Bump PyTensor dependency

conda-envs/environment-dev.yml

@@ -13,7 +13,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.23,<2.24
+- pytensor>=2.25.1,<2.26
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/environment-docs.yml

@@ -11,7 +11,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.23,<2.24
+- pytensor>=2.25.1,<2.26
 - python-graphviz
 - rich>=13.7.1
 - scipy>=1.4.1

conda-envs/environment-jax.yml

@@ -20,7 +20,7 @@ dependencies:
 - numpyro>=0.8.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.23,<2.24
+- pytensor>=2.25.1,<2.26
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/environment-test.yml

@@ -16,7 +16,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.23,<2.24
+- pytensor>=2.25.1,<2.26
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/windows-environment-dev.yml

@@ -13,7 +13,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.23,<2.24
+- pytensor>=2.25.1,<2.26
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/windows-environment-test.yml

@@ -16,7 +16,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.23,<2.24
+- pytensor>=2.25.1,<2.26
 - python-graphviz
 - networkx
 - rich>=13.7.1

pymc/distributions/distribution.py

@@ -836,11 +836,11 @@ def create_partial_observed_rv(
     if can_rewrite:
         masked_rv = rv[mask]
         fgraph = FunctionGraph(outputs=[masked_rv], clone=False, features=[ShapeFeature()])
-        [unobserved_rv] = local_subtensor_rv_lift.transform(fgraph, fgraph.outputs[0].owner)
+        unobserved_rv = local_subtensor_rv_lift.transform(fgraph, masked_rv.owner)[masked_rv]
 
         antimasked_rv = rv[antimask]
         fgraph = FunctionGraph(outputs=[antimasked_rv], clone=False, features=[ShapeFeature()])
-        [observed_rv] = local_subtensor_rv_lift.transform(fgraph, fgraph.outputs[0].owner)
+        observed_rv = local_subtensor_rv_lift.transform(fgraph, antimasked_rv.owner)[antimasked_rv]
 
         # Make a clone of the observedRV, with a distinct rng so that observed and
         # unobserved are never treated as equivalent (and mergeable) nodes by pytensor.

pymc/logprob/order.py

@@ -99,18 +99,20 @@ def find_measurable_max(fgraph: FunctionGraph, node: Apply) -> list[TensorVariab
         if not all(params.type.broadcastable):
             return None
 
-    # Check whether axis covers all dimensions
-    axis = set(node.op.axis)
-    base_var_dims = set(range(base_var.ndim))
-    if axis != base_var_dims:
-        return None
+    if node.op.axis is None:
+        axis = tuple(range(base_var.ndim))
+    else:
+        # Check whether axis covers all dimensions
+        axis = tuple(sorted(node.op.axis))
+        if axis != tuple(range(base_var.ndim)):
+            return None
 
     # distinguish measurable discrete and continuous (because logprob is different)
     measurable_max: Max
     if base_var.type.dtype.startswith("int"):
-        measurable_max = MeasurableMaxDiscrete(list(axis))
+        measurable_max = MeasurableMaxDiscrete(axis)
     else:
-        measurable_max = MeasurableMax(list(axis))
+        measurable_max = MeasurableMax(axis)
 
     max_rv_node = measurable_max.make_node(base_var)
     max_rv = max_rv_node.outputs
@@ -206,21 +208,23 @@ def find_measurable_max_neg(fgraph: FunctionGraph, node: Apply) -> list[TensorVa
         if not all(params.type.broadcastable):
             return None
 
-    # Check whether axis is supported or not
-    axis = set(node.op.axis)
-    base_var_dims = set(range(base_var.ndim))
-    if axis != base_var_dims:
-        return None
+    if node.op.axis is None:
+        axis = tuple(range(base_var.ndim))
+    else:
+        # Check whether axis is supported or not
+        axis = tuple(sorted(node.op.axis))
+        if axis != tuple(range(base_var.ndim)):
+            return None
 
     if not rv_map_feature.request_measurable([base_rv]):
         return None
 
     # distinguish measurable discrete and continuous (because logprob is different)
     measurable_min: Max
     if base_rv.type.dtype.startswith("int"):
-        measurable_min = MeasurableDiscreteMaxNeg(list(axis))
+        measurable_min = MeasurableDiscreteMaxNeg(axis)
     else:
-        measurable_min = MeasurableMaxNeg(list(axis))
+        measurable_min = MeasurableMaxNeg(axis)
 
