Delete modeling_protst.py

modeling_protst.py

@@ -1,286 +0,0 @@
-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.protein_mlp = ProtSTHead(config)
-        self.residue_mlp = ProtSTHead(config)
-
-        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)
-
-        # For ProtST pretrain and zero-shot
-        protein_feature = self.protein_mlp(protein_feature)
-        residue_feature = self.residue_mlp(residue_feature)
-
-        
-        return EsmProteinRepresentationOutput(
-            protein_feature=protein_feature, residue_feature=residue_feature
-        )
-
-
-class ProtSTPreTrainedModel(PreTrainedModel):
-    config_class = ProtSTConfig
-
-    def _compute_protein_feature(self, 
-        protein_input_ids, protein_attention_mask, protein_position_ids, 
-        output_attentions, output_hidden_states
-    ):
-
-        protein_outputs = self.protein_model(
-            protein_input_ids,
-            attention_mask=protein_attention_mask,
-            position_ids=protein_position_ids,
-            head_mask=None,
-            inputs_embeds=None,
-            encoder_hidden_states=None,
-            encoder_attention_mask=None,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=None,
-        )
-        
-        return protein_outputs
-
-    def _compute_text_feature(self, 
-        text_input_ids, text_attention_mask, text_position_ids,
-        output_attentions, output_hidden_states
-    ):
-        text_outputs = self.text_model(
-            text_input_ids,
-            attention_mask=text_attention_mask,
-            position_ids=text_position_ids,
-            head_mask=None,
-            inputs_embeds=None,
-            encoder_hidden_states=None,
-            encoder_attention_mask=None,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=None,
-        )
-
-        return text_outputs
-
-
-class ProtSTModel(ProtSTPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.config = config
-        self.protein_model = EsmForProteinRepresentation(config.protein_config)
-        self.text_model = BertForPubMed(config.text_config)
-        self.logit_scale = nn.Parameter(torch.ones([]) * math.log(1 / 0.07))
-
-        self.post_init() # NOTE
-
-    def forward(self, 
-        protein_input_ids: Optional[torch.LongTensor] = None,
-        text_input_ids: Optional[torch.LongTensor] = None,
-        protein_attention_mask: Optional[torch.Tensor] = None,
-        text_attention_mask: Optional[torch.Tensor] = None,
-        protein_position_ids: Optional[torch.LongTensor] = None,
-        text_position_ids: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ):
-        # Not implement yet
-        return None
-
-
-class ProtSTForProteinPropertyPrediction(ProtSTPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.config = config
-        self.protein_model = EsmForProteinRepresentation(config.protein_config)
-        self.text_model = BertForPubMed(config.text_config)
-        self.logit_scale = nn.Parameter(torch.ones([]) * math.log(1 / 0.07))
-        self.classifier = ProtSTHead(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)