gradio_app file

gradio_demo.py

@@ -0,0 +1,474 @@
+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(
+    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"
+)