BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms

TL;DR

BanditSpec introduces a training-free, adaptive hyperparameter tuning method for speculative decoding in large language models, utilizing bandit algorithms to optimize performance dynamically during inference.

Contribution

It formulates hyperparameter selection as a multi-armed bandit problem and develops novel algorithms with theoretical regret bounds, enhancing decoding efficiency without additional training.

Findings

Effective hyperparameter adaptation with bandit algorithms

Theoretical regret bounds established for proposed methods

Empirical results show near-oracle performance in LLM inference

Abstract

Speculative decoding has emerged as a popular method to accelerate the inference of Large Language Models (LLMs) while retaining their superior text generation performance. Previous methods either adopt a fixed speculative decoding configuration regardless of the prefix tokens, or train draft models in an offline or online manner to align them with the context. This paper proposes a training-free online learning framework to adaptively choose the configuration of the hyperparameters for speculative decoding as text is being generated. We first formulate this hyperparameter selection problem as a Multi-Armed Bandit problem and provide a general speculative decoding framework BanditSpec. Furthermore, two bandit-based hyperparameter selection algorithms, UCBSpec and EXP3Spec, are designed and analyzed in terms of a novel quantity, the stopping time regret. We upper bound this regret under both stochastic and adversarial reward settings. By deriving an information-theoretic impossibility result, it is shown that the regret performance of UCBSpec is optimal up to universal constants. Finally, extensive empirical experiments with LLaMA3 and Qwen2 demonstrate that our algorithms are effective compared to existing methods, and the throughput is close to the oracle best hyperparameter in simulated real-life LLM serving scenarios with diverse input prompts.