diff --git a/mindspore/common/parameter.py b/mindspore/common/parameter.py
index efe9e5dcc0d..6e6b7296245 100644
--- a/mindspore/common/parameter.py
+++ b/mindspore/common/parameter.py
@@ -384,6 +384,7 @@ class Parameter(Tensor_):
 
     @property
     def layerwise_parallel(self):
+        """Return whether the parameter is layerwise parallel."""
         return self.param_info.layerwise_parallel
 
     @layerwise_parallel.setter
@@ -438,10 +439,11 @@ class Parameter(Tensor_):
 
     @property
     def data(self):
+        """Return the parameter object."""
         return self
 
     def _update_tensor_data(self, data):
-        "Update the parameter by a Tensor."
+        """Update the parameter by a Tensor."""
         if isinstance(self, Tensor):
             self.init_flag = False
             self.init = None
@@ -452,7 +454,7 @@ class Parameter(Tensor_):
 
     def set_data(self, data, slice_shape=False):
         """
-        Set `set_data` of current `Parameter`.
+        Set Parameter's data.
 
         Args:
             data (Union[Tensor, int, float]): new data.
@@ -512,7 +514,7 @@ class Parameter(Tensor_):
 
     def init_data(self, layout=None, set_sliced=False):
         """
-        Initialize the parameter data.
+        Initialize the parameter's data.
 
         Args:
             layout (Union[None, list(list(int))]): Parameter slice
@@ -527,6 +529,7 @@ class Parameter(Tensor_):
 
         Raises:
             RuntimeError: If it is from Initializer, and parallel mode has changed after the Initializer created.
+            ValueError: If the length of the layout is less than 3.
 
         Returns:
             Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before,
diff --git a/mindspore/train/__init__.py b/mindspore/train/__init__.py
index 5a05c655b0c..2f567d39e64 100644
--- a/mindspore/train/__init__.py
+++ b/mindspore/train/__init__.py
@@ -23,9 +23,9 @@ from . import amp
 from .amp import build_train_network
 from .loss_scale_manager import LossScaleManager, FixedLossScaleManager, DynamicLossScaleManager
 from .serialization import save_checkpoint, load_checkpoint, load_param_into_net, export, load, parse_print,\
-    build_searched_strategy, merge_sliced_parameter, load_distributed_checkpoint
+    build_searched_strategy, merge_sliced_parameter, load_distributed_checkpoint, async_ckpt_thread_status
 
 __all__ = ["Model", "DatasetHelper", "amp", "connect_network_with_dataset", "build_train_network", "LossScaleManager",
            "FixedLossScaleManager", "DynamicLossScaleManager", "save_checkpoint", "load_checkpoint",
            "load_param_into_net", "export", "load", "parse_print", "build_searched_strategy", "merge_sliced_parameter",
-           "load_distributed_checkpoint"]
+           "load_distributed_checkpoint", "async_ckpt_thread_status"]