Updates for Aesara Solve changes

Author: brandonwillard

pymc3/distributions/dist_math.py

@@ -31,7 +31,9 @@
 from aesara.scan import until
 from aesara.tensor import gammaln
 from aesara.tensor.elemwise import Elemwise
-from aesara.tensor.slinalg import Cholesky, Solve
+from aesara.tensor.slinalg import Cholesky
+from aesara.tensor.slinalg import solve_lower_triangular as solve_lower
+from aesara.tensor.slinalg import solve_upper_triangular as solve_upper
 
 from pymc3.aesaraf import floatX
 from pymc3.distributions.shape_utils import to_tuple
@@ -267,8 +269,6 @@ def MvNormalLogp():
     delta = at.matrix("delta")
     delta.tag.test_value = floatX(np.zeros((2, 3)))
 
-    solve_lower = Solve(A_structure="lower_triangular")
-    solve_upper = Solve(A_structure="upper_triangular")
     cholesky = Cholesky(lower=True, on_error="nan")
 
     n, k = delta.shape

pymc3/distributions/multivariate.py

@@ -31,12 +31,9 @@
 from aesara.tensor.random.basic import MultinomialRV, dirichlet, multivariate_normal
 from aesara.tensor.random.op import RandomVariable, default_shape_from_params
 from aesara.tensor.random.utils import broadcast_params
-from aesara.tensor.slinalg import (
-    Cholesky,
-    Solve,
-    solve_lower_triangular,
-    solve_upper_triangular,
-)
+from aesara.tensor.slinalg import Cholesky
+from aesara.tensor.slinalg import solve_lower_triangular as solve_lower
+from aesara.tensor.slinalg import solve_upper_triangular as solve_upper
 from aesara.tensor.type import TensorType
 from scipy import linalg, stats
 
@@ -66,7 +63,6 @@
     "CAR",
 ]
 
-solve_lower = Solve(A_structure="lower_triangular")
 # Step methods and advi do not catch LinAlgErrors at the
 # moment. We work around that by using a cholesky op
 # that returns a nan as first entry instead of raising
@@ -1716,10 +1712,10 @@ def logp(value, mu, rowchol, colchol):
         delta = value - mu
 
         # Find exponent piece by piece
-        right_quaddist = solve_lower_triangular(rowchol, delta)
+        right_quaddist = solve_lower(rowchol, delta)
         quaddist = at.nlinalg.matrix_dot(right_quaddist.T, right_quaddist)
-        quaddist = solve_lower_triangular(colchol, quaddist)
-        quaddist = solve_upper_triangular(colchol.T, quaddist)
+        quaddist = solve_lower(colchol, quaddist)
+        quaddist = solve_upper(colchol.T, quaddist)
         trquaddist = at.nlinalg.trace(quaddist)
 
         coldiag = at.diag(colchol)

pymc3/gp/util.py

@@ -17,14 +17,19 @@
 import aesara.tensor as at
 import numpy as np
 
-from aesara.tensor.slinalg import Solve, cholesky  # pylint: disable=unused-import
+from aesara.tensor.slinalg import (  # noqa: W0611; pylint: disable=unused-import
+    cholesky,
+    solve,
+)
+from aesara.tensor.slinalg import (  # noqa: W0611; pylint: disable=unused-import
+    solve_lower_triangular as solve_lower,
+)
+from aesara.tensor.slinalg import (  # noqa: W0611; pylint: disable=unused-import
+    solve_upper_triangular as solve_upper,
+)
 from aesara.tensor.var import TensorConstant
 from scipy.cluster.vq import kmeans
 
-solve_lower = Solve(A_structure="lower_triangular")
-solve_upper = Solve(A_structure="upper_triangular")
-solve = Solve(A_structure="general")
-
 
 def infer_shape(X, n_points=None):
     R"""