def fit_dataloader(self,
trn: DataLoader,
criterion,
optimizer,
metric,
logger: logging.Logger,
history: History,
linear_scheduler=None,
gradient_accumulation=1,
**kwargs):
self.model.train()
timer = CountdownTimer(history.num_training_steps(len(trn), gradient_accumulation))
total_loss = 0
self.reset_metrics(metric)
for idx, batch in enumerate(trn):
output_dict = self.feed_batch(batch)
self.update_metrics(batch, output_dict, metric)
loss = output_dict['loss']
if gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
if history.step(gradient_accumulation):
self._step(optimizer, linear_scheduler)
timer.log(self.report_metrics(total_loss / (timer.current + 1), metric), ratio_percentage=None,
logger=logger)
del loss
return total_loss / timer.total
def fit_dataloader(self,
trn: DataLoader,
criterion,
optimizer,
metric,
logger: logging.Logger,
history: History,
gradient_accumulation=1,
grad_norm=None,
ratio_width=None,
eval_trn=False,
**kwargs):
optimizer, scheduler = optimizer
self.model.train()
timer = CountdownTimer(history.num_training_steps(len(trn), gradient_accumulation=gradient_accumulation))
total_loss = 0
for idx, batch in enumerate(trn):
pred, mask = self.feed_batch(batch)
loss = self.compute_loss(criterion, pred, batch['srl_id'], mask)
if gradient_accumulation and gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
if eval_trn:
prediction = self.decode_output(pred, mask, batch)
self.update_metrics(metric, prediction, batch)
if history.step(gradient_accumulation):
self._step(optimizer, scheduler, grad_norm)
report = f'loss: {total_loss / (idx + 1):.4f} {metric}' if eval_trn else f'loss: {total_loss / (idx + 1):.4f}'
timer.log(report, logger=logger, ratio_percentage=False, ratio_width=ratio_width)
del loss
del pred
del mask
def fit_dataloader(self,
trn: DataLoader,
criterion,
optimizer,
metric,
logger: logging.Logger,
history: History,
gradient_accumulation=1,
grad_norm=None,
transformer_grad_norm=None,
teacher: Tagger = None,
kd_criterion=None,
temperature_scheduler=None,
ratio_width=None,
eval_trn=True,
**kwargs):
optimizer, scheduler = optimizer
if teacher:
scheduler, lambda_scheduler = scheduler
else:
lambda_scheduler = None
self.model.train()
timer = CountdownTimer(
history.num_training_steps(
len(trn), gradient_accumulation=gradient_accumulation))
total_loss = 0
for idx, batch in enumerate(trn):
out, mask = self.feed_batch(batch)
y = batch['tag_id']
loss = self.compute_loss(criterion, out, y, mask)
if gradient_accumulation and gradient_accumulation > 1:
loss /= gradient_accumulation
if teacher:
with torch.no_grad():
out_T, _ = teacher.feed_batch(batch)
# noinspection PyNoneFunctionAssignment
kd_loss = self.compute_distill_loss(kd_criterion, out, out_T,
mask,
temperature_scheduler)
_lambda = float(lambda_scheduler)
loss = _lambda * loss + (1 - _lambda) * kd_loss
loss.backward()
total_loss += loss.item()
if eval_trn:
prediction = self.decode_output(out, mask, batch)
self.update_metrics(metric, out, y, mask, batch, prediction)
if history.step(gradient_accumulation):
self._step(optimizer, scheduler, grad_norm,
transformer_grad_norm, lambda_scheduler)
report = f'loss: {total_loss / (idx + 1):.4f} {metric if eval_trn else ""}'
timer.log(report,
logger=logger,
ratio_percentage=False,
ratio_width=ratio_width)
del loss
del out
del mask
def execute_training_loop(self,
trn: DataLoader,
dev: DataLoader,
epochs,
criterion,
optimizer,
metric,
save_dir,
logger: logging.Logger,
devices,
**kwargs):
best_epoch, best_score = 0, -1
optimizer, scheduler = optimizer
timer = CountdownTimer(epochs)
_len_trn = len(trn) // self.config.gradient_accumulation
ratio_width = len(f'{_len_trn}/{_len_trn}')
history = History()
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn, criterion, optimizer, metric, logger, history,
