Make SharedVariable.default_update a persistent property

pytensor/compile/function/pfunc.py

@@ -103,7 +103,7 @@ def rebuild_collect_shared(
         elif isinstance(v, SharedVariable):
             if v not in shared_inputs:
                 shared_inputs.append(v)
-            if hasattr(v, "default_update"):
+            if v.default_update is not None:
                 # Check that v should not be excluded from the default
                 # updates list
                 if no_default_updates is False or (

pytensor/compile/sharedvalue.py

@@ -7,8 +7,6 @@ import copy
 from contextlib import contextmanager
 from typing import List, Optional
 
-import numpy as np
-
 from pytensor.graph.basic import Variable
 from pytensor.graph.utils import add_tag_trace
 from pytensor.link.basic import Container
@@ -103,6 +101,8 @@ class SharedVariable(Variable):
         if isinstance(__SHARED_CONTEXT__, list):
             __SHARED_CONTEXT__.append(self)
 
+        self._default_update: Optional[Variable] = None
+
     def get_value(self, borrow=False, return_internal_type=False):
         """
         Get the non-symbolic value associated with this SharedVariable.
@@ -179,47 +179,23 @@ class SharedVariable(Variable):
         cp.tag = copy.copy(self.tag)
         return cp
 
-    def __getitem__(self, *args):
-        # __getitem__ is not available for generic SharedVariable objects.
-        # We raise a TypeError like Python would do if __getitem__ was not
-        # implemented at all, but with a more explicit error message to help
-        # PyTensor users figure out the root of the problem more easily.
-        value = self.get_value(borrow=True)
-        if isinstance(value, np.ndarray):
-            # Array probably had an unknown dtype.
-            msg = (
-                f"a Numpy array with dtype: '{value.dtype}'. This data type is not "
-                "currently recognized by PyTensor tensors: please cast "
-                "your data into a supported numeric type if you need "
-                "PyTensor tensor functionalities."
-            )
-        else:
-            msg = (
-                f"an object of type: {type(value)}. Did you forget to cast it into "
-                "a Numpy array before calling pytensor.shared()?"
-            )
+    @property
+    def default_update(self) -> Optional[Variable]:
+        """A default update expression for this `Variable`.
 
-        raise TypeError(
-            "The generic 'SharedVariable' object is not subscriptable. "
-            f"This shared variable contains {msg}"
-        )
-
-    def _value_get(self):
-        raise Exception(
-            "sharedvar.value does not exist anymore. Use "
-            "sharedvar.get_value() or sharedvar.set_value()"
-            " instead."
-        )
-
-    def _value_set(self, new_value):
-        raise Exception(
-            "sharedvar.value does not exist anymore. Use "
-            "sharedvar.get_value() or sharedvar.set_value()"
-            " instead."
-        )
+        If this value is non-``None``, its value will be used as the `update`
+        (see `pytensor.function`) for this `Variable` when no updates are
+        provided through `pytensor.function` and `no_default_updates` isn't
+        enabled.
+        """
+        return self._default_update
 
-    # We keep this just to raise an error
-    value = property(_value_get, _value_set)
+    @default_update.setter
+    def default_update(self, value):
+        if value is not None:
+            self._default_update = self.type.filter_variable(value, allow_convert=True)
+        else:
+            self._default_update = value
 
 
 def shared_constructor(ctor, remove=False):

pytensor/scan/basic.py

@@ -996,8 +996,8 @@ def scan(
             # We also don't want to remove a default update that applies to
             # the scope/context containing this `Scan`, so we only remove
             # default updates on "local" variables.
-            if is_local and hasattr(input.variable, "default_update"):
-                del input.variable.default_update
+            if is_local and input.variable.default_update is not None:
+                input.variable.default_update = None
 
             new_var = safe_new(input.variable)
 

tests/compile/function/test_pfunc.py

@@ -432,7 +432,8 @@ class TestPfunc:
         f()
         assert x.get_value() == 1
 
-        del x.default_update
+        x.default_update = None
+
         f()
         assert x.get_value() == 2
 

tests/scan/test_basic.py

@@ -282,10 +282,10 @@ class TestScan:
             n_steps=4,
         )
 
-        assert not hasattr(inner_rng, "default_update")
-        assert hasattr(inner_inner_rng, "default_update")
-        assert hasattr(y, "default_update")
-        assert hasattr(z_rng, "default_update")
+        assert inner_rng is None
+        assert inner_inner_rng.default_update is not None
+        assert y.default_update is not None
+        assert z_rng.default_update is not None
 
         out_fn = function([], out, mode=Mode(optimizer=None))
         res, z_res = out_fn()