modified the cn doc of scaled_dot_product_attention and add_position_encoding, test=develop (#1955)

doc/fluid/api_cn/layers_cn/add_position_encoding_cn.rst

@@ -34,14 +34,13 @@ add_position_encoding
 
 .. code-block:: python
 
-  import paddle.fluid as fluid
-     
-  tensor = fluid.layers.data(
+    import paddle.fluid as fluid
+
+    tensor = fluid.data(
         name='tensor',
-        shape=[32, 64, 512],
-        dtype='float32',
-        append_batch_size=False)
-  position_tensor = fluid.layers.add_position_encoding(
+        shape=[None, 64, 512],
+        dtype='float32')
+    position_tensor = fluid.layers.add_position_encoding(
         input=tensor, alpha=1.0, beta=1.0)
 
 
@@ -53,4 +52,3 @@ add_position_encoding
 
 
 
-

doc/fluid/api_cn/nets_cn/scaled_dot_product_attention_cn.rst

@@ -43,31 +43,12 @@ scaled_dot_product_attention
 
 .. code-block:: python
 
-          import paddle.fluid as fluid
-          
-          queries = fluid.layers.data(name="queries",
-                                      shape=[3, 5, 9],
-                                      dtype="float32",
-                                      append_batch_size=False)
-          queries.stop_gradient = False
-          keys = fluid.layers.data(name="keys",
-                                   shape=[3, 6, 9],
-                                   dtype="float32",
-                                   append_batch_size=False)
-          keys.stop_gradient = False
-          values = fluid.layers.data(name="values",
-                                     shape=[3, 6, 10],
-                                     dtype="float32",
-                                     append_batch_size=False)
-          values.stop_gradient = False
-          contexts = fluid.nets.scaled_dot_product_attention(queries, keys, values)
-          contexts.shape  # [3, 5, 10]
-
-
-
-
-
-
+    import paddle.fluid as fluid
 
+    queries = fluid.data(name="queries", shape=[3, 5, 9], dtype="float32")
+    keys = fluid.data(name="keys", shape=[3, 6, 9], dtype="float32")
+    values = fluid.data(name="values", shape=[3, 6, 10], dtype="float32")
+    contexts = fluid.nets.scaled_dot_product_attention(queries, keys, values)
+    contexts.shape  # [3, 5, 10]