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[ROCM] Optmize redudent d2d copy of moe. #38597

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benenzhu wants to merge 4 commits into from
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[ROCM] Optmize redudent d2d copy of moe. #38597

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0ba8ca3
moe opt
benenzhu Mar 31, 2026
9dd60da
moe opt
benenzhu Apr 1, 2026
b7a96e0
aiter moe
benenzhu Apr 1, 2026
53625db
Merge branch 'main' into amd/opt_moe
benenzhu Apr 2, 2026
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10 changes: 10 additions & 0 deletions vllm/model_executor/layers/fused_moe/modular_kernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
import torch

import vllm.envs as envs
from vllm._aiter_ops import rocm_aiter_ops
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe.activation import (
MoEActivation,
Expand Down Expand Up @@ -1394,6 +1395,15 @@ def apply(
expert_tokens_meta=expert_tokens_meta,
)

# Aiter don't use temp buffer, so can skip copy if output is contiguous
if (
rocm_aiter_ops.is_fused_moe_enabled()
and not self.inplace
and fused_out.shape == output.shape
and fused_out.is_contiguous()
):
output = fused_out
Comment on lines +1399 to +1405

@gemini-code-assist gemini-code-assist Bot Mar 31, 2026

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critical

Reassigning output = fused_out is highly dangerous because fused_out is a view into the transient workspace memory (allocated via current_workspace_manager). Returning this tensor means the MoE layer's output can be silently overwritten by any subsequent operation that requests workspace buffers, leading to data corruption or non-deterministic results. The copy from the workspace buffer to the persistent output tensor (allocated at line 1350) is mandatory to ensure the result persists correctly throughout the model's execution.

@bnellnm bnellnm Mar 31, 2026

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Reusing fused_out for output is dangerous beyond the fact that it might point to the temporary buffers. Doing this basically forces all the finalize methods to operate inplace which may or may not be supported.

@benenzhu benenzhu Apr 1, 2026

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Added platform.is_rocm(), only effect in rocm's finalize

@Rohan138 Rohan138 Apr 1, 2026 •
edited
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I think this check should be rocm_aiter_ops.is_fused_moe_enabled(). For other MOE backends, ROCm devices that don't support AITER, AITER fused_moe explicitly disabled, etc. we still want to keep the default behavior.

@benenzhu benenzhu Apr 1, 2026

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Yeah thanks. Should work only when AITER enabled.


return self._finalize(
output,
fused_out,
Expand Down
2 changes: 1 addition & 1 deletion vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
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Expand Up @@ -437,4 +437,4 @@ def apply(
num_local_tokens=num_local_tokens,
output_dtype=output.dtype,
)
output.copy_(result)
output.data = result

@gemini-code-assist gemini-code-assist Bot Mar 31, 2026

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high

Using output.data = result is unsafe and discouraged. This shallow pointer swap is incompatible with CUDA graphs, as the graph capture records the memory address of the tensor's storage. If result is a new allocation (which it appears to be, as rocm_aiter_fused_experts returns a new tensor), its memory address will change in subsequent iterations, invalidating the captured graph. Additionally, it detaches the tensor from the pre-allocated buffer provided by the modular kernel's workspace management. To safely avoid a copy, the underlying AITER kernel should be modified to accept the destination buffer as an argument and write into it directly.

@benenzhu benenzhu Apr 1, 2026

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Aiter will use torch.empty to create a new tensor, and it's competible with cuda graph I think.

3 changes: 2 additions & 1 deletion vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py
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Expand Up @@ -69,7 +69,8 @@ def apply(
f"But got output={output.size()}, "
f"used_expert_output={fused_expert_output.size()}"
)
output.copy_(fused_expert_output, non_blocking=True)
if output.data_ptr() != fused_expert_output.data_ptr():
output.copy_(fused_expert_output, non_blocking=True)
return output


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