[geremiiah]

It's more like OpenXLA or the PyTorch compiler, that codegens Kokkos C++ kernels from MLIR defined input programs, which for example can be outputted from PyTorch. Kokkos is common in scientific computing workloads, so outputting readable kernels is a feature in itself. Beyond that there's a lot of engineering that can go into such a compiler to specifically optimize sparse workloads.

What I am missing is a comparison with JAX/OpenXLA and PyTorch with torch.compile().

Also instead of rebuilding a whole compiler framework they could have contributed to Torch Inductor or OpenXLA, unless they had some design decisions that were incompatible. But it's quite common for academic projects to try to reinvent the wheel. It's also not necessarily a bad thing. It's a pedagogical exercise.

[convolvatron]

I think the exactly opposite, if someone was able to build a framework that doesn't overly constrain the problem, and doesn't require weeks of screwing around with the build, integration of half baked components and insane amounts of boilerplate, that would be a fantastic contribution in and of itself even it didn't advance the state of tensor compilation in any other way.