In contemporary machine learning, foundation models, vast models pretrained on copious amounts of data and then modified for downstream tasks, have become a successful paradigm. Sequence models, which operate on arbitrary sequences of inputs from a broad range of domains, including language, pictures, voice, audio, time series, and genomes, are frequently the foundation of these FMs. Even though this idea is independent of any specific model design, the Transformer and its central attention layer are the foundation for most contemporary FMs. Self-attention is effective because it can represent complicated facts by tightly routing information inside a context window.
Nevertheless, this property has two basic disadvantages. One is the quadratic scaling concerning the window length, and the second, is the inability to describe anything outside a limited window. To address these shortcomings, a vast amount of study has been conducted on more effective attention-related strategies; however, frequently at the price of the same qualities that make attention successful. These variations have yet to be demonstrated to be experimentally successful at scale across domains. Structured state space sequence models are a new and exciting family of sequence modeling architectures. These models draw influence from traditional state space models and may be seen as a hybrid of convolutional and recurrent neural networks.
This family of models has linear or almost linear scaling in sequence length and can be calculated extremely rapidly as either a recurrence or a convolution. They have also dominated benchmarks like the Long Range Arena and have defined tools for modeling long-range interdependence in certain data modalities. Numerous SSM (structured state space models) varieties have shown effectiveness in fields like audio and vision requiring continuous signal data. They have yet to be as successful in modeling discrete, information-dense material like text.
The research team from Carnegie Mellon University and Princeton University suggest a novel category of selected state space models, which enhances earlier research in several dimensions to get the Transformer-like modeling capability while maintaining a linear relationship with sequence length.
Mechanism of Selection. First, we point out a significant drawback of earlier models: their inability to effectively choose data in an input-dependent way. The research team provides a straightforward selection process by parameterizing the SSM parameters according to the input, building on understanding derived from significant synthetic tasks like selective copy and induction heads. This enables the model to retain pertinent information forever while eliminating unnecessary data.
Hardware-aware Code. This straightforward modification technically challenges the model’s calculation; all previous SSM models had to be input- and time-invariant to be computationally effective. To prevent IO access across different layers of the GPU memory hierarchy, we address this using a hardware-aware approach that computes the model recurrently using a scan rather than a convolution. However, the enlarged state is not materialized. The resultant implementation is quicker than earlier techniques on current hardware and, in theory building design.
Architecture: To provide a straightforward and homogeneous architectural design incorporating specific state spaces, we combine the design of previous SSM architectures with the MLP block of Transformers into a single block, simplifying previous deep sequence model designs.
The key qualities of Selective SSMs and the Mamba architecture allow them to be the cornerstone of broader foundation models that operate on sequences being fully recurrent models are:
(i) High quality: selectivity performs well on dense modalities like genetics and language
(ii) Fast inference and training: during inference, unrolling the model autoregressively takes just constant time per step as it does not require a cache of prior components, and computation and memory scale linearly in sequence length
(iii) Long context: performance gains on actual data up to sequence length 1M are produced by combining quality and efficiency
The research team empirically supports Mamba’s potential as a generic sequence FM backbone across various modalities and situations regarding pretraining quality and domain-specific task performance:
• Artificial materials. Mamba not only readily solves crucial synthetic tasks like copying and induction head tasks that have been suggested as essential to huge language models but can also extrapolate infinitely lengthy solutions.
• Genomics and audio. Regarding pretraining quality and downstream metrics, Mamba outperforms previous state-of-the-art models like SaShiMi, Hyena, and Transformers when modeling audio waveforms and DNA sequences. Its performance improves with more context, up to million-length sequences, in both contexts.
• Modeling language. Mamba represents the first linear-time sequence model that genuinely attains Transformer-like performance in both assessments conducted downstream and pretraining perplexity.
The research team demonstrates that Mamba outperforms many baselines, including highly powerful contemporary Transformer training recipes based on LLaMa, with scaling laws up to 1B parameters. Compared to Transformers of comparable size, their Mamba language model has a 5× generation throughput, and Mamba-3B’s quality is on par with Transformers twice its size.
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