From dca3803fc119a2fcf9f79e4948dfad419590d58a Mon Sep 17 00:00:00 2001
From: Sebastian Ament <sebastianament@fb.com>
Date: Tue, 13 Sep 2022 15:15:51 -0400
Subject: [PATCH] removing evaluate_kernel

---
 linear_operator/operators/_linear_operator.py  | 18 +++---------------
 .../operators/added_diag_linear_operator.py    |  4 ----
 2 files changed, 3 insertions(+), 19 deletions(-)

diff --git a/linear_operator/operators/_linear_operator.py b/linear_operator/operators/_linear_operator.py
index 177269ae..aceaaa5f 100644
--- a/linear_operator/operators/_linear_operator.py
+++ b/linear_operator/operators/_linear_operator.py
@@ -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:
@@ -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:
@@ -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
@@ -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(),
diff --git a/linear_operator/operators/added_diag_linear_operator.py b/linear_operator/operators/added_diag_linear_operator.py
index 4cd5f61f..325c8c08 100644
--- a/linear_operator/operators/added_diag_linear_operator.py
+++ b/linear_operator/operators/added_diag_linear_operator.py
@@ -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