[Kernels] Isolate modular kernel code from FusedMoEMethodBase subclasses. (#27123)

vllm/distributed/device_communicators/base_device_communicator.py

@@ -266,14 +266,14 @@ class DeviceCommunicatorBase:
             module
             for module in model.modules()
             # TODO(bnell): Should use isinstance but can't.  Maybe search for
-            # presence of quant_method.init_prepare_finalize?
+            # presence of quant_method.maybe_init_modular_kernel?
             if (
                 module.__class__.__name__ == "FusedMoE"
                 or module.__class__.__name__ == "SharedFusedMoE"
             )
         ]
         for module in moe_modules:
-            module.quant_method.init_prepare_finalize(module)
+            module.maybe_init_modular_kernel()
 
     def dispatch(
         self,

vllm/model_executor/layers/fused_moe/layer.py

@@ -117,10 +117,8 @@ class FusedMoeWeightScaleSupported(Enum):
 class FusedMoEMethodBase(QuantizeMethodBase):
     def __init__(self, moe: FusedMoEConfig):
         super().__init__()
-        self.moe = moe
+        self.moe: FusedMoEConfig = moe
         self.moe_quant_config: FusedMoEQuantConfig | None = None
-        self.fused_experts: FusedMoEModularKernel | None = None
-        self.topk_indices_dtype = None
 
     @abstractmethod
     def create_weights(
@@ -245,9 +243,9 @@ class FusedMoEMethodBase(QuantizeMethodBase):
         else:
             return None
 
-    # Note: init_prepare_finalize should only be called by
-    # prepare_communication_buffer_for_model.
-    def init_prepare_finalize(self, layer: torch.nn.Module):
+    def maybe_init_modular_kernel(
+        self, layer: torch.nn.Module
+    ) -> FusedMoEModularKernel | None:
         assert self.moe is not None
 
         # We must get the quant config here so that the layer is
@@ -261,17 +259,14 @@ class FusedMoEMethodBase(QuantizeMethodBase):
             logger.debug(
                 "%s for %s(%s)", prepare_finalize.__class__.__name__, self, id(self)
             )
-            assert self.topk_indices_dtype is None
-            assert self.fused_experts is None, (
-                f"Attempt to override experts for {id(self)}!"
-            )
-            self.topk_indices_dtype = prepare_finalize.topk_indices_dtype()
             experts = self.select_gemm_impl(prepare_finalize, layer)
-            self.fused_experts = FusedMoEModularKernel(
+            return FusedMoEModularKernel(
                 prepare_finalize,
                 experts,
                 layer.shared_experts,
             )
+        else:
+            return None
 
     def select_gemm_impl(
         self,
@@ -292,8 +287,16 @@ class FusedMoEMethodBase(QuantizeMethodBase):
         raise NotImplementedError
 
     @property
-    def using_modular_kernel(self) -> bool:
-        return self.fused_experts is not None
+    def topk_indices_dtype(self) -> torch.dtype | None:
+        return None
+
+    @property
+    def supports_eplb(self) -> bool:
+        return False
+
+    @property
+    def allow_inplace(self) -> bool:
+        return False
 
     @abstractmethod
     def apply(
@@ -322,6 +325,138 @@ class FusedMoEMethodBase(QuantizeMethodBase):
         raise NotImplementedError
 
