[Feature] Support noaux for eplb

custom_ops/gpu_ops/cpp_extensions.cc

@@ -647,6 +647,19 @@ std::vector<paddle::Tensor> NoauxTc(paddle::Tensor& scores,
                                     bool renormalize,
                                     float routed_scaling_factor);
 
+std::vector<paddle::Tensor> NoauxTcRedundant(
+    paddle::Tensor& scores,
+    paddle::Tensor& scores_with_bias,
+    paddle::Tensor& expert_id_to_ep_rank_array,
+    paddle::Tensor& expert_in_rank_num_list,
+    paddle::Tensor& tokens_per_expert_stats_list,
+    int n_group,
+    int topk_group,
+    int topk,
+    bool renormalize,
+    float routed_scaling_factor,
+    int redundant_ep_rank_num_plus_one);
+
 #ifdef ENABLE_FP8
 paddle::Tensor cutlass_fp8_fp8_half_gemm_func(
     const paddle::Tensor& x,
@@ -1485,6 +1498,10 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
 
   m.def("noaux_tc", &NoauxTc, "noaux_tc for Deepseekv3 MoE compute");
 
+  m.def("noaux_tc_redundant",
+        &NoauxTcRedundant,
+        "noaux_tc_redundant for MoE compute");
+
 #ifdef ENABLE_FP8
   m.def("cutlass_fp8_fp8_half_gemm_fused",
         &cutlass_fp8_fp8_half_gemm_func,

custom_ops/gpu_ops/noaux_tc_redundant.cu

@@ -0,0 +1,103 @@
+// Copyright (c) 2025 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.
+
+#include <algorithm>
+#include <optional>
+
+#include "helper.h"
+#include "noauxtc_kernel.h"
+
+std::vector<paddle::Tensor> NoauxTcRedundant(
+    paddle::Tensor& scores,
+    paddle::Tensor& scores_with_bias,
+    paddle::Tensor& expert_id_to_ep_rank_array,
+    paddle::Tensor& expert_in_rank_num_list,
+    paddle::Tensor& tokens_per_expert_stats_list,
+    int n_group,
+    int topk_group,
+    int topk,
+    bool renormalize,
+    float routed_scaling_factor,
+    int redundant_ep_rank_num_plus_one) {
+  auto input_shape = scores_with_bias.shape();
+  PD_CHECK(input_shape.size() == 2);
+  int64_t num_tokens = input_shape[0];
+  int64_t num_experts = input_shape[1];
+  auto input_type = scores_with_bias.dtype();
+  auto place = scores_with_bias.place();
+  auto group_scores = paddle::empty({num_tokens, n_group}, input_type, place);
+  auto topk_values = paddle::empty({num_tokens, topk}, input_type, place);
+  auto topk_indices =
+      paddle::empty({num_tokens, topk}, paddle::DataType::INT64, place);
+  auto stream = scores_with_bias.stream();
+
+  invokeNoAuxTcRedundant<float, int64_t>(
+      reinterpret_cast<float*>(scores.data<float>()),
+      reinterpret_cast<float*>(group_scores.data<float>()),
+      reinterpret_cast<float*>(topk_values.data<float>()),
+      reinterpret_cast<int64_t*>(topk_indices.data<int64_t>()),
+      reinterpret_cast<float*>(scores_with_bias.data<float>()),
+      reinterpret_cast<int*>(expert_id_to_ep_rank_array.data<int>()),
+      reinterpret_cast<int*>(expert_in_rank_num_list.data<int>()),
+      reinterpret_cast<int*>(tokens_per_expert_stats_list.data<int>()),
+      num_tokens,
+      num_experts,
+      n_group,
+      topk_group,
+      topk,
+      renormalize,
+      routed_scaling_factor,
+      redundant_ep_rank_num_plus_one,
+      stream);
+
+  return {scores, topk_values, topk_indices};
+}
+
+std::vector<paddle::DataType> NoauxTcRedundantInferDtype(
+    const paddle::DataType& scores_dtype,
+    const paddle::DataType& scores_with_bias_dtype) {
+  return {scores_dtype, scores_dtype, paddle::DataType::INT64};
+}
+
+std::vector<std::vector<int64_t>> NoauxTcRedundantInferShape(
+    const std::vector<int64_t>& scores_shape,
+    const std::vector<int64_t>&,
+    const int topk) {
+  auto num_tokens = scores_shape[0];
+  auto topk_values_shape = std::vector<int64_t>{num_tokens, topk};
+  auto topk_indices_shape = std::vector<int64_t>{num_tokens, topk};
+  return {scores_shape, topk_values_shape, topk_indices_shape};
+}
+
+PD_BUILD_STATIC_OP(noaux_tc_redundant)
+    .Inputs({"scores",
+             "scores_with_bias",
+             "expert_id_to_ep_rank_array",
+             "expert_in_rank_num_list",
+             "tokens_per_expert_stats_list"})
+    .Outputs({"output_tensor",
+              "topk_values",
+              "topk_indices",
+              "tokens_per_expert_stats_list_out"})
+    .Attrs({"n_group: int",
+            "topk_group: int",
+            "topk:int",
+            "renormalize: bool",
+            "routed_scaling_factor: float",
+            "redundant_ep_rank_num_plus_one:int"})
+    .SetInplaceMap({{"tokens_per_expert_stats_list",
+                     "tokens_per_expert_stats_list_out"}})
+    .SetKernelFn(PD_KERNEL(NoauxTcRedundant))
+    .SetInferShapeFn(PD_INFER_SHAPE(NoauxTcRedundantInferShape))
+    .SetInferDtypeFn(PD_INFER_DTYPE(NoauxTcRedundantInferDtype));