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109 lines
4.4 KiB
109 lines
4.4 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 <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/distributed_ops/send_recv_util.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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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<int64_t>>("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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auto height = x->height();
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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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auto& id = x_rows[i];
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PADDLE_ENFORCE_LT(id, height,
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platform::errors::OutOfRange(
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"Each row_id in x.rows must be less than x.height. "
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"But received x.rows[%d] = %d, x.height = %d",
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i, id, height));
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int out_idx = GetSectionIndex(id, abs_sections);
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outs_rows_idx[out_idx].push_back(id);
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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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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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outs[i]->mutable_rows()->clear();
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if (rows_idx.size() > 0) {
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for (auto idx : rows_idx) {
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auto id_offset = idx - abs_sections[i];
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PADDLE_ENFORCE_LT(
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id_offset, height_sections[i],
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platform::errors::OutOfRange("Each row_id in out.rows must be "
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"less than out.height. But recived "
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"out.rows = [%d], out.height = [%d]",
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id_offset, height_sections[i]));
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outs[i]->mutable_rows()->push_back(id_offset);
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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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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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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(platform::errors::Unavailable(
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"Paddle is not compiled with CUDA. Cannot visit cuda device"));
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#endif
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}
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}
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}
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PADDLE_ENFORCE_EQ(rows_idx.size(), outs[i]->rows().size(),
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platform::errors::InvalidArgument(
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"rows should has the same size with tensor dim 0. "
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"But received rows = %d, tensor's dim[0] = %d.",
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rows_idx.size(), outs[i]->rows().size()));
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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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