def main():
args = parse()
# Load a conf file
if args.resume:
conf = load_config(
os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k != 'resume':
setattr(args, k, v)
# Load dataset
train_set = Dataset(corpus=args.corpus,
tsv_path=args.train_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
n_epochs=args.n_epochs,
min_n_tokens=args.min_n_tokens,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
dev_set = Dataset(corpus=args.corpus,
tsv_path=args.dev_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
eval_sets = []
for s in args.eval_sets:
eval_sets += [
Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=1,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
]
args.vocab = train_set.vocab
# Set save path
if args.resume:
save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(save_path)
else:
dir_name = make_model_name(args)
save_path = mkdir_join(
args.model,
'_'.join(os.path.basename(args.train_set).split('.')[:-1]),
dir_name)
save_path = set_save_path(save_path) # avoid overwriting
# Set logger
logger = set_logger(os.path.join(save_path, 'train.log'), key='training')
# Model setting
if 'gated_conv' in args.lm_type:
model = GatedConvLM(args)
else:
model = RNNLM(args)
model.save_path = save_path
if args.resume:
# Set optimizer
epoch = int(args.resume.split('-')[-1])
model.set_optimizer(
optimizer='sgd'
if epoch > conf['convert_to_sgd_epoch'] + 1 else conf['optimizer'],
learning_rate=float(conf['learning_rate']), # on-the-fly
weight_decay=float(conf['weight_decay']))
# Restore the last saved model
model, checkpoint = load_checkpoint(model, args.resume, resume=True)
lr_controller = checkpoint['lr_controller']
epoch = checkpoint['epoch']
step = checkpoint['step']
ppl_dev_best = checkpoint['metric_dev_best']
# Resume between convert_to_sgd_epoch and convert_to_sgd_epoch + 1
if epoch == conf['convert_to_sgd_epoch'] + 1:
model.set_optimizer(optimizer='sgd',
learning_rate=args.learning_rate,
weight_decay=float(conf['weight_decay']))
logger.info('========== Convert to SGD ==========')
else:
# Save the conf file as a yaml file
save_config(vars(args), os.path.join(model.save_path, 'conf.yml'))
# Save the nlsyms, dictionar, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(model.save_path,
'nlsyms.txt'))
shutil.copy(args.dict, os.path.join(model.save_path, 'dict.txt'))
if args.unit == 'wp':
shutil.copy(args.wp_model, os.path.join(model.save_path,
'wp.model'))
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
nparams = model.num_params_dict[n]
logger.info("%s %d" % (n, nparams))
logger.info("Total %.2f M parameters" %
(model.total_parameters / 1000000))
logger.info(model)
# Set optimizer
model.set_optimizer(optimizer=args.optimizer,
learning_rate=float(args.learning_rate),
weight_decay=float(args.weight_decay))
epoch, step = 1, 1
ppl_dev_best = 10000
# Set learning rate controller
lr_controller = Controller(
learning_rate=float(args.learning_rate),
decay_type=args.decay_type,
decay_start_epoch=args.decay_start_epoch,
decay_rate=args.decay_rate,
decay_patient_n_epochs=args.decay_patient_n_epochs,
lower_better=True,
best_value=ppl_dev_best)
train_set.epoch = epoch - 1 # start from index:0
# GPU setting
if args.n_gpus >= 1:
model = CustomDataParallel(model,
device_ids=list(range(0, args.n_gpus, 1)),
deterministic=False,
benchmark=True)
model.cuda()
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
# Set process name
if args.job_name:
setproctitle(args.job_name)
else:
setproctitle(dir_name)
# Set reporter
reporter = Reporter(model.module.save_path, tensorboard=True)
hidden = None
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
not_improved_epoch = 0
pbar_epoch = tqdm(total=len(train_set))
while True:
# Compute loss in the training set
ys_train, is_new_epoch = train_set.next()
model.module.optimizer.zero_grad()
loss, hidden, reporter = model(ys_train, hidden, reporter)
if len(model.device_ids) > 1:
loss.backward(torch.ones(len(model.device_ids)))
else:
loss.backward()
loss.detach() # Trancate the graph
if args.clip_grad_norm > 0:
torch.nn.utils.clip_grad_norm_(model.module.parameters(),
args.clip_grad_norm)
model.module.optimizer.step()
loss_train = loss.item()
del loss
if 'gated_conv' not in args.lm_type:
hidden = model.module.repackage_hidden(hidden)
reporter.step(is_eval=False)
if step % args.print_step == 0:
# Compute loss in the dev set
ys_dev = dev_set.next()[0]
loss, _, reporter = model(ys_dev, None, reporter, is_eval=True)
loss_dev = loss.item()
del loss
reporter.step(is_eval=True)
duration_step = time.time() - start_time_step
logger.info(
"step:%d(ep:%.2f) loss:%.3f(%.3f)/ppl:%.3f(%.3f)/lr:%.5f/bs:%d (%.2f min)"
% (step, train_set.epoch_detail, loss_train, loss_dev,
np.exp(loss_train), np.exp(loss_dev), lr_controller.lr,
ys_train.shape[0], duration_step / 60))
start_time_step = time.time()
step += args.n_gpus
pbar_epoch.update(ys_train.shape[0] * (ys_train.shape[1] - 1))
# Save fugures of loss and accuracy
if step % (args.print_step * 10) == 0:
reporter.snapshot()
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('========== EPOCH:%d (%.2f min) ==========' %
(epoch, duration_epoch / 60))
if epoch < args.eval_start_epoch:
# Save the model
save_checkpoint(model.module,
model.module.save_path,
lr_controller,
epoch,
step - 1,
ppl_dev_best,
remove_old_checkpoints=True)
else:
start_time_eval = time.time()
# dev
ppl_dev, _ = eval_ppl([model.module],
dev_set,
batch_size=1,
bptt=args.bptt)
logger.info('PPL (%s): %.2f' % (dev_set.set, ppl_dev))
# Update learning rate
model.module.optimizer = lr_controller.decay(
model.module.optimizer, epoch=epoch, value=ppl_dev)
if ppl_dev < ppl_dev_best:
ppl_dev_best = ppl_dev
not_improved_epoch = 0
logger.info('||||| Best Score |||||')
# Save the model
save_checkpoint(model.module,
model.module.save_path,
lr_controller,
epoch,
step - 1,
ppl_dev_best,
remove_old_checkpoints=True)
# test
ppl_test_avg = 0.
for eval_set in eval_sets:
ppl_test, _ = eval_ppl([model.module],
eval_set,
batch_size=1,
bptt=args.bptt)
logger.info('PPL (%s): %.2f' %
(eval_set.set, ppl_test))
ppl_test_avg += ppl_test
if len(eval_sets) > 0:
logger.info('PPL (avg.): %.2f' %
(ppl_test_avg / len(eval_sets)))
else:
not_improved_epoch += 1
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if not_improved_epoch == args.not_improved_patient_n_epochs:
break
# Convert to fine-tuning stage
if epoch == args.convert_to_sgd_epoch:
model.module.set_optimizer(
'sgd',
learning_rate=args.learning_rate,
weight_decay=float(args.weight_decay))
lr_controller = Controller(
learning_rate=args.learning_rate,
decay_type='epoch',
decay_start_epoch=epoch,
decay_rate=0.5,
lower_better=True)
logger.info('========== Convert to SGD ==========')
pbar_epoch = tqdm(total=len(train_set))
if epoch == args.n_epochs:
break
start_time_step = time.time()
start_time_epoch = time.time()
epoch += 1
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
if reporter.tensorboard:
reporter.tf_writer.close()
pbar_epoch.close()
return model.module.save_path
