A library for PyTorch providing sparse, differentiable CSR support.
Clone normally and install with pip,
pip3 install .
If CUDA is not detected on your system, this will silently default to compiling only CPU
implementations: you can run pip with verbose (-v) for a sanity check on this.
Run tests using pytest like
pytest numml/tests
Note that the test cases will assume you are running on a machine with CUDA installed and you have compiled with CUDA support.
Optimized Sparse Matrix Operations for Reverse Mode Automatic Differentiation
@misc{NytkoSparse2022,
doi = {10.48550/ARXIV.2212.05159},
url = {https://arxiv.org/abs/2212.05159},
author = {Nytko, Nicolas and Taghibakhshi, Ali and Zaman, Tareq Uz and MacLachlan, Scott and Olson, Luke N. and West, Matt},
keywords = {Machine Learning (cs.LG), Mathematical Software (cs.MS), Numerical Analysis (math.NA), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Mathematics, FOS: Mathematics},
title = {Optimized Sparse Matrix Operations for Reverse Mode Automatic Differentiation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
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