GitHub - microsoft/ParrotServe: [OSDI'24] Serving LLM-based Applications Efficiently with Semantic Variable (original) (raw)

🦜 Parrot: Efficient Serving of LLM-based Application with Semantic Variable

Paper | Documentation | Slides | Poster

This repo is currently a research prototype and is not actively maintained. Please open issue or contact the authors when you need help.

Parrot is a distributed, multi-tenant serving system for LLM-based Applications. With the Semantic Variable abstraction, Parrot can easily grasp the app-level information like LLM computation graph (DAG) or the prompt structure, which enables many interesting features like:

We also provide a Python friendly frontend for users to program:

from parrot import P # 'P' is the alias of pfunc lang.

@P.semantic_function() def tell_me_a_joke(topic: P.Input, joke: P.Output): """Tell the me a joke about {{topic}}: {{joke}}."""

@P.native_function() def format_joke(joke: P.Input, formatted_joke: P.Output): ret = "Here is the joke for you\n---\n" + joke # Directly use string built-in methods formatted_joke.set(ret) # Use set to assign value to output

def main(): # Orchestrator function joke = tell_me_a_joke(topic="chicken") joke1 = format_joke(joke) joke_str = joke1.get() print(joke_str)

What's LLM Applications?

The powerful language understanding capability of large language models (LLMs) has enabled a new application paradigm, where one or multiple application entities, known as AI agents or co-pilots, communicate with LLMs via natural language, known as “prompts”, to accomplish a task collaboratively.

Parrot is designed to serve these LLM-based applications efficiently by adding Semantic Variable in current OpenAI-style API, exposing richer application-level knowledge to backend systems and engines for better optimization.

For more background knowledge and our motivation, please refer our OSDI'24 paper Parrot: Efficient Serving of LLM-based Applications with Semantic Variable.

🚀 News

Install

See INSTALL for installation instructions.

Documentations

Acknowledgement

We learned a lot from the following projects when developing Parrot.

Reference

If you find Parrot useful or relevant to your research, please cite our paper as below:

@inproceedings{lin2024parrot,
    author = {Chaofan Lin and Zhenhua Han and Chengruidong Zhang and Yuqing Yang and Fan Yang and Chen Chen and Lili Qiu},
    title = {Parrot: Efficient Serving of LLM-based Applications with Semantic Variable},
    booktitle = {18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)},
    year = {2024},
    address = {Santa Clara, CA},
    publisher = {USENIX Association},
    url = {https://www.usenix.org/conference/osdi24/presentation/lin-chaofan},
    month = jul
}

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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