Triton-fused DeepseekScalingRotaryEmbedding #19771
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Summary of Changes
Hello @ekuznetsov139, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
I've implemented a significant performance optimization for the DeepseekScalingRotaryEmbedding operation, particularly for DeepSeek-V2/V3 models. By leveraging Triton to fuse this operation, specifically for non-NeoX style rotary embeddings, I aim to reduce the overhead associated with multiple kernel launches, thereby improving the overall efficiency of attention mechanisms.
Highlights
- Performance Enhancement: I've implemented a Triton-fused kernel for
DeepseekScalingRotaryEmbeddingwhenis_neox_styleisFalse, targeting DeepSeek-V3 models. - Kernel Reduction: This change achieves a substantial reduction in the number of distinct kernel launches, from approximately 37 to 25, during the execution of
DeepseekV2MLAAttention.forward. - Triton Kernel Integration: I've introduced
deepseek_scaling_rotary_emb_kernel_gptj, a new Triton kernel, to perform the fused rotary embedding computation for query and key tensors. - Conditional Execution: The Triton-fused path is conditionally applied only when running on a CUDA device and for non-NeoX style rotary embeddings, ensuring compatibility and falling back to the original PyTorch implementation otherwise.
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Code Review
This PR introduces a Triton-fused kernel for DeepseekScalingRotaryEmbedding to improve performance. The changes involve adding a new Triton kernel and modifying the forward pass. Key suggestions include adding tests, improving kernel clarity, and addressing production considerations.
| def deepseek_scaling_rotary_emb_kernel_gptj(cos_sin, q, | ||
| stride1: int, | ||
| stride2: int, | ||
| stride_cs: int, | ||
| dim1: int, | ||
| dim2: int, | ||
| dim3: int, | ||
| BLOCK_SIZE: tl.constexpr): |
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Consider adding a docstring to the deepseek_scaling_rotary_emb_kernel_gptj function to explain its purpose, arguments, and any specific implementation details. This will improve readability and maintainability.
| dim2=q.shape[1], | ||
| dim3=q.shape[2]//2, | ||
| BLOCK_SIZE=BLOCK_SIZE, | ||
| num_warps=1 |
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The num_warps parameter is hardcoded to 1. Consider benchmarking different values to find the optimal setting for various hardware configurations. Add a comment explaining the choice if num_warps=1 is indeed optimal.
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Signed-off-by: Eugene Kuznetsov <eugene.kuznetsov@amd.com>
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can this just be done/generated by torch compile? |
This PR uses Triton to fuse DeepseekScalingRotaryEmbedding operation with is_neox_style=False (observed in DeepSeek-V3). It substantially reduces the number of distinct kernels in DeepSeek-V2/V3 profile (the number of kernel launches per execution of DeepseekV2MLAAttention.forward is reduced approximately from 37 to 25).