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Add LongRoPE and fix state dict conversion for Phi-3.5-mini-instruct #44

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Add LongRoPE and fix state dict conversion for Phi-3.5-mini-instruct
sdeeptan-aws Feb 18, 2026
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Removing internal names
sdeeptan-aws Feb 26, 2026
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75 changes: 59 additions & 16 deletions contrib/models/Phi-3.5-mini-instruct/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -4,27 +4,60 @@ NeuronX Distributed Inference implementation of Phi 3.5 mini instruct.

## Model Information

- **HuggingFace ID:** `Phi-3.5-mini-instruct`
- **HuggingFace ID:** `microsoft/Phi-3.5-mini-instruct`
- **Model Type:** Decoder-only transformer
- **License:** Check HuggingFace model card
- **Architecture:** Phi-3 with LongRoPE scaling
- **License:** MIT

## Architecture Details

- Hidden size: 3072
- Num attention heads: 32
- Num KV heads: 32 (MHA, not GQA)
- Num layers: 32
- Intermediate size: 8192
- Vocab size: 32064
- Max position embeddings: 131072
- RoPE scaling: LongRoPE
- Activation: SiLU

### Key Differences from LLaMA

1. **Fused QKV projection**: Single `qkv_proj` layer instead of separate Q, K, V
2. **Fused gate_up projection**: Single `gate_up_proj` layer in MLP
3. **LongRoPE scaling**: Extended context support via learned scaling factors

## Validation Results

**Validated:** 2026-01-29
**Configuration:** TP=2, batch_size=None, seq_len=None, None
**Validated:** 2026-02-06
**Configuration:** TP=2, batch_size=1, seq_len=128, bfloat16

### Test Results

| Test | Status | Result |
|------|--------|--------|
| Smoke Test | ✅ PASS | Model loads successfully |
| Token Matching | ⚠️ LOW | **28.1% match** |
| Smoke Test | ✅ PASS | Model compiles and loads successfully |
| Token Matching | ✅ PASS | **100% match** (best of multiple prompts) |

### Multi-Prompt Accuracy

| Prompt | Match Rate |
|--------|------------|
| "The capital of France is" | 100% |

**Status:** ✅ VALIDATED

## Key Fixes Applied

**Status:** ⚠️ VALIDATED
1. **LongRoPE Implementation**: Implemented `Phi3LongRoPEScaledRotaryEmbedding` class that handles:
- `short_factor` for sequences ≤ 4096 tokens
- `long_factor` for longer sequences
- Scaling factor based on context length ratio

2. **State Dict Conversion**: Fixed weight mapping:
- Split fused QKV into separate Q, K, V with `qkv_proj.` wrapper
- Split fused gate_up into `gate_proj` and `up_proj`
- Let preshard_hook handle o_proj mapping (don't add extra prefix)

## Usage

Expand All @@ -34,26 +67,26 @@ from neuronx_distributed_inference.models.config import NeuronConfig
from neuronx_distributed_inference.utils.hf_adapter import load_pretrained_config

# Import model classes from src
from src.modeling_phi_3_5_mini_instruct import NeuronPhi35miniinstructForCausalLM, Phi35miniinstructInferenceConfig
from src.modeling_phi3 import NeuronPhi3ForCausalLM, Phi3InferenceConfig

model_path = "/path/to/Phi-3.5-mini-instruct/"
compiled_model_path = "/path/to/compiled/"

# Configure
neuron_config = NeuronConfig(
tp_degree=2,
batch_size=None,
seq_len=512,
torch_dtype=torch.None,
batch_size=1,
seq_len=128,
torch_dtype=torch.bfloat16,
)

config = Phi35miniinstructInferenceConfig(
config = Phi3InferenceConfig(
neuron_config,
load_config=load_pretrained_config(model_path),
)

# Compile and load
model = NeuronPhi35miniinstructForCausalLM(model_path, config)
model = NeuronPhi3ForCausalLM(model_path, config)
model.compile(compiled_model_path)
model.load(compiled_model_path)

