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import torch |
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from transformers import ( |
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LlamaForCausalLM, |
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LlamaTokenizer, |
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StoppingCriteria, |
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) |
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import gradio as gr |
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import argparse |
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import os |
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from queue import Queue |
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from threading import Thread |
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import traceback |
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import gc |
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import torch |
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from auto_gptq import AutoGPTQForCausalLM |
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from langchain import HuggingFacePipeline, PromptTemplate |
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from langchain.chains import RetrievalQA |
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from langchain.document_loaders import PyPDFDirectoryLoader, DirectoryLoader |
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from langchain.embeddings import HuggingFaceInstructEmbeddings |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import Chroma |
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from pdf2image import convert_from_path |
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from transformers import AutoTokenizer, TextStreamer, pipeline |
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DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu" |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'--base_model', |
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default=None, |
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type=str, |
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help='Base model path') |
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parser.add_argument('--lora_model', default=None, type=str, |
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help="If None, perform inference on the base model") |
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parser.add_argument( |
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'--tokenizer_path', |
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default=None, |
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type=str, |
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help='If None, lora model path or base model path will be used') |
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parser.add_argument( |
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'--gpus', |
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default="0", |
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type=str, |
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help='If None, cuda:0 will be used. Inference using multi-cards: --gpus=0,1,... ') |
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parser.add_argument('--share', default=True, help='Share gradio domain name') |
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parser.add_argument('--port', default=19324, type=int, help='Port of gradio demo') |
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parser.add_argument( |
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'--max_memory', |
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default=256, |
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type=int, |
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help='Maximum input prompt length, if exceeded model will receive prompt[-max_memory:]') |
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parser.add_argument( |
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'--load_in_8bit', |
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action='store_true', |
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help='Use 8 bit quantified model') |
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parser.add_argument( |
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'--only_cpu', |
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action='store_true', |
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help='Only use CPU for inference') |
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parser.add_argument( |
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'--alpha', |
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type=str, |
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default="1.0", |
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help="The scaling factor of NTK method, can be a float or 'auto'. ") |
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args = parser.parse_args() |
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if args.only_cpu is True: |
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args.gpus = "" |
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus |
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from peft import PeftModel |
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def setup(): |
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global tokenizer, model, device, share, port, max_memory, vector_store |
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max_memory = args.max_memory |
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port = args.port |
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share = args.share |
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load_in_8bit = args.load_in_8bit |
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load_type = torch.float16 |
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if torch.cuda.is_available(): |
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device = torch.device(0) |
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else: |
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device = torch.device('cpu') |
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""" |
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if args.tokenizer_path is None: |
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args.tokenizer_path = args.lora_model |
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if args.lora_model is None: |
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args.tokenizer_path = args.base_model |
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""" |
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embeddings = HuggingFaceInstructEmbeddings( |
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model_name="BAAI/bge-large-en-v1.5", model_kwargs={"device": DEVICE} |
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) |
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if os.path.exists("/home/ywang/db")==False: |
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loader = DirectoryLoader("kb") |
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docs = loader.load() |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64) |
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texts = text_splitter.split_documents(docs) |
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vector_store = Chroma.from_documents(texts, embeddings, persist_directory="db") |
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else: |
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vector_store=Chroma(persist_directory="db", embedding_function=embeddings) |
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model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ" |
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model_basename = "model" |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) |
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base_model = AutoGPTQForCausalLM.from_quantized( |
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model_name_or_path, |
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revision="gptq-4bit-128g-actorder_True", |
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model_basename=model_basename, |
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use_safetensors=True, |
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trust_remote_code=True, |
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inject_fused_attention=False, |
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device=DEVICE, |
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quantize_config=None, |
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) |
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model_vocab_size = base_model.get_input_embeddings().weight.size(0) |
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tokenzier_vocab_size = len(tokenizer) |
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print(f"Vocab of the base model: {model_vocab_size}") |
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print(f"Vocab of the tokenizer: {tokenzier_vocab_size}") |
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if model_vocab_size != tokenzier_vocab_size: |
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assert tokenzier_vocab_size > model_vocab_size |
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print("Resize model embeddings to fit tokenizer") |
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base_model.resize_token_embeddings(tokenzier_vocab_size) |
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if args.lora_model is not None: |
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print("loading peft model") |
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model = PeftModel.from_pretrained( |
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base_model, |
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args.lora_model, |
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torch_dtype=load_type, |
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device_map='auto', |
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) |
