run_av.py

# -*- coding: utf-8 -*-
# Copyright (c) 2021 by Phuc Phan

import os
import glob
import torch
import pickle
import random
import timeit
import logging
import argparse
import numpy as np

from tqdm import tqdm, trange
from evaluator import eval_squad
from torch.utils.data import (
    DataLoader, 
    RandomSampler, 
    SequentialSampler, 
    DistributedSampler
)
from transformers import (
    WEIGHTS_NAME, 
    BertConfig,
    BertTokenizer,
    RobertaConfig,
    RobertaTokenizer,
    XLMRobertaConfig,
    XLMRobertaForQuestionAnswering,
    XLMRobertaTokenizer,
    AlbertConfig,
    AlbertTokenizer,
    AutoTokenizer,
    ElectraConfig,
    ElectraTokenizer,
    GPT2Tokenizer,
    GPT2Model,
    GPT2ForSequenceClassification
)
from transformers import (
    AdamW, 
    get_linear_schedule_with_warmup,
    squad_convert_examples_to_features
)
from transformers.data.processors.squad import (
    SquadV1Processor, 
    SquadV2Processor, 
    SquadResult
)
from transformers.data.metrics.squad_metrics import (
    compute_predictions_logits, 
    compute_predictions_log_probs, 
    squad_evaluate
)
from modeling_bert import BertForQuestionAnsweringAVPool
from modeling_albert import AlbertForQuestionAnsweringAVPool
from modeling_electra import ElectraForQuestionAnsweringAVPool
from modeling_roberta import RobertaForQuestionAnsweringAVPool
from modeling_xlm_roberta import XLMRobertaForQuestionAnsweringAVPool, XLMRobertaForQuestionAnsweringAVDep, XLMRobertaForQuestionAnsweringAVDep2

try:
    from torch.utils.tensorboard import SummaryWriter
except:
    from tensorboardX import SummaryWriter

logger = logging.getLogger(__name__)

MODEL_CLASSES = {
    'bert': (BertConfig, BertForQuestionAnsweringAVPool, BertTokenizer),
    'albert': (AlbertConfig, AlbertForQuestionAnsweringAVPool, AlbertTokenizer),
    'phobert': (RobertaConfig, RobertaForQuestionAnsweringAVPool, AutoTokenizer),
    'bartpho': (XLMRobertaConfig, XLMRobertaForQuestionAnsweringAVPool, AutoTokenizer), 
    'xlm-roberta': (XLMRobertaConfig, XLMRobertaForQuestionAnsweringAVPool, XLMRobertaTokenizer),
    'xlm-roberta-dep': (XLMRobertaConfig, XLMRobertaForQuestionAnsweringAVDep, XLMRobertaTokenizer),
    'xlm-roberta-dep2': (XLMRobertaConfig, XLMRobertaForQuestionAnsweringAVDep2, XLMRobertaTokenizer),
    'electra-base': (ElectraConfig, ElectraForQuestionAnsweringAVPool, ElectraTokenizer)
}

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):
    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 = 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)


    print(model)
    
    # 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 = 1
    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],
                'is_impossibles': batch[5]
            }
            # if args.model_type != 'xlm-roberta':
            #     inputs['is_impossibles'] = batch[5]

            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[6], 'p_mask': batch[7]})

            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

                # Log metrics
                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    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

                # Save model checkpoint
                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    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)
    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # multi-gpu evaluate
    if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
        model = torch.nn.DataParallel(model)

    # 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 = []
    start_time = timeit.default_timer()

    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]
            
            # XLNet and XLM use more arguments for their predictions
            if args.model_type in ['xlnet', 'xlm']:
                inputs.update({'cls_index': batch[5], 'p_mask': batch[6]})

            outputs = model(**inputs)

        for i, example_index in enumerate(example_indices):
            eval_feature = features[example_index.item()]
            unique_id = int(eval_feature.unique_id)

            output = [to_list(output[i]) for output in outputs]

            # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
            # models only use two.
            if len(output) >= 5:
                start_logits = output[0]
                start_top_index = output[1]
                end_logits = output[2]
                end_top_index = output[3]
                cls_logits = output[4]

                result = SquadResult(
                    unique_id, start_logits, end_logits, 
                    start_top_index=start_top_index, 
                    end_top_index=end_top_index, 
                    cls_logits=cls_logits
                )

            else:
                start_logits, end_logits, choice_logits  = output
                result = SquadResult(
                    unique_id, start_logits, end_logits, choice_logits
                )

            all_results.append(result)

    evalTime = timeit.default_timer() - start_time
    logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))

