Metadata-Version: 2.4
Name: transformer_engine
Version: 2.1.0
Summary: Transformer acceleration library
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8, <3.13
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: transformer_engine_rocm==2.1.0
Provides-Extra: pytorch
Requires-Dist: transformer_engine_torch==2.1.0; extra == "pytorch"
Provides-Extra: jax
Requires-Dist: transformer_engine_jax==2.1.0; extra == "jax"
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: license-file
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

..
    This file was modified to include portability information to AMDGPU.

    Copyright (c) 2023-2025, Advanced Micro Devices, Inc. All rights reserved.

    Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

    See LICENSE for license information.

|License|

Transformer Engine On ROCm and AMDGPU
*************************************

This repository enables Transformer Engine (TE) on ROCm as a library to accelerate Transformer models on AMD GPUs, including using 8-bit floating point (FP8) precision on MI300 GPUs, to provide better performance with lower memory utilization in both training and inference. 
One of the missions is to provide an alternative to accelerate Transformer models that were previously run on NVIDIA GPUs like Hopper with best efforts to make the migration frictionless. 
Moreover, we add optimizations specific to AMD GPUs to get the best performance benefits out of AMD GPUs.

Feature Support Status
======================

* Activation, cast, fused softmax, layernorm, rmsnorm, transpose, fused rope, fp8 recipe, HipRTC: fully supported
* GEMM: partially supported with following input/output types: (fp32/fp32), (fp16/fp16), (bf16/bf16), (fp8, bf8/fp16, bf16, fp32) **Note:** Support for rocBLAS as a GEMM backend has been removed; `hipBLASLt` is the only backend.
* Attention (Flash Attention, Fused Multihead Attention): partially supported: Fused Attention with AOTriton and CK backends, FlashAttention-2 without variable sequence length feature
* HipGraph, HipTX: partially supported
* Tensor Parallelism, Sequence Parallelism, Context Parallelism: supported

Installation
============

Execute the following commands to install ROCm Transformer Engine from source on AMDGPUs:

Known Issue with ROCm 6.4 PyTorch Release
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Using the docker image ``rocm/pytorch:rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.5.1`` triggers a failure in the unit-test ``tests/pytorch/test_permutation.py`` (tracked in Jira ticket SWDEV-534311).  

Rebuilding PyTorch at commit ``f929e0d602a71aa393ca2e6097674b210bdf321c`` resolves the issue.

Re-install PyTorch
^^^^^^^^^^^^^^^^^^

.. code-block:: bash

  # Remove the pre-installed pytorch
  pip uninstall -y torch

  # Clone PyTorch and check out the working commit
  export PYTORCH_COMMIT=f929e0d602a71aa393ca2e6097674b210bdf321c
  git clone https://github.com/pytorch/pytorch
  cd pytorch
  git fetch origin ${PYTORCH_COMMIT}
  git checkout -q ${PYTORCH_COMMIT}
  git submodule update --recursive --init

  # Build and install
  ./tools/amd_build/build_amd.py
  BUILD_TEST=0 python3 setup.py install

Install TE
^^^^^^^^^^^^^^^^^^

.. code-block:: bash

  # Clone TE repo and submodules
  git clone --recursive https://github.com/ROCm/TransformerEngine.git
  
  cd TransformerEngine
  export NVTE_FRAMEWORK=pytorch,jax #optionally set framework, currently only support pytorch and jax; if not set will try to detect installed frameworks
  export NVTE_ROCM_ARCH=gfx942 # CK fused attn only support MI200 and MI300 and fp8 features are only supported on MI300
  
  # Build Platform Selection (optional)
  # Note: Useful when both ROCm and CUDA platforms are present in the Docker
  export NVTE_USE_ROCM=1  #Use 1 for ROCm, or set to 0 to use CUDA; If not set will try to detect installed platform, prioritizing ROCm

  pip install .

Test
====

Framework Agnostic C++ library unittests
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

After a successful Transformer Engine installation via `pip install`, execute the following commands to build and test the framework agnostic C++ library:

.. code-block:: bash

  cd tests/cpp
  mkdir build
  cd build
  cmake ../
  make
  make test

Note that some of operator unit tests fail in hipBLASLt config due to limited input data configurations support

Pytorch framework integration tests
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Pytorch integration pytests under tests/pytorch/ and tests/pytorch/fused_attn/ are supported.

Jax framework integration tests
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

All JAX pytests are supported. 

Examples
========

Pytorch
^^^^^^^
MNIST with optional FP8
~~~~~~~~~~~~~~~~~~~~~~~
.. code-block:: bash
  
  cd examples/pytorch/mnist
  python main.py
  python main.py --use-te   # Linear layers from TransformerEngine
  python main.py --use-fp8  # FP8 + TransformerEngine for Linear layers

Sort with minGPT
~~~~~~~~~~~~~~~~
.. code-block:: bash
  
  cd examples/pytorch/minGPT
  python gptSort.py --use-te # Linear and layernorm from TransformerEngine
  python gptSort.py --use-te --ln-mlp # In addition, use LayernormMLP from transformer engine
  python gptSort.py --use-te --ln-mlp --use-fp8 # In addition, use fp8

Jax
^^^
Flax
~~~~
.. code-block:: python
  
  import flax
  import jax
  import jax.numpy as jnp
  import transformer_engine.jax as te
  import transformer_engine.jax.flax as te_flax
  from transformer_engine.common import recipe

  BATCH = 32
  SEQLEN = 128
  HIDDEN = 1024

  # Initialize RNG and inputs.
  rng = jax.random.PRNGKey(0)
  init_rng, data_rng = jax.random.split(rng)
  inp = jax.random.normal(data_rng, [BATCH, SEQLEN, HIDDEN], jnp.float32)

  # Create an FP8 recipe. Note: All input args are optional.
  fp8_recipe = recipe.DelayedScaling(margin=0, interval=1, fp8_format=recipe.Format.HYBRID)

  # Enable autocasting for the forward pass
  with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
      model = te_flax.DenseGeneral(features=HIDDEN)

      def loss_fn(params, other_vars, inp):
        out = model.apply({'params':params, **other_vars}, inp)
        return jnp.mean(out)

      # Initialize models.
      variables = model.init(init_rng, inp)
      other_variables, params = flax.core.pop(variables, 'params')

      # Construct the forward and backward function
      fwd_bwd_fn = jax.value_and_grad(loss_fn, argnums=(0, 1))

      for _ in range(10):
        loss, (param_grads, other_grads) = fwd_bwd_fn(params, other_variables, inp)
        # Update FP8 metas
        other_variables = te.update_fp8_metas(other_grads)

MNIST
~~~~~
.. code-block:: bash
  
  cd examples/jax/mnist
  python test_single_gpu_mnist.py # Use Flax to train MNIST with BF16 as usual
  python test_single_gpu_mnist.py --use-te # Use `te.DenseGeneral` provided by Transformer Engine to train MNIST with BF16
  python test_single_gpu_mnist.py --use-fp8 # Use `te.DenseGeneral` provided by Transformer Engine to train MNIST and enable FP8 training and evaluation.

