From f8fb17ed17717724dcf9b3b9ac89d20f0ab97ae1 Mon Sep 17 00:00:00 2001
From: asfiyab-nvidia <117682710+asfiyab-nvidia@users.noreply.github.com>
Date: Tue, 13 Dec 2022 19:16:40 -0800
Subject: [PATCH 01/16] Add ONNX export support for TE modules (#1)

* Add TorchScript Operators
* Add symbolic methods to ONNX exporter
* Add tests for the ONNX export

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 .gitignore                                    |   1 +
 libcustom_ort_fp8_qdq_ops.so                  | Bin 0 -> 675736 bytes
 qa/L0_unittest/test.sh                        |   3 +-
 setup.py                                      |   3 +-
 tests/test_onnx_export.py                     | 870 ++++++++++++++++++
 transformer_engine/pytorch/__init__.py        |   2 +
 transformer_engine/pytorch/cpp_extensions.py  |  76 +-
 transformer_engine/pytorch/csrc/common.h      |   2 +
 transformer_engine/pytorch/csrc/extensions.cu |  27 +
 transformer_engine/pytorch/csrc/extensions.h  |  14 +
 transformer_engine/pytorch/csrc/ts_fp8_op.cpp | 178 ++++
 transformer_engine/pytorch/module.py          | 505 ++++++----
 transformer_engine/pytorch/softmax.py         |  77 +-
 .../pytorch/te_onnx_extensions.py             | 170 ++++
 14 files changed, 1727 insertions(+), 201 deletions(-)
 create mode 100755 libcustom_ort_fp8_qdq_ops.so
 create mode 100644 tests/test_onnx_export.py
 create mode 100755 transformer_engine/pytorch/csrc/ts_fp8_op.cpp
 create mode 100755 transformer_engine/pytorch/te_onnx_extensions.py

diff --git a/.gitignore b/.gitignore
index 4ece24d518..68bba92666 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,6 +8,7 @@
 *.nsys-rep
 *.ncu-rep
 *.sqlite
+*.onnx
 .eggs
 build/
 *.so
diff --git a/libcustom_ort_fp8_qdq_ops.so b/libcustom_ort_fp8_qdq_ops.so
new file mode 100755
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diff --git a/qa/L0_unittest/test.sh b/qa/L0_unittest/test.sh
index 121555a3d2..7e2020d4c4 100644
--- a/qa/L0_unittest/test.sh
+++ b/qa/L0_unittest/test.sh
@@ -6,5 +6,6 @@ set -e
 
 : ${TE_PATH:=/opt/transformerengine}
 
-pip install pytest==6.2.5
+pip install pytest==6.2.5 onnxruntime
 pytest -v -s $TE_PATH/tests/test_transformerengine.py
+pytest -v -s $TE_PATH/tests/test_onnx_export.py
diff --git a/setup.py b/setup.py
index 2145025789..5f824c63e2 100644
--- a/setup.py
+++ b/setup.py
@@ -14,7 +14,7 @@
 from setuptools.command.build_ext import build_ext
 from distutils.version import LooseVersion
 from distutils.file_util import copy_file
-from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
+from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
 
 path = os.path.dirname(os.path.realpath(__file__))
 
@@ -85,6 +85,7 @@ def make_abs_path(l):
 pytorch_sources = [
     "transformer_engine/pytorch/csrc/extensions.cu",
     "transformer_engine/pytorch/csrc/common.cu",
+    "transformer_engine/pytorch/csrc/ts_fp8_op.cpp",
 ]
 pytorch_sources = make_abs_path(pytorch_sources)
 
diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
new file mode 100644
index 0000000000..15c309ccdb
--- /dev/null
+++ b/tests/test_onnx_export.py
@@ -0,0 +1,870 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+"""
+This file contains tests for exporting TransformerEngine models to ONNX.
+"""
+
+import os
+import pytest
+import warnings
+import numpy as np
+import math
+import onnxruntime as ort
+import torch
+from torch import nn as nn
+from typing import Union, Tuple
+import transformer_engine.pytorch as te
+from transformer_engine.common import recipe
+import transformer_engine_extensions as tex
+from transformer_engine.pytorch.cpp_extensions import *
+from transformer_engine.pytorch.module import get_workspace
+import transformer_engine.pytorch.cpp_extensions as texcpp
+import transformer_engine.pytorch.softmax as softmax_defs
+from transformer_engine.pytorch.utils import get_default_init_method
+
+
+# Directory where generated ONNX test models are stored.
+ONNX_FILES_DIR = "./gen_onnx_models"
+
+# Shared library implementing custom FP8 Q/DQ operators for ONNX Runtime (ORT).
+ORT_CUSTOM_OPS_LIB = "./libcustom_ort_fp8_qdq_ops.so"
+
+# ScaledUpperTriangMaskedSoftmax is exported via ONNX::Trilu which was introduced in opset 14.
+TRILU_OPSET = 14
+# Opset used in the ONNX files generated by the tests.
+OPSET = 15
+assert OPSET >= TRILU_OPSET
+
+
+def create_fp8_recipe():
+    return recipe.DelayedScaling(margin=0, interval=1, fp8_format=recipe.Format.E4M3)
+
+
+def do_export(
+    model: torch.nn.Module,
+    inp: torch.Tensor,
+    fname: str,
+    use_fp8: bool=True,
+    opset: int=OPSET,
+    input_names: list=["input"],
+    output_names: list=["output"],
+):
+    """Export to ONNX"""
+    fp8_recipe = create_fp8_recipe()
+
+    with torch.inference_mode(), te.fp8_autocast(enabled=use_fp8, fp8_recipe=fp8_recipe), warnings.catch_warnings():
+        warnings.filterwarnings(
+            action='ignore',
+            category=torch.jit.TracerWarning,
+            module=r'.*'
+        )
+
+        model.cuda().eval()
+        os.makedirs(ONNX_FILES_DIR, exist_ok=True)
+        fname = os.path.join(ONNX_FILES_DIR, fname)
+        torch.onnx.export(model,
+                          inp if isinstance(inp, list) or isinstance(inp, tuple) else (inp,),
+                          fname,
+                          verbose=False,
+                          opset_version=opset,
+                          input_names=input_names,
+                          output_names=output_names,
+                          do_constant_folding=True,
+                          operator_export_type=torch.onnx.OperatorExportTypes.ONNX_FALLTHROUGH)
+
+
+def to_numpy(tensor):
+    return tensor.cpu().numpy()
+
+
+def set_layer_scale(module: torch.nn.Module, scale: float):
+    module.fp8_init()
+    module.fp8_meta["scaling_fwd"].scale = torch.ones(
+        2, dtype=torch.float32, device="cuda") / scale
+    module.fp8_meta["scaling_fwd"].scale_inv = torch.ones(
+        2, dtype=torch.float32, device="cuda") * scale
+
+
+def te_infer(model: torch.nn.Module, inps: Union[Tuple[torch.tensor], torch.tensor], is_fp8: bool):
+    """Transformer Engine forward prpoagtation.
+
+    Return results after copying to the CPU and converting to numpy.
+    """
+    fp8_recipe = create_fp8_recipe()
+    with torch.inference_mode(), te.fp8_autocast(enabled=is_fp8, fp8_recipe=fp8_recipe), warnings.catch_warnings():
+        te_outputs = model(*inps if isinstance(inps, tuple) else (inps,))
+        if not isinstance(te_outputs, tuple):
+            te_outputs = (te_outputs,)
+        te_outputs_np = [to_numpy(te_output) for te_output in te_outputs]
+        return te_outputs_np
+
+
+def validate_result(
+    fname: str,
+    inps: Union[Tuple[torch.Tensor], torch.Tensor],
+    model: torch.nn.Module,
+    atol: float=1.e-8, # np.isclose default atol
+    rtol: float=1.e-5, # np.isclose default rtol
+    max_errors_printed: int=10,
+    is_fp8: bool=False,
+):
+    """Validate the outputs of an ONNX model vs. ONNX Runtime."""
+
+    def create_ort_session(fname: str, is_fp8: bool):
+        def load_custom_ops(session_opts: ort.SessionOptions):
+            """For FP8 validation with ORT we need to load our custom FP8 Q/DQ extension."""
+            if not os.path.exists(ORT_CUSTOM_OPS_LIB):
+                raise FileNotFoundError(f"Unable to find {ORT_CUSTOM_OPS_LIB}")
+            session_opts.register_custom_ops_library(ORT_CUSTOM_OPS_LIB)
+            print("registered custom FP8 Q/DQ ops!")
+
+        """Create an ONNX Runtime session for validation."""
+        if is_fp8:
+            sess_options = ort.SessionOptions()
+            load_custom_ops(sess_options)
+            # Model loading successfully indicates that the custom op node could be resolved successfully
+            s = ort.InferenceSession(fname, sess_options=sess_options)
+        else:
+            s = ort.InferenceSession(fname)
+        return s
+
+    def create_ort_input_dict(session, inps):
+        inp_dict = {}
+        if isinstance(inps, tuple) or isinstance(inps, list):
+            for idx, inp in enumerate(inps):
+                if inp is None:
+                    continue
+                inp_dict[session.get_inputs()[idx].name] = to_numpy(inp)
+        else:
+            inp_dict[session.get_inputs()[0].name] = to_numpy(inps)
+        return inp_dict
+
+    # Run ORT session and TE model.
+    fname = os.path.join(ONNX_FILES_DIR, fname)
+    ort_s = create_ort_session(fname, is_fp8)
+    onnx_outputs = ort_s.run(None, input_feed=create_ort_input_dict(ort_s, inps))
+    te_outputs = te_infer(model, inps, is_fp8)
+
+    # Compare ORT and TE outputs.
+    assert len(onnx_outputs) == len(te_outputs)
+    for onnx_output, te_output in zip(onnx_outputs, te_outputs):
+
+        # Compare ORT and PyTorch outputs.
+        # np.isclose: abs(a - b) <= (atol + rtol * abs(b))
+        ac = ~np.isclose(onnx_output, te_output, atol=atol, rtol=rtol)
+
+        mismatches = ac.nonzero()
+        mismatched_ids = [loc for loc in zip(*mismatches)]
+        if mismatched_ids:
+            # Log some information in case of error.
+            print("*" * 100)
+            print(onnx_output.shape)
+            nb_vals = min(len(mismatched_ids), max_errors_printed)
+            print(f"Detected {len(mismatched_ids)} diverging values.\nShowing first {nb_vals} errors (ONNX -- TE):")
+            abs_err = abs(onnx_output - te_output)
+            for loc in mismatched_ids[:nb_vals]:
+                ref = te_output[loc]
+                print(f"{onnx_output[loc]} -- {te_output[loc]} err={abs_err[loc]} > {atol + rtol * abs(ref)}")
+            raise ValueError(f"Output validation of {fname} failed with {len(mismatched_ids)} errors")
+
+
+def create_meta(scale_factor: float, size: int=1):
+    meta = tex.FP8TensorMeta()
+    meta.amax_history = torch.zeros(1, size, dtype=torch.float32, device="cuda")
+    meta.scale_inv = torch.ones(size, dtype=torch.float32, device="cuda") / scale_factor
+    meta.scale = torch.ones(size, dtype=torch.float32, device="cuda") * scale_factor
+    return meta
+
+
+def dtype2str(dtype: torch.dtype):
+    return {
+        torch.float32: "_fp32",
+        torch.float16: "_fp16",
+        torch.bfloat16: "_bf16",
+    }[dtype]
+
+
+def as_te_type(dtype: torch.dtype):
+    return {
+        torch.float32: tex.DType.kFloat32,
+        torch.float16: tex.DType.kFloat16,
+        torch.bfloat16: tex.DType.kBFloat16,
+    }[dtype]
+
+
+@pytest.mark.parametrize("scale_factor, atol", [
+    (1, 1e-7),
+    (224, 1e-7)
+])
+@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
+def test_export_cast_ops(scale_factor: float, atol: float, precision: torch.dtype):
+    class TestFP8_QDQ(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.fp8_tensor = 0
+            self.meta = create_meta(scale_factor)
+            self.highprec_type = as_te_type(precision)
+            self.fp8_type = tex.DType.kFloat8E4M3
+
+        def forward(self, inp):
+            ret = cast_to_fp8(
+                inp,
+                self.meta,
+                self.fp8_tensor,
+                self.fp8_type)
+
+            ret = cast_from_fp8(
+                ret,
+                self.meta,
+                self.fp8_tensor,
+                self.fp8_type,
+                self.highprec_type)
+            return ret
+
+    # Set dimensions (these are arbitrary).
+    in_features = 64
+    hidden_size = 256
+    inp = torch.randn(hidden_size, in_features, device="cuda", dtype=precision)
+    high_prec_str = dtype2str(precision)
+    fname = f"te.cast_fp8.s_{scale_factor}{high_prec_str}.onnx"
+    model = TestFP8_QDQ()
+    do_export(model, inp, fname)
+    validate_result(fname, inp, model, atol=atol, is_fp8=True)
+
+
+@pytest.mark.parametrize("scale_factor", [448])
+@pytest.mark.parametrize("precision, atol", [
+    [torch.float32, 1e-7], [torch.float16, 2e-3]]
+)
+def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
+    class TestFP8_Gelu(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.fp8_tensor = 0
+            self.meta = create_meta(scale_factor)
+            self.highprec_type = as_te_type(precision)
+            self.fp8_type = tex.DType.kFloat8E4M3
+
+        def forward(self, inp):
+            ret = fp8_gelu(
+                inp,
+                self.meta,
+                self.fp8_tensor,
+                self.fp8_type)
+            ret = cast_from_fp8(
+                ret,
+                self.meta,
+                self.fp8_tensor,
+                self.fp8_type,
+                self.highprec_type)
+            return ret
+
+    # Set dimensions (these are arbitrary).
+    in_features = 64
+    hidden_size = 256
+    inp = torch.randn(hidden_size, in_features, device="cuda", dtype=precision)
+    high_prec_str = dtype2str(precision)
+    fname = f"te.gelu_fp8{high_prec_str}.onnx"
+    model = TestFP8_Gelu()
+    do_export(model, inp, fname)
+    validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True)
+
+
+@pytest.mark.parametrize("scale_factors", [(224, 224,), ])
+@pytest.mark.parametrize(
+    "precision,     use_fp8, use_bias, use_gelu", [
+    (torch.float32, False,   False,    False),
+    (torch.float16, False,   False,    False),
+    (torch.float32, False,   True,     False),
+    (torch.float16, False,   True,     False),
+    (torch.float32, False,   True,     True),
+    (torch.float16, False,   True,     True),
+
+    # For FP8 GEMM GeLU is not used.
+    (torch.float32, True,    False,    False),
+    (torch.float16, True,    False,    False),
+    # When enabling bias we must use float16 or bfloat16 (because of kernel limitations)
+    (torch.float16, True,    True,     False),
+    (torch.bfloat16, True,   True,     False),
+])
+def test_export_gemm(
+    precision, # Precision of inputs, weights, output and bias
+    use_fp8,
+    use_bias,
+    use_gelu,
+    scale_factors
+):
+    class TestFP8_GEMM(nn.Module):
+        def __init__(self, precision, use_bias, gelu, scale_factors):
+            super().__init__()
+            self.use_bias = use_bias
+            self.gelu = gelu
+            self.precision = precision
+
+            self.fp8_tensor_inp = tex.FP8FwdTensors.GEMM1_INPUT
+            self.fp8_tensor_weight = tex.FP8FwdTensors.GEMM1_WEIGHT
+            nb_inp_scales, nb_weight_scales = 1, out_features
+            act_scale_factor, weight_scale_factor = scale_factors
+            self.meta_inp = create_meta(act_scale_factor, nb_inp_scales)
+            self.meta_weight = create_meta(weight_scale_factor, nb_weight_scales)
+
+            bias_size = nb_weight_scales
+            self.bias = torch.randn(bias_size, dtype=precision, device="cuda")
+            self.gelu_input = torch.randn(hidden_size, out_features, dtype=precision, device="cuda")
+
+            self.inp_type = tex.DType.kFloat8E4M3
+            self.weights_type = tex.DType.kFloat8E4M3
+            self.outp_type = precision
+
+        def forward(self, inp, weight):
+            inp_fp8 = cast_to_fp8(
+                inp,
+                self.meta_inp,
+                self.fp8_tensor_inp,
+                self.inp_type)
+
+            weight_fp8 = cast_to_fp8(
+                weight,
+                self.meta_weight,
+                self.fp8_tensor_weight,
+                self.weights_type)
+
+            ret = fp8_gemm(
+                weight_fp8,
+                self.meta_weight.scale_inv,
+                self.fp8_tensor_weight,
+                self.inp_type,
+                inp_fp8,
+                self.meta_inp.scale_inv,
+                self.fp8_tensor_inp,
+                self.weights_type,
+                self.outp_type,
+                get_workspace(),
+                bias=self.bias,
+                use_bias=self.use_bias,
+                fp32_output=(self.precision==torch.float32),
+                use_split_accumulator=False)
+            return ret
+
+    class Test_GEMM(nn.Module):
+        def __init__(self, precision, use_bias=False, gelu=False):
+            super().__init__()
+            self.use_bias = use_bias
+            self.gelu = gelu
+            self.precision = precision
+            bias_size = out_features
+            self.bias = torch.randn(bias_size, dtype=precision, device="cuda")
+            self.gelu_input = torch.randn(hidden_size, out_features, dtype=precision, device="cuda")
+
+        def forward(self, inp, weight):
+            outp_type = self.precision
+
+            # note: due to logic in lines 104:116 and L129 in cpp_extensions.py
+            # it appears either bias OR gelu can be activated, not both
+            ret, _, _ = gemm(
+                weight,
+                inp,
+                outp_type,
+                get_workspace(),
+
+                # test bias
+                bias=self.bias,
+                use_bias=self.use_bias,
+
+                # test gelu
+                gelu=self.gelu,
+                gelu_input=self.gelu_input,
+                grad=False # only True for backward pass
+            )
+            return ret
+
+    # If gelu is applied then bias must be added, as defined by TE kernel.
+    if use_gelu: assert use_bias
+    # Set dimensions (these are arbitrary).
+    out_features = 128
+    hidden_size = 256
+    in_features = 64
+    inp = torch.randn(hidden_size, in_features, dtype=precision, device="cuda")
+    weight = torch.randn(out_features, in_features, dtype=precision, device="cuda")
+    fp8_str = "_fp8" if use_fp8 else ""
+    bias_str = "_bias" if use_bias else ""
+    gelu_str = "_gelu" if use_gelu else ""
+    high_prec_str = dtype2str(precision)
+    fname = f"te.gemm{fp8_str}{bias_str}{gelu_str}{high_prec_str}.onnx"
+    if use_fp8:
+        model = TestFP8_GEMM(precision, use_bias, use_gelu, scale_factors)
+        do_export(model, (inp, weight), fname, use_fp8)
+        if precision not in (torch.bfloat16, torch.float16):
+            validate_result(fname, (inp, weight), model, rtol=1e-2, atol=1e-2, is_fp8=True)
+    else:
+        model = Test_GEMM(precision, use_bias, use_gelu)
+        do_export(model, (inp, weight), fname, use_fp8)
+        validate_result(fname, (inp, weight), model, rtol=1e-2, atol=1e-2)
+
+
+@pytest.mark.parametrize("use_fp8", [False, True])
+@pytest.mark.parametrize("scale_factor", [448, 112])
+@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
+def test_export_layernorm(
+    use_fp8: bool,
+    scale_factor: float,
+    precision: torch.dtype
+):
+    # Set dimensions (these are arbitrary).
+    inp_shape = [64, 32]
+
+    class Test_Layernorm(nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            normalized_shape = torch.Size(inp.shape[1:])
+            self.weight = torch.randn(*normalized_shape, dtype=precision, device="cuda")
+            self.bias = torch.zeros(*normalized_shape, dtype=precision, device="cuda")
+            self.eps = 1e-6 # An arbitrary small value
+
+        def forward(self, inp):
+            ret = texcpp.layernorm_fwd_inf(
+                inp,
+                self.weight,
+                self.bias,
+                self.eps)
+            return ret
+
+    class TestFP8_Layernorm(nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            normalized_shape = torch.Size(inp.shape[1:])
+            self.weight = torch.randn(*normalized_shape, dtype=precision, device="cuda")
+            self.bias = torch.zeros(*normalized_shape, dtype=precision, device="cuda")
+            self.eps = 1e-6 # An arbitrary small value
+
+            self.fp8_tensor = tex.FP8FwdTensors.GEMM1_INPUT
+            self.meta = create_meta(scale_factor)
+            self.fp8_type = tex.DType.kFloat8E4M3
+
+        def forward(self, inp):
+            ret = texcpp.layernorm_fwd_fp8_inf(
+                inp,
+                self.weight,
+                self.bias,
+                self.eps,
+                self.meta,
+                self.fp8_type)
+
+            ret = cast_from_fp8(
+                ret,
+                self.meta,
+                self.fp8_tensor,
+                self.fp8_type,
+                tex.DType.kFloat32 if precision == torch.float32 else tex.DType.kFloat16)
+            return ret
+
+    inp = torch.randn(*inp_shape, device="cuda", dtype=precision)
+    high_prec_str = dtype2str(precision)
+    if use_fp8:
+        fname = f"te.layernorm_fwd_fp8{high_prec_str}.onnx"
+        model = TestFP8_Layernorm()
+    else:
+        fname = f"te.layernorm_fwd{high_prec_str}.onnx"
+        model = Test_Layernorm()
+
+    do_export(model, inp, fname)
+    if precision not in (torch.bfloat16, ):
+        validate_result(fname, inp, model, atol=1e-5, is_fp8=use_fp8)
+
+
+@pytest.mark.parametrize("softmax_def", [
+    softmax_defs.ScaledUpperTriangMaskedSoftmax,
+    softmax_defs.ScaledMaskedSoftmax,
+    softmax_defs.ScaledSoftmax,
+])
+# Softmax kernel only supports FP16 or BF16!
+@pytest.mark.parametrize("precision", [torch.float16, torch.bfloat16])
+def test_export_softmax(softmax_def, precision):
+    class Test_Softmax(nn.Module):
+        def __init__(self, softmax_function, mask_inp=False):
+            super().__init__()
+            self.softmax_fn = softmax_function
+            self.mask_inp = mask_inp
+
+        def forward(self, inp, mask):
+            scale_factor = 8 # arbitrary value
+            if self.mask_inp:
+                ret = self.softmax_fn.apply(inp, mask, scale_factor)
+            else:
+                ret = self.softmax_fn.apply(inp, scale_factor)
+            return ret
+
+    # Set dimensions (these are arbitrary).
+    in_features = 64
+    hidden_size = 256
+    mask = None
+    input_names = ["input"]
+    inp_shape = [hidden_size, in_features, in_features, in_features]
+    if softmax_def == softmax_defs.ScaledUpperTriangMaskedSoftmax:
+        inp_shape = [hidden_size, in_features, in_features]
+        kernel_str = "te.ScaledUpperTriangMaskedSoftmax"
+        model = Test_Softmax(softmax_def)
+    elif softmax_def == softmax_defs.ScaledMaskedSoftmax:
+        # Generate a random mask with 50% probability for 0 or 1.
+        probs = 0.5 * torch.ones(hidden_size, 1, in_features, in_features, device="cuda", dtype=precision)
+        mask = torch.bernoulli(probs).to("cuda", dtype=torch.bool)
+        input_names.append("mask")
+        kernel_str = "te.ScaledMaskedSoftmax"
+        model = Test_Softmax(softmax_def, mask_inp=True)
+    elif softmax_def == softmax_defs.ScaledSoftmax:
+        kernel_str = "te.ScaledSoftmax"
+        model = Test_Softmax(softmax_def)
+    input_tensor = torch.randn(*inp_shape, device="cuda")
+    input_tensor = input_tensor.to(torch.bfloat16) if precision == torch.bfloat16 else input_tensor.half()
+    high_prec_str = dtype2str(precision)
+    fname = f"{kernel_str}{high_prec_str}.onnx"
+    inp = (input_tensor, mask)
+    do_export(model, inp, fname, input_names=input_names)
+    if precision != torch.bfloat16:
+        validate_result(fname, inp, model, atol=1e-3)
+
+
+@pytest.mark.parametrize("scale_factor", [1])
+@pytest.mark.parametrize("use_fp8", [False, True])
+@pytest.mark.parametrize("return_bias", [False, True])
+@pytest.mark.parametrize(
+    "precision,     use_bias",[
+    (torch.float32, False),
+    (torch.float32, True),
+    # Todo: cannot configure FP16/BF16 when bias is disabled -
+    # AssertionError: Data type for activations and buffers must match when outside of autocasted region
+    # (torch.float16, False),
+    (torch.float16, True),
+    #(torch.bfloat16, False),
+    # Todo: cannot configure BF16 when bias is enabled (ORT issue?)
+    # (torch.bfloat16, True),
+])
+def test_export_linear(
+    scale_factor: float,
+    use_fp8: bool,
+    use_bias: bool,
+    return_bias: bool,
+    precision: torch.dtype
+):
+    # Set dimensions (these are arbitrary).
+    in_features = 64
+    out_features = 256
+    hidden_size = 256
+
+    inp = torch.randn(hidden_size, in_features, device="cuda", dtype=precision)
+    fp8_str = "_fp8" if use_fp8 else ""
+    bias_str = "_bias" if use_bias else ""
+    high_prec_str = dtype2str(precision)
+    fname = f"te.linear{fp8_str}{bias_str}{high_prec_str}.onnx"
+    with te.fp8_autocast(enabled=use_fp8):
+        model = te.Linear(
+            in_features,
+            out_features,
+            bias=use_bias,
+            return_bias=return_bias,
+            params_dtype=precision
+        ).to(device='cuda')
+        if use_fp8:
+            set_layer_scale(model, scale_factor)
+        do_export(model, inp, fname, use_fp8)
+
+        if not use_fp8:
+            validate_result(fname, inp, model, atol=1e-3)
+        elif precision not in (torch.bfloat16, ):
+            validate_result(fname, inp, model, atol=1e-3, is_fp8=use_fp8)
+
+
+@pytest.mark.parametrize("scale_factor", [112])
+@pytest.mark.parametrize("use_fp8", [False, True])
+# Todo: handle case of True
+@pytest.mark.parametrize("return_bias", [False])
+@pytest.mark.parametrize("return_layernorm_output", [False])
+@pytest.mark.parametrize(
+    "precision,     use_bias",[
+    (torch.float32, False),
+    (torch.float32, True),
+    (torch.float16, True),
+    # Todo: cannot configure FP16 when bias is disabled
+    #(torch.float16, False),
+])
+def test_export_layernorm_linear(
+    scale_factor: float,
+    use_fp8: bool,
+    use_bias: bool,
+    return_bias: bool,
+    return_layernorm_output: bool,
+    precision: torch.dtype
+):
+    # Set dimensions (these are arbitrary).
+    in_features = 64
+    out_features = 256
+    hidden_size = 256
+
+    inp = torch.randn(in_features, out_features, device="cuda", dtype=precision)
+    fp8_str = "_fp8" if use_fp8 else ""
+    bias_str = "_bias" if use_bias else ""
+    high_prec_str = dtype2str(precision)
+    fname = f"te.layernorm_linear{fp8_str}{bias_str}{high_prec_str}.onnx"
+    with te.fp8_autocast(enabled=use_fp8):
+        model = te.LayerNormLinear(
+            hidden_size,
+            3 * hidden_size,
+            bias=use_bias,
+            return_bias=return_bias,
+            return_layernorm_output=return_layernorm_output,
+            params_dtype=precision,
+        ).to(device='cuda')
+        if use_fp8:
+            set_layer_scale(model, scale_factor)
+        do_export(model, inp, fname, use_fp8)
+        if not use_fp8:
+            validate_result(fname, inp, model, atol=1e-3)
+        elif precision not in (torch.bfloat16,):
+            validate_result(fname, inp, model, atol=1e-3, is_fp8=use_fp8)
+
+
+@pytest.mark.parametrize("scale_factor", [112])
+@pytest.mark.parametrize("use_fp8", [False, True])
+# Todo: handle case of True
+@pytest.mark.parametrize("return_bias", [False])
+@pytest.mark.parametrize("return_layernorm_output", [False])
+# Todo: cannot handle FP16 for some reason
+@pytest.mark.parametrize(
+    "precision,     use_bias",[
+    (torch.float32, False),
+    (torch.float32, True),
+    (torch.float16, True),
+    # Todo: cannot configure FP16 when bias is disabled
+    #(torch.float16, False),
+])
+def test_export_layernorm_mlp(
+    scale_factor: float,
+    use_fp8: bool,
+    use_bias: bool,
+    return_bias: bool,
+    return_layernorm_output: bool,
+    precision: torch.dtype
+):
+    # Set dimensions (these are arbitrary).
+    in_features = 64
+    out_features = 256
+    hidden_size = 256
+    ffn_hidden_size = 256
+
+    inp = torch.randn(in_features, out_features, device="cuda", dtype=precision)
+    fp8_str = "_fp8" if use_fp8 else ""
+    bias_str = "_bias" if use_bias else ""
+    high_prec_str = dtype2str(precision)
+    fname = f"te.layernorm_mlp{fp8_str}{bias_str}{high_prec_str}.onnx"
+    with te.fp8_autocast(enabled=use_fp8):
+        model = te.LayerNormMLP(
+            hidden_size,
+            ffn_hidden_size,
+            bias=use_bias,
+            return_bias=return_bias,
+            return_layernorm_output=return_layernorm_output,
+            params_dtype=precision,
+        ).to(device='cuda')
+        if use_fp8:
+            set_layer_scale(model, scale_factor)
+        do_export(model, inp, fname, use_fp8)
+        if not use_fp8:
+            validate_result(fname, inp, model, atol=1e-3)
+        else:
+            validate_result(fname, inp, model, atol=1e-2, is_fp8=use_fp8)
+
+
+@pytest.mark.parametrize(
+    "precision,     use_mask, attn_mask_type", [
+    (torch.float32, False,    None),      # calls forward_torch_softmax
+    (torch.float32, True,     None),      # calls forward_torch_softmax
+    (torch.float16, False,    "causal"),  # calls ScaledUpperTriangMaskedSoftmax
+    (torch.float16, True,     "padding"), # calls ScaledMaskedSoftmax
+    (torch.float16, False,    "padding"), # calls ScaledSoftmax
+])
+@pytest.mark.parametrize("attention_softmax_in_fp32", [True, False])
+@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
+def test_export_core_attention(
+    precision: torch.dtype,
+    use_mask: bool,
+    attn_mask_type: str,
+    attention_softmax_in_fp32: bool,
+    apply_query_key_layer_scaling: bool,
+):
+    if attn_mask_type is None:
+        attn_mask_type = 'causal'
+
+    # Set dimensions (these are arbitrary).
+    kv_channels = 64
+    num_attention_heads = 1
+    qkv_size = (2048, 4, num_attention_heads, kv_channels)
+
+    query_layer = torch.randn(qkv_size, dtype=precision, device="cuda")
+    key_layer = torch.randn(qkv_size, dtype=precision, device="cuda")
+    value_layer = torch.randn(qkv_size, dtype=precision, device="cuda")
+    input_names = ["query", "key", "value"]
+    attention_mask = None
+    if use_mask:
+        # Generate a random mask with 50% probability for 0 or 1.
+        probs = 0.5 * torch.ones(qkv_size[1], qkv_size[2], qkv_size[0], qkv_size[0], device="cuda", dtype=precision)
+        attention_mask = torch.bernoulli(probs).to("cuda", dtype=torch.bool)
+        input_names.append("attention_mask")
+    inp = (query_layer, key_layer, value_layer, attention_mask)
+
+    sm_prec_str = "_fp32" if attention_softmax_in_fp32 else "_fp16"
+    qk_scaling_str = "_qk_scaling" if apply_query_key_layer_scaling else ""
+    mask_str = "_masked" if use_mask else \
+                "_upper_trian_masked" if attn_mask_type=="causal" and precision == torch.float16 else \
+                ""
+    high_prec_str = dtype2str(precision)
+    fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
+
+    model = te.transformer.CoreAttention(
+        num_attention_heads=num_attention_heads,
+        kv_channels=kv_channels,
+        attention_dropout=0.5,
+        attn_mask_type=attn_mask_type,
+        attention_softmax_in_fp32=attention_softmax_in_fp32,
+        apply_query_key_layer_scaling=apply_query_key_layer_scaling,
+    ).to(device='cuda')
+    do_export(model,
+            inp,
+            fname,
+            input_names=input_names,
+            use_fp8=True)
+    validate_result(fname, inp, model, atol=1e-2)
+
+
+test_configs_multihead_attention = [
+    (False, "causal"),  # calls ScaledUpperTriangMaskedSoftmax
+    (True,  "padding"), # calls ScaledMaskedSoftmax
+    (False, "padding"), # calls ScaledSoftmax
+]
+@pytest.mark.parametrize("use_fp8", [False, True])
+@pytest.mark.parametrize("use_mask, attn_mask_type", test_configs_multihead_attention)
+@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
+@pytest.mark.parametrize("input_layernorm", [True, False])
+@pytest.mark.parametrize("return_layernorm_output", [False])
+@pytest.mark.parametrize("attention_type", [
+    "self",
+    #"cross" # TODO: handle this ORT error
+])
+@pytest.mark.parametrize("fuse_qkv_params", [False, True])
+def test_export_multihead_attention(
+    use_fp8: bool,
+    use_mask: bool,
+    attn_mask_type: str,
+    precision: torch.dtype,
+    return_layernorm_output: bool,
+    input_layernorm: bool,
+    attention_type: str,
+    fuse_qkv_params: bool
+):
+    hidden_size = 256
+    sequence_length = 128
+    batch_size = 4
+    num_attention_heads = 32
+    kv_channels = 8
+    attention_dropout = 0.1
+    layernorm_epsilon = 1e-5
+    init_method = output_layer_init_method = get_default_init_method()
+    attention_args = (
+        hidden_size,
+        num_attention_heads,
+        kv_channels,
+        attention_dropout,
+        layernorm_epsilon,
+        init_method,
+        output_layer_init_method,
+    )
+    hidden_states = torch.randn(sequence_length, batch_size, hidden_size, dtype=precision, device="cuda")
+    input_names = ["hidden_states"]
+    attention_mask = None
+    if use_mask and attn_mask_type != "causal":
+        # Generate a random mask with 50% probability for 0 or 1.
+        probs = 0.5 * torch.ones(batch_size, 1, sequence_length, sequence_length, device="cuda", dtype=precision)
+        attention_mask = torch.bernoulli(probs).to("cuda", dtype=torch.bool)
+        input_names.append("attention_mask")
+    inp = (hidden_states, attention_mask)
+
+    fp8_str = "_fp8" if use_fp8 else ""
+    dtype_str = dtype2str(precision)
+    attn_type_str = "_self_attention" if attention_type == "self" else "_cross_attention"
+    fuse_qkv_str = "_fused" if fuse_qkv_params else ""
+    attn_mask_type_str = f"_{attn_mask_type}" if (use_mask and attn_mask_type != "") else ""
+    fname = f"te.multihead_attention{fp8_str}{attn_mask_type_str}{attn_type_str}{fuse_qkv_str}{dtype_str}.onnx"
+
+    model = te.transformer.MultiHeadAttention(
+        *attention_args,
+        attn_mask_type=attn_mask_type,
+        params_dtype=precision,
+        return_layernorm_output=return_layernorm_output,
+        input_layernorm=input_layernorm,
+        attention_type=attention_type,
+        fuse_qkv_params=fuse_qkv_params,
+    ).to(device='cuda')
+    do_export(model, inp, fname, use_fp8, input_names=input_names)
+    if not use_fp8:
+        validate_result(fname, inp, model, atol=1e-3)
+    elif precision != torch.float16:
+        validate_result(fname, inp, model, atol=1e-2, is_fp8=use_fp8)
+
+
+@pytest.mark.parametrize("use_fp8", [False, True])
+@pytest.mark.parametrize("use_mask, attn_mask_type", test_configs_multihead_attention)
+@pytest.mark.parametrize("output_layernorm", [
+    #True, # TO DO: handle this
+    False
+])
+@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
+@pytest.mark.parametrize("fuse_qkv_params", [False, True])
+@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
+def test_export_transformer_layer(
+    use_fp8: bool,
+    use_mask: bool,
+    attn_mask_type: str,
+    output_layernorm: bool,
+    precision: torch.dtype,
+    fuse_qkv_params: bool,
+    apply_query_key_layer_scaling: bool
+):
+    # Layer configuration
+    hidden_size = 64
+    sequence_length = 128
+    batch_size = 1
+    ffn_hidden_size = 256
+    num_attention_heads = 4
+
+    input_tensor = torch.rand(sequence_length, batch_size, hidden_size, dtype=precision, device="cuda")
+    input_names = ["input"]
+    attention_mask = None
+    if use_mask and attn_mask_type != "causal":
+        # Generate a random mask with 50% probability for 0 or 1.
+        probs = 0.5 * torch.ones(batch_size, 1, sequence_length, sequence_length, device="cuda", dtype=precision)
+        attention_mask = torch.bernoulli(probs).to("cuda", dtype=torch.bool)
+        input_names.append("attention_mask")
+    inp = (input_tensor, attention_mask)
+
+    fp8_str = "_fp8" if use_fp8 else ""
+    fuse_qkv_params_str = "_fuse-qkv" if fuse_qkv_params else ""
+    qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
+    high_prec_str = dtype2str(precision)
+    attn_mask_type_str = f"_{attn_mask_type}" if (use_mask and attn_mask_type != "") else ""
+    fname = f"te.transformer_layer{fp8_str}{attn_mask_type_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
+
+    model = te.TransformerLayer(
+        hidden_size,
+        ffn_hidden_size,
+        num_attention_heads,
+        self_attn_mask_type=attn_mask_type,
+        output_layernorm=output_layernorm,
+        params_dtype=precision,
+        fuse_qkv_params=fuse_qkv_params,
+        apply_query_key_layer_scaling=apply_query_key_layer_scaling).to(device='cuda')
+    do_export(model, inp, fname, use_fp8)
+    if not use_fp8:
+        validate_result(fname, inp, model, atol=1e-3)
+    elif precision != torch.float16:
+        validate_result(fname, inp, model, atol=5e-1, is_fp8=use_fp8)
diff --git a/transformer_engine/pytorch/__init__.py b/transformer_engine/pytorch/__init__.py
index 1c5ddd5c09..b941896d49 100644
--- a/transformer_engine/pytorch/__init__.py
+++ b/transformer_engine/pytorch/__init__.py
@@ -10,3 +10,5 @@
 from .transformer import TransformerLayer
 from .fp8 import fp8_autocast
 from .distributed import checkpoint
+# Register custom op symbolic ONNX functions
+from .te_onnx_extensions import *
diff --git a/transformer_engine/pytorch/cpp_extensions.py b/transformer_engine/pytorch/cpp_extensions.py
index 0db38d25ad..6d3ffc0839 100644
--- a/transformer_engine/pytorch/cpp_extensions.py
+++ b/transformer_engine/pytorch/cpp_extensions.py
@@ -12,9 +12,11 @@
 def fp8_gemm(
     A: torch.Tensor,
     A_scale_inv: torch.Tensor,
+    A_fp8_tensor: Union[tex.FP8FwdTensors, tex.FP8BwdTensors],
     A_dtype: tex.DType,
     B: torch.Tensor,
     B_scale_inv: torch.Tensor,
+    B_fp8_tensor: Union[tex.FP8FwdTensors, tex.FP8BwdTensors],
     B_dtype: tex.DType,
     out_dtype: torch.dtype,
     workspace: torch.Tensor,
@@ -41,19 +43,21 @@ def fp8_gemm(
 
     out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 
-    tex.te_gemm(
+    _ = torch.ops.tex_ts.te_gemm_ts(
         A,
         A_scale_inv,
+        A_fp8_tensor,
         A_dtype,
         True,  # transa
         B,
         B_scale_inv,
+        B_fp8_tensor,
         B_dtype,
         False,  # transb
         out,
         out_dtype,
         bias if use_bias else empty_tensor,
-        empty_tensor,
+        empty_tensor,  # this is pre_gelu_out
         False,  # grad
         workspace,
         workspace.shape[0],
@@ -87,6 +91,7 @@ def gemm(
     transa = layout[0] == "T"
     transb = layout[1] == "T"
     empty_tensor = torch.Tensor()
+    fp8_index = -1 # dummy index
 
     input_dtype = TE_DType[dtype]
     output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
@@ -115,13 +120,15 @@ def gemm(
 
     bias = bias if use_bias else empty_tensor
 
-    tex.te_gemm(
+    _ = torch.ops.tex_ts.te_gemm_ts(
         A,
         empty_tensor,
+        fp8_index,
         input_dtype,
         transa,
         B,
         empty_tensor,
+        fp8_index,
         input_dtype,
         transb,
         out,
@@ -214,11 +221,12 @@ def fp8_gelu(
     otype: tex.DType,
 ) -> torch.Tensor:
     """GeLU with FP8 output"""
-    return tex.fp8_gelu(
+    return torch.ops.tex_ts.fp8_gelu_ts(
         inp,
-        fp8_meta_tensor.scale[fp8_tensor],
-        fp8_meta_tensor.amax_history[0][fp8_tensor],
-        fp8_meta_tensor.scale_inv[fp8_tensor],
+        fp8_meta_tensor.scale,
+        fp8_meta_tensor.amax_history,
+        fp8_meta_tensor.scale_inv,
+        fp8_tensor,
         otype,
     )
 
