Do not coerce gradients to TensorVariable (#1685)

pytensor/gradient.py

@@ -494,22 +494,25 @@ def Lop(
         coordinates of the tensor elements.
         If `f` is a list/tuple, then return a list/tuple with the results.
     """
-    if not isinstance(eval_points, list | tuple):
-        _eval_points: list[Variable] = [pytensor.tensor.as_tensor_variable(eval_points)]
-    else:
-        _eval_points = [pytensor.tensor.as_tensor_variable(x) for x in eval_points]
+    from pytensor.tensor import as_tensor_variable
 
-    if not isinstance(f, list | tuple):
-        _f: list[Variable] = [pytensor.tensor.as_tensor_variable(f)]
-    else:
-        _f = [pytensor.tensor.as_tensor_variable(x) for x in f]
+    if not isinstance(eval_points, Sequence):
+        eval_points = [eval_points]
+    _eval_points = [
+        x if isinstance(x, Variable) else as_tensor_variable(x) for x in eval_points
+    ]
+
+    if not isinstance(f, Sequence):
+        f = [f]
+    _f = [x if isinstance(x, Variable) else as_tensor_variable(x) for x in f]
 
     grads = list(_eval_points)
 
-    if not isinstance(wrt, list | tuple):
-        _wrt: list[Variable] = [pytensor.tensor.as_tensor_variable(wrt)]
-    else:
-        _wrt = [pytensor.tensor.as_tensor_variable(x) for x in wrt]
+    using_list = isinstance(wrt, list)
+    using_tuple = isinstance(wrt, tuple)
+    if not isinstance(wrt, Sequence):
+        wrt = [wrt]
+    _wrt = [x if isinstance(x, Variable) else as_tensor_variable(x) for x in wrt]
 
     assert len(_f) == len(grads)
     known = dict(zip(_f, grads, strict=True))
@@ -523,8 +526,6 @@ def Lop(
         return_disconnected=return_disconnected,
     )
 
-    using_list = isinstance(wrt, list)
-    using_tuple = isinstance(wrt, tuple)
     return as_list_or_tuple(using_list, using_tuple, ret)
 
 

tests/test_gradient.py

@@ -11,6 +11,7 @@ from pytensor.gradient import (
     DisconnectedType,
     GradClip,
     GradScale,
+    Lop,
     NullTypeGradError,
     Rop,
     UndefinedGrad,
@@ -32,6 +33,7 @@ from pytensor.graph.basic import Apply
 from pytensor.graph.null_type import NullType
 from pytensor.graph.op import Op
 from pytensor.graph.traversal import graph_inputs
+from pytensor.scalar import float64
 from pytensor.scan.op import Scan
 from pytensor.tensor.math import add, dot, exp, outer, sigmoid, sqr, sqrt, tanh
 from pytensor.tensor.math import sum as pt_sum
@@ -1207,3 +1209,13 @@ class TestHessianVectorProduct:
         hessp_x_eval, hessp_y_eval = hessp_fn(**test)
         np.testing.assert_allclose(hessp_x_eval, [2, 4, 6])
         np.testing.assert_allclose(hessp_y_eval, [-6, -4, -2])
+
+
+def test_scalar_Lop():
+    xtm1 = float64("xtm1")
+    xt = xtm1**2
+
+    dout_dxt = float64("dout_dxt")
+    dout_dxtm1 = Lop(xt, wrt=xtm1, eval_points=dout_dxt)
+    assert dout_dxtm1.type == dout_dxt.type
+    assert dout_dxtm1.eval({xtm1: 3.0, dout_dxt: 1.5}) == 2 * 3.0 * 1.5