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[Bugfix] Fix precision corruption when shared_experts_stream=None #28942

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Merged
merged 2 commits into from
Nov 19, 2025
Merged

[Bugfix] Fix precision corruption when shared_experts_stream=None #28942

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6c13d63
[Bugfix] fix(fused_moe): Fix precision corruption when shared_experts…
Nov 18, 2025
2a16631
Merge branch 'main' into fix_shared_experts_stream
DarkLight1337 Nov 19, 2025
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11 changes: 7 additions & 4 deletions vllm/model_executor/layers/fused_moe/layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -371,8 +371,8 @@ def __init__(
logger.info_once("Disabling MoE shared_experts cuda stream")
self.shared_experts_stream = None
else:
# TODO(rob): enable shared expert overlap with non-cuda.
# aux_stream() returns None on non-cuda platforms.
# TODO(rob): enable shared expert overlap with non-cuda-alike.
# aux_stream() returns None on non-cuda-alike platforms.
self.shared_experts_stream = aux_stream()
if self.shared_experts_stream is not None:
logger.info_once("Enabled separate cuda stream for MoE shared_experts")
Expand Down Expand Up @@ -1865,6 +1865,11 @@ def forward_impl(
hidden_states_combined, router_logits = get_ep_group().dispatch(
hidden_states, router_logits, self.is_sequence_parallel
)
# Run shared experts before matrix multiply.
# because matrix multiply maybe modify the hidden_states.
if has_separate_shared_experts and not use_shared_experts_stream:
assert self.shared_experts is not None
shared_output = self.shared_experts(hidden_states)

# Matrix multiply.
final_hidden_states = self.quant_method.apply(
Expand Down Expand Up @@ -1908,8 +1913,6 @@ def forward_impl(
# conflict with the main stream
shared_output = self.shared_experts(hidden_states_clone)
current_stream().wait_stream(self.shared_experts_stream)
else:
shared_output = self.shared_experts(hidden_states)

final_hidden_states = (
shared_output,
Expand Down
3 changes: 1 addition & 2 deletions vllm/utils/torch_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -426,8 +426,7 @@ def aux_stream() -> torch.cuda.Stream | None:

from vllm.platforms import current_platform

# TODO: validate this works properly on ROCm platform.
if _aux_stream is None and current_platform.is_cuda():
if _aux_stream is None and current_platform.is_cuda_alike():
_aux_stream = torch.cuda.Stream()

return _aux_stream
Expand Down