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100 lines
3.0 KiB
100 lines
3.0 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/lod_tensor.h"
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namespace paddle {
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namespace framework {
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// NOTE The vector<LoDTensor> can't be replaced with the class LoDTensorArray
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// directly, because there are many vector<LoDTensor> used accross the project,
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// and some of them are treated as LoDTensorArray.
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#if !defined(PADDLE_ON_INFERENCE)
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using LoDTensorArray = std::vector<LoDTensor>;
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#else // !PADDLE_ON_INFERENCE
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#pragma message "LoDTensorArray is replaced with the inference one."
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/*
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* A LoDTensorArray which will not deallocate buffer when resized, fix the data
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* diff in inference, and more performance friendly in the concurrency
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* scenerios.
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*/
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class LoDTensorArray {
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public:
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LoDTensorArray() = default;
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using iterator = std::vector<LoDTensor>::iterator;
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using const_iterator = std::vector<LoDTensor>::const_iterator;
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const_iterator begin() const { return array_.begin(); }
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const_iterator end() const { return array_.begin() + size_; }
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iterator begin() { return array_.begin(); }
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iterator end() { return array_.begin() + size_; }
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void push_back(const LoDTensor& x) {
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if (size_ < array_.size()) {
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array_[size_++] = x;
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} else {
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array_.push_back(x);
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++size_;
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}
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}
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void resize(size_t size) {
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if (array_.size() < size) {
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array_.resize(size);
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}
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size_ = size;
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}
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void emplace_back() { array_.emplace_back(); }
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void emplace_back(LoDTensor&& x) { array_.emplace_back(std::move(x)); }
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LoDTensor& back() { return array_.back(); }
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size_t space() const { return array_.size(); }
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void reserve(size_t size) {
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// Naive warning to tell user this array might be to large. The memory and
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// buffer used by this TensorArray will not be deleted during the training
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// and inference phase, so attention not to make it expand too long.
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if (size > 800UL) {
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LOG(WARNING) << "TensorArray has more than 800 items";
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}
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array_.reserve(size);
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}
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bool empty() const { return size_ == 0UL; }
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void clear() { size_ = 0UL; }
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LoDTensor& operator[](size_t id) { return array_[id]; }
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const LoDTensor& operator[](size_t id) const { return array_[id]; }
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LoDTensor& at(size_t id) { return array_.at(id); }
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const LoDTensor& at(size_t id) const { return array_.at(id); }
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size_t size() const { return size_; }
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private:
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size_t size_{0};
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std::vector<LoDTensor> array_;
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};
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#endif // !PADDLE_ON_INFERENCE
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} // namespace framework
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} // namespace paddle
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