Encoder
~~~~~~~
.. code-block:: bash
  
  cd examples/jax/encoder
  python test_single_gpu_encoder.py
  python test_single_gpu_encoder.py --use-fp8

Features on ROCm Platform
=========================

GEMM tuning with hipBlasLt
^^^^^^^^^^^^^^^^^^^^^^^^^^
TE provides an ability to manually or automatically select a GPU algorithm to use from a list generated by hipBlasLt. Selected algorithms info can be stored to file and read on further applications run.
This ability is controlled by environment variables when calling GEMM operations with a specific config for the first time.

* TE_HIPBLASLT_ALGO_SELECTION - algorithm index to use in the list returned by hipBlasLt for the config or the first algorithm to select from if auto-selection is enabled; default=0.
* TE_HIPBLASLT_TUNING_RUN_COUNT - number of profiling loops for algorithm auto-selection; default=0 which means no auto-selection. For small tasks where run-to-run time variation is relatively high, using higher number of loops may give better auto-selection results.
* TE_HIPBLASLT_TUNING_ALGO_COUNT - maximal number of algorithms to check when auto-selection is enabled; default=16.
* TE_HIPBLASLT_ALGO_LOAD - filename of algorithm selection data saved by previous GEMM operation runs; if file does not exist, algorithm selection logic proceeds as if no filename were specified
* TE_HIPBLASLT_ALGO_SAVE - filename to save algorithm selection data to; can be the same as a filename to load in which case the file will be read first and then overwritten with updated results; filename may contain %i, that is replaced with the process ID. For example `auto_tune_%i.csv`.

It is not guaranteed that the algorithm selection data file created with one version of TE or hipBlasLt will work with other versions. Even if it works, it is highly recommended to perform algorithm selection tuning again when switching to new libraries versions because newer hipBLASLt versions may have optimized algorithms.

Typical usage is as follows:

1. Run single iteration of training enabling algorithm selection autotuning and saving:

.. code-block:: bash

  export TE_HIPBLASLT_TUNING_RUN_COUNT=20
  export TE_HIPBLASLT_TUNING_ALGO_COUNT=400
  export TE_HIPBLASLT_ALGO_SAVE=algo_tune.csv
  some_training_app

2. Use resulting algo_tune.csv for further training runs

.. code-block:: bash

  unset TE_HIPBLASLT_TUNING_RUN_COUNT TE_HIPBLASLT_TUNING_ALGO_COUNT TE_HIPBLASLT_ALGO_SAVE #these variables are not needed anymore
  export TE_HIPBLASLT_ALGO_LOAD=algo_tune.csv
  some_training_app

If you want to check that only previously tuned algorithms are used by your application, it can be done by keeping selection data saving enabled. 

.. code-block:: bash

  export TE_HIPBLASLT_ALGO_SAVE=algo_tune_check.csv
  export TE_HIPBLASLT_ALGO_LOAD=algo_tune.csv
  some_training_app
  #If the files are different, some not previously cached algorithms are probably used
  diff algo_tune.csv algo_tune_check.csv


Fused Attention Backends on ROCm
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Currently ROCm TE supports two backends, AOTriton and CK, for fused attention. 
To enable specific backends in compilation and/or in runtime, the following environment variables can be used:

* NVTE_FUSED_ATTN - enable the fused attention, default = 1;
* NVTE_FUSED_ATTN_CK - enable the CK backend, default = 1;
* NVTE_FUSED_ATTN_AOTRITON - enable the AOTriton backend, default = 1.

Setting env NVTE_FUSED_ATTN_<BACKEND>=0 in compilation will skip the build of the specific backend, which saves the overall building time. 
Setting env NVTE_FUSED_ATTN_<BACKEND>=0 in runtime provides the option to choose specific backends in runtime. 
Note that one backend can be enabled in compilation but disabled in runtime. 
However, if one backend is disabled in compilation, the same env NVTE_FUSED_ATTN_<BACKEND>=0 is required during runtime. 
