# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. # #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. import numpy as np import os from functools import partial import logging import time import paddle import paddle.fluid as fluid import argparse import network import reader import sys logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger("fluid") logger.setLevel(logging.INFO) def parse_args(): parser = argparse.ArgumentParser("gnn") parser.add_argument( '--train_path', type=str, default='./data/diginetica/train.txt', help='dir of training data') parser.add_argument( '--config_path', type=str, default='./data/diginetica/config.txt', help='dir of config') parser.add_argument( '--model_path', type=str, default='./saved_model', help="path of model parameters") parser.add_argument( '--epoch_num', type=int, default=30, help='number of epochs to train for') parser.add_argument( '--batch_size', type=int, default=100, help='input batch size') parser.add_argument( '--hidden_size', type=int, default=100, help='hidden state size') parser.add_argument( '--l2', type=float, default=1e-5, help='l2 penalty') parser.add_argument( '--lr', type=float, default=0.001, help='learning rate') parser.add_argument( '--step', type=int, default=1, help='gnn propogation steps') parser.add_argument( '--lr_dc', type=float, default=0.1, help='learning rate decay rate') parser.add_argument( '--lr_dc_step', type=int, default=3, help='the number of steps after which the learning rate decay') parser.add_argument( '--use_cuda', type=int, default=0, help='whether to use gpu') parser.add_argument( '--use_parallel', type=int, default=1, help='whether to use parallel executor') parser.add_argument( '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.') return parser.parse_args() def train(): args = parse_args() if args.enable_ce: SEED = 102 fluid.default_main_program().random_seed = SEED fluid.default_startup_program().random_seed = SEED batch_size = args.batch_size items_num = reader.read_config(args.config_path) loss, acc, py_reader, feed_datas = network.network(items_num, args.hidden_size, args.step, batch_size) data_reader = reader.Data(args.train_path, True) logger.info("load data complete") use_cuda = True if args.use_cuda else False use_parallel = True if args.use_parallel else False place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) step_per_epoch = data_reader.length // batch_size optimizer = fluid.optimizer.Adam( learning_rate=fluid.layers.exponential_decay( learning_rate=args.lr, decay_steps=step_per_epoch * args.lr_dc_step, decay_rate=args.lr_dc), regularization=fluid.regularizer.L2DecayRegularizer( regularization_coeff=args.l2)) optimizer.minimize(loss) exe.run(fluid.default_startup_program()) all_vocab = fluid.global_scope().var("all_vocab").get_tensor() all_vocab.set( np.arange(1, items_num).astype("int64").reshape((-1)), place) feed_list = [e.name for e in feed_datas] if use_parallel: exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = 1 if os.name == 'nt' else 0 train_exe = fluid.ParallelExecutor( use_cuda=use_cuda, loss_name=loss.name, exec_strategy=exec_strategy) else: train_exe = exe logger.info("begin train") total_time = [] ce_info = [] start_time = time.time() loss_sum = 0.0 acc_sum = 0.0 global_step = 0 PRINT_STEP = 500 py_reader.decorate_paddle_reader(data_reader.reader(batch_size, batch_size * 20, True)) for i in range(args.epoch_num): epoch_sum = [] py_reader.start() try: while True: res = train_exe.run(fetch_list=[loss.name, acc.name]) loss_sum += res[0].mean() acc_sum += res[1].mean() epoch_sum.append(res[0].mean()) global_step += 1 if global_step % PRINT_STEP == 0: ce_info.append([loss_sum / PRINT_STEP, acc_sum / PRINT_STEP]) total_time.append(time.time() - start_time) logger.info("global_step: %d, loss: %.4lf, train_acc: %.4lf" % ( global_step, loss_sum / PRINT_STEP, acc_sum / PRINT_STEP)) loss_sum = 0.0 acc_sum = 0.0 start_time = time.time() except fluid.core.EOFException: py_reader.reset() logger.info("epoch loss: %.4lf" % (np.mean(epoch_sum))) save_dir = args.model_path + "/epoch_" + str(i) fetch_vars = [loss, acc] fluid.io.save_inference_model(save_dir, feed_list, fetch_vars, exe) logger.info("model saved in " + save_dir) # only for ce if args.enable_ce: gpu_num = get_cards(args) ce_loss = 0 ce_acc = 0 ce_time = 0 try: ce_loss = ce_info[-1][0] ce_acc = ce_info[-1][1] ce_time = total_time[-1] except: print("ce info error") print("kpis\teach_pass_duration_card%s\t%s" % (gpu_num, ce_time)) print("kpis\ttrain_loss_card%s\t%f" % (gpu_num, ce_loss)) print("kpis\ttrain_acc_card%s\t%f" % (gpu_num, ce_acc)) def get_cards(args): num = 0 cards = os.environ.get('CUDA_VISIBLE_DEVICES') num = len(cards.split(",")) return num def check_version(): """ Log error and exit when the installed version of paddlepaddle is not satisfied. """ err = "PaddlePaddle version 1.6 or higher is required, " \ "or a suitable develop version is satisfied as well. \n" \ "Please make sure the version is good with your code." \ try: fluid.require_version('1.6.0') except Exception as e: logger.error(err) sys.exit(1) if __name__ == "__main__": check_version() train()