/* Copyright (c) 2016 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. */ #include #include #include "paddle/fluid/framework/data_feed_factory.h" #include "paddle/fluid/framework/device_worker_factory.h" #include "paddle/fluid/framework/trainer.h" #include "paddle/fluid/framework/trainer_factory.h" namespace paddle { namespace framework { void MultiTrainer::Initialize(const TrainerDesc& trainer_desc) { thread_num_ = trainer_desc.thread_num(); // get filelist from trainer_desc here workers_.resize(thread_num_); readers_.resize(thread_num_); for (int i = 0; i < thread_num_; ++i) { workers_[i] = DeviceWorkerFactory::CreateDeviceWorker( trainer_desc.device_worker_name()); readers_[i] = DataFeedFactory::CreateDataFeed(trainer_desc.data_desc().name()); workers_[i]->SetDeviceIndex(i); readers_[i]->Init(trainer_desc.data_desc()); workers_[i]->SetDataFeed(readers_[i]); } std::vector filelist_vec; for (unsigned i = 0; i < trainer_desc.filelist_size(); ++i) { filelist_vec.push_back(trainer_desc.filelist(i)); } } // call only after all resources are set in current trainer void MultiTrainer::InitTrainerEnv(const ProgramDesc& main_program, const platform::Place& place) { for (int i = 0; i < thread_num_; ++i) { workers_[i]->SetPlace(place); workers_[i]->SetRootScope(root_scope_); workers_[i]->CreateDeviceResource(main_program); // Program workers_[i]->BindingDataFeedMemory(); } } void MultiTrainer::Run() { for (int thidx = 0; thidx < thread_num_; ++thidx) { threads_.push_back( std::thread(&DeviceWorker::TrainFiles, workers_[thidx].get())); } } void MultiTrainer::Finalize() { for (auto& th : threads_) { th.join(); } } REGISTER_TRAINER_CLASS(MultiTrainer); } // end namespace framework } // end namespace paddle