Improving Goodput-Optimized LLM Inference with DistServe’s Prefill Disaggregation and Decoding

DistServe is a novel approach to optimizing the goodput of large language model (LLM) inference by disaggregating the prefill and decoding processes. In this tutorial, we will discuss how DistServe works and how to implement it in your own system.

The key idea behind DistServe is to separate the prefill process, which generates candidate completions for a given prompt, from the decoding process, which selects the best completion based on a scoring function. By disaggregating these two processes, DistServe aims to improve the efficiency of LLM inference by optimizing the goodput, which is the rate at which useful work is performed.

To implement DistServe, you will need to set up a server-client architecture where the server is responsible for prefilling candidate completions and the client is responsible for decoding the best completion. Here is a step-by-step guide to implementing DistServe:

1. Server Setup:
– Choose a powerful machine or cluster to serve as the server for prefilling candidate completions.
– Install the appropriate version of your chosen LLM model (e.g., GPT-3, BERT) on the server.
– Implement a prefilling algorithm that generates candidate completions for a given prompt. This algorithm can be optimized for speed and scalability to handle a high volume of incoming requests.

2. Client Setup:
– Choose one or more client machines to serve as the decoding units for selecting the best completion from the prefilling candidates.
– Install the same version of the LLM model on the client machines as on the server.
– Implement a decoding algorithm that selects the best completion based on a scoring function. This algorithm should be optimized for efficiency to make quick decisions on which completion to choose.

3. Communication Protocol:
– Set up a communication protocol between the server and clients to exchange data. This protocol should include sending prompts from clients to the server, receiving prefilling candidates from the server, and sending the final completion back to the server.

4. Load Balancing:
– Implement a load balancing strategy to distribute incoming requests across multiple server instances for parallel prefilling. This can help optimize the overall throughput of the system by making efficient use of the available computational resources.

5. Monitoring and Optimization:
– Set up monitoring tools to track the performance of the system, including latency, throughput, and resource utilization. Use this data to identify bottlenecks and optimize the system for better performance.
– Experiment with different prefilling and decoding algorithms to find the optimal configuration for maximizing the goodput of the system.

By following these steps, you can implement DistServe in your own system to optimize the goodput of LLM inference. This approach can help improve the efficiency and scalability of large language model deployments, enabling faster and more reliable completion of prompts in diverse applications.