Deprecate `pytensor.get_underlying_scalar_constant`

pytensor/__init__.py

@@ -24,6 +24,7 @@ __docformat__ = "restructuredtext en"
 # pytensor code, since this code may want to log some messages.
 import logging
 import sys
+import warnings
 from functools import singledispatch
 from pathlib import Path
 from typing import Any, NoReturn, Optional
@@ -148,13 +149,13 @@ def get_underlying_scalar_constant(v):
     If `v` is not some view of constant data, then raise a
     `NotScalarConstantError`.
     """
-    # Is it necessary to test for presence of pytensor.sparse at runtime?
-    sparse = globals().get("sparse")
-    if sparse and isinstance(v.type, sparse.SparseTensorType):
-        if v.owner is not None and isinstance(v.owner.op, sparse.CSM):
-            data = v.owner.inputs[0]
-            return tensor.get_underlying_scalar_constant_value(data)
-    return tensor.get_underlying_scalar_constant_value(v)
+    warnings.warn(
+        "get_underlying_scalar_constant is deprecated. Use tensor.get_underlying_scalar_constant_value instead.",
+        FutureWarning,
+    )
+    from pytensor.tensor.basic import get_underlying_scalar_constant_value
+
+    return get_underlying_scalar_constant_value(v)
 
 
 # isort: off

pytensor/gradient.py

@@ -1329,7 +1329,7 @@ def _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name=None):
                                 f" {i}. Since this input is only connected "
                                 "to integer-valued outputs, it should "
                                 "evaluate to zeros, but it evaluates to"
-                                f"{pytensor.get_underlying_scalar_constant(term)}."
+                                f"{pytensor.get_underlying_scalar_constant_value(term)}."
                             )
                             raise ValueError(msg)
 
@@ -2157,6 +2157,9 @@ def _is_zero(x):
     'maybe' means that x is an expression that is complicated enough
     that we can't tell that it simplifies to 0.
     """
+    from pytensor.tensor import get_underlying_scalar_constant_value
+    from pytensor.tensor.exceptions import NotScalarConstantError
+
     if not hasattr(x, "type"):
         return np.all(x == 0.0)
     if isinstance(x.type, NullType):
@@ -2166,9 +2169,9 @@ def _is_zero(x):
 
     no_constant_value = True
     try:
-        constant_value = pytensor.get_underlying_scalar_constant(x)
+        constant_value = get_underlying_scalar_constant_value(x)
         no_constant_value = False
-    except pytensor.tensor.exceptions.NotScalarConstantError:
+    except NotScalarConstantError:
         pass
 
     if no_constant_value:

pytensor/tensor/basic.py

@@ -320,6 +320,8 @@ def get_underlying_scalar_constant_value(
 
     """
     from pytensor.compile.ops import DeepCopyOp, OutputGuard
+    from pytensor.sparse import CSM
+    from pytensor.tensor.subtensor import Subtensor
 
     v = orig_v
     while True:
@@ -350,16 +352,16 @@ def get_underlying_scalar_constant_value(
             raise NotScalarConstantError()
 
