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import torch.nn as nn
import torch
from torch.nn import CrossEntropyLoss
from transformers import RobertaModel
from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel
from src.utils.mapper import configmapper
@configmapper.map("models", "roberta_token_spans")
class RobertaModelForTokenAndSpans(RobertaPreTrainedModel):
def __init__(self, config, num_token_labels=2, num_qa_labels=2):
super(RobertaModelForTokenAndSpans, self).__init__(config)
self.roberta = RobertaModel(config)
self.num_token_labels = num_token_labels
self.num_qa_labels = num_qa_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, num_token_labels)
self.qa_outputs = nn.Linear(config.hidden_size, num_qa_labels)
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
labels=None, # Token Wise Labels
output_attentions=None,
output_hidden_states=None,
):
outputs = self.roberta(
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=None,
)
sequence_output = outputs[0]
qa_logits = self.qa_outputs(sequence_output)
start_logits, end_logits = qa_logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
sequence_output = self.dropout(sequence_output)
token_logits = self.classifier(sequence_output)
total_loss = None
if (
start_positions is not None
and end_positions is not None
and labels is not None
):
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
loss_fct = CrossEntropyLoss()
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = token_logits.view(-1, self.num_token_labels)
active_labels = torch.where(
active_loss,
labels.view(-1),
torch.tensor(loss_fct.ignore_index).type_as(labels),
)
token_loss = loss_fct(active_logits, active_labels)
else:
token_loss = loss_fct(
token_logits.view(-1, self.num_token_labels), labels.view(-1)
)
total_loss = (start_loss + end_loss) / 2 + token_loss
output = (start_logits, end_logits, token_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output |