File size: 3,383 Bytes
714cf46 | 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 | import torch
import torch.nn as nn
import re
from typing import Optional, Union, List, Dict
from transformers import T5EncoderModel, T5Tokenizer
from .t5 import T5ForSequenceClassification, T5ForTokenClassification
from .base_tokenizer import BaseSequenceTokenizer
presets = {
'ProtT5': 'Rostlab/prot_t5_xl_half_uniref50-enc',
'ProtT5-XL-UniRef50-full-prec': 'Rostlab/prot_t5_xl_uniref50',
'ProtT5-XXL-UniRef50': 'Rostlab/prot_t5_xxl_uniref50',
'ProtT5-XL-BFD': 'Rostlab/prot_t5_xl_bfd',
'ProtT5-XXL-BFD': 'Rostlab/prot_t5_xxl_bfd',
}
class T5TokenizerWrapper(BaseSequenceTokenizer):
def __init__(self, tokenizer: T5Tokenizer):
super().__init__(tokenizer)
def __call__(self, sequences: Union[str, List[str]], **kwargs) -> Dict[str, torch.Tensor]:
if isinstance(sequences, str):
sequences = [sequences]
kwargs.setdefault('return_tensors', 'pt')
kwargs.setdefault('padding', 'longest')
kwargs.setdefault('add_special_tokens', True)
sequences = [re.sub(r"[UZOB]", "X", seq) for seq in sequences]
sequences = [' '.join(seq) for seq in sequences]
tokenized = self.tokenizer(sequences, **kwargs)
return tokenized
class Prott5ForEmbedding(nn.Module):
def __init__(self, model_path: str, dtype: torch.dtype = None):
super().__init__()
self.plm = T5EncoderModel.from_pretrained(model_path, dtype=dtype)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = False,
**kwargs,
) -> torch.Tensor:
if output_attentions:
out = self.plm(input_ids, attention_mask=attention_mask, output_attentions=output_attentions)
return out.last_hidden_state, out.attentions
else:
return self.plm(input_ids, attention_mask=attention_mask).last_hidden_state
def get_prott5_tokenizer(preset: str, model_path: str = None):
return T5TokenizerWrapper(T5Tokenizer.from_pretrained(model_path or presets[preset]))
def build_prott5_model(preset: str, masked_lm: bool = False, dtype: torch.dtype = None, model_path: str = None, **kwargs):
model_path = model_path or presets[preset]
model = Prott5ForEmbedding(model_path, dtype=dtype).eval()
tokenizer = get_prott5_tokenizer(preset)
return model, tokenizer
def get_prott5_for_training(preset: str, tokenwise: bool = False, num_labels: int = None, hybrid: bool = False, dtype: torch.dtype = None, model_path: str = None):
model_path = model_path or presets[preset]
if hybrid:
model = T5EncoderModel.from_pretrained(model_path, dtype=dtype).eval()
else:
if tokenwise:
model = T5ForTokenClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval()
else:
model = T5ForSequenceClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval()
tokenizer = get_prott5_tokenizer(preset)
return model, tokenizer
if __name__ == '__main__':
# py -m src.protify.base_models.prott5
model, tokenizer = build_prott5_model('ProtT5')
print(model)
print(tokenizer)
print(tokenizer('MEKVQYLTRSAIRRASTIEMPQQARQKLQNLFINFCLILICBBOLLICIIVMLL'))
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