Fix local_subtensor_SpecifyShape_lift

aesara/tensor/subtensor_opt.py

@@ -1632,7 +1632,7 @@ def local_subtensor_shape_constant(fgraph, node):
 @register_canonicalize
 @local_optimizer([Subtensor])
 def local_subtensor_SpecifyShape_lift(fgraph, node):
-    """Lift ``specify_shape(x, s)[i]`` to ``specify_shape(x[i], s[i])``."""
+    """Lift ``specify_shape(x, s)[i_1, ..., i_n]`` to ``specify_shape(x[i1, ... , i_n], s[n:])``."""
 
     if not isinstance(node.op, Subtensor):
         return False
@@ -1650,19 +1650,14 @@ def local_subtensor_SpecifyShape_lift(fgraph, node):
 
     indices = get_idx_list(node.inputs, node.op.idx_list)
 
-    if len(indices) > 1 or len(indices) == 0:
-        return False
-
-    if isinstance(indices[0], slice) or isinstance(
-        getattr(indices[0], "type", None), SliceType
+    if any(
+        isinstance(index, slice) or isinstance(getattr(index, "type", None), SliceType)
+        for index in indices
     ):
         return False
 
     new_obj_arg = obj_arg[indices]
-
+    # No need to specify shape for scalar outputs
     if new_obj_arg.ndim == 0:
-        new_shape_arg = as_tensor([], dtype=np.int64)
-    else:
-        new_shape_arg = as_tensor(shape_arg[indices], dtype=np.int64, ndim=1)
-
-    return [specify_shape(new_obj_arg, new_shape_arg)]
+        return [new_obj_arg]
+    return [specify_shape(new_obj_arg, shape_arg[len(indices) :])]

tests/tensor/test_subtensor_opt.py

@@ -2102,6 +2102,13 @@ def test_local_subtensor_shape_constant():
 @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()),
@@ -2110,16 +2117,38 @@ def test_local_subtensor_shape_constant():
             np.array([2, 2], dtype=np.int64),
         ),
         (
-            vector(),
-            (iscalar(),),
-            (1,),
-            np.array([1, 2], dtype=config.floatX),
-            np.array([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)
 
     opts = OptimizationQuery(include=[None])
     no_opt_mode = Mode(optimizer=opts)
@@ -2130,7 +2159,12 @@ def test_local_subtensor_SpecifyShape_lift(x, s, idx, x_val, s_val):
     # This optimization should appear in the canonicalizations
     y_opt = optimize_graph(y, clone=False)
 
-    assert isinstance(y_opt.owner.op, SpecifyShape)
+    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] + list(s), y_opt, on_unused_input="ignore")
     y_opt_val = y_opt_fn(*([x_val] + [s_ for s_ in s_val]))