run_ner_span.py

#!/usr/bin/env python
# encoding: utf-8
'''
@author: zessay
@license: (C) Copyright Sogou.
@contact: zessay@sogou-inc.com
@file: run_ner_span.py
@time: 2020/3/5 17:38
@description: 
'''

import argparse
import glob
import logging
import os
import json
import time
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler

sys.path.append(os.path.dirname(os.getcwd()))

from albert_pytorch.callback.progressbar import ProgressBar
from albert_pytorch.model.file_utils import WEIGHTS_NAME
from albert_pytorch.model.modeling_albert_bright import AlbertConfig, AlbertSpanForNer
from albert_pytorch.model.modeling_bert import BertConfig, BertSpanForNer
from albert_pytorch.processors.utils_ner import CNerTokenizer
from albert_pytorch.processors.ner_seq import convert_examples_to_features
from albert_pytorch.processors.ner_seq import ner_processors as processors
from albert_pytorch.processors.ner_seq import collate_fn
from albert_pytorch.metrics.ner_metrics import SpanEntityScore

from snlp.optimizer import AdamW
from snlp.schedule import get_linear_schedule_with_warmup
from snlp.tools.log import logger, init_logger
from snlp.tools.common import seed_everything

ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig,)), ())

MODEL_CLASSES = {
    'albert': (AlbertConfig, AlbertSpanForNer, CNerTokenizer),
    'bert': (BertConfig, BertSpanForNer, CNerTokenizer),
}

# -------------------------------------------------------------------------------

def train(args, train_dataset, model, tokenizer):
    """ Train the model """
    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=collate_fn)
    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},
    ]
    args.warmup_steps = int(t_total * args.warmup_proportion)
    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)
    # Check if saved optimizer or scheduler states exist
    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
        os.path.join(args.model_name_or_path, "scheduler.pt")):
        # Load in optimizer and scheduler states
        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
    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
    steps_trained_in_current_epoch = 0
    # Check if continuing training from a checkpoint
    if os.path.exists(args.model_name_or_path) and "checkpoint" in args.model_name_or_path:
        # set global_step to gobal_step of last saved checkpoint from model path
        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
        logger.info("  Continuing training from epoch %d", epochs_trained)
        logger.info("  Continuing training from global step %d", global_step)
        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
    seed_everything(args.seed)  # Added here for reproductibility (even between python 2 and 3)
    for _ in range(int(args.num_train_epochs)):
        pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
        for step, batch in enumerate(train_dataloader):
            # Skip past any already trained steps if resuming training
            if steps_trained_in_current_epoch > 0:
                steps_trained_in_current_epoch -= 1
                continue
            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":
                # XLM and RoBERTa don"t use segment_ids
                inputs["token_type_ids"] = (batch[2] if args.model_type in ["bert", "xlnet"] else None)
            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
            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()
            pbar(step, {'loss': loss.item()})
            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)
                scheduler.step()  # Update learning rate schedule
                optimizer.step()
                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
                    print(" ")
                    if args.local_rank == -1:
                        # Only evaluate when single GPU otherwise metrics may not average well
                        evaluate(args, model, tokenizer)
                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"))
                    tokenizer.save_vocabulary(output_dir)
                    logger.info("Saving model checkpoint to %s", output_dir)
                    torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                    torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
        print(" ")
        if 'cuda' in str(args.device):
            torch.cuda.empty_cache()
    return global_step, tr_loss / global_step

def evaluate(args, model, tokenizer, prefix=""):
    metric = SpanEntityScore(args.id2label)
    eval_output_dir = args.output_dir
    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(eval_output_dir)
    eval_features = load_and_cache_examples(args, args.task_name,tokenizer, data_type='dev')
    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Eval!
    logger.info("***** Running evaluation %s *****", prefix)
    logger.info("  Num examples = %d", len(eval_features))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    pbar = ProgressBar(n_total=len(eval_features), desc="Evaluating")
    for step, f in enumerate(eval_features):
        input_lens = f.input_len
        input_ids = torch.tensor([f.input_ids[:input_lens]], dtype=torch.long).to(args.device)
        input_mask = torch.tensor([f.input_mask[:input_lens]], dtype=torch.long).to(args.device)
        segment_ids = torch.tensor([f.segment_ids[:input_lens]], dtype=torch.long).to(args.device)
        start_ids = torch.tensor([f.start_ids[:input_lens]], dtype=torch.long).to(args.device)
        end_ids = torch.tensor([f.end_ids[:input_lens]], dtype=torch.long).to(args.device)
        subjects = f.subjects
        model.eval()
        with torch.no_grad():
            inputs = {"input_ids": input_ids, "attention_mask": input_mask,
                      "start_positions": start_ids,"end_positions": end_ids}
            if args.model_type != "distilbert":
                # XLM and RoBERTa don"t use segment_ids
                inputs["token_type_ids"] = (segment_ids if args.model_type in ["bert", "xlnet"] else None)
            outputs = model(**inputs)
            tmp_eval_loss, start_logits, end_logits = outputs[:3]
            R = bert_extract_item(start_logits, end_logits)
            T = subjects
            metric.update(true_subject=T, pred_subject=R)
            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
        pbar(step)
    print(' ')
    eval_loss = eval_loss / nb_eval_steps
    eval_info, entity_info = metric.result()
    results = {f'{key}': value for key, value in eval_info.items()}
    results['loss'] = eval_loss
    logger.info("***** Eval results %s *****", prefix)
    info = "-".join([f' {key}: {value:.4f} ' for key, value in results.items()])
    logger.info(info)
    logger.info("***** Entity results %s *****", prefix)
    for key in sorted(entity_info.keys()):
        logger.info("******* %s results ********"%key)
        info = "-".join([f' {key}: {value:.4f} ' for key, value in entity_info[key].items()])
        logger.info(info)
    return results

