gradio_demo.py

import torch
from transformers import (
    LlamaForCausalLM,
    LlamaTokenizer,
    StoppingCriteria,
)
import gradio as gr
import argparse
import os
from queue import Queue
from threading import Thread
import traceback
import gc


import torch
from auto_gptq import AutoGPTQForCausalLM
from langchain import HuggingFacePipeline, PromptTemplate
from langchain.chains import RetrievalQA
from langchain.document_loaders import PyPDFDirectoryLoader, DirectoryLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from pdf2image import convert_from_path
from transformers import AutoTokenizer, TextStreamer, pipeline

DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"

# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument(
    '--base_model',
    default=None,
    type=str,
    help='Base model path')
parser.add_argument('--lora_model', default=None, type=str,
                    help="If None, perform inference on the base model")
parser.add_argument(
    '--tokenizer_path',
    default=None,
    type=str,
    help='If None, lora model path or base model path will be used')
parser.add_argument(
    '--gpus',
    default="0",
    type=str,
    help='If None, cuda:0 will be used. Inference using multi-cards: --gpus=0,1,... ')
parser.add_argument('--share', default=True, help='Share gradio domain name')
parser.add_argument('--port', default=19324, type=int, help='Port of gradio demo')
parser.add_argument(
    '--max_memory',
    default=256,
    type=int,
    help='Maximum input prompt length, if exceeded model will receive prompt[-max_memory:]')
parser.add_argument(
    '--load_in_8bit',
    action='store_true',
    help='Use 8 bit quantified model')
parser.add_argument(
    '--only_cpu',
    action='store_true',
    help='Only use CPU for inference')
parser.add_argument(
    '--alpha',
    type=str,
    default="1.0",
    help="The scaling factor of NTK method, can be a float or 'auto'. ")
args = parser.parse_args()
if args.only_cpu is True:
    args.gpus = ""

#from patches import apply_attention_patch, apply_ntk_scaling_patch
#apply_attention_patch(use_memory_efficient_attention=True)
#apply_ntk_scaling_patch(args.alpha)

# Set CUDA devices if available
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus


# Peft library can only import after setting CUDA devices
from peft import PeftModel


# Set up the required components: model and tokenizer

def setup():
    global tokenizer, model, device, share, port, max_memory, vector_store
    max_memory = args.max_memory
    port = args.port
    share = args.share
    load_in_8bit = args.load_in_8bit
    load_type = torch.float16
    if torch.cuda.is_available():
        device = torch.device(0)
    else:
        device = torch.device('cpu')
    """
    if args.tokenizer_path is None:
    args.tokenizer_path = args.lora_model
    if args.lora_model is None:
        args.tokenizer_path = args.base_model
    """

            
    #先读取embedding模型
    embeddings = HuggingFaceInstructEmbeddings(
        model_name="BAAI/bge-large-en-v1.5", model_kwargs={"device": DEVICE}
        )
    #如果之前没有本地的faiss仓库，就把doc读取到向量库后，再把向量库保存到本地
    if os.path.exists("/home/ywang/db")==False:
        #=======加载知识库=======
        loader = DirectoryLoader("kb")
        docs = loader.load()
        # splitting pdf into chunks with size of 1024
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
        texts = text_splitter.split_documents(docs)
        vector_store = Chroma.from_documents(texts, embeddings, persist_directory="db")        
    #如果本地已经有faiss仓库了，说明之前已经保存过了，就直接读取
    else:
        vector_store=Chroma(persist_directory="db", embedding_function=embeddings)
    
    model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ"
    model_basename = "model"
    
    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

    base_model = AutoGPTQForCausalLM.from_quantized(
                                                    model_name_or_path,
                                                    revision="gptq-4bit-128g-actorder_True",
                                                    model_basename=model_basename,
                                                    use_safetensors=True,
                                                    trust_remote_code=True,
                                                    inject_fused_attention=False,
                                                    device=DEVICE,
                                                    quantize_config=None,
                                                )

    model_vocab_size = base_model.get_input_embeddings().weight.size(0)
    tokenzier_vocab_size = len(tokenizer)
    print(f"Vocab of the base model: {model_vocab_size}")
    print(f"Vocab of the tokenizer: {tokenzier_vocab_size}")
    if model_vocab_size != tokenzier_vocab_size:
        assert tokenzier_vocab_size > model_vocab_size
        print("Resize model embeddings to fit tokenizer")
        base_model.resize_token_embeddings(tokenzier_vocab_size)
    if args.lora_model is not None:
        print("loading peft model")
        model = PeftModel.from_pretrained(
            base_model,
            args.lora_model,
            torch_dtype=load_type,
            device_map='auto',
        )
    else:
        model = base_model

    if device == torch.device('cpu'):
        model.float()

    model.eval()

    DEFAULT_SYSTEM_PROMPT = """
    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.

    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.
    """.strip()


    def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str:
        return f"""
                [INST] <<SYS>>
                {system_prompt}
                <</SYS>>
                {prompt} [/INST]
                """.strip()
    
    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    text_pipeline = pipeline(
                                "text-generation",
                                model=model,
                                tokenizer=tokenizer,
                                max_new_tokens=2048,
                                temperature=0,
                                top_p=0.95,
                                repetition_penalty=1.15,
                                streamer=streamer,
                            )
    llm = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 0})
    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."
    template = generate_prompt(
        """
    {context}

    Question: {question}
    """,
        system_prompt=SYSTEM_PROMPT,
    )
    prompt = PromptTemplate(template=template, input_variables=["context", "question"])
    qa_chain = RetrievalQA.from_chain_type(
                                                llm=llm,
                                                chain_type="stuff",
                                                retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
                                                return_source_documents=True,
                                                chain_type_kwargs={"prompt": prompt},
                                            )

# Reset the user input
def reset_user_input():
    return gr.update(value='')


# Reset the state
def reset_state():
    return []


# Generate the prompt for the input of LM model
"""
def generate_prompt(instruction,my_input):
    return f"Instruction：{my_input}\n Response：{instruction}"
"""


# User interaction function for chat
def user(user_message, history):
    return gr.update(value="", interactive=False), history + \
        [[user_message, None]]


class Stream(StoppingCriteria):
    def __init__(self, callback_func=None):
        self.callback_func = callback_func

    def __call__(self, input_ids, scores) -> bool:
        if self.callback_func is not None:
            self.callback_func(input_ids[0])
        return False


class Iteratorize:
    """
    Transforms a function that takes a callback
    into a lazy iterator (generator).

    Adapted from: https://stackoverflow.com/a/9969000
    """
    def __init__(self, func, kwargs=None, callback=None):
        self.mfunc = func
        self.c_callback = callback
        self.q = Queue()
        self.sentinel = object()
        self.kwargs = kwargs or {}
        self.stop_now = False

        def _callback(val):
            if self.stop_now:
                raise ValueError
            self.q.put(val)

        def gentask():
            try:
                ret = self.mfunc(callback=_callback, **self.kwargs)
            except ValueError:
                pass
            except Exception:
                traceback.print_exc()

            clear_torch_cache()
            self.q.put(self.sentinel)
            if self.c_callback:
                self.c_callback(ret)

        self.thread = Thread(target=gentask)
        self.thread.start()

    def __iter__(self):
        return self

    def __next__(self):
        obj = self.q.get(True, None)
        if obj is self.sentinel:
            raise StopIteration
        else:
            return obj

    def __del__(self):
        clear_torch_cache()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.stop_now = True
        clear_torch_cache()


def clear_torch_cache():
    gc.collect()
    if torch.cuda.device_count() > 0:
        torch.cuda.empty_cache()


