Chimera: A Lossless Decoding Method for Accelerating Large Language Models Inference by Fusing all Tokens
This addresses the inference efficiency problem for users of large language models, representing an incremental improvement over existing speculative sampling methods.
The paper tackles the resource-intensive decoding process of large language models by proposing Chimera, a speculative sampling framework that uses a lightweight draft model to predict subsequent tokens, achieving an average latency speedup ratio of 2.7x compared to vanilla auto-regressive decoding.
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their widespread application is hindered by the resource-intensive decoding process. To address this challenge, current approaches have incorporated additional decoding heads to enable parallel prediction of multiple subsequent tokens, thereby achieving inference acceleration. Nevertheless, the accuracy of these decoding heads falls short of the auto-regressive decoding approach. In light of these limitations, we propose Chimera, a novel framework specifically designed for speculative sampling. Within this framework, we introduce a lightweight draft model that effectively utilizes previously generated tokens to predict subsequent words. To ensure both accuracy and efficiency, we present two strategies within the lightweight draft model. Firstly, we focus on capturing short-range dependencies at the bottom layer. Secondly, we leverage the readily available representations from the original LLM.Through empirical evaluation on the Vicuna and LlaMA-2 series, Chimera demonstrates impressive results, achieving an average latency speedup ratio of 2.7x compared to the vanilla auto-regressive decoding approach. This highlights the potential of our proposed framework in significantly improving the efficiency of large language models during the decoding process.