Retiring deprecated versions ofpsd_safe_cholesky, NotPSDError, and assert_allclose

gpytorch/kernels/inducing_point_kernel.py

@@ -12,14 +12,14 @@
     LowRankRootLinearOperator,
     MatmulLinearOperator,
 )
+from linear_operator.utils.cholesky import psd_safe_cholesky
 from torch import Tensor
 
 from .. import settings
 from ..distributions import MultivariateNormal
 from ..likelihoods import Likelihood
 from ..mlls import InducingPointKernelAddedLossTerm
 from ..models import exact_prediction_strategies
-from ..utils.cholesky import psd_safe_cholesky
 from .kernel import Kernel
 
 

gpytorch/models/exact_prediction_strategies.py

@@ -17,12 +17,12 @@
     RootLinearOperator,
     ZeroLinearOperator,
 )
+from linear_operator.utils.cholesky import psd_safe_cholesky
 from linear_operator.utils.interpolation import left_interp, left_t_interp
 from torch import Tensor
 
 from .. import settings
 from ..lazy import LazyEvaluatedKernelTensor
-from ..utils.cholesky import psd_safe_cholesky
 from ..utils.memoize import add_to_cache, cached, clear_cache_hook, pop_from_cache
 
 

gpytorch/priors/lkj_prior.py

@@ -1,11 +1,11 @@
 #!/usr/bin/env python3
 
 import torch
+from linear_operator.utils.cholesky import psd_safe_cholesky
 from torch.distributions import LKJCholesky, constraints
 from torch.nn import Module as TModule
 
 from .. import settings
-from ..utils.cholesky import psd_safe_cholesky
 from .prior import Prior
 
 

gpytorch/variational/natural_variational_distribution.py

@@ -4,9 +4,9 @@
 
 import torch
 from linear_operator.operators import CholLinearOperator, TriangularLinearOperator
+from linear_operator.utils.cholesky import psd_safe_cholesky
 
 from ..distributions import MultivariateNormal
-from ..utils.cholesky import psd_safe_cholesky
 from ._variational_distribution import _VariationalDistribution
 
 

gpytorch/variational/nearest_neighbor_variational_strategy.py

@@ -4,10 +4,10 @@
 import torch
 from linear_operator import to_dense
 from linear_operator.operators import DiagLinearOperator, TriangularLinearOperator
+from linear_operator.utils.cholesky import psd_safe_cholesky
 
 from .. import settings
 from ..distributions import MultivariateNormal
-from ..utils.cholesky import psd_safe_cholesky
 from ..utils.errors import CachingError
 from ..utils.memoize import add_to_cache, cached, pop_from_cache
 from ..utils.nearest_neighbors import NNUtil

gpytorch/variational/unwhitened_variational_strategy.py

@@ -12,11 +12,11 @@
     TriangularLinearOperator,
     ZeroLinearOperator,
 )
+from linear_operator.utils.cholesky import psd_safe_cholesky
+from linear_operator.utils.errors import NotPSDError
 
 from .. import settings
 from ..distributions import MultivariateNormal
-from ..utils.cholesky import psd_safe_cholesky
-from ..utils.errors import NotPSDError
 from ..utils.memoize import add_to_cache, cached
 from ._variational_strategy import _VariationalStrategy
 from .cholesky_variational_distribution import CholeskyVariationalDistribution

gpytorch/variational/variational_strategy.py

@@ -12,19 +12,18 @@
     SumLinearOperator,
     TriangularLinearOperator,
 )
+from linear_operator.utils.cholesky import psd_safe_cholesky
+from linear_operator.utils.errors import NotPSDError
 
 from gpytorch.variational._variational_strategy import _VariationalStrategy
 from gpytorch.variational.cholesky_variational_distribution import CholeskyVariationalDistribution
 
 from ..distributions import MultivariateNormal
 from ..settings import _linalg_dtype_cholesky, trace_mode
-from ..utils.cholesky import psd_safe_cholesky
-from ..utils.errors import CachingError, NotPSDError
+from ..utils.errors import CachingError
 from ..utils.memoize import cached, clear_cache_hook, pop_from_cache_ignore_args
 from ..utils.warnings import OldVersionWarning
 
-# from ._variational_strategy import _VariationalStrategy
-
 
 def _ensure_updated_strategy_flag_set(
     state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs

test/likelihoods/test_gaussian_likelihood.py

@@ -202,7 +202,7 @@ def test_missing_value_inference(self):
         likelihood.MISSING_VALUE_FILL = -999.0
         like_init_minus = likelihood.log_marginal(samples, mvn).sum().data
 
-        torch.testing.assert_allclose(like_init_plus, like_init_minus)
+        torch.testing.assert_close(like_init_plus, like_init_minus)
 
         # check that the correct noise sd is recovered