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"""
GPT2 Language Models:
--------------------------
"""
import os
import torch
from textattack.shared import utils
from .language_model_constraint import LanguageModelConstraint
# temporarily silence W&B to ignore log-in warning
os.environ["WANDB_SILENT"] = "1"
class GPT2(LanguageModelConstraint):
"""A constraint based on the GPT-2 language model.
from "Better Language Models and Their Implications"
(openai.com/blog/better-language-models/)
Args:
model_name: id of GPT2 model
"""
def __init__(self, model_name="gpt2", **kwargs):
import transformers
# re-enable notifications
os.environ["WANDB_SILENT"] = "0"
self.model = transformers.GPT2LMHeadModel.from_pretrained(model_name)
self.model.to(utils.device)
self.tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name)
super().__init__(**kwargs)
def get_log_probs_at_index(self, text_list, word_index):
"""Gets the probability of the word at index `word_index` according to
GPT-2.
Assumes that all items in `text_list` have the same prefix up
until `word_index`.
"""
prefix = text_list[0].text_until_word_index(word_index)
if not utils.has_letter(prefix):
# This language model perplexity is not defined with respect to
# a word without a prefix. If the prefix is null, just return the
# log-probability 0.0.
return torch.zeros(len(text_list), dtype=torch.float)
token_ids = self.tokenizer.encode(prefix)
tokens_tensor = torch.tensor([token_ids])
tokens_tensor = tokens_tensor.to(utils.device)
with torch.no_grad():
outputs = self.model(tokens_tensor)
predictions = outputs[0]
probs = []
for attacked_text in text_list:
next_word_ids = self.tokenizer.encode(attacked_text.words[word_index])
next_word_prob = predictions[0, -1, next_word_ids[0]]
probs.append(next_word_prob)
return probs
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