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We use the FastPLM implementation of DPLM2.
"""
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 dplm2_fastplms.modeling_dplm2 import (
DPLM2ForMaskedLM,
DPLM2ForSequenceClassification,
DPLM2ForTokenClassification,
)
from transformers import EsmTokenizer
from .base_tokenizer import BaseSequenceTokenizer
presets = {
"DPLM2-150": "airkingbd/dplm2_150m",
"DPLM2-650": "airkingbd/dplm2_650m",
"DPLM2-3B": "airkingbd/dplm2_3b",
}
class DPLM2TokenizerWrapper(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 DPLM2ForEmbedding(nn.Module):
def __init__(self, model_path: str, dtype: torch.dtype = None):
super().__init__()
self.dplm2 = DPLM2ForMaskedLM.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:
out = self.dplm2(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if output_attentions:
return out.last_hidden_state, out.attentions
return out.last_hidden_state
def get_dplm2_tokenizer(preset: str, model_path: str = None):
return DPLM2TokenizerWrapper(EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D"))
def build_dplm2_model(preset: str, masked_lm: bool = False, dtype: torch.dtype = None, model_path: str = None, **kwargs):
model_path = model_path or presets[preset]
if masked_lm:
model = DPLM2ForMaskedLM.from_pretrained(model_path, dtype=dtype).eval()
else:
model = DPLM2ForEmbedding(model_path, dtype=dtype).eval()
tokenizer = get_dplm2_tokenizer(preset)
return model, tokenizer
def get_dplm2_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 = DPLM2ForMaskedLM.from_pretrained(model_path, dtype=dtype).eval()
else:
if tokenwise:
model = DPLM2ForTokenClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval()
else:
model = DPLM2ForSequenceClassification.from_pretrained(model_path, num_labels=num_labels, dtype=dtype).eval()
tokenizer = get_dplm2_tokenizer(preset)
return model, tokenizer
if __name__ == "__main__":
# py -m src.protify.base_models.dplm2
model, tokenizer = build_dplm2_model("DPLM2-150")
print(model)
print(tokenizer)
print(tokenizer("MEKVQYLTRSAIRRASTIEMPQQARQKLQNLFINFCLILICBBOLLICIIVMLL"))
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