@@ -245,6 +253,46 @@ def layernorm_fwd_fp8(
     )
 
 
+def layernorm_fwd_fp8_inf(
+    inp: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float,
+    fp8_meta_tensor: tex.FP8TensorMeta,
+    otype: tex.DType,
+) -> torch.Tensor:
+    """LayerNorm with FP8 output.
+
+    This version of layernorm_fwd_fp8 is specialized for inference, and returns
+    only the normalized output.
+    """
+    ret = torch.ops.tex_ts.layernorm_fwd_fp8_inf_ts(
+        inp,
+        weight,
+        bias,
+        eps,
+        fp8_meta_tensor.scale,
+        fp8_meta_tensor.amax_history,
+        fp8_meta_tensor.scale_inv,
+        otype)
+    return ret
+
+
+def layernorm_fwd_inf(
+    inp: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float,
+) -> torch.Tensor:
+    """LayerNorm with FP8 output"""
+    return torch.ops.tex_ts.layernorm_fwd_inf_ts(
+        inp,
+        weight,
+        bias,
+        eps,
+    )
+
+
 def cast_to_fp8(
     inp: torch.Tensor,
     fp8_meta_tensor: tex.FP8TensorMeta,
@@ -252,11 +300,12 @@ def cast_to_fp8(
     otype: tex.DType,
 ) -> torch.Tensor:
     """Cast input to FP8"""
-    return tex.cast_to_fp8(
+    return torch.ops.tex_ts.cast_to_fp8_ts(
         inp,
-        fp8_meta_tensor.scale[fp8_tensor],
-        fp8_meta_tensor.amax_history[0][fp8_tensor],
-        fp8_meta_tensor.scale_inv[fp8_tensor],
+        fp8_meta_tensor.scale,
+        fp8_meta_tensor.amax_history,
+        fp8_meta_tensor.scale_inv,
+        fp8_tensor,
         otype,
     )
 
@@ -269,9 +318,10 @@ def cast_from_fp8(
     otype: tex.DType,
 ) -> torch.Tensor:
     """Cast input from FP8"""
-    return tex.cast_from_fp8(
+    return torch.ops.tex_ts.cast_from_fp8_ts(
         inp,
-        fp8_meta_tensor.scale_inv[fp8_tensor],
+        fp8_meta_tensor.scale_inv,
+        fp8_tensor,
         itype,
         otype,
     )
diff --git a/transformer_engine/pytorch/csrc/common.h b/transformer_engine/pytorch/csrc/common.h
index 43389294dd..a4e8fb8a7a 100644
--- a/transformer_engine/pytorch/csrc/common.h
+++ b/transformer_engine/pytorch/csrc/common.h
@@ -94,6 +94,8 @@ inline transformer_engine::DType GetTransformerEngineDType(at::ScalarType t) {
             return transformer_engine::DType::kFloat32;
         case at::kBFloat16:
             return transformer_engine::DType::kBFloat16;
+        case at::kBool:
+            return transformer_engine::DType::kByte;
         default:
             NVTE_ERROR("Invalid type");
     }
diff --git a/transformer_engine/pytorch/csrc/extensions.cu b/transformer_engine/pytorch/csrc/extensions.cu
index 4dc18d3fae..ebc5c6d7fa 100644
--- a/transformer_engine/pytorch/csrc/extensions.cu
+++ b/transformer_engine/pytorch/csrc/extensions.cu
@@ -397,6 +397,23 @@ std::vector<at::Tensor> layernorm_fwd_fp8(const at::Tensor &input,
 }
 
 
+at::Tensor layernorm_fwd_fp8_inf(const at::Tensor &input,
+                                 const at::Tensor &weight,
+                                 const at::Tensor &bias,
+                                 float eps,
+                                 at::Tensor scale,
+                                 at::Tensor amax,
+                                 at::Tensor scale_inv,
+                                 transformer_engine::DType otype
+) {
+    // This is a specialized version of layernorm_fwd_fp8, optimized for inference,
+    // which only returns the normalized output.
+    std::vector<at::Tensor> out = layernorm_fwd_fp8(
+      input, weight, bias, eps, scale, amax, scale_inv, otype);
+    return out[0];
+}
+
+
 std::vector<at::Tensor> layernorm_fwd(const at::Tensor &input,
                                       const at::Tensor &weight,
                                       const at::Tensor &bias,
@@ -428,6 +445,16 @@ std::vector<at::Tensor> layernorm_fwd(const at::Tensor &input,
     return {ln_out, mu, rsigma};
 }
 
+at::Tensor layernorm_fwd_inf(const at::Tensor &input,
+                             const at::Tensor &weight,
+                             const at::Tensor &bias,
+                             float eps
+) {
+    // This is a specialized version of layernorm_fwd, optimized for inference,
+    // which only returns the normalized output.
+    std::vector<at::Tensor> out = layernorm_fwd(input, weight, bias, eps);
+    return out[0];
+}
 
 at::Tensor cast_to_fp8(const at::Tensor &input,
                        const at::Tensor &scale,
diff --git a/transformer_engine/pytorch/csrc/extensions.h b/transformer_engine/pytorch/csrc/extensions.h
index e2717203d5..434eacb8eb 100644
--- a/transformer_engine/pytorch/csrc/extensions.h
+++ b/transformer_engine/pytorch/csrc/extensions.h
@@ -95,6 +95,15 @@ std::vector<at::Tensor> layernorm_fwd_fp8(const at::Tensor &input,
                                           transformer_engine::DType otype
 );
 
+at::Tensor layernorm_fwd_fp8_inf(const at::Tensor &input,
+                                 const at::Tensor &weight,
+                                 const at::Tensor &bias,
+                                 float eps,
+                                 at::Tensor scale,
+                                 at::Tensor amax,
+                                 at::Tensor scale_inv,
+                                 transformer_engine::DType otype
+);
 
 std::vector<at::Tensor> layernorm_fwd(const at::Tensor &input,
                                       const at::Tensor &weight,
@@ -102,6 +111,11 @@ std::vector<at::Tensor> layernorm_fwd(const at::Tensor &input,
                                       float eps
 );
 
+at::Tensor layernorm_fwd_inf(const at::Tensor &input,
+                             const at::Tensor &weight,
+                             const at::Tensor &bias,
+                             float eps
+);
 
 at::Tensor cast_to_fp8(const at::Tensor &input,
                        const at::Tensor &scale,
diff --git a/transformer_engine/pytorch/csrc/ts_fp8_op.cpp b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
new file mode 100755
index 0000000000..94e46f0dce
--- /dev/null
+++ b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
@@ -0,0 +1,178 @@
+/*************************************************************************
+ * Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ *
+ * See LICENSE for license information.
+ ************************************************************************/
+
+#include <torch/script.h>
+#include "extensions.h"
+
+transformer_engine::DType reverse_map_dtype(int64_t dtype) {
+  switch (dtype) {
+    case static_cast<int64_t>(transformer_engine::DType::kByte):
+        return transformer_engine::DType::kByte;
+    case static_cast<int64_t>(transformer_engine::DType::kInt32):
+        return transformer_engine::DType::kInt32;
+    case static_cast<int64_t>(transformer_engine::DType::kFloat32):
+        return transformer_engine::DType::kFloat32;
+    case static_cast<int64_t>(transformer_engine::DType::kFloat16):
+        return transformer_engine::DType::kFloat16;
+    case static_cast<int64_t>(transformer_engine::DType::kBFloat16):
+        return transformer_engine::DType::kBFloat16;
+    case static_cast<int64_t>(transformer_engine::DType::kFloat8E4M3):
+        return transformer_engine::DType::kFloat8E4M3;
+    case static_cast<int64_t>(transformer_engine::DType::kFloat8E5M2):
+        return transformer_engine::DType::kFloat8E5M2;
+    default:
+        NVTE_ERROR("Type not supported.");
+  }
+}
+
+
+at::Tensor cast_to_fp8_ts(const at::Tensor &input,
+                          const at::Tensor &scale,
+                          const at::Tensor &amax,
+                          const at::Tensor &scale_inv,
+                          int64_t fp8_tensor,
+                          int64_t otype) {
+  transformer_engine::DType otype_arg = reverse_map_dtype(otype);
+  at::Tensor output =  cast_to_fp8(input,
+                                   scale[fp8_tensor],
+                                   amax[0][fp8_tensor],
+                                   scale_inv[fp8_tensor],
+                                   otype_arg);
+  return output.clone();
+}
+
+at::Tensor cast_from_fp8_ts(const at::Tensor &input,
+                            const at::Tensor &scale_inv,
+                            int64_t fp8_tensor,
+                            int64_t itype,
+                            int64_t otype) {
+  transformer_engine::DType itype_arg = reverse_map_dtype(itype);
+  transformer_engine::DType otype_arg = reverse_map_dtype(otype);
+  at::Tensor output = cast_from_fp8(input,
+                                    scale_inv[fp8_tensor],
+                                    itype_arg,
+                                    otype_arg);
+  return output.clone();
+}
+
+at::Tensor fp8_gelu_ts(at::Tensor input,
+                       at::Tensor scale,
+                       at::Tensor amax,
+                       at::Tensor scale_inv,
+                       int64_t fp8_tensor,
+                       int64_t otype) {
+  transformer_engine::DType otype_arg = reverse_map_dtype(otype);
+  at::Tensor output = fp8_gelu(input,
+                               scale[fp8_tensor],
+                               amax[0][fp8_tensor],
+                               scale_inv[fp8_tensor],
+                               otype_arg);
+  return output.clone();
+}
+
+at::Tensor te_gemm_ts(at::Tensor A,
+                      at::Tensor A_scale_inverse,
+                      int64_t A_fp8_tensor,
+                      int64_t A_type,
+                      int64_t transa,
+                      at::Tensor B,
+                      at::Tensor B_scale_inverse,
+                      int64_t B_fp8_tensor,
+                      int64_t B_type,
+                      int64_t transb,
+                      at::Tensor D,
+                      int64_t D_type,
+                      at::Tensor bias,
+                      at::Tensor pre_gelu_out,
+                      int64_t grad,
+                      at::Tensor workspace,
+                      int64_t workspaceSize,
+                      int64_t accumulate,
+                      int64_t use_split_accumulator) {
+  // cast inputs to types accepted by te_gemm
+  transformer_engine::DType A_type_arg = reverse_map_dtype(A_type);
+  bool transa_arg = static_cast<bool>(transa);
+  transformer_engine::DType B_type_arg = reverse_map_dtype(B_type);
+  bool transb_arg = static_cast<bool>(transb);
+  transformer_engine::DType D_type_arg = reverse_map_dtype(D_type);
+  bool grad_arg = static_cast<bool>(grad);
+  size_t workspaceSize_arg = static_cast<size_t>(workspaceSize);
+  bool accumulate_arg = static_cast<bool>(accumulate);
+  bool use_split_accumulator_arg = static_cast<bool>(use_split_accumulator);
+
+  at::Tensor A_scale_inverse_arg = A_scale_inverse.clone();
+  if (A_scale_inverse.numel())
+    A_scale_inverse_arg = A_scale_inverse[A_fp8_tensor];
+
+  at::Tensor B_scale_inverse_arg = B_scale_inverse.clone();
+  if (B_scale_inverse.numel())
+    B_scale_inverse_arg = B_scale_inverse[B_fp8_tensor];
+
+  te_gemm(A,
+          A_scale_inverse_arg,
+          A_type_arg,
+          transa_arg,
+          B,
+          B_scale_inverse_arg,
+          B_type_arg,
+          transb_arg,
+          D,
+          D_type_arg,
+          bias,
+          pre_gelu_out,
+          grad_arg,
+          workspace,
+          workspaceSize_arg,
+          accumulate_arg,
+          use_split_accumulator_arg);
+  return D;
+}
+
+at::Tensor layernorm_fwd_fp8_inf_ts(const at::Tensor &input,
+                                    const at::Tensor &weight,
+                                    const at::Tensor &bias,
+                                    double eps,
+                                    at::Tensor scale,
+                                    at::Tensor amax,
+                                    at::Tensor scale_inv,
+                                    int64_t otype) {
+  transformer_engine::DType otype_arg = reverse_map_dtype(otype);
+  float eps_float = static_cast<float>(eps);
+
+  at::Tensor output = layernorm_fwd_fp8_inf(input,
+                                            weight,
+                                            bias,
+                                            eps_float,
+                                            scale,
+                                            amax,
+                                            scale_inv,
+                                            otype_arg);
+
+  return output.clone();
+}
+
+at::Tensor layernorm_fwd_inf_ts(const at::Tensor &input,
+                                const at::Tensor &weight,
+                                const at::Tensor &bias,
+                                double eps) {
+  float eps_float = static_cast<float>(eps);
+
+  at::Tensor output = layernorm_fwd_inf(input,
+                                        weight,
+                                        bias,
+                                        eps_float);
+
+  return output.clone();
+}
+
+TORCH_LIBRARY(tex_ts, m) {
+  m.def("cast_to_fp8_ts", &cast_to_fp8_ts);
+  m.def("cast_from_fp8_ts", &cast_from_fp8_ts);
+  m.def("fp8_gelu_ts", &fp8_gelu_ts);
+  m.def("te_gemm_ts", &te_gemm_ts);
+  m.def("layernorm_fwd_fp8_inf_ts", &layernorm_fwd_fp8_inf_ts);
+  m.def("layernorm_fwd_inf_ts", &layernorm_fwd_inf_ts);
+}
diff --git a/transformer_engine/pytorch/module.py b/transformer_engine/pytorch/module.py
index dfbfadbb36..d0b66923e6 100644
--- a/transformer_engine/pytorch/module.py
+++ b/transformer_engine/pytorch/module.py
@@ -4,11 +4,12 @@
 
 """Top level Transformer Engine PyTorch modules"""
 import os
+import pickle
 import warnings
 from abc import ABC, abstractmethod
-from typing import Union, Optional, Callable, Tuple, Dict, List, Any, Mapping
+from typing import Union, Optional, Callable, Tuple, Dict, Any
 from functools import partial
-
+import numpy as np
 import torch
 from torch.nn.parameter import Parameter
 from torch.nn import init
@@ -69,6 +70,8 @@
     fp8_gelu,
     fp8_cast_transpose_bgrad_dgelu_fused,
     layernorm_fwd_fp8,
+    layernorm_fwd_fp8_inf,
+    layernorm_fwd_inf,
     cast_to_fp8,
     cast_from_fp8,
 )
@@ -144,8 +147,9 @@ def init_fp8_meta_tensors(self) -> None:
         self.set_meta_tensor(True)
         self.set_meta_tensor(False)
 
-    def get_extra_state(self) -> Union[List[Any], None]:
+    def get_extra_state(self) -> torch.Tensor:
         """Save before checkpointing."""
+        state = None
         if self.fp8:
             state = {}
             state["scale_fwd"] = self.fp8_meta["scaling_fwd"].scale
@@ -162,10 +166,12 @@ def get_extra_state(self) -> Union[List[Any], None]:
                     extra[k] = v
             state["extra_fp8_variables"] = extra
 
-            return state
-        return None
+        state_serialized = pickle.dumps(state)
+        state_tensor = torch.tensor(np.frombuffer(state_serialized, dtype=np.uint8), device='cuda')
+
+        return state_tensor
 
-    def set_extra_state(self, state: Union[List[Any], None]) -> None:
+    def set_extra_state(self, state: torch.Tensor) -> None:
         """Load previous state."""
         if state is None:
             return
@@ -204,6 +210,11 @@ def set_extra_state(self, state: Union[List[Any], None]) -> None:
             self.fp8_meta["autocast_id_bwd"] = state[9]
             return
 
+        if isinstance(state, torch.Tensor):
+            state = pickle.loads(state.cpu().detach().numpy().tobytes())
+            if state is None:
+                return
+
         # Restore global FP8 buffer states.
         set_global_fp8_buffer(state["global_fp8_buffer"])
         set_global_fp8_recompute_buffer(state["global_fp8_recompute_buffer"])
@@ -521,13 +532,13 @@ def grad_output_preprocess(
                     fp8_dtype_backward,
                 )
             else:
+                grad_output_t = None
                 grad_output_c = cast_to_fp8(
                     grad_output_mat,
                     ctx.fp8_meta["scaling_bwd"],
                     tex.FP8BwdTensors.GRAD_OUTPUT1,
                     fp8_dtype_backward,
                 )
-                grad_output_t = None
             grad_bias = None
 
         return grad_output_mat, grad_output_c, grad_output_t, grad_bias
@@ -537,6 +548,7 @@ def forward(self):
         """Needs override."""
 
 
+
 class _LayerNormLinear(torch.autograd.Function):
     """LayerNormLinear semi-top level module
     Calls custom cuda extensions.
@@ -564,6 +576,7 @@ def forward(
         activation_dtype: torch.dtype,
         parallel_mode: Union[str, None],
         return_layernorm_output: bool,
+        is_training: bool
     ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
         # Make sure input dimensions are compatible
         in_features = ln_weight.numel()
@@ -584,19 +597,36 @@ def forward(
             fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
 
             if not return_layernorm_output:
-                ln_out, mu, rsigma = layernorm_fwd_fp8(
-                    inputmat,
-                    ln_weight,
-                    ln_bias,
-                    eps,
-                    fp8_meta["scaling_fwd"],
-                    tex.FP8FwdTensors.GEMM1_INPUT,
-                    fp8_dtype_forward,
-                )
+                if is_training:
+                    ln_out, mu, rsigma = layernorm_fwd_fp8(
+                        inputmat,
+                        ln_weight,
+                        ln_bias,
+                        eps,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_INPUT,
+                        fp8_dtype_forward,
+                    )
+                else:
+                    mu = rsigma = None
+                    ln_out = layernorm_fwd_fp8_inf(
+                        inputmat,
+                        ln_weight,
+                        ln_bias,
+                        eps,
+                        fp8_meta["scaling_fwd"],
+                        fp8_dtype_forward,
+                    )
             else:
-                ln_out_return, mu, rsigma = tex.layernorm_fwd(
-                    inputmat, ln_weight, ln_bias, eps
-                )
+                if is_training:
+                    ln_out_return, mu, rsigma = tex.layernorm_fwd(
+                        inputmat, ln_weight, ln_bias, eps
+                    )
+                else:
+                    ln_out_return, mu, rsigma = layernorm_fwd_inf(
+                        inputmat, ln_weight, ln_bias, eps
+                    ), None, None
+
                 ln_out = cast_to_fp8(
                     ln_out_return,
                     fp8_meta["scaling_fwd"],
@@ -604,7 +634,12 @@ def forward(
                     fp8_dtype_forward,
                 )
         else:
-            ln_out, mu, rsigma = tex.layernorm_fwd(inputmat, ln_weight, ln_bias, eps)
+            if is_training:
+                ln_out, mu, rsigma = tex.layernorm_fwd(inputmat, ln_weight, ln_bias, eps)
+            else:
+                ln_out, mu, rsigma = layernorm_fwd_inf(
+                        inputmat, ln_weight, ln_bias, eps
+                ), None, None
             ln_out_return = ln_out
 
         # Column Parallel Linear
@@ -622,21 +657,31 @@ def forward(
             bias = cast_if_needed(bias, bias_dtype) if use_bias else bias
 
             if update_fp8_weights:
-                fp8_cast_transpose_fused(
-                    weight,
-                    fp8_meta["scaling_fwd"],
-                    tex.FP8FwdTensors.GEMM1_WEIGHT,
-                    fp8_dtype_forward,
-                    cast_out=weight_fp8,
-                    transpose_out=weight_t_fp8,
-                )
+                if is_training:
+                    fp8_cast_transpose_fused(
+                        weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_WEIGHT,
+                        fp8_dtype_forward,
+                        cast_out=weight_fp8,
+                        transpose_out=weight_t_fp8,
+                    )
+                else:
+                    weight_t_fp8 = None
+                    weight_fp8 = cast_to_fp8(
+                        weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_WEIGHT,
+                        fp8_dtype_forward)
 
             out = fp8_gemm(
                 weight_fp8,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM1_WEIGHT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM1_WEIGHT,
                 fp8_dtype_forward,
                 ln_out_total,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM1_INPUT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM1_INPUT,
                 fp8_dtype_forward,
                 activation_dtype,
                 get_workspace(),
@@ -658,29 +703,30 @@ def forward(
                 use_bias=use_bias,
             )
 
