Bump PyTensor dependency

The JAX implementation of Dirichlet is a bit more prone to round-off errors than other backends, and the new PyTensor implementation of Multinomial does not mask invalid p. The test with extreme values was tweaked to avoid `nan`

Author: ricardoV94

conda-envs/environment-alternative-backends.yml

@@ -22,7 +22,7 @@ dependencies:
 - numpyro>=0.8.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.28.3,<2.29
+- pytensor>=2.30.1,<2.31
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/environment-dev.yml

@@ -12,7 +12,7 @@ dependencies:
 - numpy>=1.25.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.28.3,<2.29
+- pytensor>=2.30.1,<2.31
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/environment-docs.yml

@@ -11,7 +11,7 @@ dependencies:
 - numpy>=1.25.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.28.3,<2.29
+- pytensor>=2.30.1,<2.31
 - python-graphviz
 - rich>=13.7.1
 - scipy>=1.4.1

conda-envs/environment-test.yml

@@ -14,7 +14,7 @@ dependencies:
 - pandas>=0.24.0
 - pip
 - polyagamma
-- pytensor>=2.28.3,<2.29
+- pytensor>=2.30.1,<2.31
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/windows-environment-dev.yml

@@ -12,7 +12,7 @@ dependencies:
 - numpy>=1.25.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.28.3,<2.29
+- pytensor>=2.30.1,<2.31
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/windows-environment-test.yml

@@ -15,7 +15,7 @@ dependencies:
 - pandas>=0.24.0
 - pip
 - polyagamma
-- pytensor>=2.28.3,<2.29
+- pytensor>=2.30.1,<2.31
 - python-graphviz
 - networkx
 - rich>=13.7.1

pymc/variational/opvi.py

@@ -63,7 +63,6 @@
 from pytensor.graph.replace import graph_replace
 from pytensor.scalar.basic import identity as scalar_identity
 from pytensor.tensor.elemwise import Elemwise
-from pytensor.tensor.shape import unbroadcast
 
 import pymc as pm
 
@@ -1096,13 +1095,6 @@ def make_size_and_deterministic_replacements(self, s, d, more_replacements=None)
         """
         initial = self._new_initial(s, d, more_replacements)
         initial = pt.specify_shape(initial, self.symbolic_initial.type.shape)
-        # The static shape of initial may be more precise than self.symbolic_initial,
-        # and reveal previously unknown broadcastable dimensions. We have to mask those again.
-        if initial.type.broadcastable != self.symbolic_initial.type.broadcastable:
-            unbroadcast_axes = (
-                i for i, b in enumerate(self.symbolic_initial.type.broadcastable) if not b
-            )
-            initial = unbroadcast(initial, *unbroadcast_axes)
         if more_replacements:
             initial = graph_replace(initial, more_replacements, strict=False)
         return {self.symbolic_initial: initial}

requirements-dev.txt

@@ -16,7 +16,7 @@ numpydoc
 pandas>=0.24.0
 polyagamma
 pre-commit>=2.8.0
-pytensor>=2.28.3,<2.29
+pytensor>=2.30.1,<2.31
 pytest-cov>=2.5
 pytest>=3.0
 rich>=13.7.1

requirements.txt

@@ -3,7 +3,7 @@ cachetools>=4.2.1
 cloudpickle
 numpy>=1.25.0
 pandas>=0.24.0
-pytensor>=2.28.2,<2.29
+pytensor>=2.30.1,<2.31
 rich>=13.7.1
 scipy>=1.4.1
 threadpoolctl>=3.1.0,<4.0.0

tests/distributions/test_random_alternative_backends.py

@@ -32,7 +32,7 @@ def mode(request):
 
 def test_dirichlet_multinomial(mode):
     """Test we can draw from a DM in the JAX backend if the shape is constant."""
-    dm = DirichletMultinomial.dist(n=5, a=np.eye(3) * 1e6 + 0.01)
+    dm = DirichletMultinomial.dist(n=5, a=np.eye(3) * 1e6 + 0.02)
     dm_draws = pm.draw(dm, mode=mode)
     np.testing.assert_equal(dm_draws, np.eye(3) * 5)