diff --git a/python/paddle/static/io.py b/python/paddle/static/io.py
index cfaa6d9470439ea4c05c12892ad7db4c0d887c60..e88a052730414192cd4bc99b3abc2c6b46377c3e 100644
--- a/python/paddle/static/io.py
+++ b/python/paddle/static/io.py
@@ -204,7 +204,7 @@ def is_persistable(var):
 
 
 @static_only
-def serialize_program(feed_vars, fetch_vars):
+def serialize_program(feed_vars, fetch_vars, **kwargs):
     """
     :api_attr: Static Graph
 
@@ -213,6 +213,10 @@ def serialize_program(feed_vars, fetch_vars):
     Args:
         feed_vars(Variable | list[Variable]): Variables needed by inference.
         fetch_vars(Variable | list[Variable]): Variables returned by inference.
+        kwargs: Supported keys including 'program'.
+                Attention please, kwargs is used for backward compatibility mainly.
+          - program(Program): specify a program if you don't want to use default main program.
+
     Returns:
         bytes: serialized program.
 
@@ -235,7 +239,6 @@ def serialize_program(feed_vars, fetch_vars):
             predict = paddle.static.nn.fc(image, 10, activation='softmax')
 
             loss = paddle.nn.functional.cross_entropy(predict, label)
-            avg_loss = paddle.tensor.stat.mean(loss)
 
             exe = paddle.static.Executor(paddle.CPUPlace())
             exe.run(paddle.static.default_startup_program())
@@ -252,7 +255,7 @@ def serialize_program(feed_vars, fetch_vars):
     # verify fetch_vars
     _check_vars('fetch_vars', fetch_vars)
 
-    program = _get_valid_program()
+    program = _get_valid_program(kwargs.get('program', None))
     program = _normalize_program(program, feed_vars, fetch_vars)
     return _serialize_program(program)
 
@@ -265,7 +268,7 @@ def _serialize_program(program):
 
 
 @static_only
-def serialize_persistables(feed_vars, fetch_vars, executor):
+def serialize_persistables(feed_vars, fetch_vars, executor, **kwargs):
     """
     :api_attr: Static Graph
 
@@ -274,6 +277,10 @@ def serialize_persistables(feed_vars, fetch_vars, executor):
     Args:
         feed_vars(Variable | list[Variable]): Variables needed by inference.
         fetch_vars(Variable | list[Variable]): Variables returned by inference.
+        kwargs: Supported keys including 'program'.
+                Attention please, kwargs is used for backward compatibility mainly.
+          - program(Program): specify a program if you don't want to use default main program.
+
     Returns:
         bytes: serialized program.
 
@@ -296,7 +303,6 @@ def serialize_persistables(feed_vars, fetch_vars, executor):
             predict = paddle.static.nn.fc(image, 10, activation='softmax')
 
             loss = paddle.nn.functional.cross_entropy(predict, label)
-            avg_loss = paddle.tensor.stat.mean(loss)
 
             exe = paddle.static.Executor(paddle.CPUPlace())
             exe.run(paddle.static.default_startup_program())
@@ -314,7 +320,7 @@ def serialize_persistables(feed_vars, fetch_vars, executor):
     # verify fetch_vars
     _check_vars('fetch_vars', fetch_vars)
 
-    program = _get_valid_program()
+    program = _get_valid_program(kwargs.get('program', None))
     program = _normalize_program(program, feed_vars, fetch_vars)
     return _serialize_persistables(program, executor)
 
@@ -380,7 +386,8 @@ def save_to_file(path, content):
 
 
 @static_only
-def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
+def save_inference_model(path_prefix, feed_vars, fetch_vars, executor,
+                         **kwargs):
     """
     :api_attr: Static Graph
 
@@ -396,6 +403,9 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
         fetch_vars(Variable | list[Variable]): Variables returned by inference.
         executor(Executor): The executor that saves the inference model. You can refer
                             to :ref:`api_guide_executor_en` for more details.
+        kwargs: Supported keys including 'program'.
+                Attention please, kwargs is used for backward compatibility mainly.
+          - program(Program): specify a program if you don't want to use default main program.
     Returns:
         None
 
@@ -418,7 +428,6 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
             predict = paddle.static.nn.fc(image, 10, activation='softmax')
 
             loss = paddle.nn.functional.cross_entropy(predict, label)
-            avg_loss = paddle.tensor.stat.mean(loss)
 
             exe = paddle.static.Executor(paddle.CPUPlace())
             exe.run(paddle.static.default_startup_program())
@@ -456,7 +465,7 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
     # verify fetch_vars
     _check_vars('fetch_vars', fetch_vars)
 
