metadata
license: apache-2.0
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss, KLDivLoss
from transformers.modeling_outputs import TokenClassifierOutput
from transformers import BertModel, BertPreTrainedModel
class BertForHighlightPrediction(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config, **model_kwargs):
super().__init__(config)
# self.model_args = model_kargs["model_args"]
self.num_labels = config.num_labels
self.bert = BertModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.tokens_clf = nn.Linear(config.hidden_size, config.num_labels)
self.tau = model_kwargs.pop('tau', 1)
self.gamma = model_kwargs.pop('gamma', 1)
self.soft_labeling = model_kwargs.pop('soft_labeling', False)
self.init_weights()
self.softmax = nn.Softmax(dim=-1)
def forward(self,
input_ids=None,
probs=None, # soft-labeling
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
tokens_output = outputs[0]
highlight_logits = self.tokens_clf(self.dropout(tokens_output))
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
active_loss = attention_mask.view(-1) == 1
active_logits = highlight_logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss,
labels.view(-1),
torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss_ce = loss_fct(active_logits, active_labels)
loss_kl = 0
if self.soft_labeling:
loss_fct = KLDivLoss(reduction='sum')
active_mask = (attention_mask * token_type_ids).view(-1, 1) # BL 1
n_active = (active_mask == 1).sum()
active_mask = active_mask.repeat(1, 2) # BL 2
input_logp = F.log_softmax(active_logits / self.tau, -1) # BL 2
target_p = torch.cat(( (1-probs).view(-1, 1), probs.view(-1, 1)), -1) # BL 2
loss_kl = loss_fct(input_logp, target_p * active_mask) / n_active
loss = self.gamma * loss_ce + (1-self.gamma) * loss_kl
# print("Loss:\n")
# print(loss)
# print(loss_kl)
# print(loss_ce)
return TokenClassifierOutput(
loss=loss,
logits=highlight_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@torch.no_grad()
def inference(self, outputs):
with torch.no_grad():
outputs = self.forward(**batch_inputs)
probabilities = self.softmax(self.tokens_clf(outputs.hidden_states[-1]))
predictions = torch.argmax(probabilities, dim=-1)
# active filtering
active_tokens = batch_inputs['attention_mask'] == 1
active_predictions = torch.where(
active_tokens,
predictions,
torch.tensor(-1).type_as(predictions)
)
outputs = {
"probabilities": probabilities[:, :, 1].detach(), # shape: (batch, length)
"active_predictions": predictions.detach(),
"active_tokens": active_tokens,
}
return outputs