def main(): parser = argparse.ArgumentParser() parser.add_argument("--task_name",default='ner',type=str) parser.add_argument("--do_test",action='store_true') parser.add_argument("--do_eval",action='store_true') parser.add_argument('--seed',default=42,type=str) args = parser.parse_args() seed_everything(seed=args.seed) dt = str(datetime.today()).split(" ")[0] test_path = config['data_dir'] / 'test.txt' test_result_path = config['result'] / f'{dt}_submit_test.txt' processors = {"ner": NerProcessor} task_name = args.task_name.lower() processor = processors[task_name]() label_list = processor.get_labels() id2label = {i: label for i, label in enumerate(label_list, 0)} test_data = [] with open(str(test_path), 'r') as fr: for line in fr: line = line.strip("\n") test_data.append(line) fw = open(str(test_result_path), 'w') cv_test_pred = [] for file in glob(f"{str(config['result']/ '*.pkl')}"): data = load_pickle(file) cv_test_pred.append(data) vote_pred = [] for i in range(len(test_data)): t = [np.array([x[i]]).T for x in cv_test_pred] t2 = np.concatenate(t, axis=1) t3 = [] for line in t2: c = Counter() c.update(line) t3.append(c.most_common(1)[0][0]) vote_pred.append(t3) for tag,line in zip(vote_pred,test_data): token_a = line.split("_") label_entities = get_entities(tag, id2label) if len(label_entities) == 0: record = "_".join(token_a) + "/o" else: labels = [] label_entities = sorted(label_entities, key=lambda x: x[1]) o_s = 0 for i, entity in enumerate(label_entities): begin = entity[1] end = entity[2] tag = entity[0] if begin != o_s: labels.append("_".join(token_a[o_s:begin]) + "/o") labels.append("_".join(token_a[begin:end + 1]) + f"/{tag}") o_s = end + 1 if i == len(label_entities) - 1: if o_s <= len(token_a) - 1: labels.append("_".join(token_a[o_s:]) + "/o") record = " ".join(labels) fw.write(record + "\n") fw.close()
def main(): parser = ArgumentParser() parser.add_argument("--arch", default='bert_lstm_span', type=str) parser.add_argument("--do_train", action='store_true') parser.add_argument("--do_test", action='store_true') parser.add_argument("--save_best", action='store_true') parser.add_argument("--do_lower_case", action='store_true') parser.add_argument('--soft_label', action='store_true') parser.add_argument('--data_name', default='datagrand', type=str) parser.add_argument('--optimizer', default='adam', type=str, choices=['adam', 'lookahead']) parser.add_argument('--markup', default='bios', type=str, choices=['bio', 'bios']) parser.add_argument('--checkpoint', default=900000, type=int) parser.add_argument('--fold', default=0, type=int) parser.add_argument("--epochs", default=50.0, type=int) parser.add_argument("--resume_path", default='', type=str) parser.add_argument("--mode", default='max', type=str) parser.add_argument("--monitor", default='valid_f1', type=str) parser.add_argument("--local_rank", type=int, default=-1) parser.add_argument("--sorted", default=1, type=int, help='1 : True 0:False ') parser.add_argument("--n_gpu", type=str, default='0', help='"0,1,.." or "0" or "" ') parser.add_argument('--gradient_accumulation_steps', type=int, default=1) parser.add_argument("--train_batch_size", default=24, type=int) parser.add_argument('--eval_batch_size', default=48, type=int) parser.add_argument("--train_max_seq_len", default=128, type=int) parser.add_argument("--eval_max_seq_len", default=512, type=int) parser.add_argument('--loss_scale', type=float, default=0) parser.add_argument("--warmup_proportion", default=0.1, type=float) parser.add_argument("--weight_decay", default=0.01, type=float) parser.add_argument("--adam_epsilon", default=1e-8, type=float) parser.add_argument("--grad_clip", default=5.0, type=float) parser.add_argument("--learning_rate", default=1e-4, type=float) parser.add_argument('--seed', type=int, default=42) parser.add_argument("--no_cuda", action='store_true') parser.add_argument('--fp16', action='store_true') parser.add_argument('--fp16_opt_level', type=str, default='O1') args = parser.parse_args() args.pretrain_model = config[ 'checkpoint_dir'] / f'lm-checkpoint-{args.checkpoint}' args.device = torch.device( f"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.arch = args.arch + f"_{args.markup}_fold_{args.fold}" if args.optimizer == 'lookahead': args.arch += "_lah" args.model_path = config['checkpoint_dir'] / args.arch args.model_path.mkdir(exist_ok=True) # Good practice: save your training arguments together with the trained model torch.save(args, config['checkpoint_dir'] / 'training_args.bin') seed_everything(args.seed) init_logger(log_file=config['log_dir'] / f"{args.arch}.log") logger.info("Training/evaluation parameters %s", args) if args.do_train: run_train(args) if args.do_test: run_test(args)
def main(): parser = ArgumentParser() parser.add_argument("--do_data", default=False, action='store_true') parser.add_argument("--do_corpus", default=False, action='store_true') parser.add_argument("--do_vocab", default=False, action='store_true') parser.add_argument("--do_split", default=False, action='store_true') parser.add_argument('--seed', default=42, type=int) parser.add_argument('--min_freq', default=0, type=int) parser.add_argument("--line_per_file", default=1000000000, type=int) parser.add_argument("--file_num", type=int, default=10, help="Number of dynamic masking to pregenerate") parser.add_argument("--max_seq_len", type=int, default=128) parser.add_argument( "--short_seq_prob", type=float, default=0.1, help="Probability of making a short sentence as a training example") parser.add_argument( "--masked_lm_prob", type=float, default=0.15, help="Probability of masking each token for the LM task") parser.add_argument( "--max_predictions_per_seq", type=int, default=20, help="Maximum number of tokens to mask in each sequence") args = parser.parse_args() seed_everything(args.seed) vocab = Vocabulary(min_freq=args.min_freq, add_unused=False) if args.do_corpus: corpus = [] train_path = str(config['data_dir'] / 'train.txt') with open(train_path, 'r') as fr: for ex_id, line in enumerate(fr): line = line.strip("\n") lines = [ " ".join(x.split("/")[0].split("_")) for x in line.split(" ") ] if ex_id == 0: logger.info(f"Train example: {' '.join(lines)}") corpus.append(" ".join(lines)) test_path = str(config['data_dir'] / 'test.txt') with open(test_path, 'r') as fr: for ex_id, line in enumerate(fr): line = line.strip("\n") lines = line.split("_") if ex_id == 0: logger.info(f"Test example: {' '.join(lines)}") corpus.append(" ".join(lines)) corpus_path = str(config['data_dir'] / 'corpus.txt') with open(corpus_path, 'r') as fr: for ex_id, line in enumerate(fr): line = line.strip("\n") lines = line.split("_") if ex_id == 0: logger.info(f"Corpus example: {' '.join(lines)}") corpus.append(" ".join(lines)) corpus = list(set(corpus)) logger.info(f"corpus size: {len(corpus)}") random_order = list(range(len(corpus))) np.random.shuffle(random_order) corpus = [corpus[i] for i in random_order] new_corpus_path = config['data_dir'] / "corpus/corpus.txt" if not new_corpus_path.exists(): new_corpus_path.parent.mkdir(exist_ok=True) with open(new_corpus_path, 'w') as fr: for line in corpus: fr.write(line + "\n") if args.do_split: new_corpus_path = config['data_dir'] / "corpus/corpus.txt" split_save_path = config['data_dir'] / "corpus/train" if not split_save_path.exists(): split_save_path.mkdir(exist_ok=True) line_per_file = args.line_per_file command = f'split -a 4 -l {line_per_file} -d {new_corpus_path} {split_save_path}/shard_' os.system(f"{command}") if args.do_vocab: vocab.read_data(data_path=config['data_dir'] / "corpus/train") vocab.build_vocab() vocab.save(file_path=config['data_dir'] / 'corpus/vocab_mapping.pkl') vocab.save_bert_vocab(file_path=config['checkpoint_dir'] / 'vocab.txt') logger.info(f"vocab size: {len(vocab)}") bert_base_config['vocab_size'] = len(vocab) save_json(data=bert_base_config, file_path=config['checkpoint_dir'] / 'config.json') if args.do_data: vocab_list = vocab.load_bert_vocab(config['checkpoint_dir'] / 'vocab.txt') data_path = config['data_dir'] / "corpus/train" files = sorted([ f for f in data_path.iterdir() if f.exists() and "." not in str(f) ]) logger.info("--- pregenerate training data parameters ---") logger.info(f'max_seq_len: {args.max_seq_len}') logger.info(f"max_predictions_per_seq: {args.max_predictions_per_seq}") logger.info(f"masked_lm_prob: {args.masked_lm_prob}") logger.info(f"seed: {args.seed}") logger.info(f"file num : {args.file_num}") for idx in range(args.file_num): logger.info(f"pregenetate file_{idx}.json") save_filename = data_path / f"file_{idx}.json" num_instances = 0 with save_filename.open('w') as fw: for file_idx in range(len(files)): file_path = files[file_idx] file_examples = build_examples( file_path, max_seq_len=args.max_seq_len, masked_lm_prob=args.masked_lm_prob, max_predictions_per_seq=args.max_predictions_per_seq, vocab_list=vocab_list) file_examples = [ json.dumps(instance) for instance in file_examples ] for instance in file_examples: fw.write(instance + '\n') num_instances += 1 metrics_file = data_path / f"file_{idx}_metrics.json" print(f"num_instances: {num_instances}") with metrics_file.open('w') as metrics_file: metrics = { "num_training_examples": num_instances, "max_seq_len": args.max_seq_len } metrics_file.write(json.dumps(metrics))
def main(): parser = ArgumentParser() parser.add_argument("--file_num", type=int, default=10, help="Number of pregenerate file") parser.add_argument("--reduce_memory", action="store_true", help="Store training data as on-disc memmaps to massively reduce memory usage") parser.add_argument("--epochs", type=int, default=4, help="Number of epochs to train for") parser.add_argument('--num_eval_steps', default=2000) parser.add_argument('--num_save_steps', default=5000) parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument("--train_batch_size", default=18, type=int, help="Total batch size for training.") parser.add_argument('--loss_scale', type=float, default=0, help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Linear warmup over warmup_steps.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument('--max_grad_norm', default=1.0, type=float) parser.add_argument("--learning_rate", default=2e-4, type=float, help="The initial learning rate for Adam.") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--fp16_opt_level', type=str, default='O2', help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html") parser.add_argument('--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") args = parser.parse_args() pregenerated_data = config['data_dir'] / "corpus/train" assert pregenerated_data.is_dir(), \ "--pregenerated_data should point to the folder of files made by prepare_lm_data_mask.py!" samples_per_epoch = 0 for i in range(args.file_num): data_file = pregenerated_data / f"file_{i}.json" metrics_file = pregenerated_data / f"file_{i}_metrics.json" if data_file.is_file() and metrics_file.is_file(): metrics = json.loads(metrics_file.read_text()) samples_per_epoch += metrics['num_training_examples'] else: if i == 0: exit("No training data was found!") print(f"Warning! There are fewer epochs of pregenerated data ({i}) than training epochs ({args.epochs}).") print("This script will loop over the available data, but training diversity may be negatively impacted.") break logger.info(f"samples_per_epoch: {samples_per_epoch}") if args.local_rank == -1 or args.no_cuda: device = torch.device(f"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) args.n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logger.info( f"device: {device} , distributed training: {bool(args.local_rank != -1)}, 16-bits training: {args.fp16}") if args.gradient_accumulation_steps < 1: raise ValueError( f"Invalid gradient_accumulation_steps