Make shape_tuple return all static shape information

aesara/tensor/shape.py

@@ -8,6 +8,7 @@ import numpy as np
 import aesara
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply, Variable
+from aesara.graph.type import HasShape
 from aesara.link.c.op import COp
 from aesara.link.c.params_type import ParamsType
 from aesara.misc.safe_asarray import _asarray
@@ -158,18 +159,28 @@ def _get_vector_length_Shape(op, var):
 
 
 def shape_tuple(x: TensorVariable) -> Tuple[Variable, ...]:
-    """Get a tuple of symbolic shape values.
+    r"""Get a tuple of symbolic shape values.
+
+    This will return `ScalarConstant`\s for static shape values.
 
-    This will return a `ScalarConstant` with the value ``1`` wherever
-    broadcastable is ``True``.
     """
-    one_at = aesara.scalar.ScalarConstant(aesara.scalar.int64, 1)
-    return tuple(
-        one_at if getattr(sh, "value", sh) == 1 or bcast else sh
-        for sh, bcast in zip(
-            shape(x), getattr(x, "broadcastable", (False,) * x.type.ndim)
-        )
-    )
+    if not isinstance(x.type, HasShape):
+        # We assume/call it a scalar
+        return ()
+
+    res = ()
+    symbolic_shape = shape(x)
+    static_shape = x.type.shape
+    for i in range(x.type.ndim):
+        shape_val = static_shape[i]
+
+        if shape_val is not None:
+            # TODO: Why not use uint64?
+            res += (aesara.scalar.ScalarConstant(aesara.scalar.int64, shape_val),)
+        else:
+            res += (symbolic_shape[i],)
+
+    return res
 
 
 class Shape_i(COp):

tests/tensor/test_shape.py

@@ -9,6 +9,7 @@ from aesara.graph.basic import Variable
 from aesara.graph.fg import FunctionGraph
 from aesara.graph.type import Type
 from aesara.misc.safe_asarray import _asarray
+from aesara.scalar.basic import ScalarConstant
 from aesara.tensor import as_tensor_variable, get_vector_length, row
 from aesara.tensor.basic import MakeVector, constant
 from aesara.tensor.elemwise import DimShuffle, Elemwise
@@ -22,6 +23,7 @@ from aesara.tensor.shape import (
     reshape,
     shape,
     shape_i,
+    shape_tuple,
     specify_broadcastable,
     specify_shape,
     unbroadcast,
@@ -46,6 +48,7 @@ from aesara.tensor.type_other import NoneConst
 from aesara.tensor.var import TensorVariable
 from aesara.typed_list import make_list
 from tests import unittest_tools as utt
+from tests.graph.utils import MyType2
 from tests.tensor.utils import eval_outputs, random
 from tests.test_rop import RopLopChecker
 
@@ -657,3 +660,18 @@ class TestUnbroadcastInferShape(utt.InferShapeTester):
             Unbroadcast,
             warn=False,
         )
+
+
+def test_shape_tuple():
+
+    x = Variable(MyType2(), None, None)
+    assert shape_tuple(x) == ()
+
+    x = tensor(np.float64, shape=(1, 2, None))
+    res = shape_tuple(x)
+    assert isinstance(res, tuple)
+    assert isinstance(res[0], ScalarConstant)
+    assert res[0].data == 1
+    assert isinstance(res[1], ScalarConstant)
+    assert res[1].data == 2
+    assert not isinstance(res[2], ScalarConstant)