# 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 sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa).""" from __future__ import absolute_import, division, print_function import argparse import glob import logging import os import random import pdb cwd = os.getcwd() print(f"Current working dir is {cwd}") import sys sys.path.append('./') pt_path = os.path.join( cwd, 'pytorch_transformers') sys.path.append(pt_path) print(f"Pytorch Transformer {pt_path}") import numpy as np import torch from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset) from torch.utils.data.distributed import DistributedSampler from tensorboardX import SummaryWriter from tqdm import tqdm, trange from pytorch_transformers import (WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer,BertForSequenceClassificationLatentConnector, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer) from pytorch_transformers import AdamW, WarmupLinearSchedule from utils_glue import (compute_metrics, convert_examples_to_features, output_modes, processors) logger = logging.getLogger(__name__) ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig)), ()) MODEL_CLASSES = { 'bert': (BertConfig, BertForSequenceClassificationLatentConnector, BertTokenizer), 'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer), 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer), 'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer), } 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 load_and_cache_examples(args, task, tokenizer, file_txt, evaluate=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 processor = processors[task]() output_mode = output_modes[task] label_list = processor.get_labels() if task in ['mnli', 'mnli-mm'] and args.model_type in ['roberta']: # HACK(label indices are swapped in RoBERTa pretrained model) label_list[1], label_list[2] = label_list[2], label_list[1] examples = processor.get_train_examples(args.data_dir, args.percentage_per_label, args.sample_per_label) # Chunyuan: convert examples into text lines here # write data in a file. for item in examples: # pdb.set_trace() if item.text_b: line = item.text_a + " " + tokenizer.sep_token + " " + item.text_b + "\n" else: line = item.text_a + " \n" file_txt.write(line) file_txt.close() def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") parser.add_argument('--gloabl_step_eval', type=int, default=661, help="Evaluate the results at the given global step") 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)) ## 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("--max_seq_length", default=128, 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_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--percentage_per_label", type=float, default=1.0, help="Set this value (<1.0), if you are using a subset of training dataset.") parser.add_argument("--sample_per_label", type=int, default=-1, help="Set this value, if you are using a subset of training dataset, and a fixed number of samples are specified.") parser.add_argument("--use_freeze", action='store_true', help="Set this flag if you are not updating the model.") 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("--use_philly", action='store_true', help="Use Philly for computing.") parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") 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 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) # Set seed set_seed(args) ## Tokenizer args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path, do_lower_case=args.do_lower_case) # 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 if not os.path.isdir(args.output_dir): os.mkdir(args.output_dir) logger.info("Parameters %s", args) # Prepare GLUE task TASK_NAME = ['CoLA', 'SST-2', 'MRPC', 'STS-B', 'QQP', 'MNLI', 'QNLI', 'RTE', 'WNLI'] parent_path = args.data_dir for task_ in TASK_NAME: args.data_dir = os.path.join(parent_path, task_) args.task_name = task_.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] args.output_file_name = os.path.join(args.output_dir, f"{args.task_name}.txt") logger.info("Dataset input file at %s", args.data_dir) logger.info("Dataset ouput file at %s", args.output_file_name) file_txt = open(args.output_file_name, "w") load_and_cache_examples(args, args.task_name, tokenizer, file_txt, evaluate=False) if __name__ == "__main__": main()