def main():
args = parse()
# Load a conf file
if args.resume:
conf = load_config(
os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k != 'resume':
setattr(args, k, v)
# Load dataset
train_set = Dataset(corpus=args.corpus,
tsv_path=args.train_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
n_epochs=args.n_epochs,
min_n_tokens=args.min_n_tokens,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
dev_set = Dataset(corpus=args.corpus,
tsv_path=args.dev_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
eval_sets = []
for s in args.eval_sets:
eval_sets += [
Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=1,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
]
args.vocab = train_set.vocab
# Set save path
if args.resume:
save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(save_path)
else:
dir_name = set_lm_name(args)
save_path = mkdir_join(
args.model_save_dir,
'_'.join(os.path.basename(args.train_set).split('.')[:-1]),
dir_name)
save_path = set_save_path(save_path) # avoid overwriting
# Set logger
logger = set_logger(os.path.join(save_path, 'train.log'),
key='training',
stdout=args.stdout)
# Model setting
model = select_lm(args, save_path)
if args.resume:
# Set optimizer
epoch = int(args.resume.split('-')[-1])
optimizer = set_optimizer(
model, 'sgd' if epoch > conf['convert_to_sgd_epoch'] else
conf['optimizer'], conf['lr'], conf['weight_decay'])
# Restore the last saved model
model, optimizer = load_checkpoint(model,
args.resume,
optimizer,
resume=True)
# Resume between convert_to_sgd_epoch -1 and convert_to_sgd_epoch
if epoch == conf['convert_to_sgd_epoch']:
optimizer = set_optimizer(model, 'sgd', args.lr,
conf['weight_decay'])
optimizer = LRScheduler(optimizer,
args.lr,
decay_type='always',
decay_start_epoch=0,
decay_rate=0.5)
logger.info('========== Convert to SGD ==========')
else:
# Save the conf file as a yaml file
save_config(vars(args), os.path.join(save_path, 'conf.yml'))
# Save the nlsyms, dictionar, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(save_path, 'nlsyms.txt'))
shutil.copy(args.dict, os.path.join(save_path, 'dict.txt'))
if args.unit == 'wp':
shutil.copy(args.wp_model, os.path.join(save_path, 'wp.model'))
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
n_params = model.num_params_dict[n]
logger.info("%s %d" % (n, n_params))
logger.info("Total %.2f M parameters" %
(model.total_parameters / 1000000))
logger.info(model)
# Set optimizer
optimizer = set_optimizer(model, args.optimizer, args.lr,
args.weight_decay)
# Wrap optimizer by learning rate scheduler
optimizer = LRScheduler(
optimizer,
args.lr,
decay_type=args.lr_decay_type,
decay_start_epoch=args.lr_decay_start_epoch,
decay_rate=args.lr_decay_rate,
decay_patient_n_epochs=args.lr_decay_patient_n_epochs,
early_stop_patient_n_epochs=args.early_stop_patient_n_epochs,
warmup_start_lr=args.warmup_start_lr,
warmup_n_steps=args.warmup_n_steps,
model_size=args.d_model,
factor=args.lr_factor,
noam=args.lm_type == 'transformer')
# GPU setting
if args.n_gpus >= 1:
model = CustomDataParallel(model,
device_ids=list(range(0, args.n_gpus, 1)),
deterministic=False,
benchmark=True)
model.cuda()
# Set process name
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
setproctitle(args.job_name if args.job_name else dir_name)
# Set reporter
reporter = Reporter(save_path, tensorboard=True)
hidden = None
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
pbar_epoch = tqdm(total=len(train_set))
accum_n_tokens = 0
while True:
