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 json
import requests
from typing import Iterable, List
import subprocess


# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument(
    '--base_model',
    default=None,
    type=str,
    required=True,
    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'. ")
parser.add_argument(
    "--use_vllm",
    action='store_true',
    help="Use vLLM as back-end LLM service.")
parser.add_argument(
    "--post_host",
    type=str,
    default="localhost",
    help="Host of vLLM service.")
parser.add_argument(
    "--post_port",
    type=int,
    default=8000,
    help="Port of vLLM service.")
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
    if args.use_vllm:
        # global share, port, max_memory
        max_memory = args.max_memory
        port = args.port
        share = args.share

        if args.lora_model is not None:
            raise ValueError("vLLM currently does not support LoRA, please merge the LoRA weights to the base model.")
        if args.load_in_8bit:
            raise ValueError("vLLM currently does not support quantization, please use fp16 (default) or unuse --use_vllm.")
        if args.only_cpu:
            raise ValueError("vLLM requires GPUs with compute capability not less than 7.0. If you want to run only on CPU, please unuse --use_vllm.")

        if args.tokenizer_path is None:
            args.tokenizer_path = args.base_model

        print("Start launch vllm server.")
        cmd = [
            f"python -m vllm.entrypoints.api_server",
            f"--model={args.base_model}",
            f"--tokenizer={args.tokenizer_path}",
            "--tokenizer-mode=slow",
            f"--tensor-parallel-size={len(args.gpus.split(','))}",
            "&",
        ]
        subprocess.call(cmd)
    else:
        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
        tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path)

        base_model = LlamaForCausalLM.from_pretrained(
            args.base_model,
            load_in_8bit=load_in_8bit,
            torch_dtype=load_type,
            low_cpu_mem_usage=True,
            device_map='auto',
        )

        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()


# 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):
    return f"""
Below is an instruction that describes a task. Write a response that appropriately completes the request.
{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()


def post_http_request(prompt: str,
                      api_url: str,
                      n: int = 1,
                      top_p: float = 0.9,
                      top_k: int = 40,
                      temperature: float = 0.7,
                      max_tokens: int = 512,
                      presence_penalty: float = 1.0,
                      use_beam_search: bool = False,
                      stream: bool = False) -> requests.Response:
    headers = {"User-Agent": "Test Client"}
    pload = {
        "prompt": prompt,
        "n": n,
        "top_p": 1 if use_beam_search else top_p,
        "top_k": -1 if use_beam_search else top_k,
        "temperature": 0 if use_beam_search else temperature,
        "max_tokens": max_tokens,
        "use_beam_search": use_beam_search,
        "best_of": 5 if use_beam_search else n,
        "presence_penalty": presence_penalty,
        "stream": stream,
    }
    print(pload)

    response = requests.post(api_url, headers=headers, json=pload, stream=True)
    return response


def get_streaming_response(response: requests.Response) -> Iterable[List[str]]:
    for chunk in response.iter_lines(chunk_size=8192,
                                     decode_unicode=False,
                                     delimiter=b"\0"):
        if chunk:
            data = json.loads(chunk.decode("utf-8"))
            output = data["text"]
            yield output


# 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,
    presence_penalty=0.0,
):
    history[-1][1] = ""
    if len(history) != 0:
        input = "".join(["### Instruction:\n" +
                         i[0] +
                         "\n\n" +
                         "### Response: " +
                         i[1] +
                         ("\n\n" if i[1] != "" else "") for i in history])
        if len(input) > max_memory:
            input = input[-max_memory:]
    prompt = generate_prompt(input)

    if args.use_vllm:
        generate_params = {
            'max_tokens': max_new_tokens,
            'top_p': top_p,
            'temperature': temperature,
            'top_k': top_k,
            "use_beam_search": not do_sample,
            'presence_penalty': presence_penalty,
        }

        api_url = f"http://{args.post_host}:{args.post_port}/generate"


        response = post_http_request(prompt, api_url, **generate_params, stream=True)

        for h in get_streaming_response(response):
            for i, line in enumerate(h):
                line = line.replace(prompt, '')
                history[-1][1] = line
                yield history

    else:
        inputs = tokenizer(prompt, 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


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


# Create the Gradio interface
with gr.Blocks() as demo:
    github_banner_path = 'https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca/main/pics/banner.png'
    gr.HTML(f'<p align="center"><a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca"><img src={github_banner_path} width="700"/></a></p>')
    gr.Markdown("> 为了促进大模型在中文NLP社区的开放研究，本项目开源了中文LLaMA模型和指令精调的Alpaca大模型。这些模型在原版LLaMA的基础上扩充了中文词表并使用了中文数据进行二次预训练，进一步提升了中文基础语义理解能力。同时，中文Alpaca模型进一步使用了中文指令数据进行精调，显著提升了模型对指令的理解和执行能力。")
    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发送消息...",
                    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,
                visible=False if args.use_vllm else True)
            presence_penalty = gr.Slider(
                -2.0,
                2.0,
                value=1.0,
                step=0.1,
                label="Presence Penalty",
                interactive=True,
                visible=True if args.use_vllm else False)

    params = [user_input, chatbot]
    predict_params = [
        chatbot,
        max_new_token,
        top_p,
        temperature,
        top_k,
        do_sample,
        repetition_penalty,
        presence_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=False,
    server_name='0.0.0.0',
    server_port=port)