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Re:MLX: An array framework for Apple Silicon by brrrrrm on Wednesday December 06, 2023 @09:48AM (#38544591)
factorization methods are somewhat uncommonly used in deep learning (the likely target of this framework) and have compute properties (such as approximate outputs, non-deterministic number of iterations) that make them unlike the BLAS++ standard APIs.

einsum seems like a reasonable thing to request, but it's hard to be performant across the entire surface exposed by the operation.

Re:MLX: An array framework for Apple Silicon by thanatosmin on Wednesday December 06, 2023 @10:10AM (#38544825)
Exactly right that this targets a narrower surface to enable many deep learning models. I wonder how uncommon it is to hit some operation that is not included, though? It seems pretty common from a PyTorch MPS tracking issue:

https://github.com/pytorch/pytorch/issues/77764

NVIDIA's moat is not just in providing BLAS++ operations, but extending this to a wider range of cuSPARSE, cuSOLVE, cuTENSOR, etc. Without these, it feels like Apple is just trying to play catch up with whatever is popular and unsupported...