[Model][gpt-oss] Support DP+EP for GPT-OSS with FlashInfer trtllm-gen MoE (#23819)

Signed-off-by: Po-Han Huang <pohanh@nvidia.com>

vllm/model_executor/layers/fused_moe/config.py

@@ -190,12 +190,6 @@ class FusedMoEParallelConfig:
         return (self.use_all2all_kernels
                 and envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency")
 
-    @property
-    def use_flashinfer_cutlass_kernels(self):
-        return (envs.VLLM_USE_FLASHINFER_MOE_FP4
-                and has_flashinfer_cutlass_fused_moe()
-                and envs.VLLM_FLASHINFER_MOE_BACKEND == "throughput")
-
     @staticmethod
     def make(tp_size_: int, dp_size_: int,
              vllm_parallel_config: ParallelConfig) -> "FusedMoEParallelConfig":
@@ -404,7 +398,14 @@ class FusedMoEConfig:
 
     @property
     def use_flashinfer_cutlass_kernels(self):
-        return self.moe_parallel_config.use_flashinfer_cutlass_kernels
+        """
+        Whether to use FlashInfer cutlass kernels for NVFP4 MoE.
+        """
+        return (self.quant_config is not None
+                and self.quant_config.quant_dtype == "nvfp4"
+                and envs.VLLM_USE_FLASHINFER_MOE_FP4
+                and has_flashinfer_cutlass_fused_moe()
+                and envs.VLLM_FLASHINFER_MOE_BACKEND == "throughput")
 
     @staticmethod
     def make(

vllm/model_executor/layers/fused_moe/layer.py

@@ -920,7 +920,7 @@ class FusedMoE(CustomOp):
         self.batched_router_logits: Optional[torch.Tensor] = None
         if (self.moe_parallel_config.use_pplx_kernels
                 or self.moe_parallel_config.use_deepep_ll_kernels
-                or self.moe_parallel_config.use_flashinfer_cutlass_kernels):
+                or self.moe_config.use_flashinfer_cutlass_kernels):
             self.batched_hidden_states = torch.zeros(
                 (moe.max_num_tokens, self.hidden_size),
                 dtype=moe.in_dtype,
@@ -974,7 +974,7 @@ class FusedMoE(CustomOp):
 
     @property
     def use_flashinfer_cutlass_kernels(self):
-        return self.moe_parallel_config.use_flashinfer_cutlass_kernels
+        return self.moe_config.use_flashinfer_cutlass_kernels
 
     def update_expert_map(self):
         # ep_size and ep_rank should already be updated
@@ -1665,7 +1665,7 @@ class FusedMoE(CustomOp):
         # only when data parallelism (DP) is enabled.
         use_flashinfer_cutlass_kernels = (
             self.dp_size > 1
-            and self.moe_parallel_config.use_flashinfer_cutlass_kernels)
+            and self.moe_config.use_flashinfer_cutlass_kernels)
         if (self.moe_parallel_config.use_pplx_kernels
                 or self.moe_parallel_config.use_deepep_ll_kernels
                 or use_flashinfer_cutlass_kernels):
@@ -1674,7 +1674,7 @@ class FusedMoE(CustomOp):
         do_naive_dispatch_combine: bool = (
             self.dp_size > 1
             and not self.moe_parallel_config.use_deepep_ht_kernels
-            and not self.moe_parallel_config.use_flashinfer_cutlass_kernels)
+            and not self.moe_config.use_flashinfer_cutlass_kernels)
         if do_naive_dispatch_combine:
             hidden_states, router_logits = get_ep_group().dispatch(
                 hidden_states, router_logits)

vllm/model_executor/layers/quantization/mxfp4.py

@@ -623,8 +623,6 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
 
         if should_use_flashinfer_mxfp4():
             from flashinfer import mxfp8_quantize, trtllm_fp4_block_scale_moe
-            assert not self.moe.use_ep, (
-                "EP is not supported for flashinfer mxfp4 moe backend yet.")
             if _should_use_flashinfer_mxfp4_bf16():
                 assert x.dtype == torch.bfloat16
                 x_quant = x
@@ -650,12 +648,12 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 None,  # output1_scale_scalar
                 None,  # output1_scale_gate_scalar
                 None,  # output2_scale_scalar
-                self.num_experts,
+                global_num_experts,
                 top_k,
                 None,  # n_group
                 None,  # topk_group
                 self.intermediate_size,  # padded to multiple of 256
-                0,  # local_expert_offset
+                layer.ep_rank * layer.local_num_experts,  # local_expert_offset
                 self.num_experts,  # local num experts
                 None,
                 self._get_tile_tokens_dim(x, top_k),