[Bugfix] Revert "Zero-init MLA attention output buffers to prevent NaN from CUDA graph padding"#38359
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tlrmchlsmth merged 3 commits intovllm-project:mainfrom Apr 1, 2026
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…N from CUDA graph padding (vllm-project#37442)" This reverts commit ef2c4f7. The zero-init workaround is unnecessary — the NaN was caused by a different issue (int64 expert IDs in the routing simulator). Reverting to restore the original torch.empty allocation which avoids the overhead of pre-allocated zero-init buffers. Signed-off-by: Elvir Crncevic <elvircrn@gmail.com> Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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This pull request removes pre-allocated output buffers and simplifies tensor allocation logic across the CUTLASS and FlashInfer MLA backends. In cutlass_mla.py, the _decode_out buffer is replaced with a direct new_empty allocation, while in flashinfer_mla.py, the manual buffer management and padding zeroing workarounds in forward_mqa are removed. I have no feedback to provide.
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This helped but did not address the core issue. Related: #38148 has a real NaN fix but is insufficient
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…N from CUDA graph padding" (vllm-project#38359) Signed-off-by: Elvir Crncevic <elvircrn@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
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Summary
torch.emptyallocation, removing the overhead of pre-allocated zero-init buffers and theout=workaround in FlashInfer MLA.Test plan
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