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""" |
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@author:XuMing([email protected]) |
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@description: |
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""" |
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import argparse |
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from threading import Thread |
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from typing import Union, List |
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import torch |
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from loguru import logger |
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from peft import PeftModel |
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from similarities import Similarity |
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from transformers import ( |
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AutoModel, |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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BloomForCausalLM, |
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BloomTokenizerFast, |
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LlamaTokenizer, |
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LlamaForCausalLM, |
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TextIteratorStreamer, |
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GenerationConfig, |
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) |
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MODEL_CLASSES = { |
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"bloom": (BloomForCausalLM, BloomTokenizerFast), |
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"chatglm": (AutoModel, AutoTokenizer), |
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"llama": (LlamaForCausalLM, LlamaTokenizer), |
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"baichuan": (AutoModelForCausalLM, AutoTokenizer), |
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"auto": (AutoModelForCausalLM, AutoTokenizer), |
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} |
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LLAMA_TEMPLATE = """[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. |
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n""" |
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PROMPT_TEMPLATE = """基于以下已知信息，简洁和专业的来回答用户的问题。 |
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如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。 |
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已知内容: |
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{context_str} |
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问题: |
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{query_str} |
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""" |
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class ChatPDF: |
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def __init__( |
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self, |
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sim_model_name_or_path: str = "shibing624/text2vec-base-chinese", |
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gen_model_type: str = "baichuan", |
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gen_model_name_or_path: str = "baichuan-inc/Baichuan-13B-Chat", |
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lora_model_name_or_path: str = None, |
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device: str = None, |
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int8: bool = False, |
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int4: bool = False, |
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): |
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default_device = torch.device('cpu') |
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if torch.cuda.is_available(): |
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default_device = torch.device(0) |
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elif torch.backends.mps.is_available(): |
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default_device = 'mps' |
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self.device = device or default_device |
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self.sim_model = Similarity(model_name_or_path=sim_model_name_or_path, device=self.device) |
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self.gen_model, self.tokenizer = self._init_gen_model( |
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gen_model_type, |
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gen_model_name_or_path, |
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peft_name=lora_model_name_or_path, |
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int8=int8, |
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int4=int4, |
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) |
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self.history = [] |
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self.doc_files = None |
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def _init_gen_model( |
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self, |
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gen_model_type: str, |
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gen_model_name_or_path: str, |
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peft_name: str = None, |
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int8: bool = False, |
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int4: bool = False, |
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): |
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"""Init generate model.""" |
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if int8 or int4: |
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device_map = None |
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else: |
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device_map = "auto" |
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model_class, tokenizer_class = MODEL_CLASSES[gen_model_type] |
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tokenizer = tokenizer_class.from_pretrained(gen_model_name_or_path, trust_remote_code=True) |
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model = model_class.from_pretrained( |
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gen_model_name_or_path, |
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load_in_8bit=int8 if gen_model_type not in ['baichuan', 'chatglm'] else False, |
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load_in_4bit=int4 if gen_model_type not in ['baichuan', 'chatglm'] else False, |
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torch_dtype=torch.float16, |
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low_cpu_mem_usage=True, |
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device_map=device_map, |
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trust_remote_code=True, |
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) |
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if self.device == torch.device('cpu'): |
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model.float() |
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if gen_model_type in ['baichuan', 'chatglm']: |
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if int4: |
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model = model.quantize(4).cuda() |
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elif int8: |
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model = model.quantize(8).cuda() |
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try: |
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model.generation_config = GenerationConfig.from_pretrained(gen_model_name_or_path, trust_remote_code=True) |
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except Exception as e: |
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logger.warning(f"Failed to load generation config from {gen_model_name_or_path}, {e}") |
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if peft_name: |
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model = PeftModel.from_pretrained( |
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model, |
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peft_name, |
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torch_dtype=torch.float16, |
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) |
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logger.info(f"Loaded peft model from {peft_name}") |
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model.eval() |
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return model, tokenizer |
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@torch.inference_mode() |
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def stream_generate_answer( |
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self, |
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prompt, |
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max_new_tokens=512, |
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temperature=0.7, |
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repetition_penalty=1.0, |
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context_len=2048 |
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): |
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streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) |
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input_ids = self.tokenizer(prompt).input_ids |
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max_src_len = context_len - max_new_tokens - 8 |
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input_ids = input_ids[-max_src_len:] |
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generation_kwargs = dict( |
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input_ids=torch.as_tensor([input_ids]).to(self.device), |
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max_new_tokens=max_new_tokens, |
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temperature=temperature, |
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repetition_penalty=repetition_penalty, |
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streamer=streamer, |
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) |
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thread = Thread(target=self.gen_model.generate, kwargs=generation_kwargs) |
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thread.start() |
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yield from streamer |
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def load_doc_files(self, doc_files: Union[str, List[str]]): |
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"""Load document files.""" |
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if isinstance(doc_files, str): |
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doc_files = [doc_files] |
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for doc_file in doc_files: |
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if doc_file.endswith('.pdf'): |
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corpus = self.extract_text_from_pdf(doc_file) |
