAD9/wQA+/8FAP//AQAAAP//AQACAAEAAAADAPz/AwAAAAEAAgABAP7/AgD9/wMA//8AAAEA/v8DAP//AAD//wEA/P8EAPv/AwD//wAAAQABAAIA/v8DAP3/BgD8/wYA/P8DAP7/AgAAAP//AQD+/wAAAAD//wIA/v8CAPv/BgD7/wQA////////AQD+/wAAAwD7/wMAAAABAAEA//8GAPn/CQD5/wEAAQABAP3/BQD7/wUA//8EAPz/BQD7/wMA/P8DAP3///8AAPz/AAD+//3/BAD4/wUA/P8CAP///P8DAP3/AgD///////8EAPz/AwD8/wMA/v8BAP3/AQD9/wQA+/8EAP7//v8EAP7/AgD//wEAAgD8/wcA/f8FAP7/AQAAAAEAAAAAAAIA/f8DAPr/BgD3/wgA+/8BAAIAAAD//wMAAAAAAAYA+f8KAP3/BAABAP//AgADAP7/AwAAAAEA/v8GAPz/BwD5/wYA/v8DAAEA/v8BAP3/BAD5/wkA9P8HAPn/AwD///7/AAD9////AAD8/wQA+P8HAPf/CgD3/wYA/f8CAP//AQD//wEAAwD9/wMA///9/wQA/f8FAP3/AQAAAP3/BAD7/wIAAAD9/wMA//8AAAIA+v8EAPv/AgABAPn/BgD2/wcA9/8EAPz/AgD//wAA/v8CAP//AAABAPz/BAD//wEAAAACAAEAAgD+/wMA/v8DAP7/AAAAAAIA/P8DAP3/AAABAP7/AQD+/wMA/f8EAP3/BAD+/wQA+v8EAP3/BgD8/wMA+/8FAP3/AQD//wEAAAADAAAABAAAAP//BgD9/wUA/v8FAAAAAwAEAP3/BwD8/wkA/P8FAP//AAABAP7/AQD+/wEAAQD8/wMA/P8BAP7/AQD+/wAAAAD8/wMA+/8DAPv/AgD8/wEA//8BAP3/AAACAP7/AQD+/wAAAAD9/wMA+////////P8AAAMA+P8EAPn/BAD7/wEAAAD7/wQA+f8BAP3//v8AAP///f8EAPn/BgD7/wUA/f8BAAEA//8EAP3/AwD//wMAAAD//wcA+f8FAPv/AgD//wEAAAD//wYA+v8LAPv/BwD//wMAAgACAAIAAgAAAAIA+f8JAPn/BgD8/wMA/v8EAPz/BQD9/wEAAgAAAAQA/f8CAP3/AwAAAP7/AgD+/wAAAQD///////8AAP7/AgD8/wMA/P8FAPr/BgD8/wUA//8BAAEAAQD//wQA/f8EAPz/BgD8/wIA//8AAAIA///+/wIA/v8AAP7/AAABAAAA///+////AQD9/wIA/P8DAPv/AAD+//7/AgD7/wIA+/8BAP7/AAD+/wAA/v///wAA+/8DAPz/AAD+/wAA/f8CAPz/AAAAAP///v/9/wMA+/8EAPz/AgABAP3/BgD4/woA+f8HAP//AQACAAEAAQAEAP//AAABAAAAAgACAP3/BwD8/wcA+/8FAAAAAQAEAPz/BwD+/wMABAD//wYAAAAFAAAABgD+/wcAAgABAAYAAAACAAUA/v8FAP//BAD+/wEAAgAAAAIA//8CAP3/BgD3/wYA/P8DAP7///8CAP////8BAP3/AQAAAP//AQAAAP3/AgD9////AAD/////AwD7/wEA//////7/AgD9/wMA/P8AAAEA/f8BAPr/AwD5/wQA+f8CAPv/AwD9////AgD//wAAAwD9/wIAAgD//wEABAD+/wYA/v8DAAEAAwAAAAcA/f8EAP//AwACAAMA//8DAAEAAAAEAP7/BwD7/wgA+v8EAP7///8EAP3/AAAAAPv/AwD8////AQD+//z/AgD5/wcA+v8EAP///v8BAAIA/P8HAPn/BgD9/wMAAQADAAAA//8EAP7/AgACAP//BgD//wIAAwD8/woA+P8JAPz/BwD9/wUAAgAAAAQA//8AAAcA+v8JAPz/BgAAAAIAAgD9/wgA9/8KAPn/BwD+/wMA/v8FAP3/BwD9/wMABAD8/wcA/P8DAP//AQAAAAEA///+/wMA/v8CAP7/AgD7/wYA/P8EAP3/AgD//wMA/f8DAP7/AAACAPv/BAD8/wIAAgD8/wUA+v8HAPf/CAD5/wQA+v8DAPr/BQD7/wUA+/8HAPz/BAABAP3/BwD7/wQAAQD7/wkA9/8HAPn/AwD+/wEA///+/wEAAwD+/wAAAgD7/wYA+v8FAP3/AQD//wAA/v8CAP7/AwD//wIAAQABAAMA+v8IAPv/BAACAPn/BQD6/wUA/f8CAAAAAgD//wUA+v8FAP3/AQAEAPz/BQD6/wYA+/8CAP7///8AAP7/AgD+/wIA//8AAAAAAQACAP//BQD+/wQA/v8DAAIA//8DAP//AAACAP//BQD7/wIA//8AAAIA//8CAP//AQAAAP7/BQD5/wgA+/8CAP3/AwAAAP//AQD8/wUA+v8HAPr/AwD+/wAAAAD+/wQA+v8GAPz/AQADAPr/BgD8/wcA+/8BAAEA/v8CAP7//v8CAPv/BAD6/wQA+f8EAP7//v8AAPv/AwD9////AAABAP3/BQD8/wQA/P8BAAEA/P8EAPn/AwD9///////9/wIA/P8DAPv/BQD9/wMA/////wIA/f8AAAAA//8CAP7/AAD+////AgABAAQA/f8EAAEAAwABAP3/AwD9/wUA//8CAP7/AgD5/wcA+v8CAAEA/v8CAAAAAAABAAEA/f8DAP//AQD/////AwD9/wEAAAD+/wEAAgAAAAAAAAD//wEAAQD9/wMA/P8CAAAAAAD//////////wEA/f8CAP7/AwD8/wIAAQABAAQA/P8FAPr/CQD3/wkA9/8IAPv/BAD+/wMA/v8FAPr/BwD8/wIAAAACAPv/BwD8/wUA+/8CAPr/BQD6/wEA/P///////v8AAP///v8AAP3///////7/AAD8//7/AAD+/wAA/P8CAP///v8AAAAA+/8EAPf/BwD3/wQA+/8BAP////8AAP3/BQAAAAQAAAABAAIAAAAEAP//AwACAP3/BQD7/wUA/v8CAAAAAgD+/wQA/P8GAPv/BgD7/wUA/P8FAP////8CAPz/AwD7/wEA///9/wIA/f///wIA/P8EAP7/AwD6/wYA+P8GAPv/BAD7/wMA/f///wEA/v///wAA/v////3/AQD9//3/BAD6/wQA/v8CAP//BQD//wQAAgD//wYA/P8FAAAABAD//wIA//8DAP7/AQD//wMA//8DAP7/BAD+/wAAAwD8/wYA/P8BAAEA+/8JAPT/DAD3/wYA/P8AAP////8AAP7/AwD+//3/BQD2/wsA9f8GAPv/AgD9/wEAAQD4/wYA+f8CAP///v////7////9//3////+/wIA/f8CAP7/AQD+/wYA/P8IAPr/CAD//wEAAwD9/wcA/P8GAP7/BAABAAMAAAACAAMA//8EAAEA/v8DAAEABAAAAAIA/v8AAAMA/v8CAP3///8CAPz/BQD7/wEAAQD9/wEA/////wMA+/8DAPr/BgD7/wMA/f///wAA///+//7/AAD7/wIA///9/wIA+/8FAPz/AgD8/wIA/P8AAAIA/P8GAPj/BQD9/wMA/f8CAP//AgD9/wMA/f8FAP//AAACAP//BgD9/wkA/P8GAP7/BQD//wUA/v8GAAEAAgAEAP//AQACAP7/BAD+/wMA/f8EAP//AQADAAAAAwAAAAMA//8CAAIA/P8GAPn/CAD4/wcA+v8CAAEA/f8CAP7/AAD+/wEA/f////7/AAABAAAA/f8AAP7//////wEA/f8BAAAA/P8CAPv///////z/AgD9////AAD//wMA+/8DAP//AAAFAPv/AgACAP3/AgAAAP7//v8CAP3/AwD8////AgAAAP////8BAAEA//8BAP//AAAEAPz/AQD///v/BQD6/wQA///8/wMA/f8CAAAA//8CAP3/BgD7/wUA+/8GAPz/BgD8/wUAAwAAAAUA/v8DAP//BAD//wQA//8DAP7/BwD4/woA+f8HAPn/BgD4/wcA+P8GAPr/AQD///7/AwD9////AwD7/wEA/v///wIA///8/wMA/f8CAAAA/f8BAAAA/v////3/BAD4/wcA9f8FAPz///8BAPr/AwD5/wUA9/8CAP3//v8BAP///f8DAPn/AQD9///////+//7//v8BAPn/BQD7/wEA/////wAA////////AQD9/wQAAAD//wUA+v8IAPz/BAD+/wEAAgABAP3/BgD8/wMAAgD8/wQA+v8IAPj/CQD5/wQA/P8EAP3/AQACAP//AAABAP////8CAPz//v8EAPr/BwD+/wEA//8FAP3/BwD6/wkA+v8HAPz/AwACAP//AwD/////AwD9/wEAAQD8/wIA/f8BAP//AwD7/wMA+/8AAAIA+/8DAP3/AAAAAAEA/v///////f8CAPv/AwD+//////8AAP7/AgD///7/AAD/////AwD7/wMA/v/+/wEAAAD9/wQA+f/+/wEA/f/9/wEA+P8DAPv/AgD6/wMA+P8DAP3/AgD//wMA/f8BAAEA/v8CAAAA/P8EAPv/BAD+//7/BAD9/wMA/v///wAAAAD+////AwD6/wYA+v8CAAIA//8AAAIA/f8CAP7/AAABAP//AAAAAAEA/v8AAAIA//8FAPz/AQAFAPz/BQAAAP7/BwD5/wkA/f8DAAEAAgABAP3/AwD8/wMA/v///wMA+/8DAP3/AwD//wAAAQABAAAAAQD//wAAAgAAAAAAAQD//wIAAAADAPr/BgD7/wIAAAD9/wMA/P8DAP3/BAD8////AwD8/wQA/P///wAAAAD+//z/AQD4/wUA+//9/wUA9v8JAPf/CAD4/wYA/v8BAAIA/P8CAP7/AgD+/wEA//////7////+/wAA/P8FAPf/BwD4/wQA/f8AAP7/AgD//wEA/v/9/wMA/v8FAPr/AwD8/wEA/v8CAP3///8CAPr/BgD8/wQA/////wUA/v8GAPv/BAADAPv/CQD5/wYA//8AAAEAAAD//wAAAAAAAP7/BAD9////BgD5/wkA/P8CAAIAAQABAP////8AAAAAAAD//////v8CAP3/AgD+//7/AgD9/wQA/v8DAAQA//8CAP//AwAAAAMAAwD+/wYA/P8GAP3/BQD+//7/BgD7/wYA/f8DAP3/BAD/////BgD6/wYA/f8AAAAA/v8BAPz/AAD3/wQA+////////f8AAP//+/8EAP3/AAADAPz/BQD9/wQA/f8EAP7/AAADAP7/AQAAAP//AAADAPr/BAD4/wEA/v/9/wAA/v////7/AgD9/wIAAgD7/wgA+v8IAPz/AQADAP7/AQACAPz/AwD+/wYA+v8JAPj/BgD//wMAAAAEAAEAAwACAAUAAAACAAEA/v8HAPv/AwD+//3/AQD/////AwD6/wUA+v8FAP7/AQADAP//BQD//wEAAwAAAAMAAAADAAAAAgACAPz/BAD//wAAAgD9/wMA/v8CAAUA/P8GAP7/AQD//wIA//8BAAIA/P8EAP3/BQD8/wAAAQD8/wMA/P///wUA/P8EAP7/AwAAAAIA//8DAAIAAwD//wQA/v8EAP7/AwD9/wUA/v8GAP//AAADAAEAAQACAP//AwABAP//AAADAPz/AgD9/wIA/f8BAAAA/f8EAPv/BQD8/wQA/v8AAAUA+/8FAPz/AgD//wEAAgD9/wYA+v8FAP3/AgABAPz/AwD9/wAA///+/wIA/v/+/wEA/v8EAPz/AwD7/wIA//8AAAIAAQAAAP7/BAD9/wUA/v8CAAAAAgD//wMA/f8EAP7/BAABAP//AwD//wIAAwACAAIAAQAEAP7/BQD///7/BgD7/wUA/P8DAP//AgD9/wIAAAAAAAEA//8DAP//AAACAP//BQD8/wQAAgD+/wMA/v8BAP//AwD+//7/AAD+/wAA/v8AAAAA/v8CAP////8EAPv/BgD8/wUA+v8HAPz/AwAAAP//AwD//wAABAD8/wQA/P8HAPv/BwD+/wEAAwABAAMAAgACAAMAAwAAAAUA/v8HAP3/AwABAAQA//8FAP3/AwD9/wEA//8BAP7/AQAAAP///////wIA/P8BAP///v8DAP3/AQD9/wIA/f8CAPn/BQD6/wUA/P8BAP7///8CAPz/BQD6/wUA//8CAP//BQD7/wYA+v8DAPz/AQD+/////v///////v8BAP3/BAD7/wQA/P8FAPz/BQD8/wcA+/8IAPr/CAD8/wEAAwD//wUA/v8FAPv/BwD7/wUA//8FAPv/CwD2/w0A+f8GAAAA/v8FAPz/BQD6/wYA+v8HAPr/AwD5/wYA9/8FAPn/AgD9//7/BAD6/wUA/P8EAPz/BAD6/wYA+P8GAPr/AwABAP7/BAD9/wEAAAD9/wYA+f8GAPz/AgD//wIA/v8BAAAAAQD//wIA/v8AAAAA///9/wMA+f8FAPv/AgD+////AQD+/wMA+/8EAP3/BAD8/wIA/v8AAAIA+v8CAP3/AQD//////P8BAAEA/P8CAP3/AAAAAAAA/f8BAPz/AQD+/wAA/v/9/wAA/P8BAAAA/P8BAPz//v////7//v/+//r/AwD6/wUA/P8DAP7/AQD+/wAAAAD//wMA+/8FAPz/AgD+//7/BAD9/wIA//8AAAIA/v8BAPn/AgD8/wYA/P8FAP//AQAAAAAA/P8AAP7/9/8FAPX/AwD7/wMAAAAAAAMA+f8KAPv/BwD9/wEAAwD//wEA///+/wAA/v/+//7//f////v/AwD4/wcA+P8GAPv/AAAAAPv/BAD8/wUA+/8EAPv/BAD+/wMA/v8DAAAAAAAAAP///v8BAAAA/v8DAPv/BgD6/wQA+/8DAP3//P8BAPf/AwD5//3//f/7/wAA+f/+//7/+/8CAPr/AwD3/wcA+/8FAP3/AgD//wUA/P8KAPr/BgD9/wQA/v8CAP//AgD4/wcA+f8DAPf//f/9//3/+/8AAPv/AgAAAP////8BAP//BAD//wAA///9/wMA/f////7//v8AAAAA/v///////P8AAPj///////n/BgD3/wAAAgD6/wYA+/8GAP7/AgD//wIAAAAAAAEA/v8BAAIA///+/wQA/v8BAAQA//8FAP//AwABAAIABQD+/wEAAAD+/wAA/v8BAP7/AwD+/wIAAAAGAPz/CwD9/wgA/v///wUA+/8BAAAA+/8FAPn/AgACAPv/BQD8/wAAAwD5/wcA9P8JAPX/BgD5/wQA+v8FAPv/BAD5/woA9v8IAPn/BgD7/wYA/f/+/wUA/P8BAP7/AgD8/wYA+f8IAPn/BwD6/wQA///9/wgA+f8JAPv/AwABAP3/BgD7/wMA+v8JAPb/CwD5/wIAAwD5/wkA9/8HAP3/AwD8/wUA/P8IAPv/AQD///3/BQD+/wYA+v8IAPv/AwD+/wIABAD8/wUA//8EAAEAAAD//wQA//8GAAAAAQAAAAMABAABAAIAAQADAAQAAgAFAAIAAQADAP//AwACAP//AgACAP7/BgD9/wUA/P8EAP////8FAPn/BQD9/wIAAQD9/wYA+f8JAPv/AwD//wEAAAADAP3/BQD8/wUA/v//////AQD+/wMA/P8GAPv/BQD8/wAAAAD+////AAD+//z/AgD6/wUA+v///wIA/f8FAP3/AQAGAPz/BwAAAAEAAgADAP3/CAD7/wMAAQD8/wAABAD9/wMAAAD8/wcA//8CAAUA+P8HAPz/AwD//wAAAQABAAAABAAAAP7/AQD+/wEA/v/+/wIA/v8FAPv/BAD6/wMA+v8FAP3/AgAAAAIA/P8JAPv/AAAGAPn/CQD9/wYA//8AAAcA+v8JAP3/AQAFAPn/CQD6/wcA+v8DAPz/AAD+/wAA/P8CAPn/BgD6/wcA+v8FAP7/AwACAP//BQD//wMAAgD//wkA+/8GAAIA/v8JAPv/BAAEAP3/BQADAP7/BgD9/wQAAQAAAP7/AwAAAAEAAAD7/wMA+/8DAPz////+/////v/+/////f/8/////v///wEA/P8DAPz/AQD7/wQA+/8FAPn/CAD6/wcA/P8FAPf/CQD1/wYA/f/+////AAD8/wEA+v8AAPv/AQABAP7//f8CAPz/AwD9//v/BgD4/wIA///9//7/AAD//wEAAQADAPv/CAD3/w0A+v8GAAIAAAABAAEAAAD//wUA/f8CAAQA/f8FAPn/CQD3/wkA+/8CAP7/AwD///7/AgD//wIAAQD9/wIAAAAAAAEAAAD//wAA/v8DAAAAAQAEAP//BAAFAPz/CQD8/wUA/v8BAP7/BwD7/wgA/P8CAAIA/P8CAAEA/P8EAAEAAAAEAP3/AQAAAP///f8FAPn/BAD8/wIAAQD8/wgA/P8EAP//AAACAAEA/P8EAPr/BAD8/wAA/////wEA///+/wAAAQD8/wUA+v8HAPz//v/9//r/AQD5/wIA+f8DAPn/AQACAP3/AwD7/wQA+f8JAPf/BwD5/wMAAgD//////v//////AgD7/wMA+v8EAP///v8IAPj/CAAAAAIAAwD+/wMA+v8AAAAA/v8EAP3//v8FAPv/BwD8/wQAAgADAAMA//8GAP3/BgD//wQAAgAAAAQAAgAFAP3/BAD+/wEAAgAAAPv/BAD6/wQA/v8CAAAABAD4/wsA9v8HAPv///8AAPz/AwD9/wQA+/8DAPn/AgD8/wMA/P///wAA/P8DAP///v8AAP//AQD+////AAABAP7/AwD+//7/AgACAP7/AQD7/wQA//8CAP//AgD/////BQD+/wIA//8BAP7/AwD4/wUA9v8HAPf/AwD9/wEA/P8CAP////8EAPb/AgADAPr/CwD0/woA//8FAAEAAAAFAAAABAD7/wEAAAD3/wQA9/8CAPn/AAD8/wMA+///////AQD+/wIA/f/8/wQA+f8HAPr/AwADAPn/CgD3/wsA9v8KAPv/BQAAAAEAAAAAAAQA+v8LAPX/CgD7/wIABwD7/wgA+P8LAPv/BAD9/wEA/P8EAPn/AwD9//////8CAPz/BQD8/wIAAgADAP3/BQD8/wMA+/8CAPz/AwD7/wMA/v8AAAMA+/8IAPn/BQD9/wAAAwD9/wUA9v8MAPT/CQD5/wAAAQD7/wEAAQD9/wQA+/8AAP///v///wEA/f///wAA//8EAAIA/v8DAP//AAACAP3/AgABAPn/AgD///7/AgD6/wQA/v/7/wYA9f8KAPj/AgACAP7/AQD//wAABAD8/wcA9/8JAP3/AwAFAP7/CgD6/wcA/f8AAAUA/f8FAAIA/f8MAPr/CQD6/wMABAAAAP//BAD6/wkA9f8IAPf/BAD4/wAAAQD8/wEA+/8CAP3/BAD8/wUA/////wEAAAACAPr/BgD4/wMAAgD5/wcA+/8AAAIA+f8IAPj/BwD8//7/BAD6/wMAAQD8/wYA/v/9/wUA/f8FAPj/BgD3/wcA+v8BAPn/BAD7/wMA///7/wQA/f8FAP7/AwD+/wQA//8DAP3/BgACAP7/BgD6/wUA+/8BAAEA/v8BAAQA/P8DAP3/BAD8/wYA+P8EAPz/AQACAPf/BwD7/wMAAAD+/wMA/f8EAP7/AAD+//3//v8EAPv/AQADAP3/BwD8/wQAAAACAAEAAgD+/wQA/f8DAP7/BAABAAIAAAAAAP//AQAAAAEA+/8EAPf/DAD2/wUAAAD8/wUA//8AAAQA+f8BAP7/AQD8/wIA/f8BAPz/AAD6/wIA+/////r/AwD7/wIAAAD/////AgD//wMAAQAAAAAAAwD8/wQAAgD7/wkA9/8CAAAA/P8BAPz//v8BAAAAAQABAPv/BwD8/wQA/v/9/wQA/P8DAP3/AwD7/wYA+v8FAP3/AgAAAAEAAgACAAAABAD+/wMA//8CAPz/BQD7/wUAAAACAP//AAAAAAEAAAD9//7/AgD8/wMA+v8AAPz//P8BAPr/AgD8/wAA/f8AAAAA/v/+/wIA//////7/AAD//wIA+v8GAPv/BQD8/wIA/f8BAP3/AwAAAAAA/v8BAAAA///+//7/AgD///3//v////z/BAD5/wIA+/8AAP3///8BAPz/BAD7/wMA/P8FAPr/BgD///7/BAD8/wMA/v/7/wYA+/8HAPr///8EAPv/CAD9/wEAAgD9/wMAAQAAAAUA/v8JAP3/BgD+/wIABAD9/wAAAgD7/wYA/P8CAP//AgADAP//AQD//wQA//8BAAEA+/8FAPn/BAD7/////v/+//7/AAAAAP7/AgD9/wMA//8AAP3/CAD0/woA9v8EAPz//f/9/////P/+//7/+f8BAPj////6//3/+//6/wEA+v8AAP3//v////3/AAABAP3/BAD5/wUA/P8BAAMA/v8FAP3/AQABAP//BgD9/wEAAgD+/wcA/P8KAP7/AwAEAP//BgD8/wQA/f8BAP//AgAAAP//AgD//wcA/f8JAPj/BQABAP7/BgD4/wYA/f8AAP3/BAD7/wYA+/8EAPv/BAD5/wYA+//9/wMA+v8GAPz/BQD9/wMAAQD8/wQAAAAAAAUAAAABAAAABAD8/wsA9/8JAPr/AQAEAP7///8DAPr/AwAAAPr/CQD6/wUA+/8CAAAAAAD//wAA//8DAAIA+f8JAPz//v8EAPj/AwD///v/AwD9////AAAAAAIA/f///wAAAAAAAP////////z////9//3/AAD7/////v8BAP///P/8/wIA+v8GAPj/BwD7/wQAAQD//wMAAQD//wQA/f8DAAMAAQD//wUA/f8AAAYA/P8MAPP/DAD7/wMABwD+/wgA+/8GAP7/BAAAAP7/AQD+/wMA//////v/BAD6/wQA/f8AAAAA//8DAPv/BgD5/wYA/f8GAP3/BAD//wUA//8CAAEAAgD+/wQA/f8FAP3/AgD//wMAAwABAAAABAD9/wUAAQD7/wMA/f8EAP7/AAAEAPr/CQD6/wQAAAD+/wQA9/8MAPb/CQD7/wEAAgD//wIAAgD//wUA/v8DAPj/CAD7/wUA///+/wIA/f8EAPz/AAABAPz/AgD/////AQD9//3/AQD+/wAA///+/wAA/f///wAA/v8CAPz/BAD9/wIAAQD9/wcA+P8IAPv/AwABAP//BAD//wAAAwD8/wUA/f8AAAIA/f8CAP//AQD8/wUA+P8HAPv/BAD9/wEAAgAAAAIA///+/wEAAQADAP7/AQD///7/BQD6/wYA+/8EAAEAAAAFAP3/BAACAP//AgADAAAABAABAAQAAAADAAAABAABAAEAAAADAPz/AgD+////AwD8/wIA/v8AAP3/AgD8/wMA/f///wEA/P8BAP///P8CAP3///8BAP7/AAD9/wYA+f8GAPr/AgD/////AAD///r/AwD5/wUA+/8CAPr/AQD+//7/AQD///3/AQD7/wAA/P8BAP7/AAD9/wEA/v8CAP7/BAD7/wUA/f8BAAYA+P8IAPz/AgAEAPv/CgD4/wMA/v///wQA+/8EAP//AwAAAAMAAQADAAIAAAAFAP//BQD9/wUA/v/+/wIAAQD//wMA/f8BAAIAAAADAPz/AwAAAAEABAD9/wEAAAD+/wMA/////wAAAQD+/wIA/f8AAAEA/f8BAP///v8EAP3/AAAAAAIA//8FAP3/AwD+/wQA/P8IAPj/CAD6/wYA/P8DAP7/AgD+/wEA//8BAP3/AQD9/wQA/P8CAP//+/8GAPj/BwD4/wUA+////wEA+/8DAPz/AQD9/////////wAA//////7////9/wAAAgD4/wYA+P8EAP3////+/wAA//////7/AAD9/wMA/f8DAP7/AAADAP7/AgACAP7/BgD9/wUA//8DAAIAAAACAP7/AgACAAAAAQADAP//BAD+/wMAAgD//wUA/P8GAAAAAgADAAIAAgAEAAMAAQAFAP//BwABAAQAAwACAAEABgD9/wYA/f8FAP//AQACAP7/BQD7/wYA/P8FAPj/BQD8/wYA+/8AAAIA//8AAP///v8DAPz/AwD9/wIA///9/wMA+f8FAP3///8DAPr/AwD+////AAD+/wEAAAD8/wMA/P8DAPz//P8DAPf/BwD3/wUA+P8EAP3/AAABAP//AgD//wIA/f8GAPv/BwD7/woA+v8HAP3/BQD//wUAAAAEAP//AQAEAP7/CAD6/wgA/f8DAAAAAgAEAP7/AwD//wIA+/8GAPv/BgD8//3/AQD8/wQA+v8DAPr/AQD///z/BAD8/wEAAgD7/wUAAAD8/wUA+/8EAAEAAAAAAAUA/f8GAPr/CAD5/wgA/v8CAAQA//8EAP3/BQAAAAEAAwACAP7/CAD+/wIAAwABAP//BQAAAP//CAD7/wgA/v8EAPz/CAD2/w0A9v8JAPz/BAAAAAIA//8HAPv/CAD9/wQA//8FAPn/BwD7/wQA//8AAPz/BQD8/wIAAAD//wEA/f8EAP3/BAD9/wEAAgD//wIA///9/wQA/P8FAPr/BAD9/wMA/////wMA+/8EAPz/AQD+/////v8CAP7/AAACAAAAAwD//wMA/f8GAPz/BQD+//7/BgD6/wQA/P8AAAEA/v8DAPn/CAD6/wUA/v8CAP7///8BAP//AwD8/wUA+P8HAPn/BQD//wIA//8EAP7/AwD+/wMA//8CAAAA/f8CAPz/BAD+/wEAAAABAAIAAAACAPz/AwD+/wUA/P8GAPf/CwD2/wcA+f8CAP7/AQAAAP//AQD+/wMA/P8HAPr/CQD7/wUAAAABAAIAAgD+/wQA//8BAAAAAgAAAP//AQD9/wUA/P8BAAMA/f8FAPr/BQD9/wMA/v8BAP//AQD//wMA/P8DAPz/AwD+/wIA/////wEA/v8CAPv/BwD4/wcA+/8DAP////8CAP//BAD8/wQA+/8FAPv/AgD+/wAA/f8AAAAA/v/+/wAAAAD+/wEA+////wMA+P8KAPf/BQD9/wMA//8BAP//AAD9/wQA9/8IAPn/AQD///z/AAABAP3/AgAAAP//AQACAPz/BAD9////AgD+/wEA//8AAP///v8CAAEAAgAEAPv/CAD9/wUA+/8FAPz/BwD7/wYA+v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8GAP3/BQD//wIAAAADAP3/AwD+/wMA//8AAP//AgD+/wEA//8AAAAA/v8AAP7/AgD6/wIA+f8FAPn/AwD7/wIA//////7/AwD5/wYA+f8DAP7//f8CAPv/AwD5/wIA//8CAP3/AgD9/wQA/f8FAP7/AwAAAAAAAQADAPv/BAD7/wIA/f8BAP3/AAACAPn/BwD4/wgA+/8FAP3//v8FAPv/BQD8/wMA/////wMA+v8KAPr/BwD7/wUA/P8CAPz/BAD7/wQA/f/9/wQA/f8AAAAAAAD9/wEA/v/9/wQA+//+/wAA+f8CAPz//v/+//7///////7/AgD9/wQA+/8AAAIA+f8IAPj/BAD8/wEAAQAAAAEAAQAAAAMA/v8EAP7/AgD+/wAABAD9/wMA/v8BAAEAAQAAAP7/AgD6/wcA+/8EAPz///8EAP3/AgD9/wEAAAABAP//AwD//wEAAQACAP7/BAD+/wMA/f8EAPz/AAABAPr/AwD9/////f8AAP7/AAD+/wAA+/8GAPv/AQAAAP3/AwAAAP7/BAD7/wMA+v8HAPb/CQD5/wIAAAD9/wMA/////wAAAAD+/wMA/P8EAP//AgAAAAEAAQD///7/AQD+/wIA/P8FAPn/BQD5/wQA+v8DAPz/AwD7/wMA/P/9/wEA/P///////f/+////AAD8/wAA/f////3/AAD6/wQA9/8EAPj/AAD+//3/AwD7/wQA/P8FAPf/CAD3/wkA/P///wIA/f8FAP3/AQAAAP//AgD//wIA/P8GAPv/AwABAP3/AQAAAP///v8AAAEA/v8EAPz//v8EAPr/BgD6/wIA/f///wAA/f8CAPv/AwD9/wAA//8BAAAA//8CAP3/AQAAAAEA//8BAP//AAAAAP///f8DAPv/BQD+//3////9/wIA/v/9/wMA+/8DAPv/AAD+/wIA/P8CAP7/AAAAAP7/AwD9/wMA////////AwD7/wcA+/8DAP7/AAAAAP//AAD7/wMA+v8EAP7/AAACAP3//f8EAPz/AgD//////v8BAPr/AwD6/wQA+/8DAPv///////7/AQD7/wQA+P8HAPf/AQD+////AwD7/wQA/P8FAP7/BAD9/wYAAAD+/wQA/f8EAAAA/v8EAAAA/v8GAPf/BgD///z/BQD8/wIAAgD+/wAABAAAAAAAAwD9/wQA/v8BAAIA/f8BAP//AQD+/wIA+/8IAPr/BAAAAP7/BQD+/wEAAQABAPz/BQD7/wYA+/8FAPz/BAD9/wMAAQD+/wUA/P8CAAAA/v8EAP3/AgD+/wAA/v8DAPv/BQD4/wcA+/8CAP3/AgD9/wYA9/8GAPr///8DAPn/AwD+//3/BwD6/wcA+/8FAPz/BAD8/wQAAQAAAAEAAAADAP//AwD9/wQA//8EAP3/AwABAP3/BAD+/wAAAgD//wQA/v8FAPz/CAD6/wkA/P8IAPz/CgD3/woA/P8DAAMA//8EAAAAAAD//wMAAAAAAAQA//8CAAAAAgAAAAEAAgD//wEA///+/wQA+v8HAPn/BAD8/wIA/P8DAP3/AQAAAAAAAAABAP7///8AAP////8AAP////8DAPv/BAD+/wEAAQAAAAEAAgACAAAABgD9/wgA/f8FAP//BgD//wQA/v8FAP//BgABAAEABAACAAMAAQAAAAQAAAAEAP//AwABAAEABQD+/wQAAAD+/wYA/v8BAAMA/f8DAP3/AwD+/wIA/P8FAPv/CAD2/wgA9v8KAPz/AgAAAAEA/v8EAPr/BAD6/wMA/P8CAPz/AQD+/wMA/v8FAP7/AwADAP//CQD6/wQA//8BAAMAAQD+/wQAAAAGAP7/BQD+/wQAAgD9/wYA/f8HAPz/BAABAAAABQD+/wYAAQAFAP//BAAAAAEAAAD+/wMA+v8EAPv/BQD6/wIA//8AAAAA//8BAPv/CAD5/wUA/f///wMA+/8DAPn/AwD9/wEAAgD5/wUA/f8DAP7/AQAAAAAAAQACAAEABQAAAAUA//8HAAIAAgAFAP3/CAABAAQAAwACAAUAAQACAAEABAD//wgA/f8CAAUA/v8FAAAA//8GAP7/AgD//wIAAAABAAIA/f8GAPv/AQD//wIA/f8BAP3/AQAEAPv/BAD7/wMA//8CAAAAAwD+/wMA/f8BAAEA//8CAP3/AgD///7/AgD8/wEA/f8BAP///P8DAPv/AgAAAP7/AwD+/wEA/v///wEA/v8BAAAA/v8CAAAAAAABAAIA/v8DAP3/BgD6/wcA/f8BAAIAAQACAAMAAAACAAMAAQADAP3/BAD6/wgA+/8BAP3//v8AAAEA+v8CAPz///8AAP3///8BAPz/AQD9////AgD9/wMA/v8BAAMA/f8DAAAAAQABAP//AwD+/wgA/P/+//v//f8AAP7/AAAAAP7/AwD7/wUA+/8BAPv////+//7//P8CAP//AAAGAPr/BQD//wAABQD9/wUA//8AAAIA//8CAAIA/P8DAP7/AwD9/wMA//8DAAEAAAABAAUA/P8JAPr/BwD9/wUA//8CAAAAAQABAAMAAgABAAMA/v8FAP3/AgAAAP//AwD+/wEAAAD+/wMA/f///wEA/P8BAPr/AAD7/wAA+/8CAP3//v8BAPz/AQD///7/AAD/////AgD+/wMA/v8CAAAAAQAEAP///v8EAP3/AgD+/wAAAAD+/wIA/v///////P8DAPn/BQD5/wQA/v8BAAQA+/8FAP3/AwD+/wEA//8BAAEA+v8HAPf/BgD+//7/AQD6/wMA+//8//7/+v8CAPj/AgD6/wIA/f8AAAEA/f8GAPz/AgACAP3/BAD//wEAAQAAAP//AwD8/wUA/////wQA/f8GAP//BQD//wQAAgABAAIAAQAAAAIA/P8EAP//AwACAAEAAwADAAMABQADAAQAAQACAP7/BAD6/wQA/v///wAA////////AgD//wIA/v8BAAAA/v8AAP7/AAD+/wMA+f8GAPr/AQD9//7/AgD8/wQA/P8DAP7/AwD7/wMA///8/wQA+P8FAPr/BAD8/wAA//8AAAMA+/8EAPv/BQD8/wQA/P8FAPv/BwD6/wUA+f8LAPf/CgD4/wYA/f8BAP7/AgD+/wQA/P8BAAMA/P8HAPz/AAABAP7/AwD//wQA/f8DAAAA//8CAAIAAAD//wUA/P8HAP3/AQACAP7/BQD8/wQA/f8CAAIAAAACAAIAAAACAAMAAQADAAIA/v8EAP3/AQD//wAAAQD9/wQA/P8FAPz/BQD7/wYA+/8FAPr/BAD8/wMA/////wMA/v8BAAMA+/8FAPz/AgABAP//AwD8/wUA+P8IAPj/BgD/////AgAAAAMAAAACAP//AQAAAAAA//8BAPz/AQD+/////f//////AQACAP//AQABAAIA//8FAP7/BAAAAP7/BwD6/wUA/v///wIA//8DAP3/AwD+/wAABgD5/wgA9/8DAP7/AAD//wEA+v8EAPz/BQD8///////9/wEA/f/9/wIA+v8EAPr/AQD8/wAA/f8CAPz/AwAAAAAAAAACAP7/AgABAP3/BgD7/wcA+/8CAAEAAAADAP////8FAPn/CgD4/wgA+P8HAPn/AwABAPv/BwD6/wIAAAD//wIA//8BAP7/AgD+/wEAAQD//wMA/f8FAP//AwACAAAAAwACAAAABQD+/wYA/P8GAP7/AwACAP7/BAD8/wMA/f8DAP3/AQD7/wEA/f8DAPz///8BAPv/BQD6/wMA/f/8/wEA/v///wEA/f8AAAAA/f///wIA/v8DAP7/AgAAAAMA/P8GAPf/BgD5/wUA+v8CAPz/AAD+//3//f/9//r////9/wEA+v8CAPz/AgD8/wAAAAD+////AQD9////AAD//wAAAAABAP7/AwD9/wMAAAAAAAEA//8AAAIA/v8AAAEA/v8DAP//AQAAAAIA//8EAPv/CAD4/wUA/v///wAA/v8BAP7/BAD8/wUA/f8BAP//AgD//wEAAAD//wMAAAAFAP7/BgD+/wMABAD+/wUA/P8EAP7/AwACAP7/BQD8/wMAAAD+/wUA+f8HAPz/BgD+/wIA/v///wEA+/8AAP///v///wIA+v8FAPz/AgACAP3/BAD+/wQA/f8DAP3/AQD8/wMA+v8DAP3//f8DAPj/BgD7/////v8AAP3/AQD5//3////8/wAA/P8BAPv/AwD7/wEA/v8AAAEA/P8DAPr/BAD8/wEA//8CAPz/BAD8/wAAAwD9/wIA/f8DAAAA//8DAP3/BAADAP7/BAD9/wIA/v///wEA/v8DAPv/AwD9/wMA//8AAAIAAAAEAP7/BAACAAAAAwABAAIAAQABAAIAAwABAP//AgAAAP//AQAAAP3/AQD+/wEAAgD+/wIAAAD+/wAAAAD//////v/+//////8AAAAA///+//3/AwD9/wMA/f8CAP//AgD9/wYA+/8FAPv/BwD6/wQA/v8BAAIAAgAAAAAA//8BAAAA/P8CAPz/AwD8/wEA//8AAP////8AAAAA//////3////9//7//f/9//7////8/wEA///9/wYA+P8IAPj/AwAAAP7/BAD8/wQAAQADAP//AwD//wUAAAD//wMA+v8CAPv/AQD+//7/AQD9/wIA/v8AAP///f8EAPz/AQD9//7/AgAAAP//AQABAP7/BAD+/wQA/f8HAP7/AgADAP//BQD9/wcA+v8HAP7/AgABAAAAAQABAAIA/P8DAP3/AQD8/wMA+P8EAPr/AgD8/wIA/v8AAAAA//8AAAIA//8DAP///v8GAPj/AgAAAPv/BAD6/wQA/f8EAPz/AgADAP7/AwD6/wMA//8AAAYA9v8KAPX/BwD7/wAA/f////z/BAD7/wMA/f//////AAAAAAEA/v8BAP//BAD8/wkA9/8LAPb/CgD4/wcA//8AAAIA/P8CAAAAAAABAP3/AwD8/wIA/v///wIA/P8DAP7/////////AQD+/wUA+P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type=\"audio/x-wav\" />\n", + " Your browser does not support the audio element.\n", + " </audio>\n", + " " + ], + "text/plain": [ + "<IPython.lib.display.Audio object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "# Display Results\n", + "current_subject = None\n", + "print(\"-\"*60)\n", + "print(\"g_500000 Inference Results\")\n", + "print(\"-\"*60)\n", + "OUTPUT_DIR = os.path.join(PROJECT_DIR, \"output/g_500000\")\n", + "\n", + "\n", + "for pair in file_pairs:\n", + " if pair['subject'] != current_subject:\n", + " current_subject = pair['subject']\n", + " print(\"\\n\" + \"#\"*80)\n", + " print(f\" SUBJECT: {current_subject}\")\n", + " print(\"#\"*80 + \"\\n\")\n", + "\n", + " input_base = os.path.basename(pair['pt']).replace(\"_preprocessed.pt\", \"\")\n", + " # Based on inference_unit2a.py logic, if checkpoint has no numbers, suffix is 'unknown_step'\n", + " output_filename = f\"{input_base}_500000step.wav\"\n", + " output_path = os.path.join(OUTPUT_DIR, output_filename)\n", + " \n", + " print(\"=\"*60)\n", + " print(f\"File ID: {input_base}\")\n", + " \n", + " # 1. Original Audio\n", + " print(f\"Original Audio: {os.path.basename(pair['wav'])}\")\n", + " ipd.display(ipd.Audio(pair['wav'], rate=16000))\n", + " \n", + " # 2. Synthesized Audio\n", + " if os.path.exists(output_path):\n", + " print(f\"Synthesized Audio: {output_filename}\")\n", + " ipd.display(ipd.Audio(output_path, rate=16000))\n", + " else:\n", + " print(f\"generated file not found at: {output_path}\")\n", + " print(\"\\n\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gyucheol", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/sample_utut.ipynb b/notebooks/sample_utut.ipynb new file mode 100644 index 0000000..4306543 --- /dev/null +++ b/notebooks/sample_utut.ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a0b0c0d0", + "metadata": {}, + "source": [ + "# Unit-to-Unit Translation Inference (EN → KO)\n", + "\n", + "**Pipeline**: EN unit (text) → UTUT Translation → KO unit (predicted) → CodeHiFiGAN Vocoder → Waveform\n", + "\n", + "**Ground Truth**: KO unit (text) & KO WAV for comparison\n", + "\n", + "**Data**: `aihub_a2a_unit` (en/ko unit text) + `aihub_a2a_wav` (ko wav)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a1b1c1d1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/2022113135/.conda/envs/gyucheol/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import json\n", + "import subprocess\n", + "import numpy as np\n", + "import torch\n", + "import soundfile as sf\n", + "import IPython.display as ipd\n", + "from collections import defaultdict\n", + "\n", + "# Project paths\n", + "ROOT_DIR = \"/home/2022113135\"\n", + "PROJECT_DIR = os.path.join(ROOT_DIR, \"gyucheol/NetfLips/av2av-main\")\n", + "JJS_DIR = os.path.join(ROOT_DIR, \"jjs/av2av\")\n", + "\n", + "INFERENCE_SCRIPT = os.path.join(PROJECT_DIR, \"inference_unit2a.py\")\n", + "\n", + "sys.path.insert(0, PROJECT_DIR)\n", + "sys.path.insert(0, JJS_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a2b2c2d2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Device: cuda\n", + "UTUT checkpoint: /home/2022113135/jjs/av2av/unit2unit/utut_finetune/utut_additional_ckpt/unit_mbart_multilingual_ft/en_ko/checkpoint_best.pt\n", + "Vocoder checkpoint: /home/2022113135/gyucheol/NetfLips/av2av-main/unit2av/checkpoint/zeroth-hubert/g_00500000\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Configuration\n", + "# ============================================================\n", + "\n", + "# --- Data Paths ---\n", + "EN_WAV_DIR = os.path.join(ROOT_DIR, \"datasets/aihub_a2a_wav/test/en\") # SOURCE\n", + "EN_UNIT_DIR = os.path.join(ROOT_DIR, \"datasets/aihub_a2a_unit/test/en\")\n", + "KO_UNIT_DIR = os.path.join(ROOT_DIR, \"datasets/aihub_a2a_unit/test/ko\") # GT\n", + "KO_WAV_DIR = os.path.join(ROOT_DIR, \"datasets/aihub_a2a_wav/test/ko\") # GT\n", + "\n", + "# --- UTUT (Unit-to-Unit Translation) ---\n", + "UTUT_CHECKPOINT = os.path.join(\n", + " JJS_DIR, \"unit2unit/utut_finetune/utut_additional_ckpt/unit_mbart_multilingual_ft/en_ko/checkpoint_best.pt\"\n", + ")\n", + "SRC_LANG = \"en\"\n", + "TGT_LANG = \"ko\"\n", + "\n", + "# --- Vocoder (CodeHiFiGAN) ---\n", + "VOCODER_CHECKPOINT = os.path.join(PROJECT_DIR, \"unit2av/checkpoint/zeroth-hubert/g_00500000\")\n", + "VOCODER_CONFIG = os.path.join(PROJECT_DIR, \"unit2av/checkpoint/zeroth-hubert/config.json\")\n", + "\n", + "# --- Speaker Encoder ---\n", + "SPEAKER_ENCODER_PATH = os.path.join(PROJECT_DIR, \"unit2av/encoder.pt\")\n", + "\n", + "# --- Output ---\n", + "OUTPUT_DIR = os.path.join(PROJECT_DIR, \"output/unit2unit_inference\")\n", + "os.makedirs(OUTPUT_DIR, exist_ok=True)\n", + "\n", + "# --- Inference settings ---\n", + "MAX_SAMPLES_PER_SUBJECT = 1 # max samples to display per subject\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + "print(f\"Device: {DEVICE}\")\n", + "print(f\"UTUT checkpoint: {UTUT_CHECKPOINT}\")\n", + "print(f\"Vocoder checkpoint: {VOCODER_CHECKPOINT}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a3b3c3d3", + "metadata": {}, + "source": [ + "## 1. Find All Subjects & Build File Triplets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a4b4c4d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total triplets found: 9412\n", + "Total subjects: 32\n", + "\n", + "Subjects (count):\n", + " et_c_005: 27 files\n", + " et_c_010: 4 files\n", + " et_c_012: 58 files\n", + " et_c_014: 106 files\n", + " et_k_003: 3173 files\n", + " et_k_004: 408 files\n", + " et_m_008: 328 files\n", + " et_m_010: 714 files\n", + " iv_K_018: 11 files\n", + " iv_k_018: 1939 files\n", + " iv_k_019: 381 files\n", + " iv_m_001: 712 files\n", + " md_c_001: 30 files\n", + " md_c_002: 29 files\n", + " md_c_007: 46 files\n", + " md_c_012: 24 files\n", + " md_c_013: 22 files\n", + " md_c_016: 30 files\n", + " md_c_018: 44 files\n", + " md_k_018: 52 files\n", + " md_p_001: 343 files\n", + " md_s_003: 46 files\n", + " md_s_005: 49 files\n", + " md_s_006: 48 files\n", + " md_s_007: 35 files\n", + " md_s_008: 32 files\n", + " md_s_010: 80 files\n", + " md_s_011: 40 files\n", + " md_s_014: 30 files\n", + " md_s_016: 42 files\n", + " md_t_001: 251 files\n", + " md_t_002: 278 files\n" + ] + } + ], + "source": [ + "# Scan EN unit dir and find matching KO unit + KO wav triplets\n", + "en_files = sorted([f for f in os.listdir(EN_UNIT_DIR) if f.endswith('.txt')])\n", + "\n", + "triplets = [] # list of dicts\n", + "subject_map = defaultdict(list) # subject -> list of triplet indices\n", + "\n", + "for fname in en_files:\n", + " base = fname[:-4] # strip .txt\n", + " en_wav_path = os.path.join(EN_WAV_DIR, base + \"_en.wav\")\n", + " en_unit_path = os.path.join(EN_UNIT_DIR, fname)\n", + " ko_unit_path = os.path.join(KO_UNIT_DIR, fname) # same filename\n", + " ko_wav_path = os.path.join(KO_WAV_DIR, base + \".wav\") # .txt -> .wav\n", + "\n", + " if not os.path.exists(ko_unit_path):\n", + " continue\n", + " if not os.path.exists(ko_wav_path):\n", + " continue\n", + "\n", + " # Extract subject: e.g. 