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import torch.nn as nn
from torch.nn import BCEWithLogitsLoss
from transformers import RobertaModel
from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel
from src.utils.mapper import configmapper
@configmapper.map("models", "roberta_multi_spans")
class RobertaForMultiSpans(RobertaPreTrainedModel):
def __init__(self, config):
super(RobertaForMultiSpans, self).__init__(config)
self.roberta = RobertaModel(config)
self.num_labels = config.num_labels
# TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
# self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_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,
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]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1) # batch_size
# print(start_logits.shape, end_logits.shape, start_positions.shape, end_positions.shape)
total_loss = None
if (
start_positions is not None and end_positions is not None
): # [batch_size/seq_length]
# # 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)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
# ignored_index = start_logits.size(1)
# start_positions.clamp_(0, ignored_index)
# end_positions.clamp_(0, ignored_index)
# start_positions = start_logits.view()
loss_fct = BCEWithLogitsLoss()
start_loss = loss = loss_fct(
start_logits,
start_positions.float(),
)
end_loss = loss = loss_fct(
end_logits,
end_positions.float(),
)
total_loss = (start_loss + end_loss) / 2
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output |