Do not return Constants in shape Op

Author: ricardoV94

pytensor/tensor/shape.py

@@ -146,14 +146,7 @@ def shape(x: Union[np.ndarray, Number, Variable]) -> Variable:
     if not isinstance(x, Variable):
         x = at.as_tensor_variable(x)
 
-    x_type = x.type
-
-    if isinstance(x_type, TensorType) and all(s is not None for s in x_type.shape):
-        res = at.as_tensor_variable(x_type.shape, ndim=1, dtype=np.int64)
-    else:
-        res = _shape(x)
-
-    return res
+    return _shape(x)
 
 
 @_get_vector_length.register(Shape)

pytensor/tensor/var.py

@@ -12,7 +12,7 @@
 from pytensor.graph.basic import Constant, OptionalApplyType, Variable
 from pytensor.graph.utils import MetaType
 from pytensor.scalar import ComplexError, IntegerDivisionError
-from pytensor.tensor import _get_vector_length, as_tensor_variable
+from pytensor.tensor import _get_vector_length
 from pytensor.tensor.exceptions import AdvancedIndexingError
 from pytensor.tensor.type import TensorType
 from pytensor.tensor.type_other import NoneConst
@@ -259,9 +259,6 @@ def transpose(self, *axes):
 
     @property
     def shape(self):
-        if not any(s is None for s in self.type.shape):
-            return as_tensor_variable(self.type.shape, ndim=1, dtype=np.int64)
-
         return at.shape(self)
 
     @property

tests/scan/test_rewriting.py

@@ -1477,13 +1477,12 @@ def test_while_scan_taps_and_map(self):
             f(x0=0, seq=test_seq, n_steps=0)
 
         # Evaluate the shape of ys_trace and len_zs to confirm the rewrite worked correctly.
-        # If a MissingInputError is raised, it means the rewrite failed
         [scan_node] = (n for n in f.maker.fgraph.apply_nodes if isinstance(n.op, Scan))
         _, _, ys_trace, len_zs = scan_node.inputs
         debug_fn = pytensor.function(
-            [n_steps], [ys_trace.shape[0], len_zs], accept_inplace=True
+            [x0, n_steps], [ys_trace.shape[0], len_zs], accept_inplace=True
         )
-        stored_ys_steps, stored_zs_steps = debug_fn(n_steps=200)
+        stored_ys_steps, stored_zs_steps = debug_fn(x0=0, n_steps=200)
         assert stored_ys_steps == 2
         assert stored_zs_steps == 1
 

tests/tensor/test_basic.py

@@ -3486,7 +3486,7 @@ def test_vector(self):
     def test_scalar(self):
         x = scalar()
         y = np.array(7, dtype=config.floatX)
-        assert y.size == function([], x.size)()
+        assert y.size == function([x], x.size)(y)
 
     def test_shared(self):
         # NB: we also test higher order tensors at the same time.

tests/tensor/test_shape.py

@@ -397,7 +397,6 @@ def test_fixed_shapes(self):
         shape = as_tensor_variable([2])
         y = specify_shape(x, shape)
         assert y.type.shape == (2,)
-        assert y.shape.equals(shape)
 
     def test_fixed_partial_shapes(self):
         x = TensorType("floatX", (None, None))("x")

tests/tensor/test_var.py

@@ -6,6 +6,7 @@
 
 import pytensor
 import tests.unittest_tools as utt
+from pytensor.compile import DeepCopyOp
 from pytensor.compile.mode import get_default_mode
 from pytensor.graph.basic import Constant, equal_computations
 from pytensor.tensor import get_vector_length
@@ -245,8 +246,13 @@ def test__getitem__newaxis(x, indices, new_order):
 
 def test_fixed_shape_variable_basic():
     x = TensorVariable(TensorType("int64", shape=(4,)), None)
-    assert isinstance(x.shape, Constant)
-    assert np.array_equal(x.shape.data, (4,))
+    assert x.type.shape == (4,)
+
+    shape_fn = pytensor.function([x], x.shape)
+    opt_shape = shape_fn.maker.fgraph.outputs[0]
+    assert isinstance(opt_shape.owner.op, DeepCopyOp)
+    assert isinstance(opt_shape.owner.inputs[0], Constant)
+    assert np.array_equal(opt_shape.owner.inputs[0].data, (4,))
 
     x = TensorConstant(
         TensorType("int64", shape=(None, None)), np.array([[1, 2], [2, 3]])