Text-code/NL-code-search-WebQuery/code/run.py

# 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.

from __future__ import absolute_import, division, print_function

import argparse
import glob
import logging
import os
import random
import pickle

import numpy as np
import torch
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler, 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, get_linear_schedule_with_warmup, AdamW,
                          RobertaConfig,
                          RobertaModel,
                          RobertaTokenizer)

from models import Model
from utils import acc_and_f1, TextDataset
import multiprocessing
cpu_cont = multiprocessing.cpu_count()

logger = logging.getLogger(__name__)

MODEL_CLASSES = {'roberta': (RobertaConfig, RobertaModel, RobertaTokenizer)}


def set_seed(seed=42):
    random.seed(seed)
    os.environ['PYHTONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True


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, num_workers=4, pin_memory=True)

    args.save_steps = len(train_dataloader) if args.save_steps<=0 else args.save_steps
    args.warmup_steps = len(train_dataloader) if args.warmup_steps<=0 else args.warmup_steps
    args.logging_steps = len(train_dataloader)

    if args.max_steps > 0:
        t_total = args.max_steps
        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps)
    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, args.warmup_steps, t_total)

    model.to(args.device)
    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)

    checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last')
    scheduler_last = os.path.join(checkpoint_last, 'scheduler.pt')
    if os.path.exists(scheduler_last):
        scheduler.load_state_dict(torch.load(scheduler_last))

    # 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 = args.start_step
    tr_loss, logging_loss, avg_loss, tr_nb, tr_num, train_loss = 0.0, 0.0, 0.0, 0, 0, 0
    best_results = {"acc": 0.0, "precision": 0.0, "recall": 0.0, "f1": 0.0, "acc_and_f1": 0.0}
    model.zero_grad()
    train_iterator = trange(args.start_epoch, int(args.num_train_epochs), desc="Epoch",
                            disable=args.local_rank not in [-1, 0])
    model.train()
    logger.info(model)

    for idx in train_iterator:
        bar = tqdm(enumerate(train_dataloader))
        tr_num=0
        train_loss=0
        for step, batch in bar:

            code_inputs = batch[0].to(args.device)
            nl_inputs = batch[1].to(args.device)
            labels = batch[2].to(args.device)
            loss, predictions = model(code_inputs, nl_inputs, labels)

            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:
                try:
                    from apex import amp
                except ImportError:
                    raise ImportError(
                        "Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
            else:
                loss.backward()
                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

            tr_loss += loss.item()
            tr_num += 1
            train_loss += loss.item()
            if avg_loss == 0:
                avg_loss = tr_loss
            avg_loss = round(train_loss/tr_num, 5)
            bar.set_description("epoch {} step {} loss {}".format(idx, step+1, avg_loss))

            if (step + 1) % args.gradient_accumulation_steps == 0:
                optimizer.step()
                optimizer.zero_grad()
                scheduler.step()
                global_step += 1
                avg_loss = round(np.exp((tr_loss - logging_loss) / (global_step - tr_nb)), 4)
                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    logging_loss = tr_loss
                    tr_nb = global_step

                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_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, eval_when_training=True)
                        for key, value in results.items():
                            logger.info("  %s = %s", key, round(value,4))
                        # Save model checkpoint
                        if results['acc_and_f1'] >= best_results['acc_and_f1']:
                            best_results = results

                            # save
                            checkpoint_prefix = 'checkpoint-best-aver'
                            output_dir = os.path.join(args.output_dir, checkpoint_prefix)
                            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

                            torch.save(model_to_save.state_dict(), os.path.join(output_dir, 'pytorch_model.bin'))
                            tokenizer.save_pretrained(output_dir)
                            torch.save(args, os.path.join(output_dir, 'training_{}.bin'.format(idx)))
                            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)

                    if args.local_rank == -1:
                        checkpoint_prefix = 'checkpoint-last'
                        output_dir = os.path.join(args.output_dir, checkpoint_prefix)
                        if not os.path.exists(output_dir):
                            os.makedirs(output_dir)
                        model_to_save = model.module if hasattr(model, 'module') else model
                        torch.save(model_to_save.state_dict(), os.path.join(output_dir, 'pytorch_model.bin'))
                        tokenizer.save_pretrained(output_dir)

