WIP

gpytorch/kernels/additive_structure_kernel.py

@@ -34,13 +34,6 @@ class AdditiveStructureKernel(Kernel):
             Passed down to the `base_kernel`.
     """
 
-    @property
-    def is_stationary(self) -> bool:
-        """
-        Kernel is stationary if the base kernel is stationary.
-        """
-        return self.base_kernel.is_stationary
-
     def __init__(
         self,
         base_kernel: Kernel,
@@ -51,6 +44,17 @@ def __init__(
         self.base_kernel = base_kernel
         self.num_dims = num_dims
 
+    @property
+    def _lazily_evaluate(self) -> bool:
+        return self.base_kernel._lazily_evaluate
+
+    @property
+    def is_stationary(self) -> bool:
+        """
+        Kernel is stationary if the base kernel is stationary.
+        """
+        return self.base_kernel.is_stationary
+
     def forward(self, x1, x2, diag=False, last_dim_is_batch=False, **params):
         if last_dim_is_batch:
             raise RuntimeError("AdditiveStructureKernel does not accept the last_dim_is_batch argument.")

gpytorch/kernels/cosine_kernel.py

@@ -56,8 +56,6 @@ class CosineKernel(Kernel):
         >>> covar = covar_module(x)  # Output: LazyVariable of size (2 x 10 x 10)
     """
 
-    is_stationary = True
-
     def __init__(
         self,
         period_length_prior: Optional[Prior] = None,
@@ -85,6 +83,10 @@ def __init__(
 
         self.register_constraint("raw_period_length", period_length_constraint)
 
+    @property
+    def is_stationary(self):
+        return True
+
     @property
     def period_length(self):
         return self.raw_period_length_constraint.transform(self.raw_period_length)

gpytorch/kernels/cylindrical_kernel.py

@@ -4,7 +4,6 @@
 
 import torch
 
-from .. import settings
 from ..constraints import Interval, Positive
 from ..priors import Prior
 from .kernel import Kernel
@@ -152,8 +151,7 @@ def forward(self, x1: torch.Tensor, x2: torch.Tensor, diag: Optional[bool] = Fal
                 else:
                     angular_kernel = angular_kernel + self.angular_weights[..., p, None].mul(gram_mat.pow(p))
 
-        with settings.lazily_evaluate_kernels(False):
-            radial_kernel = self.radial_base_kernel(self.kuma(r1), self.kuma(r2), diag=diag, **params)
+        radial_kernel = self.radial_base_kernel.forward(self.kuma(r1), self.kuma(r2), diag=diag, **params)
         return radial_kernel.mul(angular_kernel)
 
     def kuma(self, x: torch.Tensor) -> torch.Tensor:

gpytorch/kernels/grid_interpolation_kernel.py

@@ -121,6 +121,13 @@ def __init__(
         )
         self.register_buffer("has_initialized_grid", torch.tensor(has_initialized_grid, dtype=torch.bool))
 
+    @property
+    def _lazily_evaluate(self) -> bool:
+        # GridInterpolationKernels should not lazily evaluate; there are few gains (the inducing point kernel
+        # matrix always needs to be evaluated; regardless of the size of x1 and x2), and the
+        # InterpolatedLinearOperator structure is needed for fast predictions.
+        return False
+
     @property
     def _tight_grid_bounds(self):
         grid_spacings = tuple((bound[1] - bound[0]) / self.grid_sizes[i] for i, bound in enumerate(self.grid_bounds))

gpytorch/kernels/grid_kernel.py

@@ -44,8 +44,6 @@ class GridKernel(Kernel):
         http://www.cs.cmu.edu/~andrewgw/manet.pdf
     """
 
-    is_stationary = True
-
     def __init__(
         self,
         base_kernel: Kernel,
@@ -66,6 +64,15 @@ def __init__(
         if not self.interpolation_mode:
             self.register_buffer("full_grid", create_data_from_grid(grid))
 
+    @property
+    def _lazily_evaluate(self) -> bool:
+        # Toeplitz structure is very efficient; no need to lazily evaluate
+        return False
+
+    @property
+    def is_stationary(self) -> bool:
+        return True
+
     def _clear_cache(self):
         if hasattr(self, "_cached_kernel_mat"):
             del self._cached_kernel_mat

gpytorch/kernels/index_kernel.py

@@ -76,6 +76,12 @@ def __init__(
 
         self.register_constraint("raw_var", var_constraint)
 
+    @property
+    def _lazily_evaluate(self) -> bool:
+        # IndexKernel does not need lazy evaluation, since the complete BB^T + D_v` is always
+        # computed regardless of x1 and x2
+        return False
+
     @property
     def var(self):
         return self.raw_var_constraint.transform(self.raw_var)

gpytorch/kernels/inducing_point_kernel.py

@@ -47,6 +47,12 @@ def _clear_cache(self):
         if hasattr(self, "_cached_kernel_inv_root"):
             del self._cached_kernel_inv_root
 
+    @property
+    def _lazily_evaluate(self) -> bool:
+        # InducingPointKernels kernels should not lazily evaluate; to use the Woodbury formula,
+        # we want the Kernel to return a LowRankLinearOperator, not a KernelLinaerOperator.
+        return False
+
     @property
     def _inducing_mat(self):
         if not self.training and hasattr(self, "_cached_kernel_mat"):

gpytorch/kernels/keops/matern_kernel.py

@@ -2,7 +2,7 @@
 import math
 
 import torch
-from linear_operator.operators import KeOpsLinearOperator
+from linear_operator.operators import KernelLinearOperator
 
 from ... import settings
 from .keops_kernel import KeOpsKernel
@@ -92,7 +92,7 @@ def forward(self, x1, x2, diag=False, **params):
                 return self.covar_func(x1_, x2_, diag=True)
 
             covar_func = lambda x1, x2, diag=False: self.covar_func(x1, x2, diag)
-            return KeOpsLinearOperator(x1_, x2_, covar_func)
+            return KernelLinearOperator(x1_, x2_, covar_func=covar_func)
 
 except ImportError:
 

gpytorch/kernels/keops/rbf_kernel.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
 import torch
-from linear_operator.operators import KeOpsLinearOperator
+from linear_operator.operators import KernelLinearOperator
 
 from ... import settings
 from ..rbf_kernel import postprocess_rbf
@@ -54,7 +54,7 @@ def forward(self, x1, x2, diag=False, **params):
             if diag:
                 return covar_func(x1_, x2_, diag=True)
 
-            return KeOpsLinearOperator(x1_, x2_, covar_func)
+            return KernelLinearOperator(x1_, x2_, covar_func=covar_func)
 
 except ImportError: