Opt kimi_k2_thinking biased topk module (#13150)

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

@@ -600,6 +600,48 @@ def grouped_topk_cpu(
     )
 
 
+@torch.compile(dynamic=True, backend=get_compiler_backend(), disable=_is_npu)
+def kimi_k2_biased_topk_impl(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    correction_bias: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    routed_scaling_factor: Optional[float] = None,
+    num_token_non_padded: Optional[torch.Tensor] = None,
+    expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
+    apply_routed_scaling_factor_on_output: Optional[bool] = False,
+):
+    """
+    Optimized version for num_expert_group=1 case (e.g., Kimi K2 with 384 experts).
+    Simplifies the grouped topk logic by removing unnecessary group masking operations.
+    Note: This function assumes num_fused_shared_experts=0.
+    """
+    assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
+
+    scores = gating_output.sigmoid()
+    num_token = scores.shape[0]
+
+    # When num_expert_group=1, no need for group masking
+    # Directly compute scores with correction bias
+    tmp_scores = scores.view(num_token, -1) + correction_bias.unsqueeze(0)
+
+    # Directly select topk experts (no need to sort since num_fused_shared_experts=0)
+    _, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
+    topk_weights = scores.gather(1, topk_ids)
+
+    if renormalize:
+        topk_weights_sum = topk_weights.sum(dim=-1, keepdim=True)
+        topk_weights = topk_weights / topk_weights_sum
+        if apply_routed_scaling_factor_on_output:
+            topk_weights *= routed_scaling_factor
+
+    topk_weights, topk_ids = topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+    topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
+    _mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
+    return topk_weights, topk_ids
+
+
 @torch.compile(dynamic=True, backend=get_compiler_backend(), disable=_is_npu)
 def biased_grouped_topk_impl(
     hidden_states: torch.Tensor,
@@ -760,20 +802,35 @@ def biased_grouped_topk_gpu(
         )
         return topk_weights, topk_ids
     else:
-        return biased_grouped_topk_impl(
-            hidden_states,
-            gating_output,
-            correction_bias,
-            topk,
-            renormalize,
-            num_expert_group,
-            topk_group,
-            num_fused_shared_experts=num_fused_shared_experts,
-            routed_scaling_factor=routed_scaling_factor,
-            num_token_non_padded=num_token_non_padded,
-            expert_location_dispatch_info=expert_location_dispatch_info,
-            apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
-        )
+        # Use optimized path for Kimi K2 (384 experts with num_expert_group=1)
+        num_experts = gating_output.shape[1]
+        if num_experts == 384 and num_expert_group == 1:
+            return kimi_k2_biased_topk_impl(
+                hidden_states,
+                gating_output,
+                correction_bias,
+                topk,
+                renormalize,
+                routed_scaling_factor=routed_scaling_factor,
+                num_token_non_padded=num_token_non_padded,
+                expert_location_dispatch_info=expert_location_dispatch_info,
+                apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
+            )
+        else:
+            return biased_grouped_topk_impl(
+                hidden_states,
+                gating_output,
+                correction_bias,
+                topk,
+                renormalize,
+                num_expert_group,
+                topk_group,
+                num_fused_shared_experts=num_fused_shared_experts,
+                routed_scaling_factor=routed_scaling_factor,
+                num_token_non_padded=num_token_non_padded,
+                expert_location_dispatch_info=expert_location_dispatch_info,
+                apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
+            )
 
 
 def biased_grouped_topk_cpu(