Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding
(2402.12374)Abstract
As the usage of LLMs grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged as a promising direction for speeding up inference, existing methods are limited in their ability to scale to larger speculation budgets, and adapt to different hyperparameters and hardware. This paper introduces Sequoia, a scalable, robust, and hardware-aware algorithm for speculative decoding. To attain better scalability, Sequoia introduces a dynamic programming algorithm to find the optimal tree structure for the speculated tokens. To achieve robust speculative performance, Sequoia uses a novel sampling and verification method that outperforms prior work across different decoding temperatures. Finally, Sequoia introduces a hardware-aware tree optimizer that maximizes speculative performance by automatically selecting the token tree size and depth for a given hardware platform. Evaluation shows that Sequoia improves the decoding speed of Llama2-7B, Llama2-13B, and Vicuna-33B on an A100 by up to $4.04\times$, $3.73\times$, and $2.27\times$. For offloading setting on L40, Sequoia achieves as low as 0.56 s/token for exact Llama2-70B inference latency, which is $9.96\times$ on our optimized offloading system (5.6 s/token), $9.7\times$ than DeepSpeed-Zero-Inference, $19.5\times$ than Huggingface Accelerate.
Overview
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Sequoia introduces a dynamic programming approach to speculative decoding in LLMs, focusing on scalability, robustness, and hardware optimization.
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The method effectively increases the number of tokens generated relative to tree size, ensuring scalability even in memory-bound scenarios.
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A novel tree verification method enhances robustness by using sampling without replacement, increasing acceptance rates across various inference settings.
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Sequoia's hardware-aware tree optimization tailors tree size and depth to specific hardware configurations, enhancing speedup and efficiency in real-world applications.
Exploring Sequoia: A New Frontier in Speculative Decoding for LLMs
Introduction
In the landscape of LLMs, the quest for optimizing inference time without sacrificing output quality is a critical yet challenging endeavor. Recent advancements have heralded speculative decoding as a beacon of hope, though its application has been marred by scalability issues, a lack of robustness across various inference settings, and a non-consideration for the underlying hardware. Addressing these gaps, the recent introduction of Sequoia by Zhuoming Chen et al., marks a significant stride in speculative decoding. Sequoia proposes a dynamic programming solution that is scalable, robust across inference settings, and uniquely attuned to specific hardware configurations, demonstrating impressive speedups across a range of LLMs and hardware setups.
Scalable Tree Construction Method
Sequoia innovates the speculative decoding landscape with its tree construction algorithm. By leveraging a dynamic programming approach, Sequoia overcomes the limitations of previous methods, ensuring the number of tokens generated continues to increase with the tree size. This property is essential for maintaining speculative decoding's feasibility and effectiveness, especially in memory-bound scenarios like offloading. Empirical results underscore Sequoia's superior performance compared to existing methods, further bolstered by its theoretical foundation that guarantees unbounded growth in the number of generated tokens in relation to tree size.
Robust Tree Verification Method
The robustness of Sequoia's tree verification process is another hallmark of its design. It debuts a novel sampling and verification methodology that significantly raises acceptance rates across a wide temperature spectrum. Unlike its predecessors that falter at ensuring robust performance across different settings, Sequoia introduces sampling without replacement from the draft model. This innovation curtails the probability of repeated sampling of low-quality tokens, ensuring high acceptance rates and maintaining the fidelity of the target model's output distribution.
Hardware-aware Tree Optimizer
A unique feature of Sequoia is its hardware-aware tree optimization capability, which allows it to adaptively select the optimal tree size and depth for a given hardware configuration. This is critical for speculative decoding's real-world application as it enables Sequoia to maximize speedups by considering the specific characteristics and limitations of the inference hardware. Empirical evidence shows that Sequoia's hardware-aware tree optimizer can further enhance speedups, demonstrating the practical benefits of this approach.
Empirical Validation and Future Directions
Extensive experimental evaluations show Sequoia's promise, achieving up to 4.04× speedup on GPU for on-chip settings and up to 10.33× in offloading scenarios. These results not only validate Sequoia's theoretical underpinnings but also highlight its practical efficacy in accelerating LLM inference across various hardware platforms. Looking ahead, Sequoia's introduction prompts future research into speculative decoding, including exploring its applicability to other types of neural networks and further optimizing its components for even greater efficiency and adaptability.
Conclusion
Sequoia represents a significant leap forward in speculative decoding for LLMs. By addressing key challenges related to scalability, robustness, and hardware-aware optimization, it sets a new benchmark for accelerating LLM inference. Its dynamic programming-based approach, novel tree verification method, and hardware-aware optimization collectively offer a comprehensive solution that could spur further innovations in LLM efficiency and applicability.