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from typing import Union
import numpy as np
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from config import huggingface_config
from .utils import assert_tokenizer_consistency
from .metrics import perplexity, entropy
torch.set_grad_enabled(False)
# selected using Falcon-7B and Falcon-7B-Instruct at bfloat16
BINOCULARS_ACCURACY_THRESHOLD = 0.9015310749276843 # optimized for f1-score
BINOCULARS_FPR_THRESHOLD = 0.8536432310785527 # optimized for low-fpr
DEVICE_1 = "cuda:0" if torch.cuda.is_available() else "cpu"
DEVICE_2 = "cuda:1" if torch.cuda.device_count() > 1 else DEVICE_1
class Binoculars(object):
def __init__(self,
observer_name_or_path: str = "tiiuae/falcon-7b",
performer_name_or_path: str = "tiiuae/falcon-7b-instruct",
use_bfloat16: bool = True,
max_token_observed: int = 512,
mode: str = "low-fpr",
) -> None:
assert_tokenizer_consistency(observer_name_or_path, performer_name_or_path)
if mode == "low-fpr":
self.threshold = BINOCULARS_FPR_THRESHOLD
elif mode == "accuracy":
self.threshold = BINOCULARS_ACCURACY_THRESHOLD
else:
raise ValueError(f"Invalid mode: {mode}")
self.observer_model = AutoModelForCausalLM.from_pretrained(observer_name_or_path,
device_map={"": DEVICE_1},
trust_remote_code=True,
torch_dtype=torch.bfloat16 if use_bfloat16
else torch.float32,
token=huggingface_config["TOKEN"]
)
self.performer_model = AutoModelForCausalLM.from_pretrained(performer_name_or_path,
device_map={"": DEVICE_2},
trust_remote_code=True,
torch_dtype=torch.bfloat16 if use_bfloat16
else torch.float32,
token=huggingface_config["TOKEN"]
)
self.observer_model.eval()
self.performer_model.eval()
self.tokenizer = AutoTokenizer.from_pretrained(observer_name_or_path)
if not self.tokenizer.pad_token:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.max_token_observed = max_token_observed
def change_mode(self, mode: str) -> None:
if mode == "low-fpr":
self.threshold = BINOCULARS_FPR_THRESHOLD
elif mode == "accuracy":
self.threshold = BINOCULARS_ACCURACY_THRESHOLD
else:
raise ValueError(f"Invalid mode: {mode}")
def _tokenize(self, batch: list[str]) -> transformers.BatchEncoding:
batch_size = len(batch)
encodings = self.tokenizer(
batch,
return_tensors="pt",
padding="longest" if batch_size > 1 else False,
truncation=True,
max_length=self.max_token_observed,
return_token_type_ids=False).to(self.observer_model.device)
return encodings
@torch.inference_mode()
def _get_logits(self, encodings: transformers.BatchEncoding) -> torch.Tensor:
observer_logits = self.observer_model(**encodings.to(DEVICE_1)).logits
performer_logits = self.performer_model(**encodings.to(DEVICE_2)).logits
if DEVICE_1 != "cpu":
torch.cuda.synchronize()
return observer_logits, performer_logits
def compute_score(self, input_text: Union[list[str], str]) -> Union[float, list[float]]:
batch = [input_text] if isinstance(input_text, str) else input_text
encodings = self._tokenize(batch)
observer_logits, performer_logits = self._get_logits(encodings)
ppl = perplexity(encodings, performer_logits)
x_ppl = entropy(observer_logits.to(DEVICE_1), performer_logits.to(DEVICE_1),
encodings.to(DEVICE_1), self.tokenizer.pad_token_id)
binoculars_scores = ppl / x_ppl
binoculars_scores = binoculars_scores.tolist()
return binoculars_scores[0] if isinstance(input_text, str) else binoculars_scores
def predict(self, input_text: Union[list[str], str]) -> Union[list[str], str]:
# binoculars_scores = np.array(self.compute_score(input_text))
binoculars_scores = self.compute_score(input_text)
# pred = np.where(binoculars_scores < self.threshold,
# "Most likely AI-generated",
# "Most likely human-generated"
# ).tolist()
# return pred
return binoculars_scores
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