[NVIDIA] Fix trtllm fp4 moe backend when used in MTP (#9384)

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -783,13 +783,17 @@ class DeepEPMoE(EPMoE):
         return hidden_states
 
 
-def get_moe_impl_class():
+def get_moe_impl_class(quant_config: Optional[QuantizationConfig] = None):
     if get_moe_a2a_backend().is_deepep():
         return DeepEPMoE
 
     # NEW: Direct FP4 detection (bypasses EP requirements)
     # Check for FP4 quantization with TRTLLM flag, regardless of EP
     if get_moe_runner_backend().is_flashinfer_trtllm():
+        # FlashInferFP4MoE must be paired with ModelOptNvFp4FusedMoEMethod.
+        # If UnquantizedFusedMoEMethod is detected, fall back to FusedMoE instead.
+        if quant_config is None:
+            return FusedMoE
         try:
             # Check the quantization argument directly
             quantization = global_server_args_dict.get("quantization")

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -1008,6 +1008,8 @@ class FlashInferFP4MoE(FusedMoE):
             hidden_states: Input tensor
             topk_output: TopKOutput object with Bypassed format
         """
+        assert isinstance(self.quant_method, ModelOptNvFp4FusedMoEMethod)
+
         assert TopKOutputChecker.format_is_bypassed(topk_output)
 
         router_logits = topk_output.router_logits

python/sglang/srt/layers/moe/topk.py

@@ -198,6 +198,7 @@ class TopK(CustomOp):
         correction_bias: Optional[torch.Tensor] = None,
         routed_scaling_factor: Optional[float] = None,
         apply_routed_scaling_factor_on_output: Optional[bool] = False,
+        force_topk: bool = False,
     ):
         # NOTE: scoring_func is not used for now, but we keep it for future use
         # see https://github.com/sgl-project/sglang/pull/4505 for more details
@@ -220,6 +221,7 @@ class TopK(CustomOp):
         )
 
         self.use_triton_kernels = get_moe_runner_backend().is_triton_kernel()
+        self.force_topk = force_topk
 
     def forward_native(
         self,
@@ -254,7 +256,7 @@ class TopK(CustomOp):
                 sm_first=not self.topk_config.renormalize,
             )
             return TritonKernelTopKOutput(routing_data, gather_idx, scatter_idx)
-        elif (
+        elif not self.force_topk and (
             should_use_flashinfer_trtllm_moe()
             or get_moe_runner_backend().is_flashinfer_mxfp4()
         ):

python/sglang/srt/models/deepseek_v2.py

@@ -319,7 +319,7 @@ class DeepseekV2MoE(nn.Module):
             config=config, prefix=add_prefix("gate", prefix), is_nextn=is_nextn
         )
 
-        self.experts = get_moe_impl_class()(
+        self.experts = get_moe_impl_class(quant_config)(
             num_experts=config.n_routed_experts
             + self.num_fused_shared_experts
             + global_server_args_dict["ep_num_redundant_experts"],
@@ -343,6 +343,7 @@ class DeepseekV2MoE(nn.Module):
             correction_bias=self.gate.e_score_correction_bias,
             routed_scaling_factor=self.routed_scaling_factor,
             apply_routed_scaling_factor_on_output=self.experts.should_fuse_routed_scaling_factor_in_topk(),
+            force_topk=quant_config is None,
         )
 
         self.shared_experts_is_int8 = False