'et_c_005' from 'et_c_005_002_009_0026'\n", + " parts = base.split('_')\n", + " subject = '_'.join(parts[:3]) # et_c_005, et_k_003, ...\n", + "\n", + " triplets.append({\n", + " \"id\": base,\n", + " \"subject\": subject,\n", + " \"en_wav_path\": en_wav_path,\n", + " \"en_unit_path\": en_unit_path,\n", + " \"ko_unit_path\": ko_unit_path,\n", + " \"ko_wav_path\": ko_wav_path,\n", + " })\n", + " subject_map[subject].append(len(triplets) - 1)\n", + "\n", + "subjects = sorted(subject_map.keys())\n", + "\n", + "print(f\"Total triplets found: {len(triplets)}\")\n", + "print(f\"Total subjects: {len(subjects)}\")\n", + "print(f\"\\nSubjects (count):\")\n", + "for s in subjects:\n", + " print(f\" {s}: {len(subject_map[s])} files\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a5b5c5d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected 32 samples for inference (1 per subject)\n" + ] + } + ], + "source": [ + "# Select a subset: up to MAX_SAMPLES_PER_SUBJECT per subject\n", + "selected_triplets = []\n", + "for s in subjects:\n", + " indices = subject_map[s][:MAX_SAMPLES_PER_SUBJECT]\n", + " for idx in indices:\n", + " selected_triplets.append(triplets[idx])\n", + "\n", + "print(f\"Selected {len(selected_triplets)} samples for inference ({MAX_SAMPLES_PER_SUBJECT} per subject)\")" + ] + }, + { + "cell_type": "markdown", + "id": "a6b6c6d6", + "metadata": {}, + "source": [ + "## 2. Load Models\n", + "\n", + "- **UTUT**: loaded in-process (fairseq)\n", + "- **Speaker Encoder**: loaded in-process (to extract speaker embedding → `.pt`)\n", + "- **Vocoder**: called via `inference_unit2a.py` subprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a7b7c7d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading UTUT translation model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-04 06:51:25 | INFO | fairseq.tasks.translation | [en] dictionary: 1004 types\n", + "2026-02-04 06:51:25 | INFO | fairseq.tasks.translation | [ko] dictionary: 1004 types\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UTUT model loaded.\n" + ] + } + ], + "source": [ + "# --- 2-1. Load UTUT Translation Model ---\n", + "from unit2unit.inference import load_model as load_utut_model\n", + "from util import process_units\n", + "from fairseq import utils\n", + "from fairseq_cli.generate import get_symbols_to_strip_from_output\n", + "\n", + "print(\"Loading UTUT translation model...\")\n", + "utut_task, utut_generator = load_utut_model(\n", + " UTUT_CHECKPOINT, SRC_LANG, TGT_LANG, use_cuda=(DEVICE == \"cuda\")\n", + ")\n", + "print(\"UTUT model loaded.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a8b8c8d8", + "metadata": {}, + "outputs": [], + "source": [ + "# # --- 2-2. Load CodeHiFiGAN Vocoder ---\n", + "# from unit2av.model import CodeHiFiGANModel_spk\n", + "# from unit2av.utils import AttrDict\n", + "\n", + "# print(f\"Loading vocoder config from {VOCODER_CONFIG}...\")\n", + "# with open(VOCODER_CONFIG) as f:\n", + "# h = AttrDict(json.loads(f.read()))\n", + "\n", + "# print(\"Initializing CodeHiFiGAN vocoder...\")\n", + "# vocoder = CodeHiFiGANModel_spk(dict(h)).to(DEVICE)\n", + "\n", + "# state_dict = torch.load(VOCODER_CHECKPOINT, map_location=DEVICE)\n", + "# if 'generator' in state_dict:\n", + "# vocoder.load_state_dict(state_dict['generator'])\n", + "# else:\n", + "# vocoder.load_state_dict(state_dict)\n", + "\n", + "# vocoder.eval()\n", + "# vocoder.remove_weight_norm()\n", + "# print(\"Vocoder loaded.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a9b9c9d9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/2022113135/gyucheol/NetfLips/av2av-main/unit2av/model_speaker_encoder.py:19: UserWarning: Unable to import 'webrtcvad'. This package enables noise removal and is recommended.\n", + " warn(\"Unable to import 'webrtcvad'. This package enables noise removal and is recommended.\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading speaker encoder...\n", + "Speaker encoder loaded.\n" + ] + } + ], + "source": [ + "# --- 2-3. Load Speaker Encoder ---\n", + "from unit2av.model_speaker_encoder import SpeakerEncoder\n", + "SPEAKER_ENCODER_PATH=\"/home/2022113135/gyucheol/NetfLips/av2av-main/unit2av/encoder.pt\"\n", + "\n", + "print(\"Loading speaker encoder...\")\n", + "speaker_encoder = SpeakerEncoder(SPEAKER_ENCODER_PATH)\n", + "if DEVICE == \"cuda\":\n", + " speaker_encoder = speaker_encoder.cuda()\n", + "print(\"Speaker encoder loaded.\")" + ] + }, + { + "cell_type": "markdown", + "id": "b0b0c0d0", + "metadata": {}, + "source": [ + "## 3. Inference Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "56013a95", + "metadata": {}, + "outputs": [], + "source": [ + "def read_unit_text(path):\n", + " \"\"\"Read a unit text file and return list of int.\"\"\"\n", + " with open(path) as f:\n", + " units = list(map(int, f.readline().strip().split()))\n", + " return units\n", + "\n", + "\n", + "def translate_units(en_units, utut_task, utut_generator, use_cuda=True):\n", + " \"\"\"\n", + " Unit-to-Unit Translation: EN units -> KO units.\n", + " Returns predicted unit string and list of ints.\n", + " \"\"\"\n", + " # Reduce consecutive duplicates and encode\n", + " reduced = process_units(en_units, reduce=True)\n", + " unit_tensor = utut_task.source_dictionary.encode_line(\n", + " \" \".join(map(str, reduced)),\n", + " add_if_not_exist=False,\n", + " append_eos=True,\n", + " ).long()\n", + "\n", + " # Prepend BOS, append source language tag\n", + " unit_tensor = torch.cat([\n", + " unit_tensor.new([utut_task.source_dictionary.bos()]),\n", + " unit_tensor,\n", + " unit_tensor.new([utut_task.source_dictionary.index(f\"[{SRC_LANG}]\")])\n", + " ])\n", + "\n", + " sample = {\"net_input\": {\n", + " \"src_tokens\": unit_tensor.view(1, -1),\n", + " }}\n", + " if use_cuda:\n", + " sample = utils.move_to_cuda(sample)\n", + "\n", + " # Run translation\n", + " pred = utut_task.inference_step(\n", + " utut_generator,\n", + " None,\n", + " sample,\n", + " )[0][0]\n", + "\n", + " # Decode predicted tokens to unit string\n", + " pred_str = utut_task.target_dictionary.string(\n", + " pred[\"tokens\"].int().cpu(),\n", + " extra_symbols_to_ignore=get_symbols_to_strip_from_output(utut_generator)\n", + " )\n", + "\n", + " # Convert to list of int\n", + " pred_units = [int(x) for x in pred_str.strip().split() if x.isdigit()]\n", + " return pred_units, pred_str\n", + "\n", + "\n", + "def save_input_pt(units, ko_wav_path, speaker_encoder, output_pt_path):\n", + " \"\"\"\n", + " Save predicted units + speaker embedding as .pt file\n", + " for inference_unit2a.py consumption.\n", + " \"\"\"\n", + " code = torch.LongTensor(units)\n", + " spkr_embed = speaker_encoder.get_embed(ko_wav_path)\n", + " # Before (2D — causes IndexError on transpose(1,2)):\n", + " # spkr_tensor = torch.from_numpy(spkr_embed).float().view(1, -1) # (1, 256)\n", + "\n", + " # After (3D — matches what the model expects):\n", + " spkr_tensor = torch.from_numpy(spkr_embed).float().view(1, 1, -1) # (1, 1, 256)\n", + "\n", + "\n", + " torch.save({\"code\": code, \"spkr\": spkr_tensor}, output_pt_path)\n", + "\n", + "\n", + "def run_vocoder(input_pt_path, output_dir, device=\"cuda\"):\n", + " \"\"\"\n", + " Call inference_unit2a.py via subprocess to synthesize waveform.\n", + " Returns the generated wav path.\n", + " \"\"\"\n", + " command = [\n", + " \"python\", INFERENCE_SCRIPT,\n", + " \"--checkpoint\", VOCODER_CHECKPOINT,\n", + " \"--config\", VOCODER_CONFIG,\n", + " \"--input_file\", input_pt_path,\n", + " \"--output_folder\", output_dir,\n", + " \"--device\", device,\n", + " ]\n", + " result = subprocess.run(command, capture_output=True, text=True, cwd=PROJECT_DIR)\n", + " if result.returncode != 0:\n", + " print(f\" [ERROR] {result.stderr}\")\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "id": "b2b2c2d2", + "metadata": {}, + "source": [ + "## 4. Run Inference: Unit-to-Unit Translation + Waveform Synthesis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3b3c3d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1/32] et_c_005_002_009_0026\n", + "[2/32] et_c_010_002_004_0014\n" + ] + } + ], + "source": [ + "results = []\n", + "use_cuda = (DEVICE == \"cuda\")\n", + "\n", + "# Temp dir for .pt files passed to inference_unit2a.py\n", + "PT_DIR = os.path.join(OUTPUT_DIR, \"pt_inputs\")\n", + "os.makedirs(PT_DIR, exist_ok=True)\n", + "\n", + "for i, triplet in enumerate(selected_triplets):\n", + " sample_id = triplet[\"id\"]\n", + " print(f\"[{i+1}/{len(selected_triplets)}] {sample_id}\")\n", + "\n", + " # 1) Read EN units\n", + " en_units = read_unit_text(triplet[\"en_unit_path\"])\n", + "\n", + " # 2) Read GT KO units\n", + " gt_ko_units = read_unit_text(triplet[\"ko_unit_path\"])\n", + "\n", + " # 3) UTUT Translation: EN -> KO (predicted)\n", + " pred_ko_units, pred_ko_str = translate_units(\n", + " en_units, utut_task, utut_generator, use_cuda=use_cuda\n", + " )\n", + "\n", + " # 4) Save predicted units + speaker embedding as .pt\n", + " # inference_unit2a.py strips last 13 chars (\"_preprocessed\") from basename\n", + " pt_path = os.path.join(PT_DIR, f\"{sample_id}_preprocessed.pt\")\n", + " save_input_pt(pred_ko_units, triplet[\"ko_wav_path\"], speaker_encoder, pt_path)\n", + "\n", + " # 5) Run vocoder via inference_unit2a.py subprocess\n", + " run_vocoder(pt_path, OUTPUT_DIR, device=DEVICE)\n", + "\n", + " # 6) Locate generated wav (inference_unit2a.py naming: {base}_{step}step.wav)\n", + " output_wav_path = os.path.join(OUTPUT_DIR, f\"{sample_id}_500000step.wav\")\n", + "\n", + " # 7) Save predicted unit text\n", + " output_unit_path = os.path.join(OUTPUT_DIR, f\"{sample_id}_pred_unit.txt\")\n", + " with open(output_unit_path, 'w') as f:\n", + " f.write(' '.join(map(str, pred_ko_units)))\n", + "\n", + " results.append({\n", + " **triplet,\n", + " \"en_units\": en_units,\n", + " \"gt_ko_units\": gt_ko_units,\n", + " \"pred_ko_units\": pred_ko_units,\n", + " \"synth_wav_path\": output_wav_path,\n", + " })\n", + "\n", + "print(f\"\\nDone. {len(results)} samples processed.\")\n", + "print(f\"Output directory: {OUTPUT_DIR}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b4b4c4d4", + "metadata": {}, + "source": [ + "## 5. Display Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5b5c5d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Unit-to-Unit Translation Inference Results (EN -> KO)\n", + "--------------------------------------------------------------------------------\n", + "\n", + "################################################################################\n", + " SUBJECT: et_c_005\n", + "################################################################################\n", + "\n", + "======================================================================\n", + " File ID: et_c_005_002_009_0026\n", + " EN units (first 20): [501, 501, 501, 501, 501, 501, 991, 991, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501] ...\n", + "\n", + " GT KO units (first 20): [43, 843, 474, 825, 825, 825, 825, 825, 681, 359, 874, 822, 255, 416, 565, 565, 565, 565, 217, 217] ...\n", + "\n", + " Pred KO units (first 20): [501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501, 501] ...\n", + " EN unit length: 334 | GT KO: 140 | Pred KO: 199\n", + "\n", + " [Src] English Audio: et_c_005_002_009_0026.wav\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/home/2022113135/datasets/aihub_a2a_wav/test/en/et_c_005_002_009_0026.wav'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-11-abd3b397a673>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Source EN audio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\" [Src] English Audio: {os.path.basename(r['en_wav_path'])}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mipd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mipd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'en_wav_path'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.conda/envs/gyucheol/lib/python3.8/site-packages/IPython/lib/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, filename, url, embed, rate, autoplay, normalize, element_id)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoplay\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautoplay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0melement_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melement_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAudio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 661\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 663\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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{current_subject}\")\n", + " print(\"#\" * 80 + \"\\n\")\n", + "\n", + " print(\"=\" * 70)\n", + " print(f\" File ID: {r['id']}\")\n", + " print(f\" EN units (first 20): {r['en_units'][:20]} ...\")\n", + " print(f\"\\n GT KO units (first 20): {r['gt_ko_units'][:20]} ...\")\n", + " print(f\"\\n Pred KO units (first 20): {r['pred_ko_units'][:20]} ...\")\n", + " print(f\" EN unit length: {len(r['en_units'])} | GT KO: {len(r['gt_ko_units'])} | Pred KO: {len(r['pred_ko_units'])}\")\n", + " print()\n", + "\n", + " # Source EN audio\n", + " print(f\" [Src] English Audio: {os.path.basename(r['en_wav_path'])}\")\n", + " ipd.display(ipd.Audio(filename=r['en_wav_path']))\n", + "\n", + "\n", + " # GT KO audio\n", + " print(f\" [GT] Korean Audio: {os.path.basename(r['ko_wav_path'])}\")\n", + " ipd.display(ipd.Audio(r[\"ko_wav_path\"], rate=16000))\n", + "\n", + " # Synthesized audio\n", + " synth_path = r[\"synth_wav_path\"]\n", + " if os.path.exists(synth_path):\n", + " print(f\" [Pred] Synthesized Audio: {os.path.basename(synth_path)}\")\n", + " ipd.display(ipd.Audio(synth_path, rate=16000))\n", + " else:\n", + " print(f\" [Pred] Generated file not found at: {synth_path}\")\n", + "\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6fb0b71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <audio controls=\"controls\" >\n", + " <source 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0000000..6b888de --- /dev/null +++ b/requirements.txt @@ -0,0 +1,21 @@ +# --- Core Libraries (버전 고정 필수) --- +numpy<1.24 +scipy==1.10.0 +librosa==0.8.1 +resampy==0.4.3 +opencv-python==4.5.4.60 +tensorboard +tensorboardX + +# --- Audio & Video Processing --- +python-speech-features==0.6 +soundfile +av +ffmpeg-python +amfm_decompy +matplotlib +tqdm + +# --- System & Config --- +omegaconf==2.0.6 +hydra-core==1.0.7 \ No newline at end of file diff --git a/samples/en/TRajLqEaWhQ_00002.bbox.pkl b/samples/en/TRajLqEaWhQ_00002.bbox.pkl new file mode 100644 index 0000000..d337ebf Binary files /dev/null and b/samples/en/TRajLqEaWhQ_00002.bbox.pkl differ diff --git a/scripts/lip_detect/README.md b/scripts/lip_detect/README.md new file mode 100644 index 0000000..fe5bad1 --- /dev/null +++ b/scripts/lip_detect/README.md @@ -0,0 +1,91 @@ +# Lip Detection and Extraction Scripts + +Scripts for detecting faces, extracting lip regions, and managing bounding box metadata for video processing. + +## Environment Setup + +Create and activate a conda environment for the lip extraction scripts: + +```bash +conda env create -f environment.yml +conda activate lip_extraction +``` + + +## Core Extraction Scripts + +These scripts process an input video and produce two outputs: +- A `.lip.mp4` video: Cropped and resized (96x96) lip region. +- A `.bbox.pkl` file: A pickle file containing the bounding box coordinates of the detected face for each frame. + +### 1. `extract_lip_yolo.py` +Uses YOLOv8 for robust face tracking and combines it with landmark detection to isolate lips. +- **Landmark Methods**: Supports both `face-alignment` (S3FD) and `mediapipe`. +- **Speaker Tracking**: Uses Mouth Aspect Ratio (MAR) variance over time to identify and track the active speaker in multi-person videos. +- **Usage**: + ```bash + python extract_lip_yolo.py --input path/to/video.mp4 --output_dir ./output --landmark_method face_alignment --device cuda + ``` + +### 2. `extract_lip_yolo_filtered.py` +An extension of the YOLO script that adds a "speaking threshold." +- **Feature**: If the speaker's MAR variance (speaking activity) falls below `--min_speaking_threshold`, the frame is saved as black and coordinates as zeros. This is useful for pruning silent or inactive segments. +- **Usage**: + ```bash + python extract_lip_yolo_filtered.py --input path/to/vid.mp4 --output_dir ./out --min_speaking_threshold 0.01 + ``` + +### 3. `extract_lip_mediapipe.py` +Relies entirely on the MediaPipe Tasks API for face landmarker detection. +- **Feature**: Fast and lightweight. It automatically downloads the necessary `.task` model file. It selects the face with the highest MAR (most active mouth) in each frame. +- **Usage**: + ```bash + python extract_lip_mediapipe.py --input path/to/video.mp4 --output_dir ./output + ``` + +### 4. `extract_lip_s3fd.py` +Uses the `face-alignment` library (S3FD detector) to detect landmarks. +- **Feature**: Highly accurate landmarking, though slower than MediaPipe. It selects the largest detected face. +- **Usage**: + ```bash + python extract_lip_s3fd.py --input path/to/video.mp4 --output_dir ./output --device cuda + ``` + +--- + +## Visualization & Inspection + +Tools to verify the accuracy of the extraction process. + +### `visualize_bbox.py` +Overlays the bounding boxes from a `.pkl` file onto the original video to check if the detection is correct. +- **Usage**: Edit the `video_path` and `pkl_path` variables in the script and run: + ```bash + python visualize_bbox.py + ``` + +### `inspect_bbox.py` +A quick diagnostic script to print the structure and sample data of a `.bbox.pkl` file. +- **Usage**: Update the `pkl_path` in the script and run: + ```bash + python inspect_bbox.py + ``` + +--- + +## Utility & Metadata Management + +Scripts for post-processing and cleaning up metadata. + +### `edit_bbox_pickle.py` +Manually "mute" specific segments of a video by setting their bounding box data to `None`. +- **Use Case**: Removing incorrectly detected frames or segments where the speaker is not actually speaking despite being detected. +- **Usage**: Configure the `target_pickle_file` and `ranges_to_set_none` (tuple of start/end frames) in the script and run. + +### `change_numpylist_to_py_list.py` +Recursively converts numpy arrays inside all `.pkl` files in a directory into standard Python lists. +- **Use Case**: Eliminating `numpy` dependencies for downstream tasks or ensuring cross-version compatibility for pickle files. +- **Usage**: Set `TARGET_DIR` in the script and run: + ```bash + python change_numpylist_to_py_list.py + ``` \ No newline at end of file diff --git a/scripts/lip_detect/change_numpylist_to_py_list.py b/scripts/lip_detect/change_numpylist_to_py_list.py new file mode 100644 index 0000000..e2ceb07 --- /dev/null +++ b/scripts/lip_detect/change_numpylist_to_py_list.py @@ -0,0 +1,65 @@ +import os +import pickle +import numpy as np +from tqdm import tqdm + +TARGET_DIR = "/home/2022113135/gyucheol/NetfLips/data" + +def convert_to_list(data): + """ + Recursively convert numpy arrays to lists. + """ + if isinstance(data, np.ndarray): + return data.tolist() + elif isinstance(data, list): + return [convert_to_list(item) for item in data] + elif isinstance(data, tuple): + return tuple(convert_to_list(item) for item in data) + elif isinstance(data, dict): + return {k: convert_to_list(v) for k, v in data.items()} + else: + return data + +def main(): + if not os.path.exists(TARGET_DIR): + print(f"Error: Directory {TARGET_DIR} does not exist.") + return + + pkl_files = [] + for root, dirs, files in os.walk(TARGET_DIR): + for file in files: + if file.endswith(".pkl"): + pkl_files.append(os.path.join(root, file)) + + print(f"Found {len(pkl_files)} pickle files in {TARGET_DIR}") + + success_count = 0 + fail_count = 0 + + for pkl_path in tqdm(pkl_files): + try: + # Load the data + # Note: This requires the environment to have the SAME numpy version as the one that created it + # if the file contains numpy arrays. + with open(pkl_path, 'rb') as f: + data = pickle.load(f) + + # Convert to list + new_data = convert_to_list(data) + + # Save it back + with open(pkl_path, 'wb') as f: + pickle.dump(new_data, f) + + success_count += 1 + + except Exception as e: + print(f"Failed to process {pkl_path}: {e}") + fail_count += 1 + + print(f"\nProcessing complete.") + print(f"Successfully converted: {success_count}") + print(f"Failed: {fail_count}") + +if __name__ == "__main__": + main() diff --git a/scripts/lip_detect/edit_bbox_pickle.py b/scripts/lip_detect/edit_bbox_pickle.py new file mode 100644 index 0000000..e7e5f8d --- /dev/null +++ b/scripts/lip_detect/edit_bbox_pickle.py @@ -0,0 +1,77 @@ +import pickle +import sys +import os +import shutil + +def modify_bbox_pickle(pkl_path, modifications): + """ + Modifies a bbox pickle file by setting specific frame ranges to None. + + Args: + pkl_path (str): Path to the .bbox.pkl file. + modifications (list of tuple): List of (start_frame, end_frame) tuples. + Frames in the range [start_frame, end_frame) will be set to None. + """ + if not os.path.exists(pkl_path): + print(f"Error: File not found at {pkl_path}") + return + + # 1. Load the pickle file + print(f"Loading {pkl_path}...") + with open(pkl_path, 'rb') as f: + bbox_data = pickle.load(f) + + total_frames = len(bbox_data) + print(f"Total frames: {total_frames}") + + # 2. Apply modifications + modified_count = 0 + for start, end in modifications: + # Clamp indices to valid range + start = max(0, start) + end = min(total_frames, end) + + if start >= end: + print(f"Warning: Invalid range ({start}, {end}). Skipping.") + continue + + print(f"Setting frames {start} to {end-1} -> None") + for i in range(start, end): + if bbox_data[i] is not None: + bbox_data[i] = None + modified_count += 1 + + print(f"Total frames modified: {modified_count}") + + # 3. Create a backup + backup_path = pkl_path + ".bak" + shutil.copy2(pkl_path, backup_path) + print(f"Backup created at {backup_path}") + + # 4. Save the modified data + with open(pkl_path, 'wb') as f: + pickle.dump(bbox_data, f) + + print(f"Successfully saved modified pickle to {pkl_path}") + +if __name__ == "__main__": + # --- CONFIGURATION --- + # Change these values to match your needs + + # Path to your .bbox.pkl file + target_pickle_file = "/home/2022113135/gyucheol/NetfLips/data/final_bbox/hulk_h264_part2.bbox.pkl" + + # List of ranges to set to None. Format: (start_index, end_index) + # The end_index is exclusive (Python slice style). + # Example: Set frames 100 to 109 to None -> (100, 110) + ranges_to_set_none = [ + # (start_frame, end_frame), + (41, 84), + (161, 224) + ] + # --------------------- + + if target_pickle_file == "path/to/your/video.bbox.pkl": + print("Please edit the script to specify the 'target_pickle_file' and 'ranges_to_set_none' first.") + else: + modify_bbox_pickle(target_pickle_file, ranges_to_set_none) diff --git a/scripts/lip_detect/environment.yml b/scripts/lip_detect/environment.yml new file mode 100644 index 0000000..eea8f21 --- /dev/null +++ b/scripts/lip_detect/environment.yml @@ -0,0 +1,118 @@ +name: lip_extraction +channels: + - defaults +dependencies: + - _libgcc_mutex=0.1 + - _openmp_mutex=5.1 + - bzip2=1.0.8 + - ca-certificates=2025.12.2 + - expat=2.7.4 + - ld_impl_linux-64=2.44 + - libexpat=2.7.4 + - libffi=3.4.4 + - libgcc=15.2.0 + - libgcc-ng=15.2.0 + - libgomp=15.2.0 + - libnsl=2.0.0 + - libstdcxx=15.2.0 + - libstdcxx-ng=15.2.0 + - libuuid=1.41.5 + - libxcb=1.17.0 + - libzlib=1.3.1 + - ncurses=6.5 + - openssl=3.0.19 + - packaging=25.0 + - pip=25.3 + - pthread-stubs=0.3 + - python=3.10.19 + - readline=8.3 + - setuptools=80.10.1 + - sqlite=3.51.1 + - tk=8.6.15 + - tzdata=2025c + - wheel=0.46.3 + - xorg-libx11=1.8.12 + - xorg-libxau=1.0.12 + - xorg-libxdmcp=1.1.5 + - xorg-xorgproto=2024.1 + - xz=5.6.4 + - zlib=1.3.1 + - pip: + - absl-py==2.4.0 + - anyio==4.12.1 + - certifi==2026.1.4 + - cffi==2.0.0 + - charset-normalizer==3.4.4 + - click==8.3.1 + - contourpy==1.3.2 + - cuda-bindings==12.9.4 + - cuda-pathfinder==1.3.3 + - cycler==0.12.1 + - exceptiongroup==1.3.1 + - face-alignment==1.4.1 + - filelock==3.20.3 + - flatbuffers==25.12.19 + - fonttools==4.61.1 + - fsspec==2026.1.0 + - h11==0.16.0 + - hf-xet==1.2.0 + - httpcore==1.0.9 + - httpx==0.28.1 + - huggingface-hub==1.3.7 + - idna==3.11 + - imageio==2.37.2 + - jinja2==3.1.6 + - kiwisolver==1.4.9 + - lap==0.5.12 + - lazy-loader==0.4 + - llvmlite==0.46.0 + - markupsafe==3.0.3 + - matplotlib==3.10.8 + - mediapipe==0.10.32 + - mpmath==1.3.0 + - networkx==3.4.2 + - numba==0.63.1 + - numpy==2.2.6 + - nvidia-cublas-cu12==12.8.4.1 + - nvidia-cuda-cupti-cu12==12.8.90 + - nvidia-cuda-nvrtc-cu12==12.8.93 + - nvidia-cuda-runtime-cu12==12.8.90 + - nvidia-cudnn-cu12==9.10.2.21 + - nvidia-cufft-cu12==11.3.3.83 + - nvidia-cufile-cu12==1.13.1.3 + - nvidia-curand-cu12==10.3.9.90 + - nvidia-cusolver-cu12==11.7.3.90 + - nvidia-cusparse-cu12==12.5.8.93 + - nvidia-cusparselt-cu12==0.7.1 + - nvidia-nccl-cu12==2.27.5 + - nvidia-nvjitlink-cu12==12.8.93 + - nvidia-nvshmem-cu12==3.4.5 + - nvidia-nvtx-cu12==12.8.90 + - opencv-contrib-python==4.13.0.90 + - opencv-python==4.13.0.90 + - pillow==12.1.0 + - polars==1.37.1 + - polars-runtime-32==1.37.1 + - psutil==7.2.2 + - pycparser==3.0 + - pyparsing==3.3.2 + - python-dateutil==2.9.0.post0 + - pyyaml==6.0.3 + - requests==2.32.5 + - scikit-image==0.25.2 + - scipy==1.15.3 + - shellingham==1.5.4 + - six==1.17.0 + - sounddevice==0.5.5 + - sympy==1.14.0 + - tifffile==2025.5.10 + - torch==2.10.0 + - torchaudio==2.10.0 + - torchvision==0.25.0 + - tqdm==4.67.2 + - triton==3.6.0 + - typer-slim==0.21.1 + - typing-extensions==4.15.0 + - ultralytics==8.4.11 + - ultralytics-thop==2.0.18 + - urllib3==2.6.3 diff --git a/scripts/lip_detect/extract_lip_mediapipe.py b/scripts/lip_detect/extract_lip_mediapipe.py new file mode 100644 index 0000000..c3c5cde --- /dev/null +++ b/scripts/lip_detect/extract_lip_mediapipe.py @@ -0,0 +1,286 @@ + +import cv2 +import mediapipe as mp +import numpy as np +import pickle +import argparse +import os +import sys +import urllib.request + +# Use the new Tasks API as 'solutions' is not available in this environment +from mediapipe.tasks import python +from mediapipe.tasks.python import vision + +# Define lip landmarks indices +# These indices are consistent with the mesh topology used by FaceLandmarker (478 landmarks) +LIP_INDICES = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95, 185] + +def get_lip_bbox_and_mar(landmarks, frame_w, frame_h): + """ + Calculates the bounding box (square, centered) and MAR (Mouth Aspect Ratio). + Returns: + bbox: np.array([x1, y1, x2, y2], dtype=float32) + mar: float (height / width aspect ratio of the lip cloud) + """ + # Extract lip point coordinates + lip_pts = [] + for idx in LIP_INDICES: + # Safety check for index bound + if idx < len(landmarks): + pt = landmarks[idx] + # pt.x and pt.y are normalized [0, 1] + lip_pts.append([pt.x * frame_w, pt.y * frame_h]) + + if not lip_pts: + return None, 0.0 + + lip_pts = np.array(lip_pts, dtype=np.float32) + + # Determine bounds of the lip points + min_x, min_y = np.min(lip_pts, axis=0) + max_x, max_y = np.max(lip_pts, axis=0) + + w = max_x - min_x + h = max_y - min_y + + # Calculate MAR + # Avoid division by zero + mar = h / w if w > 1e-5 else 0.0 + + # Calculate Center + center_x = (min_x + max_x) / 2.0 + center_y = (min_y + max_y) / 2.0 + + # Determine BBox size + # 1.5x of the max dimension is a safe margin. + size = max(w, h) * 1.5 + + # Ensure square 1:1 + half_size = size / 2.0 + + x1 = center_x - half_size + y1 = center_y - half_size + x2 = center_x + half_size + y2 = center_y + half_size + + return np.array([x1, y1, x2, y2], dtype=np.float32), mar + +def get_face_bbox(landmarks, frame_w, frame_h): + """ + Calculates the bounding box for the entire face based on all landmarks. + """ + pts = [] + for pt in landmarks: + pts.append([pt.x * frame_w, pt.y * frame_h]) + + pts = np.array(pts, dtype=np.float32) + min_x, min_y = np.min(pts, axis=0) + max_x, max_y = np.max(pts, axis=0) + + # Return as [x1, y1, x2, y2] + return np.array([min_x, min_y, max_x, max_y], dtype=np.float32) + + +def crop_and_resize(frame, bbox, target_size=(96, 96)): + """ + Crops the frame based on bbox and resizes it to target_size. + Handles boundaries by padding with zeros. + """ + if bbox is None: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + fh, fw, _ = frame.shape + x1, y1, x2, y2 = bbox + + # Convert to integer coordinates for array indexing + ix1, iy1 = int(round(x1)), int(round(y1)) + ix2, iy2 = int(round(x2)), int(round(y2)) + + bw = ix2 - ix1 + bh = iy2 - iy1 + + if bw <= 0 or bh <= 0: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + # Calculate intersection with frame + src_x1 = max(0, ix1) + src_y1 = max(0, iy1) + src_x2 = min(fw, ix2) + src_y2 = min(fh, iy2) + + # Calculate placement on the destination canvas + dst_x1 = src_x1 - ix1 + dst_y1 = src_y1 - iy1 + dst_x2 = dst_x1 + (src_x2 - src_x1) + dst_y2 = dst_y1 + (src_y2 - src_y1) + + # Initialize canvas (black padding) + crop = np.zeros((bh, bw, 3), dtype=frame.dtype) + + # Copy valids pixels + if src_x2 > src_x1 and src_y2 > src_y1: + crop[dst_y1:dst_y2, dst_x1:dst_x2] = frame[src_y1:src_y2, src_x1:src_x2] + + # Resize to target + try: + resized = cv2.resize(crop, target_size, interpolation=cv2.INTER_LINEAR) + except Exception: + resized = np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + return resized + +def main(): + parser = argparse.ArgumentParser(description="Extract lip region and generate bbox coordinates using MediaPipe Tasks.") + parser.add_argument("--input", type=str, required=True, help="Path to input video file") + parser.add_argument("--output_dir", type=str, required=True, help="Directory to save outputs") + args = parser.parse_args() + + input_path = args.input + output_dir = args.output_dir + + if not os.path.isfile(input_path): + print(f"Error: Input file '{input_path}' not found.") + sys.exit(1) + + os.makedirs(output_dir, exist_ok=True) + + # --- 1. Ensure Model Asset Exists --- + # The new Tasks API requires a binary model bundle. + model_filename = "face_landmarker.task" + # Save it in the same folder as this script for reuse + script_dir = os.path.dirname(os.path.abspath(__file__)) + model_asset_path = os.path.join(script_dir, model_filename) + + if not os.path.exists(model_asset_path): + print(f"Model file '{model_filename}' not found.") + url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task" + print(f"Downloading from {url}...") + try: + urllib.request.urlretrieve(url, model_asset_path) + print("Download complete.") + except Exception as e: + print(f"Error downloading model: {e}") + sys.exit(1) + + # --- 2. Initialize MediaPipe FaceLandmarker --- + base_options = python.BaseOptions(model_asset_path=model_asset_path) + options = vision.FaceLandmarkerOptions( + base_options=base_options, + output_face_blendshapes=False, + output_facial_transformation_matrixes=False, + num_faces=5, + #min_face_detection_confidence=0.5, + #min_face_presence_confidence=0.5, + min_face_detection_confidence=0.2, # Lowered for better long-range detection + min_face_presence_confidence=0.2, + min_tracking_confidence=0.2, + # Use VIDEO mode for temporal consistency + running_mode=vision.RunningMode.VIDEO) + + # --- 3. Process Video --- + cap = cv2.VideoCapture(input_path) + if not cap.isOpened(): + print(f"Error: Could not open video '{input_path}'.") + sys.exit(1) + + fps = cap.get(cv2.CAP_PROP_FPS) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) + height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + + print(f"Processing '{input_path}'") + print(f"Resolution: {int(width)}x{int(height)}, FPS: {fps}, Frames: {total_frames}") + + # 1. args.input에서 경로를 제외한 '파일명.확장자'만 추출 + base_name = os.path.basename(args.input) + + # 2. 확장자를 제거하고 이름만 추출 + file_stem = os.path.splitext(base_name)[0] + + # 3. 새로운 파일명 생성 및 출력 폴더와 결합 + out_vid_path = os.path.join(output_dir, f"{file_stem}.lip.mp4") + # out_vid_path = os.path.join(output_dir, f"{os.path.splitext(args.input)[0]}.lip.mp4") + fourcc = cv2.VideoWriter_fourcc(*'mp4v') # or 'avc1' + out_vid = cv2.VideoWriter(out_vid_path, fourcc, fps, (96, 96)) + + coords_list = [] + + with vision.FaceLandmarker.create_from_options(options) as landmarker: + frame_idx = 0 + while True: + ret, frame = cap.read() + if not ret: + break + + # MediaPipe Tasks requires an RGB MediaPipe Image + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame_rgb) + + # Timestamp in ms required for VIDEO mode + # frame_idx / fps * 1000 + if fps > 0: + timestamp_ms = int((frame_idx / fps) * 1000) + else: + timestamp_ms = frame_idx * 33 # assume 30fps fallback + + try: + detection_result = landmarker.detect_for_video(mp_image, timestamp_ms) + + best_bbox = np.zeros(4, dtype=np.float32) + best_mar = -1.0 + detected = False + + if detection_result.face_landmarks: + for face_landmarks in detection_result.face_landmarks: + # face_landmarks is a list of NormalizedLandmark objects + bbox, mar = get_lip_bbox_and_mar(face_landmarks, width, height) + + if bbox is not None: + if mar > best_mar: + best_mar = mar + best_bbox = bbox + detected = True + # Calculate face bbox for the best face + best_face_bbox = get_face_bbox(face_landmarks, width, height) + + # Store coordinates (Store FACE bbox if detected, else zeros) + if detected: + coords_list.append(best_face_bbox) + else: + coords_list.append(np.zeros(4, dtype=np.float32)) + + # Write Video Frame + if detected: + out_frame = crop_and_resize(frame, best_bbox, (96, 96)) + else: + out_frame = np.zeros((96, 96, 3), dtype=np.uint8) + + out_vid.write(out_frame) + + frame_idx += 1 + if frame_idx % 50 == 0: + print(f"Processed {frame_idx}/{total_frames} frames", end='\r') + + except Exception as e: + # Basic error handling to keep going + print(f"\nError processing frame {frame_idx}: {e}") + coords_list.append(np.zeros(4, dtype=np.float32)) + out_vid.write(np.zeros((96, 96, 3), dtype=np.uint8)) + frame_idx += 1 + continue + + cap.release() + out_vid.release() + + # Save Coordinates + out_pkl_path = os.path.join(output_dir, f"{file_stem}.bbox.pkl") + with open(out_pkl_path, 'wb') as f: + pickle.dump(coords_list, f) + + print(f"\nProcessing complete.") + print(f"Video saved to: {out_vid_path}") + print(f"Coords saved to: {out_pkl_path}") + +if __name__ == "__main__": + main() diff --git a/scripts/lip_detect/extract_lip_s3fd.py b/scripts/lip_detect/extract_lip_s3fd.py new file mode 100644 index 0000000..07168ca --- /dev/null +++ b/scripts/lip_detect/extract_lip_s3fd.py @@ -0,0 +1,245 @@ + +import face_alignment +import cv2 +import numpy as np +import pickle +import argparse +import os +import sys +import torch +from tqdm import tqdm + +def get_lip_bbox(landmarks, frame_w, frame_h): + """ + Calculates the bounding box (square, centered) for the lip region. + landmarks: shape (68, 2) + Indices for lips in 68-point model: + Outer: 48-59 + Inner: 60-67 + """ + # Combine outer and inner lip points + lip_indices = list(range(48, 68)) + lip_pts = landmarks[lip_indices] + + # Bounds + min_x, min_y = np.min(lip_pts, axis=0) + max_x, max_y = np.max(lip_pts, axis=0) + + w = max_x - min_x + h = max_y - min_y + + # Calculate Center + center_x = (min_x + max_x) / 2.0 + center_y = (min_y + max_y) / 2.0 + + # Determine BBox size + # 1.5x of using the max dimension is a safe margin usually. + # For S3FD which is more precise, we can stick to 96x96 relative scale. + size = max(w, h) * 1.5 + + # Ensure square 1:1 + half_size = size / 2.0 + + x1 = center_x - half_size + y1 = center_y - half_size + x2 = center_x + half_size + y2 = center_y + half_size + + return np.array([x1, y1, x2, y2], dtype=np.float32) + +def get_face_bbox(landmarks, frame_w, frame_h): + """ + Calculates the bounding box for the entire face based on all landmarks. + landmarks: shape (68, 2) + """ + min_x, min_y = np.min(landmarks, axis=0) + max_x, max_y = np.max(landmarks, axis=0) + + return np.array([min_x, min_y, max_x, max_y], dtype=np.float32) + + +def crop_and_resize(frame, bbox, target_size=(96, 96)): + """ + Crops the frame based on bbox and resizes it to target_size. + Handles boundaries by padding with zeros. + """ + if bbox is None: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + fh, fw, _ = frame.shape + x1, y1, x2, y2 = bbox + + # Convert to integer coordinates + ix1, iy1 = int(round(x1)), int(round(y1)) + ix2, iy2 = int(round(x2)), int(round(y2)) + + bw = ix2 - ix1 + bh = iy2 - iy1 + + if bw <= 0 or bh <= 0: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + # Calculate intersection + src_x1 = max(0, ix1) + src_y1 = max(0, iy1) + src_x2 = min(fw, ix2) + src_y2 = min(fh, iy2) + + # Calculate destination + dst_x1 = src_x1 - ix1 + dst_y1 = src_y1 - iy1 + dst_x2 = dst_x1 + (src_x2 - src_x1) + dst_y2 = dst_y1 + (src_y2 - src_y1) + + # Initialize canvas + crop = np.zeros((bh, bw, 3), dtype=frame.dtype) + + # Copy pixels + if src_x2 > src_x1 and src_y2 > src_y1: + crop[dst_y1:dst_y2, dst_x1:dst_x2] = frame[src_y1:src_y2, src_x1:src_x2] + + # Resize + try: + resized = cv2.resize(crop, target_size, interpolation=cv2.INTER_LINEAR) + except Exception: + resized = np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + return resized + +def main(): + parser = argparse.ArgumentParser(description="Extract lip region using S3FD (face-alignment).") + parser.add_argument("--input", type=str, required=True, help="Path to input video file") + parser.add_argument("--output_dir", type=str, required=True, help="Directory to save outputs") + parser.add_argument("--device", type=str, default='cuda', help="Device to use (cuda or cpu)") + args = parser.parse_args() + + input_path = args.input + output_dir = args.output_dir + device = args.device + + if not os.path.isfile(input_path): + print(f"Error: Input file '{input_path}' not found.") + sys.exit(1) + + os.makedirs(output_dir, exist_ok=True) + + # Check device availability + if device == 'cuda' and not torch.cuda.is_available(): + print("Warning: CUDA not available, switching to CPU.") + device = 'cpu' + + print(f"Initializing FaceAlignment on {device}...") + try: + # S3FD is the default face detector + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False, device=device) + except Exception as e: + print(f"Error initializing FaceAlignment: {e}") + sys.exit(1) + + cap = cv2.VideoCapture(input_path) + if not cap.isOpened(): + print(f"Error: Could not open video '{input_path}'.") + sys.exit(1) + + fps = cap.get(cv2.CAP_PROP_FPS) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) + height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + + print(f"Processing '{input_path}'") + print(f"Resolution: {int(width)}x{int(height)}, FPS: {fps}, Frames: {total_frames}") + + # 1. args.input에서 경로를 제외한 '파일명.확장자'만 추출 + base_name = os.path.basename(args.input) + + # 2. 확장자를 제거하고 이름만 추출 + file_stem = os.path.splitext(base_name)[0] + + # 3. 새로운 파일명 생성 및 출력 폴더와 결합 + out_vid_path = os.path.join(output_dir, f"{file_stem}.lip.mp4") + # out_vid_path = os.path.join(output_dir, f"{os.path.splitext(args.input)[0]}.lip.mp4") + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out_vid = cv2.VideoWriter(out_vid_path, fourcc, fps, (96, 96)) + + coords_list = [] + + # Read all frames first to process in batch if needed? + # face-alignment can process batches, which is faster. + # But for simplicity and memory safety on large videos, let's do frame by frame or small batches. + # Let's do frame by frame for now to keep logic simple and consistent with previous script. + + frame_idx = 0 + with tqdm(total=total_frames) as pbar: + while True: + ret, frame = cap.read() + if not ret: + break + + # S3FD expects RGB + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + try: + # get_landmarks returns a list of ndarrays, one for each face + # or None if no face detected + preds = fa.get_landmarks(frame_rgb) + + best_bbox = np.zeros(4, dtype=np.float32) + detected = False + + if preds: + # If multiple faces, we need a strategy. + # Simple strategy: Choose the largest face (S3FD is good at finding faces) + # Or closest to center. + # Let's pick the one with the largest lip area or just the first one if unsure. + # Usually for single speaker videos, first one is fine. + # Let's calculate area for all and pick max. + + max_area = 0 + for landmarks in preds: + bbox = get_lip_bbox(landmarks, width, height) + w = bbox[2] - bbox[0] + h = bbox[3] - bbox[1] + area = w * h + + if area > max_area: + max_area = area + best_bbox = bbox + detected = True + # Calculate face bbox + best_face_bbox = get_face_bbox(landmarks, width, height) + + if detected: + coords_list.append(best_face_bbox) + else: + coords_list.append(np.zeros(4, dtype=np.float32)) + + if detected: + out_frame = crop_and_resize(frame, best_bbox, (96, 96)) + else: + out_frame = np.zeros((96, 96, 3), dtype=np.uint8) + + out_vid.write(out_frame) + + except Exception as e: + print(f"Error processing frame {frame_idx}: {e}") + coords_list.append(np.zeros(4, dtype=np.float32)) + out_vid.write(np.zeros((96, 96, 3), dtype=np.uint8)) + + frame_idx += 1 + pbar.update(1) + + cap.release() + out_vid.release() + + # Save Coordinates + out_pkl_path = os.path.join(output_dir, f"{file_stem}.bbox.pkl") + with open(out_pkl_path, 'wb') as f: + pickle.dump(coords_list, f) + + print(f"\nProcessing complete.") + print(f"Video saved to: {out_vid_path}") + print(f"Coords saved to: {out_pkl_path}") + +if __name__ == "__main__": + main() diff --git a/scripts/lip_detect/extract_lip_yolo.py b/scripts/lip_detect/extract_lip_yolo.py new file mode 100644 index 0000000..97e3957 --- /dev/null +++ b/scripts/lip_detect/extract_lip_yolo.py @@ -0,0 +1,381 @@ + +import cv2 +import numpy as np +import pickle +import argparse +import os +import sys +import torch +from PIL import Image +from tqdm import tqdm +from collections import deque + +# Hugging Face & Ultralytics +from huggingface_hub import hf_hub_download +from ultralytics import YOLO + +# Landmark detectors +import face_alignment +import mediapipe as mp +from mediapipe.tasks import python +from mediapipe.tasks.python import vision + +# --- Constants & Configuration --- +LIP_INDICES_MEDIAPIPE = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95, 185] +# FaceAlignment (68 points) lip indices: Outer (48-59), Inner (60-67) +LIP_INDICES_FACEALIGNMENT = list(range(48, 68)) + +def calculate_mar(lip_pts): + """ + Calculates Mouth Aspect Ratio (MAR) from a set of lip points. + Simple heuristic: (Height) / (Width) + """ + if len(lip_pts) == 0: + return 0.0 + + lip_pts = np.array(lip_pts) + min_x, min_y = np.min(lip_pts, axis=0) + max_x, max_y = np.max(lip_pts, axis=0) + + w = max_x - min_x + h = max_y - min_y + + if w < 1e-5: + return 0.0 + + return h / w + +def get_lip_bbox(lip_pts): + """ + Calculates the 1:1 bounding box centered on the lips. + """ + if len(lip_pts) == 0: + return None + + lip_pts = np.array(lip_pts) + min_x, min_y = np.min(lip_pts, axis=0) + max_x, max_y = np.max(lip_pts, axis=0) + + center_x = (min_x + max_x) / 2.0 + center_y = (min_y + max_y) / 2.0 + + w = max_x - min_x + h = max_y - min_y + + # Use 1.5x margin of the largest dimension + size = max(w, h) * 1.5 + half_size = size / 2.0 + + x1 = center_x - half_size + y1 = center_y - half_size + x2 = center_x + half_size + y2 = center_y + half_size + + return np.array([x1, y1, x2, y2], dtype=np.float32) + +def crop_and_resize(frame, bbox, target_size=(96, 96)): + """ + Crops the frame based on bbox and resizes it to target_size. + Handles boundaries by padding with zeros. + """ + if bbox is None: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + fh, fw, _ = frame.shape + x1, y1, x2, y2 = bbox + + ix1, iy1 = int(round(x1)), int(round(y1)) + ix2, iy2 = int(round(x2)), int(round(y2)) + + bw = ix2 - ix1 + bh = iy2 - iy1 + + if bw <= 0 or bh <= 0: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + src_x1 = max(0, ix1) + src_y1 = max(0, iy1) + src_x2 = min(fw, ix2) + src_y2 = min(fh, iy2) + + dst_x1 = src_x1 - ix1 + dst_y1 = src_y1 - iy1 + dst_x2 = dst_x1 + (src_x2 - src_x1) + dst_y2 = dst_y1 + (src_y2 - src_y1) + + crop = np.zeros((bh, bw, 3), dtype=frame.dtype) + + if src_x2 > src_x1 and src_y2 > src_y1: + crop[dst_y1:dst_y2, dst_x1:dst_x2] = frame[src_y1:src_y2, src_x1:src_x2] + + try: + resized = cv2.resize(crop, target_size, interpolation=cv2.INTER_LINEAR) + except Exception: + resized = np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + return resized + +class FaceAlignmentHandler: + def __init__(self, device='cuda'): + print(f"Initializing FaceAlignment on {device}...") + try: + # S3FD is accurate but crashes on too small inputs + self.fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False, device=device) + except Exception as e: + print(f"Error initializing FaceAlignment: {e}") + sys.exit(1) + + def get_landmarks(self, frame_rgb, bbox_xyxy): + x1, y1, x2, y2 = [int(v) for v in bbox_xyxy] + h, w, _ = frame_rgb.shape + + # Add some margin + margin_x = (x2 - x1) * 0.1 + margin_y = (y2 - y1) * 0.1 + cx1 = max(0, int(x1 - margin_x)) + cy1 = max(0, int(y1 - margin_y)) + cx2 = min(w, int(x2 + margin_x)) + cy2 = min(h, int(y2 + margin_y)) + + face_crop = frame_rgb[cy1:cy2, cx1:cx2] + + # FIX: Avoid tiny crops that crash S3FD + if face_crop.shape[0] < 32 or face_crop.shape[1] < 32: + return None + + # OPTIMIZATION: Skip S3FD detection inside face_alignment. + # Since we already cropped the face, we tell it the face is the entire crop. + # detected_faces format: [(x1, y1, x2, y2)] + h_crop, w_crop, _ = face_crop.shape + detected_faces = [(0, 0, w_crop, h_crop)] + + preds = self.fa.get_landmarks(face_crop, detected_faces=detected_faces) + + if preds: + landmarks = preds[0] + landmarks[:, 0] += cx1 + landmarks[:, 1] += cy1 + return landmarks + return None + + def get_lip_points(self, landmarks): + if landmarks is None: + return [] + return landmarks[LIP_INDICES_FACEALIGNMENT] + +class MediaPipeHandler: + def __init__(self): + print("Initializing MediaPipe FaceLandmarker...") + model_filename = "face_landmarker.task" + script_dir = os.path.dirname(os.path.abspath(__file__)) + model_asset_path = os.path.join(script_dir, model_filename) + + if not os.path.exists(model_asset_path): + print(f"Downloading MediaPipe model...") + url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task" + import urllib.request + urllib.request.urlretrieve(url, model_asset_path) + + base_options = python.BaseOptions(model_asset_path=model_asset_path) + options = vision.FaceLandmarkerOptions( + base_options=base_options, + output_face_blendshapes=False, + output_facial_transformation_matrixes=False, + num_faces=1, + min_face_detection_confidence=0.3, + min_face_presence_confidence=0.3, + min_tracking_confidence=0.3, + running_mode=vision.RunningMode.IMAGE) + + self.landmarker = vision.FaceLandmarker.create_from_options(options) + + def get_landmarks(self, frame_rgb, bbox_xyxy): + x1, y1, x2, y2 = [int(v) for v in bbox_xyxy] + h, w, _ = frame_rgb.shape + + margin_x = (x2 - x1) * 0.1 + margin_y = (y2 - y1) * 0.1 + cx1 = max(0, int(x1 - margin_x)) + cy1 = max(0, int(y1 - margin_y)) + cx2 = min(w, int(x2 + margin_x)) + cy2 = min(h, int(y2 + margin_y)) + + face_crop = frame_rgb[cy1:cy2, cx1:cx2] + if face_crop.size == 0: + return None + + mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=face_crop) + detection_result = self.landmarker.detect(mp_image) + + if detection_result.face_landmarks: + landmarks_norm = detection_result.face_landmarks[0] + crop_h, crop_w, _ = face_crop.shape + + landmarks = [] + for norm_pt in landmarks_norm: + px = norm_pt.x * crop_w + cx1 + py = norm_pt.y * crop_h + cy1 + landmarks.append([px, py]) + + return np.array(landmarks) + return None + + def get_lip_points(self, landmarks): + if landmarks is None: + return [] + lip_pts = [] + for idx in LIP_INDICES_MEDIAPIPE: + lip_pts.append(landmarks[idx]) + return np.array(lip_pts) + + +def main(): + parser = argparse.ArgumentParser(description="Extract lip region using YOLOv8 face detection + Landmarks.") + parser.add_argument("--input", type=str, required=True, help="Path to input video file") + parser.add_argument("--output_dir", type=str, required=True, help="Directory to save outputs") + parser.add_argument("--landmark_method", type=str, default="face_alignment", choices=["face_alignment", "mediapipe"], help="Landmark detection method") + parser.add_argument("--device", type=str, default='cuda', help="Device for FaceAlignment/YOLO (cuda or cpu)") + args = parser.parse_args() + + input_path = args.input + output_dir = args.output_dir + device_name = args.device + + if not os.path.isfile(input_path): + print(f"Error: Input file '{input_path}' not found.") + sys.exit(1) + + os.makedirs(output_dir, exist_ok=True) + + # --- 1. Load YOLO Model --- + print("Loading YOLOv8-Face-Detection model...") + try: + model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt") + yolo_model = YOLO(model_path) + yolo_model.to(device_name if torch.cuda.is_available() and device_name == 'cuda' else 'cpu') + except Exception as e: + print(f"Error loading YOLO model: {e}") + sys.exit(1) + + # --- 2. Initialize Landmark Detector --- + if args.landmark_method == "face_alignment": + landmark_detector = FaceAlignmentHandler(device=device_name) + else: + landmark_detector = MediaPipeHandler() + + # --- 3. Process Video --- + cap = cv2.VideoCapture(input_path) + fps = cap.get(cv2.CAP_PROP_FPS) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) + height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + + print(f"Processing '{input_path}' with {args.landmark_method}") + print(f"Resolution: {int(width)}x{int(height)}, FPS: {fps}, Frames: {total_frames}") + # 1. args.input에서 경로를 제외한 '파일명.