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Add SVD primitive (#809)
Add SVD op using Accelerate's LAPACK following https://developer.apple.com/documentation/accelerate/ compressing_an_image_using_linear_algebra Co-authored-by: Nicolo Valigi <nvaligi@apple.com>
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@@ -16,6 +16,13 @@ using namespace py::literals;
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using namespace mlx::core;
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using namespace mlx::core::linalg;
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namespace {
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py::tuple svd_helper(const array& a, StreamOrDevice s /* = {} */) {
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const auto result = svd(a, s);
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return py::make_tuple(result.at(0), result.at(1), result.at(2));
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}
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} // namespace
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void init_linalg(py::module_& parent_module) {
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py::options options;
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options.disable_function_signatures();
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@@ -186,7 +193,7 @@ void init_linalg(py::module_& parent_module) {
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R"pbdoc(
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qr(a: array, *, stream: Union[None, Stream, Device] = None) -> (array, array)
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The QR factorizatoin of the input matrix.
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The QR factorization of the input matrix.
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This function supports arrays with at least 2 dimensions. The matrices
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which are factorized are assumed to be in the last two dimensions of
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@@ -210,4 +217,28 @@ void init_linalg(py::module_& parent_module) {
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array([[-2.23607, -3.57771],
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[0, 0.447214]], dtype=float32)
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)pbdoc");
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m.def(
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"svd",
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&svd_helper,
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"a"_a,
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py::kw_only(),
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"stream"_a = none,
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R"pbdoc(
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svd(a: array, *, stream: Union[None, Stream, Device] = None) -> (array, array, array)
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The Singular Value Decomposition (SVD) of the input matrix.
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This function supports arrays with at least 2 dimensions. When the input
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has more than two dimensions, the function iterates over all indices of the first
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a.ndim - 2 dimensions and for each combination SVD is applied to the last two indices.
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Args:
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a (array): Input array.
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stream (Stream, optional): Stream or device. Defaults to ``None``
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in which case the default stream of the default device is used.
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Returns:
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tuple(array, array, array): The ``U``, ``S``, and ``Vt`` matrices, such that
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``A = U @ diag(S) @ Vt``
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)pbdoc");
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}
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