def train_iters(ae_model, dis_model):
train_data_loader = non_pair_data_loader(
batch_size=args.batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
train_data_loader.create_batches(args.train_file_list,
args.train_label_list,
if_shuffle=True)
add_log("Start train process.")
ae_model.train()
dis_model.train()
ae_optimizer = NoamOpt(
ae_model.src_embed[0].d_model, 1, 2000,
torch.optim.Adam(ae_model.parameters(),
lr=0,
betas=(0.9, 0.98),
eps=1e-9))
dis_optimizer = torch.optim.Adam(dis_model.parameters(), lr=0.0001)
ae_criterion = get_cuda(
LabelSmoothing(size=args.vocab_size,
padding_idx=args.id_pad,
smoothing=0.1))
dis_criterion = nn.BCELoss(size_average=True)
for epoch in range(200):
print('-' * 94)
epoch_start_time = time.time()
for it in range(train_data_loader.num_batch):
batch_sentences, tensor_labels, \
tensor_src, tensor_src_mask, tensor_tgt, tensor_tgt_y, \
tensor_tgt_mask, tensor_ntokens = train_data_loader.next_batch()
# For debug
# print(batch_sentences[0])
# print(tensor_src[0])
# print(tensor_src_mask[0])
# print("tensor_src_mask", tensor_src_mask.size())
# print(tensor_tgt[0])
# print(tensor_tgt_y[0])
# print(tensor_tgt_mask[0])
# print(batch_ntokens)
# Forward pass
latent, out = ae_model.forward(tensor_src, tensor_tgt,
tensor_src_mask, tensor_tgt_mask)
# print(latent.size()) # (batch_size, max_src_seq, d_model)
# print(out.size()) # (batch_size, max_tgt_seq, vocab_size)
# Loss calculation
loss_rec = ae_criterion(
out.contiguous().view(-1, out.size(-1)),
tensor_tgt_y.contiguous().view(-1)) / tensor_ntokens.data
# loss_all = loss_rec + loss_dis
ae_optimizer.optimizer.zero_grad()
loss_rec.backward()
ae_optimizer.step()
# Classifier
dis_lop = dis_model.forward(to_var(latent.clone()))
loss_dis = dis_criterion(dis_lop, tensor_labels)
dis_optimizer.zero_grad()
loss_dis.backward()
dis_optimizer.step()
if it % 200 == 0:
add_log(
'| epoch {:3d} | {:5d}/{:5d} batches | rec loss {:5.4f} | dis loss {:5.4f} |'
.format(epoch, it, train_data_loader.num_batch, loss_rec,
loss_dis))
print(id2text_sentence(tensor_tgt_y[0], args.id_to_word))
generator_text = ae_model.greedy_decode(
latent,
max_len=args.max_sequence_length,
start_id=args.id_bos)
print(id2text_sentence(generator_text[0], args.id_to_word))
add_log('| end of epoch {:3d} | time: {:5.2f}s |'.format(
epoch, (time.time() - epoch_start_time)))
# Save model
torch.save(ae_model.state_dict(),
args.current_save_path + 'ae_model_params.pkl')
torch.save(dis_model.state_dict(),
args.current_save_path + 'dis_model_params.pkl')
return
def train_iters(ae_model, dis_model):
if args.use_albert:
tokenizer = BertTokenizer.from_pretrained("clue/albert_chinese_tiny",
do_lower_case=True)
elif args.use_tiny_bert:
tokenizer = AutoTokenizer.from_pretrained(
"google/bert_uncased_L-2_H-256_A-4", do_lower_case=True)
elif args.use_distil_bert:
tokenizer = DistilBertTokenizer.from_pretrained(
'distilbert-base-uncased', do_lower_case=True)
# tokenizer = BertTokenizer.from_pretrained(args.PRETRAINED_MODEL_NAME, do_lower_case=True)
tokenizer.add_tokens('[EOS]')
bos_id = tokenizer.convert_tokens_to_ids(['[CLS]'])[0]
ae_model.bert_encoder.resize_token_embeddings(len(tokenizer))
#print("[CLS] ID: ", bos_id)
print("Load trainData...")
if args.load_trainData and os.path.exists('./{}_trainData.pkl'.format(
args.task)):
with open('./{}_trainData.pkl'.format(args.task), 'rb') as f:
trainData = pickle.load(f)
else:
trainData = TextDataset(batch_size=args.batch_size,
id_bos='[CLS]',
id_eos='[EOS]',
id_unk='[UNK]',
max_sequence_length=args.max_sequence_length,
vocab_size=0,
file_list=args.train_file_list,
label_list=args.train_label_list,
tokenizer=tokenizer)
with open('./{}_trainData.pkl'.format(args.task), 'wb') as f:
pickle.dump(trainData, f)
add_log("Start train process.")
