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87 lines
2.9 KiB
87 lines
2.9 KiB
7 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <algorithm>
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#include <iostream>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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template <typename DeviceContext, typename T>
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class ArgsortKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* input = ctx.Input<Tensor>("X");
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auto* output = ctx.Output<Tensor>("Out");
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auto* indices = ctx.Output<Tensor>("Indices");
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int axis = static_cast<int>(ctx.Attr<int>("axis"));
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auto in_dims = input->dims();
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axis = (axis == -1) ? (in_dims.size() - 1) : axis;
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const T* in_data = input->data<T>();
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T* out_data = output->mutable_data<T>(ctx.GetPlace());
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int64_t* idx_data = indices->mutable_data<int64_t>(ctx.GetPlace());
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int64_t part_dims_prod = input->numel() / in_dims[axis];
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for (int64_t i = 0; i < part_dims_prod; ++i) {
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int64_t idx = i;
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std::vector<int64_t> idx_vec(in_dims.size(), 0);
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for (int64_t dim = in_dims.size() - 1; dim >= 0; --dim) {
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if (dim != axis) {
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idx_vec[dim] = idx % in_dims[dim];
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idx /= in_dims[dim];
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}
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}
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std::vector<std::pair<T, int64_t>> in_vec;
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std::vector<int64_t> org_index_vec(in_dims[axis], 0);
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for (int64_t j = 0; j < in_dims[axis]; ++j) {
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idx_vec[axis] = j;
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int64_t index = idx_vec[0];
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for (int64_t dim = 0; dim < in_dims.size() - 1; ++dim) {
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index = index * in_dims[dim + 1] + idx_vec[dim + 1];
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}
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in_vec.push_back(std::pair<T, int64_t>(in_data[index], j));
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org_index_vec[j] = index;
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}
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std::sort(
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in_vec.begin(), in_vec.end(),
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[](const std::pair<T, int64_t>& v1, const std::pair<T, int64_t>& v2) {
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return v1.first < v2.first;
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});
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for (size_t j = 0; j < org_index_vec.size(); ++j) {
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int64_t index = org_index_vec[j];
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out_data[index] = in_vec[j].first;
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idx_data[index] = in_vec[j].second;
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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