File size: 3,166 Bytes
2eb3475 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | """Architecture-agnostic loader for fine-tuned classification checkpoints.
Handles BERT / RoBERTa / ELECTRA / any HF AutoModelForSequenceClassification.
Tokenizer is loaded either from the model dir (if it has tokenizer files) or
from a configured base-tokenizer name (e.g. ``roberta-base``).
Also supports PEFT LoRA adapters: pass ``is_peft=True`` and the adapter path,
and we'll load the base model first then apply the adapter.
"""
from __future__ import annotations
from typing import Optional, Tuple
import torch
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
def load_classification_model(
pretrained_path: str,
*,
num_labels: Optional[int] = None,
base_tokenizer: Optional[str] = None,
do_lower_case: Optional[bool] = None,
is_peft: bool = False,
base_model: Optional[str] = None,
) -> Tuple[torch.nn.Module, "AutoTokenizer"]:
config_kwargs = {"output_attentions": True, "output_hidden_states": True}
if num_labels is not None:
config_kwargs["num_labels"] = num_labels
if is_peft:
# Load the base model first, then apply the PEFT adapter.
from peft import PeftModel, PeftConfig
peft_config = PeftConfig.from_pretrained(pretrained_path)
base_name = base_model or peft_config.base_model_name_or_path
base_cfg = AutoConfig.from_pretrained(base_name, **config_kwargs)
base = AutoModelForSequenceClassification.from_pretrained(base_name, config=base_cfg)
model = PeftModel.from_pretrained(base, pretrained_path)
# Merge the adapter so attention/hidden_state outputs work cleanly.
model = model.merge_and_unload()
model.eval()
# Tokenizer comes from the base model.
tok_kwargs = {}
if do_lower_case is not None:
tok_kwargs["do_lower_case"] = do_lower_case
tokenizer = AutoTokenizer.from_pretrained(base_tokenizer or base_name, **tok_kwargs)
return model, tokenizer
config = AutoConfig.from_pretrained(pretrained_path, **config_kwargs)
model = AutoModelForSequenceClassification.from_pretrained(pretrained_path, config=config)
model.eval()
tok_kwargs = {}
if do_lower_case is not None:
tok_kwargs["do_lower_case"] = do_lower_case
# Try the model dir first; fall back to ``base_tokenizer`` if it lacks tokenizer files.
try:
tokenizer = AutoTokenizer.from_pretrained(pretrained_path, **tok_kwargs)
except (OSError, ValueError):
if not base_tokenizer:
raise
tokenizer = AutoTokenizer.from_pretrained(base_tokenizer, **tok_kwargs)
return model, tokenizer
# Back-compat alias used by 01_extract_predictions_and_attention.py.
def load_bert_for_classification(
pretrained_path: str,
num_labels: int = 2,
do_lower_case: bool = False,
) -> Tuple[torch.nn.Module, "AutoTokenizer"]:
return load_classification_model(
pretrained_path,
num_labels=num_labels,
base_tokenizer=None,
do_lower_case=do_lower_case,
)
def move(model: torch.nn.Module, device: torch.device) -> torch.nn.Module:
return model.to(device)
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