# Compute loss in the training set
ys_train, is_new_epoch = train_set.next()
accum_n_tokens += sum([len(y) for y in ys_train])
optimizer.zero_grad()
loss, hidden, reporter = model(ys_train, hidden, reporter)
# loss /= args.accum_grad_n_steps
if len(model.device_ids) > 1:
loss.backward(torch.ones(len(model.device_ids)))
else:
loss.backward()
loss.detach() # Trancate the graph
if args.accum_grad_n_tokens == 0 or accum_n_tokens >= args.accum_grad_n_tokens:
if args.clip_grad_norm > 0:
torch.nn.utils.clip_grad_norm_(model.module.parameters(),
args.clip_grad_norm)
optimizer.step()
optimizer.zero_grad()
accum_n_tokens = 0
loss_train = loss.item()
del loss
hidden = model.module.repackage_state(hidden)
reporter.step()
if optimizer.n_steps % args.print_step == 0:
# Compute loss in the dev set
ys_dev = dev_set.next()[0]
loss, _, reporter = model(ys_dev, None, reporter, is_eval=True)
loss_dev = loss.item()
del loss
reporter.step(is_eval=True)
duration_step = time.time() - start_time_step
logger.info(
"step:%d(ep:%.2f) loss:%.3f(%.3f)/ppl:%.3f(%.3f)/lr:%.5f/bs:%d (%.2f min)"
% (optimizer.n_steps,
optimizer.n_epochs + train_set.epoch_detail, loss_train,
loss_dev, np.exp(loss_train), np.exp(loss_dev),
optimizer.lr, ys_train.shape[0], duration_step / 60))
start_time_step = time.time()
pbar_epoch.update(ys_train.shape[0] * (ys_train.shape[1] - 1))
# Save fugures of loss and accuracy
if optimizer.n_steps % (args.print_step * 10) == 0:
reporter.snapshot()
if args.lm_type == 'transformer':
model.module.plot_attention()
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('========== EPOCH:%d (%.2f min) ==========' %
(optimizer.n_epochs + 1, duration_epoch / 60))
if optimizer.n_epochs + 1 < args.eval_start_epoch:
optimizer.epoch()
reporter.epoch()
# Save the model
save_checkpoint(
model,
save_path,
optimizer,
optimizer.n_epochs,
remove_old_checkpoints=args.lm_type != 'transformer')
else:
start_time_eval = time.time()
# dev
ppl_dev, _ = eval_ppl([model.module],
dev_set,
batch_size=1,
bptt=args.bptt)
logger.info('PPL (%s): %.2f' % (dev_set.set, ppl_dev))
optimizer.epoch(ppl_dev)
reporter.epoch(ppl_dev, name='perplexity')
if optimizer.is_best:
# Save the model
save_checkpoint(
model,
save_path,
optimizer,
optimizer.n_epochs,
remove_old_checkpoints=args.lm_type != 'transformer')
# test
ppl_test_avg = 0.
for eval_set in eval_sets:
ppl_test, _ = eval_ppl([model.module],
eval_set,
batch_size=1,
bptt=args.bptt)
logger.info('PPL (%s): %.2f' %
(eval_set.set, ppl_test))
ppl_test_avg += ppl_test
if len(eval_sets) > 0:
logger.info('PPL (avg.): %.2f' %
(ppl_test_avg / len(eval_sets)))
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if optimizer.is_early_stop:
break
# Convert to fine-tuning stage
if optimizer.n_epochs == args.convert_to_sgd_epoch:
optimizer = set_optimizer(model, 'sgd', args.lr,
args.weight_decay)
optimizer = LRScheduler(optimizer,
args.lr,
decay_type='always',
decay_start_epoch=0,
decay_rate=0.5)
logger.info('========== Convert to SGD ==========')
pbar_epoch = tqdm(total=len(train_set))
if optimizer.n_epochs == args.n_epochs:
break
start_time_step = time.time()
start_time_epoch = time.time()
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
if reporter.tensorboard:
reporter.tf_writer.close()
pbar_epoch.close()
return save_path
def main():
# Load a config file
if args.resume_model is None:
config = load_config(args.config)
else:
# Restart from the last checkpoint
config = load_config(os.path.join(args.resume_model, 'config.yml'))