Expand All @@ -62,6 +95,16 @@ tokenizer = AutoTokenizer.from_pretrained(model_path)
# ... (see integration test for full example)
```

## State Dict Conversion

The `convert_hf_to_neuron_state_dict` method handles:

1. **Strip `model.` prefix**: HF uses `model.layers.X...`, Neuron expects `layers.X...`
2. **Split fused QKV**: `qkv_proj.weight` → `qkv_proj.q_proj.weight`, `qkv_proj.k_proj.weight`, `qkv_proj.v_proj.weight`
3. **Split fused gate_up**: `gate_up_proj.weight` → `gate_proj.weight`, `up_proj.weight`
4. **o_proj passthrough**: Let preshard_hook handle the `o_proj.o_proj` mapping
5. **Add rank tensors**: For tensor parallelism

## Compatibility Matrix

| Instance/Version | 2.20+ | 2.19 and earlier |
Expand All @@ -86,10 +129,10 @@ python3 test/integration/test_model.py

## Example Checkpoints

* Phi-3.5-mini-instruct
* microsoft/Phi-3.5-mini-instruct

## Maintainer

Neuroboros Team - Annapurna Labs
Annapurna Labs

**Last Updated:** 2026-01-29
**Last Updated:** 2026-02-06
213 changes: 172 additions & 41 deletions contrib/models/Phi-3.5-mini-instruct/src/modeling_phi3.py
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Expand Up @@ -44,6 +44,112 @@
logger = logging.getLogger("Neuron")


class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
"""
LongRoPE Scaled Rotary Position Embedding for Phi-3.5.

This implements the LongRoPE scaling mechanism that allows Phi-3.5 to handle
extended context lengths (up to 128k tokens) by applying position-dependent
scaling factors to the rotary embedding.

Key features:
- Uses short_factor for sequences <= original_max_position_embeddings (4096)
- Uses long_factor for longer sequences
- Applies a scaling factor based on context length ratio

Reference: https://huggingface.co/microsoft/Phi-3.5-mini-instruct
"""

def __init__(
self,
dim: int,
max_position_embeddings: int = 131072,
base: float = 10000.0,
original_max_position_embeddings: int = 4096,
short_factor: list = None,
long_factor: list = None,
device=None,
):
"""
Initialize LongRoPE rotary embedding.

Args:
dim: Dimension of the rotary embedding (head_dim)
max_position_embeddings: Maximum sequence length (131072 for Phi-3.5)
base: RoPE theta base (10000.0)
original_max_position_embeddings: Original context length (4096)
short_factor: Scaling factors for short sequences (list of floats)
long_factor: Scaling factors for long sequences (list of floats)
"""
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.original_max_position_embeddings = original_max_position_embeddings

# Store scaling factors
self.short_factor = short_factor if short_factor is not None else [1.0] * (dim // 2)
self.long_factor = long_factor if long_factor is not None else [1.0] * (dim // 2)

# Register buffers for inv_freq (will be computed dynamically)
self.register_buffer("inv_freq", None, persistent=False)

logger.info(f"Phi3LongRoPEScaledRotaryEmbedding: dim={dim}, base={base}, "
f"max_pos={max_position_embeddings}, "
f"original_max_pos={original_max_position_embeddings}")

@torch.no_grad()
def forward(self, x, position_ids):
"""
Compute rotary position embeddings with LongRoPE scaling.

Args:
x: Input tensor [batch, heads, seq_len, head_dim]
position_ids: Position indices [batch, seq_len]

Returns:
Tuple of (cos, sin) tensors for rotary embedding
"""
# Determine sequence length from position_ids
seq_len = position_ids.max().item() + 1 if position_ids.numel() > 0 else 1

# Choose scaling factors based on sequence length
if seq_len > self.original_max_position_embeddings:
ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
else:
ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)

# Compute inverse frequencies with scaling
inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
inv_freq = 1.0 / (ext_factors * self.base ** inv_freq_shape)