                        idx_file = os.path.join(output_dir, 'idx_file.txt')
                        with open(idx_file, 'w', encoding='utf-8') as idxf:
                            idxf.write(str(args.start_epoch + idx) + '\n')
                        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)
                        step_file = os.path.join(output_dir, 'step_file.txt')
                        with open(step_file, 'w', encoding='utf-8') as stepf:
                            stepf.write(str(global_step) + '\n')

            if args.max_steps > 0 and global_step > args.max_steps:
                train_iterator.close()
                break

        # 每一轮记录checkpoint
        output_dir = os.path.join(args.output_dir, 'epoch_{}'.format(idx+1))
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
        model_to_save = model.module if hasattr(model, 'module') else model
        ckpt_output_path = os.path.join(output_dir, 'subject_model.pth')
        logger.info("Saving model checkpoint to %s", ckpt_output_path)
        torch.save(model_to_save.state_dict(), ckpt_output_path)
        # 每一轮记录表征
        # logger.info("Saving training feature")
        # train_dataloader_bs1 = DataLoader(train_dataset, sampler=train_sampler, batch_size=1, num_workers=4,
        #                                   pin_memory=True)
        # code_feature, nl_feature = [], []
        # for batch in tqdm(train_dataloader_bs1):
        #     code_inputs = batch[0].to(args.device)
        #     nl_inputs = batch[1].to(args.device)
        #     labels = batch[2].to(args.device)
        #     model.eval()
        #     with torch.no_grad():
        #         _, cf, nf = model(code_inputs=code_inputs, nl_inputs=nl_inputs, labels=labels, do_my_test=True)
        #     code_feature.append(cf.cpu().detach().numpy())
        #     nl_feature.append(nf.cpu().detach().numpy())
        # code_feature_output_path = os.path.join(output_dir, 'code_feature.pkl')
        # nl_feature_output_path = os.path.join(output_dir, 'nl_feature.pkl')
        # with open(code_feature_output_path, 'wb') as f1, open(nl_feature_output_path, 'wb') as f2:
        #     pickle.dump(code_feature, f1)
        #     pickle.dump(code_feature, f2)

def evaluate(args, model, tokenizer,eval_when_training=False):
    eval_output_dir = args.output_dir
    eval_data_path = os.path.join(args.data_dir, args.dev_file)
    eval_dataset = TextDataset(tokenizer, args, eval_data_path, type='eval')

    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(eval_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(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, num_workers=4, pin_memory=True)

    # multi-gpu evaluate
    if args.n_gpu > 1 and eval_when_training is False:
        model = torch.nn.DataParallel(model)

    # Eval!
    logger.info("***** Running evaluation *****")
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    model.eval()
    all_predictions = []
    all_labels = []
    for batch in eval_dataloader:
        code_inputs = batch[0].to(args.device)
        nl_inputs = batch[1].to(args.device)
        labels = batch[2].to(args.device)
        with torch.no_grad():
            lm_loss, predictions = model(code_inputs, nl_inputs, labels)
            # lm_loss,code_vec,nl_vec = model(code_inputs,nl_inputs)
            eval_loss += lm_loss.mean().item()
            all_predictions.append(predictions.cpu())
            all_labels.append(labels.cpu())
        nb_eval_steps += 1
    all_predictions = torch.cat(all_predictions, 0).squeeze().numpy()
    all_labels = torch.cat(all_labels, 0).squeeze().numpy()
    eval_loss = torch.tensor(eval_loss / nb_eval_steps)

    results = acc_and_f1(all_predictions, all_labels)
    results.update({"eval_loss": float(eval_loss)})
    return results


def test(args, model, tokenizer):
    if not args.prediction_file:
        args.prediction_file = os.path.join(args.output_dir, 'predictions.txt')
    if not os.path.exists(os.path.dirname(args.prediction_file)):
        os.makedirs(os.path.dirname(args.prediction_file))
    if not args.answer_file:
        args.answer_file = os.path.join(args.output_dir, 'golds.txt')
    if not os.path.exists(os.path.dirname(args.answer_file)):
        os.makedirs(os.path.dirname(args.answer_file))

    test_data_path = os.path.join(args.data_dir, args.test_file)
    eval_dataset = TextDataset(tokenizer, args, test_data_path) #, type='test')

