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mlx/benchmarks/python/comparative
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Angelos Katharopoulos c15fe3e61b
Allow arbitrary first dimension in quantization kernels. (#458) 
* Allow arbitrary first dim on qmm_t and qmv
* Allow arbitrary first dim on qmm and qvm
* Specialized aligned vs unaligned case
* Add more checks for valid quantizations
2024-01-16 00:46:21 -08:00
..
bench_mlx.py Allow arbitrary first dimension in quantization kernels. (#458) 2024-01-16 00:46:21 -08:00
bench_torch.py An initial quantized matmul implementation (#205) 2023-12-18 23:18:57 -08:00
compare.py Spelling (#342) 2024-01-01 21:08:17 -08:00
README.md awni's commit files 2023-11-29 10:30:41 -08:00

README.md

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.