linkedin / linkedin/Liger-Kernel
[AMD] Implement Flash Attention in Triton to enable transformers to run with Flash Attention on AMD GPUs.
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- Dominant language
- Python
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Description
🚀 The feature, motivation and pitch
The official implementation of flash attention is in CUDA, so in AMD GPUs, users cannot easily use flash attention on transformers to training LLM. With the supports, we can unlock many exciting use cases on AMD. The code is already there at https://triton-lang.org/main/getting-started/tutorials/06-fused-attention.html.
Another option is to use flex-attn from PyTorch team, which uses torch.compile to optimize on top of existing handwritten triton kernels
Alternatives
No response
Additional context
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Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the Triton fused-attention tutorial linked in the issue and compare it with the flex-attn alternative from PyTorch. Determine how the implementation should integrate with transformers on AMD GPUs; done means Flash Attention is usable for transformer LLM training on AMD.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100