[Cherry-Pick][RL] cherry-pick #7218 support moe-topk use topk_reduce_func

fastdeploy/model_executor/layers/moe/ep.py

@@ -508,6 +508,7 @@ def moe_select(self, layer: nn.Layer, gate_out: paddle.Tensor):
                     expert_in_rank_num_list=expert_in_rank_num_list,
                     tokens_per_expert_stats_list=tokens_per_expert_stats_list,
                     redundant_ep_rank_num_plus_one=layer.fd_config.eplb_config.redundant_experts_num + 1,
+                    topk_reduce_func=getattr(layer, "topk_reduce_func", None),
                 )
             else:
                 topk_idx, topk_weights = fastdeploy.model_executor.ops.gpu.moe_redundant_topk_select(
@@ -533,6 +534,7 @@ def moe_select(self, layer: nn.Layer, gate_out: paddle.Tensor):
                     layer.routed_scaling_factor,
                     layer.gate_correction_bias,
                     getattr(layer, "renormalize", True),
+                    topk_reduce_func=getattr(layer, "topk_reduce_func", None),
                 )
             else:
                 topk_idx, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(

fastdeploy/model_executor/layers/moe/fused_moe_cutlass_backend.py

@@ -301,6 +301,7 @@ def apply_tp(
                 layer.routed_scaling_factor,
                 layer.gate_correction_bias,
                 getattr(layer, "renormalize", True),
+                topk_reduce_func=getattr(layer, "topk_reduce_func", None),
             )
             if current_platform.is_iluvatar():
                 (

fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py

@@ -147,67 +147,6 @@ def m_grouped_fp8_gemm_nt_contiguous_custom_python_op(
     return ffn_out
 
 
-def moe_topk_select(
-    gating_output: paddle.Tensor,
-    n_group: int,
-    topk_group: int,
-    top_k: int,
-    routed_scaling_factor: float,
-    e_score_correction_bias: paddle.Tensor,
-    renormalize: bool = False,
-):
-    """
-    Topk selection using paddle PHI topk API.
-
-    Args:
-        gating_output: gate output logits, shape [seq_len, n_experts]
-        n_group: number of expert groups
-        topk_group: number of top-k groups to select
-        top_k: number of top experts per token
-        routed_scaling_factor: scaling factor for routed experts
-        e_score_correction_bias: bias for expert selection
-        renormalize: whether to renormalize topk probabilities
-
-    Returns:
-        topk_weights: normalized topk probabilities, shape [seq_len, top_k]
-        topk_ids: topk expert indices, shape [seq_len, top_k]
-    """
-    # compute gate probs via sigmoid
-    gate_probs = paddle.nn.functional.sigmoid(gating_output)
-    # probs_for_choice includes correction bias for topk selection
-    probs_for_choice = gate_probs + e_score_correction_bias if e_score_correction_bias is not None else gate_probs
-    # group-based topk selection
-    n_group = n_group if n_group > 0 else 1
-    topk_group = topk_group if topk_group > 0 else 1
-    if n_group > 1 and topk_group < n_group:
-        seq_length, n_experts = probs_for_choice.shape
-        group_scores = (
-            probs_for_choice.reshape([seq_length, n_group, -1]).topk(2, axis=-1)[0].sum(axis=-1)
-        )  # [seq_len, n_group]
-        group_idx = paddle.topk(group_scores, k=topk_group, axis=-1, sorted=True)[1]  # [seq_len, topk_group]
-        group_mask = paddle.sum(
-            paddle.nn.functional.one_hot(group_idx, num_classes=n_group).cast(group_scores.dtype),
-            axis=1,  # Sum over topk_group dimension -> [seq_len, n_group]
-        )
-        score_mask = (
-            group_mask.unsqueeze(-1).expand([seq_length, n_group, n_experts // n_group]).reshape([seq_length, -1])
-        )  # [seq_len, n_experts]
-        probs_for_choice = probs_for_choice.masked_fill(~score_mask.astype(paddle.bool), float("-inf"))
-
-    _, topk_ids = paddle.topk(probs_for_choice, top_k, axis=-1)
-    topk_weights = paddle.index_sample(gate_probs, topk_ids)
-
-    # normalize combine weights
-    if renormalize:
-        topk_weights = topk_weights / paddle.clip(topk_weights.sum(-1, keepdim=True), min=1e-12)
-
-    # apply routed scaling factor
-    if routed_scaling_factor:
-        topk_weights = topk_weights * routed_scaling_factor
-
-    return topk_weights, topk_ids
-
-
 class DeepGemmFusedMoeMethod(MoEMethodBase):
     """
     DeepGemmFusedMoeMethod is a class that implements the MoEMethodBase interface for DeepGemm backend.
@@ -329,22 +268,7 @@ def apply_ep_prefill(
         hidden_size = x.shape[1]
 
