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README.md

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+---
+title: Selective Context
+emoji: ⚡
+colorFrom: green
+colorTo: green
+sdk: streamlit
+sdk_version: 1.19.0
+app_file: app.py
+pinned: false
+license: cc-by-2.0
+duplicated_from: liyucheng/selective_context
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from transformers import GPT2Tokenizer, GPT2LMHeadModel, BertTokenizer
+import torch
+import streamlit as st
+import re
+from typing import List, Tuple
+import spacy
+import numpy as np
+from dataclasses import dataclass
+from nltk.tokenize import sent_tokenize, word_tokenize
+
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+st.set_page_config(layout="wide")
+
+@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'):
+
+        self.model_type = model_type
+        self.lang = lang
+
+        # 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 = "<deleted>"
+
+        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"])
+
+    def _prepare_model(self):
+        if self.model_type == 'gpt2':
+            if self.lang == 'zh':
+                self.model = GPT2LMHeadModel.from_pretrained('uer/gpt2-chinese-cluecorpussmall')
+                self.tokenizer = BertTokenizer.from_pretrained('uer/gpt2-chinese-cluecorpussmall')
+            else:
+                self.model = GPT2LMHeadModel.from_pretrained('gpt2')
+                self.tokenizer = GPT2Tokenizer.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
+    
+    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 _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':
+            return self.sent_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)
+        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
+    
+# streamlit app.py
+# here we ask the user to input the text and the reduce ratio
+# then we call the SelectiveContext to compress the text
+
+st.title("Selective Context: Compress your prompt")
+st.markdown("This is a demo for the **Selective Context** algorithm.")
+st.markdown("Use this algorithm to **compress** your prompt, so that LLMs can deal with **2x more context**!")
+st.markdown("- The algorithm filters out the content that is less informative. \n - You can also choose to filter out phrases or tokens instead of sentences. \n - Checkout the paper for details and experiments! [https://arxiv.org/abs/2304.12102](https://arxiv.org/abs/2304.12102).")
+st.write("")
+
+st.subheader("Demo")
+
+lang = st.radio("Please choose the language: ", ('en', 'zh'))
+ratio = st.radio("Please choose the compress ratio [we recommend 0.5]: ", (0.5, 0.2, 0.35, 0.65, 0.8))
+reduce_level = st.radio("Please choose the reduce level: ", ('phrase', 'token', 'sent'))
+
+text = st.text_area("Please input your text here", height=300)
+
+@st.cache_resource()
+def load_model(lang):
+    model = SelectiveContext(lang=lang)
+    return model
+
+if st.button("Compress"):
+    model = load_model(lang)
+    context, masked_sents = model(text, reduce_ratio=ratio, reduce_level=reduce_level)
+    st.subheader("The compressed context is:")
+    st.code(context)
+    # st.divider()
+    st.subheader("The filtered out content is:")
+    st.write(masked_sents)

requirements.txt

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+transformers
+spacy
+https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz#en_core_web_sm
+nltk
+torch
+numpy