# Copyright (c) 2018 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import time import os import random import math import contextlib from distutils.dir_util import mkpath import paddle import paddle.fluid as fluid from paddle.fluid import profiler import paddle.fluid.framework as framework import paddle.fluid.profiler as profiler from paddle.fluid.executor import Executor import reader import sys if sys.version[0] == '2': reload(sys) sys.setdefaultencoding("utf-8") sys.path.append('../') import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" from args import * from models.model_check import check_cuda, check_version from models.language_model import lm_model from config import RNNConfig import logging import pickle SEED = 123 @contextlib.contextmanager def profile_context(profile=True, profiler_path='/tmp/paddingrnn.profile'): if profile: with profiler.profiler('All', 'total', profiler_path): yield else: yield def get_current_model_para(train_prog, train_exe): param_list = train_prog.block(0).all_parameters() param_name_list = [p.name for p in param_list] vals = {} for p_name in param_name_list: p_array = np.array(fluid.global_scope().find_var(p_name).get_tensor()) vals[p_name] = p_array return vals def save_para_npz(train_prog, train_exe): print("begin to save model to model_base") param_list = train_prog.block(0).all_parameters() param_name_list = [p.name for p in param_list] vals = {} for p_name in param_name_list: p_array = np.array(fluid.global_scope().find_var(p_name).get_tensor()) vals[p_name] = p_array emb = vals["embedding_para"] print("begin to save model to model_base") np.savez("mode_base", **vals) def main(): args = parse_args() # check if set use_gpu=True in paddlepaddle cpu version check_cuda(args.use_gpu) # check if paddlepaddle version is satisfied check_version() logger = logging.getLogger("lm") logger.setLevel(logging.INFO) formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s - %(message)s') if args.log_path: file_handler = logging.FileHandler(args.log_path) file_handler.setLevel(logging.INFO) file_handler.setFormatter(formatter) logger.addHandler(file_handler) else: console_handler = logging.StreamHandler() console_handler.setLevel(logging.INFO) console_handler.setFormatter(formatter) logger.addHandler(console_handler) logger.info('Running with args : {}'.format(args)) config = RNNConfig(args) if not os.path.exists(args.save_model_dir): mkpath(args.save_model_dir) # define train program main_program = fluid.Program() startup_program = fluid.Program() if args.enable_ce: startup_program.random_seed = SEED with fluid.program_guard(main_program, startup_program): with fluid.unique_name.guard(): res_vars = lm_model.lm_model( config.hidden_size, config.vocab_size, num_layers=config.num_layers, num_steps=config.num_steps, init_scale=config.init_scale, dropout=config.dropout, rnn_model=config.rnn_model, use_dataloader=args.use_dataloader) if args.use_dataloader: dataloader = res_vars[-1] res_vars = res_vars[:-1] loss, last_hidden, last_cell, feed_order = res_vars fluid.clip.set_gradient_clip( clip=fluid.clip.GradientClipByGlobalNorm( clip_norm=config.max_grad_norm)) learning_rate = fluid.layers.create_global_var( name="learning_rate", shape=[1], value=1.0, dtype='float32', persistable=True) optimizer = fluid.optimizer.SGD(learning_rate=learning_rate) optimizer.minimize(loss) # define inference program inference_program = fluid.Program() inference_startup_program = fluid.Program() with fluid.program_guard(inference_program, inference_startup_program): with fluid.unique_name.guard(): lm_model.lm_model( config.hidden_size, config.vocab_size, num_layers=config.num_layers, num_steps=config.num_steps, init_scale=config.init_scale, dropout=config.dropout, rnn_model=config.rnn_model, use_dataloader=False) # Some op behaves differently for train and inference, we need to call # this clone function to ensure every op is right for inference. inference_program = inference_program.clone(for_test=True) place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() exe = Executor(place) exe.run(startup_program) if args.init_from_pretrain_model: if not os.path.exists(args.init_from_pretrain_model + '.pdparams'): print(args.init_from_pretrain_model) raise Warning("The pretrained params do not exist.") return fluid.load(main_program, args.init_from_pretrain_model) print("finish initing model from pretrained params from %s" % (args.init_from_pretrain_model)) device_count = len(fluid.cuda_places()) if args.use_gpu else len( fluid.cpu_places()) exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = device_count exec_strategy.num_iteration_per_drop_scope = 100 build_strategy = fluid.BuildStrategy() build_strategy.fuse_all_optimizer_ops = True if args.parallel: train_program = fluid.compiler.CompiledProgram( main_program).with_data_parallel( loss_name=loss.name, build_strategy=build_strategy, exec_strategy=exec_strategy) else: train_program = fluid.compiler.CompiledProgram(main_program) data_path = args.data_path print("begin to load data") ptb_data = reader.get_ptb_data(data_path) print("finished load data") train_data, valid_data, test_data = ptb_data def generate_init_data(): batch_size = config.batch_size * device_count init_hidden = np.zeros( (batch_size, config.num_layers, config.hidden_size), dtype='float32') init_cell = np.zeros( (batch_size, config.num_layers, config.hidden_size), dtype='float32') return init_hidden, init_cell def generate_new_lr(epoch_id=0, device_count=1): new_lr = config.base_learning_rate * (config.lr_decay**max( epoch_id + 1 - config.epoch_start_decay, 0.0)) lr = np.ones((device_count), dtype='float32') * new_lr return lr def prepare_input(batch, init_hidden=None, init_cell=None, epoch_id=0, with_lr=True, device_count=1): x, y = batch x = x.reshape((-1, config.num_steps, 1)) y = y.reshape((-1, 1)) res = {} res['x'] = x res['y'] = y if init_hidden is not None: res['init_hidden'] = init_hidden if init_cell is not None: res['init_cell'] = init_cell if with_lr: res['learning_rate'] = generate_new_lr(epoch_id, device_count) return res def eval(data): # when eval the batch_size set to 1 eval_data_iter = reader.get_data_iter(data, config.batch_size * device_count, config.num_steps) total_loss = 0.0 iters = 0 init_hidden, init_cell = generate_init_data() for batch_id, batch in enumerate(eval_data_iter): input_data_feed = prepare_input( batch, init_hidden, init_cell, epoch_id=0, with_lr=False) fetch_outs = exe.run( program=inference_program, feed=input_data_feed, fetch_list=[loss.name, last_hidden.name, last_cell.name], use_program_cache=False) cost_eval = np.array(fetch_outs[0]) init_hidden = np.array(fetch_outs[1]) init_cell = np.array(fetch_outs[2]) total_loss += cost_eval iters += config.num_steps ppl = np.exp(total_loss / iters) return ppl def get_log_interval(data_len): num_batchs = data_len // config.batch_size epoch_size = (num_batchs - 1) // config.num_steps log_interval = max(1, epoch_size // 10) return log_interval def train_an_epoch(epoch_id, batch_times): # get train epoch size log_interval = get_log_interval(len(train_data)) train_data_iter = reader.get_data_iter(train_data, config.batch_size * device_count, config.num_steps) total_loss = 0 iters = 0 init_hidden, init_cell = generate_init_data() for batch_id, batch in enumerate(train_data_iter): input_data_feed = prepare_input( batch, init_hidden=init_hidden, init_cell=init_cell, epoch_id=epoch_id, with_lr=True, device_count=device_count) batch_start_time = time.time() fetch_outs = exe.run(train_program, feed=input_data_feed, fetch_list=[ loss.name, "learning_rate", last_hidden.name, last_cell.name ], use_program_cache=True) batch_time = time.time() - batch_start_time batch_times.append(batch_time) cost_train = np.array(fetch_outs[0]) lr = np.array(fetch_outs[1]) init_hidden = np.array(fetch_outs[2]) init_cell = np.array(fetch_outs[3]) total_loss += cost_train iters += config.num_steps if batch_id > 0 and batch_id % log_interval == 0: ppl = np.exp(total_loss / iters) print( "-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f, lr: %.5f" % (epoch_id, batch_id, batch_time, ppl[0], lr[0])) # profiler tools for benchmark if args.profile and batch_id == log_interval: profiler.reset_profiler() elif args.profile and batch_id == (log_interval + 5): break ppl = np.exp(total_loss / iters) return ppl def train_an_epoch_dataloader(epoch_id, batch_times): # get train epoch size log_interval = get_log_interval(len(train_data)) init_hidden, init_cell = generate_init_data() total_loss = 0 iters = 0 dataloader.start() batch_id = 0 try: while True: data_feeds = {} if batch_id == 0: batch_time = 0 batch_start_time = time.time() else: batch_time = time.time() - batch_start_time batch_times.append(batch_time) batch_start_time = time.time() new_lr = generate_new_lr(epoch_id, device_count) data_feeds['learning_rate'] = new_lr data_feeds["init_hidden"] = init_hidden data_feeds["init_cell"] = init_cell fetch_outs = exe.run(train_program, feed=data_feeds, fetch_list=[ loss.name, "learning_rate", last_hidden.name, last_cell.name ], use_program_cache=True) cost_train = np.array(fetch_outs[0]) lr = np.array(fetch_outs[1]) init_hidden = np.array(fetch_outs[2]) init_cell = np.array(fetch_outs[3]) total_loss += cost_train iters += config.num_steps if batch_id > 0 and (log_interval == 0 or batch_id % log_interval == 0): ppl = np.exp(total_loss / iters) print( "-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f, lr: %.5f" % (epoch_id, batch_id, batch_time, ppl[0], lr[0])) batch_id += 1 # profiler tools for benchmark if args.profile and batch_id == log_interval: profiler.reset_profiler() elif args.profile and batch_id == (log_interval + 5): break except fluid.core.EOFException: dataloader.reset() batch_times.append(time.time() - batch_start_time) ppl = np.exp(total_loss / iters) return ppl def train(): if args.use_dataloader: def data_gen(): data_iter_size = config.batch_size train_batches = reader.get_data_iter(train_data, data_iter_size, config.num_steps) for batch in train_batches: x, y = batch x = x.reshape((-1, config.num_steps, 1)) y = y.reshape((-1, 1)) yield x, y dataloader.set_batch_generator(data_gen) total_time = 0.0 for epoch_id in range(config.max_epoch): batch_times = [] epoch_start_time = time.time() if args.use_dataloader: train_ppl = train_an_epoch_dataloader(epoch_id, batch_times) else: train_ppl = train_an_epoch(epoch_id, batch_times) epoch_time = time.time() - epoch_start_time total_time += epoch_time print( "\nTrain epoch:[%d]; epoch Time: %.5f; ppl: %.5f; avg_time: %.5f steps/s \n" % (epoch_id, epoch_time, train_ppl[0], len(batch_times) / sum(batch_times))) # FIXME(zjl): ppl[0] increases as batch_size increases. # We should find a better way to calculate ppl by normalizing batch_size. if device_count == 1 and config.batch_size <= 20 and epoch_id == 0 and train_ppl[ 0] > 1000: # for bad init, after first epoch, the loss is over 1000 # no more need to continue print( "Parameters are randomly initialized and not good this time because the loss is over 1000 after the first epoch." ) print("Abort this training process and please start again.") return if epoch_id == config.max_epoch - 1 and args.enable_ce: # kpis print("ptblm\tlstm_language_model_%s_duration_card%d\t%s" % (args.rnn_model, device_count, total_time / config.max_epoch)) print("ptblm\tlstm_language_model_%s_loss_card%d\t%s" % (args.rnn_model, device_count, train_ppl[0])) # NOTE(zjl): sometimes we have not enough data for eval if batch_size is large, i.e., 2100 # Just skip to avoid error def is_valid_data(data, batch_size, num_steps): data_len = len(data) batch_len = data_len // batch_size epoch_size = (batch_len - 1) // num_steps return epoch_size >= 1 valid_data_valid = is_valid_data(valid_data, config.batch_size, config.num_steps) if valid_data_valid: valid_ppl = eval(valid_data) print("Valid ppl: %.5f" % valid_ppl[0]) else: print( 'WARNING: length of valid_data is {}, which is not enough for batch_size {} and num_steps {}'. format( len(valid_data), config.batch_size, config.num_steps)) save_model_dir = os.path.join(args.save_model_dir, str(epoch_id)) if not os.path.exists(save_model_dir): mkpath(save_model_dir) save_model_dir = os.path.join(save_model_dir, 'params') fluid.save(main_program, save_model_dir) print("Saved model to: %s.\n" % save_model_dir) with profile_context(args.profile, args.profiler_path): train() test_ppl = eval(test_data) print("Test ppl:", test_ppl[0]) if __name__ == '__main__': main()