확장자'만 추출 + base_name = os.path.basename(args.input) + + # 2. 확장자를 제거하고 이름만 추출 + file_stem = os.path.splitext(base_name)[0] + + # 3. 새로운 파일명 생성 및 출력 폴더와 결합 + out_vid_path = os.path.join(output_dir, f"{file_stem}.lip.mp4") + # out_vid_path = os.path.join(output_dir, f"{os.path.splitext(args.input)[0]}.lip.mp4") + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out_vid = cv2.VideoWriter(out_vid_path, fourcc, fps, (96, 96)) + + coords_list = [] + + # Speaker Identification State + mar_histories = {} # {track_id: deque(maxlen=10)} + + frame_idx = 0 + with tqdm(total=total_frames) as pbar: + while True: + ret, frame = cap.read() + if not ret: + break + + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + # --- YOLO Face Tracking --- + results = yolo_model.track(frame_rgb, persist=True, verbose=False) + + current_frame_mar = {} + current_frame_bboxes = {} + current_frame_face_bboxes = {} + + if results and results[0].boxes is not None and len(results[0].boxes) > 0: + boxes = results[0].boxes + + for i in range(len(boxes)): + box = boxes[i] + xyxy = box.xyxy[0].cpu().numpy() + + # Get persistent id + track_id = int(box.id[0].cpu().numpy()) if box.id is not None else int(i + 1000) + + if track_id not in mar_histories: + mar_histories[track_id] = deque(maxlen=10) + + # Extract Landmarks + landmarks = landmark_detector.get_landmarks(frame_rgb, xyxy) + if landmarks is not None: + lip_pts = landmark_detector.get_lip_points(landmarks) + mar = calculate_mar(lip_pts) + mar_histories[track_id].append(mar) + + current_frame_mar[track_id] = mar + current_frame_bboxes[track_id] = get_lip_bbox(lip_pts) + current_frame_face_bboxes[track_id] = xyxy + + # --- Speaker Selection (MAR Variance) --- + winner_id = None + max_var = -1.0 + + for tid, history in mar_histories.items(): + if tid in current_frame_bboxes: + if len(history) >= 2: + # Use standard deviation as the 'speaking' score + score = np.std(history) + else: + score = 0.0 + + if score > max_var: + max_var = score + winner_id = tid + + # Fallback if no one is clearly talking (low variance) + if winner_id is not None and max_var < 0.01: + # Pick the one with the highest current MAR (as a secondary heuristic) + winner_id = max(current_frame_mar, key=current_frame_mar.get) if current_frame_mar else winner_id + + best_bbox_coords = current_frame_bboxes.get(winner_id) if winner_id is not None else None + best_face_bbox = current_frame_face_bboxes.get(winner_id) if winner_id is not None else None + + if best_bbox_coords is not None: + coords_list.append(best_face_bbox.tolist()) + out_frame = crop_and_resize(frame, best_bbox_coords, (96, 96)) + else: + coords_list.append([0.0, 0.0, 0.0, 0.0]) + out_frame = np.zeros((96, 96, 3), dtype=np.uint8) + + out_vid.write(out_frame) + + frame_idx += 1 + pbar.update(1) + + cap.release() + out_vid.release() + + # Save Coordinates + out_pkl_path = os.path.join(output_dir, f"{file_stem}.bbox.pkl") + with open(out_pkl_path, 'wb') as f: + pickle.dump(coords_list, f) + + print(f"\nProcessing complete.") + print(f"Video saved to: {out_vid_path}") + print(f"Coords saved to: {out_pkl_path}") + +if __name__ == "__main__": + main() diff --git a/scripts/lip_detect/extract_lip_yolo_filtered.py b/scripts/lip_detect/extract_lip_yolo_filtered.py new file mode 100644 index 0000000..1a8a1ba --- /dev/null +++ b/scripts/lip_detect/extract_lip_yolo_filtered.py @@ -0,0 +1,383 @@ + +import cv2 +import numpy as np +import pickle +import argparse +import os +import sys +import torch +from PIL import Image +from tqdm import tqdm +from collections import deque + +# Hugging Face & Ultralytics +from huggingface_hub import hf_hub_download +from ultralytics import YOLO + +# Landmark detectors +import face_alignment +import mediapipe as mp +from mediapipe.tasks import python +from mediapipe.tasks.python import vision + +# --- Constants & Configuration --- +LIP_INDICES_MEDIAPIPE = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95, 185] +# FaceAlignment (68 points) lip indices: Outer (48-59), Inner (60-67) +LIP_INDICES_FACEALIGNMENT = list(range(48, 68)) + +def calculate_mar(lip_pts): + """ + Calculates Mouth Aspect Ratio (MAR) from a set of lip points. + Simple heuristic: (Height) / (Width) + """ + if len(lip_pts) == 0: + return 0.0 + + lip_pts = np.array(lip_pts) + min_x, min_y = np.min(lip_pts, axis=0) + max_x, max_y = np.max(lip_pts, axis=0) + + w = max_x - min_x + h = max_y - min_y + + if w < 1e-5: + return 0.0 + + return h / w + +def get_lip_bbox(lip_pts): + """ + Calculates the 1:1 bounding box centered on the lips. + """ + if len(lip_pts) == 0: + return None + + lip_pts = np.array(lip_pts) + min_x, min_y = np.min(lip_pts, axis=0) + max_x, max_y = np.max(lip_pts, axis=0) + + center_x = (min_x + max_x) / 2.0 + center_y = (min_y + max_y) / 2.0 + + w = max_x - min_x + h = max_y - min_y + + # Use 1.5x margin of the largest dimension + size = max(w, h) * 1.5 + half_size = size / 2.0 + + x1 = center_x - half_size + y1 = center_y - half_size + x2 = center_x + half_size + y2 = center_y + half_size + + return np.array([x1, y1, x2, y2], dtype=np.float32) + +def crop_and_resize(frame, bbox, target_size=(96, 96)): + """ + Crops the frame based on bbox and resizes it to target_size. + Handles boundaries by padding with zeros. + """ + if bbox is None: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + fh, fw, _ = frame.shape + x1, y1, x2, y2 = bbox + + ix1, iy1 = int(round(x1)), int(round(y1)) + ix2, iy2 = int(round(x2)), int(round(y2)) + + bw = ix2 - ix1 + bh = iy2 - iy1 + + if bw <= 0 or bh <= 0: + return np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + src_x1 = max(0, ix1) + src_y1 = max(0, iy1) + src_x2 = min(fw, ix2) + src_y2 = min(fh, iy2) + + dst_x1 = src_x1 - ix1 + dst_y1 = src_y1 - iy1 + dst_x2 = dst_x1 + (src_x2 - src_x1) + dst_y2 = dst_y1 + (src_y2 - src_y1) + + crop = np.zeros((bh, bw, 3), dtype=frame.dtype) + + if src_x2 > src_x1 and src_y2 > src_y1: + crop[dst_y1:dst_y2, dst_x1:dst_x2] = frame[src_y1:src_y2, src_x1:src_x2] + + try: + resized = cv2.resize(crop, target_size, interpolation=cv2.INTER_LINEAR) + except Exception: + resized = np.zeros((target_size[1], target_size[0], 3), dtype=np.uint8) + + return resized + +class FaceAlignmentHandler: + def __init__(self, device='cuda'): + print(f"Initializing FaceAlignment on {device}...") + try: + # S3FD is accurate but crashes on too small inputs + self.fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False, device=device) + except Exception as e: + print(f"Error initializing FaceAlignment: {e}") + sys.exit(1) + + def get_landmarks(self, frame_rgb, bbox_xyxy): + x1, y1, x2, y2 = [int(v) for v in bbox_xyxy] + h, w, _ = frame_rgb.shape + + # Add some margin + margin_x = (x2 - x1) * 0.1 + margin_y = (y2 - y1) * 0.1 + cx1 = max(0, int(x1 - margin_x)) + cy1 = max(0, int(y1 - margin_y)) + cx2 = min(w, int(x2 + margin_x)) + cy2 = min(h, int(y2 + margin_y)) + + face_crop = frame_rgb[cy1:cy2, cx1:cx2] + + # FIX: Avoid tiny crops that crash S3FD + if face_crop.shape[0] < 32 or face_crop.shape[1] < 32: + return None + + # OPTIMIZATION: Skip S3FD detection inside face_alignment. + # Since we already cropped the face, we tell it the face is the entire crop. + # detected_faces format: [(x1, y1, x2, y2)] + h_crop, w_crop, _ = face_crop.shape + detected_faces = [(0, 0, w_crop, h_crop)] + + preds = self.fa.get_landmarks(face_crop, detected_faces=detected_faces) + + if preds: + landmarks = preds[0] + landmarks[:, 0] += cx1 + landmarks[:, 1] += cy1 + return landmarks + return None + + def get_lip_points(self, landmarks): + if landmarks is None: + return [] + return landmarks[LIP_INDICES_FACEALIGNMENT] + +class MediaPipeHandler: + def __init__(self): + print("Initializing MediaPipe FaceLandmarker...") + model_filename = "face_landmarker.task" + script_dir = os.path.dirname(os.path.abspath(__file__)) + model_asset_path = os.path.join(script_dir, model_filename) + + if not os.path.exists(model_asset_path): + print(f"Downloading MediaPipe model...") + url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task" + import urllib.request + urllib.request.urlretrieve(url, model_asset_path) + + base_options = python.BaseOptions(model_asset_path=model_asset_path) + options = vision.FaceLandmarkerOptions( + base_options=base_options, + output_face_blendshapes=False, + output_facial_transformation_matrixes=False, + num_faces=1, + min_face_detection_confidence=0.3, + min_face_presence_confidence=0.3, + min_tracking_confidence=0.3, + running_mode=vision.RunningMode.IMAGE) + + self.landmarker = vision.FaceLandmarker.create_from_options(options) + + def get_landmarks(self, frame_rgb, bbox_xyxy): + x1, y1, x2, y2 = [int(v) for v in bbox_xyxy] + h, w, _ = frame_rgb.shape + + margin_x = (x2 - x1) * 0.1 + margin_y = (y2 - y1) * 0.1 + cx1 = max(0, int(x1 - margin_x)) + cy1 = max(0, int(y1 - margin_y)) + cx2 = min(w, int(x2 + margin_x)) + cy2 = min(h, int(y2 + margin_y)) + + face_crop = frame_rgb[cy1:cy2, cx1:cx2] + if face_crop.size == 0: + return None + + mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=face_crop) + detection_result = self.landmarker.detect(mp_image) + + if detection_result.face_landmarks: + landmarks_norm = detection_result.face_landmarks[0] + crop_h, crop_w, _ = face_crop.shape + + landmarks = [] + for norm_pt in landmarks_norm: + px = norm_pt.x * crop_w + cx1 + py = norm_pt.y * crop_h + cy1 + landmarks.append([px, py]) + + return np.array(landmarks) + return None + + def get_lip_points(self, landmarks): + if landmarks is None: + return [] + lip_pts = [] + for idx in LIP_INDICES_MEDIAPIPE: + lip_pts.append(landmarks[idx]) + return np.array(lip_pts) + + +def main(): + parser = argparse.ArgumentParser(description="Extract lip region using YOLOv8 face detection + Landmarks (Filtered).") + parser.add_argument("--input", type=str, required=True, help="Path to input video file") + parser.add_argument("--output_dir", type=str, required=True, help="Directory to save outputs") + parser.add_argument("--landmark_method", type=str, default="face_alignment", choices=["face_alignment", "mediapipe"], help="Landmark detection method") + parser.add_argument("--device", type=str, default='cuda', help="Device for FaceAlignment/YOLO (cuda or cpu)") + parser.add_argument("--min_speaking_threshold", type=float, default=0.01, help="Minimum MAR variance to consider a face as speaking. Below this, output is black.") + args = parser.parse_args() + + input_path = args.input + output_dir = args.output_dir + device_name = args.device + + if not os.path.isfile(input_path): + print(f"Error: Input file '{input_path}' not found.") + sys.exit(1) + + os.makedirs(output_dir, exist_ok=True) + + # --- 1. Load YOLO Model --- + print("Loading YOLOv8-Face-Detection model...") + try: + model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt") + yolo_model = YOLO(model_path) + yolo_model.to(device_name if torch.cuda.is_available() and device_name == 'cuda' else 'cpu') + except Exception as e: + print(f"Error loading YOLO model: {e}") + sys.exit(1) + + # --- 2. Initialize Landmark Detector --- + if args.landmark_method == "face_alignment": + landmark_detector = FaceAlignmentHandler(device=device_name) + else: + landmark_detector = MediaPipeHandler() + + # --- 3. Process Video --- + cap = cv2.VideoCapture(input_path) + fps = cap.get(cv2.CAP_PROP_FPS) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) + height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + + print(f"Processing '{input_path}' with {args.landmark_method}") + print(f"Resolution: {int(width)}x{int(height)}, FPS: {fps}, Frames: {total_frames}") + + # 1. args.input에서 경로를 제외한 '파일명.확장자'만 추출 + base_name = os.path.basename(args.input) + + # 2. 확장자를 제거하고 이름만 추출 + file_stem = os.path.splitext(base_name)[0] + + # 3. 새로운 파일명 생성 및 출력 폴더와 결합 + out_vid_path = os.path.join(output_dir, f"{file_stem}.lip.mp4") + # out_vid_path = os.path.join(output_dir, f"{os.path.splitext(args.input)[0]}.lip.mp4") + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out_vid = cv2.VideoWriter(out_vid_path, fourcc, fps, (96, 96)) + + coords_list = [] + + # Speaker Identification State + mar_histories = {} # {track_id: deque(maxlen=10)} + + frame_idx = 0 + with tqdm(total=total_frames) as pbar: + while True: + ret, frame = cap.read() + if not ret: + break + + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + # --- YOLO Face Tracking --- + results = yolo_model.track(frame_rgb, persist=True, verbose=False) + + current_frame_mar = {} + current_frame_bboxes = {} + current_frame_face_bboxes = {} + + if results and results[0].boxes is not None and len(results[0].boxes) > 0: + boxes = results[0].boxes + + for i in range(len(boxes)): + box = boxes[i] + xyxy = box.xyxy[0].cpu().numpy() + + # Get persistent id + track_id = int(box.id[0].cpu().numpy()) if box.id is not None else int(i + 1000) + + if track_id not in mar_histories: + mar_histories[track_id] = deque(maxlen=10) + + # Extract Landmarks + landmarks = landmark_detector.get_landmarks(frame_rgb, xyxy) + if landmarks is not None: + lip_pts = landmark_detector.get_lip_points(landmarks) + mar = calculate_mar(lip_pts) + mar_histories[track_id].append(mar) + + current_frame_mar[track_id] = mar + current_frame_bboxes[track_id] = get_lip_bbox(lip_pts) + current_frame_face_bboxes[track_id] = xyxy + + # --- Speaker Selection (MAR Variance) --- + winner_id = None + max_var = -1.0 + + for tid, history in mar_histories.items(): + if tid in current_frame_bboxes: + if len(history) >= 2: + # Use standard deviation as the 'speaking' score + score = np.std(history) + else: + score = 0.0 + + if score > max_var: + max_var = score + winner_id = tid + + # Filter: Check if the 'winner' is actually speaking + if max_var < args.min_speaking_threshold: + # NO ONE IS SPEAKING (or just reacting/listening) + winner_id = None + + best_bbox_coords = current_frame_bboxes.get(winner_id) if winner_id is not None else None + best_face_bbox = current_frame_face_bboxes.get(winner_id) if winner_id is not None else None + + if best_bbox_coords is not None: + coords_list.append(best_face_bbox) + out_frame = crop_and_resize(frame, best_bbox_coords, (96, 96)) + else: + coords_list.append(np.zeros(4, dtype=np.float32)) + out_frame = np.zeros((96, 96, 3), dtype=np.uint8) + + out_vid.write(out_frame) + + frame_idx += 1 + pbar.update(1) + + cap.release() + out_vid.release() + + # Save Coordinates + out_pkl_path = os.path.join(output_dir, f"{file_stem}.bbox.pkl") + with open(out_pkl_path, 'wb') as f: + pickle.dump(coords_list, f) + + print(f"\nProcessing complete.") + print(f"Video saved to: {out_vid_path}") + print(f"Coords saved to: {out_pkl_path}") + +if __name__ == "__main__": + main() diff --git a/scripts/lip_detect/face_landmarker.task b/scripts/lip_detect/face_landmarker.task new file mode 100644 index 0000000..c50c845 Binary files /dev/null and b/scripts/lip_detect/face_landmarker.task differ diff --git a/scripts/lip_detect/inspect_bbox.py b/scripts/lip_detect/inspect_bbox.py new file mode 100644 index 0000000..e744660 --- /dev/null +++ b/scripts/lip_detect/inspect_bbox.py @@ -0,0 +1,24 @@ +import pickle +import os + +pkl_path = "/home/2022113135/gyucheol/NetfLips/av2av-main/assets/samples/en/TRajLqEaWhQ_00002.bbox.pkl" + +with open(pkl_path, 'rb') as f: + data = pickle.load(f) + +print(f"Type of data: {type(data)}") +if isinstance(data, list): + print(f"Length of list: {len(data)}") + if len(data) > 0: + print(f"First element: {data[0]}") +elif isinstance(data, dict): + print(f"Keys: {data.keys()}") + for k, v in data.items(): + if isinstance(v, (list, tuple)): + print(f"{k} length: {len(v)}") + if len(v) > 0: + print(f"{k} first element: {v[0]}") + else: + print(f"{k}: {v}") +else: + print(data) diff --git a/scripts/lip_detect/visualize_bbox.py b/scripts/lip_detect/visualize_bbox.py new file mode 100644 index 0000000..a909794 --- /dev/null +++ b/scripts/lip_detect/visualize_bbox.py @@ -0,0 +1,62 @@ +import cv2 +import pickle +import numpy as np +import os + +def visualize_bbox(video_path, pkl_path, output_path): + # Load bboxes + with open(pkl_path, 'rb') as f: + bboxes = pickle.load(f) + + # Open video + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + print(f"Error: Could not open video {video_path}") + return + + # Get video properties + width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) + frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + print(f"Video: {width}x{height}, {fps} FPS, {frame_count} frames") + print(f"BBoxes: {len(bboxes)} items") + + # Define codec and create VideoWriter object + # Using 'mp4v' or 'avc1' for mp4 format + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) + + frame_idx = 0 + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + if frame_idx < len(bboxes): + bbox = bboxes[frame_idx] + # Handle different formats if necessary. Assuming [x1, y1, x2, y2] + if bbox is not None: + if len(bbox) == 4: + x1, y1, x2, y2 = map(int, bbox) + # Draw rectangle (color: green (0, 255, 0), thickness: 2) + cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) + # Add frame index text + cv2.putText(frame, f"Frame: {frame_idx}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + else: + cv2.putText(frame, f"Frame: {frame_idx} (No BBox)", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) + + out.write(frame) + frame_idx += 1 + + cap.release() + out.release() + print(f"Saved visualized video to {output_path}") + +if __name__ == "__main__": + video_path = "/home/2022113135/gyucheol/NetfLips/data/final_segments/hulk_h264_part2.mp4" + pkl_path = "/home/2022113135/gyucheol/NetfLips/data/final_bbox/hulk_h264_part2.bbox.pkl" + output_path = "/home/2022113135/gyucheol/NetfLips/data/final_bbox/hulk_h264_part2_bbox_visualized.mp4" + + visualize_bbox(video_path, pkl_path, output_path) diff --git a/scripts/preprocess_aihub.py b/scripts/preprocess_aihub.py new file mode 100644 index 0000000..9c4b54d --- /dev/null +++ b/scripts/preprocess_aihub.py @@ -0,0 +1,339 @@ + +""" +AIHub 립리딩(Lip Reading) 데이터셋을 LRS3 데이터셋 형식으로 전처리하는 스크립트. +주요 작업: +1. .tar 압축 파일 해제 (필요한 경우) +2. JSON(라벨링) 및 MP4(영상) 매칭 +3. 영상에서 입술/얼굴 영역 크롭 및 리사이즈 +4. 영상 프레임 레이트(FPS) 변환 +5. 오디오 슬라이싱 및 샘플링 레이트 변환 +6. 전처리된 데이터 저장 (MP4, WAV, TXT) +""" + +import os +import json +import glob +import tarfile +import argparse +import subprocess +import shutil +import numpy as np +import cv2 +import math +from tqdm import tqdm +from scipy.io import wavfile +from scipy import signal +import tempfile + +def get_parser(): + parser = argparse.ArgumentParser(description="Preprocess AIHub Lip Reading Dataset for AV2AV") + parser.add_argument("--data-root", type=str, required=True, help="Root directory containing .tar files or extracted folders") + parser.add_argument("--save-dir", type=str, required=True, help="Output directory for preprocessed data") + parser.add_argument("--temp-dir", type=str, default="./temp_extract", help="Temporary directory for extracting tar files") + parser.add_argument("--fps", type=int, default=25, help="Target Video FPS") + parser.add_argument("--sample-rate", type=int, default=16000, help="Target Audio Sample Rate") + parser.add_argument("--crop-size", type=int, default=96, help="Target Face Crop Size (Square)") + parser.add_argument("--padding", type=float, default=0.5, help="Padding in seconds to add to start/end of clip") + parser.add_argument("--no-tar-extract", action="store_true", help="Skip tar extraction if data is already extracted") + return parser + +def extract_tar(tar_path, extract_path): + """ + tar 압축 파일을 지정된 경로에 해제. + + Args: + tar_path: tar 파일 경로 + extract_path: 압축을 해제할 경로 + """ + try: + if not os.path.exists(extract_path): + os.makedirs(extract_path, exist_ok=True) + + print(f"Extracting {tar_path}...") + with tarfile.open(tar_path, 'r') as tar: + tar.extractall(path=extract_path) + print(f"Extracted to {extract_path}") + return True + except Exception as e: + print(f"Error extracting {tar_path}: {e}") + return False + +def resample_audio(audio_path, target_sr=16000): + """ + ffmpeg을 사용해서 오디오를 목표 샘플링 레이트(target_sr)와 모노 채널로 변환. + """ + try: + # Robust한 처리를 위해 ffmpeg 사용 + out_path = audio_path.replace(".wav", f"_{target_sr}.wav") + cmd = [ + "ffmpeg", "-y", + "-i", audio_path, + "-ac", "1", # Mono + "-ar", str(target_sr), + "-vn", # No video + "-loglevel", "error", + out_path + ] + subprocess.run(cmd, check=True) + return out_path + except Exception as e: + print(f"Error processing audio {audio_path}: {e}") + return None + +def process_video_frames(video_path, bboxes, start_time, end_time, src_fps=30, tgt_fps=25, crop_size=96): + """ + 비디오를 읽고, 프레임별 BBox 정보를 바탕으로 얼굴 영역을 크롭한 후 FPS를 변환. + + Args: + video_path: 원본 MP4 파일 경로 + bboxes: 프레임별 바운딩 박스 목록 [[y1, x1, y2, x2], ...] + start_time: 시작 시간 (초) + end_time: 종료 시간 (초) + src_fps: 원본 영상의 FPS + tgt_fps: 목표 FPS (기본 25) + crop_size: 출력 이미지 크기 (기본 96x96) + + Returns: + 전처리된 프레임들의 Numpy array (T, H, W) + """ + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + print(f"Failed to open video: {video_path}") + return None + + frames = [] + + start_frame = int(start_time * src_fps) + end_frame = int(end_time * src_fps) + + # 시작 프레임 위치로 이동 + cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) + + curr_frame_idx = start_frame + + while curr_frame_idx <= end_frame: + ret, frame = cap.read() + if not ret: + break + + # 해당 프레임의 BBox 정보 확인 + if curr_frame_idx < len(bboxes): + bbox = bboxes[curr_frame_idx] + try: + # AIHub JSON BBox 형식: [y1, x1, y2, x2] (top, left, bottom, right) + y1, x1, y2, x2 = bbox + + # 영상 범위를 벗어나지 않도록 클리핑 + h, w, _ = frame.shape + x1 = max(0, x1); y1 = max(0, y1) + x2 = min(w, x2); y2 = min(h, y2) + + face_img = frame[y1:y2, x1:x2] + + # 목표 크기로 리사이즈 + face_img = cv2.resize(face_img, (crop_size, crop_size), interpolation=cv2.INTER_LINEAR) + + # AV-HuBERT 호환을 위해 그레이스케일로 변환 + face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY) + + frames.append(face_img) + + except Exception as e: + print(f"Error cropping frame {curr_frame_idx}: {e}") + else: + # BBox 정보가 없는 프레임은 건너뜀 + pass + + curr_frame_idx += 1 + + cap.release() + + if not frames: + return None + + frames = np.array(frames) # (T_src, H, W) + + # Video FPS 변환 (시간축 보간법 사용) + # 예: 30 FPS -> 25 FPS + if src_fps != tgt_fps: + sec = len(frames) / src_fps + tgt_frames_len = int(sec * tgt_fps) + + # 선형 보간을 위한 인덱스 생성 + indices = np.linspace(0, len(frames)-1, tgt_frames_len) + + new_frames = [] + for i in indices: + low = int(math.floor(i)) + high = int(math.ceil(i)) + weight = i - low + + if low == high: + new_frames.append(frames[low]) + else: + # 두 프레임 사이를 비중(weight)에 따라 혼합 + blended = (frames[low] * (1-weight) + frames[high] * weight).astype(np.uint8) + new_frames.append(blended) + + return np.array(new_frames) + + return frames + +def save_video(frames, out_path, fps=25): + """프레임 배열을 MP4 동영상 파일로 저장.""" + if len(frames) == 0: return + + h, w = frames.shape[1], frames.shape[2] + + # OpenCV VideoWriter를 사용하여 MP4v 코덱으로 저장 (그레이스케일) + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out = cv2.VideoWriter(out_path, fourcc, fps, (w, h), False) # False: isColor=False + + for frame in frames: + out.write(frame) + + out.release() + +def process_session(json_path, video_path, args, speaker_id): + """ + 하나의 세션(비디오 1개 + JSON 1개)을 처리하여 문장 단위로 데이터를 분리 + """ + + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + # AIHub 데이터 형식에 따라 리스트인 경우 처리 + if isinstance(data, list): + if len(data) == 0: return + data = data[0] + + # 메타데이터 파싱 + try: + # BBox 정보: 'Bounding_box_info' 하위 필드 확인 + bbox_data = data.get('Bounding_box_info', {}).get('Face_bounding_box') + if isinstance(bbox_data, dict): + bboxes = bbox_data.get('xtl_ytl_xbr_ybr', []) + else: + bboxes = bbox_data + + sentences = data.get('Sentence_info', []) + + except Exception as e: + print(f"Error parsing JSON {json_path}: {e}") + return + + # 오디오 추출 및 임시 저장 (전체 영상을 한 번에 처리 후 메모리에서 슬라이싱) + temp_wav = video_path.replace(".mp4", "_temp.wav") + try: + subprocess.run(["ffmpeg", "-y", "-i", video_path, "-vn", "-ac", "1", "-ar", "48000", "-loglevel", "error", temp_wav], check=True) + except: + return # 오디오 추출 실패 시 세션 스킵 + + # 슬라이싱을 위해 오디오 데이터 로드 + sr, audio_data = wavfile.read(temp_wav) + + for sent in sentences: + try: + sent_id = sent['ID'] + # 패딩(padding) 추가하여 시작/종료 시간 설정 + start_t = max(0, sent['start_time'] - args.padding) + end_t = sent['end_time'] + args.padding + text = sent['sentence_text'] + + # 출력 경로 설정 (save_dir/speaker_id/...) + spk_dir = os.path.join(args.save_dir, speaker_id) + os.makedirs(spk_dir, exist_ok=True) + + out_vid_name = f"{speaker_id}_{sent_id:04d}.mp4" + out_wav_name = f"{speaker_id}_{sent_id:04d}.wav" + out_txt_name = f"{speaker_id}_{sent_id:04d}.txt" + + output_vid_path = os.path.join(spk_dir, out_vid_name) + output_wav_path = os.path.join(spk_dir, out_wav_name) + output_txt_path = os.path.join(spk_dir, out_txt_name) + + if os.path.exists(output_vid_path): continue + + # 1. 