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99 lines
3.4 KiB
99 lines
3.4 KiB
/* 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 T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename DeviceContext, typename T>
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class TopkKernel : 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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// Get the top k elements of each row of input tensor
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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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size_t k = static_cast<int>(ctx.Attr<int>("k"));
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auto* k_t = ctx.Input<Tensor>("K");
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if (k_t) {
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k = k_t->data<int>()[0];
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framework::DDim output_dims = output->dims();
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output_dims[output_dims.size() - 1] = k;
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output->Resize(output_dims);
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indices->Resize(output_dims);
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}
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T* output_data = output->mutable_data<T>(ctx.GetPlace());
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int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
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// reshape input to a flattern matrix(like flat_inner_dims)
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framework::DDim inputdims = input->dims();
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const size_t row = framework::product(
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framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
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const size_t col = inputdims[inputdims.size() - 1];
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Eigen::DSizes<int, 2> flat2dims(row, col);
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// NOTE: eigen shape doesn't affect paddle tensor.
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for
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#endif
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for (size_t i = 0; i < row; i++) {
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std::vector<std::pair<T, size_t>> vec;
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vec.reserve(col);
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// 1D vector
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if (inputdims.size() == 1) {
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auto eg_input = EigenVector<T>::Flatten(*input);
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for (size_t j = 0; j < col; j++) {
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vec.push_back(std::pair<T, size_t>(eg_input(j), j));
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}
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} else {
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auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
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for (size_t j = 0; j < col; j++) {
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vec.push_back(std::pair<T, size_t>(eg_input(i, j), j));
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}
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}
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std::partial_sort(
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vec.begin(), vec.begin() + k, vec.end(),
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[](const std::pair<T, size_t>& l, const std::pair<T, size_t>& r) {
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return l.first > r.first;
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});
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for (size_t j = 0; j < k; j++) {
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output_data[i * k + j] = vec[j].first;
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indices_data[i * k + j] = int64_t(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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