Revert "[MoE Refactor] Combine MoERunnerBase + DefaultMoERunner" (#40560)#40668
Revert "[MoE Refactor] Combine MoERunnerBase + DefaultMoERunner" (#40560)#40668vllm-agent wants to merge 1 commit intovllm-project:mainfrom
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…-project#40560)" This reverts commit 809d83c.
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Code Review
This pull request refactors the MoE runner architecture by introducing a factory pattern and splitting the monolithic MoERunner into an abstract base class, a common base implementation (MoERunnerBase), and a concrete DefaultMoERunner. Feedback on the MoERunnerBase implementation identifies several critical issues: the _fused_output_is_reduced attribute should be a property to avoid stale state when quantization methods are updated, the float16 scaling logic incorrectly ignores the scaling factor when shared experts are absent, and an incorrect assertion in _maybe_reduce_shared_expert_output causes crashes for models without shared experts.
| self._fused_output_is_reduced = ( | ||
| self.quant_method.moe_kernel is not None | ||
| and self.quant_method.moe_kernel.output_is_reduced() | ||
| ) |
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The _fused_output_is_reduced attribute is initialized in __init__ based on the state of quant_method at creation time. However, quant_method can be updated later via _replace_quant_method (e.g., when applying LoRA or swapping kernels), which would leave this attribute stale. This should be implemented as a property to ensure it always reflects the current quantization method's requirements, as it was in the implementation being reverted.
| if fused_output.dtype != torch.float16: | ||
| fused_output *= self.routed_scaling_factor | ||
| elif shared_output is not None: | ||
| shared_output *= 1.0 / self.routed_scaling_factor |
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The scaling logic for float16 is missing a critical check. If shared_output is None (meaning there are no shared experts), the routed_scaling_factor is currently ignored for float16 tensors. The scaling factor must be applied to fused_output whenever shared_output is absent, regardless of the data type, because the 'compensation' logic (dividing shared output) is not applicable. This logic was correctly handled in the refactored version being reverted: if fused_output.dtype != torch.float16 or shared_output is None:.
| if fused_output.dtype != torch.float16: | |
| fused_output *= self.routed_scaling_factor | |
| elif shared_output is not None: | |
| shared_output *= 1.0 / self.routed_scaling_factor | |
| if fused_output.dtype != torch.float16 or shared_output is None: | |
| fused_output *= self.routed_scaling_factor | |
| elif shared_output is not None: | |
| shared_output *= 1.0 / self.routed_scaling_factor |
| def _maybe_reduce_shared_expert_output( | ||
| self, | ||
| shared_output: torch.Tensor | None, | ||
| ) -> torch.Tensor | None: | ||
| """All-reduce shared expert output when the combine kernel already | ||
| reduced fused output. | ||
|
|
||
| This is the "early" all-reduce path. When the combine kernel produces | ||
| already-reduced fused output, shared output must be reduced separately | ||
| to match. | ||
| """ | ||
| if self._fused_output_is_reduced: | ||
| assert shared_output is not None | ||
| shared_output = tensor_model_parallel_all_reduce(shared_output) | ||
| return shared_output |
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This method contains an incorrect assertion that will cause a crash for MoE models without shared experts if the quantization kernel performs reduction. If shared_output is None, no reduction is needed for it even if the fused output was reduced. I am also including the fix to turn _fused_output_is_reduced into a property here to resolve the staleness issue identified in __init__.
@property
def _fused_output_is_reduced(self) -> bool:
return (
self.quant_method.moe_kernel is not None
and self.quant_method.moe_kernel.output_is_reduced()
)
def _maybe_reduce_shared_expert_output(
self,
shared_output: torch.Tensor | None,
) -> torch.Tensor | None:
"""All-reduce shared expert output when the combine kernel already
reduced fused output.
This is the "early" all-reduce path. When the combine kernel produces
already-reduced fused output, shared output must be reduced separately
to match.
"""
if shared_output is not None and self._fused_output_is_reduced:
shared_output = tensor_model_parallel_all_reduce(shared_output)
return shared_output1 participant
Revert of #40560
This reverts commit 809d83c.
Original PR: #40560
Reason: Linked to 1 new CI failure in nightly build #62566:
deepep_high_throughputbackend. This PR refactors MoE runner base classes used by all MoE models, potentially affecting the expert parallel computation path.Auto-generated by CI failure analyzer.