# Check differences between args and yaml comfiguraiton
for k, v in vars(args).items():
if k not in config.keys():
warnings.warn("key %s is automatically set to %s" % (k, str(v)))
# Merge config with args
for k, v in config.items():
setattr(args, k, v)
# Load dataset
train_set = Dataset(csv_path=args.train_set,
dict_path=args.dict,
label_type=args.label_type,
batch_size=args.batch_size * args.ngpus,
bptt=args.bptt,
eos=args.eos,
max_epoch=args.num_epochs,
shuffle=True)
dev_set = Dataset(csv_path=args.dev_set,
dict_path=args.dict,
label_type=args.label_type,
batch_size=args.batch_size * args.ngpus,
bptt=args.bptt,
eos=args.eos,
shuffle=True)
eval_sets = []
for set in args.eval_sets:
eval_sets += [Dataset(csv_path=set,
dict_path=args.dict,
label_type=args.label_type,
batch_size=1,
bptt=args.bptt,
eos=args.eos,
is_test=True)]
args.num_classes = train_set.num_classes
# Model setting
model = RNNLM(args)
model.name = args.rnn_type
model.name += str(args.num_units) + 'H'
model.name += str(args.num_projs) + 'P'
model.name += str(args.num_layers) + 'L'
model.name += '_emb' + str(args.emb_dim)
model.name += '_' + args.optimizer
model.name += '_lr' + str(args.learning_rate)
model.name += '_bs' + str(args.batch_size)
if args.tie_weights:
model.name += '_tie'
if args.residual:
model.name += '_residual'
if args.backward:
model.name += '_bwd'
if args.resume_model is None:
# Set save path
save_path = mkdir_join(args.model, '_'.join(os.path.basename(args.train_set).split('.')[:-1]), model.name)
model.set_save_path(save_path) # avoid overwriting
# Save the config file as a yaml file
save_config(vars(args), model.save_path)
# Save the dictionary & wp_model
shutil.copy(args.dict, os.path.join(save_path, 'dict.txt'))
if args.label_type == 'wordpiece':
shutil.copy(args.wp_model, os.path.join(save_path, 'wp.model'))
# Setting for logging
logger = set_logger(os.path.join(model.save_path, 'train.log'), key='training')
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for name in sorted(list(model.num_params_dict.keys())):
num_params = model.num_params_dict[name]
logger.info("%s %d" % (name, num_params))
logger.info("Total %.2f M parameters" % (model.total_parameters / 1000000))
# Set optimizer
model.set_optimizer(optimizer=args.optimizer,
learning_rate_init=float(args.learning_rate),
weight_decay=float(args.weight_decay),
clip_grad_norm=args.clip_grad_norm,
lr_schedule=False,
factor=args.decay_rate,
patience_epoch=args.decay_patient_epoch)
epoch, step = 1, 0
learning_rate = float(args.learning_rate)
metric_dev_best = 10000
else:
raise NotImplementedError()
train_set.epoch = epoch - 1
# GPU setting
if args.ngpus >= 1:
model = CustomDataParallel(model,
device_ids=list(range(0, args.ngpus, 1)),
deterministic=True,
benchmark=False)
model.cuda()
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
# Set process name
# setproctitle(args.job_name)
# Set learning rate controller
lr_controller = Controller(learning_rate_init=learning_rate,
decay_type=args.decay_type,
decay_start_epoch=args.decay_start_epoch,
decay_rate=args.decay_rate,
decay_patient_epoch=args.decay_patient_epoch,
lower_better=True,
best_value=metric_dev_best)
# Set reporter
reporter = Reporter(model.module.save_path, max_loss=10)
# Set the updater
updater = Updater(args.clip_grad_norm)
# Setting for tensorboard
tf_writer = SummaryWriter(model.module.save_path)
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
not_improved_epoch = 0
loss_train_mean, acc_train_mean = 0., 0.
