Fix TensorType dtype validation issues and refactor tests

aesara/tensor/type.py

@@ -92,9 +92,12 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
             )
             shape = broadcastable
 
-        if isinstance(dtype, str) and dtype == "floatX":
+        if dtype == "floatX":
             self.dtype = config.floatX
         else:
+            if np.obj2sctype(dtype) is None:
+                raise TypeError(f"Invalid dtype: {dtype}")
+
             self.dtype = np.dtype(dtype).name
 
         def parse_bcast_and_shape(s):

tests/tensor/test_basic.py

@@ -1069,8 +1069,9 @@ class TestCast:
         f = function([x], y)
         assert f(np.array([1, 2], dtype=np.int32)).dtype == np.int64
 
-    def test_good_between_real_types(self):
-        good = itertools.chain(
+    @pytest.mark.parametrize(
+        "test_name, obj_dtype",
+        itertools.chain(
             multi_dtype_cast_checks((2,), dtypes=REAL_DTYPES),
             # Casts from foo to foo
             [
@@ -1080,8 +1081,10 @@ class TestCast:
                 )
                 for dtype in ALL_DTYPES
             ],
+        ),
     )
-        for testname, (obj, dtype) in good:
+    def test_good_between_real_types(self, test_name, obj_dtype):
+        (obj, dtype) = obj_dtype
         inp = vector(dtype=obj.dtype)
         out = cast(inp, dtype=dtype)
         f = function([inp], out)
@@ -1091,21 +1094,21 @@ class TestCast:
         out2 = inp.astype(dtype=dtype)
         assert out2.type == out.type
 
-    def test_cast_from_real_to_complex(self):
-        for real_dtype in REAL_DTYPES:
-            for complex_dtype in COMPLEX_DTYPES:
+    @pytest.mark.parametrize("real_dtype", REAL_DTYPES)
+    @pytest.mark.parametrize("complex_dtype", COMPLEX_DTYPES)
+    def test_cast_from_real_to_complex(self, real_dtype, complex_dtype):
         inp = vector(dtype=real_dtype)
         out = cast(inp, dtype=complex_dtype)
         f = function([inp], out)
         obj = random_of_dtype((2,), real_dtype)
         assert f(obj).dtype == np.dtype(complex_dtype)
 
-    def test_cast_from_complex_to_real_raises_error(self):
-        for real_dtype in REAL_DTYPES:
-            for complex_dtype in COMPLEX_DTYPES:
-                inp = vector(dtype=real_dtype)
+    @pytest.mark.parametrize("real_dtype", REAL_DTYPES)
+    @pytest.mark.parametrize("complex_dtype", COMPLEX_DTYPES)
+    def test_cast_from_complex_to_real_raises_error(self, real_dtype, complex_dtype):
+        inp = vector(dtype=complex_dtype)
         with pytest.raises(TypeError):
-                    tensor(cast(inp, dtype=complex_dtype))
+            cast(inp, dtype=real_dtype)
 
 
 # TODO: consider moving this function / functionality to gradient.py