CloneDetectionModel.py

"""
Original work:
https://github.com/sangHa0411/CloneDetection/blob/main/models/codebert.py#L169

Copyright (c) 2022 Sangha Park(sangha110495), Young Jin Ahn(snoop2head)

All credits to the original authors.
"""
import torch.nn as nn
from transformers import (
    RobertaPreTrainedModel,
    RobertaModel,
)
from transformers.modeling_outputs import SequenceClassifierOutput


class CloneDetectionModel(RobertaPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.roberta = RobertaModel(config, add_pooling_layer=False)
        self.net = nn.Sequential(
            nn.Dropout(config.hidden_dropout_prob),
            nn.Linear(config.hidden_size, config.hidden_size),
            nn.ReLU(),
        )
        self.classifier = nn.Linear(config.hidden_size * 4, config.num_labels)

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        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=return_dict,
        )

        hidden_states = outputs[0]
        batch_size, _, hidden_size = hidden_states.shape

        # CLS code1 SEP SEP code2 SEP
        cls_flag = input_ids == self.config.tokenizer_cls_token_id  # cls token
        sep_flag = input_ids == self.config.tokenizer_sep_token_id  # sep token

        special_token_states = hidden_states[cls_flag + sep_flag].view(
            batch_size, -1, hidden_size
        )  # (batch_size, 4, hidden_size)
        special_hidden_states = self.net(
            special_token_states
        )  # (batch_size, 4, hidden_size)

        pooled_output = special_hidden_states.view(
            batch_size, -1
        )  # (batch_size, hidden_size * 4)
        logits = self.classifier(pooled_output)  # (batch_size, num_labels)

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        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,
        )