def run_test(args): from pybert.io.task_data_label import TaskData from pybert.test.predictor import Predictor import pickle import os 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)} test_data = processor.get_train(config['data_dir'] / f"{args.data_name}.label_test.pkl") print("Test data is:") print(test_data) print("Label list is:") print(label_list) print("----------------------------------------") # test_data = processor.get_test(lines=lines) test_examples = processor.create_examples( lines=test_data, example_type='test', cached_examples_file=config['data_cache'] / f"cached_test_examples_label_finetune{args.arch}") test_features = processor.create_features( examples=test_examples, max_seq_len=args.eval_max_seq_len, cached_features_file=config['data_cache'] / "cached_test_features_label_finetune{}_{}".format( args.eval_max_seq_len, args.arch)) test_dataset = processor.create_dataset(test_features) test_sampler = SequentialSampler(test_dataset) test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.train_batch_size) model = BertForMultiLable.from_pretrained(config['checkpoint_dir'], num_labels=len(label_list)) # ----------- predicting logger.info('model predicting....') predictor = Predictor(model=model, logger=logger, n_gpu=args.n_gpu, batch_metrics=[AccuracyThresh(thresh=0.5)], epoch_metrics=[ AUC(average='micro', task_type='binary'), MultiLabelReport(id2label=id2label) ]) result, test_predicted, test_true = predictor.predict(data=test_dataloader) pickle.dump( test_true, open(os.path.join(config["test/checkpoint_dir"], "test_true.p"), "wb")) pickle.dump( test_predicted, open(os.path.join(config["test/checkpoint_dir"], "test_predicted.p"), "wb")) pickle.dump( id2label, open(os.path.join(config["test/checkpoint_dir"], "id2label.p"), "wb")) print("Predictor results:") print(result) print("-----------------------------------------------")
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}.label_train.pkl") print("Train data is:") print(train_data) train_examples = processor.create_examples( lines=train_data, example_type='train', cached_examples_file=config['data_cache'] / f"cached_train_label_examples_finetune{args.arch}") # print ("Training examples are:") # print (train_examples) train_features = processor.create_features( examples=train_examples, max_seq_len=args.train_max_seq_len, cached_features_file=config['data_cache'] / "cached_train_label_features_finetune{}_{}".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}.label_valid.pkl") valid_examples = processor.create_examples( lines=valid_data, example_type='valid', cached_examples_file=config['data_cache'] / f"cached_valid_examples_label_finetune{args.arch}") valid_features = processor.create_features( examples=valid_examples, max_seq_len=args.eval_max_seq_len, cached_features_file=config['data_cache'] / "cached_valid_features_label_finetune{}_{}".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 = BertForMultiLable.from_pretrained(args.resume_path, num_labels=len(label_list)) else: print("Labels are:") print(label_list) # model = BertForMultiLable.from_pretrained(config['bert_model_dir'], num_labels=len(label_list)) model = BertForMultiLable.from_pretrained("bert-base-uncased", 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=BCEWithLogLoss(), 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, batch_metrics=[AccuracyThresh(thresh=0.5)], epoch_metrics=[ AUC(average='micro', task_type='binary'), MultiLabelReport(id2label=id2label) ]) # embeddings_dict = pickle.load(open("/home/rgaonkar/context_home/rgaonkar/label_embeddings/code/Bert_Masked_LM/label_embeddings_dict.p", "rb")) # label_similarity_matrix = get_label_similarity_matrix(embeddings_dict, label_list) trainer.train(train_data=train_dataloader, valid_data=valid_dataloader, seed=args.seed)
def main(): # **************************** Log initial data *********************** logger = init_logger(log_name=config['model']['arch'], log_dir=config['output']['log_dir']) logger.info(f"seed is {config['train']['seed']}") device = f"cuda: {config['train']['n_gpu'][0] if len(config['train']['n_gpu']) else 'cpu'}" seed_everything(seed=config['train']['seed'], device=device) logger.info('starting load data from disk') id2label = {value: key for key, value in config['label2id'].items()} DT = DataTransformer(logger=logger, seed=config['train']['seed']) targets, sentences = DT.read_data( raw_data_path=config['data']['raw_data_path'], preprocessor=EnglishPreProcessor(), is_train=True) train, valid = DT.train_val_split( X=sentences, y=targets, save=True, shuffle=True, stratify=False, valid_size=config['train']['valid_size'], train_path=config['data']['train_file_path'], valid_path=config['data']['valid_file_path']) tokenizer = BertTokenizer( vocab_file=config['pretrained']['bert']['vocab_path'], do_lower_case=config['train']['do_lower_case']) # train train_dataset = CreateDataset(data=train, tokenizer=tokenizer, max_seq_len=config['train']['max_seq_len'], seed=config['train']['seed'], example_type='train') # valid valid_dataset = CreateDataset(data=valid, tokenizer=tokenizer, max_seq_len=config['train']['max_seq_len'], seed=config['train']['seed'], example_type='valid') # train loader train_loader = DataLoader(dataset=train_dataset, batch_size=config['train']['batch_size'], num_workers=config['train']['num_workers'], shuffle=True, drop_last=False, pin_memory=False) # validation set loader valid_loader = DataLoader(dataset=valid_dataset, batch_size=config['train']['batch_size'], num_workers=config['train']['num_workers'], shuffle=False, drop_last=False, pin_memory=False) # **************************** initialize model *********************** logger.info("initializing model") model = BertFine.from_pretrained( config['pretrained']['bert']['bert_model_dir'], cache_dir=config['output']['cache_dir'], num_classes=len(id2label)) # ************************** set params ************************* param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.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': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] num_train_steps = int( len(train_dataset.examples) / config['train']['batch_size'] / config['train']['gradient_accumulation_steps'] * config['train']['epochs']) # t_total: total number of training steps for the learning rate schedule # warmup: portion of t_total for the warmup optimizer = BertAdam(optimizer_grouped_parameters, lr=config['train']['learning_rate'], warmup=config['train']['warmup_proportion'], t_total=num_train_steps) # **************************** callbacks *********************** logger.info("initializing callbacks") # model checkpoint model_checkpoint = ModelCheckpoint( checkpoint_dir=config['output']['checkpoint_dir'], mode=config['callbacks']['mode'], monitor=config['callbacks']['monitor'], save_best_only=config['callbacks']['save_best_only'], arch=config['model']['arch'], logger=logger) # monitor train_monitor = TrainingMonitor(file_dir=config['output']['figure_dir'], arch=config['model']['arch']) # learning rate scheduler lr_scheduler = BertLR(optimizer=optimizer, learning_rate=config['train']['learning_rate'], t_total=num_train_steps, warmup=config['train']['warmup_proportion']) # **************************** training model *********************** logger.info('training model....') train_configs = { 'model': model, 'logger': logger, 'optimizer': optimizer, 'resume': config['train']['resume'], 'epochs': config['train']['epochs'], 'n_gpu': config['train']['n_gpu'], 'gradient_accumulation_steps': config['train']['gradient_accumulation_steps'], 'epoch_metrics': [F1Score(average='micro', task_type='binary')], 'batch_metrics': [AccuracyThresh(thresh=0.5)], 'criterion': BCEWithLogLoss(), 'model_checkpoint': model_checkpoint, 'training_monitor': train_monitor, 'lr_scheduler': lr_scheduler, 'early_stopping': None, 'verbose': 1 } trainer = Trainer(train_configs=train_configs) trainer.train(train_data=train_loader, valid_data=valid_loader) if len(config['train']['n_gpu']) > 0: torch.cuda.empty_cache()
def run_train(args): # --------- data --------- processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case) idx2word = {} for (w, i) in processor.tokenizer.vocab.items(): idx2word[i] = w label_list = processor.get_labels(label_path=config['data_label_path']) idx2label = {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 = BertForMultiLable.from_pretrained(args.resume_path, num_labels=len(label_list)) else: model = BertForMultiLable.from_pretrained(config['bert_model_dir'], num_labels=len(label_list)) for p in model.parameters(): p.requires_grad = False # training last 2 fc layers model.classifier.weight.requires_grad = True model.classifier_1.weight.requires_grad = True 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 }] t_total = int( len(train_dataloader) / args.gradient_accumulation_steps * args.epochs) 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 *********************** writer = SummaryWriter() 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, i2w=idx2word, i2l=idx2label, model=model, epochs=args.epochs, logger=logger, criterion=BCEWithLogLoss(), 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, batch_metrics=[AccuracyThresh(thresh=0.5)], epoch_metrics=[], writer=writer, ) trainer.train(train_data=train_dataloader, valid_data=valid_dataloader, seed=args.seed)
