add defect-detection

Code-Code/Defect-detection/code/eval.sh

@@ -0,0 +1,18 @@
+CUDA_VISIBLE_DEVICES=0,1  python run.py \
+    --output_dir=../model \
+    --model_type=roberta \
+    --tokenizer_name=microsoft/codebert-base \
+    --model_name_or_path=microsoft/codebert-base \
+    --do_eval \
+    --do_test \
+    --train_data_file=../dataset/train.jsonl \
+    --eval_data_file=../dataset/valid.jsonl \
+    --test_data_file=../dataset/valid.jsonl \
+    --epoch 5 \
+    --block_size 400 \
+    --train_batch_size 32 \
+    --eval_batch_size 64 \
+    --learning_rate 2e-5 \
+    --max_grad_norm 1.0 \
+    --evaluate_during_training \
+    --seed 123456

Code-Code/Defect-detection/code/evaluate.sh

@@ -0,0 +1 @@
+python evaluator.py -a ../dataset/valid.jsonl -p ../model/predictions.txt

Code-Code/Defect-detection/code/evaluator.py

@@ -0,0 +1,52 @@
+# Copyright (c) Microsoft Corporation. 
+# Licensed under the MIT license.
+import logging
+import sys
+import json
+import numpy as np
+
+def read_answers(filename):
+    answers={}
+    with open(filename) as f:
+        for line in f:
+            line=line.strip()
+            js=json.loads(line)
+            answers[js['idx']]=js['target']
+    return answers
+
+def read_predictions(filename):
+    predictions={}
+    with open(filename) as f:
+        for line in f:
+            line=line.strip()
+            idx,label=line.split()
+            predictions[int(idx)]=int(label)
+    return predictions
+
+def calculate_scores(answers,predictions):
+    Acc=[]
+    for key in answers:
+        if key not in predictions:
+            logging.error("Missing prediction for index {}.".format(key))
+            sys.exit()
+        Acc.append(answers[key]==predictions[key])
+
+    scores={}
+    scores['Acc']=np.mean(Acc)
+    return scores
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description='Evaluate leaderboard predictions for Defect Detection dataset.')
+    parser.add_argument('--answers', '-a',help="filename of the labels, in txt format.")
+    parser.add_argument('--predictions', '-p',help="filename of the leaderboard predictions, in txt format.")
+    
+
+    args = parser.parse_args()
+    answers=read_answers(args.answers)
+    predictions=read_predictions(args.predictions)
+    scores=calculate_scores(answers,predictions)
+    print(scores)
+
+if __name__ == '__main__':
+    main()

Code-Code/Defect-detection/code/model.py

@@ -0,0 +1,45 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+import torch
+import torch.nn as nn
+import torch
+from torch.autograd import Variable
+import copy
+from torch.nn import CrossEntropyLoss, MSELoss
+
+    
+    
+class Model(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
+    
+        # Define dropout layer, dropout_probability is taken from args.
+        self.dropout = nn.Dropout(args.dropout_probability)
+
+        
+    def forward(self, input_ids=None,labels=None, return_vec=None): 
+        outputs=self.encoder(input_ids,attention_mask=input_ids.ne(1))
+
+        if return_vec:
+            return outputs.pooler_output
+        outputs = outputs[0]
+        
+        # Apply dropout
+        outputs = self.dropout(outputs)
+
+        logits=outputs
+        prob=torch.sigmoid(logits)
+        if labels is not None:
+            labels=labels.float()
+            loss=torch.log(prob[:,0]+1e-10)*labels+torch.log((1-prob)[:,0]+1e-10)*(1-labels)
+            loss=-loss.mean()
+            return loss,prob
+        else:
+            return prob
+      
+        
+ 

