Fix Type conversion and checking in IfElse.make_node

aesara/ifelse.py

@@ -175,6 +175,11 @@ class IfElse(_NoPythonOp):
             if not isinstance(input_f, Variable):
                 input_f = as_symbolic(input_f)
 
+            if type(input_f.type) != type(input_t.type):  # noqa: E721
+                raise TypeError(
+                    f"Input types {type(input_t.type)} and {type(input_f.type)} do not match."
+                )
+
             if isinstance(input_t.type, HasDataType) and isinstance(
                 input_f.type, HasDataType
             ):
@@ -207,18 +212,18 @@ class IfElse(_NoPythonOp):
                 # TODO FIXME: The presence of this keyword is a strong
                 # assumption.  Find something that's guaranteed by the/a
                 # confirmed interface.
-                output_type_t = input_t.type.clone(shape=new_shape)()
-                output_type_f = input_f.type.clone(shape=new_shape)()
+                output_var_t = input_t.type.clone(shape=new_shape)()
+                output_var_f = input_f.type.clone(shape=new_shape)()
             else:
-                output_type_t = input_t.type()
-                output_type_f = input_f.type()
+                output_var_t = input_t.type()
+                output_var_f = input_f.type()
 
-            input_t = output_type_f.type.convert_variable(input_t)
-            input_f = output_type_t.type.convert_variable(input_f)
+            input_t_ = output_var_f.type.filter_variable(input_t)
+            input_f_ = output_var_t.type.filter_variable(input_f)
 
-            new_inputs_true_branch.append(input_t)
-            new_inputs_false_branch.append(input_f)
-            output_vars.append(output_type_t)
+            new_inputs_true_branch.append(input_t_)
+            new_inputs_false_branch.append(input_f_)
+            output_vars.append(output_var_t)
 
         return Apply(
             self,

tests/test_ifelse.py

@@ -325,22 +325,24 @@ class TestIfelse(utt.OptimizationTestMixin):
         with pytest.raises(TypeError):
             ifelse(cond, y, x)
 
-    def test_sparse_tensor_error(self):
+    def test_sparse_conversions(self):
+
+        from aesara.sparse import matrix
 
         rng = np.random.default_rng(utt.fetch_seed())
         data = rng.random((2, 3)).astype(self.dtype)
         x = self.shared(data)
-        y = aesara.sparse.matrix("csc", dtype=self.dtype, name="y")
-        z = aesara.sparse.matrix("csr", dtype=self.dtype, name="z")
+        y = matrix("csc", dtype=self.dtype, name="y")
+        z = matrix("csr", dtype=self.dtype, name="z")
         cond = iscalar("cond")
 
-        with pytest.raises(NotImplementedError):
+        with pytest.raises(TypeError, match=".*do not match."):
             ifelse(cond, x, y)
-        with pytest.raises(NotImplementedError):
+        with pytest.raises(TypeError, match=".*do not match."):
             ifelse(cond, y, x)
-        with pytest.raises(NotImplementedError):
+        with pytest.raises(TypeError):
             ifelse(cond, x, z)
-        with pytest.raises(NotImplementedError):
+        with pytest.raises(TypeError):
             ifelse(cond, z, x)
         with pytest.raises(TypeError):
             ifelse(cond, y, z)
@@ -534,6 +536,8 @@ class TestIfelse(utt.OptimizationTestMixin):
         [
             ((2,), (3,), np.r_[1.0, 2.0], np.r_[1.0, 2.0, 3.0], (None,)),
             ((None,), (3,), np.r_[1.0, 2.0], np.r_[1.0, 2.0, 3.0], (None,)),
+            ((3,), (None,), np.r_[1.0, 2.0, 3.0], np.r_[1.0, 2.0], (None,)),
+            ((2, 1), (None, 1), np.c_[[1.0, 2.0]], np.c_[[1.0, 2.0, 3.0]], (None, 1)),
             ((3,), (3,), np.r_[1.0, 2.0, 3.0], np.r_[1.0, 2.0, 3.0], (3,)),
             ((1,), (3,), np.r_[1.0], np.r_[1.0, 2.0, 3.0], (None,)),
         ],