diff --git a/mindspore/ops/composite/array_ops.py b/mindspore/ops/composite/array_ops.py
index 51921e061e..f3bbb3af5e 100644
--- a/mindspore/ops/composite/array_ops.py
+++ b/mindspore/ops/composite/array_ops.py
@@ -112,12 +112,12 @@ def sequence_mask(lengths, maxlen):
     If lengths has shape [d_1, d_2, ..., d_n], then the resulting tensor mask has type dtype and shape
     [d_1, d_2, ..., d_n, maxlen], with mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])
 
-    Args:
-        length (Tensor): Tensor to calculate the mask for. All values in this tensor must be
+    Inputs:
+        - **lengths** (Tensor) - Tensor to calculate the mask for. All values in this tensor must be
           less than or equal to `maxlen`. Must be type int32 or int64.
 
-        maxlen (int): size of the last dimension of returned tensor. Must be positive and same
-          type as elements in `lengths`.
+        - **maxlen** (int) - size of the last dimension of returned tensor. Must be positive and same
+          type as elements in `lengths`. Default is the maximum value in lengths.
 
     Outputs:
         One mask tensor of shape lengths.shape + (maxlen,).
@@ -126,9 +126,8 @@ def sequence_mask(lengths, maxlen):
         ``GPU``
 
     Examples:
-        >>> x = Tensor(np.array([[1, 3], [2, 0]])
-        >>> sequence_mask = P.SequenceMask()
-        >>> output = sequence_mask(x, 3)
+        >>> x = Tensor(np.array([[1, 3], [2, 0]]))
+        >>> output = C.sequence_mask(x, 3)
         >>> print(output)
         [[[True, False, False],
           [True, True, True]],
diff --git a/mindspore/ops/operations/_inner_ops.py b/mindspore/ops/operations/_inner_ops.py
index 75e41af212..32d8f4e4cc 100644
--- a/mindspore/ops/operations/_inner_ops.py
+++ b/mindspore/ops/operations/_inner_ops.py
@@ -690,10 +690,10 @@ class SequenceMask(PrimitiveWithCheck):
 
     Inputs:
         - **lengths** (Tensor) - Tensor to calculate the mask for. All values in this tensor must be
-          less than `maxlen`. Must be type int32 or int64.
+          less than or equal to `maxlen`. Must be type int32 or int64.
 
         - **maxlen** (int) - size of the last dimension of returned tensor. Must be positive and same
-          type as elements in `lengths`.
+          type as elements in `lengths`. Default is the maximum value in lengths.
 
     Outputs:
         One mask tensor of shape lengths.shape + (maxlen,).
@@ -702,8 +702,8 @@ class SequenceMask(PrimitiveWithCheck):
         ``GPU``
 
     Examples:
-        >>> x = Tensor(np.array([[1, 3], [2, 0]])
-        >>> sequence_mask = P.SequenceMask()
+        >>> x = Tensor(np.array([[1, 3], [2, 0]]))
+        >>> sequence_mask = ops.SequenceMask()
         >>> output = sequence_mask(x, 3)
         >>> print(output)
         [[[True, False, False],
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index c2f7082b99..41d772586e 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -5689,9 +5689,10 @@ class LARSUpdate(PrimitiveWithInfer):
         ...         super(Net, self).__init__()
         ...         self.lars = ops.LARSUpdate()
         ...         self.reduce = ops.ReduceSum()
+        ...         self.square = ops.Square()
         ...     def construct(self, weight, gradient):
-        ...         w_square_sum = self.reduce(ops.Square()(weight))
-        ...         grad_square_sum = self.reduce(ops.Square()(gradient))
+        ...         w_square_sum = self.reduce(self.square(weight))
+        ...         grad_square_sum = self.reduce(self.square(gradient))
         ...         grad_t = self.lars(weight, gradient, w_square_sum, grad_square_sum, 0.0, 1.0)
         ...         return grad_t
         ...