diff --git a/src/operators/math/selected_rows_functor.cc b/src/operators/math/selected_rows_functor.cc
deleted file mode 100644
index 1e9516e3b25be2e588fc2ddc45a034241cde4072..0000000000000000000000000000000000000000
--- a/src/operators/math/selected_rows_functor.cc
+++ /dev/null
@@ -1,294 +0,0 @@
-/* Copyright (c) 2018 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 <set>
-
-#include "operators/math/math_function.h"
-#include "operators/math/selected_rows_functor.h"
-
-namespace paddle_mobile {
-namespace operators {
-namespace math {
-// template <typename T>
-// struct SelectedRowsAdd<T> {
-//  void operator()(
-//                  const framework::SelectedRows& input1,
-//                  const framework::SelectedRows& input2,
-//                  framework::SelectedRows* output) {
-//    auto in1_height = input1.height();
-//    PADDLE_MOBILE_ENFORCE(in1_height == input2.height());
-//    output->set_height(in1_height);
-//
-//    auto& in1_rows = input1.rows();
-//    auto& in2_rows = input2.rows();
-//    std::vector<int64_t> out_rows;
-//    out_rows.reserve(in1_rows.size() + in2_rows.size());
-//
-//    // concat rows
-//    out_rows.insert(out_rows.end(), in1_rows.begin(), in1_rows.end());
-//    out_rows.insert(out_rows.end(), in2_rows.begin(), in2_rows.end());
-//    output->set_rows(out_rows);
-//
-//    auto* out_value = output->mutable_value();
-//    auto& in1_value = input1.value();
-//    auto& in2_value = input2.value();
-//
-//    auto in1_row_numel = in1_value.numel() / in1_rows.size();
-//    PADDLE_MOBILE_ENFORCE(in1_row_numel == in2_value.numel() /
-//    in2_rows.size());
-//      PADDLE_MOBILE_ENFORCE(in1_row_numel == out_value->numel() /
-//      out_rows.size());
-//
-////    auto in1_place = input1.place();
-////      PADDLE_MOBILE_ENFORCE(platform::is_cpu_place(in1_place));
-////    auto in2_place = input2.place();
-////      PADDLE_MOBILE_ENFORCE(platform::is_cpu_place(in2_place));
-////    auto out_place = context.GetPlace();
-////      PADDLE_MOBILE_ENFORCE(platform::is_cpu_place(out_place));
-//
-//    auto* out_data = out_value->data<T>();
-//    auto* in1_data = in1_value.data<T>();
-//    memory::Copy(out_data, in1_data,
-//                 in1_value.numel() * sizeof(T));
-//
-//    auto* in2_data = in2_value.data<T>();
-//    memory::Copy(
-//                 out_data + in1_value.numel(),
-//                 in2_data,
-//                 in2_value.numel() * sizeof(T));
-//  }
-//};
-//
-// template struct SelectedRowsAdd<float>;
-// template struct SelectedRowsAdd<double>;
-////
-////template <typename T>
-////struct SelectedRowsAddTensor<T> {
-////  void operator()(
-////                  const framework::SelectedRows& input1,
-////                  const framework::Tensor& input2, framework::Tensor*
-/// output) { /    auto in1_height = input1.height(); /    auto in2_dims =
-/// input2.dims(); /    auto out_dims = output->dims(); /
-/// PADDLE_MOBILE_ENFORCE(in1_height == in2_dims[0]); /
-/// PADDLE_MOBILE_ENFORCE(in1_height == out_dims[0]);
-////
-////    auto& in1_value = input1.value();
-////    auto& in1_rows = input1.rows();
-////
-////    int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
-////      PADDLE_MOBILE_ENFORCE(in1_row_numel == input2.numel() / in1_height);
-////      PADDLE_MOBILE_ENFORCE(in1_row_numel == output->numel() / in1_height);
-////
-////    SetConstant<T> functor;
-////    functor(output, 0.0);