linear_scheduler=scheduler if self.use_transformer else None, **kwargs)
if dev:
metric = self.evaluate_dataloader(dev, criterion, metric, logger, ratio_width=ratio_width)
report = f'{timer.elapsed_human}/{timer.total_time_human}'
dev_score = sum(x.score for x in metric) / len(metric)
if not self.use_transformer:
scheduler.step(dev_score)
if dev_score > best_score:
self.save_weights(save_dir)
best_score = dev_score
report += ' [red]saved[/red]'
timer.log(report, ratio_percentage=False, newline=True, ratio=False)
def execute_training_loop(self,
trn: DataLoader,
dev: DataLoader,
epochs,
criterion,
optimizer,
metric,
save_dir,
logger: logging.Logger,
devices,
patience=0.5,
**kwargs):
if isinstance(patience, float):
patience = int(patience * epochs)
best_epoch, best_metric = 0, -1
timer = CountdownTimer(epochs)
ratio_width = len(f'{len(trn)}/{len(trn)}')
epoch = 0
history = History()
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn,
criterion,
optimizer,
metric,
logger,
history,
ratio_width=ratio_width,
**self.config)
if dev:
self.evaluate_dataloader(dev,
criterion,
metric,
logger,
ratio_width=ratio_width,
input='dev')
report = f'{timer.elapsed_human}/{timer.total_time_human}'
dev_score = metric.score
if dev_score > best_metric:
self.save_weights(save_dir)
best_metric = dev_score
best_epoch = epoch
report += ' [red]saved[/red]'
else:
report += f' ({epoch - best_epoch})'
if epoch - best_epoch >= patience:
report += ' early stop'
break
timer.log(report,
ratio_percentage=False,
newline=True,
ratio=False)
for d in [trn, dev]:
self._close_dataloader(d)
if best_epoch != epoch:
logger.info(
f'Restoring best model saved [red]{epoch - best_epoch}[/red] epochs ago'
)
self.load_weights(save_dir)
return best_metric
def fit_dataloader(self,
trn: DataLoader,
criterion,
optimizer,
metric,
logger: logging.Logger,
history: History,
ratio_width=None,
gradient_accumulation=1,
encoder_grad_norm=None,
decoder_grad_norm=None,
patience=0.5,
eval_trn=False,
**kwargs):
self.model.train()
encoder_optimizer, encoder_scheduler, decoder_optimizers = optimizer
timer = CountdownTimer(len(trn))
total_loss = 0
self.reset_metrics(metric)
model = self.model_
encoder_parameters = model.encoder.parameters()
decoder_parameters = model.decoders.parameters()
for idx, (task_name, batch) in enumerate(trn):
decoder_optimizer = decoder_optimizers.get(task_name, None)
output_dict, _ = self.feed_batch(batch, task_name)
loss = self.compute_loss(batch, output_dict[task_name]['output'], criterion[task_name],
self.tasks[task_name])
if gradient_accumulation and gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += float(loss.item())
if history.step(gradient_accumulation):
if self.config.get('grad_norm', None):
clip_grad_norm(model, self.config.grad_norm)
if encoder_grad_norm:
torch.nn.utils.clip_grad_norm_(encoder_parameters, encoder_grad_norm)
if decoder_grad_norm:
torch.nn.utils.clip_grad_norm_(decoder_parameters, decoder_grad_norm)
encoder_optimizer.step()
encoder_optimizer.zero_grad()
encoder_scheduler.step()
if decoder_optimizer:
if isinstance(decoder_optimizer, tuple):
decoder_optimizer, decoder_scheduler = decoder_optimizer
else:
decoder_scheduler = None
decoder_optimizer.step()
decoder_optimizer.zero_grad()
if decoder_scheduler:
decoder_scheduler.step()
if eval_trn:
self.decode_output(output_dict, batch, task_name)
self.update_metrics(batch, output_dict, metric, task_name)
timer.log(self.report_metrics(total_loss / (timer.current + 1), metric if eval_trn else None),
ratio_percentage=None,
ratio_width=ratio_width,
logger=logger)
del loss
del output_dict
return total_loss / timer.total
def execute_training_loop(self, trn, dev, devices, epochs, logger,
patience, save_dir, optimizer,
gradient_accumulation, **kwargs):
optimizer, scheduler, transformer_optimizer, transformer_scheduler = optimizer
criterion = self.build_criterion()
best_e, best_metric = 0, self.build_metric()
timer = CountdownTimer(epochs)
history = History()
ratio_width = len(
f'{len(trn) // gradient_accumulation}/{len(trn) // gradient_accumulation}'
)
for epoch in range(1, epochs + 1):
# train one epoch and update the parameters
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn,
optimizer,
scheduler,
criterion,
epoch,
logger,
history,
transformer_optimizer,
transformer_scheduler,
gradient_accumulation=gradient_accumulation,
eval_trn=self.config.eval_trn)
loss, dev_metric = self.evaluate_dataloader(
dev, criterion, ratio_width=ratio_width, logger=logger)
timer.update()
# logger.info(f"{'Dev' + ' ' * ratio_width} loss: {loss:.4f} {dev_metric}")
# save the model if it is the best so far
report = f"{timer.elapsed_human} / {timer.total_time_human} ETA: {timer.eta_human}"
if dev_metric > best_metric:
best_e, best_metric = epoch, dev_metric
self.save_weights(save_dir)
report += ' ([red]saved[/red])'
else:
if patience != epochs:
report += f' ({epoch - best_e}/{patience})'
else:
report += f' ({epoch - best_e})'
logger.info(report)
if patience is not None and epoch - best_e >= patience:
logger.info(
f'LAS has stopped improving for {patience} epochs, early stop.'
)
break
timer.stop()
if not best_e:
self.save_weights(save_dir)
elif best_e != epoch:
self.load_weights(save_dir)
logger.info(
f"Max score of dev is {best_metric.score:.2%} at epoch {best_e}")
logger.info(
f"Average time of each epoch is {timer.elapsed_average_human}")
logger.info(f"{timer.elapsed_human} elapsed")
def fit_dataloader(self,
trn,
optimizer,
scheduler,
criterion,
epoch,
logger,
history: History,
transformer_optimizer=None,
transformer_scheduler=None,
gradient_accumulation=1,
eval_trn=False,
**kwargs):
self.model.train()
timer = CountdownTimer(
history.num_training_steps(len(trn), gradient_accumulation))
metric = self.build_metric(training=True)
total_loss = 0
for idx, batch in enumerate(trn):
optimizer.zero_grad()
(s_arc, s_sib, s_rel), mask, puncts = self.feed_batch(batch)
arcs, sibs, rels = batch['arc'], batch['sib_id'], batch['rel_id']
loss, s_arc = self.compute_loss(s_arc, s_sib, s_rel, arcs, sibs,
rels, mask)
if gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
if eval_trn:
arc_preds, rel_preds = self.decode(s_arc, s_sib, s_rel, mask)
self.update_metric(arc_preds, rel_preds, arcs, rels, mask,
puncts, metric)
if history.step(gradient_accumulation):
self._step(optimizer, scheduler, transformer_optimizer,
transformer_scheduler)
report = self._report(total_loss / (timer.current + 1),
metric if eval_trn else None)
lr = scheduler.get_last_lr()[0]
report += f' lr: {lr:.4e}'
timer.log(report, ratio_percentage=False, logger=logger)
del loss
def execute_training_loop(self,
trn: DataLoader,
dev: DataLoader,
epochs,
criterion,
optimizer,
metric,
save_dir,
logger: logging.Logger,
devices,
ratio_width=None,
patience=5,
teacher=None,
kd_criterion=None,
eval_trn=True,
**kwargs):
best_epoch, best_metric = 0, -1
timer = CountdownTimer(epochs)
history = History()
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn,
criterion,
optimizer,
metric,
logger,
history=history,
ratio_width=ratio_width,
eval_trn=eval_trn,
**self.config)
loss, dev_metric = self.evaluate_dataloader(
dev, criterion, logger=logger, ratio_width=ratio_width)
timer.update()
report = f"{timer.elapsed_human} / {timer.total_time_human} ETA: {timer.eta_human}"
if dev_metric > best_metric:
best_epoch, best_metric = epoch, dev_metric
self.save_weights(save_dir)
report += ' [red](saved)[/red]'
else:
report += f' ({epoch - best_epoch})'
if epoch - best_epoch >= patience:
report += ' early stop'
logger.info(report)
if epoch - best_epoch >= patience:
