File size: 4,514 Bytes
07eb921 | 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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | import json
import re
import torch
from string import Template
from typing import List
def remove_citations(sent):
return (
re.sub(r"\[\d+", "", re.sub(r" \[\d+", "", sent))
.replace(" |", "")
.replace("]", "")
)
class BaseGenerator:
def __init__(self, prompt_path: str):
self.prompt_path = prompt_path
self.instruction, self.prompt_format = self._load_prompt(prompt_path)
def _load_prompt(self, path: str):
with open(path, "r") as f:
prompt_config = json.load(f)
instruction = prompt_config["instruction"]
prompt_format = Template(prompt_config["prompt_format"])
return instruction, prompt_format
def _format_documents(self, doc_list: list) -> str:
documents = ""
for idx, doc in enumerate(doc_list):
title = doc["title"]
text = doc["text"]
documents += f"Document [{idx + 1}](Title: {title}): {text}\n"
return documents
class SentenceGenerator(BaseGenerator):
def __init__(self, model, tokenizer, prompt_path: str):
super().__init__(prompt_path)
self.model = model
self.tokenizer = tokenizer
def format_prompt(self, question: str, doc_list: list) -> str:
prompt = self.prompt_format.substitute(
instruction=self.instruction,
question=question,
documents=self._format_documents(doc_list),
)
return prompt
@torch.inference_mode()
def generate(self, prompt: str) -> (str, List[int]):
# * tokenization
input_ids = self.tokenizer(prompt, return_tensors="pt")["input_ids"].to(
self.model.device
)
prompt_len = input_ids.shape[1]
# * generate until the end of the sentence
outputs = self.model.generate(
input_ids,
max_new_tokens=128,
eos_token_id=[self.tokenizer.eos_token_id, 29889],
).to("cpu")
# * decode and remove the prompt
sentence = self.tokenizer.decode(
outputs[0][prompt_len:], skip_special_tokens=True
).strip()
# * make sure that only one sentence is generated
sentence = (
sentence[: sentence.index("].") + 2] if "]." in sentence else sentence
)
# * remove citations
sentence = remove_citations(sentence)
return sentence, outputs[0]
class CitationGenerator(BaseGenerator):
def __init__(self, model, tokenizer, prompt_path: str):
super().__init__(prompt_path)
self.model = model
self.tokenizer = tokenizer
def format_prompt(self, doc_list: list, sentence: str) -> str:
prompt = self.prompt_format.substitute(
instruction=self.instruction,
documents=self._format_documents(doc_list),
sentence=sentence,
)
return prompt
@torch.inference_mode()
def generate(self, prompt: str) -> str:
# * tokenization
input_ids = self.tokenizer(prompt, return_tensors="pt")["input_ids"].to(
self.model.device
)
prompt_len = input_ids.shape[1]
# * generate cited sentence
outputs = self.model.generate(input_ids, max_new_tokens=128).to("cpu")
# * decode and remove the prompt
cited_sentence = self.tokenizer.decode(
outputs[0][prompt_len:], skip_special_tokens=True
).strip()
return cited_sentence
class QueryGenerator(BaseGenerator):
def __init__(self, model, tokenizer, prompt_path: str):
super().__init__(prompt_path)
self.model = model
self.tokenizer = tokenizer
def format_prompt(self, question: str, context: str, claim: str, query_num: int) -> str:
prompt = self.prompt_format.substitute(
question=question,
context=context,
claim=claim,
query_num=query_num
)
return prompt
@torch.inference_mode()
def generate(self, prompt: str) -> str:
# * tokenization
input_ids = self.tokenizer(prompt, return_tensors="pt")["input_ids"].to(
self.model.device
)
prompt_len = input_ids.shape[1]
# * generate query
outputs = self.model.generate(input_ids, max_new_tokens=256).to("cpu")
# * decode and remove the prompt
query = self.tokenizer.decode(
outputs[0][prompt_len:], skip_special_tokens=True
).strip()
return query
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