 
+@CustomOp.register("modular_fused_moe")
+class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
+    def __init__(
+        self,
+        old_quant_method: FusedMoEMethodBase,
+        fused_experts: FusedMoEModularKernel,
+    ):
+        super().__init__(old_quant_method.moe)
+        # Find better way to copy attributes?  Should we even copy attributes?
+        # self.__dict__.update(old_quant_method.__dict__)
+        self.moe_quant_config = old_quant_method.moe_quant_config
+        self.fused_experts = fused_experts
+        self.disable_expert_map = getattr(
+            old_quant_method,
+            "disable_expert_map",
+            not fused_experts.supports_expert_map(),
+        )
+        self.old_quant_method = old_quant_method
+        logger.debug("Swapping out %s", self.old_quant_method.__class__.__name__)
+
+    @property
+    def topk_indices_dtype(self) -> torch.dtype | None:
+        return self.fused_experts.prepare_finalize.topk_indices_dtype()
+
+    @property
+    def supports_eplb(self) -> bool:
+        return self.old_quant_method.supports_eplb
+
+    @property
+    def allow_inplace(self) -> bool:
+        return self.old_quant_method.allow_inplace
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        raise NotImplementedError
+
+    def get_fused_moe_quant_config(
+        self, layer: torch.nn.Module
+    ) -> FusedMoEQuantConfig | None:
+        return self.moe_quant_config
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: int | None = None,
+        num_expert_group: int | None = None,
+        global_num_experts: int = -1,
+        expert_map: torch.Tensor | None = None,
+        custom_routing_function: Callable | None = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: torch.Tensor | None = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: torch.Tensor | None = None,
+        logical_to_physical_map: torch.Tensor | None = None,
+        logical_replica_count: torch.Tensor | None = None,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        # Is getattr needed?
+        zero_expert_num = getattr(layer, "zero_expert_num", 0)
+        zero_expert_type = getattr(layer, "zero_expert_type", None)
+
+        if enable_eplb:
+            if self.supports_eplb:
+                assert expert_load_view is not None
+                assert logical_to_physical_map is not None
+                assert logical_replica_count is not None
+                assert isinstance(layer, FusedMoE)
+            else:
+                raise NotImplementedError(
+                    "EPLB is not supported for "
+                    f"{self.old_quant_method.__class__.__name__}."
+                )
+
+        topk_weights, topk_ids, zero_expert_result = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+            indices_type=self.topk_indices_dtype,
+            enable_eplb=enable_eplb,
+            expert_map=expert_map,
+            expert_load_view=expert_load_view,
+            logical_to_physical_map=logical_to_physical_map,
+            logical_replica_count=logical_replica_count,
+            global_num_experts=global_num_experts,
+            zero_expert_num=zero_expert_num,
+            zero_expert_type=zero_expert_type,
+        )
+
+        result = self.fused_experts(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=self.allow_inplace,
+            activation=activation,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=None if self.disable_expert_map else expert_map,
+        )
+
+        if zero_expert_num != 0 and zero_expert_type is not None:
+            assert not isinstance(result, tuple), (
+                "Shared + zero experts are mutually exclusive not yet supported"
+            )
+            return result, zero_expert_result
+        else:
+            return result
+
+
 @CustomOp.register("unquantized_fused_moe")
 class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
     """MoE method without quantization."""
@@ -378,6 +513,14 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                 )
             self.flashinfer_cutlass_moe = None  # type: ignore
 
+    @property
+    def supports_eplb(self) -> bool:
+        return True
+
+    @property
+    def allow_inplace(self) -> bool:
+        return True
+
     def maybe_make_prepare_finalize(self) -> FusedMoEPrepareAndFinalize | None:
         if self.rocm_aiter_moe_enabled:
             return None
@@ -650,7 +793,6 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         )
 
         if self.rocm_aiter_moe_enabled:
-            assert self.fused_experts is None
             result = self.rocm_aiter_fused_experts(
                 hidden_states=x,
                 w1=layer.w13_weight,
@@ -671,21 +813,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                 activation=activation,
                 apply_router_weight_on_input=apply_router_weight_on_input,
             )
-        elif self.fused_experts is not None:
-            result = self.fused_experts(
-                hidden_states=x,
-                w1=layer.w13_weight,
-                w2=layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                inplace=True,
-                activation=activation,
-                apply_router_weight_on_input=apply_router_weight_on_input,
-                global_num_experts=global_num_experts,
-                expert_map=expert_map,
-            )
         else:
-            assert fused_experts is not None
             result = fused_experts(
                 hidden_states=x,
                 w1=layer.w13_weight,
@@ -1267,7 +1395,7 @@ class FusedMoE(CustomOp):
                 "Only softmax scoring function is supported for non-grouped topk."
             )
 
