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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__':
# py -m src.protify.base_models.esmc
model, tokenizer = build_esmc_model('ESMC-300')
print(model)
print(tokenizer)
print(tokenizer('MEKVQYLTRSAIRRASTIEMPQQARQKLQNLFINFCLILICBBOLLICIIVMLL'))
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