diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index f72a8a9e76..04c4b0de81 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -149,7 +149,7 @@ class LogSoftmax(PrimitiveWithInfer):
     Log Softmax activation function.
 
     Applies the Log Softmax function to the input tensor on the specified axis.
-    Suppose a slice in the given aixs :math:`x` then for each element :math:`x_i`
+    Suppose a slice in the given aixs, :math:`x` for each element :math:`x_i`,
     the Log Softmax function is shown as follows:
 
     .. math::
@@ -158,7 +158,7 @@ class LogSoftmax(PrimitiveWithInfer):
     where :math:`N` is the length of the Tensor.
 
     Args:
-        axis (int): The axis to do the Log softmax operation. Default: -1.
+        axis (int): The axis to perform the Log softmax operation. Default: -1.
 
     Inputs:
         - **logits** (Tensor) - The input of Log Softmax, with float16 or float32 data type.
@@ -2253,7 +2253,7 @@ class L2Normalize(PrimitiveWithInfer):
     r"""
     L2 normalization Operator.
 
-    This operator will normalizes the input using the given axis. The function is shown as follows:
+    This operator will normalize the input using the given axis. The function is shown as follows:
 
     .. math::
         \text{output} = \frac{x}{\sqrt{\text{max}(\text{sum} (\text{input_x}^2), \epsilon)}},
@@ -2261,7 +2261,7 @@ class L2Normalize(PrimitiveWithInfer):
     where :math:`\epsilon` is epsilon.
 
     Args:
-        axis (int): The begin axis for the input to apply L2 normalize. Default: 0.
+        axis (int): The starting axis for the input to apply the L2 normalization. Default: 0.
         epsilon (float): A small value added for numerical stability. Default: 1e-4.
 
     Inputs:
@@ -2657,7 +2657,7 @@ class LSTM(PrimitiveWithInfer):
     """
     Performs the long short term memory(LSTM) on the input.
 
-    Detailed information, please refer to `nn.LSTM`.
+    For detailed information, please refer to `nn.LSTM`.
     """
 
     @prim_attr_register
@@ -4803,7 +4803,7 @@ class ApplyProximalGradientDescent(PrimitiveWithInfer):
 
 class LARSUpdate(PrimitiveWithInfer):
     """
-    Conduct lars (layer-wise adaptive rate scaling) update on the square sum of gradient.
+    Conduct lars (layer-wise adaptive rate scaling) update on the sum of squares of gradient.
 
     Args:
         epsilon (float): Term added to the denominator to improve numerical stability. Default: 1e-05.
@@ -4813,8 +4813,8 @@ class LARSUpdate(PrimitiveWithInfer):
     Inputs:
         - **weight** (Tensor) - The weight to be updated.
         - **gradient** (Tensor) - The gradient of weight, which has the same shape and dtype with weight.
-        - **norm_weight** (Tensor) - A scalar tensor, representing the square sum of weight.
-        - **norm_gradient** (Tensor) - A scalar tensor, representing the square sum of gradient.
+        - **norm_weight** (Tensor) - A scalar tensor, representing the sum of squares of weight.
+        - **norm_gradient** (Tensor) - A scalar tensor, representing the sum of squares of gradient.
         - **weight_decay** (Union[Number, Tensor]) - Weight decay. It should be a scalar tensor or number.
         - **learning_rate** (Union[Number, Tensor]) - Learning rate. It should be a scalar tensor or number.
 
@@ -5576,10 +5576,10 @@ class InTopK(PrimitiveWithInfer):
 
 class LRN(PrimitiveWithInfer):
     r"""
-    Local Response Normalization
+    Local Response Normalization.
 
     Args:
-        depth_radius (int): Half-width of the 1-D normalization window. Shape of 0-D.
+        depth_radius (int): Half-width of the 1-D normalization window with the shape of 0-D.
         bias (float): An offset (usually positive to avoid dividing by 0).
         alpha (float): A scale factor, usually positive.
         beta (float): An exponent.
@@ -5589,7 +5589,7 @@ class LRN(PrimitiveWithInfer):
         - **x** (Tensor) - A 4D Tensor with float16 or float32 data type.
 
     Outputs:
-        Tensor, With shape and data type same as the input tensor.
+        Tensor, with the same shape and data type as the input tensor.
 
     Examples:
         >>> x = Tensor(np.random.rand(1, 10, 4, 4)), mindspore.float32)
diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py
index 8552c41cdf..1da039fbf9 100644
--- a/mindspore/ops/operations/other_ops.py
+++ b/mindspore/ops/operations/other_ops.py
@@ -299,7 +299,7 @@ class MakeRefKey(Primitive):
         tag (str): Parameter name to make the RefKey.
 
     Inputs:
-        No input.
+        No inputs.
 
     Outputs:
         RefKeyType, made from the Parameter name.
diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py
index 7f137a4816..40354ddb17 100644
--- a/mindspore/ops/operations/random_ops.py
+++ b/mindspore/ops/operations/random_ops.py
@@ -72,7 +72,7 @@ class Laplace(PrimitiveWithInfer):
         \text{f}(x;μ,λ) = \frac{1}{2λ}\exp(-\frac{|x-μ|}{λ}),
 
     Args:
-        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
+        seed (int): Seed data is used as entropy source for Random number engines to generate pseudo-random numbers.
           Default: 0.
 
     Inputs:
@@ -83,7 +83,7 @@ class Laplace(PrimitiveWithInfer):
           variance of Laplace distribution is equal to twice the square of lambda_param. With float32 data type.
 
     Outputs:
-        Tensor, has the shape 'shape' input and dtype as float32.
+        Tensor, has the specified shape and its dtype is float32.
 
     Examples:
         >>> shape = (4, 16)
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 544afef7bf..f8abd7e1b6 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -138,7 +138,7 @@ def _exec_save(ckpt_file_name, data_list):
 
     except BaseException as e:
         logger.error("Failed to save the checkpoint file %s.", ckpt_file_name)
-        raise RuntimeError(e.__str__())
+        raise e
 
 
 def save_checkpoint(save_obj, ckpt_file_name, integrated_save=True, async_save=False):
@@ -146,10 +146,10 @@ def save_checkpoint(save_obj, ckpt_file_name, integrated_save=True, async_save=F
     Saves checkpoint info to a specified file.
 
     Args:
-        save_obj (nn.Cell or list): The train network for training or parameters list(each element is a dictionary,
-                                    like {"name":xx, "data":xx}.)
-        ckpt_file_name (str): Checkpoint file name.
-        integrated_save (bool): Whether to integrated save in automatic model parallel scene. Default: True
+        save_obj (nn.Cell or list): The cell object or parameters list(each element is a dictionary,
+                                    like {"name": param_name, "data": param_data}.)
+        ckpt_file_name (str): Checkpoint file name. If the file name already exists, it will be overwritten.
+        integrated_save (bool): Whether to integrated save in automatic model parallel scene.
         async_save (bool): Whether asynchronous execution saves the checkpoint to a file. Default: False
 
     Raises: