print('Loading dependencies...')
from transformers import GPT2Tokenizer, GPT2LMHeadModel, BertTokenizer, LlamaForCausalLM, LlamaTokenizer
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
import torch
import re
from typing import List, Tuple
import spacy
import numpy as np
import os
from dataclasses import dataclass
from nltk.tokenize import sent_tokenize, word_tokenize
import time
DEVICE = torch.device('cpu')
@dataclass
class LexicalUnits:
unit_type: str
text: List[str]
self_info: List[float] = None
def __add__(self, other):
assert self.unit_type == other.unit_type, 'Cannot add two different unit types'
return LexicalUnits(self.unit_type, self.text + other.text, self.self_info + other.self_info)
def __radd__(self, other):
if other == 0:
return self
return NotImplementedError()
def add_to_head(self, token, self_info):
return LexicalUnits(self.unit_type, [token] + self.text, [self_info] + self.self_info)
def add_to_tail(self, token, self_info):
return LexicalUnits(self.unit_type, self.text + [token], self.self_info + [self_info])
class SelectiveContext:
def __init__(self, model_type = 'gpt2', lang = 'en', device = 'cpu'):
self.model_type = model_type
self.lang = lang
global DEVICE
DEVICE = device
# this means we calculate self-information sentence by sentence
self.sent_level_self_info = True
self._prepare_phrase_tokenizer()
self.sent_tokenize_pattern = r"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s"
self.phrase_mask_token = ''
self.sent_mask_token = "<...some content omitted.>"
self._prepare_model()
def _prepare_phrase_tokenizer(self):
# we use space to tokenize sentence into phrases
# for English, we should use `spacy.load("en_core_web_sm").add_pipe('merge_noun_chunks')`
# for Chinese, use `nlp = spacy.load('zh_core_web_sm')`` directly
lang = self.lang
if lang == "en":
self.nlp = spacy.load("en_core_web_sm", disable=["ner"])
self.nlp.add_pipe('merge_noun_chunks')
elif lang == "zh":
self.nlp = spacy.load('zh_core_web_sm', disable=["ner"])
# elif self.model_type == 'llama':
# self.nlp = spacy.load('en_core_web_sm', disable=["ner"])
def _prepare_model(self):
# Load tokenizer
if self.lang == 'zh':
self.tokenizer = BertTokenizer.from_pretrained('uer/gpt2-chinese-cluecorpussmall')
elif self.lang == 'en':
self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
else:
raise NotImplementedError()
if self.model_type == 'gpt2':
if self.lang == 'zh':
self.model = GPT2LMHeadModel.from_pretrained('uer/gpt2-chinese-cluecorpussmall')
else:
self.model = GPT2LMHeadModel.from_pretrained('gpt2')
self.model.to(DEVICE)
self.model.eval()
print('model loaded')
self.max_token_length = self.model.config.n_positions
self.get_self_information = self._get_self_info_via_gpt2
elif self.model_type == 'curie':
global openai
import openai
self.max_token_length = 2048
self.get_self_information = self._get_self_info_via_curie
elif self.model_type == 'llama':
print("Before tokernizer")
self.tokenizer = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-7b-chat-hf', token='LLaMA TOKEN')
print("Before model")
config = AutoConfig.from_pretrained('meta-llama/Llama-2-7b-chat-hf', token='LLaMA TOKEN')
print("After config")
self.model = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-chat-hf', config=config, token='LLaMA TOKEN')
print("Before DEVICE")
self.model.to(DEVICE)
print("Before eval")
self.model.eval()
print('model loaded')
self.max_token_length = self.model.config.max_position_embeddings
self.get_self_information = self._get_self_info_via_llama
def get_self_information(self, text: str) -> Tuple[List[str], List[float]]:
# it takes text as input, and return a list of words and a list of self-information scores
raise NotImplementedError
def _get_self_info_via_gpt2(self, text: str) -> Tuple[List[str], List[float]]:
if self.lang == 'en':