def predict(args, model, tokenizer, prefix=""):
    pred_output_dir = args.output_dir
    if not os.path.exists(pred_output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(pred_output_dir)
    test_dataset = load_and_cache_examples(args, args.task_name,tokenizer, data_type='test')
    print(len(test_dataset))
    # Note that DistributedSampler samples randomly
    test_sampler = SequentialSampler(test_dataset) if args.local_rank == -1 else DistributedSampler(test_dataset)
    test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=1,collate_fn=collate_fn)
    # Eval!
    logger.info("***** Running prediction %s *****", prefix)
    logger.info("  Num examples = %d", len(test_dataset))
    logger.info("  Batch size = %d", 1)

    results = []
    output_submit_file = os.path.join(pred_output_dir, prefix, "test_prediction.json")
    pbar = ProgressBar(n_total=len(test_dataloader), desc="Predicting")
    for step, batch in enumerate(test_dataloader):
        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],"start_positions": None,"end_positions": None}
            if args.model_type != "distilbert":
                # XLM and RoBERTa don"t use segment_ids
                inputs["token_type_ids"] = (batch[2] if args.model_type in ["bert", "xlnet"] else None)
            outputs = model(**inputs)
        start_logits, end_logits = outputs[:2]
        R = bert_extract_item(start_logits, end_logits)
        if R:
            label_entities = [[args.id2label[x[0]],x[1],x[2]] for x in R]
        else:
            label_entities = []
        json_d = {}
        json_d['id'] = step
        json_d['entities'] = label_entities
        results.append(json_d)
        pbar(step)
    print(" ")
    with open(output_submit_file, "w") as writer:
        for record in results:
            writer.write(json.dumps(record) + '\n')

def load_and_cache_examples(args, task, tokenizer, data_type='train'):
    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]()
    # Load data features from cache or dataset file
    cached_features_file = os.path.join(args.data_dir, 'cached_span-{}_{}_{}_{}'.format(
        data_type,
        list(filter(None, args.model_name_or_path.split('/'))).pop(),
        str(args.train_max_seq_length if data_type=='train' else args.eval_max_seq_length),
        str(task)))
    if os.path.exists(cached_features_file) and not args.overwrite_cache:
        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", args.data_dir)
        label_list = processor.get_labels()
        if data_type == 'train':
            examples = processor.get_train_examples(args.data_dir)
        elif data_type == 'dev':
            examples = processor.get_dev_examples(args.data_dir)
        else:
            examples = processor.get_test_examples(args.data_dir)
        features = convert_examples_to_features(examples=examples,
                                                tokenizer=tokenizer,
                                                label_list=label_list,
                                                max_seq_length=args.train_max_seq_length if data_type=='train' \
                                                               else args.eval_max_seq_length,
                                                cls_token_at_end=bool(args.model_type in ["xlnet"]),
                                                pad_on_left=bool(args.model_type in ['xlnet']),
                                                cls_token = tokenizer.cls_token,
                                                cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
                                                sep_token=tokenizer.sep_token,
                                                # pad on the left for xlnet
                                                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
                                                pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
                                                )
        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
    if data_type =='dev':
        return features
    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_start_ids = torch.tensor([f.start_ids for f in features], dtype=torch.long)
    all_end_ids = torch.tensor([f.end_ids for f in features], dtype=torch.long)
    all_input_lens = torch.tensor([f.input_len for f in features], dtype=torch.long)
    dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_ids,all_end_ids,all_input_lens)
    return dataset

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

    # Required parameters
    parser.add_argument("--task_name", default=None, type=str, required=True,
                        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
    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('--markup', default='bios', type=str, choices=['bios', 'bio'])
    parser.add_argument('--loss_type', default='ce', type=str, choices=['lsr', 'focal', 'ce'])
    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("--train_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("--eval_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=0.01, 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_proportion", default=0.1, type=float,
                        help="Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training.")
    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('--predict_all_checkpoints',action="store_true",
                        help="Predict 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()

    args.output_dir = args.output_dir + '{}'.format(args.model_type)
    if not os.path.exists(args.output_dir):
        os.mkdir(args.output_dir)
    init_logger(log_file=args.output_dir + '/{}-{}-{}.log'.format(args.model_type, args.task_name,
                                                                  time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())))
    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
    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
    seed_everything(args.seed)
    # Prepare NER task
    args.task_name = args.task_name.lower()
    if args.task_name not in processors:
        raise ValueError("Task not found: %s" % (args.task_name))
    processor = processors[args.task_name]()
    label_list = processor.get_labels()
    args.id2label = {i: label for i, label in enumerate(label_list)}
    args.label2id = {label: i for i, label in enumerate(label_list)}
    num_labels = len(label_list)

    # 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,loss_type = args.loss_type,
                                          cache_dir=args.cache_dir if args.cache_dir else None,
                                          soft_label = True)
    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)
    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 = load_and_cache_examples(args, args.task_name,tokenizer, data_type='train')
        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
        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_vocabulary(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 ""
            prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
            model = model_class.from_pretrained(checkpoint)
            model.to(args.device)
            result = evaluate(args, model, tokenizer, prefix=prefix)
            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.output_dir, do_lower_case=args.do_lower_case)
        checkpoints = [args.output_dir]
        if args.predict_all_checkpoints > 0:
            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 logging
            checkpoints = [x for x in checkpoints if x.split('-')[-1] == str(args.predict_checkpoints)]
        logger.info("Predict the following checkpoints: %s", checkpoints)
        for checkpoint in checkpoints:
            prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
            model = model_class.from_pretrained(checkpoint)
            model.to(args.device)
            predict(args, model, tokenizer, prefix=prefix)

if __name__ == "__main__":
    main()