Optimize topk sigmoid in minimax_m2 (#14047)

Co-authored-by: xuebi <xuebi@minimaxi.com>

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

@@ -29,7 +29,6 @@ from typing import (
 )
 
 import torch
-import torch.nn.functional as F
 
 from sglang.srt.custom_op import CustomOp
 from sglang.srt.distributed import get_tp_group
@@ -81,7 +80,7 @@ if _is_cuda:
         pass
 
 if _is_cuda or _is_hip:
-    from sgl_kernel import topk_softmax
+    from sgl_kernel import topk_sigmoid, topk_softmax
 if _use_aiter:
     try:
         from aiter import biased_grouped_topk as aiter_biased_grouped_topk
@@ -109,6 +108,7 @@ class TopKConfig:
     apply_routed_scaling_factor_on_output: bool = False
     fused_shared_experts_scaling_factor: Optional[float] = None
     output_format: Optional[TopKOutputFormat] = None
+    scoring_func: str = "softmax"
 
 
 # -------------------------------- TopKOutput ---------------------------------------
@@ -244,6 +244,7 @@ class TopK(CustomOp):
             apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
             fused_shared_experts_scaling_factor=fused_shared_experts_scaling_factor,
             output_format=output_format,
+            scoring_func=scoring_func,
         )
 
     def forward_native(
@@ -430,10 +431,19 @@ def fused_topk_torch_native(
     topk: int,
     renormalize: bool,
     correction_bias: torch.Tensor = None,
+    scoring_func: str = "softmax",
 ):
+    def scoring_func_impl(gating_output: torch.Tensor) -> torch.Tensor:
+        if scoring_func == "softmax":
+            return gating_output.softmax(dim=-1)
+        elif scoring_func == "sigmoid":
+            return gating_output.sigmoid()
+        else:
+            raise ValueError(f"Invalid scoring function: {scoring_func}")
+
     if correction_bias is not None:
         n_routed_experts = gating_output.shape[-1]
-        scores = gating_output.softmax(dim=-1)
+        scores = scoring_func_impl(gating_output)
         scores_for_choice = scores.view(
             -1, n_routed_experts
         ) + correction_bias.unsqueeze(0)
@@ -448,7 +458,7 @@ def fused_topk_torch_native(
             M, topk, dtype=torch.float32, device=hidden_states.device
         )
         topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
-        topk_weights = F.softmax(gating_output.float(), dim=-1)
+        topk_weights = scoring_func_impl(gating_output.float())
         topk_weights, topk_ids = torch.topk(topk_weights, topk, dim=-1)
 
     if renormalize:
@@ -464,6 +474,7 @@ def fused_topk_cpu(
     num_token_non_padded: Optional[torch.Tensor] = None,
     expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
     correction_bias: torch.Tensor = None,
+    scoring_func: str = "softmax",
 ):
     topk_weights, topk_ids = torch.ops.sgl_kernel.topk_softmax_cpu(
         hidden_states=hidden_states,
@@ -494,8 +505,10 @@ def fused_topk(
     gating_output: torch.Tensor,
     topk: int,
     renormalize: bool,
+    correction_bias: Optional[torch.Tensor] = None,
     num_token_non_padded: Optional[torch.Tensor] = None,
     expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
+    scoring_func: str = "softmax",
 ):
     assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
 
@@ -506,12 +519,23 @@ def fused_topk(
     )
     topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
 
+    if scoring_func == "softmax":
         topk_softmax(
             topk_weights,
             topk_ids,
             gating_output,
             renormalize,
         )
+    elif scoring_func == "sigmoid":
+        topk_sigmoid(
+            topk_weights,
+            topk_ids,
+            gating_output,
+            renormalize,
+            correction_bias,
+        )
+    else:
+        raise ValueError(f"Invalid scoring function: {scoring_func}")
 
     topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
     _mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
@@ -916,6 +940,7 @@ def select_experts(
     fused_shared_experts_scaling_factor = (
         topk_config.fused_shared_experts_scaling_factor
     )
+    scoring_func = topk_config.scoring_func
 
     router_logits, correction_bias = (
         expert_location_dispatch.transform_select_experts_inputs(
@@ -972,6 +997,7 @@ def select_experts(
             topk=num_routed_topk if _use_aiter else top_k,
             renormalize=renormalize,
             correction_bias=correction_bias,
+            scoring_func=scoring_func,
         )
     elif custom_routing_function is None:
         assert not apply_routed_scaling_factor_on_output, "Not implemented"
@@ -981,8 +1007,10 @@ def select_experts(
             gating_output=router_logits,
             topk=num_routed_topk if _use_aiter else top_k,
             renormalize=renormalize,
+            correction_bias=correction_bias,
             num_token_non_padded=num_token_non_padded,
             expert_location_dispatch_info=expert_location_dispatch_info,
+            scoring_func=scoring_func,
         )
     else:
         assert (

python/sglang/srt/models/minimax_m2.py

@@ -167,9 +167,6 @@ class MiniMaxM2MoE(nn.Module):
             top_k=config.num_experts_per_tok,
             renormalize=True,
             scoring_func=config.scoring_func,
-            use_grouped_topk=True,  # TODO: Use "grouped top-k" flag only for hardcoded sigmoid scoring
-            num_expert_group=1,
-            topk_group=1,
             correction_bias=self.e_score_correction_bias,
             routed_scaling_factor=1.0,
         )