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78102a47ad
* Organize and collect metal subroutine templates and elements in `metal/kernels/steel/` * Update gemm elements for better performance * Add split-K specialization for gemm * Add `addmm` primitive, op and bindings for fused matmul and bias addition * Update tests and benchmarks as needed
Microbenchmarks comparing MLX to PyTorch
Implement the same microbenchmarks in MLX and PyTorch to compare and make a list of the biggest possible performance improvements and/or regressions.
Run with python bench_mlx.py sum_axis --size 8x1024x128 --axis 2 --cpu for
instance to measure the times it takes to sum across the 3rd axis of the above
tensor on the cpu.
compare.py runs several benchmarks and compares the speed-up or lack thereof
in comparison to PyTorch.
Each bench script can be run with --print-pid to print the PID and wait for a
key in order to ease attaching a debugger.