# 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. """train auto dialogue evaluation task""" import os import sys import six import time import numpy as np import multiprocessing import paddle import paddle.fluid as fluid import ade.reader as reader from ade_net import create_net, set_word_embedding from ade.utils.configure import PDConfig from ade.utils.input_field import InputField from ade.utils.model_check import check_cuda import ade.utils.save_load_io as save_load_io try: import cPickle as pickle #python 2 except ImportError as e: import pickle #python 3 def do_train(args): """train function""" train_prog = fluid.default_main_program() startup_prog = fluid.default_startup_program() with fluid.program_guard(train_prog, startup_prog): train_prog.random_seed = args.random_seed startup_prog.random_seed = args.random_seed with fluid.unique_name.guard(): context_wordseq = fluid.data( name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1) response_wordseq = fluid.data( name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1) labels = fluid.data( name='labels', shape=[-1, 1], dtype='int64') input_inst = [context_wordseq, response_wordseq, labels] input_field = InputField(input_inst) data_reader = fluid.io.PyReader(feed_list=input_inst, capacity=4, iterable=False) loss = create_net( is_training=True, model_input=input_field, args=args ) loss.persistable = True # gradient clipping fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue( max=1.0, min=-1.0)) optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate) optimizer.minimize(loss) if args.use_cuda: dev_count = fluid.core.get_cuda_device_count() place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) else: dev_count = int( os.environ.get('CPU_NUM', multiprocessing.cpu_count())) place = fluid.CPUPlace() processor = reader.DataProcessor( data_path=args.training_file, max_seq_length=args.max_seq_len, batch_size=args.batch_size) batch_generator = processor.data_generator( place=place, phase="train", shuffle=True, sample_pro=args.sample_pro) num_train_examples = processor.get_num_examples(phase='train') max_train_steps = args.epoch * num_train_examples // dev_count // args.batch_size print("Num train examples: %d" % num_train_examples) print("Max train steps: %d" % max_train_steps) data_reader.decorate_batch_generator(batch_generator) exe = fluid.Executor(place) exe.run(startup_prog) assert (args.init_from_checkpoint == "") or ( args.init_from_pretrain_model == "") #init from some checkpoint, to resume the previous training if args.init_from_checkpoint: save_load_io.init_from_checkpoint(args, exe, train_prog) #init from some pretrain models, to better solve the current task if args.init_from_pretrain_model: save_load_io.init_from_pretrain_model(args, exe, train_prog) if args.word_emb_init: print("start loading word embedding init ...") if six.PY2: word_emb = np.array(pickle.load(open(args.word_emb_init, 'rb'))).astype('float32') else: word_emb = np.array(pickle.load(open(args.word_emb_init, 'rb'), encoding="bytes")).astype('float32') set_word_embedding(word_emb, place) print("finish init word embedding ...") build_strategy = fluid.compiler.BuildStrategy() build_strategy.enable_inplace = True compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel( loss_name=loss.name, build_strategy=build_strategy) steps = 0 begin_time = time.time() time_begin = time.time() for epoch_step in range(args.epoch): data_reader.start() sum_loss = 0.0 ce_loss = 0.0 while True: try: fetch_list = [loss.name] outputs = exe.run(compiled_train_prog, fetch_list=fetch_list) np_loss = outputs sum_loss += np.array(np_loss).mean() ce_loss = np.array(np_loss).mean() if steps % args.print_steps == 0: time_end = time.time() used_time = time_end - time_begin current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) print('%s epoch: %d, step: %s, avg loss %s, speed: %f steps/s' % (current_time, epoch_step, steps, sum_loss / args.print_steps, args.print_steps / used_time)) sum_loss = 0.0 time_begin = time.time() if steps % args.save_steps == 0: if args.save_checkpoint: save_load_io.save_checkpoint(args, exe, train_prog, "step_" + str(steps)) if args.save_param: save_load_io.save_param(args, exe, train_prog, "step_" + str(steps)) steps += 1 except fluid.core.EOFException: data_reader.reset() break if args.save_checkpoint: save_load_io.save_checkpoint(args, exe, train_prog, "step_final") if args.save_param: save_load_io.save_param(args, exe, train_prog, "step_final") def get_cards(): num = 0 cards = os.environ.get('CUDA_VISIBLE_DEVICES', '') if cards != '': num = len(cards.split(",")) return num if args.enable_ce: card_num = get_cards() pass_time_cost = time.time() - begin_time print("test_card_num", card_num) print("kpis\ttrain_duration_card%s\t%s" % (card_num, pass_time_cost)) print("kpis\ttrain_loss_card%s\t%f" % (card_num, ce_loss)) if __name__ == '__main__': args = PDConfig(yaml_file="./data/config/ade.yaml") args.build() args.Print() check_cuda(args.use_cuda) do_train(args)