Paper | Documentation | Slides | Poster
This repo is current 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:
- Automatically parallelize and batch LLM requests in complex LLM applications. Asynchronous communication between dependent requests.
- Performance objective deduction and DAG-aware scheduling.
- Sharing common prompt prefix between requests with optimized attention kernel, Context-aware scheduling.
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.
- [2024/08] A Post about Parrot is published in the WeChat account of MSRA (Microsoft Research Asia)!
- [2024/07] Parrot is published and presented in OSDI'24. Please find the paper and presentation Here if you are interested!
- [2024/06] Blogs about the technical details behind Parrot: Blog (Chinese) or Blog Zhihu (Chinese).
- [2024/04] Release Parrot as a Microsoft open source project.
- [2024/03] Parrot has been accepted to OSDI'24!
See INSTALL for installation instructions.
- Get Started: In this chapter, you will learn how to install Parrot and run your first application using Parrot.
- Documentation for Users: User documentation of Parrot. It contains the API specification of Parrot's OpenAI-like API, and the grammar of Parrot's frontend
pfunc. - Parrot System Design: If you want to hack/modify Parrot, it's what you need. This chapter offers an overview of Parrot's system architecture and provides detailed explanation of Parrot's internal code organization and implementation.
- Version Drafts: Learn about the refactor history and some of our brainstorm ideas when developing Parrot from these drafts.
We learned a lot from the following projects when developing Parrot.
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
}
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