def run_train(args): # --------- data processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case) label_list = processor.get_labels() label2id = {label: i for i, label in enumerate(label_list)} id2label = {i: label for i, label in enumerate(label_list)} train_data = processor.get_train(config['data_dir'] / f"{args.data_name}.train.pkl") train_examples = processor.create_examples(lines=train_data, example_type='train', cached_examples_file=config['data_dir'] / f"cached_train_examples_{args.arch}") train_features = processor.create_features(examples=train_examples, max_seq_len=args.train_max_seq_len, cached_features_file=config[ 'data_dir'] / "cached_train_features_{}_{}".format( args.train_max_seq_len, args.arch )) train_dataset = processor.create_dataset(train_features, is_sorted=args.sorted) if args.sorted: train_sampler = SequentialSampler(train_dataset) else: train_sampler = RandomSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) valid_data = processor.get_dev(config['data_dir'] / f"{args.data_name}.valid.pkl") valid_examples = processor.create_examples(lines=valid_data, example_type='valid', cached_examples_file=config['data_dir'] / f"cached_valid_examples_{args.arch}") valid_features = processor.create_features(examples=valid_examples, max_seq_len=args.eval_max_seq_len, cached_features_file=config['data_dir'] / "cached_valid_features_{}_{}".format( args.eval_max_seq_len, args.arch)) valid_dataset = processor.create_dataset(valid_features) valid_sampler = SequentialSampler(valid_dataset) valid_dataloader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=args.eval_batch_size) # ------- model logger.info("initializing model") if args.resume_path: args.resume_path = Path(args.resume_path) model = BertForMultiClass.from_pretrained(args.resume_path, num_labels=len(label_list)) else: model = BertForMultiClass.from_pretrained(config['bert_model_dir'], num_labels=len(label_list)) t_total = int(len(train_dataloader) / args.gradient_accumulation_steps * args.epochs) param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] warmup_steps = int(t_total * args.warmup_proportion) optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) lr_scheduler = WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps, t_total=t_total) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # ---- callbacks logger.info("initializing callbacks") train_monitor = TrainingMonitor(file_dir=config['figure_dir'], arch=args.arch) model_checkpoint = ModelCheckpoint(checkpoint_dir=config['checkpoint_dir'], mode=args.mode, monitor=args.monitor, arch=args.arch, save_best_only=args.save_best) # **************************** training model *********************** logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Num Epochs = %d", args.epochs) logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * ( torch.distributed.get_world_size() if args.local_rank != -1 else 1)) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) trainer = Trainer(n_gpu=args.n_gpu, model=model, epochs=args.epochs, logger=logger, criterion=CrossEntropy(), optimizer=optimizer, lr_scheduler=lr_scheduler, early_stopping=None, training_monitor=train_monitor, fp16=args.fp16, resume_path=args.resume_path, grad_clip=args.grad_clip, model_checkpoint=model_checkpoint, gradient_accumulation_steps=args.gradient_accumulation_steps, evaluate=F1Score(), class_report=ClassReport(target_names=[id2label[x] for x in range(len(label2id))])) trainer.train(train_data=train_dataloader, valid_data=valid_dataloader, seed=args.seed)
def run_train(args): # --------- data processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case) label_list = processor.get_labels(args.task_type) label2id = {label: i for i, label in enumerate(label_list)} id2label = {i: label for i, label in enumerate(label_list)} train_data = processor.get_train( config['data_dir'] / f"{args.data_name}.train.{args.task_type}.pkl") train_examples = processor.create_examples( lines=train_data, example_type=f'train_{args.task_type}', cached_examples_file=config['data_dir'] / f"cached_train_{args.task_type}_examples_{args.arch}") train_features = processor.create_features( examples=train_examples, max_seq_len=args.train_max_seq_len, cached_features_file=config['data_dir'] / "cached_train_{}_features_{}_{}".format( args.task_type, args.train_max_seq_len, args.arch)) train_dataset = processor.create_dataset(train_features, is_sorted=args.sorted) if args.sorted: train_sampler = SequentialSampler(train_dataset) else: train_sampler = RandomSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate_fn) valid_data = processor.get_dev( config['data_dir'] / f"{args.data_name}.valid.{args.task_type}.pkl") valid_examples = processor.create_examples( lines=valid_data, example_type=f'valid_{args.task_type}', cached_examples_file=config['data_dir'] / f"cached_valid_{args.task_type}_examples_{args.arch}") valid_features = processor.create_features( examples=valid_examples, max_seq_len=args.eval_max_seq_len, cached_features_file=config['data_dir'] / "cached_valid_{}_features_{}_{}".format( args.task_type, args.eval_max_seq_len, args.arch)) valid_dataset = processor.create_dataset(valid_features) valid_sampler = SequentialSampler(valid_dataset) valid_dataloader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=args.eval_batch_size, collate_fn=collate_fn) # ------- model logger.info("initializing model") if args.resume_path: args.resume_path = Path(args.resume_path) model = BertForMultiLable.from_pretrained(args.resume_path, num_labels=len(label_list)) else: if args.task_type == 'trans': model = BertForMultiLable_Fewshot.from_pretrained( Path('pybert/output/checkpoints/bert/base'), num_labels=len(label_list)) #model = BertForMultiLable.from_pretrained(config['bert_model_dir'], num_labels=len(label_list)) else: model = BertForMultiLable.from_pretrained( config['bert_model_dir'], num_labels=len(label_list)) t_total = int( len(train_dataloader) / args.gradient_accumulation_steps * args.epochs) # 下面是optimizer和scheduler的设计 param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] warmup_steps = int(t_total * args.warmup_proportion) optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total) if args.fp16: try: from apex import amp except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use fp16 training." ) model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # ---- callbacks logger.info("initializing callbacks") train_monitor = TrainingMonitor( file_dir=config['figure_dir'], arch=args.arch ) # TODO: 理解train_monitor的作用,感觉就是一个用来绘图的东西,用于记录每一个epoch中得到的结果 model_checkpoint = ModelCheckpoint(checkpoint_dir=config['checkpoint_dir'], mode=args.mode, monitor=args.monitor, arch=args.arch, save_best_only=args.save_best) # **************************** training model *********************** logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Num Epochs = %d", args.epochs) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) trainer = Trainer( args=args, model=model, logger=logger, criterion=BCEWithLogLoss(), optimizer=optimizer, scheduler=scheduler, early_stopping=None, training_monitor=train_monitor, model_checkpoint=model_checkpoint, batch_metrics=[ AccuracyThresh(thresh=0.5) ], # 作用于batch之上的metrics,在每次loss.backward()之后都会执行计算,记得区分它与loss epoch_metrics=[ AUC(average='micro', task_type='binary'), # 作用于epoch之上的metrics MultiLabelReport(id2label=id2label), F1Score(task_type='binary', average='micro', search_thresh=True) ]) # TODO: 考虑是否应该使用F1-score替代指标 trainer.train(train_data=train_dataloader, valid_data=valid_dataloader)