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
4.2 Language Modeling
We evaluate the Mamba architecture on standard autoregressive language modeling against other architectures, on both pretraining metrics (perplexity) and zero-shot evaluations. We set the model sizes (depth and width) to mirror GPT3 specifications. We use the Pile dataset (L. Gao, Biderman, et al. 2020), and follow the training recipe described in Brown et al. (2020). All training details are in Appendix E.2.
4.2.1 Scaling Laws
For baselines, we compare against the standard Transformer architecture (GPT3 architecture), as well as the strongest Transformer recipe we know of (here referred to as Transformer++), based on the PaLM and LLaMa
architectures (e.g. rotary embedding, SwiGLU MLP, RMSNorm instead of LayerNorm, no linear bias, and higher learning rates). We also compare against other recent subquadratic architectures (Figure 4). All model details are in Appendix E.2
4.2.2 Downstream Evaluations
Table 3 shows the performance of Mamba on a range of popular downstream zero-shot evaluation tasks. We compare against the most well-known open source models at these sizes, most importantly Pythia (Biderman et al. 2023) and RWKV (B. Peng et al. 2023) which were trained with the same tokenizer, dataset, and training length (300B tokens) as our models. (Note that Mamba and Pythia are trained with context length 2048, while RWKV was trained with context length 1024.)
This paper is available on arxiv under CC BY 4.0 DEED license.
