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95 lines
3.2 KiB
95 lines
3.2 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <map>
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#include <vector>
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#include "paddle/fluid/framework/details/reduce_and_gather.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/selected_rows.h"
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namespace paddle {
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namespace framework {
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namespace details {
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struct ReduceLoDTensor {
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const std::vector<const LoDTensor *> &src_tensors_;
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LoDTensor &dst_tensor_;
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ReduceLoDTensor(const std::vector<const LoDTensor *> &src, LoDTensor *dst)
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: src_tensors_(src), dst_tensor_(*dst) {}
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template <typename T>
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void operator()() const {
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PADDLE_ENFORCE(!src_tensors_.empty());
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auto &t0 = *src_tensors_[0];
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PADDLE_ENFORCE_NE(t0.numel(), 0);
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dst_tensor_.Resize(t0.dims());
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T *dst = dst_tensor_.mutable_data<T>(platform::CPUPlace());
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std::copy(t0.data<T>(), t0.data<T>() + t0.numel(), dst);
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for (size_t i = 1; i < src_tensors_.size(); ++i) {
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auto &t = *src_tensors_[i];
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PADDLE_ENFORCE_EQ(t.dims(), t0.dims());
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PADDLE_ENFORCE_EQ(t.type(), t0.type());
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std::transform(t.data<T>(), t.data<T>() + t.numel(), dst, dst,
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[](T a, T b) -> T { return a + b; });
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}
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}
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};
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inline void GatherSelectedRows(
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const std::vector<const SelectedRows *> &src_selecte_rows_,
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const std::vector<platform::Place> &in_places,
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const std::unordered_map<platform::Place, platform::DeviceContext *,
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platform::PlaceHash> &dev_ctxes,
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const platform::Place &out_place, SelectedRows *dst_selecte_rows) {
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PADDLE_ENFORCE(!src_selecte_rows_.empty());
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std::vector<Tensor> in_tensors;
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std::vector<int64_t> out_rows;
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for (auto in_sr_ptr : src_selecte_rows_) {
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auto &in_sr = *in_sr_ptr;
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in_tensors.emplace_back(in_sr.value());
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out_rows.insert(out_rows.end(), in_sr.rows().begin(), in_sr.rows().end());
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}
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auto &pre_in = src_selecte_rows_[0];
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auto &dst_tensor = *dst_selecte_rows;
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dst_tensor.set_height(pre_in->height());
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dst_tensor.set_rows(out_rows);
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size_t rows = out_rows.size();
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DDim out_dim = pre_in->GetCompleteDims();
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out_dim[0] = static_cast<int64_t>(rows);
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dst_tensor.mutable_value()->Resize(out_dim);
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dst_tensor.mutable_value()->mutable_data(out_place, pre_in->value().type());
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Tensor *out_tensor = dst_tensor.mutable_value();
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// copy
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int s = 0, e = 0;
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for (size_t j = 0; j < in_tensors.size(); ++j) {
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e += in_tensors[j].dims()[0];
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auto sub_out = out_tensor->Slice(s, e);
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paddle::framework::TensorCopy(in_tensors[j], out_place,
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*(dev_ctxes.at(in_places[j])), &sub_out);
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s = e;
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}
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}
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} // namespace details
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} // namespace framework
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} // namespace paddle
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