[2.0 api] fix the example codes in doc strings (#26428)

* fix the example codes, test=develop

python/paddle/fluid/tests/unittests/test_expand_as_v2_op.py

@@ -93,8 +93,19 @@ class TestExpandAsOpRank4(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestExpandAsV2Error(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            x1 = fluid.layers.data(name='x1', shape=[4], dtype="uint8")
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
+            self.assertRaises(TypeError, paddle.tensor.expand_as, x1, x2)
+            x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool")
+            x3.stop_gradient = False
+            self.assertRaises(ValueError, paddle.tensor.expand_as, x3, x2)
+
+
 # Test python API
-class TestExpandAPI(unittest.TestCase):
+class TestExpandAsV2API(unittest.TestCase):
     def test_api(self):
         input1 = np.random.random([12, 14]).astype("float32")
         input2 = np.random.random([2, 12, 14]).astype("float32")

python/paddle/fluid/tests/unittests/test_tile_op.py

@@ -215,6 +215,16 @@ class TestTileError(unittest.TestCase):
             self.assertRaises(ValueError, paddle.tile, x3, repeat_times)
 
 
+class TestTileAPIStatic(unittest.TestCase):
+    def test_api(self):
+        with program_guard(Program(), Program()):
+            repeat_times = [2, 2]
+            x1 = fluid.layers.data(name='x1', shape=[4], dtype="int32")
+            out = paddle.tile(x1, repeat_times)
+            positive_2 = fluid.layers.fill_constant([1], dtype="int32", value=2)
+            out2 = paddle.tile(x1, repeat_times=[positive_2, 2])
+
+
 # Test python API
 class TestTileAPI(unittest.TestCase):
     def test_api(self):

python/paddle/tensor/manipulation.py

@@ -841,7 +841,7 @@ def tile(x, repeat_times, name=None):
     """
 
     Construct a new Tensor by repeating ``x`` the number of times given by ``repeat_times``.
-    After tiling, the number of elements of the i'th dimension of the output is equal to ``x.dims[i] * repeat_times[i]``.
+    After tiling, the value of the i'th dimension of the output is equal to ``x.shape[i]*repeat_times[i]``.
 
     Both the number of dimensions of ``x`` and the number of elements in ``repeat_times`` should be less than or equal to 6.
 
@@ -862,9 +862,9 @@ def tile(x, repeat_times, name=None):
 
             paddle.disable_static()
             np_data = np.array([1, 2, 3]).astype('int32')
-            data = paddle.to_variable(np_data)
+            data = paddle.to_tensor(np_data)
             out = paddle.tile(data, repeat_times=[2, 1])
-			np_out = out1.numpy()
+			np_out = out.numpy()
             # [[1, 2, 3], [1, 2, 3]]
 
             out = paddle.tile(data, repeat_times=[2, 2])
@@ -872,7 +872,7 @@ def tile(x, repeat_times, name=None):
             # [[1, 2, 3, 1, 2, 3], [1, 2, 3, 1, 2, 3]]
 
             np_repeat_times = np.array([2, 1]).astype("int32")
-            repeat_times = paddle.to_variable(np_repeat_times)
+            repeat_times = paddle.to_tensor(np_repeat_times)
             out = paddle.tile(data, repeat_times=repeat_times)
 			np_out = out.numpy()
             # [[1, 2, 3], [1, 2, 3]]
@@ -884,9 +884,12 @@ def tile(x, repeat_times, name=None):
         raise ValueError(
             "When the date type is bool for the input 'x' of tile op, you "
             "must set its stop_gradient to be True by "
-            "some_var.stop_gradient == True supporting some_var as the input.")
+            "some_var.stop_gradient == True supporting some_var is the input.")
+
+    if in_dygraph_mode():
+        return core.ops.tile(x, 'repeat_times', repeat_times)
 
-    helper = LayerHelper('tile', input=x, **locals())
+    helper = LayerHelper('tile', **locals())
 
     inputs = {"X": [x]}
     attrs = {}
@@ -928,7 +931,7 @@ def expand_as(x, y, name=None):
 
     Args:
         x (Tensor): The input tensor, its data type is bool, float32, float64, int32 or int64.
-        y (Tensor): The input tensor gives the shape that ``x`` to expand to.
+        y (Tensor): The input tensor that gives the shape to expand to.
         name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -942,10 +945,10 @@ def expand_as(x, y, name=None):
 
             paddle.disable_static()
 
-            np_data_x = np.array([1, 2, 3]).astype=('int32)
-            np_data_y = np.array([[1, 2, 3], [4, 5, 6]]).astype=('int32)
-            data_x = paddle.to_variable(np_data_x)
-            data_y = paddle.to_variable(np_data_y)
+            np_data_x = np.array([1, 2, 3]).astype('int32')
+            np_data_y = np.array([[1, 2, 3], [4, 5, 6]]).astype('int32')
+            data_x = paddle.to_tensor(np_data_x)
+            data_y = paddle.to_tensor(np_data_y)
             out = paddle.expand_as(data_x, data_y)
 			np_out = out.numpy()
             # [[1, 2, 3], [1, 2, 3]]
@@ -962,7 +965,10 @@ def expand_as(x, y, name=None):
             "some_var as the input 'x'.")
     inputs = {"X": [x], "target_tensor": [y]}
 
-    helper = LayerHelper('expand_as', input=x, **locals())
+    if in_dygraph_mode():
+        return core.ops.expand_as_v2(x, y)
+
+    helper = LayerHelper('expand_as', **locals())
     dtype = helper.input_dtype(input_param_name='x')
     out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(type='expand_as_v2', inputs=inputs, outputs={'Out': out})
@@ -994,16 +1000,16 @@ def expand(x, shape, name=None):
             import paddle
 
             paddle.disable_static()
-            np_data = np.array([1, 2, 3]).astype=('int32)
-            data = paddle.to_variable(np_data)
+            np_data = np.array([1, 2, 3]).astype('int32')
+            data = paddle.to_tensor(np_data)
             out = paddle.expand(data, shape=[2, 3])
 			out = out.numpy()
             # [[1, 2, 3], [1, 2, 3]]
 
-            np_shape = np.array([2, 3]).astype=('int32)
-            shape = paddle.to_variable(np_shape)
+            np_shape = np.array([2, 3]).astype('int32')
+            shape = paddle.to_tensor(np_shape)
             out = paddle.expand(data, shape=shape)
-			out = out.numpy
+			out = out.numpy()
             # [[1, 2, 3], [1, 2, 3]]
     """
     check_variable_and_dtype(
@@ -1018,7 +1024,10 @@ def expand(x, shape, name=None):
                          "some_var.stop_gradient = True, supporting "
                          "some_var as the input.")
 
-    helper = LayerHelper('expand', input=x, **locals())
+    if in_dygraph_mode():
+        return core.ops.expand_v2(x, 'shape', shape)
+
+    helper = LayerHelper('expand', **locals())
 
     def get_attr_expand_shape(list_expand_shape):
         attrs_expand_shape = []