app.py

import subprocess
import os
import torch

import gradio as gr
import os
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
from transformers.utils.import_utils import _is_package_available

# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)


DESCRIPTION = """
# [MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention](https://aka.ms/MInference) (NeurIPS'24 Spotlight)

_Huiqiang Jiang†, Yucheng Li†, Chengruidong Zhang†, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhua Han, Amir H. Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu_

<h3 style="text-align: center;"><a href="https://github.com/microsoft/MInference" target="blank"> [Code]</a> 
<a href="https://aka.ms/MInference" target="blank"> [Project Page]</a>
<a href="https://arxiv.org/abs/2407.02490" target="blank"> [Paper]</a></h3>

## News
- 🧤 [24/09/26] MInference has been accepted as **spotlight** at **NeurIPS'24**. See you in Vancouver!
- 👘 [24/09/16] We are pleased to announce the release of our KV cache offloading work, [RetrievalAttention](https://aka.ms/RetrievalAttention), which accelerates long-context LLM inference via vector retrieval.
- 🥤 [24/07/24] MInference support [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) now.
- 🪗 [24/07/07] Thanks @AK for sponsoring. You can now use MInference online in the [HF Demo](https://huggingface.co/spaces/microsoft/MInference) with ZeroGPU.
- 📃 [24/07/03] Due to an issue with arXiv, the PDF is currently unavailable there. You can find the paper at this [link](https://export.arxiv.org/pdf/2407.02490).
- 🧩 [24/07/03] We will present **MInference 1.0** at the _**Microsoft Booth**_ and _**ES-FoMo**_ at ICML'24. See you in Vienna!

<font color="brown"><b>This is only a deployment demo. You can follow the code below to try MInference locally.</b></font>

```bash
git clone https://huggingface.co/spaces/microsoft/MInference
cd MInference
pip install -r requirments.txt
pip install flash_attn pycuda==2023.1
python app.py
```
"""

LICENSE = """
<div style="text-align: center;">
 <p>© 2024 Microsoft</p>
</div>
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
 <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">LLaMA-3-8B-Gradient-1M w/ MInference</h1>
 <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
</div>
"""


css = """
h1 {
 text-align: center;
 display: block;
}
"""

# Load the tokenizer and model
model_name = "gradientai/Llama-3-8B-Instruct-Gradient-1048k" if torch.cuda.is_available() else "Qwen/Qwen2-0.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
 model_name, torch_dtype="auto", device_map="auto"
) # to("cuda:0")

if torch.cuda.is_available() and _is_package_available("pycuda"):
 from minference import MInference

 minference_patch = MInference("minference", model_name)
 model = minference_patch(model)

terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]


@spaces.GPU(duration=120)
def chat_llama3_8b(
 message: str, history: list, temperature: float, max_new_tokens: int
) -> str:
 """
 Generate a streaming response using the llama3-8b model.
 Args:
 message (str): The input message.
 history (list): The conversation history used by ChatInterface.
 temperature (float): The temperature for generating the response.
 max_new_tokens (int): The maximum number of new tokens to generate.
 Returns:
 str: The generated response.
 """
 # global model
 conversation = []
 for user, assistant in history:
 conversation.extend(
 [
 {"role": "user", "content": user},
 {"role": "assistant", "content": assistant},
 ]
 )
 conversation.append({"role": "user", "content": message})

 input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(
 model.device
 )

 streamer = TextIteratorStreamer(
 tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
 )

 generate_kwargs = dict(
 input_ids=input_ids,
 streamer=streamer,
 max_new_tokens=max_new_tokens,
 do_sample=True,
 temperature=temperature,
 eos_token_id=terminators,
 )
 # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
 if temperature == 0:
 generate_kwargs["do_sample"] = False

 t = Thread(target=model.generate, kwargs=generate_kwargs)
 t.start()

 outputs = []
 for text in streamer:
 outputs.append(text)
 # print(outputs)
 yield "".join(outputs)


# Gradio block
chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label="Gradio ChatInterface")

with gr.Blocks(fill_height=True, css=css) as demo:

 gr.Markdown(DESCRIPTION)
 gr.ChatInterface(
 fn=chat_llama3_8b,
 chatbot=chatbot,
 fill_height=True,
 additional_inputs_accordion=gr.Accordion(
 label="⚙️ Parameters", open=False, render=False
 ),
 additional_inputs=[
 gr.Slider(
 minimum=0,
 maximum=1,
 step=0.1,
 value=0.95,
 label="Temperature",
 render=False,
 ),
 gr.Slider(
 minimum=128,
 maximum=4096,
 step=1,
 value=512,
 label="Max new tokens",
 render=False,
 ),
 ],
 examples=[
 ["How to setup a human base on Mars? Give short answer."],
 ["Explain theory of relativity to me like I’m 8 years old."],
 ["What is 9,000 * 9,000?"],
 ["Write a pun-filled happy birthday message to my friend Alex."],
 ["Justify why a penguin might make a good king of the jungle."],
 ],
 cache_examples=False,
 )

 gr.Markdown(LICENSE)

if __name__ == "__main__":
 demo.launch(share=False)