def fb_on_batch(self,
annotated_insts: List[ParseInstance],
training=True,
loss_factor=1.,
**kwargs):
self.refresh_batch(training)
# encode
input_repr, enc_repr, jpos_pack, mask_arr = self.bter.run(
annotated_insts, training)
mask_expr = BK.input_real(mask_arr)
# the parsing loss
arc_score = self.scorer_helper.score_arc(enc_repr)
lab_score = self.scorer_helper.score_label(enc_repr)
full_score = arc_score + lab_score
parsing_loss, info = self._loss(annotated_insts, full_score, mask_expr)
# other loss?
jpos_loss = self.jpos_loss(jpos_pack, mask_expr)
reg_loss = self.reg_scores_loss(arc_score, lab_score)
#
info["loss_parse"] = BK.get_value(parsing_loss).item()
final_loss = parsing_loss
if jpos_loss is not None:
info["loss_jpos"] = BK.get_value(jpos_loss).item()
final_loss = parsing_loss + jpos_loss
if reg_loss is not None:
final_loss = final_loss + reg_loss
info["fb"] = 1
if training:
BK.backward(final_loss, loss_factor)
return info
def fb_on_batch(self,
annotated_insts: List[ParseInstance],
training=True,
loss_factor=1.,
**kwargs):
self.refresh_batch(training)
# todo(note): here always using training lambdas
full_score, original_scores, jpos_pack, mask_expr, valid_mask_d, _ = \
self._score(annotated_insts, False, self.lambda_g1_arc_training, self.lambda_g1_lab_training)
parsing_loss, info = self._loss(annotated_insts, full_score, mask_expr,
valid_mask_d)
# other loss?
jpos_loss = self.jpos_loss(jpos_pack, mask_expr)
reg_loss = self.reg_scores_loss(*original_scores)
#
info["loss_parse"] = BK.get_value(parsing_loss).item()
final_loss = parsing_loss
if jpos_loss is not None:
info["loss_jpos"] = BK.get_value(jpos_loss).item()
final_loss = parsing_loss + jpos_loss
if reg_loss is not None:
final_loss = final_loss + reg_loss
info["fb"] = 1
if training:
BK.backward(final_loss, loss_factor)
return info
def collect_loss_and_backward(self, loss_info_cols: List[Dict],
training: bool, loss_factor: float):
final_loss_dict = LossHelper.combine_multiple(
loss_info_cols) # loss_name -> {}
if len(final_loss_dict) <= 0:
return {} # no loss!
final_losses = []
ret_info_vals = OrderedDict()
for loss_name, loss_info in final_loss_dict.items():
final_losses.append(loss_info['sum'] / (loss_info['count'] + 1e-5))
for k in loss_info.keys():
one_item = loss_info[k]
ret_info_vals[f"loss:{loss_name}_{k}"] = one_item.item(
) if hasattr(one_item, "item") else float(one_item)
final_loss = BK.stack(final_losses).sum()
if training and final_loss.requires_grad:
BK.backward(final_loss, loss_factor)
return ret_info_vals