비디오 처리 (크롭 -> 리사이즈 -> FPS 변환) + frames = process_video_frames(video_path, bboxes, start_t, end_t, src_fps=30, tgt_fps=args.fps, crop_size=args.crop_size) + + if frames is None: continue + + # 2. 오디오 처리 (슬라이싱 -> 샘플링 레이트 변환) + start_sample = int(start_t * sr) + end_sample = int(end_t * sr) + sliced_audio = audio_data[start_sample:end_sample] + + # 목표 샘플링 레이트로 리샘플링 (예: 48k -> 16k) + if sr != args.sample_rate: + num_samples = int(len(sliced_audio) * args.sample_rate / sr) + sliced_audio = signal.resample(sliced_audio, num_samples).astype(np.int16) + + # 3. 결과 저장 + save_video(frames, output_vid_path, fps=args.fps) + wavfile.write(output_wav_path, args.sample_rate, sliced_audio) + + with open(output_txt_path, 'w', encoding='utf-8') as tf: + tf.write(text) + + except Exception as e: + print(f"Error processing sentence {sent_id} in {os.path.basename(video_path)}: {e}") + + # 임시 오디오 파일 삭제 + if os.path.exists(temp_wav): os.remove(temp_wav) + +def main(): + parser = get_parser() + args = parser.parse_args() + + # 1. 압축 파일(.tar) 해제 처리 + search_root = args.data_root + tar_files = glob.glob(os.path.join(args.data_root, "**/*.tar"), recursive=True) + + if tar_files and not args.no_tar_extract: + print(f"Found {len(tar_files)} tar files. Extracting...") + for tar_f in tqdm(tar_files): + # 파일명으로 서브폴더 생성하여 중복 방지 + tar_name = os.path.splitext(os.path.basename(tar_f))[0] + extract_to = os.path.join(args.temp_dir, tar_name) + extract_tar(tar_f, extract_to) + + search_root = args.temp_dir + + # 2. JSON(라벨) 및 MP4(원본) 페어 찾기 + json_files = glob.glob(os.path.join(search_root, "**/*.json"), recursive=True) + + print(f"Found {len(json_files)} labeling files. Processing...") + + for json_path in tqdm(json_files): + # 동일한 경로 명에서 .json만 .mp4로 변경하여 비디오 파일 탐색 + base_no_ext = os.path.splitext(json_path)[0] + video_path = base_no_ext + ".mp4" + + if not os.path.exists(video_path): + # AIHub 특성상 '라벨링데이터'와 '원천데이터' 폴더가 나뉜 경우 경로 보정 + # TL(Label) -> TS(Source) 매핑 처리 + video_path = video_path.replace("라벨링데이터", "원천데이터") + video_path = video_path.replace("TL", "TS") + + if not os.path.exists(video_path): + # 비디오를 찾을 수 없는 경우 건너뜀 + continue + + # 화자 ID(Speaker ID) 추출: 파일 경로 또는 JSON 메타데이터에서 확인 + with open(json_path, 'r', encoding='utf-8') as f: + try: + meta = json.load(f) + if isinstance(meta, list): + meta = meta[0] + speaker_id = meta.get('speaker_info', {}).get('speaker_ID', 'Unknown') + except: + speaker_id = "Unknown" + + # 실제 세션 처리 시작 + process_session(json_path, video_path, args, speaker_id) + + print("Preprocessing Complete.") + +if __name__ == "__main__": + main() diff --git a/unit2av/README.md b/unit2av/README.md new file mode 100644 index 0000000..3bb04cd --- /dev/null +++ b/unit2av/README.md @@ -0,0 +1,144 @@ +# 1. UNIT2A +## 2-1. Train + +### 1. 학습 데이터 경로 포함하는 manifest 생성 + +`make_manifest.py` 실행해서 원본 오디오, 해당하는 유닛코드 파일 묶은 `.txt` 파일 생성(매니페스트) + +```bash +python unit2av/make_manifest.py \ + --audio_root /home/2022113135/datasets/zeroth/train_data_01/003 \ + --unit_root /home/2022113135/jihye/preprocessed_mavhubert_unit2a \ + --output_file train_hubert_new.txt +``` + +### 2. config.json 수정 + +먼저 `config.json` 에 학습/검증 데이터 매니페스트 `.txt` 파일 경로 넣어줘야함 + +```json +{ + "input_training_file": "train_hubert_2000.txt", + "input_validation_file": "train_hubert_2000.txt", + /// ... /// +} +``` + +### 3. 학습 실행 + +```bash +cd gyucheol/NetfLips/av2av-main + +CUDA_VISIBLE_DEVICES=<GPU번호설정> python train_unit2a.py \ + --config unit2av/config_hubert.json \ + --checkpoint_path unit2av/checkpoint/seamless-unit-2000 \ + --validation_interval 20 \ + --training_steps 200000 \ + --checkpoint_interval 1000 +``` + +## 2-2. Inference + +```bash +python inference_unit2a.py + --checkpoint "path/to/your/checkpoint" + --config "path/to/your/config.json" + --input_file "path/to/your/input.pt" + --output_folder "path/to/output/folder" +``` + +# 3. UNIT2AV +## Inference +```bash +python inference_unit2av.py + --in-unit-path "path/to/your/units.txt" + --in-vid-path "path/to/original_video.mp4" + --in-bbox-path "path/to/modified.bbox.pkl" + --out-vid-path "path/to/output_video.mp4" + --tgt-lang "en" + --unit2av-path "path/to/unit2av_model.pt" +``` + +## Explanation of Arguments +- `--in-unit-path`: The text file with the number sequence (speech units). +- `--in-vid-path`: Your original input video (used for Speaker Encoder). +- `--in-bbox-path`: Your modified pickle file with the None frames. +- `--unit2av-path`: Path to the .pt checkpoint file you are using. +- `--tgt-lang`: The target language (e.g., en, ko, etc.). + +# 4. 원본 코드 수정한 부분 + +### `unit2av/model.py` + +1. **불필요한 루프 주석 처리 및 삭제** + + ```python + class CodeHiFiGANModel_spk(CodeHiFiGANModel): + def forward(self, **kwargs): + # ... 중략 ... + for k, feat in kwargs.items(): + if k in ["spkr", "code", "f0", "dur_prediction"]: + continue + + feat = self._upsample(feat, x.shape[-1]) + x = torch.cat([x, feat], dim=1) + + return super(CodeHiFiGANModel, self).forward(x), torch.repeat_interleave(kwargs["code"], dur_out.view(-1)) + ``` + + - **원본**: kwargs를 돌며 spkr, code, f0 등을 제외한 나머지 특징량을 모두 업샘플링해서 x에 이어붙이는(concatenate) 로직 + - 목적 : "나머지 처음 보는 데이터(feat)가 들어오면, 무조건 **오디오(x) 길이에 맞춰 늘려서(upsample) 모델 입력에 이어붙여(concat) 버리자!**" + - **수정본**: 이 부분 주석 처리 +2. **학습 시 Duration Loss 계산 로직 추가** + + ```python + if self.dur_predictor and self.training: + # ... 중략 ... + return super(CodeHiFiGANModel,self).forward(x), dur_losses + ``` + + `self.dur_predictor`가 있고 모델이 학습 상태(`self.training`)일 때 실행되는 분기 추가 + + - `process_duration` 함수를 사용해 실제 Duration 값을 추출 + - `self.dur_predictor`를 통해 예측된 값과 실제 값 사이의 **MSE Loss(dur_losses)**를 계산 + - 결과값으로 `super().forward(x)`와 함께 계산된 **`dur_losses`를 반환** +3. **반환값(Return Value)의 세분화** + + 상황에 따라 모델이 반환하는 두 번째 인자값이 달라지도록 변경되었습니다. + + - **학습 시**: `dur_losses` 반환 + - **추론 시 (`dur_prediction=True`)**: `dur_out`에 맞춰 확장된(repeat_interleave)  code 반환 (FaceRenderer가 사용) + - **기본/평가 시**: 확장되지 않은 원본  `kwargs["code"]`반환 (이전에는 무조건 확장을 시도했으나 이제 조건부로 바뀜) + +### Dur_prediction에 관하여… + +**1. 첫 번째 분기: `if self.dur_predictor and self.training:`** + +```python +if self.dur_predictor and self.training: +# ... 코드 ... +return super(CodeHiFiGANModel,self).forward(x), dur_losses + +``` + +- **언제**: 모델을 **학습시킬 때** +- **이유**: + - 오디오 생성(HiFi-GAN)과 길이 예측(Duration Predictor)을 **동시에** 학습하고 있음 + - 오디오 생성은 **forward(x)**로 수행하고, 얼마나 길게 말해야 할지 맞추는 연습은 `dur_losses`로 + - 그래서 학습 결과로 "오디오 신호"와 "길이 예측 오차(Loss)"를 모두 반환해야 학습이 됨 + +**2. 두 번째 분기: `if self.dur_predictor and kwargs.get("dur_prediction", False):`** + +```python +if self.dur_predictor and kwargs.get("dur_prediction",False): +# ... 코드 ... +return..., torch.repeat_interleave(...) + +``` + +- **언제**: 학습이 끝난 후 Inference 단계 +- **조건**: 코드를 호출할 때 `dur_prediction=True`라고 명시했을 때 (UTUT에서 번역된 후 Unit2A 수행할 때) +- **이유**: + - 입력받은 유닛 코드에는 시간 정보가 없음 ← *UTUT로 번역돼서 왔기 때문* + - 그래서 "이 유닛은 3프레임, 저 유닛은 5프레임..." 하고 모델이 직접 길이를 예측해서 유닛을 복사함 + - 그래야 이 늘려진 코드를 받아서 얼굴 생성기(FaceRenderer) 등이 영상의 길이를 맞출 수 있습니다. diff --git a/unit2av/__init__.py b/unit2av/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/unit2av/config.json b/unit2av/config.json new file mode 100644 index 0000000..889820d --- /dev/null +++ b/unit2av/config.json @@ -0,0 +1,53 @@ +{ + "resblock": "1", + "num_gpus": 0, + "batch_size": 16, + "learning_rate": 0.0002, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + + "upsample_rates": [5,4,4,2,2], + "upsample_kernel_sizes": [11,8,8,4,4], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [3,7,11], + "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], + "num_embeddings": 1000, + "embedding_dim": 128, + "model_in_dim": 256, + + "multispkr": true, + "embedder_params": true, + "embedder_dim": 256, + + "segment_size": 8960, + "code_hop_size": 320, + "f0": false, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 256, + "win_size": 1024, + + "dur_prediction_weight": 1.0, + "dur_predictor_params": { + "encoder_embed_dim": 128, + "var_pred_hidden_dim": 128, + "var_pred_kernel_size": 3, + "var_pred_dropout": 0.5 + }, + + "sampling_rate": 16000, + + "fmin": 0, + "fmax": 8000, + "fmax_for_loss": null, + + "num_workers": 4, + + "dist_config": { + "dist_backend": "nccl", + "dist_url": "env://" + } +} diff --git a/unit2av/config_hubert.json b/unit2av/config_hubert.json new file mode 100644 index 0000000..a35707e --- /dev/null +++ b/unit2av/config_hubert.json @@ -0,0 +1,79 @@ +{ + "input_training_file": "train_hubert_2000.txt", + "input_validation_file": "train_hubert_2000.txt", + "resblock": "1", + "num_gpus": 0, + "batch_size": 32, + "learning_rate": 0.0002, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + "upsample_rates": [ + 5, + 4, + 4, + 2, + 2 + ], + "upsample_kernel_sizes": [ + 11, + 8, + 8, + 4, + 4 + ], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ], + "num_embeddings": 1024, + "embedding_dim": 128, + "model_in_dim": 256, + "multispkr": true, + "embedder_params": true, + "embedder_dim": 256, + "segment_size": 8960, + "code_hop_size": 320, + "f0": false, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 256, + "win_size": 1024, + "dur_prediction_weight": 1.0, + "dur_predictor_params": { + "encoder_embed_dim": 128, + "var_pred_hidden_dim": 128, + "var_pred_kernel_size": 3, + "var_pred_dropout": 0.5 + }, + "sampling_rate": 16000, + "fmin": 0, + "fmax": 8000, + "fmax_for_loss": null, + "num_workers": 4, + "dist_config": { + "dist_backend": "nccl", + "dist_url": "env://" + } +} \ No newline at end of file diff --git a/unit2av/config_seamless.json b/unit2av/config_seamless.json new file mode 100644 index 0000000..bf4843b --- /dev/null +++ b/unit2av/config_seamless.json @@ -0,0 +1,79 @@ +{ + "input_training_file": "train_seamless_2000.txt", + "input_validation_file": "train_seamless_2000.txt", + "resblock": "1", + "num_gpus": 0, + "batch_size": 32, + "learning_rate": 0.0002, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + "upsample_rates": [ + 5, + 4, + 4, + 2, + 2 + ], + "upsample_kernel_sizes": [ + 11, + 8, + 8, + 4, + 4 + ], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ], + "num_embeddings": 10000, + "embedding_dim": 128, + "model_in_dim": 256, + "multispkr": true, + "embedder_params": true, + "embedder_dim": 256, + "segment_size": 8960, + "code_hop_size": 320, + "f0": false, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 256, + "win_size": 1024, + "dur_prediction_weight": 1.0, + "dur_predictor_params": { + "encoder_embed_dim": 128, + "var_pred_hidden_dim": 128, + "var_pred_kernel_size": 3, + "var_pred_dropout": 0.5 + }, + "sampling_rate": 16000, + "fmin": 0, + "fmax": 8000, + "fmax_for_loss": null, + "num_workers": 4, + "dist_config": { + "dist_backend": "nccl", + "dist_url": "env://" + } +} \ No newline at end of file diff --git a/unit2av/config_zeroth.json b/unit2av/config_zeroth.json new file mode 100644 index 0000000..1d994ef --- /dev/null +++ b/unit2av/config_zeroth.json @@ -0,0 +1,79 @@ +{ + "input_training_file": "data/zeroth_train.txt", + "input_validation_file": "data/zeroth_val.txt", + "resblock": "1", + "num_gpus": 0, + "batch_size": 32, + "learning_rate": 0.0002, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + "upsample_rates": [ + 5, + 4, + 4, + 2, + 2 + ], + "upsample_kernel_sizes": [ + 11, + 8, + 8, + 4, + 4 + ], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ], + "num_embeddings": 1024, + "embedding_dim": 128, + "model_in_dim": 256, + "multispkr": true, + "embedder_params": true, + "embedder_dim": 256, + "segment_size": 8960, + "code_hop_size": 320, + "f0": false, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 256, + "win_size": 1024, + "dur_prediction_weight": 1.0, + "dur_predictor_params": { + "encoder_embed_dim": 128, + "var_pred_hidden_dim": 128, + "var_pred_kernel_size": 3, + "var_pred_dropout": 0.5 + }, + "sampling_rate": 16000, + "fmin": 0, + "fmax": 8000, + "fmax_for_loss": null, + "num_workers": 4, + "dist_config": { + "dist_backend": "nccl", + "dist_url": "env://" + } +} \ No newline at end of file diff --git a/unit2av/dataset.py b/unit2av/dataset.py new file mode 100644 index 0000000..b134e66 --- /dev/null +++ b/unit2av/dataset.py @@ -0,0 +1,473 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Adapted from https://github.com/jik876/hifi-gan + +import random +from pathlib import Path + +import amfm_decompy.basic_tools as basic +import amfm_decompy.pYAAPT as pYAAPT +import numpy as np +import soundfile as sf +import torch +import torch.utils.data +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from librosa.util import normalize + +MAX_WAV_VALUE = 32768.0 + +# [FIX] mel_spectrogram 함수에서 사용하는 전역변수를 함수 정의 전에 초기화 +# 기존에는 함수 뒤에 정의되어 있어 "mel_basis is not defined" 에러 발생 가능 +mel_basis = {} +hann_window = {} + + +def get_yaapt_f0(audio, rate=16000, interp=False): + frame_length = 20.0 + to_pad = int(frame_length / 1000 * rate) // 2 + + f0s = [] + for y in audio.astype(np.float64): + y_pad = np.pad(y.squeeze(), (to_pad, to_pad), "constant", constant_values=0) + signal = basic.SignalObj(y_pad, rate) + pitch = pYAAPT.yaapt(signal, **{'frame_length': frame_length, 'frame_space': 5.0, 'nccf_thresh1': 0.25, + 'tda_frame_length': 25.0}) + if interp: + f0s += [pitch.samp_interp[None, None, :]] + else: + f0s += [pitch.samp_values[None, None, :]] + + f0 = np.vstack(f0s) + return f0 + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + global mel_basis, hann_window + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], + center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + + spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) + + spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +def load_audio(full_path): + data, sampling_rate = sf.read(full_path, dtype='int16') + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +def parse_manifest(manifest): + audio_files = [] + codes = [] + + with open(manifest) as info: + for line in info.readlines(): + if line[0] == '{': + sample = eval(line.strip()) + if 'unit_path' in sample: + # Store path directly + codes += [sample['unit_path']] + else: + if 'cpc_km100' in sample: + k = 'cpc_km100' + elif 'vqvae256' in sample: + k = 'vqvae256' + elif 'hubert' in sample: + k = 'hubert' + else: + k = 'codes' + + codes += [torch.LongTensor( + [int(x) for x in sample[k].split(' ')] + ).numpy()] + + audio_files += [Path(sample["audio"])] + else: + audio_files += [Path(line.strip())] + + return audio_files, codes + + +def get_dataset_filelist(h): + training_files, training_codes = parse_manifest(h.input_training_file) + validation_files, validation_codes = parse_manifest(h.input_validation_file) + + return (training_files, training_codes), (validation_files, validation_codes) + + +def parse_speaker(path, method): + if type(path) == str: + path = Path(path) + + if method == 'parent_name': + return path.parent.name + elif method == 'parent_parent_name': + return path.parent.parent.name + elif method == '_': + return path.name.split('_')[0] + elif method == 'single': + return 'A' + elif callable(method): + return method(path) + else: + raise NotImplementedError() + + +class CodeDataset(torch.utils.data.Dataset): + def __init__(self, training_files, segment_size, code_hop_size, n_fft, num_mels, + hop_size, win_size, sampling_rate, fmin, fmax, split=True, n_cache_reuse=1, + device=None, fmax_loss=None, f0=None, multispkr=False, pad=None, + f0_stats=None, f0_normalize=False, f0_feats=False, f0_median=False, + f0_interp=False, vqvae=False): + self.audio_files, self.codes = training_files + # self.codes can be a list of unit paths or list of codes + random.seed(1234) + self.segment_size = segment_size + self.code_hop_size = code_hop_size + self.sampling_rate = sampling_rate + self.split = split + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.fmax_loss = fmax_loss + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.vqvae = vqvae + self.f0 = f0 + self.f0_normalize = f0_normalize + self.f0_feats = f0_feats + self.f0_stats = None + self.f0_interp = f0_interp + self.f0_median = f0_median + if f0_stats: + self.f0_stats = torch.load(f0_stats) + self.multispkr = multispkr + self.pad = pad + if self.multispkr and isinstance(self.multispkr, str): # Only if multispkr is a method string + spkrs = [parse_speaker(f, self.multispkr) for f in self.audio_files] + spkrs = list(set(spkrs)) + spkrs.sort() + + self.id_to_spkr = spkrs + self.spkr_to_id = {k: v for v, k in enumerate(self.id_to_spkr)} + + def _sample_interval(self, seqs, seq_len=None): + N = max([v.shape[-1] for v in seqs]) + if seq_len is None: + seq_len = self.segment_size if self.segment_size > 0 else N + + hops = [N // v.shape[-1] for v in seqs] + lcm = np.lcm.reduce(hops) + + # Randomly pickup with the batch_max_steps length of the part + interval_start = 0 + interval_end = N // lcm - seq_len // lcm + + start_step = random.randint(interval_start, interval_end) + + new_seqs = [] + for i, v in enumerate(seqs): + start = start_step * (lcm // hops[i]) + end = (start_step + seq_len // lcm) * (lcm // hops[i]) + new_seqs += [v[..., start:end]] + + return new_seqs + + def __getitem__(self, index): + filename = self.audio_files[index] + code_data = self.codes[index] + + # Determine if code_data is a path (str) or codes (numpy array) + unit_path = None + loaded_spkr = None + + if isinstance(code_data, str) and code_data.endswith('.pt'): + unit_path = code_data + #pt_data = torch.load(unit_path) + pt_data = torch.load(unit_path, map_location='cpu') + + code = pt_data['code'].squeeze() + if 'spkr' in pt_data: + loaded_spkr = pt_data['spkr'] + else: + code = code_data + + if self._cache_ref_count == 0: + audio, sampling_rate = load_audio(filename) + if sampling_rate != self.sampling_rate: + # raise ValueError("{} SR doesn't match target {} SR".format( + # sampling_rate, self.sampling_rate)) + import resampy + audio = resampy.resample(audio, sampling_rate, self.sampling_rate) + + if self.pad: + padding = self.pad - (audio.shape[-1] % self.pad) + audio = np.pad(audio, (0, padding), "constant", constant_values=0) + audio = audio / MAX_WAV_VALUE + audio = normalize(audio) * 0.95 + self.cached_wav = audio + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + # Trim audio ending + if self.vqvae: + code_length = audio.shape[0] // self.code_hop_size + else: + code_length = min(audio.shape[0] // self.code_hop_size, code.shape[0]) + code = code[:code_length] + audio = audio[:code_length * self.code_hop_size] + assert self.vqvae or audio.shape[0] // self.code_hop_size == code.shape[0], "Code audio mismatch" + + while audio.shape[0] < self.segment_size: + audio = np.hstack([audio, audio]) + if not self.vqvae: + code = np.hstack([code, code]) + + audio = torch.FloatTensor(audio) + audio = audio.unsqueeze(0) + + assert audio.size(1) >= self.segment_size, "Padding not supported!!" + if self.vqvae: + audio = self._sample_interval([audio])[0] + else: + if isinstance(code, torch.Tensor): + code = code.numpy() + # If code is int, expand it + if len(code.shape) == 0: + code = code[None] + audio, code = self._sample_interval([audio, code]) + + mel_loss = mel_spectrogram(audio, self.n_fft, self.num_mels, + self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax_loss, + center=False) + + if self.vqvae: + feats = { + "code": audio.view(1, -1).numpy() + } + else: + feats = {"code": code.squeeze()} + + if self.f0: + try: + f0 = get_yaapt_f0(audio.numpy(), rate=self.sampling_rate, interp=self.f0_interp) + except: + f0 = np.zeros((1, 1, audio.shape[-1] // 80)) + f0 = f0.astype(np.float32) + feats['f0'] = f0.squeeze(0) + + if self.multispkr: + if loaded_spkr is not None: + # Add dimension: [256] -> [1, 256] + feats['spkr'] = loaded_spkr.view(1, -1) + else: + feats['spkr'] = self._get_spkr(index) + + if self.f0_normalize: + spkr_id = self._get_spkr(index).item() + + if spkr_id not in self.f0_stats: + mean = self.f0_stats['f0_mean'] + std = self.f0_stats['f0_std'] + else: + mean = self.f0_stats[spkr_id]['f0_mean'] + std = self.f0_stats[spkr_id]['f0_std'] + ii = feats['f0'] != 0 + + if self.f0_median: + med = np.median(feats['f0'][ii]) + feats['f0'][~ii] = med + feats['f0'][~ii] = (feats['f0'][~ii] - mean) / std + + feats['f0'][ii] = (feats['f0'][ii] - mean) / std + + if self.f0_feats: + feats['f0_stats'] = torch.FloatTensor([mean, std]).view(-1).numpy() + + return feats, audio.squeeze(0), str(filename), mel_loss.squeeze() + + def _get_spkr(self, idx): + spkr_name = parse_speaker(self.audio_files[idx], self.multispkr) + spkr_id = torch.LongTensor([self.spkr_to_id[spkr_name]]).view(1).numpy() + return spkr_id + + def __len__(self): + return len(self.audio_files) + + +class F0Dataset(torch.utils.data.Dataset): + def __init__(self, training_files, segment_size, sampling_rate, + split=True, n_cache_reuse=1, device=None, multispkr=False, + pad=None, f0_stats=None, f0_normalize=False, f0_feats=False, + f0_median=False, f0_interp=False, vqvae=False): + self.audio_files, _ = training_files + random.seed(1234) + self.segment_size = segment_size + self.sampling_rate = sampling_rate + self.split = split + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.vqvae = vqvae + self.f0_normalize = f0_normalize + self.f0_feats = f0_feats + self.f0_stats = None + self.f0_interp = f0_interp + self.f0_median = f0_median + if f0_stats: + self.f0_stats = torch.load(f0_stats) + self.pad = pad + self.multispkr = multispkr + if self.multispkr: + spkrs = [parse_speaker(f, self.multispkr) for f in self.audio_files] + spkrs = list(set(spkrs)) + spkrs.sort() + + self.id_to_spkr = spkrs + self.spkr_to_id = {k: v for v, k in enumerate(self.id_to_spkr)} + + def _sample_interval(self, seqs, seq_len=None): + N = max([v.shape[-1] for v in seqs]) + if seq_len is None: + seq_len = self.segment_size if self.segment_size > 0 else N + + hops = [N // v.shape[-1] for v in seqs] + lcm = np.lcm.reduce(hops) + + # Randomly pickup with the batch_max_steps length of the part + interval_start = 0 + interval_end = N // lcm - seq_len // lcm + + start_step = random.randint(interval_start, interval_end) + + new_seqs = [] + for i, v in enumerate(seqs): + start = start_step * (lcm // hops[i]) + end = (start_step + seq_len // lcm) * (lcm // hops[i]) + new_seqs += [v[..., start:end]] + + return new_seqs + + def __getitem__(self, index): + filename = self.audio_files[index] + if self._cache_ref_count == 0: + audio, sampling_rate = load_audio(filename) + if self.pad: + padding = self.pad - (audio.shape[-1] % self.pad) + audio = np.pad(audio, (0, padding), "constant", constant_values=0) + audio = audio / MAX_WAV_VALUE + audio = normalize(audio) * 0.95 + self.cached_wav = audio + if sampling_rate != self.sampling_rate: + raise ValueError("{} SR doesn't match target {} SR".format( + sampling_rate, self.sampling_rate)) + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + while audio.shape[0] < self.segment_size: + audio = np.hstack([audio, audio]) + + audio = torch.FloatTensor(audio) + audio = audio.unsqueeze(0) + + assert audio.size(1) >= self.segment_size, "Padding not supported!!" + audio = self._sample_interval([audio])[0] + + feats = {} + try: + f0 = get_yaapt_f0(audio.numpy(), rate=self.sampling_rate, interp=self.f0_interp) + except: + f0 = np.zeros((1, 1, audio.shape[-1] // 80)) + f0 = f0.astype(np.float32) + feats['f0'] = f0.squeeze(0) + + if self.multispkr: + feats['spkr'] = self._get_spkr(index) + + if self.f0_normalize: + spkr_id = self._get_spkr(index).item() + + if spkr_id not in self.f0_stats: + mean = self.f0_stats['f0_mean'] + std = self.f0_stats['f0_std'] + else: + mean = self.f0_stats[spkr_id]['f0_mean'] + std = self.f0_stats[spkr_id]['f0_std'] + ii = feats['f0'] != 0 + + if self.f0_median: + med = np.median(feats['f0'][ii]) + feats['f0'][~ii] = med + feats['f0'][~ii] = (feats['f0'][~ii] - mean) / std + + feats['f0'][ii] = (feats['f0'][ii] - mean) / std + + if self.f0_feats: + feats['f0_stats'] = torch.FloatTensor([mean, std]).view(-1).numpy() + + return feats, feats['f0'], str(filename) + + def _get_spkr(self, idx): + spkr_name = parse_speaker(self.audio_files[idx], self.multispkr) + spkr_id = torch.LongTensor([self.spkr_to_id[spkr_name]]).view(1).numpy() + return spkr_id + + def __len__(self): + return len(self.audio_files) \ No newline at end of file diff --git a/unit2av/discriminators.py b/unit2av/discriminators.py new file mode 100644 index 0000000..ecb0273 --- /dev/null +++ b/unit2av/discriminators.py @@ -0,0 +1,387 @@ +# adapted from https://github.com/jik876/hifi-gan + +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm + +from .modules.jukebox import Encoder, Decoder +from .utils import init_weights, get_padding, AttrDict +from .modules.vq import Bottleneck + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), weight_norm( + Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), weight_norm( + Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2])))]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)))]) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), weight_norm( + Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1])))]) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm( + Conv1d(getattr(h, "model_in_dim", 128), h.upsample_initial_channel, 7, 1, padding=3)) + resblock = ResBlock1 if h.resblock == '1' else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append(weight_norm( + ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)), k, + u, padding=(k - u) // 2))) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class CodeGenerator(Generator): + def __init__(self, h): + super().