Otherwise TE will error out that the specific backend is not compiled. 

NVTE_FUSED_ATTN has higher priority than NVTE_FUSED_ATTN_CK and NVTE_FUSED_ATTN_AOTRITON. 
NVTE_FUSED_ATTN=0 will use the TE unfused attention even if NVTE_FUSED_ATTN_CK or NVTE_FUSED_ATTN_AOTRITON is set. 
Fused attention backends are chosen according to the match results between the actual problem config and the support matrix of the specific backend. 
For the scenario that both backends are enabled and match the problem configuration, the CK backend will be chosen with higher priority. 

FA v3 Backward Kernels in CK Backend
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ROCm TE provides experimental support for flash-attention v3 bwd kernels using the ck backend for limited fused attention configs (currently only for hdim=128). 
To enable FA v3 kernels, the following environment variables can be used:

* NVTE_CK_USES_BWD_V3 - by default 0, if set to 1, some cases will call the bwd v3 dqdkdv kernel;
* NVTE_CK_IS_V3_ATOMIC_FP32 - by default 1, if set to 0 will use atomic fp16/bf16(w/o convert_dq kernel) when NVTE_CK_USES_BWD_V3 is set to 1;
* NVTE_CK_HOW_V3_BF16_CVT - by default 1, float to bf16 convert type when bwd_v3 is set to 1, 0:RTNE; 1:RTNA; 2:RTZ.

Float to BFloat16 Conversion in CK Backend
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
How fp32 converts to bf16 affects both the performance and accuracy in ck fused attn. 
ROCm TE provides the compile-time env NVTE_CK_FUSED_ATTN_FLOAT_TO_BFLOAT16_DEFAULT with the following values available to choose from: 

* 0 - standard;
* 1 - truncate with nan;
* 2 - truncate;
* 3 - standard asm, default;
* 4 - rta_asm.

Experimental Triton Kernels on ROCm
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Most CUDA kernels in Transformer Engine are hipified to run on ROCm. While the hipifiled CUDA kernels are functional, they are not necessarily optimal on ROCm. 
We added some Triton kernels to TE ROCm to improve the performance over the hipified kernels. 
Currently, we have integrated Triton kernels for cast_transpose and cast_transpose_bgrad, which are commonly used in fp8 training, and also rmsnorm kernels. 
This feature is still experimental as it requires relatievely newer version of Pytorch (with version >= 2.4) and Triton. 
Also, it only works on Pytorch extension as JAX extension does not use it.

At runtime, you can enable specific triton kernels using the specific environment variables:

* NVTE_USE_CAST_TRANSPOSE_TRITON=1 can be used to enable cast transpose (bgrad) triton kernels; 
* NVTE_USE_LAYERNORM_TRITON=1 can be used to enable layernorm triton kernels.
* NVTE_USE_RMSNORM_TRITON=1 can be used to enable rmsnorm triton kernels.


Transformer Engine
******************

`Quickstart <#examples>`_ | `Installation <#installation>`_ | `User Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/index.html>`_ | `Examples <https://github.com/NVIDIA/TransformerEngine/tree/main/examples>`_ | `FP8 Convergence <#fp8-convergence>`_ | `Integrations <#integrations>`_ | `Release notes <https://docs.nvidia.com/deeplearning/transformer-engine/release-notes/index.html>`_

Latest News
===========

* [03/2024] `Turbocharged Training: Optimizing the Databricks Mosaic AI stack with FP8 <https://www.databricks.com/blog/turbocharged-training-optimizing-databricks-mosaic-ai-stack-fp8>`_
* [03/2024] `FP8 Training Support in SageMaker Model Parallelism Library <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-release-notes.html>`_
* [12/2023] `New NVIDIA NeMo Framework Features and NVIDIA H200 <https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/>`_

.. image:: docs/examples/H200-NeMo-performance.png
  :width: 600
  :alt: H200

* [11/2023] `Inflection-2: The Next Step Up <https://inflection.ai/inflection-2>`_
