Authors:
(1) Albert Gu, Machine Learning Department, Carnegie Mellon University with Equal contribution ([email protected]);
(2) Tri Dao, Department of Computer Science, Princeton University with Equal contribution ([email protected]).
Table of Links
3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression
3.2 Improving SSMs with Selection
3.3 Efficient Implementation of Selective SSMs
3.4 A Simplifed SSM Architecture
3.5 Properties of Selection Mechanisms
4 Empirical Evaluation and 4.1 Synthetic Tasks
4.4 Audio Modeling and Generation
4.5 Speed and Memory Benchmarks
6 Conclusion, Acknowledgments and References
A Discussion: Selection Mechanism
B Related Work and B.1 S4 Variants and Derivatives
B.4 Linear Attention and B.5 Long Context Models
D Hardware-aware Algorithm For Selective SSMs
E Experimental Details and Additional Results and E.1 Synthetic Tasks
3.6 Additional Model Details
Real vs. Complex. Most prior SSMs use complex numbers in their state ℎ, which is necessary for strong performance on many tasks (Gu, Goel, and Ré 2022). However, it has been empirically observed that completely real-valued SSMs seem to work fine, and possibly even better, in some settings (Ma et al. 2023). We use real values as the default, which work well for all but one of our tasks; we hypothesize that the complex-real tradeoff is related to the continuous-discrete spectrum in data modalities, where complex numbers are helpful for continuous modalities (e.g. audio, video) but not discrete (e.g. text, DNA).
Remark 3.1. For brevity in our experimental results, we sometimes abbreviate selective SSMs as S6 models, because they are S4 models with a selection mechanism and computed with a scan.
This paper is available on arxiv under CC BY 4.0 DEED license.