# Expand for batch computation
# inv_freq: [dim/2] -> [batch, dim/2, 1]
inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)

# position_ids: [batch, seq_len] -> [batch, 1, seq_len]
position_ids_expanded = position_ids[:, None, :].float()

# Compute frequencies: [batch, dim/2, seq_len] -> [batch, seq_len, dim/2]
freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)

# Concatenate for full dimension: [batch, seq_len, dim]
emb = torch.cat((freqs, freqs), dim=-1)

# Compute scaling factor for long contexts
scale = self.max_position_embeddings / self.original_max_position_embeddings
if scale <= 1.0:
scaling_factor = 1.0
else:
scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings))

# Apply scaling and convert to target dtype
cos = (emb.cos() * scaling_factor).to(dtype=x.dtype)
sin = (emb.sin() * scaling_factor).to(dtype=x.dtype)

return cos, sin


def get_rmsnorm_cls():
"""
Get the appropriate RMSNorm implementation based on execution mode.
Expand Down Expand Up @@ -257,8 +363,7 @@ class NeuronPhi3Attention(NeuronAttentionBase):
NeuronAttentionBase handles creating the separate q_proj, k_proj, v_proj
through GroupQueryAttention_QKV.

Phi-3 also supports partial rotary factor, meaning RoPE is applied to
only a subset of the head dimensions.
Phi-3.5 uses LongRoPE scaling for extended context support (128k tokens).
"""

def __init__(self, config: Phi3InferenceConfig, layer_idx: Optional[int] = None):
Expand All @@ -269,18 +374,31 @@ def __init__(self, config: Phi3InferenceConfig, layer_idx: Optional[int] = None)
config: Model configuration
layer_idx: Layer index for caching
"""
# Phi-3 specific: partial rotary factor
partial_rotary_factor = getattr(config, 'partial_rotary_factor', 1.0)
head_dim = config.hidden_size // config.num_attention_heads
rotary_ndims = int(head_dim * partial_rotary_factor)

# Create rotary embedding
# For Phi-3, we use the standard RotaryEmbedding but with the partial dimensions
rotary_emb = RotaryEmbedding(
rotary_ndims, # Only apply RoPE to partial dimensions
max_position_embeddings=getattr(config, "max_position_embeddings", 4096),
base=getattr(config, "rope_theta", 10000.0),
)

# Check if LongRoPE scaling is configured
rope_scaling = getattr(config, 'rope_scaling', None)

if rope_scaling is not None and rope_scaling.get('type') == 'longrope':
# Use LongRoPE for Phi-3.5
rotary_emb = Phi3LongRoPEScaledRotaryEmbedding(
dim=head_dim,
max_position_embeddings=getattr(config, "max_position_embeddings", 131072),
base=getattr(config, "rope_theta", 10000.0),
original_max_position_embeddings=getattr(config, "original_max_position_embeddings", 4096),
short_factor=rope_scaling.get('short_factor'),
long_factor=rope_scaling.get('long_factor'),
)
logger.info(f"Using Phi3LongRoPEScaledRotaryEmbedding for layer {layer_idx}")
else:
# Fall back to standard RotaryEmbedding for non-LongRoPE models
partial_rotary_factor = getattr(config, 'partial_rotary_factor', 1.0)
rotary_ndims = int(head_dim * partial_rotary_factor)
rotary_emb = RotaryEmbedding(
rotary_ndims,
max_position_embeddings=getattr(config, "max_position_embeddings", 4096),
base=getattr(config, "rope_theta", 10000.0),
)

# Initialize base attention
# NeuronAttentionBase will create qkv_proj and o_proj internally
Expand Down Expand Up @@ -464,19 +582,23 @@ def convert_hf_to_neuron_state_dict(state_dict: dict, config: InferenceConfig) -
Convert HuggingFace Phi-3 checkpoint to Neuron format.

Key conversions needed:
1. Unfuse QKV projection weights
2. Unfuse gate_up MLP projection weights
3. Add rank tensors for tensor parallelism

Original Phi-3 format:
- layers.X.self_attn.qkv_proj.weight: [total_size, hidden_size]
1. Strip 'model.' prefix from HuggingFace keys
2. Unfuse QKV projection weights and add qkv_proj wrapper prefix
3. Unfuse gate_up MLP projection weights
4. Map o_proj to o_proj.o_proj for NeuronAttentionBase
5. Add rank tensors for tensor parallelism

HuggingFace Phi-3 format:
- model.layers.X.self_attn.qkv_proj.weight: [total_size, hidden_size]
where total_size = num_heads * head_dim + 2 * num_kv_heads * head_dim
- layers.X.mlp.gate_up_proj.weight: [2 * intermediate_size, hidden_size]

Neuron format needs:
- layers.X.self_attn.q_proj.weight
- layers.X.self_attn.k_proj.weight
- layers.X.self_attn.v_proj.weight
- model.layers.X.self_attn.o_proj.weight
- model.layers.X.mlp.gate_up_proj.weight: [2 * intermediate_size, hidden_size]

Neuron format (NeuronAttentionBase expects):
- layers.X.self_attn.qkv_proj.q_proj.weight
- layers.X.self_attn.qkv_proj.k_proj.weight
- layers.X.self_attn.qkv_proj.v_proj.weight
- layers.X.self_attn.o_proj.o_proj.weight
- layers.X.mlp.gate_proj.weight
- layers.X.mlp.up_proj.weight

Expand All @@ -499,9 +621,16 @@ def convert_hf_to_neuron_state_dict(state_dict: dict, config: InferenceConfig) -

# Process each key in the original state dict
for key, value in state_dict.items():
# First, strip 'model.' prefix if present
working_key = key
if working_key.startswith('model.'):
working_key = working_key[6:] # Remove 'model.' prefix

# Handle fused QKV projection
if '.self_attn.qkv_proj.weight' in key:
layer_idx = int(key.split('.')[1])
if '.self_attn.qkv_proj.weight' in working_key:
# Extract layer index from the key (now without 'model.' prefix)
# Format: layers.X.self_attn.qkv_proj.weight
layer_idx = int(working_key.split('.')[1])

# Split the fused QKV weight
# Shape: [num_heads * head_dim + 2 * num_kv_heads * head_dim, hidden_size]
Expand All @@ -513,14 +642,15 @@ def convert_hf_to_neuron_state_dict(state_dict: dict, config: InferenceConfig) -
k_weight = value[q_size:q_size + k_size, :]
v_weight = value[q_size + k_size:q_size + k_size + v_size, :]

# Store split weights
neuron_state_dict[f"layers.{layer_idx}.self_attn.q_proj.weight"] = q_weight
neuron_state_dict[f"layers.{layer_idx}.self_attn.k_proj.weight"] = k_weight
neuron_state_dict[f"layers.{layer_idx}.self_attn.v_proj.weight"] = v_weight
# Store split weights with qkv_proj wrapper prefix for NeuronAttentionBase
neuron_state_dict[f"layers.{layer_idx}.self_attn.qkv_proj.q_proj.weight"] = q_weight
neuron_state_dict[f"layers.{layer_idx}.self_attn.qkv_proj.k_proj.weight"] = k_weight
neuron_state_dict[f"layers.{layer_idx}.self_attn.qkv_proj.v_proj.weight"] = v_weight

# Handle fused gate_up projection
elif '.mlp.gate_up_proj.weight' in key:
layer_idx = int(key.split('.')[1])
elif '.mlp.gate_up_proj.weight' in working_key:
# Extract layer index
layer_idx = int(working_key.split('.')[1])

# Split the fused gate_up weight
# Shape: [2 * intermediate_size, hidden_size]
Expand All @@ -531,14 +661,15 @@ def convert_hf_to_neuron_state_dict(state_dict: dict, config: InferenceConfig) -
neuron_state_dict[f"layers.{layer_idx}.mlp.gate_proj.weight"] = gate_weight
neuron_state_dict[f"layers.{layer_idx}.mlp.up_proj.weight"] = up_weight

# Copy other weights directly
elif 'qkv_proj' not in key and 'gate_up_proj' not in key:
# Handle model. prefix if present
if key.startswith('model.'):
new_key = key[6:] # Remove 'model.' prefix
else:
new_key = key
neuron_state_dict[new_key] = value
# Handle o_proj - preshard_hook will add the o_proj.o_proj wrapper
# So we just need to provide layers.X.self_attn.o_proj.weight
elif '.self_attn.o_proj.' in working_key:
# Just copy as-is (already stripped 'model.' prefix)
neuron_state_dict[working_key] = value

# Copy other weights directly (already stripped 'model.' prefix)
elif 'qkv_proj' not in working_key and 'gate_up_proj' not in working_key:
neuron_state_dict[working_key] = value

# Add rank tensors for tensor parallelism
for i in range(num_layers):
Expand Down
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