Mamba: Linear-Time Sequence Modeling with Selective State Spaces

 Foundation models, now powering most of the exciting applications in deep learning, are almost universally

based on the Transformer architecture and its core attention module. Many subquadratic-time architectures

such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs)

have been developed to address Transformers’ computational inefficiency on long sequences, but they have not

performed as well as attention on important modalities such as language. We identify that a key weakness of

such models is their inability to perform content-based reasoning, and make several improvements. First, simply

letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing

the model to selectively propagate or forget information along the sequence length dimension depending on

the current token. Second, even though this change prevents the use of efficient convolutions, we design a

hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified

end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast

inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance

improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves

state-of-the-art performance across several modalities such as language, audio, and genomics. On language

modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice

its size, both in pretraining and downstream evaluation.

https://arxiv.org/pdf/2312.00752v1.pdf