What are the main arguments for and against the hype surrounding Mamba vs. Transformers in the context of machine learning advancements?

Have you guys heard about Mamba, the new player in sequence modeling? They say it’s faster, handles longer sequences better than Transformers, and might even outperform them in some tasks. But is it really a game-changer or just a passing trend?

From what I gather:

Pros: Mamba uses memory efficiently, scales linearly with sequence length, and shows impressive results in language and DNA modeling. Plus, it skips the attention mechanism, potentially speeding up inference.

Cons: It’s still early days, so we’re not sure about its long-term stability across different tasks. And while it’s attention-free, its state space approach could be harder to grasp for some.

For the AI enthusiasts out there, is Mamba just another shiny new toy or a real breakthrough in sequence modeling? Will it overthrow Transformers or find its niche as a specialized tool? I’m curious to hear your thoughts.

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I’ve gone through the paper. In Mamba, the S6 layers use a memory mechanism that adapts with each new token. This approach is typical for state space modeling networks, but what sets S6 apart is its ability to control how it remembers each token, rather than attempting to memorize a compressed version of the entire input sequence. This theoretically allows it to retain crucial information from tokens far back in the sequence, while only holding onto short-term details as needed.

Whether this theoretical advantage translates into practical benefits for models of feasible size remains uncertain. Nonetheless, there’s optimism that even if it doesn’t currently achieve this, future iterations of the memory mechanism will be more sophisticated. The concept of processing inputs token-by-token and maintaining an internal memory, rather than ingesting the entire uncompressed sequence at once like attention mechanisms do, intuitively seems promising to me.

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I find this approach intuitively sensible. One question I have though: I haven’t delved deeply into the Mamba paper, but how does Mamba differ from LSTM and similar models?

Regarding hardware efficiency as claimed in the paper, is Mamba genuinely more hardware-friendly compared to LSTM?

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As far as I understand it, Mamba differs by using a linear activation function between each hidden state, unlike LSTM and RNN which employ nonlinearities. This linear approach makes backpropagation through time more stable for Mamba.

Additionally, Mamba can compute its operations in a single forward pass using a parallel scan (prefix sum) operation, unlike RNNs and LSTMs where each timestep depends on the previous one. The authors of Mamba also developed a hardware-aware algorithm similar to FlashAttention, enhancing efficiency further.

The authors note that RNN variants like QRNN, which lack time-wise nonlinearities, are closest to Mamba. However, QRNNs do not utilize state expansion or selective B and C parameters, and they rely on a heuristic gating mechanism. In contrast, Mamba’s parameterizations and initializations are grounded in structured state space modeling theory.

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How does removing non-linearities impact performance? The core principle of efficient deep learning lies in using non-linearities to map data into a more disentangled space. How does abandoning this assumption still result in a neural network that performs comparably?

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While Mamba lacks time-wise nonlinearities, it still incorporates nonlinearities between its layers.

In my opinion, rather than outright overthrowing Transformers like the original GPT series, Mamba could complement them by offering enhanced capabilities in targeted areas without replacing the broader utility of Transformer-based models.