* [11/2023] `Unleashing The Power Of Transformers With NVIDIA Transformer Engine <https://lambdalabs.com/blog/unleashing-the-power-of-transformers-with-nvidia-transformer-engine>`_
* [11/2023] `Accelerating PyTorch Training Workloads with FP8 <https://towardsdatascience.com/accelerating-pytorch-training-workloads-with-fp8-5a5123aec7d7>`_
* [09/2023] `Transformer Engine added to AWS DL Container for PyTorch Training <https://github.com/aws/deep-learning-containers/pull/3315>`_
* [06/2023] `Breaking MLPerf Training Records with NVIDIA H100 GPUs <https://developer.nvidia.com/blog/breaking-mlperf-training-records-with-nvidia-h100-gpus/>`_
* [04/2023] `Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1) <https://www.mosaicml.com/blog/coreweave-nvidia-h100-part-1>`_

What is Transformer Engine?
===========================
.. overview-begin-marker-do-not-remove

Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including
using 8-bit floating point (FP8) precision on Hopper, Ada, and Blackwell GPUs, to provide better
performance with lower memory utilization in both training and inference. TE provides a collection
of highly optimized building blocks for popular Transformer architectures and an automatic mixed
precision-like API that can be used seamlessly with your framework-specific code. TE also includes a
framework agnostic C++ API that can be integrated with other deep learning libraries to enable FP8
support for Transformers.

As the number of parameters in Transformer models continues to grow, training and inference for
architectures such as BERT, GPT and T5 become very memory and compute-intensive. Most deep learning
frameworks train with FP32 by default. This is not essential, however, to achieve full accuracy for
many deep learning models. Using mixed-precision training, which combines single-precision (FP32)
with lower precision (e.g. FP16) format when training a model, results in significant speedups with
minimal differences in accuracy as compared to FP32 training. With Hopper GPU
architecture FP8 precision was introduced, which offers improved performance over FP16 with no
degradation in accuracy. Although all major deep learning frameworks support FP16, FP8 support is
not available natively in frameworks today.

TE addresses the problem of FP8 support by providing APIs that integrate with popular Large Language
Model (LLM) libraries. It provides a Python API consisting of modules to easily build a Transformer
layer as well as a framework-agnostic library in C++ including structs and kernels needed for FP8
support. Modules provided by TE internally maintain scaling factors and other values needed for FP8
training, greatly simplifying mixed precision training for users.

Highlights
==========

* Easy-to-use modules for building Transformer layers with FP8 support
* Optimizations (e.g. fused kernels) for Transformer models
* Support for FP8 on NVIDIA Hopper, Ada, and Blackwell GPUs
* Support for optimizations across all precisions (FP16, BF16) on NVIDIA Ampere GPU architecture generations and later

Examples
========

PyTorch
^^^^^^^

.. code-block:: python

  import torch
  import transformer_engine.pytorch as te
  from transformer_engine.common import recipe

  # Set dimensions.
  in_features = 768
  out_features = 3072
  hidden_size = 2048

  # Initialize model and inputs.
  model = te.Linear(in_features, out_features, bias=True)
  inp = torch.randn(hidden_size, in_features, device="cuda")

  # Create an FP8 recipe. Note: All input args are optional.
  fp8_recipe = recipe.DelayedScaling(margin=0, fp8_format=recipe.Format.E4M3)

  # Enable autocasting for the forward pass
  with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
      out = model(inp)

  loss = out.sum()
  loss.backward()


JAX
^^^

Flax
~~~~

