!13714 update documentation of Index_add operator,input of Slice,example of Unstack.

From: @mind-lh
Reviewed-by: 
Signed-off-by:

mindspore/ops/operations/array_ops.py

@@ -2546,8 +2546,7 @@ class Unstack(PrimitiveWithInfer):
         >>> input_x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
         >>> output = unstack(input_x)
         >>> print(output)
-        (Tensor(shape=[4], dtype=Int32, value= [1, 1, 1, 1]),
-         Tensor(shape=[4], dtype=Int32, value= [2, 2, 2, 2]))
+        (Tensor(shape=[4], dtype=Int64, value= [1, 1, 1, 1]), Tensor(shape=[4], dtype=Int64, value= [2, 2, 2, 2]))
     """
 
     @prim_attr_register
@@ -2599,8 +2598,8 @@ class Slice(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Tensor): The target tensor.
-        - **begin** (tuple, list): The beginning of the slice. Only constant value is allowed.
-        - **size** (tuple, list): The size of the slice. Only constant value is allowed.
+        - **begin** (Union[tuple, list]): The beginning of the slice. Only constant value is allowed.
+        - **size** (Union[tuple, list]): The size of the slice. Only constant value is allowed.
 
     Outputs:
         Tensor, the shape is : input `size`, the data type is the same as `input_x`.
@@ -5230,6 +5229,9 @@ class Range(PrimitiveWithCheck):
     Outputs:
        A 1-D Tensor, with the same type as the inputs.
 
+    Supported Platforms:
+        ``GPU``
+
     Examples:
         >>> start = Tensor(0, mstype.int32)
         >>> limit = Tensor(10, mstype.int32)
@@ -5237,9 +5239,6 @@ class Range(PrimitiveWithCheck):
         >>> output = ops.Range()(start, limit, delta)
         >>> print(output)
         [0, 4, 8]
-
-    Supported Platforms:
-        ``GPU`` ``CPU``
     """
 
     @prim_attr_register

mindspore/ops/operations/math_ops.py

@@ -884,12 +884,20 @@ class BatchMatMul(MatMul):
         >>> output = batmatmul(input_x, input_y)
         >>> print(output)
         [[[[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
-          [[3. 3. 3. 3.]]],
+
+          [[3. 3. 3. 3.]]]
+
+
          [[[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]]]
         >>>
         >>> input_x = Tensor(np.ones(shape=[2, 4, 3, 1]), mindspore.float32)
@@ -898,12 +906,20 @@ class BatchMatMul(MatMul):
         >>> output = batmatmul(input_x, input_y)
         >>> print(output)
         [[[[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
-          [[3. 3. 3. 3.]]],
+
+          [[3. 3. 3. 3.]]]
+
+
          [[[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]
+
           [[3. 3. 3. 3.]]]]
     """
 
@@ -4520,7 +4536,8 @@ class MatrixInverse(PrimitiveWithInfer):
 
 class IndexAdd(PrimitiveWithInfer):
     """
-    Adds tensor y to specified axis and indices of tensor x.
+    Adds tensor y to specified axis and indices of tensor x. The axis should be in the range from 0 to len(x.dim) - 1,
+    and indices should be in the range from 0 to the size of x at the axis dimension.
 
     Args:
         axis (int): The dimension along which to index.
@@ -4529,7 +4546,7 @@ class IndexAdd(PrimitiveWithInfer):
         - **input_x** (Parameter) - The input tensor to add to, with data type float64, float32, float16, int32, int16,
           int8, uint8.
         - **indices** (Tensor) - The index of `input_x` on the `axis`th dimension to add to, with data type int32.
-          The `indices` must be 1D with the size same as the size of the `axis`th dimension of `input_y`. The values
+          The `indices` must be 1D with the same size as the size of the `axis`th dimension of `input_y`. The values
           of `indices` should be in the range of 0 to the size of the `axis`th dimension of `input_x`.
         - **input_y** (Tensor) - The input tensor with the value to add. Must have same data type as `input_x`.
           The shape must be the same as `input_x` except the `axis`th dimension.
@@ -4537,19 +4554,32 @@ class IndexAdd(PrimitiveWithInfer):
     Outputs:
         Tensor, has the same shape and dtype as input_x.
 
