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107 lines
3.6 KiB
107 lines
3.6 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 <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/selected_rows_functor.h"
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namespace paddle {
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namespace operators {
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static int FindOutIdx(int row, const std::vector<int>& abs_sections) {
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for (size_t i = 1; i < abs_sections.size(); ++i) {
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if (row < abs_sections[i]) {
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return i - 1;
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}
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}
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return abs_sections.size() - 1;
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}
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static std::vector<int> ToAbsoluteSection(
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const std::vector<int>& height_sections) {
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std::vector<int> abs_sections;
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abs_sections.resize(height_sections.size());
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abs_sections[0] = 0;
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for (size_t i = 1; i < height_sections.size(); ++i) {
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abs_sections[i] = height_sections[i - 1] + abs_sections[i - 1];
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}
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return abs_sections;
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}
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template <typename DeviceContext, typename T>
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class SplitSelectedRowsOpKernel : 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* x = ctx.Input<framework::SelectedRows>("X");
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auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
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auto height_sections = ctx.Attr<std::vector<int>>("height_sections");
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auto abs_sections = ToAbsoluteSection(height_sections);
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auto x_rows = x->rows();
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std::vector<std::vector<int>> outs_rows_idx;
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std::vector<std::vector<int>> outs_dense_idx;
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outs_rows_idx.resize(outs.size());
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outs_dense_idx.resize(outs.size());
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auto row_numel = x->value().numel() / x->value().dims()[0];
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auto src = x->value().data<T>();
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// split rows index into output sparse vars
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for (size_t i = 0; i < x_rows.size(); ++i) {
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int out_idx = FindOutIdx(x_rows[i], abs_sections);
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outs_rows_idx[out_idx].push_back(x_rows[i]);
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outs_dense_idx[out_idx].push_back(i);
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}
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auto place = ctx.GetPlace();
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for (size_t i = 0; i < outs_rows_idx.size(); ++i) {
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auto rows_idx = outs_rows_idx[i];
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outs[i]->set_height(height_sections[i]);
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if (rows_idx.size() > 0) {
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auto dims = x->GetCompleteDims();
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dims[0] = rows_idx.size();
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outs[i]->mutable_value()->mutable_data<T>(dims, x->place());
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for (auto idx : rows_idx) {
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outs[i]->mutable_rows()->push_back(idx - abs_sections[i]);
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}
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auto dst = outs[i]->mutable_value()->mutable_data<T>(ctx.GetPlace());
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for (size_t j = 0; j < rows_idx.size(); j++) {
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if (platform::is_cpu_place(place)) {
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memory::Copy(
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platform::CPUPlace(), dst + j * row_numel, platform::CPUPlace(),
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src + outs_dense_idx[i][j] * row_numel, sizeof(T) * row_numel);
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} else {
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#ifdef PADDLE_WITH_CUDA
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auto stream = ctx.cuda_device_context().stream();
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memory::Copy(platform::CUDAPlace(), dst + j * row_numel,
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platform::CUDAPlace(),
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src + outs_dense_idx[i][j] * row_numel,
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sizeof(T) * row_numel, stream);
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#else
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PADDLE_THROW("Paddle is not compiled with GPU");
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#endif
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}
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}
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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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