__init__(h) + self.dict = nn.Embedding(h.num_embeddings, h.embedding_dim) + self.f0 = h.get('f0', None) + self.multispkr = h.get('multispkr', None) + + if self.multispkr: + self.spkr = nn.Embedding(200, h.embedding_dim) + + self.encoder = None + self.vq = None + if h.get("lambda_commit", None): + assert self.f0, "Requires F0 set" + self.encoder = Encoder(**h.f0_encoder_params) + self.vq = Bottleneck(**h.f0_vq_params) + + self.code_encoder = None + self.code_vq = None + if h.get('lambda_commit_code', None): + self.code_encoder = Encoder(**h.code_encoder_params) + self.code_vq = Bottleneck(**h.code_vq_params) + self.dict = None + + self.quantizer = None + if h.get('f0_quantizer_path', None): + assert self.f0, "Requires F0 set" + self.quantizer = Quantizer(AttrDict(h.f0_quantizer)) + quantizer_state = torch.load(h.f0_quantizer_path, map_location='cpu') + self.quantizer.load_state_dict(quantizer_state['generator']) + self.quantizer.eval() + self.f0_dict = nn.Embedding(h.f0_quantizer['f0_vq_params']['l_bins'], h.embedding_dim) + + @staticmethod + def _upsample(signal, max_frames): + if signal.dim() == 3: + bsz, channels, cond_length = signal.size() + elif signal.dim() == 2: + signal = signal.unsqueeze(2) + bsz, channels, cond_length = signal.size() + else: + signal = signal.view(-1, 1, 1) + bsz, channels, cond_length = signal.size() + + signal = signal.unsqueeze(3).repeat(1, 1, 1, max_frames // cond_length) + + # pad zeros as needed (if signal's shape does not divide completely with max_frames) + reminder = (max_frames - signal.shape[2] * signal.shape[3]) // signal.shape[3] + if reminder > 0: + raise NotImplementedError('Padding condition signal - misalignment between condition features.') + + signal = signal.view(bsz, channels, max_frames) + return signal + + def forward(self, **kwargs): + code_commit_losses = None + code_metrics = None + if self.code_vq and kwargs['code'].dtype is torch.int64: + x = self.code_vq.level_blocks[0].k[kwargs['code']].transpose(1, 2) + elif self.code_vq: + code_h = self.code_encoder(kwargs['code']) + _, code_h_q, code_commit_losses, code_metrics = self.code_vq(code_h) + x = code_h_q[0] + else: + x = self.dict(kwargs['code']).transpose(1, 2) + + f0_commit_losses = None + f0_metrics = None + if self.vq: + f0_h = self.encoder(kwargs['f0']) + _, f0_h_q, f0_commit_losses, f0_metrics = self.vq(f0_h) + kwargs['f0'] = f0_h_q[0] + elif self.quantizer: + self.quantizer.eval() + assert not self.quantizer.training, "VQ is in training status!!!" + f0_h = self.quantizer.encoder(kwargs['f0']) + f0_h = [x.detach() for x in f0_h] + zs, _, _, _ = self.quantizer.vq(f0_h) + zs = [x.detach() for x in zs] + f0_h_q = self.f0_dict(zs[0].detach()).transpose(1, 2) + kwargs['f0'] = f0_h_q + + if self.f0: + if x.shape[-1] < kwargs['f0'].shape[-1]: + x = self._upsample(x, kwargs['f0'].shape[-1]) + else: + kwargs['f0'] = self._upsample(kwargs['f0'], x.shape[-1]) + x = torch.cat([x, kwargs['f0']], dim=1) + + if self.multispkr: + spkr = self.spkr(kwargs['spkr']).transpose(1, 2) + spkr = self._upsample(spkr, x.shape[-1]) + x = torch.cat([x, spkr], dim=1) + + for k, feat in kwargs.items(): + if k in ['spkr', 'code', 'f0']: + continue + + feat = self._upsample(feat, x.shape[-1]) + x = torch.cat([x, feat], dim=1) + + if self.vq or self.code_vq: + return super().forward(x), (code_commit_losses, f0_commit_losses), (code_metrics, f0_metrics) + else: + return super().forward(x) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiPeriodDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [DiscriminatorP(2), DiscriminatorP(3), DiscriminatorP(5), DiscriminatorP(7), DiscriminatorP(11), ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [norm_f(Conv1d(1, 128, 15, 1, padding=7)), norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [DiscriminatorS(use_spectral_norm=True), DiscriminatorS(), DiscriminatorS(), ]) + self.meanpools = nn.ModuleList([AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class Quantizer(nn.Module): + def __init__(self, h): + super().__init__() + + self.encoder = Encoder(**h.f0_encoder_params) + self.vq = Bottleneck(**h.f0_vq_params) + self.decoder = Decoder(**h.f0_decoder_params) + + def forward(self, **kwargs): + f0_h = self.encoder(kwargs['f0']) + _, f0_h_q, f0_commit_losses, f0_metrics = self.vq(f0_h) + f0 = self.decoder(f0_h_q) + + return f0, f0_commit_losses, f0_metrics + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg ** 2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/unit2av/environment.yml b/unit2av/environment.yml new file mode 100644 index 0000000..110a64c --- /dev/null +++ b/unit2av/environment.yml @@ -0,0 +1,9 @@ +name: unit2a +channels: + - conda-forge + - defaults +dependencies: + - python=3.8 + - pip + - ffmpeg + - ninja \ No newline at end of file diff --git a/unit2av/inference_unit2a.py b/unit2av/inference_unit2a.py new file mode 100644 index 0000000..9883bbe --- /dev/null +++ b/unit2av/inference_unit2a.py @@ -0,0 +1,108 @@ +import argparse +import json +import torch +import soundfile as sf +import re +import os +from model import CodeHiFiGANModel_spk +from utils import AttrDict + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--checkpoint', required=True, help='Path to the model checkpoint (e.g. g_00011000)') + parser.add_argument('--config', required=True, help='Path to config.json') + parser.add_argument('--input_file', required=True, help='Path to input .pt file containing code and optional spkr') + parser.add_argument('--output_folder', default='output/unit2a', help='Output wav folder') + parser.add_argument('--device', default='cuda', help='Device to use (cuda/cpu)') + + args = parser.parse_args() + + device = torch.device(args.device if torch.cuda.is_available() else 'cpu') + + # 1. Load Configuration + print(f"Loading config from {args.config}...") + with open(args.config) as f: + data = f.read() + json_config = json.loads(data) + h = AttrDict(json_config) + + # 2. Initialize Model + print("Initializing model...") + generator = CodeHiFiGANModel_spk(dict(h)).to(device) + + # 3. Load Checkpoint + print(f"Loading checkpoint from {args.checkpoint}...") + state_dict = torch.load(args.checkpoint, map_location=device) + + if 'generator' in state_dict: + generator.load_state_dict(state_dict['generator']) + else: + # Just in case the checkpoint structure is different + generator.load_state_dict(state_dict) + + generator.eval() + generator.remove_weight_norm() + + # 4. Load Input Data + print(f"Loading input units from {args.input_file}...") + # Expecting the .pt file format used in training (containing 'code' and optionally 'spkr') + data = torch.load(args.input_file, map_location='cpu') + + if isinstance(data, dict): + code = data.get('code') + spkr = data.get('spkr') + else: + # Fallback if it's just a code tensor + code = data + spkr = None + + if code is None: + raise ValueError("Could not find 'code' in the input file.") + + # Prepare input for model + # Model expects batch dimension + if code.dim() == 1: + code = code.unsqueeze(0) + + x = {'code': code.to(device)} + + if h.get('multispkr') and spkr is not None: + if spkr.dim() == 1: + spkr = spkr.unsqueeze(0) + x['spkr'] = spkr.to(device) + print("Using speaker embedding from input file.") + elif h.get('multispkr'): + print("Warning: Model expects speaker embedding but none provided in input file. This may cause errors.") + + # 5. Run Inference + print("Generating audio...") + with torch.no_grad(): + # returns (wav, dedup_code) + y_g_hat, _ = generator(**x) + + audio = y_g_hat.squeeze() + audio = audio.cpu().numpy() + + # 6. Save Output + os.makedirs(args.output_folder, exist_ok=True) + + input_base = os.path.splitext(os.path.basename(args.input_file))[0][:-13] + + # Extract step from checkpoint filename + ckpt_name = os.path.basename(args.checkpoint) + match = re.search(r'(\d+)', ckpt_name) + if match: + step = int(match.group(1)) + suffix = f"{step}step" + else: + suffix = "unknown_step" + + output_filename = f"{input_base}_{suffix}.wav" + output_path = os.path.join(args.output_folder, output_filename) + + print(f"Saving audio to {output_path}...") + sf.write(output_path, audio, h.sampling_rate) + print("Done!") + +if __name__ == '__main__': + main() diff --git a/unit2av/inference_unit2av.py b/unit2av/inference_unit2av.py new file mode 100644 index 0000000..d9f2e86 --- /dev/null +++ b/unit2av/inference_unit2av.py @@ -0,0 +1,84 @@ +# This code is from https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/generate_waveform_from_code.py + +import argparse +import os +import sys + +import json +import torch + +from fairseq import utils +from model import UnitAVRenderer +from model_speaker_encoder import SpeakerEncoder + +sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) +from util import save_video, extract_audio_from_video + +def load_model(model_path, cfg_path, lang, use_cuda=False): + with open(cfg_path) as f: + vocoder_cfg = json.load(f) + vocoder = UnitAVRenderer(model_path, vocoder_cfg, lang) + if use_cuda: + vocoder = vocoder.cuda() + return vocoder + +def load_speaker_encoder_model(model_path, use_cuda=False): + speaker_encoder = SpeakerEncoder(model_path) + if use_cuda: + speaker_encoder = speaker_encoder.cuda() + return speaker_encoder + +def main(args): + use_cuda = torch.cuda.is_available() and not args.cpu + + cfg_path = os.path.join(os.path.dirname(__file__), "config.json") + vocoder = load_model(args.unit2av_path, cfg_path, args.tgt_lang, use_cuda=use_cuda) + speaker_encoder = load_speaker_encoder_model(os.path.join(os.path.dirname(__file__), "encoder.pt"), use_cuda=use_cuda) + + temp_audio_path = os.path.splitext(args.in_vid_path)[0]+".temp.wav" + bbox_path = os.path.splitext(args.in_vid_path)[0]+".bbox.pkl" + extract_audio_from_video(args.in_vid_path, temp_audio_path) + + with open(args.in_unit_path) as f: + unit = list(map(int, f.readline().strip().split())) + + sample = { + "code": torch.LongTensor(unit).view(1,-1), + "spkr": torch.from_numpy(speaker_encoder.get_embed(args.in_vid_path)).view(1,1,-1), + } + sample = utils.move_to_cuda(sample) if use_cuda else sample + + wav, video, full_video, bbox = vocoder(sample, args.in_vid_path, bbox_path, dur_prediction=True) + + save_video(wav, video, full_video, bbox, args.out_vid_path) + + os.remove(temp_audio_path) + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-unit-path", type=str, required=True, help="File path of unit input" + ) + parser.add_argument( + "--in-vid-path", type=str, required=True, help="File path of video input" + ) + parser.add_argument( + "--in-bbox-path", type=str, required=True, help="File path of bounding box" + ) + parser.add_argument( + "--out-vid-path", type=str, required=True, help="File path of video output" + ) + parser.add_argument( + "--tgt-lang", type=str, required=True, + choices=["en","es","fr","it","pt", "ko"], + help="target language" + ) + parser.add_argument( + "--unit2av-path", type=str, required=True, help="path to the Unit AV Renderer" + ) + parser.add_argument("--cpu", action="store_true", help="run on CPU") + args = parser.parse_args() + main(args) + +if __name__ == "__main__": + cli_main() \ No newline at end of file diff --git a/unit2av/make_manifest.py b/unit2av/make_manifest.py new file mode 100644 index 0000000..1bc1520 --- /dev/null +++ b/unit2av/make_manifest.py @@ -0,0 +1,81 @@ +import os +import glob +import torch +import random + +''' +train_unit2a.py 기대하는 학습 데이터 형식 맞추는 스크립트 +train_unit2a.py는 매니페스트 파일(텍스트) 내에 오디오 경로와 코드(Unit) 시퀀스가 텍스트 형태로 구성되어야함 +-> 유닛 코드(.pt 파일) + 원본 오디오 경로 -> 텍스트 파일로 변환 +''' + +import argparse + +# Argument Parser 설정 +parser = argparse.ArgumentParser(description='Create manifest file for unit2av training') +parser.add_argument('--audio_root', type=str, required=True, help='Root directory of audio files') +parser.add_argument('--unit_root', type=str, required=True, help='Root directory of unit (.pt) files') +parser.add_argument('--output_file', type=str, default='train_hubert.txt', help='Output manifest file path') + +args = parser.parse_args() + +# 경로 설정 +audio_root = args.audio_root +unit_root = args.unit_root +output_file = args.output_file + +# 1. 유닛 파일(.pt) 검색 +# 유닛 파일이 "선별된" 데이터이므로, 유닛 파일을 기준으로 오디오를 매칭합니다. +unit_files = sorted(glob.glob(os.path.join(unit_root, '*.pt'))) +print(f"Total unit files found: {len(unit_files)}") + +# 2. 100개만 선별 +target_unit_files = unit_files + +lines = [] +for unit_path in target_unit_files: + # unit_path: .../113_003_0012.pt + fname = os.path.basename(unit_path)[:-16]+ ".pt" + # fname: 113_003_0012.pt + + # 오디오 파일명 추론: 113_003_0012.wav + wav_fname = fname.replace('.pt', '.wav') + + # 폴더 구조 추론: 113_003_0012 -> speaker: 113 + # 오디오 경로는 audio_root + speaker + wav_fname + parts = fname.split('_') + if len(parts) >= 1: + speaker_id = parts[0] + audio_path = os.path.join(audio_root, speaker_id, wav_fname) + + if os.path.exists(audio_path): + try: + # 3. .pt 파일 로드 및 코드 추출 + data = torch.load(unit_path) + # 사용자 데이터 키: 'code' -> 모델이 기대하는 키: 'codes' + code_tensor = data['code'] + + # 텐서를 공백으로 구분된 문자열로 변환 + code_list = code_tensor.squeeze().tolist() + if isinstance(code_list, int): code_list = [code_list] + code_str = ' '.join(map(str, code_list)) + + # 4. 딕셔너리 포맷으로 저장 + # unit_path를 저장하여 dataset.py에서 직접 .pt를 로드하도록 함 + entry = { + 'audio': audio_path, + 'unit_path': unit_path + } + lines.append(str(entry)) + except Exception as e: + print(f"Error reading {unit_path}: {e}") + else: + print(f"Audio file not found for unit: {audio_path}") + else: + print(f"Cannot parse speaker id from {fname}") + +# 5. 파일 저장 +with open(output_file, 'w') as f: + f.write('\n'.join(lines)) + +print(f"Saved {len(lines)} samples to {output_file}") \ No newline at end of file diff --git a/unit2av/model.py b/unit2av/model.py new file mode 100644 index 0000000..875f846 --- /dev/null +++ b/unit2av/model.py @@ -0,0 +1,374 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Dict +from fairseq.models.text_to_speech.codehifigan import CodeGenerator as CodeHiFiGANModel +from fairseq.models.text_to_speech.vocoder import CodeHiFiGANVocoder + +import torchvision +import pickle +import numpy as np +import cv2 + +def process_duration(code, code_feat): + ''' + 새로 추가한 부분 + from speech-resynthesis의 DurationCodeGenerator + + + ''' + uniq_code_count = [] + uniq_code_feat = [] + for i in range(code.size(0)): + _, count = torch.unique_consecutive(code[i, :], return_counts=True) + + if len(count) > 2: + # remove first and last code as segment sampling may cause incomplete segment length + uniq_code_count.append(count[1:-1]) + uniq_code_idx = count.cumsum(dim=0)[:-2] + else: + uniq_code_count.append(count) + uniq_code_idx = count.cumsum(dim=0) - 1 + uniq_code_feat.append(code_feat[i, uniq_code_idx, :].view(-1, code_feat.size(2))) + + uniq_code_count = torch.cat(uniq_code_count) + + # collate feat + max_len = max(feat.size(0) for feat in uniq_code_feat) + out = uniq_code_feat[0].new_zeros((len(uniq_code_feat), max_len, uniq_code_feat[0].size(1))) + mask = torch.arange(max_len).repeat(len(uniq_code_feat), 1) + for i, v in enumerate(uniq_code_feat): + out[i, : v.size(0)] = v + mask[i, :] = mask[i, :] < v.size(0) + + return out, mask.bool(), uniq_code_count.float() + +class UnitAVRenderer(CodeHiFiGANVocoder): + def __init__( + self, checkpoint_path: str, model_cfg: Dict[str, str], lang: str, fp16: bool = False + ) -> None: + super(CodeHiFiGANVocoder, self).__init__() + self.model = CodeHiFiGANModel_spk(model_cfg) + if torch.cuda.is_available(): + state_dict = torch.load(checkpoint_path) + else: + state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu")) + self.model.load_state_dict(state_dict["audio"][lang]) + self.model.eval() + + self.face_model = FaceRenderer(unit_num=model_cfg["num_embeddings"]) + face_state_dict = state_dict["video"] + + # unit_embed만 특별 처리 + current_embed = self.face_model.unit_embed.weight.data + checkpoint_embed = face_state_dict['unit_embed.weight'] + + # 기존 1000개는 체크포인트 값 사용 + current_embed[:1000] = checkpoint_embed + + # 새로운 24개는 기존 임베딩의 평균이나 랜덤으로 초기화 + # 옵션 1: 마지막 임베딩 복사 + current_embed[1000:] = checkpoint_embed[-1].unsqueeze(0).repeat(24, 1) + + # unit_embed.weight를 state_dict에서 제거하고 나머지 로드 + face_state_dict.pop('unit_embed.weight') + self.face_model.load_state_dict(face_state_dict, strict=False) + + self.face_model.eval() + + if fp16: + self.model.half() + self.face_model.half() + self.model.remove_weight_norm() + + units_per_second = 50 + frames_per_second = 25 + self.num_frames = 10 + self.code_frame_ratio = units_per_second // frames_per_second + self.num_units = self.num_frames * self.code_frame_ratio + + def get_crops(self, bbox_path): + bbs = pickle.load(open(bbox_path, 'rb')) + return np.array(bbs, dtype=object) + + def read_window(self, frames, crops): + window = [] + for img, crop in zip(frames, crops): + # modified : if bbox is None, write black image + if crop is None: + window.append(np.zeros((96, 96, 3), dtype=np.uint8)) + continue + x1, y1, x2, y2 = crop + img = img[max(int(y1), 0): int(y2), max(int(x1), 0):int(x2)] + if img.size == 0: + img = np.zeros((96, 96, 3), dtype=np.uint8) + else: + img = cv2.resize(img, (96, 96)) + window.append(img) + return window + + def prepare_window(self, window): + # 3 x T x H x W + x = np.asarray(window) / 255. + x = np.transpose(x, (3, 0, 1, 2)) + return x + + def forward(self, x: Dict[str, torch.Tensor], video_path: str, bbox_path: str, dur_prediction=False) -> torch.Tensor: + assert "code" in x + x["dur_prediction"] = dur_prediction + + if dur_prediction: + x["code"] = torch.unique_consecutive(x["code"]) + + # remove invalid code + mask = x["code"] >= 0 + x["code"] = x["code"][mask].unsqueeze(dim=0) + if "f0" in x: + f0_up_ratio = x["f0"].size(1) // x["code"].size(1) + mask = mask.unsqueeze(2).repeat(1, 1, f0_up_ratio).view(-1, x["f0"].size(1)) + x["f0"] = x["f0"][mask].unsqueeze(dim=0) + + gen_wav, dedup_code = self.model(**x) + gen_wav = gen_wav.detach().squeeze().cpu().numpy() + + tgt_len = len(dedup_code) // self.code_frame_ratio + remain = len(dedup_code) % self.num_units + if remain != 0: + repeat_num = self.num_units - remain + dedup_code = torch.cat([dedup_code, dedup_code[-1].repeat(repeat_num)]) + padded_tgt_len = len(dedup_code) // self.code_frame_ratio + + frames = torchvision.io.read_video(video_path, pts_unit="sec")[0] + len_frames = len(frames) + reverse_frames = frames.flip(0) + repeated_frames = torch.cat((reverse_frames[1:], frames[1:])) + while len(frames) < padded_tgt_len: + frames = torch.cat([frames, repeated_frames]) + frames = frames[:padded_tgt_len] + frames = frames.flip(-1) + + crops = self.get_crops(bbox_path) + assert len(crops) == len_frames + reverse_crops = crops[::-1] + repeated_crops = np.concatenate([reverse_crops[1:], crops[1:]]) + while len(crops) < padded_tgt_len: + crops = np.concatenate([crops, repeated_crops]) + crops = crops[:padded_tgt_len] + + frames_numpy = np.array(frames) + window = self.read_window(frames_numpy, crops) + wrong_window = window.copy() + + dedup_code_seq = dedup_code.view(-1, self.num_units) + + window = self.prepare_window(window) + window[:, :, window.shape[2] // 2:] = 0. + wrong_window = self.prepare_window(wrong_window) + windows = np.concatenate([window, wrong_window], axis=0) + windows = torch.FloatTensor(windows).to(dedup_code_seq.device) + windows = windows.transpose(1,0) + + gen_vid = self.face_model(dedup_code_seq, windows) + gen_vid = (gen_vid.detach().cpu().numpy().transpose(0,2,3,1)* 255.).astype(np.uint8) + + return gen_wav, gen_vid[:tgt_len], frames_numpy[:tgt_len], crops[:tgt_len] + + +class CodeHiFiGANModel_spk(CodeHiFiGANModel): + def forward(self, **kwargs): + x = self.dict(kwargs["code"]).transpose(1, 2) + + if self.dur_predictor and kwargs.get("dur_prediction", False): + assert x.size(0) == 1, "only support single sample" + log_dur_pred = self.dur_predictor(x.transpose(1, 2)) + dur_out = torch.clamp( + torch.round((torch.exp(log_dur_pred) - 1)).long(), min=1 + ) + # B x C x T + x = torch.repeat_interleave(x, dur_out.view(-1), dim=2) + + if self.f0: + if self.f0_quant_embed: + kwargs["f0"] = self.f0_quant_embed(kwargs["f0"].long()).transpose(1, 2) + else: + kwargs["f0"] = kwargs["f0"].unsqueeze(1) + + if x.shape[-1] < kwargs["f0"].shape[-1]: + x = self._upsample(x, kwargs["f0"].shape[-1]) + elif x.shape[-1] > kwargs["f0"].shape[-1]: + kwargs["f0"] = self._upsample(kwargs["f0"], x.shape[-1]) + x = torch.cat([x, kwargs["f0"]], dim=1) + + if self.multispkr: + assert ( + "spkr" in kwargs + ), 'require "spkr" input for multispeaker CodeHiFiGAN vocoder' + spkr = self.spkr(kwargs["spkr"]).transpose(1, 2) + spkr = self._upsample(spkr, x.shape[-1]) + x = torch.cat([x, spkr], dim=1) +# for k, feat in kwargs.items(): +# if k in ["spkr", "code", "f0", "dur_prediction"]: +# continue +# feat = self._upsample(feat, x.shape[-1]) +# x = torch.cat([x, feat], dim=1) + + dur_losses = None + if self.dur_predictor and self.training: + # Re-calculate unique code features for duration loss calculation + # This is duplicate work if we already did it above but CodeHiFiGANModel_spk + # structure doesn't easily allow passing it down. + # Assuming 'code' in kwargs is the repeated/aligned code suitable for audio gen. + + # We need to extract unique codes to train the predictor. + # (Re-using the logic from DurationCodeGenerator) + x_for_dur = self.dict(kwargs["code"]).transpose(1, 2) + uniq_code_feat, uniq_code_mask, dur = process_duration( + kwargs['code'], x_for_dur.transpose(1, 2)) + log_dur_pred = self.dur_predictor(uniq_code_feat) + log_dur_pred = log_dur_pred[uniq_code_mask] + log_dur = torch.log(dur + 1) + dur_losses = F.mse_loss(log_dur_pred, log_dur, reduction="mean") + + return super(CodeHiFiGANModel, self).forward(x), dur_losses + + if self.dur_predictor and kwargs.get("dur_prediction", False): + # Inference with duration prediction: Return expanded code for FaceRenderer + return super(CodeHiFiGANModel, self).forward(x), torch.repeat_interleave(kwargs["code"], dur_out.view(-1)) + + # Default / Evaluation without Duration Prediction: Return original code + return super(CodeHiFiGANModel, self).forward(x), kwargs["code"] + + +class FaceRenderer(nn.Module): + def __init__(self, unit_num): + super(FaceRenderer, self).__init__() + self.unit_num = unit_num + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), + + nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), + + nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0)), ]) + + self.unit_embed = nn.Embedding(self.unit_num, 512) + self.unit2lip = nn.TransformerEncoderLayer(d_model=512, nhead=1, dim_feedforward=1024, dropout=0.1, activation='relu') + + self.face_decoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0), ), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), + + nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), ), + + nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), ), + + nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), ), + + nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), ), ]) + + self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1), + nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0), + nn.Sigmoid()) + + def forward(self, audio_sequences, face_sequences): + audio_sequences = self.unit_embed(audio_sequences) # B,20,512 / T/10,20,512 + audio_sequences = F.interpolate(audio_sequences.permute(0, 2, 1), scale_factor=0.5, mode='linear') # B,512,10 / T/10,512,10 + audio_sequences = audio_sequences.permute(2, 0, 1) # 10,B,512 / 10,T/10,512 + audio_embedding = self.unit2lip(audio_sequences).permute(1,0,2) # B,10,512 + audio_embedding = audio_embedding.contiguous().view(-1, 512).unsqueeze(-1).unsqueeze(-1) + + feats = [] + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + feats.append(x) + + x = audio_embedding + for f in self.face_decoder_blocks: + x = f(x) + try: + x = torch.cat((x, feats[-1]), dim=1) + except Exception as e: + print(x.size()) + print(feats[-1].size()) + raise e + + feats.pop() + + outputs = self.output_block(x) + return outputs + +class nonorm_Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + ) + self.act = nn.LeakyReLU(0.01, inplace=True) + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) + +class Conv2dTranspose(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) + +class Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + self.residual = residual + + def forward(self, x): + out = self.conv_block(x) + if self.residual: + out = out + x + return self.act(out) + \ No newline at end of file diff --git a/unit2av/model_speaker_encoder.py b/unit2av/model_speaker_encoder.py new file mode 100644 index 0000000..26c25a9 --- /dev/null +++ b/unit2av/model_speaker_encoder.py @@ -0,0 +1,318 @@ +""" +Modified from https://github.com/CorentinJ/Real-Time-Voice-Cloning +""" + +import torch +from torch import nn +from dataclasses import dataclass +from scipy.ndimage.morphology import binary_dilation +from pathlib import Path +from typing import Optional, Union +from warnings import warn +import numpy as np +import librosa +import struct + +try: + import webrtcvad +except: + warn("Unable to import 'webrtcvad'. This package enables noise removal and is recommended.") + webrtcvad=None + +@dataclass +class SpeakerEncoderConfig: + + ## Model parameters + model_hidden_size = 256 + model_embedding_size = 256 + model_num_layers = 3 + + ## Mel-filterbank + mel_window_length = 25 # In milliseconds + mel_window_step = 10 # In milliseconds + mel_n_channels = 40 + + ## Audio + sampling_rate = 16000 + # Number of spectrogram frames in a partial utterance + partials_n_frames = 160 # 1600 ms + # Number of spectrogram frames at inference + inference_n_frames = 80 # 800 ms + + ## Voice Activation Detection + # Window size of the VAD. Must be either 10, 20 or 30 milliseconds. + # This sets the granularity of the VAD. Should not need to be changed. + vad_window_length = 30 # In milliseconds + # Number of frames to average together when performing the moving average smoothing. + # The larger this value, the larger the VAD variations must be to not get smoothed out. + vad_moving_average_width = 8 + # Maximum number of consecutive silent frames a segment can have. + vad_max_silence_length = 6 + + ## Audio volume normalization + audio_norm_target_dBFS = -30 + + int16_max = (2 ** 15) - 1 + + +class SpeakerEncoder(nn.Module): + def __init__(self, checkpoint_path: str): + super().