wehub-resource-sync
233 lines
8.5 KiB
C++
233 lines
8.5 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 "paddle/fluid/framework/threadpool.h"
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#include "paddle/fluid/framework/convert_utils.h"
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#include "paddle/fluid/framework/device_worker_factory.h"
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#include "paddle/fluid/framework/trainer.h"
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namespace paddle::framework {
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void DistMultiTrainer::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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user_define_dump_filename_ = trainer_desc.user_define_dump_filename();
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const std::vector<paddle::framework::DataFeed *> readers =
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dataset->GetReaders();
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RegisterHeterCallback();
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thread_num_ = static_cast<int>(readers.size());
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workers_.resize(thread_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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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]->SetDeviceIndex(i);
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workers_[i]->SetDataFeed(readers[i]);
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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]->SetWorkerNum(thread_num_);
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}
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VLOG(3) << "going to initialize pull dense worker";
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pull_dense_worker_ = PullDenseWorker::GetInstance();
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pull_dense_worker_->Initialize(trainer_desc);
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VLOG(3) << "initialize pull dense worker";
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SetDebug(trainer_desc.debug());
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}
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void DistMultiTrainer::RegisterHeterCallback() {
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auto fleet_ptr = FleetWrapper::GetInstance();
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fleet_ptr->RegisterHeterCallback(
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[this](int worker, int taskid) { workers_[worker]->Schedule(taskid); });
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}
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void DistMultiTrainer::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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void DistMultiTrainer::InitTrainerEnv(const ProgramDesc &main_program,
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const Place &place) {
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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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#if defined(PADDLE_WITH_PSLIB) || defined(PADDLE_WITH_PSCORE)
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workers_[i]->CacheProgram(main_program);
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#endif
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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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// Scope* -> thread id, it will be used in push_dense op
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for (int i = 0; i < thread_num_; ++i) {
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Scope *thread_scope = workers_[i]->GetThreadScope();
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pull_dense_worker_->SetThreadIdByScope(thread_scope, i);
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}
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}
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void DistMultiTrainer::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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pull_dense_worker_->SetRootScope(root_scope_);
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#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_CUDA)
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pull_dense_worker_->CreatePinVar();
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#endif
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pull_dense_worker_->Start();
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#if defined(PADDLE_WITH_PSLIB) || defined(PADDLE_WITH_PSCORE)
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for (int i = 0; i < thread_num_; ++i) {
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workers_[i]->GetXpuOpIndex();
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}
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#endif
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VLOG(3) << "init other env done.";
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}
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void DistMultiTrainer::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_) { // NOLINT
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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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}
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Scope *DistMultiTrainer::GetWorkerScope(int thread_id) {
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return workers_[thread_id]->GetThreadScope();
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}
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void DistMultiTrainer::Finalize() {
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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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DenseTensor *thread_tensor = thread_var->GetMutable<DenseTensor>();
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if (root_tensor->numel() != thread_tensor->numel()) {
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continue;
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}
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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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if (need_dump_field_ || need_dump_param_) {
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FinalizeDumpEnv();
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}
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pull_dense_worker_->Stop();
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root_scope_->DropKids();
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// flush local client push queue
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auto fleet_ptr_ = FleetWrapper::GetInstance();
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fleet_ptr_->ClientFlush();
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}
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template <typename T>
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void DistMultiTrainer::MergeToRootScope(DenseTensor *root_tensor,
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DenseTensor *tensor) {
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T *root_data = root_tensor->data<T>();
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T *data = tensor->data<T>();
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for (int i = 0; i < tensor->numel(); i++) {
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root_data[i] += data[i];
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}
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}
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} // namespace paddle::framework
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