# 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. """ Finetuning the library models for question-answering on SQuAD (DistilBERT, Bert, XLM, XLNet).""" from __future__ import absolute_import, division, print_function import argparse import logging import os import random import glob import numpy as np import torch from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset) from torch.utils.data.distributed import DistributedSampler try: from torch.utils.tensorboard import SummaryWriter except: from tensorboardX import SummaryWriter from tqdm import tqdm, trange from transformers import (WEIGHTS_NAME, BertConfig, BertForQuestionAnswering, BertTokenizer, XLMConfig, XLMForQuestionAnswering, XLMTokenizer, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer, DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer) from transformers import AdamW, WarmupLinearSchedule from utils_squad import (read_squad_examples, convert_examples_to_features, RawResult, write_predictions, RawResultExtended, write_predictions_extended) # The follwing import is the official SQuAD evaluation script (2.0). # You can remove it from the dependencies if you are using this script outside of the library # We've added it here for automated tests (see examples/test_examples.py file) from utils_squad_evaluate import EVAL_OPTS, main as evaluate_on_squad logger = logging.getLogger(__name__) ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \ for conf in (BertConfig, XLNetConfig, XLMConfig)), ()) MODEL_CLASSES = { 'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer), 'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer), 'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer), 'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer) } 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 to_list(tensor): return tensor.detach().cpu().tolist() def train(args, train_dataset, model, tokenizer): """ Train the model """ if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() 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) 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 = WarmupLinearSchedule(optimizer, warmup_steps=args.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) # 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() batch = tuple(t.to(args.device) for t in batch) inputs = {'input_ids': batch[0], 'attention_mask': batch[1], 'start_positions': batch[3], 'end_positions': batch[4]} if args.model_type != 'distilbert': inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2] if args.model_type in ['xlnet', 'xlm']: inputs.update({'cls_index': batch[5], 'p_mask': batch[6]}) outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) 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 == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) 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) 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, prefix=""): dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) all_results = [] for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {'input_ids': batch[0], 'attention_mask': batch[1] } if args.model_type != 'distilbert': inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids example_indices = batch[3] if args.model_type in ['xlnet', 'xlm']: inputs.update({'cls_index': batch[4], 'p_mask': batch[5]}) outputs = model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) if args.model_type in ['xlnet', 'xlm']: # XLNet uses a more complex post-processing procedure result = RawResultExtended(unique_id = unique_id, start_top_log_probs = to_list(outputs[0][i]), start_top_index = to_list(outputs[1][i]), end_top_log_probs = to_list(outputs[2][i]), end_top_index = to_list(outputs[3][i]), cls_logits = to_list(outputs[4][i])) else: result = RawResult(unique_id = unique_id, start_logits = to_list(outputs[0][i]), end_logits = to_list(outputs[1][i])) all_results.append(result) # Compute predictions output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix)) output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix)) if args.version_2_with_negative: output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix)) else: output_null_log_odds_file = None if args.model_type in ['xlnet', 'xlm']: # XLNet uses a more complex post-processing procedure write_predictions_extended(examples, features, all_results, args.n_best_size, args.max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.predict_file, model.config.start_n_top, model.config.end_n_top, args.version_2_with_negative, tokenizer, args.verbose_logging) else: write_predictions(examples, features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold) # Evaluate with the official SQuAD script evaluate_options = EVAL_OPTS(data_file=args.predict_file, pred_file=output_prediction_file, na_prob_file=output_null_log_odds_file) results = evaluate_on_squad(evaluate_options) return results def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file input_file = args.predict_file if evaluate else args.train_file cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format( 'dev' if evaluate else 'train', list(filter(None, args.model_name_or_path.split('/'))).pop(), str(args.max_seq_length))) if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", input_file) examples = read_squad_examples(input_file=input_file, is_training=not evaluate, version_2_with_negative=args.version_2_with_negative) features = convert_examples_to_features(examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long) all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float) if evaluate: all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index, all_cls_index, all_p_mask) else: all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long) all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions, all_cls_index, all_p_mask) if output_examples: return dataset, examples, features return dataset def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--train_file", default=None, type=str, required=True, help="SQuAD json for training. E.g., train-v1.1.json") parser.add_argument("--predict_file", default=None, type=str, required=True, help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") 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 checkpoints and predictions will be written.") ## Other parameters 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('--version_2_with_negative', action='store_true', help='If true, the SQuAD examples contain some that do not have an answer.') parser.add_argument('--null_score_diff_threshold', type=float, default=0.0, help="If null_score - best_non_null is greater than the threshold predict null.") parser.add_argument("--max_seq_length", default=384, type=int, help="The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded.") parser.add_argument("--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.") parser.add_argument("--max_query_length", default=64, type=int, help="The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") 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("--evaluate_during_training", action='store_true', help="Rul 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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") 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("--weight_decay", default=0.0, type=float, help="Weight deay 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("--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json output file.") parser.add_argument("--max_answer_length", default=30, type=int, help="The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another.") parser.add_argument("--verbose_logging", action='store_true', help="If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") 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="Whether not to use 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("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") 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('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and 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)) # 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 = 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(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) # 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) 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) model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) 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) # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set. # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will # remove the need for this code, but it is still valid. if args.fp16: try: import apex apex.amp.register_half_function(torch, 'einsum') except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer 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')) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: 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("transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items()) results.update(result) logger.info("Results: {}".format(results)) return results if __name__ == "__main__": main()