Summary Mamba Linear-Time Sequence Modeling with Selective State Spaces arxiv.org
18,444 words - PDF document - View PDF document
One Line
Mamba is a superior linear-time sequence model that surpasses Transformers in different domains by incorporating a selection mechanism for state space models.
Slides
Slide Presentation (13 slides)
Key Points
- The Mamba model is a linear-time sequence model that improves on the computational inefficiency of the Transformer architecture.
- Mamba introduces a selection mechanism that allows the model to selectively propagate or forget information along the sequence length dimension based on the current token.
- Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics.
- The selection mechanism in selective SSMs allows the model to filter out irrelevant information and remember relevant information indefinitely.
- Mamba achieves superior performance compared to other models in terms of accuracy, perplexity, and downstream evaluation metrics.
Summaries
26 word summary
Mamba is a linear-time sequence model that enhances language modeling performance by introducing a selection mechanism for state space models. It outperforms Transformers in various domains.
67 word summary
Mamba is a linear-time sequence model that improves computational efficiency compared to Transformers. It introduces a selection mechanism for selective state space models, enabling content-based reasoning and enhancing language modeling performance. Mamba achieves state-of-the-art performance in language, audio, and genomics, outperforming Transformers. The paper provides an overview of SSMs, highlighting the selection mechanism's ability to filter out irrelevant information. Empirical evaluation demonstrates Mamba's effectiveness and computational efficiency.
137 word summary
The Mamba model is a linear-time sequence model that improves computational efficiency compared to the Transformer architecture. It introduces a selection mechanism for selective state space models (SSMs) that enables content-based reasoning and enhances language modeling performance. The model utilizes a hardware-aware algorithm that computes SSMs recurrently with a scan, taking advantage of modern hardware's memory hierarchy for faster computation. Mamba achieves state-of-the-art performance in language, audio, and genomics, outperforming Transformers of the same size. It also exhibits improved performance on common sense reasoning tasks. The paper provides an overview of SSMs and their computation, highlighting the selection mechanism's ability to filter out irrelevant information. Empirical evaluation demonstrates Mamba's effectiveness across various domains and its computational efficiency. The paper concludes by discussing related work, limitations, and future directions, emphasizing the broad applications of selective state space models.
562 word summary
The Mamba model is a linear-time sequence model that improves computational efficiency in comparison to the Transformer architecture. It introduces a selection mechanism that allows the model to selectively propagate or forget information based on the current token, enabling content-based reasoning and improving language modeling performance.
To compute the selective state space models (SSMs) efficiently, a hardware-aware algorithm is designed that computes the model recurrently with a scan instead of convolution. This algorithm takes advantage of modern hardware's memory hierarchy and achieves faster computation compared to previous methods. The selective SSMs are integrated into a simplified end-to-end neural network architecture called Mamba, which achieves fast inference and linear scaling in sequence length.
Mamba serves as a general sequence model backbone and achieves state-of-the-art performance across several modalities such as language, audio, and genomics. It outperforms Transformers of the same size in language modeling and matches Transformers twice its size in both pretraining and downstream evaluation. The Mamba-3B model has 5x higher throughput than similar-sized Transformers and exhibits improved performance on common sense reasoning tasks.
The paper also provides an overview of structured state space models (SSMs) and their computation. SSMs combine recurrent neural networks (RNNs) and convolutional neural networks (CNNs) with inspiration from classical state space models. They have been successful in continuous signal domains but less effective in modeling discrete and information-dense data such as text. The selection mechanism in selective SSMs allows the model to filter out irrelevant information and remember relevant information indefinitely.
The empirical evaluation of Mamba demonstrates its effectiveness on synthetic tasks, language modeling, DNA sequence modeling, and audio waveform modeling. It achieves superior performance compared to other models in terms of accuracy, perplexity, and downstream evaluation metrics. It also exhibits computational efficiency, with faster training and inference times compared to previous methods.
In conclusion, Mamba is a linear-time sequence model that combines selective state space models with a simplified architecture. It achieves state-of-the-art performance across multiple domains and demonstrates improved computational efficiency. The selection mechanism enhances the model's ability to reason and filter information, making it a powerful backbone for general sequence models.
The paper introduces a selection mechanism for structured state space models (SSMs) called Mamba. This mechanism allows SSMs to perform context-dependent reasoning while scaling linearly in sequence length. Mamba is incorporated into a simple attention-free architecture and achieves state-of-the-art results on various domains, matching or exceeding the performance of strong Transformer models.
Mamba's pretraining perplexity improves smoothly with model size and scales better than other models. At the largest model size, Mamba can match the performance of these models with significantly fewer parameters.
Mamba-UNet outperforms larger GAN- and diffusion-based models in autoregressive speech generation using the SC09 benchmark dataset. A larger parameter-matched Mamba model further improves the metrics dramatically.
Different architectures for the outer and center stages of the Mamba model are evaluated. Mamba outperforms other architectures in the outer blocks and shows competitive performance in the center blocks.
Ablation studies investigate the parameterization, expressivity, and state dimension of Mamba. The results show that selective parameters and larger state dimensions can significantly improve performance.
