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Removing evaluate_kernel #15

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Removing evaluate_kernel #15

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18 changes: 3 additions & 15 deletions linear_operator/operators/_linear_operator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -469,12 +469,10 @@ def _cholesky(self, upper: bool = False) -> "TriangularLinearOperator": # noqa
from .keops_linear_operator import KeOpsLinearOperator
from .triangular_linear_operator import TriangularLinearOperator

evaluated_kern_mat = self.evaluate_kernel()

if any(isinstance(sub_mat, KeOpsLinearOperator) for sub_mat in evaluated_kern_mat._args):
if any(isinstance(sub_mat, KeOpsLinearOperator) for sub_mat in self._args):
raise RuntimeError("Cannot run Cholesky with KeOps: it will either be really slow or not work.")

evaluated_mat = evaluated_kern_mat.to_dense()
evaluated_mat = self.to_dense()

# if the tensor is a scalar, we can just take the square root
if evaluated_mat.size(-1) == 1:
Expand Down Expand Up @@ -554,8 +552,6 @@ def _mul_matrix(self, other: Union[torch.Tensor, "LinearOperator"]) -> LinearOpe
from .dense_linear_operator import DenseLinearOperator
from .mul_linear_operator import MulLinearOperator

self = self.evaluate_kernel()
other = other.evaluate_kernel()
if isinstance(self, DenseLinearOperator) or isinstance(other, DenseLinearOperator):
return DenseLinearOperator(self.to_dense() * other.to_dense())
else:
Expand Down Expand Up @@ -1445,14 +1441,6 @@ def eigvalsh(self) -> torch.Tensor:
pass
return self._symeig(eigenvectors=False)[0]

# TODO: remove
def evaluate_kernel(self):
"""
Return a new LinearOperator representing the same one as this one, but with
all lazily evaluated kernels actually evaluated.
"""
return self.representation_tree()(*self.representation())

@_implements(torch.exp)
def exp(self) -> "LinearOperator":
# Only implemented by some LinearOperator subclasses
Expand Down Expand Up @@ -2522,7 +2510,7 @@ def zero_mean_mvn_samples(self, num_samples: int) -> torch.Tensor:
base_samples = base_samples.permute(-1, *range(self.dim() - 1)).contiguous()
base_samples = base_samples.unsqueeze(-1)
solves, weights, _, _ = contour_integral_quad(
self.evaluate_kernel(),
self,
base_samples,
inverse=False,
num_contour_quadrature=settings.num_contour_quadrature.value(),
Expand Down
4 changes: 0 additions & 4 deletions linear_operator/operators/added_diag_linear_operator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,7 +186,3 @@ def _symeig(self, eigenvectors: bool = False) -> Tuple[Tensor, Optional[LinearOp
evals = evals_ + self._diag_tensor._diagonal()
return evals, evecs
return super()._symeig(eigenvectors=eigenvectors)

def evaluate_kernel(self) -> LinearOperator:
added_diag_linear_op = self.representation_tree()(*self.representation())
return added_diag_linear_op._linear_op + added_diag_linear_op._diag_tensor