fix: flashinfer_cutlass_moe: Use max of global expert scales instead of local...

fix: flashinfer_cutlass_moe: Use max of global expert scales instead of local for input scale (#10296)

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -503,8 +503,14 @@ class FusedMoE(torch.nn.Module):
                 param.data[:, :dim1, :dim2].copy_(loaded_weight)
             return
 
+        # ModelOptNvFp4FusedMoEMethod uses max of global expert scaling factors for input scaling factor
+        load_global_experts = (
+            isinstance(self.quant_method, ModelOptNvFp4FusedMoEMethod)
+            and "input_scale" in weight_name
+        )
+
         global_expert_location_metadata = get_global_expert_location_metadata()
-        if global_expert_location_metadata is None:
+        if global_expert_location_metadata is None or load_global_experts:
             self._weight_loader_impl(
                 param=param,
                 loaded_weight=loaded_weight,

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

@@ -996,13 +996,13 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         )
 
         w13_input_scale = PerTensorScaleParameter(
-            data=torch.empty(layer.num_local_experts, 2, dtype=torch.float32),
+            data=torch.empty(layer.num_experts, 2, dtype=torch.float32),
             weight_loader=weight_loader,
         )
         layer.register_parameter("w13_input_scale", w13_input_scale)
 
         w2_input_scale = PerTensorScaleParameter(
-            data=torch.empty(layer.num_local_experts, dtype=torch.float32),
+            data=torch.empty(layer.num_experts, dtype=torch.float32),
             weight_loader=weight_loader,
         )
         layer.register_parameter("w2_input_scale", w2_input_scale)