fix duplicate slice logic in _grad

python/paddle/fluid/tests/unittests/autograd/test_autograd_functional_dynamic.py

@@ -223,6 +223,11 @@ def test_all_cases(self):
         self.func_vjp_nested()
         self.func_vjp_aliased_input()
 
+    def test_input_single_tensor(self):
+        self.assertIsInstance(
+            paddle.incubate.autograd.vjp(paddle.tanh, paddle.rand((3, 4)))[1],
+            paddle.fluid.framework.Variable)
+
 
 @utils.place(config.DEVICES)
 @utils.parameterize(

python/paddle/incubate/autograd/functional.py

@@ -565,13 +565,15 @@ def _grad(ys, xs, v=None):
             inputs.
     """
     if paddle.fluid._non_static_mode():
+        # paddle.grad returns a list though the inputs is a signle Tensor. The
+        # follow code snippet fixes the problem by return the first element of
+        # xs_grad when the xs is a signle Tensor.
         xs_grad = paddle.grad(ys, xs, v, create_graph=True, allow_unused=True)
+        if isinstance(xs, paddle.fluid.framework.Variable) and isinstance(
+                xs_grad, typing.Sequence) and len(xs_grad) > 0:
+            xs_grad = xs_grad[0]
     else:
         xs_grad = paddle.incubate.autograd.grad(ys, xs, v)
-
-    if isinstance(xs, paddle.fluid.framework.Variable):
-        xs_grad = xs_grad[0]
-
     return _replace_none_with_zero_tensor(xs_grad, xs)
 
 

python/paddle/incubate/autograd/primapi.py

@@ -132,9 +132,16 @@ def grad(outputs, inputs, grad_outputs=None):
             paddle.incubate.autograd.disable_prim()
             paddle.disable_static()
     """
-
     if not utils.prim_enabled():
-        return backward.gradients(outputs, inputs, grad_outputs)
+        grad_inputs = backward.gradients(outputs, inputs, grad_outputs)
+        # backward.gradients returns a list though the inputs is a signle Tensor.
+        # The follow code snippet fixes the problem by return the first element
+        # of grad_inputs when the inputs is a signle Tensor.
+        if isinstance(inputs, framework.Variable) and isinstance(
+                grad_inputs, typing.Sequence) and len(grad_inputs) > 0:
+            return grad_inputs[0]
+        else:
+            return grad_inputs
 
     if not isinstance(outputs, (framework.Variable, typing.Sequence)):
         raise TypeError(f'Expected outputs is Tensor|Sequence[Tesnor], '