.. code-block:: python

  import flax
  import jax
  import jax.numpy as jnp
  import transformer_engine.jax as te
  import transformer_engine.jax.flax as te_flax
  from transformer_engine.common import recipe

  BATCH = 32
  SEQLEN = 128
  HIDDEN = 1024

  # Initialize RNG and inputs.
  rng = jax.random.PRNGKey(0)
  init_rng, data_rng = jax.random.split(rng)
  inp = jax.random.normal(data_rng, [BATCH, SEQLEN, HIDDEN], jnp.float32)

  # Create an FP8 recipe. Note: All input args are optional.
  fp8_recipe = recipe.DelayedScaling(margin=0, fp8_format=recipe.Format.HYBRID)

  # Enable autocasting for the forward pass
  with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
      model = te_flax.DenseGeneral(features=HIDDEN)

      def loss_fn(params, other_vars, inp):
        out = model.apply({'params':params, **other_vars}, inp)
        return jnp.mean(out)

      # Initialize models.
      variables = model.init(init_rng, inp)
      other_variables, params = flax.core.pop(variables, 'params')

      # Construct the forward and backward function
      fwd_bwd_fn = jax.value_and_grad(loss_fn, argnums=(0, 1))

      for _ in range(10):
        loss, (param_grads, other_grads) = fwd_bwd_fn(params, other_variables, inp)

.. overview-end-marker-do-not-remove

Installation
============
.. installation

Pre-requisites
^^^^^^^^^^^^^^^^^^^^
* Linux x86_64
* CUDA 12.1+ (CUDA 12.8+ for Blackwell)
* NVIDIA Driver supporting CUDA 12.1 or later
* cuDNN 9.3 or later

Docker
^^^^^^^^^^^^^^^^^^^^

The quickest way to get started with Transformer Engine is by using Docker images on
`NVIDIA GPU Cloud (NGC) Catalog <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch>`_.
For example to use the NGC PyTorch container interactively,

.. code-block:: bash

    docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:25.01-py3

Where 25.01 (corresponding to January 2025 release) is the container version.

pip
^^^^^^^^^^^^^^^^^^^^
To install the latest stable version of Transformer Engine,

.. code-block:: bash

    pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable

This will automatically detect if any supported deep learning frameworks are installed and build
Transformer Engine support for them. To explicitly specify frameworks, set the environment variable
NVTE_FRAMEWORK to a comma-separated list (e.g. NVTE_FRAMEWORK=jax,pytorch).

Alternatively, the package can be directly installed from
`Transformer Engine's PyPI <https://pypi.org/project/transformer-engine/>`_, e.g.

.. code-block:: bash

    pip install transformer_engine[pytorch]

To obtain the necessary Python bindings for Transformer Engine, the frameworks needed must be
explicitly specified as extra dependencies in a comma-separated list (e.g. [jax,pytorch]).
Transformer Engine ships wheels for the core library. Source distributions are shipped for the JAX
and PyTorch extensions.

From source
^^^^^^^^^^^
`See the installation guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/installation.html#installation-from-source>`_.

Compiling with FlashAttention-2
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Transformer Engine release v0.11.0 added support for FlashAttention-2 in PyTorch for improved performance.

It is a known issue that FlashAttention-2 compilation is resource-intensive and requires a large amount of RAM (see `bug <https://github.com/Dao-AILab/flash-attention/issues/358>`_), which may lead to out of memory errors during the installation of Transformer Engine. Please try setting **MAX_JOBS=1** in the environment to circumvent the issue.

Note that NGC PyTorch 23.08+ containers include FlashAttention-2.

Breaking Changes
================

v1.7: Padding mask definition for PyTorch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In an effort to unify the definition and usage of the attention mask across all three frameworks in Transformer Engine, the padding mask has changed from `True` meaning inclusion of the corresponding position in attention to exclusion of that position in our PyTorch implementation. Since v1.7, all attention mask types follow the same definition where `True` means masking out the corresponding position and `False` means including that position in attention calculation.

An example of this change is,

.. code-block:: bash

    # for a batch of 3 sequences where `a`s, `b`s and `c`s are the useful tokens
    # and `0`s are the padding tokens,
    [a, a, a, 0, 0,
     b, b, 0, 0, 0,
     c, c, c, c, 0]
    # the padding mask for this batch before v1.7 is,
    [ True,  True,  True, False, False,
      True,  True, False, False, False,
      True,  True,  True,  True, False]
    # and for v1.7 onwards it should be,
    [False, False, False,  True,  True,
     False, False,  True,  True,  True,
     False, False, False, False,  True]

FP8 Convergence
===============

FP8 has been tested extensively across different model architectures and configurations and we found **no significant difference** between FP8 and BF16 training loss curves. FP8 has also been validated for accuracy on downstream LLM tasks (e.g. LAMBADA and WikiText). Below are examples of models tested for convergence across different frameworks.

