Group subtensor specify_shape lift tests in class

tests/tensor/rewriting/test_subtensor_lift.py

@@ -247,106 +247,106 @@ def test_local_subtensor_of_alloc():
                 assert xval.__getitem__(slices).shape == val.shape
 
 
-@pytest.mark.parametrize(
-    "x, s, idx, x_val, s_val",
-    [
-        (
-            vector(),
-            (iscalar(),),
-            (1,),
-            np.array([1, 2], dtype=config.floatX),
-            np.array([2], dtype=np.int64),
-        ),
-        (
-            matrix(),
-            (iscalar(), iscalar()),
-            (1,),
-            np.array([[1, 2], [3, 4]], dtype=config.floatX),
-            np.array([2, 2], dtype=np.int64),
-        ),
-        (
-            matrix(),
-            (iscalar(), iscalar()),
-            (0,),
-            np.array([[1, 2, 3], [4, 5, 6]], dtype=config.floatX),
-            np.array([2, 3], dtype=np.int64),
-        ),
-        (
-            matrix(),
-            (iscalar(), iscalar()),
-            (1, 1),
-            np.array([[1, 2, 3], [4, 5, 6]], dtype=config.floatX),
-            np.array([2, 3], dtype=np.int64),
-        ),
-        (
-            tensor3(),
-            (iscalar(), iscalar(), iscalar()),
-            (-1,),
-            np.arange(2 * 3 * 5, dtype=config.floatX).reshape((2, 3, 5)),
-            np.array([2, 3, 5], dtype=np.int64),
-        ),
-        (
-            tensor3(),
-            (iscalar(), iscalar(), iscalar()),
-            (-1, 0),
-            np.arange(2 * 3 * 5, dtype=config.floatX).reshape((2, 3, 5)),
-            np.array([2, 3, 5], dtype=np.int64),
-        ),
-    ],
-)
-def test_local_subtensor_SpecifyShape_lift(x, s, idx, x_val, s_val):
-    y = specify_shape(x, s)[idx]
-    assert isinstance(y.owner.inputs[0].owner.op, SpecifyShape)
-
-    rewrites = RewriteDatabaseQuery(include=[None])
-    no_rewrites_mode = Mode(optimizer=rewrites)
-
-    y_val_fn = function([x, *s], y, on_unused_input="ignore", mode=no_rewrites_mode)
-    y_val = y_val_fn(*([x_val, *s_val]))
-
-    # This optimization should appear in the canonicalizations
-    y_opt = rewrite_graph(y, clone=False)
-
-    if y.ndim == 0:
-        # SpecifyShape should be removed altogether
-        assert isinstance(y_opt.owner.op, Subtensor)
-        assert y_opt.owner.inputs[0] is x
-    else:
-        assert isinstance(y_opt.owner.op, SpecifyShape)
-
-    y_opt_fn = function([x, *s], y_opt, on_unused_input="ignore")
-    y_opt_val = y_opt_fn(*([x_val, *s_val]))
-
-    assert np.allclose(y_val, y_opt_val)
-
-
-@pytest.mark.parametrize(
-    "x, s, idx",
-    [
-        (
-            matrix(),
-            (iscalar(), iscalar()),
-            (slice(1, None),),
-        ),
-        (
-            matrix(),
-            (iscalar(), iscalar()),
-            (slicetype(),),
-        ),
-        (
-            matrix(),
-            (iscalar(), iscalar()),
-            (1, 0),
-        ),
-    ],
-)
-def test_local_subtensor_SpecifyShape_lift_fail(x, s, idx):
-    y = specify_shape(x, s)[idx]
-
-    # This optimization should appear in the canonicalizations
-    y_opt = rewrite_graph(y, clone=False)
-
-    assert not isinstance(y_opt.owner.op, SpecifyShape)
+class TestLocalSubtensorSpecifyShapeLift:
+    @pytest.mark.parametrize(
+        "x, s, idx, x_val, s_val",
+        [
+            (
+                vector(),
+                (iscalar(),),
+                (1,),
+                np.array([1, 2], dtype=config.floatX),
+                np.array([2], dtype=np.int64),
+            ),
+            (
+                matrix(),
+                (iscalar(), iscalar()),
+                (1,),
+                np.array([[1, 2], [3, 4]], dtype=config.floatX),
+                np.array([2, 2], dtype=np.int64),
+            ),
+            (
+                matrix(),
+                (iscalar(), iscalar()),
+                (0,),
+                np.array([[1, 2, 3], [4, 5, 6]], dtype=config.floatX),
+                np.array([2, 3], dtype=np.int64),
+            ),
+            (
+                matrix(),
+                (iscalar(), iscalar()),
+                (1, 1),
+                np.array([[1, 2, 3], [4, 5, 6]], dtype=config.floatX),
+                np.array([2, 3], dtype=np.int64),
+            ),
+            (
+                tensor3(),
+                (iscalar(), iscalar(), iscalar()),
+                (-1,),
+                np.arange(2 * 3 * 5, dtype=config.floatX).reshape((2, 3, 5)),
+                np.array([2, 3, 5], dtype=np.int64),
+            ),
+            (
+                tensor3(),
+                (iscalar(), iscalar(), iscalar()),
+                (-1, 0),
+                np.arange(2 * 3 * 5, dtype=config.floatX).reshape((2, 3, 5)),
+                np.array([2, 3, 5], dtype=np.int64),
+            ),
+        ],
+    )
+    def test_local_subtensor_SpecifyShape_lift(self, x, s, idx, x_val, s_val):
+        y = specify_shape(x, s)[idx]
+        assert isinstance(y.owner.inputs[0].owner.op, SpecifyShape)
+
+        rewrites = RewriteDatabaseQuery(include=[None])
+        no_rewrites_mode = Mode(optimizer=rewrites)
+
+        y_val_fn = function([x, *s], y, on_unused_input="ignore", mode=no_rewrites_mode)
+        y_val = y_val_fn(*([x_val, *s_val]))
+
+        # This optimization should appear in the canonicalizations
+        y_opt = rewrite_graph(y, clone=False)
+
+        if y.ndim == 0:
+            # SpecifyShape should be removed altogether
+            assert isinstance(y_opt.owner.op, Subtensor)
+            assert y_opt.owner.inputs[0] is x
+        else:
+            assert isinstance(y_opt.owner.op, SpecifyShape)
+
+        y_opt_fn = function([x, *s], y_opt, on_unused_input="ignore")
+        y_opt_val = y_opt_fn(*([x_val, *s_val]))
+
+        assert np.allclose(y_val, y_opt_val)
+
+    @pytest.mark.parametrize(
+        "x, s, idx",
+        [
+            (
+                matrix(),
+                (iscalar(), iscalar()),
+                (slice(1, None),),
+            ),
+            (
+                matrix(),
+                (iscalar(), iscalar()),
+                (slicetype(),),
+            ),
+            (
+                matrix(),
+                (iscalar(), iscalar()),
+                (1, 0),
+            ),
+        ],
+    )
+    def test_local_subtensor_SpecifyShape_lift_fail(self, x, s, idx):
+        y = specify_shape(x, s)[idx]
+
+        # This optimization should appear in the canonicalizations
+        y_opt = rewrite_graph(y, clone=False)
+
+        assert not isinstance(y_opt.owner.op, SpecifyShape)
 
 
 class TestLocalSubtensorMakeVector: