run_concord_finetune_cc_cxg.py

from __future__ import absolute_import, division, print_function

import argparse
import logging
import os
import random


import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler,TensorDataset
from torch.utils.data.distributed import DistributedSampler
import json

from tqdm import tqdm, trange
import multiprocessing
cpu_cont = multiprocessing.cpu_count()
from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup, AutoTokenizer, AutoConfig)

from concord.modeling_concord import ConcordForEncoder

logger = logging.getLogger(__name__)


class Model(torch.nn.Module):
    def __init__(self, encoder, config, tokenizer, args):
        super(Model, self).__init__()
        self.encoder = encoder
        self.config = config
        self.tokenizer = tokenizer
        self.args = args

    def forward(self, input_ids=None, p_input_ids=None, n_input_ids=None, labels=None):
        bs, _ = input_ids.size()
        input_ids = torch.cat((input_ids, p_input_ids, n_input_ids), 0)

        outputs = self.encoder(input_ids, attention_mask=input_ids.ne(0)) # Note that the pad token id is 0 in our tokenizer
        outputs = outputs.split(bs, 0)
        prob_1 = (outputs[0] * outputs[1]).sum(-1)
        prob_2 = (outputs[0] * outputs[2]).sum(-1)
        temp = torch.cat((outputs[0], outputs[1]), 0)
        temp_labels = torch.cat((labels, labels), 0)
        prob_3 = torch.mm(outputs[0], temp.t())
        mask = labels[:, None] == temp_labels[None, :]
        prob_3 = prob_3 * (1 - mask.float()) - 1e9 * mask.float()

        prob = torch.softmax(torch.cat((prob_1[:, None], prob_2[:, None], prob_3), -1), -1)
        loss = torch.log(prob[:, 0] + 1e-10)
        loss = -loss.mean()
        return loss, outputs[0]

class InputFeatures(object):
    """A single training/test features for a example."""
    def __init__(self,
                 input_tokens,
                 input_ids,
                 index,
                 label,

    ):
        self.input_tokens = input_tokens
        self.input_ids = input_ids
        self.index=index
        self.label=label

        
def convert_examples_to_features(js,tokenizer,args):
    #source
    code=' '.join(js['code'].split())
    code_tokens=code.split()[:args.block_size-2]
    source_tokens = [tokenizer.cls_token] + code_tokens + [tokenizer.sep_token]
    source_ids = tokenizer.convert_tokens_to_ids(source_tokens)
    padding_length = args.block_size - len(source_ids)
    source_ids += [tokenizer.pad_token_id] * padding_length
    return InputFeatures(source_tokens, source_ids, js['index'], int(js['label']))

class TextDataset(Dataset):
    def __init__(self, tokenizer, args, file_path=None):
        self.examples = []
        data=[]
        with open(file_path) as f:
            for line in f:
                line=line.strip()
                js=json.loads(line)
                data.append(js)
        for js in data:
            self.examples.append(convert_examples_to_features(js,tokenizer,args))
        if 'train' in file_path:
            for idx, example in enumerate(self.examples[:3]):
                    logger.info("*** Example ***")
                    logger.info("idx: {}".format(idx))
                    logger.info("label: {}".format(example.label))
                    logger.info("input_tokens: {}".format([x.replace('\u0120','_') for x in example.input_tokens]))
                    logger.info("input_ids: {}".format(' '.join(map(str, example.input_ids))))
        self.label_examples={}
        for e in self.examples:
            if e.label not in self.label_examples:
                self.label_examples[e.label]=[]
            self.label_examples[e.label].append(e)
        
    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i):   
        label=self.examples[i].label
        index=self.examples[i].index
        labels=list(self.label_examples)
        labels.remove(label)
        while True:
            shuffle_example=random.sample(self.label_examples[label],1)[0]
            if shuffle_example.index!=index:
                p_example=shuffle_example
                break
        # Note: We take this implementation directly from CodeXGLUE, and the randomness
        # below generate different set of samples during training and evaluation (after reloading the best model)
        # Therefore, we will see different "eval" results during training and evaluation. But the test results are stable.
        n_example=random.sample(self.label_examples[random.sample(labels,1)[0]],1)[0]
        
        return (torch.tensor(self.examples[i].input_ids),torch.tensor(p_example.input_ids),
                torch.tensor(n_example.input_ids),torch.tensor(label))
            

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 """
    
    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)
    train_steps = args.num_train_epochs * len(train_dataloader)
    warmup_steps = int(args.warmup_ratio * train_steps) if args.warmup_ratio is not None else 0

    args.logging_steps = len(train_dataloader)

    model.to(args.device)
    # 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=warmup_steps,
                                                num_training_steps=train_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.")
        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')
    optimizer_last = os.path.join(checkpoint_last, 'optimizer.pt')
    if os.path.exists(scheduler_last):
        scheduler.load_state_dict(torch.load(scheduler_last))
    if os.path.exists(optimizer_last):
        optimizer.load_state_dict(torch.load(optimizer_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", train_steps)
    
    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_acc=0.0
    # model.resize_token_embeddings(len(tokenizer))
    model.zero_grad()
    for idx in range(args.start_epoch, int(args.num_train_epochs)): 
        bar = train_dataloader
        tr_num = 0
        train_loss = 0
        for step, batch in enumerate(bar):
            inputs = batch[0].to(args.device)    
            p_inputs = batch[1].to(args.device)
            n_inputs = batch[2].to(args.device)
            labels = batch[3].to(args.device)
            model.train()
            loss,vec = model(inputs,p_inputs,n_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:
                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)
            if (step+1)% 100==0:
                logger.info("epoch {} step {} loss {}".format(idx,step+1,avg_loss))
            #bar.set_description("epoch {} loss {}".format(idx,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:
                    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
                    tr_num = 0
                    train_loss = 0
 
                    if results['eval_map']>best_acc:
                        best_acc=results['eval_map']
                        logger.info("  "+"*"*20)  
                        logger.info("  Best map:%s",round(best_acc,4))
                        logger.info("  "+"*"*20)                          
                        
                        checkpoint_prefix = 'checkpoint-best-map'
                        output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))                        
                        if not os.path.exists(output_dir):
                            os.makedirs(output_dir)                        
                        model_to_save = model.module if hasattr(model,'module') else model
                        output_dir = os.path.join(output_dir, '{}'.format('model.bin'))   
                        torch.save(model_to_save.state_dict(), output_dir)
                        logger.info("Saving model checkpoint to %s", output_dir)


eval_dataset=None
def evaluate(args, model, tokenizer,eval_when_training=False):
    # Loop to handle MNLI double evaluation (matched, mis-matched)
    eval_output_dir = args.output_dir
    global eval_dataset
    if eval_dataset is None:
        eval_dataset = TextDataset(tokenizer, args,args.eval_data_file)

    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()
    vecs=[] 
    labels=[]
    for batch in eval_dataloader:
        inputs = batch[0].to(args.device)    
        p_inputs = batch[1].to(args.device)
        n_inputs = batch[2].to(args.device)
        label = batch[3].to(args.device)
        with torch.no_grad():
            lm_loss,vec = model(inputs, p_inputs, n_inputs, label)
            eval_loss += lm_loss.mean().item()
            vecs.append(vec.cpu().numpy())
            labels.append(label.cpu().numpy())
        nb_eval_steps += 1
    vecs=np.concatenate(vecs,0)
    labels=np.concatenate(labels,0)
    eval_loss = eval_loss / nb_eval_steps
    perplexity = torch.tensor(eval_loss)

