489 lines
18 KiB
Python
489 lines
18 KiB
Python
from collections import OrderedDict
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import git
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import os
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import logging
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from tqdm import tqdm
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from sklearn.metrics import precision_score, recall_score, f1_score, \
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roc_auc_score, matthews_corrcoef, brier_score_loss, confusion_matrix
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from util import *
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from transformers import (AdamW, get_linear_schedule_with_warmup,
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RobertaConfig, RobertaTokenizer, RobertaModel, AutoModel)
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import random
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torch.utils.data import Dataset, DataLoader, SequentialSampler, RandomSampler
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from torch.nn.utils.rnn import pad_sequence
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from transformers import BertTokenizer, BertModel, AutoModel
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from transformers import logging as lg
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from nltk.corpus import stopwords
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from nltk import word_tokenize
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import nltk.data
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import re
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import os
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tokenizer_RoBERTa = RobertaTokenizer.from_pretrained('../pretrained-model/roberta-large')
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stemmer = nltk.stem.SnowballStemmer('english')
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lg.set_verbosity_error()
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# device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') # added by me
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# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# n_gpu = torch.cuda.device_count()
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os.environ['CUDA_VISIBLE_DEVICES'] = '4'
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device = torch.device("cuda")
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n_gpu = 1
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class Roberta(nn.Module):
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def __init__(self):
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super(Roberta, self).__init__()
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config = RobertaConfig.from_pretrained('../pretrained-model/roberta-large')
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# config4Code = RobertaConfig.from_pretrained('microsoft/codebert-base')
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config.num_labels = 2
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self.Roberta = AutoModel.from_pretrained('../pretrained-model/roberta-large', config=config)
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# self.CodeBERT = AutoModel.from_pretrained('microsoft/codebert-base',config=config4Code)
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# self.text_hidden_size = text_hidden_size
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# self.code_hidden_size = code_hidden_size
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# self.num_class = num_class
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for param in self.Roberta.parameters():
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param.requires_grad = True
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# for param in self.CodeBERT.parameters():
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# param.requires_grad = True
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self.linear1 = nn.Linear(1024, 512)
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self.linear2 = nn.Linear(512, 256)
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self.linear3 = nn.Linear(256, 32)
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self.linear4 = nn.Linear(32, 2)
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def forward(self, input1, labels):
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text_output = self.Roberta(input1, attention_mask=input1.ne(1))[1] # [batch_size, hiddensize]
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logits = self.linear2(self.linear1(text_output))
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logits = self.linear3(logits)
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logits = self.linear4(logits)
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prob = torch.softmax(logits, -1) # -1 means cal for each row
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if labels is not None:
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loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
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loss = loss_fct(logits, labels)
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return loss, prob
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else:
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return prob
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def savefile(data, path):
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with open(path, 'wb') as f:
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pickle.dump(data, f)
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def readfile(path):
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f = open(path, 'rb')
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data = pickle.load(f)
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f.close()
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return data
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def clean_en_text(text):
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# keep English, digital and space
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comp = re.compile('[^A-Z^a-z^0-9^ ]')
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return comp.sub(' ', text)
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def RemoveTag(str1, key): # change any word in 'key-[0-9]*' to ' '
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if type(str1) != str:
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str1 = str(str1)
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keys = key.split("/")
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patterns = [k + '-[0-9]*' for k in keys]
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pattern = ""
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for ip in range(len(patterns)):
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if ip == 0:
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pattern = patterns[ip]
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else:
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pattern = pattern + "|" + patterns[ip]
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return re.sub(pattern, ' ', str1)
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def RemoveHttp(str1):
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if type(str1) != str:
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str1 = str(str1)
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httpPattern = '[a-zA-z]+://[^\s]*'
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return re.sub(httpPattern, ' ', str1)
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def RemoveGit(str1):
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if type(str1) != str:
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str1 = str(str1)
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gitPattern = '[Gg]it-svn-id'
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return re.sub(gitPattern, ' ', str1)
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def textProcess(text):
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key = 'full' # for issue_text or commit_text or commit_code
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final = []
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# remove
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text = RemoveHttp(text)
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text = RemoveTag(text, key)
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text = RemoveGit(text)
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sentences = tokenizer_RoBERTa.tokenize(text) # divide into sentence
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for sentence in sentences:
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if len(final) >= 600:
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break
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sentence = clean_en_text(sentence)
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word_tokens = word_tokenize(sentence) # divide into word
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word_tokens = [word for word in word_tokens if word.lower() not in stopwords.words('english')]
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for word in word_tokens:
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if word in stopwords.words('english'):
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continue
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else:
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final.append(str(stemmer.stem(word)))
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if len(final) == 0:
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text = ' '
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else:
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text = ' '.join(final)
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return text
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class InputFeatures(object):
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"""A single training/test features for a example."""
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def __init__(self,
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input_tokens,
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input_ids,
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label,
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issue_key,
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commit_sha,
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):
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self.input_tokens = input_tokens
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self.input_ids = input_ids
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self.label = label
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self.issue_key = issue_key,
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self.commit_sha = commit_sha,
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def convert_examples_to_features(row, tokenizer, commitType): # commit_text/commit_code
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max_seq_length = 512
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# issue_text = textProcess(row['Issue_Text'])
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# issue_text = textProcess(row['desc'])
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# commit_text = textProcess(row[commitType])
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# issue_token = tokenizer.tokenize(issue_text)
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# commit_token = tokenizer.tokenize(commit_text)
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issue_token = eval(row['desc'])
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commit_token = eval(row[commitType])
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if len(issue_token) + len(commit_token) > max_seq_length - 3:
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if len(issue_token) > (max_seq_length - 3) / 2 and len(commit_token) > (max_seq_length - 3) / 2:
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issue_token = issue_token[:int((max_seq_length - 3) / 2)]
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commit_token = commit_token[:max_seq_length - 3 - len(issue_token)]
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elif len(issue_token) > (max_seq_length - 3) / 2:
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issue_token = issue_token[:max_seq_length - 3 - len(commit_token)]
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elif len(commit_token) > (max_seq_length - 3) / 2:
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commit_token = commit_token[:max_seq_length - 3 - len(issue_token)]
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combined_token = [tokenizer.cls_token] + issue_token + [tokenizer.sep_token] + commit_token + [tokenizer.sep_token]
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combined_ids = tokenizer.convert_tokens_to_ids(combined_token)
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# padding
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if len(combined_ids) < max_seq_length:
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padding_length = max_seq_length - len(combined_ids)
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combined_ids += [tokenizer.pad_token_id] * padding_length
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return InputFeatures(combined_token, combined_ids, row['label'], row['cve'], row['commit'])
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class TextDataset_RoBERTa(Dataset):
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def __init__(self, df, tokenizer=tokenizer_RoBERTa):
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self.text_examples = []
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self.code_examples = []
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# if 'TRAIN' in file_path:
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# # under sample
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# # df_link = MySubSampler(pd.read_csv(file_path), args.seed)
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# df_link = MySubSampler(df, 1)
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# else:
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# df_link = pd.df
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# token + id + label
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for i_row, row in tqdm(df.iterrows(), total=df.shape[0]):
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# self.text_examples.append(convert_examples_to_features(row, tokenizer, 'Commit_Text'))
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# self.code_examples.append(convert_examples_to_features(row, tokenizer, 'Commit_Code'))
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self.text_examples.append(convert_examples_to_features(row, tokenizer, 'mess'))
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self.code_examples.append(convert_examples_to_features(row, tokenizer, 'commit_code'))
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assert len(self.text_examples) == len(self.code_examples), 'ErrorLength'
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def __len__(self):
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return len(self.text_examples)
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def __getitem__(self, i):
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return (torch.tensor(self.text_examples[i].input_ids),
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torch.tensor(self.code_examples[i].input_ids),
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torch.tensor(self.text_examples[i].label))
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def add_desc(df, filepath='../data/cve_desc.csv'):
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desc = pd.read_csv(filepath, encoding='latin1')
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df = df.merge(desc, on='cve', how='left')
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return df
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def add_mess(df, gitdir='../gitrepo/'):
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def get_commit_message(reponame, commit):
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gitrepo = git.Repo(gitdir + reponame)
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temp_commit = gitrepo.commit(commit)
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mess = temp_commit.message
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return mess
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df['mess'] = df.apply(
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lambda row: get_commit_message(row['repo'], row['commit']), axis=1)
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return df
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def prepare_RoBERTa_encoding():
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if not os.path.exists('../data/RoBERTa-encode/RoBERTa_Embedding.csv'):
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df = pd.read_csv('../data/Dataset_150.csv')
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df = df[['cve', 'repo', 'commit']]
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df = add_desc(df)
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df = add_mess(df) # load bert tokenizer
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desc = {'desc': []}
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list1 = []
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commit_code = {'commit_code': []}
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list2 = []
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commit = {'mess': []}
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list3 = []
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for idx, row in tqdm(df.iterrows(), total=df.shape[0]):
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issue_text = textProcess(row['desc'])
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code_text = textProcess(row['commit_code'])
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commit_text = textProcess(row['mess'])
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issue_token = tokenizer_RoBERTa.tokenize(issue_text)
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commit_token = tokenizer_RoBERTa.tokenize(commit_text)
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code_token = tokenizer_RoBERTa.tokenize(code_text)
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# use RoBERTa tokenizer
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list1.append(issue_token)
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list2.append(code_token)
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list3.append(commit_token)
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desc['desc'] = list1
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commit_code['commit_code'] = list2
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commit['commit'] = list3
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df['desc'] = pd.DataFrame(desc)['desc']
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df['commit_code'] = pd.DataFrame(commit_code)["commit_code"]
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df['mess'] = pd.DataFrame(commit)['mess']
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df.to_csv('../data/RoBERTa-encode/RoBERTa_Embedding.csv', index=False)
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def create_RoBERTa_encode_dataset(train_df, test_df, trainDatasetPath='../data/RoBERTa-encode/RoBERTa_enc_train',
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testDatasetPath='../data/RoBERTa-encode/RoBERTa_enc_test', note='0'):
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prepare_RoBERTa_encoding()
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df = pd.read_csv('../data/RoBERTa-encode/RoBERTa_Embedding.csv')
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train_df = pd.merge(left=train_df, right=df, on=['cve', 'repo', 'commit'])
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test_df = pd.merge(left=test_df, right=df, on=['cve', 'repo', 'commit'])
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print('train_df')
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trainDataset = TextDataset_RoBERTa(train_df)
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print('test_df')
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testDataset = TextDataset_RoBERTa(test_df)
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savefile(trainDataset, trainDatasetPath)
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positive_samples = train_df[train_df['label'] == 1]
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negative_samples = train_df[train_df['label'] == 0]
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balanced_negative_samples = pd.DataFrame() # save selected negative samples
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for cve_id, group in negative_samples.groupby('cve'):
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if len(group) > 10:
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sampled_group = group.sample(n=10, random_state=42)
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else:
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sampled_group = group
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balanced_negative_samples = pd.concat([balanced_negative_samples, sampled_group])
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train_df = pd.concat([positive_samples, balanced_negative_samples])
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trainDataset_10 = TextDataset_RoBERTa(train_df)
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savefile(trainDataset_10, '../data/RoBERTa-encode/RoBERTa_enc_train_' + note)
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savefile(testDataset, testDatasetPath)
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def train_enc_RoBERTa(trainDatasetPath,
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testDatasetPath,
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criterion=None,
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optimizer=None,
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num_epochs=10,
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batch_size=8,
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shuffle=False,
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num_workers=1,
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weight_decay=0.0,
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adam_epsilon=1e-8,
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learning_rate=1e-5,
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max_grad_norm=0.1,
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note='0'):
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trainDataset = readfile(trainDatasetPath)
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train_sampler = RandomSampler(trainDataset)
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trainDataLoader = DataLoader(trainDataset, sampler=train_sampler,
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batch_size=batch_size, num_workers=4, pin_memory=True)
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model = Roberta().to(device)
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if n_gpu > 1:
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model = torch.nn.DataParallel(model)
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no_decay = ['bias', 'LayerNorm.weight']
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optimizer_grouped_parameters = [
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{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],'weight_decay': weight_decay},
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{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate, eps=adam_epsilon)
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max_steps = len(trainDataLoader) * num_epochs
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=max_steps * 0.1, num_training_steps=max_steps)
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model.zero_grad()
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model.train()
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for epoch in range(num_epochs):
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t1 = time.time()
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loss_sum = 0
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bar = tqdm(trainDataLoader, total=len(trainDataLoader))
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losses = []
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for i, (data1, data2, label) in enumerate(bar):
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data1 = data1.to(device)
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# data2 = data2.to(device)
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label = label.to(device)
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loss, logits = model(data1, label)
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# loss = criterion(pred, label)
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if n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel training
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
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losses.append(loss.item())
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optimizer.step()
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optimizer.zero_grad()
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scheduler.step()
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t2 = time.time()
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logging.info('Epoch [{:2}/{:2}], Loss: {:.4f}, Time: {:4}s'.format(epoch + 1, num_epochs, loss.item(), int(t2 - t1)))
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if epoch == num_epochs - 1:
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torch.save(model.state_dict(), '../data/RoBERTa-encode/RoBERTa_{}_epoch_{}_{}.ckpt'.format(note, num_epochs, epoch))
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def RoBERTa_evaluate(model, testDataset):
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eval_dataset = testDataset
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seed = 1
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eval_sampler = SequentialSampler(eval_dataset)
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eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=16, num_workers=4, pin_memory=True)
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# Eval!