-        moe = FusedMoEConfig(
+        self.moe_config: FusedMoEConfig = FusedMoEConfig(
             num_experts=self.global_num_experts,
             experts_per_token=top_k,
             hidden_dim=hidden_size,
@@ -1279,24 +1407,26 @@ class FusedMoE(CustomOp):
             is_act_and_mul=is_act_and_mul,
             is_lora_enabled=vllm_config.lora_config is not None,
         )
-        self.moe_config: FusedMoEConfig = moe
+
         self.moe_quant_config: FusedMoEQuantConfig | None = None
         self.quant_config = quant_config
 
-        # Note: get_quant_method will look at the layer's local_num_experts
-        # for heuristic purposes, so it must be initialized first.
-        quant_method: QuantizeMethodBase | None = None
-        quant_method = (
-            UnquantizedFusedMoEMethod(moe)
-            if quant_config is None
-            else quant_config.get_quant_method(self, prefix)
-        )
+        def _get_quant_method() -> FusedMoEMethodBase:
+            """
+            Helper method to ensure self.quant_method is never None and
+            of the proper type.
+            """
+            quant_method = None
+            if self.quant_config is not None:
+                quant_method = self.quant_config.get_quant_method(self, prefix)
             if quant_method is None:
-            quant_method = UnquantizedFusedMoEMethod(moe)
-
-        assert quant_method is not None
+                quant_method = UnquantizedFusedMoEMethod(self.moe_config)
             assert isinstance(quant_method, FusedMoEMethodBase)
-        self.quant_method = quant_method
+            return quant_method
+
+        # Note: get_quant_method will look at the layer's local_num_experts
+        # for heuristic purposes, so it must be initialized first.
+        self.quant_method: FusedMoEMethodBase = _get_quant_method()
 
         if not self.moe_config.is_act_and_mul:
             # Avoid circular import
@@ -1305,7 +1435,7 @@ class FusedMoE(CustomOp):
             )
 
             if not isinstance(
-                quant_method, (UnquantizedFusedMoEMethod, ModelOptFp8MoEMethod)
+                self.quant_method, (UnquantizedFusedMoEMethod, ModelOptFp8MoEMethod)
             ):
                 raise NotImplementedError(
                     "is_act_and_mul=False is supported only for unquantized "
@@ -1316,10 +1446,7 @@ class FusedMoE(CustomOp):
                     "is_act_and_mul=False is supported only for CUDA for now"
                 )
 
-        if self.enable_eplb:
-            from vllm.model_executor.layers.quantization.fp8 import Fp8MoEMethod
-
-            if not isinstance(quant_method, (Fp8MoEMethod, UnquantizedFusedMoEMethod)):
+        if self.enable_eplb and not self.quant_method.supports_eplb:
             # TODO: Add support for additional quantization methods.
             # The implementation for other quantization methods does not
             # contain essential differences, but the current quant API
@@ -1328,6 +1455,7 @@ class FusedMoE(CustomOp):
             # If you plan to add support for more quantization methods,
             # please refer to the implementation in `Fp8MoEMethod`.
             raise NotImplementedError(
+                f"EPLB is not supported {self.quant_method.__class__.__name__}. "
                 "EPLB is only supported for FP8 quantization for now."
             )
 
@@ -1353,6 +1481,15 @@ class FusedMoE(CustomOp):
         self.batched_hidden_states: torch.Tensor | None = None
         self.batched_router_logits: torch.Tensor | None = None
 
+    # Note: maybe_init_modular_kernel should only be called by
+    # prepare_communication_buffer_for_model.
+    # This is called after all weight loading and post-processing, so it
+    # should be safe to swap out the quant_method.
+    def maybe_init_modular_kernel(self) -> None:
+        mk = self.quant_method.maybe_init_modular_kernel(self)
+        if mk is not None:
+            self.quant_method = FusedMoEModularMethod(self.quant_method, mk)
+
     @property
     def shared_experts(self) -> torch.nn.Module | None:
         return None
@@ -2167,7 +2304,7 @@ class FusedMoE(CustomOp):
         """
         assert self.quant_method is not None
         return (
-            self.quant_method.fused_experts is not None
+            isinstance(self.quant_method, FusedMoEModularMethod)
             and self.quant_method.fused_experts.output_is_reduced()
         )
 
