!45687 Remove functional interface of maskedscatter

Merge pull request !45687 from panzhihui/maskedscatter_ops

mindspore/python/mindspore/ops/_grad_experimental/grad_array_ops.py

@@ -46,6 +46,7 @@ from mindspore.ops.operations.array_ops import AffineGrid
 from mindspore.ops.operations.array_ops import Im2Col
 from mindspore.ops.operations.array_ops import Col2Im
 from mindspore.ops.operations.array_ops import StridedSliceV2
+from mindspore.ops.operations.array_ops import MaskedScatter
 from mindspore.ops.operations._grad_ops import StridedSliceV2Grad
 from mindspore.ops.operations.random_ops import LogNormalReverse
 from mindspore.ops.operations import _inner_ops as inner
@@ -124,11 +125,11 @@ def get_bprop_masked_select(self):
     return bprop
 
 
-@bprop_getters.register(P.MaskedScatter)
+@bprop_getters.register(MaskedScatter)
 def get_bprop_masked_scatter(self):
     """Generate bprop for MaskedScatter"""
     sort_ = P.Sort(descending=True)
-    masked_scatter = P.MaskedScatter()
+    masked_scatter = MaskedScatter()
     masked_fill = P.MaskedFill()
     masked_select = P.MaskedSelect()
     size = P.Size()

mindspore/python/mindspore/ops/_op_impl/aicpu/__init__.py

@@ -42,7 +42,6 @@ from .print_tensor import _print_aicpu
 from .topk import _top_k_aicpu
 from .log1p import _log1p_aicpu
 from .asin import _asin_aicpu
-from .masked_scatter import _masked_scatter_aicpu
 from .is_finite import _is_finite_aicpu
 from .is_inf import _is_inf_aicpu
 from .is_nan import _is_nan_aicpu

mindspore/python/mindspore/ops/function/__init__.py

@@ -102,7 +102,6 @@ from .array_func import (
     matrix_diag_part,
     matrix_set_diag,
     diag,
-    masked_scatter,
     masked_select,
     meshgrid,
     affine_grid,

mindspore/python/mindspore/ops/function/array_func.py

@@ -25,7 +25,6 @@ from mindspore.ops.operations.array_ops import (
     UniqueConsecutive,
     SearchSorted,
     NonZero,
-    MaskedScatter,
     MatrixDiagV3,
     MatrixDiagPartV3,
     MatrixSetDiagV3,
@@ -4133,43 +4132,6 @@ def tuple_to_array(input_x):
     return tuple_to_array_(input_x)
 
 
-def masked_scatter(x, mask, updates):
-    """
-    Updates the value in the input with the updates value according to the mask.
-    The shapes of `mask` and `x` must be the same or broadcastable.
-
-    Args:
-        x (Tensor): The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
-        mask (Tensor[bool]): A bool tensor with a shape broadcastable to x.
-        updates (Tensor): A tensor with the same data type as x. The
-            number of elements must be greater than or equal to the number of True's in `mask`.
-
-    Outputs:
-        y (Tensor), with the same type and shape as x.
-
-    Raises:
-        TypeError: If `x`, `mask` or `updates` is not a Tensor.
-        TypeError: If data type of `x` is not be supported.
-        TypeError: If dtype of `mask` is not bool.
-        TypeError: If the dim of `x` less than the dim of `mask`.
-        ValueError: If `mask` can not be broadcastable to `x`.
-        ValueError: If the number of elements in `updates` is less than the number required for the updates.
-
-    Supported Platforms:
-        ``CPU``
-
-    Examples:
-        >>> x= Tensor(np.array([1., 2., 3., 4.]), mindspore.float32)
-        >>> mask = Tensor(np.array([True, True, False, True]), mindspore.bool_)
-        >>> updates = Tensor(np.array([5., 6., 7.]), mindspore.float32)
-        >>> output = ops.MaskedScatter()(input_X, mask, updates)
-        >>> print(output)
-        [5. 6. 3. 7.]
-    """
-    masked_scatter_ = MaskedScatter()
-    return masked_scatter_(x, mask, updates)
-
-
 def masked_select(x, mask):
     """
     Returns a new 1-D Tensor which indexes the `x` tensor according to the boolean `mask`.
@@ -5105,7 +5067,6 @@ __all__ = [
     'gather_nd',
     'one_hot',
     'masked_fill',
-    'masked_scatter',
     'masked_select',
     'narrow',
     'scatter_add',

mindspore/python/mindspore/ops/operations/__init__.py

@@ -35,7 +35,7 @@ from .array_ops import (ArgMaxWithValue, ArgMinWithValue, Argmax, Argmin, BatchT
                         Eye, Fill, Gather, GatherD, GatherNd, GatherV2, Identity, Im2Col, InvertPermutation,
                         LowerBound, Lstsq, MaskedFill, MaskedSelect, Meshgrid, Mvlgamma, Ones, OnesLike,
                         Pack, Padding, ParallelConcat, PopulationCount, Range, Rank, Reshape, ResizeNearestNeighbor,
-                        ReverseSequence, ReverseV2, Rint, ScalarToTensor, ScatterAdd, MaskedScatter,
+                        ReverseSequence, ReverseV2, Rint, ScalarToTensor, ScatterAdd,
                         ScatterDiv, ScatterMax, ScatterMin, ScatterMul, ScatterNd, ScatterNdAdd, ScatterNdDiv,
                         ScatterNdMax, ScatterNdMin, ScatterNdSub, ScatterNdUpdate, ScatterNonAliasingAdd, ScatterSub,
                         ScatterUpdate, SearchSorted, Select, Shape, Size, Slice, Sort, SpaceToBatch, SpaceToBatchND,
@@ -151,7 +151,6 @@ __all__ = [
     'BatchMatMul',
     'Mul',
     'MaskedFill',
-    'MaskedScatter',
     'MaskedSelect',
     'Meshgrid',
     'MultiMarginLoss',

mindspore/python/mindspore/ops/operations/array_ops.py

@@ -6030,7 +6030,22 @@ class MaskedScatter(Primitive):
     Updates the value in the input with the updates value according to the mask.
     The shapes of `mask` and `x` must be the same or broadcastable.
 
-    Refer to :func:`mindspore.ops.masked_scatter' for more details.
+    Inputs:
+        - **x** (Tensor): The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
+        - **mask** (Tensor[bool]): A bool tensor with a shape broadcastable to x.
+        - **updates** (Tensor): A tensor with the same data type as x. The
+          number of elements must be greater than or equal to the number of True's in `mask`.
+
+    Outputs:
+        Tensor, with the same type and shape as x.
+
+    Raises:
+        TypeError: If `x`, `mask` or `updates` is not a Tensor.
+        TypeError: If data type of `x` is not be supported.
+        TypeError: If dtype of `mask` is not bool.
+        TypeError: If the dim of `x` less than the dim of `mask`.
+        ValueError: If `mask` can not be broadcastable to `x`.
+        ValueError: If the number of elements in `updates` is less than the number required for the updates.
 
     Supported Platforms:
         ``CPU``
@@ -6048,7 +6063,6 @@ class MaskedScatter(Primitive):
     def __init__(self):
         """Initialize MaskedScatter"""
         self.init_prim_io_names(inputs=['x', 'mask', 'updates'], outputs=['y'])
-        self.add_prim_attr("cust_aicpu", "MaskedScatter")
 
 
 class MaskedSelect(PrimitiveWithCheck):