// Copyright (c) 2018 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 #include #include #include "ThreadPool.h" #include "paddle/fluid/framework/reader.h" #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/platform/gpu_info.h" #endif namespace paddle { namespace operators { namespace reader { class BufferedReader : public framework::DecoratedReader { using TensorVec = std::vector; using VecFuture = std::future; public: BufferedReader(const std::shared_ptr& reader, const platform::Place& place, size_t buffer_size); ~BufferedReader() override; private: void ReadTillBufferFullAsync(); void ReadAsync(size_t i); protected: void ShutdownImpl() override; void StartImpl() override; void ReadNextImpl(std::vector* out) override; private: ThreadPool thread_pool_; platform::Place place_; const size_t buffer_size_; std::queue> position_; // The buffer for reading data. // NOTE: the simplest way to implement buffered reader is do not use any // buffer, just read async and create futures as buffer size. However, to // malloc tensors every time is extremely slow. Here we store all data in // buffers and prevent alloc every time. std::vector cpu_buffer_; std::vector gpu_buffer_; size_t prev_pos_{-1UL}; #ifdef PADDLE_WITH_CUDA cudaStream_t stream_; cudaStream_t compute_stream_; std::vector events_; #endif }; } // namespace reader } // namespace operators } // namespace paddle