Benchmarking shows that Mamba's SSM scan operation is faster than other attention implementations, and Mamba achieves higher inference throughput than Transformer models of similar size.
The paper concludes by discussing related work, limitations, and future directions. It emphasizes the broad applications of selective state space models in different domains.
631 word summary
The Mamba model is a linear-time sequence model that improves on the computational inefficiency of the Transformer architecture. It introduces a selection mechanism that allows the model to selectively propagate or forget information based on the current token, enabling content-based reasoning and improving performance in language modeling.
To compute the selective state space models (SSMs) efficiently, a hardware-aware algorithm is designed that computes the model recurrently with a scan instead of convolution. This algorithm takes advantage of modern hardware's memory hierarchy and achieves faster computation compared to previous methods. The selective SSMs are integrated into a simplified end-to-end neural network architecture called Mamba, which achieves fast inference and linear scaling in sequence length.
Mamba serves as a general sequence model backbone and achieves state-of-the-art performance across several modalities such as language, audio, and genomics. It outperforms Transformers of the same size in language modeling and matches Transformers twice its size in both pretraining and downstream evaluation. The Mamba-3B model has 5x higher throughput than similar-sized Transformers and exhibits improved performance on common sense reasoning tasks.
The paper also provides an overview of structured state space models (SSMs) and their computation. SSMs combine recurrent neural networks (RNNs) and convolutional neural networks (CNNs) with inspiration from classical state space models. They have been successful in continuous signal domains but less effective in modeling discrete and information-dense data such as text. The selection mechanism in selective SSMs allows the model to filter out irrelevant information and remember relevant information indefinitely.
The empirical evaluation of Mamba demonstrates its effectiveness on synthetic tasks, language modeling, DNA sequence modeling, and audio waveform modeling. It achieves superior performance compared to other models in terms of accuracy, perplexity, and downstream evaluation metrics. It also exhibits computational efficiency, with faster training and inference times compared to previous methods.
In conclusion, Mamba is a linear-time sequence model that combines selective state space models with a simplified architecture. It achieves state-of-the-art performance across multiple domains and demonstrates improved computational efficiency. The selection mechanism enhances the model's ability to reason and filter information, making it a powerful backbone for general sequence models.
The paper introduces a selection mechanism for structured state space models (SSMs) called Mamba. This mechanism allows SSMs to perform context-dependent reasoning while scaling linearly in sequence length. Mamba is incorporated into a simple attention-free architecture and achieves state-of-the-art results on various domains, matching or exceeding the performance of strong Transformer models.
The paper presents experimental results on pretraining perplexity and scaling with respect to model size and sequence length. Mamba's pretraining perplexity improves smoothly with model size and scales better than other models. At the largest model size, Mamba can match the performance of these models with significantly fewer parameters.
Mamba is able to make use of longer context even up to extremely long sequences of length 1M, while other models perform worse with longer sequences. This is because Mamba's selection mechanism allows it to focus on relevant information.
Mamba-UNet, a small Mamba model, outperforms larger GAN- and diffusion-based models in terms of fidelity metrics in autoregressive speech generation using the SC09 benchmark dataset. A larger parameter-matched Mamba model further improves the metrics dramatically.
Different architectures for the outer and center stages of the Mamba model are evaluated. Mamba outperforms other architectures in the outer blocks and shows competitive performance in the center blocks.
Ablation studies investigate the parameterization, expressivity, and state dimension of Mamba. The results show that selective parameters and larger state dimensions can significantly improve performance.
Benchmarking shows that Mamba's SSM scan operation is faster than other attention implementations, and Mamba achieves higher inference throughput than Transformer models of similar size.
The paper concludes by discussing related work, limitations, and future directions. It emphasizes the broad applications of selective state space models in different domains and suggests
1181 word summary
The Mamba model is a linear-time sequence model that improves on the computational inefficiency of the Transformer architecture. It addresses the weakness of other subquadratic-time architectures by introducing a selection mechanism that allows the model to selectively propagate or forget information along the sequence length dimension based on the current token. This mechanism enables the model to perform content-based reasoning and improves its performance on important modalities such as language.
To compute the selective state space models (SSMs) efficiently, a hardware-aware algorithm is designed that computes the model recurrently with a scan instead of convolution. This algorithm takes advantage of the memory hierarchy on modern hardware and achieves faster computation compared to previous methods. The selective SSMs are integrated into a simplified end-to-end neural network architecture called Mamba, which does not use attention or MLP blocks. Mamba achieves fast inference and linear scaling in sequence length, and its performance improves on real data up to million-length sequences.
Mamba serves as a general sequence model backbone and achieves state-of-the-art performance across several modalities such as language, audio, and genomics. In language modeling, Mamba outperforms Transformers of the same size and matches Transformers twice its size in both pretraining and downstream evaluation. The Mamba-3B model has 5x higher throughput than similar-sized Transformers and exhibits improved performance on common sense reasoning tasks.