Code-Code/Defect-detection/code/run.py

@@ -0,0 +1,598 @@
+# 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.
+"""
+Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
+GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
+using a masked language modeling (MLM) loss.
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import glob
+import logging
+import os
+import pickle
+import random
+import re
+import shutil
+
+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
+try:
+    from torch.utils.tensorboard import SummaryWriter
+except:
+    from tensorboardX import SummaryWriter
+
+from tqdm import tqdm, trange
+import multiprocessing
+from model import Model
+cpu_cont = multiprocessing.cpu_count()
+from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
+                          BertConfig, BertForMaskedLM, BertTokenizer, BertForSequenceClassification,
+                          GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
+                          OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
+                          RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer,
+                          DistilBertConfig, DistilBertForMaskedLM, DistilBertForSequenceClassification, DistilBertTokenizer)
+
+logger = logging.getLogger(__name__)
+
+MODEL_CLASSES = {
+    'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
+    'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
+    'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
+    'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
+    'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
+}
+
+
+
+class InputFeatures(object):
+    """A single training/test features for a example."""
+    def __init__(self,
+                 input_tokens,
+                 input_ids,
+                 idx,
+                 label,
+
+    ):
+        self.input_tokens = input_tokens
+        self.input_ids = input_ids
+        self.idx=str(idx)
+        self.label=label
+
+        
+def convert_examples_to_features(js,tokenizer,args):
+    #source
+    code=' '.join(js['func'].split())
+    code_tokens=tokenizer.tokenize(code)[: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['idx'],js['target'])
+
+class TextDataset(Dataset):
+    def __init__(self, tokenizer, args, file_path=None):
+        self.examples = []
+        with open(file_path) as f:
+            for line in f:
+                js=json.loads(line.strip())
+                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))))
+
+    def __len__(self):
+        return len(self.examples)
+
+    def __getitem__(self, i):       
+        return torch.tensor(self.examples[i].input_ids),torch.tensor(self.examples[i].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)
+    args.max_steps=args.epoch*len( train_dataloader)
+    args.save_steps=len( train_dataloader)
+    args.warmup_steps=len( train_dataloader)
+    args.logging_steps=len( train_dataloader)
+    args.num_train_epochs=args.epoch
+    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=args.max_steps*0.1,
+                                                num_training_steps=args.max_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", args.max_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_mrr=0.0
+    best_acc=0.0
+    # model.resize_token_embeddings(len(tokenizer))
+    model.zero_grad()
+
+    # Initialize early stopping parameters at the start of training
+    early_stopping_counter = 0
+    best_loss = None
+ 
+    for idx in range(args.start_epoch, int(args.num_train_epochs)): 
+        bar = tqdm(train_dataloader,total=len(train_dataloader))
+        tr_num=0
+        train_loss=0
+        for step, batch in enumerate(bar):
+            inputs = batch[0].to(args.device)        
+            labels=batch[1].to(args.device) 
+            model.train()
+            loss,logits = model(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)
+            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
+                output_flag=True
+                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['eval_acc']>best_acc:
+                        best_acc=results['eval_acc']
+                        logger.info("  "+"*"*20)  
+                        logger.info("  Best acc:%s",round(best_acc,4))
+                        logger.info("  "+"*"*20)                          
+                        
+                        checkpoint_prefix = 'checkpoint-best-acc'
+                        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)
+
+        # 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)
+
+        # Calculate average loss for the epoch
+        avg_loss = train_loss / tr_num
+
+        # Check for early stopping condition
+        if args.early_stopping_patience is not None:
+            if best_loss is None or avg_loss < best_loss - args.min_loss_delta:
+                best_loss = avg_loss
+                early_stopping_counter = 0
+            else:
+                early_stopping_counter += 1
+                if early_stopping_counter >= args.early_stopping_patience:
+                    logger.info("Early stopping")
+                    break  # Exit the loop early
+                        
+
+
+
+def evaluate(args, model, tokenizer,eval_when_training=False):
+    # Loop to handle MNLI double evaluation (matched, mis-matched)
+    eval_output_dir = args.output_dir
+
+    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()
+    logits=[] 
+    labels=[]
+    for batch in eval_dataloader:
+        inputs = batch[0].to(args.device)        
+        label=batch[1].to(args.device) 
+        with torch.no_grad():
+            lm_loss,logit = model(inputs,label)
+            eval_loss += lm_loss.mean().item()
+            logits.append(logit.cpu().numpy())
+            labels.append(label.cpu().numpy())
+        nb_eval_steps += 1
+    logits=np.concatenate(logits,0)
+    labels=np.concatenate(labels,0)
+    preds=logits[:,0]>0.5
+    eval_acc=np.mean(labels==preds)
