wehub-resource-sync
253 lines
9.2 KiB
C++
253 lines
9.2 KiB
C++
/* 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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#include <string>
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#include "paddle/common/flags.h"
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#include "paddle/fluid/framework/device_worker_factory.h"
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#include "paddle/fluid/framework/program_utils.h"
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#include "paddle/fluid/framework/threadpool.h"
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#include "paddle/fluid/framework/trainer.h"
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#include "paddle/fluid/platform/densetensor_printer.h"
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PHI_DEFINE_EXPORTED_bool(enable_dump_main_program,
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false,
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"enable dump main program, default false");
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namespace paddle::framework {
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extern Barrier g_barrier;
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void MultiTrainer::Initialize(const TrainerDesc& trainer_desc,
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Dataset* dataset) {
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thread_num_ = trainer_desc.thread_num();
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SetDataset(dataset);
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ParseDumpConfig(trainer_desc);
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mpi_rank_ = trainer_desc.mpi_rank();
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mpi_size_ = trainer_desc.mpi_size();
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dump_file_num_ = trainer_desc.dump_file_num();
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for (int i = 0; i < trainer_desc.downpour_param().stat_var_names_size();
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i++) {
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need_merge_var_names_.push_back(
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trainer_desc.downpour_param().stat_var_names(i));
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}
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use_ps_gpu_ = trainer_desc.use_ps_gpu();
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use_gpu_graph_ = trainer_desc.use_gpu_graph();
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VLOG(3) << "Initialize use_ps_gpu_:" << use_ps_gpu_
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<< "; use_gpu_graph_:" << use_gpu_graph_;
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user_define_dump_filename_ = trainer_desc.user_define_dump_filename();
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// get filelist from trainer_desc here
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const std::vector<paddle::framework::DataFeed*> readers =
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dataset->GetReaders();
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VLOG(3) << "readers num: " << readers.size();
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// change thread num to readers num
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thread_num_ = static_cast<int>(readers.size());
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VLOG(3) << "worker thread num: " << thread_num_;
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workers_.resize(thread_num_);
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g_barrier.reset(thread_num_);
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for (int i = 0; i < thread_num_; ++i) {
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workers_[i] = DeviceWorkerFactory::CreateDeviceWorker(
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trainer_desc.device_worker_name());
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workers_[i]->SetNeedDumpField(need_dump_field_);
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workers_[i]->SetNeedDumpParam(need_dump_param_);
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workers_[i]->SetDumpFieldVector(dump_fields_);
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workers_[i]->SetDumpParamVector(dump_param_);
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workers_[i]->InitRandomDumpConfig(trainer_desc);
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workers_[i]->Initialize(trainer_desc);
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workers_[i]->SetDeviceIndex(i);
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workers_[i]->SetDataFeed(readers[i]);
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workers_[i]->SetThreadNum(thread_num_);
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}
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// set debug here
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SetDebug(trainer_desc.debug());
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}
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std::string MultiTrainer::GetDumpPath(int tid) {
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if (!user_define_dump_filename_.empty()) {
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return string::format_string("%s/part-%s-%05d",
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dump_fields_path_.c_str(),
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user_define_dump_filename_.c_str(),
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tid);
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}
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return string::format_string(
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"%s/part-%03d-%05d", dump_fields_path_.c_str(), mpi_rank_, tid);
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}
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void MultiTrainer::InitDumpEnv() {
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queue_ = paddle::framework::MakeChannel<std::string>();
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for (int i = 0; i < thread_num_; ++i) {
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workers_[i]->SetChannelWriter(queue_.get());
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}
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dump_thread_num_ = 1;
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if (dump_file_num_ > mpi_size_) {
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dump_thread_num_ = dump_file_num_ / mpi_size_;
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if (dump_file_num_ % mpi_size_ > mpi_rank_) {
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dump_thread_num_ += 1;
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}
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}
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for (int i = 0; i < dump_thread_num_; i++) {
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dump_thread_.emplace_back([this, i] { DumpWork(i); });
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}
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}
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inline std::vector<std::shared_ptr<phi::ThreadPool>>& GetThreadPool(
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int thread_num) {
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static std::vector<std::shared_ptr<phi::ThreadPool>> thread_pools;
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if (!thread_pools.empty()) {
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return thread_pools;
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}
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thread_pools.resize(thread_num);
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for (int i = 0; i < thread_num; ++i) {
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thread_pools[i].reset(new phi::ThreadPool(1));
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}
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return thread_pools;
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}
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// call only after all resources are set in current trainer
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void MultiTrainer::InitTrainerEnv(const ProgramDesc& main_program,
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const Place& place) {
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// multi thread load
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auto pool = GetThreadPool(thread_num_);
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std::vector<std::future<void>> wait_futures;
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PADDLE_ENFORCE_EQ(static_cast<int>(pool.size()),
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thread_num_,
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common::errors::InvalidArgument(
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"static_cast<int>(pool.size()) is invalid, "
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"expected %d but received %d",
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thread_num_,
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static_cast<int>(pool.size())));
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for (int i = 0; i < thread_num_; ++i) {
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wait_futures.emplace_back(pool[i]->Run([this, i, &main_program, &place]() {
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workers_[i]->SetPlace(place);
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workers_[i]->SetReaderPlace(place);
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workers_[i]->SetRootScope(root_scope_);
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workers_[i]->CreateDeviceResource(main_program); // Program
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workers_[i]->BindingDataFeedMemory();
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workers_[i]->CacheProgram(main_program);
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}));
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}
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for (auto& th : wait_futures) {
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th.get();
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}
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if (!use_gpu_graph_) { // cpups mode
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for (auto& var : main_program.Block(0).AllVars()) {
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if (var->Persistable()) {
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auto it = std::find(need_merge_var_names_.begin(),
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need_merge_var_names_.end(),
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var->Name());
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if (it == need_merge_var_names_.end() &&
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var->GetType() != proto::VarType::SELECTED_ROWS) {
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VLOG(2) << "train param: " << var->Name();
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trainable_param_.push_back(var->Name());
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}
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}
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}
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}
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}
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void MultiTrainer::InitOtherEnv(const ProgramDesc& main_program) {
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if (need_dump_field_ || need_dump_param_) {
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InitDumpEnv();
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}
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}
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Scope* MultiTrainer::GetWorkerScope(int thread_id) {
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return workers_[thread_id]->GetThreadScope();
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}
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void MultiTrainer::Run() {
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VLOG(3) << "Going to run";
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auto pool = GetThreadPool(thread_num_);
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std::vector<std::future<void>> wait_futures;
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PADDLE_ENFORCE_EQ(static_cast<int>(pool.size()),
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thread_num_,
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common::errors::InvalidArgument(
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"static_cast<int>(pool.size()) is invalid, "
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"expected %d but received %d",
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thread_num_,
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static_cast<int>(pool.size())));
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for (int i = 0; i < thread_num_; ++i) {
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if (!debug_) {
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wait_futures.emplace_back(
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pool[i]->Run([this, i]() { workers_[i]->TrainFiles(); }));
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} else {
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wait_futures.emplace_back(
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pool[i]->Run([this, i]() { workers_[i]->TrainFilesWithProfiler(); }));
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}
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}
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for (auto& th : wait_futures) {
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th.get();
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}
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// merge worker vars
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MergeWorkerVars();
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}
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template <typename T>
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void MultiTrainer::MergeToRootScope(DenseTensor* root_tensor,
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DenseTensor* tensor) {
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DenseTensor tmp_root;
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TensorCopy(*root_tensor, CPUPlace(), &tmp_root);
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T* tmp_root_data = tmp_root.data<T>();
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DenseTensor tmp_tensor;
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TensorCopy(*tensor, CPUPlace(), &tmp_tensor);
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T* data = tmp_tensor.data<T>();
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for (int i = 0; i < tmp_tensor.numel(); i++) {
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tmp_root_data[i] += data[i];
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}
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TensorCopy(tmp_root, CPUPlace(), root_tensor);
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}
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void MultiTrainer::MergeWorkerVars() {
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for (size_t i = 0; i < need_merge_var_names_.size(); i++) {
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Variable* root_var = root_scope_->FindVar(need_merge_var_names_[i]);
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if (root_var == nullptr) {
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continue;
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}
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DenseTensor* root_tensor = root_var->GetMutable<DenseTensor>();
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for (int j = 1; j < thread_num_; j++) {
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Scope* cur_thread_scope = workers_[j]->GetThreadScope();
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Variable* thread_var =
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cur_thread_scope->FindVar(need_merge_var_names_[i]);
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if (thread_var == nullptr) {
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continue;
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}
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DenseTensor* thread_tensor = thread_var->GetMutable<DenseTensor>();
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#define MergeCallback(cpp_type, proto_type) \
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do { \
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if (framework::TransToProtoVarType(root_tensor->dtype()) == proto_type) { \
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if (framework::TransToProtoVarType(thread_tensor->dtype()) != \
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proto_type) { \
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VLOG(0) << "Error: thread id=" << j << ", need_merge_var_names_[" << i \
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<< "] " << need_merge_var_names_[i] \
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<< ", root tensor type=" << root_tensor->dtype() \
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<< ", thread tensor type=" << thread_tensor->dtype(); \
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exit(-1); \
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} \
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MergeToRootScope<cpp_type>(root_tensor, thread_tensor); \
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} \
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} while (0)
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_ForEachDataType_(MergeCallback);
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}
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}
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}
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void MultiTrainer::Finalize() {
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if (need_dump_field_ || need_dump_param_) {
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FinalizeDumpEnv();
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}
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root_scope_->DropKids();
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}
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} // namespace paddle::framework
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