On the Parameterization and Initialization of Diagonal State Space Models

TL;DR

This paper systematically analyzes how to effectively parameterize and initialize diagonal state space models, demonstrating that a simple diagonal version of S4, called S4D, performs comparably to the original on various long-range sequence tasks.

Contribution

It provides a detailed understanding of diagonal SSM parameterization and initialization, introducing S4D, a simple yet effective diagonal S4 variant with state-of-the-art results.

Findings

S4D matches S4 performance across multiple domains.

Initialization is critical for diagonal SSM effectiveness.

S4D kernel computation is extremely simple, requiring just 2 lines of code.

Abstract

State space models (SSM) have recently been shown to be very effective as a deep learning layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers. The first version to show this potential was the S4 model, which is particularly effective on tasks involving long-range dependencies by using a prescribed state matrix called the HiPPO matrix. While this has an interpretable mathematical mechanism for modeling long dependencies, it introduces a custom representation and algorithm that can be difficult to implement. On the other hand, a recent variant of S4 called DSS showed that restricting the state matrix to be fully diagonal can still preserve the performance of the original model when using a specific initialization based on approximating S4's matrix. This work seeks to systematically understand how to parameterize and initialize such diagonal state space models. While it follows from classical results that almost all SSMs have an equivalent diagonal form, we show that the initialization is critical for performance. We explain why DSS works mathematically, by showing that the diagonal restriction of S4's matrix surprisingly recovers the same kernel in the limit of infinite state dimension. We also systematically describe various design choices in parameterizing and computing diagonal SSMs, and perform a controlled empirical study ablating the effects of these choices. Our final model S4D is a simple diagonal version of S4 whose kernel computation requires just 2 lines of code and performs comparably to S4 in almost all settings, with state-of-the-art results for image, audio, and medical time-series domains, and averaging 85\% on the Long Range Arena benchmark.