The Scalability Challenge of State Space Models (SSMs)
State Space Models (SSMs) and Transformers have emerged as pivotal components in sequential modeling. The challenge lies in optimizing the scalability of SSMs, which have shown promising potential but are yet to surpass the dominance of Transformers.
SSMs have gained attention as a family of architectures, blending the characteristics of RNNs and CNNs, rooted in control theory. Recent breakthroughs have facilitated the scaling of deep SSMs to billions of parameters, ensuring computational efficiency and robust performance.
Mamba: Advancements in Scaling Deep SSMs
Mamba, an extension of SSMs, introduces linear-time inference and hardware-aware design, mitigating the impact of sequential recurrence. The innovative approach to state compression and a selective information propagation mechanism makes Mamba a promising sequence modeling backbone, rivaling or surpassing established Transformer models across diverse domains.
The Fusion of MoE and SSMs: Introducing MoE-Mamba
A team of researchers has proposed combining MoE with SSMs to unlock the potential of SSMs for scaling up. The model developed, MoE-Mamba, combines Mamba with a MoE layer and achieves remarkable performance, outperforming Mamba and Transformer-MoE.
Enhancing the Mamba Architecture: Exploring Conditional Computation
The research extends beyond the fusion of MoE with SSMs and delves into enhancing the Mamba architecture. A pivotal aspect is the exploration of conditional computation in Mamba’s block design. This modification is anticipated to enhance the overall architecture, creating a need for further investigation into the synergies between conditional computation and MoE within SSMs, facilitating more efficient scaling to larger language models.
MoE-Mamba: Unlocking the Potential of SSMs for Scaling
While the integration of MoE into the Mamba layer shows promising results, especially when using a performant sparse MoE feed-forward layer, one limitation to note is that in the case of a dense setting, Mamba performs slightly better without the feed-forward layer.
In summary, the MoE-Mamba model combines MoE with the Mamba architecture, surpassing both Mamba and Transformer-MoE. It achieves parity with Mamba in 2.2x fewer training steps while maintaining Mamba’s inference superiority over the Transformer. The authors anticipate that this study will serve as a catalyst, inspiring further exploration into the synergy of conditional computation, especially MoE, with SSMs.
Analyst comment
Positive
As an analyst, the market for State Space Models (SSMs) and Transformers is expected to be impacted positively by the advancements in scaling deep SSMs, the introduction of Mamba and MoE-Mamba models. These innovations have the potential to surpass the dominance of Transformers and create more efficient and scalable sequence modeling architectures.