@@ -2403,7 +2540,7 @@ class FusedMoE(CustomOp):
         self.ensure_dp_chunking_init()
 
         has_separate_shared_experts = (
-            not isinstance(self.quant_method.fused_experts, FusedMoEModularKernel)
+            not isinstance(self.quant_method, FusedMoEModularMethod)
             and self.shared_experts is not None
         )
 
@@ -2430,8 +2567,8 @@ class FusedMoE(CustomOp):
                 hidden_states, router_logits, has_separate_shared_experts
             )
 
-        do_naive_dispatch_combine: bool = (
-            self.dp_size > 1 and not self.quant_method.using_modular_kernel
+        do_naive_dispatch_combine: bool = self.dp_size > 1 and not isinstance(
+            self.quant_method, FusedMoEModularMethod
         )
 
         # If there are shared experts but we are not using a modular kernel, the

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -707,6 +707,12 @@ class FusedMoEModularKernel(torch.nn.Module):
             f"{fused_experts.activation_formats[0]}"
         )
 
+    def supports_expert_map(self) -> bool:
+        """
+        A flag indicating whether or not this class supports expert maps.
+        """
+        return self.fused_experts.supports_expert_map()
+
     def output_is_reduced(self) -> bool:
         """
         Indicates whether or not the output of fused MoE kernel

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -617,8 +617,6 @@ class AWQMoEMethod(FusedMoEMethodBase):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError("EPLB not supported for `AWQMoEMethod` yet.")
 

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -518,12 +518,11 @@ class BitsAndBytesMoEMethod(FusedMoEMethodBase):
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         from vllm.model_executor.layers.fused_moe import fused_experts
 
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `BitsAndBytesMoEMethod` yet."
             )
+
         topk_weights, topk_ids, _ = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -462,12 +462,7 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
             indices_type=self.topk_indices_dtype,
         )
 
-        #
-        # Note: the order here is important. self.fused_experts can override
-        # flashinfer cutlass, cutlass fp4 or fused_experts but not marlin.
-        #
         if self.use_marlin:
-            assert self.fused_experts is None
             return fused_marlin_moe(
                 x,
                 layer.w13_weight,
@@ -488,24 +483,6 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
                 workspace=layer.workspace,
             )
 
-        elif self.fused_experts is not None:
-            assert is_valid_flashinfer_cutlass_fused_moe(
-                x, layer.w13_weight, layer.w2_weight
-            ), "Flashinfer CUTLASS Fused MoE not applicable!"
-
-            return self.fused_experts(
-                hidden_states=x,
-                w1=layer.w13_weight,
-                w2=layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                inplace=False,  # TODO(shuw): fix later, now output is high prec
-                activation=activation,
-                global_num_experts=global_num_experts,
-                expert_map=expert_map,
-                apply_router_weight_on_input=apply_router_weight_on_input,
-            )
-
         # FlashInfer fused experts path
         elif self.allow_flashinfer:
             from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
@@ -1066,13 +1043,8 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
         per_act_token = self.input_quant.strategy == QuantizationStrategy.TOKEN
         per_channel_quant = self.weight_quant.strategy == QuantizationStrategy.CHANNEL
 
-        #
-        # Note: the order here is important. self.fused_experts can override
-        # cutlass fp8 or fused_experts but not marlin or rocm.
-        #
         if self.use_marlin:
             assert activation == "silu", f"{activation} not supported for Marlin MoE."
-            assert self.fused_experts is None
             return fused_marlin_moe(
                 x,
                 layer.w13_weight,
@@ -1098,7 +1070,6 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
 
             assert per_act_token == per_channel_quant
             assert self.moe_quant_config is not None
-            assert self.fused_experts is None
             return rocm_aiter_fused_experts(
                 hidden_states=x,
                 w1=layer.w13_weight,
@@ -1111,18 +1082,6 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                 quant_config=self.moe_quant_config,
             )
 
