[Performance][Fix] update nvfp4 code to support renorm routing

vllm/model_executor/layers/quantization/modelopt.py

@@ -15,6 +15,7 @@
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEQuantConfig,
+    RoutingMethodType,
     fp8_w8a8_moe_quant_config,
     nvfp4_moe_quant_config,
 )
@@ -1650,16 +1651,19 @@ def apply(
             use_llama4_routing = (
                 custom_routing_function is Llama4MoE.custom_routing_function
             )
-            routing_method_type = flashinfer.RoutingMethodType.DeepSeekV3
+            routing_method_type = layer.routing_method_type
             if use_llama4_routing:
-                routing_method_type = flashinfer.RoutingMethodType.Llama4
+                routing_method_type = RoutingMethodType.Llama4
+            router_logits = (
+                router_logits.to(torch.float32)
+                if routing_method_type == RoutingMethodType.DeepSeekV3
+                else router_logits
+            )
             routing_bias = e_score_correction_bias
             if routing_bias is not None:
                 routing_bias = routing_bias.to(torch.bfloat16)
             out = flashinfer.fused_moe.trtllm_fp4_block_scale_moe(
-                routing_logits=router_logits
-                if use_llama4_routing
-                else router_logits.to(torch.float32),
+                routing_logits=router_logits,
                 routing_bias=routing_bias,
                 hidden_states=hidden_states_fp4,
                 hidden_states_scale=hidden_states_scale_linear_fp4.view(
@@ -1683,8 +1687,8 @@ def apply(
                 output2_scale_scalar=layer.g2_alphas.data,
                 num_experts=global_num_experts,
                 top_k=top_k,
-                n_group=num_expert_group if num_expert_group is not None else 0,
-                topk_group=topk_group if topk_group is not None else 0,
+                n_group=num_expert_group,
+                topk_group=topk_group,
                 intermediate_size=layer.intermediate_size_per_partition,
                 local_expert_offset=layer.ep_rank * layer.local_num_experts,
                 local_num_experts=layer.local_num_experts,

vllm/model_executor/layers/quantization/utils/flashinfer_utils.py

@@ -291,5 +291,8 @@ def get_flashinfer_moe_backend() -> FlashinferMoeBackend:
 
 def is_flashinfer_supporting_global_sf(backend: FlashinferMoeBackend | None) -> bool:
     # TODO(shuw@nvidia): Update when new backends are added.
-    backends_supporting_global_sf = (FlashinferMoeBackend.CUTLASS,)
+    backends_supporting_global_sf = (
+        FlashinferMoeBackend.CUTLASS,
+        FlashinferMoeBackend.TENSORRT_LLM,
+    )
     return backend in backends_supporting_global_sf