Bump aesara dependency

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
       - types-filelock
       - types-setuptools
       - arviz
-      - aesara==2.7.5
+      - aesara==2.7.7
       - aeppl==0.0.32
       always_run: true
       require_serial: true

conda-envs/environment-dev.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
 # Base dependencies
 - aeppl=0.0.32
-- aesara=2.7.5
+- aesara=2.7.7
 - arviz>=0.12.0
 - blas
 - cachetools>=4.2.1

conda-envs/environment-test.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
 # Base dependencies
 - aeppl=0.0.32
-- aesara=2.7.5
+- aesara=2.7.7
 - arviz>=0.12.0
 - blas
 - cachetools>=4.2.1

conda-envs/windows-environment-dev.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
 # Base dependencies (see install guide for Windows)
 - aeppl=0.0.32
-- aesara=2.7.5
+- aesara=2.7.7
 - arviz>=0.12.0
 - blas
 - cachetools>=4.2.1

conda-envs/windows-environment-test.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
 # Base dependencies (see install guide for Windows)
 - aeppl=0.0.32
-- aesara=2.7.5
+- aesara=2.7.7
 - arviz>=0.12.0
 - blas
 - cachetools>=4.2.1

pymc/data.py

@@ -310,6 +310,7 @@ def __init__(
         batch_size=128,
         dtype=None,
         broadcastable=None,
+        shape=None,
         name="Minibatch",
         random_seed=42,
         update_shared_f=None,
@@ -324,9 +325,15 @@ def __init__(
         self.update_shared_f = update_shared_f
         self.random_slc = self.make_random_slices(self.shared.shape, batch_size, random_seed)
         minibatch = self.shared[self.random_slc]
-        if broadcastable is None:
-            broadcastable = (False,) * minibatch.ndim
-        minibatch = at.patternbroadcast(minibatch, broadcastable)
+        if broadcastable is not None:
+            warnings.warn(
+                "Minibatch `broadcastable` argument is deprecated. Use `shape` instead",
+                FutureWarning,
+            )
+            assert shape is None
+            shape = [1 if b else None for b in broadcastable]
+        if shape is not None:
+            minibatch = at.specify_shape(minibatch, shape)
         self.minibatch = minibatch
         super().__init__(self.minibatch.type, None, None, name=name)
         Apply(aesara.compile.view_op, inputs=[self.minibatch], outputs=[self])

pymc/ode/ode.py

@@ -158,11 +158,11 @@ def __call__(self, y0, theta, return_sens=False, **kwargs):
             )
 
         # convert inputs to tensors (and check their types)
-        y0 = at.cast(at.unbroadcast(at.as_tensor_variable(y0), 0), floatX)
-        theta = at.cast(at.unbroadcast(at.as_tensor_variable(theta), 0), floatX)
+        y0 = at.cast(at.as_tensor_variable(y0), floatX)
+        theta = at.cast(at.as_tensor_variable(theta), floatX)
         inputs = [y0, theta]
         for i, (input_val, itype) in enumerate(zip(inputs, self._itypes)):
-            if not input_val.type.in_same_class(itype):
+            if not itype.is_super(input_val.type):
                 raise ValueError(
                     f"Input {i} of type {input_val.type} does not have the expected type of {itype}"
                 )

pymc/variational/opvi.py

@@ -1030,7 +1030,7 @@ def symbolic_sample_over_posterior(self, node):
         """
         node = self.to_flat_input(node)
         random = self.symbolic_random.astype(self.symbolic_initial.dtype)
-        random = at.patternbroadcast(random, self.symbolic_initial.broadcastable)
+        random = at.specify_shape(random, self.symbolic_initial.type.shape)
 
         def sample(post, node):
             return aesara.clone_replace(node, {self.input: post})
@@ -1065,7 +1065,7 @@ def make_size_and_deterministic_replacements(self, s, d, more_replacements=None)
         dict with replacements for initial
         """
         initial = self._new_initial(s, d, more_replacements)
-        initial = at.patternbroadcast(initial, self.symbolic_initial.broadcastable)
+        initial = at.specify_shape(initial, self.symbolic_initial.type.shape)
         if more_replacements:
             initial = aesara.clone_replace(initial, more_replacements)
         return {self.symbolic_initial: initial}

requirements-dev.txt

@@ -2,7 +2,7 @@
 # See that file for comments about the need/usage of each dependency.
 
 aeppl==0.0.32
-aesara==2.7.5
+aesara==2.7.7
 arviz>=0.12.0
 cachetools>=4.2.1
 cloudpickle

requirements.txt

@@ -1,5 +1,5 @@
 aeppl==0.0.32
-aesara==2.7.5
+aesara==2.7.7
 arviz>=0.12.0
 cachetools>=4.2.1
 cloudpickle