deprecate tests that depend on the value setter

Author: Abhinav-Khot

doc/library/compile/io.rst

@@ -189,9 +189,9 @@ To show some examples of these access methods...
 >>> fn = function([a, b, ((c, c+a+b), 10.0)], [])
 
 >>> # the value associated with c is accessible in 3 ways
->>> fn['s'] is fn.value[c]
+>>> fn['s'] is fn.data[c]
 True
->>> fn['s'] is fn.container[c].value
+>>> fn['s'] is fn.container[c].data
 True
 
 >>> fn['s']
@@ -205,14 +205,14 @@ array(13.0)
 []
 >>> fn['s']
 array(100.0)
->>> fn.value[c] = 99.0
+>>> fn.data[c] = 99.0
 >>> fn(1,0)
 []
 >>> fn['s']
 array(100.0)
->>> fn['s'] == fn.value[c]
+>>> fn['s'] == fn.data[c]
 True
->>> fn['s'] == fn.container[c].value
+>>> fn['s'] == fn.container[c].data
 True
 
 

doc/library/tensor/basic.rst

@@ -287,10 +287,10 @@ precise) is by calling :func:`pytensor.shared`
 
     x = pytensor.shared(np.random.standard_normal((3, 4)))
 
-This will return a :term:`shared variable <shared variable>` whose ``.value`` is
+This will return a :term:`shared variable <shared variable>` whose ``.data`` is
 a NumPy `ndarray`.  The number of dimensions and dtype of the `Variable` are
 inferred from the `ndarray` argument.  The argument to `shared` *will not be
-copied*, and subsequent changes will be reflected in ``x.value``.
+copied*, and subsequent changes will be reflected in ``x.data``.
 
 For additional information, see the :func:`shared() <shared.shared>` documentation.
 

pytensor/compile/function/types.py

@@ -508,7 +508,7 @@ def __init__(
                         # there is no need to refeed the default value.
                         assert not refeed
                     else:
-                        c.value = value
+                        c.data = value
                 c.required = required
                 c.implicit = input.implicit
                 # this is a count of how many times the input has been
@@ -531,7 +531,7 @@ def __init__(
         self.inv_finder = inv_finder
 
         # this class is important in overriding the square-bracket notation:
-        #     fn.value[x]
+        #     fn.data[x]
         # self reference is available via the closure on the class
         class ValueAttribute:
             def __getitem__(self, item):
@@ -546,7 +546,7 @@ def __getitem__(self, item):
                         "for duplicates."
                     )
                 if isinstance(s, Container):
-                    return s.value
+                    return s.data
                 else:
                     raise NotImplementedError
 
@@ -564,7 +564,7 @@ def __setitem__(self, item, value):
                         "for duplicates."
                     )
                 if isinstance(s, Container):
-                    s.value = value
+                    s.data = value
                     s.provided += 1
                 else:
                     s(value)
@@ -1624,11 +1624,11 @@ def __init__(
         self.name = name
         self.trust_input = trust_input
 
-        self.required = [(i.value is None) for i in self.inputs]
+        self.required = [(i.data is None) for i in self.inputs]
         self.refeed = [
             (
-                i.value is not None
-                and not isinstance(i.value, Container)
+                i.data is not None
+                and not isinstance(i.data, Container)
                 and i.update is None
             )
             for i in self.inputs
@@ -1898,10 +1898,10 @@ def convert_function_input(input):
             if len(input) == 1:
                 return input[0]
             elif len(input) == 2:
-                input, value = input
+                input, data = input
                 if name is not None:
                     input.name = name
-                input.value = value
+                input.data = data
                 return input
         else:
             raise TypeError(f"The input specification is not valid: {input}")

pytensor/link/numba/dispatch/sparse.py

@@ -108,10 +108,10 @@ def unbox_matrix(typ, obj, c):
     indptr = c.pyapi.object_getattr_string(obj, "indptr")
     shape = c.pyapi.object_getattr_string(obj, "shape")
 
-    struct_ptr.data = c.unbox(typ.data, data).value
-    struct_ptr.indices = c.unbox(typ.indices, indices).value
-    struct_ptr.indptr = c.unbox(typ.indptr, indptr).value
-    struct_ptr.shape = c.unbox(typ.shape, shape).value
+    struct_ptr.data = c.unbox(typ.data, data).data
+    struct_ptr.indices = c.unbox(typ.indices, indices).data
+    struct_ptr.indptr = c.unbox(typ.indptr, indptr).data
+    struct_ptr.shape = c.unbox(typ.shape, shape).data
 
     c.pyapi.decref(data)
     c.pyapi.decref(indices)

pytensor/scan/rewriting.py

@@ -1442,7 +1442,7 @@ def scan_save_mem(fgraph, node):
                 if (
                     i <= op.info.n_mit_mot
                     and isinstance(this_slice[0], ScalarConstant)
-                    and this_slice[0].value == -1
+                    and this_slice[0].data == -1
                 ):
                     start = nw_steps - 1
                 else:
@@ -1728,7 +1728,7 @@ def scan_save_mem(fgraph, node):
                         # Special case when only last value is requested
                         if (
                             isinstance(old_slices[0], ScalarConstant)
-                            and old_slices[0].value == -1
+                            and old_slices[0].data == -1
                         ):
                             position = old_slices[0]
                         else:

pytensor/tensor/rewriting/math.py

@@ -2016,7 +2016,7 @@ def local_mul_to_sqr(fgraph, node):
 @node_rewriter([int_div])
 def local_intdiv_by_one(fgraph, node):
     """x // 1 -> x"""
-    if isinstance(node.inputs[1], TensorConstant) and np.all(node.inputs[1].value == 1):
+    if isinstance(node.inputs[1], TensorConstant) and np.all(node.inputs[1].data == 1):
         return [node.inputs[0].astype(node.outputs[0].dtype)]