    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # multi-gpu evaluate
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Eval!
    logger.info("***** Running Test *****")
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)

    nb_eval_steps = 0
    all_predictions = []
    all_golds = []
    for batch in eval_dataloader:
        code_inputs = batch[0].to(args.device)
        nl_inputs = batch[1].to(args.device)
        labels = batch[2].to(args.device)
        with torch.no_grad():
            _, predictions = model(code_inputs, nl_inputs, labels)
            all_predictions.append(predictions.cpu())
            all_golds.append(labels.cpu())
        nb_eval_steps += 1
    all_predictions = torch.cat(all_predictions, 0).squeeze().numpy()
    all_golds = torch.cat(all_golds, 0).squeeze().numpy()

    logger.info("***** Saving Test Result *****")
    with open(args.prediction_file,'w') as f:
        for example, pred in zip(eval_dataset.examples, all_predictions.tolist()):
            f.write(str(example.idx)+'\t'+str(int(pred))+'\n')
    with open(args.answer_file,'w') as f:
        for example, gold in zip(eval_dataset.examples, all_golds.tolist()):
            f.write(str(example.idx)+'\t'+str(int(gold))+'\n')


def check_feature():
    code_feature = pickle.load(file=open('model_codesearchnet/checkpoint-all/epoch_0/code_feature.pkl', 'rb'))
    print(len(code_feature))
    print(code_feature[0].shape)


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("--train_file", default=None, type=str,
                        help="The input training data file (a text file).")
    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("--dev_file", default=None, type=str,
                        help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
    parser.add_argument("--test_file", default=None, type=str,
                        help="An optional input evaluation data file to evaluate the perplexity on (a text file).")

    parser.add_argument("--model_type", default="roberta", type=str,
                        help="The model architecture to be fine-tuned.")
    parser.add_argument("--pn_weight", type=float, default=1.0,
                        help="Ratio of positive examples in the sum of bce loss")
    parser.add_argument("--encoder_name_or_path", default=None, type=str,
                        help="The model checkpoint for weights initialization.")
    parser.add_argument("--checkpoint_path", default=None, type=str,
                        help="The checkpoint path of model to continue training.")

    parser.add_argument("--mlm", action='store_true',
                        help="Train with masked-language modeling loss instead of language modeling.")
    parser.add_argument("--mlm_probability", type=float, default=0.15,
                        help="Ratio of tokens to mask for masked language modeling loss")

    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="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)")
    parser.add_argument("--max_seq_length", default=-1, type=int,
                        help="Optional input sequence length after tokenization."
                             "The training dataset will be truncated in block of this size for training."
                             "Default to the model max input length for single sentence inputs (take into account special tokens).")
    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 predict on the test 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('--gradient_accumulation_steps', type=int, default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
    parser.add_argument("--learning_rate", default=5e-5, type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--weight_decay", default=0.0, type=float,
                        help="Weight 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, type=int,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--max_steps", default=-1, type=int,
                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
    parser.add_argument("--warmup_steps", default=0, type=int,
                        help="Linear warmup over warmup_steps.")

    parser.add_argument('--logging_steps', type=int, default=50,
                        help="Log every X updates steps.")
    parser.add_argument('--save_steps', type=int, default=0,
                        help="Save checkpoint every X updates steps.")
    parser.add_argument('--save_total_limit', type=int, default=None,
                        help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default')
    parser.add_argument("--eval_all_checkpoints", action='store_true',
                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
    parser.add_argument("--no_cuda", action='store_true',
                        help="Avoid using CUDA when available")
    parser.add_argument('--overwrite_output_dir', action='store_true',
                        help="Overwrite the content of the output directory")
    parser.add_argument('--overwrite_cache', action='store_true',
                        help="Overwrite the cached training and evaluation sets")
    parser.add_argument('--seed', type=int, default=42,
                        help="random seed for initialization")

    parser.add_argument('--fp16', action='store_true',
                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
    parser.add_argument('--fp16_opt_level', type=str, default='O1',
                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                             "See details at https://nvidia.github.io/apex/amp.html")
    parser.add_argument("--local_rank", type=int, default=-1,
                        help="For distributed training: local_rank")
    parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
    parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
    parser.add_argument("--pred_model_dir", default=None, type=str,
                        help='model for prediction')
    parser.add_argument("--test_result_dir", default='test_results.tsv', type=str,
                        help='path to store test result')
    parser.add_argument("--prediction_file", default=None, type=str,
                        help='path to save predictions result, note to specify task name')
    parser.add_argument("--answer_file", default=None, type=str,
                        help='path to save gold result, note to specify task name')
    
    args = parser.parse_args()