-        elif self.fused_experts is not None:
-            return self.fused_experts(
-                x,
-                layer.w13_weight,
-                layer.w2_weight,
-                topk_weights,
-                topk_ids,
-                activation=activation,
-                global_num_experts=global_num_experts,
-                expert_map=None if self.disable_expert_map else expert_map,
-            )
-
         # cutlass path
         elif self.use_cutlass:
             assert self.moe_quant_config is not None
@@ -1318,8 +1277,6 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `CompressedTensorsW8A8Int8MoEMethod` yet."
@@ -1636,8 +1593,6 @@ class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `CompressedTensorsWNA16MarlinMoEMethod` yet."
@@ -1901,8 +1856,6 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `CompressedTensorsWNA16MoEMethod` yet."

vllm/model_executor/layers/quantization/experts_int8.py

@@ -158,8 +158,6 @@ class ExpertsInt8MoEMethod(FusedMoEMethodBase):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `ExpertsInt8MoEMethod` yet."

vllm/model_executor/layers/quantization/fp8.py

@@ -703,9 +703,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
         self.quant_config = quant_config
         self.weight_block_size = self.quant_config.weight_block_size
         self.block_quant: bool = self.weight_block_size is not None
-
-        self.fused_experts: mk.FusedMoEModularKernel | None = None  # type: ignore
-
         self.fp8_backend = get_fp8_moe_backend(self.block_quant)
 
         self.use_marlin = self.fp8_backend == Fp8MoeBackend.MARLIN
@@ -1181,6 +1178,14 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             block_shape=self.weight_block_size,
         )
 
+    @property
+    def supports_eplb(self) -> bool:
+        return True
+
+    @property
+    def allow_inplace(self) -> bool:
+        return True
+
     def apply(
         self,
         layer: torch.nn.Module,
@@ -1210,10 +1215,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             assert logical_replica_count is not None
             assert isinstance(layer, FusedMoE)
 
-        if (
-            self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
-            and self.fused_experts is None
-        ):
+        if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
             assert activation == "silu", (
                 f"Expected 'silu' activation but got {activation}"
             )
@@ -1290,10 +1292,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             num_fused_shared_experts=layer.num_fused_shared_experts,
         )
 
-        #
-        # Note: the order of checks is important since self.fused_experts
-        # can override fused_experts or cutlass but not rocm or marlin.
-        #
         topk_weights, topk_ids, zero_expert_result = select_result
 
         if self.rocm_aiter_moe_enabled:
@@ -1301,7 +1299,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                 rocm_aiter_fused_experts,
             )
 
-            assert self.fused_experts is None
             result = rocm_aiter_fused_experts(
                 x,
                 layer.w13_weight,
@@ -1315,7 +1312,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             )
         elif self.use_marlin:
             assert activation == "silu", f"{activation} not supported for Marlin MoE."
-            assert self.fused_experts is None
             result = fused_marlin_moe(
                 x,
                 layer.w13_weight,
@@ -1333,19 +1329,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                 expert_map=expert_map,
                 workspace=layer.workspace,
             )
-        elif self.fused_experts:
-            result = self.fused_experts(
-                hidden_states=x,
-                w1=layer.w13_weight,
-                w2=layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                inplace=True,
-                activation=activation,
-                global_num_experts=global_num_experts,
-                apply_router_weight_on_input=apply_router_weight_on_input,
-                expert_map=expert_map,
-            )
         elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
             assert not self.block_quant
             assert not renormalize and custom_routing_function is not None

vllm/model_executor/layers/quantization/gguf.py

@@ -585,8 +585,6 @@ class GGUFMoEMethod(FusedMoEMethodBase):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError("EPLB not supported for `GGUFMoEMethod` yet.")
 