break
if not best_epoch:
self.save_weights(save_dir)
elif best_epoch != epoch:
self.load_weights(save_dir)
logger.info(f"Max score of dev is {best_metric} at epoch {best_epoch}")
logger.info(
f"Average time of each epoch is {timer.elapsed_average_human}")
logger.info(f"{timer.elapsed_human} elapsed")
return best_metric
def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric, logger: logging.Logger,
history: History = None, gradient_accumulation=1, ratio_percentage=None, **kwargs):
optimizer, scheduler = optimizer
self.model.train()
timer = CountdownTimer(history.num_training_steps(len(trn), gradient_accumulation=gradient_accumulation))
total_loss = 0
for batch in trn:
output_dict = self.feed_batch(batch)
loss = output_dict['loss']
if gradient_accumulation and gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
if history.step(gradient_accumulation):
self._step(optimizer, scheduler)
timer.log(self.report_metrics(total_loss / (timer.current + 1)),
ratio_percentage=ratio_percentage, logger=logger)
del loss
del output_dict
return total_loss / max(timer.total, 1)
def fit_dataloader(self,
trn: DataLoader,
criterion,
optimizer,
metric: MetricDict,
logger: logging.Logger,
history: History,
gradient_accumulation=1,
grad_norm=None,
ratio_width=None,
eval_trn=True,
**kwargs):
optimizer, scheduler = optimizer
metric.reset()
self.model.train()
timer = CountdownTimer(
history.num_training_steps(
len(trn), gradient_accumulation=gradient_accumulation))
total_loss = 0
for idx, batch in enumerate(trn):
out, mask = self.feed_batch(batch)
loss = out['loss']
if gradient_accumulation and gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
if eval_trn:
self.decode_output(out, mask, batch)
self.update_metrics(metric, batch, out, mask)
if history.step(gradient_accumulation):
self._step(optimizer, scheduler, grad_norm)
report = f'loss: {total_loss / (idx + 1):.4f} {metric.cstr()}' if eval_trn \
else f'loss: {total_loss / (idx + 1):.4f}'
timer.log(report,
logger=logger,
ratio_percentage=False,
ratio_width=ratio_width)
del loss
del out
del mask
def fit_dataloader(self,
trn: DataLoader,
criterion,
optimizer,
metric,
logger: logging.Logger,
linear_scheduler=None,
history: History = None,
gradient_accumulation=1,
**kwargs):
self.model.train()
timer = CountdownTimer(
history.num_training_steps(
len(trn), gradient_accumulation=gradient_accumulation))
total_loss = 0
self.reset_metrics(metric)
for batch in trn:
optimizer.zero_grad()
output_dict = self.feed_batch(batch)
self.update_metrics(batch, output_dict, metric)
loss = output_dict['loss']
if gradient_accumulation and gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
if history.step(gradient_accumulation):
if self.config.grad_norm:
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.config.grad_norm)
optimizer.step()
if linear_scheduler:
linear_scheduler.step()
timer.log(self.report_metrics(total_loss / (timer.current + 1),
metric),
ratio_percentage=None,
logger=logger)
del loss
return total_loss / timer.total
def fit_dataloader(self,
trn,
optimizer,
scheduler,
criterion,
epoch,
logger,
history: History,
transformer_optimizer=None,
transformer_scheduler=None,
gradient_accumulation=1,
**kwargs):
self.model.train()
timer = CountdownTimer(
history.num_training_steps(len(trn), gradient_accumulation))
metric = self.build_metric(training=True)
total_loss = 0
for idx, batch in enumerate(trn):
arc_scores, rel_scores, mask, puncts = self.feed_batch(batch)
arcs, rels = batch['arc'], batch['rel_id']
loss = self.compute_loss(arc_scores, rel_scores, arcs, rels, mask,
criterion, batch)
if gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
arc_preds, rel_preds = self.decode(arc_scores, rel_scores, mask,
batch)
self.update_metric(arc_preds, rel_preds, arcs, rels, mask, puncts,
