Bump PyTensor dependency to 2.13

conda-envs/environment-dev.yml

@@ -14,7 +14,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.12.0,<2.13
+- pytensor>=2.13.0,<2.14
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/environment-docs.yml

@@ -12,7 +12,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.12.0,<2.13
+- pytensor>=2.13.0,<2.14
 - python-graphviz
 - scipy>=1.4.1
 - typing-extensions>=3.7.4

conda-envs/environment-test.yml

@@ -17,7 +17,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.12.0,<2.13
+- pytensor>=2.13.0,<2.14
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/windows-environment-dev.yml

@@ -14,7 +14,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.12.0,<2.13
+- pytensor>=2.13.0,<2.14
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/windows-environment-test.yml

@@ -17,7 +17,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.12.0,<2.13
+- pytensor>=2.13.0,<2.14
 - python-graphviz
 - networkx
 - scipy>=1.4.1

pymc/distributions/distribution.py

@@ -34,6 +34,7 @@
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.random.rewriting import local_subtensor_rv_lift
 from pytensor.tensor.random.utils import normalize_size_param
+from pytensor.tensor.rewriting.shape import ShapeFeature
 from pytensor.tensor.var import TensorVariable
 from typing_extensions import TypeAlias
 
@@ -1229,15 +1230,12 @@ def create_partial_observed_rv(
                     can_rewrite = True
 
     if can_rewrite:
-        # Rewrite doesn't work with boolean masks. Should be fixed after https://github.com/pymc-devs/pytensor/pull/329
-        mask, antimask = mask.nonzero(), antimask.nonzero()
-
         masked_rv = rv[mask]
-        fgraph = FunctionGraph(outputs=[masked_rv], clone=False)
+        fgraph = FunctionGraph(outputs=[masked_rv], clone=False, features=[ShapeFeature()])
         [unobserved_rv] = local_subtensor_rv_lift.transform(fgraph, fgraph.outputs[0].owner)
 
         antimasked_rv = rv[antimask]
-        fgraph = FunctionGraph(outputs=[antimasked_rv], clone=False)
+        fgraph = FunctionGraph(outputs=[antimasked_rv], clone=False, features=[ShapeFeature()])
         [observed_rv] = local_subtensor_rv_lift.transform(fgraph, fgraph.outputs[0].owner)
 
         # Make a clone of the observedRV, with a distinct rng so that observed and

pymc/logprob/mixture.py

@@ -52,6 +52,7 @@
     local_rv_size_lift,
     local_subtensor_rv_lift,
 )
+from pytensor.tensor.rewriting.shape import ShapeFeature
 from pytensor.tensor.shape import shape_tuple
 from pytensor.tensor.subtensor import (
     AdvancedSubtensor,
@@ -205,7 +206,7 @@ def expand_indices(
 
 def rv_pull_down(x: TensorVariable, dont_touch_vars=None) -> TensorVariable:
     """Pull a ``RandomVariable`` ``Op`` down through a graph, when possible."""
-    fgraph = FunctionGraph(outputs=dont_touch_vars or [], clone=False)
+    fgraph = FunctionGraph(outputs=dont_touch_vars or [], clone=False, features=[ShapeFeature()])
 
     return pre_greedy_node_rewriter(
         fgraph,

pymc/math.py

@@ -75,13 +75,14 @@
     where,
     zeros_like,
 )
+from pytensor.tensor.special import log_softmax, softmax
 
 try:
     from pytensor.tensor.basic import extract_diag
 except ImportError:
     from pytensor.tensor.nlinalg import extract_diag
 
-from pytensor.tensor.nlinalg import det, matrix_dot, matrix_inverse, trace
+from pytensor.tensor.nlinalg import matrix_inverse
 from scipy.linalg import block_diag as scipy_block_diag
 
 from pymc.pytensorf import floatX, ix_, largest_common_dtype
@@ -267,31 +268,7 @@ def logdiffexp_numpy(a, b):
     return a + log1mexp_numpy(b - a, negative_input=True)
 
 
-def invlogit(x, eps=None):
-    """The inverse of the logit function, 1 / (1 + exp(-x))."""
-    if eps is not None:
-        warnings.warn(
-            "pymc.math.invlogit no longer supports the ``eps`` argument and it will be ignored.",
-            FutureWarning,
-            stacklevel=2,
-        )
-    return pt.sigmoid(x)
-
-
-def softmax(x, axis=None):
-    # Ignore vector case UserWarning issued by PyTensor. This can be removed once PyTensor
-    # drops that warning
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", UserWarning)
-        return pt.special.softmax(x, axis=axis)
-
-
-def log_softmax(x, axis=None):
-    # Ignore vector case UserWarning issued by PyTensor. This can be removed once PyTensor
-    # drops that warning
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", UserWarning)
-        return pt.special.log_softmax(x, axis=axis)
+invlogit = sigmoid
 
 
 def logbern(log_p):

requirements-dev.txt

@@ -18,7 +18,7 @@ numpydoc
 pandas>=0.24.0
 polyagamma
 pre-commit>=2.8.0
-pytensor>=2.12.0,<2.13
+pytensor>=2.13.0,<2.14
 pytest-cov>=2.5
 pytest>=3.0
 scipy>=1.4.1

requirements.txt

@@ -4,6 +4,6 @@ cloudpickle
 fastprogress>=0.2.0
 numpy>=1.15.0
 pandas>=0.24.0
-pytensor>=2.12.0,<2.13
+pytensor>=2.13.0,<2.14
 scipy>=1.4.1
 typing-extensions>=3.7.4

tests/distributions/test_dist_math.py

@@ -224,7 +224,7 @@ def check_vals(fn1, fn2, *args):
 
 
 def test_multigamma():
-    x = pt.vector("x")
+    x = pt.vector("x", shape=(1,))
     p = pt.scalar("p")
 
     xvals = [np.array([v], dtype=config.floatX) for v in [0.1, 2, 5, 10, 50, 100]]

tests/test_math.py

@@ -262,37 +262,3 @@ def test_expand_packed_triangular():
     assert np.all(expand_upper.eval({packed: upper_packed}) == upper)
     assert np.all(expand_diag_lower.eval({packed: lower_packed}) == floatX(np.diag(vals)))
     assert np.all(expand_diag_upper.eval({packed: upper_packed}) == floatX(np.diag(vals)))
-
-
-def test_invlogit_deprecation_warning():
-    with pytest.warns(
-        FutureWarning,
-        match="pymc.math.invlogit no longer supports the",
-    ):
-        res = invlogit(np.array(-750.0), 1e-5).eval()
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        res_zero_eps = invlogit(np.array(-750.0)).eval()
-
-    assert np.isclose(res, res_zero_eps)
-
-
-@pytest.mark.parametrize(
-    "pytensor_function, pymc_wrapper",
-    [
-        (pt.special.softmax, softmax),
-        (pt.special.log_softmax, log_softmax),
-    ],
-)
-def test_softmax_logsoftmax_no_warnings(pytensor_function, pymc_wrapper):
-    """Test that wrappers for pytensor functions do not issue Warnings"""
-
-    vector = pt.vector("vector")
-    with pytest.warns(Warning) as record:
-        pytensor_function(vector)
-    assert {w.category for w in record.list} == {UserWarning, FutureWarning}
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        pymc_wrapper(vector)