// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include "paddle/fluid/eager/grad_node_info.h" namespace egr { /** * Input Buffer is designed for backward grad accumulate. * Since we will have one output used by multi preceding ops in forward pass, * we will meet a problem that we need to accumulate multiple grads into one. * * GradTensorHolder should have as same format as forward output **/ class GradTensorHolder { public: explicit GradTensorHolder(const std::vector& meta) { VLOG(7) << "Init GradTensorHolder with meta size: " << meta.size(); buffer_.resize(meta.size()); for (size_t i = 0; i < buffer_.size(); i++) { VLOG(7) << "Init GradTensorHolder with meta rank: " << meta[i].Size(); buffer_[i].resize(meta[i].Size()); } } GradTensorHolder(const GradTensorHolder& other) = default; explicit GradTensorHolder( std::vector>&& inputs) : buffer_(std::move(inputs)) {} GradTensorHolder& operator=(const GradTensorHolder& other) = default; // Create new tensor and copy tensor->impl void add(size_t slot_id, size_t rank, const paddle::experimental::Tensor& t, bool fill_one = false); const std::vector& operator[]( const size_t& pos) { return buffer_[pos]; } const std::vector>& Buffers() { return buffer_; } private: std::vector> buffer_; }; } // namespace egr