# 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. """Finetuning on classification tasks.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os import time import logging import multiprocessing # NOTE(paddle-dev): All of these flags should be # set before `import paddle`. Otherwise, it would # not take any effect. os.environ['FLAGS_eager_delete_tensor_gb'] = '0' # enable gc import paddle.fluid as fluid import reader.task_reader as task_reader from model.ernie import ErnieConfig from finetune.mrc import create_model, evaluate from optimization import optimization from utils.args import print_arguments, prepare_logger from utils.init import init_pretraining_params, init_checkpoint from finetune_args import parser args = parser.parse_args() log = logging.getLogger() def main(args): ernie_config = ErnieConfig(args.ernie_config_path) ernie_config.print_config() if args.use_cuda: dev_list = fluid.cuda_places() place = dev_list[0] dev_count = len(dev_list) else: place = fluid.CPUPlace() dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) exe = fluid.Executor(place) reader = task_reader.MRCReader( vocab_path=args.vocab_path, label_map_config=args.label_map_config, max_seq_len=args.max_seq_len, do_lower_case=args.do_lower_case, in_tokens=args.in_tokens, random_seed=args.random_seed, tokenizer=args.tokenizer, is_classify=args.is_classify, is_regression=args.is_regression, for_cn=args.for_cn, task_id=args.task_id, doc_stride=args.doc_stride, max_query_length=args.max_query_length) if not (args.do_train or args.do_val or args.do_test): raise ValueError("For args `do_train`, `do_val` and `do_test`, at " "least one of them must be True.") if args.do_test: assert args.test_save is not None startup_prog = fluid.Program() if args.random_seed is not None: startup_prog.random_seed = args.random_seed if args.predict_batch_size == None: args.predict_batch_size = args.batch_size if args.do_train: trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", "1")) train_data_generator = reader.data_generator( input_file=args.train_set, batch_size=args.batch_size, epoch=args.epoch, dev_count=trainers_num, shuffle=True, phase="train") num_train_examples = reader.get_num_examples("train") if args.in_tokens: max_train_steps = args.epoch * num_train_examples // ( args.batch_size // args.max_seq_len) // dev_count else: max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count warmup_steps = int(max_train_steps * args.warmup_proportion) log.info("Device count: %d" % dev_count) log.info("Num train examples: %d" % num_train_examples) log.info("Max train steps: %d" % max_train_steps) log.info("Num warmup steps: %d" % warmup_steps) train_program = fluid.Program() with fluid.program_guard(train_program, startup_prog): with fluid.unique_name.guard(): train_pyreader, graph_vars = create_model( args, pyreader_name='train_reader', ernie_config=ernie_config, is_training=True) scheduled_lr, _ = optimization( loss=graph_vars["loss"], warmup_steps=warmup_steps, num_train_steps=max_train_steps, learning_rate=args.learning_rate, train_program=train_program, startup_prog=startup_prog, weight_decay=args.weight_decay, scheduler=args.lr_scheduler, use_fp16=args.use_fp16, use_dynamic_loss_scaling=args.use_dynamic_loss_scaling, init_loss_scaling=args.init_loss_scaling, incr_every_n_steps=args.incr_every_n_steps, decr_every_n_nan_or_inf=args.decr_every_n_nan_or_inf, incr_ratio=args.incr_ratio, decr_ratio=args.decr_ratio) if args.verbose: if args.in_tokens: lower_mem, upper_mem, unit = fluid.contrib.memory_usage( program=train_program, batch_size=args.batch_size // args.max_seq_len) else: lower_mem, upper_mem, unit = fluid.contrib.memory_usage( program=train_program, batch_size=args.batch_size) log.info("Theoretical memory usage in training: %.3f - %.3f %s" % (lower_mem, upper_mem, unit)) if args.do_val or args.do_test: test_prog = fluid.Program() with fluid.program_guard(test_prog, startup_prog): with fluid.unique_name.guard(): test_pyreader, test_graph_vars = create_model( args, pyreader_name='test_reader', ernie_config=ernie_config, is_training=False) test_prog = test_prog.clone(for_test=True) nccl2_num_trainers = 1 nccl2_trainer_id = 0 if args.is_distributed: trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0")) worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS") current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT") worker_endpoints = worker_endpoints_env.split(",") trainers_num = len(worker_endpoints) log.info("worker_endpoints:{} trainers_num:{} current_endpoint:{} \ trainer_id:{}".format(worker_endpoints, trainers_num, current_endpoint, trainer_id)) # prepare nccl2 env. config = fluid.DistributeTranspilerConfig() config.mode = "nccl2" t = fluid.DistributeTranspiler(config=config) t.transpile( trainer_id, trainers=worker_endpoints_env, current_endpoint=current_endpoint, program=train_program if args.do_train else test_prog, startup_program=startup_prog) nccl2_num_trainers = trainers_num nccl2_trainer_id = trainer_id exe = fluid.Executor(place) exe.run(startup_prog) if args.do_train: if args.init_checkpoint and args.init_pretraining_params: log.warning( "WARNING: args 'init_checkpoint' and 'init_pretraining_params' " "both are set! Only arg 'init_checkpoint' is made valid.") if args.init_checkpoint: init_checkpoint( exe, args.init_checkpoint, main_program=startup_prog, use_fp16=args.use_fp16) elif args.init_pretraining_params: init_pretraining_params( exe, args.init_pretraining_params, main_program=startup_prog, use_fp16=args.use_fp16) elif args.do_val or args.do_test: if not args.init_checkpoint: raise ValueError("args 'init_checkpoint' should be set if" "only doing validation or testing!") init_checkpoint( exe, args.init_checkpoint, main_program=startup_prog, use_fp16=args.use_fp16) if args.do_train: exec_strategy = fluid.ExecutionStrategy() if args.use_fast_executor: exec_strategy.use_experimental_executor = True exec_strategy.num_threads = dev_count exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope train_exe = fluid.ParallelExecutor( use_cuda=args.use_cuda, loss_name=graph_vars["loss"].name, exec_strategy=exec_strategy, main_program=train_program, num_trainers=nccl2_num_trainers, trainer_id=nccl2_trainer_id) train_pyreader.decorate_tensor_provider(train_data_generator) else: train_exe = None if args.do_train: train_pyreader.start() steps = 0 if warmup_steps > 0: graph_vars["learning_rate"] = scheduled_lr time_begin = time.time() while True: try: steps += 1 if steps % args.skip_steps != 0: train_exe.run(fetch_list=[]) else: outputs = evaluate(train_exe, train_program, train_pyreader, graph_vars, "train") if args.verbose: verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size( ) verbose += "learning rate: %f" % ( outputs["learning_rate"] if warmup_steps > 0 else args.learning_rate) log.info(verbose) current_example, current_epoch = reader.get_train_progress() time_end = time.time() used_time = time_end - time_begin log.info("epoch: %d, progress: %d/%d, step: %d, ave loss: %f, " "speed: %f steps/s" % (current_epoch, current_example, num_train_examples, steps, outputs["loss"], args.skip_steps / used_time)) time_begin = time.time() if steps % args.save_steps == 0: save_path = os.path.join(args.checkpoints, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) if steps % args.validation_steps == 0: if args.do_val: test_pyreader.decorate_tensor_provider( reader.data_generator( args.dev_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False, phase="dev")) evaluate( exe, test_prog, test_pyreader, test_graph_vars, str(steps) + "_dev", examples=reader.get_examples("dev"), features=reader.get_features("dev"), args=args) if args.do_test: test_pyreader.decorate_tensor_provider( reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False, phase="test")) evaluate( exe, test_prog, test_pyreader, test_graph_vars, str(steps) + "_test", examples=reader.get_examples("test"), features=reader.get_features("test"), args=args) except fluid.core.EOFException: save_path = os.path.join(args.checkpoints, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) train_pyreader.reset() break # final eval on dev set if args.do_val: log.info("Final validation result:") test_pyreader.decorate_tensor_provider( reader.data_generator( args.dev_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False, phase="dev")) evaluate( exe, test_prog, test_pyreader, test_graph_vars, "dev", examples=reader.get_examples("dev"), features=reader.get_features("dev"), args=args) # final eval on test set if args.do_test: log.info("Final test result:") test_pyreader.decorate_tensor_provider( reader.data_generator( args.test_set, batch_size=args.batch_size, epoch=1, dev_count=1, shuffle=False, phase="test")) evaluate( exe, test_prog, test_pyreader, test_graph_vars, "test", examples=reader.get_examples("test"), features=reader.get_features("test"), args=args) if __name__ == '__main__': prepare_logger(log) print_arguments(args) while True: scope = fluid.core.Scope() with fluid.scope_guard(scope): main(args)