[NVIDIA] [3/N] Nvfp4 Masked Gemm: Add flashinfer grouped_gemm_nt_masked (#9199)

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -878,6 +878,13 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         """Access the global enable_flashinfer_cutlass_moe setting."""
         return get_moe_runner_backend().is_flashinfer_cutlass()
 
+    @property
+    def enable_flashinfer_cutedsl_moe(self) -> bool:
+        from sglang.srt.layers.moe import get_moe_runner_backend
+
+        """Access the global enable_flashinfer_cutedsl_moe setting."""
+        return get_moe_runner_backend().is_flashinfer_cutedsl()
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -1398,5 +1405,38 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             apply_router_weight_on_input=moe_runner_config.apply_router_weight_on_input,
         ).to(x.dtype)
         # Scale by routed_scaling_factor is fused into select_experts.
-
         return StandardCombineInput(hidden_states=output)
+
+    def apply_without_routing_weights(
+        self,
+        layer: FusedMoE,
+        x: torch.Tensor,
+        masked_m: torch.Tensor,
+        moe_runner_config: MoeRunnerConfig,
+    ) -> torch.Tensor:
+        assert (
+            moe_runner_config.activation == "silu"
+        ), "Only SiLU activation is supported."
+
+        assert self.enable_flashinfer_cutedsl_moe, "only support flashinfer cutedsl moe"
+        assert (
+            not moe_runner_config.apply_router_weight_on_input
+        ), "apply_router_weight_on_input is not supported for Flashinfer"
+
+        from sglang.srt.layers.moe.flashinfer_cutedsl_moe import (
+            flashinfer_cutedsl_moe_masked,
+        )
+
+        out = flashinfer_cutedsl_moe_masked(
+            hidden_states=x,
+            input_global_scale=layer.w13_input_scale_quant,
+            w1=layer.w13_weight,
+            w1_blockscale=layer.w13_blockscale_swizzled,
+            w1_alpha=layer.g1_alphas,
+            w2=layer.w2_weight,
+            a2_global_scale=layer.w2_input_scale_quant,
+            w2_blockscale=layer.w2_blockscale_swizzled,
+            w2_alpha=layer.g2_alphas,
+            masked_m=masked_m,
+        )
+        return out