| 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__': |
| |
| model, tokenizer = build_prott5_model('ProtT5') |
| print(model) |
| print(tokenizer) |
| print(tokenizer('MEKVQYLTRSAIRRASTIEMPQQARQKLQNLFINFCLILICBBOLLICIIVMLL')) |
|
|