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elif doc_file.endswith('.docx'): |
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corpus = self.extract_text_from_docx(doc_file) |
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elif doc_file.endswith('.md'): |
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corpus = self.extract_text_from_markdown(doc_file) |
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else: |
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corpus = self.extract_text_from_txt(doc_file) |
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self.sim_model.add_corpus(corpus) |
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self.doc_files = doc_files |
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@staticmethod |
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def extract_text_from_pdf(file_path: str): |
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"""Extract text content from a PDF file.""" |
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import PyPDF2 |
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contents = [] |
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with open(file_path, 'rb') as f: |
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pdf_reader = PyPDF2.PdfReader(f) |
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for page in pdf_reader.pages: |
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page_text = page.extract_text().strip() |
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raw_text = [text.strip() for text in page_text.splitlines() if text.strip()] |
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new_text = '' |
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for text in raw_text: |
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new_text += text |
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if text[-1] in ['.', '!', '?', '。', '！', '？', '…', ';', '；', ':', '：', '”', '’', '）', '】', '》', '」', |
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'』', '〕', '〉', '》', '〗', '〞', '〟', '»', '"', "'", ')', ']', '}']: |
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contents.append(new_text) |
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new_text = '' |
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if new_text: |
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contents.append(new_text) |
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return contents |
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@staticmethod |
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def extract_text_from_txt(file_path: str): |
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"""Extract text content from a TXT file.""" |
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contents = [] |
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with open(file_path, 'r', encoding='utf-8') as f: |
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contents = [text.strip() for text in f.readlines() if text.strip()] |
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return contents |
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@staticmethod |
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def extract_text_from_docx(file_path: str): |
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"""Extract text content from a DOCX file.""" |
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import docx |
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document = docx.Document(file_path) |
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contents = [paragraph.text.strip() for paragraph in document.paragraphs if paragraph.text.strip()] |
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return contents |
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@staticmethod |
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def extract_text_from_markdown(file_path: str): |
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"""Extract text content from a Markdown file.""" |
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import markdown |
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from bs4 import BeautifulSoup |
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with open(file_path, 'r', encoding='utf-8') as f: |
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markdown_text = f.read() |
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html = markdown.markdown(markdown_text) |
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soup = BeautifulSoup(html, 'html.parser') |
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contents = [text.strip() for text in soup.get_text().splitlines() if text.strip()] |
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return contents |
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@staticmethod |
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def _add_source_numbers(lst): |
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"""Add source numbers to a list of strings.""" |
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return [f'[{idx + 1}]\t "{item}"' for idx, item in enumerate(lst)] |
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def predict( |
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self, |
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query: str, |
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topn: int = 5, |
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max_length: int = 512, |
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context_len: int = 2048, |
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temperature: float = 0.7, |
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do_print: bool = True, |
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): |
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"""Query from corpus.""" |
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sim_contents = self.sim_model.most_similar(query, topn=topn) |
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reference_results = [] |
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for query_id, id_score_dict in sim_contents.items(): |
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for corpus_id, s in id_score_dict.items(): |
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reference_results.append(self.sim_model.corpus[corpus_id]) |
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if not reference_results: |
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return '没有提供足够的相关信息', reference_results |
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reference_results = self._add_source_numbers(reference_results) |
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context_str = '\n'.join(reference_results)[:(context_len - len(PROMPT_TEMPLATE))] |
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prompt = PROMPT_TEMPLATE.format(context_str=context_str, query_str=query) |
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self.history.append([prompt, '']) |
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response = "" |
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for new_text in self.stream_generate_answer( |
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prompt, |
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max_new_tokens=max_length, |
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temperature=temperature, |
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context_len=context_len, |
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): |
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response += new_text |
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if do_print: |
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print(new_text, end="", flush=True) |
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if do_print: |
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print("", flush=True) |
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response = response.strip() |
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self.history[-1][1] = response |
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return response, reference_results |
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def save_index(self, index_path=None): |
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"""Save model.""" |
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if index_path is None: |
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index_path = '.'.join(self.doc_files.split('.')[:-1]) + '_index.json' |
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self.sim_model.save_index(index_path) |
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def load_index(self, index_path=None): |
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"""Load model.""" |
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if index_path is None: |
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index_path = '.'.join(self.doc_files.split('.')[:-1]) + '_index.json' |
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self.sim_model.load_index(index_path) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--sim_model", type=str, default="shibing624/text2vec-base-chinese") |
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parser.add_argument("--gen_model_type", type=str, default="baichuan") |
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parser.add_argument("--gen_model", type=str, default="baichuan-inc/Baichuan-13B-Chat") |
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parser.add_argument("--lora_model", type=str, default=None) |
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parser.add_argument("--device", type=str, default=None) |
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parser.add_argument("--int4", action='store_true', help="use int4 quantization") |
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parser.add_argument("--int8", action='store_true', help="use int8 quantization") |
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args = parser.parse_args() |
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print(args) |
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m = ChatPDF( |
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sim_model_name_or_path=args.sim_model, |
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gen_model_type=args.gen_model_type, |
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gen_model_name_or_path=args.gen_model, |
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lora_model_name_or_path=args.lora_model, |
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device=args.device, |
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int4=args.int4, |
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int8=args.int8 |
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) |
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m.load_doc_files(doc_files='sample.pdf') |
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m.predict('自然语言中的非平行迁移是指什么？', do_print=True) |
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while True: |
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query = input("> ") |
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if query == 'exit': |
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break |
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m.predict(query, do_print=True) |
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