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -742,8 +742,6 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `GPTQMarlinMoEMethod` yet."

vllm/model_executor/layers/quantization/modelopt.py

@@ -18,9 +18,6 @@ from vllm.model_executor.layers.fused_moe.config import (
     fp8_w8a8_moe_quant_config,
     nvfp4_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
-    is_valid_flashinfer_cutlass_fused_moe,
-)
 from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE,
@@ -605,7 +602,6 @@ class ModelOptFp8MoEMethod(FusedMoEMethodBase):
             )
 
         if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
-            assert self.fused_experts is None
             assert activation == "silu", (
                 f"Expected 'silu' activation but got {activation}"
             )
@@ -638,24 +634,7 @@ class ModelOptFp8MoEMethod(FusedMoEMethodBase):
             indices_type=self.topk_indices_dtype,
         )
 
-        #
-        # Note: the order here is important. self.fused_experts can override
-        # cutlass or fused_experts.
-        #
-        if self.fused_experts is not None:
-            return self.fused_experts(
-                x,
-                layer.w13_weight,
-                layer.w2_weight,
-                topk_weights,
-                topk_ids,
-                inplace=False,
-                activation=activation,
-                global_num_experts=global_num_experts,
-                expert_map=expert_map,
-                apply_router_weight_on_input=apply_router_weight_on_input,
-            )
-        elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
+        if self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
             assert not renormalize
             assert activation == "silu", (
                 f"Expected 'silu' activation but got {activation}"
@@ -1647,8 +1626,6 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
 
             from vllm.model_executor.models.llama4 import Llama4MoE
 
-            assert self.fused_experts is None
-
             a1_gscale = layer.w13_input_scale_quant
             (hidden_states_fp4, hidden_states_scale_linear_fp4) = (
                 flashinfer.fp4_quantize(
@@ -1720,13 +1697,7 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
             indices_type=self.topk_indices_dtype,
         )
 
-        #
-        # Note: the order here is important. self.fused_experts can override
-        # flashinfer cutlass, cutlass fp4 or fused_experts but not marlin or
-        # trtllm.
-        #
         if self.use_marlin:
-            assert self.fused_experts is None
             return fused_marlin_moe(
                 x,
                 layer.w13_weight,
@@ -1747,23 +1718,24 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
                 workspace=layer.workspace,
             )
 
-        elif self.fused_experts is not None:
-            assert (
+        elif (
             self.allow_flashinfer
             and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS
+        ):
+            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
+                flashinfer_cutlass_moe_fp4,
             )
 
-            assert is_valid_flashinfer_cutlass_fused_moe(
-                x, layer.w13_weight, layer.w2_weight
-            ), "Flashinfer CUTLASS Fused MoE not applicable!"
+            assert self.moe_quant_config is not None
 
-            return self.fused_experts(
+            return flashinfer_cutlass_moe_fp4(
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
                 topk_weights=topk_weights,
                 topk_ids=topk_ids,
-                inplace=False,  # TODO(shuw): fix later, now output is high prec
+                quant_config=self.moe_quant_config,
+                inplace=False,
                 activation=activation,
                 global_num_experts=global_num_experts,
                 expert_map=expert_map,

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -226,7 +226,6 @@ class MoeWNA16Method(FusedMoEMethodBase):
         params_dtype: torch.dtype,
         **extra_weight_attrs,
     ):
-        self.moe = layer
         layer.quant_config = self.quant_config
         bit8_pack_factor = self.quant_config.bit8_pack_factor
         group_size = self.quant_config.group_size
@@ -381,7 +380,6 @@ class MoeWNA16Method(FusedMoEMethodBase):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
         if enable_eplb:
             raise NotImplementedError("EPLB not supported for `MoeWNA16Method` yet.")
 

vllm/model_executor/layers/quantization/mxfp4.py

@@ -197,8 +197,6 @@ class Mxfp4Config(QuantizationConfig):
 class Mxfp4MoEMethod(FusedMoEMethodBase):
     def __init__(self, moe: FusedMoEConfig):
         super().__init__(moe)
-        self.topk_indices_dtype = None
-        self.moe = moe
         self.mxfp4_backend = get_mxfp4_backend(moe.is_lora_enabled)
         self.max_capture_size = (
             get_current_vllm_config().compilation_config.max_cudagraph_capture_size
@@ -815,6 +813,18 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                     "EP batched experts format"
                 )
         else:
+            layer.w13_weight = (
+                self.w13_weight_triton_tensor
+                if layer.w13_weight is None
+                else layer.w13_weight
+            )
+            layer.w2_weight = (
+                self.w2_weight_triton_tensor
+                if layer.w2_weight is None
+                else layer.w2_weight
+            )
+            assert all([w is not None for w in [layer.w13_weight, layer.w2_weight]])
+
             assert self.moe_quant_config is not None
             if (
                 self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
@@ -838,71 +848,9 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                     f"Incompatible Mxfp4 backend ({self.mxfp4_backend}) for EP"
                 )
 
-    def _route_and_experts(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        router_logits: torch.Tensor,
-        top_k: int,
-        renormalize: bool,
-        use_grouped_topk: bool = False,
-        topk_group: int | None = None,
-        num_expert_group: int | None = None,
-        global_num_experts: int = -1,
-        expert_map: torch.Tensor | None = None,
-        custom_routing_function: Callable | None = None,
-        scoring_func: str = "softmax",
-        e_score_correction_bias: torch.Tensor | None = None,
-        apply_router_weight_on_input: bool = False,
-        activation: str = "silu",
-        enable_eplb: bool = False,
-        expert_load_view: torch.Tensor | None = None,
-        logical_to_physical_map: torch.Tensor | None = None,
-        logical_replica_count: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        assert isinstance(self.fused_experts, mk.FusedMoEModularKernel)
-
-        topk_weights, topk_ids, _ = FusedMoE.select_experts(
-            hidden_states=x,
-            router_logits=router_logits,
-            use_grouped_topk=use_grouped_topk,
-            top_k=top_k,
-            renormalize=renormalize,
-            topk_group=topk_group,
-            num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function,
-            scoring_func=scoring_func,
-            e_score_correction_bias=e_score_correction_bias,
-            indices_type=self.topk_indices_dtype,
-            enable_eplb=enable_eplb,
-            expert_map=expert_map,
-            expert_load_view=expert_load_view,
-            logical_to_physical_map=logical_to_physical_map,
-            logical_replica_count=logical_replica_count,
-        )
-
-        w13_weight = (
-            self.w13_weight_triton_tensor
-            if layer.w13_weight is None
-            else layer.w13_weight
-        )
-        w2_weight = (
-            self.w2_weight_triton_tensor if layer.w2_weight is None else layer.w2_weight
-        )
-        assert all([w is not None for w in [w13_weight, w2_weight]])
-
-        return self.fused_experts(
-            hidden_states=x,
-            w1=w13_weight,
-            w2=w2_weight,
-            topk_weights=topk_weights,
-            topk_ids=topk_ids,
-            inplace=True,
-            activation=activation,
-            global_num_experts=global_num_experts,
-            expert_map=expert_map,
-            apply_router_weight_on_input=apply_router_weight_on_input,
-        )
+    @property
+    def allow_inplace(self) -> bool:
+        return True
 
     def apply(
         self,
@@ -930,29 +878,6 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         if enable_eplb:
             raise NotImplementedError("EPLB is not supported for mxfp4")
 
-        if self.fused_experts is not None:
-            return self._route_and_experts(
-                layer,
-                x,
-                router_logits,
-                top_k,
-                renormalize,
-                use_grouped_topk,
-                topk_group,
-                num_expert_group,
-                global_num_experts,
-                expert_map,
-                custom_routing_function,
-                scoring_func,
-                e_score_correction_bias,
-                apply_router_weight_on_input,
-                activation,
-                enable_eplb,
-                expert_load_view,
-                logical_to_physical_map,
-                logical_replica_count,
-            )
-
         if self.mxfp4_backend == Mxfp4Backend.MARLIN:
             topk_weights, topk_ids, _ = FusedMoE.select_experts(
                 hidden_states=x,

