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We use the FastPLM implementation of DPLM.
"""
import sys
import os
import torch
import torch.nn as nn
from typing import List, Optional, Union, 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 dplm_fastplms.modeling_dplm import (
DPLMForMaskedLM,
DPLMForSequenceClassification,
DPLMForTokenClassification,
)
from transformers import EsmTokenizer
from .base_tokenizer import BaseSequenceTokenizer
presets = {
'DPLM-150': 'airkingbd/dplm_150m',
'DPLM-650': 'airkingbd/dplm_650m',
'DPLM-3B': 'airkingbd/dplm_3b',
}
class DPLMTokenizerWrapper(BaseSequenceTokenizer):
def __init__(self, tokenizer: EsmTokenizer):
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 DPLMForEmbedding(nn.Module):
def __init__(self, model_path: str, return_logits: bool = False, dtype: torch.dtype = None):
super().__init__()
self.dplm = DPLMForMaskedLM.from_pretrained(model_path, dtype=dtype)
self.return_logits = return_logits
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.dplm(input_ids, attention_mask=attention_mask, output_attentions=output_attentions)
return out.last_hidden_state, out.attentions
out = self.dplm(input_ids, attention_mask=attention_mask)
if self.return_logits:
return out.last_hidden_state, out.logits
return out.last_hidden_state
def get_dplm_tokenizer(preset: str, model_path: str = None):
return DPLMTokenizerWrapper(EsmTokenizer.from_pretrained('facebook/esm2_t6_8M_UR50D'))
def build_dplm_model(preset: str, masked_lm: bool = False, dtype: torch.dtype = None, model_path: str = None, **kwargs):
model = DPLMForEmbedding(model_path or presets[preset], return_logits=masked_lm, dtype=dtype).eval()
tokenizer = get_dplm_tokenizer(preset)
return model, tokenizer
def get_dplm_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 = DPLMForMaskedLM.from_pretrained(model_path, dtype=dtype).eval()
else:
if tokenwise:
model = DPLMForTokenClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval()
else:
model = DPLMForSequenceClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval()
tokenizer = get_dplm_tokenizer(preset)
return model, tokenizer
if __name__ == '__main__':
# py -m src.protify.base_models.dplm
model, tokenizer = build_dplm_model('DPLM-150')
print(model)
print(tokenizer)
print(tokenizer('MEKVQYLTRSAIRRASTIEMPQQARQKLQNLFINFCLILICBBOLLICIIVMLL'))
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