Replace TensorConstant.tag.unique_value with a get_unique_value function

aesara/scan/opt.py

@@ -52,7 +52,7 @@ from aesara.tensor.subtensor import (
     get_idx_list,
     set_subtensor,
 )
-from aesara.tensor.var import TensorConstant
+from aesara.tensor.var import TensorConstant, get_unique_value
 
 
 _logger = logging.getLogger("aesara.scan.opt")
@@ -118,7 +118,7 @@ def remove_constants_and_unused_inputs_scan(fgraph, node):
         node_inp = node.inputs[idx + 1]
         if (
             isinstance(node_inp, TensorConstant)
-            and node_inp.tag.unique_value is not None
+            and get_unique_value(node_inp) is not None
         ):
             try:
                 # This works if input is a constant that has all entries

aesara/tensor/basic.py

@@ -60,7 +60,7 @@ from aesara.tensor.type import (
     uint_dtypes,
     values_eq_approx_always_true,
 )
-from aesara.tensor.var import TensorConstant, TensorVariable
+from aesara.tensor.var import TensorConstant, TensorVariable, get_unique_value
 
 
 _logger = logging.getLogger("aesara.tensor.basic")
@@ -323,8 +323,9 @@ def get_scalar_constant_value(
                 raise NotScalarConstantError()
 
         if isinstance(v, Constant):
-            if getattr(v.tag, "unique_value", None) is not None:
-                data = v.tag.unique_value
+            unique_value = get_unique_value(v)
+            if unique_value is not None:
+                data = unique_value
             else:
                 data = v.data
 

aesara/tensor/math_opt.py

@@ -92,7 +92,7 @@ from aesara.tensor.type import (
     values_eq_approx_remove_inf_nan,
     values_eq_approx_remove_nan,
 )
-from aesara.tensor.var import TensorConstant
+from aesara.tensor.var import TensorConstant, get_unique_value
 from aesara.utils import NoDuplicateOptWarningFilter
 
 
@@ -129,8 +129,9 @@ def get_constant(v):
 
     """
     if isinstance(v, Constant):
-        if getattr(v.tag, "unique_value", None) is not None:
-            data = v.tag.unique_value
+        unique_value = get_unique_value(v)
+        if unique_value is not None:
+            data = unique_value
         else:
             data = v.data
         if data.ndim == 0:

aesara/tensor/var.py

@@ -2,6 +2,8 @@ import copy
 import traceback as tb
 import warnings
 from collections.abc import Iterable
+from numbers import Number
+from typing import Optional
 
 import numpy as np
 
@@ -957,6 +959,20 @@ class TensorConstantSignature(tuple):
     no_nan = property(_get_no_nan)
 
 
+def get_unique_value(x: TensorVariable) -> Optional[Number]:
+    """Return the unique value of a tensor, if there is one"""
+    if isinstance(x, Constant):
+        data = x.data
+
+        if isinstance(data, np.ndarray) and data.ndim > 0:
+            flat_data = data.ravel()
+            if flat_data.shape[0]:
+                if (flat_data == flat_data[0]).all():
+                    return flat_data[0]
+
+    return None
+
+
 class TensorConstant(TensorVariable, Constant):
     """Subclass to add the tensor operators to the basic `Constant` class.
 
@@ -966,16 +982,11 @@ class TensorConstant(TensorVariable, Constant):
 
     def __init__(self, type, data, name=None):
         Constant.__init__(self, type, data, name)
-        self.tag.unique_value = None
-        if isinstance(data, np.ndarray) and data.ndim > 0:
-            flat_data = data.ravel()
-            if flat_data.shape[0]:
-                if (flat_data == flat_data[0]).all():
-                    self.tag.unique_value = flat_data[0]
 
     def __str__(self):
-        if self.tag.unique_value is not None:
-            name = f"{self.data.shape} of {self.tag.unique_value}"
+        unique_val = get_unique_value(self)
+        if unique_val is not None:
+            name = f"{self.data.shape} of {unique_val}"
         else:
             name = f"{self.data}"
         if len(name) > 20: