dnabert_layer.py

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,
        )