Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond (2410.12982v1)

Abstract: While transformers have been at the core of most recent advancements in sequence generative models, their computational cost remains quadratic in sequence length. Several subquadratic architectures have been proposed to address this computational issue. Some of them, including long convolution sequence models (LCSMs), such as Hyena, address this issue at training time but remain quadratic during inference. We propose a method for speeding up LCSMs' exact inference to quasilinear $O(L\log^2L)$ time, identify the key properties that make this possible, and propose a general framework that exploits these. Our approach, inspired by previous work on relaxed polynomial interpolation, is based on a tiling which helps decrease memory movement and share computation. It has the added benefit of allowing for almost complete parallelization across layers of the position-mixing part of the architecture. Empirically, we provide a proof of concept implementation for Hyena, which gets up to $1.6\times$ end-to-end improvement over standard inference by improving $50\times$ within the position-mixing part.

• The paper presents the first quasilinear-time inference algorithm for long convolution sequence models, reducing complexity to O(L log² L).
• It outlines a general framework using dynamic FFT and tiling techniques for efficient and parallelizable inference across model layers.
• Empirical results demonstrate up to 1.6× end-to-end and 50× position-mixing speed improvements, paving the way for real-time applications.

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond

The paper presents an innovative method for accelerating inference in Long Convolution Sequence Models (LCSMs), addressing the computational inefficiencies inherent in current transformer-based architectures. By introducing a framework that achieves quasilinear time complexity, specifically $O(L\log^2 L)$, this work provides significant improvements over traditional quadratic time complexities.

Key Contributions

1. Quasilinear Inference Algorithm: The paper outlines the first quasilinear-time inference algorithm for LCSMs. This advancement is crucial for sequence models like Hyena and others seeking computational efficiency during inference.
2. General Framework for Efficiency: Beyond LCSMs, the paper proposes a general framework that identifies criteria for achieving inference speedups. This framework is applicable to future architecture designs aiming for both training and inference efficiency.
3. Parallelization Potential: The method allows for substantial parallelization across layers within the position-mixing architecture. This characteristic is pivotal for optimizing computation resources and time.
4. Empirical Validation: The method empirically demonstrates up to $1.6\times$ improvement in end-to-end inference speeds and up to $50\times$ within the position-mixing part, showcasing practical efficacy.

Methodology

The core of the proposed method revolves around leveraging relaxed polynomial interpolation, building on prior work to adapt FFT for dynamic inputs. This adaptation enables significant performance benefits by reducing the theoretical complexity associated with incremental sequence processing. The contribution is technically achieved through:

• Tiling Technique: The approach involves a strategic tiling in the computation space, reducing memory movement and sharing computations effectively.
• Dynamic FFT Use: By employing a "dynamic FFT" approach, the method efficiently balances computational workload across layers, capitalizing on data and processing flow parallelization.

Implications and Future Directions

The implications of this work are multifaceted:

• Efficiency in LCSMs: Direct benefits include enhanced computational efficiency in LCSMs, paving the way for real-time applications requiring swift and accurate processing of long sequences.
• Broader Applicability: The general framework proposed has the potential to inspire new architectural innovations spanning beyond LCSMs, influencing broader AI research domains.
• Data Reduction: The approach allows for efficient handling of data movement and storage, which are critical in hardware-constrained environments, enhancing scalability.

As for future directions, the potential for integrating data-dependent filters in a causal, efficient manner remains an intriguing area for expansion. Further, designing novel architectures aligned with the framework’s principles can harness these efficiency gains from inception.

Conclusion

"Flash Inference" proposes a significant methodological advancement in efficiently handling long sequence models. The quasilinear approach not only optimizes existing architectures like Hyena but also sets a precedent for future AI model designs aiming for computationally efficient inference. This work, through its detailed theoretical grounding and empirical validation, opens pathways for substantial enhancements in sequence data processing tasks across various domains.