optimize update verify #10277
optimize update verify #10277
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Summary of Changes
Hello @yizhang2077, 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!
This pull request focuses on enhancing the inference performance of the Qwen3 model. It achieves this by restructuring how certain attention-related tensors are processed, leading to a measurable increase in token throughput. The changes are designed to optimize the underlying computations, particularly for CUDA graph execution paths.
Highlights
- Performance Optimization: Improved token processing speed from 148.46 token/s to 159.06 token/s by optimizing decode and target verification in the Qwen3 model.
- Code Refactoring: Refactored the fused_qkvzba_split_reshape_cat_kernel and fused_qkvzba_split_reshape_cat functions to handle QKVZ and BA components separately, enhancing modularity and potential for further optimization.
- CUDA Graph Integration: Updated the forward method to leverage the newly optimized fused operation specifically when executing within a CUDA graph, ensuring performance benefits are applied in relevant scenarios.
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Code Review
This pull request optimizes the fused_qkvzba_split_reshape_cat operation by splitting the input tensor and enabling the fused Triton kernel for more cases, including TARGET_VERIFY mode. This is a good optimization that, according to the PR description, improves performance. The changes are well-contained and the logic is sound. I have one minor stylistic suggestion.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Stefan He <hebiaobuaa@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
2 participants
Motivation
optimize decode and target verify.
MTP 3/1/4
Modifications
Accuracy Tests
Benchmarking and Profiling
Checklist