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[Bugfix] Fix grouped_topk pytorch impl when num_experts can't be grouped properly (#29439)

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Signed-off-by: Divakar Verma <divakar.verma@amd.com>
Co-authored-by: Gregory Shtrasberg <156009573+gshtras@users.noreply.github.com>
Co-authored-by: TJian <tunjian.tan@embeddedllm.com>
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Divakar Verma 2025-12-10 21:47:18 -06:00 committed by GitHub
parent b51255f369
commit d1e1fb4363
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1 changed files with 9 additions and 1 deletions
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10
vllm/model_executor/layers/fused_moe/layer.py
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@ -1556,6 +1556,14 @@ class FusedMoE(CustomOp):
f"EPLB is not supported for {self.quant_method.method_name}."
)
def valid_grouping() -> bool:
# Check if num_experts is greater than num_expert_group
# and is divisible by num_expert_group
num_experts = router_logits.shape[-1]
if num_experts <= self.num_expert_group:
return False
return num_experts % self.num_expert_group == 0
indices_type = self.quant_method.topk_indices_dtype
# Check if we should use a routing simulation strategy
@ -1570,7 +1578,7 @@ class FusedMoE(CustomOp):
)
# DeepSeekv2 uses grouped_top_k
elif self.use_grouped_topk:
elif self.use_grouped_topk and valid_grouping():
assert self.topk_group is not None
assert self.num_expert_group is not None
if rocm_aiter_ops.is_fused_moe_enabled():