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main_clone.py
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from myOpenDelta.opendelta import AdapterModel , LoraModel , PrefixModel
import argparse
import logging
import os
import torch
import numpy as np
from model import Model_classification
from tqdm import tqdm
import torch.nn as nn
import transformers
from torch.nn.functional import binary_cross_entropy , binary_cross_entropy_with_logits
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
from transformers import (WEIGHTS_NAME, get_linear_schedule_with_warmup, RobertaConfig, RobertaModel, RobertaTokenizer , RobertaForSequenceClassification , AutoModel , AutoConfig , AutoTokenizer)
import torch.distributed as dis
from torch.nn.parallel import DistributedDataParallel as DDP
from utilities import *
from optimization import *
from sklearn.metrics import recall_score, precision_score, f1_score
os.environ["TOKENIZERS_PARALLELISM"] = "false"
transformers.utils.logging.set_verbosity_error()
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("name")
def train_clone(args, model, tokenizer, train_dataloader , eval_dataloader , test_dataloader=None):
""" Train the model """
optimizer =torch.optim.Adam(model.parameters(), lr=args.learning_rate )
max_steps = len(train_dataloader) * args.num_train_epochs
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=max_steps*0.1, num_training_steps=max_steps)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataloader.dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Total train batch size = %d", args.train_batch_size)
logger.info(" Total optimization steps = %d", len(train_dataloader)*args.num_train_epochs)
best_acc= - np.inf
model.zero_grad()
loss_fn = nn.BCELoss()
early_stopper = EarlyStopper(patience=3, min_delta=0.03)
results = {}
for idx in range(args.num_train_epochs):
LOSSes, ACCs = [], []
#bar = tqdm(train_dataloader,total=len(train_dataloader))
for step, batch in enumerate(train_dataloader) : #enumerate(bar):
model.train()
code_inputs = batch[0].to(args.device)
labels = batch[1].to(args.device)
labels= labels.float().squeeze()
logits = model(code_inputs=code_inputs)
loss = loss_fn(logits,labels)
accuracy = (logits.round() == labels ).float().mean().item()*100.0
# perfom a backward step
LOSSes.append(loss.item() )
# add current accuracies to accuracy arrays
ACCs.append(accuracy)
# update progress bar
#bar.set_description("Epoch {} Train Loss {} Accuracy {} ".format(idx, round(np.mean(LOSSes), 3) , np.round(np.mean(ACCs))))
if (step+1)%100 == 0:
logger.info("Epoch {} Step {} Train Loss {} Accuracy {} ".format(idx, step, round(np.mean(LOSSes), 3) , round(np.mean(ACCs), 3) ))
loss.backward()
# optimizer step
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
optimizer.zero_grad()
scheduler.step()
results.setdefault('train_loss', []).append(round(np.mean(LOSSes),3))
results.setdefault('train_acc', []).append(round(np.mean(ACCs),3))
eval_results = evaluate_clone(args, model, eval_dataloader)
results.setdefault('eval_loss', []).append(eval_results['eval_loss'])
results.setdefault('eval_acc', []).append(eval_results['eval_acc'])
results.setdefault('eval_f1', []).append(eval_results['f1_score'])
results.setdefault('eval_precision', []).append(eval_results['precision'])
results.setdefault('eval_recall', []).append(eval_results['recall'])
for key, value in eval_results.items():
logger.info(" %s = %s", key, value)
if eval_results['f1_score']>best_acc:
best_acc=eval_results['f1_score']
logger.info("\n "+"*"*30)
logger.info(" Best F1 score :%s",round(best_acc,4))
logger.info(" "+"*"*30)
if not args.do_optimization :
#save_best_model(model, args , checkpoint_prefix="models/best_model_clone")
test_result = test_clone(args, model, test_dataloader)
#if early_stopper.early_stop(round(eval_results['eval_loss'],3)):
#break
if not args.do_optimization :
save_best_model(model, args , checkpoint_prefix="models/final_model_clone")
final_test_result = test_clone(args, model, test_dataloader)
return results
# run validation for both tasks
def evaluate_clone(args, model, eval_dataloader_clone ):
logger.info("***** Running evaluation *****")
logger.info(" Num examples clone detection = %d", len(eval_dataloader_clone.dataset))
logger.info(" Batch size = %d ", args.eval_batch_size)
model.eval()
loss_fn = nn.BCELoss()
eval_loss = 0.0
nb_eval_steps = 0
logits = []
labels = []
for batch in eval_dataloader_clone:
inputs = batch[0].to(args.device)
label = batch[1].to(args.device)
with torch.no_grad():
logit = model(code_inputs=inputs)
label = label.float().squeeze()
lm_loss = loss_fn(logit, 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.round()
eval_acc = np.mean(labels == preds)
eval_loss = eval_loss / nb_eval_steps
perplexity = torch.tensor(eval_loss)
recall = recall_score(labels , preds)
precision = precision_score(labels , preds , zero_division=0)
f1 = f1_score(labels , preds)
result = {
"eval_loss": round(float(perplexity),4),
"eval_acc": round(eval_acc, 4),
"f1_score" : round(f1, 4),
"recall" : round(recall,4),
"precision" : round(precision,4)}
return result
# Run test for one task
def test_clone(args, model, test_dataloader):
logits = []
labels = []
for batch in test_dataloader:
inputs = batch[0].to(args.device)
label = batch[1].to(args.device)
with torch.no_grad():
logit = model(code_inputs=inputs)
label = label.float().squeeze()
logits.append(logit.cpu().numpy())
labels.append(label.cpu().numpy())
logits = np.concatenate(logits, 0)
labels = np.concatenate(labels, 0)
preds = logits.round()
acc = np.mean(labels == preds)
recall = recall_score(labels , preds)
precision = precision_score(labels , preds , zero_division=0)
f1 = f1_score(labels , preds)
result = {
"test_acc": round(acc, 4),
"test_f1_score" : round(f1, 4),
"test_recall" : round(recall,4),
"test_precision" : round(precision,4)
}
logger.info("\n***** Test Results for clone detection ")
logger.info("\n{}\n".format(result ))
return result
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--task", default="clone_detection", type=str,
help="Name of the task")
parser.add_argument("--train_data_file", default="./datasets/dataset_clone/train.txt", type=str,
help="The input training data file (a json file).")
parser.add_argument("--output_dir", default='./', type=str,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--num_classes", default=1, type=int,
help="The number of classes for the classification model")
parser.add_argument("--eval_data_file", default="./datasets/dataset_clone/valid.txt", type=str,
help="An optional input evaluation data file to evaluate the MRR(a jsonl file).")
parser.add_argument("--test_data_file", default="./datasets/dataset_clone/test.txt", type=str,
help="An optional input test data file to test the MRR(a josnl file).")
parser.add_argument("--codebase_file", default=None, type=str,
help="An optional input test data file to codebase (a jsonl file).")
parser.add_argument("--model_name_or_path", default='microsoft/graphcodebert-base', 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("--nl_length", default=128, type=int,
help="Optional NL input sequence length after tokenization.")
parser.add_argument("--code_length", default=512, type=int,
help="Optional Code input sequence length after tokenization.")
parser.add_argument("--do_optimization", default=None, type=bool,
help="Whether to run adapter optimization")
parser.add_argument("--do_train", default=None, type=bool,
help="Whether to run training.")
parser.add_argument("--do_eval", default=None, type=bool,
help="Whether to run eval on the dev set.")
parser.add_argument("--do_test", default=None, type=bool,
help="Whether to run eval on the test set.")
parser.add_argument("--train_batch_size", default=32, type=int,
help="Batch size for training.")
parser.add_argument("--eval_batch_size", default=32, type=int,
help="Batch size for evaluation.")