# Perform prediction based on the user input and history
@torch.no_grad()
def predict(
    history,
    max_new_tokens=128,
    top_p=0.75,
    temperature=0.1,
    top_k=40,
    do_sample=True,
    repetition_penalty=1.0
):
    history[-1][1] = ""
    
    history[-1][1] = qa_chain(history[-1][0])['result']
    """
    #history的格式：[[query1,response1],[query2,response2],[query3,response3]……]
    docs=vector_store.similarity_search(history[-1][0])
    context=[doc.page_content for doc in docs]
    #使用下面的方式，把多轮对话转为单轮对话
    input = f"### Instruction:{history[-1][0]} ### Response:{history[-1][1]}"
    prompt = generate_prompt(input,"".join(context))
    inputs = tokenizer(qa_chain, return_tensors="pt")
    input_ids = inputs["input_ids"].to(device)

    generate_params = {
        'input_ids': input_ids,
        'max_new_tokens': max_new_tokens,
        'top_p': top_p,
        'temperature': temperature,
        'top_k': top_k,
        'do_sample': do_sample,
        'repetition_penalty': repetition_penalty,
    }

    def generate_with_callback(callback=None, **kwargs):
        if 'stopping_criteria' in kwargs:
            kwargs['stopping_criteria'].append(Stream(callback_func=callback))
        else:
            kwargs['stopping_criteria'] = [Stream(callback_func=callback)]
        clear_torch_cache()
        with torch.no_grad():
            model.generate(**kwargs)

    def generate_with_streaming(**kwargs):
        return Iteratorize(generate_with_callback, kwargs, callback=None)

    with generate_with_streaming(**generate_params) as generator:
        for output in generator:
            next_token_ids = output[len(input_ids[0]):]
            if next_token_ids[0] == tokenizer.eos_token_id:
                break
            new_tokens = tokenizer.decode(
                next_token_ids, skip_special_tokens=True)
            if isinstance(tokenizer, LlamaTokenizer) and len(next_token_ids) > 0:
                if tokenizer.convert_ids_to_tokens(int(next_token_ids[0])).startswith('▁'):
                    new_tokens = ' ' + new_tokens

            history[-1][1] = new_tokens
            yield history
            if len(next_token_ids) >= max_new_tokens:
                break
    """
    yield history

# Call the setup function to initialize the components
setup()


# Create the Gradio interface
with gr.Blocks() as demo:
    github_banner_path = 'https://radformation.com/images/radformation-logo-white.svg'
    #gr.HTML(f'<p align="center"><a href="https://radformation.com/"><img src={github_banner_path} width="700"/></a></p>')
    gr.Markdown("> Radformation Q&A bot")
    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(
                    show_label=False,
                    placeholder="Shift + Enter, to send message...",
                    lines=10).style(
                    container=False)
            with gr.Column(min_width=32, scale=1):
                submitBtn = gr.Button("Submit", variant="primary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_new_token = gr.Slider(
                0,
                4096,
                value=512,
                step=1.0,
                label="Maximum New Token Length",
                interactive=True)
            top_p = gr.Slider(0, 1, value=0.9, step=0.01,
                              label="Top P", interactive=True)
            temperature = gr.Slider(
                0,
                1,
                value=0.5,
                step=0.01,
                label="Temperature",
                interactive=True)
            top_k = gr.Slider(1, 40, value=40, step=1,
                              label="Top K", interactive=True)
            do_sample = gr.Checkbox(
                value=True,
                label="Do Sample",
                info="use random sample strategy",
                interactive=True)
            repetition_penalty = gr.Slider(
                1.0,
                3.0,
                value=1.1,
                step=0.1,
                label="Repetition Penalty",
                interactive=True)

    params = [user_input, chatbot]
    predict_params = [
        chatbot,
        max_new_token,
        top_p,
        temperature,
        top_k,
        do_sample,
        repetition_penalty]

    submitBtn.click(
        user,
        params,
        params,
        queue=False).then(
        predict,
        predict_params,
        chatbot).then(
            lambda: gr.update(
                interactive=True),
        None,
        [user_input],
        queue=False)

    user_input.submit(
        user,
        params,
        params,
        queue=False).then(
        predict,
        predict_params,
        chatbot).then(
            lambda: gr.update(
                interactive=True),
        None,
        [user_input],
        queue=False)

    submitBtn.click(reset_user_input, [], [user_input])

    emptyBtn.click(reset_state, outputs=[chatbot], show_progress=True)


# Launch the Gradio interface
demo.queue().launch()
"""
demo.queue().launch(
    share=share,
    inbrowser=True,
    server_name='0.0.0.0',
    server_port=port)
"""
"""
demo.queue().launch(    
    root_path="/etc/nginx/sites-available/radllama2_gradio_app"
)

"""