__init__() + + self.cfg = SpeakerEncoderConfig() + + # Network defition + self.lstm = nn.LSTM(input_size=self.cfg.mel_n_channels, + hidden_size=self.cfg.model_hidden_size, + num_layers=self.cfg.model_num_layers, + batch_first=True) + self.linear = nn.Linear(in_features=self.cfg.model_hidden_size, + out_features=self.cfg.model_embedding_size) + self.relu = torch.nn.ReLU() + + # Cosine similarity scaling (with fixed initial parameter values) + self.similarity_weight = nn.Parameter(torch.tensor([10.])) + self.similarity_bias = nn.Parameter(torch.tensor([-5.])) + + if torch.cuda.is_available(): + state_dict = torch.load(checkpoint_path) + else: + state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu")) + self.load_state_dict(state_dict["model_state"]) + self.eval() + + def forward(self, utterances, hidden_init=None): + """ + Computes the embeddings of a batch of utterance spectrograms. + + :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape + (batch_size, n_frames, n_channels) + :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, + batch_size, hidden_size). Will default to a tensor of zeros if None. + :return: the embeddings as a tensor of shape (batch_size, embedding_size) + """ + # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state + # and the final cell state. + out, (hidden, cell) = self.lstm(utterances, hidden_init) + + # We take only the hidden state of the last layer + embeds_raw = self.relu(self.linear(hidden[-1])) + + # L2-normalize it + embeds = embeds_raw / (torch.norm(embeds_raw, dim=1, keepdim=True) + 1e-5) + + return embeds + + + def preprocess_wav( + self, + fpath_or_wav: Union[str, Path, np.ndarray], + source_sr: Optional[int] = None, + normalize: Optional[bool] = True, + trim_silence: Optional[bool] = True): + """ + Applies the preprocessing operations used in training the Speaker Encoder to a waveform + either on disk or in memory. The waveform will be resampled to match the data hyperparameters. + + :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not + just .wav), either the waveform as a numpy array of floats. + :param source_sr: if passing an audio waveform, the sampling rate of the waveform before + preprocessing. After preprocessing, the waveform's sampling rate will match the data + hyperparameters. If passing a filepath, the sampling rate will be automatically detected and + this argument will be ignored. + """ + # Load the wav from disk if needed + if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path): + wav, source_sr = librosa.load(str(fpath_or_wav), sr=None) + else: + wav = fpath_or_wav + + # Resample the wav if needed + if source_sr is not None and source_sr != self.cfg.sampling_rate: + wav = librosa.resample(wav, source_sr, self.cfg.sampling_rate) + + # Apply the preprocessing: normalize volume and shorten long silences + if normalize: + wav = self.normalize_volume(wav, self.cfg.audio_norm_target_dBFS, increase_only=True) + if webrtcvad and trim_silence: + wav = self.trim_long_silences(wav) + + return wav + + + def wav_to_mel_spectrogram(self, wav): + """ + Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform. + Note: this not a log-mel spectrogram. + """ + frames = librosa.feature.melspectrogram( + wav, + self.cfg.sampling_rate, + n_fft=int(self.cfg.sampling_rate * self.cfg.mel_window_length / 1000), + hop_length=int(self.cfg.sampling_rate * self.cfg.mel_window_step / 1000), + n_mels=self.cfg.mel_n_channels + ) + return frames.astype(np.float32).T + + + def trim_long_silences(self, wav): + """ + Ensures that segments without voice in the waveform remain no longer than a + threshold determined by the VAD parameters in params.py. + + :param wav: the raw waveform as a numpy array of floats + :return: the same waveform with silences trimmed away (length <= original wav length) + """ + # Compute the voice detection window size + samples_per_window = (self.cfg.vad_window_length * self.cfg.sampling_rate) // 1000 + + # Trim the end of the audio to have a multiple of the window size + wav = wav[:len(wav) - (len(wav) % samples_per_window)] + + # Convert the float waveform to 16-bit mono PCM + pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * self.cfg.int16_max)).astype(np.int16)) + + # Perform voice activation detection + voice_flags = [] + vad = webrtcvad.Vad(mode=3) + for window_start in range(0, len(wav), samples_per_window): + window_end = window_start + samples_per_window + voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], + sample_rate=self.cfg.sampling_rate)) + voice_flags = np.array(voice_flags) + + # Smooth the voice detection with a moving average + def moving_average(array, width): + array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) + ret = np.cumsum(array_padded, dtype=float) + ret[width:] = ret[width:] - ret[:-width] + return ret[width - 1:] / width + + audio_mask = moving_average(voice_flags, self.cfg.vad_moving_average_width) + audio_mask = np.round(audio_mask).astype(np.bool) + + # Dilate the voiced regions + audio_mask = binary_dilation(audio_mask, np.ones(self.cfg.vad_max_silence_length + 1)) + audio_mask = np.repeat(audio_mask, samples_per_window) + + return wav[audio_mask == True] + + + def normalize_volume(self, wav, target_dBFS, increase_only=False, decrease_only=False): + if increase_only and decrease_only: + raise ValueError("Both increase only and decrease only are set") + dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2)) + if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only): + return wav + return wav * (10 ** (dBFS_change / 20)) + + + def embed_frames_batch(self, frames_batch): + """ + Computes embeddings for a batch of mel spectrogram. + + :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape + (batch_size, n_frames, n_channels) + :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size) + """ + frames = torch.from_numpy(frames_batch).to(next(self.parameters()).device) + embed = self.forward(frames).detach().cpu().numpy() + return embed + + + def compute_partial_slices(self, n_samples, partial_utterance_n_frames=None, + min_pad_coverage=0.75, overlap=0.5): + """ + Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain + partial utterances of <partial_utterance_n_frames> each. Both the waveform and the mel + spectrogram slices are returned, so as to make each partial utterance waveform correspond to + its spectrogram. This function assumes that the mel spectrogram parameters used are those + defined in params_data.py. + + The returned ranges may be indexing further than the length of the waveform. It is + recommended that you pad the waveform with zeros up to wave_slices[-1].stop. + + :param n_samples: the number of samples in the waveform + :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial + utterance + :param min_pad_coverage: when reaching the last partial utterance, it may or may not have + enough frames. If at least <min_pad_coverage> of <partial_utterance_n_frames> are present, + then the last partial utterance will be considered, as if we padded the audio. Otherwise, + it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial + utterance, this parameter is ignored so that the function always returns at least 1 slice. + :param overlap: by how much the partial utterance should overlap. If set to 0, the partial + utterances are entirely disjoint. + :return: the waveform slices and mel spectrogram slices as lists of array slices. Index + respectively the waveform and the mel spectrogram with these slices to obtain the partial + utterances. + """ + if partial_utterance_n_frames is None: + partial_utterance_n_frames = self.cfg.partials_n_frames + + assert 0 <= overlap < 1 + assert 0 < min_pad_coverage <= 1 + + samples_per_frame = int((self.cfg.sampling_rate * self.cfg.mel_window_step / 1000)) + n_frames = int(np.ceil((n_samples + 1) / samples_per_frame)) + frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1) + + # Compute the slices + wav_slices, mel_slices = [], [] + steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1) + for i in range(0, steps, frame_step): + mel_range = np.array([i, i + partial_utterance_n_frames]) + wav_range = mel_range * samples_per_frame + mel_slices.append(slice(*mel_range)) + wav_slices.append(slice(*wav_range)) + + # Evaluate whether extra padding is warranted or not + last_wav_range = wav_slices[-1] + coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start) + if coverage < min_pad_coverage and len(mel_slices) > 1: + mel_slices = mel_slices[:-1] + wav_slices = wav_slices[:-1] + + return wav_slices, mel_slices + + + def embed_utterance(self, wav, **kwargs): + """ + Computes an embedding for a single utterance. + + # TODO: handle multiple wavs to benefit from batching on GPU + :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32 + :param using_partials: if True, then the utterance is split in partial utterances of + <partial_utterance_n_frames> frames and the utterance embedding is computed from their + normalized average. If False, the utterance is instead computed from feeding the entire + spectogram to the network. + :param return_partials: if True, the partial embeddings will also be returned along with the + wav slices that correspond to the partial embeddings. + :param kwargs: additional arguments to compute_partial_splits() + :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If + <return_partials> is True, the partial utterances as a numpy array of float32 of shape + (n_partials, model_embedding_size) and the wav partials as a list of slices will also be + returned. If <using_partials> is simultaneously set to False, both these values will be None + instead. + """ + + # Compute where to split the utterance into partials and pad if necessary + wave_slices, mel_slices = self.compute_partial_slices(len(wav), **kwargs) + max_wave_length = wave_slices[-1].stop + if max_wave_length >= len(wav): + wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant") + + # Split the utterance into partials + frames = self.wav_to_mel_spectrogram(wav) + frames_batch = np.array([frames[s] for s in mel_slices]) + partial_embeds = self.embed_frames_batch(frames_batch) + + # Compute the utterance embedding from the partial embeddings + raw_embed = np.mean(partial_embeds, axis=0) + embed = raw_embed / np.linalg.norm(raw_embed, 2) + + return embed + + def get_embed(self, wav_path): + wav_preprocessed = self.preprocess_wav(wav_path) + embed = self.embed_utterance(wav_preprocessed) + return embed diff --git a/unit2av/modules/__init__.py b/unit2av/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/unit2av/modules/dist.py b/unit2av/modules/dist.py new file mode 100644 index 0000000..d2d4f2e --- /dev/null +++ b/unit2av/modules/dist.py @@ -0,0 +1,108 @@ +# Adapted from https://github.com/openai/jukebox + +from enum import Enum + +import torch.distributed as dist + + +class ReduceOp(Enum): + SUM = 0, + PRODUCT = 1, + MIN = 2, + MAX = 3 + + def ToDistOp(self): + return { + self.SUM: dist.ReduceOp.SUM, + self.PRODUCT: dist.ReduceOp.PRODUCT, + self.MIN: dist.ReduceOp.MIN, + self.MAX: dist.ReduceOp.MAX + }[self] + + +def is_available(): + return dist.is_initialized() + + +def get_rank(): + if is_available(): + return _get_rank() + else: + return 0 + + +def get_world_size(): + if is_available(): + return _get_world_size() + else: + return 1 + + +def barrier(): + if is_available(): + return _barrier() + # else: do nothing + + +def all_gather(tensor_list, tensor): + if is_available(): + return _all_gather(tensor_list, tensor) + else: + tensor_list[0] = tensor + + +def all_reduce(tensor, op=ReduceOp.SUM): + if is_available(): + return _all_reduce(tensor, op) + # else: do nothing + + +def reduce(tensor, dst, op=ReduceOp.SUM): + if is_available(): + return _reduce(tensor, dst, op) + # else: do nothing + + +def broadcast(tensor, src): + if is_available(): + return _broadcast(tensor, src) + # else: do nothing + + +def init_process_group(backend, init_method): + if is_available(): + return _init_process_group(backend, init_method) + # else: do nothing + + +def _get_rank(): + return dist.get_rank() + + +def _barrier(): + return dist.barrier() + + +def _get_world_size(): + return dist.get_world_size() + + +def _all_gather(tensor_list, tensor): + return dist.all_gather(tensor_list, tensor) + + +def _all_reduce(tensor, op): + return dist.all_reduce(tensor, op.ToDistOp()) + + +def _reduce(tensor, dst, op): + return dist.reduce(tensor, dst, op.ToDistOp()) + + +def _broadcast(tensor, src): + return dist.broadcast(tensor, src) + + +def _init_process_group(backend, init_method): + return dist.init_process_group(backend, init_method) + diff --git a/unit2av/modules/jukebox.py b/unit2av/modules/jukebox.py new file mode 100644 index 0000000..ada7beb --- /dev/null +++ b/unit2av/modules/jukebox.py @@ -0,0 +1,178 @@ +# Adapted from https://github.com/openai/jukebox + +import numpy as np +import torch.nn as nn +from .resnet import Resnet1D + + +def assert_shape(x, exp_shape): + assert x.shape == exp_shape, f"Expected {exp_shape} got {x.shape}" + + +class EncoderConvBlock(nn.Module): + def __init__(self, input_emb_width, output_emb_width, down_t, stride_t, width, depth, m_conv, + dilation_growth_rate=1, dilation_cycle=None, zero_out=False, res_scale=False): + super().__init__() + blocks = [] + if type(stride_t) is tuple or type(stride_t) is list: + start = True + for s_t, d_t in zip(stride_t, down_t): + if s_t % 2 == 0: + filter_t, pad_t = s_t * 2, s_t // 2 + else: + filter_t, pad_t = s_t * 2 + 1, s_t // 2 + 1 + if d_t > 0: + for i in range(d_t): + block = nn.Sequential( + nn.Conv1d(input_emb_width if i == 0 and start else width, width, filter_t, s_t, pad_t), + Resnet1D(width, depth, m_conv, dilation_growth_rate, dilation_cycle, zero_out, res_scale), ) + blocks.append(block) + start = False + block = nn.Conv1d(width, output_emb_width, 3, 1, 1) + blocks.append(block) + else: + filter_t, pad_t = stride_t * 2, stride_t // 2 + if down_t > 0: + for i in range(down_t): + block = nn.Sequential( + nn.Conv1d(input_emb_width if i == 0 else width, width, filter_t, stride_t, pad_t), + Resnet1D(width, depth, m_conv, dilation_growth_rate, dilation_cycle, zero_out, res_scale), ) + blocks.append(block) + block = nn.Conv1d(width, output_emb_width, 3, 1, 1) + blocks.append(block) + self.model = nn.Sequential(*blocks) + + def forward(self, x): + return self.model(x) + + +class DecoderConvBock(nn.Module): + def __init__(self, input_emb_width, output_emb_width, down_t, stride_t, width, depth, m_conv, + dilation_growth_rate=1, dilation_cycle=None, zero_out=False, res_scale=False, + reverse_decoder_dilation=False, checkpoint_res=False): + super().__init__() + blocks = [] + + if type(stride_t) is tuple or type(stride_t) is list: + block = nn.Conv1d(output_emb_width, width, 3, 1, 1) + blocks.append(block) + for k, (s_t, d_t) in enumerate(zip(stride_t, down_t)): + if d_t > 0: + if s_t % 2 == 0: + filter_t, pad_t = s_t * 2, s_t // 2 + else: + filter_t, pad_t = s_t * 2 + 1, s_t // 2 + 1 + end = k == len(stride_t) - 1 + for i in range(d_t): + block = nn.Sequential( + Resnet1D(width, depth, m_conv, dilation_growth_rate, dilation_cycle, zero_out=zero_out, + res_scale=res_scale, reverse_dilation=reverse_decoder_dilation, + checkpoint_res=checkpoint_res), + nn.ConvTranspose1d(width, input_emb_width if i == (d_t - 1) and end else width, filter_t, + s_t, pad_t)) + blocks.append(block) + else: + if down_t > 0: + filter_t, pad_t = stride_t * 2, stride_t // 2 + block = nn.Conv1d(output_emb_width, width, 3, 1, 1) + blocks.append(block) + for i in range(down_t): + block = nn.Sequential( + Resnet1D(width, depth, m_conv, dilation_growth_rate, dilation_cycle, zero_out=zero_out, + res_scale=res_scale, reverse_dilation=reverse_decoder_dilation, + checkpoint_res=checkpoint_res), + nn.ConvTranspose1d(width, input_emb_width if i == (down_t - 1) else width, filter_t, stride_t, + pad_t)) + blocks.append(block) + self.model = nn.Sequential(*blocks) + + def forward(self, x): + return self.model(x) + + +class Encoder(nn.Module): + def __init__(self, input_emb_width, output_emb_width, levels, downs_t, strides_t, **block_kwargs): + super().__init__() + self.input_emb_width = input_emb_width + self.output_emb_width = output_emb_width + self.levels = levels + self.downs_t = downs_t + self.strides_t = strides_t + + block_kwargs_copy = dict(**block_kwargs) + if 'reverse_decoder_dilation' in block_kwargs_copy: + del block_kwargs_copy['reverse_decoder_dilation'] + level_block = lambda level, down_t, stride_t: EncoderConvBlock( + input_emb_width if level == 0 else output_emb_width, output_emb_width, down_t, stride_t, + **block_kwargs_copy) + self.level_blocks = nn.ModuleList() + iterator = zip(list(range(self.levels)), downs_t, strides_t) + for level, down_t, stride_t in iterator: + self.level_blocks.append(level_block(level, down_t, stride_t)) + + def forward(self, x): + N, T = x.shape[0], x.shape[-1] + emb = self.input_emb_width + assert_shape(x, (N, emb, T)) + xs = [] + + # 64, 32, ... + iterator = zip(list(range(self.levels)), self.downs_t, self.strides_t) + for level, down_t, stride_t in iterator: + level_block = self.level_blocks[level] + x = level_block(x) + if type(stride_t) is tuple or type(stride_t) is list: + emb, T = self.output_emb_width, T // np.prod([s ** d for s, d in zip(stride_t, down_t)]) + else: + emb, T = self.output_emb_width, T // (stride_t ** down_t) + assert_shape(x, (N, emb, T)) + xs.append(x) + + return xs + + +class Decoder(nn.Module): + def __init__(self, input_emb_width, output_emb_width, levels, downs_t, strides_t, **block_kwargs): + super().__init__() + self.input_emb_width = input_emb_width + self.output_emb_width = output_emb_width + self.levels = levels + + self.downs_t = downs_t + + self.strides_t = strides_t + + level_block = lambda level, down_t, stride_t: DecoderConvBock(output_emb_width, output_emb_width, down_t, + stride_t, **block_kwargs) + self.level_blocks = nn.ModuleList() + iterator = zip(list(range(self.levels)), downs_t, strides_t) + for level, down_t, stride_t in iterator: + self.level_blocks.append(level_block(level, down_t, stride_t)) + + self.out = nn.Conv1d(output_emb_width, input_emb_width, 3, 1, 1) + + def forward(self, xs, all_levels=True): + if all_levels: + assert len(xs) == self.levels + else: + assert len(xs) == 1 + x = xs[-1] + N, T = x.shape[0], x.shape[-1] + emb = self.output_emb_width + assert_shape(x, (N, emb, T)) + + # 32, 64 ... + iterator = reversed(list(zip(list(range(self.levels)), self.downs_t, self.strides_t))) + for level, down_t, stride_t in iterator: + level_block = self.level_blocks[level] + x = level_block(x) + if type(stride_t) is tuple or type(stride_t) is list: + emb, T = self.output_emb_width, T * np.prod([s ** d for s, d in zip(stride_t, down_t)]) + else: + emb, T = self.output_emb_width, T * (stride_t ** down_t) + assert_shape(x, (N, emb, T)) + if level != 0 and all_levels: + x = x + xs[level - 1] + + x = self.out(x) + return x diff --git a/unit2av/modules/resnet.py b/unit2av/modules/resnet.py new file mode 100644 index 0000000..18253c3 --- /dev/null +++ b/unit2av/modules/resnet.py @@ -0,0 +1,82 @@ +# Adapted from https://github.com/openai/jukebox + +import math +import torch.nn as nn + +from . import dist + + +class ResConvBlock(nn.Module): + def __init__(self, n_in, n_state): + super().__init__() + self.model = nn.Sequential( + nn.ReLU(), + nn.Conv2d(n_in, n_state, 3, 1, 1), + nn.ReLU(), + nn.Conv2d(n_state, n_in, 1, 1, 0), + ) + + def forward(self, x): + return x + self.model(x) + + +class Resnet(nn.Module): + def __init__(self, n_in, n_depth, m_conv=1.0): + super().__init__() + self.model = nn.Sequential(*[ResConvBlock(n_in, int(m_conv * n_in)) for _ in range(n_depth)]) + + def forward(self, x): + return self.model(x) + + +class ResConv1DBlock(nn.Module): + def __init__(self, n_in, n_state, dilation=1, zero_out=False, res_scale=1.0): + super().__init__() + padding = dilation + self.model = nn.Sequential( + nn.ReLU(), + nn.Conv1d(n_in, n_state, 3, 1, padding, dilation), + nn.ReLU(), + nn.Conv1d(n_state, n_in, 1, 1, 0), + ) + if zero_out: + out = self.model[-1] + nn.init.zeros_(out.weight) + nn.init.zeros_(out.bias) + self.res_scale = res_scale + + def forward(self, x): + return x + self.res_scale * self.model(x) + + +class Resnet1D(nn.Module): + def __init__(self, n_in, n_depth, m_conv=1.0, dilation_growth_rate=1, dilation_cycle=None, zero_out=False, + res_scale=False, reverse_dilation=False, checkpoint_res=False): + super().__init__() + + def _get_depth(depth): + if dilation_cycle is None: + return depth + else: + return depth % dilation_cycle + + blocks = [ResConv1DBlock(n_in, int(m_conv * n_in), + dilation=dilation_growth_rate ** _get_depth(depth), + zero_out=zero_out, + res_scale=1.0 if not res_scale else 1.0 / math.sqrt(n_depth)) + for depth in range(n_depth)] + if reverse_dilation: + blocks = blocks[::-1] + self.checkpoint_res = checkpoint_res + if self.checkpoint_res == 1: + if dist.get_rank() == 0: + print("Checkpointing convs") + self.blocks = nn.ModuleList(blocks) + else: + self.model = nn.Sequential(*blocks) + + def forward(self, x): + if self.checkpoint_res == 1: + raise NotImplementedError("Checkpoint not implemented") + else: + return self.model(x) diff --git a/unit2av/modules/vq.py b/unit2av/modules/vq.py new file mode 100644 index 0000000..d3fff84 --- /dev/null +++ b/unit2av/modules/vq.py @@ -0,0 +1,249 @@ +# Adapted from https://github.com/openai/jukebox + +import numpy as np +import torch as t +import torch.nn as nn +import torch.nn.functional as F + +from . import dist + + +class BottleneckBlock(nn.Module): + def __init__(self, k_bins, emb_width, mu): + super().