Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE

Abstract: Speculative decoding (SD) accelerates LLM inference by using a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model. However, the limited capacity of the draft model often necessitates tree-based sampling to improve prediction accuracy, where multiple candidates are generated at each step. We identify a key limitation in this approach: the candidates at the same step are derived from the same representation, limiting diversity and reducing overall effectiveness. To address this, we propose Jakiro, leveraging Mixture of Experts (MoE), where independent experts generate diverse predictions, effectively decoupling correlations among candidates. Furthermore, we introduce a hybrid inference strategy, combining autoregressive decoding for initial tokens with parallel decoding for subsequent stages, and enhance the latter with contrastive mechanism in features to improve accuracy. Our method significantly boosts prediction accuracy and achieves higher inference speedups. Extensive experiments across diverse models validate the effectiveness and robustness of our approach, establishing a new SOTA in speculative decoding. Our codes are available at https://github.com/haiduo/Jakiro.

• The paper introduces Jakiro, which uses a Mixture of Experts (MoE) framework to decouple token generation in LLM speculative decoding.
• Jakiro employs a hybrid inference strategy and contrastive mechanism, achieving significant speedups over existing methods on various benchmarks.
• The method improves real-world LLM applications by accelerating inference and suggests further research on integrating MoE with other model specialization.

An Analytical Overview of "Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE"

The paper "Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE" introduces an advanced methodology for enhancing speculative decoding (SD) in LLM inference processes. At the core, speculative decoding accelerates inference by employing a smaller draft model to propose multiple tokens, which are then verified by the larger and more accurate target model. However, an inherent limitation identified in this methodology is the coupled nature of token generation, which derives potential candidates from the same representation, thereby limiting diversity.

Jakiro innovatively addresses this issue by leveraging the Mixture of Experts (MoE) framework. Unlike typical methods constrained by joint representations, Jakiro employs independent experts to generate predictions, effectively decoupling the correlations among candidates. This method is enhanced by a hybrid inference strategy that merges autoregressive decoding with parallel decoding and integrates a contrastive mechanism to boost prediction accuracy further. This integration results in significant improvements in speed and efficacy, establishing a new state-of-the-art in speculative decoding.

Key Methodological Innovations

1. Mixture of Experts (MoE) in Draft Attention Tree: Jakiro utilizes an MoE-based dynamic decoupling mechanism for draft attention tree construction. The use of multiple experts allows for more independent and diverse token representations, surpassing the constraints of coupled predictions in other speculative decoding methods.
2. Hybrid Inference Strategy: The dual-stage inference approach, consisting of autoregressive token prediction followed by parallel decoding, optimizes the speed and accuracy of generating token sequences. The incorporation of a contrastive mechanism in the output features during parallel decoding further enhances Jakiro's performance.
3. Comprehensive Benchmark Evaluation: Experiments across various LLMs and benchmark datasets demonstrate Jakiro's effectiveness, highlighting improvements over existing state-of-the-art methods in speculative decoding.

Numerical Evaluation and Results

The empirical validation of Jakiro reveals notable performance enhancements. For instance, in experiments involving models such as Vicuna and LLaMA2-chat, significant increases in speedup ratios were observed compared to previous methods, including Eagle and Medusa frameworks. The draft model's prediction accuracy is improved due to the dynamic decoupling in token generation, translating to faster end-to-end inference speeds without sacrificing the fidelity of the output distribution expected from the target model.

Theoretical and Practical Implications

Theoretically, Jakiro contributes to a refined understanding of how decoupling in token generation can enhance LLM inference. The application of MoE helps in maintaining robustness against variance in token dependencies, thus allowing better exploration of token diversities during non-greedy sampling modes. Practically, this results in accelerated inference in real-world applications such as dialogue systems and code synthesis, allowing for improved user interactions with LLMs.

Future Directions

The research opens pathways for further exploration into the integration of MoE architectures with other forms of model specialization and token diversification strategies. The demonstrated robustness of Jakiro in various tasks suggests its potential applicability in other domains requiring fast yet accurate LLM outputs. Additionally, optimizing the balance between number of experts and computational cost remains a valuable area for future study to maximize the efficiency benefits of MoE-driven speculative decoding.

In conclusion, Jakiro represents a substantive advancement in speculative decoding methodologies, offering substantial improvements through the application of dynamic, expert-driven decoupling mechanisms. This work not only advances the current understanding and application of speculative decoding but also sets a foundation for future explorations in enhancing LLMs' efficiency and performance.