Deprecate redundant utilities for extracting constants

pytensor/__init__.py

@@ -24,6 +24,7 @@
 # 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 try_to_copy_if_needed(var):
                                 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/link/jax/dispatch/tensor_basic.py

@@ -18,7 +18,7 @@
     Split,
     TensorFromScalar,
     Tri,
-    get_underlying_scalar_constant_value,
+    get_scalar_constant_value,
 )
 from pytensor.tensor.exceptions import NotScalarConstantError
 from pytensor.tensor.shape import Shape_i
@@ -103,7 +103,7 @@ def join(axis, *tensors):
 def jax_funcify_Split(op: Split, node, **kwargs):
     _, axis, splits = node.inputs
     try:
-        constant_axis = get_underlying_scalar_constant_value(axis)
+        constant_axis = get_scalar_constant_value(axis)
     except NotScalarConstantError:
         constant_axis = None
         warnings.warn(
@@ -113,7 +113,7 @@ def jax_funcify_Split(op: Split, node, **kwargs):
     try:
         constant_splits = np.array(
             [
-                get_underlying_scalar_constant_value(splits[i])
+                get_scalar_constant_value(splits[i])
                 for i in range(get_vector_length(splits))
             ]
         )

pytensor/scan/basic.py

@@ -484,7 +484,7 @@ def wrap_into_list(x):
         n_fixed_steps = int(n_steps)
     else:
         try:
-            n_fixed_steps = pt.get_underlying_scalar_constant_value(n_steps)
+            n_fixed_steps = pt.get_scalar_constant_value(n_steps)
         except NotScalarConstantError:
             n_fixed_steps = None
 

pytensor/scan/rewriting.py

@@ -54,7 +54,7 @@
 from pytensor.tensor.basic import (
     Alloc,
     AllocEmpty,
-    get_underlying_scalar_constant_value,
+    get_scalar_constant_value,
 )
 from pytensor.tensor.elemwise import DimShuffle, Elemwise
 from pytensor.tensor.exceptions import NotScalarConstantError
@@ -71,7 +71,7 @@
     get_slice_elements,
     set_subtensor,
 )
-from pytensor.tensor.variable import TensorConstant, get_unique_constant_value
+from pytensor.tensor.variable import TensorConstant
 
 
 list_opt_slice = [
@@ -136,10 +136,7 @@ def remove_constants_and_unused_inputs_scan(fgraph, node):
     all_ins = list(graph_inputs(op_outs))
     for idx in range(op_info.n_seqs):
         node_inp = node.inputs[idx + 1]
-        if (
-            isinstance(node_inp, TensorConstant)
-            and get_unique_constant_value(node_inp) is not None
-        ):
+        if isinstance(node_inp, TensorConstant) and node_inp.unique_value is not None:
             try:
                 # This works if input is a constant that has all entries
                 # equal
@@ -668,8 +665,10 @@ def inner_sitsot_only_last_step_used(
         client = fgraph.clients[outer_var][0][0]
         if isinstance(client, Apply) and isinstance(client.op, Subtensor):
             lst = get_idx_list(client.inputs, client.op.idx_list)
-            if len(lst) == 1 and pt.extract_constant(lst[0]) == -1:
-                return True
+            return (
+                len(lst) == 1
+                and get_scalar_constant_value(lst[0], raise_not_constant=False) == -1
+            )
 
     return False
 
@@ -1344,10 +1343,17 @@ def scan_save_mem(fgraph, node):
                 if isinstance(this_slice[0], slice) and this_slice[0].stop is None:
                     global_nsteps = None
                 if isinstance(cf_slice[0], slice):
-                    stop = pt.extract_constant(cf_slice[0].stop)
+                    stop = get_scalar_constant_value(
+                        cf_slice[0].stop, raise_not_constant=False
+                    )
                 else:
-                    stop = pt.extract_constant(cf_slice[0]) + 1
-                if stop == maxsize or stop == pt.extract_constant(length):
+                    stop = (
+                        get_scalar_constant_value(cf_slice[0], raise_not_constant=False)
+                        + 1
+                    )
+                if stop == maxsize or stop == get_scalar_constant_value(
+                    length, raise_not_constant=False
+                ):
                     stop = None
                 else:
                     # there is a **gotcha** here ! Namely, scan returns an
@@ -1451,9 +1457,13 @@ def scan_save_mem(fgraph, node):
                     cf_slice = get_canonical_form_slice(this_slice[0], length)
 
                     if isinstance(cf_slice[0], slice):
-                        start = pt.extract_constant(cf_slice[0].start)
+                        start = pt.get_scalar_constant_value(
+                            cf_slice[0].start, raise_not_constant=False
+                        )
                     else:
-                        start = pt.extract_constant(cf_slice[0])
+                        start = pt.get_scalar_constant_value(
+                            cf_slice[0], raise_not_constant=False
+                        )
 
                 if start == 0 or store_steps[i] == 0:
                     store_steps[i] = 0
@@ -1628,7 +1638,7 @@ def scan_save_mem(fgraph, node):
         # 3.6 Compose the new scan
         # TODO: currently we don't support scan with 0 step. So
         # don't create one.
-        if pt.extract_constant(node_ins[0]) == 0:
+        if get_scalar_constant_value(node_ins[0], raise_not_constant=False) == 0:
             return False
 
         # Do not call make_node for test_value
@@ -1965,13 +1975,13 @@ def belongs_to_set(self, node, set_nodes):
 
         nsteps = node.inputs[0]
         try:
-            nsteps = int(get_underlying_scalar_constant_value(nsteps))
+            nsteps = int(get_scalar_constant_value(nsteps))
         except NotScalarConstantError:
             pass
 
         rep_nsteps = rep_node.inputs[0]
         try:
-            rep_nsteps = int(get_underlying_scalar_constant_value(rep_nsteps))
+            rep_nsteps = int(get_scalar_constant_value(rep_nsteps))
         except NotScalarConstantError:
             pass
 

pytensor/sparse/basic.py

@@ -491,6 +491,10 @@ def __str__(self):
     def __repr__(self):
         return str(self)
 
+    @property
+    def unique_value(self):
+        return None
+
 
 SparseTensorType.variable_type = SparseVariable
 SparseTensorType.constant_type = SparseConstant