    # 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.seed)

    # 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.start_epoch = 0
    args.start_step = 0
    checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last')
    if os.path.exists(checkpoint_last) and os.listdir(checkpoint_last):
        # args.encoder_name_or_path = os.path.join(checkpoint_last, 'pytorch_model.bin')
        args.config_name = os.path.join(checkpoint_last, 'config.json')
        idx_file = os.path.join(checkpoint_last, 'idx_file.txt')
        with open(idx_file, encoding='utf-8') as idxf:
            args.start_epoch = int(idxf.readlines()[0].strip()) + 1

        step_file = os.path.join(checkpoint_last, 'step_file.txt')
        if os.path.exists(step_file):
            with open(step_file, encoding='utf-8') as stepf:
                args.start_step = int(stepf.readlines()[0].strip())

        logger.info("reload model from {}, resume from {} epoch".format(checkpoint_last, args.start_epoch))



    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.encoder_name_or_path,
                                          cache_dir=args.cache_dir if args.cache_dir else None)
    config.num_labels = 2
    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.encoder_name_or_path,
                                                do_lower_case=args.do_lower_case,
                                                cache_dir=args.cache_dir if args.cache_dir else None)
    if args.max_seq_length <= 0:
        args.max_seq_length = tokenizer.max_len_single_sentence  # Our input block size will be the max possible for the model
    args.max_seq_length = min(args.max_seq_length, tokenizer.max_len_single_sentence)
    if args.encoder_name_or_path:
        model = model_class.from_pretrained(args.encoder_name_or_path,
                                            from_tf=bool('.ckpt' in args.encoder_name_or_path),
                                            config=config,
                                            cache_dir=args.cache_dir if args.cache_dir else None)
    else:
        model = model_class(config)

    model = Model(model, config, tokenizer, args)

    if args.checkpoint_path:
        model.load_state_dict(torch.load(os.path.join(args.checkpoint_path, 'pytorch_model.bin')))
    if args.local_rank == 0:
        torch.distributed.barrier()  # End of barrier to make sure only the first process in distributed training download model & vocab

    logger.info("Training/evaluation parameters %s", args)

    # Training
    if args.do_train:
        if args.local_rank not in [-1, 0]:
            torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
        train_data_path = os.path.join(args.data_dir, args.train_file)
        train_dataset = TextDataset(tokenizer, args, train_data_path, type='train')
        train(args, train_dataset, model, tokenizer)

    # Evaluation
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
        checkpoint_prefix = 'checkpoint-best-aver'
        output_dir = os.path.join(args.output_dir, checkpoint_prefix)
        model.load_state_dict(torch.load(os.path.join(output_dir, 'pytorch_model.bin')))
        tokenizer = tokenizer.from_pretrained(output_dir)
        model.to(args.device)
        results = evaluate(args, model, tokenizer)
        logger.info("***** Eval results *****")
        for key in results.keys():
            logger.info("  Eval %s = %s", key, str(results[key]))
        logger.info("Eval Model From: {}".format(os.path.join(output_dir, 'pytorch_model.bin')))
        logger.info("***** Eval results *****")

    if args.do_predict and args.local_rank in [-1, 0]:
        logger.info("***** Testing results *****")
        checkpoint_prefix = 'checkpoint-best-aver'
        if checkpoint_prefix not in args.output_dir and \
                os.path.exists(os.path.join(args.output_dir, checkpoint_prefix)):
            output_dir = os.path.join(args.output_dir, checkpoint_prefix)
        else:
            output_dir = args.output_dir
        if not args.pred_model_dir:
            model_path = os.path.join(output_dir, 'pytorch_model.bin')
        else:
            model_path = os.path.join(args.pred_model_dir, 'pytorch_model.bin')
        model.load_state_dict(torch.load(model_path))
        tokenizer = tokenizer.from_pretrained(output_dir)
        model.to(args.device)
        test(args, model, tokenizer)
        logger.info("Test Model From: {}".format(model_path))
    return results


if __name__ == "__main__":
    main()