fix:reorder topk experts to ensure shared expert replaces minimal score (#8125)

python/sglang/srt/layers/moe/topk.py

@@ -397,7 +397,9 @@ def grouped_topk_gpu(
         .reshape(num_token, -1)
     )  # [n, e]
     tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
-    topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
+    topk_weights, topk_ids = torch.topk(
+        tmp_scores, k=topk, dim=-1, sorted=num_fused_shared_experts > 0
+    )
     if num_fused_shared_experts:
         topk_ids[:, -1] = torch.randint(
             low=num_experts,
@@ -486,7 +488,9 @@ def biased_grouped_topk_impl(
     tmp_scores = scores_for_choice.masked_fill(
         ~score_mask.bool(), float("-inf")
     )  # [n, e]
-    _, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
+    _, topk_ids = torch.topk(
+        tmp_scores, k=topk, dim=-1, sorted=num_fused_shared_experts > 0
+    )
     topk_weights = scores.gather(1, topk_ids)
 
     if num_fused_shared_experts: