Fix RoutingMethodType logic

docker/Dockerfile

@@ -582,7 +582,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # This is ~1.1GB and only changes when FlashInfer version bumps
 # https://docs.flashinfer.ai/installation.html
 # From versions.json: .flashinfer.version
-ARG FLASHINFER_VERSION=0.6.2
+ARG FLASHINFER_VERSION=0.6.3
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system flashinfer-cubin==${FLASHINFER_VERSION} \
     && uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \

docker/Dockerfile.nightly_torch

@@ -217,13 +217,13 @@ RUN pip install setuptools==75.6.0 packaging==23.2 ninja==1.11.1.3 build==1.2.2.
 
 
 # build flashinfer for torch nightly from source around 10 mins
-# release version: v0.6.2
+# release version: v0.6.3
 # todo(elainewy): cache flashinfer build result for faster build
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
     echo "git clone flashinfer..." \
-    && git clone --depth 1 --branch v0.6.2 --recursive https://github.com/flashinfer-ai/flashinfer.git \
+    && git clone --depth 1 --branch v0.6.3 --recursive https://github.com/flashinfer-ai/flashinfer.git \
     && cd flashinfer \
     && git submodule update --init --recursive \
     && echo "finish git clone flashinfer..." \

docker/versions.json

@@ -68,7 +68,7 @@
       "default": "true"
     },
     "FLASHINFER_VERSION": {
-      "default": "0.6.2"
+      "default": "0.6.3"
     },
     "GDRCOPY_CUDA_VERSION": {
       "default": "12.8"

requirements/cuda.txt

@@ -10,4 +10,4 @@ torchaudio==2.9.1
 # These must be updated alongside torch
 torchvision==0.24.1 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 # FlashInfer should be updated together with the Dockerfile
-flashinfer-python==0.6.2
+flashinfer-python==0.6.3

vllm/model_executor/layers/fused_moe/config.py

@@ -124,6 +124,23 @@ class RoutingMethodType(IntEnum):
     Unspecified = 8.0
 
 
+def get_routing_method_type(
+    scoring_func: str, top_k: int, renormalize: bool
+) -> RoutingMethodType:
+    if scoring_func == "sigmoid":
+        if top_k == 1:
+            return RoutingMethodType.Llama4
+        else:
+            return RoutingMethodType.DeepSeekV3
+    elif scoring_func == "softmax":
+        if renormalize:
+            return RoutingMethodType.Renormalize
+        else:
+            return RoutingMethodType.Default
+    else:
+        return RoutingMethodType.Unspecified
+
+
 @dataclass
 class FusedMoEQuantDesc:
     """

vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py

@@ -61,6 +61,8 @@ def _supports_routing_method(
     routing_method: RoutingMethodType,
 ) -> bool:
     """Monolithic kernels need to express router support."""
+    # NOTE(dbari): TopK routing could also be enabled, but need to validate models
+    # NOTE(dbari): Default is not implemented and should not be enabled until it is
     if (weight_key, activation_key) == (kFp8Static128BlockSym, kFp8Dynamic128Sym):
         # NOTE(rob): potentially allow others here. This is a conservative list.
         return routing_method in [
@@ -72,10 +74,8 @@ def _supports_routing_method(
         # NOTE(dbari): as above, potentially allow others here.
         return routing_method in [
             RoutingMethodType.Llama4,
-            # NOTE(mgoin): Disabled to investigate accuracy issues.
-            # See https://github.com/vllm-project/vllm/issues/33532
-            # RoutingMethodType.Renormalize,
-            # RoutingMethodType.RenormalizeNaive,
+            RoutingMethodType.Renormalize,
+            RoutingMethodType.RenormalizeNaive,
         ]
     else:
         raise ValueError("Unsupported quantization scheme.")
@@ -302,6 +302,8 @@ def fi_trtllm_fp8_per_tensor_moe(
         per_act_token_quant=False,
     )
 
+    from flashinfer.fused_moe.core import ActivationType
+
     from vllm.utils.flashinfer import flashinfer_trtllm_fp8_per_tensor_scale_moe
 
     return flashinfer_trtllm_fp8_per_tensor_scale_moe(
@@ -323,6 +325,9 @@ def fi_trtllm_fp8_per_tensor_moe(
         routed_scaling_factor=routed_scaling_factor,
         use_routing_scales_on_input=use_routing_scales_on_input,
         routing_method_type=routing_method_type,
+        # TODO: Required for flashinfer==0.6.3, remove with update
+        # https://github.com/flashinfer-ai/flashinfer/pull/2508
+        activation_type=ActivationType.Swiglu,
     )
 
 

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

@@ -10,7 +10,10 @@
 from vllm.model_executor.layers.batch_invariant import (
     vllm_is_batch_invariant,
 )
-from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
+from vllm.model_executor.layers.fused_moe.config import (
+    RoutingMethodType,
+    get_routing_method_type,
+)
 from vllm.model_executor.layers.fused_moe.router.base_router import BaseRouter
 
 
@@ -158,10 +161,10 @@ def __init__(
 
     @property
     def routing_method_type(self) -> RoutingMethodType:
-        return (
-            RoutingMethodType.Renormalize
-            if not self.renormalize
-            else RoutingMethodType.RenormalizeNaive
+        return get_routing_method_type(
+            scoring_func=self.scoring_func,
+            top_k=self.top_k,
+            renormalize=self.renormalize,
         )
 
     def _compute_routing(

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

@@ -7,7 +7,10 @@
 import vllm._custom_ops as ops
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.distributed.eplb.eplb_state import EplbLayerState
-from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
+from vllm.model_executor.layers.fused_moe.config import (
+    RoutingMethodType,
+    get_routing_method_type,
+)
 from vllm.model_executor.layers.fused_moe.router.base_router import BaseRouter
 
 
@@ -135,10 +138,10 @@ def __init__(
 
     @property
     def routing_method_type(self) -> RoutingMethodType:
-        return (
-            RoutingMethodType.Renormalize
-            if not self.renormalize
-            else RoutingMethodType.RenormalizeNaive
+        return get_routing_method_type(
+            scoring_func=self.scoring_func,
+            top_k=self.top_k,
+            renormalize=self.renormalize,
         )
 
     def _compute_routing(

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

@@ -6,6 +6,7 @@
 
 import vllm.envs as envs
 from vllm.distributed.eplb.eplb_state import EplbLayerState
+from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
 from vllm.model_executor.layers.fused_moe.router.custom_routing_router import (
     CustomRoutingRouter,
 )
@@ -106,7 +107,7 @@ def create_fused_moe_router(
                 "num_expert_group and topk_group must be provided when "
                 "use_grouped_topk is True"
             )
-        return GroupedTopKRouter(
+        grouped_topk_router = GroupedTopKRouter(
             top_k=top_k,
             global_num_experts=global_num_experts,
             eplb_state=eplb_state,
@@ -120,6 +121,18 @@ def create_fused_moe_router(
             enable_eplb=enable_eplb,
             indices_type_getter=indices_type_getter,
         )
+        if (
+            grouped_topk_router.routing_method_type != RoutingMethodType.Unspecified
+            or num_expert_group > 1
+            or topk_group > 1
+        ):
+            return grouped_topk_router
+
+        # If routing_method for GroupedTopKRouter is Unspecified and there is only
+        # one group, fallback to standard top-k routing
+        use_grouped_topk = False
+        num_expert_group = None
+        topk_group = None
 
     if custom_routing_function is not None:
         return CustomRoutingRouter(