text = f"<|endoftext|>{text}"
elif self.lang == 'zh':
text = f"[CLS]{text}"
with torch.no_grad():
encoding = self.tokenizer(text, add_special_tokens=False, return_tensors='pt')
encoding = encoding.to(DEVICE)
outputs = self.model(**encoding)
logits = outputs.logits
probs = torch.softmax(logits, dim=-1)
self_info = -torch.log(probs)
input_ids = encoding['input_ids']
input_ids_expaned = input_ids[:, 1:].unsqueeze(-1)
tokens = [self.tokenizer.decode(token_) for token_ in input_ids.squeeze().tolist()[1:]]
return tokens, self_info[:, :-1].gather(-1, input_ids_expaned).squeeze(-1).squeeze(0).tolist()
def _get_self_info_via_curie(self, text: str) -> Tuple[List[str], List[float]]:
num_retry = 3
openai.api_key = os.environ["OPENAI_API_KEY"]
for _ in range(num_retry):
try:
r = openai.Completion.create(
model="curie",
prompt=f"<|endoftext|>{text}",
max_tokens=0,
temperature=0,
echo=True,
logprobs=0,
)
break
except Exception as e:
print(e)
time.sleep(1)
result = r['choices'][0]
tokens, logprobs = result["logprobs"]["tokens"][1:], result["logprobs"]["token_logprobs"][1:]
assert len(tokens) == len(logprobs), f"Expected {len(tokens)} logprobs, got {len(logprobs)}"
self_info = [ -logprob for logprob in logprobs]
return tokens, self_info
def _get_self_info_via_llama(self, text: str) -> Tuple[List[str], List[float]]:
inputs = self.tokenizer.encode_plus(text, return_tensors="pt")
input_ids = inputs.input_ids.to(DEVICE)
attention_mask = inputs.attention_mask.to(DEVICE)
with torch.no_grad():
outputs = self.model(input_ids, attention_mask=attention_mask)
logits = outputs.logits
probs = torch.softmax(logits, dim=-1)
self_info = -torch.log(probs)
input_ids = input_ids.squeeze()
self_info = self_info.squeeze()
tokens = self.tokenizer.convert_ids_to_tokens(input_ids)
return tokens, self_info.tolist()
def _lexical_unit(self, sents):
if self.sent_level_self_info:
sent_self_info = []
all_noun_phrases = []
all_noun_phrases_info = []
all_tokens = []
all_token_self_info = []
for sent in sents:
# print(sent)
tokens, self_info = self.get_self_information(sent)
sent_self_info.append(np.mean(self_info))
all_tokens.extend(tokens)
all_token_self_info.extend(self_info)
noun_phrases, noun_phrases_info = self._calculate_lexical_unit(tokens, self_info)
# We need to add a space before the first noun phrase for every sentence except the first one
if len(all_noun_phrases) != 0:
noun_phrases[0] = f" {noun_phrases[0]}"
all_noun_phrases.extend(noun_phrases)
all_noun_phrases_info.extend(noun_phrases_info)
return [
LexicalUnits('sent', text=sents, self_info=sent_self_info),
LexicalUnits('phrase', text=all_noun_phrases, self_info=all_noun_phrases_info),
LexicalUnits('token', text=all_tokens, self_info=all_token_self_info)
]
def _calculate_lexical_unit(self, tokens, self_info):
def _unit_info(tokens, self_info, units):
current_unit_idx = 0
current_position = 0
unit_self_info = [[] for _ in range(len(units))]
for idx, (token, info) in enumerate(zip(tokens, self_info)):
current_position += len(token)
if current_position == len(units[current_unit_idx]):
unit_self_info[current_unit_idx].append(info)
current_position = current_position - len(units[current_unit_idx])
current_unit_idx += 1
elif current_position > len(units[current_unit_idx]):
counter_ = 1
current_position = current_position - len(units[current_unit_idx])
current_unit_idx += 1
while current_position >= len(units[current_unit_idx]):
counter_ += 1
current_position = current_position - len(units[current_unit_idx])
current_unit_idx += 1
if current_unit_idx >= len(units):
break
partial_info = info/counter_
for _ in range(counter_):
unit_self_info[(current_unit_idx-1) - _].append(partial_info)
else:
if token == " ":
continue
unit_self_info[current_unit_idx].append(info)
unit_self_info_ = [np.mean(info) for info in unit_self_info]