ae_model.train()
dis_model.train()
ae_model.to(device)
dis_model.to(device)
'''
Fixing or distilling BERT encoder
'''
if args.fix_first_6:
print("Try fixing first 6 bertlayers")
for layer in range(6):
for param in ae_model.bert_encoder.encoder.layer[layer].parameters(
):
param.requires_grad = False
elif args.fix_last_6:
print("Try fixing last 6 bertlayers")
for layer in range(6, 12):
for param in ae_model.bert_encoder.encoder.layer[layer].parameters(
):
param.requires_grad = False
if args.distill_2:
print("Get result from layer 2")
for layer in range(2, 12):
for param in ae_model.bert_encoder.encoder.layer[layer].parameters(
):
param.requires_grad = False
ae_optimizer = NoamOpt(
ae_model.d_model, 1, 2000,
torch.optim.Adam(ae_model.parameters(),
lr=0,
betas=(0.9, 0.98),
eps=1e-9))
dis_optimizer = torch.optim.Adam(dis_model.parameters(), lr=0.0001)
#ae_criterion = get_cuda(LabelSmoothing(size=args.vocab_size, padding_idx=args.id_pad, smoothing=0.1))
ae_criterion = LabelSmoothing(size=ae_model.bert_encoder.config.vocab_size,
padding_idx=0,
smoothing=0.1).to(device)
dis_criterion = nn.BCELoss(reduction='mean')
history = {'train': []}
for epoch in range(args.epochs):
print('-' * 94)
epoch_start_time = time.time()
total_rec_loss = 0
total_dis_loss = 0
train_data_loader = DataLoader(trainData,
batch_size=args.batch_size,
shuffle=True,
collate_fn=trainData.collate_fn,
num_workers=4)
num_batch = len(train_data_loader)
trange = tqdm(enumerate(train_data_loader),
total=num_batch,
desc='Training',
file=sys.stdout,
position=0,
leave=True)
for it, data in trange:
batch_sentences, tensor_labels, tensor_src, tensor_src_mask, tensor_tgt, tensor_tgt_y, tensor_tgt_mask, tensor_ntokens = data
tensor_labels = tensor_labels.to(device)
tensor_src = tensor_src.to(device)
tensor_tgt = tensor_tgt.to(device)
tensor_tgt_y = tensor_tgt_y.to(device)
tensor_src_mask = tensor_src_mask.to(device)
tensor_tgt_mask = tensor_tgt_mask.to(device)
# Forward pass
latent, out = ae_model.forward(tensor_src, tensor_tgt,
tensor_src_mask, tensor_tgt_mask)
# Loss calculation
loss_rec = ae_criterion(
out.contiguous().view(-1, out.size(-1)),
tensor_tgt_y.contiguous().view(-1)) / tensor_ntokens.data
ae_optimizer.optimizer.zero_grad()
loss_rec.backward()
ae_optimizer.step()
latent = latent.detach()
next_latent = latent.to(device)
# Classifier
dis_lop = dis_model.forward(next_latent)
loss_dis = dis_criterion(dis_lop, tensor_labels)
dis_optimizer.zero_grad()
loss_dis.backward()
dis_optimizer.step()
total_rec_loss += loss_rec.item()
total_dis_loss += loss_dis.item()
trange.set_postfix(total_rec_loss=total_rec_loss / (it + 1),
total_dis_loss=total_dis_loss / (it + 1))
if it % 100 == 0:
add_log(
'| epoch {:3d} | {:5d}/{:5d} batches | rec loss {:5.4f} | dis loss {:5.4f} |'
.format(epoch, it, num_batch, loss_rec, loss_dis))
print(id2text_sentence(tensor_tgt_y[0], tokenizer, args.task))
generator_text = ae_model.greedy_decode(
latent, max_len=args.max_sequence_length, start_id=bos_id)
print(id2text_sentence(generator_text[0], tokenizer,
args.task))
# Save model
#torch.save(ae_model.state_dict(), args.current_save_path / 'ae_model_params.pkl')
#torch.save(dis_model.state_dict(), args.current_save_path / 'dis_model_params.pkl')
history['train'].append({
'epoch': epoch,
'total_rec_loss': total_rec_loss / len(trange),
'total_dis_loss': total_dis_loss / len(trange)
})
add_log('| end of epoch {:3d} | time: {:5.2f}s |'.format(
epoch, (time.time() - epoch_start_time)))
# Save model
torch.save(ae_model.state_dict(),
args.current_save_path / 'ae_model_params.pkl')
torch.save(dis_model.state_dict(),
args.current_save_path / 'dis_model_params.pkl')
print("Save in ", args.current_save_path)
return