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Deprecate make_positive_semidefinite in EigenConfig #86

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Deprecate make_positive_semidefinite in EigenConfig #86

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14 changes: 2 additions & 12 deletions matrix_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,6 @@ def matrix_inverse_root(
A=A,
root=root,
epsilon=epsilon,
make_positive_semidefinite=root_inv_config.make_positive_semidefinite,
retry_double_precision=root_inv_config.retry_double_precision,
eigen_decomp_offload_device=root_inv_config.eigen_decomp_offload_device,
)
Expand Down Expand Up @@ -210,7 +209,6 @@ def _matrix_inverse_root_eigen(
A: Tensor,
root: Fraction,
epsilon: float = 0.0,
make_positive_semidefinite: bool = True,
retry_double_precision: bool = True,
eigen_decomp_offload_device: torch.device | str = "",
) -> tuple[Tensor, Tensor, Tensor]:
Expand All @@ -224,7 +222,6 @@ def _matrix_inverse_root_eigen(
A (Tensor): Square matrix of interest.
root (Fraction): Root of interest. Any rational number.
epsilon (float): Adds epsilon * I to matrix before taking matrix root. (Default: 0.0)
make_positive_semidefinite (bool): Perturbs matrix eigenvalues to ensure it is numerically positive semi-definite. (Default: True)
retry_double_precision (bool): Flag for re-trying eigendecomposition with higher precision if lower precision fails due
to CuSOLVER failure. (Default: True)
eigen_decomp_offload_device (torch.device | str): Device to offload eigen decomposition computation. If value is empty string, do not perform offloading. (Default: "")
Expand All @@ -248,14 +245,8 @@ def _matrix_inverse_root_eigen(
eigen_decomp_offload_device=eigen_decomp_offload_device,
)

lambda_min = torch.min(L)

# make eigenvalues >= 0 (if necessary)
if make_positive_semidefinite:
L += -torch.minimum(lambda_min, torch.as_tensor(0.0))

# add epsilon
L += epsilon
# make eigenvalues > 0 (if necessary)
L += -torch.minimum(torch.min(L) - epsilon, torch.as_tensor(0.0))

# compute inverse preconditioner
X = Q * L.pow(torch.as_tensor(-1.0 / root)).unsqueeze(0) @ Q.T
Expand Down Expand Up @@ -596,7 +587,6 @@ def compute_matrix_root_inverse_residuals(
X_hat.double(),
root=root,
epsilon=0.0,
make_positive_semidefinite=True,
eigen_decomp_offload_device=root_inv_config.eigen_decomp_offload_device,
)

Expand Down
2 changes: 0 additions & 2 deletions matrix_functions_types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,13 +54,11 @@ class EigenConfig(RootInvConfig, EigenvalueDecompositionConfig):
retry_double_precision (bool): Whether to re-trying eigendecomposition with higher (double) precision if lower precision fails due
to CuSOLVER failure. (Default: True)
eigen_decomp_offload_device (torch.device | str): Device to offload eigen decomposition to. If value is empty string, we don't perform offloading. (Default: "")
make_positive_semidefinite (bool): Perturbs matrix eigenvalues to ensure it is numerically positive semi-definite. (Default: True)
exponent_multiplier (float): Number to be multiplied to the numerator of the inverse root, i.e., eta where the
exponent is -eta / (2 * p). (Default: 1.0)

"""

make_positive_semidefinite: bool = True
exponent_multiplier: float = 1.0


Expand Down
13 changes: 1 addition & 12 deletions tests/matrix_functions_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -333,7 +333,6 @@ def _test_eigen_root(
self,
A: torch.Tensor,
root: int,
make_positive_semidefinite: bool,
epsilon: float,
tolerance: float,
eig_sols: Tensor,
Expand All @@ -342,7 +341,6 @@ def _test_eigen_root(
A=A,
root=Fraction(root),
epsilon=epsilon,
make_positive_semidefinite=make_positive_semidefinite,
)
abs_error = torch.dist(torch.linalg.matrix_power(X, -root), A, p=torch.inf)
A_norm = torch.linalg.norm(A, ord=torch.inf)
Expand All @@ -355,7 +353,6 @@ def _test_eigen_root_multi_dim(
A: Callable[[int], Tensor],
dims: list[int],
roots: list[int],
make_positive_semidefinite: bool,
epsilons: list[float],
tolerance: float,
eig_sols: Callable[[int], Tensor],
Expand All @@ -365,7 +362,6 @@ def _test_eigen_root_multi_dim(
self._test_eigen_root(
A(n),
root,
make_positive_semidefinite,
epsilon,
tolerance,
eig_sols(n),
Expand All @@ -376,24 +372,20 @@ def test_eigen_root_identity(self) -> None:
dims = [10, 100]
roots = [1, 2, 4, 8]
epsilons = [0.0]
make_positive_semidefinite = False

def eig_sols(n: int) -> Tensor:
return torch.ones(n)

def A(n: int) -> Tensor:
return torch.eye(n)

self._test_eigen_root_multi_dim(
A, dims, roots, make_positive_semidefinite, epsilons, tolerance, eig_sols
)
self._test_eigen_root_multi_dim(A, dims, roots, epsilons, tolerance, eig_sols)

def test_eigen_root_tridiagonal_1(self) -> None:
tolerance = 1e-4
dims = [10, 100]
roots = [1, 2, 4, 8]
epsilons = [0.0]
make_positive_semidefinite = False

for alpha, beta in itertools.product(
[0.001, 0.01, 0.1, 1.0, 10.0, 100.0], repeat=2
Expand Down Expand Up @@ -425,7 +417,6 @@ def A(n: int, alpha: float, beta: float) -> Tensor:
partial(A, alpha=alpha, beta=beta),
dims,
roots,
make_positive_semidefinite,
epsilons,
tolerance,
partial(eig_sols, alpha=alpha, beta=beta),
Expand All @@ -436,7 +427,6 @@ def test_eigen_root_tridiagonal_2(self) -> None:
dims = [10, 100]
roots = [1, 2, 4, 8]
epsilons = [0.0]
make_positive_semidefinite = False

for alpha, beta in itertools.product(
[0.001, 0.01, 0.1, 1.0, 10.0, 100.0], repeat=2
Expand Down Expand Up @@ -471,7 +461,6 @@ def A(n: int, alpha: float, beta: float) -> Tensor:
partial(A, alpha=alpha, beta=beta),
dims,
roots,
make_positive_semidefinite,
epsilons,
tolerance,
partial(eig_sols, alpha=alpha, beta=beta),
Expand Down
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