update cann warning info

mindspore/ops/operations/array_ops.py

@@ -2389,7 +2389,7 @@ class Concat(PrimitiveWithInfer):
         (x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)
 
     .. warning::
-        "axis" is in the range [-len(x.shape), len(x.shape)].
+        The value range of "axis" is [-dims, dims - 1]. "dims" is the dimension length of "input_x".
 
     Args:
         axis (int): The specified axis. Default: 0.
@@ -2402,8 +2402,8 @@ class Concat(PrimitiveWithInfer):
           where the :math:`R` indicates the last axis.
 
     Outputs:
-        Tensor, the shape is :math:`(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)`.
+        - Tensor, the shape is :math:`(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)`.
-        The data type is the same with `input_x`.
+          The data type is the same with `input_x`.
 
     Raises:
         TypeError: If `axis` is not an int.
@@ -2828,7 +2828,7 @@ class ReverseV2(PrimitiveWithInfer):
     Reverses specific dimensions of a tensor.
 
     .. warning::
-        "axis" must be within the rank of "input_x".
+        The value range of "axis" is [-dims, dims - 1]. "dims" is the dimension length of "input_x".
 
     Args:
         axis (Union[tuple(int), list(int)): The indices of the dimensions to reverse.

mindspore/ops/operations/math_ops.py

@@ -2832,7 +2832,7 @@ class TruncateMod(_MathBinaryOp):
 
     .. warning::
         - The input data does not support 0.
-        - When NUM exceeds 2048 , the accuracy of operator cannot guarantee the requirement of
+        - When the elements of input exceeds 2048 , the accuracy of operator cannot guarantee the requirement of
           double thousandths in the mini form.
         - Due to different architectures, the calculation results of this operator on NPU and CPU may be inconsistent.
         - If shape is expressed as (D1,D2... ,Dn), then D1\*D2... \*DN<=1000000,n<=8.
@@ -2878,7 +2878,7 @@ class Mod(_MathBinaryOp):
 
     .. warning::
         - The input data does not support 0.
-        - When NUM exceeds 2048 , the accuracy of operator cannot guarantee the requirement of
+        - When the elements of input exceeds 2048 , the accuracy of operator cannot guarantee the requirement of
           double thousandths in the mini form.
         - Due to different architectures, the calculation results of this operator on NPU and CPU may be inconsistent.
         - If shape is expressed as (D1,D2... ,Dn), then D1\*D2... \*DN<=1000000,n<=8.
@@ -2978,7 +2978,7 @@ class FloorMod(_MathBinaryOp):
 
     .. warning::
         - The input data does not support 0.
-        - When NUM exceeds 2048 , the accuracy of operator cannot guarantee the requirement of
+        - When the elements of input exceeds 2048 , the accuracy of operator cannot guarantee the requirement of
           double thousandths in the mini form.
         - Due to different architectures, the calculation results of this operator on NPU and CPU may be inconsistent.
         - If shape is expressed as (D1,D2... ,Dn), then D1\*D2... \*DN<=1000000,n<=8.
@@ -3807,9 +3807,6 @@ class LogicalNot(PrimitiveWithInfer):
 
         out_{i} = \\neg x_{i}
 
-    .. warning::
-        The input and output values are "1" or "0", corresponding to bool values "true" and "false".
-
     Inputs:
         - **x** (Tensor) - The input tensor whose dtype is bool.
           :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
@@ -4442,7 +4439,7 @@ class NMSWithMask(PrimitiveWithInfer):
         \text{IOU} = \frac{\text{Area of Overlap}}{\text{Area of Union}}
 
     .. warning::
-        Only supports 2864 input boxes at one time.
+        Only supports up to 2864 input boxes at one time.
 
     Args:
         iou_threshold (float): Specifies the threshold of overlap boxes with respect to

mindspore/ops/operations/nn_ops.py

@@ -2027,7 +2027,7 @@ class AvgPool(_Pool):
         - Only single input and single output are supported.
         - Global pooling is supported.
         - The height of "kernel_size" and the weight of "kernel_size" are positive integers within the range [1, 255].
-          ksize_H * ksize_W < 256.
+          ksize_h * ksize_w < 256.
         - Due to instruction restrictions, the values of "strides_h" and "strides_w" are
           positive integers within the range [1, 63].
 
@@ -3230,9 +3230,9 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
         second moment. This often helps with training, but is slightly more exapnsive interms of computation and memory.
 
     .. warning::
-        In dense implementation of this algorithm, mean_gradient, mean_square, and moment will update
+        In dense implementation of this algorithm, `mean_gradient`, `mean_square`, and `moment` will update
-        even if the grad is zero. But in this sparse implementation, mean_gradient, mean_square, and moment
+        even if the `grad` is zero. But in this sparse implementation, `mean_gradient`, `mean_square`, and `moment`
-        will not update in iterations during which the grad is zero.
+        will not update in iterations during which the `grad` is zero.
 
     Args:
         use_locking (bool): Whether to enable a lock to protect the variable and accumlation tensors