!23550 add calculation picture of ScatterNd and add CPU platform for LogSigmoid, DenseBnAct, FTRL, etc.

Merge pull request !23550 from wangshuide/code_docs_wsd_master

.jenkins/OWNERS

@@ -17,7 +17,6 @@ approvers:
 - john_tzanakakis
 - jpc_chenjianping
 - kingxian
-- leonwanghui
 - liangchenghui
 - lilongfei15
 - limingqi107

OWNERS

@@ -3,7 +3,6 @@ approvers:
 - guoqi1024
 - baochong
 - zhaizhiqiang
-- leonwanghui
 
 files:
   "akg":

mindspore/nn/layer/activation.py

@@ -734,7 +734,7 @@ class LogSigmoid(Cell):
         TypeError: If dtype of `x` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> net = nn.LogSigmoid()

mindspore/nn/layer/combined.py

@@ -187,7 +187,7 @@ class DenseBnAct(Cell):
         ValueError: If `momentum` is not in range [0, 1.0].
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> net = nn.DenseBnAct(3, 4)

mindspore/nn/layer/conv.py

@@ -737,7 +737,7 @@ class Conv3dTranspose(_Conv):
         Tensor, the shape is :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Raises:
         TypeError: If `in_channels`, `out_channels` or `group` is not an int.

mindspore/nn/loss/loss.py

@@ -699,7 +699,7 @@ class MultiClassDiceLoss(LossBase):
         ValueError: If `weights` is a tensor, but its dimension is not 2.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> loss = nn.MultiClassDiceLoss(weights=None, ignore_indiex=None, activation="softmax")
@@ -1095,7 +1095,7 @@ class CosineEmbeddingLoss(LossBase):
         ValueError: If `margin` is not in range [-1, 1].
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> logits_x1 = Tensor(np.array([[0.3, 0.8], [0.4, 0.3]]), mindspore.float32)

mindspore/nn/optim/ftrl.py

@@ -170,7 +170,7 @@ class FTRL(Optimizer):
         ValueError: If `initial_accum`, `l1` or `l2` is less than 0.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> net = Net()

mindspore/nn/optim/lamb.py

@@ -241,7 +241,7 @@ class Lamb(Optimizer):
         ValueError: If `weight_decay` is less than 0.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> net = Net()

mindspore/ops/operations/array_ops.py

@@ -3475,6 +3475,11 @@ class ScatterNd(PrimitiveWithInfer):
 
     `updates` is a tensor of rank `Q-1+P-N`. Its shape is: :math:`(i_0, i_1, ..., i_{Q-2}, shape_N, ..., shape_{P-1})`.
 
+    The following figure shows the calculation process of inserting two slices in the first dimension of a rank-3
+    with two matrices of new values:
+
+    .. image:: api_img/ScatterNd.png
+
     Inputs:
         - **indices** (Tensor) - The index of scattering in the new tensor with int32 or int64 data type.
           The rank of indices must be at least 2 and `indices_shape[-1] <= len(shape)`.
@@ -3498,6 +3503,30 @@ class ScatterNd(PrimitiveWithInfer):
 
     Examples:
         >>> op = ops.ScatterNd()
+        >>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
+        >>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2],
+        ...                             [3, 3, 3, 3], [4, 4, 4, 4]],
+        ...                            [[1, 1, 1, 1], [2, 2, 2, 2],
+        ...                             [3, 3, 3, 3], [4, 4, 4, 4]]]), mindspore.float32)
+        >>> shape = (4, 4, 4)
+        >>> output = op(indices, updates, shape)
+        >>> print(output)
+        [[[1. 1. 1. 1.]
+          [2. 2. 2. 2.]
+          [3. 3. 3. 3.]
+          [4. 4. 4. 4.]]
+         [[0. 0. 0. 0.]
+          [0. 0. 0. 0.]
+          [0. 0. 0. 0.]
+          [0. 0. 0. 0.]]
+         [[1. 1. 1. 1.]
+          [2. 2. 2. 2.]
+          [3. 3. 3. 3.]
+          [4. 4. 4. 4.]]
+         [[0. 0. 0. 0.]
+          [0. 0. 0. 0.]
+          [0. 0. 0. 0.]
+          [0. 0. 0. 0.]]]
         >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32)
         >>> updates = Tensor(np.array([3.2, 1.1]), mindspore.float32)
         >>> shape = (3, 3)
@@ -5690,7 +5719,7 @@ class Sort(PrimitiveWithInfer):
         TypeError: If dtype of `x` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> x = Tensor(np.array([[8, 2, 1], [5, 9, 3], [4, 6, 7]]), mindspore.float16)

mindspore/ops/operations/nn_ops.py

@@ -8243,7 +8243,7 @@ class Conv3D(PrimitiveWithInfer):
         ValueError: If `data_format` is not 'NCDHW'.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> x = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float16)

mindspore/ops/operations/random_ops.py

@@ -301,7 +301,7 @@ class UniformInt(PrimitiveWithInfer):
         Tensor. The shape is the same as the input 'shape', and the data type is int32.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> shape = (2, 4)
@@ -362,7 +362,7 @@ class UniformReal(StandardNormal):
         ValueError: If `shape` is not a constant value.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> shape = (2, 2)

tests/OWNERS

@@ -17,7 +17,6 @@ approvers:
 - john_tzanakakis
 - jpc_chenjianping
 - kingxian
-- leonwanghui
 - liangchenghui
 - lilongfei15
 - limingqi107