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from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.models.bert.modeling_bert import BertModel as TransformersBertModel
from transformers.models.bert.modeling_bert import BertForMaskedLM as TransformersBertForMaskedLM
from transformers.models.bert.modeling_bert import BertForPreTraining as TransformersBertForPreTraining
from transformers.models.bert.modeling_bert import BertPreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput
class BertModel(TransformersBertModel):
def __init__(self, config):
super().__init__(config)
class BertForMaskedLM(TransformersBertForMaskedLM):
def __init__(self, config):
super().__init__(config)
class BertForPreTraining(TransformersBertForPreTraining):
def __init__(self, config):
super().__init__(config)
class DNABertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = BertModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# get the size of input_ids
batch_size, seq_len = input_ids.shape
if seq_len > 512:
assert seq_len % 512 == 0, "seq_len should be a multiple of 512"
# split the input_ids into multiple chunks
input_ids = input_ids.view(-1, 512)
attention_mask = attention_mask.view(-1, 512) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, 512) if token_type_ids is not None else None
position_ids = 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,
)
pooled_output = outputs[1]
if seq_len > 512:
# reshape the pooled_output
pooled_output = pooled_output.view(batch_size, -1, pooled_output.shape[-1])
# take the mean of the pooled_output
pooled_output = torch.mean(pooled_output, dim=1)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) |