-        ctx.save_for_backward(
-            inputmat,
-            ln_weight,
-            mu,
-            rsigma,
-            weight,
-            weight_t_fp8,
-            ln_out,
-            fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
-        )
+        if is_training:
+            ctx.save_for_backward(
+                inputmat,
+                ln_weight,
+                mu,
+                rsigma,
+                weight,
+                weight_t_fp8,
+                ln_out,
+                fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
+            )
 
-        ctx.activation_dtype = activation_dtype
-        ctx.fp8 = fp8
-        ctx.fp8_meta = fp8_meta
-        ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
-        ctx.is_first_microbatch = is_first_microbatch
-        ctx.use_bias = use_bias
-        ctx.sequence_parallel = sequence_parallel
-        ctx.tensor_parallel = tensor_parallel
-        ctx.inp_shape = inp.shape
-        ctx.parallel_mode = parallel_mode
-        ctx.tp_group = tp_group
-        ctx.return_layernorm_output = return_layernorm_output
+            ctx.activation_dtype = activation_dtype
+            ctx.fp8 = fp8
+            ctx.fp8_meta = fp8_meta
+            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
+            ctx.is_first_microbatch = is_first_microbatch
+            ctx.use_bias = use_bias
+            ctx.sequence_parallel = sequence_parallel
+            ctx.tensor_parallel = tensor_parallel
+            ctx.inp_shape = inp.shape
+            ctx.parallel_mode = parallel_mode
+            ctx.tp_group = tp_group
+            ctx.return_layernorm_output = return_layernorm_output
 
         # Row Parallel Linear
         if parallel_mode == "row" and sequence_parallel:
@@ -695,6 +741,7 @@ def forward(
             return out, ln_out_return.view_as(inp)
         return out
 
+
     @staticmethod
     def backward(
         ctx, *grad_outputs: Tuple[torch.Tensor, ...]
@@ -748,10 +795,12 @@ def backward(
             # DGRAD: Evaluated unconditionally to feed into Linear backward
             dgrad = fp8_gemm(
                 weight_t_fp8,
-                fwd_scale_inverses[tex.FP8FwdTensors.GEMM1_WEIGHT],
+                fwd_scale_inverses,
+                tex.FP8FwdTensors.GEMM1_WEIGHT,
                 fp8_dtype_forward,
                 grad_output_c,
-                ctx.fp8_meta["scaling_bwd"].scale_inv[tex.FP8BwdTensors.GRAD_OUTPUT1],
+                ctx.fp8_meta["scaling_bwd"].scale_inv,
+                tex.FP8BwdTensors.GRAD_OUTPUT1,
                 fp8_dtype_backward,
                 ctx.activation_dtype,
                 get_workspace(),
@@ -784,12 +833,12 @@ def backward(
                     ln_out_total_t = tex.fp8_transpose(ln_out_total, fp8_dtype_forward)
                     wgrad = fp8_gemm(
                         ln_out_total_t,
-                        fwd_scale_inverses[tex.FP8FwdTensors.GEMM1_INPUT],
+                        fwd_scale_inverses,
+                        tex.FP8FwdTensors.GEMM1_INPUT,
                         fp8_dtype_forward,
                         grad_output_t,
-                        ctx.fp8_meta["scaling_bwd"].scale_inv[
-                            tex.FP8BwdTensors.GRAD_OUTPUT1
-                        ],
+                        ctx.fp8_meta["scaling_bwd"].scale_inv,
+                        tex.FP8BwdTensors.GRAD_OUTPUT1,
                         fp8_dtype_backward,
                         ctx.activation_dtype,
                         get_workspace(),
@@ -874,6 +923,7 @@ def backward(
             None,
             None,
             None,
+            None,
         )
 
 
@@ -1094,8 +1144,13 @@ def forward(
         inp = self.pre_forward(inp)
 
         bias_tensor = bias if bias is not None else self.bias
-
-        out = _LayerNormLinear.apply(
+        if self.training:
+            fwd_fn = _LayerNormLinear.apply
+            args = []
+        else:
+            fwd_fn = _LayerNormLinear.forward
+            args = [None]
+        args += (
             inp,
             self.layer_norm_weight,
             self.layer_norm_bias,
@@ -1115,7 +1170,9 @@ def forward(
             self.activation_dtype,
             self.parallel_mode,
             self.return_layernorm_output,
+            self.training,
         )
+        out = fwd_fn(*args)
 
         self.post_forward()
 
@@ -1133,7 +1190,6 @@ def forward(
             return out, ln_out
         return out
 
-
 class _Linear(torch.autograd.Function):
     """Linear semi-top level module
     Calls custom cuda extensions.
@@ -1157,6 +1213,7 @@ def forward(
         tensor_parallel: bool,
         activation_dtype: torch.dtype,
         parallel_mode: Union[str, None],
+        is_training: bool,
     ) -> torch.Tensor:
         # Make sure input dimensions are compatible
         in_features = weight.shape[-1]
@@ -1173,19 +1230,27 @@ def forward(
             fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
 
             if not fp8_meta["recipe"].override_linear_precision.wgrad:
-                inputmat, inputmat_t = fp8_cast_transpose_fused(
-                    inputmat,
-                    fp8_meta["scaling_fwd"],
-                    tex.FP8FwdTensors.GEMM1_INPUT,
-                    fp8_dtype_forward,
-                )
+                if is_training:
+                    inputmat, inputmat_t = fp8_cast_transpose_fused(
+                        inputmat,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_INPUT,
+                        fp8_dtype_forward,
+                    )
+                else:
+                    inputmat = cast_to_fp8(
+                        inputmat,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_INPUT,
+                        fp8_dtype_forward,
+                    )
             else:
-                inputmat = cast_to_fp8(
+                inputmat, inputmat_t = cast_to_fp8(
                     inputmat,
                     fp8_meta["scaling_fwd"],
                     tex.FP8FwdTensors.GEMM1_INPUT,
                     fp8_dtype_forward,
-                )
+                ), None
 
         # Column Parallel Linear
         if parallel_mode == "column" and sequence_parallel:
@@ -1202,21 +1267,32 @@ def forward(
             bias = cast_if_needed(bias, bias_dtype) if use_bias else bias
 
             if update_fp8_weights:
-                fp8_cast_transpose_fused(
-                    weight,
-                    fp8_meta["scaling_fwd"],
-                    tex.FP8FwdTensors.GEMM1_WEIGHT,
-                    fp8_dtype_forward,
-                    cast_out=weight_fp8,
-                    transpose_out=weight_t_fp8,
-                )
+                if is_training:
+                    fp8_cast_transpose_fused(
+                        weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_WEIGHT,
+                        fp8_dtype_forward,
+                        cast_out=weight_fp8,
+                        transpose_out=weight_t_fp8,
+                    )
+                else:
+                    weight_t_fp8 = None
+                    weight_fp8 = cast_to_fp8(
+                        weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_WEIGHT,
+                        fp8_dtype_forward,
+                    )
 
             out = fp8_gemm(
                 weight_fp8,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM1_WEIGHT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM1_WEIGHT,
                 fp8_dtype_forward,
                 inputmat,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM1_INPUT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM1_INPUT,
                 fp8_dtype_forward,
                 activation_dtype,
                 get_workspace(),
@@ -1238,28 +1314,29 @@ def forward(
                 use_bias=use_bias,
             )
 
-        ctx.save_for_backward(
-            inputmat_no_fp8
-            if not fp8 or fp8_meta["recipe"].override_linear_precision.wgrad
-            else None,
-            inputmat_t
-            if fp8 and not fp8_meta["recipe"].override_linear_precision.wgrad
-            else None,
-            weight,
-            weight_t_fp8,
-            fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
-        )
-        ctx.activation_dtype = activation_dtype
-        ctx.fp8 = fp8
-        ctx.fp8_meta = fp8_meta
-        ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
-        ctx.is_first_microbatch = is_first_microbatch
-        ctx.use_bias = use_bias
-        ctx.sequence_parallel = sequence_parallel
-        ctx.tensor_parallel = tensor_parallel
-        ctx.inp_shape = inp.shape
-        ctx.parallel_mode = parallel_mode
-        ctx.tp_group = tp_group
+        if is_training:
+            ctx.save_for_backward(
+                inputmat_no_fp8
+                if not fp8 or fp8_meta["recipe"].override_linear_precision.wgrad
+                else None,
+                inputmat_t
+                if fp8 and not fp8_meta["recipe"].override_linear_precision.wgrad
+                else None,
+                weight,
+                weight_t_fp8,
+                fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
+            )
+            ctx.activation_dtype = activation_dtype
+            ctx.fp8 = fp8
+            ctx.fp8_meta = fp8_meta
+            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
+            ctx.is_first_microbatch = is_first_microbatch
+            ctx.use_bias = use_bias
+            ctx.sequence_parallel = sequence_parallel
+            ctx.tensor_parallel = tensor_parallel
+            ctx.inp_shape = inp.shape
+            ctx.parallel_mode = parallel_mode
+            ctx.tp_group = tp_group
 
         # Row Parallel Linear
         if parallel_mode == "row" and sequence_parallel:
@@ -1270,6 +1347,7 @@ def forward(
         # [*, in_features] -> [*, out_features] except first dimension changes for SP
         return out.view(-1, *inp.shape[1:-1], out.shape[-1])
 
+
     @staticmethod
     def backward(
         ctx, grad_output: torch.Tensor
@@ -1326,10 +1404,12 @@ def backward(
             # DGRAD
             dgrad = fp8_gemm(
                 weight_t_fp8,
-                fwd_scale_inverses[tex.FP8FwdTensors.GEMM1_WEIGHT],
+                fwd_scale_inverses,
+                tex.FP8FwdTensors.GEMM1_WEIGHT,
                 fp8_dtype_forward,
                 grad_output_c,
-                ctx.fp8_meta["scaling_bwd"].scale_inv[tex.FP8BwdTensors.GRAD_OUTPUT1],
+                ctx.fp8_meta["scaling_bwd"].scale_inv,
+                tex.FP8BwdTensors.GRAD_OUTPUT1,
                 fp8_dtype_backward,
                 ctx.activation_dtype,
                 get_workspace(),
@@ -1361,12 +1441,12 @@ def backward(
                 if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
                     wgrad = fp8_gemm(
                         inputmat_t_total,
-                        fwd_scale_inverses[tex.FP8FwdTensors.GEMM1_INPUT],
+                        fwd_scale_inverses,
+                        tex.FP8FwdTensors.GEMM1_INPUT,
                         fp8_dtype_forward,
                         grad_output_t,
-                        ctx.fp8_meta["scaling_bwd"].scale_inv[
-                            tex.FP8BwdTensors.GRAD_OUTPUT1
-                        ],
+                        ctx.fp8_meta["scaling_bwd"].scale_inv,
+                        tex.FP8BwdTensors.GRAD_OUTPUT1,
                         fp8_dtype_backward,
                         ctx.activation_dtype,
                         get_workspace(),
@@ -1429,6 +1509,7 @@ def backward(
             None,
             None,
             None,
+            None,
         )
 
 
@@ -1620,8 +1701,13 @@ def forward(
         inp = self.pre_forward(inp)
 
         bias_tensor = bias if bias is not None else self.bias
-
-        out = _Linear.apply(
+        if self.training:
+            linear_fn = _Linear.apply
+            args = []
+        else:
+            linear_fn = _Linear.forward
+            args = [None]
+        args += (
             weight if weight is not None else self.weight,
             self.weight1_fp8 if self.fp8 else None,
             self.weight1_t_fp8 if self.fp8 else None,
@@ -1637,8 +1723,9 @@ def forward(
             self.tp_size > 1,
             self.activation_dtype,
             self.parallel_mode,
+            self.training,
         )
-
+        out = linear_fn(*args)
         self.post_forward()
 
         if self.gemm_bias_unfused_add:
@@ -1681,6 +1768,7 @@ def forward(
         return_layernorm_output: bool,
         bias_gelu_nvfusion: bool,
         set_parallel_mode: bool,
+        is_training: bool
     ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
         # Make sure input dimensions are compatible
         in_features = ln_weight.numel()
@@ -1700,15 +1788,25 @@ def forward(
         if fp8:
             fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
             if not return_layernorm_output:
-                ln_out, mu, rsigma = layernorm_fwd_fp8(
-                    inputmat,
-                    ln_weight,
-                    ln_bias,
-                    eps,
-                    fp8_meta["scaling_fwd"],
-                    tex.FP8FwdTensors.GEMM1_INPUT,
-                    fp8_dtype_forward,
-                )
+                if is_training:
+                    ln_out, mu, rsigma = layernorm_fwd_fp8(
+                        inputmat,
+                        ln_weight,
+                        ln_bias,
+                        eps,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_INPUT,
+                        fp8_dtype_forward,
+                    )
+                else:
+                    ln_out = layernorm_fwd_fp8_inf(
+                        inputmat,
+                        ln_weight,
+                        ln_bias,
+                        eps,
+                        fp8_meta["scaling_fwd"],
+                        fp8_dtype_forward,
+                    )
             else:
                 ln_out_return, mu, rsigma = tex.layernorm_fwd(
                     inputmat, ln_weight, ln_bias, eps
@@ -1720,9 +1818,14 @@ def forward(
                     fp8_dtype_forward,
                 )
         else:
-            ln_out, mu, rsigma = tex.layernorm_fwd(inputmat, ln_weight, ln_bias, eps)
-            ln_out_return = ln_out
+            if is_training:
+                ln_out, mu, rsigma = tex.layernorm_fwd(inputmat, ln_weight, ln_bias, eps)
+            else:
+                ln_out, mu, rsigma = layernorm_fwd_inf(
+                        inputmat, ln_weight, ln_bias, eps
+                        ), None, None
 
+            ln_out_return = ln_out
         # Column Parallel Linear
         if set_parallel_mode and sequence_parallel:
             ln_out_total, _ = gather_along_first_dim(ln_out, tp_group)
@@ -1739,30 +1842,48 @@ def forward(
             fc2_bias = cast_if_needed(fc2_bias, bias_dtype) if use_bias else fc2_bias
 
             if update_fp8_weights:
-                fp8_cast_transpose_fused(
-                    fc1_weight,
-                    fp8_meta["scaling_fwd"],
-                    tex.FP8FwdTensors.GEMM1_WEIGHT,
-                    fp8_dtype_forward,
-                    cast_out=fc1_weight_fp8,
-                    transpose_out=fc1_weight_t_fp8,
-                )
+                if is_training:
+                    fp8_cast_transpose_fused(
+                        fc1_weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_WEIGHT,
+                        fp8_dtype_forward,
+                        cast_out=fc1_weight_fp8,
+                        transpose_out=fc1_weight_t_fp8,
+                    )
 
-                fp8_cast_transpose_fused(
-                    fc2_weight,
-                    fp8_meta["scaling_fwd"],
-                    tex.FP8FwdTensors.GEMM2_WEIGHT,
-                    fp8_dtype_forward,
-                    cast_out=fc2_weight_fp8,
-                    transpose_out=fc2_weight_t_fp8,
-                )
+                    fp8_cast_transpose_fused(
+                        fc2_weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM2_WEIGHT,
+                        fp8_dtype_forward,
+                        cast_out=fc2_weight_fp8,
+                        transpose_out=fc2_weight_t_fp8,
+                    )
+                else:
+                    fc1_weight_t_fp8 = None
+                    fc1_weight_fp8 = cast_to_fp8(
+                        fc1_weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_WEIGHT,
+                        fp8_dtype_forward,
+                    )
+                    fc2_weight_t_fp8 = None
+                    fc2_weight_fp8 = cast_to_fp8(
+                        fc2_weight,
+                        fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM2_WEIGHT,
+                        fp8_dtype_forward,
+                    )
 
             fc1_out = fp8_gemm(
                 fc1_weight_fp8,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM1_WEIGHT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM1_WEIGHT,
                 fp8_dtype_forward,
                 ln_out_total,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM1_INPUT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM1_INPUT,
                 fp8_dtype_forward,
                 activation_dtype,
                 get_workspace(),
@@ -1780,10 +1901,12 @@ def forward(
 
             fc2_out = fp8_gemm(
                 fc2_weight_fp8,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM2_WEIGHT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM2_WEIGHT,
                 fp8_dtype_forward,
                 gelu_out,
-                fp8_meta["scaling_fwd"].scale_inv[tex.FP8FwdTensors.GEMM2_INPUT],
+                fp8_meta["scaling_fwd"].scale_inv,
+                tex.FP8FwdTensors.GEMM2_INPUT,
                 fp8_dtype_forward,
                 activation_dtype,
                 get_workspace(),
@@ -1810,7 +1933,7 @@ def forward(
                 gelu=not bias_gelu_nvfusion,
             )
 
-            if bias_gelu_nvfusion:
+            if bias_gelu_nvfusion and is_training:
                 fc1_out, _, _ = fc1_outputs
                 gelu_out = bias_gelu_fused(fc1_out, fc1_bias)
             else:
@@ -1824,35 +1947,35 @@ def forward(
                 bias=fc2_bias,
                 use_bias=use_bias,
             )
-
-        ctx.save_for_backward(
-            inputmat,
-            ln_weight,
-            mu,
-            rsigma,
-            ln_out,
-            fc1_out,
-            gelu_out,
-            fc1_weight,
-            fc1_weight_t_fp8,
-            fc2_weight,
-            fc2_weight_t_fp8,
-            fc1_bias,
-            fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
-        )
-        ctx.activation_dtype = activation_dtype
-        ctx.fp8 = fp8
-        ctx.fp8_meta = fp8_meta
-        ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
-        ctx.is_first_microbatch = is_first_microbatch
-        ctx.use_bias = use_bias
-        ctx.sequence_parallel = sequence_parallel
-        ctx.tensor_parallel = tensor_parallel
-        ctx.inp_shape = inp.shape
-        ctx.tp_group = tp_group
-        ctx.bias_gelu_nvfusion = bias_gelu_nvfusion
-        ctx.return_layernorm_output = return_layernorm_output
-        ctx.set_parallel_mode = set_parallel_mode
+        if is_training:
+            ctx.save_for_backward(
+                inputmat,
+                ln_weight,
+                mu,
+                rsigma,
+                ln_out,
+                fc1_out,
+                gelu_out,
+                fc1_weight,
+                fc1_weight_t_fp8,
+                fc2_weight,
+                fc2_weight_t_fp8,
+                fc1_bias,
+                fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None,
+            )
+            ctx.activation_dtype = activation_dtype
+            ctx.fp8 = fp8
+            ctx.fp8_meta = fp8_meta
+            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
+            ctx.is_first_microbatch = is_first_microbatch
+            ctx.use_bias = use_bias
+            ctx.sequence_parallel = sequence_parallel
+            ctx.tensor_parallel = tensor_parallel
+            ctx.inp_shape = inp.shape
+            ctx.tp_group = tp_group
+            ctx.bias_gelu_nvfusion = bias_gelu_nvfusion
+            ctx.return_layernorm_output = return_layernorm_output
+            ctx.set_parallel_mode = set_parallel_mode
 
         # Row Parallel Linear
         if set_parallel_mode and sequence_parallel:
@@ -1867,6 +1990,7 @@ def forward(
             return fc2_out, ln_out_return.view_as(inp)
         return fc2_out
 
