[Performance] Support FP8 flashinfer TRTLLM MOE on Qwen3 and Qwen-3next (#27492)

Signed-off-by: jiahanc <173873397+jiahanc@users.noreply.github.com>

vllm/model_executor/models/qwen3_moe.py

@@ -43,6 +43,7 @@ from vllm.distributed import (
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -171,6 +172,7 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
             is_sequence_parallel=self.is_sequence_parallel,
+            routing_method_type=RoutingMethodType.Renormalize,
         )
 
         self.gate = ReplicatedLinear(

vllm/model_executor/models/qwen3_next.py

@@ -34,6 +34,7 @@ from vllm.model_executor.layers.fla.ops import (
     fused_recurrent_gated_delta_rule,
 )
 from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
 from vllm.model_executor.layers.layernorm import (
     GemmaRMSNorm as Qwen3NextRMSNorm,
 )
@@ -173,6 +174,7 @@ class Qwen3NextSparseMoeBlock(nn.Module):
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
             is_sequence_parallel=self.is_sequence_parallel,
+            routing_method_type=RoutingMethodType.Renormalize,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: