[MoE Refactor] Move select_experts from FusedMoEQuantMethod -> FusedMoE (#31996)

Signed-off-by: Bill Nell <bnell@redhat.com>

vllm/model_executor/layers/quantization/mxfp4.py

@@ -14,7 +14,6 @@ from vllm.model_executor.layers.fused_moe import (
     FusedMoE,
     FusedMoEConfig,
     FusedMoEMethodBase,
-    FusedMoERouter,
 )
 from vllm.model_executor.layers.fused_moe import modular_kernel as mk
 from vllm.model_executor.layers.fused_moe.config import (
@@ -890,22 +889,26 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
     def allow_inplace(self) -> bool:
         return True
 
+    @property
+    def is_monolithic(self) -> bool:
+        return (
+            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
+            or self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
+            or self.mxfp4_backend == Mxfp4Backend.TRITON
+        )
+
     def apply(
         self,
         layer: FusedMoE,
-        router: FusedMoERouter,
         x: torch.Tensor,
-        router_logits: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        assert not self.is_monolithic
         if layer.enable_eplb:
             raise NotImplementedError("EPLB is not supported for mxfp4")
 
         if self.mxfp4_backend == Mxfp4Backend.MARLIN:
-            topk_weights, topk_ids = router.select_experts(
-                hidden_states=x,
-                router_logits=router_logits,
-            )
-
             return fused_marlin_moe(
                 x,
                 layer.w13_weight,
@@ -914,7 +917,6 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 layer.w2_bias,
                 layer.w13_weight_scale,
                 layer.w2_weight_scale,
-                router_logits,
                 topk_weights,
                 topk_ids,
                 global_scale1=None,
@@ -942,6 +944,98 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             layer.eplb_state.logical_replica_count,
         ), "MXFP4 are not supported with this configuration."
 
