File size: 7,757 Bytes
bfca2b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
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) |