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,
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: 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,
**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()
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}'
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: SpanMetric,
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)
y = batch['chart_id']
loss, span_probs = self.compute_loss(out, y, mask)
if gradient_accumulation and gradient_accumulation > 1:
loss /= gradient_accumulation
loss.backward()
total_loss += loss.item()
if eval_trn:
prediction = self.decode_output(out, mask, batch, span_probs)
self.update_metrics(metric, batch, prediction)
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 out
del mask
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 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