         if not only_process_constants and getattr(v, "owner", None) and max_recur > 0:
+            op = v.owner.op
             max_recur -= 1
             if isinstance(
-                v.owner.op,
-                Alloc | DimShuffle | Unbroadcast | OutputGuard | DeepCopyOp,
+                op, Alloc | DimShuffle | Unbroadcast | OutputGuard | DeepCopyOp
             ):
                 # OutputGuard is only used in debugmode but we
                 # keep it here to avoid problems with old pickles
                 v = v.owner.inputs[0]
                 continue
-            elif isinstance(v.owner.op, Shape_i):
+            elif isinstance(op, Shape_i):
                 i = v.owner.op.i
                 inp = v.owner.inputs[0]
                 if isinstance(inp, Constant):
@@ -373,10 +375,10 @@ def get_underlying_scalar_constant_value(
             # mess with the stabilization optimization and be too slow.
             # We put all the scalar Ops used by get_canonical_form_slice()
             # to allow it to determine the broadcast pattern correctly.
-            elif isinstance(v.owner.op, ScalarFromTensor | TensorFromScalar):
+            elif isinstance(op, ScalarFromTensor | TensorFromScalar):
                 v = v.owner.inputs[0]
                 continue
-            elif isinstance(v.owner.op, CheckAndRaise):
+            elif isinstance(op, CheckAndRaise):
                 # check if all conditions are constant and true
                 conds = [
                     get_underlying_scalar_constant_value(c, max_recur=max_recur)
@@ -385,7 +387,7 @@ def get_underlying_scalar_constant_value(
                 if builtins.all(0 == c.ndim and c != 0 for c in conds):
                     v = v.owner.inputs[0]
                     continue
-            elif isinstance(v.owner.op, ps.ScalarOp):
+            elif isinstance(op, ps.ScalarOp):
                 if isinstance(v.owner.op, ps.Second):
                     # We don't need both input to be constant for second
                     shp, val = v.owner.inputs
@@ -402,7 +404,7 @@ def get_underlying_scalar_constant_value(
             # In fast_compile, we don't enable local_fill_to_alloc, so
             # we need to investigate Second as Alloc. So elemwise
             # don't disable the check for Second.
-            elif isinstance(v.owner.op, Elemwise):
+            elif isinstance(op, Elemwise):
                 if isinstance(v.owner.op.scalar_op, ps.Second):
                     # We don't need both input to be constant for second
                     shp, val = v.owner.inputs
@@ -418,10 +420,7 @@ def get_underlying_scalar_constant_value(
                     ret = [[None]]
                     v.owner.op.perform(v.owner, const, ret)
                     return np.asarray(ret[0][0].copy())
-            elif (
-                isinstance(v.owner.op, pytensor.tensor.subtensor.Subtensor)
-                and v.ndim == 0
-            ):
+            elif isinstance(op, Subtensor) and v.ndim == 0:
                 if isinstance(v.owner.inputs[0], TensorConstant):
                     from pytensor.tensor.subtensor import get_constant_idx
 
@@ -545,6 +544,14 @@ def get_underlying_scalar_constant_value(
 
                     if isinstance(grandparent, Constant):
                         return np.asarray(np.shape(grandparent.data)[idx])
+            elif isinstance(op, CSM):
+                data = get_underlying_scalar_constant_value(
+                    v.owner.inputs, elemwise=elemwise, max_recur=max_recur
+                )
+                # Sparse variable can only be constant if zero (or I guess if homogeneously dense)
+                if data == 0:
+                    return data
+                break
 
         raise NotScalarConstantError()
 
@@ -4071,7 +4078,7 @@ class Choose(Op):
         static_out_shape = ()
         for s in out_shape:
             try:
-                s_val = pytensor.get_underlying_scalar_constant(s)
+                s_val = get_underlying_scalar_constant_value(s)
             except (NotScalarConstantError, AttributeError):
                 s_val = None
 

tests/tensor/test_elemwise.py

@@ -19,7 +19,7 @@ from pytensor.graph.replace import vectorize_node
 from pytensor.link.basic import PerformLinker
 from pytensor.link.c.basic import CLinker, OpWiseCLinker
 from pytensor.tensor import as_tensor_variable
-from pytensor.tensor.basic import second
+from pytensor.tensor.basic import get_scalar_constant_value, second
 from pytensor.tensor.elemwise import CAReduce, DimShuffle, Elemwise
 from pytensor.tensor.math import Any, Sum, exp
 from pytensor.tensor.math import all as pt_all
@@ -807,8 +807,8 @@ class TestElemwise(unittest_tools.InferShapeTester):
 
         assert len(res_shape) == 1
         assert len(res_shape[0]) == 2
-        assert pytensor.get_underlying_scalar_constant(res_shape[0][0]) == 1
-        assert pytensor.get_underlying_scalar_constant(res_shape[0][1]) == 1
+        assert get_scalar_constant_value(res_shape[0][0]) == 1
+        assert get_scalar_constant_value(res_shape[0][1]) == 1
 
     def test_infer_shape_multi_output(self):
         class CustomElemwise(Elemwise):