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train_model.py
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train_model.py
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# coding=utf-8
import os
import logging
import argparse
import math
import numpy as np
from tqdm import tqdm
import multiprocessing
import time
import torch
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from transformers import AdamW, get_linear_schedule_with_warmup
from models import build_or_load_gen_model
from evaluator import smooth_bleu
from data_handling import get_filenames, get_elapse_time, load_and_cache_gen_data
from configs import add_args, set_seed, set_dist
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
def eval_ppl_epoch(args, eval_data, eval_examples, model, tokenizer):
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size,
num_workers=4, pin_memory=True)
# Start evaluating model
logger.info(" " + "***** Running ppl evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_loss, batch_num = 0, 0
for batch in tqdm(eval_dataloader, total=len(eval_dataloader), desc="Eval ppl"):
batch = tuple(t.to(args.device) for t in batch)
source_ids, target_ids = batch
source_mask = source_ids.ne(tokenizer.pad_token_id)
target_mask = target_ids.ne(tokenizer.pad_token_id)
with torch.no_grad():
loss, _, _ = model(source_ids=source_ids, source_mask=source_mask,
target_ids=target_ids, target_mask=target_mask)
eval_loss += loss.item()
batch_num += 1
eval_loss = eval_loss / batch_num
eval_ppl = round(np.exp(eval_loss), 5)
return eval_ppl
def eval_bleu_epoch(args, eval_data, eval_examples, model, tokenizer, split_tag, criteria):
logger.info(
" ***** Running bleu evaluation on {} data*****".format(split_tag))
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_sampler = SequentialSampler(eval_data)
if args.data_num == -1:
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size,
num_workers=4, pin_memory=True)
else:
eval_dataloader = DataLoader(
eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
pred_ids = []
bleu = 0.0
for batch in tqdm(eval_dataloader, total=len(eval_dataloader), desc="Eval bleu for {} set".format(split_tag)):
source_ids = batch[0].to(args.device)
source_mask = source_ids.ne(tokenizer.pad_token_id)
with torch.no_grad():
preds = model(source_ids=source_ids, source_mask=source_mask)
top_preds = [pred[0].cpu().numpy() for pred in preds]
pred_ids.extend(top_preds)
pred_nls = [tokenizer.decode(
id, skip_special_tokens=True, clean_up_tokenization_spaces=False) for id in pred_ids]
output_fn = os.path.join(args.res_dir, "test_{}.output".format(criteria))
gold_fn = os.path.join(args.res_dir, "test_{}.gold".format(criteria))
src_fn = os.path.join(args.res_dir, "test_{}.src".format(criteria))
dev_accs, predictions = [], []
with open(output_fn, 'w') as f, open(gold_fn, 'w') as f1, open(src_fn, 'w') as f2:
for pred_nl, gold in zip(pred_nls, eval_examples):
dev_accs.append(pred_nl.strip() == gold.target.strip())
# for smooth-bleu4 evaluation
predictions.append(str(gold.idx) + '\t' + pred_nl)
f.write(str(gold.idx) + '\t' + pred_nl.strip() + '\n')
f1.write(str(gold.idx) + '\t' + gold.target.strip() + '\n')
f2.write(str(gold.idx) + '\t' + gold.source.strip() + '\n')
(goldMap, predictionMap) = smooth_bleu.computeMaps(predictions, gold_fn)
bleu = round(smooth_bleu.bleuFromMaps(
goldMap, predictionMap)[0], 2)
result = {'em': np.mean(dev_accs) * 100, 'bleu': bleu}
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(round(result[key], 4)))
return result
def main():
parser = argparse.ArgumentParser()
args = add_args(parser)
logger.info(args)
t0 = time.time()
set_dist(args)
set_seed(args)
config, model, tokenizer = build_or_load_gen_model(args)
model.to(args.device) # moves the model to cpu
# pool for future integrations, right now just 1
pool = multiprocessing.Pool(args.cpu_cont)
args.train_filename, args.dev_filename, args.test_filename = get_filenames(
args.data_dir, args.task, args.sub_task)
fa = open(os.path.join(args.output_dir, 'summary.log'), 'a+')
if args.do_train:
summary_fn = '{}/{}'.format(args.summary_dir,
'/'.join(args.output_dir.split('/')[1:]))
tb_writer = SummaryWriter(summary_fn)
# Prepare training data loader
train_examples, train_data = load_and_cache_gen_data(
args, args.train_filename, pool, tokenizer, 'train')
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size,
num_workers=4, pin_memory=True)
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
# this should match the generated pair of TensorDataset
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(
nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate, eps=args.adam_epsilon)
num_train_optimization_steps = args.num_train_epochs * \
len(train_dataloader)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_train_optimization_steps)
# Start training
train_example_num = len(train_data)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", train_example_num)
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Batch num = %d", math.ceil(
train_example_num / args.train_batch_size))
logger.info(" Num epoch = %d", args.num_train_epochs)
dev_dataset = {}