pbar_epoch = tqdm(total=len(train_set))
pbar_all = tqdm(total=len(train_set) * args.num_epochs)
while True:
# Compute loss in the training set (including parameter update)
ys_train, is_new_epoch = train_set.next()
model, loss_train, acc_train = updater(model, ys_train, args.bptt)
loss_train_mean += loss_train
acc_train_mean += acc_train
pbar_epoch.update(np.sum([len(y) for y in ys_train]))
if (step + 1) % args.print_step == 0:
# Compute loss in the dev set
ys_dev = dev_set.next()[0]
model, loss_dev, acc_dev = updater(model, ys_dev, args.bptt, is_eval=True)
loss_train_mean /= args.print_step
acc_train_mean /= args.print_step
reporter.step(step, loss_train_mean, loss_dev, acc_train_mean, acc_dev)
# Logging by tensorboard
tf_writer.add_scalar('train/loss', loss_train_mean, step + 1)
tf_writer.add_scalar('dev/loss', loss_dev, step + 1)
for n, p in model.module.named_parameters():
n = n.replace('.', '/')
if p.grad is not None:
tf_writer.add_histogram(n, p.data.cpu().numpy(), step + 1)
tf_writer.add_histogram(n + '/grad', p.grad.data.cpu().numpy(), step + 1)
duration_step = time.time() - start_time_step
logger.info("...Step:%d(ep:%.2f) loss:%.2f(%.2f)/acc:%.2f(%.2f)/ppl:%.2f(%.2f)/lr:%.5f/bs:%d (%.2f min)" %
(step + 1, train_set.epoch_detail,
loss_train_mean, loss_dev, acc_train_mean, acc_dev,
math.exp(loss_train_mean), math.exp(loss_dev),
learning_rate, len(ys_train), duration_step / 60))
start_time_step = time.time()
loss_train_mean, acc_train_mean = 0., 0.
step += args.ngpus
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('===== EPOCH:%d (%.2f min) =====' % (epoch, duration_epoch / 60))
# Save fugures of loss and accuracy
reporter.epoch()
if epoch < args.eval_start_epoch:
# Save the model
model.module.save_checkpoint(model.module.save_path, epoch, step,
learning_rate, metric_dev_best)
else:
start_time_eval = time.time()
# dev
ppl_dev = eval_ppl([model.module], dev_set, args.bptt)
logger.info(' PPL (%s): %.3f' % (dev_set.set, ppl_dev))
if ppl_dev < metric_dev_best:
metric_dev_best = ppl_dev
not_improved_epoch = 0
logger.info('||||| Best Score |||||')
# Update learning rate
model.module.optimizer, learning_rate = lr_controller.decay_lr(
optimizer=model.module.optimizer,
learning_rate=learning_rate,
epoch=epoch,
value=ppl_dev)
# Save the model
model.module.save_checkpoint(model.module.save_path, epoch, step,
learning_rate, metric_dev_best)
# test
ppl_test_mean = 0.
for eval_set in eval_sets:
ppl_test = eval_ppl([model.module], eval_set, args.bptt)
logger.info(' PPL (%s): %.3f' % (eval_set.set, ppl_test))
ppl_test_mean += ppl_test
if len(eval_sets) > 0:
logger.info(' PPL (mean): %.3f' % (ppl_test_mean / len(eval_sets)))
else:
# Update learning rate
model.module.optimizer, learning_rate = lr_controller.decay_lr(
optimizer=model.module.optimizer,
learning_rate=learning_rate,
epoch=epoch,
value=ppl_dev)
not_improved_epoch += 1
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if not_improved_epoch == args.not_improved_patient_epoch:
break
if epoch == args.convert_to_sgd_epoch:
# Convert to fine-tuning stage
model.module.set_optimizer(
'sgd',
learning_rate_init=float(args.learning_rate), # TODO: ?
