text: /* Copyright (c) 2016 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. */
  
  #pragma once
  #include <algorithm>
  #include <utility>
  #include <vector>
  #include "paddle/fluid/framework/eigen.h"
  #include "paddle/fluid/framework/op_registry.h"
  #include "paddle/fluid/operators/transpose_op.h"
  
  namespace paddle {
  namespace operators {
  
  template <typename T, int MajorType = Eigen::RowMajor,
            typename IndexType = Eigen::DenseIndex>
  using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
  
  template <typename T, int MajorType = Eigen::RowMajor,
            typename IndexType = Eigen::DenseIndex>
  using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
  
  using Tensor = framework::Tensor;
  
  template <typename T, typename Type>
  static void FullSort(Type input_height, Type input_width, int input_dim,
                       const framework::Tensor* input, T* t_out, Type* t_indices,
                       bool descending) {
  #ifdef PADDLE_WITH_MKLML
  #pragma omp parallel for
  #endif
    for (Type i = 0; i < input_height; ++i) {
      std::vector<std::pair<T, Type>> col_vec;
      col_vec.reserve(input_width);
      if (input_dim == 1) {
        auto e_input = EigenVector<T>::Flatten(*input);
        for (Type j = 0; j < input_width; ++j) {
          col_vec.push_back(std::pair<T, Type>(e_input(j), j));
        }
      } else {
        auto e_input = EigenMatrix<T>::Reshape(*input, input_dim - 1);
        for (Type j = 0; j < input_width; ++j) {
          col_vec.push_back(std::pair<T, Type>(e_input(i, j), j));
        }
      }
      std::sort(col_vec.begin(), col_vec.end(),
                [&](const std::pair<T, Type>& l, const std::pair<T, Type>& r) {
                  if (descending)
                    return l.first > r.first;
                  else
                    return l.first < r.first;
                });
  
      for (Type j = 0; j < input_width; ++j) {
        t_out[i * input_width + j] = col_vec[j].first;
        t_indices[i * input_width + j] = col_vec[j].second;
      }
    }
  }
  template <typename DeviceContext, typename T>
  class ArgsortKernel : public framework::OpKernel<T> {
   public:
    void Compute(const framework::ExecutionContext& ctx) const override {
      auto* input = ctx.Input<framework::Tensor>("X");
      auto* output = ctx.Output<framework::Tensor>("Out");
      auto* indices = ctx.Output<framework::Tensor>("Indices");
      int axis = ctx.Attr<int>("axis");
      bool descending = ctx.Attr<bool>("descending");
  
      auto in_dims = input->dims();
      axis = (axis < 0) ? (in_dims.size() + axis) : axis;
  
      T* out_data = output->mutable_data<T>(ctx.GetPlace());
  
      // Do full sort
      if (axis == -1 || axis + 1 == in_dims.size()) {
        const int64_t input_height = framework::product(
            framework::slice_ddim(in_dims, 0, in_dims.size() - 1));
        const int64_t input_width = in_dims[in_dims.size() - 1];
  
        int64_t* ids_data = indices->mutable_data<int64_t>(ctx.GetPlace());
        FullSort<T, int64_t>(input_height, input_width, in_dims.size(), input,
                             out_data, ids_data, descending);
      } else {
        // If not full sort do transpose
        std::vector<int> trans;
        for (int i = 0; i < axis; i++) {
          trans.push_back(i);
        }
        trans.push_back(in_dims.size() - 1);
        for (int i = axis + 1; i < in_dims.size() - 1; i++) {
          trans.push_back(i);
        }
        trans.push_back(axis);
        framework::DDim trans_dims(in_dims);
        for (int i = 0; i < trans.size(); i++) {
          trans_dims[i] = in_dims[trans[i]];
        }
  
        Tensor trans_inp;
        trans_inp.mutable_data<T>(trans_dims, ctx.GetPlace());
        int ndims = trans.size();
        auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
        // Do transpose
        TransCompute<platform::CPUDeviceContext, T>(ndims, dev_ctx, *input,
                                                    &trans_inp, trans);
  
        const int64_t input_height = framework::product(
            framework::slice_ddim(trans_dims, 0, trans_dims.size() - 1));
        const int64_t input_width = trans_dims[trans_dims.size() - 1];
  
        Tensor tmp_out;
        T* t_out = tmp_out.mutable_data<T>(trans_dims, ctx.GetPlace());
        output->mutable_data<T>(ctx.GetPlace());
  
        Tensor tmp_indices;
  
        auto* t_ind =
            tmp_indices.mutable_data<int64_t>(trans_dims, ctx.GetPlace());
  
        FullSort<T, int64_t>(input_height, input_width, in_dims.size(),
                             &trans_inp, t_out, t_ind, descending);
  
        indices->mutable_data<int64_t>(ctx.GetPlace());
        TransCompute<platform::CPUDeviceContext, int64_t>(
            ndims, dev_ctx, tmp_indices, indices, trans);
        // transpose back
        TransCompute<platform::CPUDeviceContext, T>(ndims, dev_ctx, tmp_out,
                                                    output, trans);
      }
    }
  };
  
  }  // namespace operators
  }  // namespace paddle
  