def run_train(args, data_names): # --------- data # processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case) processor = BertProcessor() 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"{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, # collate_fn=collate_fn) # # valid_data = processor.get_dev(config['data_dir'] / f"{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, # 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: # model = BertForMultiLable.from_pretrained(config['bert_model_dir'], num_labels=len(label_list)) model = BertForMultiLable.from_pretrained("bert-base-multilingual-cased", num_labels=len(label_list)) #t_total = int(len(train_dataloader) / args.gradient_accumulation_steps * args.epochs) t_total = 200000 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) 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)], epoch_metrics=[AUC(average='micro', task_type='binary'), MultiLabelReport(id2label=id2label), F1Score(average='micro', task_type='binary')]) trainer.model.zero_grad() seed_everything(trainer.args.seed) # Added here for reproductibility (even between python 2 a iter_num = 0 valid_dataloader = get_valid_dataloader(args) for epoch in range(trainer.start_epoch, trainer.start_epoch + trainer.args.epochs): trainer.logger.info(f"Epoch {epoch}/{trainer.args.epochs}") update_epoch = True for i, data_name in enumerate(data_names): filename_int = int(data_name) if filename_int > 3500: continue trainer.logger.info(f"Epoch {epoch} - summary {i+1}/{len(data_names)}"+ f": summary_{data_name}") # train_dataloader, valid_dataloader = get_dataloader(args, data_name) train_dataloader = get_dataloader(args, data_name) # train_log, valid_log = trainer.train(train_data=train_dataloader, valid_data=valid_dataloader, epoch=update_epoch) train_log = trainer.train(train_data=train_dataloader, epoch=update_epoch) update_epoch = False # if train_log == None: # continue iter_num += 1 # logs = dict(train_log) # show_info = f'\nEpoch: {epoch} - ' + "-".join([f' {key}: {value:.4f} ' for key, value in logs.items()]) # trainer.logger.info(show_info) if iter_num % 50 == 0: valid_log = trainer.valid_epoch(valid_dataloader) logs = dict(valid_log) show_info = f'\nEpoch: {epoch} - ' + "-".join([f' {key}: {value:.4f} ' for key, value in logs.items()]) trainer.logger.info(show_info) # save if trainer.training_monitor: trainer.training_monitor.epoch_step(logs) # save model if trainer.model_checkpoint: if iter_num % 50 == 0: # state = trainer.save_info(epoch, best=logs[trainer.model_checkpoint.monitor]) state = trainer.save_info(iter_num, best=logs[trainer.model_checkpoint.monitor]) trainer.model_checkpoint.bert_epoch_step(current=logs[trainer.model_checkpoint.monitor], state=state) # early_stopping if trainer.early_stopping: trainer.early_stopping.epoch_step(epoch=epoch, current=logs[trainer.early_stopping.monitor]) if trainer.early_stopping.stop_training: break
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)
def run_train(args): # --------- data model_to_use = "roberta-large" processor = RobertaProcessor(model_type=model_to_use) #vocab_path=config['roberta_vocab_path'], merge_path=config['roberta_merge_path']) 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) 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.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, collate_fn=collate_fn) # ------- model logger.info("initializing model") if args.resume_path: args.resume_path = Path(args.resume_path) model = RobertaForMultiLable.from_pretrained(args.resume_path, num_labels=len(label_list)) else: model = RobertaForMultiLable.from_pretrained(model_to_use, num_labels=len(label_list)) #config['roberta_model_dir'] print("""\n\nname module\n----------------------""") for name, module in model.named_children(): if name == "roberta": for n, _ in module.named_children(): print(f"{name}:{n}") else: print("{:15} {}".format(name, module)) print() #return # print("================= train dataloader length is", len(train_dataloader), "=================\n") 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) and 'classifier.weight' not in n], '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}, {'params': model.classifier.weight, 'lr': 5e-4} # best: 5e-4 # {'params': model.classifier.bias, 'lr': 5e-4, 'weight_decay': 0.0} ] # model.parameters() 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) 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)], epoch_metrics=[AUC(average='weighted', task_type='binary'), # average='micro' MultiLabelReport(id2label=id2label)]) trainer.train(train_data=train_dataloader, valid_data=valid_dataloader)