    # 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

    # XLNet and XLM use a more complex post-processing procedure
    # for i in range(0.0, 1.0, 0.01):
    if args.model_type in ['xlnet', 'xlm']:
        start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top
        end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top

        predictions = compute_predictions_log_probs(examples, features, all_results, args.n_best_size,
                        args.max_answer_length, output_prediction_file,
                        output_nbest_file, output_null_log_odds_file,
                        start_n_top, end_n_top,
                        args.version_2_with_negative, tokenizer, args.verbose_logging)
    else:
        predictions = compute_predictions_logits(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, tokenizer)

    with open(os.path.join(args.output_dir, str(prefix) + "_eval_examples.pkl"), 'wb') as f:
        pickle.dump(examples, f)
    with open(os.path.join(args.output_dir, str(prefix) + "_eval_features.pkl"), 'wb') as f:
        pickle.dump(features, f)
    with open(os.path.join(args.output_dir, str(prefix) + "_res.pkl"), 'wb') as f:
        pickle.dump(all_results, f)

    # Compute the F1 and exact scores.
    #results = squad_evaluate(examples, predictions)
    #SQuAD 2.0
    # results = eval_squad(args.predict_file, output_prediction_file, output_null_log_odds_file,
    #                         args.null_score_diff_threshold)
    results = eval_squad(os.path.join(args.data_dir, args.predict_file), output_prediction_file, output_null_log_odds_file,
                            args.null_score_diff_threshold)
        # print("\n> ", results)
    
    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_dir = args.data_dir if args.data_dir else "."
    
    cached_features_file = os.path.join(input_dir, 'bce_reader_cached_{}_{}_{}_{}'.format(
        args.predict_file if evaluate else args.train_file,
        list(filter(None, args.model_name_or_path.split('/'))).pop(),
        str(args.max_seq_length), str(args.doc_stride)),
    )

    # Init features and dataset from cache if it exists
    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_and_dataset = torch.load(cached_features_file)
        features, dataset = features_and_dataset["features"], features_and_dataset["dataset"]
    else:
        logger.info("Creating features from dataset file at %s", input_dir)

        processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()

        print(f"FILES: {args.train_file} | {args.predict_file}")

        if evaluate:
            examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file)
        else:
            examples = processor.get_train_examples(args.data_dir, filename=args.train_file)

        features, dataset = squad_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,
            return_dataset='pt'
        )

        # if args.local_rank in [-1, 0]:
        #     logger.info("Saving features into cached file %s", cached_features_file)
        #     torch.save({"features": features, "dataset": dataset}, 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

    if output_examples:
        return dataset, examples, features
    
    return dataset

def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
    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.")
    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model checkpoints and predictions will be written.")

    ## Others parameters
    parser.add_argument("--padding_side", default="right", type=str,
                        help="right/left, padding_side of passage / question")
    parser.add_argument("--data_dir", default="", type=str,
                        help="The input data dir. Should contain the .json files for the task.")
    parser.add_argument("--train_file", default=None, type=str,
                        help="The input training file. If a data dir is specified, will look for the file there.")
    parser.add_argument("--predict_file", default=None, type=str,
                        help="The input evaluation file. If a data dir is specified, will look for the file there.")
    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=0.01, 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("--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=5000,
                        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")

    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)
    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,
                                          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, use_fast=False)
    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)

    # 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.")
    start_time = timeit.default_timer()
    # 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, force_download=True)
        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]:

        if args.do_train:
            logger.info("Loading checkpoints saved during training for evaluation")
            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
        else:
            if args.eval_all_checkpoints:
                checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
                logger.info("Loading checkpoint %s for evaluation", checkpoints)
                logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
            else:
                logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path)
                checkpoints = [args.model_name_or_path]

        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, force_download=True)
            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))
    with open(os.path.join(args.output_dir, "result.txt"), "a") as writer:
        for key in sorted(results.keys()):
            logger.info("  %s = %s", key, str(results[key]))
            writer.write("%s = %s\t" % (key, str(results[key])))
            writer.write("\t\n")

    runtime = timeit.default_timer() - start_time
    logger.info(f"Running time: {runtime}")

    return results

if __name__ == "__main__":
    main()