# Copyright (c) 2019 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. # -*- coding: UTF-8 -*- import os import sys import math import time import random import argparse import multiprocessing import numpy as np import paddle import paddle.fluid as fluid import reader import utils import creator from eval import test_process sys.path.append('../models/') from model_check import check_cuda # the function to train model def do_train(args): train_program = fluid.default_main_program() startup_program = fluid.default_startup_program() dataset = reader.Dataset(args) with fluid.program_guard(train_program, startup_program): train_program.random_seed = args.random_seed startup_program.random_seed = args.random_seed with fluid.unique_name.guard(): train_ret = creator.create_model( args, dataset.vocab_size, dataset.num_labels, mode='train') test_program = train_program.clone(for_test=True) optimizer = fluid.optimizer.Adam(learning_rate=args.base_learning_rate) optimizer.minimize(train_ret["avg_cost"]) # init executor if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) dev_count = fluid.core.get_cuda_device_count() else: dev_count = min(multiprocessing.cpu_count(), args.cpu_num) if (dev_count < args.cpu_num): print("WARNING: The total CPU NUM in this machine is %d, which is less than cpu_num parameter you set. " "Change the cpu_num from %d to %d" % (dev_count, args.cpu_num, dev_count)) os.environ['CPU_NUM'] = str(dev_count) place = fluid.CPUPlace() train_reader = creator.create_pyreader(args, file_name=args.train_data, feed_list=train_ret['feed_list'], place=place, model='lac', reader=dataset) test_reader = creator.create_pyreader(args, file_name=args.test_data, feed_list=train_ret['feed_list'], place=place, model='lac', reader=dataset, mode='test') exe = fluid.Executor(place) exe.run(startup_program) if args.init_checkpoint: utils.init_checkpoint(exe, args.init_checkpoint, train_program) if dev_count>1: device = "GPU" if args.use_cuda else "CPU" print("%d %s are used to train model"%(dev_count, device)) # multi cpu/gpu config exec_strategy = fluid.ExecutionStrategy() # exec_strategy.num_threads = dev_count * 6 build_strategy = fluid.compiler.BuildStrategy() compiled_prog = fluid.compiler.CompiledProgram(train_program).with_data_parallel( loss_name=train_ret['avg_cost'].name, build_strategy=build_strategy, exec_strategy=exec_strategy ) else: compiled_prog = fluid.compiler.CompiledProgram(train_program) # start training num_train_examples = dataset.get_num_examples(args.train_data) max_train_steps = args.epoch * num_train_examples // args.batch_size print("Num train examples: %d" % num_train_examples) print("Max train steps: %d" % max_train_steps) ce_info = [] step = 0 for epoch_id in range(args.epoch): ce_time = 0 for data in train_reader(): # this is for minimizing the fetching op, saving the training speed. if step % args.print_steps == 0: fetch_list = [ train_ret["avg_cost"], train_ret["precision"], train_ret["recall"], train_ret["f1_score"] ] else: fetch_list = [] start_time = time.time() outputs = exe.run( compiled_prog, fetch_list=fetch_list, feed=data[0], ) end_time = time.time() if step % args.print_steps == 0: avg_cost, precision, recall, f1_score = [np.mean(x) for x in outputs] print("[train] step = %d, loss = %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time %.5f" % ( step, avg_cost, precision, recall, f1_score, end_time - start_time)) if step % args.validation_steps == 0: test_process(exe, test_program, test_reader, train_ret) ce_time += end_time - start_time ce_info.append([ce_time, avg_cost, precision, recall, f1_score]) # save checkpoints if step % args.save_steps == 0 and step != 0: save_path = os.path.join(args.model_save_dir, "step_" + str(step)) fluid.io.save_persistables(exe, save_path, train_program) step += 1 if args.enable_ce: card_num = get_cards() ce_cost = 0 ce_f1 = 0 ce_p = 0 ce_r = 0 ce_time = 0 try: ce_time = ce_info[-2][0] ce_cost = ce_info[-2][1] ce_p = ce_info[-2][2] ce_r = ce_info[-2][3] ce_f1 = ce_info[-2][4] except: print("ce info error") print("kpis\teach_step_duration_card%s\t%s" % (card_num, ce_time)) print("kpis\ttrain_cost_card%s\t%f" % (card_num, ce_cost)) print("kpis\ttrain_precision_card%s\t%f" % (card_num, ce_p)) print("kpis\ttrain_recall_card%s\t%f" % (card_num, ce_r)) print("kpis\ttrain_f1_card%s\t%f" % (card_num, ce_f1)) def get_cards(): num = 0 cards = os.environ.get('CUDA_VISIBLE_DEVICES', '') if cards != '': num = len(cards.split(",")) return num if __name__ == "__main__": # 参数控制可以根据需求使用argparse，yaml或者json # 对NLP任务推荐使用PALM下定义的configure，可以统一argparse，yaml或者json格式的配置文件。 parser = argparse.ArgumentParser(__doc__) utils.load_yaml(parser,'conf/args.yaml') args = parser.parse_args() check_cuda(args.use_cuda) print(args) do_train(args)