+        assert (
+            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
+            or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16
+        )
+        from vllm.utils.flashinfer import flashinfer_cutlass_fused_moe
+
+        # Backend-specific preparation
+        if self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS:
+            from flashinfer import mxfp8_quantize
+
+            x_quant, x_scale = mxfp8_quantize(x, True, 32)
+
+            fake_input_scale = torch.ones(self.num_experts, device=x.device)
+            quant_scales = [
+                layer.w13_weight_scale.contiguous().view(torch.int32),
+                fake_input_scale,
+                layer.w2_weight_scale.contiguous().view(torch.int32),
+                fake_input_scale,
+            ]
+
+            fi_input = x_quant
+            extra_kwargs = dict(
+                use_mxfp8_act_scaling=True,
+                input_sf=x_scale,
+                fc1_expert_weights=layer.w13_weight.contiguous().view(torch.long),
+                fc2_expert_weights=layer.w2_weight.contiguous().view(torch.long),
+            )
+        elif self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16:
+            assert x.dtype == torch.bfloat16
+
+            quant_scales = [
+                layer.w13_weight_scale,
+                layer.w2_weight_scale,
+            ]
+
+            fi_input = x
+            extra_kwargs = dict(
+                use_w4_group_scaling=True,
+                fc1_expert_weights=layer.w13_weight,
+                fc2_expert_weights=layer.w2_weight,
+            )
+
+        output = torch.empty_like(x, dtype=torch.bfloat16)
+
+        flashinfer_cutlass_fused_moe(
+            input=fi_input,
+            token_selected_experts=topk_ids.to(torch.int).contiguous(),
+            token_final_scales=topk_weights,
+            output_dtype=torch.bfloat16,
+            output=output,
+            quant_scales=quant_scales,
+            fc1_expert_biases=layer.w13_bias,
+            fc2_expert_biases=layer.w2_bias,
+            swiglu_alpha=layer.gemm1_alpha,
+            swiglu_beta=layer.gemm1_beta,
+            swiglu_limit=layer.gemm1_clamp_limit,
+            tp_size=self.moe.tp_size,
+            tp_rank=self.moe.tp_rank,
+            ep_size=self.moe.ep_size,
+            ep_rank=self.moe.ep_rank,
+            tune_max_num_tokens=max(self.max_capture_size, 1),
+            **extra_kwargs,
+        )
+
+        return output
+
+    def apply_monolithic(
+        self,
+        layer: FusedMoE,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        assert self.is_monolithic
+
+        if layer.enable_eplb:
+            raise NotImplementedError("EPLB is not supported for mxfp4")
+
+        assert _can_support_mxfp4(
+            layer.use_grouped_topk,
+            layer.topk_group,
+            layer.num_expert_group,
+            layer.expert_map,
+            layer.custom_routing_function,
+            layer.e_score_correction_bias,
+            layer.apply_router_weight_on_input,
+            layer.scoring_func,
+            layer.activation,
+            layer.eplb_state.expert_load_view,
+            layer.eplb_state.logical_to_physical_map,
+            layer.eplb_state.logical_replica_count,
+        ), "MXFP4 are not supported with this configuration."
+
         if (
             self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
             or self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
@@ -988,75 +1082,6 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 tune_max_num_tokens=max(self.max_capture_size, 1),
             )[0]
             return trtllm_gen_output
-        elif (
-            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
-            or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16
-        ):
-            from vllm.utils.flashinfer import flashinfer_cutlass_fused_moe
-
-            topk_weights, topk_ids = router.select_experts(
-                hidden_states=x,
-                router_logits=router_logits,
-            )
-
-            # Backend-specific preparation
-            if self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS:
-                from flashinfer import mxfp8_quantize
-
-                x_quant, x_scale = mxfp8_quantize(x, True, 32)
-
-                fake_input_scale = torch.ones(self.num_experts, device=x.device)
-                quant_scales = [
-                    layer.w13_weight_scale.contiguous().view(torch.int32),
-                    fake_input_scale,
-                    layer.w2_weight_scale.contiguous().view(torch.int32),
-                    fake_input_scale,
-                ]
-
-                fi_input = x_quant
-                extra_kwargs = dict(
-                    use_mxfp8_act_scaling=True,
-                    input_sf=x_scale,
-                    fc1_expert_weights=layer.w13_weight.contiguous().view(torch.long),
-                    fc2_expert_weights=layer.w2_weight.contiguous().view(torch.long),
-                )
-            elif self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16:
-                assert x.dtype == torch.bfloat16
-
-                quant_scales = [
-                    layer.w13_weight_scale,
-                    layer.w2_weight_scale,
-                ]
-
-                fi_input = x
-                extra_kwargs = dict(
-                    use_w4_group_scaling=True,
-                    fc1_expert_weights=layer.w13_weight,
-                    fc2_expert_weights=layer.w2_weight,
-                )
-
-            output = torch.empty_like(x, dtype=torch.bfloat16)
-            _ = flashinfer_cutlass_fused_moe(
-                input=fi_input,
-                token_selected_experts=topk_ids.to(torch.int).contiguous(),
-                token_final_scales=topk_weights,
-                output_dtype=torch.bfloat16,
-                output=output,
-                quant_scales=quant_scales,
-                fc1_expert_biases=layer.w13_bias,
-                fc2_expert_biases=layer.w2_bias,
-                swiglu_alpha=layer.gemm1_alpha,
-                swiglu_beta=layer.gemm1_beta,
-                swiglu_limit=layer.gemm1_clamp_limit,
-                tp_size=self.moe.tp_size,
-                tp_rank=self.moe.tp_rank,
-                ep_size=self.moe.ep_size,
-                ep_rank=self.moe.ep_rank,
-                tune_max_num_tokens=max(self.max_capture_size, 1),
-                **extra_kwargs,
-            )
-
-            return output
         elif self.mxfp4_backend == Mxfp4Backend.TRITON:
             from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (  # noqa: E501
                 triton_kernel_moe_forward,
@@ -1119,10 +1144,13 @@ class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
             experts_start_id=ep_rank_start,
         )
 
-    def apply(
+    @property
+    def is_monolithic(self) -> bool:
+        return True
+
+    def apply_monolithic(
         self,
         layer: FusedMoE,
-        router: FusedMoERouter,
         x: torch.Tensor,
         router_logits: torch.Tensor,
     ) -> torch.Tensor: