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126 lines
3.8 KiB
126 lines
3.8 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#pragma once
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#include "paddle/utils/Util.h"
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#include <stdio.h>
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#include "hl_gpu.h"
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#include "paddle/gserver/dataproviders/DataProvider.h"
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#include "paddle/gserver/gradientmachines/GradientMachine.h"
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#include <stdlib.h>
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#include <fstream>
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#include "ParameterUpdater.h"
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#include "TrainerConfig.pb.h"
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#include "TrainerConfigHelper.h"
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namespace paddle {
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/**
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* Configuration for parameter utils.
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*/
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struct ParameterUtilConfig {
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DISABLE_COPY(ParameterUtilConfig);
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ParameterUtilConfig(bool save_only_one,
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int saving_period,
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bool load_save_parameters_in_pserver,
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std::string config)
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: save_only_one_(save_only_one),
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saving_period_(saving_period),
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load_save_param_pserver_(load_save_parameters_in_pserver),
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config_(config) {}
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bool save_only_one_;
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int saving_period_;
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bool load_save_param_pserver_;
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std::string config_;
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};
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/**
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* ParameterUtil
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* Utility class for loading and saving parameters
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*/
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class ParameterUtil {
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public:
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/**
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* Ctor.
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*
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* @param config
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* @param intconfig
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* @param gradientMachine
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* @param parameterUpdater
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* @return
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*/
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ParameterUtil(const std::shared_ptr<TrainerConfigHelper> &config,
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std::unique_ptr<ParameterUtilConfig> &&intconfig,
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const GradientMachinePtr &gradientMachine,
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const std::shared_ptr<ParameterUpdater> ¶meterUpdater);
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/// Load parameter from the saved parameter file as pass passId
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/// if loadsave_parameters_in_pserver is set, some parameters MUST
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/// load in pserver, which is "remote".
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/// loadParameters can choose to load local/remote parameter, or both.
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bool loadParameters(int passId, bool local = true, bool remote = false);
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/// load parameters given path info
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void loadParametersWithPath(const std::string &dir,
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bool local = true,
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bool remote = false);
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/// Save parameter to dist for pass passId
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/// passInnerId means saving times in one pass, some users want to
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/// save parameters when have processed some batches in one pass
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/// passInnerId = 0 means do not need to save in one inner pass
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void saveParameters(int passId, int passInnerId = 0);
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/// save parameters for one pass, when passInnerId > 0 means saving
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/// the passInnerId times in one pass
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void saveParametersOnePass(int passId, int passInnerId = 0);
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/// delete parameter from disk via passId
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void deleteParameters(int passId, int passInnerId = 0);
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/// save config given path info
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void saveConfigWithPath(const std::string &path);
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/**
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* Try to load parameter from config.
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* @return true if can load from trainer config.
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*/
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inline bool tryLoadParametersFromConfig() {
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auto &c = config_->getConfig();
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if (!c.init_model_path().empty()) {
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loadParametersWithPath(c.init_model_path());
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return true;
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} else if (c.start_pass() > 0) {
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CHECK(loadParameters(c.start_pass() - 1));
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return true;
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} else {
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return false;
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}
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}
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private:
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std::shared_ptr<TrainerConfigHelper> config_;
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std::unique_ptr<ParameterUtilConfig> intConfig_;
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GradientMachinePtr gserver_;
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std::shared_ptr<ParameterUpdater> pUpdater_;
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};
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} // namespace paddle
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