+
     @staticmethod
     def backward(
         ctx, *grad_outputs: Tuple[torch.Tensor, ...]
@@ -1925,10 +2049,12 @@ def backward(
             # FC2 DGRAD; Unconditional
             fc2_dgrad = fp8_gemm(
                 fc2_weight_t_fp8,
-                fwd_scale_inverses[tex.FP8FwdTensors.GEMM2_WEIGHT],
+                fwd_scale_inverses,
+                tex.FP8FwdTensors.GEMM2_WEIGHT,
                 fp8_dtype_forward,
                 grad_output_c,
-                ctx.fp8_meta["scaling_bwd"].scale_inv[tex.FP8BwdTensors.GRAD_OUTPUT1],
+                ctx.fp8_meta["scaling_bwd"].scale_inv,
+                tex.FP8BwdTensors.GRAD_OUTPUT1,
                 fp8_dtype_backward,
                 ctx.activation_dtype,
                 get_workspace(),
@@ -1941,12 +2067,12 @@ def backward(
                     gelu_out_t = tex.fp8_transpose(gelu_out, fp8_dtype_forward)
                     fc2_wgrad = fp8_gemm(
                         gelu_out_t,
-                        fwd_scale_inverses[tex.FP8FwdTensors.GEMM2_INPUT],
+                        fwd_scale_inverses,
+                        tex.FP8FwdTensors.GEMM2_INPUT,
                         fp8_dtype_forward,
                         grad_output_t,
-                        ctx.fp8_meta["scaling_bwd"].scale_inv[
-                            tex.FP8BwdTensors.GRAD_OUTPUT1
-                        ],
+                        ctx.fp8_meta["scaling_bwd"].scale_inv,
+                        tex.FP8BwdTensors.GRAD_OUTPUT1,
                         fp8_dtype_backward,
                         ctx.activation_dtype,
                         get_workspace(),
@@ -2004,10 +2130,12 @@ def backward(
             # FC1 DGRAD: Unconditional
             fc1_dgrad = fp8_gemm(
                 fc1_weight_t_fp8,
-                fwd_scale_inverses[tex.FP8FwdTensors.GEMM1_WEIGHT],
+                fwd_scale_inverses,
+                tex.FP8FwdTensors.GEMM1_WEIGHT,
                 fp8_dtype_forward,
                 dgelu,
-                ctx.fp8_meta["scaling_bwd"].scale_inv[tex.FP8BwdTensors.GRAD_OUTPUT2],
+                ctx.fp8_meta["scaling_bwd"].scale_inv,
+                tex.FP8BwdTensors.GRAD_OUTPUT2,
                 fp8_dtype_backward,
                 ctx.activation_dtype,
                 get_workspace(),
@@ -2072,12 +2200,12 @@ def backward(
                     ln_out_total_t = tex.fp8_transpose(ln_out_total, fp8_dtype_forward)
                     fc1_wgrad = fp8_gemm(
                         ln_out_total_t,
-                        fwd_scale_inverses[tex.FP8FwdTensors.GEMM1_INPUT],
+                        fwd_scale_inverses,
+                        tex.FP8FwdTensors.GEMM1_INPUT,
                         fp8_dtype_forward,
                         dgelu_t,
-                        ctx.fp8_meta["scaling_bwd"].scale_inv[
-                            tex.FP8BwdTensors.GRAD_OUTPUT2
-                        ],
+                        ctx.fp8_meta["scaling_bwd"].scale_inv,
+                        tex.FP8BwdTensors.GRAD_OUTPUT2,
                         fp8_dtype_backward,
                         ctx.activation_dtype,
                         get_workspace(),
@@ -2176,6 +2304,7 @@ def backward(
             None,
             None,
             None,
+            None,
         )
 
 
@@ -2422,7 +2551,13 @@ def forward(
 
         inp = self.pre_forward(inp, num_gemms=2)
 
-        out = _LayerNormMLP.apply(
+        if self.training:
+            fwd_fn = _LayerNormMLP.apply
+            args = []
+        else:
+            fwd_fn = _LayerNormMLP.forward
+            args = [None]
+        args += (
             inp,
             self.layer_norm_weight,
             self.layer_norm_bias,
@@ -2447,7 +2582,9 @@ def forward(
             self.return_layernorm_output,
             self.bias_gelu_nvfusion,
             self.set_parallel_mode,
+            self.training,
         )
+        out = fwd_fn(*args)
 
         self.post_forward()
 
diff --git a/transformer_engine/pytorch/softmax.py b/transformer_engine/pytorch/softmax.py
index 8a34615d7f..a33cf5a85f 100644
--- a/transformer_engine/pytorch/softmax.py
+++ b/transformer_engine/pytorch/softmax.py
@@ -5,9 +5,10 @@
 """Fused scaled masked softmax functions"""
 import os
 from typing import Callable, Tuple, Union
-
 import torch
 from torch import nn
+import torch._C._onnx as _C_onnx
+from torch.onnx import _type_utils
 
 import transformer_engine_extensions as tex
 
@@ -46,6 +47,36 @@ def backward(
 
         return input_grads, None
 
+    @staticmethod
+    def symbolic(g: torch.Graph, inputs: torch._C.Value, scale: float) -> torch._C.Value:
+        """ScaledUpperTriangMaskedSoftmax symbolic method"""
+        def triangular_mask():
+            dtype =  _type_utils.JitScalarType.INT64
+            ones = torch.onnx.symbolic_opset9.ones_like(g, inputs, dtype)
+            k = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
+            mask = g.op("Trilu", ones, k, upper_i=1)
+            mask = g.op("Cast", mask, to_i=_C_onnx.TensorProtoDataType.BOOL)
+            return mask
+
+        # Captures the logic of function scaled_upper_triang_masked_softmax_warp_forward
+        if inputs.type().scalarType() == "BFloat16":
+            inputs = g.op("Cast", inputs, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+        mask = triangular_mask()
+        one = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
+        inv_mask = g.op("Sub", one, mask)
+
+        neg_tenK = g.op("Constant", value_t=torch.tensor(-10000., dtype=torch.float16))
+        softmax_mask = g.op("Mul", mask, neg_tenK)
+
+        scale_input = g.op("Constant", value_t=torch.tensor(scale, dtype=torch.float16))
+        scaled = g.op("Mul", inputs, scale_input)
+        masked_scaled = g.op("Mul", inv_mask, scaled)
+        masked = g.op("Add", masked_scaled, softmax_mask)
+        out = g.op("Softmax", masked)
+        if inputs.type().scalarType() == "BFloat16":
+            out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+        return out
+
 
 class ScaledMaskedSoftmax(torch.autograd.Function):
     """
@@ -78,6 +109,35 @@ def backward(
         )
         return input_grads, None, None
 
+    @staticmethod
+    def symbolic(
+        g: torch.Graph,
+        inputs: torch._C.Value,
+        mask: torch._C.Value,
+        scale: float) -> torch._C.Value:
+        """ScaledMaskedSoftmax symbolic method"""
+        # Captures the logic of function scaled_masked_softmax_warp_forward.
+        # output = softmax(mask(input*scale)
+        # Computed as:
+        #   masked_scaled = (1 - mask)*(input*scale)
+        #   softmax_mask = mask * -10000
+        #   output = softmax(masked_scaled + softmax_mask)
+        if inputs.type().scalarType() == "BFloat16":
+            inputs = g.op("Cast", inputs, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+        scale_input = g.op("Constant", value_t=torch.tensor(scale, dtype=torch.float16))
+        scaled = g.op("Mul", inputs, scale_input)
+        one = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
+        inv_mask = g.op("Sub", one, mask)
+        # Todo: type is hard coded because softmax uses FP16 or BF16
+        neg_tenK = g.op("Constant", value_t=torch.tensor(-10000., dtype=torch.float16))
+        softmax_mask = g.op("Mul", mask, neg_tenK)
+        masked_scaled = g.op("Mul", inv_mask, scaled)
+        masked = g.op("Add", masked_scaled, softmax_mask)
+        out = g.op("Softmax", masked)
+        if inputs.type().scalarType() == "BFloat16":
+            out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+        return out
+
 
 class ScaledSoftmax(torch.autograd.Function):
     """
@@ -107,6 +167,19 @@ def backward(
         )
         return input_grads, None, None
 
+    @staticmethod
+    def symbolic(g: torch.Graph, inputs: torch._C.Value, scale: float) -> torch._C.Value:
+        """ScaledSoftmax symbolic method"""
+        if inputs.type().scalarType() == "BFloat16":
+            inputs = g.op("Cast", inputs, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+        scale_input = g.op("Constant", value_t=torch.tensor(scale, dtype=torch.float16))
+        scaled = g.op("Mul", inputs, scale_input)
+        out = g.op("Softmax", scaled)
+        if inputs.type().scalarType() == "BFloat16":
+            out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+        return out
+
+
 
 class FusedScaleMaskSoftmax(nn.Module):
     """
@@ -163,7 +236,7 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
             and attn_batches % 4 == 0  # np * b must be divisor of 4
         ):
             if 0 <= sk <= 4096:
-                batch_per_block = self.get_batch_per_block(sk)
+                batch_per_block = self.get_batch_per_block(int(sk))
 
                 if self.attn_mask_type == "causal":
                     if attn_batches % batch_per_block == 0:
diff --git a/transformer_engine/pytorch/te_onnx_extensions.py b/transformer_engine/pytorch/te_onnx_extensions.py
new file mode 100755
index 0000000000..1315150bec
--- /dev/null
+++ b/transformer_engine/pytorch/te_onnx_extensions.py
@@ -0,0 +1,170 @@
+# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+"""
+ONNX symbolic functions for Transformer Engine
+
+Warnings of the type pasted below are a known Pytorch issue
+(https://github.com/pytorch/pytorch/issues/81693):
+
+tests/test_onnx_export.py::test_export_cast_ops[112]
+  /opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py:649:
+  UserWarning: The shape inference of trt::TRT_FP8DequantizeLinear type is missing,
+  so it may result in wrong shape inference for the exported graph.
+  Please consider adding it in symbolic function. (Triggered internally at
+  /opt/pytorch/pytorch/torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1880.)
+    _C._jit_pass_onnx_graph_shape_type_inference(
+
+"""
+
+import torch
+from torch.onnx import symbolic_helper, register_custom_op_symbolic
+import torch._C._onnx as _C_onnx
+import transformer_engine_extensions as tex
+
+# This file registers custom op symbolic ONNX functions and does not export any symbols.
+__all__ = []
+
+
+# Custom ops spec version
+VER = 1
+
+
+def make_op_name(op_name: str) -> str:
+    """custom op name"""
+    return "trt::" + op_name
+
+
+@symbolic_helper.parse_args("v", "v", "v", "v", "i", "i")
+def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
+    """ONNX graph for cast_to_fp8"""
+    output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
+    if inputs.type().scalarType() == "Half":
+        # Q inputs are currently constrained to FP32 due to a similar limitation in ORT custom ops.
+        inputs = g.op("Cast", inputs, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    return g.op(make_op_name("TRT_FP8QuantizeLinear"), inputs, scale_inv).setType(
+            inputs.type().with_dtype(torch.uint8).with_sizes(output_shape))
+
+
+@symbolic_helper.parse_args("v", "v", "i", "i", "i")
+def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
+    """ONNX graph for cast_from_fp8"""
+    output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
+    out = g.op(make_op_name("TRT_FP8DequantizeLinear"), inputs, scale_inv).setType(
+        inputs.type().with_dtype(torch.float32).with_sizes(output_shape))
+    if otype == int(tex.DType.kFloat16):
+        # DQ outputs are currently constrained to FP32 due to a similar limitation in ORT
+        # custom ops, so cast the output.
+        out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+    return out
+
+
+@symbolic_helper.parse_args("v", "v", "v", "v", "i", "i")
+def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
+    """ONNX graph for fp8_gelu"""
+    output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
+    gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
+    if inputs.type().scalarType() == "Half":
+        gelu = g.op("Cast", gelu, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    out = g.op(make_op_name("TRT_FP8QuantizeLinear"), gelu, scale_inv).setType(
+        inputs.type().with_dtype(torch.uint8).with_sizes(output_shape))
+    return out
+
+
+@symbolic_helper.parse_args("v", "v", "i", "i", "i",
+                             "v", "v", "i", "i", "i",
+                             "v", "i", "v", "v", "i",
+                             "v", "i", "i", "i")
+def onnx_te_gemm(
+    g,
+    weight,
+    weight_scale_inverse,
+    weight_fp8_tensor,
+    weight_type,
+    trans_weight,
+    inputs,
+    input_scale_inverse,
+    input_fp8_tensor,
+    input_type,
+    trans_input,
+    out,
+    out_type,
+    bias,
+    pre_gelu_out,
+    grad,
+    workspace,
+    workspaceSize,
+    accumulate,
+    use_split_accumulator):
+    """ONNX graph for te_gemm"""
+    is_fp16 = bias.type().scalarType() == "Half"
+    if input_type == int(tex.DType.kFloat8E4M3):
+        inputs = g.op(make_op_name("TRT_FP8DequantizeLinear"), inputs, input_scale_inverse)
+
+    if weight_type == int(tex.DType.kFloat8E4M3):
+        weight = g.op(make_op_name("TRT_FP8DequantizeLinear"), weight, weight_scale_inverse)
+
+    output = g.op("Gemm", inputs, weight, transA_i=trans_input, transB_i=trans_weight)
+
+    empty_tensor_size = [0]
+    bias_empty = torch.onnx.symbolic_helper._get_tensor_sizes(bias) == empty_tensor_size
+    pre_gelu_out_empty = torch.onnx.symbolic_helper._get_tensor_sizes(pre_gelu_out) \
+        == empty_tensor_size
+    if not bias_empty:
+        if pre_gelu_out_empty:
+            if is_fp16:
+                output = g.op("Cast", output, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+            output = g.op('Add', output, bias)
+        else:
+            if is_fp16:
+                output = g.op("Cast", output, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+            output = g.op('Add', output, bias)
+            output = torch.onnx.symbolic_opset9.gelu(g, output)
+    else:
+        if is_fp16:
+            output = g.op("Cast", output, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+    return output
+
+
+@symbolic_helper.parse_args("v", "v", "v", "f", "v", "v", "v",  "i")
+def onnx_layernorm_fwd_fp8(g, inputs, weight, bias, eps, scale, amax, scale_inv, otype):
+    """ONNX graph for layernorm_fwd_fp8"""
+    ln = onnx_layernorm_fwd(g, inputs, weight, bias, eps)
+    output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
+    if inputs.type().scalarType() == "Half":
+        ln = g.op("Cast", ln, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    fp8_ln = g.op(make_op_name("TRT_FP8QuantizeLinear"), ln, scale_inv).setType(
+        inputs.type().with_dtype(torch.uint8).with_sizes(output_shape))
+    return fp8_ln
+
+
+@symbolic_helper.parse_args("v", "v", "v", "f")
+def onnx_layernorm_fwd(g, inputs, weight, bias, eps):
+    """ONNX graph for layernorm_fwd"""
+    normalized_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
+    if normalized_shape is None:
+        ndim = torch.onnx.symbolic_helper._get_tensor_rank(inputs)
+        assert ndim is not None
+        normalized_shape = list(range(0, ndim))
+    # Normalization axis = 0, so normalized_shape uses all dims except dim = 0
+    normalized_shape = normalized_shape[1:]
+
+    ln = torch.onnx.symbolic_opset9.layer_norm(
+        g,
+        inputs,
+        normalized_shape,
+        weight,
+        bias,
+        eps,
+        False # cudnn_enable (not relevant)
+    )
+    return ln
+
+
+register_custom_op_symbolic('tex_ts::cast_to_fp8_ts', onnx_cast_to_fp8, VER)
+register_custom_op_symbolic('tex_ts::cast_from_fp8_ts', onnx_cast_from_fp8, VER)
+register_custom_op_symbolic('tex_ts::fp8_gelu_ts', onnx_fp8_gelu, VER)
+register_custom_op_symbolic('tex_ts::te_gemm_ts', onnx_te_gemm, VER)
+register_custom_op_symbolic('tex_ts::layernorm_fwd_fp8_inf_ts', onnx_layernorm_fwd_fp8, VER)
+register_custom_op_symbolic('tex_ts::layernorm_fwd_inf_ts', onnx_layernorm_fwd, VER)

From b2568676ac8fe0978bd1fd5f451b30366bec25e1 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Wed, 14 Dec 2022 18:49:08 +0000
Subject: [PATCH 02/16] fixes for pylint tests

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 transformer_engine/pytorch/te_onnx_extensions.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/transformer_engine/pytorch/te_onnx_extensions.py b/transformer_engine/pytorch/te_onnx_extensions.py
index 1315150bec..2424c62a64 100755
--- a/transformer_engine/pytorch/te_onnx_extensions.py
+++ b/transformer_engine/pytorch/te_onnx_extensions.py
@@ -39,6 +39,7 @@ def make_op_name(op_name: str) -> str:
 @symbolic_helper.parse_args("v", "v", "v", "v", "i", "i")
 def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
     """ONNX graph for cast_to_fp8"""
+    # pylint: disable=unused-argument
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     if inputs.type().scalarType() == "Half":
         # Q inputs are currently constrained to FP32 due to a similar limitation in ORT custom ops.
@@ -50,6 +51,7 @@ def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 @symbolic_helper.parse_args("v", "v", "i", "i", "i")
 def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
     """ONNX graph for cast_from_fp8"""
+    # pylint: disable=unused-argument
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     out = g.op(make_op_name("TRT_FP8DequantizeLinear"), inputs, scale_inv).setType(
         inputs.type().with_dtype(torch.float32).with_sizes(output_shape))
@@ -63,6 +65,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
 @symbolic_helper.parse_args("v", "v", "v", "v", "i", "i")
 def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
     """ONNX graph for fp8_gelu"""
+    # pylint: disable=unused-argument
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
     if inputs.type().scalarType() == "Half":
@@ -98,6 +101,7 @@ def onnx_te_gemm(
     accumulate,
     use_split_accumulator):
     """ONNX graph for te_gemm"""
+    # pylint: disable=unused-argument
     is_fp16 = bias.type().scalarType() == "Half"
     if input_type == int(tex.DType.kFloat8E4M3):
         inputs = g.op(make_op_name("TRT_FP8DequantizeLinear"), inputs, input_scale_inverse)
@@ -130,6 +134,7 @@ def onnx_te_gemm(
 @symbolic_helper.parse_args("v", "v", "v", "f", "v", "v", "v",  "i")
 def onnx_layernorm_fwd_fp8(g, inputs, weight, bias, eps, scale, amax, scale_inv, otype):
     """ONNX graph for layernorm_fwd_fp8"""
+    # pylint: disable=unused-argument
     ln = onnx_layernorm_fwd(g, inputs, weight, bias, eps)
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     if inputs.type().scalarType() == "Half":
@@ -142,6 +147,7 @@ def onnx_layernorm_fwd_fp8(g, inputs, weight, bias, eps, scale, amax, scale_inv,
 @symbolic_helper.parse_args("v", "v", "v", "f")
 def onnx_layernorm_fwd(g, inputs, weight, bias, eps):
     """ONNX graph for layernorm_fwd"""
+    # pylint: disable=unused-argument
     normalized_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     if normalized_shape is None:
         ndim = torch.onnx.symbolic_helper._get_tensor_rank(inputs)

From 65231f43ffca5f739d7660082250ee11a09795a5 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Wed, 14 Dec 2022 20:32:46 +0000
Subject: [PATCH 03/16] fix pylint warning in softmax.py

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 transformer_engine/pytorch/softmax.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/transformer_engine/pytorch/softmax.py b/transformer_engine/pytorch/softmax.py
index a33cf5a85f..2e9cf61388 100644
--- a/transformer_engine/pytorch/softmax.py
+++ b/transformer_engine/pytorch/softmax.py
@@ -128,7 +128,7 @@ def symbolic(
         scaled = g.op("Mul", inputs, scale_input)
         one = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
         inv_mask = g.op("Sub", one, mask)
-        # Todo: type is hard coded because softmax uses FP16 or BF16
+        # Note: type is hard coded because softmax uses FP16 or BF16
         neg_tenK = g.op("Constant", value_t=torch.tensor(-10000., dtype=torch.float16))
         softmax_mask = g.op("Mul", mask, neg_tenK)
         masked_scaled = g.op("Mul", inv_mask, scaled)

From 291638875063462b08d6cb5ed9bb2b57e72aa037 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Wed, 14 Dec 2022 20:37:49 +0000
Subject: [PATCH 04/16] move FP8 ORT lib inside tests/

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 .../libcustom_ort_fp8_qdq_ops.so                    | Bin
 tests/test_onnx_export.py                           |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename libcustom_ort_fp8_qdq_ops.so => tests/libcustom_ort_fp8_qdq_ops.so (100%)

diff --git a/libcustom_ort_fp8_qdq_ops.so b/tests/libcustom_ort_fp8_qdq_ops.so
similarity index 100%
rename from libcustom_ort_fp8_qdq_ops.so
rename to tests/libcustom_ort_fp8_qdq_ops.so
diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index 15c309ccdb..8213e0f752 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -29,7 +29,7 @@
 ONNX_FILES_DIR = "./gen_onnx_models"
 
 # Shared library implementing custom FP8 Q/DQ operators for ONNX Runtime (ORT).
-ORT_CUSTOM_OPS_LIB = "./libcustom_ort_fp8_qdq_ops.so"
+ORT_CUSTOM_OPS_LIB = "./tests/libcustom_ort_fp8_qdq_ops.so"
 
