Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments

TL;DR

This work addresses the challenge of selecting optimal acceleration methods in decentralized systems by introducing a meta-learning-based framework that automates the selection process by learning from historical performance data of various acceleration techniques across different tasks and consistently outperforms conventional methods in terms of efficiency and performance.

Abstract

The deployment of large-scale models, such as large language models (LLMs) and sophisticated image generation systems, incurs substantial costs due to their computational demands. To mitigate these costs and address challenges related to scalability and data security, there is a growing shift towards decentralized systems for deploying such models. In these decentralized environments, efficient inference acceleration becomes crucial to manage computational resources effectively and enhance system responsiveness. In this work, we address the challenge of selecting optimal acceleration methods in decentralized systems by introducing a meta-learning-based framework. This framework automates the selection process by learning from historical performance data of various acceleration techniques across different tasks. Unlike traditional methods that rely on random selection or expert intuition, our approach systematically identifies the best acceleration strategies based on the specific characteristics of each task. We demonstrate that our meta-learning framework not only streamlines the decision-making process but also consistently outperforms conventional methods in terms of efficiency and performance. Our results highlight the potential of meta-learning to revolutionize inference acceleration in decentralized AI systems, offering a path towards more democratic and economically feasible artificial intelligence solutions.

Figures (3)

• Figure 1: BSNS framework overview: a. Interaction flow within the Nesa chain highlights the sequence from dApp interaction to final output. It begins with a query through a dApp and wallet, integrates with the dApp SDK, and involves a DHT lookup across the Nesa chain for blockchain transactions. b. Shows BSNS framework's role in distributed model inference, where different consumers, each managing a model shard, process an inference request in parallel. Blocks are distributed among swarm nodes, with the activations sequentially processed and managed through gRPC communication. c. For an LLM text generation query, the agent reads the completed generate response from the queue and performs a DHT lookup to validate the transaction against the Nesa chain, and sequentially delivers the response back to the dApp. d. Shows the message queuing system within Nesa's architecture, where an agent cluster publishes requests to a broker cluster. The broker prioritizes and routes these requests to appropriate consumer groups based on resource allocation, reputation scores and model requirements.
• Figure 2: Motivating example: Performance comparison of fast inference methods on Llama 3.1 8B.
• Figure 3: MetaInf overview (§ \ref{['subsec:overview']}); offline meta-training phase is shown on the top (§ \ref{['subsec:meta-train']})--the key is to train a meta performance predictor $f$ (denoted in ) to map language embeddings of the datasets and models to their performance $\mathbf{P}$; the online model selection (§ \ref{['subsec:model-selection']}) is shown at the bottom by transferring the meta-predictor $f$ to predict the test data paired with acceleration methods and hardware settings for selection.