Create modeling_protst.py

modeling_protst.py

@@ -0,0 +1,214 @@
+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.logit_scale = nn.Parameter(torch.ones([]) * math.log(1 / 0.07))
+        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)