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> while keeping a constant state size for generating the output Isn't Mamba's attention mechanism linear relative to the input? The innovation as I understand
by erichocean 3y ago
> while keeping a constant state size for generating the output
Isn't Mamba's attention mechanism linear relative to the input? The innovation as I understand it is that attention in Mamba *isn't* quadratic (like transformers).
- junipertea 3y agoState size is constant (a matrix), attention is linear (a single unidirectional scan over all states) Both statements are correct
- prefer_to_ask 3y agoThe innovation is that they managed to write a hardware-aware kernel to make it run fast/efficiently on GPUs. The authors of Mamba are the same authors of FlashAttention which was a performance optimization kernel written to reduce IO while computing normal O(N^2) attention. As I understand it, previously SSM models were not as easily parallelizable as the Transformer architecture.
- kelseyfrog 3y agoYes and no. There's an dual connection between ssms and convolutional models if certain constraints are met. Training convolutionally and inferring sequentially seeks a compromise between the two sides. I think we're about to find out the degree to which those constraints impact "easily".
- wityl 3y agoThey were previously parallelizable (via fft), but performed poorly on language modeling tasks. Mamba adds a dependence on the inputs that makes language modeling competitive with transformers, but that prevents using the fft approach. So they switch to a method using parallel prefix scan.