         # 1. Select topk experts and weights
-        if (
-            fastdeploy.envs.FD_USE_PHI_MOE_TOPK
-            and layer.redundant_table_manger is None
-            and layer.topk_method == "noaux_tc"
-        ):
-            topk_weights, topk_idx = moe_topk_select(
-                gate_out,
-                layer.n_group,
-                layer.topk_group,
-                layer.top_k,
-                layer.routed_scaling_factor,
-                layer.gate_correction_bias,
-                getattr(layer, "renormalize", True),
-            )
-        else:
-            topk_idx, topk_weights = self.ep_prefill_runner.moe_select(layer, gate_out)
+        topk_idx, topk_weights = self.ep_prefill_runner.moe_select(layer, gate_out)
 
         if topk_ids_hookfunc is not None:
             topk_ids_hookfunc(topk_ids=topk_idx)
@@ -619,28 +543,16 @@ def apply_tp(
         gate_out = gate(x.cast("float32"))
 
         if layer.topk_method == "noaux_tc":
-
-            if not fastdeploy.envs.FD_USE_PHI_MOE_TOPK:
-                _, topk_weights, topk_ids = fastdeploy.model_executor.layers.moe.moe.get_moe_scores(
-                    gate_out,
-                    layer.n_group,
-                    layer.topk_group,
-                    layer.top_k,
-                    layer.routed_scaling_factor,
-                    layer.gate_correction_bias,
-                    getattr(layer, "renormalize", True),
-                )
-            else:
-                topk_weights, topk_ids = moe_topk_select(
-                    gate_out,
-                    layer.n_group,
-                    layer.topk_group,
-                    layer.top_k,
-                    layer.routed_scaling_factor,
-                    layer.gate_correction_bias,
-                    getattr(layer, "renormalize", True),
-                )
-
+            _, topk_weights, topk_ids = fastdeploy.model_executor.layers.moe.moe.get_moe_scores(
+                gate_out,
+                layer.n_group,
+                layer.topk_group,
+                layer.top_k,
+                layer.routed_scaling_factor,
+                layer.gate_correction_bias,
+                getattr(layer, "renormalize", True),
+                topk_reduce_func=getattr(layer, "topk_reduce_func", None),
+            )
         else:
             topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
                 gate_out,