+    Raises:
+        TypeError: If dtype of `input_x` is not one of: float64, float32, float16, int32, int16, int8, uint8.
+        TypeError: If neither `indices` nor `input_y` is a Tensor.
+        TypeError: If shape of `input_y` is not same as the `input_x`.
+
     Supported Platforms:
         ``GPU``
 
     Examples:
-        >>> input_x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [6, 7, 8]]), mindspore.float32)
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self.index_add = ops.IndexAdd(axis=1)
+        ...         self.input_x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32))
+        ...         self.indices = Tensor(np.array([0, 2]), mindspore.int32)
+        ...
+        ...     def construct(self, input_y):
+        ...         return self.index_add(self.input_x, self.indices, input_y)
+        ...
         >>> input_y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32)
-        >>> indices = Tensor(np.array([0, 2]), mindspore.int32)
-        >>> index_add = ops.IndexAdd(axis=1)
-        >>> output = index_add(input_x, indices, input_y)
+        >>> net = Net()
+        >>> output = net(input_y)
         >>> print(output)
         [[ 1.5  2.   4. ]
          [ 5.   5.   7.5]
-         [ 8.   7.  10.5]]
+         [ 9.   8.  11.5]]
     """
     __mindspore_signature__ = (
         sig.make_sig('input_x', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),

mindspore/ops/operations/nn_ops.py

@@ -978,9 +978,14 @@ class BNTrainingReduce(PrimitiveWithInfer):
         >>> bn_training_reduce = ops.BNTrainingReduce()
         >>> output = bn_training_reduce(input_x)
         >>> print(output)
-        (Tensor(shape=[3], dtype=Float32, value=
-        [ 1.22880000e+04,  1.22880000e+04,  1.22880000e+04]), Tensor(shape=[3], dtype=Float32, value=
-        [ 1.22880000e+04,  1.22880000e+04,  1.22880000e+04]))
+        (Tensor(shape=[1, 2, 2, 2], dtype=Float32, value=
+        [[[[ 2.73200464e+00, 2,73200464e+00],
+           [ 2.73200464e+00, 2,73200464e+00]],
+          [[ 2.73200464e+00, 2,73200464e+00],
+           [ 2.73200464e+00, 2,73200464e+00]]]]), Tensor(shape=[2], dtype=Float32, value= [ 9.24999952e-01,
+        9.24999952e-01]), Tensor(shape=[2], dtype=Float32, value= [ 9.24999952e-01, 9.24999952e-01]),
+        Tensor(shape=[2], dtype=Float32, value= [ 2.50000000e-01, 2.50000000e-01]), Tensor(shape=[2], dtype=Float32,
+        value= [ 1.87500000e-01, 1.87500000-01]))
     """
 
     @prim_attr_register
@@ -1036,7 +1041,7 @@ class BNTrainingUpdate(PrimitiveWithInfer):
         TypeError: If dtype of `epsilon` or `factor` is not float.
         TypeError: If `x`, `sum`, `square_sum`, `scale`, `offset`, `mean` or `variance` is not a Tensor.
         TypeError: If dtype of `x`, `sum`, `square_sum`, `scale`, `offset`, `mean` or `variance` is neither float16 nor
-        float32.
+                   float32.
 
     Supported Platforms:
         ``Ascend``
@@ -2227,9 +2232,9 @@ class NLLLoss(PrimitiveWithInfer):
         >>> nll_loss = ops.NLLLoss(reduction="mean")
         >>> loss, weight = nll_loss(input, target, weight)
         >>> print(loss)
-        [-0.52507716]
+        -0.52507716
         >>> print(weight)
-        [1.1503246 0.79172504]
+        1.1503246
     """
 
     @prim_attr_register
@@ -3006,7 +3011,7 @@ class LayerNorm(Primitive):
          [2.]]
         >>> print(variance)
         [[0.6666667]
-         [0.6666667]])
+         [0.6666667]]
     """
 
     @prim_attr_register

mindspore/ops/primitive.py

@@ -38,8 +38,9 @@ class Primitive(Primitive_):
         >>> class Add(Primitive):
         ...     @prim_attr_register
         ...     def __init__(self, attr1, attr2):
-        ...         # check attr1 and attr2 or do some initializations
-        >>> # init a Primitive obj with attr1=1 and attr2=2
+        ...         '''init for add'''
+        ...     # check attr1 and attr2 or do some initializations
+        ...     # init a Primitive obj with attr1=1 and attr2=2
         >>> add = Add(attr1=1, attr2=2)
     """
     _repr_ignore_list = ['input_names', 'output_names']