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else: |
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model = base_model |
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if device == torch.device('cpu'): |
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model.float() |
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model.eval() |
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DEFAULT_SYSTEM_PROMPT = """ |
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You 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. |
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""".strip() |
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def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str: |
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return f""" |
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[INST] <<SYS>> |
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{system_prompt} |
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<</SYS>> |
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{prompt} [/INST] |
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""".strip() |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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text_pipeline = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_new_tokens=2048, |
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temperature=0, |
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top_p=0.95, |
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repetition_penalty=1.15, |
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streamer=streamer, |
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) |
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llm = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 0}) |
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SYSTEM_PROMPT = "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer." |
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template = generate_prompt( |
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""" |
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{context} |
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Question: {question} |
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""", |
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system_prompt=SYSTEM_PROMPT, |
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) |
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prompt = PromptTemplate(template=template, input_variables=["context", "question"]) |
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qa_chain = RetrievalQA.from_chain_type( |
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llm=llm, |
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chain_type="stuff", |
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retriever=vector_store.as_retriever(search_kwargs={"k": 2}), |
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return_source_documents=True, |
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chain_type_kwargs={"prompt": prompt}, |
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) |
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def reset_user_input(): |
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return gr.update(value='') |
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def reset_state(): |
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return [] |
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""" |
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def generate_prompt(instruction,my_input): |
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return f"Instruction：{my_input}\n Response：{instruction}" |
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""" |
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def user(user_message, history): |
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return gr.update(value="", interactive=False), history + \ |
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[[user_message, None]] |
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class Stream(StoppingCriteria): |
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def __init__(self, callback_func=None): |
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self.callback_func = callback_func |
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def __call__(self, input_ids, scores) -> bool: |
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if self.callback_func is not None: |
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self.callback_func(input_ids[0]) |
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return False |
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class Iteratorize: |
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""" |
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Transforms a function that takes a callback |
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into a lazy iterator (generator). |
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Adapted from: https://stackoverflow.com/a/9969000 |
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""" |
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def __init__(self, func, kwargs=None, callback=None): |
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self.mfunc = func |
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self.c_callback = callback |
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self.q = Queue() |
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self.sentinel = object() |
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self.kwargs = kwargs or {} |
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self.stop_now = False |
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def _callback(val): |
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if self.stop_now: |
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raise ValueError |
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self.q.put(val) |
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def gentask(): |
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try: |
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ret = self.mfunc(callback=_callback, **self.kwargs) |
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except ValueError: |
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pass |
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except Exception: |
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traceback.print_exc() |
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clear_torch_cache() |
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self.q.put(self.sentinel) |
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if self.c_callback: |
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self.c_callback(ret) |
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self.thread = Thread(target=gentask) |
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self.thread.start() |
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def __iter__(self): |
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return self |
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def __next__(self): |
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obj = self.q.get(True, None) |
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if obj is self.sentinel: |
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raise StopIteration |
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else: |
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return obj |
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def __del__(self): |
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clear_torch_cache() |
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def __enter__(self): |
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return self |
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def __exit__(self, exc_type, exc_val, exc_tb): |
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self.stop_now = True |
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clear_torch_cache() |
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def clear_torch_cache(): |
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gc.collect() |
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if torch.cuda.device_count() > 0: |
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torch.cuda.empty_cache() |
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@torch.no_grad() |
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def predict( |
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history, |
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max_new_tokens=128, |
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top_p=0.75, |
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temperature=0.1, |
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top_k=40, |
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do_sample=True, |
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repetition_penalty=1.0 |
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): |
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history[-1][1] = "" |
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history[-1][1] = qa_chain(history[-1][0])['result'] |
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""" |
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#history的格式：[[query1,response1],[query2,response2],[query3,response3]……] |
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docs=vector_store.similarity_search(history[-1][0]) |
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context=[doc.page_content for doc in docs] |
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#使用下面的方式，把多轮对话转为单轮对话 |
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input = f"### Instruction:{history[-1][0]} ### Response:{history[-1][1]}" |
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prompt = generate_prompt(input,"".join(context)) |
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inputs = tokenizer(qa_chain, return_tensors="pt") |
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input_ids = inputs["input_ids"].to(device) |
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generate_params = { |
