Add routed_scaling_factor to MoE grouped topk (#23123)

Signed-off-by: Xin Yang <xyangx@amazon.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1011,7 +1011,8 @@ def grouped_topk(
     if renormalize:
         topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
 
-    topk_weights = topk_weights * routed_scaling_factor
+    if routed_scaling_factor != 1.0:
+        topk_weights = topk_weights * routed_scaling_factor
     return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
 
 
@@ -1790,8 +1791,8 @@ def fused_moe(
         Defaults to False.
     - global_num_experts (int): The total number of experts in the global
         expert space.
-    - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices 
-        from the global expert space to the local expert space of the expert 
+    - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices
+        from the global expert space to the local expert space of the expert
         parallel shard.
     - w1_scale (Optional[torch.Tensor]): Optional scale to be used for
         w1.