+------------+------------------+---------------------------------------------------------------------------------------------------------+
| Model      | Framework        | Source                                                                                                  |
+============+==================+=========================================================================================================+
| T5-770M    |  JAX/T5x         | https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/t5x#convergence-and-performance|
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| MPT-1.3B   |  Mosaic Composer | https://www.mosaicml.com/blog/coreweave-nvidia-h100-part-1                                              |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-5B     |  JAX/Paxml       | https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/pax#h100-results               |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-5B     |  NeMo Framework  | Available on request                                                                                    |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| LLama2-7B  |  Alibaba Pai     | https://mp.weixin.qq.com/s/NQT0uKXLbXyh5031zBdeBQ                                                       |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| T5-11B     |  JAX/T5x         | Available on request                                                                                    |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| MPT-13B    |  Mosaic Composer | https://www.databricks.com/blog/turbocharged-training-optimizing-databricks-mosaic-ai-stack-fp8         |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-22B    |  NeMo Framework  | Available on request                                                                                    |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| LLama2-70B |  Alibaba Pai     | https://mp.weixin.qq.com/s/NQT0uKXLbXyh5031zBdeBQ                                                       |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-175B   |  JAX/Paxml       | https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/pax#h100-results               |
+------------+------------------+---------------------------------------------------------------------------------------------------------+

Integrations
============

Transformer Engine has been integrated with popular LLM frameworks such as:

* `DeepSpeed <https://github.com/microsoft/DeepSpeed/pull/3731>`_
* `Hugging Face Accelerate <https://github.com/huggingface/accelerate/releases/tag/v0.17.0>`_
* `Lightning <https://github.com/Lightning-AI/lightning/issues/17172>`_
* `MosaicML Composer <https://github.com/mosaicml/composer/releases/tag/v0.13.1>`_
* `NVIDIA JAX Toolbox <https://github.com/NVIDIA/JAX-Toolbox>`_
* `NVIDIA Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_
* `NVIDIA NeMo Framework <https://github.com/NVIDIA/NeMo-Megatron-Launcher>`_
* `Amazon SageMaker Model Parallel Library <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features-v2-tensor-parallelism.html>`_
* `Levanter <https://github.com/stanford-crfm/levanter>`_
* `GPT-NeoX <https://github.com/EleutherAI/gpt-neox>`_
* `Hugging Face Nanotron <https://github.com/huggingface/nanotron>`_ - Coming soon!
* `Colossal-AI <https://github.com/hpcaitech/ColossalAI>`_ - Coming soon!
* `PeriFlow <https://github.com/friendliai/periflow-python-sdk>`_ - Coming soon!


Contributing
============

We welcome contributions to Transformer Engine! To contribute to Transformer Engine and make pull requests,
follow the guidelines outlined in the `<CONTRIBUTING.rst>`_ guide.

Papers
======

* `Attention original paper <https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf>`_
* `Megatron-LM tensor parallel <https://arxiv.org/pdf/1909.08053.pdf>`_
* `Megatron-LM sequence parallel <https://arxiv.org/pdf/2205.05198.pdf>`_
* `FP8 Formats for Deep Learning <https://arxiv.org/abs/2209.05433>`_

Videos
======

* `What's New in Transformer Engine and FP8 Training | GTC 2024 <https://www.nvidia.com/en-us/on-demand/session/gtc24-s62457/>`_
* `FP8 Training with Transformer Engine | GTC 2023 <https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51393>`_
* `FP8 for Deep Learning | GTC 2023 <https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s52166/>`_
* `Inside the Hopper Architecture <https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s42663/>`_

.. |License| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
   :target: https://opensource.org/licenses/Apache-2.0
