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[ORTModule] ATen Efficient Attention and Triton Flash Attention #17959

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merged 4 commits into from
Oct 27, 2023

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@er3x3 er3x3 commented Oct 16, 2023

This PR is to support efficient attention and flash attention in ORTModule, including:

  • Use ATen to call efficient attention, which requires PyTorch 2.2.0 dev or newer. ORTMODULE_USE_EFFICIENT_ATTENTION=1 to enable.
  • Integrate Triton Flash attention, which requires triton==2.0.0.dev20221202. Need A100 or H100. ORTMODULE_USE_FLASH_ATTENTION=1 to enable.
  • A python transformer tool to match sub-graph by config and write transformer quickly.

Current transformers supports attention mask for both efficient attn and flash attn, and dropout for efficient attn only. To support more training scenarios (such as causal mask in GPT2), more transformers need to be added.

The feature is guarded by system environment variables, it won't effect any current behavior if not enabled. Since it requires specific PyTorch/Triton versions, related tests is not added for now.

@er3x3 er3x3 requested a review from askhade October 16, 2023 08:27
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lintrunner found more than 10 potential problems in the proposed changes. Check the Files changed tab for more details.

@er3x3 er3x3 merged commit b7408f7 into main Oct 27, 2023
87 of 90 checks passed
@er3x3 er3x3 deleted the weicwang/attn branch October 27, 2023 02:29
kleiti pushed a commit to kleiti/onnxruntime that referenced this pull request Mar 22, 2024
…osoft#17959)

This PR is to support efficient attention and flash attention in
ORTModule, including:
- Use ATen to call efficient attention, which requires PyTorch 2.2.0 dev
or newer. ORTMODULE_USE_EFFICIENT_ATTENTION=1 to enable.
- Integrate Triton Flash attention, which requires
triton==2.0.0.dev20221202. Need A100 or H100.
ORTMODULE_USE_FLASH_ATTENTION=1 to enable.
- A python transformer tool to match sub-graph by config and write
transformer quickly.

Current transformers supports attention mask for both efficient attn and
flash attn, and dropout for efficient attn only. To support more
training scenarios (such as causal mask in GPT2), more transformers need
to be added.

The feature is guarded by system environment variables, it won't effect
any current behavior if not enabled. Since it requires specific
PyTorch/Triton versions, related tests is not added for now.
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2 participants