print("WARNING: You have a CUDA device, so you should probably run with --cuda")
else:
torch.cuda.manual_seed(args.seed)
# Load Dictionary
assert os.path.exists(args.train_data)
assert os.path.exists(args.val_data)
dictionary = Dictionary(join_path(data_dir,'data/atec_nlp_sim_train.csv'))
args.vocab_size = len(dictionary)
best_val_loss = None
best_f1 = None
n_token = len(dictionary)
model = ESIM(args)
if torch.cuda.is_available():
model = model.cuda()
print(model)
print('Begin to load data.')
train_data = MyDataset(args.train_data, args.sequence_length, dictionary.word2idx, args.char_model)
val_data = MyDataset(args.val_data, args.sequence_length, dictionary.word2idx, args.char_model)
train_loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=16)
val_loader = DataLoader(val_data, batch_size=1, shuffle=False)
try:
for epoch in range(args.epochs):
train(train_loader, val_loader, model, args)
except KeyboardInterrupt:
print('-' * 89)
print('Exit from training early.')
def main():
parser = ArgumentParser()
parser.add_argument("--epoch", type=int, required=True)
parser.add_argument("--seed", type=int, required=True)
parser.add_argument("--emb_file", type=str, required=True)
parser.add_argument("--checkpoint", type=str, required=True)
parser.add_argument("--save_dir", type=str, required=True)
parser.add_argument("--train_file", type=str, required=True)
parser.add_argument("--log_file", type=str, required=False)
parser.add_argument("--ratio", type=str, required=True)
parser.add_argument("--vocab_size", type=int, required=True)
parser.add_argument("--emb_size", type=int, required=True)
parser.add_argument("--learning_rate", type=float, required=True)
parser.add_argument("--batch_size", type=int, required=True)
parser.add_argument("--max_length", type=int, required=True)
parser.add_argument("--max_grad_norm", type=int, required=True)
args = parser.parse_args()
split_ratio = [float(val) for val in args.ratio.split(",")]
has_cuda = torch.cuda.is_available()
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
LOG_FORMAT = "%(asctime)s - %(levelname)s - %(message)s"
DATE_FORMAT = "%m/%d/%Y %H:%M:%S %p"
logging.basicConfig(filename=args.log_file,
level=logging.INFO,
format=LOG_FORMAT,
datefmt=DATE_FORMAT)
logging.info("start preparing data")
data_preprocessor = DataPreprocess()
emb, word_idx_map = data_preprocessor.build_emb_vocab(args.emb_file)
data_preprocessor.load(args.train_file, use_mask=False, is_test=False)
train_dataset, dev_dataset = data_preprocessor.generate_train_dev_dataset(
ratio=split_ratio)
train_dataset, dev_dataset = CompDataSet(
train_dataset,
word_idx_map,
max_len=args.max_length,
emb_size=args.emb_size), CompDataSet(dev_dataset,
word_idx_map,
max_len=args.max_length,
emb_size=args.emb_size)
train_dataset = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True)
dev_dataset = DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=True)
logging.info("init model")
start_epoch = 0
if args.checkpoint:
model = torch.load(args.checkpoint)
start_epoch = re.findall("\d+(?=\_\d+.pt)", args.checkpoint)
start_epoch = int(start_epoch[0]) + 1
else:
model = ESIM(args.vocab_size,
args.emb_size,
emb,
max_len=args.max_length)
optimizer = AdamW(model.parameters(), lr=args.learning_rate)
criterion = FocalLoss()
if has_cuda:
model = model.cuda()
logging.info("start training")
neg_auc, pos_auc = validate(model, dev_dataset)
logging.info(f"pre-train neg_auc {str(neg_auc)} pos_auc {str(pos_auc)}")
for epoch in range(start_epoch, args.epoch):
running_loss = 0.0
for step, data in enumerate(train_dataset):
model.train()
start_time = time.time()
optimizer.zero_grad()
outputs = model(data["premise"], data["premise_mask"],
data["hypothese"], data["hypothese_mask"])
loss = criterion(outputs["probs"], data["label"])
loss.backward()
clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
end_time = time.time()
running_loss += loss.item()
if step % 100 == 99:
logging.info(
f"epoch: {epoch}, step: {step}, time: {end_time - start_time} loss: {running_loss / 100}"
)
running_loss = 0
if step % 500 == 499:
neg_auc, pos_auc = validate(model, dev_dataset)
logging.info(
f"pre-train neg_auc {str(neg_auc)} pos_auc {str(pos_auc)}")
torch.save(model, Path(args.save_dir) / f"{epoch}_{step}.pt")