return unit_self_info_
def _noun_phrases(sent):
noun_phrases = []
doc = self.nlp(sent)
for index, chunk in enumerate(doc):
if index == 0:
noun_phrases.append(chunk.text)
else:
noun_phrases.append(doc[index-1].whitespace_ + chunk.text)
return noun_phrases
if self.sent_level_self_info:
# in this case, the self_info is for each sentence
# we only need to calculate the self_info for each phrase
sent = ''.join(tokens)
# noun_phrases = [chunk.text for chunk in self.nlp(sent).noun_chunks]
noun_phrases = _noun_phrases(sent)
# noun_phrases[-1] = noun_phrases[-1] + ' '
noun_phrases_info = _unit_info(tokens, self_info, noun_phrases)
return noun_phrases, noun_phrases_info
def beautify_context(self, context: str) -> str:
context = re.sub(r"\s+", " ", context)
return context
def self_info_mask(self, sents: List[str], self_info: List[float], mask_level):
# mask_level: mask sentences, phrases, or tokens
sents_after_mask = []
masked_sents = []
self.ppl_threshold = np.nanpercentile(self_info, self.mask_ratio * 100)
# if title is not None:
# with open(os.path.join(self.path, title+'_prob_token.tsv'), 'w', encoding='utf-8') as f:
# for token, info in zip(tokens, self_info):
# f.write(f"{token}\t{info}\n")
# with open(os.path.join(self.path, title+'_prob_sent.tsv'), 'w', encoding='utf-8') as f:
# for sent, info in zip(sents, sent_self_info):
# f.write(f"{sent}\n{info}\n\n")
for sent, info in zip(sents, self_info):
if info < self.ppl_threshold:
masked_sents.append(sent)
sents_after_mask.append(self.mask_a_sent(sent, mask_level))
else:
sents_after_mask.append(sent)
masked_context = " ".join(sents_after_mask) if mask_level == 'sent' else "".join(sents_after_mask)
return masked_context, masked_sents
def mask_a_sent(self, sent, level):
if level == 'phrase':
return self.phrase_mask_token
elif level == 'sent':
if self.keep_leading_word:
leading_few_words = " ".join(word_tokenize(sent)[:self.num_lead_words]) + " "
else:
leading_few_words = ""
return leading_few_words + self.mask_token
elif level == 'token':
return ''
def __call__(self, text: str, reduce_ratio: float = 0.35, reduce_level :str = 'phrase') -> List[str]:
context = self.beautify_context(text)
self.mask_ratio = reduce_ratio
sents = re.split(self.sent_tokenize_pattern, context)
sents = [sent.strip() for sent in sents if sent.strip()]
# You want the reduce happen at sentence level, phrase level, or token level?
assert reduce_level in ['sent', 'phrase', 'token'], f"reduce_level should be one of ['sent', 'phrase', 'token'], got {reduce_level}"
sent_lus, phrase_lus, token_lus = self._lexical_unit(sents)
# print(phrase_lus, '^^^^')
lexical_level = {
'sent': sent_lus,
'phrase': phrase_lus,
'token': token_lus
}
# context is the reduced context, masked_sents denotes what context has been filtered out
context, masked_sents = self.self_info_mask(lexical_level[reduce_level].text, lexical_level[reduce_level].self_info, reduce_level)
return context, masked_sents
def main(
model_type = 'gpt2', # you can choose from ['gpt2', 'curie']
lang = 'en', # currenlty only support en and zh
file_to_process: str = None,
file_to_save: str = None,
):
global DEVICE
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {DEVICE}")
sc = SelectiveContext(model_type=model_type, lang=lang)
if file_to_process is None:
while True:
text = input("Please input the text you want to reduce: ")
if text == 'exit':
break
context, masked_sents = sc(text)
print('***********\nThe resultsing context is: \n')
print(context, '\n\n')
print('***********\nThe content that has been filtered out is: \n')
print(masked_sents, '\n\n')
else:
with open(file_to_process, 'r') as f:
text = f.read()
context, masked_sents = sc(text)
with open(file_to_save, 'w') as f:
f.write(context)
if __name__ == "__main__":
main(model_type='gpt2', lang = 'zh')