 # ScaledUpperTriangMaskedSoftmax is exported via ONNX::Trilu which was introduced in opset 14.
 TRILU_OPSET = 14

From 91f6c39749808cb7d49c22d51ad7680df5c7c742 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Thu, 15 Dec 2022 01:46:43 +0000
Subject: [PATCH 05/16] enable cross attention tests

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py | 33 +++++++++++++++++++++++----------
 1 file changed, 23 insertions(+), 10 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index 8213e0f752..c0e3997355 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -133,10 +133,12 @@ def load_custom_ops(session_opts: ort.SessionOptions):
     def create_ort_input_dict(session, inps):
         inp_dict = {}
         if isinstance(inps, tuple) or isinstance(inps, list):
+            nonetype_inputs = 0
             for idx, inp in enumerate(inps):
                 if inp is None:
+                    nonetype_inputs += 1
                     continue
-                inp_dict[session.get_inputs()[idx].name] = to_numpy(inp)
+                inp_dict[session.get_inputs()[idx - nonetype_inputs].name] = to_numpy(inp)
         else:
             inp_dict[session.get_inputs()[0].name] = to_numpy(inps)
         return inp_dict
@@ -742,16 +744,23 @@ def test_export_core_attention(
     (True,  "padding"), # calls ScaledMaskedSoftmax
     (False, "padding"), # calls ScaledSoftmax
 ]
+test_configs_attention_type = [
+    (True, "self", True),
+    (False, "self", True),
+    (True, "self", False),
+    (False, "self", False),
+    # disabled because query_bias (reqd for cross attention) is defined when fuse_qkv_params is False
+    # ("cross", True),
+    (True, "cross", False),
+    # disabled because TypeError: cannot assign 'transformer_engine.pytorch.module.Linear'
+    # as parameter 'query' (torch.nn.Parameter or None expected)
+    # (False, "cross", False),
+]
 @pytest.mark.parametrize("use_fp8", [False, True])
 @pytest.mark.parametrize("use_mask, attn_mask_type", test_configs_multihead_attention)
 @pytest.mark.parametrize("precision", [torch.float32, torch.float16])
-@pytest.mark.parametrize("input_layernorm", [True, False])
 @pytest.mark.parametrize("return_layernorm_output", [False])
-@pytest.mark.parametrize("attention_type", [
-    "self",
-    #"cross" # TODO: handle this ORT error
-])
-@pytest.mark.parametrize("fuse_qkv_params", [False, True])
+@pytest.mark.parametrize("input_layernorm, attention_type, fuse_qkv_params", test_configs_attention_type)
 def test_export_multihead_attention(
     use_fp8: bool,
     use_mask: bool,
@@ -780,14 +789,18 @@ def test_export_multihead_attention(
         output_layer_init_method,
     )
     hidden_states = torch.randn(sequence_length, batch_size, hidden_size, dtype=precision, device="cuda")
-    input_names = ["hidden_states"]
+
     attention_mask = None
     if use_mask and attn_mask_type != "causal":
         # Generate a random mask with 50% probability for 0 or 1.
         probs = 0.5 * torch.ones(batch_size, 1, sequence_length, sequence_length, device="cuda", dtype=precision)
         attention_mask = torch.bernoulli(probs).to("cuda", dtype=torch.bool)
-        input_names.append("attention_mask")
-    inp = (hidden_states, attention_mask)
+
+    encoder_output = None
+    if attention_type == "cross":
+        encoder_output = torch.randn(sequence_length, batch_size, hidden_size, dtype=precision, device="cuda")
+    inp = (hidden_states, attention_mask, encoder_output)
+    input_names = ["hidden_states", "attention_mask", "encoder_output"]
 
     fp8_str = "_fp8" if use_fp8 else ""
     dtype_str = dtype2str(precision)

From 35f897d843cfea2b7d0d91d2aba30015cc487f53 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Thu, 15 Dec 2022 18:27:50 +0000
Subject: [PATCH 06/16] refactor code by @nzmora * Increase layernorm FP16
 threshold * Normalize onnx file names: _ separates configs; - separates words
 in a single config * Add get_attn_mask_str and fix mask string * Add missing
 ONNX files * Moved generated ONNX files to tests/gen_onnx_models/

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py | 111 +++++++++++++++++++++-----------------
 1 file changed, 63 insertions(+), 48 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index c0e3997355..d17f7af523 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -26,7 +26,8 @@
 
 
 # Directory where generated ONNX test models are stored.
-ONNX_FILES_DIR = "./gen_onnx_models"
+TESTS_DIR = os.path.dirname(os.path.abspath(__file__))
+ONNX_FILES_DIR = os.path.join(TESTS_DIR, "./gen_onnx_models")
 
 # Shared library implementing custom FP8 Q/DQ operators for ONNX Runtime (ORT).
 ORT_CUSTOM_OPS_LIB = "./tests/libcustom_ort_fp8_qdq_ops.so"
@@ -196,6 +197,16 @@ def as_te_type(dtype: torch.dtype):
     }[dtype]
 
 
+def get_attn_mask_str(use_mask, attn_mask_type):
+    # See FusedScaleMaskSoftmax::forward_fused_softmax for logic behind names.
+    if attn_mask_type is None:
+        return "_mask" if use_mask else "_no-mask"
+    attn_mask_str = "_padding-no-mask"
+    attn_mask_str = "_causal-mask" if attn_mask_type == "causal" else attn_mask_str
+    attn_mask_str = "_padding-mask" if use_mask and attn_mask_type == "padding" else attn_mask_str
+    return attn_mask_str
+
+
 @pytest.mark.parametrize("scale_factor, atol", [
     (1, 1e-7),
     (224, 1e-7)
@@ -230,16 +241,18 @@ def forward(self, inp):
     hidden_size = 256
     inp = torch.randn(hidden_size, in_features, device="cuda", dtype=precision)
     high_prec_str = dtype2str(precision)
-    fname = f"te.cast_fp8.s_{scale_factor}{high_prec_str}.onnx"
+    fname = f"te.cast_fp8_{scale_factor}{high_prec_str}.onnx"
     model = TestFP8_QDQ()
     do_export(model, inp, fname)
     validate_result(fname, inp, model, atol=atol, is_fp8=True)
 
 
 @pytest.mark.parametrize("scale_factor", [448])
-@pytest.mark.parametrize("precision, atol", [
-    [torch.float32, 1e-7], [torch.float16, 2e-3]]
-)
+@pytest.mark.parametrize(
+    "precision,     atol", [
+    [torch.float32, 1e-7],
+    [torch.float16, 2e-3]
+])
 def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
     class TestFP8_Gelu(nn.Module):
         def __init__(self):
@@ -268,13 +281,15 @@ def forward(self, inp):
     hidden_size = 256
     inp = torch.randn(hidden_size, in_features, device="cuda", dtype=precision)
     high_prec_str = dtype2str(precision)
-    fname = f"te.gelu_fp8{high_prec_str}.onnx"
+    fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
     model = TestFP8_Gelu()
     do_export(model, inp, fname)
     validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True)
 
 
-@pytest.mark.parametrize("scale_factors", [(224, 224,), ])
+@pytest.mark.parametrize("scale_factors",
+    [(224, 224,),
+])
 @pytest.mark.parametrize(
     "precision,     use_fp8, use_bias, use_gelu", [
     (torch.float32, False,   False,    False),
@@ -463,17 +478,13 @@ def forward(self, inp):
             return ret
 
     inp = torch.randn(*inp_shape, device="cuda", dtype=precision)
+    model = TestFP8_Layernorm() if use_fp8 else Test_Layernorm()
     high_prec_str = dtype2str(precision)
-    if use_fp8:
-        fname = f"te.layernorm_fwd_fp8{high_prec_str}.onnx"
-        model = TestFP8_Layernorm()
-    else:
-        fname = f"te.layernorm_fwd{high_prec_str}.onnx"
-        model = Test_Layernorm()
-
+    fp8_str = "_fp8" if use_fp8 else ""
+    fname = f"te.layernorm{fp8_str}_{scale_factor}{high_prec_str}.onnx"
     do_export(model, inp, fname)
     if precision not in (torch.bfloat16, ):
-        validate_result(fname, inp, model, atol=1e-5, is_fp8=use_fp8)
+        validate_result(fname, inp, model, atol=5e-4, is_fp8=use_fp8)
 
 
 @pytest.mark.parametrize("softmax_def", [
@@ -506,17 +517,17 @@ def forward(self, inp, mask):
     inp_shape = [hidden_size, in_features, in_features, in_features]
     if softmax_def == softmax_defs.ScaledUpperTriangMaskedSoftmax:
         inp_shape = [hidden_size, in_features, in_features]
-        kernel_str = "te.ScaledUpperTriangMaskedSoftmax"
+        kernel_str = "ScaledUpperTriangMaskedSoftmax"
         model = Test_Softmax(softmax_def)
     elif softmax_def == softmax_defs.ScaledMaskedSoftmax:
         # Generate a random mask with 50% probability for 0 or 1.
         probs = 0.5 * torch.ones(hidden_size, 1, in_features, in_features, device="cuda", dtype=precision)
         mask = torch.bernoulli(probs).to("cuda", dtype=torch.bool)
         input_names.append("mask")
-        kernel_str = "te.ScaledMaskedSoftmax"
+        kernel_str = "ScaledMaskedSoftmax"
         model = Test_Softmax(softmax_def, mask_inp=True)
     elif softmax_def == softmax_defs.ScaledSoftmax:
-        kernel_str = "te.ScaledSoftmax"
+        kernel_str = "ScaledSoftmax"
         model = Test_Softmax(softmax_def)
     input_tensor = torch.randn(*inp_shape, device="cuda")
     input_tensor = input_tensor.to(torch.bfloat16) if precision == torch.bfloat16 else input_tensor.half()
@@ -530,7 +541,8 @@ def forward(self, inp, mask):
 
 @pytest.mark.parametrize("scale_factor", [1])
 @pytest.mark.parametrize("use_fp8", [False, True])
-@pytest.mark.parametrize("return_bias", [False, True])
+# Returning the bias is a TE fusion optimization we don't care about.
+@pytest.mark.parametrize("return_bias", [False])
 @pytest.mark.parametrize(
     "precision,     use_bias",[
     (torch.float32, False),
@@ -580,7 +592,7 @@ def test_export_linear(
 
 @pytest.mark.parametrize("scale_factor", [112])
 @pytest.mark.parametrize("use_fp8", [False, True])
-# Todo: handle case of True
+# Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
 @pytest.mark.parametrize("return_layernorm_output", [False])
 @pytest.mark.parametrize(
@@ -629,7 +641,7 @@ def test_export_layernorm_linear(
 
 @pytest.mark.parametrize("scale_factor", [112])
 @pytest.mark.parametrize("use_fp8", [False, True])
-# Todo: handle case of True
+# Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
 @pytest.mark.parametrize("return_layernorm_output", [False])
 # Todo: cannot handle FP16 for some reason
@@ -686,8 +698,10 @@ def test_export_layernorm_mlp(
     (torch.float16, True,     "padding"), # calls ScaledMaskedSoftmax
     (torch.float16, False,    "padding"), # calls ScaledSoftmax
 ])
-@pytest.mark.parametrize("attention_softmax_in_fp32", [True, False])
-@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
+@pytest.mark.parametrize("attention_softmax_in_fp32",
+    [True, False])
+@pytest.mark.parametrize("apply_query_key_layer_scaling",
+    [True, False])
 def test_export_core_attention(
     precision: torch.dtype,
     use_mask: bool,
@@ -695,9 +709,6 @@ def test_export_core_attention(
     attention_softmax_in_fp32: bool,
     apply_query_key_layer_scaling: bool,
 ):
-    if attn_mask_type is None:
-        attn_mask_type = 'causal'
-
     # Set dimensions (these are arbitrary).
     kv_channels = 64
     num_attention_heads = 1
@@ -715,14 +726,14 @@ def test_export_core_attention(
         input_names.append("attention_mask")
     inp = (query_layer, key_layer, value_layer, attention_mask)
 
-    sm_prec_str = "_fp32" if attention_softmax_in_fp32 else "_fp16"
-    qk_scaling_str = "_qk_scaling" if apply_query_key_layer_scaling else ""
-    mask_str = "_masked" if use_mask else \
-                "_upper_trian_masked" if attn_mask_type=="causal" and precision == torch.float16 else \
-                ""
+    sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
+    qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
+    mask_str = get_attn_mask_str(use_mask, attn_mask_type)
     high_prec_str = dtype2str(precision)
     fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 
+    if attn_mask_type is None:
+        attn_mask_type = 'causal'
     model = te.transformer.CoreAttention(
         num_attention_heads=num_attention_heads,
         kv_channels=kv_channels,
@@ -740,21 +751,24 @@ def test_export_core_attention(
 
 
 test_configs_multihead_attention = [
-    (False, "causal"),  # calls ScaledUpperTriangMaskedSoftmax
-    (True,  "padding"), # calls ScaledMaskedSoftmax
-    (False, "padding"), # calls ScaledSoftmax
+    #"use_mask, attn_mask_type"
+    (False,    "causal"),  # calls ScaledUpperTriangMaskedSoftmax
+    (True,     "padding"), # calls ScaledMaskedSoftmax
+    (False,    "padding"), # calls ScaledSoftmax
 ]
 test_configs_attention_type = [
-    (True, "self", True),
-    (False, "self", True),
-    (True, "self", False),
-    (False, "self", False),
+    #"input_layernorm, attention_type, fuse_qkv_params"
+    (True,             "self",         True),
+    (False,            "self",         True),
+    (True,             "self",         False),
+    (False,            "self",         False),
     # disabled because query_bias (reqd for cross attention) is defined when fuse_qkv_params is False
-    # ("cross", True),
-    (True, "cross", False),
+    # (True,           "cross",        True),
+    # (False,          "cross",        True),
+    (True,             "cross",        False),
     # disabled because TypeError: cannot assign 'transformer_engine.pytorch.module.Linear'
     # as parameter 'query' (torch.nn.Parameter or None expected)
-    # (False, "cross", False),
+    # (False,          "cross",        False),
 ]
 @pytest.mark.parametrize("use_fp8", [False, True])
 @pytest.mark.parametrize("use_mask, attn_mask_type", test_configs_multihead_attention)
@@ -804,10 +818,11 @@ def test_export_multihead_attention(
 
     fp8_str = "_fp8" if use_fp8 else ""
     dtype_str = dtype2str(precision)
-    attn_type_str = "_self_attention" if attention_type == "self" else "_cross_attention"
-    fuse_qkv_str = "_fused" if fuse_qkv_params else ""
-    attn_mask_type_str = f"_{attn_mask_type}" if (use_mask and attn_mask_type != "") else ""
-    fname = f"te.multihead_attention{fp8_str}{attn_mask_type_str}{attn_type_str}{fuse_qkv_str}{dtype_str}.onnx"
+    attn_type_str = "_self-attention" if attention_type == "self" else "_cross-attention"
+    fuse_qkv_str = "_fused-qkv" if fuse_qkv_params else ""
+    attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
+    input_ln_str = "_input-ln" if input_layernorm else ""
+    fname = f"te.multihead_attention{fp8_str}{attn_mask_str}{attn_type_str}{input_ln_str}{fuse_qkv_str}{dtype_str}.onnx"
 
     model = te.transformer.MultiHeadAttention(
         *attention_args,
@@ -861,11 +876,11 @@ def test_export_transformer_layer(
     inp = (input_tensor, attention_mask)
 
     fp8_str = "_fp8" if use_fp8 else ""
-    fuse_qkv_params_str = "_fuse-qkv" if fuse_qkv_params else ""
+    fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
     qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
     high_prec_str = dtype2str(precision)
-    attn_mask_type_str = f"_{attn_mask_type}" if (use_mask and attn_mask_type != "") else ""
-    fname = f"te.transformer_layer{fp8_str}{attn_mask_type_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
+    attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
+    fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 
     model = te.TransformerLayer(
         hidden_size,

From 697f7e1c791d8a8df6306b7e588a4b04d5df5e94 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Tue, 27 Dec 2022 19:25:36 +0000
Subject: [PATCH 07/16] fix merge conflict changes

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 transformer_engine/pytorch/module.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/transformer_engine/pytorch/module.py b/transformer_engine/pytorch/module.py
index d0b66923e6..5141358272 100644
--- a/transformer_engine/pytorch/module.py
+++ b/transformer_engine/pytorch/module.py
@@ -7,7 +7,7 @@
 import pickle
 import warnings
 from abc import ABC, abstractmethod
-from typing import Union, Optional, Callable, Tuple, Dict, Any
+from typing import Union, Optional, Callable, Tuple, Dict, List, Any, Mapping
 from functools import partial
 import numpy as np
 import torch

From ebf781a2164d4125b48027df35c81ddbbf75a214 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Tue, 27 Dec 2022 19:56:06 +0000
Subject: [PATCH 08/16] fix Q/DQ scale input

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py                     | 44 ++++++++---
 transformer_engine/pytorch/cpp_extensions.py  |  2 +
 transformer_engine/pytorch/csrc/ts_fp8_op.cpp |  1 +
 transformer_engine/pytorch/module.py          |  2 +
 .../pytorch/te_onnx_extensions.py             | 78 ++++++++++++-------
 5 files changed, 87 insertions(+), 40 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index d17f7af523..5371d6fb79 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -418,7 +418,7 @@ def forward(self, inp, weight):
     else:
         model = Test_GEMM(precision, use_bias, use_gelu)
         do_export(model, (inp, weight), fname, use_fp8)
-        validate_result(fname, (inp, weight), model, rtol=1e-2, atol=1e-2)
+        validate_result(fname, (inp, weight), model, rtol=1e-2, atol=2e-2)
 
 
 @pytest.mark.parametrize("use_fp8", [False, True])
@@ -467,6 +467,7 @@ def forward(self, inp):
                 self.bias,
                 self.eps,
                 self.meta,
+                self.fp8_tensor,
                 self.fp8_type)
 
             ret = cast_from_fp8(
@@ -480,11 +481,12 @@ def forward(self, inp):
     inp = torch.randn(*inp_shape, device="cuda", dtype=precision)
     model = TestFP8_Layernorm() if use_fp8 else Test_Layernorm()
     high_prec_str = dtype2str(precision)
-    fp8_str = "_fp8" if use_fp8 else ""
-    fname = f"te.layernorm{fp8_str}_{scale_factor}{high_prec_str}.onnx"
+    fp8_str = f"_fp8-{scale_factor}" if use_fp8 else ""
+    fname = f"te.layernorm{fp8_str}{high_prec_str}.onnx"
     do_export(model, inp, fname)
     if precision not in (torch.bfloat16, ):
-        validate_result(fname, inp, model, atol=5e-4, is_fp8=use_fp8)
+        # TODO: FP32 has a small threshold (1e-5)
+        validate_result(fname, inp, model, atol=1e-3, is_fp8=use_fp8)
 
 
 @pytest.mark.parametrize("softmax_def", [
@@ -567,21 +569,43 @@ def test_export_linear(
     out_features = 256
     hidden_size = 256
 
+    class Test_Linear(nn.Module):
+        def __init__(self,
+                in_features,
+                out_features,
+                use_bias,
+                return_bias,
+                precision
+            ):
+            super().__init__()
+            self.linear = te.Linear(
+                in_features,
+                out_features,
+                bias=use_bias,
+                return_bias=return_bias,
+                params_dtype=precision
+            )
+
+        def forward(self, inp):
+            ret = self.linear(inp)
+            return ret
+
+
     inp = torch.randn(hidden_size, in_features, device="cuda", dtype=precision)
     fp8_str = "_fp8" if use_fp8 else ""
     bias_str = "_bias" if use_bias else ""
     high_prec_str = dtype2str(precision)
     fname = f"te.linear{fp8_str}{bias_str}{high_prec_str}.onnx"
     with te.fp8_autocast(enabled=use_fp8):
-        model = te.Linear(
+        model = Test_Linear(
             in_features,
             out_features,
-            bias=use_bias,
-            return_bias=return_bias,
-            params_dtype=precision
+            use_bias,
+            return_bias,
+            precision
         ).to(device='cuda')
         if use_fp8:
-            set_layer_scale(model, scale_factor)
+            set_layer_scale(model.linear, scale_factor)
         do_export(model, inp, fname, use_fp8)
 
         if not use_fp8:
@@ -687,7 +711,7 @@ def test_export_layernorm_mlp(
         if not use_fp8:
             validate_result(fname, inp, model, atol=1e-3)
         else:
-            validate_result(fname, inp, model, atol=1e-2, is_fp8=use_fp8)
+            validate_result(fname, inp, model, atol=2e-2, is_fp8=use_fp8)
 
