diff --git a/mindspore/train/callback.py b/mindspore/train/callback.py
index c8ce5d22ef4..0b5e05e6a01 100644
--- a/mindspore/train/callback.py
+++ b/mindspore/train/callback.py
@@ -150,8 +150,8 @@ class CheckpointConfig:
         keep_checkpoint_max (int): Maximum step to save checkpoint. Default: 5.
         keep_checkpoint_per_n_minutes (int): Keep one checkpoint every n minutes. Default: 0.
             Can't be used with keep_checkpoint_max at the same time.
-        integrated_save (bool): Whether to intergrated save in automatic model parall scene. Default: True.
-            Integrated save function is only supported in automatic parall scene, not supported in manual parallel.
+        integrated_save (bool): Whether to intergrated save in automatic model parallel scene. Default: True.
+            Integrated save function is only supported in automatic parallel scene, not supported in manual parallel.
 
     Raises:
         ValueError: If the input_param is None or 0.
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 5c1cc5eba07..df1a79fb132 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -230,15 +230,6 @@ def load_param_into_net(net, parameter_dict):
         raise TypeError(msg)
 
     logger.info("Execute load parameter into net process.")
-    for name in parameter_dict:
-        for _, param in net.parameters_and_names():
-            if name == param.name and param.layerwise_parallel:
-                # layerwise parallel parameter data loaded from checkpoint file,
-                # was a complete(merged) data, need to be splited
-                new_param = parameter_dict[param.name]
-                _load_tensor_for_layerwise(new_param, param)
-                break
-
     param_not_load = []
     for _, param in net.parameters_and_names():
         if param.name in parameter_dict:
@@ -368,34 +359,6 @@ def _get_merged_param_data(net, param_name, param_data):
     return param_data
 
 
-def _load_tensor_for_layerwise(new_param, old_param):
-    """
-    Replaces parameters with sliced tensors by layerwise parallel strategies.
-
-    Args:
-        new_param (Parameter): The new layerwise parallel parameter, will be loaded into net.
-        old_param(Parameter): The current parameter in the net.
-    """
-    if not isinstance(new_param.data, Tensor) or not isinstance(old_param.data, Tensor):
-        logger.error("Failed to combine the net and the parameters.")
-        msg = ("layerwise parallel parameter should be a Tensor, but got {}.".format(type(new_param.data)))
-        raise TypeError(msg)
-
-    if old_param.data.shape() == new_param.data.shape():
-        return
-
-    from mindspore.parallel._tensor import _load_tensor
-    from mindspore.communication.management import get_group_size
-    dev_mat = [get_group_size()]
-    shape = new_param.data.shape()
-    for x in range(len(shape)):  # dim 0 set 0, others set -1
-        if x:
-            tensor_map.append(-1)
-
-    new_tensor = _load_tensor(new_param.data, dev_mat, tensor_map)
-    new_param.set_parameter_data(new_tensor)
-
-
 def _fill_param_into_net(net, parameter_list):
     """
     Fills parameter_list into net.