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[Bugfix] CustomAR + TritonAttn[AMPERE] + FULL_CG - gpt-oss #30650

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[Bugfix] CustomAR + TritonAttn[AMPERE] + FULL_CG - gpt-oss #30650

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7 changes: 4 additions & 3 deletions vllm/distributed/device_communicators/custom_all_reduce.py
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Original file line number Diff line number Diff line change
Expand Up @@ -273,9 +273,10 @@ def custom_all_reduce(self, input: torch.Tensor) -> torch.Tensor | None:
if torch.cuda.is_current_stream_capturing():
return self.all_reduce(input, registered=True)
else:
# If warm up, mimic the allocation pattern since custom
# allreduce is out-of-place.
return torch.empty_like(input)
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@bbrowning bbrowning Dec 16, 2025

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This could maybe accomplish the same thing if it was torch.zeros_like(input)? Initial tests seem promising, but I'm not sure how to interpret what that means as far as the root issue here and what this actually fixes.

# During warmup, we need to run the all_reduce operation so
# that the allocation, copy, and collective behaviors
# are consistent between warmup, capture, and replay.
return self.all_reduce(input, registered=False)
else:
# Note: outside of cuda graph context, custom allreduce incurs a
# cost of cudaMemcpy, which should be small (<=1% of overall
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