 
 @pytest.mark.parametrize(
diff --git a/transformer_engine/pytorch/cpp_extensions.py b/transformer_engine/pytorch/cpp_extensions.py
index 6d3ffc0839..babd1ec7f7 100644
--- a/transformer_engine/pytorch/cpp_extensions.py
+++ b/transformer_engine/pytorch/cpp_extensions.py
@@ -259,6 +259,7 @@ def layernorm_fwd_fp8_inf(
     bias: torch.Tensor,
     eps: float,
     fp8_meta_tensor: tex.FP8TensorMeta,
+    fp8_tensor: Union[tex.FP8FwdTensors, tex.FP8BwdTensors],
     otype: tex.DType,
 ) -> torch.Tensor:
     """LayerNorm with FP8 output.
@@ -274,6 +275,7 @@ def layernorm_fwd_fp8_inf(
         fp8_meta_tensor.scale,
         fp8_meta_tensor.amax_history,
         fp8_meta_tensor.scale_inv,
+        fp8_tensor,
         otype)
     return ret
 
diff --git a/transformer_engine/pytorch/csrc/ts_fp8_op.cpp b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
index 94e46f0dce..a50242dc0d 100755
--- a/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
+++ b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
@@ -138,6 +138,7 @@ at::Tensor layernorm_fwd_fp8_inf_ts(const at::Tensor &input,
                                     at::Tensor scale,
                                     at::Tensor amax,
                                     at::Tensor scale_inv,
+                                    int64_t fp8_tensor,
                                     int64_t otype) {
   transformer_engine::DType otype_arg = reverse_map_dtype(otype);
   float eps_float = static_cast<float>(eps);
diff --git a/transformer_engine/pytorch/module.py b/transformer_engine/pytorch/module.py
index 5141358272..f3b9f0df54 100644
--- a/transformer_engine/pytorch/module.py
+++ b/transformer_engine/pytorch/module.py
@@ -615,6 +615,7 @@ def forward(
                         ln_bias,
                         eps,
                         fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_INPUT,
                         fp8_dtype_forward,
                     )
             else:
@@ -1805,6 +1806,7 @@ def forward(
                         ln_bias,
                         eps,
                         fp8_meta["scaling_fwd"],
+                        tex.FP8FwdTensors.GEMM1_INPUT,
                         fp8_dtype_forward,
                     )
             else:
diff --git a/transformer_engine/pytorch/te_onnx_extensions.py b/transformer_engine/pytorch/te_onnx_extensions.py
index 2424c62a64..bbdefdc26b 100755
--- a/transformer_engine/pytorch/te_onnx_extensions.py
+++ b/transformer_engine/pytorch/te_onnx_extensions.py
@@ -16,6 +16,11 @@
   /opt/pytorch/pytorch/torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1880.)
     _C._jit_pass_onnx_graph_shape_type_inference(
 
+
+Scale tensors are treated as lists ("fs") instead of tensors ("v") because we need to access
+specific entries using the index passes as `fp8_tensor`. If you fail to do this you will get
+the following error when accessing a sepcific scale element (e.g. `scale_inv[fp8_tensor]`):
+    TypeError: 'torch._C.Value' object is not subscriptable
 """
 
 import torch
@@ -30,55 +35,71 @@
 # Custom ops spec version
 VER = 1
 
+UNSPECIFIED_TYPE = -1
+
 
 def make_op_name(op_name: str) -> str:
     """custom op name"""
     return "trt::" + op_name
 
 
-@symbolic_helper.parse_args("v", "v", "v", "v", "i", "i")
-def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
-    """ONNX graph for cast_to_fp8"""
-    # pylint: disable=unused-argument
+def quantize(g, inputs, scale_inv, fp8_tensor):
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
+
+    # Q inputs are currently constrained to FP32 due to a similar limitation in ORT
+    # custom ops, so cast the input if needed.
     if inputs.type().scalarType() == "Half":
-        # Q inputs are currently constrained to FP32 due to a similar limitation in ORT custom ops.
         inputs = g.op("Cast", inputs, to_i=_C_onnx.TensorProtoDataType.FLOAT)
-    return g.op(make_op_name("TRT_FP8QuantizeLinear"), inputs, scale_inv).setType(
+
+    scale = g.op("Constant", value_t=torch.tensor(scale_inv[fp8_tensor]))
+    q_op = g.op(
+        make_op_name("TRT_FP8QuantizeLinear"), inputs, scale).setType(
             inputs.type().with_dtype(torch.uint8).with_sizes(output_shape))
+    return q_op
 
 
-@symbolic_helper.parse_args("v", "v", "i", "i", "i")
-def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
-    """ONNX graph for cast_from_fp8"""
-    # pylint: disable=unused-argument
+def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
-    out = g.op(make_op_name("TRT_FP8DequantizeLinear"), inputs, scale_inv).setType(
+
+    scale = g.op("Constant", value_t=torch.tensor(scale_inv[fp8_tensor]))
+    out = g.op(make_op_name("TRT_FP8DequantizeLinear"), inputs, scale).setType(
         inputs.type().with_dtype(torch.float32).with_sizes(output_shape))
+
+    # DQ outputs are currently constrained to FP32 due to a similar limitation in ORT
+    # custom ops, so cast the output if needed.
     if otype == int(tex.DType.kFloat16):
-        # DQ outputs are currently constrained to FP32 due to a similar limitation in ORT
-        # custom ops, so cast the output.
         out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
     return out
 
 
-@symbolic_helper.parse_args("v", "v", "v", "v", "i", "i")
+@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
+def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
+    """ONNX graph for cast_to_fp8"""
+    # pylint: disable=unused-argument
+    return quantize(g, inputs, scale_inv, fp8_tensor)
+
+
+@symbolic_helper.parse_args("v", "fs", "i", "i", "i")
+def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
+    """ONNX graph for cast_from_fp8"""
+    # pylint: disable=unused-argument
+    return dequantize(g, inputs, scale_inv, fp8_tensor, otype)
+
+
+@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
 def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
     """ONNX graph for fp8_gelu"""
     # pylint: disable=unused-argument
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
-    if inputs.type().scalarType() == "Half":
-        gelu = g.op("Cast", gelu, to_i=_C_onnx.TensorProtoDataType.FLOAT)
-    out = g.op(make_op_name("TRT_FP8QuantizeLinear"), gelu, scale_inv).setType(
-        inputs.type().with_dtype(torch.uint8).with_sizes(output_shape))
+    out = quantize(g, gelu, scale_inv, fp8_tensor)
     return out
 
 
-@symbolic_helper.parse_args("v", "v", "i", "i", "i",
-                             "v", "v", "i", "i", "i",
-                             "v", "i", "v", "v", "i",
-                             "v", "i", "i", "i")
+@symbolic_helper.parse_args("v", "fs", "i", "i", "i",
+                            "v", "fs", "i", "i", "i",
+                            "v", "i", "v", "v", "i",
+                            "v", "i", "i", "i")
 def onnx_te_gemm(
     g,
     weight,
@@ -104,10 +125,10 @@ def onnx_te_gemm(
     # pylint: disable=unused-argument
     is_fp16 = bias.type().scalarType() == "Half"
     if input_type == int(tex.DType.kFloat8E4M3):
-        inputs = g.op(make_op_name("TRT_FP8DequantizeLinear"), inputs, input_scale_inverse)
+        inputs = dequantize(g, inputs, input_scale_inverse, input_fp8_tensor, UNSPECIFIED_TYPE)
 
     if weight_type == int(tex.DType.kFloat8E4M3):
-        weight = g.op(make_op_name("TRT_FP8DequantizeLinear"), weight, weight_scale_inverse)
+        weight = dequantize(g, weight, weight_scale_inverse, weight_fp8_tensor, UNSPECIFIED_TYPE)
 
     output = g.op("Gemm", inputs, weight, transA_i=trans_input, transB_i=trans_weight)
 
@@ -131,16 +152,13 @@ def onnx_te_gemm(
     return output
 
 
-@symbolic_helper.parse_args("v", "v", "v", "f", "v", "v", "v",  "i")
-def onnx_layernorm_fwd_fp8(g, inputs, weight, bias, eps, scale, amax, scale_inv, otype):
+@symbolic_helper.parse_args("v", "v", "v", "f", "v", "v", "fs", "i", "i")
+def onnx_layernorm_fwd_fp8(g, inputs, weight, bias, eps, scale, amax, scale_inv, fp8_tensor, otype):
     """ONNX graph for layernorm_fwd_fp8"""
     # pylint: disable=unused-argument
     ln = onnx_layernorm_fwd(g, inputs, weight, bias, eps)
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
-    if inputs.type().scalarType() == "Half":
-        ln = g.op("Cast", ln, to_i=_C_onnx.TensorProtoDataType.FLOAT)
-    fp8_ln = g.op(make_op_name("TRT_FP8QuantizeLinear"), ln, scale_inv).setType(
-        inputs.type().with_dtype(torch.uint8).with_sizes(output_shape))
+    fp8_ln = quantize(g, ln, scale_inv, fp8_tensor)
     return fp8_ln
 
 

From b9b5477337ef83b7f5765f46da9acca3cde4bd1e Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Tue, 3 Jan 2023 20:15:54 +0000
Subject: [PATCH 09/16] enable FP16 config when bias is disabled

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py            | 18 ++++++++----------
 transformer_engine/pytorch/module.py |  6 +++---
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index 5371d6fb79..3bbe224055 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -549,11 +549,10 @@ def forward(self, inp, mask):
     "precision,     use_bias",[
     (torch.float32, False),
     (torch.float32, True),
-    # Todo: cannot configure FP16/BF16 when bias is disabled -
-    # AssertionError: Data type for activations and buffers must match when outside of autocasted region
-    # (torch.float16, False),
+    (torch.float16, False),
     (torch.float16, True),
-    #(torch.bfloat16, False),
+    # Todo: cannot configure BF16 when bias is disabled (ORT issue?)
+    (torch.bfloat16, False),
     # Todo: cannot configure BF16 when bias is enabled (ORT issue?)
     # (torch.bfloat16, True),
 ])
@@ -608,9 +607,11 @@ def forward(self, inp):
             set_layer_scale(model.linear, scale_factor)
         do_export(model, inp, fname, use_fp8)
 
+        if precision in (torch.bfloat16, ):
+            return
         if not use_fp8:
             validate_result(fname, inp, model, atol=1e-3)
-        elif precision not in (torch.bfloat16, ):
+        else:
             validate_result(fname, inp, model, atol=1e-3, is_fp8=use_fp8)
 
 
@@ -624,8 +625,7 @@ def forward(self, inp):
     (torch.float32, False),
     (torch.float32, True),
     (torch.float16, True),
-    # Todo: cannot configure FP16 when bias is disabled
-    #(torch.float16, False),
+    (torch.float16, False),
 ])
 def test_export_layernorm_linear(
     scale_factor: float,
@@ -668,14 +668,12 @@ def test_export_layernorm_linear(
 # Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
 @pytest.mark.parametrize("return_layernorm_output", [False])
-# Todo: cannot handle FP16 for some reason
 @pytest.mark.parametrize(
     "precision,     use_bias",[
     (torch.float32, False),
     (torch.float32, True),
     (torch.float16, True),
-    # Todo: cannot configure FP16 when bias is disabled
-    #(torch.float16, False),
+    (torch.float16, False),
 ])
 def test_export_layernorm_mlp(
     scale_factor: float,
diff --git a/transformer_engine/pytorch/module.py b/transformer_engine/pytorch/module.py
index f3b9f0df54..6ab26d3fdf 100644
--- a/transformer_engine/pytorch/module.py
+++ b/transformer_engine/pytorch/module.py
@@ -1086,7 +1086,7 @@ def __init__(
                 if self.parallel_mode == "column":
                     set_tensor_model_parallel_attributes(self.bias, True, 0, 1)
             else:
-                self.register_buffer("bias", torch.Tensor(), persistent=False)
+                self.register_buffer("bias", torch.Tensor().type(params_dtype), persistent=False)
 
             with torch.no_grad():
                 self.bias.zero_()
@@ -1648,7 +1648,7 @@ def __init__(
                 if self.parallel_mode == "column":
                     set_tensor_model_parallel_attributes(self.bias, True, 0, 1)
             else:
-                self.register_buffer("bias", torch.Tensor(), persistent=False)
+                self.register_buffer("bias", torch.Tensor().type(params_dtype), persistent=False)
 
             with torch.no_grad():
                 self.bias.zero_()
@@ -2501,7 +2501,7 @@ def __init__(
                 )
             )
         else:
-            self.register_buffer("fc2_bias", torch.Tensor(), persistent=False)
+            self.register_buffer("fc2_bias", torch.Tensor().type(params_dtype), persistent=False)
 
         # For RPL, bias has to be added after TP collectives
         # So it cannot be fused with the GEMM

From 4812408c78e630205b35239a07adb42ba15e2677 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Wed, 4 Jan 2023 21:39:37 +0000
Subject: [PATCH 10/16] fix pylint check errors

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 transformer_engine/pytorch/te_onnx_extensions.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/transformer_engine/pytorch/te_onnx_extensions.py b/transformer_engine/pytorch/te_onnx_extensions.py
index bbdefdc26b..765e30e7fc 100755
--- a/transformer_engine/pytorch/te_onnx_extensions.py
+++ b/transformer_engine/pytorch/te_onnx_extensions.py
@@ -44,6 +44,7 @@ def make_op_name(op_name: str) -> str:
 
 
 def quantize(g, inputs, scale_inv, fp8_tensor):
+    """Helper Function for Quantization"""
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
 
     # Q inputs are currently constrained to FP32 due to a similar limitation in ORT
@@ -59,6 +60,7 @@ def quantize(g, inputs, scale_inv, fp8_tensor):
 
 
 def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
+    """Helper Function for Dequantization"""
     output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
 
     scale = g.op("Constant", value_t=torch.tensor(scale_inv[fp8_tensor]))
@@ -90,7 +92,6 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
 def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
     """ONNX graph for fp8_gelu"""
     # pylint: disable=unused-argument
-    output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
     out = quantize(g, gelu, scale_inv, fp8_tensor)
     return out
@@ -157,7 +158,6 @@ def onnx_layernorm_fwd_fp8(g, inputs, weight, bias, eps, scale, amax, scale_inv,
     """ONNX graph for layernorm_fwd_fp8"""
     # pylint: disable=unused-argument
     ln = onnx_layernorm_fwd(g, inputs, weight, bias, eps)
-    output_shape = torch.onnx.symbolic_helper._get_tensor_sizes(inputs)
     fp8_ln = quantize(g, ln, scale_inv, fp8_tensor)
     return fp8_ln
 

From 9a7198fdbd9f32fd908aa755a4564108b9b7920a Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Fri, 6 Jan 2023 01:03:37 +0000
Subject: [PATCH 11/16] updates 1. remove List import for pylint failure 2.
 address comments: remove state tensors from GPU 3. address comments: Update
 reverse_map_dtype function and add to namespace

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 transformer_engine/pytorch/csrc/ts_fp8_op.cpp | 25 ++++++-------------
 transformer_engine/pytorch/module.py          |  6 ++---
 2 files changed, 10 insertions(+), 21 deletions(-)

diff --git a/transformer_engine/pytorch/csrc/ts_fp8_op.cpp b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
index a50242dc0d..29a093d27d 100755
--- a/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
+++ b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
@@ -7,24 +7,13 @@
 #include <torch/script.h>
 #include "extensions.h"
 
-transformer_engine::DType reverse_map_dtype(int64_t dtype) {
-  switch (dtype) {
-    case static_cast<int64_t>(transformer_engine::DType::kByte):
-        return transformer_engine::DType::kByte;
-    case static_cast<int64_t>(transformer_engine::DType::kInt32):
-        return transformer_engine::DType::kInt32;
-    case static_cast<int64_t>(transformer_engine::DType::kFloat32):
-        return transformer_engine::DType::kFloat32;
-    case static_cast<int64_t>(transformer_engine::DType::kFloat16):
-        return transformer_engine::DType::kFloat16;
-    case static_cast<int64_t>(transformer_engine::DType::kBFloat16):
-        return transformer_engine::DType::kBFloat16;
-    case static_cast<int64_t>(transformer_engine::DType::kFloat8E4M3):
-        return transformer_engine::DType::kFloat8E4M3;
-    case static_cast<int64_t>(transformer_engine::DType::kFloat8E5M2):
-        return transformer_engine::DType::kFloat8E5M2;
-    default:
-        NVTE_ERROR("Type not supported.");
+namespace {
+  transformer_engine::DType reverse_map_dtype(int64_t dtype) {
+    if (dtype >= 0 && dtype < static_cast<int64_t>(transformer_engine::DType::kNumTypes)) {
+      return static_cast<transformer_engine::DType>(dtype);
+    } else {
+      NVTE_ERROR("Type not supported.");
+    }
   }
 }
 
diff --git a/transformer_engine/pytorch/module.py b/transformer_engine/pytorch/module.py
index 6ab26d3fdf..96b89bad11 100644
--- a/transformer_engine/pytorch/module.py
+++ b/transformer_engine/pytorch/module.py
@@ -7,7 +7,7 @@
 import pickle
 import warnings
 from abc import ABC, abstractmethod
-from typing import Union, Optional, Callable, Tuple, Dict, List, Any, Mapping
+from typing import Union, Optional, Callable, Tuple, Dict, Any, Mapping
 from functools import partial
 import numpy as np
 import torch
@@ -167,7 +167,7 @@ def get_extra_state(self) -> torch.Tensor:
             state["extra_fp8_variables"] = extra
 
         state_serialized = pickle.dumps(state)
-        state_tensor = torch.tensor(np.frombuffer(state_serialized, dtype=np.uint8), device='cuda')
+        state_tensor = torch.tensor(np.frombuffer(state_serialized, dtype=np.uint8))
 
         return state_tensor
 
@@ -211,7 +211,7 @@ def set_extra_state(self, state: torch.Tensor) -> None:
             return
 
         if isinstance(state, torch.Tensor):
-            state = pickle.loads(state.cpu().detach().numpy().tobytes())
+            state = pickle.loads(state.detach().numpy().tobytes())
             if state is None:
                 return
 

From 882c462035eacb5c188262bdef9c3a04d8ee98f1 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Fri, 6 Jan 2023 01:09:20 +0000
Subject: [PATCH 12/16] minor fix: coding guidelines

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 transformer_engine/pytorch/csrc/ts_fp8_op.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/transformer_engine/pytorch/csrc/ts_fp8_op.cpp b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
index 29a093d27d..b4e6b6900a 100755
--- a/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
+++ b/transformer_engine/pytorch/csrc/ts_fp8_op.cpp
@@ -15,7 +15,7 @@ namespace {
       NVTE_ERROR("Type not supported.");
     }
   }
-}
+} //namespace
 
 
 at::Tensor cast_to_fp8_ts(const at::Tensor &input,

From 2ef1fa4058570e9160e6122b06f3f0c108c5f5ae Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Wed, 4 Jan 2023 21:30:56 +0000
Subject: [PATCH 13/16] fix scale re-init issues

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py | 41 ++++++++++++++++++++++-----------------
 1 file changed, 23 insertions(+), 18 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index 3bbe224055..a9079da0e1 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -80,12 +80,17 @@ def to_numpy(tensor):
     return tensor.cpu().numpy()
 
 
-def set_layer_scale(module: torch.nn.Module, scale: float):
-    module.fp8_init()
-    module.fp8_meta["scaling_fwd"].scale = torch.ones(
-        2, dtype=torch.float32, device="cuda") / scale
-    module.fp8_meta["scaling_fwd"].scale_inv = torch.ones(
-        2, dtype=torch.float32, device="cuda") * scale
+def set_layer_scale(module: torch.nn.Module, scales: float, num_gemms: int=1):
+    module.fp8_init(num_gemms=num_gemms)
+    assert len(scales) == num_gemms * 2, "Each gemm should be accompanied by 2 scales"
+    num_fp8_tensors = len(scales)
+    scale = torch.ones(num_fp8_tensors, dtype=torch.float32, device="cuda")
+    scale_inv = torch.ones(num_fp8_tensors, dtype=torch.float32, device="cuda")
+    for i, s in enumerate(scales):
+       scale[i] *= s
+       scale_inv[i] /= s
+    module.fp8_meta["scaling_fwd"].scale = scale
+    module.fp8_meta["scaling_fwd"].scale_inv = scale_inv
 
 
 def te_infer(model: torch.nn.Module, inps: Union[Tuple[torch.tensor], torch.tensor], is_fp8: bool):
@@ -541,7 +546,7 @@ def forward(self, inp, mask):
         validate_result(fname, inp, model, atol=1e-3)
 