+    eval_loss = eval_loss / nb_eval_steps
+    perplexity = torch.tensor(eval_loss)
+            
+    result = {
+        "eval_loss": float(perplexity),
+        "eval_acc":round(eval_acc,4),
+    }
+    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()
+    logits=[]   
+    labels=[]
+    for batch in tqdm(eval_dataloader,total=len(eval_dataloader)):
+        inputs = batch[0].to(args.device)        
+        label=batch[1].to(args.device) 
+        with torch.no_grad():
+            logit = model(inputs)
+            logits.append(logit.cpu().numpy())
+            labels.append(label.cpu().numpy())
+
+    logits=np.concatenate(logits,0)
+    labels=np.concatenate(labels,0)
+    preds=logits[:,0]>0.5
+    with open(os.path.join(args.output_dir,"predictions.txt"),'w') as f:
+        for example,pred in zip(eval_dataset.examples,preds):
+            if pred:
+                f.write(example.idx+'\t1\n')
+            else:
+                f.write(example.idx+'\t0\n')    
+    
+                        
+                        
+def main():
+    parser = argparse.ArgumentParser()
+
+    ## Required parameters
+    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.")
+
+    ## Other parameters
+    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_type", default="bert", type=str,
+                        help="The model architecture to be fine-tuned.")
+    parser.add_argument("--model_name_or_path", default=None, type=str,
+                        help="The model checkpoint for weights initialization.")
+
+    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="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("--do_lower_case", action='store_true',
+                        help="Set this flag if you are using an uncased model.")
+
+    parser.add_argument("--train_batch_size", default=4, type=int,
+                        help="Batch size per GPU/CPU for training.")
+    parser.add_argument("--eval_batch_size", default=4, 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=1.0, type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument("--max_steps", default=-1, type=int,
+                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
+    parser.add_argument("--warmup_steps", default=0, type=int,
+                        help="Linear warmup over warmup_steps.")
+
+    parser.add_argument('--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('--epoch', 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.")
+
+    # Add early stopping parameters and dropout probability parameters
+    parser.add_argument("--early_stopping_patience", type=int, default=None,
+                        help="Number of epochs with no improvement after which training will be stopped.")
+    parser.add_argument("--min_loss_delta", type=float, default=0.001,
+                        help="Minimum change in the loss required to qualify as an improvement.")
+    parser.add_argument('--dropout_probability', type=float, default=0, help='dropout probability')
+
+
+    
+
+    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
+    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))
+
+    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
+                                          cache_dir=args.cache_dir if args.cache_dir else None)
+    config.num_labels=1
+    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name,
+                                                do_lower_case=args.do_lower_case,
+                                                cache_dir=args.cache_dir if args.cache_dir else None)
+    if args.block_size <= 0:
+        args.block_size = tokenizer.max_len_single_sentence  # Our input block size will be the max possible for the model
+    args.block_size = min(args.block_size, tokenizer.max_len_single_sentence)
+    if args.model_name_or_path:
+        model = model_class.from_pretrained(args.model_name_or_path,
+                                            from_tf=bool('.ckpt' in args.model_name_or_path),
+                                            config=config,
+                                            cache_dir=args.cache_dir if args.cache_dir else None)    
+    else:
+        model = model_class(config)
+
+    model=Model(model,config,tokenizer,args)
+    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 = 'epoch_5/subject_model.pth'
+            output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
+            model.load_state_dict(torch.load(output_dir))      
+            model.to(args.device)
+            result=evaluate(args, model, tokenizer)
+            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 = 'epoch_5/subject_model.pth'
+            output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
+            model.load_state_dict(torch.load(output_dir))                  
+            model.to(args.device)
+            test(args, model, tokenizer)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
+
+

Code-Code/Defect-detection/code/train.sh

@@ -0,0 +1,17 @@
+CUDA_VISIBLE_DEVICES=0,1  python run.py \
+    --output_dir=../model \
+    --model_type=roberta \
+    --tokenizer_name=microsoft/codebert-base \
+    --model_name_or_path=microsoft/codebert-base \
+    --do_train \
+    --train_data_file=../dataset/train.jsonl \
+    --eval_data_file=../dataset/valid.jsonl \
+    --test_data_file=../dataset/test.jsonl \
+    --epoch 5 \
+    --block_size 400 \
+    --train_batch_size 32 \
+    --eval_batch_size 64 \
+    --learning_rate 2e-5 \
+    --max_grad_norm 1.0 \
+    --evaluate_during_training \
+    --seed 123456

Code-Code/Defect-detection/dataset.zip

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Code-Code/Defect-detection/model/epoch_1/subject_model.pth

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Code-Code/Defect-detection/model/epoch_3/subject_model.pth

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Code-Code/Defect-detection/model/epoch_4/subject_model.pth

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