parameter: {args.gradient_accumulation_steps}, should be >= 1") args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps seed_everything(args.seed) tokenizer = BertTokenizer(vocab_file=config['checkpoint_dir'] / 'vocab.txt') total_train_examples = samples_per_epoch * args.epochs num_train_optimization_steps = int( total_train_examples / args.train_batch_size / args.gradient_accumulation_steps) if args.local_rank != -1: num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() args.warmup_steps = int(num_train_optimization_steps * args.warmup_proportion) # Prepare model with open(str(config['checkpoint_dir'] / 'config.json'), "r", encoding='utf-8') as reader: json_config = json.loads(reader.read()) print(json_config) bert_config = BertConfig.from_json_file(str(config['checkpoint_dir'] / 'config.json')) model = BertForMaskedLM(config=bert_config) # model = BertForMaskedLM.from_pretrained(config['checkpoint_dir'] / 'checkpoint-580000') model.to(device) # Prepare optimizer 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} ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) lr_scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=num_train_optimization_steps) 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) if args.n_gpu > 1: model = torch.nn.DataParallel(model) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank) global_step = 0 metric = LMAccuracy() tr_acc = AverageMeter() tr_loss = AverageMeter() train_logs = {} logger.info("***** Running training *****") logger.info(f" Num examples = {total_train_examples}") logger.info(f" Batch size = {args.train_batch_size}") logger.info(f" Num steps = {num_train_optimization_steps}") logger.info(f" warmup_steps = {args.warmup_steps}") seed_everything(args.seed) # Added here for reproducibility for epoch in range(args.epochs): for idx in range(args.file_num): epoch_dataset = PregeneratedDataset(file_id=idx, training_path=pregenerated_data, tokenizer=tokenizer, reduce_memory=args.reduce_memory) if args.local_rank == -1: train_sampler = RandomSampler(epoch_dataset) else: train_sampler = DistributedSampler(epoch_dataset) train_dataloader = DataLoader(epoch_dataset, sampler=train_sampler, batch_size=args.train_batch_size) model.train() nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate(train_dataloader): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, lm_label_ids = batch outputs = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, masked_lm_labels=lm_label_ids) pred_output = outputs[1] loss = outputs[0] metric(logits=pred_output.view(-1, bert_config.vocab_size), target=lm_label_ids.view(-1)) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() nb_tr_steps += 1 tr_acc.update(metric.value(), n=input_ids.size(0)) tr_loss.update(loss.item(), n=1) if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) lr_scheduler.step() optimizer.step() optimizer.zero_grad() global_step += 1 if global_step % args.num_eval_steps == 0: train_logs['loss'] = tr_loss.avg train_logs['acc'] = tr_acc.avg show_info = f'\n[Training]:[{epoch}/{args.epochs}]{global_step}/{num_train_optimization_steps} ' + "-".join( [f' {key}: {value:.4f} ' for key, value in train_logs.items()]) logger.info(show_info) tr_acc.reset() tr_loss.reset() if global_step % args.num_save_steps == 0: if args.local_rank in [-1, 0] and args.num_save_steps > 0: # Save model checkpoint output_dir = config['checkpoint_dir'] / f'lm-checkpoint-{global_step}' if not output_dir.exists(): output_dir.mkdir() # save model model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training model_to_save.save_pretrained(str(output_dir)) torch.save(args, str(output_dir / 'training_args.bin')) logger.info("Saving model checkpoint to %s", output_dir) # save config output_config_file = output_dir / CONFIG_NAME with open(str(output_config_file), 'w') as f: f.write(model_to_save.config.to_json_string()) # save vocab tokenizer.save_vocabulary(output_dir)