As LLMs become increasingly complex and powerful, their inference process, i.e., generating text given a prompt, becomes computationally expensive and time-consuming. Many applications, such as real-time translation, dialogue systems, or interactive content generation, require quick responses. Additionally, slow inference consumes substantial computational resources, leading to higher operational costs.Â
Researchers from the Dalian University of Technology, China have addressed the challenge of high inference latency in Large Language Models (LLMs) caused by their autoregressive decoding nature, which requires tokens to be generated sequentially. Current methods like speculative decoding (an approach that involves a draft model predicting multiple future tokens for verification by the target LLM) have been introduced to mitigate this latency. Still, its full potential has yet to be fully explored. Specifically, the single-layer draft head used in speculative decoding has a performance gap due to limited parameter count and inadequate training methods, resulting in inefficient acceleration of LLM inference.
Researchers introduce KOALA (K-layer Optimized Adversarial Learning Architecture), a novel approach that optimizes the draft head for speculative decoding. KOALA enhances the traditional single-layer draft head by expanding it into a multi-layer architecture, thereby reducing the performance gap with the target LLM. Additionally, KOALA integrates adversarial learning into the training process, encouraging the draft head to better capture the token generation process of the target LLM, thus improving prediction accuracy. The multi-layer structure, and adversarial learning, allow KOALA to generate more accurate tokens per draft-then-verify cycle, reducing the number of iterations needed for decoding and consequently enhancing LLM inference speed.
KOALA is evaluated through comprehensive experiments with Medusa and EAGLE as non-autoregressive and autoregressive draft heads, respectively, with Vicuna models (7B, 13B, 33B) as target LLMs. Evaluations conducted on the MT-bench demonstrate that KOALA achieves a latency speedup ratio improvement of 0.24x-0.41x, which translates to being 10.57%-14.09% faster than the original draft heads. These results underscore KOALA’s ability to enhance the efficiency of speculative decoding across various LLM sizes and tasks, with the multi-layer architecture and adversarial learning both contributing to these gains.
In conclusion, KOALA presents a significant advancement in optimizing draft heads for speculative decoding in LLMs. By introducing a multi-layer structure and incorporating adversarial learning into the training process, KOALA reduces the performance gap between draft heads and target LLMs, leading to faster inference speeds. The experimental results validate KOALA’s efficacy, showing observable improvements in latency speedup ratios. Although KOALA causes a slight increase in drafting overhead, this is outweighed by the substantial acceleration of LLM inference, making KOALA a promising technique for enhancing the efficiency of LLMs in real-world applications.
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