demo.py

"""A simple web interactive chat demo based on gradio."""

from argparse import ArgumentParser
from threading import Thread

import gradio as gr
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer,
)


class StopOnTokens(StoppingCriteria):
    def __call__(
        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
    ) -> bool:
        stop_ids = (
            [2, 6, 7, 8],
        )  # "<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|im_sep|>"
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

class StoppingCriteriaSub(StoppingCriteria):
    def __init__(self, stops = [], encounters=1):
        super().__init__()
        self.stops = [stop.to("cuda") for stop in stops]

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
        last_token = input_ids[0][-1]
        for stop in self.stops:
            if tokenizer.decode(stop) == tokenizer.decode(last_token):
                return True
        return False


def parse_text(text):
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split("`")
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f"<br></code></pre>"
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>" + line
    text = "".join(lines)
    return text


def predict(history, max_length, top_p, temperature):
    stop = StopOnTokens()
    # messages = [{"role": "system", "content": "You are a helpful assistant"}]
    messages = [{"role": "system", "content": ""}]
    # messages = []   
    for idx, (user_msg, model_msg) in enumerate(history):
        if idx == len(history) - 1 and not model_msg:
            messages.append({"role": "user", "content": user_msg})
            break
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if model_msg:
            messages.append({"role": "assistant", "content": model_msg})

    print("\n\n====conversation====\n", messages)
    model_inputs = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, tokenize=True, return_tensors="pt"
    ).to(next(model.parameters()).device)
    streamer = TextIteratorStreamer(
        tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True
    )

    # stop_words = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|im_sep|>"]
    stop_words = ["</s>"]
    stop_words_ids = [tokenizer(stop_word, return_tensors='pt', add_special_tokens=False)['input_ids'].squeeze() for stop_word in stop_words]
    stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])

    generate_kwargs = {
        "input_ids": model_inputs,
        "streamer": streamer,
        "max_new_tokens": max_length,
        "do_sample": True,
        "top_p": top_p,
        "temperature": temperature,
        "stopping_criteria": stopping_criteria,
        "repetition_penalty": 1.1,
    }
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    for new_token in streamer:
        if new_token != "":
            history[-1][1] += new_token
            yield history


def main(args):
    with gr.Blocks() as demo:
#         gr.Markdown(
#             """\
# <p align="center"><img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" style="height: 80px"/><p>"""
#         )
        # gr.Markdown("""<center><font size=8>Yi-Chat Bot</center>""")
        gr.Markdown("""<center><font size=8>🦣MAmmoTH2</center>""")
#         gr.Markdown(
#             """\
# <center><font size=3>This WebUI is based on Yi-Chat, developed by 01-AI.</center>"""
#         )
        gr.Markdown(
            """\
<center><font size=4>
MAmmoTH2-8x7B-Plus <a style="text-decoration: none" href="https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus/">🤗</a> """
# <a style="text-decoration: none" href="https://www.modelscope.cn/models/01ai/Yi-34B-Chat/summary">🤖</a>&nbsp  
# &nbsp<a style="text-decoration: none" href="https://github.com/01-ai/Yi">Yi GitHub</a></center>

        )

        chatbot = gr.Chatbot()

        with gr.Row():
            with gr.Column(scale=4):
                with gr.Column(scale=12):
                    user_input = gr.Textbox(
                        show_label=False,
                        placeholder="Input...",
                        lines=10,
                        container=False,
                    )
                with gr.Column(min_width=32, scale=1):
                    submitBtn = gr.Button("🚀 Submit")
            with gr.Column(scale=1):
                emptyBtn = gr.Button("🧹 Clear History")
                max_length = gr.Slider(
                    0,
                    32768,
                    value=4096,
                    step=1.0,
                    label="Maximum length",
                    interactive=True,
                )
                top_p = gr.Slider(
                    0, 1, value=1.0, step=0.01, label="Top P", interactive=True
                )
                temperature = gr.Slider(
                    0.01, 1, value=0.7, step=0.01, label="Temperature", interactive=True
                )

        def user(query, history):
            # return "", history + [[parse_text(query), ""]]
            return "", history + [[query, ""]]

        submitBtn.click(
            user, [user_input, chatbot], [user_input, chatbot], queue=False
        ).then(predict, [chatbot, max_length, top_p, temperature], chatbot)
        user_input.submit(
            user, [user_input, chatbot], [user_input, chatbot], queue=False
        ).then(predict, [chatbot, max_length, top_p, temperature], chatbot)
        emptyBtn.click(lambda: None, None, chatbot, queue=False)

    demo.queue()

    demo.launch(
        server_name=args.server_name,
        server_port=args.server_port,
        inbrowser=args.inbrowser,
        share=args.share
    )


if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument(
        "-c",
        "--checkpoint-path",
        type=str,
        default="TIGER-Lab/MAmmoTH2-8B-Plus",
        help="Checkpoint name or path, default to %(default)r",
    )
    parser.add_argument(
        "--cpu-only", action="store_true", help="Run demo with CPU only"
    )
    parser.add_argument(
        "--share",
        action="store_true",
        default=False,
        help="Create a publicly shareable link for the interface.",
    )
    parser.add_argument(
        "--inbrowser",
        action="store_true",
        default=True,
        help="Automatically launch the interface in a new tab on the default browser.",
    )
    parser.add_argument(
        "--server-port", type=int, default=8110, help="Demo server port."
    )
    parser.add_argument(
        "--server-name", type=str, default="127.0.0.1", help="Demo server name."
    )

    args = parser.parse_args()

    tokenizer = AutoTokenizer.from_pretrained(
        args.checkpoint_path, trust_remote_code=True
    )

    if args.cpu_only:
        device_map = "cpu"
    else:
        device_map = "auto"

    model = AutoModelForCausalLM.from_pretrained(
        args.checkpoint_path,
        device_map=device_map,
        torch_dtype="auto",
        trust_remote_code=True,
    ).eval()

    main(args)