metric, batch)
if history.step(gradient_accumulation):
self._step(optimizer, scheduler, transformer_optimizer,
transformer_scheduler)
report = self._report(total_loss / (timer.current + 1), metric)
timer.log(report, ratio_percentage=False, logger=logger)
del loss
def execute_training_loop(self,
trn: DataLoader,
dev: DataLoader,
epochs,
criterion,
optimizer,
metric,
save_dir,
logger: logging.Logger,
devices,
ratio_width=None,
gradient_accumulation=1,
**kwargs):
best_epoch, best_metric = 0, -1
timer = CountdownTimer(epochs)
history = History()
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn,
criterion,
optimizer,
metric,
logger,
ratio_width=ratio_width,
gradient_accumulation=gradient_accumulation,
history=history,
save_dir=save_dir)
report = f'{timer.elapsed_human}/{timer.total_time_human}'
self.evaluate_dataloader(dev,
logger,
ratio_width=ratio_width,
save_dir=save_dir,
metric=metric)
if metric > best_metric:
self.save_weights(save_dir)
best_metric = float(metric)
best_epoch = epoch
report += ' [red]saved[/red]'
timer.log(report,
ratio_percentage=False,
newline=True,
ratio=False)
if best_epoch and best_epoch != epochs:
logger.info(
f'Restored the best model with {best_metric} saved {epochs - best_epoch} epochs ago'
)
self.load_weights(save_dir)
def execute_training_loop(self,
trn: DataLoader,
dev: DataLoader,
epochs,
criterion,
optimizer,
metric,
save_dir,
logger: logging.Logger,
devices,
gradient_accumulation=1,
**kwargs):
best_epoch, best_metric = 0, -1
optimizer, scheduler = optimizer
history = History()
timer = CountdownTimer(epochs)
ratio_width = len(f'{len(trn)}/{len(trn)}')
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn,
criterion,
optimizer,
metric,
logger,
history=history,
gradient_accumulation=gradient_accumulation,
linear_scheduler=scheduler
if self._get_transformer() else None)
if dev:
self.evaluate_dataloader(dev,
criterion,
metric,
logger,
ratio_width=ratio_width)
report = f'{timer.elapsed_human}/{timer.total_time_human}'
dev_score = metric.score
if not self._get_transformer():
scheduler.step(dev_score)
if dev_score > best_metric:
self.save_weights(save_dir)
best_metric = dev_score
report += ' [red]saved[/red]'
timer.log(report,
ratio_percentage=False,
newline=True,
ratio=False)
return best_metric
def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epochs, criterion, optimizer, metric, save_dir,
logger: logging.Logger, devices, ratio_width=None, dev_data=None, eval_after=None,
**kwargs):
best_epoch, best_metric = 0, -1
if isinstance(eval_after, float):
eval_after = int(epochs * eval_after)
timer = CountdownTimer(epochs)
history = History()
for epoch in range(1, epochs + 1):
logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]")
self.fit_dataloader(trn, criterion, optimizer, metric, logger, history=history, ratio_width=ratio_width,
**self.config)
if epoch > eval_after:
dev_metric = self.evaluate_dataloader(dev, criterion, logger=logger, ratio_width=ratio_width,
output=os.path.join(save_dir, 'dev.pred.txt'),
input=dev_data, use_fast=True)
timer.update()
report = f"{timer.elapsed_human} / {timer.total_time_human} ETA: {timer.eta_human}"
if epoch > eval_after:
if dev_metric > best_metric:
best_epoch, best_metric = epoch, dev_metric
self.save_weights(save_dir)
report += ' [red](saved)[/red]'
else:
report += f' ({epoch - best_epoch})'
# if epoch - best_epoch >= patience:
# report += ' early stop'
logger.info(report)
# if epoch - best_epoch >= patience:
# break
if not best_epoch:
self.save_weights(save_dir)
elif best_epoch != epoch:
self.load_weights(save_dir)
logger.info(f"Max score of dev is {best_metric} at epoch {best_epoch}")
logger.info(f"Average time of each epoch is {timer.elapsed_average_human}")
logger.info(f"{timer.elapsed_human} elapsed")
return best_metric