weight_decay=float(args.weight_decay),
clip_grad_norm=args.clip_grad_norm,
lr_schedule=False,
factor=args.decay_rate,
patience_epoch=args.decay_patient_epoch)
logger.info('========== Convert to SGD ==========')
pbar_epoch = tqdm(total=len(train_set))
pbar_all.update(len(train_set))
if epoch == args.num_epoch:
break
start_time_step = time.time()
start_time_epoch = time.time()
epoch += 1
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
tf_writer.close()
pbar_epoch.close()
pbar_all.close()
return model.module.save_path
def main():
args = parse()
# Load a conf file
dir_name = os.path.dirname(args.recog_model[0])
conf = load_config(os.path.join(dir_name, 'conf.yml'))
# Overwrite conf
for k, v in conf.items():
if 'recog' not in k:
setattr(args, k, v)
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
logger = set_logger(os.path.join(args.recog_dir, 'plot.log'),
key='decoding')
for i, s in enumerate(args.recog_sets):
# Load dataset
dataset = Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
batch_size=args.recog_batch_size,
bptt=args.bptt,
serialize=args.serialize,
is_test=True)
if i == 0:
# Load the LM
if args.lm_type == 'gated_cnn':
model = GatedConvLM(args)
else:
model = RNNLM(args)
epoch = model.load_checkpoint(args.recog_model[0])['epoch']
model.save_path = dir_name
logger.info('epoch: %d' % (epoch - 1))
logger.info('batch size: %d' % args.recog_batch_size)
# logger.info('recog unit: %s' % args.recog_unit)
# logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('BPTT: %d' % (args.bptt))
logger.info('cache size: %d' % (args.recog_n_caches))
logger.info('cache theta: %.3f' % (args.recog_cache_theta))
logger.info('cache lambda: %.3f' % (args.recog_cache_lambda))
model.cache_theta = args.recog_cache_theta
model.cache_lambda = args.recog_cache_lambda
# GPU setting
model.cuda()
assert args.recog_n_caches > 0
save_path = mkdir_join(args.recog_dir, 'cache')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
if args.unit == 'word':
idx2token = dataset.idx2word
elif args.unit == 'wp':
idx2token = dataset.idx2wp
elif args.unit == 'char':
idx2token = dataset.idx2char
elif args.unit == 'phone':
idx2token = dataset.idx2phone
else:
raise NotImplementedError(args.unit)
hidden = None
fig_count = 0
toknen_count = 0
n_tokens = args.recog_n_caches
while True:
ys, is_new_epoch = dataset.next()
for t in range(ys.shape[1] - 1):
loss, hidden = model(ys[:, t:t + 2],
hidden,
is_eval=True,
n_caches=args.recog_n_caches)[:2]
if len(model.cache_attn) > 0:
if toknen_count == n_tokens:
tokens_keys = idx2token(
model.cache_ids[:args.recog_n_caches],
return_list=True)
tokens_query = idx2token(model.cache_ids[-n_tokens:],
return_list=True)
# Slide attention matrix
n_keys = len(tokens_keys)
n_queries = len(tokens_query)
cache_probs = np.zeros(
(n_keys, n_queries)) # `[n_keys, n_queries]`
mask = np.zeros((n_keys, n_queries))
for i, aw in enumerate(model.cache_attn[-n_tokens:]):
cache_probs[:(n_keys - n_queries + i + 1),
i] = aw[0,
-(n_keys - n_queries + i + 1):]
mask[(n_keys - n_queries + i + 1):, i] = 1
plot_cache_weights(cache_probs,
keys=tokens_keys,
queries=tokens_query,
save_path=mkdir_join(
save_path,
str(fig_count) + '.png'),
figsize=(40, 16),
mask=mask)
toknen_count = 0
fig_count += 1
else:
toknen_count += 1
if is_new_epoch:
break