    scores=np.matmul(vecs,vecs.T)
    dic={}
    for i in range(scores.shape[0]):
        scores[i,i]=-1000000
        if int(labels[i]) not in dic:
            dic[int(labels[i])]=-1
        dic[int(labels[i])]+=1
    sort_ids=np.argsort(scores, axis=-1, kind='quicksort', order=None)[:,::-1]
    MAP=[]
    for i in range(scores.shape[0]):
        cont=0
        label=int(labels[i])
        Avep = []
        for j in range(dic[label]):
            index=sort_ids[i,j]
            if int(labels[index])==label:
                Avep.append((len(Avep)+1)/(j+1))
        MAP.append(sum(Avep)/dic[label])
          
    result = {
        "eval_loss": float(perplexity),
        "eval_map":float(np.mean(MAP))
    }


    return result

def test_codenet(args, model, tokenizer):
    # Loop to handle MNLI double evaluation (matched, mis-matched)
    eval_dataset = TextDataset(tokenizer, args,args.test_data_file)

    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:
        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)
    eval_loss = 0.0
    nb_eval_steps = 0
    model.eval()
    vecs=[] 
    labels=[]
    for batch in eval_dataloader:
        inputs = batch[0].to(args.device)    
        p_inputs = batch[1].to(args.device)
        n_inputs = batch[2].to(args.device)
        label = batch[3].to(args.device)
        with torch.no_grad():
            lm_loss,vec = model(inputs, p_inputs, n_inputs, label)
            eval_loss += lm_loss.mean().item()
            vecs.append(vec.cpu().numpy())
            labels.append(label.cpu().numpy())
        nb_eval_steps += 1
    vecs=np.concatenate(vecs,0)
    labels=np.concatenate(labels,0)
    eval_loss = eval_loss / nb_eval_steps
    perplexity = torch.tensor(eval_loss)

    scores=np.matmul(vecs,vecs.T)
    dic={}
    for i in range(scores.shape[0]):
        scores[i,i]=-1000000
        if int(labels[i]) not in dic:
            dic[int(labels[i])]=-1
        dic[int(labels[i])]+=1
    sort_ids=np.argsort(scores, axis=-1, kind='quicksort', order=None)[:,::-1]
    MAP=[]
    for i in range(scores.shape[0]):
        cont=0
        label=int(labels[i])
        Avep = []
        for j in range(dic[label]):
            index=sort_ids[i,j]
            if int(labels[index])==label:
                Avep.append((len(Avep)+1)/(j+1))
        MAP.append(sum(Avep)/dic[label])
          
    result = {
        "test_loss": float(perplexity),
        "test_map":float(np.mean(MAP))
    }


    return result

def test(args, model, tokenizer):
    # Loop to handle MNLI double evaluation (matched, mis-matched)
    eval_dataset = TextDataset(tokenizer, args,args.test_data_file)


    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)
    eval_loss = 0.0
    nb_eval_steps = 0
    model.eval()
    vecs=[] 
    labels=[]
    for batch in eval_dataloader:
        inputs = batch[0].to(args.device)    
        p_inputs = batch[1].to(args.device)
        n_inputs = batch[2].to(args.device)
        label = batch[3].to(args.device)
        with torch.no_grad():
            lm_loss,vec = model(inputs,p_inputs,n_inputs,label)
            eval_loss += lm_loss.mean().item()
            vecs.append(vec.cpu().numpy())
            labels.append(label.cpu().numpy())
        nb_eval_steps += 1
    vecs=np.concatenate(vecs,0)
    labels=np.concatenate(labels,0)
    eval_loss = eval_loss / nb_eval_steps
    perplexity = torch.tensor(eval_loss)

    scores=np.matmul(vecs,vecs.T)
    for i in range(scores.shape[0]):
        scores[i,i]=-1000000
    sort_ids=np.argsort(scores, axis=-1, kind='quicksort', order=None)[:,::-1]
    indexs=[]
    for example in eval_dataset.examples:
        indexs.append(example.index)
    with open(os.path.join(args.output_dir,"predictions.jsonl"),'w') as f:
        for index,sort_id in zip(indexs,sort_ids):
            js={}
            js['index']=index
            js['answers']=[]
            for idx in sort_id[:499]:
                js['answers'].append(indexs[int(idx)])
            f.write(json.dumps(js)+'\n')

