[ROCm] Use unreg path for custom all-reduce during CUDA graph capture#19162
[ROCm] Use unreg path for custom all-reduce during CUDA graph capture#19162HaiShaw merged 1 commit intosgl-project:mainfrom
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zyzshishui
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Summary of ChangesHello @zyzshishui, 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 introduces a small but critical fix to the custom all-reduce mechanism, specifically targeting ROCm environments during CUDA graph capture. The change ensures proper interaction with Highlights
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Code Review
This pull request introduces a small but important fix for custom all-reduce on ROCm platforms during CUDA graph capture. By using the unregistered all-reduce path when tms_cudagraph is enabled, it aligns the behavior with the existing CUDA implementation, which is a good consistency improvement. The change is correct and well-motivated. I've added a minor suggestion to improve code conciseness.
| if self.tms_cudagraph: | ||
| return self.all_reduce_unreg(input) | ||
| return self.all_reduce_reg(input) |
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For better readability and conciseness, you can use a conditional expression (ternary operator) to simplify this logic.
return self.all_reduce_unreg(input) if self.tms_cudagraph else self.all_reduce_reg(input)
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…o xverse_moe * 'xverse_moe' of https://github.com/xiaobaicxy/sglang: (275 commits) fix: add missing blank line after docstring in serving_transcription.py (sgl-project#19206) Whisper model support & `/v1/audio/transcriptions` endpoint & benchmark (sgl-project#16983) fix: patch docker image fixes (sgl-project#19100) [PD-Disagg] Unify prefill info data transition flow, all with `PrefillServerInfo` (sgl-project#19195) [CI] Tiny enhance the dp attention load blance benchmark (sgl-project#19194) add new ci user (sgl-project#19133) [CI] fix the teardown output of disaggregation test (sgl-project#19193) [PD-Disagg] Support query dp rank from bootstrap server. (sgl-project#19168) [Kernel Slimming] Migrate AWQ marlin repack kernel to JIT (sgl-project#18949) [Diffusion] Match rotary_embedding module name style (sgl-project#19179) [Refactor] Split rotary_embedding.py into a modular package (sgl-project#19144) [NPU] bump sgl-kernel-npu to 2026.02.01.post2 (sgl-project#19178) Use single mma warp group for short q_len in FA to optimize decoding performance (sgl-project#18985) Reorganize topk logic to clean up code and expose logical experts (sgl-project#16945) [ROCm] Use unreg path for custom all-reduce during CUDA graph capture (sgl-project#19162) [diffusion] feat: detect Flux2 custom VAE path from component_paths (sgl-project#19170) [AMD] ENV flags tuning and cleanup (sgl-project#19176) Fix bench_one_batch_server by moving the print statements (sgl-project#19175) Update rocm7.2 Dockerfile to install amdsmi for QuickReduce Initialization (sgl-project#19091) Revert "Refactor graph input buffers (sgl-project#18991)" (sgl-project#19173) ...
2 participants
Motivation
Super tiny fix, needed to be compatible with torch_memory_saver, also aligns with cuda's behavior
Modifications
Accuracy Tests
Benchmarking and Profiling
Checklist
Review Process
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