# 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. import os import sys import time import six import numpy as np import math import argparse import logging import paddle.fluid as fluid import paddle import time import reader as reader from nets import MultiviewSimnet, SimpleEncoderFactory logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger("fluid") logger.setLevel(logging.INFO) def parse_args(): parser = argparse.ArgumentParser("multi-view simnet") parser.add_argument("--train_file", type=str, help="Training file") parser.add_argument("--valid_file", type=str, help="Validation file") parser.add_argument( "--epochs", type=int, default=10, help="Number of epochs for training") parser.add_argument( "--model_output_dir", type=str, default='model_output', help="Model output folder") parser.add_argument( "--query_slots", type=int, default=1, help="Number of query slots") parser.add_argument( "--title_slots", type=int, default=1, help="Number of title slots") parser.add_argument( "--query_encoder", type=str, default="bow", help="Encoder module for slot encoding") parser.add_argument( "--title_encoder", type=str, default="bow", help="Encoder module for slot encoding") parser.add_argument( "--query_encode_dim", type=int, default=128, help="Dimension of query encoder output") parser.add_argument( "--title_encode_dim", type=int, default=128, help="Dimension of title encoder output") parser.add_argument( "--batch_size", type=int, default=128, help="Batch size for training") parser.add_argument( "--embedding_dim", type=int, default=128, help="Default Dimension of Embedding") parser.add_argument( "--sparse_feature_dim", type=int, default=1000001, help="Sparse feature hashing space" "for index processing") parser.add_argument( "--hidden_size", type=int, default=128, help="Hidden dim") parser.add_argument( '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.') return parser.parse_args() 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) def start_train(args): if args.enable_ce: SEED = 102 fluid.default_startup_program().random_seed = SEED fluid.default_startup_program().random_seed = SEED dataset = reader.SyntheticDataset(args.sparse_feature_dim, args.query_slots, args.title_slots) train_reader = fluid.io.batch( fluid.io.shuffle( dataset.train(), buf_size=args.batch_size * 100), batch_size=args.batch_size) place = fluid.CPUPlace() factory = SimpleEncoderFactory() query_encoders = [ factory.create(args.query_encoder, args.query_encode_dim) for i in range(args.query_slots) ] title_encoders = [ factory.create(args.title_encoder, args.title_encode_dim) for i in range(args.title_slots) ] m_simnet = MultiviewSimnet(args.sparse_feature_dim, args.embedding_dim, args.hidden_size) m_simnet.set_query_encoder(query_encoders) m_simnet.set_title_encoder(title_encoders) all_slots, avg_cost, correct = m_simnet.train_net() optimizer = fluid.optimizer.Adam(learning_rate=1e-4) optimizer.minimize(avg_cost) startup_program = fluid.default_startup_program() loop_program = fluid.default_main_program() exe = fluid.Executor(place) exe.run(startup_program) loader = fluid.io.DataLoader.from_generator( feed_list=all_slots, capacity=10000, iterable=True) loader.set_sample_list_generator(train_reader, places=place) total_time = 0 ce_info = [] for pass_id in range(args.epochs): start_time = time.time() for batch_id, data in enumerate(loader()): loss_val, correct_val = exe.run(loop_program, feed=data, fetch_list=[avg_cost, correct]) logger.info("TRAIN --> pass: {} batch_id: {} avg_cost: {}, acc: {}" .format(pass_id, batch_id, loss_val, float(correct_val) / args.batch_size)) ce_info.append(loss_val[0]) end_time = time.time() total_time += end_time - start_time fluid.io.save_inference_model(args.model_output_dir, [val.name for val in all_slots], [avg_cost, correct], exe) # only for ce if args.enable_ce: threads_num, cpu_num = get_cards(args) epoch_idx = args.epochs ce_loss = 0 try: ce_loss = ce_info[-2] except: logger.error("ce info error") print("kpis\teach_pass_duration_cpu%s_thread%s\t%s" % (cpu_num, threads_num, total_time / epoch_idx)) print("kpis\ttrain_loss_cpu%s_thread%s\t%s" % (cpu_num, threads_num, ce_loss)) def get_cards(args): threads_num = os.environ.get('NUM_THREADS', 1) cpu_num = os.environ.get('CPU_NUM', 1) return int(threads_num), int(cpu_num) def main(): args = parse_args() start_train(args) if __name__ == "__main__": check_version() main()