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[Fix]Fix one-sided MoE padding sentinel for local expert maps #42034

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[Fix]Fix one-sided MoE padding sentinel for local expert maps #42034

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634dca5
Fix one-sided MoE padding sentinel for local expert maps (#64)
Kevin-XiongC May 6, 2026
4365528
Delete tests/kernels/moe/test_flashinfer_nvlink_one_sided.py
Kevin-XiongC May 8, 2026
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17 changes: 16 additions & 1 deletion vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,12 +118,27 @@ def prepare(
payloads.append(topk_ids)
payloads.append(topk_weights)

invalid_token_expert_id = num_experts
if expert_map is not None and self.all2all_manager.world_size > 1:
assert num_experts % self.all2all_manager.world_size == 0, (
"FlashInfer one-sided all2all expects experts to be evenly "
"partitioned across EP ranks"
)
experts_per_rank = num_experts // self.all2all_manager.world_size
invalid_token_expert_id = (
(self.all2all_manager.rank + 1) % self.all2all_manager.world_size
) * experts_per_rank

assert self.all2all_manager.moe_alltoall is not None # type: ignore[attr-defined]
recv_payloads = self.all2all_manager.moe_alltoall.dispatch( # type: ignore[attr-defined]
token_selected_experts=topk_ids,
input_payloads=payloads,
runtime_max_tokens_per_rank=self.runtime_max_tokens_per_rank,
invalid_token_expert_id=-1, # Follow TRTLLM Pattern
# The one-sided kernel pads each source rank to
# runtime_max_tokens_per_rank. Use an expert that is invalid for
# the local rank when expert_map is present; this avoids negative
# IDs entering kernels that index expert_map before filtering.
invalid_token_expert_id=invalid_token_expert_id,
expert_id_payload_index=topk_ids_payload_index,
)
if a1q_scale is not None:
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