fix(moe): support Llama4 NVFP4 router input weights on SM120 (#35504)

Co-authored-by: Po-Han Huang (NVIDIA) <53919306+nvpohanh@users.noreply.github.com>

python/sglang/srt/layers/moe/moe_runner/flashinfer_cutlass.py

@@ -160,6 +160,36 @@ def _maybe_apply_routed_scaling_factor(
     return output
 
 
+def _prescale_router_weight_on_input(
+    dispatch_output: StandardDispatchOutput | FlashinferDispatchOutput,
+    runner_config: MoeRunnerConfig,
+) -> StandardDispatchOutput | FlashinferDispatchOutput:
+    if not runner_config.apply_router_weight_on_input:
+        return dispatch_output
+
+    topk_output = dispatch_output.topk_output
+    topk_weights = topk_output.topk_weights
+
+    if dispatch_output.hidden_states_scale is not None:
+        raise NotImplementedError(
+            "apply_router_weight_on_input is not supported when activations are "
+            "quantized before dispatch (flashinfer_cutlass fp4 all-gather path)."
+        )
+
+    assert topk_weights.dim() == 2 and topk_weights.shape[-1] == 1, (
+        "apply_router_weight_on_input requires topk=1"
+    )
+
+    hidden_states = dispatch_output.hidden_states * topk_weights.to(
+        dispatch_output.hidden_states.dtype
+    )
+    unit_scales = torch.ones_like(topk_weights, dtype=torch.float32)
+    return dispatch_output._replace(
+        hidden_states=hidden_states,
+        topk_output=topk_output._replace(topk_weights=unit_scales),
+    )
+
+
 def _prepare_input(
     dispatch_output,
     quant_info: FlashInferCutlassMoeQuantInfo,
@@ -197,8 +227,9 @@ def _run_flashinfer_cutlass(
 ) -> torch.Tensor:
     flashinfer_cutlass_fused_moe, _ = _flashinfer_cutlass_fused_moe()
 
+    dispatch_output = _prescale_router_weight_on_input(dispatch_output, runner_config)
     topk_output = dispatch_output.topk_output
-    topk_weights = topk_output.topk_weights
+    topk_weights = topk_output.topk_weights.to(torch.float32)
     topk_ids = topk_output.topk_ids
     x, x_sf, output_dtype, output_col = _prepare_input(
         dispatch_output, quant_info, runner_config
@@ -267,9 +298,6 @@ def fused_experts_none_to_flashinfer_cutlass(
     assert isinstance(quant_info, FlashInferCutlassMoeQuantInfo), (
         f"Unexpected quant_info type for flashinfer_cutlass: {type(quant_info)}"
     )
-    assert not runner_config.apply_router_weight_on_input, (
-        "apply_router_weight_on_input is not supported for FlashInfer CUTLASS"
-    )
 
     output = _run_flashinfer_cutlass(
         dispatch_output=dispatch_output,
@@ -292,9 +320,6 @@ def fused_experts_flashinfer_to_flashinfer_cutlass(
     assert isinstance(quant_info, FlashInferCutlassMoeQuantInfo), (
         f"Unexpected quant_info type for flashinfer_cutlass: {type(quant_info)}"
     )
-    assert not runner_config.apply_router_weight_on_input, (
-        "apply_router_weight_on_input is not supported for FlashInfer CUTLASS"
-    )
 
     output = _run_flashinfer_cutlass(
         dispatch_output=dispatch_output,

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

@@ -3053,7 +3053,6 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         assert activation in _SUPPORTED_ACT_STRS or (
             activation == "situ" and moe_runner_backend.is_flashinfer_trtllm()
         ), f"{activation=} is unsupported by {moe_runner_backend}"
-        moe_runner_config = self.moe_runner_config
 
         if moe_runner_backend.is_flashinfer_megamoe():
             from sglang.srt.layers.moe.flashinfer_megamoe import (
@@ -3179,9 +3178,6 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
                 FlashInferCutlassMoeQuantInfo,
             )
 
-            assert not moe_runner_config.apply_router_weight_on_input, (
-                "apply_router_weight_on_input is not supported for Flashinfer"
-            )
             quant_info = FlashInferCutlassMoeQuantInfo(
                 quant_type="fp4",
                 w13_weight=layer.w13_weight,