global_step, best_bleu_em, best_ppl = 0, -1, 1e6
not_loss_dec_cnt, not_bleu_em_inc_cnt = 0, 0 if args.do_eval_bleu else 1e6
for cur_epoch in range(args.start_epoch, int(args.num_train_epochs)):
bar = tqdm(train_dataloader, total=len(
train_dataloader), desc="Training")
nb_tr_examples, nb_tr_steps, tr_loss = 0, 0, 0
model.train()
for step, batch in enumerate(bar):
batch = tuple(t.to(args.device) for t in batch)
source_ids, target_ids = batch
source_mask = source_ids.ne(tokenizer.pad_token_id)
target_mask = target_ids.ne(tokenizer.pad_token_id)
loss, _, _ = model(source_ids=source_ids, source_mask=source_mask,
target_ids=target_ids, target_mask=target_mask)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
nb_tr_examples += source_ids.size(0)
nb_tr_steps += 1
loss.backward()
if nb_tr_steps % args.gradient_accumulation_steps == 0:
# Update parameters
optimizer.step()
optimizer.zero_grad()
scheduler.step()
global_step += 1
train_loss = round(
tr_loss * args.gradient_accumulation_steps / (nb_tr_steps + 1), 4)
bar.set_description("[{}] Train loss {}".format(
cur_epoch, round(train_loss, 3)))
if True:
# Eval model with dev dataset
if 'dev_loss' in dev_dataset:
eval_examples, eval_data = dev_dataset['dev_loss']
else:
eval_examples, eval_data = load_and_cache_gen_data(
args, args.dev_filename, pool, tokenizer, 'dev')
dev_dataset['dev_loss'] = eval_examples, eval_data
eval_ppl = eval_ppl_epoch(
args, eval_data, eval_examples, model, tokenizer)
result = {'epoch': cur_epoch,
'global_step': global_step, 'eval_ppl': eval_ppl}
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
logger.info(" " + "*" * 20)
if args.data_num == -1:
tb_writer.add_scalar('dev_ppl', eval_ppl, cur_epoch)
# save last checkpoint
if args.save_last_checkpoints:
last_output_dir = os.path.join(
args.output_dir, 'checkpoint-last')
if not os.path.exists(last_output_dir):
os.makedirs(last_output_dir)
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = os.path.join(
last_output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
logger.info("Save the last model into %s",
output_model_file)
if eval_ppl < best_ppl:
not_loss_dec_cnt = 0
logger.info(" Best ppl:%s", eval_ppl)
logger.info(" " + "*" * 20)
fa.write("[%d] Best ppl changed into %.4f\n" %
(cur_epoch, eval_ppl))
best_ppl = eval_ppl
# Save best checkpoint for best ppl
output_dir = os.path.join(
args.output_dir, 'checkpoint-best-ppl')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if args.always_save_model:
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = os.path.join(
output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(),
output_model_file)
model.save_pretrained('./')
tokenizer.save_pretrained('./')
logger.info(
"Save the best ppl model into %s", output_model_file)
else:
not_loss_dec_cnt += 1
logger.info(
"Ppl does not decrease for %d epochs", not_loss_dec_cnt)
if all([x > args.patience for x in [not_bleu_em_inc_cnt, not_loss_dec_cnt]]):
early_stop_str = "[%d] Early stop as not_bleu_em_inc_cnt=%d, and not_loss_dec_cnt=%d\n" % (
cur_epoch, not_bleu_em_inc_cnt, not_loss_dec_cnt)
logger.info(early_stop_str)
fa.write(early_stop_str)
break
if args.do_eval_bleu:
eval_examples, eval_data = load_and_cache_gen_data(args, args.dev_filename, pool, tokenizer, 'dev',
only_src=True, is_sample=True)
result = eval_bleu_epoch(
args, eval_data, eval_examples, model, tokenizer, 'dev', 'e%d' % cur_epoch)
dev_bleu, dev_em = result['bleu'], result['em']
dev_bleu_em = dev_bleu
if args.data_num == -1:
tb_writer.add_scalar(
'dev_bleu_em', dev_bleu_em, cur_epoch)
if dev_bleu_em > best_bleu_em:
not_bleu_em_inc_cnt = 0
logger.info(" [%d] Best bleu+em: %.2f (bleu: %.2f, em: %.2f)",
cur_epoch, dev_bleu_em, dev_bleu, dev_em)
logger.info(" " + "*" * 20)
best_bleu_em = dev_bleu_em
fa.write("[%d] Best bleu+em changed into %.2f (bleu: %.2f, em: %.2f)\n" % (
cur_epoch, best_bleu_em, dev_bleu, dev_em))
# Save best checkpoint for best bleu
output_dir = os.path.join(
args.output_dir, 'checkpoint-best-bleu')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if args.data_num == -1 or args.always_save_model:
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = os.path.join(
output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(),
output_model_file)
logger.info(
"Save the best bleu model into %s", output_model_file)
else:
not_bleu_em_inc_cnt += 1
logger.info(
"Bleu does not increase for %d epochs", not_bleu_em_inc_cnt)
fa.write(
"[%d] Best bleu+em (%.2f) does not drop changed for %d epochs, cur bleu+em: %.2f (bleu: %.2f, em: %.2f)\n" % (
cur_epoch, best_bleu_em, not_bleu_em_inc_cnt, dev_bleu_em, dev_bleu, dev_em))
if all([x > args.patience for x in [not_bleu_em_inc_cnt, not_loss_dec_cnt]]):
stop_early_str = "[%d] Early stop as not_bleu_em_inc_cnt=%d, and not_loss_dec_cnt=%d\n" % (
cur_epoch, not_bleu_em_inc_cnt, not_loss_dec_cnt)
logger.info(stop_early_str)
fa.write(stop_early_str)
break
if args.data_num == -1:
tb_writer.close()
logger.info("Finish training and take %s", get_elapse_time(t0))
logger.info("Finish and take {}".format(get_elapse_time(t0)))
fa.write("Finish and take {}".format(get_elapse_time(t0)))
fa.close()
if __name__ == "__main__":
main()