fastdeploy/model_executor/layers/moe/moe.py

@@ -86,13 +86,22 @@ def get_moe_scores(
     expert_in_rank_num_list: paddle.Tensor = None,
     tokens_per_expert_stats_list: paddle.Tensor = None,
     redundant_ep_rank_num_plus_one: int = 1,
+    topk_reduce_func: Callable = lambda x: x.sum(axis=-1, keepdim=True) + 1e-20,
 ) -> paddle.Tensor:
     """
     compute moe scores using e_score_correction_bias.
     """
     scores = paddle.nn.functional.sigmoid(gating_output)
     assert e_score_correction_bias is not None, "e_score_correction_bias is none!"
     scores_with_bias = scores + e_score_correction_bias
+
+    if envs.FD_USE_PHI_MOE_TOPK:
+        # calculate renormalize and routed_scaling_factor value outside the noaux_tc
+        original_renormalize = renormalize
+        original_routed_scaling_factor = routed_scaling_factor
+        renormalize = False
+        routed_scaling_factor = 1.0
+
     if expert_id_to_ep_rank_array is None:
         scores, topk_values, topk_idx = noaux_tc(
             scores,
@@ -119,6 +128,16 @@ def get_moe_scores(
             routed_scaling_factor,
             redundant_ep_rank_num_plus_one,
         )
+    if envs.FD_USE_PHI_MOE_TOPK:
+        if original_renormalize:
+            if topk_reduce_func is not None:
+                topk_values = topk_values / topk_reduce_func(topk_values)
+            else:
+                # 使用默认的 sum + epsilon
+                topk_values = topk_values / (topk_values.sum(axis=-1, keepdim=True) + 1e-20)
+
+            if original_routed_scaling_factor != 1.0:
+                topk_values *= original_routed_scaling_factor
     return scores, topk_values, topk_idx
 
 
@@ -148,6 +167,8 @@ def __init__(
         with_bias: bool = False,
         activation="swiglu",
         model_format: Optional[str] = None,
+        topk_reduce_func: Callable = lambda x: x.sum(axis=-1, keepdim=True)
+        + 1e-20,  # only used when FD_USE_PHI_MOE_TOPK=1, default is same as noaux_tc kernel
     ):
         """
         Initialize the Moe layer with given parameters.
@@ -193,6 +214,7 @@ def __init__(
         self.moe_tag = moe_tag
         self.with_bias = with_bias
         self.activation = activation
+        self.topk_reduce_func = topk_reduce_func
 
         if self.ep_size > 1:
             expert_id_offset = expert_id_offset + self.ep_rank * self.num_local_experts

fastdeploy/model_executor/models/glm4_moe.py

@@ -170,6 +170,7 @@ def __init__(
             layer_idx=layer_id,
             gate_correction_bias=self.gate.e_score_correction_bias,
             weight_key_map=weight_key_map,
+            topk_reduce_func=lambda x: x.sum(axis=-1, keepdim=True) + 1e-20,
         )
 
         if self.n_shared_experts > 0:

tests/operators/test_noaux_tc_redundant.py

@@ -1,10 +1,22 @@
+# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import unittest
+from unittest import mock
 
 import paddle
 
-from fastdeploy.model_executor.layers.moe.fused_moe_deepgemm_backend import (
-    moe_topk_select,
-)
 from fastdeploy.model_executor.layers.moe.moe import get_moe_scores
 
 
@@ -135,15 +147,17 @@ def test_group_topk_using_phi_topk(self):
                     e_score_correction_bias=e_score_correction_bias,
                 )
 
-                topk_values, topk_idx = moe_topk_select(
-                    gating_output=gating_output,
-                    n_group=n_group,
-                    topk_group=topk_group,
-                    top_k=top_k,
-                    routed_scaling_factor=routed_scaling_factor,
-                    e_score_correction_bias=e_score_correction_bias,
-                    renormalize=renormalize,
-                )
+                with mock.patch.dict("os.environ", {"FD_USE_PHI_MOE_TOPK": "1"}):
+                    new_score, topk_values, topk_idx = get_moe_scores(
+                        gating_output=gating_output,
+                        n_group=n_group,
+                        topk_group=topk_group,
+                        top_k=top_k,
+                        routed_scaling_factor=routed_scaling_factor,
+                        e_score_correction_bias=e_score_correction_bias,
+                        renormalize=renormalize,
+                        topk_reduce_func=lambda x: x.sum(axis=-1, keepdim=True) + 1e-20,
+                    )
 
                 equal_topk_value = paddle.allclose(topk_values, ref_topk_values, atol=1e-03, rtol=1e-03).item()
                 equal_topk_ids = paddle.allclose(