The paper also provides an overview of structured state space models (SSMs) and their computation. SSMs are a class of sequence models that combine recurrent neural networks (RNNs) and convolutional neural networks (CNNs) with inspiration from classical state space models. They can be computed efficiently as either a recurrence or convolution, with linear or near-linear scaling in sequence length. The SSMs have been successful in domains involving continuous signal data such as audio and vision but less effective in modeling discrete and information-dense data such as text.
The selection mechanism in selective SSMs allows the model to filter out irrelevant information and remember relevant information indefinitely. It can be incorporated into different types of sequence models, such as RNNs or CNNs, by making the parameters input-dependent. The selection mechanism has properties such as variable spacing, filtering context, and boundary resetting, which enhance the model's ability to reason and generalize.
The empirical evaluation of Mamba demonstrates its effectiveness on synthetic tasks, language modeling, DNA sequence modeling, and audio waveform modeling. Mamba achieves superior performance compared to other models in terms of accuracy, perplexity, and downstream evaluation metrics. It also exhibits computational efficiency, with faster training and inference times compared to previous methods.
In conclusion, Mamba is a linear-time sequence model that combines selective state space models with a simplified architecture. It achieves state-of-the-art performance across multiple domains and demonstrates improved computational efficiency. The selection mechanism enhances the model's ability to reason and filter information, making it a powerful backbone for general sequence models.
The paper introduces a selection mechanism for structured state space models (SSMs) called Mamba. This mechanism allows SSMs to perform context-dependent reasoning while scaling linearly in sequence length. Mamba is incorporated into a simple attention-free architecture and achieves state-of-the-art results on various domains, matching or exceeding the performance of strong Transformer models. The selection mechanism overcomes the weaknesses of SSMs on discrete modalities such as text and DNA. The paper presents experimental results on pretraining perplexity and scaling with respect to model size and sequence length. Mamba's pretraining perplexity improves smoothly with model size and scales better than other models, such as HyenaDNA and Transformer++. At the largest model size, Mamba can match the performance of these models with significantly fewer parameters. In terms of scaling with respect to sequence length, Mamba is compared to the HyenaDNA model. The results show that Mamba is able to make use of longer context even up to extremely long sequences of length 1M, while HyenaDNA performs worse with longer sequences. This is because LTI models cannot selectively ignore information, whereas Mamba's selection mechanism allows it to focus on relevant information. The paper also discusses autoregressive speech generation using the SC09 benchmark dataset. Mamba-UNet, a small Mamba model, outperforms larger GAN- and diffusion-based models in terms of fidelity metrics. A larger parameter-matched Mamba model further improves the metrics dramatically. Further experiments are conducted to evaluate different architectures for the outer and center stages of the Mamba model. The results show that Mamba outperforms S4+MLP in the outer blocks and S4+MLP and MHA+MLP in the center blocks. The paper includes ablation studies that investigate the parameterization, expressivity, and state dimension of Mamba. The results show that selective parameters and larger state dimensions can significantly improve performance. The speed and memory benchmarks demonstrate that Mamba's SSM scan operation is faster than other attention implementations, and Mamba achieves higher inference throughput than Transformer models of similar size. The paper concludes by discussing related work, limitations, and future directions. It emphasizes the broad applications of selective state space models in different domains and suggests that Mamba is a strong candidate as a general sequence model backbone.
Gating mechanisms in RNNs such as LSTM and GRU have been interpreted as a way to control the flow of input into the hidden state, but the concept of gating has expanded to include any multiplicative interaction in neural network architectures. Hypernetworks are neural networks whose parameters are generated by smaller neural networks, and data-dependence refers to any notion where some parameters of the model depend on the data. The GLU activation function is an example that satisfies the common meanings of gating, hypernetworks, and data-dependence. However, it is often considered just an activation function instead of a meaningful layer. Selection mechanisms, which involve selecting or ignoring inputs to facilitate data interaction along the sequence length, can be seen as a special case of gating, hypernetworks, or data-dependence. The term "gating" is often overloaded and can be confusing, so the term "selection" is used instead to clarify the concept. Selective SSMs (Structured State-Space Models) are closely related to the gating mechanism of traditional RNNs and have connections to variable discretization. Other constructions, such as input-dependent convolutions and attention mechanisms, can also be considered as special cases. Several prior works related to selective SSMs are overviewed, including S4, DSS, S5, Mega, Liquid S4, SGConv, Hyena, H3, Selective S4, RetNet, and RWKV. These models incorporate SSMs as black box layers in neural network architectures. RNNs and SSMs both involve recurrence on a latent state, but SSMs provide better parameterizations and initializations and have overcome efficiency issues associated with RNNs. Orthogonal RNNs and other approaches that constrain the transition matrix also have limitations. Linear attention is a framework that relates to recurrent autoregressive models and has many variants proposed. Long context models have become popular, but the scalability of these models is often from a computational standpoint and has not been extensively validated. The scaling laws of SSM architectures, such as Transformer++, HyenaDNA, and Mamba, are explored with different model sizes and sequence lengths. Efficient implementations of the core operations of selective SSMs, such as parallel scan, convolution, and attention, are benchmarked. The memory footprint of Mamba is comparable to a highly optimized Transformer implementation.