app.py

import spaces
import gradio as gr

import time
from threading import Thread

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer


model_id = "Qwen/Qwen2.5-32B-Instruct"
assistant_id = "Qwen/Qwen2.5-0.5B-Instruct"

model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")
assistant_model = AutoModelForCausalLM.from_pretrained(assistant_id).to(device=model.device, dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(model_id)


@spaces.GPU
def run_generation(user_text, use_assistant, temperature, max_new_tokens):
    if temperature < 0.1:
        do_sample = False
    else:
        do_sample = True

    # Get the model and tokenizer, and tokenize the user text.
    model_inputs = tokenizer([user_text], return_tensors="pt").to(model.device)

    # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
    # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        assistant_model=assistant_model if use_assistant else None,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=do_sample,
        top_p=0.95,
        temperature=float(temperature),
        top_k=50,
        eos_token_id=-1,  # ensures `max_new_tokens` new tokens are always generated, can't reach EOS
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    start = time.time()
    t.start()

    # Pull the generated text from the streamer, and update the model output. Return the model output and time
    # spent so far.
    model_output = ""
    for new_text in streamer:
        model_output += new_text
        time_so_far = time.time() - start
        tokens_so_far = tokenizer(model_output, return_tensors="pt").input_ids.shape[1]
        yield [model_output, round(tokens_so_far/time_so_far, 2)]


def reset_textbox():
    return gr.update(value='')


with gr.Blocks() as demo:
    gr.Markdown(
        "# 🤗 Assisted Generation Demo\n"
        f"- Model: {model_id} (4-bit quant, ~16GB)\n"
        f"- Assistant Model: {assistant_id} (FP16, ~1GB)\n"
        "- Recipe for speedup: a) >10x model size difference in parameters; b) assistant trained similarly; c) CPU is not a bottleneck"
    )

    with gr.Row():
        with gr.Column(scale=4):
            user_text = gr.Textbox(
                value="A sequence: one, two, three, ",
                label="Prompt"
            )
            model_output = gr.Textbox(label="Model output", lines=10, interactive=False)
            button_submit = gr.Button(value="Submit")

        with gr.Column(scale=1, min_width=200):
            gr.Markdown("### Generation Settings")
            use_assistant = gr.Checkbox(label="Use Assisted Generation", value=True)
            max_new_tokens = gr.Slider(
                minimum=1, maximum=500, value=100, step=1, interactive=True, label="Max New Tokens",
            )
            temperature = gr.Slider(
                minimum=0.0, maximum=2.0, value=0.6, step=0.05, interactive=True, label="Temperature (0.0 = Greedy)",
            )
            gr.Markdown("### Tokens per second")
            tokens_per_second = gr.Textbox(lines=1, interactive=False, show_label=False)

    generate_inputs = [user_text, use_assistant, temperature, max_new_tokens]
    generate_outputs = [model_output, tokens_per_second]
    user_text.submit(run_generation, generate_inputs, generate_outputs)
    button_submit.click(run_generation, generate_inputs, generate_outputs)

    demo.queue(max_size=16).launch()