diff --git a/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py b/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py
index 3170f3f62cc8521cd55783f5c645515ac048b0ba..190f0eaac8911ca035c3388fa6cfcde759d042ed 100644
--- a/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py
+++ b/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py
@@ -1528,6 +1528,24 @@ class TestSundryAPI(unittest.TestCase):
             origin_result.numpy(), out3.numpy(), rtol=1e-05
         )
 
+    def test_upsample(self):
+        from paddle.nn.functional import upsample
+
+        input_x = paddle.rand([2, 3, 6, 6])
+        input_x.stop_gradient = False
+
+        output_size = [
+            paddle.full([], 12, dtype="int32"),
+            paddle.full([], 12, dtype="int32"),
+        ]
+        out1 = upsample(
+            x=input_x, size=output_size, mode="bilinear", align_corners=False
+        )
+        out1.backward()
+
+        self.assertEqual(out1.shape, [2, 3, 12, 12])
+        self.assertEqual(input_x.grad.shape, [2, 3, 6, 6])
+
     def test_maseked_select(self):
         x = paddle.rand([])
         x.stop_gradient = False
@@ -2512,6 +2530,28 @@ class TestSundryAPIStatic(unittest.TestCase):
         self.assertEqual(res2[0].shape, (2, 3, 12, 12))
         self.assertEqual(res2[1].shape, (2, 3, 6, 6))
 
+    @prog_scope()
+    def test_upsample(self):
+        from paddle.nn.functional import upsample
+
+        input_x = paddle.rand([2, 3, 6, 6])
+        input_x.stop_gradient = False
+
+        output_size = [
+            paddle.full([], 12, dtype="int32"),
+            paddle.full([], 12, dtype="int32"),
+        ]
+
+        out1 = upsample(
+            x=input_x, size=output_size, mode="bilinear", align_corners=False
+        )
+        paddle.static.append_backward(out1.sum())
+        prog = paddle.static.default_main_program()
+        res1 = self.exe.run(prog, feed={}, fetch_list=[out1, input_x.grad_name])
+
+        self.assertEqual(res1[0].shape, (2, 3, 12, 12))
+        self.assertEqual(res1[1].shape, (2, 3, 6, 6))
+
     @prog_scope()
     def test_maseked_select(self):
         x = paddle.rand([])
diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py
index 05968d593ebbb896a61001b889cd0709517ad965..5fa2c0eda0dbe7248e3b98b4410b282bdc8f39a6 100644
--- a/python/paddle/nn/functional/common.py
+++ b/python/paddle/nn/functional/common.py
@@ -318,11 +318,11 @@ def interpolate(
         size (list|tuple|Tensor|None): Output shape of image resize
              layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w)
              when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
-             Default: None. If a list/tuple, each element can be an integer or a Tensor of shape: [1].
+             Default: None. If a list/tuple, each element can be an integer or a Tensor of shape: [1] or [].
              If a Tensor, its dimensions size should be a 1.
         scale_factor (float|Tensor|list|tuple|None): The multiplier for the input height or width. At
              least one of :attr:`size` or :attr:`scale_factor` must be set.
-             And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if it is either a list or a tuple or a Tensor.
+             And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if it is either a list or a tuple or a Tensor.If a list/tuple, each element can be an integer or a Tensor of shape: [1] or [].
              Default: None.
         mode (str): The resample method. It supports 'linear', 'area', 'nearest', 'bilinear',
                        'bicubic' and 'trilinear' currently. Default: 'nearest'
@@ -870,12 +870,12 @@ def upsample(
         size (list|tuple|Tensor|None, optional): Output shape of image resize
              layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w)
              when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
-             Default: None. If a list/tuple, each element can be an integer or a Tensor of shape: [1].
+             Default: None. If a list/tuple, each element can be an integer or a Tensor of shape: [1] or [].
              If a Tensor , its dimensions size should be a 1.
         scale_factor (float|Tensor|list|tuple|None, optional): The multiplier for the input height or width. At
              least one of :attr:`size` or :attr:`scale_factor` must be set.
              And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if
-             it is either a list or a tuple or a Tensor.
+             it is either a list or a tuple or a Tensor. If a list/tuple, each element can be an integer or a Tensor of shape: [1] or [].
              Default: None.
         mode (str, optional): The resample method. It supports 'linear', 'nearest', 'bilinear',
                        'bicubic' and 'trilinear' currently. Default: 'nearest'