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from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import ModernBertModel, ModernBertPreTrainedModel, ModernBertConfig
from transformers.modeling_outputs import QuestionAnsweringModelOutput
from transformers.models.modernbert.modeling_modernbert import ModernBertPredictionHead
class ModernBertForQuestionAnswering(ModernBertPreTrainedModel):
def __init__(self, config: ModernBertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.model = ModernBertModel(config)
self.head = ModernBertPredictionHead(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2) # 2 for start/end position logits
self.qa_outputs.weight.data.normal_(mean=0.0, std=0.02)
self.qa_outputs.bias.data.zero_()
self.drop = torch.nn.Dropout(config.classifier_dropout)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
sliding_window_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
indices: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
batch_size: Optional[int] = None,
seq_len: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
self._maybe_set_compile()
outputs = self.model(
input_ids,
attention_mask=attention_mask,
sliding_window_mask=sliding_window_mask,
position_ids=position_ids,
indices=indices,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
batch_size=batch_size,
seq_len=seq_len,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
sequence_output = outputs[0]
sequence_output = self.drop(self.head(sequence_output))
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions 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)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = 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)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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