-////
-////    auto* in1_data = in1_value.data<T>();
-////    auto* out_data = output->data<T>();
-////
-////    for (size_t i = 0; i < in1_rows.size(); i++) {
-////      for (int64_t j = 0; j < in1_row_numel; j++) {
-////        out_data[in1_rows[i] * in1_row_numel + j] +=
-////            in1_data[i * in1_row_numel + j];
-////      }
-////    }
-////
-////    auto out_eigen = framework::EigenVector<T>::Flatten(*output);
-////    auto in2_eigen = framework::EigenVector<T>::Flatten(input2);
-////    out_eigen.device(*context.eigen_device()) = out_eigen + in2_eigen;
-////  }
-////};
-////
-////template struct SelectedRowsAddTensor< float>;
-////template struct SelectedRowsAddTensor<double>;
-//
-// template <typename T>
-// struct SelectedRowsAddTo {
-//  void operator()(
-//                  const framework::SelectedRows& input1,
-//                  const int64_t input2_offset,
-//                  framework::SelectedRows* input2) {
-//    auto in1_height = input1.height();
-//      PADDLE_MOBILE_ENFORCE(in1_height == input2->height());
-//
-//    auto& in1_rows = input1.rows();
-//    auto& in2_rows = *(input2->mutable_rows());
-//
-//    auto& in1_value = input1.value();
-//    auto* in2_value = input2->mutable_value();
-//
-//    // concat rows
-//    in2_rows.Extend(in1_rows.begin(), in1_rows.end());
-//
-////    auto in1_place = input1.place();
-////    PADDLE_ENFORCE(platform::is_cpu_place(in1_place));
-////    auto in2_place = input2->place();
-////    PADDLE_ENFORCE(platform::is_cpu_place(in2_place));
-//
-//    auto* in1_data = in1_value.data<T>();
-//    auto* in2_data = in2_value->data<T>();
-//    memory::Copy(
-//                 in2_data + input2_offset,
-//                  in1_data,
-//                 in1_value.numel() * sizeof(T));
-//  }
-//};
-//
-// template struct SelectedRowsAddTo<float>;
-// template struct SelectedRowsAddTo<double>;
-// template struct SelectedRowsAddTo<int>;
-// template struct SelectedRowsAddTo<int64_t>;
-//
-// template <typename T>
-// struct SelectedRowsAddToTensor<T> {
-//  void operator()(const framework::SelectedRows& input1,
-//                  framework::Tensor* input2) {
-//    auto in1_height = input1.height();
-//    auto in2_dims = input2->dims();
-//      PADDLE_MOBILE_ENFORCE(in1_height == in2_dims[0]);
-//
-//    auto& in1_value = input1.value();
-//    auto& in1_rows = input1.rows();
-//
-//    int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
-//      PADDLE_MOBILE_ENFORCE(in1_row_numel == input2->numel() / in1_height);
-//
-//    auto* in1_data = in1_value.data<T>();
-//    auto* input2_data = input2->data<T>();
-//
-//    for (size_t i = 0; i < in1_rows.size(); i++) {
-//      for (int64_t j = 0; j < in1_row_numel; j++) {
-//        input2_data[in1_rows[i] * in1_row_numel + j] +=
-//            in1_data[i * in1_row_numel + j];
-//      }
-//    }
-//  }
-//};
-//
-// template struct SelectedRowsAddToTensor< float>;
-// template struct SelectedRowsAddToTensor<double>;
-// template struct SelectedRowsAddToTensor< int>;
-// template struct SelectedRowsAddToTensor< int64_t>;
-//
-//// This is a separated namespace for manipulate SelectedRows typed
-//// data. Like merge duplicated rows, adding two SelectedRows etc.
-////
-//// Another group of functors is called "scatter updates", which means
-//// use SelectedRows to update a dense tensor with different Ops, like
-//// add or mul.
