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Mamba layers are efficient alternatives to standard attention: their training complexity is linear in sequence length, while inference is sequence-length-independent and only requires a small cache. I will discuss a selection of IBM's ongoing work in advancing the state of mamba training in pytorch, including: context-parallel training for long-sequence data, mamba + mixture-of-expert support with expert parallelism, torch-native associative scan ops, and improved DTensor op support.