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -310,7 +310,6 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
         # Property to determine if AITER is used
         if self.rocm_aiter_moe_enabled:
             from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa E501
-                rocm_aiter_fused_experts,
                 shuffle_weights,
             )
 
@@ -322,17 +321,11 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
             layer.w13_weight = torch.nn.Parameter(shuffled_w13, requires_grad=False)
             layer.w2_weight = torch.nn.Parameter(shuffled_w2, requires_grad=False)
 
-            self.rocm_aiter_fused_experts_func = rocm_aiter_fused_experts
         elif self.use_marlin:
             prepare_moe_fp8_layer_for_marlin(layer, False)
             # Activations not quantized for marlin.
             del layer.w13_input_scale
             del layer.w2_input_scale
-            self.fused_experts_func = None
-        else:
-            from vllm.model_executor.layers.fused_moe import fused_experts
-
-            self.fused_experts_func = fused_experts
 
     def get_fused_moe_quant_config(
         self, layer: torch.nn.Module
@@ -369,8 +362,6 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `QuarkW8A8Fp8MoEMethod` yet."
@@ -392,7 +383,11 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
         )
 
         if self.rocm_aiter_moe_enabled:
-            return self.rocm_aiter_fused_experts_func(
+            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+                rocm_aiter_fused_experts,
+            )
+
+            return rocm_aiter_fused_experts(
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
@@ -403,7 +398,7 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
                 quant_config=self.moe_quant_config,
                 expert_map=expert_map,
             )
-        if self.use_marlin:
+        elif self.use_marlin:
             assert activation == "silu", f"{activation} not supported for Marlin MoE."
             return fused_marlin_moe(
                 x,
@@ -421,10 +416,10 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
                 global_num_experts=global_num_experts,
                 expert_map=expert_map,
             )
+        else:
+            from vllm.model_executor.layers.fused_moe import fused_experts
 
-        assert self.fused_experts_func is not None
-
-        return self.fused_experts_func(
+            return fused_experts(
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
@@ -601,6 +596,10 @@ class QuarkOCP_MX_MoEMethod(QuarkMoEMethod):
             block_shape=None,
         )
 
+    @property
+    def allow_inplace(self) -> bool:
+        return True
+
     def apply(
         self,
         layer: torch.nn.Module,
@@ -624,8 +623,6 @@ class QuarkOCP_MX_MoEMethod(QuarkMoEMethod):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError(
                 "EPLB not supported for `QuarkOCP_MX_MoEMethod` yet."

vllm/model_executor/layers/quantization/rtn.py

@@ -377,8 +377,6 @@ class RTNMoEMethod(FusedMoEMethodBase):
         logical_to_physical_map: torch.Tensor | None = None,
         logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        assert self.fused_experts is None
-
         if enable_eplb:
             raise NotImplementedError("EPLB not supported for `RTNMoEMethod` yet.")
 

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -13,7 +13,7 @@ import vllm.envs as envs
 from vllm.distributed.parallel_state import get_dp_group
 from vllm.model_executor.layers.fused_moe.deep_gemm_moe import DeepGemmExperts
 from vllm.model_executor.layers.fused_moe.deep_gemm_utils import compute_aligned_M
-from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+from vllm.model_executor.layers.fused_moe.layer import FusedMoE, FusedMoEModularMethod
 from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEModularKernel
 from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
     TritonOrDeepGemmExperts,
@@ -160,8 +160,8 @@ def _fused_moe_grouped_gemm_may_use_deep_gemm(module: torch.nn.Module) -> bool:
     ):
         return False
 
-    if not isinstance(module.quant_method.fused_experts, FusedMoEModularKernel):
-        # fused_experts could invoke deep_gemm_moe_fp8
+    if not isinstance(module.quant_method, FusedMoEModularMethod):
+        # modular kernels could invoke deep_gemm_moe_fp8
         return True
 
     mk: FusedMoEModularKernel = module.quant_method.fused_experts