-//
-////namespace scatter {
-////
-////size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
-////  return std::find(rows.begin(), rows.end(), value) - rows.begin();
-////}
-//
-////template <typename T>
-////struct MergeAdd<platform::CPUDeviceContext, T> {
-////  framework::SelectedRows operator()(const platform::CPUDeviceContext&
-/// context, /                                     const
-/// framework::SelectedRows&  input) { /    framework::SelectedRows out; / auto
-/// input_rows =  input.rows(); /    std::set<int64_t>
-/// row_set(input_rows.begin(), input_rows.end()); /    std::vector<int64_t>
-/// merge_rows(row_set.begin(), row_set.end());
-////
-////    auto input_width = input.value().dims()[1];
-////    out.set_rows(merge_rows);
-////    out.set_height(input.height());
-////    out.mutable_value()->mutable_data<T>(
-////        framework::make_ddim(
-////            {static_cast<int64_t>(merge_rows.size()), input_width}),
-////        context.GetPlace());
-////
-////    math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
-////    constant_functor(context, out.mutable_value(), 0.0);
-////
-////    auto* out_data = out.mutable_value()->data<T>();
-////    auto* input_data = input.value().data<T>();
-////
-////    for (size_t i = 0; i < input_rows.size(); i++) {
-////      size_t out_i = FindPos(merge_rows, input_rows[i]);
-////      for (int64_t j = 0; j < input_width; j++) {
-////        out_data[out_i * input_width + j] += input_data[i * input_width +
-/// j]; /      } /    } /    return out; /  }
-////};
-////
-////template struct MergeAdd<platform::CPUDeviceContext, float>;
-////template struct MergeAdd<platform::CPUDeviceContext, double>;
-////template struct MergeAdd<platform::CPUDeviceContext, int>;
-////template struct MergeAdd<platform::CPUDeviceContext, int64_t>;
-////
-////template <typename T>
-////struct UpdateToTensor<platform::CPUDeviceContext, T> {
-////  void operator()(const platform::CPUDeviceContext& context,
-////                  const ScatterOps& op, const framework::SelectedRows&
-/// input1, /                  framework::Tensor* input2) { /    auto in1_height
-///= input1.height(); /    auto in2_dims = input2->dims(); /
-/// PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
-////
-////    auto& in1_value = input1.value();
-////    auto& in1_rows = input1.rows();
-////
-////    int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
-////    PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
-////
-////    auto* in1_data = in1_value.data<T>();
-////    auto* input2_data = input2->data<T>();
-////
-////    // FIXME(typhoonzero): use macro fix the below messy code.
-////    switch (op) {
-////      case ScatterOps::ASSIGN:
-////        INLINE_FOR2(in1_rows.size(), in1_row_numel)
-////        input2_data[in1_rows[i] * in1_row_numel + j] =
-////            in1_data[i * in1_row_numel + j];
-////        break;
-////      case ScatterOps::ADD:
-////        INLINE_FOR2(in1_rows.size(), in1_row_numel)
-////        input2_data[in1_rows[i] * in1_row_numel + j] +=
-////            in1_data[i * in1_row_numel + j];
-////        break;
-////      case ScatterOps::SUB:
-////        INLINE_FOR2(in1_rows.size(), in1_row_numel)
-////        input2_data[in1_rows[i] * in1_row_numel + j] -=
-////            in1_data[i * in1_row_numel + j];
-////        break;
-////      case ScatterOps::SUBBY:
-////        INLINE_FOR2(in1_rows.size(), in1_row_numel)
-////        input2_data[in1_rows[i] * in1_row_numel + j] =
-////            in1_data[i * in1_row_numel + j] -
-////            input2_data[in1_rows[i] * in1_row_numel + j];
-////        break;
-////      case ScatterOps::MUL:
-////        INLINE_FOR2(in1_rows.size(), in1_row_numel)
-////        input2_data[in1_rows[i] * in1_row_numel + j] *=
-////            in1_data[i * in1_row_numel + j];
-////        break;
-////      case ScatterOps::DIV:
-////        INLINE_FOR2(in1_rows.size(), in1_row_numel)
-////        input2_data[in1_rows[i] * in1_row_numel + j] /=
-////            in1_data[i * in1_row_numel + j];
-////        break;
-////      case ScatterOps::DIVBY:
-////        INLINE_FOR2(in1_rows.size(), in1_row_numel)
-////        input2_data[in1_rows[i] * in1_row_numel + j] =
-////            in1_data[i * in1_row_numel + j] /
-////            input2_data[in1_rows[i] * in1_row_numel + j];
-////        break;
-////    }
-////  }
-////};
-//
-//  // namespace scatter
-}  // namespace math
-}  // namespace operators
-}  // namespace paddle_mobile