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import math |
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import torch |
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import torch.nn as nn |
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from typing import Optional, Tuple, Union |
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from dataclasses import dataclass |
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from transformers import PreTrainedModel |
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from transformers.modeling_outputs import ModelOutput |
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from transformers.models.esm import EsmPreTrainedModel, EsmModel |
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from transformers.models.bert import BertPreTrainedModel, BertModel |
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from .configuration_protst import ProtSTConfig |
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@dataclass |
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class EsmProteinRepresentationOutput(ModelOutput): |
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protein_feature: torch.FloatTensor = None |
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residue_feature: torch.FloatTensor = None |
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@dataclass |
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class BertTextRepresentationOutput(ModelOutput): |
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text_feature: torch.FloatTensor = None |
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word_feature: torch.FloatTensor = None |
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@dataclass |
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class ProtSTClassificationOutput(ModelOutput): |
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loss: Optional[torch.FloatTensor] = None |
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logits: torch.FloatTensor = None |
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class ProtSTHead(nn.Module): |
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def __init__(self, config, out_dim=512): |
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super().__init__() |
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self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
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self.out_proj = nn.Linear(config.hidden_size, out_dim) |
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def forward(self, x): |
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x = self.dense(x) |
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x = nn.functional.relu(x) |
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x = self.out_proj(x) |
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return x |
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class BertForPubMed(BertPreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.pad_token_id = config.pad_token_id |
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self.cls_token_id = config.cls_token_id |
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self.sep_token_id = config.sep_token_id |
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self.bert = BertModel(config, add_pooling_layer=False) |
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self.text_mlp = ProtSTHead(config) |
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self.word_mlp = ProtSTHead(config) |
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self.post_init() |
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def forward( |
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self, |
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input_ids: Optional[torch.Tensor] = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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token_type_ids: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.Tensor] = None, |
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head_mask: Optional[torch.Tensor] = None, |
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inputs_embeds: Optional[torch.Tensor] = None, |
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encoder_hidden_states: Optional[torch.Tensor] = None, |
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encoder_attention_mask: Optional[torch.Tensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple[torch.Tensor], ModelOutput]: |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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outputs = self.bert( |
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input_ids, |
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attention_mask=attention_mask, |
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token_type_ids=token_type_ids, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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encoder_hidden_states=encoder_hidden_states, |
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encoder_attention_mask=encoder_attention_mask, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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word_feature = outputs.last_hidden_state |
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is_special = (input_ids == self.cls_token_id) | (input_ids == self.sep_token_id) | (input_ids == self.pad_token_id) |
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special_mask = (~is_special).to(torch.int64).unsqueeze(-1) |
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pooled_feature = ((word_feature * special_mask).sum(1) / (special_mask.sum(1) + 1.0e-6)).to(word_feature.dtype) |
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pooled_feature = self.text_mlp(pooled_feature) |
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word_feature = self.word_mlp(word_feature) |
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if not return_dict: |
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return (pooled_feature, word_feature) |
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return BertTextRepresentationOutput(text_feature=pooled_feature, word_feature=word_feature) |
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class EsmForProteinRepresentation(EsmPreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.cls_token_id = config.cls_token_id |
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self.pad_token_id = config.pad_token_id |
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self.eos_token_id = config.eos_token_id |
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self.esm = EsmModel(config, add_pooling_layer=False) |
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self.post_init() |
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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head_mask: Optional[torch.Tensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple, EsmProteinRepresentationOutput]: |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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outputs = self.esm( |
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input_ids, |
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attention_mask=attention_mask, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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residue_feature = outputs.last_hidden_state |
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is_special = ( |
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(input_ids == self.cls_token_id) | (input_ids == self.eos_token_id) | (input_ids == self.pad_token_id) |
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) |
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special_mask = (~is_special).to(torch.int64).unsqueeze(-1) |
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protein_feature = ((residue_feature * special_mask).sum(1) / (special_mask.sum(1) + 1.0e-6)).to(residue_feature.dtype) |
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return EsmProteinRepresentationOutput( |
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protein_feature=protein_feature, residue_feature=residue_feature |
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) |
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class ProtSTPreTrainedModel(PreTrainedModel): |
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config_class = ProtSTConfig |
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class ProtSTForProteinPropertyPrediction(ProtSTPreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.config = config |
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self.protein_model = EsmForProteinRepresentation(config.protein_config) |
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self.classifier = ProtSTHead(config.protein_config, out_dim=config.num_labels) |
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self.post_init() |
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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head_mask: Optional[torch.Tensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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labels: Optional[torch.LongTensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple, ProtSTClassificationOutput]: |
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r""" |
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
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Labels for computing the protein classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. |
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Returns: |
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Examples: |
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""" |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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outputs = self.protein_model( |
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input_ids, |
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attention_mask=attention_mask, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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logits = self.classifier(outputs.protein_feature) |
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loss = None |
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if labels is not None: |
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loss_fct = nn.CrossEntropyLoss() |
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labels = labels.to(logits.device) |
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loss = loss_fct(logits.view(-1, logits.shape[-1]), labels.view(-1)) |
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if not return_dict: |
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output = (logits,) |
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return ((loss,) + output) if loss is not None else output |
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return ProtSTClassificationOutput(loss=loss, logits=logits) |