-    program = _get_valid_program()
+    program = _get_valid_program(kwargs.get('program', None))
     program = _normalize_program(program, feed_vars, fetch_vars)
     # serialize and save program
     program_bytes = _serialize_program(program)
@@ -475,8 +484,35 @@ def deserialize_program(data):
 
     Args:
         data(bytes): serialized program.
+
     Returns:
         Program: deserialized program.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+
+            path_prefix = "./infer_model"
+
+            # User defined network, here a softmax regession example
+            image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
+            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+            predict = paddle.static.nn.fc(image, 10, activation='softmax')
+
+            loss = paddle.nn.functional.cross_entropy(predict, label)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            # serialize the default main program to bytes.
+            serialized_program = paddle.static.serialize_program([image], [predict])
+
+            # deserialize bytes to program
+            deserialized_program = paddle.static.deserialize_program(serialized_program)
+
     """
     program = Program.parse_from_string(data)
     if not core._is_program_version_supported(program._version()):
@@ -496,8 +532,37 @@ def deserialize_persistables(program, data, executor):
         program(Program): program that contains parameter names (to deserialize).
         data(bytes): serialized parameters.
         executor(Executor): executor used to run load op.
+
     Returns:
         Program: deserialized program.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+
+            path_prefix = "./infer_model"
+
+            # User defined network, here a softmax regession example
+            image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
+            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+            predict = paddle.static.nn.fc(image, 10, activation='softmax')
+
+            loss = paddle.nn.functional.cross_entropy(predict, label)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            # serialize parameters to bytes.
+            serialized_params = paddle.static.serialize_persistables([image], [predict], exe)
+
+            # deserialize bytes to parameters.
+            main_program = paddle.static.default_main_program()
+            deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe)
+
+
     """
     if not isinstance(program, Program):
         raise TypeError(
@@ -567,7 +632,7 @@ def load_from_file(path):
 
 
 @static_only
-def load_inference_model(path_prefix, executor, **configs):
+def load_inference_model(path_prefix, executor, **kwargs):
     """
     :api_attr: Static Graph
 
@@ -580,6 +645,10 @@ def load_inference_model(path_prefix, executor, **configs):
           - Set to None when reading the model from memory.
         executor(Executor): The executor to run for loading inference model.
                             See :ref:`api_guide_executor_en` for more details about it.
+        kwargs: Supported keys including 'model_filename', 'params_filename'.
+                Attention please, kwargs is used for backward compatibility mainly.
+          - model_filename(str): specify model_filename if you don't want to use default name.
+          - params_filename(str): specify params_filename if you don't want to use default name.
 
     Returns:
         list: The return of this API is a list with three elements:
@@ -631,17 +700,17 @@ def load_inference_model(path_prefix, executor, **configs):
             # fetch_targets, we can use an executor to run the inference
             # program to get the inference result.
     """
-    # check configs
+    # check kwargs
     supported_args = ('model_filename', 'params_filename')
     deprecated_args = ('pserver_endpoints', )
     caller = inspect.currentframe().f_code.co_name
-    _check_args(caller, configs, supported_args, deprecated_args)
+    _check_args(caller, kwargs, supported_args, deprecated_args)
 
     # load from memory
     if path_prefix is None:
         _logger.warning("Load inference model from memory is deprecated.")
-        model_filename = configs.get('model_filename', None)
-        params_filename = configs.get('params_filename', None)
+        model_filename = kwargs.get('model_filename', None)
+        params_filename = kwargs.get('params_filename', None)
         if params_filename is None:
             raise ValueError(
                 "params_filename cannot be None when path_prefix is None.")
@@ -655,14 +724,14 @@ def load_inference_model(path_prefix, executor, **configs):
 
         # set model_path and params_path in new way,
         # path_prefix represents a file path without suffix in this case.
-        if not configs:
+        if not kwargs:
             model_path = path_prefix + ".pdmodel"
             params_path = path_prefix + ".pdiparams"
         # set model_path and params_path in old way for compatible,
         # path_prefix represents a directory path.
         else:
-            model_filename = configs.get('model_filename', None)
-            params_filename = configs.get('params_filename', None)
+            model_filename = kwargs.get('model_filename', None)
+            params_filename = kwargs.get('params_filename', None)
             # set model_path
             if model_filename is None:
                 model_path = os.path.join(path_prefix, "__model__")