def train(model, tokenizer, train_data, valid_data, args, eos=False):
model.train()
train_dataset = TextDataset(train_data)
train_dataloader = DataLoader(train_dataset, sampler=RandomSampler(train_dataset),
batch_size=args.train_batch_size, num_workers=args.num_workers,
collate_fn=lambda x: collate_fn(x, tokenizer, args.max_seq_length, eos=eos, tokenizer_type=args.tokenizer))
valid_dataset = TextDataset(valid_data)
valid_dataloader = DataLoader(valid_dataset, sampler=SequentialSampler(valid_dataset),
batch_size=args.eval_batch_size, num_workers=args.num_workers,
collate_fn=lambda x: collate_fn(x, tokenizer, args.max_seq_length, eos=eos, tokenizer_type=args.tokenizer))
valid_noisy = [x['noisy'] for x in valid_data]
valid_clean = [x['clean'] for x in valid_data]
epochs = (args.max_steps - 1) // len(train_dataloader) + 1
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,
betas=eval(args.adam_betas), eps=args.eps,
weight_decay=args.weight_decay)
lr_lambda = lambda x: x / args.num_warmup_steps if x <= args.num_warmup_steps else (x / args.num_warmup_steps) ** -0.5
scheduler = LambdaLR(optimizer, lr_lambda)
step = 0
best_val_gleu = -float("inf")
meter = Meter()
for epoch in range(1, epochs + 1):
print("===EPOCH: ", epoch)
for batch in train_dataloader:
step += 1
batch = tuple(t.to(args.device) for t in batch)
loss, items = calc_loss(model, batch)
meter.add(*items)
loss.backward()
if args.max_grad_norm > 0:
nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
model.zero_grad()
scheduler.step()
if step % args.log_interval == 0:
lr = scheduler.get_lr()[0]
loss_sent, loss_token = meter.average()
logger.info(f' [{step:5d}] lr {lr:.6f} | {meter.print_str(True)}')
nsml.report(step=step, scope=locals(), summary=True,
train__lr=lr, train__loss_sent=loss_sent, train__token_ppl=math.exp(loss_token))
meter.init()
if step % args.eval_interval == 0:
start_eval = time.time()
(val_loss, val_loss_token), valid_str = evaluate(model, valid_dataloader, args)
prediction = correct(model, tokenizer, valid_noisy, args, eos=eos, length_limit=0.1)
val_em = em(prediction, valid_clean)
cnt = 0
for noisy, pred, clean in zip(valid_noisy, prediction, valid_clean):
print(f'[{noisy}], [{pred}], [{clean}]')
# 10개만 출력하기
cnt += 1
if cnt == 20:
break
val_gleu = gleu(prediction, valid_clean)
logger.info('-' * 89)
logger.info(f' [{step:6d}] valid | {valid_str} | em {val_em:5.2f} | gleu {val_gleu:5.2f}')
logger.info('-' * 89)
nsml.report(step=step, scope=locals(), summary=True,
valid__loss_sent=val_loss, valid__token_ppl=math.exp(val_loss_token),
valid__em=val_em, valid__gleu=val_gleu)
if val_gleu > best_val_gleu:
best_val_gleu = val_gleu
nsml.save("best")
meter.start += time.time() - start_eval
if step >= args.max_steps:
break
#nsml.save(epoch)
if step >= args.max_steps:
break
def train(model, tokenizer, train_data, valid_data, args):
model.train()
train_dataset = TextDataset(train_data)
train_dataloader = DataLoader(train_dataset,
sampler=RandomSampler(train_dataset),
batch_size=args.train_batch_size,
num_workers=args.num_workers,
collate_fn=lambda x: collate_fn_bert(
x, tokenizer, args.max_seq_length))
valid_dataset = TextDataset(valid_data)
valid_dataloader = DataLoader(valid_dataset,
sampler=SequentialSampler(valid_dataset),
batch_size=args.eval_batch_size,
num_workers=args.num_workers,
collate_fn=lambda x: collate_fn_bert(
x, tokenizer, args.max_seq_length))
valid_noisy = [x['noisy'] for x in valid_data]
valid_clean = [x['clean'] for x in valid_data]
epochs = (args.max_steps - 1) // len(train_dataloader) + 1
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,
# betas=eval(args.adam_betas), eps=args.eps,
# weight_decay=args.weight_decay)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
lr_lambda = lambda x: x / args.num_warmup_steps if x <= args.num_warmup_steps else (
x / args.num_warmup_steps)**-0.5
scheduler = LambdaLR(optimizer, lr_lambda)
step = 0
best_val_gleu = -float("inf")
meter = Meter()
for epoch in range(1, epochs + 1):
for batch in train_dataloader:
step += 1
batch = tuple(t.to(args.device) for t in batch)
noise_input_ids, clean_input_ids, noise_mask, clean_mask = batch
#print("noise shape: {}, clean shape: {}".format(noise_input_ids.shape, clean_input_ids.shape))
outputs = model(noise_input_ids,
labels=clean_input_ids,
attention_mask=noise_mask)
loss = outputs[0]
predict_score = outputs[1]
bsz = clean_input_ids.size(0)
items = [loss.data.item(), bsz, clean_mask.sum().item()]
#print("items: ", items)
meter.add(*items)
loss.backward()
if args.max_grad_norm > 0:
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
model.zero_grad()
scheduler.step()
if step % args.log_interval == 0:
lr = scheduler.get_lr()[0]
loss_sent, loss_token = meter.average()
logger.info(
f' [{step:5d}] lr {lr:.6f} | {meter.print_str(True)}')
nsml.report(step=step,
scope=locals(),
summary=True,
train__lr=lr,
train__loss_sent=loss_sent,
train__token_ppl=math.exp(loss_token))
meter.init()
if step % args.eval_interval == 0:
start_eval = time.time()
(val_loss, val_loss_token), valid_str = evaluate_kcBert(
model, valid_dataloader, args)
prediction = correct_kcBert(model,
tokenizer,
valid_noisy,
args,
length_limit=0.1)
val_em = em(prediction, valid_clean)
cnt = 0
for noisy, pred, clean in zip(valid_noisy, prediction,
valid_clean):
print(f'[{noisy}], [{pred}], [{clean}]')
# 10개만 출력하기
cnt += 1
if cnt == 20:
break
# print("len of prediction: {}, len of valid_clean: {}", len(prediction), len(valid_clean))
val_gleu = gleu(prediction, valid_clean)
logger.info('-' * 89)
logger.info(
f' [{step:6d}] valid | {valid_str} | em {val_em:5.2f} | gleu {val_gleu:5.2f}'
)
logger.info('-' * 89)
nsml.report(step=step,
scope=locals(),
summary=True,
valid__loss_sent=val_loss,
valid__token_ppl=math.exp(val_loss_token),
valid__em=val_em,
valid__gleu=val_gleu)
if val_gleu > best_val_gleu:
best_val_gleu = val_gleu
nsml.save("best")
meter.start += time.time() - start_eval
if step >= args.max_steps:
break
if step >= args.max_steps:
break