Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference (2409.16560v1)

Abstract: LLMs have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slow and costly. Speculative decoding has emerged as a promising solution, leveraging a smaller auxiliary model to draft future tokens, which are then validated simultaneously by the larger model, achieving a speed-up of 1-2x. Although speculative decoding matches the same distribution as multinomial sampling, multinomial sampling itself is prone to suboptimal outputs, whereas beam sampling is widely recognized for producing higher-quality results by maintaining multiple candidate sequences at each step. This paper explores the novel integration of speculative decoding with beam sampling. However, there are four key challenges: (1) how to generate multiple sequences from the larger model's distribution given drafts sequences from the small model; (2) how to dynamically optimize the number of beams to balance efficiency and accuracy; (3) how to efficiently verify the multiple drafts in parallel; and (4) how to address the extra memory costs inherent in beam sampling. To address these challenges, we propose dynamic-width speculative beam decoding (DSBD). Specifically, we first introduce a novel draft and verification scheme that generates multiple sequences following the large model's distribution based on beam sampling trajectories from the small model. Then, we introduce an adaptive mechanism to dynamically tune the number of beams based on the context, optimizing efficiency and effectiveness. Besides, we extend tree-based parallel verification to handle multiple trees simultaneously, accelerating the verification process. Finally, we illustrate a simple modification to our algorithm to mitigate the memory overhead of beam sampling...

Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference

The paper presents a novel approach in efficient inference for LLMs by integrating speculative decoding with beam sampling. Termed Dynamic-Width Speculative Beam Decoding (DSBD), this method aims to leverage the advantages of both speculative decoding, which accelerates inference through parallel verification, and beam sampling, renowned for maintaining multiple candidate sequences to enhance output quality.

Key Contributions

1. Draft and Verification Scheme: A central innovation is the draft and verification scheme that combines the generation of multiple sequences using a smaller auxiliary model with the validation of these sequences using a larger model. This scheme effectively aligns with the beam sampling distribution without additional computational overhead.
2. Adaptive Beam Management: DSBD introduces a dynamic adjustment of the beam width during decoding based on the contextual alignment between the small and large models' distributions. This adaptability addresses the inefficiencies of fixed beam width, optimizing both the correctness of outputs and computational efficiency.
3. Forest-Based Parallel Verification: Extending tree-based parallel computing to handle multiple 'trees' allows DSBD to process extensive draft forests efficiently. This enables parallel verification, significantly enhancing the speed of inference by reducing redundant computations.
4. Memory Optimization: The method offers a modification to limit memory overhead, aligning closer to the hardware constraints of typical inference setups without losing the quality benefits of beam sampling.

Experimental Insights

The experimental evaluations demonstrate that DSBD achieves a speed-up in inference by a factor of 1.5 to 1.9, and reduces energy consumption by 1.8 to 2.5 times compared to traditional beam sampling, all while maintaining comparable downstream task performance. These results are robust across different model sizes and datasets, including SQuAD and Spider, indicating broad applicability.

Implications and Future Directions

The introduction of DSBD presents significant implications for the deployment of LLMs in resource-constrained environments. By achieving more efficient inference with high-quality output, this method addresses a critical barrier to the scalar deployment of LLMs.

Theoretically, this work bridges the gap between speculative and beam sampling, providing a framework for future methodologies that harness both efficiency and quality. The adaptive width mechanism, in particular, points towards a more generalized approach to dynamic resource allocation during inference.

Potential future research could explore further integration of task-specific optimizations and enhancing the adaptability features of DSBD. Additionally, investigating the trade-offs between model accuracy and resource consumption in more varying conditions may yield insights into scaling these approaches across different application domains.

In summary, this paper presents a compelling advancement in LLM inference, promising both practical deployment benefits and theoretical enrichment of decoding strategies in neural models.