| """ |
| We use the FastPLM implementation of ESMC. |
| """ |
| import sys |
| import os |
| import torch |
| import torch.nn as nn |
| from typing import Optional, Union, List, Dict |
|
|
| _FASTPLMS = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'FastPLMs') |
| if _FASTPLMS not in sys.path: |
| sys.path.insert(0, _FASTPLMS) |
|
|
| from esm_plusplus.modeling_esm_plusplus import ( |
| ESMplusplusModel, |
| ESMplusplusForMaskedLM, |
| ESMplusplusForSequenceClassification, |
| ESMplusplusForTokenClassification, |
| ) |
| from .base_tokenizer import BaseSequenceTokenizer |
| from .esmc_utils import EsmSequenceTokenizer |
|
|
|
|
| presets = { |
| 'ESMC-300': 'Synthyra/ESMplusplus_small', |
| 'ESMC-600': 'Synthyra/ESMplusplus_large', |
| } |
|
|
|
|
| class ESMTokenizerWrapper(BaseSequenceTokenizer): |
| def __init__(self, tokenizer: EsmSequenceTokenizer): |
| 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) |
| tokenized = self.tokenizer(sequences, **kwargs) |
| return tokenized |
|
|
|
|
| class ESMplusplusForEmbedding(nn.Module): |
| def __init__(self, model_path: str, dtype: torch.dtype = None): |
| super().__init__() |
| self.esm = ESMplusplusModel.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.esm(input_ids=input_ids, attention_mask=attention_mask, output_attentions=output_attentions) |
| return out.last_hidden_state, out.attentions |
| else: |
| return self.esm(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state |
|
|
|
|
| def get_esmc_tokenizer(preset: str, model_path: str = None): |
| tokenizer = EsmSequenceTokenizer() |
| return ESMTokenizerWrapper(tokenizer) |
|
|
|
|
| def build_esmc_model(preset: str, masked_lm: bool = False, dtype: torch.dtype = None, model_path: str = None, **kwargs): |
| path = model_path or presets[preset] |
| if masked_lm: |
| model = ESMplusplusForMaskedLM.from_pretrained(path, dtype=dtype).eval() |
| else: |
| model = ESMplusplusForEmbedding(path, dtype=dtype).eval() |
| tokenizer = get_esmc_tokenizer(preset) |
| return model, tokenizer |
|
|
|
|
| def get_esmc_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 = ESMplusplusModel.from_pretrained(model_path, dtype=dtype).eval() |
| else: |
| if tokenwise: |
| model = ESMplusplusForTokenClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval() |
| else: |
| model = ESMplusplusForSequenceClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval() |
| tokenizer = get_esmc_tokenizer(preset) |
| return model, tokenizer |
|
|
|
|
| if __name__ == '__main__': |
| |
| model, tokenizer = build_esmc_model('ESMC-300') |
| print(model) |
| print(tokenizer) |
| print(tokenizer('MEKVQYLTRSAIRRASTIEMPQQARQKLQNLFINFCLILICBBOLLICIIVMLL')) |
|
|
