modeling_protst.py

import math
import torch
import torch.nn as nn
from typing import Optional, Tuple, Union
from dataclasses import dataclass
from transformers import PreTrainedModel
from transformers.modeling_outputs import ModelOutput
from transformers.models.esm import EsmPreTrainedModel, EsmModel
from transformers.models.bert import BertPreTrainedModel, BertModel
from .configuration_protst import ProtSTConfig


@dataclass
class EsmProteinRepresentationOutput(ModelOutput):

    protein_feature: torch.FloatTensor = None
    residue_feature: torch.FloatTensor = None


@dataclass
class BertTextRepresentationOutput(ModelOutput):

    text_feature: torch.FloatTensor = None
    word_feature: torch.FloatTensor = None


@dataclass
class ProtSTClassificationOutput(ModelOutput):

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None

class ProtSTHead(nn.Module):
    def __init__(self, config, out_dim=512):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.out_proj = nn.Linear(config.hidden_size, out_dim)

    def forward(self, x):
        x = self.dense(x)
        x = nn.functional.relu(x)
        x = self.out_proj(x)
        return x


class BertForPubMed(BertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.pad_token_id = config.pad_token_id
        self.cls_token_id = config.cls_token_id
        self.sep_token_id = config.sep_token_id

        self.bert = BertModel(config, add_pooling_layer=False)
        self.text_mlp = ProtSTHead(config)
        self.word_mlp = ProtSTHead(config)

        self.post_init() # NOTE

    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,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], ModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        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,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        word_feature = outputs.last_hidden_state
        is_special = (input_ids == self.cls_token_id) | (input_ids == self.sep_token_id) | (input_ids == self.pad_token_id)
        special_mask = (~is_special).to(torch.int64).unsqueeze(-1)
        pooled_feature = ((word_feature * special_mask).sum(1) / (special_mask.sum(1) + 1.0e-6)).to(word_feature.dtype)
        pooled_feature = self.text_mlp(pooled_feature)
        word_feature = self.word_mlp(word_feature)

        if not return_dict:
            return (pooled_feature, word_feature)

        return BertTextRepresentationOutput(text_feature=pooled_feature, word_feature=word_feature)
        



class EsmForProteinRepresentation(EsmPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.cls_token_id = config.cls_token_id
        self.pad_token_id = config.pad_token_id
        self.eos_token_id = config.eos_token_id

        self.esm = EsmModel(config, add_pooling_layer=False)

        self.post_init() # NOTE

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, EsmProteinRepresentationOutput]:

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

        outputs = self.esm(
            input_ids,
            attention_mask=attention_mask,
            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,
        )

        residue_feature = outputs.last_hidden_state  # [batch_size, seq_len, hidden_dim]

        # mean readout
        is_special = (
            (input_ids == self.cls_token_id) | (input_ids == self.eos_token_id) | (input_ids == self.pad_token_id)
        )
        special_mask = (~is_special).to(torch.int64).unsqueeze(-1)
        protein_feature = ((residue_feature * special_mask).sum(1) / (special_mask.sum(1) + 1.0e-6)).to(residue_feature.dtype)

        return EsmProteinRepresentationOutput(
            protein_feature=protein_feature, residue_feature=residue_feature
        )


class ProtSTPreTrainedModel(PreTrainedModel):
    config_class = ProtSTConfig


class ProtSTForProteinPropertyPrediction(ProtSTPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.config = config
        self.protein_model = EsmForProteinRepresentation(config.protein_config)
        self.classifier = ProtSTHead(config.protein_config, out_dim=config.num_labels)

        self.post_init() # NOTE

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, ProtSTClassificationOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the protein classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        Returns:
        Examples:
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.protein_model(
            input_ids,
            attention_mask=attention_mask,
            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,
        )

        logits = self.classifier(outputs.protein_feature) # [bsz, xxx] -> [bsz, num_labels]

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()

            labels = labels.to(logits.device)
            loss = loss_fct(logits.view(-1, logits.shape[-1]), labels.view(-1))

        if not return_dict:
            output = (logits,)
            return ((loss,) + output) if loss is not None else output

        return ProtSTClassificationOutput(loss=loss, logits=logits)