feat: update grouped_topk to support softmax and sigmoid (#3680)

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

@@ -75,7 +75,6 @@ def fused_topk(
     return topk_weights, topk_ids
 
 
-# This is used by the Deepseek V2/V3/R1 series models
 @torch.compile(dynamic=True, backend=get_compiler_backend())
 def grouped_topk(
     hidden_states: torch.Tensor,
@@ -84,10 +83,17 @@ def grouped_topk(
     renormalize: bool,
     num_expert_group: int = 0,
     topk_group: int = 0,
+    scoring_func: str = "softmax",
 ):
     assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
 
-    scores = torch.softmax(gating_output, dim=-1)
+    if scoring_func == "softmax":
+        scores = torch.softmax(gating_output, dim=-1)
+    elif scoring_func == "sigmoid":
+        scores = gating_output.sigmoid()
+    else:
+        raise ValueError(f"Scoring function '{scoring_func}' is not supported.")
+
     num_token = scores.shape[0]
     group_scores = (
         scores.view(num_token, num_expert_group, -1).max(dim=-1).values
@@ -111,6 +117,7 @@ def grouped_topk(
     return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
 
 
+# DeepSeek V2/V3/R1 uses biased_grouped_top
 @torch.compile(dynamic=True, backend=get_compiler_backend())
 def biased_grouped_topk(
     hidden_states: torch.Tensor,
@@ -165,7 +172,7 @@ def select_experts(
     correction_bias: Optional[torch.Tensor] = None,
     torch_native: bool = False,
 ):
-    # DeekSeekv2 uses grouped_top_k
+    # DeepSeek V2/V3/R1 uses biased_grouped_top
     if use_grouped_topk:
         assert topk_group is not None
         assert num_expert_group is not None