/* 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. */ #include #include "paddle/fluid/framework/device_worker.h" namespace paddle { namespace framework { std::shared_ptr PullDenseWorker::s_instance_ = NULL; std::mutex PullDenseWorker::mutex_for_version_; std::map PullDenseWorker::last_versions_; std::map PullDenseWorker::current_version_; std::map> PullDenseWorker::training_versions_; std::map> PullDenseWorker::dense_value_names_; void PullDenseWorker::Initialize(const TrainerDesc& param) { running_ = false; param_ = param.pull_dense_param(); dwp_param_ = param.downpour_param(); threshold_ = param_.threshold(); thread_num_ = param_.device_num(); sleep_time_ms_ = param_.sleep_time_ms(); for (size_t i = 0; i < dwp_param_.program_config(0).pull_dense_table_id_size(); ++i) { uint64_t tid = static_cast( dwp_param_.program_config(0).pull_dense_table_id(i)); TableParameter table; for (auto i : param_.dense_table()) { if (i.table_id() == tid) { table = i; break; } } // setup dense variables for each table int var_num = table.dense_value_name_size(); dense_value_names_[tid].resize(var_num); for (int j = 0; j < var_num; ++j) { dense_value_names_[tid][j] = table.dense_value_name(j); } // setup training version for each table training_versions_[tid].resize(thread_num_, 0); last_versions_[tid] = 0; current_version_[tid] = 0; } fleet_ptr_ = FleetWrapper::GetInstance(); } void PullDenseWorker::Wait(std::vector<::std::future>* status_vec) { for (auto& t : *status_vec) { t.wait(); auto status = t.get(); if (status != 0) { LOG(WARNING) << "Current Pull Dense Thread Failed Times" << ++pull_dense_fail_times_; } } int MAX_FAIL_NUM = 20; if (pull_dense_fail_times_ > MAX_FAIL_NUM) { LOG(FATAL) << "Pull Dense Failed Times More Than " << MAX_FAIL_NUM << " Times"; exit(-1); } status_vec->resize(0); } void PullDenseWorker::Stop() { if (running_) { running_ = false; t_.join(); } } void PullDenseWorker::PullDense(bool force_update) { pull_dense_status_.resize(0); for (size_t i = 0; i < dwp_param_.program_config(0).pull_dense_table_id_size(); ++i) { uint64_t tid = static_cast( dwp_param_.program_config(0).pull_dense_table_id(i)); if (force_update || CheckUpdateParam(tid)) { fleet_ptr_->PullDenseVarsAsync(*root_scope_, tid, dense_value_names_[tid], &pull_dense_status_); ResetThreadVersion(tid); } } if (pull_dense_status_.size() != 0) { Wait(&pull_dense_status_); } } int PullDenseWorker::Start() { running_ = true; // before training, we can pull dense from pserver first. PullDense(true); t_ = std::thread(&PullDenseWorker::Run, this); return 0; } void PullDenseWorker::Run() { while (running_) { PullDense(false); #ifndef _WIN32 usleep(sleep_time_ms_ * 1000); #endif } } void PullDenseWorker::IncreaseThreadVersion(int thread_id, uint64_t table_id) { std::lock_guard lock(mutex_for_version_); training_versions_[table_id][thread_id]++; } bool PullDenseWorker::CheckUpdateParam(uint64_t table_id) { std::lock_guard lock(mutex_for_version_); auto& version = training_versions_[table_id]; current_version_[table_id] = *(std::min_element(version.begin(), version.end())); if (current_version_[table_id] - last_versions_[table_id] < threshold_) { return false; } return true; } void PullDenseWorker::ResetThreadVersion(uint64_t table_id) { std::lock_guard lock(mutex_for_version_); last_versions_[table_id] = current_version_[table_id]; } } // namespace framework } // namespace paddle