     return measurable_min.make_node(base_rv).outputs
 

pymc/logprob/rewriting.py

@@ -72,7 +72,6 @@
 from pytensor.tensor.rewriting.basic import register_canonicalize
 from pytensor.tensor.rewriting.math import local_exp_over_1_plus_exp
 from pytensor.tensor.rewriting.shape import ShapeFeature
-from pytensor.tensor.rewriting.uncanonicalize import local_max_and_argmax
 from pytensor.tensor.subtensor import (
     AdvancedIncSubtensor,
     AdvancedIncSubtensor1,
@@ -374,12 +373,6 @@ def incsubtensor_rv_replace(fgraph, node):
 
 logprob_rewrites_db.register("measurable_ir_rewrites", measurable_ir_rewrites_db, "basic")
 
-# Split max_and_argmax
-# We only register this in the measurable IR db because max does not have a grad implemented
-# And running this on any MaxAndArgmax would lead to issues: https://github.com/pymc-devs/pymc/issues/7251
-# This special registering can be removed after https://github.com/pymc-devs/pytensor/issues/334 is fixed
-measurable_ir_rewrites_db.register("local_max_and_argmax", local_max_and_argmax, "basic")
-
 # These rewrites push random/measurable variables "down", making them closer to
 # (or eventually) the graph outputs.  Often this is done by lifting other `Op`s
 # "up" through the random/measurable variables and into their inputs.

pymc/logprob/tensor.py

@@ -35,6 +35,8 @@
 #   SOFTWARE.
 
 
+from pathlib import Path
+
 import pytensor
 
 from pytensor import tensor as pt
@@ -237,7 +239,7 @@ class MeasurableDimShuffle(DimShuffle):
 
     # Need to get the absolute path of `c_func_file`, otherwise it tries to
     # find it locally and fails when a new `Op` is initialized
-    c_func_file = DimShuffle.get_path(DimShuffle.c_func_file)
+    c_func_file = str(DimShuffle.get_path(Path(DimShuffle.c_func_file)))
 
 
 MeasurableVariable.register(MeasurableDimShuffle)

pymc/pytensorf.py

@@ -36,7 +36,7 @@
     graph_inputs,
     walk,
 )
-from pytensor.graph.fg import FunctionGraph
+from pytensor.graph.fg import FunctionGraph, Output
 from pytensor.graph.op import Op
 from pytensor.scalar.basic import Cast
 from pytensor.scan.op import Scan
@@ -897,7 +897,7 @@ def find_default_update(clients, rng: Variable) -> None | Variable:
         [client, _] = rng_clients[0]
 
         # RNG is an output of the function, this is not a problem
-        if client == "output":
+        if isinstance(client.op, Output):
             return rng
 
         # RNG is used by another operator, which should output an update for the RNG

requirements-dev.txt

@@ -17,7 +17,7 @@ numpydoc
 pandas>=0.24.0
 polyagamma
 pre-commit>=2.8.0
-pytensor>=2.23,<2.24
+pytensor>=2.25.1,<2.26
 pytest-cov>=2.5
 pytest>=3.0
 rich>=13.7.1

requirements.txt

@@ -3,7 +3,7 @@ cachetools>=4.2.1
 cloudpickle
 numpy>=1.15.0
 pandas>=0.24.0
-pytensor>=2.23,<2.24
+pytensor>=2.25.1,<2.26
 rich>=13.7.1
 scipy>=1.4.1
 threadpoolctl>=3.1.0,<4.0.0

tests/distributions/test_continuous.py

@@ -592,7 +592,7 @@ def test_skewstudentt_logcdf(self):
         check_logcdf(
             pm.SkewStudentT,
             R,
-            {"a": Rplus, "b": Rplus, "mu": R, "sigma": Rplus},
+            {"a": Rplus, "b": Rplus, "mu": R, "sigma": Rplusbig},
             lambda value, a, b, mu, sigma: st.jf_skew_t.logcdf(value, a, b, mu, sigma),
         )
 

tests/distributions/test_timeseries.py

@@ -797,7 +797,7 @@ def test_batched_size(self, explicit_shape, batched_param):
         with Model() as t0:
             y = GARCH11("y", **kwargs0)
 
-        y_eval = draw(y, draws=2)
+        y_eval = draw(y, draws=2, random_seed=800)
         assert y_eval[0].shape == (batch_size, steps)
         assert not np.any(np.isclose(y_eval[0], y_eval[1]))