Sketched matrix decompositions for PyTorch
Project description
skerch: Sketched matrix decompositions for PyTorch
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skerch is a Python package to compute diagonal decompositions (SVD, Hermitian Eigendecomposition) of linear operators via sketched methods.
- Built on top of PyTorch, with natural support for CPU and CUDA interoperability, and very few dependencies otherwise
- Works on matrices and matrix-free operators of potentially very large dimensionality
- Support for sketched measurements in a fully distributed fashion via HDF5 databases
See the documentation for more details.
Installation and basic usage
Install via:
pip install skerch
The sketched SVD of a linear operator op can be then computed simply via:
q, u, s, vt, pt = ssvd(
op,
op_device=DEVICE,
op_dtype=DTYPE,
outer_dim=NUM_OUTER,
inner_dim=NUM_INNER,
)
Where q @ u @ diag(s) @ vt @ pt approximates linop and the number of outer and inner measurements for the sketch is specified.
See Getting Started, Examples, and API docs for more details.
Developers
Contributions are most welcome under this repo's LICENSE. Feel free to open an issue with bug reports, features requests, etc.
The documentation contains a For Developers section with useful guidelines to interact with this repo.
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