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main_multiple.py
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main_multiple.py
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import torch
import options
from dataloader import generate_dataloader
from buildmodel import buildBERT
import random
import numpy as np
from timerecord import format_time
from tqdm import tqdm
from torch.optim import AdamW
import time
from AL import virtual_adversarial_training
from DropAL import dropAlloss
from torch.cuda.amp import autocast as autocast, GradScaler
import os
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist
def search_path(file, directory):
count = 0
for filename in os.listdir(directory):
if file in filename:
count += 1
return count
class Config:
def __init__(self):
pass
def training_config(
self,
batch_size,
epochs,
learning_rate,
weight_decay,
device,
):
self.batch_size = batch_size
self.epochs = epochs
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.device = device
def train(model, train_dataloader, train_sampler, config, optimizer, optimizer_con, scaler, data_step):
"""
:param model: nn.Module
:param train_dataloader: DataLoader
:param config: Config
"""
if not len(train_dataloader):
raise EOFError("Empty train_dataloader.")
time_t0 = time.time()
training_loss = []
model.train()
for step, batch in enumerate(tqdm(train_dataloader, desc='Step')):
train_sampler.set_epoch(step)
input_ids, attention_mask, token_type_ids, labels = batch[0].long().to(config.device), \
batch[1].long().to(config.device), batch[2].long().to(config.device), batch[3].long().to(config.device)
with autocast():
output = model(input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
labels=labels,
output_hidden_states=True,
)
loss = output[0]
logits = output[1]
hidden_states = output[2][0]
adv_loss = virtual_adversarial_training(model, hidden_states, token_type_ids, attention_mask, logits)
if adv_loss:
loss = adv_loss + 10 * loss
else:
pass
optimizer.zero_grad()
scaler.scale(loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0, norm_type=2)
scaler.step(optimizer)
scaler.update()
with autocast():
contrastive_loss = dropAlloss(model, input_ids, token_type_ids, attention_mask, config.batch_size, config.device)
optimizer_con.zero_grad()
scaler.scale(contrastive_loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0, norm_type=2)
scaler.step(optimizer_con)
scaler.update()
model.zero_grad()
loss = loss + contrastive_loss
training_loss.append(loss.item())
if (step+1) % 500 == 0:
print("Step {} training loss: {} adv loss:{} contrastive loss :{}".format(step+1, training_loss[-1],adv_loss.item(), contrastive_loss.item()))
train_loss = sum(training_loss)/len(training_loss)
time_t1 = time.time()
cost_time = format_time(time_t1 - time_t0)
print("Training loss: {}; Data iteration{} cost time: {}".format(train_loss, data_step, cost_time))
def evaluate(model, val_dataloader, val_sampler, config, data_step):
time_t0 = time.time()
evaluation_loss = []
model.eval()
with torch.no_grad():
for step, batch in enumerate(tqdm(val_dataloader, desc='Step')):
val_sampler.set_epoch(step)
input_ids, attention_mask, token_type_ids, labels = batch[0].long().to(config.device), \
batch[1].long().to(config.device), batch[2].long().to(config.device), batch[3].long().to(config.device)
with autocast():
output = model(input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
labels=labels,
output_hidden_states=True,
)
loss = output[0]
evaluation_loss.append(loss.item())
if (step+1) % 500 == 0:
print("Step {} evaluation loss: {}".format(step+1, sum(evaluation_loss)/len(evaluation_loss)))
eval_loss = sum(evaluation_loss)/len(evaluation_loss)
time_t1 = time.time()
cost_time = format_time(time_t1 - time_t0)
print("Evaluation loss: {} ; Data iteration{} cost time: {}".format(eval_loss, data_step, cost_time))
def main(args):
if args.local_rank >= 0:
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend='nccl')
dist.barrier()
ddp = True
else:
ddp = False
batch_size = args.batch_size
vocab_size = 5000
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
model = buildBERT(vocab_size)
model.cuda(args.local_rank)
if ddp:
model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank)
gpu_num = torch.distributed.get_world_size()
else:
gpu_num = 1
train_iter = search_path("attention", "./tokenized_data/train/")
val_iter = search_path("attention", "./tokenized_data/val/")
optimizer = AdamW(model.parameters(), lr=args.lr / 5, weight_decay=args.weight_decay)
optimizer_con = AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
scaler = GradScaler()
for epoch in tqdm(range(args.epochs), desc="Training Epoch"):
for step in tqdm(range(train_iter), desc="training iteration"):
input_ids_train = torch.load("./tokenized_data/train/train_input_ids{}.pt".format(step))
attention_masks_train = torch.load("./tokenized_data/train/train_attention_mask{}.pt".format(step))
token_type_ids_train = torch.load("./tokenized_data/train/train_token_type_ids{}.pt".format(step))
labels_train = torch.load("./tokenized_data/train/train_labels{}.pt".format(step))
input_ids_train = torch.cat(input_ids_train, dim=0)
attention_masks_train = torch.cat(attention_masks_train, dim=0)
token_type_ids_train = torch.cat(token_type_ids_train, dim=0)
labels_train = torch.cat(labels_train, dim=0)
train_list = [input_ids_train, attention_masks_train, token_type_ids_train, labels_train]
train_dataloader, train_sampler = generate_dataloader(train_list, "train", ddp, batch_size)
config = Config()
config.training_config(batch_size=args.batch_size, epochs=args.epochs, learning_rate=args.lr, weight_decay=args.weight_decay, device=device)
train(model, train_dataloader, train_sampler, config, optimizer, optimizer_con, scaler, step)
for step in tqdm(range(val_iter), desc="evaluation iteration"):
input_ids_val = torch.load("./tokenized_data/val/val_input_ids{}.pt".format(step))
attention_masks_val = torch.load("./tokenized_data/val/val_attention_mask{}.pt".format(step))
token_type_ids_val = torch.load("./tokenized_data/val/val_token_type_ids{}.pt".format(step))
labels_val = torch.load("./tokenized_data/val/val_labels{}.pt".format(step))
input_ids_val = torch.cat(input_ids_val, dim=0)
attention_masks_val = torch.cat(attention_masks_val, dim=0)
token_type_ids_val = torch.cat(token_type_ids_val, dim=0)
labels_val = torch.cat(labels_val, dim=0)
val_list = [input_ids_val, attention_masks_val, token_type_ids_val, labels_val]
val_dataloader, val_sampler = generate_dataloader(val_list, "val", ddp, batch_size)
config = Config()
config.training_config(batch_size=args.batch_size, epochs=args.epochs, learning_rate=args.lr, weight_decay=args.weight_decay, device=device)
evaluate(model, val_dataloader, val_sampler, config, step)
torch.save(model, 'bert_model/urlBERT.pt')
if __name__ == "__main__":
seed_val = 2024
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
args = options.args_parser()
main(args)