Fix nan in global scaling factor for large scale nvfp4 EP (#13162)

python/sglang/srt/eplb/expert_location.py

@@ -284,9 +284,17 @@ class ExpertLocationMetadata:
     # -------------------------------- usage ------------------------------------
 
     def logical_to_all_physical(
-        self, layer_id: int, logical_expert_id: int
+        self,
+        layer_id: int,
+        logical_expert_id: int,
+        require_global_experts: bool = False,
     ) -> List[int]:
         # Use CPU copy to avoid GPU→CPU sync on every call, which is expensive in update weights scenario
+        if require_global_experts:
+            num_physical_experts = self.logical_to_all_physical_map_cpu[layer_id].shape[
+                -1
+            ]
+            return list(torch.arange(0, num_physical_experts))
         return [
             physical_expert_id
             for physical_expert_id in self.logical_to_all_physical_map_cpu[
@@ -355,14 +363,10 @@ def _compute_logical_to_all_physical_map(
                 )
 
                 # Replace by the nearest physical expert
-                mapped_physical_experts = logical_to_all_physical_map[layer_id][
-                    logical_expert_id
-                ]
-                if (
-                    nearest_expert != -1
-                    and nearest_expert not in mapped_physical_experts
-                ):
-                    mapped_physical_experts[0] = nearest_expert
+                if nearest_expert != -1:
+                    logical_to_all_physical_map[layer_id][logical_expert_id] = [
+                        nearest_expert
+                    ]
 
     logical_to_all_physical_map = _pad_nested_array(
         logical_to_all_physical_map, pad_value=-1