[Bugfix] Update TrtLLM MoE routing methods

vllm/model_executor/layers/fused_moe/config.py

@@ -102,23 +102,26 @@ def _quant_flags_to_group_shape(
 class RoutingMethodType(IntEnum):
     # Default: Softmax -> TopK
     Default = (0,)
-    # Renormalize: TopK -> Softmax/Sigmoid
+    # Renormalize: TopK -> Softmax
     Renormalize = (1,)
     # DeepSeekV3: Sigmoid -> RoutingBiasAdd -> Top2 in group -> Top4 groups
     # -> Top8 experts from the Top4 groups
     DeepSeekV3 = (2,)
     # Llama4: Top1 -> Sigmoid
     Llama4 = (3,)
-    # RenormalizeNaive: Softmax/Sigmoid -> TopK -> Renormalize
+    # RenormalizeNaive: Softmax -> TopK -> Renormalize
     RenormalizeNaive = (4,)
     # TopK: TopK (no softmax)
     TopK = (5,)
     # SigmoidRenorm: Sigmoid -> TopK -> Renormalize (divide by sum of top-K)
     SigmoidRenorm = (6,)
     # MiniMax2: Sigmoid + Bias -> TopK -> ScaledSumNormalize
+    # (routeScale=1.0, epsilon=1e-20)
     MiniMax2 = (7,)
+    # Sigmoid: Sigmoid -> TopK (no renormalization)
+    Sigmoid = (8,)
     # Unspecified
-    Unspecified = (8,)
+    Unspecified = (9,)
     # other routing types (not passed to FlashInfer kernels)
     # Deepseek V4 -> sqrtsoftplus + Bias + Normalize
     DeepseekV4 = (100,)
@@ -132,6 +135,7 @@ def get_routing_method_type(
     renormalize: bool,
     num_expert_group: int | None,
     has_e_score_bias: bool,
+    routed_scaling_factor: float | None = 1.0,
 ) -> RoutingMethodType:
     if scoring_func == "sqrtsoftplus":
         # DeepSeek V4 uses sqrtsoftplus routing with optional routing bias
@@ -142,20 +146,21 @@ def get_routing_method_type(
             return RoutingMethodType.Unspecified
 
     if has_e_score_bias:
-        if (num_expert_group or 0) > 0 and scoring_func == "sigmoid":
-            return RoutingMethodType.DeepSeekV3
-        elif scoring_func == "sigmoid":
-            return RoutingMethodType.MiniMax2
+        if scoring_func == "sigmoid":
+            if not renormalize:
+                return RoutingMethodType.Unspecified
+            if (num_expert_group or 0) > 0:
+                return RoutingMethodType.DeepSeekV3
+            if routed_scaling_factor in (None, 1.0):
+                return RoutingMethodType.MiniMax2
+            return RoutingMethodType.Unspecified
         else:
             return RoutingMethodType.Unspecified
 
     if scoring_func == "sigmoid":
-        if top_k == 1:
-            return RoutingMethodType.Llama4
-        elif renormalize:
+        if renormalize:
             return RoutingMethodType.SigmoidRenorm
-        else:
-            return RoutingMethodType.Unspecified
+        return RoutingMethodType.Sigmoid
 
     if scoring_func == "softmax":
         if renormalize:

vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py

@@ -257,7 +257,7 @@ def _supports_router_logits_dtype(
         router_logits_dtype: torch.dtype | None,
         routing_method: RoutingMethodType,
     ) -> bool:
-        return router_logits_dtype != torch.float32
+        return router_logits_dtype in [torch.bfloat16, torch.float32]
 
     @staticmethod
     def _supports_routing_method(
@@ -279,6 +279,7 @@ def _supports_routing_method(
                 RoutingMethodType.Renormalize,
                 RoutingMethodType.RenormalizeNaive,
                 RoutingMethodType.SigmoidRenorm,
+                RoutingMethodType.Sigmoid,
                 RoutingMethodType.MiniMax2,
                 RoutingMethodType.Simulated,
             ]
@@ -290,6 +291,7 @@ def _supports_routing_method(
                 RoutingMethodType.Renormalize,
                 RoutingMethodType.RenormalizeNaive,
                 RoutingMethodType.SigmoidRenorm,
+                RoutingMethodType.Sigmoid,
                 RoutingMethodType.MiniMax2,
                 RoutingMethodType.Simulated,
             ]
@@ -402,11 +404,6 @@ def _apply_per_tensor(
         else:
             assert not apply_router_weight_on_input
 
-        # Currently FI requires bfloat16 routing bias.
-        # https://github.com/flashinfer-ai/flashinfer/issues/2909
-        if e_score_correction_bias is not None:
-            e_score_correction_bias = e_score_correction_bias.to(torch.bfloat16)
-
         out = flashinfer.fused_moe.trtllm_fp8_per_tensor_scale_moe(
             routing_logits=router_logits,
             routing_bias=e_score_correction_bias,

vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py

@@ -360,7 +360,7 @@ def _supports_router_logits_dtype(
         router_logits_dtype: torch.dtype | None,
         routing_method: RoutingMethodType,
     ) -> bool:
-        return router_logits_dtype != torch.float32
+        return router_logits_dtype in [torch.bfloat16, torch.float32]
 
     def apply(
         self,
@@ -393,11 +393,6 @@ def apply(
             and self.routing_method_type != RoutingMethodType.Llama4
         )
 
-        # Currently FI requires bfloat16 routing bias.
-        # https://github.com/flashinfer-ai/flashinfer/issues/2909
-        if e_score_correction_bias is not None:
-            e_score_correction_bias = e_score_correction_bias.to(torch.bfloat16)
-
         output1_scale_gate_scalar = self.quant_config.g1_alphas
 
         # Invoke kernel.

vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py

@@ -326,6 +326,7 @@ def routing_method_type(self) -> RoutingMethodType:
             renormalize=self.renormalize,
             num_expert_group=None,
             has_e_score_bias=True,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
     def _compute_routing(

vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py

@@ -283,6 +283,7 @@ def routing_method_type(self) -> RoutingMethodType:
             renormalize=self.renormalize,
             num_expert_group=self.num_expert_group,
             has_e_score_bias=self.e_score_correction_bias is not None,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
     def _compute_routing(

vllm/model_executor/layers/fused_moe/router/zero_expert_router.py

@@ -63,6 +63,7 @@ def routing_method_type(self) -> RoutingMethodType:
             renormalize=self.renormalize,
             num_expert_group=None,
             has_e_score_bias=True,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
     def _compute_routing(