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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.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) |