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print("***** Running evaluation *****")
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print(" Num examples = {}".format(len(eval_dataset)))
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print(" Batch size = 16")
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eval_loss = 0.0
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nb_eval_steps = 0
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model.eval()
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logits = []
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labels = []
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for batch in eval_dataloader:
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text_inputs = batch[0].to(device)
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code_inputs = batch[1].to(device)
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label = batch[2].to(device)
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with torch.no_grad():
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lm_loss, logit = model(text_inputs, code_inputs, label)
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eval_loss += lm_loss.mean().item()
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logits.append(logit.cpu().numpy())
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labels.append(label.cpu().numpy())
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nb_eval_steps += 1
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logits = np.concatenate(logits, 0)
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labels = np.concatenate(labels, 0)
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preds = logits.argmax(-1)
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print('Predictions', preds[:25])
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print('Labels:', labels[:25])
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eval_acc = np.mean(labels == preds)
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eval_loss = eval_loss / nb_eval_steps
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perplexity = torch.tensor(eval_loss)
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eval_precision = precision_score(labels, preds)
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eval_recall = recall_score(labels, preds)
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eval_f1 = f1_score(labels, preds)
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eval_auc = roc_auc_score(labels, preds)
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eval_mcc = matthews_corrcoef(labels, preds)
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tn, fp, fn, tp = confusion_matrix(y_true=labels, y_pred=preds).ravel()
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eval_pf = fp / (fp + tn)
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eval_brier = brier_score_loss(labels, preds)
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result = {
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"eval_loss": float(perplexity),
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"eval_acc": round(float(eval_acc), 4),
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"eval_precision": round(eval_precision, 4),
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"eval_recall": round(eval_recall, 4),
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"eval_f1": round(eval_f1, 4),
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"eval_auc": round(eval_auc, 4),
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"eval_mcc": round(eval_mcc, 4),
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"eval_brier": round(eval_brier, 4),
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"eval_pf": round(eval_pf, 4),
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}
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return result
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def get_RoBERTa_embedding(model,
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dataset_path,
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batch_size=8,
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shuffle=False,
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num_workers=1,
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note='data',
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outpath='../data/RoBERTa-encode/'):
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dataset = readfile(dataset_path)
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dataloader = DataLoader(dataset,
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batch_size=batch_size,
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shuffle=shuffle)
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model.eval()
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# data1_embedding = []
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# data2_embedding = []
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out_embedding = []
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with torch.no_grad():
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bar = tqdm(dataloader, total=len(dataloader))
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for i, (data1, data2, label) in enumerate(bar):
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data1 = data1.to(device)
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out1 = model.Roberta(data1, attention_mask=data1.ne(1))[1]
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out = model.linear3(model.linear2(model.linear1(out1)))
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out_embedding.extend(out.cpu().numpy())
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out_embedding = np.array(out_embedding)
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savefile(out_embedding, outpath + 'RoBERTa_embedding_' + note)
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def RoBERTa_encoding(train_df, test_df, note):
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if not os.path.exists('../data/RoBERTa-encode'):
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os.makedirs('../data/RoBERTa-encode')
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trainDatasetPath = '../data/RoBERTa-encode/RoBERTa_enc_trainALL_' + note
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testDatasetPath = '../data/RoBERTa-encode/RoBERTa_enc_test_' + note
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print("Create dataset {}".format(str(note)))
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logging.info("Create dataset")
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create_RoBERTa_encode_dataset(train_df, test_df, trainDatasetPath, testDatasetPath, note=note)
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print("Train encoding module {}".format(str(note)))
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logging.info("Train encoding module")
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train_enc_RoBERTa(trainDatasetPath='../data/RoBERTa-encode/RoBERTa_enc_train_' + note,
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testDatasetPath=testDatasetPath, note=note) # RoBERTa fine-tune
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print("Text encoding {}".format(str(note)))
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logging.info("Text encoding")
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model = Roberta().to(device)
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if n_gpu > 1:
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new_state_dict = OrderedDict()
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for key, value in torch.load('../data/RoBERTa-encode/RoBERTa_{}_epoch_10_9.ckpt'.format(note)).items():
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name = key[7:]
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new_state_dict[name] = value
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model.load_state_dict(new_state_dict)
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else:
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model.load_state_dict(torch.load('../data/RoBERTa-encode/RoBERTa_{}_epoch_10_9.ckpt'.format(note)))
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# if n_gpu > 1:
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# model = torch.nn.DataParallel(model)
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note_train = 'train_' + note
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note_test = 'test_' + note
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print('get train embedding')
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get_RoBERTa_embedding(model, trainDatasetPath, note=note_train) # vuln_embedding_train, commit_embedding_train
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print('get test embedding')
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get_RoBERTa_embedding(model, testDatasetPath, note=note_test) |