parser.add_argument("--train_data_rate_clone", default=0.0001, type= float,
help="Data size for train")
parser.add_argument("--learning_rate", default=1e-4, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--nb_samples", default=100, type=int,
help="Total number of train samples.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=5, type=int,
help="Total number of training epochs to perform.")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--local_rank', default=-1 ,type=int,
help="random seed for initialization")
parser.add_argument('--population_size', default=3 ,type=int,
help="population size on the evolutionary optimization algorithm")
parser.add_argument('--sample_size', default=2 ,type=int,
help="sample size on the evolutionary optimization algorithm")
parser.add_argument('--cycles', default=2 ,type=int,
help="number of cycles on the evolutionary optimization algorithm")
parser.add_argument('--optimization_history_file', default=None ,type=str,
help="saving the history of optimization")
parser.add_argument('--stats_file', default=None ,type=str,
help="saving the optimization statistics ")
args = parser.parse_args()
set_seed(seed=args.seed)
device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")
args.n_gpu = 1 #torch.cuda.device_count()
args.device = device
logger.info("device: %s, n_gpu: %s", device, args.n_gpu)
config = AutoConfig.from_pretrained(args.model_name_or_path , num_labels = args.num_classes , trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path , trust_remote_code=True)
model = AutoModel.from_pretrained(args.model_name_or_path,config=config , trust_remote_code=True)
train_dataset=TextDataset_clone(tokenizer, args, args.train_data_file, nb_samples = None) #args.nb_samples)
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size,num_workers=4,pin_memory=True )
eval_dataset = TextDataset_clone(tokenizer, args,args.eval_data_file , nb_samples=41541 )
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset , sampler=eval_sampler, batch_size=args.eval_batch_size,num_workers=4,pin_memory=True)
test_dataset = TextDataset_clone(tokenizer, args,args.test_data_file ,nb_samples= 41541 )
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.eval_batch_size , num_workers=4,pin_memory=True)
if args.do_optimization:
logger.info("Starting optimization...")
history, population, best_of_all, stats = regularized_evolution(args, config, train_dataloader , eval_dataloader )
else :
# enable to insert default adapter / lora/ prefix , with a fixed adapter across all layers
#delta = AdapterModel(model , bottleneck_dim=[24])
#delta = LoraModel(model)
#delta = PrefixModel(model)
#delta.freeze_module(exclude=["deltas" ])
#delta.log()
#model = Model_classification( model , config)
#if args.n_gpu > 1:
#model = torch.nn.DataParallel(model, device_ids=[0,1])
#model.to(args.device)
# top 3 adapter configs
x_list = [
[{'insert_modules': ('attention.self', 'intermediate', 'output'), 'bottleneck_dim': (16, 64, 128), 'non_linearity': 'gelu', 'dropout_rate': 0.2, 'normalization': 'layer_norm', 'skip_connection': True}, 0, 0, {'insert_modules': ('intermediate', 'attention.self'), 'bottleneck_dim': (64, 32), 'non_linearity': 'swish', 'dropout_rate': 0.3, 'normalization': 'layer_norm', 'skip_connection': True}, 0, 0, 0, 0, 0, 0, {'insert_modules': ('attention.output', 'intermediate', 'attention.self'), 'bottleneck_dim': (32, 64, 16), 'non_linearity': 'silu', 'dropout_rate': 0.0, 'normalization': None, 'skip_connection': True}, {'insert_modules': ('output', 'attention.self'), 'bottleneck_dim': (256, 16), 'non_linearity': 'leakyrelu', 'dropout_rate': 0.1, 'normalization': 'layer_norm', 'skip_connection': True}]
]
if args.do_train:
for x in x_list :
set_seed(seed=args.seed)
model = AutoModel.from_pretrained(args.model_name_or_path,config=config , trust_remote_code=True)
logger.info(x)
model = get_delta_model(model , x, args.device)
model = Model_classification( model , config)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, device_ids=[1])
model.to(args.device)
results = train_clone(args , model ,tokenizer ,
train_dataloader ,
eval_dataloader ,
test_dataloader)
logger.info("train results : \n {}\n".format(results))
logger.info("*"*130)
if args.do_eval:
checkpoint_prefix = 'models/final_model_clone/model.bin'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
model.load_state_dict(torch.load(output_dir) , strict=False)
eval_dataset_clone= TextDataset_clone(tokenizer, args,args.eval_data_file_clone)
eval_dataloader_clone = DataLoader(eval_dataset_clone , sampler=SequentialSampler(eval_dataset_clone ), batch_size=args.eval_batch_size,num_workers=4,pin_memory=True)
result_task1= evaluate_clone(args, model, eval_dataloader_clone )
logger.info("\n***** Eval results *****")
for key , value in result_task1.items() :
logger.info(" %s = %s", key, str(value))
if args.do_test:
checkpoint_prefix = 'models/best_model_clone/model.bin'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
model.load_state_dict(torch.load(output_dir), strict=False)
test_dataset_clone= TextDataset_clone(tokenizer, args,args.test_data_file_clone)
test_dataloader_clone = DataLoader(test_dataset_clone , sampler=SequentialSampler(test_dataset_clone ), batch_size=args.eval_batch_size,num_workers=4,pin_memory=True)
task1_test_result = test_clone(args, model, test_dataloader_clone )
if __name__ == "__main__":
main()