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

    parser.add_argument("--train_data_file", default=None, type=str, required=True,
                        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.")

    parser.add_argument("--task", default="poj104", type=str, choices=["poj104", "codenet"],
                        help="The input training data file (a text file).")
    parser.add_argument("--eval_data_file", default=None, type=str,
                        help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
    parser.add_argument("--test_data_file", default=None, type=str,
                        help="An optional input evaluation data file to evaluate the perplexity on (a text file).")

    parser.add_argument("--model_name_or_path", default=None, type=str,
                        help="The model checkpoint for weights initialization.")

    parser.add_argument("--config_name", default="", type=str,
                        help="Optional pretrained config name or path if not the same as model_name_or_path")
    parser.add_argument("--tokenizer_name", default="", type=str,
                        help="Optional pretrained tokenizer name or path if not the same as model_name_or_path")
    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("--block_size", 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_test", action='store_true',
                        help="Whether to run eval on the dev set.")    
    parser.add_argument("--evaluate_during_training", action='store_true',
                        help="Run evaluation during training at each logging step.")

    parser.add_argument("--train_batch_size", default=None, type=int,
                        help="Batch size per GPU/CPU for training.")
    parser.add_argument("--eval_batch_size", default=None, 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=8e-6, 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("--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=50,
                        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_or_path 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('--num_train_epochs', type=int, default=10,
                        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('--warmup_ratio', type=float, default=0,
                        help="warm up ratio")
    parser.add_argument("--new_pooler", action='store_true', help="Whether use new pooler for finetuning")

    args = parser.parse_args()

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    args.n_gpu = torch.cuda.device_count()
    args.device = device
    logger.info("device: %s, n_gpu: %s", device, args.n_gpu)

    args.per_gpu_train_batch_size=args.train_batch_size//args.n_gpu
    args.per_gpu_eval_batch_size=args.eval_batch_size//args.n_gpu
    # 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()  # Barrier to make sure only the first process in distributed training 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.model_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))

    # build model
    config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)

    tokenizer_kwargs = {
        "use_fast": False,
        "config": config,
        "do_lower_case": False,
        "do_basic_tokenize": False,
        "tokenize_chinese_chars": False,
        "bos_token": "<s>",
        "eos_token": "</s>"
    }
    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, **tokenizer_kwargs)
    model = ConcordForEncoder.from_pretrained(args.model_name_or_path, config=config, new_pooler=args.new_pooler if args.new_pooler else None)
    model = Model(model, config, tokenizer, args)
    logger.info("Training/evaluation parameters %s", args)
    model.to(args.device)

    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_dataset = TextDataset(tokenizer, args,args.train_data_file)
        if args.local_rank == 0:
            torch.distributed.barrier()

        train(args, train_dataset, model, tokenizer)



    # Evaluation
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
        checkpoint_prefix = 'checkpoint-best-map/model.bin'
        output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
        model.load_state_dict(torch.load(output_dir),strict=True)      
        model.to(args.device)
        result=evaluate(args, model, tokenizer, eval_when_training=True)
        logger.info("***** Eval results *****")
        for key in sorted(result.keys()):
            logger.info("  %s = %s", key, str(round(result[key],4)))
            
    if args.do_test and args.local_rank in [-1, 0]:
        checkpoint_prefix = 'checkpoint-best-map/model.bin'
        output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
        model.load_state_dict(torch.load(output_dir),strict=True)                  
        model.to(args.device)
        if args.task == "poj104":
            test(args, model, tokenizer)
        elif args.task == "codenet":
            result = test_codenet(args, model, tokenizer)
            logger.info("***** Test results *****")
            for key in sorted(result.keys()):
                logger.info("  %s = %s", key, str(round(result[key],4)))

    return results


if __name__ == "__main__":
    main()