__init__() + self.k_bins = k_bins + self.emb_width = emb_width + self.mu = mu + self.reset_k() + self.threshold = 1.0 + + def reset_k(self): + self.init = False + self.k_sum = None + self.k_elem = None + self.register_buffer('k', t.zeros(self.k_bins, self.emb_width).cuda()) + + def _tile(self, x): + d, ew = x.shape + if d < self.k_bins: + n_repeats = (self.k_bins + d - 1) // d + std = 0.01 / np.sqrt(ew) + x = x.repeat(n_repeats, 1) + x = x + t.randn_like(x) * std + return x + + def init_k(self, x): + mu, emb_width, k_bins = self.mu, self.emb_width, self.k_bins + self.init = True + # init k_w using random vectors from x + y = self._tile(x) + _k_rand = y[t.randperm(y.shape[0])][:k_bins] + dist.broadcast(_k_rand, 0) + self.k = _k_rand + assert self.k.shape == (k_bins, emb_width) + self.k_sum = self.k + self.k_elem = t.ones(k_bins, device=self.k.device) + + def restore_k(self, num_tokens=None, threshold=1.0): + mu, emb_width, k_bins = self.mu, self.emb_width, self.k_bins + self.init = True + assert self.k.shape == (k_bins, emb_width) + self.k_sum = self.k.clone() + self.k_elem = t.ones(k_bins, device=self.k.device) + if num_tokens is not None: + expected_usage = num_tokens / k_bins + self.k_elem.data.mul_(expected_usage) + self.k_sum.data.mul_(expected_usage) + self.threshold = threshold + + def update_k(self, x, x_l): + mu, emb_width, k_bins = self.mu, self.emb_width, self.k_bins + with t.no_grad(): + # Calculate new centres + x_l_onehot = t.zeros(k_bins, x.shape[0], device=x.device) # k_bins, N * L + x_l_onehot.scatter_(0, x_l.view(1, x.shape[0]), 1) + + _k_sum = t.matmul(x_l_onehot, x) # k_bins, w + _k_elem = x_l_onehot.sum(dim=-1) # k_bins + y = self._tile(x) + _k_rand = y[t.randperm(y.shape[0])][:k_bins] + + dist.broadcast(_k_rand, 0) + dist.all_reduce(_k_sum) + dist.all_reduce(_k_elem) + + # Update centres + old_k = self.k + self.k_sum = mu * self.k_sum + (1. - mu) * _k_sum # w, k_bins + self.k_elem = mu * self.k_elem + (1. - mu) * _k_elem # k_bins + usage = (self.k_elem.view(k_bins, 1) >= self.threshold).float() + self.k = usage * (self.k_sum.view(k_bins, emb_width) / self.k_elem.view(k_bins, 1)) \ + + (1 - usage) * _k_rand + _k_prob = _k_elem / t.sum(_k_elem) # x_l_onehot.mean(dim=-1) # prob of each bin + entropy = -t.sum(_k_prob * t.log(_k_prob + 1e-8)) # entropy ie how diverse + used_curr = (_k_elem >= self.threshold).sum() + usage = t.sum(usage) + dk = t.norm(self.k - old_k) / np.sqrt(np.prod(old_k.shape)) + return dict(entropy=entropy, + used_curr=used_curr, + usage=usage, + dk=dk) + + def preprocess(self, x): + # NCT -> NTC -> [NT, C] + x = x.permute(0, 2, 1).contiguous() + x = x.view(-1, x.shape[-1]) # x_en = (N * L, w), k_j = (w, k_bins) + + if x.shape[-1] == self.emb_width: + prenorm = t.norm(x - t.mean(x)) / np.sqrt(np.prod(x.shape)) + elif x.shape[-1] == 2 * self.emb_width: + x1, x2 = x[..., :self.emb_width], x[..., self.emb_width:] + prenorm = (t.norm(x1 - t.mean(x1)) / np.sqrt(np.prod(x1.shape))) + ( + t.norm(x2 - t.mean(x2)) / np.sqrt(np.prod(x2.shape))) + + # Normalise + x = x1 + x2 + else: + assert False, f"Expected {x.shape[-1]} to be (1 or 2) * {self.emb_width}" + return x, prenorm + + def postprocess(self, x_l, x_d, x_shape): + # [NT, C] -> NTC -> NCT + N, T = x_shape + x_d = x_d.view(N, T, -1).permute(0, 2, 1).contiguous() + x_l = x_l.view(N, T) + return x_l, x_d + + def quantise(self, x): + # Calculate latent code x_l + k_w = self.k.t() + distance = t.sum(x ** 2, dim=-1, keepdim=True) - 2 * t.matmul(x, k_w) + t.sum(k_w ** 2, dim=0, + keepdim=True) # (N * L, b) + min_distance, x_l = t.min(distance, dim=-1) + fit = t.mean(min_distance) + return x_l, fit + + def dequantise(self, x_l): + x = F.embedding(x_l, self.k) + return x + + def encode(self, x): + N, width, T = x.shape + + # Preprocess. + x, prenorm = self.preprocess(x) + + # Quantise + x_l, fit = self.quantise(x) + + # Postprocess. + x_l = x_l.view(N, T) + return x_l + + def decode(self, x_l): + N, T = x_l.shape + width = self.emb_width + + # Dequantise + x_d = self.dequantise(x_l) + + # Postprocess + x_d = x_d.view(N, T, width).permute(0, 2, 1).contiguous() + return x_d + + def forward(self, x, update_k=True): + N, width, T = x.shape + + # Preprocess + x, prenorm = self.preprocess(x) + + # Init k if not inited + if update_k and not self.init: + self.init_k(x) + + # Quantise and dequantise through bottleneck + x_l, fit = self.quantise(x) + x_d = self.dequantise(x_l) + + # Update embeddings + if update_k and self.training: + update_metrics = self.update_k(x, x_l) + else: + update_metrics = {} + + # Loss + commit_loss = t.norm(x_d.detach() - x) ** 2 / np.prod(x.shape) + + # Passthrough + x_d = x + (x_d - x).detach() + + # Postprocess + x_l, x_d = self.postprocess(x_l, x_d, (N, T)) + return x_l, x_d, commit_loss, dict(fit=fit, + pn=prenorm, + **update_metrics) + + +class Bottleneck(nn.Module): + def __init__(self, l_bins, emb_width, mu, levels): + super().__init__() + self.levels = levels + level_block = lambda level: BottleneckBlock(l_bins, emb_width, mu) + self.level_blocks = nn.ModuleList() + for level in range(self.levels): + self.level_blocks.append(level_block(level)) + + def encode(self, xs): + zs = [level_block.encode(x) for (level_block, x) in zip(self.level_blocks, xs)] + return zs + + def decode(self, zs, start_level=0, end_level=None): + if end_level is None: + end_level = self.levels + xs_quantised = [level_block.decode(z) for (level_block, z) in zip(self.level_blocks[start_level:end_level], zs)] + return xs_quantised + + def forward(self, xs): + zs, xs_quantised, commit_losses, metrics = [], [], [], [] + for level in range(self.levels): + level_block = self.level_blocks[level] + x = xs[level] + z, x_quantised, commit_loss, metric = level_block(x, update_k=self.training) + zs.append(z) + if not self.training: + # Be extra paranoid and make sure the encoder weights can't + # change from straight-through estimator + x_quantised = x_quantised.detach() + xs_quantised.append(x_quantised) + commit_losses.append(commit_loss) + if self.training: + metrics.append(metric) + return zs, xs_quantised, commit_losses, metrics + + +class NoBottleneckBlock(nn.Module): + def restore_k(self): + pass + + +class NoBottleneck(nn.Module): + def __init__(self, levels): + super().__init__() + self.level_blocks = nn.ModuleList() + self.levels = levels + for level in range(levels): + self.level_blocks.append(NoBottleneckBlock()) + + def encode(self, xs): + return xs + + def decode(self, zs, start_level=0, end_level=None): + if end_level is None: + end_level = self.levels + return zs + + def forward(self, xs): + zero = t.zeros(()).cuda() + commit_losses = [zero for _ in range(self.levels)] + metrics = [dict(entropy=zero, usage=zero, used_curr=zero, pn=zero, dk=zero) for _ in range(self.levels)] + return xs, xs, commit_losses, metrics diff --git a/unit2av/requirements.txt b/unit2av/requirements.txt new file mode 100644 index 0000000..6b888de --- /dev/null +++ b/unit2av/requirements.txt @@ -0,0 +1,21 @@ +# --- Core Libraries (버전 고정 필수) --- +numpy<1.24 +scipy==1.10.0 +librosa==0.8.1 +resampy==0.4.3 +opencv-python==4.5.4.60 +tensorboard +tensorboardX + +# --- Audio & Video Processing --- +python-speech-features==0.6 +soundfile +av +ffmpeg-python +amfm_decompy +matplotlib +tqdm + +# --- System & Config --- +omegaconf==2.0.6 +hydra-core==1.0.7 \ No newline at end of file diff --git a/unit2av/train_unit2a.py b/unit2av/train_unit2a.py new file mode 100644 index 0000000..a8b6c07 --- /dev/null +++ b/unit2av/train_unit2a.py @@ -0,0 +1,334 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Adapted from https://github.com/jik876/hifi-gan + +import warnings +warnings.simplefilter(action='ignore', category=FutureWarning) +warnings.filterwarnings(action='ignore', message='.*kernel_size exceeds volume extent.*') + +import itertools +import os +import time +import argparse +import json +import torch +import torch.nn.functional as F +from torch.utils.tensorboard import SummaryWriter +from torch.utils.data import DistributedSampler, DataLoader +from torch.distributed import init_process_group +from torch.nn.parallel import DistributedDataParallel +from dataset import CodeDataset, mel_spectrogram, get_dataset_filelist +from discriminators import MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss, discriminator_loss # models -> discriminators 로 변경됨 +from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint, build_env, AttrDict +from model import CodeHiFiGANModel_spk + +torch.backends.cudnn.benchmark = True + + +def train(rank, local_rank, a, h): + if h.num_gpus > 1: + init_process_group( + backend=h.dist_config['dist_backend'], + init_method=h.dist_config['dist_url'], + rank=rank, + world_size=h.num_gpus, + ) + + torch.cuda.manual_seed(h.seed) + device = torch.device('cuda:{:d}'.format(local_rank)) + + #generator = DurationCodeGenerator(h).to(device) + generator = CodeHiFiGANModel_spk(dict(h)).to(device) + mpd = MultiPeriodDiscriminator().to(device) + msd = MultiScaleDiscriminator().to(device) + + if rank == 0: + print(generator) + os.makedirs(a.checkpoint_path, exist_ok=True) + print("checkpoints directory : ", a.checkpoint_path) + + # [FIX] checkpoint_path가 존재하지 않을 경우를 대비해 변수 초기화 + cp_g = None + cp_do = None + if os.path.isdir(a.checkpoint_path): + cp_g = scan_checkpoint(a.checkpoint_path, 'g_') + cp_do = scan_checkpoint(a.checkpoint_path, 'do_') + + steps = 0 + # Best model tracking (based on validation mel_error, like HuggingFace's load_best_model_at_end) + best_val_error = float('inf') + + if cp_g is None or cp_do is None: + state_dict_do = None + last_epoch = -1 + else: + state_dict_g = load_checkpoint(cp_g, device) + state_dict_do = load_checkpoint(cp_do, device) + generator.load_state_dict(state_dict_g['generator']) + mpd.load_state_dict(state_dict_do['mpd']) + msd.load_state_dict(state_dict_do['msd']) + steps = state_dict_do['steps'] + 1 + last_epoch = state_dict_do['epoch'] + # Restore best_val_error if available (for continued training) + if 'best_val_error' in state_dict_do: + best_val_error = state_dict_do['best_val_error'] + + if h.num_gpus > 1: + generator = DistributedDataParallel( + generator, + device_ids=[local_rank], + find_unused_parameters=('f0_quantizer' in h), + ).to(device) + mpd = DistributedDataParallel(mpd, device_ids=[local_rank]).to(device) + msd = DistributedDataParallel(msd, device_ids=[local_rank]).to(device) + + optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) + optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()), h.learning_rate, + betas=[h.adam_b1, h.adam_b2]) + + if state_dict_do is not None: + optim_g.load_state_dict(state_dict_do['optim_g']) + optim_d.load_state_dict(state_dict_do['optim_d']) + + scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch) + scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch) + + training_filelist, validation_filelist = get_dataset_filelist(h) + + trainset = CodeDataset(training_filelist, h.segment_size, h.code_hop_size, h.n_fft, h.num_mels, h.hop_size, + h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0, fmax_loss=h.fmax_for_loss, + device=device, f0=h.get('f0', None), multispkr=h.get('multispkr', None), + f0_stats=h.get('f0_stats', None), + f0_normalize=h.get('f0_normalize', False), f0_feats=h.get('f0_feats', False), + f0_median=h.get('f0_median', False), f0_interp=h.get('f0_interp', False), + vqvae=h.get('code_vq_params', False)) + + train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None + + train_loader = DataLoader(trainset, num_workers=0, shuffle=False, sampler=train_sampler, + batch_size=h.batch_size, pin_memory=True, drop_last=True) + + if rank == 0: + validset = CodeDataset(validation_filelist, h.segment_size, h.code_hop_size, h.n_fft, h.num_mels, h.hop_size, + h.win_size, h.sampling_rate, h.fmin, h.fmax, False, n_cache_reuse=0, + fmax_loss=h.fmax_for_loss, device=device, f0=h.get('f0', None), + multispkr=h.get('multispkr', None), + f0_stats=h.get('f0_stats', None), f0_normalize=h.get('f0_normalize', False), + f0_feats=h.get('f0_feats', False), f0_median=h.get('f0_median', False), + f0_interp=h.get('f0_interp', False), vqvae=h.get('code_vq_params', False)) + validation_loader = DataLoader(validset, num_workers=0, shuffle=False, sampler=None, + batch_size=h.batch_size, pin_memory=True, drop_last=True) + + sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs')) + + generator.train() + mpd.train() + msd.train() + for epoch in range(max(0, last_epoch), a.training_epochs): + if rank == 0: + start = time.time() + print("Epoch: {}".format(epoch + 1)) + + if h.num_gpus > 1: + train_sampler.set_epoch(epoch) + + for i, batch in enumerate(train_loader): + if rank == 0: + start_b = time.time() + x, y, _, y_mel = batch + y = torch.autograd.Variable(y.to(device, non_blocking=False)) + y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=False)) + y = y.unsqueeze(1) + x = {k: torch.autograd.Variable(v.to(device, non_blocking=False)) for k, v in x.items()} + + y_g_hat, dur_losses = generator(**x) + + assert y_g_hat.shape == y.shape, f"Mismatch in vocoder output shape - {y_g_hat.shape} != {y.shape}" + + y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, + h.win_size, h.fmin, h.fmax_for_loss) + + optim_d.zero_grad() + + # MPD + y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach()) + loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g) + + # MSD + y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach()) + loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g) + + loss_disc_all = loss_disc_s + loss_disc_f + + loss_disc_all.backward() + optim_d.step() + + # Generator + optim_g.zero_grad() + + # L1 Mel-Spectrogram Loss + loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 + + y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat) + y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat) + loss_fm_f = feature_loss(fmap_f_r, fmap_f_g) + loss_fm_s = feature_loss(fmap_s_r, fmap_s_g) + loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g) + loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g) + loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel + if h.get('dur_prediction_weight', None): + loss_gen_all += dur_losses * h.get('dur_prediction_weight', None) + + + loss_gen_all.backward() + optim_g.step() + + if rank == 0: + # STDOUT logging + if steps % a.stdout_interval == 0: + with torch.no_grad(): + mel_error = F.l1_loss(y_mel, y_g_hat_mel).item() + + print( + 'Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'.format(steps, + loss_gen_all, + mel_error, + time.time() - start_b)) + + # checkpointing + if steps % a.checkpoint_interval == 0 and steps != 0: + checkpoint_path = "{}/g_{:08d}".format(a.checkpoint_path, steps) + save_checkpoint(checkpoint_path, + {'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()}) + checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps) + save_checkpoint(checkpoint_path, {'mpd': (mpd.module if h.num_gpus > 1 else mpd).state_dict(), + 'msd': (msd.module if h.num_gpus > 1 else msd).state_dict(), + 'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), + 'steps': steps, 'epoch': epoch, 'best_val_error': best_val_error}) + + # Tensorboard summary logging + if steps % a.summary_interval == 0: + sw.add_scalar("training/gen_loss_total", loss_gen_all, steps) + sw.add_scalar("training/mel_spec_error", mel_error, steps) + sw.add_scalar("training/gen_loss_f", loss_gen_f, steps) + sw.add_scalar("training/gen_loss_s", loss_gen_s, steps) + sw.add_scalar("training/fm_loss_f", loss_fm_f, steps) + sw.add_scalar("training/fm_loss_s", loss_fm_s, steps) + + # [FIX] VQ-VAE 관련 로깅 코드 삭제 - f0_commit_loss, f0_metrics, + # code_commit_loss, code_metrics 변수가 정의되지 않아 에러 발생 + # VQ-VAE 사용 시 generator 출력에서 해당 값들을 받아와야 함 + + # Validation + if steps % a.validation_interval == 0: # and steps != 0: + generator.eval() + torch.cuda.empty_cache() + val_err_tot = 0 + with torch.no_grad(): + for j, batch in enumerate(validation_loader): + x, y, _, y_mel = batch + x = {k: v.to(device, non_blocking=False) for k, v in x.items()} + + y_g_hat, dur_losses = generator(**x) + y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=False)) + y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, + h.hop_size, h.win_size, h.fmin, h.fmax_for_loss) + val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item() + + if j <= 4: + if steps == 0: + sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate) + sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(y_mel[0].cpu()), steps) + + sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate) + y_hat_spec = mel_spectrogram(y_g_hat[:1].squeeze(1), h.n_fft, h.num_mels, + h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax) + sw.add_figure('generated/y_hat_spec_{}'.format(j), + plot_spectrogram(y_hat_spec[:1].squeeze(0).cpu().numpy()), steps) + + val_err = val_err_tot / (j + 1) + sw.add_scalar("validation/mel_spec_error", val_err, steps) + + # Save best model based on validation mel_error (like HuggingFace's load_best_model_at_end) + if val_err < best_val_error: + best_val_error = val_err + # Save generator + checkpoint_path = "{}/g_best".format(a.checkpoint_path) + save_checkpoint(checkpoint_path, + {'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()}) + # Save discriminator & optimizer state + checkpoint_path = "{}/do_best".format(a.checkpoint_path) + save_checkpoint(checkpoint_path, + {'mpd': (mpd.module if h.num_gpus > 1 else mpd).state_dict(), + 'msd': (msd.module if h.num_gpus > 1 else msd).state_dict(), + 'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), + 'steps': steps, 'epoch': epoch, 'best_val_error': best_val_error}) + print(f"Steps : {steps}, New Best Val Mel Error : {best_val_error:.4f} -> Saved best model.") + + generator.train() + + steps += 1 + if steps >= a.training_steps: + break + + scheduler_g.step() + scheduler_d.step() + + if rank == 0: + print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start))) + + if rank == 0: + print('Finished training') + + +def main(): + print('Initializing Training Process..') + + parser = argparse.ArgumentParser() + + parser.add_argument('--group_name', default=None) + parser.add_argument('--checkpoint_path', default='cp_hifigan') + parser.add_argument('--config', default='') + parser.add_argument('--training_epochs', default=2000, type=int) + parser.add_argument('--training_steps', default=500000, type=int) + parser.add_argument('--stdout_interval', default=5, type=int) + parser.add_argument('--checkpoint_interval', default=50000, type=int) + parser.add_argument('--summary_interval', default=100, type=int) + parser.add_argument('--validation_interval', default=5000, type=int) + parser.add_argument('--fine_tuning', default=False, type=bool) + parser.add_argument('--local_rank', default=0, type=int) + parser.add_argument('--distributed-world-size', type=int) + parser.add_argument('--distributed-port', type=int) + + a = parser.parse_args() + + with open(a.config) as f: + data = f.read() + + json_config = json.loads(data) + h = AttrDict(json_config) + build_env(a.config, 'config.json', a.checkpoint_path) + + torch.manual_seed(h.seed) + if torch.cuda.is_available() and 'WORLD_SIZE' in os.environ: + torch.cuda.manual_seed(h.seed) + h.num_gpus = int(os.environ['WORLD_SIZE']) + h.batch_size = int(h.batch_size / h.num_gpus) + local_rank = a.local_rank + rank = a.local_rank + print('Batch size per GPU :', h.batch_size) + else: + # [FIX] 단일 GPU 환경에서 num_gpus 설정 누락 수정 + h.num_gpus = 1 + rank = 0 + local_rank = 0 + + train(rank, local_rank, a, h) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/unit2av/utils.py b/unit2av/utils.py new file mode 100644 index 0000000..a4262be --- /dev/null +++ b/unit2av/utils.py @@ -0,0 +1,80 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Adapted from https://github.com/jik876/hifi-gan + +import glob +import os +import shutil + +import matplotlib +import torch +from torch.nn.utils import weight_norm +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self \ No newline at end of file diff --git a/unit2unit/inference.py b/unit2unit/inference.py new file mode 100644 index 0000000..43ecf9a --- /dev/null +++ b/unit2unit/inference.py @@ -0,0 +1,99 @@ +import argparse +import numpy as np +import torch + +from fairseq import checkpoint_utils, utils +from fairseq_cli.generate import get_symbols_to_strip_from_output + +from unit2unit.task import UTUTPretrainingTask +from util import process_units, save_unit + +def load_model(model_path, src_lang, tgt_lang, use_cuda=False): + models, cfg, task = checkpoint_utils.load_model_ensemble_and_task([model_path]) + + # Fix seed for stochastic decoding + if cfg.common.seed is not None and not cfg.generation.no_seed_provided: + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) + + for model in models: + if cfg.common.fp16: + model.half() + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: + model.cuda() + model.prepare_for_inference_(cfg) + + task.source_language = src_lang + task.target_language = tgt_lang + + generator = task.build_generator( + models, cfg.generation + ) + + return task, generator + +def main(args): + use_cuda = torch.cuda.is_available() and not args.cpu + + task, generator = load_model(args.utut_path, args.src_lang, args.tgt_lang, use_cuda=use_cuda) + + with open(args.in_unit_path) as f: + unit = list(map(int, f.readline().strip().split())) + unit = task.source_dictionary.encode_line( + " ".join(map(lambda x: str(x), process_units(unit, reduce=True))), + add_if_not_exist=False, + append_eos=True, + ).long() + unit = torch.cat([ + unit.new([task.source_dictionary.bos()]), + unit, + unit.new([task.source_dictionary.index("[{}]".format(task.source_language))]) + ]) + + sample = {"net_input": { + "src_tokens": torch.LongTensor(unit).view(1,-1), + }} + sample = utils.move_to_cuda(sample) if use_cuda else sample + + pred = task.inference_step( + generator, + None, + sample, + )[0][0] + + pred_str = task.target_dictionary.string( + pred["tokens"].int().cpu(), + extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator) + ) + + save_unit(pred_str, args.out_unit_path) + +def cli_main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-unit-path", type=str, required=True, help="File path of source unit input" + ) + parser.add_argument( + "--out-unit-path", type=str, required=True, help="File path of target unit output" + ) + parser.add_argument( + "--utut-path", type=str, required=True, help="path to the UTUT pre-trained model" + ) + parser.add_argument( + "--src-lang", type=str, required=True, + choices=["en","es","fr","it","pt"], + help="source language" + ) + parser.add_argument( + "--tgt-lang", type=str, required=True, + choices=["en","es","fr","it","pt"], + help="target language" + ) + parser.add_argument("--cpu", action="store_true", help="run on CPU") + + args = parser.parse_args() + + main(args) + +if __name__ == "__main__": + cli_main() diff --git a/unit2unit/task.py b/unit2unit/task.py new file mode 100644 index 0000000..65b3d4d --- /dev/null +++ b/unit2unit/task.py @@ -0,0 +1,39 @@ +import logging +from fairseq.tasks import register_task +from fairseq.tasks.multilingual_denoising import MultilingualDenoisingConfig, MultilingualDenoisingTask + +logger = logging.getLogger(__name__) + +@register_task("utut_pretraining", dataclass=MultilingualDenoisingConfig) +class UTUTPretrainingTask(MultilingualDenoisingTask): + def build_generator( + self, + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=None, + ): + lang_list = self.cfg.langs.split(",") + + lang_token_ids = { + self.dictionary.index("[{}]".format(lang)) + for lang in lang_list + } + + if extra_gen_cls_kwargs is None: + extra_gen_cls_kwargs = {} + + extra_gen_cls_kwargs["symbols_to_strip_from_output"] = lang_token_ids + + extra_gen_cls_kwargs["eos"] = self.dictionary.index("[{}]".format(self.target_language)) + + extra_gen_cls_kwargs["tokens_to_suppress"] = [ + "[{}]".format(lang) for lang in lang_list if lang != self.target_language + ] + [self.dictionary[self.mask_idx]] + + return super().build_generator( + models, + args, + seq_gen_cls=seq_gen_cls, + extra_gen_cls_kwargs=extra_gen_cls_kwargs, + ) diff --git a/util.py b/util.py new file mode 100644 index 0000000..4a4669e --- /dev/null +++ b/util.py @@ -0,0 +1,79 @@ +import os +import soundfile as sf +import cv2 +import ffmpeg + +def process_units(units, reduce=False): + if not reduce: + return units + + out = [u for i, u in enumerate(units) if i == 0 or u != units[i - 1]] + return out + +def save_unit(unit, unit_path): + os.makedirs(os.path.dirname(unit_path), exist_ok=True) + with open(unit_path, "w") as f: + f.write(unit) + +def save_audio(audio, audio_path, sampling_rate=16000): + os.makedirs(os.path.dirname(audio_path), exist_ok=True) + sf.write( + audio_path, + audio, + sampling_rate, + ) + +def extract_audio_from_video(video_path, save_audio_path, sampling_rate=16000): + os.makedirs(os.path.dirname(save_audio_path), exist_ok=True) + ( + ffmpeg.input(video_path) + .output( + save_audio_path, + acodec="pcm_s16le", + ac=1, + ar=sampling_rate, + loglevel="panic", + ) + .run(overwrite_output=True) + ) + +def save_video(audio, video, full_video, bbox, save_video_path, sampling_rate=16000, fps=25, vcodec="libx264"): + os.makedirs(os.path.dirname(save_video_path), exist_ok=True) + temp_audio_path = os.path.splitext(save_video_path)[0]+".temp.wav" + temp_video_path = os.path.splitext(save_video_path)[0]+".temp.avi" + + save_audio(audio, temp_audio_path, sampling_rate) + + frame_h, frame_w = full_video.shape[1], full_video.shape[2] + out = cv2.VideoWriter(temp_video_path, cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h)) + + for p, f, c in zip(video, full_video, bbox): + #modified : if bbox is None, write original frame + if c is None: + out.write(f) + continue + x1, y1, x2, y2 = [max(int(_), 0) for _ in c] + if x2 - x1 > 0 and y2 - y1 > 0: + p = cv2.resize(p, (x2 - x1, y2 - y1)) + try: + f[y1:y2, x1:x2] = p + except: + height, width, c = f[y1:y2, x1:x2].shape + p = cv2.resize(p, (width, height)) + f[y1:y2, x1:x2] = p + out.write(f) + + out.release() + + ffmpeg.output( + ffmpeg.input(temp_video_path), + ffmpeg.input(temp_audio_path), + save_video_path, + vcodec="libx264", + acodec="aac", + loglevel="panic", + ).run(overwrite_output=True) + + os.remove(temp_audio_path) + os.remove(temp_video_path) +