[MoE] Add RoutingMethodType.Simulated to TRT-LLM FP8/NVFP4 kernel allowlists (#38329)

Signed-off-by: Jaewon Lee <jaewon@meta.com>

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

@@ -256,13 +256,18 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
     ) -> bool:
         """
         The FlashInfer TRTLLM FP8 kernel expects bfloat16 router_logits by default.
-        Only DeepSeekV3 routing supports float32 router_logits (which is converted
-        internally in the kernel).
+        DeepSeekV3 routing supports float32 router_logits (converted internally).
+        Simulated routing generates synthetic decisions and is agnostic to dtype.
         """
         if router_logits_dtype == torch.float32:
-            # Only DeepSeekV3 routing handles float32 logits
+            # DeepSeekV3 routing handles float32 logits internally.
+            # Simulated routing generates synthetic decisions, so the
+            # kernel doesn't care about the actual logits dtype.
             # https://github.com/flashinfer-ai/flashinfer/issues/2469
-            return routing_method == RoutingMethodType.DeepSeekV3
+            return routing_method in (
+                RoutingMethodType.DeepSeekV3,
+                RoutingMethodType.Simulated,
+            )
         return True
 
     @staticmethod
@@ -288,12 +293,14 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
             # NOTE(rob): potentially allow others here. This is a conservative list.
             return routing_method in [
                 RoutingMethodType.DeepSeekV3,
+                RoutingMethodType.Simulated,
             ]
         elif (weight_key, activation_key) == (kFp8StaticTensorSym, kFp8StaticTensorSym):
             # NOTE(dbari): as above, potentially allow others here.
             return routing_method in [
                 RoutingMethodType.DeepSeekV3,
                 RoutingMethodType.Llama4,
+                RoutingMethodType.Simulated,
             ]
         else:
             raise ValueError("Unsupported quantization scheme.")

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

@@ -255,6 +255,7 @@ class TrtLlmNvFp4ExpertsMonolithic(
             RoutingMethodType.Renormalize,
             RoutingMethodType.RenormalizeNaive,
             RoutingMethodType.Llama4,
+            RoutingMethodType.Simulated,
         ]
 
     @staticmethod
@@ -264,13 +265,18 @@ class TrtLlmNvFp4ExpertsMonolithic(
     ) -> bool:
         """
         The FlashInfer TRTLLM NvFp4 kernel expects bfloat16 router_logits by default.
-        Only DeepSeekV3 routing supports float32 router_logits (which is converted
-        internally in the kernel).
+        DeepSeekV3 routing supports float32 router_logits (converted internally).
+        Simulated routing generates synthetic decisions and is agnostic to dtype.
         """
         if router_logits_dtype == torch.float32:
-            # Only DeepSeekV3 routing handles float32 logits
+            # DeepSeekV3 routing handles float32 logits internally.
+            # Simulated routing generates synthetic decisions, so the
+            # kernel doesn't care about the actual logits dtype.
             # https://github.com/flashinfer-ai/flashinfer/issues/2469
-            return routing_method == RoutingMethodType.DeepSeekV3
+            return routing_method in (
+                RoutingMethodType.DeepSeekV3,
+                RoutingMethodType.Simulated,
+            )
         return True
 
     def apply(