Remove rarely used shape_i helpers

pytensor/tensor/rewriting/shape.py

@@ -42,7 +42,6 @@ from pytensor.tensor.shape import (
     Shape_i,
     SpecifyShape,
     Unbroadcast,
-    shape_i,
     specify_shape,
     unbroadcast,
 )
@@ -1060,7 +1059,7 @@ def local_Shape_of_SpecifyShape(fgraph, node):
     # Replace `NoneConst` by `shape_i`
     for i, sh in enumerate(shape):
         if NoneConst.equals(sh):
-            shape[i] = shape_i(x, i, fgraph)
+            shape[i] = x.shape[i]
 
     return [stack(shape).astype(np.int64)]
 

pytensor/tensor/shape.py

@@ -363,16 +363,6 @@ def shape_i(var, i, fgraph=None):
     return shape(var)[i]
 
 
-def shape_i_op(i):
-    key = i
-    if key not in shape_i_op.cache:
-        shape_i_op.cache[key] = Shape_i(i)
-    return shape_i_op.cache[key]
-
-
-shape_i_op.cache = {}  # type: ignore
-
-
 def register_shape_i_c_code(typ, code, check_input, version=()):
     """
     Tell Shape_i how to generate C code for an PyTensor Type.

pytensor/tensor/subtensor.py

@@ -38,7 +38,7 @@ from pytensor.tensor.blockwise import vectorize_node_fallback
 from pytensor.tensor.elemwise import DimShuffle
 from pytensor.tensor.exceptions import AdvancedIndexingError, NotScalarConstantError
 from pytensor.tensor.math import clip
-from pytensor.tensor.shape import Reshape, shape_i, specify_broadcastable
+from pytensor.tensor.shape import Reshape, Shape_i, specify_broadcastable
 from pytensor.tensor.type import (
     TensorType,
     bscalar,
@@ -2705,10 +2705,9 @@ class AdvancedSubtensor(Op):
         index_shapes = []
         for idx, ishape in zip(indices, ishapes[1:]):
             # Mixed bool indexes are converted to nonzero entries
+            shape0_op = Shape_i(0)
             if is_bool_index(idx):
-                index_shapes.extend(
-                    (shape_i(nz_dim, 0, fgraph=fgraph),) for nz_dim in nonzero(idx)
-                )
+                index_shapes.extend((shape0_op(nz_dim),) for nz_dim in nonzero(idx))
             # The `ishapes` entries for `SliceType`s will be None, and
             # we need to give `indexed_result_shape` the actual slices.
             elif isinstance(getattr(idx, "type", None), SliceType):

tests/tensor/test_shape.py

@@ -8,7 +8,6 @@ from pytensor import Mode, function, grad
 from pytensor.compile.ops import DeepCopyOp
 from pytensor.configdefaults import config
 from pytensor.graph.basic import Variable, equal_computations
-from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.replace import clone_replace, vectorize_node
 from pytensor.graph.type import Type
 from pytensor.misc.safe_asarray import _asarray
@@ -16,7 +15,6 @@ from pytensor.scalar.basic import ScalarConstant
 from pytensor.tensor import as_tensor_variable, broadcast_to, get_vector_length, row
 from pytensor.tensor.basic import MakeVector, constant, stack
 from pytensor.tensor.elemwise import DimShuffle, Elemwise
-from pytensor.tensor.rewriting.shape import ShapeFeature
 from pytensor.tensor.shape import (
     Reshape,
     Shape,
@@ -26,7 +24,6 @@ from pytensor.tensor.shape import (
     _specify_shape,
     reshape,
     shape,
-    shape_i,
     shape_tuple,
     specify_broadcastable,
     specify_shape,
@@ -633,13 +630,12 @@ def test_nonstandard_shapes():
     tl_shape = shape(tl)
     assert np.array_equal(tl_shape.get_test_value(), (2, 2, 3, 4))
 
-    # There's no `FunctionGraph`, so it should return a `Subtensor`
-    tl_shape_i = shape_i(tl, 0)
+    # Test specific dim
+    tl_shape_i = shape(tl)[0]
     assert isinstance(tl_shape_i.owner.op, Subtensor)
     assert tl_shape_i.get_test_value() == 2
 
-    tl_fg = FunctionGraph([a, b], [tl], features=[ShapeFeature()])
-    tl_shape_i = shape_i(tl, 0, fgraph=tl_fg)
+    tl_shape_i = Shape_i(0)(tl)
     assert not isinstance(tl_shape_i.owner.op, Subtensor)
     assert tl_shape_i.get_test_value() == 2