 
-@pytest.mark.parametrize("scale_factor", [1])
+@pytest.mark.parametrize("scale_factor", [[448, 448]])
 @pytest.mark.parametrize("use_fp8", [False, True])
 # Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
@@ -557,7 +562,7 @@ def forward(self, inp, mask):
     # (torch.bfloat16, True),
 ])
 def test_export_linear(
-    scale_factor: float,
+    scale_factor: list,
     use_fp8: bool,
     use_bias: bool,
     return_bias: bool,
@@ -595,7 +600,7 @@ def forward(self, inp):
     bias_str = "_bias" if use_bias else ""
     high_prec_str = dtype2str(precision)
     fname = f"te.linear{fp8_str}{bias_str}{high_prec_str}.onnx"
-    with te.fp8_autocast(enabled=use_fp8):
+    with te.fp8_autocast(enabled=use_fp8, fp8_recipe=create_fp8_recipe()):
         model = Test_Linear(
             in_features,
             out_features,
@@ -610,12 +615,12 @@ def forward(self, inp):
         if precision in (torch.bfloat16, ):
             return
         if not use_fp8:
-            validate_result(fname, inp, model, atol=1e-3)
+            validate_result(fname, inp, model, atol=5e-4)
         else:
-            validate_result(fname, inp, model, atol=1e-3, is_fp8=use_fp8)
+            validate_result(fname, inp, model, atol=5e-4, is_fp8=use_fp8)
 
 
-@pytest.mark.parametrize("scale_factor", [112])
+@pytest.mark.parametrize("scale_factor", [[448, 448]])
 @pytest.mark.parametrize("use_fp8", [False, True])
 # Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
@@ -645,7 +650,7 @@ def test_export_layernorm_linear(
     bias_str = "_bias" if use_bias else ""
     high_prec_str = dtype2str(precision)
     fname = f"te.layernorm_linear{fp8_str}{bias_str}{high_prec_str}.onnx"
-    with te.fp8_autocast(enabled=use_fp8):
+    with te.fp8_autocast(enabled=use_fp8, fp8_recipe=create_fp8_recipe()):
         model = te.LayerNormLinear(
             hidden_size,
             3 * hidden_size,
@@ -663,7 +668,7 @@ def test_export_layernorm_linear(
             validate_result(fname, inp, model, atol=1e-3, is_fp8=use_fp8)
 
 
-@pytest.mark.parametrize("scale_factor", [112])
+@pytest.mark.parametrize("scale_factor", [[224, 224, 448, 448]])
 @pytest.mark.parametrize("use_fp8", [False, True])
 # Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
@@ -694,7 +699,7 @@ def test_export_layernorm_mlp(
     bias_str = "_bias" if use_bias else ""
     high_prec_str = dtype2str(precision)
     fname = f"te.layernorm_mlp{fp8_str}{bias_str}{high_prec_str}.onnx"
-    with te.fp8_autocast(enabled=use_fp8):
+    with te.fp8_autocast(enabled=use_fp8, fp8_recipe=create_fp8_recipe()):
         model = te.LayerNormMLP(
             hidden_size,
             ffn_hidden_size,
@@ -704,12 +709,12 @@ def test_export_layernorm_mlp(
             params_dtype=precision,
         ).to(device='cuda')
         if use_fp8:
-            set_layer_scale(model, scale_factor)
+            set_layer_scale(model, scale_factor, num_gemms=2)
         do_export(model, inp, fname, use_fp8)
         if not use_fp8:
-            validate_result(fname, inp, model, atol=1e-3)
+            validate_result(fname, inp, model, atol=5e-4)
         else:
-            validate_result(fname, inp, model, atol=2e-2, is_fp8=use_fp8)
+            validate_result(fname, inp, model, atol=7e-3, is_fp8=use_fp8)
 
 
 @pytest.mark.parametrize(

From 58920bbd0a21c1cdd33fad887eaa0b395a042f52 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Wed, 4 Jan 2023 21:41:55 +0000
Subject: [PATCH 14/16] update scale arg type in definition

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index a9079da0e1..2cb09c80c2 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -633,7 +633,7 @@ def forward(self, inp):
     (torch.float16, False),
 ])
 def test_export_layernorm_linear(
-    scale_factor: float,
+    scale_factor: list,
     use_fp8: bool,
     use_bias: bool,
     return_bias: bool,
@@ -681,7 +681,7 @@ def test_export_layernorm_linear(
     (torch.float16, False),
 ])
 def test_export_layernorm_mlp(
-    scale_factor: float,
+    scale_factor: list,
     use_fp8: bool,
     use_bias: bool,
     return_bias: bool,

From 486db6bd588f2cd0f967cb9752f6d6ef6f4b4109 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Thu, 5 Jan 2023 04:41:51 +0000
Subject: [PATCH 15/16] add scales to mha and transformer layer submodules

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py | 136 +++++++++++++++++++++++++++++---------
 1 file changed, 106 insertions(+), 30 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index 2cb09c80c2..d2d5fc2e47 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -777,6 +777,33 @@ def test_export_core_attention(
     validate_result(fname, inp, model, atol=1e-2)
 
 
+def set_mha_scales(module,
+    scale_factor_qkv: list=[448, 448],
+    scale_factor_query: list=[112, 112],
+    scale_factor_kv: list=[224, 224],
+    scale_factor_proj: list=[448, 448]
+):
+    if module.attention_type == "self":
+        if module.input_layernorm:
+            # LayernormLinear layer scale init
+            set_layer_scale(module.layernorm_qkv, scale_factor_qkv)
+        else:
+            # Linear layer scale init
+            set_layer_scale(module.qkv, scale_factor_qkv)
+    else:
+        if module.input_layernorm:
+            # LayernormLinear layer scale init
+            set_layer_scale(module.layernorm_query, scale_factor_query)
+        else:
+            # Linear layer scale init
+            set_layer_scale(module.query_layer, scale_factor_query)
+
+        # Linear layer scale init
+        set_layer_scale(module.key_value, scale_factor_kv)
+
+    # Linear layer scale init
+    set_layer_scale(module.proj, scale_factor_proj)
+
 test_configs_multihead_attention = [
     #"use_mask, attn_mask_type"
     (False,    "causal"),  # calls ScaledUpperTriangMaskedSoftmax
@@ -802,6 +829,10 @@ def test_export_core_attention(
 @pytest.mark.parametrize("precision", [torch.float32, torch.float16])
 @pytest.mark.parametrize("return_layernorm_output", [False])
 @pytest.mark.parametrize("input_layernorm, attention_type, fuse_qkv_params", test_configs_attention_type)
+@pytest.mark.parametrize("scale_factor_qkv", [[448, 448]])
+@pytest.mark.parametrize("scale_factor_query", [[112, 112]])
+@pytest.mark.parametrize("scale_factor_kv", [[224, 224]])
+@pytest.mark.parametrize("scale_factor_proj", [[448, 448]])
 def test_export_multihead_attention(
     use_fp8: bool,
     use_mask: bool,
@@ -810,7 +841,11 @@ def test_export_multihead_attention(
     return_layernorm_output: bool,
     input_layernorm: bool,
     attention_type: str,
-    fuse_qkv_params: bool
+    fuse_qkv_params: bool,
+    scale_factor_qkv: list,
+    scale_factor_query: list,
+    scale_factor_kv: list,
+    scale_factor_proj: list,
 ):
     hidden_size = 256
     sequence_length = 128
@@ -851,21 +886,39 @@ def test_export_multihead_attention(
     input_ln_str = "_input-ln" if input_layernorm else ""
     fname = f"te.multihead_attention{fp8_str}{attn_mask_str}{attn_type_str}{input_ln_str}{fuse_qkv_str}{dtype_str}.onnx"
 
-    model = te.transformer.MultiHeadAttention(
-        *attention_args,
-        attn_mask_type=attn_mask_type,
-        params_dtype=precision,
-        return_layernorm_output=return_layernorm_output,
-        input_layernorm=input_layernorm,
-        attention_type=attention_type,
-        fuse_qkv_params=fuse_qkv_params,
-    ).to(device='cuda')
-    do_export(model, inp, fname, use_fp8, input_names=input_names)
-    if not use_fp8:
-        validate_result(fname, inp, model, atol=1e-3)
-    elif precision != torch.float16:
-        validate_result(fname, inp, model, atol=1e-2, is_fp8=use_fp8)
+    with te.fp8_autocast(enabled=use_fp8, fp8_recipe=create_fp8_recipe()):
+        model = te.transformer.MultiHeadAttention(
+            *attention_args,
+            attn_mask_type=attn_mask_type,
+            params_dtype=precision,
+            return_layernorm_output=return_layernorm_output,
+            input_layernorm=input_layernorm,
+            attention_type=attention_type,
+            fuse_qkv_params=fuse_qkv_params,
+        ).to(device='cuda')
+        if use_fp8:
+            set_mha_scales(model,
+                scale_factor_qkv,
+                scale_factor_query,
+                scale_factor_kv,
+                scale_factor_proj)
 
+        do_export(model, inp, fname, use_fp8, input_names=input_names)
+        if not use_fp8:
+            validate_result(fname, inp, model, atol=1e-3)
+        elif precision != torch.float16:
+            validate_result(fname, inp, model, atol=5e-3, is_fp8=use_fp8)
+
+def set_transformer_layer_scales(module,
+    scales_self_attn: list,
+    scales_inter_attn: list,
+    scales_layernorm_mlp: list=[224, 224, 448, 448]):
+    # set mha scales
+    set_mha_scales(module.self_attention, *scales_self_attn)
+    if module.layer_type == "decoder":
+        set_mha_scales(module.inter_attention, *scales_inter_attn)
+    # set layernorm mlp scales
+    set_layer_scale(module.layernorm_mlp, scales_layernorm_mlp, num_gemms=2)
 
 @pytest.mark.parametrize("use_fp8", [False, True])
 @pytest.mark.parametrize("use_mask, attn_mask_type", test_configs_multihead_attention)
@@ -876,6 +929,11 @@ def test_export_multihead_attention(
 @pytest.mark.parametrize("precision", [torch.float32, torch.float16])
 @pytest.mark.parametrize("fuse_qkv_params", [False, True])
 @pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
+@pytest.mark.parametrize("scale_factor_qkv", [[448, 448]])
+@pytest.mark.parametrize("scale_factor_query", [[112, 112]])
+@pytest.mark.parametrize("scale_factor_kv", [[224, 224]])
+@pytest.mark.parametrize("scale_factor_proj", [[448, 448]])
+@pytest.mark.parametrize("scale_factor_layernorm_mlp", [[224, 224, 448, 448]])
 def test_export_transformer_layer(
     use_fp8: bool,
     use_mask: bool,
@@ -883,7 +941,12 @@ def test_export_transformer_layer(
     output_layernorm: bool,
     precision: torch.dtype,
     fuse_qkv_params: bool,
-    apply_query_key_layer_scaling: bool
+    apply_query_key_layer_scaling: bool,
+    scale_factor_qkv: list,
+    scale_factor_query: list,
+    scale_factor_kv: list,
+    scale_factor_proj: list,
+    scale_factor_layernorm_mlp: list,
 ):
     # Layer configuration
     hidden_size = 64
@@ -909,17 +972,30 @@ def test_export_transformer_layer(
     attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
     fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 
-    model = te.TransformerLayer(
-        hidden_size,
-        ffn_hidden_size,
-        num_attention_heads,
-        self_attn_mask_type=attn_mask_type,
-        output_layernorm=output_layernorm,
-        params_dtype=precision,
-        fuse_qkv_params=fuse_qkv_params,
-        apply_query_key_layer_scaling=apply_query_key_layer_scaling).to(device='cuda')
-    do_export(model, inp, fname, use_fp8)
-    if not use_fp8:
-        validate_result(fname, inp, model, atol=1e-3)
-    elif precision != torch.float16:
-        validate_result(fname, inp, model, atol=5e-1, is_fp8=use_fp8)
+    with te.fp8_autocast(enabled=use_fp8, fp8_recipe=create_fp8_recipe()):
+        model = te.TransformerLayer(
+            hidden_size,
+            ffn_hidden_size,
+            num_attention_heads,
+            self_attn_mask_type=attn_mask_type,
+            output_layernorm=output_layernorm,
+            params_dtype=precision,
+            fuse_qkv_params=fuse_qkv_params,
+            apply_query_key_layer_scaling=apply_query_key_layer_scaling).to(device='cuda')
+        if use_fp8:
+            mha_scales = [
+                scale_factor_qkv,
+                scale_factor_query,
+                scale_factor_kv,
+                scale_factor_proj
+            ]
+            set_transformer_layer_scales(model,
+                scales_self_attn=mha_scales,
+                scales_inter_attn=mha_scales,
+                scales_layernorm_mlp=scale_factor_layernorm_mlp)
+
+        do_export(model, inp, fname, use_fp8)
+        if not use_fp8:
+            validate_result(fname, inp, model, atol=1e-3)
+        elif precision != torch.float16:
+            validate_result(fname, inp, model, atol=1e-2, is_fp8=use_fp8)

From e83246e25701aa4d7e082e55d410c88364f34625 Mon Sep 17 00:00:00 2001
From: Asfiya Baig <asfiyab@nvidia.com>
Date: Mon, 9 Jan 2023 20:10:41 +0000
Subject: [PATCH 16/16] address comments 1. replace variable scale_factor with
 scale_factors 2. Update type hints for scale_factors to be List[float] 3.
 Remove use of num_gemms param and add amax_history assignment

Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
---
 tests/test_onnx_export.py | 56 ++++++++++++++++++++-------------------
 1 file changed, 29 insertions(+), 27 deletions(-)

diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index d2d5fc2e47..789eccf09f 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -14,7 +14,7 @@
 import onnxruntime as ort
 import torch
 from torch import nn as nn
-from typing import Union, Tuple
+from typing import Union, Tuple, List
 import transformer_engine.pytorch as te
 from transformer_engine.common import recipe
 import transformer_engine_extensions as tex
@@ -80,17 +80,19 @@ def to_numpy(tensor):
     return tensor.cpu().numpy()
 
 
-def set_layer_scale(module: torch.nn.Module, scales: float, num_gemms: int=1):
-    module.fp8_init(num_gemms=num_gemms)
-    assert len(scales) == num_gemms * 2, "Each gemm should be accompanied by 2 scales"
+def set_layer_scale(module: torch.nn.Module, scales: List[float]):
+    module.fp8_init()
     num_fp8_tensors = len(scales)
     scale = torch.ones(num_fp8_tensors, dtype=torch.float32, device="cuda")
     scale_inv = torch.ones(num_fp8_tensors, dtype=torch.float32, device="cuda")
+    amax_history_len = module.fp8_meta["recipe"].amax_history_len
+    amax_history = torch.zeros(amax_history_len, num_fp8_tensors, dtype=torch.float32, device="cuda")
     for i, s in enumerate(scales):
        scale[i] *= s
        scale_inv[i] /= s
     module.fp8_meta["scaling_fwd"].scale = scale
     module.fp8_meta["scaling_fwd"].scale_inv = scale_inv
+    module.fp8_meta["scaling_fwd"].amax_history = amax_history
 
 
 def te_infer(model: torch.nn.Module, inps: Union[Tuple[torch.tensor], torch.tensor], is_fp8: bool):
@@ -546,7 +548,7 @@ def forward(self, inp, mask):
         validate_result(fname, inp, model, atol=1e-3)
 
 
-@pytest.mark.parametrize("scale_factor", [[448, 448]])
+@pytest.mark.parametrize("scale_factors", [[448, 448]])
 @pytest.mark.parametrize("use_fp8", [False, True])
 # Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
@@ -562,7 +564,7 @@ def forward(self, inp, mask):
     # (torch.bfloat16, True),
 ])
 def test_export_linear(
-    scale_factor: list,
+    scale_factors: List[float],
     use_fp8: bool,
     use_bias: bool,
     return_bias: bool,
@@ -609,7 +611,7 @@ def forward(self, inp):
             precision
         ).to(device='cuda')
         if use_fp8:
-            set_layer_scale(model.linear, scale_factor)
+            set_layer_scale(model.linear, scale_factors)
         do_export(model, inp, fname, use_fp8)
 
         if precision in (torch.bfloat16, ):
@@ -620,7 +622,7 @@ def forward(self, inp):
             validate_result(fname, inp, model, atol=5e-4, is_fp8=use_fp8)
 
 
-@pytest.mark.parametrize("scale_factor", [[448, 448]])
+@pytest.mark.parametrize("scale_factors", [[448, 448]])
 @pytest.mark.parametrize("use_fp8", [False, True])
 # Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
@@ -633,7 +635,7 @@ def forward(self, inp):
     (torch.float16, False),
 ])
 def test_export_layernorm_linear(
-    scale_factor: list,
+    scale_factors: List[float],
     use_fp8: bool,
     use_bias: bool,
     return_bias: bool,
@@ -660,7 +662,7 @@ def test_export_layernorm_linear(
             params_dtype=precision,
         ).to(device='cuda')
         if use_fp8:
-            set_layer_scale(model, scale_factor)
+            set_layer_scale(model, scale_factors)
         do_export(model, inp, fname, use_fp8)
         if not use_fp8:
             validate_result(fname, inp, model, atol=1e-3)
@@ -668,7 +670,7 @@ def test_export_layernorm_linear(
             validate_result(fname, inp, model, atol=1e-3, is_fp8=use_fp8)
 
 
-@pytest.mark.parametrize("scale_factor", [[224, 224, 448, 448]])
+@pytest.mark.parametrize("scale_factors", [[224, 224, 448, 448]])
 @pytest.mark.parametrize("use_fp8", [False, True])
 # Returning the bias is a TE fusion optimization we don't care about.
 @pytest.mark.parametrize("return_bias", [False])
@@ -681,7 +683,7 @@ def test_export_layernorm_linear(
     (torch.float16, False),
 ])
 def test_export_layernorm_mlp(
-    scale_factor: list,
+    scale_factors: List[float],
     use_fp8: bool,
     use_bias: bool,
     return_bias: bool,
@@ -709,7 +711,7 @@ def test_export_layernorm_mlp(
             params_dtype=precision,
         ).to(device='cuda')
         if use_fp8:
-            set_layer_scale(model, scale_factor, num_gemms=2)
+            set_layer_scale(model, scale_factors)
         do_export(model, inp, fname, use_fp8)
         if not use_fp8:
             validate_result(fname, inp, model, atol=5e-4)
@@ -778,10 +780,10 @@ def test_export_core_attention(
 
 
 def set_mha_scales(module,
-    scale_factor_qkv: list=[448, 448],
-    scale_factor_query: list=[112, 112],
-    scale_factor_kv: list=[224, 224],
-    scale_factor_proj: list=[448, 448]
+    scale_factor_qkv: List[float]=[448, 448],
+    scale_factor_query: List[float]=[112, 112],
+    scale_factor_kv: List[float]=[224, 224],
+    scale_factor_proj: List[float]=[448, 448]
 ):
     if module.attention_type == "self":
         if module.input_layernorm:
@@ -842,10 +844,10 @@ def test_export_multihead_attention(
     input_layernorm: bool,
     attention_type: str,
     fuse_qkv_params: bool,
-    scale_factor_qkv: list,
-    scale_factor_query: list,
-    scale_factor_kv: list,
-    scale_factor_proj: list,
+    scale_factor_qkv: List[float],
+    scale_factor_query: List[float],
+    scale_factor_kv: List[float],
+    scale_factor_proj: List[float],
 ):
     hidden_size = 256
     sequence_length = 128
@@ -918,7 +920,7 @@ def set_transformer_layer_scales(module,
     if module.layer_type == "decoder":
         set_mha_scales(module.inter_attention, *scales_inter_attn)
     # set layernorm mlp scales
-    set_layer_scale(module.layernorm_mlp, scales_layernorm_mlp, num_gemms=2)
+    set_layer_scale(module.layernorm_mlp, scales_layernorm_mlp)
 
 @pytest.mark.parametrize("use_fp8", [False, True])
 @pytest.mark.parametrize("use_mask, attn_mask_type", test_configs_multihead_attention)
@@ -942,11 +944,11 @@ def test_export_transformer_layer(
     precision: torch.dtype,
     fuse_qkv_params: bool,
     apply_query_key_layer_scaling: bool,
-    scale_factor_qkv: list,
-    scale_factor_query: list,
-    scale_factor_kv: list,
-    scale_factor_proj: list,
-    scale_factor_layernorm_mlp: list,
+    scale_factor_qkv: List[float],
+    scale_factor_query: List[float],
+    scale_factor_kv: List[float],
+    scale_factor_proj: List[float],
+    scale_factor_layernorm_mlp: List[float],
 ):
     # Layer configuration
     hidden_size = 64