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'input_ids': input_ids, |
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'max_new_tokens': max_new_tokens, |
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'top_p': top_p, |
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'temperature': temperature, |
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'top_k': top_k, |
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'do_sample': do_sample, |
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'repetition_penalty': repetition_penalty, |
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} |
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def generate_with_callback(callback=None, **kwargs): |
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if 'stopping_criteria' in kwargs: |
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kwargs['stopping_criteria'].append(Stream(callback_func=callback)) |
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else: |
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kwargs['stopping_criteria'] = [Stream(callback_func=callback)] |
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clear_torch_cache() |
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with torch.no_grad(): |
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model.generate(**kwargs) |
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def generate_with_streaming(**kwargs): |
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return Iteratorize(generate_with_callback, kwargs, callback=None) |
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with generate_with_streaming(**generate_params) as generator: |
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for output in generator: |
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next_token_ids = output[len(input_ids[0]):] |
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if next_token_ids[0] == tokenizer.eos_token_id: |
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break |
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new_tokens = tokenizer.decode( |
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next_token_ids, skip_special_tokens=True) |
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if isinstance(tokenizer, LlamaTokenizer) and len(next_token_ids) > 0: |
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if tokenizer.convert_ids_to_tokens(int(next_token_ids[0])).startswith('▁'): |
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new_tokens = ' ' + new_tokens |
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history[-1][1] = new_tokens |
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yield history |
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if len(next_token_ids) >= max_new_tokens: |
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break |
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""" |
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yield history |
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setup() |
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with gr.Blocks() as demo: |
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github_banner_path = 'https://radformation.com/images/radformation-logo-white.svg' |
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gr.Markdown("> Radformation Q&A bot") |
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chatbot = gr.Chatbot() |
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with gr.Row(): |
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with gr.Column(scale=4): |
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with gr.Column(scale=12): |
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user_input = gr.Textbox( |
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show_label=False, |
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placeholder="Shift + Enter, to send message...", |
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lines=10).style( |
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container=False) |
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with gr.Column(min_width=32, scale=1): |
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submitBtn = gr.Button("Submit", variant="primary") |
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with gr.Column(scale=1): |
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emptyBtn = gr.Button("Clear History") |
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max_new_token = gr.Slider( |
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0, |
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4096, |
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value=512, |
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step=1.0, |
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label="Maximum New Token Length", |
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interactive=True) |
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top_p = gr.Slider(0, 1, value=0.9, step=0.01, |
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label="Top P", interactive=True) |
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temperature = gr.Slider( |
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0, |
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1, |
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value=0.5, |
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step=0.01, |
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label="Temperature", |
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interactive=True) |
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top_k = gr.Slider(1, 40, value=40, step=1, |
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label="Top K", interactive=True) |
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do_sample = gr.Checkbox( |
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value=True, |
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label="Do Sample", |
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info="use random sample strategy", |
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interactive=True) |
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repetition_penalty = gr.Slider( |
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1.0, |
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3.0, |
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value=1.1, |
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step=0.1, |
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label="Repetition Penalty", |
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interactive=True) |
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params = [user_input, chatbot] |
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predict_params = [ |
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chatbot, |
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max_new_token, |
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top_p, |
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temperature, |
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top_k, |
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do_sample, |
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repetition_penalty] |
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submitBtn.click( |
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user, |
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params, |
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params, |
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queue=False).then( |
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predict, |
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predict_params, |
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chatbot).then( |
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lambda: gr.update( |
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interactive=True), |
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None, |
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[user_input], |
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queue=False) |
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user_input.submit( |
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user, |
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params, |
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params, |
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queue=False).then( |
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predict, |
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predict_params, |
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chatbot).then( |
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lambda: gr.update( |
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interactive=True), |
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None, |
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[user_input], |
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queue=False) |
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submitBtn.click(reset_user_input, [], [user_input]) |
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emptyBtn.click(reset_state, outputs=[chatbot], show_progress=True) |
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demo.queue().launch() |
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""" |
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demo.queue().launch( |
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share=share, |
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inbrowser=True, |
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server_name='0.0.0.0', |
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server_port=port) |
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""" |
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""" |
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demo.queue().launch( |
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root_path="/etc/nginx/sites-available/radllama2_gradio_app" |
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) |
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""" |
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