Make Elemwise.infer_shape return TensorType-ed values

aesara/tensor/elemwise.py

 from copy import copy
-from typing import Tuple, Union
+from typing import List, Tuple, Union
 
 import numpy as np
 
@@ -29,6 +29,7 @@ from aesara.tensor.type import (
     float_dtypes,
     lvector,
 )
+from aesara.tensor.var import TensorVariable
 from aesara.utils import uniq
 
 
@@ -802,7 +803,7 @@ class Elemwise(OpenMPOp):
             else:
                 storage[0] = variable
 
-    def infer_shape(self, fgraph, node, i_shapes):
+    def infer_shape(self, fgraph, node, i_shapes) -> List[Tuple[TensorVariable, ...]]:
 
         if len(node.outputs) > 1:
             from aesara.tensor.basic_opt import ShapeError
@@ -813,7 +814,8 @@ class Elemwise(OpenMPOp):
 
         out_shape = aesara.tensor.broadcast_shape(*i_shapes, arrays_are_shapes=True)
 
-        return [out_shape]
+        # The `as_tensor_variable` should convert `ScalarType`s to `TensorType`s
+        return [tuple(as_tensor_variable(s) for s in out_shape)]
 
     def _c_all(self, node, nodename, inames, onames, sub):
         # Some `Op`s directly call `Elemwise._c_all` or `Elemwise.c_code`

tests/tensor/test_elemwise.py

@@ -26,6 +26,7 @@ from aesara.tensor.type import (
     bmatrix,
     bscalar,
     discrete_dtypes,
+    lscalar,
     matrix,
     scalar,
     tensor,
@@ -815,8 +816,8 @@ class TestElemwise(unittest_tools.InferShapeTester):
 
         assert len(res_shape) == 1
         assert len(res_shape[0]) == 2
-        assert res_shape[0][0].data == 1
-        assert res_shape[0][1].data == 1
+        assert aesara.get_scalar_constant_value(res_shape[0][0]) == 1
+        assert aesara.get_scalar_constant_value(res_shape[0][1]) == 1
 
     def test_multi_output(self):
         class CustomElemwise(Elemwise):
@@ -841,6 +842,18 @@ class TestElemwise(unittest_tools.InferShapeTester):
         with pytest.raises(ShapeError):
             z_1.owner.op.infer_shape(None, z_1.owner, [in_1_shape, in_1_shape])
 
+    def test_shape_types(self):
+        x = tensor(np.float64, (None, 1))
+        y = tensor(np.float64, (50, 10))
+
+        z = x * y
+
+        assert isinstance(z.owner.op, Elemwise)
+
+        (out_shape,) = z.owner.op.infer_shape(None, z.owner, [(lscalar(), 1), (50, 10)])
+
+        assert all(isinstance(v.type, TensorType) for v in out_shape)
+
 
 def test_not_implemented_elemwise_grad():
     # Regression test for unimplemented gradient in an Elemwise Op.