# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. 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. """ Fine-tuning the library models for named entity recognition on CoNLL-2003 (Bert or Roberta). """ from __future__ import absolute_import, division, print_function import argparse import glob import logging import os import random import shutil import numpy as np import torch from seqeval.metrics import ( classification_report, f1_score, precision_score, recall_score, ) from tensorboardX import SummaryWriter from torch.nn import CrossEntropyLoss from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange from transformers import ( WEIGHTS_NAME, AdamW, BertConfig, BertForTokenClassification, BertTokenizer, RobertaConfig, RobertaForTokenClassification, RobertaTokenizer, get_linear_schedule_with_warmup, ) from layoutlm import FunsdDataset, LayoutlmConfig, LayoutlmForTokenClassification logger = logging.getLogger(__name__) ALL_MODELS = sum( ( tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, LayoutlmConfig) ), (), ) MODEL_CLASSES = { "bert": (BertConfig, BertForTokenClassification, BertTokenizer), "roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer), "layoutlm": (LayoutlmConfig, LayoutlmForTokenClassification, BertTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def collate_fn(data): batch = [i for i in zip(*data)] for i in range(len(batch)): if i < len(batch) - 2: batch[i] = torch.stack(batch[i], 0) return tuple(batch) def get_labels(path): with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels def train( # noqa C901 args, train_dataset, model, tokenizer, labels, pad_token_label_id ): """ Train the model """ if args.local_rank in [-1, 0]: tb_writer = SummaryWriter(logdir="runs/" + os.path.basename(args.output_dir)) args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = ( RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) ) train_dataloader = DataLoader( train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=None, ) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = ( args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 ) else: t_total = ( len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs ) # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [ p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) ], "weight_decay": args.weight_decay, }, { "params": [ p for n, p in model.named_parameters() 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 ) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.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 ) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info( " Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size ) 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) global_step = 0 tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] ) set_seed(args) # Added here for reproductibility (even between python 2 and 3) for _ in train_iterator: epoch_iterator = tqdm( train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0] ) for step, batch in enumerate(epoch_iterator): model.train() inputs = { "input_ids": batch[0].to(args.device), "attention_mask": batch[1].to(args.device), "labels": batch[3].to(args.device), } if args.model_type in ["layoutlm"]: inputs["bbox"] = batch[4].to(args.device) inputs["token_type_ids"] = ( batch[2].to(args.device) if args.model_type in ["bert", "layoutlm"] else None ) # RoBERTa don"t use segment_ids outputs = model(**inputs) # model outputs are always tuple in pytorch-transformers (see doc) loss = outputs[0] if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training 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() tr_loss += loss.item() 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 ) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if ( args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0 ): # Log metrics if ( args.local_rank in [-1, 0] and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results, _ = evaluate( args, model, tokenizer, labels, pad_token_label_id, mode="dev", ) for key, value in results.items(): tb_writer.add_scalar( "eval_{}".format(key), value, global_step ) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) tb_writer.add_scalar( "loss", (tr_loss - logging_loss) / args.logging_steps, global_step, ) logging_loss = tr_loss if ( args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0 ): # Save model checkpoint output_dir = os.path.join( args.output_dir, "checkpoint-{}".format(global_step) ) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""): eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=mode) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader( eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=None, ) # Eval! logger.info("***** Running evaluation %s *****", prefix) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None model.eval() for batch in tqdm(eval_dataloader, desc="Evaluating"): with torch.no_grad(): inputs = { "input_ids": batch[0].to(args.device), "attention_mask": batch[1].to(args.device), "labels": batch[3].to(args.device), } if args.model_type in ["layoutlm"]: inputs["bbox"] = batch[4].to(args.device) inputs["token_type_ids"] = ( batch[2].to(args.device) if args.model_type in ["bert", "layoutlm"] else None ) # RoBERTa don"t use segment_ids outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] if args.n_gpu > 1: tmp_eval_loss = ( tmp_eval_loss.mean() ) # mean() to average on multi-gpu parallel evaluating eval_loss += tmp_eval_loss.item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append( out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0 ) eval_loss = eval_loss / nb_eval_steps preds = np.argmax(preds, axis=2) label_map = {i: label for i, label in enumerate(labels)} out_label_list = [[] for _ in range(out_label_ids.shape[0])] preds_list = [[] for _ in range(out_label_ids.shape[0])] for i in range(out_label_ids.shape[0]): for j in range(out_label_ids.shape[1]): if out_label_ids[i, j] != pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]]) preds_list[i].append(label_map[preds[i][j]]) results = { "loss": eval_loss, "precision": precision_score(out_label_list, preds_list), "recall": recall_score(out_label_list, preds_list), "f1": f1_score(out_label_list, preds_list), } report = classification_report(out_label_list, preds_list) logger.info("\n" + report) logger.info("***** Eval results %s *****", prefix) for key in sorted(results.keys()): logger.info(" %s = %s", key, str(results[key])) return results, preds_list def main(): # noqa C901 parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) ## Other parameters parser.add_argument( "--labels", default="", type=str, help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.", ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3", ) parser.add_argument( "--max_seq_length", default=512, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.", ) parser.add_argument( "--do_train", action="store_true", help="Whether to run training." ) parser.add_argument( "--do_eval", action="store_true", help="Whether to run eval on the dev set." ) parser.add_argument( "--do_predict", action="store_true", help="Whether to run predictions on the test set.", ) parser.add_argument( "--evaluate_during_training", action="store_true", help="Whether to run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.", ) parser.add_argument( "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.", ) 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( "--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.", ) parser.add_argument( "--weight_decay", default=0.0, type=float, help="Weight decay if we apply some." ) 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, help="Max gradient norm." ) parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument( "--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps." ) parser.add_argument( "--logging_steps", type=int, default=50, help="Log every X updates steps." ) parser.add_argument( "--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.", ) parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument( "--no_cuda", action="store_true", help="Avoid using CUDA when available" ) parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets", ) parser.add_argument( "--seed", type=int, default=42, help="random seed for initialization" ) parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", 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( "--local_rank", type=int, default=-1, help="For distributed training: local_rank", ) parser.add_argument( "--server_ip", type=str, default="", help="For distant debugging." ) parser.add_argument( "--server_port", type=str, default="", help="For distant debugging." ) args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train ): if not args.overwrite_output_dir: raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) else: if args.local_rank in [-1, 0]: shutil.rmtree(args.output_dir) if not os.path.exists(args.output_dir) and (args.do_eval or args.do_predict): raise ValueError( "Output directory ({}) does not exist. Please train and save the model before inference stage.".format( args.output_dir ) ) if ( not os.path.exists(args.output_dir) and args.do_train and args.local_rank in [-1, 0] ): os.makedirs(args.output_dir) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach( address=(args.server_ip, args.server_port), redirect_output=True ) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device( "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" ) args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( filename=os.path.join(args.output_dir, "train.log") if args.local_rank in [-1, 0] else None, format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set seed set_seed(args) labels = get_labels(args.labels) num_labels = len(labels) # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later pad_token_label_id = CrossEntropyLoss().ignore_index # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = FunsdDataset( args, tokenizer, labels, pad_token_label_id, mode="train" ) global_step, tr_loss = train( args, train_dataset, model, tokenizer, labels, pad_token_label_id ) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Create output directory if needed if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained( args.output_dir, do_lower_case=args.do_lower_case ) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted( glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True) ) ) logging.getLogger("pytorch_transformers.modeling_utils").setLevel( logging.WARN ) # Reduce logging logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) result, _ = evaluate( args, model, tokenizer, labels, pad_token_label_id, mode="test", prefix=global_step, ) if global_step: result = {"{}_{}".format(global_step, k): v for k, v in result.items()} results.update(result) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: for key in sorted(results.keys()): writer.write("{} = {}\n".format(key, str(results[key]))) if args.do_predict and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained( args.model_name_or_path, do_lower_case=args.do_lower_case ) model = model_class.from_pretrained(args.output_dir) model.to(args.device) result, predictions = evaluate( args, model, tokenizer, labels, pad_token_label_id, mode="test" ) # Save results output_test_results_file = os.path.join(args.output_dir, "test_results.txt") with open(output_test_results_file, "w") as writer: for key in sorted(result.keys()): writer.write("{} = {}\n".format(key, str(result[key]))) # Save predictions output_test_predictions_file = os.path.join( args.output_dir, "test_predictions.txt" ) with open(output_test_predictions_file, "w", encoding="utf8") as writer: with open( os.path.join(args.data_dir, "test.txt"), "r", encoding="utf8" ) as f: example_id = 0 for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": writer.write(line) if not predictions[example_id]: example_id += 1 elif predictions[example_id]: output_line = ( line.split()[0] + " " + predictions[example_id].pop(0) + "\n" ) writer.write(output_line) else: logger.warning( "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0], ) return results if __name__ == "__main__": main()