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test: add cuda graph to comm test #1366
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Summary of Changes
Hello @yyihuang, 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 updated the communication test suite to include CUDA graph functionality for the trtllm_allreduce_fusion
operation. This enhancement aims to validate the correctness and performance of this critical fusion pattern when executed within a CUDA graph, which is essential for optimizing deep learning workloads. The changes involve restructuring the test flow to allow for graph capture, including a warmup phase, and then replaying the captured graph to verify results.
Highlights
- CUDA Graph Integration: I've modified the
_run_correctness_worker
function to incorporate CUDA graph capture and replay. This change allows us to test thetrtllm_allreduce_fusion
operation within a CUDA graph, ensuring its compatibility and performance benefits in such an environment. - Warmup Phase for CUDA Graph: To properly utilize CUDA graphs, I've introduced a 'warmup' phase before the graph capture. This ensures that all necessary CUDA kernels are loaded and initialized, preventing any first-run overhead from affecting the captured graph's performance.
- Test Loop Refactoring: The test logic has been refactored to move tensor initialization outside the main test loop. This is a prerequisite for effective CUDA graph capture, as graph-captured operations typically require static memory allocations and consistent input shapes.
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Code Review
This pull request adds CUDA graph testing to the trtllm_allreduce_fusion
communication test. The overall approach is sound, but there's significant code duplication between the warmup and graph capture sections. I've provided a suggestion to refactor this by extracting the duplicated logic into a helper function, which will improve the code's maintainability.
π Description
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